From 9e737cbadcdc89c23b119701815275e7c209ff00 Mon Sep 17 00:00:00 2001
From: Alexandre Simard <as.simard@outlook.com>
Date: Mon, 26 Sep 2022 17:18:57 -0400
Subject: Solve issue #962

Fix by @MrAcademy
---
 .gitignore       | 3 ++-
 javascript/ui.js | 5 ++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.gitignore b/.gitignore
index 9d78853a..fa1ab43e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,4 +19,5 @@ __pycache__
 /webui-user.sh
 /interrogate
 /user.css
-/.idea
\ No newline at end of file
+/.idea
+/SwinIR
diff --git a/javascript/ui.js b/javascript/ui.js
index 076e9436..7db4db48 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -1,9 +1,8 @@
 // various functions for interation with ui.py not large enough to warrant putting them in separate files
 
 function selected_gallery_index(){
-    var gr = gradioApp()
-    var buttons = gradioApp().querySelectorAll(".gallery-item")
-    var button = gr.querySelector(".gallery-item.\\!ring-2")
+    var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item')
+    var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2')
 
     var result = -1
     buttons.forEach(function(v, i){ if(v==button) { result = i } })
-- 
cgit v1.2.1


From 03ee67bfd34b9e872b33eb05fef5db83410b16f3 Mon Sep 17 00:00:00 2001
From: WDevelopsWebApps <97454358+WDevelopsWebApps@users.noreply.github.com>
Date: Wed, 28 Sep 2022 10:53:40 +0200
Subject: add advanced saving for save button

---
 modules/images.py |  5 ++++-
 modules/ui.py     | 37 +++++++++++++++++++++++++++++--------
 2 files changed, 33 insertions(+), 9 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index 9458bf8d..923f81df 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -290,7 +290,10 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = x.replace("[cfg]", str(p.cfg_scale))
         x = x.replace("[width]", str(p.width))
         x = x.replace("[height]", str(p.height))
-        x = x.replace("[styles]", sanitize_filename_part(", ".join(p.styles), replace_spaces=False))
+        #currently disabled if using the save button, will work otherwise 
+        # if enabled it will cause a bug because styles is not included in the save_files data dictionary
+        if hasattr(p, "styles"):
+            x = x.replace("[styles]", sanitize_filename_part(", ".join(p.styles), replace_spaces=False))
         x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
 
     x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
diff --git a/modules/ui.py b/modules/ui.py
index 7db8edbd..87a86a45 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -28,6 +28,7 @@ import modules.gfpgan_model
 import modules.codeformer_model
 import modules.styles
 import modules.generation_parameters_copypaste
+from modules.images import apply_filename_pattern, get_next_sequence_number
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
 mimetypes.init()
@@ -90,13 +91,26 @@ def send_gradio_gallery_to_image(x):
 
 
 def save_files(js_data, images, index):
-    import csv
-
-    os.makedirs(opts.outdir_save, exist_ok=True)
-
+    import csv    
     filenames = []
 
+    #quick dictionary to class object conversion. Its neccesary due apply_filename_pattern requiring it
+    class MyObject:
+        def __init__(self, d=None):
+            if d is not None:
+                for key, value in d.items():
+                    setattr(self, key, value)
+
     data = json.loads(js_data)
+    p = MyObject(data)
+    path = opts.outdir_save
+    save_to_dirs = opts.save_to_dirs
+
+    if save_to_dirs:
+        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, p.seed, p.prompt)
+        path = os.path.join(opts.outdir_save, dirname)
+
+    os.makedirs(path, exist_ok=True)
     
     if index > -1 and opts.save_selected_only and (index > 0 or not opts.return_grid): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
         images = [images[index]]
@@ -107,11 +121,18 @@ def save_files(js_data, images, index):
         writer = csv.writer(file)
         if at_start:
             writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
-
-        filename_base = str(int(time.time() * 1000))
+        file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
+        if file_decoration != "":
+            file_decoration = "-" + file_decoration.lower()
+        file_decoration = apply_filename_pattern(file_decoration, p, p.seed, p.prompt)
+        truncated = (file_decoration[:240] + '..') if len(file_decoration) > 240 else file_decoration
+        filename_base = truncated
+
+        basecount = get_next_sequence_number(path, "")
         for i, filedata in enumerate(images):
-            filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + ".png"
-            filepath = os.path.join(opts.outdir_save, filename)
+            file_number = f"{basecount+i:05}"
+            filename = file_number + filename_base + ".png"
+            filepath = os.path.join(path, filename)
 
             if filedata.startswith("data:image/png;base64,"):
                 filedata = filedata[len("data:image/png;base64,"):]
-- 
cgit v1.2.1


From c938679de7b87b4f14894d9f57fe0f40dd6e3c06 Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Wed, 28 Sep 2022 22:14:13 -0300
Subject: Fix memory leak and reduce memory usage

---
 modules/codeformer_model.py |  6 ++++--
 modules/devices.py          |  3 ++-
 modules/extras.py           |  2 ++
 modules/gfpgan_model.py     | 11 +++++------
 modules/processing.py       | 33 ++++++++++++++++++++++++++-------
 webui.py                    |  3 +++
 6 files changed, 42 insertions(+), 16 deletions(-)

diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
index 8fbdea24..2177291a 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -89,7 +89,7 @@ def setup_codeformer():
                             output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
                             restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
                         del output
-                        torch.cuda.empty_cache()
+                        devices.torch_gc()
                     except Exception as error:
                         print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
                         restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
@@ -106,7 +106,9 @@ def setup_codeformer():
                     restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
 
                 if shared.opts.face_restoration_unload:
-                    self.net.to(devices.cpu)
+                    self.net = None
+                    self.face_helper = None
+                    devices.torch_gc()
 
                 return restored_img
 
diff --git a/modules/devices.py b/modules/devices.py
index 07bb2339..df63dd88 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,4 +1,5 @@
 import torch
+import gc
 
 # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
 from modules import errors
@@ -17,8 +18,8 @@ def get_optimal_device():
 
     return cpu
 
-
 def torch_gc():
+    gc.collect()
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()
diff --git a/modules/extras.py b/modules/extras.py
index 9a825530..38b86167 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -98,6 +98,8 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
 
         outputs.append(image)
 
+    devices.torch_gc()
+
     return outputs, plaintext_to_html(info), ''
 
 
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index 44c5dc6c..b1288f0c 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -49,6 +49,7 @@ def gfpgan():
 
 
 def gfpgan_fix_faces(np_image):
+    global loaded_gfpgan_model
     model = gfpgan()
 
     np_image_bgr = np_image[:, :, ::-1]
@@ -56,7 +57,9 @@ def gfpgan_fix_faces(np_image):
     np_image = gfpgan_output_bgr[:, :, ::-1]
 
     if shared.opts.face_restoration_unload:
-        model.gfpgan.to(devices.cpu)
+        del model
+        loaded_gfpgan_model = None
+        devices.torch_gc()
 
     return np_image
 
@@ -83,11 +86,7 @@ def setup_gfpgan():
                 return "GFPGAN"
 
             def restore(self, np_image):
-                np_image_bgr = np_image[:, :, ::-1]
-                cropped_faces, restored_faces, gfpgan_output_bgr = gfpgan().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
-                np_image = gfpgan_output_bgr[:, :, ::-1]
-
-                return np_image
+                return gfpgan_fix_faces(np_image)
 
         shared.face_restorers.append(FaceRestorerGFPGAN())
     except Exception:
diff --git a/modules/processing.py b/modules/processing.py
index 4ecdfcd2..de5cda79 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -12,7 +12,7 @@ import cv2
 from skimage import exposure
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking
+from modules import devices, prompt_parser, masking, lowvram
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
 from modules.shared import opts, cmd_opts, state
@@ -335,7 +335,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         if state.job_count == -1:
             state.job_count = p.n_iter
 
-        for n in range(p.n_iter):
+    for n in range(p.n_iter):
+        with torch.no_grad(), precision_scope("cuda"), ema_scope():
             if state.interrupted:
                 break
 
@@ -368,22 +369,32 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
+            del samples_ddim
+
+            if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+                lowvram.send_everything_to_cpu()
+
+            devices.torch_gc()
+
             if opts.filter_nsfw:
                 import modules.safety as safety
                 x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)
 
-            for i, x_sample in enumerate(x_samples_ddim):
+        for i, x_sample in enumerate(x_samples_ddim):
+            with torch.no_grad(), precision_scope("cuda"), ema_scope():
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                 x_sample = x_sample.astype(np.uint8)
 
-                if p.restore_faces:
+            if p.restore_faces:
+                with torch.no_grad(), precision_scope("cuda"), ema_scope():
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
                         images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
 
-                    devices.torch_gc()
-
                     x_sample = modules.face_restoration.restore_faces(x_sample)
 
+                devices.torch_gc()
+
+            with torch.no_grad(), precision_scope("cuda"), ema_scope():
                 image = Image.fromarray(x_sample)
 
                 if p.color_corrections is not None and i < len(p.color_corrections):
@@ -411,8 +422,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 infotexts.append(infotext(n, i))
                 output_images.append(image)
 
-            state.nextjob()
+        del x_samples_ddim 
 
+        devices.torch_gc()
+
+        state.nextjob()
+
+    with torch.no_grad(), precision_scope("cuda"), ema_scope():
         p.color_corrections = None
 
         index_of_first_image = 0
@@ -648,4 +664,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         if self.mask is not None:
             samples = samples * self.nmask + self.init_latent * self.mask
 
+        del x
+        devices.torch_gc()
+
         return samples
diff --git a/webui.py b/webui.py
index c70a11c7..b61a318d 100644
--- a/webui.py
+++ b/webui.py
@@ -22,7 +22,10 @@ import modules.txt2img
 import modules.img2img
 import modules.swinir as swinir
 import modules.sd_models
+from torch.nn.functional import silu
+import ldm
 
+ldm.modules.diffusionmodules.model.nonlinearity = silu
 
 modules.codeformer_model.setup_codeformer()
 modules.gfpgan_model.setup_gfpgan()
-- 
cgit v1.2.1


From c2d5b29040132c171bc4d77f1f63da972306f22c Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Thu, 29 Sep 2022 01:14:54 -0300
Subject: Move silu to sd_hijack

---
 modules/sd_hijack.py | 12 +++---------
 webui.py             |  3 ---
 2 files changed, 3 insertions(+), 12 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index bfbd07f9..4bc58fa2 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -12,6 +12,7 @@ from ldm.util import default
 from einops import rearrange
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
+from torch.nn.functional import silu
 
 
 # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
@@ -100,14 +101,6 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     return self.to_out(r2)
 
-def nonlinearity_hijack(x):
-    # swish
-    t = torch.sigmoid(x)
-    x *= t
-    del t
-
-    return x
-
 def cross_attention_attnblock_forward(self, x):
         h_ = x
         h_ = self.norm(h_)
@@ -245,11 +238,12 @@ class StableDiffusionModelHijack:
         m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
         self.clip = m.cond_stage_model
 
+        ldm.modules.diffusionmodules.model.nonlinearity = silu
+
         if cmd_opts.opt_split_attention_v1:
             ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
         elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
             ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
-            ldm.modules.diffusionmodules.model.nonlinearity = nonlinearity_hijack
             ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
 
         def flatten(el):
diff --git a/webui.py b/webui.py
index b61a318d..c70a11c7 100644
--- a/webui.py
+++ b/webui.py
@@ -22,10 +22,7 @@ import modules.txt2img
 import modules.img2img
 import modules.swinir as swinir
 import modules.sd_models
-from torch.nn.functional import silu
-import ldm
 
-ldm.modules.diffusionmodules.model.nonlinearity = silu
 
 modules.codeformer_model.setup_codeformer()
 modules.gfpgan_model.setup_gfpgan()
-- 
cgit v1.2.1


From e82ea202997cbcd2ab72891cd075d9ba270eb67d Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 15:26:18 -0500
Subject: Optimize model loader

Child classes only get populated to __subclassess__ when they are imported. We don't actually need to import any of them to webui any more, so clean up webUI imports and make sure loader imports children.

Also, fix command line paths not actually being passed to the scalers.
---
 modules/modelloader.py | 19 ++++++++++++++++---
 webui.py               | 13 +++----------
 2 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/modules/modelloader.py b/modules/modelloader.py
index 1106aeb7..b1721671 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -4,7 +4,6 @@ import importlib
 from urllib.parse import urlparse
 
 from basicsr.utils.download_util import load_file_from_url
-
 from modules import shared
 from modules.upscaler import Upscaler
 from modules.paths import script_path, models_path
@@ -120,16 +119,30 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
 
 
 def load_upscalers():
+    sd = shared.script_path
+    # We can only do this 'magic' method to dynamically load upscalers if they are referenced,
+    # so we'll try to import any _model.py files before looking in __subclasses__
+    modules_dir = os.path.join(sd, "modules")
+    for file in os.listdir(modules_dir):
+        if "_model.py" in file:
+            model_name = file.replace("_model.py", "")
+            full_model = f"modules.{model_name}_model"
+            try:
+                importlib.import_module(full_model)
+            except:
+                pass
     datas = []
+    c_o = vars(shared.cmd_opts)
     for cls in Upscaler.__subclasses__():
         name = cls.__name__
         module_name = cls.__module__
         module = importlib.import_module(module_name)
         class_ = getattr(module, name)
-        cmd_name = f"{name.lower().replace('upscaler', '')}-models-path"
+        cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
         opt_string = None
         try:
-            opt_string = shared.opts.__getattr__(cmd_name)
+            if cmd_name in c_o:
+                opt_string = c_o[cmd_name]
         except:
             pass
         scaler = class_(opt_string)
diff --git a/webui.py b/webui.py
index b8cccd54..ebe39a17 100644
--- a/webui.py
+++ b/webui.py
@@ -1,28 +1,21 @@
 import os
-import threading
-
-from modules import devices
-from modules.paths import script_path
 import signal
 import threading
-import modules.paths
+
 import modules.codeformer_model as codeformer
-import modules.esrgan_model as esrgan
-import modules.bsrgan_model as bsrgan
 import modules.extras
 import modules.face_restoration
 import modules.gfpgan_model as gfpgan
 import modules.img2img
-import modules.ldsr_model as ldsr
 import modules.lowvram
-import modules.realesrgan_model as realesrgan
+import modules.paths
 import modules.scripts
 import modules.sd_hijack
 import modules.sd_models
 import modules.shared as shared
-import modules.swinir_model as swinir
 import modules.txt2img
 import modules.ui
+from modules import devices
 from modules import modelloader
 from modules.paths import script_path
 from modules.shared import cmd_opts
-- 
cgit v1.2.1


From 8deae077004f0332ca607fc3a5d568b1a4705bec Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Fri, 30 Sep 2022 15:28:37 -0500
Subject: Add ScuNET DeNoiser/Upscaler

Q&D Implementation of ScuNET, thanks to our handy model loader. :P

https://github.com/cszn/SCUNet
---
 modules/scunet_model.py      |  90 +++++++++++++++
 modules/scunet_model_arch.py | 265 +++++++++++++++++++++++++++++++++++++++++++
 modules/shared.py            |   1 +
 3 files changed, 356 insertions(+)
 create mode 100644 modules/scunet_model.py
 create mode 100644 modules/scunet_model_arch.py

diff --git a/modules/scunet_model.py b/modules/scunet_model.py
new file mode 100644
index 00000000..7987ac14
--- /dev/null
+++ b/modules/scunet_model.py
@@ -0,0 +1,90 @@
+import os.path
+import sys
+import traceback
+
+import PIL.Image
+import numpy as np
+import torch
+from basicsr.utils.download_util import load_file_from_url
+
+import modules.upscaler
+from modules import shared, modelloader
+from modules.paths import models_path
+from modules.scunet_model_arch import SCUNet as net
+
+
+class UpscalerScuNET(modules.upscaler.Upscaler):
+    def __init__(self, dirname):
+        self.name = "ScuNET"
+        self.model_path = os.path.join(models_path, self.name)
+        self.model_name = "ScuNET GAN"
+        self.model_name2 = "ScuNET PSNR"
+        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
+        self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
+        self.user_path = dirname
+        super().__init__()
+        model_paths = self.find_models(ext_filter=[".pth"])
+        scalers = []
+        add_model2 = True
+        for file in model_paths:
+            if "http" in file:
+                name = self.model_name
+            else:
+                name = modelloader.friendly_name(file)
+            if name == self.model_name2 or file == self.model_url2:
+                add_model2 = False
+            try:
+                scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
+                scalers.append(scaler_data)
+            except Exception:
+                print(f"Error loading ScuNET model: {file}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+        if add_model2:
+            scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
+            scalers.append(scaler_data2)
+        self.scalers = scalers
+
+    def do_upscale(self, img: PIL.Image, selected_file):
+        torch.cuda.empty_cache()
+
+        model = self.load_model(selected_file)
+        if model is None:
+            return img
+
+        device = shared.device
+        img = np.array(img)
+        img = img[:, :, ::-1]
+        img = np.moveaxis(img, 2, 0) / 255
+        img = torch.from_numpy(img).float()
+        img = img.unsqueeze(0).to(shared.device)
+
+        img = img.to(device)
+        with torch.no_grad():
+            output = model(img)
+        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
+        output = 255. * np.moveaxis(output, 0, 2)
+        output = output.astype(np.uint8)
+        output = output[:, :, ::-1]
+        torch.cuda.empty_cache()
+        return PIL.Image.fromarray(output, 'RGB')
+
+    def load_model(self, path: str):
+        device = shared.device
+        if "http" in path:
+            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
+                                          progress=True)
+        else:
+            filename = path
+        if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
+            print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
+            return None
+
+        model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
+        model.load_state_dict(torch.load(filename), strict=True)
+        model.eval()
+        for k, v in model.named_parameters():
+            v.requires_grad = False
+        model = model.to(device)
+
+        return model
+
diff --git a/modules/scunet_model_arch.py b/modules/scunet_model_arch.py
new file mode 100644
index 00000000..972a2639
--- /dev/null
+++ b/modules/scunet_model_arch.py
@@ -0,0 +1,265 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+from timm.models.layers import trunc_normal_, DropPath
+
+
+class WMSA(nn.Module):
+    """ Self-attention module in Swin Transformer
+    """
+
+    def __init__(self, input_dim, output_dim, head_dim, window_size, type):
+        super(WMSA, self).__init__()
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.head_dim = head_dim
+        self.scale = self.head_dim ** -0.5
+        self.n_heads = input_dim // head_dim
+        self.window_size = window_size
+        self.type = type
+        self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
+
+        self.relative_position_params = nn.Parameter(
+            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
+
+        self.linear = nn.Linear(self.input_dim, self.output_dim)
+
+        trunc_normal_(self.relative_position_params, std=.02)
+        self.relative_position_params = torch.nn.Parameter(
+            self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
+                                                                                                                 2).transpose(
+                0, 1))
+
+    def generate_mask(self, h, w, p, shift):
+        """ generating the mask of SW-MSA
+        Args:
+            shift: shift parameters in CyclicShift.
+        Returns:
+            attn_mask: should be (1 1 w p p),
+        """
+        # supporting sqaure.
+        attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
+        if self.type == 'W':
+            return attn_mask
+
+        s = p - shift
+        attn_mask[-1, :, :s, :, s:, :] = True
+        attn_mask[-1, :, s:, :, :s, :] = True
+        attn_mask[:, -1, :, :s, :, s:] = True
+        attn_mask[:, -1, :, s:, :, :s] = True
+        attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
+        return attn_mask
+
+    def forward(self, x):
+        """ Forward pass of Window Multi-head Self-attention module.
+        Args:
+            x: input tensor with shape of [b h w c];
+            attn_mask: attention mask, fill -inf where the value is True;
+        Returns:
+            output: tensor shape [b h w c]
+        """
+        if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
+        x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
+        h_windows = x.size(1)
+        w_windows = x.size(2)
+        # sqaure validation
+        # assert h_windows == w_windows
+
+        x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
+        qkv = self.embedding_layer(x)
+        q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
+        sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
+        # Adding learnable relative embedding
+        sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
+        # Using Attn Mask to distinguish different subwindows.
+        if self.type != 'W':
+            attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
+            sim = sim.masked_fill_(attn_mask, float("-inf"))
+
+        probs = nn.functional.softmax(sim, dim=-1)
+        output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
+        output = rearrange(output, 'h b w p c -> b w p (h c)')
+        output = self.linear(output)
+        output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
+
+        if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
+                                                 dims=(1, 2))
+        return output
+
+    def relative_embedding(self):
+        cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
+        relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
+        # negative is allowed
+        return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
+
+
+class Block(nn.Module):
+    def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+        """ SwinTransformer Block
+        """
+        super(Block, self).__init__()
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        assert type in ['W', 'SW']
+        self.type = type
+        if input_resolution <= window_size:
+            self.type = 'W'
+
+        self.ln1 = nn.LayerNorm(input_dim)
+        self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.ln2 = nn.LayerNorm(input_dim)
+        self.mlp = nn.Sequential(
+            nn.Linear(input_dim, 4 * input_dim),
+            nn.GELU(),
+            nn.Linear(4 * input_dim, output_dim),
+        )
+
+    def forward(self, x):
+        x = x + self.drop_path(self.msa(self.ln1(x)))
+        x = x + self.drop_path(self.mlp(self.ln2(x)))
+        return x
+
+
+class ConvTransBlock(nn.Module):
+    def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+        """ SwinTransformer and Conv Block
+        """
+        super(ConvTransBlock, self).__init__()
+        self.conv_dim = conv_dim
+        self.trans_dim = trans_dim
+        self.head_dim = head_dim
+        self.window_size = window_size
+        self.drop_path = drop_path
+        self.type = type
+        self.input_resolution = input_resolution
+
+        assert self.type in ['W', 'SW']
+        if self.input_resolution <= self.window_size:
+            self.type = 'W'
+
+        self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
+                                 self.type, self.input_resolution)
+        self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+        self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+
+        self.conv_block = nn.Sequential(
+            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
+            nn.ReLU(True),
+            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
+        )
+
+    def forward(self, x):
+        conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
+        conv_x = self.conv_block(conv_x) + conv_x
+        trans_x = Rearrange('b c h w -> b h w c')(trans_x)
+        trans_x = self.trans_block(trans_x)
+        trans_x = Rearrange('b h w c -> b c h w')(trans_x)
+        res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
+        x = x + res
+
+        return x
+
+
+class SCUNet(nn.Module):
+    # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
+    def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
+        super(SCUNet, self).__init__()
+        if config is None:
+            config = [2, 2, 2, 2, 2, 2, 2]
+        self.config = config
+        self.dim = dim
+        self.head_dim = 32
+        self.window_size = 8
+
+        # drop path rate for each layer
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
+
+        self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
+
+        begin = 0
+        self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution)
+                        for i in range(config[0])] + \
+                       [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[0]
+        self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution // 2)
+                        for i in range(config[1])] + \
+                       [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[1]
+        self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution // 4)
+                        for i in range(config[2])] + \
+                       [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[2]
+        self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                      'W' if not i % 2 else 'SW', input_resolution // 8)
+                       for i in range(config[3])]
+
+        begin += config[3]
+        self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution // 4)
+                      for i in range(config[4])]
+
+        begin += config[4]
+        self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution // 2)
+                      for i in range(config[5])]
+
+        begin += config[5]
+        self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution)
+                      for i in range(config[6])]
+
+        self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
+
+        self.m_head = nn.Sequential(*self.m_head)
+        self.m_down1 = nn.Sequential(*self.m_down1)
+        self.m_down2 = nn.Sequential(*self.m_down2)
+        self.m_down3 = nn.Sequential(*self.m_down3)
+        self.m_body = nn.Sequential(*self.m_body)
+        self.m_up3 = nn.Sequential(*self.m_up3)
+        self.m_up2 = nn.Sequential(*self.m_up2)
+        self.m_up1 = nn.Sequential(*self.m_up1)
+        self.m_tail = nn.Sequential(*self.m_tail)
+        # self.apply(self._init_weights)
+
+    def forward(self, x0):
+
+        h, w = x0.size()[-2:]
+        paddingBottom = int(np.ceil(h / 64) * 64 - h)
+        paddingRight = int(np.ceil(w / 64) * 64 - w)
+        x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
+
+        x1 = self.m_head(x0)
+        x2 = self.m_down1(x1)
+        x3 = self.m_down2(x2)
+        x4 = self.m_down3(x3)
+        x = self.m_body(x4)
+        x = self.m_up3(x + x4)
+        x = self.m_up2(x + x3)
+        x = self.m_up1(x + x2)
+        x = self.m_tail(x + x1)
+
+        x = x[..., :h, :w]
+
+        return x
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
\ No newline at end of file
diff --git a/modules/shared.py b/modules/shared.py
index 8428c7a3..a48b995a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -40,6 +40,7 @@ parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory wi
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))
 parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN'))
 parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))
+parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(model_path, 'ScuNET'))
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR'))
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
-- 
cgit v1.2.1


From abdbf1de646f007b6d76cfb3f416fdfaadb57903 Mon Sep 17 00:00:00 2001
From: Liam <liamthekerr@gmail.com>
Date: Thu, 29 Sep 2022 14:40:47 -0400
Subject: token counters now update when roll artist and style buttons are
 pressed
 https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/1194#issuecomment-1261203893

---
 javascript/ui.js | 28 ++++++++++++++++++++++------
 modules/ui.py    |  6 +++++-
 2 files changed, 27 insertions(+), 7 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index bfe02410..88fd45ae 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -199,12 +199,21 @@ let txt2img_textarea, img2img_textarea = undefined;
 let wait_time = 800
 let token_timeout;
 
-function submit_prompt(event, generate_button_id) {
-    if (event.altKey && event.keyCode === 13) {
-        event.preventDefault();
-        gradioApp().getElementById(generate_button_id).click();
-        return;
-    }
+function roll_artist_txt2img(prompt_text) {
+	update_token_counter("txt2img_token_button")
+	return prompt_text;
+}
+function roll_artist_img2img(prompt_text) {
+	update_token_counter("img2img_token_button")
+	return prompt_text;
+}
+function update_style_txt2img(prompt_text, negative_prompt, style1, style2) {
+	update_token_counter("txt2img_token_button")
+	return [prompt_text, negative_prompt, style1, style2]
+}
+function update_style_img2img(prompt_text, negative_prompt, style1, style2) {
+	update_token_counter("img2img_token_button")
+	return [prompt_text, negative_prompt, style1, style2]
 }
 
 function update_token_counter(button_id) {
@@ -212,3 +221,10 @@ function update_token_counter(button_id) {
 		clearTimeout(token_timeout);
 	token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
 }
+function submit_prompt(event, generate_button_id) {
+    if (event.altKey && event.keyCode === 13) {
+        event.preventDefault();
+        gradioApp().getElementById(generate_button_id).click();
+        return;
+    }
+}
\ No newline at end of file
diff --git a/modules/ui.py b/modules/ui.py
index 15572bb0..5eea1860 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -539,6 +539,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
             roll.click(
                 fn=roll_artist,
+                _js="roll_artist_txt2img",
                 inputs=[
                     txt2img_prompt,
                 ],
@@ -743,6 +744,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
             roll.click(
                 fn=roll_artist,
+                _js="roll_artist_img2img",
                 inputs=[
                     img2img_prompt,
                 ],
@@ -753,6 +755,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
             prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
             style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
+            style_js_funcs = ["update_style_txt2img", "update_style_img2img"]
 
             for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
                 button.click(
@@ -764,9 +767,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                     outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
                 )
 
-            for button, (prompt, negative_prompt), (style1, style2) in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns):
+            for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
                 button.click(
                     fn=apply_styles,
+                    _js=js_func,
                     inputs=[prompt, negative_prompt, style1, style2],
                     outputs=[prompt, negative_prompt, style1, style2],
                 )
-- 
cgit v1.2.1


From ff8dc1908af088d0ed43fb85baad662733c5ca9c Mon Sep 17 00:00:00 2001
From: Liam <liamthekerr@gmail.com>
Date: Thu, 29 Sep 2022 15:47:06 -0400
Subject: fixed token counter for prompt editing

---
 modules/ui.py | 20 +++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 5eea1860..6bf28562 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -11,6 +11,7 @@ import time
 import traceback
 import platform
 import subprocess as sp
+from functools import reduce
 
 import numpy as np
 import torch
@@ -32,6 +33,7 @@ import modules.gfpgan_model
 import modules.codeformer_model
 import modules.styles
 import modules.generation_parameters_copypaste
+from modules.prompt_parser import get_learned_conditioning_prompt_schedules
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
 mimetypes.init()
@@ -345,8 +347,11 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
         outputs=[seed, dummy_component]
     )
 
-def update_token_counter(text):
-    tokens, token_count, max_length = model_hijack.tokenize(text)
+def update_token_counter(text, steps):
+    prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
+    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
+    prompts = [prompt_text for step,prompt_text in flat_prompts]
+    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
     style_class = ' class="red"' if (token_count > max_length) else ""
     return f"<span {style_class}>{token_count}/{max_length}</span>"
 
@@ -364,8 +369,7 @@ def create_toprow(is_img2img):
                     roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
                     paste = gr.Button(value=paste_symbol, elem_id="paste")
                     token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
-                    hidden_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-                    hidden_button.click(fn=update_token_counter, inputs=[prompt], outputs=[token_counter])
+                    token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
 
                 with gr.Column(scale=10, elem_id="style_pos_col"):
                     prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
@@ -396,7 +400,7 @@ def create_toprow(is_img2img):
                 prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
                 save_style = gr.Button('Create style', elem_id="style_create")
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste, token_counter, token_button
 
 
 def setup_progressbar(progressbar, preview, id_part):
@@ -419,7 +423,7 @@ def setup_progressbar(progressbar, preview, id_part):
 
 def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste = create_toprow(is_img2img=False)
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
 
         with gr.Row(elem_id='txt2img_progress_row'):
@@ -568,9 +572,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
+            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste = create_toprow(is_img2img=True)
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
 
         with gr.Row(elem_id='img2img_progress_row'):
             with gr.Column(scale=1):
@@ -793,6 +798,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 (denoising_strength, "Denoising strength"),
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, img2img_paste_fields, img2img_prompt)
+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
-- 
cgit v1.2.1


From 3c6a049fc3c6b54ada3736710a7e86663ea7f3d9 Mon Sep 17 00:00:00 2001
From: Liam <liamthekerr@gmail.com>
Date: Fri, 30 Sep 2022 12:12:44 -0400
Subject: consolidated token counter functions

---
 javascript/ui.js | 21 +++++++++------------
 modules/ui.py    |  6 +++---
 2 files changed, 12 insertions(+), 15 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 88fd45ae..f94ed081 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -199,21 +199,18 @@ let txt2img_textarea, img2img_textarea = undefined;
 let wait_time = 800
 let token_timeout;
 
-function roll_artist_txt2img(prompt_text) {
+function update_txt2img_tokens(...args) {
 	update_token_counter("txt2img_token_button")
-	return prompt_text;
+	if (args.length == 2)
+		return args[0]
+	return args;
 }
-function roll_artist_img2img(prompt_text) {
-	update_token_counter("img2img_token_button")
-	return prompt_text;
-}
-function update_style_txt2img(prompt_text, negative_prompt, style1, style2) {
-	update_token_counter("txt2img_token_button")
-	return [prompt_text, negative_prompt, style1, style2]
-}
-function update_style_img2img(prompt_text, negative_prompt, style1, style2) {
+
+function update_img2img_tokens(...args) {
 	update_token_counter("img2img_token_button")
-	return [prompt_text, negative_prompt, style1, style2]
+	if (args.length == 2)
+		return args[0]
+	return args;
 }
 
 function update_token_counter(button_id) {
diff --git a/modules/ui.py b/modules/ui.py
index 6bf28562..40c08984 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -543,7 +543,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
             roll.click(
                 fn=roll_artist,
-                _js="roll_artist_txt2img",
+                _js="update_txt2img_tokens",
                 inputs=[
                     txt2img_prompt,
                 ],
@@ -749,7 +749,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
             roll.click(
                 fn=roll_artist,
-                _js="roll_artist_img2img",
+                _js="update_img2img_tokens",
                 inputs=[
                     img2img_prompt,
                 ],
@@ -760,7 +760,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
 
             prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
             style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
-            style_js_funcs = ["update_style_txt2img", "update_style_img2img"]
+            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
 
             for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
                 button.click(
-- 
cgit v1.2.1


From bdaa36c84470adbdce3e98c01a69af5e95adfb02 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Fri, 30 Sep 2022 23:53:25 -0400
Subject: When device is MPS, use CPU for GFPGAN instead

GFPGAN will not work if the device is MPS, so default to CPU instead.
---
 modules/devices.py      | 2 +-
 modules/gfpgan_model.py | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 07bb2339..08bb26d6 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -34,7 +34,7 @@ errors.run(enable_tf32, "Enabling TF32")
 
 
 device = get_optimal_device()
-device_codeformer = cpu if has_mps else device
+device_gfpgan = device_codeformer = cpu if device.type == 'mps' else device
 
 
 def randn(seed, shape):
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index bb30d733..fcd8544a 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -21,7 +21,7 @@ def gfpgann():
     global loaded_gfpgan_model
     global model_path
     if loaded_gfpgan_model is not None:
-        loaded_gfpgan_model.gfpgan.to(shared.device)
+        loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
         return loaded_gfpgan_model
 
     if gfpgan_constructor is None:
@@ -36,8 +36,8 @@ def gfpgann():
     else:
         print("Unable to load gfpgan model!")
         return None
-    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
-    model.gfpgan.to(shared.device)
+    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
+    model.gfpgan.to(devices.device_gfpgan)
     loaded_gfpgan_model = model
 
     return model
-- 
cgit v1.2.1


From 27fbf3de4adf6ba8dfa43876db3599bb8159ef44 Mon Sep 17 00:00:00 2001
From: shirase-0 <ultra.gamer.ebf@gmail.com>
Date: Sun, 2 Oct 2022 00:43:24 +1000
Subject: Added tag parsing for prompts from file

---
 scripts/prompts_from_file.py | 58 +++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 57 insertions(+), 1 deletion(-)

diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 513d9a1c..36e199b3 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -2,6 +2,7 @@ import math
 import os
 import sys
 import traceback
+from xml.etree.ElementTree import tostring
 
 import modules.scripts as scripts
 import gradio as gr
@@ -29,6 +30,44 @@ class Script(scripts.Script):
         checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt])
         return [checkbox_txt, file, prompt_txt]
 
+    def process_string_tag(self, tag):
+        return tag[1:-2]
+
+    def process_int_tag(self, tag):
+        return int(tag)
+
+    def process_float_tag(self, tag):
+        return float(tag)
+
+    def process_boolean_tag(self, tag):
+        return True if (tag == "true") else False
+
+    prompt_tags = {
+        "sd_model": None,
+        "outpath_samples": process_string_tag,
+        "outpath_grids": process_string_tag,
+        "prompt_for_display": process_string_tag,
+        "prompt": process_string_tag,
+        "negative_prompt": process_string_tag,
+        "styles": process_string_tag,
+        "seed": process_int_tag,
+        "subseed_strength": process_float_tag,
+        "subseed": process_int_tag,
+        "seed_resize_from_h": process_int_tag,
+        "seed_resize_from_w": process_int_tag,
+        "sampler_index": process_int_tag,
+        "batch_size": process_int_tag,
+        "n_iter": process_int_tag,
+        "steps": process_int_tag,
+        "cfg_scale": process_float_tag,
+        "width": process_int_tag,
+        "height": process_int_tag,
+        "restore_faces": process_boolean_tag,
+        "tiling": process_boolean_tag,
+        "do_not_save_samples": process_boolean_tag,
+        "do_not_save_grid": process_boolean_tag
+    }
+
     def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
         if (checkbox_txt):
             lines = [x.strip() for x in prompt_txt.splitlines()]
@@ -39,6 +78,7 @@ class Script(scripts.Script):
         img_count = len(lines) * p.n_iter
         batch_count = math.ceil(img_count / p.batch_size)
         loop_count = math.ceil(batch_count / p.n_iter)
+        # These numbers no longer accurately reflect the total images and number of batches
         print(f"Will process {img_count} images in {batch_count} batches.")
 
         p.do_not_save_grid = True
@@ -48,7 +88,23 @@ class Script(scripts.Script):
         images = []
         for loop_no in range(loop_count):
             state.job = f"{loop_no + 1} out of {loop_count}"
-            p.prompt = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter
+            # The following line may need revising to remove batch_size references
+            current_line = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter
+            if(current_line[0][:2] != "--"):
+                p.prompt = current_line
+            else:
+                tokenized_line = current_line[0].split("--")
+
+                for tag in tokenized_line:
+                    tag_split = tag.split(" ", 1)
+                    if(tag_split[0] != ''):
+                        value_func = self.prompt_tags.get(tag_split[0], None)
+                        if(value_func != None):
+                            value = value_func(self, tag_split[1])
+                            setattr(p, tag_split[0], value)
+                        else:
+                            print(f"Unknown option \"{tag_split}\"")
+
             proc = process_images(p)
             images += proc.images
 
-- 
cgit v1.2.1


From 0e77ee24b0b651d6a564245243850e4fb9831e31 Mon Sep 17 00:00:00 2001
From: shirase-0 <ultra.gamer.ebf@gmail.com>
Date: Sun, 2 Oct 2022 00:57:29 +1000
Subject: Removed unnecessary library call and added some comments

---
 scripts/prompts_from_file.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 36e199b3..0a862a5b 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -2,7 +2,6 @@ import math
 import os
 import sys
 import traceback
-from xml.etree.ElementTree import tostring
 
 import modules.scripts as scripts
 import gradio as gr
@@ -90,6 +89,8 @@ class Script(scripts.Script):
             state.job = f"{loop_no + 1} out of {loop_count}"
             # The following line may need revising to remove batch_size references
             current_line = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter
+
+            # If the current line has no tags, parse the whole line as a prompt, else parse each tag
             if(current_line[0][:2] != "--"):
                 p.prompt = current_line
             else:
-- 
cgit v1.2.1


From 4c2478a68a4f11959fe4887d38e0436eac19f97e Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 1 Oct 2022 18:30:53 +0100
Subject: add script reload method

---
 modules/scripts.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/modules/scripts.py b/modules/scripts.py
index 7c3bd5e7..3c14b9e3 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -165,3 +165,12 @@ class ScriptRunner:
 
 scripts_txt2img = ScriptRunner()
 scripts_img2img = ScriptRunner()
+
+def reload_scripts(basedir):
+  global scripts_txt2img,scripts_img2img
+
+  scripts_data.clear()
+  load_scripts(basedir)
+
+  scripts_txt2img = ScriptRunner()
+  scripts_img2img = ScriptRunner()
-- 
cgit v1.2.1


From 95f35d04ab1636e08f69ca9c0ae2446714870e80 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 1 Oct 2022 18:31:58 +0100
Subject: Host busy thread, check for reload

---
 webui.py | 48 ++++++++++++++++++++++++++++++++----------------
 1 file changed, 32 insertions(+), 16 deletions(-)

diff --git a/webui.py b/webui.py
index b8cccd54..4948c394 100644
--- a/webui.py
+++ b/webui.py
@@ -86,22 +86,38 @@ def webui():
 
     signal.signal(signal.SIGINT, sigint_handler)
 
-    demo = modules.ui.create_ui(
-        txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
-        img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
-        run_extras=wrap_gradio_gpu_call(modules.extras.run_extras),
-        run_pnginfo=modules.extras.run_pnginfo,
-        run_modelmerger=modules.extras.run_modelmerger
-    )
-
-    demo.launch(
-        share=cmd_opts.share,
-        server_name="0.0.0.0" if cmd_opts.listen else None,
-        server_port=cmd_opts.port,
-        debug=cmd_opts.gradio_debug,
-        auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
-        inbrowser=cmd_opts.autolaunch,
-    )
+    while 1:
+
+      demo = modules.ui.create_ui(
+          txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
+          img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
+          run_extras=wrap_gradio_gpu_call(modules.extras.run_extras),
+          run_pnginfo=modules.extras.run_pnginfo,
+          run_modelmerger=modules.extras.run_modelmerger
+      )
+
+
+      demo.launch(
+          share=cmd_opts.share,
+          server_name="0.0.0.0" if cmd_opts.listen else None,
+          server_port=cmd_opts.port,
+          debug=cmd_opts.gradio_debug,
+          auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
+          inbrowser=cmd_opts.autolaunch,
+          prevent_thread_lock=True
+      )
+
+      while 1:
+        time.sleep(0.5)
+        if getattr(demo,'do_restart',False):
+          time.sleep(0.5)
+          demo.close()
+          time.sleep(0.5)
+          break
+
+      print('Reloading Scripts')
+      modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
+      print('Restarting Gradio')
 
 
 if __name__ == "__main__":
-- 
cgit v1.2.1


From 4f8490cd5630823ac44de8b5c5e4325bdbbea7fa Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 1 Oct 2022 18:33:31 +0100
Subject: add restart button

---
 modules/ui.py | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 15572bb0..ec6aaa28 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1002,6 +1002,17 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             _js='function(){}'
         )
 
+        def request_restart():
+          settings_interface.gradio_ref.do_restart = True
+
+        restart_gradio = gr.Button(value='Restart Gradio and Refresh Scripts')
+        restart_gradio.click(
+            fn=request_restart,
+            inputs=[],
+            outputs=[],
+            _js='function(){document.body.innerHTML=\'<h1 style="color:white;text-align: center;">Reloading</h1>\';setTimeout(function(){location.reload()},2000)}'
+        )
+        
         if column is not None:
             column.__exit__()
 
@@ -1026,7 +1037,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         css += css_hide_progressbar
 
     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
-
+        
+        settings_interface.gradio_ref = demo
+        
         with gr.Tabs() as tabs:
             for interface, label, ifid in interfaces:
                 with gr.TabItem(label, id=ifid):
-- 
cgit v1.2.1


From 121ed7d36febe94995774973b5edc1ba2ba84aad Mon Sep 17 00:00:00 2001
From: Alexandre Simard <as.simard@outlook.com>
Date: Sat, 1 Oct 2022 14:04:20 -0400
Subject: Add progress bar for SwinIR in cmd

I do not know how to add them to the UI...
---
 modules/swinir_model.py | 27 +++++++++++++++------------
 webui-user.bat          |  2 +-
 2 files changed, 16 insertions(+), 13 deletions(-)

diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index 41fda5a7..9bd454c6 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -5,6 +5,7 @@ import numpy as np
 import torch
 from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
+from tqdm import tqdm
 
 from modules import modelloader
 from modules.paths import models_path
@@ -122,18 +123,20 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
     E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
     W = torch.zeros_like(E, dtype=torch.half, device=device)
 
-    for h_idx in h_idx_list:
-        for w_idx in w_idx_list:
-            in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
-            out_patch = model(in_patch)
-            out_patch_mask = torch.ones_like(out_patch)
-
-            E[
-            ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-            ].add_(out_patch)
-            W[
-            ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-            ].add_(out_patch_mask)
+    with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
+        for h_idx in h_idx_list:
+            for w_idx in w_idx_list:
+                in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+                out_patch = model(in_patch)
+                out_patch_mask = torch.ones_like(out_patch)
+
+                E[
+                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                ].add_(out_patch)
+                W[
+                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                ].add_(out_patch_mask)
+                pbar.update(1)
     output = E.div_(W)
 
     return output
diff --git a/webui-user.bat b/webui-user.bat
index e5a257be..5c778953 100644
--- a/webui-user.bat
+++ b/webui-user.bat
@@ -3,6 +3,6 @@
 set PYTHON=
 set GIT=
 set VENV_DIR=
-set COMMANDLINE_ARGS=
+set COMMANDLINE_ARGS=--autolaunch
 
 call webui.bat
-- 
cgit v1.2.1


From b8a2b0453b62e4e99d0e5c049313402bc79056b5 Mon Sep 17 00:00:00 2001
From: Alexandre Simard <as.simard@outlook.com>
Date: Sat, 1 Oct 2022 14:07:20 -0400
Subject: Set launch options to default

---
 webui-user.bat | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/webui-user.bat b/webui-user.bat
index 5c778953..e5a257be 100644
--- a/webui-user.bat
+++ b/webui-user.bat
@@ -3,6 +3,6 @@
 set PYTHON=
 set GIT=
 set VENV_DIR=
-set COMMANDLINE_ARGS=--autolaunch
+set COMMANDLINE_ARGS=
 
 call webui.bat
-- 
cgit v1.2.1


From a9044475c06204deb886d2a69467d0d3a9f5c9be Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 1 Oct 2022 21:47:42 +0100
Subject: add time import

---
 webui.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/webui.py b/webui.py
index 4948c394..e2c4c2ba 100644
--- a/webui.py
+++ b/webui.py
@@ -1,5 +1,6 @@
 import os
 import threading
+import time
 
 from modules import devices
 from modules.paths import script_path
-- 
cgit v1.2.1


From afaa03c5fd05f48ed9c9f15558ea6f0bc4f61628 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 1 Oct 2022 22:43:45 +0100
Subject: add redefinition guard to gradio_routes_templates_response

---
 modules/ui.py | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index ec6aaa28..fd057916 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1219,12 +1219,13 @@ for filename in sorted(os.listdir(jsdir)):
         javascript += f"\n<script>{jsfile.read()}</script>"
 
 
-def template_response(*args, **kwargs):
-    res = gradio_routes_templates_response(*args, **kwargs)
-    res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))
-    res.init_headers()
-    return res
+if 'gradio_routes_templates_response' not in globals():
+  def template_response(*args, **kwargs):
+      res = gradio_routes_templates_response(*args, **kwargs)
+      res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))
+      res.init_headers()
+      return res
 
+  gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
+  gradio.routes.templates.TemplateResponse = template_response
 
-gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
-gradio.routes.templates.TemplateResponse = template_response
-- 
cgit v1.2.1


From 30f2e3565840544dd66470c6ef216ec664db6432 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 1 Oct 2022 22:50:03 +0100
Subject: add importlib.reload

---
 webui.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/webui.py b/webui.py
index e2c4c2ba..ab200045 100644
--- a/webui.py
+++ b/webui.py
@@ -1,7 +1,7 @@
 import os
 import threading
 import time
-
+import importlib
 from modules import devices
 from modules.paths import script_path
 import signal
@@ -116,8 +116,10 @@ def webui():
           time.sleep(0.5)
           break
 
-      print('Reloading Scripts')
+      print('Reloading Custom Scripts')
       modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
+      print('Reloading modules: modules.ui')
+      importlib.reload(modules.ui)
       print('Restarting Gradio')
 
 
-- 
cgit v1.2.1


From 6048002dade91b82b1ce9fea3c6ff5b5c1f8c990 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 1 Oct 2022 23:10:07 +0100
Subject: Add scope warning to refresh button

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index fd057916..72846a12 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1005,7 +1005,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         def request_restart():
           settings_interface.gradio_ref.do_restart = True
 
-        restart_gradio = gr.Button(value='Restart Gradio and Refresh Scripts')
+        restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
         restart_gradio.click(
             fn=request_restart,
             inputs=[],
-- 
cgit v1.2.1


From 027c5aae5546ff3650347cb3c2b87df4415ab900 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 1 Oct 2022 23:29:26 +0100
Subject: update reloading message style

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 72846a12..7b2359c2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1010,7 +1010,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             fn=request_restart,
             inputs=[],
             outputs=[],
-            _js='function(){document.body.innerHTML=\'<h1 style="color:white;text-align: center;">Reloading</h1>\';setTimeout(function(){location.reload()},2000)}'
+            _js='function(){document.body.innerHTML=\'<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>\';setTimeout(function(){location.reload()},2000)}'
         )
         
         if column is not None:
-- 
cgit v1.2.1


From 55b046312c51bb7b2329d3b5b7f1c05956f821bf Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 2 Oct 2022 00:12:49 +0100
Subject: move JavaScript into ui.js

---
 javascript/ui.js | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/javascript/ui.js b/javascript/ui.js
index bfe02410..e8f289b4 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -212,3 +212,8 @@ function update_token_counter(button_id) {
 		clearTimeout(token_timeout);
 	token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
 }
+
+function restart_reload(){
+    document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
+    setTimeout(function(){location.reload()},2000)
+}
-- 
cgit v1.2.1


From 0aa354bd5e811e2b41b17a3052cf5d4c8190d533 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 2 Oct 2022 00:13:47 +0100
Subject: remove styling from python side

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 7b2359c2..cb859ac4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1010,7 +1010,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             fn=request_restart,
             inputs=[],
             outputs=[],
-            _js='function(){document.body.innerHTML=\'<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>\';setTimeout(function(){location.reload()},2000)}'
+            _js='function(){restart_reload()}'
         )
         
         if column is not None:
-- 
cgit v1.2.1


From cf33268d686986a24f2e04eb615f01ed53bfe308 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 2 Oct 2022 01:18:42 +0100
Subject: add script body only refresh

---
 modules/scripts.py | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/modules/scripts.py b/modules/scripts.py
index 3c14b9e3..788397f5 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -162,10 +162,33 @@ class ScriptRunner:
 
         return processed
 
+    def reload_sources(self):
+      for si,script in list(enumerate(self.scripts)):
+        with open(script.filename, "r", encoding="utf8") as file:
+            args_from = script.args_from
+            args_to   = script.args_to 
+            filename  = script.filename
+            text = file.read()
+
+            from types import ModuleType
+            compiled = compile(text, filename, 'exec')
+            module = ModuleType(script.filename)
+            exec(compiled, module.__dict__)
+
+            for key, script_class in module.__dict__.items():
+                if type(script_class) == type and issubclass(script_class, Script):
+                    self.scripts[si] = script_class()
+                    self.scripts[si].filename = filename
+                    self.scripts[si].args_from = args_from
+                    self.scripts[si].args_to = args_to
 
 scripts_txt2img = ScriptRunner()
 scripts_img2img = ScriptRunner()
 
+def reload_script_body_only():
+  scripts_txt2img.reload_sources()
+  scripts_img2img.reload_sources()
+
 def reload_scripts(basedir):
   global scripts_txt2img,scripts_img2img
 
-- 
cgit v1.2.1


From 07e40ad7f23472fc1c781fe1cc6c1ee403413918 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 2 Oct 2022 01:19:55 +0100
Subject: add custom script body only refresh option

---
 modules/ui.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/modules/ui.py b/modules/ui.py
index cb859ac4..eb7c0585 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1012,6 +1012,17 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             outputs=[],
             _js='function(){restart_reload()}'
         )
+
+        def reload_scripts():
+          modules.scripts.reload_script_body_only()
+
+        reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='primary')
+        reload_script_bodies.click(
+            fn=reload_scripts,
+            inputs=[],
+            outputs=[],
+            _js='function(){}'
+        )
         
         if column is not None:
             column.__exit__()
-- 
cgit v1.2.1


From 2deea867814272f1f089b60e9ba8d587c16b2fb1 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 2 Oct 2022 01:36:30 +0100
Subject: Put reload buttons in row and add secondary style

---
 modules/ui.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index eb7c0585..963a2c61 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1002,27 +1002,30 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             _js='function(){}'
         )
 
-        def request_restart():
-          settings_interface.gradio_ref.do_restart = True
+        with gr.Row():
+          reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
+          restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
 
-        restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
-        restart_gradio.click(
-            fn=request_restart,
-            inputs=[],
-            outputs=[],
-            _js='function(){restart_reload()}'
-        )
 
         def reload_scripts():
           modules.scripts.reload_script_body_only()
 
-        reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='primary')
         reload_script_bodies.click(
             fn=reload_scripts,
             inputs=[],
             outputs=[],
             _js='function(){}'
         )
+
+        def request_restart():
+          settings_interface.gradio_ref.do_restart = True
+
+        restart_gradio.click(
+            fn=request_restart,
+            inputs=[],
+            outputs=[],
+            _js='function(){restart_reload()}'
+        )
         
         if column is not None:
             column.__exit__()
-- 
cgit v1.2.1


From 3cf1a96006daffedb8ecd0ae142eca4c4da06105 Mon Sep 17 00:00:00 2001
From: RnDMonkey <ryandrutledge@gmail.com>
Date: Sat, 1 Oct 2022 21:11:03 -0700
Subject: added safety for blank directory naming patterns

---
 modules/images.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index f1aed5d6..e7894b4c 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -311,7 +311,7 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = x.replace("[cfg]", str(p.cfg_scale))
         x = x.replace("[width]", str(p.width))
         x = x.replace("[height]", str(p.height))
-        x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]), replace_spaces=False))
+        x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "No styles", replace_spaces=False))
         x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
 
     x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
@@ -374,7 +374,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
 
     if save_to_dirs:
-        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt)
+        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ ')
         path = os.path.join(path, dirname)
 
     os.makedirs(path, exist_ok=True)
-- 
cgit v1.2.1


From 70f526704721a303ae045f6406439dcceee4302e Mon Sep 17 00:00:00 2001
From: RnDMonkey <ryandrutledge@gmail.com>
Date: Sat, 1 Oct 2022 21:18:15 -0700
Subject: use os.path.normpath for better safety checking

---
 modules/images.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index e7894b4c..5ef7eb92 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -374,8 +374,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
 
     if save_to_dirs:
-        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ ')
-        path = os.path.join(path, dirname)
+        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt)
+        path = os.path.normpath(os.path.join(path, dirname))
 
     os.makedirs(path, exist_ok=True)
 
-- 
cgit v1.2.1


From 32edf1732f27a1fad5133667c22b948adda1b070 Mon Sep 17 00:00:00 2001
From: RnDMonkey <ryandrutledge@gmail.com>
Date: Sat, 1 Oct 2022 21:37:14 -0700
Subject: os.path.normpath wasn't working, reverting to manual strip

---
 modules/images.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index 5ef7eb92..4998e92c 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -374,8 +374,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
 
     if save_to_dirs:
-        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt)
-        path = os.path.normpath(os.path.join(path, dirname))
+        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /')
+        path = os.path.join(path, dirname)
 
     os.makedirs(path, exist_ok=True)
 
-- 
cgit v1.2.1


From 820f1dc96b1979d7e92170c161db281ee8bd988b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 15:03:39 +0300
Subject: initial support for training textual inversion

---
 .gitignore                                        |   1 +
 javascript/progressbar.js                         |   1 +
 javascript/textualInversion.js                    |   8 +
 modules/devices.py                                |   3 +-
 modules/processing.py                             |  13 +-
 modules/sd_hijack.py                              | 324 ++++------------------
 modules/sd_hijack_optimizations.py                | 164 +++++++++++
 modules/sd_models.py                              |   4 +-
 modules/shared.py                                 |   3 +-
 modules/textual_inversion/dataset.py              |  76 +++++
 modules/textual_inversion/textual_inversion.py    | 258 +++++++++++++++++
 modules/textual_inversion/ui.py                   |  32 +++
 modules/ui.py                                     | 139 ++++++++--
 style.css                                         |  10 +-
 textual_inversion_templates/style.txt             |  19 ++
 textual_inversion_templates/style_filewords.txt   |  19 ++
 textual_inversion_templates/subject.txt           |  27 ++
 textual_inversion_templates/subject_filewords.txt |  27 ++
 webui.py                                          |  15 +-
 19 files changed, 828 insertions(+), 315 deletions(-)
 create mode 100644 javascript/textualInversion.js
 create mode 100644 modules/sd_hijack_optimizations.py
 create mode 100644 modules/textual_inversion/dataset.py
 create mode 100644 modules/textual_inversion/textual_inversion.py
 create mode 100644 modules/textual_inversion/ui.py
 create mode 100644 textual_inversion_templates/style.txt
 create mode 100644 textual_inversion_templates/style_filewords.txt
 create mode 100644 textual_inversion_templates/subject.txt
 create mode 100644 textual_inversion_templates/subject_filewords.txt

diff --git a/.gitignore b/.gitignore
index 3532dab3..7afc9395 100644
--- a/.gitignore
+++ b/.gitignore
@@ -25,3 +25,4 @@ __pycache__
 /.idea
 notification.mp3
 /SwinIR
+/textual_inversion
diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 21f25b38..1e297abb 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -30,6 +30,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_inte
 onUiUpdate(function(){
     check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery')
     check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery')
+    check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', 'ti_interrupt', 'ti_preview', 'ti_gallery')
 })
 
 function requestMoreProgress(id_part, id_progressbar_span, id_interrupt){
diff --git a/javascript/textualInversion.js b/javascript/textualInversion.js
new file mode 100644
index 00000000..8061be08
--- /dev/null
+++ b/javascript/textualInversion.js
@@ -0,0 +1,8 @@
+
+
+function start_training_textual_inversion(){
+    requestProgress('ti')
+    gradioApp().querySelector('#ti_error').innerHTML=''
+
+    return args_to_array(arguments)
+}
diff --git a/modules/devices.py b/modules/devices.py
index 07bb2339..ff82f2f6 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -32,10 +32,9 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-
 device = get_optimal_device()
 device_codeformer = cpu if has_mps else device
-
+dtype = torch.float16
 
 def randn(seed, shape):
     # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
diff --git a/modules/processing.py b/modules/processing.py
index 7eeb5191..8223423a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -56,7 +56,7 @@ class StableDiffusionProcessing:
         self.prompt: str = prompt
         self.prompt_for_display: str = None
         self.negative_prompt: str = (negative_prompt or "")
-        self.styles: str = styles
+        self.styles: list = styles or []
         self.seed: int = seed
         self.subseed: int = subseed
         self.subseed_strength: float = subseed_strength
@@ -271,7 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
-        "Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
+        "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
     }
 
     generation_params.update(p.extra_generation_params)
@@ -295,8 +295,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     fix_seed(p)
 
-    os.makedirs(p.outpath_samples, exist_ok=True)
-    os.makedirs(p.outpath_grids, exist_ok=True)
+    if p.outpath_samples is not None:
+        os.makedirs(p.outpath_samples, exist_ok=True)
+
+    if p.outpath_grids is not None:
+        os.makedirs(p.outpath_grids, exist_ok=True)
 
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
 
@@ -323,7 +326,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
 
     if os.path.exists(cmd_opts.embeddings_dir):
-        model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)
+        model_hijack.embedding_db.load_textual_inversion_embeddings()
 
     infotexts = []
     output_images = []
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index fa7eaeb8..fd57e5c5 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -6,244 +6,41 @@ import torch
 import numpy as np
 from torch import einsum
 
-from modules import prompt_parser
+import modules.textual_inversion.textual_inversion
+from modules import prompt_parser, devices, sd_hijack_optimizations, shared
 from modules.shared import opts, device, cmd_opts
 
-from ldm.util import default
-from einops import rearrange
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 
+attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
+diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
+diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
 
-# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
-def split_cross_attention_forward_v1(self, x, context=None, mask=None):
-    h = self.heads
 
-    q = self.to_q(x)
-    context = default(context, x)
-    k = self.to_k(context)
-    v = self.to_v(context)
-    del context, x
+def apply_optimizations():
+    if cmd_opts.opt_split_attention_v1:
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
+        ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
 
-    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
-    for i in range(0, q.shape[0], 2):
-        end = i + 2
-        s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
-        s1 *= self.scale
+def undo_optimizations():
+    ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward
+    ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
+    ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
-        s2 = s1.softmax(dim=-1)
-        del s1
-
-        r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
-        del s2
-
-    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
-    del r1
-
-    return self.to_out(r2)
-
-
-# taken from https://github.com/Doggettx/stable-diffusion
-def split_cross_attention_forward(self, x, context=None, mask=None):
-    h = self.heads
-
-    q_in = self.to_q(x)
-    context = default(context, x)
-    k_in = self.to_k(context) * self.scale
-    v_in = self.to_v(context)
-    del context, x
-
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
-    del q_in, k_in, v_in
-
-    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-
-    stats = torch.cuda.memory_stats(q.device)
-    mem_active = stats['active_bytes.all.current']
-    mem_reserved = stats['reserved_bytes.all.current']
-    mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
-    mem_free_torch = mem_reserved - mem_active
-    mem_free_total = mem_free_cuda + mem_free_torch
-
-    gb = 1024 ** 3
-    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
-    modifier = 3 if q.element_size() == 2 else 2.5
-    mem_required = tensor_size * modifier
-    steps = 1
-
-    if mem_required > mem_free_total:
-        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
-        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
-        #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
-
-    if steps > 64:
-        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
-        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
-                           f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
-
-    slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
-    for i in range(0, q.shape[1], slice_size):
-        end = i + slice_size
-        s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
-
-        s2 = s1.softmax(dim=-1, dtype=q.dtype)
-        del s1
-
-        r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
-        del s2
-
-    del q, k, v
-
-    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
-    del r1
-
-    return self.to_out(r2)
-
-def nonlinearity_hijack(x):
-    # swish
-    t = torch.sigmoid(x)
-    x *= t
-    del t
-
-    return x
-
-def cross_attention_attnblock_forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q1 = self.q(h_)
-        k1 = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b, c, h, w = q1.shape
-
-        q2 = q1.reshape(b, c, h*w)
-        del q1
-
-        q = q2.permute(0, 2, 1)   # b,hw,c
-        del q2
-
-        k = k1.reshape(b, c, h*w) # b,c,hw
-        del k1
-
-        h_ = torch.zeros_like(k, device=q.device)
-
-        stats = torch.cuda.memory_stats(q.device)
-        mem_active = stats['active_bytes.all.current']
-        mem_reserved = stats['reserved_bytes.all.current']
-        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
-        mem_free_torch = mem_reserved - mem_active
-        mem_free_total = mem_free_cuda + mem_free_torch
-
-        tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
-        mem_required = tensor_size * 2.5
-        steps = 1
-
-        if mem_required > mem_free_total:
-            steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
-
-        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
-        for i in range(0, q.shape[1], slice_size):
-            end = i + slice_size
-
-            w1 = torch.bmm(q[:, i:end], k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-            w2 = w1 * (int(c)**(-0.5))
-            del w1
-            w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
-            del w2
-
-            # attend to values
-            v1 = v.reshape(b, c, h*w)
-            w4 = w3.permute(0, 2, 1)   # b,hw,hw (first hw of k, second of q)
-            del w3
-
-            h_[:, :, i:end] = torch.bmm(v1, w4)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-            del v1, w4
-
-        h2 = h_.reshape(b, c, h, w)
-        del h_
-
-        h3 = self.proj_out(h2)
-        del h2
-
-        h3 += x
-
-        return h3
 
 class StableDiffusionModelHijack:
-    ids_lookup = {}
-    word_embeddings = {}
-    word_embeddings_checksums = {}
     fixes = None
     comments = []
-    dir_mtime = None
     layers = None
     circular_enabled = False
     clip = None
 
-    def load_textual_inversion_embeddings(self, dirname, model):
-        mt = os.path.getmtime(dirname)
-        if self.dir_mtime is not None and mt <= self.dir_mtime:
-            return
-
-        self.dir_mtime = mt
-        self.ids_lookup.clear()
-        self.word_embeddings.clear()
-
-        tokenizer = model.cond_stage_model.tokenizer
-
-        def const_hash(a):
-            r = 0
-            for v in a:
-                r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
-            return r
-
-        def process_file(path, filename):
-            name = os.path.splitext(filename)[0]
-
-            data = torch.load(path, map_location="cpu")
-
-            # textual inversion embeddings
-            if 'string_to_param' in data:
-                param_dict = data['string_to_param']
-                if hasattr(param_dict, '_parameters'):
-                    param_dict = getattr(param_dict, '_parameters')  # fix for torch 1.12.1 loading saved file from torch 1.11
-                assert len(param_dict) == 1, 'embedding file has multiple terms in it'
-                emb = next(iter(param_dict.items()))[1]
-            # diffuser concepts
-            elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
-                assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
-
-                emb = next(iter(data.values()))
-                if len(emb.shape) == 1:
-                    emb = emb.unsqueeze(0)
-
-            self.word_embeddings[name] = emb.detach().to(device)
-            self.word_embeddings_checksums[name] = f'{const_hash(emb.reshape(-1)*100)&0xffff:04x}'
-
-            ids = tokenizer([name], add_special_tokens=False)['input_ids'][0]
-
-            first_id = ids[0]
-            if first_id not in self.ids_lookup:
-                self.ids_lookup[first_id] = []
-            self.ids_lookup[first_id].append((ids, name))
-
-        for fn in os.listdir(dirname):
-            try:
-                fullfn = os.path.join(dirname, fn)
-
-                if os.stat(fullfn).st_size == 0:
-                    continue
-
-                process_file(fullfn, fn)
-            except Exception:
-                print(f"Error loading emedding {fn}:", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-                continue
-
-        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
+    embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
 
     def hijack(self, m):
         model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
@@ -253,12 +50,7 @@ class StableDiffusionModelHijack:
 
         self.clip = m.cond_stage_model
 
-        if cmd_opts.opt_split_attention_v1:
-            ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
-        elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-            ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
-            ldm.modules.diffusionmodules.model.nonlinearity = nonlinearity_hijack
-            ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
+        apply_optimizations()
 
         def flatten(el):
             flattened = [flatten(children) for children in el.children()]
@@ -296,7 +88,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def __init__(self, wrapped, hijack):
         super().__init__()
         self.wrapped = wrapped
-        self.hijack = hijack
+        self.hijack: StableDiffusionModelHijack = hijack
         self.tokenizer = wrapped.tokenizer
         self.max_length = wrapped.max_length
         self.token_mults = {}
@@ -317,7 +109,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
-
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
         id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
@@ -339,28 +130,19 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             while i < len(tokens):
                 token = tokens[i]
 
-                possible_matches = self.hijack.ids_lookup.get(token, None)
+                embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
 
-                if possible_matches is None:
+                if embedding is None:
                     remade_tokens.append(token)
                     multipliers.append(weight)
+                    i += 1
                 else:
-                    found = False
-                    for ids, word in possible_matches:
-                        if tokens[i:i + len(ids)] == ids:
-                            emb_len = int(self.hijack.word_embeddings[word].shape[0])
-                            fixes.append((len(remade_tokens), word))
-                            remade_tokens += [0] * emb_len
-                            multipliers += [weight] * emb_len
-                            i += len(ids) - 1
-                            found = True
-                            used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
-                            break
-
-                    if not found:
-                        remade_tokens.append(token)
-                        multipliers.append(weight)
-                i += 1
+                    emb_len = int(embedding.vec.shape[0])
+                    fixes.append((len(remade_tokens), embedding))
+                    remade_tokens += [0] * emb_len
+                    multipliers += [weight] * emb_len
+                    used_custom_terms.append((embedding.name, embedding.checksum()))
+                    i += emb_len
 
         if len(remade_tokens) > maxlen - 2:
             vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
@@ -431,32 +213,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 while i < len(tokens):
                     token = tokens[i]
 
-                    possible_matches = self.hijack.ids_lookup.get(token, None)
+                    embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
 
                     mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
                     if mult_change is not None:
                         mult *= mult_change
-                    elif possible_matches is None:
+                        i += 1
+                    elif embedding is None:
                         remade_tokens.append(token)
                         multipliers.append(mult)
+                        i += 1
                     else:
-                        found = False
-                        for ids, word in possible_matches:
-                            if tokens[i:i+len(ids)] == ids:
-                                emb_len = int(self.hijack.word_embeddings[word].shape[0])
-                                fixes.append((len(remade_tokens), word))
-                                remade_tokens += [0] * emb_len
-                                multipliers += [mult] * emb_len
-                                i += len(ids) - 1
-                                found = True
-                                used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
-                                break
-
-                        if not found:
-                            remade_tokens.append(token)
-                            multipliers.append(mult)
-
-                    i += 1
+                        emb_len = int(embedding.vec.shape[0])
+                        fixes.append((len(remade_tokens), embedding))
+                        remade_tokens += [0] * emb_len
+                        multipliers += [mult] * emb_len
+                        used_custom_terms.append((embedding.name, embedding.checksum()))
+                        i += emb_len
 
                 if len(remade_tokens) > maxlen - 2:
                     vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
@@ -464,6 +237,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     overflowing_words = [vocab.get(int(x), "") for x in ovf]
                     overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
                     hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
+
                 token_count = len(remade_tokens)
                 remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
                 remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
@@ -484,7 +258,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         else:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
 
-
         self.hijack.fixes = hijack_fixes
         self.hijack.comments = hijack_comments
 
@@ -517,14 +290,19 @@ class EmbeddingsWithFixes(torch.nn.Module):
 
         inputs_embeds = self.wrapped(input_ids)
 
-        if batch_fixes is not None:
-            for fixes, tensor in zip(batch_fixes, inputs_embeds):
-                for offset, word in fixes:
-                    emb = self.embeddings.word_embeddings[word]
-                    emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
-                    tensor[offset+1:offset+1+emb_len] = self.embeddings.word_embeddings[word][0:emb_len]
+        if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
+            return inputs_embeds
+
+        vecs = []
+        for fixes, tensor in zip(batch_fixes, inputs_embeds):
+            for offset, embedding in fixes:
+                emb = embedding.vec
+                emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
+                tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])
+
+            vecs.append(tensor)
 
-        return inputs_embeds
+        return torch.stack(vecs)
 
 
 def add_circular_option_to_conv_2d():
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
new file mode 100644
index 00000000..9c079e57
--- /dev/null
+++ b/modules/sd_hijack_optimizations.py
@@ -0,0 +1,164 @@
+import math
+import torch
+from torch import einsum
+
+from ldm.util import default
+from einops import rearrange
+
+
+# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
+def split_cross_attention_forward_v1(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
+    k = self.to_k(context)
+    v = self.to_v(context)
+    del context, x
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
+    for i in range(0, q.shape[0], 2):
+        end = i + 2
+        s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
+        s1 *= self.scale
+
+        s2 = s1.softmax(dim=-1)
+        del s1
+
+        r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
+        del s2
+
+    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
+    del r1
+
+    return self.to_out(r2)
+
+
+# taken from https://github.com/Doggettx/stable-diffusion
+def split_cross_attention_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q_in = self.to_q(x)
+    context = default(context, x)
+    k_in = self.to_k(context) * self.scale
+    v_in = self.to_v(context)
+    del context, x
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    del q_in, k_in, v_in
+
+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+
+    stats = torch.cuda.memory_stats(q.device)
+    mem_active = stats['active_bytes.all.current']
+    mem_reserved = stats['reserved_bytes.all.current']
+    mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
+    mem_free_torch = mem_reserved - mem_active
+    mem_free_total = mem_free_cuda + mem_free_torch
+
+    gb = 1024 ** 3
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
+    modifier = 3 if q.element_size() == 2 else 2.5
+    mem_required = tensor_size * modifier
+    steps = 1
+
+    if mem_required > mem_free_total:
+        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
+        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
+        #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
+
+    if steps > 64:
+        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
+                           f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
+
+    slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+    for i in range(0, q.shape[1], slice_size):
+        end = i + slice_size
+        s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
+
+        s2 = s1.softmax(dim=-1, dtype=q.dtype)
+        del s1
+
+        r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
+        del s2
+
+    del q, k, v
+
+    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
+    del r1
+
+    return self.to_out(r2)
+
+def nonlinearity_hijack(x):
+    # swish
+    t = torch.sigmoid(x)
+    x *= t
+    del t
+
+    return x
+
+def cross_attention_attnblock_forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q1 = self.q(h_)
+        k1 = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, h, w = q1.shape
+
+        q2 = q1.reshape(b, c, h*w)
+        del q1
+
+        q = q2.permute(0, 2, 1)   # b,hw,c
+        del q2
+
+        k = k1.reshape(b, c, h*w) # b,c,hw
+        del k1
+
+        h_ = torch.zeros_like(k, device=q.device)
+
+        stats = torch.cuda.memory_stats(q.device)
+        mem_active = stats['active_bytes.all.current']
+        mem_reserved = stats['reserved_bytes.all.current']
+        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
+        mem_free_torch = mem_reserved - mem_active
+        mem_free_total = mem_free_cuda + mem_free_torch
+
+        tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
+        mem_required = tensor_size * 2.5
+        steps = 1
+
+        if mem_required > mem_free_total:
+            steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
+
+        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+        for i in range(0, q.shape[1], slice_size):
+            end = i + slice_size
+
+            w1 = torch.bmm(q[:, i:end], k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+            w2 = w1 * (int(c)**(-0.5))
+            del w1
+            w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
+            del w2
+
+            # attend to values
+            v1 = v.reshape(b, c, h*w)
+            w4 = w3.permute(0, 2, 1)   # b,hw,hw (first hw of k, second of q)
+            del w3
+
+            h_[:, :, i:end] = torch.bmm(v1, w4)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+            del v1, w4
+
+        h2 = h_.reshape(b, c, h, w)
+        del h_
+
+        h3 = self.proj_out(h2)
+        del h2
+
+        h3 += x
+
+        return h3
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2539f14c..5b3dbdc7 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -8,7 +8,7 @@ from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader
+from modules import shared, modelloader, devices
 from modules.paths import models_path
 
 model_dir = "Stable-diffusion"
@@ -134,6 +134,8 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
     if not shared.cmd_opts.no_half:
         model.half()
 
+    devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
+
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpint = checkpoint_file
 
diff --git a/modules/shared.py b/modules/shared.py
index ac968b2d..ac0bc480 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -78,6 +78,7 @@ class State:
     current_latent = None
     current_image = None
     current_image_sampling_step = 0
+    textinfo = None
 
     def interrupt(self):
         self.interrupted = True
@@ -88,7 +89,7 @@ class State:
         self.current_image_sampling_step = 0
         
     def get_job_timestamp(self):
-        return datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+        return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?
 
 
 state = State()
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
new file mode 100644
index 00000000..7e134a08
--- /dev/null
+++ b/modules/textual_inversion/dataset.py
@@ -0,0 +1,76 @@
+import os
+import numpy as np
+import PIL
+import torch
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+import random
+import tqdm
+
+
+class PersonalizedBase(Dataset):
+    def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
+
+        self.placeholder_token = placeholder_token
+
+        self.size = size
+        self.width = width
+        self.height = height
+        self.flip = transforms.RandomHorizontalFlip(p=flip_p)
+
+        self.dataset = []
+
+        with open(template_file, "r") as file:
+            lines = [x.strip() for x in file.readlines()]
+
+        self.lines = lines
+
+        assert data_root, 'dataset directory not specified'
+
+        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
+        print("Preparing dataset...")
+        for path in tqdm.tqdm(self.image_paths):
+            image = Image.open(path)
+            image = image.convert('RGB')
+            image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
+
+            filename = os.path.basename(path)
+            filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-')
+            filename_tokens = [token for token in filename_tokens if token.isalpha()]
+
+            npimage = np.array(image).astype(np.uint8)
+            npimage = (npimage / 127.5 - 1.0).astype(np.float32)
+
+            torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32)
+            torchdata = torch.moveaxis(torchdata, 2, 0)
+
+            init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
+
+            self.dataset.append((init_latent, filename_tokens))
+
+        self.length = len(self.dataset) * repeats
+
+        self.initial_indexes = np.arange(self.length) % len(self.dataset)
+        self.indexes = None
+        self.shuffle()
+
+    def shuffle(self):
+        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, i):
+        if i % len(self.dataset) == 0:
+            self.shuffle()
+
+        index = self.indexes[i % len(self.indexes)]
+        x, filename_tokens = self.dataset[index]
+
+        text = random.choice(self.lines)
+        text = text.replace("[name]", self.placeholder_token)
+        text = text.replace("[filewords]", ' '.join(filename_tokens))
+
+        return x, text
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
new file mode 100644
index 00000000..c0baaace
--- /dev/null
+++ b/modules/textual_inversion/textual_inversion.py
@@ -0,0 +1,258 @@
+import os
+import sys
+import traceback
+
+import torch
+import tqdm
+import html
+import datetime
+
+from modules import shared, devices, sd_hijack, processing
+import modules.textual_inversion.dataset
+
+
+class Embedding:
+    def __init__(self, vec, name, step=None):
+        self.vec = vec
+        self.name = name
+        self.step = step
+        self.cached_checksum = None
+
+    def save(self, filename):
+        embedding_data = {
+            "string_to_token": {"*": 265},
+            "string_to_param": {"*": self.vec},
+            "name": self.name,
+            "step": self.step,
+        }
+
+        torch.save(embedding_data, filename)
+
+    def checksum(self):
+        if self.cached_checksum is not None:
+            return self.cached_checksum
+
+        def const_hash(a):
+            r = 0
+            for v in a:
+                r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
+            return r
+
+        self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
+        return self.cached_checksum
+
+class EmbeddingDatabase:
+    def __init__(self, embeddings_dir):
+        self.ids_lookup = {}
+        self.word_embeddings = {}
+        self.dir_mtime = None
+        self.embeddings_dir = embeddings_dir
+
+    def register_embedding(self, embedding, model):
+
+        self.word_embeddings[embedding.name] = embedding
+
+        ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0]
+
+        first_id = ids[0]
+        if first_id not in self.ids_lookup:
+            self.ids_lookup[first_id] = []
+        self.ids_lookup[first_id].append((ids, embedding))
+
+        return embedding
+
+    def load_textual_inversion_embeddings(self):
+        mt = os.path.getmtime(self.embeddings_dir)
+        if self.dir_mtime is not None and mt <= self.dir_mtime:
+            return
+
+        self.dir_mtime = mt
+        self.ids_lookup.clear()
+        self.word_embeddings.clear()
+
+        def process_file(path, filename):
+            name = os.path.splitext(filename)[0]
+
+            data = torch.load(path, map_location="cpu")
+
+            # textual inversion embeddings
+            if 'string_to_param' in data:
+                param_dict = data['string_to_param']
+                if hasattr(param_dict, '_parameters'):
+                    param_dict = getattr(param_dict, '_parameters')  # fix for torch 1.12.1 loading saved file from torch 1.11
+                assert len(param_dict) == 1, 'embedding file has multiple terms in it'
+                emb = next(iter(param_dict.items()))[1]
+            # diffuser concepts
+            elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
+                assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
+
+                emb = next(iter(data.values()))
+                if len(emb.shape) == 1:
+                    emb = emb.unsqueeze(0)
+            else:
+                raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
+
+            vec = emb.detach().to(devices.device, dtype=torch.float32)
+            embedding = Embedding(vec, name)
+            embedding.step = data.get('step', None)
+            self.register_embedding(embedding, shared.sd_model)
+
+        for fn in os.listdir(self.embeddings_dir):
+            try:
+                fullfn = os.path.join(self.embeddings_dir, fn)
+
+                if os.stat(fullfn).st_size == 0:
+                    continue
+
+                process_file(fullfn, fn)
+            except Exception:
+                print(f"Error loading emedding {fn}:", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+                continue
+
+        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
+
+    def find_embedding_at_position(self, tokens, offset):
+        token = tokens[offset]
+        possible_matches = self.ids_lookup.get(token, None)
+
+        if possible_matches is None:
+            return None
+
+        for ids, embedding in possible_matches:
+            if tokens[offset:offset + len(ids)] == ids:
+                return embedding
+
+        return None
+
+
+
+def create_embedding(name, num_vectors_per_token):
+    init_text = '*'
+
+    cond_model = shared.sd_model.cond_stage_model
+    embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
+
+    ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
+    embedded = embedding_layer(ids.to(devices.device)).squeeze(0)
+    vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
+
+    for i in range(num_vectors_per_token):
+        vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
+
+    fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
+    assert not os.path.exists(fn), f"file {fn} already exists"
+
+    embedding = Embedding(vec, name)
+    embedding.step = 0
+    embedding.save(fn)
+
+    return fn
+
+
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
+    assert embedding_name, 'embedding not selected'
+
+    shared.state.textinfo = "Initializing textual inversion training..."
+    shared.state.job_count = steps
+
+    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
+
+    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%d-%m"), embedding_name)
+
+    if save_embedding_every > 0:
+        embedding_dir = os.path.join(log_directory, "embeddings")
+        os.makedirs(embedding_dir, exist_ok=True)
+    else:
+        embedding_dir = None
+
+    if create_image_every > 0:
+        images_dir = os.path.join(log_directory, "images")
+        os.makedirs(images_dir, exist_ok=True)
+    else:
+        images_dir = None
+
+    cond_model = shared.sd_model.cond_stage_model
+
+    shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
+    with torch.autocast("cuda"):
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+
+    hijack = sd_hijack.model_hijack
+
+    embedding = hijack.embedding_db.word_embeddings[embedding_name]
+    embedding.vec.requires_grad = True
+
+    optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
+
+    losses = torch.zeros((32,))
+
+    last_saved_file = "<none>"
+    last_saved_image = "<none>"
+
+    ititial_step = embedding.step or 0
+    if ititial_step > steps:
+        return embedding, filename
+
+    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
+    for i, (x, text) in pbar:
+        embedding.step = i + ititial_step
+
+        if embedding.step > steps:
+            break
+
+        if shared.state.interrupted:
+            break
+
+        with torch.autocast("cuda"):
+            c = cond_model([text])
+            loss = shared.sd_model(x.unsqueeze(0), c)[0]
+
+            losses[embedding.step % losses.shape[0]] = loss.item()
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        pbar.set_description(f"loss: {losses.mean():.7f}")
+
+        if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
+            last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
+            embedding.save(last_saved_file)
+
+        if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
+            last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
+
+            p = processing.StableDiffusionProcessingTxt2Img(
+                sd_model=shared.sd_model,
+                prompt=text,
+                steps=20,
+                do_not_save_grid=True,
+                do_not_save_samples=True,
+            )
+
+            processed = processing.process_images(p)
+            image = processed.images[0]
+
+            shared.state.current_image = image
+            image.save(last_saved_image)
+
+            last_saved_image += f", prompt: {text}"
+
+        shared.state.job_no = embedding.step
+
+        shared.state.textinfo = f"""
+<p>
+Loss: {losses.mean():.7f}<br/>
+Step: {embedding.step}<br/>
+Last prompt: {html.escape(text)}<br/>
+Last saved embedding: {html.escape(last_saved_file)}<br/>
+Last saved image: {html.escape(last_saved_image)}<br/>
+</p>
+"""
+
+    embedding.cached_checksum = None
+    embedding.save(filename)
+
+    return embedding, filename
+
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
new file mode 100644
index 00000000..ce3677a9
--- /dev/null
+++ b/modules/textual_inversion/ui.py
@@ -0,0 +1,32 @@
+import html
+
+import gradio as gr
+
+import modules.textual_inversion.textual_inversion as ti
+from modules import sd_hijack, shared
+
+
+def create_embedding(name, nvpt):
+    filename = ti.create_embedding(name, nvpt)
+
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+    return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
+
+
+def train_embedding(*args):
+
+    try:
+        sd_hijack.undo_optimizations()
+
+        embedding, filename = ti.train_embedding(*args)
+
+        res = f"""
+Training {'interrupted' if shared.state.interrupted else 'finished'} after {embedding.step} steps.
+Embedding saved to {html.escape(filename)}
+"""
+        return res, ""
+    except Exception:
+        raise
+    finally:
+        sd_hijack.apply_optimizations()
diff --git a/modules/ui.py b/modules/ui.py
index 15572bb0..57aef6ff 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -21,6 +21,7 @@ import gradio as gr
 import gradio.utils
 import gradio.routes
 
+from modules import sd_hijack
 from modules.paths import script_path
 from modules.shared import opts, cmd_opts
 import modules.shared as shared
@@ -32,6 +33,7 @@ import modules.gfpgan_model
 import modules.codeformer_model
 import modules.styles
 import modules.generation_parameters_copypaste
+import modules.textual_inversion.ui
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
 mimetypes.init()
@@ -142,8 +144,8 @@ def save_files(js_data, images, index):
     return '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
 
-def wrap_gradio_call(func):
-    def f(*args, **kwargs):
+def wrap_gradio_call(func, extra_outputs=None):
+    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
         run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled
         if run_memmon:
             shared.mem_mon.monitor()
@@ -159,7 +161,10 @@ def wrap_gradio_call(func):
             shared.state.job = ""
             shared.state.job_count = 0
 
-            res = [None, '', f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
+            if extra_outputs_array is None:
+                extra_outputs_array = [None, '']
+
+            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
 
         elapsed = time.perf_counter() - t
 
@@ -179,6 +184,7 @@ def wrap_gradio_call(func):
         res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"
 
         shared.state.interrupted = False
+        shared.state.job_count = 0
 
         return tuple(res)
 
@@ -187,7 +193,7 @@ def wrap_gradio_call(func):
 
 def check_progress_call(id_part):
     if shared.state.job_count == 0:
-        return "", gr_show(False), gr_show(False)
+        return "", gr_show(False), gr_show(False), gr_show(False)
 
     progress = 0
 
@@ -219,13 +225,19 @@ def check_progress_call(id_part):
         else:
             preview_visibility = gr_show(True)
 
-    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image
+    if shared.state.textinfo is not None:
+        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
+    else:
+        textinfo_result = gr_show(False)
+
+    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
 
 
 def check_progress_call_initial(id_part):
     shared.state.job_count = -1
     shared.state.current_latent = None
     shared.state.current_image = None
+    shared.state.textinfo = None
 
     return check_progress_call(id_part)
 
@@ -399,13 +411,16 @@ def create_toprow(is_img2img):
     return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste
 
 
-def setup_progressbar(progressbar, preview, id_part):
+def setup_progressbar(progressbar, preview, id_part, textinfo=None):
+    if textinfo is None:
+        textinfo = gr.HTML(visible=False)
+
     check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
     check_progress.click(
         fn=lambda: check_progress_call(id_part),
         show_progress=False,
         inputs=[],
-        outputs=[progressbar, preview, preview],
+        outputs=[progressbar, preview, preview, textinfo],
     )
 
     check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
@@ -413,11 +428,14 @@ def setup_progressbar(progressbar, preview, id_part):
         fn=lambda: check_progress_call_initial(id_part),
         show_progress=False,
         inputs=[],
-        outputs=[progressbar, preview, preview],
+        outputs=[progressbar, preview, preview, textinfo],
     )
 
 
-def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
+def create_ui(wrap_gradio_gpu_call):
+    import modules.img2img
+    import modules.txt2img
+
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
@@ -483,7 +501,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
             txt2img_args = dict(
-                fn=txt2img,
+                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
                 _js="submit",
                 inputs=[
                     txt2img_prompt,
@@ -675,7 +693,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
             )
 
             img2img_args = dict(
-                fn=img2img,
+                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
                 _js="submit_img2img",
                 inputs=[
                     dummy_component,
@@ -828,7 +846,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
 
         submit.click(
-            fn=run_extras,
+            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
             _js="get_extras_tab_index",
             inputs=[
                 dummy_component,
@@ -878,7 +896,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                     pnginfo_send_to_img2img = gr.Button('Send to img2img')
 
         image.change(
-            fn=wrap_gradio_call(run_pnginfo),
+            fn=wrap_gradio_call(modules.extras.run_pnginfo),
             inputs=[image],
             outputs=[html, generation_info, html2],
         )
@@ -887,7 +905,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
                 gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
-                
+
                 with gr.Row():
                     primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name")
                     secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")
@@ -896,10 +914,96 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
                 interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
                 save_as_half = gr.Checkbox(value=False, label="Safe as float16")
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
-            
+
             with gr.Column(variant='panel'):
                 submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
 
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+    with gr.Blocks() as textual_inversion_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column():
+                with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")
+
+                    new_embedding_name = gr.Textbox(label="Name")
+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_embedding = gr.Button(value="Create", variant='primary')
+
+                with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
+                    train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                    learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
+                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+                    steps = gr.Number(label='Max steps', value=100000, precision=0)
+                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=1000, precision=0)
+                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=1000, precision=0)
+
+                    with gr.Row():
+                        with gr.Column(scale=2):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            with gr.Row():
+                                interrupt_training = gr.Button(value="Interrupt")
+                                train_embedding = gr.Button(value="Train", variant='primary')
+
+            with gr.Column():
+                progressbar = gr.HTML(elem_id="ti_progressbar")
+                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
+
+                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
+                ti_preview = gr.Image(elem_id='ti_preview', visible=False)
+                ti_progress = gr.HTML(elem_id="ti_progress", value="")
+                ti_outcome = gr.HTML(elem_id="ti_error", value="")
+                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
+
+        create_embedding.click(
+            fn=modules.textual_inversion.ui.create_embedding,
+            inputs=[
+                new_embedding_name,
+                nvpt,
+            ],
+            outputs=[
+                train_embedding_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        train_embedding.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                train_embedding_name,
+                learn_rate,
+                dataset_directory,
+                log_directory,
+                steps,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        interrupt_training.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
     def create_setting_component(key):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
@@ -1011,6 +1115,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+        (textual_inversion_interface, "Textual inversion", "ti"),
         (settings_interface, "Settings", "settings"),
     ]
 
@@ -1044,11 +1149,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         
         def modelmerger(*args):
             try:
-                results = run_modelmerger(*args)
+                results = modules.extras.run_modelmerger(*args)
             except Exception as e:
                 print("Error loading/saving model file:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
-                modules.sd_models.list_models() #To remove the potentially missing models from the list
+                modules.sd_models.list_models()  # to remove the potentially missing models from the list
                 return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
             return results
 
diff --git a/style.css b/style.css
index 79d6bb0d..39586bf1 100644
--- a/style.css
+++ b/style.css
@@ -157,7 +157,7 @@ button{
     max-width: 10em;
 }
 
-#txt2img_preview, #img2img_preview{
+#txt2img_preview, #img2img_preview, #ti_preview{
     position: absolute;
     width: 320px;
     left: 0;
@@ -172,18 +172,18 @@ button{
 }
 
 @media screen and (min-width: 768px) {
-    #txt2img_preview, #img2img_preview {
+    #txt2img_preview, #img2img_preview, #ti_preview {
         position: absolute;
     }
 }
 
 @media screen and (max-width: 767px) {
-    #txt2img_preview, #img2img_preview {
+    #txt2img_preview, #img2img_preview, #ti_preview {
         position: relative;
     }
 }
 
-#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0{
+#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0, #ti_preview div.left-0.top-0{
     display: none;
 }
 
@@ -247,7 +247,7 @@ input[type="range"]{
 #txt2img_negative_prompt, #img2img_negative_prompt{
 }
 
-#txt2img_progressbar, #img2img_progressbar{
+#txt2img_progressbar, #img2img_progressbar, #ti_progressbar{
   position: absolute;
   z-index: 1000;
   right: 0;
diff --git a/textual_inversion_templates/style.txt b/textual_inversion_templates/style.txt
new file mode 100644
index 00000000..15af2d6b
--- /dev/null
+++ b/textual_inversion_templates/style.txt
@@ -0,0 +1,19 @@
+a painting, art by [name]
+a rendering, art by [name]
+a cropped painting, art by [name]
+the painting, art by [name]
+a clean painting, art by [name]
+a dirty painting, art by [name]
+a dark painting, art by [name]
+a picture, art by [name]
+a cool painting, art by [name]
+a close-up painting, art by [name]
+a bright painting, art by [name]
+a cropped painting, art by [name]
+a good painting, art by [name]
+a close-up painting, art by [name]
+a rendition, art by [name]
+a nice painting, art by [name]
+a small painting, art by [name]
+a weird painting, art by [name]
+a large painting, art by [name]
diff --git a/textual_inversion_templates/style_filewords.txt b/textual_inversion_templates/style_filewords.txt
new file mode 100644
index 00000000..b3a8159a
--- /dev/null
+++ b/textual_inversion_templates/style_filewords.txt
@@ -0,0 +1,19 @@
+a painting of [filewords], art by [name]
+a rendering of [filewords], art by [name]
+a cropped painting of [filewords], art by [name]
+the painting of [filewords], art by [name]
+a clean painting of [filewords], art by [name]
+a dirty painting of [filewords], art by [name]
+a dark painting of [filewords], art by [name]
+a picture of [filewords], art by [name]
+a cool painting of [filewords], art by [name]
+a close-up painting of [filewords], art by [name]
+a bright painting of [filewords], art by [name]
+a cropped painting of [filewords], art by [name]
+a good painting of [filewords], art by [name]
+a close-up painting of [filewords], art by [name]
+a rendition of [filewords], art by [name]
+a nice painting of [filewords], art by [name]
+a small painting of [filewords], art by [name]
+a weird painting of [filewords], art by [name]
+a large painting of [filewords], art by [name]
diff --git a/textual_inversion_templates/subject.txt b/textual_inversion_templates/subject.txt
new file mode 100644
index 00000000..79f36aa0
--- /dev/null
+++ b/textual_inversion_templates/subject.txt
@@ -0,0 +1,27 @@
+a photo of a [name]
+a rendering of a [name]
+a cropped photo of the [name]
+the photo of a [name]
+a photo of a clean [name]
+a photo of a dirty [name]
+a dark photo of the [name]
+a photo of my [name]
+a photo of the cool [name]
+a close-up photo of a [name]
+a bright photo of the [name]
+a cropped photo of a [name]
+a photo of the [name]
+a good photo of the [name]
+a photo of one [name]
+a close-up photo of the [name]
+a rendition of the [name]
+a photo of the clean [name]
+a rendition of a [name]
+a photo of a nice [name]
+a good photo of a [name]
+a photo of the nice [name]
+a photo of the small [name]
+a photo of the weird [name]
+a photo of the large [name]
+a photo of a cool [name]
+a photo of a small [name]
diff --git a/textual_inversion_templates/subject_filewords.txt b/textual_inversion_templates/subject_filewords.txt
new file mode 100644
index 00000000..008652a6
--- /dev/null
+++ b/textual_inversion_templates/subject_filewords.txt
@@ -0,0 +1,27 @@
+a photo of a [name], [filewords]
+a rendering of a [name], [filewords]
+a cropped photo of the [name], [filewords]
+the photo of a [name], [filewords]
+a photo of a clean [name], [filewords]
+a photo of a dirty [name], [filewords]
+a dark photo of the [name], [filewords]
+a photo of my [name], [filewords]
+a photo of the cool [name], [filewords]
+a close-up photo of a [name], [filewords]
+a bright photo of the [name], [filewords]
+a cropped photo of a [name], [filewords]
+a photo of the [name], [filewords]
+a good photo of the [name], [filewords]
+a photo of one [name], [filewords]
+a close-up photo of the [name], [filewords]
+a rendition of the [name], [filewords]
+a photo of the clean [name], [filewords]
+a rendition of a [name], [filewords]
+a photo of a nice [name], [filewords]
+a good photo of a [name], [filewords]
+a photo of the nice [name], [filewords]
+a photo of the small [name], [filewords]
+a photo of the weird [name], [filewords]
+a photo of the large [name], [filewords]
+a photo of a cool [name], [filewords]
+a photo of a small [name], [filewords]
diff --git a/webui.py b/webui.py
index b8cccd54..19fdcdd4 100644
--- a/webui.py
+++ b/webui.py
@@ -12,7 +12,6 @@ import modules.bsrgan_model as bsrgan
 import modules.extras
 import modules.face_restoration
 import modules.gfpgan_model as gfpgan
-import modules.img2img
 import modules.ldsr_model as ldsr
 import modules.lowvram
 import modules.realesrgan_model as realesrgan
@@ -21,7 +20,6 @@ import modules.sd_hijack
 import modules.sd_models
 import modules.shared as shared
 import modules.swinir_model as swinir
-import modules.txt2img
 import modules.ui
 from modules import modelloader
 from modules.paths import script_path
@@ -46,7 +44,7 @@ def wrap_queued_call(func):
     return f
 
 
-def wrap_gradio_gpu_call(func):
+def wrap_gradio_gpu_call(func, extra_outputs=None):
     def f(*args, **kwargs):
         devices.torch_gc()
 
@@ -58,6 +56,7 @@ def wrap_gradio_gpu_call(func):
         shared.state.current_image = None
         shared.state.current_image_sampling_step = 0
         shared.state.interrupted = False
+        shared.state.textinfo = None
 
         with queue_lock:
             res = func(*args, **kwargs)
@@ -69,7 +68,7 @@ def wrap_gradio_gpu_call(func):
 
         return res
 
-    return modules.ui.wrap_gradio_call(f)
+    return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
 
 
 modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
@@ -86,13 +85,7 @@ def webui():
 
     signal.signal(signal.SIGINT, sigint_handler)
 
-    demo = modules.ui.create_ui(
-        txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
-        img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
-        run_extras=wrap_gradio_gpu_call(modules.extras.run_extras),
-        run_pnginfo=modules.extras.run_pnginfo,
-        run_modelmerger=modules.extras.run_modelmerger
-    )
+    demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
 
     demo.launch(
         share=cmd_opts.share,
-- 
cgit v1.2.1


From 0114057ad672a581bd0b598870b58b674b1a3624 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 15:49:42 +0300
Subject: fix incorrect use of glob in modelloader for #1410

---
 modules/modelloader.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/modelloader.py b/modules/modelloader.py
index 8c862b42..015aeafa 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -43,7 +43,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
         for place in places:
             if os.path.exists(place):
                 for file in glob.iglob(place + '**/**', recursive=True):
-                    full_path = os.path.join(place, file)
+                    full_path = file
                     if os.path.isdir(full_path):
                         continue
                     if len(ext_filter) != 0:
-- 
cgit v1.2.1


From 4e72a1aab6d1b3a8d8c09fadc81843a07c05cc18 Mon Sep 17 00:00:00 2001
From: ClashSAN <98228077+ClashSAN@users.noreply.github.com>
Date: Sat, 1 Oct 2022 00:15:43 +0000
Subject: Grammar Fix

---
 README.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 5ded94f9..15e224e8 100644
--- a/README.md
+++ b/README.md
@@ -11,12 +11,12 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - One click install and run script (but you still must install python and git)
 - Outpainting
 - Inpainting
-- Prompt
-- Stable Diffusion upscale
+- Prompt Matrix
+- Stable Diffusion Upscale
 - Attention, specify parts of text that the model should pay more attention to
-    - a man in a ((txuedo)) - will pay more attentinoto tuxedo
-    - a man in a (txuedo:1.21) - alternative syntax
-- Loopback, run img2img procvessing multiple times
+    - a man in a ((tuxedo)) - will pay more attention to tuxedo
+    - a man in a (tuxedo:1.21) - alternative syntax
+- Loopback, run img2img processing multiple times
 - X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
 - Textual Inversion
     - have as many embeddings as you want and use any names you like for them
@@ -35,15 +35,15 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - 4GB video card support (also reports of 2GB working)
 - Correct seeds for batches 
 - Prompt length validation
-     - get length of prompt in tokensas you type
-     - get a warning after geenration if some text was truncated
+     - get length of prompt in tokens as you type
+     - get a warning after generation if some text was truncated
 - Generation parameters
      - parameters you used to generate images are saved with that image
      - in PNG chunks for PNG, in EXIF for JPEG
      - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
      - can be disabled in settings
 - Settings page
-- Running arbitrary python code from UI (must run with commandline flag to enable)
+- Running arbitrary python code from UI (must run with --allow-code to enable)
 - Mouseover hints for most UI elements
 - Possible to change defaults/mix/max/step values for UI elements via text config
 - Random artist button
-- 
cgit v1.2.1


From 0758f6e641b5790ce566a998d43e0ea74a627766 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 17:24:50 +0300
Subject: fix --ckpt option breaking model selection

---
 modules/sd_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5b3dbdc7..9259d69e 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -69,7 +69,7 @@ def list_models():
         h = model_hash(cmd_ckpt)
         title, short_model_name = modeltitle(cmd_ckpt, h)
         checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
-        shared.opts.sd_model_checkpoint = title
+        shared.opts.data['sd_model_checkpoint'] = title
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
     for filename in model_list:
-- 
cgit v1.2.1


From 53a3dc601fb734ce433505b1ca68770919106bad Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 18:21:56 +0300
Subject: move CLIP out of requirements and into launcher to make it possible
 to launch the program offline

---
 launch.py                 | 4 ++++
 requirements.txt          | 2 --
 requirements_versions.txt | 1 -
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/launch.py b/launch.py
index d2793ed2..57405fea 100644
--- a/launch.py
+++ b/launch.py
@@ -15,6 +15,7 @@ requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
 commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
 
 gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
+clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
 
 stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
 taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
@@ -111,6 +112,9 @@ if not skip_torch_cuda_test:
 if not is_installed("gfpgan"):
     run_pip(f"install {gfpgan_package}", "gfpgan")
 
+if not is_installed("clip"):
+    run_pip(f"install {clip_package}", "clip")
+
 os.makedirs(dir_repos, exist_ok=True)
 
 git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
diff --git a/requirements.txt b/requirements.txt
index 7cb9d329..d4b337fc 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -13,14 +13,12 @@ Pillow
 pytorch_lightning
 realesrgan
 scikit-image>=0.19
-git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379
 timm==0.4.12
 transformers==4.19.2
 torch
 einops
 jsonmerge
 clean-fid
-git+https://github.com/openai/CLIP@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
 resize-right
 torchdiffeq
 kornia
diff --git a/requirements_versions.txt b/requirements_versions.txt
index 1e8006e0..8a9acf20 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -18,7 +18,6 @@ piexif==1.1.3
 einops==0.4.1
 jsonmerge==1.8.0
 clean-fid==0.1.29
-git+https://github.com/openai/CLIP@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
 resize-right==0.0.2
 torchdiffeq==0.2.3
 kornia==0.6.7
-- 
cgit v1.2.1


From 88ec0cf5571883d84abd09196652b3679e359f2e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 19:40:51 +0300
Subject: fix for incorrect embedding token length calculation (will break
 seeds that use embeddings, you're welcome!) add option to input
 initialization text for embeddings

---
 modules/sd_hijack.py                           |  8 ++++----
 modules/textual_inversion/textual_inversion.py | 13 +++++--------
 modules/textual_inversion/ui.py                |  4 ++--
 modules/ui.py                                  |  2 ++
 4 files changed, 13 insertions(+), 14 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index fd57e5c5..3fa06242 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -130,7 +130,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             while i < len(tokens):
                 token = tokens[i]
 
-                embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
 
                 if embedding is None:
                     remade_tokens.append(token)
@@ -142,7 +142,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     remade_tokens += [0] * emb_len
                     multipliers += [weight] * emb_len
                     used_custom_terms.append((embedding.name, embedding.checksum()))
-                    i += emb_len
+                    i += embedding_length_in_tokens
 
         if len(remade_tokens) > maxlen - 2:
             vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
@@ -213,7 +213,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 while i < len(tokens):
                     token = tokens[i]
 
-                    embedding = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
 
                     mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
                     if mult_change is not None:
@@ -229,7 +229,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                         remade_tokens += [0] * emb_len
                         multipliers += [mult] * emb_len
                         used_custom_terms.append((embedding.name, embedding.checksum()))
-                        i += emb_len
+                        i += embedding_length_in_tokens
 
                 if len(remade_tokens) > maxlen - 2:
                     vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index c0baaace..0c50161d 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -117,24 +117,21 @@ class EmbeddingDatabase:
         possible_matches = self.ids_lookup.get(token, None)
 
         if possible_matches is None:
-            return None
+            return None, None
 
         for ids, embedding in possible_matches:
             if tokens[offset:offset + len(ids)] == ids:
-                return embedding
+                return embedding, len(ids)
 
-        return None
+        return None, None
 
 
-
-def create_embedding(name, num_vectors_per_token):
-    init_text = '*'
-
+def create_embedding(name, num_vectors_per_token, init_text='*'):
     cond_model = shared.sd_model.cond_stage_model
     embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
 
     ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
-    embedded = embedding_layer(ids.to(devices.device)).squeeze(0)
+    embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
     vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
 
     for i in range(num_vectors_per_token):
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index ce3677a9..66c43ffb 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -6,8 +6,8 @@ import modules.textual_inversion.textual_inversion as ti
 from modules import sd_hijack, shared
 
 
-def create_embedding(name, nvpt):
-    filename = ti.create_embedding(name, nvpt)
+def create_embedding(name, initialization_text, nvpt):
+    filename = ti.create_embedding(name, nvpt, init_text=initialization_text)
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
 
diff --git a/modules/ui.py b/modules/ui.py
index 3b81a4f7..eca50df0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -954,6 +954,7 @@ def create_ui(wrap_gradio_gpu_call):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")
 
                     new_embedding_name = gr.Textbox(label="Name")
+                    initialization_text = gr.Textbox(label="Initialization text", value="*")
                     nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
 
                     with gr.Row():
@@ -997,6 +998,7 @@ def create_ui(wrap_gradio_gpu_call):
             fn=modules.textual_inversion.ui.create_embedding,
             inputs=[
                 new_embedding_name,
+                initialization_text,
                 nvpt,
             ],
             outputs=[
-- 
cgit v1.2.1


From 71fe7fa49f5eb1a2c89932a9d217ed153c12fc8b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 19:56:37 +0300
Subject: fix using aaaa-100 embedding when the prompt has aaaa-10000 and you
 have both aaaa-100 and aaaa-10000 in the directory with embeddings.

---
 modules/textual_inversion/textual_inversion.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 0c50161d..9d2241ce 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -57,7 +57,8 @@ class EmbeddingDatabase:
         first_id = ids[0]
         if first_id not in self.ids_lookup:
             self.ids_lookup[first_id] = []
-        self.ids_lookup[first_id].append((ids, embedding))
+
+        self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True)
 
         return embedding
 
-- 
cgit v1.2.1


From 4ec4af6e0b7addeee5221a03f32d117ccdc875d9 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 20:15:25 +0300
Subject: add checkpoint info to saved embeddings

---
 modules/textual_inversion/textual_inversion.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 9d2241ce..1183aab7 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,7 +7,7 @@ import tqdm
 import html
 import datetime
 
-from modules import shared, devices, sd_hijack, processing
+from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
 
@@ -17,6 +17,8 @@ class Embedding:
         self.name = name
         self.step = step
         self.cached_checksum = None
+        self.sd_checkpoint = None
+        self.sd_checkpoint_name = None
 
     def save(self, filename):
         embedding_data = {
@@ -24,6 +26,8 @@ class Embedding:
             "string_to_param": {"*": self.vec},
             "name": self.name,
             "step": self.step,
+            "sd_checkpoint": self.sd_checkpoint,
+            "sd_checkpoint_name": self.sd_checkpoint_name,
         }
 
         torch.save(embedding_data, filename)
@@ -41,6 +45,7 @@ class Embedding:
         self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
         return self.cached_checksum
 
+
 class EmbeddingDatabase:
     def __init__(self, embeddings_dir):
         self.ids_lookup = {}
@@ -96,6 +101,8 @@ class EmbeddingDatabase:
             vec = emb.detach().to(devices.device, dtype=torch.float32)
             embedding = Embedding(vec, name)
             embedding.step = data.get('step', None)
+            embedding.sd_checkpoint = data.get('hash', None)
+            embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
             self.register_embedding(embedding, shared.sd_model)
 
         for fn in os.listdir(self.embeddings_dir):
@@ -249,6 +256,10 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
 
+    checkpoint = sd_models.select_checkpoint()
+
+    embedding.sd_checkpoint = checkpoint.hash
+    embedding.sd_checkpoint_name = checkpoint.model_name
     embedding.cached_checksum = None
     embedding.save(filename)
 
-- 
cgit v1.2.1


From 3ff0de2c594b786ef948a89efb1814c59bb42117 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 20:23:40 +0300
Subject: added --disable-console-progressbars to disable progressbars in
 console disabled printing prompts to console by default, enabled by
 --enable-console-prompts

---
 modules/img2img.py     | 4 +++-
 modules/sd_samplers.py | 8 ++++++--
 modules/shared.py      | 7 +++++--
 modules/txt2img.py     | 4 +++-
 4 files changed, 17 insertions(+), 6 deletions(-)

diff --git a/modules/img2img.py b/modules/img2img.py
index 03e934e9..f4455c90 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -103,7 +103,9 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         inpaint_full_res_padding=inpaint_full_res_padding,
         inpainting_mask_invert=inpainting_mask_invert,
     )
-    print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
+
+    if shared.cmd_opts.enable_console_prompts:
+        print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
 
     p.extra_generation_params["Mask blur"] = mask_blur
 
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 92522214..9316875a 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -77,7 +77,9 @@ def extended_tdqm(sequence, *args, desc=None, **kwargs):
     state.sampling_steps = len(sequence)
     state.sampling_step = 0
 
-    for x in tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs):
+    seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
+
+    for x in seq:
         if state.interrupted:
             break
 
@@ -207,7 +209,9 @@ def extended_trange(sampler, count, *args, **kwargs):
     state.sampling_steps = count
     state.sampling_step = 0
 
-    for x in tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs):
+    seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
+
+    for x in seq:
         if state.interrupted:
             break
 
diff --git a/modules/shared.py b/modules/shared.py
index 5a591dc9..1bf7a6c1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -58,6 +58,9 @@ parser.add_argument("--opt-channelslast", action='store_true', help="change memo
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
+parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
+parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
+
 
 cmd_opts = parser.parse_args()
 device = get_optimal_device()
@@ -320,14 +323,14 @@ class TotalTQDM:
         )
 
     def update(self):
-        if not opts.multiple_tqdm:
+        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
             return
         if self._tqdm is None:
             self.reset()
         self._tqdm.update()
 
     def updateTotal(self, new_total):
-        if not opts.multiple_tqdm:
+        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
             return
         if self._tqdm is None:
             self.reset()
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 5368e4d0..d4406c3c 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -34,7 +34,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         denoising_strength=denoising_strength if enable_hr else None,
     )
 
-    print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
+    if cmd_opts.enable_console_prompts:
+        print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
+
     processed = modules.scripts.scripts_txt2img.run(p, *args)
 
     if processed is None:
-- 
cgit v1.2.1


From 6365a41f5981efa506dfe4e8fa878b43ca2d8d0c Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Sun, 2 Oct 2022 12:58:17 -0500
Subject: Update esrgan_model.py

Use alternate ESRGAN Model download path.
---
 modules/esrgan_model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index ea91abfe..4aed9283 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -73,8 +73,8 @@ def fix_model_layers(crt_model, pretrained_net):
 class UpscalerESRGAN(Upscaler):
     def __init__(self, dirname):
         self.name = "ESRGAN"
-        self.model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
-        self.model_name = "ESRGAN 4x"
+        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"
+        self.model_name = "ESRGAN_4x"
         self.scalers = []
         self.user_path = dirname
         self.model_path = os.path.join(models_path, self.name)
-- 
cgit v1.2.1


From a1cde7e6468f80584030525a1b07cbf0f4ee42eb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 21:09:10 +0300
Subject: disabled SD model download after multiple complaints

---
 modules/sd_models.py            | 18 ++++++++----------
 modules/textual_inversion/ui.py |  2 +-
 webui.py                        |  2 +-
 3 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 9259d69e..9a6b568f 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -13,9 +13,6 @@ from modules.paths import models_path
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
-model_name = "sd-v1-4.ckpt"
-model_url = "https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl&mode=grid&download=1"
-user_dir = None
 
 CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
 checkpoints_list = {}
@@ -30,12 +27,10 @@ except Exception:
     pass
 
 
-def setup_model(dirname):
-    global user_dir
-    user_dir = dirname
+def setup_model():
     if not os.path.exists(model_path):
         os.makedirs(model_path)
-    checkpoints_list.clear()
+
     list_models()
 
 
@@ -45,7 +40,7 @@ def checkpoint_tiles():
 
 def list_models():
     checkpoints_list.clear()
-    model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=user_dir, ext_filter=[".ckpt"], download_name=model_name)
+    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"])
 
     def modeltitle(path, shorthash):
         abspath = os.path.abspath(path)
@@ -106,8 +101,11 @@ def select_checkpoint():
 
     if len(checkpoints_list) == 0:
         print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
-        print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
-        print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
+        if shared.cmd_opts.ckpt is not None:
+            print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
+        print(f" - directory {model_path}", file=sys.stderr)
+        if shared.cmd_opts.ckpt_dir is not None:
+            print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
         print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
         exit(1)
 
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index 66c43ffb..633037d8 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -22,7 +22,7 @@ def train_embedding(*args):
         embedding, filename = ti.train_embedding(*args)
 
         res = f"""
-Training {'interrupted' if shared.state.interrupted else 'finished'} after {embedding.step} steps.
+Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps.
 Embedding saved to {html.escape(filename)}
 """
         return res, ""
diff --git a/webui.py b/webui.py
index 424ab975..dc72ceb8 100644
--- a/webui.py
+++ b/webui.py
@@ -23,7 +23,7 @@ from modules.paths import script_path
 from modules.shared import cmd_opts
 
 modelloader.cleanup_models()
-modules.sd_models.setup_model(cmd_opts.ckpt_dir)
+modules.sd_models.setup_model()
 codeformer.setup_model(cmd_opts.codeformer_models_path)
 gfpgan.setup_model(cmd_opts.gfpgan_models_path)
 shared.face_restorers.append(modules.face_restoration.FaceRestoration())
-- 
cgit v1.2.1


From 852fd90c0dcda9cb5fbbfdf0c7308ce58034935c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 21:22:20 +0300
Subject: emergency fix for disabling SD model download after multiple
 complaints

---
 modules/sd_models.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 9a6b568f..5f992064 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -45,8 +45,8 @@ def list_models():
     def modeltitle(path, shorthash):
         abspath = os.path.abspath(path)
 
-        if user_dir is not None and abspath.startswith(user_dir):
-            name = abspath.replace(user_dir, '')
+        if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
+            name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
         elif abspath.startswith(model_path):
             name = abspath.replace(model_path, '')
         else:
-- 
cgit v1.2.1


From e808096cf641d868f88465515d70d40fc46125d4 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 2 Oct 2022 19:26:06 +0100
Subject: correct indent

---
 modules/scripts.py | 48 +++++++++++++++++++++++++-----------------------
 modules/ui.py      | 25 ++++++++++++-------------
 2 files changed, 37 insertions(+), 36 deletions(-)

diff --git a/modules/scripts.py b/modules/scripts.py
index 788397f5..45230f9a 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -163,37 +163,39 @@ class ScriptRunner:
         return processed
 
     def reload_sources(self):
-      for si,script in list(enumerate(self.scripts)):
-        with open(script.filename, "r", encoding="utf8") as file:
-            args_from = script.args_from
-            args_to   = script.args_to 
-            filename  = script.filename
-            text = file.read()
+        for si, script in list(enumerate(self.scripts)):
+            with open(script.filename, "r", encoding="utf8") as file:
+                args_from = script.args_from
+                args_to = script.args_to
+                filename = script.filename
+                text = file.read()
 
-            from types import ModuleType
-            compiled = compile(text, filename, 'exec')
-            module = ModuleType(script.filename)
-            exec(compiled, module.__dict__)
+                from types import ModuleType
 
-            for key, script_class in module.__dict__.items():
-                if type(script_class) == type and issubclass(script_class, Script):
-                    self.scripts[si] = script_class()
-                    self.scripts[si].filename = filename
-                    self.scripts[si].args_from = args_from
-                    self.scripts[si].args_to = args_to
+                compiled = compile(text, filename, 'exec')
+                module = ModuleType(script.filename)
+                exec(compiled, module.__dict__)
+
+                for key, script_class in module.__dict__.items():
+                    if type(script_class) == type and issubclass(script_class, Script):
+                        self.scripts[si] = script_class()
+                        self.scripts[si].filename = filename
+                        self.scripts[si].args_from = args_from
+                        self.scripts[si].args_to = args_to
 
 scripts_txt2img = ScriptRunner()
 scripts_img2img = ScriptRunner()
 
 def reload_script_body_only():
-  scripts_txt2img.reload_sources()
-  scripts_img2img.reload_sources()
+    scripts_txt2img.reload_sources()
+    scripts_img2img.reload_sources()
+
 
 def reload_scripts(basedir):
-  global scripts_txt2img,scripts_img2img
+    global scripts_txt2img, scripts_img2img
 
-  scripts_data.clear()
-  load_scripts(basedir)
+    scripts_data.clear()
+    load_scripts(basedir)
 
-  scripts_txt2img = ScriptRunner()
-  scripts_img2img = ScriptRunner()
+    scripts_txt2img = ScriptRunner()
+    scripts_img2img = ScriptRunner()
diff --git a/modules/ui.py b/modules/ui.py
index 963a2c61..6b30f84b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1003,12 +1003,12 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         )
 
         with gr.Row():
-          reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
-          restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
+            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
+            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
 
 
         def reload_scripts():
-          modules.scripts.reload_script_body_only()
+            modules.scripts.reload_script_body_only()
 
         reload_script_bodies.click(
             fn=reload_scripts,
@@ -1018,7 +1018,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
         )
 
         def request_restart():
-          settings_interface.gradio_ref.do_restart = True
+            settings_interface.gradio_ref.do_restart = True
 
         restart_gradio.click(
             fn=request_restart,
@@ -1234,12 +1234,11 @@ for filename in sorted(os.listdir(jsdir)):
 
 
 if 'gradio_routes_templates_response' not in globals():
-  def template_response(*args, **kwargs):
-      res = gradio_routes_templates_response(*args, **kwargs)
-      res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))
-      res.init_headers()
-      return res
-
-  gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
-  gradio.routes.templates.TemplateResponse = template_response
-
+    def template_response(*args, **kwargs):
+        res = gradio_routes_templates_response(*args, **kwargs)
+        res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))
+        res.init_headers()
+        return res
+
+    gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
+    gradio.routes.templates.TemplateResponse = template_response
-- 
cgit v1.2.1


From a634c3226fd69486ce96df56f95f3fd63172305c Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 2 Oct 2022 19:26:38 +0100
Subject: correct indent

---
 webui.py | 64 ++++++++++++++++++++++++++++++++--------------------------------
 1 file changed, 32 insertions(+), 32 deletions(-)

diff --git a/webui.py b/webui.py
index ab200045..140040ca 100644
--- a/webui.py
+++ b/webui.py
@@ -89,38 +89,38 @@ def webui():
 
     while 1:
 
-      demo = modules.ui.create_ui(
-          txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
-          img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
-          run_extras=wrap_gradio_gpu_call(modules.extras.run_extras),
-          run_pnginfo=modules.extras.run_pnginfo,
-          run_modelmerger=modules.extras.run_modelmerger
-      )
-
-
-      demo.launch(
-          share=cmd_opts.share,
-          server_name="0.0.0.0" if cmd_opts.listen else None,
-          server_port=cmd_opts.port,
-          debug=cmd_opts.gradio_debug,
-          auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
-          inbrowser=cmd_opts.autolaunch,
-          prevent_thread_lock=True
-      )
-
-      while 1:
-        time.sleep(0.5)
-        if getattr(demo,'do_restart',False):
-          time.sleep(0.5)
-          demo.close()
-          time.sleep(0.5)
-          break
-
-      print('Reloading Custom Scripts')
-      modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
-      print('Reloading modules: modules.ui')
-      importlib.reload(modules.ui)
-      print('Restarting Gradio')
+        demo = modules.ui.create_ui(
+            txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
+            img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
+            run_extras=wrap_gradio_gpu_call(modules.extras.run_extras),
+            run_pnginfo=modules.extras.run_pnginfo,
+            run_modelmerger=modules.extras.run_modelmerger
+        )
+
+
+        demo.launch(
+            share=cmd_opts.share,
+            server_name="0.0.0.0" if cmd_opts.listen else None,
+            server_port=cmd_opts.port,
+            debug=cmd_opts.gradio_debug,
+            auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
+            inbrowser=cmd_opts.autolaunch,
+            prevent_thread_lock=True
+        )
+
+        while 1:
+            time.sleep(0.5)
+                if getattr(demo,'do_restart',False):
+                    time.sleep(0.5)
+                    demo.close()
+                    time.sleep(0.5)
+                    break
+
+        print('Reloading Custom Scripts')
+        modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
+        print('Reloading modules: modules.ui')
+        importlib.reload(modules.ui)
+        print('Restarting Gradio')
 
 
 if __name__ == "__main__":
-- 
cgit v1.2.1


From c0389eb3071870240bc158263e5dfb4351ec8eba Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 21:35:29 +0300
Subject: hello

---
 webui.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/webui.py b/webui.py
index 63495697..47848ba5 100644
--- a/webui.py
+++ b/webui.py
@@ -103,11 +103,11 @@ def webui():
 
         while 1:
             time.sleep(0.5)
-                if getattr(demo,'do_restart',False):
-                    time.sleep(0.5)
-                    demo.close()
-                    time.sleep(0.5)
-                    break
+            if getattr(demo, 'do_restart', False):
+                time.sleep(0.5)
+                demo.close()
+                time.sleep(0.5)
+                break
 
         print('Reloading Custom Scripts')
         modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
-- 
cgit v1.2.1


From 2ef69df9a7c7b6793401f29ced71fb8a781fad4c Mon Sep 17 00:00:00 2001
From: Jocke <joakim.lovgren@protonmail.com>
Date: Sun, 2 Oct 2022 16:10:41 +0200
Subject: Prevent upscaling when None is selected for SD upscale

---
 scripts/sd_upscale.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
index 2653e2d4..cb37ff7e 100644
--- a/scripts/sd_upscale.py
+++ b/scripts/sd_upscale.py
@@ -34,7 +34,11 @@ class Script(scripts.Script):
         seed = p.seed
 
         init_img = p.init_images[0]
-        img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)
+        
+        if(upscaler.name != "None"): 
+            img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)
+        else:
+            img = init_img
 
         devices.torch_gc()
 
-- 
cgit v1.2.1


From 91f327f22bb2feb780c424c74723cc0629dc34a1 Mon Sep 17 00:00:00 2001
From: Lopyter <git@lopyter.dev>
Date: Sun, 2 Oct 2022 18:15:31 +0200
Subject: make save to dirs optional for imgs saved from ui

---
 modules/shared.py | 1 +
 modules/ui.py     | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 1bf7a6c1..785e7af6 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -173,6 +173,7 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
     "grid_save_to_dirs": OptionInfo(False, "Save grids to subdirectory"),
     "directories_filename_pattern": OptionInfo("", "Directory name pattern"),
     "directories_max_prompt_words": OptionInfo(8, "Max prompt words", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
+    "use_save_to_dirs_for_ui": OptionInfo(False, "Use \"Save images to a subdirectory\" option for images saved from UI"),
 }))
 
 options_templates.update(options_section(('upscaling', "Upscaling"), {
diff --git a/modules/ui.py b/modules/ui.py
index 78a15d83..8912deff 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -113,7 +113,7 @@ def save_files(js_data, images, index):
 
     p = MyObject(data)
     path = opts.outdir_save
-    save_to_dirs = opts.save_to_dirs
+    save_to_dirs = opts.use_save_to_dirs_for_ui
 
     if save_to_dirs:
         dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, p.seed, p.prompt)
-- 
cgit v1.2.1


From c4445225f79f1c57afe52358ff4b205864eb7aac Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 21:50:14 +0300
Subject: change wording for options

---
 modules/shared.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 785e7af6..7246eadc 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -170,10 +170,10 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
 
 options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
     "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
-    "grid_save_to_dirs": OptionInfo(False, "Save grids to subdirectory"),
+    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
+    "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
     "directories_filename_pattern": OptionInfo("", "Directory name pattern"),
-    "directories_max_prompt_words": OptionInfo(8, "Max prompt words", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
-    "use_save_to_dirs_for_ui": OptionInfo(False, "Use \"Save images to a subdirectory\" option for images saved from UI"),
+    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
 }))
 
 options_templates.update(options_section(('upscaling', "Upscaling"), {
-- 
cgit v1.2.1


From c7543d4940da672d970124ae8f2fec9de7bdc1da Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 22:41:21 +0300
Subject: preprocessing for textual inversion added

---
 modules/interrogate.py                         |  1 +
 modules/textual_inversion/preprocess.py        | 75 ++++++++++++++++++++++++++
 modules/textual_inversion/textual_inversion.py |  1 +
 modules/textual_inversion/ui.py                | 14 +++--
 modules/ui.py                                  | 36 +++++++++++++
 5 files changed, 124 insertions(+), 3 deletions(-)
 create mode 100644 modules/textual_inversion/preprocess.py

diff --git a/modules/interrogate.py b/modules/interrogate.py
index f62a4745..eed87144 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -21,6 +21,7 @@ Category = namedtuple("Category", ["name", "topn", "items"])
 
 re_topn = re.compile(r"\.top(\d+)\.")
 
+
 class InterrogateModels:
     blip_model = None
     clip_model = None
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
new file mode 100644
index 00000000..209e928f
--- /dev/null
+++ b/modules/textual_inversion/preprocess.py
@@ -0,0 +1,75 @@
+import os
+from PIL import Image, ImageOps
+import tqdm
+
+from modules import shared, images
+
+
+def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
+    size = 512
+    src = os.path.abspath(process_src)
+    dst = os.path.abspath(process_dst)
+
+    assert src != dst, 'same directory specified as source and desitnation'
+
+    os.makedirs(dst, exist_ok=True)
+
+    files = os.listdir(src)
+
+    shared.state.textinfo = "Preprocessing..."
+    shared.state.job_count = len(files)
+
+    if process_caption:
+        shared.interrogator.load()
+
+    def save_pic_with_caption(image, index):
+        if process_caption:
+            caption = "-" + shared.interrogator.generate_caption(image)
+        else:
+            caption = ""
+
+        image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
+        subindex[0] += 1
+
+    def save_pic(image, index):
+        save_pic_with_caption(image, index)
+
+        if process_flip:
+            save_pic_with_caption(ImageOps.mirror(image), index)
+
+    for index, imagefile in enumerate(tqdm.tqdm(files)):
+        subindex = [0]
+        filename = os.path.join(src, imagefile)
+        img = Image.open(filename).convert("RGB")
+
+        if shared.state.interrupted:
+            break
+
+        ratio = img.height / img.width
+        is_tall = ratio > 1.35
+        is_wide = ratio < 1 / 1.35
+
+        if process_split and is_tall:
+            img = img.resize((size, size * img.height // img.width))
+
+            top = img.crop((0, 0, size, size))
+            save_pic(top, index)
+
+            bot = img.crop((0, img.height - size, size, img.height))
+            save_pic(bot, index)
+        elif process_split and is_wide:
+            img = img.resize((size * img.width // img.height, size))
+
+            left = img.crop((0, 0, size, size))
+            save_pic(left, index)
+
+            right = img.crop((img.width - size, 0, img.width, size))
+            save_pic(right, index)
+        else:
+            img = images.resize_image(1, img, size, size)
+            save_pic(img, index)
+
+        shared.state.nextjob()
+
+    if process_caption:
+        shared.interrogator.send_blip_to_ram()
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 1183aab7..d4e250d8 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,6 +7,7 @@ import tqdm
 import html
 import datetime
 
+
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index 633037d8..f19ac5e0 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -2,24 +2,31 @@ import html
 
 import gradio as gr
 
-import modules.textual_inversion.textual_inversion as ti
+import modules.textual_inversion.textual_inversion
+import modules.textual_inversion.preprocess
 from modules import sd_hijack, shared
 
 
 def create_embedding(name, initialization_text, nvpt):
-    filename = ti.create_embedding(name, nvpt, init_text=initialization_text)
+    filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text)
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
 
     return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
 
 
+def preprocess(*args):
+    modules.textual_inversion.preprocess.preprocess(*args)
+
+    return "Preprocessing finished.", ""
+
+
 def train_embedding(*args):
 
     try:
         sd_hijack.undo_optimizations()
 
-        embedding, filename = ti.train_embedding(*args)
+        embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
 
         res = f"""
 Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps.
@@ -30,3 +37,4 @@ Embedding saved to {html.escape(filename)}
         raise
     finally:
         sd_hijack.apply_optimizations()
+
diff --git a/modules/ui.py b/modules/ui.py
index 8912deff..e7bde53b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -961,6 +961,8 @@ def create_ui(wrap_gradio_gpu_call):
         with gr.Row().style(equal_height=False):
             with gr.Column():
                 with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")
 
                     new_embedding_name = gr.Textbox(label="Name")
@@ -974,6 +976,24 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create", variant='primary')
 
+                with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
+
+                    process_src = gr.Textbox(label='Source directory')
+                    process_dst = gr.Textbox(label='Destination directory')
+
+                    with gr.Row():
+                        process_flip = gr.Checkbox(label='Flip')
+                        process_split = gr.Checkbox(label='Split into two')
+                        process_caption = gr.Checkbox(label='Add caption')
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            run_preprocess = gr.Button(value="Preprocess", variant='primary')
+
                 with gr.Group():
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
@@ -1018,6 +1038,22 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
+        run_preprocess.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                process_src,
+                process_dst,
+                process_flip,
+                process_split,
+                process_caption,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ],
+        )
+
         train_embedding.click(
             fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
-- 
cgit v1.2.1


From 6785331e22d6a488fbf5905fab56d7fec867e038 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 22:59:01 +0300
Subject: keep textual inversion dataset latents in CPU memory to save a bit of
 VRAM

---
 modules/textual_inversion/dataset.py           | 2 ++
 modules/textual_inversion/textual_inversion.py | 3 +++
 modules/ui.py                                  | 4 ++--
 3 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 7e134a08..e8394ff6 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -8,6 +8,7 @@ from torchvision import transforms
 
 import random
 import tqdm
+from modules import devices
 
 
 class PersonalizedBase(Dataset):
@@ -47,6 +48,7 @@ class PersonalizedBase(Dataset):
             torchdata = torch.moveaxis(torchdata, 2, 0)
 
             init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
+            init_latent = init_latent.to(devices.cpu)
 
             self.dataset.append((init_latent, filename_tokens))
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index d4e250d8..8686f534 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -212,7 +212,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
         with torch.autocast("cuda"):
             c = cond_model([text])
+
+            x = x.to(devices.device)
             loss = shared.sd_model(x.unsqueeze(0), c)[0]
+            del x
 
             losses[embedding.step % losses.shape[0]] = loss.item()
 
diff --git a/modules/ui.py b/modules/ui.py
index e7bde53b..d9d02ece 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1002,8 +1002,8 @@ def create_ui(wrap_gradio_gpu_call):
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
-                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=1000, precision=0)
-                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=1000, precision=0)
+                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
+                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
 
                     with gr.Row():
                         with gr.Column(scale=2):
-- 
cgit v1.2.1


From 166283653cfe7521a422c91e8fb801f3ecb4adc8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 23:18:13 +0300
Subject: remove LDSR warning

---
 modules/paths.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/paths.py b/modules/paths.py
index ceb80417..606f7d66 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -20,7 +20,6 @@ path_dirs = [
     (os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers', []),
     (os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
     (os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
-    (os.path.join(sd_path, '../latent-diffusion'), 'LDSR.py', 'LDSR', []),
     (os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
 ]
 
-- 
cgit v1.2.1


From 4c2eccf8e96825333ed400f8a8a2be78141ed8ec Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 2 Oct 2022 23:22:48 +0300
Subject: credit Rinon Gal

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 15e224e8..ec3d7532 100644
--- a/README.md
+++ b/README.md
@@ -113,6 +113,7 @@ The documentation was moved from this README over to the project's [wiki](https:
 - LDSR - https://github.com/Hafiidz/latent-diffusion
 - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
 - Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
+- Rinon Gal - Textual Inversion - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
 - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
 - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
 - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
-- 
cgit v1.2.1


From 138662734c25dab4e73e632b7eaff9ad9c0ce2b4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 3 Oct 2022 07:57:59 +0300
Subject: use dropdown instead of radio for img2img upscaler selection

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 7246eadc..2a599e9c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -183,7 +183,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
-    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
+    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
-- 
cgit v1.2.1


From e615d4f9d101e2712c7c2d0e3e8feb19cb430c74 Mon Sep 17 00:00:00 2001
From: Hanusz Leszek <leszek.hanusz@gmail.com>
Date: Sun, 2 Oct 2022 21:08:23 +0200
Subject: Convert folder icon surrogate pair to valid utf8

---
 javascript/hints.js | 2 +-
 modules/ui.py       | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index 84694eeb..e72e9338 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -15,7 +15,7 @@ titles = {
     "\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed",
     "\u{1f3a8}": "Add a random artist to the prompt.",
     "\u2199\ufe0f": "Read generation parameters from prompt into user interface.",
-    "\uD83D\uDCC2": "Open images output directory",
+    "\u{1f4c2}": "Open images output directory",
 
     "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
     "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
diff --git a/modules/ui.py b/modules/ui.py
index d9d02ece..16432151 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -69,7 +69,7 @@ random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
 reuse_symbol = '\u267b\ufe0f'  # ♻️
 art_symbol = '\U0001f3a8'  # 🎨
 paste_symbol = '\u2199\ufe0f'  # ↙
-folder_symbol = '\uD83D\uDCC2'
+folder_symbol = '\U0001f4c2'  # 📂
 
 def plaintext_to_html(text):
     text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
-- 
cgit v1.2.1


From 34c638142eaa57f89b86545ba3c72085036398bb Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Fri, 30 Sep 2022 22:38:14 +0100
Subject: Fixed when eta = 0

Unexpected behavior when using eta = 0 in something like XY, but your default eta was set to something not 0.
---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 9316875a..dbf570d2 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -127,7 +127,7 @@ class VanillaStableDiffusionSampler:
         return res
 
     def initialize(self, p):
-        self.eta = p.eta or opts.eta_ddim
+        self.eta = p.eta if p.eta is not None else opts.eta_ddim
 
         for fieldname in ['p_sample_ddim', 'p_sample_plms']:
             if hasattr(self.sampler, fieldname):
-- 
cgit v1.2.1


From 36ea4ac0f5844e5c8dec124edbdb714ccdd6013c Mon Sep 17 00:00:00 2001
From: RnDMonkey <ryandrutledge@gmail.com>
Date: Sun, 2 Oct 2022 22:21:16 -0700
Subject: moved no-style return outside join function

---
 modules/images.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/images.py b/modules/images.py
index bba55158..1a046aca 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -315,7 +315,7 @@ def apply_filename_pattern(x, p, seed, prompt):
         #currently disabled if using the save button, will work otherwise 
         # if enabled it will cause a bug because styles is not included in the save_files data dictionary
         if hasattr(p, "styles"):
-            x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"] or "None"), replace_spaces=False))
+            x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
 
         x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
 
-- 
cgit v1.2.1


From 6491b09c24ea77f1f69990ea80a216f9ce319589 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 3 Oct 2022 08:53:52 +0300
Subject: use existing function for gfpgan

---
 modules/gfpgan_model.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index bb30d733..dd3fbcab 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -97,11 +97,7 @@ def setup_model(dirname):
                 return "GFPGAN"
 
             def restore(self, np_image):
-                np_image_bgr = np_image[:, :, ::-1]
-                cropped_faces, restored_faces, gfpgan_output_bgr = gfpgann().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
-                np_image = gfpgan_output_bgr[:, :, ::-1]
-
-                return np_image
+                return gfpgan_fix_faces(np_image)
 
         shared.face_restorers.append(FaceRestorerGFPGAN())
     except Exception:
-- 
cgit v1.2.1


From 43a74fa595003321200a40bd2431e56c245e75ed Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 3 Oct 2022 11:48:19 +0300
Subject: batch processing for img2img with an empty output directory, by
 request

---
 modules/img2img.py | 7 +++++--
 modules/ui.py      | 2 +-
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/modules/img2img.py b/modules/img2img.py
index f4455c90..2ff8e261 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -23,8 +23,10 @@ def process_batch(p, input_dir, output_dir, args):
 
     print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
 
+    save_normally = output_dir == ''
+
     p.do_not_save_grid = True
-    p.do_not_save_samples = True
+    p.do_not_save_samples = not save_normally
 
     state.job_count = len(images) * p.n_iter
 
@@ -48,7 +50,8 @@ def process_batch(p, input_dir, output_dir, args):
                 left, right = os.path.splitext(filename)
                 filename = f"{left}-{n}{right}"
 
-            processed_image.save(os.path.join(output_dir, filename))
+            if not save_normally:
+                processed_image.save(os.path.join(output_dir, filename))
 
 
 def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
diff --git a/modules/ui.py b/modules/ui.py
index 16432151..55f7aa95 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -658,7 +658,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                     with gr.TabItem('Batch img2img', id='batch'):
                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
-                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.{hidden}</p>")
+                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
                         img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
                         img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
 
-- 
cgit v1.2.1


From 2865ef4b9ab16d56326cc805541bebcf01d099bc Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 3 Oct 2022 13:10:03 +0300
Subject: fix broken date in TI

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 8686f534..cd9f3498 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -164,7 +164,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
 
-    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%d-%m"), embedding_name)
+    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
 
     if save_embedding_every > 0:
         embedding_dir = os.path.join(log_directory, "embeddings")
-- 
cgit v1.2.1


From 2a7f48cdb8dcf9acb02610cccae0d1ee5d260bc2 Mon Sep 17 00:00:00 2001
From: fuzzytent <fuzzy.tent6960@fastmail.com>
Date: Fri, 30 Sep 2022 16:02:16 +0200
Subject: Improve styling of gallery items, particularly in dark mode

---
 style.css | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/style.css b/style.css
index 9709c4ee..e11316b9 100644
--- a/style.css
+++ b/style.css
@@ -403,3 +403,7 @@ input[type="range"]{
 .red {
 	color: red;
 }
+
+.gallery-item {
+    --tw-bg-opacity: 0 !important;
+}
-- 
cgit v1.2.1


From 5ef0baf5eaec7f21a1666af424405cbee19f3764 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 08:52:11 +0300
Subject: add support for gelbooru tags in filenames for textual inversion

---
 modules/textual_inversion/dataset.py    | 7 +++++--
 modules/textual_inversion/preprocess.py | 4 +++-
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index e8394ff6..7c44ea5b 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -9,6 +9,9 @@ from torchvision import transforms
 import random
 import tqdm
 from modules import devices
+import re
+
+re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 class PersonalizedBase(Dataset):
@@ -38,8 +41,8 @@ class PersonalizedBase(Dataset):
             image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
 
             filename = os.path.basename(path)
-            filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-')
-            filename_tokens = [token for token in filename_tokens if token.isalpha()]
+            filename_tokens = os.path.splitext(filename)[0]
+            filename_tokens = re_tag.findall(filename_tokens)
 
             npimage = np.array(image).astype(np.uint8)
             npimage = (npimage / 127.5 - 1.0).astype(np.float32)
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 209e928f..f545a993 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -26,7 +26,9 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
         if process_caption:
             caption = "-" + shared.interrogator.generate_caption(image)
         else:
-            caption = ""
+            caption = filename
+            caption = os.path.splitext(caption)[0]
+            caption = os.path.basename(caption)
 
         image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
         subindex[0] += 1
-- 
cgit v1.2.1


From 1c5604791da7e57f40880698666b6617a1754c65 Mon Sep 17 00:00:00 2001
From: DoTheSneedful <DoTheSneedful@outlook.com>
Date: Mon, 3 Oct 2022 22:20:09 -0400
Subject: Add a prompt order option to XY plot script

---
 scripts/xy_grid.py | 40 ++++++++++++++++++++++++++++++++++++++--
 1 file changed, 38 insertions(+), 2 deletions(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 146663b0..044c30e6 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -1,5 +1,6 @@
 from collections import namedtuple
 from copy import copy
+from itertools import permutations
 import random
 
 from PIL import Image
@@ -28,6 +29,27 @@ def apply_prompt(p, x, xs):
     p.prompt = p.prompt.replace(xs[0], x)
     p.negative_prompt = p.negative_prompt.replace(xs[0], x)
 
+def apply_order(p, x, xs):
+    token_order = []
+
+    # Initally grab the tokens from the prompt so they can be later be replaced in order of earliest seen in the prompt
+    for token in x:
+        token_order.append((p.prompt.find(token), token))
+
+    token_order.sort(key=lambda t: t[0])
+
+    search_from_pos = 0
+    for idx, token in enumerate(x):
+        original_pos, old_token = token_order[idx]
+
+        # Get position of the token again as it will likely change as tokens are being replaced
+        pos = p.prompt.find(old_token)
+        if original_pos >= 0:
+            # Avoid trying to replace what was just replaced by searching later in the prompt string
+            p.prompt = p.prompt[0:search_from_pos] + p.prompt[search_from_pos:].replace(old_token, token, 1)
+
+        search_from_pos = pos + len(token)
+
 
 samplers_dict = {}
 for i, sampler in enumerate(modules.sd_samplers.samplers):
@@ -60,7 +82,8 @@ def format_value_add_label(p, opt, x):
 def format_value(p, opt, x):
     if type(x) == float:
         x = round(x, 8)
-
+    if type(x) == type(list()):
+        x = str(x)
     return x
 
 def do_nothing(p, x, xs):
@@ -89,6 +112,7 @@ axis_options = [
     AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
     AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
     AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
+    AxisOption("Prompt order", type(list()), apply_order, format_value),
     AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
 ]
 
@@ -159,7 +183,11 @@ class Script(scripts.Script):
             if opt.label == 'Nothing':
                 return [0]
 
-            valslist = [x.strip() for x in vals.split(",")]
+            if opt.type == type(list()):
+                valslist = [x for x in vals]
+            else:
+                valslist = [x.strip() for x in vals.split(",")]
+
 
             if opt.type == int:
                 valslist_ext = []
@@ -212,9 +240,17 @@ class Script(scripts.Script):
             return valslist
 
         x_opt = axis_options[x_type]
+
+        if x_opt.label == "Prompt order":
+            x_values = list(permutations([x.strip() for x in x_values.split(",")]))
+
         xs = process_axis(x_opt, x_values)
 
         y_opt = axis_options[y_type]
+
+        if y_opt.label == "Prompt order":
+            y_values = list(permutations([y.strip() for y in y_values.split(",")]))
+
         ys = process_axis(y_opt, y_values)
 
         def fix_axis_seeds(axis_opt, axis_list):
-- 
cgit v1.2.1


From 1a6d40db35656083d5bf9d3a3430b45fda4e85eb Mon Sep 17 00:00:00 2001
From: DoTheSneedful <DoTheSneedful@outlook.com>
Date: Tue, 4 Oct 2022 00:18:15 -0400
Subject: Fix token ordering in prompt order XY plot

---
 scripts/xy_grid.py | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 044c30e6..5bcd3921 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -32,24 +32,21 @@ def apply_prompt(p, x, xs):
 def apply_order(p, x, xs):
     token_order = []
 
-    # Initally grab the tokens from the prompt so they can be later be replaced in order of earliest seen in the prompt
+    # Initally grab the tokens from the prompt so they can be be replaced in order of earliest seen
     for token in x:
         token_order.append((p.prompt.find(token), token))
 
     token_order.sort(key=lambda t: t[0])
 
     search_from_pos = 0
-    for idx, token in enumerate(x):
-        original_pos, old_token = token_order[idx]
-
+    for idx, (original_pos, old_token) in enumerate(token_order):
         # Get position of the token again as it will likely change as tokens are being replaced
-        pos = p.prompt.find(old_token)
+        pos = search_from_pos + p.prompt[search_from_pos:].find(old_token)
         if original_pos >= 0:
             # Avoid trying to replace what was just replaced by searching later in the prompt string
-            p.prompt = p.prompt[0:search_from_pos] + p.prompt[search_from_pos:].replace(old_token, token, 1)
-
-        search_from_pos = pos + len(token)
+            p.prompt = p.prompt[0:search_from_pos] + p.prompt[search_from_pos:].replace(old_token, x[idx], 1)
 
+        search_from_pos = pos + len(x[idx])
 
 samplers_dict = {}
 for i, sampler in enumerate(modules.sd_samplers.samplers):
-- 
cgit v1.2.1


From 56371153b545e3a43c3a5f206264019af361f3af Mon Sep 17 00:00:00 2001
From: DoTheSneedful <DoTheSneedful@outlook.com>
Date: Tue, 4 Oct 2022 01:07:36 -0400
Subject: XY plot prompt order simplify logic

---
 scripts/xy_grid.py | 24 +++++++++++++++---------
 1 file changed, 15 insertions(+), 9 deletions(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 5bcd3921..7def47f5 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -38,15 +38,21 @@ def apply_order(p, x, xs):
 
     token_order.sort(key=lambda t: t[0])
 
-    search_from_pos = 0
-    for idx, (original_pos, old_token) in enumerate(token_order):
-        # Get position of the token again as it will likely change as tokens are being replaced
-        pos = search_from_pos + p.prompt[search_from_pos:].find(old_token)
-        if original_pos >= 0:
-            # Avoid trying to replace what was just replaced by searching later in the prompt string
-            p.prompt = p.prompt[0:search_from_pos] + p.prompt[search_from_pos:].replace(old_token, x[idx], 1)
-
-        search_from_pos = pos + len(x[idx])
+    prompt_parts = []
+
+    # Split the prompt up, taking out the tokens
+    for _, token in token_order:
+        n = p.prompt.find(token)
+        prompt_parts.append(p.prompt[0:n])
+        p.prompt = p.prompt[n + len(token):]
+
+    # Rebuild the prompt with the tokens in the order we want
+    prompt_tmp = ""
+    for idx, part in enumerate(prompt_parts):
+        prompt_tmp += part
+        prompt_tmp += x[idx]
+    p.prompt = prompt_tmp + p.prompt
+    
 
 samplers_dict = {}
 for i, sampler in enumerate(modules.sd_samplers.samplers):
-- 
cgit v1.2.1


From 556c36b9607e3f4eacdddc85f8e7a78b29476ea7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 09:18:00 +0300
Subject: add hint, refactor code for #1607

---
 javascript/hints.js |  1 +
 scripts/xy_grid.py  | 35 ++++++++++++++++++-----------------
 2 files changed, 19 insertions(+), 17 deletions(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index e72e9338..8adcd983 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -47,6 +47,7 @@ titles = {
     "Custom code": "Run Python code. Advanced user only. Must run program with --allow-code for this to work",
 
     "Prompt S/R": "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others",
+    "Prompt order": "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order",
 
     "Tiling": "Produce an image that can be tiled.",
     "Tile overlap": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.",
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 7def47f5..1237e754 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -29,10 +29,11 @@ def apply_prompt(p, x, xs):
     p.prompt = p.prompt.replace(xs[0], x)
     p.negative_prompt = p.negative_prompt.replace(xs[0], x)
 
+
 def apply_order(p, x, xs):
     token_order = []
 
-    # Initally grab the tokens from the prompt so they can be be replaced in order of earliest seen
+    # Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
     for token in x:
         token_order.append((p.prompt.find(token), token))
 
@@ -85,17 +86,26 @@ def format_value_add_label(p, opt, x):
 def format_value(p, opt, x):
     if type(x) == float:
         x = round(x, 8)
-    if type(x) == type(list()):
-        x = str(x)
     return x
 
+
+def format_value_join_list(p, opt, x):
+    return ", ".join(x)
+
+
 def do_nothing(p, x, xs):
     pass
 
+
 def format_nothing(p, opt, x):
     return ""
 
 
+def str_permutations(x):
+    """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
+    return x
+
+
 AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value"])
 AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value"])
 
@@ -108,6 +118,7 @@ axis_options = [
     AxisOption("Steps", int, apply_field("steps"), format_value_add_label),
     AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label),
     AxisOption("Prompt S/R", str, apply_prompt, format_value),
+    AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list),
     AxisOption("Sampler", str, apply_sampler, format_value),
     AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
     AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
@@ -115,7 +126,6 @@ axis_options = [
     AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
     AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
     AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
-    AxisOption("Prompt order", type(list()), apply_order, format_value),
     AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
 ]
 
@@ -158,6 +168,7 @@ re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d
 re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
 re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
 
+
 class Script(scripts.Script):
     def title(self):
         return "X/Y plot"
@@ -186,11 +197,7 @@ class Script(scripts.Script):
             if opt.label == 'Nothing':
                 return [0]
 
-            if opt.type == type(list()):
-                valslist = [x for x in vals]
-            else:
-                valslist = [x.strip() for x in vals.split(",")]
-
+            valslist = [x.strip() for x in vals.split(",")]
 
             if opt.type == int:
                 valslist_ext = []
@@ -237,23 +244,17 @@ class Script(scripts.Script):
                         valslist_ext.append(val)
 
                 valslist = valslist_ext
+            elif opt.type == str_permutations:
+                valslist = list(permutations(valslist))
 
             valslist = [opt.type(x) for x in valslist]
 
             return valslist
 
         x_opt = axis_options[x_type]
-
-        if x_opt.label == "Prompt order":
-            x_values = list(permutations([x.strip() for x in x_values.split(",")]))
-
         xs = process_axis(x_opt, x_values)
 
         y_opt = axis_options[y_type]
-
-        if y_opt.label == "Prompt order":
-            y_values = list(permutations([y.strip() for y in y_values.split(",")]))
-
         ys = process_axis(y_opt, y_values)
 
         def fix_axis_seeds(axis_opt, axis_list):
-- 
cgit v1.2.1


From eeab7aedf532680a6ae9058ee272450bb07e41eb Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 4 Oct 2022 04:24:35 -0400
Subject: Add --use-cpu command line option

Remove MPS detection to use CPU for GFPGAN / CodeFormer and add a --use-cpu command line option.
---
 modules/devices.py      | 5 ++---
 modules/esrgan_model.py | 9 ++++-----
 modules/scunet_model.py | 8 ++++----
 modules/shared.py       | 9 +++++++--
 4 files changed, 17 insertions(+), 14 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 5d9c7a07..b5a0cd29 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,8 +1,8 @@
 import torch
 
-# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
 from modules import errors
 
+# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
 has_mps = getattr(torch, 'has_mps', False)
 
 cpu = torch.device("cpu")
@@ -32,8 +32,7 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-device = get_optimal_device()
-device_gfpgan = device_codeformer = cpu if device.type == 'mps' else device
+device = device_gfpgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
 dtype = torch.float16
 
 def randn(seed, shape):
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 4aed9283..d17e730f 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -6,8 +6,7 @@ from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgam_model_arch as arch
-from modules import shared, modelloader, images
-from modules.devices import has_mps
+from modules import shared, modelloader, images, devices
 from modules.paths import models_path
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
@@ -97,7 +96,7 @@ class UpscalerESRGAN(Upscaler):
         model = self.load_model(selected_model)
         if model is None:
             return img
-        model.to(shared.device)
+        model.to(devices.device_esrgan)
         img = esrgan_upscale(model, img)
         return img
 
@@ -112,7 +111,7 @@ class UpscalerESRGAN(Upscaler):
             print("Unable to load %s from %s" % (self.model_path, filename))
             return None
 
-        pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
+        pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None)
         crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
 
         pretrained_net = fix_model_layers(crt_model, pretrained_net)
@@ -127,7 +126,7 @@ def upscale_without_tiling(model, img):
     img = img[:, :, ::-1]
     img = np.moveaxis(img, 2, 0) / 255
     img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(shared.device)
+    img = img.unsqueeze(0).to(devices.device_esrgan)
     with torch.no_grad():
         output = model(img)
     output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/scunet_model.py b/modules/scunet_model.py
index 7987ac14..fb64b740 100644
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@@ -8,7 +8,7 @@ import torch
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.upscaler
-from modules import shared, modelloader
+from modules import devices, modelloader
 from modules.paths import models_path
 from modules.scunet_model_arch import SCUNet as net
 
@@ -51,12 +51,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         if model is None:
             return img
 
-        device = shared.device
+        device = devices.device_scunet
         img = np.array(img)
         img = img[:, :, ::-1]
         img = np.moveaxis(img, 2, 0) / 255
         img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(shared.device)
+        img = img.unsqueeze(0).to(device)
 
         img = img.to(device)
         with torch.no_grad():
@@ -69,7 +69,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         return PIL.Image.fromarray(output, 'RGB')
 
     def load_model(self, path: str):
-        device = shared.device
+        device = devices.device_scunet
         if "http" in path:
             filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
                                           progress=True)
diff --git a/modules/shared.py b/modules/shared.py
index 2a599e9c..7899ab8d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -12,7 +12,7 @@ import modules.interrogate
 import modules.memmon
 import modules.sd_models
 import modules.styles
-from modules.devices import get_optimal_device
+import modules.devices as devices
 from modules.paths import script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -46,6 +46,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
+parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU for specified modules", default=[])
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -63,7 +64,11 @@ parser.add_argument("--enable-console-prompts", action='store_true', help="print
 
 
 cmd_opts = parser.parse_args()
-device = get_optimal_device()
+
+devices.device, devices.device_gfpgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
+(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'])
+
+device = devices.device
 
 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
 parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
-- 
cgit v1.2.1


From 27ddc24fdee1fbe709054a43235ab7f9c51b3e9f Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 4 Oct 2022 05:18:17 -0400
Subject: Add BSRGAN to --add-cpu

---
 modules/bsrgan_model.py | 6 +++---
 modules/devices.py      | 2 +-
 modules/shared.py       | 6 +++---
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
index e62c6657..3bd80791 100644
--- a/modules/bsrgan_model.py
+++ b/modules/bsrgan_model.py
@@ -8,7 +8,7 @@ import torch
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.upscaler
-from modules import shared, modelloader
+from modules import devices, modelloader
 from modules.bsrgan_model_arch import RRDBNet
 from modules.paths import models_path
 
@@ -44,13 +44,13 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler):
         model = self.load_model(selected_file)
         if model is None:
             return img
-        model.to(shared.device)
+        model.to(devices.device_bsrgan)
         torch.cuda.empty_cache()
         img = np.array(img)
         img = img[:, :, ::-1]
         img = np.moveaxis(img, 2, 0) / 255
         img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(shared.device)
+        img = img.unsqueeze(0).to(devices.device_bsrgan)
         with torch.no_grad():
             output = model(img)
         output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/devices.py b/modules/devices.py
index b5a0cd29..b7899632 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -32,7 +32,7 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-device = device_gfpgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
+device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
 dtype = torch.float16
 
 def randn(seed, shape):
diff --git a/modules/shared.py b/modules/shared.py
index 7899ab8d..95b98a06 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -46,7 +46,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU for specified modules", default=[])
+parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU for specified modules", default=[])
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -65,8 +65,8 @@ parser.add_argument("--enable-console-prompts", action='store_true', help="print
 
 cmd_opts = parser.parse_args()
 
-devices.device, devices.device_gfpgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
-(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'])
+devices.device, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
+(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'])
 
 device = devices.device
 
-- 
cgit v1.2.1


From dc9c5a97742e3a34d37da7108642d8adc0dc5858 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 4 Oct 2022 05:22:50 -0400
Subject: Modify --add-cpu description

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 95b98a06..25aff5b0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -46,7 +46,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU for specified modules", default=[])
+parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[])
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
-- 
cgit v1.2.1


From 6c6ae28bf5fd1e8bc3e8f64a3430b6f29f338f77 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 12:32:22 +0300
Subject: send all three of GFPGAN's and codeformer's models to CPU memory
 instead of just one for #1283

---
 modules/codeformer_model.py | 12 ++++++++++--
 modules/devices.py          | 10 ++++++++++
 modules/gfpgan_model.py     | 14 ++++++++++++--
 modules/processing.py       | 16 +++++++++-------
 4 files changed, 41 insertions(+), 11 deletions(-)

diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
index a29f3855..e6d9fa4f 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -69,10 +69,14 @@ def setup_model(dirname):
 
                 self.net = net
                 self.face_helper = face_helper
-                self.net.to(devices.device_codeformer)
 
                 return net, face_helper
 
+            def send_model_to(self, device):
+                self.net.to(device)
+                self.face_helper.face_det.to(device)
+                self.face_helper.face_parse.to(device)
+
             def restore(self, np_image, w=None):
                 np_image = np_image[:, :, ::-1]
 
@@ -82,6 +86,8 @@ def setup_model(dirname):
                 if self.net is None or self.face_helper is None:
                     return np_image
 
+                self.send_model_to(devices.device_codeformer)
+
                 self.face_helper.clean_all()
                 self.face_helper.read_image(np_image)
                 self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
@@ -113,8 +119,10 @@ def setup_model(dirname):
                 if original_resolution != restored_img.shape[0:2]:
                     restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
 
+                self.face_helper.clean_all()
+
                 if shared.opts.face_restoration_unload:
-                    self.net.to(devices.cpu)
+                    self.send_model_to(devices.cpu)
 
                 return restored_img
 
diff --git a/modules/devices.py b/modules/devices.py
index ff82f2f6..12aab665 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,3 +1,5 @@
+import contextlib
+
 import torch
 
 # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
@@ -57,3 +59,11 @@ def randn_without_seed(shape):
 
     return torch.randn(shape, device=device)
 
+
+def autocast():
+    from modules import shared
+
+    if dtype == torch.float32 or shared.cmd_opts.precision == "full":
+        return contextlib.nullcontext()
+
+    return torch.autocast("cuda")
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index dd3fbcab..5586b554 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -37,22 +37,32 @@ def gfpgann():
         print("Unable to load gfpgan model!")
         return None
     model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
-    model.gfpgan.to(shared.device)
     loaded_gfpgan_model = model
 
     return model
 
 
+def send_model_to(model, device):
+    model.gfpgan.to(device)
+    model.face_helper.face_det.to(device)
+    model.face_helper.face_parse.to(device)
+
+
 def gfpgan_fix_faces(np_image):
     model = gfpgann()
     if model is None:
         return np_image
+
+    send_model_to(model, devices.device)
+
     np_image_bgr = np_image[:, :, ::-1]
     cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
     np_image = gfpgan_output_bgr[:, :, ::-1]
 
+    model.face_helper.clean_all()
+
     if shared.opts.face_restoration_unload:
-        model.gfpgan.to(devices.cpu)
+        send_model_to(model, devices.cpu)
 
     return np_image
 
diff --git a/modules/processing.py b/modules/processing.py
index 0a4b6198..9cbecdd8 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1,4 +1,3 @@
-import contextlib
 import json
 import math
 import os
@@ -330,9 +329,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     infotexts = []
     output_images = []
-    precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
-    ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope)
-    with torch.no_grad(), precision_scope("cuda"), ema_scope():
+
+    with torch.no_grad():
         p.init(all_prompts, all_seeds, all_subseeds)
 
         if state.job_count == -1:
@@ -351,8 +349,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
             #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
             #c = p.sd_model.get_learned_conditioning(prompts)
-            uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
-            c = prompt_parser.get_learned_conditioning(prompts, p.steps)
+            with devices.autocast():
+                uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
+                c = prompt_parser.get_learned_conditioning(prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
                 for comment in model_hijack.comments:
@@ -361,7 +360,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if p.n_iter > 1:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
-            samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+            with devices.autocast():
+                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength).to(devices.dtype)
+
             if state.interrupted:
 
                 # if we are interruped, sample returns just noise
@@ -386,6 +387,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                     devices.torch_gc()
 
                     x_sample = modules.face_restoration.restore_faces(x_sample)
+                    devices.torch_gc()
 
                 image = Image.fromarray(x_sample)
 
-- 
cgit v1.2.1


From 2f1b61d97987ae0a52a7dfc6bc99c68928bdb594 Mon Sep 17 00:00:00 2001
From: dan <guaneec@gmail.com>
Date: Mon, 3 Oct 2022 19:25:36 +0800
Subject: Allow nested structures inside schedules

---
 modules/prompt_parser.py  | 119 +++++++++++++++++++++-------------------------
 requirements.txt          |   1 +
 requirements_versions.txt |   1 +
 3 files changed, 55 insertions(+), 66 deletions(-)

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index e811eb9e..99c8ed99 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -1,20 +1,11 @@
 import re
 from collections import namedtuple
 import torch
+from lark import Lark, Transformer, Visitor
+import functools
 
 import modules.shared as shared
 
-re_prompt = re.compile(r'''
-(.*?)
-\[
-    ([^]:]+):
-    (?:([^]:]*):)?
-    ([0-9]*\.?[0-9]+)
-]
-|
-(.+)
-''', re.X)
-
 # a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
 # will be represented with prompt_schedule like this (assuming steps=100):
 # [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
@@ -25,61 +16,57 @@ re_prompt = re.compile(r'''
 
 
 def get_learned_conditioning_prompt_schedules(prompts, steps):
-    res = []
-    cache = {}
-
-    for prompt in prompts:
-        prompt_schedule: list[list[str | int]] = [[steps, ""]]
-
-        cached = cache.get(prompt, None)
-        if cached is not None:
-            res.append(cached)
-            continue
-
-        for m in re_prompt.finditer(prompt):
-            plaintext = m.group(1) if m.group(5) is None else m.group(5)
-            concept_from = m.group(2)
-            concept_to = m.group(3)
-            if concept_to is None:
-                concept_to = concept_from
-                concept_from = ""
-            swap_position = float(m.group(4)) if m.group(4) is not None else None
-
-            if swap_position is not None:
-                if swap_position < 1:
-                    swap_position = swap_position * steps
-                swap_position = int(min(swap_position, steps))
-
-            swap_index = None
-            found_exact_index = False
-            for i in range(len(prompt_schedule)):
-                end_step = prompt_schedule[i][0]
-                prompt_schedule[i][1] += plaintext
-
-                if swap_position is not None and swap_index is None:
-                    if swap_position == end_step:
-                        swap_index = i
-                        found_exact_index = True
-
-                    if swap_position < end_step:
-                        swap_index = i
-
-            if swap_index is not None:
-                if not found_exact_index:
-                    prompt_schedule.insert(swap_index, [swap_position, prompt_schedule[swap_index][1]])
-
-                for i in range(len(prompt_schedule)):
-                    end_step = prompt_schedule[i][0]
-                    must_replace = swap_position < end_step
-
-                    prompt_schedule[i][1] += concept_to if must_replace else concept_from
-
-        res.append(prompt_schedule)
-        cache[prompt] = prompt_schedule
-        #for t in prompt_schedule:
-        #    print(t)
-
-    return res
+    grammar = r"""
+    start: prompt
+    prompt: (emphasized | scheduled | weighted | plain)*
+    !emphasized: "(" prompt ")"
+            | "(" prompt ":" prompt ")"
+            | "[" prompt "]"
+    scheduled: "[" (prompt ":")? prompt ":" NUMBER "]"
+    !weighted: "{" weighted_item ("|" weighted_item)* "}"
+    !weighted_item: prompt (":" prompt)?
+    plain: /([^\\\[\](){}:|]|\\.)+/
+    %import common.SIGNED_NUMBER -> NUMBER
+    """
+    parser = Lark(grammar, parser='lalr')
+    def collect_steps(steps, tree):
+        l = [steps]
+        class CollectSteps(Visitor):
+            def scheduled(self, tree):
+                tree.children[-1] = float(tree.children[-1])
+                if tree.children[-1] < 1:
+                    tree.children[-1] *= steps
+                tree.children[-1] = min(steps, int(tree.children[-1]))
+                l.append(tree.children[-1])
+        CollectSteps().visit(tree)
+        return sorted(set(l))
+    def at_step(step, tree):
+        class AtStep(Transformer):
+            def scheduled(self, args):
+                if len(args) == 2:
+                    before, after, when = (), *args
+                else:
+                    before, after, when = args
+                yield before if step <= when else after
+            def start(self, args):
+                def flatten(x):
+                    if type(x) == str:
+                        yield x
+                    else:
+                        for gen in x:
+                            yield from flatten(gen)
+                return ''.join(flatten(args[0]))
+            def plain(self, args):
+                yield args[0].value
+            def __default__(self, data, children, meta):
+                for child in children:
+                    yield from child
+        return AtStep().transform(tree)
+    @functools.cache
+    def get_schedule(prompt):
+        tree = parser.parse(prompt)
+        return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
+    return [get_schedule(prompt) for prompt in prompts]
 
 
 ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
diff --git a/requirements.txt b/requirements.txt
index d4b337fc..631fe616 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,3 +22,4 @@ clean-fid
 resize-right
 torchdiffeq
 kornia
+lark
diff --git a/requirements_versions.txt b/requirements_versions.txt
index 8a9acf20..fdff2687 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -21,3 +21,4 @@ clean-fid==0.1.29
 resize-right==0.0.2
 torchdiffeq==0.2.3
 kornia==0.6.7
+lark==1.1.2
-- 
cgit v1.2.1


From 61652461242951966e5b4cee83ce359cefa91c17 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 14:23:22 +0300
Subject: support interrupting after the previous change

---
 modules/processing.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 9cbecdd8..6f5599c7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -361,7 +361,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
             with devices.autocast():
-                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength).to(devices.dtype)
+                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
 
             if state.interrupted:
 
@@ -369,6 +369,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 # use the image collected previously in sampler loop
                 samples_ddim = shared.state.current_latent
 
+            samples_ddim = samples_ddim.to(devices.dtype)
+
             x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
-- 
cgit v1.2.1


From d5bba20a58f43a9f984bb67b4e17f48661f6b818 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 14:35:12 +0300
Subject: ignore errors in parse for purposes of token counting for #1564

---
 modules/ui.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 55f7aa95..20dc8c37 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -386,14 +386,22 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
         outputs=[seed, dummy_component]
     )
 
+
 def update_token_counter(text, steps):
-    prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
+    try:
+        prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
+    except Exception:
+        # a parsing error can happen here during typing, and we don't want to bother the user with
+        # messages related to it in console
+        prompt_schedules = [[[steps, text]]]
+
     flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
-    prompts = [prompt_text for step,prompt_text in flat_prompts]
+    prompts = [prompt_text for step, prompt_text in flat_prompts]
     tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
     style_class = ' class="red"' if (token_count > max_length) else ""
     return f"<span {style_class}>{token_count}/{max_length}</span>"
 
+
 def create_toprow(is_img2img):
     id_part = "img2img" if is_img2img else "txt2img"
 
-- 
cgit v1.2.1


From accd00d6b8258c12b5168918a4c546b02357924a Mon Sep 17 00:00:00 2001
From: Justin Riddiough <jriddiough@gmail.com>
Date: Tue, 4 Oct 2022 01:14:28 -0500
Subject: Explain how to use second progress bar in pycharm

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 25aff5b0..11bdf01a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -200,7 +200,7 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
 options_templates.update(options_section(('system', "System"), {
     "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
     "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
-    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),
+    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. In PyCharm select 'emulate terminal in console output'."),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-- 
cgit v1.2.1


From ea6b0d98a64290a0305e27126ea59ce1da7959a2 Mon Sep 17 00:00:00 2001
From: Justin Riddiough <jriddiough@gmail.com>
Date: Tue, 4 Oct 2022 06:38:45 -0500
Subject: Remove pycharm note, fix typo

---
 modules/shared.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 11bdf01a..a7d13b2d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -200,7 +200,7 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
 options_templates.update(options_section(('system', "System"), {
     "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
     "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
-    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. In PyCharm select 'emulate terminal in console output'."),
+    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
@@ -209,7 +209,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
-    "enable_emphasis": OptionInfo(True, "Eemphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+    "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
-- 
cgit v1.2.1


From eec1b39bd54711ca31e43022d2d6ac8c6d7281da Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Tue, 4 Oct 2022 20:16:52 +0900
Subject: Apply prompt pattern last

---
 modules/images.py | 39 ++++++++++++++++++++-------------------
 1 file changed, 20 insertions(+), 19 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index bba55158..5b56c7e3 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -287,6 +287,25 @@ def apply_filename_pattern(x, p, seed, prompt):
     if seed is not None:
         x = x.replace("[seed]", str(seed))
 
+    if p is not None:
+        x = x.replace("[steps]", str(p.steps))
+        x = x.replace("[cfg]", str(p.cfg_scale))
+        x = x.replace("[width]", str(p.width))
+        x = x.replace("[height]", str(p.height))
+
+        #currently disabled if using the save button, will work otherwise
+        # if enabled it will cause a bug because styles is not included in the save_files data dictionary
+        if hasattr(p, "styles"):
+            x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"] or "None"), replace_spaces=False))
+
+        x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
+
+    x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
+    x = x.replace("[date]", datetime.date.today().isoformat())
+    x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
+    x = x.replace("[job_timestamp]", shared.state.job_timestamp)
+
+    # Apply [prompt] at last. Because it may contain any replacement word.^M
     if prompt is not None:
         x = x.replace("[prompt]", sanitize_filename_part(prompt))
         if "[prompt_no_styles]" in x:
@@ -295,7 +314,7 @@ def apply_filename_pattern(x, p, seed, prompt):
                 if len(style) > 0:
                     style_parts = [y for y in style.split("{prompt}")]
                     for part in style_parts:
-                        prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')                        
+                        prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
             prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip()
             x = x.replace("[prompt_no_styles]", sanitize_filename_part(prompt_no_style, replace_spaces=False))
 
@@ -306,24 +325,6 @@ def apply_filename_pattern(x, p, seed, prompt):
                 words = ["empty"]
             x = x.replace("[prompt_words]", sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))
 
-    if p is not None:
-        x = x.replace("[steps]", str(p.steps))
-        x = x.replace("[cfg]", str(p.cfg_scale))
-        x = x.replace("[width]", str(p.width))
-        x = x.replace("[height]", str(p.height))
-        
-        #currently disabled if using the save button, will work otherwise 
-        # if enabled it will cause a bug because styles is not included in the save_files data dictionary
-        if hasattr(p, "styles"):
-            x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"] or "None"), replace_spaces=False))
-
-        x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
-
-    x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
-    x = x.replace("[date]", datetime.date.today().isoformat())
-    x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
-    x = x.replace("[job_timestamp]", shared.state.job_timestamp)
-
     if cmd_opts.hide_ui_dir_config:
         x = re.sub(r'^[\\/]+|\.{2,}[\\/]+|[\\/]+\.{2,}', '', x)
 
-- 
cgit v1.2.1


From 52cef36f6ba169a8e606ecdcaed73d47378f0e8e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 16:54:31 +0300
Subject: emergency fix for img2img

---
 modules/processing.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 6f5599c7..e9c45394 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -331,7 +331,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     output_images = []
 
     with torch.no_grad():
-        p.init(all_prompts, all_seeds, all_subseeds)
+        with devices.autocast():
+            p.init(all_prompts, all_seeds, all_subseeds)
 
         if state.job_count == -1:
             state.job_count = p.n_iter
-- 
cgit v1.2.1


From 957e29a8e9cb8ca069799ec69263e188c89ed6a6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 17:23:48 +0300
Subject: option to not show images in web ui

---
 modules/img2img.py | 3 +++
 modules/shared.py  | 1 +
 modules/txt2img.py | 3 +++
 3 files changed, 7 insertions(+)

diff --git a/modules/img2img.py b/modules/img2img.py
index 2ff8e261..da212d72 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -129,4 +129,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
     if opts.samples_log_stdout:
         print(generation_info_js)
 
+    if opts.do_not_show_images:
+        processed.images = []
+
     return processed.images, generation_info_js, plaintext_to_html(processed.info)
diff --git a/modules/shared.py b/modules/shared.py
index a7d13b2d..ff4e5fa3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -229,6 +229,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
+    "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
diff --git a/modules/txt2img.py b/modules/txt2img.py
index d4406c3c..e985242b 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -48,5 +48,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     if opts.samples_log_stdout:
         print(generation_info_js)
 
+    if opts.do_not_show_images:
+        processed.images = []
+
     return processed.images, generation_info_js, plaintext_to_html(processed.info)
 
-- 
cgit v1.2.1


From e1b128d8e46bddb9c0b2fd3ee0eefd57e0527ee0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 17:36:39 +0300
Subject: do not touch p.seed/p.subseed during processing #1181

---
 modules/processing.py | 26 +++++++++++++++++---------
 1 file changed, 17 insertions(+), 9 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index e9c45394..8180c63d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -248,9 +248,16 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     return x
 
 
+def get_fixed_seed(seed):
+    if seed is None or seed == '' or seed == -1:
+        return int(random.randrange(4294967294))
+
+    return seed
+
+
 def fix_seed(p):
-    p.seed = int(random.randrange(4294967294)) if p.seed is None or p.seed == '' or p.seed == -1 else p.seed
-    p.subseed = int(random.randrange(4294967294)) if p.subseed is None or p.subseed == '' or p.subseed == -1 else p.subseed
+    p.seed = get_fixed_seed(p.seed)
+    p.subseed = get_fixed_seed(p.subseed)
 
 
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
@@ -292,7 +299,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         
     devices.torch_gc()
 
-    fix_seed(p)
+    seed = get_fixed_seed(p.seed)
+    subseed = get_fixed_seed(p.subseed)
 
     if p.outpath_samples is not None:
         os.makedirs(p.outpath_samples, exist_ok=True)
@@ -311,15 +319,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     else:
         all_prompts = p.batch_size * p.n_iter * [p.prompt]
 
-    if type(p.seed) == list:
-        all_seeds = p.seed
+    if type(seed) == list:
+        all_seeds = seed
     else:
-        all_seeds = [int(p.seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
+        all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
 
-    if type(p.subseed) == list:
-        all_subseeds = p.subseed
+    if type(subseed) == list:
+        all_subseeds = subseed
     else:
-        all_subseeds = [int(p.subseed) + x for x in range(len(all_prompts))]
+        all_subseeds = [int(subseed) + x for x in range(len(all_prompts))]
 
     def infotext(iteration=0, position_in_batch=0):
         return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
-- 
cgit v1.2.1


From 1eb588cbf19924333b88beaa1ac0041904966640 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 18:02:01 +0300
Subject: remove functools.cache as some people are having issues with it

---
 modules/prompt_parser.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 99c8ed99..5d58c4ed 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -29,6 +29,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
     %import common.SIGNED_NUMBER -> NUMBER
     """
     parser = Lark(grammar, parser='lalr')
+
     def collect_steps(steps, tree):
         l = [steps]
         class CollectSteps(Visitor):
@@ -40,6 +41,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
                 l.append(tree.children[-1])
         CollectSteps().visit(tree)
         return sorted(set(l))
+
     def at_step(step, tree):
         class AtStep(Transformer):
             def scheduled(self, args):
@@ -62,11 +64,13 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
                 for child in children:
                     yield from child
         return AtStep().transform(tree)
-    @functools.cache
+    
     def get_schedule(prompt):
         tree = parser.parse(prompt)
         return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
-    return [get_schedule(prompt) for prompt in prompts]
+
+    promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}
+    return [promptdict[prompt] for prompt in prompts]
 
 
 ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
-- 
cgit v1.2.1


From 90e911fd546e76f879b38a764473569911a0f845 Mon Sep 17 00:00:00 2001
From: Rae Fu <rraefu@gmail.com>
Date: Tue, 4 Oct 2022 09:49:51 -0600
Subject: prompt_parser: allow spaces in schedules, add test, log/ignore errors

Only build the parser once (at import time) instead of for each step.

doctest is run by simply executing modules/prompt_parser.py
---
 modules/processing.py    |  10 ++--
 modules/prompt_parser.py | 139 ++++++++++++++++++++++++++++++-----------------
 2 files changed, 95 insertions(+), 54 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 8180c63d..bb94033b 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -84,7 +84,7 @@ class StableDiffusionProcessing:
         self.s_tmin = opts.s_tmin
         self.s_tmax = float('inf')  # not representable as a standard ui option
         self.s_noise = opts.s_noise
-        
+
         if not seed_enable_extras:
             self.subseed = -1
             self.subseed_strength = 0
@@ -296,7 +296,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         assert(len(p.prompt) > 0)
     else:
         assert p.prompt is not None
-        
+
     devices.torch_gc()
 
     seed = get_fixed_seed(p.seed)
@@ -359,8 +359,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
             #c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
-                uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
-                c = prompt_parser.get_learned_conditioning(prompts, p.steps)
+                uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
+                c = prompt_parser.get_learned_conditioning(shared.sd_model, prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
                 for comment in model_hijack.comments:
@@ -527,7 +527,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         # GC now before running the next img2img to prevent running out of memory
         x = None
         devices.torch_gc()
-        
+
         samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
 
         return samples
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 5d58c4ed..a3b12421 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -1,10 +1,7 @@
 import re
 from collections import namedtuple
-import torch
-from lark import Lark, Transformer, Visitor
-import functools
 
-import modules.shared as shared
+import lark
 
 # a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
 # will be represented with prompt_schedule like this (assuming steps=100):
@@ -14,25 +11,48 @@ import modules.shared as shared
 # [75, 'fantasy landscape with a lake and an oak in background masterful']
 # [100, 'fantasy landscape with a lake and a christmas tree in background masterful']
 
+schedule_parser = lark.Lark(r"""
+!start: (prompt | /[][():]/+)*
+prompt: (emphasized | scheduled | plain | WHITESPACE)*
+!emphasized: "(" prompt ")"
+        | "(" prompt ":" prompt ")"
+        | "[" prompt "]"
+scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
+WHITESPACE: /\s+/
+plain: /([^\\\[\]():]|\\.)+/
+%import common.SIGNED_NUMBER -> NUMBER
+""")
 
 def get_learned_conditioning_prompt_schedules(prompts, steps):
-    grammar = r"""
-    start: prompt
-    prompt: (emphasized | scheduled | weighted | plain)*
-    !emphasized: "(" prompt ")"
-            | "(" prompt ":" prompt ")"
-            | "[" prompt "]"
-    scheduled: "[" (prompt ":")? prompt ":" NUMBER "]"
-    !weighted: "{" weighted_item ("|" weighted_item)* "}"
-    !weighted_item: prompt (":" prompt)?
-    plain: /([^\\\[\](){}:|]|\\.)+/
-    %import common.SIGNED_NUMBER -> NUMBER
     """
-    parser = Lark(grammar, parser='lalr')
+    >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
+    >>> g("test")
+    [[10, 'test']]
+    >>> g("a [b:3]")
+    [[3, 'a '], [10, 'a b']]
+    >>> g("a [b: 3]")
+    [[3, 'a '], [10, 'a b']]
+    >>> g("a [[[b]]:2]")
+    [[2, 'a '], [10, 'a [[b]]']]
+    >>> g("[(a:2):3]")
+    [[3, ''], [10, '(a:2)']]
+    >>> g("a [b : c : 1] d")
+    [[1, 'a b  d'], [10, 'a  c  d']]
+    >>> g("a[b:[c:d:2]:1]e")
+    [[1, 'abe'], [2, 'ace'], [10, 'ade']]
+    >>> g("a [unbalanced")
+    [[10, 'a [unbalanced']]
+    >>> g("a [b:.5] c")
+    [[5, 'a  c'], [10, 'a b c']]
+    >>> g("a [{b|d{:.5] c")  # not handling this right now
+    [[5, 'a  c'], [10, 'a {b|d{ c']]
+    >>> g("((a][:b:c [d:3]")
+    [[3, '((a][:b:c '], [10, '((a][:b:c d']]
+    """
 
     def collect_steps(steps, tree):
         l = [steps]
-        class CollectSteps(Visitor):
+        class CollectSteps(lark.Visitor):
             def scheduled(self, tree):
                 tree.children[-1] = float(tree.children[-1])
                 if tree.children[-1] < 1:
@@ -43,13 +63,10 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
         return sorted(set(l))
 
     def at_step(step, tree):
-        class AtStep(Transformer):
+        class AtStep(lark.Transformer):
             def scheduled(self, args):
-                if len(args) == 2:
-                    before, after, when = (), *args
-                else:
-                    before, after, when = args
-                yield before if step <= when else after
+                before, after, _, when = args
+                yield before or () if step <= when else after
             def start(self, args):
                 def flatten(x):
                     if type(x) == str:
@@ -57,16 +74,22 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
                     else:
                         for gen in x:
                             yield from flatten(gen)
-                return ''.join(flatten(args[0]))
+                return ''.join(flatten(args))
             def plain(self, args):
                 yield args[0].value
             def __default__(self, data, children, meta):
                 for child in children:
                     yield from child
         return AtStep().transform(tree)
-    
+
     def get_schedule(prompt):
-        tree = parser.parse(prompt)
+        try:
+            tree = schedule_parser.parse(prompt)
+        except lark.exceptions.LarkError as e:
+            if 0:
+                import traceback
+                traceback.print_exc()
+            return [[steps, prompt]]
         return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
 
     promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}
@@ -77,8 +100,7 @@ ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at
 ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"])
 
 
-def get_learned_conditioning(prompts, steps):
-
+def get_learned_conditioning(model, prompts, steps):
     res = []
 
     prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
@@ -92,7 +114,7 @@ def get_learned_conditioning(prompts, steps):
             continue
 
         texts = [x[1] for x in prompt_schedule]
-        conds = shared.sd_model.get_learned_conditioning(texts)
+        conds = model.get_learned_conditioning(texts)
 
         cond_schedule = []
         for i, (end_at_step, text) in enumerate(prompt_schedule):
@@ -105,12 +127,13 @@ def get_learned_conditioning(prompts, steps):
 
 
 def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
-    res = torch.zeros(c.shape, device=shared.device, dtype=next(shared.sd_model.parameters()).dtype)
+    param = c.schedules[0][0].cond
+    res = torch.zeros(c.shape, device=param.device, dtype=param.dtype)
     for i, cond_schedule in enumerate(c.schedules):
         target_index = 0
-        for curret_index, (end_at, cond) in enumerate(cond_schedule):
+        for current, (end_at, cond) in enumerate(cond_schedule):
             if current_step <= end_at:
-                target_index = curret_index
+                target_index = current
                 break
         res[i] = cond_schedule[target_index].cond
 
@@ -148,23 +171,26 @@ def parse_prompt_attention(text):
       \\ - literal character '\'
       anything else - just text
 
-    Example:
-
-        'a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).'
-
-    produces:
-
-    [
-        ['a ', 1.0],
-        ['house', 1.5730000000000004],
-        [' ', 1.1],
-        ['on', 1.0],
-        [' a ', 1.1],
-        ['hill', 0.55],
-        [', sun, ', 1.1],
-        ['sky', 1.4641000000000006],
-        ['.', 1.1]
-    ]
+    >>> parse_prompt_attention('normal text')
+    [['normal text', 1.0]]
+    >>> parse_prompt_attention('an (important) word')
+    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
+    >>> parse_prompt_attention('(unbalanced')
+    [['unbalanced', 1.1]]
+    >>> parse_prompt_attention('\(literal\]')
+    [['(literal]', 1.0]]
+    >>> parse_prompt_attention('(unnecessary)(parens)')
+    [['unnecessaryparens', 1.1]]
+    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
+    [['a ', 1.0],
+     ['house', 1.5730000000000004],
+     [' ', 1.1],
+     ['on', 1.0],
+     [' a ', 1.1],
+     ['hill', 0.55],
+     [', sun, ', 1.1],
+     ['sky', 1.4641000000000006],
+     ['.', 1.1]]
     """
 
     res = []
@@ -206,4 +232,19 @@ def parse_prompt_attention(text):
     if len(res) == 0:
         res = [["", 1.0]]
 
+    # merge runs of identical weights
+    i = 0
+    while i + 1 < len(res):
+        if res[i][1] == res[i + 1][1]:
+            res[i][0] += res[i + 1][0]
+            res.pop(i + 1)
+        else:
+            i += 1
+
     return res
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
+else:
+    import torch  # doctest faster
-- 
cgit v1.2.1


From b32852ef037251eb3d846af76e2965594e1ac7a5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 4 Oct 2022 20:49:54 +0300
Subject: add editor to img2img

---
 modules/shared.py | 1 +
 modules/ui.py     | 2 +-
 style.css         | 4 ++++
 3 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index ff4e5fa3..e52c9b1d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -55,6 +55,7 @@ parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide dire
 parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="color-sketch")
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
diff --git a/modules/ui.py b/modules/ui.py
index 20dc8c37..6cd6761b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -644,7 +644,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
                     with gr.TabItem('img2img', id='img2img'):
-                        init_img = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil")
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool)
 
                     with gr.TabItem('Inpaint', id='inpaint'):
                         init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA")
diff --git a/style.css b/style.css
index 39586bf1..e8f4cb75 100644
--- a/style.css
+++ b/style.css
@@ -403,3 +403,7 @@ input[type="range"]{
 .red {
 	color: red;
 }
+
+#img2img_image div.h-60{
+    height: 480px;
+}
\ No newline at end of file
-- 
cgit v1.2.1


From ef40e4cd4d383a3405e03f1da3f5b5a1820a8f53 Mon Sep 17 00:00:00 2001
From: xpscyho <xpsychonauticonx@gmail.com>
Date: Tue, 4 Oct 2022 15:12:38 -0400
Subject: Display time taken in mins, secs when relevant Fixes #1656

---
 modules/ui.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 6cd6761b..de6342a4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -196,6 +196,11 @@ def wrap_gradio_call(func, extra_outputs=None):
             res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
 
         elapsed = time.perf_counter() - t
+        elapsed_m = int(elapsed // 60)
+        elapsed_s = elapsed % 60
+        elapsed_text = f"{elapsed_s:.2f}s"
+        if (elapsed_m > 0):
+            elapsed_text = f"{elapsed_m}m "+elapsed_text
 
         if run_memmon:
             mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
@@ -210,7 +215,7 @@ def wrap_gradio_call(func, extra_outputs=None):
             vram_html = ''
 
         # last item is always HTML
-        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"
+        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
 
         shared.state.interrupted = False
         shared.state.job_count = 0
-- 
cgit v1.2.1


From 82380d9ac18614c87bebba1b4cfd4b147cc76a18 Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Tue, 4 Oct 2022 22:28:50 -0300
Subject: Removing parts no longer needed to fix vram

---
 modules/devices.py    |  3 +--
 modules/processing.py | 21 ++++++++-------------
 2 files changed, 9 insertions(+), 15 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 6db4e57c..0158b11f 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,7 +1,6 @@
 import contextlib
 
 import torch
-import gc
 
 from modules import errors
 
@@ -20,8 +19,8 @@ def get_optimal_device():
 
     return cpu
 
+
 def torch_gc():
-    gc.collect()
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()
diff --git a/modules/processing.py b/modules/processing.py
index e7f9c85e..f666ba81 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -345,8 +345,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         if state.job_count == -1:
             state.job_count = p.n_iter
 
-    for n in range(p.n_iter):
-        with torch.no_grad(), precision_scope("cuda"), ema_scope():
+        for n in range(p.n_iter):
             if state.interrupted:
                 break
 
@@ -395,22 +394,19 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 import modules.safety as safety
                 x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)
 
-        for i, x_sample in enumerate(x_samples_ddim):
-            with torch.no_grad(), precision_scope("cuda"), ema_scope():
+            for i, x_sample in enumerate(x_samples_ddim):
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                 x_sample = x_sample.astype(np.uint8)
 
-            if p.restore_faces:
-                with torch.no_grad(), precision_scope("cuda"), ema_scope():
+                if p.restore_faces:
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
                         images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
 
-                    x_sample = modules.face_restoration.restore_faces(x_sample)
                     devices.torch_gc()
 
-                devices.torch_gc()
+                    x_sample = modules.face_restoration.restore_faces(x_sample)
+                    devices.torch_gc()
 
-            with torch.no_grad(), precision_scope("cuda"), ema_scope():
                 image = Image.fromarray(x_sample)
 
                 if p.color_corrections is not None and i < len(p.color_corrections):
@@ -438,13 +434,12 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 infotexts.append(infotext(n, i))
                 output_images.append(image)
 
-        del x_samples_ddim 
+            del x_samples_ddim 
 
-        devices.torch_gc()
+            devices.torch_gc()
 
-        state.nextjob()
+            state.nextjob()
 
-    with torch.no_grad(), precision_scope("cuda"), ema_scope():
         p.color_corrections = None
 
         index_of_first_image = 0
-- 
cgit v1.2.1


From bbdbbd36eda870cf0bd49fdf28476c78919a123e Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 5 Oct 2022 04:43:05 +0100
Subject: shared.state.interrupt when restart is requested

---
 modules/ui.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/ui.py b/modules/ui.py
index de6342a4..523ab25b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1210,6 +1210,7 @@ def create_ui(wrap_gradio_gpu_call):
         )
 
         def request_restart():
+            shared.state.interrupt()
             settings_interface.gradio_ref.do_restart = True
 
         restart_gradio.click(
-- 
cgit v1.2.1


From 67d011b02eddc20202b654dfea56528de3d5edf7 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 5 Oct 2022 04:44:22 +0100
Subject: Show generation progress in window title

---
 javascript/progressbar.js | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 1e297abb..3e3220c3 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -4,6 +4,21 @@ global_progressbars = {}
 function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_interrupt, id_preview, id_gallery){
     var progressbar = gradioApp().getElementById(id_progressbar)
     var interrupt = gradioApp().getElementById(id_interrupt)
+    
+    if(progressbar && progressbar.offsetParent){
+        if(progressbar.innerText){
+            let newtitle = 'Stable Diffusion - ' + progressbar.innerText
+            if(document.title != newtitle){
+                document.title =  newtitle;          
+            }
+        }else{
+            let newtitle = 'Stable Diffusion'
+            if(document.title != newtitle){
+                document.title =  newtitle;          
+            }
+        }
+    }
+    
 	if(progressbar!= null && progressbar != global_progressbars[id_progressbar]){
 	    global_progressbars[id_progressbar] = progressbar
 
-- 
cgit v1.2.1


From 59a2b9e5afc27d2fda72069ca0635070535d18fe Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Wed, 5 Oct 2022 20:50:10 +0200
Subject: deepdanbooru interrogator

---
 ... your deepbooru release project folder here.txt |  0
 modules/deepbooru.py                               | 60 ++++++++++++++++++++++
 modules/ui.py                                      | 24 +++++++--
 requirements.txt                                   |  3 ++
 requirements_versions.txt                          |  3 ++
 style.css                                          |  7 ++-
 6 files changed, 91 insertions(+), 6 deletions(-)
 create mode 100644 models/deepbooru/Put your deepbooru release project folder here.txt
 create mode 100644 modules/deepbooru.py

diff --git a/models/deepbooru/Put your deepbooru release project folder here.txt b/models/deepbooru/Put your deepbooru release project folder here.txt
new file mode 100644
index 00000000..e69de29b
diff --git a/modules/deepbooru.py b/modules/deepbooru.py
new file mode 100644
index 00000000..958b1c3d
--- /dev/null
+++ b/modules/deepbooru.py
@@ -0,0 +1,60 @@
+import os.path
+from concurrent.futures import ProcessPoolExecutor
+
+import numpy as np
+import deepdanbooru as dd
+import tensorflow as tf
+
+
+def _load_tf_and_return_tags(pil_image, threshold):
+    this_folder = os.path.dirname(__file__)
+    model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28')
+    if not os.path.exists(model_path):
+        return "Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru"
+
+    tags = dd.project.load_tags_from_project(model_path)
+    model = dd.project.load_model_from_project(
+        model_path, compile_model=True
+    )
+
+    width = model.input_shape[2]
+    height = model.input_shape[1]
+    image = np.array(pil_image)
+    image = tf.image.resize(
+        image,
+        size=(height, width),
+        method=tf.image.ResizeMethod.AREA,
+        preserve_aspect_ratio=True,
+    )
+    image = image.numpy()  # EagerTensor to np.array
+    image = dd.image.transform_and_pad_image(image, width, height)
+    image = image / 255.0
+    image_shape = image.shape
+    image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
+
+    y = model.predict(image)[0]
+
+    result_dict = {}
+
+    for i, tag in enumerate(tags):
+        result_dict[tag] = y[i]
+
+
+
+    result_tags_out = []
+    result_tags_print = []
+    for tag in tags:
+        if result_dict[tag] >= threshold:
+            result_tags_out.append(tag)
+            result_tags_print.append(f'{result_dict[tag]} {tag}')
+
+    print('\n'.join(sorted(result_tags_print, reverse=True)))
+
+    return ', '.join(result_tags_out)
+
+
+def get_deepbooru_tags(pil_image, threshold=0.5):
+    with ProcessPoolExecutor() as executor:
+        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold)
+        ret = f.result()  # will rethrow any exceptions
+    return ret
\ No newline at end of file
diff --git a/modules/ui.py b/modules/ui.py
index 20dc8c37..ae98219a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -23,6 +23,7 @@ import gradio.utils
 import gradio.routes
 
 from modules import sd_hijack
+from modules.deepbooru import get_deepbooru_tags
 from modules.paths import script_path
 from modules.shared import opts, cmd_opts
 import modules.shared as shared
@@ -312,6 +313,11 @@ def interrogate(image):
     return gr_show(True) if prompt is None else prompt
 
 
+def interrogate_deepbooru(image):
+    prompt = get_deepbooru_tags(image)
+    return gr_show(True) if prompt is None else prompt
+
+
 def create_seed_inputs():
     with gr.Row():
         with gr.Box():
@@ -439,15 +445,17 @@ def create_toprow(is_img2img):
                     outputs=[],
                 )
 
-            with gr.Row():
+            with gr.Row(scale=1):
                 if is_img2img:
-                    interrogate = gr.Button('Interrogate', elem_id="interrogate")
+                    interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+                    deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
                 else:
                     interrogate = None
+                    deepbooru = None
                 prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
                 save_style = gr.Button('Create style', elem_id="style_create")
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste, token_counter, token_button
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
 
 
 def setup_progressbar(progressbar, preview, id_part, textinfo=None):
@@ -476,7 +484,7 @@ def create_ui(wrap_gradio_gpu_call):
     import modules.txt2img
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
 
         with gr.Row(elem_id='txt2img_progress_row'):
@@ -628,7 +636,7 @@ def create_ui(wrap_gradio_gpu_call):
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
 
         with gr.Row(elem_id='img2img_progress_row'):
             with gr.Column(scale=1):
@@ -785,6 +793,12 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[img2img_prompt],
             )
 
+            img2img_deepbooru.click(
+                fn=interrogate_deepbooru,
+                inputs=[init_img],
+                outputs=[img2img_prompt],
+            )
+
             save.click(
                 fn=wrap_gradio_call(save_files),
                 _js="(x, y, z) => [x, y, selected_gallery_index()]",
diff --git a/requirements.txt b/requirements.txt
index 631fe616..cab101f8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,3 +23,6 @@ resize-right
 torchdiffeq
 kornia
 lark
+deepdanbooru
+tensorflow
+tensorflow-io
diff --git a/requirements_versions.txt b/requirements_versions.txt
index fdff2687..811953c6 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -22,3 +22,6 @@ resize-right==0.0.2
 torchdiffeq==0.2.3
 kornia==0.6.7
 lark==1.1.2
+git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow]
+tensorflow==2.10.0
+tensorflow-io==0.27.0
diff --git a/style.css b/style.css
index 39586bf1..2fd351f9 100644
--- a/style.css
+++ b/style.css
@@ -103,7 +103,12 @@
 
 #style_apply, #style_create, #interrogate{
     margin: 0.75em 0.25em 0.25em 0.25em;
-    min-width: 3em;
+    min-width: 5em;
+}
+
+#style_apply, #style_create, #deepbooru{
+    margin: 0.75em 0.25em 0.25em 0.25em;
+    min-width: 5em;
 }
 
 #style_pos_col, #style_neg_col{
-- 
cgit v1.2.1


From 1506fab29ad54beb9f52236912abc432209c8089 Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Wed, 5 Oct 2022 21:15:08 +0200
Subject: removing problematic tag

---
 modules/deepbooru.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 958b1c3d..841cb9c5 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -38,13 +38,12 @@ def _load_tf_and_return_tags(pil_image, threshold):
 
     for i, tag in enumerate(tags):
         result_dict[tag] = y[i]
-
-
-
     result_tags_out = []
     result_tags_print = []
     for tag in tags:
         if result_dict[tag] >= threshold:
+            if tag.startswith("rating:"):
+                continue
             result_tags_out.append(tag)
             result_tags_print.append(f'{result_dict[tag]} {tag}')
 
-- 
cgit v1.2.1


From 17a99baf0c929e5df4dfc4b2a96aa3890a141112 Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Wed, 5 Oct 2022 22:05:24 +0200
Subject: better model search

---
 modules/deepbooru.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 841cb9c5..a64fd9cd 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -9,8 +9,15 @@ import tensorflow as tf
 def _load_tf_and_return_tags(pil_image, threshold):
     this_folder = os.path.dirname(__file__)
     model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28')
-    if not os.path.exists(model_path):
-        return "Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru"
+
+    model_good = False
+    for path_candidate in [model_path, os.path.dirname(model_path)]:
+        if os.path.exists(os.path.join(path_candidate, 'project.json')):
+            model_path = path_candidate
+            model_good = True
+    if not model_good:
+        return ("Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/"
+                "deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru")
 
     tags = dd.project.load_tags_from_project(model_path)
     model = dd.project.load_model_from_project(
-- 
cgit v1.2.1


From c26732fbee2a57e621ac22bf70decf7496daa4cd Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 5 Oct 2022 23:16:27 +0300
Subject: added support for AND from
 https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/

---
 modules/processing.py    |   2 +-
 modules/prompt_parser.py | 114 ++++++++++++++++++++++++++++++++++++++++++++---
 modules/sd_samplers.py   |  35 ++++++++++-----
 modules/ui.py            |   6 ++-
 4 files changed, 138 insertions(+), 19 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index bb94033b..d8c6b8d5 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -360,7 +360,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             #c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
-                c = prompt_parser.get_learned_conditioning(shared.sd_model, prompts, p.steps)
+                c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
                 for comment in model_hijack.comments:
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index a3b12421..f7420daf 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -97,10 +97,26 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
 
 
 ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
-ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"])
 
 
 def get_learned_conditioning(model, prompts, steps):
+    """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
+    and the sampling step at which this condition is to be replaced by the next one.
+
+    Input:
+    (model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20)
+
+    Output:
+    [
+        [
+            ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886,  0.0229, -0.0523,  ..., -0.4901, -0.3066,  0.0674], ..., [ 0.3317, -0.5102, -0.4066,  ...,  0.4119, -0.7647, -1.0160]], device='cuda:0'))
+        ],
+        [
+            ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886,  0.0229, -0.0522,  ..., -0.4901, -0.3067,  0.0673], ..., [-0.0192,  0.3867, -0.4644,  ...,  0.1135, -0.3696, -0.4625]], device='cuda:0')),
+            ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886,  0.0229, -0.0522,  ..., -0.4901, -0.3067,  0.0673], ..., [-0.7352, -0.4356, -0.7888,  ...,  0.6994, -0.4312, -1.2593]], device='cuda:0'))
+        ]
+    ]
+    """
     res = []
 
     prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
@@ -123,13 +139,75 @@ def get_learned_conditioning(model, prompts, steps):
         cache[prompt] = cond_schedule
         res.append(cond_schedule)
 
-    return ScheduledPromptBatch((len(prompts),) + res[0][0].cond.shape, res)
+    return res
+
+
+re_AND = re.compile(r"\bAND\b")
+re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?\s*(?:\d+|\d*\.\d+)?))?\s*$")
+
+
+def get_multicond_prompt_list(prompts):
+    res_indexes = []
+
+    prompt_flat_list = []
+    prompt_indexes = {}
+
+    for prompt in prompts:
+        subprompts = re_AND.split(prompt)
+
+        indexes = []
+        for subprompt in subprompts:
+            text, weight = re_weight.search(subprompt).groups()
+
+            weight = float(weight) if weight is not None else 1.0
+
+            index = prompt_indexes.get(text, None)
+            if index is None:
+                index = len(prompt_flat_list)
+                prompt_flat_list.append(text)
+                prompt_indexes[text] = index
+
+            indexes.append((index, weight))
+
+        res_indexes.append(indexes)
+
+    return res_indexes, prompt_flat_list, prompt_indexes
+
+
+class ComposableScheduledPromptConditioning:
+    def __init__(self, schedules, weight=1.0):
+        self.schedules: list[ScheduledPromptConditioning] = schedules
+        self.weight: float = weight
+
+
+class MulticondLearnedConditioning:
+    def __init__(self, shape, batch):
+        self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS
+        self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
 
 
-def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
-    param = c.schedules[0][0].cond
-    res = torch.zeros(c.shape, device=param.device, dtype=param.dtype)
-    for i, cond_schedule in enumerate(c.schedules):
+def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
+    """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
+    For each prompt, the list is obtained by splitting the prompt using the AND separator.
+
+    https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
+    """
+
+    res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)
+
+    learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
+
+    res = []
+    for indexes in res_indexes:
+        res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes])
+
+    return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
+
+
+def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
+    param = c[0][0].cond
+    res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
+    for i, cond_schedule in enumerate(c):
         target_index = 0
         for current, (end_at, cond) in enumerate(cond_schedule):
             if current_step <= end_at:
@@ -140,6 +218,30 @@ def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
     return res
 
 
+def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
+    param = c.batch[0][0].schedules[0].cond
+
+    tensors = []
+    conds_list = []
+
+    for batch_no, composable_prompts in enumerate(c.batch):
+        conds_for_batch = []
+
+        for cond_index, composable_prompt in enumerate(composable_prompts):
+            target_index = 0
+            for current, (end_at, cond) in enumerate(composable_prompt.schedules):
+                if current_step <= end_at:
+                    target_index = current
+                    break
+
+            conds_for_batch.append((len(tensors), composable_prompt.weight))
+            tensors.append(composable_prompt.schedules[target_index].cond)
+
+        conds_list.append(conds_for_batch)
+
+    return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
+
+
 re_attention = re.compile(r"""
 \\\(|
 \\\)|
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index dbf570d2..d27c547b 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -109,9 +109,12 @@ class VanillaStableDiffusionSampler:
         return 0
 
     def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
-        cond = prompt_parser.reconstruct_cond_batch(cond, self.step)
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
 
+        assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
+        cond = tensor
+
         if self.mask is not None:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)
             x_dec = img_orig * self.mask + self.nmask * x_dec
@@ -183,19 +186,31 @@ class CFGDenoiser(torch.nn.Module):
         self.step = 0
 
     def forward(self, x, sigma, uncond, cond, cond_scale):
-        cond = prompt_parser.reconstruct_cond_batch(cond, self.step)
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
 
+        batch_size = len(conds_list)
+        repeats = [len(conds_list[i]) for i in range(batch_size)]
+
+        x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+        sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+        cond_in = torch.cat([tensor, uncond])
+
         if shared.batch_cond_uncond:
-            x_in = torch.cat([x] * 2)
-            sigma_in = torch.cat([sigma] * 2)
-            cond_in = torch.cat([uncond, cond])
-            uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
-            denoised = uncond + (cond - uncond) * cond_scale
+            x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
         else:
-            uncond = self.inner_model(x, sigma, cond=uncond)
-            cond = self.inner_model(x, sigma, cond=cond)
-            denoised = uncond + (cond - uncond) * cond_scale
+            x_out = torch.zeros_like(x_in)
+            for batch_offset in range(0, x_out.shape[0], batch_size):
+                a = batch_offset
+                b = a + batch_size
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
+
+        denoised_uncond = x_out[-batch_size:]
+        denoised = torch.clone(denoised_uncond)
+
+        for i, conds in enumerate(conds_list):
+            for cond_index, weight in conds:
+                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
 
         if self.mask is not None:
             denoised = self.init_latent * self.mask + self.nmask * denoised
diff --git a/modules/ui.py b/modules/ui.py
index 523ab25b..9620350f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -34,7 +34,7 @@ import modules.gfpgan_model
 import modules.codeformer_model
 import modules.styles
 import modules.generation_parameters_copypaste
-from modules.prompt_parser import get_learned_conditioning_prompt_schedules
+from modules import prompt_parser
 from modules.images import apply_filename_pattern, get_next_sequence_number
 import modules.textual_inversion.ui
 
@@ -394,7 +394,9 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
 
 def update_token_counter(text, steps):
     try:
-        prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
+        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
+
     except Exception:
         # a parsing error can happen here during typing, and we don't want to bother the user with
         # messages related to it in console
-- 
cgit v1.2.1


From 4320f386d9641c7c234589c4cb0c0c6cbeb156ad Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Wed, 5 Oct 2022 22:39:32 +0200
Subject: removing underscores and colons

---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index a64fd9cd..fb5018a6 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -56,7 +56,7 @@ def _load_tf_and_return_tags(pil_image, threshold):
 
     print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    return ', '.join(result_tags_out)
+    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
 
 
 def get_deepbooru_tags(pil_image, threshold=0.5):
-- 
cgit v1.2.1


From f8e41a96bb30a04dd5e294c7e1178c1c3b09d481 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 5 Oct 2022 23:52:05 +0300
Subject: fix various float parsing errors

---
 modules/prompt_parser.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index f7420daf..800b12c7 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -143,8 +143,7 @@ def get_learned_conditioning(model, prompts, steps):
 
 
 re_AND = re.compile(r"\bAND\b")
-re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?\s*(?:\d+|\d*\.\d+)?))?\s*$")
-
+re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
 
 def get_multicond_prompt_list(prompts):
     res_indexes = []
-- 
cgit v1.2.1


From 20f8ec877a99ce2ebf193cb1e2e773cfc77b7c41 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 00:09:32 +0300
Subject: remove type annotations in new code because presumably they don't
 work in 3.7

---
 modules/prompt_parser.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 800b12c7..ee4c5d02 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -175,14 +175,14 @@ def get_multicond_prompt_list(prompts):
 
 class ComposableScheduledPromptConditioning:
     def __init__(self, schedules, weight=1.0):
-        self.schedules: list[ScheduledPromptConditioning] = schedules
+        self.schedules = schedules  # : list[ScheduledPromptConditioning]
         self.weight: float = weight
 
 
 class MulticondLearnedConditioning:
     def __init__(self, shape, batch):
         self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS
-        self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
+        self.batch = batch  # : list[list[ComposableScheduledPromptConditioning]]
 
 
 def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
@@ -203,7 +203,7 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne
     return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
 
 
-def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
+def reconstruct_cond_batch(c, current_step):  # c: list[list[ScheduledPromptConditioning]]
     param = c[0][0].cond
     res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
     for i, cond_schedule in enumerate(c):
-- 
cgit v1.2.1


From 34c358d10d52817f7a889ae4c52096ee654f3fe6 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 5 Oct 2022 22:11:30 +0100
Subject: use typing.list in prompt_parser.py for wider python version support

---
 modules/prompt_parser.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 800b12c7..fdfa21ae 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -1,6 +1,6 @@
 import re
 from collections import namedtuple
-
+from typing import List
 import lark
 
 # a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
@@ -175,14 +175,14 @@ def get_multicond_prompt_list(prompts):
 
 class ComposableScheduledPromptConditioning:
     def __init__(self, schedules, weight=1.0):
-        self.schedules: list[ScheduledPromptConditioning] = schedules
+        self.schedules: List[ScheduledPromptConditioning] = schedules
         self.weight: float = weight
 
 
 class MulticondLearnedConditioning:
     def __init__(self, shape, batch):
         self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS
-        self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
+        self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
 
 
 def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
@@ -203,7 +203,7 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne
     return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
 
 
-def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
+def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
     param = c[0][0].cond
     res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
     for i, cond_schedule in enumerate(c):
-- 
cgit v1.2.1


From 55400c981b7c1389482057a35ed6ea11f08da194 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 6 Oct 2022 03:11:15 +0100
Subject: Set gradio-img2img-tool default to 'editor'

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index e52c9b1d..bab0fe6e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -55,7 +55,7 @@ parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide dire
 parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="color-sketch")
+parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor")
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
-- 
cgit v1.2.1


From 2499fb4e1910d31ff12c24110f161b20641b8835 Mon Sep 17 00:00:00 2001
From: Raphael Stoeckli <github@rabanti.ch>
Date: Wed, 5 Oct 2022 21:57:18 +0200
Subject: Add sanitizer for captions in Textual inversion

---
 modules/textual_inversion/preprocess.py | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index f545a993..4f3df4bd 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,5 +1,8 @@
+from cmath import log
 import os
 from PIL import Image, ImageOps
+import platform
+import sys
 import tqdm
 
 from modules import shared, images
@@ -25,6 +28,7 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
     def save_pic_with_caption(image, index):
         if process_caption:
             caption = "-" + shared.interrogator.generate_caption(image)
+            caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
         else:
             caption = filename
             caption = os.path.splitext(caption)[0]
@@ -75,3 +79,27 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
 
     if process_caption:
         shared.interrogator.send_blip_to_ram()
+
+def sanitize_caption(base_path, original_caption, suffix):
+    operating_system = platform.system().lower()
+    if (operating_system == "windows"):
+        invalid_path_characters = "\\/:*?\"<>|"
+        max_path_length = 259
+    else:
+        invalid_path_characters = "/" #linux/macos
+        max_path_length = 1023
+    caption = original_caption
+    for invalid_character in invalid_path_characters:
+        caption = caption.replace(invalid_character, "")
+    fixed_path_length = len(base_path) + len(suffix) 
+    if fixed_path_length + len(caption) <= max_path_length:
+        return caption
+    caption_tokens = caption.split()
+    new_caption = ""
+    for token in caption_tokens:
+        last_caption = new_caption
+        new_caption = new_caption + token + " "
+        if (len(new_caption) + fixed_path_length - 1  > max_path_length):
+            break
+    print(f"\nPath will be too long. Truncated caption: {original_caption}\nto: {last_caption}", file=sys.stderr)
+    return last_caption.strip()
-- 
cgit v1.2.1


From 4288e53fc2ea25fa49715bf5b7f14603553c9e38 Mon Sep 17 00:00:00 2001
From: Raphael Stoeckli <github@rabanti.ch>
Date: Wed, 5 Oct 2022 23:11:32 +0200
Subject: removed unused import, fixed typo

---
 modules/textual_inversion/preprocess.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 4f3df4bd..f1c002a2 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,4 +1,3 @@
-from cmath import log
 import os
 from PIL import Image, ImageOps
 import platform
@@ -13,7 +12,7 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
 
-    assert src != dst, 'same directory specified as source and desitnation'
+    assert src != dst, 'same directory specified as source and destination'
 
     os.makedirs(dst, exist_ok=True)
 
-- 
cgit v1.2.1


From a93c3ffbfd264ed6b5d989922352300c9d3efbe4 Mon Sep 17 00:00:00 2001
From: Jocke <joakim.lovgren@protonmail.com>
Date: Wed, 5 Oct 2022 16:31:48 +0200
Subject: Outpainting mk2, prevent generation of a completely random image
 every time even when global seed is static

---
 scripts/outpainting_mk_2.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py
index 11613ca3..a6468e09 100644
--- a/scripts/outpainting_mk_2.py
+++ b/scripts/outpainting_mk_2.py
@@ -85,8 +85,11 @@ def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.0
     src_dist = np.absolute(src_fft)
     src_phase = src_fft / src_dist
 
+    # create a generator with a static seed to make outpainting deterministic / only follow global seed
+    rng = np.random.default_rng(0)
+
     noise_window = _get_gaussian_window(width, height, mode=1)  # start with simple gaussian noise
-    noise_rgb = np.random.random_sample((width, height, num_channels))
+    noise_rgb = rng.random((width, height, num_channels))
     noise_grey = (np.sum(noise_rgb, axis=2) / 3.)
     noise_rgb *= color_variation  # the colorfulness of the starting noise is blended to greyscale with a parameter
     for c in range(num_channels):
-- 
cgit v1.2.1


From 6e7057b31b9762a9720282c7da486e4f264dee28 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 12:08:06 +0300
Subject: support for downloading new commit hash for git repos

---
 launch.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/launch.py b/launch.py
index 57405fea..2f91f586 100644
--- a/launch.py
+++ b/launch.py
@@ -86,6 +86,15 @@ def git_clone(url, dir, name, commithash=None):
     # TODO clone into temporary dir and move if successful
 
     if os.path.exists(dir):
+        if commithash is None:
+            return
+
+        current_hash = run(f'"{git}" -C {dir} rev-parse HEAD', None, "Couldn't determine {name}'s hash: {commithash}").strip()
+        if current_hash == commithash:
+            return
+
+        run(f'"{git}" -C {dir} fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
+        run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commint for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
         return
 
     run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
-- 
cgit v1.2.1


From 5f24b7bcf4a074fbdec757617fcd1bc82e76551b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 12:08:48 +0300
Subject: option to let users select which samplers they want to hide

---
 modules/processing.py  | 13 ++++++-------
 modules/sd_samplers.py | 19 +++++++++++++++++--
 modules/shared.py      | 15 +++++++++------
 webui.py               |  4 +++-
 4 files changed, 35 insertions(+), 16 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index d8c6b8d5..e01c8b3f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -11,9 +11,8 @@ import cv2
 from skimage import exposure
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking
+from modules import devices, prompt_parser, masking, sd_samplers
 from modules.sd_hijack import model_hijack
-from modules.sd_samplers import samplers, samplers_for_img2img
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.face_restoration
@@ -110,7 +109,7 @@ class Processed:
         self.width = p.width
         self.height = p.height
         self.sampler_index = p.sampler_index
-        self.sampler = samplers[p.sampler_index].name
+        self.sampler = sd_samplers.samplers[p.sampler_index].name
         self.cfg_scale = p.cfg_scale
         self.steps = p.steps
         self.batch_size = p.batch_size
@@ -265,7 +264,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params = {
         "Steps": p.steps,
-        "Sampler": samplers[p.sampler_index].name,
+        "Sampler": sd_samplers.samplers[p.sampler_index].name,
         "CFG scale": p.cfg_scale,
         "Seed": all_seeds[index],
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
@@ -478,7 +477,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.firstphase_height_truncated = int(scale * self.height)
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
@@ -521,7 +520,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         shared.state.nextjob()
 
-        self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         # GC now before running the next img2img to prevent running out of memory
@@ -556,7 +555,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.nmask = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
-        self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
         crop_region = None
 
         if self.image_mask is not None:
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d27c547b..2e1f7715 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -32,12 +32,27 @@ samplers_data_k_diffusion = [
     if hasattr(k_diffusion.sampling, funcname)
 ]
 
-samplers = [
+all_samplers = [
     *samplers_data_k_diffusion,
     SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
     SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
 ]
-samplers_for_img2img = [x for x in samplers if x.name not in ['PLMS', 'DPM fast', 'DPM adaptive']]
+
+samplers = []
+samplers_for_img2img = []
+
+
+def set_samplers():
+    global samplers, samplers_for_img2img
+
+    hidden = set(opts.hide_samplers)
+    hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive'])
+
+    samplers = [x for x in all_samplers if x.name not in hidden]
+    samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
+
+
+set_samplers()
 
 sampler_extra_params = {
     'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
diff --git a/modules/shared.py b/modules/shared.py
index bab0fe6e..ca2e4c74 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,6 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
+from modules import sd_samplers
 from modules.paths import script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -238,14 +239,16 @@ options_templates.update(options_section(('ui', "User interface"), {
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-  "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-  "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-  "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
-  's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-  's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-  's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in sd_samplers.all_samplers]}),
+    "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
+    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
 }))
 
+
 class Options:
     data = None
     data_labels = options_templates
diff --git a/webui.py b/webui.py
index 47848ba5..9ef12427 100644
--- a/webui.py
+++ b/webui.py
@@ -2,7 +2,7 @@ import os
 import threading
 import time
 import importlib
-from modules import devices
+from modules import devices, sd_samplers
 from modules.paths import script_path
 import signal
 import threading
@@ -109,6 +109,8 @@ def webui():
                 time.sleep(0.5)
                 break
 
+        sd_samplers.set_samplers()
+
         print('Reloading Custom Scripts')
         modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
         print('Reloading modules: modules.ui')
-- 
cgit v1.2.1


From 2d3ea42a2d1e909bbccdb6b49561b187c60a9402 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 13:21:12 +0300
Subject: workaround for a mysterious bug where prompt weights can't be matched

---
 modules/prompt_parser.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index a7a6aa31..f00256f2 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -156,7 +156,9 @@ def get_multicond_prompt_list(prompts):
 
         indexes = []
         for subprompt in subprompts:
-            text, weight = re_weight.search(subprompt).groups()
+            match = re_weight.search(subprompt)
+
+            text, weight = match.groups() if match is not None else (subprompt, 1.0)
 
             weight = float(weight) if weight is not None else 1.0
 
-- 
cgit v1.2.1


From 2a532804957e47bc36c67c8f5b104dcfa8e8f3f0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 13:21:32 +0300
Subject: reorder imports to fix the bug with k-diffusion on some version

---
 webui.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/webui.py b/webui.py
index 9ef12427..480360fe 100644
--- a/webui.py
+++ b/webui.py
@@ -2,11 +2,12 @@ import os
 import threading
 import time
 import importlib
-from modules import devices, sd_samplers
-from modules.paths import script_path
 import signal
 import threading
 
+from modules.paths import script_path
+
+from modules import devices, sd_samplers
 import modules.codeformer_model as codeformer
 import modules.extras
 import modules.face_restoration
-- 
cgit v1.2.1


From c30c06db207a580d76544fd10fc1e03cd58ce85e Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 3 Oct 2022 12:48:16 +0300
Subject: update k-diffusion

---
 launch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index 2f91f586..c2713c64 100644
--- a/launch.py
+++ b/launch.py
@@ -19,7 +19,7 @@ clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLI
 
 stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
 taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
-k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "a7ec1974d4ccb394c2dca275f42cd97490618924")
+k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "567e11f7062ba20ae32b5a8cd07fb0fc4b9410cf")
 codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
 blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
-- 
cgit v1.2.1


From c1a068ed0acc788774afc1541ca69342fd1d94ad Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 3 Oct 2022 12:49:17 +0300
Subject: Create alternate_sampler_noise_schedules.py

---
 scripts/alternate_sampler_noise_schedules.py | 53 ++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 scripts/alternate_sampler_noise_schedules.py

diff --git a/scripts/alternate_sampler_noise_schedules.py b/scripts/alternate_sampler_noise_schedules.py
new file mode 100644
index 00000000..4f3ed8fb
--- /dev/null
+++ b/scripts/alternate_sampler_noise_schedules.py
@@ -0,0 +1,53 @@
+import inspect
+from modules.processing import Processed, process_images
+import gradio as gr
+import modules.scripts as scripts
+import k_diffusion.sampling
+import torch
+
+
+class Script(scripts.Script):
+
+    def title(self):
+        return "Alternate Sampler Noise Schedules"
+
+    def ui(self, is_img2img):
+      noise_scheduler = gr.Dropdown(label="Noise Scheduler", choices=['Default','Karras','Exponential', 'Variance Preserving'], value='Default', type="index")
+      sched_smin     = gr.Slider(value=0.1,   label="Sigma min",                  minimum=0.0,   maximum=100.0, step=0.5,)
+      sched_smax     = gr.Slider(value=10.0,  label="Sigma max",                  minimum=0.0,   maximum=100.0, step=0.5)
+      sched_rho      = gr.Slider(value=7.0,   label="Sigma rho (Karras only)",    minimum=7.0,   maximum=100.0, step=0.5)
+      sched_beta_d   = gr.Slider(value=19.9,  label="Beta distribution (VP only)",minimum=0.0,   maximum=40.0, step=0.5)
+      sched_beta_min = gr.Slider(value=0.1,   label="Beta min (VP only)",         minimum=0.0,   maximum=40.0, step=0.1)
+      sched_eps_s    = gr.Slider(value=0.001, label="Epsilon (VP only)",          minimum=0.001, maximum=1.0,   step=0.001)
+
+      return [noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s]
+
+    def run(self, p, noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s):
+
+      noise_scheduler_func_name = ['-','get_sigmas_karras','get_sigmas_exponential','get_sigmas_vp'][noise_scheduler]
+
+      base_params = {
+        "sigma_min":sched_smin, 
+        "sigma_max":sched_smax, 
+        "rho":sched_rho, 
+        "beta_d":sched_beta_d, 
+        "beta_min":sched_beta_min, 
+        "eps_s":sched_eps_s,
+        "device":"cuda" if torch.cuda.is_available() else "cpu"
+      }
+
+      if hasattr(k_diffusion.sampling,noise_scheduler_func_name):
+
+        sigma_func  = getattr(k_diffusion.sampling,noise_scheduler_func_name)
+        sigma_func_kwargs = {}
+
+        for k,v in base_params.items():
+          if k in inspect.signature(sigma_func).parameters:
+            sigma_func_kwargs[k] = v
+
+        def substitute_noise_scheduler(n):
+          return sigma_func(n,**sigma_func_kwargs)
+
+        p.sampler_noise_scheduler_override = substitute_noise_scheduler
+
+      return process_images(p)
-- 
cgit v1.2.1


From 71901b3d3bea1d035bf4a7229d19356b4b062151 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Wed, 5 Oct 2022 14:30:57 +0300
Subject: add karras scheduling variants

---
 modules/sd_samplers.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 2e1f7715..8d6eb762 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -26,6 +26,17 @@ samplers_k_diffusion = [
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
 ]
 
+if opts.show_karras_scheduler_variants:
+    k_diffusion.sampling.sample_dpm_2_ka = k_diffusion.sampling.sample_dpm_2
+    k_diffusion.sampling.sample_dpm_2_ancestral_ka = k_diffusion.sampling.sample_dpm_2_ancestral
+    k_diffusion.sampling.sample_lms_ka = k_diffusion.sampling.sample_lms
+    samplers_k_diffusion_ka = [
+        ('LMS K Scheduling', 'sample_lms_ka', ['k_lms_ka']),
+        ('DPM2 K Scheduling', 'sample_dpm_2_ka', ['k_dpm_2_ka']),
+        ('DPM2 a K Scheduling', 'sample_dpm_2_ancestral_ka', ['k_dpm_2_a_ka']),
+    ]
+    samplers_k_diffusion.extend(samplers_k_diffusion_ka)
+
 samplers_data_k_diffusion = [
     SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)
     for label, funcname, aliases in samplers_k_diffusion
@@ -345,6 +356,8 @@ class KDiffusionSampler:
 
         if p.sampler_noise_scheduler_override:
           sigmas = p.sampler_noise_scheduler_override(steps)
+        elif self.funcname.endswith('ka'):
+          sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
         else:
           sigmas = self.model_wrap.get_sigmas(steps)
         x = x * sigmas[0]
-- 
cgit v1.2.1


From 3ddf80a9db8793188e2fe9488233d2b272cceb33 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Wed, 5 Oct 2022 14:31:51 +0300
Subject: add variant setting

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/shared.py b/modules/shared.py
index ca2e4c74..9e4860a2 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -236,6 +236,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
+    "show_karras_scheduler_variants": OptionInfo(True, "Show Karras scheduling variants for select samplers. Try these variants if your K sampled images suffer from excessive noise."),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-- 
cgit v1.2.1


From a971e4a767118ec41ec0f129770122babfb16a16 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Thu, 6 Oct 2022 13:34:42 +0300
Subject: update k-diff once again

---
 launch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index c2713c64..9fe0fd67 100644
--- a/launch.py
+++ b/launch.py
@@ -19,7 +19,7 @@ clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLI
 
 stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
 taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
-k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "567e11f7062ba20ae32b5a8cd07fb0fc4b9410cf")
+k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
 codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
 blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
-- 
cgit v1.2.1


From 5993df24a1026225cb8af89237547c1d9101ce69 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 14:12:52 +0300
Subject: integrate the new samplers PR

---
 modules/processing.py                        |  7 ++--
 modules/sd_samplers.py                       | 59 +++++++++++++++-------------
 modules/shared.py                            |  1 -
 scripts/alternate_sampler_noise_schedules.py | 53 -------------------------
 scripts/img2imgalt.py                        |  3 +-
 5 files changed, 36 insertions(+), 87 deletions(-)
 delete mode 100644 scripts/alternate_sampler_noise_schedules.py

diff --git a/modules/processing.py b/modules/processing.py
index e01c8b3f..e567956c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -477,7 +477,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.firstphase_height_truncated = int(scale * self.height)
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
@@ -520,7 +520,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         shared.state.nextjob()
 
-        self.sampler = sd_samplers.samplers[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
+
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         # GC now before running the next img2img to prevent running out of memory
@@ -555,7 +556,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.nmask = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
-        self.sampler = sd_samplers.samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
         crop_region = None
 
         if self.image_mask is not None:
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 8d6eb762..497df943 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -13,46 +13,46 @@ from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 
 
-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases'])
+SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
 
 samplers_k_diffusion = [
-    ('Euler a', 'sample_euler_ancestral', ['k_euler_a']),
-    ('Euler', 'sample_euler', ['k_euler']),
-    ('LMS', 'sample_lms', ['k_lms']),
-    ('Heun', 'sample_heun', ['k_heun']),
-    ('DPM2', 'sample_dpm_2', ['k_dpm_2']),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
-    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast']),
-    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}),
+    ('Euler', 'sample_euler', ['k_euler'], {}),
+    ('LMS', 'sample_lms', ['k_lms'], {}),
+    ('Heun', 'sample_heun', ['k_heun'], {}),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
+    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
+    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
+    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
 ]
 
-if opts.show_karras_scheduler_variants:
-    k_diffusion.sampling.sample_dpm_2_ka = k_diffusion.sampling.sample_dpm_2
-    k_diffusion.sampling.sample_dpm_2_ancestral_ka = k_diffusion.sampling.sample_dpm_2_ancestral
-    k_diffusion.sampling.sample_lms_ka = k_diffusion.sampling.sample_lms
-    samplers_k_diffusion_ka = [
-        ('LMS K Scheduling', 'sample_lms_ka', ['k_lms_ka']),
-        ('DPM2 K Scheduling', 'sample_dpm_2_ka', ['k_dpm_2_ka']),
-        ('DPM2 a K Scheduling', 'sample_dpm_2_ancestral_ka', ['k_dpm_2_a_ka']),
-    ]
-    samplers_k_diffusion.extend(samplers_k_diffusion_ka)
-
 samplers_data_k_diffusion = [
-    SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)
-    for label, funcname, aliases in samplers_k_diffusion
+    SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
+    for label, funcname, aliases, options in samplers_k_diffusion
     if hasattr(k_diffusion.sampling, funcname)
 ]
 
 all_samplers = [
     *samplers_data_k_diffusion,
-    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
-    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
+    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
+    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
 ]
 
 samplers = []
 samplers_for_img2img = []
 
 
+def create_sampler_with_index(list_of_configs, index, model):
+    config = list_of_configs[index]
+    sampler = config.constructor(model)
+    sampler.config = config
+    
+    return sampler
+
+
 def set_samplers():
     global samplers, samplers_for_img2img
 
@@ -130,6 +130,7 @@ class VanillaStableDiffusionSampler:
         self.step = 0
         self.eta = None
         self.default_eta = 0.0
+        self.config = None
 
     def number_of_needed_noises(self, p):
         return 0
@@ -291,6 +292,7 @@ class KDiffusionSampler:
         self.stop_at = None
         self.eta = None
         self.default_eta = 1.0
+        self.config = None
 
     def callback_state(self, d):
         store_latent(d["denoised"])
@@ -355,11 +357,12 @@ class KDiffusionSampler:
         steps = steps or p.steps
 
         if p.sampler_noise_scheduler_override:
-          sigmas = p.sampler_noise_scheduler_override(steps)
-        elif self.funcname.endswith('ka'):
-          sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
+            sigmas = p.sampler_noise_scheduler_override(steps)
+        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
         else:
-          sigmas = self.model_wrap.get_sigmas(steps)
+            sigmas = self.model_wrap.get_sigmas(steps)
+
         x = x * sigmas[0]
 
         extra_params_kwargs = self.initialize(p)
diff --git a/modules/shared.py b/modules/shared.py
index 9e4860a2..ca2e4c74 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -236,7 +236,6 @@ options_templates.update(options_section(('ui', "User interface"), {
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-    "show_karras_scheduler_variants": OptionInfo(True, "Show Karras scheduling variants for select samplers. Try these variants if your K sampled images suffer from excessive noise."),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
diff --git a/scripts/alternate_sampler_noise_schedules.py b/scripts/alternate_sampler_noise_schedules.py
deleted file mode 100644
index 4f3ed8fb..00000000
--- a/scripts/alternate_sampler_noise_schedules.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import inspect
-from modules.processing import Processed, process_images
-import gradio as gr
-import modules.scripts as scripts
-import k_diffusion.sampling
-import torch
-
-
-class Script(scripts.Script):
-
-    def title(self):
-        return "Alternate Sampler Noise Schedules"
-
-    def ui(self, is_img2img):
-      noise_scheduler = gr.Dropdown(label="Noise Scheduler", choices=['Default','Karras','Exponential', 'Variance Preserving'], value='Default', type="index")
-      sched_smin     = gr.Slider(value=0.1,   label="Sigma min",                  minimum=0.0,   maximum=100.0, step=0.5,)
-      sched_smax     = gr.Slider(value=10.0,  label="Sigma max",                  minimum=0.0,   maximum=100.0, step=0.5)
-      sched_rho      = gr.Slider(value=7.0,   label="Sigma rho (Karras only)",    minimum=7.0,   maximum=100.0, step=0.5)
-      sched_beta_d   = gr.Slider(value=19.9,  label="Beta distribution (VP only)",minimum=0.0,   maximum=40.0, step=0.5)
-      sched_beta_min = gr.Slider(value=0.1,   label="Beta min (VP only)",         minimum=0.0,   maximum=40.0, step=0.1)
-      sched_eps_s    = gr.Slider(value=0.001, label="Epsilon (VP only)",          minimum=0.001, maximum=1.0,   step=0.001)
-
-      return [noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s]
-
-    def run(self, p, noise_scheduler, sched_smin, sched_smax, sched_rho, sched_beta_d, sched_beta_min, sched_eps_s):
-
-      noise_scheduler_func_name = ['-','get_sigmas_karras','get_sigmas_exponential','get_sigmas_vp'][noise_scheduler]
-
-      base_params = {
-        "sigma_min":sched_smin, 
-        "sigma_max":sched_smax, 
-        "rho":sched_rho, 
-        "beta_d":sched_beta_d, 
-        "beta_min":sched_beta_min, 
-        "eps_s":sched_eps_s,
-        "device":"cuda" if torch.cuda.is_available() else "cpu"
-      }
-
-      if hasattr(k_diffusion.sampling,noise_scheduler_func_name):
-
-        sigma_func  = getattr(k_diffusion.sampling,noise_scheduler_func_name)
-        sigma_func_kwargs = {}
-
-        for k,v in base_params.items():
-          if k in inspect.signature(sigma_func).parameters:
-            sigma_func_kwargs[k] = v
-
-        def substitute_noise_scheduler(n):
-          return sigma_func(n,**sigma_func_kwargs)
-
-        p.sampler_noise_scheduler_override = substitute_noise_scheduler
-
-      return process_images(p)
diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index 0ef137f7..f9894cb0 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -8,7 +8,6 @@ import gradio as gr
 
 from modules import processing, shared, sd_samplers, prompt_parser
 from modules.processing import Processed
-from modules.sd_samplers import samplers
 from modules.shared import opts, cmd_opts, state
 
 import torch
@@ -159,7 +158,7 @@ class Script(scripts.Script):
             
             combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
             
-            sampler = samplers[p.sampler_index].constructor(p.sd_model)
+            sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)
 
             sigmas = sampler.model_wrap.get_sigmas(p.steps)
             
-- 
cgit v1.2.1


From f5490674a8fd84162b4e80c045e675633afb9ee7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 17:41:49 +0300
Subject: fix bad output for error when updating a git repo

---
 launch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index 9fe0fd67..75edb66a 100644
--- a/launch.py
+++ b/launch.py
@@ -89,7 +89,7 @@ def git_clone(url, dir, name, commithash=None):
         if commithash is None:
             return
 
-        current_hash = run(f'"{git}" -C {dir} rev-parse HEAD', None, "Couldn't determine {name}'s hash: {commithash}").strip()
+        current_hash = run(f'"{git}" -C {dir} rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
         if current_hash == commithash:
             return
 
-- 
cgit v1.2.1


From be71115b1a1201d04f0e2a11e718fb31cbd26474 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 6 Oct 2022 01:09:44 +0100
Subject: Update shared.py

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/shared.py b/modules/shared.py
index ca2e4c74..9f7c6efe 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -236,6 +236,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
+    "show_progress_in_title": OptionInfo(False, "Show generation progress in window title."),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-- 
cgit v1.2.1


From c06298d1d003aa034007978ee7508af636c18124 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 6 Oct 2022 01:10:38 +0100
Subject: add check for progress in title setting

---
 javascript/progressbar.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 3e3220c3..f9e9290e 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -5,7 +5,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_inte
     var progressbar = gradioApp().getElementById(id_progressbar)
     var interrupt = gradioApp().getElementById(id_interrupt)
     
-    if(progressbar && progressbar.offsetParent){
+    if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){
         if(progressbar.innerText){
             let newtitle = 'Stable Diffusion - ' + progressbar.innerText
             if(document.title != newtitle){
-- 
cgit v1.2.1


From fec71e4de24b65b0f205a3c071b71651bbcb0dfc Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 6 Oct 2022 01:35:07 +0100
Subject: Default window title progress updates on

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 9f7c6efe..5c16f025 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -236,7 +236,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-    "show_progress_in_title": OptionInfo(False, "Show generation progress in window title."),
+    "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-- 
cgit v1.2.1


From 5d0e6ab8567bda2ee8f5ed31f332ca07c1b84b98 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 6 Oct 2022 04:04:50 +0100
Subject: Allow escaping of commas in xy_grid

---
 scripts/xy_grid.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 1237e754..210829a7 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -168,6 +168,7 @@ re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d
 re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
 re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
 
+re_non_escaped_comma = re.compile(r"(?<!\\),")
 
 class Script(scripts.Script):
     def title(self):
@@ -197,7 +198,7 @@ class Script(scripts.Script):
             if opt.label == 'Nothing':
                 return [0]
 
-            valslist = [x.strip() for x in vals.split(",")]
+            valslist = [x.replace('\\,',',').strip() for x in re_non_escaped_comma.split(vals)]
 
             if opt.type == int:
                 valslist_ext = []
-- 
cgit v1.2.1


From efa61d31682fb589956cbffa60243d68d9a0daa7 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 6 Oct 2022 11:55:21 +0100
Subject: use csv.reader

---
 scripts/xy_grid.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 210829a7..1a625898 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -1,8 +1,9 @@
 from collections import namedtuple
 from copy import copy
-from itertools import permutations
+from itertools import permutations, chain
 import random
-
+import csv
+from io import StringIO
 from PIL import Image
 import numpy as np
 
@@ -168,8 +169,6 @@ re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d
 re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
 re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
 
-re_non_escaped_comma = re.compile(r"(?<!\\),")
-
 class Script(scripts.Script):
     def title(self):
         return "X/Y plot"
@@ -198,7 +197,7 @@ class Script(scripts.Script):
             if opt.label == 'Nothing':
                 return [0]
 
-            valslist = [x.replace('\\,',',').strip() for x in re_non_escaped_comma.split(vals)]
+            valslist = list(chain.from_iterable(csv.reader(StringIO(s))))
 
             if opt.type == int:
                 valslist_ext = []
-- 
cgit v1.2.1


From fd9e0491681c027bd62267fef4b632d12af7af3b Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 6 Oct 2022 12:32:17 +0100
Subject: strip() split comma delimited lines

---
 scripts/xy_grid.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 1a625898..ec27e58b 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -197,7 +197,7 @@ class Script(scripts.Script):
             if opt.label == 'Nothing':
                 return [0]
 
-            valslist = list(chain.from_iterable(csv.reader(StringIO(s))))
+            valslist = list(map(str.strip,chain.from_iterable(csv.reader(StringIO(s)))))
 
             if opt.type == int:
                 valslist_ext = []
-- 
cgit v1.2.1


From 82eb8ea452b1e63535c58d15ec6db2ad2342faa8 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 6 Oct 2022 15:22:51 +0100
Subject: Update xy_grid.py

split vals not 's' from tests
---
 scripts/xy_grid.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index ec27e58b..210c7b6e 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -197,7 +197,7 @@ class Script(scripts.Script):
             if opt.label == 'Nothing':
                 return [0]
 
-            valslist = list(map(str.strip,chain.from_iterable(csv.reader(StringIO(s)))))
+            valslist = list(map(str.strip,chain.from_iterable(csv.reader(StringIO(vals)))))
 
             if opt.type == int:
                 valslist_ext = []
-- 
cgit v1.2.1


From 0bb458f0ca06a7be27cf1a1003c536d1f06a5bd3 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Wed, 5 Oct 2022 01:19:50 +0900
Subject: Removed duplicate image saving codes

Use `modules.images.save_image()` instead.
---
 modules/images.py |  7 ++++---
 modules/ui.py     | 46 ++++++++++------------------------------------
 2 files changed, 14 insertions(+), 39 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index c2fadab9..810f1446 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -353,7 +353,7 @@ def get_next_sequence_number(path, basename):
     return result + 1
 
 
-def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix=""):
+def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
     if short_filename or prompt is None or seed is None:
         file_decoration = ""
     elif opts.save_to_dirs:
@@ -377,7 +377,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     else:
         pnginfo = None
 
-    save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
+    if save_to_dirs is None:
+        save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
 
     if save_to_dirs:
         dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /')
@@ -431,4 +432,4 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:
             file.write(info + "\n")
 
-
+    return fullfn
diff --git a/modules/ui.py b/modules/ui.py
index 9620350f..4f18126f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -35,7 +35,7 @@ import modules.codeformer_model
 import modules.styles
 import modules.generation_parameters_copypaste
 from modules import prompt_parser
-from modules.images import apply_filename_pattern, get_next_sequence_number
+from modules.images import save_image
 import modules.textual_inversion.ui
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
@@ -114,20 +114,13 @@ def save_files(js_data, images, index):
     p = MyObject(data)
     path = opts.outdir_save
     save_to_dirs = opts.use_save_to_dirs_for_ui
-
-    if save_to_dirs:
-        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, p.seed, p.prompt)
-        path = os.path.join(opts.outdir_save, dirname)
-
-    os.makedirs(path, exist_ok=True)
-  
+    extension: str = opts.samples_format
+    start_index = 0
 
     if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
 
         images = [images[index]]
-        infotexts = [data["infotexts"][index]]
-    else:
-        infotexts = data["infotexts"]
+        start_index = index
 
     with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
         at_start = file.tell() == 0
@@ -135,37 +128,18 @@ def save_files(js_data, images, index):
         if at_start:
             writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
 
-        file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
-        if file_decoration != "":
-            file_decoration = "-" + file_decoration.lower()
-        file_decoration = apply_filename_pattern(file_decoration, p, p.seed, p.prompt)
-        truncated = (file_decoration[:240] + '..') if len(file_decoration) > 240 else file_decoration
-        filename_base = truncated
-        extension = opts.samples_format.lower()
-
-        basecount = get_next_sequence_number(path, "")
-        for i, filedata in enumerate(images):
-            file_number = f"{basecount+i:05}"
-            filename = file_number + filename_base + f".{extension}"
-            filepath = os.path.join(path, filename)
-
-
+        for image_index, filedata in enumerate(images, start_index):
             if filedata.startswith("data:image/png;base64,"):
                 filedata = filedata[len("data:image/png;base64,"):]
 
             image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8'))))
-            if opts.enable_pnginfo and extension == 'png':
-                pnginfo = PngImagePlugin.PngInfo()
-                pnginfo.add_text('parameters', infotexts[i])
-                image.save(filepath, pnginfo=pnginfo)
-            else:
-                image.save(filepath, quality=opts.jpeg_quality)
 
-            if opts.enable_pnginfo and extension in ("jpg", "jpeg", "webp"):
-                piexif.insert(piexif.dump({"Exif": {
-                    piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(infotexts[i], encoding="unicode")
-                }}), filepath)
+            is_grid = image_index < p.index_of_first_image
+            i = 0 if is_grid else (image_index - p.index_of_first_image)
+
+            fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
 
+            filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
 
         writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
-- 
cgit v1.2.1


From 1069ec49a35d04c1e85c92534e92a2d6aa59cb75 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 20:16:21 +0300
Subject: revert back to using list comprehension rather than list and map

---
 scripts/xy_grid.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 210c7b6e..6344e612 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -197,7 +197,7 @@ class Script(scripts.Script):
             if opt.label == 'Nothing':
                 return [0]
 
-            valslist = list(map(str.strip,chain.from_iterable(csv.reader(StringIO(vals)))))
+            valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]
 
             if opt.type == int:
                 valslist_ext = []
-- 
cgit v1.2.1


From dbc8a4d35129b08eab30776bbbaf3a2e7ac10a6c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 20:27:50 +0300
Subject: add generation parameters to images shown in web ui

---
 modules/processing.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index de818d5b..8faf9095 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -430,7 +430,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 if opts.samples_save and not p.do_not_save_samples:
                     images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
 
-                infotexts.append(infotext(n, i))
+                text = infotext(n, i)
+                infotexts.append(text)
+                image.info["parameters"] = text
                 output_images.append(image)
 
             del x_samples_ddim 
@@ -447,7 +449,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             grid = images.image_grid(output_images, p.batch_size)
 
             if opts.return_grid:
-                infotexts.insert(0, infotext())
+                text = infotext()
+                infotexts.insert(0, text)
+                grid.info["parameters"] = text
                 output_images.insert(0, grid)
                 index_of_first_image = 1
 
-- 
cgit v1.2.1


From cf7c784fcc0c84a8a4edd8d3aca4dda4c7025c43 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Fri, 7 Oct 2022 00:19:52 +0900
Subject: Removed duplicate defined models_path

Use `modules.paths.models_path` instead `modules.shared.model_path`.
---
 modules/shared.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 5c16f025..25bb6e6c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -14,11 +14,10 @@ import modules.sd_models
 import modules.styles
 import modules.devices as devices
 from modules import sd_samplers
-from modules.paths import script_path, sd_path
+from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
-model_path = os.path.join(script_path, 'models')
 parser = argparse.ArgumentParser()
 parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
@@ -36,14 +35,14 @@ parser.add_argument("--always-batch-cond-uncond", action='store_true', help="dis
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
-parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(model_path, 'Codeformer'))
-parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(model_path, 'GFPGAN'))
-parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))
-parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN'))
-parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))
-parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(model_path, 'ScuNET'))
-parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))
-parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR'))
+parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
+parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
+parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
+parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
+parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
+parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
+parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
+parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-- 
cgit v1.2.1


From 070b7d60cf5dac6387b3bfc8f3b3977b620e4fd5 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Wed, 5 Oct 2022 02:13:09 +0900
Subject: Added styles to Processed

So `[styles]` pattern can use in saving image UI.
---
 modules/images.py     | 7 +------
 modules/processing.py | 2 ++
 2 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index 810f1446..fa0714fd 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -292,12 +292,7 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = x.replace("[cfg]", str(p.cfg_scale))
         x = x.replace("[width]", str(p.width))
         x = x.replace("[height]", str(p.height))
-
-        #currently disabled if using the save button, will work otherwise
-        # if enabled it will cause a bug because styles is not included in the save_files data dictionary
-        if hasattr(p, "styles"):
-            x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
-
+        x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
         x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
 
     x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
diff --git a/modules/processing.py b/modules/processing.py
index 8faf9095..706dbfa8 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -121,6 +121,7 @@ class Processed:
         self.denoising_strength = getattr(p, 'denoising_strength', None)
         self.extra_generation_params = p.extra_generation_params
         self.index_of_first_image = index_of_first_image
+        self.styles = p.styles
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -165,6 +166,7 @@ class Processed:
             "extra_generation_params": self.extra_generation_params,
             "index_of_first_image": self.index_of_first_image,
             "infotexts": self.infotexts,
+            "styles": self.styles,
         }
 
         return json.dumps(obj)
-- 
cgit v1.2.1


From 1cc36d170ac15e7f04208df32db27af1b10c867c Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Wed, 5 Oct 2022 02:17:15 +0900
Subject: Added job_timestamp to Processed

So `[job_timestamp]` pattern can use in saving image UI.
---
 modules/images.py     | 2 +-
 modules/processing.py | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/images.py b/modules/images.py
index fa0714fd..669d76af 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -298,7 +298,7 @@ def apply_filename_pattern(x, p, seed, prompt):
     x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
     x = x.replace("[date]", datetime.date.today().isoformat())
     x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
-    x = x.replace("[job_timestamp]", shared.state.job_timestamp)
+    x = x.replace("[job_timestamp]", getattr(p, "job_timestamp", shared.state.job_timestamp))
 
     # Apply [prompt] at last. Because it may contain any replacement word.^M
     if prompt is not None:
diff --git a/modules/processing.py b/modules/processing.py
index 706dbfa8..f773a30e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -122,6 +122,7 @@ class Processed:
         self.extra_generation_params = p.extra_generation_params
         self.index_of_first_image = index_of_first_image
         self.styles = p.styles
+        self.job_timestamp = state.job_timestamp
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -167,6 +168,7 @@ class Processed:
             "index_of_first_image": self.index_of_first_image,
             "infotexts": self.infotexts,
             "styles": self.styles,
+            "job_timestamp": self.job_timestamp,
         }
 
         return json.dumps(obj)
-- 
cgit v1.2.1


From 405c8171d1acbb994084d98770bbcb97d01d9406 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Thu, 6 Oct 2022 00:59:04 +0900
Subject: Prefer using `Processed.sd_model_hash` attribute when filename
 pattern

---
 modules/images.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/images.py b/modules/images.py
index 669d76af..29c5ee24 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -295,7 +295,7 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
         x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
 
-    x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
+    x = x.replace("[model_hash]", getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash))
     x = x.replace("[date]", datetime.date.today().isoformat())
     x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
     x = x.replace("[job_timestamp]", getattr(p, "job_timestamp", shared.state.job_timestamp))
-- 
cgit v1.2.1


From b34b25b4c941819d34f29be6c4c1ec01e64585b4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 23:27:01 +0300
Subject: karras samplers for img2img?

---
 modules/sd_samplers.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 497df943..df17e93c 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -338,9 +338,11 @@ class KDiffusionSampler:
         steps, t_enc = setup_img2img_steps(p, steps)
 
         if p.sampler_noise_scheduler_override:
-          sigmas = p.sampler_noise_scheduler_override(steps)
+            sigmas = p.sampler_noise_scheduler_override(steps)
+        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
         else:
-          sigmas = self.model_wrap.get_sigmas(steps)
+            sigmas = self.model_wrap.get_sigmas(steps)
 
         noise = noise * sigmas[steps - t_enc - 1]
         xi = x + noise
-- 
cgit v1.2.1


From 2995107fa24cfd72b0a991e18271dcde148c2807 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 6 Oct 2022 23:44:54 +0300
Subject: added ctrl+up or ctrl+down hotkeys for attention

---
 README.md                    |  4 ++++
 javascript/edit-attention.js | 41 +++++++++++++++++++++++++++++++++++++++++
 2 files changed, 45 insertions(+)
 create mode 100644 javascript/edit-attention.js

diff --git a/README.md b/README.md
index ec3d7532..a14a6330 100644
--- a/README.md
+++ b/README.md
@@ -16,6 +16,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - Attention, specify parts of text that the model should pay more attention to
     - a man in a ((tuxedo)) - will pay more attention to tuxedo
     - a man in a (tuxedo:1.21) - alternative syntax
+    - select text and press ctrl+up or ctrl+down to aduotmatically adjust attention to selected text
 - Loopback, run img2img processing multiple times
 - X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
 - Textual Inversion
@@ -61,6 +62,9 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - Reloading checkpoints on the fly
 - Checkpoint Merger, a tab that allows you to merge two checkpoints into one
 - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
+- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
+     - separate prompts using uppercase `AND`
+     - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js
new file mode 100644
index 00000000..c67ed579
--- /dev/null
+++ b/javascript/edit-attention.js
@@ -0,0 +1,41 @@
+addEventListener('keydown', (event) => {
+	let target = event.originalTarget;
+	if (!target.hasAttribute("placeholder")) return;
+	if (!target.placeholder.toLowerCase().includes("prompt")) return;
+
+	let plus = "ArrowUp"
+	let minus = "ArrowDown"
+	if (event.key != plus && event.key != minus) return;
+
+	selectionStart = target.selectionStart;
+	selectionEnd = target.selectionEnd;
+	if(selectionStart == selectionEnd) return;
+
+	event.preventDefault();
+
+	if (selectionStart == 0 || target.value[selectionStart - 1] != "(") {
+		target.value = target.value.slice(0, selectionStart) +
+			"(" + target.value.slice(selectionStart, selectionEnd) + ":1.0)" +
+			target.value.slice(selectionEnd);
+
+		target.focus();
+		target.selectionStart = selectionStart + 1;
+		target.selectionEnd = selectionEnd + 1;
+
+	} else {
+		end = target.value.slice(selectionEnd + 1).indexOf(")") + 1;
+		weight = parseFloat(target.value.slice(selectionEnd + 1, selectionEnd + 1 + end));
+		if (event.key == minus) weight -= 0.1;
+		if (event.key == plus) weight += 0.1;
+
+		weight = parseFloat(weight.toPrecision(12));
+
+		target.value = target.value.slice(0, selectionEnd + 1) +
+			weight +
+			target.value.slice(selectionEnd + 1 + end - 1);
+
+		target.focus();
+		target.selectionStart = selectionStart;
+		target.selectionEnd = selectionEnd;
+	}
+});
-- 
cgit v1.2.1


From f174fb29228a04955fb951b32b0bab79e33ec2b8 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Fri, 7 Oct 2022 05:21:49 +0300
Subject: add xformers attention

---
 modules/sd_hijack_optimizations.py | 39 +++++++++++++++++++++++++++++++++++++-
 1 file changed, 38 insertions(+), 1 deletion(-)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index ea4cfdfc..da1b76e1 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,7 +1,9 @@
 import math
 import torch
 from torch import einsum
-
+import xformers.ops
+import functorch
+xformers._is_functorch_available=True
 from ldm.util import default
 from einops import rearrange
 
@@ -92,6 +94,41 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     return self.to_out(r2)
 
+def _maybe_init(self, x):
+    """
+    Initialize the attention operator, if required We expect the head dimension to be exposed here, meaning that x
+    : B, Head, Length
+    """
+    if self.attention_op is not None:
+        return
+    _, M, K = x.shape
+    try:
+        self.attention_op = xformers.ops.AttentionOpDispatch(
+            dtype=x.dtype,
+            device=x.device,
+            k=K,
+            attn_bias_type=type(None),
+            has_dropout=False,
+            kv_len=M,
+            q_len=M,
+        ).op
+    except NotImplementedError as err:
+        raise NotImplementedError(f"Please install xformers with the flash attention / cutlass components.\n{err}")
+
+def xformers_attention_forward(self, x, context=None, mask=None):
+    h = self.heads
+    q_in = self.to_q(x)
+    context = default(context, x)
+    k_in = self.to_k(context)
+    v_in = self.to_v(context)
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    del q_in, k_in, v_in
+    self._maybe_init(q)
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+
+    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+    return self.to_out(out)
+
 def cross_attention_attnblock_forward(self, x):
         h_ = x
         h_ = self.norm(h_)
-- 
cgit v1.2.1


From 2eb911b056ce6ff4434f673366782ed34f2b2f12 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Fri, 7 Oct 2022 05:22:28 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index a6fa890c..6221ed5a 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -20,12 +20,17 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 
 
 def apply_optimizations():
-    ldm.modules.diffusionmodules.model.nonlinearity = silu
-
     if cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
+    if cmd_opts.opt_split_attention:
+        ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
+        ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
+    elif not cmd_opts.disable_opt_xformers_attention:
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
+        ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init
+        ldm.modules.attention.CrossAttention.attention_op = None
+        ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
 
-- 
cgit v1.2.1


From da4ab2707b4cb0611cf181ba248a271d1937433e Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Fri, 7 Oct 2022 05:23:06 +0300
Subject: Update shared.py

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/shared.py b/modules/shared.py
index 25bb6e6c..8cc3b2fe 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -43,6 +43,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
 parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
+parser.add_argument("--disable-opt-xformers-attention", action='store_true', help="force-disables xformers attention optimization")
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-- 
cgit v1.2.1


From cd8bb597c6bcb6c59b538b7a1ab8f2face764fc5 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Fri, 7 Oct 2022 05:23:25 +0300
Subject: Update requirements.txt

---
 requirements.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index 631fe616..304a066a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,3 +23,5 @@ resize-right
 torchdiffeq
 kornia
 lark
+functorch
+#xformers?
-- 
cgit v1.2.1


From 35d6b231628d18d53d166c3a92fea1523e88d51e Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Fri, 7 Oct 2022 05:31:53 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 6221ed5a..a006c0a3 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -20,17 +20,16 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 
 
 def apply_optimizations():
+    ldm.modules.diffusionmodules.model.nonlinearity = silu
     if cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     if cmd_opts.opt_split_attention:
         ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
-        ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
     elif not cmd_opts.disable_opt_xformers_attention:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init
         ldm.modules.attention.CrossAttention.attention_op = None
-        ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
 
-- 
cgit v1.2.1


From 5303df24282ba06abb34a423f2967354d37d078e Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Fri, 7 Oct 2022 06:01:14 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index a006c0a3..ddacb0ad 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -23,10 +23,10 @@ def apply_optimizations():
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     if cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-    if cmd_opts.opt_split_attention:
+    elif cmd_opts.opt_split_attention:
         ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
-    elif not cmd_opts.disable_opt_xformers_attention:
+    elif not cmd_opts.disable_opt_xformers_attention and not cmd_opts.opt_split_attention:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init
         ldm.modules.attention.CrossAttention.attention_op = None
-- 
cgit v1.2.1


From 5e3ff846c56dc8e1d5c76ea04a8f2f74d7da07fc Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Fri, 7 Oct 2022 06:38:01 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index ddacb0ad..cbdb9d3c 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -26,7 +26,7 @@ def apply_optimizations():
     elif cmd_opts.opt_split_attention:
         ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
-    elif not cmd_opts.disable_opt_xformers_attention and not cmd_opts.opt_split_attention:
+    elif not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip):
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init
         ldm.modules.attention.CrossAttention.attention_op = None
-- 
cgit v1.2.1


From bad7cb29cecac51c5c0f39afec332b007ed73133 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 7 Oct 2022 10:17:52 +0300
Subject: added support for hypernetworks (???)

---
 modules/hypernetwork.py            | 55 ++++++++++++++++++++++++++++++++++++++
 modules/sd_hijack_optimizations.py | 17 ++++++++++--
 modules/shared.py                  |  9 ++++++-
 scripts/xy_grid.py                 | 10 +++++++
 4 files changed, 88 insertions(+), 3 deletions(-)
 create mode 100644 modules/hypernetwork.py

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
new file mode 100644
index 00000000..9ed1eed9
--- /dev/null
+++ b/modules/hypernetwork.py
@@ -0,0 +1,55 @@
+import glob
+import os
+import torch
+from modules import devices
+
+
+class HypernetworkModule(torch.nn.Module):
+    def __init__(self, dim, state_dict):
+        super().__init__()
+
+        self.linear1 = torch.nn.Linear(dim, dim * 2)
+        self.linear2 = torch.nn.Linear(dim * 2, dim)
+
+        self.load_state_dict(state_dict, strict=True)
+        self.to(devices.device)
+
+    def forward(self, x):
+        return x + (self.linear2(self.linear1(x)))
+
+
+class Hypernetwork:
+    filename = None
+    name = None
+
+    def __init__(self, filename):
+        self.filename = filename
+        self.name = os.path.splitext(os.path.basename(filename))[0]
+        self.layers = {}
+
+        state_dict = torch.load(filename, map_location='cpu')
+        for size, sd in state_dict.items():
+            self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
+
+
+def load_hypernetworks(path):
+    res = {}
+
+    for filename in glob.iglob(path + '**/*.pt', recursive=True):
+        hn = Hypernetwork(filename)
+        res[hn.name] = hn
+
+    return res
+
+def apply(self, x, context=None, mask=None, original=None):
+
+
+    if CrossAttention.hypernetwork is not None and context.shape[2] in CrossAttention.hypernetwork:
+        if context.shape[1] == 77 and CrossAttention.noise_cond:
+            context = context + (torch.randn_like(context) * 0.1)
+        h_k, h_v = CrossAttention.hypernetwork[context.shape[2]]
+        k = self.to_k(h_k(context))
+        v = self.to_v(h_v(context))
+    else:
+        k = self.to_k(context)
+        v = self.to_v(context)
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index ea4cfdfc..d9cca485 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -5,6 +5,8 @@ from torch import einsum
 from ldm.util import default
 from einops import rearrange
 
+from modules import shared
+
 
 # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
 def split_cross_attention_forward_v1(self, x, context=None, mask=None):
@@ -42,8 +44,19 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     q_in = self.to_q(x)
     context = default(context, x)
-    k_in = self.to_k(context) * self.scale
-    v_in = self.to_v(context)
+
+    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        k_in = self.to_k(hypernetwork_layers[0](context))
+        v_in = self.to_v(hypernetwork_layers[1](context))
+    else:
+        k_in = self.to_k(context)
+        v_in = self.to_v(context)
+
+    k_in *= self.scale
+
     del context, x
 
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
diff --git a/modules/shared.py b/modules/shared.py
index 25bb6e6c..879d8424 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers
+from modules import sd_samplers, hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -76,6 +76,12 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 
 config_filename = cmd_opts.ui_settings_file
 
+hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks'))
+
+
+def selected_hypernetwork():
+    return hypernetworks.get(opts.sd_hypernetwork, None)
+
 
 class State:
     interrupted = False
@@ -206,6 +212,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
+    "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 6344e612..c0c364df 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -77,6 +77,11 @@ def apply_checkpoint(p, x, xs):
     modules.sd_models.reload_model_weights(shared.sd_model, info)
 
 
+def apply_hypernetwork(p, x, xs):
+    hn = shared.hypernetworks.get(x, None)
+    opts.data["sd_hypernetwork"] = hn.name if hn is not None else 'None'
+
+
 def format_value_add_label(p, opt, x):
     if type(x) == float:
         x = round(x, 8)
@@ -122,6 +127,7 @@ axis_options = [
     AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list),
     AxisOption("Sampler", str, apply_sampler, format_value),
     AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
+    AxisOption("Hypernetwork", str, apply_hypernetwork, format_value),
     AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
     AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
     AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
@@ -193,6 +199,8 @@ class Script(scripts.Script):
         modules.processing.fix_seed(p)
         p.batch_size = 1
 
+        initial_hn = opts.sd_hypernetwork
+
         def process_axis(opt, vals):
             if opt.label == 'Nothing':
                 return [0]
@@ -300,4 +308,6 @@ class Script(scripts.Script):
         # restore checkpoint in case it was changed by axes
         modules.sd_models.reload_model_weights(shared.sd_model)
 
+        opts.data["sd_hypernetwork"] = initial_hn
+
         return processed
-- 
cgit v1.2.1


From d15b3ec0013c10f02f0fb80e8448bac8872a151f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 7 Oct 2022 10:40:22 +0300
Subject: support loading VAE

---
 modules/sd_models.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5f992064..8f794b47 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -134,6 +134,14 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
 
     devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
 
+    vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
+    if os.path.exists(vae_file):
+        print(f"Loading VAE weights from: {vae_file}")
+        vae_ckpt = torch.load(vae_file, map_location="cpu")
+        vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
+
+        model.first_stage_model.load_state_dict(vae_dict)
+
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpint = checkpoint_file
 
-- 
cgit v1.2.1


From 97bc0b9504572d2df80598d0b694703bcd626de6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 7 Oct 2022 13:22:50 +0300
Subject: do not stop working on failed hypernetwork load

---
 modules/hypernetwork.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
index 9ed1eed9..c5cf4afa 100644
--- a/modules/hypernetwork.py
+++ b/modules/hypernetwork.py
@@ -1,5 +1,8 @@
 import glob
 import os
+import sys
+import traceback
+
 import torch
 from modules import devices
 
@@ -36,8 +39,12 @@ def load_hypernetworks(path):
     res = {}
 
     for filename in glob.iglob(path + '**/*.pt', recursive=True):
-        hn = Hypernetwork(filename)
-        res[hn.name] = hn
+        try:
+            hn = Hypernetwork(filename)
+            res[hn.name] = hn
+        except Exception:
+            print(f"Error loading hypernetwork {filename}", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
 
     return res
 
-- 
cgit v1.2.1


From f7c787eb7c295c27439f4fbdf78c26b8389560be Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 7 Oct 2022 16:39:51 +0300
Subject: make it possible to use hypernetworks without opt split attention

---
 modules/hypernetwork.py | 42 ++++++++++++++++++++++++++++++++++--------
 modules/sd_hijack.py    |  6 ++++--
 2 files changed, 38 insertions(+), 10 deletions(-)

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
index c5cf4afa..c7b86682 100644
--- a/modules/hypernetwork.py
+++ b/modules/hypernetwork.py
@@ -4,7 +4,12 @@ import sys
 import traceback
 
 import torch
-from modules import devices
+
+from ldm.util import default
+from modules import devices, shared
+import torch
+from torch import einsum
+from einops import rearrange, repeat
 
 
 class HypernetworkModule(torch.nn.Module):
@@ -48,15 +53,36 @@ def load_hypernetworks(path):
 
     return res
 
-def apply(self, x, context=None, mask=None, original=None):
 
+def attention_CrossAttention_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
 
-    if CrossAttention.hypernetwork is not None and context.shape[2] in CrossAttention.hypernetwork:
-        if context.shape[1] == 77 and CrossAttention.noise_cond:
-            context = context + (torch.randn_like(context) * 0.1)
-        h_k, h_v = CrossAttention.hypernetwork[context.shape[2]]
-        k = self.to_k(h_k(context))
-        v = self.to_v(h_v(context))
+    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        k = self.to_k(hypernetwork_layers[0](context))
+        v = self.to_v(hypernetwork_layers[1](context))
     else:
         k = self.to_k(context)
         v = self.to_v(context)
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+    if mask is not None:
+        mask = rearrange(mask, 'b ... -> b (...)')
+        max_neg_value = -torch.finfo(sim.dtype).max
+        mask = repeat(mask, 'b j -> (b h) () j', h=h)
+        sim.masked_fill_(~mask, max_neg_value)
+
+    # attention, what we cannot get enough of
+    attn = sim.softmax(dim=-1)
+
+    out = einsum('b i j, b j d -> b i d', attn, v)
+    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+    return self.to_out(out)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index a6fa890c..d68f89cc 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -8,7 +8,7 @@ from torch import einsum
 from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
-from modules import prompt_parser, devices, sd_hijack_optimizations, shared
+from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork
 from modules.shared import opts, device, cmd_opts
 
 import ldm.modules.attention
@@ -20,6 +20,8 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 
 
 def apply_optimizations():
+    undo_optimizations()
+
     ldm.modules.diffusionmodules.model.nonlinearity = silu
 
     if cmd_opts.opt_split_attention_v1:
@@ -30,7 +32,7 @@ def apply_optimizations():
 
 
 def undo_optimizations():
-    ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward
+    ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
     ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
-- 
cgit v1.2.1


From 54fa613c8391e3973cca9d94cdf539061932508b Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Fri, 7 Oct 2022 20:37:43 +0200
Subject: loading tf only in interrogation process

---
 modules/deepbooru.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index fb5018a6..79dc59bd 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -1,12 +1,13 @@
 import os.path
 from concurrent.futures import ProcessPoolExecutor
 
-import numpy as np
-import deepdanbooru as dd
-import tensorflow as tf
 
 
 def _load_tf_and_return_tags(pil_image, threshold):
+    import deepdanbooru as dd
+    import tensorflow as tf
+    import numpy as np
+
     this_folder = os.path.dirname(__file__)
     model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28')
 
-- 
cgit v1.2.1


From fa2ea648db81f5723bb5d722f2fe0ebd7dfc319a Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Fri, 7 Oct 2022 20:46:38 +0200
Subject: even more powerfull fix

---
 modules/deepbooru.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 79dc59bd..60094336 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -60,8 +60,13 @@ def _load_tf_and_return_tags(pil_image, threshold):
     return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
 
 
+def subprocess_init_no_cuda():
+    import os
+    os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+
 def get_deepbooru_tags(pil_image, threshold=0.5):
-    with ProcessPoolExecutor() as executor:
-        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold)
+    with ProcessPoolExecutor(initializer=subprocess_init_no_cuda) as executor:
+        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
         ret = f.result()  # will rethrow any exceptions
     return ret
\ No newline at end of file
-- 
cgit v1.2.1


From 5f12e7efd92ad802742f96788b4be3249ad02829 Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Fri, 7 Oct 2022 20:58:30 +0200
Subject: linux test

---
 modules/deepbooru.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 60094336..781b2249 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -1,6 +1,6 @@
 import os.path
 from concurrent.futures import ProcessPoolExecutor
-
+from multiprocessing import get_context
 
 
 def _load_tf_and_return_tags(pil_image, threshold):
@@ -66,7 +66,8 @@ def subprocess_init_no_cuda():
 
 
 def get_deepbooru_tags(pil_image, threshold=0.5):
-    with ProcessPoolExecutor(initializer=subprocess_init_no_cuda) as executor:
+    context = get_context('spawn')
+    with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
         f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
         ret = f.result()  # will rethrow any exceptions
     return ret
\ No newline at end of file
-- 
cgit v1.2.1


From 065364445d4ea1ddec44c3f87d1b6b8acda592a6 Mon Sep 17 00:00:00 2001
From: EternalNooblet <deadmine5@protonmail.com>
Date: Fri, 7 Oct 2022 15:25:01 -0400
Subject: added a flag to run as root if needed

---
 webui.sh | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/webui.sh b/webui.sh
index 05ca497d..41649b9a 100755
--- a/webui.sh
+++ b/webui.sh
@@ -3,6 +3,7 @@
 # Please do not make any changes to this file,  #
 # change the variables in webui-user.sh instead #
 #################################################
+
 # Read variables from webui-user.sh
 # shellcheck source=/dev/null
 if [[ -f webui-user.sh ]]
@@ -46,6 +47,17 @@ then
     LAUNCH_SCRIPT="launch.py"
 fi
 
+# this script cannot be run as root by default
+can_run_as_root=0
+
+# read any command line flags to the webui.sh script
+while getopts "f" flag
+do
+    case ${flag} in
+        f) can_run_as_root=1;;
+    esac
+done
+
 # Disable sentry logging
 export ERROR_REPORTING=FALSE
 
@@ -61,7 +73,7 @@ printf "\e[1m\e[34mTested on Debian 11 (Bullseye)\e[0m"
 printf "\n%s\n" "${delimiter}"
 
 # Do not run as root
-if [[ $(id -u) -eq 0 ]]
+if [[ $(id -u) -eq 0 && can_run_as_root -eq 0 ]]
 then
     printf "\n%s\n" "${delimiter}"
     printf "\e[1m\e[31mERROR: This script must not be launched as root, aborting...\e[0m"
-- 
cgit v1.2.1


From 12c4d5c6b5bf9dd50d0601c36af4f99b65316d58 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 7 Oct 2022 23:22:22 +0300
Subject: hypernetwork training mk1

---
 modules/hypernetwork.py                      |  88 ---------
 modules/hypernetwork/hypernetwork.py         | 267 +++++++++++++++++++++++++++
 modules/hypernetwork/ui.py                   |  43 +++++
 modules/sd_hijack.py                         |   4 +-
 modules/sd_hijack_optimizations.py           |   3 +-
 modules/shared.py                            |  13 +-
 modules/textual_inversion/ui.py              |   1 -
 modules/ui.py                                |  58 +++++-
 scripts/xy_grid.py                           |   7 +-
 textual_inversion_templates/hypernetwork.txt |  27 +++
 textual_inversion_templates/none.txt         |   1 +
 webui.py                                     |   9 +
 12 files changed, 414 insertions(+), 107 deletions(-)
 delete mode 100644 modules/hypernetwork.py
 create mode 100644 modules/hypernetwork/hypernetwork.py
 create mode 100644 modules/hypernetwork/ui.py
 create mode 100644 textual_inversion_templates/hypernetwork.txt
 create mode 100644 textual_inversion_templates/none.txt

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
deleted file mode 100644
index c7b86682..00000000
--- a/modules/hypernetwork.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import glob
-import os
-import sys
-import traceback
-
-import torch
-
-from ldm.util import default
-from modules import devices, shared
-import torch
-from torch import einsum
-from einops import rearrange, repeat
-
-
-class HypernetworkModule(torch.nn.Module):
-    def __init__(self, dim, state_dict):
-        super().__init__()
-
-        self.linear1 = torch.nn.Linear(dim, dim * 2)
-        self.linear2 = torch.nn.Linear(dim * 2, dim)
-
-        self.load_state_dict(state_dict, strict=True)
-        self.to(devices.device)
-
-    def forward(self, x):
-        return x + (self.linear2(self.linear1(x)))
-
-
-class Hypernetwork:
-    filename = None
-    name = None
-
-    def __init__(self, filename):
-        self.filename = filename
-        self.name = os.path.splitext(os.path.basename(filename))[0]
-        self.layers = {}
-
-        state_dict = torch.load(filename, map_location='cpu')
-        for size, sd in state_dict.items():
-            self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
-
-
-def load_hypernetworks(path):
-    res = {}
-
-    for filename in glob.iglob(path + '**/*.pt', recursive=True):
-        try:
-            hn = Hypernetwork(filename)
-            res[hn.name] = hn
-        except Exception:
-            print(f"Error loading hypernetwork {filename}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-
-    return res
-
-
-def attention_CrossAttention_forward(self, x, context=None, mask=None):
-    h = self.heads
-
-    q = self.to_q(x)
-    context = default(context, x)
-
-    hypernetwork = shared.selected_hypernetwork()
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is not None:
-        k = self.to_k(hypernetwork_layers[0](context))
-        v = self.to_v(hypernetwork_layers[1](context))
-    else:
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-
-    if mask is not None:
-        mask = rearrange(mask, 'b ... -> b (...)')
-        max_neg_value = -torch.finfo(sim.dtype).max
-        mask = repeat(mask, 'b j -> (b h) () j', h=h)
-        sim.masked_fill_(~mask, max_neg_value)
-
-    # attention, what we cannot get enough of
-    attn = sim.softmax(dim=-1)
-
-    out = einsum('b i j, b j d -> b i d', attn, v)
-    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-    return self.to_out(out)
diff --git a/modules/hypernetwork/hypernetwork.py b/modules/hypernetwork/hypernetwork.py
new file mode 100644
index 00000000..a3d6a47e
--- /dev/null
+++ b/modules/hypernetwork/hypernetwork.py
@@ -0,0 +1,267 @@
+import datetime
+import glob
+import html
+import os
+import sys
+import traceback
+import tqdm
+
+import torch
+
+from ldm.util import default
+from modules import devices, shared, processing, sd_models
+import torch
+from torch import einsum
+from einops import rearrange, repeat
+import modules.textual_inversion.dataset
+
+
+class HypernetworkModule(torch.nn.Module):
+    def __init__(self, dim, state_dict=None):
+        super().__init__()
+
+        self.linear1 = torch.nn.Linear(dim, dim * 2)
+        self.linear2 = torch.nn.Linear(dim * 2, dim)
+
+        if state_dict is not None:
+            self.load_state_dict(state_dict, strict=True)
+        else:
+            self.linear1.weight.data.fill_(0.0001)
+            self.linear1.bias.data.fill_(0.0001)
+            self.linear2.weight.data.fill_(0.0001)
+            self.linear2.bias.data.fill_(0.0001)
+
+        self.to(devices.device)
+
+    def forward(self, x):
+        return x + (self.linear2(self.linear1(x)))
+
+
+class Hypernetwork:
+    filename = None
+    name = None
+
+    def __init__(self, name=None):
+        self.filename = None
+        self.name = name
+        self.layers = {}
+        self.step = 0
+        self.sd_checkpoint = None
+        self.sd_checkpoint_name = None
+
+        for size in [320, 640, 768, 1280]:
+            self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
+
+    def weights(self):
+        res = []
+
+        for k, layers in self.layers.items():
+            for layer in layers:
+                layer.train()
+                res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]
+
+        return res
+
+    def save(self, filename):
+        state_dict = {}
+
+        for k, v in self.layers.items():
+            state_dict[k] = (v[0].state_dict(), v[1].state_dict())
+
+        state_dict['step'] = self.step
+        state_dict['name'] = self.name
+        state_dict['sd_checkpoint'] = self.sd_checkpoint
+        state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
+
+        torch.save(state_dict, filename)
+
+    def load(self, filename):
+        self.filename = filename
+        if self.name is None:
+            self.name = os.path.splitext(os.path.basename(filename))[0]
+
+        state_dict = torch.load(filename, map_location='cpu')
+
+        for size, sd in state_dict.items():
+            if type(size) == int:
+                self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
+
+        self.name = state_dict.get('name', self.name)
+        self.step = state_dict.get('step', 0)
+        self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
+        self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
+
+
+def load_hypernetworks(path):
+    res = {}
+
+    for filename in glob.iglob(path + '**/*.pt', recursive=True):
+        try:
+            hn = Hypernetwork()
+            hn.load(filename)
+            res[hn.name] = hn
+        except Exception:
+            print(f"Error loading hypernetwork {filename}", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    return res
+
+
+def attention_CrossAttention_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
+
+    hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        hypernetwork_k, hypernetwork_v = hypernetwork_layers
+
+        self.hypernetwork_k = hypernetwork_k
+        self.hypernetwork_v = hypernetwork_v
+
+        context_k = hypernetwork_k(context)
+        context_v = hypernetwork_v(context)
+    else:
+        context_k = context
+        context_v = context
+
+    k = self.to_k(context_k)
+    v = self.to_v(context_v)
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+    if mask is not None:
+        mask = rearrange(mask, 'b ... -> b (...)')
+        max_neg_value = -torch.finfo(sim.dtype).max
+        mask = repeat(mask, 'b j -> (b h) () j', h=h)
+        sim.masked_fill_(~mask, max_neg_value)
+
+    # attention, what we cannot get enough of
+    attn = sim.softmax(dim=-1)
+
+    out = einsum('b i j, b j d -> b i d', attn, v)
+    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+    return self.to_out(out)
+
+
+def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
+    assert hypernetwork_name, 'embedding not selected'
+
+    shared.hypernetwork = shared.hypernetworks[hypernetwork_name]
+
+    shared.state.textinfo = "Initializing hypernetwork training..."
+    shared.state.job_count = steps
+
+    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+
+    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
+
+    if save_hypernetwork_every > 0:
+        hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
+        os.makedirs(hypernetwork_dir, exist_ok=True)
+    else:
+        hypernetwork_dir = None
+
+    if create_image_every > 0:
+        images_dir = os.path.join(log_directory, "images")
+        os.makedirs(images_dir, exist_ok=True)
+    else:
+        images_dir = None
+
+    cond_model = shared.sd_model.cond_stage_model
+
+    shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
+    with torch.autocast("cuda"):
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+
+    hypernetwork = shared.hypernetworks[hypernetwork_name]
+    weights = hypernetwork.weights()
+    for weight in weights:
+        weight.requires_grad = True
+
+    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
+
+    losses = torch.zeros((32,))
+
+    last_saved_file = "<none>"
+    last_saved_image = "<none>"
+
+    ititial_step = hypernetwork.step or 0
+    if ititial_step > steps:
+        return hypernetwork, filename
+
+    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
+    for i, (x, text) in pbar:
+        hypernetwork.step = i + ititial_step
+
+        if hypernetwork.step > steps:
+            break
+
+        if shared.state.interrupted:
+            break
+
+        with torch.autocast("cuda"):
+            c = cond_model([text])
+
+            x = x.to(devices.device)
+            loss = shared.sd_model(x.unsqueeze(0), c)[0]
+            del x
+
+            losses[hypernetwork.step % losses.shape[0]] = loss.item()
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        pbar.set_description(f"loss: {losses.mean():.7f}")
+
+        if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
+            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
+            hypernetwork.save(last_saved_file)
+
+        if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
+            last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
+
+            preview_text = text if preview_image_prompt == "" else preview_image_prompt
+
+            p = processing.StableDiffusionProcessingTxt2Img(
+                sd_model=shared.sd_model,
+                prompt=preview_text,
+                steps=20,
+                do_not_save_grid=True,
+                do_not_save_samples=True,
+            )
+
+            processed = processing.process_images(p)
+            image = processed.images[0]
+
+            shared.state.current_image = image
+            image.save(last_saved_image)
+
+            last_saved_image += f", prompt: {preview_text}"
+
+        shared.state.job_no = hypernetwork.step
+
+        shared.state.textinfo = f"""
+<p>
+Loss: {losses.mean():.7f}<br/>
+Step: {hypernetwork.step}<br/>
+Last prompt: {html.escape(text)}<br/>
+Last saved embedding: {html.escape(last_saved_file)}<br/>
+Last saved image: {html.escape(last_saved_image)}<br/>
+</p>
+"""
+
+    checkpoint = sd_models.select_checkpoint()
+
+    hypernetwork.sd_checkpoint = checkpoint.hash
+    hypernetwork.sd_checkpoint_name = checkpoint.model_name
+    hypernetwork.save(filename)
+
+    return hypernetwork, filename
+
+
diff --git a/modules/hypernetwork/ui.py b/modules/hypernetwork/ui.py
new file mode 100644
index 00000000..525f978c
--- /dev/null
+++ b/modules/hypernetwork/ui.py
@@ -0,0 +1,43 @@
+import html
+import os
+
+import gradio as gr
+
+import modules.textual_inversion.textual_inversion
+import modules.textual_inversion.preprocess
+from modules import sd_hijack, shared
+
+
+def create_hypernetwork(name):
+    fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
+    assert not os.path.exists(fn), f"file {fn} already exists"
+
+    hypernetwork = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
+    hypernetwork.save(fn)
+
+    shared.reload_hypernetworks()
+    shared.hypernetwork = shared.hypernetworks.get(shared.opts.sd_hypernetwork, None)
+
+    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
+
+
+def train_hypernetwork(*args):
+
+    initial_hypernetwork = shared.hypernetwork
+
+    try:
+        sd_hijack.undo_optimizations()
+
+        hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args)
+
+        res = f"""
+Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
+Hypernetwork saved to {html.escape(filename)}
+"""
+        return res, ""
+    except Exception:
+        raise
+    finally:
+        shared.hypernetwork = initial_hypernetwork
+        sd_hijack.apply_optimizations()
+
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index d68f89cc..ec8c9d4b 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -8,7 +8,7 @@ from torch import einsum
 from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
-from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork
+from modules import prompt_parser, devices, sd_hijack_optimizations, shared
 from modules.shared import opts, device, cmd_opts
 
 import ldm.modules.attention
@@ -32,6 +32,8 @@ def apply_optimizations():
 
 
 def undo_optimizations():
+    from modules.hypernetwork import hypernetwork
+
     ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
     ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index d9cca485..3f32e020 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -45,8 +45,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
     q_in = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.selected_hypernetwork()
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+    hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None)
 
     if hypernetwork_layers is not None:
         k_in = self.to_k(hypernetwork_layers[0](context))
diff --git a/modules/shared.py b/modules/shared.py
index 879d8424..c5a893e8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, hypernetwork
+from modules import sd_samplers
 from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -28,6 +28,7 @@ parser.add_argument("--no-half", action='store_true', help="do not switch the mo
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
+parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
@@ -76,11 +77,15 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 
 config_filename = cmd_opts.ui_settings_file
 
-hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks'))
 
+def reload_hypernetworks():
+    from modules.hypernetwork import hypernetwork
+    hypernetworks.clear()
+    hypernetworks.update(hypernetwork.load_hypernetworks(cmd_opts.hypernetwork_dir))
 
-def selected_hypernetwork():
-    return hypernetworks.get(opts.sd_hypernetwork, None)
+
+hypernetworks = {}
+hypernetwork = None
 
 
 class State:
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index f19ac5e0..c57de1f9 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -22,7 +22,6 @@ def preprocess(*args):
 
 
 def train_embedding(*args):
-
     try:
         sd_hijack.undo_optimizations()
 
diff --git a/modules/ui.py b/modules/ui.py
index 4f18126f..051908c1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -37,6 +37,7 @@ import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
+import modules.hypernetwork.ui
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
 mimetypes.init()
@@ -965,6 +966,18 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create", variant='primary')
 
+                with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
+
+                    new_hypernetwork_name = gr.Textbox(label="Name")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_hypernetwork = gr.Button(value="Create", variant='primary')
+
                 with gr.Group():
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
 
@@ -986,6 +999,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                    train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
                     learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
@@ -993,15 +1007,12 @@ def create_ui(wrap_gradio_gpu_call):
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
+                    preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
 
                     with gr.Row():
-                        with gr.Column(scale=2):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            with gr.Row():
-                                interrupt_training = gr.Button(value="Interrupt")
-                                train_embedding = gr.Button(value="Train", variant='primary')
+                        interrupt_training = gr.Button(value="Interrupt")
+                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
+                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
 
             with gr.Column():
                 progressbar = gr.HTML(elem_id="ti_progressbar")
@@ -1027,6 +1038,18 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
+        create_hypernetwork.click(
+            fn=modules.hypernetwork.ui.create_hypernetwork,
+            inputs=[
+                new_hypernetwork_name,
+            ],
+            outputs=[
+                train_hypernetwork_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
         run_preprocess.click(
             fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
@@ -1062,12 +1085,33 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
+        train_hypernetwork.click(
+            fn=wrap_gradio_gpu_call(modules.hypernetwork.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                train_hypernetwork_name,
+                learn_rate,
+                dataset_directory,
+                log_directory,
+                steps,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                preview_image_prompt,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
         interrupt_training.click(
             fn=lambda: shared.state.interrupt(),
             inputs=[],
             outputs=[],
         )
 
+
     def create_setting_component(key):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index c0c364df..5b504de6 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -78,8 +78,7 @@ def apply_checkpoint(p, x, xs):
 
 
 def apply_hypernetwork(p, x, xs):
-    hn = shared.hypernetworks.get(x, None)
-    opts.data["sd_hypernetwork"] = hn.name if hn is not None else 'None'
+    shared.hypernetwork = shared.hypernetworks.get(x, None)
 
 
 def format_value_add_label(p, opt, x):
@@ -199,7 +198,7 @@ class Script(scripts.Script):
         modules.processing.fix_seed(p)
         p.batch_size = 1
 
-        initial_hn = opts.sd_hypernetwork
+        initial_hn = shared.hypernetwork
 
         def process_axis(opt, vals):
             if opt.label == 'Nothing':
@@ -308,6 +307,6 @@ class Script(scripts.Script):
         # restore checkpoint in case it was changed by axes
         modules.sd_models.reload_model_weights(shared.sd_model)
 
-        opts.data["sd_hypernetwork"] = initial_hn
+        shared.hypernetwork = initial_hn
 
         return processed
diff --git a/textual_inversion_templates/hypernetwork.txt b/textual_inversion_templates/hypernetwork.txt
new file mode 100644
index 00000000..91e06890
--- /dev/null
+++ b/textual_inversion_templates/hypernetwork.txt
@@ -0,0 +1,27 @@
+a photo of a [filewords]
+a rendering of a [filewords]
+a cropped photo of the [filewords]
+the photo of a [filewords]
+a photo of a clean [filewords]
+a photo of a dirty [filewords]
+a dark photo of the [filewords]
+a photo of my [filewords]
+a photo of the cool [filewords]
+a close-up photo of a [filewords]
+a bright photo of the [filewords]
+a cropped photo of a [filewords]
+a photo of the [filewords]
+a good photo of the [filewords]
+a photo of one [filewords]
+a close-up photo of the [filewords]
+a rendition of the [filewords]
+a photo of the clean [filewords]
+a rendition of a [filewords]
+a photo of a nice [filewords]
+a good photo of a [filewords]
+a photo of the nice [filewords]
+a photo of the small [filewords]
+a photo of the weird [filewords]
+a photo of the large [filewords]
+a photo of a cool [filewords]
+a photo of a small [filewords]
diff --git a/textual_inversion_templates/none.txt b/textual_inversion_templates/none.txt
new file mode 100644
index 00000000..f77af461
--- /dev/null
+++ b/textual_inversion_templates/none.txt
@@ -0,0 +1 @@
+picture
diff --git a/webui.py b/webui.py
index 480360fe..60f9061f 100644
--- a/webui.py
+++ b/webui.py
@@ -74,6 +74,15 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
     return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
 
 
+def set_hypernetwork():
+    shared.hypernetwork = shared.hypernetworks.get(shared.opts.sd_hypernetwork, None)
+
+
+shared.reload_hypernetworks()
+shared.opts.onchange("sd_hypernetwork", set_hypernetwork)
+set_hypernetwork()
+
+
 modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
 
 shared.sd_model = modules.sd_models.load_model()
-- 
cgit v1.2.1


From c9cc65b201679ea43c763b0d85e749d40bbc5433 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 04:09:18 +0300
Subject: switch to the proper way of calling xformers

---
 modules/sd_hijack_optimizations.py | 28 +++-------------------------
 1 file changed, 3 insertions(+), 25 deletions(-)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index da1b76e1..7fb4a45e 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -94,39 +94,17 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     return self.to_out(r2)
 
-def _maybe_init(self, x):
-    """
-    Initialize the attention operator, if required We expect the head dimension to be exposed here, meaning that x
-    : B, Head, Length
-    """
-    if self.attention_op is not None:
-        return
-    _, M, K = x.shape
-    try:
-        self.attention_op = xformers.ops.AttentionOpDispatch(
-            dtype=x.dtype,
-            device=x.device,
-            k=K,
-            attn_bias_type=type(None),
-            has_dropout=False,
-            kv_len=M,
-            q_len=M,
-        ).op
-    except NotImplementedError as err:
-        raise NotImplementedError(f"Please install xformers with the flash attention / cutlass components.\n{err}")
-
 def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
     q_in = self.to_q(x)
     context = default(context, x)
     k_in = self.to_k(context)
     v_in = self.to_v(context)
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
-    self._maybe_init(q)
-    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
 
-    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+    out = rearrange(out, 'b n h d -> b n (h d)', h=h)
     return self.to_out(out)
 
 def cross_attention_attnblock_forward(self, x):
-- 
cgit v1.2.1


From b70eaeb2005a5a9593119e7fd32b8072c2a208d5 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 04:10:35 +0300
Subject: delete broken and unnecessary aliases

---
 modules/sd_hijack.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index cbdb9d3c..0e99c319 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -21,16 +21,14 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 
 def apply_optimizations():
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if cmd_opts.opt_split_attention_v1:
+    if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip):
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
+    elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif cmd_opts.opt_split_attention:
         ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
-    elif not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip):
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
-        ldm.modules.attention.CrossAttention._maybe_init = sd_hijack_optimizations._maybe_init
-        ldm.modules.attention.CrossAttention.attention_op = None
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
 
 def undo_optimizations():
-- 
cgit v1.2.1


From a958f9b3fdea95c01d360aba1b6fe0ce3ea6b349 Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Fri, 7 Oct 2022 20:05:47 -0300
Subject: edit-attention browser compatibility and readme typo

---
 README.md                    | 2 +-
 javascript/edit-attention.js | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index a14a6330..0516c2cd 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - Attention, specify parts of text that the model should pay more attention to
     - a man in a ((tuxedo)) - will pay more attention to tuxedo
     - a man in a (tuxedo:1.21) - alternative syntax
-    - select text and press ctrl+up or ctrl+down to aduotmatically adjust attention to selected text
+    - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text
 - Loopback, run img2img processing multiple times
 - X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
 - Textual Inversion
diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js
index c67ed579..0280c603 100644
--- a/javascript/edit-attention.js
+++ b/javascript/edit-attention.js
@@ -1,5 +1,5 @@
 addEventListener('keydown', (event) => {
-	let target = event.originalTarget;
+	let target = event.originalTarget || event.composedPath()[0];
 	if (!target.hasAttribute("placeholder")) return;
 	if (!target.placeholder.toLowerCase().includes("prompt")) return;
 
-- 
cgit v1.2.1


From f2055cb1d4ce45d7aaacc49d8ab5bec7791a8f47 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Sat, 8 Oct 2022 01:47:02 -0400
Subject: Add hypernetwork support to split cross attention v1

* Add hypernetwork support to split_cross_attention_forward_v1
* Fix device check in esrgan_model.py to use devices.device_esrgan instead of shared.device
---
 modules/esrgan_model.py            |  2 +-
 modules/sd_hijack_optimizations.py | 18 ++++++++++++++----
 2 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index d17e730f..28548124 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -111,7 +111,7 @@ class UpscalerESRGAN(Upscaler):
             print("Unable to load %s from %s" % (self.model_path, filename))
             return None
 
-        pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None)
+        pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
         crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
 
         pretrained_net = fix_model_layers(crt_model, pretrained_net)
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index d9cca485..3351c740 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -12,13 +12,22 @@ from modules import shared
 def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     h = self.heads
 
-    q = self.to_q(x)
+    q_in = self.to_q(x)
     context = default(context, x)
-    k = self.to_k(context)
-    v = self.to_v(context)
+
+    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        k_in = self.to_k(hypernetwork_layers[0](context))
+        v_in = self.to_v(hypernetwork_layers[1](context))
+    else:
+        k_in = self.to_k(context)
+        v_in = self.to_v(context)
     del context, x
 
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    del q_in, k_in, v_in
 
     r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
     for i in range(0, q.shape[0], 2):
@@ -31,6 +40,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
 
         r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
         del s2
+    del q, k, v
 
     r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
     del r1
-- 
cgit v1.2.1


From e21e4732531299ef4895baccdb7a6493a3886924 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 8 Oct 2022 05:34:17 +0100
Subject: Context Menus

---
 javascript/contextMenus.js | 165 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 165 insertions(+)
 create mode 100644 javascript/contextMenus.js

diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js
new file mode 100644
index 00000000..99d1d3f7
--- /dev/null
+++ b/javascript/contextMenus.js
@@ -0,0 +1,165 @@
+
+contextMenuInit = function(){
+  let eventListenerApplied=false;
+  let menuSpecs = new Map();
+
+  const uid = function(){
+    return Date.now().toString(36) + Math.random().toString(36).substr(2);
+  }
+
+  function showContextMenu(event,element,menuEntries){
+    let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
+    let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop; 
+
+    let oldMenu = gradioApp().querySelector('#context-menu')
+    if(oldMenu){
+      oldMenu.remove()
+    }
+
+    let tabButton = gradioApp().querySelector('button')
+    let baseStyle = window.getComputedStyle(tabButton)
+
+    const contextMenu = document.createElement('nav')
+    contextMenu.id = "context-menu"
+    contextMenu.style.background = baseStyle.background
+    contextMenu.style.color = baseStyle.color
+    contextMenu.style.fontFamily = baseStyle.fontFamily
+    contextMenu.style.top = posy+'px'
+    contextMenu.style.left = posx+'px'
+
+
+
+    const contextMenuList = document.createElement('ul')
+    contextMenuList.className = 'context-menu-items';
+    contextMenu.append(contextMenuList);
+
+    menuEntries.forEach(function(entry){
+      let contextMenuEntry = document.createElement('a')
+      contextMenuEntry.innerHTML = entry['name']
+      contextMenuEntry.addEventListener("click", function(e) {
+        entry['func']();
+      })
+      contextMenuList.append(contextMenuEntry);
+
+    })
+
+    gradioApp().getRootNode().appendChild(contextMenu)
+
+    let menuWidth = contextMenu.offsetWidth + 4;
+    let menuHeight = contextMenu.offsetHeight + 4;
+
+    let windowWidth = window.innerWidth;
+    let windowHeight = window.innerHeight;
+
+    if ( (windowWidth - posx) < menuWidth ) {
+      contextMenu.style.left = windowWidth - menuWidth + "px";
+    }
+
+    if ( (windowHeight - posy) < menuHeight ) {
+      contextMenu.style.top = windowHeight - menuHeight + "px";
+    }
+
+  }
+
+  function appendContextMenuOption(targetEmementSelector,entryName,entryFunction){
+    
+    currentItems = menuSpecs.get(targetEmementSelector)
+    
+    if(!currentItems){
+      currentItems = []
+      menuSpecs.set(targetEmementSelector,currentItems);
+    }
+    let newItem = {'id':targetEmementSelector+'_'+uid(), 
+                   'name':entryName,
+                   'func':entryFunction,
+                   'isNew':true}
+
+    currentItems.push(newItem)
+    return newItem['id']
+  }
+
+  function removeContextMenuOption(uid){
+
+  }
+
+  function addContextMenuEventListener(){
+    if(eventListenerApplied){
+      return;
+    }
+    gradioApp().addEventListener("click", function(e) {
+      let source = e.composedPath()[0]
+      if(source.id && source.indexOf('check_progress')>-1){
+        return
+      }
+      
+      let oldMenu = gradioApp().querySelector('#context-menu')
+      if(oldMenu){
+        oldMenu.remove()
+      }
+    });
+    gradioApp().addEventListener("contextmenu", function(e) {
+      let oldMenu = gradioApp().querySelector('#context-menu')
+      if(oldMenu){
+        oldMenu.remove()
+      }
+      menuSpecs.forEach(function(v,k) {
+        if(e.composedPath()[0].matches(k)){
+          showContextMenu(e,e.composedPath()[0],v)
+          e.preventDefault()
+          return
+        }
+      })
+    });
+    eventListenerApplied=true
+  
+  }
+
+  return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
+}
+
+initResponse = contextMenuInit()
+appendContextMenuOption     = initResponse[0]
+removeContextMenuOption     = initResponse[1]
+addContextMenuEventListener = initResponse[2]
+
+
+//Start example Context Menu Items
+generateOnRepeatId = appendContextMenuOption('#txt2img_generate','Generate forever',function(){
+  let genbutton = gradioApp().querySelector('#txt2img_generate');
+  let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
+  if(!interruptbutton.offsetParent){
+    genbutton.click();
+  }
+  clearInterval(window.generateOnRepeatInterval)
+  window.generateOnRepeatInterval = setInterval(function(){
+    if(!interruptbutton.offsetParent){
+      genbutton.click();
+    }
+  },
+  500)}
+)
+
+cancelGenerateForever = function(){ 
+  clearInterval(window.generateOnRepeatInterval) 
+  let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
+  if(interruptbutton.offsetParent){
+      interruptbutton.click();
+  }
+}
+
+appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
+appendContextMenuOption('#txt2img_generate','Cancel generate forever',cancelGenerateForever)
+
+appendContextMenuOption('#roll','Roll three',
+  function(){ 
+    let rollbutton = gradioApp().querySelector('#roll');
+    setTimeout(function(){rollbutton.click()},100)
+    setTimeout(function(){rollbutton.click()},200)
+    setTimeout(function(){rollbutton.click()},300)
+  }
+)
+//End example Context Menu Items
+
+onUiUpdate(function(){
+  addContextMenuEventListener()
+});
\ No newline at end of file
-- 
cgit v1.2.1


From 83749bfc72923b946abb825ebf4fdcc8b6035c8e Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 8 Oct 2022 05:35:03 +0100
Subject: context menu styling

---
 style.css | 29 ++++++++++++++++++++++++++++-
 1 file changed, 28 insertions(+), 1 deletion(-)

diff --git a/style.css b/style.css
index da0729a2..50c5e557 100644
--- a/style.css
+++ b/style.css
@@ -410,4 +410,31 @@ input[type="range"]{
 
 #img2img_image div.h-60{
     height: 480px;
-}
\ No newline at end of file
+}
+
+#context-menu{
+    z-index:9999;
+    position:absolute;
+    display:block;
+    padding:0px 0;
+    border:2px solid #a55000;
+    border-radius:8px;
+    box-shadow:1px 1px 2px #CE6400;
+    width: 200px;
+}
+
+.context-menu-items{
+    list-style: none;
+    margin: 0;
+    padding: 0;
+}
+
+.context-menu-items a{
+    display:block;
+    padding:5px;
+    cursor:pointer;
+}
+
+.context-menu-items a:hover{
+    background: #a55000;
+}
-- 
cgit v1.2.1


From 21679435e531e729a4aea494e6cb9b7152ecdf75 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 8 Oct 2022 05:46:42 +0100
Subject: implement removal

---
 javascript/contextMenus.js | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js
index 99d1d3f7..2d82269f 100644
--- a/javascript/contextMenus.js
+++ b/javascript/contextMenus.js
@@ -79,7 +79,13 @@ contextMenuInit = function(){
   }
 
   function removeContextMenuOption(uid){
-
+    menuSpecs.forEach(function(v,k) {
+      let index = -1
+      v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
+      if(index>=0){
+        v.splice(index, 1);
+      }
+    })
   }
 
   function addContextMenuEventListener(){
@@ -148,7 +154,8 @@ cancelGenerateForever = function(){
 }
 
 appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
-appendContextMenuOption('#txt2img_generate','Cancel generate forever',cancelGenerateForever)
+appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
+
 
 appendContextMenuOption('#roll','Roll three',
   function(){ 
@@ -162,4 +169,4 @@ appendContextMenuOption('#roll','Roll three',
 
 onUiUpdate(function(){
   addContextMenuEventListener()
-});
\ No newline at end of file
+});
-- 
cgit v1.2.1


From 87db6f01cc6b118fe0c82c36c6686d72d060c417 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 10:15:29 +0300
Subject: add info about cross attention javascript shortcut code

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 0516c2cd..d6e1d50b 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - Attention, specify parts of text that the model should pay more attention to
     - a man in a ((tuxedo)) - will pay more attention to tuxedo
     - a man in a (tuxedo:1.21) - alternative syntax
-    - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text
+    - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)
 - Loopback, run img2img processing multiple times
 - X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
 - Textual Inversion
-- 
cgit v1.2.1


From 5d54f35c583bd5a3b0ee271a862827f1ca81ef09 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 11:55:02 +0300
Subject: add xformers attnblock and hypernetwork support

---
 modules/sd_hijack_optimizations.py | 20 ++++++++++++++++++--
 1 file changed, 18 insertions(+), 2 deletions(-)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 7fb4a45e..c78d5838 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -98,8 +98,14 @@ def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
     q_in = self.to_q(x)
     context = default(context, x)
-    k_in = self.to_k(context)
-    v_in = self.to_v(context)
+    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+    if hypernetwork_layers is not None:
+        k_in = self.to_k(hypernetwork_layers[0](context))
+        v_in = self.to_v(hypernetwork_layers[1](context))
+    else:
+        k_in = self.to_k(context)
+        v_in = self.to_v(context)
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
@@ -169,3 +175,13 @@ def cross_attention_attnblock_forward(self, x):
         h3 += x
 
         return h3
+    
+    def xformers_attnblock_forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q1 = self.q(h_).contiguous()
+        k1 = self.k(h_).contiguous()
+        v = self.v(h_).contiguous()
+        out = xformers.ops.memory_efficient_attention(q1, k1, v)
+        out = self.proj_out(out)
+        return x+out
-- 
cgit v1.2.1


From 76a616fa6b814c681eaf6edc87eb3001b8c2b6be Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 11:55:38 +0300
Subject: Update sd_hijack_optimizations.py

---
 modules/sd_hijack_optimizations.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index c78d5838..ee58c7e4 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -176,7 +176,7 @@ def cross_attention_attnblock_forward(self, x):
 
         return h3
     
-    def xformers_attnblock_forward(self, x):
+def xformers_attnblock_forward(self, x):
         h_ = x
         h_ = self.norm(h_)
         q1 = self.q(h_).contiguous()
-- 
cgit v1.2.1


From 91d66f5520df416db718103d460550ad495e952d Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 11:56:01 +0300
Subject: use new attnblock for xformers path

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 0e99c319..3da8c8ce 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -23,7 +23,7 @@ def apply_optimizations():
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip):
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif cmd_opts.opt_split_attention:
-- 
cgit v1.2.1


From 616b7218f7c469d25c138634472017a7e18e742e Mon Sep 17 00:00:00 2001
From: leko <rexx0520@gmail.com>
Date: Fri, 7 Oct 2022 23:09:21 +0800
Subject: fix: handles when state_dict does not exist

---
 modules/sd_models.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 8f794b47..9409d070 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -122,7 +122,11 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
     pl_sd = torch.load(checkpoint_file, map_location="cpu")
     if "global_step" in pl_sd:
         print(f"Global Step: {pl_sd['global_step']}")
-    sd = pl_sd["state_dict"]
+    
+    if "state_dict" in pl_sd:
+        sd = pl_sd["state_dict"]
+    else:
+        sd = pl_sd
 
     model.load_state_dict(sd, strict=False)
 
-- 
cgit v1.2.1


From 706d5944a075a6523ea7f00165d630efc085ca22 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 13:38:57 +0300
Subject: let user choose his own prompt token count limit

---
 modules/processing.py |  6 ++++++
 modules/sd_hijack.py  | 13 +++++++------
 modules/shared.py     |  5 +++--
 3 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index f773a30e..d814d5ac 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -123,6 +123,7 @@ class Processed:
         self.index_of_first_image = index_of_first_image
         self.styles = p.styles
         self.job_timestamp = state.job_timestamp
+        self.max_prompt_tokens = opts.max_prompt_tokens
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -141,6 +142,7 @@ class Processed:
         self.all_subseeds = all_subseeds or [self.subseed]
         self.infotexts = infotexts or [info]
 
+
     def js(self):
         obj = {
             "prompt": self.prompt,
@@ -169,6 +171,7 @@ class Processed:
             "infotexts": self.infotexts,
             "styles": self.styles,
             "job_timestamp": self.job_timestamp,
+            "max_prompt_tokens": self.max_prompt_tokens,
         }
 
         return json.dumps(obj)
@@ -266,6 +269,8 @@ def fix_seed(p):
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
     index = position_in_batch + iteration * p.batch_size
 
+    max_tokens = getattr(p, 'max_prompt_tokens', opts.max_prompt_tokens)
+
     generation_params = {
         "Steps": p.steps,
         "Sampler": sd_samplers.samplers[p.sampler_index].name,
@@ -281,6 +286,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
+        "Max tokens": (None if max_tokens == shared.vanilla_max_prompt_tokens else max_tokens)
     }
 
     generation_params.update(p.extra_generation_params)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index d68f89cc..340329c0 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -18,7 +18,6 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
 diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
 
-
 def apply_optimizations():
     undo_optimizations()
 
@@ -83,7 +82,7 @@ class StableDiffusionModelHijack:
             layer.padding_mode = 'circular' if enable else 'zeros'
 
     def tokenize(self, text):
-        max_length = self.clip.max_length - 2
+        max_length = opts.max_prompt_tokens - 2
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
         return remade_batch_tokens[0], token_count, max_length
 
@@ -94,7 +93,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         self.wrapped = wrapped
         self.hijack: StableDiffusionModelHijack = hijack
         self.tokenizer = wrapped.tokenizer
-        self.max_length = wrapped.max_length
         self.token_mults = {}
 
         tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
@@ -116,7 +114,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
         id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
-        maxlen = self.wrapped.max_length
+        maxlen = opts.max_prompt_tokens
 
         if opts.enable_emphasis:
             parsed = prompt_parser.parse_prompt_attention(line)
@@ -191,7 +189,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def process_text_old(self, text):
         id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
-        maxlen = self.wrapped.max_length
+        maxlen = self.wrapped.max_length  # you get to stay at 77
         used_custom_terms = []
         remade_batch_tokens = []
         overflowing_words = []
@@ -268,8 +266,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         if len(used_custom_terms) > 0:
             self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
 
+        position_ids_array = [min(x, 75) for x in range(len(remade_batch_tokens[0])-1)] + [76]
+        position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
+
         tokens = torch.asarray(remade_batch_tokens).to(device)
-        outputs = self.wrapped.transformer(input_ids=tokens)
+        outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
         z = outputs.last_hidden_state
 
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
diff --git a/modules/shared.py b/modules/shared.py
index 879d8424..864e772c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -118,8 +118,8 @@ prompt_styles = modules.styles.StyleDatabase(styles_filename)
 interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
-# This was moved to webui.py with the other model "setup" calls.
-# modules.sd_models.list_models()
+
+vanilla_max_prompt_tokens = 77
 
 
 def realesrgan_models_names():
@@ -221,6 +221,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
+    "max_prompt_tokens": OptionInfo(vanilla_max_prompt_tokens, f"Max prompt token count. Two tokens are reserved for for start and end. Default is {vanilla_max_prompt_tokens}. Setting this to a different value will result in different pictures for same seed.", gr.Number, {"precision": 0}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-- 
cgit v1.2.1


From 786d9f63aaa4515df82eb2cf357ea92f3dae1e29 Mon Sep 17 00:00:00 2001
From: Trung Ngo <codem01@gmail.com>
Date: Tue, 4 Oct 2022 22:56:30 -0500
Subject: Add button to skip the current iteration

---
 javascript/hints.js       |  1 +
 javascript/progressbar.js | 20 ++++++++++++++------
 modules/img2img.py        |  4 ++++
 modules/processing.py     |  4 ++++
 modules/shared.py         |  5 +++++
 modules/ui.py             |  8 ++++++++
 style.css                 | 14 ++++++++++++--
 webui.py                  |  1 +
 8 files changed, 49 insertions(+), 8 deletions(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index 8adcd983..8e352e94 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -35,6 +35,7 @@ titles = {
     "Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
     "Denoising strength change factor": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.",
 
+    "Skip": "Stop processing current image and continue processing.",
     "Interrupt": "Stop processing images and return any results accumulated so far.",
     "Save": "Write image to a directory (default - log/images) and generation parameters into csv file.",
 
diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index f9e9290e..4395a215 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -1,8 +1,9 @@
 // code related to showing and updating progressbar shown as the image is being made
 global_progressbars = {}
 
-function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_interrupt, id_preview, id_gallery){
+function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
     var progressbar = gradioApp().getElementById(id_progressbar)
+    var skip = id_skip ? gradioApp().getElementById(id_skip) : null
     var interrupt = gradioApp().getElementById(id_interrupt)
     
     if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){
@@ -32,30 +33,37 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_inte
 
                 var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
                 if(!progressDiv){
+                    if (skip) {
+                        skip.style.display = "none"
+                    }
                     interrupt.style.display = "none"
                 }
             }
 
-            window.setTimeout(function(){ requestMoreProgress(id_part, id_progressbar_span, id_interrupt) }, 500)
+            window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500)
         });
         mutationObserver.observe( progressbar, { childList:true, subtree:true })
 	}
 }
 
 onUiUpdate(function(){
-    check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery')
-    check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery')
-    check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', 'ti_interrupt', 'ti_preview', 'ti_gallery')
+    check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_skip', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery')
+    check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_skip', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery')
+    check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', '', 'ti_interrupt', 'ti_preview', 'ti_gallery')
 })
 
-function requestMoreProgress(id_part, id_progressbar_span, id_interrupt){
+function requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt){
     btn = gradioApp().getElementById(id_part+"_check_progress");
     if(btn==null) return;
 
     btn.click();
     var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
+    var skip = id_skip ? gradioApp().getElementById(id_skip) : null
     var interrupt = gradioApp().getElementById(id_interrupt)
     if(progressDiv && interrupt){
+        if (skip) {
+            skip.style.display = "block"
+        }
         interrupt.style.display = "block"
     }
 }
diff --git a/modules/img2img.py b/modules/img2img.py
index da212d72..e60b7e0f 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -32,6 +32,10 @@ def process_batch(p, input_dir, output_dir, args):
 
     for i, image in enumerate(images):
         state.job = f"{i+1} out of {len(images)}"
+        if state.skipped:
+            state.skipped = False
+            state.interrupted = False
+            continue
 
         if state.interrupted:
             break
diff --git a/modules/processing.py b/modules/processing.py
index d814d5ac..6805039c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -355,6 +355,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             state.job_count = p.n_iter
 
         for n in range(p.n_iter):
+            if state.skipped:
+                state.skipped = False
+                state.interrupted = False
+            
             if state.interrupted:
                 break
 
diff --git a/modules/shared.py b/modules/shared.py
index 864e772c..7f802bd9 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -84,6 +84,7 @@ def selected_hypernetwork():
 
 
 class State:
+    skipped = False
     interrupted = False
     job = ""
     job_no = 0
@@ -96,6 +97,10 @@ class State:
     current_image_sampling_step = 0
     textinfo = None
 
+    def skip(self):
+        self.skipped = True
+        self.interrupted = True
+
     def interrupt(self):
         self.interrupted = True
 
diff --git a/modules/ui.py b/modules/ui.py
index 4f18126f..e3e62fdd 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -191,6 +191,7 @@ def wrap_gradio_call(func, extra_outputs=None):
         # last item is always HTML
         res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
 
+        shared.state.skipped = False
         shared.state.interrupted = False
         shared.state.job_count = 0
 
@@ -411,9 +412,16 @@ def create_toprow(is_img2img):
 
         with gr.Column(scale=1):
             with gr.Row():
+                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
                 interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
                 submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
 
+                skip.click(
+                    fn=lambda: shared.state.skip(),
+                    inputs=[],
+                    outputs=[],
+                )
+
                 interrupt.click(
                     fn=lambda: shared.state.interrupt(),
                     inputs=[],
diff --git a/style.css b/style.css
index 50c5e557..6904fc50 100644
--- a/style.css
+++ b/style.css
@@ -393,10 +393,20 @@ input[type="range"]{
 
 #txt2img_interrupt, #img2img_interrupt{
   position: absolute;
-  width: 100%;
+  width: 50%;
   height: 72px;
   background: #b4c0cc;
-  border-radius: 8px;
+  border-radius: 0px;
+  display: none;
+}
+
+#txt2img_skip, #img2img_skip{
+  position: absolute;
+  width: 50%;
+  right: 0px;
+  height: 72px;
+  background: #b4c0cc;
+  border-radius: 0px;
   display: none;
 }
 
diff --git a/webui.py b/webui.py
index 480360fe..3b4cf5e9 100644
--- a/webui.py
+++ b/webui.py
@@ -58,6 +58,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
         shared.state.current_latent = None
         shared.state.current_image = None
         shared.state.current_image_sampling_step = 0
+        shared.state.skipped = False
         shared.state.interrupted = False
         shared.state.textinfo = None
 
-- 
cgit v1.2.1


From 00117a07efbbe8482add12262a179326541467de Mon Sep 17 00:00:00 2001
From: Trung Ngo <codem01@gmail.com>
Date: Sat, 8 Oct 2022 05:33:21 -0500
Subject: check specifically for skipped

---
 modules/img2img.py     | 2 --
 modules/processing.py  | 3 +--
 modules/sd_samplers.py | 4 ++--
 modules/shared.py      | 1 -
 4 files changed, 3 insertions(+), 7 deletions(-)

diff --git a/modules/img2img.py b/modules/img2img.py
index e60b7e0f..24126774 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -34,8 +34,6 @@ def process_batch(p, input_dir, output_dir, args):
         state.job = f"{i+1} out of {len(images)}"
         if state.skipped:
             state.skipped = False
-            state.interrupted = False
-            continue
 
         if state.interrupted:
             break
diff --git a/modules/processing.py b/modules/processing.py
index 6805039c..3657fe69 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -357,7 +357,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         for n in range(p.n_iter):
             if state.skipped:
                 state.skipped = False
-                state.interrupted = False
             
             if state.interrupted:
                 break
@@ -385,7 +384,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             with devices.autocast():
                 samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
 
-            if state.interrupted:
+            if state.interrupted or state.skipped:
 
                 # if we are interruped, sample returns just noise
                 # use the image collected previously in sampler loop
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index df17e93c..13a8b322 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -106,7 +106,7 @@ def extended_tdqm(sequence, *args, desc=None, **kwargs):
     seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
 
     for x in seq:
-        if state.interrupted:
+        if state.interrupted or state.skipped:
             break
 
         yield x
@@ -254,7 +254,7 @@ def extended_trange(sampler, count, *args, **kwargs):
     seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
 
     for x in seq:
-        if state.interrupted:
+        if state.interrupted or state.skipped:
             break
 
         if sampler.stop_at is not None and x > sampler.stop_at:
diff --git a/modules/shared.py b/modules/shared.py
index 7f802bd9..ca462628 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -99,7 +99,6 @@ class State:
 
     def skip(self):
         self.skipped = True
-        self.interrupted = True
 
     def interrupt(self):
         self.interrupted = True
-- 
cgit v1.2.1


From 4999eb2ef9b30e8c42ca7e4a94d4bbffe4d1f015 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 14:25:47 +0300
Subject: do not let user choose his own prompt token count limit

---
 README.md             |  1 +
 modules/processing.py |  5 -----
 modules/sd_hijack.py  | 25 ++++++++++++-------------
 modules/shared.py     |  3 ---
 4 files changed, 13 insertions(+), 21 deletions(-)

diff --git a/README.md b/README.md
index d6e1d50b..ef9b5e31 100644
--- a/README.md
+++ b/README.md
@@ -65,6 +65,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
      - separate prompts using uppercase `AND`
      - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
+- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
diff --git a/modules/processing.py b/modules/processing.py
index 3657fe69..d5162ddc 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -123,7 +123,6 @@ class Processed:
         self.index_of_first_image = index_of_first_image
         self.styles = p.styles
         self.job_timestamp = state.job_timestamp
-        self.max_prompt_tokens = opts.max_prompt_tokens
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -171,7 +170,6 @@ class Processed:
             "infotexts": self.infotexts,
             "styles": self.styles,
             "job_timestamp": self.job_timestamp,
-            "max_prompt_tokens": self.max_prompt_tokens,
         }
 
         return json.dumps(obj)
@@ -269,8 +267,6 @@ def fix_seed(p):
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
     index = position_in_batch + iteration * p.batch_size
 
-    max_tokens = getattr(p, 'max_prompt_tokens', opts.max_prompt_tokens)
-
     generation_params = {
         "Steps": p.steps,
         "Sampler": sd_samplers.samplers[p.sampler_index].name,
@@ -286,7 +282,6 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
-        "Max tokens": (None if max_tokens == shared.vanilla_max_prompt_tokens else max_tokens)
     }
 
     generation_params.update(p.extra_generation_params)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 340329c0..2c1332c9 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -36,6 +36,13 @@ def undo_optimizations():
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
 
+def get_target_prompt_token_count(token_count):
+    if token_count < 75:
+        return 75
+
+    return math.ceil(token_count / 10) * 10
+
+
 class StableDiffusionModelHijack:
     fixes = None
     comments = []
@@ -84,7 +91,7 @@ class StableDiffusionModelHijack:
     def tokenize(self, text):
         max_length = opts.max_prompt_tokens - 2
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
-        return remade_batch_tokens[0], token_count, max_length
+        return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
 
 
 class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
@@ -114,7 +121,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
         id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
-        maxlen = opts.max_prompt_tokens
 
         if opts.enable_emphasis:
             parsed = prompt_parser.parse_prompt_attention(line)
@@ -146,19 +152,12 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     used_custom_terms.append((embedding.name, embedding.checksum()))
                     i += embedding_length_in_tokens
 
-        if len(remade_tokens) > maxlen - 2:
-            vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
-            ovf = remade_tokens[maxlen - 2:]
-            overflowing_words = [vocab.get(int(x), "") for x in ovf]
-            overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
-            hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
-
         token_count = len(remade_tokens)
-        remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
-        remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
+        prompt_target_length = get_target_prompt_token_count(token_count)
+        tokens_to_add = prompt_target_length - len(remade_tokens) + 1
 
-        multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
-        multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
+        remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add
+        multipliers = [1.0] + multipliers + [1.0] * tokens_to_add
 
         return remade_tokens, fixes, multipliers, token_count
 
diff --git a/modules/shared.py b/modules/shared.py
index ca462628..475d7e52 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -123,8 +123,6 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
 
-vanilla_max_prompt_tokens = 77
-
 
 def realesrgan_models_names():
     import modules.realesrgan_model
@@ -225,7 +223,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
-    "max_prompt_tokens": OptionInfo(vanilla_max_prompt_tokens, f"Max prompt token count. Two tokens are reserved for for start and end. Default is {vanilla_max_prompt_tokens}. Setting this to a different value will result in different pictures for same seed.", gr.Number, {"precision": 0}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-- 
cgit v1.2.1


From 4201fd14f5769a4cf6723d2bc5495c3c84a2cd00 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 14:42:34 +0300
Subject: install xformers

---
 launch.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/launch.py b/launch.py
index 75edb66a..f3fbe16a 100644
--- a/launch.py
+++ b/launch.py
@@ -124,6 +124,9 @@ if not is_installed("gfpgan"):
 if not is_installed("clip"):
     run_pip(f"install {clip_package}", "clip")
 
+if not is_installed("xformers"):
+    run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
+
 os.makedirs(dir_repos, exist_ok=True)
 
 git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
-- 
cgit v1.2.1


From 3f166be1b60ff2ab33a6d2646809ec3f48796303 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 14:42:50 +0300
Subject: Update requirements.txt

---
 requirements.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 304a066a..81641d68 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -24,4 +24,3 @@ torchdiffeq
 kornia
 lark
 functorch
-#xformers?
-- 
cgit v1.2.1


From 77f4237d1c3af1756e7dab2699e3dcebad5619d6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 15:25:59 +0300
Subject: fix bugs related to variable prompt lengths

---
 modules/sd_hijack.py   | 14 +++++++++-----
 modules/sd_samplers.py | 35 ++++++++++++++++++++++++++++-------
 2 files changed, 37 insertions(+), 12 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 2c1332c9..7e7fde0f 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -89,7 +89,6 @@ class StableDiffusionModelHijack:
             layer.padding_mode = 'circular' if enable else 'zeros'
 
     def tokenize(self, text):
-        max_length = opts.max_prompt_tokens - 2
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
         return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
 
@@ -174,7 +173,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if line in cache:
                 remade_tokens, fixes, multipliers = cache[line]
             else:
-                remade_tokens, fixes, multipliers, token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
+                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
+                token_count = max(current_token_count, token_count)
 
                 cache[line] = (remade_tokens, fixes, multipliers)
 
@@ -265,15 +265,19 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         if len(used_custom_terms) > 0:
             self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
 
-        position_ids_array = [min(x, 75) for x in range(len(remade_batch_tokens[0])-1)] + [76]
+        target_token_count = get_target_prompt_token_count(token_count) + 2
+
+        position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76]
         position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
 
-        tokens = torch.asarray(remade_batch_tokens).to(device)
+        remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
+        tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
         outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
         z = outputs.last_hidden_state
 
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
-        batch_multipliers = torch.asarray(batch_multipliers).to(device)
+        batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
+        batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
         original_mean = z.mean()
         z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
         new_mean = z.mean()
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 13a8b322..eade0dbb 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -142,6 +142,16 @@ class VanillaStableDiffusionSampler:
         assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
         cond = tensor
 
+        # for DDIM, shapes must match, we can't just process cond and uncond independently;
+        # filling unconditional_conditioning with repeats of the last vector to match length is
+        # not 100% correct but should work well enough
+        if unconditional_conditioning.shape[1] < cond.shape[1]:
+            last_vector = unconditional_conditioning[:, -1:]
+            last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
+            unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
+        elif unconditional_conditioning.shape[1] > cond.shape[1]:
+            unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
+
         if self.mask is not None:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)
             x_dec = img_orig * self.mask + self.nmask * x_dec
@@ -221,18 +231,29 @@ class CFGDenoiser(torch.nn.Module):
 
         x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
-        cond_in = torch.cat([tensor, uncond])
 
-        if shared.batch_cond_uncond:
-            x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
+        if tensor.shape[1] == uncond.shape[1]:
+            cond_in = torch.cat([tensor, uncond])
+
+            if shared.batch_cond_uncond:
+                x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
+            else:
+                x_out = torch.zeros_like(x_in)
+                for batch_offset in range(0, x_out.shape[0], batch_size):
+                    a = batch_offset
+                    b = a + batch_size
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
         else:
             x_out = torch.zeros_like(x_in)
-            for batch_offset in range(0, x_out.shape[0], batch_size):
+            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
+            for batch_offset in range(0, tensor.shape[0], batch_size):
                 a = batch_offset
-                b = a + batch_size
-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
+                b = min(a + batch_size, tensor.shape[0])
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])
+
+            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)
 
-        denoised_uncond = x_out[-batch_size:]
+        denoised_uncond = x_out[-uncond.shape[0]:]
         denoised = torch.clone(denoised_uncond)
 
         for i, conds in enumerate(conds_list):
-- 
cgit v1.2.1


From 7001bffe0247804793dfabb69ac96d832572ccd0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 15:43:25 +0300
Subject: fix AND broken for long prompts

---
 modules/prompt_parser.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index f00256f2..15666073 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -239,6 +239,15 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
 
         conds_list.append(conds_for_batch)
 
+    # if prompts have wildly different lengths above the limit we'll get tensors fo different shapes
+    # and won't be able to torch.stack them. So this fixes that.
+    token_count = max([x.shape[0] for x in tensors])
+    for i in range(len(tensors)):
+        if tensors[i].shape[0] != token_count:
+            last_vector = tensors[i][-1:]
+            last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
+            tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
+
     return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
 
 
-- 
cgit v1.2.1


From 772db721a52da374d627b60994222051f26c27a7 Mon Sep 17 00:00:00 2001
From: ddPn08 <pyn.g.1125@gmail.com>
Date: Fri, 7 Oct 2022 23:02:07 +0900
Subject: fix glob path in hypernetwork.py

---
 modules/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
index c7b86682..7f062242 100644
--- a/modules/hypernetwork.py
+++ b/modules/hypernetwork.py
@@ -43,7 +43,7 @@ class Hypernetwork:
 def load_hypernetworks(path):
     res = {}
 
-    for filename in glob.iglob(path + '**/*.pt', recursive=True):
+    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
         try:
             hn = Hypernetwork(filename)
             res[hn.name] = hn
-- 
cgit v1.2.1


From 32e428ff19c28c87bb2ed362316b928b372e3a70 Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Sat, 8 Oct 2022 16:01:34 +0800
Subject: Remove duplicate event listeners

---
 javascript/imageviewer.js | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js
index 3a0baac8..4c0e8f4b 100644
--- a/javascript/imageviewer.js
+++ b/javascript/imageviewer.js
@@ -86,6 +86,9 @@ function showGalleryImage(){
         
         if(fullImg_preview != null){
             fullImg_preview.forEach(function function_name(e) {
+                if (e.dataset.modded)
+                    return;
+                e.dataset.modded = true;
                 if(e && e.parentElement.tagName == 'DIV'){
 
                     e.style.cursor='pointer'
-- 
cgit v1.2.1


From 5f85a74b00c0154bfd559dc67edfa7e30342b7c9 Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Fri, 7 Oct 2022 17:48:34 -0400
Subject: fix bug where when using prompt composition, hijack_comments
 generated before the final AND will be dropped

---
 modules/processing.py | 1 +
 modules/sd_hijack.py  | 5 ++++-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index d5162ddc..8240ee27 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -313,6 +313,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         os.makedirs(p.outpath_grids, exist_ok=True)
 
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
+    modules.sd_hijack.model_hijack.clear_comments()
 
     comments = {}
 
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 7e7fde0f..ba808a39 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -88,6 +88,9 @@ class StableDiffusionModelHijack:
         for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
             layer.padding_mode = 'circular' if enable else 'zeros'
 
+    def clear_comments(self):
+        self.comments = []
+
     def tokenize(self, text):
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
         return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
@@ -260,7 +263,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
 
         self.hijack.fixes = hijack_fixes
-        self.hijack.comments = hijack_comments
+        self.hijack.comments += hijack_comments
 
         if len(used_custom_terms) > 0:
             self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
-- 
cgit v1.2.1


From d0e85873ac72416d32dee8720dc9e93ab3d3e236 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 16:13:26 +0300
Subject: check for OS and env variable

---
 launch.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/launch.py b/launch.py
index f3fbe16a..a2089b3b 100644
--- a/launch.py
+++ b/launch.py
@@ -4,6 +4,7 @@ import os
 import sys
 import importlib.util
 import shlex
+import platform
 
 dir_repos = "repositories"
 dir_tmp = "tmp"
@@ -31,6 +32,7 @@ def extract_arg(args, name):
 
 
 args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
+args, xformers = extract_arg(args, '--xformers')
 
 
 def repo_dir(name):
@@ -124,8 +126,11 @@ if not is_installed("gfpgan"):
 if not is_installed("clip"):
     run_pip(f"install {clip_package}", "clip")
 
-if not is_installed("xformers"):
-    run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
+if not is_installed("xformers") and xformers:
+    if platform.system() == "Windows":
+        run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
+    elif:
+        run_pip("install xformers", "xformers")
 
 os.makedirs(dir_repos, exist_ok=True)
 
-- 
cgit v1.2.1


From 26b459a3799c5cdf71ca8ed5315a99f69c69f02c Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 16:20:04 +0300
Subject: default to split attention if cuda is available and xformers is not

---
 modules/sd_hijack.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 3da8c8ce..04adcf03 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -21,12 +21,12 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 
 def apply_optimizations():
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip):
+    if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip or shared.xformers_available):
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-    elif cmd_opts.opt_split_attention:
+    elif cmd_opts.opt_split_attention or torch.cuda.is_available():
         ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
-- 
cgit v1.2.1


From ddfa9a97865c732193023a71521c5b7b53d8571b Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 16:20:41 +0300
Subject: add xformers_available shared variable

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 8cc3b2fe..6ed4b802 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -74,7 +74,7 @@ device = devices.device
 
 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
 parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
-
+xformers_available = False
 config_filename = cmd_opts.ui_settings_file
 
 
-- 
cgit v1.2.1


From 69d0053583757ce2942d62de81e8b89e6be07840 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 16:21:40 +0300
Subject: update sd_hijack_opt to respect new env variables

---
 modules/sd_hijack_optimizations.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index ee58c7e4..be09ec8f 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,9 +1,14 @@
 import math
 import torch
 from torch import einsum
-import xformers.ops
-import functorch
-xformers._is_functorch_available=True
+try:
+    import xformers.ops
+    import functorch
+    xformers._is_functorch_available = True
+    shared.xformers_available = True
+except:
+    print('Cannot find xformers, defaulting to split attention. Try setting --xformers in your webui-user file if you wish to install it.')
+    continue
 from ldm.util import default
 from einops import rearrange
 
-- 
cgit v1.2.1


From ca5f0f149c29c344a6badd055b15b5e5fcd6e938 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 16:22:38 +0300
Subject: Update launch.py

---
 launch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index a2089b3b..a592e1ba 100644
--- a/launch.py
+++ b/launch.py
@@ -129,7 +129,7 @@ if not is_installed("clip"):
 if not is_installed("xformers") and xformers:
     if platform.system() == "Windows":
         run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
-    elif:
+    elif platform.system() == "Linux":
         run_pip("install xformers", "xformers")
 
 os.makedirs(dir_repos, exist_ok=True)
-- 
cgit v1.2.1


From 7ffea1507813540b8cd9e73feb7bf23de1ac4e27 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 16:24:06 +0300
Subject: Update requirements_versions.txt

---
 requirements_versions.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements_versions.txt b/requirements_versions.txt
index fdff2687..fec3e9d5 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -22,3 +22,4 @@ resize-right==0.0.2
 torchdiffeq==0.2.3
 kornia==0.6.7
 lark==1.1.2
+functorch==0.2.1
-- 
cgit v1.2.1


From 970de9ee6891ff586821d0d80dde01c2f6c681b3 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 16:29:43 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 04adcf03..5b30539f 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -21,7 +21,7 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 
 def apply_optimizations():
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip or shared.xformers_available):
+    if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip) and shared.xformers_available:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
-- 
cgit v1.2.1


From 7ff1170a2e11b6f00f587407326db0b9f8f51adf Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 16:33:39 +0300
Subject: emergency fix for xformers (continue + shared)

---
 modules/sd_hijack_optimizations.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index e43e2c7a..05023b6f 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,19 +1,19 @@
 import math
 import torch
 from torch import einsum
-try:
-    import xformers.ops
-    import functorch
-    xformers._is_functorch_available = True
-    shared.xformers_available = True
-except:
-    print('Cannot find xformers, defaulting to split attention. Try setting --xformers in your webui-user file if you wish to install it.')
-    continue
+
 from ldm.util import default
 from einops import rearrange
 
 from modules import shared
 
+try:
+    import xformers.ops
+    import functorch
+    xformers._is_functorch_available = True
+    shared.xformers_available = True
+except Exception:
+    print('Cannot find xformers, defaulting to split attention. Try adding --xformers commandline argument to your webui-user file if you wish to install it.')
 
 # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
 def split_cross_attention_forward_v1(self, x, context=None, mask=None):
-- 
cgit v1.2.1


From dc1117233ef8f9b25ff1ac40b158f20b70ba2fcb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 17:02:18 +0300
Subject: simplify xfrmers options: --xformers to enable and that's it

---
 launch.py                          |  2 +-
 modules/sd_hijack.py               |  2 +-
 modules/sd_hijack_optimizations.py | 20 +++++++++++++-------
 modules/shared.py                  |  2 +-
 4 files changed, 16 insertions(+), 10 deletions(-)

diff --git a/launch.py b/launch.py
index a592e1ba..61f62096 100644
--- a/launch.py
+++ b/launch.py
@@ -32,7 +32,7 @@ def extract_arg(args, name):
 
 
 args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
-args, xformers = extract_arg(args, '--xformers')
+xformers = '--xformers' in args
 
 
 def repo_dir(name):
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 5d93f7f6..91e98c16 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -22,7 +22,7 @@ def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip) and shared.xformers_available:
+    if cmd_opts.xformers and shared.xformers_available and not torch.version.hip:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 05023b6f..d23d733b 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,4 +1,7 @@
 import math
+import sys
+import traceback
+
 import torch
 from torch import einsum
 
@@ -7,13 +10,16 @@ from einops import rearrange
 
 from modules import shared
 
-try:
-    import xformers.ops
-    import functorch
-    xformers._is_functorch_available = True
-    shared.xformers_available = True
-except Exception:
-    print('Cannot find xformers, defaulting to split attention. Try adding --xformers commandline argument to your webui-user file if you wish to install it.')
+if shared.cmd_opts.xformers:
+    try:
+        import xformers.ops
+        import functorch
+        xformers._is_functorch_available = True
+        shared.xformers_available = True
+    except Exception:
+        print("Cannot import xformers", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
 
 # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
 def split_cross_attention_forward_v1(self, x, context=None, mask=None):
diff --git a/modules/shared.py b/modules/shared.py
index d68df751..02cb2722 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -43,7 +43,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
 parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
-parser.add_argument("--disable-opt-xformers-attention", action='store_true', help="force-disables xformers attention optimization")
+parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-- 
cgit v1.2.1


From 27032c47df9c07ac21dd5b89fa7dc247bb8705b6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 17:10:05 +0300
Subject: restore old opt_split_attention/disable_opt_split_attention logic

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 91e98c16..335a2bcf 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -27,7 +27,7 @@ def apply_optimizations():
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-    elif cmd_opts.opt_split_attention or torch.cuda.is_available():
+    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
         ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
-- 
cgit v1.2.1


From 4f33289d0fc5aa3a197f4a4c926d03d44f0d597e Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Sat, 8 Oct 2022 22:48:15 +0900
Subject: Fixed typo

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index e3e62fdd..ffd75f6a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -946,7 +946,7 @@ def create_ui(wrap_gradio_gpu_call):
                 custom_name = gr.Textbox(label="Custom Name (Optional)")
                 interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
                 interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
-                save_as_half = gr.Checkbox(value=False, label="Safe as float16")
+                save_as_half = gr.Checkbox(value=False, label="Save as float16")
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
 
             with gr.Column(variant='panel'):
-- 
cgit v1.2.1


From cfc33f99d47d1f45af15499e5965834089d11858 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 17:28:58 +0300
Subject: why did you do this

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 335a2bcf..ed271976 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -28,7 +28,7 @@ def apply_optimizations():
     elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-        ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
 
-- 
cgit v1.2.1


From 7e639cd49855ef59e087ae9a9122756a937007eb Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 17:22:20 +0300
Subject: check for 3.10

---
 launch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index 61f62096..1d65a779 100644
--- a/launch.py
+++ b/launch.py
@@ -126,7 +126,7 @@ if not is_installed("gfpgan"):
 if not is_installed("clip"):
     run_pip(f"install {clip_package}", "clip")
 
-if not is_installed("xformers") and xformers:
+if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
     if platform.system() == "Windows":
         run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
     elif platform.system() == "Linux":
-- 
cgit v1.2.1


From 017b6b8744f0771e498656ec043e12d5cc6969a7 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 17:27:21 +0300
Subject: check for ampere

---
 modules/sd_hijack.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index ed271976..5e266d5e 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -22,9 +22,10 @@ def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if cmd_opts.xformers and shared.xformers_available and not torch.version.hip:
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
+    if cmd_opts.xformers and shared.xformers_available and torch.version.cuda:
+        if torch.cuda.get_device_capability(shared.device) == (8, 6):
+            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
+            ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-- 
cgit v1.2.1


From cc0258aea7b6605be3648900063cfa96ed7c5ffa Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 8 Oct 2022 17:44:53 +0300
Subject: check for ampere without destroying the optimizations. again.

---
 modules/sd_hijack.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 5e266d5e..a3e374f0 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -22,10 +22,9 @@ def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if cmd_opts.xformers and shared.xformers_available and torch.version.cuda:
-        if torch.cuda.get_device_capability(shared.device) == (8, 6):
-            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
-            ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
+    if cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6):
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-- 
cgit v1.2.1


From 34acad1628e98a5e0cbd459fa69ded915864f53d Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 7 Oct 2022 22:56:00 +0100
Subject: Add GZipMiddleware to root demo

---
 webui.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/webui.py b/webui.py
index 3b4cf5e9..18de8e16 100644
--- a/webui.py
+++ b/webui.py
@@ -5,6 +5,8 @@ import importlib
 import signal
 import threading
 
+from fastapi.middleware.gzip import GZipMiddleware
+
 from modules.paths import script_path
 
 from modules import devices, sd_samplers
@@ -93,7 +95,7 @@ def webui():
 
         demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
         
-        demo.launch(
+        app,local_url,share_url = demo.launch(
             share=cmd_opts.share,
             server_name="0.0.0.0" if cmd_opts.listen else None,
             server_port=cmd_opts.port,
@@ -102,6 +104,8 @@ def webui():
             inbrowser=cmd_opts.autolaunch,
             prevent_thread_lock=True
         )
+        
+        app.add_middleware(GZipMiddleware,minimum_size=1000)
 
         while 1:
             time.sleep(0.5)
-- 
cgit v1.2.1


From a5550f0213c3f145b1c984816ebcef92c48853ee Mon Sep 17 00:00:00 2001
From: Artem Zagidulin <p0chta@yandex.ru>
Date: Wed, 5 Oct 2022 19:10:39 +0300
Subject: alternate prompt

---
 modules/prompt_parser.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 15666073..919d5d31 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -13,13 +13,14 @@ import lark
 
 schedule_parser = lark.Lark(r"""
 !start: (prompt | /[][():]/+)*
-prompt: (emphasized | scheduled | plain | WHITESPACE)*
+prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*
 !emphasized: "(" prompt ")"
         | "(" prompt ":" prompt ")"
         | "[" prompt "]"
 scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
+alternate: "[" prompt ("|" prompt)+ "]"
 WHITESPACE: /\s+/
-plain: /([^\\\[\]():]|\\.)+/
+plain: /([^\\\[\]():|]|\\.)+/
 %import common.SIGNED_NUMBER -> NUMBER
 """)
 
@@ -59,6 +60,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
                     tree.children[-1] *= steps
                 tree.children[-1] = min(steps, int(tree.children[-1]))
                 l.append(tree.children[-1])
+            def alternate(self, tree):
+                l.extend(range(1, steps+1))
         CollectSteps().visit(tree)
         return sorted(set(l))
 
@@ -67,6 +70,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
             def scheduled(self, args):
                 before, after, _, when = args
                 yield before or () if step <= when else after
+            def alternate(self, args):
+                yield next(args[(step - 1)%len(args)])
             def start(self, args):
                 def flatten(x):
                     if type(x) == str:
-- 
cgit v1.2.1


From 01f8cb44474e454903c11718e6a4f33dbde34bb8 Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Sat, 8 Oct 2022 18:02:56 +0200
Subject: made deepdanbooru optional, added to readme, automatic download of
 deepbooru model

---
 README.md                 |  2 ++
 launch.py                 |  4 ++++
 modules/deepbooru.py      | 20 ++++++++++----------
 modules/shared.py         |  1 +
 modules/ui.py             | 19 ++++++++++++-------
 requirements.txt          |  3 ---
 requirements_versions.txt |  3 ---
 7 files changed, 29 insertions(+), 23 deletions(-)

diff --git a/README.md b/README.md
index ef9b5e31..6cd7a1f9 100644
--- a/README.md
+++ b/README.md
@@ -66,6 +66,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
      - separate prompts using uppercase `AND`
      - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
+- DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
@@ -123,4 +124,5 @@ The documentation was moved from this README over to the project's [wiki](https:
 - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
 - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
+- DeepDanbooru - interrogator for anime diffusors https://github.com/KichangKim/DeepDanbooru
 - (You)
diff --git a/launch.py b/launch.py
index 61f62096..d46426eb 100644
--- a/launch.py
+++ b/launch.py
@@ -33,6 +33,7 @@ def extract_arg(args, name):
 
 args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
 xformers = '--xformers' in args
+deepdanbooru = '--deepdanbooru' in args
 
 
 def repo_dir(name):
@@ -132,6 +133,9 @@ if not is_installed("xformers") and xformers:
     elif platform.system() == "Linux":
         run_pip("install xformers", "xformers")
 
+if not is_installed("deepdanbooru") and deepdanbooru:
+    run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
+
 os.makedirs(dir_repos, exist_ok=True)
 
 git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 781b2249..7e3c0618 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -9,16 +9,16 @@ def _load_tf_and_return_tags(pil_image, threshold):
     import numpy as np
 
     this_folder = os.path.dirname(__file__)
-    model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28')
-
-    model_good = False
-    for path_candidate in [model_path, os.path.dirname(model_path)]:
-        if os.path.exists(os.path.join(path_candidate, 'project.json')):
-            model_path = path_candidate
-            model_good = True
-    if not model_good:
-        return ("Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/"
-                "deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru")
+    model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
+    if not os.path.exists(os.path.join(model_path, 'project.json')):
+        # there is no point importing these every time
+        import zipfile
+        from basicsr.utils.download_util import load_file_from_url
+        load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
+                           model_path)
+        with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
+            zip_ref.extractall(model_path)
+        os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
 
     tags = dd.project.load_tags_from_project(model_path)
     model = dd.project.load_model_from_project(
diff --git a/modules/shared.py b/modules/shared.py
index 02cb2722..c87b726e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -44,6 +44,7 @@ parser.add_argument("--scunet-models-path", type=str, help="Path to directory wi
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
+parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
diff --git a/modules/ui.py b/modules/ui.py
index 30583fe9..c5c11c3c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -23,9 +23,10 @@ import gradio.utils
 import gradio.routes
 
 from modules import sd_hijack
-from modules.deepbooru import get_deepbooru_tags
 from modules.paths import script_path
 from modules.shared import opts, cmd_opts
+if cmd_opts.deepdanbooru:
+    from modules.deepbooru import get_deepbooru_tags
 import modules.shared as shared
 from modules.sd_samplers import samplers, samplers_for_img2img
 from modules.sd_hijack import model_hijack
@@ -437,7 +438,10 @@ def create_toprow(is_img2img):
             with gr.Row(scale=1):
                 if is_img2img:
                     interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-                    deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+                    if cmd_opts.deepdanbooru:
+                        deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+                    else:
+                        deepbooru = None
                 else:
                     interrogate = None
                     deepbooru = None
@@ -782,11 +786,12 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[img2img_prompt],
             )
 
-            img2img_deepbooru.click(
-                fn=interrogate_deepbooru,
-                inputs=[init_img],
-                outputs=[img2img_prompt],
-            )
+            if cmd_opts.deepdanbooru:
+                img2img_deepbooru.click(
+                    fn=interrogate_deepbooru,
+                    inputs=[init_img],
+                    outputs=[img2img_prompt],
+                )
 
             save.click(
                 fn=wrap_gradio_call(save_files),
diff --git a/requirements.txt b/requirements.txt
index cd3953c6..81641d68 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,7 +23,4 @@ resize-right
 torchdiffeq
 kornia
 lark
-deepdanbooru
-tensorflow
-tensorflow-io
 functorch
diff --git a/requirements_versions.txt b/requirements_versions.txt
index 2d256a54..fec3e9d5 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -22,7 +22,4 @@ resize-right==0.0.2
 torchdiffeq==0.2.3
 kornia==0.6.7
 lark==1.1.2
-git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow]
-tensorflow==2.10.0
-tensorflow-io==0.27.0
 functorch==0.2.1
-- 
cgit v1.2.1


From f9c5da159245bb1e7603b3c8b9e0703bcb1c2ff5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 19:05:19 +0300
Subject: add fallback for xformers_attnblock_forward

---
 modules/sd_hijack_optimizations.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index d23d733b..dba21192 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -211,6 +211,7 @@ def cross_attention_attnblock_forward(self, x):
         return h3
     
 def xformers_attnblock_forward(self, x):
+    try:
         h_ = x
         h_ = self.norm(h_)
         q1 = self.q(h_).contiguous()
@@ -218,4 +219,6 @@ def xformers_attnblock_forward(self, x):
         v = self.v(h_).contiguous()
         out = xformers.ops.memory_efficient_attention(q1, k1, v)
         out = self.proj_out(out)
-        return x+out
+        return x + out
+    except NotImplementedError:
+        return cross_attention_attnblock_forward(self, x)
-- 
cgit v1.2.1


From 3061cdb7b610d4ba7f1ea695d9d6364b591e5bc7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 19:22:15 +0300
Subject: add --force-enable-xformers option and also add messages to console
 regarding cross attention optimizations

---
 modules/sd_hijack.py | 6 +++++-
 modules/shared.py    | 1 +
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index a3e374f0..307cc67d 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -22,12 +22,16 @@ def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    if cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6):
+
+    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6)):
+        print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
+        print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
+        print("Applying cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
diff --git a/modules/shared.py b/modules/shared.py
index 02cb2722..8f941226 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -44,6 +44,7 @@ parser.add_argument("--scunet-models-path", type=str, help="Path to directory wi
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
+parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-- 
cgit v1.2.1


From 15c4278f1a18b8104e135dd82690d10cff39a2e7 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 8 Oct 2022 17:50:01 +0100
Subject: TI preprocess wording
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

I had to check the code to work out what splitting was 🤷🏿
---
 modules/ui.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index ffd75f6a..d52d74c6 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -980,9 +980,9 @@ def create_ui(wrap_gradio_gpu_call):
                     process_dst = gr.Textbox(label='Destination directory')
 
                     with gr.Row():
-                        process_flip = gr.Checkbox(label='Flip')
-                        process_split = gr.Checkbox(label='Split into two')
-                        process_caption = gr.Checkbox(label='Add caption')
+                        process_flip = gr.Checkbox(label='Create flipped copies')
+                        process_split = gr.Checkbox(label='Split oversized images into two')
+                        process_caption = gr.Checkbox(label='Use CLIP caption as filename')
 
                     with gr.Row():
                         with gr.Column(scale=3):
-- 
cgit v1.2.1


From b458fa48fe5734a872bca83061d702609cb52940 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 8 Oct 2022 17:56:28 +0100
Subject: Update ui.py

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index d52d74c6..b09359aa 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -982,7 +982,7 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
                         process_split = gr.Checkbox(label='Split oversized images into two')
-                        process_caption = gr.Checkbox(label='Use CLIP caption as filename')
+                        process_caption = gr.Checkbox(label='Use BLIP caption as filename')
 
                     with gr.Row():
                         with gr.Column(scale=3):
-- 
cgit v1.2.1


From 1371d7608b402d6f15c200ec2f5fde4579836a05 Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sat, 8 Oct 2022 14:28:22 -0400
Subject: Added ability to ignore last n layers in FrozenCLIPEmbedder

---
 modules/sd_hijack.py | 11 +++++++++--
 modules/shared.py    |  1 +
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 307cc67d..f12a9696 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -281,8 +281,15 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
         remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
         tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
-        outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
-        z = outputs.last_hidden_state
+
+        tmp = -opts.CLIP_ignore_last_layers
+        if (opts.CLIP_ignore_last_layers == 0):
+            outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
+            z = outputs.last_hidden_state
+        else:
+            outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp)
+            z = outputs.hidden_states[tmp]
+            z = self.wrapped.transformer.text_model.final_layer_norm(z)
 
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
diff --git a/modules/shared.py b/modules/shared.py
index 8f941226..af8dc744 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -225,6 +225,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
+    'CLIP_ignore_last_layers': OptionInfo(0, "Ignore last layers of CLIP model", gr.Slider, {"minimum": 0, "maximum": 5, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-- 
cgit v1.2.1


From e6e42f98df2c928c4f49351ad6b466387ce87d42 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 19:25:10 +0300
Subject: make --force-enable-xformers work without needing --xformers

---
 modules/sd_hijack_optimizations.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index dba21192..c4396bb9 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -10,7 +10,7 @@ from einops import rearrange
 
 from modules import shared
 
-if shared.cmd_opts.xformers:
+if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
     try:
         import xformers.ops
         import functorch
-- 
cgit v1.2.1


From 3b2141c5fb6a3c2b8ab4b1e759a97ead77260129 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 22:21:15 +0300
Subject: add 'Ignore last layers of CLIP model' option as a parameter to the
 infotext

---
 modules/processing.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 8240ee27..515fc91a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -123,6 +123,7 @@ class Processed:
         self.index_of_first_image = index_of_first_image
         self.styles = p.styles
         self.job_timestamp = state.job_timestamp
+        self.clip_skip = opts.CLIP_ignore_last_layers
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -141,7 +142,6 @@ class Processed:
         self.all_subseeds = all_subseeds or [self.subseed]
         self.infotexts = infotexts or [info]
 
-
     def js(self):
         obj = {
             "prompt": self.prompt,
@@ -170,6 +170,7 @@ class Processed:
             "infotexts": self.infotexts,
             "styles": self.styles,
             "job_timestamp": self.job_timestamp,
+            "clip_skip": self.clip_skip,
         }
 
         return json.dumps(obj)
@@ -267,6 +268,8 @@ def fix_seed(p):
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
     index = position_in_batch + iteration * p.batch_size
 
+    clip_skip = getattr(p, 'clip_skip', opts.CLIP_ignore_last_layers)
+
     generation_params = {
         "Steps": p.steps,
         "Sampler": sd_samplers.samplers[p.sampler_index].name,
@@ -282,6 +285,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
+        "Clip skip": None if clip_skip==0 else clip_skip,
     }
 
     generation_params.update(p.extra_generation_params)
-- 
cgit v1.2.1


From 610a7f4e1480c0ffeedb2a07dc27ae86bf03c3a8 Mon Sep 17 00:00:00 2001
From: Edouard Leurent <eleurent@gmail.com>
Date: Sat, 8 Oct 2022 16:49:43 +0100
Subject: Break after finding the local directory of stable diffusion

Otherwise, we may override it with one of the next two path (. or ..) if it is present there, and then the local paths of other modules (taming transformers, codeformers, etc.) wont be found in sd_path/../.

Fix https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/1085
---
 modules/paths.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/paths.py b/modules/paths.py
index 606f7d66..0519caa0 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -12,6 +12,7 @@ possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion'),
 for possible_sd_path in possible_sd_paths:
     if os.path.exists(os.path.join(possible_sd_path, 'ldm/models/diffusion/ddpm.py')):
         sd_path = os.path.abspath(possible_sd_path)
+        break
 
 assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths)
 
-- 
cgit v1.2.1


From 432782163ae53e605470bcefc9a6f796c4556912 Mon Sep 17 00:00:00 2001
From: Aidan Holland <thehappydinoa@gmail.com>
Date: Sat, 8 Oct 2022 15:12:24 -0400
Subject: chore: Fix typos

---
 README.md                    | 2 +-
 javascript/imageviewer.js    | 2 +-
 modules/interrogate.py       | 4 ++--
 modules/processing.py        | 2 +-
 modules/scunet_model_arch.py | 4 ++--
 modules/sd_models.py         | 4 ++--
 modules/sd_samplers.py       | 4 ++--
 modules/shared.py            | 6 +++---
 modules/swinir_model_arch.py | 2 +-
 modules/ui.py                | 4 ++--
 10 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/README.md b/README.md
index ef9b5e31..63dd0c18 100644
--- a/README.md
+++ b/README.md
@@ -34,7 +34,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - Sampling method selection
 - Interrupt processing at any time
 - 4GB video card support (also reports of 2GB working)
-- Correct seeds for batches 
+- Correct seeds for batches
 - Prompt length validation
      - get length of prompt in tokens as you type
      - get a warning after generation if some text was truncated
diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js
index 4c0e8f4b..6a00c0da 100644
--- a/javascript/imageviewer.js
+++ b/javascript/imageviewer.js
@@ -95,7 +95,7 @@ function showGalleryImage(){
 
                     e.addEventListener('click', function (evt) {
                         if(!opts.js_modal_lightbox) return;
-                        modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initialy_zoomed)
+                        modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
                         showModal(evt)
                     },true);
                 }
diff --git a/modules/interrogate.py b/modules/interrogate.py
index eed87144..635e266e 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -140,11 +140,11 @@ class InterrogateModels:
 
             res = caption
 
-            cilp_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+            clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
 
             precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
             with torch.no_grad(), precision_scope("cuda"):
-                image_features = self.clip_model.encode_image(cilp_image).type(self.dtype)
+                image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
 
                 image_features /= image_features.norm(dim=-1, keepdim=True)
 
diff --git a/modules/processing.py b/modules/processing.py
index 515fc91a..31220881 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -386,7 +386,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
             if state.interrupted or state.skipped:
 
-                # if we are interruped, sample returns just noise
+                # if we are interrupted, sample returns just noise
                 # use the image collected previously in sampler loop
                 samples_ddim = shared.state.current_latent
 
diff --git a/modules/scunet_model_arch.py b/modules/scunet_model_arch.py
index 972a2639..43ca8d36 100644
--- a/modules/scunet_model_arch.py
+++ b/modules/scunet_model_arch.py
@@ -40,7 +40,7 @@ class WMSA(nn.Module):
         Returns:
             attn_mask: should be (1 1 w p p),
         """
-        # supporting sqaure.
+        # supporting square.
         attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
         if self.type == 'W':
             return attn_mask
@@ -65,7 +65,7 @@ class WMSA(nn.Module):
         x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
         h_windows = x.size(1)
         w_windows = x.size(2)
-        # sqaure validation
+        # square validation
         # assert h_windows == w_windows
 
         x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 9409d070..a09866ce 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -147,7 +147,7 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
         model.first_stage_model.load_state_dict(vae_dict)
 
     model.sd_model_hash = sd_model_hash
-    model.sd_model_checkpint = checkpoint_file
+    model.sd_model_checkpoint = checkpoint_file
 
 
 def load_model():
@@ -175,7 +175,7 @@ def reload_model_weights(sd_model, info=None):
     from modules import lowvram, devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
 
-    if sd_model.sd_model_checkpint == checkpoint_info.filename:
+    if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index eade0dbb..6e743f7e 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -181,7 +181,7 @@ class VanillaStableDiffusionSampler:
 
         self.initialize(p)
 
-        # existing code fails with cetain step counts, like 9
+        # existing code fails with certain step counts, like 9
         try:
             self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
         except Exception:
@@ -204,7 +204,7 @@ class VanillaStableDiffusionSampler:
 
         steps = steps or p.steps
 
-        # existing code fails with cetin step counts, like 9
+        # existing code fails with certain step counts, like 9
         try:
             samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
         except Exception:
diff --git a/modules/shared.py b/modules/shared.py
index af8dc744..2dc092d6 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -141,9 +141,9 @@ class OptionInfo:
         self.section = None
 
 
-def options_section(section_identifer, options_dict):
+def options_section(section_identifier, options_dict):
     for k, v in options_dict.items():
-        v.section = section_identifer
+        v.section = section_identifier
 
     return options_dict
 
@@ -246,7 +246,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
-    "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
+    "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
 }))
 
diff --git a/modules/swinir_model_arch.py b/modules/swinir_model_arch.py
index 461fb354..863f42db 100644
--- a/modules/swinir_model_arch.py
+++ b/modules/swinir_model_arch.py
@@ -166,7 +166,7 @@ class SwinTransformerBlock(nn.Module):
 
     Args:
         dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
+        input_resolution (tuple[int]): Input resolution.
         num_heads (int): Number of attention heads.
         window_size (int): Window size.
         shift_size (int): Shift size for SW-MSA.
diff --git a/modules/ui.py b/modules/ui.py
index b09359aa..b51af121 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -38,7 +38,7 @@ from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
 
-# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
 mimetypes.add_type('application/javascript', '.js')
 
@@ -102,7 +102,7 @@ def save_files(js_data, images, index):
     import csv    
     filenames = []
 
-    #quick dictionary to class object conversion. Its neccesary due apply_filename_pattern requiring it
+    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
     class MyObject:
         def __init__(self, d=None):
             if d is not None:
-- 
cgit v1.2.1


From 050a6a798cec90ae2f881c2ddd3f0221e69907dc Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 8 Oct 2022 23:26:48 +0300
Subject: support loading .yaml config with same name as model support EMA
 weights in processing (????)

---
 modules/processing.py |  2 +-
 modules/sd_models.py  | 30 +++++++++++++++++++++++-------
 2 files changed, 24 insertions(+), 8 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 31220881..4fea6d56 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -347,7 +347,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     infotexts = []
     output_images = []
 
-    with torch.no_grad():
+    with torch.no_grad(), p.sd_model.ema_scope():
         with devices.autocast():
             p.init(all_prompts, all_seeds, all_subseeds)
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index a09866ce..cb3982b1 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -14,7 +14,7 @@ from modules.paths import models_path
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
 checkpoints_list = {}
 
 try:
@@ -63,14 +63,20 @@ def list_models():
     if os.path.exists(cmd_ckpt):
         h = model_hash(cmd_ckpt)
         title, short_model_name = modeltitle(cmd_ckpt, h)
-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
         shared.opts.data['sd_model_checkpoint'] = title
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
     for filename in model_list:
         h = model_hash(filename)
         title, short_model_name = modeltitle(filename, h)
-        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
+
+        basename, _ = os.path.splitext(filename)
+        config = basename + ".yaml"
+        if not os.path.exists(config):
+            config = shared.cmd_opts.config
+
+        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
 
 
 def get_closet_checkpoint_match(searchString):
@@ -116,7 +122,10 @@ def select_checkpoint():
     return checkpoint_info
 
 
-def load_model_weights(model, checkpoint_file, sd_model_hash):
+def load_model_weights(model, checkpoint_info):
+    checkpoint_file = checkpoint_info.filename
+    sd_model_hash = checkpoint_info.hash
+
     print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
     pl_sd = torch.load(checkpoint_file, map_location="cpu")
@@ -148,15 +157,19 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
+    model.sd_checkpoint_info = checkpoint_info
 
 
 def load_model():
     from modules import lowvram, sd_hijack
     checkpoint_info = select_checkpoint()
 
-    sd_config = OmegaConf.load(shared.cmd_opts.config)
+    if checkpoint_info.config != shared.cmd_opts.config:
+        print(f"Loading config from: {shared.cmd_opts.config}")
+
+    sd_config = OmegaConf.load(checkpoint_info.config)
     sd_model = instantiate_from_config(sd_config.model)
-    load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
+    load_model_weights(sd_model, checkpoint_info)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
@@ -178,6 +191,9 @@ def reload_model_weights(sd_model, info=None):
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
+    if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
+        return load_model()
+
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.send_everything_to_cpu()
     else:
@@ -185,7 +201,7 @@ def reload_model_weights(sd_model, info=None):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
+    load_model_weights(sd_model, checkpoint_info)
 
     sd_hijack.model_hijack.hijack(sd_model)
 
-- 
cgit v1.2.1


From 5841990b0df04906da7321beef6f7f7902b7d57b Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 05:38:38 +0100
Subject: Update textual_inversion.py

---
 modules/textual_inversion/textual_inversion.py | 25 ++++++++++++++++++++++---
 1 file changed, 22 insertions(+), 3 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index cd9f3498..f6316020 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,6 +7,9 @@ import tqdm
 import html
 import datetime
 
+from PIL import Image, PngImagePlugin
+import base64
+from io import BytesIO
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
@@ -80,7 +83,15 @@ class EmbeddingDatabase:
         def process_file(path, filename):
             name = os.path.splitext(filename)[0]
 
-            data = torch.load(path, map_location="cpu")
+            data = []
+
+            if filename.upper().endswith('.PNG'):
+                embed_image = Image.open(path)
+                if 'sd-embedding' in embed_image.text:
+                    embeddingData = base64.b64decode(embed_image.text['sd-embedding'])
+                    data = torch.load(BytesIO(embeddingData), map_location="cpu")
+            else:
+                data = torch.load(path, map_location="cpu")
 
             # textual inversion embeddings
             if 'string_to_param' in data:
@@ -156,7 +167,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -244,7 +255,15 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
             image = processed.images[0]
 
             shared.state.current_image = image
-            image.save(last_saved_image)
+
+            if save_image_with_stored_embedding:
+                info = PngImagePlugin.PngInfo()
+                info.add_text("sd-embedding", base64.b64encode(open(last_saved_file,'rb').read()))
+                image.save(last_saved_image, "PNG", pnginfo=info)
+            else:
+                image.save(last_saved_image)
+
+
 
             last_saved_image += f", prompt: {text}"
 
-- 
cgit v1.2.1


From cd8673bd9b2e59bddefee8d307340d643695fe11 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 05:40:57 +0100
Subject: add embed embedding to ui

---
 modules/ui.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index b51af121..a5983204 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1001,7 +1001,8 @@ def create_ui(wrap_gradio_gpu_call):
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-
+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
+                    
                     with gr.Row():
                         with gr.Column(scale=2):
                             gr.HTML(value="")
@@ -1063,6 +1064,7 @@ def create_ui(wrap_gradio_gpu_call):
                 create_image_every,
                 save_embedding_every,
                 template_file,
+                save_image_with_stored_embedding,
             ],
             outputs=[
                 ti_output,
-- 
cgit v1.2.1


From c77c89cc83c618472ad352cf8a28fde28c3a1377 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 10:23:31 +0300
Subject: make main model loading and model merger use the same code

---
 modules/extras.py    |  6 +++---
 modules/sd_models.py | 14 +++++++++-----
 2 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 1d9e64e5..ef6e6de7 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -169,9 +169,9 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
 
     print(f"Loading {secondary_model_info.filename}...")
     secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
-   
-    theta_0 = primary_model['state_dict']
-    theta_1 = secondary_model['state_dict']
+
+    theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
+    theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
 
     theta_funcs = {
         "Weighted Sum": weighted_sum,
diff --git a/modules/sd_models.py b/modules/sd_models.py
index cb3982b1..18fb8c2e 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -122,6 +122,13 @@ def select_checkpoint():
     return checkpoint_info
 
 
+def get_state_dict_from_checkpoint(pl_sd):
+    if "state_dict" in pl_sd:
+        return pl_sd["state_dict"]
+
+    return pl_sd
+
+
 def load_model_weights(model, checkpoint_info):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
@@ -131,11 +138,8 @@ def load_model_weights(model, checkpoint_info):
     pl_sd = torch.load(checkpoint_file, map_location="cpu")
     if "global_step" in pl_sd:
         print(f"Global Step: {pl_sd['global_step']}")
-    
-    if "state_dict" in pl_sd:
-        sd = pl_sd["state_dict"]
-    else:
-        sd = pl_sd
+
+    sd = get_state_dict_from_checkpoint(pl_sd)
 
     model.load_state_dict(sd, strict=False)
 
-- 
cgit v1.2.1


From 4e569fd888f8e3c5632a072d51abbb6e4d17abd6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 10:31:47 +0300
Subject: fixed incorrect message about loading config; thanks anon!

---
 modules/sd_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 18fb8c2e..2101b18d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -169,7 +169,7 @@ def load_model():
     checkpoint_info = select_checkpoint()
 
     if checkpoint_info.config != shared.cmd_opts.config:
-        print(f"Loading config from: {shared.cmd_opts.config}")
+        print(f"Loading config from: {checkpoint_info.config}")
 
     sd_config = OmegaConf.load(checkpoint_info.config)
     sd_model = instantiate_from_config(sd_config.model)
-- 
cgit v1.2.1


From 5ab7e88d9b0bb0125af9f7237242a00a93360ce5 Mon Sep 17 00:00:00 2001
From: aoirusann <82883326+aoirusann@users.noreply.github.com>
Date: Sat, 8 Oct 2022 13:09:29 +0800
Subject: Add `Download` & `Download as zip`

---
 modules/ui.py | 39 ++++++++++++++++++++++++++++++++++-----
 1 file changed, 34 insertions(+), 5 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index b51af121..fe7f10a7 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -98,9 +98,10 @@ def send_gradio_gallery_to_image(x):
     return image_from_url_text(x[0])
 
 
-def save_files(js_data, images, index):
+def save_files(js_data, images, do_make_zip, index):
     import csv    
     filenames = []
+    fullfns = []
 
     #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
     class MyObject:
@@ -141,10 +142,22 @@ def save_files(js_data, images, index):
 
             filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
+            fullfns.append(fullfn)
 
         writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
 
-    return '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+    # Make Zip
+    if do_make_zip:
+        zip_filepath = os.path.join(path, "images.zip")
+
+        from zipfile import ZipFile
+        with ZipFile(zip_filepath, "w") as zip_file:
+            for i in range(len(fullfns)):
+                with open(fullfns[i], mode="rb") as f:
+                    zip_file.writestr(filenames[i], f.read())
+        fullfns.insert(0, zip_filepath)
+
+    return fullfns, '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
 
 def wrap_gradio_call(func, extra_outputs=None):
@@ -521,6 +534,12 @@ def create_ui(wrap_gradio_gpu_call):
                         button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
                         open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
 
+                    with gr.Row():
+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+                    
+                    with gr.Row():
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False)
+
                 with gr.Group():
                     html_info = gr.HTML()
                     generation_info = gr.Textbox(visible=False)
@@ -570,13 +589,15 @@ def create_ui(wrap_gradio_gpu_call):
 
             save.click(
                 fn=wrap_gradio_call(save_files),
-                _js="(x, y, z) => [x, y, selected_gallery_index()]",
+                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
                 inputs=[
                     generation_info,
                     txt2img_gallery,
+                    do_make_zip,
                     html_info,
                 ],
                 outputs=[
+                    download_files,
                     html_info,
                     html_info,
                     html_info,
@@ -701,6 +722,12 @@ def create_ui(wrap_gradio_gpu_call):
                         button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
                         open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
 
+                    with gr.Row():
+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+                    
+                    with gr.Row():
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False)
+
                 with gr.Group():
                     html_info = gr.HTML()
                     generation_info = gr.Textbox(visible=False)
@@ -776,13 +803,15 @@ def create_ui(wrap_gradio_gpu_call):
 
             save.click(
                 fn=wrap_gradio_call(save_files),
-                _js="(x, y, z) => [x, y, selected_gallery_index()]",
+                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
                 inputs=[
                     generation_info,
                     img2img_gallery,
-                    html_info
+                    do_make_zip,
+                    html_info,
                 ],
                 outputs=[
+                    download_files,
                     html_info,
                     html_info,
                     html_info,
-- 
cgit v1.2.1


From 14192c5b207b16b1ec7a4c9c4ea538d1a6811a4d Mon Sep 17 00:00:00 2001
From: aoirusann <yimodo.siki@gmail.com>
Date: Sun, 9 Oct 2022 13:01:10 +0800
Subject: Support `Download` for txt files.

---
 modules/images.py | 39 +++++++++++++++++++++++++++++++++++++--
 modules/ui.py     |  5 ++++-
 2 files changed, 41 insertions(+), 3 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index 29c5ee24..c0a90676 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -349,6 +349,38 @@ def get_next_sequence_number(path, basename):
 
 
 def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
+    '''Save an image.
+
+    Args:
+        image (`PIL.Image`):
+            The image to be saved.
+        path (`str`):
+            The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
+        basename (`str`):
+            The base filename which will be applied to `filename pattern`.
+        seed, prompt, short_filename, 
+        extension (`str`):
+            Image file extension, default is `png`.
+        pngsectionname (`str`):
+            Specify the name of the section which `info` will be saved in.
+        info (`str` or `PngImagePlugin.iTXt`):
+            PNG info chunks.
+        existing_info (`dict`):
+            Additional PNG info. `existing_info == {pngsectionname: info, ...}`
+        no_prompt:
+            TODO I don't know its meaning.
+        p (`StableDiffusionProcessing`)
+        forced_filename (`str`):
+            If specified, `basename` and filename pattern will be ignored.
+        save_to_dirs (bool):
+            If true, the image will be saved into a subdirectory of `path`.
+
+    Returns: (fullfn, txt_fullfn)
+        fullfn (`str`):
+            The full path of the saved imaged.
+        txt_fullfn (`str` or None):
+            If a text file is saved for this image, this will be its full path. Otherwise None.
+    '''
     if short_filename or prompt is None or seed is None:
         file_decoration = ""
     elif opts.save_to_dirs:
@@ -424,7 +456,10 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             piexif.insert(exif_bytes(), fullfn_without_extension + ".jpg")
 
     if opts.save_txt and info is not None:
-        with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:
+        txt_fullfn = f"{fullfn_without_extension}.txt"
+        with open(txt_fullfn, "w", encoding="utf8") as file:
             file.write(info + "\n")
+    else:
+        txt_fullfn = None
 
-    return fullfn
+    return fullfn, txt_fullfn
diff --git a/modules/ui.py b/modules/ui.py
index fe7f10a7..debd8873 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -138,11 +138,14 @@ def save_files(js_data, images, do_make_zip, index):
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
 
-            fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
 
             filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
             fullfns.append(fullfn)
+            if txt_fullfn:
+                filenames.append(os.path.basename(txt_fullfn))
+                fullfns.append(txt_fullfn)
 
         writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
 
-- 
cgit v1.2.1


From 122d42687b97ec4df4c2a8c335d2de385cd1f1a1 Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sat, 8 Oct 2022 22:37:35 -0400
Subject: Fix VRAM Issue by only loading in hypernetwork when selected in
 settings

---
 modules/hypernetwork.py            | 23 +++++++++++++++--------
 modules/sd_hijack_optimizations.py |  6 +++---
 modules/shared.py                  |  7 ++-----
 webui.py                           |  3 +++
 4 files changed, 23 insertions(+), 16 deletions(-)

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
index 7f062242..19f1c227 100644
--- a/modules/hypernetwork.py
+++ b/modules/hypernetwork.py
@@ -40,18 +40,25 @@ class Hypernetwork:
             self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
 
 
-def load_hypernetworks(path):
+def list_hypernetworks(path):
     res = {}
-
     for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
+        name = os.path.splitext(os.path.basename(filename))[0]
+        res[name] = filename
+    return res
+
+
+def load_hypernetwork(filename):
+    print(f"Loading hypernetwork {filename}")
+    path = shared.hypernetworks.get(filename, None)
+    if (path is not None):
         try:
-            hn = Hypernetwork(filename)
-            res[hn.name] = hn
+            shared.loaded_hypernetwork = Hypernetwork(path)
         except Exception:
-            print(f"Error loading hypernetwork {filename}", file=sys.stderr)
+            print(f"Error loading hypernetwork {path}", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
-
-    return res
+    else:
+        shared.loaded_hypernetwork = None
 
 
 def attention_CrossAttention_forward(self, x, context=None, mask=None):
@@ -60,7 +67,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     q = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork = shared.loaded_hypernetwork
     hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
 
     if hypernetwork_layers is not None:
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index c4396bb9..634fb4b2 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -28,7 +28,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     q_in = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork = shared.loaded_hypernetwork
     hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
 
     if hypernetwork_layers is not None:
@@ -68,7 +68,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
     q_in = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork = shared.loaded_hypernetwork
     hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
 
     if hypernetwork_layers is not None:
@@ -132,7 +132,7 @@ def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
     q_in = self.to_q(x)
     context = default(context, x)
-    hypernetwork = shared.selected_hypernetwork()
+    hypernetwork = shared.loaded_hypernetwork
     hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
     if hypernetwork_layers is not None:
         k_in = self.to_k(hypernetwork_layers[0](context))
diff --git a/modules/shared.py b/modules/shared.py
index b2c76a32..9dce6cb7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -79,11 +79,8 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 xformers_available = False
 config_filename = cmd_opts.ui_settings_file
 
-hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks'))
-
-
-def selected_hypernetwork():
-    return hypernetworks.get(opts.sd_hypernetwork, None)
+hypernetworks = hypernetwork.list_hypernetworks(os.path.join(models_path, 'hypernetworks'))
+loaded_hypernetwork = None
 
 
 class State:
diff --git a/webui.py b/webui.py
index 18de8e16..270584f7 100644
--- a/webui.py
+++ b/webui.py
@@ -82,6 +82,9 @@ modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
 shared.sd_model = modules.sd_models.load_model()
 shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
 
+loaded_hypernetwork = modules.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)
+shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
+
 
 def webui():
     # make the program just exit at ctrl+c without waiting for anything
-- 
cgit v1.2.1


From 03e570886f430f39020e504aba057a95f2e62484 Mon Sep 17 00:00:00 2001
From: frostydad <64224601+Cyberes@users.noreply.github.com>
Date: Sat, 8 Oct 2022 18:13:13 -0600
Subject: Fix incorrect sampler name in output

---
 modules/processing.py |  9 ++++++++-
 scripts/xy_grid.py    | 16 +++++++++-------
 2 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 4fea6d56..6b8664a0 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1,3 +1,4 @@
+
 import json
 import math
 import os
@@ -46,6 +47,12 @@ def apply_color_correction(correction, image):
     return image
 
 
+def get_correct_sampler(p):
+    if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
+        return sd_samplers.samplers
+    elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
+        return sd_samplers.samplers_for_img2img
+
 class StableDiffusionProcessing:
     def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
         self.sd_model = sd_model
@@ -272,7 +279,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params = {
         "Steps": p.steps,
-        "Sampler": sd_samplers.samplers[p.sampler_index].name,
+        "Sampler": get_correct_sampler(p)[p.sampler_index].name,
         "CFG scale": p.cfg_scale,
         "Seed": all_seeds[index],
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index c0c364df..26ae2199 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -11,7 +11,7 @@ import modules.scripts as scripts
 import gradio as gr
 
 from modules import images
-from modules.processing import process_images, Processed
+from modules.processing import process_images, Processed, get_correct_sampler
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.sd_samplers
@@ -56,15 +56,17 @@ def apply_order(p, x, xs):
     p.prompt = prompt_tmp + p.prompt
     
 
-samplers_dict = {}
-for i, sampler in enumerate(modules.sd_samplers.samplers):
-    samplers_dict[sampler.name.lower()] = i
-    for alias in sampler.aliases:
-        samplers_dict[alias.lower()] = i
+def build_samplers_dict(p):
+    samplers_dict = {}
+    for i, sampler in enumerate(get_correct_sampler(p)):
+        samplers_dict[sampler.name.lower()] = i
+        for alias in sampler.aliases:
+            samplers_dict[alias.lower()] = i
+    return samplers_dict
 
 
 def apply_sampler(p, x, xs):
-    sampler_index = samplers_dict.get(x.lower(), None)
+    sampler_index = build_samplers_dict(p).get(x.lower(), None)
     if sampler_index is None:
         raise RuntimeError(f"Unknown sampler: {x}")
 
-- 
cgit v1.2.1


From ef93acdc731b7a2b3c13651b6de1bce58af989d4 Mon Sep 17 00:00:00 2001
From: frostydad <64224601+Cyberes@users.noreply.github.com>
Date: Sat, 8 Oct 2022 18:15:35 -0600
Subject: remove line break

---
 modules/processing.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 6b8664a0..7fa1144e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -1,4 +1,3 @@
-
 import json
 import math
 import os
-- 
cgit v1.2.1


From 1ffeb42d38d9276dc28918189d32f60d593a162c Mon Sep 17 00:00:00 2001
From: Nicolas Noullet <nicolas.noullet@gmail.com>
Date: Sun, 9 Oct 2022 00:18:45 +0200
Subject: Fix typo

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 9dce6cb7..dffa0094 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -238,7 +238,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
-    "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
+    "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
-- 
cgit v1.2.1


From e2930f9821c197da94e208b5ae73711002844efc Mon Sep 17 00:00:00 2001
From: Tony Beeman <beeman@gmail.com>
Date: Fri, 7 Oct 2022 17:46:39 -0700
Subject: Fix for Prompts_from_file showing extra textbox.

---
 modules/scripts.py           | 30 ++++++++++++++++++++++++++----
 scripts/prompts_from_file.py |  4 ++++
 2 files changed, 30 insertions(+), 4 deletions(-)

diff --git a/modules/scripts.py b/modules/scripts.py
index 45230f9a..d8f87927 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -1,4 +1,5 @@
 import os
+from pydoc import visiblename
 import sys
 import traceback
 
@@ -31,6 +32,15 @@ class Script:
     def show(self, is_img2img):
         return True
 
+
+    # Called when the ui for this script has been shown.
+    # Useful for hiding some controls, since the scripts module sets visibility to
+    # everything to true. The parameters will be the parameters returned by the ui method
+    # The return value should be gradio updates, similar to what you would return
+    # from a Gradio event handler.
+    def on_show(self, *args):
+        return [ui.gr_show(True)] * len(args)
+
     # This is where the additional processing is implemented. The parameters include
     # self, the model object "p" (a StableDiffusionProcessing class, see
     # processing.py), and the parameters returned by the ui method.
@@ -125,20 +135,32 @@ class ScriptRunner:
             inputs += controls
             script.args_to = len(inputs)
 
-        def select_script(script_index):
+        def select_script(*args):
+            script_index = args[0]
+            on_show_updates = []
             if 0 < script_index <= len(self.scripts):
                 script = self.scripts[script_index-1]
                 args_from = script.args_from
                 args_to = script.args_to
+                script_args = args[args_from:args_to]
+                on_show_updates = wrap_call(script.on_show, script.filename, "on_show", *script_args)
             else:
                 args_from = 0
                 args_to = 0
 
-            return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
+            ret = [ ui.gr_show(True)] # always show the dropdown
+            for i in range(1, len(inputs)):
+                if (args_from <= i < args_to):
+                    ret.append( on_show_updates[i - args_from] )
+                else:
+                    ret.append(ui.gr_show(False))
+            return ret
+
+            # return [ui.gr_show(True if (i == 0) else on_show_updates[i - args_from] if args_from <= i < args_to else False) for i in range(len(inputs))]
 
         dropdown.change(
             fn=select_script,
-            inputs=[dropdown],
+            inputs=inputs,
             outputs=inputs
         )
 
@@ -198,4 +220,4 @@ def reload_scripts(basedir):
     load_scripts(basedir)
 
     scripts_txt2img = ScriptRunner()
-    scripts_img2img = ScriptRunner()
+    scripts_img2img = ScriptRunner()
\ No newline at end of file
diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 513d9a1c..110889a6 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -10,6 +10,7 @@ from modules.processing import Processed, process_images
 from PIL import Image
 from modules.shared import opts, cmd_opts, state
 
+g_txt_mode = False
 
 class Script(scripts.Script):
     def title(self):
@@ -29,6 +30,9 @@ class Script(scripts.Script):
         checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt])
         return [checkbox_txt, file, prompt_txt]
 
+    def on_show(self, checkbox_txt, file, prompt_txt):
+        return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ]
+
     def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
         if (checkbox_txt):
             lines = [x.strip() for x in prompt_txt.splitlines()]
-- 
cgit v1.2.1


From 86cb16886f8f48169cee4658ad0c5e5443beed2a Mon Sep 17 00:00:00 2001
From: Tony Beeman <beeman@gmail.com>
Date: Fri, 7 Oct 2022 23:51:50 -0700
Subject: Pull Request Code Review Fixes

---
 modules/scripts.py           | 1 -
 scripts/prompts_from_file.py | 2 --
 2 files changed, 3 deletions(-)

diff --git a/modules/scripts.py b/modules/scripts.py
index d8f87927..8dfd4de9 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -1,5 +1,4 @@
 import os
-from pydoc import visiblename
 import sys
 import traceback
 
diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 110889a6..b24f1a80 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -10,8 +10,6 @@ from modules.processing import Processed, process_images
 from PIL import Image
 from modules.shared import opts, cmd_opts, state
 
-g_txt_mode = False
-
 class Script(scripts.Script):
     def title(self):
         return "Prompts from file or textbox"
-- 
cgit v1.2.1


From cbf6dad02d04d98e5a2d5e870777ab99b5796b2d Mon Sep 17 00:00:00 2001
From: Tony Beeman <beeman@gmail.com>
Date: Sat, 8 Oct 2022 10:40:30 -0700
Subject: Handle case where on_show returns the wrong number of arguments

---
 modules/scripts.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/modules/scripts.py b/modules/scripts.py
index 8dfd4de9..7d89979d 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -143,6 +143,8 @@ class ScriptRunner:
                 args_to = script.args_to
                 script_args = args[args_from:args_to]
                 on_show_updates = wrap_call(script.on_show, script.filename, "on_show", *script_args)
+                if (len(on_show_updates) != (args_to - args_from)):
+                    print("Error in custom script (" + script.filename + "): on_show() method should return the same number of arguments as ui().", file=sys.stderr)
             else:
                 args_from = 0
                 args_to = 0
@@ -150,13 +152,14 @@ class ScriptRunner:
             ret = [ ui.gr_show(True)] # always show the dropdown
             for i in range(1, len(inputs)):
                 if (args_from <= i < args_to):
-                    ret.append( on_show_updates[i - args_from] )
+                    if (i - args_from) < len(on_show_updates):
+                        ret.append( on_show_updates[i - args_from] )
+                    else:
+                        ret.append(ui.gr_show(True))
                 else:
                     ret.append(ui.gr_show(False))
             return ret
 
-            # return [ui.gr_show(True if (i == 0) else on_show_updates[i - args_from] if args_from <= i < args_to else False) for i in range(len(inputs))]
-
         dropdown.change(
             fn=select_script,
             inputs=inputs,
-- 
cgit v1.2.1


From ab4fe4f44c3d2675a351269fe2ff1ddeac557aa6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 11:59:41 +0300
Subject: hide filenames for save button by default

---
 modules/ui.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 8071b1cb..e1ab2665 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -162,7 +162,7 @@ def save_files(js_data, images, do_make_zip, index):
                     zip_file.writestr(filenames[i], f.read())
         fullfns.insert(0, zip_filepath)
 
-    return fullfns, '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
 
 def wrap_gradio_call(func, extra_outputs=None):
@@ -553,7 +553,7 @@ def create_ui(wrap_gradio_gpu_call):
                         do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
                     
                     with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False)
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
                 with gr.Group():
                     html_info = gr.HTML()
@@ -741,7 +741,7 @@ def create_ui(wrap_gradio_gpu_call):
                         do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
                     
                     with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False)
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
                 with gr.Group():
                     html_info = gr.HTML()
-- 
cgit v1.2.1


From 0241d811d23427b99f6b1eda1540bdf8d87963d5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 12:04:44 +0300
Subject: Revert "Fix for Prompts_from_file showing extra textbox."

This reverts commit e2930f9821c197da94e208b5ae73711002844efc.
---
 modules/scripts.py | 32 ++++----------------------------
 1 file changed, 4 insertions(+), 28 deletions(-)

diff --git a/modules/scripts.py b/modules/scripts.py
index 7d89979d..45230f9a 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -31,15 +31,6 @@ class Script:
     def show(self, is_img2img):
         return True
 
-
-    # Called when the ui for this script has been shown.
-    # Useful for hiding some controls, since the scripts module sets visibility to
-    # everything to true. The parameters will be the parameters returned by the ui method
-    # The return value should be gradio updates, similar to what you would return
-    # from a Gradio event handler.
-    def on_show(self, *args):
-        return [ui.gr_show(True)] * len(args)
-
     # This is where the additional processing is implemented. The parameters include
     # self, the model object "p" (a StableDiffusionProcessing class, see
     # processing.py), and the parameters returned by the ui method.
@@ -134,35 +125,20 @@ class ScriptRunner:
             inputs += controls
             script.args_to = len(inputs)
 
-        def select_script(*args):
-            script_index = args[0]
-            on_show_updates = []
+        def select_script(script_index):
             if 0 < script_index <= len(self.scripts):
                 script = self.scripts[script_index-1]
                 args_from = script.args_from
                 args_to = script.args_to
-                script_args = args[args_from:args_to]
-                on_show_updates = wrap_call(script.on_show, script.filename, "on_show", *script_args)
-                if (len(on_show_updates) != (args_to - args_from)):
-                    print("Error in custom script (" + script.filename + "): on_show() method should return the same number of arguments as ui().", file=sys.stderr)
             else:
                 args_from = 0
                 args_to = 0
 
-            ret = [ ui.gr_show(True)] # always show the dropdown
-            for i in range(1, len(inputs)):
-                if (args_from <= i < args_to):
-                    if (i - args_from) < len(on_show_updates):
-                        ret.append( on_show_updates[i - args_from] )
-                    else:
-                        ret.append(ui.gr_show(True))
-                else:
-                    ret.append(ui.gr_show(False))
-            return ret
+            return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
 
         dropdown.change(
             fn=select_script,
-            inputs=inputs,
+            inputs=[dropdown],
             outputs=inputs
         )
 
@@ -222,4 +198,4 @@ def reload_scripts(basedir):
     load_scripts(basedir)
 
     scripts_txt2img = ScriptRunner()
-    scripts_img2img = ScriptRunner()
\ No newline at end of file
+    scripts_img2img = ScriptRunner()
-- 
cgit v1.2.1


From 6f6798ddabe10d320fe8ea05edf0fdcef0c51a8e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 12:33:37 +0300
Subject: prevent a possible code execution error (thanks, RyotaK)

---
 modules/ui.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/modules/ui.py b/modules/ui.py
index e1ab2665..dad509f3 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1153,6 +1153,15 @@ def create_ui(wrap_gradio_gpu_call):
     component_dict = {}
 
     def open_folder(f):
+        if not os.path.isdir(f):
+            print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+            return
+
         if not shared.cmd_opts.hide_ui_dir_config:
             path = os.path.normpath(f)
             if platform.system() == "Windows":
-- 
cgit v1.2.1


From d74c38108f95e44d83a1706ee5ab218124972868 Mon Sep 17 00:00:00 2001
From: Jesse Williams <33797815+xram64@users.noreply.github.com>
Date: Sat, 8 Oct 2022 01:30:49 -0400
Subject: Confirm that options are valid before starting

When using the 'Sampler' or 'Checkpoint' options, if one of the entered
names has a typo, an error will only be thrown once the `draw_xy_grid`
loop reaches that name. This can waste a lot of time for large grids
with a typo near the end of a list, since the script needs to start over
and re-generate any earlier images to finish making the grid.

Also fixing typo in variable name in `draw_xy_grid`.
---
 scripts/xy_grid.py | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 26ae2199..07040886 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -145,7 +145,7 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
     ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
     hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
 
-    first_pocessed = None
+    first_processed = None
 
     state.job_count = len(xs) * len(ys) * p.n_iter
 
@@ -154,8 +154,8 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
             state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
 
             processed = cell(x, y)
-            if first_pocessed is None:
-                first_pocessed = processed
+            if first_processed is None:
+                first_processed = processed
 
             try:
               res.append(processed.images[0])
@@ -166,9 +166,9 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
     if draw_legend:
         grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
 
-    first_pocessed.images = [grid]
+    first_processed.images = [grid]
 
-    return first_pocessed
+    return first_processed
 
 
 re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
@@ -216,7 +216,6 @@ class Script(scripts.Script):
                     m = re_range.fullmatch(val)
                     mc = re_range_count.fullmatch(val)
                     if m is not None:
-
                         start = int(m.group(1))
                         end = int(m.group(2))+1
                         step = int(m.group(3)) if m.group(3) is not None else 1
@@ -258,6 +257,16 @@ class Script(scripts.Script):
                 valslist = list(permutations(valslist))
 
             valslist = [opt.type(x) for x in valslist]
+            
+            # Confirm options are valid before starting
+            if opt.label == "Sampler":
+                for sampler_val in valslist:
+                    if sampler_val.lower() not in samplers_dict.keys():
+                        raise RuntimeError(f"Unknown sampler: {sampler_val}")
+            elif opt.label == "Checkpoint name":
+                for ckpt_val in valslist:
+                    if modules.sd_models.get_closet_checkpoint_match(ckpt_val) is None:
+                        raise RuntimeError(f"Checkpoint for {ckpt_val} not found")
 
             return valslist
 
-- 
cgit v1.2.1


From a65a45272e8f26ee3bc52a5300b396266508a9a5 Mon Sep 17 00:00:00 2001
From: Brendan Byrd <GitHub@ResonatorSoft.org>
Date: Thu, 6 Oct 2022 19:31:36 -0400
Subject: Don't change the seed initially if "Keep -1 for seeds" is checked

Fixes #1049
---
 scripts/xy_grid.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 07040886..a8f53bef 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -198,7 +198,9 @@ class Script(scripts.Script):
         return [x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds]
 
     def run(self, p, x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds):
-        modules.processing.fix_seed(p)
+        if not no_fixed_seeds:
+            modules.processing.fix_seed(p)
+
         p.batch_size = 1
 
         initial_hn = opts.sd_hypernetwork
-- 
cgit v1.2.1


From 0609ce06c0778536cb368ac3867292f87c6d9fc7 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Fri, 7 Oct 2022 03:36:08 +0900
Subject: Removed duplicate definition model_path

---
 modules/bsrgan_model.py     | 2 --
 modules/esrgan_model.py     | 2 --
 modules/ldsr_model.py       | 2 --
 modules/realesrgan_model.py | 2 --
 modules/scunet_model.py     | 2 --
 modules/swinir_model.py     | 2 --
 modules/upscaler.py         | 7 ++++---
 7 files changed, 4 insertions(+), 15 deletions(-)

diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
index 3bd80791..737e1a76 100644
--- a/modules/bsrgan_model.py
+++ b/modules/bsrgan_model.py
@@ -10,13 +10,11 @@ from basicsr.utils.download_util import load_file_from_url
 import modules.upscaler
 from modules import devices, modelloader
 from modules.bsrgan_model_arch import RRDBNet
-from modules.paths import models_path
 
 
 class UpscalerBSRGAN(modules.upscaler.Upscaler):
     def __init__(self, dirname):
         self.name = "BSRGAN"
-        self.model_path = os.path.join(models_path, self.name)
         self.model_name = "BSRGAN 4x"
         self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth"
         self.user_path = dirname
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 28548124..3970e6e4 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -7,7 +7,6 @@ from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgam_model_arch as arch
 from modules import shared, modelloader, images, devices
-from modules.paths import models_path
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
 
@@ -76,7 +75,6 @@ class UpscalerESRGAN(Upscaler):
         self.model_name = "ESRGAN_4x"
         self.scalers = []
         self.user_path = dirname
-        self.model_path = os.path.join(models_path, self.name)
         super().__init__()
         model_paths = self.find_models(ext_filter=[".pt", ".pth"])
         scalers = []
diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py
index 1c1070fc..8c4db44a 100644
--- a/modules/ldsr_model.py
+++ b/modules/ldsr_model.py
@@ -7,13 +7,11 @@ from basicsr.utils.download_util import load_file_from_url
 from modules.upscaler import Upscaler, UpscalerData
 from modules.ldsr_model_arch import LDSR
 from modules import shared
-from modules.paths import models_path
 
 
 class UpscalerLDSR(Upscaler):
     def __init__(self, user_path):
         self.name = "LDSR"
-        self.model_path = os.path.join(models_path, self.name)
         self.user_path = user_path
         self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
         self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py
index dc0123e0..3ac0b97a 100644
--- a/modules/realesrgan_model.py
+++ b/modules/realesrgan_model.py
@@ -8,14 +8,12 @@ from basicsr.utils.download_util import load_file_from_url
 from realesrgan import RealESRGANer
 
 from modules.upscaler import Upscaler, UpscalerData
-from modules.paths import models_path
 from modules.shared import cmd_opts, opts
 
 
 class UpscalerRealESRGAN(Upscaler):
     def __init__(self, path):
         self.name = "RealESRGAN"
-        self.model_path = os.path.join(models_path, self.name)
         self.user_path = path
         super().__init__()
         try:
diff --git a/modules/scunet_model.py b/modules/scunet_model.py
index fb64b740..36a996bf 100644
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@@ -9,14 +9,12 @@ from basicsr.utils.download_util import load_file_from_url
 
 import modules.upscaler
 from modules import devices, modelloader
-from modules.paths import models_path
 from modules.scunet_model_arch import SCUNet as net
 
 
 class UpscalerScuNET(modules.upscaler.Upscaler):
     def __init__(self, dirname):
         self.name = "ScuNET"
-        self.model_path = os.path.join(models_path, self.name)
         self.model_name = "ScuNET GAN"
         self.model_name2 = "ScuNET PSNR"
         self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index 9bd454c6..fbd11f84 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -8,7 +8,6 @@ from basicsr.utils.download_util import load_file_from_url
 from tqdm import tqdm
 
 from modules import modelloader
-from modules.paths import models_path
 from modules.shared import cmd_opts, opts, device
 from modules.swinir_model_arch import SwinIR as net
 from modules.upscaler import Upscaler, UpscalerData
@@ -25,7 +24,6 @@ class UpscalerSwinIR(Upscaler):
                          "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
                          "-L_x4_GAN.pth "
         self.model_name = "SwinIR 4x"
-        self.model_path = os.path.join(models_path, self.name)
         self.user_path = dirname
         super().__init__()
         scalers = []
diff --git a/modules/upscaler.py b/modules/upscaler.py
index d9d7c5e2..34672be7 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -36,10 +36,11 @@ class Upscaler:
         self.half = not modules.shared.cmd_opts.no_half
         self.pre_pad = 0
         self.mod_scale = None
-        if self.name is not None and create_dirs:
+
+        if self.model_path is not None and self.name:
             self.model_path = os.path.join(models_path, self.name)
-            if not os.path.exists(self.model_path):
-                os.makedirs(self.model_path)
+        if self.model_path and create_dirs:
+            os.makedirs(self.model_path, exist_ok=True)
 
         try:
             import cv2
-- 
cgit v1.2.1


From bd833409ac7b8337040d521f6b65ced51e1b2ea8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 13:10:15 +0300
Subject: additional changes for saving pnginfo for #1803

---
 modules/extras.py     | 4 ++++
 modules/processing.py | 6 ++++--
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index ef6e6de7..39dd3806 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -98,6 +98,10 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
                           no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,
                           forced_filename=image_name if opts.use_original_name_batch else None)
 
+        if opts.enable_pnginfo:
+            image.info = existing_pnginfo
+            image.info["extras"] = info
+
         outputs.append(image)
 
     devices.torch_gc()
diff --git a/modules/processing.py b/modules/processing.py
index 7fa1144e..2c991317 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -451,7 +451,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
                 text = infotext(n, i)
                 infotexts.append(text)
-                image.info["parameters"] = text
+                if opts.enable_pnginfo:
+                    image.info["parameters"] = text
                 output_images.append(image)
 
             del x_samples_ddim 
@@ -470,7 +471,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if opts.return_grid:
                 text = infotext()
                 infotexts.insert(0, text)
-                grid.info["parameters"] = text
+                if opts.enable_pnginfo:
+                    grid.info["parameters"] = text
                 output_images.insert(0, grid)
                 index_of_first_image = 1
 
-- 
cgit v1.2.1


From f4578b343ded3b8ccd1879ea0c0b3cdadfcc3a5f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 13:23:30 +0300
Subject: fix model switching not working properly if there is a different yaml
 config

---
 modules/sd_models.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2101b18d..d0c74dd8 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -196,7 +196,8 @@ def reload_model_weights(sd_model, info=None):
         return
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
-        return load_model()
+        shared.sd_model = load_model()
+        return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.send_everything_to_cpu()
-- 
cgit v1.2.1


From 77a719648db515f10136e8b8483d5b16bda2eaeb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 13:48:04 +0300
Subject: fix logic error in #1832

---
 modules/upscaler.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/upscaler.py b/modules/upscaler.py
index 34672be7..6ab2fb40 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -37,7 +37,7 @@ class Upscaler:
         self.pre_pad = 0
         self.mod_scale = None
 
-        if self.model_path is not None and self.name:
+        if self.model_path is None and self.name:
             self.model_path = os.path.join(models_path, self.name)
         if self.model_path and create_dirs:
             os.makedirs(self.model_path, exist_ok=True)
-- 
cgit v1.2.1


From ad4de819c43997f2666b5bad95301f5c37f9018e Mon Sep 17 00:00:00 2001
From: victorca25 <victorca25@users.noreply.github.com>
Date: Sun, 9 Oct 2022 13:02:12 +0200
Subject: update ESRGAN architecture and model to support all ESRGAN models in
 the DB, BSRGAN and real-ESRGAN models

---
 modules/bsrgan_model.py      |  76 -------
 modules/bsrgan_model_arch.py | 102 ----------
 modules/esrgam_model_arch.py |  80 --------
 modules/esrgan_model.py      | 190 ++++++++++++------
 modules/esrgan_model_arch.py | 463 +++++++++++++++++++++++++++++++++++++++++++
 5 files changed, 591 insertions(+), 320 deletions(-)
 delete mode 100644 modules/bsrgan_model.py
 delete mode 100644 modules/bsrgan_model_arch.py
 delete mode 100644 modules/esrgam_model_arch.py
 create mode 100644 modules/esrgan_model_arch.py

diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py
deleted file mode 100644
index 737e1a76..00000000
--- a/modules/bsrgan_model.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import os.path
-import sys
-import traceback
-
-import PIL.Image
-import numpy as np
-import torch
-from basicsr.utils.download_util import load_file_from_url
-
-import modules.upscaler
-from modules import devices, modelloader
-from modules.bsrgan_model_arch import RRDBNet
-
-
-class UpscalerBSRGAN(modules.upscaler.Upscaler):
-    def __init__(self, dirname):
-        self.name = "BSRGAN"
-        self.model_name = "BSRGAN 4x"
-        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth"
-        self.user_path = dirname
-        super().__init__()
-        model_paths = self.find_models(ext_filter=[".pt", ".pth"])
-        scalers = []
-        if len(model_paths) == 0:
-            scaler_data = modules.upscaler.UpscalerData(self.model_name, self.model_url, self, 4)
-            scalers.append(scaler_data)
-        for file in model_paths:
-            if "http" in file:
-                name = self.model_name
-            else:
-                name = modelloader.friendly_name(file)
-            try:
-                scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
-                scalers.append(scaler_data)
-            except Exception:
-                print(f"Error loading BSRGAN model: {file}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-        self.scalers = scalers
-
-    def do_upscale(self, img: PIL.Image, selected_file):
-        torch.cuda.empty_cache()
-        model = self.load_model(selected_file)
-        if model is None:
-            return img
-        model.to(devices.device_bsrgan)
-        torch.cuda.empty_cache()
-        img = np.array(img)
-        img = img[:, :, ::-1]
-        img = np.moveaxis(img, 2, 0) / 255
-        img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(devices.device_bsrgan)
-        with torch.no_grad():
-            output = model(img)
-        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
-        output = 255. * np.moveaxis(output, 0, 2)
-        output = output.astype(np.uint8)
-        output = output[:, :, ::-1]
-        torch.cuda.empty_cache()
-        return PIL.Image.fromarray(output, 'RGB')
-
-    def load_model(self, path: str):
-        if "http" in path:
-            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
-                                          progress=True)
-        else:
-            filename = path
-        if not os.path.exists(filename) or filename is None:
-            print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr)
-            return None
-        model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4)  # define network
-        model.load_state_dict(torch.load(filename), strict=True)
-        model.eval()
-        for k, v in model.named_parameters():
-            v.requires_grad = False
-        return model
-
diff --git a/modules/bsrgan_model_arch.py b/modules/bsrgan_model_arch.py
deleted file mode 100644
index cb4d1c13..00000000
--- a/modules/bsrgan_model_arch.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import functools
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.nn.init as init
-
-
-def initialize_weights(net_l, scale=1):
-    if not isinstance(net_l, list):
-        net_l = [net_l]
-    for net in net_l:
-        for m in net.modules():
-            if isinstance(m, nn.Conv2d):
-                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
-                m.weight.data *= scale  # for residual block
-                if m.bias is not None:
-                    m.bias.data.zero_()
-            elif isinstance(m, nn.Linear):
-                init.kaiming_normal_(m.weight, a=0, mode='fan_in')
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.zero_()
-            elif isinstance(m, nn.BatchNorm2d):
-                init.constant_(m.weight, 1)
-                init.constant_(m.bias.data, 0.0)
-
-
-def make_layer(block, n_layers):
-    layers = []
-    for _ in range(n_layers):
-        layers.append(block())
-    return nn.Sequential(*layers)
-
-
-class ResidualDenseBlock_5C(nn.Module):
-    def __init__(self, nf=64, gc=32, bias=True):
-        super(ResidualDenseBlock_5C, self).__init__()
-        # gc: growth channel, i.e. intermediate channels
-        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
-        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
-        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    '''Residual in Residual Dense Block'''
-
-    def __init__(self, nf, gc=32):
-        super(RRDB, self).__init__()
-        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
-
-    def forward(self, x):
-        out = self.RDB1(x)
-        out = self.RDB2(out)
-        out = self.RDB3(out)
-        return out * 0.2 + x
-
-
-class RRDBNet(nn.Module):
-    def __init__(self, in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4):
-        super(RRDBNet, self).__init__()
-        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
-        self.sf = sf
-
-        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
-        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
-        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        #### upsampling
-        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        if self.sf==4:
-            self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        fea = self.conv_first(x)
-        trunk = self.trunk_conv(self.RRDB_trunk(fea))
-        fea = fea + trunk
-
-        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        if self.sf==4:
-            fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.HRconv(fea)))
-
-        return out
\ No newline at end of file
diff --git a/modules/esrgam_model_arch.py b/modules/esrgam_model_arch.py
deleted file mode 100644
index e413d36e..00000000
--- a/modules/esrgam_model_arch.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# this file is taken from https://github.com/xinntao/ESRGAN
-
-import functools
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def make_layer(block, n_layers):
-    layers = []
-    for _ in range(n_layers):
-        layers.append(block())
-    return nn.Sequential(*layers)
-
-
-class ResidualDenseBlock_5C(nn.Module):
-    def __init__(self, nf=64, gc=32, bias=True):
-        super(ResidualDenseBlock_5C, self).__init__()
-        # gc: growth channel, i.e. intermediate channels
-        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
-        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
-        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    '''Residual in Residual Dense Block'''
-
-    def __init__(self, nf, gc=32):
-        super(RRDB, self).__init__()
-        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
-
-    def forward(self, x):
-        out = self.RDB1(x)
-        out = self.RDB2(out)
-        out = self.RDB3(out)
-        return out * 0.2 + x
-
-
-class RRDBNet(nn.Module):
-    def __init__(self, in_nc, out_nc, nf, nb, gc=32):
-        super(RRDBNet, self).__init__()
-        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
-
-        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
-        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
-        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        #### upsampling
-        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        fea = self.conv_first(x)
-        trunk = self.trunk_conv(self.RRDB_trunk(fea))
-        fea = fea + trunk
-
-        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.HRconv(fea)))
-
-        return out
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 3970e6e4..a49e2258 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -5,68 +5,115 @@ import torch
 from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 
-import modules.esrgam_model_arch as arch
+import modules.esrgan_model_arch as arch
 from modules import shared, modelloader, images, devices
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
 
 
-def fix_model_layers(crt_model, pretrained_net):
-    # this code is adapted from https://github.com/xinntao/ESRGAN
-    if 'conv_first.weight' in pretrained_net:
-        return pretrained_net
 
-    if 'model.0.weight' not in pretrained_net:
-        is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"]
-        if is_realesrgan:
-            raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
-        else:
-            raise Exception("The file is not a ESRGAN model.")
+def mod2normal(state_dict):
+    # this code is copied from https://github.com/victorca25/iNNfer
+    if 'conv_first.weight' in state_dict:
+        crt_net = {}
+        items = []
+        for k, v in state_dict.items():
+            items.append(k)
+
+        crt_net['model.0.weight'] = state_dict['conv_first.weight']
+        crt_net['model.0.bias'] = state_dict['conv_first.bias']
+
+        for k in items.copy():
+            if 'RDB' in k:
+                ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
+                if '.weight' in k:
+                    ori_k = ori_k.replace('.weight', '.0.weight')
+                elif '.bias' in k:
+                    ori_k = ori_k.replace('.bias', '.0.bias')
+                crt_net[ori_k] = state_dict[k]
+                items.remove(k)
+
+        crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
+        crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
+        crt_net['model.3.weight'] = state_dict['upconv1.weight']
+        crt_net['model.3.bias'] = state_dict['upconv1.bias']
+        crt_net['model.6.weight'] = state_dict['upconv2.weight']
+        crt_net['model.6.bias'] = state_dict['upconv2.bias']
+        crt_net['model.8.weight'] = state_dict['HRconv.weight']
+        crt_net['model.8.bias'] = state_dict['HRconv.bias']
+        crt_net['model.10.weight'] = state_dict['conv_last.weight']
+        crt_net['model.10.bias'] = state_dict['conv_last.bias']
+        state_dict = crt_net
+    return state_dict
+
+
+def resrgan2normal(state_dict, nb=23):
+    # this code is copied from https://github.com/victorca25/iNNfer
+    if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
+        crt_net = {}
+        items = []
+        for k, v in state_dict.items():
+            items.append(k)
+
+        crt_net['model.0.weight'] = state_dict['conv_first.weight']
+        crt_net['model.0.bias'] = state_dict['conv_first.bias']
+
+        for k in items.copy():
+            if "rdb" in k:
+                ori_k = k.replace('body.', 'model.1.sub.')
+                ori_k = ori_k.replace('.rdb', '.RDB')
+                if '.weight' in k:
+                    ori_k = ori_k.replace('.weight', '.0.weight')
+                elif '.bias' in k:
+                    ori_k = ori_k.replace('.bias', '.0.bias')
+                crt_net[ori_k] = state_dict[k]
+                items.remove(k)
+
+        crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
+        crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
+        crt_net['model.3.weight'] = state_dict['conv_up1.weight']
+        crt_net['model.3.bias'] = state_dict['conv_up1.bias']
+        crt_net['model.6.weight'] = state_dict['conv_up2.weight']
+        crt_net['model.6.bias'] = state_dict['conv_up2.bias']
+        crt_net['model.8.weight'] = state_dict['conv_hr.weight']
+        crt_net['model.8.bias'] = state_dict['conv_hr.bias']
+        crt_net['model.10.weight'] = state_dict['conv_last.weight']
+        crt_net['model.10.bias'] = state_dict['conv_last.bias']
+        state_dict = crt_net
+    return state_dict
+
+
+def infer_params(state_dict):
+    # this code is copied from https://github.com/victorca25/iNNfer
+    scale2x = 0
+    scalemin = 6
+    n_uplayer = 0
+    plus = False
+
+    for block in list(state_dict):
+        parts = block.split(".")
+        n_parts = len(parts)
+        if n_parts == 5 and parts[2] == "sub":
+            nb = int(parts[3])
+        elif n_parts == 3:
+            part_num = int(parts[1])
+            if (part_num > scalemin
+                and parts[0] == "model"
+                and parts[2] == "weight"):
+                scale2x += 1
+            if part_num > n_uplayer:
+                n_uplayer = part_num
+                out_nc = state_dict[block].shape[0]
+        if not plus and "conv1x1" in block:
+            plus = True
+
+    nf = state_dict["model.0.weight"].shape[0]
+    in_nc = state_dict["model.0.weight"].shape[1]
+    out_nc = out_nc
+    scale = 2 ** scale2x
+
+    return in_nc, out_nc, nf, nb, plus, scale
 
-    crt_net = crt_model.state_dict()
-    load_net_clean = {}
-    for k, v in pretrained_net.items():
-        if k.startswith('module.'):
-            load_net_clean[k[7:]] = v
-        else:
-            load_net_clean[k] = v
-    pretrained_net = load_net_clean
-
-    tbd = []
-    for k, v in crt_net.items():
-        tbd.append(k)
-
-    # directly copy
-    for k, v in crt_net.items():
-        if k in pretrained_net and pretrained_net[k].size() == v.size():
-            crt_net[k] = pretrained_net[k]
-            tbd.remove(k)
-
-    crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
-    crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
-
-    for k in tbd.copy():
-        if 'RDB' in k:
-            ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
-            if '.weight' in k:
-                ori_k = ori_k.replace('.weight', '.0.weight')
-            elif '.bias' in k:
-                ori_k = ori_k.replace('.bias', '.0.bias')
-            crt_net[k] = pretrained_net[ori_k]
-            tbd.remove(k)
-
-    crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
-    crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
-    crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
-    crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
-    crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
-    crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
-    crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
-    crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
-    crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
-    crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
-
-    return crt_net
 
 class UpscalerESRGAN(Upscaler):
     def __init__(self, dirname):
@@ -109,20 +156,39 @@ class UpscalerESRGAN(Upscaler):
             print("Unable to load %s from %s" % (self.model_path, filename))
             return None
 
-        pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
-        crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
+        state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
+
+        if "params_ema" in state_dict:
+            state_dict = state_dict["params_ema"]
+        elif "params" in state_dict:
+            state_dict = state_dict["params"]
+            num_conv = 16 if "realesr-animevideov3" in filename else 32
+            model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
+            model.load_state_dict(state_dict)
+            model.eval()
+            return model
+
+        if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
+            nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23
+            state_dict = resrgan2normal(state_dict, nb)
+        elif "conv_first.weight" in state_dict:
+            state_dict = mod2normal(state_dict)
+        elif "model.0.weight" not in state_dict:
+            raise Exception("The file is not a recognized ESRGAN model.")
+
+        in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)
 
-        pretrained_net = fix_model_layers(crt_model, pretrained_net)
-        crt_model.load_state_dict(pretrained_net)
-        crt_model.eval()
+        model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
+        model.load_state_dict(state_dict)
+        model.eval()
 
-        return crt_model
+        return model
 
 
 def upscale_without_tiling(model, img):
     img = np.array(img)
     img = img[:, :, ::-1]
-    img = np.moveaxis(img, 2, 0) / 255
+    img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
     img = torch.from_numpy(img).float()
     img = img.unsqueeze(0).to(devices.device_esrgan)
     with torch.no_grad():
diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py
new file mode 100644
index 00000000..bc9ceb2a
--- /dev/null
+++ b/modules/esrgan_model_arch.py
@@ -0,0 +1,463 @@
+# this file is adapted from https://github.com/victorca25/iNNfer
+
+import math
+import functools
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+####################
+# RRDBNet Generator
+####################
+
+class RRDBNet(nn.Module):
+    def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None,
+            act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D',
+            finalact=None, gaussian_noise=False, plus=False):
+        super(RRDBNet, self).__init__()
+        n_upscale = int(math.log(upscale, 2))
+        if upscale == 3:
+            n_upscale = 1
+
+        self.resrgan_scale = 0
+        if in_nc % 16 == 0:
+            self.resrgan_scale = 1
+        elif in_nc != 4 and in_nc % 4 == 0:
+            self.resrgan_scale = 2
+
+        fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
+        rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
+            norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype,
+            gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)]
+        LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype)
+
+        if upsample_mode == 'upconv':
+            upsample_block = upconv_block
+        elif upsample_mode == 'pixelshuffle':
+            upsample_block = pixelshuffle_block
+        else:
+            raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
+        if upscale == 3:
+            upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
+        else:
+            upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)]
+        HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype)
+        HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
+
+        outact = act(finalact) if finalact else None
+
+        self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),
+            *upsampler, HR_conv0, HR_conv1, outact)
+
+    def forward(self, x, outm=None):
+        if self.resrgan_scale == 1:
+            feat = pixel_unshuffle(x, scale=4)
+        elif self.resrgan_scale == 2:
+            feat = pixel_unshuffle(x, scale=2)
+        else:
+            feat = x
+
+        return self.model(feat)
+
+
+class RRDB(nn.Module):
+    """
+    Residual in Residual Dense Block
+    (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
+    """
+
+    def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
+            norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
+            spectral_norm=False, gaussian_noise=False, plus=False):
+        super(RRDB, self).__init__()
+        # This is for backwards compatibility with existing models
+        if nr == 3:
+            self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+                    gaussian_noise=gaussian_noise, plus=plus)
+            self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+                    gaussian_noise=gaussian_noise, plus=plus)
+            self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+                    gaussian_noise=gaussian_noise, plus=plus)
+        else:
+            RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+                                              norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+                                              gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)]
+            self.RDBs = nn.Sequential(*RDB_list)
+
+    def forward(self, x):
+        if hasattr(self, 'RDB1'):
+            out = self.RDB1(x)
+            out = self.RDB2(out)
+            out = self.RDB3(out)
+        else:
+            out = self.RDBs(x)
+        return out * 0.2 + x
+
+
+class ResidualDenseBlock_5C(nn.Module):
+    """
+    Residual Dense Block
+    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
+    Modified options that can be used:
+        - "Partial Convolution based Padding" arXiv:1811.11718
+        - "Spectral normalization" arXiv:1802.05957
+        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. 
+            {Rakotonirina} and A. {Rasoanaivo}
+    """
+
+    def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
+            norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
+            spectral_norm=False, gaussian_noise=False, plus=False):
+        super(ResidualDenseBlock_5C, self).__init__()
+
+        self.noise = GaussianNoise() if gaussian_noise else None
+        self.conv1x1 = conv1x1(nf, gc) if plus else None
+
+        self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+            spectral_norm=spectral_norm)
+        self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+            spectral_norm=spectral_norm)
+        self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+            spectral_norm=spectral_norm)
+        self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+            spectral_norm=spectral_norm)
+        if mode == 'CNA':
+            last_act = None
+        else:
+            last_act = act_type
+        self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type,
+            norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype,
+            spectral_norm=spectral_norm)
+
+    def forward(self, x):
+        x1 = self.conv1(x)
+        x2 = self.conv2(torch.cat((x, x1), 1))
+        if self.conv1x1:
+            x2 = x2 + self.conv1x1(x)
+        x3 = self.conv3(torch.cat((x, x1, x2), 1))
+        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
+        if self.conv1x1:
+            x4 = x4 + x2
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        if self.noise:
+            return self.noise(x5.mul(0.2) + x)
+        else:
+            return x5 * 0.2 + x
+
+
+####################
+# ESRGANplus
+####################
+
+class GaussianNoise(nn.Module):
+    def __init__(self, sigma=0.1, is_relative_detach=False):
+        super().__init__()
+        self.sigma = sigma
+        self.is_relative_detach = is_relative_detach
+        self.noise = torch.tensor(0, dtype=torch.float)
+
+    def forward(self, x):
+        if self.training and self.sigma != 0:
+            self.noise = self.noise.to(x.device)
+            scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
+            sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
+            x = x + sampled_noise
+        return x 
+
+def conv1x1(in_planes, out_planes, stride=1):
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+####################
+# SRVGGNetCompact
+####################
+
+class SRVGGNetCompact(nn.Module):
+    """A compact VGG-style network structure for super-resolution.
+    This class is copied from https://github.com/xinntao/Real-ESRGAN
+    """
+
+    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
+        super(SRVGGNetCompact, self).__init__()
+        self.num_in_ch = num_in_ch
+        self.num_out_ch = num_out_ch
+        self.num_feat = num_feat
+        self.num_conv = num_conv
+        self.upscale = upscale
+        self.act_type = act_type
+
+        self.body = nn.ModuleList()
+        # the first conv
+        self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
+        # the first activation
+        if act_type == 'relu':
+            activation = nn.ReLU(inplace=True)
+        elif act_type == 'prelu':
+            activation = nn.PReLU(num_parameters=num_feat)
+        elif act_type == 'leakyrelu':
+            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+        self.body.append(activation)
+
+        # the body structure
+        for _ in range(num_conv):
+            self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
+            # activation
+            if act_type == 'relu':
+                activation = nn.ReLU(inplace=True)
+            elif act_type == 'prelu':
+                activation = nn.PReLU(num_parameters=num_feat)
+            elif act_type == 'leakyrelu':
+                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+            self.body.append(activation)
+
+        # the last conv
+        self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
+        # upsample
+        self.upsampler = nn.PixelShuffle(upscale)
+
+    def forward(self, x):
+        out = x
+        for i in range(0, len(self.body)):
+            out = self.body[i](out)
+
+        out = self.upsampler(out)
+        # add the nearest upsampled image, so that the network learns the residual
+        base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
+        out += base
+        return out
+
+
+####################
+# Upsampler
+####################
+
+class Upsample(nn.Module):
+    r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
+    The input data is assumed to be of the form
+    `minibatch x channels x [optional depth] x [optional height] x width`.
+    """
+
+    def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None):
+        super(Upsample, self).__init__()
+        if isinstance(scale_factor, tuple):
+            self.scale_factor = tuple(float(factor) for factor in scale_factor)
+        else:
+            self.scale_factor = float(scale_factor) if scale_factor else None
+        self.mode = mode
+        self.size = size
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
+
+    def extra_repr(self):
+        if self.scale_factor is not None:
+            info = 'scale_factor=' + str(self.scale_factor)
+        else:
+            info = 'size=' + str(self.size)
+        info += ', mode=' + self.mode
+        return info
+
+
+def pixel_unshuffle(x, scale):
+    """ Pixel unshuffle.
+    Args:
+        x (Tensor): Input feature with shape (b, c, hh, hw).
+        scale (int): Downsample ratio.
+    Returns:
+        Tensor: the pixel unshuffled feature.
+    """
+    b, c, hh, hw = x.size()
+    out_channel = c * (scale**2)
+    assert hh % scale == 0 and hw % scale == 0
+    h = hh // scale
+    w = hw // scale
+    x_view = x.view(b, c, h, scale, w, scale)
+    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
+
+
+def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
+                        pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'):
+    """
+    Pixel shuffle layer
+    (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
+    Neural Network, CVPR17)
+    """
+    conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
+                        pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype)
+    pixel_shuffle = nn.PixelShuffle(upscale_factor)
+
+    n = norm(norm_type, out_nc) if norm_type else None
+    a = act(act_type) if act_type else None
+    return sequential(conv, pixel_shuffle, n, a)
+
+
+def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
+                pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'):
+    """ Upconv layer """
+    upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor
+    upsample = Upsample(scale_factor=upscale_factor, mode=mode)
+    conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
+                        pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype)
+    return sequential(upsample, conv)
+
+
+
+
+
+
+
+
+####################
+# Basic blocks
+####################
+
+
+def make_layer(basic_block, num_basic_block, **kwarg):
+    """Make layers by stacking the same blocks.
+    Args:
+        basic_block (nn.module): nn.module class for basic block. (block)
+        num_basic_block (int): number of blocks. (n_layers)
+    Returns:
+        nn.Sequential: Stacked blocks in nn.Sequential.
+    """
+    layers = []
+    for _ in range(num_basic_block):
+        layers.append(basic_block(**kwarg))
+    return nn.Sequential(*layers)
+
+
+def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
+    """ activation helper """
+    act_type = act_type.lower()
+    if act_type == 'relu':
+        layer = nn.ReLU(inplace)
+    elif act_type in ('leakyrelu', 'lrelu'):
+        layer = nn.LeakyReLU(neg_slope, inplace)
+    elif act_type == 'prelu':
+        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
+    elif act_type == 'tanh':  # [-1, 1] range output
+        layer = nn.Tanh()
+    elif act_type == 'sigmoid':  # [0, 1] range output
+        layer = nn.Sigmoid()
+    else:
+        raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
+    return layer
+
+
+class Identity(nn.Module):
+    def __init__(self, *kwargs):
+        super(Identity, self).__init__()
+
+    def forward(self, x, *kwargs):
+        return x
+
+
+def norm(norm_type, nc):
+    """ Return a normalization layer """
+    norm_type = norm_type.lower()
+    if norm_type == 'batch':
+        layer = nn.BatchNorm2d(nc, affine=True)
+    elif norm_type == 'instance':
+        layer = nn.InstanceNorm2d(nc, affine=False)
+    elif norm_type == 'none':
+        def norm_layer(x): return Identity()
+    else:
+        raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))
+    return layer
+
+
+def pad(pad_type, padding):
+    """ padding layer helper """
+    pad_type = pad_type.lower()
+    if padding == 0:
+        return None
+    if pad_type == 'reflect':
+        layer = nn.ReflectionPad2d(padding)
+    elif pad_type == 'replicate':
+        layer = nn.ReplicationPad2d(padding)
+    elif pad_type == 'zero':
+        layer = nn.ZeroPad2d(padding)
+    else:
+        raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))
+    return layer
+
+
+def get_valid_padding(kernel_size, dilation):
+    kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
+    padding = (kernel_size - 1) // 2
+    return padding
+
+
+class ShortcutBlock(nn.Module):
+    """ Elementwise sum the output of a submodule to its input """
+    def __init__(self, submodule):
+        super(ShortcutBlock, self).__init__()
+        self.sub = submodule
+
+    def forward(self, x):
+        output = x + self.sub(x)
+        return output
+
+    def __repr__(self):
+        return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|')
+
+
+def sequential(*args):
+    """ Flatten Sequential. It unwraps nn.Sequential. """
+    if len(args) == 1:
+        if isinstance(args[0], OrderedDict):
+            raise NotImplementedError('sequential does not support OrderedDict input.')
+        return args[0]  # No sequential is needed.
+    modules = []
+    for module in args:
+        if isinstance(module, nn.Sequential):
+            for submodule in module.children():
+                modules.append(submodule)
+        elif isinstance(module, nn.Module):
+            modules.append(module)
+    return nn.Sequential(*modules)
+
+
+def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
+               pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
+               spectral_norm=False):
+    """ Conv layer with padding, normalization, activation """
+    assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode)
+    padding = get_valid_padding(kernel_size, dilation)
+    p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
+    padding = padding if pad_type == 'zero' else 0
+
+    if convtype=='PartialConv2D':
+        c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+               dilation=dilation, bias=bias, groups=groups)
+    elif convtype=='DeformConv2D':
+        c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+               dilation=dilation, bias=bias, groups=groups)
+    elif convtype=='Conv3D':
+        c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+                dilation=dilation, bias=bias, groups=groups)
+    else:
+        c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+                dilation=dilation, bias=bias, groups=groups)
+
+    if spectral_norm:
+        c = nn.utils.spectral_norm(c)
+
+    a = act(act_type) if act_type else None
+    if 'CNA' in mode:
+        n = norm(norm_type, out_nc) if norm_type else None
+        return sequential(p, c, n, a)
+    elif mode == 'NAC':
+        if norm_type is None and act_type is not None:
+            a = act(act_type, inplace=False)
+        n = norm(norm_type, in_nc) if norm_type else None
+        return sequential(n, a, p, c)
-- 
cgit v1.2.1


From 542a3d3a4a00c1383fbdaf938ceefef87cf834bb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 14:33:22 +0300
Subject: fix btoken hypernetworks in XY plot

---
 modules/hypernetwork.py | 7 +++++--
 scripts/xy_grid.py      | 9 +++------
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
index 19f1c227..498bc9d8 100644
--- a/modules/hypernetwork.py
+++ b/modules/hypernetwork.py
@@ -49,15 +49,18 @@ def list_hypernetworks(path):
 
 
 def load_hypernetwork(filename):
-    print(f"Loading hypernetwork {filename}")
     path = shared.hypernetworks.get(filename, None)
-    if (path is not None):
+    if path is not None:
+        print(f"Loading hypernetwork {filename}")
         try:
             shared.loaded_hypernetwork = Hypernetwork(path)
         except Exception:
             print(f"Error loading hypernetwork {path}", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
     else:
+        if shared.loaded_hypernetwork is not None:
+            print(f"Unloading hypernetwork")
+
         shared.loaded_hypernetwork = None
 
 
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index a8f53bef..fe949067 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -10,7 +10,7 @@ import numpy as np
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import images
+from modules import images, hypernetwork
 from modules.processing import process_images, Processed, get_correct_sampler
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -80,8 +80,7 @@ def apply_checkpoint(p, x, xs):
 
 
 def apply_hypernetwork(p, x, xs):
-    hn = shared.hypernetworks.get(x, None)
-    opts.data["sd_hypernetwork"] = hn.name if hn is not None else 'None'
+    hypernetwork.load_hypernetwork(x)
 
 
 def format_value_add_label(p, opt, x):
@@ -203,8 +202,6 @@ class Script(scripts.Script):
 
         p.batch_size = 1
 
-        initial_hn = opts.sd_hypernetwork
-
         def process_axis(opt, vals):
             if opt.label == 'Nothing':
                 return [0]
@@ -321,6 +318,6 @@ class Script(scripts.Script):
         # restore checkpoint in case it was changed by axes
         modules.sd_models.reload_model_weights(shared.sd_model)
 
-        opts.data["sd_hypernetwork"] = initial_hn
+        hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
 
         return processed
-- 
cgit v1.2.1


From d6d10a37bfd21568e74efb46137f906da96d5fdb Mon Sep 17 00:00:00 2001
From: William Moorehouse <moorehousew@gmail.com>
Date: Sun, 9 Oct 2022 04:58:40 -0400
Subject: Added extended model details to infotext

---
 modules/processing.py | 3 +++
 modules/sd_models.py  | 3 ++-
 modules/shared.py     | 1 +
 3 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 2c991317..d1bcee4a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -284,6 +284,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
+        "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_name else shared.sd_model.sd_model_name),
+        "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name),
+        "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
diff --git a/modules/sd_models.py b/modules/sd_models.py
index d0c74dd8..3fa42329 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,7 +4,7 @@ import sys
 from collections import namedtuple
 import torch
 from omegaconf import OmegaConf
-
+from pathlib import Path
 
 from ldm.util import instantiate_from_config
 
@@ -158,6 +158,7 @@ def load_model_weights(model, checkpoint_info):
         vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
         model.first_stage_model.load_state_dict(vae_dict)
+        model.sd_model_vae_name = Path(vae_file).stem
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
diff --git a/modules/shared.py b/modules/shared.py
index dffa0094..ca63f7d8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -242,6 +242,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
+    "add_extended_model_details_to_info": OptionInfo(False, "Add extended model details to generation information (model name, VAE, hypernetwork)"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-- 
cgit v1.2.1


From 006791c13d70e582eee766b7d0499e9821a86bf9 Mon Sep 17 00:00:00 2001
From: William Moorehouse <moorehousew@gmail.com>
Date: Sun, 9 Oct 2022 05:09:18 -0400
Subject: Fix grabbing the model name for infotext

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index d1bcee4a..c035c990 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -284,7 +284,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
-        "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_name else shared.sd_model.sd_model_name),
+        "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name),
         "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name),
         "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
-- 
cgit v1.2.1


From 594cbfd8fbe4078b43ceccf01509eeef3d6790c6 Mon Sep 17 00:00:00 2001
From: William Moorehouse <moorehousew@gmail.com>
Date: Sun, 9 Oct 2022 07:27:11 -0400
Subject: Sanitize infotext output (for now)

---
 modules/processing.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index c035c990..049f3769 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -284,9 +284,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
-        "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name),
-        "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name),
-        "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork),
+        "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
+        "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name.replace(',', '').replace(':', '')),
+        "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork.replace(',', '').replace(':', '')),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.1


From e6e8cabe0c9c335e0d72345602c069b198558b53 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 14:57:48 +0300
Subject: change up #2056 to make it work how i want it to plus make xy plot
 write correct values to images

---
 modules/processing.py | 5 ++---
 modules/sd_models.py  | 2 --
 modules/shared.py     | 2 +-
 3 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 049f3769..04aed989 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -284,9 +284,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
-        "Model": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Model VAE": (None if not opts.add_extended_model_details_to_info or not shared.sd_model.sd_model_vae_name else shared.sd_model.sd_model_vae_name.replace(',', '').replace(':', '')),
-        "Model hypernetwork": (None if not opts.add_extended_model_details_to_info or not opts.sd_hypernetwork else opts.sd_hypernetwork.replace(',', '').replace(':', '')),
+        "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
+        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 3fa42329..e63d3c29 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,7 +4,6 @@ import sys
 from collections import namedtuple
 import torch
 from omegaconf import OmegaConf
-from pathlib import Path
 
 from ldm.util import instantiate_from_config
 
@@ -158,7 +157,6 @@ def load_model_weights(model, checkpoint_info):
         vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
         model.first_stage_model.load_state_dict(vae_dict)
-        model.sd_model_vae_name = Path(vae_file).stem
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
diff --git a/modules/shared.py b/modules/shared.py
index ca63f7d8..6ecc2503 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -242,7 +242,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
-    "add_extended_model_details_to_info": OptionInfo(False, "Add extended model details to generation information (model name, VAE, hypernetwork)"),
+    "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-- 
cgit v1.2.1


From 2c52f4da7ff80a3ec277105f4db6146c6379898a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 15:01:42 +0300
Subject: fix broken samplers in XY plot

---
 scripts/xy_grid.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index fe949067..c89ca1a9 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -259,6 +259,7 @@ class Script(scripts.Script):
             
             # Confirm options are valid before starting
             if opt.label == "Sampler":
+                samplers_dict = build_samplers_dict(p)
                 for sampler_val in valslist:
                     if sampler_val.lower() not in samplers_dict.keys():
                         raise RuntimeError(f"Unknown sampler: {sampler_val}")
-- 
cgit v1.2.1


From 9d1138e2940c4ddcd2685bcba12c7d407e9e0ec5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 15:08:10 +0300
Subject: fix typo in filename for ESRGAN arch

---
 modules/esrgam_model_arch.py | 80 --------------------------------------------
 modules/esrgan_model.py      |  2 +-
 modules/esrgan_model_arch.py | 80 ++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 81 insertions(+), 81 deletions(-)
 delete mode 100644 modules/esrgam_model_arch.py
 create mode 100644 modules/esrgan_model_arch.py

diff --git a/modules/esrgam_model_arch.py b/modules/esrgam_model_arch.py
deleted file mode 100644
index e413d36e..00000000
--- a/modules/esrgam_model_arch.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# this file is taken from https://github.com/xinntao/ESRGAN
-
-import functools
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def make_layer(block, n_layers):
-    layers = []
-    for _ in range(n_layers):
-        layers.append(block())
-    return nn.Sequential(*layers)
-
-
-class ResidualDenseBlock_5C(nn.Module):
-    def __init__(self, nf=64, gc=32, bias=True):
-        super(ResidualDenseBlock_5C, self).__init__()
-        # gc: growth channel, i.e. intermediate channels
-        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
-        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
-        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
-        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    '''Residual in Residual Dense Block'''
-
-    def __init__(self, nf, gc=32):
-        super(RRDB, self).__init__()
-        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
-        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
-
-    def forward(self, x):
-        out = self.RDB1(x)
-        out = self.RDB2(out)
-        out = self.RDB3(out)
-        return out * 0.2 + x
-
-
-class RRDBNet(nn.Module):
-    def __init__(self, in_nc, out_nc, nf, nb, gc=32):
-        super(RRDBNet, self).__init__()
-        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
-
-        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
-        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
-        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        #### upsampling
-        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
-        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        fea = self.conv_first(x)
-        trunk = self.trunk_conv(self.RRDB_trunk(fea))
-        fea = fea + trunk
-
-        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.HRconv(fea)))
-
-        return out
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index 3970e6e4..46ad0da3 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -5,7 +5,7 @@ import torch
 from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 
-import modules.esrgam_model_arch as arch
+import modules.esrgan_model_arch as arch
 from modules import shared, modelloader, images, devices
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py
new file mode 100644
index 00000000..e413d36e
--- /dev/null
+++ b/modules/esrgan_model_arch.py
@@ -0,0 +1,80 @@
+# this file is taken from https://github.com/xinntao/ESRGAN
+
+import functools
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def make_layer(block, n_layers):
+    layers = []
+    for _ in range(n_layers):
+        layers.append(block())
+    return nn.Sequential(*layers)
+
+
+class ResidualDenseBlock_5C(nn.Module):
+    def __init__(self, nf=64, gc=32, bias=True):
+        super(ResidualDenseBlock_5C, self).__init__()
+        # gc: growth channel, i.e. intermediate channels
+        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
+        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
+        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+        # initialization
+        # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
+
+    def forward(self, x):
+        x1 = self.lrelu(self.conv1(x))
+        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
+        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
+        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        return x5 * 0.2 + x
+
+
+class RRDB(nn.Module):
+    '''Residual in Residual Dense Block'''
+
+    def __init__(self, nf, gc=32):
+        super(RRDB, self).__init__()
+        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
+
+    def forward(self, x):
+        out = self.RDB1(x)
+        out = self.RDB2(out)
+        out = self.RDB3(out)
+        return out * 0.2 + x
+
+
+class RRDBNet(nn.Module):
+    def __init__(self, in_nc, out_nc, nf, nb, gc=32):
+        super(RRDBNet, self).__init__()
+        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
+
+        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
+        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
+        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        #### upsampling
+        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        fea = self.conv_first(x)
+        trunk = self.trunk_conv(self.RRDB_trunk(fea))
+        fea = fea + trunk
+
+        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
+        fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
+        out = self.conv_last(self.lrelu(self.HRconv(fea)))
+
+        return out
-- 
cgit v1.2.1


From f8197976ef5f0523faffb2b237e9166fb2bedecd Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Sun, 9 Oct 2022 13:44:13 +0200
Subject: Shielded launch enviroment creation stuff from multiprocessing

---
 launch.py | 174 ++++++++++++++++++++++++++++++--------------------------------
 1 file changed, 85 insertions(+), 89 deletions(-)

diff --git a/launch.py b/launch.py
index b0a59b6a..d1a4fd6a 100644
--- a/launch.py
+++ b/launch.py
@@ -6,40 +6,11 @@ import importlib.util
 import shlex
 import platform
 
-dir_repos = "repositories"
-dir_tmp = "tmp"
-
-python = sys.executable
-git = os.environ.get('GIT', "git")
-torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
-requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
-commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
-
-gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
-clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
-
-stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
-taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
-k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
-codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
-blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
-
-args = shlex.split(commandline_args)
-
 
 def extract_arg(args, name):
     return [x for x in args if x != name], name in args
 
 
-args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
-xformers = '--xformers' in args
-deepdanbooru = '--deepdanbooru' in args
-
-
-def repo_dir(name):
-    return os.path.join(dir_repos, name)
-
-
 def run(command, desc=None, errdesc=None):
     if desc is not None:
         print(desc)
@@ -59,23 +30,11 @@ stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.st
     return result.stdout.decode(encoding="utf8", errors="ignore")
 
 
-def run_python(code, desc=None, errdesc=None):
-    return run(f'"{python}" -c "{code}"', desc, errdesc)
-
-
-def run_pip(args, desc=None):
-    return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
-
-
 def check_run(command):
     result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
     return result.returncode == 0
 
 
-def check_run_python(code):
-    return check_run(f'"{python}" -c "{code}"')
-
-
 def is_installed(package):
     try:
         spec = importlib.util.find_spec(package)
@@ -85,80 +44,117 @@ def is_installed(package):
     return spec is not None
 
 
-def git_clone(url, dir, name, commithash=None):
-    # TODO clone into temporary dir and move if successful
+def prepare_enviroment():
+    dir_repos = "repositories"
 
-    if os.path.exists(dir):
-        if commithash is None:
-            return
+    python = sys.executable
+    git = os.environ.get('GIT', "git")
+    torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
+    requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
+    commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
 
-        current_hash = run(f'"{git}" -C {dir} rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
-        if current_hash == commithash:
-            return
+    gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
+    clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
+
+    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
+    taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
+    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
+    codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
+    blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
+
+    args = shlex.split(commandline_args)
+
+    args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
+    xformers = '--xformers' in args
+    deepdanbooru = '--deepdanbooru' in args
+
+    def repo_dir(name):
+        return os.path.join(dir_repos, name)
+
+    def run_python(code, desc=None, errdesc=None):
+        return run(f'"{python}" -c "{code}"', desc, errdesc)
 
-        run(f'"{git}" -C {dir} fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
-        run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commint for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
-        return
+    def run_pip(args, desc=None):
+        return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
 
-    run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
+    def check_run_python(code):
+        return check_run(f'"{python}" -c "{code}"')
 
-    if commithash is not None:
-        run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
+    def git_clone(url, dir, name, commithash=None):
+        # TODO clone into temporary dir and move if successful
 
+        if os.path.exists(dir):
+            if commithash is None:
+                return
 
-try:
-    commit = run(f"{git} rev-parse HEAD").strip()
-except Exception:
-    commit = "<none>"
+            current_hash = run(f'"{git}" -C {dir} rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
+            if current_hash == commithash:
+                return
 
-print(f"Python {sys.version}")
-print(f"Commit hash: {commit}")
+            run(f'"{git}" -C {dir} fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
+            run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commint for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
+            return
+
+        run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
 
+        if commithash is not None:
+            run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
+
+    try:
+        commit = run(f"{git} rev-parse HEAD").strip()
+    except Exception:
+        commit = "<none>"
 
-if not is_installed("torch") or not is_installed("torchvision"):
-    run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
+    print(f"Python {sys.version}")
+    print(f"Commit hash: {commit}")
 
-if not skip_torch_cuda_test:
-    run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
+    if not is_installed("torch") or not is_installed("torchvision"):
+        run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
 
-if not is_installed("gfpgan"):
-    run_pip(f"install {gfpgan_package}", "gfpgan")
+    if not skip_torch_cuda_test:
+        run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
 
-if not is_installed("clip"):
-    run_pip(f"install {clip_package}", "clip")
+    if not is_installed("gfpgan"):
+        run_pip(f"install {gfpgan_package}", "gfpgan")
 
-if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
-    if platform.system() == "Windows":
-        run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
-    elif platform.system() == "Linux":
-        run_pip("install xformers", "xformers")
+    if not is_installed("clip"):
+        run_pip(f"install {clip_package}", "clip")
 
-if not is_installed("deepdanbooru") and deepdanbooru:
-    run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
+    if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
+        if platform.system() == "Windows":
+            run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
+        elif platform.system() == "Linux":
+            run_pip("install xformers", "xformers")
 
-os.makedirs(dir_repos, exist_ok=True)
+    if not is_installed("deepdanbooru") and deepdanbooru:
+        run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
 
-git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
-git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
-git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
-git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
-git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
+    os.makedirs(dir_repos, exist_ok=True)
 
-if not is_installed("lpips"):
-    run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
+    git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
+    git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
+    git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
+    git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
+    git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
 
-run_pip(f"install -r {requirements_file}", "requirements for Web UI")
+    if not is_installed("lpips"):
+        run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
 
-sys.argv += args
+    run_pip(f"install -r {requirements_file}", "requirements for Web UI")
+
+    sys.argv += args
+
+    if "--exit" in args:
+        print("Exiting because of --exit argument")
+        exit(0)
 
-if "--exit" in args:
-    print("Exiting because of --exit argument")
-    exit(0)
 
 def start_webui():
     print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}")
     import webui
     webui.webui()
 
+
 if __name__ == "__main__":
+    prepare_enviroment()
     start_webui()
-- 
cgit v1.2.1


From bba2ac8324ccd1a67c78e5f59babae8323ec7dc6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 15:22:51 +0300
Subject: reshuffle the code a bit in launcher to keep functions in one place
 for #2069

---
 launch.py | 77 ++++++++++++++++++++++++++++++++++-----------------------------
 1 file changed, 41 insertions(+), 36 deletions(-)

diff --git a/launch.py b/launch.py
index d1a4fd6a..f42f557d 100644
--- a/launch.py
+++ b/launch.py
@@ -6,6 +6,10 @@ import importlib.util
 import shlex
 import platform
 
+dir_repos = "repositories"
+python = sys.executable
+git = os.environ.get('GIT', "git")
+
 
 def extract_arg(args, name):
     return [x for x in args if x != name], name in args
@@ -44,11 +48,44 @@ def is_installed(package):
     return spec is not None
 
 
-def prepare_enviroment():
-    dir_repos = "repositories"
+def repo_dir(name):
+    return os.path.join(dir_repos, name)
+
+
+def run_python(code, desc=None, errdesc=None):
+    return run(f'"{python}" -c "{code}"', desc, errdesc)
+
+
+def run_pip(args, desc=None):
+    return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
+
+
+def check_run_python(code):
+    return check_run(f'"{python}" -c "{code}"')
+
+
+def git_clone(url, dir, name, commithash=None):
+    # TODO clone into temporary dir and move if successful
+
+    if os.path.exists(dir):
+        if commithash is None:
+            return
+
+        current_hash = run(f'"{git}" -C {dir} rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
+        if current_hash == commithash:
+            return
+
+        run(f'"{git}" -C {dir} fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
+        run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commint for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
+        return
 
-    python = sys.executable
-    git = os.environ.get('GIT', "git")
+    run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
+
+    if commithash is not None:
+        run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
+
+
+def prepare_enviroment():
     torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
     commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
@@ -68,38 +105,6 @@ def prepare_enviroment():
     xformers = '--xformers' in args
     deepdanbooru = '--deepdanbooru' in args
 
-    def repo_dir(name):
-        return os.path.join(dir_repos, name)
-
-    def run_python(code, desc=None, errdesc=None):
-        return run(f'"{python}" -c "{code}"', desc, errdesc)
-
-    def run_pip(args, desc=None):
-        return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
-
-    def check_run_python(code):
-        return check_run(f'"{python}" -c "{code}"')
-
-    def git_clone(url, dir, name, commithash=None):
-        # TODO clone into temporary dir and move if successful
-
-        if os.path.exists(dir):
-            if commithash is None:
-                return
-
-            current_hash = run(f'"{git}" -C {dir} rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
-            if current_hash == commithash:
-                return
-
-            run(f'"{git}" -C {dir} fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
-            run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commint for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
-            return
-
-        run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
-
-        if commithash is not None:
-            run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
-
     try:
         commit = run(f"{git} rev-parse HEAD").strip()
     except Exception:
-- 
cgit v1.2.1


From 875ddfeecfaffad9eee24813301637cba310337d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 17:58:43 +0300
Subject: added guard for torch.load to prevent loading pickles with unknown
 content

---
 modules/paths.py  |  1 +
 modules/safe.py   | 89 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 modules/shared.py |  1 +
 3 files changed, 91 insertions(+)
 create mode 100644 modules/safe.py

diff --git a/modules/paths.py b/modules/paths.py
index 0519caa0..1e7a2fbc 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -1,6 +1,7 @@
 import argparse
 import os
 import sys
+import modules.safe
 
 script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
 models_path = os.path.join(script_path, "models")
diff --git a/modules/safe.py b/modules/safe.py
new file mode 100644
index 00000000..2d2c1371
--- /dev/null
+++ b/modules/safe.py
@@ -0,0 +1,89 @@
+# this code is adapted from the script contributed by anon from /h/
+
+import io
+import pickle
+import collections
+import sys
+import traceback
+
+import torch
+import numpy
+import _codecs
+import zipfile
+
+
+def encode(*args):
+    out = _codecs.encode(*args)
+    return out
+
+
+class RestrictedUnpickler(pickle.Unpickler):
+    def persistent_load(self, saved_id):
+        assert saved_id[0] == 'storage'
+        return torch.storage._TypedStorage()
+
+    def find_class(self, module, name):
+        if module == 'collections' and name == 'OrderedDict':
+            return getattr(collections, name)
+        if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
+            return getattr(torch._utils, name)
+        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage']:
+            return getattr(torch, name)
+        if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
+            return getattr(torch.nn.modules.container, name)
+        if module == 'numpy.core.multiarray' and name == 'scalar':
+            return numpy.core.multiarray.scalar
+        if module == 'numpy' and name == 'dtype':
+            return numpy.dtype
+        if module == '_codecs' and name == 'encode':
+            return encode
+        if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':
+            import pytorch_lightning.callbacks
+            return pytorch_lightning.callbacks.model_checkpoint
+        if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint':
+            import pytorch_lightning.callbacks.model_checkpoint
+            return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint
+        if module == "__builtin__" and name == 'set':
+            return set
+
+        # Forbid everything else.
+        raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")
+
+
+def check_pt(filename):
+    try:
+
+        # new pytorch format is a zip file
+        with zipfile.ZipFile(filename) as z:
+            with z.open('archive/data.pkl') as file:
+                unpickler = RestrictedUnpickler(file)
+                unpickler.load()
+
+    except zipfile.BadZipfile:
+
+        # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
+        with open(filename, "rb") as file:
+            unpickler = RestrictedUnpickler(file)
+            for i in range(5):
+                unpickler.load()
+
+
+def load(filename, *args, **kwargs):
+    from modules import shared
+
+    try:
+        if not shared.cmd_opts.disable_safe_unpickle:
+            check_pt(filename)
+
+    except Exception:
+        print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+        print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
+        print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr)
+        return None
+
+    return unsafe_torch_load(filename, *args, **kwargs)
+
+
+unsafe_torch_load = torch.load
+torch.load = load
diff --git a/modules/shared.py b/modules/shared.py
index 6ecc2503..3d7f08e1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -65,6 +65,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
+parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 
 
 cmd_opts = parser.parse_args()
-- 
cgit v1.2.1


From d3cd46b0388918128af203fda37fa63461c46611 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 16:19:33 +0100
Subject: Update lightbox to change displayed image as soon as generation is
 complete (#1933)

* add updateOnBackgroundChange
* typo fixes.
* reindent to 4 spaces
---
 javascript/imageviewer.js | 174 ++++++++++++++++++++++++++--------------------
 1 file changed, 99 insertions(+), 75 deletions(-)

diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js
index 6a00c0da..65a33dd7 100644
--- a/javascript/imageviewer.js
+++ b/javascript/imageviewer.js
@@ -1,72 +1,97 @@
 // A full size 'lightbox' preview modal shown when left clicking on gallery previews
-
 function closeModal() {
-  gradioApp().getElementById("lightboxModal").style.display = "none";
+    gradioApp().getElementById("lightboxModal").style.display = "none";
 }
 
 function showModal(event) {
-  const source = event.target || event.srcElement;
-  const modalImage = gradioApp().getElementById("modalImage")
-  const lb = gradioApp().getElementById("lightboxModal")
-  modalImage.src = source.src
-  if (modalImage.style.display === 'none') {
-    lb.style.setProperty('background-image', 'url(' + source.src + ')');
-  }
-  lb.style.display = "block";
-  lb.focus()
-  event.stopPropagation()
+    const source = event.target || event.srcElement;
+    const modalImage = gradioApp().getElementById("modalImage")
+    const lb = gradioApp().getElementById("lightboxModal")
+    modalImage.src = source.src
+    if (modalImage.style.display === 'none') {
+        lb.style.setProperty('background-image', 'url(' + source.src + ')');
+    }
+    lb.style.display = "block";
+    lb.focus()
+    event.stopPropagation()
 }
 
 function negmod(n, m) {
-  return ((n % m) + m) % m;
+    return ((n % m) + m) % m;
 }
 
-function modalImageSwitch(offset){
-  var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all")
-  var galleryButtons = []
-  allgalleryButtons.forEach(function(elem){
-    if(elem.parentElement.offsetParent){
-        galleryButtons.push(elem);
+function updateOnBackgroundChange() {
+    const modalImage = gradioApp().getElementById("modalImage")
+    if (modalImage && modalImage.offsetParent) {
+        let allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
+        let currentButton = null
+        allcurrentButtons.forEach(function(elem) {
+            if (elem.parentElement.offsetParent) {
+                currentButton = elem;
+            }
+        })
+
+        if (modalImage.src != currentButton.children[0].src) {
+            modalImage.src = currentButton.children[0].src;
+            if (modalImage.style.display === 'none') {
+                modal.style.setProperty('background-image', `url(${modalImage.src})`)
+            }
+        }
     }
-  })
-
-  if(galleryButtons.length>1){
-      var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
-      var currentButton = null
-      allcurrentButtons.forEach(function(elem){
-        if(elem.parentElement.offsetParent){
-            currentButton = elem;
+}
+
+function modalImageSwitch(offset) {
+    var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all")
+    var galleryButtons = []
+    allgalleryButtons.forEach(function(elem) {
+        if (elem.parentElement.offsetParent) {
+            galleryButtons.push(elem);
         }
-      })
-
-      var result = -1
-      galleryButtons.forEach(function(v, i){ if(v==currentButton) { result = i } })
-
-      if(result != -1){
-        nextButton = galleryButtons[negmod((result+offset),galleryButtons.length)]
-        nextButton.click()
-        const modalImage = gradioApp().getElementById("modalImage");
-        const modal = gradioApp().getElementById("lightboxModal");
-        modalImage.src = nextButton.children[0].src;
-        if (modalImage.style.display === 'none') {
-            modal.style.setProperty('background-image', `url(${modalImage.src})`)
+    })
+
+    if (galleryButtons.length > 1) {
+        var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
+        var currentButton = null
+        allcurrentButtons.forEach(function(elem) {
+            if (elem.parentElement.offsetParent) {
+                currentButton = elem;
+            }
+        })
+
+        var result = -1
+        galleryButtons.forEach(function(v, i) {
+            if (v == currentButton) {
+                result = i
+            }
+        })
+
+        if (result != -1) {
+            nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
+            nextButton.click()
+            const modalImage = gradioApp().getElementById("modalImage");
+            const modal = gradioApp().getElementById("lightboxModal");
+            modalImage.src = nextButton.children[0].src;
+            if (modalImage.style.display === 'none') {
+                modal.style.setProperty('background-image', `url(${modalImage.src})`)
+            }
+            setTimeout(function() {
+                modal.focus()
+            }, 10)
         }
-        setTimeout( function(){modal.focus()},10)
-      }
-  }
+    }
 }
 
-function modalNextImage(event){
-  modalImageSwitch(1)
-  event.stopPropagation()
+function modalNextImage(event) {
+    modalImageSwitch(1)
+    event.stopPropagation()
 }
 
-function modalPrevImage(event){
-  modalImageSwitch(-1)  
-  event.stopPropagation()
+function modalPrevImage(event) {
+    modalImageSwitch(-1)
+    event.stopPropagation()
 }
 
-function modalKeyHandler(event){
+function modalKeyHandler(event) {
     switch (event.key) {
         case "ArrowLeft":
             modalPrevImage(event)
@@ -80,24 +105,22 @@ function modalKeyHandler(event){
     }
 }
 
-function showGalleryImage(){
+function showGalleryImage() {
     setTimeout(function() {
         fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain')
-        
-        if(fullImg_preview != null){
+
+        if (fullImg_preview != null) {
             fullImg_preview.forEach(function function_name(e) {
                 if (e.dataset.modded)
                     return;
                 e.dataset.modded = true;
                 if(e && e.parentElement.tagName == 'DIV'){
-
                     e.style.cursor='pointer'
-
                     e.addEventListener('click', function (evt) {
                         if(!opts.js_modal_lightbox) return;
                         modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
                         showModal(evt)
-                    },true);
+                    }, true);
                 }
             });
         }
@@ -105,21 +128,21 @@ function showGalleryImage(){
     }, 100);
 }
 
-function modalZoomSet(modalImage, enable){
-    if( enable ){
+function modalZoomSet(modalImage, enable) {
+    if (enable) {
         modalImage.classList.add('modalImageFullscreen');
-    } else{
+    } else {
         modalImage.classList.remove('modalImageFullscreen');
     }
 }
 
-function modalZoomToggle(event){
+function modalZoomToggle(event) {
     modalImage = gradioApp().getElementById("modalImage");
     modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'))
     event.stopPropagation()
 }
 
-function modalTileImageToggle(event){
+function modalTileImageToggle(event) {
     const modalImage = gradioApp().getElementById("modalImage");
     const modal = gradioApp().getElementById("lightboxModal");
     const isTiling = modalImage.style.display === 'none';
@@ -134,17 +157,18 @@ function modalTileImageToggle(event){
     event.stopPropagation()
 }
 
-function galleryImageHandler(e){
-    if(e && e.parentElement.tagName == 'BUTTON'){
+function galleryImageHandler(e) {
+    if (e && e.parentElement.tagName == 'BUTTON') {
         e.onclick = showGalleryImage;
     }
 }
 
-onUiUpdate(function(){
+onUiUpdate(function() {
     fullImg_preview = gradioApp().querySelectorAll('img.w-full')
-        if(fullImg_preview != null){
-	    fullImg_preview.forEach(galleryImageHandler);
+    if (fullImg_preview != null) {
+        fullImg_preview.forEach(galleryImageHandler);
     }
+    updateOnBackgroundChange();
 })
 
 document.addEventListener("DOMContentLoaded", function() {
@@ -152,13 +176,13 @@ document.addEventListener("DOMContentLoaded", function() {
     const modal = document.createElement('div')
     modal.onclick = closeModal;
     modal.id = "lightboxModal";
-    modal.tabIndex=0
+    modal.tabIndex = 0
     modal.addEventListener('keydown', modalKeyHandler, true)
 
     const modalControls = document.createElement('div')
     modalControls.className = 'modalControls gradio-container';
     modal.append(modalControls);
-    
+
     const modalZoom = document.createElement('span')
     modalZoom.className = 'modalZoom cursor';
     modalZoom.innerHTML = '&#10529;'
@@ -183,30 +207,30 @@ document.addEventListener("DOMContentLoaded", function() {
     const modalImage = document.createElement('img')
     modalImage.id = 'modalImage';
     modalImage.onclick = closeModal;
-    modalImage.tabIndex=0
+    modalImage.tabIndex = 0
     modalImage.addEventListener('keydown', modalKeyHandler, true)
     modal.appendChild(modalImage)
 
     const modalPrev = document.createElement('a')
     modalPrev.className = 'modalPrev';
     modalPrev.innerHTML = '&#10094;'
-    modalPrev.tabIndex=0
-    modalPrev.addEventListener('click',modalPrevImage,true);
+    modalPrev.tabIndex = 0
+    modalPrev.addEventListener('click', modalPrevImage, true);
     modalPrev.addEventListener('keydown', modalKeyHandler, true)
     modal.appendChild(modalPrev)
 
     const modalNext = document.createElement('a')
     modalNext.className = 'modalNext';
     modalNext.innerHTML = '&#10095;'
-    modalNext.tabIndex=0
-    modalNext.addEventListener('click',modalNextImage,true);
+    modalNext.tabIndex = 0
+    modalNext.addEventListener('click', modalNextImage, true);
     modalNext.addEventListener('keydown', modalKeyHandler, true)
 
     modal.appendChild(modalNext)
 
 
     gradioApp().getRootNode().appendChild(modal)
-    
+
     document.body.appendChild(modalFragment);
-	
+
 });
-- 
cgit v1.2.1


From 9ecea0a8d6bdc434755e11128487fd62f1ff130f Mon Sep 17 00:00:00 2001
From: Artem Zagidulin <p0chta@yandex.ru>
Date: Sun, 9 Oct 2022 16:14:56 +0300
Subject: fix missing png info when Extras Batch Process

---
 modules/extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/extras.py b/modules/extras.py
index 39dd3806..41e8612c 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -29,7 +29,7 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
     if extras_mode == 1:
         #convert file to pillow image
         for img in image_folder:
-            image = Image.fromarray(np.array(Image.open(img)))
+            image = Image.open(img)
             imageArr.append(image)
             imageNameArr.append(os.path.splitext(img.orig_name)[0])
     else:
-- 
cgit v1.2.1


From a2d70f25bf51264d8d68f4f36937b390f79334a7 Mon Sep 17 00:00:00 2001
From: supersteve3d <39339941+supersteve3d@users.noreply.github.com>
Date: Sun, 9 Oct 2022 23:40:18 +0800
Subject: Add files via upload

Updated txt2img screenshot (UI as of Oct 9th) for github webui / README.md
---
 txt2img_Screenshot.png | Bin 539132 -> 337094 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/txt2img_Screenshot.png b/txt2img_Screenshot.png
index fedd538e..6e2759a4 100644
Binary files a/txt2img_Screenshot.png and b/txt2img_Screenshot.png differ
-- 
cgit v1.2.1


From 45bf9a6264b3507473e02cc3f9aa36559f24aca2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 18:58:55 +0300
Subject: added clip skip to XY plot

---
 scripts/xy_grid.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index c89ca1a9..7b0d9083 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -83,6 +83,10 @@ def apply_hypernetwork(p, x, xs):
     hypernetwork.load_hypernetwork(x)
 
 
+def apply_clip_skip(p, x, xs):
+    opts.data["CLIP_ignore_last_layers"] = x
+
+
 def format_value_add_label(p, opt, x):
     if type(x) == float:
         x = round(x, 8)
@@ -134,6 +138,7 @@ axis_options = [
     AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
     AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
     AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
+    AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label),
     AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
 ]
 
@@ -201,6 +206,7 @@ class Script(scripts.Script):
             modules.processing.fix_seed(p)
 
         p.batch_size = 1
+        CLIP_ignore_last_layers = opts.CLIP_ignore_last_layers
 
         def process_axis(opt, vals):
             if opt.label == 'Nothing':
@@ -321,4 +327,6 @@ class Script(scripts.Script):
 
         hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
 
+        opts.data["CLIP_ignore_last_layers"] = CLIP_ignore_last_layers
+
         return processed
-- 
cgit v1.2.1


From 6c383d2e82045fc4475d665f83bdeeac8fd844d9 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 22:24:07 +0300
Subject: show model selection setting on top of page

---
 modules/shared.py |  5 +++--
 modules/ui.py     | 54 +++++++++++++++++++++++++++++++++++++++++++++---------
 style.css         |  9 +++++++++
 3 files changed, 57 insertions(+), 11 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 3d7f08e1..270fa402 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -131,13 +131,14 @@ def realesrgan_models_names():
 
 
 class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None):
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False):
         self.default = default
         self.label = label
         self.component = component
         self.component_args = component_args
         self.onchange = onchange
         self.section = None
+        self.show_on_main_page = show_on_main_page
 
 
 def options_section(section_identifier, options_dict):
@@ -214,7 +215,7 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True),
     "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
diff --git a/modules/ui.py b/modules/ui.py
index dad509f3..2231a8ed 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1175,10 +1175,13 @@ Requested path was: {f}
         changed = 0
 
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            if not opts.same_type(value, opts.data_labels[key].default):
-                return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
+            if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
+                return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
 
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
+            if comp == dummy_component:
+                continue
+
             comp_args = opts.data_labels[key].component_args
             if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
                 continue
@@ -1196,6 +1199,21 @@ Requested path was: {f}
 
         return f'{changed} settings changed.', opts.dumpjson()
 
+    def run_settings_single(value, key):
+        if not opts.same_type(value, opts.data_labels[key].default):
+            return gr.update(visible=True), opts.dumpjson()
+
+        oldval = opts.data.get(key, None)
+        opts.data[key] = value
+
+        if oldval != value:
+            if opts.data_labels[key].onchange is not None:
+                opts.data_labels[key].onchange()
+
+        opts.save(shared.config_filename)
+
+        return gr.update(value=value), opts.dumpjson()
+
     with gr.Blocks(analytics_enabled=False) as settings_interface:
         settings_submit = gr.Button(value="Apply settings", variant='primary')
         result = gr.HTML()
@@ -1203,6 +1221,8 @@ Requested path was: {f}
         settings_cols = 3
         items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
 
+        quicksettings_list = []
+
         cols_displayed = 0
         items_displayed = 0
         previous_section = None
@@ -1225,10 +1245,14 @@ Requested path was: {f}
 
                     gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
 
-                component = create_setting_component(k)
-                component_dict[k] = component
-                components.append(component)
-                items_displayed += 1
+                if item.show_on_main_page:
+                    quicksettings_list.append((i, k, item))
+                    components.append(dummy_component)
+                else:
+                    component = create_setting_component(k)
+                    component_dict[k] = component
+                    components.append(component)
+                    items_displayed += 1
 
         request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
         request_notifications.click(
@@ -1242,7 +1266,6 @@ Requested path was: {f}
             reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
             restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
 
-
         def reload_scripts():
             modules.scripts.reload_script_body_only()
 
@@ -1289,7 +1312,11 @@ Requested path was: {f}
         css += css_hide_progressbar
 
     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
-        
+        with gr.Row(elem_id="quicksettings"):
+            for i, k, item in quicksettings_list:
+                component = create_setting_component(k)
+                component_dict[k] = component
+
         settings_interface.gradio_ref = demo
         
         with gr.Tabs() as tabs:
@@ -1306,7 +1333,16 @@ Requested path was: {f}
             inputs=components,
             outputs=[result, text_settings],
         )
-        
+
+        for i, k, item in quicksettings_list:
+            component = component_dict[k]
+
+            component.change(
+                fn=lambda value, k=k: run_settings_single(value, key=k),
+                inputs=[component],
+                outputs=[component, text_settings],
+            )
+
         def modelmerger(*args):
             try:
                 results = modules.extras.run_modelmerger(*args)
diff --git a/style.css b/style.css
index 101d2052..28160bdf 100644
--- a/style.css
+++ b/style.css
@@ -453,3 +453,12 @@ input[type="range"]{
 .context-menu-items a:hover{
     background: #a55000;
 }
+
+#quicksettings > div{
+    border: none;
+}
+
+#quicksettings > div > div{
+    max-width: 32em;
+    padding: 0;
+}
-- 
cgit v1.2.1


From e59c66c0088422b27f64b401ef42c242f836725a Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sat, 8 Oct 2022 16:32:05 -0400
Subject: Optimized code for Ignoring last CLIP layers

---
 modules/sd_hijack.py | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index f12a9696..4a2d2153 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -282,14 +282,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
         tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
 
-        tmp = -opts.CLIP_ignore_last_layers
-        if (opts.CLIP_ignore_last_layers == 0):
-            outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
-            z = outputs.last_hidden_state
-        else:
-            outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp)
-            z = outputs.hidden_states[tmp]
-            z = self.wrapped.transformer.text_model.final_layer_norm(z)
+        tmp = -opts.CLIP_stop_at_last_layers
+        outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp)
+        z = outputs.hidden_states[tmp]
+        z = self.wrapped.transformer.text_model.final_layer_norm(z)
 
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
-- 
cgit v1.2.1


From a14f7bf113a2af9e06a1c4d06c2efa244f9c5730 Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sat, 8 Oct 2022 16:33:06 -0400
Subject: Corrected CLIP Layer Ignore description and updated its range to the
 max possible

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 270fa402..1995a99a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -225,7 +225,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
-    'CLIP_ignore_last_layers': OptionInfo(0, "Ignore last layers of CLIP model", gr.Slider, {"minimum": 0, "maximum": 5, "step": 1}),
+    'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-- 
cgit v1.2.1


From ec2bd9be75865c9f3a8c898163ab381688c03b6e Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sat, 8 Oct 2022 17:28:42 -0400
Subject: Fix issues with CLIP ignore option name change

---
 modules/processing.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 04aed989..92a105a2 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -129,7 +129,7 @@ class Processed:
         self.index_of_first_image = index_of_first_image
         self.styles = p.styles
         self.job_timestamp = state.job_timestamp
-        self.clip_skip = opts.CLIP_ignore_last_layers
+        self.clip_skip = opts.CLIP_stop_at_last_layers
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -274,7 +274,7 @@ def fix_seed(p):
 def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
     index = position_in_batch + iteration * p.batch_size
 
-    clip_skip = getattr(p, 'clip_skip', opts.CLIP_ignore_last_layers)
+    clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
 
     generation_params = {
         "Steps": p.steps,
-- 
cgit v1.2.1


From ad3ae441081155dcd4fde805279e5082ca264695 Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sun, 9 Oct 2022 04:32:40 -0400
Subject: Updated code for legibility

---
 modules/sd_hijack.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 4a2d2153..7793d25b 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -284,8 +284,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
         tmp = -opts.CLIP_stop_at_last_layers
         outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp)
-        z = outputs.hidden_states[tmp]
-        z = self.wrapped.transformer.text_model.final_layer_norm(z)
+        if tmp < -1:
+            z = outputs.hidden_states[tmp]
+            z = self.wrapped.transformer.text_model.final_layer_norm(z)
+        else:
+            z = outputs.last_hidden_state
 
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
-- 
cgit v1.2.1


From 1824e9ee3ab4f94aee8908a62ea2569a01aeb3d7 Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sun, 9 Oct 2022 14:15:43 -0400
Subject: Removed unnecessary tmp variable

---
 modules/sd_hijack.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 7793d25b..437acce4 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -282,10 +282,9 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
         tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
 
-        tmp = -opts.CLIP_stop_at_last_layers
-        outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp)
-        if tmp < -1:
-            z = outputs.hidden_states[tmp]
+        outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=-opts.CLIP_stop_at_last_layers)
+        if opts.CLIP_stop_at_last_layers > 1:
+            z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
             z = self.wrapped.transformer.text_model.final_layer_norm(z)
         else:
             z = outputs.last_hidden_state
-- 
cgit v1.2.1


From 84ddd44113b36062e8ba6cb2e5db0fce4f48efb8 Mon Sep 17 00:00:00 2001
From: Fampai <unknown>
Date: Sun, 9 Oct 2022 14:57:17 -0400
Subject: Clip skip variable name change breaks x/y plot script. This fixes
 that

---
 scripts/xy_grid.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 7b0d9083..771eb8e4 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -84,7 +84,7 @@ def apply_hypernetwork(p, x, xs):
 
 
 def apply_clip_skip(p, x, xs):
-    opts.data["CLIP_ignore_last_layers"] = x
+    opts.data["CLIP_stop_at_last_layers"] = x
 
 
 def format_value_add_label(p, opt, x):
@@ -206,7 +206,7 @@ class Script(scripts.Script):
             modules.processing.fix_seed(p)
 
         p.batch_size = 1
-        CLIP_ignore_last_layers = opts.CLIP_ignore_last_layers
+        CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
 
         def process_axis(opt, vals):
             if opt.label == 'Nothing':
@@ -327,6 +327,6 @@ class Script(scripts.Script):
 
         hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
 
-        opts.data["CLIP_ignore_last_layers"] = CLIP_ignore_last_layers
+        opts.data["CLIP_stop_at_last_layers"] = CLIP_stop_at_last_layers
 
         return processed
-- 
cgit v1.2.1


From 8d340cfb884e1dbff5b6f477f4ecf7d104279115 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 22:30:59 +0300
Subject: do not add clip skip to parameters if it's 1 or 0

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 92a105a2..94d2dd62 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -293,7 +293,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
-        "Clip skip": None if clip_skip==0 else clip_skip,
+        "Clip skip": None if clip_skip <= 1 else clip_skip,
     }
 
     generation_params.update(p.extra_generation_params)
-- 
cgit v1.2.1


From fa0c5eb81b72bc1e562d0b9bbd92f30945d78b4e Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 20:41:22 +0100
Subject: Add pretty image captioning functions

---
 modules/images.py | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/modules/images.py b/modules/images.py
index 29c5ee24..10963dc7 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -428,3 +428,34 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             file.write(info + "\n")
 
     return fullfn
+
+def addCaptionLines(lines,image,initialx,textfont):
+    draw = ImageDraw.Draw(image)
+    hstart =initialx
+    for fill,line in lines:
+        fontSize = 32
+        font = ImageFont.truetype(textfont, fontSize)
+        _,_,w, h = draw.textbbox((0,0),line,font=font)
+        fontSize =  min( int(fontSize * ((image.size[0]-35)/w) ), 28)
+        font = ImageFont.truetype(textfont, fontSize)
+        _,_,w,h = draw.textbbox((0,0),line,font=font)
+        draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill)
+        hstart += h
+    return hstart
+
+def captionImge(image,prelines,postlines,background=(51, 51, 51),font=None):
+    if font is None:
+        try:
+            font = ImageFont.truetype(opts.font or Roboto, fontsize)
+            font = opts.font or Roboto
+        except Exception:
+            font = Roboto
+
+    sampleImage = image
+    background = Image.new("RGBA", (sampleImage.size[0],sampleImage.size[1]+1024), background)
+    hoffset = addCaptionLines(prelines,background,5,font)+16
+    background.paste(sampleImage,(0,hoffset))
+    hoffset = hoffset+sampleImage.size[1]+8
+    hoffset = addCaptionLines(postlines,background,hoffset,font)
+    background = background.crop((0,0,sampleImage.size[0],hoffset+8))
+    return background
-- 
cgit v1.2.1


From a65476718f08a35f527b973ef731e6f488bace5e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 9 Oct 2022 23:38:49 +0300
Subject: add DoubleStorage to list of allowed classes for pickle

---
 modules/safe.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/safe.py b/modules/safe.py
index 2d2c1371..4d06f2a5 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -27,7 +27,7 @@ class RestrictedUnpickler(pickle.Unpickler):
             return getattr(collections, name)
         if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
             return getattr(torch._utils, name)
-        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage']:
+        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']:
             return getattr(torch, name)
         if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
             return getattr(torch.nn.modules.container, name)
-- 
cgit v1.2.1


From 03694e1f9915e34cf7d9a31073f1a1a9def2909f Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 21:58:14 +0100
Subject: add embedding load and save from b64 json

---
 modules/textual_inversion/textual_inversion.py | 30 ++++++++++++++++++--------
 1 file changed, 21 insertions(+), 9 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index f6316020..1b7f8906 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,9 +7,11 @@ import tqdm
 import html
 import datetime
 
-from PIL import Image, PngImagePlugin
+from PIL import Image,PngImagePlugin
+from ..images import captionImge
+import numpy as np
 import base64
-from io import BytesIO
+import json
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
@@ -87,9 +89,9 @@ class EmbeddingDatabase:
 
             if filename.upper().endswith('.PNG'):
                 embed_image = Image.open(path)
-                if 'sd-embedding' in embed_image.text:
-                    embeddingData = base64.b64decode(embed_image.text['sd-embedding'])
-                    data = torch.load(BytesIO(embeddingData), map_location="cpu")
+                if 'sd-ti-embedding' in embed_image.text:
+                    data = embeddingFromB64(embed_image.text['sd-ti-embedding'])
+                    name = data.get('name',name)
             else:
                 data = torch.load(path, map_location="cpu")
 
@@ -258,13 +260,23 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
             if save_image_with_stored_embedding:
                 info = PngImagePlugin.PngInfo()
-                info.add_text("sd-embedding", base64.b64encode(open(last_saved_file,'rb').read()))
-                image.save(last_saved_image, "PNG", pnginfo=info)
+                data = torch.load(last_saved_file)
+                info.add_text("sd-ti-embedding", embeddingToB64(data))
+
+                pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))]
+                
+                caption_checkpoint_hash = data.get('sd_checkpoint','UNK')
+                caption_checkpoint_hash = caption_checkpoint_hash.upper() if caption_checkpoint_hash else 'UNK'
+                caption_stepcount = data.get('step',0)
+                caption_stepcount = caption_stepcount if caption_stepcount else 0
+
+                post_lines = [((240, 223, 175),"Trained against checkpoint [{}] for {} steps".format(caption_checkpoint_hash,
+                                                                                                  caption_stepcount))]
+                captioned_image = captionImge(image,prelines=pre_lines,postlines=post_lines)
+                captioned_image.save(last_saved_image, "PNG", pnginfo=info)
             else:
                 image.save(last_saved_image)
 
-
-
             last_saved_image += f", prompt: {text}"
 
         shared.state.job_no = embedding.step
-- 
cgit v1.2.1


From 969bd8256e5b4f1007d3cc653723d4ad50a92528 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 22:02:28 +0100
Subject: add alternate checkpoint hash source

---
 modules/textual_inversion/textual_inversion.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 1b7f8906..d7813084 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -265,8 +265,11 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
                 pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))]
                 
-                caption_checkpoint_hash = data.get('sd_checkpoint','UNK')
-                caption_checkpoint_hash = caption_checkpoint_hash.upper() if caption_checkpoint_hash else 'UNK'
+                caption_checkpoint_hash = data.get('sd_checkpoint')
+                if caption_checkpoint_hash is None:
+                    caption_checkpoint_hash = data.get('hash')
+                caption_checkpoint_hash = caption_checkpoint_hash.upper() if caption_checkpoint_hash else 'UNKNOWN'
+                
                 caption_stepcount = data.get('step',0)
                 caption_stepcount = caption_stepcount if caption_stepcount else 0
 
-- 
cgit v1.2.1


From 5d12ec82d3e13f5ff4c55db2930e4e10aed7015a Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 22:05:09 +0100
Subject: add encoder and decoder classes

---
 modules/textual_inversion/textual_inversion.py | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index d7813084..44d4e08b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -16,6 +16,27 @@ import json
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
+class EmbeddingEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, torch.Tensor):
+            return {'EMBEDDINGTENSOR':obj.cpu().detach().numpy().tolist()}
+        return json.JSONEncoder.default(self, o)
+
+class EmbeddingDecoder(json.JSONDecoder):
+    def __init__(self, *args, **kwargs):
+        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
+    def object_hook(self, d):
+        if 'EMBEDDINGTENSOR' in d:
+            return torch.from_numpy(np.array(d['EMBEDDINGTENSOR']))
+        return d
+
+def embeddingToB64(data):
+    d = json.dumps(data,cls=EmbeddingEncoder)
+    return base64.b64encode(d.encode())
+
+def EmbeddingFromB64(data):
+    d = base64.b64decode(data)
+    return json.loads(d,cls=EmbeddingDecoder)
 
 class Embedding:
     def __init__(self, vec, name, step=None):
-- 
cgit v1.2.1


From d0184b8f76ce492da699f1926f34b57cd095242e Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 22:06:12 +0100
Subject: change json tensor key name

---
 modules/textual_inversion/textual_inversion.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 44d4e08b..ae8d207d 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -19,15 +19,15 @@ import modules.textual_inversion.dataset
 class EmbeddingEncoder(json.JSONEncoder):
     def default(self, obj):
         if isinstance(obj, torch.Tensor):
-            return {'EMBEDDINGTENSOR':obj.cpu().detach().numpy().tolist()}
+            return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()}
         return json.JSONEncoder.default(self, o)
 
 class EmbeddingDecoder(json.JSONDecoder):
     def __init__(self, *args, **kwargs):
         json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
     def object_hook(self, d):
-        if 'EMBEDDINGTENSOR' in d:
-            return torch.from_numpy(np.array(d['EMBEDDINGTENSOR']))
+        if 'TORCHTENSOR' in d:
+            return torch.from_numpy(np.array(d['TORCHTENSOR']))
         return d
 
 def embeddingToB64(data):
-- 
cgit v1.2.1


From 66846105103cfc282434d0dc2102910160b7a633 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 22:06:42 +0100
Subject: correct case on embeddingFromB64

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index ae8d207d..d2b95fa3 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -34,7 +34,7 @@ def embeddingToB64(data):
     d = json.dumps(data,cls=EmbeddingEncoder)
     return base64.b64encode(d.encode())
 
-def EmbeddingFromB64(data):
+def embeddingFromB64(data):
     d = base64.b64decode(data)
     return json.loads(d,cls=EmbeddingDecoder)
 
-- 
cgit v1.2.1


From 96f1e6be59316ec640cab2435fa95b3688194906 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 22:14:50 +0100
Subject: source checkpoint hash from current checkpoint

---
 modules/textual_inversion/textual_inversion.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index d2b95fa3..b16fa84e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -286,10 +286,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
                 pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))]
                 
-                caption_checkpoint_hash = data.get('sd_checkpoint')
-                if caption_checkpoint_hash is None:
-                    caption_checkpoint_hash = data.get('hash')
-                caption_checkpoint_hash = caption_checkpoint_hash.upper() if caption_checkpoint_hash else 'UNKNOWN'
+                checkpoint = sd_models.select_checkpoint()
+                caption_checkpoint_hash = checkpoint.hash
                 
                 caption_stepcount = data.get('step',0)
                 caption_stepcount = caption_stepcount if caption_stepcount else 0
-- 
cgit v1.2.1


From 01fd9cf0d28d8b71a113ab1aa62accfe7f0d9c51 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 9 Oct 2022 22:17:02 +0100
Subject: change source of step count

---
 modules/textual_inversion/textual_inversion.py | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index b16fa84e..e4f339b8 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -285,15 +285,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
                 info.add_text("sd-ti-embedding", embeddingToB64(data))
 
                 pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))]
-                
                 checkpoint = sd_models.select_checkpoint()
-                caption_checkpoint_hash = checkpoint.hash
-                
-                caption_stepcount = data.get('step',0)
-                caption_stepcount = caption_stepcount if caption_stepcount else 0
-
-                post_lines = [((240, 223, 175),"Trained against checkpoint [{}] for {} steps".format(caption_checkpoint_hash,
-                                                                                                  caption_stepcount))]
+                post_lines = [((240, 223, 175),"Trained against checkpoint [{}] for {} steps".format(checkpoint.hash,
+                                                                                                     embedding.step))]
                 captioned_image = captionImge(image,prelines=pre_lines,postlines=post_lines)
                 captioned_image.save(last_saved_image, "PNG", pnginfo=info)
             else:
-- 
cgit v1.2.1


From 45fbd1c5fec887988ab555aac75a999d4f3aff40 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 10 Oct 2022 00:42:18 +0300
Subject: remove background for quicksettings row (for dark theme)

---
 style.css | 1 +
 1 file changed, 1 insertion(+)

diff --git a/style.css b/style.css
index 28160bdf..c0c3f2bb 100644
--- a/style.css
+++ b/style.css
@@ -456,6 +456,7 @@ input[type="range"]{
 
 #quicksettings > div{
     border: none;
+    background: none;
 }
 
 #quicksettings > div > div{
-- 
cgit v1.2.1


From 0ac3a07eecbd7b98f3a19d01dc46f02dcda3443b Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 00:05:36 +0100
Subject: add caption image with overlay

---
 modules/images.py | 46 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)

diff --git a/modules/images.py b/modules/images.py
index 10963dc7..4a4fc977 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -459,3 +459,49 @@ def captionImge(image,prelines,postlines,background=(51, 51, 51),font=None):
     hoffset = addCaptionLines(postlines,background,hoffset,font)
     background = background.crop((0,0,sampleImage.size[0],hoffset+8))
     return background
+
+def captionImageOverlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None):
+    from math import cos
+
+    image = srcimage.copy()
+
+    if textfont is None:
+        try:
+            textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
+            textfont = opts.font or Roboto
+        except Exception:
+            textfont = Roboto
+
+    factor = 1.5
+    gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0))
+    for y in range(image.size[1]):
+        mag = 1-cos(y/image.size[1]*factor)
+        mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
+        gradient.putpixel((0, y), (0,0,0,int(mag*255)))
+    image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
+
+    draw = ImageDraw.Draw(image)
+    fontSize = 32
+    font = ImageFont.truetype(textfont, fontSize)
+    padding = 10
+
+    _,_,w, h = draw.textbbox((0,0),title,font=font)
+    fontSize =  min( int(fontSize * (((image.size[0]*0.75)-(padding*4))/w) ), 72)
+    font = ImageFont.truetype(textfont, fontSize)
+    _,_,w,h = draw.textbbox((0,0),title,font=font)
+    draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230))
+
+    _,_,w, h = draw.textbbox((0,0),footerLeft,font=font)
+    fontSizeleft =  min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72)
+    _,_,w, h = draw.textbbox((0,0),footerMid,font=font)
+    fontSizemid =  min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72)
+    _,_,w, h = draw.textbbox((0,0),footerRight,font=font)
+    fontSizeright =  min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72)
+
+    font = ImageFont.truetype(textfont, min(fontSizeleft,fontSizemid,fontSizeright))
+
+    draw.text((padding,image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255,255,255,230))
+    draw.text((image.size[0]/2,image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255,255,255,230))
+    draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230))
+
+    return image
-- 
cgit v1.2.1


From d6a599ef9ba18a66ae79b50f2945af5788fdda8f Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 00:07:52 +0100
Subject: change caption method

---
 modules/textual_inversion/textual_inversion.py | 30 ++++++++++++++++++--------
 1 file changed, 21 insertions(+), 9 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e4f339b8..21596e78 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -8,7 +8,7 @@ import html
 import datetime
 
 from PIL import Image,PngImagePlugin
-from ..images import captionImge
+from ..images import captionImageOverlay
 import numpy as np
 import base64
 import json
@@ -212,6 +212,12 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
     else:
         images_dir = None
 
+    if create_image_every > 0 and save_image_with_stored_embedding:
+        images_embeds_dir = os.path.join(log_directory, "image_embeddings")
+        os.makedirs(images_embeds_dir, exist_ok=True)
+    else:
+        images_embeds_dir = None
+        
     cond_model = shared.sd_model.cond_stage_model
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
@@ -279,19 +285,25 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
             shared.state.current_image = image
 
-            if save_image_with_stored_embedding:
+            if save_image_with_stored_embedding and os.path.exists(last_saved_file):
+                
+                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
+
                 info = PngImagePlugin.PngInfo()
                 data = torch.load(last_saved_file)
                 info.add_text("sd-ti-embedding", embeddingToB64(data))
 
-                pre_lines = [((255, 207, 175),"<{}>".format(data.get('name','???')))]
+                title = "<{}>".format(data.get('name','???'))
                 checkpoint = sd_models.select_checkpoint()
-                post_lines = [((240, 223, 175),"Trained against checkpoint [{}] for {} steps".format(checkpoint.hash,
-                                                                                                     embedding.step))]
-                captioned_image = captionImge(image,prelines=pre_lines,postlines=post_lines)
-                captioned_image.save(last_saved_image, "PNG", pnginfo=info)
-            else:
-                image.save(last_saved_image)
+                footer_left = checkpoint.model_name
+                footer_mid = '[{}]'.format(checkpoint.hash)
+                footer_right = '[{}]'.format(embedding.step)
+
+                captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right)
+
+                captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+            
+            image.save(last_saved_image)
 
             last_saved_image += f", prompt: {text}"
 
-- 
cgit v1.2.1


From e2c2925eb4d634b186de2c76798162ec56e2f869 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 00:12:53 +0100
Subject: remove braces from steps

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 21596e78..9a18ee5c 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -297,7 +297,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
                 checkpoint = sd_models.select_checkpoint()
                 footer_left = checkpoint.model_name
                 footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '[{}]'.format(embedding.step)
+                footer_right = '{}'.format(embedding.step)
 
                 captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right)
 
-- 
cgit v1.2.1


From 6435691bb11c5a35703720bfd2a875f24c066f86 Mon Sep 17 00:00:00 2001
From: Justin Maier <Zipp425@gmail.com>
Date: Sun, 9 Oct 2022 19:26:52 -0600
Subject: Add "Scale to" option to Extras

---
 javascript/ui.js  |  3 ++-
 modules/extras.py | 28 +++++++++++++++++++++++-----
 modules/ui.py     | 38 +++++++++++++++++++++++++-------------
 3 files changed, 50 insertions(+), 19 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index b1053201..4100944e 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -101,7 +101,8 @@ function create_tab_index_args(tabId, args){
 }
 
 function get_extras_tab_index(){
-    return create_tab_index_args('mode_extras', arguments)
+    const [,,...args] = [...arguments]
+    return [get_tab_index('mode_extras'), get_tab_index('extras_resize_mode'), ...args]
 }
 
 function create_submit_args(args){
diff --git a/modules/extras.py b/modules/extras.py
index 41e8612c..83ca7049 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -1,3 +1,4 @@
+import math
 import os
 
 import numpy as np
@@ -19,7 +20,7 @@ import gradio as gr
 cached_images = {}
 
 
-def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
+def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
     devices.torch_gc()
 
     imageArr = []
@@ -67,8 +68,23 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
             info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
             image = res
 
+        if resize_mode == 1:
+            upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
+            crop_info = " (crop)" if upscaling_crop else ""
+            info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
+
+            def crop_upscaled_center(image, resize_w, resize_h):
+                left = int(math.ceil((image.width - resize_w) / 2))
+                right = image.width - int(math.floor((image.width - resize_w) / 2))
+                top = int(math.ceil((image.height - resize_h) / 2))
+                bottom = image.height - int(math.floor((image.height - resize_h) / 2))
+
+                image = image.crop((left, top, right, bottom))
+                return image
+
+
         if upscaling_resize != 1.0:
-            def upscale(image, scaler_index, resize):
+            def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
                 small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
                 pixels = tuple(np.array(small).flatten().tolist())
                 key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
@@ -77,15 +93,17 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
                 if c is None:
                     upscaler = shared.sd_upscalers[scaler_index]
                     c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+                    if mode == 1 and crop:
+                        c = crop_upscaled_center(c, resize_w, resize_h)
                     cached_images[key] = c
 
                 return c
 
             info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
-            res = upscale(image, extras_upscaler_1, upscaling_resize)
+            res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
 
             if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-                res2 = upscale(image, extras_upscaler_2, upscaling_resize)
+                res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
                 info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
                 res = Image.blend(res, res2, extras_upscaler_2_visibility)
 
@@ -190,7 +208,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
             theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
-    
+
     for key in theta_1.keys():
         if 'model' in key and key not in theta_0:
             theta_0[key] = theta_1[key]
diff --git a/modules/ui.py b/modules/ui.py
index 2231a8ed..4bb2892b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -101,7 +101,7 @@ def send_gradio_gallery_to_image(x):
 
 
 def save_files(js_data, images, do_make_zip, index):
-    import csv    
+    import csv
     filenames = []
     fullfns = []
 
@@ -551,7 +551,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                     with gr.Row():
                         do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-                    
+
                     with gr.Row():
                         download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
@@ -739,7 +739,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                     with gr.Row():
                         do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-                    
+
                     with gr.Row():
                         download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
@@ -903,7 +903,15 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.TabItem('Batch Process'):
                         image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
 
-                upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
+                with gr.Tabs(elem_id="extras_resize_mode"):
+                    with gr.TabItem('Scale by'):
+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
+                    with gr.TabItem('Scale to'):
+                        with gr.Group():
+                            with gr.Row():
+                                upscaling_resize_w = gr.Number(label="Width", value=512)
+                                upscaling_resize_h = gr.Number(label="Height", value=512)
+                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
 
                 with gr.Group():
                     extras_upscaler_1 = gr.Radio(label='Upscaler 1', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
@@ -934,6 +942,7 @@ def create_ui(wrap_gradio_gpu_call):
             fn=wrap_gradio_gpu_call(modules.extras.run_extras),
             _js="get_extras_tab_index",
             inputs=[
+                dummy_component,
                 dummy_component,
                 extras_image,
                 image_batch,
@@ -941,6 +950,9 @@ def create_ui(wrap_gradio_gpu_call):
                 codeformer_visibility,
                 codeformer_weight,
                 upscaling_resize,
+                upscaling_resize_w,
+                upscaling_resize_h,
+                upscaling_crop,
                 extras_upscaler_1,
                 extras_upscaler_2,
                 extras_upscaler_2_visibility,
@@ -951,14 +963,14 @@ def create_ui(wrap_gradio_gpu_call):
                 html_info,
             ]
         )
-     
+
         extras_send_to_img2img.click(
             fn=lambda x: image_from_url_text(x),
             _js="extract_image_from_gallery_img2img",
             inputs=[result_images],
             outputs=[init_img],
         )
-        
+
         extras_send_to_inpaint.click(
             fn=lambda x: image_from_url_text(x),
             _js="extract_image_from_gallery_img2img",
@@ -1286,7 +1298,7 @@ Requested path was: {f}
             outputs=[],
             _js='function(){restart_reload()}'
         )
-        
+
         if column is not None:
             column.__exit__()
 
@@ -1318,12 +1330,12 @@ Requested path was: {f}
                 component_dict[k] = component
 
         settings_interface.gradio_ref = demo
-        
+
         with gr.Tabs() as tabs:
             for interface, label, ifid in interfaces:
                 with gr.TabItem(label, id=ifid):
                     interface.render()
-        
+
         if os.path.exists(os.path.join(script_path, "notification.mp3")):
             audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
 
@@ -1456,10 +1468,10 @@ Requested path was: {f}
 
             if getattr(obj,'custom_script_source',None) is not None:
               key = 'customscript/' + obj.custom_script_source + '/' + key
-            
+
             if getattr(obj, 'do_not_save_to_config', False):
                 return
-            
+
             saved_value = ui_settings.get(key, None)
             if saved_value is None:
                 ui_settings[key] = getattr(obj, field)
@@ -1483,10 +1495,10 @@ Requested path was: {f}
 
         if type(x) == gr.Textbox:
             apply_field(x, 'value')
-        
+
         if type(x) == gr.Number:
             apply_field(x, 'value')
-        
+
     visit(txt2img_interface, loadsave, "txt2img")
     visit(img2img_interface, loadsave, "img2img")
     visit(extras_interface, loadsave, "extras")
-- 
cgit v1.2.1


From cc92dc1f8d73dd4d574c4c8ccab78b7fc61e440b Mon Sep 17 00:00:00 2001
From: ssysm <me@theeditorstudio.com>
Date: Sun, 9 Oct 2022 23:17:29 -0400
Subject: add vae path args

---
 modules/sd_models.py | 2 +-
 modules/shared.py    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index cb3982b1..b6979432 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -147,7 +147,7 @@ def load_model_weights(model, checkpoint_info):
 
     devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
 
-    vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
+    vae_file = shared.cmd_opts.vae_path or os.path.splitext(checkpoint_file)[0] + ".vae.pt"
     if os.path.exists(vae_file):
         print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location="cpu")
diff --git a/modules/shared.py b/modules/shared.py
index 2dc092d6..52ccfa6e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -64,7 +64,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
-
+parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 
 cmd_opts = parser.parse_args()
 
-- 
cgit v1.2.1


From 1f92336be768d235c18a82acb2195b7135101ae7 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Sun, 9 Oct 2022 23:58:18 -0500
Subject: refactored the deepbooru module to improve speed on running multiple
 interogations in a row.  Added the option to generate deepbooru tags for
 textual inversion preproccessing.

---
 modules/deepbooru.py                    | 84 +++++++++++++++++++++++++--------
 modules/textual_inversion/preprocess.py | 22 ++++++++-
 modules/ui.py                           | 52 ++++++++++++++------
 3 files changed, 122 insertions(+), 36 deletions(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 7e3c0618..cee4a3b4 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -1,21 +1,74 @@
 import os.path
 from concurrent.futures import ProcessPoolExecutor
-from multiprocessing import get_context
+import multiprocessing
 
 
-def _load_tf_and_return_tags(pil_image, threshold):
+def get_deepbooru_tags(pil_image, threshold=0.5):
+    """
+    This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
+    """
+    from modules import shared  # prevents circular reference
+    create_deepbooru_process(threshold)
+    shared.deepbooru_process_return["value"] = -1
+    shared.deepbooru_process_queue.put(pil_image)
+    while shared.deepbooru_process_return["value"] == -1:
+        time.sleep(0.2)
+    release_process()
+    return ret
+
+
+def deepbooru_process(queue, deepbooru_process_return, threshold):
+    model, tags = get_deepbooru_tags_model()
+    while True: # while process is running, keep monitoring queue for new image
+        pil_image = queue.get()
+        if pil_image == "QUIT":
+            break
+        else:
+            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold)
+
+
+def create_deepbooru_process(threshold=0.5):
+    """
+    Creates deepbooru process.  A queue is created to send images into the process.  This enables multiple images
+    to be processed in a row without reloading the model or creating a new process.  To return the data, a shared
+    dictionary is created to hold the tags created.  To wait for tags to be returned, a value of -1 is assigned
+    to the dictionary and the method adding the image to the queue should wait for this value to be updated with
+    the tags.
+    """
+    from modules import shared  # prevents circular reference
+    shared.deepbooru_process_manager = multiprocessing.Manager()
+    shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
+    shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
+    shared.deepbooru_process_return["value"] = -1
+    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold))
+    shared.deepbooru_process.start()
+
+
+def release_process():
+    """
+    Stops the deepbooru process to return used memory
+    """
+    from modules import shared  # prevents circular reference
+    shared.deepbooru_process_queue.put("QUIT")
+    shared.deepbooru_process.join()
+    shared.deepbooru_process_queue = None
+    shared.deepbooru_process = None
+    shared.deepbooru_process_return = None
+    shared.deepbooru_process_manager = None
+
+def get_deepbooru_tags_model():
     import deepdanbooru as dd
     import tensorflow as tf
     import numpy as np
-
     this_folder = os.path.dirname(__file__)
     model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
     if not os.path.exists(os.path.join(model_path, 'project.json')):
         # there is no point importing these every time
         import zipfile
         from basicsr.utils.download_util import load_file_from_url
-        load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
-                           model_path)
+        load_file_from_url(
+            r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
+            model_path)
         with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
             zip_ref.extractall(model_path)
         os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
@@ -24,7 +77,13 @@ def _load_tf_and_return_tags(pil_image, threshold):
     model = dd.project.load_model_from_project(
         model_path, compile_model=True
     )
+    return model, tags
 
+
+def get_deepbooru_tags_from_model(model, tags, pil_image, threshold=0.5):
+    import deepdanbooru as dd
+    import tensorflow as tf
+    import numpy as np
     width = model.input_shape[2]
     height = model.input_shape[1]
     image = np.array(pil_image)
@@ -57,17 +116,4 @@ def _load_tf_and_return_tags(pil_image, threshold):
 
     print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
-
-
-def subprocess_init_no_cuda():
-    import os
-    os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-
-
-def get_deepbooru_tags(pil_image, threshold=0.5):
-    context = get_context('spawn')
-    with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
-        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
-        ret = f.result()  # will rethrow any exceptions
-    return ret
\ No newline at end of file
+    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
\ No newline at end of file
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index f1c002a2..9f63c9a4 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -3,11 +3,14 @@ from PIL import Image, ImageOps
 import platform
 import sys
 import tqdm
+import time
 
 from modules import shared, images
+from modules.shared import opts, cmd_opts
+if cmd_opts.deepdanbooru:
+    import modules.deepbooru as deepbooru
 
-
-def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
+def preprocess(process_src, process_dst, process_flip, process_split, process_caption, process_caption_deepbooru=False):
     size = 512
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
@@ -24,10 +27,21 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
     if process_caption:
         shared.interrogator.load()
 
+    if process_caption_deepbooru:
+        deepbooru.create_deepbooru_process()
+
     def save_pic_with_caption(image, index):
         if process_caption:
             caption = "-" + shared.interrogator.generate_caption(image)
             caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
+        elif process_caption_deepbooru:
+            shared.deepbooru_process_return["value"] = -1
+            shared.deepbooru_process_queue.put(image)
+            while shared.deepbooru_process_return["value"] == -1:
+                time.sleep(0.2)
+            caption = "-" + shared.deepbooru_process_return["value"]
+            caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
+            shared.deepbooru_process_return["value"] = -1
         else:
             caption = filename
             caption = os.path.splitext(caption)[0]
@@ -79,6 +93,10 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
     if process_caption:
         shared.interrogator.send_blip_to_ram()
 
+    if process_caption_deepbooru:
+        deepbooru.release_process()
+
+
 def sanitize_caption(base_path, original_caption, suffix):
     operating_system = platform.system().lower()
     if (operating_system == "windows"):
diff --git a/modules/ui.py b/modules/ui.py
index 2231a8ed..179e3a83 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1034,6 +1034,9 @@ def create_ui(wrap_gradio_gpu_call):
                         process_flip = gr.Checkbox(label='Create flipped copies')
                         process_split = gr.Checkbox(label='Split oversized images into two')
                         process_caption = gr.Checkbox(label='Use BLIP caption as filename')
+                        if cmd_opts.deepdanbooru:
+                            process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename')
+
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1086,21 +1089,40 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
-        run_preprocess.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                process_src,
-                process_dst,
-                process_flip,
-                process_split,
-                process_caption,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ],
-        )
+        if cmd_opts.deepdanbooru:
+            # if process_caption_deepbooru is None, it will cause an error, as a result only include it if it is enabled
+            run_preprocess.click(
+                fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+                _js="start_training_textual_inversion",
+                inputs=[
+                    process_src,
+                    process_dst,
+                    process_flip,
+                    process_split,
+                    process_caption,
+                    process_caption_deepbooru,
+                ],
+                outputs=[
+                    ti_output,
+                    ti_outcome,
+                ],
+            )
+        else:
+            run_preprocess.click(
+                fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+                _js="start_training_textual_inversion",
+                inputs=[
+                    process_src,
+                    process_dst,
+                    process_flip,
+                    process_split,
+                    process_caption,
+                ],
+                outputs=[
+                    ti_output,
+                    ti_outcome,
+                ],
+            )
 
         train_embedding.click(
             fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
-- 
cgit v1.2.1


From 8acc901ba3a252dc6ab4fabcb41644cf64d1774c Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 10 Oct 2022 00:38:55 -0400
Subject: Newer versions of PyTorch use TypedStorage instead

Pytorch 1.13 and later will rename _TypedStorage to TypedStorage, so check for TypedStorage and use _TypedStorage if it is not available. Currently this is needed so that nightly builds of PyTorch work correctly.
---
 modules/safe.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/modules/safe.py b/modules/safe.py
index 4d06f2a5..05917463 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -12,6 +12,10 @@ import _codecs
 import zipfile
 
 
+# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
+TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
+
+
 def encode(*args):
     out = _codecs.encode(*args)
     return out
@@ -20,7 +24,7 @@ def encode(*args):
 class RestrictedUnpickler(pickle.Unpickler):
     def persistent_load(self, saved_id):
         assert saved_id[0] == 'storage'
-        return torch.storage._TypedStorage()
+        return TypedStorage()
 
     def find_class(self, module, name):
         if module == 'collections' and name == 'OrderedDict':
-- 
cgit v1.2.1


From 8a7c07a2140c98bceca858087525d77fd0352fda Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 10 Oct 2022 15:39:39 +0800
Subject: show image history

---
 javascript/images_history.js   |  66 ++++++++++++++++++++++
 javascript/jquery-3.6.0.min.js |   2 +
 modules/images_history.py      |  90 ++++++++++++++++++++++++++++++
 modules/ui.py                  |  11 +++-
 repositorieslatent-diffusion   |   1 +
 testui.py                      | 124 +++++++++++++++++++++++++++++++++++++++++
 6 files changed, 292 insertions(+), 2 deletions(-)
 create mode 100644 javascript/images_history.js
 create mode 100644 javascript/jquery-3.6.0.min.js
 create mode 100644 modules/images_history.py
 create mode 160000 repositorieslatent-diffusion
 create mode 100644 testui.py

diff --git a/javascript/images_history.js b/javascript/images_history.js
new file mode 100644
index 00000000..f30b7eff
--- /dev/null
+++ b/javascript/images_history.js
@@ -0,0 +1,66 @@
+function init_images_history(){ 
+	if (gradioApp().getElementById('txt2img_images_history_first_page') == null) {
+		setTimeout(init_images_history, 1000)
+	} else {
+		tab_list = ["txt2img", "img2img"]
+		for (i in tab_list){
+			tab = tab_list[i]
+			gradioApp().getElementById(tab + "_images_history_first_page").click()
+			$(gradioApp().getElementById(tab + '_images_history')).addClass("images_history_gallery")
+			item = $(gradioApp().getElementById(tab + '_images_history_set_index'))
+			item.addClass("images_history_set_index")
+			item.hide()
+		}		
+	}
+	
+}
+setTimeout(init_images_history, 1000)
+onUiUpdate(function(){
+    fullImg_preview = gradioApp().querySelectorAll('#txt2img_images_history img.w-full')
+        if(fullImg_preview.length > 0){
+	    	fullImg_preview.forEach(set_history_index_from_img);
+    }
+    fullImg_preview = gradioApp().querySelectorAll('#img2img_images_history img.w-full')
+        if(fullImg_preview.length > 0){
+	    	fullImg_preview.forEach(set_history_index_from_img);
+    }
+})
+
+function set_history_gallery_index(item){
+	buttons = item.find(".gallery-item")
+	// alert(item.attr("id") + " " + buttons.length)
+	index = -1
+	i = 0
+	buttons.each(function(){
+		if($(this).hasClass("!ring-2")){ index = i }
+		i += 1
+	})
+	if (index == -1){
+		setTimeout(set_history_gallery_index, 10, item)     
+	} else {
+		item = item.find(".images_history_set_index").first()
+    	item.attr("img_index", index)
+    	item.click()
+	}
+}
+function set_history_index_from_img(e){
+    if(e && e.parentElement.tagName == 'BUTTON'){
+    	bnt = $(e).parent()
+    	if (bnt.hasClass("transform")){
+    		bnt.off("click").on("click",function(){
+    		set_history_gallery_index($(this).parents(".images_history_gallery").first())  
+     		})
+    	} else {
+    		bnt.off("mousedown").on("mousedown", function(){
+    		set_history_gallery_index($(this).parents(".images_history_gallery").first())  
+    		})
+
+		}  		
+    }
+}
+function images_history_get_current_img(is_image2image){
+	head = is_image2image?"img2img":"txt2img"
+	s =  $(gradioApp().getElementById(head + '_images_history_set_index')).attr("img_index")
+	return s
+}
+
diff --git a/javascript/jquery-3.6.0.min.js b/javascript/jquery-3.6.0.min.js
new file mode 100644
index 00000000..c4c6022f
--- /dev/null
+++ b/javascript/jquery-3.6.0.min.js
@@ -0,0 +1,2 @@
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diff --git a/modules/images_history.py b/modules/images_history.py
new file mode 100644
index 00000000..23d83557
--- /dev/null
+++ b/modules/images_history.py
@@ -0,0 +1,90 @@
+import os
+def get_recent_images(is_img2img, dir_name, page_index, step):
+	page_index = int(page_index)
+	f_list = os.listdir(dir_name)
+	file_list = []
+	for file in f_list:
+		if file[-4:] == ".txt":
+			continue
+		file_list.append(file)	
+	file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+	num = 24
+	max_page_index = len(file_list) // num + 1	
+	page_index = max_page_index if page_index == -1 else page_index + step
+	page_index = 1 if page_index < 1 else page_index	
+	page_index = max_page_index if page_index > max_page_index else page_index
+	idx_frm = (page_index - 1) * num
+	file_list = file_list[idx_frm:idx_frm + num]
+	print(f"Loading history page {page_index}")	
+	return [os.path.join(dir_name, file) for file in file_list], page_index, file_list
+def first_page_click(is_img2img, dir_name):
+	return get_recent_images(is_img2img, dir_name, 1, 0)
+def end_page_click(is_img2img, dir_name):
+	return get_recent_images(is_img2img, dir_name, -1, 0)
+def prev_page_click(is_img2img, dir_name, page_index):
+	return get_recent_images(is_img2img, dir_name, page_index, -1)
+def next_page_click(is_img2img, dir_name, page_index):	
+	return get_recent_images(is_img2img, dir_name, page_index, 1)
+def page_index_change(is_img2img, dir_name, page_index):	
+	return get_recent_images(is_img2img, dir_name, page_index, 0)
+def show_image_info(num, filenames):
+	return filenames[int(num)]
+def delete_image(is_img2img, dir_name, name, page_index, filenames):
+	path = os.path.join(dir_name, name)		
+	if os.path.exists(path):
+		print(f"Delete file {path}")
+		os.remove(path)	
+	i = 0
+	for f in filenames:
+		if f == name:
+			break
+		i += 1
+	images, page_index, file_list = get_recent_images(is_img2img, dir_name, page_index, 0)
+	current_file =  file_list[i] if i < len(file_list) else None
+	return images, page_index, file_list, current_file
+
+
+def show_images_history(gr, opts, is_img2img):
+		def id_name(is_img2img, name):	
+			return ("img2img" if is_img2img else "txt2img") + "_" + name
+		with gr.Row():				
+			if is_img2img:
+				dir_name = opts.outdir_img2img_samples
+			else:
+				dir_name = opts.outdir_txt2img_samples							
+			first_page = gr.Button('First Page', elem_id=id_name(is_img2img,"images_history_first_page"))
+			prev_page = gr.Button('Prev Page') 
+			page_index = gr.Number(value=1)
+			next_page = gr.Button('Next Page') 
+			end_page = gr.Button('End Page')  
+		with gr.Row():	      
+			delete = gr.Button('Delete')
+			Send = gr.Button('Send')
+		with gr.Row():	
+			with gr.Column(elem_id=id_name(is_img2img,"images_history")):	
+				history_gallery = gr.Gallery(label="Images history").style(grid=6)
+				img_file_name = gr.Textbox()	
+				img_file_info = gr.Textbox(dir_name)
+				img_path = gr.Textbox(dir_name, visible=False)					
+				set_index = gr.Button('set_index',  elem_id=id_name(is_img2img,"images_history_set_index"))
+				is_img2img_flag = gr.Checkbox(is_img2img, visible=False)
+		filenames = gr.State()
+		first_page.click(first_page_click, inputs=[is_img2img_flag, img_path], outputs=[history_gallery, page_index, filenames])
+		next_page.click(next_page_click, inputs=[is_img2img_flag, img_path, page_index], outputs=[history_gallery, page_index, filenames])
+		prev_page.click(prev_page_click, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index, filenames])		
+		end_page.click(end_page_click, inputs=[is_img2img_flag, img_path], outputs=[history_gallery, page_index, filenames])
+		page_index.submit(page_index_change, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index, filenames])
+		set_index.click(show_image_info, _js="images_history_get_current_img",inputs=[is_img2img_flag, filenames], outputs=img_file_name)	
+		delete.click(delete_image, inputs=[is_img2img_flag, img_path, img_file_name, page_index, filenames], outputs=[history_gallery, page_index, filenames,img_file_name]) 
+		#page_index.change(page_index_change, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index])
+		
+def create_history_tabs(gr, opts):
+	with gr.Blocks(analytics_enabled=False) as images_history:
+		with gr.Tabs() as tabs:			
+			with gr.Tab("txt2img history", id="images_history_txt2img"):
+				with gr.Blocks(analytics_enabled=False) as images_history_txt2img:   
+					show_images_history(gr, opts, is_img2img=False) 
+			with gr.Tab("img2img history", id="images_history_img2img"):
+				with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+					show_images_history(gr, opts, is_img2img=True)
+	return images_history
diff --git a/modules/ui.py b/modules/ui.py
index 4f18126f..8762fcf5 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -37,6 +37,7 @@ import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
+import modules.images_history as img_his
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
 mimetypes.init()
@@ -499,7 +500,6 @@ def create_ui(wrap_gradio_gpu_call):
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
             with gr.Column(variant='panel'):
-
                 with gr.Group():
                     txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
                     txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
@@ -516,6 +516,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     html_info = gr.HTML()
                     generation_info = gr.Textbox(visible=False)
+                
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -607,6 +608,7 @@ def create_ui(wrap_gradio_gpu_call):
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
+        
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
@@ -696,6 +698,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     html_info = gr.HTML()
                     generation_info = gr.Textbox(visible=False)
+                
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -1126,8 +1129,10 @@ def create_ui(wrap_gradio_gpu_call):
 
         opts.save(shared.config_filename)
 
-        return f'{changed} settings changed.', opts.dumpjson()
+        return f'{changed} settings changed.', opts.dumpjson()    
 
+    
+    images_history = img_his.create_history_tabs(gr, opts)    
     with gr.Blocks(analytics_enabled=False) as settings_interface:
         settings_submit = gr.Button(value="Apply settings", variant='primary')
         result = gr.HTML()
@@ -1206,7 +1211,9 @@ def create_ui(wrap_gradio_gpu_call):
         (pnginfo_interface, "PNG Info", "pnginfo"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (textual_inversion_interface, "Textual inversion", "ti"),
+        (images_history, "History", "images_history"),
         (settings_interface, "Settings", "settings"),
+
     ]
 
     with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
diff --git a/repositorieslatent-diffusion b/repositorieslatent-diffusion
new file mode 160000
index 00000000..abf33e70
--- /dev/null
+++ b/repositorieslatent-diffusion
@@ -0,0 +1 @@
+Subproject commit abf33e7002d59d9085081bce93ec798dcabd49af
diff --git a/testui.py b/testui.py
new file mode 100644
index 00000000..f54e4a62
--- /dev/null
+++ b/testui.py
@@ -0,0 +1,124 @@
+import os
+import threading
+import time
+import importlib
+import signal
+import threading
+
+from modules.paths import script_path
+
+from modules import devices, sd_samplers
+import modules.codeformer_model as codeformer
+import modules.extras
+import modules.face_restoration
+import modules.gfpgan_model as gfpgan
+import modules.img2img
+
+import modules.lowvram
+import modules.paths
+import modules.scripts
+import modules.sd_hijack
+import modules.sd_models
+import modules.shared as shared
+import modules.txt2img
+
+import modules.ui
+from modules import devices
+from modules import modelloader
+from modules.paths import script_path
+from modules.shared import cmd_opts
+
+modelloader.cleanup_models()
+modules.sd_models.setup_model()
+codeformer.setup_model(cmd_opts.codeformer_models_path)
+gfpgan.setup_model(cmd_opts.gfpgan_models_path)
+shared.face_restorers.append(modules.face_restoration.FaceRestoration())
+modelloader.load_upscalers()
+queue_lock = threading.Lock()
+
+
+def wrap_queued_call(func):
+    def f(*args, **kwargs):
+        with queue_lock:
+            res = func(*args, **kwargs)
+
+        return res
+
+    return f
+
+
+def wrap_gradio_gpu_call(func, extra_outputs=None):
+    def f(*args, **kwargs):
+        devices.torch_gc()
+
+        shared.state.sampling_step = 0
+        shared.state.job_count = -1
+        shared.state.job_no = 0
+        shared.state.job_timestamp = shared.state.get_job_timestamp()
+        shared.state.current_latent = None
+        shared.state.current_image = None
+        shared.state.current_image_sampling_step = 0
+        shared.state.interrupted = False
+        shared.state.textinfo = None
+
+        with queue_lock:
+            res = func(*args, **kwargs)
+
+        shared.state.job = ""
+        shared.state.job_count = 0
+
+        devices.torch_gc()
+
+        return res
+
+    return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
+
+
+modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
+
+shared.sd_model = None #modules.sd_models.load_model()
+#shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
+
+
+def webui():
+    # make the program just exit at ctrl+c without waiting for anything
+    def sigint_handler(sig, frame):
+        print(f'Interrupted with signal {sig} in {frame}')
+        os._exit(0)
+
+    signal.signal(signal.SIGINT, sigint_handler)
+
+    while 1:
+
+        demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
+        
+        demo.launch(
+            share=cmd_opts.share,
+            server_name="0.0.0.0" if cmd_opts.listen else None,
+            server_port=cmd_opts.port,
+            debug=cmd_opts.gradio_debug,
+            auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
+            inbrowser=cmd_opts.autolaunch,
+            prevent_thread_lock=True
+        )
+
+        while 1:
+            time.sleep(0.5)
+            if getattr(demo, 'do_restart', False):
+                time.sleep(0.5)
+                demo.close()
+                time.sleep(0.5)
+                break
+
+        sd_samplers.set_samplers()
+
+        print('Reloading Custom Scripts')
+        modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
+        print('Reloading modules: modules.ui')
+        importlib.reload(modules.ui)
+        print('Restarting Gradio')
+
+
+
+if __name__ == "__main__":
+    webui()
-- 
cgit v1.2.1


From 3110f895b2718a3a25aae419fdf5c87c177ec9f4 Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Mon, 10 Oct 2022 17:07:46 +0900
Subject: Textual Inversion: Added custom training image size and number of
 repeats per input image in a single epoch

---
 modules/textual_inversion/dataset.py           |  6 +++---
 modules/textual_inversion/preprocess.py        |  4 ++--
 modules/textual_inversion/textual_inversion.py | 15 ++++++++++++---
 modules/ui.py                                  |  8 +++++++-
 4 files changed, 24 insertions(+), 9 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 7c44ea5b..acc4ce59 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -15,13 +15,13 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
+    def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
 
         self.placeholder_token = placeholder_token
 
         self.size = size
-        self.width = width
-        self.height = height
+        self.width = size
+        self.height = size
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
         self.dataset = []
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index f1c002a2..b3de6fd7 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -7,8 +7,8 @@ import tqdm
 from modules import shared, images
 
 
-def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
-    size = 512
+def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption):
+    size = process_size
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index cd9f3498..e34dc2e8 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -6,6 +6,7 @@ import torch
 import tqdm
 import html
 import datetime
+import math
 
 
 from modules import shared, devices, sd_hijack, processing, sd_models
@@ -156,7 +157,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -182,7 +183,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
 
     hijack = sd_hijack.model_hijack
 
@@ -200,6 +201,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
     if ititial_step > steps:
         return embedding, filename
 
+    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
+    epoch_len = (tr_img_len * num_repeats) + tr_img_len
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, (x, text) in pbar:
         embedding.step = i + ititial_step
@@ -223,7 +227,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
             loss.backward()
             optimizer.step()
 
-        pbar.set_description(f"loss: {losses.mean():.7f}")
+        epoch_num = math.floor(embedding.step / epoch_len)
+        epoch_step = embedding.step - (epoch_num * epoch_len)
+
+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
 
         if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
@@ -236,6 +243,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
                 sd_model=shared.sd_model,
                 prompt=text,
                 steps=20,
+                height=training_size,
+                width=training_size,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
diff --git a/modules/ui.py b/modules/ui.py
index 2231a8ed..f821fd8d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1029,6 +1029,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
+                    process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
@@ -1043,13 +1044,15 @@ def create_ui(wrap_gradio_gpu_call):
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                 with gr.Group():
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                     learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+                    training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
+                    num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
 
@@ -1092,6 +1095,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 process_src,
                 process_dst,
+                process_size,
                 process_flip,
                 process_split,
                 process_caption,
@@ -1110,7 +1114,9 @@ def create_ui(wrap_gradio_gpu_call):
                 learn_rate,
                 dataset_directory,
                 log_directory,
+                training_size,
                 steps,
+                num_repeats,
                 create_image_every,
                 save_embedding_every,
                 template_file,
-- 
cgit v1.2.1


From 8ec069e64df48f8f202f8b93a08e91b69448eb39 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Mon, 10 Oct 2022 03:23:24 -0500
Subject: removed duplicate run_preprocess.click by creating
 run_preprocess_inputs list and appending deepbooru variable to input list if
 in scope

---
 modules/ui.py | 49 +++++++++++++++++--------------------------------
 1 file changed, 17 insertions(+), 32 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 179e3a83..22ca74c2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1089,40 +1089,25 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
+        run_preprocess_inputs = [
+            process_src,
+            process_dst,
+            process_flip,
+            process_split,
+            process_caption,
+        ]
         if cmd_opts.deepdanbooru:
             # if process_caption_deepbooru is None, it will cause an error, as a result only include it if it is enabled
-            run_preprocess.click(
-                fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
-                _js="start_training_textual_inversion",
-                inputs=[
-                    process_src,
-                    process_dst,
-                    process_flip,
-                    process_split,
-                    process_caption,
-                    process_caption_deepbooru,
-                ],
-                outputs=[
-                    ti_output,
-                    ti_outcome,
-                ],
-            )
-        else:
-            run_preprocess.click(
-                fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
-                _js="start_training_textual_inversion",
-                inputs=[
-                    process_src,
-                    process_dst,
-                    process_flip,
-                    process_split,
-                    process_caption,
-                ],
-                outputs=[
-                    ti_output,
-                    ti_outcome,
-                ],
-            )
+            run_preprocess_inputs.append(process_caption_deepbooru)
+        run_preprocess.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=run_preprocess_inputs,
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ],
+        )
 
         train_embedding.click(
             fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
-- 
cgit v1.2.1


From 4ee7519fc2e459ce8eff1f61f1655afba393357c Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Mon, 10 Oct 2022 17:31:33 +0900
Subject: Fixed progress bar output for epoch

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e34dc2e8..769682ea 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -228,7 +228,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             optimizer.step()
 
         epoch_num = math.floor(embedding.step / epoch_len)
-        epoch_step = embedding.step - (epoch_num * epoch_len)
+        epoch_step = embedding.step - (epoch_num * epoch_len) + 1
 
         pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
 
-- 
cgit v1.2.1


From 2f94331df2cb1181439adecc28cfd758049f6501 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Mon, 10 Oct 2022 03:34:00 -0500
Subject: removed change in last commit, simplified to adding the visible
 argument to process_caption_deepbooru and it set to False if deepdanbooru
 argument is not set

---
 modules/ui.py | 22 ++++++++++------------
 1 file changed, 10 insertions(+), 12 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 22ca74c2..f8adafb3 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1036,7 +1036,8 @@ def create_ui(wrap_gradio_gpu_call):
                         process_caption = gr.Checkbox(label='Use BLIP caption as filename')
                         if cmd_opts.deepdanbooru:
                             process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename')
-
+                        else:
+                            process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename', visible=False)
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1089,20 +1090,17 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
-        run_preprocess_inputs = [
-            process_src,
-            process_dst,
-            process_flip,
-            process_split,
-            process_caption,
-        ]
-        if cmd_opts.deepdanbooru:
-            # if process_caption_deepbooru is None, it will cause an error, as a result only include it if it is enabled
-            run_preprocess_inputs.append(process_caption_deepbooru)
         run_preprocess.click(
             fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
-            inputs=run_preprocess_inputs,
+            inputs=[
+                process_src,
+                process_dst,
+                process_flip,
+                process_split,
+                process_caption,
+                process_caption_deepbooru
+            ],
             outputs=[
                 ti_output,
                 ti_outcome,
-- 
cgit v1.2.1


From 23f2989799ee3911d2959cfceb74b921f20c9a51 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 10 Oct 2022 18:33:49 +0800
Subject: images history over

---
 javascript/images_history.js |   4 +-
 modules/images_history.py    | 141 ++++++++++++++++++++++++++-----------------
 modules/ui.py                |   9 ++-
 testui.py                    | 124 -------------------------------------
 4 files changed, 94 insertions(+), 184 deletions(-)
 delete mode 100644 testui.py

diff --git a/javascript/images_history.js b/javascript/images_history.js
index f30b7eff..93d2b89a 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -58,9 +58,9 @@ function set_history_index_from_img(e){
 		}  		
     }
 }
-function images_history_get_current_img(is_image2image){
+function images_history_get_current_img(is_image2image, image_path, files){
 	head = is_image2image?"img2img":"txt2img"
 	s =  $(gradioApp().getElementById(head + '_images_history_set_index')).attr("img_index")
-	return s
+	return [s, image_path, files]
 }
 
diff --git a/modules/images_history.py b/modules/images_history.py
index 23d83557..0e0a48f3 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,5 +1,6 @@
 import os
-def get_recent_images(is_img2img, dir_name, page_index, step):
+def get_recent_images(dir_name, page_index, step, image_index):
+	print(image_index)
 	page_index = int(page_index)
 	f_list = os.listdir(dir_name)
 	file_list = []
@@ -8,7 +9,7 @@ def get_recent_images(is_img2img, dir_name, page_index, step):
 			continue
 		file_list.append(file)	
 	file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
-	num = 24
+	num = 48
 	max_page_index = len(file_list) // num + 1	
 	page_index = max_page_index if page_index == -1 else page_index + step
 	page_index = 1 if page_index < 1 else page_index	
@@ -16,75 +17,101 @@ def get_recent_images(is_img2img, dir_name, page_index, step):
 	idx_frm = (page_index - 1) * num
 	file_list = file_list[idx_frm:idx_frm + num]
 	print(f"Loading history page {page_index}")	
-	return [os.path.join(dir_name, file) for file in file_list], page_index, file_list
-def first_page_click(is_img2img, dir_name):
-	return get_recent_images(is_img2img, dir_name, 1, 0)
-def end_page_click(is_img2img, dir_name):
-	return get_recent_images(is_img2img, dir_name, -1, 0)
-def prev_page_click(is_img2img, dir_name, page_index):
-	return get_recent_images(is_img2img, dir_name, page_index, -1)
-def next_page_click(is_img2img, dir_name, page_index):	
-	return get_recent_images(is_img2img, dir_name, page_index, 1)
-def page_index_change(is_img2img, dir_name, page_index):	
-	return get_recent_images(is_img2img, dir_name, page_index, 0)
-def show_image_info(num, filenames):
-	return filenames[int(num)]
-def delete_image(is_img2img, dir_name, name, page_index, filenames):
+	image_index = int(image_index)
+	if image_index < 0 or image_index > len(file_list)  - 1:
+		current_file = None 
+		hide_image = None
+	else:
+		current_file =  file_list[int(image_index)]
+		hide_image = os.path.join(dir_name, current_file)
+	return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image
+def first_page_click(dir_name, page_index, image_index):
+	return get_recent_images(dir_name, 1, 0, image_index)
+def end_page_click(dir_name, page_index, image_index):
+	return get_recent_images(dir_name, -1, 0, image_index)
+def prev_page_click(dir_name, page_index, image_index):
+	return get_recent_images(dir_name, page_index, -1, image_index)
+def next_page_click(dir_name, page_index, image_index):	
+	return get_recent_images(dir_name, page_index, 1, image_index)
+def page_index_change(dir_name, page_index, image_index):	
+	return get_recent_images(dir_name, page_index, 0, image_index)
+
+def show_image_info(num, image_path, filenames):
+	file = filenames[int(num)]
+	return file, num, os.path.join(image_path, file)
+def delete_image(is_img2img, dir_name, name, page_index, filenames, image_index):
+	print("filename", name)
 	path = os.path.join(dir_name, name)		
 	if os.path.exists(path):
 		print(f"Delete file {path}")
 		os.remove(path)	
-	i = 0
-	for f in filenames:
-		if f == name:
-			break
-		i += 1
-	images, page_index, file_list = get_recent_images(is_img2img, dir_name, page_index, 0)
-	current_file =  file_list[i] if i < len(file_list) else None
-	return images, page_index, file_list, current_file
+	images, page_index, file_list, current_file, hide_image = get_recent_images(dir_name, page_index, 0, image_index)
+	return images, page_index, file_list, current_file, hide_image
 
 
-def show_images_history(gr, opts, is_img2img):
+def show_images_history(gr, opts, is_img2img, run_pnginfo, switch_dict):
 		def id_name(is_img2img, name):	
 			return ("img2img" if is_img2img else "txt2img") + "_" + name
-		with gr.Row():				
-			if is_img2img:
-				dir_name = opts.outdir_img2img_samples
-			else:
-				dir_name = opts.outdir_txt2img_samples							
-			first_page = gr.Button('First Page', elem_id=id_name(is_img2img,"images_history_first_page"))
-			prev_page = gr.Button('Prev Page') 
-			page_index = gr.Number(value=1)
-			next_page = gr.Button('Next Page') 
-			end_page = gr.Button('End Page')  
-		with gr.Row():	      
-			delete = gr.Button('Delete')
-			Send = gr.Button('Send')
-		with gr.Row():	
-			with gr.Column(elem_id=id_name(is_img2img,"images_history")):	
-				history_gallery = gr.Gallery(label="Images history").style(grid=6)
-				img_file_name = gr.Textbox()	
-				img_file_info = gr.Textbox(dir_name)
-				img_path = gr.Textbox(dir_name, visible=False)					
-				set_index = gr.Button('set_index',  elem_id=id_name(is_img2img,"images_history_set_index"))
-				is_img2img_flag = gr.Checkbox(is_img2img, visible=False)
-		filenames = gr.State()
-		first_page.click(first_page_click, inputs=[is_img2img_flag, img_path], outputs=[history_gallery, page_index, filenames])
-		next_page.click(next_page_click, inputs=[is_img2img_flag, img_path, page_index], outputs=[history_gallery, page_index, filenames])
-		prev_page.click(prev_page_click, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index, filenames])		
-		end_page.click(end_page_click, inputs=[is_img2img_flag, img_path], outputs=[history_gallery, page_index, filenames])
-		page_index.submit(page_index_change, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index, filenames])
-		set_index.click(show_image_info, _js="images_history_get_current_img",inputs=[is_img2img_flag, filenames], outputs=img_file_name)	
-		delete.click(delete_image, inputs=[is_img2img_flag, img_path, img_file_name, page_index, filenames], outputs=[history_gallery, page_index, filenames,img_file_name]) 
+		if is_img2img:
+			dir_name = opts.outdir_img2img_samples
+		else:
+			dir_name = opts.outdir_txt2img_samples
+		with gr.Row():	 
+				first_page = gr.Button('First', elem_id=id_name(is_img2img,"images_history_first_page"))
+				prev_page = gr.Button('Prev') 
+				page_index = gr.Number(value=1, label="Page Index")
+				next_page = gr.Button('Next') 
+				end_page = gr.Button('End')	
+		with gr.Row(elem_id=id_name(is_img2img,"images_history")):			
+			with gr.Row():	 
+				with gr.Column():
+					history_gallery = gr.Gallery(show_label=False).style(grid=6)
+				with gr.Column():	 
+					with gr.Row():	      
+						delete = gr.Button('Delete')
+						pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
+						pnginfo_send_to_img2img = gr.Button('Send to img2img')
+					with gr.Row():
+						with gr.Column():
+							img_file_info = gr.Textbox(dir_name, label="Generate Info")
+							img_file_name = gr.Textbox(label="File Name")	
+					with gr.Row():	
+							# hiden items
+							img_path = gr.Textbox(dir_name, visible=False)				
+							is_img2img_flag = gr.Checkbox(is_img2img, visible=False)	
+							image_index = gr.Textbox(value=-1, visible=False)						
+							set_index = gr.Button('set_index',  elem_id=id_name(is_img2img,"images_history_set_index"))
+							filenames = gr.State()
+							hide_image = gr.Image(visible=False, type="pil")
+							info1 = gr.Textbox(visible=False)
+							info2 = gr.Textbox(visible=False)
+
+				
+		# turn pages
+		gallery_inputs = [img_path, page_index, image_index]
+		gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image]
+		first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+		next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+		prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)		
+		end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+		page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
 		#page_index.change(page_index_change, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index])
+
+		#other funcitons
+		set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[is_img2img_flag,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
+		delete.click(delete_image, inputs=[is_img2img_flag, img_path, img_file_name, page_index, filenames, image_index], outputs=gallery_outputs) 
+		hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
+		switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
+		switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
+	
 		
-def create_history_tabs(gr, opts):
+def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
 	with gr.Blocks(analytics_enabled=False) as images_history:
 		with gr.Tabs() as tabs:			
 			with gr.Tab("txt2img history", id="images_history_txt2img"):
 				with gr.Blocks(analytics_enabled=False) as images_history_txt2img:   
-					show_images_history(gr, opts, is_img2img=False) 
+					show_images_history(gr, opts, False, run_pnginfo, switch_dict) 
 			with gr.Tab("img2img history", id="images_history_img2img"):
 				with gr.Blocks(analytics_enabled=False) as images_history_img2img:
-					show_images_history(gr, opts, is_img2img=True)
+					show_images_history(gr, opts, True, run_pnginfo, switch_dict)
 	return images_history
diff --git a/modules/ui.py b/modules/ui.py
index 8762fcf5..21c9236b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1131,8 +1131,15 @@ def create_ui(wrap_gradio_gpu_call):
 
         return f'{changed} settings changed.', opts.dumpjson()    
 
+    #images history
+    images_history_switch_dict = {
+        "fn":modules.generation_parameters_copypaste.connect_paste,
+        "t2i":txt2img_paste_fields,
+        "i2i":img2img_paste_fields
+    }
+    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)    
     
-    images_history = img_his.create_history_tabs(gr, opts)    
+
     with gr.Blocks(analytics_enabled=False) as settings_interface:
         settings_submit = gr.Button(value="Apply settings", variant='primary')
         result = gr.HTML()
diff --git a/testui.py b/testui.py
deleted file mode 100644
index f54e4a62..00000000
--- a/testui.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import os
-import threading
-import time
-import importlib
-import signal
-import threading
-
-from modules.paths import script_path
-
-from modules import devices, sd_samplers
-import modules.codeformer_model as codeformer
-import modules.extras
-import modules.face_restoration
-import modules.gfpgan_model as gfpgan
-import modules.img2img
-
-import modules.lowvram
-import modules.paths
-import modules.scripts
-import modules.sd_hijack
-import modules.sd_models
-import modules.shared as shared
-import modules.txt2img
-
-import modules.ui
-from modules import devices
-from modules import modelloader
-from modules.paths import script_path
-from modules.shared import cmd_opts
-
-modelloader.cleanup_models()
-modules.sd_models.setup_model()
-codeformer.setup_model(cmd_opts.codeformer_models_path)
-gfpgan.setup_model(cmd_opts.gfpgan_models_path)
-shared.face_restorers.append(modules.face_restoration.FaceRestoration())
-modelloader.load_upscalers()
-queue_lock = threading.Lock()
-
-
-def wrap_queued_call(func):
-    def f(*args, **kwargs):
-        with queue_lock:
-            res = func(*args, **kwargs)
-
-        return res
-
-    return f
-
-
-def wrap_gradio_gpu_call(func, extra_outputs=None):
-    def f(*args, **kwargs):
-        devices.torch_gc()
-
-        shared.state.sampling_step = 0
-        shared.state.job_count = -1
-        shared.state.job_no = 0
-        shared.state.job_timestamp = shared.state.get_job_timestamp()
-        shared.state.current_latent = None
-        shared.state.current_image = None
-        shared.state.current_image_sampling_step = 0
-        shared.state.interrupted = False
-        shared.state.textinfo = None
-
-        with queue_lock:
-            res = func(*args, **kwargs)
-
-        shared.state.job = ""
-        shared.state.job_count = 0
-
-        devices.torch_gc()
-
-        return res
-
-    return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
-
-
-modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
-
-shared.sd_model = None #modules.sd_models.load_model()
-#shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
-
-
-def webui():
-    # make the program just exit at ctrl+c without waiting for anything
-    def sigint_handler(sig, frame):
-        print(f'Interrupted with signal {sig} in {frame}')
-        os._exit(0)
-
-    signal.signal(signal.SIGINT, sigint_handler)
-
-    while 1:
-
-        demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
-        
-        demo.launch(
-            share=cmd_opts.share,
-            server_name="0.0.0.0" if cmd_opts.listen else None,
-            server_port=cmd_opts.port,
-            debug=cmd_opts.gradio_debug,
-            auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
-            inbrowser=cmd_opts.autolaunch,
-            prevent_thread_lock=True
-        )
-
-        while 1:
-            time.sleep(0.5)
-            if getattr(demo, 'do_restart', False):
-                time.sleep(0.5)
-                demo.close()
-                time.sleep(0.5)
-                break
-
-        sd_samplers.set_samplers()
-
-        print('Reloading Custom Scripts')
-        modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
-        print('Reloading modules: modules.ui')
-        importlib.reload(modules.ui)
-        print('Restarting Gradio')
-
-
-
-if __name__ == "__main__":
-    webui()
-- 
cgit v1.2.1


From a3578233395e585e68c2118d3630cb2a961d4a36 Mon Sep 17 00:00:00 2001
From: Bepis <36346617+bbepis@users.noreply.github.com>
Date: Mon, 10 Oct 2022 23:12:29 +1100
Subject: Add a pull request template

---
 .../PULL_REQUEST_TEMPLATE/pull_request_template.md | 28 ++++++++++++++++++++++
 1 file changed, 28 insertions(+)
 create mode 100644 .github/PULL_REQUEST_TEMPLATE/pull_request_template.md

diff --git a/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md
new file mode 100644
index 00000000..86009613
--- /dev/null
+++ b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md
@@ -0,0 +1,28 @@
+# Please read the [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) before submitting a pull request!
+
+If you have a large change, pay special attention to this paragraph:
+
+> Before making changes, if you think that your feature will result in more than 100 lines changing, find me and talk to me about the feature you are proposing. It pains me to reject the hard work someone else did, but I won't add everything to the repo, and it's better if the rejection happens before you have to waste time working on the feature.
+
+Otherwise, after making sure you're following the rules described in wiki page, remove this section and continue on.
+
+**Describe what this pull request is trying to achieve.**
+
+A clear and concise description of what you're trying to accomplish with this, so your intent doesn't have to be extracted from your code.
+
+**Additional notes and description of your changes**
+
+More technical discussion about your changes go here, plus anything that a maintainer might have to specifically take a look at, or be wary of.
+
+**Environment this was tested in**
+
+List the environment you have developed / tested this on. As per the contributing page, changes should be able to work on Windows out of the box.
+ - OS: [e.g. Windows, Linux]
+ - Browser [e.g. chrome, safari]
+ - Graphics card [e.g. NVIDIA RTX 2080 8GB, AMD RX 6600 8GB]
+
+**Screenshots or videos of your changes**
+
+If applicable, screenshots or a video showing off your changes. If it edits an existing UI, it should ideally contain a comparison of what used to be there, before your changes were made.
+
+This is **required** for anything that touches the user interface.
\ No newline at end of file
-- 
cgit v1.2.1


From 7349088d32b080f64058b6e5de5f0380a71ecd09 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 10 Oct 2022 16:11:14 +0300
Subject: --no-half-vae

---
 modules/devices.py     |  6 +++++-
 modules/processing.py  | 11 +++++++++--
 modules/sd_models.py   |  3 +++
 modules/sd_samplers.py |  4 ++--
 modules/shared.py      |  1 +
 5 files changed, 20 insertions(+), 5 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 0158b11f..03ef58f1 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -36,6 +36,7 @@ errors.run(enable_tf32, "Enabling TF32")
 
 device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
 dtype = torch.float16
+dtype_vae = torch.float16
 
 def randn(seed, shape):
     # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
@@ -59,9 +60,12 @@ def randn_without_seed(shape):
     return torch.randn(shape, device=device)
 
 
-def autocast():
+def autocast(disable=False):
     from modules import shared
 
+    if disable:
+        return contextlib.nullcontext()
+
     if dtype == torch.float32 or shared.cmd_opts.precision == "full":
         return contextlib.nullcontext()
 
diff --git a/modules/processing.py b/modules/processing.py
index 94d2dd62..ec8651ae 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -259,6 +259,13 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     return x
 
 
+def decode_first_stage(model, x):
+    with devices.autocast(disable=x.dtype == devices.dtype_vae):
+        x = model.decode_first_stage(x)
+
+    return x
+
+
 def get_fixed_seed(seed):
     if seed is None or seed == '' or seed == -1:
         return int(random.randrange(4294967294))
@@ -400,7 +407,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
             samples_ddim = samples_ddim.to(devices.dtype)
 
-            x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
+            x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
             del samples_ddim
@@ -533,7 +540,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         if self.scale_latent:
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
         else:
-            decoded_samples = self.sd_model.decode_first_stage(samples)
+            decoded_samples = decode_first_stage(self.sd_model, samples)
 
             if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
                 decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
diff --git a/modules/sd_models.py b/modules/sd_models.py
index e63d3c29..2cdcd84f 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -149,6 +149,7 @@ def load_model_weights(model, checkpoint_info):
         model.half()
 
     devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
+    devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
     vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
     if os.path.exists(vae_file):
@@ -158,6 +159,8 @@ def load_model_weights(model, checkpoint_info):
 
         model.first_stage_model.load_state_dict(vae_dict)
 
+    model.first_stage_model.to(devices.dtype_vae)
+
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 6e743f7e..d168b938 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -7,7 +7,7 @@ import inspect
 import k_diffusion.sampling
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
-from modules import prompt_parser
+from modules import prompt_parser, devices, processing
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -83,7 +83,7 @@ def setup_img2img_steps(p, steps=None):
 
 
 def sample_to_image(samples):
-    x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0]
+    x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]
     x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
     x_sample = x_sample.astype(np.uint8)
diff --git a/modules/shared.py b/modules/shared.py
index 1995a99a..5dfc344c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -25,6 +25,7 @@ parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to director
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
 parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
 parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
+parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
-- 
cgit v1.2.1


From 04c745ea4f81518999927fee5f78500560c25e29 Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Mon, 10 Oct 2022 22:35:35 +0900
Subject: Custom Width and Height

---
 modules/textual_inversion/dataset.py           |  7 +++----
 modules/textual_inversion/preprocess.py        | 19 ++++++++++---------
 modules/textual_inversion/textual_inversion.py | 11 +++++------
 modules/ui.py                                  | 12 ++++++++----
 4 files changed, 26 insertions(+), 23 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index acc4ce59..bcf772d2 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -15,13 +15,12 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
 
         self.placeholder_token = placeholder_token
 
-        self.size = size
-        self.width = size
-        self.height = size
+        self.width = width
+        self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
         self.dataset = []
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index b3de6fd7..d7efdef2 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -7,8 +7,9 @@ import tqdm
 from modules import shared, images
 
 
-def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption):
-    size = process_size
+def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption):
+    width = process_width
+    height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
 
@@ -55,23 +56,23 @@ def preprocess(process_src, process_dst, process_size, process_flip, process_spl
         is_wide = ratio < 1 / 1.35
 
         if process_split and is_tall:
-            img = img.resize((size, size * img.height // img.width))
+            img = img.resize((width, height * img.height // img.width))
 
-            top = img.crop((0, 0, size, size))
+            top = img.crop((0, 0, width, height))
             save_pic(top, index)
 
-            bot = img.crop((0, img.height - size, size, img.height))
+            bot = img.crop((0, img.height - height, width, img.height))
             save_pic(bot, index)
         elif process_split and is_wide:
-            img = img.resize((size * img.width // img.height, size))
+            img = img.resize((width * img.width // img.height, height))
 
-            left = img.crop((0, 0, size, size))
+            left = img.crop((0, 0, width, height))
             save_pic(left, index)
 
-            right = img.crop((img.width - size, 0, img.width, size))
+            right = img.crop((img.width - width, 0, img.width, height))
             save_pic(right, index)
         else:
-            img = images.resize_image(1, img, size, size)
+            img = images.resize_image(1, img, width, height)
             save_pic(img, index)
 
         shared.state.nextjob()
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 769682ea..5965c5a0 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -6,7 +6,6 @@ import torch
 import tqdm
 import html
 import datetime
-import math
 
 
 from modules import shared, devices, sd_hijack, processing, sd_models
@@ -157,7 +156,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -183,7 +182,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
 
     hijack = sd_hijack.model_hijack
 
@@ -227,7 +226,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             loss.backward()
             optimizer.step()
 
-        epoch_num = math.floor(embedding.step / epoch_len)
+        epoch_num = embedding.step // epoch_len
         epoch_step = embedding.step - (epoch_num * epoch_len) + 1
 
         pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
@@ -243,8 +242,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 sd_model=shared.sd_model,
                 prompt=text,
                 steps=20,
-                height=training_size,
-                width=training_size,
+				height=training_height,
+				width=training_width,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
diff --git a/modules/ui.py b/modules/ui.py
index f821fd8d..8c06ad7c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1029,7 +1029,8 @@ def create_ui(wrap_gradio_gpu_call):
 
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
-                    process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
@@ -1050,7 +1051,8 @@ def create_ui(wrap_gradio_gpu_call):
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
@@ -1095,7 +1097,8 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 process_src,
                 process_dst,
-                process_size,
+                process_width,
+                process_height,
                 process_flip,
                 process_split,
                 process_caption,
@@ -1114,7 +1117,8 @@ def create_ui(wrap_gradio_gpu_call):
                 learn_rate,
                 dataset_directory,
                 log_directory,
-                training_size,
+                training_width,
+                training_height,
                 steps,
                 num_repeats,
                 create_image_every,
-- 
cgit v1.2.1


From 8f1efdc130cf7ff47cb8d3722cdfc0dbeba3069e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 10 Oct 2022 17:03:45 +0300
Subject: --no-half-vae pt2

---
 modules/processing.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index ec8651ae..50ba4fc5 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -405,8 +405,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 # use the image collected previously in sampler loop
                 samples_ddim = shared.state.current_latent
 
-            samples_ddim = samples_ddim.to(devices.dtype)
-
+            samples_ddim = samples_ddim.to(devices.dtype_vae)
             x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
-- 
cgit v1.2.1


From ea00c1624bbb0dcb5be07f59c9509061baddf5b1 Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Mon, 10 Oct 2022 17:07:46 +0900
Subject: Textual Inversion: Added custom training image size and number of
 repeats per input image in a single epoch

---
 modules/textual_inversion/dataset.py           |  6 +++---
 modules/textual_inversion/preprocess.py        |  4 ++--
 modules/textual_inversion/textual_inversion.py | 15 ++++++++++++---
 modules/ui.py                                  |  8 +++++++-
 4 files changed, 24 insertions(+), 9 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 7c44ea5b..acc4ce59 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -15,13 +15,13 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
+    def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
 
         self.placeholder_token = placeholder_token
 
         self.size = size
-        self.width = width
-        self.height = height
+        self.width = size
+        self.height = size
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
         self.dataset = []
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index f1c002a2..b3de6fd7 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -7,8 +7,8 @@ import tqdm
 from modules import shared, images
 
 
-def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
-    size = 512
+def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption):
+    size = process_size
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index cd9f3498..e34dc2e8 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -6,6 +6,7 @@ import torch
 import tqdm
 import html
 import datetime
+import math
 
 
 from modules import shared, devices, sd_hijack, processing, sd_models
@@ -156,7 +157,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -182,7 +183,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
 
     hijack = sd_hijack.model_hijack
 
@@ -200,6 +201,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
     if ititial_step > steps:
         return embedding, filename
 
+    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
+    epoch_len = (tr_img_len * num_repeats) + tr_img_len
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, (x, text) in pbar:
         embedding.step = i + ititial_step
@@ -223,7 +227,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
             loss.backward()
             optimizer.step()
 
-        pbar.set_description(f"loss: {losses.mean():.7f}")
+        epoch_num = math.floor(embedding.step / epoch_len)
+        epoch_step = embedding.step - (epoch_num * epoch_len)
+
+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
 
         if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
@@ -236,6 +243,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
                 sd_model=shared.sd_model,
                 prompt=text,
                 steps=20,
+                height=training_size,
+                width=training_size,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
diff --git a/modules/ui.py b/modules/ui.py
index 2231a8ed..f821fd8d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1029,6 +1029,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
+                    process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
@@ -1043,13 +1044,15 @@ def create_ui(wrap_gradio_gpu_call):
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                 with gr.Group():
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                     learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+                    training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
+                    num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
 
@@ -1092,6 +1095,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 process_src,
                 process_dst,
+                process_size,
                 process_flip,
                 process_split,
                 process_caption,
@@ -1110,7 +1114,9 @@ def create_ui(wrap_gradio_gpu_call):
                 learn_rate,
                 dataset_directory,
                 log_directory,
+                training_size,
                 steps,
+                num_repeats,
                 create_image_every,
                 save_embedding_every,
                 template_file,
-- 
cgit v1.2.1


From 6ad3a53e368d36535de1a4fca73b3bb78fd40654 Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Mon, 10 Oct 2022 17:31:33 +0900
Subject: Fixed progress bar output for epoch

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e34dc2e8..769682ea 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -228,7 +228,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             optimizer.step()
 
         epoch_num = math.floor(embedding.step / epoch_len)
-        epoch_step = embedding.step - (epoch_num * epoch_len)
+        epoch_step = embedding.step - (epoch_num * epoch_len) + 1
 
         pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
 
-- 
cgit v1.2.1


From 7a20f914eddfdf09c0ccced157ec108205bc3d0f Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Mon, 10 Oct 2022 22:35:35 +0900
Subject: Custom Width and Height

---
 modules/textual_inversion/dataset.py           |  7 +++----
 modules/textual_inversion/preprocess.py        | 19 ++++++++++---------
 modules/textual_inversion/textual_inversion.py | 11 +++++------
 modules/ui.py                                  | 12 ++++++++----
 4 files changed, 26 insertions(+), 23 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index acc4ce59..bcf772d2 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -15,13 +15,12 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
 
         self.placeholder_token = placeholder_token
 
-        self.size = size
-        self.width = size
-        self.height = size
+        self.width = width
+        self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
         self.dataset = []
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index b3de6fd7..d7efdef2 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -7,8 +7,9 @@ import tqdm
 from modules import shared, images
 
 
-def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption):
-    size = process_size
+def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption):
+    width = process_width
+    height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
 
@@ -55,23 +56,23 @@ def preprocess(process_src, process_dst, process_size, process_flip, process_spl
         is_wide = ratio < 1 / 1.35
 
         if process_split and is_tall:
-            img = img.resize((size, size * img.height // img.width))
+            img = img.resize((width, height * img.height // img.width))
 
-            top = img.crop((0, 0, size, size))
+            top = img.crop((0, 0, width, height))
             save_pic(top, index)
 
-            bot = img.crop((0, img.height - size, size, img.height))
+            bot = img.crop((0, img.height - height, width, img.height))
             save_pic(bot, index)
         elif process_split and is_wide:
-            img = img.resize((size * img.width // img.height, size))
+            img = img.resize((width * img.width // img.height, height))
 
-            left = img.crop((0, 0, size, size))
+            left = img.crop((0, 0, width, height))
             save_pic(left, index)
 
-            right = img.crop((img.width - size, 0, img.width, size))
+            right = img.crop((img.width - width, 0, img.width, height))
             save_pic(right, index)
         else:
-            img = images.resize_image(1, img, size, size)
+            img = images.resize_image(1, img, width, height)
             save_pic(img, index)
 
         shared.state.nextjob()
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 769682ea..5965c5a0 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -6,7 +6,6 @@ import torch
 import tqdm
 import html
 import datetime
-import math
 
 
 from modules import shared, devices, sd_hijack, processing, sd_models
@@ -157,7 +156,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -183,7 +182,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
 
     hijack = sd_hijack.model_hijack
 
@@ -227,7 +226,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             loss.backward()
             optimizer.step()
 
-        epoch_num = math.floor(embedding.step / epoch_len)
+        epoch_num = embedding.step // epoch_len
         epoch_step = embedding.step - (epoch_num * epoch_len) + 1
 
         pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
@@ -243,8 +242,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 sd_model=shared.sd_model,
                 prompt=text,
                 steps=20,
-                height=training_size,
-                width=training_size,
+				height=training_height,
+				width=training_width,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
diff --git a/modules/ui.py b/modules/ui.py
index f821fd8d..8c06ad7c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1029,7 +1029,8 @@ def create_ui(wrap_gradio_gpu_call):
 
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
-                    process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
@@ -1050,7 +1051,8 @@ def create_ui(wrap_gradio_gpu_call):
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
@@ -1095,7 +1097,8 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 process_src,
                 process_dst,
-                process_size,
+                process_width,
+                process_height,
                 process_flip,
                 process_split,
                 process_caption,
@@ -1114,7 +1117,8 @@ def create_ui(wrap_gradio_gpu_call):
                 learn_rate,
                 dataset_directory,
                 log_directory,
-                training_size,
+                training_width,
+                training_height,
                 steps,
                 num_repeats,
                 create_image_every,
-- 
cgit v1.2.1


From ce37fdd30e9fc0fe0bc5805a068ce8b11b42b5a3 Mon Sep 17 00:00:00 2001
From: Ben <110583491+TheLastBen@users.noreply.github.com>
Date: Sat, 8 Oct 2022 22:03:00 +0100
Subject: maximize the view

---
 style.css | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/style.css b/style.css
index c0c3f2bb..04bb9576 100644
--- a/style.css
+++ b/style.css
@@ -1,3 +1,7 @@
+.container {
+    max-width: 100%;
+}
+
 .output-html p {margin: 0 0.5em;}
 
 .row > *,
-- 
cgit v1.2.1


From 707a431100362645e914042bb344d08439f48ac8 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 15:34:49 +0100
Subject: add pixel data footer

---
 modules/textual_inversion/textual_inversion.py | 48 ++++++++++++++++++++++++--
 1 file changed, 46 insertions(+), 2 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 7a24192e..6fb64691 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -12,6 +12,7 @@ from ..images import captionImageOverlay
 import numpy as np
 import base64
 import json
+import zlib
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
@@ -20,7 +21,7 @@ class EmbeddingEncoder(json.JSONEncoder):
     def default(self, obj):
         if isinstance(obj, torch.Tensor):
             return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()}
-        return json.JSONEncoder.default(self, o)
+        return json.JSONEncoder.default(self, obj)
 
 class EmbeddingDecoder(json.JSONDecoder):
     def __init__(self, *args, **kwargs):
@@ -38,6 +39,45 @@ def embeddingFromB64(data):
     d = base64.b64decode(data)
     return json.loads(d,cls=EmbeddingDecoder)
 
+def appendImageDataFooter(image,data):
+    d = 3
+    data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9)
+    dnp = np.frombuffer(data_compressed,np.uint8).copy()
+    w = image.size[0]
+    next_size = dnp.shape[0] + (w-(dnp.shape[0]%w))
+    next_size = next_size + ((w*d)-(next_size%(w*d)))
+    dnp.resize(next_size)
+    dnp = dnp.reshape((-1,w,d))
+    print(dnp.shape)
+    im = Image.fromarray(dnp,mode='RGB')
+    background = Image.new('RGB',(image.size[0],image.size[1]+im.size[1]+1),(0,0,0))
+    background.paste(image,(0,0))
+    background.paste(im,(0,image.size[1]+1))
+    return background
+
+def crop_black(img,tol=0):
+    mask = (img>tol).all(2)
+    mask0,mask1 = mask.any(0),mask.any(1)
+    col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax()
+    row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax()
+    return img[row_start:row_end,col_start:col_end]
+
+def extractImageDataFooter(image):
+    d=3
+    outarr = crop_black(np.array(image.getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) )
+    lastRow = np.where( np.sum(outarr, axis=(1,2))==0)
+    if lastRow[0].shape[0] == 0:
+        print('Image data block not found.')
+        return None
+    lastRow = lastRow[0]
+    
+    lastRow = lastRow.max()
+
+    dataBlock = outarr[lastRow+1::].astype(np.uint8).flatten().tobytes()
+    print(lastRow)
+    data = zlib.decompress(dataBlock)
+    return json.loads(data,cls=EmbeddingDecoder)
+
 class Embedding:
     def __init__(self, vec, name, step=None):
         self.vec = vec
@@ -113,6 +153,9 @@ class EmbeddingDatabase:
                 if 'sd-ti-embedding' in embed_image.text:
                     data = embeddingFromB64(embed_image.text['sd-ti-embedding'])
                     name = data.get('name',name)
+                else:
+                    data = extractImageDataFooter(embed_image)
+                    name = data.get('name',name)
             else:
                 data = torch.load(path, map_location="cpu")
 
@@ -190,7 +233,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -308,6 +351,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 footer_right = '{}'.format(embedding.step)
 
                 captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right)
+                captioned_image = appendImageDataFooter(captioned_image,data)
 
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
             
-- 
cgit v1.2.1


From df6d0d9286279c41c4c67460c3158fa268697524 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 15:43:09 +0100
Subject: convert back to rgb as some hosts add alpha

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 6fb64691..667a7cf2 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -64,7 +64,7 @@ def crop_black(img,tol=0):
 
 def extractImageDataFooter(image):
     d=3
-    outarr = crop_black(np.array(image.getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) )
+    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) )
     lastRow = np.where( np.sum(outarr, axis=(1,2))==0)
     if lastRow[0].shape[0] == 0:
         print('Image data block not found.')
-- 
cgit v1.2.1


From f347ddfd808c56bb1bacdec0c4bedf826ff85cd8 Mon Sep 17 00:00:00 2001
From: RW21 <rinze.watanabe.translation@gmail.com>
Date: Mon, 10 Oct 2022 10:44:11 +0900
Subject: Remove max_batch_count from ui.py

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 8c06ad7c..8ba84911 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -524,7 +524,7 @@ def create_ui(wrap_gradio_gpu_call):
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
                 with gr.Row():
-                    batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
 
                 cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
@@ -710,7 +710,7 @@ def create_ui(wrap_gradio_gpu_call):
                     tiling = gr.Checkbox(label='Tiling', value=False)
 
                 with gr.Row():
-                    batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
 
                 with gr.Group():
-- 
cgit v1.2.1


From b340439586d844e76782149ca1857c8de35773ec Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Mon, 10 Oct 2022 05:28:06 +0100
Subject: Unlimited Token Works

Unlimited tokens actually work now. Works with textual inversion too. Replaces the previous not-so-much-working implementation.
---
 modules/sd_hijack.py | 69 ++++++++++++++++++++++++++++++++++------------------
 1 file changed, 46 insertions(+), 23 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 437acce4..8d5c77d8 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -43,10 +43,7 @@ def undo_optimizations():
 
 
 def get_target_prompt_token_count(token_count):
-    if token_count < 75:
-        return 75
-
-    return math.ceil(token_count / 10) * 10
+    return math.ceil(max(token_count, 1) / 75) * 75
 
 
 class StableDiffusionModelHijack:
@@ -127,7 +124,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 self.token_mults[ident] = mult
 
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
-        id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
 
         if opts.enable_emphasis:
@@ -154,7 +150,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     i += 1
                 else:
                     emb_len = int(embedding.vec.shape[0])
-                    fixes.append((len(remade_tokens), embedding))
+                    iteration = len(remade_tokens) // 75
+                    fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
                     remade_tokens += [0] * emb_len
                     multipliers += [weight] * emb_len
                     used_custom_terms.append((embedding.name, embedding.checksum()))
@@ -162,10 +159,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
         token_count = len(remade_tokens)
         prompt_target_length = get_target_prompt_token_count(token_count)
-        tokens_to_add = prompt_target_length - len(remade_tokens) + 1
+        tokens_to_add = prompt_target_length - len(remade_tokens)
 
-        remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add
-        multipliers = [1.0] + multipliers + [1.0] * tokens_to_add
+        remade_tokens = remade_tokens + [id_end] * tokens_to_add
+        multipliers = multipliers + [1.0] * tokens_to_add
 
         return remade_tokens, fixes, multipliers, token_count
 
@@ -260,29 +257,55 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             hijack_fixes.append(fixes)
             batch_multipliers.append(multipliers)
         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
-
+    
     def forward(self, text):
-
-        if opts.use_old_emphasis_implementation:
+        use_old = opts.use_old_emphasis_implementation
+        if use_old:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
         else:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
 
-        self.hijack.fixes = hijack_fixes
         self.hijack.comments += hijack_comments
 
         if len(used_custom_terms) > 0:
             self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
+        
+        if use_old:
+            self.hijack.fixes = hijack_fixes
+            return self.process_tokens(remade_batch_tokens, batch_multipliers)
+        
+        z = None
+        i = 0
+        while max(map(len, remade_batch_tokens)) != 0:
+            rem_tokens = [x[75:] for x in remade_batch_tokens]
+            rem_multipliers = [x[75:] for x in batch_multipliers]
+            
+            self.hijack.fixes = []
+            for unfiltered in hijack_fixes:
+                fixes = []
+                for fix in unfiltered:
+                    if fix[0] == i:
+                        fixes.append(fix[1])
+                self.hijack.fixes.append(fixes)
+            
+            z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers])
+            z = z1 if z is None else torch.cat((z, z1), axis=-2)
+            
+            remade_batch_tokens = rem_tokens
+            batch_multipliers = rem_multipliers
+            i += 1
+        
+        return z
+        
+    
+    def process_tokens(self, remade_batch_tokens, batch_multipliers):
+        if not opts.use_old_emphasis_implementation:
+            remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
+            batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
+        
+        tokens = torch.asarray(remade_batch_tokens).to(device)
+        outputs = self.wrapped.transformer(input_ids=tokens)
 
-        target_token_count = get_target_prompt_token_count(token_count) + 2
-
-        position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76]
-        position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
-
-        remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
-        tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
-
-        outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=-opts.CLIP_stop_at_last_layers)
         if opts.CLIP_stop_at_last_layers > 1:
             z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
             z = self.wrapped.transformer.text_model.final_layer_norm(z)
@@ -290,7 +313,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             z = outputs.last_hidden_state
 
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
-        batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
+        batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
         batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
         original_mean = z.mean()
         z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
-- 
cgit v1.2.1


From 460bbae58726c177beddfcddf351f27e205d3fb2 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Mon, 10 Oct 2022 16:09:06 +0100
Subject: Pad beginning of textual inversion embedding

---
 modules/sd_hijack.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 8d5c77d8..3a60cd63 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -151,6 +151,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 else:
                     emb_len = int(embedding.vec.shape[0])
                     iteration = len(remade_tokens) // 75
+                    if (len(remade_tokens) + emb_len) // 75 != iteration:
+                        rem = (75 * (iteration + 1) - len(remade_tokens))
+                        remade_tokens += [id_end] * rem
+                        multipliers += [1.0] * rem
+                        iteration += 1
                     fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
                     remade_tokens += [0] * emb_len
                     multipliers += [weight] * emb_len
-- 
cgit v1.2.1


From d5c14365fd468dbf89fa12a68bea5b217077273c Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Mon, 10 Oct 2022 16:13:47 +0100
Subject: Add back in output hidden states parameter

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 3a60cd63..3edc0e9d 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -309,7 +309,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
         
         tokens = torch.asarray(remade_batch_tokens).to(device)
-        outputs = self.wrapped.transformer(input_ids=tokens)
+        outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
 
         if opts.CLIP_stop_at_last_layers > 1:
             z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
-- 
cgit v1.2.1


From 6c36fe5719a824fa18f6ad3e02727783f095bc5f Mon Sep 17 00:00:00 2001
From: Melan <alexleander91@gmail.com>
Date: Mon, 10 Oct 2022 18:16:04 +0200
Subject: Add ctrl+enter as a shortcut to quickly start a generation.

---
 script.js | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/script.js b/script.js
index cf989605..a92c0f77 100644
--- a/script.js
+++ b/script.js
@@ -40,6 +40,22 @@ document.addEventListener("DOMContentLoaded", function() {
     mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
 });
 
+/**
+ * Add a ctrl+enter as a shortcut to start a generation
+ */
+ document.addEventListener('keydown', function(e) {
+    var handled = false;
+    if (e.key !== undefined) {
+        if((e.key == "Enter" && (e.metaKey || e.ctrlKey))) handled = true;
+    } else if (e.keyCode !== undefined) {
+        if((e.keyCode == 13 && (e.metaKey || e.ctrlKey))) handled = true;
+    }
+    if (handled) { 
+        gradioApp().querySelector("#txt2img_generate").click(); 
+        e.preventDefault();
+    }
+})
+
 /**
  * checks that a UI element is not in another hidden element or tab content
  */
-- 
cgit v1.2.1


From 9d33baba587637815d818e5e641d8f8b74c4900d Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Mon, 10 Oct 2022 18:46:48 +0300
Subject: Always show previous mask and fix extras_send dest

---
 modules/ui.py | 2 +-
 style.css     | 7 +++++++
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 8ba84911..e8039d76 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -961,7 +961,7 @@ def create_ui(wrap_gradio_gpu_call):
         
         extras_send_to_inpaint.click(
             fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_img2img",
+            _js="extract_image_from_gallery_inpaint",
             inputs=[result_images],
             outputs=[init_img_with_mask],
         )
diff --git a/style.css b/style.css
index 04bb9576..00a3d07f 100644
--- a/style.css
+++ b/style.css
@@ -467,3 +467,10 @@ input[type="range"]{
     max-width: 32em;
     padding: 0;
 }
+
+canvas[key="mask"] {
+    z-index: 12 !important;
+    filter: invert();
+    mix-blend-mode: multiply;
+    pointer-events: none;
+}
-- 
cgit v1.2.1


From b8c38f2bbfa28904f67f0c4f9cabab4d85ebced2 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 10 Oct 2022 17:44:58 +0300
Subject: change prebuilt wheel

---
 launch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index f42f557d..e1000f55 100644
--- a/launch.py
+++ b/launch.py
@@ -127,7 +127,7 @@ def prepare_enviroment():
 
     if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
         if platform.system() == "Windows":
-            run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
+            run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/c/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
         elif platform.system() == "Linux":
             run_pip("install xformers", "xformers")
 
-- 
cgit v1.2.1


From 623251ce2b8d152e242011f62984a8247a14a389 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 10 Oct 2022 17:45:38 +0300
Subject: allow pascal onwards

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 3edc0e9d..827bf304 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -23,7 +23,7 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
 
-    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6)):
+    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
-- 
cgit v1.2.1


From 3e7a981194ed9c454e951365846e4eba66fa7095 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 10 Oct 2022 17:51:05 +0300
Subject: remove functorch

---
 modules/sd_hijack_optimizations.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 634fb4b2..18408e62 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -13,8 +13,6 @@ from modules import shared
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
     try:
         import xformers.ops
-        import functorch
-        xformers._is_functorch_available = True
         shared.xformers_available = True
     except Exception:
         print("Cannot import xformers", file=sys.stderr)
-- 
cgit v1.2.1


From 5c3254b3ee62ef46cb2e3a6ed14182efeb868f30 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 10 Oct 2022 17:51:41 +0300
Subject: Update requirements.txt

---
 requirements.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 81641d68..631fe616 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,4 +23,3 @@ resize-right
 torchdiffeq
 kornia
 lark
-functorch
-- 
cgit v1.2.1


From e37d0cdd06772c8d6edb2272c0ef25c46c74cc6d Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 10 Oct 2022 17:51:53 +0300
Subject: Update requirements_versions.txt

---
 requirements_versions.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/requirements_versions.txt b/requirements_versions.txt
index fec3e9d5..fdff2687 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -22,4 +22,3 @@ resize-right==0.0.2
 torchdiffeq==0.2.3
 kornia==0.6.7
 lark==1.1.2
-functorch==0.2.1
-- 
cgit v1.2.1


From ece27fe98933eb0eda8ea94dc496dd7554f3a08f Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sun, 9 Oct 2022 18:55:33 +0300
Subject: Add files via upload

---
 modules/swinir_model_arch_v2.py | 1017 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 1017 insertions(+)
 create mode 100644 modules/swinir_model_arch_v2.py

diff --git a/modules/swinir_model_arch_v2.py b/modules/swinir_model_arch_v2.py
new file mode 100644
index 00000000..0e28ae6e
--- /dev/null
+++ b/modules/swinir_model_arch_v2.py
@@ -0,0 +1,1017 @@
+# -----------------------------------------------------------------------------------
+# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/
+# Written by Conde and Choi et al.
+# -----------------------------------------------------------------------------------
+
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
+                 pretrained_window_size=[0, 0]):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.pretrained_window_size = pretrained_window_size
+        self.num_heads = num_heads
+
+        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
+
+        # mlp to generate continuous relative position bias
+        self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
+                                     nn.ReLU(inplace=True),
+                                     nn.Linear(512, num_heads, bias=False))
+
+        # get relative_coords_table
+        relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
+        relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
+        relative_coords_table = torch.stack(
+            torch.meshgrid([relative_coords_h,
+                            relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
+        if pretrained_window_size[0] > 0:
+            relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
+            relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
+        else:
+            relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
+            relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
+        relative_coords_table *= 8  # normalize to -8, 8
+        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
+            torch.abs(relative_coords_table) + 1.0) / np.log2(8)
+
+        self.register_buffer("relative_coords_table", relative_coords_table)
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=False)
+        if qkv_bias:
+            self.q_bias = nn.Parameter(torch.zeros(dim))
+            self.v_bias = nn.Parameter(torch.zeros(dim))
+        else:
+            self.q_bias = None
+            self.v_bias = None
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv_bias = None
+        if self.q_bias is not None:
+            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        # cosine attention
+        attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
+        logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()
+        attn = attn * logit_scale
+
+        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
+        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, ' \
+               f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+        pretrained_window_size (int): Window size in pre-training.
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
+            pretrained_window_size=to_2tuple(pretrained_window_size))
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            attn_mask = self.calculate_mask(self.input_resolution)
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+        
+    def calculate_mask(self, x_size):
+        # calculate attention mask for SW-MSA
+        H, W = x_size
+        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+        return attn_mask        
+
+    def forward(self, x, x_size):
+        H, W = x_size
+        B, L, C = x.shape
+        #assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_x = x
+
+        # partition windows
+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+        if self.input_resolution == x_size:
+            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+        else:
+            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
+            
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+        x = x.view(B, H * W, C)
+        x = shortcut + self.drop_path(self.norm1(x))
+
+        # FFN
+        x = x + self.drop_path(self.norm2(self.mlp(x)))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        nW = H * W / self.window_size / self.window_size
+        flops += nW * self.attn.flops(self.window_size * self.window_size)
+        # mlp
+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+        # norm2
+        flops += self.dim * H * W
+        return flops
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(2 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.reduction(x)
+        x = self.norm(x)
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        flops += H * W * self.dim // 2
+        return flops    
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        pretrained_window_size (int): Local window size in pre-training.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 pretrained_window_size=0):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer,
+                                 pretrained_window_size=pretrained_window_size)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, x_size):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, x_size)
+            else:
+                x = blk(x, x_size)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        if self.downsample is not None:
+            flops += self.downsample.flops()
+        return flops
+
+    def _init_respostnorm(self):
+        for blk in self.blocks:
+            nn.init.constant_(blk.norm1.bias, 0)
+            nn.init.constant_(blk.norm1.weight, 0)
+            nn.init.constant_(blk.norm2.bias, 0)
+            nn.init.constant_(blk.norm2.weight, 0)
+            
+class PatchEmbed(nn.Module):
+    r""" Image to Patch Embedding
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        # assert H == self.img_size[0] and W == self.img_size[1],
+        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self):
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops           
+
+class RSTB(nn.Module):
+    """Residual Swin Transformer Block (RSTB).
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        img_size: Input image size.
+        patch_size: Patch size.
+        resi_connection: The convolutional block before residual connection.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 img_size=224, patch_size=4, resi_connection='1conv'):
+        super(RSTB, self).__init__()
+
+        self.dim = dim
+        self.input_resolution = input_resolution
+
+        self.residual_group = BasicLayer(dim=dim,
+                                         input_resolution=input_resolution,
+                                         depth=depth,
+                                         num_heads=num_heads,
+                                         window_size=window_size,
+                                         mlp_ratio=mlp_ratio,
+                                         qkv_bias=qkv_bias, 
+                                         drop=drop, attn_drop=attn_drop,
+                                         drop_path=drop_path,
+                                         norm_layer=norm_layer,
+                                         downsample=downsample,
+                                         use_checkpoint=use_checkpoint)
+
+        if resi_connection == '1conv':
+            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
+                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+            norm_layer=None)
+
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+            norm_layer=None)
+
+    def forward(self, x, x_size):
+        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
+
+    def flops(self):
+        flops = 0
+        flops += self.residual_group.flops()
+        H, W = self.input_resolution
+        flops += H * W * self.dim * self.dim * 9
+        flops += self.patch_embed.flops()
+        flops += self.patch_unembed.flops()
+
+        return flops
+
+class PatchUnEmbed(nn.Module):
+    r""" Image to Patch Unembedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+    def forward(self, x, x_size):
+        B, HW, C = x.shape
+        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
+        return x
+
+    def flops(self):
+        flops = 0
+        return flops
+
+
+class Upsample(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+        super(Upsample, self).__init__(*m)
+        
+class Upsample_hf(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+        super(Upsample_hf, self).__init__(*m)        
+
+
+class UpsampleOneStep(nn.Sequential):
+    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+       Used in lightweight SR to save parameters.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+
+    """
+
+    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
+        self.num_feat = num_feat
+        self.input_resolution = input_resolution
+        m = []
+        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+        m.append(nn.PixelShuffle(scale))
+        super(UpsampleOneStep, self).__init__(*m)
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.num_feat * 3 * 9
+        return flops
+    
+    
+
+class Swin2SR(nn.Module):
+    r""" Swin2SR
+        A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
+
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 64
+        patch_size (int | tuple(int)): Patch size. Default: 1
+        in_chans (int): Number of input image channels. Default: 3
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_size (int): Window size. Default: 7
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+        img_range: Image range. 1. or 255.
+        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
+    """
+
+    def __init__(self, img_size=64, patch_size=1, in_chans=3,
+                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 window_size=7, mlp_ratio=4., qkv_bias=True, 
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
+                 **kwargs):
+        super(Swin2SR, self).__init__()
+        num_in_ch = in_chans
+        num_out_ch = in_chans
+        num_feat = 64
+        self.img_range = img_range
+        if in_chans == 3:
+            rgb_mean = (0.4488, 0.4371, 0.4040)
+            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+        else:
+            self.mean = torch.zeros(1, 1, 1, 1)
+        self.upscale = upscale
+        self.upsampler = upsampler
+        self.window_size = window_size
+
+        #####################################################################################################
+        ################################### 1, shallow feature extraction ###################################
+        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
+
+        #####################################################################################################
+        ################################### 2, deep feature extraction ######################################
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = embed_dim
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # merge non-overlapping patches into image
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # build Residual Swin Transformer blocks (RSTB)
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = RSTB(dim=embed_dim,
+                         input_resolution=(patches_resolution[0],
+                                           patches_resolution[1]),
+                         depth=depths[i_layer],
+                         num_heads=num_heads[i_layer],
+                         window_size=window_size,
+                         mlp_ratio=self.mlp_ratio,
+                         qkv_bias=qkv_bias, 
+                         drop=drop_rate, attn_drop=attn_drop_rate,
+                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
+                         norm_layer=norm_layer,
+                         downsample=None,
+                         use_checkpoint=use_checkpoint,
+                         img_size=img_size,
+                         patch_size=patch_size,
+                         resi_connection=resi_connection
+
+                         )
+            self.layers.append(layer)
+            
+        if self.upsampler == 'pixelshuffle_hf':
+            self.layers_hf = nn.ModuleList()
+            for i_layer in range(self.num_layers):
+                layer = RSTB(dim=embed_dim,
+                             input_resolution=(patches_resolution[0],
+                                               patches_resolution[1]),
+                             depth=depths[i_layer],
+                             num_heads=num_heads[i_layer],
+                             window_size=window_size,
+                             mlp_ratio=self.mlp_ratio,
+                             qkv_bias=qkv_bias, 
+                             drop=drop_rate, attn_drop=attn_drop_rate,
+                             drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
+                             norm_layer=norm_layer,
+                             downsample=None,
+                             use_checkpoint=use_checkpoint,
+                             img_size=img_size,
+                             patch_size=patch_size,
+                             resi_connection=resi_connection
+
+                             )
+                self.layers_hf.append(layer)
+                        
+        self.norm = norm_layer(self.num_features)
+
+        # build the last conv layer in deep feature extraction
+        if resi_connection == '1conv':
+            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
+
+        #####################################################################################################
+        ################################ 3, high quality image reconstruction ################################
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.upsample = Upsample(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+        elif self.upsampler == 'pixelshuffle_aux':
+            self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+            self.conv_before_upsample = nn.Sequential(
+                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                nn.LeakyReLU(inplace=True))
+            self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.conv_after_aux = nn.Sequential(
+                nn.Conv2d(3, num_feat, 3, 1, 1),
+                nn.LeakyReLU(inplace=True))            
+            self.upsample = Upsample(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            
+        elif self.upsampler == 'pixelshuffle_hf':
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.upsample = Upsample(upscale, num_feat)
+            self.upsample_hf = Upsample_hf(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+            self.conv_before_upsample_hf = nn.Sequential(
+                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                nn.LeakyReLU(inplace=True))
+            self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR (to save parameters)
+            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
+                                            (patches_resolution[0], patches_resolution[1]))
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR (less artifacts)
+            assert self.upscale == 4, 'only support x4 now.'
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def check_image_size(self, x):
+        _, _, h, w = x.size()
+        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
+        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
+        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+        return x
+
+    def forward_features(self, x):
+        x_size = (x.shape[2], x.shape[3])
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers:
+            x = layer(x, x_size)
+
+        x = self.norm(x)  # B L C
+        x = self.patch_unembed(x, x_size)
+
+        return x
+    
+    def forward_features_hf(self, x):
+        x_size = (x.shape[2], x.shape[3])
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers_hf:
+            x = layer(x, x_size)
+
+        x = self.norm(x)  # B L C
+        x = self.patch_unembed(x, x_size)
+
+        return x    
+
+    def forward(self, x):
+        H, W = x.shape[2:]
+        x = self.check_image_size(x)
+
+        self.mean = self.mean.type_as(x)
+        x = (x - self.mean) * self.img_range
+
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.conv_last(self.upsample(x))
+        elif self.upsampler == 'pixelshuffle_aux':
+            bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
+            bicubic = self.conv_bicubic(bicubic)
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            aux = self.conv_aux(x) # b, 3, LR_H, LR_W
+            x = self.conv_after_aux(aux)
+            x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
+            x = self.conv_last(x)
+            aux = aux / self.img_range + self.mean
+        elif self.upsampler == 'pixelshuffle_hf':
+            # for classical SR with HF
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x_before = self.conv_before_upsample(x)
+            x_out = self.conv_last(self.upsample(x_before))
+            
+            x_hf = self.conv_first_hf(x_before)
+            x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
+            x_hf = self.conv_before_upsample_hf(x_hf)
+            x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
+            x = x_out + x_hf
+            x_hf = x_hf / self.img_range + self.mean
+
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.upsample(x)
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.conv_last(self.lrelu(self.conv_hr(x)))
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            x_first = self.conv_first(x)
+            res = self.conv_after_body(self.forward_features(x_first)) + x_first
+            x = x + self.conv_last(res)
+        
+        x = x / self.img_range + self.mean
+        if self.upsampler == "pixelshuffle_aux":
+            return x[:, :, :H*self.upscale, :W*self.upscale], aux
+        
+        elif self.upsampler == "pixelshuffle_hf":
+            x_out = x_out / self.img_range + self.mean
+            return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
+        
+        else:
+            return x[:, :, :H*self.upscale, :W*self.upscale]
+
+    def flops(self):
+        flops = 0
+        H, W = self.patches_resolution
+        flops += H * W * 3 * self.embed_dim * 9
+        flops += self.patch_embed.flops()
+        for i, layer in enumerate(self.layers):
+            flops += layer.flops()
+        flops += H * W * 3 * self.embed_dim * self.embed_dim
+        flops += self.upsample.flops()
+        return flops
+
+
+if __name__ == '__main__':
+    upscale = 4
+    window_size = 8
+    height = (1024 // upscale // window_size + 1) * window_size
+    width = (720 // upscale // window_size + 1) * window_size
+    model = Swin2SR(upscale=2, img_size=(height, width),
+                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
+                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+    print(model)
+    print(height, width, model.flops() / 1e9)
+
+    x = torch.randn((1, 3, height, width))
+    x = model(x)
+    print(x.shape)
\ No newline at end of file
-- 
cgit v1.2.1


From ed769977f0d0f201d8e361d365102f18775fc62c Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sun, 9 Oct 2022 18:56:59 +0300
Subject: add swinir v2 support

---
 modules/swinir_model.py | 35 ++++++++++++++++++++++++++++-------
 1 file changed, 28 insertions(+), 7 deletions(-)

diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index fbd11f84..baa02e3d 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -10,6 +10,7 @@ from tqdm import tqdm
 from modules import modelloader
 from modules.shared import cmd_opts, opts, device
 from modules.swinir_model_arch import SwinIR as net
+from modules.swinir_model_arch_v2 import Swin2SR as net2
 from modules.upscaler import Upscaler, UpscalerData
 
 precision_scope = (
@@ -57,22 +58,42 @@ class UpscalerSwinIR(Upscaler):
             filename = path
         if filename is None or not os.path.exists(filename):
             return None
-        model = net(
+        if filename.endswith(".v2.pth"):
+            model = net2(
             upscale=scale,
             in_chans=3,
             img_size=64,
             window_size=8,
             img_range=1.0,
-            depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
-            embed_dim=240,
-            num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
+            depths=[6, 6, 6, 6, 6, 6],
+            embed_dim=180,
+            num_heads=[6, 6, 6, 6, 6, 6],
             mlp_ratio=2,
             upsampler="nearest+conv",
-            resi_connection="3conv",
-        )
+            resi_connection="1conv",
+            )
+            params = None
+        else:
+            model = net(
+                upscale=scale,
+                in_chans=3,
+                img_size=64,
+                window_size=8,
+                img_range=1.0,
+                depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
+                embed_dim=240,
+                num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
+                mlp_ratio=2,
+                upsampler="nearest+conv",
+                resi_connection="3conv",
+            )
+            params = "params_ema"
 
         pretrained_model = torch.load(filename)
-        model.load_state_dict(pretrained_model["params_ema"], strict=True)
+        if params is not None:
+            model.load_state_dict(pretrained_model[params], strict=True)
+        else:
+            model.load_state_dict(pretrained_model, strict=True)
         if not cmd_opts.no_half:
             model = model.half()
         return model
-- 
cgit v1.2.1


From af62ad4d25dcd0454944368f4925d83101cdedbc Mon Sep 17 00:00:00 2001
From: ssysm <me@theeditorstudio.com>
Date: Mon, 10 Oct 2022 13:25:28 -0400
Subject: change vae loading method

---
 modules/sd_models.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index b0e1d8bd..7a42d924 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -150,9 +150,16 @@ def load_model_weights(model, checkpoint_info):
 
     devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
 
-    vae_file = shared.cmd_opts.vae_path or os.path.splitext(checkpoint_file)[0] + ".vae.pt"
+    vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
+    if os.path.exists(vae_file):
+        print(f"Found VAE Weights: {vae_file}")
+    elif shared.cmd_opts.vae_path != None:
+        vae_file = shared.cmd_opts.vae_path
+        print(f'No VAE found for inside the model folder. Using CLI specified : {vae_file}')
+    else:
+        print("No VAE found for inside the model folder. Passing.")
+
     if os.path.exists(vae_file):
-        print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location="cpu")
         vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
-- 
cgit v1.2.1


From 39919c40dd18f5a14ae21403efea1b0f819756c7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 10 Oct 2022 20:32:37 +0300
Subject: add eta noise seed delta option

---
 javascript/hints.js   | 1 +
 modules/processing.py | 6 +++++-
 modules/shared.py     | 1 +
 3 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index 8e352e94..47b80776 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -79,6 +79,7 @@ titles = {
     "Highres. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
     "Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.",
 
+    "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
 }
 
 
diff --git a/modules/processing.py b/modules/processing.py
index 50ba4fc5..698b3069 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -207,7 +207,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     # enables the generation of additional tensors with noise that the sampler will use during its processing.
     # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
     # produce the same images as with two batches [100], [101].
-    if p is not None and p.sampler is not None and len(seeds) > 1 and opts.enable_batch_seeds:
+    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
         sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
     else:
         sampler_noises = None
@@ -247,6 +247,9 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
         if sampler_noises is not None:
             cnt = p.sampler.number_of_needed_noises(p)
 
+            if opts.eta_noise_seed_delta > 0:
+                torch.manual_seed(seed + opts.eta_noise_seed_delta)
+
             for j in range(cnt):
                 sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
 
@@ -301,6 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
         "Clip skip": None if clip_skip <= 1 else clip_skip,
+        "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
     }
 
     generation_params.update(p.extra_generation_params)
diff --git a/modules/shared.py b/modules/shared.py
index 5dfc344c..b1c65ecf 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -260,6 +260,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
 
-- 
cgit v1.2.1


From 727e4d108674dc2813507e2a973a733ef21e8d53 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 10 Oct 2022 20:46:55 +0300
Subject: no to different messages plus fix using != to compare to None

---
 modules/sd_models.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 4c06051e..0a55b4c3 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -152,15 +152,12 @@ def load_model_weights(model, checkpoint_info):
     devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
     vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
-    if os.path.exists(vae_file):
-        print(f"Found VAE Weights: {vae_file}")
-    elif shared.cmd_opts.vae_path != None:
+
+    if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
         vae_file = shared.cmd_opts.vae_path
-        print(f'No VAE found for inside the model folder. Using CLI specified : {vae_file}')
-    else:
-        print("No VAE found for inside the model folder. Passing.")
 
     if os.path.exists(vae_file):
+        print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location="cpu")
         vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
-- 
cgit v1.2.1


From 1d64976dbc5a0f3124567b91fadd5014a9d93c5f Mon Sep 17 00:00:00 2001
From: Justin Maier <Zipp425@gmail.com>
Date: Mon, 10 Oct 2022 12:04:21 -0600
Subject: Simplify crop logic

---
 modules/extras.py | 14 +++-----------
 modules/ui.py     |  4 ++--
 2 files changed, 5 insertions(+), 13 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 83ca7049..b24d7de3 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -73,16 +73,6 @@ def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility,
             crop_info = " (crop)" if upscaling_crop else ""
             info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
 
-            def crop_upscaled_center(image, resize_w, resize_h):
-                left = int(math.ceil((image.width - resize_w) / 2))
-                right = image.width - int(math.floor((image.width - resize_w) / 2))
-                top = int(math.ceil((image.height - resize_h) / 2))
-                bottom = image.height - int(math.floor((image.height - resize_h) / 2))
-
-                image = image.crop((left, top, right, bottom))
-                return image
-
-
         if upscaling_resize != 1.0:
             def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
                 small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
@@ -94,7 +84,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility,
                     upscaler = shared.sd_upscalers[scaler_index]
                     c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
                     if mode == 1 and crop:
-                        c = crop_upscaled_center(c, resize_w, resize_h)
+                        cropped = Image.new("RGB", (resize_w, resize_h))
+                        cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
+                        c = cropped
                     cached_images[key] = c
 
                 return c
diff --git a/modules/ui.py b/modules/ui.py
index 4bb2892b..1aabe18d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -909,8 +909,8 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.TabItem('Scale to'):
                         with gr.Group():
                             with gr.Row():
-                                upscaling_resize_w = gr.Number(label="Width", value=512)
-                                upscaling_resize_h = gr.Number(label="Height", value=512)
+                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
+                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
 
                 with gr.Group():
-- 
cgit v1.2.1


From 5da1ba0e91a81804dc911d34c9a2e6956a23199c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 10 Oct 2022 21:24:11 +0300
Subject: remove batch size restriction from X/Y plot

---
 scripts/xy_grid.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 771eb8e4..42e1489c 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -205,7 +205,10 @@ class Script(scripts.Script):
         if not no_fixed_seeds:
             modules.processing.fix_seed(p)
 
-        p.batch_size = 1
+        if not opts.return_grid:
+            p.batch_size = 1
+
+
         CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
 
         def process_axis(opt, vals):
-- 
cgit v1.2.1


From bc3e183b739913e7be91213a256f038b10eb71e9 Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Tue, 11 Oct 2022 04:30:13 +0900
Subject: Textual Inversion: Preprocess and Training will only pick-up image
 files

---
 modules/textual_inversion/dataset.py           | 3 ++-
 modules/textual_inversion/preprocess.py        | 3 ++-
 modules/textual_inversion/textual_inversion.py | 3 ++-
 3 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index bcf772d2..d4baf066 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -22,6 +22,7 @@ class PersonalizedBase(Dataset):
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
+        self.extns = [".jpg",".jpeg",".png"]
 
         self.dataset = []
 
@@ -32,7 +33,7 @@ class PersonalizedBase(Dataset):
 
         assert data_root, 'dataset directory not specified'
 
-        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
+        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root) if os.path.splitext(file_path.casefold())[1] in self.extns]
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
             image = Image.open(path)
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index d7efdef2..b6c78cf8 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -12,12 +12,13 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
     height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
+    extns = [".jpg",".jpeg",".png"]
 
     assert src != dst, 'same directory specified as source and destination'
 
     os.makedirs(dst, exist_ok=True)
 
-    files = os.listdir(src)
+    files = [i for i in os.listdir(src) if os.path.splitext(i.casefold())[1] in extns]
 
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 5965c5a0..45397be9 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -161,6 +161,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
+    extns = [".jpg",".jpeg",".png"]
 
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
 
@@ -200,7 +201,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     if ititial_step > steps:
         return embedding, filename
 
-    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
+    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root) if os.path.splitext(file_path.casefold())[1] in extns])
     epoch_len = (tr_img_len * num_repeats) + tr_img_len
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-- 
cgit v1.2.1


From f98338faa84ecce503e68d8ba13d5f7bbae52730 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 10 Oct 2022 23:15:48 +0300
Subject: add an option to not add watermark to created images

---
 javascript/hints.js | 1 +
 modules/shared.py   | 1 +
 2 files changed, 2 insertions(+)

diff --git a/javascript/hints.js b/javascript/hints.js
index 47b80776..045f2d3c 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -80,6 +80,7 @@ titles = {
     "Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.",
 
     "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
+    "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be bevaing in an unethical manner.",
 }
 
 
diff --git a/modules/shared.py b/modules/shared.py
index da389f9c..ecd15ef5 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -173,6 +173,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
 
     "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
+    "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
 }))
 
 options_templates.update(options_section(('saving-paths', "Paths for saving"), {
-- 
cgit v1.2.1


From 42bf5fa3256bff5e4640e5a626e750d4e49e01e1 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 21:54:21 +0100
Subject: Make cancel generate forever let the current gen complete (#2206)

---
 javascript/contextMenus.js | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js
index 2d82269f..7852793c 100644
--- a/javascript/contextMenus.js
+++ b/javascript/contextMenus.js
@@ -147,10 +147,6 @@ generateOnRepeatId = appendContextMenuOption('#txt2img_generate','Generate forev
 
 cancelGenerateForever = function(){ 
   clearInterval(window.generateOnRepeatInterval) 
-  let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
-  if(interruptbutton.offsetParent){
-      interruptbutton.click();
-  }
 }
 
 appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
-- 
cgit v1.2.1


From 2536ecbb1790da2af0d61b6a26f38732cba665cd Mon Sep 17 00:00:00 2001
From: Fampai <>
Date: Mon, 10 Oct 2022 17:10:29 -0400
Subject: Refactored learning rate code

---
 modules/textual_inversion/textual_inversion.py | 51 ++++++++++++++++++++++++--
 modules/ui.py                                  |  2 +-
 2 files changed, 48 insertions(+), 5 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 5965c5a0..c64a4598 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -189,8 +189,6 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     embedding = hijack.embedding_db.word_embeddings[embedding_name]
     embedding.vec.requires_grad = True
 
-    optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
-
     losses = torch.zeros((32,))
 
     last_saved_file = "<none>"
@@ -203,12 +201,24 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
     epoch_len = (tr_img_len * num_repeats) + tr_img_len
 
+    scheduleIter = iter(LearnSchedule(learn_rate, steps, ititial_step))
+    (learn_rate, end_step) = next(scheduleIter)
+    print(f'Training at rate of {learn_rate} until step {end_step}')
+
+    optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, (x, text) in pbar:
         embedding.step = i + ititial_step
 
-        if embedding.step > steps:
-            break
+        if embedding.step > end_step:
+            try:
+                (learn_rate, end_step) = next(scheduleIter)
+            except:
+                break
+            tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
+            for pg in optimizer.param_groups:
+                pg['lr'] = learn_rate
 
         if shared.state.interrupted:
             break
@@ -277,3 +287,36 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     return embedding, filename
 
+class LearnSchedule:
+    def __init__(self, learn_rate, max_steps, cur_step=0):
+        pairs = learn_rate.split(',')
+        self.rates = []
+        self.it = 0
+        self.maxit = 0
+        for i, pair in enumerate(pairs):
+            tmp = pair.split(':')
+            if len(tmp) == 2:
+                step = int(tmp[1])
+                if step > cur_step:
+                    self.rates.append((float(tmp[0]), min(step, max_steps)))
+                    self.maxit += 1
+                    if step > max_steps:
+                        return
+                elif step == -1:
+                    self.rates.append((float(tmp[0]), max_steps))
+                    self.maxit += 1
+                    return
+            else:
+                self.rates.append((float(tmp[0]), max_steps))
+                self.maxit += 1
+                return
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.it < self.maxit:
+            self.it += 1
+            return self.rates[self.it - 1]
+        else:
+            raise StopIteration
diff --git a/modules/ui.py b/modules/ui.py
index 8c06ad7c..c9e8355b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1047,7 +1047,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
-                    learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
+                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value = "5.0e-03")
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-- 
cgit v1.2.1


From 907a88b2d0be320575c2129d8d6a1d4f3a68f9eb Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Tue, 11 Oct 2022 06:33:08 +0900
Subject: Added .webp .bmp

---
 modules/textual_inversion/dataset.py           | 2 +-
 modules/textual_inversion/preprocess.py        | 2 +-
 modules/textual_inversion/textual_inversion.py | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index d4baf066..0dc54fb7 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -22,7 +22,7 @@ class PersonalizedBase(Dataset):
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
-        self.extns = [".jpg",".jpeg",".png"]
+        self.extns = [".jpg",".jpeg",".png",".webp",".bmp"]
 
         self.dataset = []
 
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index b6c78cf8..8290abe8 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -12,7 +12,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
     height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
-    extns = [".jpg",".jpeg",".png"]
+    extns = [".jpg",".jpeg",".png",".webp",".bmp"]
 
     assert src != dst, 'same directory specified as source and destination'
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index a03b299c..33c923d1 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -161,7 +161,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
-    extns = [".jpg",".jpeg",".png"]
+    extns = [".jpg",".jpeg",".png",".webp",".bmp"]
 
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
 
-- 
cgit v1.2.1


From a1a05ad2d13d0b995dbf8ecead6315f17837ef81 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Mon, 10 Oct 2022 16:47:58 -0500
Subject: import time missing, added to deepbooru fixxing error on
 get_deepbooru_tags

---
 modules/deepbooru.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index cee4a3b4..12555b2e 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -1,6 +1,7 @@
 import os.path
 from concurrent.futures import ProcessPoolExecutor
 import multiprocessing
+import time
 
 
 def get_deepbooru_tags(pil_image, threshold=0.5):
-- 
cgit v1.2.1


From b980e7188c671fc55b26557f097076fb5c976ba0 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Mon, 10 Oct 2022 16:52:54 -0500
Subject: corrected tag return in get_deepbooru_tags

---
 modules/deepbooru.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 12555b2e..ebdba5e0 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -15,7 +15,6 @@ def get_deepbooru_tags(pil_image, threshold=0.5):
     while shared.deepbooru_process_return["value"] == -1:
         time.sleep(0.2)
     release_process()
-    return ret
 
 
 def deepbooru_process(queue, deepbooru_process_return, threshold):
-- 
cgit v1.2.1


From 315d5a8ed975c88f670bc484f40a23fbf3a77b63 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 23:14:44 +0100
Subject: update data dis[play style

---
 modules/textual_inversion/textual_inversion.py | 88 +++++++++++++++++++-------
 1 file changed, 65 insertions(+), 23 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 667a7cf2..95eebea7 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -39,20 +39,59 @@ def embeddingFromB64(data):
     d = base64.b64decode(data)
     return json.loads(d,cls=EmbeddingDecoder)
 
-def appendImageDataFooter(image,data):
+def xorBlock(block):
+    return np.bitwise_xor(block.astype(np.uint8),
+                          ((np.random.RandomState(0xDEADBEEF).random(block.shape)*255).astype(np.uint8)) & 0x0F )
+
+def styleBlock(block,sequence):
+    im = Image.new('RGB',(block.shape[1],block.shape[0]))
+    draw = ImageDraw.Draw(im)
+    i=0
+    for x in range(-6,im.size[0],8):
+        for yi,y in enumerate(range(-6,im.size[1],8)):
+            offset=0
+            if yi%2==0:
+                offset=4
+            shade = sequence[i%len(sequence)]
+            i+=1
+            draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) )
+
+    fg = np.array(im).astype(np.uint8) & 0xF0
+    return block ^ fg
+
+def insertImageDataEmbed(image,data):
     d = 3
     data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9)
     dnp = np.frombuffer(data_compressed,np.uint8).copy()
-    w = image.size[0]
-    next_size = dnp.shape[0] + (w-(dnp.shape[0]%w))
-    next_size = next_size + ((w*d)-(next_size%(w*d)))
-    dnp.resize(next_size)
-    dnp = dnp.reshape((-1,w,d))
-    print(dnp.shape)
-    im = Image.fromarray(dnp,mode='RGB')
-    background = Image.new('RGB',(image.size[0],image.size[1]+im.size[1]+1),(0,0,0))
-    background.paste(image,(0,0))
-    background.paste(im,(0,image.size[1]+1))
+    dnphigh = dnp >> 4
+    dnplow  = dnp & 0x0F
+    
+    h = image.size[1]
+    next_size = dnplow.shape[0] + (h-(dnplow.shape[0]%h))
+    next_size = next_size + ((h*d)-(next_size%(h*d)))
+
+    dnplow.resize(next_size)
+    dnplow = dnplow.reshape((h,-1,d))
+
+    dnphigh.resize(next_size)
+    dnphigh = dnphigh.reshape((h,-1,d))
+
+    edgeStyleWeights = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
+    edgeStyleWeights = (np.abs(edgeStyleWeights)/np.max(np.abs(edgeStyleWeights))*255).astype(np.uint8)
+
+    dnplow   = styleBlock(dnplow,sequence=edgeStyleWeights)
+    dnplow   = xorBlock(dnplow)    
+    dnphigh  = styleBlock(dnphigh,sequence=edgeStyleWeights[::-1])
+    dnphigh  = xorBlock(dnphigh)
+
+    imlow  = Image.fromarray(dnplow,mode='RGB')
+    imhigh = Image.fromarray(dnphigh,mode='RGB')
+
+    background = Image.new('RGB',(image.size[0]+imlow.size[0]+imhigh.size[0]+2,image.size[1]),(0,0,0))
+    background.paste(imlow,(0,0))
+    background.paste(image,(imlow.size[0]+1,0))
+    background.paste(imhigh,(imlow.size[0]+1+image.size[0]+1,0))
+
     return background
 
 def crop_black(img,tol=0):
@@ -62,19 +101,22 @@ def crop_black(img,tol=0):
     row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax()
     return img[row_start:row_end,col_start:col_end]
 
-def extractImageDataFooter(image):
+def extractImageDataEmbed(image):
     d=3
-    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) )
-    lastRow = np.where( np.sum(outarr, axis=(1,2))==0)
-    if lastRow[0].shape[0] == 0:
-        print('Image data block not found.')
+    outarr = crop_black(np.array(image.getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
+    blackCols = np.where( np.sum(outarr, axis=(0,2))==0)
+    if blackCols[0].shape[0] < 2:
+        print('No Image data blocks found.')
         return None
-    lastRow = lastRow[0]
-    
-    lastRow = lastRow.max()
 
-    dataBlock = outarr[lastRow+1::].astype(np.uint8).flatten().tobytes()
-    print(lastRow)
+    dataBlocklower = outarr[:,:blackCols[0].min(),:].astype(np.uint8)
+    dataBlockupper = outarr[:,blackCols[0].max()+1:,:].astype(np.uint8)
+
+    dataBlocklower = xorBlock(dataBlocklower)
+    dataBlockupper = xorBlock(dataBlockupper)
+    
+    dataBlock = (dataBlockupper << 4) | (dataBlocklower)
+    dataBlock = dataBlock.flatten().tobytes()   
     data = zlib.decompress(dataBlock)
     return json.loads(data,cls=EmbeddingDecoder)
 
@@ -154,7 +196,7 @@ class EmbeddingDatabase:
                     data = embeddingFromB64(embed_image.text['sd-ti-embedding'])
                     name = data.get('name',name)
                 else:
-                    data = extractImageDataFooter(embed_image)
+                    data = extractImageDataEmbed(embed_image)
                     name = data.get('name',name)
             else:
                 data = torch.load(path, map_location="cpu")
@@ -351,7 +393,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 footer_right = '{}'.format(embedding.step)
 
                 captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right)
-                captioned_image = appendImageDataFooter(captioned_image,data)
+                captioned_image = insertImageDataEmbed(captioned_image,data)
 
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
             
-- 
cgit v1.2.1


From 767202a4c324f9b49f63ab4dabbb5736fe9df6e5 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 23:20:52 +0100
Subject: add dependency

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 95eebea7..f3cacaa0 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,7 +7,7 @@ import tqdm
 import html
 import datetime
 
-from PIL import Image,PngImagePlugin
+from PIL import Image,PngImagePlugin,ImageDraw
 from ..images import captionImageOverlay
 import numpy as np
 import base64
-- 
cgit v1.2.1


From e0fbe6d27e7b4505766c8cb5a4264e1114cf3721 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 23:26:24 +0100
Subject: colour depth conversion fix

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index f3cacaa0..ae807268 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -103,7 +103,7 @@ def crop_black(img,tol=0):
 
 def extractImageDataEmbed(image):
     d=3
-    outarr = crop_black(np.array(image.getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
+    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
     blackCols = np.where( np.sum(outarr, axis=(0,2))==0)
     if blackCols[0].shape[0] < 2:
         print('No Image data blocks found.')
-- 
cgit v1.2.1


From 76ef3d75f61253516c024553335d9083d9660a8a Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Mon, 10 Oct 2022 18:01:49 -0500
Subject: added deepbooru settings (threshold and sort by alpha or likelyhood)

---
 modules/deepbooru.py | 36 +++++++++++++++++++++++++-----------
 modules/shared.py    |  6 ++++++
 2 files changed, 31 insertions(+), 11 deletions(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index ebdba5e0..e31e92c0 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -3,31 +3,32 @@ from concurrent.futures import ProcessPoolExecutor
 import multiprocessing
 import time
 
-
-def get_deepbooru_tags(pil_image, threshold=0.5):
+def get_deepbooru_tags(pil_image):
     """
     This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
     """
     from modules import shared  # prevents circular reference
-    create_deepbooru_process(threshold)
+    create_deepbooru_process(shared.opts.deepbooru_threshold, shared.opts.deepbooru_sort_alpha)
     shared.deepbooru_process_return["value"] = -1
     shared.deepbooru_process_queue.put(pil_image)
     while shared.deepbooru_process_return["value"] == -1:
         time.sleep(0.2)
+    tags = shared.deepbooru_process_return["value"]
     release_process()
+    return tags
 
 
-def deepbooru_process(queue, deepbooru_process_return, threshold):
+def deepbooru_process(queue, deepbooru_process_return, threshold, alpha_sort):
     model, tags = get_deepbooru_tags_model()
     while True: # while process is running, keep monitoring queue for new image
         pil_image = queue.get()
         if pil_image == "QUIT":
             break
         else:
-            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold)
+            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort)
 
 
-def create_deepbooru_process(threshold=0.5):
+def create_deepbooru_process(threshold, alpha_sort):
     """
     Creates deepbooru process.  A queue is created to send images into the process.  This enables multiple images
     to be processed in a row without reloading the model or creating a new process.  To return the data, a shared
@@ -40,7 +41,7 @@ def create_deepbooru_process(threshold=0.5):
     shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
     shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
     shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold))
+    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, alpha_sort))
     shared.deepbooru_process.start()
 
 
@@ -80,7 +81,7 @@ def get_deepbooru_tags_model():
     return model, tags
 
 
-def get_deepbooru_tags_from_model(model, tags, pil_image, threshold=0.5):
+def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort):
     import deepdanbooru as dd
     import tensorflow as tf
     import numpy as np
@@ -105,15 +106,28 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold=0.5):
 
     for i, tag in enumerate(tags):
         result_dict[tag] = y[i]
-    result_tags_out = []
+
+    unsorted_tags_in_theshold = []
     result_tags_print = []
     for tag in tags:
         if result_dict[tag] >= threshold:
             if tag.startswith("rating:"):
                 continue
-            result_tags_out.append(tag)
+            unsorted_tags_in_theshold.append((result_dict[tag], tag))
             result_tags_print.append(f'{result_dict[tag]} {tag}')
 
+    # sort tags
+    result_tags_out = []
+    sort_ndx = 0
+    print(alpha_sort)
+    if alpha_sort:
+        sort_ndx = 1
+
+    # sort by reverse by likelihood and normal for alpha
+    unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
+    for weight, tag in unsorted_tags_in_theshold:
+        result_tags_out.append(tag)
+
     print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
\ No newline at end of file
+    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
diff --git a/modules/shared.py b/modules/shared.py
index 1995a99a..2e307809 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -261,6 +261,12 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
 }))
 
+if cmd_opts.deepdanbooru:
+    options_templates.update(options_section(('deepbooru-params', "DeepBooru parameters"), {
+        "deepbooru_sort_alpha": OptionInfo(True, "Sort Alphabetical", gr.Checkbox),
+        'deepbooru_threshold': OptionInfo(0.5, "Threshold", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    }))
+
 
 class Options:
     data = None
-- 
cgit v1.2.1


From 70b50b1dfcb0ce0f87998c994f4855014bc7e26b Mon Sep 17 00:00:00 2001
From: ClashSAN <98228077+ClashSAN@users.noreply.github.com>
Date: Mon, 10 Oct 2022 23:23:12 +0000
Subject: add features, credit for Composable Diffusion

to readme

https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/2171
---
 README.md | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 561eb03d..0e938768 100644
--- a/README.md
+++ b/README.md
@@ -28,10 +28,12 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
     - CodeFormer, face restoration tool as an alternative to GFPGAN
     - RealESRGAN, neural network upscaler
     - ESRGAN, neural network upscaler with a lot of third party models
-    - SwinIR, neural network upscaler
+    - SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
     - LDSR, Latent diffusion super resolution upscaling
 - Resizing aspect ratio options
 - Sampling method selection
+    - Adjust sampler eta values (noise multiplier)
+    - More advanced noise setting options
 - Interrupt processing at any time
 - 4GB video card support (also reports of 2GB working)
 - Correct seeds for batches
@@ -67,6 +69,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
      - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
 - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
+- [xformers](https://github.com/mv-lab/swin2sr), major speed increase for select cards: (add --xformers to commandline args)
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
@@ -116,6 +119,7 @@ The documentation was moved from this README over to the project's [wiki](https:
 - CodeFormer - https://github.com/sczhou/CodeFormer
 - ESRGAN - https://github.com/xinntao/ESRGAN
 - SwinIR - https://github.com/JingyunLiang/SwinIR
+- Swin2SR - https://github.com/mv-lab/swin2sr
 - LDSR - https://github.com/Hafiidz/latent-diffusion
 - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
 - Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
@@ -123,6 +127,8 @@ The documentation was moved from this README over to the project's [wiki](https:
 - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
 - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
 - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
+- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
+- xformers - https://github.com/facebookresearch/xformers
+- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
-- DeepDanbooru - interrogator for anime diffusors https://github.com/KichangKim/DeepDanbooru
 - (You)
-- 
cgit v1.2.1


From bb932dbf9faf43ba918daa4791873078797b2a48 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Mon, 10 Oct 2022 18:37:52 -0500
Subject: added alpha sort and threshold variables to create process method in
 preprocessing

---
 modules/textual_inversion/preprocess.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 4a2194da..c0af729b 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -29,7 +29,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
         shared.interrogator.load()
 
     if process_caption_deepbooru:
-        deepbooru.create_deepbooru_process()
+        deepbooru.create_deepbooru_process(opts.deepbooru_threshold, opts.deepbooru_sort_alpha)
 
     def save_pic_with_caption(image, index):
         if process_caption:
-- 
cgit v1.2.1


From 1add3cff84b7e2436d69b1e97ae689281e4a7c33 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Mon, 10 Oct 2022 19:57:43 -0500
Subject: Refresh list of models/ckpts upon hitting restart gradio in the
 settings pane

---
 modules/ui.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/modules/ui.py b/modules/ui.py
index e8039d76..06ff118f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -39,6 +39,7 @@ import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
+from modules.sd_models import list_models
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -1290,6 +1291,9 @@ Requested path was: {f}
             shared.state.interrupt()
             settings_interface.gradio_ref.do_restart = True
 
+            # refresh models so that new models/.ckpt's show up on reload
+            list_models()
+
         restart_gradio.click(
             fn=request_restart,
             inputs=[],
-- 
cgit v1.2.1


From 7aa8fcac1e45c3ad9c6a40df0e44a346afcd5032 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 04:17:36 +0100
Subject: use simple lcg in xor

---
 modules/textual_inversion/textual_inversion.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index ae807268..13416a08 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -39,9 +39,15 @@ def embeddingFromB64(data):
     d = base64.b64decode(data)
     return json.loads(d,cls=EmbeddingDecoder)
 
+def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
+    while True:
+        seed = (a * seed + c) % m
+        yield seed
+
 def xorBlock(block):
-    return np.bitwise_xor(block.astype(np.uint8),
-                          ((np.random.RandomState(0xDEADBEEF).random(block.shape)*255).astype(np.uint8)) & 0x0F )
+    g = lcg()
+    randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
+    return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F) 
 
 def styleBlock(block,sequence):
     im = Image.new('RGB',(block.shape[1],block.shape[0]))
-- 
cgit v1.2.1


From 8b7d3f1bef47bbe048f644ed0d8dd3ad46554045 Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Tue, 11 Oct 2022 02:22:46 -0300
Subject: Make the ctrl+enter shortcut use the generate button on the current
 tab

---
 modules/ui.py |  2 +-
 script.js     | 11 +++++++++--
 2 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index e8039d76..cafda884 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1331,7 +1331,7 @@ Requested path was: {f}
         
         with gr.Tabs() as tabs:
             for interface, label, ifid in interfaces:
-                with gr.TabItem(label, id=ifid):
+                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
                     interface.render()
         
         if os.path.exists(os.path.join(script_path, "notification.mp3")):
diff --git a/script.js b/script.js
index a92c0f77..9543cbe6 100644
--- a/script.js
+++ b/script.js
@@ -6,6 +6,10 @@ function get_uiCurrentTab() {
     return gradioApp().querySelector('.tabs button:not(.border-transparent)')
 }
 
+function get_uiCurrentTabContent() {
+    return gradioApp().querySelector('.tabitem[id^=tab_]:not([style*="display: none"])')
+}
+
 uiUpdateCallbacks = []
 uiTabChangeCallbacks = []
 let uiCurrentTab = null
@@ -50,8 +54,11 @@ document.addEventListener("DOMContentLoaded", function() {
     } else if (e.keyCode !== undefined) {
         if((e.keyCode == 13 && (e.metaKey || e.ctrlKey))) handled = true;
     }
-    if (handled) { 
-        gradioApp().querySelector("#txt2img_generate").click(); 
+    if (handled) {
+        button = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
+        if (button) {
+            button.click();
+        }
         e.preventDefault();
     }
 })
-- 
cgit v1.2.1


From 8617396c6df71074c7fd3d39419802026874712a Mon Sep 17 00:00:00 2001
From: Kenneth <kenneth.mccarthy24@yahoo.com>
Date: Mon, 10 Oct 2022 17:23:07 -0600
Subject: Added slider for deepbooru score threshold in settings

---
 modules/shared.py | 1 +
 modules/ui.py     | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index ecd15ef5..e0830e28 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -239,6 +239,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
     "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
+    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
 }))
 
 options_templates.update(options_section(('ui', "User interface"), {
diff --git a/modules/ui.py b/modules/ui.py
index cafda884..ca3151c4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -311,7 +311,7 @@ def interrogate(image):
 
 
 def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image)
+    prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold)
     return gr_show(True) if prompt is None else prompt
 
 
-- 
cgit v1.2.1


From 5e2627a1a63e4c9f87e6e604ecc24e9936f149de Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Tue, 11 Oct 2022 07:55:28 +0100
Subject: Comma backtrack padding (#2192)

Comma backtrack padding
---
 modules/sd_hijack.py | 19 ++++++++++++++++++-
 modules/shared.py    |  1 +
 2 files changed, 19 insertions(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 827bf304..aa4d2cbc 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -107,6 +107,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         self.tokenizer = wrapped.tokenizer
         self.token_mults = {}
 
+        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
+
         tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
         for text, ident in tokens_with_parens:
             mult = 1.0
@@ -136,6 +138,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         fixes = []
         remade_tokens = []
         multipliers = []
+        last_comma = -1
 
         for tokens, (text, weight) in zip(tokenized, parsed):
             i = 0
@@ -144,6 +147,20 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
                 embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
 
+                if token == self.comma_token:
+                    last_comma = len(remade_tokens)
+                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
+                    last_comma += 1
+                    reloc_tokens = remade_tokens[last_comma:]
+                    reloc_mults = multipliers[last_comma:]
+
+                    remade_tokens = remade_tokens[:last_comma]
+                    length = len(remade_tokens)
+                    
+                    rem = int(math.ceil(length / 75)) * 75 - length
+                    remade_tokens += [id_end] * rem + reloc_tokens
+                    multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
+                
                 if embedding is None:
                     remade_tokens.append(token)
                     multipliers.append(weight)
@@ -284,7 +301,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         while max(map(len, remade_batch_tokens)) != 0:
             rem_tokens = [x[75:] for x in remade_batch_tokens]
             rem_multipliers = [x[75:] for x in batch_multipliers]
-            
+
             self.hijack.fixes = []
             for unfiltered in hijack_fixes:
                 fixes = []
diff --git a/modules/shared.py b/modules/shared.py
index e0830e28..14b40d70 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -227,6 +227,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
+    "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
-- 
cgit v1.2.1


From 948533950c9db5069a874d925fadd50bac00fdb5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 11:09:51 +0300
Subject: replace duplicate code with a function

---
 modules/hypernetwork.py            | 23 ++++++++++++--------
 modules/sd_hijack_optimizations.py | 44 +++++++++++++-------------------------
 2 files changed, 29 insertions(+), 38 deletions(-)

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
index 498bc9d8..7bbc443e 100644
--- a/modules/hypernetwork.py
+++ b/modules/hypernetwork.py
@@ -64,21 +64,26 @@ def load_hypernetwork(filename):
         shared.loaded_hypernetwork = None
 
 
+def apply_hypernetwork(hypernetwork, context):
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is None:
+        return context, context
+
+    context_k = hypernetwork_layers[0](context)
+    context_v = hypernetwork_layers[1](context)
+    return context_k, context_v
+
+
 def attention_CrossAttention_forward(self, x, context=None, mask=None):
     h = self.heads
 
     q = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.loaded_hypernetwork
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is not None:
-        k = self.to_k(hypernetwork_layers[0](context))
-        v = self.to_v(hypernetwork_layers[1](context))
-    else:
-        k = self.to_k(context)
-        v = self.to_v(context)
+    context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context)
+    k = self.to_k(context_k)
+    v = self.to_v(context_v)
 
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
 
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 18408e62..25cb67a4 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -8,7 +8,8 @@ from torch import einsum
 from ldm.util import default
 from einops import rearrange
 
-from modules import shared
+from modules import shared, hypernetwork
+
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
     try:
@@ -26,16 +27,10 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     q_in = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.loaded_hypernetwork
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is not None:
-        k_in = self.to_k(hypernetwork_layers[0](context))
-        v_in = self.to_v(hypernetwork_layers[1](context))
-    else:
-        k_in = self.to_k(context)
-        v_in = self.to_v(context)
-    del context, x
+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
+    k_in = self.to_k(context_k)
+    v_in = self.to_v(context_v)
+    del context, context_k, context_v, x
 
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
@@ -59,22 +54,16 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     return self.to_out(r2)
 
 
-# taken from https://github.com/Doggettx/stable-diffusion
+# taken from https://github.com/Doggettx/stable-diffusion and modified
 def split_cross_attention_forward(self, x, context=None, mask=None):
     h = self.heads
 
     q_in = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.loaded_hypernetwork
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is not None:
-        k_in = self.to_k(hypernetwork_layers[0](context))
-        v_in = self.to_v(hypernetwork_layers[1](context))
-    else:
-        k_in = self.to_k(context)
-        v_in = self.to_v(context)
+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
+    k_in = self.to_k(context_k)
+    v_in = self.to_v(context_v)
 
     k_in *= self.scale
 
@@ -130,14 +119,11 @@ def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
     q_in = self.to_q(x)
     context = default(context, x)
-    hypernetwork = shared.loaded_hypernetwork
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-    if hypernetwork_layers is not None:
-        k_in = self.to_k(hypernetwork_layers[0](context))
-        v_in = self.to_v(hypernetwork_layers[1](context))
-    else:
-        k_in = self.to_k(context)
-        v_in = self.to_v(context)
+
+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
+    k_in = self.to_k(context_k)
+    v_in = self.to_v(context_v)
+
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
-- 
cgit v1.2.1


From b2368a3bce663f19a7209d9cb38617e635ca6e3c Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Tue, 11 Oct 2022 17:32:46 +0900
Subject: Switched to exception handling

---
 modules/textual_inversion/dataset.py           | 10 +++++-----
 modules/textual_inversion/preprocess.py        |  8 +++++---
 modules/textual_inversion/textual_inversion.py | 18 ++++++++----------
 3 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 0dc54fb7..4d006366 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -22,7 +22,6 @@ class PersonalizedBase(Dataset):
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
-        self.extns = [".jpg",".jpeg",".png",".webp",".bmp"]
 
         self.dataset = []
 
@@ -33,12 +32,13 @@ class PersonalizedBase(Dataset):
 
         assert data_root, 'dataset directory not specified'
 
-        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root) if os.path.splitext(file_path.casefold())[1] in self.extns]
+        self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
-            image = Image.open(path)
-            image = image.convert('RGB')
-            image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
+            try:
+                image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
+            except Exception:
+                continue
 
             filename = os.path.basename(path)
             filename_tokens = os.path.splitext(filename)[0]
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 8290abe8..1a672725 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -12,13 +12,12 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
     height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
-    extns = [".jpg",".jpeg",".png",".webp",".bmp"]
 
     assert src != dst, 'same directory specified as source and destination'
 
     os.makedirs(dst, exist_ok=True)
 
-    files = [i for i in os.listdir(src) if os.path.splitext(i.casefold())[1] in extns]
+    files = os.listdir(src)
 
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
@@ -47,7 +46,10 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
     for index, imagefile in enumerate(tqdm.tqdm(files)):
         subindex = [0]
         filename = os.path.join(src, imagefile)
-        img = Image.open(filename).convert("RGB")
+        try:
+            img = Image.open(filename).convert("RGB")
+        except Exception:
+            continue
 
         if shared.state.interrupted:
             break
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 33c923d1..91cde04b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -161,7 +161,6 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
-    extns = [".jpg",".jpeg",".png",".webp",".bmp"]
 
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
 
@@ -201,10 +200,6 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     if ititial_step > steps:
         return embedding, filename
 
-    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root) if os.path.splitext(file_path.casefold())[1] in extns])
-
-    epoch_len = (tr_img_len * num_repeats) + tr_img_len
-
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, (x, text) in pbar:
         embedding.step = i + ititial_step
@@ -228,10 +223,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             loss.backward()
             optimizer.step()
 
-        epoch_num = embedding.step // epoch_len
-        epoch_step = embedding.step - (epoch_num * epoch_len) + 1
+        epoch_num = embedding.step // len(ds)
+        epoch_step = embedding.step - (epoch_num * len(ds)) + 1
 
-        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")
 
         if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
@@ -243,9 +238,12 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
                 prompt=text,
-                steps=20,
-				height=training_height,
+                steps=28,
+				height=768,
 				width=training_width,
+                negative_prompt="lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name",
+                cfg_scale=7.0,
+                sampler_index=0,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
-- 
cgit v1.2.1


From 8bacbca0a1ab9aabcb0ad0cbf070e0006991e98a Mon Sep 17 00:00:00 2001
From: alg-wiki <alg.4chan@gmail.com>
Date: Tue, 11 Oct 2022 17:35:09 +0900
Subject: Removed my local edits to checkpoint image generation

---
 modules/textual_inversion/textual_inversion.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 91cde04b..e9ff80c2 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -238,12 +238,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
                 prompt=text,
-                steps=28,
-				height=768,
+                steps=20,
+				height=training_height,
 				width=training_width,
-                negative_prompt="lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, artist name",
-                cfg_scale=7.0,
-                sampler_index=0,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
-- 
cgit v1.2.1


From 255be75d30f41e089e499ec1c8462d6bf64dec24 Mon Sep 17 00:00:00 2001
From: aperullo <18688190+aperullo@users.noreply.github.com>
Date: Tue, 11 Oct 2022 06:16:57 -0400
Subject: Error if prompt missing SR token to prevent mis-gens (#2209)

---
 scripts/xy_grid.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 42e1489c..10a82dc9 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -27,9 +27,16 @@ def apply_field(field):
 
 
 def apply_prompt(p, x, xs):
+
+    orig_prompt = p.prompt
+    orig_negative_prompt = p.negative_prompt
+
     p.prompt = p.prompt.replace(xs[0], x)
     p.negative_prompt = p.negative_prompt.replace(xs[0], x)
 
+    if p.prompt == orig_prompt and p.negative_prompt == orig_negative_prompt:
+        raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt. Did you forget to add the token?")
+
 
 def apply_order(p, x, xs):
     token_order = []
-- 
cgit v1.2.1


From 4b460fcb1a0224772949556fe0469da93245c532 Mon Sep 17 00:00:00 2001
From: Rory Grieve <rhrg500@gmail.com>
Date: Tue, 11 Oct 2022 11:23:47 +0100
Subject: Reset init img in loopback at start of each batch (#2214)

Before a new batch would use the last image from the previous batch. Now
each batch will use the original image for the init image at the start of the
batch.
---
 scripts/loopback.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/scripts/loopback.py b/scripts/loopback.py
index e90b58d4..d8c68af8 100644
--- a/scripts/loopback.py
+++ b/scripts/loopback.py
@@ -38,6 +38,7 @@ class Script(scripts.Script):
 
         grids = []
         all_images = []
+        original_init_image = p.init_images
         state.job_count = loops * batch_count
 
         initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
@@ -45,6 +46,9 @@ class Script(scripts.Script):
         for n in range(batch_count):
             history = []
 
+            # Reset to original init image at the start of each batch
+            p.init_images = original_init_image
+
             for i in range(loops):
                 p.n_iter = 1
                 p.batch_size = 1
-- 
cgit v1.2.1


From a8490e4019c359ff24824e004059744d7164361b Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 11:42:41 +0100
Subject: revert sr warning

---
 scripts/xy_grid.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 10a82dc9..99b3c4f6 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -35,7 +35,8 @@ def apply_prompt(p, x, xs):
     p.negative_prompt = p.negative_prompt.replace(xs[0], x)
 
     if p.prompt == orig_prompt and p.negative_prompt == orig_negative_prompt:
-        raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt. Did you forget to add the token?")
+        pass
+        #raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt. Did you forget to add the token?")
 
 
 def apply_order(p, x, xs):
-- 
cgit v1.2.1


From 1a0a6a84c3149e236211d547471f5416cd1129f3 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 11:59:56 +0100
Subject: add incorrect start word guard to xy_grid (#2259)

---
 scripts/xy_grid.py | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 99b3c4f6..9d4d6187 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -27,17 +27,12 @@ def apply_field(field):
 
 
 def apply_prompt(p, x, xs):
-
-    orig_prompt = p.prompt
-    orig_negative_prompt = p.negative_prompt
+    if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:
+        raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")
 
     p.prompt = p.prompt.replace(xs[0], x)
     p.negative_prompt = p.negative_prompt.replace(xs[0], x)
 
-    if p.prompt == orig_prompt and p.negative_prompt == orig_negative_prompt:
-        pass
-        #raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt. Did you forget to add the token?")
-
 
 def apply_order(p, x, xs):
     token_order = []
-- 
cgit v1.2.1


From 530103b586109c11fd068eb70ef09503ec6a4caf Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 14:53:02 +0300
Subject: fixes related to merge

---
 modules/hypernetwork.py                        | 103 -------------------------
 modules/hypernetwork/hypernetwork.py           |  74 +++++++++++-------
 modules/hypernetwork/ui.py                     |  10 +--
 modules/sd_hijack_optimizations.py             |   3 +-
 modules/shared.py                              |  13 +++-
 modules/textual_inversion/textual_inversion.py |  12 +--
 modules/ui.py                                  |   5 +-
 scripts/xy_grid.py                             |   3 +-
 webui.py                                       |  15 +---
 9 files changed, 78 insertions(+), 160 deletions(-)
 delete mode 100644 modules/hypernetwork.py

diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py
deleted file mode 100644
index 7bbc443e..00000000
--- a/modules/hypernetwork.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import glob
-import os
-import sys
-import traceback
-
-import torch
-
-from ldm.util import default
-from modules import devices, shared
-import torch
-from torch import einsum
-from einops import rearrange, repeat
-
-
-class HypernetworkModule(torch.nn.Module):
-    def __init__(self, dim, state_dict):
-        super().__init__()
-
-        self.linear1 = torch.nn.Linear(dim, dim * 2)
-        self.linear2 = torch.nn.Linear(dim * 2, dim)
-
-        self.load_state_dict(state_dict, strict=True)
-        self.to(devices.device)
-
-    def forward(self, x):
-        return x + (self.linear2(self.linear1(x)))
-
-
-class Hypernetwork:
-    filename = None
-    name = None
-
-    def __init__(self, filename):
-        self.filename = filename
-        self.name = os.path.splitext(os.path.basename(filename))[0]
-        self.layers = {}
-
-        state_dict = torch.load(filename, map_location='cpu')
-        for size, sd in state_dict.items():
-            self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
-
-
-def list_hypernetworks(path):
-    res = {}
-    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
-        name = os.path.splitext(os.path.basename(filename))[0]
-        res[name] = filename
-    return res
-
-
-def load_hypernetwork(filename):
-    path = shared.hypernetworks.get(filename, None)
-    if path is not None:
-        print(f"Loading hypernetwork {filename}")
-        try:
-            shared.loaded_hypernetwork = Hypernetwork(path)
-        except Exception:
-            print(f"Error loading hypernetwork {path}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-    else:
-        if shared.loaded_hypernetwork is not None:
-            print(f"Unloading hypernetwork")
-
-        shared.loaded_hypernetwork = None
-
-
-def apply_hypernetwork(hypernetwork, context):
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is None:
-        return context, context
-
-    context_k = hypernetwork_layers[0](context)
-    context_v = hypernetwork_layers[1](context)
-    return context_k, context_v
-
-
-def attention_CrossAttention_forward(self, x, context=None, mask=None):
-    h = self.heads
-
-    q = self.to_q(x)
-    context = default(context, x)
-
-    context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context)
-    k = self.to_k(context_k)
-    v = self.to_v(context_v)
-
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-
-    if mask is not None:
-        mask = rearrange(mask, 'b ... -> b (...)')
-        max_neg_value = -torch.finfo(sim.dtype).max
-        mask = repeat(mask, 'b j -> (b h) () j', h=h)
-        sim.masked_fill_(~mask, max_neg_value)
-
-    # attention, what we cannot get enough of
-    attn = sim.softmax(dim=-1)
-
-    out = einsum('b i j, b j d -> b i d', attn, v)
-    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-    return self.to_out(out)
diff --git a/modules/hypernetwork/hypernetwork.py b/modules/hypernetwork/hypernetwork.py
index a3d6a47e..aa701bda 100644
--- a/modules/hypernetwork/hypernetwork.py
+++ b/modules/hypernetwork/hypernetwork.py
@@ -26,10 +26,11 @@ class HypernetworkModule(torch.nn.Module):
         if state_dict is not None:
             self.load_state_dict(state_dict, strict=True)
         else:
-            self.linear1.weight.data.fill_(0.0001)
-            self.linear1.bias.data.fill_(0.0001)
-            self.linear2.weight.data.fill_(0.0001)
-            self.linear2.bias.data.fill_(0.0001)
+
+            self.linear1.weight.data.normal_(mean=0.0, std=0.01)
+            self.linear1.bias.data.zero_()
+            self.linear2.weight.data.normal_(mean=0.0, std=0.01)
+            self.linear2.bias.data.zero_()
 
         self.to(devices.device)
 
@@ -92,41 +93,54 @@ class Hypernetwork:
         self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
 
 
-def load_hypernetworks(path):
+def list_hypernetworks(path):
     res = {}
+    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
+        name = os.path.splitext(os.path.basename(filename))[0]
+        res[name] = filename
+    return res
 
-    for filename in glob.iglob(path + '**/*.pt', recursive=True):
+
+def load_hypernetwork(filename):
+    path = shared.hypernetworks.get(filename, None)
+    if path is not None:
+        print(f"Loading hypernetwork {filename}")
         try:
-            hn = Hypernetwork()
-            hn.load(filename)
-            res[hn.name] = hn
+            shared.loaded_hypernetwork = Hypernetwork()
+            shared.loaded_hypernetwork.load(path)
+
         except Exception:
-            print(f"Error loading hypernetwork {filename}", file=sys.stderr)
+            print(f"Error loading hypernetwork {path}", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
+    else:
+        if shared.loaded_hypernetwork is not None:
+            print(f"Unloading hypernetwork")
 
-    return res
+        shared.loaded_hypernetwork = None
 
 
-def attention_CrossAttention_forward(self, x, context=None, mask=None):
-    h = self.heads
+def apply_hypernetwork(hypernetwork, context, layer=None):
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
 
-    q = self.to_q(x)
-    context = default(context, x)
+    if hypernetwork_layers is None:
+        return context, context
 
-    hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None)
+    if layer is not None:
+        layer.hyper_k = hypernetwork_layers[0]
+        layer.hyper_v = hypernetwork_layers[1]
 
-    if hypernetwork_layers is not None:
-        hypernetwork_k, hypernetwork_v = hypernetwork_layers
+    context_k = hypernetwork_layers[0](context)
+    context_v = hypernetwork_layers[1](context)
+    return context_k, context_v
 
-        self.hypernetwork_k = hypernetwork_k
-        self.hypernetwork_v = hypernetwork_v
 
-        context_k = hypernetwork_k(context)
-        context_v = hypernetwork_v(context)
-    else:
-        context_k = context
-        context_v = context
+def attention_CrossAttention_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
 
+    context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self)
     k = self.to_k(context_k)
     v = self.to_v(context_v)
 
@@ -151,7 +165,9 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
 def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
     assert hypernetwork_name, 'embedding not selected'
 
-    shared.hypernetwork = shared.hypernetworks[hypernetwork_name]
+    path = shared.hypernetworks.get(hypernetwork_name, None)
+    shared.loaded_hypernetwork = Hypernetwork()
+    shared.loaded_hypernetwork.load(path)
 
     shared.state.textinfo = "Initializing hypernetwork training..."
     shared.state.job_count = steps
@@ -176,9 +192,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
 
-    hypernetwork = shared.hypernetworks[hypernetwork_name]
+    hypernetwork = shared.loaded_hypernetwork
     weights = hypernetwork.weights()
     for weight in weights:
         weight.requires_grad = True
@@ -194,7 +210,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     if ititial_step > steps:
         return hypernetwork, filename
 
-    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
+    pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, (x, text) in pbar:
         hypernetwork.step = i + ititial_step
 
diff --git a/modules/hypernetwork/ui.py b/modules/hypernetwork/ui.py
index 525f978c..f6d1d0a3 100644
--- a/modules/hypernetwork/ui.py
+++ b/modules/hypernetwork/ui.py
@@ -6,24 +6,24 @@ import gradio as gr
 import modules.textual_inversion.textual_inversion
 import modules.textual_inversion.preprocess
 from modules import sd_hijack, shared
+from modules.hypernetwork import hypernetwork
 
 
 def create_hypernetwork(name):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
-    hypernetwork = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
-    hypernetwork.save(fn)
+    hypernet = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
+    hypernet.save(fn)
 
     shared.reload_hypernetworks()
-    shared.hypernetwork = shared.hypernetworks.get(shared.opts.sd_hypernetwork, None)
 
     return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
 
 
 def train_hypernetwork(*args):
 
-    initial_hypernetwork = shared.hypernetwork
+    initial_hypernetwork = shared.loaded_hypernetwork
 
     try:
         sd_hijack.undo_optimizations()
@@ -38,6 +38,6 @@ Hypernetwork saved to {html.escape(filename)}
     except Exception:
         raise
     finally:
-        shared.hypernetwork = initial_hypernetwork
+        shared.loaded_hypernetwork = initial_hypernetwork
         sd_hijack.apply_optimizations()
 
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 25cb67a4..27e571fc 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -8,7 +8,8 @@ from torch import einsum
 from ldm.util import default
 from einops import rearrange
 
-from modules import shared, hypernetwork
+from modules import shared
+from modules.hypernetwork import hypernetwork
 
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
diff --git a/modules/shared.py b/modules/shared.py
index 14b40d70..8753015e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,8 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, hypernetwork
+from modules import sd_samplers
+from modules.hypernetwork import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -29,6 +30,7 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
+parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
@@ -82,10 +84,17 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 xformers_available = False
 config_filename = cmd_opts.ui_settings_file
 
-hypernetworks = hypernetwork.list_hypernetworks(os.path.join(models_path, 'hypernetworks'))
+hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
 
+def reload_hypernetworks():
+    global hypernetworks
+
+    hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
+    hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
+
+
 class State:
     skipped = False
     interrupted = False
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 5965c5a0..d6977950 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -156,7 +156,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, preview_image_prompt):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -238,12 +238,14 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
 
+            preview_text = text if preview_image_prompt == "" else preview_image_prompt
+
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
-                prompt=text,
+                prompt=preview_text,
                 steps=20,
-				height=training_height,
-				width=training_width,
+                height=training_height,
+                width=training_width,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
@@ -254,7 +256,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             shared.state.current_image = image
             image.save(last_saved_image)
 
-            last_saved_image += f", prompt: {text}"
+            last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = embedding.step
 
diff --git a/modules/ui.py b/modules/ui.py
index 10b1ee3a..df653059 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1023,7 +1023,7 @@ def create_ui(wrap_gradio_gpu_call):
                             gr.HTML(value="")
 
                         with gr.Column():
-                            create_embedding = gr.Button(value="Create", variant='primary')
+                            create_embedding = gr.Button(value="Create embedding", variant='primary')
 
                 with gr.Group():
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
@@ -1035,7 +1035,7 @@ def create_ui(wrap_gradio_gpu_call):
                             gr.HTML(value="")
 
                         with gr.Column():
-                            create_hypernetwork = gr.Button(value="Create", variant='primary')
+                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
 
                 with gr.Group():
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
@@ -1147,6 +1147,7 @@ def create_ui(wrap_gradio_gpu_call):
                 create_image_every,
                 save_embedding_every,
                 template_file,
+                preview_image_prompt,
             ],
             outputs=[
                 ti_output,
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 42e1489c..0af5993c 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -10,7 +10,8 @@ import numpy as np
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import images, hypernetwork
+from modules import images
+from modules.hypernetwork import hypernetwork
 from modules.processing import process_images, Processed, get_correct_sampler
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
diff --git a/webui.py b/webui.py
index 7c200551..ba2156c8 100644
--- a/webui.py
+++ b/webui.py
@@ -29,6 +29,7 @@ from modules import devices
 from modules import modelloader
 from modules.paths import script_path
 from modules.shared import cmd_opts
+import modules.hypernetwork.hypernetwork
 
 modelloader.cleanup_models()
 modules.sd_models.setup_model()
@@ -77,22 +78,12 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
     return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
 
 
-def set_hypernetwork():
-    shared.hypernetwork = shared.hypernetworks.get(shared.opts.sd_hypernetwork, None)
-
-
-shared.reload_hypernetworks()
-shared.opts.onchange("sd_hypernetwork", set_hypernetwork)
-set_hypernetwork()
-
-
 modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
 
 shared.sd_model = modules.sd_models.load_model()
 shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
 
-loaded_hypernetwork = modules.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)
-shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
+shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetwork.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
 
 
 def webui():
@@ -117,7 +108,7 @@ def webui():
             prevent_thread_lock=True
         )
         
-        app.add_middleware(GZipMiddleware,minimum_size=1000)
+        app.add_middleware(GZipMiddleware, minimum_size=1000)
 
         while 1:
             time.sleep(0.5)
-- 
cgit v1.2.1


From 7b1db45e1fda8603d4617affd976066be5e5b821 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Tue, 11 Oct 2022 20:17:27 +0800
Subject: images history improvement

---
 javascript/images_history.js   | 170 ++++++++++++++++++++----------
 javascript/jquery-3.6.0.min.js |   2 -
 modules/images_history.py      | 229 ++++++++++++++++++++++-------------------
 style.css                      |   3 +
 4 files changed, 238 insertions(+), 166 deletions(-)
 delete mode 100644 javascript/jquery-3.6.0.min.js

diff --git a/javascript/images_history.js b/javascript/images_history.js
index 93d2b89a..9a3e00a0 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -1,66 +1,124 @@
-function init_images_history(){ 
-	if (gradioApp().getElementById('txt2img_images_history_first_page') == null) {
-		setTimeout(init_images_history, 1000)
-	} else {
-		tab_list = ["txt2img", "img2img"]
-		for (i in tab_list){
-			tab = tab_list[i]
-			gradioApp().getElementById(tab + "_images_history_first_page").click()
-			$(gradioApp().getElementById(tab + '_images_history')).addClass("images_history_gallery")
-			item = $(gradioApp().getElementById(tab + '_images_history_set_index'))
-			item.addClass("images_history_set_index")
-			item.hide()
-		}		
-	}
-	
+images_history_tab_list = ["txt2img", "img2img", "extras"]
+function images_history_init(){ 
+    if (gradioApp().getElementById('txt2img_images_history_first_page') == null) {
+        setTimeout(images_history_init, 500)
+    } else {        
+        for (i in images_history_tab_list ){
+            tab = images_history_tab_list[i]
+            gradioApp().getElementById(tab + '_images_history').classList.add("images_history_gallery")
+            gradioApp().getElementById(tab + '_images_history_set_index').classList.add("images_history_set_index")      
+            
+        }
+        gradioApp().getElementById("txt2img_images_history_first_page").click()     
+    }    
+}
+setTimeout(images_history_init, 500)
+var images_history_button_actions = function(){
+    if (!this.classList.contains("transform")){        
+        gallery = this.parentElement
+        while(!gallery.classList.contains("images_history_gallery")){gallery = gallery.parentElement}        
+        buttons = gallery.querySelectorAll(".gallery-item")
+        i = 0
+        hidden_list = []
+        buttons.forEach(function(e){
+            if (e.style.display == "none"){
+                hidden_list.push(i)
+            }
+            i += 1
+        })
+        if (hidden_list.length > 0){
+            setTimeout(images_history_hide_buttons, 10, hidden_list, gallery)
+        }
+        
+    }    
+    images_history_set_image_info(this) 
+
 }
-setTimeout(init_images_history, 1000)
 onUiUpdate(function(){
-    fullImg_preview = gradioApp().querySelectorAll('#txt2img_images_history img.w-full')
-        if(fullImg_preview.length > 0){
-	    	fullImg_preview.forEach(set_history_index_from_img);
-    }
-    fullImg_preview = gradioApp().querySelectorAll('#img2img_images_history img.w-full')
-        if(fullImg_preview.length > 0){
-	    	fullImg_preview.forEach(set_history_index_from_img);
+    for (i in images_history_tab_list ){
+        tab = images_history_tab_list[i]
+        buttons = gradioApp().querySelectorAll('#' + tab + '_images_history .gallery-item')
+        buttons.forEach(function(bnt){    
+            bnt.addEventListener('click', images_history_button_actions, true)
+         });
     }
 })
+function images_history_hide_buttons(hidden_list, gallery){
+    buttons = gallery.querySelectorAll(".gallery-item")
+    num = 0
+    buttons.forEach(function(e){
+        if (e.style.display == "none"){
+            num += 1
+        }
+    })
+    if (num == hidden_list.length){
+        setTimeout(images_history_hide_buttons, 10, hidden_list, gallery)
+    } 
+    for( i in hidden_list){
+        buttons[hidden_list[i]].style.display = "none"
+    }    
+}
 
-function set_history_gallery_index(item){
-	buttons = item.find(".gallery-item")
-	// alert(item.attr("id") + " " + buttons.length)
-	index = -1
-	i = 0
-	buttons.each(function(){
-		if($(this).hasClass("!ring-2")){ index = i }
-		i += 1
-	})
-	if (index == -1){
-		setTimeout(set_history_gallery_index, 10, item)     
-	} else {
-		item = item.find(".images_history_set_index").first()
-    	item.attr("img_index", index)
-    	item.click()
-	}
+function images_history_set_image_info(button){
+    item = button.parentElement
+    while(item.tagName != "DIV"){item = item.parentElement}    
+    buttons = item.querySelectorAll(".gallery-item")
+    index = -1
+    i = 0
+    buttons.forEach(function(e){
+        if(e==button){index = i}
+        if(e.style.display != "none"){
+            i += 1
+        }        
+    })
+    gallery = button.parentElement
+    while(!gallery.classList.contains("images_history_gallery")){gallery = gallery.parentElement}    
+    set_btn = gallery.querySelector(".images_history_set_index")
+    set_btn.setAttribute("img_index", index)
+    set_btn.click()
 }
-function set_history_index_from_img(e){
-    if(e && e.parentElement.tagName == 'BUTTON'){
-    	bnt = $(e).parent()
-    	if (bnt.hasClass("transform")){
-    		bnt.off("click").on("click",function(){
-    		set_history_gallery_index($(this).parents(".images_history_gallery").first())  
-     		})
-    	} else {
-    		bnt.off("mousedown").on("mousedown", function(){
-    		set_history_gallery_index($(this).parents(".images_history_gallery").first())  
-    		})
 
-		}  		
-    }
+function images_history_get_current_img(tabname, image_path, files){
+    s =  gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index")
+    return [s, image_path, files]
 }
-function images_history_get_current_img(is_image2image, image_path, files){
-	head = is_image2image?"img2img":"txt2img"
-	s =  $(gradioApp().getElementById(head + '_images_history_set_index')).attr("img_index")
-	return [s, image_path, files]
+
+function images_history_delete(tabname, img_path, img_file_name, page_index, filenames, image_index){
+    image_index = parseInt(image_index)
+    tab = gradioApp().getElementById(tabname + '_images_history')
+    set_btn = tab.querySelector(".images_history_set_index")
+    buttons = []
+    tab.querySelectorAll(".gallery-item").forEach(function(e){
+        if (e.style.display != 'none'){
+            buttons.push(e)
+        }
+    })
+
+    
+    img_num = buttons.length / 2
+    if (img_num == 1){
+            setTimeout(function(tabname){
+                gradioApp().getElementById(tabname + '_images_history_renew_page').click()
+            }, 30, tabname) 
+    } else {
+        buttons[image_index].style.display = 'none'
+        buttons[image_index + img_num].style.display = 'none'
+        if (image_index  >= img_num - 1){
+            console.log(buttons.length, img_num)
+            btn = buttons[img_num - 2]
+        } else {
+            btn = buttons[image_index + 1]           
+        }    
+        setTimeout(function(btn){btn.click()}, 30, btn)
+    }  
+    
+    return [tabname, img_path, img_file_name, page_index, filenames, image_index]
 }
 
+function images_history_turnpage(img_path, page_index, image_index, tabname){
+    buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item")
+    buttons.forEach(function(elem) {
+         elem.style.display = 'block'
+    })
+    return [img_path, page_index, image_index, tabname]
+}
diff --git a/javascript/jquery-3.6.0.min.js b/javascript/jquery-3.6.0.min.js
deleted file mode 100644
index c4c6022f..00000000
--- a/javascript/jquery-3.6.0.min.js
+++ /dev/null
@@ -1,2 +0,0 @@
-/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
-!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof 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diff --git a/modules/images_history.py b/modules/images_history.py
index 0e0a48f3..01d11a01 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,117 +1,130 @@
 import os
 def get_recent_images(dir_name, page_index, step, image_index):
-	print(image_index)
-	page_index = int(page_index)
-	f_list = os.listdir(dir_name)
-	file_list = []
-	for file in f_list:
-		if file[-4:] == ".txt":
-			continue
-		file_list.append(file)	
-	file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
-	num = 48
-	max_page_index = len(file_list) // num + 1	
-	page_index = max_page_index if page_index == -1 else page_index + step
-	page_index = 1 if page_index < 1 else page_index	
-	page_index = max_page_index if page_index > max_page_index else page_index
-	idx_frm = (page_index - 1) * num
-	file_list = file_list[idx_frm:idx_frm + num]
-	print(f"Loading history page {page_index}")	
-	image_index = int(image_index)
-	if image_index < 0 or image_index > len(file_list)  - 1:
-		current_file = None 
-		hide_image = None
-	else:
-		current_file =  file_list[int(image_index)]
-		hide_image = os.path.join(dir_name, current_file)
-	return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image
-def first_page_click(dir_name, page_index, image_index):
-	return get_recent_images(dir_name, 1, 0, image_index)
-def end_page_click(dir_name, page_index, image_index):
-	return get_recent_images(dir_name, -1, 0, image_index)
-def prev_page_click(dir_name, page_index, image_index):
-	return get_recent_images(dir_name, page_index, -1, image_index)
-def next_page_click(dir_name, page_index, image_index):	
-	return get_recent_images(dir_name, page_index, 1, image_index)
-def page_index_change(dir_name, page_index, image_index):	
-	return get_recent_images(dir_name, page_index, 0, image_index)
+    #print(image_index)
+    page_index = int(page_index)
+    f_list = os.listdir(dir_name)
+    file_list = []
+    for file in f_list:
+        if file[-4:] == ".txt":
+            continue
+        file_list.append(file)    
+    file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+    num = 48
+    max_page_index = len(file_list) // num + 1    
+    page_index = max_page_index if page_index == -1 else page_index + step
+    page_index = 1 if page_index < 1 else page_index    
+    page_index = max_page_index if page_index > max_page_index else page_index
+    idx_frm = (page_index - 1) * num
+    file_list = file_list[idx_frm:idx_frm + num]
+    #print(f"Loading history page {page_index}")    
+    image_index = int(image_index)
+    if image_index < 0 or image_index > len(file_list)  - 1:
+        current_file = None 
+        hide_image = None
+    else:
+        current_file =  file_list[int(image_index)]
+        hide_image = os.path.join(dir_name, current_file)
+    return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image
+def first_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, 1, 0, image_index)
+def end_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, -1, 0, image_index)
+def prev_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, page_index, -1, image_index)
+def next_page_click(dir_name, page_index, image_index, tabname):    
+    return get_recent_images(dir_name, page_index, 1, image_index)
+def page_index_change(dir_name, page_index, image_index, tabname):    
+    return get_recent_images(dir_name, page_index, 0, image_index)
 
 def show_image_info(num, image_path, filenames):
-	file = filenames[int(num)]
-	return file, num, os.path.join(image_path, file)
-def delete_image(is_img2img, dir_name, name, page_index, filenames, image_index):
-	print("filename", name)
-	path = os.path.join(dir_name, name)		
-	if os.path.exists(path):
-		print(f"Delete file {path}")
-		os.remove(path)	
-	images, page_index, file_list, current_file, hide_image = get_recent_images(dir_name, page_index, 0, image_index)
-	return images, page_index, file_list, current_file, hide_image
+    #print("set img",num)
+    file = filenames[int(num)]
+    return file, num, os.path.join(image_path, file)
+def delete_image(tabname, dir_name, name, page_index, filenames, image_index):
+    #print("filename", name)
+    path = os.path.join(dir_name, name)           
+    if os.path.exists(path):
+        print(f"Delete file {path}")
+        os.remove(path)    
+        new_file_list = []
+        for f in filenames:
+            if f == name:
+                continue
+            new_file_list.append(f)
+    else:
+        print(f"Not exists file {path}")
+        new_file_list = filenames
+    return page_index, new_file_list
 
+def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
+        if tabname == "txt2img":
+            dir_name = opts.outdir_txt2img_samples
+        elif tabname == "img2img":
+            dir_name = opts.outdir_img2img_samples
+        elif tabname == "extras":
+            dir_name = opts.outdir_extras_samples
+        with gr.Row():    
+                renew_page = gr.Button('Renew', elem_id=tabname + "_images_history_renew_page") 
+                first_page = gr.Button('First', elem_id=tabname + "_images_history_first_page")
+                prev_page = gr.Button('Prev') 
+                page_index = gr.Number(value=1, label="Page Index")
+                next_page = gr.Button('Next', elem_id=tabname + "_images_history_next_page") 
+                end_page = gr.Button('End')    
+        with gr.Row(elem_id=tabname + "_images_history"):            
+            with gr.Row():     
+                with gr.Column():
+                    history_gallery = gr.Gallery(show_label=False).style(grid=6)
+                with gr.Column():     
+                    with gr.Row():          
+                        delete = gr.Button('Delete')
+                        pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
+                        pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                    with gr.Row():
+                        with gr.Column():
+                            img_file_info = gr.Textbox(label="Generate Info")
+                            img_file_name = gr.Textbox(label="File Name")    
+                    with gr.Row():    
+                            # hiden items
+                            img_path = gr.Textbox(dir_name, visible=False)                
+                            tabname_box = gr.Textbox(tabname, visible=False)    
+                            image_index = gr.Textbox(value=-1, visible=False)                        
+                            set_index = gr.Button('set_index',  elem_id=tabname + "_images_history_set_index", visible=False)
+                            filenames = gr.State()
+                            hide_image = gr.Image(visible=False, type="pil")
+                            info1 = gr.Textbox(visible=False)
+                            info2 = gr.Textbox(visible=False)
 
-def show_images_history(gr, opts, is_img2img, run_pnginfo, switch_dict):
-		def id_name(is_img2img, name):	
-			return ("img2img" if is_img2img else "txt2img") + "_" + name
-		if is_img2img:
-			dir_name = opts.outdir_img2img_samples
-		else:
-			dir_name = opts.outdir_txt2img_samples
-		with gr.Row():	 
-				first_page = gr.Button('First', elem_id=id_name(is_img2img,"images_history_first_page"))
-				prev_page = gr.Button('Prev') 
-				page_index = gr.Number(value=1, label="Page Index")
-				next_page = gr.Button('Next') 
-				end_page = gr.Button('End')	
-		with gr.Row(elem_id=id_name(is_img2img,"images_history")):			
-			with gr.Row():	 
-				with gr.Column():
-					history_gallery = gr.Gallery(show_label=False).style(grid=6)
-				with gr.Column():	 
-					with gr.Row():	      
-						delete = gr.Button('Delete')
-						pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-						pnginfo_send_to_img2img = gr.Button('Send to img2img')
-					with gr.Row():
-						with gr.Column():
-							img_file_info = gr.Textbox(dir_name, label="Generate Info")
-							img_file_name = gr.Textbox(label="File Name")	
-					with gr.Row():	
-							# hiden items
-							img_path = gr.Textbox(dir_name, visible=False)				
-							is_img2img_flag = gr.Checkbox(is_img2img, visible=False)	
-							image_index = gr.Textbox(value=-1, visible=False)						
-							set_index = gr.Button('set_index',  elem_id=id_name(is_img2img,"images_history_set_index"))
-							filenames = gr.State()
-							hide_image = gr.Image(visible=False, type="pil")
-							info1 = gr.Textbox(visible=False)
-							info2 = gr.Textbox(visible=False)
+                
+        # turn pages
+        gallery_inputs = [img_path, page_index, image_index, tabname_box]
+        gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image]
 
-				
-		# turn pages
-		gallery_inputs = [img_path, page_index, image_index]
-		gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image]
-		first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-		next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-		prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)		
-		end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-		page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
-		#page_index.change(page_index_change, inputs=[is_img2img_flag, img_path,  page_index], outputs=[history_gallery, page_index])
+        first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+        next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+        prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)        
+        end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+        page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+        renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+        #page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
 
-		#other funcitons
-		set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[is_img2img_flag,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
-		delete.click(delete_image, inputs=[is_img2img_flag, img_path, img_file_name, page_index, filenames, image_index], outputs=gallery_outputs) 
-		hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
-		switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
-		switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
-	
-		
+        #other funcitons
+        set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
+        delete.click(delete_image,_js="images_history_delete", inputs=[tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames]) 
+        hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
+        switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
+        switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
+    
+        
 def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
-	with gr.Blocks(analytics_enabled=False) as images_history:
-		with gr.Tabs() as tabs:			
-			with gr.Tab("txt2img history", id="images_history_txt2img"):
-				with gr.Blocks(analytics_enabled=False) as images_history_txt2img:   
-					show_images_history(gr, opts, False, run_pnginfo, switch_dict) 
-			with gr.Tab("img2img history", id="images_history_img2img"):
-				with gr.Blocks(analytics_enabled=False) as images_history_img2img:
-					show_images_history(gr, opts, True, run_pnginfo, switch_dict)
-	return images_history
+    with gr.Blocks(analytics_enabled=False) as images_history:
+        with gr.Tabs() as tabs:            
+            with gr.Tab("txt2img history"):
+                with gr.Blocks(analytics_enabled=False) as images_history_txt2img:   
+                    show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict) 
+            with gr.Tab("img2img history"):
+                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+                    show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict)
+            with gr.Tab("extras history"):
+                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+                    show_images_history(gr, opts, "extras", run_pnginfo, switch_dict)
+    return images_history
diff --git a/style.css b/style.css
index c0c3f2bb..ca1cdba1 100644
--- a/style.css
+++ b/style.css
@@ -463,3 +463,6 @@ input[type="range"]{
     max-width: 32em;
     padding: 0;
 }
+.images-history-hidden{
+    display: none;
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 45ada1c91025e221df04f911de6377e419f19e3f Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 13:10:11 +0100
Subject: Correct list style, apply gen forever to both tabs, roll3 on both
 tabs

---
 javascript/contextMenus.js | 24 ++++++++++++++++--------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js
index 7852793c..4e772065 100644
--- a/javascript/contextMenus.js
+++ b/javascript/contextMenus.js
@@ -16,7 +16,7 @@ contextMenuInit = function(){
       oldMenu.remove()
     }
 
-    let tabButton = gradioApp().querySelector('button')
+    let tabButton = uiCurrentTab
     let baseStyle = window.getComputedStyle(tabButton)
 
     const contextMenu = document.createElement('nav')
@@ -130,9 +130,9 @@ addContextMenuEventListener = initResponse[2]
 
 
 //Start example Context Menu Items
-generateOnRepeatId = appendContextMenuOption('#txt2img_generate','Generate forever',function(){
-  let genbutton = gradioApp().querySelector('#txt2img_generate');
-  let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
+generateOnRepeat = function(genbuttonid,interruptbuttonid){
+  let genbutton = gradioApp().querySelector(genbuttonid);
+  let interruptbutton = gradioApp().querySelector(interruptbuttonid);
   if(!interruptbutton.offsetParent){
     genbutton.click();
   }
@@ -142,8 +142,15 @@ generateOnRepeatId = appendContextMenuOption('#txt2img_generate','Generate forev
       genbutton.click();
     }
   },
-  500)}
-)
+  500)
+}
+
+generateOnRepeatId = appendContextMenuOption('#txt2img_generate','Generate forever',function(){
+  generateOnRepeat('#txt2img_generate','#txt2img_interrupt');
+})
+generateOnRepeatId = appendContextMenuOption('#img2img_generate','Generate forever',function(){
+  generateOnRepeat('#img2img_generate','#img2img_interrupt');
+})
 
 cancelGenerateForever = function(){ 
   clearInterval(window.generateOnRepeatInterval) 
@@ -151,11 +158,12 @@ cancelGenerateForever = function(){
 
 appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
 appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
-
+appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
+appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
 
 appendContextMenuOption('#roll','Roll three',
   function(){ 
-    let rollbutton = gradioApp().querySelector('#roll');
+    let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
     setTimeout(function(){rollbutton.click()},100)
     setTimeout(function(){rollbutton.click()},200)
     setTimeout(function(){rollbutton.click()},300)
-- 
cgit v1.2.1


From 9b8faefde05464fe6ba51668fe1d361e4fe22339 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 13:19:16 +0100
Subject: context menus closure

---
 javascript/contextMenus.js | 81 +++++++++++++++++++++++-----------------------
 1 file changed, 41 insertions(+), 40 deletions(-)

diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js
index 4e772065..7636c4b3 100644
--- a/javascript/contextMenus.js
+++ b/javascript/contextMenus.js
@@ -123,52 +123,53 @@ contextMenuInit = function(){
   return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
 }
 
-initResponse = contextMenuInit()
-appendContextMenuOption     = initResponse[0]
-removeContextMenuOption     = initResponse[1]
-addContextMenuEventListener = initResponse[2]
-
-
-//Start example Context Menu Items
-generateOnRepeat = function(genbuttonid,interruptbuttonid){
-  let genbutton = gradioApp().querySelector(genbuttonid);
-  let interruptbutton = gradioApp().querySelector(interruptbuttonid);
-  if(!interruptbutton.offsetParent){
-    genbutton.click();
-  }
-  clearInterval(window.generateOnRepeatInterval)
-  window.generateOnRepeatInterval = setInterval(function(){
+initResponse = contextMenuInit();
+appendContextMenuOption     = initResponse[0];
+removeContextMenuOption     = initResponse[1];
+addContextMenuEventListener = initResponse[2];
+
+(function(){
+  //Start example Context Menu Items
+  let generateOnRepeat = function(genbuttonid,interruptbuttonid){
+    let genbutton = gradioApp().querySelector(genbuttonid);
+    let interruptbutton = gradioApp().querySelector(interruptbuttonid);
     if(!interruptbutton.offsetParent){
       genbutton.click();
     }
-  },
-  500)
-}
-
-generateOnRepeatId = appendContextMenuOption('#txt2img_generate','Generate forever',function(){
-  generateOnRepeat('#txt2img_generate','#txt2img_interrupt');
-})
-generateOnRepeatId = appendContextMenuOption('#img2img_generate','Generate forever',function(){
-  generateOnRepeat('#img2img_generate','#img2img_interrupt');
-})
+    clearInterval(window.generateOnRepeatInterval)
+    window.generateOnRepeatInterval = setInterval(function(){
+      if(!interruptbutton.offsetParent){
+        genbutton.click();
+      }
+    },
+    500)
+  }
 
-cancelGenerateForever = function(){ 
-  clearInterval(window.generateOnRepeatInterval) 
-}
+  appendContextMenuOption('#txt2img_generate','Generate forever',function(){
+    generateOnRepeat('#txt2img_generate','#txt2img_interrupt');
+  })
+  appendContextMenuOption('#img2img_generate','Generate forever',function(){
+    generateOnRepeat('#img2img_generate','#img2img_interrupt');
+  })
 
-appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
-appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
-appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
-appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
-
-appendContextMenuOption('#roll','Roll three',
-  function(){ 
-    let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
-    setTimeout(function(){rollbutton.click()},100)
-    setTimeout(function(){rollbutton.click()},200)
-    setTimeout(function(){rollbutton.click()},300)
+  let cancelGenerateForever = function(){ 
+    clearInterval(window.generateOnRepeatInterval) 
   }
-)
+
+  appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
+  appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
+  appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
+  appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
+
+  appendContextMenuOption('#roll','Roll three',
+    function(){ 
+      let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
+      setTimeout(function(){rollbutton.click()},100)
+      setTimeout(function(){rollbutton.click()},200)
+      setTimeout(function(){rollbutton.click()},300)
+    }
+  )
+})();
 //End example Context Menu Items
 
 onUiUpdate(function(){
-- 
cgit v1.2.1


From 92d7a138857b308c97a8d009848f642aeb93d6c8 Mon Sep 17 00:00:00 2001
From: Martin Cairns <Martin.Cairns+github@gmail.com>
Date: Tue, 11 Oct 2022 00:02:44 +0100
Subject: Handle different parameters for DPM fast & adaptive

---
 modules/sd_samplers.py | 25 ++++++++++++++++++-------
 1 file changed, 18 insertions(+), 7 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d168b938..eee52e7d 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -57,7 +57,7 @@ def set_samplers():
     global samplers, samplers_for_img2img
 
     hidden = set(opts.hide_samplers)
-    hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive'])
+    hidden_img2img = set(opts.hide_samplers + ['PLMS'])
 
     samplers = [x for x in all_samplers if x.name not in hidden]
     samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
@@ -365,16 +365,27 @@ class KDiffusionSampler:
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
 
-        noise = noise * sigmas[steps - t_enc - 1]
-        xi = x + noise
-
-        extra_params_kwargs = self.initialize(p)
-
         sigma_sched = sigmas[steps - t_enc - 1:]
+        print('check values same', sigmas[steps - t_enc - 1] , sigma_sched[0], sigmas[steps - t_enc - 1] - sigma_sched[0])
+        xi = x + noise * sigma_sched[0]
+        
+        extra_params_kwargs = self.initialize(p)
+        if 'sigma_min' in inspect.signature(self.func).parameters:
+            ## last sigma is zero which is allowed by DPM Fast & Adaptive so taking value before last
+            extra_params_kwargs['sigma_min'] = sigma_sched[-2]
+        if 'sigma_max' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigma_max'] = sigma_sched[0]
+        if 'n' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['n'] = len(sigma_sched) - 1
+        if 'sigma_sched' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigma_sched'] = sigma_sched
+        if 'sigmas' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['sigmas'] = sigma_sched
 
         self.model_wrap_cfg.init_latent = x
 
-        return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+        return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+
 
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
         steps = steps or p.steps
-- 
cgit v1.2.1


From 1eae3076078f00ecc5d0fac3c77fffb85cd2eb77 Mon Sep 17 00:00:00 2001
From: Martin Cairns <Martin.Cairns+github@gmail.com>
Date: Tue, 11 Oct 2022 00:04:06 +0100
Subject: Remove debug code for checking that first sigma value is same after
 code cleanup

---
 modules/sd_samplers.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index eee52e7d..32272916 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -366,7 +366,6 @@ class KDiffusionSampler:
             sigmas = self.model_wrap.get_sigmas(steps)
 
         sigma_sched = sigmas[steps - t_enc - 1:]
-        print('check values same', sigmas[steps - t_enc - 1] , sigma_sched[0], sigmas[steps - t_enc - 1] - sigma_sched[0])
         xi = x + noise * sigma_sched[0]
         
         extra_params_kwargs = self.initialize(p)
-- 
cgit v1.2.1


From eacc03b16730bcc5be95cda2d7c966ff1b4a8263 Mon Sep 17 00:00:00 2001
From: Martin Cairns <Martin.Cairns+github@gmail.com>
Date: Tue, 11 Oct 2022 00:36:00 +0100
Subject: Fix typo in comments

---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 32272916..20309e06 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -370,7 +370,7 @@ class KDiffusionSampler:
         
         extra_params_kwargs = self.initialize(p)
         if 'sigma_min' in inspect.signature(self.func).parameters:
-            ## last sigma is zero which is allowed by DPM Fast & Adaptive so taking value before last
+            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
             extra_params_kwargs['sigma_min'] = sigma_sched[-2]
         if 'sigma_max' in inspect.signature(self.func).parameters:
             extra_params_kwargs['sigma_max'] = sigma_sched[0]
-- 
cgit v1.2.1


From 87d63bbab5c973ac5cec777ef7304d28f1ab3f24 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Tue, 11 Oct 2022 20:37:03 +0800
Subject: images history improvement

---
 javascript/images_history.js |  6 ++----
 modules/images_history.py    | 30 +++++++++++++++---------------
 2 files changed, 17 insertions(+), 19 deletions(-)

diff --git a/javascript/images_history.js b/javascript/images_history.js
index 9a3e00a0..d62eb181 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -93,7 +93,6 @@ function images_history_delete(tabname, img_path, img_file_name, page_index, fil
             buttons.push(e)
         }
     })
-
     
     img_num = buttons.length / 2
     if (img_num == 1){
@@ -110,15 +109,14 @@ function images_history_delete(tabname, img_path, img_file_name, page_index, fil
             btn = buttons[image_index + 1]           
         }    
         setTimeout(function(btn){btn.click()}, 30, btn)
-    }  
-    
+    }      
     return [tabname, img_path, img_file_name, page_index, filenames, image_index]
 }
 
 function images_history_turnpage(img_path, page_index, image_index, tabname){
     buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item")
     buttons.forEach(function(elem) {
-         elem.style.display = 'block'
+        elem.style.display = 'block'
     })
     return [img_path, page_index, image_index, tabname]
 }
diff --git a/modules/images_history.py b/modules/images_history.py
index 01d11a01..23f55b30 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -64,12 +64,12 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         elif tabname == "extras":
             dir_name = opts.outdir_extras_samples
         with gr.Row():    
-                renew_page = gr.Button('Renew', elem_id=tabname + "_images_history_renew_page") 
-                first_page = gr.Button('First', elem_id=tabname + "_images_history_first_page")
-                prev_page = gr.Button('Prev') 
-                page_index = gr.Number(value=1, label="Page Index")
-                next_page = gr.Button('Next', elem_id=tabname + "_images_history_next_page") 
-                end_page = gr.Button('End')    
+            renew_page = gr.Button('Renew', elem_id=tabname + "_images_history_renew_page") 
+            first_page = gr.Button('First', elem_id=tabname + "_images_history_first_page")
+            prev_page = gr.Button('Prev') 
+            page_index = gr.Number(value=1, label="Page Index")
+            next_page = gr.Button('Next', elem_id=tabname + "_images_history_next_page") 
+            end_page = gr.Button('End')    
         with gr.Row(elem_id=tabname + "_images_history"):            
             with gr.Row():     
                 with gr.Column():
@@ -84,15 +84,15 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                             img_file_info = gr.Textbox(label="Generate Info")
                             img_file_name = gr.Textbox(label="File Name")    
                     with gr.Row():    
-                            # hiden items
-                            img_path = gr.Textbox(dir_name, visible=False)                
-                            tabname_box = gr.Textbox(tabname, visible=False)    
-                            image_index = gr.Textbox(value=-1, visible=False)                        
-                            set_index = gr.Button('set_index',  elem_id=tabname + "_images_history_set_index", visible=False)
-                            filenames = gr.State()
-                            hide_image = gr.Image(visible=False, type="pil")
-                            info1 = gr.Textbox(visible=False)
-                            info2 = gr.Textbox(visible=False)
+                        # hiden items
+                        img_path = gr.Textbox(dir_name, visible=False)                
+                        tabname_box = gr.Textbox(tabname, visible=False)    
+                        image_index = gr.Textbox(value=-1, visible=False)                        
+                        set_index = gr.Button('set_index',  elem_id=tabname + "_images_history_set_index", visible=False)
+                        filenames = gr.State()
+                        hide_image = gr.Image(visible=False, type="pil")
+                        info1 = gr.Textbox(visible=False)
+                        info2 = gr.Textbox(visible=False)
 
                 
         # turn pages
-- 
cgit v1.2.1


From b372f5538bee4feba87080af4f3acf1e437accc6 Mon Sep 17 00:00:00 2001
From: Ben <110583491+TheLastBen@users.noreply.github.com>
Date: Mon, 10 Oct 2022 19:34:07 +0100
Subject: Save some space

---
 style.css | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/style.css b/style.css
index 00a3d07f..38410ca4 100644
--- a/style.css
+++ b/style.css
@@ -2,6 +2,18 @@
     max-width: 100%;
 }
 
+#txt2img_token_counter {
+    height: 0px;
+}
+
+#img2img_token_counter {
+    height: 0px;
+}
+
+#negative_prompt {
+    width: 97.9%;
+}
+
 .output-html p {margin: 0 0.5em;}
 
 .row > *,
-- 
cgit v1.2.1


From 87b77cad5f3017c952a7dfec0e7904a9df5b72fd Mon Sep 17 00:00:00 2001
From: Ben <110583491+TheLastBen@users.noreply.github.com>
Date: Mon, 10 Oct 2022 19:37:16 +0100
Subject: Layout fix

---
 modules/ui.py | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index df653059..de4cd7f2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -550,15 +550,15 @@ def create_ui(wrap_gradio_gpu_call):
                         button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
                         open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
 
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+                with gr.Row():
+                    do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
                     
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+                with gr.Row():
+                    download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
-                with gr.Group():
-                    html_info = gr.HTML()
-                    generation_info = gr.Textbox(visible=False)
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -738,15 +738,15 @@ def create_ui(wrap_gradio_gpu_call):
                         button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
                         open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
 
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+                with gr.Row():
+                    do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
                     
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+                with gr.Row():
+                    download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
-                with gr.Group():
-                    html_info = gr.HTML()
-                    generation_info = gr.Textbox(visible=False)
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-- 
cgit v1.2.1


From 861297cefe2bb663f4e09dd4778a4cb93ebe8ff1 Mon Sep 17 00:00:00 2001
From: Ben <110583491+TheLastBen@users.noreply.github.com>
Date: Tue, 11 Oct 2022 08:08:45 +0100
Subject: add a space holder

---
 modules/ui.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index de4cd7f2..fc0f3d3c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -429,7 +429,10 @@ def create_toprow(is_img2img):
 
             with gr.Row():
                 with gr.Column(scale=8):
-                    negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
+                    with gr.Row():
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
+                with gr.Column(scale=1, elem_id="roll_col"):
+                    sh = gr.Button(elem_id="sh", visible=True)                           
 
                 with gr.Column(scale=1, elem_id="style_neg_col"):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
-- 
cgit v1.2.1


From 031dc8cd7fa6bc74b44114715b28e0737342de37 Mon Sep 17 00:00:00 2001
From: Ben <110583491+TheLastBen@users.noreply.github.com>
Date: Tue, 11 Oct 2022 08:08:47 +0100
Subject: space holder

---
 style.css | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/style.css b/style.css
index 38410ca4..d1c866fc 100644
--- a/style.css
+++ b/style.css
@@ -10,8 +10,16 @@
     height: 0px;
 }
 
-#negative_prompt {
-    width: 97.9%;
+#sh{
+    min-width: 2em;
+    min-height: 2em;
+    max-width: 2em;
+    max-height: 2em;
+    flex-grow: 0;
+    padding-left: 0.25em;
+    padding-right: 0.25em;
+    margin: 0.1em 0;
+    opacity: 0%;
 }
 
 .output-html p {margin: 0 0.5em;}
-- 
cgit v1.2.1


From 54c519943a24881ea61af5a73dedbab92f9431ce Mon Sep 17 00:00:00 2001
From: Ben <110583491+TheLastBen@users.noreply.github.com>
Date: Tue, 11 Oct 2022 10:16:53 +0100
Subject: Update style.css

---
 style.css | 1 +
 1 file changed, 1 insertion(+)

diff --git a/style.css b/style.css
index d1c866fc..ecb51bb0 100644
--- a/style.css
+++ b/style.css
@@ -20,6 +20,7 @@
     padding-right: 0.25em;
     margin: 0.1em 0;
     opacity: 0%;
+    cursor: default;	
 }
 
 .output-html p {margin: 0 0.5em;}
-- 
cgit v1.2.1


From 210fd72babb8314b280a7b5ef8603c62024a22db Mon Sep 17 00:00:00 2001
From: parsec501 <105080989+parsec501@users.noreply.github.com>
Date: Tue, 11 Oct 2022 14:37:01 +0200
Subject: Added 'suggestion' flair to suggestion template

---
 .github/ISSUE_TEMPLATE/feature_request.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
index bbcbbe7d..eda42fa7 100644
--- a/.github/ISSUE_TEMPLATE/feature_request.md
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -2,7 +2,7 @@
 name: Feature request
 about: Suggest an idea for this project
 title: ''
-labels: ''
+labels: 'suggestion'
 assignees: ''
 
 ---
-- 
cgit v1.2.1


From 4e485b79238666ace2b270045f73a12e5ccc7af9 Mon Sep 17 00:00:00 2001
From: JamnedZ <zaguzinegor@gmail.com>
Date: Tue, 11 Oct 2022 16:38:03 +0700
Subject: Added installation of pyngrok if needed

---
 launch.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/launch.py b/launch.py
index e1000f55..16627a03 100644
--- a/launch.py
+++ b/launch.py
@@ -104,6 +104,7 @@ def prepare_enviroment():
     args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
     xformers = '--xformers' in args
     deepdanbooru = '--deepdanbooru' in args
+    ngrok = '--ngrok' in args
 
     try:
         commit = run(f"{git} rev-parse HEAD").strip()
@@ -134,6 +135,9 @@ def prepare_enviroment():
     if not is_installed("deepdanbooru") and deepdanbooru:
         run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
 
+    if not is_installed("pyngrok") and ngrok:
+        run_pip("install pyngrok", "ngrok")
+
     os.makedirs(dir_repos, exist_ok=True)
 
     git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
-- 
cgit v1.2.1


From 59925644480b6fd84f6bb84b4df7d4fbc6a0cce8 Mon Sep 17 00:00:00 2001
From: JamnedZ <zaguzinegor@gmail.com>
Date: Tue, 11 Oct 2022 16:40:27 +0700
Subject: Cleaned ngrok integration

---
 modules/ngrok.py  | 15 +++++++++++++++
 modules/shared.py |  1 +
 modules/ui.py     |  5 +++++
 3 files changed, 21 insertions(+)
 create mode 100644 modules/ngrok.py

diff --git a/modules/ngrok.py b/modules/ngrok.py
new file mode 100644
index 00000000..17e6976f
--- /dev/null
+++ b/modules/ngrok.py
@@ -0,0 +1,15 @@
+from pyngrok import ngrok, conf, exception
+
+
+def connect(token, port):
+    if token == None:
+        token = 'None'
+    conf.get_default().auth_token = token
+    try:
+        public_url = ngrok.connect(port).public_url
+    except exception.PyngrokNgrokError:
+        print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
+              f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
+    else:
+        print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'
+               'You can use this link after the launch is complete.')
\ No newline at end of file
diff --git a/modules/shared.py b/modules/shared.py
index 8753015e..375e3afb 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -38,6 +38,7 @@ parser.add_argument("--always-batch-cond-uncond", action='store_true', help="dis
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
+parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
diff --git a/modules/ui.py b/modules/ui.py
index fc0f3d3c..f57f32db 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -51,6 +51,11 @@ if not cmd_opts.share and not cmd_opts.listen:
     gradio.utils.version_check = lambda: None
     gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
 
+if cmd_opts.ngrok != None:
+    import modules.ngrok as ngrok
+    print('ngrok authtoken detected, trying to connect...')
+    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860)
+
 
 def gr_show(visible=True):
     return {"visible": visible, "__type__": "update"}
-- 
cgit v1.2.1


From a004d1a855311b0d7ff2976a4e31b0247ad9d1f6 Mon Sep 17 00:00:00 2001
From: JamnedZ <zaguzinegor@gmail.com>
Date: Tue, 11 Oct 2022 16:48:27 +0700
Subject: Added new line at the end of ngrok.py

---
 modules/ngrok.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ngrok.py b/modules/ngrok.py
index 17e6976f..7d03a6df 100644
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -12,4 +12,4 @@ def connect(token, port):
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
     else:
         print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'
-               'You can use this link after the launch is complete.')
\ No newline at end of file
+               'You can use this link after the launch is complete.')
-- 
cgit v1.2.1


From 873efeed49bb5197a42da18272115b326c5d68f3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 15:51:22 +0300
Subject: rename hypernetwork dir to hypernetworks to prevent clash with an old
 filename that people who use zip instead of git clone will have

---
 modules/hypernetwork/hypernetwork.py  | 283 ----------------------------------
 modules/hypernetwork/ui.py            |  43 ------
 modules/hypernetworks/hypernetwork.py | 283 ++++++++++++++++++++++++++++++++++
 modules/hypernetworks/ui.py           |  43 ++++++
 modules/sd_hijack.py                  |   2 +-
 modules/sd_hijack_optimizations.py    |   2 +-
 modules/shared.py                     |   2 +-
 modules/ui.py                         |   2 +-
 scripts/xy_grid.py                    |   2 +-
 webui.py                              |   2 +-
 10 files changed, 332 insertions(+), 332 deletions(-)
 delete mode 100644 modules/hypernetwork/hypernetwork.py
 delete mode 100644 modules/hypernetwork/ui.py
 create mode 100644 modules/hypernetworks/hypernetwork.py
 create mode 100644 modules/hypernetworks/ui.py

diff --git a/modules/hypernetwork/hypernetwork.py b/modules/hypernetwork/hypernetwork.py
deleted file mode 100644
index aa701bda..00000000
--- a/modules/hypernetwork/hypernetwork.py
+++ /dev/null
@@ -1,283 +0,0 @@
-import datetime
-import glob
-import html
-import os
-import sys
-import traceback
-import tqdm
-
-import torch
-
-from ldm.util import default
-from modules import devices, shared, processing, sd_models
-import torch
-from torch import einsum
-from einops import rearrange, repeat
-import modules.textual_inversion.dataset
-
-
-class HypernetworkModule(torch.nn.Module):
-    def __init__(self, dim, state_dict=None):
-        super().__init__()
-
-        self.linear1 = torch.nn.Linear(dim, dim * 2)
-        self.linear2 = torch.nn.Linear(dim * 2, dim)
-
-        if state_dict is not None:
-            self.load_state_dict(state_dict, strict=True)
-        else:
-
-            self.linear1.weight.data.normal_(mean=0.0, std=0.01)
-            self.linear1.bias.data.zero_()
-            self.linear2.weight.data.normal_(mean=0.0, std=0.01)
-            self.linear2.bias.data.zero_()
-
-        self.to(devices.device)
-
-    def forward(self, x):
-        return x + (self.linear2(self.linear1(x)))
-
-
-class Hypernetwork:
-    filename = None
-    name = None
-
-    def __init__(self, name=None):
-        self.filename = None
-        self.name = name
-        self.layers = {}
-        self.step = 0
-        self.sd_checkpoint = None
-        self.sd_checkpoint_name = None
-
-        for size in [320, 640, 768, 1280]:
-            self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
-
-    def weights(self):
-        res = []
-
-        for k, layers in self.layers.items():
-            for layer in layers:
-                layer.train()
-                res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]
-
-        return res
-
-    def save(self, filename):
-        state_dict = {}
-
-        for k, v in self.layers.items():
-            state_dict[k] = (v[0].state_dict(), v[1].state_dict())
-
-        state_dict['step'] = self.step
-        state_dict['name'] = self.name
-        state_dict['sd_checkpoint'] = self.sd_checkpoint
-        state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
-
-        torch.save(state_dict, filename)
-
-    def load(self, filename):
-        self.filename = filename
-        if self.name is None:
-            self.name = os.path.splitext(os.path.basename(filename))[0]
-
-        state_dict = torch.load(filename, map_location='cpu')
-
-        for size, sd in state_dict.items():
-            if type(size) == int:
-                self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
-
-        self.name = state_dict.get('name', self.name)
-        self.step = state_dict.get('step', 0)
-        self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
-        self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
-
-
-def list_hypernetworks(path):
-    res = {}
-    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
-        name = os.path.splitext(os.path.basename(filename))[0]
-        res[name] = filename
-    return res
-
-
-def load_hypernetwork(filename):
-    path = shared.hypernetworks.get(filename, None)
-    if path is not None:
-        print(f"Loading hypernetwork {filename}")
-        try:
-            shared.loaded_hypernetwork = Hypernetwork()
-            shared.loaded_hypernetwork.load(path)
-
-        except Exception:
-            print(f"Error loading hypernetwork {path}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-    else:
-        if shared.loaded_hypernetwork is not None:
-            print(f"Unloading hypernetwork")
-
-        shared.loaded_hypernetwork = None
-
-
-def apply_hypernetwork(hypernetwork, context, layer=None):
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is None:
-        return context, context
-
-    if layer is not None:
-        layer.hyper_k = hypernetwork_layers[0]
-        layer.hyper_v = hypernetwork_layers[1]
-
-    context_k = hypernetwork_layers[0](context)
-    context_v = hypernetwork_layers[1](context)
-    return context_k, context_v
-
-
-def attention_CrossAttention_forward(self, x, context=None, mask=None):
-    h = self.heads
-
-    q = self.to_q(x)
-    context = default(context, x)
-
-    context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self)
-    k = self.to_k(context_k)
-    v = self.to_v(context_v)
-
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-
-    if mask is not None:
-        mask = rearrange(mask, 'b ... -> b (...)')
-        max_neg_value = -torch.finfo(sim.dtype).max
-        mask = repeat(mask, 'b j -> (b h) () j', h=h)
-        sim.masked_fill_(~mask, max_neg_value)
-
-    # attention, what we cannot get enough of
-    attn = sim.softmax(dim=-1)
-
-    out = einsum('b i j, b j d -> b i d', attn, v)
-    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-    return self.to_out(out)
-
-
-def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
-    assert hypernetwork_name, 'embedding not selected'
-
-    path = shared.hypernetworks.get(hypernetwork_name, None)
-    shared.loaded_hypernetwork = Hypernetwork()
-    shared.loaded_hypernetwork.load(path)
-
-    shared.state.textinfo = "Initializing hypernetwork training..."
-    shared.state.job_count = steps
-
-    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
-
-    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
-
-    if save_hypernetwork_every > 0:
-        hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
-        os.makedirs(hypernetwork_dir, exist_ok=True)
-    else:
-        hypernetwork_dir = None
-
-    if create_image_every > 0:
-        images_dir = os.path.join(log_directory, "images")
-        os.makedirs(images_dir, exist_ok=True)
-    else:
-        images_dir = None
-
-    cond_model = shared.sd_model.cond_stage_model
-
-    shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
-    with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
-
-    hypernetwork = shared.loaded_hypernetwork
-    weights = hypernetwork.weights()
-    for weight in weights:
-        weight.requires_grad = True
-
-    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
-
-    losses = torch.zeros((32,))
-
-    last_saved_file = "<none>"
-    last_saved_image = "<none>"
-
-    ititial_step = hypernetwork.step or 0
-    if ititial_step > steps:
-        return hypernetwork, filename
-
-    pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, (x, text) in pbar:
-        hypernetwork.step = i + ititial_step
-
-        if hypernetwork.step > steps:
-            break
-
-        if shared.state.interrupted:
-            break
-
-        with torch.autocast("cuda"):
-            c = cond_model([text])
-
-            x = x.to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), c)[0]
-            del x
-
-            losses[hypernetwork.step % losses.shape[0]] = loss.item()
-
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-        pbar.set_description(f"loss: {losses.mean():.7f}")
-
-        if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
-            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
-            hypernetwork.save(last_saved_file)
-
-        if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
-            last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
-
-            preview_text = text if preview_image_prompt == "" else preview_image_prompt
-
-            p = processing.StableDiffusionProcessingTxt2Img(
-                sd_model=shared.sd_model,
-                prompt=preview_text,
-                steps=20,
-                do_not_save_grid=True,
-                do_not_save_samples=True,
-            )
-
-            processed = processing.process_images(p)
-            image = processed.images[0]
-
-            shared.state.current_image = image
-            image.save(last_saved_image)
-
-            last_saved_image += f", prompt: {preview_text}"
-
-        shared.state.job_no = hypernetwork.step
-
-        shared.state.textinfo = f"""
-<p>
-Loss: {losses.mean():.7f}<br/>
-Step: {hypernetwork.step}<br/>
-Last prompt: {html.escape(text)}<br/>
-Last saved embedding: {html.escape(last_saved_file)}<br/>
-Last saved image: {html.escape(last_saved_image)}<br/>
-</p>
-"""
-
-    checkpoint = sd_models.select_checkpoint()
-
-    hypernetwork.sd_checkpoint = checkpoint.hash
-    hypernetwork.sd_checkpoint_name = checkpoint.model_name
-    hypernetwork.save(filename)
-
-    return hypernetwork, filename
-
-
diff --git a/modules/hypernetwork/ui.py b/modules/hypernetwork/ui.py
deleted file mode 100644
index f6d1d0a3..00000000
--- a/modules/hypernetwork/ui.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import html
-import os
-
-import gradio as gr
-
-import modules.textual_inversion.textual_inversion
-import modules.textual_inversion.preprocess
-from modules import sd_hijack, shared
-from modules.hypernetwork import hypernetwork
-
-
-def create_hypernetwork(name):
-    fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
-    assert not os.path.exists(fn), f"file {fn} already exists"
-
-    hypernet = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
-    hypernet.save(fn)
-
-    shared.reload_hypernetworks()
-
-    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
-
-
-def train_hypernetwork(*args):
-
-    initial_hypernetwork = shared.loaded_hypernetwork
-
-    try:
-        sd_hijack.undo_optimizations()
-
-        hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args)
-
-        res = f"""
-Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
-Hypernetwork saved to {html.escape(filename)}
-"""
-        return res, ""
-    except Exception:
-        raise
-    finally:
-        shared.loaded_hypernetwork = initial_hypernetwork
-        sd_hijack.apply_optimizations()
-
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
new file mode 100644
index 00000000..aa701bda
--- /dev/null
+++ b/modules/hypernetworks/hypernetwork.py
@@ -0,0 +1,283 @@
+import datetime
+import glob
+import html
+import os
+import sys
+import traceback
+import tqdm
+
+import torch
+
+from ldm.util import default
+from modules import devices, shared, processing, sd_models
+import torch
+from torch import einsum
+from einops import rearrange, repeat
+import modules.textual_inversion.dataset
+
+
+class HypernetworkModule(torch.nn.Module):
+    def __init__(self, dim, state_dict=None):
+        super().__init__()
+
+        self.linear1 = torch.nn.Linear(dim, dim * 2)
+        self.linear2 = torch.nn.Linear(dim * 2, dim)
+
+        if state_dict is not None:
+            self.load_state_dict(state_dict, strict=True)
+        else:
+
+            self.linear1.weight.data.normal_(mean=0.0, std=0.01)
+            self.linear1.bias.data.zero_()
+            self.linear2.weight.data.normal_(mean=0.0, std=0.01)
+            self.linear2.bias.data.zero_()
+
+        self.to(devices.device)
+
+    def forward(self, x):
+        return x + (self.linear2(self.linear1(x)))
+
+
+class Hypernetwork:
+    filename = None
+    name = None
+
+    def __init__(self, name=None):
+        self.filename = None
+        self.name = name
+        self.layers = {}
+        self.step = 0
+        self.sd_checkpoint = None
+        self.sd_checkpoint_name = None
+
+        for size in [320, 640, 768, 1280]:
+            self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
+
+    def weights(self):
+        res = []
+
+        for k, layers in self.layers.items():
+            for layer in layers:
+                layer.train()
+                res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]
+
+        return res
+
+    def save(self, filename):
+        state_dict = {}
+
+        for k, v in self.layers.items():
+            state_dict[k] = (v[0].state_dict(), v[1].state_dict())
+
+        state_dict['step'] = self.step
+        state_dict['name'] = self.name
+        state_dict['sd_checkpoint'] = self.sd_checkpoint
+        state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
+
+        torch.save(state_dict, filename)
+
+    def load(self, filename):
+        self.filename = filename
+        if self.name is None:
+            self.name = os.path.splitext(os.path.basename(filename))[0]
+
+        state_dict = torch.load(filename, map_location='cpu')
+
+        for size, sd in state_dict.items():
+            if type(size) == int:
+                self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
+
+        self.name = state_dict.get('name', self.name)
+        self.step = state_dict.get('step', 0)
+        self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
+        self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
+
+
+def list_hypernetworks(path):
+    res = {}
+    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
+        name = os.path.splitext(os.path.basename(filename))[0]
+        res[name] = filename
+    return res
+
+
+def load_hypernetwork(filename):
+    path = shared.hypernetworks.get(filename, None)
+    if path is not None:
+        print(f"Loading hypernetwork {filename}")
+        try:
+            shared.loaded_hypernetwork = Hypernetwork()
+            shared.loaded_hypernetwork.load(path)
+
+        except Exception:
+            print(f"Error loading hypernetwork {path}", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+    else:
+        if shared.loaded_hypernetwork is not None:
+            print(f"Unloading hypernetwork")
+
+        shared.loaded_hypernetwork = None
+
+
+def apply_hypernetwork(hypernetwork, context, layer=None):
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is None:
+        return context, context
+
+    if layer is not None:
+        layer.hyper_k = hypernetwork_layers[0]
+        layer.hyper_v = hypernetwork_layers[1]
+
+    context_k = hypernetwork_layers[0](context)
+    context_v = hypernetwork_layers[1](context)
+    return context_k, context_v
+
+
+def attention_CrossAttention_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
+
+    context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self)
+    k = self.to_k(context_k)
+    v = self.to_v(context_v)
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+    if mask is not None:
+        mask = rearrange(mask, 'b ... -> b (...)')
+        max_neg_value = -torch.finfo(sim.dtype).max
+        mask = repeat(mask, 'b j -> (b h) () j', h=h)
+        sim.masked_fill_(~mask, max_neg_value)
+
+    # attention, what we cannot get enough of
+    attn = sim.softmax(dim=-1)
+
+    out = einsum('b i j, b j d -> b i d', attn, v)
+    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+    return self.to_out(out)
+
+
+def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
+    assert hypernetwork_name, 'embedding not selected'
+
+    path = shared.hypernetworks.get(hypernetwork_name, None)
+    shared.loaded_hypernetwork = Hypernetwork()
+    shared.loaded_hypernetwork.load(path)
+
+    shared.state.textinfo = "Initializing hypernetwork training..."
+    shared.state.job_count = steps
+
+    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+
+    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
+
+    if save_hypernetwork_every > 0:
+        hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
+        os.makedirs(hypernetwork_dir, exist_ok=True)
+    else:
+        hypernetwork_dir = None
+
+    if create_image_every > 0:
+        images_dir = os.path.join(log_directory, "images")
+        os.makedirs(images_dir, exist_ok=True)
+    else:
+        images_dir = None
+
+    cond_model = shared.sd_model.cond_stage_model
+
+    shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
+    with torch.autocast("cuda"):
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+
+    hypernetwork = shared.loaded_hypernetwork
+    weights = hypernetwork.weights()
+    for weight in weights:
+        weight.requires_grad = True
+
+    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
+
+    losses = torch.zeros((32,))
+
+    last_saved_file = "<none>"
+    last_saved_image = "<none>"
+
+    ititial_step = hypernetwork.step or 0
+    if ititial_step > steps:
+        return hypernetwork, filename
+
+    pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
+    for i, (x, text) in pbar:
+        hypernetwork.step = i + ititial_step
+
+        if hypernetwork.step > steps:
+            break
+
+        if shared.state.interrupted:
+            break
+
+        with torch.autocast("cuda"):
+            c = cond_model([text])
+
+            x = x.to(devices.device)
+            loss = shared.sd_model(x.unsqueeze(0), c)[0]
+            del x
+
+            losses[hypernetwork.step % losses.shape[0]] = loss.item()
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        pbar.set_description(f"loss: {losses.mean():.7f}")
+
+        if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
+            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
+            hypernetwork.save(last_saved_file)
+
+        if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
+            last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
+
+            preview_text = text if preview_image_prompt == "" else preview_image_prompt
+
+            p = processing.StableDiffusionProcessingTxt2Img(
+                sd_model=shared.sd_model,
+                prompt=preview_text,
+                steps=20,
+                do_not_save_grid=True,
+                do_not_save_samples=True,
+            )
+
+            processed = processing.process_images(p)
+            image = processed.images[0]
+
+            shared.state.current_image = image
+            image.save(last_saved_image)
+
+            last_saved_image += f", prompt: {preview_text}"
+
+        shared.state.job_no = hypernetwork.step
+
+        shared.state.textinfo = f"""
+<p>
+Loss: {losses.mean():.7f}<br/>
+Step: {hypernetwork.step}<br/>
+Last prompt: {html.escape(text)}<br/>
+Last saved embedding: {html.escape(last_saved_file)}<br/>
+Last saved image: {html.escape(last_saved_image)}<br/>
+</p>
+"""
+
+    checkpoint = sd_models.select_checkpoint()
+
+    hypernetwork.sd_checkpoint = checkpoint.hash
+    hypernetwork.sd_checkpoint_name = checkpoint.model_name
+    hypernetwork.save(filename)
+
+    return hypernetwork, filename
+
+
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
new file mode 100644
index 00000000..811bc31e
--- /dev/null
+++ b/modules/hypernetworks/ui.py
@@ -0,0 +1,43 @@
+import html
+import os
+
+import gradio as gr
+
+import modules.textual_inversion.textual_inversion
+import modules.textual_inversion.preprocess
+from modules import sd_hijack, shared
+from modules.hypernetworks import hypernetwork
+
+
+def create_hypernetwork(name):
+    fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
+    assert not os.path.exists(fn), f"file {fn} already exists"
+
+    hypernet = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
+    hypernet.save(fn)
+
+    shared.reload_hypernetworks()
+
+    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
+
+
+def train_hypernetwork(*args):
+
+    initial_hypernetwork = shared.loaded_hypernetwork
+
+    try:
+        sd_hijack.undo_optimizations()
+
+        hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args)
+
+        res = f"""
+Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
+Hypernetwork saved to {html.escape(filename)}
+"""
+        return res, ""
+    except Exception:
+        raise
+    finally:
+        shared.loaded_hypernetwork = initial_hypernetwork
+        sd_hijack.apply_optimizations()
+
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index f873049a..f07ec041 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -37,7 +37,7 @@ def apply_optimizations():
 
 
 def undo_optimizations():
-    from modules.hypernetwork import hypernetwork
+    from modules.hypernetworks import hypernetwork
 
     ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
     ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 27e571fc..3349b9c3 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -9,7 +9,7 @@ from ldm.util import default
 from einops import rearrange
 
 from modules import shared
-from modules.hypernetwork import hypernetwork
+from modules.hypernetworks import hypernetwork
 
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
diff --git a/modules/shared.py b/modules/shared.py
index 375e3afb..1dc2ccf2 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -14,7 +14,7 @@ import modules.sd_models
 import modules.styles
 import modules.devices as devices
 from modules import sd_samplers
-from modules.hypernetwork import hypernetwork
+from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
diff --git a/modules/ui.py b/modules/ui.py
index f57f32db..42e5d866 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -39,7 +39,7 @@ import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
-import modules.hypernetwork.ui
+import modules.hypernetworks.ui
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 16918c99..cddb192a 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -11,7 +11,7 @@ import modules.scripts as scripts
 import gradio as gr
 
 from modules import images
-from modules.hypernetwork import hypernetwork
+from modules.hypernetworks import hypernetwork
 from modules.processing import process_images, Processed, get_correct_sampler
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
diff --git a/webui.py b/webui.py
index ba2156c8..faa38a0d 100644
--- a/webui.py
+++ b/webui.py
@@ -29,7 +29,7 @@ from modules import devices
 from modules import modelloader
 from modules.paths import script_path
 from modules.shared import cmd_opts
-import modules.hypernetwork.hypernetwork
+import modules.hypernetworks.hypernetwork
 
 modelloader.cleanup_models()
 modules.sd_models.setup_model()
-- 
cgit v1.2.1


From b0583be0884cd17dafb408fd79b52b2a0a972563 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 15:54:34 +0300
Subject: more renames

---
 modules/hypernetworks/ui.py | 4 ++--
 modules/ui.py               | 4 ++--
 webui.py                    | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 811bc31e..e7540f41 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -13,7 +13,7 @@ def create_hypernetwork(name):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
-    hypernet = modules.hypernetwork.hypernetwork.Hypernetwork(name=name)
+    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name)
     hypernet.save(fn)
 
     shared.reload_hypernetworks()
@@ -28,7 +28,7 @@ def train_hypernetwork(*args):
     try:
         sd_hijack.undo_optimizations()
 
-        hypernetwork, filename = modules.hypernetwork.hypernetwork.train_hypernetwork(*args)
+        hypernetwork, filename = modules.hypernetworks.hypernetwork.train_hypernetwork(*args)
 
         res = f"""
 Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
diff --git a/modules/ui.py b/modules/ui.py
index 42e5d866..ee333c3b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1111,7 +1111,7 @@ def create_ui(wrap_gradio_gpu_call):
         )
 
         create_hypernetwork.click(
-            fn=modules.hypernetwork.ui.create_hypernetwork,
+            fn=modules.hypernetworks.ui.create_hypernetwork,
             inputs=[
                 new_hypernetwork_name,
             ],
@@ -1164,7 +1164,7 @@ def create_ui(wrap_gradio_gpu_call):
         )
 
         train_hypernetwork.click(
-            fn=wrap_gradio_gpu_call(modules.hypernetwork.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
             inputs=[
                 train_hypernetwork_name,
diff --git a/webui.py b/webui.py
index faa38a0d..338f58e1 100644
--- a/webui.py
+++ b/webui.py
@@ -83,7 +83,7 @@ modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
 shared.sd_model = modules.sd_models.load_model()
 shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
 
-shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetwork.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
+shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
 
 
 def webui():
-- 
cgit v1.2.1


From 5766ce21abc1986c94d8bd3279b6f4d5205ba984 Mon Sep 17 00:00:00 2001
From: ClashSAN <98228077+ClashSAN@users.noreply.github.com>
Date: Tue, 11 Oct 2022 13:20:03 +0000
Subject: Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 0e938768..a10faa01 100644
--- a/README.md
+++ b/README.md
@@ -69,7 +69,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
      - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
 - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
-- [xformers](https://github.com/mv-lab/swin2sr), major speed increase for select cards: (add --xformers to commandline args)
+- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
-- 
cgit v1.2.1


From d01a2d01560b31937df1f3433d210c18f97d32fa Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Tue, 11 Oct 2022 08:03:31 -0500
Subject: move list refresh to webui.py and add stdout indicating it's doing so

---
 modules/ui.py | 3 ---
 webui.py      | 2 ++
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 06ff118f..ae9317a3 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -39,7 +39,6 @@ import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
-from modules.sd_models import list_models
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -1291,8 +1290,6 @@ Requested path was: {f}
             shared.state.interrupt()
             settings_interface.gradio_ref.do_restart = True
 
-            # refresh models so that new models/.ckpt's show up on reload
-            list_models()
 
         restart_gradio.click(
             fn=request_restart,
diff --git a/webui.py b/webui.py
index 270584f7..94098c4c 100644
--- a/webui.py
+++ b/webui.py
@@ -124,6 +124,8 @@ def webui():
         modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
         print('Reloading modules: modules.ui')
         importlib.reload(modules.ui)
+        print('Refreshing Model List')
+        modules.sd_models.list_models()
         print('Restarting Gradio')
 
 
-- 
cgit v1.2.1


From 66b7d7584f0b44ce1316425808c27ca7df38293c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 17:03:00 +0300
Subject: become even stricter with pickles no pickle shall pass thank you
 again, RyotaK

---
 modules/safe.py | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/modules/safe.py b/modules/safe.py
index 05917463..20be16a5 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -10,6 +10,7 @@ import torch
 import numpy
 import _codecs
 import zipfile
+import re
 
 
 # PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
@@ -54,11 +55,27 @@ class RestrictedUnpickler(pickle.Unpickler):
         raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")
 
 
+allowed_zip_names = ["archive/data.pkl", "archive/version"]
+allowed_zip_names_re = re.compile(r"^archive/data/\d+$")
+
+
+def check_zip_filenames(filename, names):
+    for name in names:
+        if name in allowed_zip_names:
+            continue
+        if allowed_zip_names_re.match(name):
+            continue
+
+        raise Exception(f"bad file inside {filename}: {name}")
+
+
 def check_pt(filename):
     try:
 
         # new pytorch format is a zip file
         with zipfile.ZipFile(filename) as z:
+            check_zip_filenames(filename, z.namelist())
+
             with z.open('archive/data.pkl') as file:
                 unpickler = RestrictedUnpickler(file)
                 unpickler.load()
-- 
cgit v1.2.1


From e0ee5bf703996b33e6d97aa36e0973ceedc88503 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 17:08:03 +0300
Subject: add codeowners file so stop the great guys who are collaborating on
 the project from merging in PRs.

---
 CODEOWNERS | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 CODEOWNERS

diff --git a/CODEOWNERS b/CODEOWNERS
new file mode 100644
index 00000000..935fedcf
--- /dev/null
+++ b/CODEOWNERS
@@ -0,0 +1 @@
+*       @AUTOMATIC1111
-- 
cgit v1.2.1


From c0484f1b986ce7acb0e3596f6089a191279f5442 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 10 Oct 2022 22:48:54 -0400
Subject: Add cross-attention optimization from InvokeAI

* Add cross-attention optimization from InvokeAI (~30% speed improvement on MPS)
* Add command line option for it
* Make it default when CUDA is unavailable
---
 modules/sd_hijack.py               |  5 ++-
 modules/sd_hijack_optimizations.py | 79 ++++++++++++++++++++++++++++++++++++++
 modules/shared.py                  |  5 ++-
 3 files changed, 86 insertions(+), 3 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index f07ec041..5a1b167f 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -30,8 +30,11 @@ def apply_optimizations():
     elif cmd_opts.opt_split_attention_v1:
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
+        print("Applying cross attention optimization (InvokeAI).")
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-        print("Applying cross attention optimization.")
+        print("Applying cross attention optimization (Doggettx).")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 3349b9c3..870226c5 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,6 +1,7 @@
 import math
 import sys
 import traceback
+import psutil
 
 import torch
 from torch import einsum
@@ -116,6 +117,84 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     return self.to_out(r2)
 
+# -- From https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py (with hypernetworks support added) --
+
+mem_total_gb = psutil.virtual_memory().total // (1 << 30)
+
+def einsum_op_compvis(q, k, v):
+    s = einsum('b i d, b j d -> b i j', q, k)
+    s = s.softmax(dim=-1, dtype=s.dtype)
+    return einsum('b i j, b j d -> b i d', s, v)
+
+def einsum_op_slice_0(q, k, v, slice_size):
+    r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+    for i in range(0, q.shape[0], slice_size):
+        end = i + slice_size
+        r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end])
+    return r
+
+def einsum_op_slice_1(q, k, v, slice_size):
+    r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+    for i in range(0, q.shape[1], slice_size):
+        end = i + slice_size
+        r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v)
+    return r
+
+def einsum_op_mps_v1(q, k, v):
+    if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
+        return einsum_op_compvis(q, k, v)
+    else:
+        slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
+        return einsum_op_slice_1(q, k, v, slice_size)
+
+def einsum_op_mps_v2(q, k, v):
+    if mem_total_gb > 8 and q.shape[1] <= 4096:
+        return einsum_op_compvis(q, k, v)
+    else:
+        return einsum_op_slice_0(q, k, v, 1)
+
+def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
+    size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
+    if size_mb <= max_tensor_mb:
+        return einsum_op_compvis(q, k, v)
+    div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
+    if div <= q.shape[0]:
+        return einsum_op_slice_0(q, k, v, q.shape[0] // div)
+    return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
+
+def einsum_op(q, k, v):
+    if q.device.type == 'mps':
+        if mem_total_gb >= 32:
+            return einsum_op_mps_v1(q, k, v)
+        return einsum_op_mps_v2(q, k, v)
+
+    # Smaller slices are faster due to L2/L3/SLC caches.
+    # Tested on i7 with 8MB L3 cache.
+    return einsum_op_tensor_mem(q, k, v, 32)
+
+def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
+
+    hypernetwork = shared.loaded_hypernetwork
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        k = self.to_k(hypernetwork_layers[0](context)) * self.scale
+        v = self.to_v(hypernetwork_layers[1](context))
+    else:
+        k = self.to_k(context) * self.scale
+        v = self.to_v(context)
+    del context, x
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+    r = einsum_op(q, k, v)
+    return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
+
+# -- End of code from https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py --
+
 def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
     q_in = self.to_q(x)
diff --git a/modules/shared.py b/modules/shared.py
index 1dc2ccf2..20b45f23 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -50,9 +50,10 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
 parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
-parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
-parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
+parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
+parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
+parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
 parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[])
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
-- 
cgit v1.2.1


From 98fd5cde72d5bda1620ab78416c7828fdc3dc10b Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 10 Oct 2022 23:55:48 -0400
Subject: Add check for psutil

---
 modules/sd_hijack.py               | 10 ++++++++--
 modules/sd_hijack_optimizations.py | 19 +++++++++++++++----
 2 files changed, 23 insertions(+), 6 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 5a1b167f..ac70f876 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -10,6 +10,7 @@ from torch.nn.functional import silu
 import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared
 from modules.shared import opts, device, cmd_opts
+from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
@@ -31,8 +32,13 @@ def apply_optimizations():
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
-        print("Applying cross attention optimization (InvokeAI).")
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
+        if not invokeAI_mps_available and shared.device.type == 'mps':
+            print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
+            print("Applying v1 cross attention optimization.")
+            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+        else:
+            print("Applying cross attention optimization (InvokeAI).")
+            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
         print("Applying cross attention optimization (Doggettx).")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 870226c5..2a4ac7e0 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,7 +1,7 @@
 import math
 import sys
 import traceback
-import psutil
+import importlib
 
 import torch
 from torch import einsum
@@ -117,9 +117,20 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     return self.to_out(r2)
 
-# -- From https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py (with hypernetworks support added) --
 
-mem_total_gb = psutil.virtual_memory().total // (1 << 30)
+def check_for_psutil():
+    try:
+        spec = importlib.util.find_spec('psutil')
+        return spec is not None
+    except ModuleNotFoundError:
+        return False
+
+invokeAI_mps_available = check_for_psutil()
+
+# -- Taken from https://github.com/invoke-ai/InvokeAI --
+if invokeAI_mps_available:
+    import psutil
+    mem_total_gb = psutil.virtual_memory().total // (1 << 30)
 
 def einsum_op_compvis(q, k, v):
     s = einsum('b i d, b j d -> b i j', q, k)
@@ -193,7 +204,7 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
     r = einsum_op(q, k, v)
     return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
 
-# -- End of code from https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py --
+# -- End of code from https://github.com/invoke-ai/InvokeAI --
 
 def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
-- 
cgit v1.2.1


From 574c8e554a5371eca2cbf344764cb241c6ec4efc Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 11 Oct 2022 03:32:11 -0400
Subject: Add InvokeAI and lstein to credits, add back CUDA support

---
 README.md                          |  1 +
 modules/sd_hijack_optimizations.py | 13 +++++++++++++
 2 files changed, 14 insertions(+)

diff --git a/README.md b/README.md
index a10faa01..859a91b6 100644
--- a/README.md
+++ b/README.md
@@ -123,6 +123,7 @@ The documentation was moved from this README over to the project's [wiki](https:
 - LDSR - https://github.com/Hafiidz/latent-diffusion
 - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
 - Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
+- InvokeAI, lstein - Cross Attention layer optimization - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
 - Rinon Gal - Textual Inversion - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
 - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
 - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 2a4ac7e0..f006427f 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -173,7 +173,20 @@ def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
         return einsum_op_slice_0(q, k, v, q.shape[0] // div)
     return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
 
+def einsum_op_cuda(q, k, v):
+    stats = torch.cuda.memory_stats(q.device)
+    mem_active = stats['active_bytes.all.current']
+    mem_reserved = stats['reserved_bytes.all.current']
+    mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
+    mem_free_torch = mem_reserved - mem_active
+    mem_free_total = mem_free_cuda + mem_free_torch
+    # Divide factor of safety as there's copying and fragmentation
+    return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
+
 def einsum_op(q, k, v):
+    if q.device.type == 'cuda':
+        return einsum_op_cuda(q, k, v)
+
     if q.device.type == 'mps':
         if mem_total_gb >= 32:
             return einsum_op_mps_v1(q, k, v)
-- 
cgit v1.2.1


From 861db783c7acfcb93cf0b5191db3d50f9a9bc531 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 11 Oct 2022 05:13:17 -0400
Subject: Use apply_hypernetwork function

---
 modules/sd_hijack_optimizations.py | 14 ++++----------
 1 file changed, 4 insertions(+), 10 deletions(-)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index f006427f..79405525 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -202,16 +202,10 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
     q = self.to_q(x)
     context = default(context, x)
 
-    hypernetwork = shared.loaded_hypernetwork
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is not None:
-        k = self.to_k(hypernetwork_layers[0](context)) * self.scale
-        v = self.to_v(hypernetwork_layers[1](context))
-    else:
-        k = self.to_k(context) * self.scale
-        v = self.to_v(context)
-    del context, x
+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
+    k = self.to_k(context_k) * self.scale
+    v = self.to_v(context_v)
+    del context, context_k, context_v, x
 
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
     r = einsum_op(q, k, v)
-- 
cgit v1.2.1


From 5ba23cb41f28f5856a7f64cb0d95e1e94dce90af Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 17:28:17 +0300
Subject: change default for XY plot's Y to Nothing.

---
 scripts/xy_grid.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index cddb192a..ef431105 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -197,7 +197,7 @@ class Script(scripts.Script):
             x_values = gr.Textbox(label="X values", visible=False, lines=1)
 
         with gr.Row():
-            y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[4].label, visible=False, type="index", elem_id="y_type")
+            y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, visible=False, type="index", elem_id="y_type")
             y_values = gr.Textbox(label="Y values", visible=False, lines=1)
         
         draw_legend = gr.Checkbox(label='Draw legend', value=True)
-- 
cgit v1.2.1


From d682444ecc99319fbd2b142a12727501e2884ba7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 18:04:47 +0300
Subject: add option to select hypernetwork modules when creating

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 modules/hypernetworks/ui.py           | 4 ++--
 modules/ui.py                         | 2 ++
 3 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index aa701bda..b081f14e 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -42,7 +42,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None):
+    def __init__(self, name=None, enable_sizes=None):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -50,7 +50,7 @@ class Hypernetwork:
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
 
-        for size in [320, 640, 768, 1280]:
+        for size in enable_sizes or [320, 640, 768, 1280]:
             self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
 
     def weights(self):
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index e7540f41..cdddcce1 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -9,11 +9,11 @@ from modules import sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
 
-def create_hypernetwork(name):
+def create_hypernetwork(name, enable_sizes):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
-    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name)
+    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes])
     hypernet.save(fn)
 
     shared.reload_hypernetworks()
diff --git a/modules/ui.py b/modules/ui.py
index f2d16b12..14b87b92 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1037,6 +1037,7 @@ def create_ui(wrap_gradio_gpu_call):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
 
                     new_hypernetwork_name = gr.Textbox(label="Name")
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1114,6 +1115,7 @@ def create_ui(wrap_gradio_gpu_call):
             fn=modules.hypernetworks.ui.create_hypernetwork,
             inputs=[
                 new_hypernetwork_name,
+                new_hypernetwork_sizes,
             ],
             outputs=[
                 train_hypernetwork_name,
-- 
cgit v1.2.1


From ff4ef13dd591ec52f196f344f47537695df95364 Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Tue, 11 Oct 2022 10:24:27 -0500
Subject: removed unneeded print

---
 modules/deepbooru.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index e31e92c0..89dcac3c 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -119,7 +119,6 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort)
     # sort tags
     result_tags_out = []
     sort_ndx = 0
-    print(alpha_sort)
     if alpha_sort:
         sort_ndx = 1
 
-- 
cgit v1.2.1


From 6d09b8d1df3a96e1380bb1650f5961781630af96 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 18:33:57 +0300
Subject: produce error when training with medvram/lowvram enabled

---
 modules/hypernetworks/ui.py     | 2 ++
 modules/textual_inversion/ui.py | 3 +++
 2 files changed, 5 insertions(+)

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index cdddcce1..3541a388 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -25,6 +25,8 @@ def train_hypernetwork(*args):
 
     initial_hypernetwork = shared.loaded_hypernetwork
 
+    assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible'
+
     try:
         sd_hijack.undo_optimizations()
 
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index c57de1f9..70f47343 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -22,6 +22,9 @@ def preprocess(*args):
 
 
 def train_embedding(*args):
+
+    assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible'
+
     try:
         sd_hijack.undo_optimizations()
 
-- 
cgit v1.2.1


From d4ea5f4d8631f778d11efcde397e4a5b8801d43b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 19:03:08 +0300
Subject: add an option to unload models during hypernetwork training to save
 VRAM

---
 modules/hypernetworks/hypernetwork.py          | 25 +++++++++++++++-------
 modules/hypernetworks/ui.py                    |  4 +++-
 modules/shared.py                              |  4 ++++
 modules/textual_inversion/dataset.py           | 29 ++++++++++++++++++--------
 modules/textual_inversion/textual_inversion.py |  2 +-
 5 files changed, 46 insertions(+), 18 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b081f14e..4700e1ec 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -175,6 +175,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
 
     log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
+    unload = shared.opts.unload_models_when_training
 
     if save_hypernetwork_every > 0:
         hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
@@ -188,11 +189,13 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     else:
         images_dir = None
 
-    cond_model = shared.sd_model.cond_stage_model
-
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
+
+    if unload:
+        shared.sd_model.cond_stage_model.to(devices.cpu)
+        shared.sd_model.first_stage_model.to(devices.cpu)
 
     hypernetwork = shared.loaded_hypernetwork
     weights = hypernetwork.weights()
@@ -211,7 +214,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
         return hypernetwork, filename
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, (x, text) in pbar:
+    for i, (x, text, cond) in pbar:
         hypernetwork.step = i + ititial_step
 
         if hypernetwork.step > steps:
@@ -221,11 +224,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             break
 
         with torch.autocast("cuda"):
-            c = cond_model([text])
-
+            cond = cond.to(devices.device)
             x = x.to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), c)[0]
+            loss = shared.sd_model(x.unsqueeze(0), cond)[0]
             del x
+            del cond
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
@@ -244,6 +247,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 
             preview_text = text if preview_image_prompt == "" else preview_image_prompt
 
+            optimizer.zero_grad()
+            shared.sd_model.cond_stage_model.to(devices.device)
+            shared.sd_model.first_stage_model.to(devices.device)
+
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
                 prompt=preview_text,
@@ -255,6 +262,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             processed = processing.process_images(p)
             image = processed.images[0]
 
+            if unload:
+                shared.sd_model.cond_stage_model.to(devices.cpu)
+                shared.sd_model.first_stage_model.to(devices.cpu)
+
             shared.state.current_image = image
             image.save(last_saved_image)
 
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 3541a388..c67facbb 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -5,7 +5,7 @@ import gradio as gr
 
 import modules.textual_inversion.textual_inversion
 import modules.textual_inversion.preprocess
-from modules import sd_hijack, shared
+from modules import sd_hijack, shared, devices
 from modules.hypernetworks import hypernetwork
 
 
@@ -41,5 +41,7 @@ Hypernetwork saved to {html.escape(filename)}
         raise
     finally:
         shared.loaded_hypernetwork = initial_hypernetwork
+        shared.sd_model.cond_stage_model.to(devices.device)
+        shared.sd_model.first_stage_model.to(devices.device)
         sd_hijack.apply_optimizations()
 
diff --git a/modules/shared.py b/modules/shared.py
index 20b45f23..c1092ff7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -228,6 +228,10 @@ options_templates.update(options_section(('system', "System"), {
     "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
 }))
 
+options_templates.update(options_section(('training', "Training"), {
+    "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP form VRAM when training"),
+}))
+
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True),
     "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 4d006366..f61f40d3 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -8,14 +8,14 @@ from torchvision import transforms
 
 import random
 import tqdm
-from modules import devices
+from modules import devices, shared
 import re
 
 re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False):
 
         self.placeholder_token = placeholder_token
 
@@ -32,6 +32,8 @@ class PersonalizedBase(Dataset):
 
         assert data_root, 'dataset directory not specified'
 
+        cond_model = shared.sd_model.cond_stage_model
+
         self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
@@ -53,7 +55,13 @@ class PersonalizedBase(Dataset):
             init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
             init_latent = init_latent.to(devices.cpu)
 
-            self.dataset.append((init_latent, filename_tokens))
+            if include_cond:
+                text = self.create_text(filename_tokens)
+                cond = cond_model([text]).to(devices.cpu)
+            else:
+                cond = None
+
+            self.dataset.append((init_latent, filename_tokens, cond))
 
         self.length = len(self.dataset) * repeats
 
@@ -64,6 +72,12 @@ class PersonalizedBase(Dataset):
     def shuffle(self):
         self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
 
+    def create_text(self, filename_tokens):
+        text = random.choice(self.lines)
+        text = text.replace("[name]", self.placeholder_token)
+        text = text.replace("[filewords]", ' '.join(filename_tokens))
+        return text
+
     def __len__(self):
         return self.length
 
@@ -72,10 +86,7 @@ class PersonalizedBase(Dataset):
             self.shuffle()
 
         index = self.indexes[i % len(self.indexes)]
-        x, filename_tokens = self.dataset[index]
-
-        text = random.choice(self.lines)
-        text = text.replace("[name]", self.placeholder_token)
-        text = text.replace("[filewords]", ' '.join(filename_tokens))
+        x, filename_tokens, cond = self.dataset[index]
 
-        return x, text
+        text = self.create_text(filename_tokens)
+        return x, text, cond
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index bb05cdc6..35f4bd9e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -201,7 +201,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         return embedding, filename
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-    for i, (x, text) in pbar:
+    for i, (x, text, _) in pbar:
         embedding.step = i + ititial_step
 
         if embedding.step > steps:
-- 
cgit v1.2.1


From 6a9ea5b41cf92cd9e980349bb5034439f4e7a58b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 19:22:30 +0300
Subject: prevent extra modules from being saved/loaded with hypernet

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 4700e1ec..5608e799 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -50,7 +50,7 @@ class Hypernetwork:
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
 
-        for size in enable_sizes or [320, 640, 768, 1280]:
+        for size in enable_sizes or []:
             self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
 
     def weights(self):
-- 
cgit v1.2.1


From d7474a5185df2af84a93a12bc7e140d24e0fc516 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 21:10:55 +0300
Subject: bump gradio to 3.4.1

---
 requirements.txt          |  2 +-
 requirements_versions.txt |  2 +-
 style.css                 | 11 +++++++++++
 3 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 631fe616..a0d985ce 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,7 +4,7 @@ fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
-gradio==3.4b3
+gradio==3.4.1
 invisible-watermark
 numpy
 omegaconf
diff --git a/requirements_versions.txt b/requirements_versions.txt
index fdff2687..2bbea40b 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -2,7 +2,7 @@ transformers==4.19.2
 diffusers==0.3.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.4b3
+gradio==3.4.1
 numpy==1.23.3
 Pillow==9.2.0
 realesrgan==0.3.0
diff --git a/style.css b/style.css
index ecb51bb0..e6fa10b4 100644
--- a/style.css
+++ b/style.css
@@ -240,6 +240,7 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500,  label.block s
 #settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{
     position: relative;
     border: none;
+    margin-right: 8em;
 }
 
 .gr-panel div.flex-col div.justify-between label span{
@@ -495,3 +496,13 @@ canvas[key="mask"] {
     mix-blend-mode: multiply;
     pointer-events: none;
 }
+
+
+/* gradio 3.4.1 stuff for editable scrollbar values */
+.gr-box > div > div > input.gr-text-input{
+    position: absolute;
+    right: 0.5em;
+    top: -0.6em;
+    z-index: 200;
+    width: 8em;
+}
-- 
cgit v1.2.1


From 9e5f6b558072f6cdfa0f7010fa819662952fcaf1 Mon Sep 17 00:00:00 2001
From: nai-degen <92774204+nai-degen@users.noreply.github.com>
Date: Sun, 9 Oct 2022 19:37:35 -0500
Subject: triggers 'input' event when using arrow keys to edit attention

---
 javascript/edit-attention.js | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js
index 0280c603..79566a2e 100644
--- a/javascript/edit-attention.js
+++ b/javascript/edit-attention.js
@@ -38,4 +38,7 @@ addEventListener('keydown', (event) => {
 		target.selectionStart = selectionStart;
 		target.selectionEnd = selectionEnd;
 	}
+	// Since we've modified a Gradio Textbox component manually, we need to simulate an `input` DOM event to ensure its
+	// internal Svelte data binding remains in sync.
+	target.dispatchEvent(new Event("input", { bubbles: true }));
 });
-- 
cgit v1.2.1


From c080f52ceae73b893155eff7de577aaf1a982a2f Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 19:37:58 +0100
Subject: move embedding logic to separate file

---
 modules/textual_inversion/image_embedding.py | 234 +++++++++++++++++++++++++++
 1 file changed, 234 insertions(+)
 create mode 100644 modules/textual_inversion/image_embedding.py

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
new file mode 100644
index 00000000..6ad39602
--- /dev/null
+++ b/modules/textual_inversion/image_embedding.py
@@ -0,0 +1,234 @@
+import base64
+import json
+import numpy as np
+import zlib
+from PIL import Image,PngImagePlugin,ImageDraw,ImageFont
+from fonts.ttf import Roboto
+import torch
+
+class EmbeddingEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, torch.Tensor):
+            return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()}
+        return json.JSONEncoder.default(self, obj)
+
+class EmbeddingDecoder(json.JSONDecoder):
+    def __init__(self, *args, **kwargs):
+        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
+    def object_hook(self, d):
+        if 'TORCHTENSOR' in d:
+            return torch.from_numpy(np.array(d['TORCHTENSOR']))
+        return d
+
+def embedding_to_b64(data):
+    d = json.dumps(data,cls=EmbeddingEncoder)
+    return base64.b64encode(d.encode())
+
+def embedding_from_b64(data):
+    d = base64.b64decode(data)
+    return json.loads(d,cls=EmbeddingDecoder)
+
+def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
+    while True:
+        seed = (a * seed + c) % m
+        yield seed%255
+
+def xor_block(block):
+    g = lcg()
+    randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
+    return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F) 
+
+def style_block(block,sequence):
+    im = Image.new('RGB',(block.shape[1],block.shape[0]))
+    draw = ImageDraw.Draw(im)
+    i=0
+    for x in range(-6,im.size[0],8):
+        for yi,y in enumerate(range(-6,im.size[1],8)):
+            offset=0
+            if yi%2==0:
+                offset=4
+            shade = sequence[i%len(sequence)]
+            i+=1
+            draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) )
+
+    fg = np.array(im).astype(np.uint8) & 0xF0
+
+    return block ^ fg
+
+def insert_image_data_embed(image,data):
+    d = 3
+    data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9)
+    data_np_ = np.frombuffer(data_compressed,np.uint8).copy()
+    data_np_high = data_np_ >> 4
+    data_np_low  = data_np_ & 0x0F
+    
+    h = image.size[1]
+    next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0]%h))
+    next_size = next_size + ((h*d)-(next_size%(h*d)))
+
+    data_np_low.resize(next_size)
+    data_np_low = data_np_low.reshape((h,-1,d))
+
+    data_np_high.resize(next_size)
+    data_np_high = data_np_high.reshape((h,-1,d))
+
+    edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
+    edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)
+
+    data_np_low   = style_block(data_np_low,sequence=edge_style)
+    data_np_low   = xor_block(data_np_low)    
+    data_np_high  = style_block(data_np_high,sequence=edge_style[::-1])
+    data_np_high  = xor_block(data_np_high)
+
+    im_low  = Image.fromarray(data_np_low,mode='RGB')
+    im_high = Image.fromarray(data_np_high,mode='RGB')
+
+    background = Image.new('RGB',(image.size[0]+im_low.size[0]+im_high.size[0]+2,image.size[1]),(0,0,0))
+    background.paste(im_low,(0,0))
+    background.paste(image,(im_low.size[0]+1,0))
+    background.paste(im_high,(im_low.size[0]+1+image.size[0]+1,0))
+
+    return background
+
+def crop_black(img,tol=0):
+    mask = (img>tol).all(2)
+    mask0,mask1 = mask.any(0),mask.any(1)
+    col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax()
+    row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax()
+    return img[row_start:row_end,col_start:col_end]
+
+def extract_image_data_embed(image):
+    d=3
+    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
+    black_cols = np.where( np.sum(outarr, axis=(0,2))==0)
+    if black_cols[0].shape[0] < 2:
+        print('No Image data blocks found.')
+        return None
+
+    data_block_lower = outarr[:,:black_cols[0].min(),:].astype(np.uint8)
+    data_block_upper = outarr[:,black_cols[0].max()+1:,:].astype(np.uint8)
+
+    data_block_lower = xor_block(data_block_lower)
+    data_block_upper = xor_block(data_block_upper)
+    
+    data_block = (data_block_upper << 4) | (data_block_lower)
+    data_block = data_block.flatten().tobytes()
+
+    data = zlib.decompress(data_block)
+    return json.loads(data,cls=EmbeddingDecoder)
+
+def addCaptionLines(lines,image,initialx,textfont):
+    draw = ImageDraw.Draw(image)
+    hstart =initialx
+    for fill,line in lines:
+        fontsize = 32
+        font = ImageFont.truetype(textfont, fontsize)
+        _,_,w, h = draw.textbbox((0,0),line,font=font)
+        fontsize =  min( int(fontsize * ((image.size[0]-35)/w) ), 28)
+        font = ImageFont.truetype(textfont, fontsize)
+        _,_,w,h = draw.textbbox((0,0),line,font=font)
+        draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill)
+        hstart += h
+    return hstart
+
+def caption_image(image,prelines,postlines,background=(51, 51, 51),font=None):
+    if font is None:
+        try:
+            font = ImageFont.truetype(opts.font or Roboto, fontsize)
+            font = opts.font or Roboto
+        except Exception:
+            font = Roboto
+
+    sample_image = image
+    background = Image.new("RGBA", (sample_image.size[0],sample_image.size[1]+1024), background)
+    hoffset = addCaptionLines(prelines,background,5,font)+16
+    background.paste(sample_image,(0,hoffset))
+    hoffset = hoffset+sample_image.size[1]+8
+    hoffset = addCaptionLines(postlines,background,hoffset,font)
+    background = background.crop((0,0,sample_image.size[0],hoffset+8))
+    return background
+
+def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None):
+    from math import cos
+
+    image = srcimage.copy()
+
+    if textfont is None:
+        try:
+            textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
+            textfont = opts.font or Roboto
+        except Exception:
+            textfont = Roboto
+
+    factor = 1.5
+    gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0))
+    for y in range(image.size[1]):
+        mag = 1-cos(y/image.size[1]*factor)
+        mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
+        gradient.putpixel((0, y), (0,0,0,int(mag*255)))
+    image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
+
+    draw = ImageDraw.Draw(image)
+    fontsize = 32
+    font = ImageFont.truetype(textfont, fontsize)
+    padding = 10
+
+    _,_,w, h = draw.textbbox((0,0),title,font=font)
+    fontsize =  min( int(fontsize * (((image.size[0]*0.75)-(padding*4))/w) ), 72)
+    font = ImageFont.truetype(textfont, fontsize)
+    _,_,w,h = draw.textbbox((0,0),title,font=font)
+    draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230))
+
+    _,_,w, h = draw.textbbox((0,0),footerLeft,font=font)
+    fontsize_left =  min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
+    _,_,w, h = draw.textbbox((0,0),footerMid,font=font)
+    fontsize_mid =  min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
+    _,_,w, h = draw.textbbox((0,0),footerRight,font=font)
+    fontsize_right =  min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
+
+    font = ImageFont.truetype(textfont, min(fontsize_left,fontsize_mid,fontsize_right))
+
+    draw.text((padding,image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255,255,255,230))
+    draw.text((image.size[0]/2,image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255,255,255,230))
+    draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230))
+
+    return image
+
+if __name__ == '__main__':
+
+    image = Image.new('RGBA',(512,512),(255,255,200,255))
+    caption_image(image,[((255,255,255),'line a'),((255,255,255),'line b')],
+                        [((255,255,255),'line c'),((255,255,255),'line d')])
+
+    image = Image.new('RGBA',(512,512),(255,255,200,255))
+    cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
+
+    test_embed = {'string_to_param':{'*':torch.from_numpy(np.random.random((2, 4096)))}}
+
+    embedded_image = insert_image_data_embed(cap_image, test_embed)
+
+    retrived_embed = extract_image_data_embed(embedded_image)
+
+    assert str(retrived_embed) == str(test_embed)
+
+    embedded_image2 = insert_image_data_embed(cap_image, retrived_embed)
+
+    assert embedded_image == embedded_image2
+
+    g = lcg()
+    shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()
+
+    reference_random = [253, 242, 127,  44, 157,  27, 239, 133,  38,  79, 167,   4, 177, 
+                         95, 130,  79,  78,  14,  52, 215, 220, 194, 126,  28, 240, 179, 
+                        160, 153, 149,  50, 105,  14,  21, 218, 199,  18,  54, 198, 193, 
+                         38, 128,  19,  53, 195, 124,  75, 205,  12,   6, 145,   0,  28, 
+                         30, 148,   8,  45, 218, 171,  55, 249,  97, 166,  12,  35,   0, 
+                         41, 221, 122, 215, 170,  31, 113, 186,  97, 119,  31,  23, 185, 
+                         66, 140,  30,  41,  37,  63, 137, 109, 216,  55, 159, 145,  82, 
+                         204, 86,  73, 222,  44, 198, 118, 240,  97]
+
+    assert shared_random == reference_random   
+
+    hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
+
+    assert 12731374 == hunna_kay_random_sum
\ No newline at end of file
-- 
cgit v1.2.1


From e5fbf5c755b7c306696546405385d5d2314e555b Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 19:46:33 +0100
Subject: remove embedding related image functions from images

---
 modules/images.py | 77 -------------------------------------------------------
 1 file changed, 77 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index e62eec8e..c0a90676 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -463,80 +463,3 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         txt_fullfn = None
 
     return fullfn, txt_fullfn
-
-def addCaptionLines(lines,image,initialx,textfont):
-    draw = ImageDraw.Draw(image)
-    hstart =initialx
-    for fill,line in lines:
-        fontSize = 32
-        font = ImageFont.truetype(textfont, fontSize)
-        _,_,w, h = draw.textbbox((0,0),line,font=font)
-        fontSize =  min( int(fontSize * ((image.size[0]-35)/w) ), 28)
-        font = ImageFont.truetype(textfont, fontSize)
-        _,_,w,h = draw.textbbox((0,0),line,font=font)
-        draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill)
-        hstart += h
-    return hstart
-
-def captionImge(image,prelines,postlines,background=(51, 51, 51),font=None):
-    if font is None:
-        try:
-            font = ImageFont.truetype(opts.font or Roboto, fontsize)
-            font = opts.font or Roboto
-        except Exception:
-            font = Roboto
-
-    sampleImage = image
-    background = Image.new("RGBA", (sampleImage.size[0],sampleImage.size[1]+1024), background)
-    hoffset = addCaptionLines(prelines,background,5,font)+16
-    background.paste(sampleImage,(0,hoffset))
-    hoffset = hoffset+sampleImage.size[1]+8
-    hoffset = addCaptionLines(postlines,background,hoffset,font)
-    background = background.crop((0,0,sampleImage.size[0],hoffset+8))
-    return background
-
-def captionImageOverlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None):
-    from math import cos
-
-    image = srcimage.copy()
-
-    if textfont is None:
-        try:
-            textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
-            textfont = opts.font or Roboto
-        except Exception:
-            textfont = Roboto
-
-    factor = 1.5
-    gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0))
-    for y in range(image.size[1]):
-        mag = 1-cos(y/image.size[1]*factor)
-        mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
-        gradient.putpixel((0, y), (0,0,0,int(mag*255)))
-    image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
-
-    draw = ImageDraw.Draw(image)
-    fontSize = 32
-    font = ImageFont.truetype(textfont, fontSize)
-    padding = 10
-
-    _,_,w, h = draw.textbbox((0,0),title,font=font)
-    fontSize =  min( int(fontSize * (((image.size[0]*0.75)-(padding*4))/w) ), 72)
-    font = ImageFont.truetype(textfont, fontSize)
-    _,_,w,h = draw.textbbox((0,0),title,font=font)
-    draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230))
-
-    _,_,w, h = draw.textbbox((0,0),footerLeft,font=font)
-    fontSizeleft =  min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72)
-    _,_,w, h = draw.textbbox((0,0),footerMid,font=font)
-    fontSizemid =  min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72)
-    _,_,w, h = draw.textbbox((0,0),footerRight,font=font)
-    fontSizeright =  min( int(fontSize * (((image.size[0]/3)-(padding))/w) ), 72)
-
-    font = ImageFont.truetype(textfont, min(fontSizeleft,fontSizemid,fontSizeright))
-
-    draw.text((padding,image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255,255,255,230))
-    draw.text((image.size[0]/2,image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255,255,255,230))
-    draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230))
-
-    return image
-- 
cgit v1.2.1


From 61788c0538415fa9ca1dd1b306519c116b18bd2c Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 19:50:50 +0100
Subject: shift embedding logic out of textual_inversion

---
 modules/textual_inversion/textual_inversion.py | 125 ++-----------------------
 1 file changed, 6 insertions(+), 119 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 8c66aeb5..22b4ae7f 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,124 +7,11 @@ import tqdm
 import html
 import datetime
 
-from PIL import Image,PngImagePlugin,ImageDraw
-from ..images import captionImageOverlay
-import numpy as np
-import base64
-import json
-import zlib
+from PIL import Image,PngImagePlugin
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
-class EmbeddingEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, torch.Tensor):
-            return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()}
-        return json.JSONEncoder.default(self, obj)
-
-class EmbeddingDecoder(json.JSONDecoder):
-    def __init__(self, *args, **kwargs):
-        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-    def object_hook(self, d):
-        if 'TORCHTENSOR' in d:
-            return torch.from_numpy(np.array(d['TORCHTENSOR']))
-        return d
-
-def embeddingToB64(data):
-    d = json.dumps(data,cls=EmbeddingEncoder)
-    return base64.b64encode(d.encode())
-
-def embeddingFromB64(data):
-    d = base64.b64decode(data)
-    return json.loads(d,cls=EmbeddingDecoder)
-
-def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
-    while True:
-        seed = (a * seed + c) % m
-        yield seed
-
-def xorBlock(block):
-    g = lcg()
-    randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
-    return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F) 
-
-def styleBlock(block,sequence):
-    im = Image.new('RGB',(block.shape[1],block.shape[0]))
-    draw = ImageDraw.Draw(im)
-    i=0
-    for x in range(-6,im.size[0],8):
-        for yi,y in enumerate(range(-6,im.size[1],8)):
-            offset=0
-            if yi%2==0:
-                offset=4
-            shade = sequence[i%len(sequence)]
-            i+=1
-            draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) )
-
-    fg = np.array(im).astype(np.uint8) & 0xF0
-    return block ^ fg
-
-def insertImageDataEmbed(image,data):
-    d = 3
-    data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9)
-    dnp = np.frombuffer(data_compressed,np.uint8).copy()
-    dnphigh = dnp >> 4
-    dnplow  = dnp & 0x0F
-    
-    h = image.size[1]
-    next_size = dnplow.shape[0] + (h-(dnplow.shape[0]%h))
-    next_size = next_size + ((h*d)-(next_size%(h*d)))
-
-    dnplow.resize(next_size)
-    dnplow = dnplow.reshape((h,-1,d))
-
-    dnphigh.resize(next_size)
-    dnphigh = dnphigh.reshape((h,-1,d))
-
-    edgeStyleWeights = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
-    edgeStyleWeights = (np.abs(edgeStyleWeights)/np.max(np.abs(edgeStyleWeights))*255).astype(np.uint8)
-
-    dnplow   = styleBlock(dnplow,sequence=edgeStyleWeights)
-    dnplow   = xorBlock(dnplow)    
-    dnphigh  = styleBlock(dnphigh,sequence=edgeStyleWeights[::-1])
-    dnphigh  = xorBlock(dnphigh)
-
-    imlow  = Image.fromarray(dnplow,mode='RGB')
-    imhigh = Image.fromarray(dnphigh,mode='RGB')
-
-    background = Image.new('RGB',(image.size[0]+imlow.size[0]+imhigh.size[0]+2,image.size[1]),(0,0,0))
-    background.paste(imlow,(0,0))
-    background.paste(image,(imlow.size[0]+1,0))
-    background.paste(imhigh,(imlow.size[0]+1+image.size[0]+1,0))
-
-    return background
-
-def crop_black(img,tol=0):
-    mask = (img>tol).all(2)
-    mask0,mask1 = mask.any(0),mask.any(1)
-    col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax()
-    row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax()
-    return img[row_start:row_end,col_start:col_end]
-
-def extractImageDataEmbed(image):
-    d=3
-    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
-    blackCols = np.where( np.sum(outarr, axis=(0,2))==0)
-    if blackCols[0].shape[0] < 2:
-        print('No Image data blocks found.')
-        return None
-
-    dataBlocklower = outarr[:,:blackCols[0].min(),:].astype(np.uint8)
-    dataBlockupper = outarr[:,blackCols[0].max()+1:,:].astype(np.uint8)
-
-    dataBlocklower = xorBlock(dataBlocklower)
-    dataBlockupper = xorBlock(dataBlockupper)
-    
-    dataBlock = (dataBlockupper << 4) | (dataBlocklower)
-    dataBlock = dataBlock.flatten().tobytes()   
-    data = zlib.decompress(dataBlock)
-    return json.loads(data,cls=EmbeddingDecoder)
 
 class Embedding:
     def __init__(self, vec, name, step=None):
@@ -199,10 +86,10 @@ class EmbeddingDatabase:
             if filename.upper().endswith('.PNG'):
                 embed_image = Image.open(path)
                 if 'sd-ti-embedding' in embed_image.text:
-                    data = embeddingFromB64(embed_image.text['sd-ti-embedding'])
+                    data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
                     name = data.get('name',name)
                 else:
-                    data = extractImageDataEmbed(embed_image)
+                    data = extract_image_data_embed(embed_image)
                     name = data.get('name',name)
             else:
                 data = torch.load(path, map_location="cpu")
@@ -393,7 +280,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
                 info = PngImagePlugin.PngInfo()
                 data = torch.load(last_saved_file)
-                info.add_text("sd-ti-embedding", embeddingToB64(data))
+                info.add_text("sd-ti-embedding", embedding_to_b64(data))
 
                 title = "<{}>".format(data.get('name','???'))
                 checkpoint = sd_models.select_checkpoint()
@@ -401,8 +288,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 footer_mid = '[{}]'.format(checkpoint.hash)
                 footer_right = '{}'.format(embedding.step)
 
-                captioned_image = captionImageOverlay(image,title,footer_left,footer_mid,footer_right)
-                captioned_image = insertImageDataEmbed(captioned_image,data)
+                captioned_image = caption_image_overlay(image,title,footer_left,footer_mid,footer_right)
+                captioned_image = insert_image_data_embed(captioned_image,data)
 
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
             
-- 
cgit v1.2.1


From db71290d2659d3b58ff9b57a82e4721a9eab9229 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 19:55:54 +0100
Subject: remove old caption method

---
 modules/textual_inversion/image_embedding.py | 39 ++--------------------------
 1 file changed, 2 insertions(+), 37 deletions(-)

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index 6ad39602..c67028a5 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -117,37 +117,6 @@ def extract_image_data_embed(image):
     data = zlib.decompress(data_block)
     return json.loads(data,cls=EmbeddingDecoder)
 
-def addCaptionLines(lines,image,initialx,textfont):
-    draw = ImageDraw.Draw(image)
-    hstart =initialx
-    for fill,line in lines:
-        fontsize = 32
-        font = ImageFont.truetype(textfont, fontsize)
-        _,_,w, h = draw.textbbox((0,0),line,font=font)
-        fontsize =  min( int(fontsize * ((image.size[0]-35)/w) ), 28)
-        font = ImageFont.truetype(textfont, fontsize)
-        _,_,w,h = draw.textbbox((0,0),line,font=font)
-        draw.text(((image.size[0]-w)/2,hstart), line, font=font, fill=fill)
-        hstart += h
-    return hstart
-
-def caption_image(image,prelines,postlines,background=(51, 51, 51),font=None):
-    if font is None:
-        try:
-            font = ImageFont.truetype(opts.font or Roboto, fontsize)
-            font = opts.font or Roboto
-        except Exception:
-            font = Roboto
-
-    sample_image = image
-    background = Image.new("RGBA", (sample_image.size[0],sample_image.size[1]+1024), background)
-    hoffset = addCaptionLines(prelines,background,5,font)+16
-    background.paste(sample_image,(0,hoffset))
-    hoffset = hoffset+sample_image.size[1]+8
-    hoffset = addCaptionLines(postlines,background,hoffset,font)
-    background = background.crop((0,0,sample_image.size[0],hoffset+8))
-    return background
-
 def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None):
     from math import cos
 
@@ -195,11 +164,7 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo
     return image
 
 if __name__ == '__main__':
-
-    image = Image.new('RGBA',(512,512),(255,255,200,255))
-    caption_image(image,[((255,255,255),'line a'),((255,255,255),'line b')],
-                        [((255,255,255),'line c'),((255,255,255),'line d')])
-
+    
     image = Image.new('RGBA',(512,512),(255,255,200,255))
     cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
 
@@ -231,4 +196,4 @@ if __name__ == '__main__':
 
     hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
 
-    assert 12731374 == hunna_kay_random_sum
\ No newline at end of file
+    assert 12731374 == hunna_kay_random_sum
-- 
cgit v1.2.1


From d6fcc6b87bc00fcdecea276fe5b7c7945f7a8b14 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 22:03:05 +0300
Subject: apply lr schedule to hypernets

---
 modules/hypernetworks/hypernetwork.py          | 19 ++++++++---
 modules/textual_inversion/learn_schedule.py    | 34 ++++++++++++++++++++
 modules/textual_inversion/textual_inversion.py | 44 +++-----------------------
 modules/ui.py                                  |  2 +-
 4 files changed, 54 insertions(+), 45 deletions(-)
 create mode 100644 modules/textual_inversion/learn_schedule.py

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 5608e799..470659df 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -14,6 +14,7 @@ import torch
 from torch import einsum
 from einops import rearrange, repeat
 import modules.textual_inversion.dataset
+from modules.textual_inversion.learn_schedule import LearnSchedule
 
 
 class HypernetworkModule(torch.nn.Module):
@@ -202,8 +203,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     for weight in weights:
         weight.requires_grad = True
 
-    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
-
     losses = torch.zeros((32,))
 
     last_saved_file = "<none>"
@@ -213,12 +212,24 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     if ititial_step > steps:
         return hypernetwork, filename
 
+    schedules = iter(LearnSchedule(learn_rate, steps, ititial_step))
+    (learn_rate, end_step) = next(schedules)
+    print(f'Training at rate of {learn_rate} until step {end_step}')
+
+    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, (x, text, cond) in pbar:
         hypernetwork.step = i + ititial_step
 
-        if hypernetwork.step > steps:
-            break
+        if hypernetwork.step > end_step:
+            try:
+                (learn_rate, end_step) = next(schedules)
+            except Exception:
+                break
+            tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
+            for pg in optimizer.param_groups:
+                pg['lr'] = learn_rate
 
         if shared.state.interrupted:
             break
diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
new file mode 100644
index 00000000..db720271
--- /dev/null
+++ b/modules/textual_inversion/learn_schedule.py
@@ -0,0 +1,34 @@
+
+class LearnSchedule:
+    def __init__(self, learn_rate, max_steps, cur_step=0):
+        pairs = learn_rate.split(',')
+        self.rates = []
+        self.it = 0
+        self.maxit = 0
+        for i, pair in enumerate(pairs):
+            tmp = pair.split(':')
+            if len(tmp) == 2:
+                step = int(tmp[1])
+                if step > cur_step:
+                    self.rates.append((float(tmp[0]), min(step, max_steps)))
+                    self.maxit += 1
+                    if step > max_steps:
+                        return
+                elif step == -1:
+                    self.rates.append((float(tmp[0]), max_steps))
+                    self.maxit += 1
+                    return
+            else:
+                self.rates.append((float(tmp[0]), max_steps))
+                self.maxit += 1
+                return
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.it < self.maxit:
+            self.it += 1
+            return self.rates[self.it - 1]
+        else:
+            raise StopIteration
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 47a27faf..7717837d 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -10,6 +10,7 @@ import datetime
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
+from modules.textual_inversion.learn_schedule import LearnSchedule
 
 
 class Embedding:
@@ -198,11 +199,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     if ititial_step > steps:
         return embedding, filename
 
-    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
-    epoch_len = (tr_img_len * num_repeats) + tr_img_len
-
-    scheduleIter = iter(LearnSchedule(learn_rate, steps, ititial_step))
-    (learn_rate, end_step) = next(scheduleIter)
+    schedules = iter(LearnSchedule(learn_rate, steps, ititial_step))
+    (learn_rate, end_step) = next(schedules)
     print(f'Training at rate of {learn_rate} until step {end_step}')
 
     optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
@@ -213,7 +211,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
         if embedding.step > end_step:
             try:
-                (learn_rate, end_step) = next(scheduleIter)
+                (learn_rate, end_step) = next(schedules)
             except:
                 break
             tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
@@ -288,37 +286,3 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     embedding.save(filename)
 
     return embedding, filename
-
-class LearnSchedule:
-    def __init__(self, learn_rate, max_steps, cur_step=0):
-        pairs = learn_rate.split(',')
-        self.rates = []
-        self.it = 0
-        self.maxit = 0
-        for i, pair in enumerate(pairs):
-            tmp = pair.split(':')
-            if len(tmp) == 2:
-                step = int(tmp[1])
-                if step > cur_step:
-                    self.rates.append((float(tmp[0]), min(step, max_steps)))
-                    self.maxit += 1
-                    if step > max_steps:
-                        return
-                elif step == -1:
-                    self.rates.append((float(tmp[0]), max_steps))
-                    self.maxit += 1
-                    return
-            else:
-                self.rates.append((float(tmp[0]), max_steps))
-                self.maxit += 1
-                return
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        if self.it < self.maxit:
-            self.it += 1
-            return self.rates[self.it - 1]
-        else:
-            raise StopIteration
diff --git a/modules/ui.py b/modules/ui.py
index 2b688e32..1204eef7 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1070,7 +1070,7 @@ def create_ui(wrap_gradio_gpu_call):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                     train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
-                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value = "5.0e-03")
+                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-- 
cgit v1.2.1


From aa75d5cfe8c84768b0f5d16f977ddba298677379 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 20:06:13 +0100
Subject: correct conflict resolution typo

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 22b4ae7f..789383ce 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -169,7 +169,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
 
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt)
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
-- 
cgit v1.2.1


From 91d7ee0d097a7ea203d261b570cd2b834837d9e2 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 20:09:10 +0100
Subject: update imports

---
 modules/textual_inversion/textual_inversion.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 789383ce..ff0a62b3 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -12,6 +12,9 @@ from PIL import Image,PngImagePlugin
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
+from modules.textual_inversion.image_embedding import( embedding_to_b64,embedding_from_b64,
+                                                       insert_image_data_embed,extract_image_data_embed,
+                                                       caption_image_overlay )
 
 class Embedding:
     def __init__(self, vec, name, step=None):
-- 
cgit v1.2.1


From 5f3317376bb7952bc5145f05f16c1bbd466efc85 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 20:09:49 +0100
Subject: spacing

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index ff0a62b3..485ef46c 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -12,7 +12,7 @@ from PIL import Image,PngImagePlugin
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
-from modules.textual_inversion.image_embedding import( embedding_to_b64,embedding_from_b64,
+from modules.textual_inversion.image_embedding import (embedding_to_b64,embedding_from_b64,
                                                        insert_image_data_embed,extract_image_data_embed,
                                                        caption_image_overlay )
 
-- 
cgit v1.2.1


From 7e6a6e00ad6f3b7ef43c8120db9ecac6e8d6bea5 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 20:20:46 +0100
Subject: Add files via upload

---
 modules/textual_inversion/test_embedding.png | Bin 0 -> 489220 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 modules/textual_inversion/test_embedding.png

diff --git a/modules/textual_inversion/test_embedding.png b/modules/textual_inversion/test_embedding.png
new file mode 100644
index 00000000..07e2d9af
Binary files /dev/null and b/modules/textual_inversion/test_embedding.png differ
-- 
cgit v1.2.1


From 66ec505975aaa305a217fc27281ce368cbaef281 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Tue, 11 Oct 2022 20:21:30 +0100
Subject: add file based test

---
 modules/textual_inversion/image_embedding.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index c67028a5..1224fb42 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -164,6 +164,14 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo
     return image
 
 if __name__ == '__main__':
+
+    testEmbed = Image.open('test_embedding.png')
+    
+    data = extract_image_data_embed(testEmbed)
+    assert data is not None
+
+    data = embedding_from_b64(testEmbed.text['sd-ti-embedding'])
+    assert data is not None
     
     image = Image.new('RGBA',(512,512),(255,255,200,255))
     cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
-- 
cgit v1.2.1


From 6be32b31d181e42c639dad3451229aa7b9cfd1cf Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 23:07:09 +0300
Subject: reports that training with medvram is possible.

---
 modules/hypernetworks/ui.py     | 2 +-
 modules/textual_inversion/ui.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index c67facbb..dfa599af 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -25,7 +25,7 @@ def train_hypernetwork(*args):
 
     initial_hypernetwork = shared.loaded_hypernetwork
 
-    assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible'
+    assert not shared.cmd_opts.lowvram, 'Training models with lowvram is not possible'
 
     try:
         sd_hijack.undo_optimizations()
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index 70f47343..36881e7a 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -23,7 +23,7 @@ def preprocess(*args):
 
 def train_embedding(*args):
 
-    assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible'
+    assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
 
     try:
         sd_hijack.undo_optimizations()
-- 
cgit v1.2.1


From f53f703aebc801c4204182d52bb1e0bef9808e1f Mon Sep 17 00:00:00 2001
From: JC_Array <justin318@att.net>
Date: Tue, 11 Oct 2022 18:12:12 -0500
Subject: resolved conflicts, moved settings under interrogate section,
 settings only show if deepbooru flag is enabled

---
 modules/deepbooru.py                    |  2 +-
 modules/shared.py                       | 19 +++++++++----------
 modules/textual_inversion/preprocess.py |  2 +-
 modules/ui.py                           |  2 +-
 4 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 89dcac3c..29529949 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -8,7 +8,7 @@ def get_deepbooru_tags(pil_image):
     This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
     """
     from modules import shared  # prevents circular reference
-    create_deepbooru_process(shared.opts.deepbooru_threshold, shared.opts.deepbooru_sort_alpha)
+    create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, shared.opts.deepbooru_sort_alpha)
     shared.deepbooru_process_return["value"] = -1
     shared.deepbooru_process_queue.put(pil_image)
     while shared.deepbooru_process_return["value"] == -1:
diff --git a/modules/shared.py b/modules/shared.py
index 817203f8..5456c477 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -248,15 +248,20 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-options_templates.update(options_section(('interrogate', "Interrogate Options"), {
+interrogate_option_dictionary = {
     "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
     "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
     "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
-    "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
-    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
-}))
+    "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)")
+}
+
+if cmd_opts.deepdanbooru:
+    interrogate_option_dictionary["interrogate_deepbooru_score_threshold"] = OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01})
+    interrogate_option_dictionary["deepbooru_sort_alpha"] = OptionInfo(True, "Interrogate: deepbooru sort alphabetically", gr.Checkbox)
+
+options_templates.update(options_section(('interrogate', "Interrogate Options"), interrogate_option_dictionary))
 
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
@@ -282,12 +287,6 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
-if cmd_opts.deepdanbooru:
-    options_templates.update(options_section(('deepbooru-params', "DeepBooru parameters"), {
-        "deepbooru_sort_alpha": OptionInfo(True, "Sort Alphabetical", gr.Checkbox),
-        'deepbooru_threshold': OptionInfo(0.5, "Threshold", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    }))
-
 
 class Options:
     data = None
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index a96388d6..113cecf1 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -29,7 +29,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
         shared.interrogator.load()
 
     if process_caption_deepbooru:
-        deepbooru.create_deepbooru_process(opts.deepbooru_threshold, opts.deepbooru_sort_alpha)
+        deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, opts.deepbooru_sort_alpha)
 
     def save_pic_with_caption(image, index):
         if process_caption:
diff --git a/modules/ui.py b/modules/ui.py
index 2891fc8c..fa45edca 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -317,7 +317,7 @@ def interrogate(image):
 
 
 def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold)
+    prompt = get_deepbooru_tags(image)
     return gr_show(True) if prompt is None else prompt
 
 
-- 
cgit v1.2.1


From 6d408c06c634cc96480f055941754dcc43f781d9 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 12 Oct 2022 00:19:28 +0100
Subject: Prevent nans from failed float parsing from overwriting weights

---
 javascript/edit-attention.js | 1 +
 1 file changed, 1 insertion(+)

diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js
index 79566a2e..3f1d2fbb 100644
--- a/javascript/edit-attention.js
+++ b/javascript/edit-attention.js
@@ -25,6 +25,7 @@ addEventListener('keydown', (event) => {
 	} else {
 		end = target.value.slice(selectionEnd + 1).indexOf(")") + 1;
 		weight = parseFloat(target.value.slice(selectionEnd + 1, selectionEnd + 1 + end));
+		if (isNaN(weight)) return;
 		if (event.key == minus) weight -= 0.1;
 		if (event.key == plus) weight += 0.1;
 
-- 
cgit v1.2.1


From 65b973ac4e547a325f30a05f852b161421af2041 Mon Sep 17 00:00:00 2001
From: supersteve3d <39339941+supersteve3d@users.noreply.github.com>
Date: Wed, 12 Oct 2022 08:21:52 +0800
Subject: Update shared.py

Correct typo to "Unload VAE and CLIP from VRAM when training" in settings tab.
---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index c1092ff7..46bc740c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -229,7 +229,7 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP form VRAM when training"),
+    "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-- 
cgit v1.2.1


From d717eb079cd6b7fa7a4f97c0a10d400bdec753fb Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Tue, 11 Oct 2022 18:02:41 -0700
Subject: Interrogate: add option to include ranks in output

Since the UI also allows users to specify ranks, it can be useful to show people what ranks are being returned by interrogate

This can also give much better results when feeding the interrogate results back into either img2img or txt2img, especially when trying to generate a specific character or scene for which you have a similar concept image

Testing Steps:

Launch Webui with command line arg: --deepdanbooru
Navigate to img2img tab, use interrogate DeepBooru, verify tags appears as before. Use "Interrogate CLIP", verify prompt appears as before
Navigate to Settings tab, enable new option, click "apply settings"
Navigate to img2img, Interrogate DeepBooru again, verify that weights appear and are properly formatted. Note that "Interrogate CLIP" prompt is still unchanged
In my testing, this change has no effect to "Interrogate CLIP", as it seems to generate a sentence-structured caption, and not a set of tags.

(reproduce changes from https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2149/commits/6ed4faac46c45ca7353f228aca9b436bbaba7bc7)
---
 modules/deepbooru.py   | 14 +++++++++-----
 modules/interrogate.py |  7 +++++--
 modules/shared.py      |  1 +
 modules/ui.py          |  5 ++---
 4 files changed, 17 insertions(+), 10 deletions(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 7e3c0618..32d741e2 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -3,7 +3,7 @@ from concurrent.futures import ProcessPoolExecutor
 from multiprocessing import get_context
 
 
-def _load_tf_and_return_tags(pil_image, threshold):
+def _load_tf_and_return_tags(pil_image, threshold, include_ranks):
     import deepdanbooru as dd
     import tensorflow as tf
     import numpy as np
@@ -52,12 +52,16 @@ def _load_tf_and_return_tags(pil_image, threshold):
         if result_dict[tag] >= threshold:
             if tag.startswith("rating:"):
                 continue
-            result_tags_out.append(tag)
+            tag_formatted = tag.replace('_', ' ').replace(':', ' ')
+            if include_ranks:
+                result_tags_out.append(f'({tag_formatted}:{result_dict[tag]})')
+            else:
+                result_tags_out.append(tag_formatted)
             result_tags_print.append(f'{result_dict[tag]} {tag}')
 
     print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
+    return ', '.join(result_tags_out)
 
 
 def subprocess_init_no_cuda():
@@ -65,9 +69,9 @@ def subprocess_init_no_cuda():
     os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
 
-def get_deepbooru_tags(pil_image, threshold=0.5):
+def get_deepbooru_tags(pil_image, threshold=0.5, include_ranks=False):
     context = get_context('spawn')
     with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
-        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
+        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, include_ranks)
         ret = f.result()  # will rethrow any exceptions
     return ret
\ No newline at end of file
diff --git a/modules/interrogate.py b/modules/interrogate.py
index 635e266e..af858cc0 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -123,7 +123,7 @@ class InterrogateModels:
 
         return caption[0]
 
-    def interrogate(self, pil_image):
+    def interrogate(self, pil_image, include_ranks=False):
         res = None
 
         try:
@@ -156,7 +156,10 @@ class InterrogateModels:
                 for name, topn, items in self.categories:
                     matches = self.rank(image_features, items, top_count=topn)
                     for match, score in matches:
-                        res += ", " + match
+                        if include_ranks:
+                            res += ", " + match
+                        else:
+                            res += f", ({match}:{score})"
 
         except Exception:
             print(f"Error interrogating", file=sys.stderr)
diff --git a/modules/shared.py b/modules/shared.py
index c1092ff7..3e0bfd72 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -251,6 +251,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
     "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
     "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
+    "interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
     "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
diff --git a/modules/ui.py b/modules/ui.py
index 1204eef7..f4dbe247 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -311,13 +311,12 @@ def apply_styles(prompt, prompt_neg, style1_name, style2_name):
 
 
 def interrogate(image):
-    prompt = shared.interrogator.interrogate(image)
-
+    prompt = shared.interrogator.interrogate(image, include_ranks=opts.interrogate_return_ranks)
     return gr_show(True) if prompt is None else prompt
 
 
 def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold)
+    prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold, opts.interrogate_return_ranks)
     return gr_show(True) if prompt is None else prompt
 
 
-- 
cgit v1.2.1


From 6ac2ec2b78bc5fabd09cb866dd9a71061d669269 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 07:01:20 +0300
Subject: create dir for hypernetworks

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/shared.py b/modules/shared.py
index c1092ff7..e65e77f8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -86,6 +86,7 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 xformers_available = False
 config_filename = cmd_opts.ui_settings_file
 
+os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
-- 
cgit v1.2.1


From fec2221eeaafb50afd26ba3e109bf6f928011e69 Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Tue, 11 Oct 2022 19:29:38 -0700
Subject: Truncate error text to fix service lockup / stall

What:
* Update wrap_gradio_call to add a limit to the maximum amount of text output

Why:
* wrap_gradio_call currently prints out a list of the arguments provided to the failing function.
   * if that function is save_image, this causes the entire image to be printed to stderr
    * If the image is large, this can cause the service to lock up while attempting to print all the text
 * It is easy to generate large images using the x/y plot script
 * it is easy to encounter image save exceptions, including if the output directory does not exist / cannot be written to, or if the file is too big
  * The huge amount of log spam is confusing and not particularly helpful
---
 modules/ui.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 1204eef7..33a49d3b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -181,8 +181,15 @@ def wrap_gradio_call(func, extra_outputs=None):
         try:
             res = list(func(*args, **kwargs))
         except Exception as e:
+            # When printing out our debug argument list, do not print out more than a MB of text
+            max_debug_str_len = 131072 # (1024*1024)/8
+
             print("Error completing request", file=sys.stderr)
-            print("Arguments:", args, kwargs, file=sys.stderr)
+            argStr = f"Arguments: {str(args)} {str(kwargs)}"
+            print(argStr[:max_debug_str_len], file=sys.stderr)
+            if len(argStr) > max_debug_str_len:
+                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
+
             print(traceback.format_exc(), file=sys.stderr)
 
             shared.state.job = ""
-- 
cgit v1.2.1


From 336bd8703c7b4d71f2f096f303599925a30b8167 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 09:00:07 +0300
Subject: just add the deepdanbooru settings unconditionally

---
 modules/shared.py | 13 ++++---------
 1 file changed, 4 insertions(+), 9 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index f150e024..42e99741 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -249,20 +249,15 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-interrogate_option_dictionary = {
+options_templates.update(options_section(('interrogate', "Interrogate Options"), {
     "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
     "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
     "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
-    "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)")
-}
-
-if cmd_opts.deepdanbooru:
-    interrogate_option_dictionary["interrogate_deepbooru_score_threshold"] = OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01})
-    interrogate_option_dictionary["deepbooru_sort_alpha"] = OptionInfo(True, "Interrogate: deepbooru sort alphabetically", gr.Checkbox)
-
-options_templates.update(options_section(('interrogate', "Interrogate Options"), interrogate_option_dictionary))
+    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+    "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
+}))
 
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
-- 
cgit v1.2.1


From fd07b103aeb70a80e3641068e483475e32c9750c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 09:00:39 +0300
Subject: prevent SD model from loading when running in deepdanbooru process

---
 webui.py | 26 ++++++++++++++------------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/webui.py b/webui.py
index ca278e94..32bcdb06 100644
--- a/webui.py
+++ b/webui.py
@@ -31,12 +31,7 @@ from modules.paths import script_path
 from modules.shared import cmd_opts
 import modules.hypernetworks.hypernetwork
 
-modelloader.cleanup_models()
-modules.sd_models.setup_model()
-codeformer.setup_model(cmd_opts.codeformer_models_path)
-gfpgan.setup_model(cmd_opts.gfpgan_models_path)
-shared.face_restorers.append(modules.face_restoration.FaceRestoration())
-modelloader.load_upscalers()
+
 queue_lock = threading.Lock()
 
 
@@ -78,12 +73,19 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
     return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
 
 
-modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
+def initialize():
+    modelloader.cleanup_models()
+    modules.sd_models.setup_model()
+    codeformer.setup_model(cmd_opts.codeformer_models_path)
+    gfpgan.setup_model(cmd_opts.gfpgan_models_path)
+    shared.face_restorers.append(modules.face_restoration.FaceRestoration())
+    modelloader.load_upscalers()
 
-shared.sd_model = modules.sd_models.load_model()
-shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
+    modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
 
-shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
+    shared.sd_model = modules.sd_models.load_model()
+    shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
+    shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
 
 
 def webui():
@@ -98,7 +100,7 @@ def webui():
 
         demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
         
-        app,local_url,share_url = demo.launch(
+        app, local_url, share_url = demo.launch(
             share=cmd_opts.share,
             server_name="0.0.0.0" if cmd_opts.listen else None,
             server_port=cmd_opts.port,
@@ -129,6 +131,6 @@ def webui():
         print('Restarting Gradio')
 
 
-
 if __name__ == "__main__":
+    initialize()
     webui()
-- 
cgit v1.2.1


From 7edd58d90dd08f68fab5ff84d26dedd0eb85cae3 Mon Sep 17 00:00:00 2001
From: James Noeckel <jamesn8@cs.washington.edu>
Date: Tue, 11 Oct 2022 17:48:24 -0700
Subject: update environment-wsl2.yaml

---
 environment-wsl2.yaml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/environment-wsl2.yaml b/environment-wsl2.yaml
index c9ce11df..f8872750 100644
--- a/environment-wsl2.yaml
+++ b/environment-wsl2.yaml
@@ -3,9 +3,9 @@ channels:
   - pytorch
   - defaults
 dependencies:
-  - python=3.8.5
-  - pip=20.3
+  - python=3.10
+  - pip=22.2.2
   - cudatoolkit=11.3
-  - pytorch=1.11.0
-  - torchvision=0.12.0
-  - numpy=1.19.2
+  - pytorch=1.12.1
+  - torchvision=0.13.1
+  - numpy=1.23.1
\ No newline at end of file
-- 
cgit v1.2.1


From 8aead63f1ac9fec0e5198bd626ec2c5bcbeff4d8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 09:32:14 +0300
Subject: emergency fix

---
 webui.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/webui.py b/webui.py
index 32bcdb06..33ba7905 100644
--- a/webui.py
+++ b/webui.py
@@ -89,6 +89,8 @@ def initialize():
 
 
 def webui():
+    initialize()
+    
     # make the program just exit at ctrl+c without waiting for anything
     def sigint_handler(sig, frame):
         print(f'Interrupted with signal {sig} in {frame}')
@@ -132,5 +134,4 @@ def webui():
 
 
 if __name__ == "__main__":
-    initialize()
     webui()
-- 
cgit v1.2.1


From 57e03cdd244eee4e33ccab7554b3594563a3d0cd Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Wed, 12 Oct 2022 00:54:24 -0400
Subject: Ensure the directory exists before saving to it

The directory for the images saved with the Save button may still not exist, so it needs to be created prior to opening the log.csv file.
---
 modules/ui.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modules/ui.py b/modules/ui.py
index 00bf09ae..cd67b84b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -131,6 +131,8 @@ def save_files(js_data, images, do_make_zip, index):
         images = [images[index]]
         start_index = index
 
+    os.makedirs(opts.outdir_save, exist_ok=True)
+
     with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
         at_start = file.tell() == 0
         writer = csv.writer(file)
-- 
cgit v1.2.1


From f421f2af2df41a86af1aea1e82b4c32a2d143385 Mon Sep 17 00:00:00 2001
From: aoirusann <yimodo.siki@gmail.com>
Date: Wed, 12 Oct 2022 13:02:28 +0800
Subject: [img2imgalt] Fix seed & Allow batch.

---
 scripts/img2imgalt.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index f9894cb0..313a55d2 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -129,8 +129,6 @@ class Script(scripts.Script):
         return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
 
     def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
-        p.batch_size = 1
-        p.batch_count = 1
 
 
         def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
@@ -154,7 +152,7 @@ class Script(scripts.Script):
                     rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
                 self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
 
-            rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
+            rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
             
             combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
             
-- 
cgit v1.2.1


From ca5efc316b9431746ff886d259275310f63f95fb Mon Sep 17 00:00:00 2001
From: LunixWasTaken <Lunixwastaken@protonmail.com>
Date: Tue, 11 Oct 2022 22:04:56 +0200
Subject: Typo fix in watermark hint.

---
 javascript/hints.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index 045f2d3c..b81c181b 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -80,7 +80,7 @@ titles = {
     "Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.",
 
     "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
-    "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be bevaing in an unethical manner.",
+    "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
 }
 
 
-- 
cgit v1.2.1


From 2d006ce16cd95d587533656c3ac4991495e96f23 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Mon, 10 Oct 2022 00:56:36 +0900
Subject: xy_grid: Find hypernetwork by closest name

---
 modules/hypernetworks/hypernetwork.py | 11 +++++++++++
 scripts/xy_grid.py                    |  6 +++++-
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 470659df..8f2192e2 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -120,6 +120,17 @@ def load_hypernetwork(filename):
         shared.loaded_hypernetwork = None
 
 
+def find_closest_hypernetwork_name(search: str):
+    if not search:
+        return None
+    search = search.lower()
+    applicable = [name for name in shared.hypernetworks if search in name.lower()]
+    if not applicable:
+        return None
+    applicable = sorted(applicable, key=lambda name: len(name))
+    return applicable[0]
+
+
 def apply_hypernetwork(hypernetwork, context, layer=None):
     hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
 
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index ef431105..6f4217ec 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -84,7 +84,11 @@ def apply_checkpoint(p, x, xs):
 
 
 def apply_hypernetwork(p, x, xs):
-    hypernetwork.load_hypernetwork(x)
+    if x.lower() in ["", "none"]:
+        name = None
+    else:
+        name = hypernetwork.find_closest_hypernetwork_name(x)
+    hypernetwork.load_hypernetwork(name)
 
 
 def apply_clip_skip(p, x, xs):
-- 
cgit v1.2.1


From 7dba1c07cb337114507d9c256f9b843162c187d6 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Mon, 10 Oct 2022 01:37:09 +0900
Subject: xy_grid: Confirm that hypernetwork options are valid before starting

---
 scripts/xy_grid.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 6f4217ec..b2239d0a 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -88,9 +88,19 @@ def apply_hypernetwork(p, x, xs):
         name = None
     else:
         name = hypernetwork.find_closest_hypernetwork_name(x)
+        if not name:
+            raise RuntimeError(f"Unknown hypernetwork: {x}")
     hypernetwork.load_hypernetwork(name)
 
 
+def confirm_hypernetworks(xs):
+    for x in xs:
+        if x.lower() in ["", "none"]:
+            continue
+        if not hypernetwork.find_closest_hypernetwork_name(x):
+            raise RuntimeError(f"Unknown hypernetwork: {x}")
+
+
 def apply_clip_skip(p, x, xs):
     opts.data["CLIP_stop_at_last_layers"] = x
 
@@ -284,6 +294,8 @@ class Script(scripts.Script):
                 for ckpt_val in valslist:
                     if modules.sd_models.get_closet_checkpoint_match(ckpt_val) is None:
                         raise RuntimeError(f"Checkpoint for {ckpt_val} not found")
+            elif opt.label == "Hypernetwork":
+                confirm_hypernetworks(valslist)
 
             return valslist
 
-- 
cgit v1.2.1


From 2fffd4bddce12b2c98a5bae5a2cc6d64450d65a0 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Mon, 10 Oct 2022 02:20:35 +0900
Subject: xy_grid: Refactor confirm functions

---
 scripts/xy_grid.py | 73 +++++++++++++++++++++++++++++-------------------------
 1 file changed, 39 insertions(+), 34 deletions(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index b2239d0a..3bb080bf 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -77,12 +77,26 @@ def apply_sampler(p, x, xs):
     p.sampler_index = sampler_index
 
 
+def confirm_samplers(p, xs):
+    samplers_dict = build_samplers_dict(p)
+    for x in xs:
+        if x.lower() not in samplers_dict.keys():
+            raise RuntimeError(f"Unknown sampler: {x}")
+
+
 def apply_checkpoint(p, x, xs):
     info = modules.sd_models.get_closet_checkpoint_match(x)
-    assert info is not None, f'Checkpoint for {x} not found'
+    if info is None:
+        raise RuntimeError(f"Unknown checkpoint: {x}")
     modules.sd_models.reload_model_weights(shared.sd_model, info)
 
 
+def confirm_checkpoints(p, xs):
+    for x in xs:
+        if modules.sd_models.get_closet_checkpoint_match(x) is None:
+            raise RuntimeError(f"Unknown checkpoint: {x}")
+
+
 def apply_hypernetwork(p, x, xs):
     if x.lower() in ["", "none"]:
         name = None
@@ -93,7 +107,7 @@ def apply_hypernetwork(p, x, xs):
     hypernetwork.load_hypernetwork(name)
 
 
-def confirm_hypernetworks(xs):
+def confirm_hypernetworks(p, xs):
     for x in xs:
         if x.lower() in ["", "none"]:
             continue
@@ -135,29 +149,29 @@ def str_permutations(x):
     return x
 
 
-AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value"])
-AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value"])
+AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"])
+AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"])
 
 
 axis_options = [
-    AxisOption("Nothing", str, do_nothing, format_nothing),
-    AxisOption("Seed", int, apply_field("seed"), format_value_add_label),
-    AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label),
-    AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label),
-    AxisOption("Steps", int, apply_field("steps"), format_value_add_label),
-    AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label),
-    AxisOption("Prompt S/R", str, apply_prompt, format_value),
-    AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list),
-    AxisOption("Sampler", str, apply_sampler, format_value),
-    AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
-    AxisOption("Hypernetwork", str, apply_hypernetwork, format_value),
-    AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
-    AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
-    AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
-    AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
-    AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
-    AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label),
-    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
+    AxisOption("Nothing", str, do_nothing, format_nothing, None),
+    AxisOption("Seed", int, apply_field("seed"), format_value_add_label, None),
+    AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label, None),
+    AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label, None),
+    AxisOption("Steps", int, apply_field("steps"), format_value_add_label, None),
+    AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label, None),
+    AxisOption("Prompt S/R", str, apply_prompt, format_value, None),
+    AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list, None),
+    AxisOption("Sampler", str, apply_sampler, format_value, confirm_samplers),
+    AxisOption("Checkpoint name", str, apply_checkpoint, format_value, confirm_checkpoints),
+    AxisOption("Hypernetwork", str, apply_hypernetwork, format_value, confirm_hypernetworks),
+    AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label, None),
+    AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label, None),
+    AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label, None),
+    AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label, None),
+    AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
+    AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
+    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
 ]
 
 
@@ -283,19 +297,10 @@ class Script(scripts.Script):
                 valslist = list(permutations(valslist))
 
             valslist = [opt.type(x) for x in valslist]
-            
+
             # Confirm options are valid before starting
-            if opt.label == "Sampler":
-                samplers_dict = build_samplers_dict(p)
-                for sampler_val in valslist:
-                    if sampler_val.lower() not in samplers_dict.keys():
-                        raise RuntimeError(f"Unknown sampler: {sampler_val}")
-            elif opt.label == "Checkpoint name":
-                for ckpt_val in valslist:
-                    if modules.sd_models.get_closet_checkpoint_match(ckpt_val) is None:
-                        raise RuntimeError(f"Checkpoint for {ckpt_val} not found")
-            elif opt.label == "Hypernetwork":
-                confirm_hypernetworks(valslist)
+            if opt.confirm:
+                opt.confirm(p, valslist)
 
             return valslist
 
-- 
cgit v1.2.1


From ee015a1af66a94a75c914659fa0d321e702a0a87 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 11:05:57 +0300
Subject: change textual inversion tab to train remake train interface to use
 tabs

---
 modules/hypernetworks/hypernetwork.py |  2 +-
 modules/ui.py                         | 22 +++++++++-------------
 2 files changed, 10 insertions(+), 14 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8f2192e2..8314450a 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -175,7 +175,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
 
 
 def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
-    assert hypernetwork_name, 'embedding not selected'
+    assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
     shared.loaded_hypernetwork = Hypernetwork()
diff --git a/modules/ui.py b/modules/ui.py
index 4bfdd275..86a2da6c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1035,14 +1035,14 @@ def create_ui(wrap_gradio_gpu_call):
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
 
-    with gr.Blocks() as textual_inversion_interface:
+    with gr.Blocks() as train_interface:
         with gr.Row().style(equal_height=False):
-            with gr.Column():
-                with gr.Group():
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
 
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")
+        with gr.Row().style(equal_height=False):
+            with gr.Tabs(elem_id="train_tabs"):
 
+                with gr.Tab(label="Create embedding"):
                     new_embedding_name = gr.Textbox(label="Name")
                     initialization_text = gr.Textbox(label="Initialization text", value="*")
                     nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
@@ -1054,9 +1054,7 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create embedding", variant='primary')
 
-                with gr.Group():
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
-
+                with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
 
@@ -1067,9 +1065,7 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
 
-                with gr.Group():
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
-
+                with gr.Tab(label="Preprocess images"):
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
                     process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
@@ -1091,7 +1087,7 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
-                with gr.Group():
+                with gr.Tab(label="Train"):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                     train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
@@ -1388,7 +1384,7 @@ Requested path was: {f}
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
-        (textual_inversion_interface, "Textual inversion", "ti"),
+        (train_interface, "Train", "ti"),
         (settings_interface, "Settings", "settings"),
     ]
 
-- 
cgit v1.2.1


From 80f3cf2bb2ce3f00d801cae2c3a8c20a8d4167d8 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Tue, 11 Oct 2022 19:48:53 +0100
Subject: Account when lines are mismatched

---
 modules/sd_hijack.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index ac70f876..2753d4fa 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -321,7 +321,17 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                         fixes.append(fix[1])
                 self.hijack.fixes.append(fixes)
             
-            z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers])
+            tokens = []
+            multipliers = []
+            for i in range(len(remade_batch_tokens)):
+                if len(remade_batch_tokens[i]) > 0:
+                    tokens.append(remade_batch_tokens[i][:75])
+                    multipliers.append(batch_multipliers[i][:75])
+                else:
+                    tokens.append([self.wrapped.tokenizer.eos_token_id] * 75)
+                    multipliers.append([1.0] * 75)
+
+            z1 = self.process_tokens(tokens, multipliers)
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
             
             remade_batch_tokens = rem_tokens
-- 
cgit v1.2.1


From 8561d5762b98bf7cfb764128ebf11633d8bb4405 Mon Sep 17 00:00:00 2001
From: Kalle <kalekkiorg@gmail.com>
Date: Wed, 12 Oct 2022 12:43:11 +0300
Subject: Remove duplicate artist from file

---
 artists.csv | 1 -
 1 file changed, 1 deletion(-)

diff --git a/artists.csv b/artists.csv
index 14ba2022..99cdbdc6 100644
--- a/artists.csv
+++ b/artists.csv
@@ -1045,7 +1045,6 @@ Bakemono Zukushi,0.67051035,anime
 Lucy Madox Brown,0.67032814,fineart
 Paul Wonner,0.6700563,scribbles
 Guido Borelli Da Caluso,0.66966087,digipa-high-impact
-Guido Borelli da Caluso,0.66966087,digipa-high-impact
 Emil Alzamora,0.5844039,nudity
 Heinrich Brocksieper,0.64469147,fineart
 Dan Smith,0.669563,digipa-high-impact
-- 
cgit v1.2.1


From 429442f4a6aab7301efb89d27bef524fe827e81a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 13:38:03 +0300
Subject: fix iterator bug for #2295

---
 modules/sd_hijack.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 2753d4fa..c81722a0 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -323,10 +323,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             
             tokens = []
             multipliers = []
-            for i in range(len(remade_batch_tokens)):
-                if len(remade_batch_tokens[i]) > 0:
-                    tokens.append(remade_batch_tokens[i][:75])
-                    multipliers.append(batch_multipliers[i][:75])
+            for j in range(len(remade_batch_tokens)):
+                if len(remade_batch_tokens[j]) > 0:
+                    tokens.append(remade_batch_tokens[j][:75])
+                    multipliers.append(batch_multipliers[j][:75])
                 else:
                     tokens.append([self.wrapped.tokenizer.eos_token_id] * 75)
                     multipliers.append([1.0] * 75)
-- 
cgit v1.2.1


From 50be33e953be93c40814262c6dbce36e66004528 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 12 Oct 2022 13:13:25 +0100
Subject: formatting

---
 modules/textual_inversion/image_embedding.py | 170 ++++++++++++++-------------
 1 file changed, 91 insertions(+), 79 deletions(-)

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index 1224fb42..898ce3b3 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -2,122 +2,134 @@ import base64
 import json
 import numpy as np
 import zlib
-from PIL import Image,PngImagePlugin,ImageDraw,ImageFont
+from PIL import Image, PngImagePlugin, ImageDraw, ImageFont
 from fonts.ttf import Roboto
 import torch
 
+
 class EmbeddingEncoder(json.JSONEncoder):
     def default(self, obj):
         if isinstance(obj, torch.Tensor):
-            return {'TORCHTENSOR':obj.cpu().detach().numpy().tolist()}
+            return {'TORCHTENSOR': obj.cpu().detach().numpy().tolist()}
         return json.JSONEncoder.default(self, obj)
 
+
 class EmbeddingDecoder(json.JSONDecoder):
     def __init__(self, *args, **kwargs):
         json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
+
     def object_hook(self, d):
         if 'TORCHTENSOR' in d:
             return torch.from_numpy(np.array(d['TORCHTENSOR']))
         return d
 
+
 def embedding_to_b64(data):
-    d = json.dumps(data,cls=EmbeddingEncoder)
+    d = json.dumps(data, cls=EmbeddingEncoder)
     return base64.b64encode(d.encode())
 
+
 def embedding_from_b64(data):
     d = base64.b64decode(data)
-    return json.loads(d,cls=EmbeddingDecoder)
+    return json.loads(d, cls=EmbeddingDecoder)
+
 
 def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
     while True:
         seed = (a * seed + c) % m
-        yield seed%255
+        yield seed % 255
+
 
 def xor_block(block):
     g = lcg()
     randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
-    return np.bitwise_xor(block.astype(np.uint8),randblock & 0x0F) 
+    return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F)
 
-def style_block(block,sequence):
-    im = Image.new('RGB',(block.shape[1],block.shape[0]))
+
+def style_block(block, sequence):
+    im = Image.new('RGB', (block.shape[1], block.shape[0]))
     draw = ImageDraw.Draw(im)
-    i=0
-    for x in range(-6,im.size[0],8):
-        for yi,y in enumerate(range(-6,im.size[1],8)):
-            offset=0
-            if yi%2==0:
-                offset=4
-            shade = sequence[i%len(sequence)]
-            i+=1
-            draw.ellipse((x+offset, y, x+6+offset, y+6), fill =(shade,shade,shade) )
+    i = 0
+    for x in range(-6, im.size[0], 8):
+        for yi, y in enumerate(range(-6, im.size[1], 8)):
+            offset = 0
+            if yi % 2 == 0:
+                offset = 4
+            shade = sequence[i % len(sequence)]
+            i += 1
+            draw.ellipse((x+offset, y, x+6+offset, y+6), fill=(shade, shade, shade))
 
     fg = np.array(im).astype(np.uint8) & 0xF0
 
     return block ^ fg
 
-def insert_image_data_embed(image,data):
+
+def insert_image_data_embed(image, data):
     d = 3
-    data_compressed = zlib.compress( json.dumps(data,cls=EmbeddingEncoder).encode(),level=9)
-    data_np_ = np.frombuffer(data_compressed,np.uint8).copy()
+    data_compressed = zlib.compress(json.dumps(data, cls=EmbeddingEncoder).encode(), level=9)
+    data_np_ = np.frombuffer(data_compressed, np.uint8).copy()
     data_np_high = data_np_ >> 4
-    data_np_low  = data_np_ & 0x0F
-    
+    data_np_low = data_np_ & 0x0F
+
     h = image.size[1]
-    next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0]%h))
-    next_size = next_size + ((h*d)-(next_size%(h*d)))
+    next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0] % h))
+    next_size = next_size + ((h*d)-(next_size % (h*d)))
 
     data_np_low.resize(next_size)
-    data_np_low = data_np_low.reshape((h,-1,d))
+    data_np_low = data_np_low.reshape((h, -1, d))
 
     data_np_high.resize(next_size)
-    data_np_high = data_np_high.reshape((h,-1,d))
+    data_np_high = data_np_high.reshape((h, -1, d))
 
     edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
     edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)
 
-    data_np_low   = style_block(data_np_low,sequence=edge_style)
-    data_np_low   = xor_block(data_np_low)    
-    data_np_high  = style_block(data_np_high,sequence=edge_style[::-1])
-    data_np_high  = xor_block(data_np_high)
+    data_np_low = style_block(data_np_low, sequence=edge_style)
+    data_np_low = xor_block(data_np_low)
+    data_np_high = style_block(data_np_high, sequence=edge_style[::-1])
+    data_np_high = xor_block(data_np_high)
 
-    im_low  = Image.fromarray(data_np_low,mode='RGB')
-    im_high = Image.fromarray(data_np_high,mode='RGB')
+    im_low = Image.fromarray(data_np_low, mode='RGB')
+    im_high = Image.fromarray(data_np_high, mode='RGB')
 
-    background = Image.new('RGB',(image.size[0]+im_low.size[0]+im_high.size[0]+2,image.size[1]),(0,0,0))
-    background.paste(im_low,(0,0))
-    background.paste(image,(im_low.size[0]+1,0))
-    background.paste(im_high,(im_low.size[0]+1+image.size[0]+1,0))
+    background = Image.new('RGB', (image.size[0]+im_low.size[0]+im_high.size[0]+2, image.size[1]), (0, 0, 0))
+    background.paste(im_low, (0, 0))
+    background.paste(image, (im_low.size[0]+1, 0))
+    background.paste(im_high, (im_low.size[0]+1+image.size[0]+1, 0))
 
     return background
 
-def crop_black(img,tol=0):
-    mask = (img>tol).all(2)
-    mask0,mask1 = mask.any(0),mask.any(1)
-    col_start,col_end = mask0.argmax(),mask.shape[1]-mask0[::-1].argmax()
-    row_start,row_end = mask1.argmax(),mask.shape[0]-mask1[::-1].argmax()
-    return img[row_start:row_end,col_start:col_end]
+
+def crop_black(img, tol=0):
+    mask = (img > tol).all(2)
+    mask0, mask1 = mask.any(0), mask.any(1)
+    col_start, col_end = mask0.argmax(), mask.shape[1]-mask0[::-1].argmax()
+    row_start, row_end = mask1.argmax(), mask.shape[0]-mask1[::-1].argmax()
+    return img[row_start:row_end, col_start:col_end]
+
 
 def extract_image_data_embed(image):
-    d=3
-    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1],image.size[0],d ).astype(np.uint8) ) & 0x0F
-    black_cols = np.where( np.sum(outarr, axis=(0,2))==0)
+    d = 3
+    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F
+    black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0)
     if black_cols[0].shape[0] < 2:
         print('No Image data blocks found.')
         return None
 
-    data_block_lower = outarr[:,:black_cols[0].min(),:].astype(np.uint8)
-    data_block_upper = outarr[:,black_cols[0].max()+1:,:].astype(np.uint8)
+    data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8)
+    data_block_upper = outarr[:, black_cols[0].max()+1:, :].astype(np.uint8)
 
     data_block_lower = xor_block(data_block_lower)
     data_block_upper = xor_block(data_block_upper)
-    
+
     data_block = (data_block_upper << 4) | (data_block_lower)
     data_block = data_block.flatten().tobytes()
 
     data = zlib.decompress(data_block)
-    return json.loads(data,cls=EmbeddingDecoder)
+    return json.loads(data, cls=EmbeddingDecoder)
+
 
-def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfont=None):
+def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
     from math import cos
 
     image = srcimage.copy()
@@ -130,11 +142,11 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo
             textfont = Roboto
 
     factor = 1.5
-    gradient = Image.new('RGBA', (1,image.size[1]), color=(0,0,0,0))
+    gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))
     for y in range(image.size[1]):
         mag = 1-cos(y/image.size[1]*factor)
-        mag = max(mag,1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
-        gradient.putpixel((0, y), (0,0,0,int(mag*255)))
+        mag = max(mag, 1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
+        gradient.putpixel((0, y), (0, 0, 0, int(mag*255)))
     image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
 
     draw = ImageDraw.Draw(image)
@@ -142,41 +154,41 @@ def caption_image_overlay(srcimage,title,footerLeft,footerMid,footerRight,textfo
     font = ImageFont.truetype(textfont, fontsize)
     padding = 10
 
-    _,_,w, h = draw.textbbox((0,0),title,font=font)
-    fontsize =  min( int(fontsize * (((image.size[0]*0.75)-(padding*4))/w) ), 72)
+    _, _, w, h = draw.textbbox((0, 0), title, font=font)
+    fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)
     font = ImageFont.truetype(textfont, fontsize)
-    _,_,w,h = draw.textbbox((0,0),title,font=font)
-    draw.text((padding,padding), title, anchor='lt', font=font, fill=(255,255,255,230))
+    _, _, w, h = draw.textbbox((0, 0), title, font=font)
+    draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))
 
-    _,_,w, h = draw.textbbox((0,0),footerLeft,font=font)
-    fontsize_left =  min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
-    _,_,w, h = draw.textbbox((0,0),footerMid,font=font)
-    fontsize_mid =  min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
-    _,_,w, h = draw.textbbox((0,0),footerRight,font=font)
-    fontsize_right =  min( int(fontsize * (((image.size[0]/3)-(padding))/w) ), 72)
+    _, _, w, h = draw.textbbox((0, 0), footerLeft, font=font)
+    fontsize_left = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+    _, _, w, h = draw.textbbox((0, 0), footerMid, font=font)
+    fontsize_mid = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+    _, _, w, h = draw.textbbox((0, 0), footerRight, font=font)
+    fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
 
-    font = ImageFont.truetype(textfont, min(fontsize_left,fontsize_mid,fontsize_right))
+    font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right))
 
-    draw.text((padding,image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255,255,255,230))
-    draw.text((image.size[0]/2,image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255,255,255,230))
-    draw.text((image.size[0]-padding,image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255,255,255,230))
+    draw.text((padding, image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))
+    draw.text((image.size[0]/2, image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))
+    draw.text((image.size[0]-padding, image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255, 255, 255, 230))
 
     return image
 
+
 if __name__ == '__main__':
 
     testEmbed = Image.open('test_embedding.png')
-    
     data = extract_image_data_embed(testEmbed)
     assert data is not None
 
     data = embedding_from_b64(testEmbed.text['sd-ti-embedding'])
     assert data is not None
-    
-    image = Image.new('RGBA',(512,512),(255,255,200,255))
+
+    image = Image.new('RGBA', (512, 512), (255, 255, 200, 255))
     cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
 
-    test_embed = {'string_to_param':{'*':torch.from_numpy(np.random.random((2, 4096)))}}
+    test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}}
 
     embedded_image = insert_image_data_embed(cap_image, test_embed)
 
@@ -191,16 +203,16 @@ if __name__ == '__main__':
     g = lcg()
     shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()
 
-    reference_random = [253, 242, 127,  44, 157,  27, 239, 133,  38,  79, 167,   4, 177, 
-                         95, 130,  79,  78,  14,  52, 215, 220, 194, 126,  28, 240, 179, 
-                        160, 153, 149,  50, 105,  14,  21, 218, 199,  18,  54, 198, 193, 
-                         38, 128,  19,  53, 195, 124,  75, 205,  12,   6, 145,   0,  28, 
-                         30, 148,   8,  45, 218, 171,  55, 249,  97, 166,  12,  35,   0, 
-                         41, 221, 122, 215, 170,  31, 113, 186,  97, 119,  31,  23, 185, 
-                         66, 140,  30,  41,  37,  63, 137, 109, 216,  55, 159, 145,  82, 
+    reference_random = [253, 242, 127,  44, 157,  27, 239, 133,  38,  79, 167,   4, 177,
+                         95, 130,  79,  78,  14,  52, 215, 220, 194, 126,  28, 240, 179,
+                        160, 153, 149,  50, 105,  14,  21, 218, 199,  18,  54, 198, 193,
+                         38, 128,  19,  53, 195, 124,  75, 205,  12,   6, 145,   0,  28,
+                         30, 148,   8,  45, 218, 171,  55, 249,  97, 166,  12,  35,   0,
+                         41, 221, 122, 215, 170,  31, 113, 186,  97, 119,  31,  23, 185,
+                         66, 140,  30,  41,  37,  63, 137, 109, 216,  55, 159, 145,  82,
                          204, 86,  73, 222,  44, 198, 118, 240,  97]
 
-    assert shared_random == reference_random   
+    assert shared_random == reference_random
 
     hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
 
-- 
cgit v1.2.1


From 10a2de644f8ea4cfade88e85d768da3480f4c9f0 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 12 Oct 2022 13:15:35 +0100
Subject: formatting

---
 modules/textual_inversion/textual_inversion.py | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 485ef46c..b072d745 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -7,14 +7,14 @@ import tqdm
 import html
 import datetime
 
-from PIL import Image,PngImagePlugin
+from PIL import Image, PngImagePlugin
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 
-from modules.textual_inversion.image_embedding import (embedding_to_b64,embedding_from_b64,
-                                                       insert_image_data_embed,extract_image_data_embed,
-                                                       caption_image_overlay )
+from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64,
+                                                       insert_image_data_embed, extract_image_data_embed,
+                                                       caption_image_overlay)
 
 class Embedding:
     def __init__(self, vec, name, step=None):
@@ -90,10 +90,10 @@ class EmbeddingDatabase:
                 embed_image = Image.open(path)
                 if 'sd-ti-embedding' in embed_image.text:
                     data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
-                    name = data.get('name',name)
+                    name = data.get('name', name)
                 else:
                     data = extract_image_data_embed(embed_image)
-                    name = data.get('name',name)
+                    name = data.get('name', name)
             else:
                 data = torch.load(path, map_location="cpu")
 
@@ -278,24 +278,24 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             shared.state.current_image = image
 
             if save_image_with_stored_embedding and os.path.exists(last_saved_file):
-                
+
                 last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
 
                 info = PngImagePlugin.PngInfo()
                 data = torch.load(last_saved_file)
                 info.add_text("sd-ti-embedding", embedding_to_b64(data))
 
-                title = "<{}>".format(data.get('name','???'))
+                title = "<{}>".format(data.get('name', '???'))
                 checkpoint = sd_models.select_checkpoint()
                 footer_left = checkpoint.model_name
                 footer_mid = '[{}]'.format(checkpoint.hash)
                 footer_right = '{}'.format(embedding.step)
 
-                captioned_image = caption_image_overlay(image,title,footer_left,footer_mid,footer_right)
-                captioned_image = insert_image_data_embed(captioned_image,data)
+                captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
+                captioned_image = insert_image_data_embed(captioned_image, data)
 
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
-            
+
             image.save(last_saved_image)
 
             last_saved_image += f", prompt: {preview_text}"
-- 
cgit v1.2.1


From e05573e1adc1cde1e3bd7eb651a1ab27c446b3d5 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Wed, 12 Oct 2022 20:47:55 +0800
Subject: images history improvement

---
 .gitignore                   |   1 +
 javascript/images_history.js | 222 ++++++++++++++++++++++++++++---------------
 modules/images_history.py    |  67 ++++++++-----
 3 files changed, 190 insertions(+), 100 deletions(-)

diff --git a/.gitignore b/.gitignore
index 7afc9395..434e23ce 100644
--- a/.gitignore
+++ b/.gitignore
@@ -26,3 +26,4 @@ __pycache__
 notification.mp3
 /SwinIR
 /textual_inversion
+/images_history_testui.py
diff --git a/javascript/images_history.js b/javascript/images_history.js
index d62eb181..c9a63166 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -1,122 +1,192 @@
-images_history_tab_list = ["txt2img", "img2img", "extras"]
-function images_history_init(){ 
-    if (gradioApp().getElementById('txt2img_images_history_first_page') == null) {
-        setTimeout(images_history_init, 500)
-    } else {        
-        for (i in images_history_tab_list ){
-            tab = images_history_tab_list[i]
-            gradioApp().getElementById(tab + '_images_history').classList.add("images_history_gallery")
-            gradioApp().getElementById(tab + '_images_history_set_index').classList.add("images_history_set_index")      
-            
-        }
-        gradioApp().getElementById("txt2img_images_history_first_page").click()     
-    }    
-}
-setTimeout(images_history_init, 500)
-var images_history_button_actions = function(){
+var images_history_click_image = function(){
     if (!this.classList.contains("transform")){        
-        gallery = this.parentElement
-        while(!gallery.classList.contains("images_history_gallery")){gallery = gallery.parentElement}        
-        buttons = gallery.querySelectorAll(".gallery-item")
-        i = 0
-        hidden_list = []
+        var gallery = images_history_get_parent_by_class(this, "images_history_cantainor");
+        var buttons = gallery.querySelectorAll(".gallery-item");
+        var i = 0;
+        var hidden_list = [];
         buttons.forEach(function(e){
             if (e.style.display == "none"){
-                hidden_list.push(i)
+                hidden_list.push(i);
             }
-            i += 1
+            i += 1;
         })
         if (hidden_list.length > 0){
-            setTimeout(images_history_hide_buttons, 10, hidden_list, gallery)
-        }
-        
+            setTimeout(images_history_hide_buttons, 10, hidden_list, gallery);
+        }        
     }    
-    images_history_set_image_info(this) 
+    images_history_set_image_info(this); 
+}
 
+var images_history_click_tab = function(){
+	var tabs_box = gradioApp().getElementById("images_history_tab");
+	if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
+		gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_renew_page").click();
+		tabs_box.classList.add(this.getAttribute("tabname"))
+	}	            
 }
-onUiUpdate(function(){
-    for (i in images_history_tab_list ){
-        tab = images_history_tab_list[i]
-        buttons = gradioApp().querySelectorAll('#' + tab + '_images_history .gallery-item')
-        buttons.forEach(function(bnt){    
-            bnt.addEventListener('click', images_history_button_actions, true)
-         });
+
+var images_history_close_full_view = function(){
+	var box = images_history_get_parent_by_class(this, "images_history_cantainor");
+	box.querySelector(".images_history_del_button").setAttribute("disabled", "disabled");
+}
+
+function images_history_get_parent_by_class(item, class_name){
+	var parent = item.parentElement;
+    while(!parent.classList.contains(class_name)){
+        parent = parent.parentElement;
     }
-})
+    return parent;  
+}
+
+function images_history_get_parent_by_tagname(item, tagname){
+	var parent = item.parentElement;
+	tagname = tagname.toUpperCase()
+    while(parent.tagName != tagname){
+    	console.log(parent.tagName, tagname)
+        parent = parent.parentElement;
+    }  
+    return parent;
+}
 function images_history_hide_buttons(hidden_list, gallery){
-    buttons = gallery.querySelectorAll(".gallery-item")
-    num = 0
+    var buttons = gallery.querySelectorAll(".gallery-item");
+    var num = 0;
     buttons.forEach(function(e){
         if (e.style.display == "none"){
-            num += 1
+            num += 1;
         }
     })
     if (num == hidden_list.length){
-        setTimeout(images_history_hide_buttons, 10, hidden_list, gallery)
+        setTimeout(images_history_hide_buttons, 10, hidden_list, gallery);
     } 
     for( i in hidden_list){
-        buttons[hidden_list[i]].style.display = "none"
+        buttons[hidden_list[i]].style.display = "none";
     }    
 }
 
 function images_history_set_image_info(button){
-    item = button.parentElement
-    while(item.tagName != "DIV"){item = item.parentElement}    
-    buttons = item.querySelectorAll(".gallery-item")
-    index = -1
-    i = 0
+    var buttons = images_history_get_parent_by_tagname(button, "DIV").querySelectorAll(".gallery-item");
+    var index = -1;
+    var i = 0;
     buttons.forEach(function(e){
-        if(e==button){index = i}
+        if(e == button){
+            index = i;
+        }
         if(e.style.display != "none"){
-            i += 1
+            i += 1;
         }        
     })
-    gallery = button.parentElement
-    while(!gallery.classList.contains("images_history_gallery")){gallery = gallery.parentElement}    
-    set_btn = gallery.querySelector(".images_history_set_index")
-    set_btn.setAttribute("img_index", index)
-    set_btn.click()
+    var gallery = images_history_get_parent_by_class(button, "images_history_cantainor");
+    var set_btn = gallery.querySelector(".images_history_set_index");
+    set_btn.setAttribute("img_index", index);    
+    set_btn.click();
+    gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
+    	btn.setAttribute('disabled','disabled');
+    })
+    
 }
 
 function images_history_get_current_img(tabname, image_path, files){
-    s =  gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index")
-    return [s, image_path, files]
+    return [
+        gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"), 
+        image_path, 
+        files
+    ];
 }
 
 function images_history_delete(tabname, img_path, img_file_name, page_index, filenames, image_index){
-    image_index = parseInt(image_index)
-    tab = gradioApp().getElementById(tabname + '_images_history')
-    set_btn = tab.querySelector(".images_history_set_index")
-    buttons = []
+    image_index = parseInt(image_index);
+    var tab = gradioApp().getElementById(tabname + '_images_history');
+    var set_btn = tab.querySelector(".images_history_set_index");
+    var buttons = [];
     tab.querySelectorAll(".gallery-item").forEach(function(e){
         if (e.style.display != 'none'){
-            buttons.push(e)
+            buttons.push(e);
         }
-    })
+    });
     
-    img_num = buttons.length / 2
-    if (img_num == 1){
-            setTimeout(function(tabname){
-                gradioApp().getElementById(tabname + '_images_history_renew_page').click()
-            }, 30, tabname) 
+    var img_num = buttons.length / 2;
+    if (img_num === 1){
+        setTimeout(function(tabname){
+            gradioApp().getElementById(tabname + '_images_history_renew_page').click();
+        }, 30, tabname); 
     } else {
-        buttons[image_index].style.display = 'none'
-        buttons[image_index + img_num].style.display = 'none'
-        if (image_index  >= img_num - 1){
-            console.log(buttons.length, img_num)
-            btn = buttons[img_num - 2]
+        buttons[image_index].style.display = 'none';
+        buttons[image_index + img_num].style.display = 'none';
+        var bnt;
+        if (image_index >= img_num - 1){
+            btn = buttons[img_num - 2];
         } else {
-            btn = buttons[image_index + 1]           
+            btn = buttons[image_index + 1] ;          
         }    
-        setTimeout(function(btn){btn.click()}, 30, btn)
+        setTimeout(function(btn){btn.click()}, 30, btn);
     }      
-    return [tabname, img_path, img_file_name, page_index, filenames, image_index]
+    return [tabname, img_path, img_file_name, page_index, filenames, image_index];
 }
 
 function images_history_turnpage(img_path, page_index, image_index, tabname){
-    buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item")
+    var buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item");
     buttons.forEach(function(elem) {
-        elem.style.display = 'block'
+        elem.style.display = 'block';
     })
-    return [img_path, page_index, image_index, tabname]
+    return [img_path, page_index, image_index, tabname];
 }
+
+function images_history_enable_del_buttons(){
+	gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
+    	btn.removeAttribute('disabled');
+    })
+}
+
+function images_history_init(){ 
+    if (gradioApp().getElementById('txt2img_images_history_renew_page') == null) {
+        setTimeout(images_history_init, 500);
+    } else {        
+        for (var i in images_history_tab_list ){
+            tab = images_history_tab_list[i];
+            gradioApp().getElementById(tab + '_images_history').classList.add("images_history_cantainor");
+            gradioApp().getElementById(tab + '_images_history_set_index').classList.add("images_history_set_index");
+            gradioApp().getElementById(tab + '_images_history_del_button').classList.add("images_history_del_button");
+            gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery"); 
+           
+           	      
+        }
+        var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div");
+        tabs_box.setAttribute("id", "images_history_tab");
+        tabs_box.classList.add(images_history_tab_list[0]);
+        gradioApp().getElementById("txt2img_images_history_renew_page").click();
+    }
+}
+
+var images_history_tab_list = ["txt2img", "img2img", "extras"];
+var images_history_start_flag = false;
+
+onUiUpdate(function(){
+	var tab = gradioApp().getElementById("images_history_tab");
+	if (tab) {
+		if (!images_history_start_flag){
+			images_history_init();
+			images_history_start_flag = true;
+		}
+	    var tab_btns = gradioApp().getElementById("images_history_tab").querySelectorAll("button");
+	    for (var i in images_history_tab_list ){
+	        var buttons = gradioApp().querySelectorAll('#' + images_history_tab_list[i] + '_images_history .gallery-item');
+	        buttons.forEach(function(bnt){    
+	            bnt.addEventListener('click', images_history_click_image, true);
+	         });
+	        var tabname = images_history_tab_list[i]
+	        tab_btns[i].setAttribute("tabname", tabname);
+	        tab_btns[i].addEventListener('click', images_history_click_tab, true);
+        // var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
+        // if (cls_btn){
+        // 	cls_btn.addEventListener('click', images_history_close_full_view, false);
+        // }
+        // console.log(cls_btn, cls_btn.parentElement.parentElement)
+        // if (cls_btn) {
+        // 	cls_btn = images_history_get_parent_by_tagname(cls_btn, "BUTTON");
+        // 	cls_btn.addEventListener('click', images_history_close_full_view, true);
+        // }
+    }
+
+    }
+});    
+
diff --git a/modules/images_history.py b/modules/images_history.py
index 23f55b30..77f692fe 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,15 +1,29 @@
 import os
-def get_recent_images(dir_name, page_index, step, image_index):
-    #print(image_index)
+import shutil
+def get_recent_images(dir_name, page_index, step, image_index, tabname):
+    print(f"renew page {page_index}")
     page_index = int(page_index)
     f_list = os.listdir(dir_name)
     file_list = []
     for file in f_list:
         if file[-4:] == ".txt":
             continue
-        file_list.append(file)    
+        #subdirectories        
+        if file[-10:].rfind(".") < 0:
+            sub_dir = os.path.join(dir_name, file)
+            if os.path.isfile(sub_dir):
+                continue
+            sub_file_list = os.listdir(sub_dir)
+            for sub_file in sub_file_list:
+                if sub_file[-4:] == ".txt":
+                    continue
+                if os.path.isfile(os.path.join(sub_dir, sub_file) ): 
+                    file_list.append(os.path.join(file, sub_file)) 
+            continue
+        file_list.append(file)        
+
     file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
-    num = 48
+    num = 48 if tabname != "extras" else 12
     max_page_index = len(file_list) // num + 1    
     page_index = max_page_index if page_index == -1 else page_index + step
     page_index = 1 if page_index < 1 else page_index    
@@ -26,26 +40,28 @@ def get_recent_images(dir_name, page_index, step, image_index):
         hide_image = os.path.join(dir_name, current_file)
     return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image
 def first_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, 1, 0, image_index)
+    return get_recent_images(dir_name, 1, 0, image_index, tabname)
 def end_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, -1, 0, image_index)
+    return get_recent_images(dir_name, -1, 0, image_index, tabname)
 def prev_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, -1, image_index)
+    return get_recent_images(dir_name, page_index, -1, image_index, tabname)
 def next_page_click(dir_name, page_index, image_index, tabname):    
-    return get_recent_images(dir_name, page_index, 1, image_index)
+    return get_recent_images(dir_name, page_index, 1, image_index, tabname)
 def page_index_change(dir_name, page_index, image_index, tabname):    
-    return get_recent_images(dir_name, page_index, 0, image_index)
+    return get_recent_images(dir_name, page_index, 0, image_index, tabname)
 
 def show_image_info(num, image_path, filenames):
-    #print("set img",num)
+    print(f"select image {num}")
     file = filenames[int(num)]
     return file, num, os.path.join(image_path, file)
 def delete_image(tabname, dir_name, name, page_index, filenames, image_index):
-    #print("filename", name)
     path = os.path.join(dir_name, name)           
-    if os.path.exists(path):
+    if os.path.exists(path):        
         print(f"Delete file {path}")
-        os.remove(path)    
+        os.remove(path)
+        txt_file = os.path.splitext(path)[0]  + ".txt"
+        if os.path.exists(txt_file):
+        	os.remove(txt_file)
         new_file_list = []
         for f in filenames:
             if f == name:
@@ -64,25 +80,26 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         elif tabname == "extras":
             dir_name = opts.outdir_extras_samples
         with gr.Row():    
-            renew_page = gr.Button('Renew', elem_id=tabname + "_images_history_renew_page") 
-            first_page = gr.Button('First', elem_id=tabname + "_images_history_first_page")
-            prev_page = gr.Button('Prev') 
+            renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page") 
+            first_page = gr.Button('First Page')
+            prev_page = gr.Button('Prev Page') 
             page_index = gr.Number(value=1, label="Page Index")
-            next_page = gr.Button('Next', elem_id=tabname + "_images_history_next_page") 
-            end_page = gr.Button('End')    
+            next_page = gr.Button('Next Page') 
+            end_page = gr.Button('End Page')    
         with gr.Row(elem_id=tabname + "_images_history"):            
             with gr.Row():     
-                with gr.Column():
-                    history_gallery = gr.Gallery(show_label=False).style(grid=6)
+                with gr.Column(scale=2):                	
+                    history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
+                    delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")
                 with gr.Column():     
                     with gr.Row():          
-                        delete = gr.Button('Delete')
+                        #pnginfo = gr.Button('PNG info')
                         pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
                         pnginfo_send_to_img2img = gr.Button('Send to img2img')
                     with gr.Row():
                         with gr.Column():
-                            img_file_info = gr.Textbox(label="Generate Info")
-                            img_file_name = gr.Textbox(label="File Name")    
+                            img_file_info = gr.Textbox(label="Generate Info", interactive=False)
+                            img_file_name = gr.Textbox(label="File Name", interactive=False)    
                     with gr.Row():    
                         # hiden items
                         img_path = gr.Textbox(dir_name, visible=False)                
@@ -90,7 +107,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                         image_index = gr.Textbox(value=-1, visible=False)                        
                         set_index = gr.Button('set_index',  elem_id=tabname + "_images_history_set_index", visible=False)
                         filenames = gr.State()
-                        hide_image = gr.Image(visible=False, type="pil")
+                        hide_image = gr.Image(type="pil", visible=False)
                         info1 = gr.Textbox(visible=False)
                         info2 = gr.Textbox(visible=False)
 
@@ -111,6 +128,8 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
         delete.click(delete_image,_js="images_history_delete", inputs=[tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames]) 
         hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
+        hide_image.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
+        #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
         switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
         switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
     
-- 
cgit v1.2.1


From a1a94b8b5f342f467aecc53b21b80ed0227ee76a Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 13 Oct 2022 00:19:34 +0800
Subject: images history improvement

---
 javascript/images_history.js | 125 ++++++++++++++++++++++---------------------
 modules/images_history.py    |   7 +--
 modules/ui.py                |  40 +++++++-------
 3 files changed, 88 insertions(+), 84 deletions(-)

diff --git a/javascript/images_history.js b/javascript/images_history.js
index c9a63166..620f242c 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -18,20 +18,26 @@ var images_history_click_image = function(){
 }
 
 var images_history_click_tab = function(){
-	var tabs_box = gradioApp().getElementById("images_history_tab");
-	if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
-		gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_renew_page").click();
-		tabs_box.classList.add(this.getAttribute("tabname"))
-	}	            
+    var tabs_box = gradioApp().getElementById("images_history_tab");
+    if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
+        gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_renew_page").click();
+        tabs_box.classList.add(this.getAttribute("tabname"))
+    }                
 }
 
 var images_history_close_full_view = function(){
-	var box = images_history_get_parent_by_class(this, "images_history_cantainor");
-	box.querySelector(".images_history_del_button").setAttribute("disabled", "disabled");
+    var box = images_history_get_parent_by_class(this, "images_history_cantainor");
+    box.querySelector(".images_history_del_button").setAttribute("disabled", "disabled");
+}
+
+function images_history_disabled_del(){
+    gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
+        btn.setAttribute('disabled','disabled');
+    }); 
 }
 
 function images_history_get_parent_by_class(item, class_name){
-	var parent = item.parentElement;
+    var parent = item.parentElement;
     while(!parent.classList.contains(class_name)){
         parent = parent.parentElement;
     }
@@ -39,14 +45,15 @@ function images_history_get_parent_by_class(item, class_name){
 }
 
 function images_history_get_parent_by_tagname(item, tagname){
-	var parent = item.parentElement;
-	tagname = tagname.toUpperCase()
+    var parent = item.parentElement;
+    tagname = tagname.toUpperCase()
     while(parent.tagName != tagname){
-    	console.log(parent.tagName, tagname)
+        console.log(parent.tagName, tagname)
         parent = parent.parentElement;
     }  
     return parent;
 }
+
 function images_history_hide_buttons(hidden_list, gallery){
     var buttons = gallery.querySelectorAll(".gallery-item");
     var num = 0;
@@ -54,7 +61,7 @@ function images_history_hide_buttons(hidden_list, gallery){
         if (e.style.display == "none"){
             num += 1;
         }
-    })
+    });
     if (num == hidden_list.length){
         setTimeout(images_history_hide_buttons, 10, hidden_list, gallery);
     } 
@@ -74,14 +81,15 @@ function images_history_set_image_info(button){
         if(e.style.display != "none"){
             i += 1;
         }        
-    })
+    });
     var gallery = images_history_get_parent_by_class(button, "images_history_cantainor");
     var set_btn = gallery.querySelector(".images_history_set_index");
-    set_btn.setAttribute("img_index", index);    
+    var curr_idx = set_btn.getAttribute("img_index", index);  
+    if (curr_idx != index) {
+        set_btn.setAttribute("img_index", index);        
+        images_history_disabled_del();
+    }
     set_btn.click();
-    gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
-    	btn.setAttribute('disabled','disabled');
-    })
     
 }
 
@@ -102,24 +110,24 @@ function images_history_delete(tabname, img_path, img_file_name, page_index, fil
         if (e.style.display != 'none'){
             buttons.push(e);
         }
-    });
-    
+    });    
     var img_num = buttons.length / 2;
     if (img_num === 1){
         setTimeout(function(tabname){
             gradioApp().getElementById(tabname + '_images_history_renew_page').click();
         }, 30, tabname); 
-    } else {
+    } else {        
         buttons[image_index].style.display = 'none';
         buttons[image_index + img_num].style.display = 'none';
         var bnt;
         if (image_index >= img_num - 1){
             btn = buttons[img_num - 2];
-        } else {
+        } else {            
             btn = buttons[image_index + 1] ;          
-        }    
+        } 
         setTimeout(function(btn){btn.click()}, 30, btn);
-    }      
+    }
+    images_history_disabled_del();  
     return [tabname, img_path, img_file_name, page_index, filenames, image_index];
 }
 
@@ -132,61 +140,58 @@ function images_history_turnpage(img_path, page_index, image_index, tabname){
 }
 
 function images_history_enable_del_buttons(){
-	gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
-    	btn.removeAttribute('disabled');
+    gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
+        btn.removeAttribute('disabled');
     })
 }
 
 function images_history_init(){ 
-    if (gradioApp().getElementById('txt2img_images_history_renew_page') == null) {
-        setTimeout(images_history_init, 500);
-    } else {        
+    var load_txt2img_button = gradioApp().getElementById('txt2img_images_history_renew_page')
+    if (load_txt2img_button){        
         for (var i in images_history_tab_list ){
             tab = images_history_tab_list[i];
             gradioApp().getElementById(tab + '_images_history').classList.add("images_history_cantainor");
             gradioApp().getElementById(tab + '_images_history_set_index').classList.add("images_history_set_index");
             gradioApp().getElementById(tab + '_images_history_del_button').classList.add("images_history_del_button");
-            gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery"); 
-           
-           	      
+            gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery");            
+                     
         }
         var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div");
-        tabs_box.setAttribute("id", "images_history_tab");
-        tabs_box.classList.add(images_history_tab_list[0]);
-        gradioApp().getElementById("txt2img_images_history_renew_page").click();
-    }
+        tabs_box.setAttribute("id", "images_history_tab");        
+        var tab_btns = tabs_box.querySelectorAll("button");        
+        for (var i in images_history_tab_list){               
+            var tabname = images_history_tab_list[i]
+            tab_btns[i].setAttribute("tabname", tabname);
+            tab_btns[i].addEventListener('click', images_history_click_tab);
+        }    
+        tabs_box.classList.add(images_history_tab_list[0]);   
+           load_txt2img_button.click();
+    } else {
+        setTimeout(images_history_init, 500);
+    } 
 }
 
 var images_history_tab_list = ["txt2img", "img2img", "extras"];
-var images_history_start_flag = false;
-
-onUiUpdate(function(){
-	var tab = gradioApp().getElementById("images_history_tab");
-	if (tab) {
-		if (!images_history_start_flag){
-			images_history_init();
-			images_history_start_flag = true;
-		}
-	    var tab_btns = gradioApp().getElementById("images_history_tab").querySelectorAll("button");
-	    for (var i in images_history_tab_list ){
-	        var buttons = gradioApp().querySelectorAll('#' + images_history_tab_list[i] + '_images_history .gallery-item');
-	        buttons.forEach(function(bnt){    
-	            bnt.addEventListener('click', images_history_click_image, true);
-	         });
-	        var tabname = images_history_tab_list[i]
-	        tab_btns[i].setAttribute("tabname", tabname);
-	        tab_btns[i].addEventListener('click', images_history_click_tab, true);
+setTimeout(images_history_init, 500)
+document.addEventListener("DOMContentLoaded", function() {
+    var mutationObserver = new MutationObserver(function(m){
+        for (var i in images_history_tab_list ){
+            var buttons = gradioApp().querySelectorAll('#' + images_history_tab_list[i] + '_images_history .gallery-item');
+            buttons.forEach(function(bnt){    
+                bnt.addEventListener('click', images_history_click_image, true);
+            });
         // var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
         // if (cls_btn){
-        // 	cls_btn.addEventListener('click', images_history_close_full_view, false);
+        //     cls_btn.addEventListener('click', images_history_close_full_view, false);
         // }
         // console.log(cls_btn, cls_btn.parentElement.parentElement)
         // if (cls_btn) {
-        // 	cls_btn = images_history_get_parent_by_tagname(cls_btn, "BUTTON");
-        // 	cls_btn.addEventListener('click', images_history_close_full_view, true);
-        // }
-    }
+        //     cls_btn = images_history_get_parent_by_tagname(cls_btn, "BUTTON");
+        //     cls_btn.addEventListener('click', images_history_close_full_view, true);
+        }     
+    });
+    mutationObserver.observe( gradioApp(), { childList:true, subtree:true });
+
+});
 
-    }
-});    
 
diff --git a/modules/images_history.py b/modules/images_history.py
index 2bc4b7ee..1bca0ad9 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -61,7 +61,7 @@ def delete_image(tabname, dir_name, name, page_index, filenames, image_index):
         os.remove(path)
         txt_file = os.path.splitext(path)[0]  + ".txt"
         if os.path.exists(txt_file):
-        	os.remove(txt_file)
+            os.remove(txt_file)
         new_file_list = []
         for f in filenames:
             if f == name:
@@ -88,7 +88,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
             end_page = gr.Button('End Page')    
         with gr.Row(elem_id=tabname + "_images_history"):            
             with gr.Row():     
-                with gr.Column(scale=2):                	
+                with gr.Column(scale=2):                    
                     history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
                     delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")
                 with gr.Column():     
@@ -126,9 +126,10 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
 
         #other funcitons
         set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
+        img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
         delete.click(delete_image,_js="images_history_delete", inputs=[tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames]) 
         hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
-        hide_image.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
+       
         #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
         switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
         switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
diff --git a/modules/ui.py b/modules/ui.py
index 94297ba6..8cd12b51 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -39,7 +39,7 @@ import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
-import modules.hypernetwork.ui
+import modules.hypernetworks.ui
 import modules.images_history as img_his
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
@@ -554,6 +554,7 @@ def create_ui(wrap_gradio_gpu_call):
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
             with gr.Column(variant='panel'):
+
                 with gr.Group():
                     txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
                     txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
@@ -573,9 +574,9 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.Row():
                         download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
-                with gr.Group():
-                    html_info = gr.HTML()
-                    generation_info = gr.Textbox(visible=False)
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -669,7 +670,6 @@ def create_ui(wrap_gradio_gpu_call):
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-        
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
@@ -762,10 +762,10 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.Row():
                         download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
-                with gr.Group():
-                    html_info = gr.HTML()
-                    generation_info = gr.Textbox(visible=False)
-                
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
+
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
@@ -1016,6 +1016,13 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[image],
             outputs=[html, generation_info, html2],
         )
+    #images history
+    images_history_switch_dict = {
+        "fn":modules.generation_parameters_copypaste.connect_paste,
+        "t2i":txt2img_paste_fields,
+        "i2i":img2img_paste_fields
+    }
+    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) 
 
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
@@ -1285,16 +1292,7 @@ Requested path was: {f}
 
         opts.save(shared.config_filename)
 
-        return f'{changed} settings changed.', opts.dumpjson()    
-
-    #images history
-    images_history_switch_dict = {
-        "fn":modules.generation_parameters_copypaste.connect_paste,
-        "t2i":txt2img_paste_fields,
-        "i2i":img2img_paste_fields
-    }
-    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)    
-    
+        return f'{changed} settings changed.', opts.dumpjson()
 
     def run_settings_single(value, key):
         if not opts.same_type(value, opts.data_labels[key].default):
@@ -1393,11 +1391,10 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (images_history, "History", "images_history"),
+        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
         (settings_interface, "Settings", "settings"),
-
     ]
 
     with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
@@ -1616,3 +1613,4 @@ if 'gradio_routes_templates_response' not in globals():
 
     gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
     gradio.routes.templates.TemplateResponse = template_response
+
-- 
cgit v1.2.1


From a2aa2a68bc7868320b502a78765be597e507ce45 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 13 Oct 2022 00:21:16 +0800
Subject: images history improvement

---
 modules/images_history.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/images_history.py b/modules/images_history.py
index 1bca0ad9..6408973c 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,7 +1,7 @@
 import os
 import shutil
 def get_recent_images(dir_name, page_index, step, image_index, tabname):
-    print(f"renew page {page_index}")
+    #print(f"renew page {page_index}")
     page_index = int(page_index)
     f_list = os.listdir(dir_name)
     file_list = []
@@ -51,7 +51,7 @@ def page_index_change(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, page_index, 0, image_index, tabname)
 
 def show_image_info(num, image_path, filenames):
-    print(f"select image {num}")
+    #print(f"select image {num}")
     file = filenames[int(num)]
     return file, num, os.path.join(image_path, file)
 def delete_image(tabname, dir_name, name, page_index, filenames, image_index):
-- 
cgit v1.2.1


From 717ba4c71c86cb2d49d731caeed82ce8bec0c057 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 13 Oct 2022 00:27:45 +0800
Subject: images history improvement

---
 javascript/images_history.js | 2 +-
 style.css                    | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/javascript/images_history.js b/javascript/images_history.js
index 620f242c..c5c2886e 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -172,7 +172,7 @@ function images_history_init(){
 }
 
 var images_history_tab_list = ["txt2img", "img2img", "extras"];
-setTimeout(images_history_init, 500)
+setTimeout(images_history_init, 500);
 document.addEventListener("DOMContentLoaded", function() {
     var mutationObserver = new MutationObserver(function(m){
         for (var i in images_history_tab_list ){
diff --git a/style.css b/style.css
index 7704e7bd..c75dce4c 100644
--- a/style.css
+++ b/style.css
@@ -20,7 +20,7 @@
     padding-right: 0.25em;
     margin: 0.1em 0;
     opacity: 0%;
-    cursor: default;	
+    cursor: default;    
 }
 
 .output-html p {margin: 0 0.5em;}
@@ -442,7 +442,7 @@ input[type="range"]{
 }
 
 .red {
-	color: red;
+    color: red;
 }
 
 .gallery-item {
-- 
cgit v1.2.1


From c3c8eef9fd5a0c8b26319e32ca4a19b56204e6df Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 20:49:47 +0300
Subject: train: change filename processing to be more simple and configurable
 train: make it possible to make text files with prompts train: rework
 scheduler so that there's less repeating code in textual inversion and
 hypernets train: move epochs setting to options

---
 javascript/hints.js                            |  3 ++
 modules/hypernetworks/hypernetwork.py          | 40 +++++++++-------------
 modules/shared.py                              |  3 ++
 modules/textual_inversion/dataset.py           | 47 +++++++++++++++++++-------
 modules/textual_inversion/learn_schedule.py    | 37 +++++++++++++++++++-
 modules/textual_inversion/textual_inversion.py | 35 +++++++------------
 modules/ui.py                                  |  2 --
 7 files changed, 105 insertions(+), 62 deletions(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index b81c181b..d51ee14c 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -81,6 +81,9 @@ titles = {
 
     "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
     "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
+
+    "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
+    "Filename join string": "This string will be used to hoin split words into a single line if the option above is enabled.",
 }
 
 
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8314450a..b6c06d49 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -14,7 +14,7 @@ import torch
 from torch import einsum
 from einops import rearrange, repeat
 import modules.textual_inversion.dataset
-from modules.textual_inversion.learn_schedule import LearnSchedule
+from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 
 class HypernetworkModule(torch.nn.Module):
@@ -223,31 +223,23 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     if ititial_step > steps:
         return hypernetwork, filename
 
-    schedules = iter(LearnSchedule(learn_rate, steps, ititial_step))
-    (learn_rate, end_step) = next(schedules)
-    print(f'Training at rate of {learn_rate} until step {end_step}')
-
-    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
+    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, (x, text, cond) in pbar:
+    for i, entry in pbar:
         hypernetwork.step = i + ititial_step
 
-        if hypernetwork.step > end_step:
-            try:
-                (learn_rate, end_step) = next(schedules)
-            except Exception:
-                break
-            tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
-            for pg in optimizer.param_groups:
-                pg['lr'] = learn_rate
+        scheduler.apply(optimizer, hypernetwork.step)
+        if scheduler.finished:
+            break
 
         if shared.state.interrupted:
             break
 
         with torch.autocast("cuda"):
-            cond = cond.to(devices.device)
-            x = x.to(devices.device)
+            cond = entry.cond.to(devices.device)
+            x = entry.latent.to(devices.device)
             loss = shared.sd_model(x.unsqueeze(0), cond)[0]
             del x
             del cond
@@ -267,7 +259,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
 
-            preview_text = text if preview_image_prompt == "" else preview_image_prompt
+            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
 
             optimizer.zero_grad()
             shared.sd_model.cond_stage_model.to(devices.device)
@@ -282,16 +274,16 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             )
 
             processed = processing.process_images(p)
-            image = processed.images[0]
+            image = processed.images[0] if len(processed.images)>0 else None
 
             if unload:
                 shared.sd_model.cond_stage_model.to(devices.cpu)
                 shared.sd_model.first_stage_model.to(devices.cpu)
 
-            shared.state.current_image = image
-            image.save(last_saved_image)
-
-            last_saved_image += f", prompt: {preview_text}"
+            if image is not None:
+                shared.state.current_image = image
+                image.save(last_saved_image)
+                last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = hypernetwork.step
 
@@ -299,7 +291,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {hypernetwork.step}<br/>
-Last prompt: {html.escape(text)}<br/>
+Last prompt: {html.escape(entry.cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
diff --git a/modules/shared.py b/modules/shared.py
index 42e99741..e64e69fc 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -231,6 +231,9 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),
+    "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
+    "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
+    "training_image_repeats_per_epoch": OptionInfo(100, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index f61f40d3..67e90afe 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -11,11 +11,21 @@ import tqdm
 from modules import devices, shared
 import re
 
-re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
+re_numbers_at_start = re.compile(r"^[-\d]+\s*")
+
+
+class DatasetEntry:
+    def __init__(self, filename=None, latent=None, filename_text=None):
+        self.filename = filename
+        self.latent = latent
+        self.filename_text = filename_text
+        self.cond = None
+        self.cond_text = None
 
 
 class PersonalizedBase(Dataset):
     def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False):
+        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else None
 
         self.placeholder_token = placeholder_token
 
@@ -42,9 +52,18 @@ class PersonalizedBase(Dataset):
             except Exception:
                 continue
 
+            text_filename = os.path.splitext(path)[0] + ".txt"
             filename = os.path.basename(path)
-            filename_tokens = os.path.splitext(filename)[0]
-            filename_tokens = re_tag.findall(filename_tokens)
+
+            if os.path.exists(text_filename):
+                with open(text_filename, "r", encoding="utf8") as file:
+                    filename_text = file.read()
+            else:
+                filename_text = os.path.splitext(filename)[0]
+                filename_text = re.sub(re_numbers_at_start, '', filename_text)
+                if re_word:
+                    tokens = re_word.findall(filename_text)
+                    filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens)
 
             npimage = np.array(image).astype(np.uint8)
             npimage = (npimage / 127.5 - 1.0).astype(np.float32)
@@ -55,13 +74,13 @@ class PersonalizedBase(Dataset):
             init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
             init_latent = init_latent.to(devices.cpu)
 
+            entry = DatasetEntry(filename=path, filename_text=filename_text, latent=init_latent)
+
             if include_cond:
-                text = self.create_text(filename_tokens)
-                cond = cond_model([text]).to(devices.cpu)
-            else:
-                cond = None
+                entry.cond_text = self.create_text(filename_text)
+                entry.cond = cond_model([entry.cond_text]).to(devices.cpu)
 
-            self.dataset.append((init_latent, filename_tokens, cond))
+            self.dataset.append(entry)
 
         self.length = len(self.dataset) * repeats
 
@@ -72,10 +91,10 @@ class PersonalizedBase(Dataset):
     def shuffle(self):
         self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
 
-    def create_text(self, filename_tokens):
+    def create_text(self, filename_text):
         text = random.choice(self.lines)
         text = text.replace("[name]", self.placeholder_token)
-        text = text.replace("[filewords]", ' '.join(filename_tokens))
+        text = text.replace("[filewords]", filename_text)
         return text
 
     def __len__(self):
@@ -86,7 +105,9 @@ class PersonalizedBase(Dataset):
             self.shuffle()
 
         index = self.indexes[i % len(self.indexes)]
-        x, filename_tokens, cond = self.dataset[index]
+        entry = self.dataset[index]
+
+        if entry.cond is None:
+            entry.cond_text = self.create_text(entry.filename_text)
 
-        text = self.create_text(filename_tokens)
-        return x, text, cond
+        return entry
diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index db720271..2062726a 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -1,6 +1,12 @@
+import tqdm
 
-class LearnSchedule:
+
+class LearnScheduleIterator:
     def __init__(self, learn_rate, max_steps, cur_step=0):
+        """
+        specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, 1e-5:10000 until 10000
+        """
+
         pairs = learn_rate.split(',')
         self.rates = []
         self.it = 0
@@ -32,3 +38,32 @@ class LearnSchedule:
             return self.rates[self.it - 1]
         else:
             raise StopIteration
+
+
+class LearnRateScheduler:
+    def __init__(self, learn_rate, max_steps, cur_step=0, verbose=True):
+        self.schedules = LearnScheduleIterator(learn_rate, max_steps, cur_step)
+        (self.learn_rate,  self.end_step) = next(self.schedules)
+        self.verbose = verbose
+
+        if self.verbose:
+            print(f'Training at rate of {self.learn_rate} until step {self.end_step}')
+
+        self.finished = False
+
+    def apply(self, optimizer, step_number):
+        if step_number <= self.end_step:
+            return
+
+        try:
+            (self.learn_rate, self.end_step) = next(self.schedules)
+        except Exception:
+            self.finished = True
+            return
+
+        if self.verbose:
+            tqdm.tqdm.write(f'Training at rate of {self.learn_rate} until step {self.end_step}')
+
+        for pg in optimizer.param_groups:
+            pg['lr'] = self.learn_rate
+
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index c5153e4a..fa0e33a2 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -11,7 +11,7 @@ from PIL import Image, PngImagePlugin
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
-from modules.textual_inversion.learn_schedule import LearnSchedule
+from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64,
                                                        insert_image_data_embed, extract_image_data_embed,
@@ -172,8 +172,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -205,7 +204,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
 
     hijack = sd_hijack.model_hijack
 
@@ -221,32 +220,24 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     if ititial_step > steps:
         return embedding, filename
 
-    schedules = iter(LearnSchedule(learn_rate, steps, ititial_step))
-    (learn_rate, end_step) = next(schedules)
-    print(f'Training at rate of {learn_rate} until step {end_step}')
-
-    optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
+    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-    for i, (x, text, _) in pbar:
+    for i, entry in pbar:
         embedding.step = i + ititial_step
 
-        if embedding.step > end_step:
-            try:
-                (learn_rate, end_step) = next(schedules)
-            except:
-                break
-            tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
-            for pg in optimizer.param_groups:
-                pg['lr'] = learn_rate
+        scheduler.apply(optimizer, embedding.step)
+        if scheduler.finished:
+            break
 
         if shared.state.interrupted:
             break
 
         with torch.autocast("cuda"):
-            c = cond_model([text])
+            c = cond_model([entry.cond_text])
 
-            x = x.to(devices.device)
+            x = entry.latent.to(devices.device)
             loss = shared.sd_model(x.unsqueeze(0), c)[0]
             del x
 
@@ -268,7 +259,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
 
-            preview_text = text if preview_image_prompt == "" else preview_image_prompt
+            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
 
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
@@ -314,7 +305,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {embedding.step}<br/>
-Last prompt: {html.escape(text)}<br/>
+Last prompt: {html.escape(entry.cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
diff --git a/modules/ui.py b/modules/ui.py
index 2b332267..c42535c8 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1098,7 +1098,6 @@ def create_ui(wrap_gradio_gpu_call):
                     training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
-                    num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
@@ -1176,7 +1175,6 @@ def create_ui(wrap_gradio_gpu_call):
                 training_width,
                 training_height,
                 steps,
-                num_repeats,
                 create_image_every,
                 save_embedding_every,
                 template_file,
-- 
cgit v1.2.1


From 698d303b04e293635bfb49c525409f3bcf671dce Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 21:55:43 +0300
Subject: deepbooru: added option to use spaces or underscores deepbooru: added
 option to quote (\) in tags deepbooru/BLIP: write caption to file instead of
 image filename deepbooru/BLIP: now possible to use both for captions
 deepbooru: process is stopped even if an exception occurs

---
 modules/deepbooru.py                    | 65 ++++++++++++++++++-----
 modules/shared.py                       |  2 +
 modules/textual_inversion/preprocess.py | 92 ++++++++++++++-------------------
 modules/ui.py                           |  7 +--
 4 files changed, 95 insertions(+), 71 deletions(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 29529949..419e6a9c 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -2,33 +2,44 @@ import os.path
 from concurrent.futures import ProcessPoolExecutor
 import multiprocessing
 import time
+import re
+
+re_special = re.compile(r'([\\()])')
 
 def get_deepbooru_tags(pil_image):
     """
     This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
     """
     from modules import shared  # prevents circular reference
-    create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, shared.opts.deepbooru_sort_alpha)
-    shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process_queue.put(pil_image)
-    while shared.deepbooru_process_return["value"] == -1:
-        time.sleep(0.2)
-    tags = shared.deepbooru_process_return["value"]
-    release_process()
-    return tags
 
+    try:
+        create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts())
+        return get_tags_from_process(pil_image)
+    finally:
+        release_process()
+
+
+def create_deepbooru_opts():
+    from modules import shared
 
-def deepbooru_process(queue, deepbooru_process_return, threshold, alpha_sort):
+    return {
+        "use_spaces": shared.opts.deepbooru_use_spaces,
+        "use_escape": shared.opts.deepbooru_escape,
+        "alpha_sort": shared.opts.deepbooru_sort_alpha,
+    }
+
+
+def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts):
     model, tags = get_deepbooru_tags_model()
     while True: # while process is running, keep monitoring queue for new image
         pil_image = queue.get()
         if pil_image == "QUIT":
             break
         else:
-            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort)
+            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts)
 
 
-def create_deepbooru_process(threshold, alpha_sort):
+def create_deepbooru_process(threshold, deepbooru_opts):
     """
     Creates deepbooru process.  A queue is created to send images into the process.  This enables multiple images
     to be processed in a row without reloading the model or creating a new process.  To return the data, a shared
@@ -41,10 +52,23 @@ def create_deepbooru_process(threshold, alpha_sort):
     shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
     shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
     shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, alpha_sort))
+    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
     shared.deepbooru_process.start()
 
 
+def get_tags_from_process(image):
+    from modules import shared
+
+    shared.deepbooru_process_return["value"] = -1
+    shared.deepbooru_process_queue.put(image)
+    while shared.deepbooru_process_return["value"] == -1:
+        time.sleep(0.2)
+    caption = shared.deepbooru_process_return["value"]
+    shared.deepbooru_process_return["value"] = -1
+
+    return caption
+
+
 def release_process():
     """
     Stops the deepbooru process to return used memory
@@ -81,10 +105,15 @@ def get_deepbooru_tags_model():
     return model, tags
 
 
-def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort):
+def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts):
     import deepdanbooru as dd
     import tensorflow as tf
     import numpy as np
+
+    alpha_sort = deepbooru_opts['alpha_sort']
+    use_spaces = deepbooru_opts['use_spaces']
+    use_escape = deepbooru_opts['use_escape']
+
     width = model.input_shape[2]
     height = model.input_shape[1]
     image = np.array(pil_image)
@@ -129,4 +158,12 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, alpha_sort)
 
     print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
+    tags_text = ', '.join(result_tags_out)
+
+    if use_spaces:
+        tags_text = tags_text.replace('_', ' ')
+
+    if use_escape:
+        tags_text = re.sub(re_special, r'\\\1', tags_text)
+
+    return tags_text.replace(':', ' ')
diff --git a/modules/shared.py b/modules/shared.py
index e64e69fc..78b73aae 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -260,6 +260,8 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
     "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
     "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
+    "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
+    "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
 }))
 
 options_templates.update(options_section(('ui', "User interface"), {
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 113cecf1..3047bede 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -10,7 +10,28 @@ from modules.shared import opts, cmd_opts
 if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru
 
+
 def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+    try:
+        if process_caption:
+            shared.interrogator.load()
+
+        if process_caption_deepbooru:
+            deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, deepbooru.create_deepbooru_opts())
+
+        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
+
+    finally:
+
+        if process_caption:
+            shared.interrogator.send_blip_to_ram()
+
+        if process_caption_deepbooru:
+            deepbooru.release_process()
+
+
+
+def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
     width = process_width
     height = process_height
     src = os.path.abspath(process_src)
@@ -25,30 +46,28 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
 
-    if process_caption:
-        shared.interrogator.load()
-
-    if process_caption_deepbooru:
-        deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, opts.deepbooru_sort_alpha)
-
     def save_pic_with_caption(image, index):
+        caption = ""
+
         if process_caption:
-            caption = "-" + shared.interrogator.generate_caption(image)
-            caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
-        elif process_caption_deepbooru:
-            shared.deepbooru_process_return["value"] = -1
-            shared.deepbooru_process_queue.put(image)
-            while shared.deepbooru_process_return["value"] == -1:
-                time.sleep(0.2)
-            caption = "-" + shared.deepbooru_process_return["value"]
-            caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
-            shared.deepbooru_process_return["value"] = -1
-        else:
-            caption = filename
-            caption = os.path.splitext(caption)[0]
-            caption = os.path.basename(caption)
+            caption += shared.interrogator.generate_caption(image)
+
+        if process_caption_deepbooru:
+            if len(caption) > 0:
+                caption += ", "
+            caption += deepbooru.get_tags_from_process(image)
+
+        filename_part = filename
+        filename_part = os.path.splitext(filename_part)[0]
+        filename_part = os.path.basename(filename_part)
+
+        basename = f"{index:05}-{subindex[0]}-{filename_part}"
+        image.save(os.path.join(dst, f"{basename}.png"))
+
+        if len(caption) > 0:
+            with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:
+                file.write(caption)
 
-        image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
         subindex[0] += 1
 
     def save_pic(image, index):
@@ -93,34 +112,3 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
             save_pic(img, index)
 
         shared.state.nextjob()
-
-    if process_caption:
-        shared.interrogator.send_blip_to_ram()
-
-    if process_caption_deepbooru:
-        deepbooru.release_process()
-
-
-def sanitize_caption(base_path, original_caption, suffix):
-    operating_system = platform.system().lower()
-    if (operating_system == "windows"):
-        invalid_path_characters = "\\/:*?\"<>|"
-        max_path_length = 259
-    else:
-        invalid_path_characters = "/" #linux/macos
-        max_path_length = 1023
-    caption = original_caption
-    for invalid_character in invalid_path_characters:
-        caption = caption.replace(invalid_character, "")
-    fixed_path_length = len(base_path) + len(suffix) 
-    if fixed_path_length + len(caption) <= max_path_length:
-        return caption
-    caption_tokens = caption.split()
-    new_caption = ""
-    for token in caption_tokens:
-        last_caption = new_caption
-        new_caption = new_caption + token + " "
-        if (len(new_caption) + fixed_path_length - 1  > max_path_length):
-            break
-    print(f"\nPath will be too long. Truncated caption: {original_caption}\nto: {last_caption}", file=sys.stderr)
-    return last_caption.strip()
diff --git a/modules/ui.py b/modules/ui.py
index c42535c8..e07ee0e1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1074,11 +1074,8 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
                         process_split = gr.Checkbox(label='Split oversized images into two')
-                        process_caption = gr.Checkbox(label='Use BLIP caption as filename')
-                        if cmd_opts.deepdanbooru:
-                            process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename')
-                        else:
-                            process_caption_deepbooru = gr.Checkbox(label='Use deepbooru caption as filename', visible=False)
+                        process_caption = gr.Checkbox(label='Use BLIP for caption')
+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
 
                     with gr.Row():
                         with gr.Column(scale=3):
-- 
cgit v1.2.1


From efefa4862c6c75115d3da9f768348630cc32bdea Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 13:03:00 -0700
Subject: [1/?] [wip] Reintroduce opts.interrogate_return_ranks

looks functionally correct, needs testing

Needs particular testing care around whether the colon usage (:) will break anything in whatever new use cases were introduced by https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2143
---
 modules/deepbooru.py | 25 ++++++++++++++-----------
 1 file changed, 14 insertions(+), 11 deletions(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 419e6a9c..2cbf2cab 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -26,6 +26,7 @@ def create_deepbooru_opts():
         "use_spaces": shared.opts.deepbooru_use_spaces,
         "use_escape": shared.opts.deepbooru_escape,
         "alpha_sort": shared.opts.deepbooru_sort_alpha,
+        "include_ranks": shared.opts.interrogate_return_ranks,
     }
 
 
@@ -113,6 +114,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
     alpha_sort = deepbooru_opts['alpha_sort']
     use_spaces = deepbooru_opts['use_spaces']
     use_escape = deepbooru_opts['use_escape']
+    include_ranks = deepbooru_opts['include_ranks']
 
     width = model.input_shape[2]
     height = model.input_shape[1]
@@ -151,19 +153,20 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
     if alpha_sort:
         sort_ndx = 1
 
-    # sort by reverse by likelihood and normal for alpha
+    # sort by reverse by likelihood and normal for alpha, and format tag text as requested
     unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
     for weight, tag in unsorted_tags_in_theshold:
-        result_tags_out.append(tag)
+        # note: tag_outformat will still have a colon if include_ranks is True
+        tag_outformat = tag.replace(':', ' ')
+        if use_spaces:
+            tag_outformat = tag_outformat.replace('_', ' ')
+        if use_escape:
+            tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
+        if include_ranks:
+            use_escape += f":{weight:.3f}"
 
-    print('\n'.join(sorted(result_tags_print, reverse=True)))
-
-    tags_text = ', '.join(result_tags_out)
+        result_tags_out.append(tag_outformat)
 
-    if use_spaces:
-        tags_text = tags_text.replace('_', ' ')
-
-    if use_escape:
-        tags_text = re.sub(re_special, r'\\\1', tags_text)
+    print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    return tags_text.replace(':', ' ')
+    return ', '.join(result_tags_out)
-- 
cgit v1.2.1


From f776254b12361b5bae16f6629bcdcb47b450c48d Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 13:08:06 -0700
Subject: [2/?] [wip] ignore OPT_INCLUDE_RANKS for training filenames

---
 modules/deepbooru.py                    | 3 ++-
 modules/textual_inversion/preprocess.py | 4 +++-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 2cbf2cab..fcc05819 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -19,6 +19,7 @@ def get_deepbooru_tags(pil_image):
         release_process()
 
 
+OPT_INCLUDE_RANKS = "include_ranks"
 def create_deepbooru_opts():
     from modules import shared
 
@@ -26,7 +27,7 @@ def create_deepbooru_opts():
         "use_spaces": shared.opts.deepbooru_use_spaces,
         "use_escape": shared.opts.deepbooru_escape,
         "alpha_sort": shared.opts.deepbooru_sort_alpha,
-        "include_ranks": shared.opts.interrogate_return_ranks,
+        OPT_INCLUDE_RANKS: shared.opts.interrogate_return_ranks,
     }
 
 
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 3047bede..886cf0c3 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -17,7 +17,9 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
             shared.interrogator.load()
 
         if process_caption_deepbooru:
-            deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, deepbooru.create_deepbooru_opts())
+            db_opts = deepbooru.create_deepbooru_opts()
+            db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
+            deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
 
         preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
 
-- 
cgit v1.2.1


From 514456101b142b47acf87f6de95bad1a23d73be7 Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 13:14:13 -0700
Subject: [3/?] [wip] fix incorrect variable reference

still needs testing
---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index fcc05819..c2004696 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -164,7 +164,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
         if use_escape:
             tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
         if include_ranks:
-            use_escape += f":{weight:.3f}"
+            tag_outformat += f":{weight:.3f}"
 
         result_tags_out.append(tag_outformat)
 
-- 
cgit v1.2.1


From 1cfc2a18981ee56bdb69a2de7b463a11ad05e329 Mon Sep 17 00:00:00 2001
From: Melan <alexleander91@gmail.com>
Date: Wed, 12 Oct 2022 23:36:29 +0200
Subject: Save a csv containing the loss while training

---
 modules/hypernetworks/hypernetwork.py          | 17 ++++++++++++++++-
 modules/textual_inversion/textual_inversion.py | 17 ++++++++++++++++-
 modules/ui.py                                  |  3 +++
 3 files changed, 35 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b6c06d49..6522078f 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -5,6 +5,7 @@ import os
 import sys
 import traceback
 import tqdm
+import csv
 
 import torch
 
@@ -174,7 +175,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     return self.to_out(out)
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
+def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, write_csv_every, template_file, preview_image_prompt):
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -256,6 +257,20 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
             hypernetwork.save(last_saved_file)
 
+        print(f"{write_csv_every} > {hypernetwork.step % write_csv_every == 0}, {write_csv_every}")
+        if write_csv_every > 0 and hypernetwork_dir is not None and hypernetwork.step % write_csv_every == 0:
+            write_csv_header = False if os.path.exists(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv")) else True
+            
+            with open(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv"), "a+") as fout:
+
+                csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss"])
+                
+                if write_csv_header:
+                    csv_writer.writeheader()
+
+                csv_writer.writerow({"step": hypernetwork.step, 
+                    "loss": f"{losses.mean():.7f}"})
+
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fa0e33a2..25038a89 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -6,6 +6,7 @@ import torch
 import tqdm
 import html
 import datetime
+import csv
 
 from PIL import Image, PngImagePlugin
 
@@ -172,7 +173,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, write_csv_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -256,6 +257,20 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
             embedding.save(last_saved_file)
 
+        if write_csv_every > 0 and log_directory is not None and embedding.step % write_csv_every == 0:
+            write_csv_header = False if os.path.exists(os.path.join(log_directory, "textual_inversion_loss.csv")) else True
+
+            with open(os.path.join(log_directory, "textual_inversion_loss.csv"), "a+") as fout:
+
+                csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss"])
+                
+                if write_csv_header:
+                    csv_writer.writeheader()
+
+                csv_writer.writerow({"epoch": epoch_num + 1, 
+                    "epoch_step": epoch_step - 1, 
+                    "loss": f"{losses.mean():.7f}"})
+
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
 
diff --git a/modules/ui.py b/modules/ui.py
index e07ee0e1..1195c2f1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1096,6 +1096,7 @@ def create_ui(wrap_gradio_gpu_call):
                     training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
+                    write_csv_every = gr.Number(label='Save an csv containing the loss to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
                     preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
@@ -1174,6 +1175,7 @@ def create_ui(wrap_gradio_gpu_call):
                 steps,
                 create_image_every,
                 save_embedding_every,
+                write_csv_every,
                 template_file,
                 save_image_with_stored_embedding,
                 preview_image_prompt,
@@ -1195,6 +1197,7 @@ def create_ui(wrap_gradio_gpu_call):
                 steps,
                 create_image_every,
                 save_embedding_every,
+                write_csv_every,
                 template_file,
                 preview_image_prompt,
             ],
-- 
cgit v1.2.1


From 54e0051bdd7dea7348825c09600ec61ea0771cb8 Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Wed, 12 Oct 2022 18:17:26 -0500
Subject: Add drag/drop param loading.

Drop an image or generational text onto the prompt bar, it loads the info for parsing.
---
 javascript/dragdrop.js    |  3 +++
 javascript/imageParams.js | 22 ++++++++++++++++++++++
 modules/images.py         | 20 ++++++++++++++++++++
 modules/ui.py             | 30 +++++++++++++++++++++++++++++-
 4 files changed, 74 insertions(+), 1 deletion(-)
 create mode 100644 javascript/imageParams.js

diff --git a/javascript/dragdrop.js b/javascript/dragdrop.js
index 5aac57f7..cf900f50 100644
--- a/javascript/dragdrop.js
+++ b/javascript/dragdrop.js
@@ -53,6 +53,9 @@ window.document.addEventListener('dragover', e => {
 
 window.document.addEventListener('drop', e => {
     const target = e.composedPath()[0];
+    if (target.placeholder === "Prompt") {
+        return;
+    }
     const imgWrap = target.closest('[data-testid="image"]');
     if ( !imgWrap ) {
         return;
diff --git a/javascript/imageParams.js b/javascript/imageParams.js
new file mode 100644
index 00000000..f9d0c0aa
--- /dev/null
+++ b/javascript/imageParams.js
@@ -0,0 +1,22 @@
+window.onload = (function(){
+    window.addEventListener('drop', e => {
+        const target = e.composedPath()[0];
+        const idx = selected_gallery_index();
+        let prompt_target = "txt2img_prompt_image";
+        if (idx === 1) {
+            prompt_target = "img2img_prompt_image";
+        }
+        if (target.placeholder === "Prompt") {
+            e.stopPropagation();
+            e.preventDefault();
+            const imgParent = gradioApp().getElementById(prompt_target);
+            const files = e.dataTransfer.files;
+            const fileInput = imgParent.querySelector('input[type="file"]');
+            if ( fileInput ) {
+                fileInput.files = files;
+                fileInput.dispatchEvent(new Event('change'));
+            }
+        }
+    });
+
+});
\ No newline at end of file
diff --git a/modules/images.py b/modules/images.py
index c0a90676..f1155b7f 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -463,3 +463,23 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         txt_fullfn = None
 
     return fullfn, txt_fullfn
+
+
+def image_data(image_path):
+    file, ext = os.path.splitext(image_path.name)
+    data = {}
+    if "png" in ext:
+        image = Image.open(image_path.name, "r")
+        print(f"Image data requested for {image_path.name} {image.format} of {type(image)}")
+        try:
+            data = image.text["parameters"]
+        except Exception as e:
+            print(f"Exception: {e}")
+            pass
+        print(f"Image data: {data}")
+    if "txt" in ext:
+        myfile = open(image_path.name, 'r')
+        data = myfile.read()
+        myfile.close()
+
+    return data, None
diff --git a/modules/ui.py b/modules/ui.py
index 2b332267..dd793c39 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -431,7 +431,6 @@ def create_toprow(is_img2img):
                 with gr.Column(scale=80):
                     with gr.Row():
                         prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2)
-
                 with gr.Column(scale=1, elem_id="roll_col"):
                     roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
                     paste = gr.Button(value=paste_symbol, elem_id="paste")
@@ -513,6 +512,7 @@ def create_ui(wrap_gradio_gpu_call):
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
+        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="file", visible=False)
 
         with gr.Row(elem_id='txt2img_progress_row'):
             with gr.Column(scale=1):
@@ -614,6 +614,18 @@ def create_ui(wrap_gradio_gpu_call):
             txt2img_prompt.submit(**txt2img_args)
             submit.click(**txt2img_args)
 
+            txt_prompt_img.change(
+                fn=modules.images.image_data,
+                # _js = "get_extras_tab_index",
+                inputs=[
+                    txt_prompt_img
+                ],
+                outputs=[
+                    txt2img_prompt,
+                    txt_prompt_img
+                ]
+            )
+
             enable_hr.change(
                 fn=lambda x: gr_show(x),
                 inputs=[enable_hr],
@@ -674,6 +686,9 @@ def create_ui(wrap_gradio_gpu_call):
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
 
         with gr.Row(elem_id='img2img_progress_row'):
+            img2img_prompt_img = gr.File(label="", elem_id="txt_prompt_image", file_count="single", type="file",
+                                         visible=False)
+
             with gr.Column(scale=1):
                 pass
 
@@ -768,6 +783,18 @@ def create_ui(wrap_gradio_gpu_call):
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
+            img2img_prompt_img.change(
+                fn=modules.images.image_data,
+                # _js = "get_extras_tab_index",
+                inputs=[
+                    txt_prompt_img
+                ],
+                outputs=[
+                    img2img_prompt,
+                    img2img_prompt_img
+                ]
+            )
+
             mask_mode.change(
                 lambda mode, img: {
                     init_img_with_mask: gr_show(mode == 0),
@@ -956,6 +983,7 @@ def create_ui(wrap_gradio_gpu_call):
                 button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else ''
                 open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
 
+
         submit.click(
             fn=wrap_gradio_gpu_call(modules.extras.run_extras),
             _js="get_extras_tab_index",
-- 
cgit v1.2.1


From 716a9e034f1aff434083363b218bd6043a774fc2 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 13 Oct 2022 12:19:50 +0800
Subject: images history delete a number of images  consecutively next

---
 javascript/images_history.js | 24 +++++++++++++++---------
 modules/images_history.py    | 44 ++++++++++++++++++++++++--------------------
 2 files changed, 39 insertions(+), 29 deletions(-)

diff --git a/javascript/images_history.js b/javascript/images_history.js
index c5c2886e..8fa4a15e 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -101,7 +101,7 @@ function images_history_get_current_img(tabname, image_path, files){
     ];
 }
 
-function images_history_delete(tabname, img_path, img_file_name, page_index, filenames, image_index){
+function images_history_delete(del_num, tabname, img_path, img_file_name, page_index, filenames, image_index){
     image_index = parseInt(image_index);
     var tab = gradioApp().getElementById(tabname + '_images_history');
     var set_btn = tab.querySelector(".images_history_set_index");
@@ -112,23 +112,29 @@ function images_history_delete(tabname, img_path, img_file_name, page_index, fil
         }
     });    
     var img_num = buttons.length / 2;
-    if (img_num === 1){
+    if (img_num <= del_num){
         setTimeout(function(tabname){
             gradioApp().getElementById(tabname + '_images_history_renew_page').click();
         }, 30, tabname); 
-    } else {        
-        buttons[image_index].style.display = 'none';
-        buttons[image_index + img_num].style.display = 'none';
+    } else {
+        var next_img  
+        for (var i = 0; i < del_num; i++){
+            if (image_index + i < image_index + img_num){
+                buttons[image_index + i].style.display = 'none';
+                buttons[image_index + img_num + 1].style.display = 'none';
+                next_img = image_index + i + 1
+            }
+        }
         var bnt;
-        if (image_index >= img_num - 1){
-            btn = buttons[img_num - 2];
+        if (next_img  >= img_num){
+            btn = buttons[image_index - del_num];
         } else {            
-            btn = buttons[image_index + 1] ;          
+            btn = buttons[next_img];          
         } 
         setTimeout(function(btn){btn.click()}, 30, btn);
     }
     images_history_disabled_del();  
-    return [tabname, img_path, img_file_name, page_index, filenames, image_index];
+    return [del_num, tabname, img_path, img_file_name, page_index, filenames, image_index];
 }
 
 function images_history_turnpage(img_path, page_index, image_index, tabname){
diff --git a/modules/images_history.py b/modules/images_history.py
index 6408973c..f812ea4e 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -54,23 +54,26 @@ def show_image_info(num, image_path, filenames):
     #print(f"select image {num}")
     file = filenames[int(num)]
     return file, num, os.path.join(image_path, file)
-def delete_image(tabname, dir_name, name, page_index, filenames, image_index):
-    path = os.path.join(dir_name, name)           
-    if os.path.exists(path):        
-        print(f"Delete file {path}")
-        os.remove(path)
-        txt_file = os.path.splitext(path)[0]  + ".txt"
-        if os.path.exists(txt_file):
-            os.remove(txt_file)
-        new_file_list = []
-        for f in filenames:
-            if f == name:
-                continue
-            new_file_list.append(f)
-    else:
-        print(f"Not exists file {path}")
-        new_file_list = filenames
-    return page_index, new_file_list
+def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
+    delete_num = int(delete_num)
+    index = list(filenames).index(name)
+    i = 0
+    new_file_list = []
+    for name in filenames:
+        if i  >= index and i < index + delete_num:            
+            path = os.path.join(dir_name, name)
+            if os.path.exists(path):        
+                print(f"Delete file {path}")
+                os.remove(path)
+                txt_file = os.path.splitext(path)[0]  + ".txt"
+                if os.path.exists(txt_file):
+                    os.remove(txt_file)       
+            else:
+                print(f"Not exists file {path}")
+        else:
+            new_file_list.append(name)
+        i += 1
+    return page_index, new_file_list, 1
 
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         if tabname == "txt2img":
@@ -90,10 +93,11 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
             with gr.Row():     
                 with gr.Column(scale=2):                    
                     history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
-                    delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")
+                    with gr.Row():
+                        delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")
+                        delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")                                           
                 with gr.Column():     
                     with gr.Row():          
-                        #pnginfo = gr.Button('PNG info')
                         pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
                         pnginfo_send_to_img2img = gr.Button('Send to img2img')
                     with gr.Row():
@@ -127,7 +131,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         #other funcitons
         set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
         img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
-        delete.click(delete_image,_js="images_history_delete", inputs=[tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames]) 
+        delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames, delete_num]) 
         hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
        
         #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
-- 
cgit v1.2.1


From 78592d404acba7db3baf8d78bdc19266906e684a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 13 Oct 2022 07:40:03 +0300
Subject: remove interrogate option I accidentally deleted

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/shared.py b/modules/shared.py
index 78b73aae..9bda45c1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -258,6 +258,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
     "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
+    "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
     "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
     "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
     "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
-- 
cgit v1.2.1


From 490494320ec8b5e1049c4ff35c3416258b75807b Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 13 Oct 2022 04:10:38 +0100
Subject: add missing id property

---
 javascript/contextMenus.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js
index 7636c4b3..fe67c42e 100644
--- a/javascript/contextMenus.js
+++ b/javascript/contextMenus.js
@@ -94,7 +94,7 @@ contextMenuInit = function(){
     }
     gradioApp().addEventListener("click", function(e) {
       let source = e.composedPath()[0]
-      if(source.id && source.indexOf('check_progress')>-1){
+      if(source.id && source.id.indexOf('check_progress')>-1){
         return
       }
       
-- 
cgit v1.2.1


From 04c0e643f2eec68d93a76db171b4d70595808702 Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 22:13:53 -0700
Subject: Merge branch 'master' of
 https://github.com/HunterVacui/stable-diffusion-webui

---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index c2004696..f34f3788 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -164,7 +164,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
         if use_escape:
             tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
         if include_ranks:
-            tag_outformat += f":{weight:.3f}"
+            tag_outformat = f"({tag_outformat}:{weight:.3f})"
 
         result_tags_out.append(tag_outformat)
 
-- 
cgit v1.2.1


From e72adc999b3531370eafb9d316924ac497feb445 Mon Sep 17 00:00:00 2001
From: Trung Ngo <codem01@gmail.com>
Date: Sat, 8 Oct 2022 22:57:19 -0500
Subject: Restore last generation params

---
 .gitignore                                 | 1 +
 javascript/hints.js                        | 2 +-
 modules/generation_parameters_copypaste.py | 8 ++++++++
 modules/processing.py                      | 4 ++++
 4 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 7afc9395..69785b3e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,6 +17,7 @@ __pycache__
 /webui.settings.bat
 /embeddings
 /styles.csv
+/params.txt
 /styles.csv.bak
 /webui-user.bat
 /webui-user.sh
diff --git a/javascript/hints.js b/javascript/hints.js
index d51ee14c..32f10fde 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -14,7 +14,7 @@ titles = {
     "\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
     "\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed",
     "\u{1f3a8}": "Add a random artist to the prompt.",
-    "\u2199\ufe0f": "Read generation parameters from prompt into user interface.",
+    "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
     "\u{1f4c2}": "Open images output directory",
 
     "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index ac1ba7f4..3e75aecc 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -1,5 +1,7 @@
+import os
 import re
 import gradio as gr
+from modules.shared import script_path
 
 re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)"
 re_param = re.compile(re_param_code)
@@ -61,6 +63,12 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 
 def connect_paste(button, paste_fields, input_comp, js=None):
     def paste_func(prompt):
+        if not prompt:
+            filename = os.path.join(script_path, "params.txt")
+            if os.path.exists(filename):
+                with open(filename, "r", encoding="utf8") as file:
+                    prompt = file.read()
+
         params = parse_generation_parameters(prompt)
         res = []
 
diff --git a/modules/processing.py b/modules/processing.py
index 698b3069..d5172f00 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -324,6 +324,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     else:
         assert p.prompt is not None
 
+    with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
+        processed = Processed(p, [], p.seed, "")
+        file.write(processed.infotext(p, 0))
+
     devices.torch_gc()
 
     seed = get_fixed_seed(p.seed)
-- 
cgit v1.2.1


From fde7fefa2ea23747f1107e3e46bf60c08a1134f1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 13 Oct 2022 12:26:34 +0300
Subject: update #2336 to prevent reading params.txt when --hide-ui-dir-config
 option is enabled (for servers, since this will let some users access others'
 params)

---
 modules/generation_parameters_copypaste.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 3e75aecc..c27826b6 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -2,6 +2,7 @@ import os
 import re
 import gradio as gr
 from modules.shared import script_path
+from modules import shared
 
 re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)"
 re_param = re.compile(re_param_code)
@@ -63,7 +64,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 
 def connect_paste(button, paste_fields, input_comp, js=None):
     def paste_func(prompt):
-        if not prompt:
+        if not prompt and not shared.cmd_opts.hide_ui_dir_config:
             filename = os.path.join(script_path, "params.txt")
             if os.path.exists(filename):
                 with open(filename, "r", encoding="utf8") as file:
-- 
cgit v1.2.1


From 94c01aa35656130b56f401830ad443ce3d97c364 Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Tue, 11 Oct 2022 17:05:20 -0700
Subject: draw_xy_grid provides the option to also return lone images

---
 scripts/xy_grid.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 3bb080bf..14edacc1 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -175,8 +175,9 @@ axis_options = [
 ]
 
 
-def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
+def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_images):
     res = []
+    successful_images = []
 
     ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
     hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
@@ -194,7 +195,9 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
                 first_processed = processed
 
             try:
-              res.append(processed.images[0])
+              processed_image = processed.images[0]
+              res.append(processed_image)
+              successful_images.append(processed_image)
             except:
               res.append(Image.new(res[0].mode, res[0].size))
 
@@ -203,6 +206,8 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
         grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
 
     first_processed.images = [grid]
+    if include_lone_images:
+        first_processed.images += successful_images
 
     return first_processed
 
@@ -229,11 +234,12 @@ class Script(scripts.Script):
             y_values = gr.Textbox(label="Y values", visible=False, lines=1)
         
         draw_legend = gr.Checkbox(label='Draw legend', value=True)
+        include_lone_images = gr.Checkbox(label='Include Separate Images', value=True)
         no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)
 
-        return [x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds]
+        return [x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds]
 
-    def run(self, p, x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds):
+    def run(self, p, x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds):
         if not no_fixed_seeds:
             modules.processing.fix_seed(p)
 
@@ -344,7 +350,8 @@ class Script(scripts.Script):
             x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
             y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
             cell=cell,
-            draw_legend=draw_legend
+            draw_legend=draw_legend,
+            include_lone_images=include_lone_images
         )
 
         if opts.grid_save:
-- 
cgit v1.2.1


From aeacbac218c47f61f1d0d3f3b429c9038b8faf0f Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Tue, 11 Oct 2022 19:46:33 -0700
Subject: Fix save error

---
 modules/ui.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index e07ee0e1..4fa405a9 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -148,7 +148,10 @@ def save_files(js_data, images, do_make_zip, index):
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
 
-            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+            seed = p.all_seeds[i] if len(p.all_seeds) > 1 else p.seed
+            prompt = p.all_prompts[i] if len(p.all_prompts) > 1 else p.prompt
+            info = p.infotexts[image_index] if len(p.infotexts) > 1 else p.infotexts[0]
+            fullfn, txt_fullfn = save_image(image, path, "", seed=seed, prompt=prompt, extension=extension, info=info, grid=is_grid, p=p, save_to_dirs=save_to_dirs)
 
             filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
-- 
cgit v1.2.1


From 8711c2fe0135d5c160a57db41cb79ed1942ce7fa Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 16:12:12 -0700
Subject: Fix metadata contents

---
 modules/ui.py      |  5 +----
 scripts/xy_grid.py | 52 ++++++++++++++++++++++++++++++++++------------------
 2 files changed, 35 insertions(+), 22 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 4fa405a9..e07ee0e1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -148,10 +148,7 @@ def save_files(js_data, images, do_make_zip, index):
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
 
-            seed = p.all_seeds[i] if len(p.all_seeds) > 1 else p.seed
-            prompt = p.all_prompts[i] if len(p.all_prompts) > 1 else p.prompt
-            info = p.infotexts[image_index] if len(p.infotexts) > 1 else p.infotexts[0]
-            fullfn, txt_fullfn = save_image(image, path, "", seed=seed, prompt=prompt, extension=extension, info=info, grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
 
             filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 14edacc1..02931ae6 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -176,13 +176,16 @@ axis_options = [
 
 
 def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_images):
-    res = []
-    successful_images = []
-
     ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
     hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
 
-    first_processed = None
+    # Temporary list of all the images that are generated to be populated into the grid.
+    # Will be filled with empty images for any individual step that fails to process properly
+    image_cache = []
+
+    processed_result = None
+    cell_mode = "P"
+    cell_size = (1,1)
 
     state.job_count = len(xs) * len(ys) * p.n_iter
 
@@ -190,26 +193,39 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_
         for ix, x in enumerate(xs):
             state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
 
-            processed = cell(x, y)
-            if first_processed is None:
-                first_processed = processed
-
+            processed:Processed = cell(x, y)
             try:
-              processed_image = processed.images[0]
-              res.append(processed_image)
-              successful_images.append(processed_image)
+                # this dereference will throw an exception if the image was not processed
+                # (this happens in cases such as if the user stops the process from the UI)
+                processed_image = processed.images[0]
+                
+                if processed_result is None:
+                    # Use our first valid processed result as a template container to hold our full results
+                    processed_result = copy(processed)
+                    cell_mode = processed_image.mode
+                    cell_size = processed_image.size
+                    processed_result.images = [Image.new(cell_mode, cell_size)]
+
+                image_cache.append(processed_image)
+                if include_lone_images:
+                    processed_result.images.append(processed_image)
+                    processed_result.all_prompts.append(processed.prompt)
+                    processed_result.all_seeds.append(processed.seed)
+                    processed_result.infotexts.append(processed.infotexts[0])
             except:
-              res.append(Image.new(res[0].mode, res[0].size))
+                image_cache.append(Image.new(cell_mode, cell_size))
+
+    if not processed_result:
+        print("Unexpected error: draw_xy_grid failed to return even a single processed image")
+        return Processed()
 
-    grid = images.image_grid(res, rows=len(ys))
+    grid = images.image_grid(image_cache, rows=len(ys))
     if draw_legend:
-        grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
+        grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts)
 
-    first_processed.images = [grid]
-    if include_lone_images:
-        first_processed.images += successful_images
+    processed_result.images[0] = grid
 
-    return first_processed
+    return processed_result
 
 
 re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
-- 
cgit v1.2.1


From a3f02e4690844715a510b7bc857a0971dd05c4d8 Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 16:48:53 -0700
Subject: fix prompt in log.csv

---
 modules/ui.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index e07ee0e1..edb4dab1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -139,6 +139,8 @@ def save_files(js_data, images, do_make_zip, index):
         if at_start:
             writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
 
+        log_prompt=data["prompt"]
+        log_seed=data["seed"]
         for image_index, filedata in enumerate(images, start_index):
             if filedata.startswith("data:image/png;base64,"):
                 filedata = filedata[len("data:image/png;base64,"):]
@@ -148,7 +150,9 @@ def save_files(js_data, images, do_make_zip, index):
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
 
-            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+            log_seed=p.all_seeds[i]
+            log_prompt=p.all_prompts[i]
+            fullfn, txt_fullfn = save_image(image, path, "", seed=log_seed, prompt=log_prompt, extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
 
             filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
@@ -157,7 +161,7 @@ def save_files(js_data, images, do_make_zip, index):
                 filenames.append(os.path.basename(txt_fullfn))
                 fullfns.append(txt_fullfn)
 
-        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+        writer.writerow([log_prompt, log_seed, data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
 
     # Make Zip
     if do_make_zip:
-- 
cgit v1.2.1


From fed7f0e281a42ea962bbe422e018468bafa6f1e6 Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Wed, 12 Oct 2022 23:09:30 -0700
Subject: Revert "fix prompt in log.csv"

This reverts commit e4b5d1696429ab78dae9779420ce6ec4cd9c5f67.
---
 modules/ui.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index edb4dab1..e07ee0e1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -139,8 +139,6 @@ def save_files(js_data, images, do_make_zip, index):
         if at_start:
             writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
 
-        log_prompt=data["prompt"]
-        log_seed=data["seed"]
         for image_index, filedata in enumerate(images, start_index):
             if filedata.startswith("data:image/png;base64,"):
                 filedata = filedata[len("data:image/png;base64,"):]
@@ -150,9 +148,7 @@ def save_files(js_data, images, do_make_zip, index):
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
 
-            log_seed=p.all_seeds[i]
-            log_prompt=p.all_prompts[i]
-            fullfn, txt_fullfn = save_image(image, path, "", seed=log_seed, prompt=log_prompt, extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
 
             filename = os.path.relpath(fullfn, path)
             filenames.append(filename)
@@ -161,7 +157,7 @@ def save_files(js_data, images, do_make_zip, index):
                 filenames.append(os.path.basename(txt_fullfn))
                 fullfns.append(txt_fullfn)
 
-        writer.writerow([log_prompt, log_seed, data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
 
     # Make Zip
     if do_make_zip:
-- 
cgit v1.2.1


From 8636b50aea83f9c743f005722d9f3f8ee9303e00 Mon Sep 17 00:00:00 2001
From: Melan <alexleander91@gmail.com>
Date: Thu, 13 Oct 2022 12:37:58 +0200
Subject: Add learn_rate to csv and removed a left-over debug statement

---
 modules/hypernetworks/hypernetwork.py          | 6 +++---
 modules/textual_inversion/textual_inversion.py | 5 +++--
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 6522078f..2751a8c8 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -257,19 +257,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
             hypernetwork.save(last_saved_file)
 
-        print(f"{write_csv_every} > {hypernetwork.step % write_csv_every == 0}, {write_csv_every}")
         if write_csv_every > 0 and hypernetwork_dir is not None and hypernetwork.step % write_csv_every == 0:
             write_csv_header = False if os.path.exists(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv")) else True
             
             with open(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv"), "a+") as fout:
 
-                csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss"])
+                csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss", "learn_rate"])
                 
                 if write_csv_header:
                     csv_writer.writeheader()
 
                 csv_writer.writerow({"step": hypernetwork.step, 
-                    "loss": f"{losses.mean():.7f}"})
+                    "loss": f"{losses.mean():.7f}",
+                    "learn_rate": scheduler.learn_rate})
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 25038a89..b83df079 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -262,14 +262,15 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
             with open(os.path.join(log_directory, "textual_inversion_loss.csv"), "a+") as fout:
 
-                csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss"])
+                csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss", "learn_rate"])
                 
                 if write_csv_header:
                     csv_writer.writeheader()
 
                 csv_writer.writerow({"epoch": epoch_num + 1, 
                     "epoch_step": epoch_step - 1, 
-                    "loss": f"{losses.mean():.7f}"})
+                    "loss": f"{losses.mean():.7f}",
+                    "learn_rate": scheduler.learn_rate})
 
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
-- 
cgit v1.2.1


From bb7baf6b9cb6b4b9fa09b6f07ef997db32fe6e58 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 13 Oct 2022 16:07:18 +0300
Subject: add option to change what's shown in quicksettings bar

---
 javascript/hints.js |  2 ++
 modules/shared.py   |  4 ++--
 modules/ui.py       | 16 +++++++++-------
 style.css           |  1 +
 4 files changed, 14 insertions(+), 9 deletions(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index 32f10fde..06bbd9e2 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -84,6 +84,8 @@ titles = {
 
     "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
     "Filename join string": "This string will be used to hoin split words into a single line if the option above is enabled.",
+
+    "Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual stetting tab. See modules/shared.py for setting names. Requires restart to apply."
 }
 
 
diff --git a/modules/shared.py b/modules/shared.py
index 5f6101a4..4d3ed625 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -152,7 +152,6 @@ class OptionInfo:
         self.component_args = component_args
         self.onchange = onchange
         self.section = None
-        self.show_on_main_page = show_on_main_page
 
 
 def options_section(section_identifier, options_dict):
@@ -237,7 +236,7 @@ options_templates.update(options_section(('training', "Training"), {
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
     "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
@@ -250,6 +249,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
+    'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
diff --git a/modules/ui.py b/modules/ui.py
index e07ee0e1..a0529860 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1305,6 +1305,9 @@ Requested path was: {f}
         settings_cols = 3
         items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
 
+        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
+        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
+
         quicksettings_list = []
 
         cols_displayed = 0
@@ -1329,7 +1332,7 @@ Requested path was: {f}
 
                     gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
 
-                if item.show_on_main_page:
+                if k in quicksettings_names:
                     quicksettings_list.append((i, k, item))
                     components.append(dummy_component)
                 else:
@@ -1338,7 +1341,11 @@ Requested path was: {f}
                     components.append(component)
                     items_displayed += 1
 
-        request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+        with gr.Row():
+            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
+            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
+
         request_notifications.click(
             fn=lambda: None,
             inputs=[],
@@ -1346,10 +1353,6 @@ Requested path was: {f}
             _js='function(){}'
         )
 
-        with gr.Row():
-            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
-            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
-
         def reload_scripts():
             modules.scripts.reload_script_body_only()
 
@@ -1364,7 +1367,6 @@ Requested path was: {f}
             shared.state.interrupt()
             settings_interface.gradio_ref.do_restart = True
 
-
         restart_gradio.click(
             fn=request_restart,
             inputs=[],
diff --git a/style.css b/style.css
index e6fa10b4..55c41971 100644
--- a/style.css
+++ b/style.css
@@ -488,6 +488,7 @@ input[type="range"]{
 #quicksettings > div > div{
     max-width: 32em;
     padding: 0;
+    margin-right: 0.75em;
 }
 
 canvas[key="mask"] {
-- 
cgit v1.2.1


From cf1e8fcb303a21ab626fc1e8b3bc95bb780e8758 Mon Sep 17 00:00:00 2001
From: Kalle <kalekkiorg@gmail.com>
Date: Thu, 13 Oct 2022 00:12:20 +0300
Subject: Correct img gen count in notification

Display correct count of images generated in browser notification regardless of "Show grid in results for web" setting.
---
 javascript/notification.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/javascript/notification.js b/javascript/notification.js
index bdf614ad..f96de313 100644
--- a/javascript/notification.js
+++ b/javascript/notification.js
@@ -36,7 +36,7 @@ onUiUpdate(function(){
     const notification = new Notification(
         'Stable Diffusion',
         {
-            body: `Generated ${imgs.size > 1 ? imgs.size - 1 : 1} image${imgs.size > 1 ? 's' : ''}`,
+            body: `Generated ${imgs.size > 1 ? imgs.size - opts.return_grid : 1} image${imgs.size > 1 ? 's' : ''}`,
             icon: headImg,
             image: headImg,
         }
-- 
cgit v1.2.1


From a4170875b00e5362cd252277c9830024dcea0c51 Mon Sep 17 00:00:00 2001
From: aoirusann <yimodo.siki@gmail.com>
Date: Wed, 12 Oct 2022 20:09:42 +0800
Subject: [img2imgalt] Add `override` in UI for convenience. Some params in
 img2imgalt are fixed, such as `Sampling method` and  `Denosing Strength`. And
 some params should be matched with those in decode, such as `steps`.

---
 scripts/img2imgalt.py | 35 ++++++++++++++++++++++++++++++++---
 1 file changed, 32 insertions(+), 3 deletions(-)

diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index 313a55d2..1e52f69b 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -120,15 +120,44 @@ class Script(scripts.Script):
         return is_img2img
 
     def ui(self, is_img2img):
+        info = gr.Markdown('''
+        * `Sampling method` is overriden as Euler, as this script is built on it.
+        * `CFG Scale` should be 2 or lower.
+        ''')
+
+        override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=True)
         original_prompt = gr.Textbox(label="Original prompt", lines=1)
         original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1)
-        cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
+
+        override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True)
         st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
+
+        override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True)
+
+        cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
         randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
         sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False)
-        return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
 
-    def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
+        return [
+            info, 
+            override_prompt, original_prompt, original_negative_prompt, 
+            override_steps, st,
+            override_strength,
+            cfg, randomness, sigma_adjustment,
+        ]
+
+    def run(self, p, _, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
+        # MUST Override
+        p.sampler_index = [sampler.name for sampler in sd_samplers.samplers].index("Euler")
+
+        # OPTIONAL Override
+        if override_prompt:
+            p.prompt = original_prompt
+            p.negative_prompt = original_negative_prompt
+        if override_steps:
+            p.steps = st
+        if override_strength:
+            p.denoising_strength = 1.0
 
 
         def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-- 
cgit v1.2.1


From e548fc4aca19e58fa97da5404a2116915eb85531 Mon Sep 17 00:00:00 2001
From: aoirusann <yimodo.siki@gmail.com>
Date: Thu, 13 Oct 2022 07:39:33 +0800
Subject: [img2imgalt] Make sampler's override be optional

---
 scripts/img2imgalt.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index 1e52f69b..d438175c 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -121,10 +121,11 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):
         info = gr.Markdown('''
-        * `Sampling method` is overriden as Euler, as this script is built on it.
         * `CFG Scale` should be 2 or lower.
         ''')
 
+        override_sampler = gr.Checkbox(label="Override `Sampling method` to Euler?(this method is built for it)", value=True)
+
         override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=True)
         original_prompt = gr.Textbox(label="Original prompt", lines=1)
         original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1)
@@ -140,17 +141,17 @@ class Script(scripts.Script):
 
         return [
             info, 
+            override_sampler,
             override_prompt, original_prompt, original_negative_prompt, 
             override_steps, st,
             override_strength,
             cfg, randomness, sigma_adjustment,
         ]
 
-    def run(self, p, _, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
-        # MUST Override
-        p.sampler_index = [sampler.name for sampler in sd_samplers.samplers].index("Euler")
-
-        # OPTIONAL Override
+    def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
+        # Override
+        if override_sampler:
+            p.sampler_index = [sampler.name for sampler in sd_samplers.samplers].index("Euler")
         if override_prompt:
             p.prompt = original_prompt
             p.negative_prompt = original_negative_prompt
-- 
cgit v1.2.1


From dccc181b55100b09182c1679c8dd75011aad7335 Mon Sep 17 00:00:00 2001
From: Taithrah <Taithrah@users.noreply.github.com>
Date: Thu, 13 Oct 2022 10:43:57 -0400
Subject: Update hints.js

typo
---
 javascript/hints.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index 06bbd9e2..f65e7b88 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -85,7 +85,7 @@ titles = {
     "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
     "Filename join string": "This string will be used to hoin split words into a single line if the option above is enabled.",
 
-    "Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual stetting tab. See modules/shared.py for setting names. Requires restart to apply."
+    "Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply."
 }
 
 
-- 
cgit v1.2.1


From a10b0e11fc22cc67b6a3664f2ddd17425d8433a8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 13 Oct 2022 19:22:41 +0300
Subject: options to refresh list of models and hypernetworks

---
 modules/shared.py |  9 +++++----
 modules/ui.py     | 33 +++++++++++++++++++++++++++++----
 style.css         | 21 ++++++++++++++++++++-
 3 files changed, 54 insertions(+), 9 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 4d3ed625..d8e3a286 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers
+from modules import sd_samplers, sd_models
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -145,13 +145,14 @@ def realesrgan_models_names():
 
 
 class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False):
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False, refresh=None):
         self.default = default
         self.label = label
         self.component = component
         self.component_args = component_args
         self.onchange = onchange
         self.section = None
+        self.refresh = refresh
 
 
 def options_section(section_identifier, options_dict):
@@ -236,8 +237,8 @@ options_templates.update(options_section(('training', "Training"), {
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
-    "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
+    "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
diff --git a/modules/ui.py b/modules/ui.py
index a0529860..0a58f6be 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -78,6 +78,8 @@ reuse_symbol = '\u267b\ufe0f'  # ♻️
 art_symbol = '\U0001f3a8'  # 🎨
 paste_symbol = '\u2199\ufe0f'  # ↙
 folder_symbol = '\U0001f4c2'  # 📂
+refresh_symbol = '\U0001f504'  # 🔄
+
 
 def plaintext_to_html(text):
     text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
@@ -1210,8 +1212,7 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[],
         )
 
-
-    def create_setting_component(key):
+    def create_setting_component(key, is_quicksettings=False):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
 
@@ -1231,7 +1232,31 @@ def create_ui(wrap_gradio_gpu_call):
         else:
             raise Exception(f'bad options item type: {str(t)} for key {key}')
 
-        return comp(label=info.label, value=fun, **(args or {}))
+        if info.refresh is not None:
+            if is_quicksettings:
+                res = comp(label=info.label, value=fun, **(args or {}))
+                refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_"+key)
+            else:
+                with gr.Row(variant="compact"):
+                    res = comp(label=info.label, value=fun, **(args or {}))
+                    refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_" + key)
+
+            def refresh():
+                info.refresh()
+                refreshed_args = info.component_args() if callable(info.component_args) else info.component_args
+                res.choices = refreshed_args["choices"]
+                return gr.update(**(refreshed_args or {}))
+
+            refresh_button.click(
+                fn=refresh,
+                inputs=[],
+                outputs=[res],
+            )
+        else:
+            res = comp(label=info.label, value=fun, **(args or {}))
+
+
+        return res
 
     components = []
     component_dict = {}
@@ -1401,7 +1426,7 @@ Requested path was: {f}
     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
         with gr.Row(elem_id="quicksettings"):
             for i, k, item in quicksettings_list:
-                component = create_setting_component(k)
+                component = create_setting_component(k, is_quicksettings=True)
                 component_dict[k] = component
 
         settings_interface.gradio_ref = demo
diff --git a/style.css b/style.css
index 55c41971..ad2a52cc 100644
--- a/style.css
+++ b/style.css
@@ -228,6 +228,8 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500,  label.block s
     border-top: 1px solid #eee;
     border-left: 1px solid #eee;
     border-right: 1px solid #eee;
+
+    z-index: 300;
 }
 
 .dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{
@@ -480,17 +482,30 @@ input[type="range"]{
     background: #a55000;
 }
 
+#quicksettings {
+    gap: 0.4em;
+}
+
 #quicksettings > div{
     border: none;
     background: none;
+    flex: unset;
+    gap: 0.5em;
 }
 
 #quicksettings > div > div{
     max-width: 32em;
+    min-width: 24em;
     padding: 0;
-    margin-right: 0.75em;
 }
 
+#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork{
+    max-width: 2.5em;
+    min-width: 2.5em;
+    height: 2.4em;
+}
+
+
 canvas[key="mask"] {
     z-index: 12 !important;
     filter: invert();
@@ -507,3 +522,7 @@ canvas[key="mask"] {
     z-index: 200;
     width: 8em;
 }
+
+.row.gr-compact{
+    overflow: visible;
+}
-- 
cgit v1.2.1


From 354ef0da3b1f0fa5c113d04b6c79e3908c848d23 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 13 Oct 2022 20:12:37 +0300
Subject: add hypernetwork multipliers

---
 modules/hypernetworks/hypernetwork.py | 8 +++++++-
 modules/shared.py                     | 5 ++++-
 modules/ui.py                         | 5 ++++-
 scripts/xy_grid.py                    | 9 ++++++++-
 style.css                             | 3 +++
 webui.py                              | 2 +-
 6 files changed, 27 insertions(+), 5 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b6c06d49..f1248bb7 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -18,6 +18,8 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 
 class HypernetworkModule(torch.nn.Module):
+    multiplier = 1.0
+
     def __init__(self, dim, state_dict=None):
         super().__init__()
 
@@ -36,7 +38,11 @@ class HypernetworkModule(torch.nn.Module):
         self.to(devices.device)
 
     def forward(self, x):
-        return x + (self.linear2(self.linear1(x)))
+        return x + (self.linear2(self.linear1(x))) * self.multiplier
+
+
+def apply_strength(value=None):
+    HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
 
 
 class Hypernetwork:
diff --git a/modules/shared.py b/modules/shared.py
index d8e3a286..5901e605 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -238,7 +238,8 @@ options_templates.update(options_section(('training', "Training"), {
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
-    "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+    "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+    "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
@@ -348,6 +349,8 @@ class Options:
         item = self.data_labels.get(key)
         item.onchange = func
 
+        func()
+
     def dumpjson(self):
         d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
         return json.dumps(d)
diff --git a/modules/ui.py b/modules/ui.py
index 0a58f6be..673014f2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1244,7 +1244,10 @@ def create_ui(wrap_gradio_gpu_call):
             def refresh():
                 info.refresh()
                 refreshed_args = info.component_args() if callable(info.component_args) else info.component_args
-                res.choices = refreshed_args["choices"]
+
+                for k, v in refreshed_args.items():
+                    setattr(res, k, v)
+
                 return gr.update(**(refreshed_args or {}))
 
             refresh_button.click(
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 02931ae6..efb63af5 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -107,6 +107,10 @@ def apply_hypernetwork(p, x, xs):
     hypernetwork.load_hypernetwork(name)
 
 
+def apply_hypernetwork_strength(p, x, xs):
+    hypernetwork.apply_strength(x)
+
+
 def confirm_hypernetworks(p, xs):
     for x in xs:
         if x.lower() in ["", "none"]:
@@ -165,6 +169,7 @@ axis_options = [
     AxisOption("Sampler", str, apply_sampler, format_value, confirm_samplers),
     AxisOption("Checkpoint name", str, apply_checkpoint, format_value, confirm_checkpoints),
     AxisOption("Hypernetwork", str, apply_hypernetwork, format_value, confirm_hypernetworks),
+    AxisOption("Hypernet str.", float, apply_hypernetwork_strength, format_value_add_label, None),
     AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label, None),
     AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label, None),
     AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label, None),
@@ -250,7 +255,7 @@ class Script(scripts.Script):
             y_values = gr.Textbox(label="Y values", visible=False, lines=1)
         
         draw_legend = gr.Checkbox(label='Draw legend', value=True)
-        include_lone_images = gr.Checkbox(label='Include Separate Images', value=True)
+        include_lone_images = gr.Checkbox(label='Include Separate Images', value=False)
         no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)
 
         return [x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds]
@@ -377,6 +382,8 @@ class Script(scripts.Script):
         modules.sd_models.reload_model_weights(shared.sd_model)
 
         hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
+        hypernetwork.apply_strength()
+
 
         opts.data["CLIP_stop_at_last_layers"] = CLIP_stop_at_last_layers
 
diff --git a/style.css b/style.css
index ad2a52cc..aa3d379c 100644
--- a/style.css
+++ b/style.css
@@ -522,6 +522,9 @@ canvas[key="mask"] {
     z-index: 200;
     width: 8em;
 }
+#quicksettings .gr-box > div > div > input.gr-text-input {
+  top: -1.12em;
+}
 
 .row.gr-compact{
     overflow: visible;
diff --git a/webui.py b/webui.py
index 33ba7905..fe0ce321 100644
--- a/webui.py
+++ b/webui.py
@@ -72,7 +72,6 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
     return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
 
-
 def initialize():
     modelloader.cleanup_models()
     modules.sd_models.setup_model()
@@ -86,6 +85,7 @@ def initialize():
     shared.sd_model = modules.sd_models.load_model()
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
+    shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
 
 
 def webui():
-- 
cgit v1.2.1


From 08b3f7aef15f74f4d2254b1274dd66fcc7940348 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 13 Oct 2022 20:42:27 +0300
Subject: emergency fix for broken send to buttons

---
 javascript/ui.js | 8 ++++----
 modules/ui.py    | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 4100944e..0f8fe68e 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -33,27 +33,27 @@ function args_to_array(args){
 }
 
 function switch_to_txt2img(){
-    gradioApp().querySelectorAll('button')[0].click();
+    gradioApp().querySelector('#tabs').querySelectorAll('button')[0].click();
 
     return args_to_array(arguments);
 }
 
 function switch_to_img2img_img2img(){
-    gradioApp().querySelectorAll('button')[1].click();
+    gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
     gradioApp().getElementById('mode_img2img').querySelectorAll('button')[0].click();
 
     return args_to_array(arguments);
 }
 
 function switch_to_img2img_inpaint(){
-    gradioApp().querySelectorAll('button')[1].click();
+    gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
     gradioApp().getElementById('mode_img2img').querySelectorAll('button')[1].click();
 
     return args_to_array(arguments);
 }
 
 function switch_to_extras(){
-    gradioApp().querySelectorAll('button')[2].click();
+    gradioApp().querySelector('#tabs').querySelectorAll('button')[2].click();
 
     return args_to_array(arguments);
 }
diff --git a/modules/ui.py b/modules/ui.py
index 673014f2..7446439d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1434,7 +1434,7 @@ Requested path was: {f}
 
         settings_interface.gradio_ref = demo
 
-        with gr.Tabs() as tabs:
+        with gr.Tabs(elem_id="tabs") as tabs:
             for interface, label, ifid in interfaces:
                 with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
                     interface.render()
-- 
cgit v1.2.1


From a1489f94283c07824a7a58353c03dc89541bbe49 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Fri, 14 Oct 2022 07:13:38 +0800
Subject: images history fix all known bug

---
 .gitignore                   |  1 +
 javascript/images_history.js | 23 ++++++++------------
 modules/images_history.py    | 51 +++++++++++++++++++++++---------------------
 repositorieslatent-diffusion |  1 -
 style.css                    |  6 +++---
 5 files changed, 40 insertions(+), 42 deletions(-)
 delete mode 160000 repositorieslatent-diffusion

diff --git a/.gitignore b/.gitignore
index 434e23ce..b9e23112 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,3 +27,4 @@ notification.mp3
 /SwinIR
 /textual_inversion
 /images_history_testui.py
+/repositorieslatent-diffusion
diff --git a/javascript/images_history.js b/javascript/images_history.js
index 8fa4a15e..3a20056b 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -25,11 +25,6 @@ var images_history_click_tab = function(){
     }                
 }
 
-var images_history_close_full_view = function(){
-    var box = images_history_get_parent_by_class(this, "images_history_cantainor");
-    box.querySelector(".images_history_del_button").setAttribute("disabled", "disabled");
-}
-
 function images_history_disabled_del(){
     gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
         btn.setAttribute('disabled','disabled');
@@ -182,18 +177,18 @@ setTimeout(images_history_init, 500);
 document.addEventListener("DOMContentLoaded", function() {
     var mutationObserver = new MutationObserver(function(m){
         for (var i in images_history_tab_list ){
-            var buttons = gradioApp().querySelectorAll('#' + images_history_tab_list[i] + '_images_history .gallery-item');
+            let tabname = images_history_tab_list[i]
+            var buttons = gradioApp().querySelectorAll('#' + tabname + '_images_history .gallery-item');
             buttons.forEach(function(bnt){    
                 bnt.addEventListener('click', images_history_click_image, true);
             });
-        // var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
-        // if (cls_btn){
-        //     cls_btn.addEventListener('click', images_history_close_full_view, false);
-        // }
-        // console.log(cls_btn, cls_btn.parentElement.parentElement)
-        // if (cls_btn) {
-        //     cls_btn = images_history_get_parent_by_tagname(cls_btn, "BUTTON");
-        //     cls_btn.addEventListener('click', images_history_close_full_view, true);
+            var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
+            if (cls_btn){
+                cls_btn.addEventListener('click', function(){
+                    gradioApp().getElementById(tabname + '_images_history_renew_page').click();
+                }, false);
+            }
+
         }     
     });
     mutationObserver.observe( gradioApp(), { childList:true, subtree:true });
diff --git a/modules/images_history.py b/modules/images_history.py
index f812ea4e..cdfcffed 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -38,7 +38,7 @@ def get_recent_images(dir_name, page_index, step, image_index, tabname):
     else:
         current_file =  file_list[int(image_index)]
         hide_image = os.path.join(dir_name, current_file)
-    return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image
+    return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image, ""
 def first_page_click(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, 1, 0, image_index, tabname)
 def end_page_click(dir_name, page_index, image_index, tabname):
@@ -55,25 +55,28 @@ def show_image_info(num, image_path, filenames):
     file = filenames[int(num)]
     return file, num, os.path.join(image_path, file)
 def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
-    delete_num = int(delete_num)
-    index = list(filenames).index(name)
-    i = 0
-    new_file_list = []
-    for name in filenames:
-        if i  >= index and i < index + delete_num:            
-            path = os.path.join(dir_name, name)
-            if os.path.exists(path):        
-                print(f"Delete file {path}")
-                os.remove(path)
-                txt_file = os.path.splitext(path)[0]  + ".txt"
-                if os.path.exists(txt_file):
-                    os.remove(txt_file)       
+    if name == "":
+        return filenames, delete_num
+    else:
+        delete_num = int(delete_num)
+        index = list(filenames).index(name)
+        i = 0
+        new_file_list = []
+        for name in filenames:
+            if i  >= index and i < index + delete_num:            
+                path = os.path.join(dir_name, name)
+                if os.path.exists(path):        
+                    print(f"Delete file {path}")
+                    os.remove(path)
+                    txt_file = os.path.splitext(path)[0]  + ".txt"
+                    if os.path.exists(txt_file):
+                        os.remove(txt_file)       
+                else:
+                    print(f"Not exists file {path}")
             else:
-                print(f"Not exists file {path}")
-        else:
-            new_file_list.append(name)
-        i += 1
-    return page_index, new_file_list, 1
+                new_file_list.append(name)
+            i += 1
+    return new_file_list, 1
 
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         if tabname == "txt2img":
@@ -93,9 +96,9 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
             with gr.Row():     
                 with gr.Column(scale=2):                    
                     history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
-                    with gr.Row():
-                        delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")
-                        delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")                                           
+                    with gr.Row():                        
+                        delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")   
+                        delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")                                        
                 with gr.Column():     
                     with gr.Row():          
                         pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
@@ -118,7 +121,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                 
         # turn pages
         gallery_inputs = [img_path, page_index, image_index, tabname_box]
-        gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image]
+        gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image, img_file_name]
 
         first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
         next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
@@ -131,7 +134,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         #other funcitons
         set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
         img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
-        delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[page_index, filenames, delete_num]) 
+        delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num]) 
         hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
        
         #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
diff --git a/repositorieslatent-diffusion b/repositorieslatent-diffusion
deleted file mode 160000
index abf33e70..00000000
--- a/repositorieslatent-diffusion
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit abf33e7002d59d9085081bce93ec798dcabd49af
diff --git a/style.css b/style.css
index c75dce4c..e6fa10b4 100644
--- a/style.css
+++ b/style.css
@@ -20,7 +20,7 @@
     padding-right: 0.25em;
     margin: 0.1em 0;
     opacity: 0%;
-    cursor: default;    
+    cursor: default;	
 }
 
 .output-html p {margin: 0 0.5em;}
@@ -442,7 +442,7 @@ input[type="range"]{
 }
 
 .red {
-    color: red;
+	color: red;
 }
 
 .gallery-item {
@@ -505,4 +505,4 @@ canvas[key="mask"] {
     top: -0.6em;
     z-index: 200;
     width: 8em;
-}
\ No newline at end of file
+}
-- 
cgit v1.2.1


From 4a37c7eedeab579efec03e8dae3f3f9fd4a37b02 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Fri, 14 Oct 2022 11:48:28 +0800
Subject: fix  deep nesting directories problem

---
 modules/images_history.py | 76 ++++++++++++++++++++++++++---------------------
 1 file changed, 42 insertions(+), 34 deletions(-)

diff --git a/modules/images_history.py b/modules/images_history.py
index cdfcffed..723f5301 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,44 +1,47 @@
 import os
 import shutil
-def get_recent_images(dir_name, page_index, step, image_index, tabname):
-    #print(f"renew page {page_index}")
-    page_index = int(page_index)
-    f_list = os.listdir(dir_name)
-    file_list = []
+def traverse_all_files(output_dir, image_list, curr_dir=None):
+    curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
+    try:
+        f_list = os.listdir(curr_path)
+    except:
+        if curr_dir[-10:].rfind(".") > 0 and curr_dir[-4:] != ".txt":
+            image_list.append(curr_dir)
+        return image_list
     for file in f_list:
+        file = file if curr_dir is None else os.path.join(curr_dir, file)
+        file_path = os.path.join(curr_path, file)
         if file[-4:] == ".txt":
-            continue
-        #subdirectories        
-        if file[-10:].rfind(".") < 0:
-            sub_dir = os.path.join(dir_name, file)
-            if os.path.isfile(sub_dir):
-                continue
-            sub_file_list = os.listdir(sub_dir)
-            for sub_file in sub_file_list:
-                if sub_file[-4:] == ".txt":
-                    continue
-                if os.path.isfile(os.path.join(sub_dir, sub_file) ): 
-                    file_list.append(os.path.join(file, sub_file)) 
-            continue
-        file_list.append(file)        
+            pass
+        elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0:
+            image_list.append(file)
+        else:
+            image_list = traverse_all_files(output_dir, image_list, file)            
+    return image_list
 
-    file_list = sorted(file_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+        
+def get_recent_images(dir_name, page_index, step, image_index, tabname):
+    page_index = int(page_index)
+    f_list = os.listdir(dir_name)
+    image_list = []
+    image_list = traverse_all_files(dir_name, image_list)
+    image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
     num = 48 if tabname != "extras" else 12
-    max_page_index = len(file_list) // num + 1    
+    max_page_index = len(image_list) // num + 1    
     page_index = max_page_index if page_index == -1 else page_index + step
     page_index = 1 if page_index < 1 else page_index    
     page_index = max_page_index if page_index > max_page_index else page_index
     idx_frm = (page_index - 1) * num
-    file_list = file_list[idx_frm:idx_frm + num]
-    #print(f"Loading history page {page_index}")    
+    image_list = image_list[idx_frm:idx_frm + num]
     image_index = int(image_index)
-    if image_index < 0 or image_index > len(file_list)  - 1:
+    if image_index < 0 or image_index > len(image_list)  - 1:
         current_file = None 
-        hide_image = None
+        hidden = None
     else:
-        current_file =  file_list[int(image_index)]
-        hide_image = os.path.join(dir_name, current_file)
-    return [os.path.join(dir_name, file) for file in file_list], page_index, file_list, current_file, hide_image, ""
+        current_file =  image_list[int(image_index)]
+        hidden = os.path.join(dir_name, current_file)
+    return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, ""
+
 def first_page_click(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, 1, 0, image_index, tabname)
 def end_page_click(dir_name, page_index, image_index, tabname):
@@ -85,6 +88,10 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
             dir_name = opts.outdir_img2img_samples
         elif tabname == "extras":
             dir_name = opts.outdir_extras_samples
+        d = dir_name.split("/")
+        dir_name = d[0]
+        for p in d[1:]:
+            dir_name = os.path.join(dir_name, p)
         with gr.Row():    
             renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page") 
             first_page = gr.Button('First Page')
@@ -109,19 +116,20 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                             img_file_name = gr.Textbox(label="File Name", interactive=False)    
                     with gr.Row():    
                         # hiden items
-                        img_path = gr.Textbox(dir_name, visible=False)                
+
+                        img_path = gr.Textbox(dir_name.rstrip("/") , visible=False)                
                         tabname_box = gr.Textbox(tabname, visible=False)    
                         image_index = gr.Textbox(value=-1, visible=False)                        
                         set_index = gr.Button('set_index',  elem_id=tabname + "_images_history_set_index", visible=False)
                         filenames = gr.State()
-                        hide_image = gr.Image(type="pil", visible=False)
+                        hidden = gr.Image(type="pil", visible=False)
                         info1 = gr.Textbox(visible=False)
                         info2 = gr.Textbox(visible=False)
 
                 
         # turn pages
         gallery_inputs = [img_path, page_index, image_index, tabname_box]
-        gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hide_image, img_file_name]
+        gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
 
         first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
         next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
@@ -132,12 +140,12 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         #page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
 
         #other funcitons
-        set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hide_image])
+        set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hidden])
         img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
         delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num]) 
-        hide_image.change(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
+        hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
        
-        #pnginfo.click(fn=run_pnginfo, inputs=[hide_image], outputs=[info1, img_file_info, info2])
+        #pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
         switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
         switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
     
-- 
cgit v1.2.1


From 494afccbc1d7b0aca4ffeb3d8354b09c414d95f4 Mon Sep 17 00:00:00 2001
From: crackfoo <info@zpool.ca>
Date: Thu, 13 Oct 2022 20:26:54 -0700
Subject: Update hints.js

typo
---
 javascript/hints.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index f65e7b88..94438c5c 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -83,7 +83,7 @@ titles = {
     "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
 
     "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
-    "Filename join string": "This string will be used to hoin split words into a single line if the option above is enabled.",
+    "Filename join string": "This string will be used to join split words into a single line if the option above is enabled.",
 
     "Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply."
 }
-- 
cgit v1.2.1


From fdecb636855748e03efc40c846a0043800aadfcc Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 09:05:06 +0300
Subject: add an ability to merge three checkpoints

---
 javascript/hints.js |  5 ++++-
 modules/extras.py   | 29 +++++++++++++++++++++--------
 modules/ui.py       | 11 +++++++----
 3 files changed, 32 insertions(+), 13 deletions(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index 94438c5c..af010a59 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -85,7 +85,10 @@ titles = {
     "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
     "Filename join string": "This string will be used to join split words into a single line if the option above is enabled.",
 
-    "Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply."
+    "Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
+
+    "Weighted Sum": "Result = A * (1 - M) + B * M",
+    "Add difference": "Result = A + (B - C) * (1 - M)",
 }
 
 
diff --git a/modules/extras.py b/modules/extras.py
index b24d7de3..532d869f 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -159,48 +159,61 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half, custom_name):
+def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, interp_amount, save_as_half, custom_name):
     # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
-    def weighted_sum(theta0, theta1, alpha):
+    def weighted_sum(theta0, theta1, theta2, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
     # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    def sigmoid(theta0, theta1, alpha):
+    def sigmoid(theta0, theta1, theta2, alpha):
         alpha = alpha * alpha * (3 - (2 * alpha))
         return theta0 + ((theta1 - theta0) * alpha)
 
     # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    def inv_sigmoid(theta0, theta1, alpha):
+    def inv_sigmoid(theta0, theta1, theta2, alpha):
         import math
         alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
         return theta0 + ((theta1 - theta0) * alpha)
 
+    def add_difference(theta0, theta1, theta2, alpha):
+        return theta0 + (theta1 - theta2) * (1.0 - alpha)
+
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
+    teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
 
     print(f"Loading {primary_model_info.filename}...")
     primary_model = torch.load(primary_model_info.filename, map_location='cpu')
+    theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
 
     print(f"Loading {secondary_model_info.filename}...")
     secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
-
-    theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
     theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
 
+    if teritary_model_info is not None:
+        print(f"Loading {teritary_model_info.filename}...")
+        teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
+        theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
+    else:
+        theta_2 = None
+
     theta_funcs = {
         "Weighted Sum": weighted_sum,
         "Sigmoid": sigmoid,
         "Inverse Sigmoid": inv_sigmoid,
+        "Add difference": add_difference,
     }
     theta_func = theta_funcs[interp_method]
 
     print(f"Merging...")
+
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
-            theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
+            theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key] if theta_2 else None, (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
 
+    # I believe this part should be discarded, but I'll leave it for now until I am sure
     for key in theta_1.keys():
         if 'model' in key and key not in theta_0:
             theta_0[key] = theta_1[key]
@@ -219,4 +232,4 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
     sd_models.list_models()
 
     print(f"Checkpoint saved.")
-    return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(3)]
+    return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
diff --git a/modules/ui.py b/modules/ui.py
index 7446439d..220fb80b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1024,11 +1024,12 @@ def create_ui(wrap_gradio_gpu_call):
                 gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
 
                 with gr.Row():
-                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name")
-                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")
+                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
                 custom_name = gr.Textbox(label="Custom Name (Optional)")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
-                interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation amount (1 - M)', value=0.3)
+                interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid", "Add difference"], value="Weighted Sum", label="Interpolation Method")
                 save_as_half = gr.Checkbox(value=False, label="Save as float16")
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
 
@@ -1473,6 +1474,7 @@ Requested path was: {f}
             inputs=[
                 primary_model_name,
                 secondary_model_name,
+                tertiary_model_name,
                 interp_method,
                 interp_amount,
                 save_as_half,
@@ -1482,6 +1484,7 @@ Requested path was: {f}
                 submit_result,
                 primary_model_name,
                 secondary_model_name,
+                tertiary_model_name,
                 component_dict['sd_model_checkpoint'],
             ]
         )
-- 
cgit v1.2.1


From bb57f30c2de46cfca5419ad01738a41705f96cc3 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Fri, 14 Oct 2022 10:56:41 +0200
Subject: init

---
 README.md                                      |  1 +
 aesthetic_embeddings/insert_embs_here.txt      |  0
 modules/processing.py                          | 17 +++++-
 modules/sd_hijack.py                           | 80 +++++++++++++++++++++++++-
 modules/shared.py                              |  5 ++
 modules/textual_inversion/dataset.py           |  2 +-
 modules/textual_inversion/textual_inversion.py | 35 +++++++----
 modules/txt2img.py                             | 11 +++-
 modules/ui.py                                  | 59 ++++++++++++-------
 9 files changed, 172 insertions(+), 38 deletions(-)
 create mode 100644 aesthetic_embeddings/insert_embs_here.txt

diff --git a/README.md b/README.md
index 859a91b6..7b8d018b 100644
--- a/README.md
+++ b/README.md
@@ -70,6 +70,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
 - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
 - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
+- Aesthetic, a way to generate images with a specific aesthetic by using clip images embds (implementation of https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
diff --git a/aesthetic_embeddings/insert_embs_here.txt b/aesthetic_embeddings/insert_embs_here.txt
new file mode 100644
index 00000000..e69de29b
diff --git a/modules/processing.py b/modules/processing.py
index d5172f00..9a033759 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -316,11 +316,16 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
 
-def process_images(p: StableDiffusionProcessing) -> Processed:
+def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0,
+                   aesthetic_imgs=None,aesthetic_slerp=False) -> Processed:
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
+    aesthetic_lr = float(aesthetic_lr)
+    aesthetic_weight = float(aesthetic_weight)
+    aesthetic_steps = int(aesthetic_steps)
+
     if type(p.prompt) == list:
-        assert(len(p.prompt) > 0)
+        assert (len(p.prompt) > 0)
     else:
         assert p.prompt is not None
 
@@ -394,7 +399,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
             #c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
-                uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
+                if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
+                    shared.sd_model.cond_stage_model.set_aesthetic_params(0, 0, 0)
+                uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt],
+                                                            p.steps)
+                if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
+                    shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight,
+                                                                          aesthetic_steps, aesthetic_imgs,aesthetic_slerp)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index c81722a0..6d5196fe 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -9,11 +9,14 @@ from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared
-from modules.shared import opts, device, cmd_opts
+from modules.shared import opts, device, cmd_opts, aesthetic_embeddings
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
+from transformers import CLIPVisionModel, CLIPModel
+import torch.optim as optim
+import copy
 
 attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
@@ -109,13 +112,29 @@ class StableDiffusionModelHijack:
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
         return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
 
+def slerp(low, high, val):
+    low_norm = low/torch.norm(low, dim=1, keepdim=True)
+    high_norm = high/torch.norm(high, dim=1, keepdim=True)
+    omega = torch.acos((low_norm*high_norm).sum(1))
+    so = torch.sin(omega)
+    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+    return res
 
 class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def __init__(self, wrapped, hijack):
         super().__init__()
         self.wrapped = wrapped
+        self.clipModel = CLIPModel.from_pretrained(
+            self.wrapped.transformer.name_or_path
+        )
+        del self.clipModel.vision_model
         self.hijack: StableDiffusionModelHijack = hijack
         self.tokenizer = wrapped.tokenizer
+        # self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval()
+        self.image_embs_name = None
+        self.image_embs = None
+        self.load_image_embs(None)
+
         self.token_mults = {}
 
         self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
@@ -136,6 +155,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
+    def set_aesthetic_params(self, aesthetic_lr, aesthetic_weight, aesthetic_steps, image_embs_name=None,
+                             aesthetic_slerp=True):
+        self.slerp = aesthetic_slerp
+        self.aesthetic_lr = aesthetic_lr
+        self.aesthetic_weight = aesthetic_weight
+        self.aesthetic_steps = aesthetic_steps
+        self.load_image_embs(image_embs_name)
+
+    def load_image_embs(self, image_embs_name):
+        if image_embs_name is None or len(image_embs_name) == 0:
+            image_embs_name = None
+        if image_embs_name is not None and self.image_embs_name != image_embs_name:
+            self.image_embs_name = image_embs_name
+            self.image_embs = torch.load(aesthetic_embeddings[self.image_embs_name], map_location=device)
+            self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
+            self.image_embs.requires_grad_(False)
+
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
         id_end = self.wrapped.tokenizer.eos_token_id
 
@@ -333,7 +369,47 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
             z1 = self.process_tokens(tokens, multipliers)
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
-            
+
+            if len(text[
+                       0]) != 0 and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None:
+                if not opts.use_old_emphasis_implementation:
+                    remade_batch_tokens = [
+                        [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in
+                        remade_batch_tokens]
+
+                tokens = torch.asarray(remade_batch_tokens).to(device)
+                with torch.enable_grad():
+                    model = copy.deepcopy(self.clipModel).to(device)
+                    model.requires_grad_(True)
+
+                    # We optimize the model to maximize the similarity
+                    optimizer = optim.Adam(
+                        model.text_model.parameters(), lr=self.aesthetic_lr
+                    )
+
+                    for i in range(self.aesthetic_steps):
+                        text_embs = model.get_text_features(input_ids=tokens)
+                        text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
+                        sim = text_embs @ self.image_embs.T
+                        loss = -sim
+                        optimizer.zero_grad()
+                        loss.mean().backward()
+                        optimizer.step()
+
+                    zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
+                    if opts.CLIP_stop_at_last_layers > 1:
+                        zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers]
+                        zn = model.text_model.final_layer_norm(zn)
+                    else:
+                        zn = zn.last_hidden_state
+                    model.cpu()
+                    del model
+
+                if self.slerp:
+                    z = slerp(z, zn, self.aesthetic_weight)
+                else:
+                    z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight
+
             remade_batch_tokens = rem_tokens
             batch_multipliers = rem_multipliers
             i += 1
diff --git a/modules/shared.py b/modules/shared.py
index 5901e605..cf13a10d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -30,6 +30,8 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
+parser.add_argument("--aesthetic_embeddings-dir", type=str, default=os.path.join(script_path, 'aesthetic_embeddings'),
+                    help="aesthetic_embeddings directory(default: aesthetic_embeddings)")
 parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
@@ -90,6 +92,9 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
+aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
+                        os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
+
 
 def reload_hypernetworks():
     global hypernetworks
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 67e90afe..59b2b021 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -48,7 +48,7 @@ class PersonalizedBase(Dataset):
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
             try:
-                image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
+                image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.Resampling.BICUBIC)
             except Exception:
                 continue
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fa0e33a2..b12a8e6d 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -172,7 +172,15 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
+def batched(dataset, total, n=1):
+    for ndx in range(0, total, n):
+        yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))]
+
+
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps,
+                    create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding,
+                    preview_image_prompt, batch_size=1,
+                    gradient_accumulation=1):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -204,7 +212,11 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width,
+                                                                height=training_height,
+                                                                repeats=shared.opts.training_image_repeats_per_epoch,
+                                                                placeholder_token=embedding_name, model=shared.sd_model,
+                                                                device=devices.device, template_file=template_file)
 
     hijack = sd_hijack.model_hijack
 
@@ -223,7 +235,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
 
-    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
+    pbar = tqdm.tqdm(enumerate(batched(ds, steps - ititial_step, batch_size)), total=steps - ititial_step)
     for i, entry in pbar:
         embedding.step = i + ititial_step
 
@@ -235,17 +247,20 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             break
 
         with torch.autocast("cuda"):
-            c = cond_model([entry.cond_text])
+            c = cond_model([e.cond_text for e in entry])
+
+            x = torch.stack([e.latent for e in entry]).to(devices.device)
+            loss = shared.sd_model(x, c)[0]
 
-            x = entry.latent.to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), c)[0]
             del x
 
             losses[embedding.step % losses.shape[0]] = loss.item()
 
-            optimizer.zero_grad()
             loss.backward()
-            optimizer.step()
+            if ((i + 1) % gradient_accumulation == 0) or (i + 1 == steps - ititial_step):
+                optimizer.step()
+                optimizer.zero_grad()
+
 
         epoch_num = embedding.step // len(ds)
         epoch_step = embedding.step - (epoch_num * len(ds)) + 1
@@ -259,7 +274,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
 
-            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
+            preview_text = entry[0].cond_text if preview_image_prompt == "" else preview_image_prompt
 
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
@@ -305,7 +320,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {embedding.step}<br/>
-Last prompt: {html.escape(entry.cond_text)}<br/>
+Last prompt: {html.escape(entry[-1].cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
diff --git a/modules/txt2img.py b/modules/txt2img.py
index e985242b..78342024 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -6,7 +6,14 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int,
+            restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int,
+            subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool,
+            height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float,
+            aesthetic_lr=0,
+            aesthetic_weight=0, aesthetic_steps=0,
+            aesthetic_imgs=None,
+            aesthetic_slerp=False, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -40,7 +47,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     processed = modules.scripts.scripts_txt2img.run(p, *args)
 
     if processed is None:
-        processed = process_images(p)
+        processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp)
 
     shared.total_tqdm.clear()
 
diff --git a/modules/ui.py b/modules/ui.py
index 220fb80b..d961d126 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -24,7 +24,8 @@ import gradio.routes
 
 from modules import sd_hijack
 from modules.paths import script_path
-from modules.shared import opts, cmd_opts
+from modules.shared import opts, cmd_opts,aesthetic_embeddings
+
 if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
 import modules.shared as shared
@@ -534,6 +535,14 @@ def create_ui(wrap_gradio_gpu_call):
                     width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
+                with gr.Group():
+                    aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
+                    aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.7)
+                    aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=50)
+
+                    aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None)
+                    aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
+
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
                     tiling = gr.Checkbox(label='Tiling', value=False)
@@ -586,25 +595,30 @@ def create_ui(wrap_gradio_gpu_call):
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
                 _js="submit",
                 inputs=[
-                    txt2img_prompt,
-                    txt2img_negative_prompt,
-                    txt2img_prompt_style,
-                    txt2img_prompt_style2,
-                    steps,
-                    sampler_index,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    height,
-                    width,
-                    enable_hr,
-                    scale_latent,
-                    denoising_strength,
-                ] + custom_inputs,
+                           txt2img_prompt,
+                           txt2img_negative_prompt,
+                           txt2img_prompt_style,
+                           txt2img_prompt_style2,
+                           steps,
+                           sampler_index,
+                           restore_faces,
+                           tiling,
+                           batch_count,
+                           batch_size,
+                           cfg_scale,
+                           seed,
+                           subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                           height,
+                           width,
+                           enable_hr,
+                           scale_latent,
+                           denoising_strength,
+                           aesthetic_lr,
+                           aesthetic_weight,
+                           aesthetic_steps,
+                           aesthetic_imgs,
+                           aesthetic_slerp
+                       ] + custom_inputs,
                 outputs=[
                     txt2img_gallery,
                     generation_info,
@@ -1097,6 +1111,9 @@ def create_ui(wrap_gradio_gpu_call):
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
                     training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    batch_size = gr.Slider(minimum=1, maximum=64, step=1, label="Batch Size", value=4)
+                    gradient_accumulation = gr.Slider(minimum=1, maximum=256, step=1, label="Gradient accumulation",
+                                                      value=1)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
@@ -1180,6 +1197,8 @@ def create_ui(wrap_gradio_gpu_call):
                 template_file,
                 save_image_with_stored_embedding,
                 preview_image_prompt,
+                batch_size,
+                gradient_accumulation
             ],
             outputs=[
                 ti_output,
-- 
cgit v1.2.1


From fdef8253a43ca5135923092ca9b85e878d980869 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Fri, 14 Oct 2022 04:42:53 -0400
Subject: Add 'interrogate' and 'all' choices to --use-cpu

* Add 'interrogate' and 'all' choices to --use-cpu
* Change type for --use-cpu argument to str.lower, so that choices are case insensitive
---
 modules/devices.py     |  2 +-
 modules/interrogate.py | 14 +++++++-------
 modules/shared.py      |  6 +++---
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 03ef58f1..eb422583 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -34,7 +34,7 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
+device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
 dtype = torch.float16
 dtype_vae = torch.float16
 
diff --git a/modules/interrogate.py b/modules/interrogate.py
index af858cc0..9263d65a 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -55,7 +55,7 @@ class InterrogateModels:
 
         model, preprocess = clip.load(clip_model_name)
         model.eval()
-        model = model.to(shared.device)
+        model = model.to(devices.device_interrogate)
 
         return model, preprocess
 
@@ -65,14 +65,14 @@ class InterrogateModels:
             if not shared.cmd_opts.no_half:
                 self.blip_model = self.blip_model.half()
 
-        self.blip_model = self.blip_model.to(shared.device)
+        self.blip_model = self.blip_model.to(devices.device_interrogate)
 
         if self.clip_model is None:
             self.clip_model, self.clip_preprocess = self.load_clip_model()
             if not shared.cmd_opts.no_half:
                 self.clip_model = self.clip_model.half()
 
-        self.clip_model = self.clip_model.to(shared.device)
+        self.clip_model = self.clip_model.to(devices.device_interrogate)
 
         self.dtype = next(self.clip_model.parameters()).dtype
 
@@ -99,11 +99,11 @@ class InterrogateModels:
             text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
 
         top_count = min(top_count, len(text_array))
-        text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(shared.device)
+        text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(devices.device_interrogate)
         text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
         text_features /= text_features.norm(dim=-1, keepdim=True)
 
-        similarity = torch.zeros((1, len(text_array))).to(shared.device)
+        similarity = torch.zeros((1, len(text_array))).to(devices.device_interrogate)
         for i in range(image_features.shape[0]):
             similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
         similarity /= image_features.shape[0]
@@ -116,7 +116,7 @@ class InterrogateModels:
             transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
             transforms.ToTensor(),
             transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
-        ])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+        ])(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
 
         with torch.no_grad():
             caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length)
@@ -140,7 +140,7 @@ class InterrogateModels:
 
             res = caption
 
-            clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+            clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
 
             precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
             with torch.no_grad(), precision_scope("cuda"):
diff --git a/modules/shared.py b/modules/shared.py
index 5901e605..b6a5c1a8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -54,7 +54,7 @@ parser.add_argument("--opt-split-attention", action='store_true', help="force-en
 parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
-parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[])
+parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -76,8 +76,8 @@ parser.add_argument("--disable-safe-unpickle", action='store_true', help="disabl
 
 cmd_opts = parser.parse_args()
 
-devices.device, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
-(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'])
+devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
+(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])
 
 device = devices.device
 
-- 
cgit v1.2.1


From 9e5ca5077f43bb3ec1a0ec41b47964cb38d544a6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 16:37:32 +0300
Subject: extra message for unpicking fails

---
 modules/safe.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/modules/safe.py b/modules/safe.py
index 20be16a5..399165a1 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -96,11 +96,18 @@ def load(filename, *args, **kwargs):
         if not shared.cmd_opts.disable_safe_unpickle:
             check_pt(filename)
 
+    except pickle.UnpicklingError:
+        print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+        print(f"-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
+        print(f"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
+        return None
+
     except Exception:
         print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
         print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
-        print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr)
+        print(f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
         return None
 
     return unsafe_torch_load(filename, *args, **kwargs)
-- 
cgit v1.2.1


From b2261b53ae4ad01b3713bc73ff62ab7b6f479e26 Mon Sep 17 00:00:00 2001
From: Buckzor <bucklogos@yahoo.co.uk>
Date: Thu, 13 Oct 2022 17:07:06 +0100
Subject: Added first_pass_width and height as adjustable inputs to "High Res
 Fix"

---
 modules/processing.py | 6 ++++--
 modules/txt2img.py    | 5 ++++-
 modules/ui.py         | 6 ++++++
 3 files changed, 14 insertions(+), 3 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index d5172f00..abbfdf98 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -506,11 +506,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     firstphase_width_truncated = 0
     firstphase_height_truncated = 0
 
-    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs):
+    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, first_pass_width=512, first_pass_height=512, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.scale_latent = scale_latent
         self.denoising_strength = denoising_strength
+        self.first_pass_width = first_pass_width
+        self.first_pass_height = first_pass_height
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
@@ -519,7 +521,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
-            desired_pixel_count = 512 * 512
+            desired_pixel_count = self.first_pass_width * self.first_pass_height
             actual_pixel_count = self.width * self.height
             scale = math.sqrt(desired_pixel_count / actual_pixel_count)
 
diff --git a/modules/txt2img.py b/modules/txt2img.py
index e985242b..85cbece4 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -6,7 +6,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, first_pass_width: int, first_pass_height: int, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -32,6 +32,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         enable_hr=enable_hr,
         scale_latent=scale_latent if enable_hr else None,
         denoising_strength=denoising_strength if enable_hr else None,
+		first_pass_width=first_pass_width if enable_hr else None,
+		first_pass_height=first_pass_height if enable_hr else None,
+
     )
 
     if cmd_opts.enable_console_prompts:
diff --git a/modules/ui.py b/modules/ui.py
index 220fb80b..544419b2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -540,6 +540,8 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
+                    first_pass_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
+                    first_pass_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
                     scale_latent = gr.Checkbox(label='Scale latent', value=False)
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
@@ -604,6 +606,8 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr,
                     scale_latent,
                     denoising_strength,
+                    first_pass_width,
+                    first_pass_height,
                 ] + custom_inputs,
                 outputs=[
                     txt2img_gallery,
@@ -668,6 +672,8 @@ def create_ui(wrap_gradio_gpu_call):
                 (denoising_strength, "Denoising strength"),
                 (enable_hr, lambda d: "Denoising strength" in d),
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
+                (first_pass_width, "First pass width"),
+                (first_pass_height, "First pass height"),
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-- 
cgit v1.2.1


From 40d1c6e423b4dc52b3bdae43d9e2442960760ced Mon Sep 17 00:00:00 2001
From: Buckzor <bucklogos@yahoo.co.uk>
Date: Thu, 13 Oct 2022 20:04:22 +0100
Subject: Option between stretch and crop for Highres. fix

---
 modules/processing.py | 34 ++++++++++++++++++++++------------
 modules/txt2img.py    |  7 ++++---
 modules/ui.py         | 25 ++++++++++++++++---------
 3 files changed, 42 insertions(+), 24 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index abbfdf98..0246f5dd 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -506,13 +506,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     firstphase_width_truncated = 0
     firstphase_height_truncated = 0
 
-    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, first_pass_width=512, first_pass_height=512, **kwargs):
+    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, crop_scale=False, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.scale_latent = scale_latent
         self.denoising_strength = denoising_strength
-        self.first_pass_width = first_pass_width
-        self.first_pass_height = first_pass_height
+        self.firstphase_width = firstphase_width
+        self.firstphase_height = firstphase_height
+        self.crop_scale = crop_scale
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
@@ -521,14 +522,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
-            desired_pixel_count = self.first_pass_width * self.first_pass_height
-            actual_pixel_count = self.width * self.height
-            scale = math.sqrt(desired_pixel_count / actual_pixel_count)
+            #desired_pixel_count = self.firstphase_width * self.firstphase_height
+            #actual_pixel_count = self.width * self.height
+            #scale = math.sqrt(desired_pixel_count / actual_pixel_count)
 
-            self.firstphase_width = math.ceil(scale * self.width / 64) * 64
-            self.firstphase_height = math.ceil(scale * self.height / 64) * 64
-            self.firstphase_width_truncated = int(scale * self.width)
-            self.firstphase_height_truncated = int(scale * self.height)
+            #self.firstphase_width = math.ceil(scale * self.width / 64) * 64
+            #self.firstphase_height = math.ceil(scale * self.height / 64) * 64
+            #self.firstphase_width_truncated = int(scale * self.width)
+            #self.firstphase_height_truncated = int(scale * self.height)
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
@@ -541,8 +542,17 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
 
-        truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f
-        truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f
+        truncate_x = 0
+        truncate_y = 0
+
+        if self.crop_scale:
+            if self.width/self.firstphase_width > self.height/self.firstphase_height:
+                #Crop to landscape
+                truncate_y = (self.width -  self.firstphase_width)//2 // opt_f
+
+            elif self.width/self.firstphase_width < self.height/self.firstphase_height:
+                #Crop to portrait
+                truncate_x = (self.height - self.firstphase_height)//2 // opt_f
 
         samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
 
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 85cbece4..447ec3d3 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -6,7 +6,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, first_pass_width: int, first_pass_height: int, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, crop_scale: bool, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -32,8 +32,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         enable_hr=enable_hr,
         scale_latent=scale_latent if enable_hr else None,
         denoising_strength=denoising_strength if enable_hr else None,
-		first_pass_width=first_pass_width if enable_hr else None,
-		first_pass_height=first_pass_height if enable_hr else None,
+		firstphase_width=firstphase_width if enable_hr else None,
+		firstphase_height=firstphase_height if enable_hr else None,
+        crop_scale=crop_scale if enable_hr else None,
 
     )
 
diff --git a/modules/ui.py b/modules/ui.py
index 544419b2..f2d81f68 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -540,12 +540,18 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
-                    first_pass_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
-                    first_pass_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
-                    scale_latent = gr.Checkbox(label='Scale latent', value=False)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
+                    with gr.Column(scale=1.0):
+                        firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
+                        firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
+                        
+                    with gr.Column(scale=1.0):
+                        with gr.Row():
+                            crop_scale = gr.Checkbox(label='Crop when scaling', value=False)
+                            scale_latent = gr.Checkbox(label='Scale latent', value=False)
+                        with gr.Row():
+                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
-                with gr.Row():
+                with gr.Row(equal_height=True):
                     batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
 
@@ -606,8 +612,9 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr,
                     scale_latent,
                     denoising_strength,
-                    first_pass_width,
-                    first_pass_height,
+                    firstphase_width,
+                    firstphase_height,
+                    crop_scale,
                 ] + custom_inputs,
                 outputs=[
                     txt2img_gallery,
@@ -672,8 +679,8 @@ def create_ui(wrap_gradio_gpu_call):
                 (denoising_strength, "Denoising strength"),
                 (enable_hr, lambda d: "Denoising strength" in d),
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
-                (first_pass_width, "First pass width"),
-                (first_pass_height, "First pass height"),
+                (firstphase_width, "First pass width"),
+                (firstphase_height, "First pass height"),
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-- 
cgit v1.2.1


From b382de2d77c653c565840ce92d27aa668a1934d7 Mon Sep 17 00:00:00 2001
From: Buckzor <bucklogos@yahoo.co.uk>
Date: Thu, 13 Oct 2022 22:23:22 +0100
Subject: Fixed Scale ratio problem

---
 modules/processing.py | 25 +++++++++++--------------
 1 file changed, 11 insertions(+), 14 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 0246f5dd..d9b0e0e7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -522,15 +522,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
-            #desired_pixel_count = self.firstphase_width * self.firstphase_height
-            #actual_pixel_count = self.width * self.height
-            #scale = math.sqrt(desired_pixel_count / actual_pixel_count)
-
-            #self.firstphase_width = math.ceil(scale * self.width / 64) * 64
-            #self.firstphase_height = math.ceil(scale * self.height / 64) * 64
-            #self.firstphase_width_truncated = int(scale * self.width)
-            #self.firstphase_height_truncated = int(scale * self.height)
-
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
@@ -544,17 +535,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         truncate_x = 0
         truncate_y = 0
+        width_ratio = self.width/self.firstphase_width
+        height_ratio = self.height/self.firstphase_height
 
         if self.crop_scale:
-            if self.width/self.firstphase_width > self.height/self.firstphase_height:
+            if width_ratio > height_ratio:
                 #Crop to landscape
-                truncate_y = (self.width -  self.firstphase_width)//2 // opt_f
+                truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
 
-            elif self.width/self.firstphase_width < self.height/self.firstphase_height:
+            elif width_ratio < height_ratio:
                 #Crop to portrait
-                truncate_x = (self.height - self.firstphase_height)//2 // opt_f
+                truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
+                
+            samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
+
+        
 
-        samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
+        
 
         if self.scale_latent:
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
-- 
cgit v1.2.1


From e644b5a80beb54b6df4caa63fb19d889dd4ceff6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 17:03:03 +0300
Subject: remove scale latent and no-crop options from hires fix support
 copy-pasting new parameters for hires fix

---
 modules/processing.py | 64 ++++++++++++++++++++++-----------------------------
 modules/txt2img.py    |  9 +++-----
 modules/ui.py         | 19 ++++-----------
 3 files changed, 35 insertions(+), 57 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index d9b0e0e7..100a259f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -506,14 +506,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     firstphase_width_truncated = 0
     firstphase_height_truncated = 0
 
-    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, crop_scale=False, **kwargs):
+    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
-        self.scale_latent = scale_latent
         self.denoising_strength = denoising_strength
         self.firstphase_width = firstphase_width
         self.firstphase_height = firstphase_height
-        self.crop_scale = crop_scale
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
@@ -530,6 +528,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
             return samples
 
+        self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
+
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
 
@@ -538,46 +538,36 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         width_ratio = self.width/self.firstphase_width
         height_ratio = self.height/self.firstphase_height
 
-        if self.crop_scale:
-            if width_ratio > height_ratio:
-                #Crop to landscape
-                truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
+        if width_ratio > height_ratio:
+            truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
 
-            elif width_ratio < height_ratio:
-                #Crop to portrait
-                truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
+        elif width_ratio < height_ratio:
+            truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
                 
-            samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
-
-        
+        samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
 
-        
+        decoded_samples = decode_first_stage(self.sd_model, samples)
 
-        if self.scale_latent:
-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+        if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
+            decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
         else:
-            decoded_samples = decode_first_stage(self.sd_model, samples)
+            lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
 
-            if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
-                decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
-            else:
-                lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
-                batch_images = []
-                for i, x_sample in enumerate(lowres_samples):
-                    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-                    x_sample = x_sample.astype(np.uint8)
-                    image = Image.fromarray(x_sample)
-                    image = images.resize_image(0, image, self.width, self.height)
-                    image = np.array(image).astype(np.float32) / 255.0
-                    image = np.moveaxis(image, 2, 0)
-                    batch_images.append(image)
-
-                decoded_samples = torch.from_numpy(np.array(batch_images))
-                decoded_samples = decoded_samples.to(shared.device)
-                decoded_samples = 2. * decoded_samples - 1.
-
-            samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
+            batch_images = []
+            for i, x_sample in enumerate(lowres_samples):
+                x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+                x_sample = x_sample.astype(np.uint8)
+                image = Image.fromarray(x_sample)
+                image = images.resize_image(0, image, self.width, self.height)
+                image = np.array(image).astype(np.float32) / 255.0
+                image = np.moveaxis(image, 2, 0)
+                batch_images.append(image)
+
+            decoded_samples = torch.from_numpy(np.array(batch_images))
+            decoded_samples = decoded_samples.to(shared.device)
+            decoded_samples = 2. * decoded_samples - 1.
+
+        samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
         shared.state.nextjob()
 
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 447ec3d3..2381347f 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -6,7 +6,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, crop_scale: bool, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -30,12 +30,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         restore_faces=restore_faces,
         tiling=tiling,
         enable_hr=enable_hr,
-        scale_latent=scale_latent if enable_hr else None,
         denoising_strength=denoising_strength if enable_hr else None,
-		firstphase_width=firstphase_width if enable_hr else None,
-		firstphase_height=firstphase_height if enable_hr else None,
-        crop_scale=crop_scale if enable_hr else None,
-
+        firstphase_width=firstphase_width if enable_hr else None,
+        firstphase_height=firstphase_height if enable_hr else None,
     )
 
     if cmd_opts.enable_console_prompts:
diff --git a/modules/ui.py b/modules/ui.py
index f2d81f68..d66ddc14 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -540,16 +540,9 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
-                    with gr.Column(scale=1.0):
-                        firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
-                        firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
-                        
-                    with gr.Column(scale=1.0):
-                        with gr.Row():
-                            crop_scale = gr.Checkbox(label='Crop when scaling', value=False)
-                            scale_latent = gr.Checkbox(label='Scale latent', value=False)
-                        with gr.Row():
-                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
+                    firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
+                    firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
                 with gr.Row(equal_height=True):
                     batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
@@ -610,11 +603,9 @@ def create_ui(wrap_gradio_gpu_call):
                     height,
                     width,
                     enable_hr,
-                    scale_latent,
                     denoising_strength,
                     firstphase_width,
                     firstphase_height,
-                    crop_scale,
                 ] + custom_inputs,
                 outputs=[
                     txt2img_gallery,
@@ -679,8 +670,8 @@ def create_ui(wrap_gradio_gpu_call):
                 (denoising_strength, "Denoising strength"),
                 (enable_hr, lambda d: "Denoising strength" in d),
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
-                (firstphase_width, "First pass width"),
-                (firstphase_height, "First pass height"),
+                (firstphase_width, "First pass size-1"),
+                (firstphase_height, "First pass size-2"),
             ]
             modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-- 
cgit v1.2.1


From 33ae6be55eaedabd49c8c888ec0b37c612618fdf Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 17:53:34 +0300
Subject: fix paste not working in firefox fix paste always going into txt2img
 field

---
 javascript/dragdrop.js    |  2 +-
 javascript/imageParams.js | 29 +++++++++++++----------------
 2 files changed, 14 insertions(+), 17 deletions(-)

diff --git a/javascript/dragdrop.js b/javascript/dragdrop.js
index cf900f50..fe0185a5 100644
--- a/javascript/dragdrop.js
+++ b/javascript/dragdrop.js
@@ -43,7 +43,7 @@ function dropReplaceImage( imgWrap, files ) {
 window.document.addEventListener('dragover', e => {
     const target = e.composedPath()[0];
     const imgWrap = target.closest('[data-testid="image"]');
-    if ( !imgWrap ) {
+    if ( !imgWrap && target.placeholder != "Prompt") {
         return;
     }
     e.stopPropagation();
diff --git a/javascript/imageParams.js b/javascript/imageParams.js
index f9d0c0aa..4a7b0900 100644
--- a/javascript/imageParams.js
+++ b/javascript/imageParams.js
@@ -2,21 +2,18 @@ window.onload = (function(){
     window.addEventListener('drop', e => {
         const target = e.composedPath()[0];
         const idx = selected_gallery_index();
-        let prompt_target = "txt2img_prompt_image";
-        if (idx === 1) {
-            prompt_target = "img2img_prompt_image";
-        }
-        if (target.placeholder === "Prompt") {
-            e.stopPropagation();
-            e.preventDefault();
-            const imgParent = gradioApp().getElementById(prompt_target);
-            const files = e.dataTransfer.files;
-            const fileInput = imgParent.querySelector('input[type="file"]');
-            if ( fileInput ) {
-                fileInput.files = files;
-                fileInput.dispatchEvent(new Event('change'));
-            }
+        if (target.placeholder != "Prompt") return;
+
+        let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
+
+        e.stopPropagation();
+        e.preventDefault();
+        const imgParent = gradioApp().getElementById(prompt_target);
+        const files = e.dataTransfer.files;
+        const fileInput = imgParent.querySelector('input[type="file"]');
+        if ( fileInput ) {
+            fileInput.files = files;
+            fileInput.dispatchEvent(new Event('change'));
         }
     });
-
-});
\ No newline at end of file
+});
-- 
cgit v1.2.1


From 0aec19d7837d8564355fdb286541db7165852e41 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 18:15:03 +0300
Subject: make pasting into img2img prompt work make image params request not
 use temp files

---
 modules/images.py | 36 ++++++++++++++++++------------------
 modules/ui.py     |  9 +++------
 2 files changed, 21 insertions(+), 24 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index f1155b7f..68cdbc93 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -1,4 +1,5 @@
 import datetime
+import io
 import math
 import os
 from collections import namedtuple
@@ -465,21 +466,20 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     return fullfn, txt_fullfn
 
 
-def image_data(image_path):
-    file, ext = os.path.splitext(image_path.name)
-    data = {}
-    if "png" in ext:
-        image = Image.open(image_path.name, "r")
-        print(f"Image data requested for {image_path.name} {image.format} of {type(image)}")
-        try:
-            data = image.text["parameters"]
-        except Exception as e:
-            print(f"Exception: {e}")
-            pass
-        print(f"Image data: {data}")
-    if "txt" in ext:
-        myfile = open(image_path.name, 'r')
-        data = myfile.read()
-        myfile.close()
-
-    return data, None
+def image_data(data):
+    try:
+        image = Image.open(io.BytesIO(data))
+        textinfo = image.text["parameters"]
+        return textinfo, None
+    except Exception:
+        pass
+
+    try:
+        text = data.decode('utf8')
+        assert len(text) < 10000
+        return text, None
+
+    except Exception:
+        pass
+
+    return '', None
diff --git a/modules/ui.py b/modules/ui.py
index 0a3ee887..6266db49 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -514,7 +514,7 @@ def create_ui(wrap_gradio_gpu_call):
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
-        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="file", visible=False)
+        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
 
         with gr.Row(elem_id='txt2img_progress_row'):
             with gr.Column(scale=1):
@@ -620,7 +620,6 @@ def create_ui(wrap_gradio_gpu_call):
 
             txt_prompt_img.change(
                 fn=modules.images.image_data,
-                # _js = "get_extras_tab_index",
                 inputs=[
                     txt_prompt_img
                 ],
@@ -692,8 +691,7 @@ def create_ui(wrap_gradio_gpu_call):
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
 
         with gr.Row(elem_id='img2img_progress_row'):
-            img2img_prompt_img = gr.File(label="", elem_id="txt_prompt_image", file_count="single", type="file",
-                                         visible=False)
+            img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
 
             with gr.Column(scale=1):
                 pass
@@ -791,9 +789,8 @@ def create_ui(wrap_gradio_gpu_call):
 
             img2img_prompt_img.change(
                 fn=modules.images.image_data,
-                # _js = "get_extras_tab_index",
                 inputs=[
-                    txt_prompt_img
+                    img2img_prompt_img
                 ],
                 outputs=[
                     img2img_prompt,
-- 
cgit v1.2.1


From 67f447ddcc8a17d11939c3801dca635dc22944c7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 19:30:28 +0300
Subject: possibility to load checkpoint, clip skip, and hypernet from infotext

---
 modules/ui.py | 52 +++++++++++++++++++++++++++++++++++++++++++++-------
 1 file changed, 45 insertions(+), 7 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 6266db49..a37a4e17 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -22,7 +22,7 @@ import gradio as gr
 import gradio.utils
 import gradio.routes
 
-from modules import sd_hijack
+from modules import sd_hijack, sd_models
 from modules.paths import script_path
 from modules.shared import opts, cmd_opts
 if cmd_opts.deepdanbooru:
@@ -507,12 +507,38 @@ def setup_progressbar(progressbar, preview, id_part, textinfo=None):
     )
 
 
+def apply_setting(key, value):
+    if value is None:
+        return gr.update()
+
+    if key == "sd_model_checkpoint":
+        ckpt_info = sd_models.get_closet_checkpoint_match(value)
+
+        if ckpt_info is not None:
+            value = ckpt_info.title
+        else:
+            return gr.update()
+
+    comp_args = opts.data_labels[key].component_args
+    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
+        return
+
+    valtype = type(opts.data_labels[key].default)
+    oldval = opts.data[key]
+    opts.data[key] = valtype(value) if valtype != type(None) else value
+    if oldval != value and opts.data_labels[key].onchange is not None:
+        opts.data_labels[key].onchange()
+
+    opts.save(shared.config_filename)
+    return value
+
+
 def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
 
@@ -684,11 +710,10 @@ def create_ui(wrap_gradio_gpu_call):
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
             ]
-            modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
 
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -938,7 +963,6 @@ def create_ui(wrap_gradio_gpu_call):
                 (seed_resize_from_h, "Seed resize from-2"),
                 (denoising_strength, "Denoising strength"),
             ]
-            modules.generation_parameters_copypaste.connect_paste(paste, img2img_paste_fields, img2img_prompt)
             token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as extras_interface:
@@ -1580,8 +1604,22 @@ Requested path was: {f}
             outputs=[extras_image],
         )
 
-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields, generation_info, 'switch_to_txt2img')
-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields, generation_info, 'switch_to_img2img_img2img')
+        settings_map = {
+            'sd_hypernetwork': 'Hypernet',
+            'CLIP_stop_at_last_layers': 'Clip skip',
+            'sd_model_checkpoint': 'Model hash',
+        }
+
+        settings_paste_fields = [
+            (component_dict[k], lambda d, k=k, v=v: apply_setting(k, d.get(v, None)))
+            for k, v in settings_map.items()
+        ]
+
+        modules.generation_parameters_copypaste.connect_paste(txt2img_paste, txt2img_paste_fields + settings_paste_fields, txt2img_prompt)
+        modules.generation_parameters_copypaste.connect_paste(img2img_paste, img2img_paste_fields + settings_paste_fields, img2img_prompt)
+
+        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_txt2img')
+        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_img2img_img2img')
 
     ui_config_file = cmd_opts.ui_config_file
     ui_settings = {}
-- 
cgit v1.2.1


From 6c6427946087d761d548d97164594d914fdd9b78 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 19:33:49 +0300
Subject: remove user's liners from .gitigbore - those go into
 .git/info/exclude

---
 .gitignore | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.gitignore b/.gitignore
index a6f27495..69785b3e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,5 +27,3 @@ __pycache__
 notification.mp3
 /SwinIR
 /textual_inversion
-/images_history_testui.py
-/repositorieslatent-diffusion
-- 
cgit v1.2.1


From 2fb9891af3bb4c36a6de6b44937e927bda43c10d Mon Sep 17 00:00:00 2001
From: Gugubo <29143981+Gugubo@users.noreply.github.com>
Date: Fri, 14 Oct 2022 14:19:39 +0200
Subject: Change grid row count autodetect to prevent empty spots

Instead of just rounding (sometimes resulting in grids with "empty" spots), find a divisor.
For example: 8 images will now result in a 4x2 grid instead of a 3x3 with one empty spot.
---
 modules/images.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index 68cdbc93..90eca37a 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -25,8 +25,9 @@ def image_grid(imgs, batch_size=1, rows=None):
         elif opts.n_rows == 0:
             rows = batch_size
         else:
-            rows = math.sqrt(len(imgs))
-            rows = round(rows)
+            rows = math.floor(math.sqrt(len(imgs)))
+            while len(imgs) % rows != 0:
+                rows -= 1
 
     cols = math.ceil(len(imgs) / rows)
 
-- 
cgit v1.2.1


From 43f926aad1b77a4bb642c1d173adfae1f56cf42d Mon Sep 17 00:00:00 2001
From: Gugubo <29143981+Gugubo@users.noreply.github.com>
Date: Fri, 14 Oct 2022 17:06:51 +0200
Subject: Add option to prevent empty spots in grid (1/2)

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/shared.py b/modules/shared.py
index b6a5c1a8..159f504f 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -175,6 +175,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "grid_format": OptionInfo('png', 'File format for grids'),
     "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
     "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
+    "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
     "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
 
     "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
-- 
cgit v1.2.1


From 5f87dd1ee0960963e3f756c4ebe47652ff57f715 Mon Sep 17 00:00:00 2001
From: Gugubo <29143981+Gugubo@users.noreply.github.com>
Date: Fri, 14 Oct 2022 17:07:24 +0200
Subject: Add option to prevent empty spots in grid (2/2)

---
 modules/images.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/images.py b/modules/images.py
index 90eca37a..b9589563 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -24,10 +24,13 @@ def image_grid(imgs, batch_size=1, rows=None):
             rows = opts.n_rows
         elif opts.n_rows == 0:
             rows = batch_size
-        else:
+        elif opts.grid_prevent_empty_spots:
             rows = math.floor(math.sqrt(len(imgs)))
             while len(imgs) % rows != 0:
                 rows -= 1
+        else:
+            rows = math.sqrt(len(imgs))
+            rows = round(rows)
 
     cols = math.ceil(len(imgs) / rows)
 
-- 
cgit v1.2.1


From a8eeb2b7ad0c43ad60ac2ba8bd299b9cb265fdd3 Mon Sep 17 00:00:00 2001
From: Ljzd-PRO <63289359+Ljzd-PRO@users.noreply.github.com>
Date: Thu, 13 Oct 2022 02:03:08 +0800
Subject: add `--lowram` parameter

load models to VRM instead of RAM (for machines which have bigger VRM than RAM such as free Google Colab server)
---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/shared.py b/modules/shared.py
index 159f504f..cd4a4714 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -34,6 +34,7 @@ parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
+parser.add_argument("--lowram", action='store_true', help="load models to VRM instead of RAM (for machines which have bigger VRM than RAM such as free Google Colab server)")
 parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
-- 
cgit v1.2.1


From 4a216ded433ded315106e2989c5ff7dec1c49304 Mon Sep 17 00:00:00 2001
From: Ljzd-PRO <63289359+Ljzd-PRO@users.noreply.github.com>
Date: Thu, 13 Oct 2022 02:07:49 +0800
Subject: load models to VRAM when using `--lowram` param

load models to VRM instead of RAM (for machines which have bigger VRM than RAM such as free Google Colab server)
---
 modules/sd_models.py | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 0a55b4c3..78a198b9 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -134,7 +134,12 @@ def load_model_weights(model, checkpoint_info):
 
     print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-    pl_sd = torch.load(checkpoint_file, map_location="cpu")
+    if shared.cmd_opts.lowram:
+        print("Load to VRAM if GPU is available (low RAM)")
+        pl_sd = torch.load(checkpoint_file)
+    else:
+        pl_sd = torch.load(checkpoint_file, map_location="cpu")
+
     if "global_step" in pl_sd:
         print(f"Global Step: {pl_sd['global_step']}")
 
@@ -158,7 +163,13 @@ def load_model_weights(model, checkpoint_info):
 
     if os.path.exists(vae_file):
         print(f"Loading VAE weights from: {vae_file}")
-        vae_ckpt = torch.load(vae_file, map_location="cpu")
+
+        if shared.cmd_opts.lowram:
+            print("Load to VRAM if GPU is available (low RAM)")
+            vae_ckpt = torch.load(vae_file)
+        else:
+            vae_ckpt = torch.load(vae_file, map_location="cpu")
+
         vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
         model.first_stage_model.load_state_dict(vae_dict)
-- 
cgit v1.2.1


From bb295f54785ac36dc6aa6f7103a3431464440fc3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 20:03:41 +0300
Subject: rework the code for lowram a bit

---
 modules/sd_models.py | 12 ++----------
 modules/shared.py    |  3 ++-
 2 files changed, 4 insertions(+), 11 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 78a198b9..3a01c93d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -134,11 +134,7 @@ def load_model_weights(model, checkpoint_info):
 
     print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-    if shared.cmd_opts.lowram:
-        print("Load to VRAM if GPU is available (low RAM)")
-        pl_sd = torch.load(checkpoint_file)
-    else:
-        pl_sd = torch.load(checkpoint_file, map_location="cpu")
+    pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
 
     if "global_step" in pl_sd:
         print(f"Global Step: {pl_sd['global_step']}")
@@ -164,11 +160,7 @@ def load_model_weights(model, checkpoint_info):
     if os.path.exists(vae_file):
         print(f"Loading VAE weights from: {vae_file}")
 
-        if shared.cmd_opts.lowram:
-            print("Load to VRAM if GPU is available (low RAM)")
-            vae_ckpt = torch.load(vae_file)
-        else:
-            vae_ckpt = torch.load(vae_file, map_location="cpu")
+        vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
 
         vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
diff --git a/modules/shared.py b/modules/shared.py
index cd4a4714..695d29b6 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -34,7 +34,7 @@ parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
-parser.add_argument("--lowram", action='store_true', help="load models to VRM instead of RAM (for machines which have bigger VRM than RAM such as free Google Colab server)")
+parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
 parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
@@ -81,6 +81,7 @@ devices.device, devices.device_interrogate, devices.device_gfpgan, devices.devic
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])
 
 device = devices.device
+weight_load_location = None if cmd_opts.lowram else "cpu"
 
 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
 parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
-- 
cgit v1.2.1


From c344ba3b325459abbf9b0df2c1b18f7bf99805b2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 20:31:49 +0300
Subject: add option to read generation params for learning previews from
 txt2img

---
 modules/hypernetworks/hypernetwork.py          | 21 ++++++++++++++++-----
 modules/textual_inversion/textual_inversion.py | 25 ++++++++++++++++++-------
 modules/ui.py                                  | 20 +++++++++++++++++---
 3 files changed, 51 insertions(+), 15 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index f1248bb7..e5cb1817 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -180,7 +180,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     return self.to_out(out)
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
+def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -265,20 +265,31 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
 
-            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
-
             optimizer.zero_grad()
             shared.sd_model.cond_stage_model.to(devices.device)
             shared.sd_model.first_stage_model.to(devices.device)
 
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
-                prompt=preview_text,
-                steps=20,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
 
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entry.cond_text
+                p.steps = 20
+
+            preview_text = p.prompt
+
             processed = processing.process_images(p)
             image = processed.images[0] if len(processed.images)>0 else None
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fa0e33a2..3d835358 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -172,7 +172,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -259,18 +259,29 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
 
-            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
-
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
-                prompt=preview_text,
-                steps=20,
-                height=training_height,
-                width=training_width,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
 
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entry.cond_text
+                p.steps = 20
+                p.width = training_width
+                p.height = training_height
+
+            preview_text = p.prompt
+
             processed = processing.process_images(p)
             image = processed.images[0]
 
diff --git a/modules/ui.py b/modules/ui.py
index 828bfeea..4a04c2cc 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -711,6 +711,18 @@ def create_ui(wrap_gradio_gpu_call):
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
             ]
+
+            txt2img_preview_params = [
+                txt2img_prompt,
+                txt2img_negative_prompt,
+                steps,
+                sampler_index,
+                cfg_scale,
+                seed,
+                width,
+                height,
+            ]
+
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
@@ -1162,7 +1174,7 @@ def create_ui(wrap_gradio_gpu_call):
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
-                    preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
 
                     with gr.Row():
                         interrupt_training = gr.Button(value="Interrupt")
@@ -1240,7 +1252,8 @@ def create_ui(wrap_gradio_gpu_call):
                 save_embedding_every,
                 template_file,
                 save_image_with_stored_embedding,
-                preview_image_prompt,
+                preview_from_txt2img,
+                *txt2img_preview_params,
             ],
             outputs=[
                 ti_output,
@@ -1260,7 +1273,8 @@ def create_ui(wrap_gradio_gpu_call):
                 create_image_every,
                 save_embedding_every,
                 template_file,
-                preview_image_prompt,
+                preview_from_txt2img,
+                *txt2img_preview_params,
             ],
             outputs=[
                 ti_output,
-- 
cgit v1.2.1


From 6cdf55627cb4eb156fb7d8c010d396f93011c04e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 21:12:52 +0300
Subject: restore borders for prompts

---
 style.css | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/style.css b/style.css
index aa3d379c..2306c002 100644
--- a/style.css
+++ b/style.css
@@ -167,14 +167,6 @@ button{
     align-self: stretch !important;
 }
 
-#prompt, #negative_prompt{
-    border: none !important;
-}
-#prompt textarea, #negative_prompt textarea{
-    border: none !important;
-}
-
-
 #img2maskimg .h-60{
     height: 30rem;
 }
-- 
cgit v1.2.1


From 2f0e089c7c8e1ad7d2ad658971c6fdec9622e3ab Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 21:20:28 +0300
Subject: should fix the issue with missing layers in chechpoint merger

---
 modules/extras.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/modules/extras.py b/modules/extras.py
index 532d869f..2e7b3751 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -209,7 +209,12 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
-            theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key] if theta_2 else None, (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
+            t2 = (theta_2 or {}).get(key)
+            if t2 is None:
+                t2 = torch.zeros_like(theta_0[key])
+
+            theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
+
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
 
-- 
cgit v1.2.1


From 9b75ab144f5fa3669166374dacd5ffc340984078 Mon Sep 17 00:00:00 2001
From: ChucklesTheBeard <ChucklesTheBeard@users.noreply.github.com>
Date: Thu, 13 Oct 2022 16:45:02 -0400
Subject: fix typo

---
 launch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index 16627a03..a7c5807b 100644
--- a/launch.py
+++ b/launch.py
@@ -76,7 +76,7 @@ def git_clone(url, dir, name, commithash=None):
             return
 
         run(f'"{git}" -C {dir} fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
-        run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commint for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
+        run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
         return
 
     run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
-- 
cgit v1.2.1


From 02382f7ce462a360e8aea9ee3178da48b564f70a Mon Sep 17 00:00:00 2001
From: RnDMonkey <ryandrutledge@gmail.com>
Date: Wed, 12 Oct 2022 16:35:36 -0700
Subject: regression in xy_grid Var. seed fixing

---
 scripts/xy_grid.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index efb63af5..5700b007 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -338,7 +338,7 @@ class Script(scripts.Script):
         ys = process_axis(y_opt, y_values)
 
         def fix_axis_seeds(axis_opt, axis_list):
-            if axis_opt.label == 'Seed':
+            if axis_opt.label in ['Seed','Var. seed']:
                 return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
             else:
                 return axis_list
-- 
cgit v1.2.1


From c250cb289c97fe303cef69064bf45899406f6a40 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 22:01:49 +0300
Subject: change checkpoint merger to work in a more obvious way remove sigmoid
 and inverse sigmoid because they just did the same thing as weighed sum only
 with changed multiplier

---
 javascript/hints.js |  4 ++--
 modules/extras.py   | 24 +++++-------------------
 modules/ui.py       |  4 ++--
 3 files changed, 9 insertions(+), 23 deletions(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index af010a59..8fec907d 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -87,8 +87,8 @@ titles = {
 
     "Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
 
-    "Weighted Sum": "Result = A * (1 - M) + B * M",
-    "Add difference": "Result = A + (B - C) * (1 - M)",
+    "Weighted sum": "Result = A * (1 - M) + B * M",
+    "Add difference": "Result = A + (B - C) * M",
 }
 
 
diff --git a/modules/extras.py b/modules/extras.py
index 2e7b3751..f2f5a7b0 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -159,24 +159,12 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, interp_amount, save_as_half, custom_name):
-    # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
+def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
     def weighted_sum(theta0, theta1, theta2, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
-    # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    def sigmoid(theta0, theta1, theta2, alpha):
-        alpha = alpha * alpha * (3 - (2 * alpha))
-        return theta0 + ((theta1 - theta0) * alpha)
-
-    # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    def inv_sigmoid(theta0, theta1, theta2, alpha):
-        import math
-        alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
-        return theta0 + ((theta1 - theta0) * alpha)
-
     def add_difference(theta0, theta1, theta2, alpha):
-        return theta0 + (theta1 - theta2) * (1.0 - alpha)
+        return theta0 + (theta1 - theta2) * alpha
 
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
@@ -198,9 +186,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
         theta_2 = None
 
     theta_funcs = {
-        "Weighted Sum": weighted_sum,
-        "Sigmoid": sigmoid,
-        "Inverse Sigmoid": inv_sigmoid,
+        "Weighted sum": weighted_sum,
         "Add difference": add_difference,
     }
     theta_func = theta_funcs[interp_method]
@@ -213,7 +199,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
             if t2 is None:
                 t2 = torch.zeros_like(theta_0[key])
 
-            theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
+            theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, multiplier)
 
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
@@ -227,7 +213,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
 
-    filename = primary_model_info.model_name + '_' + str(round(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
+    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
     filename = filename if custom_name == '' else (custom_name + '.ckpt')
     output_modelname = os.path.join(ckpt_dir, filename)
 
diff --git a/modules/ui.py b/modules/ui.py
index 4a04c2cc..a08ffc9b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1101,8 +1101,8 @@ def create_ui(wrap_gradio_gpu_call):
                     secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
                     tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
                 custom_name = gr.Textbox(label="Custom Name (Optional)")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation amount (1 - M)', value=0.3)
-                interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid", "Add difference"], value="Weighted Sum", label="Interpolation Method")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
+                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
                 save_as_half = gr.Checkbox(value=False, label="Save as float16")
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
 
-- 
cgit v1.2.1


From 4cc37e4cdf2ce5f5b753786b55ae1d4abd530c01 Mon Sep 17 00:00:00 2001
From: Naeaeaeaeae <niels.reisch@gmx.de>
Date: Thu, 13 Oct 2022 18:49:58 +0200
Subject: [xy_grid.py] add option denoising_strength

---
 scripts/xy_grid.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 5700b007..fda2b71d 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -176,6 +176,7 @@ axis_options = [
     AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label, None),
     AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
     AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
+    AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
     AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
 ]
 
-- 
cgit v1.2.1


From 989a552de3d1fcd1f178fe873713b884e192dd61 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 22:04:08 +0300
Subject: remove the other Denoising

---
 scripts/xy_grid.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index fda2b71d..8c7da6bb 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -177,7 +177,6 @@ axis_options = [
     AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
     AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
     AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
-    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
 ]
 
 
-- 
cgit v1.2.1


From 03d62538aebeff51713619fe808c953bdb70193d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 22:43:55 +0300
Subject: remove duplicate code for log loss, add step, make it read from
 options rather than gradio input

---
 modules/hypernetworks/hypernetwork.py          | 20 ++++--------
 modules/shared.py                              |  3 +-
 modules/textual_inversion/textual_inversion.py | 44 ++++++++++++++++++--------
 modules/ui.py                                  |  3 --
 4 files changed, 38 insertions(+), 32 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index edb8cba1..59c7ac6e 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -15,6 +15,7 @@ import torch
 from torch import einsum
 from einops import rearrange, repeat
 import modules.textual_inversion.dataset
+from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 
@@ -210,7 +211,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
@@ -263,19 +264,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
             hypernetwork.save(last_saved_file)
 
-        if write_csv_every > 0 and hypernetwork_dir is not None and hypernetwork.step % write_csv_every == 0:
-            write_csv_header = False if os.path.exists(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv")) else True
-            
-            with open(os.path.join(hypernetwork_dir, "hypernetwork_loss.csv"), "a+") as fout:
-
-                csv_writer = csv.DictWriter(fout, fieldnames=["step", "loss", "learn_rate"])
-                
-                if write_csv_header:
-                    csv_writer.writeheader()
-
-                csv_writer.writerow({"step": hypernetwork.step, 
-                    "loss": f"{losses.mean():.7f}",
-                    "learn_rate": scheduler.learn_rate})
+        textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
+            "loss": f"{losses.mean():.7f}",
+            "learn_rate": scheduler.learn_rate
+        })
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
diff --git a/modules/shared.py b/modules/shared.py
index 695d29b6..d41a7ab3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -236,7 +236,8 @@ options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
-    "training_image_repeats_per_epoch": OptionInfo(100, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
+    "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
+    "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 1f5ace6f..da0d77a0 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -173,6 +173,32 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
+def write_loss(log_directory, filename, step, epoch_len, values):
+    if shared.opts.training_write_csv_every == 0:
+        return
+
+    if step % shared.opts.training_write_csv_every != 0:
+        return
+
+    write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
+
+    with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
+        csv_writer = csv.DictWriter(fout, fieldnames=["step", "epoch", "epoch_step", *(values.keys())])
+
+        if write_csv_header:
+            csv_writer.writeheader()
+
+        epoch = step // epoch_len
+        epoch_step = step - epoch * epoch_len
+
+        csv_writer.writerow({
+            "step": step + 1,
+            "epoch": epoch + 1,
+            "epoch_step": epoch_step + 1,
+            **values,
+        })
+
+
 def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert embedding_name, 'embedding not selected'
 
@@ -257,20 +283,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
             embedding.save(last_saved_file)
 
-        if write_csv_every > 0 and log_directory is not None and embedding.step % write_csv_every == 0:
-            write_csv_header = False if os.path.exists(os.path.join(log_directory, "textual_inversion_loss.csv")) else True
-
-            with open(os.path.join(log_directory, "textual_inversion_loss.csv"), "a+") as fout:
-
-                csv_writer = csv.DictWriter(fout, fieldnames=["epoch", "epoch_step", "loss", "learn_rate"])
-                
-                if write_csv_header:
-                    csv_writer.writeheader()
-
-                csv_writer.writerow({"epoch": epoch_num + 1, 
-                    "epoch_step": epoch_step - 1, 
-                    "loss": f"{losses.mean():.7f}",
-                    "learn_rate": scheduler.learn_rate})
+        write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
+            "loss": f"{losses.mean():.7f}",
+            "learn_rate": scheduler.learn_rate
+        })
 
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
diff --git a/modules/ui.py b/modules/ui.py
index be4a43a7..a08ffc9b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1172,7 +1172,6 @@ def create_ui(wrap_gradio_gpu_call):
                     training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
-                    write_csv_every = gr.Number(label='Save an csv containing the loss to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
                     preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
@@ -1251,7 +1250,6 @@ def create_ui(wrap_gradio_gpu_call):
                 steps,
                 create_image_every,
                 save_embedding_every,
-                write_csv_every,
                 template_file,
                 save_image_with_stored_embedding,
                 preview_from_txt2img,
@@ -1274,7 +1272,6 @@ def create_ui(wrap_gradio_gpu_call):
                 steps,
                 create_image_every,
                 save_embedding_every,
-                write_csv_every,
                 template_file,
                 preview_from_txt2img,
                 *txt2img_preview_params,
-- 
cgit v1.2.1


From e21f01f64504bc651da6e85216474bbd35ee010d Mon Sep 17 00:00:00 2001
From: Rae Fu <rraefu@gmail.com>
Date: Thu, 13 Oct 2022 23:00:38 -0600
Subject: add checkpoint cache option to UI for faster model switching

switching time reduced from ~1500ms to ~280ms
---
 modules/sd_models.py | 54 +++++++++++++++++++++++++++++++---------------------
 modules/shared.py    |  1 +
 2 files changed, 33 insertions(+), 22 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 0a55b4c3..f3660d8d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1,4 +1,4 @@
-import glob
+import collections
 import os.path
 import sys
 from collections import namedtuple
@@ -15,6 +15,7 @@ model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
 CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
 checkpoints_list = {}
+checkpoints_loaded = collections.OrderedDict()
 
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -132,38 +133,46 @@ def load_model_weights(model, checkpoint_info):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
+    if checkpoint_info not in checkpoints_loaded:
+        print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-    pl_sd = torch.load(checkpoint_file, map_location="cpu")
-    if "global_step" in pl_sd:
-        print(f"Global Step: {pl_sd['global_step']}")
+        pl_sd = torch.load(checkpoint_file, map_location="cpu")
+        if "global_step" in pl_sd:
+            print(f"Global Step: {pl_sd['global_step']}")
 
-    sd = get_state_dict_from_checkpoint(pl_sd)
+        sd = get_state_dict_from_checkpoint(pl_sd)
+        model.load_state_dict(sd, strict=False)
 
-    model.load_state_dict(sd, strict=False)
+        if shared.cmd_opts.opt_channelslast:
+            model.to(memory_format=torch.channels_last)
 
-    if shared.cmd_opts.opt_channelslast:
-        model.to(memory_format=torch.channels_last)
+        if not shared.cmd_opts.no_half:
+            model.half()
 
-    if not shared.cmd_opts.no_half:
-        model.half()
+        devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
+        devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
-    devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
-    devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
+        vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
 
-    vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
+        if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
+            vae_file = shared.cmd_opts.vae_path
 
-    if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
-        vae_file = shared.cmd_opts.vae_path
+        if os.path.exists(vae_file):
+            print(f"Loading VAE weights from: {vae_file}")
+            vae_ckpt = torch.load(vae_file, map_location="cpu")
+            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
 
-    if os.path.exists(vae_file):
-        print(f"Loading VAE weights from: {vae_file}")
-        vae_ckpt = torch.load(vae_file, map_location="cpu")
-        vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
+            model.first_stage_model.load_state_dict(vae_dict)
 
-        model.first_stage_model.load_state_dict(vae_dict)
+        model.first_stage_model.to(devices.dtype_vae)
 
-    model.first_stage_model.to(devices.dtype_vae)
+        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+            checkpoints_loaded.popitem(last=False)  # LRU
+    else:
+        print(f"Loading weights [{sd_model_hash}] from cache")
+        checkpoints_loaded.move_to_end(checkpoint_info)
+        model.load_state_dict(checkpoints_loaded[checkpoint_info])
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
@@ -202,6 +211,7 @@ def reload_model_weights(sd_model, info=None):
         return
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
+        checkpoints_loaded.clear()
         shared.sd_model = load_model()
         return shared.sd_model
 
diff --git a/modules/shared.py b/modules/shared.py
index 5901e605..b2090da1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -238,6 +238,7 @@ options_templates.update(options_section(('training', "Training"), {
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
+    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
-- 
cgit v1.2.1


From cd58e44051f658f2efb544203a92837f43786372 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 23:17:28 +0300
Subject: disabling history - i knew it was slow as fuck but i didn't realize
 it would also show galleries on launch

---
 modules/ui.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index a08ffc9b..6d193955 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1089,7 +1089,8 @@ def create_ui(wrap_gradio_gpu_call):
         "t2i":txt2img_paste_fields,
         "i2i":img2img_paste_fields
     }
-    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) 
+
+    #images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
 
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
@@ -1486,7 +1487,7 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        (images_history, "History", "images_history"),
+        #(images_history, "History", "images_history"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
         (settings_interface, "Settings", "settings"),
-- 
cgit v1.2.1


From 368f4cc4c73509c1968cd9defe068d8bf4ff7c4f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 14 Oct 2022 23:19:05 +0300
Subject: set firstpass w/h to 0 by default and rever to old behavior when any
 are 0

---
 modules/processing.py | 49 ++++++++++++++++++++++++++++++-------------------
 modules/ui.py         |  4 ++--
 2 files changed, 32 insertions(+), 21 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 100a259f..a75b9f84 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -501,17 +501,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
-    firstphase_width = 0
-    firstphase_height = 0
-    firstphase_width_truncated = 0
-    firstphase_height_truncated = 0
 
-    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, **kwargs):
+    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.denoising_strength = denoising_strength
         self.firstphase_width = firstphase_width
         self.firstphase_height = firstphase_height
+        self.truncate_x = 0
+        self.truncate_y = 0
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
@@ -520,6 +518,32 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
+            if self.firstphase_width == 0 or self.firstphase_height == 0:
+                desired_pixel_count = 512 * 512
+                actual_pixel_count = self.width * self.height
+                scale = math.sqrt(desired_pixel_count / actual_pixel_count)
+                self.firstphase_width = math.ceil(scale * self.width / 64) * 64
+                self.firstphase_height = math.ceil(scale * self.height / 64) * 64
+                firstphase_width_truncated = int(scale * self.width)
+                firstphase_height_truncated = int(scale * self.height)
+
+            else:
+                self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
+
+                width_ratio = self.width / self.firstphase_width
+                height_ratio = self.height / self.firstphase_height
+
+                if width_ratio > height_ratio:
+                    firstphase_width_truncated = self.firstphase_width
+                    firstphase_height_truncated = self.firstphase_width * self.height / self.width
+                else:
+                    firstphase_width_truncated = self.firstphase_height * self.width / self.height
+                    firstphase_height_truncated = self.firstphase_height
+
+            self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
+            self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
+
+
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
@@ -528,23 +552,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
             return samples
 
-        self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
-
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
 
-        truncate_x = 0
-        truncate_y = 0
-        width_ratio = self.width/self.firstphase_width
-        height_ratio = self.height/self.firstphase_height
-
-        if width_ratio > height_ratio:
-            truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
-
-        elif width_ratio < height_ratio:
-            truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
-                
-        samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
+        samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
         decoded_samples = decode_first_stage(self.sd_model, samples)
 
diff --git a/modules/ui.py b/modules/ui.py
index 6d193955..a1d18be9 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -567,8 +567,8 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
-                    firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass width", value=0)
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass height", value=0)
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
                 with gr.Row(equal_height=True):
-- 
cgit v1.2.1


From 4d19f3b7d461fe0f63e7ccff936909b0ce0c6126 Mon Sep 17 00:00:00 2001
From: Melan <alexleander91@gmail.com>
Date: Fri, 14 Oct 2022 22:45:26 +0200
Subject: Raise an assertion error if no training images have been found.

---
 modules/textual_inversion/dataset.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 67e90afe..12e2f43b 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -81,7 +81,8 @@ class PersonalizedBase(Dataset):
                 entry.cond = cond_model([entry.cond_text]).to(devices.cpu)
 
             self.dataset.append(entry)
-
+        
+        assert len(self.dataset) > 1, "No images have been found in the dataset."
         self.length = len(self.dataset) * repeats
 
         self.initial_indexes = np.arange(self.length) % len(self.dataset)
-- 
cgit v1.2.1


From 4dc426509918e90bf4557ecfd1f84031362360c0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 00:21:48 +0300
Subject: rename firstpass w/h to discard old user settings

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index a1d18be9..c5d295ea 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -567,8 +567,8 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="First pass height", value=0)
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
                 with gr.Row(equal_height=True):
-- 
cgit v1.2.1


From 4bbe5d62e042e78cfe1dc83492c2398a39a2455c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 00:25:09 +0300
Subject: reformat lines in images_history.py

---
 modules/images_history.py | 182 +++++++++++++++++++++++++---------------------
 1 file changed, 98 insertions(+), 84 deletions(-)

diff --git a/modules/images_history.py b/modules/images_history.py
index 723f5301..f5ef44fe 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,5 +1,7 @@
 import os
 import shutil
+
+
 def traverse_all_files(output_dir, image_list, curr_dir=None):
     curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
     try:
@@ -16,10 +18,10 @@ def traverse_all_files(output_dir, image_list, curr_dir=None):
         elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0:
             image_list.append(file)
         else:
-            image_list = traverse_all_files(output_dir, image_list, file)            
+            image_list = traverse_all_files(output_dir, image_list, file)
     return image_list
 
-        
+
 def get_recent_images(dir_name, page_index, step, image_index, tabname):
     page_index = int(page_index)
     f_list = os.listdir(dir_name)
@@ -27,36 +29,48 @@ def get_recent_images(dir_name, page_index, step, image_index, tabname):
     image_list = traverse_all_files(dir_name, image_list)
     image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
     num = 48 if tabname != "extras" else 12
-    max_page_index = len(image_list) // num + 1    
+    max_page_index = len(image_list) // num + 1
     page_index = max_page_index if page_index == -1 else page_index + step
-    page_index = 1 if page_index < 1 else page_index    
+    page_index = 1 if page_index < 1 else page_index
     page_index = max_page_index if page_index > max_page_index else page_index
     idx_frm = (page_index - 1) * num
     image_list = image_list[idx_frm:idx_frm + num]
     image_index = int(image_index)
-    if image_index < 0 or image_index > len(image_list)  - 1:
-        current_file = None 
+    if image_index < 0 or image_index > len(image_list) - 1:
+        current_file = None
         hidden = None
     else:
-        current_file =  image_list[int(image_index)]
+        current_file = image_list[int(image_index)]
         hidden = os.path.join(dir_name, current_file)
     return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, ""
 
+
 def first_page_click(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, 1, 0, image_index, tabname)
+
+
 def end_page_click(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, -1, 0, image_index, tabname)
+
+
 def prev_page_click(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, page_index, -1, image_index, tabname)
-def next_page_click(dir_name, page_index, image_index, tabname):    
+
+
+def next_page_click(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, page_index, 1, image_index, tabname)
-def page_index_change(dir_name, page_index, image_index, tabname):    
+
+
+def page_index_change(dir_name, page_index, image_index, tabname):
     return get_recent_images(dir_name, page_index, 0, image_index, tabname)
 
+
 def show_image_info(num, image_path, filenames):
-    #print(f"select image {num}")
+    # print(f"select image {num}")
     file = filenames[int(num)]
     return file, num, os.path.join(image_path, file)
+
+
 def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
     if name == "":
         return filenames, delete_num
@@ -66,14 +80,14 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima
         i = 0
         new_file_list = []
         for name in filenames:
-            if i  >= index and i < index + delete_num:            
+            if i >= index and i < index + delete_num:
                 path = os.path.join(dir_name, name)
-                if os.path.exists(path):        
+                if os.path.exists(path):
                     print(f"Delete file {path}")
                     os.remove(path)
-                    txt_file = os.path.splitext(path)[0]  + ".txt"
+                    txt_file = os.path.splitext(path)[0] + ".txt"
                     if os.path.exists(txt_file):
-                        os.remove(txt_file)       
+                        os.remove(txt_file)
                 else:
                     print(f"Not exists file {path}")
             else:
@@ -81,81 +95,81 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima
             i += 1
     return new_file_list, 1
 
+
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
-        if tabname == "txt2img":
-            dir_name = opts.outdir_txt2img_samples
-        elif tabname == "img2img":
-            dir_name = opts.outdir_img2img_samples
-        elif tabname == "extras":
-            dir_name = opts.outdir_extras_samples
-        d = dir_name.split("/")
-        dir_name = d[0]
-        for p in d[1:]:
-            dir_name = os.path.join(dir_name, p)
-        with gr.Row():    
-            renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page") 
-            first_page = gr.Button('First Page')
-            prev_page = gr.Button('Prev Page') 
-            page_index = gr.Number(value=1, label="Page Index")
-            next_page = gr.Button('Next Page') 
-            end_page = gr.Button('End Page')    
-        with gr.Row(elem_id=tabname + "_images_history"):            
-            with gr.Row():     
-                with gr.Column(scale=2):                    
-                    history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
-                    with gr.Row():                        
-                        delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")   
-                        delete = gr.Button('Delete',  elem_id=tabname + "_images_history_del_button")                                        
-                with gr.Column():     
-                    with gr.Row():          
-                        pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                        pnginfo_send_to_img2img = gr.Button('Send to img2img')
-                    with gr.Row():
-                        with gr.Column():
-                            img_file_info = gr.Textbox(label="Generate Info", interactive=False)
-                            img_file_name = gr.Textbox(label="File Name", interactive=False)    
-                    with gr.Row():    
-                        # hiden items
-
-                        img_path = gr.Textbox(dir_name.rstrip("/") , visible=False)                
-                        tabname_box = gr.Textbox(tabname, visible=False)    
-                        image_index = gr.Textbox(value=-1, visible=False)                        
-                        set_index = gr.Button('set_index',  elem_id=tabname + "_images_history_set_index", visible=False)
-                        filenames = gr.State()
-                        hidden = gr.Image(type="pil", visible=False)
-                        info1 = gr.Textbox(visible=False)
-                        info2 = gr.Textbox(visible=False)
-
-                
-        # turn pages
-        gallery_inputs = [img_path, page_index, image_index, tabname_box]
-        gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
-
-        first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-        next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-        prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)        
-        end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-        page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-        renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-        #page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
-
-        #other funcitons
-        set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box,  img_path, filenames], outputs=[img_file_name, image_index, hidden])
-        img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
-        delete.click(delete_image,_js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num]) 
-        hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
-       
-        #pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
-        switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
-        switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
-    
-        
+    if tabname == "txt2img":
+        dir_name = opts.outdir_txt2img_samples
+    elif tabname == "img2img":
+        dir_name = opts.outdir_img2img_samples
+    elif tabname == "extras":
+        dir_name = opts.outdir_extras_samples
+    d = dir_name.split("/")
+    dir_name = d[0]
+    for p in d[1:]:
+        dir_name = os.path.join(dir_name, p)
+    with gr.Row():
+        renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page")
+        first_page = gr.Button('First Page')
+        prev_page = gr.Button('Prev Page')
+        page_index = gr.Number(value=1, label="Page Index")
+        next_page = gr.Button('Next Page')
+        end_page = gr.Button('End Page')
+    with gr.Row(elem_id=tabname + "_images_history"):
+        with gr.Row():
+            with gr.Column(scale=2):
+                history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
+                with gr.Row():
+                    delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
+                    delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
+            with gr.Column():
+                with gr.Row():
+                    pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
+                    pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                with gr.Row():
+                    with gr.Column():
+                        img_file_info = gr.Textbox(label="Generate Info", interactive=False)
+                        img_file_name = gr.Textbox(label="File Name", interactive=False)
+                with gr.Row():
+                    # hiden items
+
+                    img_path = gr.Textbox(dir_name.rstrip("/"), visible=False)
+                    tabname_box = gr.Textbox(tabname, visible=False)
+                    image_index = gr.Textbox(value=-1, visible=False)
+                    set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False)
+                    filenames = gr.State()
+                    hidden = gr.Image(type="pil", visible=False)
+                    info1 = gr.Textbox(visible=False)
+                    info2 = gr.Textbox(visible=False)
+
+    # turn pages
+    gallery_inputs = [img_path, page_index, image_index, tabname_box]
+    gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
+
+    first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    # page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
+
+    # other funcitons
+    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hidden])
+    img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
+    delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num])
+    hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
+
+    # pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
+    switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
+    switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
+
+
 def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
     with gr.Blocks(analytics_enabled=False) as images_history:
         with gr.Tabs() as tabs:
             with gr.Tab("txt2img history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_txt2img:   
-                    show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict) 
+                with gr.Blocks(analytics_enabled=False) as images_history_txt2img:
+                    show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict)
             with gr.Tab("img2img history"):
                 with gr.Blocks(analytics_enabled=False) as images_history_img2img:
                     show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict)
-- 
cgit v1.2.1


From a8f7722e4e7460122b44589c3718eee0c597009d Mon Sep 17 00:00:00 2001
From: space-nuko <24979496+space-nuko@users.noreply.github.com>
Date: Fri, 14 Oct 2022 14:26:38 -0700
Subject: Fix XY-plot steps if highres fix is enabled

---
 scripts/xy_grid.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 8c7da6bb..88ad3bf7 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -12,7 +12,7 @@ import gradio as gr
 
 from modules import images
 from modules.hypernetworks import hypernetwork
-from modules.processing import process_images, Processed, get_correct_sampler
+from modules.processing import process_images, Processed, get_correct_sampler, StableDiffusionProcessingTxt2Img
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.sd_samplers
@@ -354,6 +354,9 @@ class Script(scripts.Script):
         else:
             total_steps = p.steps * len(xs) * len(ys)
 
+        if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
+            total_steps *= 2
+
         print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})")
         shared.total_tqdm.updateTotal(total_steps * p.n_iter)
 
-- 
cgit v1.2.1


From acedbe67d2b8a3af99ca3b9a2f809e7a2db285d1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 00:43:15 +0300
Subject: bring history tab back, make it behave; it's still slow but won't
 fuck anything up until you use it

---
 javascript/images_history.js | 16 ++++++++++++----
 modules/ui.py                |  4 ++--
 2 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/javascript/images_history.js b/javascript/images_history.js
index 3a20056b..f7d052c3 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -163,10 +163,15 @@ function images_history_init(){
         for (var i in images_history_tab_list){               
             var tabname = images_history_tab_list[i]
             tab_btns[i].setAttribute("tabname", tabname);
-            tab_btns[i].addEventListener('click', images_history_click_tab);
+
+            // this refreshes history upon tab switch
+            // until the history is known to work well, which is not the case now, we do not do this at startup
+            //tab_btns[i].addEventListener('click', images_history_click_tab);
         }    
-        tabs_box.classList.add(images_history_tab_list[0]);   
-           load_txt2img_button.click();
+        tabs_box.classList.add(images_history_tab_list[0]);
+
+        // same as above, at page load
+        //load_txt2img_button.click();
     } else {
         setTimeout(images_history_init, 500);
     } 
@@ -182,12 +187,15 @@ document.addEventListener("DOMContentLoaded", function() {
             buttons.forEach(function(bnt){    
                 bnt.addEventListener('click', images_history_click_image, true);
             });
+
+            // same as load_txt2img_button.click() above
+            /*
             var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
             if (cls_btn){
                 cls_btn.addEventListener('click', function(){
                     gradioApp().getElementById(tabname + '_images_history_renew_page').click();
                 }, false);
-            }
+            }*/
 
         }     
     });
diff --git a/modules/ui.py b/modules/ui.py
index c5d295ea..1bc919c7 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1090,7 +1090,7 @@ def create_ui(wrap_gradio_gpu_call):
         "i2i":img2img_paste_fields
     }
 
-    #images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
+    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
 
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
@@ -1487,7 +1487,7 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        #(images_history, "History", "images_history"),
+        (images_history, "History", "images_history"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
         (settings_interface, "Settings", "settings"),
-- 
cgit v1.2.1


From c7a86f7fe9c0b8967a87e8d709f507d2f44400d8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 09:24:59 +0300
Subject: add option to use batch size for training

---
 modules/hypernetworks/hypernetwork.py          | 33 +++++++++++++++++++-------
 modules/textual_inversion/dataset.py           | 31 ++++++++++++++----------
 modules/textual_inversion/textual_inversion.py | 17 +++++++------
 modules/ui.py                                  |  3 +++
 4 files changed, 54 insertions(+), 30 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 59c7ac6e..a2b3bc0a 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -182,7 +182,21 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     return self.to_out(out)
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def stack_conds(conds):
+    if len(conds) == 1:
+        return torch.stack(conds)
+
+    # same as in reconstruct_multicond_batch
+    token_count = max([x.shape[0] for x in conds])
+    for i in range(len(conds)):
+        if conds[i].shape[0] != token_count:
+            last_vector = conds[i][-1:]
+            last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1])
+            conds[i] = torch.vstack([conds[i], last_vector_repeated])
+
+    return torch.stack(conds)
+
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -211,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
@@ -235,7 +249,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, entry in pbar:
+    for i, entries in pbar:
         hypernetwork.step = i + ititial_step
 
         scheduler.apply(optimizer, hypernetwork.step)
@@ -246,11 +260,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             break
 
         with torch.autocast("cuda"):
-            cond = entry.cond.to(devices.device)
-            x = entry.latent.to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), cond)[0]
+            c = stack_conds([entry.cond for entry in entries]).to(devices.device)
+#            c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
+            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+            loss = shared.sd_model(x, c)[0]
             del x
-            del cond
+            del c
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
@@ -292,7 +307,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
                 p.width = preview_width
                 p.height = preview_height
             else:
-                p.prompt = entry.cond_text
+                p.prompt = entries[0].cond_text
                 p.steps = 20
 
             preview_text = p.prompt
@@ -315,7 +330,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {hypernetwork.step}<br/>
-Last prompt: {html.escape(entry.cond_text)}<br/>
+Last prompt: {html.escape(entries[0].cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 67e90afe..bd99c0cb 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -24,11 +24,12 @@ class DatasetEntry:
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False):
-        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else None
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):
+        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
 
         self.placeholder_token = placeholder_token
 
+        self.batch_size = batch_size
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
@@ -78,13 +79,13 @@ class PersonalizedBase(Dataset):
 
             if include_cond:
                 entry.cond_text = self.create_text(filename_text)
-                entry.cond = cond_model([entry.cond_text]).to(devices.cpu)
+                entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
 
             self.dataset.append(entry)
 
-        self.length = len(self.dataset) * repeats
+        self.length = len(self.dataset) * repeats // batch_size
 
-        self.initial_indexes = np.arange(self.length) % len(self.dataset)
+        self.initial_indexes = np.arange(len(self.dataset))
         self.indexes = None
         self.shuffle()
 
@@ -101,13 +102,19 @@ class PersonalizedBase(Dataset):
         return self.length
 
     def __getitem__(self, i):
-        if i % len(self.dataset) == 0:
-            self.shuffle()
+        res = []
 
-        index = self.indexes[i % len(self.indexes)]
-        entry = self.dataset[index]
+        for j in range(self.batch_size):
+            position = i * self.batch_size + j
+            if position % len(self.indexes) == 0:
+                self.shuffle()
 
-        if entry.cond is None:
-            entry.cond_text = self.create_text(entry.filename_text)
+            index = self.indexes[position % len(self.indexes)]
+            entry = self.dataset[index]
 
-        return entry
+            if entry.cond is None:
+                entry.cond_text = self.create_text(entry.filename_text)
+
+            res.append(entry)
+
+        return res
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index da0d77a0..e754747e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -199,7 +199,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
         })
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -231,7 +231,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
 
     hijack = sd_hijack.model_hijack
 
@@ -251,7 +251,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-    for i, entry in pbar:
+    for i, entries in pbar:
         embedding.step = i + ititial_step
 
         scheduler.apply(optimizer, embedding.step)
@@ -262,10 +262,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             break
 
         with torch.autocast("cuda"):
-            c = cond_model([entry.cond_text])
-
-            x = entry.latent.to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), c)[0]
+            c = cond_model([entry.cond_text for entry in entries])
+            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+            loss = shared.sd_model(x, c)[0]
             del x
 
             losses[embedding.step % losses.shape[0]] = loss.item()
@@ -307,7 +306,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 p.width = preview_width
                 p.height = preview_height
             else:
-                p.prompt = entry.cond_text
+                p.prompt = entries[0].cond_text
                 p.steps = 20
                 p.width = training_width
                 p.height = training_height
@@ -348,7 +347,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {embedding.step}<br/>
-Last prompt: {html.escape(entry.cond_text)}<br/>
+Last prompt: {html.escape(entries[0].cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
diff --git a/modules/ui.py b/modules/ui.py
index 1bc919c7..45550ea8 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1166,6 +1166,7 @@ def create_ui(wrap_gradio_gpu_call):
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                     train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
                     learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
+                    batch_size = gr.Number(label='Batch size', value=1, precision=0)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
@@ -1244,6 +1245,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 train_embedding_name,
                 learn_rate,
+                batch_size,
                 dataset_directory,
                 log_directory,
                 training_width,
@@ -1268,6 +1270,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 train_hypernetwork_name,
                 learn_rate,
+                batch_size,
                 dataset_directory,
                 log_directory,
                 steps,
-- 
cgit v1.2.1


From 3bd40bb77ff274f2a09efa07b759eebf6dc40b58 Mon Sep 17 00:00:00 2001
From: ruocaled <ruocaled@gmail.com>
Date: Fri, 14 Oct 2022 11:05:14 -0700
Subject: auto re-open selected image after re-generation

attach an observer of gallery when generation in progress, if there was a image selected in gallery and gallery has only 1 image, auto re-select/open that image.

This matches behavior of prior to Gradio 3.4.1 version bump, is a quality of life feature many people enjoyed.
---
 javascript/progressbar.js | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 4395a215..4994b476 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -1,5 +1,7 @@
 // code related to showing and updating progressbar shown as the image is being made
 global_progressbars = {}
+galleries = {}
+galleryObservers = {}
 
 function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
     var progressbar = gradioApp().getElementById(id_progressbar)
@@ -31,6 +33,9 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
                 preview.style.width = gallery.clientWidth + "px"
                 preview.style.height = gallery.clientHeight + "px"
 
+				//only watch gallery if there is a generation process going on
+                check_gallery(id_gallery);    
+
                 var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
                 if(!progressDiv){
                     if (skip) {
@@ -38,6 +43,12 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
                     }
                     interrupt.style.display = "none"
                 }
+
+				//disconnect observer once generation finished, so user can close selected image if they want
+				if (galleryObservers[id_gallery]) {
+					galleryObservers[id_gallery].disconnect();
+					galleries[id_gallery] = null;
+				}    
             }
 
             window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500)
@@ -46,6 +57,27 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
 	}
 }
 
+function check_gallery(id_gallery){
+    let gallery = gradioApp().getElementById(id_gallery)
+    // if gallery has no change, no need to setting up observer again.
+    if (gallery && galleries[id_gallery] !== gallery){
+        galleries[id_gallery] = gallery;
+        if(galleryObservers[id_gallery]){
+            galleryObservers[id_gallery].disconnect();
+        }
+        galleryObservers[id_gallery] = new MutationObserver(function (){
+            let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
+            let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
+            if (galleryButtons.length === 1 && !galleryBtnSelected) {
+                //automatically open when there is only 1 gallery btn, and was previously selected
+                galleryButtons[0].click();
+                console.log('clicked');
+            }
+        })
+        galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
+    }
+}
+
 onUiUpdate(function(){
     check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_skip', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery')
     check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_skip', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery')
-- 
cgit v1.2.1


From 6b5c54c187796900bf677c8c14b62a166eb53b24 Mon Sep 17 00:00:00 2001
From: ruocaled <ruocaled@gmail.com>
Date: Fri, 14 Oct 2022 11:06:38 -0700
Subject: remove console.log

---
 javascript/progressbar.js | 1 -
 1 file changed, 1 deletion(-)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 4994b476..b4925e99 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -71,7 +71,6 @@ function check_gallery(id_gallery){
             if (galleryButtons.length === 1 && !galleryBtnSelected) {
                 //automatically open when there is only 1 gallery btn, and was previously selected
                 galleryButtons[0].click();
-                console.log('clicked');
             }
         })
         galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
-- 
cgit v1.2.1


From c84eef8195b2bae4f4b4d1785159ae9efd937abe Mon Sep 17 00:00:00 2001
From: ruocaled <ruocaled@gmail.com>
Date: Fri, 14 Oct 2022 11:10:26 -0700
Subject: fix observer disconnect logic

---
 javascript/progressbar.js | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index b4925e99..196fe507 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -42,13 +42,15 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
                         skip.style.display = "none"
                     }
                     interrupt.style.display = "none"
+			
+                    //disconnect observer once generation finished, so user can close selected image if they want
+                    if (galleryObservers[id_gallery]) {
+                        galleryObservers[id_gallery].disconnect();
+                        galleries[id_gallery] = null;
+                    }    
                 }
 
-				//disconnect observer once generation finished, so user can close selected image if they want
-				if (galleryObservers[id_gallery]) {
-					galleryObservers[id_gallery].disconnect();
-					galleries[id_gallery] = null;
-				}    
+
             }
 
             window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500)
-- 
cgit v1.2.1


From b26efff8c496309329cd1982aee55e81bf81a655 Mon Sep 17 00:00:00 2001
From: ruocaled <ruocaled@gmail.com>
Date: Fri, 14 Oct 2022 17:14:59 -0700
Subject: allow re-open for multiple images gallery

---
 javascript/progressbar.js | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 196fe507..574fd549 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -67,12 +67,13 @@ function check_gallery(id_gallery){
         if(galleryObservers[id_gallery]){
             galleryObservers[id_gallery].disconnect();
         }
+        let prevSelectedIndex = selected_gallery_index();
         galleryObservers[id_gallery] = new MutationObserver(function (){
             let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
             let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
-            if (galleryButtons.length === 1 && !galleryBtnSelected) {
-                //automatically open when there is only 1 gallery btn, and was previously selected
-                galleryButtons[0].click();
+            if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
+                //automatically re-open previously selected index (if exists)
+                galleryButtons[prevSelectedIndex].click();
             }
         })
         galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
-- 
cgit v1.2.1


From c7cd2fda5a6c9c97d5c238e0f2e1146d346e0e93 Mon Sep 17 00:00:00 2001
From: ruocaled <ruocaled@gmail.com>
Date: Fri, 14 Oct 2022 19:05:41 -0700
Subject: re-attach full screen zoom listeners

---
 javascript/progressbar.js | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 574fd549..35f20b15 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -74,6 +74,9 @@ function check_gallery(id_gallery){
             if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
                 //automatically re-open previously selected index (if exists)
                 galleryButtons[prevSelectedIndex].click();
+		setTimeout(function (){
+                    showGalleryImage()
+                },100)
             }
         })
         galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
-- 
cgit v1.2.1


From 661a61985c7bee34a67390a05761e25830a6b918 Mon Sep 17 00:00:00 2001
From: ruocaled <ruocaled@gmail.com>
Date: Fri, 14 Oct 2022 19:25:30 -0700
Subject: remove extra 100ms timeout

---
 javascript/progressbar.js | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 35f20b15..076f0a97 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -74,9 +74,7 @@ function check_gallery(id_gallery){
             if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
                 //automatically re-open previously selected index (if exists)
                 galleryButtons[prevSelectedIndex].click();
-		setTimeout(function (){
-                    showGalleryImage()
-                },100)
+		showGalleryImage();
             }
         })
         galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
-- 
cgit v1.2.1


From db27b987a97fc8b7894a9dd34bd7641536f9c424 Mon Sep 17 00:00:00 2001
From: aoirusann <yimodo.siki@gmail.com>
Date: Sat, 15 Oct 2022 11:48:13 +0800
Subject: Add hint for `ctrl/alt enter` And duplicate implementations are
 removed

---
 javascript/ui.js | 10 ----------
 modules/ui.py    | 10 ++++++++--
 script.js        |  4 ++--
 3 files changed, 10 insertions(+), 14 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 0f8fe68e..56f4216f 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -187,12 +187,10 @@ onUiUpdate(function(){
 	if (!txt2img_textarea) {
 		txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea");
 		txt2img_textarea?.addEventListener("input", () => update_token_counter("txt2img_token_button"));
-        txt2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "txt2img_generate"));
 	}
 	if (!img2img_textarea) {
 		img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
 		img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
-        img2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "img2img_generate"));
 	}
 })
 
@@ -220,14 +218,6 @@ function update_token_counter(button_id) {
 	token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
 }
 
-function submit_prompt(event, generate_button_id) {
-    if (event.altKey && event.keyCode === 13) {
-        event.preventDefault();
-        gradioApp().getElementById(generate_button_id).click();
-        return;
-    }
-}
-
 function restart_reload(){
     document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
     setTimeout(function(){location.reload()},2000)
diff --git a/modules/ui.py b/modules/ui.py
index 45550ea8..baf4c397 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -433,7 +433,10 @@ def create_toprow(is_img2img):
             with gr.Row():
                 with gr.Column(scale=80):
                     with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2)
+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2, 
+                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
+                        )
+
                 with gr.Column(scale=1, elem_id="roll_col"):
                     roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
                     paste = gr.Button(value=paste_symbol, elem_id="paste")
@@ -446,7 +449,10 @@ def create_toprow(is_img2img):
             with gr.Row():
                 with gr.Column(scale=8):
                     with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, 
+                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
+                        )
+
                 with gr.Column(scale=1, elem_id="roll_col"):
                     sh = gr.Button(elem_id="sh", visible=True)                           
 
diff --git a/script.js b/script.js
index 9543cbe6..88f2c839 100644
--- a/script.js
+++ b/script.js
@@ -50,9 +50,9 @@ document.addEventListener("DOMContentLoaded", function() {
  document.addEventListener('keydown', function(e) {
     var handled = false;
     if (e.key !== undefined) {
-        if((e.key == "Enter" && (e.metaKey || e.ctrlKey))) handled = true;
+        if((e.key == "Enter" && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
     } else if (e.keyCode !== undefined) {
-        if((e.keyCode == 13 && (e.metaKey || e.ctrlKey))) handled = true;
+        if((e.keyCode == 13 && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
     }
     if (handled) {
         button = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
-- 
cgit v1.2.1


From cd28465bf87d911965790513c37e6881e4231523 Mon Sep 17 00:00:00 2001
From: ddPn08 <pyn.g.1125@gmail.com>
Date: Sat, 15 Oct 2022 10:56:02 +0900
Subject: do not force relative paths in image history

---
 modules/images_history.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/images_history.py b/modules/images_history.py
index f5ef44fe..09c749fe 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -104,7 +104,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     elif tabname == "extras":
         dir_name = opts.outdir_extras_samples
     d = dir_name.split("/")
-    dir_name = d[0]
+    dir_name = "/" if dir_name.startswith("/") else d[0]
     for p in d[1:]:
         dir_name = os.path.join(dir_name, p)
     with gr.Row():
-- 
cgit v1.2.1


From 0da6c1809996f0f696d4047faf4b9c9939e26daa Mon Sep 17 00:00:00 2001
From: ddPn08 <pyn.g.1125@gmail.com>
Date: Sat, 15 Oct 2022 11:22:05 +0900
Subject: use "outdir_samples" if specified

---
 modules/images_history.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/images_history.py b/modules/images_history.py
index 09c749fe..9260df8a 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -97,7 +97,9 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima
 
 
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
-    if tabname == "txt2img":
+    if opts.outdir_samples != "":
+        dir_name = opts.outdir_samples
+    elif tabname == "txt2img":
         dir_name = opts.outdir_txt2img_samples
     elif tabname == "img2img":
         dir_name = opts.outdir_img2img_samples
-- 
cgit v1.2.1


From 77bf3525f894e89e8f3d812319e822e44419f5ea Mon Sep 17 00:00:00 2001
From: Cassy-Lee <104408348+Cassy-Lee@users.noreply.github.com>
Date: Fri, 14 Oct 2022 17:31:39 +0800
Subject: Update launch.py

Allow change set --index-url for pip.
---
 launch.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/launch.py b/launch.py
index a7c5807b..b753efc1 100644
--- a/launch.py
+++ b/launch.py
@@ -89,6 +89,7 @@ def prepare_enviroment():
     torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
     commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
+    index_url = os.environ.get('INDEX_URL',"")
 
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
     clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
@@ -121,22 +122,22 @@ def prepare_enviroment():
         run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
 
     if not is_installed("gfpgan"):
-        run_pip(f"install {gfpgan_package}", "gfpgan")
+        run_pip(f"install {gfpgan_package}{f' --index-url {index_url}' if index_url!='' else ''}", "gfpgan")
 
     if not is_installed("clip"):
-        run_pip(f"install {clip_package}", "clip")
+        run_pip(f"install {clip_package}{f' --index-url {index_url}' if index_url!='' else ''}", "clip")
 
     if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
         if platform.system() == "Windows":
             run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/c/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
         elif platform.system() == "Linux":
-            run_pip("install xformers", "xformers")
+            run_pip("install xformers{f' --index-url {index_url}' if index_url!='' else ''}", "xformers")
 
     if not is_installed("deepdanbooru") and deepdanbooru:
         run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
 
     if not is_installed("pyngrok") and ngrok:
-        run_pip("install pyngrok", "ngrok")
+        run_pip("install pyngrok{f' --index-url {index_url}' if index_url!='' else ''}", "ngrok")
 
     os.makedirs(dir_repos, exist_ok=True)
 
@@ -147,9 +148,9 @@ def prepare_enviroment():
     git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
 
     if not is_installed("lpips"):
-        run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
+        run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}{f' --index-url {index_url}' if index_url!='' else ''}", "requirements for CodeFormer")
 
-    run_pip(f"install -r {requirements_file}", "requirements for Web UI")
+    run_pip(f"install -r {requirements_file}{f' --index-url {index_url}' if index_url!='' else ''}", "requirements for Web UI")
 
     sys.argv += args
 
-- 
cgit v1.2.1


From 7855993bef0a16c235649027527b0f3ad7cca757 Mon Sep 17 00:00:00 2001
From: Cassy-Lee <104408348+Cassy-Lee@users.noreply.github.com>
Date: Sat, 15 Oct 2022 10:02:18 +0800
Subject: Move index_url args into run_pip.

---
 launch.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/launch.py b/launch.py
index b753efc1..42072f33 100644
--- a/launch.py
+++ b/launch.py
@@ -9,6 +9,7 @@ import platform
 dir_repos = "repositories"
 python = sys.executable
 git = os.environ.get('GIT', "git")
+index_url = os.environ.get('INDEX_URL',"")
 
 
 def extract_arg(args, name):
@@ -57,7 +58,7 @@ def run_python(code, desc=None, errdesc=None):
 
 
 def run_pip(args, desc=None):
-    return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
+    return run(f'"{python}" -m pip {args} --prefer-binary{f' --index-url {index_url}' if index_url!='' else ''}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
 
 
 def check_run_python(code):
@@ -89,7 +90,6 @@ def prepare_enviroment():
     torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
     commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
-    index_url = os.environ.get('INDEX_URL',"")
 
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
     clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
@@ -122,22 +122,22 @@ def prepare_enviroment():
         run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
 
     if not is_installed("gfpgan"):
-        run_pip(f"install {gfpgan_package}{f' --index-url {index_url}' if index_url!='' else ''}", "gfpgan")
+        run_pip(f"install {gfpgan_package}", "gfpgan")
 
     if not is_installed("clip"):
-        run_pip(f"install {clip_package}{f' --index-url {index_url}' if index_url!='' else ''}", "clip")
+        run_pip(f"install {clip_package}", "clip")
 
     if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
         if platform.system() == "Windows":
             run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/c/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
         elif platform.system() == "Linux":
-            run_pip("install xformers{f' --index-url {index_url}' if index_url!='' else ''}", "xformers")
+            run_pip("install xformers", "xformers")
 
     if not is_installed("deepdanbooru") and deepdanbooru:
         run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
 
     if not is_installed("pyngrok") and ngrok:
-        run_pip("install pyngrok{f' --index-url {index_url}' if index_url!='' else ''}", "ngrok")
+        run_pip("install pyngrok", "ngrok")
 
     os.makedirs(dir_repos, exist_ok=True)
 
@@ -148,9 +148,9 @@ def prepare_enviroment():
     git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
 
     if not is_installed("lpips"):
-        run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}{f' --index-url {index_url}' if index_url!='' else ''}", "requirements for CodeFormer")
+        run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
 
-    run_pip(f"install -r {requirements_file}{f' --index-url {index_url}' if index_url!='' else ''}", "requirements for Web UI")
+    run_pip(f"install -r {requirements_file}", "requirements for Web UI")
 
     sys.argv += args
 
-- 
cgit v1.2.1


From a13af34b902bebc5df9509228380206a01f1245b Mon Sep 17 00:00:00 2001
From: githublsx <xiaolinshen@outlook.com>
Date: Thu, 13 Oct 2022 20:05:07 -0700
Subject: Set to -1 when seed input is none

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index a75b9f84..7e2a416d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -140,7 +140,7 @@ class Processed:
         self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
         self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
-        self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
+        self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
         self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
 
         self.all_prompts = all_prompts or [self.prompt]
-- 
cgit v1.2.1


From c24df4b486a48c60f48276f7760a9acb4a13e22d Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sat, 15 Oct 2022 03:26:36 +0900
Subject: Disable compiling deepbooru model

This is only necessary when you have to train,
and compiling model produces warning.
---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index f34f3788..4ad334a1 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -102,7 +102,7 @@ def get_deepbooru_tags_model():
 
     tags = dd.project.load_tags_from_project(model_path)
     model = dd.project.load_model_from_project(
-        model_path, compile_model=True
+        model_path, compile_model=False
     )
     return model, tags
 
-- 
cgit v1.2.1


From f756bc540a849039d88c19378419838fe87f15b0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 10:28:20 +0300
Subject: fix #2588 breaking launch.py (. . .)

---
 launch.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/launch.py b/launch.py
index 42072f33..537670a3 100644
--- a/launch.py
+++ b/launch.py
@@ -9,7 +9,7 @@ import platform
 dir_repos = "repositories"
 python = sys.executable
 git = os.environ.get('GIT', "git")
-index_url = os.environ.get('INDEX_URL',"")
+index_url = os.environ.get('INDEX_URL', "")
 
 
 def extract_arg(args, name):
@@ -58,7 +58,8 @@ def run_python(code, desc=None, errdesc=None):
 
 
 def run_pip(args, desc=None):
-    return run(f'"{python}" -m pip {args} --prefer-binary{f' --index-url {index_url}' if index_url!='' else ''}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
+    index_url_line = f' --index-url {index_url}' if index_url != '' else ''
+    return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
 
 
 def check_run_python(code):
-- 
cgit v1.2.1


From 6a4e84671016d38c10a55fedcdf09321dba737ae Mon Sep 17 00:00:00 2001
From: Daniel M <danielm-github@dnml.de>
Date: Fri, 14 Oct 2022 20:50:21 +0200
Subject: Fix prerequisites check in webui.sh

- Check the actually used `$python_cmd` and `$GIT` executables instead
  of the hardcoded ones
- Fix typo in comment
---
 webui.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/webui.sh b/webui.sh
index 05ca497d..980c0aaf 100755
--- a/webui.sh
+++ b/webui.sh
@@ -82,8 +82,8 @@ then
     clone_dir="${PWD##*/}"
 fi
 
-# Check prequisites
-for preq in git python3
+# Check prerequisites
+for preq in "${GIT}" "${python_cmd}"
 do
     if ! hash "${preq}" &>/dev/null
     then
-- 
cgit v1.2.1


From 7d6042b908c064774ee10961309d396eabdc6c4a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 12:00:31 +0300
Subject: update for commandline args for btch prompts to parse string properly

---
 scripts/prompts_from_file.py | 172 ++++++++++++++++++++++++++-----------------
 1 file changed, 104 insertions(+), 68 deletions(-)

diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 5732623f..1266be6f 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -1,7 +1,9 @@
+import copy
 import math
 import os
 import sys
 import traceback
+import shlex
 
 import modules.scripts as scripts
 import gradio as gr
@@ -10,6 +12,75 @@ from modules.processing import Processed, process_images
 from PIL import Image
 from modules.shared import opts, cmd_opts, state
 
+
+def process_string_tag(tag):
+    return tag
+
+
+def process_int_tag(tag):
+    return int(tag)
+
+
+def process_float_tag(tag):
+    return float(tag)
+
+
+def process_boolean_tag(tag):
+    return True if (tag == "true") else False
+
+
+prompt_tags = {
+    "sd_model": None,
+    "outpath_samples": process_string_tag,
+    "outpath_grids": process_string_tag,
+    "prompt_for_display": process_string_tag,
+    "prompt": process_string_tag,
+    "negative_prompt": process_string_tag,
+    "styles": process_string_tag,
+    "seed": process_int_tag,
+    "subseed_strength": process_float_tag,
+    "subseed": process_int_tag,
+    "seed_resize_from_h": process_int_tag,
+    "seed_resize_from_w": process_int_tag,
+    "sampler_index": process_int_tag,
+    "batch_size": process_int_tag,
+    "n_iter": process_int_tag,
+    "steps": process_int_tag,
+    "cfg_scale": process_float_tag,
+    "width": process_int_tag,
+    "height": process_int_tag,
+    "restore_faces": process_boolean_tag,
+    "tiling": process_boolean_tag,
+    "do_not_save_samples": process_boolean_tag,
+    "do_not_save_grid": process_boolean_tag
+}
+
+
+def cmdargs(line):
+    args = shlex.split(line)
+    pos = 0
+    res = {}
+
+    while pos < len(args):
+        arg = args[pos]
+
+        assert arg.startswith("--"), f'must start with "--": {arg}'
+        tag = arg[2:]
+
+        func = prompt_tags.get(tag, None)
+        assert func, f'unknown commandline option: {arg}'
+
+        assert pos+1 < len(args), f'missing argument for command line option {arg}'
+
+        val = args[pos+1]
+
+        res[tag] = func(val)
+
+        pos += 2
+
+    return res
+
+
 class Script(scripts.Script):
     def title(self):
         return "Prompts from file or textbox"
@@ -28,87 +99,52 @@ class Script(scripts.Script):
         checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt])
         return [checkbox_txt, file, prompt_txt]
 
-    def process_string_tag(self, tag):
-        return tag[1:-2]
-
-    def process_int_tag(self, tag):
-        return int(tag)
-
-    def process_float_tag(self, tag):
-        return float(tag)
-
-    def process_boolean_tag(self, tag):
-        return True if (tag == "true") else False
-
-    prompt_tags = {
-        "sd_model": None,
-        "outpath_samples": process_string_tag,
-        "outpath_grids": process_string_tag,
-        "prompt_for_display": process_string_tag,
-        "prompt": process_string_tag,
-        "negative_prompt": process_string_tag,
-        "styles": process_string_tag,
-        "seed": process_int_tag,
-        "subseed_strength": process_float_tag,
-        "subseed": process_int_tag,
-        "seed_resize_from_h": process_int_tag,
-        "seed_resize_from_w": process_int_tag,
-        "sampler_index": process_int_tag,
-        "batch_size": process_int_tag,
-        "n_iter": process_int_tag,
-        "steps": process_int_tag,
-        "cfg_scale": process_float_tag,
-        "width": process_int_tag,
-        "height": process_int_tag,
-        "restore_faces": process_boolean_tag,
-        "tiling": process_boolean_tag,
-        "do_not_save_samples": process_boolean_tag,
-        "do_not_save_grid": process_boolean_tag
-    }
-
     def on_show(self, checkbox_txt, file, prompt_txt):
         return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ]
 
     def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
-        if (checkbox_txt):
+        if checkbox_txt:
             lines = [x.strip() for x in prompt_txt.splitlines()]
         else:
             lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
         lines = [x for x in lines if len(x) > 0]
 
-        img_count = len(lines) * p.n_iter
-        batch_count = math.ceil(img_count / p.batch_size)
-        loop_count = math.ceil(batch_count / p.n_iter)
-        # These numbers no longer accurately reflect the total images and number of batches
-        print(f"Will process {img_count} images in {batch_count} batches.")
-
         p.do_not_save_grid = True
 
-        state.job_count = batch_count
+        job_count = 0
+        jobs = []
+
+        for line in lines:
+            if "--" in line:
+                try:
+                    args = cmdargs(line)
+                except Exception:
+                    print(f"Error parsing line [line] as commandline:", file=sys.stderr)
+                    print(traceback.format_exc(), file=sys.stderr)
+                    args = {"prompt": line}
+            else:
+                args = {"prompt": line}
 
-        images = []
-        for loop_no in range(loop_count):
-            state.job = f"{loop_no + 1} out of {loop_count}"
-            # The following line may need revising to remove batch_size references
-            current_line = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter
-
-            # If the current line has no tags, parse the whole line as a prompt, else parse each tag
-            if(current_line[0][:2] != "--"):
-                p.prompt = current_line
+            n_iter = args.get("n_iter", 1)
+            if n_iter != 1:
+                job_count += n_iter
             else:
-                tokenized_line = current_line[0].split("--")
-
-                for tag in tokenized_line:
-                    tag_split = tag.split(" ", 1)
-                    if(tag_split[0] != ''):
-                        value_func = self.prompt_tags.get(tag_split[0], None)
-                        if(value_func != None):
-                            value = value_func(self, tag_split[1])
-                            setattr(p, tag_split[0], value)
-                        else:
-                            print(f"Unknown option \"{tag_split}\"")
-
-            proc = process_images(p)
+                job_count += 1
+
+            jobs.append(args)
+
+        print(f"Will process {len(lines)} lines in {job_count} jobs.")
+        state.job_count = job_count
+
+        images = []
+        for n, args in enumerate(jobs):
+            state.job = f"{state.job_no + 1} out of {state.job_count}"
+
+            copy_p = copy.copy(p)
+            for k, v in args.items():
+                setattr(copy_p, k, v)
+
+            proc = process_images(copy_p)
             images += proc.images
 
         return Processed(p, images, p.seed, "")
-- 
cgit v1.2.1


From 4ed99d599640bb86bc793aa3cbed31c6d0bd6957 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 12:10:52 +0300
Subject: bump gradio to 3.5

---
 requirements.txt          | 2 +-
 requirements_versions.txt | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index a0d985ce..cf583de9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,7 +4,7 @@ fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
-gradio==3.4.1
+gradio==3.5
 invisible-watermark
 numpy
 omegaconf
diff --git a/requirements_versions.txt b/requirements_versions.txt
index 2bbea40b..abadcb58 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -2,7 +2,7 @@ transformers==4.19.2
 diffusers==0.3.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.4.1
+gradio==3.5
 numpy==1.23.3
 Pillow==9.2.0
 realesrgan==0.3.0
-- 
cgit v1.2.1


From e8729dd0511f8410db967d9ef192645cfef1be8a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 12:54:23 +0300
Subject: re-apply height hacks to work with new gradio

---
 modules/ui.py |  4 ++--
 style.css     | 16 ++++++++--------
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index baf4c397..9c7a67dd 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -750,10 +750,10 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
                     with gr.TabItem('img2img', id='img2img'):
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool)
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
 
                     with gr.TabItem('Inpaint', id='inpaint'):
-                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA")
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
 
                         init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
                         init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
diff --git a/style.css b/style.css
index 2306c002..c27e53f8 100644
--- a/style.css
+++ b/style.css
@@ -167,10 +167,6 @@ button{
     align-self: stretch !important;
 }
 
-#img2maskimg .h-60{
-    height: 30rem;
-}
-
 .overflow-hidden, .gr-panel{
     overflow: visible !important;
 }
@@ -443,10 +439,6 @@ input[type="range"]{
     --tw-bg-opacity: 0 !important;
 }
 
-#img2img_image div.h-60{
-    height: 480px;
-}
-
 #context-menu{
     z-index:9999;
     position:absolute;
@@ -521,3 +513,11 @@ canvas[key="mask"] {
 .row.gr-compact{
     overflow: visible;
 }
+
+#img2img_image, #img2img_image > .h-60, #img2img_image > .h-60 > div, #img2img_image > .h-60 > div > img,
+img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h-60 > div > img
+{
+    height: 480px !important;
+    max-height: 480px !important;
+    min-height: 480px !important;
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 3631adfe96dd7746f7e23a3cf5802d8f4a95a532 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 12:58:53 +0300
Subject: make dragging to prompt work again

---
 javascript/dragdrop.js    | 4 ++--
 javascript/imageParams.js | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/javascript/dragdrop.js b/javascript/dragdrop.js
index fe0185a5..070cf255 100644
--- a/javascript/dragdrop.js
+++ b/javascript/dragdrop.js
@@ -43,7 +43,7 @@ function dropReplaceImage( imgWrap, files ) {
 window.document.addEventListener('dragover', e => {
     const target = e.composedPath()[0];
     const imgWrap = target.closest('[data-testid="image"]');
-    if ( !imgWrap && target.placeholder != "Prompt") {
+    if ( !imgWrap && target.placeholder.indexOf("Prompt") == -1) {
         return;
     }
     e.stopPropagation();
@@ -53,7 +53,7 @@ window.document.addEventListener('dragover', e => {
 
 window.document.addEventListener('drop', e => {
     const target = e.composedPath()[0];
-    if (target.placeholder === "Prompt") {
+    if (target.placeholder.indexOf("Prompt") == -1) {
         return;
     }
     const imgWrap = target.closest('[data-testid="image"]');
diff --git a/javascript/imageParams.js b/javascript/imageParams.js
index 4a7b0900..67404a89 100644
--- a/javascript/imageParams.js
+++ b/javascript/imageParams.js
@@ -2,7 +2,7 @@ window.onload = (function(){
     window.addEventListener('drop', e => {
         const target = e.composedPath()[0];
         const idx = selected_gallery_index();
-        if (target.placeholder != "Prompt") return;
+        if (target.placeholder.indexOf("Prompt") == -1) return;
 
         let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
 
-- 
cgit v1.2.1


From 5967d07d1aa4e2fef031a57b1612b1ab04a3cd78 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 13:11:28 +0300
Subject: fix new gradio failing to preserve image params

---
 modules/ui.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/modules/ui.py b/modules/ui.py
index 9c7a67dd..de5ab929 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -7,6 +7,7 @@ import mimetypes
 import os
 import random
 import sys
+import tempfile
 import time
 import traceback
 import platform
@@ -176,6 +177,23 @@ def save_files(js_data, images, do_make_zip, index):
     return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
 
+def save_pil_to_file(pil_image, dir=None):
+    use_metadata = False
+    metadata = PngImagePlugin.PngInfo()
+    for key, value in pil_image.info.items():
+        if isinstance(key, str) and isinstance(value, str):
+            metadata.add_text(key, value)
+            use_metadata = True
+
+    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
+    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
+    return file_obj
+
+
+# override save to file function so that it also writes PNG info
+gr.processing_utils.save_pil_to_file = save_pil_to_file
+
+
 def wrap_gradio_call(func, extra_outputs=None):
     def f(*args, extra_outputs_array=extra_outputs, **kwargs):
         run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled
-- 
cgit v1.2.1


From f7ca63937ac83d32483285c3af09afaa356d6276 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 13:23:12 +0300
Subject: bring back scale latent option in settings

---
 modules/processing.py | 8 ++++----
 modules/shared.py     | 3 ++-
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 7e2a416d..b9a1660e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -557,11 +557,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
-        decoded_samples = decode_first_stage(self.sd_model, samples)
+        if opts.use_scale_latent_for_hires_fix:
+            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
 
-        if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
-            decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
         else:
+            decoded_samples = decode_first_stage(self.sd_model, samples)
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
 
             batch_images = []
@@ -578,7 +578,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             decoded_samples = decoded_samples.to(shared.device)
             decoded_samples = 2. * decoded_samples - 1.
 
-        samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
+            samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
         shared.state.nextjob()
 
diff --git a/modules/shared.py b/modules/shared.py
index aa69bedf..b4141e67 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -218,6 +218,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
+    "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space iamge when doing hires. fix"),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
@@ -256,7 +257,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
-    'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
@@ -284,6 +284,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+    'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-- 
cgit v1.2.1


From d3463bc59a44d62c2de8b357184c49876d84f654 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 14:22:30 +0300
Subject: change styling for top right corner UI made save style button not die
 when you cancel

---
 javascript/hints.js |  2 ++
 javascript/ui.js    |  2 +-
 modules/ui.py       | 57 ++++++++++++++++++++++++++---------------------------
 style.css           | 16 +++++++--------
 4 files changed, 39 insertions(+), 38 deletions(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index 8fec907d..b98012f5 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -16,6 +16,8 @@ titles = {
     "\u{1f3a8}": "Add a random artist to the prompt.",
     "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
     "\u{1f4c2}": "Open images output directory",
+    "\u{1f4be}": "Save style",
+    "\u{1f4cb}": "Apply selected styles to current prompt",
 
     "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
     "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
diff --git a/javascript/ui.js b/javascript/ui.js
index 56f4216f..9e1bed4c 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -141,7 +141,7 @@ function submit_img2img(){
 
 function ask_for_style_name(_, prompt_text, negative_prompt_text) {
     name_ = prompt('Style name:')
-    return name_ === null ? [null, null, null]: [name_, prompt_text, negative_prompt_text]
+    return [name_, prompt_text, negative_prompt_text]
 }
 
 
diff --git a/modules/ui.py b/modules/ui.py
index de5ab929..cab8ab11 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -81,6 +81,8 @@ art_symbol = '\U0001f3a8'  # 🎨
 paste_symbol = '\u2199\ufe0f'  # ↙
 folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
+save_style_symbol = '\U0001f4be'  # 💾
+apply_style_symbol = '\U0001f4cb'  # 📋
 
 
 def plaintext_to_html(text):
@@ -322,7 +324,7 @@ def visit(x, func, path=""):
 
 def add_style(name: str, prompt: str, negative_prompt: str):
     if name is None:
-        return [gr_show(), gr_show()]
+        return [gr_show() for x in range(4)]
 
     style = modules.styles.PromptStyle(name, prompt, negative_prompt)
     shared.prompt_styles.styles[style.name] = style
@@ -447,7 +449,7 @@ def create_toprow(is_img2img):
     id_part = "img2img" if is_img2img else "txt2img"
 
     with gr.Row(elem_id="toprow"):
-        with gr.Column(scale=4):
+        with gr.Column(scale=6):
             with gr.Row():
                 with gr.Column(scale=80):
                     with gr.Row():
@@ -455,27 +457,30 @@ def create_toprow(is_img2img):
                             placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
                         )
 
-                with gr.Column(scale=1, elem_id="roll_col"):
-                    roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
-                    paste = gr.Button(value=paste_symbol, elem_id="paste")
-                    token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
-                    token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-
-                with gr.Column(scale=10, elem_id="style_pos_col"):
-                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
-
             with gr.Row():
-                with gr.Column(scale=8):
+                with gr.Column(scale=80):
                     with gr.Row():
                         negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, 
                             placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
                         )
 
-                with gr.Column(scale=1, elem_id="roll_col"):
-                    sh = gr.Button(elem_id="sh", visible=True)                           
+        with gr.Column(scale=1, elem_id="roll_col"):
+            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
+            paste = gr.Button(value=paste_symbol, elem_id="paste")
+            save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
+            prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
 
-                with gr.Column(scale=1, elem_id="style_neg_col"):
-                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
+            token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
+            token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+
+        button_interrogate = None
+        button_deepbooru = None
+        if is_img2img:
+            with gr.Column(scale=1, elem_id="interrogate_col"):
+                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+
+                if cmd_opts.deepdanbooru:
+                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
 
         with gr.Column(scale=1):
             with gr.Row():
@@ -495,20 +500,14 @@ def create_toprow(is_img2img):
                     outputs=[],
                 )
 
-            with gr.Row(scale=1):
-                if is_img2img:
-                    interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-                    if cmd_opts.deepdanbooru:
-                        deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-                    else:
-                        deepbooru = None
-                else:
-                    interrogate = None
-                    deepbooru = None
-                prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
-                save_style = gr.Button('Create style', elem_id="style_create")
+            with gr.Row():
+                with gr.Column(scale=1, elem_id="style_pos_col"):
+                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+
+                with gr.Column(scale=1, elem_id="style_neg_col"):
+                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
 
 
 def setup_progressbar(progressbar, preview, id_part, textinfo=None):
diff --git a/style.css b/style.css
index c27e53f8..b534f950 100644
--- a/style.css
+++ b/style.css
@@ -115,7 +115,7 @@
     padding: 0.4em 0;
 }
 
-#roll, #paste{
+#roll, #paste, #style_create, #style_apply{
     min-width: 2em;
     min-height: 2em;
     max-width: 2em;
@@ -126,14 +126,14 @@
     margin: 0.1em 0;
 }
 
-#style_apply, #style_create, #interrogate{
-    margin: 0.75em 0.25em 0.25em 0.25em;
-    min-width: 5em;
+#interrogate_col{
+    min-width: 0 !important;
+    max-width: 8em !important;
 }
-
-#style_apply, #style_create, #deepbooru{
-    margin: 0.75em 0.25em 0.25em 0.25em;
-    min-width: 5em;
+#interrogate, #deepbooru{
+    margin: 0em 0.25em 0.9em 0.25em;
+    min-width: 8em;
+    max-width: 8em;
 }
 
 #style_pos_col, #style_neg_col{
-- 
cgit v1.2.1


From 20a1f68c752f8e37492ea00911c97bfc572a6e67 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 15:44:46 +0300
Subject: fix gadio issue with sending files between tabs

---
 modules/ui.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/modules/ui.py b/modules/ui.py
index cab8ab11..c9b53247 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -91,6 +91,14 @@ def plaintext_to_html(text):
 
 
 def image_from_url_text(filedata):
+    if type(filedata) == dict and filedata["is_file"]:
+        filename = filedata["name"]
+        tempdir = os.path.normpath(tempfile.gettempdir())
+        normfn = os.path.normpath(filename)
+        assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
+
+        return Image.open(filename)
+
     if type(filedata) == list:
         if len(filedata) == 0:
             return None
-- 
cgit v1.2.1


From 97f0727489ddd3d7ca264c54ed0f63b6847502e2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 15:47:02 +0300
Subject: add First pass size always regardless of whether it was auto chosen
 or specified

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index b9a1660e..941ae089 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -528,7 +528,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 firstphase_height_truncated = int(scale * self.height)
 
             else:
-                self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
 
                 width_ratio = self.width / self.firstphase_width
                 height_ratio = self.height / self.firstphase_height
@@ -540,6 +539,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                     firstphase_width_truncated = self.firstphase_height * self.width / self.height
                     firstphase_height_truncated = self.firstphase_height
 
+            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-- 
cgit v1.2.1


From 73901c3f011a2510d65de1c99b4958cd9b559264 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 15:51:57 +0300
Subject: make attention edit only work with ctrl as was initially intended

---
 javascript/edit-attention.js | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js
index 3f1d2fbb..67084e7a 100644
--- a/javascript/edit-attention.js
+++ b/javascript/edit-attention.js
@@ -2,6 +2,8 @@ addEventListener('keydown', (event) => {
 	let target = event.originalTarget || event.composedPath()[0];
 	if (!target.hasAttribute("placeholder")) return;
 	if (!target.placeholder.toLowerCase().includes("prompt")) return;
+	if (! (event.metaKey || event.ctrlKey)) return;
+
 
 	let plus = "ArrowUp"
 	let minus = "ArrowDown"
-- 
cgit v1.2.1


From eef3bc649069d6caaef1274f132de28e528bfa7d Mon Sep 17 00:00:00 2001
From: NO_ob <15161159+NO-ob@users.noreply.github.com>
Date: Sat, 15 Oct 2022 11:43:30 +0100
Subject: typo

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index b4141e67..fa30bbb0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -218,7 +218,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
-    "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space iamge when doing hires. fix"),
+    "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
-- 
cgit v1.2.1


From d3ffc962dd1d5c8d0ed763a9d05832c153ff15ea Mon Sep 17 00:00:00 2001
From: Robert Smieja <robertsmieja@users.noreply.github.com>
Date: Sat, 15 Oct 2022 04:19:16 -0400
Subject: Add basic Pylint to catch syntax errors on PRs

---
 .github/workflows/on_pull_request.yaml | 36 ++++++++++++++++++++++++++++++++++
 .pylintrc                              |  3 +++
 2 files changed, 39 insertions(+)
 create mode 100644 .github/workflows/on_pull_request.yaml
 create mode 100644 .pylintrc

diff --git a/.github/workflows/on_pull_request.yaml b/.github/workflows/on_pull_request.yaml
new file mode 100644
index 00000000..5270cba4
--- /dev/null
+++ b/.github/workflows/on_pull_request.yaml
@@ -0,0 +1,36 @@
+# See https://github.com/actions/starter-workflows/blob/1067f16ad8a1eac328834e4b0ae24f7d206f810d/ci/pylint.yml for original reference file
+name: Run Linting/Formatting on Pull Requests
+
+on:
+  - push
+  - pull_request
+  # See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onpull_requestpull_request_targetbranchesbranches-ignore for syntax docs
+  # if you want to filter out branches, delete the `- pull_request` and uncomment these lines :
+  # pull_request:  
+  #  branches:
+  #    - master
+  #  branches-ignore:
+  #    - development
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v3
+        with:
+          python-version: 3.10.6
+      - name: Install PyLint
+        run: | 
+          python -m pip install --upgrade pip
+          pip install pylint
+      # This lets PyLint check to see if it can resolve imports
+      - name: Install dependencies
+        run : |
+          export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
+          python launch.py
+      - name: Analysing the code with pylint
+        run: |
+          pylint $(git ls-files '*.py')
diff --git a/.pylintrc b/.pylintrc
new file mode 100644
index 00000000..53254e5d
--- /dev/null
+++ b/.pylintrc
@@ -0,0 +1,3 @@
+# See https://pylint.pycqa.org/en/latest/user_guide/messages/message_control.html
+[MESSAGES CONTROL]
+disable=C,R,W,E,I
-- 
cgit v1.2.1


From 37d7ffb415cd8c69b3c0bb5f61844dde0b169f78 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sat, 15 Oct 2022 15:59:37 +0200
Subject: fix to tokens lenght, addend embs generator, add new features to edit
 the embedding before the generation using text

---
 modules/aesthetic_clip.py |  78 ++++++++++++++++++++++++
 modules/processing.py     | 148 +++++++++++++++++++++++++++++++---------------
 modules/sd_hijack.py      | 111 ++++++++++++++++++++++------------
 modules/shared.py         |   4 ++
 modules/txt2img.py        |  10 +++-
 modules/ui.py             |  47 ++++++++++++---
 6 files changed, 302 insertions(+), 96 deletions(-)
 create mode 100644 modules/aesthetic_clip.py

diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
new file mode 100644
index 00000000..f15cfd47
--- /dev/null
+++ b/modules/aesthetic_clip.py
@@ -0,0 +1,78 @@
+import itertools
+import os
+from pathlib import Path
+import html
+import gc
+
+import gradio as gr
+import torch
+from PIL import Image
+from modules import shared
+from modules.shared import device, aesthetic_embeddings
+from transformers import CLIPModel, CLIPProcessor
+
+from tqdm.auto import tqdm
+
+
+def get_all_images_in_folder(folder):
+    return [os.path.join(folder, f) for f in os.listdir(folder) if
+            os.path.isfile(os.path.join(folder, f)) and check_is_valid_image_file(f)]
+
+
+def check_is_valid_image_file(filename):
+    return filename.lower().endswith(('.png', '.jpg', '.jpeg'))
+
+
+def batched(dataset, total, n=1):
+    for ndx in range(0, total, n):
+        yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))]
+
+
+def iter_to_batched(iterable, n=1):
+    it = iter(iterable)
+    while True:
+        chunk = tuple(itertools.islice(it, n))
+        if not chunk:
+            return
+        yield chunk
+
+
+def generate_imgs_embd(name, folder, batch_size):
+    # clipModel = CLIPModel.from_pretrained(
+    #     shared.sd_model.cond_stage_model.clipModel.name_or_path
+    # )
+    model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path).to(device)
+    processor = CLIPProcessor.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path)
+
+    with torch.no_grad():
+        embs = []
+        for paths in tqdm(iter_to_batched(get_all_images_in_folder(folder), batch_size),
+                          desc=f"Generating embeddings for {name}"):
+            if shared.state.interrupted:
+                break
+            inputs = processor(images=[Image.open(path) for path in paths], return_tensors="pt").to(device)
+            outputs = model.get_image_features(**inputs).cpu()
+            embs.append(torch.clone(outputs))
+            inputs.to("cpu")
+            del inputs, outputs
+
+        embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True)
+
+        # The generated embedding will be located here
+        path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt")
+        torch.save(embs, path)
+
+        model = model.cpu()
+        del model
+        del processor
+        del embs
+        gc.collect()
+        torch.cuda.empty_cache()
+        res = f"""
+        Done generating embedding for {name}!
+        Hypernetwork saved to {html.escape(path)}
+        """
+        shared.update_aesthetic_embeddings()
+        return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding",
+                           value=sorted(aesthetic_embeddings.keys())[0] if len(
+                               aesthetic_embeddings) > 0 else None), res, ""
diff --git a/modules/processing.py b/modules/processing.py
index 9a033759..ab68d63a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -20,7 +20,6 @@ import modules.images as images
 import modules.styles
 import logging
 
-
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
 opt_f = 8
@@ -52,8 +51,13 @@ def get_correct_sampler(p):
     elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
         return sd_samplers.samplers_for_img2img
 
+
 class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1,
+                 subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True,
+                 sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512,
+                 restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False,
+                 extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -104,7 +108,8 @@ class StableDiffusionProcessing:
 
 
 class Processed:
-    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
+    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None,
+                 all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
         self.images = images_list
         self.prompt = p.prompt
         self.negative_prompt = p.negative_prompt
@@ -141,7 +146,8 @@ class Processed:
         self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
         self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
-        self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
+        self.subseed = int(
+            self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
 
         self.all_prompts = all_prompts or [self.prompt]
         self.all_seeds = all_seeds or [self.seed]
@@ -181,39 +187,43 @@ class Processed:
 
         return json.dumps(obj)
 
-    def infotext(self,  p: StableDiffusionProcessing, index):
-        return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
+    def infotext(self, p: StableDiffusionProcessing, index):
+        return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[],
+                               position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
 
 
 # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
 def slerp(val, low, high):
-    low_norm = low/torch.norm(low, dim=1, keepdim=True)
-    high_norm = high/torch.norm(high, dim=1, keepdim=True)
-    dot = (low_norm*high_norm).sum(1)
+    low_norm = low / torch.norm(low, dim=1, keepdim=True)
+    high_norm = high / torch.norm(high, dim=1, keepdim=True)
+    dot = (low_norm * high_norm).sum(1)
 
     if dot.mean() > 0.9995:
         return low * val + high * (1 - val)
 
     omega = torch.acos(dot)
     so = torch.sin(omega)
-    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
     return res
 
 
-def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
+def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0,
+                          p=None):
     xs = []
 
     # if we have multiple seeds, this means we are working with batch size>1; this then
     # enables the generation of additional tensors with noise that the sampler will use during its processing.
     # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
     # produce the same images as with two batches [100], [101].
-    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
+    if p is not None and p.sampler is not None and (
+            len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
         sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
     else:
         sampler_noises = None
 
     for i, seed in enumerate(seeds):
-        noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
+        noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (
+            shape[0], seed_resize_from_h // 8, seed_resize_from_w // 8)
 
         subnoise = None
         if subseeds is not None:
@@ -241,7 +251,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
             dx = max(-dx, 0)
             dy = max(-dy, 0)
 
-            x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
+            x[:, ty:ty + h, tx:tx + w] = noise[:, dy:dy + h, dx:dx + w]
             noise = x
 
         if sampler_noises is not None:
@@ -293,14 +303,20 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Seed": all_seeds[index],
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
-        "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
-        "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')),
+        "Model hash": getattr(p, 'sd_model_hash',
+                              None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
+        "Model": (
+            None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(
+                ',', '').replace(':', '')),
+        "Hypernet": (
+            None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(
+                ':', '')),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
-        "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
+        "Seed resize from": (
+            None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
         "Clip skip": None if clip_skip <= 1 else clip_skip,
@@ -309,7 +325,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params.update(p.extra_generation_params)
 
-    generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
+    generation_params_text = ", ".join(
+        [k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
 
     negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
 
@@ -317,7 +334,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
 
 def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0,
-                   aesthetic_imgs=None,aesthetic_slerp=False) -> Processed:
+                   aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="",
+                   aesthetic_slerp_angle=0.15,
+                   aesthetic_text_negative=False) -> Processed:
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
     aesthetic_lr = float(aesthetic_lr)
@@ -385,7 +404,7 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
         for n in range(p.n_iter):
             if state.skipped:
                 state.skipped = False
-            
+
             if state.interrupted:
                 break
 
@@ -396,16 +415,19 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
             if (len(prompts) == 0):
                 break
 
-            #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
-            #c = p.sd_model.get_learned_conditioning(prompts)
+            # uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
+            # c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
                 if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
-                    shared.sd_model.cond_stage_model.set_aesthetic_params(0, 0, 0)
+                    shared.sd_model.cond_stage_model.set_aesthetic_params()
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt],
                                                             p.steps)
                 if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
                     shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight,
-                                                                          aesthetic_steps, aesthetic_imgs,aesthetic_slerp)
+                                                                          aesthetic_steps, aesthetic_imgs,
+                                                                          aesthetic_slerp, aesthetic_imgs_text,
+                                                                          aesthetic_slerp_angle,
+                                                                          aesthetic_text_negative)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
@@ -413,13 +435,13 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
                     comments[comment] = 1
 
             if p.n_iter > 1:
-                shared.state.job = f"Batch {n+1} out of {p.n_iter}"
+                shared.state.job = f"Batch {n + 1} out of {p.n_iter}"
 
             with devices.autocast():
-                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
+                                        subseed_strength=p.subseed_strength)
 
             if state.interrupted or state.skipped:
-
                 # if we are interrupted, sample returns just noise
                 # use the image collected previously in sampler loop
                 samples_ddim = shared.state.current_latent
@@ -445,7 +467,9 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
 
                 if p.restore_faces:
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
-                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
+                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i],
+                                          opts.samples_format, info=infotext(n, i), p=p,
+                                          suffix="-before-face-restoration")
 
                     devices.torch_gc()
 
@@ -456,7 +480,8 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
 
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
-                        images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+                        images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format,
+                                          info=infotext(n, i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
                 if p.overlay_images is not None and i < len(p.overlay_images):
@@ -474,7 +499,8 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
                     image = image.convert('RGB')
 
                 if opts.samples_save and not p.do_not_save_samples:
-                    images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
+                    images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format,
+                                      info=infotext(n, i), p=p)
 
                 text = infotext(n, i)
                 infotexts.append(text)
@@ -482,7 +508,7 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
                     image.info["parameters"] = text
                 output_images.append(image)
 
-            del x_samples_ddim 
+            del x_samples_ddim
 
             devices.torch_gc()
 
@@ -504,10 +530,13 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
                 index_of_first_image = 1
 
             if opts.grid_save:
-                images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
+                images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format,
+                                  info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     devices.torch_gc()
-    return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+    return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]),
+                     subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds,
+                     index_of_first_image=index_of_first_image, infotexts=infotexts)
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
@@ -543,25 +572,34 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
-            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds,
+                                      subseeds=subseeds, subseed_strength=self.subseed_strength,
+                                      seed_resize_from_h=self.seed_resize_from_h,
+                                      seed_resize_from_w=self.seed_resize_from_w, p=self)
             samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
             return samples
 
-        x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds,
+                                  subseeds=subseeds, subseed_strength=self.subseed_strength,
+                                  seed_resize_from_h=self.seed_resize_from_h,
+                                  seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
 
         truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f
         truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f
 
-        samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
+        samples = samples[:, :, truncate_y // 2:samples.shape[2] - truncate_y // 2,
+                  truncate_x // 2:samples.shape[3] - truncate_x // 2]
 
         if self.scale_latent:
-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f),
+                                                      mode="bilinear")
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
 
             if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
-                decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
+                decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width),
+                                                                  mode="bilinear")
             else:
                 lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
 
@@ -585,13 +623,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
-        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds,
+                                      subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h,
+                                      seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         # GC now before running the next img2img to prevent running out of memory
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning,
+                                              steps=self.steps)
 
         return samples
 
@@ -599,7 +640,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs):
+    def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4,
+                 inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0,
+                 **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
@@ -607,7 +650,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.denoising_strength: float = denoising_strength
         self.init_latent = None
         self.image_mask = mask
-        #self.image_unblurred_mask = None
+        # self.image_unblurred_mask = None
         self.latent_mask = None
         self.mask_for_overlay = None
         self.mask_blur = mask_blur
@@ -619,7 +662,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.nmask = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index,
+                                                             self.sd_model)
         crop_region = None
 
         if self.image_mask is not None:
@@ -628,7 +672,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             if self.inpainting_mask_invert:
                 self.image_mask = ImageOps.invert(self.image_mask)
 
-            #self.image_unblurred_mask = self.image_mask
+            # self.image_unblurred_mask = self.image_mask
 
             if self.mask_blur > 0:
                 self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
@@ -642,7 +686,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
                 mask = mask.crop(crop_region)
                 self.image_mask = images.resize_image(2, mask, self.width, self.height)
-                self.paste_to = (x1, y1, x2-x1, y2-y1)
+                self.paste_to = (x1, y1, x2 - x1, y2 - y1)
             else:
                 self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
                 np_mask = np.array(self.image_mask)
@@ -665,7 +709,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
             if self.image_mask is not None:
                 image_masked = Image.new('RGBa', (image.width, image.height))
-                image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
+                image_masked.paste(image.convert("RGBA").convert("RGBa"),
+                                   mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
 
                 self.overlay_images.append(image_masked.convert('RGBA'))
 
@@ -714,12 +759,17 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
             # this needs to be fixed to be done in sample() using actual seeds for batches
             if self.inpainting_fill == 2:
-                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
+                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:],
+                                                                                        all_seeds[
+                                                                                        0:self.init_latent.shape[
+                                                                                            0]]) * self.nmask
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds,
+                                  subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h,
+                                  seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)
 
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 6d5196fe..192883b2 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -14,7 +14,8 @@ from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
-from transformers import CLIPVisionModel, CLIPModel
+from tqdm import trange
+from transformers import CLIPVisionModel, CLIPModel, CLIPTokenizer
 import torch.optim as optim
 import copy
 
@@ -22,21 +23,25 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
 diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
 
+
 def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
 
-    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
+    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (
+            6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
+    elif not cmd_opts.disable_opt_split_attention and (
+            cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
         if not invokeAI_mps_available and shared.device.type == 'mps':
-            print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
+            print(
+                "The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
             print("Applying v1 cross attention optimization.")
             ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
         else:
@@ -112,14 +117,16 @@ class StableDiffusionModelHijack:
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
         return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
 
+
 def slerp(low, high, val):
-    low_norm = low/torch.norm(low, dim=1, keepdim=True)
-    high_norm = high/torch.norm(high, dim=1, keepdim=True)
-    omega = torch.acos((low_norm*high_norm).sum(1))
+    low_norm = low / torch.norm(low, dim=1, keepdim=True)
+    high_norm = high / torch.norm(high, dim=1, keepdim=True)
+    omega = torch.acos((low_norm * high_norm).sum(1))
     so = torch.sin(omega)
-    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
     return res
 
+
 class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def __init__(self, wrapped, hijack):
         super().__init__()
@@ -128,6 +135,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             self.wrapped.transformer.name_or_path
         )
         del self.clipModel.vision_model
+        self.tokenizer = CLIPTokenizer.from_pretrained(self.wrapped.transformer.name_or_path)
         self.hijack: StableDiffusionModelHijack = hijack
         self.tokenizer = wrapped.tokenizer
         # self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval()
@@ -139,7 +147,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
         self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
 
-        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
+        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if
+                              '(' in k or ')' in k or '[' in k or ']' in k]
         for text, ident in tokens_with_parens:
             mult = 1.0
             for c in text:
@@ -155,8 +164,13 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
-    def set_aesthetic_params(self, aesthetic_lr, aesthetic_weight, aesthetic_steps, image_embs_name=None,
-                             aesthetic_slerp=True):
+    def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
+                             aesthetic_slerp=True, aesthetic_imgs_text="",
+                             aesthetic_slerp_angle=0.15,
+                             aesthetic_text_negative=False):
+        self.aesthetic_imgs_text = aesthetic_imgs_text
+        self.aesthetic_slerp_angle = aesthetic_slerp_angle
+        self.aesthetic_text_negative = aesthetic_text_negative
         self.slerp = aesthetic_slerp
         self.aesthetic_lr = aesthetic_lr
         self.aesthetic_weight = aesthetic_weight
@@ -180,7 +194,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         else:
             parsed = [[line, 1.0]]
 
-        tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"]
+        tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)[
+            "input_ids"]
 
         fixes = []
         remade_tokens = []
@@ -196,18 +211,20 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
                 if token == self.comma_token:
                     last_comma = len(remade_tokens)
-                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
+                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens),
+                                                               1) % 75 == 0 and last_comma != -1 and len(
+                    remade_tokens) - last_comma <= opts.comma_padding_backtrack:
                     last_comma += 1
                     reloc_tokens = remade_tokens[last_comma:]
                     reloc_mults = multipliers[last_comma:]
 
                     remade_tokens = remade_tokens[:last_comma]
                     length = len(remade_tokens)
-                    
+
                     rem = int(math.ceil(length / 75)) * 75 - length
                     remade_tokens += [id_end] * rem + reloc_tokens
                     multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
-                
+
                 if embedding is None:
                     remade_tokens.append(token)
                     multipliers.append(weight)
@@ -248,7 +265,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if line in cache:
                 remade_tokens, fixes, multipliers = cache[line]
             else:
-                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
+                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms,
+                                                                                            hijack_comments)
                 token_count = max(current_token_count, token_count)
 
                 cache[line] = (remade_tokens, fixes, multipliers)
@@ -259,7 +277,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
 
-
     def process_text_old(self, text):
         id_start = self.wrapped.tokenizer.bos_token_id
         id_end = self.wrapped.tokenizer.eos_token_id
@@ -289,7 +306,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                 while i < len(tokens):
                     token = tokens[i]
 
-                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens,
+                                                                                                                i)
 
                     mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
                     if mult_change is not None:
@@ -312,11 +330,12 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     ovf = remade_tokens[maxlen - 2:]
                     overflowing_words = [vocab.get(int(x), "") for x in ovf]
                     overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
-                    hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
+                    hijack_comments.append(
+                        f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
 
                 token_count = len(remade_tokens)
                 remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
-                remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
+                remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
                 cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
 
             multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
@@ -326,23 +345,26 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             hijack_fixes.append(fixes)
             batch_multipliers.append(multipliers)
         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
-    
+
     def forward(self, text):
         use_old = opts.use_old_emphasis_implementation
         if use_old:
-            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
+            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(
+                text)
         else:
-            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
+            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(
+                text)
 
         self.hijack.comments += hijack_comments
 
         if len(used_custom_terms) > 0:
-            self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
-        
+            self.hijack.comments.append(
+                "Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
+
         if use_old:
             self.hijack.fixes = hijack_fixes
             return self.process_tokens(remade_batch_tokens, batch_multipliers)
-        
+
         z = None
         i = 0
         while max(map(len, remade_batch_tokens)) != 0:
@@ -356,7 +378,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     if fix[0] == i:
                         fixes.append(fix[1])
                 self.hijack.fixes.append(fixes)
-            
+
             tokens = []
             multipliers = []
             for j in range(len(remade_batch_tokens)):
@@ -378,19 +400,30 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                         remade_batch_tokens]
 
                 tokens = torch.asarray(remade_batch_tokens).to(device)
+
+                model = copy.deepcopy(self.clipModel).to(device)
+                model.requires_grad_(True)
+                if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
+                    text_embs_2 = model.get_text_features(
+                        **self.tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
+                    if self.aesthetic_text_negative:
+                        text_embs_2 = self.image_embs - text_embs_2
+                        text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
+                    img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
+                else:
+                    img_embs = self.image_embs
+
                 with torch.enable_grad():
-                    model = copy.deepcopy(self.clipModel).to(device)
-                    model.requires_grad_(True)
 
                     # We optimize the model to maximize the similarity
                     optimizer = optim.Adam(
                         model.text_model.parameters(), lr=self.aesthetic_lr
                     )
 
-                    for i in range(self.aesthetic_steps):
+                    for i in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
                         text_embs = model.get_text_features(input_ids=tokens)
                         text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
-                        sim = text_embs @ self.image_embs.T
+                        sim = text_embs @ img_embs.T
                         loss = -sim
                         optimizer.zero_grad()
                         loss.mean().backward()
@@ -405,6 +438,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     model.cpu()
                     del model
 
+                zn = torch.concat([zn for i in range(z.shape[1] // 77)], 1)
                 if self.slerp:
                     z = slerp(z, zn, self.aesthetic_weight)
                 else:
@@ -413,15 +447,16 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             remade_batch_tokens = rem_tokens
             batch_multipliers = rem_multipliers
             i += 1
-        
+
         return z
-        
-    
+
     def process_tokens(self, remade_batch_tokens, batch_multipliers):
         if not opts.use_old_emphasis_implementation:
-            remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
+            remade_batch_tokens = [
+                [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in
+                remade_batch_tokens]
             batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
-        
+
         tokens = torch.asarray(remade_batch_tokens).to(device)
         outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
 
@@ -461,8 +496,8 @@ class EmbeddingsWithFixes(torch.nn.Module):
         for fixes, tensor in zip(batch_fixes, inputs_embeds):
             for offset, embedding in fixes:
                 emb = embedding.vec
-                emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
-                tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])
+                emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
+                tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
 
             vecs.append(tensor)
 
diff --git a/modules/shared.py b/modules/shared.py
index cf13a10d..7cd608ca 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -95,6 +95,10 @@ loaded_hypernetwork = None
 aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
                         os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
 
+def update_aesthetic_embeddings():
+    global aesthetic_embeddings
+    aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
+                            os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
 
 def reload_hypernetworks():
     global hypernetworks
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 78342024..eedcdfe0 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -13,7 +13,11 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
             aesthetic_lr=0,
             aesthetic_weight=0, aesthetic_steps=0,
             aesthetic_imgs=None,
-            aesthetic_slerp=False, *args):
+            aesthetic_slerp=False,
+            aesthetic_imgs_text="",
+            aesthetic_slerp_angle=0.15,
+            aesthetic_text_negative=False,
+            *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -47,7 +51,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     processed = modules.scripts.scripts_txt2img.run(p, *args)
 
     if processed is None:
-        processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp)
+        processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp,aesthetic_imgs_text,
+                           aesthetic_slerp_angle,
+                           aesthetic_text_negative)
 
     shared.total_tqdm.clear()
 
diff --git a/modules/ui.py b/modules/ui.py
index d961d126..e98e2113 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -41,6 +41,7 @@ from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
+import modules.aesthetic_clip
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -449,7 +450,7 @@ def create_toprow(is_img2img):
                     with gr.Row():
                         negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
                 with gr.Column(scale=1, elem_id="roll_col"):
-                    sh = gr.Button(elem_id="sh", visible=True)                           
+                    sh = gr.Button(elem_id="sh", visible=True)
 
                 with gr.Column(scale=1, elem_id="style_neg_col"):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
@@ -536,9 +537,13 @@ def create_ui(wrap_gradio_gpu_call):
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
                 with gr.Group():
-                    aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
-                    aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.7)
-                    aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=50)
+                    aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001")
+                    aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
+                    aesthetic_steps = gr.Slider(minimum=0, maximum=256, step=1, label="Aesthetic steps", value=5)
+                    with gr.Row():
+                        aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
+                        aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
+                        aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
 
                     aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None)
                     aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
@@ -617,7 +622,10 @@ def create_ui(wrap_gradio_gpu_call):
                            aesthetic_weight,
                            aesthetic_steps,
                            aesthetic_imgs,
-                           aesthetic_slerp
+                           aesthetic_slerp,
+                           aesthetic_imgs_text,
+                           aesthetic_slerp_angle,
+                           aesthetic_text_negative
                        ] + custom_inputs,
                 outputs=[
                     txt2img_gallery,
@@ -721,7 +729,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                         with gr.Row():
                             inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
-                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
+                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=1024, step=4, value=32)
 
                     with gr.TabItem('Batch img2img', id='batch'):
                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
@@ -1071,6 +1079,17 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create embedding", variant='primary')
 
+                with gr.Tab(label="Create images embedding"):
+                    new_embedding_name_ae = gr.Textbox(label="Name")
+                    process_src_ae = gr.Textbox(label='Source directory')
+                    batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256)
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_embedding_ae = gr.Button(value="Create images embedding", variant='primary')
+
                 with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
@@ -1139,7 +1158,7 @@ def create_ui(wrap_gradio_gpu_call):
             fn=modules.textual_inversion.ui.create_embedding,
             inputs=[
                 new_embedding_name,
-                initialization_text,
+                process_src,
                 nvpt,
             ],
             outputs=[
@@ -1149,6 +1168,20 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
+        create_embedding_ae.click(
+            fn=modules.aesthetic_clip.generate_imgs_embd,
+            inputs=[
+                new_embedding_name_ae,
+                process_src_ae,
+                batch_ae
+            ],
+            outputs=[
+                aesthetic_imgs,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
         create_hypernetwork.click(
             fn=modules.hypernetworks.ui.create_hypernetwork,
             inputs=[
-- 
cgit v1.2.1


From 5fd638f14d75a71a37157ded5d33c716ab9eb8ca Mon Sep 17 00:00:00 2001
From: ruocaled <ruocaled@gmail.com>
Date: Sat, 15 Oct 2022 02:00:46 -0700
Subject: fix download section layout

---
 modules/ui.py | 4 ++--
 style.css     | 7 -------
 2 files changed, 2 insertions(+), 9 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index c9b53247..3206113e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -619,7 +619,7 @@ def create_ui(wrap_gradio_gpu_call):
                     txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
                     txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
 
-                with gr.Group():
+                with gr.Column():
                     with gr.Row():
                         save = gr.Button('Save')
                         send_to_img2img = gr.Button('Send to img2img')
@@ -834,7 +834,7 @@ def create_ui(wrap_gradio_gpu_call):
                     img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
                     img2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='img2img_gallery').style(grid=4)
 
-                with gr.Group():
+                with gr.Column():
                     with gr.Row():
                         save = gr.Button('Save')
                         img2img_send_to_img2img = gr.Button('Send to img2img')
diff --git a/style.css b/style.css
index b534f950..920c32ab 100644
--- a/style.css
+++ b/style.css
@@ -237,13 +237,6 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500,  label.block s
     margin: 0;
 }
 
-.gr-panel div.flex-col div.justify-between div{
-  position: absolute;
-  top: -0.1em;
-  right: 1em;
-  padding: 0 0.5em;
-}
-
 #settings .gr-panel div.flex-col div.justify-between div{
     position: relative;
     z-index: 200;
-- 
cgit v1.2.1


From 703e6d9e4e161d36b9328eefb5200e1c44fb4afd Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sat, 15 Oct 2022 21:47:08 +0900
Subject: check NaN for hypernetwork tuning

---
 modules/hypernetworks/hypernetwork.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index a2b3bc0a..4905710e 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -272,15 +272,17 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             optimizer.zero_grad()
             loss.backward()
             optimizer.step()
-
-        pbar.set_description(f"loss: {losses.mean():.7f}")
+        mean_loss = losses.mean()
+        if torch.isnan(mean_loss):
+            raise RuntimeError("Loss diverged.")
+        pbar.set_description(f"loss: {mean_loss:.7f}")
 
         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
             hypernetwork.save(last_saved_file)
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
-            "loss": f"{losses.mean():.7f}",
+            "loss": f"{mean_loss:.7f}",
             "learn_rate": scheduler.learn_rate
         })
 
@@ -328,7 +330,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         shared.state.textinfo = f"""
 <p>
-Loss: {losses.mean():.7f}<br/>
+Loss: {mean_loss:.7f}<br/>
 Step: {hypernetwork.step}<br/>
 Last prompt: {html.escape(entries[0].cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
-- 
cgit v1.2.1


From 9e846083b702a498fdb60accd72f075fa26701d9 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 14 Oct 2022 14:50:25 +0100
Subject: add vector size to embed text

---
 modules/textual_inversion/textual_inversion.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e754747e..6f549d62 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -327,10 +327,16 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 info.add_text("sd-ti-embedding", embedding_to_b64(data))
 
                 title = "<{}>".format(data.get('name', '???'))
+
+                try:
+                    vectorSize = list(data['string_to_param'].values())[0].shape[0]
+                except Exception as e:
+                    vectorSize = '?'
+
                 checkpoint = sd_models.select_checkpoint()
                 footer_left = checkpoint.model_name
                 footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '{}'.format(embedding.step)
+                footer_right = 'v{} {}s'.format(vectorSize, embedding.step)
 
                 captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
                 captioned_image = insert_image_data_embed(captioned_image, data)
-- 
cgit v1.2.1


From 939f16529a72fe48c2ce3ef31bdaba785925a33c Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 14 Oct 2022 14:55:05 +0100
Subject: only save 1 image per embedding

---
 modules/textual_inversion/textual_inversion.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 6f549d62..1d697c90 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -242,6 +242,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
     last_saved_file = "<none>"
     last_saved_image = "<none>"
+    embedding_yet_to_be_embedded = False
 
     ititial_step = embedding.step or 0
     if ititial_step > steps:
@@ -281,6 +282,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
             embedding.save(last_saved_file)
+            embedding_yet_to_be_embedded = True
 
         write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
             "loss": f"{losses.mean():.7f}",
@@ -318,7 +320,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
             shared.state.current_image = image
 
-            if save_image_with_stored_embedding and os.path.exists(last_saved_file):
+            if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
 
                 last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
 
@@ -342,6 +344,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 captioned_image = insert_image_data_embed(captioned_image, data)
 
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+                embedding_yet_to_be_embedded = False
 
             image.save(last_saved_image)
 
-- 
cgit v1.2.1


From 9a1dcd78edbf9caf68b9e6286d7b5ca81500e243 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 14 Oct 2022 18:14:02 +0100
Subject: add webp for embed load

---
 modules/textual_inversion/textual_inversion.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 1d697c90..c07bffc3 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -96,6 +96,10 @@ class EmbeddingDatabase:
                 else:
                     data = extract_image_data_embed(embed_image)
                     name = data.get('name', name)
+            elif filename.upper().endswith('.WEBP'):
+                embed_image = Image.open(path)
+                data = extract_image_data_embed(embed_image)
+                name = data.get('name', name)
             else:
                 data = torch.load(path, map_location="cpu")
 
-- 
cgit v1.2.1


From ddf6899df0cf87d4da77cb2ce223061f4a5edf18 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 14 Oct 2022 18:23:20 +0100
Subject: generalise to popular lossless formats

---
 modules/textual_inversion/textual_inversion.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index c07bffc3..b99df3b1 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -88,18 +88,14 @@ class EmbeddingDatabase:
 
             data = []
 
-            if filename.upper().endswith('.PNG'):
+            if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
                 embed_image = Image.open(path)
-                if 'sd-ti-embedding' in embed_image.text:
+                if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
                     data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
                     name = data.get('name', name)
                 else:
                     data = extract_image_data_embed(embed_image)
                     name = data.get('name', name)
-            elif filename.upper().endswith('.WEBP'):
-                embed_image = Image.open(path)
-                data = extract_image_data_embed(embed_image)
-                name = data.get('name', name)
             else:
                 data = torch.load(path, map_location="cpu")
 
-- 
cgit v1.2.1


From b6e3b96dab94a00f51725f9cc977eebc6b4072ab Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sat, 15 Oct 2022 15:17:21 +0100
Subject: Change vector size footer label

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index b99df3b1..2ed345b1 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -338,7 +338,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 checkpoint = sd_models.select_checkpoint()
                 footer_left = checkpoint.model_name
                 footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = 'v{} {}s'.format(vectorSize, embedding.step)
+                footer_right = '{}v {}s'.format(vectorSize, embedding.step)
 
                 captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
                 captioned_image = insert_image_data_embed(captioned_image, data)
-- 
cgit v1.2.1


From 606519813dd998140a741096f9029c732ee52d2a Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Sat, 15 Oct 2022 22:10:39 +0800
Subject: Prevent modal content from being selected

---
 style.css | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/style.css b/style.css
index 920c32ab..33832ebf 100644
--- a/style.css
+++ b/style.css
@@ -309,6 +309,8 @@ input[type="range"]{
   height: 100%;
   overflow: auto;
   background-color: rgba(20, 20, 20, 0.95);
+  user-select: none;
+  -webkit-user-select: none;
 }
 
 .modalControls {
@@ -513,4 +515,4 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
     height: 480px !important;
     max-height: 480px !important;
     min-height: 480px !important;
-}
\ No newline at end of file
+}
-- 
cgit v1.2.1


From 6e4f5566b58e36aede83427df6c69eba8517af28 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sat, 15 Oct 2022 23:53:49 +0800
Subject: sorting files

---
 javascript/images_history.js | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/javascript/images_history.js b/javascript/images_history.js
index f7d052c3..7f0d8f42 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -96,7 +96,7 @@ function images_history_get_current_img(tabname, image_path, files){
     ];
 }
 
-function images_history_delete(del_num, tabname, img_path, img_file_name, page_index, filenames, image_index){
+function images_history_delete(del_num, tabname, img_file_name, page_index, filenames, image_index){
     image_index = parseInt(image_index);
     var tab = gradioApp().getElementById(tabname + '_images_history');
     var set_btn = tab.querySelector(".images_history_set_index");
@@ -132,12 +132,12 @@ function images_history_delete(del_num, tabname, img_path, img_file_name, page_i
     return [del_num, tabname, img_path, img_file_name, page_index, filenames, image_index];
 }
 
-function images_history_turnpage(img_path, page_index, image_index, tabname){
+function images_history_turnpage(img_path, page_index, image_index, tabname, date_from, date_to){
     var buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item");
     buttons.forEach(function(elem) {
         elem.style.display = 'block';
     })
-    return [img_path, page_index, image_index, tabname];
+    return [img_path, page_index, image_index, tabname, date_from, date_to];
 }
 
 function images_history_enable_del_buttons(){
-- 
cgit v1.2.1


From 4387e4fe6479c08f7bc7e42924c3a1093e3a1872 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sat, 15 Oct 2022 18:39:29 +0200
Subject: Update modules/ui.py
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Víctor Gallego <vicgalle@ucm.es>
---
 modules/ui.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index d0696101..5bb961b2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -599,7 +599,8 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001")
                     aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
-                    aesthetic_steps = gr.Slider(minimum=0, maximum=256, step=1, label="Aesthetic steps", value=5)
+                    aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
+
                     with gr.Row():
                         aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
                         aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
-- 
cgit v1.2.1


From f7df06a98180a2a8769b3ceebf7b6a35eca8ffc5 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sat, 15 Oct 2022 18:40:06 +0200
Subject: Update README.md
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Víctor Gallego <vicgalle@ucm.es>
---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 7b8d018b..40104833 100644
--- a/README.md
+++ b/README.md
@@ -70,7 +70,8 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
 - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
 - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
-- Aesthetic, a way to generate images with a specific aesthetic by using clip images embds (implementation of https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)
+- Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
+
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
-- 
cgit v1.2.1


From 9b7705e0573bddde26df4575c71f994d73a4d519 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sat, 15 Oct 2022 18:40:34 +0200
Subject: Update modules/aesthetic_clip.py
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Víctor Gallego <vicgalle@ucm.es>
---
 modules/aesthetic_clip.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
index f15cfd47..bcf2b073 100644
--- a/modules/aesthetic_clip.py
+++ b/modules/aesthetic_clip.py
@@ -70,7 +70,7 @@ def generate_imgs_embd(name, folder, batch_size):
         torch.cuda.empty_cache()
         res = f"""
         Done generating embedding for {name}!
-        Hypernetwork saved to {html.escape(path)}
+        Aesthetic embedding saved to {html.escape(path)}
         """
         shared.update_aesthetic_embeddings()
         return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding",
-- 
cgit v1.2.1


From 0d4f5db235357aeb4c7a8738179ba33aaf5a6b75 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sat, 15 Oct 2022 18:40:58 +0200
Subject: Update modules/ui.py
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Víctor Gallego <vicgalle@ucm.es>
---
 modules/ui.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 5bb961b2..25eba548 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -597,7 +597,8 @@ def create_ui(wrap_gradio_gpu_call):
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
                 with gr.Group():
-                    aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001")
+                    aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001")
+
                     aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
                     aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
 
-- 
cgit v1.2.1


From ad9bc604a8fadcfebe72be37f66cec51e7e87fb5 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sat, 15 Oct 2022 18:41:18 +0200
Subject: Update modules/ui.py
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Víctor Gallego <vicgalle@ucm.es>
---
 modules/ui.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 25eba548..3b28b69c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -607,7 +607,8 @@ def create_ui(wrap_gradio_gpu_call):
                         aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
                         aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
 
-                    aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None)
+                    aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Aesthetic imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None)
+
                     aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
 
                 with gr.Row():
-- 
cgit v1.2.1


From 3f5c3b981e46c16bb10948d012575b25170efb3b Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sat, 15 Oct 2022 18:41:46 +0200
Subject: Update modules/ui.py
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Víctor Gallego <vicgalle@ucm.es>
---
 modules/ui.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 3b28b69c..1f6fcdc9 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1190,7 +1190,8 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create embedding", variant='primary')
 
-                with gr.Tab(label="Create images embedding"):
+                with gr.Tab(label="Create aesthetic images embedding"):
+
                     new_embedding_name_ae = gr.Textbox(label="Name")
                     process_src_ae = gr.Textbox(label='Source directory')
                     batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256)
-- 
cgit v1.2.1


From 74a9ee70020ffa2746c82300c533de3f7e523f22 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 15 Oct 2022 17:25:35 +0300
Subject: fix saving images compatibility with gradio update

---
 modules/ui.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 3206113e..b867d40f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -154,10 +154,7 @@ def save_files(js_data, images, do_make_zip, index):
             writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
 
         for image_index, filedata in enumerate(images, start_index):
-            if filedata.startswith("data:image/png;base64,"):
-                filedata = filedata[len("data:image/png;base64,"):]
-
-            image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8'))))
+            image = image_from_url_text(filedata)
 
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
-- 
cgit v1.2.1


From 09814e3cf384bf4189d57d1573483f72b38fb99f Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 15 Oct 2022 19:06:34 +0300
Subject: Update launch.py

---
 launch.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/launch.py b/launch.py
index 537670a3..e3208553 100644
--- a/launch.py
+++ b/launch.py
@@ -107,6 +107,7 @@ def prepare_enviroment():
     xformers = '--xformers' in args
     deepdanbooru = '--deepdanbooru' in args
     ngrok = '--ngrok' in args
+    reinstall_xformers = '--reinstall-xformers' in args
 
     try:
         commit = run(f"{git} rev-parse HEAD").strip()
@@ -128,9 +129,9 @@ def prepare_enviroment():
     if not is_installed("clip"):
         run_pip(f"install {clip_package}", "clip")
 
-    if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
+    if (not is_installed("xformers") or reinstall_xformers) and xformers and platform.python_version().startswith("3.10"):
         if platform.system() == "Windows":
-            run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/c/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
+            run_pip("install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
         elif platform.system() == "Linux":
             run_pip("install xformers", "xformers")
 
-- 
cgit v1.2.1


From 529afbf4d70165a0dfd19eb9c2ec22416b794a1d Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 15 Oct 2022 19:19:54 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index c81722a0..984b35c4 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -24,7 +24,7 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
 
-    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
+    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
-- 
cgit v1.2.1


From 8fb0b991522658d938ae43de77f708555aa1902b Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 15 Oct 2022 20:17:51 +0300
Subject: Update launch.py

---
 launch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index e3208553..5ec2b926 100644
--- a/launch.py
+++ b/launch.py
@@ -104,10 +104,10 @@ def prepare_enviroment():
     args = shlex.split(commandline_args)
 
     args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
+    args, reinstall_xformers = extract_argg(args, '--reinstall-xformers')
     xformers = '--xformers' in args
     deepdanbooru = '--deepdanbooru' in args
     ngrok = '--ngrok' in args
-    reinstall_xformers = '--reinstall-xformers' in args
 
     try:
         commit = run(f"{git} rev-parse HEAD").strip()
-- 
cgit v1.2.1


From be1596ce30b1ead6998da0c62003003dcce5eb2c Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Sat, 15 Oct 2022 20:19:16 +0300
Subject: fix typo

---
 launch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index 5ec2b926..2e6b3369 100644
--- a/launch.py
+++ b/launch.py
@@ -104,7 +104,7 @@ def prepare_enviroment():
     args = shlex.split(commandline_args)
 
     args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
-    args, reinstall_xformers = extract_argg(args, '--reinstall-xformers')
+    args, reinstall_xformers = extract_arg(args, '--reinstall-xformers')
     xformers = '--xformers' in args
     deepdanbooru = '--deepdanbooru' in args
     ngrok = '--ngrok' in args
-- 
cgit v1.2.1


From 9a33292ce41b01252cdb8ab6214a11d274e32fa0 Mon Sep 17 00:00:00 2001
From: zhengxiaoyao0716 <1499383852@qq.com>
Date: Sat, 15 Oct 2022 01:04:47 +0800
Subject: reload javascript files when custom script bodies

---
 modules/ui.py | 28 ++++++++++++++++------------
 1 file changed, 16 insertions(+), 12 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index b867d40f..90b8646b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -12,7 +12,7 @@ import time
 import traceback
 import platform
 import subprocess as sp
-from functools import reduce
+from functools import partial, reduce
 
 import numpy as np
 import torch
@@ -1491,6 +1491,7 @@ Requested path was: {f}
 
         def reload_scripts():
             modules.scripts.reload_script_body_only()
+            reload_javascript() # need to refresh the html page
 
         reload_script_bodies.click(
             fn=reload_scripts,
@@ -1738,22 +1739,25 @@ Requested path was: {f}
     return demo
 
 
-with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
-    javascript = f'<script>{jsfile.read()}</script>'
+def load_javascript(raw_response):
+    with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
+        javascript = f'<script>{jsfile.read()}</script>'
 
-jsdir = os.path.join(script_path, "javascript")
-for filename in sorted(os.listdir(jsdir)):
-    with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
-        javascript += f"\n<script>{jsfile.read()}</script>"
+    jsdir = os.path.join(script_path, "javascript")
+    for filename in sorted(os.listdir(jsdir)):
+        with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
+            javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
 
-
-if 'gradio_routes_templates_response' not in globals():
     def template_response(*args, **kwargs):
-        res = gradio_routes_templates_response(*args, **kwargs)
-        res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))
+        res = raw_response(*args, **kwargs)
+        res.body = res.body.replace(
+            b'</head>', f'{javascript}</head>'.encode("utf8"))
         res.init_headers()
         return res
 
-    gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
     gradio.routes.templates.TemplateResponse = template_response
 
+
+reload_javascript = partial(load_javascript,
+                            gradio.routes.templates.TemplateResponse)
+reload_javascript()
-- 
cgit v1.2.1


From 3d21684ee30ca5734126b8d08c05b3a0f513fe75 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sun, 16 Oct 2022 00:01:00 +0200
Subject: Add support to other img format, fixed dropbox update

---
 modules/aesthetic_clip.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
index bcf2b073..68264284 100644
--- a/modules/aesthetic_clip.py
+++ b/modules/aesthetic_clip.py
@@ -8,7 +8,7 @@ import gradio as gr
 import torch
 from PIL import Image
 from modules import shared
-from modules.shared import device, aesthetic_embeddings
+from modules.shared import device
 from transformers import CLIPModel, CLIPProcessor
 
 from tqdm.auto import tqdm
@@ -20,7 +20,7 @@ def get_all_images_in_folder(folder):
 
 
 def check_is_valid_image_file(filename):
-    return filename.lower().endswith(('.png', '.jpg', '.jpeg'))
+    return filename.lower().endswith(('.png', '.jpg', '.jpeg', ".gif", ".tiff", ".webp"))
 
 
 def batched(dataset, total, n=1):
@@ -73,6 +73,6 @@ def generate_imgs_embd(name, folder, batch_size):
         Aesthetic embedding saved to {html.escape(path)}
         """
         shared.update_aesthetic_embeddings()
-        return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding",
-                           value=sorted(aesthetic_embeddings.keys())[0] if len(
-                               aesthetic_embeddings) > 0 else None), res, ""
+        return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding",
+                           value=sorted(shared.aesthetic_embeddings.keys())[0] if len(
+                               shared.aesthetic_embeddings) > 0 else None), res, ""
-- 
cgit v1.2.1


From 9325c85f780c569d1823e422eaf51b2e497e0d3e Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sun, 16 Oct 2022 00:23:47 +0200
Subject: fixed dropbox update

---
 modules/sd_hijack.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 192883b2..491312b4 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -9,7 +9,7 @@ from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared
-from modules.shared import opts, device, cmd_opts, aesthetic_embeddings
+from modules.shared import opts, device, cmd_opts
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
@@ -182,7 +182,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             image_embs_name = None
         if image_embs_name is not None and self.image_embs_name != image_embs_name:
             self.image_embs_name = image_embs_name
-            self.image_embs = torch.load(aesthetic_embeddings[self.image_embs_name], map_location=device)
+            self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device)
             self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
             self.image_embs.requires_grad_(False)
 
-- 
cgit v1.2.1


From 763b893f319cee280b86e63025eb55e7c16b02e7 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sun, 16 Oct 2022 10:03:09 +0800
Subject: images history sorting files by date

---
 javascript/images_history.js |  12 +-
 modules/images_history.py    | 261 ++++++++++++++++++++++++++++++++-----------
 2 files changed, 202 insertions(+), 71 deletions(-)

diff --git a/javascript/images_history.js b/javascript/images_history.js
index 7f0d8f42..ac5834c7 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -88,10 +88,10 @@ function images_history_set_image_info(button){
     
 }
 
-function images_history_get_current_img(tabname, image_path, files){
+function images_history_get_current_img(tabname, img_index, files){
     return [
-        gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"), 
-        image_path, 
+        tabname, 
+        gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"),         
         files
     ];
 }
@@ -129,7 +129,7 @@ function images_history_delete(del_num, tabname, img_file_name, page_index, file
         setTimeout(function(btn){btn.click()}, 30, btn);
     }
     images_history_disabled_del();  
-    return [del_num, tabname, img_path, img_file_name, page_index, filenames, image_index];
+    return [del_num, tabname, img_file_name, page_index, filenames, image_index];
 }
 
 function images_history_turnpage(img_path, page_index, image_index, tabname, date_from, date_to){
@@ -170,8 +170,8 @@ function images_history_init(){
         }    
         tabs_box.classList.add(images_history_tab_list[0]);
 
-        // same as above, at page load
-        //load_txt2img_button.click();
+        // same as above, at page load-- load very fast now
+        load_txt2img_button.click();
     } else {
         setTimeout(images_history_init, 500);
     } 
diff --git a/modules/images_history.py b/modules/images_history.py
index f5ef44fe..533cf51b 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,33 +1,74 @@
 import os
 import shutil
+import time
+import hashlib
+import gradio
+show_max_dates_num = 3
+system_bak_path = "webui_log_and_bak"
+def is_valid_date(date):
+    try:
+        time.strptime(date, "%Y%m%d")
+        return True
+    except:
+        return False
 
+def reduplicative_file_move(src, dst):
+    def same_name_file(basename, path):
+        name, ext = os.path.splitext(basename)
+        f_list = os.listdir(path)
+        max_num = 0
+        for f in f_list:
+            if len(f) <= len(basename):
+                continue
+            f_ext = f[-len(ext):] if len(ext) > 0 else ""
+            if f[:len(name)] == name and f_ext == ext:                
+                if f[len(name)] == "(" and f[-len(ext)-1] == ")":
+                    number = f[len(name)+1:-len(ext)-1]
+                    if number.isdigit():
+                        if int(number) > max_num:
+                            max_num = int(number)
+        return f"{name}({max_num + 1}){ext}"
+    name = os.path.basename(src)
+    save_name = os.path.join(dst, name)
+    if not os.path.exists(save_name):
+        shutil.move(src, dst)
+    else:
+        name = same_name_file(name, dst)
+        shutil.move(src, os.path.join(dst, name))
 
-def traverse_all_files(output_dir, image_list, curr_dir=None):
-    curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
+def traverse_all_files(curr_path, image_list, all_type=False):
     try:
         f_list = os.listdir(curr_path)
     except:
-        if curr_dir[-10:].rfind(".") > 0 and curr_dir[-4:] != ".txt":
-            image_list.append(curr_dir)
+        if all_type or curr_path[-10:].rfind(".") > 0 and curr_path[-4:] != ".txt":
+            image_list.append(curr_path)
         return image_list
     for file in f_list:
-        file = file if curr_dir is None else os.path.join(curr_dir, file)
-        file_path = os.path.join(curr_path, file)
-        if file[-4:] == ".txt":
+        file = os.path.join(curr_path, file)
+        if (not all_type) and file[-4:] == ".txt":
             pass
-        elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0:
+        elif os.path.isfile(file) and file[-10:].rfind(".") > 0:
             image_list.append(file)
         else:
-            image_list = traverse_all_files(output_dir, image_list, file)
+            image_list = traverse_all_files(file, image_list)
     return image_list
 
-
-def get_recent_images(dir_name, page_index, step, image_index, tabname):
-    page_index = int(page_index)
-    f_list = os.listdir(dir_name)
+def get_recent_images(dir_name, page_index, step, image_index, tabname, date_from, date_to):
+    #print(f"turn_page {page_index}",date_from)
+    if date_from is None or date_from == "":
+        return None, 1, None, ""
     image_list = []
-    image_list = traverse_all_files(dir_name, image_list)
-    image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+    date_list = auto_sorting(dir_name)
+    page_index = int(page_index)
+    today = time.strftime("%Y%m%d",time.localtime(time.time()))
+    for date in date_list:
+        if date >= date_from and date <= date_to:
+            path = os.path.join(dir_name, date)
+            if date == today and not os.path.exists(path):
+                continue
+            image_list = traverse_all_files(path, image_list)
+
+    image_list = sorted(image_list, key=lambda file: -os.path.getctime(file))
     num = 48 if tabname != "extras" else 12
     max_page_index = len(image_list) // num + 1
     page_index = max_page_index if page_index == -1 else page_index + step
@@ -38,40 +79,101 @@ def get_recent_images(dir_name, page_index, step, image_index, tabname):
     image_index = int(image_index)
     if image_index < 0 or image_index > len(image_list) - 1:
         current_file = None
-        hidden = None
     else:
-        current_file = image_list[int(image_index)]
-        hidden = os.path.join(dir_name, current_file)
-    return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, ""
+        current_file = image_list[image_index]
+    return image_list, page_index, image_list,  ""
 
+def auto_sorting(dir_name):
+    #print(f"auto sorting")
+    bak_path = os.path.join(dir_name, system_bak_path)
+    if not os.path.exists(bak_path):
+        os.mkdir(bak_path)
+    log_file = None 
+    files_list = []    
+    f_list = os.listdir(dir_name)
+    for file in f_list:   
+        if file == system_bak_path:
+            continue     
+        file_path = os.path.join(dir_name, file)
+        if not is_valid_date(file):
+            if file[-10:].rfind(".") > 0:
+                files_list.append(file_path)
+            else:
+                files_list = traverse_all_files(file_path, files_list, all_type=True)
+
+    for file in files_list:        
+        date_str = time.strftime("%Y%m%d",time.localtime(os.path.getctime(file)))
+        file_path = os.path.dirname(file)
+        hash_path = hashlib.md5(file_path.encode()).hexdigest()
+        path = os.path.join(dir_name, date_str, hash_path)
+        if not os.path.exists(path):
+            os.makedirs(path)
+        if log_file is None:
+            log_file = open(os.path.join(bak_path,"path_mapping.csv"),"a") 
+        log_file.write(f"{hash_path},{file_path}\n")
+        reduplicative_file_move(file, path)
+       
+    date_list = []
+    f_list = os.listdir(dir_name)
+    for f in f_list:
+        if is_valid_date(f):
+            date_list.append(f)
+        elif f == system_bak_path:
+            continue
+        else:
+            reduplicative_file_move(os.path.join(dir_name, f), bak_path)           
+            
+    today = time.strftime("%Y%m%d",time.localtime(time.time()))
+    if today not in date_list:
+        date_list.append(today)
+    return sorted(date_list)
 
-def first_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, 1, 0, image_index, tabname)
 
 
-def end_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, -1, 0, image_index, tabname)
+def archive_images(dir_name):
+    date_list = auto_sorting(dir_name)
+    date_from = date_list[-show_max_dates_num] if len(date_list) > show_max_dates_num else date_list[0]
+    return (
+        gradio.update(visible=False), 
+        gradio.update(visible=True), 
+        gradio.Dropdown.update(choices=date_list, value=date_list[-1]),
+        gradio.Dropdown.update(choices=date_list, value=date_from)
+    )
 
+def date_to_change(dir_name, page_index, image_index, tabname, date_from, date_to):
+    #print("date_to", date_to)
+    date_list = auto_sorting(dir_name)
+    date_from_list = [date for date in date_list if date <= date_to]
+    date_from = date_from_list[0] if len(date_from_list) < show_max_dates_num else date_from_list[-show_max_dates_num]
+    image_list, page_index, image_list, _  =get_recent_images(dir_name, 1, 0, image_index, tabname, date_from, date_to)
+    return image_list, page_index, image_list, _, gradio.Dropdown.update(choices=date_from_list, value=date_from)
 
-def prev_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, -1, image_index, tabname)
+def first_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
+    return get_recent_images(dir_name, 1, 0, image_index, tabname, date_from, date_to)
 
 
-def next_page_click(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, 1, image_index, tabname)
+def end_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
+    return get_recent_images(dir_name, -1, 0, image_index, tabname, date_from, date_to)
 
 
-def page_index_change(dir_name, page_index, image_index, tabname):
-    return get_recent_images(dir_name, page_index, 0, image_index, tabname)
+def prev_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
+    return get_recent_images(dir_name, page_index, -1, image_index, tabname, date_from, date_to)
 
 
-def show_image_info(num, image_path, filenames):
-    # print(f"select image {num}")
-    file = filenames[int(num)]
-    return file, num, os.path.join(image_path, file)
+def next_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
+    return get_recent_images(dir_name, page_index, 1, image_index, tabname, date_from, date_to)
+
+
+def page_index_change(dir_name, page_index, image_index, tabname, date_from, date_to):
+    return get_recent_images(dir_name, page_index, 0, image_index, tabname, date_from, date_to)
 
 
-def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
+def show_image_info(tabname_box, num, filenames):
+    # #print(f"select image {num}")
+    file = filenames[int(num)]
+    return file, num, file
+
+def delete_image(delete_num, tabname, name, page_index, filenames, image_index):
     if name == "":
         return filenames, delete_num
     else:
@@ -81,21 +183,19 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima
         new_file_list = []
         for name in filenames:
             if i >= index and i < index + delete_num:
-                path = os.path.join(dir_name, name)
-                if os.path.exists(path):
-                    print(f"Delete file {path}")
-                    os.remove(path)
-                    txt_file = os.path.splitext(path)[0] + ".txt"
+                if os.path.exists(name):
+                    #print(f"Delete file {name}")
+                    os.remove(name)
+                    txt_file = os.path.splitext(name)[0] + ".txt"
                     if os.path.exists(txt_file):
                         os.remove(txt_file)
                 else:
-                    print(f"Not exists file {path}")
+                    #print(f"Not exists file {name}")
             else:
                 new_file_list.append(name)
             i += 1
     return new_file_list, 1
 
-
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     if tabname == "txt2img":
         dir_name = opts.outdir_txt2img_samples
@@ -107,16 +207,32 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     dir_name = d[0]
     for p in d[1:]:
         dir_name = os.path.join(dir_name, p)
-    with gr.Row():
-        renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page")
-        first_page = gr.Button('First Page')
-        prev_page = gr.Button('Prev Page')
-        page_index = gr.Number(value=1, label="Page Index")
-        next_page = gr.Button('Next Page')
-        end_page = gr.Button('End Page')
-    with gr.Row(elem_id=tabname + "_images_history"):
+
+    f_list = os.listdir(dir_name)
+    sorted_flag = os.path.exists(os.path.join(dir_name, system_bak_path)) or len(f_list) == 0 
+    date_list, date_from, date_to = None, None, None
+    if sorted_flag:
+        #print(sorted_flag)
+        date_list = auto_sorting(dir_name)
+        date_to = date_list[-1]
+        date_from = date_list[-show_max_dates_num] if len(date_list) > show_max_dates_num else date_list[0] 
+
+    with gr.Column(visible=sorted_flag) as page_panel:
         with gr.Row():
+            renew_page = gr.Button('Refresh', elem_id=tabname + "_images_history_renew_page", interactive=sorted_flag)
+            first_page = gr.Button('First Page')
+            prev_page = gr.Button('Prev Page')
+            page_index = gr.Number(value=1, label="Page Index")
+            next_page = gr.Button('Next Page')
+            end_page = gr.Button('End Page')
+
+        with gr.Row(elem_id=tabname + "_images_history"):
             with gr.Column(scale=2):
+                with gr.Row():
+                    newest = gr.Button('Newest')
+                    date_to = gr.Dropdown(choices=date_list, value=date_to, label="Date to")
+                    date_from = gr.Dropdown(choices=date_list, value=date_from, label="Date from")                    
+
                 history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
                 with gr.Row():
                     delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
@@ -128,22 +244,31 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                 with gr.Row():
                     with gr.Column():
                         img_file_info = gr.Textbox(label="Generate Info", interactive=False)
-                        img_file_name = gr.Textbox(label="File Name", interactive=False)
-                with gr.Row():
+                        img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
                     # hiden items
+                    with gr.Row(visible=False):
+                        img_path = gr.Textbox(dir_name)
+                        tabname_box = gr.Textbox(tabname)
+                        image_index = gr.Textbox(value=-1)
+                        set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
+                        filenames = gr.State()
+                        hidden = gr.Image(type="pil")
+                        info1 = gr.Textbox()
+                        info2 = gr.Textbox()
+    with gr.Column(visible=not sorted_flag) as init_warning:
+        with gr.Row():
+            gr.Textbox("The system needs to archive the files according to the date. This requires changing the directory structure of the files",
+             label="Waring",
+             css="")
+        with gr.Row():
+            sorted_button = gr.Button('Confirme')
 
-                    img_path = gr.Textbox(dir_name.rstrip("/"), visible=False)
-                    tabname_box = gr.Textbox(tabname, visible=False)
-                    image_index = gr.Textbox(value=-1, visible=False)
-                    set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False)
-                    filenames = gr.State()
-                    hidden = gr.Image(type="pil", visible=False)
-                    info1 = gr.Textbox(visible=False)
-                    info2 = gr.Textbox(visible=False)
-
+                  
+           
+   
     # turn pages
-    gallery_inputs = [img_path, page_index, image_index, tabname_box]
-    gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
+    gallery_inputs = [img_path, page_index, image_index, tabname_box, date_from, date_to]
+    gallery_outputs = [history_gallery, page_index, filenames, img_file_name]
 
     first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
     next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
@@ -154,15 +279,21 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     # page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
 
     # other funcitons
-    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hidden])
+    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, filenames], outputs=[img_file_name, image_index, hidden])
     img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
-    delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num])
+    delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num])
     hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
-
+    date_to.change(date_to_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs + [date_from])
     # pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
     switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
     switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
 
+    sorted_button.click(archive_images, inputs=[img_path], outputs=[init_warning, page_panel, date_to, date_from])
+    newest.click(archive_images, inputs=[img_path], outputs=[init_warning, page_panel, date_to, date_from])
+   
+    
+    
+
 
 def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
     with gr.Blocks(analytics_enabled=False) as images_history:
-- 
cgit v1.2.1


From 0c5fa9a681672508adadbe1e10fc16d7fe0ed6dd Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 16 Oct 2022 08:51:24 +0300
Subject: do not reload embeddings from disk when doing textual inversion

---
 modules/processing.py                          | 5 +++--
 modules/textual_inversion/textual_inversion.py | 1 +
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 941ae089..833fed8a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -53,7 +53,7 @@ def get_correct_sampler(p):
         return sd_samplers.samplers_for_img2img
 
 class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None, do_not_reload_embeddings=False):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -80,6 +80,7 @@ class StableDiffusionProcessing:
         self.extra_generation_params: dict = extra_generation_params or {}
         self.overlay_images = overlay_images
         self.eta = eta
+        self.do_not_reload_embeddings = do_not_reload_embeddings
         self.paste_to = None
         self.color_corrections = None
         self.denoising_strength: float = 0
@@ -364,7 +365,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     def infotext(iteration=0, position_in_batch=0):
         return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
 
-    if os.path.exists(cmd_opts.embeddings_dir):
+    if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
     infotexts = []
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 2ed345b1..7ec75018 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -296,6 +296,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 sd_model=shared.sd_model,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
+                do_not_reload_embeddings=True,
             )
 
             if preview_from_txt2img:
-- 
cgit v1.2.1


From 2ce27728f6433911274efa67856315d22df56629 Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Sun, 16 Oct 2022 13:50:55 +0900
Subject: added extras batch work from directory

---
 modules/extras.py | 23 ++++++++++++++++++-----
 modules/ui.py     | 12 ++++++++++++
 2 files changed, 30 insertions(+), 5 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index f2f5a7b0..5b52b27d 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -20,26 +20,38 @@ import gradio as gr
 cached_images = {}
 
 
-def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
     devices.torch_gc()
 
     imageArr = []
     # Also keep track of original file names
     imageNameArr = []
-
+    outputs = []
+    
     if extras_mode == 1:
         #convert file to pillow image
         for img in image_folder:
             image = Image.open(img)
             imageArr.append(image)
             imageNameArr.append(os.path.splitext(img.orig_name)[0])
+    elif extras_mode == 2:
+        if input_dir == '':
+            return outputs, "Please select an input directory.", ''
+        image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
+        for img in image_list:
+            image = Image.open(img)
+            imageArr.append(image)
+            imageNameArr.append(img)
     else:
         imageArr.append(image)
         imageNameArr.append(None)
 
-    outpath = opts.outdir_samples or opts.outdir_extras_samples
+    if extras_mode == 2 and output_dir != '':
+        outpath = output_dir
+    else:
+        outpath = opts.outdir_samples or opts.outdir_extras_samples
 
-    outputs = []
+    
     for image, image_name in zip(imageArr, imageNameArr):
         if image is None:
             return outputs, "Please select an input image.", ''
@@ -112,7 +124,8 @@ def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility,
             image.info = existing_pnginfo
             image.info["extras"] = info
 
-        outputs.append(image)
+        if extras_mode != 2 or show_extras_results :
+            outputs.append(image)
 
     devices.torch_gc()
 
diff --git a/modules/ui.py b/modules/ui.py
index b867d40f..08fa72c6 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1016,6 +1016,15 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.TabItem('Batch Process'):
                         image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
 
+                    with gr.TabItem('Batch from Directory'):
+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs,
+                            placeholder="A directory on the same machine where the server is running."
+                        )
+                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs,
+                            placeholder="Leave blank to save images to the default path."
+                        )
+                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
+
                 with gr.Tabs(elem_id="extras_resize_mode"):
                     with gr.TabItem('Scale by'):
                         upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
@@ -1060,6 +1069,9 @@ def create_ui(wrap_gradio_gpu_call):
                 dummy_component,
                 extras_image,
                 image_batch,
+                extras_batch_input_dir,
+                extras_batch_output_dir,
+                show_extras_results,
                 gfpgan_visibility,
                 codeformer_visibility,
                 codeformer_weight,
-- 
cgit v1.2.1


From 179e3ca752d0133470fd3ae44153ee0b71450c9f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 16 Oct 2022 09:51:01 +0300
Subject: honor --hide-ui-dir-config option for #2807

---
 modules/extras.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modules/extras.py b/modules/extras.py
index 5b52b27d..0819ed37 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -35,6 +35,8 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
             imageArr.append(image)
             imageNameArr.append(os.path.splitext(img.orig_name)[0])
     elif extras_mode == 2:
+        assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
+
         if input_dir == '':
             return outputs, "Please select an input directory.", ''
         image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
-- 
cgit v1.2.1


From 3395ba493f93214cf037d084d45693a37610bd85 Mon Sep 17 00:00:00 2001
From: ddPn08 <pyn.g.1125@gmail.com>
Date: Sun, 16 Oct 2022 09:24:01 +0900
Subject: Allow specifying the region of ngrok.

---
 modules/ngrok.py  | 8 +++++---
 modules/shared.py | 1 +
 modules/ui.py     | 2 +-
 3 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/modules/ngrok.py b/modules/ngrok.py
index 7d03a6df..5c5f349a 100644
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -1,12 +1,14 @@
 from pyngrok import ngrok, conf, exception
 
 
-def connect(token, port):
+def connect(token, port, region):
     if token == None:
         token = 'None'
-    conf.get_default().auth_token = token
+    config = conf.PyngrokConfig(
+        auth_token=token, region=region
+    )
     try:
-        public_url = ngrok.connect(port).public_url
+        public_url = ngrok.connect(port, pyngrok_config=config).public_url
     except exception.PyngrokNgrokError:
         print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
diff --git a/modules/shared.py b/modules/shared.py
index fa30bbb0..dcab0af9 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -40,6 +40,7 @@ parser.add_argument("--unload-gfpgan", action='store_true', help="does not do an
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
+parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
diff --git a/modules/ui.py b/modules/ui.py
index 08fa72c6..5c0eaf73 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -56,7 +56,7 @@ if not cmd_opts.share and not cmd_opts.listen:
 if cmd_opts.ngrok != None:
     import modules.ngrok as ngrok
     print('ngrok authtoken detected, trying to connect...')
-    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860)
+    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
 
 
 def gr_show(visible=True):
-- 
cgit v1.2.1


From 20bf99052a9d50b5f99d199f4c449ef1ddd6e3cb Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sun, 16 Oct 2022 04:47:03 +0900
Subject: Make style configurable in ui-config.json

---
 modules/ui.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/modules/ui.py b/modules/ui.py
index 5c0eaf73..78096f27 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -508,9 +508,11 @@ def create_toprow(is_img2img):
             with gr.Row():
                 with gr.Column(scale=1, elem_id="style_pos_col"):
                     prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style.save_to_config = True
 
                 with gr.Column(scale=1, elem_id="style_neg_col"):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style2.save_to_config = True
 
     return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
 
@@ -1739,6 +1741,11 @@ Requested path was: {f}
         if type(x) == gr.Number:
             apply_field(x, 'value')
 
+        # Since there are many dropdowns that shouldn't be saved,
+        # we only mark dropdowns that should be saved.
+        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
+            apply_field(x, 'value')
+
     visit(txt2img_interface, loadsave, "txt2img")
     visit(img2img_interface, loadsave, "img2img")
     visit(extras_interface, loadsave, "extras")
-- 
cgit v1.2.1


From b65a3101ce82b42b4ccc525044548e66cc44ae4a Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sun, 16 Oct 2022 04:54:53 +0900
Subject: Use default value when dropdown ui setting is bad

Default value is the first value of selectables.
Particually, None in styles.
---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 78096f27..c8e68bd6 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1744,7 +1744,7 @@ Requested path was: {f}
         # Since there are many dropdowns that shouldn't be saved,
         # we only mark dropdowns that should be saved.
         if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
-            apply_field(x, 'value')
+            apply_field(x, 'value', lambda val: val in x.choices)
 
     visit(txt2img_interface, loadsave, "txt2img")
     visit(img2img_interface, loadsave, "img2img")
-- 
cgit v1.2.1


From 9258a33e3755c76922cd47a03cd59419b6426304 Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sun, 16 Oct 2022 05:09:11 +0900
Subject: Warn when user uses bad ui setting

---
 modules/ui.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index c8e68bd6..10bdf121 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1717,7 +1717,9 @@ Requested path was: {f}
             saved_value = ui_settings.get(key, None)
             if saved_value is None:
                 ui_settings[key] = getattr(obj, field)
-            elif condition is None or condition(saved_value):
+            elif condition and not condition(saved_value):
+                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
+            else:
                 setattr(obj, field, saved_value)
 
         if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
-- 
cgit v1.2.1


From 863e9efc19d2811f1db5055be8e346781df3f7ce Mon Sep 17 00:00:00 2001
From: Zeithrold <41533799+zeithrold@users.noreply.github.com>
Date: Sun, 16 Oct 2022 15:13:18 +0800
Subject: Pull out some of URL to Env Variable (#2578)

* moved repository url to changeable environment variable

* move stable diffusion repo itself to env

* added missing env

* Remove default URL

Co-authored-by: AUTOMATIC1111 <16777216c@gmail.com>
---
 launch.py     | 23 ++++++++++++++++-------
 webui-user.sh | 20 +++++++++++++++++++-
 webui.sh      |  7 ++++++-
 3 files changed, 41 insertions(+), 9 deletions(-)

diff --git a/launch.py b/launch.py
index 2e6b3369..7520cfee 100644
--- a/launch.py
+++ b/launch.py
@@ -94,6 +94,15 @@ def prepare_enviroment():
 
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
     clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
+    deepdanbooru_package = os.environ.get('DEEPDANBOORU_PACKAGE', "git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26")
+
+    xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
+
+    stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/CompVis/stable-diffusion.git")
+    taming_transformers_repo = os.environ.get('TAMING_REANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
+    k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
+    codeformer_repo = os.environ.get('CODEFORMET_REPO', 'https://github.com/sczhou/CodeFormer.git')
+    blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
 
     stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
     taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
@@ -131,23 +140,23 @@ def prepare_enviroment():
 
     if (not is_installed("xformers") or reinstall_xformers) and xformers and platform.python_version().startswith("3.10"):
         if platform.system() == "Windows":
-            run_pip("install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
+            run_pip(f"install -U -I --no-deps {xformers_windows_package}", "xformers")
         elif platform.system() == "Linux":
             run_pip("install xformers", "xformers")
 
     if not is_installed("deepdanbooru") and deepdanbooru:
-        run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
+        run_pip(f"install {deepdanbooru_package}#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
 
     if not is_installed("pyngrok") and ngrok:
         run_pip("install pyngrok", "ngrok")
 
     os.makedirs(dir_repos, exist_ok=True)
 
-    git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
-    git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
-    git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
-    git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
-    git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
+    git_clone(stable_diffusion_repo, repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
+    git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
+    git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
+    git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
+    git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
 
     if not is_installed("lpips"):
         run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
diff --git a/webui-user.sh b/webui-user.sh
index 30646f5c..96293d43 100644
--- a/webui-user.sh
+++ b/webui-user.sh
@@ -12,6 +12,8 @@
 # Commandline arguments for webui.py, for example: export COMMANDLINE_ARGS="--medvram --opt-split-attention"
 export COMMANDLINE_ARGS=""
 
+#export STABLE_DIFFUSION_WEBUI_REPO="https://github.com/AUTOMATIC1111/stable-diffusion-webui.git"
+
 # python3 executable
 #python_cmd="python3"
 
@@ -30,13 +32,29 @@ export COMMANDLINE_ARGS=""
 # Requirements file to use for stable-diffusion-webui
 #export REQS_FILE="requirements_versions.txt"
 
-# Fixed git repos
+# Fixed git-based pip packages
+# Example: "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379"
 #export K_DIFFUSION_PACKAGE=""
+
 #export GFPGAN_PACKAGE=""
+#export DEEPDANBOORU_PACKAGE=""
+#export CLIP_PACKAGE=""
+
+#export XFORMERS_WINDOWS_PACKAGE=""
+
+# Fixed git repos
+# Example: "https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/c/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl"
+#export STABLE_DIFFUSION_REPO=""
+#export TAMING_REANSFORMERS_REPO=""
+#export K_DIFFUSION_REPO=""
+#export CODEFORMET_REPO=""
+#export BLIP_REPO=""
 
 # Fixed git commits
+# Example: "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc"
 #export STABLE_DIFFUSION_COMMIT_HASH=""
 #export TAMING_TRANSFORMERS_COMMIT_HASH=""
+#export K_DIFFUSION_COMMIT_HASH=""
 #export CODEFORMER_COMMIT_HASH=""
 #export BLIP_COMMIT_HASH=""
 
diff --git a/webui.sh b/webui.sh
index 980c0aaf..88a78459 100755
--- a/webui.sh
+++ b/webui.sh
@@ -41,6 +41,11 @@ then
     venv_dir="venv"
 fi
 
+if [[ -z "${STABLE_DIFFUSION_WEBUI_REPO}" ]]
+then
+    STABLE_DIFFUSION_WEBUI_REPO="https://github.com/AUTOMATIC1111/stable-diffusion-webui.git"
+fi
+
 if [[ -z "${LAUNCH_SCRIPT}" ]]
 then
     LAUNCH_SCRIPT="launch.py"
@@ -111,7 +116,7 @@ then
     cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
     "${GIT}" pull
 else
-    "${GIT}" clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git "${clone_dir}"
+    "${GIT}" clone "${STABLE_DIFFUSION_WEBUI_REPO}" "${clone_dir}"
     cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
 fi
 
-- 
cgit v1.2.1


From bd4f0fb9d9df72899c2c3a1c2bc3580bf26bb685 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 16 Oct 2022 10:14:27 +0300
Subject: revert changes to two bat files I asked to revert but the author
 couldn't in 863e9efc19d2811f1db5055be8e346781df3f7ce.

---
 webui-user.sh | 20 +-------------------
 webui.sh      |  7 +------
 2 files changed, 2 insertions(+), 25 deletions(-)

diff --git a/webui-user.sh b/webui-user.sh
index 96293d43..30646f5c 100644
--- a/webui-user.sh
+++ b/webui-user.sh
@@ -12,8 +12,6 @@
 # Commandline arguments for webui.py, for example: export COMMANDLINE_ARGS="--medvram --opt-split-attention"
 export COMMANDLINE_ARGS=""
 
-#export STABLE_DIFFUSION_WEBUI_REPO="https://github.com/AUTOMATIC1111/stable-diffusion-webui.git"
-
 # python3 executable
 #python_cmd="python3"
 
@@ -32,29 +30,13 @@ export COMMANDLINE_ARGS=""
 # Requirements file to use for stable-diffusion-webui
 #export REQS_FILE="requirements_versions.txt"
 
-# Fixed git-based pip packages
-# Example: "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379"
+# Fixed git repos
 #export K_DIFFUSION_PACKAGE=""
-
 #export GFPGAN_PACKAGE=""
-#export DEEPDANBOORU_PACKAGE=""
-#export CLIP_PACKAGE=""
-
-#export XFORMERS_WINDOWS_PACKAGE=""
-
-# Fixed git repos
-# Example: "https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/c/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl"
-#export STABLE_DIFFUSION_REPO=""
-#export TAMING_REANSFORMERS_REPO=""
-#export K_DIFFUSION_REPO=""
-#export CODEFORMET_REPO=""
-#export BLIP_REPO=""
 
 # Fixed git commits
-# Example: "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc"
 #export STABLE_DIFFUSION_COMMIT_HASH=""
 #export TAMING_TRANSFORMERS_COMMIT_HASH=""
-#export K_DIFFUSION_COMMIT_HASH=""
 #export CODEFORMER_COMMIT_HASH=""
 #export BLIP_COMMIT_HASH=""
 
diff --git a/webui.sh b/webui.sh
index 88a78459..980c0aaf 100755
--- a/webui.sh
+++ b/webui.sh
@@ -41,11 +41,6 @@ then
     venv_dir="venv"
 fi
 
-if [[ -z "${STABLE_DIFFUSION_WEBUI_REPO}" ]]
-then
-    STABLE_DIFFUSION_WEBUI_REPO="https://github.com/AUTOMATIC1111/stable-diffusion-webui.git"
-fi
-
 if [[ -z "${LAUNCH_SCRIPT}" ]]
 then
     LAUNCH_SCRIPT="launch.py"
@@ -116,7 +111,7 @@ then
     cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
     "${GIT}" pull
 else
-    "${GIT}" clone "${STABLE_DIFFUSION_WEBUI_REPO}" "${clone_dir}"
+    "${GIT}" clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git "${clone_dir}"
     cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
 fi
 
-- 
cgit v1.2.1


From 36a0ba357ab0742c3c4a28437b68fb29a235afbe Mon Sep 17 00:00:00 2001
From: Junpeng Qiu <naminoqiu@gmail.com>
Date: Sat, 15 Oct 2022 21:42:52 -0700
Subject: Added Refresh Button to embedding and hypernetwork names in Train Tab

Problem
everytime I modified pt files in embedding_dir or hypernetwork_dir, I
need to restart webui to have the new files shown in the dropdown of
Train Tab

Solution
refactored create_refresh_button out of create_setting_component so we
can use this method to create button next to gr.Dropdowns of embedding
name and hypernetworks

Extra Modification
hypernetwork pt are now sorted in alphabetic order
---
 modules/ui.py | 45 ++++++++++++++++++++++++++-------------------
 style.css     |  2 +-
 2 files changed, 27 insertions(+), 20 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 10bdf121..ee3d0248 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -568,6 +568,24 @@ def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
+    def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+        def refresh():
+            refresh_method()
+            args = refreshed_args() if callable(refreshed_args) else refreshed_args
+
+            for k, v in args.items():
+                setattr(refresh_component, k, v)
+
+            return gr.update(**(args or {}))
+
+        refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
+        refresh_button.click(
+            fn = refresh,
+            inputs = [],
+            outputs = [refresh_component]
+        )
+        return refresh_button
+
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
@@ -1205,8 +1223,12 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tab(label="Train"):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
-                    train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
-                    train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
+                    with gr.Row():
+                        train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+                    with gr.Row():
+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
+                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
                     learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
                     batch_size = gr.Number(label='Batch size', value=1, precision=0)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
@@ -1357,26 +1379,11 @@ def create_ui(wrap_gradio_gpu_call):
         if info.refresh is not None:
             if is_quicksettings:
                 res = comp(label=info.label, value=fun, **(args or {}))
-                refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_"+key)
+                refresh_button = create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
             else:
                 with gr.Row(variant="compact"):
                     res = comp(label=info.label, value=fun, **(args or {}))
-                    refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_" + key)
-
-            def refresh():
-                info.refresh()
-                refreshed_args = info.component_args() if callable(info.component_args) else info.component_args
-
-                for k, v in refreshed_args.items():
-                    setattr(res, k, v)
-
-                return gr.update(**(refreshed_args or {}))
-
-            refresh_button.click(
-                fn=refresh,
-                inputs=[],
-                outputs=[res],
-            )
+                    refresh_button = create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
         else:
             res = comp(label=info.label, value=fun, **(args or {}))
 
diff --git a/style.css b/style.css
index 33832ebf..71eb4d20 100644
--- a/style.css
+++ b/style.css
@@ -478,7 +478,7 @@ input[type="range"]{
     padding: 0;
 }
 
-#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork{
+#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name{
     max-width: 2.5em;
     min-width: 2.5em;
     height: 2.4em;
-- 
cgit v1.2.1


From 523140d7805c644700009b8a2483ff4eb4a22304 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sun, 16 Oct 2022 10:23:30 +0200
Subject: ui fix

---
 modules/aesthetic_clip.py |  3 +--
 modules/sd_hijack.py      |  3 +--
 modules/shared.py         |  2 ++
 modules/ui.py             | 24 ++++++++++++++----------
 4 files changed, 18 insertions(+), 14 deletions(-)

diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
index 68264284..ccb35c73 100644
--- a/modules/aesthetic_clip.py
+++ b/modules/aesthetic_clip.py
@@ -74,5 +74,4 @@ def generate_imgs_embd(name, folder, batch_size):
         """
         shared.update_aesthetic_embeddings()
         return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding",
-                           value=sorted(shared.aesthetic_embeddings.keys())[0] if len(
-                               shared.aesthetic_embeddings) > 0 else None), res, ""
+                           value="None"), res, ""
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 01fcb78f..2de2eed5 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -392,8 +392,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             z1 = self.process_tokens(tokens, multipliers)
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
 
-            if len(text[
-                       0]) != 0 and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None:
+            if self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None:
                 if not opts.use_old_emphasis_implementation:
                     remade_batch_tokens = [
                         [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in
diff --git a/modules/shared.py b/modules/shared.py
index 3c5ffef1..e2c98b2d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -96,11 +96,13 @@ loaded_hypernetwork = None
 
 aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
                         os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
+aesthetic_embeddings = aesthetic_embeddings | {"None": None}
 
 def update_aesthetic_embeddings():
     global aesthetic_embeddings
     aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
                             os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
+    aesthetic_embeddings = aesthetic_embeddings | {"None": None}
 
 def reload_hypernetworks():
     global hypernetworks
diff --git a/modules/ui.py b/modules/ui.py
index 13ba3142..4069f0d2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -594,19 +594,23 @@ def create_ui(wrap_gradio_gpu_call):
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
                 with gr.Group():
-                    aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001")
-
-                    aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
-                    aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
+                    with gr.Accordion("Open for Clip Aesthetic!",open=False):
+                        with gr.Row():
+                            aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
+                            aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
 
-                    with gr.Row():
-                        aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
-                        aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
-                        aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
+                        with gr.Row():
+                            aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001")
+                            aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
+                            aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()),
+                                                         label="Aesthetic imgs embedding",
+                                                         value="None")
 
-                    aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Aesthetic imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None)
+                        with gr.Row():
+                            aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
+                            aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
+                            aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
 
-                    aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-- 
cgit v1.2.1


From e4f8b5f00dd33b7547cc6b76fbed26bb83b37a64 Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sun, 16 Oct 2022 10:28:21 +0200
Subject: ui fix

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 2de2eed5..5d0590af 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -178,7 +178,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         self.load_image_embs(image_embs_name)
 
     def load_image_embs(self, image_embs_name):
-        if image_embs_name is None or len(image_embs_name) == 0:
+        if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None":
             image_embs_name = None
         if image_embs_name is not None and self.image_embs_name != image_embs_name:
             self.image_embs_name = image_embs_name
-- 
cgit v1.2.1


From f62905fdf928b54aa76765e5cbde8d538d494e49 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sun, 16 Oct 2022 21:22:38 +0800
Subject: images history speed up

---
 javascript/images_history.js |  39 ++++---
 modules/images_history.py    | 250 ++++++++++++++++++++++---------------------
 2 files changed, 147 insertions(+), 142 deletions(-)

diff --git a/javascript/images_history.js b/javascript/images_history.js
index ac5834c7..fb1356d9 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -20,7 +20,7 @@ var images_history_click_image = function(){
 var images_history_click_tab = function(){
     var tabs_box = gradioApp().getElementById("images_history_tab");
     if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
-        gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_renew_page").click();
+        gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_start").click();
         tabs_box.classList.add(this.getAttribute("tabname"))
     }                
 }
@@ -96,7 +96,7 @@ function images_history_get_current_img(tabname, img_index, files){
     ];
 }
 
-function images_history_delete(del_num, tabname, img_file_name, page_index, filenames, image_index){
+function images_history_delete(del_num, tabname, image_index){
     image_index = parseInt(image_index);
     var tab = gradioApp().getElementById(tabname + '_images_history');
     var set_btn = tab.querySelector(".images_history_set_index");
@@ -107,6 +107,7 @@ function images_history_delete(del_num, tabname, img_file_name, page_index, file
         }
     });    
     var img_num = buttons.length / 2;
+    del_num = Math.min(img_num - image_index, del_num)    
     if (img_num <= del_num){
         setTimeout(function(tabname){
             gradioApp().getElementById(tabname + '_images_history_renew_page').click();
@@ -114,30 +115,29 @@ function images_history_delete(del_num, tabname, img_file_name, page_index, file
     } else {
         var next_img  
         for (var i = 0; i < del_num; i++){
-            if (image_index + i < image_index + img_num){
-                buttons[image_index + i].style.display = 'none';
-                buttons[image_index + img_num + 1].style.display = 'none';
-                next_img = image_index + i + 1
-            }
+            buttons[image_index + i].style.display = 'none';
+            buttons[image_index + i + img_num].style.display = 'none';
+            next_img = image_index + i + 1
         }
         var bnt;
         if (next_img  >= img_num){
-            btn = buttons[image_index - del_num];
+            btn = buttons[image_index - 1];
         } else {            
             btn = buttons[next_img];          
         } 
         setTimeout(function(btn){btn.click()}, 30, btn);
     }
     images_history_disabled_del();  
-    return [del_num, tabname, img_file_name, page_index, filenames, image_index];
+   
 }
 
-function images_history_turnpage(img_path, page_index, image_index, tabname, date_from, date_to){
+function images_history_turnpage(tabname){
+    console.log("del_button")
+    gradioApp().getElementById(tabname + '_images_history_del_button').setAttribute('disabled','disabled');
     var buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item");
     buttons.forEach(function(elem) {
         elem.style.display = 'block';
-    })
-    return [img_path, page_index, image_index, tabname, date_from, date_to];
+    })   
 }
 
 function images_history_enable_del_buttons(){
@@ -147,7 +147,7 @@ function images_history_enable_del_buttons(){
 }
 
 function images_history_init(){ 
-    var load_txt2img_button = gradioApp().getElementById('txt2img_images_history_renew_page')
+    var load_txt2img_button = gradioApp().getElementById('saved_images_history_start')
     if (load_txt2img_button){        
         for (var i in images_history_tab_list ){
             tab = images_history_tab_list[i];
@@ -166,7 +166,8 @@ function images_history_init(){
 
             // this refreshes history upon tab switch
             // until the history is known to work well, which is not the case now, we do not do this at startup
-            //tab_btns[i].addEventListener('click', images_history_click_tab);
+            // -- load page very fast now,  so better user experience by automatically activating pages
+            tab_btns[i].addEventListener('click', images_history_click_tab);
         }    
         tabs_box.classList.add(images_history_tab_list[0]);
 
@@ -177,7 +178,7 @@ function images_history_init(){
     } 
 }
 
-var images_history_tab_list = ["txt2img", "img2img", "extras"];
+var images_history_tab_list = ["saved", "txt2img", "img2img", "extras"];
 setTimeout(images_history_init, 500);
 document.addEventListener("DOMContentLoaded", function() {
     var mutationObserver = new MutationObserver(function(m){
@@ -188,18 +189,16 @@ document.addEventListener("DOMContentLoaded", function() {
                 bnt.addEventListener('click', images_history_click_image, true);
             });
 
-            // same as load_txt2img_button.click() above
-            /*
             var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
             if (cls_btn){
                 cls_btn.addEventListener('click', function(){
-                    gradioApp().getElementById(tabname + '_images_history_renew_page').click();
+                    gradioApp().getElementById(tabname + '_images_history_del_button').setAttribute('disabled','disabled');
                 }, false);
-            }*/
+            }
 
         }     
     });
-    mutationObserver.observe( gradioApp(), { childList:true, subtree:true });
+    mutationObserver.observe(gradioApp(), { childList:true, subtree:true });
 
 });
 
diff --git a/modules/images_history.py b/modules/images_history.py
index 7fd75005..ae0b4e40 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -3,8 +3,10 @@ import shutil
 import time
 import hashlib
 import gradio
-show_max_dates_num = 3
+
 system_bak_path = "webui_log_and_bak"
+loads_files_num = 216
+num_of_imgs_per_page = 36
 def is_valid_date(date):
     try:
         time.strptime(date, "%Y%m%d")
@@ -53,38 +55,7 @@ def traverse_all_files(curr_path, image_list, all_type=False):
             image_list = traverse_all_files(file, image_list)
     return image_list
 
-def get_recent_images(dir_name, page_index, step, image_index, tabname, date_from, date_to):
-    #print(f"turn_page {page_index}",date_from)
-    if date_from is None or date_from == "":
-        return None, 1, None, ""
-    image_list = []
-    date_list = auto_sorting(dir_name)
-    page_index = int(page_index)
-    today = time.strftime("%Y%m%d",time.localtime(time.time()))
-    for date in date_list:
-        if date >= date_from and date <= date_to:
-            path = os.path.join(dir_name, date)
-            if date == today and not os.path.exists(path):
-                continue
-            image_list = traverse_all_files(path, image_list)
-
-    image_list = sorted(image_list, key=lambda file: -os.path.getctime(file))
-    num = 48 if tabname != "extras" else 12
-    max_page_index = len(image_list) // num + 1
-    page_index = max_page_index if page_index == -1 else page_index + step
-    page_index = 1 if page_index < 1 else page_index
-    page_index = max_page_index if page_index > max_page_index else page_index
-    idx_frm = (page_index - 1) * num
-    image_list = image_list[idx_frm:idx_frm + num]
-    image_index = int(image_index)
-    if image_index < 0 or image_index > len(image_list) - 1:
-        current_file = None
-    else:
-        current_file = image_list[image_index]
-    return image_list, page_index, image_list,  ""
-
-def auto_sorting(dir_name):
-    #print(f"auto sorting")
+def auto_sorting(dir_name):    
     bak_path = os.path.join(dir_name, system_bak_path)
     if not os.path.exists(bak_path):
         os.mkdir(bak_path)
@@ -126,102 +97,131 @@ def auto_sorting(dir_name):
     today = time.strftime("%Y%m%d",time.localtime(time.time()))
     if today not in date_list:
         date_list.append(today)
-    return sorted(date_list)
+    return sorted(date_list, reverse=True)
 
 
 
-def archive_images(dir_name):
+def archive_images(dir_name, date_to):
     date_list = auto_sorting(dir_name)
-    date_from = date_list[-show_max_dates_num] if len(date_list) > show_max_dates_num else date_list[0]
+    today = time.strftime("%Y%m%d",time.localtime(time.time()))
+    date_to = today if date_to is None  or date_to == "" else date_to
+    filenames = []
+    for date in date_list:
+        if date <= date_to:
+            path = os.path.join(dir_name, date)
+            if date == today and not os.path.exists(path):
+                continue
+            filenames = traverse_all_files(path, filenames)
+        if len(filenames) > loads_files_num:            
+            break
+    filenames = sorted(filenames, key=lambda file: -os.path.getctime(file))
+    _, image_list, _, visible_num = get_recent_images(1, 0, filenames)
     return (
         gradio.update(visible=False), 
         gradio.update(visible=True), 
-        gradio.Dropdown.update(choices=date_list, value=date_list[-1]),
-        gradio.Dropdown.update(choices=date_list, value=date_from)
+        gradio.Dropdown.update(choices=date_list, value=date_to),
+        date,
+        filenames,        
+        1,
+        image_list,
+        "",
+        visible_num
     )
+def system_init(dir_name):
+    ret =  [x for x in  archive_images(dir_name, None)]
+    ret += [gradio.update(visible=False)]
+    return ret    
+
+def newest_click(dir_name,  date_to):
+    if date_to == "start":
+         return  True,  False, "start", None, None, 1, None, ""
+    else:
+        return archive_images(dir_name, time.strftime("%Y%m%d",time.localtime(time.time())))
 
-def date_to_change(dir_name, page_index, image_index, tabname, date_from, date_to):
-    #print("date_to", date_to)
-    date_list = auto_sorting(dir_name)
-    date_from_list = [date for date in date_list if date <= date_to]
-    date_from = date_from_list[0] if len(date_from_list) < show_max_dates_num else date_from_list[-show_max_dates_num]
-    image_list, page_index, image_list, _  =get_recent_images(dir_name, 1, 0, image_index, tabname, date_from, date_to)
-    return image_list, page_index, image_list, _, gradio.Dropdown.update(choices=date_from_list, value=date_from)
-
-def first_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
-    return get_recent_images(dir_name, 1, 0, image_index, tabname, date_from, date_to)
-
-
-def end_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
-    return get_recent_images(dir_name, -1, 0, image_index, tabname, date_from, date_to)
-
-
-def prev_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
-    return get_recent_images(dir_name, page_index, -1, image_index, tabname, date_from, date_to)
-
-
-def next_page_click(dir_name, page_index, image_index, tabname, date_from, date_to):
-    return get_recent_images(dir_name, page_index, 1, image_index, tabname, date_from, date_to)
-
-
-def page_index_change(dir_name, page_index, image_index, tabname, date_from, date_to):
-    return get_recent_images(dir_name, page_index, 0, image_index, tabname, date_from, date_to)
-
-
-def show_image_info(tabname_box, num, filenames):
-    # #print(f"select image {num}")
-    file = filenames[int(num)]
-    return file, num, file
-
-def delete_image(delete_num, tabname, name, page_index, filenames, image_index):
+def delete_image(delete_num, name, filenames, image_index, visible_num):
     if name == "":
         return filenames, delete_num
     else:
         delete_num = int(delete_num)
+        visible_num = int(visible_num)
+        image_index = int(image_index)
         index = list(filenames).index(name)
         i = 0
         new_file_list = []
         for name in filenames:
             if i >= index and i < index + delete_num:
                 if os.path.exists(name):
-                    #print(f"Delete file {name}")
+                    if visible_num == image_index:
+                        new_file_list.append(name)
+                        continue                    
+                    print(f"Delete file {name}")
                     os.remove(name)
+                    visible_num -= 1
                     txt_file = os.path.splitext(name)[0] + ".txt"
                     if os.path.exists(txt_file):
                         os.remove(txt_file)
                 else:
-                    #print(f"Not exists file {name}")
+                    print(f"Not exists file {name}")
             else:
                 new_file_list.append(name)
             i += 1
-    return new_file_list, 1
+    return new_file_list, 1, visible_num
+
+def get_recent_images(page_index, step, filenames):
+    page_index = int(page_index)
+    max_page_index = len(filenames) // num_of_imgs_per_page + 1
+    page_index = max_page_index if page_index == -1 else page_index + step
+    page_index = 1 if page_index < 1 else page_index
+    page_index = max_page_index if page_index > max_page_index else page_index
+    idx_frm = (page_index - 1) * num_of_imgs_per_page
+    image_list = filenames[idx_frm:idx_frm + num_of_imgs_per_page]
+    length = len(filenames)
+    visible_num = num_of_imgs_per_page if  idx_frm + num_of_imgs_per_page <= length else length % num_of_imgs_per_page 
+    visible_num = num_of_imgs_per_page if visible_num == 0 else visible_num
+    return page_index, image_list,  "", visible_num
+
+def first_page_click(page_index, filenames):
+    return get_recent_images(1, 0, filenames)
+
+def end_page_click(page_index, filenames):
+    return get_recent_images(-1, 0, filenames)
+
+def prev_page_click(page_index, filenames):
+    return get_recent_images(page_index, -1, filenames)
+
+def next_page_click(page_index, filenames):
+    return get_recent_images(page_index, 1, filenames)
+
+def page_index_change(page_index, filenames):
+    return get_recent_images(page_index, 0, filenames)
+
+def show_image_info(tabname_box, num, page_index, filenames):
+    file = filenames[int(num) + int((page_index - 1) * num_of_imgs_per_page)] 
+    return file, num, file
 
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
-    if opts.outdir_samples != "":
-        dir_name = opts.outdir_samples
-    elif tabname == "txt2img":
+    if tabname == "txt2img":
         dir_name = opts.outdir_txt2img_samples
     elif tabname == "img2img":
         dir_name = opts.outdir_img2img_samples
     elif tabname == "extras":
         dir_name = opts.outdir_extras_samples
+    elif tabname == "saved":
+        dir_name = opts.outdir_save
+        if not os.path.exists(dir_name):
+            os.makedirs(dir_name)
     d = dir_name.split("/")
-    dir_name = "/" if dir_name.startswith("/") else d[0]
+    dir_name = d[0]
     for p in d[1:]:
         dir_name = os.path.join(dir_name, p)
 
     f_list = os.listdir(dir_name)
     sorted_flag = os.path.exists(os.path.join(dir_name, system_bak_path)) or len(f_list) == 0 
     date_list, date_from, date_to = None, None, None
-    if sorted_flag:
-        #print(sorted_flag)
-        date_list = auto_sorting(dir_name)
-        date_to = date_list[-1]
-        date_from = date_list[-show_max_dates_num] if len(date_list) > show_max_dates_num else date_list[0] 
 
     with gr.Column(visible=sorted_flag) as page_panel:
         with gr.Row():
-            renew_page = gr.Button('Refresh', elem_id=tabname + "_images_history_renew_page", interactive=sorted_flag)
+            #renew_page = gr.Button('Refresh')
             first_page = gr.Button('First Page')
             prev_page = gr.Button('Prev Page')
             page_index = gr.Number(value=1, label="Page Index")
@@ -231,9 +231,9 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         with gr.Row(elem_id=tabname + "_images_history"):
             with gr.Column(scale=2):
                 with gr.Row():
-                    newest = gr.Button('Newest')
-                    date_to = gr.Dropdown(choices=date_list, value=date_to, label="Date to")
-                    date_from = gr.Dropdown(choices=date_list, value=date_from, label="Date from")                    
+                    newest = gr.Button('Refresh', elem_id=tabname + "_images_history_start")                    
+                    date_from = gr.Textbox(label="Date from", interactive=False)  
+                    date_to = gr.Dropdown(value="start" if not sorted_flag else None, label="Date to")                  
 
                 history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
                 with gr.Row():
@@ -247,66 +247,72 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                     with gr.Column():
                         img_file_info = gr.Textbox(label="Generate Info", interactive=False)
                         img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
+                        
                     # hiden items
-                    with gr.Row(visible=False):
+                    with gr.Row(visible=False):   
+                        visible_img_num = gr.Number()                     
                         img_path = gr.Textbox(dir_name)
                         tabname_box = gr.Textbox(tabname)
                         image_index = gr.Textbox(value=-1)
                         set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
                         filenames = gr.State()
+                        all_images_list = gr.State()
                         hidden = gr.Image(type="pil")
                         info1 = gr.Textbox()
                         info2 = gr.Textbox()
+
     with gr.Column(visible=not sorted_flag) as init_warning:
         with gr.Row():
-            gr.Textbox("The system needs to archive the files according to the date. This requires changing the directory structure of the files",
-             label="Waring",
-             css="")
+            warning = gr.Textbox(
+                label="Waring",
+                value=f"The system needs to archive the files according to the date. This requires changing the directory structure of the files.If you have doubts about this operation, you can first back up the files in the '{dir_name}' directory"
+            )
+            warning.style(height=100, width=50)
         with gr.Row():
             sorted_button = gr.Button('Confirme')
 
-                  
-           
+    change_date_output = [init_warning, page_panel, date_to, date_from, filenames, page_index, history_gallery, img_file_name, visible_img_num]           
+    sorted_button.click(system_init, inputs=[img_path], outputs=change_date_output + [sorted_button]) 
+    newest.click(newest_click, inputs=[img_path, date_to], outputs=change_date_output)
+    date_to.change(archive_images, inputs=[img_path, date_to], outputs=change_date_output) 
+    date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    newest.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+
+    delete.click(delete_image, inputs=[delete_num, img_file_name, filenames, image_index, visible_img_num], outputs=[filenames, delete_num, visible_img_num])
+    delete.click(fn=None, _js="images_history_delete", inputs=[delete_num, tabname_box, image_index], outputs=None)  
+    
    
     # turn pages
-    gallery_inputs = [img_path, page_index, image_index, tabname_box, date_from, date_to]
-    gallery_outputs = [history_gallery, page_index, filenames, img_file_name]
+    gallery_inputs = [page_index, filenames]
+    gallery_outputs = [page_index, history_gallery, img_file_name, visible_img_num]
+
+    first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+    next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+    prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+    end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
+    page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
 
-    first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
-    # page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
+    first_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    next_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    prev_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    end_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    page_index.submit(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
 
     # other funcitons
-    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, filenames], outputs=[img_file_name, image_index, hidden])
-    img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
-    delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num])
+    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, image_index, hidden])
+    img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)   
     hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
-    date_to.change(date_to_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs + [date_from])
-    # pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
+
     switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
     switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
 
-    sorted_button.click(archive_images, inputs=[img_path], outputs=[init_warning, page_panel, date_to, date_from])
-    newest.click(archive_images, inputs=[img_path], outputs=[init_warning, page_panel, date_to, date_from])
-   
-    
-    
 
 
 def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
     with gr.Blocks(analytics_enabled=False) as images_history:
         with gr.Tabs() as tabs:
-            with gr.Tab("txt2img history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_txt2img:
-                    show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict)
-            with gr.Tab("img2img history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
-                    show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict)
-            with gr.Tab("extras history"):
-                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
-                    show_images_history(gr, opts, "extras", run_pnginfo, switch_dict)
+            for tab in ["saved", "txt2img", "img2img", "extras"]:
+                with gr.Tab(tab):
+                    with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+                        show_images_history(gr, opts, tab, run_pnginfo, switch_dict) 
     return images_history
-- 
cgit v1.2.1


From 91235d8008372862b1f232f7bf99da310a5955e4 Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sun, 16 Oct 2022 20:50:24 +0900
Subject: Fix FileNotFoundError in history tab

Now only traverse images when directory exists
---
 modules/images_history.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/modules/images_history.py b/modules/images_history.py
index 9260df8a..e6284142 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -1,6 +1,6 @@
 import os
 import shutil
-
+import sys
 
 def traverse_all_files(output_dir, image_list, curr_dir=None):
     curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
@@ -24,10 +24,14 @@ def traverse_all_files(output_dir, image_list, curr_dir=None):
 
 def get_recent_images(dir_name, page_index, step, image_index, tabname):
     page_index = int(page_index)
-    f_list = os.listdir(dir_name)
     image_list = []
-    image_list = traverse_all_files(dir_name, image_list)
-    image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+    if not os.path.exists(dir_name):
+        pass
+    elif os.path.isdir(dir_name):
+        image_list = traverse_all_files(dir_name, image_list)
+        image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+    else:
+        print(f"ERROR: {dir_name} is not a directory. Check the path in the settings.", file=sys.stderr)
     num = 48 if tabname != "extras" else 12
     max_page_index = len(image_list) // num + 1
     page_index = max_page_index if page_index == -1 else page_index + step
-- 
cgit v1.2.1


From c9836279f58461e04c1dda0a86e718f8bd3f41e4 Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sun, 16 Oct 2022 21:59:05 +0900
Subject: Only make output dir when creating output

---
 modules/processing.py | 6 ------
 modules/ui.py         | 5 ++++-
 2 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 833fed8a..deb6125e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -334,12 +334,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     seed = get_fixed_seed(p.seed)
     subseed = get_fixed_seed(p.subseed)
 
-    if p.outpath_samples is not None:
-        os.makedirs(p.outpath_samples, exist_ok=True)
-
-    if p.outpath_grids is not None:
-        os.makedirs(p.outpath_grids, exist_ok=True)
-
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
     modules.sd_hijack.model_hijack.clear_comments()
 
diff --git a/modules/ui.py b/modules/ui.py
index ee3d0248..fa73627a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1394,7 +1394,10 @@ def create_ui(wrap_gradio_gpu_call):
     component_dict = {}
 
     def open_folder(f):
-        if not os.path.isdir(f):
+        if not os.path.exists(f):
+            print(f"{f} doesn't exist. After you create an image, the folder will be created.")
+            return
+        elif not os.path.isdir(f):
             print(f"""
 WARNING
 An open_folder request was made with an argument that is not a folder.
-- 
cgit v1.2.1


From adc0ea74e1ee9791f15c3a74bc6c5ad789e10d17 Mon Sep 17 00:00:00 2001
From: CookieHCl <nhc7502@snu.ac.kr>
Date: Sun, 16 Oct 2022 22:03:18 +0900
Subject: Better readablity of logs

---
 modules/images_history.py | 2 +-
 modules/ui.py             | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/images_history.py b/modules/images_history.py
index e6284142..e06e07bf 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -31,7 +31,7 @@ def get_recent_images(dir_name, page_index, step, image_index, tabname):
         image_list = traverse_all_files(dir_name, image_list)
         image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
     else:
-        print(f"ERROR: {dir_name} is not a directory. Check the path in the settings.", file=sys.stderr)
+        print(f'ERROR: "{dir_name}" is not a directory. Check the path in the settings.', file=sys.stderr)
     num = 48 if tabname != "extras" else 12
     max_page_index = len(image_list) // num + 1
     page_index = max_page_index if page_index == -1 else page_index + step
diff --git a/modules/ui.py b/modules/ui.py
index fa73627a..7b0d5a92 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1395,7 +1395,7 @@ def create_ui(wrap_gradio_gpu_call):
 
     def open_folder(f):
         if not os.path.exists(f):
-            print(f"{f} doesn't exist. After you create an image, the folder will be created.")
+            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
             return
         elif not os.path.isdir(f):
             print(f"""
-- 
cgit v1.2.1


From fc220a51cf5bb5bfca83322c16e907a18ec59f6b Mon Sep 17 00:00:00 2001
From: DancingSnow <1121149616@qq.com>
Date: Sun, 16 Oct 2022 10:49:21 +0800
Subject: fix dir_path in some path like `D:/Pic/outputs`

---
 modules/images_history.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/modules/images_history.py b/modules/images_history.py
index e06e07bf..46b23e56 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -109,10 +109,8 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         dir_name = opts.outdir_img2img_samples
     elif tabname == "extras":
         dir_name = opts.outdir_extras_samples
-    d = dir_name.split("/")
-    dir_name = "/" if dir_name.startswith("/") else d[0]
-    for p in d[1:]:
-        dir_name = os.path.join(dir_name, p)
+    else:
+        return
     with gr.Row():
         renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page")
         first_page = gr.Button('First Page')
-- 
cgit v1.2.1


From c57919ea2a8e4a23a05d21f28928e08bbf34c59e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 16 Oct 2022 17:22:56 +0300
Subject: keep focus on current element when updating gallery

---
 javascript/progressbar.js | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 076f0a97..c7d0343f 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -34,7 +34,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
                 preview.style.height = gallery.clientHeight + "px"
 
 				//only watch gallery if there is a generation process going on
-                check_gallery(id_gallery);    
+                check_gallery(id_gallery);
 
                 var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
                 if(!progressDiv){
@@ -73,8 +73,10 @@ function check_gallery(id_gallery){
             let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
             if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
                 //automatically re-open previously selected index (if exists)
+                activeElement = document.activeElement;
                 galleryButtons[prevSelectedIndex].click();
-		showGalleryImage();
+                showGalleryImage();
+                if(activeElement) activeElement.focus()
             }
         })
         galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
-- 
cgit v1.2.1


From a4de699e3c235d83b5a957d08779cb41cb0781bc Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sun, 16 Oct 2022 22:37:12 +0800
Subject: Images history speed up

---
 javascript/images_history.js | 1 +
 modules/images_history.py    | 7 +++++--
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/javascript/images_history.js b/javascript/images_history.js
index fb1356d9..9d9d04fb 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -108,6 +108,7 @@ function images_history_delete(del_num, tabname, image_index){
     });    
     var img_num = buttons.length / 2;
     del_num = Math.min(img_num - image_index, del_num)    
+    console.log(del_num, img_num)
     if (img_num <= del_num){
         setTimeout(function(tabname){
             gradioApp().getElementById(tabname + '_images_history_renew_page').click();
diff --git a/modules/images_history.py b/modules/images_history.py
index ae0b4e40..94bd16a8 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -153,6 +153,7 @@ def delete_image(delete_num, name, filenames, image_index, visible_num):
                 if os.path.exists(name):
                     if visible_num == image_index:
                         new_file_list.append(name)
+                        i += 1
                         continue                    
                     print(f"Delete file {name}")
                     os.remove(name)
@@ -221,7 +222,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
 
     with gr.Column(visible=sorted_flag) as page_panel:
         with gr.Row():
-            #renew_page = gr.Button('Refresh')
+            renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
             first_page = gr.Button('First Page')
             prev_page = gr.Button('Prev Page')
             page_index = gr.Number(value=1, label="Page Index")
@@ -231,7 +232,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         with gr.Row(elem_id=tabname + "_images_history"):
             with gr.Column(scale=2):
                 with gr.Row():
-                    newest = gr.Button('Refresh', elem_id=tabname + "_images_history_start")                    
+                    newest = gr.Button('Reload', elem_id=tabname + "_images_history_start")                    
                     date_from = gr.Textbox(label="Date from", interactive=False)  
                     date_to = gr.Dropdown(value="start" if not sorted_flag else None, label="Date to")                  
 
@@ -291,12 +292,14 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
     end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
     page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
+    renew_page.click(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
 
     first_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
     next_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
     prev_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
     end_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
     page_index.submit(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
+    renew_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
 
     # other funcitons
     set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, image_index, hidden])
-- 
cgit v1.2.1


From 9324cdaa3199d65c182858785dd1eca42b192b8e Mon Sep 17 00:00:00 2001
From: MalumaDev <piano.lu92@gmail.com>
Date: Sun, 16 Oct 2022 17:53:56 +0200
Subject: ui fix, re organization of the code

---
 modules/aesthetic_clip.py            | 154 +++++++++++++++++++++++++++++++++--
 modules/img2img.py                   |  14 +++-
 modules/processing.py                |  29 ++-----
 modules/sd_hijack.py                 | 102 ++---------------------
 modules/sd_models.py                 |   5 +-
 modules/shared.py                    |  14 +++-
 modules/textual_inversion/dataset.py |   2 +-
 modules/txt2img.py                   |  18 ++--
 modules/ui.py                        |  52 +++++++-----
 9 files changed, 233 insertions(+), 157 deletions(-)

diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
index ccb35c73..34efa931 100644
--- a/modules/aesthetic_clip.py
+++ b/modules/aesthetic_clip.py
@@ -1,3 +1,4 @@
+import copy
 import itertools
 import os
 from pathlib import Path
@@ -7,11 +8,12 @@ import gc
 import gradio as gr
 import torch
 from PIL import Image
-from modules import shared
-from modules.shared import device
-from transformers import CLIPModel, CLIPProcessor
+from torch import optim
 
-from tqdm.auto import tqdm
+from modules import shared
+from transformers import CLIPModel, CLIPProcessor, CLIPTokenizer
+from tqdm.auto import tqdm, trange
+from modules.shared import opts, device
 
 
 def get_all_images_in_folder(folder):
@@ -37,12 +39,39 @@ def iter_to_batched(iterable, n=1):
         yield chunk
 
 
+def create_ui():
+    with gr.Group():
+        with gr.Accordion("Open for Clip Aesthetic!", open=False):
+            with gr.Row():
+                aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight",
+                                             value=0.9)
+                aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
+
+            with gr.Row():
+                aesthetic_lr = gr.Textbox(label='Aesthetic learning rate',
+                                          placeholder="Aesthetic learning rate", value="0.0001")
+                aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
+                aesthetic_imgs = gr.Dropdown(sorted(shared.aesthetic_embeddings.keys()),
+                                             label="Aesthetic imgs embedding",
+                                             value="None")
+
+            with gr.Row():
+                aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs',
+                                                 placeholder="This text is used to rotate the feature space of the imgs embs",
+                                                 value="")
+                aesthetic_slerp_angle = gr.Slider(label='Slerp angle', minimum=0, maximum=1, step=0.01,
+                                                  value=0.1)
+                aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
+
+    return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative
+
+
 def generate_imgs_embd(name, folder, batch_size):
     # clipModel = CLIPModel.from_pretrained(
     #     shared.sd_model.cond_stage_model.clipModel.name_or_path
     # )
-    model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path).to(device)
-    processor = CLIPProcessor.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path)
+    model = shared.clip_model.to(device)
+    processor = CLIPProcessor.from_pretrained(model.name_or_path)
 
     with torch.no_grad():
         embs = []
@@ -63,7 +92,6 @@ def generate_imgs_embd(name, folder, batch_size):
         torch.save(embs, path)
 
         model = model.cpu()
-        del model
         del processor
         del embs
         gc.collect()
@@ -74,4 +102,114 @@ def generate_imgs_embd(name, folder, batch_size):
         """
         shared.update_aesthetic_embeddings()
         return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding",
-                           value="None"), res, ""
+                                  value="None"), \
+               gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()),
+                                  label="Imgs embedding",
+                                  value="None"), res, ""
+
+
+def slerp(low, high, val):
+    low_norm = low / torch.norm(low, dim=1, keepdim=True)
+    high_norm = high / torch.norm(high, dim=1, keepdim=True)
+    omega = torch.acos((low_norm * high_norm).sum(1))
+    so = torch.sin(omega)
+    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
+    return res
+
+
+class AestheticCLIP:
+    def __init__(self):
+        self.skip = False
+        self.aesthetic_steps = 0
+        self.aesthetic_weight = 0
+        self.aesthetic_lr = 0
+        self.slerp = False
+        self.aesthetic_text_negative = ""
+        self.aesthetic_slerp_angle = 0
+        self.aesthetic_imgs_text = ""
+
+        self.image_embs_name = None
+        self.image_embs = None
+        self.load_image_embs(None)
+
+    def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
+                             aesthetic_slerp=True, aesthetic_imgs_text="",
+                             aesthetic_slerp_angle=0.15,
+                             aesthetic_text_negative=False):
+        self.aesthetic_imgs_text = aesthetic_imgs_text
+        self.aesthetic_slerp_angle = aesthetic_slerp_angle
+        self.aesthetic_text_negative = aesthetic_text_negative
+        self.slerp = aesthetic_slerp
+        self.aesthetic_lr = aesthetic_lr
+        self.aesthetic_weight = aesthetic_weight
+        self.aesthetic_steps = aesthetic_steps
+        self.load_image_embs(image_embs_name)
+
+    def set_skip(self, skip):
+        self.skip = skip
+
+    def load_image_embs(self, image_embs_name):
+        if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None":
+            image_embs_name = None
+            self.image_embs_name = None
+        if image_embs_name is not None and self.image_embs_name != image_embs_name:
+            self.image_embs_name = image_embs_name
+            self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device)
+            self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
+            self.image_embs.requires_grad_(False)
+
+    def __call__(self, z, remade_batch_tokens):
+        if not self.skip and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name is not None:
+            tokenizer = shared.sd_model.cond_stage_model.tokenizer
+            if not opts.use_old_emphasis_implementation:
+                remade_batch_tokens = [
+                    [tokenizer.bos_token_id] + x[:75] + [tokenizer.eos_token_id] for x in
+                    remade_batch_tokens]
+
+            tokens = torch.asarray(remade_batch_tokens).to(device)
+
+            model = copy.deepcopy(shared.clip_model).to(device)
+            model.requires_grad_(True)
+            if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
+                text_embs_2 = model.get_text_features(
+                    **tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
+                if self.aesthetic_text_negative:
+                    text_embs_2 = self.image_embs - text_embs_2
+                    text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
+                img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
+            else:
+                img_embs = self.image_embs
+
+            with torch.enable_grad():
+
+                # We optimize the model to maximize the similarity
+                optimizer = optim.Adam(
+                    model.text_model.parameters(), lr=self.aesthetic_lr
+                )
+
+                for _ in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
+                    text_embs = model.get_text_features(input_ids=tokens)
+                    text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
+                    sim = text_embs @ img_embs.T
+                    loss = -sim
+                    optimizer.zero_grad()
+                    loss.mean().backward()
+                    optimizer.step()
+
+                zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
+                if opts.CLIP_stop_at_last_layers > 1:
+                    zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers]
+                    zn = model.text_model.final_layer_norm(zn)
+                else:
+                    zn = zn.last_hidden_state
+                model.cpu()
+                del model
+                gc.collect()
+                torch.cuda.empty_cache()
+            zn = torch.concat([zn[77 * i:77 * (i + 1)] for i in range(max(z.shape[1] // 77, 1))], 1)
+            if self.slerp:
+                z = slerp(z, zn, self.aesthetic_weight)
+            else:
+                z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight
+
+        return z
diff --git a/modules/img2img.py b/modules/img2img.py
index 24126774..4ed80c4b 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -56,7 +56,14 @@ def process_batch(p, input_dir, output_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str,
+            aesthetic_lr=0,
+            aesthetic_weight=0, aesthetic_steps=0,
+            aesthetic_imgs=None,
+            aesthetic_slerp=False,
+            aesthetic_imgs_text="",
+            aesthetic_slerp_angle=0.15,
+            aesthetic_text_negative=False, *args):
     is_inpaint = mode == 1
     is_batch = mode == 2
 
@@ -109,6 +116,11 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         inpainting_mask_invert=inpainting_mask_invert,
     )
 
+    shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps),
+                                               aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text,
+                                               aesthetic_slerp_angle,
+                                               aesthetic_text_negative)
+
     if shared.cmd_opts.enable_console_prompts:
         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
 
diff --git a/modules/processing.py b/modules/processing.py
index 1db26c3e..685f9fcd 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -146,7 +146,8 @@ class Processed:
         self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
         self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
-        self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
+        self.subseed = int(
+            self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
 
         self.all_prompts = all_prompts or [self.prompt]
         self.all_seeds = all_seeds or [self.seed]
@@ -332,16 +333,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
 
-def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0,
-                   aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="",
-                   aesthetic_slerp_angle=0.15,
-                   aesthetic_text_negative=False) -> Processed:
+def process_images(p: StableDiffusionProcessing) -> Processed:
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
-    aesthetic_lr = float(aesthetic_lr)
-    aesthetic_weight = float(aesthetic_weight)
-    aesthetic_steps = int(aesthetic_steps)
-
     if type(p.prompt) == list:
         assert (len(p.prompt) > 0)
     else:
@@ -417,16 +411,10 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
             # uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
             # c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
-                if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
-                    shared.sd_model.cond_stage_model.set_aesthetic_params()
+                shared.aesthetic_clip.set_skip(True)
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt],
                                                             p.steps)
-                if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
-                    shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight,
-                                                                          aesthetic_steps, aesthetic_imgs,
-                                                                          aesthetic_slerp, aesthetic_imgs_text,
-                                                                          aesthetic_slerp_angle,
-                                                                          aesthetic_text_negative)
+                shared.aesthetic_clip.set_skip(False)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
@@ -582,7 +570,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
@@ -600,10 +587,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                                   seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
 
-        samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
+        samples = samples[:, :, self.truncate_y // 2:samples.shape[2] - self.truncate_y // 2,
+                  self.truncate_x // 2:samples.shape[3] - self.truncate_x // 2]
 
         if opts.use_scale_latent_for_hires_fix:
-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f),
+                                                      mode="bilinear")
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 5d0590af..227e7670 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -29,8 +29,8 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
 
-
-    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
+    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (
+    6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
@@ -118,33 +118,14 @@ class StableDiffusionModelHijack:
         return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
 
 
-def slerp(low, high, val):
-    low_norm = low / torch.norm(low, dim=1, keepdim=True)
-    high_norm = high / torch.norm(high, dim=1, keepdim=True)
-    omega = torch.acos((low_norm * high_norm).sum(1))
-    so = torch.sin(omega)
-    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
-    return res
-
-
 class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def __init__(self, wrapped, hijack):
         super().__init__()
         self.wrapped = wrapped
-        self.clipModel = CLIPModel.from_pretrained(
-            self.wrapped.transformer.name_or_path
-        )
-        del self.clipModel.vision_model
-        self.tokenizer = CLIPTokenizer.from_pretrained(self.wrapped.transformer.name_or_path)
-        self.hijack: StableDiffusionModelHijack = hijack
-        self.tokenizer = wrapped.tokenizer
-        # self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval()
-        self.image_embs_name = None
-        self.image_embs = None
-        self.load_image_embs(None)
 
         self.token_mults = {}
-
+        self.hijack: StableDiffusionModelHijack = hijack
+        self.tokenizer = wrapped.tokenizer
         self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
 
         tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if
@@ -164,28 +145,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
-    def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
-                             aesthetic_slerp=True, aesthetic_imgs_text="",
-                             aesthetic_slerp_angle=0.15,
-                             aesthetic_text_negative=False):
-        self.aesthetic_imgs_text = aesthetic_imgs_text
-        self.aesthetic_slerp_angle = aesthetic_slerp_angle
-        self.aesthetic_text_negative = aesthetic_text_negative
-        self.slerp = aesthetic_slerp
-        self.aesthetic_lr = aesthetic_lr
-        self.aesthetic_weight = aesthetic_weight
-        self.aesthetic_steps = aesthetic_steps
-        self.load_image_embs(image_embs_name)
-
-    def load_image_embs(self, image_embs_name):
-        if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None":
-            image_embs_name = None
-        if image_embs_name is not None and self.image_embs_name != image_embs_name:
-            self.image_embs_name = image_embs_name
-            self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device)
-            self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
-            self.image_embs.requires_grad_(False)
-
     def tokenize_line(self, line, used_custom_terms, hijack_comments):
         id_end = self.wrapped.tokenizer.eos_token_id
 
@@ -391,58 +350,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
             z1 = self.process_tokens(tokens, multipliers)
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
-
-            if self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None:
-                if not opts.use_old_emphasis_implementation:
-                    remade_batch_tokens = [
-                        [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in
-                        remade_batch_tokens]
-
-                tokens = torch.asarray(remade_batch_tokens).to(device)
-
-                model = copy.deepcopy(self.clipModel).to(device)
-                model.requires_grad_(True)
-                if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
-                    text_embs_2 = model.get_text_features(
-                        **self.tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
-                    if self.aesthetic_text_negative:
-                        text_embs_2 = self.image_embs - text_embs_2
-                        text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
-                    img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
-                else:
-                    img_embs = self.image_embs
-
-                with torch.enable_grad():
-
-                    # We optimize the model to maximize the similarity
-                    optimizer = optim.Adam(
-                        model.text_model.parameters(), lr=self.aesthetic_lr
-                    )
-
-                    for i in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
-                        text_embs = model.get_text_features(input_ids=tokens)
-                        text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
-                        sim = text_embs @ img_embs.T
-                        loss = -sim
-                        optimizer.zero_grad()
-                        loss.mean().backward()
-                        optimizer.step()
-
-                    zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
-                    if opts.CLIP_stop_at_last_layers > 1:
-                        zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers]
-                        zn = model.text_model.final_layer_norm(zn)
-                    else:
-                        zn = zn.last_hidden_state
-                    model.cpu()
-                    del model
-
-                zn = torch.concat([zn for i in range(z.shape[1] // 77)], 1)
-                if self.slerp:
-                    z = slerp(z, zn, self.aesthetic_weight)
-                else:
-                    z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight
-
+            z = shared.aesthetic_clip(z, remade_batch_tokens)
             remade_batch_tokens = rem_tokens
             batch_multipliers = rem_multipliers
             i += 1
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 3aa21ec1..8e4ee435 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -20,7 +20,7 @@ checkpoints_loaded = collections.OrderedDict()
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
 
-    from transformers import logging
+    from transformers import logging, CLIPModel
 
     logging.set_verbosity_error()
 except Exception:
@@ -196,6 +196,9 @@ def load_model():
 
     sd_hijack.model_hijack.hijack(sd_model)
 
+    if shared.clip_model is None or shared.clip_model.transformer.name_or_path != sd_model.cond_stage_model.wrapped.transformer.name_or_path:
+        shared.clip_model = CLIPModel.from_pretrained(sd_model.cond_stage_model.wrapped.transformer.name_or_path)
+
     sd_model.eval()
 
     print(f"Model loaded.")
diff --git a/modules/shared.py b/modules/shared.py
index e2c98b2d..e19ca779 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -3,6 +3,7 @@ import datetime
 import json
 import os
 import sys
+from collections import OrderedDict
 
 import gradio as gr
 import tqdm
@@ -94,15 +95,15 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
-aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
-                        os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
-aesthetic_embeddings = aesthetic_embeddings | {"None": None}
+aesthetic_embeddings = {}
 
 def update_aesthetic_embeddings():
     global aesthetic_embeddings
     aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
                             os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
-    aesthetic_embeddings = aesthetic_embeddings | {"None": None}
+    aesthetic_embeddings = OrderedDict(**{"None": None}, **aesthetic_embeddings)
+
+update_aesthetic_embeddings()
 
 def reload_hypernetworks():
     global hypernetworks
@@ -381,6 +382,11 @@ sd_upscalers = []
 
 sd_model = None
 
+clip_model = None
+
+from modules.aesthetic_clip import AestheticCLIP
+aesthetic_clip = AestheticCLIP()
+
 progress_print_out = sys.stdout
 
 
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 68ceffe3..23bb4b6a 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -49,7 +49,7 @@ class PersonalizedBase(Dataset):
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
             try:
-                image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.Resampling.BICUBIC)
+                image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
             except Exception:
                 continue
 
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 8f394d05..6cbc50fc 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -1,12 +1,17 @@
 import modules.scripts
-from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
+from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
+    StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, cmd_opts
 import modules.shared as shared
 import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int,aesthetic_lr=0,
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int,
+            restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int,
+            subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool,
+            height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int,
+            firstphase_height: int, aesthetic_lr=0,
             aesthetic_weight=0, aesthetic_steps=0,
             aesthetic_imgs=None,
             aesthetic_slerp=False,
@@ -41,15 +46,17 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         firstphase_height=firstphase_height if enable_hr else None,
     )
 
+    shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps),
+                                           aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle,
+                                           aesthetic_text_negative)
+
     if cmd_opts.enable_console_prompts:
         print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
 
     processed = modules.scripts.scripts_txt2img.run(p, *args)
 
     if processed is None:
-        processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp,aesthetic_imgs_text,
-                           aesthetic_slerp_angle,
-                           aesthetic_text_negative)
+        processed = process_images(p)
 
     shared.total_tqdm.clear()
 
@@ -61,4 +68,3 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         processed.images = []
 
     return processed.images, generation_info_js, plaintext_to_html(processed.info)
-
diff --git a/modules/ui.py b/modules/ui.py
index 4069f0d2..0e5d73f0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -43,7 +43,7 @@ from modules.images import save_image
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
 
-import modules.aesthetic_clip
+import modules.aesthetic_clip as aesthetic_clip
 import modules.images_history as img_his
 
 
@@ -593,23 +593,25 @@ def create_ui(wrap_gradio_gpu_call):
                     width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
-                with gr.Group():
-                    with gr.Accordion("Open for Clip Aesthetic!",open=False):
-                        with gr.Row():
-                            aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
-                            aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
-
-                        with gr.Row():
-                            aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001")
-                            aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
-                            aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()),
-                                                         label="Aesthetic imgs embedding",
-                                                         value="None")
-
-                        with gr.Row():
-                            aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
-                            aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
-                            aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
+                # with gr.Group():
+                #     with gr.Accordion("Open for Clip Aesthetic!",open=False):
+                #         with gr.Row():
+                #             aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
+                #             aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
+                #
+                #         with gr.Row():
+                #             aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001")
+                #             aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
+                #             aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()),
+                #                                          label="Aesthetic imgs embedding",
+                #                                          value="None")
+                #
+                #         with gr.Row():
+                #             aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
+                #             aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
+                #             aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
+
+                aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative = aesthetic_clip.create_ui()
 
 
                 with gr.Row():
@@ -840,6 +842,9 @@ def create_ui(wrap_gradio_gpu_call):
                     width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
 
+                aesthetic_weight_im, aesthetic_steps_im, aesthetic_lr_im, aesthetic_slerp_im, aesthetic_imgs_im, aesthetic_imgs_text_im, aesthetic_slerp_angle_im, aesthetic_text_negative_im = aesthetic_clip.create_ui()
+
+
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
                     tiling = gr.Checkbox(label='Tiling', value=False)
@@ -944,6 +949,14 @@ def create_ui(wrap_gradio_gpu_call):
                     inpainting_mask_invert,
                     img2img_batch_input_dir,
                     img2img_batch_output_dir,
+                    aesthetic_lr_im,
+                    aesthetic_weight_im,
+                    aesthetic_steps_im,
+                    aesthetic_imgs_im,
+                    aesthetic_slerp_im,
+                    aesthetic_imgs_text_im,
+                    aesthetic_slerp_angle_im,
+                    aesthetic_text_negative_im,
                 ] + custom_inputs,
                 outputs=[
                     img2img_gallery,
@@ -1283,7 +1296,7 @@ def create_ui(wrap_gradio_gpu_call):
         )
 
         create_embedding_ae.click(
-            fn=modules.aesthetic_clip.generate_imgs_embd,
+            fn=aesthetic_clip.generate_imgs_embd,
             inputs=[
                 new_embedding_name_ae,
                 process_src_ae,
@@ -1291,6 +1304,7 @@ def create_ui(wrap_gradio_gpu_call):
             ],
             outputs=[
                 aesthetic_imgs,
+                aesthetic_imgs_im,
                 ti_output,
                 ti_outcome,
             ]
-- 
cgit v1.2.1


From 26a11776e4070af30b864ab0ddf25dcda21631a7 Mon Sep 17 00:00:00 2001
From: dvsilch <743422767@qq.com>
Date: Mon, 17 Oct 2022 00:51:10 +0800
Subject: fix: add null check when start running project the currentButton is
 null

---
 javascript/imageviewer.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js
index 65a33dd7..d4ab6984 100644
--- a/javascript/imageviewer.js
+++ b/javascript/imageviewer.js
@@ -31,7 +31,7 @@ function updateOnBackgroundChange() {
             }
         })
 
-        if (modalImage.src != currentButton.children[0].src) {
+        if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
             modalImage.src = currentButton.children[0].src;
             if (modalImage.style.display === 'none') {
                 modal.style.setProperty('background-image', `url(${modalImage.src})`)
-- 
cgit v1.2.1


From c8045c5ad4f99deb3a19add06e0457de1df62b05 Mon Sep 17 00:00:00 2001
From: SGKoishi <i@sgkoi.dev>
Date: Sun, 16 Oct 2022 10:08:23 -0700
Subject: The hide_ui_dir_config flag also restrict write attempt to path
 settings

---
 modules/shared.py | 10 ++++++++++
 modules/ui.py     |  8 +++++++-
 2 files changed, 17 insertions(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index dcab0af9..c2775603 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -77,6 +77,16 @@ parser.add_argument("--disable-safe-unpickle", action='store_true', help="disabl
 
 
 cmd_opts = parser.parse_args()
+restricted_opts = [
+    "samples_filename_pattern",
+    "outdir_samples",
+    "outdir_txt2img_samples",
+    "outdir_img2img_samples",
+    "outdir_extras_samples",
+    "outdir_grids",
+    "outdir_txt2img_grids",
+    "outdir_save",
+]
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])
diff --git a/modules/ui.py b/modules/ui.py
index 7b0d5a92..43dc88fc 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -25,7 +25,7 @@ import gradio.routes
 
 from modules import sd_hijack, sd_models
 from modules.paths import script_path
-from modules.shared import opts, cmd_opts
+from modules.shared import opts, cmd_opts, restricted_opts
 if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
 import modules.shared as shared
@@ -1430,6 +1430,9 @@ Requested path was: {f}
             if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
                 continue
 
+            if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+                continue
+
             oldval = opts.data.get(key, None)
             opts.data[key] = value
 
@@ -1447,6 +1450,9 @@ Requested path was: {f}
         if not opts.same_type(value, opts.data_labels[key].default):
             return gr.update(visible=True), opts.dumpjson()
 
+        if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+            return gr.update(value=oldval), opts.dumpjson()
+
         oldval = opts.data.get(key, None)
         opts.data[key] = value
 
-- 
cgit v1.2.1


From a1d3cbf92cfde6b3e02a9c795412d01cdc268934 Mon Sep 17 00:00:00 2001
From: fortypercnt <114840933+fortypercnt@users.noreply.github.com>
Date: Mon, 17 Oct 2022 05:25:34 +0200
Subject: Fix #2750

left / top alignment was necessary with gradio 3.4.1. In gradio 3.5 the parent div of the image mask is centered, so the left / top alignment put the mask in the wrong place as described in #2750 #2795 #2805. This fix was tested on Windows 10 / Chrome.
---
 javascript/imageMaskFix.js | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/javascript/imageMaskFix.js b/javascript/imageMaskFix.js
index 3d77bfe9..9fe7a603 100644
--- a/javascript/imageMaskFix.js
+++ b/javascript/imageMaskFix.js
@@ -31,8 +31,8 @@ function imageMaskResize() {
 
     wrapper.style.width = `${wW}px`;
     wrapper.style.height = `${wH}px`;
-    wrapper.style.left = `${(w-wW)/2}px`;
-    wrapper.style.top = `${(h-wH)/2}px`;
+    wrapper.style.left = `0px`;
+    wrapper.style.top = `0px`;
 
     canvases.forEach( c => {
         c.style.width = c.style.height = '';
@@ -42,4 +42,4 @@ function imageMaskResize() {
     });
  }
   
- onUiUpdate(() => imageMaskResize());
\ No newline at end of file
+ onUiUpdate(() => imageMaskResize());
-- 
cgit v1.2.1


From 0fd130767102ebcf90e97c6c191ecf199a2d4091 Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Sun, 16 Oct 2022 18:44:39 -0400
Subject: improve performance of 3-way merge on machines with not enough ram,
 by only accessing two of the models at a time

---
 modules/extras.py | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 0819ed37..340a45fd 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -175,11 +175,14 @@ def run_pnginfo(image):
 
 
 def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
-    def weighted_sum(theta0, theta1, theta2, alpha):
+    def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
-    def add_difference(theta0, theta1, theta2, alpha):
-        return theta0 + (theta1 - theta2) * alpha
+    def get_difference(theta1, theta2):
+        return theta1 - theta2
+
+    def add_difference(theta0, theta1_2_diff, alpha):
+        return theta0 + (alpha * theta1_2_diff)
 
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
@@ -201,20 +204,24 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
         theta_2 = None
 
     theta_funcs = {
-        "Weighted sum": weighted_sum,
-        "Add difference": add_difference,
+        "Weighted sum": (None, weighted_sum),
+        "Add difference": (get_difference, add_difference),
     }
-    theta_func = theta_funcs[interp_method]
+    theta_func1, theta_func2 = theta_funcs[interp_method]
 
     print(f"Merging...")
 
+    if theta_func1:
+        for key in tqdm.tqdm(theta_1.keys()):
+            if 'model' in key:
+                t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
+                theta_1[key] = theta_func1(theta_1[key], t2)
+    del theta_2, teritary_model
+
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
-            t2 = (theta_2 or {}).get(key)
-            if t2 is None:
-                t2 = torch.zeros_like(theta_0[key])
 
-            theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, multiplier)
+            theta_0[key] = theta_func2(theta_0[key], theta_1[key], multiplier)
 
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
-- 
cgit v1.2.1


From 6f7b7a3dcdc471ebe63baa8a7731952557859c5b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 07:56:08 +0300
Subject: only read files with .py extension from  the scripts dir

---
 modules/scripts.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/scripts.py b/modules/scripts.py
index 45230f9a..ac66d448 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -58,6 +58,9 @@ def load_scripts(basedir):
     for filename in sorted(os.listdir(basedir)):
         path = os.path.join(basedir, filename)
 
+        if os.path.splitext(path)[1].lower() != '.py':
+            continue
+
         if not os.path.isfile(path):
             continue
 
-- 
cgit v1.2.1


From 8aaadf56b3d5f06b9bd86718d7518883d94d70a0 Mon Sep 17 00:00:00 2001
From: DancingSnow <1121149616@qq.com>
Date: Mon, 17 Oct 2022 09:34:18 +0800
Subject: add cache for workflow

---
 .github/workflows/on_pull_request.yaml | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/.github/workflows/on_pull_request.yaml b/.github/workflows/on_pull_request.yaml
index 5270cba4..b097d180 100644
--- a/.github/workflows/on_pull_request.yaml
+++ b/.github/workflows/on_pull_request.yaml
@@ -22,6 +22,12 @@ jobs:
         uses: actions/setup-python@v3
         with:
           python-version: 3.10.6
+      - uses: actions/cache@v2
+        with:
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-
       - name: Install PyLint
         run: | 
           python -m pip install --upgrade pip
-- 
cgit v1.2.1


From 58f3ef77336663bce2321f5b692cf2aeacd3ac1c Mon Sep 17 00:00:00 2001
From: DenkingOfficial <shershnev.andrei@gmail.com>
Date: Mon, 17 Oct 2022 03:10:59 +0500
Subject: Fix CLIP Interrogator and disable ranks for it

---
 modules/interrogate.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 9263d65a..d85d7dcc 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -157,9 +157,9 @@ class InterrogateModels:
                     matches = self.rank(image_features, items, top_count=topn)
                     for match, score in matches:
                         if include_ranks:
-                            res += ", " + match
-                        else:
                             res += f", ({match}:{score})"
+                        else:
+                            res += ", " + match
 
         except Exception:
             print(f"Error interrogating", file=sys.stderr)
-- 
cgit v1.2.1


From 5c94aaf290f8ad7bf4499a91c268ad0791b0432f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 08:28:18 +0300
Subject: fix bug for latest model merge RAM improvement

---
 modules/extras.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/extras.py b/modules/extras.py
index 340a45fd..8dbab240 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -201,6 +201,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
         teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
         theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
     else:
+        teritary_model = None
         theta_2 = None
 
     theta_funcs = {
-- 
cgit v1.2.1


From b99d3cf6dd9bc817e51d0d0a6e8eb12c7c0ac6af Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 08:41:02 +0300
Subject: make CLIP interrogate ranks output sane values

---
 modules/interrogate.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/interrogate.py b/modules/interrogate.py
index d85d7dcc..64b91eb4 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -123,7 +123,7 @@ class InterrogateModels:
 
         return caption[0]
 
-    def interrogate(self, pil_image, include_ranks=False):
+    def interrogate(self, pil_image):
         res = None
 
         try:
@@ -156,8 +156,8 @@ class InterrogateModels:
                 for name, topn, items in self.categories:
                     matches = self.rank(image_features, items, top_count=topn)
                     for match, score in matches:
-                        if include_ranks:
-                            res += f", ({match}:{score})"
+                        if shared.opts.interrogate_return_ranks:
+                            res += f", ({match}:{score/100:.3f})"
                         else:
                             res += ", " + match
 
-- 
cgit v1.2.1


From 62edfae257e8982cd620d03862c7bdd44159d18f Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 16 Oct 2022 20:28:15 +0100
Subject: print list of embeddings on reload

---
 modules/textual_inversion/textual_inversion.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 7ec75018..3be69562 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -137,6 +137,7 @@ class EmbeddingDatabase:
                 continue
 
         print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
+        print("Embeddings:", ', '.join(self.word_embeddings.keys()))
 
     def find_embedding_at_position(self, tokens, offset):
         token = tokens[offset]
-- 
cgit v1.2.1


From cccc5a20fce4bde9a4299f8790366790735f1d05 Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Sun, 16 Oct 2022 12:10:07 -0700
Subject: Safeguard setting restore logic against exceptions

also useful for keeping settings cache and restore logic together, and nice for code reuse (other third party scripts can import this class)
---
 scripts/xy_grid.py | 48 ++++++++++++++++++++++++++----------------------
 1 file changed, 26 insertions(+), 22 deletions(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 88ad3bf7..5cca168a 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -233,6 +233,21 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_
     return processed_result
 
 
+class SharedSettingsStackHelper(object):
+    def __enter__(self):
+        self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
+        self.hypernetwork = opts.sd_hypernetwork
+        self.model = shared.sd_model
+  
+    def __exit__(self, exc_type, exc_value, tb):
+        modules.sd_models.reload_model_weights(self.model)
+
+        hypernetwork.load_hypernetwork(self.hypernetwork)
+        hypernetwork.apply_strength()
+
+        opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
+
+
 re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
 re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
 
@@ -267,9 +282,6 @@ class Script(scripts.Script):
         if not opts.return_grid:
             p.batch_size = 1
 
-
-        CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
-
         def process_axis(opt, vals):
             if opt.label == 'Nothing':
                 return [0]
@@ -367,27 +379,19 @@ class Script(scripts.Script):
 
             return process_images(pc)
 
-        processed = draw_xy_grid(
-            p,
-            xs=xs,
-            ys=ys,
-            x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
-            y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
-            cell=cell,
-            draw_legend=draw_legend,
-            include_lone_images=include_lone_images
-        )
+        with SharedSettingsStackHelper():
+            processed = draw_xy_grid(
+                p,
+                xs=xs,
+                ys=ys,
+                x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
+                y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
+                cell=cell,
+                draw_legend=draw_legend,
+                include_lone_images=include_lone_images
+            )
 
         if opts.grid_save:
             images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed, grid=True, p=p)
 
-        # restore checkpoint in case it was changed by axes
-        modules.sd_models.reload_model_weights(shared.sd_model)
-
-        hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
-        hypernetwork.apply_strength()
-
-
-        opts.data["CLIP_stop_at_last_layers"] = CLIP_stop_at_last_layers
-
         return processed
-- 
cgit v1.2.1


From 9d702b16f01795c3af900e0ebd70faf4b25200f6 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 17 Oct 2022 16:11:03 +0800
Subject: fix two little bug

---
 modules/images_history.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/images_history.py b/modules/images_history.py
index 23045df1..1ae168ca 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -133,7 +133,7 @@ def archive_images(dir_name, date_to):
             date = sort_array[loads_num][2]
             filenames = [x[1] for x in sort_array]
         else:
-            date =  sort_array[loads_num][2]
+            date =  sort_array[-1][2]
             filenames = [x[1] for x in sort_array]
         filenames = [x[1] for x in sort_array if x[2]>= date]
     _, image_list, _, visible_num = get_recent_images(1, 0, filenames)
@@ -334,6 +334,6 @@ def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict):
                 with gr.Tab(tab):
                     with gr.Blocks(analytics_enabled=False) as images_history_img2img:
                         show_images_history(gr, opts, tab, run_pnginfo, switch_dict) 
-        gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory") #, visible=False)
+        gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory", visible=False)
     
     return images_history
-- 
cgit v1.2.1


From 60251c9456f5472784862896c2f97e38feb42482 Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 06:58:42 +0000
Subject: initial prototype by borrowing contracts

---
 modules/api/api.py    | 60 ++++++++++++++++++++++++++++++++++++++++++++
 modules/processing.py |  2 +-
 modules/shared.py     |  2 +-
 webui.py              | 69 +++++++++++++++++++++++++++++----------------------
 4 files changed, 102 insertions(+), 31 deletions(-)
 create mode 100644 modules/api/api.py

diff --git a/modules/api/api.py b/modules/api/api.py
new file mode 100644
index 00000000..9d7c699d
--- /dev/null
+++ b/modules/api/api.py
@@ -0,0 +1,60 @@
+from modules.api.processing import StableDiffusionProcessingAPI
+from modules.processing import StableDiffusionProcessingTxt2Img, process_images
+import modules.shared as shared
+import uvicorn
+from fastapi import FastAPI, Body, APIRouter
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, Field, Json
+import json
+import io
+import base64
+
+app = FastAPI()
+
+class TextToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: Json
+    info: Json
+
+
+class Api:
+    def __init__(self, txt2img, img2img, run_extras, run_pnginfo):
+        self.router = APIRouter()
+        app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])
+
+    def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
+        print(txt2imgreq)
+        p = StableDiffusionProcessingTxt2Img(**vars(txt2imgreq))
+        p.sd_model = shared.sd_model
+        print(p)
+        processed = process_images(p)
+        
+        b64images = []
+        for i in processed.images:
+            buffer = io.BytesIO()
+            i.save(buffer, format="png")
+            b64images.append(base64.b64encode(buffer.getvalue()))
+        
+        response = {
+            "images": b64images,
+            "info": processed.js(),
+            "parameters": json.dumps(vars(txt2imgreq))
+        }
+        
+
+        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
+        
+        
+
+    def img2imgendoint(self):
+        raise NotImplementedError
+
+    def extrasendoint(self):
+        raise NotImplementedError
+
+    def pnginfoendoint(self):
+        raise NotImplementedError
+
+    def launch(self, server_name, port):
+        app.include_router(self.router)
+        uvicorn.run(app, host=server_name, port=port)
\ No newline at end of file
diff --git a/modules/processing.py b/modules/processing.py
index deb6125e..4a7c6ccc 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -723,4 +723,4 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         del x
         devices.torch_gc()
 
-        return samples
+        return samples
\ No newline at end of file
diff --git a/modules/shared.py b/modules/shared.py
index c2775603..6c6405fd 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -74,7 +74,7 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-
+parser.add_argument("--api", action='store_true', help="use api=True to launch the api instead of the webui")
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
diff --git a/webui.py b/webui.py
index fe0ce321..cd8a99ea 100644
--- a/webui.py
+++ b/webui.py
@@ -97,40 +97,51 @@ def webui():
         os._exit(0)
 
     signal.signal(signal.SIGINT, sigint_handler)
+    
+    if cmd_opts.api:
+        from modules.api.api import Api
+        api = Api(txt2img=modules.txt2img.txt2img,
+          img2img=modules.img2img.img2img,
+          run_extras=modules.extras.run_extras,
+          run_pnginfo=modules.extras.run_pnginfo)
 
-    while 1:
-
-        demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
-        
-        app, local_url, share_url = demo.launch(
-            share=cmd_opts.share,
-            server_name="0.0.0.0" if cmd_opts.listen else None,
-            server_port=cmd_opts.port,
-            debug=cmd_opts.gradio_debug,
-            auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
-            inbrowser=cmd_opts.autolaunch,
-            prevent_thread_lock=True
-        )
-        
-        app.add_middleware(GZipMiddleware, minimum_size=1000)
+        api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1",
+                   port=cmd_opts.port if cmd_opts.port else 7861)
 
+    else:
         while 1:
-            time.sleep(0.5)
-            if getattr(demo, 'do_restart', False):
-                time.sleep(0.5)
-                demo.close()
-                time.sleep(0.5)
-                break
 
-        sd_samplers.set_samplers()
+            demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
 
-        print('Reloading Custom Scripts')
-        modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
-        print('Reloading modules: modules.ui')
-        importlib.reload(modules.ui)
-        print('Refreshing Model List')
-        modules.sd_models.list_models()
-        print('Restarting Gradio')
+            app, local_url, share_url = demo.launch(
+                share=cmd_opts.share,
+                server_name="0.0.0.0" if cmd_opts.listen else None,
+                server_port=cmd_opts.port,
+                debug=cmd_opts.gradio_debug,
+                auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
+                inbrowser=cmd_opts.autolaunch,
+                prevent_thread_lock=True
+            )
+
+            app.add_middleware(GZipMiddleware, minimum_size=1000)
+
+            while 1:
+                time.sleep(0.5)
+                if getattr(demo, 'do_restart', False):
+                    time.sleep(0.5)
+                    demo.close()
+                    time.sleep(0.5)
+                    break
+
+            sd_samplers.set_samplers()
+
+            print('Reloading Custom Scripts')
+            modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
+            print('Reloading modules: modules.ui')
+            importlib.reload(modules.ui)
+            print('Refreshing Model List')
+            modules.sd_models.list_models()
+            print('Restarting Gradio')
 
 
 if __name__ == "__main__":
-- 
cgit v1.2.1


From 9e02812afd10582f00a7fbbfa63c8f9188678e26 Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 07:02:08 +0000
Subject: pydantic instrumentation

---
 modules/api/processing.py | 99 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 99 insertions(+)
 create mode 100644 modules/api/processing.py

diff --git a/modules/api/processing.py b/modules/api/processing.py
new file mode 100644
index 00000000..459a8f49
--- /dev/null
+++ b/modules/api/processing.py
@@ -0,0 +1,99 @@
+from inflection import underscore
+from typing import Any, Dict, Optional
+from pydantic import BaseModel, Field, create_model
+from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
+import inspect
+
+
+class ModelDef(BaseModel):
+    """Assistance Class for Pydantic Dynamic Model Generation"""
+
+    field: str
+    field_alias: str
+    field_type: Any
+    field_value: Any
+
+
+class pydanticModelGenerator:
+    """
+    Takes source_data:Dict ( a single instance example of something like a JSON node) and self generates a pythonic data model with Alias to original source field names. This makes it easy to popuate or export to other systems yet handle the data in a pythonic way.
+    Being a pydantic datamodel all the richness of pydantic data validation is available and these models can easily be used in FastAPI and or a ORM
+
+    It does not process full JSON data structures but takes simple JSON document with basic elements
+
+    Provide a model_name, an example of JSON data and a dict of type overrides
+
+    Example:
+
+    source_data = {'Name': '48 Rainbow Rd',
+        'GroupAddressStyle': 'ThreeLevel',
+        'LastModified': '2020-12-21T07:02:51.2400232Z',
+        'ProjectStart': '2020-12-03T07:36:03.324856Z',
+        'Comment': '',
+        'CompletionStatus': 'Editing',
+        'LastUsedPuid': '955',
+        'Guid': '0c85957b-c2ae-4985-9752-b300ab385b36'}
+
+    source_overrides = {'Guid':{'type':uuid.UUID},
+            'LastModified':{'type':datetime },
+            'ProjectStart':{'type':datetime },
+            }
+    source_optionals = {"Comment":True}
+
+    #create Model
+    model_Project=pydanticModelGenerator(
+        model_name="Project",
+        source_data=source_data,
+        overrides=source_overrides,
+        optionals=source_optionals).generate_model()
+
+    #create instance using DynamicModel
+    project_instance=model_Project(**project_info)
+
+    """
+
+    def __init__(
+        self,
+        model_name: str = None,
+        source_data: str = None,
+        params: Dict = {},
+        overrides: Dict = {},
+        optionals: Dict = {},
+    ):
+        def field_type_generator(k, v, overrides, optionals):
+            print(k, v)
+            field_type = str if not overrides.get(k) else overrides[k]["type"]
+            if v is None:
+                field_type = Any
+            else:
+                field_type = type(v)
+            
+            return Optional[field_type]
+        
+        self._model_name = model_name
+        self._json_data = source_data
+        self._model_def = [
+            ModelDef(
+                field=underscore(k),
+                field_alias=k,
+                field_type=field_type_generator(k, v, overrides, optionals),
+                field_value=v
+            )
+            for (k,v) in source_data.items() if k in params
+        ]
+
+    def generate_model(self):
+        """
+        Creates a pydantic BaseModel
+        from the json and overrides provided at initialization
+        """
+        fields = {
+            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
+        }
+        DynamicModel = create_model(self._model_name, **fields)
+        DynamicModel.__config__.allow_population_by_field_name = True
+        return DynamicModel
+    
+StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing", 
+                                                      StableDiffusionProcessing().__dict__, 
+                                                      inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model()
\ No newline at end of file
-- 
cgit v1.2.1


From f3fe487e6340b1a2db5d2e2ddf5ae885b4eef54c Mon Sep 17 00:00:00 2001
From: Jonathan <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 03:14:53 -0400
Subject: Update webui.py

---
 webui.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/webui.py b/webui.py
index cd8a99ea..603a4ccd 100644
--- a/webui.py
+++ b/webui.py
@@ -100,10 +100,7 @@ def webui():
     
     if cmd_opts.api:
         from modules.api.api import Api
-        api = Api(txt2img=modules.txt2img.txt2img,
-          img2img=modules.img2img.img2img,
-          run_extras=modules.extras.run_extras,
-          run_pnginfo=modules.extras.run_pnginfo)
+        api = Api()
 
         api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1",
                    port=cmd_opts.port if cmd_opts.port else 7861)
-- 
cgit v1.2.1


From 832b490e5173f78c4d3aa7ca9ca9ac794d140664 Mon Sep 17 00:00:00 2001
From: Jonathan <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 03:18:41 -0400
Subject: Update processing.py

---
 modules/api/processing.py | 41 +++++------------------------------------
 1 file changed, 5 insertions(+), 36 deletions(-)

diff --git a/modules/api/processing.py b/modules/api/processing.py
index 459a8f49..4c3d0bd0 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -16,46 +16,15 @@ class ModelDef(BaseModel):
 
 class pydanticModelGenerator:
     """
-    Takes source_data:Dict ( a single instance example of something like a JSON node) and self generates a pythonic data model with Alias to original source field names. This makes it easy to popuate or export to other systems yet handle the data in a pythonic way.
-    Being a pydantic datamodel all the richness of pydantic data validation is available and these models can easily be used in FastAPI and or a ORM
-
-    It does not process full JSON data structures but takes simple JSON document with basic elements
-
-    Provide a model_name, an example of JSON data and a dict of type overrides
-
-    Example:
-
-    source_data = {'Name': '48 Rainbow Rd',
-        'GroupAddressStyle': 'ThreeLevel',
-        'LastModified': '2020-12-21T07:02:51.2400232Z',
-        'ProjectStart': '2020-12-03T07:36:03.324856Z',
-        'Comment': '',
-        'CompletionStatus': 'Editing',
-        'LastUsedPuid': '955',
-        'Guid': '0c85957b-c2ae-4985-9752-b300ab385b36'}
-
-    source_overrides = {'Guid':{'type':uuid.UUID},
-            'LastModified':{'type':datetime },
-            'ProjectStart':{'type':datetime },
-            }
-    source_optionals = {"Comment":True}
-
-    #create Model
-    model_Project=pydanticModelGenerator(
-        model_name="Project",
-        source_data=source_data,
-        overrides=source_overrides,
-        optionals=source_optionals).generate_model()
-
-    #create instance using DynamicModel
-    project_instance=model_Project(**project_info)
-
+    Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
+    source_data is a snapshot of the default values produced by the class
+    params are the names of the actual keys required by __init__
     """
 
     def __init__(
         self,
         model_name: str = None,
-        source_data: str = None,
+        source_data: {} = {},
         params: Dict = {},
         overrides: Dict = {},
         optionals: Dict = {},
@@ -96,4 +65,4 @@ class pydanticModelGenerator:
     
 StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing", 
                                                       StableDiffusionProcessing().__dict__, 
-                                                      inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model()
\ No newline at end of file
+                                                      inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model()
-- 
cgit v1.2.1


From 99013ba68a5fe1bde3621632e5539c03562a3ae8 Mon Sep 17 00:00:00 2001
From: Jonathan <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 03:20:17 -0400
Subject: Update processing.py

---
 modules/api/processing.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/api/processing.py b/modules/api/processing.py
index 4c3d0bd0..e4df93c5 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -30,7 +30,6 @@ class pydanticModelGenerator:
         optionals: Dict = {},
     ):
         def field_type_generator(k, v, overrides, optionals):
-            print(k, v)
             field_type = str if not overrides.get(k) else overrides[k]["type"]
             if v is None:
                 field_type = Any
-- 
cgit v1.2.1


From 71d42bb44b257f3fb274c3ad5075a195281ff915 Mon Sep 17 00:00:00 2001
From: Jonathan <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 03:22:19 -0400
Subject: Update api.py

---
 modules/api/api.py | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 9d7c699d..4d9619a8 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -23,10 +23,8 @@ class Api:
         app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
-        print(txt2imgreq)
         p = StableDiffusionProcessingTxt2Img(**vars(txt2imgreq))
         p.sd_model = shared.sd_model
-        print(p)
         processed = process_images(p)
         
         b64images = []
@@ -34,13 +32,6 @@ class Api:
             buffer = io.BytesIO()
             i.save(buffer, format="png")
             b64images.append(base64.b64encode(buffer.getvalue()))
-        
-        response = {
-            "images": b64images,
-            "info": processed.js(),
-            "parameters": json.dumps(vars(txt2imgreq))
-        }
-        
 
         return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
         
@@ -57,4 +48,4 @@ class Api:
 
     def launch(self, server_name, port):
         app.include_router(self.router)
-        uvicorn.run(app, host=server_name, port=port)
\ No newline at end of file
+        uvicorn.run(app, host=server_name, port=port)
-- 
cgit v1.2.1


From 964b63c0423a861bd67c40b59f767e7037051083 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 11:38:32 +0300
Subject: add api() function to return webui() to how it was

---
 webui.py | 79 +++++++++++++++++++++++++++++++++-------------------------------
 1 file changed, 41 insertions(+), 38 deletions(-)

diff --git a/webui.py b/webui.py
index 603a4ccd..16c862f0 100644
--- a/webui.py
+++ b/webui.py
@@ -87,59 +87,62 @@ def initialize():
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
 
-
-def webui():
-    initialize()
-    
     # make the program just exit at ctrl+c without waiting for anything
     def sigint_handler(sig, frame):
         print(f'Interrupted with signal {sig} in {frame}')
         os._exit(0)
 
     signal.signal(signal.SIGINT, sigint_handler)
-    
-    if cmd_opts.api:
-        from modules.api.api import Api
-        api = Api()
 
-        api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1",
-                   port=cmd_opts.port if cmd_opts.port else 7861)
 
-    else:
-        while 1:
+def api()
+    initialize()
 
-            demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
+    from modules.api.api import Api
+    api = Api()
+    api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
 
-            app, local_url, share_url = demo.launch(
-                share=cmd_opts.share,
-                server_name="0.0.0.0" if cmd_opts.listen else None,
-                server_port=cmd_opts.port,
-                debug=cmd_opts.gradio_debug,
-                auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
-                inbrowser=cmd_opts.autolaunch,
-                prevent_thread_lock=True
-            )
 
-            app.add_middleware(GZipMiddleware, minimum_size=1000)
+def webui():
+    initialize()
 
-            while 1:
+    while 1:
+
+        demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
+
+        app, local_url, share_url = demo.launch(
+            share=cmd_opts.share,
+            server_name="0.0.0.0" if cmd_opts.listen else None,
+            server_port=cmd_opts.port,
+            debug=cmd_opts.gradio_debug,
+            auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
+            inbrowser=cmd_opts.autolaunch,
+            prevent_thread_lock=True
+        )
+
+        app.add_middleware(GZipMiddleware, minimum_size=1000)
+
+        while 1:
+            time.sleep(0.5)
+            if getattr(demo, 'do_restart', False):
+                time.sleep(0.5)
+                demo.close()
                 time.sleep(0.5)
-                if getattr(demo, 'do_restart', False):
-                    time.sleep(0.5)
-                    demo.close()
-                    time.sleep(0.5)
-                    break
+                break
 
-            sd_samplers.set_samplers()
+        sd_samplers.set_samplers()
 
-            print('Reloading Custom Scripts')
-            modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
-            print('Reloading modules: modules.ui')
-            importlib.reload(modules.ui)
-            print('Refreshing Model List')
-            modules.sd_models.list_models()
-            print('Restarting Gradio')
+        print('Reloading Custom Scripts')
+        modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
+        print('Reloading modules: modules.ui')
+        importlib.reload(modules.ui)
+        print('Refreshing Model List')
+        modules.sd_models.list_models()
+        print('Restarting Gradio')
 
 
 if __name__ == "__main__":
-    webui()
+    if cmd_opts.api:
+        api()
+    else:
+        webui()
-- 
cgit v1.2.1


From d42125baf62880854ad06af06c15c23e7e50cca6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 11:50:20 +0300
Subject: add missing requirement for api and fix some typos

---
 modules/api/api.py        | 2 +-
 requirements.txt          | 1 +
 requirements_versions.txt | 1 +
 webui.py                  | 2 +-
 4 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 4d9619a8..fd09d352 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -18,7 +18,7 @@ class TextToImageResponse(BaseModel):
 
 
 class Api:
-    def __init__(self, txt2img, img2img, run_extras, run_pnginfo):
+    def __init__(self):
         self.router = APIRouter()
         app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])
 
diff --git a/requirements.txt b/requirements.txt
index cf583de9..da1969cf 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -23,3 +23,4 @@ resize-right
 torchdiffeq
 kornia
 lark
+inflection
diff --git a/requirements_versions.txt b/requirements_versions.txt
index abadcb58..72ccc5a3 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -22,3 +22,4 @@ resize-right==0.0.2
 torchdiffeq==0.2.3
 kornia==0.6.7
 lark==1.1.2
+inflection==0.5.1
diff --git a/webui.py b/webui.py
index 16c862f0..eeee44c3 100644
--- a/webui.py
+++ b/webui.py
@@ -95,7 +95,7 @@ def initialize():
     signal.signal(signal.SIGINT, sigint_handler)
 
 
-def api()
+def api():
     initialize()
 
     from modules.api.api import Api
-- 
cgit v1.2.1


From 8c6a981d5d9ef30381ac2327460285111550acbc Mon Sep 17 00:00:00 2001
From: Michoko <michoko@hotmail.com>
Date: Mon, 17 Oct 2022 11:05:05 +0200
Subject: Added dark mode switch

Launch the UI in dark mode with the --dark-mode switch
---
 javascript/ui.js  | 7 +++++++
 modules/shared.py | 2 +-
 modules/ui.py     | 2 ++
 3 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 9e1bed4c..bfa72885 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -1,5 +1,12 @@
 // various functions for interation with ui.py not large enough to warrant putting them in separate files
 
+function go_dark_mode(){
+    gradioURL = window.location.href
+    if (!gradioURL.endsWith('?__theme=dark')) {
+      window.location.replace(gradioURL + '?__theme=dark');
+    }
+}
+
 function selected_gallery_index(){
     var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item')
     var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2')
diff --git a/modules/shared.py b/modules/shared.py
index c2775603..cbf158e4 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -69,13 +69,13 @@ parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image upload
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
+parser.add_argument("--dark-mode", action='store_true', help="launches the UI in dark mode", default=False)
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 
-
 cmd_opts = parser.parse_args()
 restricted_opts = [
     "samples_filename_pattern",
diff --git a/modules/ui.py b/modules/ui.py
index 43dc88fc..a0cd052e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1783,6 +1783,8 @@ for filename in sorted(os.listdir(jsdir)):
     with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
         javascript += f"\n<script>{jsfile.read()}</script>"
 
+if cmd_opts.dark_mode:
+    javascript += "\n<script>go_dark_mode();</script>\n"
 
 if 'gradio_routes_templates_response' not in globals():
     def template_response(*args, **kwargs):
-- 
cgit v1.2.1


From af3f6489d3b229da4e688eaf439adb5d3e4f070b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 16:57:19 +0300
Subject: possibly defeat losing of focus for prompt when generating images
 with gallery open

---
 javascript/progressbar.js | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index c7d0343f..7a05726e 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -72,11 +72,17 @@ function check_gallery(id_gallery){
             let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
             let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
             if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
-                //automatically re-open previously selected index (if exists)
-                activeElement = document.activeElement;
+                // automatically re-open previously selected index (if exists)
+                activeElement = gradioApp().activeElement;
+
                 galleryButtons[prevSelectedIndex].click();
                 showGalleryImage();
-                if(activeElement) activeElement.focus()
+
+                if(activeElement){
+                    // i fought this for about an hour; i don't know why the focus is lost or why this helps recover it
+                    // if somenoe has a better solution please by all means
+                    setTimeout(function() { activeElement.focus() }, 1);
+                }
             }
         })
         galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
-- 
cgit v1.2.1


From c408a0b41cfffde184cad35b2d97346342947d83 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 17 Oct 2022 22:28:43 +0800
Subject: fix two bug

---
 launch.py                 | 1 -
 modules/images_history.py | 4 ++--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/launch.py b/launch.py
index 7520cfee..088eada1 100644
--- a/launch.py
+++ b/launch.py
@@ -11,7 +11,6 @@ python = sys.executable
 git = os.environ.get('GIT', "git")
 index_url = os.environ.get('INDEX_URL', "")
 
-
 def extract_arg(args, name):
     return [x for x in args if x != name], name in args
 
diff --git a/modules/images_history.py b/modules/images_history.py
index 1ae168ca..10e5b970 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -181,7 +181,8 @@ def delete_image(delete_num, name, filenames, image_index, visible_num):
     return new_file_list, 1, visible_num
 
 def save_image(file_name):
-    shutil.copy2(file_name, opts.outdir_save)
+    if file_name is not None and os.path.exists(file_name):
+        shutil.copy2(file_name, opts.outdir_save)
 
 def get_recent_images(page_index, step, filenames):
     page_index = int(page_index)
@@ -327,7 +328,6 @@ def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict):
     opts = sys_opts
     loads_files_num = int(opts.images_history_num_per_page)
     num_of_imgs_per_page = int(opts.images_history_num_per_page * opts.images_history_pages_num)
-    backup_flag = opts.images_history_backup
     with gr.Blocks(analytics_enabled=False) as images_history:
         with gr.Tabs() as tabs:
             for tab in ["txt2img", "img2img", "extras", "saved"]:
-- 
cgit v1.2.1


From de179cf8fd8191e1a6d288e7c29a16f53da1be88 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 17 Oct 2022 22:38:46 +0800
Subject: fix two bug

---
 launch.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/launch.py b/launch.py
index 088eada1..7520cfee 100644
--- a/launch.py
+++ b/launch.py
@@ -11,6 +11,7 @@ python = sys.executable
 git = os.environ.get('GIT', "git")
 index_url = os.environ.get('INDEX_URL', "")
 
+
 def extract_arg(args, name):
     return [x for x in args if x != name], name in args
 
-- 
cgit v1.2.1


From 2272cf2f35fafd5cd486bfb4ee89df5bbc625b97 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 17 Oct 2022 23:04:42 +0800
Subject: fix two bug

---
 modules/images_history.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/images_history.py b/modules/images_history.py
index 10e5b970..1c1790a4 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -133,7 +133,7 @@ def archive_images(dir_name, date_to):
             date = sort_array[loads_num][2]
             filenames = [x[1] for x in sort_array]
         else:
-            date =  sort_array[-1][2]
+            date =  None if len(sort_array) == 0 else sort_array[-1][2]
             filenames = [x[1] for x in sort_array]
         filenames = [x[1] for x in sort_array if x[2]>= date]
     _, image_list, _, visible_num = get_recent_images(1, 0, filenames)
-- 
cgit v1.2.1


From 2b5b62e768d892773a7ec1d5e8d8cea23aae1254 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 17 Oct 2022 23:14:03 +0800
Subject: fix two bug

---
 modules/images_history.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/images_history.py b/modules/images_history.py
index 1c1790a4..20324557 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -44,7 +44,7 @@ def traverse_all_files(curr_path, image_list, all_type=False):
         return image_list
     for file in f_list:
         file = os.path.join(curr_path, file)
-        if (not all_type) and file[-4:] == ".txt":
+        if (not all_type) and (file[-4:] == ".txt" or file[-4:] == ".csv"):
             pass
         elif os.path.isfile(file) and file[-10:].rfind(".") > 0:
             image_list.append(file)
@@ -182,7 +182,7 @@ def delete_image(delete_num, name, filenames, image_index, visible_num):
 
 def save_image(file_name):
     if file_name is not None and os.path.exists(file_name):
-        shutil.copy2(file_name, opts.outdir_save)
+        shutil.copy(file_name, opts.outdir_save)
 
 def get_recent_images(page_index, step, filenames):
     page_index = int(page_index)
-- 
cgit v1.2.1


From 665beebc0825a6fad410c8252f27f6f6f0bd900b Mon Sep 17 00:00:00 2001
From: Michoko <michoko@hotmail.com>
Date: Mon, 17 Oct 2022 18:24:24 +0200
Subject: Use of a --theme argument for more flexibility

Added possibility to set the theme (light or dark)
---
 javascript/ui.js  | 6 +++---
 modules/shared.py | 2 +-
 modules/ui.py     | 4 ++--
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index bfa72885..cfd0dcd3 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -1,9 +1,9 @@
 // various functions for interation with ui.py not large enough to warrant putting them in separate files
 
-function go_dark_mode(){
+function set_theme(theme){
     gradioURL = window.location.href
-    if (!gradioURL.endsWith('?__theme=dark')) {
-      window.location.replace(gradioURL + '?__theme=dark');
+    if (!gradioURL.includes('?__theme=')) {
+      window.location.replace(gradioURL + '?__theme=' + theme);
     }
 }
 
diff --git a/modules/shared.py b/modules/shared.py
index cbf158e4..fa084c69 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -69,7 +69,7 @@ parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image upload
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
-parser.add_argument("--dark-mode", action='store_true', help="launches the UI in dark mode", default=False)
+parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
diff --git a/modules/ui.py b/modules/ui.py
index a0cd052e..d41715fa 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1783,8 +1783,8 @@ for filename in sorted(os.listdir(jsdir)):
     with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
         javascript += f"\n<script>{jsfile.read()}</script>"
 
-if cmd_opts.dark_mode:
-    javascript += "\n<script>go_dark_mode();</script>\n"
+if cmd_opts.theme is not None:
+    javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
 
 if 'gradio_routes_templates_response' not in globals():
     def template_response(*args, **kwargs):
-- 
cgit v1.2.1


From 695377a8b9f7de28b880d96487a9ddf7230cff14 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 19:56:23 +0300
Subject: make modelmerger work with ui-config.json

---
 modules/ui.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/ui.py b/modules/ui.py
index 43dc88fc..533b1db3 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1767,6 +1767,7 @@ Requested path was: {f}
     visit(txt2img_interface, loadsave, "txt2img")
     visit(img2img_interface, loadsave, "img2img")
     visit(extras_interface, loadsave, "extras")
+    visit(modelmerger_interface, loadsave, "modelmerger")
 
     if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
         with open(ui_config_file, "w", encoding="utf8") as file:
-- 
cgit v1.2.1


From cf47d13c1e11fcb7169bac7488d2c39e579ee491 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 17 Oct 2022 21:15:32 +0300
Subject: localization support

---
 javascript/localization.js                    | 146 ++++++++++++++++++++++++++
 localizations/Put localization files here.txt |   0
 modules/localization.py                       |  31 ++++++
 modules/shared.py                             |   7 +-
 modules/ui.py                                 |  33 ++++--
 script.js                                     |  10 +-
 style.css                                     |   2 +-
 7 files changed, 211 insertions(+), 18 deletions(-)
 create mode 100644 javascript/localization.js
 create mode 100644 localizations/Put localization files here.txt
 create mode 100644 modules/localization.py

diff --git a/javascript/localization.js b/javascript/localization.js
new file mode 100644
index 00000000..e6644635
--- /dev/null
+++ b/javascript/localization.js
@@ -0,0 +1,146 @@
+
+// localization = {} -- the dict with translations is created by the backend
+
+ignore_ids_for_localization={
+    setting_sd_hypernetwork: 'OPTION',
+    setting_sd_model_checkpoint: 'OPTION',
+    setting_realesrgan_enabled_models: 'OPTION',
+    modelmerger_primary_model_name: 'OPTION',
+    modelmerger_secondary_model_name: 'OPTION',
+    modelmerger_tertiary_model_name: 'OPTION',
+    train_embedding: 'OPTION',
+    train_hypernetwork: 'OPTION',
+    txt2img_style_index: 'OPTION',
+    txt2img_style2_index: 'OPTION',
+    img2img_style_index: 'OPTION',
+    img2img_style2_index: 'OPTION',
+    setting_random_artist_categories: 'SPAN',
+    setting_face_restoration_model: 'SPAN',
+    setting_realesrgan_enabled_models: 'SPAN',
+    extras_upscaler_1: 'SPAN',
+    extras_upscaler_2: 'SPAN',
+}
+
+re_num = /^[\.\d]+$/
+re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u
+
+original_lines = {}
+translated_lines = {}
+
+function textNodesUnder(el){
+    var n, a=[], walk=document.createTreeWalker(el,NodeFilter.SHOW_TEXT,null,false);
+    while(n=walk.nextNode()) a.push(n);
+    return a;
+}
+
+function canBeTranslated(node, text){
+    if(! text) return false;
+    if(! node.parentElement) return false;
+
+    parentType = node.parentElement.nodeName
+    if(parentType=='SCRIPT' || parentType=='STYLE' || parentType=='TEXTAREA') return false;
+
+    if (parentType=='OPTION' || parentType=='SPAN'){
+        pnode = node
+        for(var level=0; level<4; level++){
+            pnode = pnode.parentElement
+            if(! pnode) break;
+
+            if(ignore_ids_for_localization[pnode.id] == parentType) return false;
+        }
+    }
+
+    if(re_num.test(text)) return false;
+    if(re_emoji.test(text)) return false;
+    return true
+}
+
+function getTranslation(text){
+    if(! text) return undefined
+
+    if(translated_lines[text] === undefined){
+        original_lines[text] = 1
+    }
+
+    tl = localization[text]
+    if(tl !== undefined){
+        translated_lines[tl] = 1
+    }
+
+    return tl
+}
+
+function processTextNode(node){
+    text = node.textContent.trim()
+
+    if(! canBeTranslated(node, text)) return
+
+    tl = getTranslation(text)
+    if(tl !== undefined){
+        node.textContent = tl
+    }
+}
+
+function processNode(node){
+    if(node.nodeType == 3){
+        processTextNode(node)
+        return
+    }
+
+    if(node.title){
+        tl = getTranslation(node.title)
+        if(tl !== undefined){
+            node.title = tl
+        }
+    }
+
+    if(node.placeholder){
+        tl = getTranslation(node.placeholder)
+        if(tl !== undefined){
+            node.placeholder = tl
+        }
+    }
+
+    textNodesUnder(node).forEach(function(node){
+        processTextNode(node)
+    })
+}
+
+function dumpTranslations(){
+    dumped = {}
+
+    Object.keys(original_lines).forEach(function(text){
+        if(dumped[text] !== undefined)  return
+
+        dumped[text] = localization[text] || text
+    })
+
+    return dumped
+}
+
+onUiUpdate(function(m){
+    m.forEach(function(mutation){
+        mutation.addedNodes.forEach(function(node){
+            processNode(node)
+        })
+    });
+})
+
+
+document.addEventListener("DOMContentLoaded", function() {
+    processNode(gradioApp())
+})
+
+function download_localization() {
+    text = JSON.stringify(dumpTranslations(), null, 4)
+
+    var element = document.createElement('a');
+    element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
+    element.setAttribute('download', "localization.json");
+    element.style.display = 'none';
+    document.body.appendChild(element);
+
+    element.click();
+
+    document.body.removeChild(element);
+}
diff --git a/localizations/Put localization files here.txt b/localizations/Put localization files here.txt
new file mode 100644
index 00000000..e69de29b
diff --git a/modules/localization.py b/modules/localization.py
new file mode 100644
index 00000000..b1810cda
--- /dev/null
+++ b/modules/localization.py
@@ -0,0 +1,31 @@
+import json
+import os
+import sys
+import traceback
+
+localizations = {}
+
+
+def list_localizations(dirname):
+    localizations.clear()
+
+    for file in os.listdir(dirname):
+        fn, ext = os.path.splitext(file)
+        if ext.lower() != ".json":
+            continue
+
+        localizations[fn] = os.path.join(dirname, file)
+
+
+def localization_js(current_localization_name):
+    fn = localizations.get(current_localization_name, None)
+    data = {}
+    if fn is not None:
+        try:
+            with open(fn, "r", encoding="utf8") as file:
+                data = json.load(file)
+        except Exception:
+            print(f"Error loading localization from {fn}:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    return f"var localization = {json.dumps(data)}\n"
diff --git a/modules/shared.py b/modules/shared.py
index c2775603..2a2b0427 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models
+from modules import sd_samplers, sd_models, localization
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -31,6 +31,7 @@ parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
 parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
+parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
@@ -103,7 +104,6 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
-
 def reload_hypernetworks():
     global hypernetworks
 
@@ -151,6 +151,8 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
 
+localization.list_localizations(cmd_opts.localizations_dir)
+
 
 def realesrgan_models_names():
     import modules.realesrgan_model
@@ -296,6 +298,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
+    'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
diff --git a/modules/ui.py b/modules/ui.py
index 533b1db3..656bab7a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -23,7 +23,7 @@ import gradio as gr
 import gradio.utils
 import gradio.routes
 
-from modules import sd_hijack, sd_models
+from modules import sd_hijack, sd_models, localization
 from modules.paths import script_path
 from modules.shared import opts, cmd_opts, restricted_opts
 if cmd_opts.deepdanbooru:
@@ -1056,10 +1056,10 @@ def create_ui(wrap_gradio_gpu_call):
                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
 
                 with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
                 with gr.Group():
-                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', celem_id="extras_upscaler_2", hoices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
                     extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
 
                 with gr.Group():
@@ -1224,10 +1224,10 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Tab(label="Train"):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     with gr.Row():
-                        train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                         create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
                     with gr.Row():
-                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
                         create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
                     learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
                     batch_size = gr.Number(label='Batch size', value=1, precision=0)
@@ -1376,16 +1376,18 @@ def create_ui(wrap_gradio_gpu_call):
         else:
             raise Exception(f'bad options item type: {str(t)} for key {key}')
 
+        elem_id = "setting_"+key
+
         if info.refresh is not None:
             if is_quicksettings:
-                res = comp(label=info.label, value=fun, **(args or {}))
-                refresh_button = create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+                res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
+                create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
             else:
                 with gr.Row(variant="compact"):
-                    res = comp(label=info.label, value=fun, **(args or {}))
-                    refresh_button = create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+                    res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
+                    create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
         else:
-            res = comp(label=info.label, value=fun, **(args or {}))
+            res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
 
 
         return res
@@ -1509,6 +1511,9 @@ Requested path was: {f}
 
         with gr.Row():
             request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+
+        with gr.Row():
             reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
             restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
 
@@ -1519,6 +1524,13 @@ Requested path was: {f}
             _js='function(){}'
         )
 
+        download_localization.click(
+            fn=lambda: None,
+            inputs=[],
+            outputs=[],
+            _js='download_localization'
+        )
+
         def reload_scripts():
             modules.scripts.reload_script_body_only()
 
@@ -1784,6 +1796,7 @@ for filename in sorted(os.listdir(jsdir)):
     with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
         javascript += f"\n<script>{jsfile.read()}</script>"
 
+javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
 
 if 'gradio_routes_templates_response' not in globals():
     def template_response(*args, **kwargs):
diff --git a/script.js b/script.js
index 88f2c839..8b3b67e3 100644
--- a/script.js
+++ b/script.js
@@ -21,20 +21,20 @@ function onUiTabChange(callback){
     uiTabChangeCallbacks.push(callback)
 }
 
-function runCallback(x){
+function runCallback(x, m){
     try {
-        x()
+        x(m)
     } catch (e) {
         (console.error || console.log).call(console, e.message, e);
     }
 }
-function executeCallbacks(queue) {
-    queue.forEach(runCallback)
+function executeCallbacks(queue, m) {
+    queue.forEach(function(x){runCallback(x, m)})
 }
 
 document.addEventListener("DOMContentLoaded", function() {
     var mutationObserver = new MutationObserver(function(m){
-        executeCallbacks(uiUpdateCallbacks);
+        executeCallbacks(uiUpdateCallbacks, m);
         const newTab = get_uiCurrentTab();
         if ( newTab && ( newTab !== uiCurrentTab ) ) {
             uiCurrentTab = newTab;
diff --git a/style.css b/style.css
index 71eb4d20..9dc4b696 100644
--- a/style.css
+++ b/style.css
@@ -478,7 +478,7 @@ input[type="range"]{
     padding: 0;
 }
 
-#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name{
+#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
     max-width: 2.5em;
     min-width: 2.5em;
     height: 2.4em;
-- 
cgit v1.2.1


From d62ef76614624cda99d842a2900242d5b7923eda Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Tue, 18 Oct 2022 03:09:50 +0800
Subject: Don't eat colons in booru tags

---
 modules/deepbooru.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 4ad334a1..de16b13f 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -157,8 +157,6 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
     # sort by reverse by likelihood and normal for alpha, and format tag text as requested
     unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
     for weight, tag in unsorted_tags_in_theshold:
-        # note: tag_outformat will still have a colon if include_ranks is True
-        tag_outformat = tag.replace(':', ' ')
         if use_spaces:
             tag_outformat = tag_outformat.replace('_', ' ')
         if use_escape:
-- 
cgit v1.2.1


From f80e914ac4aa69a9783b4040813253500b34d925 Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 19:10:36 +0000
Subject: example API working with gradio

---
 modules/api/api.py        |  9 ++++++--
 modules/api/processing.py | 56 ++++++++++++++++++++++++++++++++---------------
 modules/processing.py     | 22 +++++++++++++------
 3 files changed, 60 insertions(+), 27 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index fd09d352..5e86c3bf 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -23,8 +23,13 @@ class Api:
         app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
-        p = StableDiffusionProcessingTxt2Img(**vars(txt2imgreq))
-        p.sd_model = shared.sd_model
+        populate = txt2imgreq.copy(update={ # Override __init__ params
+            "sd_model": shared.sd_model, 
+            "sampler_index": 0,
+            }
+        )
+        p = StableDiffusionProcessingTxt2Img(**vars(populate))
+        # Override object param
         processed = process_images(p)
         
         b64images = []
diff --git a/modules/api/processing.py b/modules/api/processing.py
index e4df93c5..b6798241 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -5,6 +5,24 @@ from modules.processing import StableDiffusionProcessing, Processed, StableDiffu
 import inspect
 
 
+API_NOT_ALLOWED = [
+    "self",
+    "kwargs",
+    "sd_model",
+    "outpath_samples",
+    "outpath_grids",
+    "sampler_index",
+    "do_not_save_samples",
+    "do_not_save_grid",
+    "extra_generation_params",
+    "overlay_images",
+    "do_not_reload_embeddings",
+    "seed_enable_extras",
+    "prompt_for_display",
+    "sampler_noise_scheduler_override",
+    "ddim_discretize"
+]
+
 class ModelDef(BaseModel):
     """Assistance Class for Pydantic Dynamic Model Generation"""
 
@@ -14,7 +32,7 @@ class ModelDef(BaseModel):
     field_value: Any
 
 
-class pydanticModelGenerator:
+class PydanticModelGenerator:
     """
     Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
     source_data is a snapshot of the default values produced by the class
@@ -24,30 +42,33 @@ class pydanticModelGenerator:
     def __init__(
         self,
         model_name: str = None,
-        source_data: {} = {},
-        params: Dict = {},
-        overrides: Dict = {},
-        optionals: Dict = {},
+        class_instance = None
     ):
-        def field_type_generator(k, v, overrides, optionals):
-            field_type = str if not overrides.get(k) else overrides[k]["type"]
-            if v is None:
-                field_type = Any
-            else:
-                field_type = type(v)
+        def field_type_generator(k, v):
+            # field_type = str if not overrides.get(k) else overrides[k]["type"]
+            # print(k, v.annotation, v.default)
+            field_type = v.annotation
             
             return Optional[field_type]
         
+        def merge_class_params(class_):
+            all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
+            parameters = {}
+            for classes in all_classes:
+                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
+            return parameters
+            
+                
         self._model_name = model_name
-        self._json_data = source_data
+        self._class_data = merge_class_params(class_instance)
         self._model_def = [
             ModelDef(
                 field=underscore(k),
                 field_alias=k,
-                field_type=field_type_generator(k, v, overrides, optionals),
-                field_value=v
+                field_type=field_type_generator(k, v),
+                field_value=v.default
             )
-            for (k,v) in source_data.items() if k in params
+            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
         ]
 
     def generate_model(self):
@@ -60,8 +81,7 @@ class pydanticModelGenerator:
         }
         DynamicModel = create_model(self._model_name, **fields)
         DynamicModel.__config__.allow_population_by_field_name = True
+        DynamicModel.__config__.allow_mutation = True
         return DynamicModel
     
-StableDiffusionProcessingAPI = pydanticModelGenerator("StableDiffusionProcessing", 
-                                                      StableDiffusionProcessing().__dict__, 
-                                                      inspect.signature(StableDiffusionProcessing.__init__).parameters).generate_model()
+StableDiffusionProcessingAPI = PydanticModelGenerator("StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img).generate_model()
diff --git a/modules/processing.py b/modules/processing.py
index 4a7c6ccc..024a4fc3 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -9,6 +9,7 @@ from PIL import Image, ImageFilter, ImageOps
 import random
 import cv2
 from skimage import exposure
+from typing import Any, Dict, List, Optional
 
 import modules.sd_hijack
 from modules import devices, prompt_parser, masking, sd_samplers, lowvram
@@ -51,9 +52,15 @@ def get_correct_sampler(p):
         return sd_samplers.samplers
     elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
         return sd_samplers.samplers_for_img2img
+    elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI):
+        return sd_samplers.samplers
 
-class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None, do_not_reload_embeddings=False):
+class StableDiffusionProcessing():
+    """
+    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
+    
+    """
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str="", styles: List[str]=None, seed: int=-1, subseed: int=-1, subseed_strength: float=0, seed_resize_from_h: int=-1, seed_resize_from_w: int=-1, seed_enable_extras: bool=True, sampler_index: int=0, batch_size: int=1, n_iter: int=1, steps:int =50, cfg_scale:float=7.0, width:int=512, height:int=512, restore_faces:bool=False, tiling:bool=False, do_not_save_samples:bool=False, do_not_save_grid:bool=False, extra_generation_params: Dict[Any,Any]=None, overlay_images: Any=None, negative_prompt: str=None, eta: float =None, do_not_reload_embeddings: bool=False, denoising_strength: float = 0, ddim_discretize: str = "uniform", s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -86,10 +93,10 @@ class StableDiffusionProcessing:
         self.denoising_strength: float = 0
         self.sampler_noise_scheduler_override = None
         self.ddim_discretize = opts.ddim_discretize
-        self.s_churn = opts.s_churn
-        self.s_tmin = opts.s_tmin
-        self.s_tmax = float('inf')  # not representable as a standard ui option
-        self.s_noise = opts.s_noise
+        self.s_churn = s_churn or opts.s_churn
+        self.s_tmin = s_tmin or opts.s_tmin
+        self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
+        self.s_noise = s_noise or opts.s_noise
 
         if not seed_enable_extras:
             self.subseed = -1
@@ -97,6 +104,7 @@ class StableDiffusionProcessing:
             self.seed_resize_from_h = 0
             self.seed_resize_from_w = 0
 
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
@@ -497,7 +505,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):
+    def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.denoising_strength = denoising_strength
-- 
cgit v1.2.1


From 2e28c841f438b2090caac2b9a54eb62ddbda837c Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Tue, 18 Oct 2022 03:15:41 +0800
Subject: Oops

---
 modules/deepbooru.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index de16b13f..8914662d 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -157,6 +157,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
     # sort by reverse by likelihood and normal for alpha, and format tag text as requested
     unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
     for weight, tag in unsorted_tags_in_theshold:
+        tag_outformat = tag
         if use_spaces:
             tag_outformat = tag_outformat.replace('_', ' ')
         if use_escape:
-- 
cgit v1.2.1


From f29b16bad19b6332a15b2ef439864d866277fffb Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Mon, 17 Oct 2022 20:36:14 +0000
Subject: prevent API from saving

---
 modules/api/api.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modules/api/api.py b/modules/api/api.py
index 5e86c3bf..ce72c5ee 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -26,6 +26,8 @@ class Api:
         populate = txt2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model, 
             "sampler_index": 0,
+            "do_not_save_samples": True,
+            "do_not_save_grid": True
             }
         )
         p = StableDiffusionProcessingTxt2Img(**vars(populate))
-- 
cgit v1.2.1


From ab3f997c0c4a1423a82623ae1d4d3c66005bb8da Mon Sep 17 00:00:00 2001
From: Jordan Hall <jordan@hall05.co.uk>
Date: Mon, 17 Oct 2022 20:59:44 +0100
Subject: Fix typo in 'choices' when loading upscaler 2 config

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 656bab7a..e4ead347 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1059,7 +1059,7 @@ def create_ui(wrap_gradio_gpu_call):
                     extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
                 with gr.Group():
-                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', celem_id="extras_upscaler_2", hoices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', celem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
                     extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
 
                 with gr.Group():
-- 
cgit v1.2.1


From 43cb1ddad2af31170352394b81b9a299b151ea05 Mon Sep 17 00:00:00 2001
From: Adam Snodgrass <overra@gmail.com>
Date: Mon, 17 Oct 2022 05:21:59 -0500
Subject: prevent highlighting/selecting image

---
 javascript/imageviewer.js | 1 +
 1 file changed, 1 insertion(+)

diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js
index d4ab6984..9e380c65 100644
--- a/javascript/imageviewer.js
+++ b/javascript/imageviewer.js
@@ -116,6 +116,7 @@ function showGalleryImage() {
                 e.dataset.modded = true;
                 if(e && e.parentElement.tagName == 'DIV'){
                     e.style.cursor='pointer'
+                    e.style.userSelect='none'
                     e.addEventListener('click', function (evt) {
                         if(!opts.js_modal_lightbox) return;
                         modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
-- 
cgit v1.2.1


From d3338bdef18b3049431a0649d55ff22aa18baa68 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 17 Oct 2022 22:46:56 +0100
Subject: extras extend cache key with new upscale to options

---
 modules/extras.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/extras.py b/modules/extras.py
index 8dbab240..c908b43e 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -91,7 +91,8 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
             def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
                 small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
                 pixels = tuple(np.array(small).flatten().tolist())
-                key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
+                key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight, 
+                       resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
 
                 c = cached_images.get(key)
                 if c is None:
-- 
cgit v1.2.1


From c3851a853d99ad35ccedcdd8dbeb6cfbe273439b Mon Sep 17 00:00:00 2001
From: Ryan Voots <simcop2387@simcop2387.info>
Date: Mon, 17 Oct 2022 12:49:33 -0400
Subject: Re-use webui fastapi application rather than requiring one or the
 other, not both.

---
 modules/api/api.py |  6 ++----
 webui.py           | 14 +++++++-------
 2 files changed, 9 insertions(+), 11 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index ce72c5ee..8781cd86 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -2,15 +2,13 @@ from modules.api.processing import StableDiffusionProcessingAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, process_images
 import modules.shared as shared
 import uvicorn
-from fastapi import FastAPI, Body, APIRouter
+from fastapi import Body, APIRouter
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field, Json
 import json
 import io
 import base64
 
-app = FastAPI()
-
 class TextToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: Json
@@ -18,7 +16,7 @@ class TextToImageResponse(BaseModel):
 
 
 class Api:
-    def __init__(self):
+    def __init__(self, app):
         self.router = APIRouter()
         app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])
 
diff --git a/webui.py b/webui.py
index eeee44c3..6b55fbed 100644
--- a/webui.py
+++ b/webui.py
@@ -96,14 +96,11 @@ def initialize():
 
 
 def api():
-    initialize()
-
     from modules.api.api import Api
-    api = Api()
-    api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
+    api = Api(app)
 
 
-def webui():
+def webui(launch_api=False):
     initialize()
 
     while 1:
@@ -122,6 +119,9 @@ def webui():
 
         app.add_middleware(GZipMiddleware, minimum_size=1000)
 
+        if (launch_api):
+            api(app)
+
         while 1:
             time.sleep(0.5)
             if getattr(demo, 'do_restart', False):
@@ -143,6 +143,6 @@ def webui():
 
 if __name__ == "__main__":
     if cmd_opts.api:
-        api()
+        webui(True)
     else:
-        webui()
+        webui(False)
-- 
cgit v1.2.1


From 247aeb3aaaf2925c7d68a9cf47c975f3e6d3dd33 Mon Sep 17 00:00:00 2001
From: Ryan Voots <simcop2387@simcop2387.info>
Date: Mon, 17 Oct 2022 12:50:45 -0400
Subject: Put API under /sdapi/ so that routing is simpler in the future.  This
 means that one could allow access to /sdapi/ but not the webui.

---
 modules/api/api.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 8781cd86..14613d8c 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -18,7 +18,7 @@ class TextToImageResponse(BaseModel):
 class Api:
     def __init__(self, app):
         self.router = APIRouter()
-        app.add_api_route("/v1/txt2img", self.text2imgapi, methods=["POST"])
+        app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
         populate = txt2imgreq.copy(update={ # Override __init__ params
-- 
cgit v1.2.1


From 1df3ff25e6fe2e3f308e45f7a6dd37fb4f1988e6 Mon Sep 17 00:00:00 2001
From: Ryan Voots <simcop2387@simcop2387.info>
Date: Mon, 17 Oct 2022 12:58:34 -0400
Subject: Add --nowebui as a means of disabling the webui and run on the other
 port

---
 modules/shared.py |  3 ++-
 webui.py          | 35 +++++++++++++++++++++++++----------
 2 files changed, 27 insertions(+), 11 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 6c6405fd..8b436970 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -74,7 +74,8 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-parser.add_argument("--api", action='store_true', help="use api=True to launch the api instead of the webui")
+parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
+parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
diff --git a/webui.py b/webui.py
index 6b55fbed..6212be01 100644
--- a/webui.py
+++ b/webui.py
@@ -95,16 +95,34 @@ def initialize():
     signal.signal(signal.SIGINT, sigint_handler)
 
 
-def api():
+def create_api(app):
     from modules.api.api import Api
     api = Api(app)
+    return api
+
+def wait_on_server(demo=None):
+    while 1:
+        time.sleep(0.5)
+        if demo and getattr(demo, 'do_restart', False):
+            time.sleep(0.5)
+            demo.close()
+            time.sleep(0.5)
+            break
+
+def api_only():
+    initialize()
+
+    app = FastAPI()
+    app.add_middleware(GZipMiddleware, minimum_size=1000)
+    api = create_api(app)
+
+    api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
 
 
 def webui(launch_api=False):
     initialize()
 
     while 1:
-
         demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
 
         app, local_url, share_url = demo.launch(
@@ -120,15 +138,9 @@ def webui(launch_api=False):
         app.add_middleware(GZipMiddleware, minimum_size=1000)
 
         if (launch_api):
-            api(app)
+            create_api(app)
 
-        while 1:
-            time.sleep(0.5)
-            if getattr(demo, 'do_restart', False):
-                time.sleep(0.5)
-                demo.close()
-                time.sleep(0.5)
-                break
+        wait_on_server(demo)
 
         sd_samplers.set_samplers()
 
@@ -142,6 +154,9 @@ def webui(launch_api=False):
 
 
 if __name__ == "__main__":
+    if not cmd_opts.nowebui:
+        api_only()
+
     if cmd_opts.api:
         webui(True)
     else:
-- 
cgit v1.2.1


From 7432b6f4d2c3001895fc75411a34afae1810c1a2 Mon Sep 17 00:00:00 2001
From: Mykeehu <halasim@hotmail.hu>
Date: Tue, 18 Oct 2022 07:15:38 +0200
Subject: Fix typo "celem_id" to "elem_id"

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index e4ead347..2a7f64f9 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1059,7 +1059,7 @@ def create_ui(wrap_gradio_gpu_call):
                     extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
                 with gr.Group():
-                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', celem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
                     extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
 
                 with gr.Group():
-- 
cgit v1.2.1


From 8d5d863a9d11850464fdb6b64f34602803c15ccc Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Tue, 18 Oct 2022 06:51:53 +0000
Subject: gradio and FastAPI

---
 modules/api/api.py | 13 ++++++++-----
 webui.py           | 16 +++++++---------
 2 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 14613d8c..ce98cb8c 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -16,9 +16,11 @@ class TextToImageResponse(BaseModel):
 
 
 class Api:
-    def __init__(self, app):
+    def __init__(self, app, queue_lock):
         self.router = APIRouter()
-        app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
+        self.app = app
+        self.queue_lock = queue_lock
+        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
         populate = txt2imgreq.copy(update={ # Override __init__ params
@@ -30,7 +32,8 @@ class Api:
         )
         p = StableDiffusionProcessingTxt2Img(**vars(populate))
         # Override object param
-        processed = process_images(p)
+        with self.queue_lock:
+            processed = process_images(p)
         
         b64images = []
         for i in processed.images:
@@ -52,5 +55,5 @@ class Api:
         raise NotImplementedError
 
     def launch(self, server_name, port):
-        app.include_router(self.router)
-        uvicorn.run(app, host=server_name, port=port)
+        self.app.include_router(self.router)
+        uvicorn.run(self.app, host=server_name, port=port)
diff --git a/webui.py b/webui.py
index 6212be01..71724c3b 100644
--- a/webui.py
+++ b/webui.py
@@ -4,7 +4,7 @@ import time
 import importlib
 import signal
 import threading
-
+from fastapi import FastAPI
 from fastapi.middleware.gzip import GZipMiddleware
 
 from modules.paths import script_path
@@ -31,7 +31,6 @@ from modules.paths import script_path
 from modules.shared import cmd_opts
 import modules.hypernetworks.hypernetwork
 
-
 queue_lock = threading.Lock()
 
 
@@ -97,7 +96,7 @@ def initialize():
 
 def create_api(app):
     from modules.api.api import Api
-    api = Api(app)
+    api = Api(app, queue_lock)
     return api
 
 def wait_on_server(demo=None):
@@ -141,7 +140,7 @@ def webui(launch_api=False):
             create_api(app)
 
         wait_on_server(demo)
-
+        
         sd_samplers.set_samplers()
 
         print('Reloading Custom Scripts')
@@ -153,11 +152,10 @@ def webui(launch_api=False):
         print('Restarting Gradio')
 
 
+
+task = []
 if __name__ == "__main__":
-    if not cmd_opts.nowebui:
+    if cmd_opts.nowebui:
         api_only()
-
-    if cmd_opts.api:
-        webui(True)
     else:
-        webui(False)
+        webui(cmd_opts.api)
\ No newline at end of file
-- 
cgit v1.2.1


From 786ed499226177d71e937e0342bcb9d3b1ff260f Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 17 Oct 2022 19:48:39 +0300
Subject: use legacy attnblock

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 984b35c4..2407a461 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -27,7 +27,7 @@ def apply_optimizations():
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-- 
cgit v1.2.1


From 2043c4a231eef838bb15044f502b864b55885037 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 17 Oct 2022 19:49:11 +0300
Subject: delete xformers attnblock

---
 modules/sd_hijack_optimizations.py | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 79405525..60da7459 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -292,15 +292,3 @@ def cross_attention_attnblock_forward(self, x):
 
         return h3
     
-def xformers_attnblock_forward(self, x):
-    try:
-        h_ = x
-        h_ = self.norm(h_)
-        q1 = self.q(h_).contiguous()
-        k1 = self.k(h_).contiguous()
-        v = self.v(h_).contiguous()
-        out = xformers.ops.memory_efficient_attention(q1, k1, v)
-        out = self.proj_out(out)
-        return x + out
-    except NotImplementedError:
-        return cross_attention_attnblock_forward(self, x)
-- 
cgit v1.2.1


From 84823275e896bcc1f7cb4ce098ae3c5d05e17b9a Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 17 Oct 2022 22:18:59 +0300
Subject: readd xformers attnblock

---
 modules/sd_hijack_optimizations.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 60da7459..7ebef3f0 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -292,3 +292,18 @@ def cross_attention_attnblock_forward(self, x):
 
         return h3
     
+def xformers_attnblock_forward(self, x):
+    try:
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+        b, c, h, w = q.shape
+        q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+        out = xformers.ops.memory_efficient_attention(q, k, v)
+        out = rearrange(out, 'b (h w) c -> b c h w', h=h)
+        out = self.proj_out(out)
+        return x + out
+    except NotImplementedError:
+        return cross_attention_attnblock_forward(self, x)
-- 
cgit v1.2.1


From 73b5dbf72a93b64445551c74a4c0dc924986081d Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Mon, 17 Oct 2022 22:19:18 +0300
Subject: Update sd_hijack.py

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 2407a461..984b35c4 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -27,7 +27,7 @@ def apply_optimizations():
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
     elif cmd_opts.opt_split_attention_v1:
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-- 
cgit v1.2.1


From c71008c74156635558bb2e877d1628913f6f781e Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Tue, 18 Oct 2022 00:02:50 +0300
Subject: Update sd_hijack_optimizations.py

---
 modules/sd_hijack_optimizations.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 7ebef3f0..a3345bb9 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -301,6 +301,9 @@ def xformers_attnblock_forward(self, x):
         v = self.v(h_)
         b, c, h, w = q.shape
         q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+        q = q.contiguous()
+        k = k.contiguous()
+        v = v.contiguous()
         out = xformers.ops.memory_efficient_attention(q, k, v)
         out = rearrange(out, 'b (h w) c -> b c h w', h=h)
         out = self.proj_out(out)
-- 
cgit v1.2.1


From ca023f8a459717be19d333513a0e388cf3944e74 Mon Sep 17 00:00:00 2001
From: ClashSAN <98228077+ClashSAN@users.noreply.github.com>
Date: Tue, 18 Oct 2022 08:57:05 +0000
Subject: Update README.md

---
 README.md | 20 +++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 859a91b6..a89593bf 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - One click install and run script (but you still must install python and git)
 - Outpainting
 - Inpainting
+- Color Sketch
 - Prompt Matrix
 - Stable Diffusion Upscale
 - Attention, specify parts of text that the model should pay more attention to
@@ -37,14 +38,14 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - Interrupt processing at any time
 - 4GB video card support (also reports of 2GB working)
 - Correct seeds for batches
-- Prompt length validation
-     - get length of prompt in tokens as you type
-     - get a warning after generation if some text was truncated
+- Live prompt token length validation
 - Generation parameters
      - parameters you used to generate images are saved with that image
      - in PNG chunks for PNG, in EXIF for JPEG
      - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
      - can be disabled in settings
+     - drag and drop an image/text-parameters to promptbox
+- Read Generation Parameters Button, loads parameters in promptbox to UI
 - Settings page
 - Running arbitrary python code from UI (must run with --allow-code to enable)
 - Mouseover hints for most UI elements
@@ -62,7 +63,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - Img2img Alternative
 - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
 - Reloading checkpoints on the fly
-- Checkpoint Merger, a tab that allows you to merge two checkpoints into one
+- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
 - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
 - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
      - separate prompts using uppercase `AND`
@@ -70,14 +71,19 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
 - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
 - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
+- History tab: view, direct and delete images conveniently within the UI
+- Generate forever option
+- Training Tab
+- Preprocessing Image Datasets: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
+
+
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
 
-Alternatively, use Google Colab:
+Alternatively, use online services(like Google Colab):
 
-- [Colab, maintained by Akaibu](https://colab.research.google.com/drive/1kw3egmSn-KgWsikYvOMjJkVDsPLjEMzl)
-- [Colab, original by me, outdated](https://colab.research.google.com/drive/1Iy-xW9t1-OQWhb0hNxueGij8phCyluOh).
+- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
 
 ### Automatic Installation on Windows
 1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
-- 
cgit v1.2.1


From 7651b84968f66dd0a5c3346520aad8dac6c4464e Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Tue, 18 Oct 2022 19:07:17 +0900
Subject: Initial KR support - WIP

Localization WIP
---
 ko-KR.json | 76 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 ko-KR.json

diff --git a/ko-KR.json b/ko-KR.json
new file mode 100644
index 00000000..f93b3e16
--- /dev/null
+++ b/ko-KR.json
@@ -0,0 +1,76 @@
+{
+  "txt2img": "텍스트→이미지",
+  "img2img": "이미지→이미지",
+  "Extras": "부가기능",
+  "PNG Info": "PNG 정보",
+  "History": "기록",
+  "Checkpoint Merger": "체크포인트 병합",
+  "Train": "훈련",
+  "Settings": "설정",
+  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "Hypernetwork": "하이퍼네트워크",
+  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
+  "Generate": "생성",
+  "Style 1": "스타일 1",
+  "Style 2": "스타일 2",
+  "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
+  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
+  "Save style": "스타일 저장",
+  "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
+  "Do not do anything special": "아무것도 하지 않기",
+  "Generate forever": "반복 생성",
+  "Cancel generate forever": "반복 생성 취소",
+  "Interrupt": "중단",
+  "Skip": "건너뛰기",
+  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
+  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
+  "Prompt": "프롬프트",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Negative prompt": "네거티브 프롬프트",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Sampling Steps": "샘플링 스텝 수",
+  "Sampling method": "샘플링 방법",
+  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
+  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
+  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
+  "Width": "가로",
+  "Height": "세로",
+  "Restore faces": "얼굴 보정",
+  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
+  "Tiling": "타일링",
+  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
+  "Highres. fix": "고해상도 보정",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
+  "Firstpass width": "초기 가로길이",
+  "Firstpass height": "초기 세로길이",
+  "Denoising strength": "디노이즈 강도",
+  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
+  "Batch count": "배치 수",
+  "Batch size": "배치 크기",
+  "How many batches of images to create": "생성할 이미지 배치 수",
+  "How many image to create in a single batch": "한 배치당 이미지 수",
+  "CFG Scale": "CFG 스케일",
+  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
+  "Seed": "시드",
+  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
+  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
+  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
+  "Extra": "고급",
+  "Variation seed": "바리에이션 시드",
+  "Variation strength": "바리에이션 강도",
+  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
+  "Resize seed from height": "시드 리사이징 가로길이",
+  "Resize seed from width": "시드 리사이징 세로길이",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
+  "Script": "스크립트",
+  "Save": "저장",
+  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
+  "Send to img2img": "이미지→이미지로 전송",
+  "Send to inpaint": "인페인트로 전송",
+  "Send to extras": "부가기능으로 전송",
+  "Open images output directory": "이미지 저장 경로 열기",
+  "Make Zip when Save?": "저장 시 Zip 생성하기",
+  "Always save all generated images": "생성된 이미지 항상 저장하기"
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 50e34cf194b3e3085bc99aeea4dbfd7758dc79c8 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Tue, 18 Oct 2022 20:11:17 +0900
Subject: Update ko-KR.json

---
 localizations/ko-KR.json | 85 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 85 insertions(+)
 create mode 100644 localizations/ko-KR.json

diff --git a/localizations/ko-KR.json b/localizations/ko-KR.json
new file mode 100644
index 00000000..a4367dc5
--- /dev/null
+++ b/localizations/ko-KR.json
@@ -0,0 +1,85 @@
+{
+  "⤡": "⤡",
+  "⊞": "⊞",
+  "×": "×",
+  "❮": "❮",
+  "❯": "❯",
+  "Loading...": "로딩중...",
+  "view": "",
+  "api": "api",
+  "•": "•",
+  "txt2img": "텍스트→이미지",
+  "img2img": "이미지→이미지",
+  "Extras": "부가기능",
+  "PNG Info": "PNG 정보",
+  "History": "기록",
+  "Checkpoint Merger": "체크포인트 병합",
+  "Train": "훈련",
+  "Settings": "설정",
+  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "Hypernetwork": "하이퍼네트워크",
+  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
+  "Generate": "생성",
+  "Style 1": "스타일 1",
+  "Style 2": "스타일 2",
+  "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
+  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
+  "Save style": "스타일 저장",
+  "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
+  "Do not do anything special": "아무것도 하지 않기",
+  "Generate forever": "반복 생성",
+  "Cancel generate forever": "반복 생성 취소",
+  "Interrupt": "중단",
+  "Skip": "건너뛰기",
+  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
+  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
+  "Prompt": "프롬프트",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Negative prompt": "네거티브 프롬프트",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Sampling Steps": "샘플링 스텝 수",
+  "Sampling method": "샘플링 방법",
+  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
+  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
+  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
+  "Width": "가로",
+  "Height": "세로",
+  "Restore faces": "얼굴 보정",
+  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
+  "Tiling": "타일링",
+  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
+  "Highres. fix": "고해상도 보정",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
+  "Firstpass width": "초기 가로길이",
+  "Firstpass height": "초기 세로길이",
+  "Denoising strength": "디노이즈 강도",
+  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
+  "Batch count": "배치 수",
+  "Batch size": "배치 크기",
+  "How many batches of images to create": "생성할 이미지 배치 수",
+  "How many image to create in a single batch": "한 배치당 이미지 수",
+  "CFG Scale": "CFG 스케일",
+  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
+  "Seed": "시드",
+  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
+  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
+  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
+  "Extra": "고급",
+  "Variation seed": "바리에이션 시드",
+  "Variation strength": "바리에이션 강도",
+  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
+  "Resize seed from height": "시드 리사이징 가로길이",
+  "Resize seed from width": "시드 리사이징 세로길이",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
+  "Script": "스크립트",
+  "Save": "저장",
+  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
+  "Send to img2img": "이미지→이미지로 전송",
+  "Send to inpaint": "인페인트로 전송",
+  "Send to extras": "부가기능으로 전송",
+  "Open images output directory": "이미지 저장 경로 열기",
+  "Make Zip when Save?": "저장 시 Zip 생성하기",
+  "Always save all generated images": "생성된 이미지 항상 저장하기"
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 0530f07da3c77ed4bfa02f37de5c84562a37f470 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Tue, 18 Oct 2022 20:12:54 +0900
Subject: Move ko-KR.json

---
 ko-KR.json | 76 --------------------------------------------------------------
 1 file changed, 76 deletions(-)
 delete mode 100644 ko-KR.json

diff --git a/ko-KR.json b/ko-KR.json
deleted file mode 100644
index f93b3e16..00000000
--- a/ko-KR.json
+++ /dev/null
@@ -1,76 +0,0 @@
-{
-  "txt2img": "텍스트→이미지",
-  "img2img": "이미지→이미지",
-  "Extras": "부가기능",
-  "PNG Info": "PNG 정보",
-  "History": "기록",
-  "Checkpoint Merger": "체크포인트 병합",
-  "Train": "훈련",
-  "Settings": "설정",
-  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
-  "Hypernetwork": "하이퍼네트워크",
-  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
-  "Generate": "생성",
-  "Style 1": "스타일 1",
-  "Style 2": "스타일 2",
-  "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
-  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
-  "Save style": "스타일 저장",
-  "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
-  "Do not do anything special": "아무것도 하지 않기",
-  "Generate forever": "반복 생성",
-  "Cancel generate forever": "반복 생성 취소",
-  "Interrupt": "중단",
-  "Skip": "건너뛰기",
-  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
-  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
-  "Prompt": "프롬프트",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Negative prompt": "네거티브 프롬프트",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Sampling Steps": "샘플링 스텝 수",
-  "Sampling method": "샘플링 방법",
-  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
-  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
-  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
-  "Width": "가로",
-  "Height": "세로",
-  "Restore faces": "얼굴 보정",
-  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
-  "Tiling": "타일링",
-  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
-  "Highres. fix": "고해상도 보정",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
-  "Firstpass width": "초기 가로길이",
-  "Firstpass height": "초기 세로길이",
-  "Denoising strength": "디노이즈 강도",
-  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
-  "Batch count": "배치 수",
-  "Batch size": "배치 크기",
-  "How many batches of images to create": "생성할 이미지 배치 수",
-  "How many image to create in a single batch": "한 배치당 이미지 수",
-  "CFG Scale": "CFG 스케일",
-  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
-  "Seed": "시드",
-  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
-  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
-  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
-  "Extra": "고급",
-  "Variation seed": "바리에이션 시드",
-  "Variation strength": "바리에이션 강도",
-  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
-  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
-  "Resize seed from height": "시드 리사이징 가로길이",
-  "Resize seed from width": "시드 리사이징 세로길이",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
-  "Script": "스크립트",
-  "Save": "저장",
-  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
-  "Send to img2img": "이미지→이미지로 전송",
-  "Send to inpaint": "인페인트로 전송",
-  "Send to extras": "부가기능으로 전송",
-  "Open images output directory": "이미지 저장 경로 열기",
-  "Make Zip when Save?": "저장 시 Zip 생성하기",
-  "Always save all generated images": "생성된 이미지 항상 저장하기"
-}
\ No newline at end of file
-- 
cgit v1.2.1


From 8b02662215917d39f76f86b703a322818d5a8ad4 Mon Sep 17 00:00:00 2001
From: trufty <trufty@gmail.com>
Date: Mon, 17 Oct 2022 10:58:21 -0400
Subject: Disable auto weights swap with config option

---
 modules/shared.py | 1 +
 modules/ui.py     | 4 ++++
 2 files changed, 5 insertions(+)

diff --git a/modules/shared.py b/modules/shared.py
index 9603d26e..8a1d1881 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -266,6 +266,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
+    "disable_weights_auto_swap": OptionInfo(False, "Disable auto swapping weights to match model hash in prompts"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
diff --git a/modules/ui.py b/modules/ui.py
index 1dae4a65..75eb0b0c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -542,6 +542,10 @@ def apply_setting(key, value):
     if value is None:
         return gr.update()
 
+    # dont allow model to be swapped when model hash exists in prompt
+    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+        return gr.update()
+
     if key == "sd_model_checkpoint":
         ckpt_info = sd_models.get_closet_checkpoint_match(value)
 
-- 
cgit v1.2.1


From d2f459c5cf9f728256775dc1c3380c7e9a7e27fb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 18 Oct 2022 14:22:52 +0300
Subject: clarify the comment for the new option from #2959 and move it to UI
 section.

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 8a1d1881..c0d87168 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -266,7 +266,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
-    "disable_weights_auto_swap": OptionInfo(False, "Disable auto swapping weights to match model hash in prompts"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
@@ -294,6 +293,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
+    "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-- 
cgit v1.2.1


From 7543787a0a7d8a45c44f2293b242e128da7c5a1d Mon Sep 17 00:00:00 2001
From: Justin Maier <Zipp425@gmail.com>
Date: Mon, 17 Oct 2022 17:11:44 -0600
Subject: Auto select attention block for editing

---
 javascript/edit-attention.js | 35 ++++++++++++++++++++++++++++++++---
 1 file changed, 32 insertions(+), 3 deletions(-)

diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js
index 67084e7a..c0d29a74 100644
--- a/javascript/edit-attention.js
+++ b/javascript/edit-attention.js
@@ -9,9 +9,38 @@ addEventListener('keydown', (event) => {
 	let minus = "ArrowDown"
 	if (event.key != plus && event.key != minus) return;
 
-	selectionStart = target.selectionStart;
-	selectionEnd = target.selectionEnd;
-	if(selectionStart == selectionEnd) return;
+	let selectionStart = target.selectionStart;
+	let selectionEnd = target.selectionEnd;
+	// If the user hasn't selected anything, let's select their current parenthesis block
+	if (selectionStart === selectionEnd) {
+		// Find opening parenthesis around current cursor
+		const before = target.value.substring(0, selectionStart);
+		let beforeParen = before.lastIndexOf("(");
+		if (beforeParen == -1) return;
+		let beforeParenClose = before.lastIndexOf(")");
+		while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
+			beforeParen = before.lastIndexOf("(", beforeParen - 1);
+			beforeParenClose = before.lastIndexOf(")", beforeParenClose - 1);
+		}
+
+		// Find closing parenthesis around current cursor
+		const after = target.value.substring(selectionStart);
+		let afterParen = after.indexOf(")");
+		if (afterParen == -1) return;
+		let afterParenOpen = after.indexOf("(");
+		while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
+			afterParen = after.indexOf(")", afterParen + 1);
+			afterParenOpen = after.indexOf("(", afterParenOpen + 1);
+		}
+		if (beforeParen === -1 || afterParen === -1) return;
+
+		// Set the selection to the text between the parenthesis
+		const parenContent = target.value.substring(beforeParen + 1, selectionStart + afterParen);
+		const lastColon = parenContent.lastIndexOf(":");
+		selectionStart = beforeParen + 1;
+		selectionEnd = selectionStart + lastColon;
+		target.setSelectionRange(selectionStart, selectionEnd);
+	}
 
 	event.preventDefault();
 
-- 
cgit v1.2.1


From 428080d469a1b760d7b08a1d81fef77d3d47832a Mon Sep 17 00:00:00 2001
From: camenduru <54370274+camenduru@users.noreply.github.com>
Date: Mon, 17 Oct 2022 03:20:11 +0300
Subject: Remove duplicate artists.

---
 artists.csv | 2 --
 1 file changed, 2 deletions(-)

diff --git a/artists.csv b/artists.csv
index 99cdbdc6..858dfcd6 100644
--- a/artists.csv
+++ b/artists.csv
@@ -738,7 +738,6 @@ Abraham Mignon,0.60605425,fineart
 Albert Bloch,0.69573116,nudity
 Charles Dana Gibson,0.67155975,fineart
 Alexandre-Évariste Fragonard,0.6507174,fineart
-Alexandre-Évariste Fragonard,0.6507174,fineart
 Ernst Fuchs,0.6953538,nudity
 Alfredo Jaar,0.6952965,digipa-high-impact
 Judy Chicago,0.6952246,weird
@@ -2411,7 +2410,6 @@ Hermann Feierabend,0.5346168,digipa-high-impact
 Antonio Donghi,0.4610982,digipa-low-impact
 Adonna Khare,0.4858036,digipa-med-impact
 James Stokoe,0.5015107,digipa-med-impact
-Art & Language,0.5341332,digipa-high-impact
 Agustín Fernández,0.53403986,fineart
 Germán Londoño,0.5338712,fineart
 Emmanuelle Moureaux,0.5335641,digipa-high-impact
-- 
cgit v1.2.1


From 97d3ba3941536215ea15431886c7f28300a9d915 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E3=81=B5=E3=81=81?=
 <34892635+fa0311@users.noreply.github.com>
Date: Tue, 18 Oct 2022 17:29:42 +0900
Subject: Add scripts to ui-config,json

---
 modules/scripts.py | 15 +++++++++++++--
 modules/ui.py      |  5 +++++
 2 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/modules/scripts.py b/modules/scripts.py
index ac66d448..3402066d 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -96,6 +96,7 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
 class ScriptRunner:
     def __init__(self):
         self.scripts = []
+        self.titles = []
 
     def setup_ui(self, is_img2img):
         for script_class, path in scripts_data:
@@ -107,9 +108,10 @@ class ScriptRunner:
 
             self.scripts.append(script)
 
-        titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
+        self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
 
-        dropdown = gr.Dropdown(label="Script", choices=["None"] + titles, value="None", type="index")
+        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
+        dropdown.save_to_config = True
         inputs = [dropdown]
 
         for script in self.scripts:
@@ -139,6 +141,15 @@ class ScriptRunner:
 
             return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
 
+        def init_field(title):
+            if title == "None":
+                return
+            script_index = self.titles.index(title)
+            script = self.scripts[script_index]
+            for i in range(script.args_from, script.args_to):
+                inputs[i].visible = True
+
+        dropdown.init_field = init_field
         dropdown.change(
             fn=select_script,
             inputs=[dropdown],
diff --git a/modules/ui.py b/modules/ui.py
index 75eb0b0c..39afbc4e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1753,6 +1753,11 @@ Requested path was: {f}
                 print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
             else:
                 setattr(obj, field, saved_value)
+                if getattr(x, 'init_field', False):
+                    try:
+                        x.init_field(saved_value)
+                    except Exception:
+                        print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
 
         if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
             apply_field(x, 'visible')
-- 
cgit v1.2.1


From de29ec0743fcfb141d8891a3ccbd537ea71bf5b4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E3=81=B5=E3=81=81?=
 <34892635+fa0311@users.noreply.github.com>
Date: Tue, 18 Oct 2022 18:15:00 +0900
Subject: Remove exception handling

---
 modules/ui.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 39afbc4e..b38bfb3f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1754,10 +1754,7 @@ Requested path was: {f}
             else:
                 setattr(obj, field, saved_value)
                 if getattr(x, 'init_field', False):
-                    try:
-                        x.init_field(saved_value)
-                    except Exception:
-                        print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
+                    x.init_field(saved_value)
 
         if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
             apply_field(x, 'visible')
-- 
cgit v1.2.1


From 3003438088502774628656790d83fc8074d51ab4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E3=81=B5=E3=81=81?=
 <34892635+fa0311@users.noreply.github.com>
Date: Tue, 18 Oct 2022 18:51:57 +0900
Subject: Add visible for dropdown

---
 modules/ui.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index b38bfb3f..fb6eb5a0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1737,7 +1737,7 @@ Requested path was: {f}
         print(traceback.format_exc(), file=sys.stderr)
 
     def loadsave(path, x):
-        def apply_field(obj, field, condition=None):
+        def apply_field(obj, field, condition=None, init_field=None):
             key = path + "/" + field
 
             if getattr(obj,'custom_script_source',None) is not None:
@@ -1753,8 +1753,8 @@ Requested path was: {f}
                 print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
             else:
                 setattr(obj, field, saved_value)
-                if getattr(x, 'init_field', False):
-                    x.init_field(saved_value)
+                if init_field is not None:
+                    init_field(saved_value)
 
         if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
             apply_field(x, 'visible')
@@ -1780,7 +1780,8 @@ Requested path was: {f}
         # Since there are many dropdowns that shouldn't be saved,
         # we only mark dropdowns that should be saved.
         if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
-            apply_field(x, 'value', lambda val: val in x.choices)
+            apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
+            apply_field(x, 'visible')
 
     visit(txt2img_interface, loadsave, "txt2img")
     visit(img2img_interface, loadsave, "img2img")
-- 
cgit v1.2.1


From 02622b19191f5f5112db7633c0630e5c7df1b2f7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E3=81=B5=E3=81=81?=
 <34892635+fa0311@users.noreply.github.com>
Date: Tue, 18 Oct 2022 18:52:27 +0900
Subject: update scripts.py

---
 modules/scripts.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/scripts.py b/modules/scripts.py
index 3402066d..1039fa9c 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -142,7 +142,7 @@ class ScriptRunner:
             return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
 
         def init_field(title):
-            if title == "None":
+            if title == 'None':
                 return
             script_index = self.titles.index(title)
             script = self.scripts[script_index]
-- 
cgit v1.2.1


From 68e83f40bf143e25639875b27e8ad3e32b382db5 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 16 Oct 2022 10:49:28 +0100
Subject: add update warning to launch.py

---
 launch.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/launch.py b/launch.py
index 7520cfee..1b133633 100644
--- a/launch.py
+++ b/launch.py
@@ -5,6 +5,7 @@ import sys
 import importlib.util
 import shlex
 import platform
+import requests
 
 dir_repos = "repositories"
 python = sys.executable
@@ -126,6 +127,16 @@ def prepare_enviroment():
     print(f"Python {sys.version}")
     print(f"Commit hash: {commit}")
 
+    try:
+        commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
+        if commit != "<none>" and commits['commit']['sha'] != commit:
+            print("--------------------------------------------------------")
+            print("| You are not up to date with the most recent release. |")
+            print("| Consider running `git pull` to update.               |")
+            print("--------------------------------------------------------")
+    except Exception as e:
+        pass
+    
     if not is_installed("torch") or not is_installed("torchvision"):
         run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
 
-- 
cgit v1.2.1


From a647cbc618ea13ce2312f0291ebdfc3b9449e5a1 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 16 Oct 2022 10:54:09 +0100
Subject: move update check to after dep installation

---
 launch.py | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/launch.py b/launch.py
index 1b133633..8484d7c1 100644
--- a/launch.py
+++ b/launch.py
@@ -5,7 +5,6 @@ import sys
 import importlib.util
 import shlex
 import platform
-import requests
 
 dir_repos = "repositories"
 python = sys.executable
@@ -126,16 +125,6 @@ def prepare_enviroment():
 
     print(f"Python {sys.version}")
     print(f"Commit hash: {commit}")
-
-    try:
-        commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
-        if commit != "<none>" and commits['commit']['sha'] != commit:
-            print("--------------------------------------------------------")
-            print("| You are not up to date with the most recent release. |")
-            print("| Consider running `git pull` to update.               |")
-            print("--------------------------------------------------------")
-    except Exception as e:
-        pass
     
     if not is_installed("torch") or not is_installed("torchvision"):
         run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
@@ -176,6 +165,17 @@ def prepare_enviroment():
 
     sys.argv += args
 
+    try:
+        import requests
+        commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
+        if commit != "<none>" and commits['commit']['sha'] != commit:
+            print("--------------------------------------------------------")
+            print("| You are not up to date with the most recent release. |")
+            print("| Consider running `git pull` to update.               |")
+            print("--------------------------------------------------------")
+    except Exception as e:
+        pass
+    
     if "--exit" in args:
         print("Exiting because of --exit argument")
         exit(0)
-- 
cgit v1.2.1


From e511b867a9e85e715966c5c60052d67497a4f949 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 16 Oct 2022 17:04:09 +0100
Subject: Make update check commandline option, give response on all paths.

---
 launch.py | 29 +++++++++++++++++++----------
 1 file changed, 19 insertions(+), 10 deletions(-)

diff --git a/launch.py b/launch.py
index 8484d7c1..9d9dd1b6 100644
--- a/launch.py
+++ b/launch.py
@@ -86,7 +86,24 @@ def git_clone(url, dir, name, commithash=None):
     if commithash is not None:
         run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
 
+        
+def version_check(commit):
+    try:
+        import requests
+        commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
+        if commit != "<none>" and commits['commit']['sha'] != commit:
+            print("--------------------------------------------------------")
+            print("| You are not up to date with the most recent release. |")
+            print("| Consider running `git pull` to update.               |")
+            print("--------------------------------------------------------")
+        elif commits['commit']['sha'] == commit:
+            print("You are up to date with the most recent release.")
+        else:
+            print("Not a git clone, can't perform version check.")
+    except Exception as e:
+        print("versipm check failed",e)
 
+        
 def prepare_enviroment():
     torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
@@ -165,16 +182,8 @@ def prepare_enviroment():
 
     sys.argv += args
 
-    try:
-        import requests
-        commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
-        if commit != "<none>" and commits['commit']['sha'] != commit:
-            print("--------------------------------------------------------")
-            print("| You are not up to date with the most recent release. |")
-            print("| Consider running `git pull` to update.               |")
-            print("--------------------------------------------------------")
-    except Exception as e:
-        pass
+    if '--update-check' in args:
+        version_check(commit)
     
     if "--exit" in args:
         print("Exiting because of --exit argument")
-- 
cgit v1.2.1


From 4c605c5174a9b211c3a88e9aff5f5be92b53fd92 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 16 Oct 2022 17:24:06 +0100
Subject: add shared option for update check

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/shared.py b/modules/shared.py
index c0d87168..50dc46ae 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -76,6 +76,7 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
+parser.add_argument("--update-check", action='store_true', help="enable http check to confirm that the currently running version is the most recent release.", default=False)
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
-- 
cgit v1.2.1


From eb299527b1e5d1f83a14641647fca72e8fb305ac Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Tue, 18 Oct 2022 20:14:11 +0800
Subject: Image browser

---
 javascript/images_history.js |  19 ++--
 modules/images_history.py    | 227 ++++++++++++++++++++++++++++---------------
 modules/shared.py            |   7 +-
 modules/ui.py                |   2 +-
 uitest.bat                   |   2 +
 uitest.py                    | 124 +++++++++++++++++++++++
 6 files changed, 289 insertions(+), 92 deletions(-)
 create mode 100644 uitest.bat
 create mode 100644 uitest.py

diff --git a/javascript/images_history.js b/javascript/images_history.js
index 3c028bc6..182d730b 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -145,9 +145,10 @@ function images_history_enable_del_buttons(){
 }
 
 function images_history_init(){ 
-    var loaded = gradioApp().getElementById("images_history_reconstruct_directory")
-    if (loaded){  
-        var init_status = loaded.querySelector("input").checked       
+    // var loaded = gradioApp().getElementById("images_history_reconstruct_directory")
+    // if (loaded){  
+        // var init_status = loaded.querySelector("input").checked 
+    if (gradioApp().getElementById("images_history_finish_render")){      
         for (var i in images_history_tab_list ){
             tab = images_history_tab_list[i];
             gradioApp().getElementById(tab + '_images_history').classList.add("images_history_cantainor");
@@ -163,19 +164,17 @@ function images_history_init(){
         for (var i in images_history_tab_list){               
             var tabname = images_history_tab_list[i]
             tab_btns[i].setAttribute("tabname", tabname);
-            if (init_status){
-                tab_btns[i].addEventListener('click', images_history_click_tab);
-            }
-        }  
-        if (init_status){
-                tab_btns[0].click();
+            // if (!init_status){
+            //     tab_btns[i].addEventListener('click', images_history_click_tab);
+            // }
+            tab_btns[i].addEventListener('click', images_history_click_tab);
         }  
     } else {
         setTimeout(images_history_init, 500);
     } 
 }
 
-var images_history_tab_list = ["txt2img", "img2img", "extras", "saved"];
+var images_history_tab_list = ["custom", "txt2img", "img2img", "extras", "saved"];
 setTimeout(images_history_init, 500);
 document.addEventListener("DOMContentLoaded", function() {
     var mutationObserver = new MutationObserver(function(m){
diff --git a/modules/images_history.py b/modules/images_history.py
index 20324557..d56f3a25 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -4,6 +4,7 @@ import time
 import hashlib
 import gradio
 system_bak_path = "webui_log_and_bak"
+browser_tabname = "custom"
 def is_valid_date(date):
     try:
         time.strptime(date, "%Y%m%d")
@@ -99,13 +100,15 @@ def auto_sorting(dir_name):
         date_list.append(today)
     return sorted(date_list, reverse=True)
 
-def archive_images(dir_name, date_to): 
+def archive_images(dir_name, date_to):
+    
     filenames = []   
     loads_num =int(opts.images_history_num_per_page * opts.images_history_pages_num)
+    today = time.strftime("%Y%m%d",time.localtime(time.time()))
+    date_to = today if date_to is None  or date_to == "" else date_to
+    date_to_bak = date_to 
     if opts.images_history_reconstruct_directory:        
-        date_list = auto_sorting(dir_name)
-        today = time.strftime("%Y%m%d",time.localtime(time.time()))
-        date_to = today if date_to is None  or date_to == "" else date_to        
+        date_list = auto_sorting(dir_name)               
         for date in date_list:
             if date <= date_to:
                 path = os.path.join(dir_name, date)
@@ -120,7 +123,7 @@ def archive_images(dir_name, date_to):
         tmparray = [(os.path.getmtime(file), file) for file in filenames ]
         date_stamp = time.mktime(time.strptime(date_to, "%Y%m%d")) + 86400      
         filenames = []
-        date_list = {}
+        date_list = {date_to:None}
         date = time.strftime("%Y%m%d",time.localtime(time.time()))
         for t, f in tmparray:
             date = time.strftime("%Y%m%d",time.localtime(t))
@@ -133,22 +136,29 @@ def archive_images(dir_name, date_to):
             date = sort_array[loads_num][2]
             filenames = [x[1] for x in sort_array]
         else:
-            date =  None if len(sort_array) == 0 else sort_array[-1][2]
+            date =  date_to if len(sort_array) == 0 else sort_array[-1][2]
             filenames = [x[1] for x in sort_array]
-        filenames = [x[1] for x in sort_array if x[2]>= date]
-    _, image_list, _, visible_num = get_recent_images(1, 0, filenames)
+        filenames = [x[1] for x in sort_array if x[2]>= date]   
+    num = len(filenames)  
+    last_date_from = date_to_bak if num == 0 else time.strftime("%Y%m%d", time.localtime(time.mktime(time.strptime(date, "%Y%m%d")) - 1000))
+    date = date[:4] + "-" + date[4:6] + "-" + date[6:8]
+    date_to_bak = date_to_bak[:4] + "-" + date_to_bak[4:6] + "-" + date_to_bak[6:8]
+    load_info = f"Loaded {(num + 1) // opts.images_history_pages_num} pades, {num} images, during {date} - {date_to_bak}"
+    _, image_list, _, _, visible_num = get_recent_images(1, 0, filenames)
     return (
         gradio.Dropdown.update(choices=date_list, value=date_to),
-        date,
+        load_info,
         filenames,        
         1,
         image_list,
         "",
-        visible_num
+        "",
+        visible_num, 
+        last_date_from
     )
 
-def newest_click(dir_name,  date_to):
-        return archive_images(dir_name, time.strftime("%Y%m%d",time.localtime(time.time())))
+
+
 
 def delete_image(delete_num, name, filenames, image_index, visible_num):
     if name == "":
@@ -196,7 +206,29 @@ def get_recent_images(page_index, step, filenames):
     length = len(filenames)
     visible_num = num_of_imgs_per_page if  idx_frm + num_of_imgs_per_page <= length else length % num_of_imgs_per_page 
     visible_num = num_of_imgs_per_page if visible_num == 0 else visible_num
-    return page_index, image_list,  "", visible_num
+    return page_index, image_list,  "", "",  visible_num
+
+def newest_click(date_to):
+    if date_to is None:
+        return time.strftime("%Y%m%d",time.localtime(time.time())), []
+    else:
+        return None, []
+def forward_click(last_date_from, date_to_recorder):
+    if len(date_to_recorder) == 0:
+        return None, []
+    if last_date_from == date_to_recorder[-1]:
+        date_to_recorder = date_to_recorder[:-1]
+    if len(date_to_recorder) == 0:
+        return None, []
+    return date_to_recorder[-1], date_to_recorder[:-1]
+
+def backward_click(last_date_from, date_to_recorder):
+    if last_date_from is None or last_date_from == "":
+        return time.strftime("%Y%m%d",time.localtime(time.time())), []
+    if len(date_to_recorder) == 0 or last_date_from != date_to_recorder[-1]:
+        date_to_recorder.append(last_date_from)    
+    return last_date_from, date_to_recorder
+
 
 def first_page_click(page_index, filenames):
     return get_recent_images(1, 0, filenames)
@@ -214,13 +246,33 @@ def page_index_change(page_index, filenames):
     return get_recent_images(page_index, 0, filenames)
 
 def show_image_info(tabname_box, num, page_index, filenames):
-    file = filenames[int(num) + int((page_index - 1) * int(opts.images_history_num_per_page))]     
-    return file, num, file
+    file = filenames[int(num) + int((page_index - 1) * int(opts.images_history_num_per_page))]   
+    tm =   time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(os.path.getmtime(file)))
+    return file, tm, num, file
 
 def enable_page_buttons():
     return gradio.update(visible=True)
 
+def change_dir(img_dir, date_to):
+    warning = None
+    try:
+        if os.path.exists(img_dir):
+            try:
+                f = os.listdir(img_dir)                
+            except:
+                warning = f"'{img_dir} is not a directory"
+        else:
+            warning = "The directory is not exist"
+    except:
+        warning = "The format of the directory is incorrect"
+    if warning is None:        
+        today = time.strftime("%Y%m%d",time.localtime(time.time()))
+        return gradio.update(visible=False), gradio.update(visible=True), None,   None if date_to != today else today
+    else:
+        return gradio.update(visible=True), gradio.update(visible=False), warning, date_to
+
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
+    custom_dir = False
     if tabname == "txt2img":
         dir_name = opts.outdir_txt2img_samples
     elif tabname == "img2img":
@@ -229,69 +281,85 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         dir_name = opts.outdir_extras_samples
     elif tabname == "saved":
         dir_name = opts.outdir_save
+    else:
+        custom_dir = True
+        dir_name = None
+
+    if not custom_dir:
+        d = dir_name.split("/")
+        dir_name = d[0]
+        for p in d[1:]:
+            dir_name = os.path.join(dir_name, p)
+        if not os.path.exists(dir_name):
+            os.makedirs(dir_name)
 
-    d = dir_name.split("/")
-    dir_name = d[0]
-    for p in d[1:]:
-        dir_name = os.path.join(dir_name, p)
-    if not os.path.exists(dir_name):
-        os.makedirs(dir_name)
-
-    with gr.Column() as page_panel:
-        with gr.Row(visible=False) as turn_page_buttons:
-            renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
-            first_page = gr.Button('First Page')
-            prev_page = gr.Button('Prev Page')
-            page_index = gr.Number(value=1, label="Page Index")
-            next_page = gr.Button('Next Page')
-            end_page = gr.Button('End Page')
-
-        with gr.Row(elem_id=tabname + "_images_history"):
-            with gr.Column(scale=2):
-                with gr.Row():
-                    newest = gr.Button('Reload', elem_id=tabname + "_images_history_start")                    
-                    date_from = gr.Textbox(label="Date from", interactive=False)  
-                    date_to = gr.Dropdown(label="Date to")                  
-
-                history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
-                with gr.Row():
-                    delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
-                    delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
-                    
-            with gr.Column():
-                with gr.Row():
-                    if tabname != "saved":
-                        save_btn = gr.Button('Save')
-                    pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                    pnginfo_send_to_img2img = gr.Button('Send to img2img')
-                with gr.Row():
-                    with gr.Column():
-                        img_file_info = gr.Textbox(label="Generate Info", interactive=False)
-                        img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
+    with gr.Column() as page_panel:            
+            with gr.Row():  
+                img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory") 
+            with gr.Row(visible=False) as warning:                 
+                warning_box = gr.Textbox("Message", interactive=False)                        
 
+            with gr.Row(visible=not custom_dir, elem_id=tabname + "_images_history") as main_panel:
+                with gr.Column(scale=2):                   
+                    with gr.Row():                    
+                        backward = gr.Button('Backward')         
+                        date_to = gr.Dropdown(label="Date to") 
+                        forward = gr.Button('Forward')  
+                        newest = gr.Button('Reload', elem_id=tabname + "_images_history_start")   
+                    with gr.Row():
+                        load_info = gr.Textbox(show_label=False, interactive=False)  
+                    with gr.Row(visible=False) as turn_page_buttons:
+                        renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
+                        first_page = gr.Button('First Page')
+                        prev_page = gr.Button('Prev Page')
+                        page_index = gr.Number(value=1, label="Page Index")
+                        next_page = gr.Button('Next Page')
+                        end_page = gr.Button('End Page')                 
+
+                    history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=opts.images_history_grid_num)
+                    with gr.Row():
+                        delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
+                        delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
                         
-                    # hiden items
-                    with gr.Row(visible=False):   
-                        visible_img_num = gr.Number()                     
-                        img_path = gr.Textbox(dir_name)
-                        tabname_box = gr.Textbox(tabname)
-                        image_index = gr.Textbox(value=-1)
-                        set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
-                        filenames = gr.State()
-                        all_images_list = gr.State()
-                        hidden = gr.Image(type="pil")
-                        info1 = gr.Textbox()
-                        info2 = gr.Textbox()
+                with gr.Column():
+                    with gr.Row():
+                        if tabname != "saved":
+                            save_btn = gr.Button('Save')
+                        pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
+                        pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                    with gr.Row():
+                        with gr.Column():
+                            img_file_info = gr.Textbox(label="Generate Info", interactive=False)
+                            img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
+                            img_file_time= gr.Textbox(value="", label="Create Time", interactive=False)
 
-    
+                            
+                        # hiden items
+                        with gr.Row(): #visible=False):   
+                            visible_img_num = gr.Number()                     
+                            date_to_recorder = gr.State([])
+                            last_date_from = gr.Textbox()
+                            tabname_box = gr.Textbox(tabname)
+                            image_index = gr.Textbox(value=-1)
+                            set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
+                            filenames = gr.State()
+                            all_images_list = gr.State()
+                            hidden = gr.Image(type="pil")
+                            info1 = gr.Textbox()
+                            info2 = gr.Textbox()
+
+    img_path.submit(change_dir, inputs=[img_path, date_to], outputs=[warning, main_panel, warning_box, date_to])
     #change date
-    change_date_output = [date_to, date_from, filenames, page_index, history_gallery, img_file_name, visible_img_num]  
-    newest.click(newest_click, inputs=[img_path, date_to], outputs=change_date_output)
-    date_to.change(archive_images, inputs=[img_path, date_to], outputs=change_date_output)     
-    newest.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])
-    newest.click(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])
+    change_date_output = [date_to, load_info, filenames, page_index, history_gallery, img_file_name, img_file_time, visible_img_num, last_date_from]  
+   
+    date_to.change(archive_images, inputs=[img_path, date_to], outputs=change_date_output)   
+    date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])  
+    date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")  
+
+    newest.click(newest_click, inputs=[date_to], outputs=[date_to, date_to_recorder])
+    forward.click(forward_click, inputs=[last_date_from, date_to_recorder], outputs=[date_to, date_to_recorder])
+    backward.click(backward_click, inputs=[last_date_from, date_to_recorder], outputs=[date_to, date_to_recorder])
+
 
     #delete
     delete.click(delete_image, inputs=[delete_num, img_file_name, filenames, image_index, visible_img_num], outputs=[filenames, delete_num, visible_img_num])
@@ -301,7 +369,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
 
     #turn page
     gallery_inputs = [page_index, filenames]
-    gallery_outputs = [page_index, history_gallery, img_file_name, visible_img_num]
+    gallery_outputs = [page_index, history_gallery, img_file_name, img_file_time, visible_img_num]
     first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
     next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
     prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
@@ -317,12 +385,14 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     renew_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
 
     # other funcitons
-    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, image_index, hidden])
+    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, img_file_time, image_index, hidden])
     img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)   
     hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
     switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
     switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
 
+   
+
 def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict):
     global opts;
     opts = sys_opts
@@ -330,10 +400,11 @@ def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict):
     num_of_imgs_per_page = int(opts.images_history_num_per_page * opts.images_history_pages_num)
     with gr.Blocks(analytics_enabled=False) as images_history:
         with gr.Tabs() as tabs:
-            for tab in ["txt2img", "img2img", "extras", "saved"]:
+            for tab in [browser_tabname, "txt2img", "img2img", "extras", "saved"]:
                 with gr.Tab(tab):
-                    with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+                    with gr.Blocks(analytics_enabled=False) :
                         show_images_history(gr, opts, tab, run_pnginfo, switch_dict) 
-        gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory", visible=False)
+        #gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory", visible=False)
+        gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_finish_render", visible=False)
     
     return images_history
diff --git a/modules/shared.py b/modules/shared.py
index c2ea4186..1811018d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -309,10 +309,11 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
-options_templates.update(options_section(('images-history', "Images history"), {
-    "images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"),
+options_templates.update(options_section(('images-history', "Images Browser"), {
+    #"images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"),
     "images_history_num_per_page": OptionInfo(36, "Number of pictures displayed on each page"),
-    "images_history_pages_num": OptionInfo(6, "Maximum number of pages per load "),   
+    "images_history_pages_num": OptionInfo(6, "Minimum number of pages per load "),   
+    "images_history_grid_num": OptionInfo(6, "Number of grids in each row"),   
 
 }))
 
diff --git a/modules/ui.py b/modules/ui.py
index 43dc88fc..85abac4d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1548,7 +1548,7 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        (images_history, "History", "images_history"),
+        (images_history, "Image Browser", "images_history"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
         (settings_interface, "Settings", "settings"),
diff --git a/uitest.bat b/uitest.bat
new file mode 100644
index 00000000..ae863af6
--- /dev/null
+++ b/uitest.bat
@@ -0,0 +1,2 @@
+venv\Scripts\python.exe uitest.py
+pause
diff --git a/uitest.py b/uitest.py
new file mode 100644
index 00000000..393e2d81
--- /dev/null
+++ b/uitest.py
@@ -0,0 +1,124 @@
+import os
+import threading
+import time
+import importlib
+import signal
+import threading
+
+from modules.paths import script_path
+
+from modules import devices, sd_samplers
+import modules.codeformer_model as codeformer
+import modules.extras
+import modules.face_restoration
+import modules.gfpgan_model as gfpgan
+import modules.img2img
+
+import modules.lowvram
+import modules.paths
+import modules.scripts
+import modules.sd_hijack
+import modules.sd_models
+import modules.shared as shared
+import modules.txt2img
+
+import modules.ui
+from modules import devices
+from modules import modelloader
+from modules.paths import script_path
+from modules.shared import cmd_opts
+
+modelloader.cleanup_models()
+modules.sd_models.setup_model()
+codeformer.setup_model(cmd_opts.codeformer_models_path)
+gfpgan.setup_model(cmd_opts.gfpgan_models_path)
+shared.face_restorers.append(modules.face_restoration.FaceRestoration())
+modelloader.load_upscalers()
+queue_lock = threading.Lock()
+
+
+def wrap_queued_call(func):
+    def f(*args, **kwargs):
+        with queue_lock:
+            res = func(*args, **kwargs)
+
+        return res
+
+    return f
+
+
+def wrap_gradio_gpu_call(func, extra_outputs=None):
+    def f(*args, **kwargs):
+        devices.torch_gc()
+
+        shared.state.sampling_step = 0
+        shared.state.job_count = -1
+        shared.state.job_no = 0
+        shared.state.job_timestamp = shared.state.get_job_timestamp()
+        shared.state.current_latent = None
+        shared.state.current_image = None
+        shared.state.current_image_sampling_step = 0
+        shared.state.interrupted = False
+        shared.state.textinfo = None
+
+        with queue_lock:
+            res = func(*args, **kwargs)
+
+        shared.state.job = ""
+        shared.state.job_count = 0
+
+        devices.torch_gc()
+
+        return res
+
+    return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
+
+
+modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
+
+shared.sd_model = None #modules.sd_models.load_model()
+#shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
+
+
+def webui():
+    # make the program just exit at ctrl+c without waiting for anything
+    def sigint_handler(sig, frame):
+        print(f'Interrupted with signal {sig} in {frame}')
+        os._exit(0)
+
+    signal.signal(signal.SIGINT, sigint_handler)
+
+    while 1:
+
+        demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
+        
+        demo.launch(
+            share=cmd_opts.share,
+            server_name="0.0.0.0" if cmd_opts.listen else None,
+            server_port=cmd_opts.port,
+            debug=cmd_opts.gradio_debug,
+            auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
+            inbrowser=cmd_opts.autolaunch,
+            prevent_thread_lock=True
+        )
+
+        while 1:
+            time.sleep(0.5)
+            if getattr(demo, 'do_restart', False):
+                time.sleep(0.5)
+                demo.close()
+                time.sleep(0.5)
+                break
+
+        sd_samplers.set_samplers()
+
+        print('Reloading Custom Scripts')
+        modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
+        print('Reloading modules: modules.ui')
+        importlib.reload(modules.ui)
+        print('Restarting Gradio')
+
+
+
+if __name__ == "__main__":
+    webui()
\ No newline at end of file
-- 
cgit v1.2.1


From c6f778d9b19d7116ffb82718f6ca0b867e2f4445 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Tue, 18 Oct 2022 20:15:08 +0800
Subject: Image browser

---
 uitest.bat |   2 -
 uitest.py  | 124 -------------------------------------------------------------
 2 files changed, 126 deletions(-)
 delete mode 100644 uitest.bat
 delete mode 100644 uitest.py

diff --git a/uitest.bat b/uitest.bat
deleted file mode 100644
index ae863af6..00000000
--- a/uitest.bat
+++ /dev/null
@@ -1,2 +0,0 @@
-venv\Scripts\python.exe uitest.py
-pause
diff --git a/uitest.py b/uitest.py
deleted file mode 100644
index 393e2d81..00000000
--- a/uitest.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import os
-import threading
-import time
-import importlib
-import signal
-import threading
-
-from modules.paths import script_path
-
-from modules import devices, sd_samplers
-import modules.codeformer_model as codeformer
-import modules.extras
-import modules.face_restoration
-import modules.gfpgan_model as gfpgan
-import modules.img2img
-
-import modules.lowvram
-import modules.paths
-import modules.scripts
-import modules.sd_hijack
-import modules.sd_models
-import modules.shared as shared
-import modules.txt2img
-
-import modules.ui
-from modules import devices
-from modules import modelloader
-from modules.paths import script_path
-from modules.shared import cmd_opts
-
-modelloader.cleanup_models()
-modules.sd_models.setup_model()
-codeformer.setup_model(cmd_opts.codeformer_models_path)
-gfpgan.setup_model(cmd_opts.gfpgan_models_path)
-shared.face_restorers.append(modules.face_restoration.FaceRestoration())
-modelloader.load_upscalers()
-queue_lock = threading.Lock()
-
-
-def wrap_queued_call(func):
-    def f(*args, **kwargs):
-        with queue_lock:
-            res = func(*args, **kwargs)
-
-        return res
-
-    return f
-
-
-def wrap_gradio_gpu_call(func, extra_outputs=None):
-    def f(*args, **kwargs):
-        devices.torch_gc()
-
-        shared.state.sampling_step = 0
-        shared.state.job_count = -1
-        shared.state.job_no = 0
-        shared.state.job_timestamp = shared.state.get_job_timestamp()
-        shared.state.current_latent = None
-        shared.state.current_image = None
-        shared.state.current_image_sampling_step = 0
-        shared.state.interrupted = False
-        shared.state.textinfo = None
-
-        with queue_lock:
-            res = func(*args, **kwargs)
-
-        shared.state.job = ""
-        shared.state.job_count = 0
-
-        devices.torch_gc()
-
-        return res
-
-    return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
-
-
-modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
-
-shared.sd_model = None #modules.sd_models.load_model()
-#shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
-
-
-def webui():
-    # make the program just exit at ctrl+c without waiting for anything
-    def sigint_handler(sig, frame):
-        print(f'Interrupted with signal {sig} in {frame}')
-        os._exit(0)
-
-    signal.signal(signal.SIGINT, sigint_handler)
-
-    while 1:
-
-        demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
-        
-        demo.launch(
-            share=cmd_opts.share,
-            server_name="0.0.0.0" if cmd_opts.listen else None,
-            server_port=cmd_opts.port,
-            debug=cmd_opts.gradio_debug,
-            auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
-            inbrowser=cmd_opts.autolaunch,
-            prevent_thread_lock=True
-        )
-
-        while 1:
-            time.sleep(0.5)
-            if getattr(demo, 'do_restart', False):
-                time.sleep(0.5)
-                demo.close()
-                time.sleep(0.5)
-                break
-
-        sd_samplers.set_samplers()
-
-        print('Reloading Custom Scripts')
-        modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
-        print('Reloading modules: modules.ui')
-        importlib.reload(modules.ui)
-        print('Restarting Gradio')
-
-
-
-if __name__ == "__main__":
-    webui()
\ No newline at end of file
-- 
cgit v1.2.1


From 433a7525c1f5eb5963340e0cc45d31038ede3f7e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 18 Oct 2022 15:18:02 +0300
Subject: remove shared option for update check (because it is not an argument
 of webui) have launch.py examine both COMMANDLINE_ARGS as well as argv for
 its arguments

---
 launch.py         | 19 +++++++++----------
 modules/shared.py |  1 -
 2 files changed, 9 insertions(+), 11 deletions(-)

diff --git a/launch.py b/launch.py
index 9d9dd1b6..7b15e78e 100644
--- a/launch.py
+++ b/launch.py
@@ -127,13 +127,14 @@ def prepare_enviroment():
     codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
-    args = shlex.split(commandline_args)
+    sys.argv += shlex.split(commandline_args)
 
-    args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
-    args, reinstall_xformers = extract_arg(args, '--reinstall-xformers')
-    xformers = '--xformers' in args
-    deepdanbooru = '--deepdanbooru' in args
-    ngrok = '--ngrok' in args
+    sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
+    sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
+    sys.argv, update_check = extract_arg(sys.argv, '--update-check')
+    xformers = '--xformers' in sys.argv
+    deepdanbooru = '--deepdanbooru' in sys.argv
+    ngrok = '--ngrok' in sys.argv
 
     try:
         commit = run(f"{git} rev-parse HEAD").strip()
@@ -180,12 +181,10 @@ def prepare_enviroment():
 
     run_pip(f"install -r {requirements_file}", "requirements for Web UI")
 
-    sys.argv += args
-
-    if '--update-check' in args:
+    if update_check:
         version_check(commit)
     
-    if "--exit" in args:
+    if "--exit" in sys.argv:
         print("Exiting because of --exit argument")
         exit(0)
 
diff --git a/modules/shared.py b/modules/shared.py
index 50dc46ae..c0d87168 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -76,7 +76,6 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-parser.add_argument("--update-check", action='store_true', help="enable http check to confirm that the currently running version is the most recent release.", default=False)
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
-- 
cgit v1.2.1


From 2f448d97a9427f9a7bad19cf608561b2878ab2da Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Mon, 17 Oct 2022 23:18:21 +0900
Subject: styles.csv encoding utf8 to utf-8-sig

utf-8-bom for better compatibility for some programs
---
 modules/styles.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/styles.py b/modules/styles.py
index d44dfc1a..3bf5c5b6 100644
--- a/modules/styles.py
+++ b/modules/styles.py
@@ -45,7 +45,7 @@ class StyleDatabase:
         if not os.path.exists(path):
             return
 
-        with open(path, "r", encoding="utf8", newline='') as file:
+        with open(path, "r", encoding="utf-8-sig", newline='') as file:
             reader = csv.DictReader(file)
             for row in reader:
                 # Support loading old CSV format with "name, text"-columns
@@ -79,7 +79,7 @@ class StyleDatabase:
     def save_styles(self, path: str) -> None:
         # Write to temporary file first, so we don't nuke the file if something goes wrong
         fd, temp_path = tempfile.mkstemp(".csv")
-        with os.fdopen(fd, "w", encoding="utf8", newline='') as file:
+        with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
             # _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
             # and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
             writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
-- 
cgit v1.2.1


From e20b7e30fe17744acb74ad33c87c0963525ea921 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 18 Oct 2022 15:33:24 +0300
Subject: fix for add difference model merging

---
 modules/extras.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index c908b43e..03f6085e 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -216,8 +216,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
     if theta_func1:
         for key in tqdm.tqdm(theta_1.keys()):
             if 'model' in key:
-                t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
-                theta_1[key] = theta_func1(theta_1[key], t2)
+                if key in theta_2:
+                    t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
+                    theta_1[key] = theta_func1(theta_1[key], t2)
+                else:
+                    theta_1[key] = 0
     del theta_2, teritary_model
 
     for key in tqdm.tqdm(theta_0.keys()):
-- 
cgit v1.2.1


From 684a31c4da673961ce9e3a384132fda5d1111ab8 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Tue, 18 Oct 2022 21:50:34 +0900
Subject: update ko-KR.json

Translated all text on txt2img window, plus some extra
---
 localizations/ko-KR.json | 42 ++++++++++++++++++++++++++++++++++++++----
 1 file changed, 38 insertions(+), 4 deletions(-)

diff --git a/localizations/ko-KR.json b/localizations/ko-KR.json
index a4367dc5..c6e55bb1 100644
--- a/localizations/ko-KR.json
+++ b/localizations/ko-KR.json
@@ -4,9 +4,10 @@
   "×": "×",
   "❮": "❮",
   "❯": "❯",
-  "Loading...": "로딩중...",
-  "view": "",
-  "api": "api",
+  "Loading...": "",
+  "view": "api 보이기",
+  "hide": "api 숨기기",
+  "api": "",
   "•": "•",
   "txt2img": "텍스트→이미지",
   "img2img": "이미지→이미지",
@@ -50,7 +51,7 @@
   "Tiling": "타일링",
   "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
   "Highres. fix": "고해상도 보정",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
   "Firstpass width": "초기 가로길이",
   "Firstpass height": "초기 세로길이",
   "Denoising strength": "디노이즈 강도",
@@ -81,5 +82,38 @@
   "Send to extras": "부가기능으로 전송",
   "Open images output directory": "이미지 저장 경로 열기",
   "Make Zip when Save?": "저장 시 Zip 생성하기",
+  "Prompt matrix": "프롬프트 매트릭스",
+  "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "(|)를 이용해 프롬프트를 분리할 시 첫 프롬프트를 제외하고 모든 프롬프트의 조합마다 이미지를 생성합니다. 첫 프롬프트는 모든 조합에 포함되게 됩니다.",
+  "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
+  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
+  "Show Textbox": "텍스트박스 보이기",
+  "File with inputs": "설정값 파일",
+  "Prompts": "프롬프트",
+  "X/Y plot": "X/Y 플롯",
+  "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
+  "X type": "X축",
+  "Y type": "Y축",
+  "X values": "X 설정값",
+  "Y values": "Y 설정값",
+  "Separate values for X axis using commas.": "쉼표로 X축에 적용할 값 분리",
+  "Separate values for Y axis using commas.": "쉼표로 Y축에 적용할 값 분리",
+  "Draw legend": "범례 그리기",
+  "Include Separate Images": "분리된 이미지 포함하기",
+  "Keep -1 for seeds": "시드값 -1로 유지",
+  "Var. seed": "바리에이션 시드",
+  "Var. strength": "바리에이션 강도",
+  "Steps": "스텝 수",
+  "Prompt S/R": "프롬프트 스타일 변경",
+  "Prompt order": "프롬프트 순서",
+  "Sampler": "샘플러",
+  "Checkpoint name": "체크포인트 이름",
+  "Hypernet str.": "하이퍼네트워크 강도",
+  "Sigma Churn": "시그마 섞기",
+  "Sigma min": "시그마 최솟값",
+  "Sigma max": "시그마 최댓값",
+  "Sigma noise": "시그마 노이즈",
+  "Clip skip": "클립 건너뛰기",
+  "Denoising": "디노이징",
+  "Nothing": "없음",
   "Always save all generated images": "생성된 이미지 항상 저장하기"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From ec1924ee5789b72c31c65932b549c59ccae0cdd6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 18 Oct 2022 16:05:52 +0300
Subject: additional fix for difference model merging

---
 modules/extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/extras.py b/modules/extras.py
index 03f6085e..b853fa5b 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -220,7 +220,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
                     t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
                     theta_1[key] = theta_func1(theta_1[key], t2)
                 else:
-                    theta_1[key] = 0
+                    theta_1[key] = torch.zeros_like(theta_1[key])
     del theta_2, teritary_model
 
     for key in tqdm.tqdm(theta_0.keys()):
-- 
cgit v1.2.1


From 4f4e7fed7e4910b165c651e7618eb8e47c57ddb5 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Tue, 18 Oct 2022 22:12:41 +0900
Subject: update ko-KR.json

---
 localizations/ko-KR.json | 1 +
 1 file changed, 1 insertion(+)

diff --git a/localizations/ko-KR.json b/localizations/ko-KR.json
index c6e55bb1..b263b13c 100644
--- a/localizations/ko-KR.json
+++ b/localizations/ko-KR.json
@@ -115,5 +115,6 @@
   "Clip skip": "클립 건너뛰기",
   "Denoising": "디노이징",
   "Nothing": "없음",
+  "Apply settings": "설정 적용하기",
   "Always save all generated images": "생성된 이미지 항상 저장하기"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From b7e78ef692fe912916de6e54f6e2521b000d650c Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Tue, 18 Oct 2022 22:21:54 +0800
Subject: Image browser improve

---
 modules/images_history.py | 43 ++++++++++++++++++++++---------------------
 1 file changed, 22 insertions(+), 21 deletions(-)

diff --git a/modules/images_history.py b/modules/images_history.py
index d56f3a25..a40cdc0e 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -100,14 +100,15 @@ def auto_sorting(dir_name):
         date_list.append(today)
     return sorted(date_list, reverse=True)
 
-def archive_images(dir_name, date_to):
-    
+def archive_images(dir_name, date_to):    
     filenames = []   
-    loads_num =int(opts.images_history_num_per_page * opts.images_history_pages_num)
+    batch_size =int(opts.images_history_num_per_page * opts.images_history_pages_num)
+    if batch_size <= 0:
+        batch_size = opts.images_history_num_per_page * 6
     today = time.strftime("%Y%m%d",time.localtime(time.time()))
     date_to = today if date_to is None  or date_to == "" else date_to
     date_to_bak = date_to 
-    if opts.images_history_reconstruct_directory:        
+    if False: #opts.images_history_reconstruct_directory:        
         date_list = auto_sorting(dir_name)               
         for date in date_list:
             if date <= date_to:
@@ -115,11 +116,13 @@ def archive_images(dir_name, date_to):
                 if date == today and not os.path.exists(path):
                     continue
                 filenames = traverse_all_files(path, filenames)
-            if len(filenames) > loads_num:            
+            if len(filenames) > batch_size:            
                 break
         filenames = sorted(filenames, key=lambda file: -os.path.getmtime(file))
     else:
-        filenames = traverse_all_files(dir_name, filenames)       
+        filenames = traverse_all_files(dir_name, filenames)  
+        total_num = len(filenames) 
+        batch_count = len(filenames) + 1 //  batch_size + 1
         tmparray = [(os.path.getmtime(file), file) for file in filenames ]
         date_stamp = time.mktime(time.strptime(date_to, "%Y%m%d")) + 86400      
         filenames = []
@@ -132,8 +135,8 @@ def archive_images(dir_name, date_to):
                 filenames.append((t, f ,date))
         date_list = sorted(list(date_list.keys()), reverse=True)
         sort_array = sorted(filenames, key=lambda x:-x[0])
-        if len(sort_array) > loads_num:
-            date = sort_array[loads_num][2]
+        if len(sort_array) > batch_size:
+            date = sort_array[batch_size][2]
             filenames = [x[1] for x in sort_array]
         else:
             date =  date_to if len(sort_array) == 0 else sort_array[-1][2]
@@ -141,9 +144,9 @@ def archive_images(dir_name, date_to):
         filenames = [x[1] for x in sort_array if x[2]>= date]   
     num = len(filenames)  
     last_date_from = date_to_bak if num == 0 else time.strftime("%Y%m%d", time.localtime(time.mktime(time.strptime(date, "%Y%m%d")) - 1000))
-    date = date[:4] + "-" + date[4:6] + "-" + date[6:8]
-    date_to_bak = date_to_bak[:4] + "-" + date_to_bak[4:6] + "-" + date_to_bak[6:8]
-    load_info = f"Loaded {(num + 1) // opts.images_history_pages_num} pades, {num} images, during {date} - {date_to_bak}"
+    date = date[:4] + "/" + date[4:6] + "/" + date[6:8]
+    date_to_bak = date_to_bak[:4] + "/" + date_to_bak[4:6] + "/" + date_to_bak[6:8]
+    load_info = f"{total_num} images in this directory. Loaded {num} images during {date} - {date_to_bak}, divided into {int((num + 1) // opts.images_history_num_per_page + 1)} pages"
     _, image_list, _, _, visible_num = get_recent_images(1, 0, filenames)
     return (
         gradio.Dropdown.update(choices=date_list, value=date_to),
@@ -154,12 +157,10 @@ def archive_images(dir_name, date_to):
         "",
         "",
         visible_num, 
-        last_date_from
+        last_date_from, 
+        #gradio.update(visible=batch_count > 1)
     )
 
-
-
-
 def delete_image(delete_num, name, filenames, image_index, visible_num):
     if name == "":
         return filenames, delete_num
@@ -295,16 +296,16 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
 
     with gr.Column() as page_panel:            
             with gr.Row():  
-                img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory") 
+                img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory", interactive=custom_dir) 
             with gr.Row(visible=False) as warning:                 
                 warning_box = gr.Textbox("Message", interactive=False)                        
 
             with gr.Row(visible=not custom_dir, elem_id=tabname + "_images_history") as main_panel:
                 with gr.Column(scale=2):                   
-                    with gr.Row():                    
-                        backward = gr.Button('Backward')         
-                        date_to = gr.Dropdown(label="Date to") 
-                        forward = gr.Button('Forward')  
+                    with gr.Row() as batch_panel:                    
+                        forward = gr.Button('Forward')        
+                        date_to = gr.Dropdown(label="Date to")
+                        backward = gr.Button('Backward')                           
                         newest = gr.Button('Reload', elem_id=tabname + "_images_history_start")   
                     with gr.Row():
                         load_info = gr.Textbox(show_label=False, interactive=False)  
@@ -335,7 +336,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
 
                             
                         # hiden items
-                        with gr.Row(): #visible=False):   
+                        with gr.Row(visible=False):   
                             visible_img_num = gr.Number()                     
                             date_to_recorder = gr.State([])
                             last_date_from = gr.Textbox()
-- 
cgit v1.2.1


From cbf15edbf90a68a08eeab40af5df577ba4ac90b6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 18 Oct 2022 17:23:38 +0300
Subject: remove dependence on TQDM for sampler progress/interrupt
 functionality

---
 modules/processing.py  |   6 ---
 modules/sd_samplers.py | 107 +++++++++++++++++++++++++++----------------------
 2 files changed, 58 insertions(+), 55 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index deb6125e..346eea88 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -402,12 +402,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             with devices.autocast():
                 samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
 
-            if state.interrupted or state.skipped:
-
-                # if we are interrupted, sample returns just noise
-                # use the image collected previously in sampler loop
-                samples_ddim = shared.state.current_latent
-
             samples_ddim = samples_ddim.to(devices.dtype_vae)
             x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 20309e06..b58e810b 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -98,25 +98,8 @@ def store_latent(decoded):
             shared.state.current_image = sample_to_image(decoded)
 
 
-
-def extended_tdqm(sequence, *args, desc=None, **kwargs):
-    state.sampling_steps = len(sequence)
-    state.sampling_step = 0
-
-    seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
-
-    for x in seq:
-        if state.interrupted or state.skipped:
-            break
-
-        yield x
-
-        state.sampling_step += 1
-        shared.total_tqdm.update()
-
-
-ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
-ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
+class InterruptedException(BaseException):
+    pass
 
 
 class VanillaStableDiffusionSampler:
@@ -128,14 +111,32 @@ class VanillaStableDiffusionSampler:
         self.init_latent = None
         self.sampler_noises = None
         self.step = 0
+        self.stop_at = None
         self.eta = None
         self.default_eta = 0.0
         self.config = None
+        self.last_latent = None
 
     def number_of_needed_noises(self, p):
         return 0
 
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except InterruptedException:
+            return self.last_latent
+
     def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
+        if state.interrupted or state.skipped:
+            raise InterruptedException
+
+        if self.stop_at is not None and self.step > self.stop_at:
+            raise InterruptedException
+
+
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
 
@@ -159,11 +160,16 @@ class VanillaStableDiffusionSampler:
         res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
 
         if self.mask is not None:
-            store_latent(self.init_latent * self.mask + self.nmask * res[1])
+            self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
         else:
-            store_latent(res[1])
+            self.last_latent = res[1]
+
+        store_latent(self.last_latent)
 
         self.step += 1
+        state.sampling_step = self.step
+        shared.total_tqdm.update()
+
         return res
 
     def initialize(self, p):
@@ -192,7 +198,7 @@ class VanillaStableDiffusionSampler:
         self.init_latent = x
         self.step = 0
 
-        samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
+        samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
 
         return samples
 
@@ -206,9 +212,9 @@ class VanillaStableDiffusionSampler:
 
         # existing code fails with certain step counts, like 9
         try:
-            samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
+            samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
         except Exception:
-            samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
+            samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
 
         return samples_ddim
 
@@ -223,6 +229,9 @@ class CFGDenoiser(torch.nn.Module):
         self.step = 0
 
     def forward(self, x, sigma, uncond, cond, cond_scale):
+        if state.interrupted or state.skipped:
+            raise InterruptedException
+
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
         uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
 
@@ -268,25 +277,6 @@ class CFGDenoiser(torch.nn.Module):
         return denoised
 
 
-def extended_trange(sampler, count, *args, **kwargs):
-    state.sampling_steps = count
-    state.sampling_step = 0
-
-    seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
-
-    for x in seq:
-        if state.interrupted or state.skipped:
-            break
-
-        if sampler.stop_at is not None and x > sampler.stop_at:
-            break
-
-        yield x
-
-        state.sampling_step += 1
-        shared.total_tqdm.update()
-
-
 class TorchHijack:
     def __init__(self, kdiff_sampler):
         self.kdiff_sampler = kdiff_sampler
@@ -314,9 +304,28 @@ class KDiffusionSampler:
         self.eta = None
         self.default_eta = 1.0
         self.config = None
+        self.last_latent = None
 
     def callback_state(self, d):
-        store_latent(d["denoised"])
+        step = d['i']
+        latent = d["denoised"]
+        store_latent(latent)
+        self.last_latent = latent
+
+        if self.stop_at is not None and step > self.stop_at:
+            raise InterruptedException
+
+        state.sampling_step = step
+        shared.total_tqdm.update()
+
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except InterruptedException:
+            return self.last_latent
 
     def number_of_needed_noises(self, p):
         return p.steps
@@ -339,9 +348,6 @@ class KDiffusionSampler:
         self.sampler_noise_index = 0
         self.eta = p.eta or opts.eta_ancestral
 
-        if hasattr(k_diffusion.sampling, 'trange'):
-            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
-
         if self.sampler_noises is not None:
             k_diffusion.sampling.torch = TorchHijack(self)
 
@@ -383,8 +389,9 @@ class KDiffusionSampler:
 
         self.model_wrap_cfg.init_latent = x
 
-        return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
+        return samples
 
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
         steps = steps or p.steps
@@ -406,6 +413,8 @@ class KDiffusionSampler:
                 extra_params_kwargs['n'] = steps
         else:
             extra_params_kwargs['sigmas'] = sigmas
-        samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
+
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
         return samples
 
-- 
cgit v1.2.1


From bdf1a8903a38e6c29afdbfb6370c40976cdfd99a Mon Sep 17 00:00:00 2001
From: Matthew Clark <linux.matthew@gmail.com>
Date: Tue, 4 Oct 2022 13:44:21 -0400
Subject: Pass arguments from bash to python

---
 webui.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/webui.sh b/webui.sh
index 980c0aaf..a9f85d89 100755
--- a/webui.sh
+++ b/webui.sh
@@ -138,4 +138,4 @@ fi
 printf "\n%s\n" "${delimiter}"
 printf "Launching launch.py..."
 printf "\n%s\n" "${delimiter}"
-"${python_cmd}" "${LAUNCH_SCRIPT}"
+"${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
-- 
cgit v1.2.1


From 82589c2d5e97491b8fc741217800598ba07e2c46 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 10 Oct 2022 01:52:35 +0100
Subject: add windows equivalent

---
 webui.bat | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/webui.bat b/webui.bat
index 3f1d03f6..a38a28bb 100644
--- a/webui.bat
+++ b/webui.bat
@@ -33,7 +33,7 @@ goto :launch
 :skip_venv
 
 :launch
-%PYTHON% launch.py
+%PYTHON% launch.py %*
 pause
 exit /b
 
-- 
cgit v1.2.1


From b76c9ded4556efa8fa19bcc3e9f442dd33ed9daa Mon Sep 17 00:00:00 2001
From: supersteve3d <39339941+supersteve3d@users.noreply.github.com>
Date: Tue, 18 Oct 2022 20:14:49 +0800
Subject: Update artists.csv

---
 artists.csv | 2 --
 1 file changed, 2 deletions(-)

diff --git a/artists.csv b/artists.csv
index 858dfcd6..c54de486 100644
--- a/artists.csv
+++ b/artists.csv
@@ -523,7 +523,6 @@ Affandi,0.7170285,nudity
 Diane Arbus,0.655138,digipa-high-impact
 Joseph Ducreux,0.65247905,digipa-high-impact
 Berthe Morisot,0.7165984,fineart
-Hilma AF Klint,0.71643853,scribbles
 Hilma af Klint,0.71643853,scribbles
 Filippino Lippi,0.7163017,fineart
 Leonid Afremov,0.7163005,fineart
@@ -1521,7 +1520,6 @@ Gertrude Harvey,0.5903887,fineart
 Grant Wood,0.6266253,fineart
 Fyodor Vasilyev,0.5234919,digipa-med-impact
 Cagnaccio di San Pietro,0.6261671,fineart
-Cagnaccio Di San Pietro,0.6261671,fineart
 Doris Boulton-Maude,0.62593174,fineart
 Adolf Hirémy-Hirschl,0.5946784,fineart
 Harold von Schmidt,0.6256755,fineart
-- 
cgit v1.2.1


From c1093b8051606f0ac90506b7114c4b55d0447c70 Mon Sep 17 00:00:00 2001
From: supersteve3d <39339941+supersteve3d@users.noreply.github.com>
Date: Tue, 18 Oct 2022 20:19:12 +0800
Subject: Update artists.csv

---
 artists.csv | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/artists.csv b/artists.csv
index c54de486..1a61ed88 100644
--- a/artists.csv
+++ b/artists.csv
@@ -743,7 +743,6 @@ Judy Chicago,0.6952246,weird
 Frans van Mieris the Younger,0.6951849,fineart
 Aertgen van Leyden,0.6951305,fineart
 Emily Carr,0.69512105,fineart
-Frances Macdonald,0.6950408,scribbles
 Frances MacDonald,0.6950408,scribbles
 Hannah Höch,0.69495845,scribbles
 Gillis Rombouts,0.58770025,fineart
@@ -893,7 +892,6 @@ Richard McGuire,0.6820089,scribbles
 Anni Albers,0.65708244,digipa-high-impact
 Aleksey Savrasov,0.65207493,fineart
 Wayne Barlowe,0.6537874,fineart
-Giorgio De Chirico,0.6815907,fineart
 Giorgio de Chirico,0.6815907,fineart
 Ernest Procter,0.6815795,fineart
 Adriaen Brouwer,0.6815058,fineart
@@ -1239,7 +1237,6 @@ Betty Churcher,0.65387225,fineart
 Claes Corneliszoon Moeyaert,0.65386075,fineart
 David Bomberg,0.6537477,fineart
 Abraham Bosschaert,0.6535562,fineart
-Giuseppe De Nittis,0.65354455,fineart
 Giuseppe de Nittis,0.65354455,fineart
 John La Farge,0.65342575,fineart
 Frits Thaulow,0.65341854,fineart
-- 
cgit v1.2.1


From 6021f7a75f7b5208a2be15cda5526028152f922d Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 00:51:36 +0900
Subject: add options to custom hypernetwork layer structure

---
 .gitignore                            |  1 +
 modules/hypernetworks/hypernetwork.py | 88 ++++++++++++++++++++++++++---------
 modules/shared.py                     |  4 +-
 webui.py                              |  6 ++-
 4 files changed, 75 insertions(+), 24 deletions(-)

diff --git a/.gitignore b/.gitignore
index 69785b3e..4794865c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,3 +27,4 @@ __pycache__
 notification.mp3
 /SwinIR
 /textual_inversion
+/hypernetwork
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 4905710e..cadb9911 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -1,52 +1,98 @@
+import csv
 import datetime
 import glob
 import html
 import os
 import sys
 import traceback
-import tqdm
-import csv
 
+import modules.textual_inversion.dataset
 import torch
-
-from ldm.util import default
-from modules import devices, shared, processing, sd_models
-import torch
-from torch import einsum
+import tqdm
 from einops import rearrange, repeat
-import modules.textual_inversion.dataset
+from ldm.util import default
+from modules import devices, processing, sd_models, shared
 from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
+from torch import einsum
+
+
+def parse_layer_structure(dim, state_dict):
+    i = 0
+    res = [1]
+    while (key := "linear.{}.weight".format(i)) in state_dict:
+        weight = state_dict[key]
+        res.append(len(weight) // dim)
+        i += 1
+    return res
 
 
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
+    layer_structure = None
+    add_layer_norm = False
 
     def __init__(self, dim, state_dict=None):
         super().__init__()
+        if (state_dict is None or 'linear.0.weight' not in state_dict) and self.layer_structure is None:
+            layer_structure = (1, 2, 1)
+        else:
+            if self.layer_structure is not None:
+                assert self.layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
+                assert self.layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
+                layer_structure = self.layer_structure
+            else:
+                layer_structure = parse_layer_structure(dim, state_dict)
+
+        linears = []
+        for i in range(len(layer_structure) - 1):
+            linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
+            if self.add_layer_norm:
+                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
-        self.linear1 = torch.nn.Linear(dim, dim * 2)
-        self.linear2 = torch.nn.Linear(dim * 2, dim)
+        self.linear = torch.nn.Sequential(*linears)
 
         if state_dict is not None:
-            self.load_state_dict(state_dict, strict=True)
+            try:
+                self.load_state_dict(state_dict)
+            except RuntimeError:
+                self.try_load_previous(state_dict)
         else:
-
-            self.linear1.weight.data.normal_(mean=0.0, std=0.01)
-            self.linear1.bias.data.zero_()
-            self.linear2.weight.data.normal_(mean=0.0, std=0.01)
-            self.linear2.bias.data.zero_()
+            for layer in self.linear:
+                layer.weight.data.normal_(mean = 0.0, std = 0.01)
+                layer.bias.data.zero_()
 
         self.to(devices.device)
 
+    def try_load_previous(self, state_dict):
+        states = self.state_dict()
+        states['linear.0.bias'].copy_(state_dict['linear1.bias'])
+        states['linear.0.weight'].copy_(state_dict['linear1.weight'])
+        states['linear.1.bias'].copy_(state_dict['linear2.bias'])
+        states['linear.1.weight'].copy_(state_dict['linear2.weight'])
+
     def forward(self, x):
-        return x + (self.linear2(self.linear1(x))) * self.multiplier
+        return x + self.linear(x) * self.multiplier
+
+    def trainables(self):
+        res = []
+        for layer in self.linear:
+            res += [layer.weight, layer.bias]
+        return res
 
 
 def apply_strength(value=None):
     HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
 
 
+def apply_layer_structure(value=None):
+    HypernetworkModule.layer_structure = value if value is not None else shared.opts.sd_hypernetwork_layer_structure
+
+
+def apply_layer_norm(value=None):
+    HypernetworkModule.add_layer_norm = value if value is not None else shared.opts.sd_hypernetwork_add_layer_norm
+
+
 class Hypernetwork:
     filename = None
     name = None
@@ -68,7 +114,7 @@ class Hypernetwork:
         for k, layers in self.layers.items():
             for layer in layers:
                 layer.train()
-                res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]
+                res += layer.trainables()
 
         return res
 
@@ -226,7 +272,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
         ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
-
+        assert ds.length > 1, "Dataset should contain more than 1 images"
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
         shared.sd_model.first_stage_model.to(devices.cpu)
@@ -261,7 +307,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         with torch.autocast("cuda"):
             c = stack_conds([entry.cond for entry in entries]).to(devices.device)
-#            c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
+            c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
             x = torch.stack([entry.latent for entry in entries]).to(devices.device)
             loss = shared.sd_model(x, c)[0]
             del x
@@ -283,7 +329,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{mean_loss:.7f}",
-            "learn_rate": scheduler.learn_rate
+            "learn_rate": f"{scheduler.learn_rate:.7f}"
         })
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
diff --git a/modules/shared.py b/modules/shared.py
index c0d87168..c87ce70e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models, localization
+from modules import sd_models, sd_samplers, localization
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -258,6 +258,8 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+    "sd_hypernetwork_layer_structure": OptionInfo(None, "Hypernetwork layer structure Default: (1,2,1).", gr.Dropdown, lambda: {"choices": [(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)]}),
+    "sd_hypernetwork_add_layer_norm": OptionInfo(False, "Add layer normalization to hypernetwork architecture."),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
diff --git a/webui.py b/webui.py
index fe0ce321..86e98ad0 100644
--- a/webui.py
+++ b/webui.py
@@ -86,11 +86,13 @@ def initialize():
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
+    shared.opts.onchange("sd_hypernetwork_layer_structure", modules.hypernetworks.hypernetwork.apply_layer_structure)
+    shared.opts.onchange("sd_hypernetwork_add_layer_norm", modules.hypernetworks.hypernetwork.apply_layer_norm)
 
 
 def webui():
     initialize()
-    
+
     # make the program just exit at ctrl+c without waiting for anything
     def sigint_handler(sig, frame):
         print(f'Interrupted with signal {sig} in {frame}')
@@ -101,7 +103,7 @@ def webui():
     while 1:
 
         demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
-        
+
         app, local_url, share_url = demo.launch(
             share=cmd_opts.share,
             server_name="0.0.0.0" if cmd_opts.listen else None,
-- 
cgit v1.2.1


From a5611ea5026bd8e12d8e84023384c369d0511dda Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 01:00:01 +0900
Subject: update

---
 .gitignore                            |  1 -
 modules/hypernetworks/hypernetwork.py | 14 ++++++++------
 2 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/.gitignore b/.gitignore
index 4794865c..69785b3e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,4 +27,3 @@ __pycache__
 notification.mp3
 /SwinIR
 /textual_inversion
-/hypernetwork
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index cadb9911..c5835bce 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -1,20 +1,22 @@
-import csv
 import datetime
 import glob
 import html
 import os
 import sys
 import traceback
+import tqdm
+import csv
 
-import modules.textual_inversion.dataset
 import torch
-import tqdm
-from einops import rearrange, repeat
+
 from ldm.util import default
-from modules import devices, processing, sd_models, shared
+from modules import devices, shared, processing, sd_models
+import torch
+from torch import einsum
+from einops import rearrange, repeat
+import modules.textual_inversion.dataset
 from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
-from torch import einsum
 
 
 def parse_layer_structure(dim, state_dict):
-- 
cgit v1.2.1


From 7f2095c6c8db82a5c9cd7c7177f6ba856a2cc676 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 01:01:22 +0900
Subject: update

---
 modules/shared.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index c87ce70e..6b6d5c41 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -13,7 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_models, sd_samplers, localization
+from modules import sd_samplers, sd_models, localization
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -135,7 +135,7 @@ class State:
         self.job_no += 1
         self.sampling_step = 0
         self.current_image_sampling_step = 0
-        
+
     def get_job_timestamp(self):
         return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?
 
-- 
cgit v1.2.1


From e40ba281f1b419cf99552962ea01d87d699840a5 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 01:03:58 +0900
Subject: update

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index c5835bce..082165f4 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -309,7 +309,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         with torch.autocast("cuda"):
             c = stack_conds([entry.cond for entry in entries]).to(devices.device)
-            c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
+            # c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
             x = torch.stack([entry.latent for entry in entries]).to(devices.device)
             loss = shared.sd_model(x, c)[0]
             del x
@@ -331,7 +331,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{mean_loss:.7f}",
-            "learn_rate": f"{scheduler.learn_rate:.7f}"
+            "learn_rate": scheduler.learn_rate
         })
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
-- 
cgit v1.2.1


From e7f4808505f7a6339927c32b9a0c01bc9134bdeb Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Tue, 18 Oct 2022 19:04:56 +0000
Subject: provide sampler by name

---
 modules/api/api.py        | 12 ++++++++++--
 modules/api/processing.py | 16 ++++++++++++++--
 2 files changed, 24 insertions(+), 4 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index ce98cb8c..ff9df0d1 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,14 +1,17 @@
 from modules.api.processing import StableDiffusionProcessingAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, process_images
+from modules.sd_samplers import samplers_k_diffusion
 import modules.shared as shared
 import uvicorn
-from fastapi import Body, APIRouter
+from fastapi import Body, APIRouter, HTTPException
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field, Json
 import json
 import io
 import base64
 
+sampler_to_index = lambda name: next(filter(lambda row: name in row[1][2], enumerate(samplers_k_diffusion)), None)
+
 class TextToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: Json
@@ -23,9 +26,14 @@ class Api:
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
+        sampler_index = sampler_to_index(txt2imgreq.sampler_index)
+        
+        if sampler_index is None:
+            raise HTTPException(status_code=404, detail="Sampler not found") 
+        
         populate = txt2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model, 
-            "sampler_index": 0,
+            "sampler_index": sampler_index[0],
             "do_not_save_samples": True,
             "do_not_save_grid": True
             }
diff --git a/modules/api/processing.py b/modules/api/processing.py
index b6798241..2e6483ee 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -42,7 +42,8 @@ class PydanticModelGenerator:
     def __init__(
         self,
         model_name: str = None,
-        class_instance = None
+        class_instance = None,
+        additional_fields = None,
     ):
         def field_type_generator(k, v):
             # field_type = str if not overrides.get(k) else overrides[k]["type"]
@@ -70,6 +71,13 @@ class PydanticModelGenerator:
             )
             for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
         ]
+        
+        for fields in additional_fields:
+            self._model_def.append(ModelDef(
+                field=underscore(fields["key"]), 
+                field_alias=fields["key"], 
+                field_type=fields["type"],
+                field_value=fields["default"]))
 
     def generate_model(self):
         """
@@ -84,4 +92,8 @@ class PydanticModelGenerator:
         DynamicModel.__config__.allow_mutation = True
         return DynamicModel
     
-StableDiffusionProcessingAPI = PydanticModelGenerator("StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img).generate_model()
+StableDiffusionProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingTxt2Img", 
+    StableDiffusionProcessingTxt2Img,
+    [{"key": "sampler_index", "type": str, "default": "k_euler_a"}]
+).generate_model()
-- 
cgit v1.2.1


From 538bc89c269743e56b07ef2b471d1ce0a39b6776 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Wed, 19 Oct 2022 11:27:51 +0800
Subject: Image browser improved

---
 javascript/images_history.js |  87 ++++++++++++++--------------
 modules/images_history.py    | 135 ++++++++++++++++++++++++-------------------
 modules/shared.py            |   5 ++
 modules/ui.py                |   2 +-
 4 files changed, 123 insertions(+), 106 deletions(-)

diff --git a/javascript/images_history.js b/javascript/images_history.js
index 182d730b..c9aa76f8 100644
--- a/javascript/images_history.js
+++ b/javascript/images_history.js
@@ -17,14 +17,6 @@ var images_history_click_image = function(){
     images_history_set_image_info(this); 
 }
 
-var images_history_click_tab = function(){
-    var tabs_box = gradioApp().getElementById("images_history_tab");
-    if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
-        gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_start").click();
-        tabs_box.classList.add(this.getAttribute("tabname"))
-    }                
-}
-
 function images_history_disabled_del(){
     gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
         btn.setAttribute('disabled','disabled');
@@ -145,57 +137,64 @@ function images_history_enable_del_buttons(){
 }
 
 function images_history_init(){ 
-    // var loaded = gradioApp().getElementById("images_history_reconstruct_directory")
-    // if (loaded){  
-        // var init_status = loaded.querySelector("input").checked 
-    if (gradioApp().getElementById("images_history_finish_render")){      
+    var tabnames = gradioApp().getElementById("images_history_tabnames_list")   
+    if (tabnames){  
+        images_history_tab_list = tabnames.querySelector("textarea").value.split(",")    
         for (var i in images_history_tab_list ){
-            tab = images_history_tab_list[i];
+            var tab = images_history_tab_list[i];
             gradioApp().getElementById(tab + '_images_history').classList.add("images_history_cantainor");
             gradioApp().getElementById(tab + '_images_history_set_index').classList.add("images_history_set_index");
             gradioApp().getElementById(tab + '_images_history_del_button').classList.add("images_history_del_button");
-            gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery");            
-                     
+            gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery");  
+             gradioApp().getElementById(tab + "_images_history_start").setAttribute("style","padding:20px;font-size:25px");           
+        }
+
+        //preload
+        if (gradioApp().getElementById("images_history_preload").querySelector("input").checked ){
+            var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div");
+            tabs_box.setAttribute("id", "images_history_tab");        
+            var tab_btns = tabs_box.querySelectorAll("button"); 
+            for (var i in images_history_tab_list){               
+                var tabname = images_history_tab_list[i]
+                tab_btns[i].setAttribute("tabname", tabname);
+                tab_btns[i].addEventListener('click', function(){
+                    var tabs_box = gradioApp().getElementById("images_history_tab");
+                    if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
+                        gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_start").click();
+                        tabs_box.classList.add(this.getAttribute("tabname"))
+                    }                
+                });
+            }            
+            tab_btns[0].click()
         }
-        var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div");
-        tabs_box.setAttribute("id", "images_history_tab");        
-        var tab_btns = tabs_box.querySelectorAll("button"); 
-
-        for (var i in images_history_tab_list){               
-            var tabname = images_history_tab_list[i]
-            tab_btns[i].setAttribute("tabname", tabname);
-            // if (!init_status){
-            //     tab_btns[i].addEventListener('click', images_history_click_tab);
-            // }
-            tab_btns[i].addEventListener('click', images_history_click_tab);
-        }  
     } else {
         setTimeout(images_history_init, 500);
     } 
 }
 
-var images_history_tab_list = ["custom", "txt2img", "img2img", "extras", "saved"];
+var images_history_tab_list = "";
 setTimeout(images_history_init, 500);
 document.addEventListener("DOMContentLoaded", function() {
     var mutationObserver = new MutationObserver(function(m){
-        for (var i in images_history_tab_list ){
-            let tabname = images_history_tab_list[i]
-            var buttons = gradioApp().querySelectorAll('#' + tabname + '_images_history .gallery-item');
-            buttons.forEach(function(bnt){    
-                bnt.addEventListener('click', images_history_click_image, true);
-            });
-
-            var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
-            if (cls_btn){
-                cls_btn.addEventListener('click', function(){
-                    gradioApp().getElementById(tabname + '_images_history_del_button').setAttribute('disabled','disabled');
-                }, false);
-            }
-
-        }     
+        if (images_history_tab_list != ""){
+            for (var i in images_history_tab_list ){
+                let tabname = images_history_tab_list[i]
+                var buttons = gradioApp().querySelectorAll('#' + tabname + '_images_history .gallery-item');
+                buttons.forEach(function(bnt){    
+                    bnt.addEventListener('click', images_history_click_image, true);
+                });
+
+                var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
+                if (cls_btn){
+                    cls_btn.addEventListener('click', function(){
+                        gradioApp().getElementById(tabname + '_images_history_renew_page').click();
+                    }, false);
+                }
+
+            }     
+        }
     });
     mutationObserver.observe(gradioApp(), { childList:true, subtree:true });
-
 });
 
 
diff --git a/modules/images_history.py b/modules/images_history.py
index a40cdc0e..78fd0543 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -4,7 +4,9 @@ import time
 import hashlib
 import gradio
 system_bak_path = "webui_log_and_bak"
-browser_tabname = "custom"
+custom_tab_name = "custom fold"
+faverate_tab_name = "favorites"
+tabs_list = ["txt2img", "img2img", "extras", faverate_tab_name]
 def is_valid_date(date):
     try:
         time.strptime(date, "%Y%m%d")
@@ -122,7 +124,6 @@ def archive_images(dir_name, date_to):
     else:
         filenames = traverse_all_files(dir_name, filenames)  
         total_num = len(filenames) 
-        batch_count = len(filenames) + 1 //  batch_size + 1
         tmparray = [(os.path.getmtime(file), file) for file in filenames ]
         date_stamp = time.mktime(time.strptime(date_to, "%Y%m%d")) + 86400      
         filenames = []
@@ -146,10 +147,12 @@ def archive_images(dir_name, date_to):
     last_date_from = date_to_bak if num == 0 else time.strftime("%Y%m%d", time.localtime(time.mktime(time.strptime(date, "%Y%m%d")) - 1000))
     date = date[:4] + "/" + date[4:6] + "/" + date[6:8]
     date_to_bak = date_to_bak[:4] + "/" + date_to_bak[4:6] + "/" + date_to_bak[6:8]
-    load_info = f"{total_num} images in this directory. Loaded {num} images during {date} - {date_to_bak}, divided into {int((num + 1) // opts.images_history_num_per_page + 1)} pages"
+    load_info = "<div style='color:#999' align='center'>"
+    load_info += f"{total_num} images in this directory. Loaded {num} images during {date} - {date_to_bak}, divided into {int((num + 1) // opts.images_history_num_per_page + 1)} pages"
+    load_info += "</div>"
     _, image_list, _, _, visible_num = get_recent_images(1, 0, filenames)
     return (
-        gradio.Dropdown.update(choices=date_list, value=date_to),
+        date_to,
         load_info,
         filenames,        
         1,
@@ -158,7 +161,7 @@ def archive_images(dir_name, date_to):
         "",
         visible_num, 
         last_date_from, 
-        #gradio.update(visible=batch_count > 1)
+        gradio.update(visible=total_num > num)
     )
 
 def delete_image(delete_num, name, filenames, image_index, visible_num):
@@ -209,7 +212,7 @@ def get_recent_images(page_index, step, filenames):
     visible_num = num_of_imgs_per_page if visible_num == 0 else visible_num
     return page_index, image_list,  "", "",  visible_num
 
-def newest_click(date_to):
+def loac_batch_click(date_to):
     if date_to is None:
         return time.strftime("%Y%m%d",time.localtime(time.time())), []
     else:
@@ -248,7 +251,7 @@ def page_index_change(page_index, filenames):
 
 def show_image_info(tabname_box, num, page_index, filenames):
     file = filenames[int(num) + int((page_index - 1) * int(opts.images_history_num_per_page))]   
-    tm =   time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(os.path.getmtime(file)))
+    tm =   "<div style='color:#999' align='right'>" + time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(os.path.getmtime(file))) + "</div>"
     return file, tm, num, file
 
 def enable_page_buttons():
@@ -268,9 +271,9 @@ def change_dir(img_dir, date_to):
         warning = "The format of the directory is incorrect"
     if warning is None:        
         today = time.strftime("%Y%m%d",time.localtime(time.time()))
-        return gradio.update(visible=False), gradio.update(visible=True), None,   None if date_to != today else today
+        return gradio.update(visible=False), gradio.update(visible=True), None,   None if date_to != today else today, gradio.update(visible=True), gradio.update(visible=True)
     else:
-        return gradio.update(visible=True), gradio.update(visible=False), warning, date_to
+        return gradio.update(visible=True), gradio.update(visible=False), warning, date_to, gradio.update(visible=False), gradio.update(visible=False)
 
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
     custom_dir = False
@@ -280,7 +283,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
         dir_name = opts.outdir_img2img_samples
     elif tabname == "extras":
         dir_name = opts.outdir_extras_samples
-    elif tabname == "saved":
+    elif tabname == faverate_tab_name:
         dir_name = opts.outdir_save
     else:
         custom_dir = True
@@ -295,22 +298,26 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
             os.makedirs(dir_name)
 
     with gr.Column() as page_panel:            
-            with gr.Row():  
-                img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory", interactive=custom_dir) 
+            with gr.Row():                
+                with gr.Column(scale=1, visible=not custom_dir) as load_batch_box: 
+                    load_batch = gr.Button('Load', elem_id=tabname + "_images_history_start", full_width=True)    
+                with gr.Column(scale=4): 
+                    with gr.Row():                        
+                        img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory", interactive=custom_dir)  
+            with gr.Row():
+                with gr.Column(visible=False, scale=1) as batch_panel: 
+                    with gr.Row():
+                        forward = gr.Button('Prev batch')
+                        backward = gr.Button('Next batch')
+                with gr.Column(scale=3):
+                    load_info =  gr.HTML(visible=not custom_dir)   
             with gr.Row(visible=False) as warning:                 
                 warning_box = gr.Textbox("Message", interactive=False)                        
 
             with gr.Row(visible=not custom_dir, elem_id=tabname + "_images_history") as main_panel:
-                with gr.Column(scale=2):                   
-                    with gr.Row() as batch_panel:                    
-                        forward = gr.Button('Forward')        
-                        date_to = gr.Dropdown(label="Date to")
-                        backward = gr.Button('Backward')                           
-                        newest = gr.Button('Reload', elem_id=tabname + "_images_history_start")   
-                    with gr.Row():
-                        load_info = gr.Textbox(show_label=False, interactive=False)  
-                    with gr.Row(visible=False) as turn_page_buttons:
-                        renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
+                with gr.Column(scale=2):                     
+                    with gr.Row(visible=True) as turn_page_buttons:                        
+                        #date_to = gr.Dropdown(label="Date to")                          
                         first_page = gr.Button('First Page')
                         prev_page = gr.Button('Prev Page')
                         page_index = gr.Number(value=1, label="Page Index")
@@ -322,50 +329,54 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                         delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
                         delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
                         
-                with gr.Column():
-                    with gr.Row():
-                        if tabname != "saved":
-                            save_btn = gr.Button('Save')
-                        pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                        pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                with gr.Column():                    
                     with gr.Row():
                         with gr.Column():
-                            img_file_info = gr.Textbox(label="Generate Info", interactive=False)
+                            img_file_info = gr.Textbox(label="Generate Info", interactive=False, lines=6)
+                            gr.HTML("<hr>")                           
                             img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
-                            img_file_time= gr.Textbox(value="", label="Create Time", interactive=False)
-
+                            img_file_time= gr.HTML()
+                    with gr.Row():
+                        if tabname != faverate_tab_name:
+                            save_btn = gr.Button('Collect')
+                        pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
+                        pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                           
                             
-                        # hiden items
-                        with gr.Row(visible=False):   
-                            visible_img_num = gr.Number()                     
-                            date_to_recorder = gr.State([])
-                            last_date_from = gr.Textbox()
-                            tabname_box = gr.Textbox(tabname)
-                            image_index = gr.Textbox(value=-1)
-                            set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
-                            filenames = gr.State()
-                            all_images_list = gr.State()
-                            hidden = gr.Image(type="pil")
-                            info1 = gr.Textbox()
-                            info2 = gr.Textbox()
-
-    img_path.submit(change_dir, inputs=[img_path, date_to], outputs=[warning, main_panel, warning_box, date_to])
-    #change date
-    change_date_output = [date_to, load_info, filenames, page_index, history_gallery, img_file_name, img_file_time, visible_img_num, last_date_from]  
+                    # hiden items
+                    with gr.Row(visible=False): 
+                        renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
+                        batch_date_to = gr.Textbox(label="Date to")  
+                        visible_img_num = gr.Number()                     
+                        date_to_recorder = gr.State([])
+                        last_date_from = gr.Textbox()
+                        tabname_box = gr.Textbox(tabname)
+                        image_index = gr.Textbox(value=-1)
+                        set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
+                        filenames = gr.State()
+                        all_images_list = gr.State()
+                        hidden = gr.Image(type="pil")
+                        info1 = gr.Textbox()
+                        info2 = gr.Textbox()
+
+    img_path.submit(change_dir, inputs=[img_path, batch_date_to], outputs=[warning, main_panel, warning_box, batch_date_to, load_batch_box, load_info])
+
+    #change batch
+    change_date_output = [batch_date_to, load_info, filenames, page_index, history_gallery, img_file_name, img_file_time, visible_img_num, last_date_from,  batch_panel]  
    
-    date_to.change(archive_images, inputs=[img_path, date_to], outputs=change_date_output)   
-    date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])  
-    date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")  
+    batch_date_to.change(archive_images, inputs=[img_path, batch_date_to], outputs=change_date_output)   
+    batch_date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])  
+    batch_date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")  
 
-    newest.click(newest_click, inputs=[date_to], outputs=[date_to, date_to_recorder])
-    forward.click(forward_click, inputs=[last_date_from, date_to_recorder], outputs=[date_to, date_to_recorder])
-    backward.click(backward_click, inputs=[last_date_from, date_to_recorder], outputs=[date_to, date_to_recorder])
+    load_batch.click(loac_batch_click, inputs=[batch_date_to], outputs=[batch_date_to, date_to_recorder])
+    forward.click(forward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder])
+    backward.click(backward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder])
 
 
     #delete
     delete.click(delete_image, inputs=[delete_num, img_file_name, filenames, image_index, visible_img_num], outputs=[filenames, delete_num, visible_img_num])
     delete.click(fn=None, _js="images_history_delete", inputs=[delete_num, tabname_box, image_index], outputs=None) 
-    if tabname != "saved": 
+    if tabname != faverate_tab_name: 
         save_btn.click(save_image, inputs=[img_file_name], outputs=None)     
 
     #turn page
@@ -394,18 +405,20 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
 
    
 
-def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict):
+def create_history_tabs(gr, sys_opts, cmp_ops, run_pnginfo, switch_dict):
     global opts;
     opts = sys_opts
     loads_files_num = int(opts.images_history_num_per_page)
     num_of_imgs_per_page = int(opts.images_history_num_per_page * opts.images_history_pages_num)
+    if cmp_ops.browse_all_images:
+        tabs_list.append(custom_tab_name)
     with gr.Blocks(analytics_enabled=False) as images_history:
         with gr.Tabs() as tabs:
-            for tab in [browser_tabname, "txt2img", "img2img", "extras", "saved"]:
+            for tab in tabs_list:
                 with gr.Tab(tab):
                     with gr.Blocks(analytics_enabled=False) :
-                        show_images_history(gr, opts, tab, run_pnginfo, switch_dict) 
-        #gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory", visible=False)
-        gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_finish_render", visible=False)
-    
+                        show_images_history(gr, opts, tab, run_pnginfo, switch_dict)
+        gradio.Checkbox(opts.images_history_preload, elem_id="images_history_preload", visible=False)         
+        gradio.Textbox(",".join(tabs_list), elem_id="images_history_tabnames_list", visible=False)    
+        
     return images_history
diff --git a/modules/shared.py b/modules/shared.py
index 1811018d..4d735414 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -74,6 +74,10 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
+parser.add_argument("--browse-all-images", action='store_true', help="Allow browsing all images by Image Browser", default=False)
+
+
+cmd_opts = parser.parse_args()
 
 
 cmd_opts = parser.parse_args()
@@ -311,6 +315,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
 
 options_templates.update(options_section(('images-history', "Images Browser"), {
     #"images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"),
+    "images_history_preload": OptionInfo(False, "Preload images at startup"),
     "images_history_num_per_page": OptionInfo(36, "Number of pictures displayed on each page"),
     "images_history_pages_num": OptionInfo(6, "Minimum number of pages per load "),   
     "images_history_grid_num": OptionInfo(6, "Number of grids in each row"),   
diff --git a/modules/ui.py b/modules/ui.py
index 85abac4d..88f46659 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1150,7 +1150,7 @@ def create_ui(wrap_gradio_gpu_call):
         "i2i":img2img_paste_fields
     }
 
-    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
+    images_history = img_his.create_history_tabs(gr, opts, cmd_opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
 
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
-- 
cgit v1.2.1


From 0f0d6ab8e06898ce066251fc769fe14e77e98ced Mon Sep 17 00:00:00 2001
From: arcticfaded <jbelt021@fiu.edu>
Date: Wed, 19 Oct 2022 05:19:01 +0000
Subject: call sampler by name

---
 modules/api/api.py        | 11 ++++++-----
 modules/api/processing.py |  6 +++---
 2 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index ff9df0d1..5b0c934e 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,6 +1,7 @@
 from modules.api.processing import StableDiffusionProcessingAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, process_images
-from modules.sd_samplers import samplers_k_diffusion
+from modules.sd_samplers import all_samplers
+from modules.extras import run_pnginfo
 import modules.shared as shared
 import uvicorn
 from fastapi import Body, APIRouter, HTTPException
@@ -10,7 +11,7 @@ import json
 import io
 import base64
 
-sampler_to_index = lambda name: next(filter(lambda row: name in row[1][2], enumerate(samplers_k_diffusion)), None)
+sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
 class TextToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
@@ -53,13 +54,13 @@ class Api:
         
         
 
-    def img2imgendoint(self):
+    def img2imgapi(self):
         raise NotImplementedError
 
-    def extrasendoint(self):
+    def extrasapi(self):
         raise NotImplementedError
 
-    def pnginfoendoint(self):
+    def pnginfoapi(self):
         raise NotImplementedError
 
     def launch(self, server_name, port):
diff --git a/modules/api/processing.py b/modules/api/processing.py
index 2e6483ee..4c541241 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -1,7 +1,7 @@
 from inflection import underscore
 from typing import Any, Dict, Optional
 from pydantic import BaseModel, Field, create_model
-from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
+from modules.processing import StableDiffusionProcessingTxt2Img
 import inspect
 
 
@@ -95,5 +95,5 @@ class PydanticModelGenerator:
 StableDiffusionProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingTxt2Img", 
     StableDiffusionProcessingTxt2Img,
-    [{"key": "sampler_index", "type": str, "default": "k_euler_a"}]
-).generate_model()
+    [{"key": "sampler_index", "type": str, "default": "Euler"}]
+).generate_model()
\ No newline at end of file
-- 
cgit v1.2.1


From 10aca1ca3e81e69e08f556a500c3dc603451429b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 19 Oct 2022 08:42:22 +0300
Subject: more careful loading of model weights (eliminates some issues with
 checkpoints that have weird cond_stage_model layer names)

---
 modules/sd_models.py | 28 +++++++++++++++++++++++++---
 1 file changed, 25 insertions(+), 3 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 3aa21ec1..7ad6d474 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -122,11 +122,33 @@ def select_checkpoint():
     return checkpoint_info
 
 
+chckpoint_dict_replacements = {
+    'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
+    'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
+    'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
+}
+
+
+def transform_checkpoint_dict_key(k):
+    for text, replacement in chckpoint_dict_replacements.items():
+        if k.startswith(text):
+            k = replacement + k[len(text):]
+
+    return k
+
+
 def get_state_dict_from_checkpoint(pl_sd):
     if "state_dict" in pl_sd:
-        return pl_sd["state_dict"]
+        pl_sd = pl_sd["state_dict"]
+
+    sd = {}
+    for k, v in pl_sd.items():
+        new_key = transform_checkpoint_dict_key(k)
+
+        if new_key is not None:
+            sd[new_key] = v
 
-    return pl_sd
+    return sd
 
 
 def load_model_weights(model, checkpoint_info):
@@ -141,7 +163,7 @@ def load_model_weights(model, checkpoint_info):
             print(f"Global Step: {pl_sd['global_step']}")
 
         sd = get_state_dict_from_checkpoint(pl_sd)
-        model.load_state_dict(sd, strict=False)
+        missing, extra = model.load_state_dict(sd, strict=False)
 
         if shared.cmd_opts.opt_channelslast:
             model.to(memory_format=torch.channels_last)
-- 
cgit v1.2.1


From da72becb13e4b750fbcb3d158c3f843311ef9938 Mon Sep 17 00:00:00 2001
From: Silent <16026653+s-ilent@users.noreply.github.com>
Date: Wed, 19 Oct 2022 16:14:33 +1030
Subject: Use training width/height when training hypernetworks.

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 modules/ui.py                         | 2 ++
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 4905710e..b8695fc1 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -196,7 +196,7 @@ def stack_conds(conds):
 
     return torch.stack(conds)
 
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -225,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
diff --git a/modules/ui.py b/modules/ui.py
index fb6eb5a0..ca46343f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1341,6 +1341,8 @@ def create_ui(wrap_gradio_gpu_call):
                 batch_size,
                 dataset_directory,
                 log_directory,
+                training_width,
+                training_height,
                 steps,
                 create_image_every,
                 save_embedding_every,
-- 
cgit v1.2.1


From 2fd7935ef4ed296db5dfd8c7fea99244816f8cf0 Mon Sep 17 00:00:00 2001
From: Cheka <carlos93@hotmail.cl>
Date: Tue, 18 Oct 2022 20:28:28 -0300
Subject: Remove wrong self reference in CUDA support for invokeai

---
 modules/sd_hijack_optimizations.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index a3345bb9..98123fbf 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -181,7 +181,7 @@ def einsum_op_cuda(q, k, v):
     mem_free_torch = mem_reserved - mem_active
     mem_free_total = mem_free_cuda + mem_free_torch
     # Divide factor of safety as there's copying and fragmentation
-    return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
+    return einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
 
 def einsum_op(q, k, v):
     if q.device.type == 'cuda':
-- 
cgit v1.2.1


From bcfbb33e50a48b237d8d961cc2be038db53774d5 Mon Sep 17 00:00:00 2001
From: Anastasius <anastasiuspernat@gmail.com>
Date: Mon, 17 Oct 2022 13:35:20 -0700
Subject: Added time left estimation

---
 modules/ui.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index ca46343f..9a54aa16 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -261,6 +261,15 @@ def wrap_gradio_call(func, extra_outputs=None):
     return f
 
 
+def calc_time_left(progress):
+    if progress == 0:
+        return "N/A"
+    else:
+        time_since_start = time.time() - shared.state.time_start
+        eta = (time_since_start/progress)
+        return time.strftime('%H:%M:%S', time.gmtime(eta-time_since_start))        
+
+
 def check_progress_call(id_part):
     if shared.state.job_count == 0:
         return "", gr_show(False), gr_show(False), gr_show(False)
@@ -272,11 +281,13 @@ def check_progress_call(id_part):
     if shared.state.sampling_steps > 0:
         progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
 
+    time_left = calc_time_left( progress )
+
     progress = min(progress, 1)
 
     progressbar = ""
     if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="width:{progress * 100}%">{str(int(progress*100))+"%" if progress > 0.01 else ""}</div></div>"""
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:hidden;width:{progress * 100}%">{str(int(progress*100))+"% ETA:"+time_left if progress > 0.01 else ""}</div></div>"""
 
     image = gr_show(False)
     preview_visibility = gr_show(False)
@@ -308,6 +319,7 @@ def check_progress_call_initial(id_part):
     shared.state.current_latent = None
     shared.state.current_image = None
     shared.state.textinfo = None
+    shared.state.time_start = time.time()
 
     return check_progress_call(id_part)
 
-- 
cgit v1.2.1


From 442dbedc159bb7e9cf94f0c3626f8a409e0a50eb Mon Sep 17 00:00:00 2001
From: Anastasius <anastasiuspernat@gmail.com>
Date: Tue, 18 Oct 2022 10:38:07 -0700
Subject: Estimated time displayed if jobs take more 60 sec

---
 modules/ui.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 9a54aa16..fa54110b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -261,13 +261,17 @@ def wrap_gradio_call(func, extra_outputs=None):
     return f
 
 
-def calc_time_left(progress):
+def calc_time_left(progress, threshold, label, force_display):
     if progress == 0:
-        return "N/A"
+        return ""
     else:
         time_since_start = time.time() - shared.state.time_start
         eta = (time_since_start/progress)
-        return time.strftime('%H:%M:%S', time.gmtime(eta-time_since_start))        
+        eta_relative = eta-time_since_start
+        if eta_relative > threshold or force_display:              
+            return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))        
+        else:
+            return ""
 
 
 def check_progress_call(id_part):
@@ -281,13 +285,15 @@ def check_progress_call(id_part):
     if shared.state.sampling_steps > 0:
         progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
 
-    time_left = calc_time_left( progress )
+    time_left = calc_time_left( progress, 60, " ETA:", shared.state.time_left_force_display )
+    if time_left != "":
+        shared.state.time_left_force_display = True
 
     progress = min(progress, 1)
 
     progressbar = ""
     if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:hidden;width:{progress * 100}%">{str(int(progress*100))+"% ETA:"+time_left if progress > 0.01 else ""}</div></div>"""
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:hidden;width:{progress * 100}%">{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}</div></div>"""
 
     image = gr_show(False)
     preview_visibility = gr_show(False)
@@ -320,6 +326,7 @@ def check_progress_call_initial(id_part):
     shared.state.current_image = None
     shared.state.textinfo = None
     shared.state.time_start = time.time()
+    shared.state.time_left_force_display = False
 
     return check_progress_call(id_part)
 
-- 
cgit v1.2.1


From 1d4aa376e6111e90888a30ae24d2bcd7f978ec51 Mon Sep 17 00:00:00 2001
From: Anastasius <anastasiuspernat@gmail.com>
Date: Tue, 18 Oct 2022 12:42:39 -0700
Subject: Predictable long operation check for time estimation

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index fa54110b..38ba1138 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -268,7 +268,7 @@ def calc_time_left(progress, threshold, label, force_display):
         time_since_start = time.time() - shared.state.time_start
         eta = (time_since_start/progress)
         eta_relative = eta-time_since_start
-        if eta_relative > threshold or force_display:              
+        if (eta_relative > threshold and progress > 0.02) or force_display:           
             return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))        
         else:
             return ""
-- 
cgit v1.2.1


From 83a517eb96cc36cf1dc0915a9ebde43a7e05c7da Mon Sep 17 00:00:00 2001
From: realryo1 <60560430+realryo1@users.noreply.github.com>
Date: Wed, 19 Oct 2022 04:48:00 +0900
Subject: Fixed performance, vram style disorder

---
 style.css | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/style.css b/style.css
index 9dc4b696..9bd408cd 100644
--- a/style.css
+++ b/style.css
@@ -34,9 +34,6 @@
 .performance {
     font-size: 0.85em;
     color: #444;
-    display: flex;
-    justify-content: space-between;
-    white-space: nowrap;
 }
 
 .performance .time {
@@ -44,8 +41,6 @@
 }
 
 .performance .vram {
-    margin-left: 0;
-    text-align: right;
 }
 
 #txt2img_generate, #img2img_generate {
-- 
cgit v1.2.1


From bb0e7232b301d1706bbd0e09367dece3bb7ac07c Mon Sep 17 00:00:00 2001
From: Ikko Ashimine <eltociear@gmail.com>
Date: Wed, 19 Oct 2022 02:18:56 +0900
Subject: Fix typo in prompt_parser.py

assoicated -> associated
---
 modules/prompt_parser.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 919d5d31..f70872c4 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -275,7 +275,7 @@ re_attention = re.compile(r"""
 
 def parse_prompt_attention(text):
     """
-    Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight.
+    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
     Accepted tokens are:
       (abc) - increases attention to abc by a multiplier of 1.1
       (abc:3.12) - increases attention to abc by a multiplier of 3.12
-- 
cgit v1.2.1


From 9931c0bd48346dc5af23864117becfac33347a7c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 19 Oct 2022 12:01:31 +0300
Subject: remove the unneeded line break introduced by #3092

---
 style.css | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/style.css b/style.css
index 9bd408cd..26ae36a5 100644
--- a/style.css
+++ b/style.css
@@ -36,6 +36,10 @@
     color: #444;
 }
 
+.performance p{
+    display: inline-block;
+}
+
 .performance .time {
     margin-right: 0;
 }
-- 
cgit v1.2.1


From f894dd552f68bea27476f1f360ab8e79f3a65b4f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 19 Oct 2022 12:45:30 +0300
Subject: fix for broken checkpoint merger

---
 modules/sd_models.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 7ad6d474..eae22e87 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -148,7 +148,10 @@ def get_state_dict_from_checkpoint(pl_sd):
         if new_key is not None:
             sd[new_key] = v
 
-    return sd
+    pl_sd.clear()
+    pl_sd.update(sd)
+
+    return pl_sd
 
 
 def load_model_weights(model, checkpoint_info):
-- 
cgit v1.2.1


From abeec4b63029c2c4151a78fc395d312113881845 Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Wed, 19 Oct 2022 03:18:26 -0700
Subject: Add auto focal point cropping to Preprocess images

This algorithm plots a bunch of points of interest on the source
image and averages their locations to find a center.

Most points come from OpenCV.  One point comes from an
entropy model. OpenCV points account for 50% of the weight and the
entropy based point is the other 50%.

The center of all weighted points is calculated and a bounding box
is drawn as close to centered over that point as possible.
---
 modules/textual_inversion/preprocess.py | 151 ++++++++++++++++++++++++++++++--
 1 file changed, 146 insertions(+), 5 deletions(-)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 886cf0c3..168bfb09 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,5 +1,7 @@
 import os
-from PIL import Image, ImageOps
+import cv2
+import numpy as np
+from PIL import Image, ImageOps, ImageDraw
 import platform
 import sys
 import tqdm
@@ -11,7 +13,7 @@ if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru
 
 
-def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False, process_entropy_focus=False):
     try:
         if process_caption:
             shared.interrogator.load()
@@ -21,7 +23,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
             db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
             deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
 
-        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
+        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru, process_entropy_focus)
 
     finally:
 
@@ -33,7 +35,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
 
 
 
-def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False, process_entropy_focus=False):
     width = process_width
     height = process_height
     src = os.path.abspath(process_src)
@@ -93,6 +95,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         is_tall = ratio > 1.35
         is_wide = ratio < 1 / 1.35
 
+        processing_option_ran = False
+
         if process_split and is_tall:
             img = img.resize((width, height * img.height // img.width))
 
@@ -101,6 +105,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
 
             bot = img.crop((0, img.height - height, width, img.height))
             save_pic(bot, index)
+
+            processing_option_ran = True
         elif process_split and is_wide:
             img = img.resize((width * img.width // img.height, height))
 
@@ -109,8 +115,143 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
 
             right = img.crop((img.width - width, 0, img.width, height))
             save_pic(right, index)
-        else:
+            
+            processing_option_ran = True
+
+        if process_entropy_focus and (is_tall or is_wide):
+            if is_tall:
+                img = img.resize((width, height * img.height // img.width))
+            else:
+                img = img.resize((width * img.width // img.height, height))
+
+            x_focal_center, y_focal_center = image_central_focal_point(img, width, height)
+
+            # take the focal point and turn it into crop coordinates that try to center over the focal
+            # point but then get adjusted back into the frame
+            y_half = int(height / 2)
+            x_half = int(width / 2)
+
+            x1 = x_focal_center - x_half
+            if x1 < 0:
+                x1 = 0
+            elif x1 + width > img.width:
+                x1 = img.width - width
+
+            y1 = y_focal_center - y_half
+            if y1 < 0:
+                y1 = 0
+            elif y1 + height > img.height:
+                y1 = img.height - height
+
+            x2 = x1 + width
+            y2 = y1 + height
+
+            crop = [x1, y1, x2, y2]
+
+            focal = img.crop(tuple(crop))
+            save_pic(focal, index)
+
+            processing_option_ran = True
+
+        if not processing_option_ran:
             img = images.resize_image(1, img, width, height)
             save_pic(img, index)
 
         shared.state.nextjob()
+
+
+def image_central_focal_point(im, target_width, target_height):
+    focal_points = []
+
+    focal_points.extend(
+        image_focal_points(im)
+    )
+
+    fp_entropy = image_entropy_point(im, target_width, target_height)
+    fp_entropy['weight'] = len(focal_points) + 1 # about half of the weight to entropy
+
+    focal_points.append(fp_entropy)
+
+    weight = 0.0
+    x = 0.0
+    y = 0.0
+    for focal_point in focal_points:
+        weight += focal_point['weight']
+        x += focal_point['x'] * focal_point['weight']
+        y += focal_point['y'] * focal_point['weight']
+    avg_x = round(x // weight)
+    avg_y = round(y // weight)
+
+    return avg_x, avg_y
+
+
+def image_focal_points(im):
+    grayscale = im.convert("L")
+
+    # naive attempt at preventing focal points from collecting at watermarks near the bottom
+    gd = ImageDraw.Draw(grayscale)
+    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
+
+    np_im = np.array(grayscale)
+
+    points = cv2.goodFeaturesToTrack(
+        np_im,
+        maxCorners=50,
+        qualityLevel=0.04,
+        minDistance=min(grayscale.width, grayscale.height)*0.05,
+        useHarrisDetector=False,
+    )
+
+    if points is None:
+        return []
+
+    focal_points = []
+    for point in points:
+        x, y = point.ravel()
+        focal_points.append({
+            'x': x,
+            'y': y,
+            'weight': 1.0
+        })
+
+    return focal_points
+
+
+def image_entropy_point(im, crop_width, crop_height):
+    img = im.copy()
+    # just make it easier to slide the test crop with images oriented the same way
+    if (img.size[0] < img.size[1]):
+        portrait = True
+        img = img.rotate(90, expand=1)
+
+    e_max = 0
+    crop_current = [0, 0, crop_width, crop_height]
+    crop_best = crop_current
+    while crop_current[2] < img.size[0]:
+        crop = img.crop(tuple(crop_current))
+        e = image_entropy(crop)
+
+        if (e_max < e):
+          e_max = e
+          crop_best = list(crop_current)
+
+        crop_current[0] += 4
+        crop_current[2] += 4
+
+    x_mid = int((crop_best[2] - crop_best[0])/2)
+    y_mid = int((crop_best[3] - crop_best[1])/2)
+
+    return {
+        'x': x_mid,
+        'y': y_mid,
+        'weight': 1.0
+    }
+
+
+def image_entropy(im):
+    # greyscale image entropy
+    band = np.asarray(im.convert("L"))
+    hist, _ = np.histogram(band, bins=range(0, 256))
+    hist = hist[hist > 0]
+    return -np.log2(hist / hist.sum()).sum()
+
-- 
cgit v1.2.1


From 087609ee181a91a523647435ffffa6288a317e2f Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Wed, 19 Oct 2022 03:19:35 -0700
Subject: UI changes for focal point image cropping

---
 modules/ui.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 1ff7eb4f..b6be713b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1234,6 +1234,7 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
                         process_split = gr.Checkbox(label='Split oversized images into two')
+                        process_entropy_focus = gr.Checkbox(label='Create auto focal point crop')
                         process_caption = gr.Checkbox(label='Use BLIP for caption')
                         process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
 
@@ -1318,7 +1319,8 @@ def create_ui(wrap_gradio_gpu_call):
                 process_flip,
                 process_split,
                 process_caption,
-                process_caption_deepbooru
+                process_caption_deepbooru,
+                process_entropy_focus
             ],
             outputs=[
                 ti_output,
-- 
cgit v1.2.1


From 42fbda83bb9830af18187fddb50c1bedd01da502 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 14:30:33 +0000
Subject: layer options moves into create hnet ui

---
 modules/hypernetworks/hypernetwork.py | 64 +++++++++++++++++------------------
 modules/hypernetworks/ui.py           |  9 +++--
 modules/shared.py                     |  2 --
 modules/ui.py                         |  8 +++--
 webui.py                              |  8 ++---
 5 files changed, 48 insertions(+), 43 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 583ada31..7d519cd9 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -19,37 +19,21 @@ from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 
-def parse_layer_structure(dim, state_dict):
-    i = 0
-    res = [1]
-    while (key := "linear.{}.weight".format(i)) in state_dict:
-        weight = state_dict[key]
-        res.append(len(weight) // dim)
-        i += 1
-    return res
-
-
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
-    layer_structure = None
-    add_layer_norm = False
 
-    def __init__(self, dim, state_dict=None):
+    def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False):
         super().__init__()
-        if (state_dict is None or 'linear.0.weight' not in state_dict) and self.layer_structure is None:
-            layer_structure = (1, 2, 1)
+        if layer_structure is not None:
+            assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
+            assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
         else:
-            if self.layer_structure is not None:
-                assert self.layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
-                assert self.layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
-                layer_structure = self.layer_structure
-            else:
-                layer_structure = parse_layer_structure(dim, state_dict)
+            layer_structure = parse_layer_structure(dim, state_dict)
 
         linears = []
         for i in range(len(layer_structure) - 1):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
-            if self.add_layer_norm:
+            if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
         self.linear = torch.nn.Sequential(*linears)
@@ -77,38 +61,47 @@ class HypernetworkModule(torch.nn.Module):
         return x + self.linear(x) * self.multiplier
 
     def trainables(self):
-        res = []
+        layer_structure = []
         for layer in self.linear:
-            res += [layer.weight, layer.bias]
-        return res
+            layer_structure += [layer.weight, layer.bias]
+        return layer_structure
 
 
 def apply_strength(value=None):
     HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
 
 
-def apply_layer_structure(value=None):
-    HypernetworkModule.layer_structure = value if value is not None else shared.opts.sd_hypernetwork_layer_structure
+def parse_layer_structure(dim, state_dict):
+    i = 0
+    layer_structure = [1]
 
+    while (key := "linear.{}.weight".format(i)) in state_dict:
+        weight = state_dict[key]
+        layer_structure.append(len(weight) // dim)
+        i += 1
 
-def apply_layer_norm(value=None):
-    HypernetworkModule.add_layer_norm = value if value is not None else shared.opts.sd_hypernetwork_add_layer_norm
+    return layer_structure
 
 
 class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False):
         self.filename = None
         self.name = name
         self.layers = {}
         self.step = 0
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
+        self.layer_structure = layer_structure
+        self.add_layer_norm = add_layer_norm
 
         for size in enable_sizes or []:
-            self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
+            self.layers[size] = (
+                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm),
+                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm),
+            )
 
     def weights(self):
         res = []
@@ -128,6 +121,8 @@ class Hypernetwork:
 
         state_dict['step'] = self.step
         state_dict['name'] = self.name
+        state_dict['layer_structure'] = self.layer_structure
+        state_dict['is_layer_norm'] = self.add_layer_norm
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
 
@@ -142,10 +137,15 @@ class Hypernetwork:
 
         for size, sd in state_dict.items():
             if type(size) == int:
-                self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
+                self.layers[size] = (
+                    HypernetworkModule(size, sd[0], state_dict["layer_structure"], state_dict["is_layer_norm"]),
+                    HypernetworkModule(size, sd[1], state_dict["layer_structure"], state_dict["is_layer_norm"]),
+                )
 
         self.name = state_dict.get('name', self.name)
         self.step = state_dict.get('step', 0)
+        self.layer_structure = state_dict.get('layer_structure', None)
+        self.add_layer_norm = state_dict.get('is_layer_norm', False)
         self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
         self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
 
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index dfa599af..7e8ea95e 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -9,11 +9,16 @@ from modules import sd_hijack, shared, devices
 from modules.hypernetworks import hypernetwork
 
 
-def create_hypernetwork(name, enable_sizes):
+def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
-    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes])
+    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
+        name=name,
+        enable_sizes=[int(x) for x in enable_sizes],
+        layer_structure=layer_structure,
+        add_layer_norm=add_layer_norm,
+    )
     hypernet.save(fn)
 
     shared.reload_hypernetworks()
diff --git a/modules/shared.py b/modules/shared.py
index 0540cae9..faede821 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -260,8 +260,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
-    "sd_hypernetwork_layer_structure": OptionInfo(None, "Hypernetwork layer structure Default: (1,2,1).", gr.Dropdown, lambda: {"choices": [(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)]}),
-    "sd_hypernetwork_add_layer_norm": OptionInfo(False, "Add layer normalization to hypernetwork architecture."),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
diff --git a/modules/ui.py b/modules/ui.py
index ca46343f..d9ee462f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -458,14 +458,14 @@ def create_toprow(is_img2img):
             with gr.Row():
                 with gr.Column(scale=80):
                     with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2, 
+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
                             placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
                         )
 
             with gr.Row():
                 with gr.Column(scale=80):
                     with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, 
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
                             placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
                         )
 
@@ -1198,6 +1198,8 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
+                    new_hypernetwork_layer_structure = gr.Dropdown(label="Hypernetwork layer structure", choices=[(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)])
+                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1280,6 +1282,8 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 new_hypernetwork_name,
                 new_hypernetwork_sizes,
+                new_hypernetwork_layer_structure,
+                new_hypernetwork_add_layer_norm,
             ],
             outputs=[
                 train_hypernetwork_name,
diff --git a/webui.py b/webui.py
index c7260c7a..177bef74 100644
--- a/webui.py
+++ b/webui.py
@@ -85,9 +85,7 @@ def initialize():
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
-    shared.opts.onchange("sd_hypernetwork_layer_structure", modules.hypernetworks.hypernetwork.apply_layer_structure)
-    shared.opts.onchange("sd_hypernetwork_add_layer_norm", modules.hypernetworks.hypernetwork.apply_layer_norm)
-    
+
     # make the program just exit at ctrl+c without waiting for anything
     def sigint_handler(sig, frame):
         print(f'Interrupted with signal {sig} in {frame}')
@@ -142,7 +140,7 @@ def webui(launch_api=False):
             create_api(app)
 
         wait_on_server(demo)
-        
+
         sd_samplers.set_samplers()
 
         print('Reloading Custom Scripts')
@@ -160,4 +158,4 @@ if __name__ == "__main__":
     if cmd_opts.nowebui:
         api_only()
     else:
-        webui(cmd_opts.api)
\ No newline at end of file
+        webui(cmd_opts.api)
-- 
cgit v1.2.1


From 3770b8d2fa62066d472a04739c7b84bce8538832 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 15:28:42 +0000
Subject: enable to write layer structure of hn himself

---
 modules/hypernetworks/ui.py | 4 ++++
 modules/ui.py               | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 7e8ea95e..08f75f15 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -1,5 +1,6 @@
 import html
 import os
+import re
 
 import gradio as gr
 
@@ -13,6 +14,9 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
+    if type(layer_structure) == str:
+        layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure)))
+
     hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
         name=name,
         enable_sizes=[int(x) for x in enable_sizes],
diff --git a/modules/ui.py b/modules/ui.py
index d9ee462f..18a2add0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1198,7 +1198,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
-                    new_hypernetwork_layer_structure = gr.Dropdown(label="Hypernetwork layer structure", choices=[(1, 2, 1), (1, 2, 2, 1), (1, 2, 4, 2, 1)])
+                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
 
                     with gr.Row():
-- 
cgit v1.2.1


From 019a3a88f07766f2d32c32fbe8e41625f28ecb5e Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 19 Oct 2022 17:15:47 +0100
Subject: Update ui.py

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index d2e24880..1573ef82 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1247,7 +1247,7 @@ def create_ui(wrap_gradio_gpu_call):
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                 with gr.Tab(label="Train"):
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images<br/>Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion">wiki</a></p>")
                     with gr.Row():
                         train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                         create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
-- 
cgit v1.2.1


From c6e9fed5003631c87d548e74d6e359678959a453 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 19 Oct 2022 19:21:16 +0300
Subject: fix for #3086 failing to load any previous hypernet

---
 modules/hypernetworks/hypernetwork.py | 60 ++++++++++++++++-------------------
 1 file changed, 28 insertions(+), 32 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7d519cd9..74300122 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -24,11 +24,10 @@ class HypernetworkModule(torch.nn.Module):
 
     def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False):
         super().__init__()
-        if layer_structure is not None:
-            assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
-            assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
-        else:
-            layer_structure = parse_layer_structure(dim, state_dict)
+
+        assert layer_structure is not None, "layer_structure mut not be None"
+        assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
+        assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
 
         linears = []
         for i in range(len(layer_structure) - 1):
@@ -39,23 +38,30 @@ class HypernetworkModule(torch.nn.Module):
         self.linear = torch.nn.Sequential(*linears)
 
         if state_dict is not None:
-            try:
-                self.load_state_dict(state_dict)
-            except RuntimeError:
-                self.try_load_previous(state_dict)
+            self.fix_old_state_dict(state_dict)
+            self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                layer.weight.data.normal_(mean = 0.0, std = 0.01)
+                layer.weight.data.normal_(mean=0.0, std=0.01)
                 layer.bias.data.zero_()
 
         self.to(devices.device)
 
-    def try_load_previous(self, state_dict):
-        states = self.state_dict()
-        states['linear.0.bias'].copy_(state_dict['linear1.bias'])
-        states['linear.0.weight'].copy_(state_dict['linear1.weight'])
-        states['linear.1.bias'].copy_(state_dict['linear2.bias'])
-        states['linear.1.weight'].copy_(state_dict['linear2.weight'])
+    def fix_old_state_dict(self, state_dict):
+        changes = {
+            'linear1.bias': 'linear.0.bias',
+            'linear1.weight': 'linear.0.weight',
+            'linear2.bias': 'linear.1.bias',
+            'linear2.weight': 'linear.1.weight',
+        }
+
+        for fr, to in changes.items():
+            x = state_dict.get(fr, None)
+            if x is None:
+                continue
+
+            del state_dict[fr]
+            state_dict[to] = x
 
     def forward(self, x):
         return x + self.linear(x) * self.multiplier
@@ -71,18 +77,6 @@ def apply_strength(value=None):
     HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
 
 
-def parse_layer_structure(dim, state_dict):
-    i = 0
-    layer_structure = [1]
-
-    while (key := "linear.{}.weight".format(i)) in state_dict:
-        weight = state_dict[key]
-        layer_structure.append(len(weight) // dim)
-        i += 1
-
-    return layer_structure
-
-
 class Hypernetwork:
     filename = None
     name = None
@@ -135,17 +129,18 @@ class Hypernetwork:
 
         state_dict = torch.load(filename, map_location='cpu')
 
+        self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
+        self.add_layer_norm = state_dict.get('is_layer_norm', False)
+
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], state_dict["layer_structure"], state_dict["is_layer_norm"]),
-                    HypernetworkModule(size, sd[1], state_dict["layer_structure"], state_dict["is_layer_norm"]),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm),
                 )
 
         self.name = state_dict.get('name', self.name)
         self.step = state_dict.get('step', 0)
-        self.layer_structure = state_dict.get('layer_structure', None)
-        self.add_layer_norm = state_dict.get('is_layer_norm', False)
         self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
         self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
 
@@ -244,6 +239,7 @@ def stack_conds(conds):
 
     return torch.stack(conds)
 
+
 def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert hypernetwork_name, 'hypernetwork not selected'
 
-- 
cgit v1.2.1


From 2ce52d32e41fb523d1494f45073fd18496e52d35 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Wed, 19 Oct 2022 16:31:12 +0000
Subject: fix for #3086 failing to load any previous hypernet

---
 modules/hypernetworks/hypernetwork.py | 60 ++++++++++++++++-------------------
 1 file changed, 28 insertions(+), 32 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7d519cd9..74300122 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -24,11 +24,10 @@ class HypernetworkModule(torch.nn.Module):
 
     def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False):
         super().__init__()
-        if layer_structure is not None:
-            assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
-            assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
-        else:
-            layer_structure = parse_layer_structure(dim, state_dict)
+
+        assert layer_structure is not None, "layer_structure mut not be None"
+        assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
+        assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
 
         linears = []
         for i in range(len(layer_structure) - 1):
@@ -39,23 +38,30 @@ class HypernetworkModule(torch.nn.Module):
         self.linear = torch.nn.Sequential(*linears)
 
         if state_dict is not None:
-            try:
-                self.load_state_dict(state_dict)
-            except RuntimeError:
-                self.try_load_previous(state_dict)
+            self.fix_old_state_dict(state_dict)
+            self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                layer.weight.data.normal_(mean = 0.0, std = 0.01)
+                layer.weight.data.normal_(mean=0.0, std=0.01)
                 layer.bias.data.zero_()
 
         self.to(devices.device)
 
-    def try_load_previous(self, state_dict):
-        states = self.state_dict()
-        states['linear.0.bias'].copy_(state_dict['linear1.bias'])
-        states['linear.0.weight'].copy_(state_dict['linear1.weight'])
-        states['linear.1.bias'].copy_(state_dict['linear2.bias'])
-        states['linear.1.weight'].copy_(state_dict['linear2.weight'])
+    def fix_old_state_dict(self, state_dict):
+        changes = {
+            'linear1.bias': 'linear.0.bias',
+            'linear1.weight': 'linear.0.weight',
+            'linear2.bias': 'linear.1.bias',
+            'linear2.weight': 'linear.1.weight',
+        }
+
+        for fr, to in changes.items():
+            x = state_dict.get(fr, None)
+            if x is None:
+                continue
+
+            del state_dict[fr]
+            state_dict[to] = x
 
     def forward(self, x):
         return x + self.linear(x) * self.multiplier
@@ -71,18 +77,6 @@ def apply_strength(value=None):
     HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
 
 
-def parse_layer_structure(dim, state_dict):
-    i = 0
-    layer_structure = [1]
-
-    while (key := "linear.{}.weight".format(i)) in state_dict:
-        weight = state_dict[key]
-        layer_structure.append(len(weight) // dim)
-        i += 1
-
-    return layer_structure
-
-
 class Hypernetwork:
     filename = None
     name = None
@@ -135,17 +129,18 @@ class Hypernetwork:
 
         state_dict = torch.load(filename, map_location='cpu')
 
+        self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
+        self.add_layer_norm = state_dict.get('is_layer_norm', False)
+
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], state_dict["layer_structure"], state_dict["is_layer_norm"]),
-                    HypernetworkModule(size, sd[1], state_dict["layer_structure"], state_dict["is_layer_norm"]),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm),
                 )
 
         self.name = state_dict.get('name', self.name)
         self.step = state_dict.get('step', 0)
-        self.layer_structure = state_dict.get('layer_structure', None)
-        self.add_layer_norm = state_dict.get('is_layer_norm', False)
         self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
         self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
 
@@ -244,6 +239,7 @@ def stack_conds(conds):
 
     return torch.stack(conds)
 
+
 def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert hypernetwork_name, 'hypernetwork not selected'
 
-- 
cgit v1.2.1


From 57eb1a64c85d995cacb4fa3832e87405bf6820b9 Mon Sep 17 00:00:00 2001
From: Alexandre Simard <as.simard@outlook.com>
Date: Wed, 19 Oct 2022 12:28:27 -0400
Subject: Update ui.py

---
 modules/ui.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index d2e24880..c9a923ab 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -268,8 +268,13 @@ def calc_time_left(progress, threshold, label, force_display):
         time_since_start = time.time() - shared.state.time_start
         eta = (time_since_start/progress)
         eta_relative = eta-time_since_start
-        if (eta_relative > threshold and progress > 0.02) or force_display:           
-            return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))        
+        if (eta_relative > threshold and progress > 0.02) or force_display:
+            if eta_relative > 3600:
+                return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
+            elif eta_relative > 60:
+                return label + time.strftime('%M:%S',  time.gmtime(eta_relative))
+            else:
+                return label + time.strftime('%Ss',  time.gmtime(eta_relative))
         else:
             return ""
 
@@ -285,7 +290,7 @@ def check_progress_call(id_part):
     if shared.state.sampling_steps > 0:
         progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
 
-    time_left = calc_time_left( progress, 60, " ETA:", shared.state.time_left_force_display )
+    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
     if time_left != "":
         shared.state.time_left_force_display = True
 
@@ -293,7 +298,7 @@ def check_progress_call(id_part):
 
     progressbar = ""
     if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:hidden;width:{progress * 100}%">{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}</div></div>"""
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}</div></div>"""
 
     image = gr_show(False)
     preview_visibility = gr_show(False)
-- 
cgit v1.2.1


From 1e4809b251d478a102fd980dcfc26e21d6d3730b Mon Sep 17 00:00:00 2001
From: Alexandre Simard <as.simard@outlook.com>
Date: Wed, 19 Oct 2022 12:53:23 -0400
Subject: Added a bit of padding to the left

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index c9a923ab..a2dbd41e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -298,7 +298,7 @@ def check_progress_call(id_part):
 
     progressbar = ""
     if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}</div></div>"""
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
 
     image = gr_show(False)
     preview_visibility = gr_show(False)
-- 
cgit v1.2.1


From 5e012e4dfa5dcfeade0394678cf14b70682dba6c Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Wed, 19 Oct 2022 06:17:47 -0700
Subject: Infotext saves more specific hypernet name.

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index ea926fc3..bcb0c32c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -304,7 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')),
+        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.filename.split('\\')[-1].split('.')[0]),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.1


From 46122c4ff6aadc0f96e657f88dbac7bbd9f9bf99 Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Wed, 19 Oct 2022 19:18:52 +0300
Subject: Send empty prompts as valid generation parameter

---
 modules/generation_parameters_copypaste.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index c27826b6..98d24406 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -45,10 +45,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         else:
             prompt += ("" if prompt == "" else "\n") + line
 
-    if len(prompt) > 0:
         res["Prompt"] = prompt
-
-    if len(negative_prompt) > 0:
         res["Negative prompt"] = negative_prompt
 
     for k, v in re_param.findall(lastline):
-- 
cgit v1.2.1


From 14c1c2b9351f16d43ba4e6b6c9062edad44a6bec Mon Sep 17 00:00:00 2001
From: Alexandre Simard <as.simard@outlook.com>
Date: Wed, 19 Oct 2022 13:53:52 -0400
Subject: Show PB texts at same time and earlier

For big tasks (1000+ steps), waiting 1 minute to see ETA is long and this changes it so the number of steps done plays a role in showing the text as well.
---
 modules/ui.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index a2dbd41e..0abd177a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -261,14 +261,14 @@ def wrap_gradio_call(func, extra_outputs=None):
     return f
 
 
-def calc_time_left(progress, threshold, label, force_display):
+def calc_time_left(progress, threshold, label, force_display, showTime):
     if progress == 0:
         return ""
     else:
         time_since_start = time.time() - shared.state.time_start
         eta = (time_since_start/progress)
         eta_relative = eta-time_since_start
-        if (eta_relative > threshold and progress > 0.02) or force_display:
+        if (eta_relative > threshold and showTime) or force_display:
             if eta_relative > 3600:
                 return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
             elif eta_relative > 60:
@@ -290,7 +290,10 @@ def check_progress_call(id_part):
     if shared.state.sampling_steps > 0:
         progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
 
-    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
+    # Show progress percentage and time left at the same moment, and base it also on steps done
+    showPBText = progress >= 0.01 or shared.state.sampling_step >= 10
+
+    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display, showPBText )
     if time_left != "":
         shared.state.time_left_force_display = True
 
@@ -298,7 +301,7 @@ def check_progress_call(id_part):
 
     progressbar = ""
     if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if showPBText else ""}</div></div>"""
 
     image = gr_show(False)
     preview_visibility = gr_show(False)
-- 
cgit v1.2.1


From 13ed73bedaa3df0f3edff41bd89bf0702f1c57b5 Mon Sep 17 00:00:00 2001
From: Greg Fuller <gfuller23@gmail.com>
Date: Tue, 18 Oct 2022 16:24:55 -0700
Subject: Update Learning Rate tooltip

---
 javascript/hints.js | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/javascript/hints.js b/javascript/hints.js
index b98012f5..a1fcc93b 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -91,6 +91,8 @@ titles = {
 
     "Weighted sum": "Result = A * (1 - M) + B * M",
     "Add difference": "Result = A + (B - C) * M",
+
+    "Learning rate": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
 }
 
 
-- 
cgit v1.2.1


From fd1008f1e0f067a99793c7885e1f5010811f1ac0 Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Sun, 16 Oct 2022 12:41:03 +0200
Subject: Better Bug report form

---
 .github/ISSUE_TEMPLATE/bug_report.md  | 32 ------------------
 .github/ISSUE_TEMPLATE/bug_report.yml | 61 +++++++++++++++++++++++++++++++++++
 .vscode/settings.json                 |  5 +++
 3 files changed, 66 insertions(+), 32 deletions(-)
 delete mode 100644 .github/ISSUE_TEMPLATE/bug_report.md
 create mode 100644 .github/ISSUE_TEMPLATE/bug_report.yml
 create mode 100644 .vscode/settings.json

diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
deleted file mode 100644
index 50c54210..00000000
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ /dev/null
@@ -1,32 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: bug-report
-assignees: ''
-
----
-
-**Describe the bug**
-A clear and concise description of what the bug is.
-
-**To Reproduce**
-Steps to reproduce the behavior:
-1. Go to '...'
-2. Click on '....'
-3. Scroll down to '....'
-4. See error
-
-**Expected behavior**
-A clear and concise description of what you expected to happen.
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
-
-**Desktop (please complete the following information):**
- - OS: [e.g. Windows, Linux]
- - Browser [e.g. chrome, safari]
- - Commit revision [looks like this: e68484500f76a33ba477d5a99340ab30451e557b; can be seen when launching webui.bat, or obtained manually by running `git rev-parse HEAD`]
-
-**Additional context**
-Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
new file mode 100644
index 00000000..9b7e224e
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -0,0 +1,61 @@
+name: Bug Report
+description: File a bug report
+title: "[Bug]: "
+labels: ["bug-report"]
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this bug report!
+  - type: textarea
+    id: what-did
+    attributes:
+      label: What did happened?
+      description: Also tell us, what did you expect to happen?
+    validations:
+      required: true
+  - type: textarea
+    id: what-should
+    attributes:
+      label: What should have happened?
+      description: tell what you think the normal behavior should be
+    validations:
+      required: true
+  - type: textarea
+    id: commit
+    attributes:
+      label: commit
+      description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
+  - type: dropdown
+    id: browsers
+    attributes:
+      label: What browsers do you use to run the UI ?
+      multiple: true
+      options:
+        - Mozilla Firefox
+        - Google Chrome
+        - Brave
+        - Apple Safari
+        - Microsoft Edge
+  - type: dropdown
+    id: os
+    attributes:
+      label: Where are you running the webui?
+      multiple: true
+      options:
+        - Colab/Cloud
+        - Windows
+        - Linux
+        - MacOS
+  - type: textarea
+    id: cmdargs
+    attributes:
+      label: Command Line Arguments
+      description: Are you using any launching parameters/command line arguments (modified webui-user.py) ? If yes, please write them below
+      render: Shell
+  - type: textarea
+    id: misc
+    attributes:
+      label: Additionnal information, context and logs
+      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 00000000..d6e94aad
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,5 @@
+{
+  "yaml.schemas": {
+    "https://json.schemastore.org/github-issue-forms.json": "file:///c%3A/AI/Repos/stable-diffusion-webui-moz/.github/ISSUE_TEMPLATE/bug_report.md"
+  }
+}
-- 
cgit v1.2.1


From 57c48093a92e58e07d5875de98ad2a790a7ceb14 Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Sun, 16 Oct 2022 12:43:06 +0200
Subject: Delete .vscode directory

---
 .vscode/settings.json | 5 -----
 1 file changed, 5 deletions(-)
 delete mode 100644 .vscode/settings.json

diff --git a/.vscode/settings.json b/.vscode/settings.json
deleted file mode 100644
index d6e94aad..00000000
--- a/.vscode/settings.json
+++ /dev/null
@@ -1,5 +0,0 @@
-{
-  "yaml.schemas": {
-    "https://json.schemastore.org/github-issue-forms.json": "file:///c%3A/AI/Repos/stable-diffusion-webui-moz/.github/ISSUE_TEMPLATE/bug_report.md"
-  }
-}
-- 
cgit v1.2.1


From d0042587adf03059977181fae9c2ede019044fe0 Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Sun, 16 Oct 2022 12:46:36 +0200
Subject: Cleaning & improvements

---
 .github/ISSUE_TEMPLATE/bug_report.yml | 26 +++++++++++---------------
 .gitignore                            |  1 +
 2 files changed, 12 insertions(+), 15 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 9b7e224e..02fc994c 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -4,10 +4,6 @@ title: "[Bug]: "
 labels: ["bug-report"]
 
 body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to fill out this bug report!
   - type: textarea
     id: what-did
     attributes:
@@ -25,8 +21,18 @@ body:
   - type: textarea
     id: commit
     attributes:
-      label: commit
+      label: Commit where the problem happens
       description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
+  - type: dropdown
+    id: os
+    attributes:
+      label: What OS do you use to run the webui?
+      multiple: true
+      options:
+        - Colab/Runpod or Cloud based
+        - Windows
+        - Linux
+        - MacOS
   - type: dropdown
     id: browsers
     attributes:
@@ -38,16 +44,6 @@ body:
         - Brave
         - Apple Safari
         - Microsoft Edge
-  - type: dropdown
-    id: os
-    attributes:
-      label: Where are you running the webui?
-      multiple: true
-      options:
-        - Colab/Cloud
-        - Windows
-        - Linux
-        - MacOS
   - type: textarea
     id: cmdargs
     attributes:
diff --git a/.gitignore b/.gitignore
index 69785b3e..f9c3357c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,3 +27,4 @@ __pycache__
 notification.mp3
 /SwinIR
 /textual_inversion
+.vscode
\ No newline at end of file
-- 
cgit v1.2.1


From dd66530a63f47aa87f4a95eaece51052d45a29f0 Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Sun, 16 Oct 2022 12:53:36 +0200
Subject: Fixes and adding step by step

---
 .github/ISSUE_TEMPLATE/bug_report.yml | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 02fc994c..3243f934 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -7,8 +7,18 @@ body:
   - type: textarea
     id: what-did
     attributes:
-      label: What did happened?
-      description: Also tell us, what did you expect to happen?
+      label: What happened?
+      description: Tell us what happened in a very clear and simple way
+    validations:
+      required: true
+  - type: textarea
+    id: steps
+    attributes:
+      label: Steps to reproduce the problem
+      description: Please provide us with precise step by step information to reproduce the bug
+      value: 1. Go to .... |
+        2. Press ....
+        3. ...
     validations:
       required: true
   - type: textarea
@@ -18,7 +28,7 @@ body:
       description: tell what you think the normal behavior should be
     validations:
       required: true
-  - type: textarea
+  - type: input
     id: commit
     attributes:
       label: Commit where the problem happens
-- 
cgit v1.2.1


From 45f188e0d3167f36c69bdf392a1141b8e5183d21 Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Sun, 16 Oct 2022 12:54:43 +0200
Subject: fixing linebreak issue

---
 .github/ISSUE_TEMPLATE/bug_report.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 3243f934..55fb2d72 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -16,7 +16,8 @@ body:
     attributes:
       label: Steps to reproduce the problem
       description: Please provide us with precise step by step information to reproduce the bug
-      value: 1. Go to .... |
+      value: |
+        1. Go to .... 
         2. Press ....
         3. ...
     validations:
-- 
cgit v1.2.1


From 03cf7cf32798d45ae92832bd22bea8b299e64a17 Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Sun, 16 Oct 2022 13:00:28 +0200
Subject: Fixes and trying to make dropdown required

---
 .github/ISSUE_TEMPLATE/bug_report.yml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 55fb2d72..201ed57d 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -15,7 +15,7 @@ body:
     id: steps
     attributes:
       label: Steps to reproduce the problem
-      description: Please provide us with precise step by step information to reproduce the bug
+      description: Please provide us with precise step by step information on how to reproduce the bug
       value: |
         1. Go to .... 
         2. Press ....
@@ -44,6 +44,7 @@ body:
         - Windows
         - Linux
         - MacOS
+      required: true
   - type: dropdown
     id: browsers
     attributes:
@@ -55,6 +56,7 @@ body:
         - Brave
         - Apple Safari
         - Microsoft Edge
+      required: true
   - type: textarea
     id: cmdargs
     attributes:
-- 
cgit v1.2.1


From ca30e67289f9e2d0aca33c9da8d915ae3bcc39cc Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Sun, 16 Oct 2022 13:03:03 +0200
Subject: removing the required tag as it obviously doesn't work, adding a top
 description

---
 .github/ISSUE_TEMPLATE/bug_report.yml | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 201ed57d..b53db6bb 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -4,6 +4,10 @@ title: "[Bug]: "
 labels: ["bug-report"]
 
 body:
+  - type: markdown
+    attributes:
+      value: |
+        Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers"
   - type: textarea
     id: what-did
     attributes:
@@ -44,7 +48,6 @@ body:
         - Windows
         - Linux
         - MacOS
-      required: true
   - type: dropdown
     id: browsers
     attributes:
@@ -56,7 +59,6 @@ body:
         - Brave
         - Apple Safari
         - Microsoft Edge
-      required: true
   - type: textarea
     id: cmdargs
     attributes:
-- 
cgit v1.2.1


From 8400e854747970ee1593619c37736c47cdac7e3e Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Sun, 16 Oct 2022 13:13:22 +0200
Subject: Adding a confirmation checkbox that the user has checked the issues &
 commits before

Also small fixes
---
 .github/ISSUE_TEMPLATE/bug_report.yml | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index b53db6bb..3e8732f9 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -1,13 +1,20 @@
 name: Bug Report
-description: File a bug report
+description: You think somethings is broken in the UI
 title: "[Bug]: "
-labels: ["bug-report"]
+labels: ["bug"]
 
 body:
   - type: markdown
     attributes:
       value: |
-        Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers"
+        Make sure this issue hasn't been posted already and wasn't solved in recent commits, then fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers"
+  - type: checkboxes
+    attributes:
+      label: Is there an existing issue for this?
+      description: Please search to see if an issue already exists for the bug you encountered, and that it hasn't been fixed in a recent build/commit.
+      options:
+        - label: I have searched the existing issues and checked the recent builds/commits
+          required: true
   - type: textarea
     id: what-did
     attributes:
-- 
cgit v1.2.1


From 62a1a97fe3e1b322e2a1c6a1fcc0272e9b132704 Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Sun, 16 Oct 2022 13:22:35 +0200
Subject: Fixed labels and created a brand new Feature Request yaml

---
 .github/ISSUE_TEMPLATE/bug_report.yml      | 10 ++++----
 .github/ISSUE_TEMPLATE/feature_request.md  | 20 ---------------
 .github/ISSUE_TEMPLATE/feature_request.yml | 40 ++++++++++++++++++++++++++++++
 3 files changed, 45 insertions(+), 25 deletions(-)
 delete mode 100644 .github/ISSUE_TEMPLATE/feature_request.md
 create mode 100644 .github/ISSUE_TEMPLATE/feature_request.yml

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 3e8732f9..04f072bc 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -1,13 +1,9 @@
 name: Bug Report
 description: You think somethings is broken in the UI
 title: "[Bug]: "
-labels: ["bug"]
+labels: ["bug-report"]
 
 body:
-  - type: markdown
-    attributes:
-      value: |
-        Make sure this issue hasn't been posted already and wasn't solved in recent commits, then fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers"
   - type: checkboxes
     attributes:
       label: Is there an existing issue for this?
@@ -15,6 +11,10 @@ body:
       options:
         - label: I have searched the existing issues and checked the recent builds/commits
           required: true
+  - type: markdown
+    attributes:
+      value: |
+        Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible*
   - type: textarea
     id: what-did
     attributes:
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
deleted file mode 100644
index eda42fa7..00000000
--- a/.github/ISSUE_TEMPLATE/feature_request.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-name: Feature request
-about: Suggest an idea for this project
-title: ''
-labels: 'suggestion'
-assignees: ''
-
----
-
-**Is your feature request related to a problem? Please describe.**
-A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
-
-**Describe the solution you'd like**
-A clear and concise description of what you want to happen.
-
-**Describe alternatives you've considered**
-A clear and concise description of any alternative solutions or features you've considered.
-
-**Additional context**
-Add any other context or screenshots about the feature request here.
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
new file mode 100644
index 00000000..045e15da
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -0,0 +1,40 @@
+name: Feature request
+description: Suggest an idea for this project
+title: "[Feature Request]: "
+labels: ["suggestion"]
+
+body:
+  - type: checkboxes
+    attributes:
+      label: Is there an existing issue for this?
+      description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit.
+      options:
+        - label: I have searched the existing issues and checked the recent builds/commits
+          required: true
+  - type: markdown
+    attributes:
+      value: |
+        Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible
+  - type: textarea
+    id: feature
+    attributes:
+      label: What would your feature do ?
+      description: Tell us about your feature in a very clear and simple way, and what problem it would solve
+    validations:
+      required: true
+  - type: textarea
+    id: workflow
+    attributes:
+      label: Proposed workflow
+      description: Please provide us with step by step information on how you'd like the feature to be accessed and used
+      value: |
+        1. Go to .... 
+        2. Press ....
+        3. ...
+    validations:
+      required: true
+  - type: textarea
+    id: misc
+    attributes:
+      label: Additionnal information
+      description: Add any other context or screenshots about the feature request here.
-- 
cgit v1.2.1


From 5292d1f0920186e53ef2280637845c8344518a89 Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Sun, 16 Oct 2022 13:26:05 +0200
Subject: Formatting the top description

---
 .github/ISSUE_TEMPLATE/bug_report.yml      | 2 +-
 .github/ISSUE_TEMPLATE/feature_request.yml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 04f072bc..84053e76 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -14,7 +14,7 @@ body:
   - type: markdown
     attributes:
       value: |
-        Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible*
+        *Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible**
   - type: textarea
     id: what-did
     attributes:
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
index 045e15da..62133a0d 100644
--- a/.github/ISSUE_TEMPLATE/feature_request.yml
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -14,7 +14,7 @@ body:
   - type: markdown
     attributes:
       value: |
-        Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible
+        *Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible*
   - type: textarea
     id: feature
     attributes:
-- 
cgit v1.2.1


From 3e2a035ffaf6f45a18b971ee0388f5ddca312714 Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Sun, 16 Oct 2022 14:30:11 +0200
Subject: Removed obsolete legacy Hlky description

---
 .github/ISSUE_TEMPLATE/bug_report.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 84053e76..629afad3 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -76,4 +76,4 @@ body:
     id: misc
     attributes:
       label: Additionnal information, context and logs
-      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
+      description: Please provide us with any relevant additional info, context or log output.
-- 
cgit v1.2.1


From a0e50d5e70094dbdfd048efee566865ab93e4c3f Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Mon, 17 Oct 2022 19:09:06 +0200
Subject: Improved the OS/Platforms field

---
 .github/ISSUE_TEMPLATE/bug_report.yml | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 629afad3..d2edb250 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -46,19 +46,21 @@ body:
       label: Commit where the problem happens
       description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
   - type: dropdown
-    id: os
+    id: platforms
     attributes:
-      label: What OS do you use to run the webui?
+      label: What platforms do you use to access UI ?
       multiple: true
       options:
-        - Colab/Runpod or Cloud based
         - Windows
         - Linux
         - MacOS
+        - iOS
+        - Android
+        - Other/Cloud
   - type: dropdown
     id: browsers
     attributes:
-      label: What browsers do you use to run the UI ?
+      label: What browsers do you use to access the UI ?
       multiple: true
       options:
         - Mozilla Firefox
-- 
cgit v1.2.1


From 5d9e3acd4e8fc9562d0b2972e79f6cf8597d3805 Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Wed, 19 Oct 2022 19:38:38 +0200
Subject: Fixed additionnnnal typo, sorry

---
 .github/ISSUE_TEMPLATE/bug_report.yml      | 2 +-
 .github/ISSUE_TEMPLATE/feature_request.yml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index d2edb250..35802a53 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -77,5 +77,5 @@ body:
   - type: textarea
     id: misc
     attributes:
-      label: Additionnal information, context and logs
+      label: Additional information, context and logs
       description: Please provide us with any relevant additional info, context or log output.
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
index 62133a0d..8ca6e21f 100644
--- a/.github/ISSUE_TEMPLATE/feature_request.yml
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -36,5 +36,5 @@ body:
   - type: textarea
     id: misc
     attributes:
-      label: Additionnal information
+      label: Additional information
       description: Add any other context or screenshots about the feature request here.
-- 
cgit v1.2.1


From b748b583c0b9f771c1be509175a6913e3f2ad97c Mon Sep 17 00:00:00 2001
From: Mackerel <mackerel@chickatrice.com>
Date: Wed, 19 Oct 2022 14:22:03 -0400
Subject: generation_parameters_copypaste.py: fix indent

---
 modules/generation_parameters_copypaste.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 98d24406..0f041449 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -45,8 +45,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         else:
             prompt += ("" if prompt == "" else "\n") + line
 
-        res["Prompt"] = prompt
-        res["Negative prompt"] = negative_prompt
+    res["Prompt"] = prompt
+    res["Negative prompt"] = negative_prompt
 
     for k, v in re_param.findall(lastline):
         m = re_imagesize.match(v)
-- 
cgit v1.2.1


From 604620a7f08d1126a8689f9f4bec8ade0801a69b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E3=81=B5=E3=81=81?=
 <34892635+fa0311@users.noreply.github.com>
Date: Thu, 20 Oct 2022 03:16:22 +0900
Subject: Add xformers message.

---
 launch.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/launch.py b/launch.py
index 7b15e78e..333f308a 100644
--- a/launch.py
+++ b/launch.py
@@ -156,9 +156,15 @@ def prepare_enviroment():
     if not is_installed("clip"):
         run_pip(f"install {clip_package}", "clip")
 
-    if (not is_installed("xformers") or reinstall_xformers) and xformers and platform.python_version().startswith("3.10"):
+    if (not is_installed("xformers") or reinstall_xformers) and xformers:
         if platform.system() == "Windows":
-            run_pip(f"install -U -I --no-deps {xformers_windows_package}", "xformers")
+            if platform.python_version().startswith("3.10"):
+                run_pip(f"install -U -I --no-deps {xformers_windows_package}", "xformers")
+            else:
+                print("Installation of xformers is not supported in this version of Python.")
+                print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
+                if not is_installed("xformers"):
+                    exit(0)
         elif platform.system() == "Linux":
             run_pip("install xformers", "xformers")
 
-- 
cgit v1.2.1


From eb7ba4b713ac2fb960ecf6365b1de0c89451e583 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 19 Oct 2022 19:50:46 +0100
Subject: update training header text

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 1573ef82..93c0767c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1247,7 +1247,7 @@ def create_ui(wrap_gradio_gpu_call):
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                 with gr.Tab(label="Train"):
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images<br/>Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion">wiki</a></p>")
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images<br/>Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
                     with gr.Row():
                         train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                         create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
-- 
cgit v1.2.1


From 4fbdbddc18b21f712acae58bf41740d27023285f Mon Sep 17 00:00:00 2001
From: Alexandre Simard <as.simard@outlook.com>
Date: Wed, 19 Oct 2022 15:21:36 -0400
Subject: Remove pad spaces from progress bar text

---
 javascript/progressbar.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 7a05726e..24ab4795 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -10,7 +10,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
     
     if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){
         if(progressbar.innerText){
-            let newtitle = 'Stable Diffusion - ' + progressbar.innerText
+            let newtitle = 'Stable Diffusion - ' + progressbar.innerText.slice(2)
             if(document.title != newtitle){
                 document.title =  newtitle;          
             }
-- 
cgit v1.2.1


From 4d663055ded968831ec97f047dfa8e94036cf1c1 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 19 Oct 2022 20:33:18 +0100
Subject: update ui with extra training options

---
 modules/ui.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 93c0767c..cdb9d335 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1206,6 +1206,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_embedding_name = gr.Textbox(label="Name")
                     initialization_text = gr.Textbox(label="Initialization text", value="*")
                     nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1219,6 +1220,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1247,14 +1249,17 @@ def create_ui(wrap_gradio_gpu_call):
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                 with gr.Tab(label="Train"):
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images<br/>Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
                     with gr.Row():
                         train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                         create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
                     with gr.Row():
                         train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
                         create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
-                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
+                    with gr.Row():
+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
+                    
                     batch_size = gr.Number(label='Batch size', value=1, precision=0)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
@@ -1288,6 +1293,7 @@ def create_ui(wrap_gradio_gpu_call):
                 new_embedding_name,
                 initialization_text,
                 nvpt,
+                overwrite_old_embedding,
             ],
             outputs=[
                 train_embedding_name,
@@ -1303,6 +1309,7 @@ def create_ui(wrap_gradio_gpu_call):
                 new_hypernetwork_sizes,
                 new_hypernetwork_layer_structure,
                 new_hypernetwork_add_layer_norm,
+                overwrite_old_hypernetwork,
             ],
             outputs=[
                 train_hypernetwork_name,
-- 
cgit v1.2.1


From 41e3877be2c667316515c86037413763eb0ba4da Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Wed, 19 Oct 2022 13:44:59 -0700
Subject: fix entropy point calculation

---
 modules/textual_inversion/preprocess.py | 34 ++++++++++++++++++---------------
 1 file changed, 19 insertions(+), 15 deletions(-)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 168bfb09..7c1a594e 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -196,9 +196,9 @@ def image_focal_points(im):
 
     points = cv2.goodFeaturesToTrack(
         np_im,
-        maxCorners=50,
+        maxCorners=100,
         qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.05,
+        minDistance=min(grayscale.width, grayscale.height)*0.07,
         useHarrisDetector=False,
     )
 
@@ -218,28 +218,32 @@ def image_focal_points(im):
 
 
 def image_entropy_point(im, crop_width, crop_height):
-    img = im.copy()
-    # just make it easier to slide the test crop with images oriented the same way
-    if (img.size[0] < img.size[1]):
-        portrait = True
-        img = img.rotate(90, expand=1)
+    landscape = im.height < im.width
+    portrait = im.height > im.width
+    if landscape:
+      move_idx = [0, 2]
+      move_max = im.size[0]
+    elif portrait:
+      move_idx = [1, 3]
+      move_max = im.size[1]
 
     e_max = 0
     crop_current = [0, 0, crop_width, crop_height]
     crop_best = crop_current
-    while crop_current[2] < img.size[0]:
-        crop = img.crop(tuple(crop_current))
+    while crop_current[move_idx[1]] < move_max:
+        crop = im.crop(tuple(crop_current))
         e = image_entropy(crop)
 
-        if (e_max < e):
+        if (e > e_max):
           e_max = e
           crop_best = list(crop_current)
 
-        crop_current[0] += 4
-        crop_current[2] += 4
+        crop_current[move_idx[0]] += 4
+        crop_current[move_idx[1]] += 4
+
+    x_mid = int(crop_best[0] + crop_width/2)
+    y_mid = int(crop_best[1] + crop_height/2)
 
-    x_mid = int((crop_best[2] - crop_best[0])/2)
-    y_mid = int((crop_best[3] - crop_best[1])/2)
 
     return {
         'x': x_mid,
@@ -250,7 +254,7 @@ def image_entropy_point(im, crop_width, crop_height):
 
 def image_entropy(im):
     # greyscale image entropy
-    band = np.asarray(im.convert("L"))
+    band = np.asarray(im.convert("1"))
     hist, _ = np.histogram(band, bins=range(0, 256))
     hist = hist[hist > 0]
     return -np.log2(hist / hist.sum()).sum()
-- 
cgit v1.2.1


From 8e7097d06a6a261580d34375c9d2a9e4ffc63ffa Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 13:47:45 -0700
Subject: Added support for RunwayML inpainting model

---
 modules/processing.py           |  34 ++++++-
 modules/sd_hijack_inpainting.py | 208 ++++++++++++++++++++++++++++++++++++++++
 modules/sd_models.py            |  16 +++-
 modules/sd_samplers.py          |  50 +++++++---
 4 files changed, 293 insertions(+), 15 deletions(-)
 create mode 100644 modules/sd_hijack_inpainting.py

diff --git a/modules/processing.py b/modules/processing.py
index bcb0c32c..a6c308f9 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -546,7 +546,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
+            
+            # The "masked-image" in this case will just be all zeros since the entire image is masked.
+            image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device)
+            image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
+
+            # Add the fake full 1s mask to the first dimension.
+            image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+            image_conditioning = image_conditioning.to(x.dtype)
+
+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=image_conditioning)
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
@@ -714,10 +723,31 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
+        if self.image_mask is not None:
+            conditioning_mask = np.array(self.image_mask.convert("L"))
+            conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+            conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+            # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+            conditioning_mask = torch.round(conditioning_mask)
+        else:
+            conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
+
+        # Create another latent image, this time with a masked version of the original input.
+        conditioning_mask = conditioning_mask.to(image.device)
+        conditioning_image = image * (1.0 - conditioning_mask)
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+
+        # Create the concatenated conditioning tensor to be fed to `c_concat`
+        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
+        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+        self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+        self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
+
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
-        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)
+        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
 
         if self.mask is not None:
             samples = samples * self.nmask + self.init_latent * self.mask
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
new file mode 100644
index 00000000..7e5670d6
--- /dev/null
+++ b/modules/sd_hijack_inpainting.py
@@ -0,0 +1,208 @@
+import torch
+import numpy as np
+
+from tqdm import tqdm
+from einops import rearrange, repeat
+from omegaconf import ListConfig
+
+from types import MethodType
+
+import ldm.models.diffusion.ddpm
+import ldm.models.diffusion.ddim
+
+from ldm.models.diffusion.ddpm import LatentDiffusion
+from ldm.models.diffusion.ddim import DDIMSampler, noise_like
+
+# =================================================================================================
+# Monkey patch DDIMSampler methods from RunwayML repo directly.
+# Adapted from:
+# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
+# =================================================================================================
+@torch.no_grad()
+def sample(
+    self,
+    S,
+    batch_size,
+    shape,
+    conditioning=None,
+    callback=None,
+    normals_sequence=None,
+    img_callback=None,
+    quantize_x0=False,
+    eta=0.,
+    mask=None,
+    x0=None,
+    temperature=1.,
+    noise_dropout=0.,
+    score_corrector=None,
+    corrector_kwargs=None,
+    verbose=True,
+    x_T=None,
+    log_every_t=100,
+    unconditional_guidance_scale=1.,
+    unconditional_conditioning=None,
+    # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+    **kwargs
+    ):
+    if conditioning is not None:
+        if isinstance(conditioning, dict):
+            ctmp = conditioning[list(conditioning.keys())[0]]
+            while isinstance(ctmp, list):
+                ctmp = elf.inpainting_fill == 2:
+                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
+            elif self.inpainting_fill == 3:
+                self.init_latent = self.init_latent * self.mask
+
+        if self.image_mask is not None:
+            conditioning_mask = np.array(self.image_mask.convert("L"))
+            conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+            conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+            # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+            conditioning_mask = torch.round(conditioning_mask)
+        else:
+            conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
+
+        # Create another latent image, this time with a masked version of the original input.
+        conditioning_mask = conditioning_mask.to(image.device)
+        conditioning_image = image * (1.0 - conditioning_mask)
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+
+        # Create the concatenated conditioning tensor to be fed to `c_concat`
+        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
+        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+        self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+        self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
+
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+        x = create_random_tensors([opctmp[0]
+            cbs = ctmp.shape[0]
+            if cbs != batch_size:
+                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+        else:
+            if conditioning.shape[0] != batch_size:
+                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+    # sampling
+    C, H, W = shape
+    size = (batch_size, C, H, W)
+    print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+
+    samples, intermediates = self.ddim_sampling(conditioning, size,
+                                                callback=callback,
+                                                img_callback=img_callback,
+                                                quantize_denoised=quantize_x0,
+                                                mask=mask, x0=x0,
+                                                ddim_use_original_steps=False,
+                                                noise_dropout=noise_dropout,
+                                                temperature=temperature,
+                                                score_corrector=score_corrector,
+                                                corrector_kwargs=corrector_kwargs,
+                                                x_T=x_T,
+                                                log_every_t=log_every_t,
+                                                unconditional_guidance_scale=unconditional_guidance_scale,
+                                                unconditional_conditioning=unconditional_conditioning,
+                                                )
+    return samples, intermediates
+
+
+@torch.no_grad()
+def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                    unconditional_guidance_scale=1., unconditional_conditioning=None):
+    b, *_, device = *x.shape, x.device
+
+    if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+        e_t = self.model.apply_model(x, t, c)
+    else:
+        x_in = torch.cat([x] * 2)
+        t_in = torch.cat([t] * 2)
+        if isinstance(c, dict):
+            assert isinstance(unconditional_conditioning, dict)
+            c_in = dict()
+            for k in c:
+                if isinstance(c[k], list):
+                    c_in[k] = [
+                        torch.cat([unconditional_conditioning[k][i], c[k][i]])
+                        for i in range(len(c[k]))
+                    ]
+                else:
+                    c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
+        else:
+            c_in = torch.cat([unconditional_conditioning, c])
+        e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+        e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+    if score_corrector is not None:
+        assert self.model.parameterization == "eps"
+        e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+    # select parameters corresponding to the currently considered timestep
+    a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+    a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+    sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+    sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+    # current prediction for x_0
+    pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+    if quantize_denoised:
+        pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+    # direction pointing to x_t
+    dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+    noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+    if noise_dropout > 0.:
+        noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+    x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+    return x_prev, pred_x0
+
+
+# =================================================================================================
+# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
+# Adapted from:
+# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py
+# =================================================================================================
+
+@torch.no_grad()
+def get_unconditional_conditioning(self, batch_size, null_label=None):
+    if null_label is not None:
+        xc = null_label
+        if isinstance(xc, ListConfig):
+            xc = list(xc)
+        if isinstance(xc, dict) or isinstance(xc, list):
+            c = self.get_learned_conditioning(xc)
+        else:
+            if hasattr(xc, "to"):
+                xc = xc.to(self.device)
+            c = self.get_learned_conditioning(xc)
+    else:
+        # todo: get null label from cond_stage_model
+        raise NotImplementedError()
+    c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
+    return c
+
+class LatentInpaintDiffusion(LatentDiffusion):
+    def __init__(
+        self,
+        concat_keys=("mask", "masked_image"),
+        masked_image_key="masked_image",
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.masked_image_key = masked_image_key
+        assert self.masked_image_key in concat_keys
+        self.concat_keys = concat_keys
+
+def should_hijack_inpainting(checkpoint_info):
+    return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml")
+
+def do_inpainting_hijack():
+    ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
+    ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
+    ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
+    ldm.models.diffusion.ddim.DDIMSampler.sample = sample
\ No newline at end of file
diff --git a/modules/sd_models.py b/modules/sd_models.py
index eae22e87..47836d25 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -9,6 +9,7 @@ from ldm.util import instantiate_from_config
 
 from modules import shared, modelloader, devices
 from modules.paths import models_path
+from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
@@ -211,6 +212,19 @@ def load_model():
         print(f"Loading config from: {checkpoint_info.config}")
 
     sd_config = OmegaConf.load(checkpoint_info.config)
+    
+    if should_hijack_inpainting(checkpoint_info):
+        do_inpainting_hijack()
+
+        # Hardcoded config for now...
+        sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
+        sd_config.model.params.use_ema = False
+        sd_config.model.params.conditioning_key = "hybrid"
+        sd_config.model.params.unet_config.params.in_channels = 9
+
+        # Create a "fake" config with a different name so that we know to unload it when switching models.
+        checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
+
     sd_model = instantiate_from_config(sd_config.model)
     load_model_weights(sd_model, checkpoint_info)
 
@@ -234,7 +248,7 @@ def reload_model_weights(sd_model, info=None):
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
-    if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
+    if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         checkpoints_loaded.clear()
         shared.sd_model = load_model()
         return shared.sd_model
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b58e810b..9d3cf289 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -136,9 +136,15 @@ class VanillaStableDiffusionSampler:
         if self.stop_at is not None and self.step > self.stop_at:
             raise InterruptedException
 
+        # Have to unwrap the inpainting conditioning here to perform pre-preocessing
+        image_conditioning = None
+        if isinstance(cond, dict):
+            image_conditioning = cond["c_concat"][0]
+            cond = cond["c_crossattn"][0]
+            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
 
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
+        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)            
 
         assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
         cond = tensor
@@ -157,6 +163,10 @@ class VanillaStableDiffusionSampler:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)
             x_dec = img_orig * self.mask + self.nmask * x_dec
 
+        if image_conditioning is not None:
+            cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
         res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
 
         if self.mask is not None:
@@ -182,7 +192,7 @@ class VanillaStableDiffusionSampler:
         self.mask = p.mask if hasattr(p, 'mask') else None
         self.nmask = p.nmask if hasattr(p, 'nmask') else None
 
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
 
         self.initialize(p)
@@ -202,7 +212,7 @@ class VanillaStableDiffusionSampler:
 
         return samples
 
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         self.initialize(p)
 
         self.init_latent = None
@@ -210,6 +220,11 @@ class VanillaStableDiffusionSampler:
 
         steps = steps or p.steps
 
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        if image_conditioning is not None:
+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
         # existing code fails with certain step counts, like 9
         try:
             samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
@@ -228,7 +243,7 @@ class CFGDenoiser(torch.nn.Module):
         self.init_latent = None
         self.step = 0
 
-    def forward(self, x, sigma, uncond, cond, cond_scale):
+    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
         if state.interrupted or state.skipped:
             raise InterruptedException
 
@@ -239,28 +254,29 @@ class CFGDenoiser(torch.nn.Module):
         repeats = [len(conds_list[i]) for i in range(batch_size)]
 
         x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
 
         if tensor.shape[1] == uncond.shape[1]:
             cond_in = torch.cat([tensor, uncond])
 
             if shared.batch_cond_uncond:
-                x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
+                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
             else:
                 x_out = torch.zeros_like(x_in)
                 for batch_offset in range(0, x_out.shape[0], batch_size):
                     a = batch_offset
                     b = a + batch_size
-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
         else:
             x_out = torch.zeros_like(x_in)
             batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
             for batch_offset in range(0, tensor.shape[0], batch_size):
                 a = batch_offset
                 b = min(a + batch_size, tensor.shape[0])
-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})
 
-            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)
+            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
 
         denoised_uncond = x_out[-uncond.shape[0]:]
         denoised = torch.clone(denoised_uncond)
@@ -361,7 +377,7 @@ class KDiffusionSampler:
 
         return extra_params_kwargs
 
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
 
         if p.sampler_noise_scheduler_override:
@@ -389,11 +405,16 @@ class KDiffusionSampler:
 
         self.model_wrap_cfg.init_latent = x
 
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
+            'cond': conditioning, 
+            'image_cond': image_conditioning, 
+            'uncond': unconditional_conditioning, 
+            'cond_scale': p.cfg_scale
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples
 
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
         steps = steps or p.steps
 
         if p.sampler_noise_scheduler_override:
@@ -414,7 +435,12 @@ class KDiffusionSampler:
         else:
             extra_params_kwargs['sigmas'] = sigmas
 
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+            'cond': conditioning, 
+            'image_cond': image_conditioning, 
+            'uncond': unconditional_conditioning, 
+            'cond_scale': p.cfg_scale
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples
 
-- 
cgit v1.2.1


From 0719c10bf1b817364a498ee11b90d30d3d527344 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 13:56:26 -0700
Subject: Fixed copying mistake

---
 modules/sd_hijack_inpainting.py | 79 +++++++++++++----------------------------
 1 file changed, 25 insertions(+), 54 deletions(-)

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 7e5670d6..d4d28d2e 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -19,63 +19,35 @@ from ldm.models.diffusion.ddim import DDIMSampler, noise_like
 # https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
 # =================================================================================================
 @torch.no_grad()
-def sample(
-    self,
-    S,
-    batch_size,
-    shape,
-    conditioning=None,
-    callback=None,
-    normals_sequence=None,
-    img_callback=None,
-    quantize_x0=False,
-    eta=0.,
-    mask=None,
-    x0=None,
-    temperature=1.,
-    noise_dropout=0.,
-    score_corrector=None,
-    corrector_kwargs=None,
-    verbose=True,
-    x_T=None,
-    log_every_t=100,
-    unconditional_guidance_scale=1.,
-    unconditional_conditioning=None,
-    # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-    **kwargs
-    ):
+def sample(self,
+            S,
+            batch_size,
+            shape,
+            conditioning=None,
+            callback=None,
+            normals_sequence=None,
+            img_callback=None,
+            quantize_x0=False,
+            eta=0.,
+            mask=None,
+            x0=None,
+            temperature=1.,
+            noise_dropout=0.,
+            score_corrector=None,
+            corrector_kwargs=None,
+            verbose=True,
+            x_T=None,
+            log_every_t=100,
+            unconditional_guidance_scale=1.,
+            unconditional_conditioning=None,
+            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+            **kwargs
+            ):
     if conditioning is not None:
         if isinstance(conditioning, dict):
             ctmp = conditioning[list(conditioning.keys())[0]]
             while isinstance(ctmp, list):
-                ctmp = elf.inpainting_fill == 2:
-                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
-            elif self.inpainting_fill == 3:
-                self.init_latent = self.init_latent * self.mask
-
-        if self.image_mask is not None:
-            conditioning_mask = np.array(self.image_mask.convert("L"))
-            conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
-            conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
-
-            # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
-            conditioning_mask = torch.round(conditioning_mask)
-        else:
-            conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
-
-        # Create another latent image, this time with a masked version of the original input.
-        conditioning_mask = conditioning_mask.to(image.device)
-        conditioning_image = image * (1.0 - conditioning_mask)
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
-
-        # Create the concatenated conditioning tensor to be fed to `c_concat`
-        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
-        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
-        self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
-        self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
-
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        x = create_random_tensors([opctmp[0]
+                ctmp = ctmp[0]
             cbs = ctmp.shape[0]
             if cbs != batch_size:
                 print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
@@ -106,7 +78,6 @@ def sample(
                                                 )
     return samples, intermediates
 
-
 @torch.no_grad()
 def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
                     temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-- 
cgit v1.2.1


From dde9f960727bfe151d418e43685a2881cf580a17 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 14:14:24 -0700
Subject: added support for ddim img2img

---
 modules/sd_samplers.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 9d3cf289..d270e4df 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -208,6 +208,12 @@ class VanillaStableDiffusionSampler:
         self.init_latent = x
         self.step = 0
 
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        if image_conditioning is not None:
+            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+            
+            
         samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
 
         return samples
-- 
cgit v1.2.1


From c418467c03db916c3e5312e6ac4a67365e196dbd Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 15:09:43 -0700
Subject: Don't compute latent mask if were not using it. Also added support
 for fixed highres_fix generation.

---
 modules/processing.py  | 72 +++++++++++++++++++++++++++++++-------------------
 modules/sd_samplers.py |  4 +++
 2 files changed, 49 insertions(+), 27 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index a6c308f9..684e5833 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -541,12 +541,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
-
-        if not self.enable_hr:
-            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            
+    def create_dummy_mask(self, x):
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
             # The "masked-image" in this case will just be all zeros since the entire image is masked.
             image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device)
             image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
@@ -555,11 +551,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
             image_conditioning = image_conditioning.to(x.dtype)
 
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=image_conditioning)
+        else:
+            # Dummy zero conditioning if we're not using inpainting model.
+            # Still takes up a bit of memory, but no encoder call.
+            image_conditioning = torch.zeros(x.shape[0], 5, x.shape[-2], x.shape[-1], dtype=x.dtype, device=x.device)
+
+        return image_conditioning
+
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
+
+        if not self.enable_hr:
+            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
@@ -596,7 +604,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=self.create_dummy_mask(samples))
 
         return samples
 
@@ -723,26 +731,36 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        if self.image_mask is not None:
-            conditioning_mask = np.array(self.image_mask.convert("L"))
-            conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
-            conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+        conditioning_key = self.sampler.conditioning_key
 
-            # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
-            conditioning_mask = torch.round(conditioning_mask)
+        if conditioning_key in {'hybrid', 'concat'}:
+            if self.image_mask is not None:
+                conditioning_mask = np.array(self.image_mask.convert("L"))
+                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+                conditioning_mask = torch.round(conditioning_mask)
+            else:
+                conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
+
+            # Create another latent image, this time with a masked version of the original input.
+            conditioning_mask = conditioning_mask.to(image.device)
+            conditioning_image = image * (1.0 - conditioning_mask)
+            conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+
+            # Create the concatenated conditioning tensor to be fed to `c_concat`
+            conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
+            conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+            self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+            self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
         else:
-            conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
-
-        # Create another latent image, this time with a masked version of the original input.
-        conditioning_mask = conditioning_mask.to(image.device)
-        conditioning_image = image * (1.0 - conditioning_mask)
-        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
-
-        # Create the concatenated conditioning tensor to be fed to `c_concat`
-        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
-        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
-        self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
-        self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
+            self.image_conditioning = torch.zeros(
+                self.init_latent.shape[0], 5, self.init_latent.shape[-2], self.init_latent.shape[-1], 
+                dtype=self.init_latent.dtype, 
+                device=self.init_latent.device
+            )
+
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d270e4df..c21be26e 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -117,6 +117,8 @@ class VanillaStableDiffusionSampler:
         self.config = None
         self.last_latent = None
 
+        self.conditioning_key = sd_model.model.conditioning_key
+
     def number_of_needed_noises(self, p):
         return 0
 
@@ -328,6 +330,8 @@ class KDiffusionSampler:
         self.config = None
         self.last_latent = None
 
+        self.conditioning_key = sd_model.model.conditioning_key
+
     def callback_state(self, d):
         step = d['i']
         latent = d["denoised"]
-- 
cgit v1.2.1


From d6ea5841374a28f3f6deb73abc251c8f0bcb240f Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:07:57 +0100
Subject: change html output

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7d519cd9..73c1cb80 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -380,7 +380,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 Loss: {mean_loss:.7f}<br/>
 Step: {hypernetwork.step}<br/>
 Last prompt: {html.escape(entries[0].cond_text)}<br/>
-Last saved embedding: {html.escape(last_saved_file)}<br/>
+Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-- 
cgit v1.2.1


From 166be3919b817cee5e702fd01c34afe9081b952c Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:09:40 +0100
Subject: allow overwrite old hn

---
 modules/hypernetworks/ui.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 08f75f15..f45345ea 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -10,9 +10,10 @@ from modules import sd_hijack, shared, devices
 from modules.hypernetworks import hypernetwork
 
 
-def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False):
+def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, add_layer_norm=False):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
-    assert not os.path.exists(fn), f"file {fn} already exists"
+    if not overwrite_old:
+        assert not os.path.exists(fn), f"file {fn} already exists"
 
     if type(layer_structure) == str:
         layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure)))
-- 
cgit v1.2.1


From 0087079c2d487b67b06ffc30f36ce486a74e6318 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:10:59 +0100
Subject: allow overwrite old embedding

---
 modules/textual_inversion/textual_inversion.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 3be69562..5776778b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -153,7 +153,7 @@ class EmbeddingDatabase:
         return None, None
 
 
-def create_embedding(name, num_vectors_per_token, init_text='*'):
+def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
     cond_model = shared.sd_model.cond_stage_model
     embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
 
@@ -165,7 +165,8 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
         vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
 
     fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
-    assert not os.path.exists(fn), f"file {fn} already exists"
+    if not overwrite_old:
+        assert not os.path.exists(fn), f"file {fn} already exists"
 
     embedding = Embedding(vec, name)
     embedding.step = 0
-- 
cgit v1.2.1


From 632e8d660293081cadb145d8062e5aff0a4a8f0d Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:19:40 +0100
Subject: split learn rates

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index cdb9d335..d07184ee 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1342,7 +1342,7 @@ def create_ui(wrap_gradio_gpu_call):
             _js="start_training_textual_inversion",
             inputs=[
                 train_embedding_name,
-                learn_rate,
+                embedding_learn_rate,
                 batch_size,
                 dataset_directory,
                 log_directory,
@@ -1367,7 +1367,7 @@ def create_ui(wrap_gradio_gpu_call):
             _js="start_training_textual_inversion",
             inputs=[
                 train_hypernetwork_name,
-                learn_rate,
+                hypernetwork_learn_rate,
                 batch_size,
                 dataset_directory,
                 log_directory,
-- 
cgit v1.2.1


From c3835ec85cbb44fa3c46fa871c622b6fee235c89 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:24:24 +0100
Subject: pass overwrite old flag

---
 modules/textual_inversion/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index 36881e7a..e712284d 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -7,8 +7,8 @@ import modules.textual_inversion.preprocess
 from modules import sd_hijack, shared
 
 
-def create_embedding(name, initialization_text, nvpt):
-    filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text)
+def create_embedding(name, initialization_text, nvpt, overwrite_old):
+    filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, overwrite_old, init_text=initialization_text)
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
 
-- 
cgit v1.2.1


From 4d6b9f76a55fd0ac0f72634071032dd9c6efb409 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:27:16 +0100
Subject: reorder create_hypernetwork params

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index d07184ee..322c082b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1307,9 +1307,9 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 new_hypernetwork_name,
                 new_hypernetwork_sizes,
+                overwrite_old_hypernetwork,
                 new_hypernetwork_layer_structure,
                 new_hypernetwork_add_layer_norm,
-                overwrite_old_hypernetwork,
             ],
             outputs=[
                 train_hypernetwork_name,
-- 
cgit v1.2.1


From fbcce66601994f6ed370db36d9c238840fed6bd2 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:46:54 +0100
Subject: add existing caption file handling

---
 modules/textual_inversion/preprocess.py | 32 ++++++++++++++++++++++++--------
 1 file changed, 24 insertions(+), 8 deletions(-)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 886cf0c3..5c43fe13 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -48,7 +48,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
 
-    def save_pic_with_caption(image, index):
+    def save_pic_with_caption(image, index, existing_caption=None):
         caption = ""
 
         if process_caption:
@@ -66,17 +66,26 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         basename = f"{index:05}-{subindex[0]}-{filename_part}"
         image.save(os.path.join(dst, f"{basename}.png"))
 
+        if preprocess_txt_action == 'prepend' and existing_caption:
+            caption = existing_caption + ' ' + caption
+        elif preprocess_txt_action == 'append' and existing_caption:
+            caption = caption + ' ' + existing_caption
+        elif preprocess_txt_action == 'copy' and existing_caption:
+            caption = existing_caption
+
+        caption = caption.strip()
+        
         if len(caption) > 0:
             with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:
                 file.write(caption)
 
         subindex[0] += 1
 
-    def save_pic(image, index):
+    def save_pic(image, index, existing_caption=None):
         save_pic_with_caption(image, index)
 
         if process_flip:
-            save_pic_with_caption(ImageOps.mirror(image), index)
+            save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption)
 
     for index, imagefile in enumerate(tqdm.tqdm(files)):
         subindex = [0]
@@ -86,6 +95,13 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         except Exception:
             continue
 
+        existing_caption = None
+
+        try:
+            existing_caption = open(os.path.splitext(filename)[0] + '.txt', 'r').read()
+        except Exception as e:
+            print(e)
+
         if shared.state.interrupted:
             break
 
@@ -97,20 +113,20 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
             img = img.resize((width, height * img.height // img.width))
 
             top = img.crop((0, 0, width, height))
-            save_pic(top, index)
+            save_pic(top, index, existing_caption=existing_caption)
 
             bot = img.crop((0, img.height - height, width, img.height))
-            save_pic(bot, index)
+            save_pic(bot, index, existing_caption=existing_caption)
         elif process_split and is_wide:
             img = img.resize((width * img.width // img.height, height))
 
             left = img.crop((0, 0, width, height))
-            save_pic(left, index)
+            save_pic(left, index, existing_caption=existing_caption)
 
             right = img.crop((img.width - width, 0, img.width, height))
-            save_pic(right, index)
+            save_pic(right, index, existing_caption=existing_caption)
         else:
             img = images.resize_image(1, img, width, height)
-            save_pic(img, index)
+            save_pic(img, index, existing_caption=existing_caption)
 
         shared.state.nextjob()
-- 
cgit v1.2.1


From ab353b141df8eee042b0964bcb645015dabf3459 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:48:07 +0100
Subject: link existing txt option

---
 modules/ui.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modules/ui.py b/modules/ui.py
index 322c082b..7f52ac0c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1234,6 +1234,7 @@ def create_ui(wrap_gradio_gpu_call):
                     process_dst = gr.Textbox(label='Destination directory')
                     process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', choices=['ignore', 'copy', 'prepend', 'append'])
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
@@ -1326,6 +1327,7 @@ def create_ui(wrap_gradio_gpu_call):
                 process_dst,
                 process_width,
                 process_height,
+                preprocess_txt_action,
                 process_flip,
                 process_split,
                 process_caption,
-- 
cgit v1.2.1


From 9b65c4ecf4f8eb6187ee721918adebe68e9bc631 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:49:23 +0100
Subject: pass preprocess_txt_action param

---
 modules/textual_inversion/preprocess.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 5c43fe13..3713bc89 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -11,7 +11,7 @@ if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru
 
 
-def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False):
     try:
         if process_caption:
             shared.interrogator.load()
@@ -21,7 +21,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
             db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
             deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
 
-        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
+        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru)
 
     finally:
 
@@ -33,7 +33,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
 
 
 
-def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False):
     width = process_width
     height = process_height
     src = os.path.abspath(process_src)
-- 
cgit v1.2.1


From 55d8c6cce6d3aef848b9f194adad2ce53064d8b7 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 00:53:29 +0100
Subject: default to ignore existing captions

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 7f52ac0c..bd5f1b05 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1234,7 +1234,7 @@ def create_ui(wrap_gradio_gpu_call):
                     process_dst = gr.Textbox(label='Destination directory')
                     process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', choices=['ignore', 'copy', 'prepend', 'append'])
+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
-- 
cgit v1.2.1


From 8b74b9aa9a20e4c5c1f72641f8b9617479eb276b Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Wed, 19 Oct 2022 19:06:14 -0500
Subject: add symbol for clear button and simplify roll_col css selector

---
 modules/ui.py | 2 ++
 style.css     | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index a2dbd41e..9f6edc5f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -83,6 +83,7 @@ folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 apply_style_symbol = '\U0001f4cb'  # 📋
+trash_prompt_symbol = '\U0001F5D1' # 🗑🗑🗑
 
 
 def plaintext_to_html(text):
@@ -498,6 +499,7 @@ def create_toprow(is_img2img):
             paste = gr.Button(value=paste_symbol, elem_id="paste")
             save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
             prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
+            trash_prompt = gr.Button(value=trash_prompt_symbol, elem_id="trash_prompt")
 
             token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
             token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
diff --git a/style.css b/style.css
index 26ae36a5..21a8911f 100644
--- a/style.css
+++ b/style.css
@@ -114,7 +114,7 @@
     padding: 0.4em 0;
 }
 
-#roll, #paste, #style_create, #style_apply{
+#roll_col > button {
     min-width: 2em;
     min-height: 2em;
     max-width: 2em;
-- 
cgit v1.2.1


From 6f98e89486f55b0e4657e96ce640cf1c4675d187 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Thu, 20 Oct 2022 00:10:45 +0000
Subject: update

---
 modules/hypernetworks/hypernetwork.py | 29 +++++++++++++++--------
 modules/hypernetworks/ui.py           |  3 ++-
 modules/ui.py                         | 43 +++++++++++++++++++----------------
 3 files changed, 44 insertions(+), 31 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 74300122..7d617680 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -22,16 +22,20 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False, activation_func=None):
         super().__init__()
 
-        assert layer_structure is not None, "layer_structure mut not be None"
+        assert layer_structure is not None, "layer_structure must not be None"
         assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
         assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
 
         linears = []
         for i in range(len(layer_structure) - 1):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
+            if activation_func == "relu":
+                linears.append(torch.nn.ReLU())
+            if activation_func == "leakyrelu":
+                linears.append(torch.nn.LeakyReLU())
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
@@ -42,8 +46,9 @@ class HypernetworkModule(torch.nn.Module):
             self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                layer.weight.data.normal_(mean=0.0, std=0.01)
-                layer.bias.data.zero_()
+                if not "ReLU" in layer.__str__():
+                    layer.weight.data.normal_(mean=0.0, std=0.01)
+                    layer.bias.data.zero_()
 
         self.to(devices.device)
 
@@ -69,7 +74,8 @@ class HypernetworkModule(torch.nn.Module):
     def trainables(self):
         layer_structure = []
         for layer in self.linear:
-            layer_structure += [layer.weight, layer.bias]
+            if not "ReLU" in layer.__str__():
+                layer_structure += [layer.weight, layer.bias]
         return layer_structure
 
 
@@ -81,7 +87,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False, activation_func=None):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -90,11 +96,12 @@ class Hypernetwork:
         self.sd_checkpoint_name = None
         self.layer_structure = layer_structure
         self.add_layer_norm = add_layer_norm
+        self.activation_func = activation_func
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm),
-                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm),
+                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func),
+                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func),
             )
 
     def weights(self):
@@ -117,6 +124,7 @@ class Hypernetwork:
         state_dict['name'] = self.name
         state_dict['layer_structure'] = self.layer_structure
         state_dict['is_layer_norm'] = self.add_layer_norm
+        state_dict['activation_func'] = self.activation_func
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
 
@@ -131,12 +139,13 @@ class Hypernetwork:
 
         self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
+        self.activation_func = state_dict.get('activation_func', None)
 
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm, self.activation_func),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm, self.activation_func),
                 )
 
         self.name = state_dict.get('name', self.name)
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 08f75f15..83f9547b 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -10,7 +10,7 @@ from modules import sd_hijack, shared, devices
 from modules.hypernetworks import hypernetwork
 
 
-def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False):
+def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False, activation_func=None):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
@@ -22,6 +22,7 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm
         enable_sizes=[int(x) for x in enable_sizes],
         layer_structure=layer_structure,
         add_layer_norm=add_layer_norm,
+        activation_func=activation_func,
     )
     hypernet.save(fn)
 
diff --git a/modules/ui.py b/modules/ui.py
index d2e24880..8751fa9c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -5,43 +5,44 @@ import json
 import math
 import mimetypes
 import os
+import platform
 import random
+import subprocess as sp
 import sys
 import tempfile
 import time
 import traceback
-import platform
-import subprocess as sp
 from functools import partial, reduce
 
+import gradio as gr
+import gradio.routes
+import gradio.utils
 import numpy as np
+import piexif
 import torch
 from PIL import Image, PngImagePlugin
-import piexif
 
-import gradio as gr
-import gradio.utils
-import gradio.routes
-
-from modules import sd_hijack, sd_models, localization
+from modules import localization, sd_hijack, sd_models
 from modules.paths import script_path
-from modules.shared import opts, cmd_opts, restricted_opts
+from modules.shared import cmd_opts, opts, restricted_opts
+
 if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
-import modules.shared as shared
-from modules.sd_samplers import samplers, samplers_for_img2img
-from modules.sd_hijack import model_hijack
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.images_history as img_his
 import modules.ldsr_model
 import modules.scripts
-import modules.gfpgan_model
-import modules.codeformer_model
+import modules.shared as shared
 import modules.styles
-import modules.generation_parameters_copypaste
+import modules.textual_inversion.ui
 from modules import prompt_parser
 from modules.images import save_image
-import modules.textual_inversion.ui
-import modules.hypernetworks.ui
-import modules.images_history as img_his
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers import samplers, samplers_for_img2img
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -268,8 +269,8 @@ def calc_time_left(progress, threshold, label, force_display):
         time_since_start = time.time() - shared.state.time_start
         eta = (time_since_start/progress)
         eta_relative = eta-time_since_start
-        if (eta_relative > threshold and progress > 0.02) or force_display:           
-            return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))        
+        if (eta_relative > threshold and progress > 0.02) or force_display:
+            return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
         else:
             return ""
 
@@ -1219,6 +1220,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["relu", "leakyrelu"])
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1303,6 +1305,7 @@ def create_ui(wrap_gradio_gpu_call):
                 new_hypernetwork_sizes,
                 new_hypernetwork_layer_structure,
                 new_hypernetwork_add_layer_norm,
+                new_hypernetwork_activation_func,
             ],
             outputs=[
                 train_hypernetwork_name,
-- 
cgit v1.2.1


From ba469343e6a1c6e23e82acf5feb65c6101dacbb2 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Thu, 20 Oct 2022 00:17:04 +0000
Subject: align ui.py imports with upstream

---
 modules/ui.py | 37 ++++++++++++++++++-------------------
 1 file changed, 18 insertions(+), 19 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 987b1d7d..913b23b4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -5,44 +5,43 @@ import json
 import math
 import mimetypes
 import os
-import platform
 import random
-import subprocess as sp
 import sys
 import tempfile
 import time
 import traceback
+import platform
+import subprocess as sp
 from functools import partial, reduce
 
-import gradio as gr
-import gradio.routes
-import gradio.utils
 import numpy as np
-import piexif
 import torch
 from PIL import Image, PngImagePlugin
+import piexif
 
-from modules import localization, sd_hijack, sd_models
-from modules.paths import script_path
-from modules.shared import cmd_opts, opts, restricted_opts
+import gradio as gr
+import gradio.utils
+import gradio.routes
 
+from modules import sd_hijack, sd_models, localization
+from modules.paths import script_path
+from modules.shared import opts, cmd_opts, restricted_opts
 if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
-
-import modules.codeformer_model
-import modules.generation_parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.images_history as img_his
+import modules.shared as shared
+from modules.sd_samplers import samplers, samplers_for_img2img
+from modules.sd_hijack import model_hijack
 import modules.ldsr_model
 import modules.scripts
-import modules.shared as shared
+import modules.gfpgan_model
+import modules.codeformer_model
 import modules.styles
-import modules.textual_inversion.ui
+import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
-from modules.sd_hijack import model_hijack
-from modules.sd_samplers import samplers, samplers_for_img2img
+import modules.textual_inversion.ui
+import modules.hypernetworks.ui
+import modules.images_history as img_his
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
-- 
cgit v1.2.1


From 59ed74438318af893d2cba552b0e28dbc2a9266c Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Wed, 19 Oct 2022 17:19:02 -0700
Subject: face detection algo, configurability, reusability

Try to move the crop in the direction of a face if it is present

More internal configuration options for choosing weights of each of the algorithm's findings

Move logic into its module
---
 modules/textual_inversion/autocrop.py   | 216 ++++++++++++++++++++++++++++++++
 modules/textual_inversion/preprocess.py | 150 +++-------------------
 2 files changed, 230 insertions(+), 136 deletions(-)
 create mode 100644 modules/textual_inversion/autocrop.py

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
new file mode 100644
index 00000000..f858a958
--- /dev/null
+++ b/modules/textual_inversion/autocrop.py
@@ -0,0 +1,216 @@
+import cv2
+from collections import defaultdict
+from math import log, sqrt
+import numpy as np
+from PIL import Image, ImageDraw
+
+GREEN = "#0F0"
+BLUE = "#00F"
+RED = "#F00"
+
+def crop_image(im, settings):
+  """ Intelligently crop an image to the subject matter """
+  if im.height > im.width:
+      im = im.resize((settings.crop_width, settings.crop_height * im.height // im.width))
+  else:
+      im = im.resize((settings.crop_width * im.width // im.height, settings.crop_height))
+
+  focus = focal_point(im, settings)
+
+  # take the focal point and turn it into crop coordinates that try to center over the focal
+  # point but then get adjusted back into the frame
+  y_half = int(settings.crop_height / 2)
+  x_half = int(settings.crop_width / 2)
+
+  x1 = focus.x - x_half
+  if x1 < 0:
+      x1 = 0
+  elif x1 + settings.crop_width > im.width:
+      x1 = im.width - settings.crop_width
+
+  y1 = focus.y - y_half
+  if y1 < 0:
+      y1 = 0
+  elif y1 + settings.crop_height > im.height:
+      y1 = im.height - settings.crop_height
+
+  x2 = x1 + settings.crop_width
+  y2 = y1 + settings.crop_height
+
+  crop = [x1, y1, x2, y2]
+
+  if settings.annotate_image:
+    d = ImageDraw.Draw(im)
+    rect = list(crop)
+    rect[2] -= 1
+    rect[3] -= 1
+    d.rectangle(rect, outline=GREEN)
+    if settings.destop_view_image:
+      im.show()
+
+  return im.crop(tuple(crop))
+
+def focal_point(im, settings):
+    corner_points = image_corner_points(im, settings)
+    entropy_points = image_entropy_points(im, settings)
+    face_points = image_face_points(im, settings)
+
+    total_points = len(corner_points) + len(entropy_points) + len(face_points)
+
+    corner_weight = settings.corner_points_weight
+    entropy_weight = settings.entropy_points_weight
+    face_weight = settings.face_points_weight
+
+    weight_pref_total = corner_weight + entropy_weight + face_weight
+
+    # weight things
+    pois = []
+    if weight_pref_total == 0 or total_points == 0: 
+      return pois
+
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (corner_weight/weight_pref_total) / (len(corner_points)/total_points) )) for p in corner_points ]
+    )
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (entropy_weight/weight_pref_total) / (len(entropy_points)/total_points) )) for p in entropy_points ]
+    )
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ]
+    )
+
+    if settings.annotate_image:
+      d = ImageDraw.Draw(im)
+
+    average_point = poi_average(pois, settings, im=im)
+
+    if settings.annotate_image:
+      d.ellipse([average_point.x - 25, average_point.y - 25, average_point.x + 25, average_point.y + 25], outline=GREEN)
+      
+    return average_point
+
+
+def image_face_points(im, settings):
+    np_im = np.array(im)
+    gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
+    classifier = cv2.CascadeClassifier(f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml')
+
+    minsize = int(min(im.width, im.height) * 0.15) # at least N percent of the smallest side
+    faces = classifier.detectMultiScale(gray, scaleFactor=1.05,
+      minNeighbors=5, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
+
+    if len(faces) == 0:
+      return []
+
+    rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
+    if settings.annotate_image:
+      for f in rects:
+        d = ImageDraw.Draw(im)
+        d.rectangle(f, outline=RED)
+    
+    return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2) for r in rects]
+
+
+def image_corner_points(im, settings):
+    grayscale = im.convert("L")
+
+    # naive attempt at preventing focal points from collecting at watermarks near the bottom
+    gd = ImageDraw.Draw(grayscale)
+    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
+
+    np_im = np.array(grayscale)
+
+    points = cv2.goodFeaturesToTrack(
+        np_im,
+        maxCorners=100,
+        qualityLevel=0.04,
+        minDistance=min(grayscale.width, grayscale.height)*0.07,
+        useHarrisDetector=False,
+    )
+
+    if points is None:
+        return []
+
+    focal_points = []
+    for point in points:
+        x, y = point.ravel()
+        focal_points.append(PointOfInterest(x, y))
+
+    return focal_points
+
+
+def image_entropy_points(im, settings):
+    landscape = im.height < im.width
+    portrait = im.height > im.width
+    if landscape:
+      move_idx = [0, 2]
+      move_max = im.size[0]
+    elif portrait:
+      move_idx = [1, 3]
+      move_max = im.size[1]
+    else:
+      return []
+
+    e_max = 0
+    crop_current = [0, 0, settings.crop_width, settings.crop_height]
+    crop_best = crop_current
+    while crop_current[move_idx[1]] < move_max:
+        crop = im.crop(tuple(crop_current))
+        e = image_entropy(crop)
+
+        if (e > e_max):
+          e_max = e
+          crop_best = list(crop_current)
+
+        crop_current[move_idx[0]] += 4
+        crop_current[move_idx[1]] += 4
+
+    x_mid = int(crop_best[0] + settings.crop_width/2)
+    y_mid = int(crop_best[1] + settings.crop_height/2)
+
+    return [PointOfInterest(x_mid, y_mid)]
+
+
+def image_entropy(im):
+    # greyscale image entropy
+    band = np.asarray(im.convert("1"))
+    hist, _ = np.histogram(band, bins=range(0, 256))
+    hist = hist[hist > 0]
+    return -np.log2(hist / hist.sum()).sum()
+
+
+def poi_average(pois, settings, im=None):
+    weight = 0.0
+    x = 0.0
+    y = 0.0
+    for pois in pois:
+        if settings.annotate_image and im is not None:
+          w = 4 * 0.5 * sqrt(pois.weight)
+          d = ImageDraw.Draw(im)
+          d.ellipse([
+            pois.x - w, pois.y - w,
+            pois.x + w, pois.y + w ], fill=BLUE)
+        weight += pois.weight
+        x += pois.x * pois.weight
+        y += pois.y * pois.weight
+    avg_x = round(x / weight)
+    avg_y = round(y / weight)
+
+    return PointOfInterest(avg_x, avg_y)
+
+
+class PointOfInterest:
+  def __init__(self, x, y, weight=1.0):
+    self.x = x
+    self.y = y
+    self.weight = weight
+
+
+class Settings:
+  def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False):
+    self.crop_width = crop_width
+    self.crop_height = crop_height
+    self.corner_points_weight = corner_points_weight
+    self.entropy_points_weight = entropy_points_weight
+    self.face_points_weight = entropy_points_weight
+    self.annotate_image = annotate_image
+    self.destop_view_image = False
\ No newline at end of file
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 7c1a594e..0c79f012 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,7 +1,5 @@
 import os
-import cv2
-import numpy as np
-from PIL import Image, ImageOps, ImageDraw
+from PIL import Image, ImageOps
 import platform
 import sys
 import tqdm
@@ -9,6 +7,7 @@ import time
 
 from modules import shared, images
 from modules.shared import opts, cmd_opts
+from modules.textual_inversion import autocrop
 if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru
 
@@ -80,6 +79,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         if process_flip:
             save_pic_with_caption(ImageOps.mirror(image), index)
 
+
     for index, imagefile in enumerate(tqdm.tqdm(files)):
         subindex = [0]
         filename = os.path.join(src, imagefile)
@@ -118,37 +118,16 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
             
             processing_option_ran = True
 
-        if process_entropy_focus and (is_tall or is_wide):
-            if is_tall:
-                img = img.resize((width, height * img.height // img.width))
-            else:
-                img = img.resize((width * img.width // img.height, height))
-
-            x_focal_center, y_focal_center = image_central_focal_point(img, width, height)
-
-            # take the focal point and turn it into crop coordinates that try to center over the focal
-            # point but then get adjusted back into the frame
-            y_half = int(height / 2)
-            x_half = int(width / 2)
-
-            x1 = x_focal_center - x_half
-            if x1 < 0:
-                x1 = 0
-            elif x1 + width > img.width:
-                x1 = img.width - width
-
-            y1 = y_focal_center - y_half
-            if y1 < 0:
-                y1 = 0
-            elif y1 + height > img.height:
-                y1 = img.height - height
-
-            x2 = x1 + width
-            y2 = y1 + height
-
-            crop = [x1, y1, x2, y2]
-
-            focal = img.crop(tuple(crop))
+        if process_entropy_focus and img.height != img.width:
+            autocrop_settings = autocrop.Settings(
+                crop_width = width,
+                crop_height = height,
+                face_points_weight = 0.9,
+                entropy_points_weight = 0.7,
+                corner_points_weight = 0.5,
+                annotate_image = False
+            )
+            focal = autocrop.crop_image(img, autocrop_settings)
             save_pic(focal, index)
 
             processing_option_ran = True
@@ -157,105 +136,4 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
             img = images.resize_image(1, img, width, height)
             save_pic(img, index)
 
-        shared.state.nextjob()
-
-
-def image_central_focal_point(im, target_width, target_height):
-    focal_points = []
-
-    focal_points.extend(
-        image_focal_points(im)
-    )
-
-    fp_entropy = image_entropy_point(im, target_width, target_height)
-    fp_entropy['weight'] = len(focal_points) + 1 # about half of the weight to entropy
-
-    focal_points.append(fp_entropy)
-
-    weight = 0.0
-    x = 0.0
-    y = 0.0
-    for focal_point in focal_points:
-        weight += focal_point['weight']
-        x += focal_point['x'] * focal_point['weight']
-        y += focal_point['y'] * focal_point['weight']
-    avg_x = round(x // weight)
-    avg_y = round(y // weight)
-
-    return avg_x, avg_y
-
-
-def image_focal_points(im):
-    grayscale = im.convert("L")
-
-    # naive attempt at preventing focal points from collecting at watermarks near the bottom
-    gd = ImageDraw.Draw(grayscale)
-    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
-
-    np_im = np.array(grayscale)
-
-    points = cv2.goodFeaturesToTrack(
-        np_im,
-        maxCorners=100,
-        qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.07,
-        useHarrisDetector=False,
-    )
-
-    if points is None:
-        return []
-
-    focal_points = []
-    for point in points:
-        x, y = point.ravel()
-        focal_points.append({
-            'x': x,
-            'y': y,
-            'weight': 1.0
-        })
-
-    return focal_points
-
-
-def image_entropy_point(im, crop_width, crop_height):
-    landscape = im.height < im.width
-    portrait = im.height > im.width
-    if landscape:
-      move_idx = [0, 2]
-      move_max = im.size[0]
-    elif portrait:
-      move_idx = [1, 3]
-      move_max = im.size[1]
-
-    e_max = 0
-    crop_current = [0, 0, crop_width, crop_height]
-    crop_best = crop_current
-    while crop_current[move_idx[1]] < move_max:
-        crop = im.crop(tuple(crop_current))
-        e = image_entropy(crop)
-
-        if (e > e_max):
-          e_max = e
-          crop_best = list(crop_current)
-
-        crop_current[move_idx[0]] += 4
-        crop_current[move_idx[1]] += 4
-
-    x_mid = int(crop_best[0] + crop_width/2)
-    y_mid = int(crop_best[1] + crop_height/2)
-
-
-    return {
-        'x': x_mid,
-        'y': y_mid,
-        'weight': 1.0
-    }
-
-
-def image_entropy(im):
-    # greyscale image entropy
-    band = np.asarray(im.convert("1"))
-    hist, _ = np.histogram(band, bins=range(0, 256))
-    hist = hist[hist > 0]
-    return -np.log2(hist / hist.sum()).sum()
-
+        shared.state.nextjob()
\ No newline at end of file
-- 
cgit v1.2.1


From 858462f719c22ca9f24b94a41699653c34b5f4fb Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Thu, 20 Oct 2022 02:57:18 +0100
Subject: do caption copy for both flips

---
 modules/textual_inversion/preprocess.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 3713bc89..6bba3852 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -82,7 +82,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
         subindex[0] += 1
 
     def save_pic(image, index, existing_caption=None):
-        save_pic_with_caption(image, index)
+        save_pic_with_caption(image, index, existing_caption=existing_caption)
 
         if process_flip:
             save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption)
-- 
cgit v1.2.1


From c6345bd445463b7aa41723d6637e80dfa293a890 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Wed, 19 Oct 2022 21:23:57 -0500
Subject: nerf line length

---
 modules/ui.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 9f6edc5f..cb9a6c6e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -83,7 +83,7 @@ folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 apply_style_symbol = '\U0001f4cb'  # 📋
-trash_prompt_symbol = '\U0001F5D1' # 🗑🗑🗑
+trash_prompt_symbol = '\U0001F5D1' #
 
 
 def plaintext_to_html(text):
@@ -617,7 +617,10 @@ def create_ui(wrap_gradio_gpu_call):
         return refresh_button
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,\
+        txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter,\
+        token_button = create_toprow(is_img2img=False)
+
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
 
-- 
cgit v1.2.1


From aa7ff2a1972f3865883e10ba28c5414cdebe8e3b Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Wed, 19 Oct 2022 21:46:13 -0700
Subject: Fixed non-square highres fix generation

---
 modules/processing.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 684e5833..3caac25e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -541,10 +541,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
 
-    def create_dummy_mask(self, x):
+    def create_dummy_mask(self, x, first_phase: bool = False):
         if self.sampler.conditioning_key in {'hybrid', 'concat'}:
+            height = self.firstphase_height if first_phase else self.height
+            width = self.firstphase_width if first_phase else self.width
+
             # The "masked-image" in this case will just be all zeros since the entire image is masked.
-            image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device)
+            image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
             image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
 
             # Add the fake full 1s mask to the first dimension.
@@ -567,7 +570,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, first_phase=True))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
-- 
cgit v1.2.1


From 930b4c64f7dbce6918894d53538003e5959fd022 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 20 Oct 2022 08:18:02 +0300
Subject: allow float sizes for hypernet's layer_structure

---
 modules/hypernetworks/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 08f75f15..e0741d08 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -15,7 +15,7 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm
     assert not os.path.exists(fn), f"file {fn} already exists"
 
     if type(layer_structure) == str:
-        layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure)))
+        layer_structure = [float(x.strip()) for x in layer_structure.split(",")]
 
     hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
         name=name,
-- 
cgit v1.2.1


From 158d678f596d7fc304a6ce2f0dc31f8abfe62250 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Thu, 20 Oct 2022 01:08:24 -0500
Subject: clear prompt button now works on both relevant tabs. Device detection
 stuff will be added later.

---
 javascript/ui.js | 28 ++++++++++++++++++++++++++++
 modules/ui.py    | 21 ++++++++++++++++++---
 2 files changed, 46 insertions(+), 3 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index cfd0dcd3..165383da 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -151,6 +151,34 @@ function ask_for_style_name(_, prompt_text, negative_prompt_text) {
     return [name_, prompt_text, negative_prompt_text]
 }
 
+// returns css id for currently selected tab in ui
+function selected_tab_id() {
+    tabs = gradioApp().querySelectorAll('#tabs div.tabitem')
+
+    for(var tab = 0; tab < tabs.length; tab++) {
+        if (tabs[tab].style.display != "none") return tabs[tab].id
+
+    }
+
+}
+
+function trash_prompt(_,_, is_img2img) {
+
+    if(selected_tab_id() == "tab_txt2img") {
+     pos_prompt = txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea");
+     neg_prompt = txt2img_textarea = gradioApp().querySelector("#txt2img_neg_prompt > label > textarea");
+
+     pos_prompt.value = ""
+     neg_prompt.value = ""
+    } else {
+     pos_prompt = txt2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
+     neg_prompt = txt2img_textarea = gradioApp().querySelector("#img2img_neg_prompt > label > textarea");
+
+     pos_prompt.value = ""
+     neg_prompt.value = ""
+    }
+}
+
 
 
 opts = {}
diff --git a/modules/ui.py b/modules/ui.py
index cb9a6c6e..bde546cc 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -424,6 +424,16 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
+# setup button for clearing prompt input boxes on client side of webui
+def connect_trash_prompt(dummy_component, button, is_img2img):
+
+    button.click(
+        fn=lambda: print("Clearing prompt"),
+        _js="trash_prompt",
+        inputs=[],
+        outputs=[],
+    )
+
 def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
     """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
         (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
@@ -540,7 +550,7 @@ def create_toprow(is_img2img):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
                     prompt_style2.save_to_config = True
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button, trash_prompt
 
 
 def setup_progressbar(progressbar, preview, id_part, textinfo=None):
@@ -619,10 +629,11 @@ def create_ui(wrap_gradio_gpu_call):
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,\
         txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter,\
-        token_button = create_toprow(is_img2img=False)
+        token_button, trash_prompt_button = create_toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
+        connect_trash_prompt(dummy_component, trash_prompt_button, False)
 
         with gr.Row(elem_id='txt2img_progress_row'):
             with gr.Column(scale=1):
@@ -807,7 +818,11 @@ def create_ui(wrap_gradio_gpu_call):
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit,\
+        img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,\
+        token_counter, token_button, trash_prompt_button = create_toprow(is_img2img=True)
+
+        connect_trash_prompt(dummy_component,trash_prompt_button, True)
 
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
-- 
cgit v1.2.1


From 0ddaf8d2028a7251e8c4ad93551a43b5d4700841 Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Thu, 20 Oct 2022 00:34:55 -0700
Subject: improve face detection a lot

---
 modules/textual_inversion/autocrop.py | 99 ++++++++++++++++++++++-------------
 1 file changed, 62 insertions(+), 37 deletions(-)

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index f858a958..5a551c25 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -8,12 +8,18 @@ GREEN = "#0F0"
 BLUE = "#00F"
 RED = "#F00"
 
+
 def crop_image(im, settings):
   """ Intelligently crop an image to the subject matter """
   if im.height > im.width:
       im = im.resize((settings.crop_width, settings.crop_height * im.height // im.width))
-  else:
+  elif im.width > im.height:
       im = im.resize((settings.crop_width * im.width // im.height, settings.crop_height))
+  else:
+      im = im.resize((settings.crop_width, settings.crop_height))
+
+  if im.height == im.width:
+    return im
 
   focus = focal_point(im, settings)
 
@@ -78,13 +84,18 @@ def focal_point(im, settings):
       [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ]
     )
 
-    if settings.annotate_image:
-      d = ImageDraw.Draw(im)
-
-    average_point = poi_average(pois, settings, im=im)
+    average_point = poi_average(pois, settings)
 
     if settings.annotate_image:
-      d.ellipse([average_point.x - 25, average_point.y - 25, average_point.x + 25, average_point.y + 25], outline=GREEN)
+      d = ImageDraw.Draw(im)
+      for f in face_points:
+        d.rectangle(f.bounding(f.size), outline=RED)
+      for f in entropy_points:
+        d.rectangle(f.bounding(30), outline=BLUE)
+      for poi in pois:
+        w = max(4, 4 * 0.5 * sqrt(poi.weight))
+        d.ellipse(poi.bounding(w), fill=BLUE)
+      d.ellipse(average_point.bounding(25), outline=GREEN)
       
     return average_point
 
@@ -92,22 +103,32 @@ def focal_point(im, settings):
 def image_face_points(im, settings):
     np_im = np.array(im)
     gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
-    classifier = cv2.CascadeClassifier(f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml')
-
-    minsize = int(min(im.width, im.height) * 0.15) # at least N percent of the smallest side
-    faces = classifier.detectMultiScale(gray, scaleFactor=1.05,
-      minNeighbors=5, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
 
-    if len(faces) == 0:
-      return []
-
-    rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
-    if settings.annotate_image:
-      for f in rects:
-        d = ImageDraw.Draw(im)
-        d.rectangle(f, outline=RED)
-    
-    return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2) for r in rects]
+    tries = [
+      [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
+    ]
+
+    for t in tries:
+      # print(t[0])
+      classifier = cv2.CascadeClassifier(t[0])
+      minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
+      try:
+        faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
+          minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
+      except:
+        continue
+
+      if len(faces) > 0:
+        rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
+        return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2])) for r in rects]
+    return []
 
 
 def image_corner_points(im, settings):
@@ -132,8 +153,8 @@ def image_corner_points(im, settings):
 
     focal_points = []
     for point in points:
-        x, y = point.ravel()
-        focal_points.append(PointOfInterest(x, y))
+      x, y = point.ravel()
+      focal_points.append(PointOfInterest(x, y, size=4))
 
     return focal_points
 
@@ -167,31 +188,26 @@ def image_entropy_points(im, settings):
     x_mid = int(crop_best[0] + settings.crop_width/2)
     y_mid = int(crop_best[1] + settings.crop_height/2)
 
-    return [PointOfInterest(x_mid, y_mid)]
+    return [PointOfInterest(x_mid, y_mid, size=25)]
 
 
 def image_entropy(im):
     # greyscale image entropy
-    band = np.asarray(im.convert("1"))
+    # band = np.asarray(im.convert("L"))
+    band = np.asarray(im.convert("1"), dtype=np.uint8)
     hist, _ = np.histogram(band, bins=range(0, 256))
     hist = hist[hist > 0]
     return -np.log2(hist / hist.sum()).sum()
 
 
-def poi_average(pois, settings, im=None):
+def poi_average(pois, settings):
     weight = 0.0
     x = 0.0
     y = 0.0
-    for pois in pois:
-        if settings.annotate_image and im is not None:
-          w = 4 * 0.5 * sqrt(pois.weight)
-          d = ImageDraw.Draw(im)
-          d.ellipse([
-            pois.x - w, pois.y - w,
-            pois.x + w, pois.y + w ], fill=BLUE)
-        weight += pois.weight
-        x += pois.x * pois.weight
-        y += pois.y * pois.weight
+    for poi in pois:
+        weight += poi.weight
+        x += poi.x * poi.weight
+        y += poi.y * poi.weight
     avg_x = round(x / weight)
     avg_y = round(y / weight)
 
@@ -199,10 +215,19 @@ def poi_average(pois, settings, im=None):
 
 
 class PointOfInterest:
-  def __init__(self, x, y, weight=1.0):
+  def __init__(self, x, y, weight=1.0, size=10):
     self.x = x
     self.y = y
     self.weight = weight
+    self.size = size
+
+  def bounding(self, size):
+    return [
+      self.x - size//2,
+      self.y - size//2,
+      self.x + size//2,
+      self.y + size//2
+    ]
 
 
 class Settings:
-- 
cgit v1.2.1


From 21364c5c39b269497944b56dd6664792d779333b Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Thu, 20 Oct 2022 19:20:39 +0900
Subject: Updated file with basic template and added new translations

Translation done in txt2img-img2img windows and following scripts
---
 localizations/ko-KR.json | 492 ++++++++++++++++++++++++++++++++++++++---------
 1 file changed, 397 insertions(+), 95 deletions(-)

diff --git a/localizations/ko-KR.json b/localizations/ko-KR.json
index b263b13c..7cc431c6 100644
--- a/localizations/ko-KR.json
+++ b/localizations/ko-KR.json
@@ -1,120 +1,422 @@
 {
-  "⤡": "⤡",
-  "⊞": "⊞",
   "×": "×",
+  "•": "•",
+  "⊞": "⊞",
   "❮": "❮",
   "❯": "❯",
-  "Loading...": "",
-  "view": "api 보이기",
-  "hide": "api 숨기기",
-  "api": "",
-  "•": "•",
-  "txt2img": "텍스트→이미지",
-  "img2img": "이미지→이미지",
-  "Extras": "부가기능",
-  "PNG Info": "PNG 정보",
-  "History": "기록",
-  "Checkpoint Merger": "체크포인트 병합",
-  "Train": "훈련",
-  "Settings": "설정",
-  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
-  "Hypernetwork": "하이퍼네트워크",
-  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
-  "Generate": "생성",
-  "Style 1": "스타일 1",
-  "Style 2": "스타일 2",
+  "⤡": "⤡",
+  "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
+  "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
+  "A merger of the two checkpoints will be generated in your": "A merger of the two checkpoints will be generated in your",
+  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
   "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
-  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
-  "Save style": "스타일 저장",
+  "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
+  "Add difference": "Add difference",
+  "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid",
+  "Add layer normalization": "Add layer normalization",
+  "Add model hash to generation information": "Add model hash to generation information",
+  "Add model name to generation information": "Add model name to generation information",
+  "Always print all generation info to standard output": "Always print all generation info to standard output",
+  "Always save all generated image grids": "Always save all generated image grids",
+  "Always save all generated images": "생성된 이미지 항상 저장하기",
+  "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
   "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
-  "Do not do anything special": "아무것도 하지 않기",
-  "Generate forever": "반복 생성",
+  "Apply settings": "설정 적용하기",
+  "BSRGAN 4x": "BSRGAN 4x",
+  "Batch Process": "Batch Process",
+  "Batch count": "배치 수",
+  "Batch from Directory": "Batch from Directory",
+  "Batch img2img": "이미지→이미지 배치",
+  "Batch size": "배치 크기",
+  "CFG Scale": "CFG 스케일",
+  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
   "Cancel generate forever": "반복 생성 취소",
-  "Interrupt": "중단",
-  "Skip": "건너뛰기",
-  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
-  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
-  "Prompt": "프롬프트",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Negative prompt": "네거티브 프롬프트",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Sampling Steps": "샘플링 스텝 수",
-  "Sampling method": "샘플링 방법",
-  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
-  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
+  "Check progress (first)": "Check progress (first)",
+  "Check progress": "Check progress",
+  "Checkpoint Merger": "체크포인트 병합",
+  "Checkpoint name": "체크포인트 이름",
+  "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
+  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
+  "Clip skip": "클립 건너뛰기",
+  "CodeFormer visibility": "CodeFormer visibility",
+  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)",
+  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
+  "Color variation": "색깔 다양성",
+  "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
+  "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
+  "Create embedding": "Create embedding",
+  "Create flipped copies": "Create flipped copies",
+  "Create hypernetwork": "Create hypernetwork",
+  "Crop and resize": "잘라낸 후 리사이징",
+  "Crop to fit": "Crop to fit",
+  "Custom Name (Optional)": "Custom Name (Optional)",
+  "DDIM": "DDIM",
+  "DPM adaptive": "DPM adaptive",
+  "DPM fast": "DPM fast",
+  "DPM2 Karras": "DPM2 Karras",
+  "DPM2 a Karras": "DPM2 a Karras",
+  "DPM2 a": "DPM2 a",
+  "DPM2": "DPM2",
+  "Dataset directory": "Dataset directory",
+  "Decode CFG scale": "디코딩 CFG 스케일",
+  "Decode steps": "디코딩 스텝 수",
+  "Delete": "Delete",
   "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
-  "Width": "가로",
-  "Height": "세로",
-  "Restore faces": "얼굴 보정",
-  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
-  "Tiling": "타일링",
-  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
-  "Highres. fix": "고해상도 보정",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
-  "Firstpass width": "초기 가로길이",
-  "Firstpass height": "초기 세로길이",
+  "Denoising strength change factor": "디노이즈 강도 변경 배수",
   "Denoising strength": "디노이즈 강도",
+  "Denoising": "디노이징",
+  "Destination directory": "Destination directory",
   "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
-  "Batch count": "배치 수",
-  "Batch size": "배치 크기",
+  "Directory for saving images using the Save button": "Directory for saving images using the Save button",
+  "Directory name pattern": "Directory name pattern",
+  "Do not add watermark to images": "Do not add watermark to images",
+  "Do not do anything special": "아무것도 하지 않기",
+  "Do not save grids consisting of one picture": "Do not save grids consisting of one picture",
+  "Do not show any images in results for web": "Do not show any images in results for web",
+  "Download localization template": "Download localization template",
+  "Draw legend": "범례 그리기",
+  "Draw mask": "마스크 직접 그리기",
+  "Drop File Here": "Drop File Here",
+  "Drop Image Here": "Drop Image Here",
+  "ESRGAN_4x": "ESRGAN_4x",
+  "Embedding": "Embedding",
+  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
+  "Enable full page image viewer": "Enable full page image viewer",
+  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
+  "End Page": "End Page",
+  "Enter hypernetwork layer structure": "Enter hypernetwork layer structure",
+  "Eta noise seed delta": "Eta noise seed delta",
+  "Eta": "Eta",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
+  "Euler a": "Euler a",
+  "Euler": "Euler",
+  "Extra": "고급",
+  "Extras": "부가기능",
+  "Face restoration": "Face restoration",
+  "Fall-off exponent (lower=higher detail)": "감쇠 지수 (낮을수록 디테일이 올라감)",
+  "File Name": "File Name",
+  "File format for grids": "File format for grids",
+  "File format for images": "File format for images",
+  "File with inputs": "설정값 파일",
+  "File": "File",
+  "Filename join string": "Filename join string",
+  "Filename word regex": "Filename word regex",
+  "Filter NSFW content": "Filter NSFW content",
+  "First Page": "First Page",
+  "Firstpass height": "초기 세로길이",
+  "Firstpass width": "초기 가로길이",
+  "Font for image grids that have text": "Font for image grids that have text",
+  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD 업스케일링에서 타일 간 몇 픽셀을 겹치게 할지 결정하는 설정값입니다. 타일들이 다시 한 이미지로 합쳐질 때, 눈에 띄는 이음매가 없도록 서로 겹치게 됩니다.",
+  "GFPGAN visibility": "GFPGAN visibility",
+  "Generate Info": "Generate Info",
+  "Generate forever": "반복 생성",
+  "Generate": "생성",
+  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
+  "Height": "세로",
+  "Heun": "Heun",
+  "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
+  "Highres. fix": "고해상도 보정",
+  "History": "기록",
   "How many batches of images to create": "생성할 이미지 배치 수",
   "How many image to create in a single batch": "한 배치당 이미지 수",
-  "CFG Scale": "CFG 스케일",
-  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
-  "Seed": "시드",
-  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
-  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
-  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
-  "Extra": "고급",
-  "Variation seed": "바리에이션 시드",
-  "Variation strength": "바리에이션 강도",
-  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
+  "How many times to repeat processing an image and using it as input for the next iteration": "이미지를 생성 후 원본으로 몇 번 반복해서 사용할지 결정하는 값",
+  "How much to blur the mask before processing, in pixels.": "이미지 생성 전 마스크를 얼마나 블러처리할지 결정하는 값. 픽셀 단위",
   "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
+  "Hypernet str.": "하이퍼네트워크 강도",
+  "Hypernetwork strength": "Hypernetwork strength",
+  "Hypernetwork": "하이퍼네트워크",
+  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
+  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
+  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
+  "Image for img2img": "Image for img2img",
+  "Image for inpainting with mask": "Image for inpainting with mask",
+  "Image": "Image",
+  "Images filename pattern": "Images filename pattern",
+  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "루프백 모드에서는 매 루프마다 디노이즈 강도에 이 값이 곱해집니다. 1보다 작을 경우 다양성이 낮아져 결과 이미지들이 고정된 형태로 모일 겁니다. 1보다 클 경우 다양성이 높아져 결과 이미지들이 갈수록 혼란스러워지겠죠.",
+  "Include Separate Images": "분리된 이미지 포함하기",
+  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
+  "Initialization text": "Initialization text",
+  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트 패딩값(픽셀 단위)",
+  "Inpaint at full resolution": "전체 해상도로 인페인트하기",
+  "Inpaint masked": "마스크만 처리",
+  "Inpaint not masked": "마스크 이외만 처리",
+  "Inpaint": "인페인트",
+  "Input directory": "인풋 이미지 경로",
+  "Interpolation Method": "Interpolation Method",
+  "Interrogate\nCLIP": "CLIP\n분석",
+  "Interrogate\nDeepBooru": "DeepBooru\n분석",
+  "Interrogate Options": "Interrogate Options",
+  "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold",
+  "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically",
+  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
+  "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM",
+  "Interrogate: maximum description length": "Interrogate: maximum description length",
+  "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
+  "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
+  "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
+  "Interrupt": "중단",
+  "Just resize": "리사이징",
+  "Keep -1 for seeds": "시드값 -1로 유지",
+  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
+  "LDSR": "LDSR",
+  "LMS Karras": "LMS Karras",
+  "LMS": "LMS",
+  "Label": "Label",
+  "Lanczos": "Lanczos",
+  "Learning rate": "Learning rate",
+  "Leave blank to save images to the default path.": "Leave blank to save images to the default path.",
+  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
+  "Loading...": "로딩 중...",
+  "Localization (requires restart)": "Localization (requires restart)",
+  "Log directory": "Log directory",
+  "Loopback": "루프백",
+  "Loops": "루프 수",
+  "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
+  "Make Zip when Save?": "저장 시 Zip 생성하기",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
+  "Mask blur": "마스크 블러",
+  "Mask mode": "Mask mode",
+  "Mask": "마스크",
+  "Masked content": "마스크된 부분",
+  "Masking mode": "Masking mode",
+  "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
+  "Max steps": "Max steps",
+  "Modules": "Modules",
+  "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
+  "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - set to 0 to get model A",
+  "Name": "Name",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Negative prompt": "네거티브 프롬프트",
+  "Next Page": "Next Page",
+  "None": "None",
+  "Nothing": "없음",
+  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
+  "Number of vectors per token": "Number of vectors per token",
+  "Open images output directory": "이미지 저장 경로 열기",
+  "Open output directory": "Open output directory",
+  "Original negative prompt": "기존 네거티브 프롬프트",
+  "Original prompt": "기존 프롬프트",
+  "Outpainting direction": "아웃페인팅 방향",
+  "Outpainting mk2": "아웃페인팅 마크 2",
+  "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
+  "Output directory for images from extras tab": "Output directory for images from extras tab",
+  "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
+  "Output directory for img2img grids": "Output directory for img2img grids",
+  "Output directory for img2img images": "Output directory for img2img images",
+  "Output directory for txt2img grids": "Output directory for txt2img grids",
+  "Output directory for txt2img images": "Output directory for txt2img images",
+  "Output directory": "이미지 저장 경로",
+  "Override `Denoising strength` to 1?": "디노이즈 강도를 1로 적용할까요?",
+  "Override `Sampling Steps` to the same value as `Decode steps`?": "샘플링 스텝 수를 디코딩 스텝 수와 동일하게 적용할까요?",
+  "Override `Sampling method` to Euler?(this method is built for it)": "샘플링 방법을 Euler로 적용할까요?(이 기능은 해당 샘플러를 위해 만들어져 있습니다)",
+  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "프롬프트 값을 기존 프롬프트와 동일하게 적용할까요?(네거티브 프롬프트 포함)",
+  "PLMS": "PLMS",
+  "PNG Info": "PNG 정보",
+  "Page Index": "Page Index",
+  "Path to directory where to write outputs": "Path to directory where to write outputs",
+  "Path to directory with input images": "Path to directory with input images",
+  "Paths for saving": "Paths for saving",
+  "Pixels to expand": "확장할 픽셀 수",
+  "Poor man's outpainting": "가난뱅이의 아웃페인팅",
+  "Preprocess images": "Preprocess images",
+  "Preprocess": "Preprocess",
+  "Prev Page": "Prev Page",
+  "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)",
+  "Primary model (A)": "Primary model (A)",
+  "Process an image, use it as an input, repeat.": "이미지를 생성하고, 생성한 이미지를 다시 원본으로 사용하는 과정을 반복합니다.",
+  "Process images in a directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리의 이미지들을 처리합니다.",
+  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Prompt S/R": "프롬프트 스타일 변경",
+  "Prompt matrix": "프롬프트 매트릭스",
+  "Prompt order": "프롬프트 순서",
+  "Prompt template file": "Prompt template file",
+  "Prompt": "프롬프트",
+  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
+  "Prompts": "프롬프트",
+  "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
+  "Quality for saved jpeg images": "Quality for saved jpeg images",
+  "Quicksettings list": "Quicksettings list",
+  "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+  "Randomness": "랜덤성",
+  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
+  "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews",
+  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "추천 설정값 - 샘플링 스텝 수 : 80-100 , 샘플러 : Euler a, 디노이즈 강도 : 0.8",
+  "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
+  "Renew Page": "Renew Page",
+  "Request browser notifications": "Request browser notifications",
+  "Resize and fill": "리사이징 후 채우기",
+  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "설정된 해상도로 이미지 리사이징을 진행합니다. 원본과 가로/세로 길이가 일치하지 않을 경우, 부정확한 화면비의 이미지를 얻게 됩니다.",
+  "Resize mode": "Resize mode",
   "Resize seed from height": "시드 리사이징 가로길이",
   "Resize seed from width": "시드 리사이징 세로길이",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
-  "Script": "스크립트",
+  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "이미지 전체가 설정된 해상도 내부에 들어가게 리사이징을 진행합니다. 빈 공간은 이미지의 색상으로 채웁니다.",
+  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "설정된 해상도 전체가 이미지로 가득차게 리사이징을 진행합니다. 튀어나오는 부분은 잘라냅니다.",
+  "Resize": "Resize",
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
+  "Restore faces": "얼굴 보정",
+  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
+  "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M",
+  "Result = A + (B - C) * M": "Result = A + (B - C) * M",
+  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
+  "Run": "Run",
+  "SD upscale": "SD 업스케일링",
+  "Sampler parameters": "Sampler parameters",
+  "Sampler": "샘플러",
+  "Sampling Steps": "샘플링 스텝 수",
+  "Sampling method": "샘플링 방법",
+  "Save a copy of embedding to log directory every N steps, 0 to disable": "Save a copy of embedding to log directory every N steps, 0 to disable",
+  "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
+  "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
+  "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
+  "Save an image to log directory every N steps, 0 to disable": "Save an image to log directory every N steps, 0 to disable",
+  "Save as float16": "Save as float16",
+  "Save grids to a subdirectory": "Save grids to a subdirectory",
+  "Save images to a subdirectory": "Save images to a subdirectory",
+  "Save images with embedding in PNG chunks": "Save images with embedding in PNG chunks",
+  "Save style": "스타일 저장",
+  "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
   "Save": "저장",
-  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
+  "Saving images/grids": "Saving images/grids",
+  "Saving to a directory": "Saving to a directory",
+  "Scale by": "Scale by",
+  "Scale to": "Scale to",
+  "Script": "스크립트",
+  "ScuNET GAN": "ScuNET GAN",
+  "ScuNET PSNR": "ScuNET PSNR",
+  "Secondary model (B)": "Secondary model (B)",
+  "See": "See",
+  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "Seed": "시드",
+  "Send to extras": "부가기능으로 전송",
   "Send to img2img": "이미지→이미지로 전송",
   "Send to inpaint": "인페인트로 전송",
-  "Send to extras": "부가기능으로 전송",
-  "Open images output directory": "이미지 저장 경로 열기",
-  "Make Zip when Save?": "저장 시 Zip 생성하기",
-  "Prompt matrix": "프롬프트 매트릭스",
+  "Send to txt2img": "텍스트→이미지로 전송",
   "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "(|)를 이용해 프롬프트를 분리할 시 첫 프롬프트를 제외하고 모든 프롬프트의 조합마다 이미지를 생성합니다. 첫 프롬프트는 모든 조합에 포함되게 됩니다.",
-  "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
-  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
-  "Show Textbox": "텍스트박스 보이기",
-  "File with inputs": "설정값 파일",
-  "Prompts": "프롬프트",
-  "X/Y plot": "X/Y 플롯",
-  "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
-  "X type": "X축",
-  "Y type": "Y축",
-  "X values": "X 설정값",
-  "Y values": "Y 설정값",
   "Separate values for X axis using commas.": "쉼표로 X축에 적용할 값 분리",
   "Separate values for Y axis using commas.": "쉼표로 Y축에 적용할 값 분리",
-  "Draw legend": "범례 그리기",
-  "Include Separate Images": "분리된 이미지 포함하기",
-  "Keep -1 for seeds": "시드값 -1로 유지",
-  "Var. seed": "바리에이션 시드",
-  "Var. strength": "바리에이션 강도",
-  "Steps": "스텝 수",
-  "Prompt S/R": "프롬프트 스타일 변경",
-  "Prompt order": "프롬프트 순서",
-  "Sampler": "샘플러",
-  "Checkpoint name": "체크포인트 이름",
-  "Hypernet str.": "하이퍼네트워크 강도",
+  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
+  "Settings": "설정",
+  "Show Textbox": "텍스트박스 보이기",
+  "Show generation progress in window title.": "Show generation progress in window title.",
+  "Show grid in results for web": "Show grid in results for web",
+  "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
+  "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
+  "Show progressbar": "Show progressbar",
+  "Show result images": "Show result images",
   "Sigma Churn": "시그마 섞기",
-  "Sigma min": "시그마 최솟값",
+  "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
   "Sigma max": "시그마 최댓값",
+  "Sigma min": "시그마 최솟값",
   "Sigma noise": "시그마 노이즈",
-  "Clip skip": "클립 건너뛰기",
-  "Denoising": "디노이징",
-  "Nothing": "없음",
-  "Apply settings": "설정 적용하기",
-  "Always save all generated images": "생성된 이미지 항상 저장하기"
+  "Single Image": "Single Image",
+  "Skip": "건너뛰기",
+  "Source directory": "Source directory",
+  "Source": "Source",
+  "Split oversized images into two": "Split oversized images into two",
+  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "Stable Diffusion": "Stable Diffusion",
+  "Steps": "스텝 수",
+  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
+  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
+  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
+  "Style 1": "스타일 1",
+  "Style 2": "스타일 2",
+  "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
+  "SwinIR 4x": "SwinIR 4x",
+  "System": "System",
+  "Tertiary model (C)": "Tertiary model (C)",
+  "Textbox": "Textbox",
+  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
+  "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
+  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
+  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
+  "Tile overlap": "타일 겹침",
+  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
+  "Tile size for all SwinIR.": "Tile size for all SwinIR.",
+  "Tiling": "타일링",
+  "Train Embedding": "Train Embedding",
+  "Train Hypernetwork": "Train Hypernetwork",
+  "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Train an embedding; must specify a directory with a set of 1:1 ratio images",
+  "Train": "훈련",
+  "Training": "Training",
+  "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training",
+  "Upload mask": "마스크 업로드하기",
+  "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
+  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
+  "Upscaler 2 visibility": "Upscaler 2 visibility",
+  "Upscaler for img2img": "Upscaler for img2img",
+  "Upscaler": "업스케일러",
+  "Upscaling": "Upscaling",
+  "Use BLIP for caption": "Use BLIP for caption",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
+  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "저장 경로를 비워두면 기본 저장 폴더에 이미지들이 저장됩니다.",
+  "Use deepbooru for caption": "Use deepbooru for caption",
+  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
+  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
+  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
+  "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
+  "User interface": "User interface",
+  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
+  "Var. seed": "바리에이션 시드",
+  "Var. strength": "바리에이션 강도",
+  "Variation seed": "바리에이션 시드",
+  "Variation strength": "바리에이션 강도",
+  "Weighted sum": "Weighted sum",
+  "What to put inside the masked area before processing it with Stable Diffusion.": "Stable Diffusion으로 이미지를 생성하기 전 마스크된 부분에 무엇을 채울지 결정하는 설정값",
+  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
+  "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
+  "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
+  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
+  "Width": "가로",
+  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "이미지를 설정된 사이즈의 2배로 업스케일합니다. 상단의 가로와 세로 슬라이더를 이용해 타일 사이즈를 지정하세요.",
+  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
+  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
+  "X type": "X축",
+  "X values": "X 설정값",
+  "X/Y plot": "X/Y 플롯",
+  "Y type": "Y축",
+  "Y values": "Y 설정값",
+  "api": "",
+  "built with gradio": "gradio로 제작되었습니다",
+  "checkpoint": "checkpoint",
+  "directory.": "directory.",
+  "down": "아래쪽",
+  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
+  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
+  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
+  "extras history": "extras history",
+  "fill it with colors of the image": "이미지의 색상으로 채우기",
+  "fill it with latent space noise": "잠재 공간 노이즈로 채우기",
+  "fill it with latent space zeroes": "잠재 공간의 0값으로 채우기",
+  "fill": "채우기",
+  "for detailed explanation.": "for detailed explanation.",
+  "hide": "api 숨기기",
+  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
+  "img2img DDIM discretize": "img2img DDIM discretize",
+  "img2img alternative test": "이미지→이미지 대체버전 테스트",
+  "img2img history": "img2img history",
+  "img2img": "이미지→이미지",
+  "keep whatever was there originally": "이미지 원본 유지",
+  "latent noise": "잠재 노이즈",
+  "latent nothing": "잠재 공백",
+  "left": "왼쪽",
+  "number of images to delete consecutively next": "number of images to delete consecutively next",
+  "or": "or",
+  "original": "원본 유지",
+  "quad": "quad",
+  "right": "오른쪽",
+  "set_index": "set_index",
+  "should be 2 or lower.": "이 2 이하여야 합니다.",
+  "sigma churn": "sigma churn",
+  "sigma noise": "sigma noise",
+  "sigma tmin": "sigma tmin",
+  "txt2img history": "txt2img history",
+  "txt2img": "텍스트→이미지",
+  "uniform": "uniform",
+  "up": "위쪽",
+  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
+  "view": "api 보이기",
+  "wiki": "wiki"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From f8733ad08be08bafb40f4299785590e11f049e96 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Thu, 20 Oct 2022 11:07:37 +0000
Subject: add linear as a act func (option for doin nothing)

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 913b23b4..716f14b8 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1224,7 +1224,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["relu", "leakyrelu"])
+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"])
 
                     with gr.Row():
                         with gr.Column(scale=3):
-- 
cgit v1.2.1


From 4281f255d5e7c67515d619f53654be59a6fc1e13 Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Thu, 20 Oct 2022 15:31:09 +0100
Subject: Implemented batch count logic to Outpainting mk2

---
 scripts/outpainting_mk_2.py | 40 ++++++++++++++++++++++++++++------------
 1 file changed, 28 insertions(+), 12 deletions(-)

diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py
index a6468e09..02e655e9 100644
--- a/scripts/outpainting_mk_2.py
+++ b/scripts/outpainting_mk_2.py
@@ -242,21 +242,37 @@ class Script(scripts.Script):
             out = out.crop((0, 0, res_w, res_h))
             return out
 
-        img = init_image
-
-        if left > 0:
-            img = expand(img, left, is_left=True)
-        if right > 0:
-            img = expand(img, right, is_right=True)
-        if up > 0:
-            img = expand(img, up, is_top=True)
-        if down > 0:
-            img = expand(img, down, is_bottom=True)
-
-        res = Processed(p, [img], initial_seed_and_info[0], initial_seed_and_info[1])
+        batch_count = p.n_iter
+        p.n_iter = 1
+        state.job_count = batch_count
+        all_images = []
+
+        for i in range(batch_count):
+            img = init_image
+            state.job = f"Batch {i + 1} out of {state.job_count}"
+
+            if left > 0:
+                img = expand(img, left, is_left=True)
+            if right > 0:
+                img = expand(img, right, is_right=True)
+            if up > 0:
+                img = expand(img, up, is_top=True)
+            if down > 0:
+                img = expand(img, down, is_bottom=True)
+
+            all_images.append(img)
+
+        combined_grid_image = images.image_grid(all_images)
+        if opts.return_grid:
+            all_images = [combined_grid_image] + all_images
+        
+        res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1])
 
         if opts.samples_save:
             images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
 
+        if opts.grid_save:
+            images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
+
         return res
 
-- 
cgit v1.2.1


From 9681419e422515e42444e0174355b760645a846f Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Thu, 20 Oct 2022 16:53:46 +0900
Subject: train: fixed preprocess image ratio

---
 modules/textual_inversion/preprocess.py | 54 +++++++++++++++++++++------------
 1 file changed, 35 insertions(+), 19 deletions(-)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 886cf0c3..2743bdeb 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,5 +1,6 @@
 import os
 from PIL import Image, ImageOps
+import math
 import platform
 import sys
 import tqdm
@@ -38,6 +39,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
     height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
+    split_threshold = 0.5
+    overlap_ratio = 0.2
 
     assert src != dst, 'same directory specified as source and destination'
 
@@ -78,6 +81,29 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         if process_flip:
             save_pic_with_caption(ImageOps.mirror(image), index)
 
+    def split_pic(image, inverse_xy):
+        if inverse_xy:
+            from_w, from_h = image.height, image.width
+            to_w, to_h = height, width
+        else:
+            from_w, from_h = image.width, image.height
+            to_w, to_h = width, height
+        h = from_h * to_w // from_w
+        if inverse_xy:
+            image = image.resize((h, to_w))
+        else:
+            image = image.resize((to_w, h))
+
+        split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
+        y_step = (h - to_h) / (split_count - 1)
+        for i in range(split_count):
+            y = int(y_step * i)
+            if inverse_xy:
+                splitted = image.crop((y, 0, y + to_h, to_w))
+            else:
+                splitted = image.crop((0, y, to_w, y + to_h))
+            yield splitted
+
     for index, imagefile in enumerate(tqdm.tqdm(files)):
         subindex = [0]
         filename = os.path.join(src, imagefile)
@@ -89,26 +115,16 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
         if shared.state.interrupted:
             break
 
-        ratio = img.height / img.width
-        is_tall = ratio > 1.35
-        is_wide = ratio < 1 / 1.35
-
-        if process_split and is_tall:
-            img = img.resize((width, height * img.height // img.width))
-
-            top = img.crop((0, 0, width, height))
-            save_pic(top, index)
-
-            bot = img.crop((0, img.height - height, width, img.height))
-            save_pic(bot, index)
-        elif process_split and is_wide:
-            img = img.resize((width * img.width // img.height, height))
-
-            left = img.crop((0, 0, width, height))
-            save_pic(left, index)
+        if img.height > img.width:
+            ratio = (img.width * height) / (img.height * width)
+            inverse_xy = False
+        else:
+            ratio = (img.height * width) / (img.width * height)
+            inverse_xy = True
 
-            right = img.crop((img.width - width, 0, img.width, height))
-            save_pic(right, index)
+        if process_split and ratio < 1.0 and ratio <= split_threshold:
+            for splitted in split_pic(img, inverse_xy):
+                save_pic(splitted, index)
         else:
             img = images.resize_image(1, img, width, height)
             save_pic(img, index)
-- 
cgit v1.2.1


From 85dd62c4c7635b8e21a75f140d093036069e97a1 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Thu, 20 Oct 2022 22:56:45 +0900
Subject: train: ui: added `Split image threshold` and `Split image overlap
 ratio` to preprocess

---
 modules/textual_inversion/preprocess.py | 10 +++++-----
 modules/ui.py                           | 16 ++++++++++++++--
 2 files changed, 19 insertions(+), 7 deletions(-)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 2743bdeb..c8df8aa0 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -12,7 +12,7 @@ if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru
 
 
-def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2):
     try:
         if process_caption:
             shared.interrogator.load()
@@ -22,7 +22,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
             db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
             deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
 
-        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
+        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio)
 
     finally:
 
@@ -34,13 +34,13 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
 
 
 
-def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2):
     width = process_width
     height = process_height
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
-    split_threshold = 0.5
-    overlap_ratio = 0.2
+    split_threshold = max(0.0, min(1.0, split_threshold))
+    overlap_ratio = max(0.0, min(0.9, overlap_ratio))
 
     assert src != dst, 'same directory specified as source and destination'
 
diff --git a/modules/ui.py b/modules/ui.py
index a2dbd41e..bc7f3330 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1240,10 +1240,14 @@ def create_ui(wrap_gradio_gpu_call):
 
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
-                        process_split = gr.Checkbox(label='Split oversized images into two')
+                        process_split = gr.Checkbox(label='Split oversized images')
                         process_caption = gr.Checkbox(label='Use BLIP for caption')
                         process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
 
+                    with gr.Row(visible=False) as process_split_extra_row:
+                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
+
                     with gr.Row():
                         with gr.Column(scale=3):
                             gr.HTML(value="")
@@ -1251,6 +1255,12 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
+                    process_split.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_split],
+                        outputs=[process_split_extra_row],
+                    )
+
                 with gr.Tab(label="Train"):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     with gr.Row():
@@ -1327,7 +1337,9 @@ def create_ui(wrap_gradio_gpu_call):
                 process_flip,
                 process_split,
                 process_caption,
-                process_caption_deepbooru
+                process_caption_deepbooru,
+                process_split_threshold,
+                process_overlap_ratio,
             ],
             outputs=[
                 ti_output,
-- 
cgit v1.2.1


From d8acd34f66ab35a91f10d66330bcc95a83bfcac6 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Thu, 20 Oct 2022 23:43:03 +0900
Subject: generalized some functions and option for ignoring first layer

---
 modules/hypernetworks/hypernetwork.py | 23 +++++++++++++++--------
 1 file changed, 15 insertions(+), 8 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7d617680..3a44b377 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -21,21 +21,27 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
-
+    activation_dict = {"relu": torch.nn.ReLU, "leakyrelu": torch.nn.LeakyReLU, "elu": torch.nn.ELU,
+                       "swish": torch.nn.Hardswish}
+    
     def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False, activation_func=None):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
         assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
         assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
-
+        
         linears = []
         for i in range(len(layer_structure) - 1):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
-            if activation_func == "relu":
-                linears.append(torch.nn.ReLU())
-            if activation_func == "leakyrelu":
-                linears.append(torch.nn.LeakyReLU())
+            # if skip_first_layer because first parameters potentially contain negative values
+            if i < 1: continue
+            if activation_func in HypernetworkModule.activation_dict:
+                linears.append(HypernetworkModule.activation_dict[activation_func]())
+            else:
+                print("Invalid key {} encountered as activation function!".format(activation_func))
+            # if use_dropout: 
+            linears.append(torch.nn.Dropout(p=0.3))
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
@@ -46,7 +52,7 @@ class HypernetworkModule(torch.nn.Module):
             self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                if not "ReLU" in layer.__str__():
+                if isinstance(layer, torch.nn.Linear):
                     layer.weight.data.normal_(mean=0.0, std=0.01)
                     layer.bias.data.zero_()
 
@@ -298,7 +304,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
+    # if optimizer == "Adam": or else Adam / AdamW / etc...
+    optimizer = torch.optim.Adam(weights, lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
-- 
cgit v1.2.1


From a71e0212363979c7cbbb797c9fbd5f8cd03b29d3 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Thu, 20 Oct 2022 23:48:52 +0900
Subject: only linear

---
 modules/hypernetworks/hypernetwork.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3a44b377..905cbeef 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -35,13 +35,13 @@ class HypernetworkModule(torch.nn.Module):
         for i in range(len(layer_structure) - 1):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
             # if skip_first_layer because first parameters potentially contain negative values
-            if i < 1: continue
+            # if i < 1: continue
             if activation_func in HypernetworkModule.activation_dict:
                 linears.append(HypernetworkModule.activation_dict[activation_func]())
             else:
                 print("Invalid key {} encountered as activation function!".format(activation_func))
             # if use_dropout: 
-            linears.append(torch.nn.Dropout(p=0.3))
+            # linears.append(torch.nn.Dropout(p=0.3))
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
@@ -80,7 +80,7 @@ class HypernetworkModule(torch.nn.Module):
     def trainables(self):
         layer_structure = []
         for layer in self.linear:
-            if not "ReLU" in layer.__str__():
+            if isinstance(layer, torch.nn.Linear):
                 layer_structure += [layer.weight, layer.bias]
         return layer_structure
 
@@ -304,8 +304,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    # if optimizer == "Adam": or else Adam / AdamW / etc...
-    optimizer = torch.optim.Adam(weights, lr=scheduler.learn_rate)
+    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
+    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
-- 
cgit v1.2.1


From 91efe138b35dda65e83070c14e9eb94f481fe476 Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Thu, 20 Oct 2022 16:02:32 +0100
Subject: Implemented batch_size logic in outpainting_mk2

---
 scripts/outpainting_mk_2.py | 118 +++++++++++++++++++++++---------------------
 1 file changed, 63 insertions(+), 55 deletions(-)

diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py
index 02e655e9..0377ab32 100644
--- a/scripts/outpainting_mk_2.py
+++ b/scripts/outpainting_mk_2.py
@@ -176,50 +176,53 @@ class Script(scripts.Script):
 
         state.job_count = (1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0)
 
-        def expand(init, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
+        def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
             is_horiz = is_left or is_right
             is_vert = is_top or is_bottom
             pixels_horiz = expand_pixels if is_horiz else 0
             pixels_vert = expand_pixels if is_vert else 0
 
-            res_w = init.width + pixels_horiz
-            res_h = init.height + pixels_vert
-            process_res_w = math.ceil(res_w / 64) * 64
-            process_res_h = math.ceil(res_h / 64) * 64
-
-            img = Image.new("RGB", (process_res_w, process_res_h))
-            img.paste(init, (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
-            mask = Image.new("RGB", (process_res_w, process_res_h), "white")
-            draw = ImageDraw.Draw(mask)
-            draw.rectangle((
-                expand_pixels + mask_blur if is_left else 0,
-                expand_pixels + mask_blur if is_top else 0,
-                mask.width - expand_pixels - mask_blur if is_right else res_w,
-                mask.height - expand_pixels - mask_blur if is_bottom else res_h,
-            ), fill="black")
-
-            np_image = (np.asarray(img) / 255.0).astype(np.float64)
-            np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
-            noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
-            out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")
-
-            target_width = min(process_width, init.width + pixels_horiz) if is_horiz else img.width
-            target_height = min(process_height, init.height + pixels_vert) if is_vert else img.height
-
-            crop_region = (
-                0 if is_left else out.width - target_width,
-                0 if is_top else out.height - target_height,
-                target_width if is_left else out.width,
-                target_height if is_top else out.height,
-            )
-
-            image_to_process = out.crop(crop_region)
-            mask = mask.crop(crop_region)
-
-            p.width = target_width if is_horiz else img.width
-            p.height = target_height if is_vert else img.height
-            p.init_images = [image_to_process]
-            p.image_mask = mask
+            images_to_process = []
+            for n in range(count):
+                res_w = init[n].width + pixels_horiz
+                res_h = init[n].height + pixels_vert
+                process_res_w = math.ceil(res_w / 64) * 64
+                process_res_h = math.ceil(res_h / 64) * 64
+
+                img = Image.new("RGB", (process_res_w, process_res_h))
+                img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
+                mask = Image.new("RGB", (process_res_w, process_res_h), "white")
+                draw = ImageDraw.Draw(mask)
+                draw.rectangle((
+                    expand_pixels + mask_blur if is_left else 0,
+                    expand_pixels + mask_blur if is_top else 0,
+                    mask.width - expand_pixels - mask_blur if is_right else res_w,
+                    mask.height - expand_pixels - mask_blur if is_bottom else res_h,
+                ), fill="black")
+
+                np_image = (np.asarray(img) / 255.0).astype(np.float64)
+                np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
+                noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
+                out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")
+
+                target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width
+                target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height
+                p.width = target_width if is_horiz else img.width
+                p.height = target_height if is_vert else img.height
+
+                crop_region = (
+                    0 if is_left else out.width - target_width,
+                    0 if is_top else out.height - target_height,
+                    target_width if is_left else out.width,
+                    target_height if is_top else out.height,
+                )
+                mask = mask.crop(crop_region)
+                p.image_mask = mask
+
+                image_to_process = out.crop(crop_region)
+                images_to_process.append(image_to_process)
+
+            p.init_images = images_to_process
 
             latent_mask = Image.new("RGB", (p.width, p.height), "white")
             draw = ImageDraw.Draw(latent_mask)
@@ -232,44 +235,49 @@ class Script(scripts.Script):
             p.latent_mask = latent_mask
 
             proc = process_images(p)
-            proc_img = proc.images[0]
 
             if initial_seed_and_info[0] is None:
                 initial_seed_and_info[0] = proc.seed
                 initial_seed_and_info[1] = proc.info
 
-            out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height))
-            out = out.crop((0, 0, res_w, res_h))
-            return out
+            for proc_img in proc.images:
+                out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height))
+                out = out.crop((0, 0, res_w, res_h))
+
+            return proc.images
 
         batch_count = p.n_iter
+        batch_size = p.batch_size
         p.n_iter = 1
         state.job_count = batch_count
-        all_images = []
+        all_processed_images = []
 
         for i in range(batch_count):
-            img = init_image
-            state.job = f"Batch {i + 1} out of {state.job_count}"
+            imgs = [init_img] * batch_size
+            state.job = f"Batch {i + 1} out of {batch_count}"
 
             if left > 0:
-                img = expand(img, left, is_left=True)
+                imgs = expand(imgs, batch_size, left, is_left=True)
             if right > 0:
-                img = expand(img, right, is_right=True)
+                imgs = expand(imgs, batch_size, right, is_right=True)
             if up > 0:
-                img = expand(img, up, is_top=True)
+                imgs = expand(imgs, batch_size, up, is_top=True)
             if down > 0:
-                img = expand(img, down, is_bottom=True)
+                imgs = expand(imgs, batch_size, down, is_bottom=True)
 
-            all_images.append(img)
+            all_processed_images += imgs
+
+        combined_grid_image = images.image_grid(all_processed_images)
+        all_images = all_processed_images
 
-        combined_grid_image = images.image_grid(all_images)
         if opts.return_grid:
-            all_images = [combined_grid_image] + all_images
-        
+            all_images = [combined_grid_image] + all_processed_images
+
         res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1])
 
         if opts.samples_save:
-            images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
+            for img in all_processed_images:
+                images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
 
         if opts.grid_save:
             images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
-- 
cgit v1.2.1


From 18df060c3e9252f1cf79b494e7173aff4181049a Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Thu, 20 Oct 2022 16:16:09 +0100
Subject: Fixed outpainting_mk2 output cropping

---
 scripts/outpainting_mk_2.py | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py
index 0377ab32..726417e7 100644
--- a/scripts/outpainting_mk_2.py
+++ b/scripts/outpainting_mk_2.py
@@ -183,6 +183,7 @@ class Script(scripts.Script):
             pixels_vert = expand_pixels if is_vert else 0
 
             images_to_process = []
+            output_images = []
             for n in range(count):
                 res_w = init[n].width + pixels_horiz
                 res_h = init[n].height + pixels_vert
@@ -203,7 +204,7 @@ class Script(scripts.Script):
                 np_image = (np.asarray(img) / 255.0).astype(np.float64)
                 np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
                 noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
-                out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")
+                output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB"))
 
                 target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width
                 target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height
@@ -211,15 +212,15 @@ class Script(scripts.Script):
                 p.height = target_height if is_vert else img.height
 
                 crop_region = (
-                    0 if is_left else out.width - target_width,
-                    0 if is_top else out.height - target_height,
-                    target_width if is_left else out.width,
-                    target_height if is_top else out.height,
+                    0 if is_left else output_images[n].width - target_width,
+                    0 if is_top else output_images[n].height - target_height,
+                    target_width if is_left else output_images[n].width,
+                    target_height if is_top else output_images[n].height,
                 )
                 mask = mask.crop(crop_region)
                 p.image_mask = mask
 
-                image_to_process = out.crop(crop_region)
+                image_to_process = output_images[n].crop(crop_region)
                 images_to_process.append(image_to_process)
 
             p.init_images = images_to_process
@@ -240,11 +241,11 @@ class Script(scripts.Script):
                 initial_seed_and_info[0] = proc.seed
                 initial_seed_and_info[1] = proc.info
 
-            for proc_img in proc.images:
-                out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height))
-                out = out.crop((0, 0, res_w, res_h))
+            for n in range(count):
+                output_images[n].paste(proc.images[n], (0 if is_left else output_images[n].width - proc.images[n].width, 0 if is_top else output_images[n].height - proc.images[n].height))
+                output_images[n] = output_images[n].crop((0, 0, res_w, res_h))
 
-            return proc.images
+            return output_images
 
         batch_count = p.n_iter
         batch_size = p.batch_size
-- 
cgit v1.2.1


From d07cb46f34b3d9fe7a78b102f899ebef352ea56b Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 20 Oct 2022 23:58:52 +0800
Subject: inspiration pull request

---
 .gitignore                           |   3 +-
 javascript/imageviewer.js            |   1 -
 javascript/inspiration.js            |  42 ++++++++++++
 modules/inspiration.py               | 122 +++++++++++++++++++++++++++++++++++
 modules/shared.py                    |   1 +
 modules/ui.py                        |  13 ++--
 scripts/create_inspiration_images.py |  45 +++++++++++++
 webui.py                             |   5 ++
 8 files changed, 225 insertions(+), 7 deletions(-)
 create mode 100644 javascript/inspiration.js
 create mode 100644 modules/inspiration.py
 create mode 100644 scripts/create_inspiration_images.py

diff --git a/.gitignore b/.gitignore
index f9c3357c..434d50b7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,4 +27,5 @@ __pycache__
 notification.mp3
 /SwinIR
 /textual_inversion
-.vscode
\ No newline at end of file
+.vscode
+/inspiration
\ No newline at end of file
diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js
index 9e380c65..d4ab6984 100644
--- a/javascript/imageviewer.js
+++ b/javascript/imageviewer.js
@@ -116,7 +116,6 @@ function showGalleryImage() {
                 e.dataset.modded = true;
                 if(e && e.parentElement.tagName == 'DIV'){
                     e.style.cursor='pointer'
-                    e.style.userSelect='none'
                     e.addEventListener('click', function (evt) {
                         if(!opts.js_modal_lightbox) return;
                         modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
diff --git a/javascript/inspiration.js b/javascript/inspiration.js
new file mode 100644
index 00000000..e1c0e114
--- /dev/null
+++ b/javascript/inspiration.js
@@ -0,0 +1,42 @@
+function public_image_index_in_gallery(item, gallery){
+    var index;
+    var i = 0;
+    gallery.querySelectorAll("img").forEach(function(e){
+        if (e == item)
+            index = i;
+        i += 1;
+    });
+    return index;
+}
+
+function inspiration_selected(name, types, name_list){
+    var btn = gradioApp().getElementById("inspiration_select_button")
+    return [gradioApp().getElementById("inspiration_select_button").getAttribute("img-index"), types];
+}    
+var inspiration_image_click = function(){
+    var index =  public_image_index_in_gallery(this, gradioApp().getElementById("inspiration_gallery"));
+    var btn = gradioApp().getElementById("inspiration_select_button")
+    btn.setAttribute("img-index", index)
+    setTimeout(function(btn){btn.click();}, 10, btn)
+}
+
+document.addEventListener("DOMContentLoaded", function() {
+    var mutationObserver = new MutationObserver(function(m){
+        var gallery = gradioApp().getElementById("inspiration_gallery")
+        if (gallery) {
+            var node = gallery.querySelector(".absolute.backdrop-blur.h-full")
+            if (node) {
+                node.style.display = "None"; //parentNode.removeChild(node) 
+            }
+            
+            gallery.querySelectorAll('img').forEach(function(e){    
+                e.onclick = inspiration_image_click
+        }) 
+
+        }
+
+
+    });
+    mutationObserver.observe( gradioApp(), { childList:true, subtree:true });
+
+});
diff --git a/modules/inspiration.py b/modules/inspiration.py
new file mode 100644
index 00000000..456bfcb5
--- /dev/null
+++ b/modules/inspiration.py
@@ -0,0 +1,122 @@
+import os
+import random
+import gradio 
+inspiration_path = "inspiration"
+inspiration_system_path = os.path.join(inspiration_path, "system")
+def read_name_list(file):
+    if not os.path.exists(file):
+        return []
+    f = open(file, "r")
+    ret = []
+    line = f.readline()
+    while len(line) > 0:
+        line = line.rstrip("\n")
+        ret.append(line)
+    print(ret)
+    return ret
+
+def save_name_list(file, name): 
+    print(file)
+    f = open(file, "a")
+    f.write(name + "\n")
+
+def get_inspiration_images(source, types):   
+    path = os.path.join(inspiration_path , types) 
+    if source == "Favorites":
+        names = read_name_list(os.path.join(inspiration_system_path, types + "_faverites.txt"))
+        names = random.sample(names, 25)
+    elif source == "Abandoned":
+        names = read_name_list(os.path.join(inspiration_system_path, types + "_abondened.txt"))
+        names = random.sample(names, 25)
+    elif source == "Exclude abandoned":
+        abondened = read_name_list(os.path.join(inspiration_system_path, types + "_abondened.txt"))        
+        all_names = os.listdir(path)
+        names = []
+        while len(names) < 25:
+            name = random.choice(all_names)
+            if name not in abondened:
+                names.append(name)
+    else:
+        names = random.sample(os.listdir(path), 25)
+    names = random.sample(names, 25)
+    image_list = []
+    for a in names:
+        image_path = os.path.join(path, a)
+        images = os.listdir(image_path)        
+        image_list.append(os.path.join(image_path, random.choice(images)))
+    return image_list, names
+
+def select_click(index, types, name_list):
+    name = name_list[int(index)]
+    path = os.path.join(inspiration_path, types, name)
+    images = os.listdir(path)
+    return name, [os.path.join(path, x) for x in images]
+
+def give_up_click(name, types):
+    file = os.path.join(inspiration_system_path, types + "_abandoned.txt")
+    name_list = read_name_list(file)
+    if name not in name_list:
+        save_name_list(file, name)
+   
+def collect_click(name, types):
+    file = os.path.join(inspiration_system_path, types + "_faverites.txt")
+    print(file)
+    name_list = read_name_list(file)
+    print(name_list)
+    if name not in name_list:
+        save_name_list(file, name)
+
+def moveout_click(name, types):
+    file = os.path.join(inspiration_system_path, types + "_faverites.txt")
+    name_list = read_name_list(file)
+    if name not in name_list:
+        save_name_list(file, name)
+
+def source_change(source):
+    if source == "Abandoned" or source == "Favorites":
+        return gradio.Button.update(visible=True, value=f"Move out {source}")
+    else:
+        return gradio.Button.update(visible=False)
+
+def ui(gr, opts):     
+    with gr.Blocks(analytics_enabled=False) as inspiration:
+        flag = os.path.exists(inspiration_path)        
+        if flag:
+            types = os.listdir(inspiration_path)
+            types = [x for x in types if x != "system"]
+            flag = len(types) > 0
+        if not flag:
+            os.mkdir(inspiration_path)
+            gr.HTML("""
+                <div align='center' width="50%>You need get "inspiration" images first. You can create these images by run "Create inspiration images" script in txt2img page, or download zip file from here and unzip these file under fold "inpiration".</div>"
+                """)           
+            return inspiration
+        if not os.path.exists(inspiration_system_path):
+            os.mkdir(inspiration_system_path)
+        gallery, names = get_inspiration_images("Exclude abandoned", types[0])   
+        with gr.Row():
+            with gr.Column(scale=2):                
+                inspiration_gallery = gr.Gallery(gallery, show_label=False, elem_id="inspiration_gallery").style(grid=5, height='auto')
+            with gr.Column(scale=1):
+                types = gr.Dropdown(choices=types, value=types[0], label="Type", visible=len(types) > 1)
+                with gr.Row():                    
+                    source = gr.Dropdown(choices=["All", "Favorites", "Exclude abandoned", "Abandoned"], value="Exclude abandoned", label="Source")
+                    get_inspiration = gr.Button("Get inspiration")
+                name = gr.Textbox(show_label=False, interactive=False)
+                with gr.Row(): 
+                    send_to_txt2img = gr.Button('to txt2img')
+                    send_to_img2img = gr.Button('to img2img')
+                style_gallery = gr.Gallery(show_label=False, elem_id="inspiration_style_gallery").style(grid=2, height='auto')                          
+               
+                collect = gr.Button('Collect')     
+                give_up = gr.Button("Don't show any more")
+                moveout = gr.Button("Move out", visible=False)
+        with gr.Row():
+            select_button = gr.Button('set button', elem_id="inspiration_select_button")
+            name_list = gr.State(names)
+        source.change(source_change, inputs=[source], outputs=[moveout])
+        get_inspiration.click(get_inspiration_images, inputs=[source, types], outputs=[inspiration_gallery, name_list])
+        select_button.click(select_click, _js="inspiration_selected", inputs=[name, types, name_list], outputs=[name, style_gallery])
+        give_up.click(give_up_click, inputs=[name, types], outputs=None)
+        collect.click(collect_click, inputs=[name, types], outputs=None)
+    return inspiration
diff --git a/modules/shared.py b/modules/shared.py
index faede821..ae033710 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -78,6 +78,7 @@ parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencode
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
+parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
diff --git a/modules/ui.py b/modules/ui.py
index a2dbd41e..6a0a3c3b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -41,7 +41,8 @@ from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
-import modules.images_history as img_his
+import modules.images_history as images_history
+import modules.inspiration as inspiration
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -1082,9 +1083,9 @@ def create_ui(wrap_gradio_gpu_call):
                                 upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
                                 upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-
+                
                 with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers] , value=shared.sd_upscalers[0].name, type="index") 
 
                 with gr.Group():
                     extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
@@ -1178,7 +1179,8 @@ def create_ui(wrap_gradio_gpu_call):
         "i2i":img2img_paste_fields
     }
 
-    images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
+    browser_interface = images_history.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
+    inspiration_interface = inspiration.ui(gr, opts)
 
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
@@ -1595,7 +1597,8 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        (images_history, "History", "images_history"),
+        (browser_interface, "History", "images_history"),
+        (inspiration_interface, "Inspiration", "inspiration"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
         (settings_interface, "Settings", "settings"),
diff --git a/scripts/create_inspiration_images.py b/scripts/create_inspiration_images.py
new file mode 100644
index 00000000..6a20def8
--- /dev/null
+++ b/scripts/create_inspiration_images.py
@@ -0,0 +1,45 @@
+import csv, os, shutil
+import modules.scripts as scripts
+from modules import processing, shared, sd_samplers, images
+from modules.processing import Processed
+
+
+class Script(scripts.Script):
+    def title(self):
+        return "Create artists style image"
+
+    def show(self, is_img2img):
+        return not is_img2img
+
+    def ui(self, is_img2img):
+       return []
+    def show(self, is_img2img):
+        return not is_img2img
+
+    def run(self, p): #, max_snapshoots_num):
+        path = os.path.join("style_snapshoot", "artist")
+        if not os.path.exists(path):
+            os.makedirs(path) 
+        p.do_not_save_samples = True
+        p.do_not_save_grid = True
+        p.negative_prompt = "portrait photo"
+        f = open('artists.csv')
+        f_csv = csv.reader(f)        
+        for row in f_csv:
+            name = row[0]
+            artist_path = os.path.join(path, name)  
+            if not os.path.exists(artist_path):
+                os.mkdir(artist_path)  
+            if len(os.listdir(artist_path)) > 0:
+                continue
+            print(name)
+            p.prompt = name
+            processed = processing.process_images(p)
+            for img in  processed.images:
+                i = 0
+                filename = os.path.join(artist_path,  format(0, "03d") + ".jpg")
+                while os.path.exists(filename):
+                    i += 1
+                    filename = os.path.join(artist_path,  format(i, "03d") + ".jpg")
+                img.save(filename, quality=70)
+        return processed
diff --git a/webui.py b/webui.py
index 177bef74..5923905f 100644
--- a/webui.py
+++ b/webui.py
@@ -72,6 +72,11 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
     return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
 
 def initialize():
+    if cmd_opts.ui_debug_mode:
+        class enmpty():
+            name = None
+        shared.sd_upscalers = [enmpty()]
+        return
     modelloader.cleanup_models()
     modules.sd_models.setup_model()
     codeformer.setup_model(cmd_opts.codeformer_models_path)
-- 
cgit v1.2.1


From 108be15500aac590b4e00420635d7b61fccfa530 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Fri, 21 Oct 2022 01:00:41 +0900
Subject: fix bugs and optimizations

---
 modules/hypernetworks/hypernetwork.py | 105 +++++++++++++++++++---------------
 1 file changed, 59 insertions(+), 46 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 905cbeef..893ba110 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -36,14 +36,14 @@ class HypernetworkModule(torch.nn.Module):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
             # if skip_first_layer because first parameters potentially contain negative values
             # if i < 1: continue
+            if add_layer_norm:
+                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
             if activation_func in HypernetworkModule.activation_dict:
                 linears.append(HypernetworkModule.activation_dict[activation_func]())
             else:
                 print("Invalid key {} encountered as activation function!".format(activation_func))
             # if use_dropout: 
             # linears.append(torch.nn.Dropout(p=0.3))
-            if add_layer_norm:
-                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
         self.linear = torch.nn.Sequential(*linears)
 
@@ -115,11 +115,24 @@ class Hypernetwork:
 
         for k, layers in self.layers.items():
             for layer in layers:
-                layer.train()
                 res += layer.trainables()
 
         return res
 
+    def eval(self):
+        for k, layers in self.layers.items():
+            for layer in layers:
+                layer.eval()
+        for items in self.weights():
+            items.requires_grad = False
+
+    def train(self):
+        for k, layers in self.layers.items():
+            for layer in layers:
+                layer.train()
+        for items in self.weights():
+            items.requires_grad = True
+
     def save(self, filename):
         state_dict = {}
 
@@ -290,10 +303,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     hypernetwork = shared.loaded_hypernetwork
-    weights = hypernetwork.weights()
-    for weight in weights:
-        weight.requires_grad = True
-
     losses = torch.zeros((32,))
 
     last_saved_file = "<none>"
@@ -304,10 +313,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
-    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
+    optimizer = torch.optim.AdamW(hypernetwork.weights(), lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
+    hypernetwork.train()
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
 
@@ -328,8 +337,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
-            optimizer.zero_grad()
+            optimizer.zero_grad(set_to_none=True)
             loss.backward()
+            del loss
             optimizer.step()
         mean_loss = losses.mean()
         if torch.isnan(mean_loss):
@@ -346,44 +356,47 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         })
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
+            torch.cuda.empty_cache()
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
+            with torch.no_grad():
+                hypernetwork.eval()
+                shared.sd_model.cond_stage_model.to(devices.device)
+                shared.sd_model.first_stage_model.to(devices.device)
+
+                p = processing.StableDiffusionProcessingTxt2Img(
+                    sd_model=shared.sd_model,
+                    do_not_save_grid=True,
+                    do_not_save_samples=True,
+                )
 
-            optimizer.zero_grad()
-            shared.sd_model.cond_stage_model.to(devices.device)
-            shared.sd_model.first_stage_model.to(devices.device)
-
-            p = processing.StableDiffusionProcessingTxt2Img(
-                sd_model=shared.sd_model,
-                do_not_save_grid=True,
-                do_not_save_samples=True,
-            )
-
-            if preview_from_txt2img:
-                p.prompt = preview_prompt
-                p.negative_prompt = preview_negative_prompt
-                p.steps = preview_steps
-                p.sampler_index = preview_sampler_index
-                p.cfg_scale = preview_cfg_scale
-                p.seed = preview_seed
-                p.width = preview_width
-                p.height = preview_height
-            else:
-                p.prompt = entries[0].cond_text
-                p.steps = 20
-
-            preview_text = p.prompt
-
-            processed = processing.process_images(p)
-            image = processed.images[0] if len(processed.images)>0 else None
-
-            if unload:
-                shared.sd_model.cond_stage_model.to(devices.cpu)
-                shared.sd_model.first_stage_model.to(devices.cpu)
-
-            if image is not None:
-                shared.state.current_image = image
-                image.save(last_saved_image)
-                last_saved_image += f", prompt: {preview_text}"
+                if preview_from_txt2img:
+                    p.prompt = preview_prompt
+                    p.negative_prompt = preview_negative_prompt
+                    p.steps = preview_steps
+                    p.sampler_index = preview_sampler_index
+                    p.cfg_scale = preview_cfg_scale
+                    p.seed = preview_seed
+                    p.width = preview_width
+                    p.height = preview_height
+                else:
+                    p.prompt = entries[0].cond_text
+                    p.steps = 20
+
+                preview_text = p.prompt
+
+                processed = processing.process_images(p)
+                image = processed.images[0] if len(processed.images)>0 else None
+
+                if unload:
+                    shared.sd_model.cond_stage_model.to(devices.cpu)
+                    shared.sd_model.first_stage_model.to(devices.cpu)
+
+                if image is not None:
+                    shared.state.current_image = image
+                    image.save(last_saved_image)
+                    last_saved_image += f", prompt: {preview_text}"
+
+            hypernetwork.train()
 
         shared.state.job_no = hypernetwork.step
 
-- 
cgit v1.2.1


From f89829ec3a0baceb445451ad98d4fb4323e922aa Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Fri, 21 Oct 2022 01:37:11 +0900
Subject: Revert "fix bugs and optimizations"

This reverts commit 108be15500aac590b4e00420635d7b61fccfa530.
---
 modules/hypernetworks/hypernetwork.py | 105 +++++++++++++++-------------------
 1 file changed, 46 insertions(+), 59 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 893ba110..905cbeef 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -36,14 +36,14 @@ class HypernetworkModule(torch.nn.Module):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
             # if skip_first_layer because first parameters potentially contain negative values
             # if i < 1: continue
-            if add_layer_norm:
-                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
             if activation_func in HypernetworkModule.activation_dict:
                 linears.append(HypernetworkModule.activation_dict[activation_func]())
             else:
                 print("Invalid key {} encountered as activation function!".format(activation_func))
             # if use_dropout: 
             # linears.append(torch.nn.Dropout(p=0.3))
+            if add_layer_norm:
+                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
         self.linear = torch.nn.Sequential(*linears)
 
@@ -115,24 +115,11 @@ class Hypernetwork:
 
         for k, layers in self.layers.items():
             for layer in layers:
+                layer.train()
                 res += layer.trainables()
 
         return res
 
-    def eval(self):
-        for k, layers in self.layers.items():
-            for layer in layers:
-                layer.eval()
-        for items in self.weights():
-            items.requires_grad = False
-
-    def train(self):
-        for k, layers in self.layers.items():
-            for layer in layers:
-                layer.train()
-        for items in self.weights():
-            items.requires_grad = True
-
     def save(self, filename):
         state_dict = {}
 
@@ -303,6 +290,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     hypernetwork = shared.loaded_hypernetwork
+    weights = hypernetwork.weights()
+    for weight in weights:
+        weight.requires_grad = True
+
     losses = torch.zeros((32,))
 
     last_saved_file = "<none>"
@@ -313,10 +304,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    optimizer = torch.optim.AdamW(hypernetwork.weights(), lr=scheduler.learn_rate)
+    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
+    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    hypernetwork.train()
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
 
@@ -337,9 +328,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
-            optimizer.zero_grad(set_to_none=True)
+            optimizer.zero_grad()
             loss.backward()
-            del loss
             optimizer.step()
         mean_loss = losses.mean()
         if torch.isnan(mean_loss):
@@ -356,47 +346,44 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         })
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
-            torch.cuda.empty_cache()
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
-            with torch.no_grad():
-                hypernetwork.eval()
-                shared.sd_model.cond_stage_model.to(devices.device)
-                shared.sd_model.first_stage_model.to(devices.device)
-
-                p = processing.StableDiffusionProcessingTxt2Img(
-                    sd_model=shared.sd_model,
-                    do_not_save_grid=True,
-                    do_not_save_samples=True,
-                )
 
-                if preview_from_txt2img:
-                    p.prompt = preview_prompt
-                    p.negative_prompt = preview_negative_prompt
-                    p.steps = preview_steps
-                    p.sampler_index = preview_sampler_index
-                    p.cfg_scale = preview_cfg_scale
-                    p.seed = preview_seed
-                    p.width = preview_width
-                    p.height = preview_height
-                else:
-                    p.prompt = entries[0].cond_text
-                    p.steps = 20
-
-                preview_text = p.prompt
-
-                processed = processing.process_images(p)
-                image = processed.images[0] if len(processed.images)>0 else None
-
-                if unload:
-                    shared.sd_model.cond_stage_model.to(devices.cpu)
-                    shared.sd_model.first_stage_model.to(devices.cpu)
-
-                if image is not None:
-                    shared.state.current_image = image
-                    image.save(last_saved_image)
-                    last_saved_image += f", prompt: {preview_text}"
-
-            hypernetwork.train()
+            optimizer.zero_grad()
+            shared.sd_model.cond_stage_model.to(devices.device)
+            shared.sd_model.first_stage_model.to(devices.device)
+
+            p = processing.StableDiffusionProcessingTxt2Img(
+                sd_model=shared.sd_model,
+                do_not_save_grid=True,
+                do_not_save_samples=True,
+            )
+
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entries[0].cond_text
+                p.steps = 20
+
+            preview_text = p.prompt
+
+            processed = processing.process_images(p)
+            image = processed.images[0] if len(processed.images)>0 else None
+
+            if unload:
+                shared.sd_model.cond_stage_model.to(devices.cpu)
+                shared.sd_model.first_stage_model.to(devices.cpu)
+
+            if image is not None:
+                shared.state.current_image = image
+                image.save(last_saved_image)
+                last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = hypernetwork.step
 
-- 
cgit v1.2.1


From 92a17a7a4a13fceb3c3e25a2e854b2a7dd6eb5df Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 20 Oct 2022 09:45:03 -0700
Subject: Made dummy latents smaller. Minor code cleanups

---
 modules/processing.py  | 7 ++++---
 modules/sd_samplers.py | 6 ++++--
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 3caac25e..539cde38 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -557,7 +557,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         else:
             # Dummy zero conditioning if we're not using inpainting model.
             # Still takes up a bit of memory, but no encoder call.
-            image_conditioning = torch.zeros(x.shape[0], 5, x.shape[-2], x.shape[-1], dtype=x.dtype, device=x.device)
+            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+            image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
 
         return image_conditioning
 
@@ -759,8 +760,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
         else:
             self.image_conditioning = torch.zeros(
-                self.init_latent.shape[0], 5, self.init_latent.shape[-2], self.init_latent.shape[-1], 
-                dtype=self.init_latent.dtype, 
+                self.init_latent.shape[0], 5, 1, 1,
+                dtype=self.init_latent.dtype,
                 device=self.init_latent.device
             )
 
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index c21be26e..cc682593 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -138,7 +138,7 @@ class VanillaStableDiffusionSampler:
         if self.stop_at is not None and self.step > self.stop_at:
             raise InterruptedException
 
-        # Have to unwrap the inpainting conditioning here to perform pre-preocessing
+        # Have to unwrap the inpainting conditioning here to perform pre-processing
         image_conditioning = None
         if isinstance(cond, dict):
             image_conditioning = cond["c_concat"][0]
@@ -146,7 +146,7 @@ class VanillaStableDiffusionSampler:
             unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
 
         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)            
+        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
 
         assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
         cond = tensor
@@ -165,6 +165,8 @@ class VanillaStableDiffusionSampler:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)
             x_dec = img_orig * self.mask + self.nmask * x_dec
 
+        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
+        # Note that they need to be lists because it just concatenates them later.
         if image_conditioning is not None:
             cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
             unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-- 
cgit v1.2.1


From d1cb08bfb221cd1b0cfc6078162b4e206ea80a5c Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Thu, 20 Oct 2022 22:49:06 +0300
Subject: fix skip and interrupt for highres. fix option

---
 modules/processing.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index bcb0c32c..6324ca91 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -587,9 +587,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
-
-        return samples
+        return self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) or samples
 
 
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
-- 
cgit v1.2.1


From 708c3a7bd8ce68cbe1aa7c268e5a4b1980affc9f Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 20 Oct 2022 13:28:43 -0700
Subject: Added PLMS hijack and made sure to always replace methods

---
 modules/sd_hijack_inpainting.py | 163 ++++++++++++++++++++++++++++++++++++++--
 modules/sd_models.py            |   3 +-
 2 files changed, 157 insertions(+), 9 deletions(-)

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index d4d28d2e..43938071 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -1,16 +1,14 @@
 import torch
-import numpy as np
 
-from tqdm import tqdm
-from einops import rearrange, repeat
+from einops import repeat
 from omegaconf import ListConfig
 
-from types import MethodType
-
 import ldm.models.diffusion.ddpm
 import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
 
 from ldm.models.diffusion.ddpm import LatentDiffusion
+from ldm.models.diffusion.plms import PLMSSampler
 from ldm.models.diffusion.ddim import DDIMSampler, noise_like
 
 # =================================================================================================
@@ -19,7 +17,7 @@ from ldm.models.diffusion.ddim import DDIMSampler, noise_like
 # https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
 # =================================================================================================
 @torch.no_grad()
-def sample(self,
+def sample_ddim(self,
             S,
             batch_size,
             shape,
@@ -132,6 +130,153 @@ def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=F
     return x_prev, pred_x0
 
 
+# =================================================================================================
+# Monkey patch PLMSSampler methods.
+# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes.
+# Adapted from:
+# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py
+# =================================================================================================
+@torch.no_grad()
+def sample_plms(self,
+            S,
+            batch_size,
+            shape,
+            conditioning=None,
+            callback=None,
+            normals_sequence=None,
+            img_callback=None,
+            quantize_x0=False,
+            eta=0.,
+            mask=None,
+            x0=None,
+            temperature=1.,
+            noise_dropout=0.,
+            score_corrector=None,
+            corrector_kwargs=None,
+            verbose=True,
+            x_T=None,
+            log_every_t=100,
+            unconditional_guidance_scale=1.,
+            unconditional_conditioning=None,
+            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+            **kwargs
+            ):
+    if conditioning is not None:
+        if isinstance(conditioning, dict):
+            ctmp = conditioning[list(conditioning.keys())[0]]
+            while isinstance(ctmp, list):
+                ctmp = ctmp[0]
+            cbs = ctmp.shape[0]
+            if cbs != batch_size:
+                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+        else:
+            if conditioning.shape[0] != batch_size:
+                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+    # sampling
+    C, H, W = shape
+    size = (batch_size, C, H, W)
+    print(f'Data shape for PLMS sampling is {size}')
+
+    samples, intermediates = self.plms_sampling(conditioning, size,
+                                                callback=callback,
+                                                img_callback=img_callback,
+                                                quantize_denoised=quantize_x0,
+                                                mask=mask, x0=x0,
+                                                ddim_use_original_steps=False,
+                                                noise_dropout=noise_dropout,
+                                                temperature=temperature,
+                                                score_corrector=score_corrector,
+                                                corrector_kwargs=corrector_kwargs,
+                                                x_T=x_T,
+                                                log_every_t=log_every_t,
+                                                unconditional_guidance_scale=unconditional_guidance_scale,
+                                                unconditional_conditioning=unconditional_conditioning,
+                                                )
+    return samples, intermediates
+
+
+@torch.no_grad()
+def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                    unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
+    b, *_, device = *x.shape, x.device
+
+    def get_model_output(x, t):
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            e_t = self.model.apply_model(x, t, c)
+        else:
+            x_in = torch.cat([x] * 2)
+            t_in = torch.cat([t] * 2)
+            
+            if isinstance(c, dict):
+                assert isinstance(unconditional_conditioning, dict)
+                c_in = dict()
+                for k in c:
+                    if isinstance(c[k], list):
+                        c_in[k] = [
+                            torch.cat([unconditional_conditioning[k][i], c[k][i]])
+                            for i in range(len(c[k]))
+                        ]
+                    else:
+                        c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
+            else:
+                c_in = torch.cat([unconditional_conditioning, c])
+
+            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+        if score_corrector is not None:
+            assert self.model.parameterization == "eps"
+            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+        return e_t
+
+    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+    def get_x_prev_and_pred_x0(e_t, index):
+        # select parameters corresponding to the currently considered timestep
+        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+        # current prediction for x_0
+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        if quantize_denoised:
+            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+        # direction pointing to x_t
+        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+        return x_prev, pred_x0
+
+    e_t = get_model_output(x, t)
+    if len(old_eps) == 0:
+        # Pseudo Improved Euler (2nd order)
+        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+        e_t_next = get_model_output(x_prev, t_next)
+        e_t_prime = (e_t + e_t_next) / 2
+    elif len(old_eps) == 1:
+        # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (3 * e_t - old_eps[-1]) / 2
+    elif len(old_eps) == 2:
+        # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+    elif len(old_eps) >= 3:
+        # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+    x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+    return x_prev, pred_x0, e_t
+    
 # =================================================================================================
 # Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
 # Adapted from:
@@ -175,5 +320,9 @@ def should_hijack_inpainting(checkpoint_info):
 def do_inpainting_hijack():
     ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
     ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
+
     ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
-    ldm.models.diffusion.ddim.DDIMSampler.sample = sample
\ No newline at end of file
+    ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
+
+    ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
+    ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
\ No newline at end of file
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 47836d25..7072db08 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -214,8 +214,6 @@ def load_model():
     sd_config = OmegaConf.load(checkpoint_info.config)
     
     if should_hijack_inpainting(checkpoint_info):
-        do_inpainting_hijack()
-
         # Hardcoded config for now...
         sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
         sd_config.model.params.use_ema = False
@@ -225,6 +223,7 @@ def load_model():
         # Create a "fake" config with a different name so that we know to unload it when switching models.
         checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
 
+    do_inpainting_hijack()
     sd_model = instantiate_from_config(sd_config.model)
     load_model_weights(sd_model, checkpoint_info)
 
-- 
cgit v1.2.1


From d23a46ceaa76af2847f11172f32c92665c268b1b Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Thu, 20 Oct 2022 23:49:14 +0300
Subject: Different approach to skip/interrupt with highres fix

---
 modules/processing.py  | 4 +++-
 modules/sd_samplers.py | 4 ++++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 6324ca91..bcb0c32c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -587,7 +587,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        return self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) or samples
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
+
+        return samples
 
 
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b58e810b..7ff77c01 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -196,6 +196,7 @@ class VanillaStableDiffusionSampler:
         x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
 
         self.init_latent = x
+        self.last_latent = x
         self.step = 0
 
         samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
@@ -206,6 +207,7 @@ class VanillaStableDiffusionSampler:
         self.initialize(p)
 
         self.init_latent = None
+        self.last_latent = x
         self.step = 0
 
         steps = steps or p.steps
@@ -388,6 +390,7 @@ class KDiffusionSampler:
             extra_params_kwargs['sigmas'] = sigma_sched
 
         self.model_wrap_cfg.init_latent = x
+        self.last_latent = x
 
         samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
@@ -414,6 +417,7 @@ class KDiffusionSampler:
         else:
             extra_params_kwargs['sigmas'] = sigmas
 
+        self.last_latent = x
         samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples
-- 
cgit v1.2.1


From 9cc4974d2362a49a505e9408a4d992f26ffad02d Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Thu, 20 Oct 2022 17:03:25 -0500
Subject: add confirmation dialogue

---
 javascript/ui.js | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/javascript/ui.js b/javascript/ui.js
index 165383da..037cffca 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -164,6 +164,8 @@ function selected_tab_id() {
 
 function trash_prompt(_,_, is_img2img) {
 
+if(!confirm("Delete prompt?")) return false
+
     if(selected_tab_id() == "tab_txt2img") {
      pos_prompt = txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea");
      neg_prompt = txt2img_textarea = gradioApp().querySelector("#txt2img_neg_prompt > label > textarea");
@@ -177,6 +179,8 @@ function trash_prompt(_,_, is_img2img) {
      pos_prompt.value = ""
      neg_prompt.value = ""
     }
+
+    return true
 }
 
 
-- 
cgit v1.2.1


From a81651498018f6a0d5144f2ba957f685d7c28028 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Thu, 20 Oct 2022 17:33:33 -0500
Subject: remove unnecessary assignment

---
 javascript/ui.js | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 037cffca..39eae1f7 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -163,21 +163,20 @@ function selected_tab_id() {
 }
 
 function trash_prompt(_,_, is_img2img) {
+//txt2img_token_button
 
 if(!confirm("Delete prompt?")) return false
 
     if(selected_tab_id() == "tab_txt2img") {
-     pos_prompt = txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea");
-     neg_prompt = txt2img_textarea = gradioApp().querySelector("#txt2img_neg_prompt > label > textarea");
+        gradioApp().querySelector("#txt2img_prompt > label > textarea").value = "";
+        gradioApp().querySelector("#txt2img_neg_prompt > label > textarea").value = "";
 
-     pos_prompt.value = ""
-     neg_prompt.value = ""
+        update_token_counter("img2img_token_button")
     } else {
-     pos_prompt = txt2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
-     neg_prompt = txt2img_textarea = gradioApp().querySelector("#img2img_neg_prompt > label > textarea");
+        gradioApp().querySelector("#img2img_prompt > label > textarea").value = "";
+        gradioApp().querySelector("#img2img_neg_prompt > label > textarea").value = "";
 
-     pos_prompt.value = ""
-     neg_prompt.value = ""
+        update_token_counter("txt2img_token_button")
     }
 
     return true
-- 
cgit v1.2.1


From 49533eed9e3aad19e9868ee140708baec4fd44be Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 20 Oct 2022 16:01:27 -0700
Subject: XY grid correctly re-assignes model when config changes

---
 modules/sd_models.py | 6 +++---
 scripts/xy_grid.py   | 1 +
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 7072db08..fea84630 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -204,9 +204,9 @@ def load_model_weights(model, checkpoint_info):
     model.sd_checkpoint_info = checkpoint_info
 
 
-def load_model():
+def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
-    checkpoint_info = select_checkpoint()
+    checkpoint_info = checkpoint_info or select_checkpoint()
 
     if checkpoint_info.config != shared.cmd_opts.config:
         print(f"Loading config from: {checkpoint_info.config}")
@@ -249,7 +249,7 @@ def reload_model_weights(sd_model, info=None):
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         checkpoints_loaded.clear()
-        shared.sd_model = load_model()
+        shared.sd_model = load_model(checkpoint_info)
         return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 5cca168a..eff0c942 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -89,6 +89,7 @@ def apply_checkpoint(p, x, xs):
     if info is None:
         raise RuntimeError(f"Unknown checkpoint: {x}")
     modules.sd_models.reload_model_weights(shared.sd_model, info)
+    p.sd_model = shared.sd_model
 
 
 def confirm_checkpoints(p, xs):
-- 
cgit v1.2.1


From a3b047b7c74dc6ca07f40aee778997fc1889d72f Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Thu, 20 Oct 2022 19:28:58 -0500
Subject: add settings option to toggle button visibility

---
 javascript/ui.js  | 1 -
 modules/shared.py | 1 +
 modules/ui.py     | 2 +-
 3 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 39eae1f7..f19af550 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -163,7 +163,6 @@ function selected_tab_id() {
 }
 
 function trash_prompt(_,_, is_img2img) {
-//txt2img_token_button
 
 if(!confirm("Delete prompt?")) return false
 
diff --git a/modules/shared.py b/modules/shared.py
index faede821..7e9c2696 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -300,6 +300,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+    "trash_prompt_visible": OptionInfo(True, "Show trash prompt button"),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
     'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
diff --git a/modules/ui.py b/modules/ui.py
index bde546cc..13c0b4ca 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -509,7 +509,7 @@ def create_toprow(is_img2img):
             paste = gr.Button(value=paste_symbol, elem_id="paste")
             save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
             prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-            trash_prompt = gr.Button(value=trash_prompt_symbol, elem_id="trash_prompt")
+            trash_prompt = gr.Button(value=trash_prompt_symbol, elem_id="trash_prompt", visible=opts.trash_prompt_visible)
 
             token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
             token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-- 
cgit v1.2.1


From 1fc278bcc642f720484a77eb169271054d3153b1 Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Fri, 21 Oct 2022 02:38:24 +0100
Subject: Fixed job count & single-output grid

---
 scripts/outpainting_mk_2.py | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py
index 726417e7..633dc119 100644
--- a/scripts/outpainting_mk_2.py
+++ b/scripts/outpainting_mk_2.py
@@ -172,10 +172,6 @@ class Script(scripts.Script):
         if down > 0:
             down = target_h - init_img.height - up
 
-        init_image = p.init_images[0]
-
-        state.job_count = (1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0)
-
         def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
             is_horiz = is_left or is_right
             is_vert = is_top or is_bottom
@@ -250,7 +246,7 @@ class Script(scripts.Script):
         batch_count = p.n_iter
         batch_size = p.batch_size
         p.n_iter = 1
-        state.job_count = batch_count
+        state.job_count = batch_count * batch_size * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0))
         all_processed_images = []
 
         for i in range(batch_count):
@@ -268,10 +264,11 @@ class Script(scripts.Script):
 
             all_processed_images += imgs
 
-        combined_grid_image = images.image_grid(all_processed_images)
         all_images = all_processed_images
 
-        if opts.return_grid:
+        combined_grid_image = images.image_grid(all_processed_images)
+        unwanted_grid_because_of_img_count = len(all_processed_images) < 2 and opts.grid_only_if_multiple
+        if opts.return_grid and not unwanted_grid_because_of_img_count:
             all_images = [combined_grid_image] + all_processed_images
 
         res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1])
@@ -280,8 +277,7 @@ class Script(scripts.Script):
             for img in all_processed_images:
                 images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
 
-        if opts.grid_save:
+        if opts.grid_save and not unwanted_grid_because_of_img_count:
             images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
 
         return res
-
-- 
cgit v1.2.1


From 0110429dc4bc004ac56573fe1a6b05cb0123678e Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Thu, 20 Oct 2022 19:10:32 +0900
Subject: Fixed path issue while extras batch processing

---
 modules/extras.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index b853fa5b..f9796624 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -118,10 +118,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         while len(cached_images) > 2:
             del cached_images[next(iter(cached_images.keys()))]
-
-        images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,
-                          forced_filename=image_name if opts.use_original_name_batch else None)
+        
+        if opts.use_original_name_batch and image_name != None:
+            basename = os.path.splitext(os.path.basename(image_name))[0]
+        else:
+            basename = ''
+
+        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo
-- 
cgit v1.2.1


From aacc4c1ecbcba3cef421d8776dc5b4b239df9b42 Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Thu, 20 Oct 2022 21:31:29 +0900
Subject: Added try except to extras batch from directory

---
 modules/extras.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index f9796624..0d817cf9 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -41,7 +41,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
             return outputs, "Please select an input directory.", ''
         image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
         for img in image_list:
-            image = Image.open(img)
+            try:
+                image = Image.open(img)
+            except Exception:
+                continue
             imageArr.append(image)
             imageNameArr.append(img)
     else:
@@ -122,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         if opts.use_original_name_batch and image_name != None:
             basename = os.path.splitext(os.path.basename(image_name))[0]
         else:
-            basename = ''
+            basename = None
 
-        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
+        images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo
-- 
cgit v1.2.1


From bc16b135b527224545dca555a9d51edb0adcee2d Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Thu, 20 Oct 2022 21:43:27 +0900
Subject: Fixed path issue while extras batch processing

---
 modules/extras.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 0d817cf9..ac85142c 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -125,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         if opts.use_original_name_batch and image_name != None:
             basename = os.path.splitext(os.path.basename(image_name))[0]
         else:
-            basename = None
+            basename = ''
 
-        images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename)
+        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo
-- 
cgit v1.2.1


From 991a595686b8d105025d68d0e833d1cbf44cb143 Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Fri, 21 Oct 2022 09:23:13 +0900
Subject: sort file list in alphabetical ordering in extras

---
 modules/extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/extras.py b/modules/extras.py
index ac85142c..22c5a1c1 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -39,7 +39,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         if input_dir == '':
             return outputs, "Please select an input directory.", ''
-        image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
+        image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)]
         for img in image_list:
             try:
                 image = Image.open(img)
-- 
cgit v1.2.1


From 45872181902ada06267e2de601586d512cf5df1a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 09:00:39 +0300
Subject: updated readme and some small stylistic changes to code

---
 README.md                       |  1 +
 modules/processing.py           | 14 ++++++--------
 modules/sd_hijack_inpainting.py |  3 +++
 3 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 859a91b6..a98bb00b 100644
--- a/README.md
+++ b/README.md
@@ -70,6 +70,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
 - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
 - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
+- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. 
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
diff --git a/modules/processing.py b/modules/processing.py
index 539cde38..21786968 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -540,11 +540,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-
-    def create_dummy_mask(self, x, first_phase: bool = False):
+    def create_dummy_mask(self, x, width=None, height=None):
         if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            height = self.firstphase_height if first_phase else self.height
-            width = self.firstphase_width if first_phase else self.width
+            height = height or self.height
+            width = width or self.width
 
             # The "masked-image" in this case will just be all zeros since the entire image is masked.
             image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
@@ -571,7 +570,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, first_phase=True))
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
@@ -634,6 +633,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.inpainting_mask_invert = inpainting_mask_invert
         self.mask = None
         self.nmask = None
+        self.image_conditioning = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
@@ -735,9 +735,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        conditioning_key = self.sampler.conditioning_key
-
-        if conditioning_key in {'hybrid', 'concat'}:
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
             if self.image_mask is not None:
                 conditioning_mask = np.array(self.image_mask.convert("L"))
                 conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 43938071..fd92a335 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -301,6 +301,7 @@ def get_unconditional_conditioning(self, batch_size, null_label=None):
     c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
     return c
 
+
 class LatentInpaintDiffusion(LatentDiffusion):
     def __init__(
         self,
@@ -314,9 +315,11 @@ class LatentInpaintDiffusion(LatentDiffusion):
         assert self.masked_image_key in concat_keys
         self.concat_keys = concat_keys
 
+
 def should_hijack_inpainting(checkpoint_info):
     return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml")
 
+
 def do_inpainting_hijack():
     ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
     ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
-- 
cgit v1.2.1


From 74088c2a06a975092806362aede22f82716cb011 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Thu, 20 Oct 2022 08:18:02 +0300
Subject: allow float sizes for hypernet's layer_structure

---
 modules/hypernetworks/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 08f75f15..e0741d08 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -15,7 +15,7 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm
     assert not os.path.exists(fn), f"file {fn} already exists"
 
     if type(layer_structure) == str:
-        layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure)))
+        layer_structure = [float(x.strip()) for x in layer_structure.split(",")]
 
     hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
         name=name,
-- 
cgit v1.2.1


From 60872c5b404114336f9ca0c671ba88fa4a8201c9 Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Thu, 20 Oct 2022 19:10:32 +0900
Subject: Fixed path issue while extras batch processing

---
 modules/extras.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index b853fa5b..f9796624 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -118,10 +118,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         while len(cached_images) > 2:
             del cached_images[next(iter(cached_images.keys()))]
-
-        images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,
-                          forced_filename=image_name if opts.use_original_name_batch else None)
+        
+        if opts.use_original_name_batch and image_name != None:
+            basename = os.path.splitext(os.path.basename(image_name))[0]
+        else:
+            basename = ''
+
+        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo
-- 
cgit v1.2.1


From fb5a8cf0d9ed027ea3aa2e5422c946d8e6e72efe Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Thu, 20 Oct 2022 21:31:29 +0900
Subject: Added try except to extras batch from directory

---
 modules/extras.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index f9796624..0d817cf9 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -41,7 +41,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
             return outputs, "Please select an input directory.", ''
         image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
         for img in image_list:
-            image = Image.open(img)
+            try:
+                image = Image.open(img)
+            except Exception:
+                continue
             imageArr.append(image)
             imageNameArr.append(img)
     else:
@@ -122,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         if opts.use_original_name_batch and image_name != None:
             basename = os.path.splitext(os.path.basename(image_name))[0]
         else:
-            basename = ''
+            basename = None
 
-        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
+        images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo
-- 
cgit v1.2.1


From a13c3bed3cec27afe3c015d3d62db36e25b10d1f Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Thu, 20 Oct 2022 21:43:27 +0900
Subject: Fixed path issue while extras batch processing

---
 modules/extras.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 0d817cf9..ac85142c 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -125,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         if opts.use_original_name_batch and image_name != None:
             basename = os.path.splitext(os.path.basename(image_name))[0]
         else:
-            basename = None
+            basename = ''
 
-        images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename)
+        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo
-- 
cgit v1.2.1


From 9d71eef02e7395e179b8d5e61e6d91ddd8928d2e Mon Sep 17 00:00:00 2001
From: winterspringsummer <linoo@naver.com>
Date: Fri, 21 Oct 2022 09:23:13 +0900
Subject: sort file list in alphabetical ordering in extras

---
 modules/extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/extras.py b/modules/extras.py
index ac85142c..22c5a1c1 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -39,7 +39,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         if input_dir == '':
             return outputs, "Please select an input directory.", ''
-        image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
+        image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)]
         for img in image_list:
             try:
                 image = Image.open(img)
-- 
cgit v1.2.1


From c23f666dba2b484d521d2dc4be91cf9e09312647 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 09:47:43 +0300
Subject: a more strict check for activation type and a more reasonable check
 for type of layer in hypernets

---
 modules/hypernetworks/hypernetwork.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7d617680..84e7e350 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -32,10 +32,16 @@ class HypernetworkModule(torch.nn.Module):
         linears = []
         for i in range(len(layer_structure) - 1):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
+
             if activation_func == "relu":
                 linears.append(torch.nn.ReLU())
-            if activation_func == "leakyrelu":
+            elif activation_func == "leakyrelu":
                 linears.append(torch.nn.LeakyReLU())
+            elif activation_func == 'linear' or activation_func is None:
+                pass
+            else:
+                raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')
+
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
@@ -46,7 +52,7 @@ class HypernetworkModule(torch.nn.Module):
             self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                if not "ReLU" in layer.__str__():
+                if type(layer) == torch.nn.Linear:
                     layer.weight.data.normal_(mean=0.0, std=0.01)
                     layer.bias.data.zero_()
 
@@ -74,7 +80,7 @@ class HypernetworkModule(torch.nn.Module):
     def trainables(self):
         layer_structure = []
         for layer in self.linear:
-            if not "ReLU" in layer.__str__():
+            if type(layer) == torch.nn.Linear:
                 layer_structure += [layer.weight, layer.bias]
         return layer_structure
 
-- 
cgit v1.2.1


From 5f4fec307c14dd7f817244ffa92e8a4a64abed0b Mon Sep 17 00:00:00 2001
From: Stephen <farrenzo47@gmail.com>
Date: Thu, 20 Oct 2022 11:32:17 -0400
Subject: [Bugfix][API] - Fix API arg in launch script

---
 webui.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/webui.py b/webui.py
index 177bef74..87589064 100644
--- a/webui.py
+++ b/webui.py
@@ -118,7 +118,8 @@ def api_only():
     api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
 
 
-def webui(launch_api=False):
+def webui():
+    launch_api = cmd_opts.api
     initialize()
 
     while 1:
@@ -158,4 +159,4 @@ if __name__ == "__main__":
     if cmd_opts.nowebui:
         api_only()
     else:
-        webui(cmd_opts.api)
+        webui()
-- 
cgit v1.2.1


From 7157e5d064741fa57ca81a2c6432a651f21ee82f Mon Sep 17 00:00:00 2001
From: Patryk Wychowaniec <pwychowaniec@pm.me>
Date: Thu, 20 Oct 2022 19:22:59 +0200
Subject: interrogate: Fix CLIP-interrogation on CPU

Currently, trying to perform CLIP interrogation on a CPU fails, saying:

```
RuntimeError: "slow_conv2d_cpu" not implemented for 'Half'
```

This merge request fixes this issue by detecting whether the target
device is CPU and, if so, force-enabling `--no-half` and passing
`device="cpu"` to `clip.load()` (which then does some extra tricks to
ensure it works correctly on CPU).
---
 modules/interrogate.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 64b91eb4..65b05d34 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -28,9 +28,11 @@ class InterrogateModels:
     clip_preprocess = None
     categories = None
     dtype = None
+    running_on_cpu = None
 
     def __init__(self, content_dir):
         self.categories = []
+        self.running_on_cpu = devices.device_interrogate == torch.device("cpu")
 
         if os.path.exists(content_dir):
             for filename in os.listdir(content_dir):
@@ -53,7 +55,11 @@ class InterrogateModels:
     def load_clip_model(self):
         import clip
 
-        model, preprocess = clip.load(clip_model_name)
+        if self.running_on_cpu:
+            model, preprocess = clip.load(clip_model_name, device="cpu")
+        else:
+            model, preprocess = clip.load(clip_model_name)
+
         model.eval()
         model = model.to(devices.device_interrogate)
 
@@ -62,14 +68,14 @@ class InterrogateModels:
     def load(self):
         if self.blip_model is None:
             self.blip_model = self.load_blip_model()
-            if not shared.cmd_opts.no_half:
+            if not shared.cmd_opts.no_half and not self.running_on_cpu:
                 self.blip_model = self.blip_model.half()
 
         self.blip_model = self.blip_model.to(devices.device_interrogate)
 
         if self.clip_model is None:
             self.clip_model, self.clip_preprocess = self.load_clip_model()
-            if not shared.cmd_opts.no_half:
+            if not shared.cmd_opts.no_half and not self.running_on_cpu:
                 self.clip_model = self.clip_model.half()
 
         self.clip_model = self.clip_model.to(devices.device_interrogate)
-- 
cgit v1.2.1


From b69c37d25e4ffc56e8f8c247fa2c38b4648cefb7 Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Thu, 20 Oct 2022 22:21:12 +0800
Subject: Allow datasets with only 1 image in TI

---
 modules/textual_inversion/dataset.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 23bb4b6a..5b1c5002 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -83,7 +83,7 @@ class PersonalizedBase(Dataset):
 
             self.dataset.append(entry)
 
-        assert len(self.dataset) > 1, "No images have been found in the dataset."
+        assert len(self.dataset) > 0, "No images have been found in the dataset."
         self.length = len(self.dataset) * repeats // batch_size
 
         self.initial_indexes = np.arange(len(self.dataset))
@@ -91,7 +91,7 @@ class PersonalizedBase(Dataset):
         self.shuffle()
 
     def shuffle(self):
-        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
+        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0]).numpy()]
 
     def create_text(self, filename_text):
         text = random.choice(self.lines)
-- 
cgit v1.2.1


From 5245c7a4935f67b677da0f5a1fc2b74c074aa0e2 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Wed, 19 Oct 2022 12:21:32 -0700
Subject: Issue #2921-Give PNG info to Hypernet previews.

---
 modules/hypernetworks/hypernetwork.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 84e7e350..68c8f26d 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -256,6 +256,9 @@ def stack_conds(conds):
 
 
 def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+    # images is required here to give training previews their infotext. Importing this at the very top causes a circular dependency.
+    from modules import images
+
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -298,6 +301,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
     last_saved_file = "<none>"
     last_saved_image = "<none>"
+    forced_filename = "<none>"
 
     ititial_step = hypernetwork.step or 0
     if ititial_step > steps:
@@ -345,7 +349,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         })
 
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
-            last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
+            forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'
+            last_saved_image = os.path.join(images_dir, forced_filename)
 
             optimizer.zero_grad()
             shared.sd_model.cond_stage_model.to(devices.device)
@@ -381,7 +386,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             if image is not None:
                 shared.state.current_image = image
-                image.save(last_saved_image)
+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename)
                 last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = hypernetwork.step
-- 
cgit v1.2.1


From 6014fb8afbe05c8d02fffe7a36a2e48128713bd2 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Wed, 19 Oct 2022 12:22:23 -0700
Subject: Do nothing if image file already exists.

---
 modules/images.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/modules/images.py b/modules/images.py
index b9589563..550e53ae 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -416,7 +416,11 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /')
         path = os.path.join(path, dirname)
 
-    os.makedirs(path, exist_ok=True)
+    try:
+        os.makedirs(path, exist_ok=True)
+    except FileExistsError:
+        # If the file already exists, continue and allow said file to be overwritten.
+        pass
 
     if forced_filename is None:
         basecount = get_next_sequence_number(path, basename)
-- 
cgit v1.2.1


From 4ff274e1e35bb642687253ce744d2cfa738ab293 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Wed, 19 Oct 2022 12:32:22 -0700
Subject: Revise comments.

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 modules/images.py                     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 68c8f26d..3f96361c 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -256,7 +256,7 @@ def stack_conds(conds):
 
 
 def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
-    # images is required here to give training previews their infotext. Importing this at the very top causes a circular dependency.
+    # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
     assert hypernetwork_name, 'hypernetwork not selected'
diff --git a/modules/images.py b/modules/images.py
index 550e53ae..b8834e3c 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -419,7 +419,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     try:
         os.makedirs(path, exist_ok=True)
     except FileExistsError:
-        # If the file already exists, continue and allow said file to be overwritten.
+        # If the file already exists, allow said file to be overwritten.
         pass
 
     if forced_filename is None:
-- 
cgit v1.2.1


From 2273e752fb3e578f1047f6d38b96330b07bf61a9 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Wed, 19 Oct 2022 14:23:48 -0700
Subject: Remove redundant try/except.

---
 modules/images.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index b8834e3c..b9589563 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -416,11 +416,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /')
         path = os.path.join(path, dirname)
 
-    try:
-        os.makedirs(path, exist_ok=True)
-    except FileExistsError:
-        # If the file already exists, allow said file to be overwritten.
-        pass
+    os.makedirs(path, exist_ok=True)
 
     if forced_filename is None:
         basecount = get_next_sequence_number(path, basename)
-- 
cgit v1.2.1


From 03a1e288c4973dd2dff57a97469b40f146b6fccf Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 10:13:24 +0300
Subject: turns out LayerNorm also has weight and bias and needs to be
 pre-multiplied and trained for hypernets

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3274a802..b1a5d0c7 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -52,7 +52,7 @@ class HypernetworkModule(torch.nn.Module):
             self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                if type(layer) == torch.nn.Linear:
+                if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
                     layer.weight.data.normal_(mean=0.0, std=0.01)
                     layer.bias.data.zero_()
 
@@ -80,7 +80,7 @@ class HypernetworkModule(torch.nn.Module):
     def trainables(self):
         layer_structure = []
         for layer in self.linear:
-            if type(layer) == torch.nn.Linear:
+            if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
                 layer_structure += [layer.weight, layer.bias]
         return layer_structure
 
-- 
cgit v1.2.1


From bf30673f5132c8f28357b31224c54331e788d3e7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 10:19:25 +0300
Subject: Fix Hypernet infotext string split bug for PR #3283

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 21786968..d1deffa9 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -304,7 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.filename.split('\\')[-1].split('.')[0]),
+        "Hypernet": (None if shared.loaded_hypernetwork is None else os.path.splitext(os.path.basename(shared.loaded_hypernetwork.filename))[0]),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.1


From 1ed227b3b57f06f4152be3bfc9f83b0a839a2604 Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Fri, 21 Oct 2022 10:57:40 +0200
Subject: wtf is happening

---
 .github/ISSUE_TEMPLATE/config.yml | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 .github/ISSUE_TEMPLATE/config.yml

diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 00000000..f58c94a9
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,5 @@
+blank_issues_enabled: false
+contact_links:
+  - name: WebUI Community Support
+    url: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions
+    about: Please ask and answer questions here.
-- 
cgit v1.2.1


From 003d2c7fe427edde299274c9e0d5fa59734e7f7e Mon Sep 17 00:00:00 2001
From: ClashSAN <98228077+ClashSAN@users.noreply.github.com>
Date: Fri, 21 Oct 2022 11:40:37 +0000
Subject: Update README.md

---
 README.md | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index a89593bf..348aaf87 100644
--- a/README.md
+++ b/README.md
@@ -24,6 +24,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
     - have as many embeddings as you want and use any names you like for them
     - use multiple embeddings with different numbers of vectors per token
     - works with half precision floating point numbers
+    - train embeddings on 8GB (also reports of 6GB working)
 - Extras tab with:
     - GFPGAN, neural network that fixes faces
     - CodeFormer, face restoration tool as an alternative to GFPGAN
@@ -60,7 +61,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - CLIP interrogator, a button that tries to guess prompt from an image
 - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
 - Batch Processing, process a group of files using img2img
-- Img2img Alternative
+- Img2img Alternative, reverse Euler method of cross attention control
 - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
 - Reloading checkpoints on the fly
 - Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
@@ -73,15 +74,22 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
 - History tab: view, direct and delete images conveniently within the UI
 - Generate forever option
-- Training Tab
-- Preprocessing Image Datasets: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
+- Training tab
+     - hypernetworks and embeddings options
+     - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
+- Clip skip
+- Use Hypernetworks
+- Use VAEs
+- Estimated completion time in progress bar
+- API
+- Support for dedicated inpainting model by RunwayML.
 
 
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
 
-Alternatively, use online services(like Google Colab):
+Alternatively, use online services (like Google Colab):
 
 - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
 
-- 
cgit v1.2.1


From df5706409386cc2e88718bd9101045587c39f8bb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 16:10:51 +0300
Subject: do not load aesthetic clip model until it's needed add refresh button
 for aesthetic embeddings add aesthetic params to images' infotext

---
 modules/aesthetic_clip.py                  | 40 +++++++++++++++++++----
 modules/generation_parameters_copypaste.py | 18 +++++++++--
 modules/img2img.py                         |  5 +--
 modules/processing.py                      |  4 +--
 modules/sd_models.py                       |  3 --
 modules/txt2img.py                         |  4 +--
 modules/ui.py                              | 52 ++++++++++++++++++++----------
 style.css                                  |  2 +-
 8 files changed, 89 insertions(+), 39 deletions(-)

diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
index 34efa931..8c828541 100644
--- a/modules/aesthetic_clip.py
+++ b/modules/aesthetic_clip.py
@@ -40,6 +40,8 @@ def iter_to_batched(iterable, n=1):
 
 
 def create_ui():
+    import modules.ui
+
     with gr.Group():
         with gr.Accordion("Open for Clip Aesthetic!", open=False):
             with gr.Row():
@@ -55,6 +57,8 @@ def create_ui():
                                              label="Aesthetic imgs embedding",
                                              value="None")
 
+                modules.ui.create_refresh_button(aesthetic_imgs, shared.update_aesthetic_embeddings, lambda: {"choices": sorted(shared.aesthetic_embeddings.keys())}, "refresh_aesthetic_embeddings")
+
             with gr.Row():
                 aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs',
                                                  placeholder="This text is used to rotate the feature space of the imgs embs",
@@ -66,11 +70,21 @@ def create_ui():
     return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative
 
 
+aesthetic_clip_model = None
+
+
+def aesthetic_clip():
+    global aesthetic_clip_model
+
+    if aesthetic_clip_model is None or aesthetic_clip_model.name_or_path != shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path:
+        aesthetic_clip_model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path)
+        aesthetic_clip_model.cpu()
+
+    return aesthetic_clip_model
+
+
 def generate_imgs_embd(name, folder, batch_size):
-    # clipModel = CLIPModel.from_pretrained(
-    #     shared.sd_model.cond_stage_model.clipModel.name_or_path
-    # )
-    model = shared.clip_model.to(device)
+    model = aesthetic_clip().to(device)
     processor = CLIPProcessor.from_pretrained(model.name_or_path)
 
     with torch.no_grad():
@@ -91,7 +105,7 @@ def generate_imgs_embd(name, folder, batch_size):
         path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt")
         torch.save(embs, path)
 
-        model = model.cpu()
+        model.cpu()
         del processor
         del embs
         gc.collect()
@@ -132,7 +146,7 @@ class AestheticCLIP:
         self.image_embs = None
         self.load_image_embs(None)
 
-    def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
+    def set_aesthetic_params(self, p, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
                              aesthetic_slerp=True, aesthetic_imgs_text="",
                              aesthetic_slerp_angle=0.15,
                              aesthetic_text_negative=False):
@@ -145,6 +159,18 @@ class AestheticCLIP:
         self.aesthetic_steps = aesthetic_steps
         self.load_image_embs(image_embs_name)
 
+        if self.image_embs_name is not None:
+            p.extra_generation_params.update({
+                "Aesthetic LR": aesthetic_lr,
+                "Aesthetic weight": aesthetic_weight,
+                "Aesthetic steps": aesthetic_steps,
+                "Aesthetic embedding": self.image_embs_name,
+                "Aesthetic slerp": aesthetic_slerp,
+                "Aesthetic text": aesthetic_imgs_text,
+                "Aesthetic text negative": aesthetic_text_negative,
+                "Aesthetic slerp angle": aesthetic_slerp_angle,
+            })
+
     def set_skip(self, skip):
         self.skip = skip
 
@@ -168,7 +194,7 @@ class AestheticCLIP:
 
             tokens = torch.asarray(remade_batch_tokens).to(device)
 
-            model = copy.deepcopy(shared.clip_model).to(device)
+            model = copy.deepcopy(aesthetic_clip()).to(device)
             model.requires_grad_(True)
             if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
                 text_embs_2 = model.get_text_features(
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 0f041449..f73647da 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -4,13 +4,22 @@ import gradio as gr
 from modules.shared import script_path
 from modules import shared
 
-re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)"
+re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
 re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
 type_of_gr_update = type(gr.update())
 
 
+def quote(text):
+    if ',' not in str(text):
+        return text
+
+    text = str(text)
+    text = text.replace('\\', '\\\\')
+    text = text.replace('"', '\\"')
+    return f'"{text}"'
+
 def parse_generation_parameters(x: str):
     """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
@@ -83,7 +92,12 @@ def connect_paste(button, paste_fields, input_comp, js=None):
             else:
                 try:
                     valtype = type(output.value)
-                    val = valtype(v)
+
+                    if valtype == bool and v == "False":
+                        val = False
+                    else:
+                        val = valtype(v)
+
                     res.append(gr.update(value=val))
                 except Exception:
                     res.append(gr.update())
diff --git a/modules/img2img.py b/modules/img2img.py
index bc7c66bc..eea5199b 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -109,10 +109,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         inpainting_mask_invert=inpainting_mask_invert,
     )
 
-    shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps),
-                                               aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text,
-                                               aesthetic_slerp_angle,
-                                               aesthetic_text_negative)
+    shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
 
     if shared.cmd_opts.enable_console_prompts:
         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
diff --git a/modules/processing.py b/modules/processing.py
index d1deffa9..f0852cd5 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -12,7 +12,7 @@ from skimage import exposure
 from typing import Any, Dict, List, Optional
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -318,7 +318,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params.update(p.extra_generation_params)
 
-    generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
+    generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
     negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 05a1df28..b1c91b0d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -234,9 +234,6 @@ def load_model(checkpoint_info=None):
 
     sd_hijack.model_hijack.hijack(sd_model)
 
-    if shared.clip_model is None or shared.clip_model.transformer.name_or_path != sd_model.cond_stage_model.wrapped.transformer.name_or_path:
-        shared.clip_model = CLIPModel.from_pretrained(sd_model.cond_stage_model.wrapped.transformer.name_or_path)
-
     sd_model.eval()
 
     print(f"Model loaded.")
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 32ed1d8d..1761cfa2 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -36,9 +36,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         firstphase_height=firstphase_height if enable_hr else None,
     )
 
-    shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps),
-                                           aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle,
-                                           aesthetic_text_negative)
+    shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
 
     if cmd_opts.enable_console_prompts:
         print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
diff --git a/modules/ui.py b/modules/ui.py
index 381ca925..0d020de6 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -597,27 +597,29 @@ def apply_setting(key, value):
     return value
 
 
-def create_ui(wrap_gradio_gpu_call):
-    import modules.img2img
-    import modules.txt2img
+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+    def refresh():
+        refresh_method()
+        args = refreshed_args() if callable(refreshed_args) else refreshed_args
 
-    def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
-        def refresh():
-            refresh_method()
-            args = refreshed_args() if callable(refreshed_args) else refreshed_args
+        for k, v in args.items():
+            setattr(refresh_component, k, v)
 
-            for k, v in args.items():
-                setattr(refresh_component, k, v)
+        return gr.update(**(args or {}))
 
-            return gr.update(**(args or {}))
+    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
+    refresh_button.click(
+        fn=refresh,
+        inputs=[],
+        outputs=[refresh_component]
+    )
+    return refresh_button
+
+
+def create_ui(wrap_gradio_gpu_call):
+    import modules.img2img
+    import modules.txt2img
 
-        refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
-        refresh_button.click(
-            fn = refresh,
-            inputs = [],
-            outputs = [refresh_component]
-        )
-        return refresh_button
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
@@ -802,6 +804,14 @@ def create_ui(wrap_gradio_gpu_call):
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
+                (aesthetic_lr, "Aesthetic LR"),
+                (aesthetic_weight, "Aesthetic weight"),
+                (aesthetic_steps, "Aesthetic steps"),
+                (aesthetic_imgs, "Aesthetic embedding"),
+                (aesthetic_slerp, "Aesthetic slerp"),
+                (aesthetic_imgs_text, "Aesthetic text"),
+                (aesthetic_text_negative, "Aesthetic text negative"),
+                (aesthetic_slerp_angle, "Aesthetic slerp angle"),
             ]
 
             txt2img_preview_params = [
@@ -1077,6 +1087,14 @@ def create_ui(wrap_gradio_gpu_call):
                 (seed_resize_from_w, "Seed resize from-1"),
                 (seed_resize_from_h, "Seed resize from-2"),
                 (denoising_strength, "Denoising strength"),
+                (aesthetic_lr_im, "Aesthetic LR"),
+                (aesthetic_weight_im, "Aesthetic weight"),
+                (aesthetic_steps_im, "Aesthetic steps"),
+                (aesthetic_imgs_im, "Aesthetic embedding"),
+                (aesthetic_slerp_im, "Aesthetic slerp"),
+                (aesthetic_imgs_text_im, "Aesthetic text"),
+                (aesthetic_text_negative_im, "Aesthetic text negative"),
+                (aesthetic_slerp_angle_im, "Aesthetic slerp angle"),
             ]
             token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
 
diff --git a/style.css b/style.css
index 26ae36a5..5d2bacc9 100644
--- a/style.css
+++ b/style.css
@@ -477,7 +477,7 @@ input[type="range"]{
     padding: 0;
 }
 
-#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
+#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization, #refresh_aesthetic_embeddings{
     max-width: 2.5em;
     min-width: 2.5em;
     height: 2.4em;
-- 
cgit v1.2.1


From 9286fe53de2eef91f13cc3ad5938ddf67ecc8413 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 16:38:06 +0300
Subject: make aestetic embedding ciompatible with prompts longer than 75
 tokens

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 36198a3c..1f8587d1 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -332,8 +332,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     multipliers.append([1.0] * 75)
 
             z1 = self.process_tokens(tokens, multipliers)
+            z1 = shared.aesthetic_clip(z1, remade_batch_tokens)
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
-            z = shared.aesthetic_clip(z, remade_batch_tokens)
 
             remade_batch_tokens = rem_tokens
             batch_multipliers = rem_multipliers
-- 
cgit v1.2.1


From d0ea471b0cdaede163c6e7f6fae8535f5c3cd226 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 21 Oct 2022 14:04:41 +0100
Subject: Use opts in textual_inversion image_embedding.py for dynamic fonts

---
 modules/textual_inversion/image_embedding.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index 898ce3b3..c50b1e7b 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -5,6 +5,7 @@ import zlib
 from PIL import Image, PngImagePlugin, ImageDraw, ImageFont
 from fonts.ttf import Roboto
 import torch
+from modules.shared import opts
 
 
 class EmbeddingEncoder(json.JSONEncoder):
-- 
cgit v1.2.1


From 306e2ff6ab8f4c7e94ab55f4f08ab8f94d73d287 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 21 Oct 2022 14:47:21 +0100
Subject: Update image_embedding.py

---
 modules/textual_inversion/image_embedding.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index c50b1e7b..ea653806 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -134,7 +134,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
     from math import cos
 
     image = srcimage.copy()
-
+    fontsize = 32
     if textfont is None:
         try:
             textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
@@ -151,7 +151,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t
     image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
 
     draw = ImageDraw.Draw(image)
-    fontsize = 32
+
     font = ImageFont.truetype(textfont, fontsize)
     padding = 10
 
-- 
cgit v1.2.1


From 85cb5918ee7c97cafafe4149880ed82f1933d919 Mon Sep 17 00:00:00 2001
From: parsec501 <105080989+parsec501@users.noreply.github.com>
Date: Fri, 21 Oct 2022 14:43:23 +0200
Subject: Make commit hash mandatory field

---
 .github/ISSUE_TEMPLATE/bug_report.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 35802a53..9c2ff313 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -45,6 +45,8 @@ body:
     attributes:
       label: Commit where the problem happens
       description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
+    validations:
+      required: true
   - type: dropdown
     id: platforms
     attributes:
-- 
cgit v1.2.1


From 51e3dc9ccad157d7161b697a246e26c868d46a7c Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Fri, 21 Oct 2022 02:11:12 -0700
Subject: Sanitize hypernet name input.

---
 modules/hypernetworks/ui.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 266f04f6..e6f50a1f 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -11,6 +11,9 @@ from modules.hypernetworks import hypernetwork
 
 
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, add_layer_norm=False, activation_func=None):
+    # Remove illegal characters from name.
+    name = "".join( x for x in name if (x.isalnum() or x in "._- "))
+
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     if not overwrite_old:
         assert not os.path.exists(fn), f"file {fn} already exists"
-- 
cgit v1.2.1


From 19818f023cfafc472c6c241cab0b72896a168481 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Fri, 21 Oct 2022 02:14:02 -0700
Subject: Match hypernet name with filename in all cases.

---
 modules/hypernetworks/hypernetwork.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b1a5d0c7..6d392be4 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -340,7 +340,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         pbar.set_description(f"loss: {mean_loss:.7f}")
 
         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
-            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
+            temp = hypernetwork.name
+            # Before saving, change name to match current checkpoint.
+            hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'
+            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
             hypernetwork.save(last_saved_file)
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
@@ -405,6 +408,9 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     hypernetwork.sd_checkpoint = checkpoint.hash
     hypernetwork.sd_checkpoint_name = checkpoint.model_name
+    # Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention).
+    hypernetwork.name = hypernetwork_name
+    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork.name}.pt')
     hypernetwork.save(filename)
 
     return hypernetwork, filename
-- 
cgit v1.2.1


From fccad18a59e3c2c33fefbbb1763c6a87a3a68eba Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Fri, 21 Oct 2022 02:17:26 -0700
Subject: Refer to Hypernet's name, sensibly, by its name variable.

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index f0852cd5..ff1ec4c9 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -304,7 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else os.path.splitext(os.path.basename(shared.loaded_hypernetwork.filename))[0]),
+        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.1


From 272fa527bbe93143668ffc16838107b7dca35b40 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Fri, 21 Oct 2022 02:41:55 -0700
Subject: Remove unused variable.

---
 modules/hypernetworks/hypernetwork.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 6d392be4..47d91ea5 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -340,7 +340,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         pbar.set_description(f"loss: {mean_loss:.7f}")
 
         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
-            temp = hypernetwork.name
             # Before saving, change name to match current checkpoint.
             hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
-- 
cgit v1.2.1


From 02e4d4694dd9254a6ca9f05c2eb7b01ea508abc7 Mon Sep 17 00:00:00 2001
From: Rcmcpe <rcmcpe-cn@outlook.com>
Date: Fri, 21 Oct 2022 15:53:35 +0800
Subject: Change option description of unload_models_when_training

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 5c675b80..41d7f08e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -266,7 +266,7 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),
+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-- 
cgit v1.2.1


From 704036ff07b71bf86cadcbbff2bcfeebdd1ed3a6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 17:11:42 +0300
Subject: make aspect ratio overlay work regardless of selected localization

---
 javascript/aspectRatioOverlay.js | 36 +++++++++++++++++-------------------
 javascript/dragdrop.js           |  2 +-
 modules/ui.py                    |  4 ++--
 3 files changed, 20 insertions(+), 22 deletions(-)

diff --git a/javascript/aspectRatioOverlay.js b/javascript/aspectRatioOverlay.js
index 96f1c00d..d3ca2781 100644
--- a/javascript/aspectRatioOverlay.js
+++ b/javascript/aspectRatioOverlay.js
@@ -3,12 +3,12 @@ let currentWidth = null;
 let currentHeight = null;
 let arFrameTimeout = setTimeout(function(){},0);
 
-function dimensionChange(e,dimname){
+function dimensionChange(e, is_width, is_height){
 
-	if(dimname == 'Width'){
+	if(is_width){
 		currentWidth = e.target.value*1.0
 	}
-	if(dimname == 'Height'){
+	if(is_height){
 		currentHeight = e.target.value*1.0
 	}
 
@@ -98,22 +98,20 @@ onUiUpdate(function(){
 	var inImg2img   = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
 	if(inImg2img){
 		let inputs = gradioApp().querySelectorAll('input');
-		inputs.forEach(function(e){ 
-			let parentLabel = e.parentElement.querySelector('label')
-			if(parentLabel && parentLabel.innerText){
-				if(!e.classList.contains('scrollwatch')){
-					if(parentLabel.innerText == 'Width' || parentLabel.innerText == 'Height'){
-						e.addEventListener('input', function(e){dimensionChange(e,parentLabel.innerText)} )
-						e.classList.add('scrollwatch')
-					}
-					if(parentLabel.innerText == 'Width'){
-						currentWidth = e.value*1.0
-					}
-					if(parentLabel.innerText == 'Height'){
-						currentHeight = e.value*1.0
-					}
-				}
-			} 
+		inputs.forEach(function(e){
+		    var is_width = e.parentElement.id == "img2img_width"
+		    var is_height = e.parentElement.id == "img2img_height"
+
+			if((is_width || is_height) && !e.classList.contains('scrollwatch')){
+				e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
+				e.classList.add('scrollwatch')
+			}
+			if(is_width){
+				currentWidth = e.value*1.0
+			}
+			if(is_height){
+				currentHeight = e.value*1.0
+			}
 		})
 	}
 });
diff --git a/javascript/dragdrop.js b/javascript/dragdrop.js
index 070cf255..3ed1cb3c 100644
--- a/javascript/dragdrop.js
+++ b/javascript/dragdrop.js
@@ -43,7 +43,7 @@ function dropReplaceImage( imgWrap, files ) {
 window.document.addEventListener('dragover', e => {
     const target = e.composedPath()[0];
     const imgWrap = target.closest('[data-testid="image"]');
-    if ( !imgWrap && target.placeholder.indexOf("Prompt") == -1) {
+    if ( !imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) {
         return;
     }
     e.stopPropagation();
diff --git a/modules/ui.py b/modules/ui.py
index 0d020de6..85f95792 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -879,8 +879,8 @@ def create_ui(wrap_gradio_gpu_call):
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-- 
cgit v1.2.1


From 3d898044e5e55dca1698e9b5b7d3558b5b78675a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 17:26:30 +0300
Subject: batch_size does not affect job count

---
 scripts/outpainting_mk_2.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py
index 633dc119..2afd4aa5 100644
--- a/scripts/outpainting_mk_2.py
+++ b/scripts/outpainting_mk_2.py
@@ -246,7 +246,7 @@ class Script(scripts.Script):
         batch_count = p.n_iter
         batch_size = p.batch_size
         p.n_iter = 1
-        state.job_count = batch_count * batch_size * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0))
+        state.job_count = batch_count * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0))
         all_processed_images = []
 
         for i in range(batch_count):
-- 
cgit v1.2.1


From ac0aa2b18efeeb9220a5994c8dd54c7cdda7cc40 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 17:35:51 +0300
Subject: loading SD VAE, see PR #3303

---
 modules/sd_models.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index b1c91b0d..d99dbce8 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -155,6 +155,9 @@ def get_state_dict_from_checkpoint(pl_sd):
     return pl_sd
 
 
+vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
+
+
 def load_model_weights(model, checkpoint_info):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
@@ -186,7 +189,7 @@ def load_model_weights(model, checkpoint_info):
         if os.path.exists(vae_file):
             print(f"Loading VAE weights from: {vae_file}")
             vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
-            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
+            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
             model.first_stage_model.load_state_dict(vae_dict)
 
         model.first_stage_model.to(devices.dtype_vae)
-- 
cgit v1.2.1


From 24ce67a13bd74202d298cd8e2a306d90214980d8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 17:41:47 +0300
Subject: make aspect ratio overlay work regardless of selected localization,
 pt2

---
 javascript/aspectRatioOverlay.js | 19 +++++--------------
 1 file changed, 5 insertions(+), 14 deletions(-)

diff --git a/javascript/aspectRatioOverlay.js b/javascript/aspectRatioOverlay.js
index d3ca2781..66f26a22 100644
--- a/javascript/aspectRatioOverlay.js
+++ b/javascript/aspectRatioOverlay.js
@@ -18,22 +18,13 @@ function dimensionChange(e, is_width, is_height){
 		return;
 	}
 
-	var img2imgMode = gradioApp().querySelector('#mode_img2img.tabs > div > button.rounded-t-lg.border-gray-200')
-	if(img2imgMode){
-		img2imgMode=img2imgMode.innerText
-	}else{
-		return;
-	}
-
-	var redrawImage = gradioApp().querySelector('div[data-testid=image] img');
-	var inpaintImage = gradioApp().querySelector('#img2maskimg div[data-testid=image] img')
-
 	var targetElement = null;
 
-	if(img2imgMode=='img2img' && redrawImage){
-		targetElement = redrawImage;
-	}else if(img2imgMode=='Inpaint' && inpaintImage){
-		targetElement = inpaintImage;
+    var tabIndex = get_tab_index('mode_img2img')
+	if(tabIndex == 0){
+		targetElement = gradioApp().querySelector('div[data-testid=image] img');
+	} else if(tabIndex == 1){
+		targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img');
 	}
 
 	if(targetElement){
-- 
cgit v1.2.1


From f49c08ea566385db339c6628f65c3a121033f67c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 18:46:02 +0300
Subject: prevent error spam when processing images without txt files for
 captions

---
 modules/textual_inversion/preprocess.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 17e4ddc1..33eaddb6 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -122,11 +122,10 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
             continue
 
         existing_caption = None
-
-        try:
-            existing_caption = open(os.path.splitext(filename)[0] + '.txt', 'r').read()
-        except Exception as e:
-            print(e)
+        existing_caption_filename = os.path.splitext(filename)[0] + '.txt'
+        if os.path.exists(existing_caption_filename):
+            with open(existing_caption_filename, 'r', encoding="utf8") as file:
+                existing_caption = file.read()
 
         if shared.state.interrupted:
             break
-- 
cgit v1.2.1


From bb0f1a2cdae3410a41d06ae878f56e29b8154c41 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sat, 22 Oct 2022 01:23:00 +0800
Subject: inspiration finished

---
 javascript/inspiration.js |  27 ++++---
 modules/inspiration.py    | 192 ++++++++++++++++++++++++++++++----------------
 modules/shared.py         |   6 ++
 modules/ui.py             |   2 +-
 webui.py                  |   3 +-
 5 files changed, 151 insertions(+), 79 deletions(-)

diff --git a/javascript/inspiration.js b/javascript/inspiration.js
index e1c0e114..791a80c9 100644
--- a/javascript/inspiration.js
+++ b/javascript/inspiration.js
@@ -1,25 +1,31 @@
 function public_image_index_in_gallery(item, gallery){
+    var imgs = gallery.querySelectorAll("img.h-full")
     var index;
     var i = 0;
-    gallery.querySelectorAll("img").forEach(function(e){
+    imgs.forEach(function(e){
         if (e == item)
             index = i;
         i += 1;
     });
+    var num = imgs.length / 2
+    index = (index < num) ? index : (index - num)
     return index;
 }
 
-function inspiration_selected(name, types, name_list){
+function inspiration_selected(name, name_list){
     var btn = gradioApp().getElementById("inspiration_select_button")
-    return [gradioApp().getElementById("inspiration_select_button").getAttribute("img-index"), types];
-}    
+    return [gradioApp().getElementById("inspiration_select_button").getAttribute("img-index")];
+}   
+function inspiration_click_get_button(){
+    gradioApp().getElementById("inspiration_get_button").click();
+}  
 var inspiration_image_click = function(){
     var index =  public_image_index_in_gallery(this, gradioApp().getElementById("inspiration_gallery"));
-    var btn = gradioApp().getElementById("inspiration_select_button")
-    btn.setAttribute("img-index", index)
-    setTimeout(function(btn){btn.click();}, 10, btn)
+    var btn = gradioApp().getElementById("inspiration_select_button");
+    btn.setAttribute("img-index", index);
+    setTimeout(function(btn){btn.click();}, 10, btn);
 }
-
+ 
 document.addEventListener("DOMContentLoaded", function() {
     var mutationObserver = new MutationObserver(function(m){
         var gallery = gradioApp().getElementById("inspiration_gallery")
@@ -27,11 +33,10 @@ document.addEventListener("DOMContentLoaded", function() {
             var node = gallery.querySelector(".absolute.backdrop-blur.h-full")
             if (node) {
                 node.style.display = "None"; //parentNode.removeChild(node) 
-            }
-            
+            } 
             gallery.querySelectorAll('img').forEach(function(e){    
                 e.onclick = inspiration_image_click
-        }) 
+            });
 
         }
 
diff --git a/modules/inspiration.py b/modules/inspiration.py
index 456bfcb5..f72ebf3a 100644
--- a/modules/inspiration.py
+++ b/modules/inspiration.py
@@ -1,122 +1,182 @@
 import os
 import random
-import gradio 
-inspiration_path = "inspiration"
-inspiration_system_path = os.path.join(inspiration_path, "system")
-def read_name_list(file):
+import gradio
+from modules.shared import opts
+inspiration_system_path = os.path.join(opts.inspiration_dir, "system")
+def read_name_list(file, types=None, keyword=None):
     if not os.path.exists(file):
         return []
-    f = open(file, "r")
     ret = []
+    f = open(file, "r")    
     line = f.readline()
     while len(line) > 0:
         line = line.rstrip("\n")
-        ret.append(line)
-    print(ret)
+        if types is not None:
+            dirname = os.path.split(line)
+            if dirname[0] in types and keyword in dirname[1]:
+                ret.append(line)
+        else:
+            ret.append(line)
+        line = f.readline()
     return ret
 
 def save_name_list(file, name): 
-    print(file)
-    f = open(file, "a")
-    f.write(name + "\n")
+    with open(file, "a") as f:
+        f.write(name + "\n")
 
-def get_inspiration_images(source, types):   
-    path = os.path.join(inspiration_path , types) 
+def get_types_list():
+    files = os.listdir(opts.inspiration_dir)
+    types = []
+    for x in files:
+        path = os.path.join(opts.inspiration_dir, x)
+        if x[0] == ".":
+            continue
+        if not os.path.isdir(path):
+            continue
+        if path == inspiration_system_path:
+            continue
+        types.append(x)
+    return types
+
+def get_inspiration_images(source, types, keyword):
+    get_num = int(opts.inspiration_rows_num * opts.inspiration_cols_num)
     if source == "Favorites":
-        names = read_name_list(os.path.join(inspiration_system_path, types + "_faverites.txt"))
-        names = random.sample(names, 25)
+        names = read_name_list(os.path.join(inspiration_system_path, "faverites.txt"), types, keyword)
+        names = random.sample(names, get_num) if len(names) > get_num else names
     elif source == "Abandoned":
-        names = read_name_list(os.path.join(inspiration_system_path, types + "_abondened.txt"))
-        names = random.sample(names, 25)
-    elif source == "Exclude abandoned":
-        abondened = read_name_list(os.path.join(inspiration_system_path, types + "_abondened.txt"))        
-        all_names = os.listdir(path)
-        names = []
-        while len(names) < 25:
-            name = random.choice(all_names)
-            if name not in abondened:
-                names.append(name)
+        names = read_name_list(os.path.join(inspiration_system_path, "abandoned.txt"), types, keyword)
+        print(names)
+        names = random.sample(names, get_num) if len(names) > get_num else names
+    elif source == "Exclude abandoned":        
+        abandoned = read_name_list(os.path.join(inspiration_system_path, "abandoned.txt"), types, keyword)  
+        all_names = []
+        for tp in types:
+            name_list = os.listdir(os.path.join(opts.inspiration_dir, tp))
+            all_names += [os.path.join(tp, x) for x in name_list if keyword in x]
+        
+        if len(all_names) > get_num:
+            names = []
+            while len(names) < get_num:
+                name = random.choice(all_names)
+                if name not in abandoned:
+                    names.append(name)
+        else:
+            names = all_names
     else:
-        names = random.sample(os.listdir(path), 25)
-    names = random.sample(names, 25)
+        all_names = []
+        for tp in types:
+            name_list = os.listdir(os.path.join(opts.inspiration_dir, tp))
+            all_names += [os.path.join(tp, x) for x in name_list if keyword in x]
+        names = random.sample(all_names, get_num) if len(all_names) > get_num else all_names
     image_list = []
     for a in names:
-        image_path = os.path.join(path, a)
+        image_path = os.path.join(opts.inspiration_dir, a)
         images = os.listdir(image_path)        
-        image_list.append(os.path.join(image_path, random.choice(images)))
-    return image_list, names
+        image_list.append((os.path.join(image_path, random.choice(images)), a))
+    return image_list, names, ""
 
-def select_click(index, types, name_list):
+def select_click(index, name_list):
     name = name_list[int(index)]
-    path = os.path.join(inspiration_path, types, name)
+    path = os.path.join(opts.inspiration_dir, name)
     images = os.listdir(path)
-    return name, [os.path.join(path, x) for x in images]
+    return name, [os.path.join(path, x) for x in images], ""
 
-def give_up_click(name, types):
-    file = os.path.join(inspiration_system_path, types + "_abandoned.txt")
+def give_up_click(name):
+    file = os.path.join(inspiration_system_path, "abandoned.txt")
     name_list = read_name_list(file)
     if name not in name_list:
         save_name_list(file, name)
+    return "Added to abandoned list"
    
-def collect_click(name, types):
-    file = os.path.join(inspiration_system_path, types + "_faverites.txt")
-    print(file)
+def collect_click(name):
+    file = os.path.join(inspiration_system_path, "faverites.txt")
     name_list = read_name_list(file)
-    print(name_list)
     if name not in name_list:
         save_name_list(file, name)
+    return "Added to faverite list"
 
-def moveout_click(name, types):
-    file = os.path.join(inspiration_system_path, types + "_faverites.txt")
+def moveout_click(name, source):
+    if source == "Abandoned":
+        file = os.path.join(inspiration_system_path, "abandoned.txt")
+    if source == "Favorites":
+        file = os.path.join(inspiration_system_path, "faverites.txt")
+    else:
+        return None
     name_list = read_name_list(file)
-    if name not in name_list:
-        save_name_list(file, name)
+    os.remove(file)
+    with open(file, "a") as f:
+        for a in name_list:
+            if a != name:
+                f.write(a)
+    return "Moved out {name} from {source} list"
 
 def source_change(source):
-    if source == "Abandoned" or source == "Favorites":
-        return gradio.Button.update(visible=True, value=f"Move out {source}")
+    if source in ["Abandoned", "Favorites"]:
+        return gradio.update(visible=True), []
     else:
-        return gradio.Button.update(visible=False)
+        return gradio.update(visible=False), []
+def add_to_prompt(name, prompt):
+    print(name, prompt)
+    name = os.path.basename(name)
+    return prompt + "," + name
 
-def ui(gr, opts):     
+def ui(gr, opts, txt2img_prompt, img2img_prompt):     
     with gr.Blocks(analytics_enabled=False) as inspiration:
-        flag = os.path.exists(inspiration_path)        
+        flag = os.path.exists(opts.inspiration_dir)        
         if flag:
-            types = os.listdir(inspiration_path)
-            types = [x for x in types if x != "system"]
+            types = get_types_list()
             flag = len(types) > 0
-        if not flag:
-            os.mkdir(inspiration_path)
+        else:
+            os.makedirs(opts.inspiration_dir)
+        if not flag:            
             gr.HTML("""
-                <div align='center' width="50%>You need get "inspiration" images first. You can create these images by run "Create inspiration images" script in txt2img page, or download zip file from here and unzip these file under fold "inpiration".</div>"
+                <div align='center' width="50%"><h2>To activate inspiration function, you need get "inspiration" images first. </h2><br>
+                You can create these images by run "Create inspiration images" script in txt2img page, <br> you can get the artists or art styles list from here<br>
+                <a>https://github.com/pharmapsychotic/clip-interrogator/tree/main/data</a><br>
+                download these files, and select these files in the "Create inspiration images" script UI<br>
+                There about 6000 artists and art styles in these files. <br>This takes server hours depending on your GPU type and how many pictures  you generate for each artist/style
+                <br>I suggest at least four images for each<br><br><br>
+                <h2>You can also download generated pictures from here:</h2><br>
+                <a>https://huggingface.co/datasets/yfszzx/inspiration</a><br>
+                unzip the file to the project directory of webui<br>
+                and restart webui, and enjoy the joy of creation!<br></div>
                 """)           
             return inspiration
         if not os.path.exists(inspiration_system_path):
             os.mkdir(inspiration_system_path)
-        gallery, names = get_inspiration_images("Exclude abandoned", types[0])   
         with gr.Row():
             with gr.Column(scale=2):                
-                inspiration_gallery = gr.Gallery(gallery, show_label=False, elem_id="inspiration_gallery").style(grid=5, height='auto')
+                inspiration_gallery = gr.Gallery(show_label=False, elem_id="inspiration_gallery").style(grid=opts.inspiration_cols_num, height='auto')
             with gr.Column(scale=1):
-                types = gr.Dropdown(choices=types, value=types[0], label="Type", visible=len(types) > 1)
+                print(types)
+                types = gr.CheckboxGroup(choices=types, value=types)
+                keyword = gr.Textbox("", label="Key word")                
                 with gr.Row():                    
                     source = gr.Dropdown(choices=["All", "Favorites", "Exclude abandoned", "Abandoned"], value="Exclude abandoned", label="Source")
-                    get_inspiration = gr.Button("Get inspiration")
+                    get_inspiration = gr.Button("Get inspiration", elem_id="inspiration_get_button")
                 name = gr.Textbox(show_label=False, interactive=False)
                 with gr.Row(): 
                     send_to_txt2img = gr.Button('to txt2img')
                     send_to_img2img = gr.Button('to img2img')
-                style_gallery = gr.Gallery(show_label=False, elem_id="inspiration_style_gallery").style(grid=2, height='auto')                          
-               
+                style_gallery = gr.Gallery(show_label=False).style(grid=2, height='auto') 
                 collect = gr.Button('Collect')     
-                give_up = gr.Button("Don't show any more")
+                give_up = gr.Button("Don't show again")
                 moveout = gr.Button("Move out", visible=False)
-        with gr.Row():
+                warning = gr.HTML()
+        with gr.Row(visible=False):
             select_button = gr.Button('set button', elem_id="inspiration_select_button")
-            name_list = gr.State(names)
-        source.change(source_change, inputs=[source], outputs=[moveout])
-        get_inspiration.click(get_inspiration_images, inputs=[source, types], outputs=[inspiration_gallery, name_list])
-        select_button.click(select_click, _js="inspiration_selected", inputs=[name, types, name_list], outputs=[name, style_gallery])
-        give_up.click(give_up_click, inputs=[name, types], outputs=None)
-        collect.click(collect_click, inputs=[name, types], outputs=None)
+            name_list = gr.State()
+        
+        get_inspiration.click(get_inspiration_images, inputs=[source, types, keyword], outputs=[inspiration_gallery, name_list, keyword])
+        source.change(source_change, inputs=[source], outputs=[moveout, style_gallery])
+        source.change(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
+        keyword.submit(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
+        select_button.click(select_click, _js="inspiration_selected", inputs=[name, name_list], outputs=[name, style_gallery, warning])
+        give_up.click(give_up_click, inputs=[name], outputs=[warning])
+        collect.click(collect_click, inputs=[name], outputs=[warning])
+        moveout.click(moveout_click, inputs=[name, source], outputs=[warning])
+        send_to_txt2img.click(add_to_prompt, inputs=[name, txt2img_prompt], outputs=[txt2img_prompt])
+        send_to_img2img.click(add_to_prompt, inputs=[name, img2img_prompt], outputs=[img2img_prompt])
+        send_to_txt2img.click(None, _js='switch_to_txt2img', inputs=None, outputs=None)
+        send_to_img2img.click(None, _js="switch_to_img2img_img2img", inputs=None, outputs=None)
     return inspiration
diff --git a/modules/shared.py b/modules/shared.py
index ae033710..564b1b8d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -316,6 +316,12 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
+options_templates.update(options_section(('inspiration', "Inspiration"), {
+    "inspiration_dir": OptionInfo("inspiration", "Directory of inspiration", component_args=hide_dirs),
+    "inspiration_max_samples": OptionInfo(4, "Maximum number of samples, used to determine which folders to skip when continue running the create script", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
+    "inspiration_rows_num":  OptionInfo(4, "Rows of inspiration interface frame", gr.Slider, {"minimum": 4, "maximum": 16, "step": 1}),
+    "inspiration_cols_num":  OptionInfo(8, "Columns of inspiration interface frame", gr.Slider, {"minimum": 4, "maximum": 16, "step": 1}),
+}))
 
 class Options:
     data = None
diff --git a/modules/ui.py b/modules/ui.py
index 6a0a3c3b..b651eb9c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1180,7 +1180,7 @@ def create_ui(wrap_gradio_gpu_call):
     }
 
     browser_interface = images_history.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
-    inspiration_interface = inspiration.ui(gr, opts)
+    inspiration_interface = inspiration.ui(gr, opts, txt2img_prompt, img2img_prompt)
 
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
diff --git a/webui.py b/webui.py
index 5923905f..5ccae715 100644
--- a/webui.py
+++ b/webui.py
@@ -72,6 +72,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
     return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
 
 def initialize():
+    modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
     if cmd_opts.ui_debug_mode:
         class enmpty():
             name = None
@@ -84,7 +85,7 @@ def initialize():
     shared.face_restorers.append(modules.face_restoration.FaceRestoration())
     modelloader.load_upscalers()
 
-    modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
+    
 
     shared.sd_model = modules.sd_models.load_model()
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
-- 
cgit v1.2.1


From 2797b2cbf29a928ea84522d8d9478d47c7feede9 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sat, 22 Oct 2022 01:28:02 +0800
Subject: inspiration finished

---
 javascript/imageviewer.js | 1 +
 1 file changed, 1 insertion(+)

diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js
index d4ab6984..9e380c65 100644
--- a/javascript/imageviewer.js
+++ b/javascript/imageviewer.js
@@ -116,6 +116,7 @@ function showGalleryImage() {
                 e.dataset.modded = true;
                 if(e && e.parentElement.tagName == 'DIV'){
                     e.style.cursor='pointer'
+                    e.style.userSelect='none'
                     e.addEventListener('click', function (evt) {
                         if(!opts.js_modal_lightbox) return;
                         modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
-- 
cgit v1.2.1


From 58ee008f0f559a947cc280a552d97050e638d611 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sat, 22 Oct 2022 01:30:12 +0800
Subject: inspiration finished

---
 scripts/create_inspiration_images.py | 76 +++++++++++++++++++++---------------
 1 file changed, 44 insertions(+), 32 deletions(-)

diff --git a/scripts/create_inspiration_images.py b/scripts/create_inspiration_images.py
index 6a20def8..2fd30578 100644
--- a/scripts/create_inspiration_images.py
+++ b/scripts/create_inspiration_images.py
@@ -2,44 +2,56 @@ import csv, os, shutil
 import modules.scripts as scripts
 from modules import processing, shared, sd_samplers, images
 from modules.processing import Processed
-
-
+from modules.shared import opts
+import gradio
 class Script(scripts.Script):
     def title(self):
-        return "Create artists style image"
+        return "Create inspiration images"
 
     def show(self, is_img2img):
-        return not is_img2img
+        return True
 
     def ui(self, is_img2img):
-       return []
-    def show(self, is_img2img):
-        return not is_img2img
+        file = gradio.Files(label="Artist or styles name list. '.txt' files with one name per line",)
+        with gradio.Row():
+            prefix = gradio.Textbox("a painting in", label="Prompt words before artist or style name", file_count="multiple")
+            suffix= gradio.Textbox("style", label="Prompt words after artist or style name")
+        negative_prompt = gradio.Textbox("picture frame, portrait photo", label="Negative Prompt")
+        with gradio.Row():
+            batch_size = gradio.Number(1, label="Batch size")
+            batch_count = gradio.Number(2, label="Batch count")            
+        return [batch_size, batch_count, prefix, suffix, negative_prompt, file]
 
-    def run(self, p): #, max_snapshoots_num):
-        path = os.path.join("style_snapshoot", "artist")
-        if not os.path.exists(path):
-            os.makedirs(path) 
+    def run(self, p, batch_size, batch_count, prefix, suffix, negative_prompt, files):
+        p.batch_size = int(batch_size)
+        p.n_iterint = int(batch_count)
+        p.negative_prompt = negative_prompt        
         p.do_not_save_samples = True
-        p.do_not_save_grid = True
-        p.negative_prompt = "portrait photo"
-        f = open('artists.csv')
-        f_csv = csv.reader(f)        
-        for row in f_csv:
-            name = row[0]
-            artist_path = os.path.join(path, name)  
-            if not os.path.exists(artist_path):
-                os.mkdir(artist_path)  
-            if len(os.listdir(artist_path)) > 0:
-                continue
-            print(name)
-            p.prompt = name
-            processed = processing.process_images(p)
-            for img in  processed.images:
-                i = 0
-                filename = os.path.join(artist_path,  format(0, "03d") + ".jpg")
-                while os.path.exists(filename):
-                    i += 1
-                    filename = os.path.join(artist_path,  format(i, "03d") + ".jpg")
-                img.save(filename, quality=70)
+        p.do_not_save_grid = True        
+        for file in files:
+            tp = file.orig_name.split(".")[0]
+            print(tp)
+            path = os.path.join(opts.inspiration_dir, tp)
+            if not os.path.exists(path):
+                os.makedirs(path) 
+            f = open(file.name, "r")
+            line = f.readline() 
+            while len(line) > 0: 
+                name = line.rstrip("\n").split(",")[0]
+                line = f.readline() 
+                artist_path = os.path.join(path, name)  
+                if not os.path.exists(artist_path):
+                    os.mkdir(artist_path)  
+                if len(os.listdir(artist_path)) >= opts.inspiration_max_samples:
+                    continue
+                p.prompt = f"{prefix} {name} {suffix}"
+                print(p.prompt)
+                processed = processing.process_images(p)
+                for img in  processed.images:
+                    i = 0
+                    filename = os.path.join(artist_path,  format(0, "03d") + ".jpg")
+                    while os.path.exists(filename):
+                        i += 1
+                        filename = os.path.join(artist_path,  format(i, "03d") + ".jpg")
+                    img.save(filename, quality=80)
         return processed
-- 
cgit v1.2.1


From 9ba372de90df81c4f1e992d8b33ae17c6630de95 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Fri, 21 Oct 2022 13:55:42 -0500
Subject: initial work on getting prompts cleared on the backend and
 synchronizing token counter

---
 javascript/ui.js | 10 +++++++---
 modules/ui.py    | 29 +++++++++++++++++++----------
 2 files changed, 26 insertions(+), 13 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index f19af550..a0f01d10 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -162,9 +162,13 @@ function selected_tab_id() {
 
 }
 
-function trash_prompt(_,_, is_img2img) {
+function trash_prompt(_, confirmed) {
 
-if(!confirm("Delete prompt?")) return false
+if(confirm("Delete prompt?")) {
+    confirmed = true
+} else {
+return [_, confirmed]
+}
 
     if(selected_tab_id() == "tab_txt2img") {
         gradioApp().querySelector("#txt2img_prompt > label > textarea").value = "";
@@ -178,7 +182,7 @@ if(!confirm("Delete prompt?")) return false
         update_token_counter("txt2img_token_button")
     }
 
-    return true
+    return [_, confirmed]
 }
 
 
diff --git a/modules/ui.py b/modules/ui.py
index d2cb528e..2748a2e0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -429,15 +429,16 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
-# setup button for clearing prompt input boxes on client side of webui
-def connect_trash_prompt(dummy_component, button, is_img2img):
+def clear_prompt(prompt):
+    print(f"type: '{prompt}'")
+    print(prompt)
+
+    # update_token_counter(prompt, steps)
+    return ""
+
+def connect_trash_prompt(prompt, confirmed):
+        if(confirmed): clear_prompt(prompt)
 
-    button.click(
-        fn=lambda: print("Clearing prompt"),
-        _js="trash_prompt",
-        inputs=[],
-        outputs=[],
-    )
 
 def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
     """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
@@ -640,7 +641,16 @@ def create_ui(wrap_gradio_gpu_call):
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
-        connect_trash_prompt(dummy_component, trash_prompt_button, False)
+
+
+        trash_prompt_button.click(
+            # fn=lambda: print("Clearing prompt"),
+            _js="trash_prompt",
+            fn=lambda: clear_prompt(),
+            inputs=[txt2img_prompt, dummy_component],
+            outputs=[txt2img_prompt, dummy_component],
+        )
+
 
         with gr.Row(elem_id='txt2img_progress_row'):
             with gr.Column(scale=1):
@@ -848,7 +858,6 @@ def create_ui(wrap_gradio_gpu_call):
         img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,\
         token_counter, token_button, trash_prompt_button = create_toprow(is_img2img=True)
 
-        connect_trash_prompt(dummy_component,trash_prompt_button, True)
 
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
-- 
cgit v1.2.1


From ee0505dd0092ae7073b77aba93a858bda000dc60 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Fri, 21 Oct 2022 14:24:14 -0500
Subject: only delete prompt on back end and remove client-side deletion

---
 javascript/ui.js |  6 ------
 modules/ui.py    | 29 +++++++++++++++--------------
 2 files changed, 15 insertions(+), 20 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index a0f01d10..29306abe 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -171,14 +171,8 @@ return [_, confirmed]
 }
 
     if(selected_tab_id() == "tab_txt2img") {
-        gradioApp().querySelector("#txt2img_prompt > label > textarea").value = "";
-        gradioApp().querySelector("#txt2img_neg_prompt > label > textarea").value = "";
-
         update_token_counter("img2img_token_button")
     } else {
-        gradioApp().querySelector("#img2img_prompt > label > textarea").value = "";
-        gradioApp().querySelector("#img2img_neg_prompt > label > textarea").value = "";
-
         update_token_counter("txt2img_token_button")
     }
 
diff --git a/modules/ui.py b/modules/ui.py
index 2748a2e0..90c338da 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -429,15 +429,21 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
-def clear_prompt(prompt):
-    print(f"type: '{prompt}'")
-    print(prompt)
 
-    # update_token_counter(prompt, steps)
-    return ""
+def connect_trash_prompt(_, confirmed):
+        if(confirmed):
+            # update_token_counter(prompt, steps)
+            return ["", confirmed]
 
-def connect_trash_prompt(prompt, confirmed):
-        if(confirmed): clear_prompt(prompt)
+def trash_prompt_click(button, prompt):
+    dummy_component = gradio.Label(visible=False)
+
+    button.click(
+        _js="trash_prompt",
+        fn=connect_trash_prompt,
+        inputs=[prompt, dummy_component],
+        outputs=[prompt, dummy_component],
+    )
 
 
 def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
@@ -643,13 +649,7 @@ def create_ui(wrap_gradio_gpu_call):
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
 
 
-        trash_prompt_button.click(
-            # fn=lambda: print("Clearing prompt"),
-            _js="trash_prompt",
-            fn=lambda: clear_prompt(),
-            inputs=[txt2img_prompt, dummy_component],
-            outputs=[txt2img_prompt, dummy_component],
-        )
+        trash_prompt_click(trash_prompt_button, txt2img_prompt)
 
 
         with gr.Row(elem_id='txt2img_progress_row'):
@@ -858,6 +858,7 @@ def create_ui(wrap_gradio_gpu_call):
         img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,\
         token_counter, token_button, trash_prompt_button = create_toprow(is_img2img=True)
 
+        trash_prompt_click(trash_prompt_button, img2img_prompt)
 
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
-- 
cgit v1.2.1


From de70ddaf58fae98c561738a54f574abfa14cd8d1 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Fri, 21 Oct 2022 15:00:35 -0500
Subject: update token counter when clearing prompt

---
 javascript/ui.js |  4 ++--
 modules/ui.py    | 17 +++++++----------
 2 files changed, 9 insertions(+), 12 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 29306abe..acd57565 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -162,7 +162,7 @@ function selected_tab_id() {
 
 }
 
-function trash_prompt(_, confirmed) {
+function trash_prompt(_, confirmed,_steps) {
 
 if(confirm("Delete prompt?")) {
     confirmed = true
@@ -176,7 +176,7 @@ return [_, confirmed]
         update_token_counter("txt2img_token_button")
     }
 
-    return [_, confirmed]
+    return [_, confirmed,_steps]
 }
 
 
diff --git a/modules/ui.py b/modules/ui.py
index 90c338da..d3a89bf7 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -430,19 +430,16 @@ def create_seed_inputs():
 
 
 
-def connect_trash_prompt(_, confirmed):
+def connect_trash_prompt(_prompt, confirmed, _token_counter):
         if(confirmed):
-            # update_token_counter(prompt, steps)
-            return ["", confirmed]
-
-def trash_prompt_click(button, prompt):
-    dummy_component = gradio.Label(visible=False)
+            return ["", confirmed, update_token_counter("", 1)]
 
+def trash_prompt_click(button, prompt, _dummy_confirmed, token_counter):
     button.click(
         _js="trash_prompt",
         fn=connect_trash_prompt,
-        inputs=[prompt, dummy_component],
-        outputs=[prompt, dummy_component],
+        inputs=[prompt, _dummy_confirmed, token_counter],
+        outputs=[prompt, _dummy_confirmed, token_counter],
     )
 
 
@@ -649,7 +646,6 @@ def create_ui(wrap_gradio_gpu_call):
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
 
 
-        trash_prompt_click(trash_prompt_button, txt2img_prompt)
 
 
         with gr.Row(elem_id='txt2img_progress_row'):
@@ -720,6 +716,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+            trash_prompt_click(trash_prompt_button, txt2img_prompt, dummy_component, token_counter)
 
             txt2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
@@ -858,7 +855,6 @@ def create_ui(wrap_gradio_gpu_call):
         img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,\
         token_counter, token_button, trash_prompt_button = create_toprow(is_img2img=True)
 
-        trash_prompt_click(trash_prompt_button, img2img_prompt)
 
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -958,6 +954,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+            trash_prompt_click(trash_prompt_button, img2img_prompt, dummy_component, token_counter)
 
             img2img_prompt_img.change(
                 fn=modules.images.image_data,
-- 
cgit v1.2.1


From 9e40520f00d836cfa93187f7f1e81e2a7bd100b9 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Fri, 21 Oct 2022 15:13:12 -0500
Subject: refactor internal terminology to use 'clear' instead of 'trash' like
 #2728

---
 javascript/ui.js  |  2 +-
 modules/shared.py |  2 +-
 modules/ui.py     | 22 +++++++++++-----------
 3 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index acd57565..45d93a5c 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -162,7 +162,7 @@ function selected_tab_id() {
 
 }
 
-function trash_prompt(_, confirmed,_steps) {
+function clear_prompt(_, confirmed,_steps) {
 
 if(confirm("Delete prompt?")) {
     confirmed = true
diff --git a/modules/shared.py b/modules/shared.py
index 1585d532..ab5a0e9a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -317,7 +317,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
-    "trash_prompt_visible": OptionInfo(True, "Show trash prompt button"),
+    "clear_prompt_visible": OptionInfo(True, "Show clear prompt button"),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
     'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
diff --git a/modules/ui.py b/modules/ui.py
index d3a89bf7..31150800 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -88,7 +88,7 @@ folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 apply_style_symbol = '\U0001f4cb'  # 📋
-trash_prompt_symbol = '\U0001F5D1' #
+clear_prompt_symbol = '\U0001F5D1' # 🗑️
 
 
 def plaintext_to_html(text):
@@ -430,14 +430,14 @@ def create_seed_inputs():
 
 
 
-def connect_trash_prompt(_prompt, confirmed, _token_counter):
+def clear_prompt(_prompt, confirmed, _token_counter):
         if(confirmed):
             return ["", confirmed, update_token_counter("", 1)]
 
-def trash_prompt_click(button, prompt, _dummy_confirmed, token_counter):
+def connect_clear_prompt(button, prompt, _dummy_confirmed, token_counter):
     button.click(
-        _js="trash_prompt",
-        fn=connect_trash_prompt,
+        _js="clear_prompt",
+        fn=clear_prompt,
         inputs=[prompt, _dummy_confirmed, token_counter],
         outputs=[prompt, _dummy_confirmed, token_counter],
     )
@@ -518,7 +518,7 @@ def create_toprow(is_img2img):
             paste = gr.Button(value=paste_symbol, elem_id="paste")
             save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
             prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-            trash_prompt = gr.Button(value=trash_prompt_symbol, elem_id="trash_prompt", visible=opts.trash_prompt_visible)
+            clear_prompt_button = gr.Button(value=clear_prompt_symbol, elem_id="clear_prompt", visible=opts.clear_prompt_visible)
 
             token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
             token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
@@ -559,7 +559,7 @@ def create_toprow(is_img2img):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
                     prompt_style2.save_to_config = True
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button, trash_prompt
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button, clear_prompt_button
 
 
 def setup_progressbar(progressbar, preview, id_part, textinfo=None):
@@ -640,7 +640,7 @@ def create_ui(wrap_gradio_gpu_call):
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,\
         txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter,\
-        token_button, trash_prompt_button = create_toprow(is_img2img=False)
+        token_button, clear_prompt_button = create_toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -716,7 +716,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            trash_prompt_click(trash_prompt_button, txt2img_prompt, dummy_component, token_counter)
+            connect_clear_prompt(clear_prompt_button, txt2img_prompt, dummy_component, token_counter)
 
             txt2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
@@ -853,7 +853,7 @@ def create_ui(wrap_gradio_gpu_call):
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit,\
         img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,\
-        token_counter, token_button, trash_prompt_button = create_toprow(is_img2img=True)
+        token_counter, token_button, clear_prompt_button = create_toprow(is_img2img=True)
 
 
         with gr.Row(elem_id='img2img_progress_row'):
@@ -954,7 +954,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            trash_prompt_click(trash_prompt_button, img2img_prompt, dummy_component, token_counter)
+            connect_clear_prompt(clear_prompt_button, img2img_prompt, dummy_component, token_counter)
 
             img2img_prompt_img.change(
                 fn=modules.images.image_data,
-- 
cgit v1.2.1


From 0c7cf08b3d27a61bab4cd8b16f8be8ae74879423 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Fri, 21 Oct 2022 15:32:26 -0500
Subject: some doc and formatting

---
 modules/ui.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 31150800..b26cf10a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -88,7 +88,7 @@ folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 apply_style_symbol = '\U0001f4cb'  # 📋
-clear_prompt_symbol = '\U0001F5D1' # 🗑️
+clear_prompt_symbol = '\U0001F5D1'  # 🗑️
 
 
 def plaintext_to_html(text):
@@ -429,12 +429,14 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
-
 def clear_prompt(_prompt, confirmed, _token_counter):
-        if(confirmed):
-            return ["", confirmed, update_token_counter("", 1)]
+    """Given confirmation from a user on the client-side, go ahead with clearing prompt"""
+    if confirmed:
+        return ["", confirmed, update_token_counter("", 1)]
+
 
 def connect_clear_prompt(button, prompt, _dummy_confirmed, token_counter):
+    """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
     button.click(
         _js="clear_prompt",
         fn=clear_prompt,
@@ -518,7 +520,12 @@ def create_toprow(is_img2img):
             paste = gr.Button(value=paste_symbol, elem_id="paste")
             save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
             prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-            clear_prompt_button = gr.Button(value=clear_prompt_symbol, elem_id="clear_prompt", visible=opts.clear_prompt_visible)
+
+            clear_prompt_button = gr.Button(
+                value=clear_prompt_symbol,
+                elem_id="clear_prompt",
+                visible=opts.clear_prompt_visible
+            )
 
             token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
             token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-- 
cgit v1.2.1


From 57eb54b838faa383c10079e1bb5471b7bee6a695 Mon Sep 17 00:00:00 2001
From: Extraltodeus <extraltodeus@gmail.com>
Date: Sat, 22 Oct 2022 00:11:07 +0200
Subject: implement CUDA device selection by ID

---
 modules/devices.py | 21 ++++++++++++++++++---
 1 file changed, 18 insertions(+), 3 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index eb422583..8a159282 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,7 +1,6 @@
+import sys, os, shlex
 import contextlib
-
 import torch
-
 from modules import errors
 
 # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
@@ -9,10 +8,26 @@ has_mps = getattr(torch, 'has_mps', False)
 
 cpu = torch.device("cpu")
 
+def extract_device_id(args, name):
+    for x in range(len(args)):
+        if name in args[x]: return args[x+1]
+    return None
 
 def get_optimal_device():
     if torch.cuda.is_available():
-        return torch.device("cuda")
+        # CUDA device selection support:
+        if "shared" not in sys.modules:
+            commandline_args = os.environ.get('COMMANDLINE_ARGS', "") #re-parse the commandline arguments because using the shared.py module creates an import loop.
+            sys.argv += shlex.split(commandline_args)
+            device_id = extract_device_id(sys.argv, '--device-id')
+        else:
+            device_id = shared.cmd_opts.device_id
+            
+        if device_id is not None:
+            cuda_device = f"cuda:{device_id}"
+            return torch.device(cuda_device)
+        else:
+            return torch.device("cuda")
 
     if has_mps:
         return torch.device("mps")
-- 
cgit v1.2.1


From 29bfacd63cb5c73b9643d94f255cca818fd49d9c Mon Sep 17 00:00:00 2001
From: Extraltodeus <extraltodeus@gmail.com>
Date: Sat, 22 Oct 2022 00:12:46 +0200
Subject: implement CUDA device selection, --device-id arg

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/shared.py b/modules/shared.py
index 41d7f08e..03032a47 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -80,6 +80,7 @@ parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencode
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
+parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
-- 
cgit v1.2.1


From 700340448baa7412c7cc5ff3d1349ac79ee8ed0c Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Fri, 21 Oct 2022 17:24:04 -0500
Subject: forgot to clear neg prompt after moving to back. Add tooltip to hints

---
 javascript/hints.js |  1 +
 javascript/ui.js    |  4 ++--
 modules/ui.py       | 14 +++++++-------
 3 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index a1fcc93b..54c8c238 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -17,6 +17,7 @@ titles = {
     "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
     "\u{1f4c2}": "Open images output directory",
     "\u{1f4be}": "Save style",
+    "\U0001F5D1": "Clear prompt"
     "\u{1f4cb}": "Apply selected styles to current prompt",
 
     "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
diff --git a/javascript/ui.js b/javascript/ui.js
index 45d93a5c..6c99824b 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -162,7 +162,7 @@ function selected_tab_id() {
 
 }
 
-function clear_prompt(_, confirmed,_steps) {
+function clear_prompt(_, _prompt_neg, confirmed,_steps) {
 
 if(confirm("Delete prompt?")) {
     confirmed = true
@@ -176,7 +176,7 @@ return [_, confirmed]
         update_token_counter("txt2img_token_button")
     }
 
-    return [_, confirmed,_steps]
+    return [_, _prompt_neg, confirmed,_steps]
 }
 
 
diff --git a/modules/ui.py b/modules/ui.py
index b26cf10a..25aeba3b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -429,19 +429,19 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
-def clear_prompt(_prompt, confirmed, _token_counter):
+def clear_prompt(_prompt, _prompt_neg, confirmed, _token_counter):
     """Given confirmation from a user on the client-side, go ahead with clearing prompt"""
     if confirmed:
-        return ["", confirmed, update_token_counter("", 1)]
+        return ["", "", confirmed, update_token_counter("", 1)]
 
 
-def connect_clear_prompt(button, prompt, _dummy_confirmed, token_counter):
+def connect_clear_prompt(button, prompt, prompt_neg, _dummy_confirmed, token_counter):
     """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
     button.click(
         _js="clear_prompt",
         fn=clear_prompt,
-        inputs=[prompt, _dummy_confirmed, token_counter],
-        outputs=[prompt, _dummy_confirmed, token_counter],
+        inputs=[prompt, prompt_neg, _dummy_confirmed, token_counter],
+        outputs=[prompt, prompt_neg, _dummy_confirmed, token_counter],
     )
 
 
@@ -723,7 +723,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            connect_clear_prompt(clear_prompt_button, txt2img_prompt, dummy_component, token_counter)
+            connect_clear_prompt(clear_prompt_button, txt2img_prompt, txt2img_negative_prompt, dummy_component, token_counter)
 
             txt2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
@@ -961,7 +961,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            connect_clear_prompt(clear_prompt_button, img2img_prompt, dummy_component, token_counter)
+            connect_clear_prompt(clear_prompt_button, img2img_prompt, img2img_negative_prompt, dummy_component, token_counter)
 
             img2img_prompt_img.change(
                 fn=modules.images.image_data,
-- 
cgit v1.2.1


From 40ddb6df61564684263c7442bacf61efe3882b87 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sat, 22 Oct 2022 10:16:22 +0800
Subject: inspiration perfected

---
 javascript/inspiration.js | 19 +++++++------
 modules/inspiration.py    | 71 ++++++++++++++++++++++++++---------------------
 2 files changed, 49 insertions(+), 41 deletions(-)

diff --git a/javascript/inspiration.js b/javascript/inspiration.js
index 791a80c9..39844544 100644
--- a/javascript/inspiration.js
+++ b/javascript/inspiration.js
@@ -1,5 +1,5 @@
 function public_image_index_in_gallery(item, gallery){
-    var imgs = gallery.querySelectorAll("img.h-full")
+    var imgs = gallery.querySelectorAll("img.h-full")    
     var index;
     var i = 0;
     imgs.forEach(function(e){
@@ -7,18 +7,23 @@ function public_image_index_in_gallery(item, gallery){
             index = i;
         i += 1;
     });
-    var num = imgs.length / 2
-    index = (index < num) ? index : (index - num)
+    var all_imgs = gallery.querySelectorAll("img")
+    if (all_imgs.length > imgs.length){
+        var num = imgs.length / 2
+        index = (index < num) ? index : (index - num)
+    }   
     return index;
 }
 
 function inspiration_selected(name, name_list){
     var btn = gradioApp().getElementById("inspiration_select_button")
     return [gradioApp().getElementById("inspiration_select_button").getAttribute("img-index")];
-}   
+}  
+
 function inspiration_click_get_button(){
     gradioApp().getElementById("inspiration_get_button").click();
 }  
+
 var inspiration_image_click = function(){
     var index =  public_image_index_in_gallery(this, gradioApp().getElementById("inspiration_gallery"));
     var btn = gradioApp().getElementById("inspiration_select_button");
@@ -32,16 +37,12 @@ document.addEventListener("DOMContentLoaded", function() {
         if (gallery) {
             var node = gallery.querySelector(".absolute.backdrop-blur.h-full")
             if (node) {
-                node.style.display = "None"; //parentNode.removeChild(node) 
+                node.style.display = "None";
             } 
             gallery.querySelectorAll('img').forEach(function(e){    
                 e.onclick = inspiration_image_click
             });
-
         }
-
-
     });
     mutationObserver.observe( gradioApp(), { childList:true, subtree:true });
-
 });
diff --git a/modules/inspiration.py b/modules/inspiration.py
index f72ebf3a..319183ab 100644
--- a/modules/inspiration.py
+++ b/modules/inspiration.py
@@ -13,7 +13,7 @@ def read_name_list(file, types=None, keyword=None):
         line = line.rstrip("\n")
         if types is not None:
             dirname = os.path.split(line)
-            if dirname[0] in types and keyword in dirname[1]:
+            if dirname[0] in types and keyword in dirname[1].lower():
                 ret.append(line)
         else:
             ret.append(line)
@@ -21,8 +21,10 @@ def read_name_list(file, types=None, keyword=None):
     return ret
 
 def save_name_list(file, name): 
-    with open(file, "a") as f:
-        f.write(name + "\n")
+    name_list = read_name_list(file)
+    if name not in name_list:
+        with open(file, "a") as f:
+            f.write(name + "\n")
 
 def get_types_list():
     files = os.listdir(opts.inspiration_dir)
@@ -39,20 +41,20 @@ def get_types_list():
     return types
 
 def get_inspiration_images(source, types, keyword):
+    keyword = keyword.strip(" ").lower()
     get_num = int(opts.inspiration_rows_num * opts.inspiration_cols_num)
     if source == "Favorites":
         names = read_name_list(os.path.join(inspiration_system_path, "faverites.txt"), types, keyword)
         names = random.sample(names, get_num) if len(names) > get_num else names
     elif source == "Abandoned":
         names = read_name_list(os.path.join(inspiration_system_path, "abandoned.txt"), types, keyword)
-        print(names)
         names = random.sample(names, get_num) if len(names) > get_num else names
     elif source == "Exclude abandoned":        
         abandoned = read_name_list(os.path.join(inspiration_system_path, "abandoned.txt"), types, keyword)  
         all_names = []
         for tp in types:
             name_list = os.listdir(os.path.join(opts.inspiration_dir, tp))
-            all_names += [os.path.join(tp, x) for x in name_list if keyword in x]
+            all_names += [os.path.join(tp, x) for x in name_list if keyword in x.lower()]
         
         if len(all_names) > get_num:
             names = []
@@ -66,14 +68,14 @@ def get_inspiration_images(source, types, keyword):
         all_names = []
         for tp in types:
             name_list = os.listdir(os.path.join(opts.inspiration_dir, tp))
-            all_names += [os.path.join(tp, x) for x in name_list if keyword in x]
+            all_names += [os.path.join(tp, x) for x in name_list if keyword in x.lower()]
         names = random.sample(all_names, get_num) if len(all_names) > get_num else all_names
     image_list = []
     for a in names:
         image_path = os.path.join(opts.inspiration_dir, a)
         images = os.listdir(image_path)        
         image_list.append((os.path.join(image_path, random.choice(images)), a))
-    return image_list, names, ""
+    return image_list, names
 
 def select_click(index, name_list):
     name = name_list[int(index)]
@@ -83,22 +85,18 @@ def select_click(index, name_list):
 
 def give_up_click(name):
     file = os.path.join(inspiration_system_path, "abandoned.txt")
-    name_list = read_name_list(file)
-    if name not in name_list:
-        save_name_list(file, name)
+    save_name_list(file, name)
     return "Added to abandoned list"
    
 def collect_click(name):
     file = os.path.join(inspiration_system_path, "faverites.txt")
-    name_list = read_name_list(file)
-    if name not in name_list:
-        save_name_list(file, name)
+    save_name_list(file, name)
     return "Added to faverite list"
 
 def moveout_click(name, source):
     if source == "Abandoned":
         file = os.path.join(inspiration_system_path, "abandoned.txt")
-    if source == "Favorites":
+    elif source == "Favorites":
         file = os.path.join(inspiration_system_path, "faverites.txt")
     else:
         return None
@@ -107,8 +105,8 @@ def moveout_click(name, source):
     with open(file, "a") as f:
         for a in name_list:
             if a != name:
-                f.write(a)
-    return "Moved out {name} from {source} list"
+                f.write(a + "\n")
+    return f"Moved out {name} from {source} list"
 
 def source_change(source):
     if source in ["Abandoned", "Favorites"]:
@@ -116,10 +114,12 @@ def source_change(source):
     else:
         return gradio.update(visible=False), []
 def add_to_prompt(name, prompt):
-    print(name, prompt)
     name = os.path.basename(name)
     return prompt + "," + name
 
+def clear_keyword():
+    return ""
+
 def ui(gr, opts, txt2img_prompt, img2img_prompt):     
     with gr.Blocks(analytics_enabled=False) as inspiration:
         flag = os.path.exists(opts.inspiration_dir)        
@@ -132,15 +132,15 @@ def ui(gr, opts, txt2img_prompt, img2img_prompt):
             gr.HTML("""
                 <div align='center' width="50%"><h2>To activate inspiration function, you need get "inspiration" images first. </h2><br>
                 You can create these images by run "Create inspiration images" script in txt2img page, <br> you can get the artists or art styles list from here<br>
-                <a>https://github.com/pharmapsychotic/clip-interrogator/tree/main/data</a><br>
+                <a href="https://github.com/pharmapsychotic/clip-interrogator/tree/main/data">https://github.com/pharmapsychotic/clip-interrogator/tree/main/data</a><br>
                 download these files, and select these files in the "Create inspiration images" script UI<br>
                 There about 6000 artists and art styles in these files. <br>This takes server hours depending on your GPU type and how many pictures  you generate for each artist/style
                 <br>I suggest at least four images for each<br><br><br>
                 <h2>You can also download generated pictures from here:</h2><br>
-                <a>https://huggingface.co/datasets/yfszzx/inspiration</a><br>
+                <a href="https://huggingface.co/datasets/yfszzx/inspiration">https://huggingface.co/datasets/yfszzx/inspiration</a><br>
                 unzip the file to the project directory of webui<br>
                 and restart webui, and enjoy the joy of creation!<br></div>
-                """)           
+                """)      
             return inspiration
         if not os.path.exists(inspiration_system_path):
             os.mkdir(inspiration_system_path)
@@ -148,35 +148,42 @@ def ui(gr, opts, txt2img_prompt, img2img_prompt):
             with gr.Column(scale=2):                
                 inspiration_gallery = gr.Gallery(show_label=False, elem_id="inspiration_gallery").style(grid=opts.inspiration_cols_num, height='auto')
             with gr.Column(scale=1):
-                print(types)
                 types = gr.CheckboxGroup(choices=types, value=types)
-                keyword = gr.Textbox("", label="Key word")                
-                with gr.Row():                    
-                    source = gr.Dropdown(choices=["All", "Favorites", "Exclude abandoned", "Abandoned"], value="Exclude abandoned", label="Source")
-                    get_inspiration = gr.Button("Get inspiration", elem_id="inspiration_get_button")
-                name = gr.Textbox(show_label=False, interactive=False)
                 with gr.Row(): 
+                    source = gr.Dropdown(choices=["All", "Favorites", "Exclude abandoned", "Abandoned"], value="Exclude abandoned", label="Source")
+                    keyword = gr.Textbox("", label="Key word")                                                
+                get_inspiration = gr.Button("Get inspiration", elem_id="inspiration_get_button")   
+                name = gr.Textbox(show_label=False, interactive=False)      
+                with gr.Row():                     
                     send_to_txt2img = gr.Button('to txt2img')
                     send_to_img2img = gr.Button('to img2img')
                 style_gallery = gr.Gallery(show_label=False).style(grid=2, height='auto') 
-                collect = gr.Button('Collect')     
-                give_up = gr.Button("Don't show again")
-                moveout = gr.Button("Move out", visible=False)
                 warning = gr.HTML()
+                with gr.Row():
+                    collect = gr.Button('Collect')     
+                    give_up = gr.Button("Don't show again")
+                moveout = gr.Button("Move out", visible=False)
+                
         with gr.Row(visible=False):
             select_button = gr.Button('set button', elem_id="inspiration_select_button")
             name_list = gr.State()
         
-        get_inspiration.click(get_inspiration_images, inputs=[source, types, keyword], outputs=[inspiration_gallery, name_list, keyword])
-        source.change(source_change, inputs=[source], outputs=[moveout, style_gallery])
-        source.change(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
+        get_inspiration.click(get_inspiration_images, inputs=[source, types, keyword], outputs=[inspiration_gallery, name_list])
         keyword.submit(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
+        source.change(source_change, inputs=[source], outputs=[moveout, style_gallery])
+        source.change(fn=clear_keyword, _js="inspiration_click_get_button", inputs=None, outputs=[keyword])       
+        types.change(fn=clear_keyword, _js="inspiration_click_get_button", inputs=None, outputs=[keyword])  
+
         select_button.click(select_click, _js="inspiration_selected", inputs=[name, name_list], outputs=[name, style_gallery, warning])
         give_up.click(give_up_click, inputs=[name], outputs=[warning])
         collect.click(collect_click, inputs=[name], outputs=[warning])
         moveout.click(moveout_click, inputs=[name, source], outputs=[warning])
+        moveout.click(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
+
         send_to_txt2img.click(add_to_prompt, inputs=[name, txt2img_prompt], outputs=[txt2img_prompt])
         send_to_img2img.click(add_to_prompt, inputs=[name, img2img_prompt], outputs=[img2img_prompt])
+        send_to_txt2img.click(collect_click, inputs=[name], outputs=[warning])
+        send_to_img2img.click(collect_click, inputs=[name], outputs=[warning])
         send_to_txt2img.click(None, _js='switch_to_txt2img', inputs=None, outputs=None)
         send_to_img2img.click(None, _js="switch_to_img2img_img2img", inputs=None, outputs=None)
     return inspiration
-- 
cgit v1.2.1


From d93ea5cdeb2fd3607b7265271ccab2c9bf4c1156 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sat, 22 Oct 2022 10:21:21 +0800
Subject: inspiration perfected

---
 modules/inspiration.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/inspiration.py b/modules/inspiration.py
index 319183ab..94ff139a 100644
--- a/modules/inspiration.py
+++ b/modules/inspiration.py
@@ -73,8 +73,11 @@ def get_inspiration_images(source, types, keyword):
     image_list = []
     for a in names:
         image_path = os.path.join(opts.inspiration_dir, a)
-        images = os.listdir(image_path)        
-        image_list.append((os.path.join(image_path, random.choice(images)), a))
+        images = os.listdir(image_path)
+        if len(images) > 0:        
+            image_list.append((os.path.join(image_path, random.choice(images)), a))
+        else:
+            print(image_path)
     return image_list, names
 
 def select_click(index, name_list):
-- 
cgit v1.2.1


From 67b78f0ea6f196bfdca49932da062631bb40d0b1 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Sat, 22 Oct 2022 10:29:23 +0800
Subject: inspiration perfected

---
 modules/inspiration.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/modules/inspiration.py b/modules/inspiration.py
index 94ff139a..29cf8297 100644
--- a/modules/inspiration.py
+++ b/modules/inspiration.py
@@ -160,12 +160,13 @@ def ui(gr, opts, txt2img_prompt, img2img_prompt):
                 with gr.Row():                     
                     send_to_txt2img = gr.Button('to txt2img')
                     send_to_img2img = gr.Button('to img2img')
-                style_gallery = gr.Gallery(show_label=False).style(grid=2, height='auto') 
-                warning = gr.HTML()
-                with gr.Row():
                     collect = gr.Button('Collect')     
                     give_up = gr.Button("Don't show again")
-                moveout = gr.Button("Move out", visible=False)
+                    moveout = gr.Button("Move out", visible=False)
+                warning = gr.HTML()
+                style_gallery = gr.Gallery(show_label=False).style(grid=2, height='auto') 
+                    
+               
                 
         with gr.Row(visible=False):
             select_button = gr.Button('set button', elem_id="inspiration_select_button")
-- 
cgit v1.2.1


From 2b91251637078e04472c91a06a8d9c4db9c1dcf0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 12:23:45 +0300
Subject: removed aesthetic gradients as built-in added support for extensions

---
 .gitignore                        |   2 +-
 extensions/put extension here.txt |   0
 modules/aesthetic_clip.py         | 241 --------------------------------------
 modules/images_history.py         |   2 +-
 modules/img2img.py                |   5 +-
 modules/processing.py             |  35 ++++--
 modules/script_callbacks.py       |  42 +++++++
 modules/scripts.py                | 210 ++++++++++++++++++++++++---------
 modules/sd_hijack.py              |   1 -
 modules/sd_models.py              |   7 +-
 modules/shared.py                 |  19 ---
 modules/txt2img.py                |   5 +-
 modules/ui.py                     |  83 ++-----------
 webui.py                          |   7 +-
 14 files changed, 249 insertions(+), 410 deletions(-)
 create mode 100644 extensions/put extension here.txt
 delete mode 100644 modules/aesthetic_clip.py
 create mode 100644 modules/script_callbacks.py

diff --git a/.gitignore b/.gitignore
index f9c3357c..2f1e08ed 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,4 +27,4 @@ __pycache__
 notification.mp3
 /SwinIR
 /textual_inversion
-.vscode
\ No newline at end of file
+.vscode
diff --git a/extensions/put extension here.txt b/extensions/put extension here.txt
new file mode 100644
index 00000000..e69de29b
diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py
deleted file mode 100644
index 8c828541..00000000
--- a/modules/aesthetic_clip.py
+++ /dev/null
@@ -1,241 +0,0 @@
-import copy
-import itertools
-import os
-from pathlib import Path
-import html
-import gc
-
-import gradio as gr
-import torch
-from PIL import Image
-from torch import optim
-
-from modules import shared
-from transformers import CLIPModel, CLIPProcessor, CLIPTokenizer
-from tqdm.auto import tqdm, trange
-from modules.shared import opts, device
-
-
-def get_all_images_in_folder(folder):
-    return [os.path.join(folder, f) for f in os.listdir(folder) if
-            os.path.isfile(os.path.join(folder, f)) and check_is_valid_image_file(f)]
-
-
-def check_is_valid_image_file(filename):
-    return filename.lower().endswith(('.png', '.jpg', '.jpeg', ".gif", ".tiff", ".webp"))
-
-
-def batched(dataset, total, n=1):
-    for ndx in range(0, total, n):
-        yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))]
-
-
-def iter_to_batched(iterable, n=1):
-    it = iter(iterable)
-    while True:
-        chunk = tuple(itertools.islice(it, n))
-        if not chunk:
-            return
-        yield chunk
-
-
-def create_ui():
-    import modules.ui
-
-    with gr.Group():
-        with gr.Accordion("Open for Clip Aesthetic!", open=False):
-            with gr.Row():
-                aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight",
-                                             value=0.9)
-                aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5)
-
-            with gr.Row():
-                aesthetic_lr = gr.Textbox(label='Aesthetic learning rate',
-                                          placeholder="Aesthetic learning rate", value="0.0001")
-                aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
-                aesthetic_imgs = gr.Dropdown(sorted(shared.aesthetic_embeddings.keys()),
-                                             label="Aesthetic imgs embedding",
-                                             value="None")
-
-                modules.ui.create_refresh_button(aesthetic_imgs, shared.update_aesthetic_embeddings, lambda: {"choices": sorted(shared.aesthetic_embeddings.keys())}, "refresh_aesthetic_embeddings")
-
-            with gr.Row():
-                aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs',
-                                                 placeholder="This text is used to rotate the feature space of the imgs embs",
-                                                 value="")
-                aesthetic_slerp_angle = gr.Slider(label='Slerp angle', minimum=0, maximum=1, step=0.01,
-                                                  value=0.1)
-                aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
-
-    return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative
-
-
-aesthetic_clip_model = None
-
-
-def aesthetic_clip():
-    global aesthetic_clip_model
-
-    if aesthetic_clip_model is None or aesthetic_clip_model.name_or_path != shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path:
-        aesthetic_clip_model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path)
-        aesthetic_clip_model.cpu()
-
-    return aesthetic_clip_model
-
-
-def generate_imgs_embd(name, folder, batch_size):
-    model = aesthetic_clip().to(device)
-    processor = CLIPProcessor.from_pretrained(model.name_or_path)
-
-    with torch.no_grad():
-        embs = []
-        for paths in tqdm(iter_to_batched(get_all_images_in_folder(folder), batch_size),
-                          desc=f"Generating embeddings for {name}"):
-            if shared.state.interrupted:
-                break
-            inputs = processor(images=[Image.open(path) for path in paths], return_tensors="pt").to(device)
-            outputs = model.get_image_features(**inputs).cpu()
-            embs.append(torch.clone(outputs))
-            inputs.to("cpu")
-            del inputs, outputs
-
-        embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True)
-
-        # The generated embedding will be located here
-        path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt")
-        torch.save(embs, path)
-
-        model.cpu()
-        del processor
-        del embs
-        gc.collect()
-        torch.cuda.empty_cache()
-        res = f"""
-        Done generating embedding for {name}!
-        Aesthetic embedding saved to {html.escape(path)}
-        """
-        shared.update_aesthetic_embeddings()
-        return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding",
-                                  value="None"), \
-               gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()),
-                                  label="Imgs embedding",
-                                  value="None"), res, ""
-
-
-def slerp(low, high, val):
-    low_norm = low / torch.norm(low, dim=1, keepdim=True)
-    high_norm = high / torch.norm(high, dim=1, keepdim=True)
-    omega = torch.acos((low_norm * high_norm).sum(1))
-    so = torch.sin(omega)
-    res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
-    return res
-
-
-class AestheticCLIP:
-    def __init__(self):
-        self.skip = False
-        self.aesthetic_steps = 0
-        self.aesthetic_weight = 0
-        self.aesthetic_lr = 0
-        self.slerp = False
-        self.aesthetic_text_negative = ""
-        self.aesthetic_slerp_angle = 0
-        self.aesthetic_imgs_text = ""
-
-        self.image_embs_name = None
-        self.image_embs = None
-        self.load_image_embs(None)
-
-    def set_aesthetic_params(self, p, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
-                             aesthetic_slerp=True, aesthetic_imgs_text="",
-                             aesthetic_slerp_angle=0.15,
-                             aesthetic_text_negative=False):
-        self.aesthetic_imgs_text = aesthetic_imgs_text
-        self.aesthetic_slerp_angle = aesthetic_slerp_angle
-        self.aesthetic_text_negative = aesthetic_text_negative
-        self.slerp = aesthetic_slerp
-        self.aesthetic_lr = aesthetic_lr
-        self.aesthetic_weight = aesthetic_weight
-        self.aesthetic_steps = aesthetic_steps
-        self.load_image_embs(image_embs_name)
-
-        if self.image_embs_name is not None:
-            p.extra_generation_params.update({
-                "Aesthetic LR": aesthetic_lr,
-                "Aesthetic weight": aesthetic_weight,
-                "Aesthetic steps": aesthetic_steps,
-                "Aesthetic embedding": self.image_embs_name,
-                "Aesthetic slerp": aesthetic_slerp,
-                "Aesthetic text": aesthetic_imgs_text,
-                "Aesthetic text negative": aesthetic_text_negative,
-                "Aesthetic slerp angle": aesthetic_slerp_angle,
-            })
-
-    def set_skip(self, skip):
-        self.skip = skip
-
-    def load_image_embs(self, image_embs_name):
-        if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None":
-            image_embs_name = None
-            self.image_embs_name = None
-        if image_embs_name is not None and self.image_embs_name != image_embs_name:
-            self.image_embs_name = image_embs_name
-            self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device)
-            self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True)
-            self.image_embs.requires_grad_(False)
-
-    def __call__(self, z, remade_batch_tokens):
-        if not self.skip and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name is not None:
-            tokenizer = shared.sd_model.cond_stage_model.tokenizer
-            if not opts.use_old_emphasis_implementation:
-                remade_batch_tokens = [
-                    [tokenizer.bos_token_id] + x[:75] + [tokenizer.eos_token_id] for x in
-                    remade_batch_tokens]
-
-            tokens = torch.asarray(remade_batch_tokens).to(device)
-
-            model = copy.deepcopy(aesthetic_clip()).to(device)
-            model.requires_grad_(True)
-            if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
-                text_embs_2 = model.get_text_features(
-                    **tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
-                if self.aesthetic_text_negative:
-                    text_embs_2 = self.image_embs - text_embs_2
-                    text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
-                img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
-            else:
-                img_embs = self.image_embs
-
-            with torch.enable_grad():
-
-                # We optimize the model to maximize the similarity
-                optimizer = optim.Adam(
-                    model.text_model.parameters(), lr=self.aesthetic_lr
-                )
-
-                for _ in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
-                    text_embs = model.get_text_features(input_ids=tokens)
-                    text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
-                    sim = text_embs @ img_embs.T
-                    loss = -sim
-                    optimizer.zero_grad()
-                    loss.mean().backward()
-                    optimizer.step()
-
-                zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
-                if opts.CLIP_stop_at_last_layers > 1:
-                    zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers]
-                    zn = model.text_model.final_layer_norm(zn)
-                else:
-                    zn = zn.last_hidden_state
-                model.cpu()
-                del model
-                gc.collect()
-                torch.cuda.empty_cache()
-            zn = torch.concat([zn[77 * i:77 * (i + 1)] for i in range(max(z.shape[1] // 77, 1))], 1)
-            if self.slerp:
-                z = slerp(z, zn, self.aesthetic_weight)
-            else:
-                z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight
-
-        return z
diff --git a/modules/images_history.py b/modules/images_history.py
index 78fd0543..bc5cf11f 100644
--- a/modules/images_history.py
+++ b/modules/images_history.py
@@ -310,7 +310,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
                         forward = gr.Button('Prev batch')
                         backward = gr.Button('Next batch')
                 with gr.Column(scale=3):
-                    load_info =  gr.HTML(visible=not custom_dir)   
+                    load_info =     gr.HTML(visible=not custom_dir)
             with gr.Row(visible=False) as warning:                 
                 warning_box = gr.Textbox("Message", interactive=False)                        
 
diff --git a/modules/img2img.py b/modules/img2img.py
index eea5199b..8d9f7cf9 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -56,7 +56,7 @@ def process_batch(p, input_dir, output_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False, *args):
+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
     is_inpaint = mode == 1
     is_batch = mode == 2
 
@@ -109,7 +109,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         inpainting_mask_invert=inpainting_mask_invert,
     )
 
-    shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
+    p.scripts = modules.scripts.scripts_txt2img
+    p.script_args = args
 
     if shared.cmd_opts.enable_console_prompts:
         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
diff --git a/modules/processing.py b/modules/processing.py
index ff1ec4c9..372489f7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -104,6 +104,12 @@ class StableDiffusionProcessing():
             self.seed_resize_from_h = 0
             self.seed_resize_from_w = 0
 
+        self.scripts = None
+        self.script_args = None
+        self.all_prompts = None
+        self.all_seeds = None
+        self.all_subseeds = None
+
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
@@ -350,32 +356,35 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     shared.prompt_styles.apply_styles(p)
 
     if type(p.prompt) == list:
-        all_prompts = p.prompt
+        p.all_prompts = p.prompt
     else:
-        all_prompts = p.batch_size * p.n_iter * [p.prompt]
+        p.all_prompts = p.batch_size * p.n_iter * [p.prompt]
 
     if type(seed) == list:
-        all_seeds = seed
+        p.all_seeds = seed
     else:
-        all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
+        p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
 
     if type(subseed) == list:
-        all_subseeds = subseed
+        p.all_subseeds = subseed
     else:
-        all_subseeds = [int(subseed) + x for x in range(len(all_prompts))]
+        p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
 
     def infotext(iteration=0, position_in_batch=0):
-        return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
+        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
 
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
+    if p.scripts is not None:
+        p.scripts.run_alwayson_scripts(p)
+
     infotexts = []
     output_images = []
 
     with torch.no_grad(), p.sd_model.ema_scope():
         with devices.autocast():
-            p.init(all_prompts, all_seeds, all_subseeds)
+            p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
 
         if state.job_count == -1:
             state.job_count = p.n_iter
@@ -387,9 +396,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if state.interrupted:
                 break
 
-            prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
-            seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
-            subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
+            prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+            seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
+            subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
             if (len(prompts) == 0):
                 break
@@ -490,10 +499,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 index_of_first_image = 1
 
             if opts.grid_save:
-                images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
+                images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     devices.torch_gc()
-    return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+    return Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
new file mode 100644
index 00000000..866b7acd
--- /dev/null
+++ b/modules/script_callbacks.py
@@ -0,0 +1,42 @@
+
+callbacks_model_loaded = []
+callbacks_ui_tabs = []
+
+
+def clear_callbacks():
+    callbacks_model_loaded.clear()
+    callbacks_ui_tabs.clear()
+
+
+def model_loaded_callback(sd_model):
+    for callback in callbacks_model_loaded:
+        callback(sd_model)
+
+
+def ui_tabs_callback():
+    res = []
+
+    for callback in callbacks_ui_tabs:
+        res += callback() or []
+
+    return res
+
+
+def on_model_loaded(callback):
+    """register a function to be called when the stable diffusion model is created; the model is
+    passed as an argument"""
+    callbacks_model_loaded.append(callback)
+
+
+def on_ui_tabs(callback):
+    """register a function to be called when the UI is creating new tabs.
+    The function must either return a None, which means no new tabs to be added, or a list, where
+    each element is a tuple:
+        (gradio_component, title, elem_id)
+
+    gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)
+    title is tab text displayed to user in the UI
+    elem_id is HTML id for the tab
+    """
+    callbacks_ui_tabs.append(callback)
+
diff --git a/modules/scripts.py b/modules/scripts.py
index 1039fa9c..65f25f49 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -1,86 +1,153 @@
 import os
 import sys
 import traceback
+from collections import namedtuple
 
 import modules.ui as ui
 import gradio as gr
 
 from modules.processing import StableDiffusionProcessing
-from modules import shared
+from modules import shared, paths, script_callbacks
+
+AlwaysVisible = object()
+
 
 class Script:
     filename = None
     args_from = None
     args_to = None
+    alwayson = False
+
+    infotext_fields = None
+    """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
+    parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
+    """
 
-    # The title of the script. This is what will be displayed in the dropdown menu.
     def title(self):
+        """this function should return the title of the script. This is what will be displayed in the dropdown menu."""
+
         raise NotImplementedError()
 
-    # How the script is displayed in the UI. See https://gradio.app/docs/#components
-    # for the different UI components you can use and how to create them.
-    # Most UI components can return a value, such as a boolean for a checkbox.
-    # The returned values are passed to the run method as parameters.
     def ui(self, is_img2img):
+        """this function should create gradio UI elements. See https://gradio.app/docs/#components
+        The return value should be an array of all components that are used in processing.
+        Values of those returned componenbts will be passed to run() and process() functions.
+        """
+
         pass
 
-    # Determines when the script should be shown in the dropdown menu via the 
-    # returned value. As an example:
-    # is_img2img is True if the current tab is img2img, and False if it is txt2img.
-    # Thus, return is_img2img to only show the script on the img2img tab.
     def show(self, is_img2img):
+        """
+        is_img2img is True if this function is called for the img2img interface, and Fasle otherwise
+
+        This function should return:
+         - False if the script should not be shown in UI at all
+         - True if the script should be shown in UI if it's scelected in the scripts drowpdown
+         - script.AlwaysVisible if the script should be shown in UI at all times
+         """
+
         return True
 
-    # This is where the additional processing is implemented. The parameters include
-    # self, the model object "p" (a StableDiffusionProcessing class, see
-    # processing.py), and the parameters returned by the ui method.
-    # Custom functions can be defined here, and additional libraries can be imported 
-    # to be used in processing. The return value should be a Processed object, which is
-    # what is returned by the process_images method.
-    def run(self, *args):
+    def run(self, p, *args):
+        """
+        This function is called if the script has been selected in the script dropdown.
+        It must do all processing and return the Processed object with results, same as
+        one returned by processing.process_images.
+
+        Usually the processing is done by calling the processing.process_images function.
+
+        args contains all values returned by components from ui()
+        """
+
         raise NotImplementedError()
 
-    # The description method is currently unused.
-    # To add a description that appears when hovering over the title, amend the "titles" 
-    # dict in script.js to include the script title (returned by title) as a key, and 
-    # your description as the value.
+    def process(self, p, *args):
+        """
+        This function is called before processing begins for AlwaysVisible scripts.
+        scripts. You can modify the processing object (p) here, inject hooks, etc.
+        """
+
+        pass
+
     def describe(self):
+        """unused"""
         return ""
 
 
+current_basedir = paths.script_path
+
+
+def basedir():
+    """returns the base directory for the current script. For scripts in the main scripts directory,
+    this is the main directory (where webui.py resides), and for scripts in extensions directory
+    (ie extensions/aesthetic/script/aesthetic.py), this is extension's directory (extensions/aesthetic)
+    """
+    return current_basedir
+
+
 scripts_data = []
+ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"])
+ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir"])
+
+
+def list_scripts(scriptdirname, extension):
+    scripts_list = []
+
+    basedir = os.path.join(paths.script_path, scriptdirname)
+    if os.path.exists(basedir):
+        for filename in sorted(os.listdir(basedir)):
+            scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
+
+    extdir = os.path.join(paths.script_path, "extensions")
+    if os.path.exists(extdir):
+        for dirname in sorted(os.listdir(extdir)):
+            dirpath = os.path.join(extdir, dirname)
+            if not os.path.isdir(dirpath):
+                continue
 
+            for filename in sorted(os.listdir(os.path.join(dirpath, scriptdirname))):
+                scripts_list.append(ScriptFile(dirpath, filename, os.path.join(dirpath, scriptdirname, filename)))
 
-def load_scripts(basedir):
-    if not os.path.exists(basedir):
-        return
+    scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
 
-    for filename in sorted(os.listdir(basedir)):
-        path = os.path.join(basedir, filename)
+    return scripts_list
 
-        if os.path.splitext(path)[1].lower() != '.py':
-            continue
 
-        if not os.path.isfile(path):
-            continue
+def load_scripts():
+    global current_basedir
+    scripts_data.clear()
+    script_callbacks.clear_callbacks()
+
+    scripts_list = list_scripts("scripts", ".py")
+
+    syspath = sys.path
 
+    for scriptfile in sorted(scripts_list):
         try:
-            with open(path, "r", encoding="utf8") as file:
+            if scriptfile.basedir != paths.script_path:
+                sys.path = [scriptfile.basedir] + sys.path
+            current_basedir = scriptfile.basedir
+
+            with open(scriptfile.path, "r", encoding="utf8") as file:
                 text = file.read()
 
             from types import ModuleType
-            compiled = compile(text, path, 'exec')
-            module = ModuleType(filename)
+            compiled = compile(text, scriptfile.path, 'exec')
+            module = ModuleType(scriptfile.filename)
             exec(compiled, module.__dict__)
 
             for key, script_class in module.__dict__.items():
                 if type(script_class) == type and issubclass(script_class, Script):
-                    scripts_data.append((script_class, path))
+                    scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir))
 
         except Exception:
-            print(f"Error loading script: {filename}", file=sys.stderr)
+            print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
+        finally:
+            sys.path = syspath
+            current_basedir = paths.script_path
+
 
 def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
     try:
@@ -96,56 +163,80 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
 class ScriptRunner:
     def __init__(self):
         self.scripts = []
+        self.selectable_scripts = []
+        self.alwayson_scripts = []
         self.titles = []
+        self.infotext_fields = []
 
     def setup_ui(self, is_img2img):
-        for script_class, path in scripts_data:
+        for script_class, path, basedir in scripts_data:
             script = script_class()
             script.filename = path
 
-            if not script.show(is_img2img):
-                continue
+            visibility = script.show(is_img2img)
 
-            self.scripts.append(script)
+            if visibility == AlwaysVisible:
+                self.scripts.append(script)
+                self.alwayson_scripts.append(script)
+                script.alwayson = True
 
-        self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts]
+            elif visibility:
+                self.scripts.append(script)
+                self.selectable_scripts.append(script)
 
-        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
-        dropdown.save_to_config = True
-        inputs = [dropdown]
+        self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
+
+        inputs = [None]
+        inputs_alwayson = [True]
 
-        for script in self.scripts:
+        def create_script_ui(script, inputs, inputs_alwayson):
             script.args_from = len(inputs)
             script.args_to = len(inputs)
 
             controls = wrap_call(script.ui, script.filename, "ui", is_img2img)
 
             if controls is None:
-                continue
+                return
 
             for control in controls:
                 control.custom_script_source = os.path.basename(script.filename)
-                control.visible = False
+                if not script.alwayson:
+                    control.visible = False
+
+            if script.infotext_fields is not None:
+                self.infotext_fields += script.infotext_fields
 
             inputs += controls
+            inputs_alwayson += [script.alwayson for _ in controls]
             script.args_to = len(inputs)
 
+        for script in self.alwayson_scripts:
+            with gr.Group():
+                create_script_ui(script, inputs, inputs_alwayson)
+
+        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
+        dropdown.save_to_config = True
+        inputs[0] = dropdown
+
+        for script in self.selectable_scripts:
+            create_script_ui(script, inputs, inputs_alwayson)
+
         def select_script(script_index):
-            if 0 < script_index <= len(self.scripts):
-                script = self.scripts[script_index-1]
+            if 0 < script_index <= len(self.selectable_scripts):
+                script = self.selectable_scripts[script_index-1]
                 args_from = script.args_from
                 args_to = script.args_to
             else:
                 args_from = 0
                 args_to = 0
 
-            return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))]
+            return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)]
 
         def init_field(title):
             if title == 'None':
                 return
             script_index = self.titles.index(title)
-            script = self.scripts[script_index]
+            script = self.selectable_scripts[script_index]
             for i in range(script.args_from, script.args_to):
                 inputs[i].visible = True
 
@@ -164,7 +255,7 @@ class ScriptRunner:
         if script_index == 0:
             return None
 
-        script = self.scripts[script_index-1]
+        script = self.selectable_scripts[script_index-1]
 
         if script is None:
             return None
@@ -176,6 +267,15 @@ class ScriptRunner:
 
         return processed
 
+    def run_alwayson_scripts(self, p):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.process(p, *script_args)
+            except Exception:
+                print(f"Error running alwayson script: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
     def reload_sources(self):
         for si, script in list(enumerate(self.scripts)):
             with open(script.filename, "r", encoding="utf8") as file:
@@ -197,19 +297,21 @@ class ScriptRunner:
                         self.scripts[si].args_from = args_from
                         self.scripts[si].args_to = args_to
 
+
 scripts_txt2img = ScriptRunner()
 scripts_img2img = ScriptRunner()
 
+
 def reload_script_body_only():
     scripts_txt2img.reload_sources()
     scripts_img2img.reload_sources()
 
 
-def reload_scripts(basedir):
+def reload_scripts():
     global scripts_txt2img, scripts_img2img
 
-    scripts_data.clear()
-    load_scripts(basedir)
+    load_scripts()
 
     scripts_txt2img = ScriptRunner()
     scripts_img2img = ScriptRunner()
+
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 1f8587d1..0f10828e 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -332,7 +332,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     multipliers.append([1.0] * 75)
 
             z1 = self.process_tokens(tokens, multipliers)
-            z1 = shared.aesthetic_clip(z1, remade_batch_tokens)
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
 
             remade_batch_tokens = rem_tokens
diff --git a/modules/sd_models.py b/modules/sd_models.py
index d99dbce8..f9b3063d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -7,7 +7,7 @@ from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader, devices
+from modules import shared, modelloader, devices, script_callbacks
 from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
 
@@ -238,6 +238,9 @@ def load_model(checkpoint_info=None):
     sd_hijack.model_hijack.hijack(sd_model)
 
     sd_model.eval()
+    shared.sd_model = sd_model
+
+    script_callbacks.model_loaded_callback(sd_model)
 
     print(f"Model loaded.")
     return sd_model
@@ -252,7 +255,7 @@ def reload_model_weights(sd_model, info=None):
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         checkpoints_loaded.clear()
-        shared.sd_model = load_model(checkpoint_info)
+        load_model(checkpoint_info)
         return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
diff --git a/modules/shared.py b/modules/shared.py
index 0dbe360d..7d786f07 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -31,7 +31,6 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
-parser.add_argument("--aesthetic_embeddings-dir", type=str, default=os.path.join(models_path, 'aesthetic_embeddings'), help="aesthetic_embeddings directory(default: aesthetic_embeddings)")
 parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
 parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
@@ -109,21 +108,6 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
-
-os.makedirs(cmd_opts.aesthetic_embeddings_dir, exist_ok=True)
-aesthetic_embeddings = {}
-
-
-def update_aesthetic_embeddings():
-    global aesthetic_embeddings
-    aesthetic_embeddings = {f.replace(".pt", ""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
-                            os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
-    aesthetic_embeddings = OrderedDict(**{"None": None}, **aesthetic_embeddings)
-
-
-update_aesthetic_embeddings()
-
-
 def reload_hypernetworks():
     global hypernetworks
 
@@ -415,9 +399,6 @@ sd_model = None
 
 clip_model = None
 
-from modules.aesthetic_clip import AestheticCLIP
-aesthetic_clip = AestheticCLIP()
-
 progress_print_out = sys.stdout
 
 
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 1761cfa2..c9d5a090 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -7,7 +7,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -36,7 +36,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         firstphase_height=firstphase_height if enable_hr else None,
     )
 
-    shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
+    p.scripts = modules.scripts.scripts_txt2img
+    p.script_args = args
 
     if cmd_opts.enable_console_prompts:
         print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
diff --git a/modules/ui.py b/modules/ui.py
index 70a9cf10..c977482c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -23,10 +23,10 @@ import gradio as gr
 import gradio.utils
 import gradio.routes
 
-from modules import sd_hijack, sd_models, localization
+from modules import sd_hijack, sd_models, localization, script_callbacks
 from modules.paths import script_path
 
-from modules.shared import opts, cmd_opts, restricted_opts, aesthetic_embeddings
+from modules.shared import opts, cmd_opts, restricted_opts
 
 if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
@@ -44,7 +44,6 @@ from modules.images import save_image
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
 
-import modules.aesthetic_clip as aesthetic_clip
 import modules.images_history as img_his
 
 
@@ -662,8 +661,6 @@ def create_ui(wrap_gradio_gpu_call):
 
                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
 
-                aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative = aesthetic_clip.create_ui()
-
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
@@ -718,14 +715,6 @@ def create_ui(wrap_gradio_gpu_call):
                     denoising_strength,
                     firstphase_width,
                     firstphase_height,
-                    aesthetic_lr,
-                    aesthetic_weight,
-                    aesthetic_steps,
-                    aesthetic_imgs,
-                    aesthetic_slerp,
-                    aesthetic_imgs_text,
-                    aesthetic_slerp_angle,
-                    aesthetic_text_negative
                 ] + custom_inputs,
 
                 outputs=[
@@ -804,14 +793,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
-                (aesthetic_lr, "Aesthetic LR"),
-                (aesthetic_weight, "Aesthetic weight"),
-                (aesthetic_steps, "Aesthetic steps"),
-                (aesthetic_imgs, "Aesthetic embedding"),
-                (aesthetic_slerp, "Aesthetic slerp"),
-                (aesthetic_imgs_text, "Aesthetic text"),
-                (aesthetic_text_negative, "Aesthetic text negative"),
-                (aesthetic_slerp_angle, "Aesthetic slerp angle"),
+                *modules.scripts.scripts_txt2img.infotext_fields
             ]
 
             txt2img_preview_params = [
@@ -896,8 +878,6 @@ def create_ui(wrap_gradio_gpu_call):
 
                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
 
-                aesthetic_weight_im, aesthetic_steps_im, aesthetic_lr_im, aesthetic_slerp_im, aesthetic_imgs_im, aesthetic_imgs_text_im, aesthetic_slerp_angle_im, aesthetic_text_negative_im = aesthetic_clip.create_ui()
-
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
 
@@ -988,14 +968,6 @@ def create_ui(wrap_gradio_gpu_call):
                     inpainting_mask_invert,
                     img2img_batch_input_dir,
                     img2img_batch_output_dir,
-                    aesthetic_lr_im,
-                    aesthetic_weight_im,
-                    aesthetic_steps_im,
-                    aesthetic_imgs_im,
-                    aesthetic_slerp_im,
-                    aesthetic_imgs_text_im,
-                    aesthetic_slerp_angle_im,
-                    aesthetic_text_negative_im,
                 ] + custom_inputs,
                 outputs=[
                     img2img_gallery,
@@ -1087,14 +1059,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (seed_resize_from_w, "Seed resize from-1"),
                 (seed_resize_from_h, "Seed resize from-2"),
                 (denoising_strength, "Denoising strength"),
-                (aesthetic_lr_im, "Aesthetic LR"),
-                (aesthetic_weight_im, "Aesthetic weight"),
-                (aesthetic_steps_im, "Aesthetic steps"),
-                (aesthetic_imgs_im, "Aesthetic embedding"),
-                (aesthetic_slerp_im, "Aesthetic slerp"),
-                (aesthetic_imgs_text_im, "Aesthetic text"),
-                (aesthetic_text_negative_im, "Aesthetic text negative"),
-                (aesthetic_slerp_angle_im, "Aesthetic slerp angle"),
+                *modules.scripts.scripts_img2img.infotext_fields
             ]
             token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
 
@@ -1217,9 +1182,9 @@ def create_ui(wrap_gradio_gpu_call):
         )
     #images history
     images_history_switch_dict = {
-        "fn":modules.generation_parameters_copypaste.connect_paste,
-        "t2i":txt2img_paste_fields,
-        "i2i":img2img_paste_fields
+        "fn": modules.generation_parameters_copypaste.connect_paste,
+        "t2i": txt2img_paste_fields,
+        "i2i": img2img_paste_fields
     }
 
     images_history = img_his.create_history_tabs(gr, opts, cmd_opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
@@ -1264,18 +1229,6 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create embedding", variant='primary')
 
-                with gr.Tab(label="Create aesthetic images embedding"):
-
-                    new_embedding_name_ae = gr.Textbox(label="Name")
-                    process_src_ae = gr.Textbox(label='Source directory')
-                    batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256)
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_embedding_ae = gr.Button(value="Create images embedding", variant='primary')
-
                 with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
@@ -1375,21 +1328,6 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
-        create_embedding_ae.click(
-            fn=aesthetic_clip.generate_imgs_embd,
-            inputs=[
-                new_embedding_name_ae,
-                process_src_ae,
-                batch_ae
-            ],
-            outputs=[
-                aesthetic_imgs,
-                aesthetic_imgs_im,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
         create_hypernetwork.click(
             fn=modules.hypernetworks.ui.create_hypernetwork,
             inputs=[
@@ -1580,10 +1518,10 @@ Requested path was: {f}
         if not opts.same_type(value, opts.data_labels[key].default):
             return gr.update(visible=True), opts.dumpjson()
 
+        oldval = opts.data.get(key, None)
         if cmd_opts.hide_ui_dir_config and key in restricted_opts:
             return gr.update(value=oldval), opts.dumpjson()
 
-        oldval = opts.data.get(key, None)
         opts.data[key] = value
 
         if oldval != value:
@@ -1692,9 +1630,12 @@ Requested path was: {f}
         (images_history, "Image Browser", "images_history"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
-        (settings_interface, "Settings", "settings"),
     ]
 
+    interfaces += script_callbacks.ui_tabs_callback()
+
+    interfaces += [(settings_interface, "Settings", "settings")]
+
     with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
         css = file.read()
 
diff --git a/webui.py b/webui.py
index 87589064..b1deca1b 100644
--- a/webui.py
+++ b/webui.py
@@ -71,6 +71,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
     return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
 
+
 def initialize():
     modelloader.cleanup_models()
     modules.sd_models.setup_model()
@@ -79,9 +80,9 @@ def initialize():
     shared.face_restorers.append(modules.face_restoration.FaceRestoration())
     modelloader.load_upscalers()
 
-    modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
+    modules.scripts.load_scripts()
 
-    shared.sd_model = modules.sd_models.load_model()
+    modules.sd_models.load_model()
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
@@ -145,7 +146,7 @@ def webui():
         sd_samplers.set_samplers()
 
         print('Reloading Custom Scripts')
-        modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
+        modules.scripts.reload_scripts()
         print('Reloading modules: modules.ui')
         importlib.reload(modules.ui)
         print('Refreshing Model List')
-- 
cgit v1.2.1


From 6398dc9b1049f242576ca309f95a3fb1e654951c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 13:34:49 +0300
Subject: further support for extensions

---
 .gitignore         |  1 +
 README.md          |  3 +--
 modules/scripts.py | 44 +++++++++++++++++++++++++++++++++++---------
 modules/ui.py      | 19 ++++++++++---------
 style.css          |  2 +-
 5 files changed, 48 insertions(+), 21 deletions(-)

diff --git a/.gitignore b/.gitignore
index 2f1e08ed..8fa05852 100644
--- a/.gitignore
+++ b/.gitignore
@@ -28,3 +28,4 @@ notification.mp3
 /SwinIR
 /textual_inversion
 .vscode
+/extensions
diff --git a/README.md b/README.md
index 5b5dc8ba..6853aea0 100644
--- a/README.md
+++ b/README.md
@@ -83,8 +83,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - Estimated completion time in progress bar
 - API
 - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. 
-- Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
-
+- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
diff --git a/modules/scripts.py b/modules/scripts.py
index 65f25f49..9323af3e 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -102,17 +102,39 @@ def list_scripts(scriptdirname, extension):
     if os.path.exists(extdir):
         for dirname in sorted(os.listdir(extdir)):
             dirpath = os.path.join(extdir, dirname)
-            if not os.path.isdir(dirpath):
+            scriptdirpath = os.path.join(dirpath, scriptdirname)
+
+            if not os.path.isdir(scriptdirpath):
                 continue
 
-            for filename in sorted(os.listdir(os.path.join(dirpath, scriptdirname))):
-                scripts_list.append(ScriptFile(dirpath, filename, os.path.join(dirpath, scriptdirname, filename)))
+            for filename in sorted(os.listdir(scriptdirpath)):
+                scripts_list.append(ScriptFile(dirpath, filename, os.path.join(scriptdirpath, filename)))
 
     scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
 
     return scripts_list
 
 
+def list_files_with_name(filename):
+    res = []
+
+    dirs = [paths.script_path]
+
+    extdir = os.path.join(paths.script_path, "extensions")
+    if os.path.exists(extdir):
+        dirs += [os.path.join(extdir, d) for d in sorted(os.listdir(extdir))]
+
+    for dirpath in dirs:
+        if not os.path.isdir(dirpath):
+            continue
+
+        path = os.path.join(dirpath, filename)
+        if os.path.isfile(filename):
+            res.append(path)
+
+    return res
+
+
 def load_scripts():
     global current_basedir
     scripts_data.clear()
@@ -276,7 +298,7 @@ class ScriptRunner:
                 print(f"Error running alwayson script: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
-    def reload_sources(self):
+    def reload_sources(self, cache):
         for si, script in list(enumerate(self.scripts)):
             with open(script.filename, "r", encoding="utf8") as file:
                 args_from = script.args_from
@@ -286,9 +308,12 @@ class ScriptRunner:
 
                 from types import ModuleType
 
-                compiled = compile(text, filename, 'exec')
-                module = ModuleType(script.filename)
-                exec(compiled, module.__dict__)
+                module = cache.get(filename, None)
+                if module is None:
+                    compiled = compile(text, filename, 'exec')
+                    module = ModuleType(script.filename)
+                    exec(compiled, module.__dict__)
+                    cache[filename] = module
 
                 for key, script_class in module.__dict__.items():
                     if type(script_class) == type and issubclass(script_class, Script):
@@ -303,8 +328,9 @@ scripts_img2img = ScriptRunner()
 
 
 def reload_script_body_only():
-    scripts_txt2img.reload_sources()
-    scripts_img2img.reload_sources()
+    cache = {}
+    scripts_txt2img.reload_sources(cache)
+    scripts_img2img.reload_sources(cache)
 
 
 def reload_scripts():
diff --git a/modules/ui.py b/modules/ui.py
index c977482c..29986124 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1636,13 +1636,15 @@ Requested path was: {f}
 
     interfaces += [(settings_interface, "Settings", "settings")]
 
-    with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
-        css = file.read()
+    css = ""
+
+    for cssfile in modules.scripts.list_files_with_name("style.css"):
+        with open(cssfile, "r", encoding="utf8") as file:
+            css += file.read() + "\n"
 
     if os.path.exists(os.path.join(script_path, "user.css")):
         with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
-            usercss = file.read()
-            css += usercss
+            css += file.read() + "\n"
 
     if not cmd_opts.no_progressbar_hiding:
         css += css_hide_progressbar
@@ -1865,9 +1867,9 @@ def load_javascript(raw_response):
     with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
         javascript = f'<script>{jsfile.read()}</script>'
 
-    jsdir = os.path.join(script_path, "javascript")
-    for filename in sorted(os.listdir(jsdir)):
-        with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
+    scripts_list = modules.scripts.list_scripts("javascript", ".js")
+    for basedir, filename, path in scripts_list:
+        with open(path, "r", encoding="utf8") as jsfile:
             javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
 
     if cmd_opts.theme is not None:
@@ -1885,6 +1887,5 @@ def load_javascript(raw_response):
     gradio.routes.templates.TemplateResponse = template_response
 
 
-reload_javascript = partial(load_javascript,
-                            gradio.routes.templates.TemplateResponse)
+reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse)
 reload_javascript()
diff --git a/style.css b/style.css
index 5d2bacc9..26ae36a5 100644
--- a/style.css
+++ b/style.css
@@ -477,7 +477,7 @@ input[type="range"]{
     padding: 0;
 }
 
-#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization, #refresh_aesthetic_embeddings{
+#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
     max-width: 2.5em;
     min-width: 2.5em;
     height: 2.4em;
-- 
cgit v1.2.1


From 5aa9525046b7520d39fe8fc8c5c6cc10ab4d5fdb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 13:40:07 +0300
Subject: updated readme with info about Aesthetic Gradients

---
 README.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/README.md b/README.md
index 6853aea0..1a0e4f6a 100644
--- a/README.md
+++ b/README.md
@@ -85,6 +85,16 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. 
 - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
 
+## Where are Aesthetic Gradients?!?!
+Aesthetic Gradients are now an extension. You can install it using git:
+
+```commandline
+git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients extensions/aesthetic-gradients
+```
+
+After running this command, make sure that you have `aesthetic-gradients` dir in webui's `extensions` directory and restart
+the UI. The interface for Aesthetic Gradients should appear exactly the same as it was.
+
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
 
-- 
cgit v1.2.1


From 50b5504401e50b6c94eba41b37fe212b2f27b792 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 14:04:14 +0300
Subject: remove parsing command line from devices.py

---
 modules/devices.py | 14 +++++---------
 modules/lowvram.py |  9 ++++-----
 2 files changed, 9 insertions(+), 14 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 8a159282..dc1f3cdd 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -15,14 +15,10 @@ def extract_device_id(args, name):
 
 def get_optimal_device():
     if torch.cuda.is_available():
-        # CUDA device selection support:
-        if "shared" not in sys.modules:
-            commandline_args = os.environ.get('COMMANDLINE_ARGS', "") #re-parse the commandline arguments because using the shared.py module creates an import loop.
-            sys.argv += shlex.split(commandline_args)
-            device_id = extract_device_id(sys.argv, '--device-id')
-        else:
-            device_id = shared.cmd_opts.device_id
-            
+        from modules import shared
+
+        device_id = shared.cmd_opts.device_id
+
         if device_id is not None:
             cuda_device = f"cuda:{device_id}"
             return torch.device(cuda_device)
@@ -49,7 +45,7 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
+device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = None
 dtype = torch.float16
 dtype_vae = torch.float16
 
diff --git a/modules/lowvram.py b/modules/lowvram.py
index 7eba1349..f327c3df 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -1,9 +1,8 @@
 import torch
-from modules.devices import get_optimal_device
+from modules import devices
 
 module_in_gpu = None
 cpu = torch.device("cpu")
-device = gpu = get_optimal_device()
 
 
 def send_everything_to_cpu():
@@ -33,7 +32,7 @@ def setup_for_low_vram(sd_model, use_medvram):
         if module_in_gpu is not None:
             module_in_gpu.to(cpu)
 
-        module.to(gpu)
+        module.to(devices.device)
         module_in_gpu = module
 
     # see below for register_forward_pre_hook;
@@ -51,7 +50,7 @@ def setup_for_low_vram(sd_model, use_medvram):
     # send the model to GPU. Then put modules back. the modules will be in CPU.
     stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model
     sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None
-    sd_model.to(device)
+    sd_model.to(devices.device)
     sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored
 
     # register hooks for those the first two models
@@ -70,7 +69,7 @@ def setup_for_low_vram(sd_model, use_medvram):
         # so that only one of them is in GPU at a time
         stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed
         diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None
-        sd_model.model.to(device)
+        sd_model.model.to(devices.device)
         diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored
 
         # install hooks for bits of third model
-- 
cgit v1.2.1


From 0e8ca8e7af05be22d7d2c07a47c3c7febe0f0ab6 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Sat, 22 Oct 2022 11:07:00 +0000
Subject: add dropout

---
 modules/hypernetworks/hypernetwork.py | 68 +++++++++++++++++++++--------------
 modules/hypernetworks/ui.py           | 10 +++---
 modules/ui.py                         | 43 +++++++++++-----------
 3 files changed, 70 insertions(+), 51 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 905cbeef..e493f366 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -1,47 +1,60 @@
+import csv
 import datetime
 import glob
 import html
 import os
 import sys
 import traceback
-import tqdm
-import csv
 
+import modules.textual_inversion.dataset
 import torch
-
-from ldm.util import default
-from modules import devices, shared, processing, sd_models
-import torch
-from torch import einsum
+import tqdm
 from einops import rearrange, repeat
-import modules.textual_inversion.dataset
+from ldm.util import default
+from modules import devices, processing, sd_models, shared
 from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
+from torch import einsum
 
 
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
-    activation_dict = {"relu": torch.nn.ReLU, "leakyrelu": torch.nn.LeakyReLU, "elu": torch.nn.ELU,
-                       "swish": torch.nn.Hardswish}
-    
-    def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False, activation_func=None):
+    activation_dict = {
+        "relu": torch.nn.ReLU,
+        "leakyrelu": torch.nn.LeakyReLU,
+        "elu": torch.nn.ELU,
+        "swish": torch.nn.Hardswish,
+    }
+
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
         assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
         assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
-        
+        assert activation_func not in self.activation_dict.keys() + "linear", f"Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'"
+
         linears = []
         for i in range(len(layer_structure) - 1):
+
+            # Add a fully-connected layer
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
-            # if skip_first_layer because first parameters potentially contain negative values
-            # if i < 1: continue
-            if activation_func in HypernetworkModule.activation_dict:
-                linears.append(HypernetworkModule.activation_dict[activation_func]())
+
+            # Add an activation func
+            if activation_func == "linear":
+                pass
+            elif activation_func in self.activation_dict:
+                linears.append(self.activation_dict[activation_func]())
             else:
-                print("Invalid key {} encountered as activation function!".format(activation_func))
-            # if use_dropout: 
-            # linears.append(torch.nn.Dropout(p=0.3))
+                raise NotImplementedError(
+                    "Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'"
+                )
+
+            # Add dropout
+            if use_dropout:
+                linears.append(torch.nn.Dropout(p=0.3))
+
+            # Add layer normalization
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
@@ -93,7 +106,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False, activation_func=None):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -101,13 +114,14 @@ class Hypernetwork:
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
         self.layer_structure = layer_structure
-        self.add_layer_norm = add_layer_norm
         self.activation_func = activation_func
+        self.add_layer_norm = add_layer_norm
+        self.use_dropout = use_dropout
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func),
-                HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
             )
 
     def weights(self):
@@ -129,8 +143,9 @@ class Hypernetwork:
         state_dict['step'] = self.step
         state_dict['name'] = self.name
         state_dict['layer_structure'] = self.layer_structure
-        state_dict['is_layer_norm'] = self.add_layer_norm
         state_dict['activation_func'] = self.activation_func
+        state_dict['is_layer_norm'] = self.add_layer_norm
+        state_dict['use_dropout'] = self.use_dropout
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
 
@@ -144,8 +159,9 @@ class Hypernetwork:
         state_dict = torch.load(filename, map_location='cpu')
 
         self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
-        self.add_layer_norm = state_dict.get('is_layer_norm', False)
         self.activation_func = state_dict.get('activation_func', None)
+        self.add_layer_norm = state_dict.get('is_layer_norm', False)
+        self.use_dropout = state_dict.get('use_dropout', False)
 
         for size, sd in state_dict.items():
             if type(size) == int:
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 1a5a27d8..5f6f17b6 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -3,14 +3,13 @@ import os
 import re
 
 import gradio as gr
-
-import modules.textual_inversion.textual_inversion
 import modules.textual_inversion.preprocess
-from modules import sd_hijack, shared, devices
+import modules.textual_inversion.textual_inversion
+from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
 
-def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False, activation_func=None):
+def create_hypernetwork(name, enable_sizes, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
     fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
     assert not os.path.exists(fn), f"file {fn} already exists"
 
@@ -21,8 +20,9 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm
         name=name,
         enable_sizes=[int(x) for x in enable_sizes],
         layer_structure=layer_structure,
-        add_layer_norm=add_layer_norm,
         activation_func=activation_func,
+        add_layer_norm=add_layer_norm,
+        use_dropout=use_dropout,
     )
     hypernet.save(fn)
 
diff --git a/modules/ui.py b/modules/ui.py
index 716f14b8..d4b32c05 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -5,43 +5,44 @@ import json
 import math
 import mimetypes
 import os
+import platform
 import random
+import subprocess as sp
 import sys
 import tempfile
 import time
 import traceback
-import platform
-import subprocess as sp
 from functools import partial, reduce
 
+import gradio as gr
+import gradio.routes
+import gradio.utils
 import numpy as np
+import piexif
 import torch
 from PIL import Image, PngImagePlugin
-import piexif
 
-import gradio as gr
-import gradio.utils
-import gradio.routes
-
-from modules import sd_hijack, sd_models, localization
+from modules import localization, sd_hijack, sd_models
 from modules.paths import script_path
-from modules.shared import opts, cmd_opts, restricted_opts
+from modules.shared import cmd_opts, opts, restricted_opts
+
 if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
-import modules.shared as shared
-from modules.sd_samplers import samplers, samplers_for_img2img
-from modules.sd_hijack import model_hijack
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.images_history as img_his
 import modules.ldsr_model
 import modules.scripts
-import modules.gfpgan_model
-import modules.codeformer_model
+import modules.shared as shared
 import modules.styles
-import modules.generation_parameters_copypaste
+import modules.textual_inversion.ui
 from modules import prompt_parser
 from modules.images import save_image
-import modules.textual_inversion.ui
-import modules.hypernetworks.ui
-import modules.images_history as img_his
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers import samplers, samplers_for_img2img
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -1223,8 +1224,9 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu", "elu", "swish"])
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"])
+                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1308,8 +1310,9 @@ def create_ui(wrap_gradio_gpu_call):
                 new_hypernetwork_name,
                 new_hypernetwork_sizes,
                 new_hypernetwork_layer_structure,
-                new_hypernetwork_add_layer_norm,
                 new_hypernetwork_activation_func,
+                new_hypernetwork_add_layer_norm,
+                new_hypernetwork_use_dropout
             ],
             outputs=[
                 train_hypernetwork_name,
-- 
cgit v1.2.1


From 1cd3ed7def40198f46d30f74dd37d2906ebdbaa6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 14:28:56 +0300
Subject: fix for extensions without style.css

---
 modules/ui.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/ui.py b/modules/ui.py
index 29986124..d8d52db1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1639,6 +1639,9 @@ Requested path was: {f}
     css = ""
 
     for cssfile in modules.scripts.list_files_with_name("style.css"):
+        if not os.path.isfile(cssfile):
+            continue
+
         with open(cssfile, "r", encoding="utf8") as file:
             css += file.read() + "\n"
 
-- 
cgit v1.2.1


From 7fd90128eb6d1820045bfe2c2c1269661023a712 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 14:48:43 +0300
Subject: added a guard for hypernet training that will stop early if weights
 are getting no gradients

---
 modules/hypernetworks/hypernetwork.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 47d91ea5..46039a49 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -310,6 +310,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
     optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
+    steps_without_grad = 0
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
@@ -332,8 +334,17 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
             optimizer.zero_grad()
+            weights[0].grad = None
             loss.backward()
+
+            if weights[0].grad is None:
+                steps_without_grad += 1
+            else:
+                steps_without_grad = 0
+            assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
+
             optimizer.step()
+
         mean_loss = losses.mean()
         if torch.isnan(mean_loss):
             raise RuntimeError("Loss diverged.")
-- 
cgit v1.2.1


From fccba4729db341a299db3343e3264fecd9459a07 Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Sat, 22 Oct 2022 12:02:41 +0000
Subject: add an option to avoid dying relu

---
 modules/hypernetworks/hypernetwork.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b7a04038..3132a56c 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -32,7 +32,6 @@ class HypernetworkModule(torch.nn.Module):
         assert layer_structure is not None, "layer_structure must not be None"
         assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
         assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
-        assert activation_func not in self.activation_dict.keys() + "linear", f"Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'"
 
         linears = []
         for i in range(len(layer_structure) - 1):
@@ -43,12 +42,13 @@ class HypernetworkModule(torch.nn.Module):
             # Add an activation func
             if activation_func == "linear" or activation_func is None:
                 pass
+            # If ReLU, Skip adding it to the first layer to avoid dying ReLU
+            elif activation_func == "relu" and i < 1:
+                pass
             elif activation_func in self.activation_dict:
                 linears.append(self.activation_dict[activation_func]())
             else:
-                raise RuntimeError(
-                    "Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'"
-                )
+                raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')
 
             # Add dropout
             if use_dropout:
@@ -166,8 +166,8 @@ class Hypernetwork:
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm, self.activation_func),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm, self.activation_func),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
                 )
 
         self.name = state_dict.get('name', self.name)
-- 
cgit v1.2.1


From 7912acef725832debef58c4c7bf8ec22fb446c0b Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Sat, 22 Oct 2022 13:00:44 +0000
Subject: small fix

---
 modules/hypernetworks/hypernetwork.py | 12 +++++-------
 modules/ui.py                         |  1 -
 2 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3132a56c..7d12e0ff 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -42,22 +42,20 @@ class HypernetworkModule(torch.nn.Module):
             # Add an activation func
             if activation_func == "linear" or activation_func is None:
                 pass
-            # If ReLU, Skip adding it to the first layer to avoid dying ReLU
-            elif activation_func == "relu" and i < 1:
-                pass
             elif activation_func in self.activation_dict:
                 linears.append(self.activation_dict[activation_func]())
             else:
                 raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')
 
-            # Add dropout
-            if use_dropout:
-                linears.append(torch.nn.Dropout(p=0.3))
-
             # Add layer normalization
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
+            # Add dropout
+            if use_dropout:
+                p = 0.5 if 0 <= i <= len(layer_structure) - 3 else 0.2
+                linears.append(torch.nn.Dropout(p=p))
+
         self.linear = torch.nn.Sequential(*linears)
 
         if state_dict is not None:
diff --git a/modules/ui.py b/modules/ui.py
index cd118552..eca887ca 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1244,7 +1244,6 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
                     overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"])
 
                     with gr.Row():
                         with gr.Column(scale=3):
-- 
cgit v1.2.1


From 6a4fa73a38935a18779ce1809892730fd1572bee Mon Sep 17 00:00:00 2001
From: discus0434 <discus0434@gmail.com>
Date: Sat, 22 Oct 2022 13:44:39 +0000
Subject: small fix

---
 modules/hypernetworks/hypernetwork.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3372aae2..3bc71ee5 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -51,10 +51,9 @@ class HypernetworkModule(torch.nn.Module):
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
-            # Add dropout
-            if use_dropout:
-                p = 0.5 if 0 <= i <= len(layer_structure) - 3 else 0.2
-                linears.append(torch.nn.Dropout(p=p))
+            # Add dropout expect last layer
+            if use_dropout and i < len(layer_structure) - 3:
+                linears.append(torch.nn.Dropout(p=0.3))
 
         self.linear = torch.nn.Sequential(*linears)
 
-- 
cgit v1.2.1


From d37cfffd537cd29309afbcb192c4f979995c6a34 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 19:18:56 +0300
Subject: added callback for creating new settings in extensions

---
 modules/script_callbacks.py | 11 +++++++++++
 modules/shared.py           | 19 +++++++++++++++++--
 modules/ui.py               |  6 +++++-
 3 files changed, 33 insertions(+), 3 deletions(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 866b7acd..1270e50f 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -1,6 +1,7 @@
 
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
+callbacks_ui_settings = []
 
 
 def clear_callbacks():
@@ -22,6 +23,11 @@ def ui_tabs_callback():
     return res
 
 
+def ui_settings_callback():
+    for callback in callbacks_ui_settings:
+        callback()
+
+
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
@@ -40,3 +46,8 @@ def on_ui_tabs(callback):
     """
     callbacks_ui_tabs.append(callback)
 
+
+def on_ui_settings(callback):
+    """register a function to be called before UI  settingsare populated; add your settings
+    by using shared.opts.add_option(shared.OptionInfo(...)) """
+    callbacks_ui_settings.append(callback)
diff --git a/modules/shared.py b/modules/shared.py
index 5d83971e..d9cb65ef 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -165,13 +165,13 @@ def realesrgan_models_names():
 
 
 class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False, refresh=None):
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None):
         self.default = default
         self.label = label
         self.component = component
         self.component_args = component_args
         self.onchange = onchange
-        self.section = None
+        self.section = section
         self.refresh = refresh
 
 
@@ -327,6 +327,7 @@ options_templates.update(options_section(('images-history', "Images Browser"), {
 
 }))
 
+
 class Options:
     data = None
     data_labels = options_templates
@@ -389,6 +390,20 @@ class Options:
         d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
         return json.dumps(d)
 
+    def add_option(self, key, info):
+        self.data_labels[key] = info
+
+    def reorder(self):
+        """reorder settings so that all items related to section always go together"""
+
+        section_ids = {}
+        settings_items = self.data_labels.items()
+        for k, item in settings_items:
+            if item.section not in section_ids:
+                section_ids[item.section] = len(section_ids)
+
+        self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}
+
 
 opts = Options()
 if os.path.exists(config_filename):
diff --git a/modules/ui.py b/modules/ui.py
index d8d52db1..2849b111 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1461,6 +1461,9 @@ def create_ui(wrap_gradio_gpu_call):
     components = []
     component_dict = {}
 
+    script_callbacks.ui_settings_callback()
+    opts.reorder()
+
     def open_folder(f):
         if not os.path.exists(f):
             print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
@@ -1564,7 +1567,8 @@ Requested path was: {f}
 
                     previous_section = item.section
 
-                    gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
+                    elem_id, text = item.section
+                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
 
                 if k in quicksettings_names:
                     quicksettings_list.append((i, k, item))
-- 
cgit v1.2.1


From dbc8ab65f6d496459a76547776b656c96ad1350d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 19:19:17 +0300
Subject: typo

---
 modules/script_callbacks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 1270e50f..5bcccd67 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -48,6 +48,6 @@ def on_ui_tabs(callback):
 
 
 def on_ui_settings(callback):
-    """register a function to be called before UI  settingsare populated; add your settings
+    """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
     callbacks_ui_settings.append(callback)
-- 
cgit v1.2.1


From 72383abacdc6a101704a6f73758ce4d0bb68c9d1 Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Sat, 22 Oct 2022 16:50:07 +0200
Subject: Deepdanbooru linux fix

---
 modules/deepbooru.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 8914662d..3c34ab7c 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -50,7 +50,8 @@ def create_deepbooru_process(threshold, deepbooru_opts):
     the tags.
     """
     from modules import shared  # prevents circular reference
-    shared.deepbooru_process_manager = multiprocessing.Manager()
+    context = multiprocessing.get_context("spawn")
+    shared.deepbooru_process_manager = context.Manager()
     shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
     shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
     shared.deepbooru_process_return["value"] = -1
-- 
cgit v1.2.1


From e38625011cd4955da4bc67fe95d1d0f4c0c53899 Mon Sep 17 00:00:00 2001
From: Greendayle <Greendayle>
Date: Sat, 22 Oct 2022 16:56:52 +0200
Subject: fix part2

---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 3c34ab7c..8bbc90a4 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -55,7 +55,7 @@ def create_deepbooru_process(threshold, deepbooru_opts):
     shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
     shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
     shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
+    shared.deepbooru_process = context.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
     shared.deepbooru_process.start()
 
 
-- 
cgit v1.2.1


From 7613ea12f267143ceb70a9aeb45eb20aca086e3e Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Fri, 21 Oct 2022 11:32:56 -0700
Subject: Fixed img2imgalt after inpainting update

---
 scripts/img2imgalt.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index d438175c..88abc093 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -34,6 +34,9 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
         sigma_in = torch.cat([sigmas[i] * s_in] * 2)
         cond_in = torch.cat([uncond, cond])
 
+        image_conditioning = torch.cat([p.image_conditioning] * 2)
+        cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
+
         c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
         t = dnw.sigma_to_t(sigma_in)
 
@@ -78,6 +81,9 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
         sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
         cond_in = torch.cat([uncond, cond])
 
+        image_conditioning = torch.cat([p.image_conditioning] * 2)
+        cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
+
         c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
 
         if i == 1:
@@ -194,7 +200,7 @@ class Script(scripts.Script):
             
             p.seed = p.seed + 1
             
-            return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning)
+            return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)
 
         p.sample = sample_extra
 
-- 
cgit v1.2.1


From 96ee7d77077eb3c1eacff802e9ccf194adc04592 Mon Sep 17 00:00:00 2001
From: Kris57 <49682577+yuuki76@users.noreply.github.com>
Date: Tue, 18 Oct 2022 14:16:26 +0900
Subject: add ja localization

---
 localizations/ja_JP.json | 413 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 413 insertions(+)
 create mode 100644 localizations/ja_JP.json

diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
new file mode 100644
index 00000000..5da28cb6
--- /dev/null
+++ b/localizations/ja_JP.json
@@ -0,0 +1,413 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "読み込み中...",
+    "view": "view",
+    "api": "api",
+    "•": "•",
+    "gradioで作ろう": "gradioで作ろう",
+    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "その他",
+    "PNG Info": "PNG Info",
+    "History": "履歴",
+    "Checkpoint Merger": "Checkpoint Merger",
+    "Train": "学習",
+    "Settings": "設定",
+    "Prompt": "プロンプト",
+    "Negative prompt": "ネガティブ プロンプト",
+    "Run": "実行",
+    "Skip": "スキップ",
+    "Interrupt": "中断",
+    "Generate": "生成！",
+    "Style 1": "スタイル 1",
+    "Style 2": "スタイル 2",
+    "Label": "Label",
+    "File": "ファイル",
+    "ここにファイルをドロップ": "ここにファイルをドロップ",
+    "-": "-",
+    "または": "または",
+    "クリックしてアップロード": "クリックしてアップロード",
+    "Image": "Image",
+    "Check progress": "Check progress",
+    "Check progress (first)": "Check progress (first)",
+    "Sampling Steps": "Sampling Steps",
+    "Sampling method": "Sampling method",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "幅",
+    "Height": "高さ",
+    "Restore faces": "顔修復",
+    "Tiling": "テクスチャ生成モード",
+    "Highres. fix": "*高解像度 fix",
+    "Firstpass width": "Firstpass width",
+    "Firstpass height": "Firstpass height",
+    "Denoising strength": "Denoising 強度",
+    "Batch count": "連続生成回数",
+    "Batch size": "同時生成枚数",
+    "CFG Scale": "CFG Scale",
+    "Seed": "Seed",
+    "Extra": "Extra",
+    "Variation seed": "Variation seed",
+    "Variation strength": "Variation 強度",
+    "Resize seed from width": "Resize seed from width",
+    "Resize seed from height": "Resize seed from height",
+    "Script": "Script",
+    "None": "None",
+    "Prompt matrix": "Prompt matrix",
+    "Prompts from file or textbox": "Prompts from file or textbox",
+    "X/Y plot": "X/Y plot",
+    "Put variable parts at start of prompt": "Put variable parts at start of prompt",
+    "Show Textbox": "Show Textbox",
+    "File with inputs": "File with inputs",
+    "Prompts": "Prompts",
+    "X type": "X type",
+    "Nothing": "Nothing",
+    "Var. seed": "Var. seed",
+    "Var. strength": "Var. 強度",
+    "Steps": "Steps",
+    "Prompt S/R": "Prompt S/R",
+    "Prompt order": "Prompt order",
+    "Sampler": "Sampler",
+    "Checkpoint name": "Checkpoint name",
+    "Hypernetwork": "Hypernetwork",
+    "Hypernet str.": "Hypernet 強度",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "Sigma min",
+    "Sigma max": "Sigma max",
+    "Sigma noise": "Sigma noise",
+    "Eta": "Eta",
+    "Clip skip": "Clip skip",
+    "Denoising": "Denoising",
+    "X values": "X values",
+    "Y type": "Y type",
+    "Y values": "Y values",
+    "Draw legend": "Draw legend",
+    "Include Separate Images": "Include Separate Images",
+    "Keep -1 for seeds": "Keep -1 for seeds",
+    "ここに画像をドロップ": "ここに画像をドロップ",
+    "Save": "保存",
+    "Send to img2img": "img2imgに送る",
+    "Send to inpaint": "inpaintに送る",
+    "Send to extras": "その他タブに送る",
+    "Make Zip when Save?": "保存するときZipも同時に作る",
+    "Textbox": "Textbox",
+    "Interrogate\nCLIP": "Interrogate\nCLIP",
+    "Interrogate\nDeepBooru": "Interrogate\nDeepBooru",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "Batch img2img",
+    "Image for img2img": "Image for img2img",
+    "Image for inpainting with mask": "Image for inpainting with mask",
+    "Mask": "Mask",
+    "Mask blur": "Mask blur",
+    "Mask mode": "Mask mode",
+    "Draw mask": "Draw mask",
+    "Upload mask": "Upload mask",
+    "Masking mode": "Masking mode",
+    "Inpaint masked": "Inpaint masked",
+    "Inpaint not masked": "Inpaint not masked",
+    "Masked content": "Masked content",
+    "fill": "fill",
+    "original": "original",
+    "latent noise": "latent noise",
+    "latent nothing": "latent nothing",
+    "Inpaint at full resolution": "Inpaint at full resolution",
+    "Inpaint at full resolution padding, pixels": "Inpaint at full resolution padding, pixels",
+    "Process images in a directory on the same machine where the server is running.": "Process images in a directory on the same machine where the server is running.",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Use an empty output directory to save pictures normally instead of writing to the output directory.",
+    "Input directory": "Input directory",
+    "Output directory": "Output directory",
+    "Resize mode": "Resize mode",
+    "Just resize": "Just resize",
+    "Crop and resize": "Crop and resize",
+    "Resize and fill": "Resize and fill",
+    "img2img alternative test": "img2img alternative test",
+    "Loopback": "Loopback",
+    "Outpainting mk2": "Outpainting mk2",
+    "Poor man's outpainting": "Poor man's outpainting",
+    "SD upscale": "SD upscale",
+    "should be 2 or lower.": "should be 2 or lower.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Override `Sampling method` to Euler?(this method is built for it)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)",
+    "Original prompt": "Original prompt",
+    "Original negative prompt": "Original negative prompt",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Override `Sampling Steps` to the same value as `Decode steps`?",
+    "Decode steps": "Decode steps",
+    "Override `Denoising strength` to 1?": "Override `Denoising strength` to 1?",
+    "Decode CFG scale": "Decode CFG scale",
+    "Randomness": "Randomness",
+    "Sigma adjustment for finding noise for image": "Sigma adjustment for finding noise for image",
+    "Loops": "Loops",
+    "Denoising strength change factor": "Denoising strength change factor",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8",
+    "Pixels to expand": "Pixels to expand",
+    "Outpainting direction": "Outpainting direction",
+    "left": "left",
+    "right": "right",
+    "up": "up",
+    "down": "down",
+    "Fall-off exponent (lower=higher detail)": "Fall-off exponent (lower=higher detail)",
+    "Color variation": "Color variation",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Will upscale the image to twice the dimensions; use width and height sliders to set tile size",
+    "Tile overlap": "Tile overlap",
+    "Upscaler": "Upscaler",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "BSRGAN 4x": "BSRGAN 4x",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR 4x": "SwinIR 4x",
+    "Single Image": "Single Image",
+    "Batch Process": "Batch Process",
+    "Batch from Directory": "Batch from Directory",
+    "Source": "Source",
+    "Show result images": "Show result images",
+    "Scale by": "Scale by",
+    "Scale to": "Scale to",
+    "Resize": "Resize",
+    "Crop to fit": "Crop to fit",
+    "Upscaler 2": "Upscaler 2",
+    "Upscaler 2 visibility": "Upscaler 2 visibility",
+    "GFPGAN visibility": "GFPGAN visibility",
+    "CodeFormer visibility": "CodeFormer visibility",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)",
+    "Open output directory": "出力フォルダを開く",
+    "Send to txt2img": "Send to txt2img",
+    "txt2img history": "txt2img history",
+    "img2img history": "img2img history",
+    "extras history": "extras history",
+    "Renew Page": "Renew Page",
+    "First Page": "First Page",
+    "Prev Page": "Prev Page",
+    "Page Index": "Page Index",
+    "Next Page": "Next Page",
+    "End Page": "End Page",
+    "number of images to delete consecutively next": "number of images to delete consecutively next",
+    "Delete": "Delete",
+    "Generate Info": "Generate Info",
+    "File Name": "File Name",
+    "set_index": "set_index",
+    "A merger of the two checkpoints will be generated in your": "A merger of the two checkpoints will be generated in your",
+    "checkpoint": "checkpoint",
+    "directory.": "directory.",
+    "Primary model (A)": "Primary model (A)",
+    "Secondary model (B)": "Secondary model (B)",
+    "Tertiary model (C)": "Tertiary model (C)",
+    "Custom Name (Optional)": "Custom Name (Optional)",
+    "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - set to 0 to get model A",
+    "Interpolation Method": "Interpolation Method",
+    "Weighted sum": "Weighted sum",
+    "Add difference": "Add difference",
+    "Save as float16": "Save as float16",
+    "See": "See",
+    "wiki": "wiki",
+    "for detailed explanation.": "for detailed explanation.",
+    "Create embedding": "Create embedding",
+    "Create hypernetwork": "Create hypernetwork",
+    "Preprocess images": "Preprocess images",
+    "Name": "Name",
+    "Initialization text": "Initialization text",
+    "Number of vectors per token": "Number of vectors per token",
+    "Modules": "Modules",
+    "Source directory": "Source directory",
+    "Destination directory": "Destination directory",
+    "Create flipped copies": "Create flipped copies",
+    "Split oversized images into two": "Split oversized images into two",
+    "Use BLIP for caption": "Use BLIP for caption",
+    "Use deepbooru for caption": "Use deepbooru for caption",
+    "Preprocess": "Preprocess",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Train an embedding; must specify a directory with a set of 1:1 ratio images",
+    "Embedding": "Embedding",
+    "Learning rate": "Learning rate",
+    "Dataset directory": "Dataset directory",
+    "Log directory": "Log directory",
+    "Prompt template file": "Prompt template file",
+    "Max steps": "Max steps",
+    "Save an image to log directory every N steps, 0 to disable": "Save an image to log directory every N steps, 0 to disable",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Save a copy of embedding to log directory every N steps, 0 to disable",
+    "Save images with embedding in PNG chunks": "Save images with embedding in PNG chunks",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews",
+    "Train Hypernetwork": "Train Hypernetwork",
+    "Train Embedding": "Train Embedding",
+    "Apply settings": "Apply settings",
+    "Saving images/grids": "Saving images/grids",
+    "Always save all generated images": "Always save all generated images",
+    "File format for images": "File format for images",
+    "Images filename pattern": "Images filename pattern",
+    "Always save all generated image grids": "Always save all generated image grids",
+    "File format for grids": "File format for grids",
+    "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid",
+    "Do not save grids consisting of one picture": "Do not save grids consisting of one picture",
+    "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
+    "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
+    "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
+    "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
+    "Quality for saved jpeg images": "Quality for saved jpeg images",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
+    "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
+    "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
+    "Do not add watermark to images": "Do not add watermark to images",
+    "Paths for saving": "Paths for saving",
+    "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
+    "Output directory for txt2img images": "Output directory for txt2img images",
+    "Output directory for img2img images": "Output directory for img2img images",
+    "Output directory for images from extras tab": "Output directory for images from extras tab",
+    "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
+    "Output directory for txt2img grids": "Output directory for txt2img grids",
+    "Output directory for img2img grids": "Output directory for img2img grids",
+    "Directory for saving images using the Save button": "Directory for saving images using the Save button",
+    "Saving to a directory": "Saving to a directory",
+    "Save images to a subdirectory": "Save images to a subdirectory",
+    "Save grids to a subdirectory": "Save grids to a subdirectory",
+    "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
+    "Directory name pattern": "Directory name pattern",
+    "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
+    "Upscaling": "Upscaling",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
+    "Tile size for all SwinIR.": "Tile size for all SwinIR.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
+    "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
+    "Upscaler for img2img": "Upscaler for img2img",
+    "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
+    "Face restoration": "Face restoration",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
+    "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
+    "System": "System",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
+    "Always print all generation info to standard output": "Always print all generation info to standard output",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
+    "Training": "Training",
+    "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training",
+    "Filename word regex": "Filename word regex",
+    "Filename join string": "Filename join string",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
+    "Hypernetwork strength": "Hypernetwork strength",
+    "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
+    "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
+    "Filter NSFW content": "Filter NSFW content",
+    "Stop At last layers of CLIP model": "Stop At last layers of CLIP model",
+    "Interrogate Options": "Interrogate Options",
+    "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM",
+    "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
+    "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
+    "Interrogate: maximum description length": "Interrogate: maximum description length",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
+    "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold",
+    "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically",
+    "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
+    "User interface": "User interface",
+    "Show progressbar": "Show progressbar",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
+    "Show grid in results for web": "Show grid in results for web",
+    "Do not show any images in results for web": "Do not show any images in results for web",
+    "Add model hash to generation information": "Add model hash to generation information",
+    "Add model name to generation information": "Add model name to generation information",
+    "Font for image grids that have text": "Font for image grids that have text",
+    "Enable full page image viewer": "Enable full page image viewer",
+    "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
+    "Show generation progress in window title.": "Show generation progress in window title.",
+    "Quicksettings list": "Quicksettings list",
+    "Localization (requires restart)": "Localization (requires restart)",
+    "ja_JP": "ja_JP",
+    "Sampler parameters": "Sampler parameters",
+    "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
+    "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
+    "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
+    "img2img DDIM discretize": "img2img DDIM discretize",
+    "uniform": "uniform",
+    "quad": "quad",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma noise",
+    "Eta noise seed delta": "Eta noise seed delta",
+    "Request browser notifications": "Request browser notifications",
+    "Download localization template": "Download localization template",
+    "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "プロンプト (Ctrl+Enter か Alt+Enter を押して生成)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "ネガティブ プロンプト (Ctrl+Enter か Alt+Enter を押して生成)",
+    "Add a random artist to the prompt.": "Add a random artist to the prompt.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
+    "Save style": "Save style",
+    "Apply selected styles to current prompt": "Apply selected styles to current prompt",
+    "Stop processing current image and continue processing.": "Stop processing current image and continue processing.",
+    "Stop processing images and return any results accumulated so far.": "Stop processing images and return any results accumulated so far.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
+    "Do not do anything special": "Do not do anything special",
+    "Which algorithm to use to produce the image": "Which algorithm to use to produce the image",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - best at inpainting",
+    "Produce an image that can be tiled.": "Produce an image that can be tiled.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
+    "How many batches of images to create": "How many batches of images to create",
+    "How many image to create in a single batch": "How many image to create in a single batch",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
+    "Set seed to -1, which will cause a new random number to be used every time": "Set seed to -1, which will cause a new random number to be used every time",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Reuse seed from last generation, mostly useful if it was randomed",
+    "Seed of a different picture to be mixed into the generation.": "Seed of a different picture to be mixed into the generation.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution",
+    "Separate values for X axis using commas.": "Separate values for X axis using commas.",
+    "Separate values for Y axis using commas.": "Separate values for Y axis using commas.",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Write image to a directory (default - log/images) and generation parameters into csv file.",
+    "Open images output directory": "Open images output directory",
+    "How much to blur the mask before processing, in pixels.": "How much to blur the mask before processing, in pixels.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "What to put inside the masked area before processing it with Stable Diffusion.",
+    "fill it with colors of the image": "fill it with colors of the image",
+    "keep whatever was there originally": "keep whatever was there originally",
+    "fill it with latent space noise": "fill it with latent space noise",
+    "fill it with latent space zeroes": "fill it with latent space zeroes",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "How many times to repeat processing an image and using it as input for the next iteration",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.",
+    "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
+    "Leave blank to save images to the default path.": "Leave blank to save images to the default path.",
+    "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Result = A + (B - C) * M",
+    "Path to directory with input images": "Path to directory with input images",
+    "Path to directory where to write outputs": "Path to directory where to write outputs",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
+    "Restore low quality faces using GFPGAN neural network": "Restore low quality faces using GFPGAN neural network",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing."
+}
\ No newline at end of file
-- 
cgit v1.2.1


From eb2dae196e5d901162d477f24c1ebb2597b13dfb Mon Sep 17 00:00:00 2001
From: Kris57 <49682577+yuuki76@users.noreply.github.com>
Date: Tue, 18 Oct 2022 14:40:07 +0900
Subject: add ja translation

---
 localizations/ja_JP.json | 48 ++++++++++++++++++++++++------------------------
 1 file changed, 24 insertions(+), 24 deletions(-)

diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
index 5da28cb6..27bd342a 100644
--- a/localizations/ja_JP.json
+++ b/localizations/ja_JP.json
@@ -13,9 +13,9 @@
     "txt2img": "txt2img",
     "img2img": "img2img",
     "Extras": "その他",
-    "PNG Info": "PNG Info",
+    "PNG Info": "PNG内の情報を表示",
     "History": "履歴",
-    "Checkpoint Merger": "Checkpoint Merger",
+    "Checkpoint Merger": "Checkpointの統合",
     "Train": "学習",
     "Settings": "設定",
     "Prompt": "プロンプト",
@@ -35,8 +35,8 @@
     "Image": "Image",
     "Check progress": "Check progress",
     "Check progress (first)": "Check progress (first)",
-    "Sampling Steps": "Sampling Steps",
-    "Sampling method": "Sampling method",
+    "Sampling Steps": "サンプリング回数",
+    "Sampling method": "サンプリングアルゴリズム",
     "Euler a": "Euler a",
     "Euler": "Euler",
     "LMS": "LMS",
@@ -57,18 +57,18 @@
     "Highres. fix": "*高解像度 fix",
     "Firstpass width": "Firstpass width",
     "Firstpass height": "Firstpass height",
-    "Denoising strength": "Denoising 強度",
+    "Denoising strength": "ノイズ除去 強度",
     "Batch count": "連続生成回数",
     "Batch size": "同時生成枚数",
     "CFG Scale": "CFG Scale",
-    "Seed": "Seed",
-    "Extra": "Extra",
-    "Variation seed": "Variation seed",
+    "Seed": "シード値",
+    "Extra": "その他",
+    "Variation seed": "Variation シード値",
     "Variation strength": "Variation 強度",
     "Resize seed from width": "Resize seed from width",
     "Resize seed from height": "Resize seed from height",
-    "Script": "Script",
-    "None": "None",
+    "Script": "スクリプト",
+    "None": "なし",
     "Prompt matrix": "Prompt matrix",
     "Prompts from file or textbox": "Prompts from file or textbox",
     "X/Y plot": "X/Y plot",
@@ -84,7 +84,7 @@
     "Prompt S/R": "Prompt S/R",
     "Prompt order": "Prompt order",
     "Sampler": "Sampler",
-    "Checkpoint name": "Checkpoint name",
+    "Checkpoint name": "Checkpoint名",
     "Hypernetwork": "Hypernetwork",
     "Hypernet str.": "Hypernet 強度",
     "Sigma Churn": "Sigma Churn",
@@ -103,13 +103,13 @@
     "ここに画像をドロップ": "ここに画像をドロップ",
     "Save": "保存",
     "Send to img2img": "img2imgに送る",
-    "Send to inpaint": "inpaintに送る",
+    "Send to inpaint": "描き直しに送る",
     "Send to extras": "その他タブに送る",
     "Make Zip when Save?": "保存するときZipも同時に作る",
     "Textbox": "Textbox",
     "Interrogate\nCLIP": "Interrogate\nCLIP",
     "Interrogate\nDeepBooru": "Interrogate\nDeepBooru",
-    "Inpaint": "Inpaint",
+    "Inpaint": "描き直し(Inpaint)",
     "Batch img2img": "Batch img2img",
     "Image for img2img": "Image for img2img",
     "Image for inpainting with mask": "Image for inpainting with mask",
@@ -132,16 +132,16 @@
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Use an empty output directory to save pictures normally instead of writing to the output directory.",
     "Input directory": "Input directory",
     "Output directory": "Output directory",
-    "Resize mode": "Resize mode",
-    "Just resize": "Just resize",
-    "Crop and resize": "Crop and resize",
-    "Resize and fill": "Resize and fill",
+    "Resize mode": "リサイズモード",
+    "Just resize": "リサイズのみ",
+    "Crop and resize": "切り取ってからリサイズ",
+    "Resize and fill": "リサイズして埋める",
     "img2img alternative test": "img2img alternative test",
     "Loopback": "Loopback",
     "Outpainting mk2": "Outpainting mk2",
     "Poor man's outpainting": "Poor man's outpainting",
-    "SD upscale": "SD upscale",
-    "should be 2 or lower.": "should be 2 or lower.",
+    "SD upscale": "SD アップスケール",
+    "should be 2 or lower.": "2以下にすること",
     "Override `Sampling method` to Euler?(this method is built for it)": "Override `Sampling method` to Euler?(this method is built for it)",
     "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)",
     "Original prompt": "Original prompt",
@@ -370,19 +370,19 @@
     "Produce an image that can be tiled.": "Produce an image that can be tiled.",
     "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
     "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
-    "How many batches of images to create": "How many batches of images to create",
-    "How many image to create in a single batch": "How many image to create in a single batch",
+    "How many batches of images to create": "バッチ処理を何回行うか",
+    "How many image to create in a single batch": "1回のバッチ処理で何枚の画像を生成するか",
     "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
     "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
-    "Set seed to -1, which will cause a new random number to be used every time": "Set seed to -1, which will cause a new random number to be used every time",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Reuse seed from last generation, mostly useful if it was randomed",
+    "Set seed to -1, which will cause a new random number to be used every time": "シード値を -1 に設定するとランダムに生成します。",
+    "Reuse seed from last generation, mostly useful if it was randomed": "前回生成時のシード値を読み出す。(ランダム生成時に便利)",
     "Seed of a different picture to be mixed into the generation.": "Seed of a different picture to be mixed into the generation.",
     "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).",
     "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution",
     "Separate values for X axis using commas.": "Separate values for X axis using commas.",
     "Separate values for Y axis using commas.": "Separate values for Y axis using commas.",
     "Write image to a directory (default - log/images) and generation parameters into csv file.": "Write image to a directory (default - log/images) and generation parameters into csv file.",
-    "Open images output directory": "Open images output directory",
+    "Open images output directory": "画像の出力フォルダを開く",
     "How much to blur the mask before processing, in pixels.": "How much to blur the mask before processing, in pixels.",
     "What to put inside the masked area before processing it with Stable Diffusion.": "What to put inside the masked area before processing it with Stable Diffusion.",
     "fill it with colors of the image": "fill it with colors of the image",
-- 
cgit v1.2.1


From 070fda592bf80fb348ffe8e17b7c71cc288db729 Mon Sep 17 00:00:00 2001
From: Kris57 <49682577+yuuki76@users.noreply.github.com>
Date: Tue, 18 Oct 2022 15:20:00 +0900
Subject: add ja translation

---
 localizations/ja_JP.json | 128 +++++++++++++++++++++++------------------------
 1 file changed, 64 insertions(+), 64 deletions(-)

diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
index 27bd342a..11f747b4 100644
--- a/localizations/ja_JP.json
+++ b/localizations/ja_JP.json
@@ -26,13 +26,13 @@
     "Generate": "生成！",
     "Style 1": "スタイル 1",
     "Style 2": "スタイル 2",
-    "Label": "Label",
+    "Label": "ラベル",
     "File": "ファイル",
     "ここにファイルをドロップ": "ここにファイルをドロップ",
     "-": "-",
     "または": "または",
     "クリックしてアップロード": "クリックしてアップロード",
-    "Image": "Image",
+    "Image": "画像",
     "Check progress": "Check progress",
     "Check progress (first)": "Check progress (first)",
     "Sampling Steps": "サンプリング回数",
@@ -54,12 +54,12 @@
     "Height": "高さ",
     "Restore faces": "顔修復",
     "Tiling": "テクスチャ生成モード",
-    "Highres. fix": "*高解像度 fix",
+    "Highres. fix": "(※)高解像度 fix",
     "Firstpass width": "Firstpass width",
     "Firstpass height": "Firstpass height",
     "Denoising strength": "ノイズ除去 強度",
-    "Batch count": "連続生成回数",
-    "Batch size": "同時生成枚数",
+    "Batch count": "バッチ生成回数",
+    "Batch size": "バッチあたり生成枚数",
     "CFG Scale": "CFG Scale",
     "Seed": "シード値",
     "Extra": "その他",
@@ -77,16 +77,16 @@
     "File with inputs": "File with inputs",
     "Prompts": "Prompts",
     "X type": "X type",
-    "Nothing": "Nothing",
+    "Nothing": "なし",
     "Var. seed": "Var. seed",
     "Var. strength": "Var. 強度",
-    "Steps": "Steps",
+    "Steps": "ステップ数",
     "Prompt S/R": "Prompt S/R",
     "Prompt order": "Prompt order",
-    "Sampler": "Sampler",
+    "Sampler": "サンプラー",
     "Checkpoint name": "Checkpoint名",
     "Hypernetwork": "Hypernetwork",
-    "Hypernet str.": "Hypernet 強度",
+    "Hypernet str.": "Hypernet強度",
     "Sigma Churn": "Sigma Churn",
     "Sigma min": "Sigma min",
     "Sigma max": "Sigma max",
@@ -192,78 +192,78 @@
     "txt2img history": "txt2img history",
     "img2img history": "img2img history",
     "extras history": "extras history",
-    "Renew Page": "Renew Page",
-    "First Page": "First Page",
-    "Prev Page": "Prev Page",
-    "Page Index": "Page Index",
-    "Next Page": "Next Page",
-    "End Page": "End Page",
+    "Renew Page": "更新",
+    "First Page": "最初のぺージへ",
+    "Prev Page": "前ページへ",
+    "Page Index": "ページ番号",
+    "Next Page": "次ページへ",
+    "End Page": "最後のページへ",
     "number of images to delete consecutively next": "number of images to delete consecutively next",
-    "Delete": "Delete",
+    "Delete": "削除",
     "Generate Info": "Generate Info",
     "File Name": "File Name",
     "set_index": "set_index",
-    "A merger of the two checkpoints will be generated in your": "A merger of the two checkpoints will be generated in your",
+    "A merger of the two checkpoints will be generated in your": "統合されたチェックポイントはあなたの",
     "checkpoint": "checkpoint",
-    "directory.": "directory.",
-    "Primary model (A)": "Primary model (A)",
-    "Secondary model (B)": "Secondary model (B)",
-    "Tertiary model (C)": "Tertiary model (C)",
-    "Custom Name (Optional)": "Custom Name (Optional)",
-    "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - set to 0 to get model A",
+    "directory.": "フォルダに保存されます.",
+    "Primary model (A)": "1つめのmodel (A)",
+    "Secondary model (B)": "2つめのmodel (B)",
+    "Tertiary model (C)": "3つめのmodel (C)",
+    "Custom Name (Optional)": "Custom Name (任意)",
+    "Multiplier (M) - set to 0 to get model A": "Multiplier (M) 0にすると完全にmodel Aとなります",
     "Interpolation Method": "Interpolation Method",
     "Weighted sum": "Weighted sum",
     "Add difference": "Add difference",
-    "Save as float16": "Save as float16",
-    "See": "See",
+    "Save as float16": "float16で保存",
+    "See": "詳細な説明については",
     "wiki": "wiki",
-    "for detailed explanation.": "for detailed explanation.",
-    "Create embedding": "Create embedding",
-    "Create hypernetwork": "Create hypernetwork",
-    "Preprocess images": "Preprocess images",
-    "Name": "Name",
+    "for detailed explanation.": "を見てください。",
+    "Create embedding": "Embeddingを作る",
+    "Create hypernetwork": "Hypernetworkを作る",
+    "Preprocess images": "画像の前処理",
+    "Name": "ファイル名",
     "Initialization text": "Initialization text",
     "Number of vectors per token": "Number of vectors per token",
     "Modules": "Modules",
-    "Source directory": "Source directory",
-    "Destination directory": "Destination directory",
-    "Create flipped copies": "Create flipped copies",
-    "Split oversized images into two": "Split oversized images into two",
-    "Use BLIP for caption": "Use BLIP for caption",
-    "Use deepbooru for caption": "Use deepbooru for caption",
-    "Preprocess": "Preprocess",
-    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Train an embedding; must specify a directory with a set of 1:1 ratio images",
+    "Source directory": "入力フォルダ",
+    "Destination directory": "出力フォルダ",
+    "Create flipped copies": "反転画像を生成する",
+    "Split oversized images into two": "大きすぎる画像を2分割する",
+    "Use BLIP for caption": "BLIPで説明をつける",
+    "Use deepbooru for caption": "deepbooruで説明をつける",
+    "Preprocess": "前処理開始",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "embeddingの学習をします;データセット内の画像は正方形でなければなりません。",
     "Embedding": "Embedding",
-    "Learning rate": "Learning rate",
-    "Dataset directory": "Dataset directory",
-    "Log directory": "Log directory",
+    "Learning rate": "学習率",
+    "Dataset directory": "データセットフォルダ",
+    "Log directory": "ログフォルダ",
     "Prompt template file": "Prompt template file",
-    "Max steps": "Max steps",
-    "Save an image to log directory every N steps, 0 to disable": "Save an image to log directory every N steps, 0 to disable",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Save a copy of embedding to log directory every N steps, 0 to disable",
-    "Save images with embedding in PNG chunks": "Save images with embedding in PNG chunks",
+    "Max steps": "最大ステップ数",
+    "Save an image to log directory every N steps, 0 to disable": "指定したステップ数ごとに画像を生成し、ログに保存する。0で無効化。",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "指定したステップ数ごとにEmbeddingのコピーをログに保存する。0で無効化。",
+    "Save images with embedding in PNG chunks": "保存する画像にembeddingを埋め込む",
     "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews",
-    "Train Hypernetwork": "Train Hypernetwork",
-    "Train Embedding": "Train Embedding",
+    "Train Hypernetwork": "Hypernetworkの学習を開始",
+    "Train Embedding": "Embeddingの学習を開始",
     "Apply settings": "Apply settings",
-    "Saving images/grids": "Saving images/grids",
-    "Always save all generated images": "Always save all generated images",
-    "File format for images": "File format for images",
-    "Images filename pattern": "Images filename pattern",
-    "Always save all generated image grids": "Always save all generated image grids",
-    "File format for grids": "File format for grids",
-    "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid",
-    "Do not save grids consisting of one picture": "Do not save grids consisting of one picture",
-    "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
-    "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
-    "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
-    "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
-    "Quality for saved jpeg images": "Quality for saved jpeg images",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
+    "Saving images/grids": "画像/グリッドの保存",
+    "Always save all generated images": "生成された画像をすべて保存する",
+    "File format for images": "画像ファイルの保存形式",
+    "Images filename pattern": "ファイル名のパターン",
+    "Always save all generated image grids": "グリッド画像を常に保存する",
+    "File format for grids": "グリッド画像の保存形式",
+    "Add extended info (seed, prompt) to filename when saving grid": "保存するグリッド画像のファイル名に追加情報(シード値、プロンプト)を加える",
+    "Do not save grids consisting of one picture": "1画像からなるグリッド画像は保存しない",
+    "Prevent empty spots in grid (when set to autodetect)": "(自動設定のとき)グリッドに空隙が生じるのを防ぐ",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "グリッドの列数; -1で自動設定、0でバッチ生成回数と同じにする",
+    "Save text information about generation parameters as chunks to png files": "生成に関するパラメーターをpng画像に含める",
+    "Create a text file next to every image with generation parameters.": "保存する画像とともに生成パラメータをテキストファイルで保存する",
+    "Save a copy of image before doing face restoration.": "顔修復を行う前にコピーを保存しておく。",
+    "Quality for saved jpeg images": "JPG保存時の画質",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "PNG画像が4MBを超えるか、どちらか1辺の長さが4000を超えたなら、ダウンスケールしてコピーを別にJPGで保存する",
     "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
     "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
-    "Do not add watermark to images": "Do not add watermark to images",
+    "Do not add watermark to images": "電子透かしを画像に追加しない",
     "Paths for saving": "Paths for saving",
     "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
     "Output directory for txt2img images": "Output directory for txt2img images",
@@ -403,7 +403,7 @@
     "Path to directory with input images": "Path to directory with input images",
     "Path to directory where to write outputs": "Path to directory where to write outputs",
     "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "このオプションを有効にすると、作成された画像にウォーターマークが追加されなくなります。警告:ウォーターマークを追加しない場合、非倫理的な行動とみなされる場合があります。",
     "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
     "Restore low quality faces using GFPGAN neural network": "Restore low quality faces using GFPGAN neural network",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
-- 
cgit v1.2.1


From 7043f4eff3913ac1ed0ae1621f622c90437c6843 Mon Sep 17 00:00:00 2001
From: Kris57 <49682577+yuuki76@users.noreply.github.com>
Date: Tue, 18 Oct 2022 15:57:39 +0900
Subject: improve ja translation

---
 localizations/ja_JP.json | 92 ++++++++++++++++++++++++------------------------
 1 file changed, 46 insertions(+), 46 deletions(-)

diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
index 11f747b4..87809d72 100644
--- a/localizations/ja_JP.json
+++ b/localizations/ja_JP.json
@@ -54,7 +54,7 @@
     "Height": "高さ",
     "Restore faces": "顔修復",
     "Tiling": "テクスチャ生成モード",
-    "Highres. fix": "(※)高解像度 fix",
+    "Highres. fix": "高解像度 fix(マウスオーバーで詳細)",
     "Firstpass width": "Firstpass width",
     "Firstpass height": "Firstpass height",
     "Denoising strength": "ノイズ除去 強度",
@@ -75,7 +75,7 @@
     "Put variable parts at start of prompt": "Put variable parts at start of prompt",
     "Show Textbox": "Show Textbox",
     "File with inputs": "File with inputs",
-    "Prompts": "Prompts",
+    "Prompts": "プロンプト",
     "X type": "X type",
     "Nothing": "なし",
     "Var. seed": "Var. seed",
@@ -123,26 +123,26 @@
     "Inpaint not masked": "Inpaint not masked",
     "Masked content": "Masked content",
     "fill": "fill",
-    "original": "original",
+    "original": "オリジナル",
     "latent noise": "latent noise",
     "latent nothing": "latent nothing",
     "Inpaint at full resolution": "Inpaint at full resolution",
     "Inpaint at full resolution padding, pixels": "Inpaint at full resolution padding, pixels",
     "Process images in a directory on the same machine where the server is running.": "Process images in a directory on the same machine where the server is running.",
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Use an empty output directory to save pictures normally instead of writing to the output directory.",
-    "Input directory": "Input directory",
-    "Output directory": "Output directory",
+    "Input directory": "入力フォルダ",
+    "Output directory": "出力フォルダ",
     "Resize mode": "リサイズモード",
     "Just resize": "リサイズのみ",
     "Crop and resize": "切り取ってからリサイズ",
     "Resize and fill": "リサイズして埋める",
     "img2img alternative test": "img2img alternative test",
-    "Loopback": "Loopback",
+    "Loopback": "ループバック",
     "Outpainting mk2": "Outpainting mk2",
     "Poor man's outpainting": "Poor man's outpainting",
     "SD upscale": "SD アップスケール",
     "should be 2 or lower.": "2以下にすること",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Override `Sampling method` to Euler?(this method is built for it)",
+    "Override `Sampling method` to Euler?(this method is built for it)": "サンプリングアルゴリズムをEulerに上書きする(そうすることを前提に設計されています)",
     "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)",
     "Original prompt": "Original prompt",
     "Original negative prompt": "Original negative prompt",
@@ -157,15 +157,15 @@
     "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8",
     "Pixels to expand": "Pixels to expand",
     "Outpainting direction": "Outpainting direction",
-    "left": "left",
-    "right": "right",
-    "up": "up",
-    "down": "down",
+    "left": "左",
+    "right": "右",
+    "up": "上",
+    "down": "下",
     "Fall-off exponent (lower=higher detail)": "Fall-off exponent (lower=higher detail)",
     "Color variation": "Color variation",
     "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Will upscale the image to twice the dimensions; use width and height sliders to set tile size",
     "Tile overlap": "Tile overlap",
-    "Upscaler": "Upscaler",
+    "Upscaler": "アップスケーラー",
     "Lanczos": "Lanczos",
     "LDSR": "LDSR",
     "BSRGAN 4x": "BSRGAN 4x",
@@ -173,25 +173,25 @@
     "ScuNET GAN": "ScuNET GAN",
     "ScuNET PSNR": "ScuNET PSNR",
     "SwinIR 4x": "SwinIR 4x",
-    "Single Image": "Single Image",
-    "Batch Process": "Batch Process",
-    "Batch from Directory": "Batch from Directory",
-    "Source": "Source",
-    "Show result images": "Show result images",
-    "Scale by": "Scale by",
-    "Scale to": "Scale to",
-    "Resize": "Resize",
-    "Crop to fit": "Crop to fit",
-    "Upscaler 2": "Upscaler 2",
+    "Single Image": "単一画像",
+    "Batch Process": "バッチ処理",
+    "Batch from Directory": "フォルダからバッチ処理",
+    "Source": "入力",
+    "Show result images": "出力画像を表示",
+    "Scale by": "倍率指定",
+    "Scale to": "解像度指定",
+    "Resize": "倍率",
+    "Crop to fit": "合うように切り抜き",
+    "Upscaler 2": "アップスケーラー 2",
     "Upscaler 2 visibility": "Upscaler 2 visibility",
     "GFPGAN visibility": "GFPGAN visibility",
     "CodeFormer visibility": "CodeFormer visibility",
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)",
     "Open output directory": "出力フォルダを開く",
-    "Send to txt2img": "Send to txt2img",
-    "txt2img history": "txt2img history",
-    "img2img history": "img2img history",
-    "extras history": "extras history",
+    "Send to txt2img": "txt2imgに送る",
+    "txt2img history": "txt2imgの履歴",
+    "img2img history": "img2imgの履歴",
+    "extras history": "その他タブの履歴",
     "Renew Page": "更新",
     "First Page": "最初のぺージへ",
     "Prev Page": "前ページへ",
@@ -200,8 +200,8 @@
     "End Page": "最後のページへ",
     "number of images to delete consecutively next": "number of images to delete consecutively next",
     "Delete": "削除",
-    "Generate Info": "Generate Info",
-    "File Name": "File Name",
+    "Generate Info": "生成情報",
+    "File Name": "ファイル名",
     "set_index": "set_index",
     "A merger of the two checkpoints will be generated in your": "統合されたチェックポイントはあなたの",
     "checkpoint": "checkpoint",
@@ -265,27 +265,27 @@
     "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
     "Do not add watermark to images": "電子透かしを画像に追加しない",
     "Paths for saving": "Paths for saving",
-    "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
-    "Output directory for txt2img images": "Output directory for txt2img images",
-    "Output directory for img2img images": "Output directory for img2img images",
-    "Output directory for images from extras tab": "Output directory for images from extras tab",
-    "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
-    "Output directory for txt2img grids": "Output directory for txt2img grids",
-    "Output directory for img2img grids": "Output directory for img2img grids",
-    "Directory for saving images using the Save button": "Directory for saving images using the Save button",
-    "Saving to a directory": "Saving to a directory",
-    "Save images to a subdirectory": "Save images to a subdirectory",
-    "Save grids to a subdirectory": "Save grids to a subdirectory",
-    "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
-    "Directory name pattern": "Directory name pattern",
+    "Output directory for images; if empty, defaults to three directories below": "画像の保存先フォルダ(下項目のデフォルト値になります)",
+    "Output directory for txt2img images": "txt2imgで作った画像の保存先フォルダ",
+    "Output directory for img2img images": "img2imgで作った画像の保存先フォルダ",
+    "Output directory for images from extras tab": "その他タブで作った画像の保存先フォルダ",
+    "Output directory for grids; if empty, defaults to two directories below": "画像の保存先フォルダ(下項目のデフォルト値になります)",
+    "Output directory for txt2img grids": "txt2imgで作ったグリッドの保存先フォルダ",
+    "Output directory for img2img grids": "img2imgで作ったグリッドの保存先フォルダ",
+    "Directory for saving images using the Save button": "保存ボタンを押したときの画像の保存先フォルダ",
+    "Saving to a directory": "フォルダについて",
+    "Save images to a subdirectory": "画像をサブフォルダに保存する",
+    "Save grids to a subdirectory": "グリッドをサブフォルダに保存する",
+    "When using \"Save\" button, save images to a subdirectory": "保存ボタンを押した時、画像をサブフォルダに保存する",
+    "Directory name pattern": "フォルダ名のパターン",
     "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
-    "Upscaling": "Upscaling",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
+    "Upscaling": "アップスケール",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGANのタイルサイズ。0とするとタイルしない。",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGANのタイルの重複部分のピクセル数。少なくするとつなぎ目が見えやすくなる。",
     "Tile size for all SwinIR.": "Tile size for all SwinIR.",
     "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
     "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
-    "Upscaler for img2img": "Upscaler for img2img",
+    "Upscaler for img2img": "img2imgで使うアップスケーラー",
     "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
     "Face restoration": "Face restoration",
     "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
@@ -368,7 +368,7 @@
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help",
     "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - best at inpainting",
     "Produce an image that can be tiled.": "Produce an image that can be tiled.",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "2ステップで、まず部分的に小さい解像度で画像を作成し、その後アップスケールすることで、構図を変えずにディテールが改善されます。",
     "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
     "How many batches of images to create": "バッチ処理を何回行うか",
     "How many image to create in a single batch": "1回のバッチ処理で何枚の画像を生成するか",
-- 
cgit v1.2.1


From 0262bf64ddbf85e05ddd120929138d3c5dac3bac Mon Sep 17 00:00:00 2001
From: Kris57 <49682577+yuuki76@users.noreply.github.com>
Date: Tue, 18 Oct 2022 16:38:31 +0900
Subject: improve ja translation

---
 localizations/ja_JP.json | 48 ++++++++++++++++++++++++------------------------
 1 file changed, 24 insertions(+), 24 deletions(-)

diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
index 87809d72..fc958656 100644
--- a/localizations/ja_JP.json
+++ b/localizations/ja_JP.json
@@ -282,19 +282,19 @@
     "Upscaling": "アップスケール",
     "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGANのタイルサイズ。0とするとタイルしない。",
     "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGANのタイルの重複部分のピクセル数。少なくするとつなぎ目が見えやすくなる。",
-    "Tile size for all SwinIR.": "Tile size for all SwinIR.",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
+    "Tile size for all SwinIR.": "SwinIRのタイルサイズ",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIRのタイルの重複部分のピクセル数。少なくするとつなぎ目が見えやすくなる。",
     "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
     "Upscaler for img2img": "img2imgで使うアップスケーラー",
-    "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
-    "Face restoration": "Face restoration",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
+    "Upscale latent space image when doing hires. fix": "高解像度 fix時に潜在空間(latent space)の画像をアップスケールする",
+    "Face restoration": "顔修復",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormerの重みパラメーター;0が最大で1が最小",
     "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
-    "System": "System",
+    "System": "システム設定",
     "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
-    "Always print all generation info to standard output": "Always print all generation info to standard output",
+    "Always print all generation info to standard output": "常にすべての生成に関する情報を標準出力(stdout)に出力する",
     "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
-    "Training": "Training",
+    "Training": "学習",
     "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training",
     "Filename word regex": "Filename word regex",
     "Filename join string": "Filename join string",
@@ -307,15 +307,15 @@
     "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "強調: (text)とするとモデルはtextをより強く扱い、[text]とするとモデルはtextをより弱く扱います。",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "古い強調の実装を使う。古い生成物を再現するのに使えます。",
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
     "Filter NSFW content": "Filter NSFW content",
-    "Stop At last layers of CLIP model": "Stop At last layers of CLIP model",
-    "Interrogate Options": "Interrogate Options",
-    "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM",
-    "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
+    "Stop At last layers of CLIP model": "最後から何層目でCLIPを止めるか(stop…layers of CLIP model)",
+    "Interrogate Options": "Interrogate 設定",
+    "Interrogate: keep models in VRAM": "Interrogate: モデルをVRAMに保持する",
+    "Interrogate: use artists from artists.csv": "Interrogate: artists.csvにある芸術家などの名称を利用する",
     "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
     "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
     "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
@@ -337,9 +337,9 @@
     "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
     "Show generation progress in window title.": "Show generation progress in window title.",
     "Quicksettings list": "Quicksettings list",
-    "Localization (requires restart)": "Localization (requires restart)",
+    "Localization (requires restart)": "言語 (プログラムの再起動が必要)",
     "ja_JP": "ja_JP",
-    "Sampler parameters": "Sampler parameters",
+    "Sampler parameters": "サンプラー parameters",
     "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
     "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
     "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
@@ -350,16 +350,16 @@
     "sigma tmin": "sigma tmin",
     "sigma noise": "sigma noise",
     "Eta noise seed delta": "Eta noise seed delta",
-    "Request browser notifications": "Request browser notifications",
-    "Download localization template": "Download localization template",
-    "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
+    "Request browser notifications": "ブラウザ通知の許可を要求する",
+    "Download localization template": "ローカライゼーション用のテンプレートをダウンロードする",
+    "Reload custom script bodies (No ui updates, No restart)": "カスタムスクリプトを再読み込み (UIは変更されず、再起動もしません。)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradioを再起動してコンポーネントをリフレッシュする (Custom Scripts, ui.py, js, cssのみ影響を受ける)",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "プロンプト (Ctrl+Enter か Alt+Enter を押して生成)",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "ネガティブ プロンプト (Ctrl+Enter か Alt+Enter を押して生成)",
     "Add a random artist to the prompt.": "Add a random artist to the prompt.",
     "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
-    "Save style": "Save style",
-    "Apply selected styles to current prompt": "Apply selected styles to current prompt",
+    "Save style": "スタイルを保存する",
+    "Apply selected styles to current prompt": "現在のプロンプトに選択したスタイルを適用する",
     "Stop processing current image and continue processing.": "Stop processing current image and continue processing.",
     "Stop processing images and return any results accumulated so far.": "Stop processing images and return any results accumulated so far.",
     "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
@@ -379,8 +379,8 @@
     "Seed of a different picture to be mixed into the generation.": "Seed of a different picture to be mixed into the generation.",
     "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).",
     "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution",
-    "Separate values for X axis using commas.": "Separate values for X axis using commas.",
-    "Separate values for Y axis using commas.": "Separate values for Y axis using commas.",
+    "Separate values for X axis using commas.": "X軸に用いる値をカンマ(,)で区切って入力してください。",
+    "Separate values for Y axis using commas.": "Y軸に用いる値をカンマ(,)で区切って入力してください。",
     "Write image to a directory (default - log/images) and generation parameters into csv file.": "Write image to a directory (default - log/images) and generation parameters into csv file.",
     "Open images output directory": "画像の出力フォルダを開く",
     "How much to blur the mask before processing, in pixels.": "How much to blur the mask before processing, in pixels.",
-- 
cgit v1.2.1


From f613c6b8c532dbcfb3570cdbb3ce56a0d2821d0b Mon Sep 17 00:00:00 2001
From: Kris57 <49682577+yuuki76@users.noreply.github.com>
Date: Tue, 18 Oct 2022 23:01:41 +0900
Subject: improve ja translation

---
 localizations/ja_JP.json | 66 ++++++++++++++++++++++++------------------------
 1 file changed, 33 insertions(+), 33 deletions(-)

diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
index fc958656..a9f6cb20 100644
--- a/localizations/ja_JP.json
+++ b/localizations/ja_JP.json
@@ -163,7 +163,7 @@
     "down": "下",
     "Fall-off exponent (lower=higher detail)": "Fall-off exponent (lower=higher detail)",
     "Color variation": "Color variation",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Will upscale the image to twice the dimensions; use width and height sliders to set tile size",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "画像を2倍の大きさにアップスケールします。幅と高さのスライダーでタイルの大きさを設定します。",
     "Tile overlap": "Tile overlap",
     "Upscaler": "アップスケーラー",
     "Lanczos": "Lanczos",
@@ -210,10 +210,10 @@
     "Secondary model (B)": "2つめのmodel (B)",
     "Tertiary model (C)": "3つめのmodel (C)",
     "Custom Name (Optional)": "Custom Name (任意)",
-    "Multiplier (M) - set to 0 to get model A": "Multiplier (M) 0にすると完全にmodel Aとなります",
-    "Interpolation Method": "Interpolation Method",
-    "Weighted sum": "Weighted sum",
-    "Add difference": "Add difference",
+    "Multiplier (M) - set to 0 to get model A": "Multiplier (M) 0にすると完全にmodel Aとなります (ツールチップ参照)",
+    "Interpolation Method": "混合(Interpolation)方式",
+    "Weighted sum": "加重平均",
+    "Add difference": "差を加える",
     "Save as float16": "float16で保存",
     "See": "詳細な説明については",
     "wiki": "wiki",
@@ -289,7 +289,7 @@
     "Upscale latent space image when doing hires. fix": "高解像度 fix時に潜在空間(latent space)の画像をアップスケールする",
     "Face restoration": "顔修復",
     "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormerの重みパラメーター;0が最大で1が最小",
-    "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
+    "Move face restoration model from VRAM into RAM after processing": "処理終了後、顔修復モデルをVRAMからRAMへと移動する",
     "System": "システム設定",
     "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
     "Always print all generation info to standard output": "常にすべての生成に関する情報を標準出力(stdout)に出力する",
@@ -301,9 +301,9 @@
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
     "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
     "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
+    "Checkpoints to cache in RAM": "RAMにキャッシュするCheckpoint数",
     "Hypernetwork strength": "Hypernetwork strength",
-    "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
+    "Apply color correction to img2img results to match original colors.": "元画像に合わせてimg2imgの結果を色補正する",
     "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
@@ -325,24 +325,24 @@
     "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically",
     "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
     "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
-    "User interface": "User interface",
-    "Show progressbar": "Show progressbar",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
-    "Show grid in results for web": "Show grid in results for web",
-    "Do not show any images in results for web": "Do not show any images in results for web",
-    "Add model hash to generation information": "Add model hash to generation information",
-    "Add model name to generation information": "Add model name to generation information",
-    "Font for image grids that have text": "Font for image grids that have text",
-    "Enable full page image viewer": "Enable full page image viewer",
-    "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
-    "Show generation progress in window title.": "Show generation progress in window title.",
+    "User interface": "UI設定",
+    "Show progressbar": "プログレスバーを表示",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "指定したステップ数ごとに画像の生成過程を表示する。0で無効化。",
+    "Show grid in results for web": "WebUI上でグリッド表示",
+    "Do not show any images in results for web": "WebUI上で一切画像を表示しない",
+    "Add model hash to generation information": "モデルのハッシュ値を生成情報に追加",
+    "Add model name to generation information": "モデルの名称を生成情報に追加",
+    "Font for image grids that have text": "画像グリッド内のテキストフォント",
+    "Enable full page image viewer": "フルページの画像ビューワーを有効化",
+    "Show images zoomed in by default in full page image viewer": "フルページ画像ビューアでデフォルトで画像を拡大して表示する",
+    "Show generation progress in window title.": "ウィンドウのタイトルで生成の進捗を表示",
     "Quicksettings list": "Quicksettings list",
     "Localization (requires restart)": "言語 (プログラムの再起動が必要)",
     "ja_JP": "ja_JP",
     "Sampler parameters": "サンプラー parameters",
-    "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
-    "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
-    "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
+    "Hide samplers in user interface (requires restart)": "使わないサンプリングアルゴリズムを隠す (再起動が必要)",
+    "eta (noise multiplier) for DDIM": "DDIMで用いるeta (noise multiplier)",
+    "eta (noise multiplier) for ancestral samplers": "ancestral サンプラーで用いるeta (noise multiplier)",
     "img2img DDIM discretize": "img2img DDIM discretize",
     "uniform": "uniform",
     "quad": "quad",
@@ -356,17 +356,17 @@
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradioを再起動してコンポーネントをリフレッシュする (Custom Scripts, ui.py, js, cssのみ影響を受ける)",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "プロンプト (Ctrl+Enter か Alt+Enter を押して生成)",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "ネガティブ プロンプト (Ctrl+Enter か Alt+Enter を押して生成)",
-    "Add a random artist to the prompt.": "Add a random artist to the prompt.",
+    "Add a random artist to the prompt.": "芸術家などの名称をプロンプトに追加",
     "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
     "Save style": "スタイルを保存する",
     "Apply selected styles to current prompt": "現在のプロンプトに選択したスタイルを適用する",
-    "Stop processing current image and continue processing.": "Stop processing current image and continue processing.",
-    "Stop processing images and return any results accumulated so far.": "Stop processing images and return any results accumulated so far.",
+    "Stop processing current image and continue processing.": "現在の処理を中断し、その後の処理は続ける",
+    "Stop processing images and return any results accumulated so far.": "処理を中断し、それまでに出来た結果を表示する",
     "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
-    "Do not do anything special": "Do not do anything special",
-    "Which algorithm to use to produce the image": "Which algorithm to use to produce the image",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - best at inpainting",
+    "Do not do anything special": "特別なことをなにもしない",
+    "Which algorithm to use to produce the image": "どのアルゴリズムを使って生成するか",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常に独創的で、ステップ数によって全く異なる画像が得られる、ステップ数を30～40より高く設定しても効果がない。",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 描き直しには最適",
     "Produce an image that can be tiled.": "Produce an image that can be tiled.",
     "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "2ステップで、まず部分的に小さい解像度で画像を作成し、その後アップスケールすることで、構図を変えずにディテールが改善されます。",
     "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
@@ -397,15 +397,15 @@
     "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.",
     "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.",
     "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
-    "Leave blank to save images to the default path.": "Leave blank to save images to the default path.",
-    "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "Result = A + (B - C) * M",
+    "Leave blank to save images to the default path.": "空欄でデフォルトの場所へ画像を保存",
+    "Result = A * (1 - M) + B * M": "結果モデル = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "結果モデル = A + (B - C) * M",
     "Path to directory with input images": "Path to directory with input images",
     "Path to directory where to write outputs": "Path to directory where to write outputs",
     "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
     "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "このオプションを有効にすると、作成された画像にウォーターマークが追加されなくなります。警告:ウォーターマークを追加しない場合、非倫理的な行動とみなされる場合があります。",
     "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
-    "Restore low quality faces using GFPGAN neural network": "Restore low quality faces using GFPGAN neural network",
+    "Restore low quality faces using GFPGAN neural network": "GFPGANを用いて低クオリティーの画像を修復",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
     "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
-- 
cgit v1.2.1


From 774be6d2f271415a82d9a83147e8ee8bbad018d0 Mon Sep 17 00:00:00 2001
From: Kris57 <49682577+yuuki76@users.noreply.github.com>
Date: Tue, 18 Oct 2022 23:29:34 +0900
Subject: improve ja translation

---
 localizations/ja_JP.json | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
index a9f6cb20..514b579e 100644
--- a/localizations/ja_JP.json
+++ b/localizations/ja_JP.json
@@ -76,7 +76,7 @@
     "Show Textbox": "Show Textbox",
     "File with inputs": "File with inputs",
     "Prompts": "プロンプト",
-    "X type": "X type",
+    "X type": "X軸の種類",
     "Nothing": "なし",
     "Var. seed": "Var. seed",
     "Var. strength": "Var. 強度",
@@ -94,12 +94,12 @@
     "Eta": "Eta",
     "Clip skip": "Clip skip",
     "Denoising": "Denoising",
-    "X values": "X values",
-    "Y type": "Y type",
-    "Y values": "Y values",
-    "Draw legend": "Draw legend",
+    "X values": "Xの値",
+    "Y type": "Y軸の種類",
+    "Y values": "Yの値",
+    "Draw legend": "凡例を描画",
     "Include Separate Images": "Include Separate Images",
-    "Keep -1 for seeds": "Keep -1 for seeds",
+    "Keep -1 for seeds": "シード値を-1で固定",
     "ここに画像をドロップ": "ここに画像をドロップ",
     "Save": "保存",
     "Send to img2img": "img2imgに送る",
@@ -295,7 +295,7 @@
     "Always print all generation info to standard output": "常にすべての生成に関する情報を標準出力(stdout)に出力する",
     "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
     "Training": "学習",
-    "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training",
+    "Unload VAE and CLIP from VRAM when training": "学習を行う際、VAEとCLIPをVRAMから削除する",
     "Filename word regex": "Filename word regex",
     "Filename join string": "Filename join string",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
@@ -304,14 +304,14 @@
     "Checkpoints to cache in RAM": "RAMにキャッシュするCheckpoint数",
     "Hypernetwork strength": "Hypernetwork strength",
     "Apply color correction to img2img results to match original colors.": "元画像に合わせてimg2imgの結果を色補正する",
-    "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
+    "Save a copy of image before applying color correction to img2img results": "色補正をする前の画像も保存する",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "img2imgでスライダーで指定されたステップ数を正確に実行する（通常は、ノイズ除去を少なくするためにより少ないステップ数で実行します）。",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "強調: (text)とするとモデルはtextをより強く扱い、[text]とするとモデルはtextをより弱く扱います。",
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "古い強調の実装を使う。古い生成物を再現するのに使えます。",
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
-    "Filter NSFW content": "Filter NSFW content",
+    "Filter NSFW content": "NSFW(≒R-18)なコンテンツを検閲する",
     "Stop At last layers of CLIP model": "最後から何層目でCLIPを止めるか(stop…layers of CLIP model)",
     "Interrogate Options": "Interrogate 設定",
     "Interrogate: keep models in VRAM": "Interrogate: モデルをVRAMに保持する",
@@ -321,10 +321,10 @@
     "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
     "Interrogate: maximum description length": "Interrogate: maximum description length",
     "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
-    "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold",
-    "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically",
-    "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
+    "Interrogate: deepbooru score threshold": "Interrogate: deepbooruで拾う単語のスコア閾値",
+    "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooruで単語をアルファベット順に並べる",
+    "use spaces for tags in deepbooru": "deepbooruのタグでスペースを使う",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "deepbooruで括弧をエスケープする(\\) (強調を示す()ではなく、文字通りの()であることをモデルに示すため)",
     "User interface": "UI設定",
     "Show progressbar": "プログレスバーを表示",
     "Show image creation progress every N sampling steps. Set 0 to disable.": "指定したステップ数ごとに画像の生成過程を表示する。0で無効化。",
-- 
cgit v1.2.1


From 324c7c732dd9afc3d4c397c354797ae5d655b514 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 20:09:37 +0300
Subject: record First pass size as 0x0 for #3328

---
 modules/processing.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 372489f7..27c669b0 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -524,6 +524,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
+            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
+
             if self.firstphase_width == 0 or self.firstphase_height == 0:
                 desired_pixel_count = 512 * 512
                 actual_pixel_count = self.width * self.height
@@ -545,7 +547,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                     firstphase_width_truncated = self.firstphase_height * self.width / self.height
                     firstphase_height_truncated = self.firstphase_height
 
-            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-- 
cgit v1.2.1


From 0df94d3fcf9d1fc47c4d39039352a3d5b3380c1f Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Sat, 22 Oct 2022 12:59:21 -0400
Subject: fix aesthetic gradients doing nothing after loading a different model

---
 modules/sd_models.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index f9b3063d..49dc3238 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -236,12 +236,11 @@ def load_model(checkpoint_info=None):
         sd_model.to(shared.device)
 
     sd_hijack.model_hijack.hijack(sd_model)
+    script_callbacks.model_loaded_callback(sd_model)
 
     sd_model.eval()
     shared.sd_model = sd_model
 
-    script_callbacks.model_loaded_callback(sd_model)
-
     print(f"Model loaded.")
     return sd_model
 
@@ -268,6 +267,7 @@ def reload_model_weights(sd_model, info=None):
     load_model_weights(sd_model, checkpoint_info)
 
     sd_hijack.model_hijack.hijack(sd_model)
+    script_callbacks.model_loaded_callback(sd_model)
 
     if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
         sd_model.to(devices.device)
-- 
cgit v1.2.1


From 321bacc6a9eaf4a25f31279f288fa752be507a20 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 20:15:12 +0300
Subject: call model_loaded_callback after setting shared.sd_model in case
 scripts refer to it using that

---
 modules/sd_models.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 49dc3238..e697bb72 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -236,11 +236,12 @@ def load_model(checkpoint_info=None):
         sd_model.to(shared.device)
 
     sd_hijack.model_hijack.hijack(sd_model)
-    script_callbacks.model_loaded_callback(sd_model)
 
     sd_model.eval()
     shared.sd_model = sd_model
 
+    script_callbacks.model_loaded_callback(sd_model)
+
     print(f"Model loaded.")
     return sd_model
 
-- 
cgit v1.2.1


From 24694e5983d0944b901892cb101878e6dec89a20 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 01:57:58 +0900
Subject: Update hypernetwork.py

---
 modules/hypernetworks/hypernetwork.py | 55 ++++++++++++++++++++++++++++-------
 1 file changed, 44 insertions(+), 11 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3bc71ee5..81132be4 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -16,6 +16,7 @@ from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
 
+from statistics import stdev, mean
 
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
@@ -268,6 +269,32 @@ def stack_conds(conds):
     return torch.stack(conds)
 
 
+def log_statistics(loss_info:dict, key, value):
+    if key not in loss_info:
+        loss_info[key] = [value]
+    else:
+        loss_info[key].append(value)
+        if len(loss_info) > 1024:
+            loss_info.pop(0)
+
+
+def statistics(data):
+    total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})"
+    recent_data = data[-32:]
+    recent_information = f"recent 32 loss:{mean(recent_data):.3f}"+u"\u00B1"+f"({stdev(recent_data)/ (len(recent_data)**0.5):.3f})"
+    return total_information, recent_information
+
+
+def report_statistics(loss_info:dict):
+    keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
+    for key in keys:
+        info, recent = statistics(loss_info[key])
+        print("Loss statistics for file " + key)
+        print(info)
+        print(recent)
+
+
+
 def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
@@ -310,7 +337,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     for weight in weights:
         weight.requires_grad = True
 
-    losses = torch.zeros((32,))
+    size = len(ds.indexes)
+    loss_dict = {}
+    losses = torch.zeros((size,))
+    previous_mean_loss = 0
+    print("Mean loss of {} elements".format(size))
 
     last_saved_file = "<none>"
     last_saved_image = "<none>"
@@ -329,7 +360,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
-
+        if loss_dict and i % size == 0:
+            previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict)
+            
         scheduler.apply(optimizer, hypernetwork.step)
         if scheduler.finished:
             break
@@ -346,7 +379,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             del c
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
-
+            for entry in entries:
+                log_statistics(loss_dict, entry.filename, loss.item())
+                
             optimizer.zero_grad()
             weights[0].grad = None
             loss.backward()
@@ -359,10 +394,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             optimizer.step()
 
-        mean_loss = losses.mean()
-        if torch.isnan(mean_loss):
+        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
             raise RuntimeError("Loss diverged.")
-        pbar.set_description(f"loss: {mean_loss:.7f}")
+        pbar.set_description(f"dataset loss: {previous_mean_loss:.7f}")
 
         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
             # Before saving, change name to match current checkpoint.
@@ -371,7 +405,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             hypernetwork.save(last_saved_file)
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
-            "loss": f"{mean_loss:.7f}",
+            "loss": f"{previous_mean_loss:.7f}",
             "learn_rate": scheduler.learn_rate
         })
 
@@ -420,14 +454,15 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         shared.state.textinfo = f"""
 <p>
-Loss: {mean_loss:.7f}<br/>
+Loss: {previous_mean_loss:.7f}<br/>
 Step: {hypernetwork.step}<br/>
 Last prompt: {html.escape(entries[0].cond_text)}<br/>
 Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-
+        
+    report_statistics(loss_dict)
     checkpoint = sd_models.select_checkpoint()
 
     hypernetwork.sd_checkpoint = checkpoint.hash
@@ -438,5 +473,3 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     hypernetwork.save(filename)
 
     return hypernetwork, filename
-
-
-- 
cgit v1.2.1


From 4fdb53c1e9962507fc8336dad9a0fabfe6c418c0 Mon Sep 17 00:00:00 2001
From: Unnoen <Unnoen@users.noreply.github.com>
Date: Wed, 19 Oct 2022 21:38:10 +1100
Subject: Generate grid preview for progress image

---
 modules/sd_samplers.py | 26 +++++++++++++++++++++++++-
 modules/shared.py      |  1 +
 modules/ui.py          |  5 ++++-
 3 files changed, 30 insertions(+), 2 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index f58a29b9..74a480e5 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -7,7 +7,7 @@ import inspect
 import k_diffusion.sampling
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
-from modules import prompt_parser, devices, processing
+from modules import prompt_parser, devices, processing, images
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -89,6 +89,30 @@ def sample_to_image(samples):
     x_sample = x_sample.astype(np.uint8)
     return Image.fromarray(x_sample)
 
+def samples_to_image_grid(samples):
+    progress_images = []
+    for i in range(len(samples)):
+        # Decode the samples individually to reduce VRAM usage at the cost of a bit of speed.
+        x_sample = processing.decode_first_stage(shared.sd_model, samples[i:i+1])[0]
+        x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+        x_sample = x_sample.astype(np.uint8)
+        progress_images.append(Image.fromarray(x_sample))
+
+    return images.image_grid(progress_images)
+
+def samples_to_image_grid_combined(samples):
+    progress_images = []
+    # Decode all samples at once to increase speed at the cost of VRAM usage.
+    x_samples = processing.decode_first_stage(shared.sd_model, samples)
+    x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
+
+    for x_sample in x_samples:
+        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+        x_sample = x_sample.astype(np.uint8)
+        progress_images.append(Image.fromarray(x_sample))
+
+    return images.image_grid(progress_images)
 
 def store_latent(decoded):
     state.current_latent = decoded
diff --git a/modules/shared.py b/modules/shared.py
index d9cb65ef..95d6e225 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -294,6 +294,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
+    "progress_decode_combined": OptionInfo(False, "Decode all progress images at once. (Slighty speeds up progress generation but consumes significantly more VRAM with large batches.)"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
diff --git a/modules/ui.py b/modules/ui.py
index 56c233ab..de0abc7e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -318,7 +318,10 @@ def check_progress_call(id_part):
         if shared.parallel_processing_allowed:
 
             if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None:
-                shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)
+                if opts.progress_decode_combined:
+                    shared.state.current_image = modules.sd_samplers.samples_to_image_grid_combined(shared.state.current_latent)
+                else:
+                    shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent)
                 shared.state.current_image_sampling_step = shared.state.sampling_step
 
         image = shared.state.current_image
-- 
cgit v1.2.1


From d213d6ca6f90094cb45c11e2f3cb37d25a8d1f94 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 20:48:13 +0300
Subject: removed the option to use 2x more memory when generating previews
 added an option to always only show one image in previews removed duplicate
 code

---
 modules/sd_samplers.py | 35 ++++++++++-------------------------
 modules/shared.py      |  2 +-
 modules/ui.py          |  6 +++---
 3 files changed, 14 insertions(+), 29 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 74a480e5..0b408a70 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -71,6 +71,7 @@ sampler_extra_params = {
     'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
 }
 
+
 def setup_img2img_steps(p, steps=None):
     if opts.img2img_fix_steps or steps is not None:
         steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
@@ -82,37 +83,21 @@ def setup_img2img_steps(p, steps=None):
     return steps, t_enc
 
 
-def sample_to_image(samples):
-    x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]
+def single_sample_to_image(sample):
+    x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
     x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
     x_sample = x_sample.astype(np.uint8)
     return Image.fromarray(x_sample)
 
+
+def sample_to_image(samples):
+    return single_sample_to_image(samples[0])
+
+
 def samples_to_image_grid(samples):
-    progress_images = []
-    for i in range(len(samples)):
-        # Decode the samples individually to reduce VRAM usage at the cost of a bit of speed.
-        x_sample = processing.decode_first_stage(shared.sd_model, samples[i:i+1])[0]
-        x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
-        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-        x_sample = x_sample.astype(np.uint8)
-        progress_images.append(Image.fromarray(x_sample))
-
-    return images.image_grid(progress_images)
-
-def samples_to_image_grid_combined(samples):
-    progress_images = []
-    # Decode all samples at once to increase speed at the cost of VRAM usage.
-    x_samples = processing.decode_first_stage(shared.sd_model, samples)
-    x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
-    for x_sample in x_samples:
-        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-        x_sample = x_sample.astype(np.uint8)
-        progress_images.append(Image.fromarray(x_sample))
-
-    return images.image_grid(progress_images)
+    return images.image_grid([single_sample_to_image(sample) for sample in samples])
+
 
 def store_latent(decoded):
     state.current_latent = decoded
diff --git a/modules/shared.py b/modules/shared.py
index 95d6e225..25bfc895 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -294,7 +294,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
-    "progress_decode_combined": OptionInfo(False, "Decode all progress images at once. (Slighty speeds up progress generation but consumes significantly more VRAM with large batches.)"),
+    "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
diff --git a/modules/ui.py b/modules/ui.py
index de0abc7e..ffa14cac 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -318,10 +318,10 @@ def check_progress_call(id_part):
         if shared.parallel_processing_allowed:
 
             if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None:
-                if opts.progress_decode_combined:
-                    shared.state.current_image = modules.sd_samplers.samples_to_image_grid_combined(shared.state.current_latent)
-                else:
+                if opts.show_progress_grid:
                     shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent)
+                else:
+                    shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)
                 shared.state.current_image_sampling_step = shared.state.sampling_step
 
         image = shared.state.current_image
-- 
cgit v1.2.1


From be748e8b086bd9834d08bdd9160649a5e7700af7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 22:05:22 +0300
Subject: add --freeze-settings commandline argument to disable changing
 settings

---
 modules/shared.py |  1 +
 modules/ui.py     | 11 +++++++++--
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 25bfc895..b55371d3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -64,6 +64,7 @@ parser.add_argument("--port", type=int, help="launch gradio with given server po
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
 parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(script_path, 'ui-config.json'))
 parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
+parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
 parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
diff --git a/modules/ui.py b/modules/ui.py
index ffa14cac..2311572c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -580,6 +580,9 @@ def apply_setting(key, value):
     if value is None:
         return gr.update()
 
+    if shared.cmd_opts.freeze_settings:
+        return gr.update()
+
     # dont allow model to be swapped when model hash exists in prompt
     if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
         return gr.update()
@@ -1501,6 +1504,8 @@ Requested path was: {f}
     def run_settings(*args):
         changed = 0
 
+        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
+
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
             if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
                 return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
@@ -1530,6 +1535,8 @@ Requested path was: {f}
         return f'{changed} settings changed.', opts.dumpjson()
 
     def run_settings_single(value, key):
+        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
+
         if not opts.same_type(value, opts.data_labels[key].default):
             return gr.update(visible=True), opts.dumpjson()
 
@@ -1582,7 +1589,7 @@ Requested path was: {f}
                     elem_id, text = item.section
                     gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
 
-                if k in quicksettings_names:
+                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
                     quicksettings_list.append((i, k, item))
                     components.append(dummy_component)
                 else:
@@ -1615,7 +1622,7 @@ Requested path was: {f}
 
         def reload_scripts():
             modules.scripts.reload_script_body_only()
-            reload_javascript() # need to refresh the html page
+            reload_javascript()  # need to refresh the html page
 
         reload_script_bodies.click(
             fn=reload_scripts,
-- 
cgit v1.2.1


From ca5a9e79dc28eeaa3a161427a82e34703bf15765 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 22 Oct 2022 22:06:54 +0300
Subject: fix for img2img color correction in a batch #3218

---
 modules/processing.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 27c669b0..b1877b80 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -403,8 +403,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if (len(prompts) == 0):
                 break
 
-            #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
-            #c = p.sd_model.get_learned_conditioning(prompts)
             with devices.autocast():
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
@@ -716,6 +714,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
             if self.overlay_images is not None:
                 self.overlay_images = self.overlay_images * self.batch_size
+
+            if self.color_corrections is not None and len(self.color_corrections) == 1:
+                self.color_corrections = self.color_corrections * self.batch_size
+
         elif len(imgs) <= self.batch_size:
             self.batch_size = len(imgs)
             batch_images = np.array(imgs)
-- 
cgit v1.2.1


From 48dbf99e84045ee7af55bc5b1b86492a240e631e Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 04:17:16 +0900
Subject: Allow tracking real-time loss

Someone had 6000 images in their dataset, and it was shown as 0, which was confusing.
This will allow tracking real time dataset-average loss for registered objects.
---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 81132be4..99fd0f8f 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -360,7 +360,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
-        if loss_dict and i % size == 0:
+        if len(loss_dict) > 0:
             previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict)
             
         scheduler.apply(optimizer, hypernetwork.step)
-- 
cgit v1.2.1


From ce42879438bf2dbd76b5b346be656292e42ffb2b Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Sat, 22 Oct 2022 14:53:37 -0500
Subject: fix js func signature and not forget to initialize confirmation var
 to prevent exception upon cancelling confirmation

---
 javascript/ui.js | 7 ++++---
 modules/ui.py    | 4 +++-
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 6c99824b..39011079 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -162,12 +162,13 @@ function selected_tab_id() {
 
 }
 
-function clear_prompt(_, _prompt_neg, confirmed,_steps) {
+function clear_prompt(_, _prompt_neg, confirmed, _token_counter) {
+confirmed = false
 
 if(confirm("Delete prompt?")) {
     confirmed = true
 } else {
-return [_, confirmed]
+return [_, _prompt_neg, confirmed, _token_counter]
 }
 
     if(selected_tab_id() == "tab_txt2img") {
@@ -176,7 +177,7 @@ return [_, confirmed]
         update_token_counter("txt2img_token_button")
     }
 
-    return [_, _prompt_neg, confirmed,_steps]
+    return [_, _prompt_neg, confirmed, _token_counter]
 }
 
 
diff --git a/modules/ui.py b/modules/ui.py
index 25aeba3b..e58f040e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -429,10 +429,12 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
-def clear_prompt(_prompt, _prompt_neg, confirmed, _token_counter):
+def clear_prompt(prompt, _prompt_neg, confirmed, _token_counter):
     """Given confirmation from a user on the client-side, go ahead with clearing prompt"""
     if confirmed:
         return ["", "", confirmed, update_token_counter("", 1)]
+    else:
+        return [prompt, _prompt_neg, confirmed, _token_counter]
 
 
 def connect_clear_prompt(button, prompt, prompt_neg, _dummy_confirmed, token_counter):
-- 
cgit v1.2.1


From 1b4d04737ac513cbd55958bb60a4f85166f3484b Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sat, 22 Oct 2022 20:13:16 -0300
Subject: Remove unused imports

---
 modules/api/api.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 5b0c934e..a5136b4b 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,11 +1,9 @@
 from modules.api.processing import StableDiffusionProcessingAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, process_images
 from modules.sd_samplers import all_samplers
-from modules.extras import run_pnginfo
 import modules.shared as shared
 import uvicorn
-from fastapi import Body, APIRouter, HTTPException
-from fastapi.responses import JSONResponse
+from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel, Field, Json
 import json
 import io
@@ -18,7 +16,6 @@ class TextToImageResponse(BaseModel):
     parameters: Json
     info: Json
 
-
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
-- 
cgit v1.2.1


From b02926df1393df311db734af149fb9faf4389cbe Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sat, 22 Oct 2022 20:24:04 -0300
Subject: Moved moodels to their own file and extracted base64 conversion to
 its own function

---
 modules/api/api.py    | 17 ++++++-----------
 modules/api/models.py |  8 ++++++++
 2 files changed, 14 insertions(+), 11 deletions(-)
 create mode 100644 modules/api/models.py

diff --git a/modules/api/api.py b/modules/api/api.py
index a5136b4b..c17d7580 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -4,17 +4,17 @@ from modules.sd_samplers import all_samplers
 import modules.shared as shared
 import uvicorn
 from fastapi import APIRouter, HTTPException
-from pydantic import BaseModel, Field, Json
 import json
 import io
 import base64
+from modules.api.models import *
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
-class TextToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: Json
-    info: Json
+def img_to_base64(img):
+    buffer = io.BytesIO()
+    img.save(buffer, format="png")
+    return base64.b64encode(buffer.getvalue())
 
 class Api:
     def __init__(self, app, queue_lock):
@@ -41,15 +41,10 @@ class Api:
         with self.queue_lock:
             processed = process_images(p)
         
-        b64images = []
-        for i in processed.images:
-            buffer = io.BytesIO()
-            i.save(buffer, format="png")
-            b64images.append(base64.b64encode(buffer.getvalue()))
+        b64images = list(map(img_to_base64, processed.images))
 
         return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
         
-        
 
     def img2imgapi(self):
         raise NotImplementedError
diff --git a/modules/api/models.py b/modules/api/models.py
new file mode 100644
index 00000000..a7d247d8
--- /dev/null
+++ b/modules/api/models.py
@@ -0,0 +1,8 @@
+from pydantic import BaseModel, Field, Json
+
+class TextToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: Json
+    info: Json
+
+    
\ No newline at end of file
-- 
cgit v1.2.1


From 28e26c2bef217ae82eb9e980cceb3f67ef22e109 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sat, 22 Oct 2022 23:13:32 -0300
Subject: Add "extra" single image operation

 - Separate extra modes into 3 endpoints so the user ddoesn't ahve to
handle so many unused parameters.
 - Add response model for codumentation
---
 modules/api/api.py    | 43 ++++++++++++++++++++++++++++++++++++++-----
 modules/api/models.py | 26 +++++++++++++++++++++++++-
 2 files changed, 63 insertions(+), 6 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index c17d7580..3b804373 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -8,20 +8,42 @@ import json
 import io
 import base64
 from modules.api.models import *
+from PIL import Image
+from modules.extras import run_extras
+
+def upscaler_to_index(name: str):
+    try:
+        return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
+    except:
+        raise HTTPException(status_code=400, detail="Upscaler not found")
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
-def img_to_base64(img):
+def img_to_base64(img: str):
     buffer = io.BytesIO()
     img.save(buffer, format="png")
     return base64.b64encode(buffer.getvalue())
 
+def base64_to_bytes(base64Img: str):
+    if "," in base64Img:
+        base64Img = base64Img.split(",")[1]
+    return io.BytesIO(base64.b64decode(base64Img))
+
+def base64_to_images(base64Imgs: list[str]):
+    imgs = []
+    for img in base64Imgs:
+        img = Image.open(base64_to_bytes(img))
+        imgs.append(img)
+    return imgs
+
+
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
-        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
+        self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -45,12 +67,23 @@ class Api:
 
         return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
         
-
     def img2imgapi(self):
         raise NotImplementedError
 
-    def extrasapi(self):
-        raise NotImplementedError
+    def extras_single_image_api(self, req: ExtrasSingleImageRequest):
+        upscaler1Index = upscaler_to_index(req.upscaler_1)
+        upscaler2Index = upscaler_to_index(req.upscaler_2)
+
+        reqDict = vars(req)
+        reqDict.pop('upscaler_1')
+        reqDict.pop('upscaler_2')
+
+        reqDict['image'] = base64_to_images([reqDict['image']])[0]
+
+        with self.queue_lock:
+            result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=0, image_folder="", input_dir="", output_dir="")
+
+        return ExtrasSingleImageResponse(image="data:image/png;base64,"+img_to_base64(result[0]), html_info_x=result[1], html_info=result[2])
 
     def pnginfoapi(self):
         raise NotImplementedError
diff --git a/modules/api/models.py b/modules/api/models.py
index a7d247d8..dcf1ab54 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,8 +1,32 @@
 from pydantic import BaseModel, Field, Json
+from typing_extensions import Literal
+from modules.shared import sd_upscalers
 
 class TextToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: Json
     info: Json
 
-    
\ No newline at end of file
+class ExtrasBaseRequest(BaseModel):
+    resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.")
+    show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?")
+    gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
+    codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.")
+    codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.")
+    upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.")
+    upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
+    upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
+    upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the choosen size?")
+    upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
+    upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
+    extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
+
+class ExtraBaseResponse(BaseModel):
+    html_info_x: str
+    html_info: str
+
+class ExtrasSingleImageRequest(ExtrasBaseRequest):
+    image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+
+class ExtrasSingleImageResponse(ExtraBaseResponse):
+    image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
\ No newline at end of file
-- 
cgit v1.2.1


From 1fbfc052eb529d8cf8ce5baf578bcf93d0280c29 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 23 Oct 2022 05:43:34 +0100
Subject: Update hypernetwork.py

---
 modules/hypernetworks/hypernetwork.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 99fd0f8f..98a7b62e 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -288,10 +288,13 @@ def statistics(data):
 def report_statistics(loss_info:dict):
     keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
     for key in keys:
-        info, recent = statistics(loss_info[key])
-        print("Loss statistics for file " + key)
-        print(info)
-        print(recent)
+        try:
+            print("Loss statistics for file " + key)
+            info, recent = statistics(loss_info[key])
+            print(info)
+            print(recent)
+        except Exception as e:
+            print(e)
 
 
 
-- 
cgit v1.2.1


From a7c213d0f5ebb10722629b8490a5863f9ce6c4fa Mon Sep 17 00:00:00 2001
From: Stephen <farrenzo47@gmail.com>
Date: Fri, 21 Oct 2022 19:27:40 -0400
Subject: [API][Feature] - Add img2img API endpoint

---
 modules/api/api.py        | 58 +++++++++++++++++++++++++++++++++++++++++++----
 modules/api/processing.py | 11 +++++++--
 modules/processing.py     |  2 +-
 3 files changed, 63 insertions(+), 8 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 5b0c934e..a04f2428 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,5 +1,5 @@
-from modules.api.processing import StableDiffusionProcessingAPI
-from modules.processing import StableDiffusionProcessingTxt2Img, process_images
+from modules.api.processing import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.sd_samplers import all_samplers
 from modules.extras import run_pnginfo
 import modules.shared as shared
@@ -10,6 +10,7 @@ from pydantic import BaseModel, Field, Json
 import json
 import io
 import base64
+from PIL import Image
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
@@ -18,6 +19,11 @@ class TextToImageResponse(BaseModel):
     parameters: Json
     info: Json
 
+class ImageToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: Json
+    info: Json
+
 
 class Api:
     def __init__(self, app, queue_lock):
@@ -25,8 +31,9 @@ class Api:
         self.app = app
         self.queue_lock = queue_lock
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
 
-    def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
+    def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
         
         if sampler_index is None:
@@ -54,8 +61,49 @@ class Api:
         
         
 
-    def img2imgapi(self):
-        raise NotImplementedError
+    def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
+        sampler_index = sampler_to_index(img2imgreq.sampler_index)
+        
+        if sampler_index is None:
+            raise HTTPException(status_code=404, detail="Sampler not found") 
+
+
+        init_images = img2imgreq.init_images
+        if init_images is None:
+            raise HTTPException(status_code=404, detail="Init image not found") 
+
+        
+        populate = img2imgreq.copy(update={ # Override __init__ params
+            "sd_model": shared.sd_model, 
+            "sampler_index": sampler_index[0],
+            "do_not_save_samples": True,
+            "do_not_save_grid": True
+            }
+        )
+        p = StableDiffusionProcessingImg2Img(**vars(populate))
+
+        imgs = []
+        for img in init_images:
+            # if has a comma, deal with prefix
+            if "," in img:
+                img = img.split(",")[1]
+            # convert base64 to PIL image
+            img = base64.b64decode(img)
+            img = Image.open(io.BytesIO(img))
+            imgs = [img] * p.batch_size
+
+        p.init_images = imgs
+        # Override object param
+        with self.queue_lock:
+            processed = process_images(p)
+        
+        b64images = []
+        for i in processed.images:
+            buffer = io.BytesIO()
+            i.save(buffer, format="png")
+            b64images.append(base64.b64encode(buffer.getvalue()))
+
+        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info))
 
     def extrasapi(self):
         raise NotImplementedError
diff --git a/modules/api/processing.py b/modules/api/processing.py
index 4c541241..9f1d65c0 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -1,7 +1,8 @@
+from array import array
 from inflection import underscore
 from typing import Any, Dict, Optional
 from pydantic import BaseModel, Field, create_model
-from modules.processing import StableDiffusionProcessingTxt2Img
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 import inspect
 
 
@@ -92,8 +93,14 @@ class PydanticModelGenerator:
         DynamicModel.__config__.allow_mutation = True
         return DynamicModel
     
-StableDiffusionProcessingAPI = PydanticModelGenerator(
+StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingTxt2Img", 
     StableDiffusionProcessingTxt2Img,
     [{"key": "sampler_index", "type": str, "default": "Euler"}]
+).generate_model()
+
+StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingImg2Img", 
+    StableDiffusionProcessingImg2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}]
 ).generate_model()
\ No newline at end of file
diff --git a/modules/processing.py b/modules/processing.py
index b1877b80..1557ed8c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -623,7 +623,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs):
+    def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: str=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
-- 
cgit v1.2.1


From 9e1a8b7734a2881451a2efbf80def011ea41ba49 Mon Sep 17 00:00:00 2001
From: Stephen <farrenzo47@gmail.com>
Date: Sat, 22 Oct 2022 15:42:00 -0400
Subject: non-implemented mask with any type

---
 modules/api/api.py        | 4 ++++
 modules/api/processing.py | 2 +-
 modules/processing.py     | 2 +-
 3 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index a04f2428..3df6ff96 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -72,6 +72,10 @@ class Api:
         if init_images is None:
             raise HTTPException(status_code=404, detail="Init image not found") 
 
+        mask = img2imgreq.mask
+        if mask:
+            raise HTTPException(status_code=400, detail="Mask not supported yet") 
+
         
         populate = img2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model, 
diff --git a/modules/api/processing.py b/modules/api/processing.py
index 9f1d65c0..f551fa35 100644
--- a/modules/api/processing.py
+++ b/modules/api/processing.py
@@ -102,5 +102,5 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
 StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingImg2Img", 
     StableDiffusionProcessingImg2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}]
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
 ).generate_model()
\ No newline at end of file
diff --git a/modules/processing.py b/modules/processing.py
index 1557ed8c..ff83023c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -623,7 +623,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: str=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
+    def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: Any=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
-- 
cgit v1.2.1


From 5dc0739ecdc1ade8fcf4eb77f2a503ef12489f32 Mon Sep 17 00:00:00 2001
From: Stephen <farrenzo47@gmail.com>
Date: Sat, 22 Oct 2022 17:10:28 -0400
Subject: working mask

---
 modules/api/api.py | 20 ++++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 3df6ff96..3caa83a4 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -33,6 +33,14 @@ class Api:
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
 
+    def __base64_to_image(self, base64_string):
+        # if has a comma, deal with prefix
+        if "," in base64_string:
+            base64_string = base64_string.split(",")[1]
+        imgdata = base64.b64decode(base64_string)
+        # convert base64 to PIL image
+        return Image.open(io.BytesIO(imgdata))
+
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
         
@@ -74,26 +82,22 @@ class Api:
 
         mask = img2imgreq.mask
         if mask:
-            raise HTTPException(status_code=400, detail="Mask not supported yet") 
+            mask = self.__base64_to_image(mask)
 
         
         populate = img2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model, 
             "sampler_index": sampler_index[0],
             "do_not_save_samples": True,
-            "do_not_save_grid": True
+            "do_not_save_grid": True, 
+            "mask": mask
             }
         )
         p = StableDiffusionProcessingImg2Img(**vars(populate))
 
         imgs = []
         for img in init_images:
-            # if has a comma, deal with prefix
-            if "," in img:
-                img = img.split(",")[1]
-            # convert base64 to PIL image
-            img = base64.b64decode(img)
-            img = Image.open(io.BytesIO(img))
+            img = self.__base64_to_image(img)
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
-- 
cgit v1.2.1


From 1ef32c8b8fa3e16a1e7b287eb19d4fc943d1f2a5 Mon Sep 17 00:00:00 2001
From: kabachuha <artemkhrapov2001@yandex.ru>
Date: Sun, 23 Oct 2022 00:01:13 +0300
Subject: Add ru_RU localization

---
 localizations/ru_RU.json | 475 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 475 insertions(+)
 create mode 100644 localizations/ru_RU.json

diff --git a/localizations/ru_RU.json b/localizations/ru_RU.json
new file mode 100644
index 00000000..664d36ea
--- /dev/null
+++ b/localizations/ru_RU.json
@@ -0,0 +1,475 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "Загрузка...",
+    "view": "просмотр",
+    "api": "api",
+    "•": "•",
+    "built with gradio": "На основе Gradio",
+    "Stable Diffusion checkpoint": "Веса Stable Diffusion",
+    "txt2img": "текст-в-рисунок",
+    "img2img": "рисунок-в-рисунок",
+    "Extras": "Дополнения",
+    "PNG Info": "Информация о PNG",
+    "Image Browser": "Просмотр изображений",
+    "History": "Журнал",
+    "Checkpoint Merger": "Слияние весов",
+    "Train": "Обучение",
+    "Create aesthetic embedding": "Создать эмбеддинг эстетики",
+    "Settings": "Настройки",
+    "Prompt": "Запрос",
+    "Negative prompt": "Исключающий запрос",
+    "Run": "Запустить",
+    "Skip": "Пропустить",
+    "Interrupt": "Прервать",
+    "Generate": "Создать",
+    "Style 1": "Стиль 1",
+    "Style 2": "Стиль 2",
+    "Label": "Метка",
+    "File": "Файл",
+    "Drop File Here": "Перетащите файл сюда",
+    "-": "-",
+    "or": "или",
+    "Click to Upload": "Нажмите, чтобы загрузить",
+    "Image": "Рисунок",
+    "Check progress": "Узнать состояние",
+    "Check progress (first)": "Узнать состояние первого",
+    "Sampling Steps": "Шагов семплера",
+    "Sampling method": "Метод семплирования",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Ширина",
+    "Height": "Высота",
+    "Restore faces": "Восстановить лица",
+    "Tiling": "Замощение",
+    "Highres. fix": "HD-режим",
+    "Firstpass width": "Ширина первого прохода",
+    "Firstpass height": "Высота первого прохода",
+    "Denoising strength": "Сила шумоподавления",
+    "Batch count": "Рисунков подряд",
+    "Batch size": "Рисунков параллельно",
+    "CFG Scale": "Близость к запросу",
+    "Seed": "Семя",
+    "Extra": "Дополнения",
+    "Variation seed": "Вариация семени",
+    "Variation strength": "Вариация шумоподавления",
+    "Resize seed from width": "Поправка в семя от ширины",
+    "Resize seed from height": "Поправка в семя от высоты",
+    "Open for Clip Aesthetic!": "Clip-эстетика!",
+    "▼": "▼",
+    "Aesthetic weight": "Вес эстетики",
+    "Aesthetic steps": "Шагов эстетики",
+    "Aesthetic learning rate": "Скорость обучения эстетики",
+    "Slerp interpolation": "Slerp-интерполяция",
+    "Aesthetic imgs embedding": "Рисунки - эмбеддинги эстетики",
+    "None": "Ничего",
+    "Aesthetic text for imgs": "Имя эстетики рисунков",
+    "Slerp angle": "Угол slerp",
+    "Is negative text": "Это текст для исключения",
+    "Script": "Скрипт",
+    "Prompt matrix": "Матрица запросов",
+    "Prompts from file or textbox": "Запросы из файла или текста",
+    "X/Y plot": "X/Y-график",
+    "Put variable parts at start of prompt": "Переменное начало запроса",
+    "Show Textbox": "Показать текстовый ввод",
+    "File with inputs": "Файл входа",
+    "Prompts": "Запросы",
+    "X type": "Ось X",
+    "Nothing": "Ничего",
+    "Var. seed": "Вариация семени",
+    "Var. strength": "Вариация силы",
+    "Steps": "Число шагов",
+    "Prompt S/R": "Вариация запроса",
+    "Prompt order": "Порядок запросов",
+    "Sampler": "Семплер",
+    "Checkpoint name": "Имя файла весов",
+    "Hypernetwork": "Гиперсеть",
+    "Hypernet str.": "Строка гиперсети",
+    "Sigma Churn": "Возмущение сигмы",
+    "Sigma min": "Мин. сигма",
+    "Sigma max": "Макс. сигма",
+    "Sigma noise": "Сигма-шум",
+    "Eta": "Расчётное время",
+    "Clip skip": "Пропустить Clip",
+    "Denoising": "Шумоподавление",
+    "X values": "Значения X",
+    "Y type": "Тип Y",
+    "Y values": "Значения Y",
+    "Draw legend": "Легенда графика",
+    "Include Separate Images": "Включить отдельные рисунки",
+    "Keep -1 for seeds": "-1 для семени",
+    "Drop Image Here": "Перетащите рисунок сюда",
+    "Save": "Сохранить",
+    "Send to img2img": "В рисунок-в-рисунок",
+    "Send to inpaint": "В режим врисовывания",
+    "Send to extras": "В дополнения",
+    "Make Zip when Save?": "Создать zip при сохранении?",
+    "Textbox": "Текст",
+    "Interrogate\nCLIP": "Распознавание\nCLIP",
+    "Interrogate\nDeepBooru": "Распознавание\nDeepBooru",
+    "Inpaint": "врисовать",
+    "Batch img2img": "рисунок-в-рисунок (набор)",
+    "Image for img2img": "рисунок-в-рисунок (вход)",
+    "Image for inpainting with mask": "врисовать (вход с трафаретом)",
+    "Mask": "Трафарет",
+    "Mask blur": "Размытие трафарета",
+    "Mask mode": "Режим трафарета",
+    "Draw mask": "Нарисовать трафарет",
+    "Upload mask": "Загрузить трафарет",
+    "Masking mode": "Режим трафарета",
+    "Inpaint masked": "Внутри трафарета",
+    "Inpaint not masked": "Вне трафарета",
+    "Masked content": "Под трафаретом",
+    "fill": "залить",
+    "original": "сохранить",
+    "latent noise": "латентный шум",
+    "latent nothing": "латентная пустота",
+    "Inpaint at full resolution": "Врисовать при полном разрешении",
+    "Inpaint at full resolution padding, pixels": "Врисовать с достройкой до полного разрешения, в пикселях",
+    "Process images in a directory on the same machine where the server is running.": "Обрабатывать рисунки на том же компьютере, где сервер",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Использовать пустую папку вместо того, чтобы выводить в output",
+    "Disabled when launched with --hide-ui-dir-config.": "Выключено при запуске с  --hide-ui-dir-config",
+    "Input directory": "Папка входа",
+    "Output directory": "Папка выхода",
+    "Resize mode": "Масштабирование",
+    "Just resize": "Только сжать",
+    "Crop and resize": "Сжать и обрезать",
+    "Resize and fill": "Сжать и залить",
+    "img2img alternative test": "рисунок-в-рисунок (альтернатива)",
+    "Loopback": "Прокручивание",
+    "Outpainting mk2": "Обрисовыватель mk2",
+    "Poor man's outpainting": "Хоть какой-то обрисовыватель",
+    "SD upscale": "SD-апскейл",
+    "should be 2 or lower.": "должно быть меньше равно 2",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Сменить метод семплирования на метод Эйлера?(скрипт строился с его учётом)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Сменить `запрос` на `изначальный запрос`?(и `запрос-исключение`)",
+    "Original prompt": "Изначальный запрос",
+    "Original negative prompt": "Изначальный запрос-исключение",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Сменить число шагов на число шагов декодирования?",
+    "Decode steps": "Шагов декодирования",
+    "Override `Denoising strength` to 1?": "Сменить силу шумоподавления на 1?",
+    "Decode CFG scale": "Близость к запросу декодирования",
+    "Randomness": "Случайность",
+    "Sigma adjustment for finding noise for image": "Поправка к сигме подбора шума для рисунка",
+    "Loops": "Циклов",
+    "Denoising strength change factor": "Множитель силы шумоподавления",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Рекоммендуемые настройки： Число шагов：80-100，Метод：Euler a，Шумоподавление：0.8",
+    "Pixels to expand": "Пикселов расширить",
+    "Outpainting direction": "Направление обрисовывания",
+    "left": "влево",
+    "right": "вправо",
+    "up": "вверх",
+    "down": "вниз",
+    "Fall-off exponent (lower=higher detail)": "Степень затухания （меньше=больше деталей）",
+    "Color variation": "Вариация цвета",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Расширит рисунок дважды； ползунки ширины и высоты устанавливают размеры плиток",
+    "Tile overlap": "Перекрытие плиток",
+    "Upscaler": "Апскейлер",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "BSRGAN 4x": "BSRGAN 4x",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR_4x": "SwinIR 4x",
+    "Single Image": "Один рисунок",
+    "Batch Process": "Набор рисунков",
+    "Batch from Directory": "Рисунки из папки",
+    "Source": "Вход",
+    "Show result images": "Показать результаты",
+    "Scale by": "Увеличить в",
+    "Scale to": "Увеличить до",
+    "Resize": "Масштабировать",
+    "Crop to fit": "Обрезать до рамки",
+    "Upscaler 2": "Апскейлер 2",
+    "Upscaler 2 visibility": "Видимость Апскейлера 2",
+    "GFPGAN visibility": "Видимость GFPGAN",
+    "CodeFormer visibility": "Видимость CodeFormer",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Вес CodeFormer (0 = максимальное действие, 1 = минимальное)",
+    "Open output directory": "Открыть папку выхода",
+    "Send to txt2img": "В текст-в-рисунок",
+    "txt2img history": "журнал текста-в-рисунок",
+    "img2img history": "журнал рисунка-в-рисунок",
+    "extras history": "журнал дополнений",
+    "Renew Page": "Обновить страницу",
+    "extras": "дополнения",
+    "favorites": "избранное",
+    "Load": "Загрузить",
+    "Images directory": "Папка с рисунками",
+    "Prev batch": "Пред. набор",
+    "Next batch": "След. набор",
+    "First Page": "Первая страница",
+    "Prev Page": "Пред. страница",
+    "Page Index": "Список страниц",
+    "Next Page": "След. страница",
+    "End Page": "Конец страницы",
+    "number of images to delete consecutively next": "сколько рисунков удалить подряд",
+    "Delete": "Удалить",
+    "Generate Info": "Сведения о генерации",
+    "File Name": "Имя файла",
+    "Collect": "Накопить",
+    "Refresh page": "Обновить страницу",
+    "Date to": "Дата",
+    "Number": "Число",
+    "set_index": "индекс",
+    "Checkbox": "Галочка",
+    "A merger of the two checkpoints will be generated in your": "Слияние весов будет создано, где хранятся",
+    "checkpoint": "ckpt",
+    "directory.": "веса",
+    "Primary model (A)": "Первичная модель (A)",
+    "Secondary model (B)": "Вторичная модель (B)",
+    "Tertiary model (C)": "Третичная модель (C)",
+    "Custom Name (Optional)": "Произвольное имя (необязательно)",
+    "Multiplier (M) - set to 0 to get model A": "Множитель (M) - 0 даст модель A",
+    "Interpolation Method": "Метод интерполяции",
+    "Weighted sum": "Взвешенная сумма",
+    "Add difference": "Сумма разностей",
+    "Save as float16": "Сохранить как float16",
+    "See": "См.",
+    "wiki": "вики",
+    "for detailed explanation.": "для подробных объяснений.",
+    "Create embedding": "Создать эмбеддинг",
+    "Create aesthetic images embedding": "Создать эмбеддинг эстетики по рисункам",
+    "Create hypernetwork": "Создать гиперсеть",
+    "Preprocess images": "Предобработать рисунки",
+    "Name": "Имя",
+    "Initialization text": "Соответствующий текст",
+    "Number of vectors per token": "Векторов на токен",
+    "Overwrite Old Embedding": "Перезаписать эмбеддинг",
+    "Source directory": "Исходная папка",
+    "Modules": "Модули",
+    "Enter hypernetwork layer structure": "Структура слоёв гиперсети",
+    "Add layer normalization": "Добавить нормализацию слоёв",
+    "Overwrite Old Hypernetwork": "Перезаписать гиперсеть",
+    "Select activation function of hypernetwork": "Функция активации гиперсети",
+    "linear": "линейная",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "Destination directory": "Папка назначения",
+    "Existing Caption txt Action": "Что делать с предыдущим текстом",
+    "ignore": "игнорировать",
+    "copy": "копировать",
+    "prepend": "в начало",
+    "append": "в конец",
+    "Create flipped copies": "Создать отражённые копии",
+    "Split oversized images into two": "Поделить слишком большие рисунки пополам",
+    "Split oversized images": "Поделить слишком большие рисунки",
+    "Use BLIP for caption": "Использовать BLIP для названий",
+    "Use deepbooru for caption": "Использовать deepbooru для тегов",
+    "Split image threshold": "Порог разделения рисунков",
+    "Split image overlap ratio": "Пропорции разделения рисунков",
+    "Preprocess": "Предобработка",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Обучить эмбеддинг； укажите папку рисунков с пропорциями 1:1",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Обучить эмбеддинг или гиперсеть； укажите папку рисунков с пропорциями 1:1",
+    "[wiki]": "[вики]",
+    "Embedding": "Эмбеддинг",
+    "Embedding Learning rate": "Скорость обучения эмбеддинга",
+    "Hypernetwork Learning rate": "Скорость обучения гиперсети",
+    "Learning rate": "Скорость обучения",
+    "Dataset directory": "Папка датасета",
+    "Log directory": "Папка журнала",
+    "Prompt template file": "Файл шаблона запроса",
+    "Max steps": "Макс. шагов",
+    "Save an image to log directory every N steps, 0 to disable": "Сохранять рисунок каждые N шагов, 0 чтобы отключить",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Сохранять эмбеддинг каждые N шагов, 0 чтобы отключить",
+    "Save images with embedding in PNG chunks": "Сохранить рисунок с эмбеддингом в виде PNG-фрагментов",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Считать параметры (запрос и т.д.) из вкладки текст-в-рисунок для предпросмотра",
+    "Train Hypernetwork": "Обучить гиперсеть",
+    "Train Embedding": "Обучить эмбеддинг",
+    "Create an aesthetic embedding out of any number of images": "Создать эмбеддинг эстетики по любому числу рисунков",
+    "Create images embedding": "Создать эмбеддинг рисунков",
+    "Apply settings": "Применить настройки",
+    "Saving images/grids": "Сохранение рисунков/таблиц",
+    "Always save all generated images": "Всегда сохранять созданные рисунки",
+    "File format for images": "Формат файла рисунков",
+    "Images filename pattern": "Формат имени файлов рисунков",
+    "Always save all generated image grids": "Всегда сохранять созданные таблицы",
+    "File format for grids": "Формат файла таблиц",
+    "Add extended info (seed, prompt) to filename when saving grid": "Вставлять доп. сведения (семя, запрос) в имя файла таблиц",
+    "Do not save grids consisting of one picture": "Не сохранять таблицы из одного рисунка",
+    "Prevent empty spots in grid (when set to autodetect)": "Не допускать пустоты в таблицах （автообнаружение）",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Число строк таблицы； -1, чтобы автоматически, 0 — размер набора",
+    "Save text information about generation parameters as chunks to png files": "Встроить сведения о генерации в файлы png",
+    "Create a text file next to every image with generation parameters.": "Создать текстовый файл для каждого рисунка с параметрами генерации",
+    "Save a copy of image before doing face restoration.": "Сохранить копию перед восстановлением лиц",
+    "Quality for saved jpeg images": "Качество jpeg-рисунков",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Если размер PNG больше 4МБ или рисунок шире 4000 пикселей, пересжать в JPEG",
+    "Use original name for output filename during batch process in extras tab": "Использовать исходное имя выходного файла для обработки набора во вкладке дополнений",
+    "When using 'Save' button, only save a single selected image": "Сохранять только один рисунок при нажатии кнопки Сохранить",
+    "Do not add watermark to images": "Не добавлять водяной знак",
+    "Paths for saving": "Папки сохранений",
+    "Output directory for images; if empty, defaults to three directories below": "Папка выхода рисунков; если пусто, использует те, что ниже",
+    "Output directory for txt2img images": "Папка выхода текста-в-рисунок",
+    "Output directory for img2img images": "Папка выхода рисунка-в-рисунок",
+    "Output directory for images from extras tab": "Папка выхода для дополнений",
+    "Output directory for grids; if empty, defaults to two directories below": "Папка выхода таблиц; если пусто, использует папки выше",
+    "Output directory for txt2img grids": "Папка выхода текста-в-рисунок",
+    "Output directory for img2img grids": "Папка выхода рисунка-в-рисунок",
+    "Directory for saving images using the Save button": "Папка выхода для кнопки Сохранить",
+    "Saving to a directory": "Сохранить в папку",
+    "Save images to a subdirectory": "Сохранить рисунки в подпапку",
+    "Save grids to a subdirectory": "Сохранить таблицы в подпапку",
+    "When using \"Save\" button, save images to a subdirectory": "При нажатии кнопки Сохранить, сложить рисунки в подпапку",
+    "Directory name pattern": "Шаблон имени папки",
+    "Max prompt words for [prompt_words] pattern": "Макс. число слов для шаблона [prompt_words]",
+    "Upscaling": "Апскейл",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Размер плитки для ESRGAN. 0 = нет замощения",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Наложение плиток ESRGAN, в пикселях. Меньше = выделеннее шов",
+    "Tile size for all SwinIR.": "Размер плиток SwinIR",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Наложение плиток SwinIR, в пикселях. Меньше = выделеннее шов",
+    "LDSR processing steps. Lower = faster": "Число шагов LDSR. Меньше = быстрее",
+    "Upscaler for img2img": "Апскейлер рисунка-в-рисунок",
+    "Upscale latent space image when doing hires. fix": "Апскейлить образ латентного пространства для HD-режима",
+    "Face restoration": "Восстановление лиц",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Вес CodeFormer； 0 = максимальное действие； 1 = минимальное",
+    "Move face restoration model from VRAM into RAM after processing": "Переместить модель восстановления лиц из ВОЗУ в ОЗУ после обработки",
+    "System": "Система",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Сколько раз в секунду следить за потреблением ВОЗУ. 0, чтобы отключить",
+    "Always print all generation info to standard output": "Выводить все сведения о генерации в стандартный вывод",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Вторая шкала прогресса для всей задачи",
+    "Training": "Обучение",
+    "Unload VAE and CLIP from VRAM when training": "Убрать VAE и CLIP из ВОЗУ на время обучения",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Переместить VAE и CLIP в ОЗУ на время обучения гиперсети. Сохраняет ВОЗУ",
+    "Filename word regex": "Regex имени файла",
+    "Filename join string": "Дополнить к имени файла",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Число повторов для каждого рисунка за эпоху; используется только, чтобы отобразить число эпохи",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Сохранять csv с параметром loss в папку журнала каждые N шагов, 0 - отключить",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "Удерживать веса в ОЗУ",
+    "Hypernetwork strength": "Сила гиперсети",
+    "Apply color correction to img2img results to match original colors.": "Цветокоррекция вывода рисунка-в-рисунок, сохраняющая исходные цвета",
+    "Save a copy of image before applying color correction to img2img results": "Сохранить копию рисунка перед цветокоррекцией",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "В режиме рисунок-в-рисунок сделать ровно указанное ползунком число шагов (обычно шумоподавление их уменьшает)",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Включить квантование К-семплерах для более резких и чистых результатов. Может потребовать поменять семя. Требует перезапуска.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Скобки: (понятие) - больше внимания к тексту, [понятие] - меньше внимания к тексту",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Включить старую обработку скобок. Может потребоваться, чтобы воспроизвести старые семена.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Заставить семплеры K-diffusion производить тот же самый рисунок в наборе, как и в единичной генерации",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Увеличить связность, достраивая запрос от последней запятой до n токенов, когда используется свыше 75 токенов",
+    "Filter NSFW content": "Фильтровать небезопасный контент",
+    "Stop At last layers of CLIP model": "Остановиться на последних слоях модели CLIP",
+    "Interrogate Options": "Опции распознавания",
+    "Interrogate: keep models in VRAM": "Распознавание: хранить модели в ВОЗУ",
+    "Interrogate: use artists from artists.csv": "Распознавание: использовать художников из artists.csv",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Распознавание: включить ранжирование совпавших тегов в результате (не работает для распознавателей-создателей заголовков)",
+    "Interrogate: num_beams for BLIP": "Распознавание: num_beams для BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Распознавание: минимальная длина описания (исключая художников и т.п.)",
+    "Interrogate: maximum description length": "Распознавание: максимальная длина описания",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: максимальное число строк в текстовом файле (0 = без ограничений)",
+    "Interrogate: deepbooru score threshold": "Распознавание: ограничение счёта deepbooru",
+    "Interrogate: deepbooru sort alphabetically": "Распознавание: сортировать deepbooru по алфавиту",
+    "use spaces for tags in deepbooru": "Пробелы для тегов deepbooru",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Использовать скобки в deepbooru как обычные скобки, а не для усиления",
+    "User interface": "Пользовательский интерфейс",
+    "Show progressbar": "Шкала прогресса",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Показывать процесс созданния рисунка каждые N шагов. 0 - отключить",
+    "Show grid in results for web": "Показать таблицу в выводе браузера",
+    "Do not show any images in results for web": "Не показывать выходные рисунки в браузере",
+    "Add model hash to generation information": "Добавить хеш весов к параметрам генерации",
+    "Add model name to generation information": "Добавить имя весов к параметрам генерации",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "При считывании параметров генерации из текста в интерфейс, не менять выбранную модель/веса.",
+    "Font for image grids that have text": "Шрифт для таблиц, содержащих текст",
+    "Enable full page image viewer": "Включить полноэкранный просмотр картинок",
+    "Show images zoomed in by default in full page image viewer": "По умолчанию увеличивать картинки в полноэкранном просмотре",
+    "Show generation progress in window title.": "Отображать прогресс в имени вкладки",
+    "Quicksettings list": "Список быстрых настроек",
+    "Localization (requires restart)": "Перевод （требует перезапуск）",
+    "Sampler parameters": "Параметры семплера",
+    "Hide samplers in user interface (requires restart)": "Убрать семплеры из интерфейса (требует перезапуск)",
+    "eta (noise multiplier) for DDIM": "eta (множитель шума) DDIM",
+    "eta (noise multiplier) for ancestral samplers": "eta (множитель шума) для ancestral-семплеров",
+    "img2img DDIM discretize": "дискретизация DDIM для рисунка-в-рисунок",
+    "uniform": "однородная",
+    "quad": "квадратичная",
+    "sigma churn": "сигма-вариация",
+    "sigma tmin": "сигма-tmin",
+    "sigma noise": "сигма-шум",
+    "Eta noise seed delta": "Eta (дельта шума семени)",
+    "Images Browser": "Просмотр изображений",
+    "Preload images at startup": "Предзагружать рисунки во время запуска",
+    "Number of pictures displayed on each page": "Число рисунков на каждой странице",
+    "Minimum number of pages per load": "Мин. число загружаемых страниц",
+    "Number of grids in each row": "Число таблиц в каждой строке",
+    "Request browser notifications": "Запросить уведомления браузера",
+    "Download localization template": "Загрузить щаблон перевода",
+    "Reload custom script bodies (No ui updates, No restart)": "Перезагрузить пользовательские скрипты (не требует обновления интерфейса и перезапуска)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Перезагрузить Gradio и обновить компоненты (только пользовательские скрипты, ui.py, js и css)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Запрос （нажмите Ctrl+Enter или Alt+Enter для генерации）",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Запрос-исключение （нажмите Ctrl+Enter или Alt+Enter для генерации）",
+    "Add a random artist to the prompt.": "Добавить случайного художника к запросу",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Считать параметры генерации из запроса или из предыдущей генерации в пользовательский интерфейс, если пусто",
+    "Save style": "Сохранить стиль",
+    "Apply selected styles to current prompt": "Применить выбранные стили к текущему промпту",
+    "Stop processing current image and continue processing.": "Прекратить обрабатывать текущий рисунок, но продолжить работу",
+    "Stop processing images and return any results accumulated so far.": "Прекратить обрабатку рисунков и вернуть всё, что успели сделать.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Стиль к применению; стили содержат как запрос, так и исключение, и применяют их оба",
+    "Do not do anything special": "Не делать ничего особенного",
+    "Which algorithm to use to produce the image": "Какой алгоритм использовать для того, чтобы произвести рисунок",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - очень творческий, в зависимости от числа шагов может привести совершенно к различным результатам, выше 30-40 лучше не ставить",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit модели - лучше всего для обрисовки",
+    "Produce an image that can be tiled.": "Сделать из рисунка непрерывную обёртку",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Применить двушаговый процесс, чтобы создать рисунок на меньшем разрешении, апскейлнуть, а затем улучшить детали без смены композиции",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Определяет, насколько сильно алгоритм будет опираться на содержание изображения. 0 - не меняет ничего, 1 - совсем не связанный выход. Меньше 1.0 процесс использует меньше шагов, чем указано их ползунком.",
+    "How many batches of images to create": "Сколько создать наборов из картинок",
+    "How many image to create in a single batch": "Сколько картинок создать в каждом наборе",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale: насколько сильно изображение должно соответсвтовать запросу — меньшие значения приведут к более свободным итогам",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Значение, которое определяет выход генератора случайных чисел — если вы создадите рисунок с теми же параметрами и семенем, как у другого изображения, вы получите тот же результат",
+    "Set seed to -1, which will cause a new random number to be used every time": "Установить семя в -1, что вызовет каждый раз случайное число",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Использовать семя предыдущей генерации, обычно полезно, если оно было случайным",
+    "Seed of a different picture to be mixed into the generation.": "Семя с другого рисунка, подмешенного в генерацию.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Насколько сильную вариацию произвести. При 0м значении действия не будет. Для 1 вы получите полноценный рисунок с семенем вариации (кроме ancestral-семплеров, где вы просто что-то получите).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Попытаться воспроизвести изображение, похожее на то, чтобы получилось с тем же семенем на выбранном разрешении",
+    "This text is used to rotate the feature space of the imgs embs": "Этот текст используется, чтобы произвести вращение пространства признаков из эмбеддинга рисунков",
+    "Separate values for X axis using commas.": "Отдельные значения оси X через запятую.",
+    "Separate values for Y axis using commas.": "Отдельные значения оси Y через запятую.",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Записать изображение в папку (по-умолчанию - log/images), а параметры генерации - в csv файл",
+    "Open images output directory": "Открыть папку сохранения изображений",
+    "How much to blur the mask before processing, in pixels.": "Насколько пикселей размыть трафарет перед обработкой",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Что поместить в область под трафаретом перед обработкой Stable Diffusion",
+    "fill it with colors of the image": "залить цветами изображения",
+    "keep whatever was there originally": "сохранить то, что было до этого",
+    "fill it with latent space noise": "залить латентным шумом",
+    "fill it with latent space zeroes": "залить латентными нулями",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "апскейл до нужного разрешения, врисовка, сжатие до начального размера и вставка в исходный рисунок",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Масшабировать изображение до нужного разрешения. Если только высота и ширина не совпадают, вы получите неверное соотношение сторон.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Масштабировать изображение так, чтобы им заполнялось всё выбранное выходное разрешение. Обрезать выступающие части",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Масштабировать изображение так, всё изображение помещалось в выбранное выходное разрешение. Заполнить пустое место цветами изображения.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Сколько раз повторить обработку изображения и использовать её как вход для следующией итерации",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "В режиме прокрутки, для каждого цикла сила шумоподавления умножается на это значение. <1 уменьшает вариации так, чтобы последовательность сошлась на какой-то одной картинке. >1 увеличивает вариации, так что ваша последовательность станет всё более и более сумбурной.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Для SD-апскейла, как много перекрытия в пикселях должно быть между плитками. Плитки перекрываются таким образом, чтобы они могли сойтись обратно в единое изображение, без видимого шва.",
+    "A directory on the same machine where the server is running.": "Папка на той же машине, где запущен сервер",
+    "Leave blank to save images to the default path.": "Оставьте пустым, чтобы сохранить рисунки в папку по-умолчанию",
+    "Result = A * (1 - M) + B * M": "Выход = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Выход = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "1я и последняя цифры должны быть 1. напр.'1, 2, 1'",
+    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "как быстро будет происходить обучение. Меньшие значения увеличат время обучения, но высокие могут нарушить сходимость модели (не будет создавать должные результаты) и/или сломать эмбеддинг. (Это случилось, если вы видете Loss: nan в текстовом окне вывода обучения. В этом случае вам придётся восстанавливать эмбеддинг вручную из старой, не повреждённой резервной копии).\n\nВы также можете указать единичное значение или последовательность из нескольких, используя следующий синтаксис:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nБудет обучаться со скоростью 0.005 первые 100 шагов, затем 1e-3 до 1000 шагов, после 1e-5 для всех оставшихся шагов.",
+    "Path to directory with input images": "Путь к папке со входными изображениями",
+    "Path to directory where to write outputs": "Путь к папке, в которую записывать результаты",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Используйте следующие теги, чтобы определить, как подбираются названия файлов для изображений: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; если пусто, используется значение по-умолчанию",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Когда эта опция включена, на созданные изображения не будет добавляться водяной знак. Предупреждение: не добавляя водяной знак, вы, вероятно, ведёте себя аморально.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Используйте следующие теги, чтобы определить, как подбираются названия подпапок для рисунков и табоиц: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; если пусто, используется значение по-умолчанию",
+    "Restore low quality faces using GFPGAN neural network": "Восстановить низкокачественные лица, используя нейросеть GFPGAN",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Это регулярное выражение будет использовано, чтобы извлечь слова из имени файла, и они будут соединены с текстом в метке ниже как вход во время обучения. Оставьте пустым, чтобы сохранить имя файла как есть",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Эта строка будет использована, чтобы объединить разделённые слова в одну строку, если включена опция выше.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Список имён настроек, разделённый запятыми, предназначенных для быстрого доступа через панель наверху, а не через привычную вкладку настроек. Для применения требует перезапуска.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Если это значение не нулевое, оно будет добавлено к семени и использовано для инициалицации ГСЧ шума семплеров с параметром Eta. Вы можете использовать это, чтобы произвести ещё больше вариаций рисунков, либо же для того, чтобы подойти близко к результатам других программ, если знаете, что делаете.",
+    "Enable Autocomplete": "Включить автодополнение",
+    "Allowed categories for random artists selection when using the Roll button": "Разрешённые категории художников для случайного выбора при использовании кнопки + три",
+    "Roll three": "+ три",
+    "Generate forever": "Непрерывная генерация",
+    "Cancel generate forever": "Отключить непрерывную генерацию"
+}
-- 
cgit v1.2.1


From 696cb33e50faf3f37859ebfba70fff902f46b8fb Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sun, 23 Oct 2022 16:46:54 +0900
Subject: after initial launch, disable --autolaunch for subsequent restarts

---
 webui.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/webui.py b/webui.py
index b1deca1b..a742c17d 100644
--- a/webui.py
+++ b/webui.py
@@ -135,6 +135,8 @@ def webui():
             inbrowser=cmd_opts.autolaunch,
             prevent_thread_lock=True
         )
+        # after initial launch, disable --autolaunch for subsequent restarts
+        cmd_opts.autolaunch = False
 
         app.add_middleware(GZipMiddleware, minimum_size=1000)
 
-- 
cgit v1.2.1


From 1be5933ba21a3badec42b7b2753d626f849b609d Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Sun, 23 Oct 2022 04:11:07 -0700
Subject: auto cropping now works with non square crops

---
 modules/textual_inversion/autocrop.py | 509 ++++++++++++++++++----------------
 1 file changed, 269 insertions(+), 240 deletions(-)

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 5a551c25..b2f9241c 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -1,241 +1,270 @@
-import cv2
-from collections import defaultdict
-from math import log, sqrt
-import numpy as np
-from PIL import Image, ImageDraw
-
-GREEN = "#0F0"
-BLUE = "#00F"
-RED = "#F00"
-
-
-def crop_image(im, settings):
-  """ Intelligently crop an image to the subject matter """
-  if im.height > im.width:
-      im = im.resize((settings.crop_width, settings.crop_height * im.height // im.width))
-  elif im.width > im.height:
-      im = im.resize((settings.crop_width * im.width // im.height, settings.crop_height))
-  else:
-      im = im.resize((settings.crop_width, settings.crop_height))
-
-  if im.height == im.width:
-    return im
-
-  focus = focal_point(im, settings)
-
-  # take the focal point and turn it into crop coordinates that try to center over the focal
-  # point but then get adjusted back into the frame
-  y_half = int(settings.crop_height / 2)
-  x_half = int(settings.crop_width / 2)
-
-  x1 = focus.x - x_half
-  if x1 < 0:
-      x1 = 0
-  elif x1 + settings.crop_width > im.width:
-      x1 = im.width - settings.crop_width
-
-  y1 = focus.y - y_half
-  if y1 < 0:
-      y1 = 0
-  elif y1 + settings.crop_height > im.height:
-      y1 = im.height - settings.crop_height
-
-  x2 = x1 + settings.crop_width
-  y2 = y1 + settings.crop_height
-
-  crop = [x1, y1, x2, y2]
-
-  if settings.annotate_image:
-    d = ImageDraw.Draw(im)
-    rect = list(crop)
-    rect[2] -= 1
-    rect[3] -= 1
-    d.rectangle(rect, outline=GREEN)
-    if settings.destop_view_image:
-      im.show()
-
-  return im.crop(tuple(crop))
-
-def focal_point(im, settings):
-    corner_points = image_corner_points(im, settings)
-    entropy_points = image_entropy_points(im, settings)
-    face_points = image_face_points(im, settings)
-
-    total_points = len(corner_points) + len(entropy_points) + len(face_points)
-
-    corner_weight = settings.corner_points_weight
-    entropy_weight = settings.entropy_points_weight
-    face_weight = settings.face_points_weight
-
-    weight_pref_total = corner_weight + entropy_weight + face_weight
-
-    # weight things
-    pois = []
-    if weight_pref_total == 0 or total_points == 0: 
-      return pois
-
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (corner_weight/weight_pref_total) / (len(corner_points)/total_points) )) for p in corner_points ]
-    )
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (entropy_weight/weight_pref_total) / (len(entropy_points)/total_points) )) for p in entropy_points ]
-    )
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ]
-    )
-
-    average_point = poi_average(pois, settings)
-
-    if settings.annotate_image:
-      d = ImageDraw.Draw(im)
-      for f in face_points:
-        d.rectangle(f.bounding(f.size), outline=RED)
-      for f in entropy_points:
-        d.rectangle(f.bounding(30), outline=BLUE)
-      for poi in pois:
-        w = max(4, 4 * 0.5 * sqrt(poi.weight))
-        d.ellipse(poi.bounding(w), fill=BLUE)
-      d.ellipse(average_point.bounding(25), outline=GREEN)
-      
-    return average_point
-
-
-def image_face_points(im, settings):
-    np_im = np.array(im)
-    gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
-
-    tries = [
-      [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
-    ]
-
-    for t in tries:
-      # print(t[0])
-      classifier = cv2.CascadeClassifier(t[0])
-      minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
-      try:
-        faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
-          minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
-      except:
-        continue
-
-      if len(faces) > 0:
-        rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
-        return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2])) for r in rects]
-    return []
-
-
-def image_corner_points(im, settings):
-    grayscale = im.convert("L")
-
-    # naive attempt at preventing focal points from collecting at watermarks near the bottom
-    gd = ImageDraw.Draw(grayscale)
-    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
-
-    np_im = np.array(grayscale)
-
-    points = cv2.goodFeaturesToTrack(
-        np_im,
-        maxCorners=100,
-        qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.07,
-        useHarrisDetector=False,
-    )
-
-    if points is None:
-        return []
-
-    focal_points = []
-    for point in points:
-      x, y = point.ravel()
-      focal_points.append(PointOfInterest(x, y, size=4))
-
-    return focal_points
-
-
-def image_entropy_points(im, settings):
-    landscape = im.height < im.width
-    portrait = im.height > im.width
-    if landscape:
-      move_idx = [0, 2]
-      move_max = im.size[0]
-    elif portrait:
-      move_idx = [1, 3]
-      move_max = im.size[1]
-    else:
-      return []
-
-    e_max = 0
-    crop_current = [0, 0, settings.crop_width, settings.crop_height]
-    crop_best = crop_current
-    while crop_current[move_idx[1]] < move_max:
-        crop = im.crop(tuple(crop_current))
-        e = image_entropy(crop)
-
-        if (e > e_max):
-          e_max = e
-          crop_best = list(crop_current)
-
-        crop_current[move_idx[0]] += 4
-        crop_current[move_idx[1]] += 4
-
-    x_mid = int(crop_best[0] + settings.crop_width/2)
-    y_mid = int(crop_best[1] + settings.crop_height/2)
-
-    return [PointOfInterest(x_mid, y_mid, size=25)]
-
-
-def image_entropy(im):
-    # greyscale image entropy
-    # band = np.asarray(im.convert("L"))
-    band = np.asarray(im.convert("1"), dtype=np.uint8)
-    hist, _ = np.histogram(band, bins=range(0, 256))
-    hist = hist[hist > 0]
-    return -np.log2(hist / hist.sum()).sum()
-
-
-def poi_average(pois, settings):
-    weight = 0.0
-    x = 0.0
-    y = 0.0
-    for poi in pois:
-        weight += poi.weight
-        x += poi.x * poi.weight
-        y += poi.y * poi.weight
-    avg_x = round(x / weight)
-    avg_y = round(y / weight)
-
-    return PointOfInterest(avg_x, avg_y)
-
-
-class PointOfInterest:
-  def __init__(self, x, y, weight=1.0, size=10):
-    self.x = x
-    self.y = y
-    self.weight = weight
-    self.size = size
-
-  def bounding(self, size):
-    return [
-      self.x - size//2,
-      self.y - size//2,
-      self.x + size//2,
-      self.y + size//2
-    ]
-
-
-class Settings:
-  def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False):
-    self.crop_width = crop_width
-    self.crop_height = crop_height
-    self.corner_points_weight = corner_points_weight
-    self.entropy_points_weight = entropy_points_weight
-    self.face_points_weight = entropy_points_weight
-    self.annotate_image = annotate_image
+import cv2
+from collections import defaultdict
+from math import log, sqrt
+import numpy as np
+from PIL import Image, ImageDraw
+
+GREEN = "#0F0"
+BLUE = "#00F"
+RED = "#F00"
+
+
+def crop_image(im, settings):
+  """ Intelligently crop an image to the subject matter """
+
+  scale_by = 1
+  if is_landscape(im.width, im.height):
+    scale_by = settings.crop_height / im.height
+  elif is_portrait(im.width, im.height):
+    scale_by = settings.crop_width / im.width
+  elif is_square(im.width, im.height):
+    if is_square(settings.crop_width, settings.crop_height):
+      scale_by = settings.crop_width / im.width
+    elif is_landscape(settings.crop_width, settings.crop_height):
+      scale_by = settings.crop_width / im.width
+    elif is_portrait(settings.crop_width, settings.crop_height):
+      scale_by = settings.crop_height / im.height
+
+  im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
+
+  if im.width == settings.crop_width and im.height == settings.crop_height:
+    if settings.annotate_image:
+      d = ImageDraw.Draw(im)
+      rect = [0, 0, im.width, im.height]
+      rect[2] -= 1
+      rect[3] -= 1
+      d.rectangle(rect, outline=GREEN)
+      if settings.destop_view_image:
+        im.show()
+    return im
+
+  focus = focal_point(im, settings)
+
+  # take the focal point and turn it into crop coordinates that try to center over the focal
+  # point but then get adjusted back into the frame
+  y_half = int(settings.crop_height / 2)
+  x_half = int(settings.crop_width / 2)
+
+  x1 = focus.x - x_half
+  if x1 < 0:
+      x1 = 0
+  elif x1 + settings.crop_width > im.width:
+      x1 = im.width - settings.crop_width
+
+  y1 = focus.y - y_half
+  if y1 < 0:
+      y1 = 0
+  elif y1 + settings.crop_height > im.height:
+      y1 = im.height - settings.crop_height
+
+  x2 = x1 + settings.crop_width
+  y2 = y1 + settings.crop_height
+
+  crop = [x1, y1, x2, y2]
+
+  if settings.annotate_image:
+    d = ImageDraw.Draw(im)
+    rect = list(crop)
+    rect[2] -= 1
+    rect[3] -= 1
+    d.rectangle(rect, outline=GREEN)
+    if settings.destop_view_image:
+      im.show()
+
+  return im.crop(tuple(crop))
+
+def focal_point(im, settings):
+    corner_points = image_corner_points(im, settings)
+    entropy_points = image_entropy_points(im, settings)
+    face_points = image_face_points(im, settings)
+
+    total_points = len(corner_points) + len(entropy_points) + len(face_points)
+
+    corner_weight = settings.corner_points_weight
+    entropy_weight = settings.entropy_points_weight
+    face_weight = settings.face_points_weight
+
+    weight_pref_total = corner_weight + entropy_weight + face_weight
+
+    # weight things
+    pois = []
+    if weight_pref_total == 0 or total_points == 0: 
+      return pois
+
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (corner_weight/weight_pref_total) / (len(corner_points)/total_points) )) for p in corner_points ]
+    )
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (entropy_weight/weight_pref_total) / (len(entropy_points)/total_points) )) for p in entropy_points ]
+    )
+    pois.extend(
+      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ]
+    )
+
+    average_point = poi_average(pois, settings)
+
+    if settings.annotate_image:
+      d = ImageDraw.Draw(im)
+      for f in face_points:
+        d.rectangle(f.bounding(f.size), outline=RED)
+      for f in entropy_points:
+        d.rectangle(f.bounding(30), outline=BLUE)
+      for poi in pois:
+        w = max(4, 4 * 0.5 * sqrt(poi.weight))
+        d.ellipse(poi.bounding(w), fill=BLUE)
+      d.ellipse(average_point.bounding(25), outline=GREEN)
+      
+    return average_point
+
+
+def image_face_points(im, settings):
+    np_im = np.array(im)
+    gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
+
+    tries = [
+      [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
+      [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
+    ]
+
+    for t in tries:
+      # print(t[0])
+      classifier = cv2.CascadeClassifier(t[0])
+      minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
+      try:
+        faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
+          minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
+      except:
+        continue
+
+      if len(faces) > 0:
+        rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
+        return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2])) for r in rects]
+    return []
+
+
+def image_corner_points(im, settings):
+    grayscale = im.convert("L")
+
+    # naive attempt at preventing focal points from collecting at watermarks near the bottom
+    gd = ImageDraw.Draw(grayscale)
+    gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
+
+    np_im = np.array(grayscale)
+
+    points = cv2.goodFeaturesToTrack(
+        np_im,
+        maxCorners=100,
+        qualityLevel=0.04,
+        minDistance=min(grayscale.width, grayscale.height)*0.07,
+        useHarrisDetector=False,
+    )
+
+    if points is None:
+        return []
+
+    focal_points = []
+    for point in points:
+      x, y = point.ravel()
+      focal_points.append(PointOfInterest(x, y, size=4))
+
+    return focal_points
+
+
+def image_entropy_points(im, settings):
+    landscape = im.height < im.width
+    portrait = im.height > im.width
+    if landscape:
+      move_idx = [0, 2]
+      move_max = im.size[0]
+    elif portrait:
+      move_idx = [1, 3]
+      move_max = im.size[1]
+    else:
+      return []
+
+    e_max = 0
+    crop_current = [0, 0, settings.crop_width, settings.crop_height]
+    crop_best = crop_current
+    while crop_current[move_idx[1]] < move_max:
+        crop = im.crop(tuple(crop_current))
+        e = image_entropy(crop)
+
+        if (e > e_max):
+          e_max = e
+          crop_best = list(crop_current)
+
+        crop_current[move_idx[0]] += 4
+        crop_current[move_idx[1]] += 4
+
+    x_mid = int(crop_best[0] + settings.crop_width/2)
+    y_mid = int(crop_best[1] + settings.crop_height/2)
+
+    return [PointOfInterest(x_mid, y_mid, size=25)]
+
+
+def image_entropy(im):
+    # greyscale image entropy
+    # band = np.asarray(im.convert("L"))
+    band = np.asarray(im.convert("1"), dtype=np.uint8)
+    hist, _ = np.histogram(band, bins=range(0, 256))
+    hist = hist[hist > 0]
+    return -np.log2(hist / hist.sum()).sum()
+
+
+def poi_average(pois, settings):
+    weight = 0.0
+    x = 0.0
+    y = 0.0
+    for poi in pois:
+        weight += poi.weight
+        x += poi.x * poi.weight
+        y += poi.y * poi.weight
+    avg_x = round(x / weight)
+    avg_y = round(y / weight)
+
+    return PointOfInterest(avg_x, avg_y)
+
+
+def is_landscape(w, h):
+  return w > h
+
+
+def is_portrait(w, h):
+  return h > w
+
+
+def is_square(w, h):
+  return w == h
+
+
+class PointOfInterest:
+  def __init__(self, x, y, weight=1.0, size=10):
+    self.x = x
+    self.y = y
+    self.weight = weight
+    self.size = size
+
+  def bounding(self, size):
+    return [
+      self.x - size//2,
+      self.y - size//2,
+      self.x + size//2,
+      self.y + size//2
+    ]
+
+
+class Settings:
+  def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False):
+    self.crop_width = crop_width
+    self.crop_height = crop_height
+    self.corner_points_weight = corner_points_weight
+    self.entropy_points_weight = entropy_points_weight
+    self.face_points_weight = entropy_points_weight
+    self.annotate_image = annotate_image
     self.destop_view_image = False
\ No newline at end of file
-- 
cgit v1.2.1


From 705bbf327f54e26facc833ddf620425453358dbc Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Sun, 23 Oct 2022 22:37:40 +0900
Subject: Rename ko-KR.json to ko_KR.json

---
 localizations/ko-KR.json | 422 -----------------------------------------------
 localizations/ko_KR.json | 422 +++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 422 insertions(+), 422 deletions(-)
 delete mode 100644 localizations/ko-KR.json
 create mode 100644 localizations/ko_KR.json

diff --git a/localizations/ko-KR.json b/localizations/ko-KR.json
deleted file mode 100644
index 7cc431c6..00000000
--- a/localizations/ko-KR.json
+++ /dev/null
@@ -1,422 +0,0 @@
-{
-  "×": "×",
-  "•": "•",
-  "⊞": "⊞",
-  "❮": "❮",
-  "❯": "❯",
-  "⤡": "⤡",
-  "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
-  "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
-  "A merger of the two checkpoints will be generated in your": "A merger of the two checkpoints will be generated in your",
-  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
-  "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
-  "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
-  "Add difference": "Add difference",
-  "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid",
-  "Add layer normalization": "Add layer normalization",
-  "Add model hash to generation information": "Add model hash to generation information",
-  "Add model name to generation information": "Add model name to generation information",
-  "Always print all generation info to standard output": "Always print all generation info to standard output",
-  "Always save all generated image grids": "Always save all generated image grids",
-  "Always save all generated images": "생성된 이미지 항상 저장하기",
-  "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
-  "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
-  "Apply settings": "설정 적용하기",
-  "BSRGAN 4x": "BSRGAN 4x",
-  "Batch Process": "Batch Process",
-  "Batch count": "배치 수",
-  "Batch from Directory": "Batch from Directory",
-  "Batch img2img": "이미지→이미지 배치",
-  "Batch size": "배치 크기",
-  "CFG Scale": "CFG 스케일",
-  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
-  "Cancel generate forever": "반복 생성 취소",
-  "Check progress (first)": "Check progress (first)",
-  "Check progress": "Check progress",
-  "Checkpoint Merger": "체크포인트 병합",
-  "Checkpoint name": "체크포인트 이름",
-  "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
-  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
-  "Clip skip": "클립 건너뛰기",
-  "CodeFormer visibility": "CodeFormer visibility",
-  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)",
-  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
-  "Color variation": "색깔 다양성",
-  "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
-  "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
-  "Create embedding": "Create embedding",
-  "Create flipped copies": "Create flipped copies",
-  "Create hypernetwork": "Create hypernetwork",
-  "Crop and resize": "잘라낸 후 리사이징",
-  "Crop to fit": "Crop to fit",
-  "Custom Name (Optional)": "Custom Name (Optional)",
-  "DDIM": "DDIM",
-  "DPM adaptive": "DPM adaptive",
-  "DPM fast": "DPM fast",
-  "DPM2 Karras": "DPM2 Karras",
-  "DPM2 a Karras": "DPM2 a Karras",
-  "DPM2 a": "DPM2 a",
-  "DPM2": "DPM2",
-  "Dataset directory": "Dataset directory",
-  "Decode CFG scale": "디코딩 CFG 스케일",
-  "Decode steps": "디코딩 스텝 수",
-  "Delete": "Delete",
-  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
-  "Denoising strength change factor": "디노이즈 강도 변경 배수",
-  "Denoising strength": "디노이즈 강도",
-  "Denoising": "디노이징",
-  "Destination directory": "Destination directory",
-  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
-  "Directory for saving images using the Save button": "Directory for saving images using the Save button",
-  "Directory name pattern": "Directory name pattern",
-  "Do not add watermark to images": "Do not add watermark to images",
-  "Do not do anything special": "아무것도 하지 않기",
-  "Do not save grids consisting of one picture": "Do not save grids consisting of one picture",
-  "Do not show any images in results for web": "Do not show any images in results for web",
-  "Download localization template": "Download localization template",
-  "Draw legend": "범례 그리기",
-  "Draw mask": "마스크 직접 그리기",
-  "Drop File Here": "Drop File Here",
-  "Drop Image Here": "Drop Image Here",
-  "ESRGAN_4x": "ESRGAN_4x",
-  "Embedding": "Embedding",
-  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
-  "Enable full page image viewer": "Enable full page image viewer",
-  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
-  "End Page": "End Page",
-  "Enter hypernetwork layer structure": "Enter hypernetwork layer structure",
-  "Eta noise seed delta": "Eta noise seed delta",
-  "Eta": "Eta",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
-  "Euler a": "Euler a",
-  "Euler": "Euler",
-  "Extra": "고급",
-  "Extras": "부가기능",
-  "Face restoration": "Face restoration",
-  "Fall-off exponent (lower=higher detail)": "감쇠 지수 (낮을수록 디테일이 올라감)",
-  "File Name": "File Name",
-  "File format for grids": "File format for grids",
-  "File format for images": "File format for images",
-  "File with inputs": "설정값 파일",
-  "File": "File",
-  "Filename join string": "Filename join string",
-  "Filename word regex": "Filename word regex",
-  "Filter NSFW content": "Filter NSFW content",
-  "First Page": "First Page",
-  "Firstpass height": "초기 세로길이",
-  "Firstpass width": "초기 가로길이",
-  "Font for image grids that have text": "Font for image grids that have text",
-  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD 업스케일링에서 타일 간 몇 픽셀을 겹치게 할지 결정하는 설정값입니다. 타일들이 다시 한 이미지로 합쳐질 때, 눈에 띄는 이음매가 없도록 서로 겹치게 됩니다.",
-  "GFPGAN visibility": "GFPGAN visibility",
-  "Generate Info": "Generate Info",
-  "Generate forever": "반복 생성",
-  "Generate": "생성",
-  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
-  "Height": "세로",
-  "Heun": "Heun",
-  "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
-  "Highres. fix": "고해상도 보정",
-  "History": "기록",
-  "How many batches of images to create": "생성할 이미지 배치 수",
-  "How many image to create in a single batch": "한 배치당 이미지 수",
-  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
-  "How many times to repeat processing an image and using it as input for the next iteration": "이미지를 생성 후 원본으로 몇 번 반복해서 사용할지 결정하는 값",
-  "How much to blur the mask before processing, in pixels.": "이미지 생성 전 마스크를 얼마나 블러처리할지 결정하는 값. 픽셀 단위",
-  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
-  "Hypernet str.": "하이퍼네트워크 강도",
-  "Hypernetwork strength": "Hypernetwork strength",
-  "Hypernetwork": "하이퍼네트워크",
-  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
-  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
-  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
-  "Image for img2img": "Image for img2img",
-  "Image for inpainting with mask": "Image for inpainting with mask",
-  "Image": "Image",
-  "Images filename pattern": "Images filename pattern",
-  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "루프백 모드에서는 매 루프마다 디노이즈 강도에 이 값이 곱해집니다. 1보다 작을 경우 다양성이 낮아져 결과 이미지들이 고정된 형태로 모일 겁니다. 1보다 클 경우 다양성이 높아져 결과 이미지들이 갈수록 혼란스러워지겠죠.",
-  "Include Separate Images": "분리된 이미지 포함하기",
-  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
-  "Initialization text": "Initialization text",
-  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트 패딩값(픽셀 단위)",
-  "Inpaint at full resolution": "전체 해상도로 인페인트하기",
-  "Inpaint masked": "마스크만 처리",
-  "Inpaint not masked": "마스크 이외만 처리",
-  "Inpaint": "인페인트",
-  "Input directory": "인풋 이미지 경로",
-  "Interpolation Method": "Interpolation Method",
-  "Interrogate\nCLIP": "CLIP\n분석",
-  "Interrogate\nDeepBooru": "DeepBooru\n분석",
-  "Interrogate Options": "Interrogate Options",
-  "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold",
-  "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically",
-  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
-  "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM",
-  "Interrogate: maximum description length": "Interrogate: maximum description length",
-  "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
-  "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
-  "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
-  "Interrupt": "중단",
-  "Just resize": "리사이징",
-  "Keep -1 for seeds": "시드값 -1로 유지",
-  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
-  "LDSR": "LDSR",
-  "LMS Karras": "LMS Karras",
-  "LMS": "LMS",
-  "Label": "Label",
-  "Lanczos": "Lanczos",
-  "Learning rate": "Learning rate",
-  "Leave blank to save images to the default path.": "Leave blank to save images to the default path.",
-  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
-  "Loading...": "로딩 중...",
-  "Localization (requires restart)": "Localization (requires restart)",
-  "Log directory": "Log directory",
-  "Loopback": "루프백",
-  "Loops": "루프 수",
-  "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
-  "Make Zip when Save?": "저장 시 Zip 생성하기",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
-  "Mask blur": "마스크 블러",
-  "Mask mode": "Mask mode",
-  "Mask": "마스크",
-  "Masked content": "마스크된 부분",
-  "Masking mode": "Masking mode",
-  "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
-  "Max steps": "Max steps",
-  "Modules": "Modules",
-  "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
-  "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - set to 0 to get model A",
-  "Name": "Name",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Negative prompt": "네거티브 프롬프트",
-  "Next Page": "Next Page",
-  "None": "None",
-  "Nothing": "없음",
-  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
-  "Number of vectors per token": "Number of vectors per token",
-  "Open images output directory": "이미지 저장 경로 열기",
-  "Open output directory": "Open output directory",
-  "Original negative prompt": "기존 네거티브 프롬프트",
-  "Original prompt": "기존 프롬프트",
-  "Outpainting direction": "아웃페인팅 방향",
-  "Outpainting mk2": "아웃페인팅 마크 2",
-  "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
-  "Output directory for images from extras tab": "Output directory for images from extras tab",
-  "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
-  "Output directory for img2img grids": "Output directory for img2img grids",
-  "Output directory for img2img images": "Output directory for img2img images",
-  "Output directory for txt2img grids": "Output directory for txt2img grids",
-  "Output directory for txt2img images": "Output directory for txt2img images",
-  "Output directory": "이미지 저장 경로",
-  "Override `Denoising strength` to 1?": "디노이즈 강도를 1로 적용할까요?",
-  "Override `Sampling Steps` to the same value as `Decode steps`?": "샘플링 스텝 수를 디코딩 스텝 수와 동일하게 적용할까요?",
-  "Override `Sampling method` to Euler?(this method is built for it)": "샘플링 방법을 Euler로 적용할까요?(이 기능은 해당 샘플러를 위해 만들어져 있습니다)",
-  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "프롬프트 값을 기존 프롬프트와 동일하게 적용할까요?(네거티브 프롬프트 포함)",
-  "PLMS": "PLMS",
-  "PNG Info": "PNG 정보",
-  "Page Index": "Page Index",
-  "Path to directory where to write outputs": "Path to directory where to write outputs",
-  "Path to directory with input images": "Path to directory with input images",
-  "Paths for saving": "Paths for saving",
-  "Pixels to expand": "확장할 픽셀 수",
-  "Poor man's outpainting": "가난뱅이의 아웃페인팅",
-  "Preprocess images": "Preprocess images",
-  "Preprocess": "Preprocess",
-  "Prev Page": "Prev Page",
-  "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)",
-  "Primary model (A)": "Primary model (A)",
-  "Process an image, use it as an input, repeat.": "이미지를 생성하고, 생성한 이미지를 다시 원본으로 사용하는 과정을 반복합니다.",
-  "Process images in a directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리의 이미지들을 처리합니다.",
-  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Prompt S/R": "프롬프트 스타일 변경",
-  "Prompt matrix": "프롬프트 매트릭스",
-  "Prompt order": "프롬프트 순서",
-  "Prompt template file": "Prompt template file",
-  "Prompt": "프롬프트",
-  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
-  "Prompts": "프롬프트",
-  "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
-  "Quality for saved jpeg images": "Quality for saved jpeg images",
-  "Quicksettings list": "Quicksettings list",
-  "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
-  "Randomness": "랜덤성",
-  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
-  "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews",
-  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "추천 설정값 - 샘플링 스텝 수 : 80-100 , 샘플러 : Euler a, 디노이즈 강도 : 0.8",
-  "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
-  "Renew Page": "Renew Page",
-  "Request browser notifications": "Request browser notifications",
-  "Resize and fill": "리사이징 후 채우기",
-  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "설정된 해상도로 이미지 리사이징을 진행합니다. 원본과 가로/세로 길이가 일치하지 않을 경우, 부정확한 화면비의 이미지를 얻게 됩니다.",
-  "Resize mode": "Resize mode",
-  "Resize seed from height": "시드 리사이징 가로길이",
-  "Resize seed from width": "시드 리사이징 세로길이",
-  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "이미지 전체가 설정된 해상도 내부에 들어가게 리사이징을 진행합니다. 빈 공간은 이미지의 색상으로 채웁니다.",
-  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "설정된 해상도 전체가 이미지로 가득차게 리사이징을 진행합니다. 튀어나오는 부분은 잘라냅니다.",
-  "Resize": "Resize",
-  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
-  "Restore faces": "얼굴 보정",
-  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
-  "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M",
-  "Result = A + (B - C) * M": "Result = A + (B - C) * M",
-  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
-  "Run": "Run",
-  "SD upscale": "SD 업스케일링",
-  "Sampler parameters": "Sampler parameters",
-  "Sampler": "샘플러",
-  "Sampling Steps": "샘플링 스텝 수",
-  "Sampling method": "샘플링 방법",
-  "Save a copy of embedding to log directory every N steps, 0 to disable": "Save a copy of embedding to log directory every N steps, 0 to disable",
-  "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
-  "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
-  "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
-  "Save an image to log directory every N steps, 0 to disable": "Save an image to log directory every N steps, 0 to disable",
-  "Save as float16": "Save as float16",
-  "Save grids to a subdirectory": "Save grids to a subdirectory",
-  "Save images to a subdirectory": "Save images to a subdirectory",
-  "Save images with embedding in PNG chunks": "Save images with embedding in PNG chunks",
-  "Save style": "스타일 저장",
-  "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
-  "Save": "저장",
-  "Saving images/grids": "Saving images/grids",
-  "Saving to a directory": "Saving to a directory",
-  "Scale by": "Scale by",
-  "Scale to": "Scale to",
-  "Script": "스크립트",
-  "ScuNET GAN": "ScuNET GAN",
-  "ScuNET PSNR": "ScuNET PSNR",
-  "Secondary model (B)": "Secondary model (B)",
-  "See": "See",
-  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
-  "Seed": "시드",
-  "Send to extras": "부가기능으로 전송",
-  "Send to img2img": "이미지→이미지로 전송",
-  "Send to inpaint": "인페인트로 전송",
-  "Send to txt2img": "텍스트→이미지로 전송",
-  "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "(|)를 이용해 프롬프트를 분리할 시 첫 프롬프트를 제외하고 모든 프롬프트의 조합마다 이미지를 생성합니다. 첫 프롬프트는 모든 조합에 포함되게 됩니다.",
-  "Separate values for X axis using commas.": "쉼표로 X축에 적용할 값 분리",
-  "Separate values for Y axis using commas.": "쉼표로 Y축에 적용할 값 분리",
-  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
-  "Settings": "설정",
-  "Show Textbox": "텍스트박스 보이기",
-  "Show generation progress in window title.": "Show generation progress in window title.",
-  "Show grid in results for web": "Show grid in results for web",
-  "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
-  "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
-  "Show progressbar": "Show progressbar",
-  "Show result images": "Show result images",
-  "Sigma Churn": "시그마 섞기",
-  "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
-  "Sigma max": "시그마 최댓값",
-  "Sigma min": "시그마 최솟값",
-  "Sigma noise": "시그마 노이즈",
-  "Single Image": "Single Image",
-  "Skip": "건너뛰기",
-  "Source directory": "Source directory",
-  "Source": "Source",
-  "Split oversized images into two": "Split oversized images into two",
-  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
-  "Stable Diffusion": "Stable Diffusion",
-  "Steps": "스텝 수",
-  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
-  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
-  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
-  "Style 1": "스타일 1",
-  "Style 2": "스타일 2",
-  "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
-  "SwinIR 4x": "SwinIR 4x",
-  "System": "System",
-  "Tertiary model (C)": "Tertiary model (C)",
-  "Textbox": "Textbox",
-  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
-  "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
-  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
-  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
-  "Tile overlap": "타일 겹침",
-  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
-  "Tile size for all SwinIR.": "Tile size for all SwinIR.",
-  "Tiling": "타일링",
-  "Train Embedding": "Train Embedding",
-  "Train Hypernetwork": "Train Hypernetwork",
-  "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Train an embedding; must specify a directory with a set of 1:1 ratio images",
-  "Train": "훈련",
-  "Training": "Training",
-  "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training",
-  "Upload mask": "마스크 업로드하기",
-  "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
-  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
-  "Upscaler 2 visibility": "Upscaler 2 visibility",
-  "Upscaler for img2img": "Upscaler for img2img",
-  "Upscaler": "업스케일러",
-  "Upscaling": "Upscaling",
-  "Use BLIP for caption": "Use BLIP for caption",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
-  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "저장 경로를 비워두면 기본 저장 폴더에 이미지들이 저장됩니다.",
-  "Use deepbooru for caption": "Use deepbooru for caption",
-  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
-  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
-  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
-  "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
-  "User interface": "User interface",
-  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
-  "Var. seed": "바리에이션 시드",
-  "Var. strength": "바리에이션 강도",
-  "Variation seed": "바리에이션 시드",
-  "Variation strength": "바리에이션 강도",
-  "Weighted sum": "Weighted sum",
-  "What to put inside the masked area before processing it with Stable Diffusion.": "Stable Diffusion으로 이미지를 생성하기 전 마스크된 부분에 무엇을 채울지 결정하는 설정값",
-  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
-  "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
-  "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
-  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
-  "Width": "가로",
-  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "이미지를 설정된 사이즈의 2배로 업스케일합니다. 상단의 가로와 세로 슬라이더를 이용해 타일 사이즈를 지정하세요.",
-  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
-  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
-  "X type": "X축",
-  "X values": "X 설정값",
-  "X/Y plot": "X/Y 플롯",
-  "Y type": "Y축",
-  "Y values": "Y 설정값",
-  "api": "",
-  "built with gradio": "gradio로 제작되었습니다",
-  "checkpoint": "checkpoint",
-  "directory.": "directory.",
-  "down": "아래쪽",
-  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
-  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
-  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
-  "extras history": "extras history",
-  "fill it with colors of the image": "이미지의 색상으로 채우기",
-  "fill it with latent space noise": "잠재 공간 노이즈로 채우기",
-  "fill it with latent space zeroes": "잠재 공간의 0값으로 채우기",
-  "fill": "채우기",
-  "for detailed explanation.": "for detailed explanation.",
-  "hide": "api 숨기기",
-  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
-  "img2img DDIM discretize": "img2img DDIM discretize",
-  "img2img alternative test": "이미지→이미지 대체버전 테스트",
-  "img2img history": "img2img history",
-  "img2img": "이미지→이미지",
-  "keep whatever was there originally": "이미지 원본 유지",
-  "latent noise": "잠재 노이즈",
-  "latent nothing": "잠재 공백",
-  "left": "왼쪽",
-  "number of images to delete consecutively next": "number of images to delete consecutively next",
-  "or": "or",
-  "original": "원본 유지",
-  "quad": "quad",
-  "right": "오른쪽",
-  "set_index": "set_index",
-  "should be 2 or lower.": "이 2 이하여야 합니다.",
-  "sigma churn": "sigma churn",
-  "sigma noise": "sigma noise",
-  "sigma tmin": "sigma tmin",
-  "txt2img history": "txt2img history",
-  "txt2img": "텍스트→이미지",
-  "uniform": "uniform",
-  "up": "위쪽",
-  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
-  "view": "api 보이기",
-  "wiki": "wiki"
-}
\ No newline at end of file
diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
new file mode 100644
index 00000000..f665042e
--- /dev/null
+++ b/localizations/ko_KR.json
@@ -0,0 +1,422 @@
+{
+  "×": "×",
+  "•": "•",
+  "⊞": "⊞",
+  "❮": "❮",
+  "❯": "❯",
+  "⤡": "⤡",
+  "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
+  "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
+  "A merger of the two checkpoints will be generated in your": "A merger of the two checkpoints will be generated in your",
+  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
+  "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
+  "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
+  "Add difference": "Add difference",
+  "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid",
+  "Add layer normalization": "Add layer normalization",
+  "Add model hash to generation information": "Add model hash to generation information",
+  "Add model name to generation information": "Add model name to generation information",
+  "Always print all generation info to standard output": "Always print all generation info to standard output",
+  "Always save all generated image grids": "Always save all generated image grids",
+  "Always save all generated images": "생성된 이미지 항상 저장하기",
+  "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
+  "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
+  "Apply settings": "설정 적용하기",
+  "BSRGAN 4x": "BSRGAN 4x",
+  "Batch Process": "Batch Process",
+  "Batch count": "배치 수",
+  "Batch from Directory": "Batch from Directory",
+  "Batch img2img": "이미지→이미지 배치",
+  "Batch size": "배치 크기",
+  "CFG Scale": "CFG 스케일",
+  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
+  "Cancel generate forever": "반복 생성 취소",
+  "Check progress (first)": "Check progress (first)",
+  "Check progress": "Check progress",
+  "Checkpoint Merger": "체크포인트 병합",
+  "Checkpoint name": "체크포인트 이름",
+  "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
+  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
+  "Clip skip": "클립 건너뛰기",
+  "CodeFormer visibility": "CodeFormer visibility",
+  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)",
+  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
+  "Color variation": "색깔 다양성",
+  "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
+  "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
+  "Create embedding": "Create embedding",
+  "Create flipped copies": "Create flipped copies",
+  "Create hypernetwork": "Create hypernetwork",
+  "Crop and resize": "잘라낸 후 리사이징",
+  "Crop to fit": "Crop to fit",
+  "Custom Name (Optional)": "Custom Name (Optional)",
+  "DDIM": "DDIM",
+  "DPM adaptive": "DPM adaptive",
+  "DPM fast": "DPM fast",
+  "DPM2 Karras": "DPM2 Karras",
+  "DPM2 a Karras": "DPM2 a Karras",
+  "DPM2 a": "DPM2 a",
+  "DPM2": "DPM2",
+  "Dataset directory": "Dataset directory",
+  "Decode CFG scale": "디코딩 CFG 스케일",
+  "Decode steps": "디코딩 스텝 수",
+  "Delete": "Delete",
+  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
+  "Denoising strength change factor": "디노이즈 강도 변경 배수",
+  "Denoising strength": "디노이즈 강도",
+  "Denoising": "디노이징",
+  "Destination directory": "Destination directory",
+  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
+  "Directory for saving images using the Save button": "Directory for saving images using the Save button",
+  "Directory name pattern": "Directory name pattern",
+  "Do not add watermark to images": "Do not add watermark to images",
+  "Do not do anything special": "아무것도 하지 않기",
+  "Do not save grids consisting of one picture": "Do not save grids consisting of one picture",
+  "Do not show any images in results for web": "Do not show any images in results for web",
+  "Download localization template": "Download localization template",
+  "Draw legend": "범례 그리기",
+  "Draw mask": "마스크 직접 그리기",
+  "Drop File Here": "Drop File Here",
+  "Drop Image Here": "Drop Image Here",
+  "ESRGAN_4x": "ESRGAN_4x",
+  "Embedding": "Embedding",
+  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
+  "Enable full page image viewer": "Enable full page image viewer",
+  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
+  "End Page": "End Page",
+  "Enter hypernetwork layer structure": "Enter hypernetwork layer structure",
+  "Eta noise seed delta": "Eta noise seed delta",
+  "Eta": "Eta",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
+  "Euler a": "Euler a",
+  "Euler": "Euler",
+  "Extra": "고급",
+  "Extras": "부가기능",
+  "Face restoration": "Face restoration",
+  "Fall-off exponent (lower=higher detail)": "감쇠 지수 (낮을수록 디테일이 올라감)",
+  "File Name": "File Name",
+  "File format for grids": "File format for grids",
+  "File format for images": "File format for images",
+  "File with inputs": "설정값 파일",
+  "File": "File",
+  "Filename join string": "Filename join string",
+  "Filename word regex": "Filename word regex",
+  "Filter NSFW content": "Filter NSFW content",
+  "First Page": "First Page",
+  "Firstpass height": "초기 세로길이",
+  "Firstpass width": "초기 가로길이",
+  "Font for image grids that have text": "Font for image grids that have text",
+  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD 업스케일링에서 타일 간 몇 픽셀을 겹치게 할지 결정하는 설정값입니다. 타일들이 다시 한 이미지로 합쳐질 때, 눈에 띄는 이음매가 없도록 서로 겹치게 됩니다.",
+  "GFPGAN visibility": "GFPGAN visibility",
+  "Generate Info": "Generate Info",
+  "Generate forever": "반복 생성",
+  "Generate": "생성",
+  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
+  "Height": "세로",
+  "Heun": "Heun",
+  "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
+  "Highres. fix": "고해상도 보정",
+  "History": "기록",
+  "How many batches of images to create": "생성할 이미지 배치 수",
+  "How many image to create in a single batch": "한 배치당 이미지 수",
+  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
+  "How many times to repeat processing an image and using it as input for the next iteration": "이미지를 생성 후 원본으로 몇 번 반복해서 사용할지 결정하는 값",
+  "How much to blur the mask before processing, in pixels.": "이미지 생성 전 마스크를 얼마나 블러처리할지 결정하는 값. 픽셀 단위",
+  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
+  "Hypernet str.": "하이퍼네트워크 강도",
+  "Hypernetwork strength": "Hypernetwork strength",
+  "Hypernetwork": "하이퍼네트워크",
+  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
+  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
+  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
+  "Image for img2img": "Image for img2img",
+  "Image for inpainting with mask": "Image for inpainting with mask",
+  "Image": "Image",
+  "Images filename pattern": "Images filename pattern",
+  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "루프백 모드에서는 매 루프마다 디노이즈 강도에 이 값이 곱해집니다. 1보다 작을 경우 다양성이 낮아져 결과 이미지들이 고정된 형태로 모일 겁니다. 1보다 클 경우 다양성이 높아져 결과 이미지들이 갈수록 혼란스러워지겠죠.",
+  "Include Separate Images": "분리된 이미지 포함하기",
+  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
+  "Initialization text": "Initialization text",
+  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트 패딩값(픽셀 단위)",
+  "Inpaint at full resolution": "전체 해상도로 인페인트하기",
+  "Inpaint masked": "마스크만 처리",
+  "Inpaint not masked": "마스크 이외만 처리",
+  "Inpaint": "인페인트",
+  "Input directory": "인풋 이미지 경로",
+  "Interpolation Method": "Interpolation Method",
+  "Interrogate\nCLIP": "CLIP\n분석",
+  "Interrogate\nDeepBooru": "DeepBooru\n분석",
+  "Interrogate Options": "Interrogate Options",
+  "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold",
+  "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically",
+  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
+  "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM",
+  "Interrogate: maximum description length": "Interrogate: maximum description length",
+  "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
+  "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
+  "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
+  "Interrupt": "중단",
+  "Just resize": "리사이징",
+  "Keep -1 for seeds": "시드값 -1로 유지",
+  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
+  "LDSR": "LDSR",
+  "LMS Karras": "LMS Karras",
+  "LMS": "LMS",
+  "Label": "Label",
+  "Lanczos": "Lanczos",
+  "Learning rate": "Learning rate",
+  "Leave blank to save images to the default path.": "Leave blank to save images to the default path.",
+  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
+  "Loading...": "로딩 중...",
+  "Localization (requires restart)": "Localization (requires restart)",
+  "Log directory": "Log directory",
+  "Loopback": "루프백",
+  "Loops": "루프 수",
+  "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
+  "Make Zip when Save?": "저장 시 Zip 생성하기",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
+  "Mask blur": "마스크 블러",
+  "Mask mode": "Mask mode",
+  "Mask": "마스크",
+  "Masked content": "마스크된 부분",
+  "Masking mode": "Masking mode",
+  "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
+  "Max steps": "Max steps",
+  "Modules": "Modules",
+  "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
+  "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - set to 0 to get model A",
+  "Name": "Name",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Negative prompt": "네거티브 프롬프트",
+  "Next Page": "Next Page",
+  "None": "None",
+  "Nothing": "없음",
+  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
+  "Number of vectors per token": "Number of vectors per token",
+  "Open images output directory": "이미지 저장 경로 열기",
+  "Open output directory": "Open output directory",
+  "Original negative prompt": "기존 네거티브 프롬프트",
+  "Original prompt": "기존 프롬프트",
+  "Outpainting direction": "아웃페인팅 방향",
+  "Outpainting mk2": "아웃페인팅 마크 2",
+  "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
+  "Output directory for images from extras tab": "Output directory for images from extras tab",
+  "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
+  "Output directory for img2img grids": "Output directory for img2img grids",
+  "Output directory for img2img images": "Output directory for img2img images",
+  "Output directory for txt2img grids": "Output directory for txt2img grids",
+  "Output directory for txt2img images": "Output directory for txt2img images",
+  "Output directory": "이미지 저장 경로",
+  "Override `Denoising strength` to 1?": "디노이즈 강도를 1로 적용할까요?",
+  "Override `Sampling Steps` to the same value as `Decode steps`?": "샘플링 스텝 수를 디코딩 스텝 수와 동일하게 적용할까요?",
+  "Override `Sampling method` to Euler?(this method is built for it)": "샘플링 방법을 Euler로 적용할까요?(이 기능은 해당 샘플러를 위해 만들어져 있습니다)",
+  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "프롬프트 값을 기존 프롬프트와 동일하게 적용할까요?(네거티브 프롬프트 포함)",
+  "PLMS": "PLMS",
+  "PNG Info": "PNG 정보",
+  "Page Index": "Page Index",
+  "Path to directory where to write outputs": "Path to directory where to write outputs",
+  "Path to directory with input images": "Path to directory with input images",
+  "Paths for saving": "Paths for saving",
+  "Pixels to expand": "확장할 픽셀 수",
+  "Poor man's outpainting": "가난뱅이의 아웃페인팅",
+  "Preprocess images": "Preprocess images",
+  "Preprocess": "Preprocess",
+  "Prev Page": "Prev Page",
+  "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)",
+  "Primary model (A)": "Primary model (A)",
+  "Process an image, use it as an input, repeat.": "이미지를 생성하고, 생성한 이미지를 다시 원본으로 사용하는 과정을 반복합니다.",
+  "Process images in a directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리의 이미지들을 처리합니다.",
+  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Prompt S/R": "프롬프트 스타일 변경",
+  "Prompt matrix": "프롬프트 매트릭스",
+  "Prompt order": "프롬프트 순서",
+  "Prompt template file": "Prompt template file",
+  "Prompt": "프롬프트",
+  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
+  "Prompts": "프롬프트",
+  "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
+  "Quality for saved jpeg images": "Quality for saved jpeg images",
+  "Quicksettings list": "Quicksettings list",
+  "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+  "Randomness": "랜덤성",
+  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
+  "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews",
+  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "추천 설정값 - 샘플링 스텝 수 : 80-100 , 샘플러 : Euler a, 디노이즈 강도 : 0.8",
+  "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
+  "Renew Page": "Renew Page",
+  "Request browser notifications": "Request browser notifications",
+  "Resize and fill": "리사이징 후 채우기",
+  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "설정된 해상도로 이미지 리사이징을 진행합니다. 원본과 가로/세로 길이가 일치하지 않을 경우, 부정확한 화면비의 이미지를 얻게 됩니다.",
+  "Resize mode": "Resize mode",
+  "Resize seed from height": "시드 리사이징 가로길이",
+  "Resize seed from width": "시드 리사이징 세로길이",
+  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "이미지 전체가 설정된 해상도 내부에 들어가게 리사이징을 진행합니다. 빈 공간은 이미지의 색상으로 채웁니다.",
+  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "설정된 해상도 전체가 이미지로 가득차게 리사이징을 진행합니다. 튀어나오는 부분은 잘라냅니다.",
+  "Resize": "Resize",
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
+  "Restore faces": "얼굴 보정",
+  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
+  "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M",
+  "Result = A + (B - C) * M": "Result = A + (B - C) * M",
+  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
+  "Run": "Run",
+  "SD upscale": "SD 업스케일링",
+  "Sampler parameters": "Sampler parameters",
+  "Sampler": "샘플러",
+  "Sampling Steps": "샘플링 스텝 수",
+  "Sampling method": "샘플링 방법",
+  "Save a copy of embedding to log directory every N steps, 0 to disable": "Save a copy of embedding to log directory every N steps, 0 to disable",
+  "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
+  "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
+  "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
+  "Save an image to log directory every N steps, 0 to disable": "Save an image to log directory every N steps, 0 to disable",
+  "Save as float16": "Save as float16",
+  "Save grids to a subdirectory": "Save grids to a subdirectory",
+  "Save images to a subdirectory": "Save images to a subdirectory",
+  "Save images with embedding in PNG chunks": "Save images with embedding in PNG chunks",
+  "Save style": "스타일 저장",
+  "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
+  "Save": "저장",
+  "Saving images/grids": "Saving images/grids",
+  "Saving to a directory": "Saving to a directory",
+  "Scale by": "Scale by",
+  "Scale to": "Scale to",
+  "Script": "스크립트",
+  "ScuNET GAN": "ScuNET GAN",
+  "ScuNET PSNR": "ScuNET PSNR",
+  "Secondary model (B)": "Secondary model (B)",
+  "See": "See",
+  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "Seed": "시드",
+  "Send to extras": "부가기능으로 전송",
+  "Send to img2img": "이미지→이미지로 전송",
+  "Send to inpaint": "인페인트로 전송",
+  "Send to txt2img": "텍스트→이미지로 전송",
+  "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "(|)를 이용해 프롬프트를 분리할 시 첫 프롬프트를 제외하고 모든 프롬프트의 조합마다 이미지를 생성합니다. 첫 프롬프트는 모든 조합에 포함되게 됩니다.",
+  "Separate values for X axis using commas.": "쉼표로 X축에 적용할 값 분리",
+  "Separate values for Y axis using commas.": "쉼표로 Y축에 적용할 값 분리",
+  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
+  "Settings": "설정",
+  "Show Textbox": "텍스트박스 보이기",
+  "Show generation progress in window title.": "Show generation progress in window title.",
+  "Show grid in results for web": "Show grid in results for web",
+  "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
+  "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
+  "Show progressbar": "Show progressbar",
+  "Show result images": "Show result images",
+  "Sigma Churn": "시그마 섞기",
+  "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
+  "Sigma max": "시그마 최댓값",
+  "Sigma min": "시그마 최솟값",
+  "Sigma noise": "시그마 노이즈",
+  "Single Image": "Single Image",
+  "Skip": "건너뛰기",
+  "Source directory": "Source directory",
+  "Source": "Source",
+  "Split oversized images into two": "Split oversized images into two",
+  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "Stable Diffusion": "Stable Diffusion",
+  "Steps": "스텝 수",
+  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
+  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
+  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
+  "Style 1": "스타일 1",
+  "Style 2": "스타일 2",
+  "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
+  "SwinIR 4x": "SwinIR 4x",
+  "System": "System",
+  "Tertiary model (C)": "Tertiary model (C)",
+  "Textbox": "Textbox",
+  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
+  "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
+  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
+  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
+  "Tile overlap": "타일 겹침",
+  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
+  "Tile size for all SwinIR.": "Tile size for all SwinIR.",
+  "Tiling": "타일링",
+  "Train Embedding": "Train Embedding",
+  "Train Hypernetwork": "Train Hypernetwork",
+  "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Train an embedding; must specify a directory with a set of 1:1 ratio images",
+  "Train": "훈련",
+  "Training": "Training",
+  "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training",
+  "Upload mask": "마스크 업로드하기",
+  "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
+  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
+  "Upscaler 2 visibility": "Upscaler 2 visibility",
+  "Upscaler for img2img": "Upscaler for img2img",
+  "Upscaler": "업스케일러",
+  "Upscaling": "Upscaling",
+  "Use BLIP for caption": "Use BLIP for caption",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
+  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "저장 경로를 비워두면 기본 저장 폴더에 이미지들이 저장됩니다.",
+  "Use deepbooru for caption": "Use deepbooru for caption",
+  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
+  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
+  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
+  "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
+  "User interface": "User interface",
+  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
+  "Var. seed": "바리에이션 시드",
+  "Var. strength": "바리에이션 강도",
+  "Variation seed": "바리에이션 시드",
+  "Variation strength": "바리에이션 강도",
+  "Weighted sum": "Weighted sum",
+  "What to put inside the masked area before processing it with Stable Diffusion.": "Stable Diffusion으로 이미지를 생성하기 전 마스크된 부분에 무엇을 채울지 결정하는 설정값",
+  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
+  "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
+  "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
+  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
+  "Width": "가로",
+  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "이미지를 설정된 사이즈의 2배로 업스케일합니다. 상단의 가로와 세로 슬라이더를 이용해 타일 사이즈를 지정하세요.",
+  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
+  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
+  "X type": "X축",
+  "X values": "X 설정값",
+  "X/Y plot": "X/Y 플롯",
+  "Y type": "Y축",
+  "Y values": "Y 설정값",
+  "api": "",
+  "built with gradio": "gradio로 제작되었습니다",
+  "checkpoint": "checkpoint",
+  "directory.": "directory.",
+  "down": "아래쪽",
+  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
+  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
+  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
+  "extras history": "extras history",
+  "fill it with colors of the image": "이미지의 색상으로 채우기",
+  "fill it with latent space noise": "잠재 공간 노이즈로 채우기",
+  "fill it with latent space zeroes": "잠재 공간의 0값으로 채우기",
+  "fill": "채우기",
+  "for detailed explanation.": "for detailed explanation.",
+  "hide": "api 숨기기",
+  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
+  "img2img DDIM discretize": "img2img DDIM discretize",
+  "img2img alternative test": "이미지→이미지 대체버전 테스트",
+  "img2img history": "img2img history",
+  "img2img": "이미지→이미지",
+  "keep whatever was there originally": "이미지 원본 유지",
+  "latent noise": "잠재 노이즈",
+  "latent nothing": "잠재 공백",
+  "left": "왼쪽",
+  "number of images to delete consecutively next": "number of images to delete consecutively next",
+  "or": "or",
+  "original": "원본 유지",
+  "quad": "quad",
+  "right": "오른쪽",
+  "set_index": "set_index",
+  "should be 2 or lower.": "이 2 이하여야 합니다.",
+  "sigma churn": "sigma churn",
+  "sigma noise": "sigma noise",
+  "sigma tmin": "sigma tmin",
+  "txt2img history": "txt2img history",
+  "txt2img": "텍스트→이미지",
+  "uniform": "uniform",
+  "up": "위쪽",
+  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
+  "view": "api 보이기",
+  "wiki": "wiki"
+}
-- 
cgit v1.2.1


From c729cd41303ee258e1fbca9d0dcf9e54c7f6993f Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Sun, 23 Oct 2022 22:38:49 +0900
Subject: Update ko_KR.json

Updated translation for everything except the Settings tab
---
 localizations/ko_KR.json | 379 +++++++++++++++++++++++++++--------------------
 1 file changed, 218 insertions(+), 161 deletions(-)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index f665042e..a48ece87 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -5,118 +5,158 @@
   "❮": "❮",
   "❯": "❯",
   "⤡": "⤡",
+  " images in this directory. Loaded ": "개의 이미지가 이 경로에 존재합니다. ",
+  " images during ": "개의 이미지를 불러왔고, 생성 기간은 ",
+  ", divided into ": "입니다. ",
+  " pages": "페이지로 나뉘어 표시합니다.",
   "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
-  "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
-  "A merger of the two checkpoints will be generated in your": "A merger of the two checkpoints will be generated in your",
+  "[wiki]": " [위키] 참조",
+  "A directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리를 선택해 주세요.",
+  "A merger of the two checkpoints will be generated in your": "체크포인트들이 병합된 결과물이 당신의",
   "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
   "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
   "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
-  "Add difference": "Add difference",
+  "Add difference": "차이점 추가",
   "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid",
-  "Add layer normalization": "Add layer normalization",
+  "Add layer normalization": "레이어 정규화(normalization) 추가",
   "Add model hash to generation information": "Add model hash to generation information",
   "Add model name to generation information": "Add model name to generation information",
+  "Aesthetic imgs embedding": "스타일 이미지 임베딩",
+  "Aesthetic learning rate": "스타일 학습 수",
+  "Aesthetic steps": "스타일 스텝 수",
+  "Aesthetic text for imgs": "스타일 텍스트",
+  "Aesthetic weight": "스타일 가중치",
   "Always print all generation info to standard output": "Always print all generation info to standard output",
   "Always save all generated image grids": "Always save all generated image grids",
   "Always save all generated images": "생성된 이미지 항상 저장하기",
+  "api": "",
+  "append": "뒤에 삽입",
   "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
   "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
   "Apply settings": "설정 적용하기",
-  "BSRGAN 4x": "BSRGAN 4x",
-  "Batch Process": "Batch Process",
   "Batch count": "배치 수",
-  "Batch from Directory": "Batch from Directory",
+  "Batch from Directory": "저장 경로로부터 여러장 처리",
   "Batch img2img": "이미지→이미지 배치",
+  "Batch Process": "이미지 여러장 처리",
   "Batch size": "배치 크기",
-  "CFG Scale": "CFG 스케일",
-  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
+  "BSRGAN 4x": "BSRGAN 4x",
+  "built with gradio": "gradio로 제작되었습니다",
   "Cancel generate forever": "반복 생성 취소",
-  "Check progress (first)": "Check progress (first)",
+  "CFG Scale": "CFG 스케일",
   "Check progress": "Check progress",
+  "Check progress (first)": "Check progress (first)",
+  "checkpoint": " 체크포인트 ",
   "Checkpoint Merger": "체크포인트 병합",
   "Checkpoint name": "체크포인트 이름",
   "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
   "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
+  "Click to Upload": "Click to Upload",
   "Clip skip": "클립 건너뛰기",
-  "CodeFormer visibility": "CodeFormer visibility",
-  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)",
+  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
+  "CodeFormer visibility": "CodeFormer 가시성",
+  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 가중치 (0 = 최대 효과, 1 = 최소 효과)",
   "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
   "Color variation": "색깔 다양성",
+  "Collect": "즐겨찾기",
+  "copy": "복사",
   "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
   "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
-  "Create embedding": "Create embedding",
-  "Create flipped copies": "Create flipped copies",
-  "Create hypernetwork": "Create hypernetwork",
+  "Create aesthetic images embedding": "Create aesthetic images embedding",
+  "Create embedding": "임베딩 생성",
+  "Create flipped copies": "좌우로 뒤집은 복사본 생성",
+  "Create hypernetwork": "하이퍼네트워크 생성",
+  "Create images embedding": "Create images embedding",
   "Crop and resize": "잘라낸 후 리사이징",
-  "Crop to fit": "Crop to fit",
-  "Custom Name (Optional)": "Custom Name (Optional)",
+  "Crop to fit": "잘라내서 맞추기",
+  "Custom Name (Optional)": "병합 모델 이름 (선택사항)",
+  "Dataset directory": "데이터셋 경로",
   "DDIM": "DDIM",
-  "DPM adaptive": "DPM adaptive",
-  "DPM fast": "DPM fast",
-  "DPM2 Karras": "DPM2 Karras",
-  "DPM2 a Karras": "DPM2 a Karras",
-  "DPM2 a": "DPM2 a",
-  "DPM2": "DPM2",
-  "Dataset directory": "Dataset directory",
   "Decode CFG scale": "디코딩 CFG 스케일",
   "Decode steps": "디코딩 스텝 수",
-  "Delete": "Delete",
+  "Delete": "삭제",
+  "Denoising": "디노이징",
   "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
-  "Denoising strength change factor": "디노이즈 강도 변경 배수",
   "Denoising strength": "디노이즈 강도",
-  "Denoising": "디노이징",
-  "Destination directory": "Destination directory",
+  "Denoising strength change factor": "디노이즈 강도 변경 배수",
+  "Destination directory": "결과물 저장 경로",
   "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
   "Directory for saving images using the Save button": "Directory for saving images using the Save button",
   "Directory name pattern": "Directory name pattern",
+  "directory.": "저장 경로에 저장됩니다.",
   "Do not add watermark to images": "Do not add watermark to images",
   "Do not do anything special": "아무것도 하지 않기",
   "Do not save grids consisting of one picture": "Do not save grids consisting of one picture",
   "Do not show any images in results for web": "Do not show any images in results for web",
+  "down": "아래쪽",
   "Download localization template": "Download localization template",
+  "Download": "다운로드",
+  "DPM adaptive": "DPM adaptive",
+  "DPM fast": "DPM fast",
+  "DPM2": "DPM2",
+  "DPM2 a": "DPM2 a",
+  "DPM2 a Karras": "DPM2 a Karras",
+  "DPM2 Karras": "DPM2 Karras",
   "Draw legend": "범례 그리기",
   "Draw mask": "마스크 직접 그리기",
   "Drop File Here": "Drop File Here",
   "Drop Image Here": "Drop Image Here",
-  "ESRGAN_4x": "ESRGAN_4x",
-  "Embedding": "Embedding",
+  "Embedding": "임베딩",
+  "Embedding Learning rate": "임베딩 학습률",
   "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
   "Enable full page image viewer": "Enable full page image viewer",
   "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
-  "End Page": "End Page",
-  "Enter hypernetwork layer structure": "Enter hypernetwork layer structure",
-  "Eta noise seed delta": "Eta noise seed delta",
+  "End Page": "마지막 페이지",
+  "Enter hypernetwork layer structure": "하이퍼네트워크 레이어 구조 입력",
+  "Error": "오류",
+  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
+  "ESRGAN_4x": "ESRGAN_4x",
   "Eta": "Eta",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
-  "Euler a": "Euler a",
+  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
+  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
+  "Eta noise seed delta": "Eta noise seed delta",
   "Euler": "Euler",
+  "Euler a": "Euler a",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
+  "Existing Caption txt Action": "이미 존재하는 캡션 텍스트 처리",
   "Extra": "고급",
   "Extras": "부가기능",
+  "extras history": "extras history",
   "Face restoration": "Face restoration",
   "Fall-off exponent (lower=higher detail)": "감쇠 지수 (낮을수록 디테일이 올라감)",
-  "File Name": "File Name",
+  "favorites": "즐겨찾기",
+  "File": "File",
   "File format for grids": "File format for grids",
   "File format for images": "File format for images",
+  "File Name": "파일 이름",
   "File with inputs": "설정값 파일",
-  "File": "File",
   "Filename join string": "Filename join string",
   "Filename word regex": "Filename word regex",
+  "fill": "채우기",
+  "fill it with colors of the image": "이미지의 색상으로 채우기",
+  "fill it with latent space noise": "잠재 공간 노이즈로 채우기",
+  "fill it with latent space zeroes": "잠재 공간의 0값으로 채우기",
   "Filter NSFW content": "Filter NSFW content",
-  "First Page": "First Page",
+  "First Page": "처음 페이지",
   "Firstpass height": "초기 세로길이",
   "Firstpass width": "초기 가로길이",
   "Font for image grids that have text": "Font for image grids that have text",
+  "for detailed explanation.": "를 참조하십시오.",
   "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD 업스케일링에서 타일 간 몇 픽셀을 겹치게 할지 결정하는 설정값입니다. 타일들이 다시 한 이미지로 합쳐질 때, 눈에 띄는 이음매가 없도록 서로 겹치게 됩니다.",
-  "GFPGAN visibility": "GFPGAN visibility",
-  "Generate Info": "Generate Info",
-  "Generate forever": "반복 생성",
   "Generate": "생성",
+  "Generate forever": "반복 생성",
+  "Generate Info": "생성 정보",
+  "GFPGAN visibility": "GFPGAN 가시성",
   "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
   "Height": "세로",
   "Heun": "Heun",
+  "hide": "api 숨기기",
   "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
   "Highres. fix": "고해상도 보정",
   "History": "기록",
+  "Image Browser": "이미지 브라우저",
+  "Images directory": "이미지 경로",
+  "extras": "부가기능",
+  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
   "How many batches of images to create": "생성할 이미지 배치 수",
   "How many image to create in a single batch": "한 배치당 이미지 수",
   "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
@@ -124,26 +164,32 @@
   "How much to blur the mask before processing, in pixels.": "이미지 생성 전 마스크를 얼마나 블러처리할지 결정하는 값. 픽셀 단위",
   "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
   "Hypernet str.": "하이퍼네트워크 강도",
-  "Hypernetwork strength": "Hypernetwork strength",
   "Hypernetwork": "하이퍼네트워크",
+  "Hypernetwork Learning rate": "하이퍼네트워크 학습률",
+  "Hypernetwork strength": "Hypernetwork strength",
   "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
   "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
   "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
+  "ignore": "무시",
+  "Image": "Image",
   "Image for img2img": "Image for img2img",
   "Image for inpainting with mask": "Image for inpainting with mask",
-  "Image": "Image",
   "Images filename pattern": "Images filename pattern",
+  "img2img": "이미지→이미지",
+  "img2img alternative test": "이미지→이미지 대체버전 테스트",
+  "img2img DDIM discretize": "img2img DDIM discretize",
+  "img2img history": "img2img history",
   "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "루프백 모드에서는 매 루프마다 디노이즈 강도에 이 값이 곱해집니다. 1보다 작을 경우 다양성이 낮아져 결과 이미지들이 고정된 형태로 모일 겁니다. 1보다 클 경우 다양성이 높아져 결과 이미지들이 갈수록 혼란스러워지겠죠.",
   "Include Separate Images": "분리된 이미지 포함하기",
   "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
-  "Initialization text": "Initialization text",
-  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트 패딩값(픽셀 단위)",
+  "Initialization text": "초기화 텍스트",
+  "Inpaint": "인페인트",
   "Inpaint at full resolution": "전체 해상도로 인페인트하기",
+  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트 패딩값(픽셀 단위)",
   "Inpaint masked": "마스크만 처리",
   "Inpaint not masked": "마스크 이외만 처리",
-  "Inpaint": "인페인트",
   "Input directory": "인풋 이미지 경로",
-  "Interpolation Method": "Interpolation Method",
+  "Interpolation Method": "보간 방법",
   "Interrogate\nCLIP": "CLIP\n분석",
   "Interrogate\nDeepBooru": "DeepBooru\n분석",
   "Interrogate Options": "Interrogate Options",
@@ -156,49 +202,68 @@
   "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
   "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
   "Interrupt": "중단",
+  "Is negative text": "네거티브 텍스트일시 체크",
   "Just resize": "리사이징",
   "Keep -1 for seeds": "시드값 -1로 유지",
-  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
-  "LDSR": "LDSR",
-  "LMS Karras": "LMS Karras",
-  "LMS": "LMS",
+  "keep whatever was there originally": "이미지 원본 유지",
   "Label": "Label",
   "Lanczos": "Lanczos",
-  "Learning rate": "Learning rate",
-  "Leave blank to save images to the default path.": "Leave blank to save images to the default path.",
+  "Last prompt:": "Last prompt:",
+  "Last saved hypernetwork:": "Last saved hypernetwork:",
+  "Last saved image:": "Last saved image:",
+  "latent noise": "잠재 노이즈",
+  "latent nothing": "잠재 공백",
+  "LDSR": "LDSR",
+  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
+  "leakyrelu": "leakyrelu",
+  "Leave blank to save images to the default path.": "기존 저장 경로에 이미지들을 저장하려면 비워두세요.",
+  "left": "왼쪽",
+  "linear": "linear",
   "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
+  "LMS": "LMS",
+  "LMS Karras": "LMS Karras",
+  "Load": "불러오기",
   "Loading...": "로딩 중...",
   "Localization (requires restart)": "Localization (requires restart)",
-  "Log directory": "Log directory",
+  "Log directory": "로그 경로",
   "Loopback": "루프백",
   "Loops": "루프 수",
+  "Loss:": "Loss:",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
   "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
   "Make Zip when Save?": "저장 시 Zip 생성하기",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
+  "Mask": "마스크",
   "Mask blur": "마스크 블러",
   "Mask mode": "Mask mode",
-  "Mask": "마스크",
   "Masked content": "마스크된 부분",
   "Masking mode": "Masking mode",
   "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
-  "Max steps": "Max steps",
-  "Modules": "Modules",
+  "Max steps": "최대 스텝 수",
+  "Modules": "모듈",
   "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
-  "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - set to 0 to get model A",
-  "Name": "Name",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.",
+  "Multiplier (M) - set to 0 to get model A": "배율 (M) - 0으로 적용하면 모델 A를 얻게 됩니다",
+  "Name": "이름",
   "Negative prompt": "네거티브 프롬프트",
-  "Next Page": "Next Page",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Next batch": "다음 묶음",
+  "Next Page": "다음 페이지",
   "None": "None",
   "Nothing": "없음",
+  "Nothing found in the image.": "Nothing found in the image.",
+  "number of images to delete consecutively next": "연속적으로 삭제할 이미지 수",
   "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
-  "Number of vectors per token": "Number of vectors per token",
+  "Number of vectors per token": "토큰별 벡터 수",
+  "Open for Clip Aesthetic!": "클립 스타일 기능을 활성화하려면 클릭!",
   "Open images output directory": "이미지 저장 경로 열기",
-  "Open output directory": "Open output directory",
+  "Open output directory": "저장 경로 열기",
+  "or": "or",
+  "original": "원본 유지",
   "Original negative prompt": "기존 네거티브 프롬프트",
   "Original prompt": "기존 프롬프트",
   "Outpainting direction": "아웃페인팅 방향",
   "Outpainting mk2": "아웃페인팅 마크 2",
+  "Output directory": "이미지 저장 경로",
   "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
   "Output directory for images from extras tab": "Output directory for images from extras tab",
   "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
@@ -206,46 +271,54 @@
   "Output directory for img2img images": "Output directory for img2img images",
   "Output directory for txt2img grids": "Output directory for txt2img grids",
   "Output directory for txt2img images": "Output directory for txt2img images",
-  "Output directory": "이미지 저장 경로",
   "Override `Denoising strength` to 1?": "디노이즈 강도를 1로 적용할까요?",
-  "Override `Sampling Steps` to the same value as `Decode steps`?": "샘플링 스텝 수를 디코딩 스텝 수와 동일하게 적용할까요?",
-  "Override `Sampling method` to Euler?(this method is built for it)": "샘플링 방법을 Euler로 적용할까요?(이 기능은 해당 샘플러를 위해 만들어져 있습니다)",
   "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "프롬프트 값을 기존 프롬프트와 동일하게 적용할까요?(네거티브 프롬프트 포함)",
-  "PLMS": "PLMS",
-  "PNG Info": "PNG 정보",
-  "Page Index": "Page Index",
+  "Override `Sampling method` to Euler?(this method is built for it)": "샘플링 방법을 Euler로 적용할까요?(이 기능은 해당 샘플러를 위해 만들어져 있습니다)",
+  "Override `Sampling Steps` to the same value as `Decode steps`?": "샘플링 스텝 수를 디코딩 스텝 수와 동일하게 적용할까요?",
+  "Overwrite Old Embedding": "기존 임베딩 덮어쓰기",
+  "Overwrite Old Hypernetwork": "기존 하이퍼네트워크 덮어쓰기",
+  "Page Index": "페이지 인덱스",
+  "parameters": "설정값",
   "Path to directory where to write outputs": "Path to directory where to write outputs",
-  "Path to directory with input images": "Path to directory with input images",
+  "Path to directory with input images": "인풋 이미지가 있는 경로",
   "Paths for saving": "Paths for saving",
   "Pixels to expand": "확장할 픽셀 수",
+  "PLMS": "PLMS",
+  "PNG Info": "PNG 정보",
   "Poor man's outpainting": "가난뱅이의 아웃페인팅",
-  "Preprocess images": "Preprocess images",
-  "Preprocess": "Preprocess",
-  "Prev Page": "Prev Page",
+  "Preparing dataset from": "Preparing dataset from",
+  "prepend": "앞에 삽입",
+  "Preprocess": "전처리",
+  "Preprocess images": "이미지 전처리",
+  "Prev batch": "이전 묶음",
+  "Prev Page": "이전 페이지",
   "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)",
-  "Primary model (A)": "Primary model (A)",
+  "Primary model (A)": "주 모델 (A)",
   "Process an image, use it as an input, repeat.": "이미지를 생성하고, 생성한 이미지를 다시 원본으로 사용하는 과정을 반복합니다.",
   "Process images in a directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리의 이미지들을 처리합니다.",
   "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
+  "Prompt": "프롬프트",
   "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Prompt S/R": "프롬프트 스타일 변경",
   "Prompt matrix": "프롬프트 매트릭스",
   "Prompt order": "프롬프트 순서",
-  "Prompt template file": "Prompt template file",
-  "Prompt": "프롬프트",
-  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
+  "Prompt S/R": "프롬프트 스타일 변경",
+  "Prompt template file": "프롬프트 템플릿 파일 경로",
   "Prompts": "프롬프트",
+  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
   "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
+  "quad": "quad",
   "Quality for saved jpeg images": "Quality for saved jpeg images",
   "Quicksettings list": "Quicksettings list",
   "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
   "Randomness": "랜덤성",
   "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
-  "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews",
+  "Read parameters (prompt, etc...) from txt2img tab when making previews": "프리뷰 이미지 생성 시 텍스트→이미지 탭에서 설정값(프롬프트 등) 읽어오기",
   "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "추천 설정값 - 샘플링 스텝 수 : 80-100 , 샘플러 : Euler a, 디노이즈 강도 : 0.8",
   "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
+  "relu": "relu",
   "Renew Page": "Renew Page",
   "Request browser notifications": "Request browser notifications",
+  "Resize": "리사이징 배수",
   "Resize and fill": "리사이징 후 채우기",
   "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "설정된 해상도로 이미지 리사이징을 진행합니다. 원본과 가로/세로 길이가 일치하지 않을 경우, 부정확한 화면비의 이미지를 얻게 됩니다.",
   "Resize mode": "Resize mode",
@@ -253,42 +326,43 @@
   "Resize seed from width": "시드 리사이징 세로길이",
   "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "이미지 전체가 설정된 해상도 내부에 들어가게 리사이징을 진행합니다. 빈 공간은 이미지의 색상으로 채웁니다.",
   "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "설정된 해상도 전체가 이미지로 가득차게 리사이징을 진행합니다. 튀어나오는 부분은 잘라냅니다.",
-  "Resize": "Resize",
   "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
   "Restore faces": "얼굴 보정",
   "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
-  "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M",
-  "Result = A + (B - C) * M": "Result = A + (B - C) * M",
+  "Result = A * (1 - M) + B * M": "결과물 = A * (1 - M) + B * M",
+  "Result = A + (B - C) * M": "결과물 = A + (B - C) * M",
   "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
-  "Run": "Run",
-  "SD upscale": "SD 업스케일링",
-  "Sampler parameters": "Sampler parameters",
+  "right": "오른쪽",
+  "Run": "가동",
   "Sampler": "샘플러",
-  "Sampling Steps": "샘플링 스텝 수",
+  "Sampler parameters": "Sampler parameters",
   "Sampling method": "샘플링 방법",
-  "Save a copy of embedding to log directory every N steps, 0 to disable": "Save a copy of embedding to log directory every N steps, 0 to disable",
+  "Sampling Steps": "샘플링 스텝 수",
+  "Save": "저장",
+  "Save a copy of embedding to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 임베딩을 저장합니다, 비활성화하려면 0으로 설정하십시오.",
   "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
   "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
   "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
-  "Save an image to log directory every N steps, 0 to disable": "Save an image to log directory every N steps, 0 to disable",
-  "Save as float16": "Save as float16",
+  "Save an image to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 이미지를 저장합니다, 비활성화하려면 0으로 설정하십시오.",
+  "Save as float16": "float16으로 저장",
   "Save grids to a subdirectory": "Save grids to a subdirectory",
   "Save images to a subdirectory": "Save images to a subdirectory",
-  "Save images with embedding in PNG chunks": "Save images with embedding in PNG chunks",
+  "Save images with embedding in PNG chunks": "PNG 청크로 이미지에 임베딩을 포함시켜 저장",
   "Save style": "스타일 저장",
   "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
-  "Save": "저장",
   "Saving images/grids": "Saving images/grids",
   "Saving to a directory": "Saving to a directory",
-  "Scale by": "Scale by",
-  "Scale to": "Scale to",
+  "Scale by": "스케일링 배수 지정",
+  "Scale to": "스케일링 사이즈 지정",
   "Script": "스크립트",
   "ScuNET GAN": "ScuNET GAN",
   "ScuNET PSNR": "ScuNET PSNR",
-  "Secondary model (B)": "Secondary model (B)",
-  "See": "See",
-  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "SD upscale": "SD 업스케일링",
+  "Secondary model (B)": "2차 모델 (B)",
+  "See": "자세한 설명은",
   "Seed": "시드",
+  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "Select activation function of hypernetwork": "하이퍼네트워크 활성화 함수 선택",
   "Send to extras": "부가기능으로 전송",
   "Send to img2img": "이미지→이미지로 전송",
   "Send to inpaint": "인페인트로 전송",
@@ -297,26 +371,36 @@
   "Separate values for X axis using commas.": "쉼표로 X축에 적용할 값 분리",
   "Separate values for Y axis using commas.": "쉼표로 Y축에 적용할 값 분리",
   "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
+  "set_index": "set_index",
   "Settings": "설정",
-  "Show Textbox": "텍스트박스 보이기",
+  "should be 2 or lower.": "이 2 이하여야 합니다.",
   "Show generation progress in window title.": "Show generation progress in window title.",
   "Show grid in results for web": "Show grid in results for web",
   "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
   "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
   "Show progressbar": "Show progressbar",
-  "Show result images": "Show result images",
-  "Sigma Churn": "시그마 섞기",
+  "Show result images": "이미지 결과 보이기",
+  "Show Textbox": "텍스트박스 보이기",
   "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
+  "Sigma Churn": "시그마 섞기",
+  "sigma churn": "sigma churn",
   "Sigma max": "시그마 최댓값",
   "Sigma min": "시그마 최솟값",
   "Sigma noise": "시그마 노이즈",
-  "Single Image": "Single Image",
+  "sigma noise": "sigma noise",
+  "sigma tmin": "sigma tmin",
+  "Single Image": "단일 이미지",
   "Skip": "건너뛰기",
-  "Source directory": "Source directory",
-  "Source": "Source",
-  "Split oversized images into two": "Split oversized images into two",
-  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "Slerp angle": "구면 선형 보간 각도",
+  "Slerp interpolation": "구면 선형 보간",
+  "Source": "원본",
+  "Source directory": "원본 경로",
+  "Split image threshold": "Split image threshold",
+  "Split image overlap ratio": "Split image overlap ratio",
+  "Split oversized images": "사이즈가 큰 이미지 분할하기",
   "Stable Diffusion": "Stable Diffusion",
+  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "Step:": "Step:",
   "Steps": "스텝 수",
   "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
   "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
@@ -325,51 +409,65 @@
   "Style 2": "스타일 2",
   "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
   "SwinIR 4x": "SwinIR 4x",
+  "Sys VRAM:": "시스템 VRAM : ",
   "System": "System",
-  "Tertiary model (C)": "Tertiary model (C)",
+  "Tertiary model (C)": "3차 모델 (C)",
   "Textbox": "Textbox",
   "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
   "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
+  "This text is used to rotate the feature space of the imgs embs": "이 텍스트는 이미지 임베딩의 특징 공간을 회전하는 데 사용됩니다.",
+  "Tile overlap": "타일 겹침",
   "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
   "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
-  "Tile overlap": "타일 겹침",
-  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
   "Tile size for all SwinIR.": "Tile size for all SwinIR.",
+  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
   "Tiling": "타일링",
-  "Train Embedding": "Train Embedding",
-  "Train Hypernetwork": "Train Hypernetwork",
-  "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Train an embedding; must specify a directory with a set of 1:1 ratio images",
+  "Time taken:": "소요 시간 : ",
+  "Torch active/reserved:": "활성화/예약된 Torch 양 : ",
+  "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "활성화된 Torch : 생성 도중 캐시된 데이터를 포함해 사용된 VRAM의 최대량\n예약된 Torch : 활성화되고 캐시된 모든 데이터를 포함해 Torch에게 할당된 VRAM의 최대량\n시스템 VRAM : 모든 어플리케이션에 할당된 VRAM 최대량 / 총 GPU VRAM (최고 이용도%)",
   "Train": "훈련",
+  "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "임베딩이나 하이퍼네트워크를 훈련시킵니다. 1:1 비율의 이미지가 있는 경로를 지정해야 합니다.",
+  "Train Embedding": "임베딩 훈련",
+  "Train Hypernetwork": "하이퍼네트워크 훈련",
   "Training": "Training",
-  "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training",
+  "txt2img": "텍스트→이미지",
+  "txt2img history": "txt2img history",
+  "uniform": "uniform",
+  "up": "위쪽",
   "Upload mask": "마스크 업로드하기",
   "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
   "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
-  "Upscaler 2 visibility": "Upscaler 2 visibility",
-  "Upscaler for img2img": "Upscaler for img2img",
   "Upscaler": "업스케일러",
+  "Upscaler 1": "업스케일러 1",
+  "Upscaler 2": "업스케일러 2",
+  "Upscaler 2 visibility": "업스케일러 2 가시성",
+  "Upscaler for img2img": "Upscaler for img2img",
   "Upscaling": "Upscaling",
-  "Use BLIP for caption": "Use BLIP for caption",
   "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
   "Use an empty output directory to save pictures normally instead of writing to the output directory.": "저장 경로를 비워두면 기본 저장 폴더에 이미지들이 저장됩니다.",
-  "Use deepbooru for caption": "Use deepbooru for caption",
+  "Use BLIP for caption": "캡션에 BLIP 사용",
+  "Use deepbooru for caption": "캡션에 deepbooru 사용",
+  "Use dropout": "드롭아웃 사용",
   "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
   "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
   "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
   "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
+  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
   "User interface": "User interface",
-  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
   "Var. seed": "바리에이션 시드",
   "Var. strength": "바리에이션 강도",
   "Variation seed": "바리에이션 시드",
   "Variation strength": "바리에이션 강도",
-  "Weighted sum": "Weighted sum",
+  "view": "api 보이기",
+  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
+  "Weighted sum": "가중 합",
   "What to put inside the masked area before processing it with Stable Diffusion.": "Stable Diffusion으로 이미지를 생성하기 전 마스크된 부분에 무엇을 채울지 결정하는 설정값",
   "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
   "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
   "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
   "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
   "Width": "가로",
+  "wiki": " 위키",
   "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "이미지를 설정된 사이즈의 2배로 업스케일합니다. 상단의 가로와 세로 슬라이더를 이용해 타일 사이즈를 지정하세요.",
   "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
   "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
@@ -377,46 +475,5 @@
   "X values": "X 설정값",
   "X/Y plot": "X/Y 플롯",
   "Y type": "Y축",
-  "Y values": "Y 설정값",
-  "api": "",
-  "built with gradio": "gradio로 제작되었습니다",
-  "checkpoint": "checkpoint",
-  "directory.": "directory.",
-  "down": "아래쪽",
-  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
-  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
-  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
-  "extras history": "extras history",
-  "fill it with colors of the image": "이미지의 색상으로 채우기",
-  "fill it with latent space noise": "잠재 공간 노이즈로 채우기",
-  "fill it with latent space zeroes": "잠재 공간의 0값으로 채우기",
-  "fill": "채우기",
-  "for detailed explanation.": "for detailed explanation.",
-  "hide": "api 숨기기",
-  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
-  "img2img DDIM discretize": "img2img DDIM discretize",
-  "img2img alternative test": "이미지→이미지 대체버전 테스트",
-  "img2img history": "img2img history",
-  "img2img": "이미지→이미지",
-  "keep whatever was there originally": "이미지 원본 유지",
-  "latent noise": "잠재 노이즈",
-  "latent nothing": "잠재 공백",
-  "left": "왼쪽",
-  "number of images to delete consecutively next": "number of images to delete consecutively next",
-  "or": "or",
-  "original": "원본 유지",
-  "quad": "quad",
-  "right": "오른쪽",
-  "set_index": "set_index",
-  "should be 2 or lower.": "이 2 이하여야 합니다.",
-  "sigma churn": "sigma churn",
-  "sigma noise": "sigma noise",
-  "sigma tmin": "sigma tmin",
-  "txt2img history": "txt2img history",
-  "txt2img": "텍스트→이미지",
-  "uniform": "uniform",
-  "up": "위쪽",
-  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
-  "view": "api 보이기",
-  "wiki": "wiki"
-}
+  "Y values": "Y 설정값"
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 0523704dade0508bf3ae0c8cb799b1ae332d449b Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sun, 23 Oct 2022 12:27:50 -0300
Subject: Update run_extras to use the temp filename

In batch mode run_extras tries to preserve the original file name of the
images. The problem is that this makes no sense since the user only gets
a list of images in the UI, trying to manually save them shows that this
images have random temp names. Also, trying to keep "orig_name" in the
API is a hassle that adds complexity to the consuming UI since the
client has to use (or emulate) an input (type=file) element in a form.
Using the normal file name not only doesn't change the output and
functionality in the original UI but also helps keep the API simple.
---
 modules/extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/extras.py b/modules/extras.py
index 22c5a1c1..29ac312e 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -33,7 +33,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         for img in image_folder:
             image = Image.open(img)
             imageArr.append(image)
-            imageNameArr.append(os.path.splitext(img.orig_name)[0])
+            imageNameArr.append(os.path.splitext(img.name)[0])
     elif extras_mode == 2:
         assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
 
-- 
cgit v1.2.1


From 4ff852ffb50859f2eae75375cab94dd790a46886 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sun, 23 Oct 2022 13:07:59 -0300
Subject: Add batch processing "extras" endpoint

---
 modules/api/api.py    | 25 +++++++++++++++++++++++--
 modules/api/models.py | 15 ++++++++++++++-
 2 files changed, 37 insertions(+), 3 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 3b804373..528134a8 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -10,6 +10,7 @@ import base64
 from modules.api.models import *
 from PIL import Image
 from modules.extras import run_extras
+from gradio import processing_utils
 
 def upscaler_to_index(name: str):
     try:
@@ -44,6 +45,7 @@ class Api:
         self.queue_lock = queue_lock
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
+        self.app.add_api_route("/sdapi/v1/extra-batch-image", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -78,12 +80,31 @@ class Api:
         reqDict.pop('upscaler_1')
         reqDict.pop('upscaler_2')
 
-        reqDict['image'] = base64_to_images([reqDict['image']])[0]
+        reqDict['image'] = processing_utils.decode_base64_to_file(reqDict['image'])
 
         with self.queue_lock:
             result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=0, image_folder="", input_dir="", output_dir="")
 
-        return ExtrasSingleImageResponse(image="data:image/png;base64,"+img_to_base64(result[0]), html_info_x=result[1], html_info=result[2])
+        return ExtrasSingleImageResponse(image=processing_utils.encode_pil_to_base64(result[0]), html_info_x=result[1], html_info=result[2])
+
+    def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
+        upscaler1Index = upscaler_to_index(req.upscaler_1)
+        upscaler2Index = upscaler_to_index(req.upscaler_2)
+
+        reqDict = vars(req)
+        reqDict.pop('upscaler_1')
+        reqDict.pop('upscaler_2')
+
+        reqDict['image_folder'] = list(map(processing_utils.decode_base64_to_file, reqDict['imageList']))
+        reqDict.pop('imageList')
+
+        with self.queue_lock:
+            result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=1, image="", input_dir="", output_dir="")
+
+        return ExtrasBatchImagesResponse(images=list(map(processing_utils.encode_pil_to_base64, result[0])), html_info_x=result[1], html_info=result[2])
+    
+    def extras_folder_processing_api(self):
+        raise NotImplementedError
 
     def pnginfoapi(self):
         raise NotImplementedError
diff --git a/modules/api/models.py b/modules/api/models.py
index dcf1ab54..bbd0ef53 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -29,4 +29,17 @@ class ExtrasSingleImageRequest(ExtrasBaseRequest):
     image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
 
 class ExtrasSingleImageResponse(ExtraBaseResponse):
-    image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
\ No newline at end of file
+    image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
+
+class SerializableImage(BaseModel):
+  path: str = Field(title="Path", description="The image's path ()")
+
+class ImageItem(BaseModel):
+    data: str = Field(title="image data")
+    name: str = Field(title="filename")
+
+class ExtrasBatchImagesRequest(ExtrasBaseRequest):
+    imageList: list[str] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
+
+class ExtrasBatchImagesResponse(ExtraBaseResponse):
+    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
\ No newline at end of file
-- 
cgit v1.2.1


From e0ca4dfbc10e0af8dfc4185e5e758f33fd2f0d81 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sun, 23 Oct 2022 15:13:37 -0300
Subject: Update endpoints to use gradio's own utils functions

---
 modules/api/api.py    | 75 +++++++++++++++++++++++++--------------------------
 modules/api/models.py |  4 +--
 2 files changed, 38 insertions(+), 41 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 3f490ce2..3acb1f36 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -20,27 +20,27 @@ def upscaler_to_index(name: str):
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
-def img_to_base64(img: str):
-    buffer = io.BytesIO()
-    img.save(buffer, format="png")
-    return base64.b64encode(buffer.getvalue())
-
-def base64_to_bytes(base64Img: str):
-    if "," in base64Img:
-        base64Img = base64Img.split(",")[1]
-    return io.BytesIO(base64.b64decode(base64Img))
-
-def base64_to_images(base64Imgs: list[str]):
-    imgs = []
-    for img in base64Imgs:
-        img = Image.open(base64_to_bytes(img))
-        imgs.append(img)
-    return imgs
+# def img_to_base64(img: str):
+#     buffer = io.BytesIO()
+#     img.save(buffer, format="png")
+#     return base64.b64encode(buffer.getvalue())
+
+# def base64_to_bytes(base64Img: str):
+#     if "," in base64Img:
+#         base64Img = base64Img.split(",")[1]
+#     return io.BytesIO(base64.b64decode(base64Img))
+
+# def base64_to_images(base64Imgs: list[str]):
+#     imgs = []
+#     for img in base64Imgs:
+#         img = Image.open(base64_to_bytes(img))
+#         imgs.append(img)
+#     return imgs
 
 class ImageToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: Json
-    info: Json
+    parameters: dict
+    info: str
 
 
 class Api:
@@ -49,17 +49,17 @@ class Api:
         self.app = app
         self.queue_lock = queue_lock
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
-        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-batch-image", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
 
-    def __base64_to_image(self, base64_string):
-        # if has a comma, deal with prefix
-        if "," in base64_string:
-            base64_string = base64_string.split(",")[1]
-        imgdata = base64.b64decode(base64_string)
-        # convert base64 to PIL image
-        return Image.open(io.BytesIO(imgdata))
+    # def __base64_to_image(self, base64_string):
+    #     # if has a comma, deal with prefix
+    #     if "," in base64_string:
+    #         base64_string = base64_string.split(",")[1]
+    #     imgdata = base64.b64decode(base64_string)
+    #     # convert base64 to PIL image
+    #     return Image.open(io.BytesIO(imgdata))
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -79,11 +79,9 @@ class Api:
         with self.queue_lock:
             processed = process_images(p)
         
-        b64images = list(map(img_to_base64, processed.images))
-
-        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
-        
+        b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
         
+        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.info)
 
     def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
         sampler_index = sampler_to_index(img2imgreq.sampler_index)
@@ -98,7 +96,7 @@ class Api:
 
         mask = img2imgreq.mask
         if mask:
-            mask = self.__base64_to_image(mask)
+            mask = processing_utils.decode_base64_to_image(mask)
 
         
         populate = img2imgreq.copy(update={ # Override __init__ params
@@ -113,7 +111,7 @@ class Api:
 
         imgs = []
         for img in init_images:
-            img = self.__base64_to_image(img)
+            img = processing_utils.decode_base64_to_image(img)
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
@@ -121,13 +119,12 @@ class Api:
         with self.queue_lock:
             processed = process_images(p)
         
-        b64images = []
-        for i in processed.images:
-            buffer = io.BytesIO()
-            i.save(buffer, format="png")
-            b64images.append(base64.b64encode(buffer.getvalue()))
-
-        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info))
+        b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
+        # for i in processed.images:
+        #     buffer = io.BytesIO()
+        #     i.save(buffer, format="png")
+        #     b64images.append(base64.b64encode(buffer.getvalue()))
+        return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.info)
 
     def extras_single_image_api(self, req: ExtrasSingleImageRequest):
         upscaler1Index = upscaler_to_index(req.upscaler_1)
diff --git a/modules/api/models.py b/modules/api/models.py
index bbd0ef53..209f8af5 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -4,8 +4,8 @@ from modules.shared import sd_upscalers
 
 class TextToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: Json
-    info: Json
+    parameters: str
+    info: str
 
 class ExtrasBaseRequest(BaseModel):
     resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.")
-- 
cgit v1.2.1


From 866b36d705a338d299aba385788729d60f7d48c8 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sun, 23 Oct 2022 15:35:49 -0300
Subject: Move processing's models into models.py

It didn't make sense to have two differente files for the same and
"models" is a more descriptive name.
---
 modules/api/api.py        |  57 ++++-------------------
 modules/api/models.py     | 112 +++++++++++++++++++++++++++++++++++++++++++++-
 modules/api/processing.py | 106 -------------------------------------------
 3 files changed, 119 insertions(+), 156 deletions(-)
 delete mode 100644 modules/api/processing.py

diff --git a/modules/api/api.py b/modules/api/api.py
index 3acb1f36..20e85e82 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,16 +1,11 @@
-from modules.api.processing import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
-from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers
-import modules.shared as shared
 import uvicorn
+from gradio import processing_utils
 from fastapi import APIRouter, HTTPException
-import json
-import io
-import base64
+import modules.shared as shared
 from modules.api.models import *
-from PIL import Image
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
+from modules.sd_samplers import all_samplers
 from modules.extras import run_extras
-from gradio import processing_utils
 
 def upscaler_to_index(name: str):
     try:
@@ -20,29 +15,6 @@ def upscaler_to_index(name: str):
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
-# def img_to_base64(img: str):
-#     buffer = io.BytesIO()
-#     img.save(buffer, format="png")
-#     return base64.b64encode(buffer.getvalue())
-
-# def base64_to_bytes(base64Img: str):
-#     if "," in base64Img:
-#         base64Img = base64Img.split(",")[1]
-#     return io.BytesIO(base64.b64decode(base64Img))
-
-# def base64_to_images(base64Imgs: list[str]):
-#     imgs = []
-#     for img in base64Imgs:
-#         img = Image.open(base64_to_bytes(img))
-#         imgs.append(img)
-#     return imgs
-
-class ImageToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: dict
-    info: str
-
-
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
@@ -51,15 +23,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
-        self.app.add_api_route("/sdapi/v1/extra-batch-image", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
-
-    # def __base64_to_image(self, base64_string):
-    #     # if has a comma, deal with prefix
-    #     if "," in base64_string:
-    #         base64_string = base64_string.split(",")[1]
-    #     imgdata = base64.b64decode(base64_string)
-    #     # convert base64 to PIL image
-    #     return Image.open(io.BytesIO(imgdata))
+        self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -81,7 +45,7 @@ class Api:
         
         b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
         
-        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.info)
+        return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.info)
 
     def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
         sampler_index = sampler_to_index(img2imgreq.sampler_index)
@@ -120,10 +84,7 @@ class Api:
             processed = process_images(p)
         
         b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
-        # for i in processed.images:
-        #     buffer = io.BytesIO()
-        #     i.save(buffer, format="png")
-        #     b64images.append(base64.b64encode(buffer.getvalue()))
+       
         return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.info)
 
     def extras_single_image_api(self, req: ExtrasSingleImageRequest):
@@ -134,12 +95,12 @@ class Api:
         reqDict.pop('upscaler_1')
         reqDict.pop('upscaler_2')
 
-        reqDict['image'] = processing_utils.decode_base64_to_file(reqDict['image'])
+        reqDict['image'] = processing_utils.decode_base64_to_image(reqDict['image'])
 
         with self.queue_lock:
             result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=0, image_folder="", input_dir="", output_dir="")
 
-        return ExtrasSingleImageResponse(image=processing_utils.encode_pil_to_base64(result[0]), html_info_x=result[1], html_info=result[2])
+        return ExtrasSingleImageResponse(image=processing_utils.encode_pil_to_base64(result[0][0]), html_info_x=result[1], html_info=result[2])
 
     def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
         upscaler1Index = upscaler_to_index(req.upscaler_1)
diff --git a/modules/api/models.py b/modules/api/models.py
index 209f8af5..362e6277 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,10 +1,118 @@
-from pydantic import BaseModel, Field, Json
+import inspect
+from pydantic import BaseModel, Field, Json, create_model
+from typing import Any, Optional
 from typing_extensions import Literal
+from inflection import underscore
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 from modules.shared import sd_upscalers
 
+API_NOT_ALLOWED = [
+    "self",
+    "kwargs",
+    "sd_model",
+    "outpath_samples",
+    "outpath_grids",
+    "sampler_index",
+    "do_not_save_samples",
+    "do_not_save_grid",
+    "extra_generation_params",
+    "overlay_images",
+    "do_not_reload_embeddings",
+    "seed_enable_extras",
+    "prompt_for_display",
+    "sampler_noise_scheduler_override",
+    "ddim_discretize"
+]
+
+class ModelDef(BaseModel):
+    """Assistance Class for Pydantic Dynamic Model Generation"""
+
+    field: str
+    field_alias: str
+    field_type: Any
+    field_value: Any
+
+
+class PydanticModelGenerator:
+    """
+    Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
+    source_data is a snapshot of the default values produced by the class
+    params are the names of the actual keys required by __init__
+    """
+
+    def __init__(
+        self,
+        model_name: str = None,
+        class_instance = None,
+        additional_fields = None,
+    ):
+        def field_type_generator(k, v):
+            # field_type = str if not overrides.get(k) else overrides[k]["type"]
+            # print(k, v.annotation, v.default)
+            field_type = v.annotation
+            
+            return Optional[field_type]
+        
+        def merge_class_params(class_):
+            all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
+            parameters = {}
+            for classes in all_classes:
+                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
+            return parameters
+            
+                
+        self._model_name = model_name
+        self._class_data = merge_class_params(class_instance)
+        self._model_def = [
+            ModelDef(
+                field=underscore(k),
+                field_alias=k,
+                field_type=field_type_generator(k, v),
+                field_value=v.default
+            )
+            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
+        ]
+        
+        for fields in additional_fields:
+            self._model_def.append(ModelDef(
+                field=underscore(fields["key"]), 
+                field_alias=fields["key"], 
+                field_type=fields["type"],
+                field_value=fields["default"]))
+
+    def generate_model(self):
+        """
+        Creates a pydantic BaseModel
+        from the json and overrides provided at initialization
+        """
+        fields = {
+            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
+        }
+        DynamicModel = create_model(self._model_name, **fields)
+        DynamicModel.__config__.allow_population_by_field_name = True
+        DynamicModel.__config__.allow_mutation = True
+        return DynamicModel
+    
+StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingTxt2Img", 
+    StableDiffusionProcessingTxt2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}]
+).generate_model()
+
+StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingImg2Img", 
+    StableDiffusionProcessingImg2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
+).generate_model()
+
 class TextToImageResponse(BaseModel):
     images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: str
+    parameters: dict
+    info: str
+
+class ImageToImageResponse(BaseModel):
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    parameters: dict
     info: str
 
 class ExtrasBaseRequest(BaseModel):
diff --git a/modules/api/processing.py b/modules/api/processing.py
deleted file mode 100644
index f551fa35..00000000
--- a/modules/api/processing.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from array import array
-from inflection import underscore
-from typing import Any, Dict, Optional
-from pydantic import BaseModel, Field, create_model
-from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
-import inspect
-
-
-API_NOT_ALLOWED = [
-    "self",
-    "kwargs",
-    "sd_model",
-    "outpath_samples",
-    "outpath_grids",
-    "sampler_index",
-    "do_not_save_samples",
-    "do_not_save_grid",
-    "extra_generation_params",
-    "overlay_images",
-    "do_not_reload_embeddings",
-    "seed_enable_extras",
-    "prompt_for_display",
-    "sampler_noise_scheduler_override",
-    "ddim_discretize"
-]
-
-class ModelDef(BaseModel):
-    """Assistance Class for Pydantic Dynamic Model Generation"""
-
-    field: str
-    field_alias: str
-    field_type: Any
-    field_value: Any
-
-
-class PydanticModelGenerator:
-    """
-    Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
-    source_data is a snapshot of the default values produced by the class
-    params are the names of the actual keys required by __init__
-    """
-
-    def __init__(
-        self,
-        model_name: str = None,
-        class_instance = None,
-        additional_fields = None,
-    ):
-        def field_type_generator(k, v):
-            # field_type = str if not overrides.get(k) else overrides[k]["type"]
-            # print(k, v.annotation, v.default)
-            field_type = v.annotation
-            
-            return Optional[field_type]
-        
-        def merge_class_params(class_):
-            all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
-            parameters = {}
-            for classes in all_classes:
-                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
-            return parameters
-            
-                
-        self._model_name = model_name
-        self._class_data = merge_class_params(class_instance)
-        self._model_def = [
-            ModelDef(
-                field=underscore(k),
-                field_alias=k,
-                field_type=field_type_generator(k, v),
-                field_value=v.default
-            )
-            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
-        ]
-        
-        for fields in additional_fields:
-            self._model_def.append(ModelDef(
-                field=underscore(fields["key"]), 
-                field_alias=fields["key"], 
-                field_type=fields["type"],
-                field_value=fields["default"]))
-
-    def generate_model(self):
-        """
-        Creates a pydantic BaseModel
-        from the json and overrides provided at initialization
-        """
-        fields = {
-            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
-        }
-        DynamicModel = create_model(self._model_name, **fields)
-        DynamicModel.__config__.allow_population_by_field_name = True
-        DynamicModel.__config__.allow_mutation = True
-        return DynamicModel
-    
-StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
-    "StableDiffusionProcessingTxt2Img", 
-    StableDiffusionProcessingTxt2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}]
-).generate_model()
-
-StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
-    "StableDiffusionProcessingImg2Img", 
-    StableDiffusionProcessingImg2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
-).generate_model()
\ No newline at end of file
-- 
cgit v1.2.1


From 1e625624ba6ab3dfc167f0a5226780bb9b50fb58 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sun, 23 Oct 2022 16:01:16 -0300
Subject: Add folder processing endpoint

Also minor refactor
---
 modules/api/api.py    | 56 +++++++++++++++++++++++++++------------------------
 modules/api/models.py |  6 +++++-
 2 files changed, 35 insertions(+), 27 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 20e85e82..7b4fbe29 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,5 +1,5 @@
 import uvicorn
-from gradio import processing_utils
+from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
 from fastapi import APIRouter, HTTPException
 import modules.shared as shared
 from modules.api.models import *
@@ -11,10 +11,18 @@ def upscaler_to_index(name: str):
     try:
         return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
     except:
-        raise HTTPException(status_code=400, detail="Upscaler not found")
+        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
+def setUpscalers(req: dict):
+    reqDict = vars(req)
+    reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1)
+    reqDict['extras_upscaler_2'] = upscaler_to_index(req.upscaler_2)
+    reqDict.pop('upscaler_1')
+    reqDict.pop('upscaler_2')
+    return reqDict
+
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
@@ -24,6 +32,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
+        self.app.add_api_route("/sdapi/v1/extra-folder-images", self.extras_folder_processing_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -43,7 +52,7 @@ class Api:
         with self.queue_lock:
             processed = process_images(p)
         
-        b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
+        b64images = list(map(encode_pil_to_base64, processed.images))
         
         return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.info)
 
@@ -60,7 +69,7 @@ class Api:
 
         mask = img2imgreq.mask
         if mask:
-            mask = processing_utils.decode_base64_to_image(mask)
+            mask = decode_base64_to_image(mask)
 
         
         populate = img2imgreq.copy(update={ # Override __init__ params
@@ -75,7 +84,7 @@ class Api:
 
         imgs = []
         for img in init_images:
-            img = processing_utils.decode_base64_to_image(img)
+            img = decode_base64_to_image(img)
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
@@ -83,43 +92,38 @@ class Api:
         with self.queue_lock:
             processed = process_images(p)
         
-        b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
+        b64images = list(map(encode_pil_to_base64, processed.images))
        
         return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.info)
 
     def extras_single_image_api(self, req: ExtrasSingleImageRequest):
-        upscaler1Index = upscaler_to_index(req.upscaler_1)
-        upscaler2Index = upscaler_to_index(req.upscaler_2)
-
-        reqDict = vars(req)
-        reqDict.pop('upscaler_1')
-        reqDict.pop('upscaler_2')
+        reqDict = setUpscalers(req)
 
-        reqDict['image'] = processing_utils.decode_base64_to_image(reqDict['image'])
+        reqDict['image'] = decode_base64_to_image(reqDict['image'])
 
         with self.queue_lock:
-            result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=0, image_folder="", input_dir="", output_dir="")
+            result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", **reqDict)
 
-        return ExtrasSingleImageResponse(image=processing_utils.encode_pil_to_base64(result[0][0]), html_info_x=result[1], html_info=result[2])
+        return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info_x=result[1], html_info=result[2])
 
     def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
-        upscaler1Index = upscaler_to_index(req.upscaler_1)
-        upscaler2Index = upscaler_to_index(req.upscaler_2)
+        reqDict = setUpscalers(req)
 
-        reqDict = vars(req)
-        reqDict.pop('upscaler_1')
-        reqDict.pop('upscaler_2')
-
-        reqDict['image_folder'] = list(map(processing_utils.decode_base64_to_file, reqDict['imageList']))
+        reqDict['image_folder'] = list(map(decode_base64_to_file, reqDict['imageList']))
         reqDict.pop('imageList')
 
         with self.queue_lock:
-            result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=1, image="", input_dir="", output_dir="")
+            result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", **reqDict)
 
-        return ExtrasBatchImagesResponse(images=list(map(processing_utils.encode_pil_to_base64, result[0])), html_info_x=result[1], html_info=result[2])
+        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info_x=result[1], html_info=result[2])
     
-    def extras_folder_processing_api(self):
-        raise NotImplementedError
+    def extras_folder_processing_api(self, req:ExtrasFoldersRequest):
+        reqDict = setUpscalers(req)
+
+        with self.queue_lock:
+            result = run_extras(extras_mode=2, image=None, image_folder=None, **reqDict)
+
+        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info_x=result[1], html_info=result[2])
 
     def pnginfoapi(self):
         raise NotImplementedError
diff --git a/modules/api/models.py b/modules/api/models.py
index 362e6277..6f096807 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -150,4 +150,8 @@ class ExtrasBatchImagesRequest(ExtrasBaseRequest):
     imageList: list[str] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
 
 class ExtrasBatchImagesResponse(ExtraBaseResponse):
-    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
\ No newline at end of file
+    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
+
+class ExtrasFoldersRequest(ExtrasBaseRequest):
+    input_dir: str = Field(title="Input directory", description="Directory path from where to take the images")
+    output_dir: str = Field(title="Output directory", description="Directory path to put the processsed images into")
-- 
cgit v1.2.1


From 90f02c75220d187e075203a4e3b450bfba392c4d Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sun, 23 Oct 2022 16:03:30 -0300
Subject: Remove unused field and class

---
 modules/api/api.py    | 6 +++---
 modules/api/models.py | 6 +-----
 2 files changed, 4 insertions(+), 8 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 7b4fbe29..799e3701 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -104,7 +104,7 @@ class Api:
         with self.queue_lock:
             result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", **reqDict)
 
-        return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info_x=result[1], html_info=result[2])
+        return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
 
     def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
         reqDict = setUpscalers(req)
@@ -115,7 +115,7 @@ class Api:
         with self.queue_lock:
             result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", **reqDict)
 
-        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info_x=result[1], html_info=result[2])
+        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
     
     def extras_folder_processing_api(self, req:ExtrasFoldersRequest):
         reqDict = setUpscalers(req)
@@ -123,7 +123,7 @@ class Api:
         with self.queue_lock:
             result = run_extras(extras_mode=2, image=None, image_folder=None, **reqDict)
 
-        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info_x=result[1], html_info=result[2])
+        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
 
     def pnginfoapi(self):
         raise NotImplementedError
diff --git a/modules/api/models.py b/modules/api/models.py
index 6f096807..e461d397 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -130,8 +130,7 @@ class ExtrasBaseRequest(BaseModel):
     extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
 
 class ExtraBaseResponse(BaseModel):
-    html_info_x: str
-    html_info: str
+    html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
 
 class ExtrasSingleImageRequest(ExtrasBaseRequest):
     image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
@@ -139,9 +138,6 @@ class ExtrasSingleImageRequest(ExtrasBaseRequest):
 class ExtrasSingleImageResponse(ExtraBaseResponse):
     image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
 
-class SerializableImage(BaseModel):
-  path: str = Field(title="Path", description="The image's path ()")
-
 class ImageItem(BaseModel):
     data: str = Field(title="image data")
     name: str = Field(title="filename")
-- 
cgit v1.2.1


From 6124575e1892259bf706db186de303acc9de47bf Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Mon, 24 Oct 2022 04:29:19 +0900
Subject: Translation complete

---
 localizations/ko_KR.json | 302 +++++++++++++++++++++++++----------------------
 1 file changed, 160 insertions(+), 142 deletions(-)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index a48ece87..6889de46 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -15,23 +15,24 @@
   "A merger of the two checkpoints will be generated in your": "체크포인트들이 병합된 결과물이 당신의",
   "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
   "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
-  "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
+  "Add a second progress bar to the console that shows progress for an entire job.": "콘솔에 전체 작업의 진행도를 보여주는 2번째 프로그레스 바 추가하기",
   "Add difference": "차이점 추가",
-  "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid",
+  "Add extended info (seed, prompt) to filename when saving grid": "그리드 저장 시 파일명에 추가 정보(시드, 프롬프트) 기입",
   "Add layer normalization": "레이어 정규화(normalization) 추가",
-  "Add model hash to generation information": "Add model hash to generation information",
-  "Add model name to generation information": "Add model name to generation information",
+  "Add model hash to generation information": "생성 정보에 모델 해시 추가",
+  "Add model name to generation information": "생성 정보에 모델 이름 추가",
   "Aesthetic imgs embedding": "스타일 이미지 임베딩",
   "Aesthetic learning rate": "스타일 학습 수",
   "Aesthetic steps": "스타일 스텝 수",
   "Aesthetic text for imgs": "스타일 텍스트",
   "Aesthetic weight": "스타일 가중치",
-  "Always print all generation info to standard output": "Always print all generation info to standard output",
-  "Always save all generated image grids": "Always save all generated image grids",
+  "Allowed categories for random artists selection when using the Roll button": "랜덤 버튼을 눌러 무작위 작가를 선택할 때 허용된 카테고리",
+  "Always print all generation info to standard output": "기본 아웃풋에 모든 생성 정보 항상 출력하기",
+  "Always save all generated image grids": "생성된 이미지 그리드 항상 저장하기",
   "Always save all generated images": "생성된 이미지 항상 저장하기",
   "api": "",
   "append": "뒤에 삽입",
-  "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
+  "Apply color correction to img2img results to match original colors.": "이미지→이미지 결과물이 기존 색상과 일치하도록 색상 보정 적용하기",
   "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
   "Apply settings": "설정 적용하기",
   "Batch count": "배치 수",
@@ -43,29 +44,29 @@
   "built with gradio": "gradio로 제작되었습니다",
   "Cancel generate forever": "반복 생성 취소",
   "CFG Scale": "CFG 스케일",
-  "Check progress": "Check progress",
-  "Check progress (first)": "Check progress (first)",
+  "Check progress": "진행도 체크",
+  "Check progress (first)": "진행도 체크 (처음)",
   "checkpoint": " 체크포인트 ",
   "Checkpoint Merger": "체크포인트 병합",
   "Checkpoint name": "체크포인트 이름",
-  "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
+  "Checkpoints to cache in RAM": "RAM에 캐싱할 체크포인트 수",
   "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
-  "Click to Upload": "Click to Upload",
+  "Click to Upload": "클릭해서 업로드하기",
   "Clip skip": "클립 건너뛰기",
-  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
+  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP : 텍스트 파일 최대 라인 수 (0 = 제한 없음)",
   "CodeFormer visibility": "CodeFormer 가시성",
   "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 가중치 (0 = 최대 효과, 1 = 최소 효과)",
-  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
+  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 가중치 설정값 (0 = 최대 효과, 1 = 최소 효과)",
   "Color variation": "색깔 다양성",
   "Collect": "즐겨찾기",
   "copy": "복사",
   "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
-  "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
-  "Create aesthetic images embedding": "Create aesthetic images embedding",
+  "Create a text file next to every image with generation parameters.": "생성된 이미지마다 생성 설정값을 담은 텍스트 파일 생성하기",
+  "Create aesthetic images embedding": "스타일 이미지 임베딩 생성하기",
   "Create embedding": "임베딩 생성",
   "Create flipped copies": "좌우로 뒤집은 복사본 생성",
   "Create hypernetwork": "하이퍼네트워크 생성",
-  "Create images embedding": "Create images embedding",
+  "Create images embedding": "이미지 임베딩 생성하기",
   "Crop and resize": "잘라낸 후 리사이징",
   "Crop to fit": "잘라내서 맞추기",
   "Custom Name (Optional)": "병합 모델 이름 (선택사항)",
@@ -80,15 +81,15 @@
   "Denoising strength change factor": "디노이즈 강도 변경 배수",
   "Destination directory": "결과물 저장 경로",
   "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
-  "Directory for saving images using the Save button": "Directory for saving images using the Save button",
-  "Directory name pattern": "Directory name pattern",
+  "Directory for saving images using the Save button": "저장 버튼을 이용해 저장하는 이미지들의 저장 경로",
+  "Directory name pattern": "디렉토리명 패턴",
   "directory.": "저장 경로에 저장됩니다.",
-  "Do not add watermark to images": "Do not add watermark to images",
+  "Do not add watermark to images": "이미지에 워터마크 추가하지 않기",
   "Do not do anything special": "아무것도 하지 않기",
-  "Do not save grids consisting of one picture": "Do not save grids consisting of one picture",
-  "Do not show any images in results for web": "Do not show any images in results for web",
+  "Do not save grids consisting of one picture": "이미지가 1개뿐인 그리드는 저장하지 않기",
+  "Do not show any images in results for web": "웹에서 결과창에 아무 이미지도 보여주지 않기",
   "down": "아래쪽",
-  "Download localization template": "Download localization template",
+  "Download localization template": "현지화 템플릿 다운로드",
   "Download": "다운로드",
   "DPM adaptive": "DPM adaptive",
   "DPM fast": "DPM fast",
@@ -98,65 +99,67 @@
   "DPM2 Karras": "DPM2 Karras",
   "Draw legend": "범례 그리기",
   "Draw mask": "마스크 직접 그리기",
-  "Drop File Here": "Drop File Here",
-  "Drop Image Here": "Drop Image Here",
+  "Drop File Here": "파일을 끌어 놓으세요",
+  "Drop Image Here": "이미지를 끌어 놓으세요",
   "Embedding": "임베딩",
   "Embedding Learning rate": "임베딩 학습률",
-  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
-  "Enable full page image viewer": "Enable full page image viewer",
-  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
+  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "강조 : (텍스트)를 이용해 모델의 텍스트에 대한 가중치를 더 강하게 주고 [텍스트]를 이용해 더 약하게 줍니다.",
+  "Enable full page image viewer": "전체 페이지 이미지 뷰어 활성화",
+  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "더 예리하고 깔끔한 결과물을 위해 K 샘플러들에 양자화를 적용합니다. 존재하는 시드가 변경될 수 있습니다. 재시작이 필요합니다.",
   "End Page": "마지막 페이지",
   "Enter hypernetwork layer structure": "하이퍼네트워크 레이어 구조 입력",
   "Error": "오류",
-  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
+  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "deepbooru에서 괄호를 역슬래시(\\)로 이스케이프 처리하기(가중치 강조가 아니라 실제 괄호로 사용되게 하기 위해)",
   "ESRGAN_4x": "ESRGAN_4x",
   "Eta": "Eta",
-  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
-  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
-  "Eta noise seed delta": "Eta noise seed delta",
+  "eta (noise multiplier) for ancestral samplers": "ancestral 샘플러를 위한 eta(노이즈 배수)값",
+  "eta (noise multiplier) for DDIM": "DDIM을 위한 eta(노이즈 배수)값",
+  "Eta noise seed delta": "Eta 노이즈 시드 변화",
   "Euler": "Euler",
   "Euler a": "Euler a",
   "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
   "Existing Caption txt Action": "이미 존재하는 캡션 텍스트 처리",
   "Extra": "고급",
   "Extras": "부가기능",
-  "extras history": "extras history",
-  "Face restoration": "Face restoration",
+  "extras history": "부가기능 기록",
+  "Face restoration": "얼굴 보정",
+  "Face restoration model": "얼굴 보정 모델",
   "Fall-off exponent (lower=higher detail)": "감쇠 지수 (낮을수록 디테일이 올라감)",
   "favorites": "즐겨찾기",
-  "File": "File",
-  "File format for grids": "File format for grids",
-  "File format for images": "File format for images",
+  "File": "파일",
+  "File format for grids": "그리드 이미지 파일 형식",
+  "File format for images": "이미지 파일 형식",
   "File Name": "파일 이름",
   "File with inputs": "설정값 파일",
-  "Filename join string": "Filename join string",
-  "Filename word regex": "Filename word regex",
+  "Filename join string": "파일명 병합 문자열",
+  "Filename word regex": "파일명 정규표현식",
   "fill": "채우기",
   "fill it with colors of the image": "이미지의 색상으로 채우기",
   "fill it with latent space noise": "잠재 공간 노이즈로 채우기",
   "fill it with latent space zeroes": "잠재 공간의 0값으로 채우기",
-  "Filter NSFW content": "Filter NSFW content",
+  "Filter NSFW content": "성인 컨텐츠 필터링하기",
   "First Page": "처음 페이지",
   "Firstpass height": "초기 세로길이",
   "Firstpass width": "초기 가로길이",
-  "Font for image grids that have text": "Font for image grids that have text",
+  "Font for image grids that have text": "텍스트가 존재하는 그리드 이미지의 폰트",
   "for detailed explanation.": "를 참조하십시오.",
   "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD 업스케일링에서 타일 간 몇 픽셀을 겹치게 할지 결정하는 설정값입니다. 타일들이 다시 한 이미지로 합쳐질 때, 눈에 띄는 이음매가 없도록 서로 겹치게 됩니다.",
   "Generate": "생성",
   "Generate forever": "반복 생성",
   "Generate Info": "생성 정보",
   "GFPGAN visibility": "GFPGAN 가시성",
-  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
+  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "그리드 세로줄 수 : -1로 설정 시 자동 감지/0으로 설정 시 배치 크기와 동일",
   "Height": "세로",
   "Heun": "Heun",
   "hide": "api 숨기기",
-  "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
+  "Hide samplers in user interface (requires restart)": "사용자 인터페이스에서 숨길 샘플러 선택(재시작 필요)",
   "Highres. fix": "고해상도 보정",
   "History": "기록",
   "Image Browser": "이미지 브라우저",
+  "Images Browser": "이미지 브라우저",
   "Images directory": "이미지 경로",
   "extras": "부가기능",
-  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
+  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "훈련이 얼마나 빨리 이루어질지 정하는 값입니다. 값이 낮을수록 훈련 시간이 길어지고, 높은 값일수록 정확한 결과를 내는 데 실패하고 임베딩을 망가뜨릴 수 있습니다(임베딩이 망가진 경우에는 훈련 정보 텍스트박스에 손실(Loss) : nan 이라고 출력되게 됩니다. 이 경우에는 망가지지 않은 이전 백업본을 불러와야 합니다).\n\n학습률은 하나의 값으로 설정할 수도 있고, 다음 문법을 사용해 여러 값을 사용할 수도 있습니다 :\n\n학습률_1:최대 스텝수_1, 학습률_2:최대 스텝수_2, ...\n\n예 : 0.005:100, 1e-3:1000, 1e-5\n\n예의 설정값은 첫 100스텝동안 0.005의 학습률로, 그 이후 1000스텝까지는 1e-3으로, 남은 스텝은 1e-5로 훈련하게 됩니다.",
   "How many batches of images to create": "생성할 이미지 배치 수",
   "How many image to create in a single batch": "한 배치당 이미지 수",
   "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
@@ -166,111 +169,114 @@
   "Hypernet str.": "하이퍼네트워크 강도",
   "Hypernetwork": "하이퍼네트워크",
   "Hypernetwork Learning rate": "하이퍼네트워크 학습률",
-  "Hypernetwork strength": "Hypernetwork strength",
-  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
-  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
-  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
+  "Hypernetwork strength": "하이퍼네트워크 강도",
+  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "PNG 이미지가 4MB보다 크거나 가로 또는 세로길이가 4000보다 클 경우, 다운스케일 후 JPG로 복사본 저장하기",
+  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "이 옵션이 활성화되면 생성된 이미지에 워터마크가 추가되지 않습니다. 경고 : 워터마크를 추가하지 않는다면, 비윤리적인 행동을 하는 중일지도 모릅니다.",
+  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "이 값이 0이 아니라면, 시드에 해당 값이 더해지고, Eta가 있는 샘플러를 사용할 때 노이즈의 RNG 조정을 위해 해당 값이 사용됩니다. 이 설정으로 더 다양한 이미지를 생성하거나, 잘 알고 계시다면 특정 소프트웨어의 결과값을 재현할 수도 있습니다.",
   "ignore": "무시",
-  "Image": "Image",
+  "Image": "이미지",
   "Image for img2img": "Image for img2img",
-  "Image for inpainting with mask": "Image for inpainting with mask",
-  "Images filename pattern": "Images filename pattern",
+  "Image for inpainting with mask": "마스크로 인페인팅할 이미지",
+  "Images filename pattern": "이미지 파일명 패턴",
   "img2img": "이미지→이미지",
   "img2img alternative test": "이미지→이미지 대체버전 테스트",
-  "img2img DDIM discretize": "img2img DDIM discretize",
-  "img2img history": "img2img history",
+  "img2img DDIM discretize": "이미지→이미지 DDIM 이산화",
+  "img2img history": "이미지→이미지 기록",
   "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "루프백 모드에서는 매 루프마다 디노이즈 강도에 이 값이 곱해집니다. 1보다 작을 경우 다양성이 낮아져 결과 이미지들이 고정된 형태로 모일 겁니다. 1보다 클 경우 다양성이 높아져 결과 이미지들이 갈수록 혼란스러워지겠죠.",
   "Include Separate Images": "분리된 이미지 포함하기",
-  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
+  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "75개보다 많은 토큰을 사용시 마지막 쉼표로부터 N개의 토큰 이내에 패딩을 추가해 통일성 증가시키기",
   "Initialization text": "초기화 텍스트",
   "Inpaint": "인페인트",
   "Inpaint at full resolution": "전체 해상도로 인페인트하기",
-  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트 패딩값(픽셀 단위)",
+  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트시 패딩값(픽셀 단위)",
   "Inpaint masked": "마스크만 처리",
   "Inpaint not masked": "마스크 이외만 처리",
   "Input directory": "인풋 이미지 경로",
   "Interpolation Method": "보간 방법",
   "Interrogate\nCLIP": "CLIP\n분석",
   "Interrogate\nDeepBooru": "DeepBooru\n분석",
-  "Interrogate Options": "Interrogate Options",
-  "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold",
-  "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically",
-  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
-  "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM",
-  "Interrogate: maximum description length": "Interrogate: maximum description length",
-  "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
-  "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
-  "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
+  "Interrogate Options": "분석 설정",
+  "Interrogate: deepbooru score threshold": "분석 : deepbooru 점수 임계값",
+  "Interrogate: deepbooru sort alphabetically": "분석 : deepbooru 알파벳 순서로 정렬하기",
+  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "분석 : 결과물에 모델 태그의 랭크 포함하기 (캡션 바탕의 분석기에는 효과 없음)",
+  "Interrogate: keep models in VRAM": "분석 : VRAM에 모델 유지하기",
+  "Interrogate: maximum description length": "분석 : 설명 최대 길이",
+  "Interrogate: minimum description length (excluding artists, etc..)": "분석 : 설명 최소 길이(작가 등등..제외)",
+  "Interrogate: num_beams for BLIP": "분석 : BLIP의 num_beams값",
+  "Interrogate: use artists from artists.csv": "분석 : artists.csv의 작가들 사용하기",
   "Interrupt": "중단",
   "Is negative text": "네거티브 텍스트일시 체크",
   "Just resize": "리사이징",
   "Keep -1 for seeds": "시드값 -1로 유지",
   "keep whatever was there originally": "이미지 원본 유지",
-  "Label": "Label",
+  "Label": "라벨",
   "Lanczos": "Lanczos",
-  "Last prompt:": "Last prompt:",
-  "Last saved hypernetwork:": "Last saved hypernetwork:",
-  "Last saved image:": "Last saved image:",
+  "Last prompt:": "마지막 프롬프트 : ",
+  "Last saved hypernetwork:": "마지막으로 저장된 하이퍼네트워크 : ",
+  "Last saved image:": "마지막으로 저장된 이미지 : ",
   "latent noise": "잠재 노이즈",
   "latent nothing": "잠재 공백",
   "LDSR": "LDSR",
-  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
+  "LDSR processing steps. Lower = faster": "LDSR 스텝 수. 낮은 값 = 빠른 속도",
   "leakyrelu": "leakyrelu",
   "Leave blank to save images to the default path.": "기존 저장 경로에 이미지들을 저장하려면 비워두세요.",
   "left": "왼쪽",
   "linear": "linear",
-  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
+  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "설정 탭이 아니라 상단의 빠른 설정 바에 위치시킬 설정 이름을 쉼표로 분리해서 입력하십시오. 설정 이름은 modules/shared.py에서 찾을 수 있습니다. 재시작이 필요합니다.",
   "LMS": "LMS",
   "LMS Karras": "LMS Karras",
   "Load": "불러오기",
   "Loading...": "로딩 중...",
-  "Localization (requires restart)": "Localization (requires restart)",
+  "Localization (requires restart)": "현지화 (재시작 필요)",
   "Log directory": "로그 경로",
   "Loopback": "루프백",
   "Loops": "루프 수",
-  "Loss:": "Loss:",
+  "Loss:": "손실(Loss) : ",
   "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
-  "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
+  "Make K-diffusion samplers produce same images in a batch as when making a single image": "K-diffusion 샘플러들이 단일 이미지를 생성하는 것처럼 배치에서도 동일한 이미지를 생성하게 하기",
   "Make Zip when Save?": "저장 시 Zip 생성하기",
   "Mask": "마스크",
   "Mask blur": "마스크 블러",
-  "Mask mode": "Mask mode",
+  "Mask mode": "마스크 모드",
   "Masked content": "마스크된 부분",
-  "Masking mode": "Masking mode",
-  "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
+  "Masking mode": "마스킹 모드",
+  "Max prompt words for [prompt_words] pattern": "[prompt_words] 패턴의 최대 프롬프트 단어 수",
   "Max steps": "최대 스텝 수",
   "Modules": "모듈",
-  "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
-  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.",
+  "Move face restoration model from VRAM into RAM after processing": "처리가 완료되면 얼굴 보정 모델을 VRAM에서 RAM으로 옮기기",
+  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "하이퍼네트워크 훈련 진행 시 VAE와 CLIP을 RAM으로 옮기기. VRAM이 절약됩니다.",
   "Multiplier (M) - set to 0 to get model A": "배율 (M) - 0으로 적용하면 모델 A를 얻게 됩니다",
   "Name": "이름",
   "Negative prompt": "네거티브 프롬프트",
   "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
   "Next batch": "다음 묶음",
   "Next Page": "다음 페이지",
-  "None": "None",
+  "None": "없음",
   "Nothing": "없음",
   "Nothing found in the image.": "Nothing found in the image.",
   "number of images to delete consecutively next": "연속적으로 삭제할 이미지 수",
-  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
+  "Number of pictures displayed on each page": "각 페이지에 표시될 이미지 수",
+  "Minimum number of pages per load": "한번 불러올 때마다 불러올 최소 페이지 수",
+  "Number of grids in each row": "각 세로줄마다 표시될 그리드 수",
+  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "세대(Epoch)당 단일 인풋 이미지의 반복 횟수 - 세대(Epoch) 숫자를 표시하는 데에만 사용됩니다. ",
   "Number of vectors per token": "토큰별 벡터 수",
   "Open for Clip Aesthetic!": "클립 스타일 기능을 활성화하려면 클릭!",
   "Open images output directory": "이미지 저장 경로 열기",
   "Open output directory": "저장 경로 열기",
-  "or": "or",
+  "or": "또는",
   "original": "원본 유지",
   "Original negative prompt": "기존 네거티브 프롬프트",
   "Original prompt": "기존 프롬프트",
   "Outpainting direction": "아웃페인팅 방향",
   "Outpainting mk2": "아웃페인팅 마크 2",
   "Output directory": "이미지 저장 경로",
-  "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
-  "Output directory for images from extras tab": "Output directory for images from extras tab",
-  "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
-  "Output directory for img2img grids": "Output directory for img2img grids",
-  "Output directory for img2img images": "Output directory for img2img images",
-  "Output directory for txt2img grids": "Output directory for txt2img grids",
-  "Output directory for txt2img images": "Output directory for txt2img images",
+  "Output directory for grids; if empty, defaults to two directories below": "그리드 이미지 저장 경로 - 비워둘 시 하단의 2가지 기본 경로로 설정됨",
+  "Output directory for images from extras tab": "부가기능 탭 저장 경로",
+  "Output directory for images; if empty, defaults to three directories below": "이미지 저장 경로 - 비워둘 시 하단의 3가지 기본 경로로 설정됨",
+  "Output directory for img2img grids": "이미지→이미지 그리드 저장 경로",
+  "Output directory for img2img images": "이미지→이미지 저장 경로",
+  "Output directory for txt2img grids": "텍스트→이미지 그리드 저장 경로",
+  "Output directory for txt2img images": "텍스트→이미지 저장 경로",
   "Override `Denoising strength` to 1?": "디노이즈 강도를 1로 적용할까요?",
   "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "프롬프트 값을 기존 프롬프트와 동일하게 적용할까요?(네거티브 프롬프트 포함)",
   "Override `Sampling method` to Euler?(this method is built for it)": "샘플링 방법을 Euler로 적용할까요?(이 기능은 해당 샘플러를 위해 만들어져 있습니다)",
@@ -279,20 +285,21 @@
   "Overwrite Old Hypernetwork": "기존 하이퍼네트워크 덮어쓰기",
   "Page Index": "페이지 인덱스",
   "parameters": "설정값",
-  "Path to directory where to write outputs": "Path to directory where to write outputs",
+  "Path to directory where to write outputs": "결과물을 출력할 경로",
   "Path to directory with input images": "인풋 이미지가 있는 경로",
-  "Paths for saving": "Paths for saving",
+  "Paths for saving": "저장 경로",
   "Pixels to expand": "확장할 픽셀 수",
   "PLMS": "PLMS",
   "PNG Info": "PNG 정보",
   "Poor man's outpainting": "가난뱅이의 아웃페인팅",
-  "Preparing dataset from": "Preparing dataset from",
+  "Preload images at startup": "WebUI 가동 시 이미지 프리로드하기",
+  "Preparing dataset from": "준비된 데이터셋 경로 : ",
   "prepend": "앞에 삽입",
   "Preprocess": "전처리",
   "Preprocess images": "이미지 전처리",
   "Prev batch": "이전 묶음",
   "Prev Page": "이전 페이지",
-  "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)",
+  "Prevent empty spots in grid (when set to autodetect)": "(자동 감지 사용시)그리드에 빈칸이 생기는 것 방지하기",
   "Primary model (A)": "주 모델 (A)",
   "Process an image, use it as an input, repeat.": "이미지를 생성하고, 생성한 이미지를 다시 원본으로 사용하는 과정을 반복합니다.",
   "Process images in a directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리의 이미지들을 처리합니다.",
@@ -307,26 +314,26 @@
   "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
   "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
   "quad": "quad",
-  "Quality for saved jpeg images": "Quality for saved jpeg images",
-  "Quicksettings list": "Quicksettings list",
+  "Quality for saved jpeg images": "저장된 jpeg 이미지들의 품질",
+  "Quicksettings list": "빠른 설정 리스트",
   "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
   "Randomness": "랜덤성",
   "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
   "Read parameters (prompt, etc...) from txt2img tab when making previews": "프리뷰 이미지 생성 시 텍스트→이미지 탭에서 설정값(프롬프트 등) 읽어오기",
   "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "추천 설정값 - 샘플링 스텝 수 : 80-100 , 샘플러 : Euler a, 디노이즈 강도 : 0.8",
-  "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
+  "Reload custom script bodies (No ui updates, No restart)": "커스텀 스크립트 리로드하기(UI 업데이트 없음, 재시작 없음)",
   "relu": "relu",
   "Renew Page": "Renew Page",
-  "Request browser notifications": "Request browser notifications",
+  "Request browser notifications": "브라우저 알림 권한 요청",
   "Resize": "리사이징 배수",
   "Resize and fill": "리사이징 후 채우기",
   "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "설정된 해상도로 이미지 리사이징을 진행합니다. 원본과 가로/세로 길이가 일치하지 않을 경우, 부정확한 화면비의 이미지를 얻게 됩니다.",
-  "Resize mode": "Resize mode",
+  "Resize mode": "리사이징 모드",
   "Resize seed from height": "시드 리사이징 가로길이",
   "Resize seed from width": "시드 리사이징 세로길이",
   "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "이미지 전체가 설정된 해상도 내부에 들어가게 리사이징을 진행합니다. 빈 공간은 이미지의 색상으로 채웁니다.",
   "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "설정된 해상도 전체가 이미지로 가득차게 리사이징을 진행합니다. 튀어나오는 부분은 잘라냅니다.",
-  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradio를 재시작하고 컴포넌트 새로고침하기 (커스텀 스크립트, ui.py, js, css만 해당됨)",
   "Restore faces": "얼굴 보정",
   "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
   "Result = A * (1 - M) + B * M": "결과물 = A * (1 - M) + B * M",
@@ -335,23 +342,23 @@
   "right": "오른쪽",
   "Run": "가동",
   "Sampler": "샘플러",
-  "Sampler parameters": "Sampler parameters",
+  "Sampler parameters": "샘플러 설정값",
   "Sampling method": "샘플링 방법",
   "Sampling Steps": "샘플링 스텝 수",
   "Save": "저장",
   "Save a copy of embedding to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 임베딩을 저장합니다, 비활성화하려면 0으로 설정하십시오.",
-  "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
-  "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
-  "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
+  "Save a copy of image before applying color correction to img2img results": "이미지→이미지 결과물에 색상 보정을 진행하기 전 이미지의 복사본을 저장하기",
+  "Save a copy of image before doing face restoration.": "얼굴 보정을 진행하기 전 이미지의 복사본을 저장하기",
+  "Save an csv containing the loss to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 손실(Loss)을 포함하는 csv 파일을 저장합니다, 비활성화하려면 0으로 설정하십시오.",
   "Save an image to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 이미지를 저장합니다, 비활성화하려면 0으로 설정하십시오.",
   "Save as float16": "float16으로 저장",
-  "Save grids to a subdirectory": "Save grids to a subdirectory",
-  "Save images to a subdirectory": "Save images to a subdirectory",
+  "Save grids to a subdirectory": "그리드 이미지를 하위 디렉토리에 저장하기",
+  "Save images to a subdirectory": "이미지를 하위 디렉토리에 저장하기",
   "Save images with embedding in PNG chunks": "PNG 청크로 이미지에 임베딩을 포함시켜 저장",
   "Save style": "스타일 저장",
-  "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
-  "Saving images/grids": "Saving images/grids",
-  "Saving to a directory": "Saving to a directory",
+  "Save text information about generation parameters as chunks to png files": "이미지 생성 설정값을 PNG 청크에 텍스트로 저장",
+  "Saving images/grids": "이미지/그리드 저장",
+  "Saving to a directory": "디렉토리에 저장",
   "Scale by": "스케일링 배수 지정",
   "Scale to": "스케일링 사이즈 지정",
   "Script": "스크립트",
@@ -363,6 +370,7 @@
   "Seed": "시드",
   "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
   "Select activation function of hypernetwork": "하이퍼네트워크 활성화 함수 선택",
+  "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "WebUI에 표시할 Real-ESRGAN 모델을 선택하십시오. (재시작 필요)",
   "Send to extras": "부가기능으로 전송",
   "Send to img2img": "이미지→이미지로 전송",
   "Send to inpaint": "인페인트로 전송",
@@ -374,29 +382,30 @@
   "set_index": "set_index",
   "Settings": "설정",
   "should be 2 or lower.": "이 2 이하여야 합니다.",
-  "Show generation progress in window title.": "Show generation progress in window title.",
-  "Show grid in results for web": "Show grid in results for web",
-  "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
-  "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
-  "Show progressbar": "Show progressbar",
+  "Show generation progress in window title.": "창 타이틀에 생성 진행도 보여주기",
+  "Show grid in results for web": "웹에서 결과창에 그리드 보여주기",
+  "Show image creation progress every N sampling steps. Set 0 to disable.": "N번째 샘플링 스텝마다 이미지 생성 과정 보이기 - 비활성화하려면 0으로 설정",
+  "Show images zoomed in by default in full page image viewer": "전체 페이지 이미지 뷰어에서 기본값으로 이미지 확대해서 보여주기",
+  "Show progressbar": "프로그레스 바 보이기",
   "Show result images": "이미지 결과 보이기",
   "Show Textbox": "텍스트박스 보이기",
+  "Show previews of all images generated in a batch as a grid": "배치에서 생성된 모든 이미지의 미리보기를 그리드 형식으로 보여주기",
   "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
   "Sigma Churn": "시그마 섞기",
-  "sigma churn": "sigma churn",
+  "sigma churn": "시그마 섞기",
   "Sigma max": "시그마 최댓값",
   "Sigma min": "시그마 최솟값",
   "Sigma noise": "시그마 노이즈",
-  "sigma noise": "sigma noise",
-  "sigma tmin": "sigma tmin",
+  "sigma noise": "시그마 노이즈",
+  "sigma tmin": "시그마 tmin",
   "Single Image": "단일 이미지",
   "Skip": "건너뛰기",
   "Slerp angle": "구면 선형 보간 각도",
   "Slerp interpolation": "구면 선형 보간",
   "Source": "원본",
   "Source directory": "원본 경로",
-  "Split image threshold": "Split image threshold",
-  "Split image overlap ratio": "Split image overlap ratio",
+  "Split image threshold": "이미지 분할 임계값",
+  "Split image overlap ratio": "이미지 분할 겹침 비율",
   "Split oversized images": "사이즈가 큰 이미지 분할하기",
   "Stable Diffusion": "Stable Diffusion",
   "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
@@ -407,20 +416,20 @@
   "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
   "Style 1": "스타일 1",
   "Style 2": "스타일 2",
-  "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
+  "Style to apply; styles have components for both positive and negative prompts and apply to both": "적용할 스타일 - 스타일은 긍정/부정 프롬프트 모두에 대한 설정값을 가지고 있고 양쪽 모두에 적용 가능합니다.",
   "SwinIR 4x": "SwinIR 4x",
   "Sys VRAM:": "시스템 VRAM : ",
-  "System": "System",
+  "System": "시스템",
   "Tertiary model (C)": "3차 모델 (C)",
-  "Textbox": "Textbox",
-  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
-  "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
+  "Textbox": "텍스트박스",
+  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "이 정규표현식은 파일명으로부터 단어를 추출하는 데 사용됩니다. 추출된 단어들은 하단의 설정을 이용해 라벨 텍스트로 변환되어 훈련에 사용됩니다. 파일명 텍스트를 유지하려면 비워두십시오.",
+  "This string will be used to join split words into a single line if the option above is enabled.": "이 문자열은 상단 설정이 활성화되어있을 때 분리된 단어들을 한 줄로 합치는 데 사용됩니다.",
   "This text is used to rotate the feature space of the imgs embs": "이 텍스트는 이미지 임베딩의 특징 공간을 회전하는 데 사용됩니다.",
   "Tile overlap": "타일 겹침",
-  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
-  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
-  "Tile size for all SwinIR.": "Tile size for all SwinIR.",
-  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
+  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN 업스케일러들의 타일 중첩 수치, 픽셀 단위. 낮은 값 = 눈에 띄는 이음매.",
+  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR의 타일 중첩 수치, 픽셀 단위. 낮은 값 = 눈에 띄는 이음매.",
+  "Tile size for all SwinIR.": "SwinIR의 타일 사이즈.",
+  "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 업스케일러들의 타일 사이즈. 0 = 타일링 없음.",
   "Tiling": "타일링",
   "Time taken:": "소요 시간 : ",
   "Torch active/reserved:": "활성화/예약된 Torch 양 : ",
@@ -429,51 +438,60 @@
   "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "임베딩이나 하이퍼네트워크를 훈련시킵니다. 1:1 비율의 이미지가 있는 경로를 지정해야 합니다.",
   "Train Embedding": "임베딩 훈련",
   "Train Hypernetwork": "하이퍼네트워크 훈련",
-  "Training": "Training",
+  "Training": "훈련",
   "txt2img": "텍스트→이미지",
-  "txt2img history": "txt2img history",
+  "txt2img history": "텍스트→이미지 기록",
   "uniform": "uniform",
   "up": "위쪽",
   "Upload mask": "마스크 업로드하기",
-  "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
+  "Upscale latent space image when doing hires. fix": "고해상도 보정 사용시 잠재 공간 이미지 업스케일하기",
   "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
   "Upscaler": "업스케일러",
   "Upscaler 1": "업스케일러 1",
   "Upscaler 2": "업스케일러 2",
   "Upscaler 2 visibility": "업스케일러 2 가시성",
-  "Upscaler for img2img": "Upscaler for img2img",
-  "Upscaling": "Upscaling",
+  "Upscaler for img2img": "이미지→이미지 업스케일러",
+  "Upscaling": "업스케일링",
   "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
   "Use an empty output directory to save pictures normally instead of writing to the output directory.": "저장 경로를 비워두면 기본 저장 폴더에 이미지들이 저장됩니다.",
   "Use BLIP for caption": "캡션에 BLIP 사용",
   "Use deepbooru for caption": "캡션에 deepbooru 사용",
   "Use dropout": "드롭아웃 사용",
-  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
-  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
-  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
-  "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
-  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
-  "User interface": "User interface",
+  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지 파일명 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
+  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지와 그리드의 하위 디렉토리명의 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
+  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "옛 방식의 강조 구현을 사용합니다. 옛 시드를 재현하는 데 효과적일 수 있습니다.",
+  "Use original name for output filename during batch process in extras tab": "부가기능 탭에서 이미지를 여러장 처리 시 결과물 파일명에 기존 파일명 사용하기",
+  "use spaces for tags in deepbooru": "deepbooru에서 태그에 공백 사용",
+  "User interface": "사용자 인터페이스",
   "Var. seed": "바리에이션 시드",
   "Var. strength": "바리에이션 강도",
   "Variation seed": "바리에이션 시드",
   "Variation strength": "바리에이션 강도",
   "view": "api 보이기",
-  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
+  "VRAM usage polls per second during generation. Set to 0 to disable.": "생성 도중 초당 VRAM 사용량 폴링 수. 비활성화하려면 0으로 설정하십시오.",
   "Weighted sum": "가중 합",
   "What to put inside the masked area before processing it with Stable Diffusion.": "Stable Diffusion으로 이미지를 생성하기 전 마스크된 부분에 무엇을 채울지 결정하는 설정값",
-  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
-  "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
-  "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
+  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "PNG 정보나 붙여넣은 텍스트로부터 생성 설정값을 읽어올 때, 선택된 모델/체크포인트는 변경하지 않기.",
+  "When using \"Save\" button, save images to a subdirectory": "저장 버튼 사용시, 이미지를 하위 디렉토리에 저장하기",
+  "When using 'Save' button, only save a single selected image": "저장 버튼 사용시, 선택된 이미지 1개만 저장하기",
   "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
   "Width": "가로",
   "wiki": " 위키",
   "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "이미지를 설정된 사이즈의 2배로 업스케일합니다. 상단의 가로와 세로 슬라이더를 이용해 타일 사이즈를 지정하세요.",
-  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
+  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "이미지→이미지 진행 시, 슬라이더로 설정한 스텝 수를 정확히 실행하기 (일반적으로 디노이즈 강도가 낮을수록 실제 설정된 스텝 수보다 적게 진행됨)",
   "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
   "X type": "X축",
   "X values": "X 설정값",
   "X/Y plot": "X/Y 플롯",
   "Y type": "Y축",
-  "Y values": "Y 설정값"
+  "Y values": "Y 설정값",
+  "step1 min/max": "스텝1 최소/최대",
+  "step2 min/max": "스텝2 최소/최대",
+  "step count": "스텝 변화 횟수",
+  "cfg1 min/max": "CFG1 최소/최대",
+  "cfg2 min/max": "CFG2 최소/최대",
+  "cfg count": "CFG 변화 횟수",
+  "x/y change": "X/Y축 변경",
+  "Random": "랜덤",
+  "Random grid": "랜덤 그리드"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From 2ce44fc48e3ee6c73042ea83748772fe3eb45b1e Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Mon, 24 Oct 2022 04:38:16 +0900
Subject: Finalize ko_KR.json

---
 localizations/ko_KR.json | 44 ++++++++++++++++++++++----------------------
 1 file changed, 22 insertions(+), 22 deletions(-)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 6889de46..ab12c37e 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -5,10 +5,10 @@
   "❮": "❮",
   "❯": "❯",
   "⤡": "⤡",
-  " images in this directory. Loaded ": "개의 이미지가 이 경로에 존재합니다. ",
   " images during ": "개의 이미지를 불러왔고, 생성 기간은 ",
-  ", divided into ": "입니다. ",
+  " images in this directory. Loaded ": "개의 이미지가 이 경로에 존재합니다. ",
   " pages": "페이지로 나뉘어 표시합니다.",
+  ", divided into ": "입니다. ",
   "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
   "[wiki]": " [위키] 참조",
   "A directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리를 선택해 주세요.",
@@ -43,7 +43,10 @@
   "BSRGAN 4x": "BSRGAN 4x",
   "built with gradio": "gradio로 제작되었습니다",
   "Cancel generate forever": "반복 생성 취소",
+  "cfg count": "CFG 변화 횟수",
   "CFG Scale": "CFG 스케일",
+  "cfg1 min/max": "CFG1 최소/최대",
+  "cfg2 min/max": "CFG2 최소/최대",
   "Check progress": "진행도 체크",
   "Check progress (first)": "진행도 체크 (처음)",
   "checkpoint": " 체크포인트 ",
@@ -57,8 +60,8 @@
   "CodeFormer visibility": "CodeFormer 가시성",
   "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 가중치 (0 = 최대 효과, 1 = 최소 효과)",
   "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 가중치 설정값 (0 = 최대 효과, 1 = 최소 효과)",
-  "Color variation": "색깔 다양성",
   "Collect": "즐겨찾기",
+  "Color variation": "색깔 다양성",
   "copy": "복사",
   "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
   "Create a text file next to every image with generation parameters.": "생성된 이미지마다 생성 설정값을 담은 텍스트 파일 생성하기",
@@ -89,8 +92,8 @@
   "Do not save grids consisting of one picture": "이미지가 1개뿐인 그리드는 저장하지 않기",
   "Do not show any images in results for web": "웹에서 결과창에 아무 이미지도 보여주지 않기",
   "down": "아래쪽",
-  "Download localization template": "현지화 템플릿 다운로드",
   "Download": "다운로드",
+  "Download localization template": "현지화 템플릿 다운로드",
   "DPM adaptive": "DPM adaptive",
   "DPM fast": "DPM fast",
   "DPM2": "DPM2",
@@ -121,6 +124,7 @@
   "Existing Caption txt Action": "이미 존재하는 캡션 텍스트 처리",
   "Extra": "고급",
   "Extras": "부가기능",
+  "extras": "부가기능",
   "extras history": "부가기능 기록",
   "Face restoration": "얼굴 보정",
   "Face restoration model": "얼굴 보정 모델",
@@ -155,10 +159,6 @@
   "Hide samplers in user interface (requires restart)": "사용자 인터페이스에서 숨길 샘플러 선택(재시작 필요)",
   "Highres. fix": "고해상도 보정",
   "History": "기록",
-  "Image Browser": "이미지 브라우저",
-  "Images Browser": "이미지 브라우저",
-  "Images directory": "이미지 경로",
-  "extras": "부가기능",
   "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "훈련이 얼마나 빨리 이루어질지 정하는 값입니다. 값이 낮을수록 훈련 시간이 길어지고, 높은 값일수록 정확한 결과를 내는 데 실패하고 임베딩을 망가뜨릴 수 있습니다(임베딩이 망가진 경우에는 훈련 정보 텍스트박스에 손실(Loss) : nan 이라고 출력되게 됩니다. 이 경우에는 망가지지 않은 이전 백업본을 불러와야 합니다).\n\n학습률은 하나의 값으로 설정할 수도 있고, 다음 문법을 사용해 여러 값을 사용할 수도 있습니다 :\n\n학습률_1:최대 스텝수_1, 학습률_2:최대 스텝수_2, ...\n\n예 : 0.005:100, 1e-3:1000, 1e-5\n\n예의 설정값은 첫 100스텝동안 0.005의 학습률로, 그 이후 1000스텝까지는 1e-3으로, 남은 스텝은 1e-5로 훈련하게 됩니다.",
   "How many batches of images to create": "생성할 이미지 배치 수",
   "How many image to create in a single batch": "한 배치당 이미지 수",
@@ -175,8 +175,11 @@
   "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "이 값이 0이 아니라면, 시드에 해당 값이 더해지고, Eta가 있는 샘플러를 사용할 때 노이즈의 RNG 조정을 위해 해당 값이 사용됩니다. 이 설정으로 더 다양한 이미지를 생성하거나, 잘 알고 계시다면 특정 소프트웨어의 결과값을 재현할 수도 있습니다.",
   "ignore": "무시",
   "Image": "이미지",
+  "Image Browser": "이미지 브라우저",
   "Image for img2img": "Image for img2img",
   "Image for inpainting with mask": "마스크로 인페인팅할 이미지",
+  "Images Browser": "이미지 브라우저",
+  "Images directory": "이미지 경로",
   "Images filename pattern": "이미지 파일명 패턴",
   "img2img": "이미지→이미지",
   "img2img alternative test": "이미지→이미지 대체버전 테스트",
@@ -242,6 +245,7 @@
   "Masking mode": "마스킹 모드",
   "Max prompt words for [prompt_words] pattern": "[prompt_words] 패턴의 최대 프롬프트 단어 수",
   "Max steps": "최대 스텝 수",
+  "Minimum number of pages per load": "한번 불러올 때마다 불러올 최소 페이지 수",
   "Modules": "모듈",
   "Move face restoration model from VRAM into RAM after processing": "처리가 완료되면 얼굴 보정 모델을 VRAM에서 RAM으로 옮기기",
   "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "하이퍼네트워크 훈련 진행 시 VAE와 CLIP을 RAM으로 옮기기. VRAM이 절약됩니다.",
@@ -254,10 +258,9 @@
   "None": "없음",
   "Nothing": "없음",
   "Nothing found in the image.": "Nothing found in the image.",
+  "Number of grids in each row": "각 세로줄마다 표시될 그리드 수",
   "number of images to delete consecutively next": "연속적으로 삭제할 이미지 수",
   "Number of pictures displayed on each page": "각 페이지에 표시될 이미지 수",
-  "Minimum number of pages per load": "한번 불러올 때마다 불러올 최소 페이지 수",
-  "Number of grids in each row": "각 세로줄마다 표시될 그리드 수",
   "Number of repeats for a single input image per epoch; used only for displaying epoch number": "세대(Epoch)당 단일 인풋 이미지의 반복 횟수 - 세대(Epoch) 숫자를 표시하는 데에만 사용됩니다. ",
   "Number of vectors per token": "토큰별 벡터 수",
   "Open for Clip Aesthetic!": "클립 스타일 기능을 활성화하려면 클릭!",
@@ -317,6 +320,8 @@
   "Quality for saved jpeg images": "저장된 jpeg 이미지들의 품질",
   "Quicksettings list": "빠른 설정 리스트",
   "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+  "Random": "랜덤",
+  "Random grid": "랜덤 그리드",
   "Randomness": "랜덤성",
   "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
   "Read parameters (prompt, etc...) from txt2img tab when making previews": "프리뷰 이미지 생성 시 텍스트→이미지 탭에서 설정값(프롬프트 등) 읽어오기",
@@ -386,10 +391,10 @@
   "Show grid in results for web": "웹에서 결과창에 그리드 보여주기",
   "Show image creation progress every N sampling steps. Set 0 to disable.": "N번째 샘플링 스텝마다 이미지 생성 과정 보이기 - 비활성화하려면 0으로 설정",
   "Show images zoomed in by default in full page image viewer": "전체 페이지 이미지 뷰어에서 기본값으로 이미지 확대해서 보여주기",
+  "Show previews of all images generated in a batch as a grid": "배치에서 생성된 모든 이미지의 미리보기를 그리드 형식으로 보여주기",
   "Show progressbar": "프로그레스 바 보이기",
   "Show result images": "이미지 결과 보이기",
   "Show Textbox": "텍스트박스 보이기",
-  "Show previews of all images generated in a batch as a grid": "배치에서 생성된 모든 이미지의 미리보기를 그리드 형식으로 보여주기",
   "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
   "Sigma Churn": "시그마 섞기",
   "sigma churn": "시그마 섞기",
@@ -404,11 +409,14 @@
   "Slerp interpolation": "구면 선형 보간",
   "Source": "원본",
   "Source directory": "원본 경로",
-  "Split image threshold": "이미지 분할 임계값",
   "Split image overlap ratio": "이미지 분할 겹침 비율",
+  "Split image threshold": "이미지 분할 임계값",
   "Split oversized images": "사이즈가 큰 이미지 분할하기",
   "Stable Diffusion": "Stable Diffusion",
   "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "step count": "스텝 변화 횟수",
+  "step1 min/max": "스텝1 최소/최대",
+  "step2 min/max": "스텝2 최소/최대",
   "Step:": "Step:",
   "Steps": "스텝 수",
   "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
@@ -482,16 +490,8 @@
   "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
   "X type": "X축",
   "X values": "X 설정값",
+  "x/y change": "X/Y축 변경",
   "X/Y plot": "X/Y 플롯",
   "Y type": "Y축",
-  "Y values": "Y 설정값",
-  "step1 min/max": "스텝1 최소/최대",
-  "step2 min/max": "스텝2 최소/최대",
-  "step count": "스텝 변화 횟수",
-  "cfg1 min/max": "CFG1 최소/최대",
-  "cfg2 min/max": "CFG2 최소/최대",
-  "cfg count": "CFG 변화 횟수",
-  "x/y change": "X/Y축 변경",
-  "Random": "랜덤",
-  "Random grid": "랜덤 그리드"
+  "Y values": "Y 설정값"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From 124e44cf1eed1edc68954f63a2a9bc428aabbcec Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 24 Oct 2022 09:51:56 +0800
Subject: remove browser to extension

---
 .gitignore                   |   1 -
 javascript/images_history.js | 200 --------------------
 javascript/inspiration.js    |  48 -----
 modules/images_history.py    | 424 -------------------------------------------
 modules/inspiration.py       | 193 --------------------
 modules/script_callbacks.py  |   2 -
 modules/shared.py            |  15 --
 modules/ui.py                |  20 +-
 8 files changed, 4 insertions(+), 899 deletions(-)
 delete mode 100644 javascript/images_history.js
 delete mode 100644 javascript/inspiration.js
 delete mode 100644 modules/images_history.py
 delete mode 100644 modules/inspiration.py

diff --git a/.gitignore b/.gitignore
index 8d01bc6a..70660c51 100644
--- a/.gitignore
+++ b/.gitignore
@@ -29,5 +29,4 @@ notification.mp3
 /textual_inversion
 .vscode
 /extensions
- /inspiration
 
diff --git a/javascript/images_history.js b/javascript/images_history.js
deleted file mode 100644
index c9aa76f8..00000000
--- a/javascript/images_history.js
+++ /dev/null
@@ -1,200 +0,0 @@
-var images_history_click_image = function(){
-    if (!this.classList.contains("transform")){        
-        var gallery = images_history_get_parent_by_class(this, "images_history_cantainor");
-        var buttons = gallery.querySelectorAll(".gallery-item");
-        var i = 0;
-        var hidden_list = [];
-        buttons.forEach(function(e){
-            if (e.style.display == "none"){
-                hidden_list.push(i);
-            }
-            i += 1;
-        })
-        if (hidden_list.length > 0){
-            setTimeout(images_history_hide_buttons, 10, hidden_list, gallery);
-        }        
-    }    
-    images_history_set_image_info(this); 
-}
-
-function images_history_disabled_del(){
-    gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
-        btn.setAttribute('disabled','disabled');
-    }); 
-}
-
-function images_history_get_parent_by_class(item, class_name){
-    var parent = item.parentElement;
-    while(!parent.classList.contains(class_name)){
-        parent = parent.parentElement;
-    }
-    return parent;  
-}
-
-function images_history_get_parent_by_tagname(item, tagname){
-    var parent = item.parentElement;
-    tagname = tagname.toUpperCase()
-    while(parent.tagName != tagname){
-        parent = parent.parentElement;
-    }  
-    return parent;
-}
-
-function images_history_hide_buttons(hidden_list, gallery){
-    var buttons = gallery.querySelectorAll(".gallery-item");
-    var num = 0;
-    buttons.forEach(function(e){
-        if (e.style.display == "none"){
-            num += 1;
-        }
-    });
-    if (num == hidden_list.length){
-        setTimeout(images_history_hide_buttons, 10, hidden_list, gallery);
-    } 
-    for( i in hidden_list){
-        buttons[hidden_list[i]].style.display = "none";
-    }    
-}
-
-function images_history_set_image_info(button){
-    var buttons = images_history_get_parent_by_tagname(button, "DIV").querySelectorAll(".gallery-item");
-    var index = -1;
-    var i = 0;
-    buttons.forEach(function(e){
-        if(e == button){
-            index = i;
-        }
-        if(e.style.display != "none"){
-            i += 1;
-        }        
-    });
-    var gallery = images_history_get_parent_by_class(button, "images_history_cantainor");
-    var set_btn = gallery.querySelector(".images_history_set_index");
-    var curr_idx = set_btn.getAttribute("img_index", index);  
-    if (curr_idx != index) {
-        set_btn.setAttribute("img_index", index);        
-        images_history_disabled_del();
-    }
-    set_btn.click();
-    
-}
-
-function images_history_get_current_img(tabname, img_index, files){
-    return [
-        tabname, 
-        gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"),         
-        files
-    ];
-}
-
-function images_history_delete(del_num, tabname, image_index){
-    image_index = parseInt(image_index);
-    var tab = gradioApp().getElementById(tabname + '_images_history');
-    var set_btn = tab.querySelector(".images_history_set_index");
-    var buttons = [];
-    tab.querySelectorAll(".gallery-item").forEach(function(e){
-        if (e.style.display != 'none'){
-            buttons.push(e);
-        }
-    });    
-    var img_num = buttons.length / 2;
-    del_num = Math.min(img_num - image_index, del_num)    
-    if (img_num <= del_num){
-        setTimeout(function(tabname){
-            gradioApp().getElementById(tabname + '_images_history_renew_page').click();
-        }, 30, tabname); 
-    } else {
-        var next_img  
-        for (var i = 0; i < del_num; i++){
-            buttons[image_index + i].style.display = 'none';
-            buttons[image_index + i + img_num].style.display = 'none';
-            next_img = image_index + i + 1
-        }
-        var bnt;
-        if (next_img  >= img_num){
-            btn = buttons[image_index - 1];
-        } else {            
-            btn = buttons[next_img];          
-        } 
-        setTimeout(function(btn){btn.click()}, 30, btn);
-    }
-    images_history_disabled_del();  
-   
-}
-
-function images_history_turnpage(tabname){
-    gradioApp().getElementById(tabname + '_images_history_del_button').setAttribute('disabled','disabled');
-    var buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item");
-    buttons.forEach(function(elem) {
-        elem.style.display = 'block';
-    })   
-}
-
-function images_history_enable_del_buttons(){
-    gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
-        btn.removeAttribute('disabled');
-    })
-}
-
-function images_history_init(){ 
-    var tabnames = gradioApp().getElementById("images_history_tabnames_list")   
-    if (tabnames){  
-        images_history_tab_list = tabnames.querySelector("textarea").value.split(",")    
-        for (var i in images_history_tab_list ){
-            var tab = images_history_tab_list[i];
-            gradioApp().getElementById(tab + '_images_history').classList.add("images_history_cantainor");
-            gradioApp().getElementById(tab + '_images_history_set_index').classList.add("images_history_set_index");
-            gradioApp().getElementById(tab + '_images_history_del_button').classList.add("images_history_del_button");
-            gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery");  
-             gradioApp().getElementById(tab + "_images_history_start").setAttribute("style","padding:20px;font-size:25px");           
-        }
-
-        //preload
-        if (gradioApp().getElementById("images_history_preload").querySelector("input").checked ){
-            var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div");
-            tabs_box.setAttribute("id", "images_history_tab");        
-            var tab_btns = tabs_box.querySelectorAll("button"); 
-            for (var i in images_history_tab_list){               
-                var tabname = images_history_tab_list[i]
-                tab_btns[i].setAttribute("tabname", tabname);
-                tab_btns[i].addEventListener('click', function(){
-                    var tabs_box = gradioApp().getElementById("images_history_tab");
-                    if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
-                        gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_start").click();
-                        tabs_box.classList.add(this.getAttribute("tabname"))
-                    }                
-                });
-            }            
-            tab_btns[0].click()
-        }
-    } else {
-        setTimeout(images_history_init, 500);
-    } 
-}
-
-var images_history_tab_list = "";
-setTimeout(images_history_init, 500);
-document.addEventListener("DOMContentLoaded", function() {
-    var mutationObserver = new MutationObserver(function(m){
-        if (images_history_tab_list != ""){
-            for (var i in images_history_tab_list ){
-                let tabname = images_history_tab_list[i]
-                var buttons = gradioApp().querySelectorAll('#' + tabname + '_images_history .gallery-item');
-                buttons.forEach(function(bnt){    
-                    bnt.addEventListener('click', images_history_click_image, true);
-                });
-
-                var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
-                if (cls_btn){
-                    cls_btn.addEventListener('click', function(){
-                        gradioApp().getElementById(tabname + '_images_history_renew_page').click();
-                    }, false);
-                }
-
-            }     
-        }
-    });
-    mutationObserver.observe(gradioApp(), { childList:true, subtree:true });
-});
-
-
diff --git a/javascript/inspiration.js b/javascript/inspiration.js
deleted file mode 100644
index 39844544..00000000
--- a/javascript/inspiration.js
+++ /dev/null
@@ -1,48 +0,0 @@
-function public_image_index_in_gallery(item, gallery){
-    var imgs = gallery.querySelectorAll("img.h-full")    
-    var index;
-    var i = 0;
-    imgs.forEach(function(e){
-        if (e == item)
-            index = i;
-        i += 1;
-    });
-    var all_imgs = gallery.querySelectorAll("img")
-    if (all_imgs.length > imgs.length){
-        var num = imgs.length / 2
-        index = (index < num) ? index : (index - num)
-    }   
-    return index;
-}
-
-function inspiration_selected(name, name_list){
-    var btn = gradioApp().getElementById("inspiration_select_button")
-    return [gradioApp().getElementById("inspiration_select_button").getAttribute("img-index")];
-}  
-
-function inspiration_click_get_button(){
-    gradioApp().getElementById("inspiration_get_button").click();
-}  
-
-var inspiration_image_click = function(){
-    var index =  public_image_index_in_gallery(this, gradioApp().getElementById("inspiration_gallery"));
-    var btn = gradioApp().getElementById("inspiration_select_button");
-    btn.setAttribute("img-index", index);
-    setTimeout(function(btn){btn.click();}, 10, btn);
-}
- 
-document.addEventListener("DOMContentLoaded", function() {
-    var mutationObserver = new MutationObserver(function(m){
-        var gallery = gradioApp().getElementById("inspiration_gallery")
-        if (gallery) {
-            var node = gallery.querySelector(".absolute.backdrop-blur.h-full")
-            if (node) {
-                node.style.display = "None";
-            } 
-            gallery.querySelectorAll('img').forEach(function(e){    
-                e.onclick = inspiration_image_click
-            });
-        }
-    });
-    mutationObserver.observe( gradioApp(), { childList:true, subtree:true });
-});
diff --git a/modules/images_history.py b/modules/images_history.py
deleted file mode 100644
index bc5cf11f..00000000
--- a/modules/images_history.py
+++ /dev/null
@@ -1,424 +0,0 @@
-import os
-import shutil
-import time
-import hashlib
-import gradio
-system_bak_path = "webui_log_and_bak"
-custom_tab_name = "custom fold"
-faverate_tab_name = "favorites"
-tabs_list = ["txt2img", "img2img", "extras", faverate_tab_name]
-def is_valid_date(date):
-    try:
-        time.strptime(date, "%Y%m%d")
-        return True
-    except:
-        return False
-
-def reduplicative_file_move(src, dst):
-    def same_name_file(basename, path):
-        name, ext = os.path.splitext(basename)
-        f_list = os.listdir(path)
-        max_num = 0
-        for f in f_list:
-            if len(f) <= len(basename):
-                continue
-            f_ext = f[-len(ext):] if len(ext) > 0 else ""
-            if f[:len(name)] == name and f_ext == ext:                
-                if f[len(name)] == "(" and f[-len(ext)-1] == ")":
-                    number = f[len(name)+1:-len(ext)-1]
-                    if number.isdigit():
-                        if int(number) > max_num:
-                            max_num = int(number)
-        return f"{name}({max_num + 1}){ext}"
-    name = os.path.basename(src)
-    save_name = os.path.join(dst, name)
-    if not os.path.exists(save_name):
-        shutil.move(src, dst)
-    else:
-        name = same_name_file(name, dst)
-        shutil.move(src, os.path.join(dst, name))
-
-def traverse_all_files(curr_path, image_list, all_type=False):
-    try:
-        f_list = os.listdir(curr_path)
-    except:
-        if all_type or (curr_path[-10:].rfind(".") > 0 and curr_path[-4:] != ".txt" and curr_path[-4:] != ".csv"):
-            image_list.append(curr_path)
-        return image_list
-    for file in f_list:
-        file = os.path.join(curr_path, file)
-        if (not all_type) and (file[-4:] == ".txt" or file[-4:] == ".csv"):
-            pass
-        elif os.path.isfile(file) and file[-10:].rfind(".") > 0:
-            image_list.append(file)
-        else:
-            image_list = traverse_all_files(file, image_list)
-    return image_list
-
-def auto_sorting(dir_name):    
-    bak_path = os.path.join(dir_name, system_bak_path)
-    if not os.path.exists(bak_path):
-        os.mkdir(bak_path)
-    log_file = None 
-    files_list = []    
-    f_list = os.listdir(dir_name)
-    for file in f_list:   
-        if file == system_bak_path:
-            continue     
-        file_path = os.path.join(dir_name, file)
-        if not is_valid_date(file):
-            if file[-10:].rfind(".") > 0:
-                files_list.append(file_path)
-            else:
-                files_list = traverse_all_files(file_path, files_list, all_type=True)
-
-    for file in files_list:        
-        date_str = time.strftime("%Y%m%d",time.localtime(os.path.getmtime(file)))
-        file_path = os.path.dirname(file)
-        hash_path = hashlib.md5(file_path.encode()).hexdigest()
-        path = os.path.join(dir_name, date_str, hash_path)
-        if not os.path.exists(path):
-            os.makedirs(path)
-        if log_file is None:
-            log_file = open(os.path.join(bak_path,"path_mapping.csv"),"a") 
-        log_file.write(f"{hash_path},{file_path}\n")
-        reduplicative_file_move(file, path)
-       
-    date_list = []
-    f_list = os.listdir(dir_name)
-    for f in f_list:
-        if is_valid_date(f):
-            date_list.append(f)
-        elif f == system_bak_path:
-            continue
-        else:
-            try:
-                reduplicative_file_move(os.path.join(dir_name, f), bak_path)  
-            except:
-                pass         
-            
-    today = time.strftime("%Y%m%d",time.localtime(time.time()))
-    if today not in date_list:
-        date_list.append(today)
-    return sorted(date_list, reverse=True)
-
-def archive_images(dir_name, date_to):    
-    filenames = []   
-    batch_size =int(opts.images_history_num_per_page * opts.images_history_pages_num)
-    if batch_size <= 0:
-        batch_size = opts.images_history_num_per_page * 6
-    today = time.strftime("%Y%m%d",time.localtime(time.time()))
-    date_to = today if date_to is None  or date_to == "" else date_to
-    date_to_bak = date_to 
-    if False: #opts.images_history_reconstruct_directory:        
-        date_list = auto_sorting(dir_name)               
-        for date in date_list:
-            if date <= date_to:
-                path = os.path.join(dir_name, date)
-                if date == today and not os.path.exists(path):
-                    continue
-                filenames = traverse_all_files(path, filenames)
-            if len(filenames) > batch_size:            
-                break
-        filenames = sorted(filenames, key=lambda file: -os.path.getmtime(file))
-    else:
-        filenames = traverse_all_files(dir_name, filenames)  
-        total_num = len(filenames) 
-        tmparray = [(os.path.getmtime(file), file) for file in filenames ]
-        date_stamp = time.mktime(time.strptime(date_to, "%Y%m%d")) + 86400      
-        filenames = []
-        date_list = {date_to:None}
-        date = time.strftime("%Y%m%d",time.localtime(time.time()))
-        for t, f in tmparray:
-            date = time.strftime("%Y%m%d",time.localtime(t))
-            date_list[date] = None
-            if t <= date_stamp:
-                filenames.append((t, f ,date))
-        date_list = sorted(list(date_list.keys()), reverse=True)
-        sort_array = sorted(filenames, key=lambda x:-x[0])
-        if len(sort_array) > batch_size:
-            date = sort_array[batch_size][2]
-            filenames = [x[1] for x in sort_array]
-        else:
-            date =  date_to if len(sort_array) == 0 else sort_array[-1][2]
-            filenames = [x[1] for x in sort_array]
-        filenames = [x[1] for x in sort_array if x[2]>= date]   
-    num = len(filenames)  
-    last_date_from = date_to_bak if num == 0 else time.strftime("%Y%m%d", time.localtime(time.mktime(time.strptime(date, "%Y%m%d")) - 1000))
-    date = date[:4] + "/" + date[4:6] + "/" + date[6:8]
-    date_to_bak = date_to_bak[:4] + "/" + date_to_bak[4:6] + "/" + date_to_bak[6:8]
-    load_info = "<div style='color:#999' align='center'>"
-    load_info += f"{total_num} images in this directory. Loaded {num} images during {date} - {date_to_bak}, divided into {int((num + 1) // opts.images_history_num_per_page + 1)} pages"
-    load_info += "</div>"
-    _, image_list, _, _, visible_num = get_recent_images(1, 0, filenames)
-    return (
-        date_to,
-        load_info,
-        filenames,        
-        1,
-        image_list,
-        "",
-        "",
-        visible_num, 
-        last_date_from, 
-        gradio.update(visible=total_num > num)
-    )
-
-def delete_image(delete_num, name, filenames, image_index, visible_num):
-    if name == "":
-        return filenames, delete_num
-    else:
-        delete_num = int(delete_num)
-        visible_num = int(visible_num)
-        image_index = int(image_index)
-        index = list(filenames).index(name)
-        i = 0
-        new_file_list = []
-        for name in filenames:
-            if i >= index and i < index + delete_num:
-                if os.path.exists(name):
-                    if visible_num == image_index:
-                        new_file_list.append(name)
-                        i += 1
-                        continue                    
-                    print(f"Delete file {name}")
-                    os.remove(name)
-                    visible_num -= 1
-                    txt_file = os.path.splitext(name)[0] + ".txt"
-                    if os.path.exists(txt_file):
-                        os.remove(txt_file)
-                else:
-                    print(f"Not exists file {name}")
-            else:
-                new_file_list.append(name)
-            i += 1
-    return new_file_list, 1, visible_num
-
-def save_image(file_name):
-    if file_name is not None and os.path.exists(file_name):
-        shutil.copy(file_name, opts.outdir_save)
-
-def get_recent_images(page_index, step, filenames):
-    page_index = int(page_index)
-    num_of_imgs_per_page = int(opts.images_history_num_per_page)
-    max_page_index = len(filenames) // num_of_imgs_per_page + 1
-    page_index = max_page_index if page_index == -1 else page_index + step
-    page_index = 1 if page_index < 1 else page_index
-    page_index = max_page_index if page_index > max_page_index else page_index
-    idx_frm = (page_index - 1) * num_of_imgs_per_page
-    image_list = filenames[idx_frm:idx_frm + num_of_imgs_per_page]
-    length = len(filenames)
-    visible_num = num_of_imgs_per_page if  idx_frm + num_of_imgs_per_page <= length else length % num_of_imgs_per_page 
-    visible_num = num_of_imgs_per_page if visible_num == 0 else visible_num
-    return page_index, image_list,  "", "",  visible_num
-
-def loac_batch_click(date_to):
-    if date_to is None:
-        return time.strftime("%Y%m%d",time.localtime(time.time())), []
-    else:
-        return None, []
-def forward_click(last_date_from, date_to_recorder):
-    if len(date_to_recorder) == 0:
-        return None, []
-    if last_date_from == date_to_recorder[-1]:
-        date_to_recorder = date_to_recorder[:-1]
-    if len(date_to_recorder) == 0:
-        return None, []
-    return date_to_recorder[-1], date_to_recorder[:-1]
-
-def backward_click(last_date_from, date_to_recorder):
-    if last_date_from is None or last_date_from == "":
-        return time.strftime("%Y%m%d",time.localtime(time.time())), []
-    if len(date_to_recorder) == 0 or last_date_from != date_to_recorder[-1]:
-        date_to_recorder.append(last_date_from)    
-    return last_date_from, date_to_recorder
-
-
-def first_page_click(page_index, filenames):
-    return get_recent_images(1, 0, filenames)
-
-def end_page_click(page_index, filenames):
-    return get_recent_images(-1, 0, filenames)
-
-def prev_page_click(page_index, filenames):
-    return get_recent_images(page_index, -1, filenames)
-
-def next_page_click(page_index, filenames):
-    return get_recent_images(page_index, 1, filenames)
-
-def page_index_change(page_index, filenames):
-    return get_recent_images(page_index, 0, filenames)
-
-def show_image_info(tabname_box, num, page_index, filenames):
-    file = filenames[int(num) + int((page_index - 1) * int(opts.images_history_num_per_page))]   
-    tm =   "<div style='color:#999' align='right'>" + time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(os.path.getmtime(file))) + "</div>"
-    return file, tm, num, file
-
-def enable_page_buttons():
-    return gradio.update(visible=True)
-
-def change_dir(img_dir, date_to):
-    warning = None
-    try:
-        if os.path.exists(img_dir):
-            try:
-                f = os.listdir(img_dir)                
-            except:
-                warning = f"'{img_dir} is not a directory"
-        else:
-            warning = "The directory is not exist"
-    except:
-        warning = "The format of the directory is incorrect"
-    if warning is None:        
-        today = time.strftime("%Y%m%d",time.localtime(time.time()))
-        return gradio.update(visible=False), gradio.update(visible=True), None,   None if date_to != today else today, gradio.update(visible=True), gradio.update(visible=True)
-    else:
-        return gradio.update(visible=True), gradio.update(visible=False), warning, date_to, gradio.update(visible=False), gradio.update(visible=False)
-
-def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
-    custom_dir = False
-    if tabname == "txt2img":
-        dir_name = opts.outdir_txt2img_samples
-    elif tabname == "img2img":
-        dir_name = opts.outdir_img2img_samples
-    elif tabname == "extras":
-        dir_name = opts.outdir_extras_samples
-    elif tabname == faverate_tab_name:
-        dir_name = opts.outdir_save
-    else:
-        custom_dir = True
-        dir_name = None
-
-    if not custom_dir:
-        d = dir_name.split("/")
-        dir_name = d[0]
-        for p in d[1:]:
-            dir_name = os.path.join(dir_name, p)
-        if not os.path.exists(dir_name):
-            os.makedirs(dir_name)
-
-    with gr.Column() as page_panel:            
-            with gr.Row():                
-                with gr.Column(scale=1, visible=not custom_dir) as load_batch_box: 
-                    load_batch = gr.Button('Load', elem_id=tabname + "_images_history_start", full_width=True)    
-                with gr.Column(scale=4): 
-                    with gr.Row():                        
-                        img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory", interactive=custom_dir)  
-            with gr.Row():
-                with gr.Column(visible=False, scale=1) as batch_panel: 
-                    with gr.Row():
-                        forward = gr.Button('Prev batch')
-                        backward = gr.Button('Next batch')
-                with gr.Column(scale=3):
-                    load_info =     gr.HTML(visible=not custom_dir)
-            with gr.Row(visible=False) as warning:                 
-                warning_box = gr.Textbox("Message", interactive=False)                        
-
-            with gr.Row(visible=not custom_dir, elem_id=tabname + "_images_history") as main_panel:
-                with gr.Column(scale=2):                     
-                    with gr.Row(visible=True) as turn_page_buttons:                        
-                        #date_to = gr.Dropdown(label="Date to")                          
-                        first_page = gr.Button('First Page')
-                        prev_page = gr.Button('Prev Page')
-                        page_index = gr.Number(value=1, label="Page Index")
-                        next_page = gr.Button('Next Page')
-                        end_page = gr.Button('End Page')                 
-
-                    history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=opts.images_history_grid_num)
-                    with gr.Row():
-                        delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
-                        delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
-                        
-                with gr.Column():                    
-                    with gr.Row():
-                        with gr.Column():
-                            img_file_info = gr.Textbox(label="Generate Info", interactive=False, lines=6)
-                            gr.HTML("<hr>")                           
-                            img_file_name = gr.Textbox(value="", label="File Name", interactive=False)
-                            img_file_time= gr.HTML()
-                    with gr.Row():
-                        if tabname != faverate_tab_name:
-                            save_btn = gr.Button('Collect')
-                        pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                        pnginfo_send_to_img2img = gr.Button('Send to img2img')
-                           
-                            
-                    # hiden items
-                    with gr.Row(visible=False): 
-                        renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page")
-                        batch_date_to = gr.Textbox(label="Date to")  
-                        visible_img_num = gr.Number()                     
-                        date_to_recorder = gr.State([])
-                        last_date_from = gr.Textbox()
-                        tabname_box = gr.Textbox(tabname)
-                        image_index = gr.Textbox(value=-1)
-                        set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index")
-                        filenames = gr.State()
-                        all_images_list = gr.State()
-                        hidden = gr.Image(type="pil")
-                        info1 = gr.Textbox()
-                        info2 = gr.Textbox()
-
-    img_path.submit(change_dir, inputs=[img_path, batch_date_to], outputs=[warning, main_panel, warning_box, batch_date_to, load_batch_box, load_info])
-
-    #change batch
-    change_date_output = [batch_date_to, load_info, filenames, page_index, history_gallery, img_file_name, img_file_time, visible_img_num, last_date_from,  batch_panel]  
-   
-    batch_date_to.change(archive_images, inputs=[img_path, batch_date_to], outputs=change_date_output)   
-    batch_date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons])  
-    batch_date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")  
-
-    load_batch.click(loac_batch_click, inputs=[batch_date_to], outputs=[batch_date_to, date_to_recorder])
-    forward.click(forward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder])
-    backward.click(backward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder])
-
-
-    #delete
-    delete.click(delete_image, inputs=[delete_num, img_file_name, filenames, image_index, visible_img_num], outputs=[filenames, delete_num, visible_img_num])
-    delete.click(fn=None, _js="images_history_delete", inputs=[delete_num, tabname_box, image_index], outputs=None) 
-    if tabname != faverate_tab_name: 
-        save_btn.click(save_image, inputs=[img_file_name], outputs=None)     
-
-    #turn page
-    gallery_inputs = [page_index, filenames]
-    gallery_outputs = [page_index, history_gallery, img_file_name, img_file_time, visible_img_num]
-    first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-    next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-    prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-    end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs)
-    page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
-    renew_page.click(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs)
-
-    first_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    next_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    prev_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    end_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    page_index.submit(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-    renew_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage")
-
-    # other funcitons
-    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, img_file_time, image_index, hidden])
-    img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)   
-    hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
-    switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
-    switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
-
-   
-
-def create_history_tabs(gr, sys_opts, cmp_ops, run_pnginfo, switch_dict):
-    global opts;
-    opts = sys_opts
-    loads_files_num = int(opts.images_history_num_per_page)
-    num_of_imgs_per_page = int(opts.images_history_num_per_page * opts.images_history_pages_num)
-    if cmp_ops.browse_all_images:
-        tabs_list.append(custom_tab_name)
-    with gr.Blocks(analytics_enabled=False) as images_history:
-        with gr.Tabs() as tabs:
-            for tab in tabs_list:
-                with gr.Tab(tab):
-                    with gr.Blocks(analytics_enabled=False) :
-                        show_images_history(gr, opts, tab, run_pnginfo, switch_dict)
-        gradio.Checkbox(opts.images_history_preload, elem_id="images_history_preload", visible=False)         
-        gradio.Textbox(",".join(tabs_list), elem_id="images_history_tabnames_list", visible=False)    
-        
-    return images_history
diff --git a/modules/inspiration.py b/modules/inspiration.py
deleted file mode 100644
index 29cf8297..00000000
--- a/modules/inspiration.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import os
-import random
-import gradio
-from modules.shared import opts
-inspiration_system_path = os.path.join(opts.inspiration_dir, "system")
-def read_name_list(file, types=None, keyword=None):
-    if not os.path.exists(file):
-        return []
-    ret = []
-    f = open(file, "r")    
-    line = f.readline()
-    while len(line) > 0:
-        line = line.rstrip("\n")
-        if types is not None:
-            dirname = os.path.split(line)
-            if dirname[0] in types and keyword in dirname[1].lower():
-                ret.append(line)
-        else:
-            ret.append(line)
-        line = f.readline()
-    return ret
-
-def save_name_list(file, name): 
-    name_list = read_name_list(file)
-    if name not in name_list:
-        with open(file, "a") as f:
-            f.write(name + "\n")
-
-def get_types_list():
-    files = os.listdir(opts.inspiration_dir)
-    types = []
-    for x in files:
-        path = os.path.join(opts.inspiration_dir, x)
-        if x[0] == ".":
-            continue
-        if not os.path.isdir(path):
-            continue
-        if path == inspiration_system_path:
-            continue
-        types.append(x)
-    return types
-
-def get_inspiration_images(source, types, keyword):
-    keyword = keyword.strip(" ").lower()
-    get_num = int(opts.inspiration_rows_num * opts.inspiration_cols_num)
-    if source == "Favorites":
-        names = read_name_list(os.path.join(inspiration_system_path, "faverites.txt"), types, keyword)
-        names = random.sample(names, get_num) if len(names) > get_num else names
-    elif source == "Abandoned":
-        names = read_name_list(os.path.join(inspiration_system_path, "abandoned.txt"), types, keyword)
-        names = random.sample(names, get_num) if len(names) > get_num else names
-    elif source == "Exclude abandoned":        
-        abandoned = read_name_list(os.path.join(inspiration_system_path, "abandoned.txt"), types, keyword)  
-        all_names = []
-        for tp in types:
-            name_list = os.listdir(os.path.join(opts.inspiration_dir, tp))
-            all_names += [os.path.join(tp, x) for x in name_list if keyword in x.lower()]
-        
-        if len(all_names) > get_num:
-            names = []
-            while len(names) < get_num:
-                name = random.choice(all_names)
-                if name not in abandoned:
-                    names.append(name)
-        else:
-            names = all_names
-    else:
-        all_names = []
-        for tp in types:
-            name_list = os.listdir(os.path.join(opts.inspiration_dir, tp))
-            all_names += [os.path.join(tp, x) for x in name_list if keyword in x.lower()]
-        names = random.sample(all_names, get_num) if len(all_names) > get_num else all_names
-    image_list = []
-    for a in names:
-        image_path = os.path.join(opts.inspiration_dir, a)
-        images = os.listdir(image_path)
-        if len(images) > 0:        
-            image_list.append((os.path.join(image_path, random.choice(images)), a))
-        else:
-            print(image_path)
-    return image_list, names
-
-def select_click(index, name_list):
-    name = name_list[int(index)]
-    path = os.path.join(opts.inspiration_dir, name)
-    images = os.listdir(path)
-    return name, [os.path.join(path, x) for x in images], ""
-
-def give_up_click(name):
-    file = os.path.join(inspiration_system_path, "abandoned.txt")
-    save_name_list(file, name)
-    return "Added to abandoned list"
-   
-def collect_click(name):
-    file = os.path.join(inspiration_system_path, "faverites.txt")
-    save_name_list(file, name)
-    return "Added to faverite list"
-
-def moveout_click(name, source):
-    if source == "Abandoned":
-        file = os.path.join(inspiration_system_path, "abandoned.txt")
-    elif source == "Favorites":
-        file = os.path.join(inspiration_system_path, "faverites.txt")
-    else:
-        return None
-    name_list = read_name_list(file)
-    os.remove(file)
-    with open(file, "a") as f:
-        for a in name_list:
-            if a != name:
-                f.write(a + "\n")
-    return f"Moved out {name} from {source} list"
-
-def source_change(source):
-    if source in ["Abandoned", "Favorites"]:
-        return gradio.update(visible=True), []
-    else:
-        return gradio.update(visible=False), []
-def add_to_prompt(name, prompt):
-    name = os.path.basename(name)
-    return prompt + "," + name
-
-def clear_keyword():
-    return ""
-
-def ui(gr, opts, txt2img_prompt, img2img_prompt):     
-    with gr.Blocks(analytics_enabled=False) as inspiration:
-        flag = os.path.exists(opts.inspiration_dir)        
-        if flag:
-            types = get_types_list()
-            flag = len(types) > 0
-        else:
-            os.makedirs(opts.inspiration_dir)
-        if not flag:            
-            gr.HTML("""
-                <div align='center' width="50%"><h2>To activate inspiration function, you need get "inspiration" images first. </h2><br>
-                You can create these images by run "Create inspiration images" script in txt2img page, <br> you can get the artists or art styles list from here<br>
-                <a href="https://github.com/pharmapsychotic/clip-interrogator/tree/main/data">https://github.com/pharmapsychotic/clip-interrogator/tree/main/data</a><br>
-                download these files, and select these files in the "Create inspiration images" script UI<br>
-                There about 6000 artists and art styles in these files. <br>This takes server hours depending on your GPU type and how many pictures  you generate for each artist/style
-                <br>I suggest at least four images for each<br><br><br>
-                <h2>You can also download generated pictures from here:</h2><br>
-                <a href="https://huggingface.co/datasets/yfszzx/inspiration">https://huggingface.co/datasets/yfszzx/inspiration</a><br>
-                unzip the file to the project directory of webui<br>
-                and restart webui, and enjoy the joy of creation!<br></div>
-                """)      
-            return inspiration
-        if not os.path.exists(inspiration_system_path):
-            os.mkdir(inspiration_system_path)
-        with gr.Row():
-            with gr.Column(scale=2):                
-                inspiration_gallery = gr.Gallery(show_label=False, elem_id="inspiration_gallery").style(grid=opts.inspiration_cols_num, height='auto')
-            with gr.Column(scale=1):
-                types = gr.CheckboxGroup(choices=types, value=types)
-                with gr.Row(): 
-                    source = gr.Dropdown(choices=["All", "Favorites", "Exclude abandoned", "Abandoned"], value="Exclude abandoned", label="Source")
-                    keyword = gr.Textbox("", label="Key word")                                                
-                get_inspiration = gr.Button("Get inspiration", elem_id="inspiration_get_button")   
-                name = gr.Textbox(show_label=False, interactive=False)      
-                with gr.Row():                     
-                    send_to_txt2img = gr.Button('to txt2img')
-                    send_to_img2img = gr.Button('to img2img')
-                    collect = gr.Button('Collect')     
-                    give_up = gr.Button("Don't show again")
-                    moveout = gr.Button("Move out", visible=False)
-                warning = gr.HTML()
-                style_gallery = gr.Gallery(show_label=False).style(grid=2, height='auto') 
-                    
-               
-                
-        with gr.Row(visible=False):
-            select_button = gr.Button('set button', elem_id="inspiration_select_button")
-            name_list = gr.State()
-        
-        get_inspiration.click(get_inspiration_images, inputs=[source, types, keyword], outputs=[inspiration_gallery, name_list])
-        keyword.submit(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
-        source.change(source_change, inputs=[source], outputs=[moveout, style_gallery])
-        source.change(fn=clear_keyword, _js="inspiration_click_get_button", inputs=None, outputs=[keyword])       
-        types.change(fn=clear_keyword, _js="inspiration_click_get_button", inputs=None, outputs=[keyword])  
-
-        select_button.click(select_click, _js="inspiration_selected", inputs=[name, name_list], outputs=[name, style_gallery, warning])
-        give_up.click(give_up_click, inputs=[name], outputs=[warning])
-        collect.click(collect_click, inputs=[name], outputs=[warning])
-        moveout.click(moveout_click, inputs=[name, source], outputs=[warning])
-        moveout.click(fn=None, _js="inspiration_click_get_button", inputs=None, outputs=None)
-
-        send_to_txt2img.click(add_to_prompt, inputs=[name, txt2img_prompt], outputs=[txt2img_prompt])
-        send_to_img2img.click(add_to_prompt, inputs=[name, img2img_prompt], outputs=[img2img_prompt])
-        send_to_txt2img.click(collect_click, inputs=[name], outputs=[warning])
-        send_to_img2img.click(collect_click, inputs=[name], outputs=[warning])
-        send_to_txt2img.click(None, _js='switch_to_txt2img', inputs=None, outputs=None)
-        send_to_img2img.click(None, _js="switch_to_img2img_img2img", inputs=None, outputs=None)
-    return inspiration
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 5bcccd67..66666a56 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -1,4 +1,3 @@
-
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
@@ -16,7 +15,6 @@ def model_loaded_callback(sd_model):
 
 def ui_tabs_callback():
     res = []
-
     for callback in callbacks_ui_tabs:
         res += callback() or []
 
diff --git a/modules/shared.py b/modules/shared.py
index 0aaaadac..5dfd7927 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -321,21 +321,6 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
-options_templates.update(options_section(('inspiration', "Inspiration"), {
-    "inspiration_dir": OptionInfo("inspiration", "Directory of inspiration", component_args=hide_dirs),
-    "inspiration_max_samples": OptionInfo(4, "Maximum number of samples, used to determine which folders to skip when continue running the create script", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
-    "inspiration_rows_num":  OptionInfo(4, "Rows of inspiration interface frame", gr.Slider, {"minimum": 4, "maximum": 16, "step": 1}),
-    "inspiration_cols_num":  OptionInfo(8, "Columns of inspiration interface frame", gr.Slider, {"minimum": 4, "maximum": 16, "step": 1}),
-}))
-
-options_templates.update(options_section(('images-history', "Images Browser"), {
-    #"images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"),
-    "images_history_preload": OptionInfo(False, "Preload images at startup"),
-    "images_history_num_per_page": OptionInfo(36, "Number of pictures displayed on each page"),
-    "images_history_pages_num": OptionInfo(6, "Minimum number of pages per load "),   
-    "images_history_grid_num": OptionInfo(6, "Number of grids in each row"),   
-
-}))
 
 class Options:
     data = None
diff --git a/modules/ui.py b/modules/ui.py
index a73175f5..fa42712e 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -49,14 +49,12 @@ from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
-import modules.images_history as images_history
-import modules.inspiration as inspiration
-
-
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
 mimetypes.add_type('application/javascript', '.js')
+txt2img_paste_fields = []
+img2img_paste_fields = []
 
 
 if not cmd_opts.share and not cmd_opts.listen:
@@ -1193,16 +1191,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[image],
             outputs=[html, generation_info, html2],
         )
-    #images history
-    images_history_switch_dict = {
-        "fn": modules.generation_parameters_copypaste.connect_paste,
-        "t2i": txt2img_paste_fields,
-        "i2i": img2img_paste_fields
-    }
-
-    browser_interface = images_history.create_history_tabs(gr, opts, cmd_opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
-    inspiration_interface = inspiration.ui(gr, opts, txt2img_prompt, img2img_prompt)
-
+   
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
@@ -1651,8 +1640,6 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        (inspiration_interface, "Inspiration", "inspiration"),
-        (browser_interface , "Image Browser", "images_history"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
     ]
@@ -1896,6 +1883,7 @@ def load_javascript(raw_response):
         javascript = f'<script>{jsfile.read()}</script>'
 
     scripts_list = modules.scripts.list_scripts("javascript", ".js")
+    scripts_list += modules.scripts.list_scripts("scripts", ".js")
     for basedir, filename, path in scripts_list:
         with open(path, "r", encoding="utf8") as jsfile:
             javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
-- 
cgit v1.2.1


From cef1b89aa2e6c7647db7e93a4cd4ec020da3f2da Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 24 Oct 2022 10:10:33 +0800
Subject: remove browser to extension

---
 modules/script_callbacks.py          |  2 ++
 modules/shared.py                    |  1 -
 modules/ui.py                        |  2 +-
 scripts/create_inspiration_images.py | 57 ------------------------------------
 4 files changed, 3 insertions(+), 59 deletions(-)
 delete mode 100644 scripts/create_inspiration_images.py

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 66666a56..f46d3d9a 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -1,3 +1,4 @@
+
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
@@ -15,6 +16,7 @@ def model_loaded_callback(sd_model):
 
 def ui_tabs_callback():
     res = []
+    
     for callback in callbacks_ui_tabs:
         res += callback() or []
 
diff --git a/modules/shared.py b/modules/shared.py
index 5dfd7927..6541e679 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -82,7 +82,6 @@ parser.add_argument("--api", action='store_true', help="use api=True to launch t
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-parser.add_argument("--browse-all-images", action='store_true', help="Allow browsing all images by Image Browser", default=False)
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
diff --git a/modules/ui.py b/modules/ui.py
index fa42712e..a32f7259 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1104,7 +1104,7 @@ def create_ui(wrap_gradio_gpu_call):
                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
                 
                 with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers] , value=shared.sd_upscalers[0].name, type="index") 
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") 
 
                 with gr.Group():
                     extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
diff --git a/scripts/create_inspiration_images.py b/scripts/create_inspiration_images.py
deleted file mode 100644
index 2fd30578..00000000
--- a/scripts/create_inspiration_images.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import csv, os, shutil
-import modules.scripts as scripts
-from modules import processing, shared, sd_samplers, images
-from modules.processing import Processed
-from modules.shared import opts
-import gradio
-class Script(scripts.Script):
-    def title(self):
-        return "Create inspiration images"
-
-    def show(self, is_img2img):
-        return True
-
-    def ui(self, is_img2img):
-        file = gradio.Files(label="Artist or styles name list. '.txt' files with one name per line",)
-        with gradio.Row():
-            prefix = gradio.Textbox("a painting in", label="Prompt words before artist or style name", file_count="multiple")
-            suffix= gradio.Textbox("style", label="Prompt words after artist or style name")
-        negative_prompt = gradio.Textbox("picture frame, portrait photo", label="Negative Prompt")
-        with gradio.Row():
-            batch_size = gradio.Number(1, label="Batch size")
-            batch_count = gradio.Number(2, label="Batch count")            
-        return [batch_size, batch_count, prefix, suffix, negative_prompt, file]
-
-    def run(self, p, batch_size, batch_count, prefix, suffix, negative_prompt, files):
-        p.batch_size = int(batch_size)
-        p.n_iterint = int(batch_count)
-        p.negative_prompt = negative_prompt        
-        p.do_not_save_samples = True
-        p.do_not_save_grid = True        
-        for file in files:
-            tp = file.orig_name.split(".")[0]
-            print(tp)
-            path = os.path.join(opts.inspiration_dir, tp)
-            if not os.path.exists(path):
-                os.makedirs(path) 
-            f = open(file.name, "r")
-            line = f.readline() 
-            while len(line) > 0: 
-                name = line.rstrip("\n").split(",")[0]
-                line = f.readline() 
-                artist_path = os.path.join(path, name)  
-                if not os.path.exists(artist_path):
-                    os.mkdir(artist_path)  
-                if len(os.listdir(artist_path)) >= opts.inspiration_max_samples:
-                    continue
-                p.prompt = f"{prefix} {name} {suffix}"
-                print(p.prompt)
-                processed = processing.process_images(p)
-                for img in  processed.images:
-                    i = 0
-                    filename = os.path.join(artist_path,  format(0, "03d") + ".jpg")
-                    while os.path.exists(filename):
-                        i += 1
-                        filename = os.path.join(artist_path,  format(i, "03d") + ".jpg")
-                    img.save(filename, quality=80)
-        return processed
-- 
cgit v1.2.1


From d7987ef9da2d89f146e091f0c727444a522245d9 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 24 Oct 2022 11:06:58 +0800
Subject: add paste_fields to global

---
 extensions/inspiration                                | 1 +
 extensions/put extension here.txt                     | 0
 extensions/stable-diffusion-webui-aesthetic-gradients | 1 +
 extensions/stable-diffusion-webui-images-browse       | 1 +
 extensions/stable-diffusion-webui-inspiration         | 1 +
 extensions/stable-diffusion-webui-wildcards           | 1 +
 6 files changed, 5 insertions(+)
 create mode 160000 extensions/inspiration
 create mode 100644 extensions/put extension here.txt
 create mode 160000 extensions/stable-diffusion-webui-aesthetic-gradients
 create mode 160000 extensions/stable-diffusion-webui-images-browse
 create mode 160000 extensions/stable-diffusion-webui-inspiration
 create mode 160000 extensions/stable-diffusion-webui-wildcards

diff --git a/extensions/inspiration b/extensions/inspiration
new file mode 160000
index 00000000..4cff5855
--- /dev/null
+++ b/extensions/inspiration
@@ -0,0 +1 @@
+Subproject commit 4cff5855f3ca658fb5c9dd9745e5f2ae7bcc7074
diff --git a/extensions/put extension here.txt b/extensions/put extension here.txt
new file mode 100644
index 00000000..e69de29b
diff --git a/extensions/stable-diffusion-webui-aesthetic-gradients b/extensions/stable-diffusion-webui-aesthetic-gradients
new file mode 160000
index 00000000..411889ca
--- /dev/null
+++ b/extensions/stable-diffusion-webui-aesthetic-gradients
@@ -0,0 +1 @@
+Subproject commit 411889ca602f20b8bb5e4d1af2b9686eab1913b1
diff --git a/extensions/stable-diffusion-webui-images-browse b/extensions/stable-diffusion-webui-images-browse
new file mode 160000
index 00000000..6b8e158d
--- /dev/null
+++ b/extensions/stable-diffusion-webui-images-browse
@@ -0,0 +1 @@
+Subproject commit 6b8e158dc174f31f0bb73d74547917f5a6fba507
diff --git a/extensions/stable-diffusion-webui-inspiration b/extensions/stable-diffusion-webui-inspiration
new file mode 160000
index 00000000..4cff5855
--- /dev/null
+++ b/extensions/stable-diffusion-webui-inspiration
@@ -0,0 +1 @@
+Subproject commit 4cff5855f3ca658fb5c9dd9745e5f2ae7bcc7074
diff --git a/extensions/stable-diffusion-webui-wildcards b/extensions/stable-diffusion-webui-wildcards
new file mode 160000
index 00000000..2c0e7d7e
--- /dev/null
+++ b/extensions/stable-diffusion-webui-wildcards
@@ -0,0 +1 @@
+Subproject commit 2c0e7d7e19e6c2b76b83189013aadb822776301f
-- 
cgit v1.2.1


From a889c93f23f1e80d0dac4e5ddbc3a26207e8cdf1 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 24 Oct 2022 11:13:16 +0800
Subject: paste_fields add to public

---
 modules/ui.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modules/ui.py b/modules/ui.py
index a32f7259..a73b9ff0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -784,6 +784,7 @@ def create_ui(wrap_gradio_gpu_call):
                 ]
             )
 
+            global txt2img_paste_fields 
             txt2img_paste_fields = [
                 (txt2img_prompt, "Prompt"),
                 (txt2img_negative_prompt, "Negative prompt"),
@@ -1054,6 +1055,7 @@ def create_ui(wrap_gradio_gpu_call):
                     outputs=[prompt, negative_prompt, style1, style2],
                 )
 
+            global img2img_paste_fields
             img2img_paste_fields = [
                 (img2img_prompt, "Prompt"),
                 (img2img_negative_prompt, "Negative prompt"),
-- 
cgit v1.2.1


From 9dd17b86017e26ccf58897142bdcaa0297f8db8d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B8=8D=E4=BC=9A=E7=94=BB=E7=94=BB=E7=9A=84=E4=B8=AD?=
 =?UTF-8?q?=E5=8C=BB=E4=B8=8D=E6=98=AF=E5=A5=BD=E7=A8=8B=E5=BA=8F=E5=91=98?=
 <yfszzx@gmail.com>
Date: Mon, 24 Oct 2022 11:19:49 +0800
Subject: fix add git add mistake

---
 extensions/inspiration                                | 1 -
 extensions/put extension here.txt                     | 0
 extensions/stable-diffusion-webui-aesthetic-gradients | 1 -
 extensions/stable-diffusion-webui-images-browse       | 1 -
 extensions/stable-diffusion-webui-inspiration         | 1 -
 extensions/stable-diffusion-webui-wildcards           | 1 -
 6 files changed, 5 deletions(-)
 delete mode 160000 extensions/inspiration
 delete mode 100644 extensions/put extension here.txt
 delete mode 160000 extensions/stable-diffusion-webui-aesthetic-gradients
 delete mode 160000 extensions/stable-diffusion-webui-images-browse
 delete mode 160000 extensions/stable-diffusion-webui-inspiration
 delete mode 160000 extensions/stable-diffusion-webui-wildcards

diff --git a/extensions/inspiration b/extensions/inspiration
deleted file mode 160000
index 4cff5855..00000000
--- a/extensions/inspiration
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 4cff5855f3ca658fb5c9dd9745e5f2ae7bcc7074
diff --git a/extensions/put extension here.txt b/extensions/put extension here.txt
deleted file mode 100644
index e69de29b..00000000
diff --git a/extensions/stable-diffusion-webui-aesthetic-gradients b/extensions/stable-diffusion-webui-aesthetic-gradients
deleted file mode 160000
index 411889ca..00000000
--- a/extensions/stable-diffusion-webui-aesthetic-gradients
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 411889ca602f20b8bb5e4d1af2b9686eab1913b1
diff --git a/extensions/stable-diffusion-webui-images-browse b/extensions/stable-diffusion-webui-images-browse
deleted file mode 160000
index 6b8e158d..00000000
--- a/extensions/stable-diffusion-webui-images-browse
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 6b8e158dc174f31f0bb73d74547917f5a6fba507
diff --git a/extensions/stable-diffusion-webui-inspiration b/extensions/stable-diffusion-webui-inspiration
deleted file mode 160000
index 4cff5855..00000000
--- a/extensions/stable-diffusion-webui-inspiration
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 4cff5855f3ca658fb5c9dd9745e5f2ae7bcc7074
diff --git a/extensions/stable-diffusion-webui-wildcards b/extensions/stable-diffusion-webui-wildcards
deleted file mode 160000
index 2c0e7d7e..00000000
--- a/extensions/stable-diffusion-webui-wildcards
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 2c0e7d7e19e6c2b76b83189013aadb822776301f
-- 
cgit v1.2.1


From 394c4986211df4f7d9d8c9c26180edf8b9946d51 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 24 Oct 2022 11:29:45 +0800
Subject: test

---
 extensions/stable-diffusion-webui-inspiration | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/extensions/stable-diffusion-webui-inspiration b/extensions/stable-diffusion-webui-inspiration
index 4cff5855..a0b96664 160000
--- a/extensions/stable-diffusion-webui-inspiration
+++ b/extensions/stable-diffusion-webui-inspiration
@@ -1 +1 @@
-Subproject commit 4cff5855f3ca658fb5c9dd9745e5f2ae7bcc7074
+Subproject commit a0b96664d2524b87916ae463fbb65411b13a569b
-- 
cgit v1.2.1


From fe9740d2f5fa057e02529f8a81de21333adf4234 Mon Sep 17 00:00:00 2001
From: judgeou <judgeou@vip.qq.com>
Date: Sun, 23 Oct 2022 20:40:23 +0800
Subject: update deepdanbooru version

---
 launch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index 333f308a..8affd410 100644
--- a/launch.py
+++ b/launch.py
@@ -111,7 +111,7 @@ def prepare_enviroment():
 
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
     clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
-    deepdanbooru_package = os.environ.get('DEEPDANBOORU_PACKAGE', "git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26")
+    deepdanbooru_package = os.environ.get('DEEPDANBOORU_PACKAGE', "git+https://github.com/KichangKim/DeepDanbooru.git@d91a2963bf87c6a770d74894667e9ffa9f6de7ff")
 
     xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
 
-- 
cgit v1.2.1


From 68e9e978996c24772016ba9e4937367e91540681 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Tue, 18 Oct 2022 19:07:17 +0900
Subject: Initial KR support - WIP

Localization WIP
---
 ko-KR.json | 76 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 ko-KR.json

diff --git a/ko-KR.json b/ko-KR.json
new file mode 100644
index 00000000..f93b3e16
--- /dev/null
+++ b/ko-KR.json
@@ -0,0 +1,76 @@
+{
+  "txt2img": "텍스트→이미지",
+  "img2img": "이미지→이미지",
+  "Extras": "부가기능",
+  "PNG Info": "PNG 정보",
+  "History": "기록",
+  "Checkpoint Merger": "체크포인트 병합",
+  "Train": "훈련",
+  "Settings": "설정",
+  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "Hypernetwork": "하이퍼네트워크",
+  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
+  "Generate": "생성",
+  "Style 1": "스타일 1",
+  "Style 2": "스타일 2",
+  "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
+  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
+  "Save style": "스타일 저장",
+  "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
+  "Do not do anything special": "아무것도 하지 않기",
+  "Generate forever": "반복 생성",
+  "Cancel generate forever": "반복 생성 취소",
+  "Interrupt": "중단",
+  "Skip": "건너뛰기",
+  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
+  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
+  "Prompt": "프롬프트",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Negative prompt": "네거티브 프롬프트",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Sampling Steps": "샘플링 스텝 수",
+  "Sampling method": "샘플링 방법",
+  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
+  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
+  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
+  "Width": "가로",
+  "Height": "세로",
+  "Restore faces": "얼굴 보정",
+  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
+  "Tiling": "타일링",
+  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
+  "Highres. fix": "고해상도 보정",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
+  "Firstpass width": "초기 가로길이",
+  "Firstpass height": "초기 세로길이",
+  "Denoising strength": "디노이즈 강도",
+  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
+  "Batch count": "배치 수",
+  "Batch size": "배치 크기",
+  "How many batches of images to create": "생성할 이미지 배치 수",
+  "How many image to create in a single batch": "한 배치당 이미지 수",
+  "CFG Scale": "CFG 스케일",
+  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
+  "Seed": "시드",
+  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
+  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
+  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
+  "Extra": "고급",
+  "Variation seed": "바리에이션 시드",
+  "Variation strength": "바리에이션 강도",
+  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
+  "Resize seed from height": "시드 리사이징 가로길이",
+  "Resize seed from width": "시드 리사이징 세로길이",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
+  "Script": "스크립트",
+  "Save": "저장",
+  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
+  "Send to img2img": "이미지→이미지로 전송",
+  "Send to inpaint": "인페인트로 전송",
+  "Send to extras": "부가기능으로 전송",
+  "Open images output directory": "이미지 저장 경로 열기",
+  "Make Zip when Save?": "저장 시 Zip 생성하기",
+  "Always save all generated images": "생성된 이미지 항상 저장하기"
+}
\ No newline at end of file
-- 
cgit v1.2.1


From e7eea555715320a7b1977bf0e12c5ca1e2774a09 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Tue, 18 Oct 2022 20:11:17 +0900
Subject: Update ko-KR.json

---
 localizations/ko-KR.json | 85 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 85 insertions(+)
 create mode 100644 localizations/ko-KR.json

diff --git a/localizations/ko-KR.json b/localizations/ko-KR.json
new file mode 100644
index 00000000..a4367dc5
--- /dev/null
+++ b/localizations/ko-KR.json
@@ -0,0 +1,85 @@
+{
+  "⤡": "⤡",
+  "⊞": "⊞",
+  "×": "×",
+  "❮": "❮",
+  "❯": "❯",
+  "Loading...": "로딩중...",
+  "view": "",
+  "api": "api",
+  "•": "•",
+  "txt2img": "텍스트→이미지",
+  "img2img": "이미지→이미지",
+  "Extras": "부가기능",
+  "PNG Info": "PNG 정보",
+  "History": "기록",
+  "Checkpoint Merger": "체크포인트 병합",
+  "Train": "훈련",
+  "Settings": "설정",
+  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "Hypernetwork": "하이퍼네트워크",
+  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
+  "Generate": "생성",
+  "Style 1": "스타일 1",
+  "Style 2": "스타일 2",
+  "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
+  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
+  "Save style": "스타일 저장",
+  "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
+  "Do not do anything special": "아무것도 하지 않기",
+  "Generate forever": "반복 생성",
+  "Cancel generate forever": "반복 생성 취소",
+  "Interrupt": "중단",
+  "Skip": "건너뛰기",
+  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
+  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
+  "Prompt": "프롬프트",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Negative prompt": "네거티브 프롬프트",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Sampling Steps": "샘플링 스텝 수",
+  "Sampling method": "샘플링 방법",
+  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
+  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
+  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
+  "Width": "가로",
+  "Height": "세로",
+  "Restore faces": "얼굴 보정",
+  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
+  "Tiling": "타일링",
+  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
+  "Highres. fix": "고해상도 보정",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
+  "Firstpass width": "초기 가로길이",
+  "Firstpass height": "초기 세로길이",
+  "Denoising strength": "디노이즈 강도",
+  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
+  "Batch count": "배치 수",
+  "Batch size": "배치 크기",
+  "How many batches of images to create": "생성할 이미지 배치 수",
+  "How many image to create in a single batch": "한 배치당 이미지 수",
+  "CFG Scale": "CFG 스케일",
+  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
+  "Seed": "시드",
+  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
+  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
+  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
+  "Extra": "고급",
+  "Variation seed": "바리에이션 시드",
+  "Variation strength": "바리에이션 강도",
+  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
+  "Resize seed from height": "시드 리사이징 가로길이",
+  "Resize seed from width": "시드 리사이징 세로길이",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
+  "Script": "스크립트",
+  "Save": "저장",
+  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
+  "Send to img2img": "이미지→이미지로 전송",
+  "Send to inpaint": "인페인트로 전송",
+  "Send to extras": "부가기능으로 전송",
+  "Open images output directory": "이미지 저장 경로 열기",
+  "Make Zip when Save?": "저장 시 Zip 생성하기",
+  "Always save all generated images": "생성된 이미지 항상 저장하기"
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 021b02751ef08f8f5fc7cc2a3d7e40c599657dc4 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Tue, 18 Oct 2022 20:12:54 +0900
Subject: Move ko-KR.json

---
 ko-KR.json | 76 --------------------------------------------------------------
 1 file changed, 76 deletions(-)
 delete mode 100644 ko-KR.json

diff --git a/ko-KR.json b/ko-KR.json
deleted file mode 100644
index f93b3e16..00000000
--- a/ko-KR.json
+++ /dev/null
@@ -1,76 +0,0 @@
-{
-  "txt2img": "텍스트→이미지",
-  "img2img": "이미지→이미지",
-  "Extras": "부가기능",
-  "PNG Info": "PNG 정보",
-  "History": "기록",
-  "Checkpoint Merger": "체크포인트 병합",
-  "Train": "훈련",
-  "Settings": "설정",
-  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
-  "Hypernetwork": "하이퍼네트워크",
-  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
-  "Generate": "생성",
-  "Style 1": "스타일 1",
-  "Style 2": "스타일 2",
-  "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
-  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
-  "Save style": "스타일 저장",
-  "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
-  "Do not do anything special": "아무것도 하지 않기",
-  "Generate forever": "반복 생성",
-  "Cancel generate forever": "반복 생성 취소",
-  "Interrupt": "중단",
-  "Skip": "건너뛰기",
-  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
-  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
-  "Prompt": "프롬프트",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Negative prompt": "네거티브 프롬프트",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Sampling Steps": "샘플링 스텝 수",
-  "Sampling method": "샘플링 방법",
-  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
-  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
-  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
-  "Width": "가로",
-  "Height": "세로",
-  "Restore faces": "얼굴 보정",
-  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
-  "Tiling": "타일링",
-  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
-  "Highres. fix": "고해상도 보정",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
-  "Firstpass width": "초기 가로길이",
-  "Firstpass height": "초기 세로길이",
-  "Denoising strength": "디노이즈 강도",
-  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
-  "Batch count": "배치 수",
-  "Batch size": "배치 크기",
-  "How many batches of images to create": "생성할 이미지 배치 수",
-  "How many image to create in a single batch": "한 배치당 이미지 수",
-  "CFG Scale": "CFG 스케일",
-  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
-  "Seed": "시드",
-  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
-  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
-  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
-  "Extra": "고급",
-  "Variation seed": "바리에이션 시드",
-  "Variation strength": "바리에이션 강도",
-  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
-  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
-  "Resize seed from height": "시드 리사이징 가로길이",
-  "Resize seed from width": "시드 리사이징 세로길이",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
-  "Script": "스크립트",
-  "Save": "저장",
-  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
-  "Send to img2img": "이미지→이미지로 전송",
-  "Send to inpaint": "인페인트로 전송",
-  "Send to extras": "부가기능으로 전송",
-  "Open images output directory": "이미지 저장 경로 열기",
-  "Make Zip when Save?": "저장 시 Zip 생성하기",
-  "Always save all generated images": "생성된 이미지 항상 저장하기"
-}
\ No newline at end of file
-- 
cgit v1.2.1


From 1a96f856c4c3348708974b80d0de5a8ac18c1799 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Tue, 18 Oct 2022 21:50:34 +0900
Subject: update ko-KR.json

Translated all text on txt2img window, plus some extra
---
 localizations/ko-KR.json | 42 ++++++++++++++++++++++++++++++++++++++----
 1 file changed, 38 insertions(+), 4 deletions(-)

diff --git a/localizations/ko-KR.json b/localizations/ko-KR.json
index a4367dc5..c6e55bb1 100644
--- a/localizations/ko-KR.json
+++ b/localizations/ko-KR.json
@@ -4,9 +4,10 @@
   "×": "×",
   "❮": "❮",
   "❯": "❯",
-  "Loading...": "로딩중...",
-  "view": "",
-  "api": "api",
+  "Loading...": "",
+  "view": "api 보이기",
+  "hide": "api 숨기기",
+  "api": "",
   "•": "•",
   "txt2img": "텍스트→이미지",
   "img2img": "이미지→이미지",
@@ -50,7 +51,7 @@
   "Tiling": "타일링",
   "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
   "Highres. fix": "고해상도 보정",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
   "Firstpass width": "초기 가로길이",
   "Firstpass height": "초기 세로길이",
   "Denoising strength": "디노이즈 강도",
@@ -81,5 +82,38 @@
   "Send to extras": "부가기능으로 전송",
   "Open images output directory": "이미지 저장 경로 열기",
   "Make Zip when Save?": "저장 시 Zip 생성하기",
+  "Prompt matrix": "프롬프트 매트릭스",
+  "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "(|)를 이용해 프롬프트를 분리할 시 첫 프롬프트를 제외하고 모든 프롬프트의 조합마다 이미지를 생성합니다. 첫 프롬프트는 모든 조합에 포함되게 됩니다.",
+  "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
+  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
+  "Show Textbox": "텍스트박스 보이기",
+  "File with inputs": "설정값 파일",
+  "Prompts": "프롬프트",
+  "X/Y plot": "X/Y 플롯",
+  "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
+  "X type": "X축",
+  "Y type": "Y축",
+  "X values": "X 설정값",
+  "Y values": "Y 설정값",
+  "Separate values for X axis using commas.": "쉼표로 X축에 적용할 값 분리",
+  "Separate values for Y axis using commas.": "쉼표로 Y축에 적용할 값 분리",
+  "Draw legend": "범례 그리기",
+  "Include Separate Images": "분리된 이미지 포함하기",
+  "Keep -1 for seeds": "시드값 -1로 유지",
+  "Var. seed": "바리에이션 시드",
+  "Var. strength": "바리에이션 강도",
+  "Steps": "스텝 수",
+  "Prompt S/R": "프롬프트 스타일 변경",
+  "Prompt order": "프롬프트 순서",
+  "Sampler": "샘플러",
+  "Checkpoint name": "체크포인트 이름",
+  "Hypernet str.": "하이퍼네트워크 강도",
+  "Sigma Churn": "시그마 섞기",
+  "Sigma min": "시그마 최솟값",
+  "Sigma max": "시그마 최댓값",
+  "Sigma noise": "시그마 노이즈",
+  "Clip skip": "클립 건너뛰기",
+  "Denoising": "디노이징",
+  "Nothing": "없음",
   "Always save all generated images": "생성된 이미지 항상 저장하기"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From e210b61d6a4189817e27b7a4f3c1028cdb67a868 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Tue, 18 Oct 2022 22:12:41 +0900
Subject: update ko-KR.json

---
 localizations/ko-KR.json | 1 +
 1 file changed, 1 insertion(+)

diff --git a/localizations/ko-KR.json b/localizations/ko-KR.json
index c6e55bb1..b263b13c 100644
--- a/localizations/ko-KR.json
+++ b/localizations/ko-KR.json
@@ -115,5 +115,6 @@
   "Clip skip": "클립 건너뛰기",
   "Denoising": "디노이징",
   "Nothing": "없음",
+  "Apply settings": "설정 적용하기",
   "Always save all generated images": "생성된 이미지 항상 저장하기"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From 499713c54697ae7ccdb264316307f4aa2c39faea Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Thu, 20 Oct 2022 19:20:39 +0900
Subject: Updated file with basic template and added new translations

Translation done in txt2img-img2img windows and following scripts
---
 localizations/ko-KR.json | 492 ++++++++++++++++++++++++++++++++++++++---------
 1 file changed, 397 insertions(+), 95 deletions(-)

diff --git a/localizations/ko-KR.json b/localizations/ko-KR.json
index b263b13c..7cc431c6 100644
--- a/localizations/ko-KR.json
+++ b/localizations/ko-KR.json
@@ -1,120 +1,422 @@
 {
-  "⤡": "⤡",
-  "⊞": "⊞",
   "×": "×",
+  "•": "•",
+  "⊞": "⊞",
   "❮": "❮",
   "❯": "❯",
-  "Loading...": "",
-  "view": "api 보이기",
-  "hide": "api 숨기기",
-  "api": "",
-  "•": "•",
-  "txt2img": "텍스트→이미지",
-  "img2img": "이미지→이미지",
-  "Extras": "부가기능",
-  "PNG Info": "PNG 정보",
-  "History": "기록",
-  "Checkpoint Merger": "체크포인트 병합",
-  "Train": "훈련",
-  "Settings": "설정",
-  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
-  "Hypernetwork": "하이퍼네트워크",
-  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
-  "Generate": "생성",
-  "Style 1": "스타일 1",
-  "Style 2": "스타일 2",
+  "⤡": "⤡",
+  "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
+  "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
+  "A merger of the two checkpoints will be generated in your": "A merger of the two checkpoints will be generated in your",
+  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
   "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
-  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
-  "Save style": "스타일 저장",
+  "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
+  "Add difference": "Add difference",
+  "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid",
+  "Add layer normalization": "Add layer normalization",
+  "Add model hash to generation information": "Add model hash to generation information",
+  "Add model name to generation information": "Add model name to generation information",
+  "Always print all generation info to standard output": "Always print all generation info to standard output",
+  "Always save all generated image grids": "Always save all generated image grids",
+  "Always save all generated images": "생성된 이미지 항상 저장하기",
+  "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
   "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
-  "Do not do anything special": "아무것도 하지 않기",
-  "Generate forever": "반복 생성",
+  "Apply settings": "설정 적용하기",
+  "BSRGAN 4x": "BSRGAN 4x",
+  "Batch Process": "Batch Process",
+  "Batch count": "배치 수",
+  "Batch from Directory": "Batch from Directory",
+  "Batch img2img": "이미지→이미지 배치",
+  "Batch size": "배치 크기",
+  "CFG Scale": "CFG 스케일",
+  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
   "Cancel generate forever": "반복 생성 취소",
-  "Interrupt": "중단",
-  "Skip": "건너뛰기",
-  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
-  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
-  "Prompt": "프롬프트",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Negative prompt": "네거티브 프롬프트",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Sampling Steps": "샘플링 스텝 수",
-  "Sampling method": "샘플링 방법",
-  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
-  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
+  "Check progress (first)": "Check progress (first)",
+  "Check progress": "Check progress",
+  "Checkpoint Merger": "체크포인트 병합",
+  "Checkpoint name": "체크포인트 이름",
+  "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
+  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
+  "Clip skip": "클립 건너뛰기",
+  "CodeFormer visibility": "CodeFormer visibility",
+  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)",
+  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
+  "Color variation": "색깔 다양성",
+  "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
+  "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
+  "Create embedding": "Create embedding",
+  "Create flipped copies": "Create flipped copies",
+  "Create hypernetwork": "Create hypernetwork",
+  "Crop and resize": "잘라낸 후 리사이징",
+  "Crop to fit": "Crop to fit",
+  "Custom Name (Optional)": "Custom Name (Optional)",
+  "DDIM": "DDIM",
+  "DPM adaptive": "DPM adaptive",
+  "DPM fast": "DPM fast",
+  "DPM2 Karras": "DPM2 Karras",
+  "DPM2 a Karras": "DPM2 a Karras",
+  "DPM2 a": "DPM2 a",
+  "DPM2": "DPM2",
+  "Dataset directory": "Dataset directory",
+  "Decode CFG scale": "디코딩 CFG 스케일",
+  "Decode steps": "디코딩 스텝 수",
+  "Delete": "Delete",
   "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
-  "Width": "가로",
-  "Height": "세로",
-  "Restore faces": "얼굴 보정",
-  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
-  "Tiling": "타일링",
-  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
-  "Highres. fix": "고해상도 보정",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
-  "Firstpass width": "초기 가로길이",
-  "Firstpass height": "초기 세로길이",
+  "Denoising strength change factor": "디노이즈 강도 변경 배수",
   "Denoising strength": "디노이즈 강도",
+  "Denoising": "디노이징",
+  "Destination directory": "Destination directory",
   "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
-  "Batch count": "배치 수",
-  "Batch size": "배치 크기",
+  "Directory for saving images using the Save button": "Directory for saving images using the Save button",
+  "Directory name pattern": "Directory name pattern",
+  "Do not add watermark to images": "Do not add watermark to images",
+  "Do not do anything special": "아무것도 하지 않기",
+  "Do not save grids consisting of one picture": "Do not save grids consisting of one picture",
+  "Do not show any images in results for web": "Do not show any images in results for web",
+  "Download localization template": "Download localization template",
+  "Draw legend": "범례 그리기",
+  "Draw mask": "마스크 직접 그리기",
+  "Drop File Here": "Drop File Here",
+  "Drop Image Here": "Drop Image Here",
+  "ESRGAN_4x": "ESRGAN_4x",
+  "Embedding": "Embedding",
+  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
+  "Enable full page image viewer": "Enable full page image viewer",
+  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
+  "End Page": "End Page",
+  "Enter hypernetwork layer structure": "Enter hypernetwork layer structure",
+  "Eta noise seed delta": "Eta noise seed delta",
+  "Eta": "Eta",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
+  "Euler a": "Euler a",
+  "Euler": "Euler",
+  "Extra": "고급",
+  "Extras": "부가기능",
+  "Face restoration": "Face restoration",
+  "Fall-off exponent (lower=higher detail)": "감쇠 지수 (낮을수록 디테일이 올라감)",
+  "File Name": "File Name",
+  "File format for grids": "File format for grids",
+  "File format for images": "File format for images",
+  "File with inputs": "설정값 파일",
+  "File": "File",
+  "Filename join string": "Filename join string",
+  "Filename word regex": "Filename word regex",
+  "Filter NSFW content": "Filter NSFW content",
+  "First Page": "First Page",
+  "Firstpass height": "초기 세로길이",
+  "Firstpass width": "초기 가로길이",
+  "Font for image grids that have text": "Font for image grids that have text",
+  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD 업스케일링에서 타일 간 몇 픽셀을 겹치게 할지 결정하는 설정값입니다. 타일들이 다시 한 이미지로 합쳐질 때, 눈에 띄는 이음매가 없도록 서로 겹치게 됩니다.",
+  "GFPGAN visibility": "GFPGAN visibility",
+  "Generate Info": "Generate Info",
+  "Generate forever": "반복 생성",
+  "Generate": "생성",
+  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
+  "Height": "세로",
+  "Heun": "Heun",
+  "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
+  "Highres. fix": "고해상도 보정",
+  "History": "기록",
   "How many batches of images to create": "생성할 이미지 배치 수",
   "How many image to create in a single batch": "한 배치당 이미지 수",
-  "CFG Scale": "CFG 스케일",
-  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
-  "Seed": "시드",
-  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
-  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
-  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
-  "Extra": "고급",
-  "Variation seed": "바리에이션 시드",
-  "Variation strength": "바리에이션 강도",
-  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
+  "How many times to repeat processing an image and using it as input for the next iteration": "이미지를 생성 후 원본으로 몇 번 반복해서 사용할지 결정하는 값",
+  "How much to blur the mask before processing, in pixels.": "이미지 생성 전 마스크를 얼마나 블러처리할지 결정하는 값. 픽셀 단위",
   "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
+  "Hypernet str.": "하이퍼네트워크 강도",
+  "Hypernetwork strength": "Hypernetwork strength",
+  "Hypernetwork": "하이퍼네트워크",
+  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
+  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
+  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
+  "Image for img2img": "Image for img2img",
+  "Image for inpainting with mask": "Image for inpainting with mask",
+  "Image": "Image",
+  "Images filename pattern": "Images filename pattern",
+  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "루프백 모드에서는 매 루프마다 디노이즈 강도에 이 값이 곱해집니다. 1보다 작을 경우 다양성이 낮아져 결과 이미지들이 고정된 형태로 모일 겁니다. 1보다 클 경우 다양성이 높아져 결과 이미지들이 갈수록 혼란스러워지겠죠.",
+  "Include Separate Images": "분리된 이미지 포함하기",
+  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
+  "Initialization text": "Initialization text",
+  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트 패딩값(픽셀 단위)",
+  "Inpaint at full resolution": "전체 해상도로 인페인트하기",
+  "Inpaint masked": "마스크만 처리",
+  "Inpaint not masked": "마스크 이외만 처리",
+  "Inpaint": "인페인트",
+  "Input directory": "인풋 이미지 경로",
+  "Interpolation Method": "Interpolation Method",
+  "Interrogate\nCLIP": "CLIP\n분석",
+  "Interrogate\nDeepBooru": "DeepBooru\n분석",
+  "Interrogate Options": "Interrogate Options",
+  "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold",
+  "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically",
+  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
+  "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM",
+  "Interrogate: maximum description length": "Interrogate: maximum description length",
+  "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
+  "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
+  "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
+  "Interrupt": "중단",
+  "Just resize": "리사이징",
+  "Keep -1 for seeds": "시드값 -1로 유지",
+  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
+  "LDSR": "LDSR",
+  "LMS Karras": "LMS Karras",
+  "LMS": "LMS",
+  "Label": "Label",
+  "Lanczos": "Lanczos",
+  "Learning rate": "Learning rate",
+  "Leave blank to save images to the default path.": "Leave blank to save images to the default path.",
+  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
+  "Loading...": "로딩 중...",
+  "Localization (requires restart)": "Localization (requires restart)",
+  "Log directory": "Log directory",
+  "Loopback": "루프백",
+  "Loops": "루프 수",
+  "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
+  "Make Zip when Save?": "저장 시 Zip 생성하기",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
+  "Mask blur": "마스크 블러",
+  "Mask mode": "Mask mode",
+  "Mask": "마스크",
+  "Masked content": "마스크된 부분",
+  "Masking mode": "Masking mode",
+  "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
+  "Max steps": "Max steps",
+  "Modules": "Modules",
+  "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
+  "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - set to 0 to get model A",
+  "Name": "Name",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Negative prompt": "네거티브 프롬프트",
+  "Next Page": "Next Page",
+  "None": "None",
+  "Nothing": "없음",
+  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
+  "Number of vectors per token": "Number of vectors per token",
+  "Open images output directory": "이미지 저장 경로 열기",
+  "Open output directory": "Open output directory",
+  "Original negative prompt": "기존 네거티브 프롬프트",
+  "Original prompt": "기존 프롬프트",
+  "Outpainting direction": "아웃페인팅 방향",
+  "Outpainting mk2": "아웃페인팅 마크 2",
+  "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
+  "Output directory for images from extras tab": "Output directory for images from extras tab",
+  "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
+  "Output directory for img2img grids": "Output directory for img2img grids",
+  "Output directory for img2img images": "Output directory for img2img images",
+  "Output directory for txt2img grids": "Output directory for txt2img grids",
+  "Output directory for txt2img images": "Output directory for txt2img images",
+  "Output directory": "이미지 저장 경로",
+  "Override `Denoising strength` to 1?": "디노이즈 강도를 1로 적용할까요?",
+  "Override `Sampling Steps` to the same value as `Decode steps`?": "샘플링 스텝 수를 디코딩 스텝 수와 동일하게 적용할까요?",
+  "Override `Sampling method` to Euler?(this method is built for it)": "샘플링 방법을 Euler로 적용할까요?(이 기능은 해당 샘플러를 위해 만들어져 있습니다)",
+  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "프롬프트 값을 기존 프롬프트와 동일하게 적용할까요?(네거티브 프롬프트 포함)",
+  "PLMS": "PLMS",
+  "PNG Info": "PNG 정보",
+  "Page Index": "Page Index",
+  "Path to directory where to write outputs": "Path to directory where to write outputs",
+  "Path to directory with input images": "Path to directory with input images",
+  "Paths for saving": "Paths for saving",
+  "Pixels to expand": "확장할 픽셀 수",
+  "Poor man's outpainting": "가난뱅이의 아웃페인팅",
+  "Preprocess images": "Preprocess images",
+  "Preprocess": "Preprocess",
+  "Prev Page": "Prev Page",
+  "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)",
+  "Primary model (A)": "Primary model (A)",
+  "Process an image, use it as an input, repeat.": "이미지를 생성하고, 생성한 이미지를 다시 원본으로 사용하는 과정을 반복합니다.",
+  "Process images in a directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리의 이미지들을 처리합니다.",
+  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Prompt S/R": "프롬프트 스타일 변경",
+  "Prompt matrix": "프롬프트 매트릭스",
+  "Prompt order": "프롬프트 순서",
+  "Prompt template file": "Prompt template file",
+  "Prompt": "프롬프트",
+  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
+  "Prompts": "프롬프트",
+  "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
+  "Quality for saved jpeg images": "Quality for saved jpeg images",
+  "Quicksettings list": "Quicksettings list",
+  "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+  "Randomness": "랜덤성",
+  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
+  "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews",
+  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "추천 설정값 - 샘플링 스텝 수 : 80-100 , 샘플러 : Euler a, 디노이즈 강도 : 0.8",
+  "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
+  "Renew Page": "Renew Page",
+  "Request browser notifications": "Request browser notifications",
+  "Resize and fill": "리사이징 후 채우기",
+  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "설정된 해상도로 이미지 리사이징을 진행합니다. 원본과 가로/세로 길이가 일치하지 않을 경우, 부정확한 화면비의 이미지를 얻게 됩니다.",
+  "Resize mode": "Resize mode",
   "Resize seed from height": "시드 리사이징 가로길이",
   "Resize seed from width": "시드 리사이징 세로길이",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
-  "Script": "스크립트",
+  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "이미지 전체가 설정된 해상도 내부에 들어가게 리사이징을 진행합니다. 빈 공간은 이미지의 색상으로 채웁니다.",
+  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "설정된 해상도 전체가 이미지로 가득차게 리사이징을 진행합니다. 튀어나오는 부분은 잘라냅니다.",
+  "Resize": "Resize",
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
+  "Restore faces": "얼굴 보정",
+  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
+  "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M",
+  "Result = A + (B - C) * M": "Result = A + (B - C) * M",
+  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
+  "Run": "Run",
+  "SD upscale": "SD 업스케일링",
+  "Sampler parameters": "Sampler parameters",
+  "Sampler": "샘플러",
+  "Sampling Steps": "샘플링 스텝 수",
+  "Sampling method": "샘플링 방법",
+  "Save a copy of embedding to log directory every N steps, 0 to disable": "Save a copy of embedding to log directory every N steps, 0 to disable",
+  "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
+  "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
+  "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
+  "Save an image to log directory every N steps, 0 to disable": "Save an image to log directory every N steps, 0 to disable",
+  "Save as float16": "Save as float16",
+  "Save grids to a subdirectory": "Save grids to a subdirectory",
+  "Save images to a subdirectory": "Save images to a subdirectory",
+  "Save images with embedding in PNG chunks": "Save images with embedding in PNG chunks",
+  "Save style": "스타일 저장",
+  "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
   "Save": "저장",
-  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
+  "Saving images/grids": "Saving images/grids",
+  "Saving to a directory": "Saving to a directory",
+  "Scale by": "Scale by",
+  "Scale to": "Scale to",
+  "Script": "스크립트",
+  "ScuNET GAN": "ScuNET GAN",
+  "ScuNET PSNR": "ScuNET PSNR",
+  "Secondary model (B)": "Secondary model (B)",
+  "See": "See",
+  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "Seed": "시드",
+  "Send to extras": "부가기능으로 전송",
   "Send to img2img": "이미지→이미지로 전송",
   "Send to inpaint": "인페인트로 전송",
-  "Send to extras": "부가기능으로 전송",
-  "Open images output directory": "이미지 저장 경로 열기",
-  "Make Zip when Save?": "저장 시 Zip 생성하기",
-  "Prompt matrix": "프롬프트 매트릭스",
+  "Send to txt2img": "텍스트→이미지로 전송",
   "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "(|)를 이용해 프롬프트를 분리할 시 첫 프롬프트를 제외하고 모든 프롬프트의 조합마다 이미지를 생성합니다. 첫 프롬프트는 모든 조합에 포함되게 됩니다.",
-  "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
-  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
-  "Show Textbox": "텍스트박스 보이기",
-  "File with inputs": "설정값 파일",
-  "Prompts": "프롬프트",
-  "X/Y plot": "X/Y 플롯",
-  "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
-  "X type": "X축",
-  "Y type": "Y축",
-  "X values": "X 설정값",
-  "Y values": "Y 설정값",
   "Separate values for X axis using commas.": "쉼표로 X축에 적용할 값 분리",
   "Separate values for Y axis using commas.": "쉼표로 Y축에 적용할 값 분리",
-  "Draw legend": "범례 그리기",
-  "Include Separate Images": "분리된 이미지 포함하기",
-  "Keep -1 for seeds": "시드값 -1로 유지",
-  "Var. seed": "바리에이션 시드",
-  "Var. strength": "바리에이션 강도",
-  "Steps": "스텝 수",
-  "Prompt S/R": "프롬프트 스타일 변경",
-  "Prompt order": "프롬프트 순서",
-  "Sampler": "샘플러",
-  "Checkpoint name": "체크포인트 이름",
-  "Hypernet str.": "하이퍼네트워크 강도",
+  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
+  "Settings": "설정",
+  "Show Textbox": "텍스트박스 보이기",
+  "Show generation progress in window title.": "Show generation progress in window title.",
+  "Show grid in results for web": "Show grid in results for web",
+  "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
+  "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
+  "Show progressbar": "Show progressbar",
+  "Show result images": "Show result images",
   "Sigma Churn": "시그마 섞기",
-  "Sigma min": "시그마 최솟값",
+  "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
   "Sigma max": "시그마 최댓값",
+  "Sigma min": "시그마 최솟값",
   "Sigma noise": "시그마 노이즈",
-  "Clip skip": "클립 건너뛰기",
-  "Denoising": "디노이징",
-  "Nothing": "없음",
-  "Apply settings": "설정 적용하기",
-  "Always save all generated images": "생성된 이미지 항상 저장하기"
+  "Single Image": "Single Image",
+  "Skip": "건너뛰기",
+  "Source directory": "Source directory",
+  "Source": "Source",
+  "Split oversized images into two": "Split oversized images into two",
+  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "Stable Diffusion": "Stable Diffusion",
+  "Steps": "스텝 수",
+  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
+  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
+  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
+  "Style 1": "스타일 1",
+  "Style 2": "스타일 2",
+  "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
+  "SwinIR 4x": "SwinIR 4x",
+  "System": "System",
+  "Tertiary model (C)": "Tertiary model (C)",
+  "Textbox": "Textbox",
+  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
+  "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
+  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
+  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
+  "Tile overlap": "타일 겹침",
+  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
+  "Tile size for all SwinIR.": "Tile size for all SwinIR.",
+  "Tiling": "타일링",
+  "Train Embedding": "Train Embedding",
+  "Train Hypernetwork": "Train Hypernetwork",
+  "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Train an embedding; must specify a directory with a set of 1:1 ratio images",
+  "Train": "훈련",
+  "Training": "Training",
+  "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training",
+  "Upload mask": "마스크 업로드하기",
+  "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
+  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
+  "Upscaler 2 visibility": "Upscaler 2 visibility",
+  "Upscaler for img2img": "Upscaler for img2img",
+  "Upscaler": "업스케일러",
+  "Upscaling": "Upscaling",
+  "Use BLIP for caption": "Use BLIP for caption",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
+  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "저장 경로를 비워두면 기본 저장 폴더에 이미지들이 저장됩니다.",
+  "Use deepbooru for caption": "Use deepbooru for caption",
+  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
+  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
+  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
+  "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
+  "User interface": "User interface",
+  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
+  "Var. seed": "바리에이션 시드",
+  "Var. strength": "바리에이션 강도",
+  "Variation seed": "바리에이션 시드",
+  "Variation strength": "바리에이션 강도",
+  "Weighted sum": "Weighted sum",
+  "What to put inside the masked area before processing it with Stable Diffusion.": "Stable Diffusion으로 이미지를 생성하기 전 마스크된 부분에 무엇을 채울지 결정하는 설정값",
+  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
+  "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
+  "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
+  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
+  "Width": "가로",
+  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "이미지를 설정된 사이즈의 2배로 업스케일합니다. 상단의 가로와 세로 슬라이더를 이용해 타일 사이즈를 지정하세요.",
+  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
+  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
+  "X type": "X축",
+  "X values": "X 설정값",
+  "X/Y plot": "X/Y 플롯",
+  "Y type": "Y축",
+  "Y values": "Y 설정값",
+  "api": "",
+  "built with gradio": "gradio로 제작되었습니다",
+  "checkpoint": "checkpoint",
+  "directory.": "directory.",
+  "down": "아래쪽",
+  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
+  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
+  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
+  "extras history": "extras history",
+  "fill it with colors of the image": "이미지의 색상으로 채우기",
+  "fill it with latent space noise": "잠재 공간 노이즈로 채우기",
+  "fill it with latent space zeroes": "잠재 공간의 0값으로 채우기",
+  "fill": "채우기",
+  "for detailed explanation.": "for detailed explanation.",
+  "hide": "api 숨기기",
+  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
+  "img2img DDIM discretize": "img2img DDIM discretize",
+  "img2img alternative test": "이미지→이미지 대체버전 테스트",
+  "img2img history": "img2img history",
+  "img2img": "이미지→이미지",
+  "keep whatever was there originally": "이미지 원본 유지",
+  "latent noise": "잠재 노이즈",
+  "latent nothing": "잠재 공백",
+  "left": "왼쪽",
+  "number of images to delete consecutively next": "number of images to delete consecutively next",
+  "or": "or",
+  "original": "원본 유지",
+  "quad": "quad",
+  "right": "오른쪽",
+  "set_index": "set_index",
+  "should be 2 or lower.": "이 2 이하여야 합니다.",
+  "sigma churn": "sigma churn",
+  "sigma noise": "sigma noise",
+  "sigma tmin": "sigma tmin",
+  "txt2img history": "txt2img history",
+  "txt2img": "텍스트→이미지",
+  "uniform": "uniform",
+  "up": "위쪽",
+  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
+  "view": "api 보이기",
+  "wiki": "wiki"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From 6cfe23a6f183be58746feb7d7d58f83e877ed630 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Sun, 23 Oct 2022 22:37:40 +0900
Subject: Rename ko-KR.json to ko_KR.json

---
 localizations/ko-KR.json | 422 -----------------------------------------------
 localizations/ko_KR.json | 422 +++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 422 insertions(+), 422 deletions(-)
 delete mode 100644 localizations/ko-KR.json
 create mode 100644 localizations/ko_KR.json

diff --git a/localizations/ko-KR.json b/localizations/ko-KR.json
deleted file mode 100644
index 7cc431c6..00000000
--- a/localizations/ko-KR.json
+++ /dev/null
@@ -1,422 +0,0 @@
-{
-  "×": "×",
-  "•": "•",
-  "⊞": "⊞",
-  "❮": "❮",
-  "❯": "❯",
-  "⤡": "⤡",
-  "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
-  "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
-  "A merger of the two checkpoints will be generated in your": "A merger of the two checkpoints will be generated in your",
-  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
-  "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
-  "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
-  "Add difference": "Add difference",
-  "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid",
-  "Add layer normalization": "Add layer normalization",
-  "Add model hash to generation information": "Add model hash to generation information",
-  "Add model name to generation information": "Add model name to generation information",
-  "Always print all generation info to standard output": "Always print all generation info to standard output",
-  "Always save all generated image grids": "Always save all generated image grids",
-  "Always save all generated images": "생성된 이미지 항상 저장하기",
-  "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
-  "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
-  "Apply settings": "설정 적용하기",
-  "BSRGAN 4x": "BSRGAN 4x",
-  "Batch Process": "Batch Process",
-  "Batch count": "배치 수",
-  "Batch from Directory": "Batch from Directory",
-  "Batch img2img": "이미지→이미지 배치",
-  "Batch size": "배치 크기",
-  "CFG Scale": "CFG 스케일",
-  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
-  "Cancel generate forever": "반복 생성 취소",
-  "Check progress (first)": "Check progress (first)",
-  "Check progress": "Check progress",
-  "Checkpoint Merger": "체크포인트 병합",
-  "Checkpoint name": "체크포인트 이름",
-  "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
-  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
-  "Clip skip": "클립 건너뛰기",
-  "CodeFormer visibility": "CodeFormer visibility",
-  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)",
-  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
-  "Color variation": "색깔 다양성",
-  "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
-  "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
-  "Create embedding": "Create embedding",
-  "Create flipped copies": "Create flipped copies",
-  "Create hypernetwork": "Create hypernetwork",
-  "Crop and resize": "잘라낸 후 리사이징",
-  "Crop to fit": "Crop to fit",
-  "Custom Name (Optional)": "Custom Name (Optional)",
-  "DDIM": "DDIM",
-  "DPM adaptive": "DPM adaptive",
-  "DPM fast": "DPM fast",
-  "DPM2 Karras": "DPM2 Karras",
-  "DPM2 a Karras": "DPM2 a Karras",
-  "DPM2 a": "DPM2 a",
-  "DPM2": "DPM2",
-  "Dataset directory": "Dataset directory",
-  "Decode CFG scale": "디코딩 CFG 스케일",
-  "Decode steps": "디코딩 스텝 수",
-  "Delete": "Delete",
-  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
-  "Denoising strength change factor": "디노이즈 강도 변경 배수",
-  "Denoising strength": "디노이즈 강도",
-  "Denoising": "디노이징",
-  "Destination directory": "Destination directory",
-  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
-  "Directory for saving images using the Save button": "Directory for saving images using the Save button",
-  "Directory name pattern": "Directory name pattern",
-  "Do not add watermark to images": "Do not add watermark to images",
-  "Do not do anything special": "아무것도 하지 않기",
-  "Do not save grids consisting of one picture": "Do not save grids consisting of one picture",
-  "Do not show any images in results for web": "Do not show any images in results for web",
-  "Download localization template": "Download localization template",
-  "Draw legend": "범례 그리기",
-  "Draw mask": "마스크 직접 그리기",
-  "Drop File Here": "Drop File Here",
-  "Drop Image Here": "Drop Image Here",
-  "ESRGAN_4x": "ESRGAN_4x",
-  "Embedding": "Embedding",
-  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
-  "Enable full page image viewer": "Enable full page image viewer",
-  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
-  "End Page": "End Page",
-  "Enter hypernetwork layer structure": "Enter hypernetwork layer structure",
-  "Eta noise seed delta": "Eta noise seed delta",
-  "Eta": "Eta",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
-  "Euler a": "Euler a",
-  "Euler": "Euler",
-  "Extra": "고급",
-  "Extras": "부가기능",
-  "Face restoration": "Face restoration",
-  "Fall-off exponent (lower=higher detail)": "감쇠 지수 (낮을수록 디테일이 올라감)",
-  "File Name": "File Name",
-  "File format for grids": "File format for grids",
-  "File format for images": "File format for images",
-  "File with inputs": "설정값 파일",
-  "File": "File",
-  "Filename join string": "Filename join string",
-  "Filename word regex": "Filename word regex",
-  "Filter NSFW content": "Filter NSFW content",
-  "First Page": "First Page",
-  "Firstpass height": "초기 세로길이",
-  "Firstpass width": "초기 가로길이",
-  "Font for image grids that have text": "Font for image grids that have text",
-  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD 업스케일링에서 타일 간 몇 픽셀을 겹치게 할지 결정하는 설정값입니다. 타일들이 다시 한 이미지로 합쳐질 때, 눈에 띄는 이음매가 없도록 서로 겹치게 됩니다.",
-  "GFPGAN visibility": "GFPGAN visibility",
-  "Generate Info": "Generate Info",
-  "Generate forever": "반복 생성",
-  "Generate": "생성",
-  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
-  "Height": "세로",
-  "Heun": "Heun",
-  "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
-  "Highres. fix": "고해상도 보정",
-  "History": "기록",
-  "How many batches of images to create": "생성할 이미지 배치 수",
-  "How many image to create in a single batch": "한 배치당 이미지 수",
-  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
-  "How many times to repeat processing an image and using it as input for the next iteration": "이미지를 생성 후 원본으로 몇 번 반복해서 사용할지 결정하는 값",
-  "How much to blur the mask before processing, in pixels.": "이미지 생성 전 마스크를 얼마나 블러처리할지 결정하는 값. 픽셀 단위",
-  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
-  "Hypernet str.": "하이퍼네트워크 강도",
-  "Hypernetwork strength": "Hypernetwork strength",
-  "Hypernetwork": "하이퍼네트워크",
-  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
-  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
-  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
-  "Image for img2img": "Image for img2img",
-  "Image for inpainting with mask": "Image for inpainting with mask",
-  "Image": "Image",
-  "Images filename pattern": "Images filename pattern",
-  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "루프백 모드에서는 매 루프마다 디노이즈 강도에 이 값이 곱해집니다. 1보다 작을 경우 다양성이 낮아져 결과 이미지들이 고정된 형태로 모일 겁니다. 1보다 클 경우 다양성이 높아져 결과 이미지들이 갈수록 혼란스러워지겠죠.",
-  "Include Separate Images": "분리된 이미지 포함하기",
-  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
-  "Initialization text": "Initialization text",
-  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트 패딩값(픽셀 단위)",
-  "Inpaint at full resolution": "전체 해상도로 인페인트하기",
-  "Inpaint masked": "마스크만 처리",
-  "Inpaint not masked": "마스크 이외만 처리",
-  "Inpaint": "인페인트",
-  "Input directory": "인풋 이미지 경로",
-  "Interpolation Method": "Interpolation Method",
-  "Interrogate\nCLIP": "CLIP\n분석",
-  "Interrogate\nDeepBooru": "DeepBooru\n분석",
-  "Interrogate Options": "Interrogate Options",
-  "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold",
-  "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically",
-  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
-  "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM",
-  "Interrogate: maximum description length": "Interrogate: maximum description length",
-  "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
-  "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
-  "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
-  "Interrupt": "중단",
-  "Just resize": "리사이징",
-  "Keep -1 for seeds": "시드값 -1로 유지",
-  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
-  "LDSR": "LDSR",
-  "LMS Karras": "LMS Karras",
-  "LMS": "LMS",
-  "Label": "Label",
-  "Lanczos": "Lanczos",
-  "Learning rate": "Learning rate",
-  "Leave blank to save images to the default path.": "Leave blank to save images to the default path.",
-  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
-  "Loading...": "로딩 중...",
-  "Localization (requires restart)": "Localization (requires restart)",
-  "Log directory": "Log directory",
-  "Loopback": "루프백",
-  "Loops": "루프 수",
-  "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
-  "Make Zip when Save?": "저장 시 Zip 생성하기",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
-  "Mask blur": "마스크 블러",
-  "Mask mode": "Mask mode",
-  "Mask": "마스크",
-  "Masked content": "마스크된 부분",
-  "Masking mode": "Masking mode",
-  "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
-  "Max steps": "Max steps",
-  "Modules": "Modules",
-  "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
-  "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - set to 0 to get model A",
-  "Name": "Name",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Negative prompt": "네거티브 프롬프트",
-  "Next Page": "Next Page",
-  "None": "None",
-  "Nothing": "없음",
-  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
-  "Number of vectors per token": "Number of vectors per token",
-  "Open images output directory": "이미지 저장 경로 열기",
-  "Open output directory": "Open output directory",
-  "Original negative prompt": "기존 네거티브 프롬프트",
-  "Original prompt": "기존 프롬프트",
-  "Outpainting direction": "아웃페인팅 방향",
-  "Outpainting mk2": "아웃페인팅 마크 2",
-  "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
-  "Output directory for images from extras tab": "Output directory for images from extras tab",
-  "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
-  "Output directory for img2img grids": "Output directory for img2img grids",
-  "Output directory for img2img images": "Output directory for img2img images",
-  "Output directory for txt2img grids": "Output directory for txt2img grids",
-  "Output directory for txt2img images": "Output directory for txt2img images",
-  "Output directory": "이미지 저장 경로",
-  "Override `Denoising strength` to 1?": "디노이즈 강도를 1로 적용할까요?",
-  "Override `Sampling Steps` to the same value as `Decode steps`?": "샘플링 스텝 수를 디코딩 스텝 수와 동일하게 적용할까요?",
-  "Override `Sampling method` to Euler?(this method is built for it)": "샘플링 방법을 Euler로 적용할까요?(이 기능은 해당 샘플러를 위해 만들어져 있습니다)",
-  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "프롬프트 값을 기존 프롬프트와 동일하게 적용할까요?(네거티브 프롬프트 포함)",
-  "PLMS": "PLMS",
-  "PNG Info": "PNG 정보",
-  "Page Index": "Page Index",
-  "Path to directory where to write outputs": "Path to directory where to write outputs",
-  "Path to directory with input images": "Path to directory with input images",
-  "Paths for saving": "Paths for saving",
-  "Pixels to expand": "확장할 픽셀 수",
-  "Poor man's outpainting": "가난뱅이의 아웃페인팅",
-  "Preprocess images": "Preprocess images",
-  "Preprocess": "Preprocess",
-  "Prev Page": "Prev Page",
-  "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)",
-  "Primary model (A)": "Primary model (A)",
-  "Process an image, use it as an input, repeat.": "이미지를 생성하고, 생성한 이미지를 다시 원본으로 사용하는 과정을 반복합니다.",
-  "Process images in a directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리의 이미지들을 처리합니다.",
-  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Prompt S/R": "프롬프트 스타일 변경",
-  "Prompt matrix": "프롬프트 매트릭스",
-  "Prompt order": "프롬프트 순서",
-  "Prompt template file": "Prompt template file",
-  "Prompt": "프롬프트",
-  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
-  "Prompts": "프롬프트",
-  "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
-  "Quality for saved jpeg images": "Quality for saved jpeg images",
-  "Quicksettings list": "Quicksettings list",
-  "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
-  "Randomness": "랜덤성",
-  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
-  "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews",
-  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "추천 설정값 - 샘플링 스텝 수 : 80-100 , 샘플러 : Euler a, 디노이즈 강도 : 0.8",
-  "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
-  "Renew Page": "Renew Page",
-  "Request browser notifications": "Request browser notifications",
-  "Resize and fill": "리사이징 후 채우기",
-  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "설정된 해상도로 이미지 리사이징을 진행합니다. 원본과 가로/세로 길이가 일치하지 않을 경우, 부정확한 화면비의 이미지를 얻게 됩니다.",
-  "Resize mode": "Resize mode",
-  "Resize seed from height": "시드 리사이징 가로길이",
-  "Resize seed from width": "시드 리사이징 세로길이",
-  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "이미지 전체가 설정된 해상도 내부에 들어가게 리사이징을 진행합니다. 빈 공간은 이미지의 색상으로 채웁니다.",
-  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "설정된 해상도 전체가 이미지로 가득차게 리사이징을 진행합니다. 튀어나오는 부분은 잘라냅니다.",
-  "Resize": "Resize",
-  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
-  "Restore faces": "얼굴 보정",
-  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
-  "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M",
-  "Result = A + (B - C) * M": "Result = A + (B - C) * M",
-  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
-  "Run": "Run",
-  "SD upscale": "SD 업스케일링",
-  "Sampler parameters": "Sampler parameters",
-  "Sampler": "샘플러",
-  "Sampling Steps": "샘플링 스텝 수",
-  "Sampling method": "샘플링 방법",
-  "Save a copy of embedding to log directory every N steps, 0 to disable": "Save a copy of embedding to log directory every N steps, 0 to disable",
-  "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
-  "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
-  "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
-  "Save an image to log directory every N steps, 0 to disable": "Save an image to log directory every N steps, 0 to disable",
-  "Save as float16": "Save as float16",
-  "Save grids to a subdirectory": "Save grids to a subdirectory",
-  "Save images to a subdirectory": "Save images to a subdirectory",
-  "Save images with embedding in PNG chunks": "Save images with embedding in PNG chunks",
-  "Save style": "스타일 저장",
-  "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
-  "Save": "저장",
-  "Saving images/grids": "Saving images/grids",
-  "Saving to a directory": "Saving to a directory",
-  "Scale by": "Scale by",
-  "Scale to": "Scale to",
-  "Script": "스크립트",
-  "ScuNET GAN": "ScuNET GAN",
-  "ScuNET PSNR": "ScuNET PSNR",
-  "Secondary model (B)": "Secondary model (B)",
-  "See": "See",
-  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
-  "Seed": "시드",
-  "Send to extras": "부가기능으로 전송",
-  "Send to img2img": "이미지→이미지로 전송",
-  "Send to inpaint": "인페인트로 전송",
-  "Send to txt2img": "텍스트→이미지로 전송",
-  "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "(|)를 이용해 프롬프트를 분리할 시 첫 프롬프트를 제외하고 모든 프롬프트의 조합마다 이미지를 생성합니다. 첫 프롬프트는 모든 조합에 포함되게 됩니다.",
-  "Separate values for X axis using commas.": "쉼표로 X축에 적용할 값 분리",
-  "Separate values for Y axis using commas.": "쉼표로 Y축에 적용할 값 분리",
-  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
-  "Settings": "설정",
-  "Show Textbox": "텍스트박스 보이기",
-  "Show generation progress in window title.": "Show generation progress in window title.",
-  "Show grid in results for web": "Show grid in results for web",
-  "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
-  "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
-  "Show progressbar": "Show progressbar",
-  "Show result images": "Show result images",
-  "Sigma Churn": "시그마 섞기",
-  "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
-  "Sigma max": "시그마 최댓값",
-  "Sigma min": "시그마 최솟값",
-  "Sigma noise": "시그마 노이즈",
-  "Single Image": "Single Image",
-  "Skip": "건너뛰기",
-  "Source directory": "Source directory",
-  "Source": "Source",
-  "Split oversized images into two": "Split oversized images into two",
-  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
-  "Stable Diffusion": "Stable Diffusion",
-  "Steps": "스텝 수",
-  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
-  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
-  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
-  "Style 1": "스타일 1",
-  "Style 2": "스타일 2",
-  "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
-  "SwinIR 4x": "SwinIR 4x",
-  "System": "System",
-  "Tertiary model (C)": "Tertiary model (C)",
-  "Textbox": "Textbox",
-  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
-  "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
-  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
-  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
-  "Tile overlap": "타일 겹침",
-  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
-  "Tile size for all SwinIR.": "Tile size for all SwinIR.",
-  "Tiling": "타일링",
-  "Train Embedding": "Train Embedding",
-  "Train Hypernetwork": "Train Hypernetwork",
-  "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Train an embedding; must specify a directory with a set of 1:1 ratio images",
-  "Train": "훈련",
-  "Training": "Training",
-  "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training",
-  "Upload mask": "마스크 업로드하기",
-  "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
-  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
-  "Upscaler 2 visibility": "Upscaler 2 visibility",
-  "Upscaler for img2img": "Upscaler for img2img",
-  "Upscaler": "업스케일러",
-  "Upscaling": "Upscaling",
-  "Use BLIP for caption": "Use BLIP for caption",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
-  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "저장 경로를 비워두면 기본 저장 폴더에 이미지들이 저장됩니다.",
-  "Use deepbooru for caption": "Use deepbooru for caption",
-  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
-  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
-  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
-  "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
-  "User interface": "User interface",
-  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
-  "Var. seed": "바리에이션 시드",
-  "Var. strength": "바리에이션 강도",
-  "Variation seed": "바리에이션 시드",
-  "Variation strength": "바리에이션 강도",
-  "Weighted sum": "Weighted sum",
-  "What to put inside the masked area before processing it with Stable Diffusion.": "Stable Diffusion으로 이미지를 생성하기 전 마스크된 부분에 무엇을 채울지 결정하는 설정값",
-  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
-  "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
-  "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
-  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
-  "Width": "가로",
-  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "이미지를 설정된 사이즈의 2배로 업스케일합니다. 상단의 가로와 세로 슬라이더를 이용해 타일 사이즈를 지정하세요.",
-  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
-  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
-  "X type": "X축",
-  "X values": "X 설정값",
-  "X/Y plot": "X/Y 플롯",
-  "Y type": "Y축",
-  "Y values": "Y 설정값",
-  "api": "",
-  "built with gradio": "gradio로 제작되었습니다",
-  "checkpoint": "checkpoint",
-  "directory.": "directory.",
-  "down": "아래쪽",
-  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
-  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
-  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
-  "extras history": "extras history",
-  "fill it with colors of the image": "이미지의 색상으로 채우기",
-  "fill it with latent space noise": "잠재 공간 노이즈로 채우기",
-  "fill it with latent space zeroes": "잠재 공간의 0값으로 채우기",
-  "fill": "채우기",
-  "for detailed explanation.": "for detailed explanation.",
-  "hide": "api 숨기기",
-  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
-  "img2img DDIM discretize": "img2img DDIM discretize",
-  "img2img alternative test": "이미지→이미지 대체버전 테스트",
-  "img2img history": "img2img history",
-  "img2img": "이미지→이미지",
-  "keep whatever was there originally": "이미지 원본 유지",
-  "latent noise": "잠재 노이즈",
-  "latent nothing": "잠재 공백",
-  "left": "왼쪽",
-  "number of images to delete consecutively next": "number of images to delete consecutively next",
-  "or": "or",
-  "original": "원본 유지",
-  "quad": "quad",
-  "right": "오른쪽",
-  "set_index": "set_index",
-  "should be 2 or lower.": "이 2 이하여야 합니다.",
-  "sigma churn": "sigma churn",
-  "sigma noise": "sigma noise",
-  "sigma tmin": "sigma tmin",
-  "txt2img history": "txt2img history",
-  "txt2img": "텍스트→이미지",
-  "uniform": "uniform",
-  "up": "위쪽",
-  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
-  "view": "api 보이기",
-  "wiki": "wiki"
-}
\ No newline at end of file
diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
new file mode 100644
index 00000000..f665042e
--- /dev/null
+++ b/localizations/ko_KR.json
@@ -0,0 +1,422 @@
+{
+  "×": "×",
+  "•": "•",
+  "⊞": "⊞",
+  "❮": "❮",
+  "❯": "❯",
+  "⤡": "⤡",
+  "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
+  "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
+  "A merger of the two checkpoints will be generated in your": "A merger of the two checkpoints will be generated in your",
+  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
+  "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
+  "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
+  "Add difference": "Add difference",
+  "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid",
+  "Add layer normalization": "Add layer normalization",
+  "Add model hash to generation information": "Add model hash to generation information",
+  "Add model name to generation information": "Add model name to generation information",
+  "Always print all generation info to standard output": "Always print all generation info to standard output",
+  "Always save all generated image grids": "Always save all generated image grids",
+  "Always save all generated images": "생성된 이미지 항상 저장하기",
+  "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
+  "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
+  "Apply settings": "설정 적용하기",
+  "BSRGAN 4x": "BSRGAN 4x",
+  "Batch Process": "Batch Process",
+  "Batch count": "배치 수",
+  "Batch from Directory": "Batch from Directory",
+  "Batch img2img": "이미지→이미지 배치",
+  "Batch size": "배치 크기",
+  "CFG Scale": "CFG 스케일",
+  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
+  "Cancel generate forever": "반복 생성 취소",
+  "Check progress (first)": "Check progress (first)",
+  "Check progress": "Check progress",
+  "Checkpoint Merger": "체크포인트 병합",
+  "Checkpoint name": "체크포인트 이름",
+  "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
+  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
+  "Clip skip": "클립 건너뛰기",
+  "CodeFormer visibility": "CodeFormer visibility",
+  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)",
+  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
+  "Color variation": "색깔 다양성",
+  "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
+  "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
+  "Create embedding": "Create embedding",
+  "Create flipped copies": "Create flipped copies",
+  "Create hypernetwork": "Create hypernetwork",
+  "Crop and resize": "잘라낸 후 리사이징",
+  "Crop to fit": "Crop to fit",
+  "Custom Name (Optional)": "Custom Name (Optional)",
+  "DDIM": "DDIM",
+  "DPM adaptive": "DPM adaptive",
+  "DPM fast": "DPM fast",
+  "DPM2 Karras": "DPM2 Karras",
+  "DPM2 a Karras": "DPM2 a Karras",
+  "DPM2 a": "DPM2 a",
+  "DPM2": "DPM2",
+  "Dataset directory": "Dataset directory",
+  "Decode CFG scale": "디코딩 CFG 스케일",
+  "Decode steps": "디코딩 스텝 수",
+  "Delete": "Delete",
+  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
+  "Denoising strength change factor": "디노이즈 강도 변경 배수",
+  "Denoising strength": "디노이즈 강도",
+  "Denoising": "디노이징",
+  "Destination directory": "Destination directory",
+  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
+  "Directory for saving images using the Save button": "Directory for saving images using the Save button",
+  "Directory name pattern": "Directory name pattern",
+  "Do not add watermark to images": "Do not add watermark to images",
+  "Do not do anything special": "아무것도 하지 않기",
+  "Do not save grids consisting of one picture": "Do not save grids consisting of one picture",
+  "Do not show any images in results for web": "Do not show any images in results for web",
+  "Download localization template": "Download localization template",
+  "Draw legend": "범례 그리기",
+  "Draw mask": "마스크 직접 그리기",
+  "Drop File Here": "Drop File Here",
+  "Drop Image Here": "Drop Image Here",
+  "ESRGAN_4x": "ESRGAN_4x",
+  "Embedding": "Embedding",
+  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
+  "Enable full page image viewer": "Enable full page image viewer",
+  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
+  "End Page": "End Page",
+  "Enter hypernetwork layer structure": "Enter hypernetwork layer structure",
+  "Eta noise seed delta": "Eta noise seed delta",
+  "Eta": "Eta",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
+  "Euler a": "Euler a",
+  "Euler": "Euler",
+  "Extra": "고급",
+  "Extras": "부가기능",
+  "Face restoration": "Face restoration",
+  "Fall-off exponent (lower=higher detail)": "감쇠 지수 (낮을수록 디테일이 올라감)",
+  "File Name": "File Name",
+  "File format for grids": "File format for grids",
+  "File format for images": "File format for images",
+  "File with inputs": "설정값 파일",
+  "File": "File",
+  "Filename join string": "Filename join string",
+  "Filename word regex": "Filename word regex",
+  "Filter NSFW content": "Filter NSFW content",
+  "First Page": "First Page",
+  "Firstpass height": "초기 세로길이",
+  "Firstpass width": "초기 가로길이",
+  "Font for image grids that have text": "Font for image grids that have text",
+  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD 업스케일링에서 타일 간 몇 픽셀을 겹치게 할지 결정하는 설정값입니다. 타일들이 다시 한 이미지로 합쳐질 때, 눈에 띄는 이음매가 없도록 서로 겹치게 됩니다.",
+  "GFPGAN visibility": "GFPGAN visibility",
+  "Generate Info": "Generate Info",
+  "Generate forever": "반복 생성",
+  "Generate": "생성",
+  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
+  "Height": "세로",
+  "Heun": "Heun",
+  "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
+  "Highres. fix": "고해상도 보정",
+  "History": "기록",
+  "How many batches of images to create": "생성할 이미지 배치 수",
+  "How many image to create in a single batch": "한 배치당 이미지 수",
+  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
+  "How many times to repeat processing an image and using it as input for the next iteration": "이미지를 생성 후 원본으로 몇 번 반복해서 사용할지 결정하는 값",
+  "How much to blur the mask before processing, in pixels.": "이미지 생성 전 마스크를 얼마나 블러처리할지 결정하는 값. 픽셀 단위",
+  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
+  "Hypernet str.": "하이퍼네트워크 강도",
+  "Hypernetwork strength": "Hypernetwork strength",
+  "Hypernetwork": "하이퍼네트워크",
+  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
+  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
+  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
+  "Image for img2img": "Image for img2img",
+  "Image for inpainting with mask": "Image for inpainting with mask",
+  "Image": "Image",
+  "Images filename pattern": "Images filename pattern",
+  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "루프백 모드에서는 매 루프마다 디노이즈 강도에 이 값이 곱해집니다. 1보다 작을 경우 다양성이 낮아져 결과 이미지들이 고정된 형태로 모일 겁니다. 1보다 클 경우 다양성이 높아져 결과 이미지들이 갈수록 혼란스러워지겠죠.",
+  "Include Separate Images": "분리된 이미지 포함하기",
+  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
+  "Initialization text": "Initialization text",
+  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트 패딩값(픽셀 단위)",
+  "Inpaint at full resolution": "전체 해상도로 인페인트하기",
+  "Inpaint masked": "마스크만 처리",
+  "Inpaint not masked": "마스크 이외만 처리",
+  "Inpaint": "인페인트",
+  "Input directory": "인풋 이미지 경로",
+  "Interpolation Method": "Interpolation Method",
+  "Interrogate\nCLIP": "CLIP\n분석",
+  "Interrogate\nDeepBooru": "DeepBooru\n분석",
+  "Interrogate Options": "Interrogate Options",
+  "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold",
+  "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically",
+  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
+  "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM",
+  "Interrogate: maximum description length": "Interrogate: maximum description length",
+  "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
+  "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
+  "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
+  "Interrupt": "중단",
+  "Just resize": "리사이징",
+  "Keep -1 for seeds": "시드값 -1로 유지",
+  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
+  "LDSR": "LDSR",
+  "LMS Karras": "LMS Karras",
+  "LMS": "LMS",
+  "Label": "Label",
+  "Lanczos": "Lanczos",
+  "Learning rate": "Learning rate",
+  "Leave blank to save images to the default path.": "Leave blank to save images to the default path.",
+  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
+  "Loading...": "로딩 중...",
+  "Localization (requires restart)": "Localization (requires restart)",
+  "Log directory": "Log directory",
+  "Loopback": "루프백",
+  "Loops": "루프 수",
+  "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
+  "Make Zip when Save?": "저장 시 Zip 생성하기",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
+  "Mask blur": "마스크 블러",
+  "Mask mode": "Mask mode",
+  "Mask": "마스크",
+  "Masked content": "마스크된 부분",
+  "Masking mode": "Masking mode",
+  "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
+  "Max steps": "Max steps",
+  "Modules": "Modules",
+  "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
+  "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - set to 0 to get model A",
+  "Name": "Name",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Negative prompt": "네거티브 프롬프트",
+  "Next Page": "Next Page",
+  "None": "None",
+  "Nothing": "없음",
+  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
+  "Number of vectors per token": "Number of vectors per token",
+  "Open images output directory": "이미지 저장 경로 열기",
+  "Open output directory": "Open output directory",
+  "Original negative prompt": "기존 네거티브 프롬프트",
+  "Original prompt": "기존 프롬프트",
+  "Outpainting direction": "아웃페인팅 방향",
+  "Outpainting mk2": "아웃페인팅 마크 2",
+  "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
+  "Output directory for images from extras tab": "Output directory for images from extras tab",
+  "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
+  "Output directory for img2img grids": "Output directory for img2img grids",
+  "Output directory for img2img images": "Output directory for img2img images",
+  "Output directory for txt2img grids": "Output directory for txt2img grids",
+  "Output directory for txt2img images": "Output directory for txt2img images",
+  "Output directory": "이미지 저장 경로",
+  "Override `Denoising strength` to 1?": "디노이즈 강도를 1로 적용할까요?",
+  "Override `Sampling Steps` to the same value as `Decode steps`?": "샘플링 스텝 수를 디코딩 스텝 수와 동일하게 적용할까요?",
+  "Override `Sampling method` to Euler?(this method is built for it)": "샘플링 방법을 Euler로 적용할까요?(이 기능은 해당 샘플러를 위해 만들어져 있습니다)",
+  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "프롬프트 값을 기존 프롬프트와 동일하게 적용할까요?(네거티브 프롬프트 포함)",
+  "PLMS": "PLMS",
+  "PNG Info": "PNG 정보",
+  "Page Index": "Page Index",
+  "Path to directory where to write outputs": "Path to directory where to write outputs",
+  "Path to directory with input images": "Path to directory with input images",
+  "Paths for saving": "Paths for saving",
+  "Pixels to expand": "확장할 픽셀 수",
+  "Poor man's outpainting": "가난뱅이의 아웃페인팅",
+  "Preprocess images": "Preprocess images",
+  "Preprocess": "Preprocess",
+  "Prev Page": "Prev Page",
+  "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)",
+  "Primary model (A)": "Primary model (A)",
+  "Process an image, use it as an input, repeat.": "이미지를 생성하고, 생성한 이미지를 다시 원본으로 사용하는 과정을 반복합니다.",
+  "Process images in a directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리의 이미지들을 처리합니다.",
+  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Prompt S/R": "프롬프트 스타일 변경",
+  "Prompt matrix": "프롬프트 매트릭스",
+  "Prompt order": "프롬프트 순서",
+  "Prompt template file": "Prompt template file",
+  "Prompt": "프롬프트",
+  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
+  "Prompts": "프롬프트",
+  "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
+  "Quality for saved jpeg images": "Quality for saved jpeg images",
+  "Quicksettings list": "Quicksettings list",
+  "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+  "Randomness": "랜덤성",
+  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
+  "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews",
+  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "추천 설정값 - 샘플링 스텝 수 : 80-100 , 샘플러 : Euler a, 디노이즈 강도 : 0.8",
+  "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
+  "Renew Page": "Renew Page",
+  "Request browser notifications": "Request browser notifications",
+  "Resize and fill": "리사이징 후 채우기",
+  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "설정된 해상도로 이미지 리사이징을 진행합니다. 원본과 가로/세로 길이가 일치하지 않을 경우, 부정확한 화면비의 이미지를 얻게 됩니다.",
+  "Resize mode": "Resize mode",
+  "Resize seed from height": "시드 리사이징 가로길이",
+  "Resize seed from width": "시드 리사이징 세로길이",
+  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "이미지 전체가 설정된 해상도 내부에 들어가게 리사이징을 진행합니다. 빈 공간은 이미지의 색상으로 채웁니다.",
+  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "설정된 해상도 전체가 이미지로 가득차게 리사이징을 진행합니다. 튀어나오는 부분은 잘라냅니다.",
+  "Resize": "Resize",
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
+  "Restore faces": "얼굴 보정",
+  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
+  "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M",
+  "Result = A + (B - C) * M": "Result = A + (B - C) * M",
+  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
+  "Run": "Run",
+  "SD upscale": "SD 업스케일링",
+  "Sampler parameters": "Sampler parameters",
+  "Sampler": "샘플러",
+  "Sampling Steps": "샘플링 스텝 수",
+  "Sampling method": "샘플링 방법",
+  "Save a copy of embedding to log directory every N steps, 0 to disable": "Save a copy of embedding to log directory every N steps, 0 to disable",
+  "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
+  "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
+  "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
+  "Save an image to log directory every N steps, 0 to disable": "Save an image to log directory every N steps, 0 to disable",
+  "Save as float16": "Save as float16",
+  "Save grids to a subdirectory": "Save grids to a subdirectory",
+  "Save images to a subdirectory": "Save images to a subdirectory",
+  "Save images with embedding in PNG chunks": "Save images with embedding in PNG chunks",
+  "Save style": "스타일 저장",
+  "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
+  "Save": "저장",
+  "Saving images/grids": "Saving images/grids",
+  "Saving to a directory": "Saving to a directory",
+  "Scale by": "Scale by",
+  "Scale to": "Scale to",
+  "Script": "스크립트",
+  "ScuNET GAN": "ScuNET GAN",
+  "ScuNET PSNR": "ScuNET PSNR",
+  "Secondary model (B)": "Secondary model (B)",
+  "See": "See",
+  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "Seed": "시드",
+  "Send to extras": "부가기능으로 전송",
+  "Send to img2img": "이미지→이미지로 전송",
+  "Send to inpaint": "인페인트로 전송",
+  "Send to txt2img": "텍스트→이미지로 전송",
+  "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "(|)를 이용해 프롬프트를 분리할 시 첫 프롬프트를 제외하고 모든 프롬프트의 조합마다 이미지를 생성합니다. 첫 프롬프트는 모든 조합에 포함되게 됩니다.",
+  "Separate values for X axis using commas.": "쉼표로 X축에 적용할 값 분리",
+  "Separate values for Y axis using commas.": "쉼표로 Y축에 적용할 값 분리",
+  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
+  "Settings": "설정",
+  "Show Textbox": "텍스트박스 보이기",
+  "Show generation progress in window title.": "Show generation progress in window title.",
+  "Show grid in results for web": "Show grid in results for web",
+  "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
+  "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
+  "Show progressbar": "Show progressbar",
+  "Show result images": "Show result images",
+  "Sigma Churn": "시그마 섞기",
+  "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
+  "Sigma max": "시그마 최댓값",
+  "Sigma min": "시그마 최솟값",
+  "Sigma noise": "시그마 노이즈",
+  "Single Image": "Single Image",
+  "Skip": "건너뛰기",
+  "Source directory": "Source directory",
+  "Source": "Source",
+  "Split oversized images into two": "Split oversized images into two",
+  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "Stable Diffusion": "Stable Diffusion",
+  "Steps": "스텝 수",
+  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
+  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
+  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
+  "Style 1": "스타일 1",
+  "Style 2": "스타일 2",
+  "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
+  "SwinIR 4x": "SwinIR 4x",
+  "System": "System",
+  "Tertiary model (C)": "Tertiary model (C)",
+  "Textbox": "Textbox",
+  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
+  "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
+  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
+  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
+  "Tile overlap": "타일 겹침",
+  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
+  "Tile size for all SwinIR.": "Tile size for all SwinIR.",
+  "Tiling": "타일링",
+  "Train Embedding": "Train Embedding",
+  "Train Hypernetwork": "Train Hypernetwork",
+  "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Train an embedding; must specify a directory with a set of 1:1 ratio images",
+  "Train": "훈련",
+  "Training": "Training",
+  "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training",
+  "Upload mask": "마스크 업로드하기",
+  "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
+  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
+  "Upscaler 2 visibility": "Upscaler 2 visibility",
+  "Upscaler for img2img": "Upscaler for img2img",
+  "Upscaler": "업스케일러",
+  "Upscaling": "Upscaling",
+  "Use BLIP for caption": "Use BLIP for caption",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
+  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "저장 경로를 비워두면 기본 저장 폴더에 이미지들이 저장됩니다.",
+  "Use deepbooru for caption": "Use deepbooru for caption",
+  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
+  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
+  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
+  "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
+  "User interface": "User interface",
+  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
+  "Var. seed": "바리에이션 시드",
+  "Var. strength": "바리에이션 강도",
+  "Variation seed": "바리에이션 시드",
+  "Variation strength": "바리에이션 강도",
+  "Weighted sum": "Weighted sum",
+  "What to put inside the masked area before processing it with Stable Diffusion.": "Stable Diffusion으로 이미지를 생성하기 전 마스크된 부분에 무엇을 채울지 결정하는 설정값",
+  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
+  "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
+  "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
+  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
+  "Width": "가로",
+  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "이미지를 설정된 사이즈의 2배로 업스케일합니다. 상단의 가로와 세로 슬라이더를 이용해 타일 사이즈를 지정하세요.",
+  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
+  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
+  "X type": "X축",
+  "X values": "X 설정값",
+  "X/Y plot": "X/Y 플롯",
+  "Y type": "Y축",
+  "Y values": "Y 설정값",
+  "api": "",
+  "built with gradio": "gradio로 제작되었습니다",
+  "checkpoint": "checkpoint",
+  "directory.": "directory.",
+  "down": "아래쪽",
+  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
+  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
+  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
+  "extras history": "extras history",
+  "fill it with colors of the image": "이미지의 색상으로 채우기",
+  "fill it with latent space noise": "잠재 공간 노이즈로 채우기",
+  "fill it with latent space zeroes": "잠재 공간의 0값으로 채우기",
+  "fill": "채우기",
+  "for detailed explanation.": "for detailed explanation.",
+  "hide": "api 숨기기",
+  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
+  "img2img DDIM discretize": "img2img DDIM discretize",
+  "img2img alternative test": "이미지→이미지 대체버전 테스트",
+  "img2img history": "img2img history",
+  "img2img": "이미지→이미지",
+  "keep whatever was there originally": "이미지 원본 유지",
+  "latent noise": "잠재 노이즈",
+  "latent nothing": "잠재 공백",
+  "left": "왼쪽",
+  "number of images to delete consecutively next": "number of images to delete consecutively next",
+  "or": "or",
+  "original": "원본 유지",
+  "quad": "quad",
+  "right": "오른쪽",
+  "set_index": "set_index",
+  "should be 2 or lower.": "이 2 이하여야 합니다.",
+  "sigma churn": "sigma churn",
+  "sigma noise": "sigma noise",
+  "sigma tmin": "sigma tmin",
+  "txt2img history": "txt2img history",
+  "txt2img": "텍스트→이미지",
+  "uniform": "uniform",
+  "up": "위쪽",
+  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
+  "view": "api 보이기",
+  "wiki": "wiki"
+}
-- 
cgit v1.2.1


From 016712fc4cd523fb18123eed4281245f0dcc5bc3 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Sun, 23 Oct 2022 22:38:49 +0900
Subject: Update ko_KR.json

Updated translation for everything except the Settings tab
---
 localizations/ko_KR.json | 379 +++++++++++++++++++++++++++--------------------
 1 file changed, 218 insertions(+), 161 deletions(-)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index f665042e..a48ece87 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -5,118 +5,158 @@
   "❮": "❮",
   "❯": "❯",
   "⤡": "⤡",
+  " images in this directory. Loaded ": "개의 이미지가 이 경로에 존재합니다. ",
+  " images during ": "개의 이미지를 불러왔고, 생성 기간은 ",
+  ", divided into ": "입니다. ",
+  " pages": "페이지로 나뉘어 표시합니다.",
   "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
-  "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
-  "A merger of the two checkpoints will be generated in your": "A merger of the two checkpoints will be generated in your",
+  "[wiki]": " [위키] 참조",
+  "A directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리를 선택해 주세요.",
+  "A merger of the two checkpoints will be generated in your": "체크포인트들이 병합된 결과물이 당신의",
   "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
   "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
   "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
-  "Add difference": "Add difference",
+  "Add difference": "차이점 추가",
   "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid",
-  "Add layer normalization": "Add layer normalization",
+  "Add layer normalization": "레이어 정규화(normalization) 추가",
   "Add model hash to generation information": "Add model hash to generation information",
   "Add model name to generation information": "Add model name to generation information",
+  "Aesthetic imgs embedding": "스타일 이미지 임베딩",
+  "Aesthetic learning rate": "스타일 학습 수",
+  "Aesthetic steps": "스타일 스텝 수",
+  "Aesthetic text for imgs": "스타일 텍스트",
+  "Aesthetic weight": "스타일 가중치",
   "Always print all generation info to standard output": "Always print all generation info to standard output",
   "Always save all generated image grids": "Always save all generated image grids",
   "Always save all generated images": "생성된 이미지 항상 저장하기",
+  "api": "",
+  "append": "뒤에 삽입",
   "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
   "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
   "Apply settings": "설정 적용하기",
-  "BSRGAN 4x": "BSRGAN 4x",
-  "Batch Process": "Batch Process",
   "Batch count": "배치 수",
-  "Batch from Directory": "Batch from Directory",
+  "Batch from Directory": "저장 경로로부터 여러장 처리",
   "Batch img2img": "이미지→이미지 배치",
+  "Batch Process": "이미지 여러장 처리",
   "Batch size": "배치 크기",
-  "CFG Scale": "CFG 스케일",
-  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
+  "BSRGAN 4x": "BSRGAN 4x",
+  "built with gradio": "gradio로 제작되었습니다",
   "Cancel generate forever": "반복 생성 취소",
-  "Check progress (first)": "Check progress (first)",
+  "CFG Scale": "CFG 스케일",
   "Check progress": "Check progress",
+  "Check progress (first)": "Check progress (first)",
+  "checkpoint": " 체크포인트 ",
   "Checkpoint Merger": "체크포인트 병합",
   "Checkpoint name": "체크포인트 이름",
   "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
   "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
+  "Click to Upload": "Click to Upload",
   "Clip skip": "클립 건너뛰기",
-  "CodeFormer visibility": "CodeFormer visibility",
-  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)",
+  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
+  "CodeFormer visibility": "CodeFormer 가시성",
+  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 가중치 (0 = 최대 효과, 1 = 최소 효과)",
   "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
   "Color variation": "색깔 다양성",
+  "Collect": "즐겨찾기",
+  "copy": "복사",
   "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
   "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
-  "Create embedding": "Create embedding",
-  "Create flipped copies": "Create flipped copies",
-  "Create hypernetwork": "Create hypernetwork",
+  "Create aesthetic images embedding": "Create aesthetic images embedding",
+  "Create embedding": "임베딩 생성",
+  "Create flipped copies": "좌우로 뒤집은 복사본 생성",
+  "Create hypernetwork": "하이퍼네트워크 생성",
+  "Create images embedding": "Create images embedding",
   "Crop and resize": "잘라낸 후 리사이징",
-  "Crop to fit": "Crop to fit",
-  "Custom Name (Optional)": "Custom Name (Optional)",
+  "Crop to fit": "잘라내서 맞추기",
+  "Custom Name (Optional)": "병합 모델 이름 (선택사항)",
+  "Dataset directory": "데이터셋 경로",
   "DDIM": "DDIM",
-  "DPM adaptive": "DPM adaptive",
-  "DPM fast": "DPM fast",
-  "DPM2 Karras": "DPM2 Karras",
-  "DPM2 a Karras": "DPM2 a Karras",
-  "DPM2 a": "DPM2 a",
-  "DPM2": "DPM2",
-  "Dataset directory": "Dataset directory",
   "Decode CFG scale": "디코딩 CFG 스케일",
   "Decode steps": "디코딩 스텝 수",
-  "Delete": "Delete",
+  "Delete": "삭제",
+  "Denoising": "디노이징",
   "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
-  "Denoising strength change factor": "디노이즈 강도 변경 배수",
   "Denoising strength": "디노이즈 강도",
-  "Denoising": "디노이징",
-  "Destination directory": "Destination directory",
+  "Denoising strength change factor": "디노이즈 강도 변경 배수",
+  "Destination directory": "결과물 저장 경로",
   "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
   "Directory for saving images using the Save button": "Directory for saving images using the Save button",
   "Directory name pattern": "Directory name pattern",
+  "directory.": "저장 경로에 저장됩니다.",
   "Do not add watermark to images": "Do not add watermark to images",
   "Do not do anything special": "아무것도 하지 않기",
   "Do not save grids consisting of one picture": "Do not save grids consisting of one picture",
   "Do not show any images in results for web": "Do not show any images in results for web",
+  "down": "아래쪽",
   "Download localization template": "Download localization template",
+  "Download": "다운로드",
+  "DPM adaptive": "DPM adaptive",
+  "DPM fast": "DPM fast",
+  "DPM2": "DPM2",
+  "DPM2 a": "DPM2 a",
+  "DPM2 a Karras": "DPM2 a Karras",
+  "DPM2 Karras": "DPM2 Karras",
   "Draw legend": "범례 그리기",
   "Draw mask": "마스크 직접 그리기",
   "Drop File Here": "Drop File Here",
   "Drop Image Here": "Drop Image Here",
-  "ESRGAN_4x": "ESRGAN_4x",
-  "Embedding": "Embedding",
+  "Embedding": "임베딩",
+  "Embedding Learning rate": "임베딩 학습률",
   "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
   "Enable full page image viewer": "Enable full page image viewer",
   "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
-  "End Page": "End Page",
-  "Enter hypernetwork layer structure": "Enter hypernetwork layer structure",
-  "Eta noise seed delta": "Eta noise seed delta",
+  "End Page": "마지막 페이지",
+  "Enter hypernetwork layer structure": "하이퍼네트워크 레이어 구조 입력",
+  "Error": "오류",
+  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
+  "ESRGAN_4x": "ESRGAN_4x",
   "Eta": "Eta",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
-  "Euler a": "Euler a",
+  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
+  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
+  "Eta noise seed delta": "Eta noise seed delta",
   "Euler": "Euler",
+  "Euler a": "Euler a",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
+  "Existing Caption txt Action": "이미 존재하는 캡션 텍스트 처리",
   "Extra": "고급",
   "Extras": "부가기능",
+  "extras history": "extras history",
   "Face restoration": "Face restoration",
   "Fall-off exponent (lower=higher detail)": "감쇠 지수 (낮을수록 디테일이 올라감)",
-  "File Name": "File Name",
+  "favorites": "즐겨찾기",
+  "File": "File",
   "File format for grids": "File format for grids",
   "File format for images": "File format for images",
+  "File Name": "파일 이름",
   "File with inputs": "설정값 파일",
-  "File": "File",
   "Filename join string": "Filename join string",
   "Filename word regex": "Filename word regex",
+  "fill": "채우기",
+  "fill it with colors of the image": "이미지의 색상으로 채우기",
+  "fill it with latent space noise": "잠재 공간 노이즈로 채우기",
+  "fill it with latent space zeroes": "잠재 공간의 0값으로 채우기",
   "Filter NSFW content": "Filter NSFW content",
-  "First Page": "First Page",
+  "First Page": "처음 페이지",
   "Firstpass height": "초기 세로길이",
   "Firstpass width": "초기 가로길이",
   "Font for image grids that have text": "Font for image grids that have text",
+  "for detailed explanation.": "를 참조하십시오.",
   "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD 업스케일링에서 타일 간 몇 픽셀을 겹치게 할지 결정하는 설정값입니다. 타일들이 다시 한 이미지로 합쳐질 때, 눈에 띄는 이음매가 없도록 서로 겹치게 됩니다.",
-  "GFPGAN visibility": "GFPGAN visibility",
-  "Generate Info": "Generate Info",
-  "Generate forever": "반복 생성",
   "Generate": "생성",
+  "Generate forever": "반복 생성",
+  "Generate Info": "생성 정보",
+  "GFPGAN visibility": "GFPGAN 가시성",
   "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
   "Height": "세로",
   "Heun": "Heun",
+  "hide": "api 숨기기",
   "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
   "Highres. fix": "고해상도 보정",
   "History": "기록",
+  "Image Browser": "이미지 브라우저",
+  "Images directory": "이미지 경로",
+  "extras": "부가기능",
+  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
   "How many batches of images to create": "생성할 이미지 배치 수",
   "How many image to create in a single batch": "한 배치당 이미지 수",
   "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
@@ -124,26 +164,32 @@
   "How much to blur the mask before processing, in pixels.": "이미지 생성 전 마스크를 얼마나 블러처리할지 결정하는 값. 픽셀 단위",
   "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
   "Hypernet str.": "하이퍼네트워크 강도",
-  "Hypernetwork strength": "Hypernetwork strength",
   "Hypernetwork": "하이퍼네트워크",
+  "Hypernetwork Learning rate": "하이퍼네트워크 학습률",
+  "Hypernetwork strength": "Hypernetwork strength",
   "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
   "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
   "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
+  "ignore": "무시",
+  "Image": "Image",
   "Image for img2img": "Image for img2img",
   "Image for inpainting with mask": "Image for inpainting with mask",
-  "Image": "Image",
   "Images filename pattern": "Images filename pattern",
+  "img2img": "이미지→이미지",
+  "img2img alternative test": "이미지→이미지 대체버전 테스트",
+  "img2img DDIM discretize": "img2img DDIM discretize",
+  "img2img history": "img2img history",
   "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "루프백 모드에서는 매 루프마다 디노이즈 강도에 이 값이 곱해집니다. 1보다 작을 경우 다양성이 낮아져 결과 이미지들이 고정된 형태로 모일 겁니다. 1보다 클 경우 다양성이 높아져 결과 이미지들이 갈수록 혼란스러워지겠죠.",
   "Include Separate Images": "분리된 이미지 포함하기",
   "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
-  "Initialization text": "Initialization text",
-  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트 패딩값(픽셀 단위)",
+  "Initialization text": "초기화 텍스트",
+  "Inpaint": "인페인트",
   "Inpaint at full resolution": "전체 해상도로 인페인트하기",
+  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트 패딩값(픽셀 단위)",
   "Inpaint masked": "마스크만 처리",
   "Inpaint not masked": "마스크 이외만 처리",
-  "Inpaint": "인페인트",
   "Input directory": "인풋 이미지 경로",
-  "Interpolation Method": "Interpolation Method",
+  "Interpolation Method": "보간 방법",
   "Interrogate\nCLIP": "CLIP\n분석",
   "Interrogate\nDeepBooru": "DeepBooru\n분석",
   "Interrogate Options": "Interrogate Options",
@@ -156,49 +202,68 @@
   "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
   "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
   "Interrupt": "중단",
+  "Is negative text": "네거티브 텍스트일시 체크",
   "Just resize": "리사이징",
   "Keep -1 for seeds": "시드값 -1로 유지",
-  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
-  "LDSR": "LDSR",
-  "LMS Karras": "LMS Karras",
-  "LMS": "LMS",
+  "keep whatever was there originally": "이미지 원본 유지",
   "Label": "Label",
   "Lanczos": "Lanczos",
-  "Learning rate": "Learning rate",
-  "Leave blank to save images to the default path.": "Leave blank to save images to the default path.",
+  "Last prompt:": "Last prompt:",
+  "Last saved hypernetwork:": "Last saved hypernetwork:",
+  "Last saved image:": "Last saved image:",
+  "latent noise": "잠재 노이즈",
+  "latent nothing": "잠재 공백",
+  "LDSR": "LDSR",
+  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
+  "leakyrelu": "leakyrelu",
+  "Leave blank to save images to the default path.": "기존 저장 경로에 이미지들을 저장하려면 비워두세요.",
+  "left": "왼쪽",
+  "linear": "linear",
   "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
+  "LMS": "LMS",
+  "LMS Karras": "LMS Karras",
+  "Load": "불러오기",
   "Loading...": "로딩 중...",
   "Localization (requires restart)": "Localization (requires restart)",
-  "Log directory": "Log directory",
+  "Log directory": "로그 경로",
   "Loopback": "루프백",
   "Loops": "루프 수",
+  "Loss:": "Loss:",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
   "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
   "Make Zip when Save?": "저장 시 Zip 생성하기",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
+  "Mask": "마스크",
   "Mask blur": "마스크 블러",
   "Mask mode": "Mask mode",
-  "Mask": "마스크",
   "Masked content": "마스크된 부분",
   "Masking mode": "Masking mode",
   "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
-  "Max steps": "Max steps",
-  "Modules": "Modules",
+  "Max steps": "최대 스텝 수",
+  "Modules": "모듈",
   "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
-  "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - set to 0 to get model A",
-  "Name": "Name",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.",
+  "Multiplier (M) - set to 0 to get model A": "배율 (M) - 0으로 적용하면 모델 A를 얻게 됩니다",
+  "Name": "이름",
   "Negative prompt": "네거티브 프롬프트",
-  "Next Page": "Next Page",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Next batch": "다음 묶음",
+  "Next Page": "다음 페이지",
   "None": "None",
   "Nothing": "없음",
+  "Nothing found in the image.": "Nothing found in the image.",
+  "number of images to delete consecutively next": "연속적으로 삭제할 이미지 수",
   "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
-  "Number of vectors per token": "Number of vectors per token",
+  "Number of vectors per token": "토큰별 벡터 수",
+  "Open for Clip Aesthetic!": "클립 스타일 기능을 활성화하려면 클릭!",
   "Open images output directory": "이미지 저장 경로 열기",
-  "Open output directory": "Open output directory",
+  "Open output directory": "저장 경로 열기",
+  "or": "or",
+  "original": "원본 유지",
   "Original negative prompt": "기존 네거티브 프롬프트",
   "Original prompt": "기존 프롬프트",
   "Outpainting direction": "아웃페인팅 방향",
   "Outpainting mk2": "아웃페인팅 마크 2",
+  "Output directory": "이미지 저장 경로",
   "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
   "Output directory for images from extras tab": "Output directory for images from extras tab",
   "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
@@ -206,46 +271,54 @@
   "Output directory for img2img images": "Output directory for img2img images",
   "Output directory for txt2img grids": "Output directory for txt2img grids",
   "Output directory for txt2img images": "Output directory for txt2img images",
-  "Output directory": "이미지 저장 경로",
   "Override `Denoising strength` to 1?": "디노이즈 강도를 1로 적용할까요?",
-  "Override `Sampling Steps` to the same value as `Decode steps`?": "샘플링 스텝 수를 디코딩 스텝 수와 동일하게 적용할까요?",
-  "Override `Sampling method` to Euler?(this method is built for it)": "샘플링 방법을 Euler로 적용할까요?(이 기능은 해당 샘플러를 위해 만들어져 있습니다)",
   "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "프롬프트 값을 기존 프롬프트와 동일하게 적용할까요?(네거티브 프롬프트 포함)",
-  "PLMS": "PLMS",
-  "PNG Info": "PNG 정보",
-  "Page Index": "Page Index",
+  "Override `Sampling method` to Euler?(this method is built for it)": "샘플링 방법을 Euler로 적용할까요?(이 기능은 해당 샘플러를 위해 만들어져 있습니다)",
+  "Override `Sampling Steps` to the same value as `Decode steps`?": "샘플링 스텝 수를 디코딩 스텝 수와 동일하게 적용할까요?",
+  "Overwrite Old Embedding": "기존 임베딩 덮어쓰기",
+  "Overwrite Old Hypernetwork": "기존 하이퍼네트워크 덮어쓰기",
+  "Page Index": "페이지 인덱스",
+  "parameters": "설정값",
   "Path to directory where to write outputs": "Path to directory where to write outputs",
-  "Path to directory with input images": "Path to directory with input images",
+  "Path to directory with input images": "인풋 이미지가 있는 경로",
   "Paths for saving": "Paths for saving",
   "Pixels to expand": "확장할 픽셀 수",
+  "PLMS": "PLMS",
+  "PNG Info": "PNG 정보",
   "Poor man's outpainting": "가난뱅이의 아웃페인팅",
-  "Preprocess images": "Preprocess images",
-  "Preprocess": "Preprocess",
-  "Prev Page": "Prev Page",
+  "Preparing dataset from": "Preparing dataset from",
+  "prepend": "앞에 삽입",
+  "Preprocess": "전처리",
+  "Preprocess images": "이미지 전처리",
+  "Prev batch": "이전 묶음",
+  "Prev Page": "이전 페이지",
   "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)",
-  "Primary model (A)": "Primary model (A)",
+  "Primary model (A)": "주 모델 (A)",
   "Process an image, use it as an input, repeat.": "이미지를 생성하고, 생성한 이미지를 다시 원본으로 사용하는 과정을 반복합니다.",
   "Process images in a directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리의 이미지들을 처리합니다.",
   "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
+  "Prompt": "프롬프트",
   "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Prompt S/R": "프롬프트 스타일 변경",
   "Prompt matrix": "프롬프트 매트릭스",
   "Prompt order": "프롬프트 순서",
-  "Prompt template file": "Prompt template file",
-  "Prompt": "프롬프트",
-  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
+  "Prompt S/R": "프롬프트 스타일 변경",
+  "Prompt template file": "프롬프트 템플릿 파일 경로",
   "Prompts": "프롬프트",
+  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
   "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
+  "quad": "quad",
   "Quality for saved jpeg images": "Quality for saved jpeg images",
   "Quicksettings list": "Quicksettings list",
   "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
   "Randomness": "랜덤성",
   "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
-  "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews",
+  "Read parameters (prompt, etc...) from txt2img tab when making previews": "프리뷰 이미지 생성 시 텍스트→이미지 탭에서 설정값(프롬프트 등) 읽어오기",
   "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "추천 설정값 - 샘플링 스텝 수 : 80-100 , 샘플러 : Euler a, 디노이즈 강도 : 0.8",
   "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
+  "relu": "relu",
   "Renew Page": "Renew Page",
   "Request browser notifications": "Request browser notifications",
+  "Resize": "리사이징 배수",
   "Resize and fill": "리사이징 후 채우기",
   "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "설정된 해상도로 이미지 리사이징을 진행합니다. 원본과 가로/세로 길이가 일치하지 않을 경우, 부정확한 화면비의 이미지를 얻게 됩니다.",
   "Resize mode": "Resize mode",
@@ -253,42 +326,43 @@
   "Resize seed from width": "시드 리사이징 세로길이",
   "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "이미지 전체가 설정된 해상도 내부에 들어가게 리사이징을 진행합니다. 빈 공간은 이미지의 색상으로 채웁니다.",
   "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "설정된 해상도 전체가 이미지로 가득차게 리사이징을 진행합니다. 튀어나오는 부분은 잘라냅니다.",
-  "Resize": "Resize",
   "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
   "Restore faces": "얼굴 보정",
   "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
-  "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M",
-  "Result = A + (B - C) * M": "Result = A + (B - C) * M",
+  "Result = A * (1 - M) + B * M": "결과물 = A * (1 - M) + B * M",
+  "Result = A + (B - C) * M": "결과물 = A + (B - C) * M",
   "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
-  "Run": "Run",
-  "SD upscale": "SD 업스케일링",
-  "Sampler parameters": "Sampler parameters",
+  "right": "오른쪽",
+  "Run": "가동",
   "Sampler": "샘플러",
-  "Sampling Steps": "샘플링 스텝 수",
+  "Sampler parameters": "Sampler parameters",
   "Sampling method": "샘플링 방법",
-  "Save a copy of embedding to log directory every N steps, 0 to disable": "Save a copy of embedding to log directory every N steps, 0 to disable",
+  "Sampling Steps": "샘플링 스텝 수",
+  "Save": "저장",
+  "Save a copy of embedding to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 임베딩을 저장합니다, 비활성화하려면 0으로 설정하십시오.",
   "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
   "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
   "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
-  "Save an image to log directory every N steps, 0 to disable": "Save an image to log directory every N steps, 0 to disable",
-  "Save as float16": "Save as float16",
+  "Save an image to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 이미지를 저장합니다, 비활성화하려면 0으로 설정하십시오.",
+  "Save as float16": "float16으로 저장",
   "Save grids to a subdirectory": "Save grids to a subdirectory",
   "Save images to a subdirectory": "Save images to a subdirectory",
-  "Save images with embedding in PNG chunks": "Save images with embedding in PNG chunks",
+  "Save images with embedding in PNG chunks": "PNG 청크로 이미지에 임베딩을 포함시켜 저장",
   "Save style": "스타일 저장",
   "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
-  "Save": "저장",
   "Saving images/grids": "Saving images/grids",
   "Saving to a directory": "Saving to a directory",
-  "Scale by": "Scale by",
-  "Scale to": "Scale to",
+  "Scale by": "스케일링 배수 지정",
+  "Scale to": "스케일링 사이즈 지정",
   "Script": "스크립트",
   "ScuNET GAN": "ScuNET GAN",
   "ScuNET PSNR": "ScuNET PSNR",
-  "Secondary model (B)": "Secondary model (B)",
-  "See": "See",
-  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "SD upscale": "SD 업스케일링",
+  "Secondary model (B)": "2차 모델 (B)",
+  "See": "자세한 설명은",
   "Seed": "시드",
+  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
+  "Select activation function of hypernetwork": "하이퍼네트워크 활성화 함수 선택",
   "Send to extras": "부가기능으로 전송",
   "Send to img2img": "이미지→이미지로 전송",
   "Send to inpaint": "인페인트로 전송",
@@ -297,26 +371,36 @@
   "Separate values for X axis using commas.": "쉼표로 X축에 적용할 값 분리",
   "Separate values for Y axis using commas.": "쉼표로 Y축에 적용할 값 분리",
   "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
+  "set_index": "set_index",
   "Settings": "설정",
-  "Show Textbox": "텍스트박스 보이기",
+  "should be 2 or lower.": "이 2 이하여야 합니다.",
   "Show generation progress in window title.": "Show generation progress in window title.",
   "Show grid in results for web": "Show grid in results for web",
   "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
   "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
   "Show progressbar": "Show progressbar",
-  "Show result images": "Show result images",
-  "Sigma Churn": "시그마 섞기",
+  "Show result images": "이미지 결과 보이기",
+  "Show Textbox": "텍스트박스 보이기",
   "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
+  "Sigma Churn": "시그마 섞기",
+  "sigma churn": "sigma churn",
   "Sigma max": "시그마 최댓값",
   "Sigma min": "시그마 최솟값",
   "Sigma noise": "시그마 노이즈",
-  "Single Image": "Single Image",
+  "sigma noise": "sigma noise",
+  "sigma tmin": "sigma tmin",
+  "Single Image": "단일 이미지",
   "Skip": "건너뛰기",
-  "Source directory": "Source directory",
-  "Source": "Source",
-  "Split oversized images into two": "Split oversized images into two",
-  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "Slerp angle": "구면 선형 보간 각도",
+  "Slerp interpolation": "구면 선형 보간",
+  "Source": "원본",
+  "Source directory": "원본 경로",
+  "Split image threshold": "Split image threshold",
+  "Split image overlap ratio": "Split image overlap ratio",
+  "Split oversized images": "사이즈가 큰 이미지 분할하기",
   "Stable Diffusion": "Stable Diffusion",
+  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "Step:": "Step:",
   "Steps": "스텝 수",
   "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
   "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
@@ -325,51 +409,65 @@
   "Style 2": "스타일 2",
   "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
   "SwinIR 4x": "SwinIR 4x",
+  "Sys VRAM:": "시스템 VRAM : ",
   "System": "System",
-  "Tertiary model (C)": "Tertiary model (C)",
+  "Tertiary model (C)": "3차 모델 (C)",
   "Textbox": "Textbox",
   "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
   "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
+  "This text is used to rotate the feature space of the imgs embs": "이 텍스트는 이미지 임베딩의 특징 공간을 회전하는 데 사용됩니다.",
+  "Tile overlap": "타일 겹침",
   "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
   "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
-  "Tile overlap": "타일 겹침",
-  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
   "Tile size for all SwinIR.": "Tile size for all SwinIR.",
+  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
   "Tiling": "타일링",
-  "Train Embedding": "Train Embedding",
-  "Train Hypernetwork": "Train Hypernetwork",
-  "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Train an embedding; must specify a directory with a set of 1:1 ratio images",
+  "Time taken:": "소요 시간 : ",
+  "Torch active/reserved:": "활성화/예약된 Torch 양 : ",
+  "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "활성화된 Torch : 생성 도중 캐시된 데이터를 포함해 사용된 VRAM의 최대량\n예약된 Torch : 활성화되고 캐시된 모든 데이터를 포함해 Torch에게 할당된 VRAM의 최대량\n시스템 VRAM : 모든 어플리케이션에 할당된 VRAM 최대량 / 총 GPU VRAM (최고 이용도%)",
   "Train": "훈련",
+  "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "임베딩이나 하이퍼네트워크를 훈련시킵니다. 1:1 비율의 이미지가 있는 경로를 지정해야 합니다.",
+  "Train Embedding": "임베딩 훈련",
+  "Train Hypernetwork": "하이퍼네트워크 훈련",
   "Training": "Training",
-  "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training",
+  "txt2img": "텍스트→이미지",
+  "txt2img history": "txt2img history",
+  "uniform": "uniform",
+  "up": "위쪽",
   "Upload mask": "마스크 업로드하기",
   "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
   "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
-  "Upscaler 2 visibility": "Upscaler 2 visibility",
-  "Upscaler for img2img": "Upscaler for img2img",
   "Upscaler": "업스케일러",
+  "Upscaler 1": "업스케일러 1",
+  "Upscaler 2": "업스케일러 2",
+  "Upscaler 2 visibility": "업스케일러 2 가시성",
+  "Upscaler for img2img": "Upscaler for img2img",
   "Upscaling": "Upscaling",
-  "Use BLIP for caption": "Use BLIP for caption",
   "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
   "Use an empty output directory to save pictures normally instead of writing to the output directory.": "저장 경로를 비워두면 기본 저장 폴더에 이미지들이 저장됩니다.",
-  "Use deepbooru for caption": "Use deepbooru for caption",
+  "Use BLIP for caption": "캡션에 BLIP 사용",
+  "Use deepbooru for caption": "캡션에 deepbooru 사용",
+  "Use dropout": "드롭아웃 사용",
   "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
   "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
   "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
   "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
+  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
   "User interface": "User interface",
-  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
   "Var. seed": "바리에이션 시드",
   "Var. strength": "바리에이션 강도",
   "Variation seed": "바리에이션 시드",
   "Variation strength": "바리에이션 강도",
-  "Weighted sum": "Weighted sum",
+  "view": "api 보이기",
+  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
+  "Weighted sum": "가중 합",
   "What to put inside the masked area before processing it with Stable Diffusion.": "Stable Diffusion으로 이미지를 생성하기 전 마스크된 부분에 무엇을 채울지 결정하는 설정값",
   "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
   "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
   "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
   "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
   "Width": "가로",
+  "wiki": " 위키",
   "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "이미지를 설정된 사이즈의 2배로 업스케일합니다. 상단의 가로와 세로 슬라이더를 이용해 타일 사이즈를 지정하세요.",
   "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
   "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
@@ -377,46 +475,5 @@
   "X values": "X 설정값",
   "X/Y plot": "X/Y 플롯",
   "Y type": "Y축",
-  "Y values": "Y 설정값",
-  "api": "",
-  "built with gradio": "gradio로 제작되었습니다",
-  "checkpoint": "checkpoint",
-  "directory.": "directory.",
-  "down": "아래쪽",
-  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
-  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
-  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
-  "extras history": "extras history",
-  "fill it with colors of the image": "이미지의 색상으로 채우기",
-  "fill it with latent space noise": "잠재 공간 노이즈로 채우기",
-  "fill it with latent space zeroes": "잠재 공간의 0값으로 채우기",
-  "fill": "채우기",
-  "for detailed explanation.": "for detailed explanation.",
-  "hide": "api 숨기기",
-  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
-  "img2img DDIM discretize": "img2img DDIM discretize",
-  "img2img alternative test": "이미지→이미지 대체버전 테스트",
-  "img2img history": "img2img history",
-  "img2img": "이미지→이미지",
-  "keep whatever was there originally": "이미지 원본 유지",
-  "latent noise": "잠재 노이즈",
-  "latent nothing": "잠재 공백",
-  "left": "왼쪽",
-  "number of images to delete consecutively next": "number of images to delete consecutively next",
-  "or": "or",
-  "original": "원본 유지",
-  "quad": "quad",
-  "right": "오른쪽",
-  "set_index": "set_index",
-  "should be 2 or lower.": "이 2 이하여야 합니다.",
-  "sigma churn": "sigma churn",
-  "sigma noise": "sigma noise",
-  "sigma tmin": "sigma tmin",
-  "txt2img history": "txt2img history",
-  "txt2img": "텍스트→이미지",
-  "uniform": "uniform",
-  "up": "위쪽",
-  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
-  "view": "api 보이기",
-  "wiki": "wiki"
-}
+  "Y values": "Y 설정값"
+}
\ No newline at end of file
-- 
cgit v1.2.1


From ae7c830c3ae7bb1ebe9b0d935cb33c254354b649 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Mon, 24 Oct 2022 04:29:19 +0900
Subject: Translation complete

---
 localizations/ko_KR.json | 302 +++++++++++++++++++++++++----------------------
 1 file changed, 160 insertions(+), 142 deletions(-)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index a48ece87..6889de46 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -15,23 +15,24 @@
   "A merger of the two checkpoints will be generated in your": "체크포인트들이 병합된 결과물이 당신의",
   "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
   "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
-  "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
+  "Add a second progress bar to the console that shows progress for an entire job.": "콘솔에 전체 작업의 진행도를 보여주는 2번째 프로그레스 바 추가하기",
   "Add difference": "차이점 추가",
-  "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid",
+  "Add extended info (seed, prompt) to filename when saving grid": "그리드 저장 시 파일명에 추가 정보(시드, 프롬프트) 기입",
   "Add layer normalization": "레이어 정규화(normalization) 추가",
-  "Add model hash to generation information": "Add model hash to generation information",
-  "Add model name to generation information": "Add model name to generation information",
+  "Add model hash to generation information": "생성 정보에 모델 해시 추가",
+  "Add model name to generation information": "생성 정보에 모델 이름 추가",
   "Aesthetic imgs embedding": "스타일 이미지 임베딩",
   "Aesthetic learning rate": "스타일 학습 수",
   "Aesthetic steps": "스타일 스텝 수",
   "Aesthetic text for imgs": "스타일 텍스트",
   "Aesthetic weight": "스타일 가중치",
-  "Always print all generation info to standard output": "Always print all generation info to standard output",
-  "Always save all generated image grids": "Always save all generated image grids",
+  "Allowed categories for random artists selection when using the Roll button": "랜덤 버튼을 눌러 무작위 작가를 선택할 때 허용된 카테고리",
+  "Always print all generation info to standard output": "기본 아웃풋에 모든 생성 정보 항상 출력하기",
+  "Always save all generated image grids": "생성된 이미지 그리드 항상 저장하기",
   "Always save all generated images": "생성된 이미지 항상 저장하기",
   "api": "",
   "append": "뒤에 삽입",
-  "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
+  "Apply color correction to img2img results to match original colors.": "이미지→이미지 결과물이 기존 색상과 일치하도록 색상 보정 적용하기",
   "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
   "Apply settings": "설정 적용하기",
   "Batch count": "배치 수",
@@ -43,29 +44,29 @@
   "built with gradio": "gradio로 제작되었습니다",
   "Cancel generate forever": "반복 생성 취소",
   "CFG Scale": "CFG 스케일",
-  "Check progress": "Check progress",
-  "Check progress (first)": "Check progress (first)",
+  "Check progress": "진행도 체크",
+  "Check progress (first)": "진행도 체크 (처음)",
   "checkpoint": " 체크포인트 ",
   "Checkpoint Merger": "체크포인트 병합",
   "Checkpoint name": "체크포인트 이름",
-  "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
+  "Checkpoints to cache in RAM": "RAM에 캐싱할 체크포인트 수",
   "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
-  "Click to Upload": "Click to Upload",
+  "Click to Upload": "클릭해서 업로드하기",
   "Clip skip": "클립 건너뛰기",
-  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
+  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP : 텍스트 파일 최대 라인 수 (0 = 제한 없음)",
   "CodeFormer visibility": "CodeFormer 가시성",
   "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 가중치 (0 = 최대 효과, 1 = 최소 효과)",
-  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
+  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 가중치 설정값 (0 = 최대 효과, 1 = 최소 효과)",
   "Color variation": "색깔 다양성",
   "Collect": "즐겨찾기",
   "copy": "복사",
   "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
-  "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
-  "Create aesthetic images embedding": "Create aesthetic images embedding",
+  "Create a text file next to every image with generation parameters.": "생성된 이미지마다 생성 설정값을 담은 텍스트 파일 생성하기",
+  "Create aesthetic images embedding": "스타일 이미지 임베딩 생성하기",
   "Create embedding": "임베딩 생성",
   "Create flipped copies": "좌우로 뒤집은 복사본 생성",
   "Create hypernetwork": "하이퍼네트워크 생성",
-  "Create images embedding": "Create images embedding",
+  "Create images embedding": "이미지 임베딩 생성하기",
   "Crop and resize": "잘라낸 후 리사이징",
   "Crop to fit": "잘라내서 맞추기",
   "Custom Name (Optional)": "병합 모델 이름 (선택사항)",
@@ -80,15 +81,15 @@
   "Denoising strength change factor": "디노이즈 강도 변경 배수",
   "Destination directory": "결과물 저장 경로",
   "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
-  "Directory for saving images using the Save button": "Directory for saving images using the Save button",
-  "Directory name pattern": "Directory name pattern",
+  "Directory for saving images using the Save button": "저장 버튼을 이용해 저장하는 이미지들의 저장 경로",
+  "Directory name pattern": "디렉토리명 패턴",
   "directory.": "저장 경로에 저장됩니다.",
-  "Do not add watermark to images": "Do not add watermark to images",
+  "Do not add watermark to images": "이미지에 워터마크 추가하지 않기",
   "Do not do anything special": "아무것도 하지 않기",
-  "Do not save grids consisting of one picture": "Do not save grids consisting of one picture",
-  "Do not show any images in results for web": "Do not show any images in results for web",
+  "Do not save grids consisting of one picture": "이미지가 1개뿐인 그리드는 저장하지 않기",
+  "Do not show any images in results for web": "웹에서 결과창에 아무 이미지도 보여주지 않기",
   "down": "아래쪽",
-  "Download localization template": "Download localization template",
+  "Download localization template": "현지화 템플릿 다운로드",
   "Download": "다운로드",
   "DPM adaptive": "DPM adaptive",
   "DPM fast": "DPM fast",
@@ -98,65 +99,67 @@
   "DPM2 Karras": "DPM2 Karras",
   "Draw legend": "범례 그리기",
   "Draw mask": "마스크 직접 그리기",
-  "Drop File Here": "Drop File Here",
-  "Drop Image Here": "Drop Image Here",
+  "Drop File Here": "파일을 끌어 놓으세요",
+  "Drop Image Here": "이미지를 끌어 놓으세요",
   "Embedding": "임베딩",
   "Embedding Learning rate": "임베딩 학습률",
-  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
-  "Enable full page image viewer": "Enable full page image viewer",
-  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
+  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "강조 : (텍스트)를 이용해 모델의 텍스트에 대한 가중치를 더 강하게 주고 [텍스트]를 이용해 더 약하게 줍니다.",
+  "Enable full page image viewer": "전체 페이지 이미지 뷰어 활성화",
+  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "더 예리하고 깔끔한 결과물을 위해 K 샘플러들에 양자화를 적용합니다. 존재하는 시드가 변경될 수 있습니다. 재시작이 필요합니다.",
   "End Page": "마지막 페이지",
   "Enter hypernetwork layer structure": "하이퍼네트워크 레이어 구조 입력",
   "Error": "오류",
-  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
+  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "deepbooru에서 괄호를 역슬래시(\\)로 이스케이프 처리하기(가중치 강조가 아니라 실제 괄호로 사용되게 하기 위해)",
   "ESRGAN_4x": "ESRGAN_4x",
   "Eta": "Eta",
-  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
-  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
-  "Eta noise seed delta": "Eta noise seed delta",
+  "eta (noise multiplier) for ancestral samplers": "ancestral 샘플러를 위한 eta(노이즈 배수)값",
+  "eta (noise multiplier) for DDIM": "DDIM을 위한 eta(노이즈 배수)값",
+  "Eta noise seed delta": "Eta 노이즈 시드 변화",
   "Euler": "Euler",
   "Euler a": "Euler a",
   "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
   "Existing Caption txt Action": "이미 존재하는 캡션 텍스트 처리",
   "Extra": "고급",
   "Extras": "부가기능",
-  "extras history": "extras history",
-  "Face restoration": "Face restoration",
+  "extras history": "부가기능 기록",
+  "Face restoration": "얼굴 보정",
+  "Face restoration model": "얼굴 보정 모델",
   "Fall-off exponent (lower=higher detail)": "감쇠 지수 (낮을수록 디테일이 올라감)",
   "favorites": "즐겨찾기",
-  "File": "File",
-  "File format for grids": "File format for grids",
-  "File format for images": "File format for images",
+  "File": "파일",
+  "File format for grids": "그리드 이미지 파일 형식",
+  "File format for images": "이미지 파일 형식",
   "File Name": "파일 이름",
   "File with inputs": "설정값 파일",
-  "Filename join string": "Filename join string",
-  "Filename word regex": "Filename word regex",
+  "Filename join string": "파일명 병합 문자열",
+  "Filename word regex": "파일명 정규표현식",
   "fill": "채우기",
   "fill it with colors of the image": "이미지의 색상으로 채우기",
   "fill it with latent space noise": "잠재 공간 노이즈로 채우기",
   "fill it with latent space zeroes": "잠재 공간의 0값으로 채우기",
-  "Filter NSFW content": "Filter NSFW content",
+  "Filter NSFW content": "성인 컨텐츠 필터링하기",
   "First Page": "처음 페이지",
   "Firstpass height": "초기 세로길이",
   "Firstpass width": "초기 가로길이",
-  "Font for image grids that have text": "Font for image grids that have text",
+  "Font for image grids that have text": "텍스트가 존재하는 그리드 이미지의 폰트",
   "for detailed explanation.": "를 참조하십시오.",
   "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD 업스케일링에서 타일 간 몇 픽셀을 겹치게 할지 결정하는 설정값입니다. 타일들이 다시 한 이미지로 합쳐질 때, 눈에 띄는 이음매가 없도록 서로 겹치게 됩니다.",
   "Generate": "생성",
   "Generate forever": "반복 생성",
   "Generate Info": "생성 정보",
   "GFPGAN visibility": "GFPGAN 가시성",
-  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
+  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "그리드 세로줄 수 : -1로 설정 시 자동 감지/0으로 설정 시 배치 크기와 동일",
   "Height": "세로",
   "Heun": "Heun",
   "hide": "api 숨기기",
-  "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
+  "Hide samplers in user interface (requires restart)": "사용자 인터페이스에서 숨길 샘플러 선택(재시작 필요)",
   "Highres. fix": "고해상도 보정",
   "History": "기록",
   "Image Browser": "이미지 브라우저",
+  "Images Browser": "이미지 브라우저",
   "Images directory": "이미지 경로",
   "extras": "부가기능",
-  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
+  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "훈련이 얼마나 빨리 이루어질지 정하는 값입니다. 값이 낮을수록 훈련 시간이 길어지고, 높은 값일수록 정확한 결과를 내는 데 실패하고 임베딩을 망가뜨릴 수 있습니다(임베딩이 망가진 경우에는 훈련 정보 텍스트박스에 손실(Loss) : nan 이라고 출력되게 됩니다. 이 경우에는 망가지지 않은 이전 백업본을 불러와야 합니다).\n\n학습률은 하나의 값으로 설정할 수도 있고, 다음 문법을 사용해 여러 값을 사용할 수도 있습니다 :\n\n학습률_1:최대 스텝수_1, 학습률_2:최대 스텝수_2, ...\n\n예 : 0.005:100, 1e-3:1000, 1e-5\n\n예의 설정값은 첫 100스텝동안 0.005의 학습률로, 그 이후 1000스텝까지는 1e-3으로, 남은 스텝은 1e-5로 훈련하게 됩니다.",
   "How many batches of images to create": "생성할 이미지 배치 수",
   "How many image to create in a single batch": "한 배치당 이미지 수",
   "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
@@ -166,111 +169,114 @@
   "Hypernet str.": "하이퍼네트워크 강도",
   "Hypernetwork": "하이퍼네트워크",
   "Hypernetwork Learning rate": "하이퍼네트워크 학습률",
-  "Hypernetwork strength": "Hypernetwork strength",
-  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
-  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
-  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
+  "Hypernetwork strength": "하이퍼네트워크 강도",
+  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "PNG 이미지가 4MB보다 크거나 가로 또는 세로길이가 4000보다 클 경우, 다운스케일 후 JPG로 복사본 저장하기",
+  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "이 옵션이 활성화되면 생성된 이미지에 워터마크가 추가되지 않습니다. 경고 : 워터마크를 추가하지 않는다면, 비윤리적인 행동을 하는 중일지도 모릅니다.",
+  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "이 값이 0이 아니라면, 시드에 해당 값이 더해지고, Eta가 있는 샘플러를 사용할 때 노이즈의 RNG 조정을 위해 해당 값이 사용됩니다. 이 설정으로 더 다양한 이미지를 생성하거나, 잘 알고 계시다면 특정 소프트웨어의 결과값을 재현할 수도 있습니다.",
   "ignore": "무시",
-  "Image": "Image",
+  "Image": "이미지",
   "Image for img2img": "Image for img2img",
-  "Image for inpainting with mask": "Image for inpainting with mask",
-  "Images filename pattern": "Images filename pattern",
+  "Image for inpainting with mask": "마스크로 인페인팅할 이미지",
+  "Images filename pattern": "이미지 파일명 패턴",
   "img2img": "이미지→이미지",
   "img2img alternative test": "이미지→이미지 대체버전 테스트",
-  "img2img DDIM discretize": "img2img DDIM discretize",
-  "img2img history": "img2img history",
+  "img2img DDIM discretize": "이미지→이미지 DDIM 이산화",
+  "img2img history": "이미지→이미지 기록",
   "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "루프백 모드에서는 매 루프마다 디노이즈 강도에 이 값이 곱해집니다. 1보다 작을 경우 다양성이 낮아져 결과 이미지들이 고정된 형태로 모일 겁니다. 1보다 클 경우 다양성이 높아져 결과 이미지들이 갈수록 혼란스러워지겠죠.",
   "Include Separate Images": "분리된 이미지 포함하기",
-  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
+  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "75개보다 많은 토큰을 사용시 마지막 쉼표로부터 N개의 토큰 이내에 패딩을 추가해 통일성 증가시키기",
   "Initialization text": "초기화 텍스트",
   "Inpaint": "인페인트",
   "Inpaint at full resolution": "전체 해상도로 인페인트하기",
-  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트 패딩값(픽셀 단위)",
+  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트시 패딩값(픽셀 단위)",
   "Inpaint masked": "마스크만 처리",
   "Inpaint not masked": "마스크 이외만 처리",
   "Input directory": "인풋 이미지 경로",
   "Interpolation Method": "보간 방법",
   "Interrogate\nCLIP": "CLIP\n분석",
   "Interrogate\nDeepBooru": "DeepBooru\n분석",
-  "Interrogate Options": "Interrogate Options",
-  "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold",
-  "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically",
-  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
-  "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM",
-  "Interrogate: maximum description length": "Interrogate: maximum description length",
-  "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
-  "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
-  "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
+  "Interrogate Options": "분석 설정",
+  "Interrogate: deepbooru score threshold": "분석 : deepbooru 점수 임계값",
+  "Interrogate: deepbooru sort alphabetically": "분석 : deepbooru 알파벳 순서로 정렬하기",
+  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "분석 : 결과물에 모델 태그의 랭크 포함하기 (캡션 바탕의 분석기에는 효과 없음)",
+  "Interrogate: keep models in VRAM": "분석 : VRAM에 모델 유지하기",
+  "Interrogate: maximum description length": "분석 : 설명 최대 길이",
+  "Interrogate: minimum description length (excluding artists, etc..)": "분석 : 설명 최소 길이(작가 등등..제외)",
+  "Interrogate: num_beams for BLIP": "분석 : BLIP의 num_beams값",
+  "Interrogate: use artists from artists.csv": "분석 : artists.csv의 작가들 사용하기",
   "Interrupt": "중단",
   "Is negative text": "네거티브 텍스트일시 체크",
   "Just resize": "리사이징",
   "Keep -1 for seeds": "시드값 -1로 유지",
   "keep whatever was there originally": "이미지 원본 유지",
-  "Label": "Label",
+  "Label": "라벨",
   "Lanczos": "Lanczos",
-  "Last prompt:": "Last prompt:",
-  "Last saved hypernetwork:": "Last saved hypernetwork:",
-  "Last saved image:": "Last saved image:",
+  "Last prompt:": "마지막 프롬프트 : ",
+  "Last saved hypernetwork:": "마지막으로 저장된 하이퍼네트워크 : ",
+  "Last saved image:": "마지막으로 저장된 이미지 : ",
   "latent noise": "잠재 노이즈",
   "latent nothing": "잠재 공백",
   "LDSR": "LDSR",
-  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
+  "LDSR processing steps. Lower = faster": "LDSR 스텝 수. 낮은 값 = 빠른 속도",
   "leakyrelu": "leakyrelu",
   "Leave blank to save images to the default path.": "기존 저장 경로에 이미지들을 저장하려면 비워두세요.",
   "left": "왼쪽",
   "linear": "linear",
-  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
+  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "설정 탭이 아니라 상단의 빠른 설정 바에 위치시킬 설정 이름을 쉼표로 분리해서 입력하십시오. 설정 이름은 modules/shared.py에서 찾을 수 있습니다. 재시작이 필요합니다.",
   "LMS": "LMS",
   "LMS Karras": "LMS Karras",
   "Load": "불러오기",
   "Loading...": "로딩 중...",
-  "Localization (requires restart)": "Localization (requires restart)",
+  "Localization (requires restart)": "현지화 (재시작 필요)",
   "Log directory": "로그 경로",
   "Loopback": "루프백",
   "Loops": "루프 수",
-  "Loss:": "Loss:",
+  "Loss:": "손실(Loss) : ",
   "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
-  "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
+  "Make K-diffusion samplers produce same images in a batch as when making a single image": "K-diffusion 샘플러들이 단일 이미지를 생성하는 것처럼 배치에서도 동일한 이미지를 생성하게 하기",
   "Make Zip when Save?": "저장 시 Zip 생성하기",
   "Mask": "마스크",
   "Mask blur": "마스크 블러",
-  "Mask mode": "Mask mode",
+  "Mask mode": "마스크 모드",
   "Masked content": "마스크된 부분",
-  "Masking mode": "Masking mode",
-  "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
+  "Masking mode": "마스킹 모드",
+  "Max prompt words for [prompt_words] pattern": "[prompt_words] 패턴의 최대 프롬프트 단어 수",
   "Max steps": "최대 스텝 수",
   "Modules": "모듈",
-  "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
-  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.",
+  "Move face restoration model from VRAM into RAM after processing": "처리가 완료되면 얼굴 보정 모델을 VRAM에서 RAM으로 옮기기",
+  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "하이퍼네트워크 훈련 진행 시 VAE와 CLIP을 RAM으로 옮기기. VRAM이 절약됩니다.",
   "Multiplier (M) - set to 0 to get model A": "배율 (M) - 0으로 적용하면 모델 A를 얻게 됩니다",
   "Name": "이름",
   "Negative prompt": "네거티브 프롬프트",
   "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
   "Next batch": "다음 묶음",
   "Next Page": "다음 페이지",
-  "None": "None",
+  "None": "없음",
   "Nothing": "없음",
   "Nothing found in the image.": "Nothing found in the image.",
   "number of images to delete consecutively next": "연속적으로 삭제할 이미지 수",
-  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
+  "Number of pictures displayed on each page": "각 페이지에 표시될 이미지 수",
+  "Minimum number of pages per load": "한번 불러올 때마다 불러올 최소 페이지 수",
+  "Number of grids in each row": "각 세로줄마다 표시될 그리드 수",
+  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "세대(Epoch)당 단일 인풋 이미지의 반복 횟수 - 세대(Epoch) 숫자를 표시하는 데에만 사용됩니다. ",
   "Number of vectors per token": "토큰별 벡터 수",
   "Open for Clip Aesthetic!": "클립 스타일 기능을 활성화하려면 클릭!",
   "Open images output directory": "이미지 저장 경로 열기",
   "Open output directory": "저장 경로 열기",
-  "or": "or",
+  "or": "또는",
   "original": "원본 유지",
   "Original negative prompt": "기존 네거티브 프롬프트",
   "Original prompt": "기존 프롬프트",
   "Outpainting direction": "아웃페인팅 방향",
   "Outpainting mk2": "아웃페인팅 마크 2",
   "Output directory": "이미지 저장 경로",
-  "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
-  "Output directory for images from extras tab": "Output directory for images from extras tab",
-  "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
-  "Output directory for img2img grids": "Output directory for img2img grids",
-  "Output directory for img2img images": "Output directory for img2img images",
-  "Output directory for txt2img grids": "Output directory for txt2img grids",
-  "Output directory for txt2img images": "Output directory for txt2img images",
+  "Output directory for grids; if empty, defaults to two directories below": "그리드 이미지 저장 경로 - 비워둘 시 하단의 2가지 기본 경로로 설정됨",
+  "Output directory for images from extras tab": "부가기능 탭 저장 경로",
+  "Output directory for images; if empty, defaults to three directories below": "이미지 저장 경로 - 비워둘 시 하단의 3가지 기본 경로로 설정됨",
+  "Output directory for img2img grids": "이미지→이미지 그리드 저장 경로",
+  "Output directory for img2img images": "이미지→이미지 저장 경로",
+  "Output directory for txt2img grids": "텍스트→이미지 그리드 저장 경로",
+  "Output directory for txt2img images": "텍스트→이미지 저장 경로",
   "Override `Denoising strength` to 1?": "디노이즈 강도를 1로 적용할까요?",
   "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "프롬프트 값을 기존 프롬프트와 동일하게 적용할까요?(네거티브 프롬프트 포함)",
   "Override `Sampling method` to Euler?(this method is built for it)": "샘플링 방법을 Euler로 적용할까요?(이 기능은 해당 샘플러를 위해 만들어져 있습니다)",
@@ -279,20 +285,21 @@
   "Overwrite Old Hypernetwork": "기존 하이퍼네트워크 덮어쓰기",
   "Page Index": "페이지 인덱스",
   "parameters": "설정값",
-  "Path to directory where to write outputs": "Path to directory where to write outputs",
+  "Path to directory where to write outputs": "결과물을 출력할 경로",
   "Path to directory with input images": "인풋 이미지가 있는 경로",
-  "Paths for saving": "Paths for saving",
+  "Paths for saving": "저장 경로",
   "Pixels to expand": "확장할 픽셀 수",
   "PLMS": "PLMS",
   "PNG Info": "PNG 정보",
   "Poor man's outpainting": "가난뱅이의 아웃페인팅",
-  "Preparing dataset from": "Preparing dataset from",
+  "Preload images at startup": "WebUI 가동 시 이미지 프리로드하기",
+  "Preparing dataset from": "준비된 데이터셋 경로 : ",
   "prepend": "앞에 삽입",
   "Preprocess": "전처리",
   "Preprocess images": "이미지 전처리",
   "Prev batch": "이전 묶음",
   "Prev Page": "이전 페이지",
-  "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)",
+  "Prevent empty spots in grid (when set to autodetect)": "(자동 감지 사용시)그리드에 빈칸이 생기는 것 방지하기",
   "Primary model (A)": "주 모델 (A)",
   "Process an image, use it as an input, repeat.": "이미지를 생성하고, 생성한 이미지를 다시 원본으로 사용하는 과정을 반복합니다.",
   "Process images in a directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리의 이미지들을 처리합니다.",
@@ -307,26 +314,26 @@
   "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
   "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
   "quad": "quad",
-  "Quality for saved jpeg images": "Quality for saved jpeg images",
-  "Quicksettings list": "Quicksettings list",
+  "Quality for saved jpeg images": "저장된 jpeg 이미지들의 품질",
+  "Quicksettings list": "빠른 설정 리스트",
   "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
   "Randomness": "랜덤성",
   "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
   "Read parameters (prompt, etc...) from txt2img tab when making previews": "프리뷰 이미지 생성 시 텍스트→이미지 탭에서 설정값(프롬프트 등) 읽어오기",
   "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "추천 설정값 - 샘플링 스텝 수 : 80-100 , 샘플러 : Euler a, 디노이즈 강도 : 0.8",
-  "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
+  "Reload custom script bodies (No ui updates, No restart)": "커스텀 스크립트 리로드하기(UI 업데이트 없음, 재시작 없음)",
   "relu": "relu",
   "Renew Page": "Renew Page",
-  "Request browser notifications": "Request browser notifications",
+  "Request browser notifications": "브라우저 알림 권한 요청",
   "Resize": "리사이징 배수",
   "Resize and fill": "리사이징 후 채우기",
   "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "설정된 해상도로 이미지 리사이징을 진행합니다. 원본과 가로/세로 길이가 일치하지 않을 경우, 부정확한 화면비의 이미지를 얻게 됩니다.",
-  "Resize mode": "Resize mode",
+  "Resize mode": "리사이징 모드",
   "Resize seed from height": "시드 리사이징 가로길이",
   "Resize seed from width": "시드 리사이징 세로길이",
   "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "이미지 전체가 설정된 해상도 내부에 들어가게 리사이징을 진행합니다. 빈 공간은 이미지의 색상으로 채웁니다.",
   "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "설정된 해상도 전체가 이미지로 가득차게 리사이징을 진행합니다. 튀어나오는 부분은 잘라냅니다.",
-  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradio를 재시작하고 컴포넌트 새로고침하기 (커스텀 스크립트, ui.py, js, css만 해당됨)",
   "Restore faces": "얼굴 보정",
   "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
   "Result = A * (1 - M) + B * M": "결과물 = A * (1 - M) + B * M",
@@ -335,23 +342,23 @@
   "right": "오른쪽",
   "Run": "가동",
   "Sampler": "샘플러",
-  "Sampler parameters": "Sampler parameters",
+  "Sampler parameters": "샘플러 설정값",
   "Sampling method": "샘플링 방법",
   "Sampling Steps": "샘플링 스텝 수",
   "Save": "저장",
   "Save a copy of embedding to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 임베딩을 저장합니다, 비활성화하려면 0으로 설정하십시오.",
-  "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
-  "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
-  "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
+  "Save a copy of image before applying color correction to img2img results": "이미지→이미지 결과물에 색상 보정을 진행하기 전 이미지의 복사본을 저장하기",
+  "Save a copy of image before doing face restoration.": "얼굴 보정을 진행하기 전 이미지의 복사본을 저장하기",
+  "Save an csv containing the loss to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 손실(Loss)을 포함하는 csv 파일을 저장합니다, 비활성화하려면 0으로 설정하십시오.",
   "Save an image to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 이미지를 저장합니다, 비활성화하려면 0으로 설정하십시오.",
   "Save as float16": "float16으로 저장",
-  "Save grids to a subdirectory": "Save grids to a subdirectory",
-  "Save images to a subdirectory": "Save images to a subdirectory",
+  "Save grids to a subdirectory": "그리드 이미지를 하위 디렉토리에 저장하기",
+  "Save images to a subdirectory": "이미지를 하위 디렉토리에 저장하기",
   "Save images with embedding in PNG chunks": "PNG 청크로 이미지에 임베딩을 포함시켜 저장",
   "Save style": "스타일 저장",
-  "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
-  "Saving images/grids": "Saving images/grids",
-  "Saving to a directory": "Saving to a directory",
+  "Save text information about generation parameters as chunks to png files": "이미지 생성 설정값을 PNG 청크에 텍스트로 저장",
+  "Saving images/grids": "이미지/그리드 저장",
+  "Saving to a directory": "디렉토리에 저장",
   "Scale by": "스케일링 배수 지정",
   "Scale to": "스케일링 사이즈 지정",
   "Script": "스크립트",
@@ -363,6 +370,7 @@
   "Seed": "시드",
   "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
   "Select activation function of hypernetwork": "하이퍼네트워크 활성화 함수 선택",
+  "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "WebUI에 표시할 Real-ESRGAN 모델을 선택하십시오. (재시작 필요)",
   "Send to extras": "부가기능으로 전송",
   "Send to img2img": "이미지→이미지로 전송",
   "Send to inpaint": "인페인트로 전송",
@@ -374,29 +382,30 @@
   "set_index": "set_index",
   "Settings": "설정",
   "should be 2 or lower.": "이 2 이하여야 합니다.",
-  "Show generation progress in window title.": "Show generation progress in window title.",
-  "Show grid in results for web": "Show grid in results for web",
-  "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
-  "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
-  "Show progressbar": "Show progressbar",
+  "Show generation progress in window title.": "창 타이틀에 생성 진행도 보여주기",
+  "Show grid in results for web": "웹에서 결과창에 그리드 보여주기",
+  "Show image creation progress every N sampling steps. Set 0 to disable.": "N번째 샘플링 스텝마다 이미지 생성 과정 보이기 - 비활성화하려면 0으로 설정",
+  "Show images zoomed in by default in full page image viewer": "전체 페이지 이미지 뷰어에서 기본값으로 이미지 확대해서 보여주기",
+  "Show progressbar": "프로그레스 바 보이기",
   "Show result images": "이미지 결과 보이기",
   "Show Textbox": "텍스트박스 보이기",
+  "Show previews of all images generated in a batch as a grid": "배치에서 생성된 모든 이미지의 미리보기를 그리드 형식으로 보여주기",
   "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
   "Sigma Churn": "시그마 섞기",
-  "sigma churn": "sigma churn",
+  "sigma churn": "시그마 섞기",
   "Sigma max": "시그마 최댓값",
   "Sigma min": "시그마 최솟값",
   "Sigma noise": "시그마 노이즈",
-  "sigma noise": "sigma noise",
-  "sigma tmin": "sigma tmin",
+  "sigma noise": "시그마 노이즈",
+  "sigma tmin": "시그마 tmin",
   "Single Image": "단일 이미지",
   "Skip": "건너뛰기",
   "Slerp angle": "구면 선형 보간 각도",
   "Slerp interpolation": "구면 선형 보간",
   "Source": "원본",
   "Source directory": "원본 경로",
-  "Split image threshold": "Split image threshold",
-  "Split image overlap ratio": "Split image overlap ratio",
+  "Split image threshold": "이미지 분할 임계값",
+  "Split image overlap ratio": "이미지 분할 겹침 비율",
   "Split oversized images": "사이즈가 큰 이미지 분할하기",
   "Stable Diffusion": "Stable Diffusion",
   "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
@@ -407,20 +416,20 @@
   "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
   "Style 1": "스타일 1",
   "Style 2": "스타일 2",
-  "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
+  "Style to apply; styles have components for both positive and negative prompts and apply to both": "적용할 스타일 - 스타일은 긍정/부정 프롬프트 모두에 대한 설정값을 가지고 있고 양쪽 모두에 적용 가능합니다.",
   "SwinIR 4x": "SwinIR 4x",
   "Sys VRAM:": "시스템 VRAM : ",
-  "System": "System",
+  "System": "시스템",
   "Tertiary model (C)": "3차 모델 (C)",
-  "Textbox": "Textbox",
-  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
-  "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
+  "Textbox": "텍스트박스",
+  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "이 정규표현식은 파일명으로부터 단어를 추출하는 데 사용됩니다. 추출된 단어들은 하단의 설정을 이용해 라벨 텍스트로 변환되어 훈련에 사용됩니다. 파일명 텍스트를 유지하려면 비워두십시오.",
+  "This string will be used to join split words into a single line if the option above is enabled.": "이 문자열은 상단 설정이 활성화되어있을 때 분리된 단어들을 한 줄로 합치는 데 사용됩니다.",
   "This text is used to rotate the feature space of the imgs embs": "이 텍스트는 이미지 임베딩의 특징 공간을 회전하는 데 사용됩니다.",
   "Tile overlap": "타일 겹침",
-  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
-  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
-  "Tile size for all SwinIR.": "Tile size for all SwinIR.",
-  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
+  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN 업스케일러들의 타일 중첩 수치, 픽셀 단위. 낮은 값 = 눈에 띄는 이음매.",
+  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR의 타일 중첩 수치, 픽셀 단위. 낮은 값 = 눈에 띄는 이음매.",
+  "Tile size for all SwinIR.": "SwinIR의 타일 사이즈.",
+  "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 업스케일러들의 타일 사이즈. 0 = 타일링 없음.",
   "Tiling": "타일링",
   "Time taken:": "소요 시간 : ",
   "Torch active/reserved:": "활성화/예약된 Torch 양 : ",
@@ -429,51 +438,60 @@
   "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "임베딩이나 하이퍼네트워크를 훈련시킵니다. 1:1 비율의 이미지가 있는 경로를 지정해야 합니다.",
   "Train Embedding": "임베딩 훈련",
   "Train Hypernetwork": "하이퍼네트워크 훈련",
-  "Training": "Training",
+  "Training": "훈련",
   "txt2img": "텍스트→이미지",
-  "txt2img history": "txt2img history",
+  "txt2img history": "텍스트→이미지 기록",
   "uniform": "uniform",
   "up": "위쪽",
   "Upload mask": "마스크 업로드하기",
-  "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
+  "Upscale latent space image when doing hires. fix": "고해상도 보정 사용시 잠재 공간 이미지 업스케일하기",
   "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
   "Upscaler": "업스케일러",
   "Upscaler 1": "업스케일러 1",
   "Upscaler 2": "업스케일러 2",
   "Upscaler 2 visibility": "업스케일러 2 가시성",
-  "Upscaler for img2img": "Upscaler for img2img",
-  "Upscaling": "Upscaling",
+  "Upscaler for img2img": "이미지→이미지 업스케일러",
+  "Upscaling": "업스케일링",
   "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
   "Use an empty output directory to save pictures normally instead of writing to the output directory.": "저장 경로를 비워두면 기본 저장 폴더에 이미지들이 저장됩니다.",
   "Use BLIP for caption": "캡션에 BLIP 사용",
   "Use deepbooru for caption": "캡션에 deepbooru 사용",
   "Use dropout": "드롭아웃 사용",
-  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
-  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
-  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
-  "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
-  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
-  "User interface": "User interface",
+  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지 파일명 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
+  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지와 그리드의 하위 디렉토리명의 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
+  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "옛 방식의 강조 구현을 사용합니다. 옛 시드를 재현하는 데 효과적일 수 있습니다.",
+  "Use original name for output filename during batch process in extras tab": "부가기능 탭에서 이미지를 여러장 처리 시 결과물 파일명에 기존 파일명 사용하기",
+  "use spaces for tags in deepbooru": "deepbooru에서 태그에 공백 사용",
+  "User interface": "사용자 인터페이스",
   "Var. seed": "바리에이션 시드",
   "Var. strength": "바리에이션 강도",
   "Variation seed": "바리에이션 시드",
   "Variation strength": "바리에이션 강도",
   "view": "api 보이기",
-  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
+  "VRAM usage polls per second during generation. Set to 0 to disable.": "생성 도중 초당 VRAM 사용량 폴링 수. 비활성화하려면 0으로 설정하십시오.",
   "Weighted sum": "가중 합",
   "What to put inside the masked area before processing it with Stable Diffusion.": "Stable Diffusion으로 이미지를 생성하기 전 마스크된 부분에 무엇을 채울지 결정하는 설정값",
-  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
-  "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
-  "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
+  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "PNG 정보나 붙여넣은 텍스트로부터 생성 설정값을 읽어올 때, 선택된 모델/체크포인트는 변경하지 않기.",
+  "When using \"Save\" button, save images to a subdirectory": "저장 버튼 사용시, 이미지를 하위 디렉토리에 저장하기",
+  "When using 'Save' button, only save a single selected image": "저장 버튼 사용시, 선택된 이미지 1개만 저장하기",
   "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
   "Width": "가로",
   "wiki": " 위키",
   "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "이미지를 설정된 사이즈의 2배로 업스케일합니다. 상단의 가로와 세로 슬라이더를 이용해 타일 사이즈를 지정하세요.",
-  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
+  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "이미지→이미지 진행 시, 슬라이더로 설정한 스텝 수를 정확히 실행하기 (일반적으로 디노이즈 강도가 낮을수록 실제 설정된 스텝 수보다 적게 진행됨)",
   "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
   "X type": "X축",
   "X values": "X 설정값",
   "X/Y plot": "X/Y 플롯",
   "Y type": "Y축",
-  "Y values": "Y 설정값"
+  "Y values": "Y 설정값",
+  "step1 min/max": "스텝1 최소/최대",
+  "step2 min/max": "스텝2 최소/최대",
+  "step count": "스텝 변화 횟수",
+  "cfg1 min/max": "CFG1 최소/최대",
+  "cfg2 min/max": "CFG2 최소/최대",
+  "cfg count": "CFG 변화 횟수",
+  "x/y change": "X/Y축 변경",
+  "Random": "랜덤",
+  "Random grid": "랜덤 그리드"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From dd25722d6c3f9d9a5f7d76307822bf7558386a0f Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Mon, 24 Oct 2022 04:38:16 +0900
Subject: Finalize ko_KR.json

---
 localizations/ko_KR.json | 44 ++++++++++++++++++++++----------------------
 1 file changed, 22 insertions(+), 22 deletions(-)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 6889de46..ab12c37e 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -5,10 +5,10 @@
   "❮": "❮",
   "❯": "❯",
   "⤡": "⤡",
-  " images in this directory. Loaded ": "개의 이미지가 이 경로에 존재합니다. ",
   " images during ": "개의 이미지를 불러왔고, 생성 기간은 ",
-  ", divided into ": "입니다. ",
+  " images in this directory. Loaded ": "개의 이미지가 이 경로에 존재합니다. ",
   " pages": "페이지로 나뉘어 표시합니다.",
+  ", divided into ": "입니다. ",
   "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
   "[wiki]": " [위키] 참조",
   "A directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리를 선택해 주세요.",
@@ -43,7 +43,10 @@
   "BSRGAN 4x": "BSRGAN 4x",
   "built with gradio": "gradio로 제작되었습니다",
   "Cancel generate forever": "반복 생성 취소",
+  "cfg count": "CFG 변화 횟수",
   "CFG Scale": "CFG 스케일",
+  "cfg1 min/max": "CFG1 최소/최대",
+  "cfg2 min/max": "CFG2 최소/최대",
   "Check progress": "진행도 체크",
   "Check progress (first)": "진행도 체크 (처음)",
   "checkpoint": " 체크포인트 ",
@@ -57,8 +60,8 @@
   "CodeFormer visibility": "CodeFormer 가시성",
   "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 가중치 (0 = 최대 효과, 1 = 최소 효과)",
   "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 가중치 설정값 (0 = 최대 효과, 1 = 최소 효과)",
-  "Color variation": "색깔 다양성",
   "Collect": "즐겨찾기",
+  "Color variation": "색깔 다양성",
   "copy": "복사",
   "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
   "Create a text file next to every image with generation parameters.": "생성된 이미지마다 생성 설정값을 담은 텍스트 파일 생성하기",
@@ -89,8 +92,8 @@
   "Do not save grids consisting of one picture": "이미지가 1개뿐인 그리드는 저장하지 않기",
   "Do not show any images in results for web": "웹에서 결과창에 아무 이미지도 보여주지 않기",
   "down": "아래쪽",
-  "Download localization template": "현지화 템플릿 다운로드",
   "Download": "다운로드",
+  "Download localization template": "현지화 템플릿 다운로드",
   "DPM adaptive": "DPM adaptive",
   "DPM fast": "DPM fast",
   "DPM2": "DPM2",
@@ -121,6 +124,7 @@
   "Existing Caption txt Action": "이미 존재하는 캡션 텍스트 처리",
   "Extra": "고급",
   "Extras": "부가기능",
+  "extras": "부가기능",
   "extras history": "부가기능 기록",
   "Face restoration": "얼굴 보정",
   "Face restoration model": "얼굴 보정 모델",
@@ -155,10 +159,6 @@
   "Hide samplers in user interface (requires restart)": "사용자 인터페이스에서 숨길 샘플러 선택(재시작 필요)",
   "Highres. fix": "고해상도 보정",
   "History": "기록",
-  "Image Browser": "이미지 브라우저",
-  "Images Browser": "이미지 브라우저",
-  "Images directory": "이미지 경로",
-  "extras": "부가기능",
   "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "훈련이 얼마나 빨리 이루어질지 정하는 값입니다. 값이 낮을수록 훈련 시간이 길어지고, 높은 값일수록 정확한 결과를 내는 데 실패하고 임베딩을 망가뜨릴 수 있습니다(임베딩이 망가진 경우에는 훈련 정보 텍스트박스에 손실(Loss) : nan 이라고 출력되게 됩니다. 이 경우에는 망가지지 않은 이전 백업본을 불러와야 합니다).\n\n학습률은 하나의 값으로 설정할 수도 있고, 다음 문법을 사용해 여러 값을 사용할 수도 있습니다 :\n\n학습률_1:최대 스텝수_1, 학습률_2:최대 스텝수_2, ...\n\n예 : 0.005:100, 1e-3:1000, 1e-5\n\n예의 설정값은 첫 100스텝동안 0.005의 학습률로, 그 이후 1000스텝까지는 1e-3으로, 남은 스텝은 1e-5로 훈련하게 됩니다.",
   "How many batches of images to create": "생성할 이미지 배치 수",
   "How many image to create in a single batch": "한 배치당 이미지 수",
@@ -175,8 +175,11 @@
   "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "이 값이 0이 아니라면, 시드에 해당 값이 더해지고, Eta가 있는 샘플러를 사용할 때 노이즈의 RNG 조정을 위해 해당 값이 사용됩니다. 이 설정으로 더 다양한 이미지를 생성하거나, 잘 알고 계시다면 특정 소프트웨어의 결과값을 재현할 수도 있습니다.",
   "ignore": "무시",
   "Image": "이미지",
+  "Image Browser": "이미지 브라우저",
   "Image for img2img": "Image for img2img",
   "Image for inpainting with mask": "마스크로 인페인팅할 이미지",
+  "Images Browser": "이미지 브라우저",
+  "Images directory": "이미지 경로",
   "Images filename pattern": "이미지 파일명 패턴",
   "img2img": "이미지→이미지",
   "img2img alternative test": "이미지→이미지 대체버전 테스트",
@@ -242,6 +245,7 @@
   "Masking mode": "마스킹 모드",
   "Max prompt words for [prompt_words] pattern": "[prompt_words] 패턴의 최대 프롬프트 단어 수",
   "Max steps": "최대 스텝 수",
+  "Minimum number of pages per load": "한번 불러올 때마다 불러올 최소 페이지 수",
   "Modules": "모듈",
   "Move face restoration model from VRAM into RAM after processing": "처리가 완료되면 얼굴 보정 모델을 VRAM에서 RAM으로 옮기기",
   "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "하이퍼네트워크 훈련 진행 시 VAE와 CLIP을 RAM으로 옮기기. VRAM이 절약됩니다.",
@@ -254,10 +258,9 @@
   "None": "없음",
   "Nothing": "없음",
   "Nothing found in the image.": "Nothing found in the image.",
+  "Number of grids in each row": "각 세로줄마다 표시될 그리드 수",
   "number of images to delete consecutively next": "연속적으로 삭제할 이미지 수",
   "Number of pictures displayed on each page": "각 페이지에 표시될 이미지 수",
-  "Minimum number of pages per load": "한번 불러올 때마다 불러올 최소 페이지 수",
-  "Number of grids in each row": "각 세로줄마다 표시될 그리드 수",
   "Number of repeats for a single input image per epoch; used only for displaying epoch number": "세대(Epoch)당 단일 인풋 이미지의 반복 횟수 - 세대(Epoch) 숫자를 표시하는 데에만 사용됩니다. ",
   "Number of vectors per token": "토큰별 벡터 수",
   "Open for Clip Aesthetic!": "클립 스타일 기능을 활성화하려면 클릭!",
@@ -317,6 +320,8 @@
   "Quality for saved jpeg images": "저장된 jpeg 이미지들의 품질",
   "Quicksettings list": "빠른 설정 리스트",
   "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+  "Random": "랜덤",
+  "Random grid": "랜덤 그리드",
   "Randomness": "랜덤성",
   "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
   "Read parameters (prompt, etc...) from txt2img tab when making previews": "프리뷰 이미지 생성 시 텍스트→이미지 탭에서 설정값(프롬프트 등) 읽어오기",
@@ -386,10 +391,10 @@
   "Show grid in results for web": "웹에서 결과창에 그리드 보여주기",
   "Show image creation progress every N sampling steps. Set 0 to disable.": "N번째 샘플링 스텝마다 이미지 생성 과정 보이기 - 비활성화하려면 0으로 설정",
   "Show images zoomed in by default in full page image viewer": "전체 페이지 이미지 뷰어에서 기본값으로 이미지 확대해서 보여주기",
+  "Show previews of all images generated in a batch as a grid": "배치에서 생성된 모든 이미지의 미리보기를 그리드 형식으로 보여주기",
   "Show progressbar": "프로그레스 바 보이기",
   "Show result images": "이미지 결과 보이기",
   "Show Textbox": "텍스트박스 보이기",
-  "Show previews of all images generated in a batch as a grid": "배치에서 생성된 모든 이미지의 미리보기를 그리드 형식으로 보여주기",
   "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
   "Sigma Churn": "시그마 섞기",
   "sigma churn": "시그마 섞기",
@@ -404,11 +409,14 @@
   "Slerp interpolation": "구면 선형 보간",
   "Source": "원본",
   "Source directory": "원본 경로",
-  "Split image threshold": "이미지 분할 임계값",
   "Split image overlap ratio": "이미지 분할 겹침 비율",
+  "Split image threshold": "이미지 분할 임계값",
   "Split oversized images": "사이즈가 큰 이미지 분할하기",
   "Stable Diffusion": "Stable Diffusion",
   "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "step count": "스텝 변화 횟수",
+  "step1 min/max": "스텝1 최소/최대",
+  "step2 min/max": "스텝2 최소/최대",
   "Step:": "Step:",
   "Steps": "스텝 수",
   "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
@@ -482,16 +490,8 @@
   "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
   "X type": "X축",
   "X values": "X 설정값",
+  "x/y change": "X/Y축 변경",
   "X/Y plot": "X/Y 플롯",
   "Y type": "Y축",
-  "Y values": "Y 설정값",
-  "step1 min/max": "스텝1 최소/최대",
-  "step2 min/max": "스텝2 최소/최대",
-  "step count": "스텝 변화 횟수",
-  "cfg1 min/max": "CFG1 최소/최대",
-  "cfg2 min/max": "CFG2 최소/최대",
-  "cfg count": "CFG 변화 횟수",
-  "x/y change": "X/Y축 변경",
-  "Random": "랜덤",
-  "Random grid": "랜덤 그리드"
+  "Y values": "Y 설정값"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From 974196932583b96b6b76632052fc0d7e70820bf3 Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sun, 23 Oct 2022 22:38:42 +0300
Subject: Save properly processed image before color correction

---
 modules/processing.py | 33 ++++++++++++++++++---------------
 1 file changed, 18 insertions(+), 15 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index ff83023c..15b639e1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -46,6 +46,20 @@ def apply_color_correction(correction, image):
 
     return image
 
+def apply_overlay(overlay_exists, overlay, paste_loc, image):
+    if overlay_exists:
+        if paste_loc is not None:
+            x, y, w, h = paste_loc
+            base_image = Image.new('RGBA', (overlay.width, overlay.height))
+            image = images.resize_image(1, image, w, h)
+            base_image.paste(image, (x, y))
+            image = base_image
+
+        image = image.convert('RGBA')
+        image.alpha_composite(overlay)
+        image = image.convert('RGB')
+
+    return image
 
 def get_correct_sampler(p):
     if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
@@ -446,25 +460,14 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                     devices.torch_gc()
 
                 image = Image.fromarray(x_sample)
-
+                
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
-                        images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+                        image_without_cc = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
+                        images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
-                if p.overlay_images is not None and i < len(p.overlay_images):
-                    overlay = p.overlay_images[i]
-
-                    if p.paste_to is not None:
-                        x, y, w, h = p.paste_to
-                        base_image = Image.new('RGBA', (overlay.width, overlay.height))
-                        image = images.resize_image(1, image, w, h)
-                        base_image.paste(image, (x, y))
-                        image = base_image
-
-                    image = image.convert('RGBA')
-                    image.alpha_composite(overlay)
-                    image = image.convert('RGB')
+                image = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
 
                 if opts.samples_save and not p.do_not_save_samples:
                     images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
-- 
cgit v1.2.1


From f2cc3f32d5bc8538e95edec54d7dc1b9efdf769a Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sun, 23 Oct 2022 22:44:46 +0300
Subject: fix whitespaces

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 15b639e1..2a332514 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -460,7 +460,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                     devices.torch_gc()
 
                 image = Image.fromarray(x_sample)
-                
+
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
                         image_without_cc = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
-- 
cgit v1.2.1


From b297cc3324979ec78d69b2d11dd18030dfad7bcc Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 20:06:42 +0900
Subject: Hypernetworks - fix KeyError in statistics caching

Statistics logging has changed to {filename : list[losses]}, so it has to use loss_info[key].pop()
---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 98a7b62e..33827210 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -274,8 +274,8 @@ def log_statistics(loss_info:dict, key, value):
         loss_info[key] = [value]
     else:
         loss_info[key].append(value)
-        if len(loss_info) > 1024:
-            loss_info.pop(0)
+        if len(loss_info[key]) > 1024:
+            loss_info[key].pop(0)
 
 
 def statistics(data):
-- 
cgit v1.2.1


From 40b56c9289bf9458ae5ef3c1990ccea851c6c3e2 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 21:07:07 +0900
Subject: cleanup some code

---
 modules/hypernetworks/hypernetwork.py | 14 +++-----------
 1 file changed, 3 insertions(+), 11 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 33827210..4072bf54 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -16,6 +16,7 @@ from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
 
+from collections import defaultdict, deque
 from statistics import stdev, mean
 
 class HypernetworkModule(torch.nn.Module):
@@ -269,15 +270,6 @@ def stack_conds(conds):
     return torch.stack(conds)
 
 
-def log_statistics(loss_info:dict, key, value):
-    if key not in loss_info:
-        loss_info[key] = [value]
-    else:
-        loss_info[key].append(value)
-        if len(loss_info[key]) > 1024:
-            loss_info[key].pop(0)
-
-
 def statistics(data):
     total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})"
     recent_data = data[-32:]
@@ -341,7 +333,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         weight.requires_grad = True
 
     size = len(ds.indexes)
-    loss_dict = {}
+    loss_dict = defaultdict(lambda : deque(maxlen = 1024))
     losses = torch.zeros((size,))
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
@@ -383,7 +375,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
             for entry in entries:
-                log_statistics(loss_dict, entry.filename, loss.item())
+                loss_dict[entry.filename].append(loss.item())
                 
             optimizer.zero_grad()
             weights[0].grad = None
-- 
cgit v1.2.1


From 348f89c8d40397c1875cff4a7331018785f9c3b8 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 21:29:53 +0900
Subject: statistics for pbar

---
 modules/hypernetworks/hypernetwork.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 4072bf54..48b56029 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -335,6 +335,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     size = len(ds.indexes)
     loss_dict = defaultdict(lambda : deque(maxlen = 1024))
     losses = torch.zeros((size,))
+    previous_mean_losses = [0]
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
 
@@ -356,7 +357,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
         if len(loss_dict) > 0:
-            previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict)
+            previous_mean_losses = [i[-1] for i in loss_dict.values()]
+            previous_mean_loss = mean(previous_mean_losses)
             
         scheduler.apply(optimizer, hypernetwork.step)
         if scheduler.finished:
@@ -391,7 +393,13 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
             raise RuntimeError("Loss diverged.")
-        pbar.set_description(f"dataset loss: {previous_mean_loss:.7f}")
+        
+        if len(previous_mean_losses) > 1:
+            std = stdev(previous_mean_losses)
+        else:
+            std = 0
+        dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
+        pbar.set_description(dataset_loss_info)
 
         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
             # Before saving, change name to match current checkpoint.
-- 
cgit v1.2.1


From 0d2e1dac407a0e2f5b148d314715f0457b2525b7 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 21:41:39 +0900
Subject: convert deque -> list

I don't feel this being efficient
---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 48b56029..fb510fa7 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -282,7 +282,7 @@ def report_statistics(loss_info:dict):
     for key in keys:
         try:
             print("Loss statistics for file " + key)
-            info, recent = statistics(loss_info[key])
+            info, recent = statistics(list(loss_info[key]))
             print(info)
             print(recent)
         except Exception as e:
-- 
cgit v1.2.1


From e9a410b5357612f63528015c5533c2185dcff92e Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sun, 23 Oct 2022 21:47:39 +0900
Subject: check length for variance

---
 modules/hypernetworks/hypernetwork.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index fb510fa7..d647ea55 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -271,9 +271,17 @@ def stack_conds(conds):
 
 
 def statistics(data):
-    total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})"
+    if len(data) < 2:
+        std = 0
+    else:
+        std = stdev(data)
+    total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"
     recent_data = data[-32:]
-    recent_information = f"recent 32 loss:{mean(recent_data):.3f}"+u"\u00B1"+f"({stdev(recent_data)/ (len(recent_data)**0.5):.3f})"
+    if len(recent_data) < 2:
+        std = 0
+    else:
+        std = stdev(recent_data)
+    recent_information = f"recent 32 loss:{mean(recent_data):.3f}" + u"\u00B1" + f"({std / (len(recent_data) ** 0.5):.3f})"
     return total_information, recent_information
 
 
-- 
cgit v1.2.1


From 6cbb04f7a5e675cf1f6dfc247aa9c9e8df7dc5ce Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 24 Oct 2022 09:15:26 +0300
Subject: fix #3517 breaking txt2img

---
 modules/processing.py | 33 +++++++++++++++++++--------------
 1 file changed, 19 insertions(+), 14 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 2a332514..c61bbfbd 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -46,18 +46,23 @@ def apply_color_correction(correction, image):
 
     return image
 
-def apply_overlay(overlay_exists, overlay, paste_loc, image):
-    if overlay_exists:
-        if paste_loc is not None:
-            x, y, w, h = paste_loc
-            base_image = Image.new('RGBA', (overlay.width, overlay.height))
-            image = images.resize_image(1, image, w, h)
-            base_image.paste(image, (x, y))
-            image = base_image
-
-        image = image.convert('RGBA')
-        image.alpha_composite(overlay)
-        image = image.convert('RGB')
+
+def apply_overlay(image, paste_loc, index, overlays):
+    if overlays is None or index >= len(overlays):
+        return image
+
+    overlay = overlays[index]
+
+    if paste_loc is not None:
+        x, y, w, h = paste_loc
+        base_image = Image.new('RGBA', (overlay.width, overlay.height))
+        image = images.resize_image(1, image, w, h)
+        base_image.paste(image, (x, y))
+        image = base_image
+
+    image = image.convert('RGBA')
+    image.alpha_composite(overlay)
+    image = image.convert('RGB')
 
     return image
 
@@ -463,11 +468,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
-                        image_without_cc = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
+                        image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
                         images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
-                image = apply_overlay(p.overlay_images is not None and i < len(p.overlay_images), p.overlay_images[i], p.paste_to, image)
+                image = apply_overlay(image, p.paste_to, i, p.overlay_images)
 
                 if opts.samples_save and not p.do_not_save_samples:
                     images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
-- 
cgit v1.2.1


From c6459986cb98211565c5a4d7596f9617e82b6d12 Mon Sep 17 00:00:00 2001
From: Kris57 <49682577+yuuki76@users.noreply.github.com>
Date: Sun, 23 Oct 2022 02:41:17 +0900
Subject: update ja translation

---
 localizations/ja_JP.json | 91 +++++++++++++++++++++++++++++++++++++++++-------
 1 file changed, 78 insertions(+), 13 deletions(-)

diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
index 514b579e..f9987473 100644
--- a/localizations/ja_JP.json
+++ b/localizations/ja_JP.json
@@ -14,9 +14,10 @@
     "img2img": "img2img",
     "Extras": "その他",
     "PNG Info": "PNG内の情報を表示",
-    "History": "履歴",
+    "Image Browser": "画像閲覧",
     "Checkpoint Merger": "Checkpointの統合",
     "Train": "学習",
+    "Create aesthetic embedding": "Create aesthetic embedding",
     "Settings": "設定",
     "Prompt": "プロンプト",
     "Negative prompt": "ネガティブ プロンプト",
@@ -67,8 +68,18 @@
     "Variation strength": "Variation 強度",
     "Resize seed from width": "Resize seed from width",
     "Resize seed from height": "Resize seed from height",
-    "Script": "スクリプト",
+    "Open for Clip Aesthetic!": "Open for Clip Aesthetic!",
+    "▼": "▼",
+    "Aesthetic weight": "Aesthetic weight",
+    "Aesthetic steps": "Aesthetic steps",
+    "Aesthetic learning rate": "Aesthetic learning rate",
+    "Slerp interpolation": "Slerp interpolation",
+    "Aesthetic imgs embedding": "Aesthetic imgs embedding",
     "None": "なし",
+    "Aesthetic text for imgs": "Aesthetic text for imgs",
+    "Slerp angle": "Slerp angle",
+    "Is negative text": "Is negative text",
+    "Script": "スクリプト",
     "Prompt matrix": "Prompt matrix",
     "Prompts from file or textbox": "Prompts from file or textbox",
     "X/Y plot": "X/Y plot",
@@ -76,6 +87,7 @@
     "Show Textbox": "Show Textbox",
     "File with inputs": "File with inputs",
     "Prompts": "プロンプト",
+    "Save images to path": "Save images to path",
     "X type": "X軸の種類",
     "Nothing": "なし",
     "Var. seed": "Var. seed",
@@ -86,7 +98,7 @@
     "Sampler": "サンプラー",
     "Checkpoint name": "Checkpoint名",
     "Hypernetwork": "Hypernetwork",
-    "Hypernet str.": "Hypernet強度",
+    "Hypernet str.": "Hypernetの強度",
     "Sigma Churn": "Sigma Churn",
     "Sigma min": "Sigma min",
     "Sigma max": "Sigma max",
@@ -141,6 +153,7 @@
     "Outpainting mk2": "Outpainting mk2",
     "Poor man's outpainting": "Poor man's outpainting",
     "SD upscale": "SD アップスケール",
+    "[C] Video to video": "[C] Video to video",
     "should be 2 or lower.": "2以下にすること",
     "Override `Sampling method` to Euler?(this method is built for it)": "サンプリングアルゴリズムをEulerに上書きする(そうすることを前提に設計されています)",
     "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)",
@@ -170,9 +183,22 @@
     "LDSR": "LDSR",
     "BSRGAN 4x": "BSRGAN 4x",
     "ESRGAN_4x": "ESRGAN_4x",
+    "R-ESRGAN General 4xV3": "R-ESRGAN General 4xV3",
+    "R-ESRGAN General WDN 4xV3": "R-ESRGAN General WDN 4xV3",
+    "R-ESRGAN AnimeVideo": "R-ESRGAN AnimeVideo",
+    "R-ESRGAN 4x+": "R-ESRGAN 4x+",
+    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+    "R-ESRGAN 2x+": "R-ESRGAN 2x+",
     "ScuNET GAN": "ScuNET GAN",
     "ScuNET PSNR": "ScuNET PSNR",
     "SwinIR 4x": "SwinIR 4x",
+    "Input file path": "Input file path",
+    "CRF (quality, less is better, x264 param)": "CRF (quality, less is better, x264 param)",
+    "FPS": "FPS",
+    "Seed step size": "Seed step size",
+    "Seed max distance": "Seed max distance",
+    "Start time": "Start time",
+    "End time": "End time",
     "Single Image": "単一画像",
     "Batch Process": "バッチ処理",
     "Batch from Directory": "フォルダからバッチ処理",
@@ -182,17 +208,18 @@
     "Scale to": "解像度指定",
     "Resize": "倍率",
     "Crop to fit": "合うように切り抜き",
-    "Upscaler 2": "アップスケーラー 2",
     "Upscaler 2 visibility": "Upscaler 2 visibility",
     "GFPGAN visibility": "GFPGAN visibility",
     "CodeFormer visibility": "CodeFormer visibility",
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)",
     "Open output directory": "出力フォルダを開く",
     "Send to txt2img": "txt2imgに送る",
-    "txt2img history": "txt2imgの履歴",
-    "img2img history": "img2imgの履歴",
-    "extras history": "その他タブの履歴",
-    "Renew Page": "更新",
+    "extras": "その他タブ",
+    "favorites": "お気に入り",
+    "Load": "読み込み",
+    "Images directory": "フォルダ",
+    "Prev batch": "前の batch",
+    "Next batch": "次の batch",
     "First Page": "最初のぺージへ",
     "Prev Page": "前ページへ",
     "Page Index": "ページ番号",
@@ -202,7 +229,12 @@
     "Delete": "削除",
     "Generate Info": "生成情報",
     "File Name": "ファイル名",
+    "Collect": "保存(お気に入り)",
+    "Refresh page": "Refresh page",
+    "Date to": "Date to",
+    "Number": "Number",
     "set_index": "set_index",
+    "Checkbox": "Checkbox",
     "A merger of the two checkpoints will be generated in your": "統合されたチェックポイントはあなたの",
     "checkpoint": "checkpoint",
     "directory.": "フォルダに保存されます.",
@@ -224,17 +256,37 @@
     "Name": "ファイル名",
     "Initialization text": "Initialization text",
     "Number of vectors per token": "Number of vectors per token",
+    "Overwrite Old Embedding": "Overwrite Old Embedding",
     "Modules": "Modules",
+    "Enter hypernetwork layer structure": "Enter hypernetwork layer structure",
+    "Select activation function of hypernetwork": "Select activation function of hypernetwork",
+    "linear": "linear",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "Add layer normalization": "Add layer normalization",
+    "Use dropout": "Use dropout",
+    "Overwrite Old Hypernetwork": "Overwrite Old Hypernetwork",
     "Source directory": "入力フォルダ",
     "Destination directory": "出力フォルダ",
+    "Existing Caption txt Action": "Existing Caption txt Action",
+    "ignore": "ignore",
+    "copy": "copy",
+    "prepend": "prepend",
+    "append": "append",
     "Create flipped copies": "反転画像を生成する",
-    "Split oversized images into two": "大きすぎる画像を2分割する",
+    "Split oversized images": "大きすぎる画像を分割する",
     "Use BLIP for caption": "BLIPで説明をつける",
     "Use deepbooru for caption": "deepbooruで説明をつける",
+    "Split image threshold": "分割する大きさの閾値",
+    "Split image overlap ratio": "Split image overlap ratio",
     "Preprocess": "前処理開始",
-    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "embeddingの学習をします;データセット内の画像は正方形でなければなりません。",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images",
+    "[wiki]": "[wiki]",
     "Embedding": "Embedding",
-    "Learning rate": "学習率",
+    "Embedding Learning rate": "Embedding Learning rate",
+    "Hypernetwork Learning rate": "Hypernetwork Learning rate",
     "Dataset directory": "データセットフォルダ",
     "Log directory": "ログフォルダ",
     "Prompt template file": "Prompt template file",
@@ -245,6 +297,8 @@
     "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews",
     "Train Hypernetwork": "Hypernetworkの学習を開始",
     "Train Embedding": "Embeddingの学習を開始",
+    "Create an aesthetic embedding out of any number of images": "Create an aesthetic embedding out of any number of images",
+    "Create images embedding": "Create images embedding",
     "Apply settings": "Apply settings",
     "Saving images/grids": "画像/グリッドの保存",
     "Always save all generated images": "生成された画像をすべて保存する",
@@ -295,7 +349,7 @@
     "Always print all generation info to standard output": "常にすべての生成に関する情報を標準出力(stdout)に出力する",
     "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
     "Training": "学習",
-    "Unload VAE and CLIP from VRAM when training": "学習を行う際、VAEとCLIPをVRAMから削除する",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "hypernetworkの学習をするとき、VAEとCLIPをRAMへ退避します。VRAMが節約できます。",
     "Filename word regex": "Filename word regex",
     "Filename join string": "Filename join string",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
@@ -332,6 +386,7 @@
     "Do not show any images in results for web": "WebUI上で一切画像を表示しない",
     "Add model hash to generation information": "モデルのハッシュ値を生成情報に追加",
     "Add model name to generation information": "モデルの名称を生成情報に追加",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
     "Font for image grids that have text": "画像グリッド内のテキストフォント",
     "Enable full page image viewer": "フルページの画像ビューワーを有効化",
     "Show images zoomed in by default in full page image viewer": "フルページ画像ビューアでデフォルトで画像を拡大して表示する",
@@ -350,10 +405,16 @@
     "sigma tmin": "sigma tmin",
     "sigma noise": "sigma noise",
     "Eta noise seed delta": "Eta noise seed delta",
+    "Images Browser": "画像閲覧",
+    "Preload images at startup": "起動時に画像を読み込んでおく",
+    "Number of pictures displayed on each page": "各ページに表示される画像の枚数",
+    "Minimum number of pages per load": "Minimum number of pages per load",
+    "Number of grids in each row": "Number of grids in each row",
     "Request browser notifications": "ブラウザ通知の許可を要求する",
     "Download localization template": "ローカライゼーション用のテンプレートをダウンロードする",
     "Reload custom script bodies (No ui updates, No restart)": "カスタムスクリプトを再読み込み (UIは変更されず、再起動もしません。)",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradioを再起動してコンポーネントをリフレッシュする (Custom Scripts, ui.py, js, cssのみ影響を受ける)",
+    "Audio": "Audio",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "プロンプト (Ctrl+Enter か Alt+Enter を押して生成)",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "ネガティブ プロンプト (Ctrl+Enter か Alt+Enter を押して生成)",
     "Add a random artist to the prompt.": "芸術家などの名称をプロンプトに追加",
@@ -379,6 +440,7 @@
     "Seed of a different picture to be mixed into the generation.": "Seed of a different picture to be mixed into the generation.",
     "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).",
     "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution",
+    "This text is used to rotate the feature space of the imgs embs": "This text is used to rotate the feature space of the imgs embs",
     "Separate values for X axis using commas.": "X軸に用いる値をカンマ(,)で区切って入力してください。",
     "Separate values for Y axis using commas.": "Y軸に用いる値をカンマ(,)で区切って入力してください。",
     "Write image to a directory (default - log/images) and generation parameters into csv file.": "Write image to a directory (default - log/images) and generation parameters into csv file.",
@@ -398,8 +460,10 @@
     "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.",
     "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
     "Leave blank to save images to the default path.": "空欄でデフォルトの場所へ画像を保存",
+    "Input images directory": "Input images directory",
     "Result = A * (1 - M) + B * M": "結果モデル = A * (1 - M) + B * M",
     "Result = A + (B - C) * M": "結果モデル = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
     "Path to directory with input images": "Path to directory with input images",
     "Path to directory where to write outputs": "Path to directory where to write outputs",
     "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
@@ -409,5 +473,6 @@
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
     "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing."
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
+    "Enable Autocomplete": "自動補完を有効化"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From a921badac3df177ab4bd8f6469dceb0342269cb7 Mon Sep 17 00:00:00 2001
From: Kris57 <49682577+yuuki76@users.noreply.github.com>
Date: Sun, 23 Oct 2022 18:12:21 +0900
Subject: update ja translation

---
 localizations/ja_JP.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
index f9987473..a790b0a6 100644
--- a/localizations/ja_JP.json
+++ b/localizations/ja_JP.json
@@ -366,7 +366,7 @@
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
     "Filter NSFW content": "NSFW(≒R-18)なコンテンツを検閲する",
-    "Stop At last layers of CLIP model": "最後から何層目でCLIPを止めるか(stop…layers of CLIP model)",
+    "Stop At last layers of CLIP model": "最後から何層目でCLIPを止めるか",
     "Interrogate Options": "Interrogate 設定",
     "Interrogate: keep models in VRAM": "Interrogate: モデルをVRAMに保持する",
     "Interrogate: use artists from artists.csv": "Interrogate: artists.csvにある芸術家などの名称を利用する",
-- 
cgit v1.2.1


From e33a05f263ff39f2750e4aa51b04d463c55cea4c Mon Sep 17 00:00:00 2001
From: Kris57 <49682577+yuuki76@users.noreply.github.com>
Date: Sun, 23 Oct 2022 21:16:44 +0900
Subject: update ja translation

---
 localizations/ja_JP.json | 209 ++++++++++++++++++++++++-----------------------
 1 file changed, 108 insertions(+), 101 deletions(-)

diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
index a790b0a6..741875c3 100644
--- a/localizations/ja_JP.json
+++ b/localizations/ja_JP.json
@@ -10,6 +10,7 @@
     "•": "•",
     "gradioで作ろう": "gradioで作ろう",
     "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
+    "Stop At last layers of CLIP model": "最後から何層目でCLIPを止めるか",
     "txt2img": "txt2img",
     "img2img": "img2img",
     "Extras": "その他",
@@ -17,7 +18,7 @@
     "Image Browser": "画像閲覧",
     "Checkpoint Merger": "Checkpointの統合",
     "Train": "学習",
-    "Create aesthetic embedding": "Create aesthetic embedding",
+    "Create aesthetic embedding": "aesthetic embeddingを作る",
     "Settings": "設定",
     "Prompt": "プロンプト",
     "Negative prompt": "ネガティブ プロンプト",
@@ -58,7 +59,7 @@
     "Highres. fix": "高解像度 fix(マウスオーバーで詳細)",
     "Firstpass width": "Firstpass width",
     "Firstpass height": "Firstpass height",
-    "Denoising strength": "ノイズ除去 強度",
+    "Denoising strength": "ノイズ除去強度",
     "Batch count": "バッチ生成回数",
     "Batch size": "バッチあたり生成枚数",
     "CFG Scale": "CFG Scale",
@@ -80,13 +81,17 @@
     "Slerp angle": "Slerp angle",
     "Is negative text": "Is negative text",
     "Script": "スクリプト",
+    "nai2SD Prompt Converter": "nai2SD Prompt Converter",
     "Prompt matrix": "Prompt matrix",
     "Prompts from file or textbox": "Prompts from file or textbox",
+    "Save steps of the sampling process to files": "Save steps of the sampling process to files",
     "X/Y plot": "X/Y plot",
+    "Prompts": "プロンプト",
+    "convert": "convert",
+    "Converted Prompts": "Converted Prompts",
     "Put variable parts at start of prompt": "Put variable parts at start of prompt",
     "Show Textbox": "Show Textbox",
     "File with inputs": "File with inputs",
-    "Prompts": "プロンプト",
     "Save images to path": "Save images to path",
     "X type": "X軸の種類",
     "Nothing": "なし",
@@ -125,23 +130,23 @@
     "Batch img2img": "Batch img2img",
     "Image for img2img": "Image for img2img",
     "Image for inpainting with mask": "Image for inpainting with mask",
-    "Mask": "Mask",
-    "Mask blur": "Mask blur",
-    "Mask mode": "Mask mode",
-    "Draw mask": "Draw mask",
-    "Upload mask": "Upload mask",
-    "Masking mode": "Masking mode",
-    "Inpaint masked": "Inpaint masked",
-    "Inpaint not masked": "Inpaint not masked",
-    "Masked content": "Masked content",
-    "fill": "fill",
+    "Mask": "マスク",
+    "Mask blur": "マスクぼかし",
+    "Mask mode": "マスクモード",
+    "Draw mask": "マスクをかける",
+    "Upload mask": "マスクをアップロードする",
+    "Masking mode": "マスキング方法",
+    "Inpaint masked": "マスクされた場所を描き直す",
+    "Inpaint not masked": "マスクされていない場所を描き直す",
+    "Masked content": "マスクされたコンテンツ",
+    "fill": "埋める",
     "original": "オリジナル",
-    "latent noise": "latent noise",
-    "latent nothing": "latent nothing",
-    "Inpaint at full resolution": "Inpaint at full resolution",
-    "Inpaint at full resolution padding, pixels": "Inpaint at full resolution padding, pixels",
-    "Process images in a directory on the same machine where the server is running.": "Process images in a directory on the same machine where the server is running.",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Use an empty output directory to save pictures normally instead of writing to the output directory.",
+    "latent noise": "潜在空間でのノイズ",
+    "latent nothing": "潜在空間での無",
+    "Inpaint at full resolution": "フル解像度で描き直す",
+    "Inpaint at full resolution padding, pixels": "フル解像度で描き直す際のパディング数。px単位。",
+    "Process images in a directory on the same machine where the server is running.": "サーバーが稼働しているマシンと同じフォルダにある画像を処理します",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "\"出力フォルダ\"を空にすると、通常の画像と同様に保存されます。",
     "Input directory": "入力フォルダ",
     "Output directory": "出力フォルダ",
     "Resize mode": "リサイズモード",
@@ -156,18 +161,18 @@
     "[C] Video to video": "[C] Video to video",
     "should be 2 or lower.": "2以下にすること",
     "Override `Sampling method` to Euler?(this method is built for it)": "サンプリングアルゴリズムをEulerに上書きする(そうすることを前提に設計されています)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)",
-    "Original prompt": "Original prompt",
-    "Original negative prompt": "Original negative prompt",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Override `Sampling Steps` to the same value as `Decode steps`?",
-    "Decode steps": "Decode steps",
-    "Override `Denoising strength` to 1?": "Override `Denoising strength` to 1?",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "プロンプトをオリジナルプロンプトと同じ値に上書きする(ネガティブプロンプトも同様)",
+    "Original prompt": "オリジナルのプロンプト",
+    "Original negative prompt": "オリジナルのネガティブプロンプト",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "サンプリング数をデコードステップ数と同じ値に上書きする",
+    "Decode steps": "デコードステップ数",
+    "Override `Denoising strength` to 1?": "ノイズ除去強度を1に上書きする",
     "Decode CFG scale": "Decode CFG scale",
-    "Randomness": "Randomness",
+    "Randomness": "ランダム性",
     "Sigma adjustment for finding noise for image": "Sigma adjustment for finding noise for image",
-    "Loops": "Loops",
+    "Loops": "ループ数",
     "Denoising strength change factor": "Denoising strength change factor",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推奨設定: サンプリング回数: 80-100, サンプリングアルゴリズム: Euler a, ノイズ除去強度: 0.8",
     "Pixels to expand": "Pixels to expand",
     "Outpainting direction": "Outpainting direction",
     "left": "左",
@@ -181,7 +186,6 @@
     "Upscaler": "アップスケーラー",
     "Lanczos": "Lanczos",
     "LDSR": "LDSR",
-    "BSRGAN 4x": "BSRGAN 4x",
     "ESRGAN_4x": "ESRGAN_4x",
     "R-ESRGAN General 4xV3": "R-ESRGAN General 4xV3",
     "R-ESRGAN General WDN 4xV3": "R-ESRGAN General WDN 4xV3",
@@ -211,7 +215,7 @@
     "Upscaler 2 visibility": "Upscaler 2 visibility",
     "GFPGAN visibility": "GFPGAN visibility",
     "CodeFormer visibility": "CodeFormer visibility",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormerの重み (注:0で最大、1で最小)",
     "Open output directory": "出力フォルダを開く",
     "Send to txt2img": "txt2imgに送る",
     "extras": "その他タブ",
@@ -225,12 +229,12 @@
     "Page Index": "ページ番号",
     "Next Page": "次ページへ",
     "End Page": "最後のページへ",
-    "number of images to delete consecutively next": "number of images to delete consecutively next",
+    "number of images to delete consecutively next": "次の削除で一度に削除する画像数",
     "Delete": "削除",
     "Generate Info": "生成情報",
     "File Name": "ファイル名",
     "Collect": "保存(お気に入り)",
-    "Refresh page": "Refresh page",
+    "Refresh page": "ページを更新",
     "Date to": "Date to",
     "Number": "Number",
     "set_index": "set_index",
@@ -253,13 +257,13 @@
     "Create embedding": "Embeddingを作る",
     "Create hypernetwork": "Hypernetworkを作る",
     "Preprocess images": "画像の前処理",
-    "Name": "ファイル名",
+    "Name": "名称",
     "Initialization text": "Initialization text",
     "Number of vectors per token": "Number of vectors per token",
-    "Overwrite Old Embedding": "Overwrite Old Embedding",
-    "Modules": "Modules",
-    "Enter hypernetwork layer structure": "Enter hypernetwork layer structure",
-    "Select activation function of hypernetwork": "Select activation function of hypernetwork",
+    "Overwrite Old Embedding": "古いEmbeddingを上書き",
+    "Modules": "モジュール",
+    "Enter hypernetwork layer structure": "Hypernetworkのレイヤー構造を入力",
+    "Select activation function of hypernetwork": "Hypernetworkの活性化関数",
     "linear": "linear",
     "relu": "relu",
     "leakyrelu": "leakyrelu",
@@ -267,14 +271,14 @@
     "swish": "swish",
     "Add layer normalization": "Add layer normalization",
     "Use dropout": "Use dropout",
-    "Overwrite Old Hypernetwork": "Overwrite Old Hypernetwork",
+    "Overwrite Old Hypernetwork": "古いHypernetworkを上書きする",
     "Source directory": "入力フォルダ",
     "Destination directory": "出力フォルダ",
-    "Existing Caption txt Action": "Existing Caption txt Action",
-    "ignore": "ignore",
-    "copy": "copy",
-    "prepend": "prepend",
-    "append": "append",
+    "Existing Caption txt Action": "既存のキャプションの取り扱い",
+    "ignore": "無視する",
+    "copy": "コピーする",
+    "prepend": "先頭に加える",
+    "append": "末尾に加える",
     "Create flipped copies": "反転画像を生成する",
     "Split oversized images": "大きすぎる画像を分割する",
     "Use BLIP for caption": "BLIPで説明をつける",
@@ -282,24 +286,24 @@
     "Split image threshold": "分割する大きさの閾値",
     "Split image overlap ratio": "Split image overlap ratio",
     "Preprocess": "前処理開始",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "EmbeddingまたはHypernetworkを学習します。1:1の比率の画像セットを含むフォルダを指定する必要があります。",
     "[wiki]": "[wiki]",
     "Embedding": "Embedding",
-    "Embedding Learning rate": "Embedding Learning rate",
-    "Hypernetwork Learning rate": "Hypernetwork Learning rate",
+    "Embedding Learning rate": "Embeddingの学習率(Learning rate)",
+    "Hypernetwork Learning rate": "Hypernetworkの学習率(Learning rate)",
     "Dataset directory": "データセットフォルダ",
     "Log directory": "ログフォルダ",
-    "Prompt template file": "Prompt template file",
+    "Prompt template file": "プロンプトのテンプレートファイル",
     "Max steps": "最大ステップ数",
     "Save an image to log directory every N steps, 0 to disable": "指定したステップ数ごとに画像を生成し、ログに保存する。0で無効化。",
     "Save a copy of embedding to log directory every N steps, 0 to disable": "指定したステップ数ごとにEmbeddingのコピーをログに保存する。0で無効化。",
     "Save images with embedding in PNG chunks": "保存する画像にembeddingを埋め込む",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "プレビューの作成にtxt2imgタブから読み込んだパラメータ(プロンプトなど)を使う",
     "Train Hypernetwork": "Hypernetworkの学習を開始",
     "Train Embedding": "Embeddingの学習を開始",
     "Create an aesthetic embedding out of any number of images": "Create an aesthetic embedding out of any number of images",
     "Create images embedding": "Create images embedding",
-    "Apply settings": "Apply settings",
+    "Apply settings": "設定を適用",
     "Saving images/grids": "画像/グリッドの保存",
     "Always save all generated images": "生成された画像をすべて保存する",
     "File format for images": "画像ファイルの保存形式",
@@ -310,15 +314,15 @@
     "Do not save grids consisting of one picture": "1画像からなるグリッド画像は保存しない",
     "Prevent empty spots in grid (when set to autodetect)": "(自動設定のとき)グリッドに空隙が生じるのを防ぐ",
     "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "グリッドの列数; -1で自動設定、0でバッチ生成回数と同じにする",
-    "Save text information about generation parameters as chunks to png files": "生成に関するパラメーターをpng画像に含める",
+    "Save text information about generation parameters as chunks to png files": "生成に関するパラメーターをPNG画像に含める",
     "Create a text file next to every image with generation parameters.": "保存する画像とともに生成パラメータをテキストファイルで保存する",
     "Save a copy of image before doing face restoration.": "顔修復を行う前にコピーを保存しておく。",
     "Quality for saved jpeg images": "JPG保存時の画質",
     "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "PNG画像が4MBを超えるか、どちらか1辺の長さが4000を超えたなら、ダウンスケールしてコピーを別にJPGで保存する",
-    "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
-    "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
+    "Use original name for output filename during batch process in extras tab": "その他タブでバッチ処理をする際、元のファイル名を出力ファイル名に使う",
+    "When using 'Save' button, only save a single selected image": "\"保存\"ボタンを使うとき、単一の選択された画像のみを保存する",
     "Do not add watermark to images": "電子透かしを画像に追加しない",
-    "Paths for saving": "Paths for saving",
+    "Paths for saving": "保存する場所",
     "Output directory for images; if empty, defaults to three directories below": "画像の保存先フォルダ(下項目のデフォルト値になります)",
     "Output directory for txt2img images": "txt2imgで作った画像の保存先フォルダ",
     "Output directory for img2img images": "img2imgで作った画像の保存先フォルダ",
@@ -345,13 +349,13 @@
     "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormerの重みパラメーター;0が最大で1が最小",
     "Move face restoration model from VRAM into RAM after processing": "処理終了後、顔修復モデルをVRAMからRAMへと移動する",
     "System": "システム設定",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成中のVRAM使用率の取得間隔。0にすると取得しない。",
     "Always print all generation info to standard output": "常にすべての生成に関する情報を標準出力(stdout)に出力する",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
+    "Add a second progress bar to the console that shows progress for an entire job.": "ジョブ全体の進捗をコンソールに表示する2つ目のプログレスバーを追加する",
     "Training": "学習",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "hypernetworkの学習をするとき、VAEとCLIPをRAMへ退避します。VRAMが節約できます。",
-    "Filename word regex": "Filename word regex",
-    "Filename join string": "Filename join string",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "hypernetworkの学習をするとき、VAEとCLIPをRAMへ退避する。VRAMが節約できます。",
+    "Filename word regex": "ファイル名の正規表現(学習用)",
+    "Filename join string": "ファイル名の結合子",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
     "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
     "Stable Diffusion": "Stable Diffusion",
@@ -360,13 +364,12 @@
     "Apply color correction to img2img results to match original colors.": "元画像に合わせてimg2imgの結果を色補正する",
     "Save a copy of image before applying color correction to img2img results": "色補正をする前の画像も保存する",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "img2imgでスライダーで指定されたステップ数を正確に実行する（通常は、ノイズ除去を少なくするためにより少ないステップ数で実行します）。",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "より良い結果を得るために、Kサンプラーで量子化を有効にします。これにより既存のシードが変更される可能性があります。適用するには再起動が必要です。",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "強調: (text)とするとモデルはtextをより強く扱い、[text]とするとモデルはtextをより弱く扱います。",
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "古い強調の実装を使う。古い生成物を再現するのに使えます。",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "K-diffusionサンプラーによるバッチ生成時に、単一画像生成時と同じ画像を生成する",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "75トークン以上を使用する場合、nトークン内の最後のカンマからパディングして一貫性を高める",
     "Filter NSFW content": "NSFW(≒R-18)なコンテンツを検閲する",
-    "Stop At last layers of CLIP model": "最後から何層目でCLIPを止めるか",
     "Interrogate Options": "Interrogate 設定",
     "Interrogate: keep models in VRAM": "Interrogate: モデルをVRAMに保持する",
     "Interrogate: use artists from artists.csv": "Interrogate: artists.csvにある芸術家などの名称を利用する",
@@ -382,18 +385,20 @@
     "User interface": "UI設定",
     "Show progressbar": "プログレスバーを表示",
     "Show image creation progress every N sampling steps. Set 0 to disable.": "指定したステップ数ごとに画像の生成過程を表示する。0で無効化。",
+    "Show previews of all images generated in a batch as a grid": "Show previews of all images generated in a batch as a grid",
     "Show grid in results for web": "WebUI上でグリッド表示",
     "Do not show any images in results for web": "WebUI上で一切画像を表示しない",
     "Add model hash to generation information": "モデルのハッシュ値を生成情報に追加",
     "Add model name to generation information": "モデルの名称を生成情報に追加",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "テキストからUIに生成パラメータを読み込む場合(PNG情報または貼り付けられたテキストから)、選択されたモデル/チェックポイントは変更しない。",
     "Font for image grids that have text": "画像グリッド内のテキストフォント",
     "Enable full page image viewer": "フルページの画像ビューワーを有効化",
     "Show images zoomed in by default in full page image viewer": "フルページ画像ビューアでデフォルトで画像を拡大して表示する",
     "Show generation progress in window title.": "ウィンドウのタイトルで生成の進捗を表示",
-    "Quicksettings list": "Quicksettings list",
+    "Quicksettings list": "クイック設定",
     "Localization (requires restart)": "言語 (プログラムの再起動が必要)",
     "ja_JP": "ja_JP",
+    "ru_RU": "ru_RU",
     "Sampler parameters": "サンプラー parameters",
     "Hide samplers in user interface (requires restart)": "使わないサンプリングアルゴリズムを隠す (再起動が必要)",
     "eta (noise multiplier) for DDIM": "DDIMで用いるeta (noise multiplier)",
@@ -414,65 +419,67 @@
     "Download localization template": "ローカライゼーション用のテンプレートをダウンロードする",
     "Reload custom script bodies (No ui updates, No restart)": "カスタムスクリプトを再読み込み (UIは変更されず、再起動もしません。)",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradioを再起動してコンポーネントをリフレッシュする (Custom Scripts, ui.py, js, cssのみ影響を受ける)",
-    "Audio": "Audio",
+    "Audio": "音声",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "プロンプト (Ctrl+Enter か Alt+Enter を押して生成)",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "ネガティブ プロンプト (Ctrl+Enter か Alt+Enter を押して生成)",
     "Add a random artist to the prompt.": "芸術家などの名称をプロンプトに追加",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "プロンプトから生成パラメータを読み込むか、プロンプトが空の場合は最後の生成パラメータをユーザーインターフェースに読み込む。",
     "Save style": "スタイルを保存する",
     "Apply selected styles to current prompt": "現在のプロンプトに選択したスタイルを適用する",
     "Stop processing current image and continue processing.": "現在の処理を中断し、その後の処理は続ける",
     "Stop processing images and return any results accumulated so far.": "処理を中断し、それまでに出来た結果を表示する",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "適用するスタイル。スタイルは、ポジティブプロンプトとネガティブプロンプトの両方のコンポーネントを持ち、両方に適用される。",
     "Do not do anything special": "特別なことをなにもしない",
     "Which algorithm to use to produce the image": "どのアルゴリズムを使って生成するか",
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常に独創的で、ステップ数によって全く異なる画像が得られる、ステップ数を30～40より高く設定しても効果がない。",
     "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 描き直しには最適",
-    "Produce an image that can be tiled.": "Produce an image that can be tiled.",
+    "Produce an image that can be tiled.": "タイルとして扱える画像を生成する",
     "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "2ステップで、まず部分的に小さい解像度で画像を作成し、その後アップスケールすることで、構図を変えずにディテールが改善されます。",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "アルゴリズムが画像の内容をどの程度参考にするかを決定します。0 にすると何も変わりませんし、 1 にすると全く無関係な画像になります。1.0未満の値ではスライダーで指定したサンプリングステップ数よりも少ないステップ数で処理が行われます。",
     "How many batches of images to create": "バッチ処理を何回行うか",
     "How many image to create in a single batch": "1回のバッチ処理で何枚の画像を生成するか",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 生成する画像がどの程度プロンプトに沿ったものになるか。 - 低い値の方がよりクリエイティブな結果を生み出します。",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "乱数発生器の出力を決定する値。同じパラメータとシードで画像を作成すれば、同じ結果が得られます。",
     "Set seed to -1, which will cause a new random number to be used every time": "シード値を -1 に設定するとランダムに生成します。",
     "Reuse seed from last generation, mostly useful if it was randomed": "前回生成時のシード値を読み出す。(ランダム生成時に便利)",
-    "Seed of a different picture to be mixed into the generation.": "Seed of a different picture to be mixed into the generation.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution",
+    "Seed of a different picture to be mixed into the generation.": "生成時に混合されることになる画像のシード値",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Variationの強度。0の場合、何の効果もありません。1では、バリエーションシードで完全な画像を得ることができます（Ancestalなアルゴリズム以外では、何か(?)を得るだけです）。",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "同じシードで指定された解像度の似た画像を生成することを試みる。",
     "This text is used to rotate the feature space of the imgs embs": "This text is used to rotate the feature space of the imgs embs",
     "Separate values for X axis using commas.": "X軸に用いる値をカンマ(,)で区切って入力してください。",
     "Separate values for Y axis using commas.": "Y軸に用いる値をカンマ(,)で区切って入力してください。",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Write image to a directory (default - log/images) and generation parameters into csv file.",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "画像はフォルダ(デフォルト:log/images)に、生成パラメータはcsvファイルに書き出します。",
     "Open images output directory": "画像の出力フォルダを開く",
-    "How much to blur the mask before processing, in pixels.": "How much to blur the mask before processing, in pixels.",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "What to put inside the masked area before processing it with Stable Diffusion.",
-    "fill it with colors of the image": "fill it with colors of the image",
-    "keep whatever was there originally": "keep whatever was there originally",
-    "fill it with latent space noise": "fill it with latent space noise",
-    "fill it with latent space zeroes": "fill it with latent space zeroes",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.",
-    "How many times to repeat processing an image and using it as input for the next iteration": "How many times to repeat processing an image and using it as input for the next iteration",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.",
-    "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
+    "How much to blur the mask before processing, in pixels.": "処理前にどれだけマスクをぼかすか。px単位。",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Stable Diffusionにわたす前にマスクされたエリアに何を書き込むか",
+    "fill it with colors of the image": "元画像の色で埋める",
+    "keep whatever was there originally": "もともとあったものをそのままにする",
+    "fill it with latent space noise": "潜在空間(latent space)におけるノイズで埋める",
+    "fill it with latent space zeroes": "潜在空間(latent space)における0で埋める",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "マスクされた領域をターゲット解像度にアップスケールし、インペイントを行い、元の解像度にダウンスケールして元の画像に貼り付けます。",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "画像をターゲット解像度にリサイズします。高さと幅が一致しない場合、アスペクト比が正しくなくなります。",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "対象の解像度に画像をフィットさせます。はみ出た部分は切り取られます。",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "画像をリサイズして、ターゲット解像度の中に収まるようにします。空白部分は画像の色で埋めます。",
+    "How many times to repeat processing an image and using it as input for the next iteration": "何回画像処理を繰り返し、次の反復処理の入力として使用するか",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "ループバックモードにおいて、各ループでのノイズ除去の強度はこの値によって乗算されます。1より小さければ変化が小さくなっていって、生成される画像は1つの画像に収束します。1より大きいとどんどん変化が大きくなるので、生成される画像はよりカオスになります。",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SDアップスケールで、どれだけタイル間の重なりを確保するか(px単位)。タイルの一部を重複させることで、1枚の画像にした時明らかな継ぎ目がなくなります。",
+    "A directory on the same machine where the server is running.": "サーバーが稼働しているのと同じマシンのあるフォルダ",
     "Leave blank to save images to the default path.": "空欄でデフォルトの場所へ画像を保存",
     "Input images directory": "Input images directory",
-    "Result = A * (1 - M) + B * M": "結果モデル = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "結果モデル = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
-    "Path to directory with input images": "Path to directory with input images",
-    "Path to directory where to write outputs": "Path to directory where to write outputs",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
+    "Result = A * (1 - M) + B * M": "出力されるモデル = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "出力されるモデル = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "最初と最後の数字は1でなければなりません。 例:'1, 2, 1'",
+    "Path to directory with input images": "入力ファイルのあるフォルダの場所",
+    "Path to directory where to write outputs": "出力を書き込むフォルダの場所",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "以下のタグを用いてファイル名パターンを決められます: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 空白でデフォルト設定。",
     "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "このオプションを有効にすると、作成された画像にウォーターマークが追加されなくなります。警告:ウォーターマークを追加しない場合、非倫理的な行動とみなされる場合があります。",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
-    "Restore low quality faces using GFPGAN neural network": "GFPGANを用いて低クオリティーの画像を修復",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "以下のタグを用いてサブフォルダのフォルダ名パターンを決められます: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 空白でデフォルト設定",
+    "Restore low quality faces using GFPGAN neural network": "GFPGANを用いて低クオリティーな顔画像を修復",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "この正規表現を使ってファイル名から単語を抽出し、以下のオプションで結合して学習用のラベルテキストにします。ファイル名のテキストをそのまま使用する場合は、空白にしてください。",
+    "This string will be used to join split words into a single line if the option above is enabled.": "この文字列は、上記のオプションが有効な場合に、分割された単語を1行に結合するために使用されます。",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "上部のクイックアクセスバーに置く設定の設定名をカンマで区切って入力。設定名については modules/shared.py を参照してください。適用するには再起動が必要です。",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "この値が0以外の場合、シードに追加され、Etaでサンプラーを使用する際のノイズ用の乱数生成器を初期化するのに使用されます。これを利用して、さらにバリエーション豊かな画像を作成したり、他のソフトの画像に合わせたりすることができます。",
+    "NAIConvert": "NAIから変換",
+    "History": "履歴",
     "Enable Autocomplete": "自動補完を有効化"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From 71d14a4c40503f0788e2881bb406911c102af40d Mon Sep 17 00:00:00 2001
From: Kris57 <49682577+yuuki76@users.noreply.github.com>
Date: Sun, 23 Oct 2022 21:25:25 +0900
Subject: cleanup ja translation

---
 localizations/ja_JP.json | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
index 741875c3..7bb9db30 100644
--- a/localizations/ja_JP.json
+++ b/localizations/ja_JP.json
@@ -81,17 +81,14 @@
     "Slerp angle": "Slerp angle",
     "Is negative text": "Is negative text",
     "Script": "スクリプト",
-    "nai2SD Prompt Converter": "nai2SD Prompt Converter",
     "Prompt matrix": "Prompt matrix",
     "Prompts from file or textbox": "Prompts from file or textbox",
     "Save steps of the sampling process to files": "Save steps of the sampling process to files",
     "X/Y plot": "X/Y plot",
-    "Prompts": "プロンプト",
-    "convert": "convert",
-    "Converted Prompts": "Converted Prompts",
     "Put variable parts at start of prompt": "Put variable parts at start of prompt",
     "Show Textbox": "Show Textbox",
     "File with inputs": "File with inputs",
+    "Prompts": "プロンプト",
     "Save images to path": "Save images to path",
     "X type": "X軸の種類",
     "Nothing": "なし",
-- 
cgit v1.2.1


From edc0c907fa257f70d63dfdbb755e674cea08f4a7 Mon Sep 17 00:00:00 2001
From: Kris57 <49682577+yuuki76@users.noreply.github.com>
Date: Sun, 23 Oct 2022 22:10:13 +0900
Subject: fix ja translation

---
 localizations/ja_JP.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
index 7bb9db30..a6cc2477 100644
--- a/localizations/ja_JP.json
+++ b/localizations/ja_JP.json
@@ -437,7 +437,7 @@
     "How many image to create in a single batch": "1回のバッチ処理で何枚の画像を生成するか",
     "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 生成する画像がどの程度プロンプトに沿ったものになるか。 - 低い値の方がよりクリエイティブな結果を生み出します。",
     "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "乱数発生器の出力を決定する値。同じパラメータとシードで画像を作成すれば、同じ結果が得られます。",
-    "Set seed to -1, which will cause a new random number to be used every time": "シード値を -1 に設定するとランダムに生成します。",
+    "Set seed to -1, which will cause a new random number to be used every time": "シード値を-1に設定。つまり、毎回ランダムに生成します。",
     "Reuse seed from last generation, mostly useful if it was randomed": "前回生成時のシード値を読み出す。(ランダム生成時に便利)",
     "Seed of a different picture to be mixed into the generation.": "生成時に混合されることになる画像のシード値",
     "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Variationの強度。0の場合、何の効果もありません。1では、バリエーションシードで完全な画像を得ることができます（Ancestalなアルゴリズム以外では、何か(?)を得るだけです）。",
-- 
cgit v1.2.1


From 734986dde3231416813f827242c111da212b2ccb Mon Sep 17 00:00:00 2001
From: Trung Ngo <codem01@gmail.com>
Date: Mon, 24 Oct 2022 01:17:09 -0500
Subject: add callback after image is saved

---
 modules/images.py           |  3 ++-
 modules/script_callbacks.py | 12 +++++++++++-
 2 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index b9589563..01c60f89 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -12,7 +12,7 @@ from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
 from fonts.ttf import Roboto
 import string
 
-from modules import sd_samplers, shared
+from modules import sd_samplers, shared, script_callbacks
 from modules.shared import opts, cmd_opts
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
@@ -467,6 +467,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     else:
         txt_fullfn = None
 
+    script_callbacks.image_saved_callback(image, p, fullfn, txt_fullfn)
     return fullfn, txt_fullfn
 
 
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 5bcccd67..5836e4b9 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -2,11 +2,12 @@
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
-
+callbacks_image_saved = []
 
 def clear_callbacks():
     callbacks_model_loaded.clear()
     callbacks_ui_tabs.clear()
+    callbacks_image_saved.clear()
 
 
 def model_loaded_callback(sd_model):
@@ -28,6 +29,10 @@ def ui_settings_callback():
         callback()
 
 
+def image_saved_callback(image, p, fullfn, txt_fullfn):
+    for callback in callbacks_image_saved:
+        callback(image, p, fullfn, txt_fullfn)
+
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
@@ -51,3 +56,8 @@ def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
     callbacks_ui_settings.append(callback)
+
+
+def on_save_imaged(callback):
+    """register a function to call after modules.images.save_image is called returning same values, original image and p """
+    callbacks_image_saved.append(callback)
-- 
cgit v1.2.1


From 876a96f0f9843382ebc8984db3de5d8af0e9ce4c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 24 Oct 2022 09:39:46 +0300
Subject: remove erroneous dir in the extension directory remove loading .js
 files from scripts dir (they go into javascript) load scripts after models,
 for scripts that depend on loaded models

---
 extensions/stable-diffusion-webui-inspiration |  1 -
 modules/ui.py                                 |  2 +-
 webui.py                                      | 11 ++++++-----
 3 files changed, 7 insertions(+), 7 deletions(-)
 delete mode 160000 extensions/stable-diffusion-webui-inspiration

diff --git a/extensions/stable-diffusion-webui-inspiration b/extensions/stable-diffusion-webui-inspiration
deleted file mode 160000
index a0b96664..00000000
--- a/extensions/stable-diffusion-webui-inspiration
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit a0b96664d2524b87916ae463fbb65411b13a569b
diff --git a/modules/ui.py b/modules/ui.py
index a73b9ff0..03528968 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1885,7 +1885,7 @@ def load_javascript(raw_response):
         javascript = f'<script>{jsfile.read()}</script>'
 
     scripts_list = modules.scripts.list_scripts("javascript", ".js")
-    scripts_list += modules.scripts.list_scripts("scripts", ".js")
+    
     for basedir, filename, path in scripts_list:
         with open(path, "r", encoding="utf8") as jsfile:
             javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
diff --git a/webui.py b/webui.py
index a0f3757f..ade7334b 100644
--- a/webui.py
+++ b/webui.py
@@ -9,7 +9,7 @@ from fastapi.middleware.gzip import GZipMiddleware
 
 from modules.paths import script_path
 
-from modules import devices, sd_samplers
+from modules import devices, sd_samplers, upscaler
 import modules.codeformer_model as codeformer
 import modules.extras
 import modules.face_restoration
@@ -73,12 +73,11 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
 
 def initialize():
-    modules.scripts.load_scripts()
     if cmd_opts.ui_debug_mode:
-        class enmpty():
-            name = None
-        shared.sd_upscalers = [enmpty()]
+        shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
+        modules.scripts.load_scripts()
         return
+
     modelloader.cleanup_models()
     modules.sd_models.setup_model()
     codeformer.setup_model(cmd_opts.codeformer_models_path)
@@ -86,6 +85,8 @@ def initialize():
     shared.face_restorers.append(modules.face_restoration.FaceRestoration())
     modelloader.load_upscalers()
 
+    modules.scripts.load_scripts()
+
     modules.sd_models.load_model()
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
-- 
cgit v1.2.1


From c623fa1f0b9a3936a29f1d1bd65f4e0fadf1c9c4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 24 Oct 2022 09:51:17 +0300
Subject: add extensions dir

---
 extensions/put extensions here.txt | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 extensions/put extensions here.txt

diff --git a/extensions/put extensions here.txt b/extensions/put extensions here.txt
new file mode 100644
index 00000000..e69de29b
-- 
cgit v1.2.1


From 3be6b29d81408d2adb741bff5b11c80214aa621e Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Mon, 24 Oct 2022 15:14:34 +0900
Subject: indent=4 config.json

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 6541e679..d6ddfe59 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -348,7 +348,7 @@ class Options:
 
     def save(self, filename):
         with open(filename, "w", encoding="utf8") as file:
-            json.dump(self.data, file)
+            json.dump(self.data, file, indent=4)
 
     def same_type(self, x, y):
         if x is None or y is None:
-- 
cgit v1.2.1


From c5d90628a4058bf49c2fdabf620a24db73407f31 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 22 Oct 2022 17:16:55 +0900
Subject: move "file_decoration" initialize section

into "if forced_filename is None:"
no need to initialize it if it's not going to be used
---
 modules/images.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index b9589563..50a59cff 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -386,18 +386,6 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         txt_fullfn (`str` or None):
             If a text file is saved for this image, this will be its full path. Otherwise None.
     '''
-    if short_filename or prompt is None or seed is None:
-        file_decoration = ""
-    elif opts.save_to_dirs:
-        file_decoration = opts.samples_filename_pattern or "[seed]"
-    else:
-        file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
-
-    if file_decoration != "":
-        file_decoration = "-" + file_decoration.lower()
-
-    file_decoration = apply_filename_pattern(file_decoration, p, seed, prompt) + suffix
-
     if extension == 'png' and opts.enable_pnginfo and info is not None:
         pnginfo = PngImagePlugin.PngInfo()
 
@@ -419,6 +407,18 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     os.makedirs(path, exist_ok=True)
 
     if forced_filename is None:
+        if short_filename or prompt is None or seed is None:
+            file_decoration = ""
+        elif opts.save_to_dirs:
+            file_decoration = opts.samples_filename_pattern or "[seed]"
+        else:
+            file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
+
+        if file_decoration != "":
+            file_decoration = "-" + file_decoration.lower()
+
+        file_decoration = apply_filename_pattern(file_decoration, p, seed, prompt) + suffix
+
         basecount = get_next_sequence_number(path, basename)
         fullfn = "a.png"
         fullfn_without_extension = "a"
-- 
cgit v1.2.1


From 7d4a4db9ea7543c079f4a4a702c2945f4b66cd11 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 22 Oct 2022 17:48:59 +0900
Subject: modify unnecessary sting assignment as it's going to get overwritten

---
 modules/images.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index 50a59cff..cc5066b1 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -420,8 +420,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         file_decoration = apply_filename_pattern(file_decoration, p, seed, prompt) + suffix
 
         basecount = get_next_sequence_number(path, basename)
-        fullfn = "a.png"
-        fullfn_without_extension = "a"
+        fullfn = None
+        fullfn_without_extension = None
         for i in range(500):
             fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
             fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
-- 
cgit v1.2.1


From 37dd6deafb831a809eaf7ae8d232937a8c7998e7 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 22 Oct 2022 21:11:15 +0900
Subject: filename pattern [datetime], extended customizable Format and Time
 Zone

format:
[datetime]
[datetime<Format>]
[datetime<Format><Time Zone>]
---
 modules/images.py | 54 +++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 53 insertions(+), 1 deletion(-)

diff --git a/modules/images.py b/modules/images.py
index cc5066b1..fb0b373d 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -1,4 +1,5 @@
 import datetime
+import pytz
 import io
 import math
 import os
@@ -302,7 +303,7 @@ def apply_filename_pattern(x, p, seed, prompt):
 
     x = x.replace("[model_hash]", getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash))
     x = x.replace("[date]", datetime.date.today().isoformat())
-    x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
+    x = replace_datetime(x)
     x = x.replace("[job_timestamp]", getattr(p, "job_timestamp", shared.state.job_timestamp))
 
     # Apply [prompt] at last. Because it may contain any replacement word.^M
@@ -353,6 +354,57 @@ def get_next_sequence_number(path, basename):
     return result + 1
 
 
+def replace_datetime(input_str: str):
+    """
+    Formats sub_string of input_str with formatted datetime with time zone support.
+    accepts sub_string format: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
+    case insensitive
+
+    e.g.
+    input: "___[Datetime<%Y_%m_%d %H-%M-%S><Asia/Tokyo>]___"
+    return: "___2022_10_22 20-40-14___"
+
+    handles invalid Formats and Time Zones
+
+    time format reference:
+    https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes
+
+    valid time zones
+    print(pytz.all_timezones)
+    https://pytz.sourceforge.net/
+    """
+    default_time_format = '%Y%m%d%H%M%S'
+    time_now = datetime.datetime.now()
+
+    # match all datetime to be replace
+    match_itr = re.finditer(r'\[datetime(?:<([^>]*)>(?:<([^>]*)>)?)?]', input_str, re.IGNORECASE)
+    for match in reversed(list(match_itr)):
+        # extract format
+        time_format = match.group(1)
+        if time_format == '':
+            # if time_format is blank use default YYYYMMDDHHMMSS
+            time_format = default_time_format
+
+        # extract timezone
+        try:
+            time_zone = pytz.timezone(match.group(2))
+        except pytz.exceptions.UnknownTimeZoneError as _:
+            # if no time_zone or invalid, use system time
+            time_zone = None
+
+        # generate time string
+        time_zone_time = time_now.astimezone(time_zone)
+        try:
+            formatted_time = time_zone_time.strftime(time_format)
+
+        except (ValueError, TypeError) as _:
+            # if format error then use default_time_format
+            formatted_time = time_zone_time.strftime(default_time_format)
+
+        input_str = input_str[:match.start()] + formatted_time + input_str[match.end():]
+    return input_str
+
+
 def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
     '''Save an image.
 
-- 
cgit v1.2.1


From 480d8e764611d7779bf8e0245640a6b84af626f9 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 22 Oct 2022 22:26:57 +0900
Subject: replace "srt.replace()" in apply_filename_pattern() with equivalent
 re.sub()

the file_decoration passed into apply_filename_pattern() is formatted to lowercase to increase compatibility
the use of case sensitive srt.replace()

but because the newly implemented "time format" is case sensitive
the lowercasing the file_decoration will cause time format to be broken

in order to resolve this issue
I decided to replace every srt.replace() and in if "str" in x to regular expression (case insensitive) equivalent
---
 modules/images.py | 35 +++++++++++++++++------------------
 1 file changed, 17 insertions(+), 18 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index fb0b373d..55d8ab5e 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -291,25 +291,24 @@ def apply_filename_pattern(x, p, seed, prompt):
     max_prompt_words = opts.directories_max_prompt_words
 
     if seed is not None:
-        x = x.replace("[seed]", str(seed))
+        x = re.sub(r'\[seed]', str(seed), x, flags=re.IGNORECASE)
 
     if p is not None:
-        x = x.replace("[steps]", str(p.steps))
-        x = x.replace("[cfg]", str(p.cfg_scale))
-        x = x.replace("[width]", str(p.width))
-        x = x.replace("[height]", str(p.height))
-        x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
-        x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
-
-    x = x.replace("[model_hash]", getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash))
-    x = x.replace("[date]", datetime.date.today().isoformat())
+        x = re.sub(r'\[steps]', str(p.steps), x, flags=re.IGNORECASE)
+        x = re.sub(r'\[cfg]', str(p.cfg_scale), x, flags=re.IGNORECASE)
+        x = re.sub(r'\[width]', str(p.width), x, flags=re.IGNORECASE)
+        x = re.sub(r'\[height]', str(p.height), x, flags=re.IGNORECASE)
+        x = re.sub(r'\[styles]', sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False), x, flags=re.IGNORECASE)
+        x = re.sub(r'\[sampler]', sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False), x, flags=re.IGNORECASE)
+
+    x = re.sub(r'\[model_hash]', getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash), x, flags=re.IGNORECASE)
+    x = re.sub(r'\[date]', datetime.date.today().isoformat(), x, flags=re.IGNORECASE)
     x = replace_datetime(x)
-    x = x.replace("[job_timestamp]", getattr(p, "job_timestamp", shared.state.job_timestamp))
-
+    x = re.sub(r'\[job_timestamp]', getattr(p, "job_timestamp", shared.state.job_timestamp), x, flags=re.IGNORECASE)
     # Apply [prompt] at last. Because it may contain any replacement word.^M
     if prompt is not None:
-        x = x.replace("[prompt]", sanitize_filename_part(prompt))
-        if "[prompt_no_styles]" in x:
+        x = re.sub(r'\[prompt]', sanitize_filename_part(prompt), x, flags=re.IGNORECASE)
+        if re.search(r'\[prompt_no_styles]', x, re.IGNORECASE):
             prompt_no_style = prompt
             for style in shared.prompt_styles.get_style_prompts(p.styles):
                 if len(style) > 0:
@@ -317,14 +316,14 @@ def apply_filename_pattern(x, p, seed, prompt):
                     for part in style_parts:
                         prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
             prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip()
-            x = x.replace("[prompt_no_styles]", sanitize_filename_part(prompt_no_style, replace_spaces=False))
+            x = re.sub(r'\[prompt_no_styles]', sanitize_filename_part(prompt_no_style, replace_spaces=False), x, flags=re.IGNORECASE)
 
-        x = x.replace("[prompt_spaces]", sanitize_filename_part(prompt, replace_spaces=False))
-        if "[prompt_words]" in x:
+        x = re.sub(r'\[prompt_spaces]', sanitize_filename_part(prompt, replace_spaces=False), x, flags=re.IGNORECASE)
+        if re.search(r'\[prompt_words]', x, re.IGNORECASE):
             words = [x for x in re_nonletters.split(prompt or "") if len(x) > 0]
             if len(words) == 0:
                 words = ["empty"]
-            x = x.replace("[prompt_words]", sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))
+            x = re.sub(r'\[prompt_words]', sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False), x, flags=re.IGNORECASE)
 
     if cmd_opts.hide_ui_dir_config:
         x = re.sub(r'^[\\/]+|\.{2,}[\\/]+|[\\/]+\.{2,}', '', x)
-- 
cgit v1.2.1


From 00952fb4a8b86b75402cc8276e00f3daadf1f459 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 22 Oct 2022 22:31:02 +0900
Subject: add sanitize_filename() to datetime

---
 modules/images.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/images.py b/modules/images.py
index 55d8ab5e..898b065b 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -400,6 +400,7 @@ def replace_datetime(input_str: str):
             # if format error then use default_time_format
             formatted_time = time_zone_time.strftime(default_time_format)
 
+        formatted_time = sanitize_filename_part(formatted_time, replace_spaces=False)
         input_str = input_str[:match.start()] + formatted_time + input_str[match.end():]
     return input_str
 
-- 
cgit v1.2.1


From 8f6af4ed651cdb4456e2f3474d05dd3a18086ac2 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 22 Oct 2022 22:32:26 +0900
Subject: remove lowercasing file_decoration as it is not needed anymore

---
 modules/images.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/images.py b/modules/images.py
index 898b065b..321439e3 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -467,7 +467,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
 
         if file_decoration != "":
-            file_decoration = "-" + file_decoration.lower()
+            file_decoration = "-" + file_decoration
 
         file_decoration = apply_filename_pattern(file_decoration, p, seed, prompt) + suffix
 
-- 
cgit v1.2.1


From 5a981310e68253c77f9fe3144d247cfd531f9e48 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sun, 23 Oct 2022 18:09:21 +0900
Subject: replace_datetime() can now accept a datetime parameter

---
 modules/images.py | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index 321439e3..ff00fc52 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -303,7 +303,7 @@ def apply_filename_pattern(x, p, seed, prompt):
 
     x = re.sub(r'\[model_hash]', getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash), x, flags=re.IGNORECASE)
     x = re.sub(r'\[date]', datetime.date.today().isoformat(), x, flags=re.IGNORECASE)
-    x = replace_datetime(x)
+    x = replace_datetime(x, datetime.datetime.now())
     x = re.sub(r'\[job_timestamp]', getattr(p, "job_timestamp", shared.state.job_timestamp), x, flags=re.IGNORECASE)
     # Apply [prompt] at last. Because it may contain any replacement word.^M
     if prompt is not None:
@@ -353,8 +353,14 @@ def get_next_sequence_number(path, basename):
     return result + 1
 
 
-def replace_datetime(input_str: str):
+def replace_datetime(input_str: str, time_datetime: datetime.datetime = None):
     """
+    Args:
+        input_str (`str`):
+            the String to be Formatted
+        time_datetime (`datetime.datetime`)
+            the time to be used, if None, use datetime.datetime.now()
+
     Formats sub_string of input_str with formatted datetime with time zone support.
     accepts sub_string format: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
     case insensitive
@@ -373,8 +379,8 @@ def replace_datetime(input_str: str):
     https://pytz.sourceforge.net/
     """
     default_time_format = '%Y%m%d%H%M%S'
-    time_now = datetime.datetime.now()
-
+    if time_datetime is None:
+        time_datetime = datetime.datetime.now()
     # match all datetime to be replace
     match_itr = re.finditer(r'\[datetime(?:<([^>]*)>(?:<([^>]*)>)?)?]', input_str, re.IGNORECASE)
     for match in reversed(list(match_itr)):
@@ -392,7 +398,7 @@ def replace_datetime(input_str: str):
             time_zone = None
 
         # generate time string
-        time_zone_time = time_now.astimezone(time_zone)
+        time_zone_time = time_datetime.astimezone(time_zone)
         try:
             formatted_time = time_zone_time.strftime(time_format)
 
-- 
cgit v1.2.1


From eb007e5884c23fbc38d7e9d1dd3669625270ca27 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sun, 23 Oct 2022 18:17:39 +0900
Subject: use the same datetime object for [date] and [datetime]

---
 modules/images.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index ff00fc52..a9b1330d 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -302,8 +302,9 @@ def apply_filename_pattern(x, p, seed, prompt):
         x = re.sub(r'\[sampler]', sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False), x, flags=re.IGNORECASE)
 
     x = re.sub(r'\[model_hash]', getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash), x, flags=re.IGNORECASE)
-    x = re.sub(r'\[date]', datetime.date.today().isoformat(), x, flags=re.IGNORECASE)
-    x = replace_datetime(x, datetime.datetime.now())
+    current_time = datetime.datetime.now()
+    x = re.sub(r'\[date]', current_time.strftime('%Y-%m-%d'), x, flags=re.IGNORECASE)
+    x = replace_datetime(x, current_time)  # replace [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
     x = re.sub(r'\[job_timestamp]', getattr(p, "job_timestamp", shared.state.job_timestamp), x, flags=re.IGNORECASE)
     # Apply [prompt] at last. Because it may contain any replacement word.^M
     if prompt is not None:
-- 
cgit v1.2.1


From 994aaadf0861366b9e6f219e1a3c25a233fbb63c Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Mon, 24 Oct 2022 16:44:36 +0800
Subject: a strange bug

---
 extensions/stable-diffusion-webui-inspiration | 2 +-
 modules/ui.py                                 | 4 ++++
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/extensions/stable-diffusion-webui-inspiration b/extensions/stable-diffusion-webui-inspiration
index a0b96664..c50c03ac 160000
--- a/extensions/stable-diffusion-webui-inspiration
+++ b/extensions/stable-diffusion-webui-inspiration
@@ -1 +1 @@
-Subproject commit a0b96664d2524b87916ae463fbb65411b13a569b
+Subproject commit c50c03ac8fd2f6317d17d0c8c7c1ce26e6fe5cd7
diff --git a/modules/ui.py b/modules/ui.py
index a73b9ff0..8c6dc026 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -55,6 +55,7 @@ mimetypes.init()
 mimetypes.add_type('application/javascript', '.js')
 txt2img_paste_fields = []
 img2img_paste_fields = []
+init_img_components = {}
 
 
 if not cmd_opts.share and not cmd_opts.listen:
@@ -1174,6 +1175,9 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[init_img_with_mask],
         )
 
+    global  init_img_components
+    init_img_components = {"img2img":init_img, "inpaint":init_img_with_mask, "extras":extras_image}
+
     with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
-- 
cgit v1.2.1


From 8da1bd48bf9d0411cd9ba87b8d9220743cb5807e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 24 Oct 2022 14:03:58 +0300
Subject: add an option to skip adding number to filenames when saving. rework
 filename pattern function go through the pattern once and not calculate any
 of replacements until they are actually encountered in the pattern.

---
 modules/images.py | 250 ++++++++++++++++++++++++++++--------------------------
 modules/shared.py |   8 +-
 2 files changed, 135 insertions(+), 123 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index a9b1330d..848ede75 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -1,4 +1,7 @@
 import datetime
+import sys
+import traceback
+
 import pytz
 import io
 import math
@@ -274,10 +277,15 @@ invalid_filename_chars = '<>:"/\\|?*\n'
 invalid_filename_prefix = ' '
 invalid_filename_postfix = ' .'
 re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
+re_pattern = re.compile(r"([^\[\]]+|\[([^]]+)]|[\[\]]*)")
+re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
 max_filename_part_length = 128
 
 
 def sanitize_filename_part(text, replace_spaces=True):
+    if text is None:
+        return None
+
     if replace_spaces:
         text = text.replace(' ', '_')
 
@@ -287,49 +295,103 @@ def sanitize_filename_part(text, replace_spaces=True):
     return text
 
 
-def apply_filename_pattern(x, p, seed, prompt):
-    max_prompt_words = opts.directories_max_prompt_words
-
-    if seed is not None:
-        x = re.sub(r'\[seed]', str(seed), x, flags=re.IGNORECASE)
-
-    if p is not None:
-        x = re.sub(r'\[steps]', str(p.steps), x, flags=re.IGNORECASE)
-        x = re.sub(r'\[cfg]', str(p.cfg_scale), x, flags=re.IGNORECASE)
-        x = re.sub(r'\[width]', str(p.width), x, flags=re.IGNORECASE)
-        x = re.sub(r'\[height]', str(p.height), x, flags=re.IGNORECASE)
-        x = re.sub(r'\[styles]', sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False), x, flags=re.IGNORECASE)
-        x = re.sub(r'\[sampler]', sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False), x, flags=re.IGNORECASE)
-
-    x = re.sub(r'\[model_hash]', getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash), x, flags=re.IGNORECASE)
-    current_time = datetime.datetime.now()
-    x = re.sub(r'\[date]', current_time.strftime('%Y-%m-%d'), x, flags=re.IGNORECASE)
-    x = replace_datetime(x, current_time)  # replace [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
-    x = re.sub(r'\[job_timestamp]', getattr(p, "job_timestamp", shared.state.job_timestamp), x, flags=re.IGNORECASE)
-    # Apply [prompt] at last. Because it may contain any replacement word.^M
-    if prompt is not None:
-        x = re.sub(r'\[prompt]', sanitize_filename_part(prompt), x, flags=re.IGNORECASE)
-        if re.search(r'\[prompt_no_styles]', x, re.IGNORECASE):
-            prompt_no_style = prompt
-            for style in shared.prompt_styles.get_style_prompts(p.styles):
-                if len(style) > 0:
-                    style_parts = [y for y in style.split("{prompt}")]
-                    for part in style_parts:
-                        prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
-            prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip()
-            x = re.sub(r'\[prompt_no_styles]', sanitize_filename_part(prompt_no_style, replace_spaces=False), x, flags=re.IGNORECASE)
-
-        x = re.sub(r'\[prompt_spaces]', sanitize_filename_part(prompt, replace_spaces=False), x, flags=re.IGNORECASE)
-        if re.search(r'\[prompt_words]', x, re.IGNORECASE):
-            words = [x for x in re_nonletters.split(prompt or "") if len(x) > 0]
-            if len(words) == 0:
-                words = ["empty"]
-            x = re.sub(r'\[prompt_words]', sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False), x, flags=re.IGNORECASE)
-
-    if cmd_opts.hide_ui_dir_config:
-        x = re.sub(r'^[\\/]+|\.{2,}[\\/]+|[\\/]+\.{2,}', '', x)
-
-    return x
+class FilenameGenerator:
+    replacements = {
+        'seed': lambda self: self.seed if self.seed is not None else '',
+        'steps': lambda self:  self.p and self.p.steps,
+        'cfg': lambda self: self.p and self.p.cfg_scale,
+        'width': lambda self: self.p and self.p.width,
+        'height': lambda self: self.p and self.p.height,
+        'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
+        'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),
+        'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
+        'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
+        'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
+        'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
+        'prompt': lambda self: sanitize_filename_part(self.prompt),
+        'prompt_no_styles': lambda self: self.prompt_no_style(),
+        'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
+        'prompt_words': lambda self: self.prompt_words(),
+    }
+    default_time_format = '%Y%m%d%H%M%S'
+
+    def __init__(self, p, seed, prompt):
+        self.p = p
+        self.seed = seed
+        self.prompt = prompt
+
+    def prompt_no_style(self):
+        if self.p is None or self.prompt is None:
+            return None
+
+        prompt_no_style = self.prompt
+        for style in shared.prompt_styles.get_style_prompts(self.p.styles):
+            if len(style) > 0:
+                for part in style.split("{prompt}"):
+                    prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
+
+                prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip()
+
+        return sanitize_filename_part(prompt_no_style, replace_spaces=False)
+
+    def prompt_words(self):
+        words = [x for x in re_nonletters.split(self.prompt or "") if len(x) > 0]
+        if len(words) == 0:
+            words = ["empty"]
+        return sanitize_filename_part(" ".join(words[0:opts.directories_max_prompt_words]), replace_spaces=False)
+
+    def datetime(self, *args):
+        time_datetime = datetime.datetime.now()
+
+        time_format = args[0] if len(args) > 0 else self.default_time_format
+        time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
+
+        time_zone_time = time_datetime.astimezone(time_zone)
+        try:
+            formatted_time = time_zone_time.strftime(time_format)
+        except (ValueError, TypeError) as _:
+            formatted_time = time_zone_time.strftime(self.default_time_format)
+
+        return sanitize_filename_part(formatted_time, replace_spaces=False)
+
+    def apply(self, x):
+        res = ''
+
+        for m in re_pattern.finditer(x):
+            text, pattern = m.groups()
+
+            if pattern is None:
+                res += text
+                continue
+
+            pattern_args = []
+            while True:
+                m = re_pattern_arg.match(pattern)
+                if m is None:
+                    break
+
+                pattern, arg = m.groups()
+                pattern_args.insert(0, arg)
+
+            fun = self.replacements.get(pattern.lower())
+            if fun is not None:
+                try:
+                    replacement = fun(self, *pattern_args)
+                except Exception:
+                    replacement = None
+                    print(f"Error adding [{pattern}] to filename", file=sys.stderr)
+                    print(traceback.format_exc(), file=sys.stderr)
+
+                if replacement is None:
+                    res += f'[{pattern}]'
+                else:
+                    res += str(replacement)
+
+                continue
+
+            res += f'[{pattern}]'
+
+        return res
 
 
 def get_next_sequence_number(path, basename):
@@ -354,66 +416,8 @@ def get_next_sequence_number(path, basename):
     return result + 1
 
 
-def replace_datetime(input_str: str, time_datetime: datetime.datetime = None):
-    """
-    Args:
-        input_str (`str`):
-            the String to be Formatted
-        time_datetime (`datetime.datetime`)
-            the time to be used, if None, use datetime.datetime.now()
-
-    Formats sub_string of input_str with formatted datetime with time zone support.
-    accepts sub_string format: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
-    case insensitive
-
-    e.g.
-    input: "___[Datetime<%Y_%m_%d %H-%M-%S><Asia/Tokyo>]___"
-    return: "___2022_10_22 20-40-14___"
-
-    handles invalid Formats and Time Zones
-
-    time format reference:
-    https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes
-
-    valid time zones
-    print(pytz.all_timezones)
-    https://pytz.sourceforge.net/
-    """
-    default_time_format = '%Y%m%d%H%M%S'
-    if time_datetime is None:
-        time_datetime = datetime.datetime.now()
-    # match all datetime to be replace
-    match_itr = re.finditer(r'\[datetime(?:<([^>]*)>(?:<([^>]*)>)?)?]', input_str, re.IGNORECASE)
-    for match in reversed(list(match_itr)):
-        # extract format
-        time_format = match.group(1)
-        if time_format == '':
-            # if time_format is blank use default YYYYMMDDHHMMSS
-            time_format = default_time_format
-
-        # extract timezone
-        try:
-            time_zone = pytz.timezone(match.group(2))
-        except pytz.exceptions.UnknownTimeZoneError as _:
-            # if no time_zone or invalid, use system time
-            time_zone = None
-
-        # generate time string
-        time_zone_time = time_datetime.astimezone(time_zone)
-        try:
-            formatted_time = time_zone_time.strftime(time_format)
-
-        except (ValueError, TypeError) as _:
-            # if format error then use default_time_format
-            formatted_time = time_zone_time.strftime(default_time_format)
-
-        formatted_time = sanitize_filename_part(formatted_time, replace_spaces=False)
-        input_str = input_str[:match.start()] + formatted_time + input_str[match.end():]
-    return input_str
-
-
 def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
-    '''Save an image.
+    """Save an image.
 
     Args:
         image (`PIL.Image`):
@@ -444,7 +448,9 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             The full path of the saved imaged.
         txt_fullfn (`str` or None):
             If a text file is saved for this image, this will be its full path. Otherwise None.
-    '''
+    """
+    namegen = FilenameGenerator(p, seed, prompt)
+
     if extension == 'png' and opts.enable_pnginfo and info is not None:
         pnginfo = PngImagePlugin.PngInfo()
 
@@ -460,33 +466,37 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
 
     if save_to_dirs:
-        dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /')
+        dirname = namegen.apply(opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /')
         path = os.path.join(path, dirname)
 
     os.makedirs(path, exist_ok=True)
 
     if forced_filename is None:
-        if short_filename or prompt is None or seed is None:
+        if short_filename or seed is None:
             file_decoration = ""
-        elif opts.save_to_dirs:
-            file_decoration = opts.samples_filename_pattern or "[seed]"
         else:
-            file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
+            file_decoration = opts.samples_filename_pattern or "[seed]"
+
+        add_number = opts.save_images_add_number or file_decoration == ''
 
-        if file_decoration != "":
+        if file_decoration != "" and add_number:
             file_decoration = "-" + file_decoration
 
-        file_decoration = apply_filename_pattern(file_decoration, p, seed, prompt) + suffix
-
-        basecount = get_next_sequence_number(path, basename)
-        fullfn = None
-        fullfn_without_extension = None
-        for i in range(500):
-            fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
-            fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
-            fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")
-            if not os.path.exists(fullfn):
-                break
+        file_decoration = namegen.apply(file_decoration) + suffix
+
+        if add_number:
+            basecount = get_next_sequence_number(path, basename)
+            fullfn = None
+            fullfn_without_extension = None
+            for i in range(500):
+                fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
+                fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
+                fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")
+                if not os.path.exists(fullfn):
+                    break
+        else:
+            fullfn = os.path.join(path, f"{file_decoration}.{extension}")
+            fullfn_without_extension = os.path.join(path, file_decoration)
     else:
         fullfn = os.path.join(path, f"{forced_filename}.{extension}")
         fullfn_without_extension = os.path.join(path, forced_filename)
diff --git a/modules/shared.py b/modules/shared.py
index d6ddfe59..76cbb1bd 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -86,6 +86,7 @@ parser.add_argument("--device-id", type=str, help="Select the default CUDA devic
 cmd_opts = parser.parse_args()
 restricted_opts = [
     "samples_filename_pattern",
+    "directories_filename_pattern",
     "outdir_samples",
     "outdir_txt2img_samples",
     "outdir_img2img_samples",
@@ -190,7 +191,8 @@ options_templates = {}
 options_templates.update(options_section(('saving-images', "Saving images/grids"), {
     "samples_save": OptionInfo(True, "Always save all generated images"),
     "samples_format": OptionInfo('png', 'File format for images'),
-    "samples_filename_pattern": OptionInfo("", "Images filename pattern"),
+    "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs),
+    "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
 
     "grid_save": OptionInfo(True, "Always save all generated image grids"),
     "grid_format": OptionInfo('png', 'File format for grids'),
@@ -225,8 +227,8 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
     "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
     "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
     "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
-    "directories_filename_pattern": OptionInfo("", "Directory name pattern"),
-    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
+    "directories_filename_pattern": OptionInfo("", "Directory name pattern", component_args=hide_dirs),
+    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
 }))
 
 options_templates.update(options_section(('upscaling', "Upscaling"), {
-- 
cgit v1.2.1


From 2c05e06ea770b013de55636f15aae0b8e60e0590 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 24 Oct 2022 14:11:14 +0300
Subject: rename api/processing to api/models for #3511

---
 modules/api/api.py        |   2 +-
 modules/api/models.py     | 106 ++++++++++++++++++++++++++++++++++++++++++++++
 modules/api/processing.py | 106 ----------------------------------------------
 3 files changed, 107 insertions(+), 107 deletions(-)
 create mode 100644 modules/api/models.py
 delete mode 100644 modules/api/processing.py

diff --git a/modules/api/api.py b/modules/api/api.py
index 3caa83a4..a860a964 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,4 +1,4 @@
-from modules.api.processing import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
+from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.sd_samplers import all_samplers
 from modules.extras import run_pnginfo
diff --git a/modules/api/models.py b/modules/api/models.py
new file mode 100644
index 00000000..f551fa35
--- /dev/null
+++ b/modules/api/models.py
@@ -0,0 +1,106 @@
+from array import array
+from inflection import underscore
+from typing import Any, Dict, Optional
+from pydantic import BaseModel, Field, create_model
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
+import inspect
+
+
+API_NOT_ALLOWED = [
+    "self",
+    "kwargs",
+    "sd_model",
+    "outpath_samples",
+    "outpath_grids",
+    "sampler_index",
+    "do_not_save_samples",
+    "do_not_save_grid",
+    "extra_generation_params",
+    "overlay_images",
+    "do_not_reload_embeddings",
+    "seed_enable_extras",
+    "prompt_for_display",
+    "sampler_noise_scheduler_override",
+    "ddim_discretize"
+]
+
+class ModelDef(BaseModel):
+    """Assistance Class for Pydantic Dynamic Model Generation"""
+
+    field: str
+    field_alias: str
+    field_type: Any
+    field_value: Any
+
+
+class PydanticModelGenerator:
+    """
+    Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
+    source_data is a snapshot of the default values produced by the class
+    params are the names of the actual keys required by __init__
+    """
+
+    def __init__(
+        self,
+        model_name: str = None,
+        class_instance = None,
+        additional_fields = None,
+    ):
+        def field_type_generator(k, v):
+            # field_type = str if not overrides.get(k) else overrides[k]["type"]
+            # print(k, v.annotation, v.default)
+            field_type = v.annotation
+            
+            return Optional[field_type]
+        
+        def merge_class_params(class_):
+            all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
+            parameters = {}
+            for classes in all_classes:
+                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
+            return parameters
+            
+                
+        self._model_name = model_name
+        self._class_data = merge_class_params(class_instance)
+        self._model_def = [
+            ModelDef(
+                field=underscore(k),
+                field_alias=k,
+                field_type=field_type_generator(k, v),
+                field_value=v.default
+            )
+            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
+        ]
+        
+        for fields in additional_fields:
+            self._model_def.append(ModelDef(
+                field=underscore(fields["key"]), 
+                field_alias=fields["key"], 
+                field_type=fields["type"],
+                field_value=fields["default"]))
+
+    def generate_model(self):
+        """
+        Creates a pydantic BaseModel
+        from the json and overrides provided at initialization
+        """
+        fields = {
+            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
+        }
+        DynamicModel = create_model(self._model_name, **fields)
+        DynamicModel.__config__.allow_population_by_field_name = True
+        DynamicModel.__config__.allow_mutation = True
+        return DynamicModel
+    
+StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingTxt2Img", 
+    StableDiffusionProcessingTxt2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}]
+).generate_model()
+
+StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
+    "StableDiffusionProcessingImg2Img", 
+    StableDiffusionProcessingImg2Img,
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
+).generate_model()
\ No newline at end of file
diff --git a/modules/api/processing.py b/modules/api/processing.py
deleted file mode 100644
index f551fa35..00000000
--- a/modules/api/processing.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from array import array
-from inflection import underscore
-from typing import Any, Dict, Optional
-from pydantic import BaseModel, Field, create_model
-from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
-import inspect
-
-
-API_NOT_ALLOWED = [
-    "self",
-    "kwargs",
-    "sd_model",
-    "outpath_samples",
-    "outpath_grids",
-    "sampler_index",
-    "do_not_save_samples",
-    "do_not_save_grid",
-    "extra_generation_params",
-    "overlay_images",
-    "do_not_reload_embeddings",
-    "seed_enable_extras",
-    "prompt_for_display",
-    "sampler_noise_scheduler_override",
-    "ddim_discretize"
-]
-
-class ModelDef(BaseModel):
-    """Assistance Class for Pydantic Dynamic Model Generation"""
-
-    field: str
-    field_alias: str
-    field_type: Any
-    field_value: Any
-
-
-class PydanticModelGenerator:
-    """
-    Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
-    source_data is a snapshot of the default values produced by the class
-    params are the names of the actual keys required by __init__
-    """
-
-    def __init__(
-        self,
-        model_name: str = None,
-        class_instance = None,
-        additional_fields = None,
-    ):
-        def field_type_generator(k, v):
-            # field_type = str if not overrides.get(k) else overrides[k]["type"]
-            # print(k, v.annotation, v.default)
-            field_type = v.annotation
-            
-            return Optional[field_type]
-        
-        def merge_class_params(class_):
-            all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
-            parameters = {}
-            for classes in all_classes:
-                parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
-            return parameters
-            
-                
-        self._model_name = model_name
-        self._class_data = merge_class_params(class_instance)
-        self._model_def = [
-            ModelDef(
-                field=underscore(k),
-                field_alias=k,
-                field_type=field_type_generator(k, v),
-                field_value=v.default
-            )
-            for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
-        ]
-        
-        for fields in additional_fields:
-            self._model_def.append(ModelDef(
-                field=underscore(fields["key"]), 
-                field_alias=fields["key"], 
-                field_type=fields["type"],
-                field_value=fields["default"]))
-
-    def generate_model(self):
-        """
-        Creates a pydantic BaseModel
-        from the json and overrides provided at initialization
-        """
-        fields = {
-            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
-        }
-        DynamicModel = create_model(self._model_name, **fields)
-        DynamicModel.__config__.allow_population_by_field_name = True
-        DynamicModel.__config__.allow_mutation = True
-        return DynamicModel
-    
-StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
-    "StableDiffusionProcessingTxt2Img", 
-    StableDiffusionProcessingTxt2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}]
-).generate_model()
-
-StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
-    "StableDiffusionProcessingImg2Img", 
-    StableDiffusionProcessingImg2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
-).generate_model()
\ No newline at end of file
-- 
cgit v1.2.1


From 5587ab7ea8c78ef186592325ffc22bdfc1677bbc Mon Sep 17 00:00:00 2001
From: blackneoo <blackneoo@gmail.com>
Date: Mon, 24 Oct 2022 14:16:13 +0400
Subject: Add Arabic localization

Arabic translation for the UI interface, however arabic language is a right to left language, so for proper display (especially when text contain both Arabic and English text) it is required to add dir="rtl" in the style, i suggest adding a checkbox in setting to trigger this for right to left languages
---
 localizations/ar_AR.json | 691 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 691 insertions(+)
 create mode 100644 localizations/ar_AR.json

diff --git a/localizations/ar_AR.json b/localizations/ar_AR.json
new file mode 100644
index 00000000..6d03e013
--- /dev/null
+++ b/localizations/ar_AR.json
@@ -0,0 +1,691 @@
+{   "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "جار التحميل...",
+    "view": "معاينة",
+    "api": "api",
+    "•": "•",
+    "built with gradio": "مبني باستخدام Gradio",
+    "Stable Diffusion checkpoint": "نماذج الانتشار المستقر",
+    "txt2img": "نص لصورة",
+    "img2img": "صورة لصورة",
+    "Extras": "الإضافات",
+    "PNG Info": "معلومات PNG",
+    "Image Browser": "مستعرض الصور",
+    "Checkpoint Merger": "دمج النماذج",
+    "Train": "التدريب",
+    "Settings": "الإعدادات",
+    "Prompt": "الموجه",
+    "Negative prompt": "الموجه السلبي",
+    "Run": "تشغيل",
+    "Skip": "تخطي",
+    "Interrupt": "إيقاف",
+    "Generate": "إنشاء",
+    "Style 1": "نمط 1",
+    "Style 2": "نمط 2",
+    "Label": "الوسم",
+    "File": "ملف",
+    "Drop File Here": "اسحب الملف هنا",
+    "-": "-",
+    "or": "أو",
+    "Click to Upload": "انقر للتحميل",
+    "Image": "صورة",
+    "Check progress": "تحقق من التقدم",
+    "Check progress (first)": "تحقق من التقدم (الأول)",
+    "Sampling Steps": "خطوات أخذ العينة",
+    "Sampling method": "نظام أخذ العينات",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "العرض",
+    "Height": "الارتفاع",
+    "Restore faces": "ترميم الوجوه",
+    "Tiling": "تبليط",
+    "Highres. fix": "إصلاح الصور عالية الدقة",
+    "Firstpass width": "عرض المرور الأول",
+    "Firstpass height": "ارتفاع المرور الأول",
+    "Denoising strength": "قوة تقليل الضوضاء",
+    "Batch count": "عدد الدُفعات",
+    "Batch size": "حجم الدفعة",
+    "CFG Scale": "مقياس التقارب من الموجه (CFG)",
+    "Seed": "البذرة",
+    "Extra": "إضافي",
+    "Variation seed": "تباين البذرة",
+    "Variation strength": "قوة التباين",
+    "Resize seed from width": "تغيير حجم البذرة من العرض",
+    "Resize seed from height": "تغيير حجم البذرة من الارتفاع",
+    "Script": "سكريبت",
+    "None": "لايوجد",
+    "Prompt matrix": "مصفوفة الموجهات",
+    "Prompts from file or textbox": "موجهات من ملف أو مربع النص",
+    "X/Y plot": "الرسم البياني X/Y",
+    "Put variable parts at start of prompt": "ضع الأجزاء المتغيرة في بداية الموجه",
+    "Show Textbox": "إظهار مربع النص",
+    "File with inputs": "ملف يحتوي المدخلات",
+    "Prompts": "الموجهات",
+    "X type": "نوع X",
+    "Nothing": "لا شئ",
+    "Var. seed": "تغير البذرة",
+    "Var. strength": "قوة التغيير",
+    "Steps": "الخطوات",
+    "Prompt S/R": "موجه S / R",
+    "Prompt order": "ترتيب الموجهات",
+    "Sampler": "نظام أخذ العينات",
+    "Checkpoint name": "اسم النموذج",
+    "Hypernetwork": "الشبكة الفائقة",
+    "Hypernet str.": "قوة الشبكة الفائقة",
+    "Sigma Churn": "دفع سيجما",
+    "Sigma min": "أصغر سيجما",
+    "Sigma max": "أكبر سيجما",
+    "Sigma noise": "ضجة سيجما",
+    "Eta": "الوقت المتوقع",
+    "Clip skip": "تخطي Clip",
+    "Denoising": "تقليل الضوضاء",
+    "X values": "قيم X",
+    "Y type": "نوع Y",
+    "Y values": "قيم Y",
+    "Draw legend": "ارسم مفتاح التوضيح",
+    "Include Separate Images": "قم بتضمين الصور منفصلة",
+    "Keep -1 for seeds": "احتفظ بـقيمة -1 للبذور",
+    "Drop Image Here": "إسقاط الصورة هنا",
+    "Save": "حفظ",
+    "Send to img2img": "أرسل إلى صورة لصورة",
+    "Send to inpaint": "أرسل إلى إعادة الرسم الجزئي",
+    "Send to extras": "أرسل إلى الإضافات",
+    "Make Zip when Save?": "إنشاء ملف مضغوط عند الحفظ؟",
+    "Textbox": "مربع النص",
+    "Interrogate\nCLIP": "استجواب\n CLIP",
+    "Inpaint": "إعادة الرسم الجزئي",
+    "Batch img2img": "دفعات صورة لصورة",
+    "Image for img2img": "صورة (صورة لصورة)",
+    "Image for inpainting with mask": "صورة (إعادة الرسم الجزئي)",
+    "Mask": "القناع",
+    "Mask blur": "ضبابية القناع",
+    "Mask mode": "أسلوب القناع",
+    "Draw mask": "رسم القناع",
+    "Upload mask": "تحميل القناع",
+    "Masking mode": "أسلوب التقنيع",
+    "Inpaint masked": "إعادة الرسم الجزئي (المنطقة المقنعة)",
+    "Inpaint not masked": "إعادة الرسم الجزئي (المنطقة الغير مقنعة)",
+    "Masked content": "المحتوى المقنع",
+    "fill": "الملأ",
+    "original": "الأصلي",
+    "latent noise": "الضوضاء الكامنة",
+    "latent nothing": "لا شيء كامن",
+    "Inpaint at full resolution": "إعادة الرسم الجزئي بدقة كاملة",
+    "Inpaint at full resolution padding, pixels": "إعادة الرسم الجزئي بدقة كاملة, الحشو, بيكسل",
+    "Process images in a directory on the same machine where the server is running.": "معالجة الصور في المجلد على نفس الجهاز حيث يتم تشغيل الخادم.",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "استخدم مجلد إخراج فارغ لحفظ الصور بشكل طبيعي بدلاً من الكتابة إلى مجلد المخرجات.",
+    "Input directory": "مجلد المدخلات",
+    "Output directory": "مجلد المخرجات",
+    "Resize mode": "وضعية تغيير الحجم",
+    "Just resize": "تغييير الحجم فقط",
+    "Crop and resize": "اقتصاص وتغيير الحجم",
+    "Resize and fill": "تغيير الحجم والتعبئة",
+    "Animator": "محرك الرسوم",
+    "Animator v2": "محرك الرسوم نسخة 2",
+    "Deforum v0.5-webui-beta": "ديفوروم 0.5",
+    "External Image Masking": "تقنيع الصور الخارجي",
+    "img2img alternative test": "صورة لصورة البديلة",
+    "Loopback": "الحلقة الراجعة",
+    "Outpainting mk2": "الرسم الخارجي نسخة 2",
+    "Poor man's outpainting": "الرسم الخارجي للفقراء",
+    "SD upscale": "ترقية الانتشار المستقر",
+    "txt2mask v0.1.1": "نص لقناع 0.1.1",
+    "[C] Video to video": "فيديو لفيديو",
+    "Videos": "فيديوهات",
+    "Render these video formats:": "رسم تنسيقات الفيديو:",
+    "GIF": "GIF",
+    "MP4": "MP4",
+    "WEBM": "WEBM",
+    "Animation Parameters": "متغيرات تحريك الرسوم",
+    "Total Animation Length (s)": "المدة الكلية للرسوم المتحركة",
+    "Framerate": "Framerate",
+    "Add_Noise": "Add_Noise",
+    "Noise Strength": "Noise Strength",
+    "Denoising_Decay": "Denoising_Decay",
+    "Denoising Decay Rate": "Denoising Decay Rate",
+    "Initial Parameters": "Initial Parameters",
+    "Denoising Strength (overrides img2img slider)": "Denoising Strength (overrides img2img slider)",
+    "Zoom Factor (scale/s)": "Zoom Factor (scale/s)",
+    "X Pixel Shift (pixels/s)": "X Pixel Shift (pixels/s)",
+    "Y Pixel Shift (pixels/s)": "Y Pixel Shift (pixels/s)",
+    "Prompt Template, applied to each keyframe below": "Prompt Template, applied to each keyframe below",
+    "Positive Prompts": "Positive Prompts",
+    "Negative Prompts": "Negative Prompts",
+    "Keyframe Format:": "Keyframe Format:",
+    "Time (s) | Desnoise | Zoom (/s) | X Shift (pix/s) | Y shift (pix/s) | Positive Prompts | Negative Prompts | Seed": "Time (s) | Desnoise | Zoom (/s) | X Shift (pix/s) | Y shift (pix/s) | Positive Prompts | Negative Prompts | Seed",
+    "Keyframes:": "Keyframes:",
+    "Rotation (deg/s)": "Rotation (deg/s)",
+    "Props": "Props",
+    "Folder:": "Folder:",
+    "Supported Keyframes:": "Supported Keyframes:",
+    "time_s | prompt | positive_prompts | negative_prompts": "time_s | prompt | positive_prompts | negative_prompts",
+    "time_s | transform | zoom | x_shift | y_shift | rotation": "time_s | transform | zoom | x_shift | y_shift | rotation",
+    "time_s | seed | new_seed_int": "time_s | seed | new_seed_int",
+    "time_s | denoise | denoise_value": "time_s | denoise | denoise_value",
+    "time_s | prop | prop_filename | x_pos | y_pos | scale | rotation": "time_s | prop | prop_filename | x_pos | y_pos | scale | rotation",
+    "time_s | set_text | textblock_name | text_prompt | x | y | fore_R | fore_G | fore_B | back_R | back_G | back_B | font_name | font_size": "time_s | set_text | textblock_name | text_prompt | x | y | fore_R | fore_G | fore_B | back_R | back_G | back_B | font_name | font_size",
+    "time_s | clear_text | textblock_name": "time_s | clear_text | textblock_name",
+    "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation": "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation",
+    "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation": "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation",
+    "time_s | clear_stamp | stamp_name": "time_s | clear_stamp | stamp_name",
+    "time_s | col_set": "time_s | col_set",
+    "time_s | col_clear": "time_s | col_clear",
+    "time_s | model | 2022-10-19T08-10-53_VV_6_training_images_1212_max_training_steps_VV_V_token_woman_class_word, 2022-10-19T09-06-28_VV_6_training_images_1212_max_training_steps_VV_V_token_woman_class_word, 2022-10-20T10-07-46_Zori_New_20_training_images_2020_max_training_steps_ZZ_W_token_woman_class_word, 2022-10-20T10-39-38_Zori_New_20_training_images_1010_max_training_steps_ZZ_W_token_woman_class_word, HD-16, L, L2_1, L2_2, LM, MT_V, Main+Trinart, Z1, Z2, model, modelZ, nai, robo-diffusion-v1, sd-v1-5-inpainting, trinart2_step115000, v1-5-pruned, v1-5-pruned-emaonly, wd-v1-2-full-ema, wd-v1-3-float16": "time_s | model | 2022-10-19T08-10-53_VV_6_training_images_1212_max_training_steps_VV_V_token_woman_class_word, 2022-10-19T09-06-28_VV_6_training_images_1212_max_training_steps_VV_V_token_woman_class_word, 2022-10-20T10-07-46_Zori_New_20_training_images_2020_max_training_steps_ZZ_W_token_woman_class_word, 2022-10-20T10-39-38_Zori_New_20_training_images_1010_max_training_steps_ZZ_W_token_woman_class_word, HD-16, L, L2_1, L2_2, LM, MT_V, Main+Trinart, Z1, Z2, model, modelZ, nai, robo-diffusion-v1, sd-v1-5-inpainting, trinart2_step115000, v1-5-pruned, v1-5-pruned-emaonly, wd-v1-2-full-ema, wd-v1-3-float16",
+    "Click here after the generation to show the video": "Click here after the generation to show the video",
+    "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha": "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha",
+    "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Original Deforum Github repo  github.com/deforum/stable-diffusion",
+    "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui": "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui",
+    "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions",
+    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
+    "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
+    "Keyframes": "Keyframes",
+    "Init": "Init",
+    "Video output": "Video output",
+    "Run settings": "Run settings",
+    "Import settings from file": "Import settings from file",
+    "Override settings": "Override settings",
+    "Custom settings file": "Custom settings file",
+    "Sampling settings": "Sampling settings",
+    "The following settings have already been set up in the webui": "The following settings have already been set up in the webui",
+    "Do you want to override them with the values above?": "Do you want to override them with the values above?",
+    "FIXME! Need to make deforum <-> webui samplers map. Before that, the sampler gets used from webui anyway. If your images are changing too adruptly, lower steps or increase strength schedule!": "FIXME! Need to make deforum <-> webui samplers map. Before that, the sampler gets used from webui anyway. If your images are changing too adruptly, lower steps or increase strength schedule!",
+    "override_these_with_webui": "override_these_with_webui",
+    "W, H, seed, sampler, steps, scale, ddim_eta, n_batch, make_grid, grid_rows": "W, H, seed, sampler, steps, scale, ddim_eta, n_batch, make_grid, grid_rows",
+    "W": "W",
+    "H": "H",
+    "seed": "seed",
+    "sampler": "sampler",
+    "klms": "klms",
+    "dpm2": "dpm2",
+    "dpm2_ancestral": "dpm2_ancestral",
+    "heun": "heun",
+    "euler": "euler",
+    "euler_ancestral": "euler_ancestral",
+    "plms": "plms",
+    "ddim": "ddim",
+    "steps": "steps",
+    "scale": "scale",
+    "ddim_eta": "ddim_eta",
+    "n_batch": "n_batch",
+    "make_grid": "make_grid",
+    "grid_rows": "grid_rows",
+    "save_settings": "save_settings",
+    "save_samples": "save_samples",
+    "display_samples": "display_samples",
+    "save_sample_per_step": "save_sample_per_step",
+    "show_sample_per_step": "show_sample_per_step",
+    "Batch settings": "Batch settings",
+    "batch_name": "batch_name",
+    "filename_format": "filename_format",
+    "seed_behavior": "seed_behavior",
+    "iter": "iter",
+    "fixed": "fixed",
+    "random": "random",
+    "schedule": "schedule",
+    "Animation settings": "Animation settings",
+    "animation_mode": "animation_mode",
+    "2D": "2D",
+    "3D": "3D",
+    "Video Input": "Video Input",
+    "max_frames": "max_frames",
+    "border": "border",
+    "replicate": "replicate",
+    "wrap": "wrap",
+    "Motion parameters:": "Motion parameters:",
+    "2D and 3D settings": "2D and 3D settings",
+    "angle": "angle",
+    "zoom": "zoom",
+    "translation_x": "translation_x",
+    "translation_y": "translation_y",
+    "3D settings": "3D settings",
+    "translation_z": "translation_z",
+    "rotation_3d_x": "rotation_3d_x",
+    "rotation_3d_y": "rotation_3d_y",
+    "rotation_3d_z": "rotation_3d_z",
+    "Prespective flip — Low VRAM pseudo-3D mode:": "Prespective flip — Low VRAM pseudo-3D mode:",
+    "flip_2d_perspective": "flip_2d_perspective",
+    "perspective_flip_theta": "perspective_flip_theta",
+    "perspective_flip_phi": "perspective_flip_phi",
+    "perspective_flip_gamma": "perspective_flip_gamma",
+    "perspective_flip_fv": "perspective_flip_fv",
+    "Generation settings:": "Generation settings:",
+    "noise_schedule": "noise_schedule",
+    "strength_schedule": "strength_schedule",
+    "contrast_schedule": "contrast_schedule",
+    "cfg_scale_schedule": "cfg_scale_schedule",
+    "To enable seed schedule select seed behavior — 'schedule'": "To enable seed schedule select seed behavior — 'schedule'",
+    "seed_schedule": "seed_schedule",
+    "Coherence:": "Coherence:",
+    "color_coherence": "color_coherence",
+    "Match Frame 0 HSV": "Match Frame 0 HSV",
+    "Match Frame 0 LAB": "Match Frame 0 LAB",
+    "Match Frame 0 RGB": "Match Frame 0 RGB",
+    "diffusion_cadence": "diffusion_cadence",
+    "3D Depth Warping:": "3D Depth Warping:",
+    "use_depth_warping": "use_depth_warping",
+    "midas_weight": "midas_weight",
+    "near_plane": "near_plane",
+    "far_plane": "far_plane",
+    "fov": "fov",
+    "padding_mode": "padding_mode",
+    "reflection": "reflection",
+    "zeros": "zeros",
+    "sampling_mode": "sampling_mode",
+    "bicubic": "bicubic",
+    "bilinear": "bilinear",
+    "nearest": "nearest",
+    "save_depth_maps": "save_depth_maps",
+    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)",
+    "*Important change from vanilla Deforum!*": "*Important change from vanilla Deforum!*",
+    "This script uses the built-in webui weighting settings.": "This script uses the built-in webui weighting settings.",
+    "So if you want to use math functions as prompt weights,": "So if you want to use math functions as prompt weights,",
+    "keep the values above zero in both parts": "keep the values above zero in both parts",
+    "Negative prompt part can be specified with --neg": "Negative prompt part can be specified with --neg",
+    "batch_prompts (disabled atm)": "batch_prompts (disabled atm)",
+    "animation_prompts": "animation_prompts",
+    "Init settings": "Init settings",
+    "use_init": "use_init",
+    "from_img2img_instead_of_link": "from_img2img_instead_of_link",
+    "strength_0_no_init": "strength_0_no_init",
+    "strength": "strength",
+    "init_image": "init_image",
+    "use_mask": "use_mask",
+    "use_alpha_as_mask": "use_alpha_as_mask",
+    "invert_mask": "invert_mask",
+    "overlay_mask": "overlay_mask",
+    "mask_file": "mask_file",
+    "mask_brightness_adjust": "mask_brightness_adjust",
+    "mask_overlay_blur": "mask_overlay_blur",
+    "Video Input:": "Video Input:",
+    "video_init_path": "video_init_path",
+    "extract_nth_frame": "extract_nth_frame",
+    "overwrite_extracted_frames": "overwrite_extracted_frames",
+    "use_mask_video": "use_mask_video",
+    "video_mask_path": "video_mask_path",
+    "Interpolation (turned off atm)": "Interpolation (turned off atm)",
+    "interpolate_key_frames": "interpolate_key_frames",
+    "interpolate_x_frames": "interpolate_x_frames",
+    "Resume animation:": "Resume animation:",
+    "resume_from_timestring": "resume_from_timestring",
+    "resume_timestring": "resume_timestring",
+    "Video output settings": "Video output settings",
+    "skip_video_for_run_all": "skip_video_for_run_all",
+    "fps": "fps",
+    "output_format": "output_format",
+    "PIL gif": "PIL gif",
+    "FFMPEG mp4": "FFMPEG mp4",
+    "ffmpeg_location": "ffmpeg_location",
+    "add_soundtrack": "add_soundtrack",
+    "soundtrack_path": "soundtrack_path",
+    "use_manual_settings": "use_manual_settings",
+    "render_steps": "render_steps",
+    "max_video_frames": "max_video_frames",
+    "path_name_modifier": "path_name_modifier",
+    "x0_pred": "x0_pred",
+    "x": "x",
+    "image_path": "image_path",
+    "mp4_path": "mp4_path",
+    "Masking preview size": "Masking preview size",
+    "Draw new mask on every run": "Draw new mask on every run",
+    "Process non-contigious masks separately": "Process non-contigious masks separately",
+    "should be 2 or lower.": "should be 2 or lower.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Override `Sampling method` to Euler?(this method is built for it)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)",
+    "Original prompt": "Original prompt",
+    "Original negative prompt": "Original negative prompt",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Override `Sampling Steps` to the same value as `Decode steps`?",
+    "Decode steps": "Decode steps",
+    "Override `Denoising strength` to 1?": "Override `Denoising strength` to 1?",
+    "Decode CFG scale": "Decode CFG scale",
+    "Randomness": "Randomness",
+    "Sigma adjustment for finding noise for image": "Sigma adjustment for finding noise for image",
+    "Loops": "Loops",
+    "Denoising strength change factor": "معامل قوة تقليل الضوضاء",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "الإعدادات الموصى بها: خطوات أخذ العينات: 80-100 ، طريقة أخذ العينات: Euler a ، تقليل الضوضاء: 0.8",
+    "Pixels to expand": "عدد البكسل للتوسيع",
+    "Outpainting direction": "إتجاه الرسم الخارجي",
+    "left": "يسار",
+    "right": "يمين",
+    "up": "فوق",
+    "down": "تحت",
+    "Fall-off exponent (lower=higher detail)": "أس التناقص (الأدنى = تفاصيل أعلى)",
+    "Color variation": "اختلاف اللون",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "سيقوم بترقية الصورة إلى ضعف الأبعاد ؛ استخدم شريط تمرير العرض والارتفاع لضبط حجم التبليط",
+    "Tile overlap": "تداخل التبليط",
+    "Upscaler": "المرقي",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "ESRGAN 4x": "ESRGAN 4x",
+    "R-ESRGAN General 4xV3": "R-ESRGAN General 4xV3",
+    "R-ESRGAN General WDN 4xV3": "R-ESRGAN General WDN 4xV3",
+    "R-ESRGAN AnimeVideo": "R-ESRGAN AnimeVideo",
+    "R-ESRGAN 4x+": "R-ESRGAN 4x+",
+    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+    "R-ESRGAN 2x+": "R-ESRGAN 2x+",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR_4x": "SwinIR_4x",
+    "Mask prompt": "Mask prompt",
+    "Negative mask prompt": "Negative mask prompt",
+    "Mask precision": "Mask precision",
+    "Mask padding": "Mask padding",
+    "Brush mask mode": "Brush mask mode",
+    "discard": "discard",
+    "add": "add",
+    "subtract": "subtract",
+    "Show mask in output?": "Show mask in output?",
+    "If you like my work, please consider showing your support on": "If you like my work, please consider showing your support on",
+    "Patreon": "Patreon",
+    "Input file path": "Input file path",
+    "CRF (quality, less is better, x264 param)": "CRF (quality, less is better, x264 param)",
+    "FPS": "FPS",
+    "Seed step size": "Seed step size",
+    "Seed max distance": "Seed max distance",
+    "Start time": "Start time",
+    "End time": "End time",
+    "End Prompt Blend Trigger Percent": "End Prompt Blend Trigger Percent",
+    "Prompt end": "Prompt end",
+    "Smooth video": "Smooth video",
+    "Seconds": "Seconds",
+    "Zoom": "Zoom",
+    "Zoom level": "Zoom level",
+    "Direction X": "Direction X",
+    "Direction Y": "Direction Y",
+    "Rotate": "Rotate",
+    "Degrees": "Degrees",
+    "Is the Image Tiled?": "Is the Image Tiled?",
+    "TranslateX": "TranslateX",
+    "Left": "Left",
+    "PercentX": "PercentX",
+    "TranslateY": "TranslateY",
+    "Up": "Up",
+    "PercentY": "PercentY",
+    "Show generated pictures in ui": "عرض الصور المنشئة في واجهة الاستخدام",
+    "Single Image": "صورة واحدة",
+    "Batch Process": "معالجة الدفعات",
+    "Batch from Directory": "دفعة من المجلد",
+    "Source": "مصدر",
+    "Show result images": "إظهار نتائج الصور ",
+    "Scale by": "رفع الحجم بمقدار",
+    "Scale to": "رفع الحجم إلى",
+    "Resize": "تغيير الحجم",
+    "Crop to fit": "اقتصاص للتوافق",
+    "Upscaler 2 visibility": "إظهار المرقي 2",
+    "GFPGAN visibility": "إظهار GFPGAN",
+    "CodeFormer visibility": "إظهار CodeFormer",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "وزن CodeFormer (0 = أقصى تأثير ، 1 = تأثير أدنى)",
+    "Open output directory": "افتح مجلد المخرجات",
+    "Send to txt2img": "أرسل إلى كتابة لصورة",
+    "extras": "إضافات",
+    "favorites": "المفضلة",
+    "Load": "تحميل",
+    "Images directory": "مجلد الصور",
+    "Prev batch": "الدفعة السابقة",
+    "Next batch": "الدفعة التالية",
+    "First Page": "الصفحة الأولى",
+    "Prev Page": "الصفحة السابقة",
+    "Page Index": "فهرس الصفحات",
+    "Next Page": "الصفحة التالية",
+    "End Page": "صفحة النهاية",
+    "number of images to delete consecutively next": "عدد الصور المطلوب حذفها على التوالي بعد ذلك",
+    "Delete": "حذف",
+    "Generate Info": "معلومات الإنشاء",
+    "File Name": "اسم الملف",
+    "Collect": "جمع",
+    "Refresh page": "إعادة تحميل الصفحة",
+    "Date to": "التاريخ إلى",
+    "Number": "الرقم",
+    "set_index": "وضع الفهرس",
+    "Checkbox": "صندوق اختيار",
+    "A merger of the two checkpoints will be generated in your": "سيتم إنشاء نموذجمدمج من النموذجين في",
+    "checkpoint": "النموذج",
+    "directory.": "المجلد.",
+    "Primary model (A)": "النموذج الأساسي (أ)",
+    "Secondary model (B)": "النموذج الثانوي (ب)",
+    "Tertiary model (C)": "النموذج الثالث (ج)",
+    "Custom Name (Optional)": "اسم مخصص (اختياري)",
+    "Multiplier (M) - set to 0 to get model A": "المضاعف (M) - اضبط على 0 للحصول على النموذج أ",
+    "Interpolation Method": "طريقة الاستنباط",
+    "Weighted sum": "المجموع الموزون",
+    "Add difference": "أضافة الاختلاف",
+    "Save as float16": "حفظ  float16",
+    "See": "شاهد",
+    "wiki": "ويكي",
+    "for detailed explanation.": "للحصول على شرح مفصل.",
+    "Create embedding": "إنشاء التضمين",
+    "Create hypernetwork": "إنشاء شبكة فائقة",
+    "Preprocess images": "تجهيز الصور",
+    "Name": "الاسم",
+    "Initialization text": "نص التهيئة",
+    "Number of vectors per token": "عدد المتجهات لكل رمز",
+    "Overwrite Old Embedding": "الكتابة فوق التضمين القديم",
+    "Modules": "الوحدات",
+    "Enter hypernetwork layer structure": "أدخل بنية طبقة الشبكة الفائقة",
+    "Select activation function of hypernetwork": "حدد وظيفة تنشيط الشبكة الفائقة",
+    "linear": "خطي (Linear)",
+    "relu": "الوحدة الخطية المعدلة (Relu)",
+    "leakyrelu": "الوحدة الخطية المعدلة المسربة (Leakyrelu)",
+    "elu": "الوحدة الأسية الخطية (Elu)",
+    "swish": "Swish",
+    "Add layer normalization": "أضف طبقة التسوية",
+    "Use dropout": "استخدم الهبوط",
+    "Overwrite Old Hypernetwork": "الكتابة فوق الشبكة الفائقة القديمة",
+    "Source directory": "مجلد المصدر",
+    "Destination directory": "مجلد النتائج",
+    "Existing Caption txt Action": "الإجراء النصي للتعليق المتوفر",
+    "ignore": "تجاهل",
+    "copy": "نسخ",
+    "prepend": "أضف قبل",
+    "append": "ألحق",
+    "Create flipped copies": "قم بإنشاء نسخ مقلوبة",
+    "Split oversized images": "تقسيم الصور كبيرة الحجم",
+    "Use BLIP for caption": "استخدم BLIP للتعليق",
+    "Use deepbooru for caption": "استخدم deepbooru للتعليق",
+    "Split image threshold": "حد تقسيم الصورة",
+    "Split image overlap ratio": "نسبة تداخل الصورة المنقسمة",
+    "Preprocess": "تجهيز الصور",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "تدريب التضمين أو الشبكة الفائقة ؛ يجب تحديد مجلد بمجموعة من الصور بنسبة أبعاد 1: 1",
+    "[wiki]": "[ويكي]",
+    "Embedding": "التضمين",
+    "Embedding Learning rate": "معدل تعلم التضمين",
+    "Hypernetwork Learning rate": "معدل تعلم الشبكة الفائقة",
+    "Dataset directory": "مجلد مجموعة البيانات",
+    "Log directory": "مجلد السجل",
+    "Prompt template file": "ملف قالب الموجهات",
+    "Max steps": "الخطوات القصوى",
+    "Save an image to log directory every N steps, 0 to disable": "حفظ صورة في مجلد السجل  كل N خطوات ، 0 للتعطيل",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "حفظ نسخة من التضمين في مجلد السجل كل N خطوات ، 0 للتعطيل",
+    "Save images with embedding in PNG chunks": "حفظ التضمين مع الصور في أجزاء PNG",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "قراءة المتغيرات (الموجه ، إلخ ...) من علامة تبويب نص لصورة عند إجراء المعاينات",
+    "Train Hypernetwork": "تدريب الشبكة الفائقة",
+    "Train Embedding": "تدريب التضمين",
+    "Apply settings": "تطبيق الإعدادات",
+    "Saving images/grids": "حفظ الصور / الإطار الشبكي",
+    "Always save all generated images": "احفظ دائمًا جميع الصور التي تم إنشائها",
+    "File format for images": "تنسيق ملفات الصور",
+    "Images filename pattern": "نمط اسم ملفات الصور",
+    "Always save all generated image grids": "احفظ دائمًا جميع الإطارات الشبكية للصور التي تم إنشاؤها",
+    "File format for grids": "تنسيق ملفات الإطارات الشبكية",
+    "Add extended info (seed, prompt) to filename when saving grid": "أضف معلومات إضافية (البذرة ، الموجه) إلى اسم الملف عند حفظ الإطار الشبكي",
+    "Do not save grids consisting of one picture": "لا تحفظ الإطارات الشبكية التي تتكون من صورة واحدة",
+    "Prevent empty spots in grid (when set to autodetect)": "منع المناطق الفارغة في الإطار الشبكي (عند الضبط على الاكتشاف التلقائي)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "عدد صفوف الإطار الشبكي استخدم -1 للاكتشاف التلقائي و 0 ليكون نفس حجم الدُفعة",
+    "Save text information about generation parameters as chunks to png files": "احفظ معلومات نصية حول متغيرات الإنشاء كمقاطع في ملفات png",
+    "Create a text file next to every image with generation parameters.": "قم بإنشاء ملف نصي بجوار كل صورة باستخدام متغيرات الإنشاء.",
+    "Save a copy of image before doing face restoration.": "احفظ نسخة من الصورة قبل القيام بترميم الوجوه.",
+    "Quality for saved jpeg images": "جودة الصور المحفوظة بتنسيق jpeg",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "إذا كانت صورة PNG أكبر من 4 ميجابايت أو كان أي بُعد أكبر من 4000 ، قم بتقليل حجم الصورة وحفظها بتنسيق JPG",
+    "Use original name for output filename during batch process in extras tab": "استخدم الاسم الأصلي لاسم ملف الإخراج أثناء عملية الدُفعات في علامة تبويب الإضافات",
+    "When using 'Save' button, only save a single selected image": "عند استخدام زر 'حفظ' ، احفظ فقط صورة واحدة محددة",
+    "Do not add watermark to images": "لا تقم بإضافة العلامة المائية للصور",
+    "Paths for saving": "مسارات الحفظ",
+    "Output directory for images; if empty, defaults to three directories below": "مجلد المخرجات للصور ؛ إذا كان فارغا ، يتم تعيينه افتراضيًا إلى المجلدات الثلاثة أدناه",
+    "Output directory for txt2img images": "مجلد المخرجات لصور نص لصورة",
+    "Output directory for img2img images": "مجلد المخرجات لصور صورة لصورة",
+    "Output directory for images from extras tab": "مجلد المخرجات لصور علامة تبويب الإضافات",
+    "Output directory for grids; if empty, defaults to two directories below": "مجلد المخرجات للإطارات الشبكية ؛ إذا كان فارغا ، يتم تعيينه افتراضيًا إلى المجلدين أدناه",
+    "Output directory for txt2img grids": "مجلد المخرجات للإطارات الشبكية نص لصورة",
+    "Output directory for img2img grids": "مجلد المخرجات للإطارات الشبكية صورة لصورة",
+    "Directory for saving images using the Save button": "مجلد لحفظ الصور باستخدام زر حفظ",
+    "Saving to a directory": "يتم الحفظ إلى المجلد..",
+    "Save images to a subdirectory": "حفظ الصور في مجلد فرعي",
+    "Save grids to a subdirectory": "حفظ الإطارات الشبكية في مجلد فرعي",
+    "When using 'Save' button, save images to a subdirectory": "عند استخدام زر 'حفظ' ، احفظ الصور في مجلد فرعي",
+    "Directory name pattern": "نمط اسم المجلد",
+    "Max prompt words for [prompt_words] pattern": "أقصى عدد لكلمات التوجيه لنمط [كلمات_التوجيه]",
+    "Upscaling": "الارتقاء",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "حجم التبليط لترقيات ESRGAN, القيمة 0= لا يوجد تبليط.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "تداخل المربعات ، بالبكسل لمرقي ESRGAN. القيم المنخفضة = تداخل مرئي.",
+    "Tile size for all SwinIR.": "حجم التليط لجميع عمليات SwinIR.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "تداخل المربعات بالبكسل لـ SwinIR. القيم المنخفضة = تداخل مرئي.",
+    "LDSR processing steps. Lower = faster": "خطوات معالجة LDSR. أقل = أسرع",
+    "Upscaler for img2img": "المرقي لصورة لصورة",
+    "Upscale latent space image when doing hires. fix": "ترقية صورة الفضاء الكامن عند التعيينات. تصليح",
+    "Face restoration": "ترميم الوجوه",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "متغيرات وزن CodeFormer ؛ 0 = أقصى تأثير ؛ 1 = الحد الأدنى من التأثير",
+    "Move face restoration model from VRAM into RAM after processing": "انقل نموذج استعادة الوجوه من VRAM إلى RAM بعد المعالجة",
+    "System": "نظام",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "استطلاعات استخدام VRAM في الثانية أثناء الإنشاء. اضبط على 0 للتعطيل.",
+    "Always print all generation info to standard output": "قم دائمًا بطباعة جميع معلومات الإنشاء إلى المخرج القياسي",
+    "Add a second progress bar to the console that shows progress for an entire job.": "أضف شريط تقدم آخر إلى وحدة التحكم يُظهر التدقم الكلي للمهمة.",
+    "Training": "التدريب",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "تفريغ VAE و CLIP من VRAM عند التدريب",
+    "Filename word regex": "اسم ملف كلمات regex",
+    "Filename join string": "سلسلة أحرف دمج أسماء الملفات",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "عدد التكرارات لصورة إدخال واحدة لكل حقبة ؛ تستخدم فقط لعرض رقم الحقبة",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "حفظ ملف csv يحتوي على الخسارة إلى مجلد السجل كل N خطوات ، 0 للتعطيل",
+    "Stable Diffusion": "انتشار مستقر",
+    "Checkpoints to cache in RAM": "التخزين المؤقت للنماذج في RAM",
+    "Hypernetwork strength": "قوة الشبكة الفائقة",
+    "Apply color correction to img2img results to match original colors.": "قم بتطبيق تصحيح الألوان على نتائج صورة لصورة لمطابقة الألوان الأصلية.",
+    "Save a copy of image before applying color correction to img2img results": "احفظ نسخة من الصورة قبل تطبيق تصحيح الألوان على نتائج  صورة لصورة",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "باستخدام صورة لصورة ، قم بالضبط بعدد الخطوات التي يحددها شريط التمرير (عادةً ما سيكون أقل مع تقليل التشويش).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "تمكين تكميم عينات K للحصول على نتائج أوضح وأنظف. هذا قد يغير البذرة المستخدمة. يتطلب إعادة التشغيل للتطبيق.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "التأكيد: استخدم (النص) لجعل النموذج يولي مزيدًا من الاهتمام للنص و استخدم [النص] لجعل النموذج يولي اهتمامًا أقل  للنص",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "استخدم طريقة التأكيد القديمة. يمكن أن يكون مفيدًا لإعادة إنتاج البذور القديمة.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "اجعل انظمة أخذ عينات الانتشار K تنتج نفس الصور في الدفعات كما هو الحال عند إنشاء صورة واحدة",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "زيادة التناسق عن طريق المساحة المتروكة من الفاصلة الأخيرة ضمن عدد n من الرموز المميزة عند استخدام أكثر من 75 رمزًا مميزًا",
+    "Filter NSFW content": "تصفية المحتوى الإباحي",
+    "Stop At last layers of CLIP model": "توقف عند آخر طبقات نموذج CLIP",
+    "Interrogate Options": "خيارات الاستجواب",
+    "Interrogate: keep models in VRAM": "الاستجواب: احتفظ بالنماذج في VRAM",
+    "Interrogate: use artists from artists.csv": "الاستجواب: استخدم  قائمة الفنانين من ملف artists.csv",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "الاستجواب: قم بتضمين رتب علامات النموذج في النتائج (ليس له تأثير على الاستجواب المستند إلى التعليقات).",
+    "Interrogate: num_beams for BLIP": "الاستجواب: عدد الحزم لـ BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "الاستجواب: الحد الأدنى لطول الوصف (باستثناء الفنانين ،إلخ..)",
+    "Interrogate: maximum description length": "الاستجواب: الحد الأقصى لطول الوصف",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: أقصى عدد من الأسطر في الملف النصي (0 = بلا حدود)",
+    "Interrogate: deepbooru score threshold": "الاستجواب: عتبة درجات deepbooru",
+    "Interrogate: deepbooru sort alphabetically": "الاستجواب: الترتيب الأبجدي لـ deepbooru",
+    "use spaces for tags in deepbooru": "استخدام مسافات للعلامات في deepbooru",
+    "User interface": "واجهة المستخدم",
+    "Show progressbar": "إظهار شريط التقدم",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "إظهار تقدم إنشاء الصورة لكل N خطوة من خطوات أخذ العينات. قم بتعيين 0 للتعطيل.",
+    "Show previews of all images generated in a batch as a grid": "إظهار معاينة لجميع الصور التي يتم إنشائها في الدفعة كإطار شبكي",
+    "Show grid in results for web": "إظهار الإطار الشبكي في نتائج الويب",
+    "Do not show any images in results for web": "لا تظهر أي صور في نتائج الويب",
+    "Add model hash to generation information": "أضف معلومات الهاش للنموذج إلى معلومات الإنشاء",
+    "Add model name to generation information": "أضف اسم النموذج إلى معلومات الإنشاء",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "عند قراءة متغيرات الإنشاء من النص إلى واجهة المستخدم (من معلومات PNG أو النص الملصق) ، لا تقم بتغيير النموذج",
+    "Font for image grids that have text": "الخط المستخدم للإطارات الشبكية للصور التي تحتوي على نص",
+    "Enable full page image viewer": "تفعيل عارض الصور ذو الصفحة الكاملة",
+    "Show images zoomed in by default in full page image viewer": "إظهار الصور التي تم تكبيرها بشكل افتراضي في عارض الصور ذو الصفحة الكاملة",
+    "Show generation progress in window title.": "إظهار تقدم الإنشاء في عنوان الشاشة.",
+    "Quicksettings list": "قائمة الإعدادات السريعة",
+    "Localization (requires restart)": "الترجمة (يتطلب إعادة التشغيل)",
+    "ar_AR": "العربية",
+    "ja_JP": "اليابانية",
+    "ru_RU": "الروسية",
+    "Sampler parameters": "متغيرات أنظمة اخذ العينات",
+    "Hide samplers in user interface (requires restart)": "إخفاء أنظمة أخذ العينات في واجهة المستخدم (يتطلب إعادة التشغيل)",
+    "eta (noise multiplier) for DDIM": "الوقت المتبقي (مضاعف الضوضاء) لـ DDIM",
+    "eta (noise multiplier) for ancestral samplers": "الوقت المتبقي (مضاعف الضوضاء) لأنظمة أخذ العينات التي تعتمد على الأجداد",
+    "img2img DDIM discretize": "تفصل DDIM لصورة لصورة",
+    "uniform": "التوحيد",
+    "quad": "رباعية",
+    "sigma churn": "دفع سيجما",
+    "sigma tmin": "سيجما tmin",
+    "sigma noise": "ضجيج سيجما",
+    "Eta noise seed delta": "الوقت المتوقع لديلتا بذرة الضجيج ",
+    "Images Browser": "مستعرض الصور",
+    "Preload images at startup": "تحميل الصور مسبقًا عند بدء التشغيل",
+    "Number of pictures displayed on each page": "عدد الصور المعروضة في كل صفحة",
+    "Minimum number of pages per load": "الحد الأدنى لعدد الصفحات لكل تحميل",
+    "Number of grids in each row": "عدد الإطارات الشبكية لكل صف",
+    "Request browser notifications": "طلب إشعارات المتصفح",
+    "Download localization template": "تنزيل نموذج الترجمة",
+    "Reload custom script bodies (No ui updates, No restart)": "إعادة تحميل السكريبتات المخصصة (لا توجد تحديثات لواجهة المستخدم ، لا إعادة تشغيل)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعادة تشغيل  Gradio و تحديث مكونات واجهة الاستخدام (السكريبتات و ui.py و js و css فقط)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "الموجه (اضغط على Ctrl + Enter أو Alt + Enter للإنشاء)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "الموجه السلبي (اضغط على Ctrl + Enter أو Alt + Enter للإنشاء)",
+    "Add a random artist to the prompt.": "أضف فنانًا عشوائيًا إلى الموجه.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "قراءة متغيرات الإنشاء من الموجه أو الإنشاء الأخير إذا كان الموجه فارغا في واجهة المستخدم.",
+    "Save style": "حفظ النمط",
+    "Apply selected styles to current prompt": "تطبيق الأنماط المحددة للموجه الحالي",
+    "Stop processing current image and continue processing.": "توقف عن معالجة الصورة الحالية واستمر في المعالجة.",
+    "Stop processing images and return any results accumulated so far.": "توقف عن معالجة الصور وقم بإرجاع أي نتائج متراكمة حتى الآن.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "النمط المطلوب تطبيقه ؛ الأنماط لها مكونات لكل من الموجهات الإيجابية والسلبية وتنطبق على كليهما",
+    "Do not do anything special": "لا تفعل أي شيء خاص",
+    "Which algorithm to use to produce the image": "الخوارزمية التي يجب استخدامها لإنتاج الصورة",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - مبدع جدًا ، يمكن لكل منها الحصول على صورة مختلفة تمامًا اعتمادًا على عدد الخطوات ، تعيين الخطوات على أعلى من 30-40 لا يعطي نتيجة مفيدة",
+    "Denoising Diffusion Implicit Models - best at inpainting": "نماذج تقليل التشويش الضمني  - أفضل اسخدام في الرسم الجزئي",
+    "Produce an image that can be tiled.": "أنتج صورة يمكن تبليطها.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "استخدم عملية من خطوتين لإنشاء صورة جزئيًا بدقة أقل ثم ترقيتها و تحسين تفاصيلها دون تغيير التكوين",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "يحدد مدى ضآلة احترام الخوارزمية لمحتوى الصورة. عند 0 ، لن يتغير شيء ، وعند 1 ستحصل على صورة لا صلة لها بالصورة الأصلية. مع وجود قيم أقل من 1.0 ، المعالجة ستأخذ خطوات أقل مما يحدده شريط تمرير خطوات المعالجة.",
+    "How many batches of images to create": "كم عدد دفعات الصور التي يتم إنشائها",
+    "How many image to create in a single batch": "كم صورة يتم إنشائها دفعة واحدة",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "مقياس التوجيه الحر للمصنف - إلى أي مدى يجب أن تتوافق الصورة مع الموجه - القيم المنخفضة تنتج نتائج أكثر إبداعًا",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "القيمة التي تحدد ناتج منشئ الأرقام العشوائية - إذا قمت بإنشاء صورة بنفس المتغيرات و بذرة الصورة ، فستحصل على نفس النتيجة",
+    "Set seed to -1, which will cause a new random number to be used every time": "عيّن البذرة إلى -1 ، مما سيؤدي إلى استخدام رقم عشوائي جديد في كل مرة",
+    "Reuse seed from last generation, mostly useful if it was randomed": "إعادة استخدام البذرة من الإنشاء الأخير ، وتكون مفيدة في الغالب إذا كانت عشوائية",
+    "Seed of a different picture to be mixed into the generation.": "استخدام بذرة من صورة مختلفة ليتم مزجها في الإنشاء.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "ما مدى قوة التباين عند الإنشاء. 0 ، لن يكون هناك أي تأثير. 1 ، ستحصل على الصورة الكاملة ببذور التباين (باستثناء أنظمة عينات الأسلاف ، حيث ستحصل على شيء ما فقط).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "قم بمحاولة لإنتاج صورة مشابهة لما تم إنتاجه بنفس البذرة بناءا على دقة محددة",
+    "Separate values for X axis using commas.": "افصل بين قيم المحور X باستخدام الفواصل.",
+    "Separate values for Y axis using commas.": "افصل بين قيم المحور Y باستخدام الفواصل.",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "اكتب الصورة إلى مجلد (الافتراضي -  log/images) وتوليد المتغيرات في ملف csv.",
+    "Open images output directory": "افتح مجلد مخرجات الصور",
+    "How much to blur the mask before processing, in pixels.": "كم يتم تضبيب القناع قبل المعالجة ، بالبكسل.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "ما يجب وضعه داخل المنطقة المقنعة قبل معالجتها باستخدام الانتشار المستقر.",
+    "fill it with colors of the image": "املأها بألوان الصورة",
+    "keep whatever was there originally": "احتفظ بكل ما كان هناك في الأصل",
+    "fill it with latent space noise": "املأه بضوضاء الفضاء الكامنة",
+    "fill it with latent space zeroes": "املأه بأصفار الفضاء الكامنة",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "قم بترقية المنطقة المقنعة إلى الدقة المستهدفة و الرسم الجزئي ثم تقليص الحجم  واللصق في الصورة الأصلية",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "تغيير حجم الصورة إلى الدقة المستهدفة. ما لم يتطابق الطول والعرض ، ستحصل على نسبة عرض إلى ارتفاع غير صحيحة.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "قم بتغيير حجم الصورة بحيث يتم ملء الدقة المستهدفة بالكامل بالصورة. يتم قطع المناطق التي تبرز.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "قم بتغيير حجم الصورة بحيث تكون الصورة بأكملها داخل الدقة المستهدفة. املأ المساحة الفارغة بألوان الصورة.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "كم مرة يتم تكرار معالجة صورة واستخدامها  كمدخل للتكرار التالي",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "في وضع حلقة الاسترجاع ، يتم ضرب قوة تقليل الضوضاء بهذه القيمة في كل حلقة. يعني <1 تقليل التنوع بحيث يتقارب التسلسل الخاص بك على صورة ثابتة. > 1 يعني زيادة التنوع بحيث يصبح تسلسلك أكثر وأكثر فوضوية.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "الترقية باستخدام الانتشار المستقر ، كم يجب أن يكون مقدار التداخل بالبكسل بين المربعات. تتداخل المربعات بحيث لا يكون هناك خط واضح للعيان عند دمجها مرة أخرى في صورة واحدة.",
+    "A directory on the same machine where the server is running.": "مجلد على نفس الجهاز حيث يتم تشغيل الخادم.",
+    "Leave blank to save images to the default path.": "اتركه فارغًا لحفظ الصور في المسار الافتراضي.",
+    "Input images directory": "مجلد الصور المدخلة",
+    "Result = A * (1 - M) + B * M": "النتيجة = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "النتيجة = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "يجب أن يكون الرقم الأول والأخير 1. على سبيل المثال: '1 ، 2 ، 1'",
+    "Path to directory with input images": "المسار إلى مجلد الصور المدخلة",
+    "Path to directory where to write outputs": "المسار إلى مجلد كتابة النتائج",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "استخدم العلامات التالية لتعريف كيفية اختيار أسماء الملفات للصور: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp] ؛ اتركه فارغا للوضع الافتراضي.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "إذا تم تمكين هذا الخيار ، فلن تتم إضافة العلامة المائية إلى الصور التي تم إنشاؤها. تحذير: إذا لم تقم بإضافة علامة مائية ، فقد تكون تتصرف بطريقة غير أخلاقية.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "استخدم العلامات التالية لتحديد كيفية اختيار المجادت الفرعية للصور و الإطارات الشبكية : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp] ؛ اتركه فارغا للوضع الافتراضي.",
+    "Restore low quality faces using GFPGAN neural network": "ترميم الوجوه منخفضة الجودة باستخدام الشبكة العصبية GFPGAN ",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "سيتم استخدام هذا التعبير العادي لاستخراج الكلمات من اسم الملف ، وسيتم ضمها باستخدام الخيار أدناه في نص التسمية المستخدم للتدريب. اتركه فارغًا للاحتفاظ بنص اسم الملف كما هو.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "سيتم استخدام هذا النص لضم الكلمات المقسمة إلى سطر واحد إذا تم تمكين الخيار أعلاه.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "قائمة أسماء الإعدادات ، مفصولة بفواصل ، للإعدادات التي يجب أن تنتقل إلى شريط الوصول السريع في الأعلى ، بدلاً من علامة تبويب الإعداد المعتادة. انظر modules / shared.py للتعرف على أسماء الإعدادات. يتطلب إعادة التشغيل للتطبيق.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "إذا كانت هذه القيم غير صفرية ، فستتم إضافتها إلى البذرة واستخدامها لتهيئة فوضى الضوضاء عند استخدام أنظمة أخذ العينات باستخدام الوقت المتبقي. يمكنك استخدام ذلك لإنتاج المزيد من التنوعات في الصور ، أو يمكنك استخدامه لمطابقة صور البرامج الأخرى إذا كنت تعرف ما تفعله."
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 595dca85af9e26b5d76cd64659a5bdd9da4f2b89 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Mon, 24 Oct 2022 08:32:18 -0300
Subject: Reverse run_extras change

Update serialization on the batch images endpoint
---
 modules/api/api.py    | 7 ++++++-
 modules/api/models.py | 8 ++++----
 modules/extras.py     | 2 +-
 3 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 799e3701..67b783de 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -109,7 +109,12 @@ class Api:
     def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
         reqDict = setUpscalers(req)
 
-        reqDict['image_folder'] = list(map(decode_base64_to_file, reqDict['imageList']))
+        def prepareFiles(file):
+            file = decode_base64_to_file(file.data, file_path=file.name)
+            file.orig_name = file.name
+            return file
+
+        reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
         reqDict.pop('imageList')
 
         with self.queue_lock:
diff --git a/modules/api/models.py b/modules/api/models.py
index e461d397..fca2f991 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -138,12 +138,12 @@ class ExtrasSingleImageRequest(ExtrasBaseRequest):
 class ExtrasSingleImageResponse(ExtraBaseResponse):
     image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
 
-class ImageItem(BaseModel):
-    data: str = Field(title="image data")
-    name: str = Field(title="filename")
+class FileData(BaseModel):
+    data: str = Field(title="File data", description="Base64 representation of the file")
+    name: str = Field(title="File name")
 
 class ExtrasBatchImagesRequest(ExtrasBaseRequest):
-    imageList: list[str] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
+    imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
 
 class ExtrasBatchImagesResponse(ExtraBaseResponse):
     images: list[str] = Field(title="Images", description="The generated images in base64 format.")
diff --git a/modules/extras.py b/modules/extras.py
index 29ac312e..22c5a1c1 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -33,7 +33,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         for img in image_folder:
             image = Image.open(img)
             imageArr.append(image)
-            imageNameArr.append(os.path.splitext(img.name)[0])
+            imageNameArr.append(os.path.splitext(img.orig_name)[0])
     elif extras_mode == 2:
         assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
 
-- 
cgit v1.2.1


From 9f79e59a95042635f5b14e1ebc691431b9e3000e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 24 Oct 2022 14:42:18 +0300
Subject: added info about History/Image browser to readme

---
 README.md | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 1a0e4f6a..55c050d5 100644
--- a/README.md
+++ b/README.md
@@ -72,7 +72,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
 - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
 - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
-- History tab: view, direct and delete images conveniently within the UI
+- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
 - Generate forever option
 - Training tab
      - hypernetworks and embeddings options
@@ -95,6 +95,16 @@ git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-grad
 After running this command, make sure that you have `aesthetic-gradients` dir in webui's `extensions` directory and restart
 the UI. The interface for Aesthetic Gradients should appear exactly the same as it was.
 
+## Where is History/Image browser?!?!
+Image browser is now an extension. You can install it using git:
+
+```commandline
+git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser extensions/images-browser
+```
+
+After running this command, make sure that you have `images-browser` dir in webui's `extensions` directory and restart
+the UI. The interface for Image browser should appear exactly the same as it was.
+
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
 
-- 
cgit v1.2.1


From 8d8d4d8a1b6401ef3c21d21d7784eb2e1f82d955 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Mon, 24 Oct 2022 20:55:21 +0900
Subject: Update new strings in Settings tab

---
 localizations/ko_KR.json | 1 +
 1 file changed, 1 insertion(+)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index ab12c37e..eb614c35 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -21,6 +21,7 @@
   "Add layer normalization": "레이어 정규화(normalization) 추가",
   "Add model hash to generation information": "생성 정보에 모델 해시 추가",
   "Add model name to generation information": "생성 정보에 모델 이름 추가",
+  "Add number to filename when saving": "이미지를 저장할 때 파일명에 숫자 추가하기",
   "Aesthetic imgs embedding": "스타일 이미지 임베딩",
   "Aesthetic learning rate": "스타일 학습 수",
   "Aesthetic steps": "스타일 스텝 수",
-- 
cgit v1.2.1


From 0c0028a9d3be4fc98bbdf975a6e0daa663bbed2d Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Mon, 24 Oct 2022 21:13:36 +0900
Subject: UnknownTimeZoneError

---
 modules/images.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/images.py b/modules/images.py
index 848ede75..9a8fe3ed 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -344,7 +344,10 @@ class FilenameGenerator:
         time_datetime = datetime.datetime.now()
 
         time_format = args[0] if len(args) > 0 else self.default_time_format
-        time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
+        try:
+            time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
+        except pytz.exceptions.UnknownTimeZoneError as _:
+            time_zone = None
 
         time_zone_time = time_datetime.astimezone(time_zone)
         try:
-- 
cgit v1.2.1


From df0a1f83815c771246a7b1bca85d63feaefad8d1 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Mon, 24 Oct 2022 21:28:57 +0900
Subject: add hints, [datetime<Format><Time Zone>]

---
 javascript/hints.js | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index a1fcc93b..6ddd6aec 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -62,8 +62,8 @@ titles = {
 
     "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",
 
-    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
-    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.",
+    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
+    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
     "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
 
     "Loopback": "Process an image, use it as an input, repeat.",
-- 
cgit v1.2.1


From e595b41c9d8a596b9b29d9505324e9afca2f12b5 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Tue, 25 Oct 2022 00:17:46 +0900
Subject: Update translations for renewed tooltip texts

---
 localizations/ko_KR.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index eb614c35..52ba4020 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -466,8 +466,8 @@
   "Use BLIP for caption": "캡션에 BLIP 사용",
   "Use deepbooru for caption": "캡션에 deepbooru 사용",
   "Use dropout": "드롭아웃 사용",
-  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지 파일명 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
-  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지와 그리드의 하위 디렉토리명의 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
+  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지 파일명 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
+  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지와 그리드의 하위 디렉토리명의 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
   "Use old emphasis implementation. Can be useful to reproduce old seeds.": "옛 방식의 강조 구현을 사용합니다. 옛 시드를 재현하는 데 효과적일 수 있습니다.",
   "Use original name for output filename during batch process in extras tab": "부가기능 탭에서 이미지를 여러장 처리 시 결과물 파일명에 기존 파일명 사용하기",
   "use spaces for tags in deepbooru": "deepbooru에서 태그에 공백 사용",
-- 
cgit v1.2.1


From ca2ebc89c2d18c02d9e337b660fdabe7178a6ccc Mon Sep 17 00:00:00 2001
From: xmodar <modar.alfadly@gmail.com>
Date: Mon, 24 Oct 2022 19:03:01 +0300
Subject: Add RTL languages support and improved Arabic localization

---
 javascript/localization.js |   20 +-
 localizations/ar_AR.json   | 1145 ++++++++++++++++++--------------------------
 localizations/ja_JP.json   |    1 +
 localizations/ko_KR.json   |    1 +
 localizations/ru_RU.json   |    1 +
 style.css                  |   74 +++
 6 files changed, 551 insertions(+), 691 deletions(-)

diff --git a/javascript/localization.js b/javascript/localization.js
index e6644635..6fb2fccd 100644
--- a/javascript/localization.js
+++ b/javascript/localization.js
@@ -107,7 +107,7 @@ function processNode(node){
 }
 
 function dumpTranslations(){
-    dumped = {}
+    dumped = { rtl: localization.rtl || false }
 
     Object.keys(original_lines).forEach(function(text){
         if(dumped[text] !== undefined)  return
@@ -129,6 +129,24 @@ onUiUpdate(function(m){
 
 document.addEventListener("DOMContentLoaded", function() {
     processNode(gradioApp())
+
+    if (localization.rtl) {  // if the language is from right to left,
+        (new MutationObserver((mutations, observer) => { // wait for the style to load
+            mutations.forEach(mutation => {
+                mutation.addedNodes.forEach(node => {
+                    if (node.tagName === 'STYLE') {
+                        observer.disconnect();
+
+                        for (const x of node.sheet.rules) {  // find all rtl media rules
+                            if (Array.from(x.media || []).includes('rtl')) {
+                                x.media.appendMedium('all');  // enable them
+                            }
+                        }
+                    }
+                })
+            });
+        })).observe(gradioApp(), { childList: true });
+    }
 })
 
 function download_localization() {
diff --git a/localizations/ar_AR.json b/localizations/ar_AR.json
index 6d03e013..1195a8e9 100644
--- a/localizations/ar_AR.json
+++ b/localizations/ar_AR.json
@@ -1,691 +1,456 @@
-{   "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "جار التحميل...",
-    "view": "معاينة",
-    "api": "api",
-    "•": "•",
-    "built with gradio": "مبني باستخدام Gradio",
-    "Stable Diffusion checkpoint": "نماذج الانتشار المستقر",
-    "txt2img": "نص لصورة",
-    "img2img": "صورة لصورة",
-    "Extras": "الإضافات",
-    "PNG Info": "معلومات PNG",
-    "Image Browser": "مستعرض الصور",
-    "Checkpoint Merger": "دمج النماذج",
-    "Train": "التدريب",
-    "Settings": "الإعدادات",
-    "Prompt": "الموجه",
-    "Negative prompt": "الموجه السلبي",
-    "Run": "تشغيل",
-    "Skip": "تخطي",
-    "Interrupt": "إيقاف",
-    "Generate": "إنشاء",
-    "Style 1": "نمط 1",
-    "Style 2": "نمط 2",
-    "Label": "الوسم",
-    "File": "ملف",
-    "Drop File Here": "اسحب الملف هنا",
-    "-": "-",
-    "or": "أو",
-    "Click to Upload": "انقر للتحميل",
-    "Image": "صورة",
-    "Check progress": "تحقق من التقدم",
-    "Check progress (first)": "تحقق من التقدم (الأول)",
-    "Sampling Steps": "خطوات أخذ العينة",
-    "Sampling method": "نظام أخذ العينات",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "العرض",
-    "Height": "الارتفاع",
-    "Restore faces": "ترميم الوجوه",
-    "Tiling": "تبليط",
-    "Highres. fix": "إصلاح الصور عالية الدقة",
-    "Firstpass width": "عرض المرور الأول",
-    "Firstpass height": "ارتفاع المرور الأول",
-    "Denoising strength": "قوة تقليل الضوضاء",
-    "Batch count": "عدد الدُفعات",
-    "Batch size": "حجم الدفعة",
-    "CFG Scale": "مقياس التقارب من الموجه (CFG)",
-    "Seed": "البذرة",
-    "Extra": "إضافي",
-    "Variation seed": "تباين البذرة",
-    "Variation strength": "قوة التباين",
-    "Resize seed from width": "تغيير حجم البذرة من العرض",
-    "Resize seed from height": "تغيير حجم البذرة من الارتفاع",
-    "Script": "سكريبت",
-    "None": "لايوجد",
-    "Prompt matrix": "مصفوفة الموجهات",
-    "Prompts from file or textbox": "موجهات من ملف أو مربع النص",
-    "X/Y plot": "الرسم البياني X/Y",
-    "Put variable parts at start of prompt": "ضع الأجزاء المتغيرة في بداية الموجه",
-    "Show Textbox": "إظهار مربع النص",
-    "File with inputs": "ملف يحتوي المدخلات",
-    "Prompts": "الموجهات",
-    "X type": "نوع X",
-    "Nothing": "لا شئ",
-    "Var. seed": "تغير البذرة",
-    "Var. strength": "قوة التغيير",
-    "Steps": "الخطوات",
-    "Prompt S/R": "موجه S / R",
-    "Prompt order": "ترتيب الموجهات",
-    "Sampler": "نظام أخذ العينات",
-    "Checkpoint name": "اسم النموذج",
-    "Hypernetwork": "الشبكة الفائقة",
-    "Hypernet str.": "قوة الشبكة الفائقة",
-    "Sigma Churn": "دفع سيجما",
-    "Sigma min": "أصغر سيجما",
-    "Sigma max": "أكبر سيجما",
-    "Sigma noise": "ضجة سيجما",
-    "Eta": "الوقت المتوقع",
-    "Clip skip": "تخطي Clip",
-    "Denoising": "تقليل الضوضاء",
-    "X values": "قيم X",
-    "Y type": "نوع Y",
-    "Y values": "قيم Y",
-    "Draw legend": "ارسم مفتاح التوضيح",
-    "Include Separate Images": "قم بتضمين الصور منفصلة",
-    "Keep -1 for seeds": "احتفظ بـقيمة -1 للبذور",
-    "Drop Image Here": "إسقاط الصورة هنا",
-    "Save": "حفظ",
-    "Send to img2img": "أرسل إلى صورة لصورة",
-    "Send to inpaint": "أرسل إلى إعادة الرسم الجزئي",
-    "Send to extras": "أرسل إلى الإضافات",
-    "Make Zip when Save?": "إنشاء ملف مضغوط عند الحفظ؟",
-    "Textbox": "مربع النص",
-    "Interrogate\nCLIP": "استجواب\n CLIP",
-    "Inpaint": "إعادة الرسم الجزئي",
-    "Batch img2img": "دفعات صورة لصورة",
-    "Image for img2img": "صورة (صورة لصورة)",
-    "Image for inpainting with mask": "صورة (إعادة الرسم الجزئي)",
-    "Mask": "القناع",
-    "Mask blur": "ضبابية القناع",
-    "Mask mode": "أسلوب القناع",
-    "Draw mask": "رسم القناع",
-    "Upload mask": "تحميل القناع",
-    "Masking mode": "أسلوب التقنيع",
-    "Inpaint masked": "إعادة الرسم الجزئي (المنطقة المقنعة)",
-    "Inpaint not masked": "إعادة الرسم الجزئي (المنطقة الغير مقنعة)",
-    "Masked content": "المحتوى المقنع",
-    "fill": "الملأ",
-    "original": "الأصلي",
-    "latent noise": "الضوضاء الكامنة",
-    "latent nothing": "لا شيء كامن",
-    "Inpaint at full resolution": "إعادة الرسم الجزئي بدقة كاملة",
-    "Inpaint at full resolution padding, pixels": "إعادة الرسم الجزئي بدقة كاملة, الحشو, بيكسل",
-    "Process images in a directory on the same machine where the server is running.": "معالجة الصور في المجلد على نفس الجهاز حيث يتم تشغيل الخادم.",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "استخدم مجلد إخراج فارغ لحفظ الصور بشكل طبيعي بدلاً من الكتابة إلى مجلد المخرجات.",
-    "Input directory": "مجلد المدخلات",
-    "Output directory": "مجلد المخرجات",
-    "Resize mode": "وضعية تغيير الحجم",
-    "Just resize": "تغييير الحجم فقط",
-    "Crop and resize": "اقتصاص وتغيير الحجم",
-    "Resize and fill": "تغيير الحجم والتعبئة",
-    "Animator": "محرك الرسوم",
-    "Animator v2": "محرك الرسوم نسخة 2",
-    "Deforum v0.5-webui-beta": "ديفوروم 0.5",
-    "External Image Masking": "تقنيع الصور الخارجي",
-    "img2img alternative test": "صورة لصورة البديلة",
-    "Loopback": "الحلقة الراجعة",
-    "Outpainting mk2": "الرسم الخارجي نسخة 2",
-    "Poor man's outpainting": "الرسم الخارجي للفقراء",
-    "SD upscale": "ترقية الانتشار المستقر",
-    "txt2mask v0.1.1": "نص لقناع 0.1.1",
-    "[C] Video to video": "فيديو لفيديو",
-    "Videos": "فيديوهات",
-    "Render these video formats:": "رسم تنسيقات الفيديو:",
-    "GIF": "GIF",
-    "MP4": "MP4",
-    "WEBM": "WEBM",
-    "Animation Parameters": "متغيرات تحريك الرسوم",
-    "Total Animation Length (s)": "المدة الكلية للرسوم المتحركة",
-    "Framerate": "Framerate",
-    "Add_Noise": "Add_Noise",
-    "Noise Strength": "Noise Strength",
-    "Denoising_Decay": "Denoising_Decay",
-    "Denoising Decay Rate": "Denoising Decay Rate",
-    "Initial Parameters": "Initial Parameters",
-    "Denoising Strength (overrides img2img slider)": "Denoising Strength (overrides img2img slider)",
-    "Zoom Factor (scale/s)": "Zoom Factor (scale/s)",
-    "X Pixel Shift (pixels/s)": "X Pixel Shift (pixels/s)",
-    "Y Pixel Shift (pixels/s)": "Y Pixel Shift (pixels/s)",
-    "Prompt Template, applied to each keyframe below": "Prompt Template, applied to each keyframe below",
-    "Positive Prompts": "Positive Prompts",
-    "Negative Prompts": "Negative Prompts",
-    "Keyframe Format:": "Keyframe Format:",
-    "Time (s) | Desnoise | Zoom (/s) | X Shift (pix/s) | Y shift (pix/s) | Positive Prompts | Negative Prompts | Seed": "Time (s) | Desnoise | Zoom (/s) | X Shift (pix/s) | Y shift (pix/s) | Positive Prompts | Negative Prompts | Seed",
-    "Keyframes:": "Keyframes:",
-    "Rotation (deg/s)": "Rotation (deg/s)",
-    "Props": "Props",
-    "Folder:": "Folder:",
-    "Supported Keyframes:": "Supported Keyframes:",
-    "time_s | prompt | positive_prompts | negative_prompts": "time_s | prompt | positive_prompts | negative_prompts",
-    "time_s | transform | zoom | x_shift | y_shift | rotation": "time_s | transform | zoom | x_shift | y_shift | rotation",
-    "time_s | seed | new_seed_int": "time_s | seed | new_seed_int",
-    "time_s | denoise | denoise_value": "time_s | denoise | denoise_value",
-    "time_s | prop | prop_filename | x_pos | y_pos | scale | rotation": "time_s | prop | prop_filename | x_pos | y_pos | scale | rotation",
-    "time_s | set_text | textblock_name | text_prompt | x | y | fore_R | fore_G | fore_B | back_R | back_G | back_B | font_name | font_size": "time_s | set_text | textblock_name | text_prompt | x | y | fore_R | fore_G | fore_B | back_R | back_G | back_B | font_name | font_size",
-    "time_s | clear_text | textblock_name": "time_s | clear_text | textblock_name",
-    "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation": "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation",
-    "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation": "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation",
-    "time_s | clear_stamp | stamp_name": "time_s | clear_stamp | stamp_name",
-    "time_s | col_set": "time_s | col_set",
-    "time_s | col_clear": "time_s | col_clear",
-    "time_s | model | 2022-10-19T08-10-53_VV_6_training_images_1212_max_training_steps_VV_V_token_woman_class_word, 2022-10-19T09-06-28_VV_6_training_images_1212_max_training_steps_VV_V_token_woman_class_word, 2022-10-20T10-07-46_Zori_New_20_training_images_2020_max_training_steps_ZZ_W_token_woman_class_word, 2022-10-20T10-39-38_Zori_New_20_training_images_1010_max_training_steps_ZZ_W_token_woman_class_word, HD-16, L, L2_1, L2_2, LM, MT_V, Main+Trinart, Z1, Z2, model, modelZ, nai, robo-diffusion-v1, sd-v1-5-inpainting, trinart2_step115000, v1-5-pruned, v1-5-pruned-emaonly, wd-v1-2-full-ema, wd-v1-3-float16": "time_s | model | 2022-10-19T08-10-53_VV_6_training_images_1212_max_training_steps_VV_V_token_woman_class_word, 2022-10-19T09-06-28_VV_6_training_images_1212_max_training_steps_VV_V_token_woman_class_word, 2022-10-20T10-07-46_Zori_New_20_training_images_2020_max_training_steps_ZZ_W_token_woman_class_word, 2022-10-20T10-39-38_Zori_New_20_training_images_1010_max_training_steps_ZZ_W_token_woman_class_word, HD-16, L, L2_1, L2_2, LM, MT_V, Main+Trinart, Z1, Z2, model, modelZ, nai, robo-diffusion-v1, sd-v1-5-inpainting, trinart2_step115000, v1-5-pruned, v1-5-pruned-emaonly, wd-v1-2-full-ema, wd-v1-3-float16",
-    "Click here after the generation to show the video": "Click here after the generation to show the video",
-    "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha": "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha",
-    "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Original Deforum Github repo  github.com/deforum/stable-diffusion",
-    "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui": "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui",
-    "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions",
-    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
-    "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
-    "Keyframes": "Keyframes",
-    "Init": "Init",
-    "Video output": "Video output",
-    "Run settings": "Run settings",
-    "Import settings from file": "Import settings from file",
-    "Override settings": "Override settings",
-    "Custom settings file": "Custom settings file",
-    "Sampling settings": "Sampling settings",
-    "The following settings have already been set up in the webui": "The following settings have already been set up in the webui",
-    "Do you want to override them with the values above?": "Do you want to override them with the values above?",
-    "FIXME! Need to make deforum <-> webui samplers map. Before that, the sampler gets used from webui anyway. If your images are changing too adruptly, lower steps or increase strength schedule!": "FIXME! Need to make deforum <-> webui samplers map. Before that, the sampler gets used from webui anyway. If your images are changing too adruptly, lower steps or increase strength schedule!",
-    "override_these_with_webui": "override_these_with_webui",
-    "W, H, seed, sampler, steps, scale, ddim_eta, n_batch, make_grid, grid_rows": "W, H, seed, sampler, steps, scale, ddim_eta, n_batch, make_grid, grid_rows",
-    "W": "W",
-    "H": "H",
-    "seed": "seed",
-    "sampler": "sampler",
-    "klms": "klms",
-    "dpm2": "dpm2",
-    "dpm2_ancestral": "dpm2_ancestral",
-    "heun": "heun",
-    "euler": "euler",
-    "euler_ancestral": "euler_ancestral",
-    "plms": "plms",
-    "ddim": "ddim",
-    "steps": "steps",
-    "scale": "scale",
-    "ddim_eta": "ddim_eta",
-    "n_batch": "n_batch",
-    "make_grid": "make_grid",
-    "grid_rows": "grid_rows",
-    "save_settings": "save_settings",
-    "save_samples": "save_samples",
-    "display_samples": "display_samples",
-    "save_sample_per_step": "save_sample_per_step",
-    "show_sample_per_step": "show_sample_per_step",
-    "Batch settings": "Batch settings",
-    "batch_name": "batch_name",
-    "filename_format": "filename_format",
-    "seed_behavior": "seed_behavior",
-    "iter": "iter",
-    "fixed": "fixed",
-    "random": "random",
-    "schedule": "schedule",
-    "Animation settings": "Animation settings",
-    "animation_mode": "animation_mode",
-    "2D": "2D",
-    "3D": "3D",
-    "Video Input": "Video Input",
-    "max_frames": "max_frames",
-    "border": "border",
-    "replicate": "replicate",
-    "wrap": "wrap",
-    "Motion parameters:": "Motion parameters:",
-    "2D and 3D settings": "2D and 3D settings",
-    "angle": "angle",
-    "zoom": "zoom",
-    "translation_x": "translation_x",
-    "translation_y": "translation_y",
-    "3D settings": "3D settings",
-    "translation_z": "translation_z",
-    "rotation_3d_x": "rotation_3d_x",
-    "rotation_3d_y": "rotation_3d_y",
-    "rotation_3d_z": "rotation_3d_z",
-    "Prespective flip — Low VRAM pseudo-3D mode:": "Prespective flip — Low VRAM pseudo-3D mode:",
-    "flip_2d_perspective": "flip_2d_perspective",
-    "perspective_flip_theta": "perspective_flip_theta",
-    "perspective_flip_phi": "perspective_flip_phi",
-    "perspective_flip_gamma": "perspective_flip_gamma",
-    "perspective_flip_fv": "perspective_flip_fv",
-    "Generation settings:": "Generation settings:",
-    "noise_schedule": "noise_schedule",
-    "strength_schedule": "strength_schedule",
-    "contrast_schedule": "contrast_schedule",
-    "cfg_scale_schedule": "cfg_scale_schedule",
-    "To enable seed schedule select seed behavior — 'schedule'": "To enable seed schedule select seed behavior — 'schedule'",
-    "seed_schedule": "seed_schedule",
-    "Coherence:": "Coherence:",
-    "color_coherence": "color_coherence",
-    "Match Frame 0 HSV": "Match Frame 0 HSV",
-    "Match Frame 0 LAB": "Match Frame 0 LAB",
-    "Match Frame 0 RGB": "Match Frame 0 RGB",
-    "diffusion_cadence": "diffusion_cadence",
-    "3D Depth Warping:": "3D Depth Warping:",
-    "use_depth_warping": "use_depth_warping",
-    "midas_weight": "midas_weight",
-    "near_plane": "near_plane",
-    "far_plane": "far_plane",
-    "fov": "fov",
-    "padding_mode": "padding_mode",
-    "reflection": "reflection",
-    "zeros": "zeros",
-    "sampling_mode": "sampling_mode",
-    "bicubic": "bicubic",
-    "bilinear": "bilinear",
-    "nearest": "nearest",
-    "save_depth_maps": "save_depth_maps",
-    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)",
-    "*Important change from vanilla Deforum!*": "*Important change from vanilla Deforum!*",
-    "This script uses the built-in webui weighting settings.": "This script uses the built-in webui weighting settings.",
-    "So if you want to use math functions as prompt weights,": "So if you want to use math functions as prompt weights,",
-    "keep the values above zero in both parts": "keep the values above zero in both parts",
-    "Negative prompt part can be specified with --neg": "Negative prompt part can be specified with --neg",
-    "batch_prompts (disabled atm)": "batch_prompts (disabled atm)",
-    "animation_prompts": "animation_prompts",
-    "Init settings": "Init settings",
-    "use_init": "use_init",
-    "from_img2img_instead_of_link": "from_img2img_instead_of_link",
-    "strength_0_no_init": "strength_0_no_init",
-    "strength": "strength",
-    "init_image": "init_image",
-    "use_mask": "use_mask",
-    "use_alpha_as_mask": "use_alpha_as_mask",
-    "invert_mask": "invert_mask",
-    "overlay_mask": "overlay_mask",
-    "mask_file": "mask_file",
-    "mask_brightness_adjust": "mask_brightness_adjust",
-    "mask_overlay_blur": "mask_overlay_blur",
-    "Video Input:": "Video Input:",
-    "video_init_path": "video_init_path",
-    "extract_nth_frame": "extract_nth_frame",
-    "overwrite_extracted_frames": "overwrite_extracted_frames",
-    "use_mask_video": "use_mask_video",
-    "video_mask_path": "video_mask_path",
-    "Interpolation (turned off atm)": "Interpolation (turned off atm)",
-    "interpolate_key_frames": "interpolate_key_frames",
-    "interpolate_x_frames": "interpolate_x_frames",
-    "Resume animation:": "Resume animation:",
-    "resume_from_timestring": "resume_from_timestring",
-    "resume_timestring": "resume_timestring",
-    "Video output settings": "Video output settings",
-    "skip_video_for_run_all": "skip_video_for_run_all",
-    "fps": "fps",
-    "output_format": "output_format",
-    "PIL gif": "PIL gif",
-    "FFMPEG mp4": "FFMPEG mp4",
-    "ffmpeg_location": "ffmpeg_location",
-    "add_soundtrack": "add_soundtrack",
-    "soundtrack_path": "soundtrack_path",
-    "use_manual_settings": "use_manual_settings",
-    "render_steps": "render_steps",
-    "max_video_frames": "max_video_frames",
-    "path_name_modifier": "path_name_modifier",
-    "x0_pred": "x0_pred",
-    "x": "x",
-    "image_path": "image_path",
-    "mp4_path": "mp4_path",
-    "Masking preview size": "Masking preview size",
-    "Draw new mask on every run": "Draw new mask on every run",
-    "Process non-contigious masks separately": "Process non-contigious masks separately",
-    "should be 2 or lower.": "should be 2 or lower.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Override `Sampling method` to Euler?(this method is built for it)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)",
-    "Original prompt": "Original prompt",
-    "Original negative prompt": "Original negative prompt",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Override `Sampling Steps` to the same value as `Decode steps`?",
-    "Decode steps": "Decode steps",
-    "Override `Denoising strength` to 1?": "Override `Denoising strength` to 1?",
-    "Decode CFG scale": "Decode CFG scale",
-    "Randomness": "Randomness",
-    "Sigma adjustment for finding noise for image": "Sigma adjustment for finding noise for image",
-    "Loops": "Loops",
-    "Denoising strength change factor": "معامل قوة تقليل الضوضاء",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "الإعدادات الموصى بها: خطوات أخذ العينات: 80-100 ، طريقة أخذ العينات: Euler a ، تقليل الضوضاء: 0.8",
-    "Pixels to expand": "عدد البكسل للتوسيع",
-    "Outpainting direction": "إتجاه الرسم الخارجي",
-    "left": "يسار",
-    "right": "يمين",
-    "up": "فوق",
-    "down": "تحت",
-    "Fall-off exponent (lower=higher detail)": "أس التناقص (الأدنى = تفاصيل أعلى)",
-    "Color variation": "اختلاف اللون",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "سيقوم بترقية الصورة إلى ضعف الأبعاد ؛ استخدم شريط تمرير العرض والارتفاع لضبط حجم التبليط",
-    "Tile overlap": "تداخل التبليط",
-    "Upscaler": "المرقي",
-    "Lanczos": "Lanczos",
-    "LDSR": "LDSR",
-    "ESRGAN 4x": "ESRGAN 4x",
-    "R-ESRGAN General 4xV3": "R-ESRGAN General 4xV3",
-    "R-ESRGAN General WDN 4xV3": "R-ESRGAN General WDN 4xV3",
-    "R-ESRGAN AnimeVideo": "R-ESRGAN AnimeVideo",
-    "R-ESRGAN 4x+": "R-ESRGAN 4x+",
-    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
-    "R-ESRGAN 2x+": "R-ESRGAN 2x+",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR_4x": "SwinIR_4x",
-    "Mask prompt": "Mask prompt",
-    "Negative mask prompt": "Negative mask prompt",
-    "Mask precision": "Mask precision",
-    "Mask padding": "Mask padding",
-    "Brush mask mode": "Brush mask mode",
-    "discard": "discard",
-    "add": "add",
-    "subtract": "subtract",
-    "Show mask in output?": "Show mask in output?",
-    "If you like my work, please consider showing your support on": "If you like my work, please consider showing your support on",
-    "Patreon": "Patreon",
-    "Input file path": "Input file path",
-    "CRF (quality, less is better, x264 param)": "CRF (quality, less is better, x264 param)",
-    "FPS": "FPS",
-    "Seed step size": "Seed step size",
-    "Seed max distance": "Seed max distance",
-    "Start time": "Start time",
-    "End time": "End time",
-    "End Prompt Blend Trigger Percent": "End Prompt Blend Trigger Percent",
-    "Prompt end": "Prompt end",
-    "Smooth video": "Smooth video",
-    "Seconds": "Seconds",
-    "Zoom": "Zoom",
-    "Zoom level": "Zoom level",
-    "Direction X": "Direction X",
-    "Direction Y": "Direction Y",
-    "Rotate": "Rotate",
-    "Degrees": "Degrees",
-    "Is the Image Tiled?": "Is the Image Tiled?",
-    "TranslateX": "TranslateX",
-    "Left": "Left",
-    "PercentX": "PercentX",
-    "TranslateY": "TranslateY",
-    "Up": "Up",
-    "PercentY": "PercentY",
-    "Show generated pictures in ui": "عرض الصور المنشئة في واجهة الاستخدام",
-    "Single Image": "صورة واحدة",
-    "Batch Process": "معالجة الدفعات",
-    "Batch from Directory": "دفعة من المجلد",
-    "Source": "مصدر",
-    "Show result images": "إظهار نتائج الصور ",
-    "Scale by": "رفع الحجم بمقدار",
-    "Scale to": "رفع الحجم إلى",
-    "Resize": "تغيير الحجم",
-    "Crop to fit": "اقتصاص للتوافق",
-    "Upscaler 2 visibility": "إظهار المرقي 2",
-    "GFPGAN visibility": "إظهار GFPGAN",
-    "CodeFormer visibility": "إظهار CodeFormer",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "وزن CodeFormer (0 = أقصى تأثير ، 1 = تأثير أدنى)",
-    "Open output directory": "افتح مجلد المخرجات",
-    "Send to txt2img": "أرسل إلى كتابة لصورة",
-    "extras": "إضافات",
-    "favorites": "المفضلة",
-    "Load": "تحميل",
-    "Images directory": "مجلد الصور",
-    "Prev batch": "الدفعة السابقة",
-    "Next batch": "الدفعة التالية",
-    "First Page": "الصفحة الأولى",
-    "Prev Page": "الصفحة السابقة",
-    "Page Index": "فهرس الصفحات",
-    "Next Page": "الصفحة التالية",
-    "End Page": "صفحة النهاية",
-    "number of images to delete consecutively next": "عدد الصور المطلوب حذفها على التوالي بعد ذلك",
-    "Delete": "حذف",
-    "Generate Info": "معلومات الإنشاء",
-    "File Name": "اسم الملف",
-    "Collect": "جمع",
-    "Refresh page": "إعادة تحميل الصفحة",
-    "Date to": "التاريخ إلى",
-    "Number": "الرقم",
-    "set_index": "وضع الفهرس",
-    "Checkbox": "صندوق اختيار",
-    "A merger of the two checkpoints will be generated in your": "سيتم إنشاء نموذجمدمج من النموذجين في",
-    "checkpoint": "النموذج",
-    "directory.": "المجلد.",
-    "Primary model (A)": "النموذج الأساسي (أ)",
-    "Secondary model (B)": "النموذج الثانوي (ب)",
-    "Tertiary model (C)": "النموذج الثالث (ج)",
-    "Custom Name (Optional)": "اسم مخصص (اختياري)",
-    "Multiplier (M) - set to 0 to get model A": "المضاعف (M) - اضبط على 0 للحصول على النموذج أ",
-    "Interpolation Method": "طريقة الاستنباط",
-    "Weighted sum": "المجموع الموزون",
-    "Add difference": "أضافة الاختلاف",
-    "Save as float16": "حفظ  float16",
-    "See": "شاهد",
-    "wiki": "ويكي",
-    "for detailed explanation.": "للحصول على شرح مفصل.",
-    "Create embedding": "إنشاء التضمين",
-    "Create hypernetwork": "إنشاء شبكة فائقة",
-    "Preprocess images": "تجهيز الصور",
-    "Name": "الاسم",
-    "Initialization text": "نص التهيئة",
-    "Number of vectors per token": "عدد المتجهات لكل رمز",
-    "Overwrite Old Embedding": "الكتابة فوق التضمين القديم",
-    "Modules": "الوحدات",
-    "Enter hypernetwork layer structure": "أدخل بنية طبقة الشبكة الفائقة",
-    "Select activation function of hypernetwork": "حدد وظيفة تنشيط الشبكة الفائقة",
-    "linear": "خطي (Linear)",
-    "relu": "الوحدة الخطية المعدلة (Relu)",
-    "leakyrelu": "الوحدة الخطية المعدلة المسربة (Leakyrelu)",
-    "elu": "الوحدة الأسية الخطية (Elu)",
-    "swish": "Swish",
-    "Add layer normalization": "أضف طبقة التسوية",
-    "Use dropout": "استخدم الهبوط",
-    "Overwrite Old Hypernetwork": "الكتابة فوق الشبكة الفائقة القديمة",
-    "Source directory": "مجلد المصدر",
-    "Destination directory": "مجلد النتائج",
-    "Existing Caption txt Action": "الإجراء النصي للتعليق المتوفر",
-    "ignore": "تجاهل",
-    "copy": "نسخ",
-    "prepend": "أضف قبل",
-    "append": "ألحق",
-    "Create flipped copies": "قم بإنشاء نسخ مقلوبة",
-    "Split oversized images": "تقسيم الصور كبيرة الحجم",
-    "Use BLIP for caption": "استخدم BLIP للتعليق",
-    "Use deepbooru for caption": "استخدم deepbooru للتعليق",
-    "Split image threshold": "حد تقسيم الصورة",
-    "Split image overlap ratio": "نسبة تداخل الصورة المنقسمة",
-    "Preprocess": "تجهيز الصور",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "تدريب التضمين أو الشبكة الفائقة ؛ يجب تحديد مجلد بمجموعة من الصور بنسبة أبعاد 1: 1",
-    "[wiki]": "[ويكي]",
-    "Embedding": "التضمين",
-    "Embedding Learning rate": "معدل تعلم التضمين",
-    "Hypernetwork Learning rate": "معدل تعلم الشبكة الفائقة",
-    "Dataset directory": "مجلد مجموعة البيانات",
-    "Log directory": "مجلد السجل",
-    "Prompt template file": "ملف قالب الموجهات",
-    "Max steps": "الخطوات القصوى",
-    "Save an image to log directory every N steps, 0 to disable": "حفظ صورة في مجلد السجل  كل N خطوات ، 0 للتعطيل",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "حفظ نسخة من التضمين في مجلد السجل كل N خطوات ، 0 للتعطيل",
-    "Save images with embedding in PNG chunks": "حفظ التضمين مع الصور في أجزاء PNG",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "قراءة المتغيرات (الموجه ، إلخ ...) من علامة تبويب نص لصورة عند إجراء المعاينات",
-    "Train Hypernetwork": "تدريب الشبكة الفائقة",
-    "Train Embedding": "تدريب التضمين",
-    "Apply settings": "تطبيق الإعدادات",
-    "Saving images/grids": "حفظ الصور / الإطار الشبكي",
-    "Always save all generated images": "احفظ دائمًا جميع الصور التي تم إنشائها",
-    "File format for images": "تنسيق ملفات الصور",
-    "Images filename pattern": "نمط اسم ملفات الصور",
-    "Always save all generated image grids": "احفظ دائمًا جميع الإطارات الشبكية للصور التي تم إنشاؤها",
-    "File format for grids": "تنسيق ملفات الإطارات الشبكية",
-    "Add extended info (seed, prompt) to filename when saving grid": "أضف معلومات إضافية (البذرة ، الموجه) إلى اسم الملف عند حفظ الإطار الشبكي",
-    "Do not save grids consisting of one picture": "لا تحفظ الإطارات الشبكية التي تتكون من صورة واحدة",
-    "Prevent empty spots in grid (when set to autodetect)": "منع المناطق الفارغة في الإطار الشبكي (عند الضبط على الاكتشاف التلقائي)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "عدد صفوف الإطار الشبكي استخدم -1 للاكتشاف التلقائي و 0 ليكون نفس حجم الدُفعة",
-    "Save text information about generation parameters as chunks to png files": "احفظ معلومات نصية حول متغيرات الإنشاء كمقاطع في ملفات png",
-    "Create a text file next to every image with generation parameters.": "قم بإنشاء ملف نصي بجوار كل صورة باستخدام متغيرات الإنشاء.",
-    "Save a copy of image before doing face restoration.": "احفظ نسخة من الصورة قبل القيام بترميم الوجوه.",
-    "Quality for saved jpeg images": "جودة الصور المحفوظة بتنسيق jpeg",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "إذا كانت صورة PNG أكبر من 4 ميجابايت أو كان أي بُعد أكبر من 4000 ، قم بتقليل حجم الصورة وحفظها بتنسيق JPG",
-    "Use original name for output filename during batch process in extras tab": "استخدم الاسم الأصلي لاسم ملف الإخراج أثناء عملية الدُفعات في علامة تبويب الإضافات",
-    "When using 'Save' button, only save a single selected image": "عند استخدام زر 'حفظ' ، احفظ فقط صورة واحدة محددة",
-    "Do not add watermark to images": "لا تقم بإضافة العلامة المائية للصور",
-    "Paths for saving": "مسارات الحفظ",
-    "Output directory for images; if empty, defaults to three directories below": "مجلد المخرجات للصور ؛ إذا كان فارغا ، يتم تعيينه افتراضيًا إلى المجلدات الثلاثة أدناه",
-    "Output directory for txt2img images": "مجلد المخرجات لصور نص لصورة",
-    "Output directory for img2img images": "مجلد المخرجات لصور صورة لصورة",
-    "Output directory for images from extras tab": "مجلد المخرجات لصور علامة تبويب الإضافات",
-    "Output directory for grids; if empty, defaults to two directories below": "مجلد المخرجات للإطارات الشبكية ؛ إذا كان فارغا ، يتم تعيينه افتراضيًا إلى المجلدين أدناه",
-    "Output directory for txt2img grids": "مجلد المخرجات للإطارات الشبكية نص لصورة",
-    "Output directory for img2img grids": "مجلد المخرجات للإطارات الشبكية صورة لصورة",
-    "Directory for saving images using the Save button": "مجلد لحفظ الصور باستخدام زر حفظ",
-    "Saving to a directory": "يتم الحفظ إلى المجلد..",
-    "Save images to a subdirectory": "حفظ الصور في مجلد فرعي",
-    "Save grids to a subdirectory": "حفظ الإطارات الشبكية في مجلد فرعي",
-    "When using 'Save' button, save images to a subdirectory": "عند استخدام زر 'حفظ' ، احفظ الصور في مجلد فرعي",
-    "Directory name pattern": "نمط اسم المجلد",
-    "Max prompt words for [prompt_words] pattern": "أقصى عدد لكلمات التوجيه لنمط [كلمات_التوجيه]",
-    "Upscaling": "الارتقاء",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "حجم التبليط لترقيات ESRGAN, القيمة 0= لا يوجد تبليط.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "تداخل المربعات ، بالبكسل لمرقي ESRGAN. القيم المنخفضة = تداخل مرئي.",
-    "Tile size for all SwinIR.": "حجم التليط لجميع عمليات SwinIR.",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "تداخل المربعات بالبكسل لـ SwinIR. القيم المنخفضة = تداخل مرئي.",
-    "LDSR processing steps. Lower = faster": "خطوات معالجة LDSR. أقل = أسرع",
-    "Upscaler for img2img": "المرقي لصورة لصورة",
-    "Upscale latent space image when doing hires. fix": "ترقية صورة الفضاء الكامن عند التعيينات. تصليح",
-    "Face restoration": "ترميم الوجوه",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "متغيرات وزن CodeFormer ؛ 0 = أقصى تأثير ؛ 1 = الحد الأدنى من التأثير",
-    "Move face restoration model from VRAM into RAM after processing": "انقل نموذج استعادة الوجوه من VRAM إلى RAM بعد المعالجة",
-    "System": "نظام",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "استطلاعات استخدام VRAM في الثانية أثناء الإنشاء. اضبط على 0 للتعطيل.",
-    "Always print all generation info to standard output": "قم دائمًا بطباعة جميع معلومات الإنشاء إلى المخرج القياسي",
-    "Add a second progress bar to the console that shows progress for an entire job.": "أضف شريط تقدم آخر إلى وحدة التحكم يُظهر التدقم الكلي للمهمة.",
-    "Training": "التدريب",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "تفريغ VAE و CLIP من VRAM عند التدريب",
-    "Filename word regex": "اسم ملف كلمات regex",
-    "Filename join string": "سلسلة أحرف دمج أسماء الملفات",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "عدد التكرارات لصورة إدخال واحدة لكل حقبة ؛ تستخدم فقط لعرض رقم الحقبة",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "حفظ ملف csv يحتوي على الخسارة إلى مجلد السجل كل N خطوات ، 0 للتعطيل",
-    "Stable Diffusion": "انتشار مستقر",
-    "Checkpoints to cache in RAM": "التخزين المؤقت للنماذج في RAM",
-    "Hypernetwork strength": "قوة الشبكة الفائقة",
-    "Apply color correction to img2img results to match original colors.": "قم بتطبيق تصحيح الألوان على نتائج صورة لصورة لمطابقة الألوان الأصلية.",
-    "Save a copy of image before applying color correction to img2img results": "احفظ نسخة من الصورة قبل تطبيق تصحيح الألوان على نتائج  صورة لصورة",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "باستخدام صورة لصورة ، قم بالضبط بعدد الخطوات التي يحددها شريط التمرير (عادةً ما سيكون أقل مع تقليل التشويش).",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "تمكين تكميم عينات K للحصول على نتائج أوضح وأنظف. هذا قد يغير البذرة المستخدمة. يتطلب إعادة التشغيل للتطبيق.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "التأكيد: استخدم (النص) لجعل النموذج يولي مزيدًا من الاهتمام للنص و استخدم [النص] لجعل النموذج يولي اهتمامًا أقل  للنص",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "استخدم طريقة التأكيد القديمة. يمكن أن يكون مفيدًا لإعادة إنتاج البذور القديمة.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "اجعل انظمة أخذ عينات الانتشار K تنتج نفس الصور في الدفعات كما هو الحال عند إنشاء صورة واحدة",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "زيادة التناسق عن طريق المساحة المتروكة من الفاصلة الأخيرة ضمن عدد n من الرموز المميزة عند استخدام أكثر من 75 رمزًا مميزًا",
-    "Filter NSFW content": "تصفية المحتوى الإباحي",
-    "Stop At last layers of CLIP model": "توقف عند آخر طبقات نموذج CLIP",
-    "Interrogate Options": "خيارات الاستجواب",
-    "Interrogate: keep models in VRAM": "الاستجواب: احتفظ بالنماذج في VRAM",
-    "Interrogate: use artists from artists.csv": "الاستجواب: استخدم  قائمة الفنانين من ملف artists.csv",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "الاستجواب: قم بتضمين رتب علامات النموذج في النتائج (ليس له تأثير على الاستجواب المستند إلى التعليقات).",
-    "Interrogate: num_beams for BLIP": "الاستجواب: عدد الحزم لـ BLIP",
-    "Interrogate: minimum description length (excluding artists, etc..)": "الاستجواب: الحد الأدنى لطول الوصف (باستثناء الفنانين ،إلخ..)",
-    "Interrogate: maximum description length": "الاستجواب: الحد الأقصى لطول الوصف",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: أقصى عدد من الأسطر في الملف النصي (0 = بلا حدود)",
-    "Interrogate: deepbooru score threshold": "الاستجواب: عتبة درجات deepbooru",
-    "Interrogate: deepbooru sort alphabetically": "الاستجواب: الترتيب الأبجدي لـ deepbooru",
-    "use spaces for tags in deepbooru": "استخدام مسافات للعلامات في deepbooru",
-    "User interface": "واجهة المستخدم",
-    "Show progressbar": "إظهار شريط التقدم",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "إظهار تقدم إنشاء الصورة لكل N خطوة من خطوات أخذ العينات. قم بتعيين 0 للتعطيل.",
-    "Show previews of all images generated in a batch as a grid": "إظهار معاينة لجميع الصور التي يتم إنشائها في الدفعة كإطار شبكي",
-    "Show grid in results for web": "إظهار الإطار الشبكي في نتائج الويب",
-    "Do not show any images in results for web": "لا تظهر أي صور في نتائج الويب",
-    "Add model hash to generation information": "أضف معلومات الهاش للنموذج إلى معلومات الإنشاء",
-    "Add model name to generation information": "أضف اسم النموذج إلى معلومات الإنشاء",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "عند قراءة متغيرات الإنشاء من النص إلى واجهة المستخدم (من معلومات PNG أو النص الملصق) ، لا تقم بتغيير النموذج",
-    "Font for image grids that have text": "الخط المستخدم للإطارات الشبكية للصور التي تحتوي على نص",
-    "Enable full page image viewer": "تفعيل عارض الصور ذو الصفحة الكاملة",
-    "Show images zoomed in by default in full page image viewer": "إظهار الصور التي تم تكبيرها بشكل افتراضي في عارض الصور ذو الصفحة الكاملة",
-    "Show generation progress in window title.": "إظهار تقدم الإنشاء في عنوان الشاشة.",
-    "Quicksettings list": "قائمة الإعدادات السريعة",
-    "Localization (requires restart)": "الترجمة (يتطلب إعادة التشغيل)",
-    "ar_AR": "العربية",
-    "ja_JP": "اليابانية",
-    "ru_RU": "الروسية",
-    "Sampler parameters": "متغيرات أنظمة اخذ العينات",
-    "Hide samplers in user interface (requires restart)": "إخفاء أنظمة أخذ العينات في واجهة المستخدم (يتطلب إعادة التشغيل)",
-    "eta (noise multiplier) for DDIM": "الوقت المتبقي (مضاعف الضوضاء) لـ DDIM",
-    "eta (noise multiplier) for ancestral samplers": "الوقت المتبقي (مضاعف الضوضاء) لأنظمة أخذ العينات التي تعتمد على الأجداد",
-    "img2img DDIM discretize": "تفصل DDIM لصورة لصورة",
-    "uniform": "التوحيد",
-    "quad": "رباعية",
-    "sigma churn": "دفع سيجما",
-    "sigma tmin": "سيجما tmin",
-    "sigma noise": "ضجيج سيجما",
-    "Eta noise seed delta": "الوقت المتوقع لديلتا بذرة الضجيج ",
-    "Images Browser": "مستعرض الصور",
-    "Preload images at startup": "تحميل الصور مسبقًا عند بدء التشغيل",
-    "Number of pictures displayed on each page": "عدد الصور المعروضة في كل صفحة",
-    "Minimum number of pages per load": "الحد الأدنى لعدد الصفحات لكل تحميل",
-    "Number of grids in each row": "عدد الإطارات الشبكية لكل صف",
-    "Request browser notifications": "طلب إشعارات المتصفح",
-    "Download localization template": "تنزيل نموذج الترجمة",
-    "Reload custom script bodies (No ui updates, No restart)": "إعادة تحميل السكريبتات المخصصة (لا توجد تحديثات لواجهة المستخدم ، لا إعادة تشغيل)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعادة تشغيل  Gradio و تحديث مكونات واجهة الاستخدام (السكريبتات و ui.py و js و css فقط)",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "الموجه (اضغط على Ctrl + Enter أو Alt + Enter للإنشاء)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "الموجه السلبي (اضغط على Ctrl + Enter أو Alt + Enter للإنشاء)",
-    "Add a random artist to the prompt.": "أضف فنانًا عشوائيًا إلى الموجه.",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "قراءة متغيرات الإنشاء من الموجه أو الإنشاء الأخير إذا كان الموجه فارغا في واجهة المستخدم.",
-    "Save style": "حفظ النمط",
-    "Apply selected styles to current prompt": "تطبيق الأنماط المحددة للموجه الحالي",
-    "Stop processing current image and continue processing.": "توقف عن معالجة الصورة الحالية واستمر في المعالجة.",
-    "Stop processing images and return any results accumulated so far.": "توقف عن معالجة الصور وقم بإرجاع أي نتائج متراكمة حتى الآن.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "النمط المطلوب تطبيقه ؛ الأنماط لها مكونات لكل من الموجهات الإيجابية والسلبية وتنطبق على كليهما",
-    "Do not do anything special": "لا تفعل أي شيء خاص",
-    "Which algorithm to use to produce the image": "الخوارزمية التي يجب استخدامها لإنتاج الصورة",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - مبدع جدًا ، يمكن لكل منها الحصول على صورة مختلفة تمامًا اعتمادًا على عدد الخطوات ، تعيين الخطوات على أعلى من 30-40 لا يعطي نتيجة مفيدة",
-    "Denoising Diffusion Implicit Models - best at inpainting": "نماذج تقليل التشويش الضمني  - أفضل اسخدام في الرسم الجزئي",
-    "Produce an image that can be tiled.": "أنتج صورة يمكن تبليطها.",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "استخدم عملية من خطوتين لإنشاء صورة جزئيًا بدقة أقل ثم ترقيتها و تحسين تفاصيلها دون تغيير التكوين",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "يحدد مدى ضآلة احترام الخوارزمية لمحتوى الصورة. عند 0 ، لن يتغير شيء ، وعند 1 ستحصل على صورة لا صلة لها بالصورة الأصلية. مع وجود قيم أقل من 1.0 ، المعالجة ستأخذ خطوات أقل مما يحدده شريط تمرير خطوات المعالجة.",
-    "How many batches of images to create": "كم عدد دفعات الصور التي يتم إنشائها",
-    "How many image to create in a single batch": "كم صورة يتم إنشائها دفعة واحدة",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "مقياس التوجيه الحر للمصنف - إلى أي مدى يجب أن تتوافق الصورة مع الموجه - القيم المنخفضة تنتج نتائج أكثر إبداعًا",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "القيمة التي تحدد ناتج منشئ الأرقام العشوائية - إذا قمت بإنشاء صورة بنفس المتغيرات و بذرة الصورة ، فستحصل على نفس النتيجة",
-    "Set seed to -1, which will cause a new random number to be used every time": "عيّن البذرة إلى -1 ، مما سيؤدي إلى استخدام رقم عشوائي جديد في كل مرة",
-    "Reuse seed from last generation, mostly useful if it was randomed": "إعادة استخدام البذرة من الإنشاء الأخير ، وتكون مفيدة في الغالب إذا كانت عشوائية",
-    "Seed of a different picture to be mixed into the generation.": "استخدام بذرة من صورة مختلفة ليتم مزجها في الإنشاء.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "ما مدى قوة التباين عند الإنشاء. 0 ، لن يكون هناك أي تأثير. 1 ، ستحصل على الصورة الكاملة ببذور التباين (باستثناء أنظمة عينات الأسلاف ، حيث ستحصل على شيء ما فقط).",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "قم بمحاولة لإنتاج صورة مشابهة لما تم إنتاجه بنفس البذرة بناءا على دقة محددة",
-    "Separate values for X axis using commas.": "افصل بين قيم المحور X باستخدام الفواصل.",
-    "Separate values for Y axis using commas.": "افصل بين قيم المحور Y باستخدام الفواصل.",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "اكتب الصورة إلى مجلد (الافتراضي -  log/images) وتوليد المتغيرات في ملف csv.",
-    "Open images output directory": "افتح مجلد مخرجات الصور",
-    "How much to blur the mask before processing, in pixels.": "كم يتم تضبيب القناع قبل المعالجة ، بالبكسل.",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "ما يجب وضعه داخل المنطقة المقنعة قبل معالجتها باستخدام الانتشار المستقر.",
-    "fill it with colors of the image": "املأها بألوان الصورة",
-    "keep whatever was there originally": "احتفظ بكل ما كان هناك في الأصل",
-    "fill it with latent space noise": "املأه بضوضاء الفضاء الكامنة",
-    "fill it with latent space zeroes": "املأه بأصفار الفضاء الكامنة",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "قم بترقية المنطقة المقنعة إلى الدقة المستهدفة و الرسم الجزئي ثم تقليص الحجم  واللصق في الصورة الأصلية",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "تغيير حجم الصورة إلى الدقة المستهدفة. ما لم يتطابق الطول والعرض ، ستحصل على نسبة عرض إلى ارتفاع غير صحيحة.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "قم بتغيير حجم الصورة بحيث يتم ملء الدقة المستهدفة بالكامل بالصورة. يتم قطع المناطق التي تبرز.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "قم بتغيير حجم الصورة بحيث تكون الصورة بأكملها داخل الدقة المستهدفة. املأ المساحة الفارغة بألوان الصورة.",
-    "How many times to repeat processing an image and using it as input for the next iteration": "كم مرة يتم تكرار معالجة صورة واستخدامها  كمدخل للتكرار التالي",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "في وضع حلقة الاسترجاع ، يتم ضرب قوة تقليل الضوضاء بهذه القيمة في كل حلقة. يعني <1 تقليل التنوع بحيث يتقارب التسلسل الخاص بك على صورة ثابتة. > 1 يعني زيادة التنوع بحيث يصبح تسلسلك أكثر وأكثر فوضوية.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "الترقية باستخدام الانتشار المستقر ، كم يجب أن يكون مقدار التداخل بالبكسل بين المربعات. تتداخل المربعات بحيث لا يكون هناك خط واضح للعيان عند دمجها مرة أخرى في صورة واحدة.",
-    "A directory on the same machine where the server is running.": "مجلد على نفس الجهاز حيث يتم تشغيل الخادم.",
-    "Leave blank to save images to the default path.": "اتركه فارغًا لحفظ الصور في المسار الافتراضي.",
-    "Input images directory": "مجلد الصور المدخلة",
-    "Result = A * (1 - M) + B * M": "النتيجة = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "النتيجة = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "يجب أن يكون الرقم الأول والأخير 1. على سبيل المثال: '1 ، 2 ، 1'",
-    "Path to directory with input images": "المسار إلى مجلد الصور المدخلة",
-    "Path to directory where to write outputs": "المسار إلى مجلد كتابة النتائج",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "استخدم العلامات التالية لتعريف كيفية اختيار أسماء الملفات للصور: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp] ؛ اتركه فارغا للوضع الافتراضي.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "إذا تم تمكين هذا الخيار ، فلن تتم إضافة العلامة المائية إلى الصور التي تم إنشاؤها. تحذير: إذا لم تقم بإضافة علامة مائية ، فقد تكون تتصرف بطريقة غير أخلاقية.",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "استخدم العلامات التالية لتحديد كيفية اختيار المجادت الفرعية للصور و الإطارات الشبكية : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp] ؛ اتركه فارغا للوضع الافتراضي.",
-    "Restore low quality faces using GFPGAN neural network": "ترميم الوجوه منخفضة الجودة باستخدام الشبكة العصبية GFPGAN ",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "سيتم استخدام هذا التعبير العادي لاستخراج الكلمات من اسم الملف ، وسيتم ضمها باستخدام الخيار أدناه في نص التسمية المستخدم للتدريب. اتركه فارغًا للاحتفاظ بنص اسم الملف كما هو.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "سيتم استخدام هذا النص لضم الكلمات المقسمة إلى سطر واحد إذا تم تمكين الخيار أعلاه.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "قائمة أسماء الإعدادات ، مفصولة بفواصل ، للإعدادات التي يجب أن تنتقل إلى شريط الوصول السريع في الأعلى ، بدلاً من علامة تبويب الإعداد المعتادة. انظر modules / shared.py للتعرف على أسماء الإعدادات. يتطلب إعادة التشغيل للتطبيق.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "إذا كانت هذه القيم غير صفرية ، فستتم إضافتها إلى البذرة واستخدامها لتهيئة فوضى الضوضاء عند استخدام أنظمة أخذ العينات باستخدام الوقت المتبقي. يمكنك استخدام ذلك لإنتاج المزيد من التنوعات في الصور ، أو يمكنك استخدامه لمطابقة صور البرامج الأخرى إذا كنت تعرف ما تفعله."
+{
+  "rtl": true,
+  "⤡": "⤡",
+  "⊞": "⊞",
+  "×": "×",
+  "❮": "❮",
+  "❯": "❯",
+  "Loading...": "جار التحميل...",
+  "view": "معاينة",
+  "api": "api",
+  "•": "•",
+  "built with gradio": "مبني باستخدام Gradio",
+  "Stable Diffusion checkpoint": "نماذج الانتشار المستقر",
+  "txt2img": "نص لصورة",
+  "img2img": "صورة لصورة",
+  "Extras": "الإضافات",
+  "PNG Info": "معلومات PNG",
+  "Checkpoint Merger": "دمج النماذج",
+  "Train": "التدريب",
+  "Create aesthetic embedding": "Create aesthetic embedding",
+  "Image Browser": "مستعرض الصور",
+  "Settings": "الإعدادات",
+  "Prompt": "الموجه",
+  "Negative prompt": "الموجه السلبي",
+  "Run": "تشغيل",
+  "Skip": "تخطي",
+  "Interrupt": "إيقاف",
+  "Generate": "إنشاء",
+  "Style 1": "نمط 1",
+  "Style 2": "نمط 2",
+  "Label": "الوسم",
+  "File": "ملف",
+  "Drop File Here": "اسحب الملف هنا",
+  "-": "-",
+  "or": "أو",
+  "Click to Upload": "انقر للتحميل",
+  "Image": "صورة",
+  "Check progress": "تحقق من التقدم",
+  "Check progress (first)": "تحقق من التقدم (الأول)",
+  "Sampling Steps": "خطوات أخذ العينة",
+  "Sampling method": "نظام أخذ العينات",
+  "Euler a": "Euler a",
+  "Euler": "Euler",
+  "LMS": "LMS",
+  "Heun": "Heun",
+  "DPM2": "DPM2",
+  "DPM2 a": "DPM2 a",
+  "DPM fast": "DPM fast",
+  "DPM adaptive": "DPM adaptive",
+  "LMS Karras": "LMS Karras",
+  "DPM2 Karras": "DPM2 Karras",
+  "DPM2 a Karras": "DPM2 a Karras",
+  "DDIM": "DDIM",
+  "PLMS": "PLMS",
+  "Width": "العرض",
+  "Height": "الارتفاع",
+  "Restore faces": "ترميم الوجوه",
+  "Tiling": "تبليط",
+  "Highres. fix": "إصلاح الصور عالية الدقة",
+  "Firstpass width": "عرض المرور الأول",
+  "Firstpass height": "ارتفاع المرور الأول",
+  "Denoising strength": "قوة تقليل الضوضاء",
+  "Batch count": "عدد الدُفعات",
+  "Batch size": "حجم الدفعة",
+  "CFG Scale": "مقياس التقارب من الموجه (CFG)",
+  "Seed": "البذرة",
+  "Extra": "إضافي",
+  "Variation seed": "تباين البذرة",
+  "Variation strength": "قوة التباين",
+  "Resize seed from width": "تغيير حجم البذرة من العرض",
+  "Resize seed from height": "تغيير حجم البذرة من الارتفاع",
+  "Open for Clip Aesthetic!": "Open for Clip Aesthetic!",
+  "▼": "▼",
+  "Aesthetic weight": "Aesthetic weight",
+  "Aesthetic steps": "Aesthetic steps",
+  "Aesthetic learning rate": "Aesthetic learning rate",
+  "Slerp interpolation": "Slerp interpolation",
+  "Aesthetic imgs embedding": "Aesthetic imgs embedding",
+  "None": "لايوجد",
+  "Aesthetic text for imgs": "Aesthetic text for imgs",
+  "Slerp angle": "Slerp angle",
+  "Is negative text": "Is negative text",
+  "Script": "سكريبت",
+  "Prompt matrix": "مصفوفة الموجهات",
+  "Prompts from file or textbox": "موجهات من ملف أو مربع النص",
+  "X/Y plot": "الرسم البياني X/Y",
+  "Put variable parts at start of prompt": "ضع الأجزاء المتغيرة في بداية الموجه",
+  "Show Textbox": "إظهار مربع النص",
+  "File with inputs": "ملف يحتوي المدخلات",
+  "Prompts": "الموجهات",
+  "X type": "نوع X",
+  "Nothing": "لا شئ",
+  "Var. seed": "تغير البذرة",
+  "Var. strength": "قوة التغيير",
+  "Steps": "الخطوات",
+  "Prompt S/R": "موجه S / R",
+  "Prompt order": "ترتيب الموجهات",
+  "Sampler": "نظام أخذ العينات",
+  "Checkpoint name": "اسم النموذج",
+  "Hypernetwork": "الشبكة الفائقة",
+  "Hypernet str.": "قوة الشبكة الفائقة",
+  "Sigma Churn": "دفع سيجما",
+  "Sigma min": "أصغر سيجما",
+  "Sigma max": "أكبر سيجما",
+  "Sigma noise": "ضجة سيجما",
+  "Eta": "الوقت المتوقع",
+  "Clip skip": "تخطي Clip",
+  "Denoising": "تقليل الضوضاء",
+  "X values": "قيم X",
+  "Y type": "نوع Y",
+  "Y values": "قيم Y",
+  "Draw legend": "ارسم مفتاح التوضيح",
+  "Include Separate Images": "قم بتضمين الصور منفصلة",
+  "Keep -1 for seeds": "احتفظ بـقيمة -1 للبذور",
+  "Drop Image Here": "إسقاط الصورة هنا",
+  "Save": "حفظ",
+  "Send to img2img": "أرسل إلى صورة لصورة",
+  "Send to inpaint": "أرسل إلى إعادة الرسم الجزئي",
+  "Send to extras": "أرسل إلى الإضافات",
+  "Make Zip when Save?": "إنشاء ملف مضغوط عند الحفظ؟",
+  "Textbox": "مربع النص",
+  "Interrogate\nCLIP": "استجواب\n CLIP",
+  "Inpaint": "إعادة الرسم الجزئي",
+  "Batch img2img": "دفعات صورة لصورة",
+  "Image for img2img": "صورة (صورة لصورة)",
+  "Image for inpainting with mask": "صورة (إعادة الرسم الجزئي)",
+  "Mask": "القناع",
+  "Mask blur": "ضبابية القناع",
+  "Mask mode": "أسلوب القناع",
+  "Draw mask": "رسم القناع",
+  "Upload mask": "تحميل القناع",
+  "Masking mode": "أسلوب التقنيع",
+  "Inpaint masked": "إعادة الرسم الجزئي (المنطقة المقنعة)",
+  "Inpaint not masked": "إعادة الرسم الجزئي (المنطقة الغير مقنعة)",
+  "Masked content": "المحتوى المقنع",
+  "fill": "الملأ",
+  "original": "الأصلي",
+  "latent noise": "الضوضاء الكامنة",
+  "latent nothing": "لا شيء كامن",
+  "Inpaint at full resolution": "إعادة الرسم الجزئي بدقة كاملة",
+  "Inpaint at full resolution padding, pixels": "إعادة الرسم الجزئي بدقة كاملة, الحشو, بيكسل",
+  "Process images in a directory on the same machine where the server is running.": "معالجة الصور في المجلد على نفس الجهاز حيث يتم تشغيل الخادم.",
+  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "استخدم مجلد إخراج فارغ لحفظ الصور بشكل طبيعي بدلاً من الكتابة إلى مجلد المخرجات.",
+  "Input directory": "مجلد المدخلات",
+  "Output directory": "مجلد المخرجات",
+  "Resize mode": "وضعية تغيير الحجم",
+  "Just resize": "تغييير الحجم فقط",
+  "Crop and resize": "اقتصاص وتغيير الحجم",
+  "Resize and fill": "تغيير الحجم والتعبئة",
+  "img2img alternative test": "صورة لصورة البديلة",
+  "Loopback": "الحلقة الراجعة",
+  "Outpainting mk2": "الرسم الخارجي نسخة 2",
+  "Poor man's outpainting": "الرسم الخارجي للفقراء",
+  "SD upscale": "ترقية الانتشار المستقر",
+  "should be 2 or lower.": "should be 2 or lower.",
+  "Override `Sampling method` to Euler?(this method is built for it)": "Override `Sampling method` to Euler?(this method is built for it)",
+  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)",
+  "Original prompt": "Original prompt",
+  "Original negative prompt": "Original negative prompt",
+  "Override `Sampling Steps` to the same value as `Decode steps`?": "Override `Sampling Steps` to the same value as `Decode steps`?",
+  "Decode steps": "Decode steps",
+  "Override `Denoising strength` to 1?": "Override `Denoising strength` to 1?",
+  "Decode CFG scale": "Decode CFG scale",
+  "Randomness": "Randomness",
+  "Sigma adjustment for finding noise for image": "Sigma adjustment for finding noise for image",
+  "Loops": "Loops",
+  "Denoising strength change factor": "معامل قوة تقليل الضوضاء",
+  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "الإعدادات الموصى بها: خطوات أخذ العينات: 80-100 ، طريقة أخذ العينات: Euler a ، تقليل الضوضاء: 0.8",
+  "Pixels to expand": "عدد البكسل للتوسيع",
+  "Outpainting direction": "إتجاه الرسم الخارجي",
+  "left": "يسار",
+  "right": "يمين",
+  "up": "فوق",
+  "down": "تحت",
+  "Fall-off exponent (lower=higher detail)": "أس التناقص (الأدنى = تفاصيل أعلى)",
+  "Color variation": "اختلاف اللون",
+  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "سيقوم بترقية الصورة إلى ضعف الأبعاد ؛ استخدم شريط تمرير العرض والارتفاع لضبط حجم التبليط",
+  "Tile overlap": "تداخل التبليط",
+  "Upscaler": "المرقي",
+  "Lanczos": "Lanczos",
+  "Single Image": "صورة واحدة",
+  "Batch Process": "معالجة الدفعات",
+  "Batch from Directory": "دفعة من المجلد",
+  "Source": "مصدر",
+  "Show result images": "إظهار نتائج الصور ",
+  "Scale by": "رفع الحجم بمقدار",
+  "Scale to": "رفع الحجم إلى",
+  "Resize": "تغيير الحجم",
+  "Crop to fit": "اقتصاص للتوافق",
+  "Upscaler 2 visibility": "إظهار المرقي 2",
+  "GFPGAN visibility": "إظهار GFPGAN",
+  "CodeFormer visibility": "إظهار CodeFormer",
+  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "وزن CodeFormer (0 = أقصى تأثير ، 1 = تأثير أدنى)",
+  "Open output directory": "افتح مجلد المخرجات",
+  "Send to txt2img": "أرسل إلى كتابة لصورة",
+  "A merger of the two checkpoints will be generated in your": "سيتم إنشاء نموذجمدمج من النموذجين في",
+  "checkpoint": "النموذج",
+  "directory.": "المجلد.",
+  "Primary model (A)": "النموذج الأساسي (أ)",
+  "Secondary model (B)": "النموذج الثانوي (ب)",
+  "Tertiary model (C)": "النموذج الثالث (ج)",
+  "Custom Name (Optional)": "اسم مخصص (اختياري)",
+  "Multiplier (M) - set to 0 to get model A": "المضاعف (M) - اضبط على 0 للحصول على النموذج أ",
+  "Interpolation Method": "طريقة الاستنباط",
+  "Weighted sum": "المجموع الموزون",
+  "Add difference": "أضافة الاختلاف",
+  "Save as float16": "حفظ  float16",
+  "See": "شاهد",
+  "wiki": "ويكي",
+  "for detailed explanation.": "للحصول على شرح مفصل.",
+  "Create embedding": "إنشاء التضمين",
+  "Create hypernetwork": "إنشاء شبكة فائقة",
+  "Preprocess images": "تجهيز الصور",
+  "Name": "الاسم",
+  "Initialization text": "نص التهيئة",
+  "Number of vectors per token": "عدد المتجهات لكل رمز",
+  "Overwrite Old Embedding": "الكتابة فوق التضمين القديم",
+  "Modules": "الوحدات",
+  "Enter hypernetwork layer structure": "أدخل بنية طبقة الشبكة الفائقة",
+  "Select activation function of hypernetwork": "حدد وظيفة تنشيط الشبكة الفائقة",
+  "linear": "خطي (Linear)",
+  "relu": "الوحدة الخطية المعدلة (Relu)",
+  "leakyrelu": "الوحدة الخطية المعدلة المسربة (Leakyrelu)",
+  "elu": "الوحدة الأسية الخطية (Elu)",
+  "swish": "Swish",
+  "Add layer normalization": "أضف طبقة التسوية",
+  "Use dropout": "استخدم الهبوط",
+  "Overwrite Old Hypernetwork": "الكتابة فوق الشبكة الفائقة القديمة",
+  "Source directory": "مجلد المصدر",
+  "Destination directory": "مجلد النتائج",
+  "Existing Caption txt Action": "الإجراء النصي للتعليق المتوفر",
+  "ignore": "تجاهل",
+  "copy": "نسخ",
+  "prepend": "أضف قبل",
+  "append": "ألحق",
+  "Create flipped copies": "قم بإنشاء نسخ مقلوبة",
+  "Split oversized images": "تقسيم الصور كبيرة الحجم",
+  "Use BLIP for caption": "استخدم BLIP للتعليق",
+  "Use deepbooru for caption": "استخدم deepbooru للتعليق",
+  "Split image threshold": "حد تقسيم الصورة",
+  "Split image overlap ratio": "نسبة تداخل الصورة المنقسمة",
+  "Preprocess": "تجهيز الصور",
+  "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "تدريب التضمين أو الشبكة الفائقة ؛ يجب تحديد مجلد بمجموعة من الصور بنسبة أبعاد 1: 1",
+  "[wiki]": "[ويكي]",
+  "Embedding": "التضمين",
+  "Embedding Learning rate": "معدل تعلم التضمين",
+  "Hypernetwork Learning rate": "معدل تعلم الشبكة الفائقة",
+  "Dataset directory": "مجلد مجموعة البيانات",
+  "Log directory": "مجلد السجل",
+  "Prompt template file": "ملف قالب الموجهات",
+  "Max steps": "الخطوات القصوى",
+  "Save an image to log directory every N steps, 0 to disable": "حفظ صورة في مجلد السجل  كل N خطوات ، 0 للتعطيل",
+  "Save a copy of embedding to log directory every N steps, 0 to disable": "حفظ نسخة من التضمين في مجلد السجل كل N خطوات ، 0 للتعطيل",
+  "Save images with embedding in PNG chunks": "حفظ التضمين مع الصور في أجزاء PNG",
+  "Read parameters (prompt, etc...) from txt2img tab when making previews": "قراءة المتغيرات (الموجه ، إلخ ...) من علامة تبويب نص لصورة عند إجراء المعاينات",
+  "Train Hypernetwork": "تدريب الشبكة الفائقة",
+  "Train Embedding": "تدريب التضمين",
+  "Create an aesthetic embedding out of any number of images": "Create an aesthetic embedding out of any number of images",
+  "Create images embedding": "Create images embedding",
+  "extras": "إضافات",
+  "favorites": "المفضلة",
+  "custom fold": "custom fold",
+  "Load": "تحميل",
+  "Images directory": "مجلد الصور",
+  "Prev batch": "الدفعة السابقة",
+  "Next batch": "الدفعة التالية",
+  "First Page": "الصفحة الأولى",
+  "Prev Page": "الصفحة السابقة",
+  "Page Index": "فهرس الصفحات",
+  "Next Page": "الصفحة التالية",
+  "End Page": "صفحة النهاية",
+  "number of images to delete consecutively next": "عدد الصور المطلوب حذفها على التوالي بعد ذلك",
+  "Delete": "حذف",
+  "Generate Info": "معلومات الإنشاء",
+  "File Name": "اسم الملف",
+  "Collect": "جمع",
+  "Refresh page": "إعادة تحميل الصفحة",
+  "Date to": "التاريخ إلى",
+  "Number": "الرقم",
+  "set_index": "وضع الفهرس",
+  "Checkbox": "صندوق اختيار",
+  "Apply settings": "تطبيق الإعدادات",
+  "Saving images/grids": "حفظ الصور / الإطار الشبكي",
+  "Always save all generated images": "احفظ دائمًا جميع الصور التي تم إنشائها",
+  "File format for images": "تنسيق ملفات الصور",
+  "Images filename pattern": "نمط اسم ملفات الصور",
+  "Add number to filename when saving": "Add number to filename when saving",
+  "Always save all generated image grids": "احفظ دائمًا جميع الإطارات الشبكية للصور التي تم إنشاؤها",
+  "File format for grids": "تنسيق ملفات الإطارات الشبكية",
+  "Add extended info (seed, prompt) to filename when saving grid": "أضف معلومات إضافية (البذرة ، الموجه) إلى اسم الملف عند حفظ الإطار الشبكي",
+  "Do not save grids consisting of one picture": "لا تحفظ الإطارات الشبكية التي تتكون من صورة واحدة",
+  "Prevent empty spots in grid (when set to autodetect)": "منع المناطق الفارغة في الإطار الشبكي (عند الضبط على الاكتشاف التلقائي)",
+  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "عدد صفوف الإطار الشبكي استخدم -1 للاكتشاف التلقائي و 0 ليكون نفس حجم الدُفعة",
+  "Save text information about generation parameters as chunks to png files": "احفظ معلومات نصية حول متغيرات الإنشاء كمقاطع في ملفات png",
+  "Create a text file next to every image with generation parameters.": "قم بإنشاء ملف نصي بجوار كل صورة باستخدام متغيرات الإنشاء.",
+  "Save a copy of image before doing face restoration.": "احفظ نسخة من الصورة قبل القيام بترميم الوجوه.",
+  "Quality for saved jpeg images": "جودة الصور المحفوظة بتنسيق jpeg",
+  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "إذا كانت صورة PNG أكبر من 4 ميجابايت أو كان أي بُعد أكبر من 4000 ، قم بتقليل حجم الصورة وحفظها بتنسيق JPG",
+  "Use original name for output filename during batch process in extras tab": "استخدم الاسم الأصلي لاسم ملف الإخراج أثناء عملية الدُفعات في علامة تبويب الإضافات",
+  "When using 'Save' button, only save a single selected image": "عند استخدام زر 'حفظ' ، احفظ فقط صورة واحدة محددة",
+  "Do not add watermark to images": "لا تقم بإضافة العلامة المائية للصور",
+  "Paths for saving": "مسارات الحفظ",
+  "Output directory for images; if empty, defaults to three directories below": "مجلد المخرجات للصور ؛ إذا كان فارغا ، يتم تعيينه افتراضيًا إلى المجلدات الثلاثة أدناه",
+  "Output directory for txt2img images": "مجلد المخرجات لصور نص لصورة",
+  "Output directory for img2img images": "مجلد المخرجات لصور صورة لصورة",
+  "Output directory for images from extras tab": "مجلد المخرجات لصور علامة تبويب الإضافات",
+  "Output directory for grids; if empty, defaults to two directories below": "مجلد المخرجات للإطارات الشبكية ؛ إذا كان فارغا ، يتم تعيينه افتراضيًا إلى المجلدين أدناه",
+  "Output directory for txt2img grids": "مجلد المخرجات للإطارات الشبكية نص لصورة",
+  "Output directory for img2img grids": "مجلد المخرجات للإطارات الشبكية صورة لصورة",
+  "Directory for saving images using the Save button": "مجلد لحفظ الصور باستخدام زر حفظ",
+  "Saving to a directory": "يتم الحفظ إلى المجلد..",
+  "Save images to a subdirectory": "حفظ الصور في مجلد فرعي",
+  "Save grids to a subdirectory": "حفظ الإطارات الشبكية في مجلد فرعي",
+  "When using \"Save\" button, save images to a subdirectory": "احفظ الصور في مجلد فرعي عند الضغط على زر الحفظ",
+  "Directory name pattern": "نمط اسم المجلد",
+  "Max prompt words for [prompt_words] pattern": "أقصى عدد لكلمات التوجيه لنمط [كلمات_التوجيه]",
+  "Upscaling": "الارتقاء",
+  "Tile size for ESRGAN upscalers. 0 = no tiling.": "حجم التبليط لترقيات ESRGAN, القيمة 0= لا يوجد تبليط.",
+  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "تداخل المربعات ، بالبكسل لمرقي ESRGAN. القيم المنخفضة = تداخل مرئي.",
+  "Tile size for all SwinIR.": "حجم التليط لجميع عمليات SwinIR.",
+  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "تداخل المربعات بالبكسل لـ SwinIR. القيم المنخفضة = تداخل مرئي.",
+  "LDSR processing steps. Lower = faster": "خطوات معالجة LDSR. أقل = أسرع",
+  "Upscaler for img2img": "المرقي لصورة لصورة",
+  "Upscale latent space image when doing hires. fix": "ترقية صورة الفضاء الكامن عند التعيينات. تصليح",
+  "Face restoration": "ترميم الوجوه",
+  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "متغيرات وزن CodeFormer ؛ 0 = أقصى تأثير ؛ 1 = الحد الأدنى من التأثير",
+  "Move face restoration model from VRAM into RAM after processing": "انقل نموذج استعادة الوجوه من VRAM إلى RAM بعد المعالجة",
+  "System": "نظام",
+  "VRAM usage polls per second during generation. Set to 0 to disable.": "استطلاعات استخدام VRAM في الثانية أثناء الإنشاء. اضبط على 0 للتعطيل.",
+  "Always print all generation info to standard output": "قم دائمًا بطباعة جميع معلومات الإنشاء إلى المخرج القياسي",
+  "Add a second progress bar to the console that shows progress for an entire job.": "أضف شريط تقدم آخر إلى وحدة التحكم يُظهر التدقم الكلي للمهمة.",
+  "Training": "التدريب",
+  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "تفريغ VAE و CLIP من VRAM عند التدريب",
+  "Filename word regex": "اسم ملف كلمات regex",
+  "Filename join string": "سلسلة أحرف دمج أسماء الملفات",
+  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "عدد التكرارات لصورة إدخال واحدة لكل حقبة ؛ تستخدم فقط لعرض رقم الحقبة",
+  "Save an csv containing the loss to log directory every N steps, 0 to disable": "حفظ ملف csv يحتوي على الخسارة إلى مجلد السجل كل N خطوات ، 0 للتعطيل",
+  "Stable Diffusion": "انتشار مستقر",
+  "Checkpoints to cache in RAM": "التخزين المؤقت للنماذج في RAM",
+  "Hypernetwork strength": "قوة الشبكة الفائقة",
+  "Apply color correction to img2img results to match original colors.": "قم بتطبيق تصحيح الألوان على نتائج صورة لصورة لمطابقة الألوان الأصلية.",
+  "Save a copy of image before applying color correction to img2img results": "احفظ نسخة من الصورة قبل تطبيق تصحيح الألوان على نتائج  صورة لصورة",
+  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "باستخدام صورة لصورة ، قم بالضبط بعدد الخطوات التي يحددها شريط التمرير (عادةً ما سيكون أقل مع تقليل التشويش).",
+  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "تمكين تكميم عينات K للحصول على نتائج أوضح وأنظف. هذا قد يغير البذرة المستخدمة. يتطلب إعادة التشغيل للتطبيق.",
+  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "التأكيد: استخدم (النص) لجعل النموذج يولي مزيدًا من الاهتمام للنص و استخدم [النص] لجعل النموذج يولي اهتمامًا أقل  للنص",
+  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "استخدم طريقة التأكيد القديمة. يمكن أن يكون مفيدًا لإعادة إنتاج البذور القديمة.",
+  "Make K-diffusion samplers produce same images in a batch as when making a single image": "اجعل انظمة أخذ عينات الانتشار K تنتج نفس الصور في الدفعات كما هو الحال عند إنشاء صورة واحدة",
+  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "زيادة التناسق عن طريق المساحة المتروكة من الفاصلة الأخيرة ضمن عدد n من الرموز المميزة عند استخدام أكثر من 75 رمزًا مميزًا",
+  "Filter NSFW content": "تصفية المحتوى الإباحي",
+  "Stop At last layers of CLIP model": "توقف عند آخر طبقات نموذج CLIP",
+  "Interrogate Options": "خيارات الاستجواب",
+  "Interrogate: keep models in VRAM": "الاستجواب: احتفظ بالنماذج في VRAM",
+  "Interrogate: use artists from artists.csv": "الاستجواب: استخدم  قائمة الفنانين من ملف artists.csv",
+  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "الاستجواب: قم بتضمين رتب علامات النموذج في النتائج (ليس له تأثير على الاستجواب المستند إلى التعليقات).",
+  "Interrogate: num_beams for BLIP": "الاستجواب: عدد الحزم لـ BLIP",
+  "Interrogate: minimum description length (excluding artists, etc..)": "الاستجواب: الحد الأدنى لطول الوصف (باستثناء الفنانين ،إلخ..)",
+  "Interrogate: maximum description length": "الاستجواب: الحد الأقصى لطول الوصف",
+  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: أقصى عدد من الأسطر في الملف النصي (0 = بلا حدود)",
+  "Interrogate: deepbooru score threshold": "الاستجواب: عتبة درجات deepbooru",
+  "Interrogate: deepbooru sort alphabetically": "الاستجواب: الترتيب الأبجدي لـ deepbooru",
+  "use spaces for tags in deepbooru": "استخدام مسافات للعلامات في deepbooru",
+  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "تجاهل الأقواس عند استخدام deepbooru (كي لا تعامل كأقواس التشديد)",
+  "User interface": "واجهة المستخدم",
+  "Show progressbar": "إظهار شريط التقدم",
+  "Show image creation progress every N sampling steps. Set 0 to disable.": "إظهار تقدم إنشاء الصورة لكل N خطوة من خطوات أخذ العينات. قم بتعيين 0 للتعطيل.",
+  "Show previews of all images generated in a batch as a grid": "إظهار معاينة لجميع الصور التي يتم إنشائها في الدفعة كإطار شبكي",
+  "Show grid in results for web": "إظهار الإطار الشبكي في نتائج الويب",
+  "Do not show any images in results for web": "لا تظهر أي صور في نتائج الويب",
+  "Add model hash to generation information": "أضف معلومات الهاش للنموذج إلى معلومات الإنشاء",
+  "Add model name to generation information": "أضف اسم النموذج إلى معلومات الإنشاء",
+  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "عند قراءة متغيرات الإنشاء من النص إلى واجهة المستخدم (من معلومات PNG أو النص الملصق) ، لا تقم بتغيير النموذج",
+  "Font for image grids that have text": "الخط المستخدم للإطارات الشبكية للصور التي تحتوي على نص",
+  "Enable full page image viewer": "تفعيل عارض الصور ذو الصفحة الكاملة",
+  "Show images zoomed in by default in full page image viewer": "إظهار الصور التي تم تكبيرها بشكل افتراضي في عارض الصور ذو الصفحة الكاملة",
+  "Show generation progress in window title.": "إظهار تقدم الإنشاء في عنوان الشاشة.",
+  "Quicksettings list": "قائمة الإعدادات السريعة",
+  "Localization (requires restart)": "الترجمة (يتطلب إعادة التشغيل)",
+  "Sampler parameters": "متغيرات أنظمة اخذ العينات",
+  "Hide samplers in user interface (requires restart)": "إخفاء أنظمة أخذ العينات في واجهة المستخدم (يتطلب إعادة التشغيل)",
+  "eta (noise multiplier) for DDIM": "الوقت المتبقي (مضاعف الضوضاء) لـ DDIM",
+  "eta (noise multiplier) for ancestral samplers": "الوقت المتبقي (مضاعف الضوضاء) لأنظمة أخذ العينات التي تعتمد على الأجداد",
+  "img2img DDIM discretize": "تفصل DDIM لصورة لصورة",
+  "uniform": "التوحيد",
+  "quad": "رباعية",
+  "sigma churn": "دفع سيجما",
+  "sigma tmin": "سيجما tmin",
+  "sigma noise": "ضجيج سيجما",
+  "Eta noise seed delta": "الوقت المتوقع لديلتا بذرة الضجيج ",
+  "Images Browser": "مستعرض الصور",
+  "Preload images at startup": "تحميل الصور مسبقًا عند بدء التشغيل",
+  "Number of columns on the page": "Number of columns on the page",
+  "Number of rows on the page": "Number of rows on the page",
+  "Minimum number of pages per load": "الحد الأدنى لعدد الصفحات لكل تحميل",
+  "Request browser notifications": "طلب إشعارات المتصفح",
+  "Download localization template": "تنزيل نموذج الترجمة",
+  "Reload custom script bodies (No ui updates, No restart)": "إعادة تحميل السكريبتات المخصصة (لا توجد تحديثات لواجهة المستخدم ، لا إعادة تشغيل)",
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعادة تشغيل  Gradio و تحديث مكونات واجهة الاستخدام (السكريبتات و ui.py و js و css فقط)",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "الموجه (اضغط على Ctrl + Enter أو Alt + Enter للإنشاء)",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "الموجه السلبي (اضغط على Ctrl + Enter أو Alt + Enter للإنشاء)",
+  "Add a random artist to the prompt.": "أضف فنانًا عشوائيًا إلى الموجه.",
+  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "قراءة متغيرات الإنشاء من الموجه أو الإنشاء الأخير إذا كان الموجه فارغا في واجهة المستخدم.",
+  "Save style": "حفظ النمط",
+  "Apply selected styles to current prompt": "تطبيق الأنماط المحددة للموجه الحالي",
+  "Stop processing current image and continue processing.": "توقف عن معالجة الصورة الحالية واستمر في المعالجة.",
+  "Stop processing images and return any results accumulated so far.": "توقف عن معالجة الصور وقم بإرجاع أي نتائج متراكمة حتى الآن.",
+  "Style to apply; styles have components for both positive and negative prompts and apply to both": "النمط المطلوب تطبيقه ؛ الأنماط لها مكونات لكل من الموجهات الإيجابية والسلبية وتنطبق على كليهما",
+  "Do not do anything special": "لا تفعل أي شيء خاص",
+  "Which algorithm to use to produce the image": "الخوارزمية التي يجب استخدامها لإنتاج الصورة",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - مبدع جدًا ، يمكن لكل منها الحصول على صورة مختلفة تمامًا اعتمادًا على عدد الخطوات ، تعيين الخطوات على أعلى من 30-40 لا يعطي نتيجة مفيدة",
+  "Denoising Diffusion Implicit Models - best at inpainting": "نماذج تقليل التشويش الضمني  - أفضل اسخدام في الرسم الجزئي",
+  "Produce an image that can be tiled.": "أنتج صورة يمكن تبليطها.",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "استخدم عملية من خطوتين لإنشاء صورة جزئيًا بدقة أقل ثم ترقيتها و تحسين تفاصيلها دون تغيير التكوين",
+  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "يحدد مدى ضآلة احترام الخوارزمية لمحتوى الصورة. عند 0 ، لن يتغير شيء ، وعند 1 ستحصل على صورة لا صلة لها بالصورة الأصلية. مع وجود قيم أقل من 1.0 ، المعالجة ستأخذ خطوات أقل مما يحدده شريط تمرير خطوات المعالجة.",
+  "How many batches of images to create": "كم عدد دفعات الصور التي يتم إنشائها",
+  "How many image to create in a single batch": "كم صورة يتم إنشائها دفعة واحدة",
+  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "مقياس التوجيه الحر للمصنف - إلى أي مدى يجب أن تتوافق الصورة مع الموجه - القيم المنخفضة تنتج نتائج أكثر إبداعًا",
+  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "القيمة التي تحدد ناتج منشئ الأرقام العشوائية - إذا قمت بإنشاء صورة بنفس المتغيرات و بذرة الصورة ، فستحصل على نفس النتيجة",
+  "Set seed to -1, which will cause a new random number to be used every time": "عيّن البذرة إلى -1 ، مما سيؤدي إلى استخدام رقم عشوائي جديد في كل مرة",
+  "Reuse seed from last generation, mostly useful if it was randomed": "إعادة استخدام البذرة من الإنشاء الأخير ، وتكون مفيدة في الغالب إذا كانت عشوائية",
+  "Seed of a different picture to be mixed into the generation.": "استخدام بذرة من صورة مختلفة ليتم مزجها في الإنشاء.",
+  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "ما مدى قوة التباين عند الإنشاء. 0 ، لن يكون هناك أي تأثير. 1 ، ستحصل على الصورة الكاملة ببذور التباين (باستثناء أنظمة عينات الأسلاف ، حيث ستحصل على شيء ما فقط).",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "قم بمحاولة لإنتاج صورة مشابهة لما تم إنتاجه بنفس البذرة بناءا على دقة محددة",
+  "This text is used to rotate the feature space of the imgs embs": "This text is used to rotate the feature space of the imgs embs",
+  "Separate values for X axis using commas.": "افصل بين قيم المحور X باستخدام الفواصل.",
+  "Separate values for Y axis using commas.": "افصل بين قيم المحور Y باستخدام الفواصل.",
+  "Write image to a directory (default - log/images) and generation parameters into csv file.": "اكتب الصورة إلى مجلد (الافتراضي -  log/images) وتوليد المتغيرات في ملف csv.",
+  "Open images output directory": "افتح مجلد مخرجات الصور",
+  "How much to blur the mask before processing, in pixels.": "كم يتم تضبيب القناع قبل المعالجة ، بالبكسل.",
+  "What to put inside the masked area before processing it with Stable Diffusion.": "ما يجب وضعه داخل المنطقة المقنعة قبل معالجتها باستخدام الانتشار المستقر.",
+  "fill it with colors of the image": "املأها بألوان الصورة",
+  "keep whatever was there originally": "احتفظ بكل ما كان هناك في الأصل",
+  "fill it with latent space noise": "املأه بضوضاء الفضاء الكامنة",
+  "fill it with latent space zeroes": "املأه بأصفار الفضاء الكامنة",
+  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "قم بترقية المنطقة المقنعة إلى الدقة المستهدفة و الرسم الجزئي ثم تقليص الحجم  واللصق في الصورة الأصلية",
+  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "تغيير حجم الصورة إلى الدقة المستهدفة. ما لم يتطابق الطول والعرض ، ستحصل على نسبة عرض إلى ارتفاع غير صحيحة.",
+  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "قم بتغيير حجم الصورة بحيث يتم ملء الدقة المستهدفة بالكامل بالصورة. يتم قطع المناطق التي تبرز.",
+  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "قم بتغيير حجم الصورة بحيث تكون الصورة بأكملها داخل الدقة المستهدفة. املأ المساحة الفارغة بألوان الصورة.",
+  "How many times to repeat processing an image and using it as input for the next iteration": "كم مرة يتم تكرار معالجة صورة واستخدامها  كمدخل للتكرار التالي",
+  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "في وضع حلقة الاسترجاع ، يتم ضرب قوة تقليل الضوضاء بهذه القيمة في كل حلقة. يعني <1 تقليل التنوع بحيث يتقارب التسلسل الخاص بك على صورة ثابتة. > 1 يعني زيادة التنوع بحيث يصبح تسلسلك أكثر وأكثر فوضوية.",
+  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "الترقية باستخدام الانتشار المستقر ، كم يجب أن يكون مقدار التداخل بالبكسل بين المربعات. تتداخل المربعات بحيث لا يكون هناك خط واضح للعيان عند دمجها مرة أخرى في صورة واحدة.",
+  "A directory on the same machine where the server is running.": "مجلد على نفس الجهاز حيث يتم تشغيل الخادم.",
+  "Leave blank to save images to the default path.": "اتركه فارغًا لحفظ الصور في المسار الافتراضي.",
+  "Result = A * (1 - M) + B * M": "النتيجة = A * (1 - M) + B * M",
+  "Result = A + (B - C) * M": "النتيجة = A + (B - C) * M",
+  "1st and last digit must be 1. ex:'1, 2, 1'": "يجب أن يكون الرقم الأول والأخير 1. على سبيل المثال: '1 ، 2 ، 1'",
+  "Path to directory with input images": "المسار إلى مجلد الصور المدخلة",
+  "Path to directory where to write outputs": "المسار إلى مجلد كتابة النتائج",
+  "Input images directory": "مجلد الصور المدخلة",
+  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم العلامات التالية لتعريف كيفية اختيار أسماء الملفات للصور: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] ؛ اتركه فارغا للوضع الافتراضي.",
+  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "إذا تم تمكين هذا الخيار ، فلن تتم إضافة العلامة المائية إلى الصور التي تم إنشاؤها. تحذير: إذا لم تقم بإضافة علامة مائية ، فقد تكون تتصرف بطريقة غير أخلاقية.",
+  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم العلامات التالية لتحديد كيفية اختيار المجادت الفرعية للصور و الإطارات الشبكية : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] ؛ اتركه فارغا للوضع الافتراضي.",
+  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "سيتم استخدام هذا التعبير العادي لاستخراج الكلمات من اسم الملف ، وسيتم ضمها باستخدام الخيار أدناه في نص التسمية المستخدم للتدريب. اتركه فارغًا للاحتفاظ بنص اسم الملف كما هو.",
+  "This string will be used to join split words into a single line if the option above is enabled.": "سيتم استخدام هذا النص لضم الكلمات المقسمة إلى سطر واحد إذا تم تمكين الخيار أعلاه.",
+  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "قائمة أسماء الإعدادات ، مفصولة بفواصل ، للإعدادات التي يجب أن تنتقل إلى شريط الوصول السريع في الأعلى ، بدلاً من علامة تبويب الإعداد المعتادة. انظر modules / shared.py للتعرف على أسماء الإعدادات. يتطلب إعادة التشغيل للتطبيق.",
+  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "إذا كانت هذه القيم غير صفرية ، فستتم إضافتها إلى البذرة واستخدامها لتهيئة فوضى الضوضاء عند استخدام أنظمة أخذ العينات باستخدام الوقت المتبقي. يمكنك استخدام ذلك لإنتاج المزيد من التنوعات في الصور ، أو يمكنك استخدامه لمطابقة صور البرامج الأخرى إذا كنت تعرف ما تفعله."
 }
\ No newline at end of file
diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
index a6cc2477..ae533e06 100644
--- a/localizations/ja_JP.json
+++ b/localizations/ja_JP.json
@@ -1,4 +1,5 @@
 {
+    "rtl": false,
     "⤡": "⤡",
     "⊞": "⊞",
     "×": "×",
diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index ab12c37e..6daad1b7 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -1,4 +1,5 @@
 {
+  "rtl": false,
   "×": "×",
   "•": "•",
   "⊞": "⊞",
diff --git a/localizations/ru_RU.json b/localizations/ru_RU.json
index 664d36ea..8ec2c5a3 100644
--- a/localizations/ru_RU.json
+++ b/localizations/ru_RU.json
@@ -1,4 +1,5 @@
 {
+    "rtl": false,
     "⤡": "⤡",
     "⊞": "⊞",
     "×": "×",
diff --git a/style.css b/style.css
index 26ae36a5..13a9fb07 100644
--- a/style.css
+++ b/style.css
@@ -515,3 +515,77 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
     max-height: 480px !important;
     min-height: 480px !important;
 }
+
+/* The following handles localization for right-to-left (RTL) languages like Arabic.
+The rtl media type will only be activated by the logic in javascript/localization.js.
+If you change anything above, you need to make sure it is RTL compliant by just running
+your changes through converters like https://cssjanus.github.io/ or https://rtlcss.com/.
+Then, you will need to add the RTL counterpart only if needed in the rtl section below.*/
+@media rtl {
+    /* this part was manualy added */
+    :host {
+        direction: rtl;
+    }
+    .output-html:has(.performance), .gr-text-input {
+        direction: ltr;
+    }
+    .gr-radio, .gr-checkbox{
+        margin-left: 0.25em;
+    }
+
+    /* this part was automatically generated with few manual modifications */
+    .performance .time {
+        margin-right: unset;
+        margin-left: 0;
+    }
+    .justify-center.overflow-x-scroll {
+        justify-content: right;
+    }
+    .justify-center.overflow-x-scroll button:first-of-type {
+        margin-left: unset;
+        margin-right: auto;
+    }
+    .justify-center.overflow-x-scroll button:last-of-type {
+        margin-right: unset;
+        margin-left: auto;
+    }
+    #settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{
+        margin-right: unset;
+        margin-left: 8em;
+    }
+    #txt2img_progressbar, #img2img_progressbar, #ti_progressbar{
+        right: unset;
+        left: 0;
+    }
+    .progressDiv .progress{
+        padding: 0 0 0 8px;
+        text-align: left;
+    }
+    #lightboxModal{
+        left: unset;
+        right: 0;
+    }
+    .modalPrev, .modalNext{
+        border-radius: 3px 0 0 3px;
+    }
+    .modalNext {
+        right: unset;
+        left: 0;
+        border-radius: 0 3px 3px 0;
+    }
+    #imageARPreview{
+        left:unset;
+        right:0px;
+    }
+    #txt2img_skip, #img2img_skip{
+        right: unset;
+        left: 0px;
+    }
+    #context-menu{
+        box-shadow:-1px 1px 2px #CE6400;
+    }
+    .gr-box > div > div > input.gr-text-input{
+        right: unset;
+        left: 0.5em;
+    }
+}
-- 
cgit v1.2.1


From d3eef0aa0c773b8b9dc3a4f119e5709744e35af6 Mon Sep 17 00:00:00 2001
From: xmodar <modar.alfadly@gmail.com>
Date: Tue, 25 Oct 2022 06:55:17 +0300
Subject: Remove rtl: false from localizations

---
 javascript/localization.js | 5 ++++-
 localizations/ja_JP.json   | 1 -
 localizations/ko_KR.json   | 1 -
 localizations/ru_RU.json   | 1 -
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/javascript/localization.js b/javascript/localization.js
index 6fb2fccd..f92d2d24 100644
--- a/javascript/localization.js
+++ b/javascript/localization.js
@@ -107,7 +107,10 @@ function processNode(node){
 }
 
 function dumpTranslations(){
-    dumped = { rtl: localization.rtl || false }
+    dumped = {}
+    if (localization.rtl) {
+        dumped.rtl = true
+    }
 
     Object.keys(original_lines).forEach(function(text){
         if(dumped[text] !== undefined)  return
diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
index ae533e06..a6cc2477 100644
--- a/localizations/ja_JP.json
+++ b/localizations/ja_JP.json
@@ -1,5 +1,4 @@
 {
-    "rtl": false,
     "⤡": "⤡",
     "⊞": "⊞",
     "×": "×",
diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 6daad1b7..ab12c37e 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -1,5 +1,4 @@
 {
-  "rtl": false,
   "×": "×",
   "•": "•",
   "⊞": "⊞",
diff --git a/localizations/ru_RU.json b/localizations/ru_RU.json
index 8ec2c5a3..664d36ea 100644
--- a/localizations/ru_RU.json
+++ b/localizations/ru_RU.json
@@ -1,5 +1,4 @@
 {
-    "rtl": false,
     "⤡": "⤡",
     "⊞": "⊞",
     "×": "×",
-- 
cgit v1.2.1


From bdc4c203f2ba4c4598da6a4ce8342ebc8ed8ee65 Mon Sep 17 00:00:00 2001
From: innovaciones <sonygarcia99@gmail.com>
Date: Mon, 24 Oct 2022 19:45:22 -0500
Subject: Add Spanish localization

---
 localizations/es_ES.json | 418 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 418 insertions(+)
 create mode 100644 localizations/es_ES.json

diff --git a/localizations/es_ES.json b/localizations/es_ES.json
new file mode 100644
index 00000000..e58a17fe
--- /dev/null
+++ b/localizations/es_ES.json
@@ -0,0 +1,418 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "Cargando...",
+    "view": "mostrar",
+    "api": "api",
+    "•": "•",
+    "built with gradio": "Construido con Gradio",
+    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "Extras",
+    "PNG Info": "PNG Info",
+    "Checkpoint Merger": "Checkpoint Merger",
+    "Train": "Train",
+    "Settings": "Ajustes",
+    "Prompt": "Prompt",
+    "Negative prompt": "Prompt negativo",
+    "Run": "Ejecutar",
+    "Skip": "Saltar",
+    "Interrupt": "Interrumpir",
+    "Generate": "Generar",
+    "Style 1": "Estilo 1",
+    "Style 2": "Estilo 2",
+    "Label": "Etiqueta",
+    "File": "Archivo",
+    "Drop File Here": "Suelta el Archivo Aquí",
+    "-": "-",
+    "o": "o",
+    "Click to Upload": "Click para Cargar",
+    "Image": "Imagen",
+    "Check progress": "Comprobar progreso",
+    "Check progress (first)": "Comprobar progreso (inicial)",
+    "Sampling Steps": "Sampling Steps",
+    "Sampling method": "Sampling method",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Ancho",
+    "Height": "Alto",
+    "Restore faces": "Restaurar rostros",
+    "Tiling": "Mosaico",
+    "Highres. fix": "Highres. fix",
+    "Firstpass width": "Ancho original",
+    "Firstpass height": "Alto original",
+    "Denoising strength": "Denoising strength",
+    "Batch count": "Batch count",
+    "Batch size": "Batch size",
+    "CFG Scale": "CFG Scale",
+    "Seed": "Seed",
+    "Extra": "Extra",
+    "Variation seed": "Variation seed",
+    "Variation strength": "Variation strength",
+    "Resize seed from width": "Redimensionar seed del ancho",
+    "Resize seed from height": "Redimensionar seed del alto",
+    "Script": "Script",
+    "None": "Ninguno",
+    "Prompt matrix": "Prompt matrix",
+    "Prompts from file or textbox": "Prompts desde archivo o campo de texto",
+    "X/Y plot": "X/Y plot",
+    "Put variable parts at start of prompt": "Poner partes variables al inicio del prompt",
+    "Show Textbox": "Mostrar Campo de texto",
+    "File with inputs": "Archivo con inputs",
+    "Prompts": "Prompts",
+    "X type": "X type",
+    "Nothing": "Nada",
+    "Var. seed": "Var. seed",
+    "Var. strength": "Var. strength",
+    "Steps": "Steps",
+    "Prompt S/R": "Prompt S/R",
+    "Prompt order": "Prompt order",
+    "Sampler": "Sampler",
+    "Checkpoint name": "Checkpoint name",
+    "Hypernetwork": "Hypernetwork",
+    "Hypernet str.": "Hypernet str.",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "Sigma min",
+    "Sigma max": "Sigma max",
+    "Sigma noise": "Sigma noise",
+    "Eta": "Eta",
+    "Clip skip": "Clip skip",
+    "Denoising": "Denoising",
+    "X values": "X values",
+    "Y type": "Y type",
+    "Y values": "Y values",
+    "Draw legend": "Agregar leyenda",
+    "Include Separate Images": "Incluir Imágenes Separadas",
+    "Keep -1 for seeds": "Mantener -1 para seeds",
+    "Drop Image Here": "Suelta la Imagen Aquí",
+    "Save": "Guardar",
+    "Send to img2img": "Enviar a img2img",
+    "Send to inpaint": "Enviar a inpaint",
+    "Send to extras": "Enviar a extras",
+    "Make Zip when Save?": "Crear Zip al Guardar?",
+    "Textbox": "Campo de texto",
+    "Interrogate\nCLIP": "Interrogar\nCLIP",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "Batch img2img",
+    "Image for img2img": "Imagen para img2img",
+    "Image for inpainting with mask": "Imagen para inpainting con mask",
+    "Mask": "Mask",
+    "Mask blur": "Mask blur",
+    "Mask mode": "Mask mode",
+    "Draw mask": "Dibujar mask",
+    "Upload mask": "Cargar mask",
+    "Masking mode": "Masking mode",
+    "Inpaint masked": "Inpaint masked",
+    "Inpaint not masked": "Inpaint not masked",
+    "Masked content": "Masked content",
+    "fill": "fill",
+    "original": "original",
+    "latent noise": "latent noise",
+    "latent nothing": "latent nothing",
+    "Inpaint at full resolution": "Inpaint a resolución completa",
+    "Inpaint at full resolution padding, pixels": "Inpaint a resolución completa padding, pixeles",
+    "Process images in a directory on the same machine where the server is running.": "Procesa imágenes en un directorio en la misma máquina donde se ejecuta el servidor.",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa un directorio de salida vacío para guardar imágenes normalmente en lugar de escribir en el directorio de salida.",
+    "Input directory": "Directorio de entrada",
+    "Output directory": "Directorio de salida",
+    "Resize mode": "Modo de cambio de tamaño",
+    "Just resize": "Solo redimensionar",
+    "Crop and resize": "Recortar y redimensionar",
+    "Resize and fill": "Redimensionar y rellenar",
+    "img2img alternative test": "img2img alternative test",
+    "Loopback": "Loopback",
+    "Outpainting mk2": "Outpainting mk2",
+    "Poor man's outpainting": "Poor man's outpainting",
+    "SD upscale": "SD upscale",
+    "should be 2 or lower.": "debe ser 2 o menos.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Anular `Sampling method` a Euler? (este método está diseñado para ello)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Anular `prompt` al mismo valor `prompt original`? (y `prompt negativo`)",
+    "Original prompt": "Prompt original",
+    "Original negative prompt": "Promp negativo original",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Anular `Sampling Steps` al mismo valor de `Decode steps`?",
+    "Decode steps": "Decode steps",
+    "Override `Denoising strength` to 1?": "Anular `Denoising strength` a 1?",
+    "Decode CFG scale": "Decode CFG scale",
+    "Randomness": "Aleatoriedad",
+    "Sigma adjustment for finding noise for image": "Ajuste Sigma para encontrar ruido para la imagen.",
+    "Loops": "Loops",
+    "Denoising strength change factor": "Denoising strength change factor",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Ajustes recomendados: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8",
+    "Pixels to expand": "Píxeles para expandir",
+    "Outpainting direction": "Dirección Outpainting",
+    "left": "izquierda",
+    "right": "derecha",
+    "up": "arriba",
+    "down": "abajo",
+    "Fall-off exponent (lower=higher detail)": "Fall-off exponent (inferior=mayor detalle)",
+    "Color variation": "Variación de color",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Mejorará la imagen al doble de las dimensiones; usa los controles deslizantes de ancho y alto para establecer el tamaño del mosaico",
+    "Tile overlap": "Solapar mosaicos",
+    "Upscaler": "Upscaler",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "SwinIR 4x": "SwinIR 4x",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "Single Image": "Imagen Única",
+    "Batch Process": "Batch Process",
+    "Batch from Directory": "Batch desde Directorio",
+    "Source": "Origen",
+    "Show result images": "Mostrar resultados de imágenes",
+    "Scale by": "Escalar por",
+    "Scale to": "Escalar a",
+    "Resize": "Redimensionar",
+    "Crop to fit": "Recortar para ajustar",
+    "Upscaler 2 visibility": "Upscaler 2 visibilidad",
+    "GFPGAN visibility": "GFPGAN visibilidad",
+    "CodeFormer visibility": "CodeFormer visibilidad",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer peso (0 = efecto máximo, 1 = efecto mínimo)",
+    "Open output directory": "Abrir directorio de salida",
+    "Send to txt2img": "Enviar a txt2img",
+    "A merger of the two checkpoints will be generated in your": "Se generará una fusión de los dos checkpoints en su",
+    "checkpoint": "checkpoint",
+    "directory.": "directorio.",
+    "Primary model (A)": "Modelo primario (A)",
+    "Secondary model (B)": "Modelo secundario (B)",
+    "Tertiary model (C)": "Modelo terciario (C)",
+    "Custom Name (Optional)": "Nombre personalizado (Opcional)",
+    "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - establecer en 0 para obtener el modelo A",
+    "Interpolation Method": "Método de interpolación",
+    "Weighted sum": "Weighted sum",
+    "Add difference": "Add difference",
+    "Save as float16": "Guardar como float16",
+    "See": "Ver",
+    "wiki": "wiki",
+    "for detailed explanation.": "para una explicación detallada.",
+    "Create embedding": "Crear embedding",
+    "Create hypernetwork": "Crear hypernetwork",
+    "Preprocess images": "Preprocesar imágenes",
+    "Name": "Nombre",
+    "Initialization text": "Texto de inicialización",
+    "Number of vectors per token": "Número de vectores por token",
+    "Overwrite Old Embedding": "Sobrescribir Embedding Anterior",
+    "Modules": "Módulos",
+    "Enter hypernetwork layer structure": "Ingresa la estructura de capa del hypernetwork",
+    "Select activation function of hypernetwork": "Seleccione la función de activación del hypernetwork",
+    "linear": "linear",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "Add layer normalization": "Agregar normalización de capa",
+    "Use dropout": "Usar dropout",
+    "Overwrite Old Hypernetwork": "Sobrescribir Hypernetwork Anterior",
+    "Source directory": "Directorio de origen",
+    "Destination directory": "Directorio de salida",
+    "Existing Caption txt Action": "Existing Caption txt Action",
+    "ignore": "ignorar",
+    "copy": "copiar",
+    "prepend": "anteponer",
+    "append": "añadir",
+    "Create flipped copies": "Crear copias volteadas",
+    "Split oversized images": "Dividir imágenes grandes",
+    "Use BLIP for caption": "Usar BLIP para subtítulo",
+    "Use deepbooru for caption": "Usar deepbooru para subtítulo",
+    "Split image threshold": "Threshold imagen dividida",
+    "Split image overlap ratio": "Overlap ratio imagen dividida",
+    "Preprocess": "Preproceso",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Entrenar un embedding o Hypernetwork; debes especificar un directorio con un conjunto de imágenes con una proporción de 1:1",
+    "[wiki]": "[wiki]",
+    "Embedding": "Embedding",
+    "Embedding Learning rate": "Embedding Learning rate",
+    "Hypernetwork Learning rate": "Hypernetwork Learning rate",
+    "Dataset directory": "Directorio dataset",
+    "Log directory": "Directorio log",
+    "Prompt template file": "Prompt archivos plantilla",
+    "Max steps": "Max steps",
+    "Save an image to log directory every N steps, 0 to disable": "Guarda una imagen en el directorio log cada N pasos, 0 para deshabilitar",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Guarda una copia de embedding en el directorio log cada N pasos, 0 para deshabilitar",
+    "Save images with embedding in PNG chunks": "Guarda imágenes con embedding en fragmentos PNG",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Leer parámetros (prompt, etc...) desde la pestaña txt2img al hacer vistas previas",
+    "Train Hypernetwork": "Train Hypernetwork",
+    "Train Embedding": "Train Embedding",
+    "Apply settings": "Aplicar ajustes",
+    "Saving images/grids": "Guardar imágenes/grids",
+    "Always save all generated images": "Siempre guardar imágenes generadas",
+    "File format for images": "Formato de archivo para imágenes",
+    "Images filename pattern": "Patrón nombre archivo imágenes",
+    "Add number to filename when saving": "Agregar número al nombre de archivo al guardar",
+    "Always save all generated image grids": "Siempre guardar grids de imágenes generadas",
+    "File format for grids": "Formato de archivo para grids",
+    "Add extended info (seed, prompt) to filename when saving grid": "Agregar información extendida (seed, prompt) al nombre del archivo al guardar grid",
+    "Do not save grids consisting of one picture": "No guardar grids que consisten en una imagen",
+    "Prevent empty spots in grid (when set to autodetect)": "Evitar espacios vacíos en grids (cuando se establece detección automática)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Recuento de filas de grids; usar -1 para la detección automática y 0 para que sea igual al batch size",
+    "Save text information about generation parameters as chunks to png files": "Guardar información de texto sobre parámetros de generación como fragmentos en archivos png",
+    "Create a text file next to every image with generation parameters.": "Crear un archivo de texto junto a cada imagen con parámetros de generación.",
+    "Save a copy of image before doing face restoration.": "Guardar una copia de la imagen antes de restaurar rostro.",
+    "Quality for saved jpeg images": "Calidad para imágenes jpeg guardadas",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Si la imagen PNG es más grande de 4 MB o cualquier dimensión es más grande que 4000, reduce la escala y guarda la copia como JPG",
+    "Use original name for output filename during batch process in extras tab": "Use el nombre original para el nombre del archivo de salida durante el batch process en la pestaña extras",
+    "When using 'Save' button, only save a single selected image": "Al usar el botón 'Guardar', solo guarda una sola imagen seleccionada",
+    "Do not add watermark to images": "No agregar watermark a las imágenes",
+    "Paths for saving": "Directorios para guardar",
+    "Output directory for images; if empty, defaults to three directories below": "Directorio de imágenes; si está vacío, se utilizan los siguientes 3 directorios",
+    "Output directory for txt2img images": "Directorio para guardar imágenes de txt2img",
+    "Output directory for img2img images": "Directorio para guardar imágenes de img2img",
+    "Output directory for images from extras tab": "Directorio para guardar imágenes de extras",
+    "Output directory for grids; if empty, defaults to two directories below": "Directorio de grids; si está vacío, se utilizan los siguientes 2 directorios",
+    "Output directory for txt2img grids": "Directorio para guardar txt2img grids",
+    "Output directory for img2img grids": "Directorio para guardar img2img grids",
+    "Directory for saving images using the Save button": "Directorio para guardar imágenes usando el botón Guardar",
+    "Saving to a directory": "Guardando a un directorio",
+    "Save images to a subdirectory": "Guardar imágenes a un subdirectorio",
+    "Save grids to a subdirectory": "Guardar grids a un subdirectorio",
+    "When using \"Save\" button, save images to a subdirectory": "Al usar el botón \"Guardar\", guarda las imágenes en un subdirectorio",
+    "Directory name pattern": "Patrón nombre directorio",
+    "Max prompt words for [prompt_words] pattern": "Máximo de palabras en prompt [prompt_words] para patrón",
+    "Upscaling": "Upscaling",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tamaño mosaico para ESRGAN upscalers. 0 = sin mosaico.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Solapar mosaico, en pixeles para ESRGAN upscalers. Valores bajos = unión visible.",
+    "Tile size for all SwinIR.": "Tamaño mosaico para SwinIR.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Solapar mosaico, en pixeles para SwinIR. Valores bajos = unión visible.",
+    "LDSR processing steps. Lower = faster": "LDSR processing steps. Más bajo = rápido",
+    "Upscaler for img2img": "Upscaler para img2img",
+    "Upscale latent space image when doing hires. fix": "Upscale latent space al aplicar hires. fix",
+    "Face restoration": "Restauración de rostro",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer parámetro peso; 0 = máximo efecto; 1 = mínimo efecto",
+    "Move face restoration model from VRAM into RAM after processing": "Mover modelo de restauración de rostro del VRAM al RAM después de procesar",
+    "System": "Sistema",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Sondeos de uso de VRAM por segundo durante la generación. Establecer en 0 para deshabilitar.",
+    "Always print all generation info to standard output": "Imprime siempre toda la información de generación en la salida estándar",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Agrega una segunda barra de progreso a la consola que muestra el progreso de un trabajo completo.",
+    "Training": "Entrenamiento",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Mover VAE y CLIP al RAM al entrenar un hypernetwork. Ahorra VRAM.",
+    "Filename word regex": "Filename word regex",
+    "Filename join string": "Filename join string",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Número de repeticiones para una sola imagen de entrada por epoch; utilizado solo para mostrar el número epoch",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Guarda un csv que contenga la pérdida en el directorio log cada N pasos, 0 para deshabilitar",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "Checkpoints al cache en RAM",
+    "Hypernetwork strength": "Hypernetwork strength",
+    "Apply color correction to img2img results to match original colors.": "Aplica la corrección de color a los resultados de img2img para que coincidan con los colores originales.",
+    "Save a copy of image before applying color correction to img2img results": "Guarda una copia de la imagen antes de aplicar la corrección de color a los resultados de img2img",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, hace exactamente la cantidad de pasos que especifica el slider (normalmente haría menos con menos eliminación de ruido).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Habilita la cuantificación en K samplers para obtener resultados más nítidos y limpios. Esto puede cambiar los seeds existentes. Requiere reiniciar para aplicar.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Énfasis: utiliza (texto) para que el modelo preste más atención al texto y [texto] para que preste menos atención",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Utiliza la implementación de énfasis antiguo. Puede ser útil para reproducir seeds anteriores.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Hace que los K-diffusion samplers produzcan las mismas imágenes en un lote que cuando se crea una sola imagen",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coherencia rellenando desde la última coma dentro de n tokens cuando se use más de 75 tokens",
+    "Filter NSFW content": "Filtrar contenido NSFW",
+    "Stop At last layers of CLIP model": "Detener en las últimas capas del modelo CLIP",
+    "Interrogate Options": "Opciones de Interrogar",
+    "Interrogate: keep models in VRAM": "Interrogar: mantener modelos en VRAM",
+    "Interrogate: use artists from artists.csv": "Interrogar: utilizar artistas de artists.csv",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogar: incluir rangos de coincidencias de etiquetas de modelos en los resultados (No tiene efecto en los interrogadores basados en subtítulos).",
+    "Interrogate: num_beams for BLIP": "Interrogar: num_beams para BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Interrogar: longitud mínima de la descripción (excluyendo artistas, etc.)",
+    "Interrogate: maximum description length": "Interrogar: longitud máxima de la descripción",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: número máximo de líneas en el archivo de texto (0 = Sin límite)",
+    "Interrogate: deepbooru score threshold": "Interrogar: deepbooru score threshold",
+    "Interrogate: deepbooru sort alphabetically": "Interrogar: deepbooru ordenar alfabéticamente",
+    "use spaces for tags in deepbooru": "usar espacios para etiquetas en deepbooru",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "corchetes de escape (\\) en deepbooru (por lo que se usan como corchetes literales y no para enfatizar)",
+    "User interface": "Interfaz de usuario",
+    "Show progressbar": "Mostrar barra de progreso",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Muestra el progreso de creación de la imagen cada N sampling steps. Establecer 0 para deshabilitar.",
+    "Show previews of all images generated in a batch as a grid": "Mostrar vistas previas de todas las imágenes generadas en un batch como un grid",
+    "Show grid in results for web": "Mostrar grids en resultados para web",
+    "Do not show any images in results for web": "No mostrar ninguna imagen en los resultados para web",
+    "Add model hash to generation information": "Agregar hash de modelo a la información de generación",
+    "Add model name to generation information": "Agregar nombre de modelo a la información de generación",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Al leer los parámetros de generación del texto en la interfaz de usuario (desde PNG Info o texto pegado), no cambia el modelo/checkpoint seleccionado.",
+    "Font for image grids that have text": "Tipografía para grids de imágenes que tienen texto",
+    "Enable full page image viewer": "Habilitar visor de imágenes de página completa",
+    "Show images zoomed in by default in full page image viewer": "Mostrar imágenes ampliadas de forma predeterminada en el visor de imágenes de página completa",
+    "Show generation progress in window title.": "Muestra el progreso de la generación en el título de la ventana del navegador.",
+    "Quicksettings list": "Lista de ajustes rápidos",
+    "Localization (requires restart)": "Traducción (requiere reiniciar)",
+    "Sampler parameters": "Parámetros sampler",
+    "Hide samplers in user interface (requires restart)": "Ocultar samplers en interfaz de usuario (requiere reiniciar)",
+    "eta (noise multiplier) for DDIM": "eta (noise multiplier) para DDIM",
+    "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) para ancestral samplers",
+    "img2img DDIM discretize": "img2img DDIM discretize",
+    "uniform": "uniform",
+    "quad": "quad",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma noise",
+    "Eta noise seed delta": "Eta noise seed delta",
+    "Request browser notifications": "Solicitar notificaciones del navegador",
+    "Download localization template": "Descargar plantilla de traducción",
+    "Reload custom script bodies (No ui updates, No restart)": "Recargar custom script bodies (Sin actualizar UI, Sin reiniciar)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Reiniciar Gradio and Refrescar componentes (Custom Scripts, ui.py, js and css only)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (presiona Ctrl+Enter o Alt+Enter para generar)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (presiona Ctrl+Enter o Alt+Enter para generar)",
+    "Add a random artist to the prompt.": "Agregar un artista aleatorio al prompt.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leer los parámetros de generación del prompt o de la última generación si el prompt está vacío en la interfaz de usuario.",
+    "Save style": "Guardar estilo",
+    "Apply selected styles to current prompt": "Aplicar estilos seleccionados al prompt",
+    "Stop processing current image and continue processing.": "Dejar de procesar la imagen actual y continuar procesando.",
+    "Stop processing images and return any results accumulated so far.": "Dejar de procesar imágenes y devuelva los resultados acumulados hasta el momento.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Estilo a aplicar; los estilos tienen componentes tanto para prompts positivos como negativos y se aplican a ambos",
+    "Do not do anything special": "No hacer nada especial",
+    "Which algorithm to use to produce the image": "Qué algoritmo usar para producir la imagen",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - muy creativo, cada uno puede obtener una imagen completamente diferente dependiendo del conteo de steps, configurar los steps a más de 30-40 no ayuda",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - funcionan mejor para inpainting",
+    "Produce an image that can be tiled.": "Produce una imagen que puede usarse como mosaico.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Usa un proceso de dos pasos para crear parcialmente una imagen con una resolución más pequeña, mejora la escala y luego mejora los detalles sin cambiar la composición",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determina qué tan poco respeto debe tener el algoritmo por el contenido de la imagen. En 0, nada cambiará y en 1 obtendrá una imagen no relacionada. Con valores por debajo de 1.0, el procesamiento tomará menos steps de los que especifica el slider de Sampling Steps.",
+    "How many batches of images to create": "Cuantos batches de imágenes para crear",
+    "How many image to create in a single batch": "Cuantas imágenes para crear en un solo batch",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - con qué fuerza debe ajustarse la imagen al prompt: los valores más bajos producen resultados más creativos",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Un valor que determina la salida del generador de números aleatorios: si creas una imagen con los mismos parámetros y el seed que otra imagen, obtendrás el mismo resultado",
+    "Set seed to -1, which will cause a new random number to be used every time": "Establece el seed a -1, lo que hará que se use un nuevo número aleatorio cada vez",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Reutilice el seed de la última generación, muy útil si fue aleatoria",
+    "Seed of a different picture to be mixed into the generation.": "Seed de una imagen diferente para ser mezclada en la generación.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Qué fuerte de una variación para producir. En 0, no habrá ningún efecto. En 1, obtendrá la imagen completa con variation seed (excepto para ancestral samplers, donde solo obtendrás algo).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Intenta producir una imagen similar a la que se habría producido con el mismo seed a la resolución especificada",
+    "Separate values for X axis using commas.": "Separar valores para X usando comas.",
+    "Separate values for Y axis using commas.": "Separar valores para Y usando comas.",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Escribe la imagen en un directorio (predeterminado: log/images) y los parámetros de generación en el archivo csv.",
+    "Open images output directory": "Abrir directorio de imágenes",
+    "How much to blur the mask before processing, in pixels.": "Cuánto blur al mask antes de procesar, en píxeles.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Qué poner dentro del área con mask antes de procesarlo con Stable Diffusion.",
+    "fill it with colors of the image": "rellenarlo con los colores de la imagen",
+    "keep whatever was there originally": "mantener lo que estaba allí originalmente",
+    "fill it with latent space noise": "rellenarlo con latent space noise",
+    "fill it with latent space zeroes": "rellenarlo con latent space zeroes",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Upscale la región con mask a la resolución objetivo, vuelve a pintar, reduce la escala hacia atrás y pégala en la imagen original",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Cambia el tamaño de la imagen a la resolución destino. A menos que la altura y el ancho coincidan, obtendrás una relación de aspecto incorrecta.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Cambia el tamaño de la imagen para que la totalidad de la resolución destino se llene con la imagen. Recorta las partes que sobresalen.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Cambia el tamaño de la imagen para que la totalidad de la imagen esté dentro de la resolución de destino. Rellena el espacio vacío con los colores de la imagen.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Cuántas veces repetir el procesamiento de una imagen y usarla como entrada para la próxima iteración",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "En modo loopback, en cada bucle, la fuerza de eliminación de ruido se multiplica por este valor. <1 significa variedad decreciente, por lo que su secuencia convergerá en una imagen fija. >1 significa aumentar la variedad, por lo que su secuencia se volverá cada vez más caótica.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Para SD upscale, cuánta superposición en píxeles debe haber entre mosaicos. Los mosaicos se superponen de modo que cuando se fusionan nuevamente en una imagen, no hay una unión claramente visible.",
+    "A directory on the same machine where the server is running.": "Un directorio en la misma máquina donde se ejecuta el servidor.",
+    "Leave blank to save images to the default path.": "Déjalo en blanco para guardar las imágenes en la ruta predeterminada.",
+    "Result = A * (1 - M) + B * M": "Resultado = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Resultado = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "Primer y último dígito debe ser 1. ej:'1, 2, 1'",
+    "Path to directory with input images": "Ruta al directorio con imágenes de entrada",
+    "Path to directory where to write outputs": "Ruta al directorio donde escribir salidas",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa las siguientes etiquetas para definir cómo se eligen los nombres de archivo para las imágenes: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; dejar vacío para utilizar predeterminados.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Si esta opción está habilitada, el watermark no se agregará a las imágenes creadas. Advertencia: si no agregas un watermark, es posible que te estés comportando de manera poco ética.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa las siguiente etiquetas para definir cómo los subdirectorios para imágenes y grids son seleccionados: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; dejar vacío para utilizar predeterminados.",
+    "Restore low quality faces using GFPGAN neural network": "Restaurar rostros de baja calidad utilizando GFPGAN neural network",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Esta expresión regular se usará para extraer palabras del nombre de archivo y se unirán usando la opción a continuación en el texto de la etiqueta que se usa para el entrenamiento. Dejar vacío para mantener el texto del nombre de archivo tal como está.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Esta cadena se usará para unir palabras divididas en una sola línea si la opción anterior está habilitada.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Lista de nombres de configuración, separados por comas, para configuraciones que deben ir a la barra de acceso rápido en la parte superior, en lugar de la pestaña de configuración habitual. Ver modules/shared.py para configurar los nombres. Requiere reiniciar para aplicar.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Si este valor no es cero, se agregará al seed y se usará para inicializar RNG para ruidos cuando se usan samplers con Eta. Puedes usar esto para producir aún más variaciones de imágenes, o puedes usar esto para hacer coincidir imágenes de otro software si sabes lo que estás haciendo."
+}
-- 
cgit v1.2.1


From 73c4a8138cd547a40337053d78a6efb4d9627352 Mon Sep 17 00:00:00 2001
From: innovaciones <sonygarcia99@gmail.com>
Date: Mon, 24 Oct 2022 21:12:08 -0500
Subject: Fixes

---
 localizations/es_ES.json | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/localizations/es_ES.json b/localizations/es_ES.json
index e58a17fe..d9ca4ef7 100644
--- a/localizations/es_ES.json
+++ b/localizations/es_ES.json
@@ -197,8 +197,8 @@
     "Weighted sum": "Weighted sum",
     "Add difference": "Add difference",
     "Save as float16": "Guardar como float16",
-    "See": "Ver",
-    "wiki": "wiki",
+    "See": "Ver ",
+    "wiki": "wiki ",
     "for detailed explanation.": "para una explicación detallada.",
     "Create embedding": "Crear embedding",
     "Create hypernetwork": "Crear hypernetwork",
@@ -209,7 +209,7 @@
     "Overwrite Old Embedding": "Sobrescribir Embedding Anterior",
     "Modules": "Módulos",
     "Enter hypernetwork layer structure": "Ingresa la estructura de capa del hypernetwork",
-    "Select activation function of hypernetwork": "Seleccione la función de activación del hypernetwork",
+    "Select activation function of hypernetwork": "Selecciona la función de activación del hypernetwork",
     "linear": "linear",
     "relu": "relu",
     "leakyrelu": "leakyrelu",
@@ -232,7 +232,7 @@
     "Split image threshold": "Threshold imagen dividida",
     "Split image overlap ratio": "Overlap ratio imagen dividida",
     "Preprocess": "Preproceso",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Entrenar un embedding o Hypernetwork; debes especificar un directorio con un conjunto de imágenes con una proporción de 1:1",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Entrenar un embedding o Hypernetwork; debes especificar un directorio con un conjunto de imágenes con una proporción de 1:1 ",
     "[wiki]": "[wiki]",
     "Embedding": "Embedding",
     "Embedding Learning rate": "Embedding Learning rate",
@@ -357,7 +357,7 @@
     "Request browser notifications": "Solicitar notificaciones del navegador",
     "Download localization template": "Descargar plantilla de traducción",
     "Reload custom script bodies (No ui updates, No restart)": "Recargar custom script bodies (Sin actualizar UI, Sin reiniciar)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Reiniciar Gradio and Refrescar componentes (Custom Scripts, ui.py, js and css only)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Reiniciar Gradio y Refrescar componentes (Custom Scripts, ui.py, js y css)",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (presiona Ctrl+Enter o Alt+Enter para generar)",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (presiona Ctrl+Enter o Alt+Enter para generar)",
     "Add a random artist to the prompt.": "Agregar un artista aleatorio al prompt.",
-- 
cgit v1.2.1


From b38370275275bf6e11575000f39c50c6e90b1f7a Mon Sep 17 00:00:00 2001
From: ritosonn <ritosonn@gmail.com>
Date: Fri, 21 Oct 2022 23:46:32 +0900
Subject: fix #3145 #3093

---
 modules/sd_samplers.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 0b408a70..3670b57d 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -228,7 +228,7 @@ class VanillaStableDiffusionSampler:
             unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
             
             
-        samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
+        samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
 
         return samples
 
@@ -429,7 +429,7 @@ class KDiffusionSampler:
         self.model_wrap_cfg.init_latent = x
         self.last_latent = x
 
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
             'cond': conditioning, 
             'image_cond': image_conditioning, 
             'uncond': unconditional_conditioning, 
-- 
cgit v1.2.1


From 77a320f406a76425176b8ca4c034c362b6734713 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 24 Oct 2022 17:23:51 +0300
Subject: do not stop execution when script's callback misbehaves and report
 which script it was

---
 modules/script_callbacks.py | 45 ++++++++++++++++++++++++++++++++++++---------
 1 file changed, 36 insertions(+), 9 deletions(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index f46d3d9a..5e9a6d4b 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -1,4 +1,15 @@
+import sys
+import traceback
+from collections import namedtuple
+import inspect
 
+
+def report_exception(c, job):
+    print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
+    print(traceback.format_exc(), file=sys.stderr)
+
+
+ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
@@ -10,28 +21,44 @@ def clear_callbacks():
 
 
 def model_loaded_callback(sd_model):
-    for callback in callbacks_model_loaded:
-        callback(sd_model)
+    for c in callbacks_model_loaded:
+        try:
+            c.callback(sd_model)
+        except Exception:
+            report_exception(c, 'model_loaded_callback')
 
 
 def ui_tabs_callback():
     res = []
     
-    for callback in callbacks_ui_tabs:
-        res += callback() or []
+    for c in callbacks_ui_tabs:
+        try:
+            res += c.callback() or []
+        except Exception:
+            report_exception(c, 'ui_tabs_callback')
 
     return res
 
 
 def ui_settings_callback():
-    for callback in callbacks_ui_settings:
-        callback()
+    for c in callbacks_ui_settings:
+        try:
+            c.callback()
+        except Exception:
+            report_exception(c, 'ui_settings_callback')
+
+
+def add_callback(callbacks, fun):
+    stack = [x for x in inspect.stack() if x.filename != __file__]
+    filename = stack[0].filename if len(stack) > 0 else 'unknown file'
+
+    callbacks.append(ScriptCallback(filename, fun))
 
 
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
-    callbacks_model_loaded.append(callback)
+    add_callback(callbacks_model_loaded, callback)
 
 
 def on_ui_tabs(callback):
@@ -44,10 +71,10 @@ def on_ui_tabs(callback):
     title is tab text displayed to user in the UI
     elem_id is HTML id for the tab
     """
-    callbacks_ui_tabs.append(callback)
+    add_callback(callbacks_ui_tabs, callback)
 
 
 def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
-    callbacks_ui_settings.append(callback)
+    add_callback(callbacks_ui_settings, callback)
-- 
cgit v1.2.1


From 4c24347e45776d505937856ab280548d9298f0a8 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 24 Oct 2022 23:04:50 -0400
Subject: Remove BSRGAN from --use-cpu, add SwinIR

---
 modules/devices.py      |  2 +-
 modules/shared.py       |  6 +++---
 modules/swinir_model.py | 12 ++++++------
 3 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index dc1f3cdd..033a42d5 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -45,7 +45,7 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = None
+device = device_interrogate = device_gfpgan = device_swinir = device_esrgan = device_scunet = device_codeformer = None
 dtype = torch.float16
 dtype_vae = torch.float16
 
diff --git a/modules/shared.py b/modules/shared.py
index 76cbb1bd..308fccce 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -58,7 +58,7 @@ parser.add_argument("--opt-split-attention", action='store_true', help="force-en
 parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
-parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)
+parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -96,8 +96,8 @@ restricted_opts = [
     "outdir_save",
 ]
 
-devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
-(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])
+devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
+(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
 
 device = devices.device
 weight_load_location = None if cmd_opts.lowram else "cpu"
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index baa02e3d..facd262d 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -7,8 +7,8 @@ from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 from tqdm import tqdm
 
-from modules import modelloader
-from modules.shared import cmd_opts, opts, device
+from modules import modelloader, devices
+from modules.shared import cmd_opts, opts
 from modules.swinir_model_arch import SwinIR as net
 from modules.swinir_model_arch_v2 import Swin2SR as net2
 from modules.upscaler import Upscaler, UpscalerData
@@ -42,7 +42,7 @@ class UpscalerSwinIR(Upscaler):
         model = self.load_model(model_file)
         if model is None:
             return img
-        model = model.to(device)
+        model = model.to(devices.device_swinir)
         img = upscale(img, model)
         try:
             torch.cuda.empty_cache()
@@ -111,7 +111,7 @@ def upscale(
     img = img[:, :, ::-1]
     img = np.moveaxis(img, 2, 0) / 255
     img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(device)
+    img = img.unsqueeze(0).to(devices.device_swinir)
     with torch.no_grad(), precision_scope("cuda"):
         _, _, h_old, w_old = img.size()
         h_pad = (h_old // window_size + 1) * window_size - h_old
@@ -139,8 +139,8 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
     stride = tile - tile_overlap
     h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
     w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
-    E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
-    W = torch.zeros_like(E, dtype=torch.half, device=device)
+    E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=devices.device_swinir).type_as(img)
+    W = torch.zeros_like(E, dtype=torch.half, device=devices.device_swinir)
 
     with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
         for h_idx in h_idx_list:
-- 
cgit v1.2.1


From faed465a0b1a7d19669568738c93e04907c10415 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 25 Oct 2022 02:01:57 -0400
Subject: MPS Upscalers Fix

Get ESRGAN, SCUNet, and SwinIR working correctly on MPS by ensuring memory is contiguous for tensor views before sending to MPS device.
---
 modules/devices.py      | 4 ++++
 modules/esrgan_model.py | 2 +-
 modules/scunet_model.py | 3 +--
 modules/swinir_model.py | 2 +-
 4 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 033a42d5..7511e1dc 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -81,3 +81,7 @@ def autocast(disable=False):
         return contextlib.nullcontext()
 
     return torch.autocast("cuda")
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
+def mps_contiguous(input_tensor, device): return input_tensor.contiguous() if device.type == 'mps' else input_tensor
+def mps_contiguous_to(input_tensor, device): return mps_contiguous(input_tensor, device).to(device)
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index a49e2258..a13cf6ac 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -190,7 +190,7 @@ def upscale_without_tiling(model, img):
     img = img[:, :, ::-1]
     img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
     img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(devices.device_esrgan)
+    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_esrgan)
     with torch.no_grad():
         output = model(img)
     output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/scunet_model.py b/modules/scunet_model.py
index 36a996bf..59532274 100644
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@@ -54,9 +54,8 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         img = img[:, :, ::-1]
         img = np.moveaxis(img, 2, 0) / 255
         img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(device)
+        img = devices.mps_contiguous_to(img.unsqueeze(0), device)
 
-        img = img.to(device)
         with torch.no_grad():
             output = model(img)
         output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index facd262d..4253b66d 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -111,7 +111,7 @@ def upscale(
     img = img[:, :, ::-1]
     img = np.moveaxis(img, 2, 0) / 255
     img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(devices.device_swinir)
+    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_swinir)
     with torch.no_grad(), precision_scope("cuda"):
         _, _, h_old, w_old = img.size()
         h_pad = (h_old // window_size + 1) * window_size - h_old
-- 
cgit v1.2.1


From 91c1e1e6a92061b99c92a5b1d548535907d2ad96 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Tue, 25 Oct 2022 00:46:28 +0900
Subject: fix default filename pattern

---
 modules/images.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/images.py b/modules/images.py
index 0e044af2..286de2ae 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -477,8 +477,10 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     if forced_filename is None:
         if short_filename or seed is None:
             file_decoration = ""
-        else:
+        elif opts.save_to_dirs:
             file_decoration = opts.samples_filename_pattern or "[seed]"
+        else:
+            file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
 
         add_number = opts.save_images_add_number or file_decoration == ''
 
-- 
cgit v1.2.1


From ff305acd51cc71c5eea8aee0f537a26a6d1ba2a1 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Tue, 25 Oct 2022 15:33:43 +0800
Subject: some rights for extensions

---
 extensions/stable-diffusion-webui-inspiration | 2 +-
 modules/shared.py                             | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/extensions/stable-diffusion-webui-inspiration b/extensions/stable-diffusion-webui-inspiration
index c50c03ac..c4b9f5c2 160000
--- a/extensions/stable-diffusion-webui-inspiration
+++ b/extensions/stable-diffusion-webui-inspiration
@@ -1 +1 @@
-Subproject commit c50c03ac8fd2f6317d17d0c8c7c1ce26e6fe5cd7
+Subproject commit c4b9f5c233c8619ee140b763e706edc26cae0f1d
diff --git a/modules/shared.py b/modules/shared.py
index 76cbb1bd..7b1fadf2 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -82,6 +82,7 @@ parser.add_argument("--api", action='store_true', help="use api=True to launch t
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
+parser.add_argument("--administrator", type=str, help="Administrator rights", default=None)
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
-- 
cgit v1.2.1


From 3e15f8e0f5cc87507f77546d92435670644dbd18 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 25 Oct 2022 12:16:17 +0300
Subject: update callbacks code for #3549

---
 modules/script_callbacks.py | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 9933fa38..dc520abc 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -49,6 +49,14 @@ def ui_settings_callback():
             report_exception(c, 'ui_settings_callback')
 
 
+def image_saved_callback(image, p, fullfn, txt_fullfn):
+    for c in callbacks_image_saved:
+        try:
+            c.callback(image, p, fullfn, txt_fullfn)
+        except Exception:
+            report_exception(c, 'image_saved_callback')
+
+
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -56,9 +64,6 @@ def add_callback(callbacks, fun):
     callbacks.append(ScriptCallback(filename, fun))
 
 
-def image_saved_callback(image, p, fullfn, txt_fullfn):
-    for callback in callbacks_image_saved:
-        callback(image, p, fullfn, txt_fullfn)
 
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
@@ -82,9 +87,14 @@ def on_ui_tabs(callback):
 def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
-    callbacks_ui_settings.append(callback)
+    add_callback(callbacks_ui_settings, callback)
 
 
 def on_save_imaged(callback):
-    """register a function to call after modules.images.save_image is called returning same values, original image and p """
-    callbacks_image_saved.append(callback)
+    """register a function to be called after modules.images.save_image is called.
+    The callback is called with three arguments:
+        - p - procesing object (or a dummy object with same fields if the image is saved using save button)
+        - fullfn - image filename
+        - txt_fullfn - text file with parameters; may be None
+    """
+    add_callback(callbacks_image_saved, callback)
-- 
cgit v1.2.1


From 46cc0b3bc6833a793179e86ffae2498a54cdddaf Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Tue, 25 Oct 2022 18:28:09 +0900
Subject: Update strings for some custom script/extensions

---
 localizations/ko_KR.json | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 52ba4020..07c2797c 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -73,6 +73,7 @@
   "Create images embedding": "이미지 임베딩 생성하기",
   "Crop and resize": "잘라낸 후 리사이징",
   "Crop to fit": "잘라내서 맞추기",
+  "custom fold": "커스텀 경로",
   "Custom Name (Optional)": "병합 모델 이름 (선택사항)",
   "Dataset directory": "데이터셋 경로",
   "DDIM": "DDIM",
@@ -108,6 +109,7 @@
   "Embedding": "임베딩",
   "Embedding Learning rate": "임베딩 학습률",
   "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "강조 : (텍스트)를 이용해 모델의 텍스트에 대한 가중치를 더 강하게 주고 [텍스트]를 이용해 더 약하게 줍니다.",
+  "Enable Autocomplete": "태그 자동완성 사용",
   "Enable full page image viewer": "전체 페이지 이미지 뷰어 활성화",
   "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "더 예리하고 깔끔한 결과물을 위해 K 샘플러들에 양자화를 적용합니다. 존재하는 시드가 변경될 수 있습니다. 재시작이 필요합니다.",
   "End Page": "마지막 페이지",
@@ -196,6 +198,7 @@
   "Inpaint masked": "마스크만 처리",
   "Inpaint not masked": "마스크 이외만 처리",
   "Input directory": "인풋 이미지 경로",
+  "Input images directory": "이미지 경로 입력",
   "Interpolation Method": "보간 방법",
   "Interrogate\nCLIP": "CLIP\n분석",
   "Interrogate\nDeepBooru": "DeepBooru\n분석",
@@ -259,10 +262,12 @@
   "None": "없음",
   "Nothing": "없음",
   "Nothing found in the image.": "Nothing found in the image.",
+  "Number of columns on the page": "각 페이지마다 표시할 가로줄 수",
   "Number of grids in each row": "각 세로줄마다 표시될 그리드 수",
   "number of images to delete consecutively next": "연속적으로 삭제할 이미지 수",
   "Number of pictures displayed on each page": "각 페이지에 표시될 이미지 수",
   "Number of repeats for a single input image per epoch; used only for displaying epoch number": "세대(Epoch)당 단일 인풋 이미지의 반복 횟수 - 세대(Epoch) 숫자를 표시하는 데에만 사용됩니다. ",
+  "Number of rows on the page": "각 페이지마다 표시할 세로줄 수",
   "Number of vectors per token": "토큰별 벡터 수",
   "Open for Clip Aesthetic!": "클립 스타일 기능을 활성화하려면 클릭!",
   "Open images output directory": "이미지 저장 경로 열기",
@@ -470,6 +475,7 @@
   "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지와 그리드의 하위 디렉토리명의 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
   "Use old emphasis implementation. Can be useful to reproduce old seeds.": "옛 방식의 강조 구현을 사용합니다. 옛 시드를 재현하는 데 효과적일 수 있습니다.",
   "Use original name for output filename during batch process in extras tab": "부가기능 탭에서 이미지를 여러장 처리 시 결과물 파일명에 기존 파일명 사용하기",
+  "Use same seed for each image": "각 이미지에 동일한 시드 사용",
   "use spaces for tags in deepbooru": "deepbooru에서 태그에 공백 사용",
   "User interface": "사용자 인터페이스",
   "Var. seed": "바리에이션 시드",
@@ -486,6 +492,7 @@
   "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
   "Width": "가로",
   "wiki": " 위키",
+  "Wildcards": "와일드카드",
   "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "이미지를 설정된 사이즈의 2배로 업스케일합니다. 상단의 가로와 세로 슬라이더를 이용해 타일 사이즈를 지정하세요.",
   "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "이미지→이미지 진행 시, 슬라이더로 설정한 스텝 수를 정확히 실행하기 (일반적으로 디노이즈 강도가 낮을수록 실제 설정된 스텝 수보다 적게 진행됨)",
   "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
-- 
cgit v1.2.1


From 9ba439b53313ef78984dd8e39f25b34501188ee2 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Tue, 25 Oct 2022 18:48:07 +0800
Subject: need some rights for extensions

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 7b1fadf2..b5975707 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -82,7 +82,7 @@ parser.add_argument("--api", action='store_true', help="use api=True to launch t
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-parser.add_argument("--administrator", type=str, help="Administrator rights", default=None)
+parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
-- 
cgit v1.2.1


From f9549d1cbb3f1d7d1f0fb70375a06e31f9c5dd9d Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Tue, 25 Oct 2022 11:14:12 -0700
Subject: Added option to use unmasked conditioning image.

---
 modules/processing.py | 6 +++++-
 modules/shared.py     | 1 +
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index c61bbfbd..96f56b0d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -768,7 +768,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
             # Create another latent image, this time with a masked version of the original input.
             conditioning_mask = conditioning_mask.to(image.device)
-            conditioning_image = image * (1.0 - conditioning_mask)
+
+            conditioning_image = image
+            if shared.opts.inpainting_mask_image:
+                conditioning_image = conditioning_image * (1.0 - conditioning_mask)                
+            
             conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
 
             # Create the concatenated conditioning tensor to be fed to `c_concat`
diff --git a/modules/shared.py b/modules/shared.py
index 308fccce..1d0ff1a1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -320,6 +320,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
+    "inpainting_mask_image": OptionInfo(True, "Mask original image for conditioning used by inpainting model."),
 }))
 
 
-- 
cgit v1.2.1


From 605d27687f433c0fefb9025aace7dc94f0ebd454 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Tue, 25 Oct 2022 12:20:54 -0700
Subject: Added conditioning image masking to xy_grid. Use `True` and `False`
 to select values.

---
 modules/processing.py | 2 +-
 scripts/xy_grid.py    | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 96f56b0d..23ee5e02 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -770,7 +770,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             conditioning_mask = conditioning_mask.to(image.device)
 
             conditioning_image = image
-            if shared.opts.inpainting_mask_image:
+            if getattr(self, "inpainting_mask_image", shared.opts.inpainting_mask_image):
                 conditioning_image = conditioning_image * (1.0 - conditioning_mask)                
             
             conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index eff0c942..0843adcc 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -153,6 +153,8 @@ def str_permutations(x):
     """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
     return x
 
+def str_to_bool(x):
+    return "true" in x.lower().strip()
 
 AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"])
 AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"])
@@ -178,6 +180,7 @@ axis_options = [
     AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
     AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
     AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
+    AxisOption("Mask Conditioning Image", str_to_bool, apply_field("inpainting_mask_image"), format_value_add_label, None),
 ]
 
 
-- 
cgit v1.2.1


From 3e6c2420c1177e9e79f2b566a5a7795b7416e34a Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Tue, 25 Oct 2022 13:10:58 -0700
Subject: improve debug markers, fix algo weighting

---
 modules/textual_inversion/autocrop.py | 207 +++++++++++++++++++++-------------
 1 file changed, 129 insertions(+), 78 deletions(-)

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index b2f9241c..caaf18c8 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -1,4 +1,5 @@
 import cv2
+import os
 from collections import defaultdict
 from math import log, sqrt
 import numpy as np
@@ -26,19 +27,9 @@ def crop_image(im, settings):
       scale_by = settings.crop_height / im.height
 
   im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
+  im_debug = im.copy()
 
-  if im.width == settings.crop_width and im.height == settings.crop_height:
-    if settings.annotate_image:
-      d = ImageDraw.Draw(im)
-      rect = [0, 0, im.width, im.height]
-      rect[2] -= 1
-      rect[3] -= 1
-      d.rectangle(rect, outline=GREEN)
-      if settings.destop_view_image:
-        im.show()
-    return im
-
-  focus = focal_point(im, settings)
+  focus = focal_point(im_debug, settings)
 
   # take the focal point and turn it into crop coordinates that try to center over the focal
   # point but then get adjusted back into the frame
@@ -62,89 +53,143 @@ def crop_image(im, settings):
 
   crop = [x1, y1, x2, y2]
 
+  results = []
+
+  results.append(im.crop(tuple(crop)))
+
   if settings.annotate_image:
-    d = ImageDraw.Draw(im)
+    d = ImageDraw.Draw(im_debug)
     rect = list(crop)
     rect[2] -= 1
     rect[3] -= 1
     d.rectangle(rect, outline=GREEN)
+    results.append(im_debug)
     if settings.destop_view_image:
-      im.show()
+      im_debug.show()
 
-  return im.crop(tuple(crop))
+  return results
 
 def focal_point(im, settings):
     corner_points = image_corner_points(im, settings)
     entropy_points = image_entropy_points(im, settings)
     face_points = image_face_points(im, settings)
 
-    total_points = len(corner_points) + len(entropy_points) + len(face_points)
-
-    corner_weight = settings.corner_points_weight
-    entropy_weight = settings.entropy_points_weight
-    face_weight = settings.face_points_weight
-
-    weight_pref_total = corner_weight + entropy_weight + face_weight
-
-    # weight things
     pois = []
-    if weight_pref_total == 0 or total_points == 0: 
-      return pois
 
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (corner_weight/weight_pref_total) / (len(corner_points)/total_points) )) for p in corner_points ]
-    )
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (entropy_weight/weight_pref_total) / (len(entropy_points)/total_points) )) for p in entropy_points ]
-    )
-    pois.extend(
-      [ PointOfInterest( p.x, p.y, weight=p.weight * ( (face_weight/weight_pref_total) / (len(face_points)/total_points) )) for p in face_points ]
-    )
+    weight_pref_total = 0
+    if len(corner_points) > 0:
+      weight_pref_total += settings.corner_points_weight
+    if len(entropy_points) > 0:
+      weight_pref_total += settings.entropy_points_weight
+    if len(face_points) > 0:
+      weight_pref_total += settings.face_points_weight
+
+    corner_centroid = None
+    if len(corner_points) > 0:
+      corner_centroid = centroid(corner_points)
+      corner_centroid.weight = settings.corner_points_weight / weight_pref_total 
+      pois.append(corner_centroid)
+
+    entropy_centroid = None
+    if len(entropy_points) > 0:
+      entropy_centroid = centroid(entropy_points)
+      entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
+      pois.append(entropy_centroid)
+
+    face_centroid = None
+    if len(face_points) > 0:
+      face_centroid = centroid(face_points)
+      face_centroid.weight = settings.face_points_weight / weight_pref_total 
+      pois.append(face_centroid)
 
     average_point = poi_average(pois, settings)
 
     if settings.annotate_image:
       d = ImageDraw.Draw(im)
-      for f in face_points:
-        d.rectangle(f.bounding(f.size), outline=RED)
-      for f in entropy_points:
-        d.rectangle(f.bounding(30), outline=BLUE)
-      for poi in pois:
-        w = max(4, 4 * 0.5 * sqrt(poi.weight))
-        d.ellipse(poi.bounding(w), fill=BLUE)
-      d.ellipse(average_point.bounding(25), outline=GREEN)
+      max_size = min(im.width, im.height) * 0.07
+      if corner_centroid is not None:
+        color = BLUE
+        box = corner_centroid.bounding(max_size * corner_centroid.weight)
+        d.text((box[0], box[1]-15), "Edge: %.02f" % corner_centroid.weight, fill=color)
+        d.ellipse(box, outline=color)
+        if len(corner_points) > 1:
+          for f in corner_points:
+            d.rectangle(f.bounding(4), outline=color)
+      if entropy_centroid is not None:
+        color = "#ff0"
+        box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
+        d.text((box[0], box[1]-15), "Entropy: %.02f" % entropy_centroid.weight, fill=color)
+        d.ellipse(box, outline=color)
+        if len(entropy_points) > 1:
+          for f in entropy_points:
+            d.rectangle(f.bounding(4), outline=color)
+      if face_centroid is not None:
+        color = RED
+        box = face_centroid.bounding(max_size * face_centroid.weight)
+        d.text((box[0], box[1]-15), "Face: %.02f" % face_centroid.weight, fill=color)
+        d.ellipse(box, outline=color)
+        if len(face_points) > 1:
+          for f in face_points:
+            d.rectangle(f.bounding(4), outline=color)
+
+      d.ellipse(average_point.bounding(max_size), outline=GREEN)
       
     return average_point
 
 
 def image_face_points(im, settings):
-    np_im = np.array(im)
-    gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
-
-    tries = [
-      [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
-      [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
-    ]
-
-    for t in tries:
-      # print(t[0])
-      classifier = cv2.CascadeClassifier(t[0])
-      minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
-      try:
-        faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
-          minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
-      except:
-        continue
-
-      if len(faces) > 0:
-        rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
-        return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2])) for r in rects]
+    if settings.dnn_model_path is not None:
+      detector = cv2.FaceDetectorYN.create(
+          settings.dnn_model_path,
+          "",
+          (im.width, im.height),
+          0.8, # score threshold
+          0.3, # nms threshold
+          5000 # keep top k before nms
+      )
+      faces = detector.detect(np.array(im))
+      results = []
+      if faces[1] is not None:
+        for face in faces[1]:
+          x = face[0]
+          y = face[1]
+          w = face[2]
+          h = face[3]
+          results.append(
+            PointOfInterest(
+              int(x + (w * 0.5)), # face focus left/right is center
+              int(y + (h * 0)), # face focus up/down is close to the top of the head
+              size = w,
+              weight = 1/len(faces[1])
+            )
+          )
+      return results
+    else:
+      np_im = np.array(im)
+      gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
+
+      tries = [
+        [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
+        [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
+        [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
+        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
+        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
+        [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
+        [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
+        [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
+      ]
+      for t in tries:
+        classifier = cv2.CascadeClassifier(t[0])
+        minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
+        try:
+          faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
+            minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
+        except:
+          continue
+
+        if len(faces) > 0:
+          rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
+          return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
     return []
 
 
@@ -161,7 +206,7 @@ def image_corner_points(im, settings):
         np_im,
         maxCorners=100,
         qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.07,
+        minDistance=min(grayscale.width, grayscale.height)*0.03,
         useHarrisDetector=False,
     )
 
@@ -171,7 +216,7 @@ def image_corner_points(im, settings):
     focal_points = []
     for point in points:
       x, y = point.ravel()
-      focal_points.append(PointOfInterest(x, y, size=4))
+      focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
 
     return focal_points
 
@@ -205,17 +250,22 @@ def image_entropy_points(im, settings):
     x_mid = int(crop_best[0] + settings.crop_width/2)
     y_mid = int(crop_best[1] + settings.crop_height/2)
 
-    return [PointOfInterest(x_mid, y_mid, size=25)]
+    return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]
 
 
 def image_entropy(im):
     # greyscale image entropy
-    # band = np.asarray(im.convert("L"))
-    band = np.asarray(im.convert("1"), dtype=np.uint8)
+    band = np.asarray(im.convert("L"))
+    # band = np.asarray(im.convert("1"), dtype=np.uint8)
     hist, _ = np.histogram(band, bins=range(0, 256))
     hist = hist[hist > 0]
     return -np.log2(hist / hist.sum()).sum()
 
+def centroid(pois):
+  x = [poi.x for poi in pois]
+  y = [poi.y for poi in pois]
+  return PointOfInterest(sum(x)/len(pois), sum(y)/len(pois))
+
 
 def poi_average(pois, settings):
     weight = 0.0
@@ -260,11 +310,12 @@ class PointOfInterest:
 
 
 class Settings:
-  def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False):
+  def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False, dnn_model_path=None):
     self.crop_width = crop_width
     self.crop_height = crop_height
     self.corner_points_weight = corner_points_weight
     self.entropy_points_weight = entropy_points_weight
-    self.face_points_weight = entropy_points_weight
+    self.face_points_weight = face_points_weight
     self.annotate_image = annotate_image
-    self.destop_view_image = False
\ No newline at end of file
+    self.destop_view_image = False
+    self.dnn_model_path = dnn_model_path
\ No newline at end of file
-- 
cgit v1.2.1


From 8b4f32779f28010fc8077e8fcfb85a3205b36bc2 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Tue, 25 Oct 2022 13:15:08 -0700
Subject: Switch to a continous blend for cond. image.

---
 modules/processing.py | 9 ++++++---
 modules/shared.py     | 2 +-
 scripts/xy_grid.py    | 5 +----
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 23ee5e02..02292bdc 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -769,9 +769,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             # Create another latent image, this time with a masked version of the original input.
             conditioning_mask = conditioning_mask.to(image.device)
 
-            conditioning_image = image
-            if getattr(self, "inpainting_mask_image", shared.opts.inpainting_mask_image):
-                conditioning_image = conditioning_image * (1.0 - conditioning_mask)                
+            # Smoothly interpolate between the masked and unmasked latent conditioning image.
+            conditioning_image = torch.lerp(
+                image,
+                image * (1.0 - conditioning_mask),
+                getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
+            )
             
             conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
 
diff --git a/modules/shared.py b/modules/shared.py
index 1d0ff1a1..e0ffb824 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -320,7 +320,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
-    "inpainting_mask_image": OptionInfo(True, "Mask original image for conditioning used by inpainting model."),
+    "inpainting_mask_weight": OptionInfo(1.0, "Blend betweeen an unmasked and masked conditioning image for inpainting models.", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
 }))
 
 
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 0843adcc..f5255786 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -153,9 +153,6 @@ def str_permutations(x):
     """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
     return x
 
-def str_to_bool(x):
-    return "true" in x.lower().strip()
-
 AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"])
 AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"])
 
@@ -180,7 +177,7 @@ axis_options = [
     AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
     AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
     AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
-    AxisOption("Mask Conditioning Image", str_to_bool, apply_field("inpainting_mask_image"), format_value_add_label, None),
+    AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight"), format_value_add_label, None),
 ]
 
 
-- 
cgit v1.2.1


From db8ed5fe5cd6e967d12d43d96b7f83083e58626c Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Tue, 25 Oct 2022 15:22:29 -0700
Subject: Focal crop UI elements

---
 modules/textual_inversion/preprocess.py | 26 +++++++++++++-------------
 modules/ui.py                           | 20 ++++++++++++++++++--
 2 files changed, 31 insertions(+), 15 deletions(-)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index a8c17c6f..1e4d4de8 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -13,7 +13,7 @@ if cmd_opts.deepdanbooru:
     import modules.deepbooru as deepbooru
 
 
-def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_entropy_focus=False):
+def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):
     try:
         if process_caption:
             shared.interrogator.load()
@@ -23,7 +23,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
             db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
             deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
 
-        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_entropy_focus)
+        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug)
 
     finally:
 
@@ -35,7 +35,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
 
 
 
-def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_entropy_focus=False):
+def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):
     width = process_width
     height = process_height
     src = os.path.abspath(process_src)
@@ -139,27 +139,27 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
             ratio = (img.height * width) / (img.width * height)
             inverse_xy = True
 
-        processing_option_ran = False
+        process_default_resize = True
 
         if process_split and ratio < 1.0 and ratio <= split_threshold:
             for splitted in split_pic(img, inverse_xy):
                 save_pic(splitted, index, existing_caption=existing_caption)
-            processing_option_ran = True
+            process_default_resize = False
 
         if process_entropy_focus and img.height != img.width:
             autocrop_settings = autocrop.Settings(
                 crop_width = width,
                 crop_height = height,
-                face_points_weight = 0.9,
-                entropy_points_weight = 0.7,
-                corner_points_weight = 0.5,
-                annotate_image = False
+                face_points_weight = process_focal_crop_face_weight,
+                entropy_points_weight = process_focal_crop_entropy_weight,
+                corner_points_weight = process_focal_crop_edges_weight,
+                annotate_image = process_focal_crop_debug
             )
-            focal = autocrop.crop_image(img, autocrop_settings)
-            save_pic(focal, index, existing_caption=existing_caption)
-            processing_option_ran = True
+            for focal in autocrop.crop_image(img, autocrop_settings):
+                save_pic(focal, index, existing_caption=existing_caption)
+            process_default_resize = False
 
-        if not processing_option_ran:
+        if process_default_resize:
             img = images.resize_image(1, img, width, height)
             save_pic(img, index, existing_caption=existing_caption)
 
diff --git a/modules/ui.py b/modules/ui.py
index 028eb4e5..95b9c703 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1260,7 +1260,7 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
                         process_split = gr.Checkbox(label='Split oversized images')
-                        process_entropy_focus = gr.Checkbox(label='Create auto focal point crop')
+                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')
                         process_caption = gr.Checkbox(label='Use BLIP for caption')
                         process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
 
@@ -1268,6 +1268,12 @@ def create_ui(wrap_gradio_gpu_call):
                         process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
                         process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
 
+                    with gr.Row(visible=False) as process_focal_crop_row:
+                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.3, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')
+
                     with gr.Row():
                         with gr.Column(scale=3):
                             gr.HTML(value="")
@@ -1281,6 +1287,12 @@ def create_ui(wrap_gradio_gpu_call):
                         outputs=[process_split_extra_row],
                     )
 
+                    process_focal_crop.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_focal_crop],
+                        outputs=[process_focal_crop_row],
+                    )
+
                 with gr.Tab(label="Train"):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
                     with gr.Row():
@@ -1368,7 +1380,11 @@ def create_ui(wrap_gradio_gpu_call):
                 process_caption_deepbooru,
                 process_split_threshold,
                 process_overlap_ratio,
-                process_entropy_focus,
+                process_focal_crop,
+                process_focal_crop_face_weight,
+                process_focal_crop_entropy_weight,
+                process_focal_crop_edges_weight,
+                process_focal_crop_debug,
             ],
             outputs=[
                 ti_output,
-- 
cgit v1.2.1


From 53b48d93fb9acd66ad631129afd837049449a76b Mon Sep 17 00:00:00 2001
From: xmodar <modar.alfadly@gmail.com>
Date: Wed, 26 Oct 2022 02:12:53 +0300
Subject: Add complete retranslation for the Arabic localization

Used the following resources:
  - https://www.almaany.com/
  - https://translate.google.com/
  - https://techtionary.thinktech.sa/
  - https://sdaia.gov.sa/files/Dictionary.pdf

The translations are ordered by the way they appear in the UI.
This version abandons literal translation and adds explainations where possible.
---
 localizations/ar_AR.json | 808 +++++++++++++++++++++++------------------------
 1 file changed, 387 insertions(+), 421 deletions(-)

diff --git a/localizations/ar_AR.json b/localizations/ar_AR.json
index 1195a8e9..21c24e62 100644
--- a/localizations/ar_AR.json
+++ b/localizations/ar_AR.json
@@ -1,456 +1,422 @@
 {
   "rtl": true,
-  "⤡": "⤡",
-  "⊞": "⊞",
-  "×": "×",
-  "❮": "❮",
-  "❯": "❯",
-  "Loading...": "جار التحميل...",
-  "view": "معاينة",
-  "api": "api",
-  "•": "•",
-  "built with gradio": "مبني باستخدام Gradio",
-  "Stable Diffusion checkpoint": "نماذج الانتشار المستقر",
-  "txt2img": "نص لصورة",
-  "img2img": "صورة لصورة",
-  "Extras": "الإضافات",
-  "PNG Info": "معلومات PNG",
-  "Checkpoint Merger": "دمج النماذج",
-  "Train": "التدريب",
-  "Create aesthetic embedding": "Create aesthetic embedding",
-  "Image Browser": "مستعرض الصور",
-  "Settings": "الإعدادات",
-  "Prompt": "الموجه",
-  "Negative prompt": "الموجه السلبي",
-  "Run": "تشغيل",
-  "Skip": "تخطي",
-  "Interrupt": "إيقاف",
-  "Generate": "إنشاء",
-  "Style 1": "نمط 1",
-  "Style 2": "نمط 2",
-  "Label": "الوسم",
-  "File": "ملف",
-  "Drop File Here": "اسحب الملف هنا",
-  "-": "-",
-  "or": "أو",
-  "Click to Upload": "انقر للتحميل",
-  "Image": "صورة",
-  "Check progress": "تحقق من التقدم",
-  "Check progress (first)": "تحقق من التقدم (الأول)",
-  "Sampling Steps": "خطوات أخذ العينة",
-  "Sampling method": "نظام أخذ العينات",
-  "Euler a": "Euler a",
-  "Euler": "Euler",
-  "LMS": "LMS",
-  "Heun": "Heun",
-  "DPM2": "DPM2",
-  "DPM2 a": "DPM2 a",
-  "DPM fast": "DPM fast",
-  "DPM adaptive": "DPM adaptive",
-  "LMS Karras": "LMS Karras",
-  "DPM2 Karras": "DPM2 Karras",
-  "DPM2 a Karras": "DPM2 a Karras",
-  "DDIM": "DDIM",
-  "PLMS": "PLMS",
+  "Loading...": "لحظة...",
+  "view": "اعرض ",
+  "api": "واجهة البرمجة",
+  "built with gradio": "مبني باستخدام gradio",
+  "Stable Diffusion checkpoint": "ملف أوزان Stable Diffusion",
+  "txt2img": "نص إلى صورة",
+  "Prompt": "الطلب",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "الطلب (لبدء الإنتاج Ctrl+Enter أو Alt+Enter اضغط)",
+  "Negative prompt": "عكس الطلب",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "عكس الطلب (لبدء الإنتاج Ctrl+Enter أو Alt+Enter اضغط)",
+  "Add a random artist to the prompt.": "أضف فنان عشوائي للطلب",
+  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "اقرأ عوامل الإنتاج من الطلب أو من الإنتاج السابق إذا كان الطلب فارغا",
+  "Save style": "احتفظ بالطلب وعكسه كإضافة",
+  "Apply selected styles to current prompt": "ألحق الإضافات المحددة إلى الطلب وعكسه",
+  "Generate": "أنتج",
+  "Skip": "تخطى",
+  "Stop processing current image and continue processing.": "تجاهل إنتاج هذة الحزمة وانتقل إلى الحزمة التالية",
+  "Interrupt": "توقف",
+  "Stop processing images and return any results accumulated so far.": "توقف عن الإنتاج واعرض ما تم إلى الآن",
+  "Style 1": "الإضافة 1",
+  "Style to apply; styles have components for both positive and negative prompts and apply to both": "الإضافات (styles) عبارة عن كلمات تتكرر كثيرا يتم إلحاقها بالطلب وعكسه عند الرغبة",
+  "Style 2": "الإضافة 2",
+  "Do not do anything special": "لا يغير شيئا",
+  "Sampling Steps": "عدد الخطوات",
+  "Sampling method": "أسلوب الخطو",
+  "Which algorithm to use to produce the image": "Sampler: اسم نظام تحديد طريقة تغيير المسافات بين الخطوات",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral: طريقة مبدعة يمكن أن تنتج صور مختلفة على حسب عدد الخطوات، لا تتغير بعد 30-40 خطوة",
+  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models: الأفضل في التحبير",
   "Width": "العرض",
-  "Height": "الارتفاع",
-  "Restore faces": "ترميم الوجوه",
-  "Tiling": "تبليط",
-  "Highres. fix": "إصلاح الصور عالية الدقة",
-  "Firstpass width": "عرض المرور الأول",
-  "Firstpass height": "ارتفاع المرور الأول",
-  "Denoising strength": "قوة تقليل الضوضاء",
-  "Batch count": "عدد الدُفعات",
-  "Batch size": "حجم الدفعة",
-  "CFG Scale": "مقياس التقارب من الموجه (CFG)",
+  "Height": "الإرتفاع",
+  "Restore faces": "تحسين الوجوه",
+  "Tiling": "ترصيف",
+  "Produce an image that can be tiled.": "أنتج صور يمكن ترصيفها بجانب بعضها كالبلاط",
+  "Highres. fix": "أقل دقة",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "أنتج صورة بدقة منخفضة ثم قم برفع الدقة فيما بعد لتسريع الإنتاج عندما تكون الدقة المطلوبة كبيرة",
+  "Firstpass width": "العرض الأولي",
+  "Firstpass height": "الإرتفاع الأولي",
+  "Denoising strength": "قسط المشوار",
+  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Denoising strength: حدد مقدار جزء المشوار المراد قطعه (1: كامل المشوار ينتج صورة مختلفة تماما، 0: ثابت لا يغير الصورة)",
+  "Batch count": "عدد الحزم",
+  "How many batches of images to create": "يتم إنتاج الصور على دفعات، كل دفعة فيها حزمة من الصور",
+  "Batch size": "حجم الحزمة",
+  "How many image to create in a single batch": "Batch size: إنتاج حزمة صور أسرع من إنتاجهم فرادى، حدد عدد الصور في كل حزمة",
+  "CFG Scale": "التركيز",
+  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "CFG scale: يحدد مقدار التركيز على تلبية الطلب وتجنب عكسه، كلما زاد قل الإبداع",
   "Seed": "البذرة",
-  "Extra": "إضافي",
-  "Variation seed": "تباين البذرة",
-  "Variation strength": "قوة التباين",
-  "Resize seed from width": "تغيير حجم البذرة من العرض",
-  "Resize seed from height": "تغيير حجم البذرة من الارتفاع",
-  "Open for Clip Aesthetic!": "Open for Clip Aesthetic!",
-  "▼": "▼",
-  "Aesthetic weight": "Aesthetic weight",
-  "Aesthetic steps": "Aesthetic steps",
-  "Aesthetic learning rate": "Aesthetic learning rate",
-  "Slerp interpolation": "Slerp interpolation",
-  "Aesthetic imgs embedding": "Aesthetic imgs embedding",
-  "None": "لايوجد",
-  "Aesthetic text for imgs": "Aesthetic text for imgs",
-  "Slerp angle": "Slerp angle",
-  "Is negative text": "Is negative text",
-  "Script": "سكريبت",
-  "Prompt matrix": "مصفوفة الموجهات",
-  "Prompts from file or textbox": "موجهات من ملف أو مربع النص",
-  "X/Y plot": "الرسم البياني X/Y",
-  "Put variable parts at start of prompt": "ضع الأجزاء المتغيرة في بداية الموجه",
-  "Show Textbox": "إظهار مربع النص",
-  "File with inputs": "ملف يحتوي المدخلات",
-  "Prompts": "الموجهات",
-  "X type": "نوع X",
-  "Nothing": "لا شئ",
-  "Var. seed": "تغير البذرة",
-  "Var. strength": "قوة التغيير",
-  "Steps": "الخطوات",
-  "Prompt S/R": "موجه S / R",
-  "Prompt order": "ترتيب الموجهات",
-  "Sampler": "نظام أخذ العينات",
-  "Checkpoint name": "اسم النموذج",
+  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Seed: رقم طبيعي عشوائي يسمح بإعادة إنتاج نفس الصورة إذا توافقت قيم العوامل الأخرى",
+  "Set seed to -1, which will cause a new random number to be used every time": "استخدم بذرة جديدة في كل مرة (نرمز لهذا الخيار بجعل قيمة البذرة 1-)",
+  "Reuse seed from last generation, mostly useful if it was randomed": "أعد استخدام البذرة من الإنتاج السابق",
+  "Extra": "مزج",
+  "Variation seed": "بذرة الممزوج",
+  "Seed of a different picture to be mixed into the generation.": "Variation seed: بذرة صورة أخرى ليتم مزجها مع الصورة الحالية",
+  "Variation strength": "أثر الممزوج",
+  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Variation seed strength: مقدار أثر الصورة المدمجة على النتيجة النهائية (0: لا أثر، 1: أثر كامل ما عدا عند استخدام أسلوب خطو سلفي Ancestral)",
+  "Resize seed from width": "عرض الممزوج",
+  "Resize seed from height": "إرتفاع الممزوج",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Seed resize from: حدد دقة صورة الممزوج (0: نفس دقة الإنتاج)",
+  "Open for Clip Aesthetic!": "تضمين تجميلي",
+  "Aesthetic weight": "أثر التضمين",
+  "Aesthetic steps": "عدد الخطوات",
+  "Aesthetic learning rate": "معدل التعلم",
+  "Slerp interpolation": "امزج بطريقة كروية",
+  "Aesthetic imgs embedding": "التضمين",
+  "None": "بدون",
+  "Aesthetic text for imgs": "الطلب (اختياري)",
+  "This text is used to rotate the feature space of the imgs embs": "لإعادة توجيه التضمين التجميلي",
+  "Slerp angle": "أثر الطلب",
+  "Is negative text": "الطلب عكسي",
+  "Script": "أداة",
+  "Prompt matrix": "مصفوفة طلبات",
+  "Put variable parts at start of prompt": "الجزء المتغير في بداية الطلب",
+  "Prompts from file or textbox": "قائمة طلبات",
+  "Show Textbox": "أظهر مربع النص",
+  "File with inputs": "ملف نصي للطلبات",
+  "Drop File Here": "اسقط ملف هنا",
+  "-": "-",
+  "or": "أو",
+  "Click to Upload": "انقر للرفع",
+  "Prompts": "قائمة الطلبات",
+  "X/Y plot": "مصفوفة عوامل",
+  "X type": "العامل الأول",
+  "Nothing": "لا شيء",
+  "Var. seed": "بذرة الممزوج",
+  "Var. strength": "أثر الممزوج",
+  "Steps": "عدد الخطوات",
+  "Prompt S/R": "كلمات بديلة",
+  "Prompt order": "ترتيب الكلمات",
+  "Sampler": "أسلوب الخطو",
+  "Checkpoint name": "ملف الأوزان",
   "Hypernetwork": "الشبكة الفائقة",
   "Hypernet str.": "قوة الشبكة الفائقة",
-  "Sigma Churn": "دفع سيجما",
-  "Sigma min": "أصغر سيجما",
-  "Sigma max": "أكبر سيجما",
-  "Sigma noise": "ضجة سيجما",
-  "Eta": "الوقت المتوقع",
-  "Clip skip": "تخطي Clip",
-  "Denoising": "تقليل الضوضاء",
-  "X values": "قيم X",
-  "Y type": "نوع Y",
-  "Y values": "قيم Y",
-  "Draw legend": "ارسم مفتاح التوضيح",
-  "Include Separate Images": "قم بتضمين الصور منفصلة",
-  "Keep -1 for seeds": "احتفظ بـقيمة -1 للبذور",
-  "Drop Image Here": "إسقاط الصورة هنا",
-  "Save": "حفظ",
-  "Send to img2img": "أرسل إلى صورة لصورة",
-  "Send to inpaint": "أرسل إلى إعادة الرسم الجزئي",
-  "Send to extras": "أرسل إلى الإضافات",
-  "Make Zip when Save?": "إنشاء ملف مضغوط عند الحفظ؟",
-  "Textbox": "مربع النص",
-  "Interrogate\nCLIP": "استجواب\n CLIP",
-  "Inpaint": "إعادة الرسم الجزئي",
-  "Batch img2img": "دفعات صورة لصورة",
-  "Image for img2img": "صورة (صورة لصورة)",
-  "Image for inpainting with mask": "صورة (إعادة الرسم الجزئي)",
-  "Mask": "القناع",
-  "Mask blur": "ضبابية القناع",
-  "Mask mode": "أسلوب القناع",
-  "Draw mask": "رسم القناع",
-  "Upload mask": "تحميل القناع",
-  "Masking mode": "أسلوب التقنيع",
-  "Inpaint masked": "إعادة الرسم الجزئي (المنطقة المقنعة)",
-  "Inpaint not masked": "إعادة الرسم الجزئي (المنطقة الغير مقنعة)",
-  "Masked content": "المحتوى المقنع",
-  "fill": "الملأ",
-  "original": "الأصلي",
-  "latent noise": "الضوضاء الكامنة",
-  "latent nothing": "لا شيء كامن",
-  "Inpaint at full resolution": "إعادة الرسم الجزئي بدقة كاملة",
-  "Inpaint at full resolution padding, pixels": "إعادة الرسم الجزئي بدقة كاملة, الحشو, بيكسل",
-  "Process images in a directory on the same machine where the server is running.": "معالجة الصور في المجلد على نفس الجهاز حيث يتم تشغيل الخادم.",
-  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "استخدم مجلد إخراج فارغ لحفظ الصور بشكل طبيعي بدلاً من الكتابة إلى مجلد المخرجات.",
-  "Input directory": "مجلد المدخلات",
-  "Output directory": "مجلد المخرجات",
-  "Resize mode": "وضعية تغيير الحجم",
-  "Just resize": "تغييير الحجم فقط",
-  "Crop and resize": "اقتصاص وتغيير الحجم",
-  "Resize and fill": "تغيير الحجم والتعبئة",
-  "img2img alternative test": "صورة لصورة البديلة",
-  "Loopback": "الحلقة الراجعة",
-  "Outpainting mk2": "الرسم الخارجي نسخة 2",
-  "Poor man's outpainting": "الرسم الخارجي للفقراء",
-  "SD upscale": "ترقية الانتشار المستقر",
-  "should be 2 or lower.": "should be 2 or lower.",
-  "Override `Sampling method` to Euler?(this method is built for it)": "Override `Sampling method` to Euler?(this method is built for it)",
-  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)",
-  "Original prompt": "Original prompt",
-  "Original negative prompt": "Original negative prompt",
-  "Override `Sampling Steps` to the same value as `Decode steps`?": "Override `Sampling Steps` to the same value as `Decode steps`?",
-  "Decode steps": "Decode steps",
-  "Override `Denoising strength` to 1?": "Override `Denoising strength` to 1?",
-  "Decode CFG scale": "Decode CFG scale",
-  "Randomness": "Randomness",
-  "Sigma adjustment for finding noise for image": "Sigma adjustment for finding noise for image",
-  "Loops": "Loops",
-  "Denoising strength change factor": "معامل قوة تقليل الضوضاء",
-  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "الإعدادات الموصى بها: خطوات أخذ العينات: 80-100 ، طريقة أخذ العينات: Euler a ، تقليل الضوضاء: 0.8",
-  "Pixels to expand": "عدد البكسل للتوسيع",
-  "Outpainting direction": "إتجاه الرسم الخارجي",
+  "Sigma Churn": "العشوائية (Schurn)",
+  "Sigma min": "أدنى تشويش (Stmin)",
+  "Sigma max": "أقصى تشويش (Stmax)",
+  "Sigma noise": "التشويش (Snoise)",
+  "Eta": "العامل Eta η",
+  "Clip skip": "تخطي آخر طبقات CLIP",
+  "Denoising": "قسط المشوار",
+  "X values": "قيم العامل الأول",
+  "Separate values for X axis using commas.": "افصل القيم بفواصل (,) من اليسار إلى اليمين",
+  "Y type": "العامل الثاني",
+  "Y values": "قيم العامل الثاني",
+  "Separate values for Y axis using commas.": "افصل القيم بفواصل (,) من الأعلى إلى الأسفل",
+  "Draw legend": "أضف الهامش",
+  "Include Separate Images": "أضف الصور منفصلة",
+  "Keep -1 for seeds": "استخدم بذور عشوائية",
+  "Save": "احفظ",
+  "Write image to a directory (default - log/images) and generation parameters into csv file.": "احفظ الصور مع ملف العوامل بصيغة CSV",
+  "Send to img2img": "أرسل لصورة إلى صورة",
+  "Send to inpaint": "أرسل للتحبير",
+  "Send to extras": "أرسل للمعالجة",
+  "Open images output directory": "افتح مجلد الصور المخرجة",
+  "Make Zip when Save?": "ضع النتائج في ملف مضغوط عند الحفظ",
+  "img2img": "صورة إلى صورة",
+  "Interrogate\nCLIP": "استجواب\nCLIP",
+  "Drop Image Here": "اسقط صورة هنا",
+  "Just resize": "تغيير الحجم فقط",
+  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "غير حجم الصورة بدون مراعات اتزان الأبعاد",
+  "Crop and resize": "تغيير الحجم وقص الأطراف",
+  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "غير حجم الصورة واقتص الأطراف الزائدة",
+  "Resize and fill": "تغيير الحجم وتبطين الأطراف",
+  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "غير حجم الصورة واملأ الأطراف الزائدة بألوان من الصورة",
+  "img2img alternative test": "استجواب الصورة (تجريبي)",
+  "should be 2 or lower.": "يفترض أن يكون 2 أو أقل",
+  "Override `Sampling method` to Euler?(this method is built for it)": "استخدم أسلوب خطو Euler (مستحسن)",
+  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "استبدل الطلب وعكسه في الأعلى بالطلب الأصلي وعكسه التاليين",
+  "Original prompt": "الطلب الأصلي",
+  "Original negative prompt": "عكس الطلب الأصلي",
+  "Override `Sampling Steps` to the same value as `Decode steps`?": "استدبل عدد الخطوات بعدد الخطوات الأصلية",
+  "Decode steps": "عدد الخطوات الأصلية",
+  "Override `Denoising strength` to 1?": "اجعل قسط المشوار 1",
+  "Decode CFG scale": "التركيز",
+  "Randomness": "العشوائية",
+  "Sigma adjustment for finding noise for image": "لا تسمح بتثبيت قيمة التباين",
+  "Loopback": "اجترار وتكرار",
+  "Loops": "عدد المرات",
+  "How many times to repeat processing an image and using it as input for the next iteration": "كم مرة يتم أخذ مخرجات الإنتاج كمدخلات وإعادة الإنتاج مرة أخرى",
+  "Denoising strength change factor": "معدل تغيير قسط المشوار",
+  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "يتم ضرب قسط المشوار بهذا الرقم في كل مرة، إذا استخدمت رقم أصغر من 1 يمكن الرسو على نتيجة، وإذا استخدمت رقم أكبر من 1 تصبح النتيجة عشوائية",
+  "Outpainting mk2": "توسيع الصورة (mk2)",
+  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "يفضل استخدام: 80-100 خطوة، أسلوب Euler a، قسط المشوار 0.8",
+  "Pixels to expand": "عدد البيكسلات",
+  "Mask blur": "تنعيم القناع",
+  "How much to blur the mask before processing, in pixels.": "مقدرا تنعيم القناع قبل استخدامه (يقاس بالبيكسل)",
+  "Outpainting direction": "اتجاه توسيع الصورة",
   "left": "يسار",
   "right": "يمين",
   "up": "فوق",
   "down": "تحت",
-  "Fall-off exponent (lower=higher detail)": "أس التناقص (الأدنى = تفاصيل أعلى)",
-  "Color variation": "اختلاف اللون",
-  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "سيقوم بترقية الصورة إلى ضعف الأبعاد ؛ استخدم شريط تمرير العرض والارتفاع لضبط حجم التبليط",
-  "Tile overlap": "تداخل التبليط",
-  "Upscaler": "المرقي",
-  "Lanczos": "Lanczos",
+  "Fall-off exponent (lower=higher detail)": "قوة السقوط (كلما قلت زادت التفاصيل)",
+  "Color variation": "تنوع الألوان",
+  "Poor man's outpainting": "توسيع الصورة (بدائي)",
+  "Masked content": "محتويات القناع",
+  "What to put inside the masked area before processing it with Stable Diffusion.": "ما يوضع مكان الفراغ في الصورة الذي نريد إنتاج محتوياته",
+  "fill": "ألوان",
+  "fill it with colors of the image": "املأ باستخدام ألوان مأخوذة من باقي الصورة",
+  "original": "بدون تغيير",
+  "keep whatever was there originally": "أبق محتويات ما تحت القناع كما هي",
+  "latent noise": "تشويش كامن",
+  "fill it with latent space noise": "املأه باستخدام تشويش من الفضاء الكامن",
+  "latent nothing": "تصفير كامن",
+  "fill it with latent space zeroes": "استبدل مكان القناع في الفضاء الكامن بأصفار",
+  "SD upscale": "مضاعفة الدقة",
+  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "سيتم تكبير حجم الصورة إلى الضعف، استخدم الطول والإرتفاع في الأعلى لتحديد حجم نافذة المكبر",
+  "Tile overlap": "تداخل النافذة",
+  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "المكبر ينظر إلى أجزاء الصورة من خلال نافذة لتكبير المحتوى ثم ينتقل إلى الجزء المجاور، يفضل أن يكون هناك تداخل بين كل رقعة لكي لا يكون هناك اختلاف واضح بينهم",
+  "Upscaler": "طريقة التكبير",
+  "Inpaint": "تحبير",
+  "Draw mask": "ارسم القناع",
+  "Upload mask": "ارفع القناع",
+  "Inpaint masked": "تحبير ما بداخل القناع",
+  "Inpaint not masked": "تحبير ما حول القناع",
+  "Inpaint at full resolution": "تحبير بالدقة الكاملة",
+  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "كبر ما يراد تحبيره ثم صغر النتيجة وألصقها في مكانها",
+  "Inpaint at full resolution padding, pixels": "عدد بيكسلات التبطين",
+  "Batch img2img": "صور إلى صور",
+  "Process images in a directory on the same machine where the server is running.": "حدد مسار مجلد صور موجود في جهاز الخادم",
+  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "يمكنك أيضا تحديد مجلد حفظ النتائج (غير الإفتراضي)",
+  "Input directory": "مجلد المدخلات",
+  "Output directory": "مجلد المخرجات",
+  "Extras": "معالجة",
   "Single Image": "صورة واحدة",
-  "Batch Process": "معالجة الدفعات",
-  "Batch from Directory": "دفعة من المجلد",
-  "Source": "مصدر",
-  "Show result images": "إظهار نتائج الصور ",
-  "Scale by": "رفع الحجم بمقدار",
-  "Scale to": "رفع الحجم إلى",
+  "Source": "المصدر",
+  "Scale by": "مضاعفة الدقة",
   "Resize": "تغيير الحجم",
-  "Crop to fit": "اقتصاص للتوافق",
-  "Upscaler 2 visibility": "إظهار المرقي 2",
-  "GFPGAN visibility": "إظهار GFPGAN",
-  "CodeFormer visibility": "إظهار CodeFormer",
-  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "وزن CodeFormer (0 = أقصى تأثير ، 1 = تأثير أدنى)",
+  "Upscaler 1": "المكبر الأول",
+  "Upscaler 2": "المكبر الثاني",
+  "Upscaler 2 visibility": "أثر المكبر الثاني",
+  "GFPGAN visibility": "أثر GFPGAN (محسن وجوه)",
+  "CodeFormer visibility": "أثر CodeFormer (محسن وجوه)",
+  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "وزن CodeFormer (يزيد التفاصيل على حساب الجودة)",
+  "Scale to": "دقة محددة",
+  "Crop to fit": "قص الأطراف الزائدة إذا لم تتناسب الأبعاد",
+  "Batch Process": "حزمة صور",
+  "Batch from Directory": "حزمة من مجلد",
+  "A directory on the same machine where the server is running.": "مسار مجلد صور موجود في جهاز الخادم",
+  "Leave blank to save images to the default path.": "اتركه فارغا لاستخدام المسار الإفتراضي",
+  "Show result images": "اعرض الصور الناتجة",
   "Open output directory": "افتح مجلد المخرجات",
-  "Send to txt2img": "أرسل إلى كتابة لصورة",
-  "A merger of the two checkpoints will be generated in your": "سيتم إنشاء نموذجمدمج من النموذجين في",
-  "checkpoint": "النموذج",
-  "directory.": "المجلد.",
-  "Primary model (A)": "النموذج الأساسي (أ)",
-  "Secondary model (B)": "النموذج الثانوي (ب)",
-  "Tertiary model (C)": "النموذج الثالث (ج)",
-  "Custom Name (Optional)": "اسم مخصص (اختياري)",
-  "Multiplier (M) - set to 0 to get model A": "المضاعف (M) - اضبط على 0 للحصول على النموذج أ",
-  "Interpolation Method": "طريقة الاستنباط",
-  "Weighted sum": "المجموع الموزون",
-  "Add difference": "أضافة الاختلاف",
-  "Save as float16": "حفظ  float16",
-  "See": "شاهد",
-  "wiki": "ويكي",
-  "for detailed explanation.": "للحصول على شرح مفصل.",
-  "Create embedding": "إنشاء التضمين",
-  "Create hypernetwork": "إنشاء شبكة فائقة",
-  "Preprocess images": "تجهيز الصور",
+  "PNG Info": "عوامل الصورة",
+  "Send to txt2img": "أرسل لنص إلى صورة",
+  "Checkpoint Merger": "مزج الأوزان",
+  "A merger of the two checkpoints will be generated in your": "سيتم مزج الأوزان التالية وحفظ الأوزان المدجمة مع ",
+  "checkpoint": "الأوزان",
+  "directory.": " مجلد.",
+  "Primary model (A)": "الأوزان الأولى (A)",
+  "Secondary model (B)": "الأوزان الثانية (B)",
+  "Tertiary model (C)": "الأوزان الثالثة (C)",
+  "Custom Name (Optional)": "الاسم الجديد (اختياري)",
+  "Multiplier (M) - set to 0 to get model A": "العامل M: مسافة الإبتعاد عن الأوزان الأولى A",
+  "Interpolation Method": "طريقة المزج",
+  "Weighted sum": "خطية",
+  "Result = A * (1 - M) + B * M": "النتيجة = A * (1 - M) + B * M",
+  "Add difference": "جمع الفرق",
+  "Result = A + (B - C) * M": "النتيجة = A + (B - C) * M",
+  "Save as float16": "احفظ بدقة float16",
+  "Run": "تشغيل",
+  "Train": "تدريب",
+  "See": "اقرأ",
+  "wiki": "الـwiki ",
+  "for detailed explanation.": "لمعرفة المزيد",
+  "Create embedding": "إنشاء تضمين",
   "Name": "الاسم",
-  "Initialization text": "نص التهيئة",
-  "Number of vectors per token": "عدد المتجهات لكل رمز",
-  "Overwrite Old Embedding": "الكتابة فوق التضمين القديم",
-  "Modules": "الوحدات",
-  "Enter hypernetwork layer structure": "أدخل بنية طبقة الشبكة الفائقة",
-  "Select activation function of hypernetwork": "حدد وظيفة تنشيط الشبكة الفائقة",
-  "linear": "خطي (Linear)",
-  "relu": "الوحدة الخطية المعدلة (Relu)",
-  "leakyrelu": "الوحدة الخطية المعدلة المسربة (Leakyrelu)",
-  "elu": "الوحدة الأسية الخطية (Elu)",
-  "swish": "Swish",
-  "Add layer normalization": "أضف طبقة التسوية",
-  "Use dropout": "استخدم الهبوط",
-  "Overwrite Old Hypernetwork": "الكتابة فوق الشبكة الفائقة القديمة",
-  "Source directory": "مجلد المصدر",
-  "Destination directory": "مجلد النتائج",
-  "Existing Caption txt Action": "الإجراء النصي للتعليق المتوفر",
+  "Initialization text": "النص المبدأي",
+  "Number of vectors per token": "عدد المتجهات لكل وحدة لغوية",
+  "Overwrite Old Embedding": "استبدل التضمين القديم",
+  "Create hypernetwork": "إنشاء شبكة فائقة",
+  "Modules": "الأجزاء",
+  "Enter hypernetwork layer structure": "ترتيب مضاعفات عرض الطبقات",
+  "1st and last digit must be 1. ex:'1, 2, 1'": "المضاعفين الأول والأخير يجب أن يكونا 1، مثال: 1, 2, 1",
+  "Select activation function of hypernetwork": "دالة التنشيط",
+  "linear": "خطية (بدون)",
+  "relu": "ريلو (ReLU)",
+  "leakyrelu": "ريلو المتسربة (LeakyReLU)",
+  "elu": "إيلو (ELU)",
+  "swish": "سويش (Swish)",
+  "Add layer normalization": "أضف تسوية الطبقات (LayerNorm)",
+  "Use dropout": "استخدم الإسقاط (Dropout)",
+  "Overwrite Old Hypernetwork": "استبدل الشبكة الفائقة القديمة",
+  "Preprocess images": "معالجة مسبقة للصور",
+  "Source directory": "مجلد المخرجات",
+  "Destination directory": "مجلد المخرجات",
+  "Existing Caption txt Action": "اذا كانت الصورة لديها توصيف (طلب)",
   "ignore": "تجاهل",
-  "copy": "نسخ",
-  "prepend": "أضف قبل",
+  "copy": "انسخ",
+  "prepend": "أسبق",
   "append": "ألحق",
-  "Create flipped copies": "قم بإنشاء نسخ مقلوبة",
-  "Split oversized images": "تقسيم الصور كبيرة الحجم",
-  "Use BLIP for caption": "استخدم BLIP للتعليق",
-  "Use deepbooru for caption": "استخدم deepbooru للتعليق",
-  "Split image threshold": "حد تقسيم الصورة",
-  "Split image overlap ratio": "نسبة تداخل الصورة المنقسمة",
-  "Preprocess": "تجهيز الصور",
-  "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "تدريب التضمين أو الشبكة الفائقة ؛ يجب تحديد مجلد بمجموعة من الصور بنسبة أبعاد 1: 1",
-  "[wiki]": "[ويكي]",
+  "Create flipped copies": "انشئ نسخ معكوسة للصور",
+  "Split oversized images": "قسّم الصور الكبيرة",
+  "Split image threshold": "حد تقسيم الصور الكبيرة",
+  "Split image overlap ratio": "نسبة تداخل اقسام الصور الكبيرة",
+  "Use BLIP for caption": "استخدم BLIP لتوصيف الصور",
+  "Use deepbooru for caption": "استخدم deepbooru لتوصيف الصور",
+  "Preprocess": "معالجة مسبقة",
+  "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "درب التضمين أو الشبكة الفائقة: يجب تحديد مجلد يحتوي صور مربعة فقط ",
+  "[wiki]": "[wiki]",
   "Embedding": "التضمين",
   "Embedding Learning rate": "معدل تعلم التضمين",
   "Hypernetwork Learning rate": "معدل تعلم الشبكة الفائقة",
   "Dataset directory": "مجلد مجموعة البيانات",
+  "Path to directory with input images": "مسار مجلد الصور المدخلة",
   "Log directory": "مجلد السجل",
-  "Prompt template file": "ملف قالب الموجهات",
-  "Max steps": "الخطوات القصوى",
-  "Save an image to log directory every N steps, 0 to disable": "حفظ صورة في مجلد السجل  كل N خطوات ، 0 للتعطيل",
-  "Save a copy of embedding to log directory every N steps, 0 to disable": "حفظ نسخة من التضمين في مجلد السجل كل N خطوات ، 0 للتعطيل",
-  "Save images with embedding in PNG chunks": "حفظ التضمين مع الصور في أجزاء PNG",
-  "Read parameters (prompt, etc...) from txt2img tab when making previews": "قراءة المتغيرات (الموجه ، إلخ ...) من علامة تبويب نص لصورة عند إجراء المعاينات",
-  "Train Hypernetwork": "تدريب الشبكة الفائقة",
-  "Train Embedding": "تدريب التضمين",
-  "Create an aesthetic embedding out of any number of images": "Create an aesthetic embedding out of any number of images",
-  "Create images embedding": "Create images embedding",
-  "extras": "إضافات",
-  "favorites": "المفضلة",
-  "custom fold": "custom fold",
-  "Load": "تحميل",
+  "Path to directory where to write outputs": "مسار مجلد الصور المخرجة",
+  "Prompt template file": "ملف صيغ الطلبات",
+  "Max steps": "أقصى عدد خطوات التدريب",
+  "Save an image to log directory every N steps, 0 to disable": "احفظ صورة في السجل بعد كل كم خطوة تدريب (لا تحفظ إذا كان 0)",
+  "Save a copy of embedding to log directory every N steps, 0 to disable": "احفظ التضمين في السجل بعد كل كم خطوة تدريب (لا تحفظ إذا كان 0)",
+  "Save images with embedding in PNG chunks": "احفظ التضمين بداخل ملف الصورة كعامل يمكن استخراجه من عوامل الصورة (صيغة PNG)",
+  "Read parameters (prompt, etc...) from txt2img tab when making previews": "استخدم قيم العوامل الموجودة في تبويب نص إلى صورة لعرض نتائجهم أثناء التدريب",
+  "Train Hypernetwork": "درّب الشبكة الفائقة",
+  "Train Embedding": "درّب التضمين",
+  "Create aesthetic embedding": "تضمين تجميلي",
+  "Create an aesthetic embedding out of any number of images": "انشئ تضمين تجميلي يعبر عن مجموعة من الصور",
+  "Create images embedding": "انشئ التضمين التجميلي",
+  "Image Browser": "معرض الصور",
+  "Load": "حمّل",
   "Images directory": "مجلد الصور",
-  "Prev batch": "الدفعة السابقة",
-  "Next batch": "الدفعة التالية",
   "First Page": "الصفحة الأولى",
   "Prev Page": "الصفحة السابقة",
-  "Page Index": "فهرس الصفحات",
+  "Page Index": "رقم الصفحة",
   "Next Page": "الصفحة التالية",
-  "End Page": "صفحة النهاية",
-  "number of images to delete consecutively next": "عدد الصور المطلوب حذفها على التوالي بعد ذلك",
-  "Delete": "حذف",
-  "Generate Info": "معلومات الإنشاء",
+  "End Page": "الصفحة الأخيرة",
+  "number of images to delete consecutively next": "عدد الصور المتتالية للحذف",
+  "Delete": "احذف",
+  "Generate Info": "معلومات عامة",
   "File Name": "اسم الملف",
-  "Collect": "جمع",
-  "Refresh page": "إعادة تحميل الصفحة",
-  "Date to": "التاريخ إلى",
-  "Number": "الرقم",
-  "set_index": "وضع الفهرس",
-  "Checkbox": "صندوق اختيار",
-  "Apply settings": "تطبيق الإعدادات",
-  "Saving images/grids": "حفظ الصور / الإطار الشبكي",
-  "Always save all generated images": "احفظ دائمًا جميع الصور التي تم إنشائها",
-  "File format for images": "تنسيق ملفات الصور",
-  "Images filename pattern": "نمط اسم ملفات الصور",
-  "Add number to filename when saving": "Add number to filename when saving",
-  "Always save all generated image grids": "احفظ دائمًا جميع الإطارات الشبكية للصور التي تم إنشاؤها",
-  "File format for grids": "تنسيق ملفات الإطارات الشبكية",
-  "Add extended info (seed, prompt) to filename when saving grid": "أضف معلومات إضافية (البذرة ، الموجه) إلى اسم الملف عند حفظ الإطار الشبكي",
-  "Do not save grids consisting of one picture": "لا تحفظ الإطارات الشبكية التي تتكون من صورة واحدة",
-  "Prevent empty spots in grid (when set to autodetect)": "منع المناطق الفارغة في الإطار الشبكي (عند الضبط على الاكتشاف التلقائي)",
-  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "عدد صفوف الإطار الشبكي استخدم -1 للاكتشاف التلقائي و 0 ليكون نفس حجم الدُفعة",
-  "Save text information about generation parameters as chunks to png files": "احفظ معلومات نصية حول متغيرات الإنشاء كمقاطع في ملفات png",
-  "Create a text file next to every image with generation parameters.": "قم بإنشاء ملف نصي بجوار كل صورة باستخدام متغيرات الإنشاء.",
-  "Save a copy of image before doing face restoration.": "احفظ نسخة من الصورة قبل القيام بترميم الوجوه.",
-  "Quality for saved jpeg images": "جودة الصور المحفوظة بتنسيق jpeg",
-  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "إذا كانت صورة PNG أكبر من 4 ميجابايت أو كان أي بُعد أكبر من 4000 ، قم بتقليل حجم الصورة وحفظها بتنسيق JPG",
-  "Use original name for output filename during batch process in extras tab": "استخدم الاسم الأصلي لاسم ملف الإخراج أثناء عملية الدُفعات في علامة تبويب الإضافات",
-  "When using 'Save' button, only save a single selected image": "عند استخدام زر 'حفظ' ، احفظ فقط صورة واحدة محددة",
-  "Do not add watermark to images": "لا تقم بإضافة العلامة المائية للصور",
-  "Paths for saving": "مسارات الحفظ",
-  "Output directory for images; if empty, defaults to three directories below": "مجلد المخرجات للصور ؛ إذا كان فارغا ، يتم تعيينه افتراضيًا إلى المجلدات الثلاثة أدناه",
-  "Output directory for txt2img images": "مجلد المخرجات لصور نص لصورة",
-  "Output directory for img2img images": "مجلد المخرجات لصور صورة لصورة",
-  "Output directory for images from extras tab": "مجلد المخرجات لصور علامة تبويب الإضافات",
-  "Output directory for grids; if empty, defaults to two directories below": "مجلد المخرجات للإطارات الشبكية ؛ إذا كان فارغا ، يتم تعيينه افتراضيًا إلى المجلدين أدناه",
-  "Output directory for txt2img grids": "مجلد المخرجات للإطارات الشبكية نص لصورة",
-  "Output directory for img2img grids": "مجلد المخرجات للإطارات الشبكية صورة لصورة",
-  "Directory for saving images using the Save button": "مجلد لحفظ الصور باستخدام زر حفظ",
-  "Saving to a directory": "يتم الحفظ إلى المجلد..",
-  "Save images to a subdirectory": "حفظ الصور في مجلد فرعي",
-  "Save grids to a subdirectory": "حفظ الإطارات الشبكية في مجلد فرعي",
+  "Collect": "اجمع",
+  "extras": "معالجة",
+  "favorites": "المفضلة",
+  "custom fold": "مجلد آخر",
+  "Input images directory": "مجلد الصور المدخلة",
+  "Settings": "إعدادات",
+  "Apply settings": "طبق الإعدادت",
+  "Saving images/grids": "حفظ الصور وجداولها",
+  "Always save all generated images": "احفظ كل الصور المنتجة",
+  "File format for images": "صيغة ملفات الصور",
+  "Images filename pattern": "نمط تسمية الصور",
+  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم الأوسمة التالية لتحديد كيفية تسمية الصور: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] أو اتركه فارغا إذا أردت",
+  "Add number to filename when saving": "دائما أضف رقم على اسم الملف",
+  "Always save all generated image grids": "احفظ جداول الصور دائما",
+  "File format for grids": "صيغة ملفات جداول الصور",
+  "Add extended info (seed, prompt) to filename when saving grid": "أضف عوامل الإنتاج (مثل الطلب والبذرة) لأسماء جداول الصور",
+  "Do not save grids consisting of one picture": "لا تحفظ جدول الصور عند إنتاج صورة واحدة فقط",
+  "Prevent empty spots in grid (when set to autodetect)": "في الوضع التلقائي امنع الفراغات في جداول الصور",
+  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "عدد صفوف جداول الصور (1-: تلقائي، 0: نفس حجم الحزمة)",
+  "Save text information about generation parameters as chunks to png files": "احفظ عوامل الإنتاج داخل ملفات الصور (صيغة PNG)",
+  "Create a text file next to every image with generation parameters.": "انشئ ملف نصي يحتوي على عوامل الإنتاج بجانب كل صورة",
+  "Save a copy of image before doing face restoration.": "احفظ نسخة من الصورة قبل تحسين الوجوه",
+  "Quality for saved jpeg images": "جودة حفظ صور JPEG",
+  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "إذا كان حجم ملف صورة PNG أكبر من 4MB أو أحد أبعاد الصورة أكبر من 4000 بيكسل، صغر الصورة واحفظها بصيغة JPEG",
+  "Use original name for output filename during batch process in extras tab": "استخدم الإسم الأصلي للصور عند معالجتهم في حزم تحت تبويب معالجة",
+  "When using 'Save' button, only save a single selected image": "احفظ صورة واحدة فقط عند الضغط على حفظ",
+  "Do not add watermark to images": "لا تضف علامة مائية على الصور",
+  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "الصور المنتجة لن تحتوي على علامة مائية عند تفعيل هذا الخيار، تحذير: ربما قد يدل هذا على أنك تتصرف بشكل غير أخلاقي",
+  "Paths for saving": "اماكن الحفظ",
+  "Output directory for images; if empty, defaults to three directories below": "مسار حفظ الصور المخرجة؛ يمكن أن يترك فارغا",
+  "Output directory for txt2img images": "مسار الصور المخرجة من تبويب نص إلى صورة",
+  "Output directory for img2img images": "مسار الصور المخرجة من تبويب صورة إلى صورة",
+  "Output directory for images from extras tab": "مسار الصور المخرجة من تبويب معالجة",
+  "Output directory for grids; if empty, defaults to two directories below": "مسار حفظ جداول الصور المخرجة؛ يمكن أن يترك فارغا",
+  "Output directory for txt2img grids": "مسار جداول الصور المخرجة من تبويب نص إلى صورة",
+  "Output directory for img2img grids": "مسار جداول الصور المخرجة من تبويب صورة إلى صورة",
+  "Directory for saving images using the Save button": "مسار حفظ الصور عند الضغط على زر الحفظ",
+  "Saving to a directory": "مجلدات الحفظ",
+  "Save images to a subdirectory": "احفظ الصور في مجلد فرعي",
+  "Save grids to a subdirectory": "احفظ جداول الصور في مجلد فرعي",
   "When using \"Save\" button, save images to a subdirectory": "احفظ الصور في مجلد فرعي عند الضغط على زر الحفظ",
   "Directory name pattern": "نمط اسم المجلد",
-  "Max prompt words for [prompt_words] pattern": "أقصى عدد لكلمات التوجيه لنمط [كلمات_التوجيه]",
-  "Upscaling": "الارتقاء",
-  "Tile size for ESRGAN upscalers. 0 = no tiling.": "حجم التبليط لترقيات ESRGAN, القيمة 0= لا يوجد تبليط.",
-  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "تداخل المربعات ، بالبكسل لمرقي ESRGAN. القيم المنخفضة = تداخل مرئي.",
-  "Tile size for all SwinIR.": "حجم التليط لجميع عمليات SwinIR.",
-  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "تداخل المربعات بالبكسل لـ SwinIR. القيم المنخفضة = تداخل مرئي.",
-  "LDSR processing steps. Lower = faster": "خطوات معالجة LDSR. أقل = أسرع",
-  "Upscaler for img2img": "المرقي لصورة لصورة",
-  "Upscale latent space image when doing hires. fix": "ترقية صورة الفضاء الكامن عند التعيينات. تصليح",
-  "Face restoration": "ترميم الوجوه",
-  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "متغيرات وزن CodeFormer ؛ 0 = أقصى تأثير ؛ 1 = الحد الأدنى من التأثير",
-  "Move face restoration model from VRAM into RAM after processing": "انقل نموذج استعادة الوجوه من VRAM إلى RAM بعد المعالجة",
-  "System": "نظام",
-  "VRAM usage polls per second during generation. Set to 0 to disable.": "استطلاعات استخدام VRAM في الثانية أثناء الإنشاء. اضبط على 0 للتعطيل.",
-  "Always print all generation info to standard output": "قم دائمًا بطباعة جميع معلومات الإنشاء إلى المخرج القياسي",
-  "Add a second progress bar to the console that shows progress for an entire job.": "أضف شريط تقدم آخر إلى وحدة التحكم يُظهر التدقم الكلي للمهمة.",
+  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم الأوسمة التالية لحديد كيفية تسمية المجلدات الفرعية للصور والصور المركبة: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] أو اتركه فارغا إذا أردت",
+  "Max prompt words for [prompt_words] pattern": "أقصى عدد كلمات الطلب عند استخدام وسم [prompt_words]",
+  "Upscaling": "تكبير الصور",
+  "Tile size for ESRGAN upscalers. 0 = no tiling.": "حجم نافذة المكبر ESRGAN (يمكن أن يكون 0)",
+  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "تداخل نافذة المكبر ESRGAN (تتضح الفواصل إذا كان قليل)",
+  "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "حدد ما تريد عرضه كخيار لمكبر من نوع Real-ESRGAN يمكن استخدامه (يتطلب إعادة تشغيل)",
+  "Tile size for all SwinIR.": "حجم نافذة المكبر SwinIR",
+  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "تداخل نافذة المكبر SwinIR (تتضح الفواصل إذا كان قليل)",
+  "LDSR processing steps. Lower = faster": "عدد خطوات مكبر LDSR (أسرع كلما قلت)",
+  "Upscaler for img2img": "طريقة التكبير تحت تبويب صورة إلى صورة",
+  "Upscale latent space image when doing hires. fix": "عند اختيار \"أقل دقة\" قم بالتكبير في الفضاء الكامن",
+  "Face restoration": "تحسين الوجوه",
+  "Face restoration model": "نموذج تحسين الوجوه",
+  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "وزن CodeFormer (يزيد التفاصيل على حساب الجودة)",
+  "Move face restoration model from VRAM into RAM after processing": "احذف محسن الوجوه من ذاكرة كرت الشاشة (VRAM) بعد استخدامه",
+  "System": "النظام",
+  "VRAM usage polls per second during generation. Set to 0 to disable.": "عدد مرات التحقق من ذاكرة كرت الشاشة المستخدمة (VRAM) في الثانية",
+  "Always print all generation info to standard output": "اطبع دائما كل عوامل الإنتاج",
+  "Add a second progress bar to the console that shows progress for an entire job.": "أضف شريط تقدم ثاني لعملية الإنتاج الكاملة",
   "Training": "التدريب",
-  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "تفريغ VAE و CLIP من VRAM عند التدريب",
-  "Filename word regex": "اسم ملف كلمات regex",
-  "Filename join string": "سلسلة أحرف دمج أسماء الملفات",
-  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "عدد التكرارات لصورة إدخال واحدة لكل حقبة ؛ تستخدم فقط لعرض رقم الحقبة",
-  "Save an csv containing the loss to log directory every N steps, 0 to disable": "حفظ ملف csv يحتوي على الخسارة إلى مجلد السجل كل N خطوات ، 0 للتعطيل",
-  "Stable Diffusion": "انتشار مستقر",
-  "Checkpoints to cache in RAM": "التخزين المؤقت للنماذج في RAM",
+  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "قلل استخدام ذاكرة كرت الشاشة (VRAM) عند تدريب الشبكة الفائقة بالتخلص من CLIP وVAE",
+  "Filename word regex": "التعبير النمطي (RegEx) لكلمات اسم الملف",
+  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "استخدم هذا التعبير لاستخراج كلمات من أسماء الملفات ليتم دمجها بالخيار التالي لتستخدم في التدريب، اتركه فارغا لتستخدم اسم الملف كما هو",
+  "Filename join string": "النص الفاصل للكلمات المدموجة",
+  "This string will be used to join split words into a single line if the option above is enabled.": "سيستخدم بين كل كلمة يتم استخراجها من الخيار السابق",
+  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "عدد مرات تكرار الصور في كل دورة (Epoch)، يستخدم للعرض فقط",
+  "Save an csv containing the loss to log directory every N steps, 0 to disable": "احفظ خسارة التدريب (Loss) في السجل بعد كل كم خطوة (لا تحفظ إذا كان 0)",
+  "Stable Diffusion": "نموذج التشعث الرزين (Stable Diffusion)",
+  "Checkpoints to cache in RAM": "عدد النماذج التي تترك في الذاكرة العشوائية (RAM)",
   "Hypernetwork strength": "قوة الشبكة الفائقة",
-  "Apply color correction to img2img results to match original colors.": "قم بتطبيق تصحيح الألوان على نتائج صورة لصورة لمطابقة الألوان الأصلية.",
-  "Save a copy of image before applying color correction to img2img results": "احفظ نسخة من الصورة قبل تطبيق تصحيح الألوان على نتائج  صورة لصورة",
-  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "باستخدام صورة لصورة ، قم بالضبط بعدد الخطوات التي يحددها شريط التمرير (عادةً ما سيكون أقل مع تقليل التشويش).",
-  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "تمكين تكميم عينات K للحصول على نتائج أوضح وأنظف. هذا قد يغير البذرة المستخدمة. يتطلب إعادة التشغيل للتطبيق.",
-  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "التأكيد: استخدم (النص) لجعل النموذج يولي مزيدًا من الاهتمام للنص و استخدم [النص] لجعل النموذج يولي اهتمامًا أقل  للنص",
-  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "استخدم طريقة التأكيد القديمة. يمكن أن يكون مفيدًا لإعادة إنتاج البذور القديمة.",
-  "Make K-diffusion samplers produce same images in a batch as when making a single image": "اجعل انظمة أخذ عينات الانتشار K تنتج نفس الصور في الدفعات كما هو الحال عند إنشاء صورة واحدة",
-  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "زيادة التناسق عن طريق المساحة المتروكة من الفاصلة الأخيرة ضمن عدد n من الرموز المميزة عند استخدام أكثر من 75 رمزًا مميزًا",
-  "Filter NSFW content": "تصفية المحتوى الإباحي",
-  "Stop At last layers of CLIP model": "توقف عند آخر طبقات نموذج CLIP",
+  "Apply color correction to img2img results to match original colors.": "صحح ألوان نتائج صورة إلى صورة لتشابه الصورة الأصلية",
+  "Save a copy of image before applying color correction to img2img results": "احفظ نسخة من الصور المنتجة من صورة إلى صورة قبل عملية تصحيح الألوان",
+  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "استخدم بالضبط عدد الخطوات المحددة بالرغم من قسط المشوار (عدد الخطوات الإفتراضي = عدد الخطوات / قسط المشوار)",
+  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "فعل التجزيء الكمي (Quantization) لأغلب أساليب الخطو (k-diffusion) للحصول على صور أنظف (يتطلب إعادة تشغيل)",
+  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "أحط أي كلمة أو عبارة في الطلب أو عكسه بأقواس () للتشديد أو [] للتيسير",
+  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "استخدم الطريقة القديمة للتشديد (إذا كانت لديك نتائج قديمة تريد تجربتها)",
+  "Make K-diffusion samplers produce same images in a batch as when making a single image": "عدم تأثير تحزيم الصور على البذور عند استخدام أساليب خطو (k-diffusion)",
+  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "عند استخدام طلب طويل أكبر من 75 وحدة لغوية، افصل الطلب عند أخر فاصلة قبل كم كلمة",
+  "Filter NSFW content": "انتق المحتوى النظيف فقط",
+  "Stop At last layers of CLIP model": "قف عند آخر كم طبقة لنموذج CLIP",
+  "Allowed categories for random artists selection when using the Roll button": "اختر اهتمامات الفنانين المسموح بإضافتهم للطلب",
   "Interrogate Options": "خيارات الاستجواب",
-  "Interrogate: keep models in VRAM": "الاستجواب: احتفظ بالنماذج في VRAM",
-  "Interrogate: use artists from artists.csv": "الاستجواب: استخدم  قائمة الفنانين من ملف artists.csv",
-  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "الاستجواب: قم بتضمين رتب علامات النموذج في النتائج (ليس له تأثير على الاستجواب المستند إلى التعليقات).",
-  "Interrogate: num_beams for BLIP": "الاستجواب: عدد الحزم لـ BLIP",
-  "Interrogate: minimum description length (excluding artists, etc..)": "الاستجواب: الحد الأدنى لطول الوصف (باستثناء الفنانين ،إلخ..)",
-  "Interrogate: maximum description length": "الاستجواب: الحد الأقصى لطول الوصف",
-  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: أقصى عدد من الأسطر في الملف النصي (0 = بلا حدود)",
-  "Interrogate: deepbooru score threshold": "الاستجواب: عتبة درجات deepbooru",
-  "Interrogate: deepbooru sort alphabetically": "الاستجواب: الترتيب الأبجدي لـ deepbooru",
-  "use spaces for tags in deepbooru": "استخدام مسافات للعلامات في deepbooru",
+  "Interrogate: keep models in VRAM": "ابق نموذج الاستجواب في ذاكرة كرت الشاشة (VRAM)",
+  "Interrogate: use artists from artists.csv": "استخدم قائمة الفنانين في الاستجواب من ملف artists.csv",
+  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "أدرج رتب الشعارات عند الاستجواب (لا تعمل مع جميع النماذج)",
+  "Interrogate: num_beams for BLIP": "عدد أشعة الاستجواب لنموذج BLIP",
+  "Interrogate: minimum description length (excluding artists, etc..)": "أقل عدد كلمات للتوصيف (لا يتضمن الفنانين وغيرهم)",
+  "Interrogate: maximum description length": "أكثر عدد كلمات للتوصيف",
+  "CLIP: maximum number of lines in text file (0 = No limit)": "أكثر عدد أسطر لملف نصي عند الاستجواب باستخدام CLIP (0 = بدون حد)",
+  "Interrogate: deepbooru score threshold": "حد درجة الاستجواب باستخدام deepbooru",
+  "Interrogate: deepbooru sort alphabetically": "رتب نتائج توصيف deepbooru أبجديا",
+  "use spaces for tags in deepbooru": "افصل شعارات deepbooru بمسافات",
   "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "تجاهل الأقواس عند استخدام deepbooru (كي لا تعامل كأقواس التشديد)",
   "User interface": "واجهة المستخدم",
-  "Show progressbar": "إظهار شريط التقدم",
-  "Show image creation progress every N sampling steps. Set 0 to disable.": "إظهار تقدم إنشاء الصورة لكل N خطوة من خطوات أخذ العينات. قم بتعيين 0 للتعطيل.",
-  "Show previews of all images generated in a batch as a grid": "إظهار معاينة لجميع الصور التي يتم إنشائها في الدفعة كإطار شبكي",
-  "Show grid in results for web": "إظهار الإطار الشبكي في نتائج الويب",
-  "Do not show any images in results for web": "لا تظهر أي صور في نتائج الويب",
-  "Add model hash to generation information": "أضف معلومات الهاش للنموذج إلى معلومات الإنشاء",
-  "Add model name to generation information": "أضف اسم النموذج إلى معلومات الإنشاء",
-  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "عند قراءة متغيرات الإنشاء من النص إلى واجهة المستخدم (من معلومات PNG أو النص الملصق) ، لا تقم بتغيير النموذج",
-  "Font for image grids that have text": "الخط المستخدم للإطارات الشبكية للصور التي تحتوي على نص",
-  "Enable full page image viewer": "تفعيل عارض الصور ذو الصفحة الكاملة",
-  "Show images zoomed in by default in full page image viewer": "إظهار الصور التي تم تكبيرها بشكل افتراضي في عارض الصور ذو الصفحة الكاملة",
-  "Show generation progress in window title.": "إظهار تقدم الإنشاء في عنوان الشاشة.",
+  "Show progressbar": "اظهر شريط التقدم",
+  "Show image creation progress every N sampling steps. Set 0 to disable.": "اعرض صورة بعد كل كم خطوة (لا تعرض إذا كان 0)",
+  "Show previews of all images generated in a batch as a grid": "اعرض كل الصور التي تم إنتاجها في حزمة كجدول",
+  "Show grid in results for web": "أظهر نتائج جداول الصور",
+  "Do not show any images in results for web": "لا تظهر نتائج الصور",
+  "Add model hash to generation information": "أضف رمز تهشير (Hash) ملف الأوزان لعوامل الإنتاج",
+  "Add model name to generation information": "أضف اسم ملف الأوزان لعوامل الإنتاج",
+  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "لا تغير الأوزان المختارة عند قراءة عوامل الإنتاج من صورة أو من ملف",
+  "Font for image grids that have text": "نوع الخط في جداول الصور التي تحتوي على نصوص",
+  "Enable full page image viewer": "اسمح بعرض الصور في وضع ملئ الشاشة",
+  "Show images zoomed in by default in full page image viewer": "اعرض الصور مقربة عند استخدام وضع ملئ الشاشة",
+  "Show generation progress in window title.": "أظهر التقدم في عنوان النافذة",
   "Quicksettings list": "قائمة الإعدادات السريعة",
-  "Localization (requires restart)": "الترجمة (يتطلب إعادة التشغيل)",
-  "Sampler parameters": "متغيرات أنظمة اخذ العينات",
-  "Hide samplers in user interface (requires restart)": "إخفاء أنظمة أخذ العينات في واجهة المستخدم (يتطلب إعادة التشغيل)",
-  "eta (noise multiplier) for DDIM": "الوقت المتبقي (مضاعف الضوضاء) لـ DDIM",
-  "eta (noise multiplier) for ancestral samplers": "الوقت المتبقي (مضاعف الضوضاء) لأنظمة أخذ العينات التي تعتمد على الأجداد",
-  "img2img DDIM discretize": "تفصل DDIM لصورة لصورة",
-  "uniform": "التوحيد",
-  "quad": "رباعية",
-  "sigma churn": "دفع سيجما",
-  "sigma tmin": "سيجما tmin",
-  "sigma noise": "ضجيج سيجما",
-  "Eta noise seed delta": "الوقت المتوقع لديلتا بذرة الضجيج ",
-  "Images Browser": "مستعرض الصور",
-  "Preload images at startup": "تحميل الصور مسبقًا عند بدء التشغيل",
-  "Number of columns on the page": "Number of columns on the page",
-  "Number of rows on the page": "Number of rows on the page",
-  "Minimum number of pages per load": "الحد الأدنى لعدد الصفحات لكل تحميل",
-  "Request browser notifications": "طلب إشعارات المتصفح",
-  "Download localization template": "تنزيل نموذج الترجمة",
-  "Reload custom script bodies (No ui updates, No restart)": "إعادة تحميل السكريبتات المخصصة (لا توجد تحديثات لواجهة المستخدم ، لا إعادة تشغيل)",
-  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعادة تشغيل  Gradio و تحديث مكونات واجهة الاستخدام (السكريبتات و ui.py و js و css فقط)",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "الموجه (اضغط على Ctrl + Enter أو Alt + Enter للإنشاء)",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "الموجه السلبي (اضغط على Ctrl + Enter أو Alt + Enter للإنشاء)",
-  "Add a random artist to the prompt.": "أضف فنانًا عشوائيًا إلى الموجه.",
-  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "قراءة متغيرات الإنشاء من الموجه أو الإنشاء الأخير إذا كان الموجه فارغا في واجهة المستخدم.",
-  "Save style": "حفظ النمط",
-  "Apply selected styles to current prompt": "تطبيق الأنماط المحددة للموجه الحالي",
-  "Stop processing current image and continue processing.": "توقف عن معالجة الصورة الحالية واستمر في المعالجة.",
-  "Stop processing images and return any results accumulated so far.": "توقف عن معالجة الصور وقم بإرجاع أي نتائج متراكمة حتى الآن.",
-  "Style to apply; styles have components for both positive and negative prompts and apply to both": "النمط المطلوب تطبيقه ؛ الأنماط لها مكونات لكل من الموجهات الإيجابية والسلبية وتنطبق على كليهما",
-  "Do not do anything special": "لا تفعل أي شيء خاص",
-  "Which algorithm to use to produce the image": "الخوارزمية التي يجب استخدامها لإنتاج الصورة",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - مبدع جدًا ، يمكن لكل منها الحصول على صورة مختلفة تمامًا اعتمادًا على عدد الخطوات ، تعيين الخطوات على أعلى من 30-40 لا يعطي نتيجة مفيدة",
-  "Denoising Diffusion Implicit Models - best at inpainting": "نماذج تقليل التشويش الضمني  - أفضل اسخدام في الرسم الجزئي",
-  "Produce an image that can be tiled.": "أنتج صورة يمكن تبليطها.",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "استخدم عملية من خطوتين لإنشاء صورة جزئيًا بدقة أقل ثم ترقيتها و تحسين تفاصيلها دون تغيير التكوين",
-  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "يحدد مدى ضآلة احترام الخوارزمية لمحتوى الصورة. عند 0 ، لن يتغير شيء ، وعند 1 ستحصل على صورة لا صلة لها بالصورة الأصلية. مع وجود قيم أقل من 1.0 ، المعالجة ستأخذ خطوات أقل مما يحدده شريط تمرير خطوات المعالجة.",
-  "How many batches of images to create": "كم عدد دفعات الصور التي يتم إنشائها",
-  "How many image to create in a single batch": "كم صورة يتم إنشائها دفعة واحدة",
-  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "مقياس التوجيه الحر للمصنف - إلى أي مدى يجب أن تتوافق الصورة مع الموجه - القيم المنخفضة تنتج نتائج أكثر إبداعًا",
-  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "القيمة التي تحدد ناتج منشئ الأرقام العشوائية - إذا قمت بإنشاء صورة بنفس المتغيرات و بذرة الصورة ، فستحصل على نفس النتيجة",
-  "Set seed to -1, which will cause a new random number to be used every time": "عيّن البذرة إلى -1 ، مما سيؤدي إلى استخدام رقم عشوائي جديد في كل مرة",
-  "Reuse seed from last generation, mostly useful if it was randomed": "إعادة استخدام البذرة من الإنشاء الأخير ، وتكون مفيدة في الغالب إذا كانت عشوائية",
-  "Seed of a different picture to be mixed into the generation.": "استخدام بذرة من صورة مختلفة ليتم مزجها في الإنشاء.",
-  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "ما مدى قوة التباين عند الإنشاء. 0 ، لن يكون هناك أي تأثير. 1 ، ستحصل على الصورة الكاملة ببذور التباين (باستثناء أنظمة عينات الأسلاف ، حيث ستحصل على شيء ما فقط).",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "قم بمحاولة لإنتاج صورة مشابهة لما تم إنتاجه بنفس البذرة بناءا على دقة محددة",
-  "This text is used to rotate the feature space of the imgs embs": "This text is used to rotate the feature space of the imgs embs",
-  "Separate values for X axis using commas.": "افصل بين قيم المحور X باستخدام الفواصل.",
-  "Separate values for Y axis using commas.": "افصل بين قيم المحور Y باستخدام الفواصل.",
-  "Write image to a directory (default - log/images) and generation parameters into csv file.": "اكتب الصورة إلى مجلد (الافتراضي -  log/images) وتوليد المتغيرات في ملف csv.",
-  "Open images output directory": "افتح مجلد مخرجات الصور",
-  "How much to blur the mask before processing, in pixels.": "كم يتم تضبيب القناع قبل المعالجة ، بالبكسل.",
-  "What to put inside the masked area before processing it with Stable Diffusion.": "ما يجب وضعه داخل المنطقة المقنعة قبل معالجتها باستخدام الانتشار المستقر.",
-  "fill it with colors of the image": "املأها بألوان الصورة",
-  "keep whatever was there originally": "احتفظ بكل ما كان هناك في الأصل",
-  "fill it with latent space noise": "املأه بضوضاء الفضاء الكامنة",
-  "fill it with latent space zeroes": "املأه بأصفار الفضاء الكامنة",
-  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "قم بترقية المنطقة المقنعة إلى الدقة المستهدفة و الرسم الجزئي ثم تقليص الحجم  واللصق في الصورة الأصلية",
-  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "تغيير حجم الصورة إلى الدقة المستهدفة. ما لم يتطابق الطول والعرض ، ستحصل على نسبة عرض إلى ارتفاع غير صحيحة.",
-  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "قم بتغيير حجم الصورة بحيث يتم ملء الدقة المستهدفة بالكامل بالصورة. يتم قطع المناطق التي تبرز.",
-  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "قم بتغيير حجم الصورة بحيث تكون الصورة بأكملها داخل الدقة المستهدفة. املأ المساحة الفارغة بألوان الصورة.",
-  "How many times to repeat processing an image and using it as input for the next iteration": "كم مرة يتم تكرار معالجة صورة واستخدامها  كمدخل للتكرار التالي",
-  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "في وضع حلقة الاسترجاع ، يتم ضرب قوة تقليل الضوضاء بهذه القيمة في كل حلقة. يعني <1 تقليل التنوع بحيث يتقارب التسلسل الخاص بك على صورة ثابتة. > 1 يعني زيادة التنوع بحيث يصبح تسلسلك أكثر وأكثر فوضوية.",
-  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "الترقية باستخدام الانتشار المستقر ، كم يجب أن يكون مقدار التداخل بالبكسل بين المربعات. تتداخل المربعات بحيث لا يكون هناك خط واضح للعيان عند دمجها مرة أخرى في صورة واحدة.",
-  "A directory on the same machine where the server is running.": "مجلد على نفس الجهاز حيث يتم تشغيل الخادم.",
-  "Leave blank to save images to the default path.": "اتركه فارغًا لحفظ الصور في المسار الافتراضي.",
-  "Result = A * (1 - M) + B * M": "النتيجة = A * (1 - M) + B * M",
-  "Result = A + (B - C) * M": "النتيجة = A + (B - C) * M",
-  "1st and last digit must be 1. ex:'1, 2, 1'": "يجب أن يكون الرقم الأول والأخير 1. على سبيل المثال: '1 ، 2 ، 1'",
-  "Path to directory with input images": "المسار إلى مجلد الصور المدخلة",
-  "Path to directory where to write outputs": "المسار إلى مجلد كتابة النتائج",
-  "Input images directory": "مجلد الصور المدخلة",
-  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم العلامات التالية لتعريف كيفية اختيار أسماء الملفات للصور: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] ؛ اتركه فارغا للوضع الافتراضي.",
-  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "إذا تم تمكين هذا الخيار ، فلن تتم إضافة العلامة المائية إلى الصور التي تم إنشاؤها. تحذير: إذا لم تقم بإضافة علامة مائية ، فقد تكون تتصرف بطريقة غير أخلاقية.",
-  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم العلامات التالية لتحديد كيفية اختيار المجادت الفرعية للصور و الإطارات الشبكية : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] ؛ اتركه فارغا للوضع الافتراضي.",
-  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "سيتم استخدام هذا التعبير العادي لاستخراج الكلمات من اسم الملف ، وسيتم ضمها باستخدام الخيار أدناه في نص التسمية المستخدم للتدريب. اتركه فارغًا للاحتفاظ بنص اسم الملف كما هو.",
-  "This string will be used to join split words into a single line if the option above is enabled.": "سيتم استخدام هذا النص لضم الكلمات المقسمة إلى سطر واحد إذا تم تمكين الخيار أعلاه.",
-  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "قائمة أسماء الإعدادات ، مفصولة بفواصل ، للإعدادات التي يجب أن تنتقل إلى شريط الوصول السريع في الأعلى ، بدلاً من علامة تبويب الإعداد المعتادة. انظر modules / shared.py للتعرف على أسماء الإعدادات. يتطلب إعادة التشغيل للتطبيق.",
-  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "إذا كانت هذه القيم غير صفرية ، فستتم إضافتها إلى البذرة واستخدامها لتهيئة فوضى الضوضاء عند استخدام أنظمة أخذ العينات باستخدام الوقت المتبقي. يمكنك استخدام ذلك لإنتاج المزيد من التنوعات في الصور ، أو يمكنك استخدامه لمطابقة صور البرامج الأخرى إذا كنت تعرف ما تفعله."
+  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "قائمة مقسمة بفواصل لأسماء الإعدادات التي يجب أن تظهر في الأعلى لتسهيل الوصول إليها، انظر إلى modules/shared.py لمعرفة الأسماء، يتطلب إعادة تشغيل",
+  "Localization (requires restart)": "اللغة/language (يتطلب إعادة تشغيل/requires restart)",
+  "ar_AR": "العربية",
+  "Sampler parameters": "عوامل أساليب الخطو",
+  "Hide samplers in user interface (requires restart)": "اخف أساليب الخطو التالية (يتطلب إعادة تشغيل)",
+  "eta (noise multiplier) for DDIM": "العامل Eta η لأسلوب الخطو DDIM",
+  "eta (noise multiplier) for ancestral samplers": "العامل Eta η لأساليب الخطو السلفية (Ancestral)",
+  "img2img DDIM discretize": "طريقة التقطيع (Discretization) لأسلوب الخطو DDIM في وضع صورة إلى صورة",
+  "uniform": "خطية",
+  "quad": "تربيعية",
+  "sigma churn": "العشوائية (Schurn)",
+  "sigma tmin": "أدنى تشويش (Stmin)",
+  "sigma noise": "التشويش (Snoise)",
+  "Eta noise seed delta": "إزاحة بذرة أساليب الخطو التي تستعمل العامل Eta η",
+  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "هذا الرقم سيتم إضافته إلى البذرة عند استخدام أحد أساليب الخطو التي تستعمل العامل Eta η، يفيد هذا الخيار في مشابهة نتائج بعض البرامج الأخرى التي تستعمله",
+  "Images Browser": "معرض الصور",
+  "Preload images at startup": "حمل الصور عند بدء التشغيل",
+  "Number of columns on the page": "عدد الأعمدة في كل صفحة",
+  "Number of rows on the page": "عدد الصفوف في كل صفحة",
+  "Minimum number of pages per load": "أقل عدد صور يتم تحميلها كل مرة",
+  "Request browser notifications": "اطلب تنبيهات المتصفح",
+  "Download localization template": "حمل ملف اللغة",
+  "Reload custom script bodies (No ui updates, No restart)": "أعد تحميل الأدوات (بدون واجهة المستخدم ولا يحتاج إعادة تشغيل)",
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعد تحميل مكونات gradio (الأدوات الخاصة وواجهة المستخدم فقط)"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From 54f0c1482427a5b3f2248b97be55878e742cbcb1 Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Tue, 25 Oct 2022 16:14:13 -0700
Subject: download better face detection module dynamically

---
 modules/textual_inversion/autocrop.py   | 20 ++++++++++++++++++++
 modules/textual_inversion/preprocess.py | 13 +++++++++++--
 requirements.txt                        |  2 ++
 3 files changed, 33 insertions(+), 2 deletions(-)

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index caaf18c8..01a92b12 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -1,4 +1,5 @@
 import cv2
+import requests
 import os
 from collections import defaultdict
 from math import log, sqrt
@@ -293,6 +294,25 @@ def is_square(w, h):
   return w == h
 
 
+def download_and_cache_models(dirname):
+  download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
+  model_file_name = 'face_detection_yunet.onnx'
+
+  if not os.path.exists(dirname):
+    os.makedirs(dirname)
+
+  cache_file = os.path.join(dirname, model_file_name)
+  if not os.path.exists(cache_file):
+    print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
+    response = requests.get(download_url)
+    with open(cache_file, "wb") as f:
+      f.write(response.content)
+
+  if os.path.exists(cache_file):
+    return cache_file
+  return None
+
+
 class PointOfInterest:
   def __init__(self, x, y, weight=1.0, size=10):
     self.x = x
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 1e4d4de8..e13b1894 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -7,6 +7,7 @@ import tqdm
 import time
 
 from modules import shared, images
+from modules.paths import models_path
 from modules.shared import opts, cmd_opts
 from modules.textual_inversion import autocrop
 if cmd_opts.deepdanbooru:
@@ -146,14 +147,22 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
                 save_pic(splitted, index, existing_caption=existing_caption)
             process_default_resize = False
 
-        if process_entropy_focus and img.height != img.width:
+        if process_focal_crop and img.height != img.width:
+
+            dnn_model_path = None
+            try:
+                dnn_model_path = autocrop.download_and_cache_models(os.path.join(models_path, "opencv"))
+            except Exception as e:
+                print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)
+
             autocrop_settings = autocrop.Settings(
                 crop_width = width,
                 crop_height = height,
                 face_points_weight = process_focal_crop_face_weight,
                 entropy_points_weight = process_focal_crop_entropy_weight,
                 corner_points_weight = process_focal_crop_edges_weight,
-                annotate_image = process_focal_crop_debug
+                annotate_image = process_focal_crop_debug,
+                dnn_model_path = dnn_model_path,
             )
             for focal in autocrop.crop_image(img, autocrop_settings):
                 save_pic(focal, index, existing_caption=existing_caption)
diff --git a/requirements.txt b/requirements.txt
index da1969cf..75b37c4f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,6 +8,8 @@ gradio==3.5
 invisible-watermark
 numpy
 omegaconf
+opencv-python
+requests
 piexif
 Pillow
 pytorch_lightning
-- 
cgit v1.2.1


From df0c5ea29d7f0c682ac81f184f3e482a6450d018 Mon Sep 17 00:00:00 2001
From: captin411 <captindave@gmail.com>
Date: Tue, 25 Oct 2022 17:06:59 -0700
Subject: update default weights

---
 modules/textual_inversion/autocrop.py | 16 ++++++++--------
 modules/ui.py                         |  2 +-
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 01a92b12..9859974a 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -71,9 +71,9 @@ def crop_image(im, settings):
   return results
 
 def focal_point(im, settings):
-    corner_points = image_corner_points(im, settings)
-    entropy_points = image_entropy_points(im, settings)
-    face_points = image_face_points(im, settings)
+    corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
+    entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
+    face_points = image_face_points(im, settings) if settings.face_points_weight > 0 else []
 
     pois = []
 
@@ -144,7 +144,7 @@ def image_face_points(im, settings):
           settings.dnn_model_path,
           "",
           (im.width, im.height),
-          0.8, # score threshold
+          0.9, # score threshold
           0.3, # nms threshold
           5000 # keep top k before nms
       )
@@ -159,7 +159,7 @@ def image_face_points(im, settings):
           results.append(
             PointOfInterest(
               int(x + (w * 0.5)), # face focus left/right is center
-              int(y + (h * 0)), # face focus up/down is close to the top of the head
+              int(y + (h * 0.33)), # face focus up/down is close to the top of the head
               size = w,
               weight = 1/len(faces[1])
             )
@@ -207,7 +207,7 @@ def image_corner_points(im, settings):
         np_im,
         maxCorners=100,
         qualityLevel=0.04,
-        minDistance=min(grayscale.width, grayscale.height)*0.03,
+        minDistance=min(grayscale.width, grayscale.height)*0.06,
         useHarrisDetector=False,
     )
 
@@ -256,8 +256,8 @@ def image_entropy_points(im, settings):
 
 def image_entropy(im):
     # greyscale image entropy
-    band = np.asarray(im.convert("L"))
-    # band = np.asarray(im.convert("1"), dtype=np.uint8)
+    # band = np.asarray(im.convert("L"))
+    band = np.asarray(im.convert("1"), dtype=np.uint8)
     hist, _ = np.histogram(band, bins=range(0, 256))
     hist = hist[hist > 0]
     return -np.log2(hist / hist.sum()).sum()
diff --git a/modules/ui.py b/modules/ui.py
index 95b9c703..095200a8 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1270,7 +1270,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                     with gr.Row(visible=False) as process_focal_crop_row:
                         process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.3, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
                         process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
                         process_focal_crop_debug = gr.Checkbox(label='Create debug image')
 
-- 
cgit v1.2.1


From 2f4c91894d4c0a055c1069b2fda0e4da8fcda188 Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Wed, 26 Oct 2022 12:10:30 +0800
Subject: Remove activation from final layer of HNs

---
 modules/hypernetworks/hypernetwork.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index d647ea55..54346b64 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -41,8 +41,8 @@ class HypernetworkModule(torch.nn.Module):
             # Add a fully-connected layer
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
 
-            # Add an activation func
-            if activation_func == "linear" or activation_func is None:
+            # Add an activation func except last layer
+            if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 3:
                 pass
             elif activation_func in self.activation_dict:
                 linears.append(self.activation_dict[activation_func]())
@@ -53,7 +53,7 @@ class HypernetworkModule(torch.nn.Module):
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
-            # Add dropout expect last layer
+            # Add dropout except last layer
             if use_dropout and i < len(layer_structure) - 3:
                 linears.append(torch.nn.Dropout(p=0.3))
 
-- 
cgit v1.2.1


From c702d4d0df21790199d199818f25c449213ffe0f Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Wed, 26 Oct 2022 13:43:04 +0800
Subject: Fix off-by-one

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 54346b64..3ce85bb5 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -42,7 +42,7 @@ class HypernetworkModule(torch.nn.Module):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
 
             # Add an activation func except last layer
-            if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 3:
+            if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 2:
                 pass
             elif activation_func in self.activation_dict:
                 linears.append(self.activation_dict[activation_func]())
@@ -54,7 +54,7 @@ class HypernetworkModule(torch.nn.Module):
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
             # Add dropout except last layer
-            if use_dropout and i < len(layer_structure) - 3:
+            if use_dropout and i < len(layer_structure) - 2:
                 linears.append(torch.nn.Dropout(p=0.3))
 
         self.linear = torch.nn.Sequential(*linears)
-- 
cgit v1.2.1


From de096d0ce752c96e45508dcc7b9e84f7dbe10cca Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Tue, 25 Oct 2022 14:48:49 +0900
Subject: Weight initialization and More activation func

add weight init

add weight init option in create_hypernetwork

fstringify hypernet info

save weight initialization info for further debugging

fill bias with zero for He/Xavier

initialize LayerNorm with Normal

fix loading weight_init
---
 modules/hypernetworks/hypernetwork.py | 47 ++++++++++++++++++++++++++++-------
 modules/hypernetworks/ui.py           |  4 ++-
 modules/ui.py                         |  4 ++-
 3 files changed, 44 insertions(+), 11 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index d647ea55..afbcdff8 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -5,6 +5,7 @@ import html
 import os
 import sys
 import traceback
+import inspect
 
 import modules.textual_inversion.dataset
 import torch
@@ -15,10 +16,12 @@ from modules import devices, processing, sd_models, shared
 from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
+from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
 
 from collections import defaultdict, deque
 from statistics import stdev, mean
 
+
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
     activation_dict = {
@@ -26,9 +29,12 @@ class HypernetworkModule(torch.nn.Module):
         "leakyrelu": torch.nn.LeakyReLU,
         "elu": torch.nn.ELU,
         "swish": torch.nn.Hardswish,
+        "tanh": torch.nn.Tanh,
+        "sigmoid": torch.nn.Sigmoid,
     }
+    activation_dict.update({cls_name: cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -65,9 +71,24 @@ class HypernetworkModule(torch.nn.Module):
         else:
             for layer in self.linear:
                 if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
-                    layer.weight.data.normal_(mean=0.0, std=0.01)
-                    layer.bias.data.zero_()
-
+                    w, b = layer.weight.data, layer.bias.data
+                    if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm:
+                        normal_(w, mean=0.0, std=0.01)
+                        normal_(b, mean=0.0, std=0.005)
+                    elif weight_init == 'XavierUniform':
+                        xavier_uniform_(w)
+                        zeros_(b)
+                    elif weight_init == 'XavierNormal':
+                        xavier_normal_(w)
+                        zeros_(b)
+                    elif weight_init == 'KaimingUniform':
+                        kaiming_uniform_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')
+                        zeros_(b)
+                    elif weight_init == 'KaimingNormal':
+                        kaiming_normal_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')
+                        zeros_(b)
+                    else:
+                        raise KeyError(f"Key {weight_init} is not defined as initialization!")
         self.to(devices.device)
 
     def fix_old_state_dict(self, state_dict):
@@ -105,7 +126,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -114,13 +135,14 @@ class Hypernetwork:
         self.sd_checkpoint_name = None
         self.layer_structure = layer_structure
         self.activation_func = activation_func
+        self.weight_init = weight_init
         self.add_layer_norm = add_layer_norm
         self.use_dropout = use_dropout
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
             )
 
     def weights(self):
@@ -144,6 +166,7 @@ class Hypernetwork:
         state_dict['layer_structure'] = self.layer_structure
         state_dict['activation_func'] = self.activation_func
         state_dict['is_layer_norm'] = self.add_layer_norm
+        state_dict['weight_initialization'] = self.weight_init
         state_dict['use_dropout'] = self.use_dropout
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
@@ -158,15 +181,21 @@ class Hypernetwork:
         state_dict = torch.load(filename, map_location='cpu')
 
         self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
+        print(self.layer_structure)
         self.activation_func = state_dict.get('activation_func', None)
+        print(f"Activation function is {self.activation_func}")
+        self.weight_init = state_dict.get('weight_initialization', 'Normal')
+        print(f"Weight initialization is {self.weight_init}")
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
+        print(f"Layer norm is set to {self.add_layer_norm}")
         self.use_dropout = state_dict.get('use_dropout', False)
+        print(f"Dropout usage is set to {self.use_dropout}" )
 
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
                 )
 
         self.name = state_dict.get('name', self.name)
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 2b472d87..2c6c0470 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -8,8 +8,9 @@ import modules.textual_inversion.textual_inversion
 from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
+keys = list(hypernetwork.HypernetworkModule.activation_dict.keys())
 
-def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
+def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
     # Remove illegal characters from name.
     name = "".join( x for x in name if (x.isalnum() or x in "._- "))
 
@@ -25,6 +26,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
         enable_sizes=[int(x) for x in enable_sizes],
         layer_structure=layer_structure,
         activation_func=activation_func,
+        weight_init=weight_init,
         add_layer_norm=add_layer_norm,
         use_dropout=use_dropout,
     )
diff --git a/modules/ui.py b/modules/ui.py
index 03528968..8e343258 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1238,7 +1238,8 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu", "elu", "swish"])
+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
                     overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
@@ -1342,6 +1343,7 @@ def create_ui(wrap_gradio_gpu_call):
                 overwrite_old_hypernetwork,
                 new_hypernetwork_layer_structure,
                 new_hypernetwork_activation_func,
+                new_hypernetwork_initialization_option,
                 new_hypernetwork_add_layer_norm,
                 new_hypernetwork_use_dropout
             ],
-- 
cgit v1.2.1


From 7207e3bf49ed000464d288cd67e02f0ba8614dc3 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Tue, 25 Oct 2022 15:24:59 +0900
Subject: remove duplicate keys and lowercase

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index afbcdff8..842b6447 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -32,7 +32,7 @@ class HypernetworkModule(torch.nn.Module):
         "tanh": torch.nn.Tanh,
         "sigmoid": torch.nn.Sigmoid,
     }
-    activation_dict.update({cls_name: cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
+    activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
     def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False):
         super().__init__()
-- 
cgit v1.2.1


From cbb857b675cf0f169b21515c29da492b513cc8c4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 26 Oct 2022 09:44:02 +0300
Subject: enable creating embedding with --medvram

---
 modules/textual_inversion/textual_inversion.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 529ed3e2..647ffe3e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -157,6 +157,9 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
     cond_model = shared.sd_model.cond_stage_model
     embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
 
+    with devices.autocast():
+        cond_model([""])  # will send cond model to GPU if lowvram/medvram is active
+
     ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
     embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
     vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
-- 
cgit v1.2.1


From db9ab1a46b5ad4d36ecce76dfee04b7164249829 Mon Sep 17 00:00:00 2001
From: Stephen <farrenzo47@gmail.com>
Date: Mon, 24 Oct 2022 11:16:07 -0400
Subject: [Bugfix][API] - Fix API response for colab users

---
 modules/api/api.py    | 17 +++++++++++++----
 modules/api/models.py | 10 ++++++----
 2 files changed, 19 insertions(+), 8 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index a860a964..ba890243 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -7,6 +7,7 @@ import uvicorn
 from fastapi import Body, APIRouter, HTTPException
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field, Json
+from typing import List
 import json
 import io
 import base64
@@ -15,12 +16,12 @@ from PIL import Image
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
 class TextToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: Json
     info: Json
 
 class ImageToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: Json
     info: Json
 
@@ -41,6 +42,9 @@ class Api:
         # convert base64 to PIL image
         return Image.open(io.BytesIO(imgdata))
 
+    def __processed_info_to_json(self, processed):
+        return json.dumps(processed.info)
+
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
         
@@ -65,7 +69,7 @@ class Api:
             i.save(buffer, format="png")
             b64images.append(base64.b64encode(buffer.getvalue()))
 
-        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
+        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.js())
         
         
 
@@ -111,7 +115,12 @@ class Api:
             i.save(buffer, format="png")
             b64images.append(base64.b64encode(buffer.getvalue()))
 
-        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info))
+        if (not img2imgreq.include_init_images):
+            # remove img2imgreq.init_images and img2imgreq.mask
+            img2imgreq.init_images = None
+            img2imgreq.mask = None
+
+        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js())
 
     def extrasapi(self):
         raise NotImplementedError
diff --git a/modules/api/models.py b/modules/api/models.py
index f551fa35..c6d43606 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -31,6 +31,7 @@ class ModelDef(BaseModel):
     field_alias: str
     field_type: Any
     field_value: Any
+    field_exclude: bool = False
 
 
 class PydanticModelGenerator:
@@ -68,7 +69,7 @@ class PydanticModelGenerator:
                 field=underscore(k),
                 field_alias=k,
                 field_type=field_type_generator(k, v),
-                field_value=v.default
+                field_value=v.default,
             )
             for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
         ]
@@ -78,7 +79,8 @@ class PydanticModelGenerator:
                 field=underscore(fields["key"]), 
                 field_alias=fields["key"], 
                 field_type=fields["type"],
-                field_value=fields["default"]))
+                field_value=fields["default"],
+                field_exclude=fields["exclude"] if "exclude" in fields else False))
 
     def generate_model(self):
         """
@@ -86,7 +88,7 @@ class PydanticModelGenerator:
         from the json and overrides provided at initialization
         """
         fields = {
-            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
+            d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def
         }
         DynamicModel = create_model(self._model_name, **fields)
         DynamicModel.__config__.allow_population_by_field_name = True
@@ -102,5 +104,5 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
 StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingImg2Img", 
     StableDiffusionProcessingImg2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}]
 ).generate_model()
\ No newline at end of file
-- 
cgit v1.2.1


From b46c64c6e5b40d69521e4d50e2d35f6a35468129 Mon Sep 17 00:00:00 2001
From: Stephen <farrenzo47@gmail.com>
Date: Mon, 24 Oct 2022 12:18:54 -0400
Subject: clean

---
 modules/api/api.py    | 4 ----
 modules/api/models.py | 2 +-
 2 files changed, 1 insertion(+), 5 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index ba890243..6e9d6097 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -42,9 +42,6 @@ class Api:
         # convert base64 to PIL image
         return Image.open(io.BytesIO(imgdata))
 
-    def __processed_info_to_json(self, processed):
-        return json.dumps(processed.info)
-
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
         
@@ -116,7 +113,6 @@ class Api:
             b64images.append(base64.b64encode(buffer.getvalue()))
 
         if (not img2imgreq.include_init_images):
-            # remove img2imgreq.init_images and img2imgreq.mask
             img2imgreq.init_images = None
             img2imgreq.mask = None
 
diff --git a/modules/api/models.py b/modules/api/models.py
index c6d43606..079e33d9 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -69,7 +69,7 @@ class PydanticModelGenerator:
                 field=underscore(k),
                 field_alias=k,
                 field_type=field_type_generator(k, v),
-                field_value=v.default,
+                field_value=v.default
             )
             for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
         ]
-- 
cgit v1.2.1


From e86e2f7181e3e4acdcd21fa949677067a4040ba0 Mon Sep 17 00:00:00 2001
From: tumbly <tumbly@example.com>
Date: Tue, 25 Oct 2022 16:58:53 +0200
Subject: Add French localization

---
 localizations/fr-FR.json | 415 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 415 insertions(+)
 create mode 100644 localizations/fr-FR.json

diff --git a/localizations/fr-FR.json b/localizations/fr-FR.json
new file mode 100644
index 00000000..81c65d5e
--- /dev/null
+++ b/localizations/fr-FR.json
@@ -0,0 +1,415 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "Chargement...",
+    "view": "查看",
+    "api": "api",
+    "•": "•",
+    "built with gradio": "Construit avec Gradio",
+    "Stable Diffusion checkpoint": "checkpoint Stable Diffusion",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "Extras",
+    "PNG Info": "Infos PNG",
+    "History": "Historique",
+    "Checkpoint Merger": "Fusion de checkpoints",
+    "Train": "Entrainer",
+    "Settings": "Paramètres",
+    "Prompt": "Prompt",
+    "Negative prompt": "Prompt négatif",
+    "Run": "Lancer",
+    "Skip": "Passer",
+    "Interrupt": "Interrrompre",
+    "Generate": "Générer",
+    "Style 1": "Style 1",
+    "Style 2": "Style 2",
+    "Label": "Etiquette",
+    "File": "Fichier",
+    "Drop File Here": "Déposer votre fichier ici",
+    "-": "-",
+    "or": "ou",
+    "Click to Upload": "Cliquer pour uploader",
+    "Image": "Image",
+    "Check progress": "Voir l'avancement",
+    "Check progress (first)": "Voir l'avancement (1er)",
+    "Sampling Steps": "Etapes d'échantillonnage",
+    "Sampling method": "Méthode d'échantillonnage",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Largeur",
+    "Height": "Hauteur",
+    "Restore faces": "Restaurer les visages",
+    "Tiling": "Mode Tuile",
+    "Highres. fix": "Correction haute résolution",
+    "Firstpass width": "Largeur première passe",
+    "Firstpass height": "Hauteur seconde passe",
+    "Denoising strength": "Puissance de réduction du bruit",
+    "Batch count": "Nombre de lots",
+    "Batch size": "Taille de lots",
+    "CFG Scale": "Echelle CFG",
+    "Seed": "Graine",
+    "Extra": "Extra",
+    "Variation seed": "Variation de la graine",
+    "Variation strength": "Puissance de variation",
+    "Resize seed from width": "Largeur de redimensionnement de la graine",
+    "Resize seed from height": "Hauteur de redimensionnement de la graine",
+    "Script": "Script",
+    "None": "Aucun",
+    "Prompt matrix": "Matrice de prompt",
+    "Prompts from file or textbox": "Prompts depuis un fichier ou une boite de dialogue",
+    "X/Y plot": "graphe X/Y",
+    "Put variable parts at start of prompt": "Mettre les mots clés variable au début du prompt",
+    "Show Textbox": "Afficher le champs texte",
+    "File with inputs": "Fichier d'entrée",
+    "Prompts": "Prompts",
+    "X type": "Axe X",
+    "Nothing": "Rien",
+    "Var. seed": "Graine variable",
+    "Var. strength": "Puissance variable",
+    "Steps": "Etapes",
+    "Prompt S/R": "Cherche et remplace dans le prompt",
+    "Prompt order": "Ordre du prompt",
+    "Sampler": "Echantilloneur",
+    "Checkpoint name": "Nom du checkpoint",
+    "Hypernetwork": "Hypernetwork",
+    "Hypernet str.": "Force de l'Hypernetwork",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "Sigma min.",
+    "Sigma max": "Sigma max.",
+    "Sigma noise": "Bruit Sigma",
+    "Eta": "Temps estimé",
+    "Clip skip": "Passer Clip",
+    "Denoising": "Réduction du bruit",
+    "X values": "Valeur X",
+    "Y type": "Type Y",
+    "Y values": "Valeur Y",
+    "Draw legend": "Afficher la légende",
+    "Include Separate Images": "Inclure les images séparées",
+    "Keep -1 for seeds": "Conserver -1 pour la graine",
+    "Drop Image Here": "Déposer l'image ici",
+    "Save": "Enregistrer",
+    "Send to img2img": "Envoyer vers img2img",
+    "Send to inpaint": "Envoyer vers inpaint",
+    "Send to extras": "Envoyer vers extras",
+    "Make Zip when Save?": "Créer un zip lors de l'enregistrement?",
+    "Textbox": "Champ texte",
+    "Interrogate\nCLIP": "Interroger\nCLIP",
+    "Interrogate\nDeepBooru": "Interroger\nDeepBooru",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "Lot img2img",
+    "Image for img2img": "Image pour img2img",
+    "Image for inpainting with mask": "Image pour inpainting avec masque",
+    "Mask": "Masque",
+    "Mask blur": "Flou masque",
+    "Mask mode": "Mode masque",
+    "Draw mask": "Dessiner masque",
+    "Upload mask": "Uploader masque",
+    "Masking mode": "Mode de masquage",
+    "Inpaint masked": "Inpaint masqué",
+    "Inpaint not masked": "Inpaint non masqué",
+    "Masked content": "Contenu masqué",
+    "fill": "remplir",
+    "original": "original",
+    "latent noise": "bruit latent",
+    "latent nothing": "latent vide",
+    "Inpaint at full resolution": "Inpaint en pleine résolution",
+    "Inpaint at full resolution padding, pixels": "Padding de l'inpaint en pleine résolution, en pixels",
+    "Process images in a directory on the same machine where the server is running.": "Traite les images dans un dossier sur la même machine où le serveur tourne",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Utiliser un dossier de sortie vide pour enregistrer les images normalement plutôt que d'écrire dans le dossier de sortie",
+    "Input directory": "Dossier d'entrée",
+    "Output directory": "Dossier de sortie",
+    "Resize mode": "Mode redimensionnement",
+    "Just resize": "Redimensionner uniquement",
+    "Crop and resize": "Recadrer et redimensionner",
+    "Resize and fill": "Redimensionner et remplir",
+    "img2img alternative test": "Test alternatif img2img",
+    "Loopback": "Bouclage",
+    "Outpainting mk2": "Outpainting v2",
+    "Poor man's outpainting": "Outpainting du pauvre",
+    "SD upscale": "Agrandissement SD",
+    "should be 2 or lower.": "doit être inférieur ou égal à 2",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Forcer `Méthode d'échantillonnage` à Euler ? (cette méthode est dédiée à cela)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Forcer le `prompt` au contenu du `prompt d'origine` ? (de même pour le `prompt négatif`)",
+    "Original prompt": "Prompt d'origine",
+    "Original negative prompt": "Prompt négatif d'origine",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Forcer le valeur d'`Etapes d'échantillonnage` à la même valeur qu'`Etapes de décodage` ?",
+    "Decode steps": "Etapes de décodage",
+    "Override `Denoising strength` to 1?": "Forcer `Puissance de réduction du bruit` à 1 ?",
+    "Decode CFG scale": "Echelle CFG de décodage",
+    "Randomness": "Aléatoire",
+    "Sigma adjustment for finding noise for image": "Ajustement Sigma lors de la recherche du bruit dans l'image",
+    "Loops": "Boucles",
+    "Denoising strength change factor": "Facteur de changement de la puissance de réduction du bruit",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Paramètres recommandés : Etapes d'échantillonnage : 80-100, Echantillonneur : Euler a, Puissance de réduction du bruit : 0.8",
+    "Pixels to expand": "Pixels à étendre",
+    "Outpainting direction": "Direction de l'outpainting",
+    "left": "gauche",
+    "right": "droite",
+    "up": "haut",
+    "down": "bas",
+    "Fall-off exponent (lower=higher detail)": "Exposant de diminution (plus petit = plus de détails)",
+    "Color variation": "Variation de couleur",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Agrandira l'image à deux fois sa taille; utilisez les glissières largeur et hauteur afin de choisir la taille de tuile",
+    "Tile overlap": "Chevauchement de tuile",
+    "Upscaler": "Agrandisseur",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "BSRGAN 4x": "BSRGAN 4x",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR 4x": "SwinIR 4x",
+    "Single Image": "Image unique",
+    "Batch Process": "Traitement par lot",
+    "Batch from Directory": "Lot depuis un dossier",
+    "Source": "Source",
+    "Show result images": "Montrez les images résultantes",
+    "Scale by": "Mise à l'échelle de",
+    "Scale to": "Mise à l'échelle à",
+    "Resize": "Redimensionner",
+    "Crop to fit": "Recadrer à la taille",
+    "Upscaler 2": "Agrandisseur 2",
+    "Upscaler 2 visibility": "Visibilité de l'agrandisseur 2",
+    "GFPGAN visibility": "Visibilité GFPGAN",
+    "CodeFormer visibility": "Visibilité CodeFormer",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Poids CodeFormer (0 = effet maximum, 1 = effet minimum)",
+    "Open output directory": "Ouvrir le dossier de sortie",
+    "Send to txt2img": "Envoyer vers txt2img",
+    "txt2img history": "historique txt2img",
+    "img2img history": "historique img2img",
+    "extras history": "historique extras",
+    "Renew Page": "Rafraichr la page",
+    "First Page": "Première page",
+    "Prev Page": "Page précendente",
+    "Page Index": "Index des pages",
+    "Next Page": "Page suivante",
+    "End Page": "Page de fin",
+    "number of images to delete consecutively next": "nombre d'image à supprimer consécutivement ensuite",
+    "Delete": "Supprimer",
+    "Generate Info": "Générer les informations",
+    "File Name": "Nom de fichier",
+    "set_index": "set_index",
+    "A merger of the two checkpoints will be generated in your": "Une fusion des deux checkpoints sera générée dans votre",
+    "checkpoint": "checkpoint",
+    "directory.": "dossier",
+    "Primary model (A)": "Modèle primaire (A)",
+    "Secondary model (B)": "Modèle secondaire (B)",
+    "Tertiary model (C)": "Modèle tertiaire (C)",
+    "Custom Name (Optional)": "Nom personnalisé (Optionel)",
+    "Multiplier (M) - set to 0 to get model A": "Multiplieur (M) - utiliser 0 pour le modèle A",
+    "Interpolation Method": "Méthode d'interpolation",
+    "Weighted sum": "Somme pondérée",
+    "Add difference": "Ajouter différence",
+    "Save as float16": "Enregistrer en tant que float16",
+    "See": "Voir",
+    "wiki": "wiki",
+    "for detailed explanation.": "pour une explication détaillée.",
+    "Create embedding": "Créer un embedding",
+    "Create hypernetwork": "Créer un hypernetwork",
+    "Preprocess images": "Pré-traite les images",
+    "Name": "Nom",
+    "Initialization text": "Texte d'initialisation",
+    "Number of vectors per token": "Nombre de vecteurs par jeton",
+    "Modules": "Modules",
+    "Source directory": "Dossier source",
+    "Destination directory": "Dossier destination",
+    "Create flipped copies": "Créer des copies en mirroir",
+    "Split oversized images into two": "Couper les images trop grandes en deux",
+    "Use BLIP for caption": "Utiliser BLIP pour les descriptions",
+    "Use deepbooru for caption": "Utiliser deepbooru pour les descriptions",
+    "Preprocess": "Pré-traite",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Entrainer un embedding ; spécifiez un dossier contenant un ensemble d'images avec un ratio de 1:1",
+    "Embedding": "Embedding",
+    "Learning rate": "Vitesse d'apprentissage",
+    "Dataset directory": "Dossier des images d'entrée",
+    "Log directory": "Dossier de journalisation",
+    "Prompt template file": "Fichier modèle de prompts",
+    "Max steps": "Etapes max.",
+    "Save an image to log directory every N steps, 0 to disable": "Enregistrer une image dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Enregistrer une copie de l'embedding dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
+    "Save images with embedding in PNG chunks": "Sauvegarder les images incluant l'embedding dans leur blocs PNG",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Lire les paramètres (prompt, etc.) depuis l'onglet txt2img lors de la génération des previews",
+    "Train Hypernetwork": "Entrainer un Hypernetwork",
+    "Train Embedding": "Entrainer un Embedding",
+    "Apply settings": "Appliquer les paramètres",
+    "Saving images/grids": "Enregistrer les images/grilles",
+    "Always save all generated images": "Toujours enregistrer toutes les images",
+    "File format for images": "Format de fichier pour les images",
+    "Images filename pattern": "Motif pour le nom de fichier des images",
+    "Always save all generated image grids": "Toujours enregistrer toutes les grilles d'images générées",
+    "File format for grids": "Format de fichier pour les grilles",
+    "Add extended info (seed, prompt) to filename when saving grid": "Ajouter les informations étendues (graine, prompt) aux noms de fichiers lors de l'enregistrement d'une grille",
+    "Do not save grids consisting of one picture": "Ne pas enregistrer les grilles contenant une seule image",
+    "Prevent empty spots in grid (when set to autodetect)": "Eviter les vides dans la grille (quand autodétection est choisie)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Nombre de colonnes de la grille; utilisez -1 pour autodétection et 0 pour qu'il soit égal à la taille du lot",
+    "Save text information about generation parameters as chunks to png files": "Enregistrer l'information du text des paramètres de génération en tant que blocs dans les fichiers PNG",
+    "Create a text file next to every image with generation parameters.": "Créer un fichier texte contenant les paramètres de génération à côté de chaque image",
+    "Save a copy of image before doing face restoration.": "Enregistrer une copie de l'image avant de lancer la restauration de visage",
+    "Quality for saved jpeg images": "Qualité pour les images jpeg enregistrées",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Si l'image PNG est plus grande que 4MB or l'une des ses dimensions supérieure à 4000, réduire sa taille et enregistrer une copie en JPG",
+    "Use original name for output filename during batch process in extras tab": "Utiliser un nom de fichier original pour les fichiers de sortie durant le traitement par lot dans l'onglet Extras",
+    "When using 'Save' button, only save a single selected image": "A l'utilisation du bouton `Enregistrer`, n'enregistrer que l'image séléctionnée",
+    "Do not add watermark to images": "Ne pas ajouter de filigrane aux images",
+    "Paths for saving": "Chemins pour l'enregistrement",
+    "Output directory for images; if empty, defaults to three directories below": "Dossier de sortie pour les images; si non spécifié, le chemin par défaut sera trois niveau en dessous",
+    "Output directory for txt2img images": "Dossier de sortie pour les images txt2img",
+    "Output directory for img2img images": "Dossier de sortie pour les images img2img",
+    "Output directory for images from extras tab": "Dossier de sortie pour les images de l'onglet Extras",
+    "Output directory for grids; if empty, defaults to two directories below": "Dossier de sortie pour les grilles; si non spécifié, le chemin par défaut sera deux niveau en dessous",
+    "Output directory for txt2img grids": "Dossier de sortie pour les grilles txt2img",
+    "Output directory for img2img grids": "Dossier de sortie pour les grilles img2img",
+    "Directory for saving images using the Save button": "Dossier de sauvegarde des images pour le bouton `Enregistrer`",
+    "Saving to a directory": "Enregistrer dans un dossier",
+    "Save images to a subdirectory": "Enregistrer les images dans un sous dossier",
+    "Save grids to a subdirectory": "Enregistrer les grilles dans un sous dossier",
+    "When using \"Save\" button, save images to a subdirectory": "Lors de l'utilisation du bouton \"Enregistrer\", sauvegarder les images dans un sous dossier",
+    "Directory name pattern": "Motif pour le nom des dossiers",
+    "Max prompt words for [prompt_words] pattern": "Maximum de mot pour le motif [prompt_words]",
+    "Upscaling": "Agrandissement",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Taille des tuile for les agrandisseurs ESRGAN. 0 = mode tuile désactivé.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Chevauchement des tuiles, en pixel pour l'agrandisseur ESRGAN. Valeur faible = couture visible",
+    "Tile size for all SwinIR.": "Taille de la tuile pour tous les agrandisseur SwinIR.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Chevauchement de tuile, en pixels pour SwinIR. Valeur faible = couture visible",
+    "LDSR processing steps. Lower = faster": "Echantillon du traitement LDSR. Valeur faible =  plus rapide",
+    "Upscaler for img2img": "Agrandisseur pour img2img",
+    "Upscale latent space image when doing hires. fix": "Agrandir l'image de l'espace latent lors de la correction haute résolution",
+    "Face restoration": "Restauration de visage",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Paramètre de poids pour CodeFormer; 0 = effet maximum ； 1 = effet minimum",
+    "Move face restoration model from VRAM into RAM after processing": "Déplacer le modèle de restauration de visage de la VRAM vers la RAM après traitement",
+    "System": "Système",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Fréquence d'interrogation par seconde pendant la génération. Mettez la valeur à 0 pour désactiver.",
+    "Always print all generation info to standard output": "Toujours afficher toutes les informations de génération dans la sortie standard",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Ajouter un seconde barre de progression dans la console montrant l'avancement pour un tâche complète.",
+    "Training": "Entrainement",
+    "Unload VAE and CLIP from VRAM when training": "Décharger VAE et CLIP de la VRAM pendant l'entrainement",
+    "Filename word regex": "Regex de mot",
+    "Filename join string": "Chaine de caractère pour lier les noms de fichier",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Nombre de répétition pour une image unique par époque; utilisé seulement pour afficher le nombre d'époques",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Enregistrer un csv contenant la perte dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "Checkpoint à mettre en cache dans la RAM",
+    "Hypernetwork strength": "Force de l'Hypernetwork",
+    "Apply color correction to img2img results to match original colors.": "Appliquer une correction de couleur aux résultats img2img afin de conserver les couleurs d'origine",
+    "Save a copy of image before applying color correction to img2img results": "Enregistrer une copie de l'image avant d'appliquer les résultats de la correction de couleur img2img",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Avec img2img, executer exactement le nombre d'étapes spécifiées par la glissière (normalement moins d'étapes sont executées quand la réduction du bruit est plus faible).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Activer la quantisation des échantillionneurs K pour des résultats plus nets et plus propres. Cela peut modifier les graines existantes. Requiert un redémarrage pour être actif.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphase : utilisez (texte) afin de forcer le modèle à porter plus d'attention au texte et [texte] afin qu'il y porte moins attention",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Utilisez l'ancienne méthode d'emphase. Peut être utile afin de reproduire d'anciennes graines.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Demander aux échantillionneurs K-diffusion de produire les mêmes dans un lot que lors de la génération d'une image unique",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Améliorer la cohérence en remplissant (padding) à partir de la dernière virgule dans les X jetons quand on en utilise plus de 75",
+    "Filter NSFW content": "Filtrer le contenu +18 (NSFW)",
+    "Stop At last layers of CLIP model": "S'arrêter aux derniers niveaux du modèle CLIP",
+    "Interrogate Options": "Options d'intérrogation",
+    "Interrogate: keep models in VRAM": "Interroger : conserver les modèles en VRAM",
+    "Interrogate: use artists from artists.csv": "Interroger : utiliser les artistes dans artists.csv",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interroger : inclure la correspondance du classement des labels de modèle dans les résultats (N'a pas d'effet sur les interrogateurs basés sur des descriptions) ",
+    "Interrogate: num_beams for BLIP": "Interroger : num_beams pour BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Interroger : longueur minimale de la description (excluant les artistes, etc.)",
+    "Interrogate: maximum description length": "Interroger : longueur maximale de la description",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP : nombre maximum de lignes dans le fichier texte (0 = pas de limite)",
+    "Interrogate: deepbooru score threshold": "Interroger : seuil du score deepbooru",
+    "Interrogate: deepbooru sort alphabetically": "Interroger : classement alphabétique deepbooru",
+    "use spaces for tags in deepbooru": "Utiliser des espaces pour les étiquettes dans deepbooru",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "échapper (\\) les crochets dans deepbooru (afin qu'ils puissent être utilisés littéralement et non pour mettre en emphase)",
+    "User interface": "Interface utilisateur",
+    "Show progressbar": "Afficher la barre de progression",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Afficher l'état d'avancement de la création d'image toutes les X étapes d'échantillionnage. Utiliser 0 pour désactiver.",
+    "Show grid in results for web": "Afficher la grille dans les résultats web",
+    "Do not show any images in results for web": "N'afficher aucune image dans les résultats web'",
+    "Add model hash to generation information": "Ajouter le hash du modèle dans l'information de génération",
+    "Add model name to generation information": "Ajouter le nom du modèle dans l'information de génération",
+    "Font for image grids that have text": "Police pour les grilles d'images contenant du texte",
+    "Enable full page image viewer": "Activer l'affichage des images en plein écran",
+    "Show images zoomed in by default in full page image viewer": "Afficher les images zoomées par défaut lors de l'affichage en plein écran",
+    "Show generation progress in window title.": "Afficher l'avancement de la génération dans le titre de la fenêtre.",
+    "Quicksettings list": "Liste de réglages rapides",
+    "Localization (requires restart)": "Localisation (requiert un redémarrage)",
+    "Sampler parameters": "Paramètres de l'échantillionneur",
+    "Hide samplers in user interface (requires restart)": "Cacher les échantillonneurs dans l'interface utilisateur (requiert un redémarrage)",
+    "eta (noise multiplier) for DDIM": "eta (multiplicateur de bruit) pour DDIM",
+    "eta (noise multiplier) for ancestral samplers": "eta (multiplicateur de bruit) poru les échantillionneurs de type 'ancestral'",
+    "img2img DDIM discretize": "Discrétisation DDIM pour img2img",
+    "uniform": "uniforme",
+    "quad": "quad",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma noise",
+    "Eta noise seed delta": "Eta noise seed delta",
+    "Request browser notifications": "Demander les notifications au navigateur",
+    "Download localization template": "Télécharger le modèle de localisation",
+    "Reload custom script bodies (No ui updates, No restart)": "Recharger le contenu des scripts personnalisés (Pas de mise à jour de l'interface, Pas de redémarrage)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Redémarrer Gradio et rafraichir les composants (Scripts personnalisés, ui.py, js et css uniquement)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (Ctrl + Entrée ou Alt + Entrée pour générer)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt négatif (Ctrl + Entrée ou Alt + Entrée pour générer)",
+    "Add a random artist to the prompt.": "Ajouter un artiste aléatoire au prompt",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Lire les paramètres de génération depuis le prompt, ou depuis la dernière génération si le prompt est vide dans l'interface utilisateur.",
+    "Save style": "Sauvegarder le style",
+    "Apply selected styles to current prompt": "Appliquer les styles séléctionnés au prompt actuel",
+    "Stop processing current image and continue processing.": "Arrêter le traitement de l'image actuelle et continuer le traitement.",
+    "Stop processing images and return any results accumulated so far.": "Arrêter le traitement des images et retourne les résultats accumulés depuis le début.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style à appliquer ; les styles sont composés de prompts positifs et négatifs et s'appliquent au deux",
+    "Do not do anything special": "Ne rien faire de particulier",
+    "Which algorithm to use to produce the image": "Quel algorithme utiliser pour produire l'image",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - très créatif, peut générer des images complètement différentes en fonction du nombre d'étapes, utiliser plus de 30 à 40 étapes n'améliore pas le résultat",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Modèles implicite de réduction du bruit à diffusion - utile pour l'inpainting",
+    "Produce an image that can be tiled.": "Produit une image qui peut être bouclée (tuile).",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilise un processus en deux étapes afin de créer partiellement une image dans une résolution plus faible, l'agrandir et améliorer ses détails sans modifier la composition",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Détermine à quel point l'algorithme doit respecter le contenu de l'image. A 0 rien ne changera, à 1 l'image sera entièrement différente. Avec des valeurs inférieures à 1.0 le traitement utilisera moins d'étapes que ce que la glissière Etapes d'échantillionnage spécifie. ",
+    "How many batches of images to create": "Combien de lots d'images créer",
+    "How many image to create in a single batch": "Combien d'images créer par lot",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - spécifie à quel point l'image doit se conformer au prompt - des valeurs plus faibles produisent des résultats plus créatifs",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Une valeur qui détermine la sortie du générateur de nombres aléatoires - si vous créez une image avec les mêmes paramètres et graine qu'une autre, le résultat sera identique",
+    "Set seed to -1, which will cause a new random number to be used every time": "Passer la graine à -1, cela causera qu'un nombre aléatoire différent sera utilisé à chaque fois",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Réutiliser la graine de la dernière génération, généralement utile uniquement si elle était randomisée",
+    "Seed of a different picture to be mixed into the generation.": "Graine d'une image différente à mélanger dans la génération",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Force de la variation à produire. A 0 il n'y aura pas d'effet. A 1 l'image sera composée uniquement de la graine variable spécifiée (à l'exception des échantillionneurs `ancestral`)",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Essayer de produire une image similaire à ce qu'elle aurait été avec la même graine, mais dans la résolution spécifiée",
+    "Separate values for X axis using commas.": "Séparer les valeurs pour l'axe X par des virgules",
+    "Separate values for Y axis using commas.": "Séparer les valeurs pour l'axe Y par des virgules",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Ecrire l'image dans un dossier (par défaut - log/images) et les paramètres de génération dans un fichier csv.",
+    "Open images output directory": "Ouvrir le dossier de sortie des images",
+    "How much to blur the mask before processing, in pixels.": "Quantité de flou à appliquer au masque avant traitement, en pixels",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Avec quoi remplir la zone masquée avant traitement par Stable Diffusion.",
+    "fill it with colors of the image": "remplir avec les couleurs de l'image",
+    "keep whatever was there originally": "conserver ce qui était présent à l'origine",
+    "fill it with latent space noise": "remplir avec le bruit de l'espace latent",
+    "fill it with latent space zeroes": "remplir avec des zéros dans l'espace latent",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Agrandir la région masquées à la résolution cible, exécuter l'inpainting, réduire à nouveau puis coller dans l'image originale",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Redimensionner l'image dans la résolution cible. A moins que la hauteur et la largeur coincident le ratio de l'image sera incorrect.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Redimensionner l'image afin que l'entièreté de la résolution cible soit remplie par l'image. Recadrer les parties qui dépassent.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Redimensionner l'image afin que l'entièreté de l'image soit contenue dans la résolution cible. Remplir l'espace vide avec les couleurs de l'image.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Combien de fois répéter le traitement d'une image et l'utiliser comme entrée pour la prochaine itération",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "En mode bouclage (Loopback), à chaque tour de la boucle la force du réducteur de bruit est multipliée par cette valeur. <1 signifie réduire la variation donc votre séquence convergera vers une image fixe. >1 signifie augmenter la variation donc votre séquence deviendra de plus en plus chaotique. ",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Pour l'agrandissement SD, de combien les tuiles doivent se chevaucher, en pixels. Les tuiles se chevauchent de manière à ce qu'il n'y ait pas de couture visible une fois fusionnées en une image. ",
+    "A directory on the same machine where the server is running.": "Un dossier sur la même machine où le serveur tourne.",
+    "Leave blank to save images to the default path.": "Laisser vide pour sauvegarder les images dans le chemin par défaut.",
+    "Result = A * (1 - M) + B * M": "Résultat = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Résultat = A + (B - C) * M",
+    "Path to directory with input images": "Chemin vers le dossier contenant les images d'entrée",
+    "Path to directory where to write outputs": "Chemin vers le dossier où écrire les sorties",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utiliser les étiquettes suivantes pour définir le nom des images : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp] ; laisser vide pour le nom par défaut.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Si cette option est activée le filigrane ne sera pas ajouté au images crées. Attention : si vous n'ajoutez pas de filigrane vous pourriez vous comporter de manière non éthique.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utiliser les étiquettes suivantes pour définir le nom des sous dossiers pour les images et les grilles  : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp] ; laisser vide pour le nom par défaut.",
+    "Restore low quality faces using GFPGAN neural network": "Restaurer les visages de basse qualité en utilisant le réseau neuronal GFPGAN",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Cette expression régulière sera utilisée pour extraire les mots depuis le nom de fichier ; ils seront joints en utilisant l'option ci dessous en une étiquette utilisée pour l'entrainement. Laisser vide pour conserver le texte du nom de fichier tel quel.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Cette chaine de caractères sera utilisée pour joindre les mots séparés en une ligne unique si l'option ci dessus est activée.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Liste des noms de paramètres, séparés par des virgules, pour les paramètres de la barre d'accès rapide en haut de page, plutôt que dans la page habituelle des paramètres. Voir modules/shared.py pour définir les noms. Requiert un redémarrage pour s'appliquer.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Si cette valeur est différente de zéro elle sera ajoutée à la graine et utilisée pour initialiser le générateur de nombres aléatoires du bruit lors de l'utilisation des échantillonneurs supportants Eta. Vous pouvez l'utiliser pour produire encore plus de variation dans les images, ou vous pouvez utiliser ceci pour faire correspondre les images avec d'autres logiciels si vous savez ce que vous faites.",
+    "Enable Autocomplete": "Activer l'autocomplétion",
+    "/0.0": "/0.0"
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 1ecfa5977ebdd59a09d52636894ab76451c9d17e Mon Sep 17 00:00:00 2001
From: tumbly <tumbly@example.com>
Date: Tue, 25 Oct 2022 21:55:58 +0200
Subject: Fix typos and proper wording per salco's comments

---
 localizations/fr-FR.json | 88 ++++++++++++++++++++++++------------------------
 1 file changed, 44 insertions(+), 44 deletions(-)

diff --git a/localizations/fr-FR.json b/localizations/fr-FR.json
index 81c65d5e..5c1e97c7 100644
--- a/localizations/fr-FR.json
+++ b/localizations/fr-FR.json
@@ -5,7 +5,7 @@
     "❮": "❮",
     "❯": "❯",
     "Loading...": "Chargement...",
-    "view": "查看",
+    "view": "vue",
     "api": "api",
     "•": "•",
     "built with gradio": "Construit avec Gradio",
@@ -18,8 +18,8 @@
     "Checkpoint Merger": "Fusion de checkpoints",
     "Train": "Entrainer",
     "Settings": "Paramètres",
-    "Prompt": "Prompt",
-    "Negative prompt": "Prompt négatif",
+    "Prompt": "Requête",
+    "Negative prompt": "Requête négative",
     "Run": "Lancer",
     "Skip": "Passer",
     "Interrupt": "Interrrompre",
@@ -35,7 +35,7 @@
     "Image": "Image",
     "Check progress": "Voir l'avancement",
     "Check progress (first)": "Voir l'avancement (1er)",
-    "Sampling Steps": "Etapes d'échantillonnage",
+    "Sampling Steps": "Étapes d'échantillonnage",
     "Sampling method": "Méthode d'échantillonnage",
     "Euler a": "Euler a",
     "Euler": "Euler",
@@ -61,28 +61,28 @@
     "Batch count": "Nombre de lots",
     "Batch size": "Taille de lots",
     "CFG Scale": "Echelle CFG",
-    "Seed": "Graine",
+    "Seed": "Valeur aléatoire",
     "Extra": "Extra",
-    "Variation seed": "Variation de la graine",
+    "Variation seed": "Variation de la valeur aléatoire",
     "Variation strength": "Puissance de variation",
-    "Resize seed from width": "Largeur de redimensionnement de la graine",
-    "Resize seed from height": "Hauteur de redimensionnement de la graine",
+    "Resize seed from width": "Largeur de redimensionnement de la valeur aléatoire",
+    "Resize seed from height": "Hauteur de redimensionnement de la valeur aléatoire",
     "Script": "Script",
     "None": "Aucun",
-    "Prompt matrix": "Matrice de prompt",
-    "Prompts from file or textbox": "Prompts depuis un fichier ou une boite de dialogue",
+    "Prompt matrix": "Matrice de requête",
+    "Prompts from file or textbox": "Requêtes depuis un fichier ou une boite de dialogue",
     "X/Y plot": "graphe X/Y",
-    "Put variable parts at start of prompt": "Mettre les mots clés variable au début du prompt",
+    "Put variable parts at start of prompt": "Mettre les mots clés variable au début de la requête",
     "Show Textbox": "Afficher le champs texte",
     "File with inputs": "Fichier d'entrée",
-    "Prompts": "Prompts",
+    "Prompts": "Requêtes",
     "X type": "Axe X",
     "Nothing": "Rien",
-    "Var. seed": "Graine variable",
+    "Var. seed": "Valeur aléatoire variable",
     "Var. strength": "Puissance variable",
-    "Steps": "Etapes",
-    "Prompt S/R": "Cherche et remplace dans le prompt",
-    "Prompt order": "Ordre du prompt",
+    "Steps": "Étapes",
+    "Prompt S/R": "Cherche et remplace dans la requête",
+    "Prompt order": "Ordre de la requête",
     "Sampler": "Echantilloneur",
     "Checkpoint name": "Nom du checkpoint",
     "Hypernetwork": "Hypernetwork",
@@ -99,7 +99,7 @@
     "Y values": "Valeur Y",
     "Draw legend": "Afficher la légende",
     "Include Separate Images": "Inclure les images séparées",
-    "Keep -1 for seeds": "Conserver -1 pour la graine",
+    "Keep -1 for seeds": "Conserver -1 pour la valeur aléatoire",
     "Drop Image Here": "Déposer l'image ici",
     "Save": "Enregistrer",
     "Send to img2img": "Envoyer vers img2img",
@@ -143,18 +143,18 @@
     "SD upscale": "Agrandissement SD",
     "should be 2 or lower.": "doit être inférieur ou égal à 2",
     "Override `Sampling method` to Euler?(this method is built for it)": "Forcer `Méthode d'échantillonnage` à Euler ? (cette méthode est dédiée à cela)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Forcer le `prompt` au contenu du `prompt d'origine` ? (de même pour le `prompt négatif`)",
-    "Original prompt": "Prompt d'origine",
-    "Original negative prompt": "Prompt négatif d'origine",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Forcer le valeur d'`Etapes d'échantillonnage` à la même valeur qu'`Etapes de décodage` ?",
-    "Decode steps": "Etapes de décodage",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Forcer la `requête` au contenu de la `requête d'origine` ? (de même pour la `requête négative`)",
+    "Original prompt": "Requête d'origine",
+    "Original negative prompt": "Requête négative d'origine",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Forcer le valeur d'`Étapes d'échantillonnage` à la même valeur qu'`Étapes de décodage` ?",
+    "Decode steps": "Étapes de décodage",
     "Override `Denoising strength` to 1?": "Forcer `Puissance de réduction du bruit` à 1 ?",
     "Decode CFG scale": "Echelle CFG de décodage",
     "Randomness": "Aléatoire",
     "Sigma adjustment for finding noise for image": "Ajustement Sigma lors de la recherche du bruit dans l'image",
     "Loops": "Boucles",
     "Denoising strength change factor": "Facteur de changement de la puissance de réduction du bruit",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Paramètres recommandés : Etapes d'échantillonnage : 80-100, Echantillonneur : Euler a, Puissance de réduction du bruit : 0.8",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Paramètres recommandés : Étapes d'échantillonnage : 80-100, Echantillonneur : Euler a, Puissance de réduction du bruit : 0.8",
     "Pixels to expand": "Pixels à étendre",
     "Outpainting direction": "Direction de l'outpainting",
     "left": "gauche",
@@ -238,12 +238,12 @@
     "Learning rate": "Vitesse d'apprentissage",
     "Dataset directory": "Dossier des images d'entrée",
     "Log directory": "Dossier de journalisation",
-    "Prompt template file": "Fichier modèle de prompts",
-    "Max steps": "Etapes max.",
+    "Prompt template file": "Fichier modèle de requêtes",
+    "Max steps": "Étapes max.",
     "Save an image to log directory every N steps, 0 to disable": "Enregistrer une image dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
     "Save a copy of embedding to log directory every N steps, 0 to disable": "Enregistrer une copie de l'embedding dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
     "Save images with embedding in PNG chunks": "Sauvegarder les images incluant l'embedding dans leur blocs PNG",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Lire les paramètres (prompt, etc.) depuis l'onglet txt2img lors de la génération des previews",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Lire les paramètres (requête, etc.) depuis l'onglet txt2img lors de la génération des previews",
     "Train Hypernetwork": "Entrainer un Hypernetwork",
     "Train Embedding": "Entrainer un Embedding",
     "Apply settings": "Appliquer les paramètres",
@@ -253,7 +253,7 @@
     "Images filename pattern": "Motif pour le nom de fichier des images",
     "Always save all generated image grids": "Toujours enregistrer toutes les grilles d'images générées",
     "File format for grids": "Format de fichier pour les grilles",
-    "Add extended info (seed, prompt) to filename when saving grid": "Ajouter les informations étendues (graine, prompt) aux noms de fichiers lors de l'enregistrement d'une grille",
+    "Add extended info (seed, prompt) to filename when saving grid": "Ajouter les informations étendues (valeur aléatoire, requête) aux noms de fichiers lors de l'enregistrement d'une grille",
     "Do not save grids consisting of one picture": "Ne pas enregistrer les grilles contenant une seule image",
     "Prevent empty spots in grid (when set to autodetect)": "Eviter les vides dans la grille (quand autodétection est choisie)",
     "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Nombre de colonnes de la grille; utilisez -1 pour autodétection et 0 pour qu'il soit égal à la taille du lot",
@@ -307,9 +307,9 @@
     "Apply color correction to img2img results to match original colors.": "Appliquer une correction de couleur aux résultats img2img afin de conserver les couleurs d'origine",
     "Save a copy of image before applying color correction to img2img results": "Enregistrer une copie de l'image avant d'appliquer les résultats de la correction de couleur img2img",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Avec img2img, executer exactement le nombre d'étapes spécifiées par la glissière (normalement moins d'étapes sont executées quand la réduction du bruit est plus faible).",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Activer la quantisation des échantillionneurs K pour des résultats plus nets et plus propres. Cela peut modifier les graines existantes. Requiert un redémarrage pour être actif.",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Activer la quantisation des échantillionneurs K pour des résultats plus nets et plus propres. Cela peut modifier les valeurs aléatoires existantes. Requiert un redémarrage pour être actif.",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphase : utilisez (texte) afin de forcer le modèle à porter plus d'attention au texte et [texte] afin qu'il y porte moins attention",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Utilisez l'ancienne méthode d'emphase. Peut être utile afin de reproduire d'anciennes graines.",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Utilisez l'ancienne méthode d'emphase. Peut être utile afin de reproduire d'anciennes valeurs aléatoires.",
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "Demander aux échantillionneurs K-diffusion de produire les mêmes dans un lot que lors de la génération d'une image unique",
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Améliorer la cohérence en remplissant (padding) à partir de la dernière virgule dans les X jetons quand on en utilise plus de 75",
     "Filter NSFW content": "Filtrer le contenu +18 (NSFW)",
@@ -354,31 +354,31 @@
     "Download localization template": "Télécharger le modèle de localisation",
     "Reload custom script bodies (No ui updates, No restart)": "Recharger le contenu des scripts personnalisés (Pas de mise à jour de l'interface, Pas de redémarrage)",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Redémarrer Gradio et rafraichir les composants (Scripts personnalisés, ui.py, js et css uniquement)",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (Ctrl + Entrée ou Alt + Entrée pour générer)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt négatif (Ctrl + Entrée ou Alt + Entrée pour générer)",
-    "Add a random artist to the prompt.": "Ajouter un artiste aléatoire au prompt",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Lire les paramètres de génération depuis le prompt, ou depuis la dernière génération si le prompt est vide dans l'interface utilisateur.",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Requête (Ctrl + Entrée ou Alt + Entrée pour générer)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Requête négative (Ctrl + Entrée ou Alt + Entrée pour générer)",
+    "Add a random artist to the prompt.": "Ajouter un artiste aléatoire à la requête",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Lire les paramètres de génération depuis la requête, ou depuis la dernière génération si la requête est vide dans l'interface utilisateur.",
     "Save style": "Sauvegarder le style",
-    "Apply selected styles to current prompt": "Appliquer les styles séléctionnés au prompt actuel",
+    "Apply selected styles to current prompt": "Appliquer les styles séléctionnés à la requête actuelle",
     "Stop processing current image and continue processing.": "Arrêter le traitement de l'image actuelle et continuer le traitement.",
     "Stop processing images and return any results accumulated so far.": "Arrêter le traitement des images et retourne les résultats accumulés depuis le début.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style à appliquer ; les styles sont composés de prompts positifs et négatifs et s'appliquent au deux",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style à appliquer ; les styles sont composés de requêtes positives et négatives et s'appliquent au deux",
     "Do not do anything special": "Ne rien faire de particulier",
     "Which algorithm to use to produce the image": "Quel algorithme utiliser pour produire l'image",
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - très créatif, peut générer des images complètement différentes en fonction du nombre d'étapes, utiliser plus de 30 à 40 étapes n'améliore pas le résultat",
     "Denoising Diffusion Implicit Models - best at inpainting": "Modèles implicite de réduction du bruit à diffusion - utile pour l'inpainting",
     "Produce an image that can be tiled.": "Produit une image qui peut être bouclée (tuile).",
     "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilise un processus en deux étapes afin de créer partiellement une image dans une résolution plus faible, l'agrandir et améliorer ses détails sans modifier la composition",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Détermine à quel point l'algorithme doit respecter le contenu de l'image. A 0 rien ne changera, à 1 l'image sera entièrement différente. Avec des valeurs inférieures à 1.0 le traitement utilisera moins d'étapes que ce que la glissière Etapes d'échantillionnage spécifie. ",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Détermine à quel point l'algorithme doit respecter le contenu de l'image. A 0 rien ne changera, à 1 l'image sera entièrement différente. Avec des valeurs inférieures à 1.0 le traitement utilisera moins d'étapes que ce que la glissière Étapes d'échantillionnage spécifie. ",
     "How many batches of images to create": "Combien de lots d'images créer",
     "How many image to create in a single batch": "Combien d'images créer par lot",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - spécifie à quel point l'image doit se conformer au prompt - des valeurs plus faibles produisent des résultats plus créatifs",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Une valeur qui détermine la sortie du générateur de nombres aléatoires - si vous créez une image avec les mêmes paramètres et graine qu'une autre, le résultat sera identique",
-    "Set seed to -1, which will cause a new random number to be used every time": "Passer la graine à -1, cela causera qu'un nombre aléatoire différent sera utilisé à chaque fois",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Réutiliser la graine de la dernière génération, généralement utile uniquement si elle était randomisée",
-    "Seed of a different picture to be mixed into the generation.": "Graine d'une image différente à mélanger dans la génération",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Force de la variation à produire. A 0 il n'y aura pas d'effet. A 1 l'image sera composée uniquement de la graine variable spécifiée (à l'exception des échantillionneurs `ancestral`)",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Essayer de produire une image similaire à ce qu'elle aurait été avec la même graine, mais dans la résolution spécifiée",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - spécifie à quel point l'image doit se conformer à la requête - des valeurs plus faibles produisent des résultats plus créatifs",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Une valeur qui détermine la sortie du générateur de nombres aléatoires - si vous créez une image avec les mêmes paramètres et valeur aléatoire qu'une autre, le résultat sera identique",
+    "Set seed to -1, which will cause a new random number to be used every time": "Passer la valeur aléatoire à -1, cela causera qu'un nombre aléatoire différent sera utilisé à chaque fois",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Réutiliser la valeur aléatoire de la dernière génération, généralement utile uniquement si elle était randomisée",
+    "Seed of a different picture to be mixed into the generation.": "Valeur aléatoire d'une image différente à mélanger dans la génération",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Force de la variation à produire. A 0 il n'y aura pas d'effet. A 1 l'image sera composée uniquement de la valeur aléatoire variable spécifiée (à l'exception des échantillionneurs `ancestral`)",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Essayer de produire une image similaire à ce qu'elle aurait été avec la même valeur aléatoire, mais dans la résolution spécifiée",
     "Separate values for X axis using commas.": "Séparer les valeurs pour l'axe X par des virgules",
     "Separate values for Y axis using commas.": "Séparer les valeurs pour l'axe Y par des virgules",
     "Write image to a directory (default - log/images) and generation parameters into csv file.": "Ecrire l'image dans un dossier (par défaut - log/images) et les paramètres de génération dans un fichier csv.",
@@ -409,7 +409,7 @@
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Cette expression régulière sera utilisée pour extraire les mots depuis le nom de fichier ; ils seront joints en utilisant l'option ci dessous en une étiquette utilisée pour l'entrainement. Laisser vide pour conserver le texte du nom de fichier tel quel.",
     "This string will be used to join split words into a single line if the option above is enabled.": "Cette chaine de caractères sera utilisée pour joindre les mots séparés en une ligne unique si l'option ci dessus est activée.",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Liste des noms de paramètres, séparés par des virgules, pour les paramètres de la barre d'accès rapide en haut de page, plutôt que dans la page habituelle des paramètres. Voir modules/shared.py pour définir les noms. Requiert un redémarrage pour s'appliquer.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Si cette valeur est différente de zéro elle sera ajoutée à la graine et utilisée pour initialiser le générateur de nombres aléatoires du bruit lors de l'utilisation des échantillonneurs supportants Eta. Vous pouvez l'utiliser pour produire encore plus de variation dans les images, ou vous pouvez utiliser ceci pour faire correspondre les images avec d'autres logiciels si vous savez ce que vous faites.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Si cette valeur est différente de zéro elle sera ajoutée à la valeur aléatoire et utilisée pour initialiser le générateur de nombres aléatoires du bruit lors de l'utilisation des échantillonneurs supportants Eta. Vous pouvez l'utiliser pour produire encore plus de variation dans les images, ou vous pouvez utiliser ceci pour faire correspondre les images avec d'autres logiciels si vous savez ce que vous faites.",
     "Enable Autocomplete": "Activer l'autocomplétion",
     "/0.0": "/0.0"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From c7af69f893227b58935fc9da083910e5c90925de Mon Sep 17 00:00:00 2001
From: tumbly <tumbly@example.com>
Date: Tue, 25 Oct 2022 22:03:02 +0200
Subject: Add missed fixes from salco's comments, tweak some

---
 localizations/fr-FR.json | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/localizations/fr-FR.json b/localizations/fr-FR.json
index 5c1e97c7..b33d0838 100644
--- a/localizations/fr-FR.json
+++ b/localizations/fr-FR.json
@@ -69,14 +69,14 @@
     "Resize seed from height": "Hauteur de redimensionnement de la valeur aléatoire",
     "Script": "Script",
     "None": "Aucun",
-    "Prompt matrix": "Matrice de requête",
-    "Prompts from file or textbox": "Requêtes depuis un fichier ou une boite de dialogue",
+    "Prompt matrix": "Matrice de requète",
+    "Prompts from file or textbox": "Requètes depuis un fichier ou une boite de dialogue",
     "X/Y plot": "graphe X/Y",
     "Put variable parts at start of prompt": "Mettre les mots clés variable au début de la requête",
     "Show Textbox": "Afficher le champs texte",
     "File with inputs": "Fichier d'entrée",
     "Prompts": "Requêtes",
-    "X type": "Axe X",
+    "X type": "Paramètre axe X",
     "Nothing": "Rien",
     "Var. seed": "Valeur aléatoire variable",
     "Var. strength": "Puissance variable",
@@ -94,9 +94,9 @@
     "Eta": "Temps estimé",
     "Clip skip": "Passer Clip",
     "Denoising": "Réduction du bruit",
-    "X values": "Valeur X",
-    "Y type": "Type Y",
-    "Y values": "Valeur Y",
+    "X values": "Valeurs X",
+    "Y type": "Paramètre axe Y",
+    "Y values": "Valeurs Y",
     "Draw legend": "Afficher la légende",
     "Include Separate Images": "Inclure les images séparées",
     "Keep -1 for seeds": "Conserver -1 pour la valeur aléatoire",
-- 
cgit v1.2.1


From 146856f66d7e06a762f5ef5bf61a226057de6757 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Tue, 25 Oct 2022 06:21:31 +0900
Subject: images: allow nested bracket in filename pattern

---
 modules/images.py | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index 286de2ae..ed448a8a 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -277,7 +277,7 @@ invalid_filename_chars = '<>:"/\\|?*\n'
 invalid_filename_prefix = ' '
 invalid_filename_postfix = ' .'
 re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
-re_pattern = re.compile(r"([^\[\]]+|\[([^]]+)]|[\[\]]*)")
+re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
 re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
 max_filename_part_length = 128
 
@@ -362,9 +362,9 @@ class FilenameGenerator:
 
         for m in re_pattern.finditer(x):
             text, pattern = m.groups()
+            res += text
 
             if pattern is None:
-                res += text
                 continue
 
             pattern_args = []
@@ -385,12 +385,9 @@ class FilenameGenerator:
                     print(f"Error adding [{pattern}] to filename", file=sys.stderr)
                     print(traceback.format_exc(), file=sys.stderr)
 
-                if replacement is None:
-                    res += f'[{pattern}]'
-                else:
+                if replacement is not None:
                     res += str(replacement)
-
-                continue
+                    continue
 
             res += f'[{pattern}]'
 
-- 
cgit v1.2.1


From 877d94f97ca5491d8779440769b191e0dcd32c8e Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Wed, 26 Oct 2022 14:50:58 +0800
Subject: Back compatibility

---
 modules/hypernetworks/hypernetwork.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3ce85bb5..dd317085 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -28,7 +28,7 @@ class HypernetworkModule(torch.nn.Module):
         "swish": torch.nn.Hardswish,
     }
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False, activate_output=False):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -42,7 +42,7 @@ class HypernetworkModule(torch.nn.Module):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
 
             # Add an activation func except last layer
-            if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 2:
+            if activation_func == "linear" or activation_func is None or (i >= len(layer_structure) - 2 and not activate_output):
                 pass
             elif activation_func in self.activation_dict:
                 linears.append(self.activation_dict[activation_func]())
@@ -105,7 +105,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False, activate_output=False):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -116,11 +116,12 @@ class Hypernetwork:
         self.activation_func = activation_func
         self.add_layer_norm = add_layer_norm
         self.use_dropout = use_dropout
+        self.activate_output = activate_output
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),
             )
 
     def weights(self):
@@ -147,6 +148,7 @@ class Hypernetwork:
         state_dict['use_dropout'] = self.use_dropout
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
+        state_dict['activate_output'] = self.activate_output
 
         torch.save(state_dict, filename)
 
@@ -161,12 +163,13 @@ class Hypernetwork:
         self.activation_func = state_dict.get('activation_func', None)
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
         self.use_dropout = state_dict.get('use_dropout', False)
+        self.activate_output = state_dict.get('activate_output', True)
 
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),
                 )
 
         self.name = state_dict.get('name', self.name)
-- 
cgit v1.2.1


From 757264c453eca533ee1c9ea7e9d9b45a009367d7 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Tue, 25 Oct 2022 23:39:21 +0900
Subject: default_time_format if format is blank

---
 modules/images.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/images.py b/modules/images.py
index ed448a8a..bfc2ba06 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -343,7 +343,7 @@ class FilenameGenerator:
     def datetime(self, *args):
         time_datetime = datetime.datetime.now()
 
-        time_format = args[0] if len(args) > 0 else self.default_time_format
+        time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
         try:
             time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
         except pytz.exceptions.UnknownTimeZoneError as _:
-- 
cgit v1.2.1


From 9d82c351ac36d1511f5f65b24443c60250ee3e9e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 26 Oct 2022 09:56:25 +0300
Subject: fix typo in on_save_imaged/on_image_saved; hope no extension is using
 it yet

---
 modules/script_callbacks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index dc520abc..6803d57b 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -90,7 +90,7 @@ def on_ui_settings(callback):
     add_callback(callbacks_ui_settings, callback)
 
 
-def on_save_imaged(callback):
+def on_image_saved(callback):
     """register a function to be called after modules.images.save_image is called.
     The callback is called with three arguments:
         - p - procesing object (or a dummy object with same fields if the image is saved using save button)
-- 
cgit v1.2.1


From 91bb35b1e6842b30ce7553009c8ecea3643de8d2 Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Wed, 26 Oct 2022 15:00:03 +0800
Subject: Merge fix

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index eab8b32f..bd171793 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -190,7 +190,7 @@ class Hypernetwork:
         print(f"Weight initialization is {self.weight_init}")
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
         print(f"Layer norm is set to {self.add_layer_norm}")
-        self.use_dropout = state_dict.get('use_dropout', False
+        self.use_dropout = state_dict.get('use_dropout', False)
         print(f"Dropout usage is set to {self.use_dropout}" )
         self.activate_output = state_dict.get('activate_output', True)
 
-- 
cgit v1.2.1


From cb49800c08a9f6619733250401952e5571dc12f8 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Tue, 25 Oct 2022 01:39:59 -0700
Subject: img2img, use smartphone photos' EXIF orientation

---
 modules/img2img.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/modules/img2img.py b/modules/img2img.py
index 8d9f7cf9..9c0cf23e 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -39,6 +39,8 @@ def process_batch(p, input_dir, output_dir, args):
             break
 
         img = Image.open(image)
+        # Use the EXIF orientation of photos taken by smartphones.
+        img = ImageOps.exif_transpose(img) 
         p.init_images = [img] * p.batch_size
 
         proc = modules.scripts.scripts_img2img.run(p, *args)
@@ -61,19 +63,25 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
     is_batch = mode == 2
 
     if is_inpaint:
+        # Drawn mask
         if mask_mode == 0:
             image = init_img_with_mask['image']
             mask = init_img_with_mask['mask']
             alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
             mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
             image = image.convert('RGB')
+        # Uploaded mask
         else:
             image = init_img_inpaint
             mask = init_mask_inpaint
+    # No mask
     else:
         image = init_img
         mask = None
 
+    # Use the EXIF orientation of photos taken by smartphones.
+    image = ImageOps.exif_transpose(image) 
+
     assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
 
     p = StableDiffusionProcessingImg2Img(
-- 
cgit v1.2.1


From a524d137d0a89bb19a6676dc9b8fbb5d1b580678 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Mon, 24 Oct 2022 23:48:05 -0700
Subject: patch bug (SeverianVoid's comment on 5245c7a)

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 842b6447..8113b35b 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -487,7 +487,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             if image is not None:
                 shared.state.current_image = image
-                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename)
+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
                 last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = hypernetwork.step
-- 
cgit v1.2.1


From c2dc9bfa89070b8e1d857f8773a790b752f1b709 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Mon, 24 Oct 2022 23:22:58 -0700
Subject: Implement PR #3189 but for embeddings.

---
 modules/textual_inversion/textual_inversion.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 647ffe3e..22c7b54b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -10,7 +10,7 @@ import csv
 
 from PIL import Image, PngImagePlugin
 
-from modules import shared, devices, sd_hijack, processing, sd_models
+from modules import shared, devices, sd_hijack, processing, sd_models, images
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -247,6 +247,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
     last_saved_file = "<none>"
     last_saved_image = "<none>"
+    forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
 
     ititial_step = embedding.step or 0
@@ -296,8 +297,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         })
 
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
-            last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
-
+            forced_filename = f'{embedding_name}-{embedding.step}'
+            last_saved_image = os.path.join(images_dir, forced_filename)
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
                 do_not_save_grid=True,
@@ -353,8 +354,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
                 embedding_yet_to_be_embedded = False
 
-            image.save(last_saved_image)
-
+            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename)
             last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = embedding.step
-- 
cgit v1.2.1


From 4875a6c217df5cc06ee2bf11fb645b172c7156a8 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Mon, 24 Oct 2022 23:38:07 -0700
Subject: Implement PR #3309 but for embeddings.

---
 modules/textual_inversion/textual_inversion.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 22c7b54b..4921bd01 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -167,6 +167,8 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
     for i in range(num_vectors_per_token):
         vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
 
+    # Remove illegal characters from name.
+    name = "".join( x for x in name if (x.isalnum() or x in "._- "))
     fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
     if not overwrite_old:
         assert not os.path.exists(fn), f"file {fn} already exists"
@@ -287,7 +289,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")
 
         if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
-            last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
+            # Before saving, change name to match current checkpoint.
+            embedding.name = f'{embedding_name}-{embedding.step}'
+            last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
             embedding.save(last_saved_file)
             embedding_yet_to_be_embedded = True
 
@@ -374,6 +378,9 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     embedding.sd_checkpoint = checkpoint.hash
     embedding.sd_checkpoint_name = checkpoint.model_name
     embedding.cached_checksum = None
+    # Before saving for the last time, change name back to base name (as opposed to the save_embedding_every step-suffixed naming convention).
+    embedding.name = embedding_name
+    filename = os.path.join(shared.cmd_opts.embedding_dir, f'{embedding.name}.pt')
     embedding.save(filename)
 
     return embedding, filename
-- 
cgit v1.2.1


From f4e14642173a04723200b131deb417c6c79cab17 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Tue, 25 Oct 2022 00:04:25 -0700
Subject: Implement PR #3625 but for embeddings.

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 4921bd01..4fcebe74 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -358,7 +358,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
                 embedding_yet_to_be_embedded = False
 
-            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename)
+            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
             last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = embedding.step
-- 
cgit v1.2.1


From b6a8bb123bd519736306417399f6441e504f1e8b Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Wed, 26 Oct 2022 15:15:19 +0800
Subject: Fix merge

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index bd171793..2997cead 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -60,7 +60,7 @@ class HypernetworkModule(torch.nn.Module):
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
             # Add dropout except last layer
-            if use_dropout and i < len(layer_structure) - 2:
+            if use_dropout and i < len(layer_structure) - 3:
                 linears.append(torch.nn.Dropout(p=0.3))
 
         self.linear = torch.nn.Sequential(*linears)
@@ -126,7 +126,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False)
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False):
         self.filename = None
         self.name = name
         self.layers = {}
-- 
cgit v1.2.1


From 1e428238db4e399b7a06ad5251cb16eef23a014d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 26 Oct 2022 11:47:07 +0300
Subject: add override_settings to API as an alternative to #3629

---
 modules/processing.py | 25 ++++++++++++++++++++-----
 modules/shared.py     |  4 ++--
 2 files changed, 22 insertions(+), 7 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index c61bbfbd..4efba946 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -77,9 +77,8 @@ def get_correct_sampler(p):
 class StableDiffusionProcessing():
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
-    
     """
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str="", styles: List[str]=None, seed: int=-1, subseed: int=-1, subseed_strength: float=0, seed_resize_from_h: int=-1, seed_resize_from_w: int=-1, seed_enable_extras: bool=True, sampler_index: int=0, batch_size: int=1, n_iter: int=1, steps:int =50, cfg_scale:float=7.0, width:int=512, height:int=512, restore_faces:bool=False, tiling:bool=False, do_not_save_samples:bool=False, do_not_save_grid:bool=False, extra_generation_params: Dict[Any,Any]=None, overlay_images: Any=None, negative_prompt: str=None, eta: float =None, do_not_reload_embeddings: bool=False, denoising_strength: float = 0, ddim_discretize: str = "uniform", s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_index: int = 0, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -109,13 +108,14 @@ class StableDiffusionProcessing():
         self.do_not_reload_embeddings = do_not_reload_embeddings
         self.paste_to = None
         self.color_corrections = None
-        self.denoising_strength: float = 0
+        self.denoising_strength: float = denoising_strength
         self.sampler_noise_scheduler_override = None
-        self.ddim_discretize = opts.ddim_discretize
+        self.ddim_discretize = ddim_discretize or opts.ddim_discretize
         self.s_churn = s_churn or opts.s_churn
         self.s_tmin = s_tmin or opts.s_tmin
         self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
         self.s_noise = s_noise or opts.s_noise
+        self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
 
         if not seed_enable_extras:
             self.subseed = -1
@@ -129,7 +129,6 @@ class StableDiffusionProcessing():
         self.all_seeds = None
         self.all_subseeds = None
 
-
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
@@ -351,6 +350,22 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
 
 def process_images(p: StableDiffusionProcessing) -> Processed:
+    stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
+
+    try:
+        for k, v in p.override_settings.items():
+            opts.data[k] = v  # we don't call onchange for simplicity which makes changing model, hypernet impossible
+
+        res = process_images_inner(p)
+
+    finally:
+        for k, v in stored_opts.items():
+            opts.data[k] = v
+
+    return res
+
+
+def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
     if type(p.prompt) == list:
diff --git a/modules/shared.py b/modules/shared.py
index 308fccce..1a9b8289 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -84,7 +84,7 @@ parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load mod
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
 
 cmd_opts = parser.parse_args()
-restricted_opts = [
+restricted_opts = {
     "samples_filename_pattern",
     "directories_filename_pattern",
     "outdir_samples",
@@ -94,7 +94,7 @@ restricted_opts = [
     "outdir_grids",
     "outdir_txt2img_grids",
     "outdir_save",
-]
+}
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
-- 
cgit v1.2.1


From d3fc0f7fc46b6f293db67604d43370db7812edb3 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Wed, 26 Oct 2022 17:59:10 +0900
Subject: Update new strings

---
 localizations/ko_KR.json | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 07c2797c..ff70f1ea 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -36,6 +36,7 @@
   "Apply color correction to img2img results to match original colors.": "이미지→이미지 결과물이 기존 색상과 일치하도록 색상 보정 적용하기",
   "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
   "Apply settings": "설정 적용하기",
+  "Auto focal point crop": "초점 기준 크롭(자동 감지)",
   "Batch count": "배치 수",
   "Batch from Directory": "저장 경로로부터 여러장 처리",
   "Batch img2img": "이미지→이미지 배치",
@@ -67,6 +68,7 @@
   "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
   "Create a text file next to every image with generation parameters.": "생성된 이미지마다 생성 설정값을 담은 텍스트 파일 생성하기",
   "Create aesthetic images embedding": "스타일 이미지 임베딩 생성하기",
+  "Create debug image": "디버그 이미지 생성",
   "Create embedding": "임베딩 생성",
   "Create flipped copies": "좌우로 뒤집은 복사본 생성",
   "Create hypernetwork": "하이퍼네트워크 생성",
@@ -148,6 +150,9 @@
   "First Page": "처음 페이지",
   "Firstpass height": "초기 세로길이",
   "Firstpass width": "초기 가로길이",
+  "Focal point edges weight": "경계면 가중치",
+  "Focal point entropy weight": "엔트로피 가중치",
+  "Focal point face weight": "얼굴 가중치",
   "Font for image grids that have text": "텍스트가 존재하는 그리드 이미지의 폰트",
   "for detailed explanation.": "를 참조하십시오.",
   "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD 업스케일링에서 타일 간 몇 픽셀을 겹치게 할지 결정하는 설정값입니다. 타일들이 다시 한 이미지로 합쳐질 때, 눈에 띄는 이음매가 없도록 서로 겹치게 됩니다.",
@@ -381,6 +386,7 @@
   "Seed": "시드",
   "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
   "Select activation function of hypernetwork": "하이퍼네트워크 활성화 함수 선택",
+  "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "레이어 가중치 초기화 방식 선택 - relu류 : Kaiming 추천, sigmoid류 : Xavier 추천",
   "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "WebUI에 표시할 Real-ESRGAN 모델을 선택하십시오. (재시작 필요)",
   "Send to extras": "부가기능으로 전송",
   "Send to img2img": "이미지→이미지로 전송",
-- 
cgit v1.2.1


From 0cd74602531a40f72d1a75b471a8a9166135d333 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 26 Oct 2022 13:12:44 +0300
Subject: add script callback for before image save and change callback for
 after image save to use a class with parameters

---
 modules/images.py           | 42 ++++++++++++++++++++++-----------------
 modules/script_callbacks.py | 48 +++++++++++++++++++++++++++++++++++++--------
 2 files changed, 64 insertions(+), 26 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index bfc2ba06..7870b5b7 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -451,17 +451,6 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     """
     namegen = FilenameGenerator(p, seed, prompt)
 
-    if extension == 'png' and opts.enable_pnginfo and info is not None:
-        pnginfo = PngImagePlugin.PngInfo()
-
-        if existing_info is not None:
-            for k, v in existing_info.items():
-                pnginfo.add_text(k, str(v))
-
-        pnginfo.add_text(pnginfo_section_name, info)
-    else:
-        pnginfo = None
-
     if save_to_dirs is None:
         save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
 
@@ -489,19 +478,27 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         if add_number:
             basecount = get_next_sequence_number(path, basename)
             fullfn = None
-            fullfn_without_extension = None
             for i in range(500):
                 fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
                 fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
-                fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")
                 if not os.path.exists(fullfn):
                     break
         else:
             fullfn = os.path.join(path, f"{file_decoration}.{extension}")
-            fullfn_without_extension = os.path.join(path, file_decoration)
     else:
         fullfn = os.path.join(path, f"{forced_filename}.{extension}")
-        fullfn_without_extension = os.path.join(path, forced_filename)
+
+    pnginfo = existing_info or {}
+    if info is not None:
+        pnginfo[pnginfo_section_name] = info
+
+    params = script_callbacks.ImageSaveParams(image, p, fullfn, pnginfo)
+    script_callbacks.before_image_saved_callback(params)
+
+    image = params.image
+    fullfn = params.filename
+    info = params.pnginfo.get(pnginfo_section_name, None)
+    fullfn_without_extension, extension = os.path.splitext(params.filename)
 
     def exif_bytes():
         return piexif.dump({
@@ -510,12 +507,20 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             },
         })
 
-    if extension.lower() in ("jpg", "jpeg", "webp"):
+    if extension.lower() == '.png':
+        pnginfo_data = PngImagePlugin.PngInfo()
+        for k, v in params.pnginfo.items():
+            pnginfo_data.add_text(k, str(v))
+
+        image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
+
+    elif extension.lower() in (".jpg", ".jpeg", ".webp"):
         image.save(fullfn, quality=opts.jpeg_quality)
+
         if opts.enable_pnginfo and info is not None:
             piexif.insert(exif_bytes(), fullfn)
     else:
-        image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo)
+        image.save(fullfn, quality=opts.jpeg_quality)
 
     target_side_length = 4000
     oversize = image.width > target_side_length or image.height > target_side_length
@@ -538,7 +543,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     else:
         txt_fullfn = None
 
-    script_callbacks.image_saved_callback(image, p, fullfn, txt_fullfn)
+    script_callbacks.image_saved_callback(params)
+
     return fullfn, txt_fullfn
 
 
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 6803d57b..6ea58d61 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -9,15 +9,34 @@ def report_exception(c, job):
     print(traceback.format_exc(), file=sys.stderr)
 
 
+class ImageSaveParams:
+    def __init__(self, image, p, filename, pnginfo):
+        self.image = image
+        """the PIL image itself"""
+
+        self.p = p
+        """p object with processing parameters; either StableDiffusionProcessing or an object with same fields"""
+
+        self.filename = filename
+        """name of file that the image would be saved to"""
+
+        self.pnginfo = pnginfo
+        """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
+
+
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
+callbacks_before_image_saved = []
 callbacks_image_saved = []
 
+
 def clear_callbacks():
     callbacks_model_loaded.clear()
     callbacks_ui_tabs.clear()
+    callbacks_ui_settings.clear()
+    callbacks_before_image_saved.clear()
     callbacks_image_saved.clear()
 
 
@@ -49,10 +68,18 @@ def ui_settings_callback():
             report_exception(c, 'ui_settings_callback')
 
 
-def image_saved_callback(image, p, fullfn, txt_fullfn):
+def before_image_saved_callback(params: ImageSaveParams):
     for c in callbacks_image_saved:
         try:
-            c.callback(image, p, fullfn, txt_fullfn)
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'before_image_saved_callback')
+
+
+def image_saved_callback(params: ImageSaveParams):
+    for c in callbacks_image_saved:
+        try:
+            c.callback(params)
         except Exception:
             report_exception(c, 'image_saved_callback')
 
@@ -64,7 +91,6 @@ def add_callback(callbacks, fun):
     callbacks.append(ScriptCallback(filename, fun))
 
 
-
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
@@ -90,11 +116,17 @@ def on_ui_settings(callback):
     add_callback(callbacks_ui_settings, callback)
 
 
+def on_before_image_saved(callback):
+    """register a function to be called before an image is saved to a file.
+    The callback is called with one argument:
+        - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
+    """
+    add_callback(callbacks_before_image_saved, callback)
+
+
 def on_image_saved(callback):
-    """register a function to be called after modules.images.save_image is called.
-    The callback is called with three arguments:
-        - p - procesing object (or a dummy object with same fields if the image is saved using save button)
-        - fullfn - image filename
-        - txt_fullfn - text file with parameters; may be None
+    """register a function to be called after an image is saved to a file.
+    The callback is called with one argument:
+        - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
     """
     add_callback(callbacks_image_saved, callback)
-- 
cgit v1.2.1


From 0c38baeb20086c05eac86a6812a4cca740f69c21 Mon Sep 17 00:00:00 2001
From: camenduru <54370274+camenduru@users.noreply.github.com>
Date: Tue, 25 Oct 2022 12:15:17 +0300
Subject: =?UTF-8?q?=F0=9F=A7=BF=20Turkish=20localization?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

translated with deepl, it needs some tweaks I will fix 🔧
---
 localizations/tr_TR.json | 423 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 423 insertions(+)
 create mode 100644 localizations/tr_TR.json

diff --git a/localizations/tr_TR.json b/localizations/tr_TR.json
new file mode 100644
index 00000000..511b8882
--- /dev/null
+++ b/localizations/tr_TR.json
@@ -0,0 +1,423 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "Yükleniyor...",
+    "view": "görünüm",
+    "api": "api",
+    "•": "-",
+    "built with gradio": "gradio ile inşa edildi",
+    "Stable Diffusion checkpoint": "Kararlı Difüzyon kontrol noktası",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "Ekstralar",
+    "PNG Info": "PNG Bilgisi",
+    "Checkpoint Merger": "Checkpoint Birleşmesi",
+    "Train": "Tren",
+    "Settings": "Ayarlar",
+    "Prompt": "İstem",
+    "Negative prompt": "Negatif istem",
+    "Run": "Koşmak",
+    "Skip": "Atla",
+    "Interrupt": "Kesinti",
+    "Generate": "Oluşturmak",
+    "Style 1": "Stil 1",
+    "Style 2": "Stil 2",
+    "Label": "Etiket",
+    "File": "Dosya",
+    "Drop File Here": "Dosyayı Buraya Bırakın",
+    "-": "-",
+    "or": "veya",
+    "Click to Upload": "Yüklemek için Tıklayınız",
+    "Image": "Resim",
+    "Check progress": "İlerlemeyi kontrol edin",
+    "Check progress (first)": "İlerlemeyi kontrol edin (önce)",
+    "Sampling Steps": "Örnekleme Adımları",
+    "Sampling method": "Örnekleme yöntemi",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM hızlı",
+    "DPM adaptive": "DPM uyarlanabilir",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Genişlik",
+    "Height": "Yükseklik",
+    "Restore faces": "Yüzleri geri yükle",
+    "Tiling": "Döşeme",
+    "Highres. fix": "Highres. düzeltme",
+    "Firstpass width": "İlk geçiş genişliği",
+    "Firstpass height": "İlk geçiş yüksekliği",
+    "Denoising strength": "Denoising gücü",
+    "Batch count": "Parti sayısı",
+    "Batch size": "Parti büyüklüğü",
+    "CFG Scale": "CFG Ölçeği",
+    "Seed": "Tohum",
+    "Extra": "Ekstra",
+    "Variation seed": "Varyasyon tohumu",
+    "Variation strength": "Varyasyon gücü",
+    "Resize seed from width": "Tohumu genişlikten yeniden boyutlandırma",
+    "Resize seed from height": "Tohumu yükseklikten yeniden boyutlandırma",
+    "Script": "Senaryo",
+    "None": "Hiçbiri",
+    "Prompt matrix": "İstem matrisi",
+    "Prompts from file or textbox": "Dosyadan veya metin kutusundan istemler",
+    "X/Y plot": "X/Y grafiği",
+    "Put variable parts at start of prompt": "Değişken parçaları komut isteminin başına koyun",
+    "Show Textbox": "Metin Kutusunu Göster",
+    "File with inputs": "Girdileri içeren dosya",
+    "Prompts": "İpuçları",
+    "X type": "X tipi",
+    "Nothing": "Hiçbir şey",
+    "Var. seed": "Var. tohum",
+    "Var. strength": "Var. güç",
+    "Steps": "Adımlar",
+    "Prompt S/R": "Hızlı S/R",
+    "Prompt order": "Hızlı sipariş",
+    "Sampler": "Örnekleyici",
+    "Checkpoint name": "Kontrol noktası adı",
+    "Hypernetwork": "Hipernetwork",
+    "Hypernet str.": "Hypernet str.",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "Sigma dakika",
+    "Sigma max": "Sigma maksimum",
+    "Sigma noise": "Sigma gürültüsü",
+    "Eta": "Eta",
+    "Clip skip": "Klip atlama",
+    "Denoising": "Denoising",
+    "X values": "X değerleri",
+    "Y type": "Y tipi",
+    "Y values": "Y değerleri",
+    "Draw legend": "Gösterge çizin",
+    "Include Separate Images": "Ayrı Görseller Ekleyin",
+    "Keep -1 for seeds": "Tohumlar için -1'i saklayın",
+    "Drop Image Here": "Resmi Buraya Bırakın",
+    "Save": "Kaydet",
+    "Send to img2img": "img2img'ye gönder",
+    "Send to inpaint": "Inpaint'e gönder",
+    "Send to extras": "Ekstralara gönder",
+    "Make Zip when Save?": "Kaydederken Zip Yap?",
+    "Textbox": "Metin Kutusu",
+    "Interrogate\nCLIP": "Sorgula\nCLIP",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "Toplu img2img",
+    "Image for img2img": "img2img için resim",
+    "Image for inpainting with mask": "Maske ile inpainting için görüntü",
+    "Mask": "Maske",
+    "Mask blur": "Maske bulanıklığı",
+    "Mask mode": "Maske modu",
+    "Draw mask": "Maske çizin",
+    "Upload mask": "Maske yükle",
+    "Masking mode": "Maskeleme modu",
+    "Inpaint masked": "Maskeli inpaint",
+    "Inpaint not masked": "Boya maskelenmemiş",
+    "Masked content": "Maskelenmiş içerik",
+    "fill": "doldurun",
+    "original": "orijinal",
+    "latent noise": "gizli gürültü",
+    "latent nothing": "gizli hiçbir şey",
+    "Inpaint at full resolution": "Tam çözünürlükte inpaint",
+    "Inpaint at full resolution padding, pixels": "Tam çözünürlükte inpaint dolgu, piksel",
+    "Process images in a directory on the same machine where the server is running.": "Görüntüleri sunucunun çalıştığı makinedeki bir dizinde işleyin.",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Resimleri çıktı dizinine yazmak yerine normal şekilde kaydetmek için boş bir çıktı dizini kullanın.",
+    "Input directory": "Girdi dizini",
+    "Output directory": "Çıktı dizini",
+    "Resize mode": "Yeniden boyutlandırma modu",
+    "Just resize": "Sadece yeniden boyutlandır",
+    "Crop and resize": "Kırpma ve yeniden boyutlandırma",
+    "Resize and fill": "Yeniden boyutlandırın ve doldurun",
+    "img2img alternative test": "img2img alternatif test",
+    "Loopback": "Geri Döngü",
+    "Outpainting mk2": "Outpainting mk2",
+    "Poor man's outpainting": "Zavallı adamın dış boyaması",
+    "SD upscale": "SD lüks",
+    "should be 2 or lower.": "2 veya daha düşük olmalıdır.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Euler için `Örnekleme yöntemini` geçersiz kılın (bu yöntem bunun için oluşturulmuştur)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Prompt` değerini `orijinal prompt` ile aynı değere geçersiz kılma (ve `negatif prompt`)",
+    "Original prompt": "Orijinal bilgi istemi",
+    "Original negative prompt": "Orijinal negatif istem",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Örnekleme Adımlarını `Kod çözme adımları` ile aynı değere mi geçersiz kılıyorsunuz?",
+    "Decode steps": "Kod çözme adımları",
+    "Override `Denoising strength` to 1?": "`Denoising strength` değerini 1 olarak geçersiz kıl?",
+    "Decode CFG scale": "CFG ölçeğinin kodunu çöz",
+    "Randomness": "Rastgelelik",
+    "Sigma adjustment for finding noise for image": "Görüntü için gürültü bulmaya yönelik Sigma ayarı",
+    "Loops": "Döngüler",
+    "Denoising strength change factor": "Denoising gücü değişim faktörü",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Önerilen ayarlar: Örnekleme Adımları: 80-100, Örnekleyici: Euler a, Denoising gücü: 0.8",
+    "Pixels to expand": "Genişletilecek pikseller",
+    "Outpainting direction": "Dış boyama yönü",
+    "left": "sol",
+    "right": "doğru",
+    "up": "yukarı",
+    "down": "aşağı",
+    "Fall-off exponent (lower=higher detail)": "Düşme üssü (düşük=daha yüksek detay)",
+    "Color variation": "Renk çeşitliliği",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Görüntüyü boyutlarının iki katına yükseltir; döşeme boyutunu ayarlamak için genişlik ve yükseklik kaydırıcılarını kullanın",
+    "Tile overlap": "Karo örtüşmesi",
+    "Upscaler": "Upscaler",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "SwinIR 4x": "SwinIR 4x",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "Single Image": "Tek Resim",
+    "Batch Process": "Toplu İşlem",
+    "Batch from Directory": "Dizinden Toplu İş",
+    "Source": "Kaynak",
+    "Show result images": "Sonuç resimlerini göster",
+    "Scale by": "Ölçek tarafından",
+    "Scale to": "Ölçeklendirmek",
+    "Resize": "Yeniden Boyutlandır",
+    "Crop to fit": "Sığdırmak için kırpın",
+    "Upscaler 2 visibility": "Upscaler 2 görünürlüğü",
+    "GFPGAN visibility": "GFPGAN görünürlüğü",
+    "CodeFormer visibility": "CodeFormer görünürlüğü",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer ağırlığı (0 = maksimum etki, 1 = minimum etki)",
+    "Open output directory": "Çıktı dizinini aç",
+    "Send to txt2img": "txt2img'ye gönder",
+    "A merger of the two checkpoints will be generated in your": "İki kontrol noktasının bir birleşimi sizin kontrol noktanızda oluşturulacaktır.",
+    "checkpoint": "kontrol noktası",
+    "directory.": "dizin.",
+    "Primary model (A)": "Birincil model (A)",
+    "Secondary model (B)": "İkincil model (B)",
+    "Tertiary model (C)": "Tersiyer model (C)",
+    "Custom Name (Optional)": "Özel Ad (İsteğe Bağlı)",
+    "Multiplier (M) - set to 0 to get model A": "Çarpan (M) - A modelini elde etmek için 0'a ayarlayın",
+    "Interpolation Method": "İnterpolasyon Yöntemi",
+    "Weighted sum": "Ağırlıklı toplam",
+    "Add difference": "Farklılık ekleyin",
+    "Save as float16": "float16 olarak kaydet",
+    "See": "Bkz.",
+    "wiki": "wiki",
+    "for detailed explanation.": "ayrıntılı açıklama için.",
+    "Create embedding": "Yerleştirme oluşturma",
+    "Create hypernetwork": "Hipernet oluşturun",
+    "Preprocess images": "Görüntüleri ön işleme",
+    "Name": "İsim",
+    "Initialization text": "Başlatma metni",
+    "Number of vectors per token": "Belirteç başına vektör sayısı",
+    "Overwrite Old Embedding": "Eski Yerleştirmenin Üzerine Yaz",
+    "Modules": "Modüller",
+    "Enter hypernetwork layer structure": "Hipernetwork katman yapısına girin",
+    "Select activation function of hypernetwork": "Hipernetwork'ün aktivasyon fonksiyonunu seçin",
+    "linear": "doğrusal",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "Add layer normalization": "Katman normalizasyonu ekleyin",
+    "Use dropout": "Bırakmayı kullanın",
+    "Overwrite Old Hypernetwork": "Eski Hipernetwork'ün Üzerine Yazma",
+    "Source directory": "Kaynak dizini",
+    "Destination directory": "Hedef dizini",
+    "Existing Caption txt Action": "Mevcut Başlık txt Eylem",
+    "ignore": "görmezden gel",
+    "copy": "kopya",
+    "prepend": "prepend",
+    "append": "ekle",
+    "Create flipped copies": "Ters çevrilmiş kopyalar oluşturun",
+    "Split oversized images": "Büyük boyutlu görüntüleri bölme",
+    "Use BLIP for caption": "Başlık için BLIP kullanın",
+    "Use deepbooru for caption": "Başlık için deepbooru kullanın",
+    "Split image threshold": "Bölünmüş görüntü eşiği",
+    "Split image overlap ratio": "Bölünmüş görüntü örtüşme oranı",
+    "Preprocess": "Ön işlem",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Bir gömme veya Hipernetwork eğitin; 1:1 oranlı görüntülerin bulunduğu bir dizin belirtmelisiniz",
+    "[wiki]": "[wiki]",
+    "Embedding": "Yerleştirme",
+    "Embedding Learning rate": "Gömme Öğrenme oranı",
+    "Hypernetwork Learning rate": "Hypernetwork Öğrenme oranı",
+    "Dataset directory": "Veri seti dizini",
+    "Log directory": "Günlük dizini",
+    "Prompt template file": "Komut istemi şablon dosyası",
+    "Max steps": "Maksimum adım",
+    "Save an image to log directory every N steps, 0 to disable": "Her N adımda bir görüntüyü günlük dizinine kaydet, 0 devre dışı bırakmak için",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Katıştırmanın bir kopyasını her N adımda bir günlük dizinine kaydedin, devre dışı bırakmak için 0",
+    "Save images with embedding in PNG chunks": "Görüntüleri PNG parçalarına yerleştirerek kaydedin",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Önizleme yaparken txt2img sekmesinden parametreleri (istem, vb...) okuma",
+    "Train Hypernetwork": "Tren Hipernetwork",
+    "Train Embedding": "Tren Gömme",
+    "Apply settings": "Ayarları uygula",
+    "Saving images/grids": "Görüntüleri/gridleri kaydetme",
+    "Always save all generated images": "Oluşturulan tüm görüntüleri her zaman kaydedin",
+    "File format for images": "Görüntüler için dosya formatı",
+    "Images filename pattern": "Görüntü dosya adı deseni",
+    "Add number to filename when saving": "Kaydederken dosya adına numara ekle",
+    "Always save all generated image grids": "Oluşturulan tüm görüntü ızgaralarını her zaman kaydedin",
+    "File format for grids": "Izgaralar için dosya formatı",
+    "Add extended info (seed, prompt) to filename when saving grid": "Izgarayı kaydederken dosya adına genişletilmiş bilgi (tohum, istem) ekleyin",
+    "Do not save grids consisting of one picture": "Tek resimden oluşan ızgaraları kaydetmeyin",
+    "Prevent empty spots in grid (when set to autodetect)": "Izgaradaki boş noktaları önleme (otomatik algılamaya ayarlandığında)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Izgara satır sayısı; otomatik algılama için -1, yığın boyutuyla aynı olması için 0 kullanın",
+    "Save text information about generation parameters as chunks to png files": "Üretim parametreleri hakkındaki metin bilgilerini png dosyalarına parçalar halinde kaydedin",
+    "Create a text file next to every image with generation parameters.": "Oluşturma parametreleri ile her görüntünün yanında bir metin dosyası oluşturun.",
+    "Save a copy of image before doing face restoration.": "Yüz restorasyonu yapmadan önce görüntünün bir kopyasını kaydedin.",
+    "Quality for saved jpeg images": "Kaydedilen jpeg görüntüleri için kalite",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "PNG görüntüsü 4MB'den büyükse veya herhangi bir boyut 4000'den büyükse, ölçeği küçültün ve kopyayı JPG olarak kaydedin",
+    "Use original name for output filename during batch process in extras tab": "Ekstralar sekmesinde toplu işlem sırasında çıktı dosya adı için orijinal adı kullan",
+    "When using 'Save' button, only save a single selected image": "'Kaydet' düğmesini kullanırken, yalnızca seçilen tek bir resmi kaydedin",
+    "Do not add watermark to images": "Görüntülere filigran eklemeyin",
+    "Paths for saving": "Tasarruf için yollar",
+    "Output directory for images; if empty, defaults to three directories below": "Görüntüler için çıktı dizini; boşsa, varsayılan olarak aşağıdaki üç dizine gider",
+    "Output directory for txt2img images": "txt2img görüntüleri için çıktı dizini",
+    "Output directory for img2img images": "img2img görüntüleri için çıktı dizini",
+    "Output directory for images from extras tab": "Ekstralar sekmesindeki görüntüler için çıktı dizini",
+    "Output directory for grids; if empty, defaults to two directories below": "Izgaralar için çıktı dizini; boşsa, varsayılan olarak aşağıdaki iki dizine gider",
+    "Output directory for txt2img grids": "txt2img ızgaraları için çıktı dizini",
+    "Output directory for img2img grids": "img2img ızgaraları için çıktı dizini",
+    "Directory for saving images using the Save button": "Kaydet düğmesini kullanarak görüntüleri kaydetmek için dizin",
+    "Saving to a directory": "Bir dizine kaydetme",
+    "Save images to a subdirectory": "Görüntüleri bir alt dizine kaydetme",
+    "Save grids to a subdirectory": "Izgaraları bir alt dizine kaydetme",
+    "When using \"Save\" button, save images to a subdirectory": "\"Kaydet\" düğmesini kullanırken, görüntüleri bir alt dizine kaydedin",
+    "Directory name pattern": "Dizin adı kalıbı",
+    "Max prompt words for [prompt_words] pattern": "prompt_words] kalıbı için maksimum istem sözcükleri",
+    "Upscaling": "Yükseltme",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN yükselticileri için döşeme boyutu. 0 = döşeme yok.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN yükselticileri için piksel cinsinden döşeme örtüşmesi. Düşük değerler = görünür dikiş.",
+    "Tile size for all SwinIR.": "Tüm SwinIR için döşeme boyutu.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR için piksel cinsinden döşeme örtüşmesi. Düşük değerler = görünür dikiş.",
+    "LDSR processing steps. Lower = faster": "LDSR işleme adımları. Düşük = daha hızlı",
+    "Upscaler for img2img": "img2img için üst ölçekleyici",
+    "Upscale latent space image when doing hires. fix": "İşe alım yaparken gizli alan görüntüsünü yükselt. düzelt",
+    "Face restoration": "Yüz restorasyonu",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer ağırlık parametresi; 0 = maksimum etki; 1 = minimum etki",
+    "Move face restoration model from VRAM into RAM after processing": "İşlemden sonra yüz restorasyon modelini VRAM'den RAM'e taşıma",
+    "System": "Sistem",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Üretim sırasında saniye başına VRAM kullanım yoklamaları. Devre dışı bırakmak için 0 olarak ayarlayın.",
+    "Always print all generation info to standard output": "Tüm üretim bilgilerini her zaman standart çıktıya yazdır",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Konsola tüm iş için ilerlemeyi gösteren ikinci bir ilerleme çubuğu ekleyin.",
+    "Training": "Eğitim",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Hiperneti eğitirken VAE ve CLIP'i RAM'e taşıyın. VRAM'den tasarruf sağlar.",
+    "Filename word regex": "Dosya adı kelime regex",
+    "Filename join string": "Dosya adı birleştirme dizesi",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Epok başına tek bir girdi görüntüsü için tekrar sayısı; yalnızca epok numarasını görüntülemek için kullanılır",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Her N adımda bir günlük dizinine kaybı içeren bir csv kaydedin, devre dışı bırakmak için 0",
+    "Stable Diffusion": "Kararlı Difüzyon",
+    "Checkpoints to cache in RAM": "RAM'de önbelleğe alınacak kontrol noktaları",
+    "Hypernetwork strength": "Hipernetwork gücü",
+    "Apply color correction to img2img results to match original colors.": "Orijinal renklerle eşleştirmek için img2img sonuçlarına renk düzeltmesi uygulayın.",
+    "Save a copy of image before applying color correction to img2img results": "img2img sonuçlarına renk düzeltmesi uygulamadan önce görüntünün bir kopyasını kaydedin",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "img2img ile, kaydırıcının belirttiği adım miktarını tam olarak yapın (normalde daha az denoising ile daha az yaparsınız).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Daha keskin ve temiz sonuçlar için K örnekleyicilerinde nicelemeyi etkinleştirin. Bu, mevcut tohumları değiştirebilir. Uygulamak için yeniden başlatma gerektirir.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Vurgu: modelin metne daha fazla dikkat etmesini sağlamak için (metin) ve daha az dikkat etmesini sağlamak için [metin] kullanın",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Eski vurgu uygulamasını kullanın. Eski tohumları yeniden üretmek faydalı olabilir.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "K-difüzyon örnekleyicilerinin tek bir görüntü oluştururken olduğu gibi toplu halde aynı görüntüleri üretmesini sağlayın",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "75'ten fazla belirteç kullanıldığında n belirteç içindeki son virgülden itibaren dolgu yaparak tutarlılığı artırın",
+    "Filter NSFW content": "NSFW içeriği filtreleme",
+    "Stop At last layers of CLIP model": "Durdur CLIP modelinin son katmanlarında",
+    "Interrogate Options": "Sorgulama Seçenekleri",
+    "Interrogate: keep models in VRAM": "Sorgula: modelleri VRAM'de tut",
+    "Interrogate: use artists from artists.csv": "Sorgula: artists.csv dosyasındaki sanatçıları kullan",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: sonuçlara eşleşen model etiketlerinin sıralarını dahil et (Başlık tabanlı sorgulayıcılar üzerinde etkisi yoktur).",
+    "Interrogate: num_beams for BLIP": "Sorgula: BLIP için num_beams",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Sorgula: minimum açıklama uzunluğu (sanatçılar vb. hariç)",
+    "Interrogate: maximum description length": "Sorgula: maksimum açıklama uzunluğu",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: metin dosyasındaki maksimum satır sayısı (0 = Sınır yok)",
+    "Interrogate: deepbooru score threshold": "Sorgula: deepbooru puan eşiği",
+    "Interrogate: deepbooru sort alphabetically": "Sorgula: deepbooru alfabetik olarak sırala",
+    "use spaces for tags in deepbooru": "deepbooru'da etiketler için boşluk kullanın",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "deepbooru'da kaçış (\\) parantezleri (böylece vurgu için değil, gerçek parantez olarak kullanılırlar)",
+    "User interface": "Kullanıcı arayüzü",
+    "Show progressbar": "İlerleme çubuğunu göster",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Her N örnekleme adımında görüntü oluşturma ilerlemesini gösterir. Devre dışı bırakmak için 0 olarak ayarlayın.",
+    "Show previews of all images generated in a batch as a grid": "Bir toplu işte oluşturulan tüm görüntülerin önizlemelerini ızgara olarak göster",
+    "Show grid in results for web": "Web için sonuçlarda ızgarayı göster",
+    "Do not show any images in results for web": "Web için sonuçlarda herhangi bir resim gösterme",
+    "Add model hash to generation information": "Üretim bilgilerine model karması ekleyin",
+    "Add model name to generation information": "Üretim bilgilerine model adı ekleme",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Üretim parametrelerini metinden kullanıcı arayüzüne okurken (PNG bilgisinden veya yapıştırılan metinden), seçilen modeli/denetim noktasını değiştirmeyin.",
+    "Font for image grids that have text": "Metin içeren görüntü ızgaraları için yazı tipi",
+    "Enable full page image viewer": "Tam sayfa resim görüntüleyiciyi etkinleştir",
+    "Show images zoomed in by default in full page image viewer": "Tam sayfa resim görüntüleyicide resimleri varsayılan olarak yakınlaştırılmış olarak gösterme",
+    "Show generation progress in window title.": "Pencere başlığında üretim ilerlemesini göster.",
+    "Quicksettings list": "Hızlı ayarlar listesi",
+    "Localization (requires restart)": "Yerelleştirme (yeniden başlatma gerektirir)",
+    "ko_KR": "ko_KR",
+    "ru_RU": "ru_RU",
+    "es_ES": "es_ES",
+    "ja_JP": "ja_JP",
+    "ar_AR": "ar_AR",
+    "Sampler parameters": "Örnekleyici parametreleri",
+    "Hide samplers in user interface (requires restart)": "Kullanıcı arayüzünde örnekleyicileri gizle (yeniden başlatma gerektirir)",
+    "eta (noise multiplier) for DDIM": "DDIM için eta (gürültü çarpanı)",
+    "eta (noise multiplier) for ancestral samplers": "atasal örnekleyiciler için eta (gürültü çarpanı)",
+    "img2img DDIM discretize": "img2img DDIM discretize",
+    "uniform": "üniforma",
+    "quad": "dörtlü",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma gürültüsü",
+    "Eta noise seed delta": "Eta gürültü tohum deltası",
+    "Request browser notifications": "Tarayıcı bildirimleri isteyin",
+    "Download localization template": "Yerelleştirme şablonunu indirin",
+    "Reload custom script bodies (No ui updates, No restart)": "Özel komut dosyası gövdelerini yeniden yükle (Kullanıcı arayüzü güncellemesi yok, yeniden başlatma yok)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradio'yu yeniden başlatın ve bileşenleri yenileyin (yalnızca Özel Komut Dosyaları, ui.py, js ve css)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "İstem (oluşturmak için Ctrl+Enter veya Alt+Enter tuşlarına basın)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Negatif istem (oluşturmak için Ctrl+Enter veya Alt+Enter tuşlarına basın)",
+    "Add a random artist to the prompt.": "Komut istemine rastgele bir sanatçı ekleyin.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Kullanıcı arayüzüne istemden veya istem boşsa son üretimden üretim parametrelerini okuyun.",
+    "Save style": "Stil kaydet",
+    "Apply selected styles to current prompt": "Seçilen stilleri geçerli komut istemine uygulama",
+    "Stop processing current image and continue processing.": "Geçerli görüntüyü işlemeyi durdurun ve işlemeye devam edin.",
+    "Stop processing images and return any results accumulated so far.": "Görüntüleri işlemeyi durdurun ve o ana kadar biriken tüm sonuçları döndürün.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Uygulanacak stil; stillerin hem pozitif hem de negatif istemler için bileşenleri vardır ve her ikisine de uygulanır",
+    "Do not do anything special": "Özel bir şey yapmayın",
+    "Which algorithm to use to produce the image": "Görüntüyü üretmek için hangi algoritmanın kullanılacağı",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - çok yaratıcı, adım sayısına bağlı olarak her biri tamamen farklı bir resim elde edebilir, adımları 30-40'tan daha yükseğe ayarlamak yardımcı olmaz",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Difüzyon Örtük Modelleri - en iyi inpainting",
+    "Produce an image that can be tiled.": "Döşenebilen bir görüntü üretin.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Bir görüntüyü kısmen daha düşük çözünürlükte oluşturmak, büyütmek ve ardından kompozisyonu değiştirmeden ayrıntıları iyileştirmek için iki adımlı bir işlem kullanın",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Algoritmanın resmin içeriğine ne kadar az saygı göstermesi gerektiğini belirler. 0'da hiçbir şey değişmez ve 1'de ilgisiz bir görüntü elde edersiniz. 1,0'ın altındaki değerlerde işleme, Örnekleme Adımları kaydırıcısının belirttiğinden daha az adım atacaktır.",
+    "How many batches of images to create": "Kaç görüntü grubu oluşturulacağı",
+    "How many image to create in a single batch": "Tek bir partide kaç görüntü oluşturulacağı",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Sınıflandırıcı Serbest Rehberlik Ölçeği - görüntünün istemle ne kadar uyumlu olması gerektiği - düşük değerler daha yaratıcı sonuçlar üretir",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Rastgele sayı üretecinin çıktısını belirleyen bir değer - başka bir resimle aynı parametrelere ve tohuma sahip bir resim oluşturursanız, aynı sonucu alırsınız",
+    "Set seed to -1, which will cause a new random number to be used every time": "Tohum değerini -1 olarak ayarlayın, bu her seferinde yeni bir rastgele sayı kullanılmasına neden olacaktır",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Son nesilden tohumu yeniden kullanın, çoğunlukla rastgele ise kullanışlıdır",
+    "Seed of a different picture to be mixed into the generation.": "Nesle karıştırılacak farklı bir resmin tohumu.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Ne kadar güçlü bir varyasyon üretileceği. 0'da hiçbir etki olmayacaktır. 1'de, varyasyon tohumu ile tam bir resim elde edersiniz (sadece bir şey alacağınız atasal örnekleyiciler hariç).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Belirtilen çözünürlükte aynı tohumla üretilecek olana benzer bir resim üretme girişiminde bulunun",
+    "Separate values for X axis using commas.": "X ekseni için değerleri virgül kullanarak ayırın.",
+    "Separate values for Y axis using commas.": "Y ekseni için değerleri virgül kullanarak ayırın.",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Görüntüyü bir dizine (varsayılan - log/images) ve üretim parametrelerini csv dosyasına yazın.",
+    "Open images output directory": "Görüntü çıktı dizinini açın",
+    "How much to blur the mask before processing, in pixels.": "İşlemeden önce maskenin piksel cinsinden ne kadar bulanıklaştırılacağı.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Kararlı Difüzyon ile işlemeden önce maskelenmiş alanın içine ne konulacağı.",
+    "fill it with colors of the image": "Görüntünün renkleriyle doldurun",
+    "keep whatever was there originally": "başlangıçta orada ne varsa saklayın",
+    "fill it with latent space noise": "gizli alan gürültüsü ile doldurun",
+    "fill it with latent space zeroes": "gizli uzay sıfırları ile doldurun",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Maskelenmiş bölgeyi hedef çözünürlüğe yükseltme, inpainting yapma, ölçeği küçültme ve orijinal görüntüye yapıştırma",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Görüntüyü hedef çözünürlüğe göre yeniden boyutlandırın. Yükseklik ve genişlik eşleşmediği sürece, yanlış en boy oranı elde edersiniz.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Görüntüyü, hedef çözünürlüğün tamamı görüntüyle dolacak şekilde yeniden boyutlandırın. Dışarıda kalan kısımları kırpın.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Görüntünün tamamı hedef çözünürlüğün içinde olacak şekilde görüntüyü yeniden boyutlandırın. Boş alanı görüntünün renkleriyle doldurun.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Bir görüntüyü işlemeyi kaç kez tekrarlamak ve bir sonraki yineleme için girdi olarak kullanmak",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "Geri döngü modunda, her döngüde denoising gücü bu değerle çarpılır. <1 çeşitliliğin azalması anlamına gelir, böylece diziniz sabit bir resme yakınsayacaktır. >1'den büyük olması çeşitliliğin artması anlamına gelir, böylece sekansınız gittikçe daha kaotik hale gelecektir.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD yükseltme için karolar arasında piksel olarak ne kadar örtüşme olmalıdır. Döşemeler, tekrar tek bir resimde birleştirildiklerinde açıkça görülebilen bir dikiş olmayacak şekilde üst üste biner.",
+    "A directory on the same machine where the server is running.": "Sunucunun çalıştığı makinedeki bir dizin.",
+    "Leave blank to save images to the default path.": "Görüntüleri varsayılan yola kaydetmek için boş bırakın.",
+    "Result = A * (1 - M) + B * M": "Sonuç = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Sonuç = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "1. ve son rakam 1 olmalıdır. örn:'1, 2, 1'",
+    "Path to directory with input images": "Girdi resimlerinin bulunduğu dizinin yolu",
+    "Path to directory where to write outputs": "Çıktıların yazılacağı dizinin yolu",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Görüntülerin dosya adlarının nasıl seçileceğini tanımlamak için aşağıdaki etiketleri kullanın: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; varsayılan için boş bırakın.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Bu seçenek etkinleştirilirse, oluşturulan görüntülere filigran eklenmeyecektir. Uyarı: filigran eklemezseniz, etik olmayan bir şekilde davranıyor olabilirsiniz.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Görüntüler ve ızgaralar için alt dizinlerin nasıl seçileceğini tanımlamak için aşağıdaki etiketleri kullanın: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; varsayılan için boş bırakın.",
+    "Restore low quality faces using GFPGAN neural network": "GFPGAN sinir ağını kullanarak düşük kaliteli yüzleri geri yükleme",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Bu düzenli ifade, dosya adından sözcükleri ayıklamak için kullanılır ve bunlar aşağıdaki seçenek kullanılarak eğitim için kullanılan etiket metnine birleştirilir. Dosya adı metnini olduğu gibi tutmak için boş bırakın.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Bu dize, yukarıdaki seçenek etkinleştirilirse bölünmüş kelimeleri tek bir satırda birleştirmek için kullanılacaktır.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Normal ayar sekmesi yerine üstteki hızlı erişim çubuğuna gitmesi gereken ayarlar için virgülle ayrılmış ayar adlarının listesi. Ayar adları için modules/shared.py dosyasına bakın. Uygulanması için yeniden başlatma gerekir.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Bu değer sıfır değilse, tohuma eklenecek ve Eta ile örnekleyiciler kullanılırken gürültüler için RNG'yi başlatmak için kullanılacaktır. Bunu daha fazla görüntü çeşitliliği üretmek için kullanabilir veya ne yaptığınızı biliyorsanız diğer yazılımların görüntülerini eşleştirmek için kullanabilirsiniz.."
+}
-- 
cgit v1.2.1


From ef53f7ffcfe022e6f392a6161b6cd926e81ad292 Mon Sep 17 00:00:00 2001
From: camenduru <54370274+camenduru@users.noreply.github.com>
Date: Wed, 26 Oct 2022 12:24:04 +0300
Subject: Fix Turkish Localization
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

it's good enough to merge 🎉
---
 localizations/tr_TR.json | 50 ++++++++++++++++++++++++------------------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/localizations/tr_TR.json b/localizations/tr_TR.json
index 511b8882..74cdf056 100644
--- a/localizations/tr_TR.json
+++ b/localizations/tr_TR.json
@@ -5,8 +5,8 @@
     "❮": "❮",
     "❯": "❯",
     "Loading...": "Yükleniyor...",
-    "view": "görünüm",
-    "api": "api",
+    "view": "arayüz",
+    "api": "",
     "•": "-",
     "built with gradio": "gradio ile inşa edildi",
     "Stable Diffusion checkpoint": "Kararlı Difüzyon kontrol noktası",
@@ -14,15 +14,15 @@
     "img2img": "img2img",
     "Extras": "Ekstralar",
     "PNG Info": "PNG Bilgisi",
-    "Checkpoint Merger": "Checkpoint Birleşmesi",
-    "Train": "Tren",
+    "Checkpoint Merger": "Checkpoint Birleştir",
+    "Train": "Eğitim",
     "Settings": "Ayarlar",
     "Prompt": "İstem",
     "Negative prompt": "Negatif istem",
     "Run": "Koşmak",
     "Skip": "Atla",
-    "Interrupt": "Kesinti",
-    "Generate": "Oluşturmak",
+    "Interrupt": "Durdur",
+    "Generate": "Oluştur",
     "Style 1": "Stil 1",
     "Style 2": "Stil 2",
     "Label": "Etiket",
@@ -33,7 +33,7 @@
     "Click to Upload": "Yüklemek için Tıklayınız",
     "Image": "Resim",
     "Check progress": "İlerlemeyi kontrol edin",
-    "Check progress (first)": "İlerlemeyi kontrol edin (önce)",
+    "Check progress (first)": "Önce ilerlemeyi kontrol edin",
     "Sampling Steps": "Örnekleme Adımları",
     "Sampling method": "Örnekleme yöntemi",
     "Euler a": "Euler a",
@@ -51,22 +51,22 @@
     "PLMS": "PLMS",
     "Width": "Genişlik",
     "Height": "Yükseklik",
-    "Restore faces": "Yüzleri geri yükle",
-    "Tiling": "Döşeme",
+    "Restore faces": "Yüzleri düzeltme",
+    "Tiling": "Döşeme Oluştur",
     "Highres. fix": "Highres. düzeltme",
     "Firstpass width": "İlk geçiş genişliği",
     "Firstpass height": "İlk geçiş yüksekliği",
-    "Denoising strength": "Denoising gücü",
-    "Batch count": "Parti sayısı",
-    "Batch size": "Parti büyüklüğü",
+    "Denoising strength": "Gürültü arındırma gücü",
+    "Batch count": "Grup sayısı",
+    "Batch size": "Grup büyüklüğü",
     "CFG Scale": "CFG Ölçeği",
     "Seed": "Tohum",
     "Extra": "Ekstra",
     "Variation seed": "Varyasyon tohumu",
     "Variation strength": "Varyasyon gücü",
-    "Resize seed from width": "Tohumu genişlikten yeniden boyutlandırma",
-    "Resize seed from height": "Tohumu yükseklikten yeniden boyutlandırma",
-    "Script": "Senaryo",
+    "Resize seed from width": "Tohumu genişlik ile yeniden boyutlandırma",
+    "Resize seed from height": "Tohumu yükseklik ile yeniden boyutlandırma",
+    "Script": "Scriptler",
     "None": "Hiçbiri",
     "Prompt matrix": "İstem matrisi",
     "Prompts from file or textbox": "Dosyadan veya metin kutusundan istemler",
@@ -80,8 +80,8 @@
     "Var. seed": "Var. tohum",
     "Var. strength": "Var. güç",
     "Steps": "Adımlar",
-    "Prompt S/R": "Hızlı S/R",
-    "Prompt order": "Hızlı sipariş",
+    "Prompt S/R": "İstem S/R",
+    "Prompt order": "İstem sırası",
     "Sampler": "Örnekleyici",
     "Checkpoint name": "Kontrol noktası adı",
     "Hypernetwork": "Hipernetwork",
@@ -176,7 +176,7 @@
     "Source": "Kaynak",
     "Show result images": "Sonuç resimlerini göster",
     "Scale by": "Ölçek tarafından",
-    "Scale to": "Ölçeklendirmek",
+    "Scale to": "Ölçeklendir",
     "Resize": "Yeniden Boyutlandır",
     "Crop to fit": "Sığdırmak için kırpın",
     "Upscaler 2 visibility": "Upscaler 2 görünürlüğü",
@@ -190,16 +190,16 @@
     "directory.": "dizin.",
     "Primary model (A)": "Birincil model (A)",
     "Secondary model (B)": "İkincil model (B)",
-    "Tertiary model (C)": "Tersiyer model (C)",
+    "Tertiary model (C)": "Üçüncü model (C)",
     "Custom Name (Optional)": "Özel Ad (İsteğe Bağlı)",
     "Multiplier (M) - set to 0 to get model A": "Çarpan (M) - A modelini elde etmek için 0'a ayarlayın",
     "Interpolation Method": "İnterpolasyon Yöntemi",
     "Weighted sum": "Ağırlıklı toplam",
     "Add difference": "Farklılık ekleyin",
     "Save as float16": "float16 olarak kaydet",
-    "See": "Bkz.",
+    "See": "Bkz. ",
     "wiki": "wiki",
-    "for detailed explanation.": "ayrıntılı açıklama için.",
+    "for detailed explanation.": " ayrıntılı açıklama için.",
     "Create embedding": "Yerleştirme oluşturma",
     "Create hypernetwork": "Hipernet oluşturun",
     "Preprocess images": "Görüntüleri ön işleme",
@@ -284,7 +284,7 @@
     "Max prompt words for [prompt_words] pattern": "prompt_words] kalıbı için maksimum istem sözcükleri",
     "Upscaling": "Yükseltme",
     "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN yükselticileri için döşeme boyutu. 0 = döşeme yok.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN yükselticileri için piksel cinsinden döşeme örtüşmesi. Düşük değerler = görünür dikiş.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN yükselticileri için piksel cinsinden döşeme örtüşmesi. Düşük değerler = görünür bağlantı hattı.",
     "Tile size for all SwinIR.": "Tüm SwinIR için döşeme boyutu.",
     "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR için piksel cinsinden döşeme örtüşmesi. Düşük değerler = görünür dikiş.",
     "LDSR processing steps. Lower = faster": "LDSR işleme adımları. Düşük = daha hızlı",
@@ -351,7 +351,7 @@
     "Sampler parameters": "Örnekleyici parametreleri",
     "Hide samplers in user interface (requires restart)": "Kullanıcı arayüzünde örnekleyicileri gizle (yeniden başlatma gerektirir)",
     "eta (noise multiplier) for DDIM": "DDIM için eta (gürültü çarpanı)",
-    "eta (noise multiplier) for ancestral samplers": "atasal örnekleyiciler için eta (gürültü çarpanı)",
+    "eta (noise multiplier) for ancestral samplers": "eta örnekleyiciler için eta (gürültü çarpanı)",
     "img2img DDIM discretize": "img2img DDIM discretize",
     "uniform": "üniforma",
     "quad": "dörtlü",
@@ -360,8 +360,8 @@
     "sigma noise": "sigma gürültüsü",
     "Eta noise seed delta": "Eta gürültü tohum deltası",
     "Request browser notifications": "Tarayıcı bildirimleri isteyin",
-    "Download localization template": "Yerelleştirme şablonunu indirin",
-    "Reload custom script bodies (No ui updates, No restart)": "Özel komut dosyası gövdelerini yeniden yükle (Kullanıcı arayüzü güncellemesi yok, yeniden başlatma yok)",
+    "Download localization template": "Dil şablonunu indirin",
+    "Reload custom script bodies (No ui updates, No restart)": "Kişisel komut dosyası gövdelerini yeniden yükle (Kullanıcı arayüzü güncellemesi yok, yeniden başlatma yok)",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradio'yu yeniden başlatın ve bileşenleri yenileyin (yalnızca Özel Komut Dosyaları, ui.py, js ve css)",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "İstem (oluşturmak için Ctrl+Enter veya Alt+Enter tuşlarına basın)",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Negatif istem (oluşturmak için Ctrl+Enter veya Alt+Enter tuşlarına basın)",
-- 
cgit v1.2.1


From 7e0e4d21d469b2d08af1dd302f6d56388c525ba3 Mon Sep 17 00:00:00 2001
From: Leo Mozoloa <leo.drrty@gmail.com>
Date: Tue, 25 Oct 2022 21:51:22 +0200
Subject: Prevent people from just saying "latest version"

in the bug report form
---
 .github/ISSUE_TEMPLATE/bug_report.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
index 9c2ff313..ed372f22 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -44,7 +44,7 @@ body:
     id: commit
     attributes:
       label: Commit where the problem happens
-      description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
+      description: Which commit are you running ? (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
     validations:
       required: true
   - type: dropdown
-- 
cgit v1.2.1


From 366fe8dbd08e88ea96d9017886e7803e2bcb0102 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Sat, 22 Oct 2022 01:29:33 +0900
Subject: Create zh-hans.json

---
 localizations/zh-hans.json | 456 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 456 insertions(+)
 create mode 100644 localizations/zh-hans.json

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
new file mode 100644
index 00000000..113df5f7
--- /dev/null
+++ b/localizations/zh-hans.json
@@ -0,0 +1,456 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "载入中...",
+    "view": "查看",
+    "api": "api",
+    "•": "•",
+    "built with gradio": "基于 Gradio 库编译",
+    "Stable Diffusion checkpoint": "Stable Diffusion 模型(ckpt)",
+    "txt2img": "文转图",
+    "img2img": "图转图",
+    "Extras": "后处理",
+    "PNG Info": "PNG 信息",
+    "History": "历史记录",
+    "Checkpoint Merger": "模型(ckpt)合并工具",
+    "Train": "训练",
+    "Settings": "设置",
+    "Prompt": "提示词",
+    "Negative prompt": "否定提示词",
+    "Run": "运行",
+    "Skip": "略过",
+    "Interrupt": "中断",
+    "Generate": "生成",
+    "Style 1": "模版风格 1",
+    "Style 2": "模版风格 2",
+    "Label": "标签",
+    "File": "文件",
+    "Drop File Here": "拖拽文件到这里",
+    "-": "-",
+    "or": "或",
+    "Click to Upload": "点击上传",
+    "Image": "图像",
+    "Check progress": "查看进度",
+    "Check progress (first)": "查看进度 (1st)",
+    "Sampling Steps": "采样迭代步数",
+    "Sampling method": "采样方法",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "宽度",
+    "Height": "高度",
+    "Restore faces": "面部修复",
+    "Tiling": "密铺",
+    "Highres. fix": "高分辨率修复",
+    "Firstpass width": "第一遍宽度",
+    "Firstpass height": "第一遍高度",
+    "Denoising strength": "降噪(模糊)强度",
+    "Batch count": "批量数量",
+    "Batch size": "批量大小",
+    "CFG Scale": "提示词相关性(CFG Scale)",
+    "Seed": "随机种子",
+    "Extra": "额外参数",
+    "Variation seed": "差异随机种子",
+    "Variation strength": "差异强度",
+    "Resize seed from width": "自宽度缩放种子",
+    "Resize seed from height": "自高度缩放种子",
+    "Open for Clip Aesthetic!": "打开美学Clip!",
+    "▼": "▼",
+    "Aesthetic weight": "美学权重",
+    "Aesthetic steps": "美学迭代步数",
+    "Aesthetic learning rate": "美学学习率",
+    "Slerp interpolation": "Slerp 插值",
+    "Aesthetic imgs embedding": "美学图片 embedding",
+    "None": "无",
+    "Aesthetic text for imgs": "描述图片的美学文字",
+    "Slerp angle": "Slerp 角度",
+    "Is negative text": "是否定类文字",
+    "Script": "脚本",
+    "Prompt matrix": "提示词矩阵",
+    "Prompts from file or textbox": "来自文本框或文件载入提示词",
+    "X/Y plot": "X/Y 图表",
+    "Put variable parts at start of prompt": "把变量部分放在提示词文本的开头",
+    "Show Textbox": "显示文本框",
+    "File with inputs": "含输入内容的文件",
+    "Prompts": "提示词",
+    "X type": "X轴类型",
+    "Nothing": "无",
+    "Var. seed": "差异种子",
+    "Var. strength": "差异强度",
+    "Steps": "迭代步数",
+    "Prompt S/R": "提示词替换",
+    "Prompt order": "提示词顺序",
+    "Sampler": "采样器",
+    "Checkpoint name": "模型(ckpt)名",
+    "Hypernetwork": "Hypernetwork",
+    "Hypernet str.": "Hypernetwork 强度",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "最小 Sigma",
+    "Sigma max": "最大 Sigma",
+    "Sigma noise": "Sigma noise",
+    "Eta": "Eta",
+    "Clip skip": "Clip 略过",
+    "Denoising": "降噪",
+    "X values": "X轴数值",
+    "Y type": "Y轴类型",
+    "Y values": "Y轴数值",
+    "Draw legend": "在图表中包括轴标题",
+    "Include Separate Images": "包括独立的图像",
+    "Keep -1 for seeds": "保持随机种子为-1",
+    "Drop Image Here": "拖拽图像到此",
+    "Save": "保存",
+    "Send to img2img": "传送到图转图",
+    "Send to inpaint": "传送到内补绘制",
+    "Send to extras": "传送到后处理",
+    "Make Zip when Save?": "保存时生成压缩档?",
+    "Textbox": "文本框",
+    "Interrogate\nCLIP": "推断提示词\nCLIP",
+    "Interrogate\nDeepBooru": "推断提示词\nDeepBooru",
+    "Inpaint": "内补绘制",
+    "Batch img2img": "批量图转图",
+    "Image for img2img": "图转图的图像",
+    "Image for inpainting with mask": "用在内补绘制蒙版内容的图像",
+    "Mask": "蒙版",
+    "Mask blur": "蒙版模糊",
+    "Mask mode": "蒙版模式",
+    "Draw mask": "绘制蒙版",
+    "Upload mask": "上传蒙版",
+    "Masking mode": "蒙版模式",
+    "Inpaint masked": "内补绘制蒙版内容",
+    "Inpaint not masked": "内补绘制非蒙版内容",
+    "Masked content": "蒙版蒙住的内容",
+    "fill": "填空",
+    "original": "原图",
+    "latent noise": "latent noise",
+    "latent nothing": "latent nothing",
+    "Inpaint at full resolution": "以完整分辨率进行内补绘制",
+    "Inpaint at full resolution padding, pixels": "以完整分辨率进行内补绘制 - 填空像素",
+    "Process images in a directory on the same machine where the server is running.": "在服务器主机上的目录中处理图像",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "指定一个空的文件夹为输出目录而非默认的 output 文件夹为输出目录",
+    "Disabled when launched with --hide-ui-dir-config.": "启动 --hide-ui-dir-config 时禁用",
+    "Input directory": "输入目录",
+    "Output directory": "输出目录",
+    "Resize mode": "缩放模式",
+    "Just resize": "只缩放",
+    "Crop and resize": "剪裁后缩放",
+    "Resize and fill": "缩放并填空",
+    "img2img alternative test": "图转图的另一种测试",
+    "Loopback": "回送",
+    "Outpainting mk2": "外补绘制第二版",
+    "Poor man's outpainting": "Poor man 的外补绘制",
+    "SD upscale": "SD 超采样",
+    "should be 2 or lower.": "必须小于等于2",
+    "Override `Sampling method` to Euler?(this method is built for it)": "覆写 `采样方法` 为 Euler?(这个方法就是为这样做设计的)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆写 `提示词` 为 `原始提示词`?(包括`否定提示词`)",
+    "Original prompt": "原始提示词",
+    "Original negative prompt": "原始否定提示词",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "覆写 `采样迭代步数` 为 `解码迭代步数`?",
+    "Decode steps": "解码迭代步数",
+    "Override `Denoising strength` to 1?": "覆写 `降噪强度` 为 1?",
+    "Decode CFG scale": "解码提示词相关性(CFG scale)",
+    "Randomness": "随机强度",
+    "Sigma adjustment for finding noise for image": "为寻找图中噪点的 Sigma 调整",
+    "Loops": "循环",
+    "Denoising strength change factor": "降噪强度的调整因数",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推荐设置：采样迭代步数：80-100，采样器：Euler a，降噪强度：0.8",
+    "Pixels to expand": "拓展的像素",
+    "Outpainting direction": "外填充的方向",
+    "left": "左",
+    "right": "右",
+    "up": "上",
+    "down": "下",
+    "Fall-off exponent (lower=higher detail)": "衰减指数（低=高细节）",
+    "Color variation": "色彩变化",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置密铺大小",
+    "Tile overlap": "密铺重叠像素",
+    "Upscaler": "放大算法",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "BSRGAN 4x": "BSRGAN 4x",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR_4x": "SwinIR 4x",
+    "Single Image": "单个图像",
+    "Batch Process": "批量处理",
+    "Batch from Directory": "从目录进行批量处理",
+    "Source": "来源",
+    "Show result images": "显示输出图像",
+    "Scale by": "缩放到",
+    "Scale to": "缩放为",
+    "Resize": "缩放",
+    "Crop to fit": "裁剪以适应",
+    "Upscaler 2": "放大算法 2",
+    "Upscaler 2 visibility": "放大算法 2 可见度",
+    "GFPGAN visibility": "GFPGAN 可见度",
+    "CodeFormer visibility": "CodeFormer 可见度",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 权重 (0 = 最大效果, 1 = 最小效果)",
+    "Open output directory": "打开输出目录",
+    "Send to txt2img": "输出到文转图",
+    "txt2img history": "文转图历史记录",
+    "img2img history": "图转图历史记录",
+    "extras history": "后处理历史记录",
+    "Renew Page": "刷新页面",
+    "First Page": "第一页",
+    "Prev Page": "上一页",
+    "Page Index": "页数",
+    "Next Page": "下一页",
+    "End Page": "尾页",
+    "number of images to delete consecutively next": "下一批要批量删除的图像数",
+    "Delete": "删除",
+    "Generate Info": "生成信息",
+    "File Name": "文件名",
+    "set_index": "设置索引",
+    "A merger of the two checkpoints will be generated in your": "合并后的模型(ckpt)会生成在你的",
+    "checkpoint": "模型(ckpt)",
+    "directory.": "目录",
+    "Primary model (A)": "主要模型 (A)",
+    "Secondary model (B)": "第二模型 (B)",
+    "Tertiary model (C)": "第三模型 (C)",
+    "Custom Name (Optional)": "自定义名字 (可选)",
+    "Multiplier (M) - set to 0 to get model A": "倍率 (M) - 设为 0 等价于模型 A",
+    "Interpolation Method": "插值方法",
+    "Weighted sum": "加权和",
+    "Add difference": "添加差分",
+    "Save as float16": "以 float16 储存",
+    "See": "查看",
+    "wiki": "wiki",
+    "for detailed explanation.": "想看详细解释的话",
+    "Create embedding": "生成 embedding",
+    "Create aesthetic images embedding": "生成美学图片 embedding",
+    "Create hypernetwork": "生成 hypernetwork",
+    "Preprocess images": "图像预处理",
+    "Name": "名称",
+    "Initialization text": "初始化文字",
+    "Number of vectors per token": "每个令牌(token)的向量数",
+    "Overwrite Old Embedding": "覆写旧的 Embedding",
+    "Source directory": "源目录",
+    "Create images embedding": "生成 embedding 图片",
+    "Modules": "模组",
+    "Enter hypernetwork layer structure": "输入 hypernetwork 层结构",
+    "Add layer normalization": "添加层标准化",
+    "Overwrite Old Hypernetwork": "Overwrite Old Hypernetwork",
+    "Select activation function of hypernetwork": "选择 hypernetwork 的激活函数",
+    "linear": "linear",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "Destination directory": "目标目录",
+    "Existing Caption txt Action": "对已有说明文字的 txt 的行为",
+    "ignore": "无视",
+    "copy": "复制",
+    "prepend": "放前面",
+    "append": "放后面",
+    "Create flipped copies": "生成镜像副本",
+    "Split oversized images into two": "将过大的图像分为两份",
+    "Split oversized images": "分割过大的图像",
+    "Use BLIP for caption": "使用 BLIP 生成说明文字(自然语言描述)",
+    "Use deepbooru for caption": "使用 deepbooru 生成说明文字(tags)",
+    "Split image threshold": "图像分割阈值",
+    "Split image overlap ratio": "分割图像重叠的比率",
+    "Preprocess": "预处理",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "训练 embedding； 必须指定一组具有 1:1 比例图像的目录",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "训练 embedding 或者 hypernetwork； 必须指定一组具有 1:1 比例图像的目录",
+    "[wiki]": "[wiki]",
+    "Embedding": "Embedding",
+    "Embedding Learning rate": "Embedding 学习率",
+    "Hypernetwork Learning rate": "Hypernetwork 学习率",
+    "Learning rate": "学习率",
+    "Dataset directory": "数据集目录",
+    "Log directory": "日志目录",
+    "Prompt template file": "提示词模版文件",
+    "Max steps": "最大迭代步数",
+    "Save an image to log directory every N steps, 0 to disable": "每 N 步保存一个图像到日志目录，0 表示禁用",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步将 embedding 的副本保存到日志目录，0 表示禁用",
+    "Save images with embedding in PNG chunks": "保存图像並在 PNG 文件中嵌入 embedding 文件",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "进行预览时从文转图选项卡中读取参数（提示词等）",
+    "Train Hypernetwork": "训练 Hypernetwork",
+    "Train Embedding": "训练 Embedding",
+    "Apply settings": "保存设置",
+    "Saving images/grids": "保存图像/概览图",
+    "Always save all generated images": "始终保存所有生成的图像",
+    "File format for images": "图像的文件格式",
+    "Images filename pattern": "图像文件名格式",
+    "Always save all generated image grids": "始终保存所有生成的概览图",
+    "File format for grids": "概览图的文件格式",
+    "Add extended info (seed, prompt) to filename when saving grid": "保存概览时将扩展信息（种子、提示词）添加到文件名",
+    "Do not save grids consisting of one picture": "不要保存由一张图像组成的概览图",
+    "Prevent empty spots in grid (when set to autodetect)": "防止概览图中出现空白点（设置为自动检测时）",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "概览行数； 使用 -1 进行自动检测，使用 0 使其与批量大小相同",
+    "Save text information about generation parameters as chunks to png files": "将有关生成参数的文本信息作为块保存到 png 文件中",
+    "Create a text file next to every image with generation parameters.": "保存图像时在每个图像旁边创建一个文本文件储存生成参数",
+    "Save a copy of image before doing face restoration.": "在进行面部恢复之前保存图像副本",
+    "Quality for saved jpeg images": "保存的 jpeg 图像的质量",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 图像大于 4MB 或宽高大于 4000，则缩小并保存副本为 JPG",
+    "Use original name for output filename during batch process in extras tab": "在后处理选项卡中的批处理过程中使用原始名称作为输出文件名",
+    "When using 'Save' button, only save a single selected image": "使用“保存”按钮时，只保存一个选定的图像",
+    "Do not add watermark to images": "不要给图像加水印",
+    "Paths for saving": "保存目录",
+    "Output directory for images; if empty, defaults to three directories below": "图像的输出目录； 如果为空，则默认为下面三个目录",
+    "Output directory for txt2img images": "文转图的输出目录",
+    "Output directory for img2img images": "图转图的输出目录",
+    "Output directory for images from extras tab": "后处理的输出目录",
+    "Output directory for grids; if empty, defaults to two directories below": "概览图的输出目录； 如果为空，则默认为以下两个目录",
+    "Output directory for txt2img grids": "文转图概览的输出目录",
+    "Output directory for img2img grids": "图转图概览的输出目录",
+    "Directory for saving images using the Save button": "使用“保存”按钮保存图像的目录",
+    "Saving to a directory": "保存到目录",
+    "Save images to a subdirectory": "将图像保存到子目录",
+    "Save grids to a subdirectory": "将概览图保存到子目录",
+    "When using \"Save\" button, save images to a subdirectory": "使用“保存”按钮时，将图像保存到子目录",
+    "Directory name pattern": "目录名称格式",
+    "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示词数量",
+    "Upscaling": "放大",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的密铺大小。 0 = 不密铺",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "密铺重叠，以 ESRGAN 的像素为单位。 低值 = 可见接缝",
+    "Tile size for all SwinIR.": "所有 SwinIR 的密铺尺寸",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "密铺重叠，以 SwinIR 的像素为单位。 低值 = 可见接缝",
+    "LDSR processing steps. Lower = faster": "LDSR 处理迭代步数。 更低 = 更快",
+    "Upscaler for img2img": "图转图的放大器",
+    "Upscale latent space image when doing hires. fix": "做高分辨率修复时也放大 latent 空间图像",
+    "Face restoration": "面部修复",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 权重参数； 0 = 最大效果； 1 = 最小效果",
+    "Move face restoration model from VRAM into RAM after processing": "人脸修复处理完成后将人脸修复模型从显存(VRAM)移至内存(RAM)",
+    "System": "系统",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成图像时每秒轮询显存(VRAM)使用情况的次数。 设置为 0 以禁用",
+    "Always print all generation info to standard output": "始终将所有生成信息输出到 standard output (一般为控制台)",
+    "Add a second progress bar to the console that shows progress for an entire job.": "向控制台添加第二个进度条，显示整个作业的进度",
+    "Training": "训练",
+    "Unload VAE and CLIP from VRAM when training": "训练时从显存(VRAM)中取消 VAE 和 CLIP 的加载",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
+    "Filename word regex": "文件名用词的正则表达式",
+    "Filename join string": "文件名连接用字符",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每个 epoch 中单个输入图像的重复次数； 仅用于显示 epoch 数",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步保存一个包含 loss 的 csv 到日志目录，0 表示禁用",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "缓存在内存(RAM)中的模型(ckpt)",
+    "Hypernetwork strength": "Hypernetwork 强度",
+    "Apply color correction to img2img results to match original colors.": "对图转图结果应用颜色校正以匹配原始颜色",
+    "Save a copy of image before applying color correction to img2img results": "在对图转图结果应用颜色校正之前保存图像副本",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图转图的时候，确切地执行滑块指定的迭代步数（通常更少的降噪会做更少的迭代步数）",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。 这可能会改变现有的种子。 需要重新启动才能应用",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。 可用于复现旧种子",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 采样器批量生成与生成单个图像时产出相同的图像",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个令牌(token)时，通过从 n 个令牌(token)中的最后一个逗号填空来提高一致性",
+    "Filter NSFW content": "过滤成人内容",
+    "Stop At last layers of CLIP model": "在 CLIP 模型的最后哪一层停下",
+    "Interrogate Options": "逆向生成提示词选项",
+    "Interrogate: keep models in VRAM": "逆向生成: 将模型保存在显存(VRAM)中",
+    "Interrogate: use artists from artists.csv": "逆向生成: 使用 artists.csv 中的艺术家",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "逆向生成: 在生成结果中包含与模型标签(tags)相匹配的等级（对基于生成自然语言描述的逆向生成没有影响）",
+    "Interrogate: num_beams for BLIP": "逆向生成: BLIP 的 num_beams",
+    "Interrogate: minimum description length (excluding artists, etc..)": "逆向生成: 最小描述长度（不包括艺术家, 等…）",
+    "Interrogate: maximum description length": "逆向生成: 最大描述长度",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: 文本文件中的最大行数（0 = 无限制）",
+    "Interrogate: deepbooru score threshold": "逆向生成: deepbooru 分数阈值",
+    "Interrogate: deepbooru sort alphabetically": "逆向生成: deepbooru 按字母顺序排序",
+    "use spaces for tags in deepbooru": "在 deepbooru 中为标签使用空格",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "在 deepbooru 中使用转义 (\\) 括号（因此它们用作文字括号而不是强调符号）",
+    "User interface": "用户界面",
+    "Show progressbar": "显示进度条",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 个采样迭代步数显示图像生成进度。 设置 0 禁用",
+    "Show grid in results for web": "在网页的结果中显示概览图",
+    "Do not show any images in results for web": "不在网页的结果中显示任何图像",
+    "Add model hash to generation information": "将模型的哈希值添加到生成信息",
+    "Add model name to generation information": "将模型名称添加到生成信息",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "当从文本读取生成参数到 UI（从 PNG 信息或粘贴文本）时，不要更改选定的模型(ckpt)",
+    "Font for image grids that have text": "有文字的概览图使用的字体",
+    "Enable full page image viewer": "启用整页图像查看器",
+    "Show images zoomed in by default in full page image viewer": "在整页图像查看器中默认放大显示图像",
+    "Show generation progress in window title.": "在窗口标题中显示生成进度",
+    "Quicksettings list": "快速设置列表",
+    "Localization (requires restart)": "本地化（需要重新启动）",
+    "Sampler parameters": "采样器参数",
+    "Hide samplers in user interface (requires restart)": "在用户界面中隐藏采样器（需要重新启动）",
+    "eta (noise multiplier) for DDIM": "DDIM 的 eta (噪声倍增器) ",
+    "eta (noise multiplier) for ancestral samplers": "ancestral 采样器的 eta (噪声倍增器)",
+    "img2img DDIM discretize": "图转图 DDIM 离散化",
+    "uniform": "均勻",
+    "quad": "二阶",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "最小(tmin) sigma",
+    "sigma noise": "sigma 噪声",
+    "Eta noise seed delta": "Eta 噪声种子偏移(noise seed delta)",
+    "Request browser notifications": "请求浏览器通知",
+    "Download localization template": "下载本地化模板",
+    "Reload custom script bodies (No ui updates, No restart)": "重新加载自定义脚本主体（无 ui 更新，无重启）",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重启 Gradio 及刷新组件（仅限自定义脚本、ui.py、js 和 css）",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示词（按 Ctrl+Enter 或 Alt+Enter 生成）",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "否定提示词（按 Ctrl+Enter 或 Alt+Enter 生成）",
+    "Add a random artist to the prompt.": "随机添加一个艺术家到提示词中",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
+    "Save style": "储存为模版风格",
+    "Apply selected styles to current prompt": "将所选样式应用于当前提示",
+    "Stop processing current image and continue processing.": "停止处理当前图像并继续处理下一个",
+    "Stop processing images and return any results accumulated so far.": "停止处理图像并返回迄今为止累积的任何结果",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要应用的模版风格； 模版风格包含正面和负面提示词，并应用于两者",
+    "Do not do anything special": "并没有做什么特别的事情",
+    "Which algorithm to use to produce the image": "使用哪种算法生成图像",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图片，将迭代步数设置为高于 30-40 不会有正面作用",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长内补绘制",
+    "Produce an image that can be tiled.": "生成可以密铺的图像",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候以较小的分辨率生成初步图像、接着上采样，然后在不更改合成的情况下改进其中的细节",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "决定算法对图像内容的影响程度。 设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。 值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
+    "How many batches of images to create": "创建多少批次的图像",
+    "How many image to create in a single batch": "每批创建多少图像",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 图像应在多大程度上服从提示词 - 较低的值会产生更有创意的结果",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一个固定随机数生成器输出的值 - 以相同参数和种子生成的图像会得到相同的结果",
+    "Set seed to -1, which will cause a new random number to be used every time": "将种子设置为-1，则每次都会使用一个新的随机数",
+    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的种子，如果它是随机的就有用",
+    "Seed of a different picture to be mixed into the generation.": "将要参与生成的另一张图的种子",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要产生多强烈的变化。 设为 0 时，将没有效果。 设为 1 时，你将获得完全产自偏差种子的图片（ancestral 采样器除外，你只是生成了一些东西）",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "尝试生成与在指定分辨率下使用相同种子生成的图片相似的图片",
+    "This text is used to rotate the feature space of the imgs embs": "此文本用于旋转imgs embs的特征空间",
+    "Separate values for X axis using commas.": "使用逗号分隔 X 轴的值",
+    "Separate values for Y axis using commas.": "使用逗号分隔 Y 轴的值",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "将图像写入目录（默认 - log/images）并将生成参数写入 csv 文件",
+    "Open images output directory": "打开图像输出目录",
+    "How much to blur the mask before processing, in pixels.": "处理前要对蒙版进行多强的模糊，以像素为单位",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 处理蒙版区域之前要在蒙版区域内放置什么",
+    "fill it with colors of the image": "用图像的颜色填充它",
+    "keep whatever was there originally": "保留原来的东西",
+    "fill it with latent space noise": "用 latent 空间的噪声填充它",
+    "fill it with latent space zeroes": "用 latent 空间的零填充它",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做内补绘制，缩小后粘贴到原始图像中",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。 除非高度和宽度匹配，否则你将获得不正确的纵横比",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。 裁剪多出来的部分",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。 用图像的颜色填充空白区域",
+    "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像多少次并将其用作下一次迭代的输入",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，降噪强度都会乘以该值。 <1 表示减少多样性，因此你的这一组图将集中在固定的图片上。 >1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "对于 SD 超采样，图块之间应该有多少像素重叠。 图块需要重叠才可以让它们合并回一张图片时，没有清晰可见的接缝",
+    "A directory on the same machine where the server is running.": "与服务器主机上的目录",
+    "Leave blank to save images to the default path.": "留空以将图像保存到默认目录",
+    "Result = A * (1 - M) + B * M": "结果 = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "结果 = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "第一个和最后一个数字必须是 1。例:'1, 2, 1'",
+    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。 低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
+    "Path to directory with input images": "带有输入图像的目录",
+    "Path to directory where to write outputs": "进行输出的目录",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像的文件名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果启用此选项，水印将不会添加到生成出来的图像中。 警告：如果你不添加水印，你的行为可能是不符合专业操守的",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像和概览图的子目录: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
+    "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络恢复低质量人脸",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。 留空以保持文件名文本不变",
+    "This string will be used to join split words into a single line if the option above is enabled.": "如果启用了上述选项，则此处的字符会用于将拆分的单词接合为同一行",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置名称列表，以逗号分隔，设置应转到顶部的快速访问栏，而不是通常的设置选项卡。 有关设置名称，请参见 modules/shared.py。 需要重新启动才能应用",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
+    "Enable Autocomplete": "开启Tag补全",
+    "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
+    "Roll three": "抽三位出來",
+    "Generate forever": "不停地生成",
+    "Cancel generate forever": "取消不停地生成"
+}
-- 
cgit v1.2.1


From 5784aae925feed97a6325cd16ccfec0488ccd40f Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Sat, 22 Oct 2022 21:58:23 +0900
Subject: Update zh-hans.json

---
 localizations/zh-hans.json | 75 +++++++++++++++++++++++++++++-----------------
 1 file changed, 47 insertions(+), 28 deletions(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 113df5f7..e2e175e2 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -8,15 +8,17 @@
     "view": "查看",
     "api": "api",
     "•": "•",
-    "built with gradio": "基于 Gradio 库编译",
+    "built with gradio": "基于 Gradio 构建",
     "Stable Diffusion checkpoint": "Stable Diffusion 模型(ckpt)",
     "txt2img": "文转图",
     "img2img": "图转图",
     "Extras": "后处理",
     "PNG Info": "PNG 信息",
+    "Image Browser": "图库浏览器",
     "History": "历史记录",
     "Checkpoint Merger": "模型(ckpt)合并工具",
     "Train": "训练",
+    "Create aesthetic embedding": "生成美术风格 embedding",
     "Settings": "设置",
     "Prompt": "提示词",
     "Negative prompt": "否定提示词",
@@ -67,15 +69,15 @@
     "Variation strength": "差异强度",
     "Resize seed from width": "自宽度缩放种子",
     "Resize seed from height": "自高度缩放种子",
-    "Open for Clip Aesthetic!": "打开美学Clip!",
+    "Open for Clip Aesthetic!": "打开美术风格 Clip!",
     "▼": "▼",
-    "Aesthetic weight": "美学权重",
-    "Aesthetic steps": "美学迭代步数",
-    "Aesthetic learning rate": "美学学习率",
+    "Aesthetic weight": "美术风格权重",
+    "Aesthetic steps": "美术风格迭代步数",
+    "Aesthetic learning rate": "美术风格学习率",
     "Slerp interpolation": "Slerp 插值",
-    "Aesthetic imgs embedding": "美学图片 embedding",
+    "Aesthetic imgs embedding": "美术风格图像 embedding",
     "None": "无",
-    "Aesthetic text for imgs": "描述图片的美学文字",
+    "Aesthetic text for imgs": "描述图像的美术风格文字",
     "Slerp angle": "Slerp 角度",
     "Is negative text": "是否定类文字",
     "Script": "脚本",
@@ -134,8 +136,8 @@
     "Masked content": "蒙版蒙住的内容",
     "fill": "填空",
     "original": "原图",
-    "latent noise": "latent noise",
-    "latent nothing": "latent nothing",
+    "latent noise": "潜空间噪声",
+    "latent nothing": "潜空间数值零",
     "Inpaint at full resolution": "以完整分辨率进行内补绘制",
     "Inpaint at full resolution padding, pixels": "以完整分辨率进行内补绘制 - 填空像素",
     "Process images in a directory on the same machine where the server is running.": "在服务器主机上的目录中处理图像",
@@ -151,7 +153,7 @@
     "Loopback": "回送",
     "Outpainting mk2": "外补绘制第二版",
     "Poor man's outpainting": "Poor man 的外补绘制",
-    "SD upscale": "SD 超采样",
+    "SD upscale": "使用 SD 放大(SD upscale)",
     "should be 2 or lower.": "必须小于等于2",
     "Override `Sampling method` to Euler?(this method is built for it)": "覆写 `采样方法` 为 Euler?(这个方法就是为这样做设计的)",
     "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆写 `提示词` 为 `原始提示词`?(包括`否定提示词`)",
@@ -167,7 +169,7 @@
     "Denoising strength change factor": "降噪强度的调整因数",
     "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推荐设置：采样迭代步数：80-100，采样器：Euler a，降噪强度：0.8",
     "Pixels to expand": "拓展的像素",
-    "Outpainting direction": "外填充的方向",
+    "Outpainting direction": "外补绘制的方向",
     "left": "左",
     "right": "右",
     "up": "上",
@@ -205,6 +207,12 @@
     "img2img history": "图转图历史记录",
     "extras history": "后处理历史记录",
     "Renew Page": "刷新页面",
+    "extras": "后处理",
+    "favorites": "我的收藏",
+    "Load": "载入",
+    "Images directory": "图像路径",
+    "Prev batch": "上一批",
+    "Next batch": "下一批",
     "First Page": "第一页",
     "Prev Page": "上一页",
     "Page Index": "页数",
@@ -214,7 +222,12 @@
     "Delete": "删除",
     "Generate Info": "生成信息",
     "File Name": "文件名",
+    "Collect": "收藏",
+    "Refresh page": "刷新页面",
+    "Date to": "日期至",
+    "Number": "数量",
     "set_index": "设置索引",
+    "Checkbox": "勾选框",
     "A merger of the two checkpoints will be generated in your": "合并后的模型(ckpt)会生成在你的",
     "checkpoint": "模型(ckpt)",
     "directory.": "目录",
@@ -229,21 +242,20 @@
     "Save as float16": "以 float16 储存",
     "See": "查看",
     "wiki": "wiki",
-    "for detailed explanation.": "想看详细解释的话",
+    "for detailed explanation.": "以了解详细说明",
     "Create embedding": "生成 embedding",
-    "Create aesthetic images embedding": "生成美学图片 embedding",
+    "Create aesthetic images embedding": "生成美术风格图像 embedding",
     "Create hypernetwork": "生成 hypernetwork",
     "Preprocess images": "图像预处理",
     "Name": "名称",
     "Initialization text": "初始化文字",
-    "Number of vectors per token": "每个令牌(token)的向量数",
+    "Number of vectors per token": "每个 token 的向量数",
     "Overwrite Old Embedding": "覆写旧的 Embedding",
     "Source directory": "源目录",
-    "Create images embedding": "生成 embedding 图片",
-    "Modules": "模组",
+    "Modules": "模块",
     "Enter hypernetwork layer structure": "输入 hypernetwork 层结构",
     "Add layer normalization": "添加层标准化",
-    "Overwrite Old Hypernetwork": "Overwrite Old Hypernetwork",
+    "Overwrite Old Hypernetwork": "覆写旧的 Hypernetwork",
     "Select activation function of hypernetwork": "选择 hypernetwork 的激活函数",
     "linear": "linear",
     "relu": "relu",
@@ -275,10 +287,12 @@
     "Max steps": "最大迭代步数",
     "Save an image to log directory every N steps, 0 to disable": "每 N 步保存一个图像到日志目录，0 表示禁用",
     "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步将 embedding 的副本保存到日志目录，0 表示禁用",
-    "Save images with embedding in PNG chunks": "保存图像並在 PNG 文件中嵌入 embedding 文件",
+    "Save images with embedding in PNG chunks": "保存图像并在 PNG 文件中嵌入 embedding 文件",
     "Read parameters (prompt, etc...) from txt2img tab when making previews": "进行预览时从文转图选项卡中读取参数（提示词等）",
     "Train Hypernetwork": "训练 Hypernetwork",
     "Train Embedding": "训练 Embedding",
+    "Create an aesthetic embedding out of any number of images": "从任意数量的图像中创建美术风格 embedding",
+    "Create images embedding": "生成 embedding 图像",
     "Apply settings": "保存设置",
     "Saving images/grids": "保存图像/概览图",
     "Always save all generated images": "始终保存所有生成的图像",
@@ -320,7 +334,7 @@
     "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "密铺重叠，以 SwinIR 的像素为单位。 低值 = 可见接缝",
     "LDSR processing steps. Lower = faster": "LDSR 处理迭代步数。 更低 = 更快",
     "Upscaler for img2img": "图转图的放大器",
-    "Upscale latent space image when doing hires. fix": "做高分辨率修复时也放大 latent 空间图像",
+    "Upscale latent space image when doing hires. fix": "做高分辨率修复时也放大潜空间图像",
     "Face restoration": "面部修复",
     "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 权重参数； 0 = 最大效果； 1 = 最小效果",
     "Move face restoration model from VRAM into RAM after processing": "人脸修复处理完成后将人脸修复模型从显存(VRAM)移至内存(RAM)",
@@ -345,7 +359,7 @@
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。 可用于复现旧种子",
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 采样器批量生成与生成单个图像时产出相同的图像",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个令牌(token)时，通过从 n 个令牌(token)中的最后一个逗号填空来提高一致性",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个 token 时，通过从 n 个 token 中的最后一个逗号填空来提高一致性",
     "Filter NSFW content": "过滤成人内容",
     "Stop At last layers of CLIP model": "在 CLIP 模型的最后哪一层停下",
     "Interrogate Options": "逆向生成提示词选项",
@@ -385,6 +399,11 @@
     "sigma tmin": "最小(tmin) sigma",
     "sigma noise": "sigma 噪声",
     "Eta noise seed delta": "Eta 噪声种子偏移(noise seed delta)",
+    "Images Browser": "图库浏览器",
+    "Preload images at startup": "在启动时预载图像",
+    "Number of pictures displayed on each page": "每页显示的图像数量",
+    "Minimum number of pages per load": "每次加载的最小页数",
+    "Number of grids in each row": "每行显示多少格",
     "Request browser notifications": "请求浏览器通知",
     "Download localization template": "下载本地化模板",
     "Reload custom script bodies (No ui updates, No restart)": "重新加载自定义脚本主体（无 ui 更新，无重启）",
@@ -400,7 +419,7 @@
     "Style to apply; styles have components for both positive and negative prompts and apply to both": "要应用的模版风格； 模版风格包含正面和负面提示词，并应用于两者",
     "Do not do anything special": "并没有做什么特别的事情",
     "Which algorithm to use to produce the image": "使用哪种算法生成图像",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图片，将迭代步数设置为高于 30-40 不会有正面作用",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图像，将迭代步数设置为高于 30-40 不会有正面作用",
     "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长内补绘制",
     "Produce an image that can be tiled.": "生成可以密铺的图像",
     "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候以较小的分辨率生成初步图像、接着上采样，然后在不更改合成的情况下改进其中的细节",
@@ -412,9 +431,9 @@
     "Set seed to -1, which will cause a new random number to be used every time": "将种子设置为-1，则每次都会使用一个新的随机数",
     "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的种子，如果它是随机的就有用",
     "Seed of a different picture to be mixed into the generation.": "将要参与生成的另一张图的种子",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要产生多强烈的变化。 设为 0 时，将没有效果。 设为 1 时，你将获得完全产自偏差种子的图片（ancestral 采样器除外，你只是生成了一些东西）",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "尝试生成与在指定分辨率下使用相同种子生成的图片相似的图片",
-    "This text is used to rotate the feature space of the imgs embs": "此文本用于旋转imgs embs的特征空间",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要产生多强烈的变化。 设为 0 时，将没有效果。 设为 1 时，你将获得完全产自偏差种子的图像（ancestral 采样器除外，你只是生成了一些东西）",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "尝试生成与在指定分辨率下使用相同种子生成的图像相似的图片",
+    "This text is used to rotate the feature space of the imgs embs": "此文本用于旋转图像 embeddings 的特征空间",
     "Separate values for X axis using commas.": "使用逗号分隔 X 轴的值",
     "Separate values for Y axis using commas.": "使用逗号分隔 Y 轴的值",
     "Write image to a directory (default - log/images) and generation parameters into csv file.": "将图像写入目录（默认 - log/images）并将生成参数写入 csv 文件",
@@ -423,15 +442,15 @@
     "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 处理蒙版区域之前要在蒙版区域内放置什么",
     "fill it with colors of the image": "用图像的颜色填充它",
     "keep whatever was there originally": "保留原来的东西",
-    "fill it with latent space noise": "用 latent 空间的噪声填充它",
-    "fill it with latent space zeroes": "用 latent 空间的零填充它",
+    "fill it with latent space noise": "用潜空间的噪声填充它",
+    "fill it with latent space zeroes": "用潜空间的零填充它",
     "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做内补绘制，缩小后粘贴到原始图像中",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。 除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。 裁剪多出来的部分",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。 用图像的颜色填充空白区域",
     "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像多少次并将其用作下一次迭代的输入",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，降噪强度都会乘以该值。 <1 表示减少多样性，因此你的这一组图将集中在固定的图片上。 >1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "对于 SD 超采样，图块之间应该有多少像素重叠。 图块需要重叠才可以让它们合并回一张图片时，没有清晰可见的接缝",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，降噪强度都会乘以该值。 <1 表示减少多样性，因此你的这一组图将集中在固定的图像上。 >1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "对于 SD 超采样，图块之间应该有多少像素重叠。 图块需要重叠才可以让它们合并回一张图像时，没有清晰可见的接缝",
     "A directory on the same machine where the server is running.": "与服务器主机上的目录",
     "Leave blank to save images to the default path.": "留空以将图像保存到默认目录",
     "Result = A * (1 - M) + B * M": "结果 = A * (1 - M) + B * M",
-- 
cgit v1.2.1


From 046bb3b16aac1f7c5fb3c93604c03bd6e74e0b16 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Sat, 22 Oct 2022 23:58:01 +0900
Subject: Update zh-hans.json

- fix translation of Tile / Tiling
- fix translation of Negative prompt
---
 localizations/zh-hans.json | 30 +++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index e2e175e2..1a9fc731 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -21,9 +21,9 @@
     "Create aesthetic embedding": "生成美术风格 embedding",
     "Settings": "设置",
     "Prompt": "提示词",
-    "Negative prompt": "否定提示词",
+    "Negative prompt": "反向提示词",
     "Run": "运行",
-    "Skip": "略过",
+    "Skip": "跳过",
     "Interrupt": "中断",
     "Generate": "生成",
     "Style 1": "模版风格 1",
@@ -55,7 +55,7 @@
     "Width": "宽度",
     "Height": "高度",
     "Restore faces": "面部修复",
-    "Tiling": "密铺",
+    "Tiling": "分块(Tiling)",
     "Highres. fix": "高分辨率修复",
     "Firstpass width": "第一遍宽度",
     "Firstpass height": "第一遍高度",
@@ -156,9 +156,9 @@
     "SD upscale": "使用 SD 放大(SD upscale)",
     "should be 2 or lower.": "必须小于等于2",
     "Override `Sampling method` to Euler?(this method is built for it)": "覆写 `采样方法` 为 Euler?(这个方法就是为这样做设计的)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆写 `提示词` 为 `原始提示词`?(包括`否定提示词`)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆写 `提示词` 为 `原始提示词`?(包括`反向提示词`)",
     "Original prompt": "原始提示词",
-    "Original negative prompt": "原始否定提示词",
+    "Original negative prompt": "原始反向提示词",
     "Override `Sampling Steps` to the same value as `Decode steps`?": "覆写 `采样迭代步数` 为 `解码迭代步数`?",
     "Decode steps": "解码迭代步数",
     "Override `Denoising strength` to 1?": "覆写 `降噪强度` 为 1?",
@@ -176,8 +176,8 @@
     "down": "下",
     "Fall-off exponent (lower=higher detail)": "衰减指数（低=高细节）",
     "Color variation": "色彩变化",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置密铺大小",
-    "Tile overlap": "密铺重叠像素",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置图块大小(tile size)",
+    "Tile overlap": "图块重叠的像素(Tile overlap)",
     "Upscaler": "放大算法",
     "Lanczos": "Lanczos",
     "LDSR": "LDSR",
@@ -328,10 +328,10 @@
     "Directory name pattern": "目录名称格式",
     "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示词数量",
     "Upscaling": "放大",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的密铺大小。 0 = 不密铺",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "密铺重叠，以 ESRGAN 的像素为单位。 低值 = 可见接缝",
-    "Tile size for all SwinIR.": "所有 SwinIR 的密铺尺寸",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "密铺重叠，以 SwinIR 的像素为单位。 低值 = 可见接缝",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的图块大小(Tile size)。 0 = 不分块(no tiling)",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "图块重叠(Tile overlap)，以 ESRGAN 的像素为单位。 低值 = 可见接缝",
+    "Tile size for all SwinIR.": "所有 SwinIR 的图块尺寸(Tile size)",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "图块重叠(Tile overlap)，以 SwinIR 的像素为单位。 低值 = 可见接缝",
     "LDSR processing steps. Lower = faster": "LDSR 处理迭代步数。 更低 = 更快",
     "Upscaler for img2img": "图转图的放大器",
     "Upscale latent space image when doing hires. fix": "做高分辨率修复时也放大潜空间图像",
@@ -409,19 +409,19 @@
     "Reload custom script bodies (No ui updates, No restart)": "重新加载自定义脚本主体（无 ui 更新，无重启）",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重启 Gradio 及刷新组件（仅限自定义脚本、ui.py、js 和 css）",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示词（按 Ctrl+Enter 或 Alt+Enter 生成）",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "否定提示词（按 Ctrl+Enter 或 Alt+Enter 生成）",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示词（按 Ctrl+Enter 或 Alt+Enter 生成）",
     "Add a random artist to the prompt.": "随机添加一个艺术家到提示词中",
     "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
     "Save style": "储存为模版风格",
     "Apply selected styles to current prompt": "将所选样式应用于当前提示",
     "Stop processing current image and continue processing.": "停止处理当前图像并继续处理下一个",
     "Stop processing images and return any results accumulated so far.": "停止处理图像并返回迄今为止累积的任何结果",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要应用的模版风格； 模版风格包含正面和负面提示词，并应用于两者",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要应用的模版风格； 模版风格包含正向和反向提示词，并应用于两者",
     "Do not do anything special": "并没有做什么特别的事情",
     "Which algorithm to use to produce the image": "使用哪种算法生成图像",
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图像，将迭代步数设置为高于 30-40 不会有正面作用",
     "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长内补绘制",
-    "Produce an image that can be tiled.": "生成可以密铺的图像",
+    "Produce an image that can be tiled.": "生成可以分块(Tiling)的图像",
     "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候以较小的分辨率生成初步图像、接着上采样，然后在不更改合成的情况下改进其中的细节",
     "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "决定算法对图像内容的影响程度。 设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。 值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
     "How many batches of images to create": "创建多少批次的图像",
@@ -450,7 +450,7 @@
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。 用图像的颜色填充空白区域",
     "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像多少次并将其用作下一次迭代的输入",
     "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，降噪强度都会乘以该值。 <1 表示减少多样性，因此你的这一组图将集中在固定的图像上。 >1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "对于 SD 超采样，图块之间应该有多少像素重叠。 图块需要重叠才可以让它们合并回一张图像时，没有清晰可见的接缝",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "对于 SD 超采样，图块(Tiles)之间应该有多少像素重叠。 图块(Tiles)需要重叠才可以让它们合并回一张图像时，没有清晰可见的接缝",
     "A directory on the same machine where the server is running.": "与服务器主机上的目录",
     "Leave blank to save images to the default path.": "留空以将图像保存到默认目录",
     "Result = A * (1 - M) + B * M": "结果 = A * (1 - M) + B * M",
-- 
cgit v1.2.1


From 69576d3398a1cc9a091ce1b24acadc404091ab39 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Sun, 23 Oct 2022 03:16:44 +0900
Subject: Update zh-hans.json

- unify the translation of upscale / upscaler
- unify the translation of seed / random seed
- correct images embedding's translation
---
 localizations/zh-hans.json | 42 +++++++++++++++++++++---------------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 1a9fc731..1bce1dae 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -67,19 +67,19 @@
     "Extra": "额外参数",
     "Variation seed": "差异随机种子",
     "Variation strength": "差异强度",
-    "Resize seed from width": "自宽度缩放种子",
-    "Resize seed from height": "自高度缩放种子",
+    "Resize seed from width": "自宽度缩放随机种子",
+    "Resize seed from height": "自高度缩放随机种子",
     "Open for Clip Aesthetic!": "打开美术风格 Clip!",
     "▼": "▼",
     "Aesthetic weight": "美术风格权重",
     "Aesthetic steps": "美术风格迭代步数",
     "Aesthetic learning rate": "美术风格学习率",
     "Slerp interpolation": "Slerp 插值",
-    "Aesthetic imgs embedding": "美术风格图像 embedding",
+    "Aesthetic imgs embedding": "美术风格 图类 embedding",
     "None": "无",
-    "Aesthetic text for imgs": "描述图像的美术风格文字",
+    "Aesthetic text for imgs": "用于 图类 embedding 的美术风格描述",
     "Slerp angle": "Slerp 角度",
-    "Is negative text": "是否定类文字",
+    "Is negative text": "属于否定类文字",
     "Script": "脚本",
     "Prompt matrix": "提示词矩阵",
     "Prompts from file or textbox": "来自文本框或文件载入提示词",
@@ -90,7 +90,7 @@
     "Prompts": "提示词",
     "X type": "X轴类型",
     "Nothing": "无",
-    "Var. seed": "差异种子",
+    "Var. seed": "差异随机种子",
     "Var. strength": "差异强度",
     "Steps": "迭代步数",
     "Prompt S/R": "提示词替换",
@@ -244,7 +244,7 @@
     "wiki": "wiki",
     "for detailed explanation.": "以了解详细说明",
     "Create embedding": "生成 embedding",
-    "Create aesthetic images embedding": "生成美术风格图像 embedding",
+    "Create aesthetic images embedding": "生成美术风格 图类 embedding",
     "Create hypernetwork": "生成 hypernetwork",
     "Preprocess images": "图像预处理",
     "Name": "名称",
@@ -292,7 +292,7 @@
     "Train Hypernetwork": "训练 Hypernetwork",
     "Train Embedding": "训练 Embedding",
     "Create an aesthetic embedding out of any number of images": "从任意数量的图像中创建美术风格 embedding",
-    "Create images embedding": "生成 embedding 图像",
+    "Create images embedding": "生成 图类 embedding",
     "Apply settings": "保存设置",
     "Saving images/grids": "保存图像/概览图",
     "Always save all generated images": "始终保存所有生成的图像",
@@ -300,7 +300,7 @@
     "Images filename pattern": "图像文件名格式",
     "Always save all generated image grids": "始终保存所有生成的概览图",
     "File format for grids": "概览图的文件格式",
-    "Add extended info (seed, prompt) to filename when saving grid": "保存概览时将扩展信息（种子、提示词）添加到文件名",
+    "Add extended info (seed, prompt) to filename when saving grid": "保存概览时将扩展信息（随机种子、提示词）添加到文件名",
     "Do not save grids consisting of one picture": "不要保存由一张图像组成的概览图",
     "Prevent empty spots in grid (when set to autodetect)": "防止概览图中出现空白点（设置为自动检测时）",
     "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "概览行数； 使用 -1 进行自动检测，使用 0 使其与批量大小相同",
@@ -333,7 +333,7 @@
     "Tile size for all SwinIR.": "所有 SwinIR 的图块尺寸(Tile size)",
     "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "图块重叠(Tile overlap)，以 SwinIR 的像素为单位。 低值 = 可见接缝",
     "LDSR processing steps. Lower = faster": "LDSR 处理迭代步数。 更低 = 更快",
-    "Upscaler for img2img": "图转图的放大器",
+    "Upscaler for img2img": "图转图的放大算法",
     "Upscale latent space image when doing hires. fix": "做高分辨率修复时也放大潜空间图像",
     "Face restoration": "面部修复",
     "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 权重参数； 0 = 最大效果； 1 = 最小效果",
@@ -355,9 +355,9 @@
     "Apply color correction to img2img results to match original colors.": "对图转图结果应用颜色校正以匹配原始颜色",
     "Save a copy of image before applying color correction to img2img results": "在对图转图结果应用颜色校正之前保存图像副本",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图转图的时候，确切地执行滑块指定的迭代步数（通常更少的降噪会做更少的迭代步数）",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。 这可能会改变现有的种子。 需要重新启动才能应用",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。 这可能会改变现有的随机种子。 需要重新启动才能应用",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。 可用于复现旧种子",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。 可用于复现旧随机种子",
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 采样器批量生成与生成单个图像时产出相同的图像",
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个 token 时，通过从 n 个 token 中的最后一个逗号填空来提高一致性",
     "Filter NSFW content": "过滤成人内容",
@@ -422,18 +422,18 @@
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图像，将迭代步数设置为高于 30-40 不会有正面作用",
     "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长内补绘制",
     "Produce an image that can be tiled.": "生成可以分块(Tiling)的图像",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候以较小的分辨率生成初步图像、接着上采样，然后在不更改合成的情况下改进其中的细节",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候以较小的分辨率生成初步图像、接着放大图片，然后在不更改构图的情况下改进其中的细节",
     "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "决定算法对图像内容的影响程度。 设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。 值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
     "How many batches of images to create": "创建多少批次的图像",
     "How many image to create in a single batch": "每批创建多少图像",
     "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 图像应在多大程度上服从提示词 - 较低的值会产生更有创意的结果",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一个固定随机数生成器输出的值 - 以相同参数和种子生成的图像会得到相同的结果",
-    "Set seed to -1, which will cause a new random number to be used every time": "将种子设置为-1，则每次都会使用一个新的随机数",
-    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的种子，如果它是随机的就有用",
-    "Seed of a different picture to be mixed into the generation.": "将要参与生成的另一张图的种子",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要产生多强烈的变化。 设为 0 时，将没有效果。 设为 1 时，你将获得完全产自偏差种子的图像（ancestral 采样器除外，你只是生成了一些东西）",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "尝试生成与在指定分辨率下使用相同种子生成的图像相似的图片",
-    "This text is used to rotate the feature space of the imgs embs": "此文本用于旋转图像 embeddings 的特征空间",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一个固定随机数生成器输出的值 - 以相同参数和随机种子生成的图像会得到相同的结果",
+    "Set seed to -1, which will cause a new random number to be used every time": "将随机种子设置为-1，则每次都会使用一个新的随机数",
+    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的随机种子，如果它是随机出来的话就有用",
+    "Seed of a different picture to be mixed into the generation.": "将要参与生成的另一张图的随机种子",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要产生多强烈的变化。 设为 0 时，将没有效果。 设为 1 时，你将获得完全产自差异随机种子的图像（ancestral 采样器除外，你只是单纯地生成了一些东西）",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "尝试生成与在指定分辨率下使用相同随机种子生成的图像相似的图片",
+    "This text is used to rotate the feature space of the imgs embs": "此文本用于旋转 图类 embeddings 的特征空间",
     "Separate values for X axis using commas.": "使用逗号分隔 X 轴的值",
     "Separate values for Y axis using commas.": "使用逗号分隔 Y 轴的值",
     "Write image to a directory (default - log/images) and generation parameters into csv file.": "将图像写入目录（默认 - log/images）并将生成参数写入 csv 文件",
@@ -450,7 +450,7 @@
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。 用图像的颜色填充空白区域",
     "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像多少次并将其用作下一次迭代的输入",
     "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，降噪强度都会乘以该值。 <1 表示减少多样性，因此你的这一组图将集中在固定的图像上。 >1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "对于 SD 超采样，图块(Tiles)之间应该有多少像素重叠。 图块(Tiles)需要重叠才可以让它们合并回一张图像时，没有清晰可见的接缝",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。 图块之间(Tiles)需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
     "A directory on the same machine where the server is running.": "与服务器主机上的目录",
     "Leave blank to save images to the default path.": "留空以将图像保存到默认目录",
     "Result = A * (1 - M) + B * M": "结果 = A * (1 - M) + B * M",
-- 
cgit v1.2.1


From 7e6a6266ad7ec77c0e40a0f39e794532cd2c2649 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Sun, 23 Oct 2022 05:35:12 +0900
Subject: Update zh-hans.json

- update to latest
---
 localizations/zh-hans.json | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 1bce1dae..2d5b4798 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -251,15 +251,18 @@
     "Initialization text": "初始化文字",
     "Number of vectors per token": "每个 token 的向量数",
     "Overwrite Old Embedding": "覆写旧的 Embedding",
-    "Source directory": "源目录",
     "Modules": "模块",
     "Enter hypernetwork layer structure": "输入 hypernetwork 层结构",
-    "Add layer normalization": "添加层标准化",
-    "Overwrite Old Hypernetwork": "覆写旧的 Hypernetwork",
     "Select activation function of hypernetwork": "选择 hypernetwork 的激活函数",
     "linear": "linear",
     "relu": "relu",
     "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "Add layer normalization": "添加层标准化",
+    "Use dropout": "采用 dropout 防止过拟合",
+    "Overwrite Old Hypernetwork": "覆写旧的 Hypernetwork",
+    "Source directory": "源目录",
     "Destination directory": "目标目录",
     "Existing Caption txt Action": "对已有说明文字的 txt 的行为",
     "ignore": "无视",
@@ -377,6 +380,7 @@
     "User interface": "用户界面",
     "Show progressbar": "显示进度条",
     "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 个采样迭代步数显示图像生成进度。 设置 0 禁用",
+    "Show previews of all images generated in a batch as a grid": "以网格的形式预览所有批量生成出来的图片",
     "Show grid in results for web": "在网页的结果中显示概览图",
     "Do not show any images in results for web": "不在网页的结果中显示任何图像",
     "Add model hash to generation information": "将模型的哈希值添加到生成信息",
@@ -404,6 +408,8 @@
     "Number of pictures displayed on each page": "每页显示的图像数量",
     "Minimum number of pages per load": "每次加载的最小页数",
     "Number of grids in each row": "每行显示多少格",
+    "Wildcards": "通配符",
+    "Use same seed for all images": "为所有图像使用同一个随机种子",
     "Request browser notifications": "请求浏览器通知",
     "Download localization template": "下载本地化模板",
     "Reload custom script bodies (No ui updates, No restart)": "重新加载自定义脚本主体（无 ui 更新，无重启）",
@@ -453,6 +459,7 @@
     "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。 图块之间(Tiles)需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
     "A directory on the same machine where the server is running.": "与服务器主机上的目录",
     "Leave blank to save images to the default path.": "留空以将图像保存到默认目录",
+    "Input images directory": "输入图像目录",
     "Result = A * (1 - M) + B * M": "结果 = A * (1 - M) + B * M",
     "Result = A + (B - C) * M": "结果 = A + (B - C) * M",
     "1st and last digit must be 1. ex:'1, 2, 1'": "第一个和最后一个数字必须是 1。例:'1, 2, 1'",
-- 
cgit v1.2.1


From e05a4a90f21bf2246f805a38286be316acde7a52 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Sun, 23 Oct 2022 17:58:09 +0800
Subject: Update localizations/zh-hans.json

Co-authored-by: liggest <43201720+liggest@users.noreply.github.com>
---
 localizations/zh-hans.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 2d5b4798..9d750ecb 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -30,7 +30,7 @@
     "Style 2": "模版风格 2",
     "Label": "标签",
     "File": "文件",
-    "Drop File Here": "拖拽文件到这里",
+    "Drop File Here": "拖拽文件到此",
     "-": "-",
     "or": "或",
     "Click to Upload": "点击上传",
-- 
cgit v1.2.1


From db23be8bcdec82ea26681c12ac6cd076164b47c8 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Sun, 23 Oct 2022 17:59:09 +0800
Subject: Update localizations/zh-hans.json

Co-authored-by: liggest <43201720+liggest@users.noreply.github.com>
---
 localizations/zh-hans.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 9d750ecb..698e8694 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -202,7 +202,7 @@
     "CodeFormer visibility": "CodeFormer 可见度",
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 权重 (0 = 最大效果, 1 = 最小效果)",
     "Open output directory": "打开输出目录",
-    "Send to txt2img": "输出到文转图",
+    "Send to txt2img": "传送到文转图",
     "txt2img history": "文转图历史记录",
     "img2img history": "图转图历史记录",
     "extras history": "后处理历史记录",
-- 
cgit v1.2.1


From e125f1cd2f8ab71b2caaa16bbd24aaf2a3cb470e Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Sun, 23 Oct 2022 18:07:12 +0800
Subject: Update localizations/zh-hans.json

Co-authored-by: liggest <43201720+liggest@users.noreply.github.com>
---
 localizations/zh-hans.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 698e8694..91e745c4 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -119,8 +119,8 @@
     "Send to extras": "传送到后处理",
     "Make Zip when Save?": "保存时生成压缩档?",
     "Textbox": "文本框",
-    "Interrogate\nCLIP": "推断提示词\nCLIP",
-    "Interrogate\nDeepBooru": "推断提示词\nDeepBooru",
+    "Interrogate\nCLIP": "CLIP\n逆向生成提示词",
+    "Interrogate\nDeepBooru": "DeepBooru\n逆向生成提示词",
     "Inpaint": "内补绘制",
     "Batch img2img": "批量图转图",
     "Image for img2img": "图转图的图像",
-- 
cgit v1.2.1


From e34653eac03aff6041fd3c32ec324b8a9d835cee Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Sun, 23 Oct 2022 19:15:17 +0900
Subject: Update zh-hans.json

- fix according to https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/3348#issuecomment-1287923525
---
 localizations/zh-hans.json | 112 ++++++++++++++++++++++-----------------------
 1 file changed, 56 insertions(+), 56 deletions(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 91e745c4..3fbcac6b 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -24,7 +24,7 @@
     "Negative prompt": "反向提示词",
     "Run": "运行",
     "Skip": "跳过",
-    "Interrupt": "中断",
+    "Interrupt": "中止",
     "Generate": "生成",
     "Style 1": "模版风格 1",
     "Style 2": "模版风格 2",
@@ -57,11 +57,11 @@
     "Restore faces": "面部修复",
     "Tiling": "分块(Tiling)",
     "Highres. fix": "高分辨率修复",
-    "Firstpass width": "第一遍宽度",
-    "Firstpass height": "第一遍高度",
+    "Firstpass width": "第一遍的宽度",
+    "Firstpass height": "第一遍的高度",
     "Denoising strength": "降噪(模糊)强度",
-    "Batch count": "批量数量",
-    "Batch size": "批量大小",
+    "Batch count": "批次",
+    "Batch size": "批量",
     "CFG Scale": "提示词相关性(CFG Scale)",
     "Seed": "随机种子",
     "Extra": "额外参数",
@@ -75,9 +75,9 @@
     "Aesthetic steps": "美术风格迭代步数",
     "Aesthetic learning rate": "美术风格学习率",
     "Slerp interpolation": "Slerp 插值",
-    "Aesthetic imgs embedding": "美术风格 图类 embedding",
+    "Aesthetic imgs embedding": "美术风格图集 embedding",
     "None": "无",
-    "Aesthetic text for imgs": "用于 图类 embedding 的美术风格描述",
+    "Aesthetic text for imgs": "用于图集的美术风格描述",
     "Slerp angle": "Slerp 角度",
     "Is negative text": "属于否定类文字",
     "Script": "脚本",
@@ -104,7 +104,7 @@
     "Sigma max": "最大 Sigma",
     "Sigma noise": "Sigma noise",
     "Eta": "Eta",
-    "Clip skip": "Clip 略过",
+    "Clip skip": "Clip 跳过",
     "Denoising": "降噪",
     "X values": "X轴数值",
     "Y type": "Y轴类型",
@@ -147,28 +147,28 @@
     "Output directory": "输出目录",
     "Resize mode": "缩放模式",
     "Just resize": "只缩放",
-    "Crop and resize": "剪裁后缩放",
+    "Crop and resize": "缩放并剪裁",
     "Resize and fill": "缩放并填空",
     "img2img alternative test": "图转图的另一种测试",
     "Loopback": "回送",
     "Outpainting mk2": "外补绘制第二版",
-    "Poor man's outpainting": "Poor man 的外补绘制",
+    "Poor man's outpainting": "效果稍差的外补绘制",
     "SD upscale": "使用 SD 放大(SD upscale)",
     "should be 2 or lower.": "必须小于等于2",
     "Override `Sampling method` to Euler?(this method is built for it)": "覆写 `采样方法` 为 Euler?(这个方法就是为这样做设计的)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆写 `提示词` 为 `原始提示词`?(包括`反向提示词`)",
-    "Original prompt": "原始提示词",
-    "Original negative prompt": "原始反向提示词",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆写 `提示词` 为 `初始提示词`?(包括`反向提示词`)",
+    "Original prompt": "初始提示词",
+    "Original negative prompt": "初始反向提示词",
     "Override `Sampling Steps` to the same value as `Decode steps`?": "覆写 `采样迭代步数` 为 `解码迭代步数`?",
     "Decode steps": "解码迭代步数",
     "Override `Denoising strength` to 1?": "覆写 `降噪强度` 为 1?",
     "Decode CFG scale": "解码提示词相关性(CFG scale)",
-    "Randomness": "随机强度",
+    "Randomness": "随机度",
     "Sigma adjustment for finding noise for image": "为寻找图中噪点的 Sigma 调整",
     "Loops": "循环",
-    "Denoising strength change factor": "降噪强度的调整因数",
+    "Denoising strength change factor": "降噪强度的调整系数",
     "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推荐设置：采样迭代步数：80-100，采样器：Euler a，降噪强度：0.8",
-    "Pixels to expand": "拓展的像素",
+    "Pixels to expand": "拓展的像素数",
     "Outpainting direction": "外补绘制的方向",
     "left": "左",
     "right": "右",
@@ -192,8 +192,8 @@
     "Batch from Directory": "从目录进行批量处理",
     "Source": "来源",
     "Show result images": "显示输出图像",
-    "Scale by": "缩放到",
-    "Scale to": "缩放为",
+    "Scale by": "等比缩放",
+    "Scale to": "指定尺寸缩放",
     "Resize": "缩放",
     "Crop to fit": "裁剪以适应",
     "Upscaler 2": "放大算法 2",
@@ -208,9 +208,9 @@
     "extras history": "后处理历史记录",
     "Renew Page": "刷新页面",
     "extras": "后处理",
-    "favorites": "我的收藏",
+    "favorites": "收藏夹",
     "Load": "载入",
-    "Images directory": "图像路径",
+    "Images directory": "图像目录",
     "Prev batch": "上一批",
     "Next batch": "下一批",
     "First Page": "第一页",
@@ -218,7 +218,7 @@
     "Page Index": "页数",
     "Next Page": "下一页",
     "End Page": "尾页",
-    "number of images to delete consecutively next": "下一批要批量删除的图像数",
+    "number of images to delete consecutively next": "接下来要连续删除的图像数",
     "Delete": "删除",
     "Generate Info": "生成信息",
     "File Name": "文件名",
@@ -244,7 +244,7 @@
     "wiki": "wiki",
     "for detailed explanation.": "以了解详细说明",
     "Create embedding": "生成 embedding",
-    "Create aesthetic images embedding": "生成美术风格 图类 embedding",
+    "Create aesthetic images embedding": "生成美术风格图集 embedding",
     "Create hypernetwork": "生成 hypernetwork",
     "Preprocess images": "图像预处理",
     "Name": "名称",
@@ -295,7 +295,7 @@
     "Train Hypernetwork": "训练 Hypernetwork",
     "Train Embedding": "训练 Embedding",
     "Create an aesthetic embedding out of any number of images": "从任意数量的图像中创建美术风格 embedding",
-    "Create images embedding": "生成 图类 embedding",
+    "Create images embedding": "生成图集 embedding",
     "Apply settings": "保存设置",
     "Saving images/grids": "保存图像/概览图",
     "Always save all generated images": "始终保存所有生成的图像",
@@ -309,13 +309,13 @@
     "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "概览行数； 使用 -1 进行自动检测，使用 0 使其与批量大小相同",
     "Save text information about generation parameters as chunks to png files": "将有关生成参数的文本信息作为块保存到 png 文件中",
     "Create a text file next to every image with generation parameters.": "保存图像时在每个图像旁边创建一个文本文件储存生成参数",
-    "Save a copy of image before doing face restoration.": "在进行面部恢复之前保存图像副本",
+    "Save a copy of image before doing face restoration.": "在进行面部修复之前保存图像副本",
     "Quality for saved jpeg images": "保存的 jpeg 图像的质量",
     "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 图像大于 4MB 或宽高大于 4000，则缩小并保存副本为 JPG",
-    "Use original name for output filename during batch process in extras tab": "在后处理选项卡中的批处理过程中使用原始名称作为输出文件名",
+    "Use original name for output filename during batch process in extras tab": "在后处理选项卡中的批量处理过程中使用原始名称作为输出文件名",
     "When using 'Save' button, only save a single selected image": "使用“保存”按钮时，只保存一个选定的图像",
     "Do not add watermark to images": "不要给图像加水印",
-    "Paths for saving": "保存目录",
+    "Paths for saving": "保存路径",
     "Output directory for images; if empty, defaults to three directories below": "图像的输出目录； 如果为空，则默认为下面三个目录",
     "Output directory for txt2img images": "文转图的输出目录",
     "Output directory for img2img images": "图转图的输出目录",
@@ -331,25 +331,25 @@
     "Directory name pattern": "目录名称格式",
     "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示词数量",
     "Upscaling": "放大",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的图块大小(Tile size)。 0 = 不分块(no tiling)",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "图块重叠(Tile overlap)，以 ESRGAN 的像素为单位。 低值 = 可见接缝",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的图块大小(Tile size)。0 = 不分块(no tiling)",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "图块重叠(Tile overlap)，以 ESRGAN 的像素为单位。低值 = 可见接缝",
     "Tile size for all SwinIR.": "所有 SwinIR 的图块尺寸(Tile size)",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "图块重叠(Tile overlap)，以 SwinIR 的像素为单位。 低值 = 可见接缝",
-    "LDSR processing steps. Lower = faster": "LDSR 处理迭代步数。 更低 = 更快",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "图块重叠(Tile overlap)，以 SwinIR 的像素为单位。低值 = 可见接缝",
+    "LDSR processing steps. Lower = faster": "LDSR 处理迭代步数。更低 = 更快",
     "Upscaler for img2img": "图转图的放大算法",
     "Upscale latent space image when doing hires. fix": "做高分辨率修复时也放大潜空间图像",
     "Face restoration": "面部修复",
     "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 权重参数； 0 = 最大效果； 1 = 最小效果",
-    "Move face restoration model from VRAM into RAM after processing": "人脸修复处理完成后将人脸修复模型从显存(VRAM)移至内存(RAM)",
+    "Move face restoration model from VRAM into RAM after processing": "面部修复处理完成后将面部修复模型从显存(VRAM)移至内存(RAM)",
     "System": "系统",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成图像时每秒轮询显存(VRAM)使用情况的次数。 设置为 0 以禁用",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成图像时每秒轮询显存(VRAM)使用情况的次数。设置为 0 以禁用",
     "Always print all generation info to standard output": "始终将所有生成信息输出到 standard output (一般为控制台)",
     "Add a second progress bar to the console that shows progress for an entire job.": "向控制台添加第二个进度条，显示整个作业的进度",
     "Training": "训练",
     "Unload VAE and CLIP from VRAM when training": "训练时从显存(VRAM)中取消 VAE 和 CLIP 的加载",
     "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
     "Filename word regex": "文件名用词的正则表达式",
-    "Filename join string": "文件名连接用字符",
+    "Filename join string": "文件名连接用字符串",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每个 epoch 中单个输入图像的重复次数； 仅用于显示 epoch 数",
     "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步保存一个包含 loss 的 csv 到日志目录，0 表示禁用",
     "Stable Diffusion": "Stable Diffusion",
@@ -358,9 +358,9 @@
     "Apply color correction to img2img results to match original colors.": "对图转图结果应用颜色校正以匹配原始颜色",
     "Save a copy of image before applying color correction to img2img results": "在对图转图结果应用颜色校正之前保存图像副本",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图转图的时候，确切地执行滑块指定的迭代步数（通常更少的降噪会做更少的迭代步数）",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。 这可能会改变现有的随机种子。 需要重新启动才能应用",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。这可能会改变现有的随机种子。需要重新启动才能应用",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。 可用于复现旧随机种子",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。可用于复现旧随机种子",
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 采样器批量生成与生成单个图像时产出相同的图像",
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个 token 时，通过从 n 个 token 中的最后一个逗号填空来提高一致性",
     "Filter NSFW content": "过滤成人内容",
@@ -379,8 +379,8 @@
     "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "在 deepbooru 中使用转义 (\\) 括号（因此它们用作文字括号而不是强调符号）",
     "User interface": "用户界面",
     "Show progressbar": "显示进度条",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 个采样迭代步数显示图像生成进度。 设置 0 禁用",
-    "Show previews of all images generated in a batch as a grid": "以网格的形式预览所有批量生成出来的图片",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 个采样迭代步数显示图像生成进度。设置 0 禁用",
+    "Show previews of all images generated in a batch as a grid": "以网格的形式预览所有批量生成出来的图像",
     "Show grid in results for web": "在网页的结果中显示概览图",
     "Do not show any images in results for web": "不在网页的结果中显示任何图像",
     "Add model hash to generation information": "将模型的哈希值添加到生成信息",
@@ -423,13 +423,13 @@
     "Stop processing current image and continue processing.": "停止处理当前图像并继续处理下一个",
     "Stop processing images and return any results accumulated so far.": "停止处理图像并返回迄今为止累积的任何结果",
     "Style to apply; styles have components for both positive and negative prompts and apply to both": "要应用的模版风格； 模版风格包含正向和反向提示词，并应用于两者",
-    "Do not do anything special": "并没有做什么特别的事情",
+    "Do not do anything special": "什么都不做",
     "Which algorithm to use to produce the image": "使用哪种算法生成图像",
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图像，将迭代步数设置为高于 30-40 不会有正面作用",
     "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长内补绘制",
     "Produce an image that can be tiled.": "生成可以分块(Tiling)的图像",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候以较小的分辨率生成初步图像、接着放大图片，然后在不更改构图的情况下改进其中的细节",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "决定算法对图像内容的影响程度。 设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。 值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候以较小的分辨率生成初步图像、接着放大图像，然后在不更改构图的情况下改进其中的细节",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "决定算法对图像内容的影响程度。设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
     "How many batches of images to create": "创建多少批次的图像",
     "How many image to create in a single batch": "每批创建多少图像",
     "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 图像应在多大程度上服从提示词 - 较低的值会产生更有创意的结果",
@@ -437,9 +437,9 @@
     "Set seed to -1, which will cause a new random number to be used every time": "将随机种子设置为-1，则每次都会使用一个新的随机数",
     "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的随机种子，如果它是随机出来的话就有用",
     "Seed of a different picture to be mixed into the generation.": "将要参与生成的另一张图的随机种子",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要产生多强烈的变化。 设为 0 时，将没有效果。 设为 1 时，你将获得完全产自差异随机种子的图像（ancestral 采样器除外，你只是单纯地生成了一些东西）",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要产生多强烈的变化。设为 0 时，将没有效果。设为 1 时，你将获得完全产自差异随机种子的图像（ancestral 采样器除外，你只是单纯地生成了一些东西）",
     "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "尝试生成与在指定分辨率下使用相同随机种子生成的图像相似的图片",
-    "This text is used to rotate the feature space of the imgs embs": "此文本用于旋转 图类 embeddings 的特征空间",
+    "This text is used to rotate the feature space of the imgs embs": "此文本用于旋转图集 embeddings 的特征空间",
     "Separate values for X axis using commas.": "使用逗号分隔 X 轴的值",
     "Separate values for Y axis using commas.": "使用逗号分隔 Y 轴的值",
     "Write image to a directory (default - log/images) and generation parameters into csv file.": "将图像写入目录（默认 - log/images）并将生成参数写入 csv 文件",
@@ -451,29 +451,29 @@
     "fill it with latent space noise": "用潜空间的噪声填充它",
     "fill it with latent space zeroes": "用潜空间的零填充它",
     "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做内补绘制，缩小后粘贴到原始图像中",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。 除非高度和宽度匹配，否则你将获得不正确的纵横比",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。 裁剪多出来的部分",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。 用图像的颜色填充空白区域",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
     "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像多少次并将其用作下一次迭代的输入",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，降噪强度都会乘以该值。 <1 表示减少多样性，因此你的这一组图将集中在固定的图像上。 >1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。 图块之间(Tiles)需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，降噪强度都会乘以该值。<1 表示减少多样性，因此你的这一组图将集中在固定的图像上。>1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。图块之间(Tiles)需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
     "A directory on the same machine where the server is running.": "与服务器主机上的目录",
-    "Leave blank to save images to the default path.": "留空以将图像保存到默认目录",
+    "Leave blank to save images to the default path.": "留空以将图像保存到默认路径",
     "Input images directory": "输入图像目录",
     "Result = A * (1 - M) + B * M": "结果 = A * (1 - M) + B * M",
     "Result = A + (B - C) * M": "结果 = A + (B - C) * M",
     "1st and last digit must be 1. ex:'1, 2, 1'": "第一个和最后一个数字必须是 1。例:'1, 2, 1'",
-    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。 低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
-    "Path to directory with input images": "带有输入图像的目录",
-    "Path to directory where to write outputs": "进行输出的目录",
+    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
+    "Path to directory with input images": "带有输入图像的路径",
+    "Path to directory where to write outputs": "进行输出的路径",
     "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像的文件名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果启用此选项，水印将不会添加到生成出来的图像中。 警告：如果你不添加水印，你的行为可能是不符合专业操守的",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果启用此选项，水印将不会添加到生成出来的图像中。警告：如果你不添加水印，你的行为可能是不符合专业操守的",
     "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像和概览图的子目录: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
-    "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络恢复低质量人脸",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。 留空以保持文件名文本不变",
+    "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络修复低质量面部",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。留空以保持文件名文本不变",
     "This string will be used to join split words into a single line if the option above is enabled.": "如果启用了上述选项，则此处的字符会用于将拆分的单词接合为同一行",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置名称列表，以逗号分隔，设置应转到顶部的快速访问栏，而不是通常的设置选项卡。 有关设置名称，请参见 modules/shared.py。 需要重新启动才能应用",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置名称列表，以逗号分隔，设置应转到顶部的快速访问栏，而不是通常的设置选项卡。有关设置名称，请参见 modules/shared.py。需要重新启动才能应用",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声。你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
     "Enable Autocomplete": "开启Tag补全",
     "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
     "Roll three": "抽三位出來",
-- 
cgit v1.2.1


From 168aa1cf67440ecc9134c9f81daf800558f67135 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Sun, 23 Oct 2022 20:11:40 +0900
Subject: Update zh-hans.json

---
 localizations/zh-hans.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 3fbcac6b..9d20fa17 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -77,9 +77,9 @@
     "Slerp interpolation": "Slerp 插值",
     "Aesthetic imgs embedding": "美术风格图集 embedding",
     "None": "无",
-    "Aesthetic text for imgs": "用于图集的美术风格描述",
+    "Aesthetic text for imgs": "该图集的美术风格描述",
     "Slerp angle": "Slerp 角度",
-    "Is negative text": "属于否定类文字",
+    "Is negative text": "是反向提示词",
     "Script": "脚本",
     "Prompt matrix": "提示词矩阵",
     "Prompts from file or textbox": "来自文本框或文件载入提示词",
-- 
cgit v1.2.1


From c7e000bedde0d8fbf50ecd7601cb8344f0ec2202 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 24 Oct 2022 04:07:28 +0900
Subject: Update zh-hans.json

---
 localizations/zh-hans.json | 80 +++++++++++++++++++++++-----------------------
 1 file changed, 40 insertions(+), 40 deletions(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 9d20fa17..6276f573 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -10,8 +10,8 @@
     "•": "•",
     "built with gradio": "基于 Gradio 构建",
     "Stable Diffusion checkpoint": "Stable Diffusion 模型(ckpt)",
-    "txt2img": "文转图",
-    "img2img": "图转图",
+    "txt2img": "文生图",
+    "img2img": "图生图",
     "Extras": "后处理",
     "PNG Info": "PNG 信息",
     "Image Browser": "图库浏览器",
@@ -82,7 +82,7 @@
     "Is negative text": "是反向提示词",
     "Script": "脚本",
     "Prompt matrix": "提示词矩阵",
-    "Prompts from file or textbox": "来自文本框或文件载入提示词",
+    "Prompts from file or textbox": "从文本框或文件载入提示词",
     "X/Y plot": "X/Y 图表",
     "Put variable parts at start of prompt": "把变量部分放在提示词文本的开头",
     "Show Textbox": "显示文本框",
@@ -114,16 +114,16 @@
     "Keep -1 for seeds": "保持随机种子为-1",
     "Drop Image Here": "拖拽图像到此",
     "Save": "保存",
-    "Send to img2img": "传送到图转图",
-    "Send to inpaint": "传送到内补绘制",
-    "Send to extras": "传送到后处理",
-    "Make Zip when Save?": "保存时生成压缩档?",
+    "Send to img2img": ">> 图生图",
+    "Send to inpaint": ">> 内补绘制",
+    "Send to extras": ">> 后处理",
+    "Make Zip when Save?": "保存时生成zip压缩文件?",
     "Textbox": "文本框",
-    "Interrogate\nCLIP": "CLIP\n逆向生成提示词",
-    "Interrogate\nDeepBooru": "DeepBooru\n逆向生成提示词",
+    "Interrogate\nCLIP": "CLIP\n反推提示词",
+    "Interrogate\nDeepBooru": "DeepBooru\n反推提示词",
     "Inpaint": "内补绘制",
-    "Batch img2img": "批量图转图",
-    "Image for img2img": "图转图的图像",
+    "Batch img2img": "批量图生图",
+    "Image for img2img": "图生图的图像",
     "Image for inpainting with mask": "用在内补绘制蒙版内容的图像",
     "Mask": "蒙版",
     "Mask blur": "蒙版模糊",
@@ -149,7 +149,7 @@
     "Just resize": "只缩放",
     "Crop and resize": "缩放并剪裁",
     "Resize and fill": "缩放并填空",
-    "img2img alternative test": "图转图的另一种测试",
+    "img2img alternative test": "图生图的另一种测试",
     "Loopback": "回送",
     "Outpainting mk2": "外补绘制第二版",
     "Poor man's outpainting": "效果稍差的外补绘制",
@@ -176,7 +176,7 @@
     "down": "下",
     "Fall-off exponent (lower=higher detail)": "衰减指数（低=高细节）",
     "Color variation": "色彩变化",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置图块大小(tile size)",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置图块尺寸(tile size)",
     "Tile overlap": "图块重叠的像素(Tile overlap)",
     "Upscaler": "放大算法",
     "Lanczos": "Lanczos",
@@ -202,9 +202,9 @@
     "CodeFormer visibility": "CodeFormer 可见度",
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 权重 (0 = 最大效果, 1 = 最小效果)",
     "Open output directory": "打开输出目录",
-    "Send to txt2img": "传送到文转图",
-    "txt2img history": "文转图历史记录",
-    "img2img history": "图转图历史记录",
+    "Send to txt2img": ">> 文生图",
+    "txt2img history": "文生图历史记录",
+    "img2img history": "图生图历史记录",
     "extras history": "后处理历史记录",
     "Renew Page": "刷新页面",
     "extras": "后处理",
@@ -291,7 +291,7 @@
     "Save an image to log directory every N steps, 0 to disable": "每 N 步保存一个图像到日志目录，0 表示禁用",
     "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步将 embedding 的副本保存到日志目录，0 表示禁用",
     "Save images with embedding in PNG chunks": "保存图像并在 PNG 文件中嵌入 embedding 文件",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "进行预览时从文转图选项卡中读取参数（提示词等）",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "进行预览时从文生图选项卡中读取参数（提示词等）",
     "Train Hypernetwork": "训练 Hypernetwork",
     "Train Embedding": "训练 Embedding",
     "Create an aesthetic embedding out of any number of images": "从任意数量的图像中创建美术风格 embedding",
@@ -317,12 +317,12 @@
     "Do not add watermark to images": "不要给图像加水印",
     "Paths for saving": "保存路径",
     "Output directory for images; if empty, defaults to three directories below": "图像的输出目录； 如果为空，则默认为下面三个目录",
-    "Output directory for txt2img images": "文转图的输出目录",
-    "Output directory for img2img images": "图转图的输出目录",
+    "Output directory for txt2img images": "文生图的输出目录",
+    "Output directory for img2img images": "图生图的输出目录",
     "Output directory for images from extras tab": "后处理的输出目录",
     "Output directory for grids; if empty, defaults to two directories below": "概览图的输出目录； 如果为空，则默认为以下两个目录",
-    "Output directory for txt2img grids": "文转图概览的输出目录",
-    "Output directory for img2img grids": "图转图概览的输出目录",
+    "Output directory for txt2img grids": "文生图概览的输出目录",
+    "Output directory for img2img grids": "图生图概览的输出目录",
     "Directory for saving images using the Save button": "使用“保存”按钮保存图像的目录",
     "Saving to a directory": "保存到目录",
     "Save images to a subdirectory": "将图像保存到子目录",
@@ -331,12 +331,12 @@
     "Directory name pattern": "目录名称格式",
     "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示词数量",
     "Upscaling": "放大",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的图块大小(Tile size)。0 = 不分块(no tiling)",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "图块重叠(Tile overlap)，以 ESRGAN 的像素为单位。低值 = 可见接缝",
-    "Tile size for all SwinIR.": "所有 SwinIR 的图块尺寸(Tile size)",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "图块重叠(Tile overlap)，以 SwinIR 的像素为单位。低值 = 可见接缝",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的图块尺寸(Tile size)。0 = 不分块(no tiling)",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN 的图块重叠(Tile overlap)像素。低值 = 可见接缝",
+    "Tile size for all SwinIR.": "适用所有 SwinIR 系算法的图块尺寸(Tile size)",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR 的图块重叠(Tile overlap)像素。低值 = 可见接缝",
     "LDSR processing steps. Lower = faster": "LDSR 处理迭代步数。更低 = 更快",
-    "Upscaler for img2img": "图转图的放大算法",
+    "Upscaler for img2img": "图生图的放大算法",
     "Upscale latent space image when doing hires. fix": "做高分辨率修复时也放大潜空间图像",
     "Face restoration": "面部修复",
     "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 权重参数； 0 = 最大效果； 1 = 最小效果",
@@ -355,9 +355,9 @@
     "Stable Diffusion": "Stable Diffusion",
     "Checkpoints to cache in RAM": "缓存在内存(RAM)中的模型(ckpt)",
     "Hypernetwork strength": "Hypernetwork 强度",
-    "Apply color correction to img2img results to match original colors.": "对图转图结果应用颜色校正以匹配原始颜色",
-    "Save a copy of image before applying color correction to img2img results": "在对图转图结果应用颜色校正之前保存图像副本",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图转图的时候，确切地执行滑块指定的迭代步数（通常更少的降噪会做更少的迭代步数）",
+    "Apply color correction to img2img results to match original colors.": "对图生图结果应用颜色校正以匹配原始颜色",
+    "Save a copy of image before applying color correction to img2img results": "在对图生图结果应用颜色校正之前保存图像副本",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（通常更少的降噪会做更少的迭代步数）",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。这可能会改变现有的随机种子。需要重新启动才能应用",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。可用于复现旧随机种子",
@@ -365,16 +365,16 @@
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个 token 时，通过从 n 个 token 中的最后一个逗号填空来提高一致性",
     "Filter NSFW content": "过滤成人内容",
     "Stop At last layers of CLIP model": "在 CLIP 模型的最后哪一层停下",
-    "Interrogate Options": "逆向生成提示词选项",
-    "Interrogate: keep models in VRAM": "逆向生成: 将模型保存在显存(VRAM)中",
-    "Interrogate: use artists from artists.csv": "逆向生成: 使用 artists.csv 中的艺术家",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "逆向生成: 在生成结果中包含与模型标签(tags)相匹配的等级（对基于生成自然语言描述的逆向生成没有影响）",
-    "Interrogate: num_beams for BLIP": "逆向生成: BLIP 的 num_beams",
-    "Interrogate: minimum description length (excluding artists, etc..)": "逆向生成: 最小描述长度（不包括艺术家, 等…）",
-    "Interrogate: maximum description length": "逆向生成: 最大描述长度",
+    "Interrogate Options": "反推提示词选项",
+    "Interrogate: keep models in VRAM": "反推: 将模型保存在显存(VRAM)中",
+    "Interrogate: use artists from artists.csv": "反推: 使用 artists.csv 中的艺术家",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "反推: 在生成结果中包含与模型标签(tags)相匹配的等级（对基于生成自然语言描述的反推没有影响）",
+    "Interrogate: num_beams for BLIP": "反推: BLIP 的 num_beams",
+    "Interrogate: minimum description length (excluding artists, etc..)": "反推: 最小描述长度（不包括艺术家, 等…）",
+    "Interrogate: maximum description length": "反推: 最大描述长度",
     "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: 文本文件中的最大行数（0 = 无限制）",
-    "Interrogate: deepbooru score threshold": "逆向生成: deepbooru 分数阈值",
-    "Interrogate: deepbooru sort alphabetically": "逆向生成: deepbooru 按字母顺序排序",
+    "Interrogate: deepbooru score threshold": "反推: deepbooru 分数阈值",
+    "Interrogate: deepbooru sort alphabetically": "反推: deepbooru 按字母顺序排序",
     "use spaces for tags in deepbooru": "在 deepbooru 中为标签使用空格",
     "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "在 deepbooru 中使用转义 (\\) 括号（因此它们用作文字括号而不是强调符号）",
     "User interface": "用户界面",
@@ -396,7 +396,7 @@
     "Hide samplers in user interface (requires restart)": "在用户界面中隐藏采样器（需要重新启动）",
     "eta (noise multiplier) for DDIM": "DDIM 的 eta (噪声倍增器) ",
     "eta (noise multiplier) for ancestral samplers": "ancestral 采样器的 eta (噪声倍增器)",
-    "img2img DDIM discretize": "图转图 DDIM 离散化",
+    "img2img DDIM discretize": "图生图 DDIM 离散化",
     "uniform": "均勻",
     "quad": "二阶",
     "sigma churn": "sigma churn",
@@ -456,7 +456,7 @@
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
     "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像多少次并将其用作下一次迭代的输入",
     "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，降噪强度都会乘以该值。<1 表示减少多样性，因此你的这一组图将集中在固定的图像上。>1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。图块之间(Tiles)需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。图块(Tiles)之间需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
     "A directory on the same machine where the server is running.": "与服务器主机上的目录",
     "Leave blank to save images to the default path.": "留空以将图像保存到默认路径",
     "Input images directory": "输入图像目录",
-- 
cgit v1.2.1


From f9b20482fc5f71a5efead408a4006e73471fa1bc Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 24 Oct 2022 04:22:43 +0900
Subject: Update zh-hans.json

---
 localizations/zh-hans.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 6276f573..e206da88 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -55,7 +55,7 @@
     "Width": "宽度",
     "Height": "高度",
     "Restore faces": "面部修复",
-    "Tiling": "分块(Tiling)",
+    "Tiling": "可平铺(Tiling)",
     "Highres. fix": "高分辨率修复",
     "Firstpass width": "第一遍的宽度",
     "Firstpass height": "第一遍的高度",
@@ -427,7 +427,7 @@
     "Which algorithm to use to produce the image": "使用哪种算法生成图像",
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图像，将迭代步数设置为高于 30-40 不会有正面作用",
     "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长内补绘制",
-    "Produce an image that can be tiled.": "生成可以分块(Tiling)的图像",
+    "Produce an image that can be tiled.": "生成可用于平铺(tiled)的图像",
     "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候以较小的分辨率生成初步图像、接着放大图像，然后在不更改构图的情况下改进其中的细节",
     "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "决定算法对图像内容的影响程度。设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
     "How many batches of images to create": "创建多少批次的图像",
-- 
cgit v1.2.1


From 57b90a8b19494dfcd8be9a00c8e591b63d4564bd Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 24 Oct 2022 09:51:05 +0800
Subject: Update localizations/zh-hans.json

Co-authored-by: liggest <43201720+liggest@users.noreply.github.com>
---
 localizations/zh-hans.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index e206da88..da020248 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -454,7 +454,7 @@
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
-    "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像多少次并将其用作下一次迭代的输入",
+    "How many times to repeat processing an image and using it as input for the next iteration": "处理图片并作为下次迭代输入的重复次数",
     "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，降噪强度都会乘以该值。<1 表示减少多样性，因此你的这一组图将集中在固定的图像上。>1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
     "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。图块(Tiles)之间需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
     "A directory on the same machine where the server is running.": "与服务器主机上的目录",
-- 
cgit v1.2.1


From f9e31c6bddb62245208ad607da4550e7559b50f9 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 24 Oct 2022 09:56:20 +0800
Subject: Update localizations/zh-hans.json

Co-authored-by: liggest <43201720+liggest@users.noreply.github.com>
---
 localizations/zh-hans.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index da020248..ab589856 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -165,7 +165,7 @@
     "Decode CFG scale": "解码提示词相关性(CFG scale)",
     "Randomness": "随机度",
     "Sigma adjustment for finding noise for image": "为寻找图中噪点的 Sigma 调整",
-    "Loops": "循环",
+    "Loops": "迭代次数",
     "Denoising strength change factor": "降噪强度的调整系数",
     "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推荐设置：采样迭代步数：80-100，采样器：Euler a，降噪强度：0.8",
     "Pixels to expand": "拓展的像素数",
-- 
cgit v1.2.1


From f1103b67500a647483538f820e48bacc8de9c478 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 24 Oct 2022 10:07:56 +0800
Subject: Update localizations/zh-hans.json

Co-authored-by: liggest <43201720+liggest@users.noreply.github.com>
---
 localizations/zh-hans.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index ab589856..42faebe2 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -174,7 +174,7 @@
     "right": "右",
     "up": "上",
     "down": "下",
-    "Fall-off exponent (lower=higher detail)": "衰减指数（低=高细节）",
+    "Fall-off exponent (lower=higher detail)": "衰减指数(越低细节越好)",
     "Color variation": "色彩变化",
     "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置图块尺寸(tile size)",
     "Tile overlap": "图块重叠的像素(Tile overlap)",
-- 
cgit v1.2.1


From dd15526e6b1159e1c124c33dae9c28f70cb6f062 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 24 Oct 2022 10:08:12 +0800
Subject: Update localizations/zh-hans.json

Co-authored-by: liggest <43201720+liggest@users.noreply.github.com>
---
 localizations/zh-hans.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 42faebe2..b78f464b 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -234,7 +234,7 @@
     "Primary model (A)": "主要模型 (A)",
     "Secondary model (B)": "第二模型 (B)",
     "Tertiary model (C)": "第三模型 (C)",
-    "Custom Name (Optional)": "自定义名字 (可选)",
+    "Custom Name (Optional)": "自定义名称 (可选)",
     "Multiplier (M) - set to 0 to get model A": "倍率 (M) - 设为 0 等价于模型 A",
     "Interpolation Method": "插值方法",
     "Weighted sum": "加权和",
-- 
cgit v1.2.1


From e53474d31c621b239efb6a30a1e49de6bb251e21 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 24 Oct 2022 10:08:32 +0800
Subject: Update localizations/zh-hans.json

Co-authored-by: liggest <43201720+liggest@users.noreply.github.com>
---
 localizations/zh-hans.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index b78f464b..eecb1b39 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -305,7 +305,7 @@
     "File format for grids": "概览图的文件格式",
     "Add extended info (seed, prompt) to filename when saving grid": "保存概览时将扩展信息（随机种子、提示词）添加到文件名",
     "Do not save grids consisting of one picture": "不要保存由一张图像组成的概览图",
-    "Prevent empty spots in grid (when set to autodetect)": "防止概览图中出现空白点（设置为自动检测时）",
+    "Prevent empty spots in grid (when set to autodetect)": "（在自动检测时）防止概览图中出现空位",
     "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "概览行数； 使用 -1 进行自动检测，使用 0 使其与批量大小相同",
     "Save text information about generation parameters as chunks to png files": "将有关生成参数的文本信息作为块保存到 png 文件中",
     "Create a text file next to every image with generation parameters.": "保存图像时在每个图像旁边创建一个文本文件储存生成参数",
-- 
cgit v1.2.1


From 0655797c53928ff7ee269f312b8a479f1177cd70 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 24 Oct 2022 10:09:01 +0800
Subject: Update localizations/zh-hans.json

Co-authored-by: liggest <43201720+liggest@users.noreply.github.com>
---
 localizations/zh-hans.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index eecb1b39..f468c024 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -316,7 +316,7 @@
     "When using 'Save' button, only save a single selected image": "使用“保存”按钮时，只保存一个选定的图像",
     "Do not add watermark to images": "不要给图像加水印",
     "Paths for saving": "保存路径",
-    "Output directory for images; if empty, defaults to three directories below": "图像的输出目录； 如果为空，则默认为下面三个目录",
+    "Output directory for images; if empty, defaults to three directories below": "图像的输出目录； 如果为空，则默认为以下三个目录",
     "Output directory for txt2img images": "文生图的输出目录",
     "Output directory for img2img images": "图生图的输出目录",
     "Output directory for images from extras tab": "后处理的输出目录",
-- 
cgit v1.2.1


From bf7b50e6bcb61048891834def8393398d40a0b50 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 24 Oct 2022 10:21:31 +0800
Subject: Update localizations/zh-hans.json

Co-authored-by: liggest <43201720+liggest@users.noreply.github.com>
---
 localizations/zh-hans.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index f468c024..363ff200 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -36,7 +36,7 @@
     "Click to Upload": "点击上传",
     "Image": "图像",
     "Check progress": "查看进度",
-    "Check progress (first)": "查看进度 (1st)",
+    "Check progress (first)": "(首次)查看进度",
     "Sampling Steps": "采样迭代步数",
     "Sampling method": "采样方法",
     "Euler a": "Euler a",
-- 
cgit v1.2.1


From 5394d0e696bbf91a7b0aff9ee40170a1166b7607 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 24 Oct 2022 16:45:30 +0900
Subject: Update zh-hans.json

---
 localizations/zh-hans.json | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 363ff200..7dae0612 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -124,7 +124,7 @@
     "Inpaint": "内补绘制",
     "Batch img2img": "批量图生图",
     "Image for img2img": "图生图的图像",
-    "Image for inpainting with mask": "用在内补绘制蒙版内容的图像",
+    "Image for inpainting with mask": "用于内补绘制蒙版内容的图像",
     "Mask": "蒙版",
     "Mask blur": "蒙版模糊",
     "Mask mode": "蒙版模式",
@@ -264,7 +264,7 @@
     "Overwrite Old Hypernetwork": "覆写旧的 Hypernetwork",
     "Source directory": "源目录",
     "Destination directory": "目标目录",
-    "Existing Caption txt Action": "对已有说明文字的 txt 的行为",
+    "Existing Caption txt Action": "对已有的 txt 说明文字的行为",
     "ignore": "无视",
     "copy": "复制",
     "prepend": "放前面",
@@ -304,7 +304,7 @@
     "Always save all generated image grids": "始终保存所有生成的概览图",
     "File format for grids": "概览图的文件格式",
     "Add extended info (seed, prompt) to filename when saving grid": "保存概览时将扩展信息（随机种子、提示词）添加到文件名",
-    "Do not save grids consisting of one picture": "不要保存由一张图像组成的概览图",
+    "Do not save grids consisting of one picture": "只有一张图片时不要保存概览图",
     "Prevent empty spots in grid (when set to autodetect)": "（在自动检测时）防止概览图中出现空位",
     "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "概览行数； 使用 -1 进行自动检测，使用 0 使其与批量大小相同",
     "Save text information about generation parameters as chunks to png files": "将有关生成参数的文本信息作为块保存到 png 文件中",
@@ -394,8 +394,8 @@
     "Localization (requires restart)": "本地化（需要重新启动）",
     "Sampler parameters": "采样器参数",
     "Hide samplers in user interface (requires restart)": "在用户界面中隐藏采样器（需要重新启动）",
-    "eta (noise multiplier) for DDIM": "DDIM 的 eta (噪声倍增器) ",
-    "eta (noise multiplier) for ancestral samplers": "ancestral 采样器的 eta (噪声倍增器)",
+    "eta (noise multiplier) for DDIM": "DDIM 的 eta (噪声乘数) ",
+    "eta (noise multiplier) for ancestral samplers": "ancestral 采样器的 eta (噪声乘数)",
     "img2img DDIM discretize": "图生图 DDIM 离散化",
     "uniform": "均勻",
     "quad": "二阶",
@@ -435,7 +435,7 @@
     "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 图像应在多大程度上服从提示词 - 较低的值会产生更有创意的结果",
     "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一个固定随机数生成器输出的值 - 以相同参数和随机种子生成的图像会得到相同的结果",
     "Set seed to -1, which will cause a new random number to be used every time": "将随机种子设置为-1，则每次都会使用一个新的随机数",
-    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的随机种子，如果它是随机出来的话就有用",
+    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的随机种子，如果想要固定结果就会很有用",
     "Seed of a different picture to be mixed into the generation.": "将要参与生成的另一张图的随机种子",
     "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要产生多强烈的变化。设为 0 时，将没有效果。设为 1 时，你将获得完全产自差异随机种子的图像（ancestral 采样器除外，你只是单纯地生成了一些东西）",
     "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "尝试生成与在指定分辨率下使用相同随机种子生成的图像相似的图片",
@@ -454,7 +454,7 @@
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
-    "How many times to repeat processing an image and using it as input for the next iteration": "处理图片并作为下次迭代输入的重复次数",
+    "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像并用作下次迭代输入的次数",
     "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，降噪强度都会乘以该值。<1 表示减少多样性，因此你的这一组图将集中在固定的图像上。>1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
     "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。图块(Tiles)之间需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
     "A directory on the same machine where the server is running.": "与服务器主机上的目录",
-- 
cgit v1.2.1


From 6cf030abfc8c91caab453d88c9a181c8f1ee9e23 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 24 Oct 2022 17:20:34 +0900
Subject: re-order some element. update to latest

---
 localizations/zh-hans.json | 62 +++++++++++++++++++++++++---------------------
 1 file changed, 34 insertions(+), 28 deletions(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 7dae0612..06f56852 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -14,11 +14,11 @@
     "img2img": "图生图",
     "Extras": "后处理",
     "PNG Info": "PNG 信息",
-    "Image Browser": "图库浏览器",
-    "History": "历史记录",
     "Checkpoint Merger": "模型(ckpt)合并工具",
     "Train": "训练",
     "Create aesthetic embedding": "生成美术风格 embedding",
+    "Image Browser": "图库浏览器",
+    "History": "历史记录",
     "Settings": "设置",
     "Prompt": "提示词",
     "Negative prompt": "反向提示词",
@@ -81,9 +81,12 @@
     "Slerp angle": "Slerp 角度",
     "Is negative text": "是反向提示词",
     "Script": "脚本",
+    "Embedding to Shareable PNG": "将 Embedding 转换为可分享的 PNG",
     "Prompt matrix": "提示词矩阵",
     "Prompts from file or textbox": "从文本框或文件载入提示词",
     "X/Y plot": "X/Y 图表",
+    "Source embedding to convert": "用于转换的源 Embedding",
+    "Embedding token": "Embedding 的 token (关键词)",
     "Put variable parts at start of prompt": "把变量部分放在提示词文本的开头",
     "Show Textbox": "显示文本框",
     "File with inputs": "含输入内容的文件",
@@ -203,31 +206,6 @@
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 权重 (0 = 最大效果, 1 = 最小效果)",
     "Open output directory": "打开输出目录",
     "Send to txt2img": ">> 文生图",
-    "txt2img history": "文生图历史记录",
-    "img2img history": "图生图历史记录",
-    "extras history": "后处理历史记录",
-    "Renew Page": "刷新页面",
-    "extras": "后处理",
-    "favorites": "收藏夹",
-    "Load": "载入",
-    "Images directory": "图像目录",
-    "Prev batch": "上一批",
-    "Next batch": "下一批",
-    "First Page": "第一页",
-    "Prev Page": "上一页",
-    "Page Index": "页数",
-    "Next Page": "下一页",
-    "End Page": "尾页",
-    "number of images to delete consecutively next": "接下来要连续删除的图像数",
-    "Delete": "删除",
-    "Generate Info": "生成信息",
-    "File Name": "文件名",
-    "Collect": "收藏",
-    "Refresh page": "刷新页面",
-    "Date to": "日期至",
-    "Number": "数量",
-    "set_index": "设置索引",
-    "Checkbox": "勾选框",
     "A merger of the two checkpoints will be generated in your": "合并后的模型(ckpt)会生成在你的",
     "checkpoint": "模型(ckpt)",
     "directory.": "目录",
@@ -296,6 +274,32 @@
     "Train Embedding": "训练 Embedding",
     "Create an aesthetic embedding out of any number of images": "从任意数量的图像中创建美术风格 embedding",
     "Create images embedding": "生成图集 embedding",
+    "txt2img history": "文生图历史记录",
+    "img2img history": "图生图历史记录",
+    "extras history": "后处理历史记录",
+    "Renew Page": "刷新页面",
+    "extras": "后处理",
+    "favorites": "收藏夹",
+    "custom fold": "自定义文件夹",
+    "Load": "载入",
+    "Images directory": "图像目录",
+    "Prev batch": "上一批",
+    "Next batch": "下一批",
+    "First Page": "第一页",
+    "Prev Page": "上一页",
+    "Page Index": "页数",
+    "Next Page": "下一页",
+    "End Page": "尾页",
+    "number of images to delete consecutively next": "接下来要连续删除的图像数",
+    "Delete": "删除",
+    "Generate Info": "生成信息",
+    "File Name": "文件名",
+    "Collect": "收藏",
+    "Refresh page": "刷新页面",
+    "Date to": "日期至",
+    "Number": "数量",
+    "set_index": "设置索引",
+    "Checkbox": "勾选框",
     "Apply settings": "保存设置",
     "Saving images/grids": "保存图像/概览图",
     "Always save all generated images": "始终保存所有生成的图像",
@@ -405,6 +409,8 @@
     "Eta noise seed delta": "Eta 噪声种子偏移(noise seed delta)",
     "Images Browser": "图库浏览器",
     "Preload images at startup": "在启动时预载图像",
+    "Number of columns on the page": "每页列数",
+    "Number of rows on the page": "每页行数",
     "Number of pictures displayed on each page": "每页显示的图像数量",
     "Minimum number of pages per load": "每次加载的最小页数",
     "Number of grids in each row": "每行显示多少格",
@@ -459,13 +465,13 @@
     "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。图块(Tiles)之间需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
     "A directory on the same machine where the server is running.": "与服务器主机上的目录",
     "Leave blank to save images to the default path.": "留空以将图像保存到默认路径",
-    "Input images directory": "输入图像目录",
     "Result = A * (1 - M) + B * M": "结果 = A * (1 - M) + B * M",
     "Result = A + (B - C) * M": "结果 = A + (B - C) * M",
     "1st and last digit must be 1. ex:'1, 2, 1'": "第一个和最后一个数字必须是 1。例:'1, 2, 1'",
     "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
     "Path to directory with input images": "带有输入图像的路径",
     "Path to directory where to write outputs": "进行输出的路径",
+    "Input images directory": "输入图像目录",
     "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像的文件名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
     "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果启用此选项，水印将不会添加到生成出来的图像中。警告：如果你不添加水印，你的行为可能是不符合专业操守的",
     "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像和概览图的子目录: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
-- 
cgit v1.2.1


From 05335805340a6f77c700a663f461ff1481fda922 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Tue, 25 Oct 2022 00:23:33 +0900
Subject: Update zh-hans.json

---
 localizations/zh-hans.json | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 06f56852..4f860286 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -137,12 +137,12 @@
     "Inpaint masked": "内补绘制蒙版内容",
     "Inpaint not masked": "内补绘制非蒙版内容",
     "Masked content": "蒙版蒙住的内容",
-    "fill": "填空",
+    "fill": "填充",
     "original": "原图",
     "latent noise": "潜空间噪声",
     "latent nothing": "潜空间数值零",
     "Inpaint at full resolution": "以完整分辨率进行内补绘制",
-    "Inpaint at full resolution padding, pixels": "以完整分辨率进行内补绘制 - 填空像素",
+    "Inpaint at full resolution padding, pixels": "以完整分辨率进行内补绘制 - 填补像素",
     "Process images in a directory on the same machine where the server is running.": "在服务器主机上的目录中处理图像",
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "指定一个空的文件夹为输出目录而非默认的 output 文件夹为输出目录",
     "Disabled when launched with --hide-ui-dir-config.": "启动 --hide-ui-dir-config 时禁用",
@@ -151,7 +151,7 @@
     "Resize mode": "缩放模式",
     "Just resize": "只缩放",
     "Crop and resize": "缩放并剪裁",
-    "Resize and fill": "缩放并填空",
+    "Resize and fill": "缩放并填充",
     "img2img alternative test": "图生图的另一种测试",
     "Loopback": "回送",
     "Outpainting mk2": "外补绘制第二版",
@@ -285,7 +285,7 @@
     "Images directory": "图像目录",
     "Prev batch": "上一批",
     "Next batch": "下一批",
-    "First Page": "第一页",
+    "First Page": "首页",
     "Prev Page": "上一页",
     "Page Index": "页数",
     "Next Page": "下一页",
@@ -366,7 +366,7 @@
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。可用于复现旧随机种子",
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 采样器批量生成与生成单个图像时产出相同的图像",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个 token 时，通过从 n 个 token 中的最后一个逗号填空来提高一致性",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个 token 时，通过从 n 个 token 中的最后一个逗号填补来提高一致性",
     "Filter NSFW content": "过滤成人内容",
     "Stop At last layers of CLIP model": "在 CLIP 模型的最后哪一层停下",
     "Interrogate Options": "反推提示词选项",
-- 
cgit v1.2.1


From 2439f4c32c4bbb8d66cad44a461d1b0229fb5e8c Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Tue, 25 Oct 2022 12:35:04 +0900
Subject: Update zh-hans.json

---
 localizations/zh-hans.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 4f860286..c91289fd 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -59,7 +59,7 @@
     "Highres. fix": "高分辨率修复",
     "Firstpass width": "第一遍的宽度",
     "Firstpass height": "第一遍的高度",
-    "Denoising strength": "降噪(模糊)强度",
+    "Denoising strength": "去噪强度",
     "Batch count": "批次",
     "Batch size": "批量",
     "CFG Scale": "提示词相关性(CFG Scale)",
-- 
cgit v1.2.1


From 68bdeb5b84abd9c345bf411a43fc9ebb73745b66 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Tue, 25 Oct 2022 12:38:29 +0900
Subject: Update zh-hans.json

---
 localizations/zh-hans.json | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index c91289fd..4d04398a 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -108,7 +108,7 @@
     "Sigma noise": "Sigma noise",
     "Eta": "Eta",
     "Clip skip": "Clip 跳过",
-    "Denoising": "降噪",
+    "Denoising": "去噪",
     "X values": "X轴数值",
     "Y type": "Y轴类型",
     "Y values": "Y轴数值",
@@ -164,13 +164,13 @@
     "Original negative prompt": "初始反向提示词",
     "Override `Sampling Steps` to the same value as `Decode steps`?": "覆写 `采样迭代步数` 为 `解码迭代步数`?",
     "Decode steps": "解码迭代步数",
-    "Override `Denoising strength` to 1?": "覆写 `降噪强度` 为 1?",
+    "Override `Denoising strength` to 1?": "覆写 `去噪强度` 为 1?",
     "Decode CFG scale": "解码提示词相关性(CFG scale)",
     "Randomness": "随机度",
     "Sigma adjustment for finding noise for image": "为寻找图中噪点的 Sigma 调整",
     "Loops": "迭代次数",
-    "Denoising strength change factor": "降噪强度的调整系数",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推荐设置：采样迭代步数：80-100，采样器：Euler a，降噪强度：0.8",
+    "Denoising strength change factor": "去噪强度的调整系数",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推荐设置：采样迭代步数：80-100，采样器：Euler a，去噪强度：0.8",
     "Pixels to expand": "拓展的像素数",
     "Outpainting direction": "外补绘制的方向",
     "left": "左",
@@ -361,7 +361,7 @@
     "Hypernetwork strength": "Hypernetwork 强度",
     "Apply color correction to img2img results to match original colors.": "对图生图结果应用颜色校正以匹配原始颜色",
     "Save a copy of image before applying color correction to img2img results": "在对图生图结果应用颜色校正之前保存图像副本",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（通常更少的降噪会做更少的迭代步数）",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（正常情況下更弱的去噪需要更少的迭代步数）",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。这可能会改变现有的随机种子。需要重新启动才能应用",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。可用于复现旧随机种子",
@@ -461,7 +461,7 @@
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
     "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像并用作下次迭代输入的次数",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，降噪强度都会乘以该值。<1 表示减少多样性，因此你的这一组图将集中在固定的图像上。>1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，去噪强度都会乘以该值。<1 表示减少多样性，因此你的这一组图将集中在固定的图像上。>1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
     "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。图块(Tiles)之间需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
     "A directory on the same machine where the server is running.": "与服务器主机上的目录",
     "Leave blank to save images to the default path.": "留空以将图像保存到默认路径",
-- 
cgit v1.2.1


From dde8c435987dcd071c63d16f247ae832bce0101f Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Tue, 25 Oct 2022 12:40:15 +0900
Subject: Update zh-hans.json

---
 localizations/zh-hans.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
index 4d04398a..e453f5e3 100644
--- a/localizations/zh-hans.json
+++ b/localizations/zh-hans.json
@@ -361,7 +361,7 @@
     "Hypernetwork strength": "Hypernetwork 强度",
     "Apply color correction to img2img results to match original colors.": "对图生图结果应用颜色校正以匹配原始颜色",
     "Save a copy of image before applying color correction to img2img results": "在对图生图结果应用颜色校正之前保存图像副本",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（正常情況下更弱的去噪需要更少的迭代步数）",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（正常情况下更弱的去噪需要更少的迭代步数）",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。这可能会改变现有的随机种子。需要重新启动才能应用",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。可用于复现旧随机种子",
-- 
cgit v1.2.1


From af73cf50fdd79c84c3439842fa0c8f1a406760d1 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Wed, 26 Oct 2022 01:14:42 +0900
Subject: change file name

---
 localizations/zh-hans.json | 488 ---------------------------------------------
 localizations/zh_CN.json   | 488 +++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 488 insertions(+), 488 deletions(-)
 delete mode 100644 localizations/zh-hans.json
 create mode 100644 localizations/zh_CN.json

diff --git a/localizations/zh-hans.json b/localizations/zh-hans.json
deleted file mode 100644
index e453f5e3..00000000
--- a/localizations/zh-hans.json
+++ /dev/null
@@ -1,488 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "载入中...",
-    "view": "查看",
-    "api": "api",
-    "•": "•",
-    "built with gradio": "基于 Gradio 构建",
-    "Stable Diffusion checkpoint": "Stable Diffusion 模型(ckpt)",
-    "txt2img": "文生图",
-    "img2img": "图生图",
-    "Extras": "后处理",
-    "PNG Info": "PNG 信息",
-    "Checkpoint Merger": "模型(ckpt)合并工具",
-    "Train": "训练",
-    "Create aesthetic embedding": "生成美术风格 embedding",
-    "Image Browser": "图库浏览器",
-    "History": "历史记录",
-    "Settings": "设置",
-    "Prompt": "提示词",
-    "Negative prompt": "反向提示词",
-    "Run": "运行",
-    "Skip": "跳过",
-    "Interrupt": "中止",
-    "Generate": "生成",
-    "Style 1": "模版风格 1",
-    "Style 2": "模版风格 2",
-    "Label": "标签",
-    "File": "文件",
-    "Drop File Here": "拖拽文件到此",
-    "-": "-",
-    "or": "或",
-    "Click to Upload": "点击上传",
-    "Image": "图像",
-    "Check progress": "查看进度",
-    "Check progress (first)": "(首次)查看进度",
-    "Sampling Steps": "采样迭代步数",
-    "Sampling method": "采样方法",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "宽度",
-    "Height": "高度",
-    "Restore faces": "面部修复",
-    "Tiling": "可平铺(Tiling)",
-    "Highres. fix": "高分辨率修复",
-    "Firstpass width": "第一遍的宽度",
-    "Firstpass height": "第一遍的高度",
-    "Denoising strength": "去噪强度",
-    "Batch count": "批次",
-    "Batch size": "批量",
-    "CFG Scale": "提示词相关性(CFG Scale)",
-    "Seed": "随机种子",
-    "Extra": "额外参数",
-    "Variation seed": "差异随机种子",
-    "Variation strength": "差异强度",
-    "Resize seed from width": "自宽度缩放随机种子",
-    "Resize seed from height": "自高度缩放随机种子",
-    "Open for Clip Aesthetic!": "打开美术风格 Clip!",
-    "▼": "▼",
-    "Aesthetic weight": "美术风格权重",
-    "Aesthetic steps": "美术风格迭代步数",
-    "Aesthetic learning rate": "美术风格学习率",
-    "Slerp interpolation": "Slerp 插值",
-    "Aesthetic imgs embedding": "美术风格图集 embedding",
-    "None": "无",
-    "Aesthetic text for imgs": "该图集的美术风格描述",
-    "Slerp angle": "Slerp 角度",
-    "Is negative text": "是反向提示词",
-    "Script": "脚本",
-    "Embedding to Shareable PNG": "将 Embedding 转换为可分享的 PNG",
-    "Prompt matrix": "提示词矩阵",
-    "Prompts from file or textbox": "从文本框或文件载入提示词",
-    "X/Y plot": "X/Y 图表",
-    "Source embedding to convert": "用于转换的源 Embedding",
-    "Embedding token": "Embedding 的 token (关键词)",
-    "Put variable parts at start of prompt": "把变量部分放在提示词文本的开头",
-    "Show Textbox": "显示文本框",
-    "File with inputs": "含输入内容的文件",
-    "Prompts": "提示词",
-    "X type": "X轴类型",
-    "Nothing": "无",
-    "Var. seed": "差异随机种子",
-    "Var. strength": "差异强度",
-    "Steps": "迭代步数",
-    "Prompt S/R": "提示词替换",
-    "Prompt order": "提示词顺序",
-    "Sampler": "采样器",
-    "Checkpoint name": "模型(ckpt)名",
-    "Hypernetwork": "Hypernetwork",
-    "Hypernet str.": "Hypernetwork 强度",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma min": "最小 Sigma",
-    "Sigma max": "最大 Sigma",
-    "Sigma noise": "Sigma noise",
-    "Eta": "Eta",
-    "Clip skip": "Clip 跳过",
-    "Denoising": "去噪",
-    "X values": "X轴数值",
-    "Y type": "Y轴类型",
-    "Y values": "Y轴数值",
-    "Draw legend": "在图表中包括轴标题",
-    "Include Separate Images": "包括独立的图像",
-    "Keep -1 for seeds": "保持随机种子为-1",
-    "Drop Image Here": "拖拽图像到此",
-    "Save": "保存",
-    "Send to img2img": ">> 图生图",
-    "Send to inpaint": ">> 内补绘制",
-    "Send to extras": ">> 后处理",
-    "Make Zip when Save?": "保存时生成zip压缩文件?",
-    "Textbox": "文本框",
-    "Interrogate\nCLIP": "CLIP\n反推提示词",
-    "Interrogate\nDeepBooru": "DeepBooru\n反推提示词",
-    "Inpaint": "内补绘制",
-    "Batch img2img": "批量图生图",
-    "Image for img2img": "图生图的图像",
-    "Image for inpainting with mask": "用于内补绘制蒙版内容的图像",
-    "Mask": "蒙版",
-    "Mask blur": "蒙版模糊",
-    "Mask mode": "蒙版模式",
-    "Draw mask": "绘制蒙版",
-    "Upload mask": "上传蒙版",
-    "Masking mode": "蒙版模式",
-    "Inpaint masked": "内补绘制蒙版内容",
-    "Inpaint not masked": "内补绘制非蒙版内容",
-    "Masked content": "蒙版蒙住的内容",
-    "fill": "填充",
-    "original": "原图",
-    "latent noise": "潜空间噪声",
-    "latent nothing": "潜空间数值零",
-    "Inpaint at full resolution": "以完整分辨率进行内补绘制",
-    "Inpaint at full resolution padding, pixels": "以完整分辨率进行内补绘制 - 填补像素",
-    "Process images in a directory on the same machine where the server is running.": "在服务器主机上的目录中处理图像",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "指定一个空的文件夹为输出目录而非默认的 output 文件夹为输出目录",
-    "Disabled when launched with --hide-ui-dir-config.": "启动 --hide-ui-dir-config 时禁用",
-    "Input directory": "输入目录",
-    "Output directory": "输出目录",
-    "Resize mode": "缩放模式",
-    "Just resize": "只缩放",
-    "Crop and resize": "缩放并剪裁",
-    "Resize and fill": "缩放并填充",
-    "img2img alternative test": "图生图的另一种测试",
-    "Loopback": "回送",
-    "Outpainting mk2": "外补绘制第二版",
-    "Poor man's outpainting": "效果稍差的外补绘制",
-    "SD upscale": "使用 SD 放大(SD upscale)",
-    "should be 2 or lower.": "必须小于等于2",
-    "Override `Sampling method` to Euler?(this method is built for it)": "覆写 `采样方法` 为 Euler?(这个方法就是为这样做设计的)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆写 `提示词` 为 `初始提示词`?(包括`反向提示词`)",
-    "Original prompt": "初始提示词",
-    "Original negative prompt": "初始反向提示词",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "覆写 `采样迭代步数` 为 `解码迭代步数`?",
-    "Decode steps": "解码迭代步数",
-    "Override `Denoising strength` to 1?": "覆写 `去噪强度` 为 1?",
-    "Decode CFG scale": "解码提示词相关性(CFG scale)",
-    "Randomness": "随机度",
-    "Sigma adjustment for finding noise for image": "为寻找图中噪点的 Sigma 调整",
-    "Loops": "迭代次数",
-    "Denoising strength change factor": "去噪强度的调整系数",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推荐设置：采样迭代步数：80-100，采样器：Euler a，去噪强度：0.8",
-    "Pixels to expand": "拓展的像素数",
-    "Outpainting direction": "外补绘制的方向",
-    "left": "左",
-    "right": "右",
-    "up": "上",
-    "down": "下",
-    "Fall-off exponent (lower=higher detail)": "衰减指数(越低细节越好)",
-    "Color variation": "色彩变化",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置图块尺寸(tile size)",
-    "Tile overlap": "图块重叠的像素(Tile overlap)",
-    "Upscaler": "放大算法",
-    "Lanczos": "Lanczos",
-    "LDSR": "LDSR",
-    "BSRGAN 4x": "BSRGAN 4x",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR_4x": "SwinIR 4x",
-    "Single Image": "单个图像",
-    "Batch Process": "批量处理",
-    "Batch from Directory": "从目录进行批量处理",
-    "Source": "来源",
-    "Show result images": "显示输出图像",
-    "Scale by": "等比缩放",
-    "Scale to": "指定尺寸缩放",
-    "Resize": "缩放",
-    "Crop to fit": "裁剪以适应",
-    "Upscaler 2": "放大算法 2",
-    "Upscaler 2 visibility": "放大算法 2 可见度",
-    "GFPGAN visibility": "GFPGAN 可见度",
-    "CodeFormer visibility": "CodeFormer 可见度",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 权重 (0 = 最大效果, 1 = 最小效果)",
-    "Open output directory": "打开输出目录",
-    "Send to txt2img": ">> 文生图",
-    "A merger of the two checkpoints will be generated in your": "合并后的模型(ckpt)会生成在你的",
-    "checkpoint": "模型(ckpt)",
-    "directory.": "目录",
-    "Primary model (A)": "主要模型 (A)",
-    "Secondary model (B)": "第二模型 (B)",
-    "Tertiary model (C)": "第三模型 (C)",
-    "Custom Name (Optional)": "自定义名称 (可选)",
-    "Multiplier (M) - set to 0 to get model A": "倍率 (M) - 设为 0 等价于模型 A",
-    "Interpolation Method": "插值方法",
-    "Weighted sum": "加权和",
-    "Add difference": "添加差分",
-    "Save as float16": "以 float16 储存",
-    "See": "查看",
-    "wiki": "wiki",
-    "for detailed explanation.": "以了解详细说明",
-    "Create embedding": "生成 embedding",
-    "Create aesthetic images embedding": "生成美术风格图集 embedding",
-    "Create hypernetwork": "生成 hypernetwork",
-    "Preprocess images": "图像预处理",
-    "Name": "名称",
-    "Initialization text": "初始化文字",
-    "Number of vectors per token": "每个 token 的向量数",
-    "Overwrite Old Embedding": "覆写旧的 Embedding",
-    "Modules": "模块",
-    "Enter hypernetwork layer structure": "输入 hypernetwork 层结构",
-    "Select activation function of hypernetwork": "选择 hypernetwork 的激活函数",
-    "linear": "linear",
-    "relu": "relu",
-    "leakyrelu": "leakyrelu",
-    "elu": "elu",
-    "swish": "swish",
-    "Add layer normalization": "添加层标准化",
-    "Use dropout": "采用 dropout 防止过拟合",
-    "Overwrite Old Hypernetwork": "覆写旧的 Hypernetwork",
-    "Source directory": "源目录",
-    "Destination directory": "目标目录",
-    "Existing Caption txt Action": "对已有的 txt 说明文字的行为",
-    "ignore": "无视",
-    "copy": "复制",
-    "prepend": "放前面",
-    "append": "放后面",
-    "Create flipped copies": "生成镜像副本",
-    "Split oversized images into two": "将过大的图像分为两份",
-    "Split oversized images": "分割过大的图像",
-    "Use BLIP for caption": "使用 BLIP 生成说明文字(自然语言描述)",
-    "Use deepbooru for caption": "使用 deepbooru 生成说明文字(tags)",
-    "Split image threshold": "图像分割阈值",
-    "Split image overlap ratio": "分割图像重叠的比率",
-    "Preprocess": "预处理",
-    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "训练 embedding； 必须指定一组具有 1:1 比例图像的目录",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "训练 embedding 或者 hypernetwork； 必须指定一组具有 1:1 比例图像的目录",
-    "[wiki]": "[wiki]",
-    "Embedding": "Embedding",
-    "Embedding Learning rate": "Embedding 学习率",
-    "Hypernetwork Learning rate": "Hypernetwork 学习率",
-    "Learning rate": "学习率",
-    "Dataset directory": "数据集目录",
-    "Log directory": "日志目录",
-    "Prompt template file": "提示词模版文件",
-    "Max steps": "最大迭代步数",
-    "Save an image to log directory every N steps, 0 to disable": "每 N 步保存一个图像到日志目录，0 表示禁用",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步将 embedding 的副本保存到日志目录，0 表示禁用",
-    "Save images with embedding in PNG chunks": "保存图像并在 PNG 文件中嵌入 embedding 文件",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "进行预览时从文生图选项卡中读取参数（提示词等）",
-    "Train Hypernetwork": "训练 Hypernetwork",
-    "Train Embedding": "训练 Embedding",
-    "Create an aesthetic embedding out of any number of images": "从任意数量的图像中创建美术风格 embedding",
-    "Create images embedding": "生成图集 embedding",
-    "txt2img history": "文生图历史记录",
-    "img2img history": "图生图历史记录",
-    "extras history": "后处理历史记录",
-    "Renew Page": "刷新页面",
-    "extras": "后处理",
-    "favorites": "收藏夹",
-    "custom fold": "自定义文件夹",
-    "Load": "载入",
-    "Images directory": "图像目录",
-    "Prev batch": "上一批",
-    "Next batch": "下一批",
-    "First Page": "首页",
-    "Prev Page": "上一页",
-    "Page Index": "页数",
-    "Next Page": "下一页",
-    "End Page": "尾页",
-    "number of images to delete consecutively next": "接下来要连续删除的图像数",
-    "Delete": "删除",
-    "Generate Info": "生成信息",
-    "File Name": "文件名",
-    "Collect": "收藏",
-    "Refresh page": "刷新页面",
-    "Date to": "日期至",
-    "Number": "数量",
-    "set_index": "设置索引",
-    "Checkbox": "勾选框",
-    "Apply settings": "保存设置",
-    "Saving images/grids": "保存图像/概览图",
-    "Always save all generated images": "始终保存所有生成的图像",
-    "File format for images": "图像的文件格式",
-    "Images filename pattern": "图像文件名格式",
-    "Always save all generated image grids": "始终保存所有生成的概览图",
-    "File format for grids": "概览图的文件格式",
-    "Add extended info (seed, prompt) to filename when saving grid": "保存概览时将扩展信息（随机种子、提示词）添加到文件名",
-    "Do not save grids consisting of one picture": "只有一张图片时不要保存概览图",
-    "Prevent empty spots in grid (when set to autodetect)": "（在自动检测时）防止概览图中出现空位",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "概览行数； 使用 -1 进行自动检测，使用 0 使其与批量大小相同",
-    "Save text information about generation parameters as chunks to png files": "将有关生成参数的文本信息作为块保存到 png 文件中",
-    "Create a text file next to every image with generation parameters.": "保存图像时在每个图像旁边创建一个文本文件储存生成参数",
-    "Save a copy of image before doing face restoration.": "在进行面部修复之前保存图像副本",
-    "Quality for saved jpeg images": "保存的 jpeg 图像的质量",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 图像大于 4MB 或宽高大于 4000，则缩小并保存副本为 JPG",
-    "Use original name for output filename during batch process in extras tab": "在后处理选项卡中的批量处理过程中使用原始名称作为输出文件名",
-    "When using 'Save' button, only save a single selected image": "使用“保存”按钮时，只保存一个选定的图像",
-    "Do not add watermark to images": "不要给图像加水印",
-    "Paths for saving": "保存路径",
-    "Output directory for images; if empty, defaults to three directories below": "图像的输出目录； 如果为空，则默认为以下三个目录",
-    "Output directory for txt2img images": "文生图的输出目录",
-    "Output directory for img2img images": "图生图的输出目录",
-    "Output directory for images from extras tab": "后处理的输出目录",
-    "Output directory for grids; if empty, defaults to two directories below": "概览图的输出目录； 如果为空，则默认为以下两个目录",
-    "Output directory for txt2img grids": "文生图概览的输出目录",
-    "Output directory for img2img grids": "图生图概览的输出目录",
-    "Directory for saving images using the Save button": "使用“保存”按钮保存图像的目录",
-    "Saving to a directory": "保存到目录",
-    "Save images to a subdirectory": "将图像保存到子目录",
-    "Save grids to a subdirectory": "将概览图保存到子目录",
-    "When using \"Save\" button, save images to a subdirectory": "使用“保存”按钮时，将图像保存到子目录",
-    "Directory name pattern": "目录名称格式",
-    "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示词数量",
-    "Upscaling": "放大",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的图块尺寸(Tile size)。0 = 不分块(no tiling)",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN 的图块重叠(Tile overlap)像素。低值 = 可见接缝",
-    "Tile size for all SwinIR.": "适用所有 SwinIR 系算法的图块尺寸(Tile size)",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR 的图块重叠(Tile overlap)像素。低值 = 可见接缝",
-    "LDSR processing steps. Lower = faster": "LDSR 处理迭代步数。更低 = 更快",
-    "Upscaler for img2img": "图生图的放大算法",
-    "Upscale latent space image when doing hires. fix": "做高分辨率修复时也放大潜空间图像",
-    "Face restoration": "面部修复",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 权重参数； 0 = 最大效果； 1 = 最小效果",
-    "Move face restoration model from VRAM into RAM after processing": "面部修复处理完成后将面部修复模型从显存(VRAM)移至内存(RAM)",
-    "System": "系统",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成图像时每秒轮询显存(VRAM)使用情况的次数。设置为 0 以禁用",
-    "Always print all generation info to standard output": "始终将所有生成信息输出到 standard output (一般为控制台)",
-    "Add a second progress bar to the console that shows progress for an entire job.": "向控制台添加第二个进度条，显示整个作业的进度",
-    "Training": "训练",
-    "Unload VAE and CLIP from VRAM when training": "训练时从显存(VRAM)中取消 VAE 和 CLIP 的加载",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
-    "Filename word regex": "文件名用词的正则表达式",
-    "Filename join string": "文件名连接用字符串",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每个 epoch 中单个输入图像的重复次数； 仅用于显示 epoch 数",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步保存一个包含 loss 的 csv 到日志目录，0 表示禁用",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "缓存在内存(RAM)中的模型(ckpt)",
-    "Hypernetwork strength": "Hypernetwork 强度",
-    "Apply color correction to img2img results to match original colors.": "对图生图结果应用颜色校正以匹配原始颜色",
-    "Save a copy of image before applying color correction to img2img results": "在对图生图结果应用颜色校正之前保存图像副本",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（正常情况下更弱的去噪需要更少的迭代步数）",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。这可能会改变现有的随机种子。需要重新启动才能应用",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。可用于复现旧随机种子",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 采样器批量生成与生成单个图像时产出相同的图像",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个 token 时，通过从 n 个 token 中的最后一个逗号填补来提高一致性",
-    "Filter NSFW content": "过滤成人内容",
-    "Stop At last layers of CLIP model": "在 CLIP 模型的最后哪一层停下",
-    "Interrogate Options": "反推提示词选项",
-    "Interrogate: keep models in VRAM": "反推: 将模型保存在显存(VRAM)中",
-    "Interrogate: use artists from artists.csv": "反推: 使用 artists.csv 中的艺术家",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "反推: 在生成结果中包含与模型标签(tags)相匹配的等级（对基于生成自然语言描述的反推没有影响）",
-    "Interrogate: num_beams for BLIP": "反推: BLIP 的 num_beams",
-    "Interrogate: minimum description length (excluding artists, etc..)": "反推: 最小描述长度（不包括艺术家, 等…）",
-    "Interrogate: maximum description length": "反推: 最大描述长度",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: 文本文件中的最大行数（0 = 无限制）",
-    "Interrogate: deepbooru score threshold": "反推: deepbooru 分数阈值",
-    "Interrogate: deepbooru sort alphabetically": "反推: deepbooru 按字母顺序排序",
-    "use spaces for tags in deepbooru": "在 deepbooru 中为标签使用空格",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "在 deepbooru 中使用转义 (\\) 括号（因此它们用作文字括号而不是强调符号）",
-    "User interface": "用户界面",
-    "Show progressbar": "显示进度条",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 个采样迭代步数显示图像生成进度。设置 0 禁用",
-    "Show previews of all images generated in a batch as a grid": "以网格的形式预览所有批量生成出来的图像",
-    "Show grid in results for web": "在网页的结果中显示概览图",
-    "Do not show any images in results for web": "不在网页的结果中显示任何图像",
-    "Add model hash to generation information": "将模型的哈希值添加到生成信息",
-    "Add model name to generation information": "将模型名称添加到生成信息",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "当从文本读取生成参数到 UI（从 PNG 信息或粘贴文本）时，不要更改选定的模型(ckpt)",
-    "Font for image grids that have text": "有文字的概览图使用的字体",
-    "Enable full page image viewer": "启用整页图像查看器",
-    "Show images zoomed in by default in full page image viewer": "在整页图像查看器中默认放大显示图像",
-    "Show generation progress in window title.": "在窗口标题中显示生成进度",
-    "Quicksettings list": "快速设置列表",
-    "Localization (requires restart)": "本地化（需要重新启动）",
-    "Sampler parameters": "采样器参数",
-    "Hide samplers in user interface (requires restart)": "在用户界面中隐藏采样器（需要重新启动）",
-    "eta (noise multiplier) for DDIM": "DDIM 的 eta (噪声乘数) ",
-    "eta (noise multiplier) for ancestral samplers": "ancestral 采样器的 eta (噪声乘数)",
-    "img2img DDIM discretize": "图生图 DDIM 离散化",
-    "uniform": "均勻",
-    "quad": "二阶",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "最小(tmin) sigma",
-    "sigma noise": "sigma 噪声",
-    "Eta noise seed delta": "Eta 噪声种子偏移(noise seed delta)",
-    "Images Browser": "图库浏览器",
-    "Preload images at startup": "在启动时预载图像",
-    "Number of columns on the page": "每页列数",
-    "Number of rows on the page": "每页行数",
-    "Number of pictures displayed on each page": "每页显示的图像数量",
-    "Minimum number of pages per load": "每次加载的最小页数",
-    "Number of grids in each row": "每行显示多少格",
-    "Wildcards": "通配符",
-    "Use same seed for all images": "为所有图像使用同一个随机种子",
-    "Request browser notifications": "请求浏览器通知",
-    "Download localization template": "下载本地化模板",
-    "Reload custom script bodies (No ui updates, No restart)": "重新加载自定义脚本主体（无 ui 更新，无重启）",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重启 Gradio 及刷新组件（仅限自定义脚本、ui.py、js 和 css）",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示词（按 Ctrl+Enter 或 Alt+Enter 生成）",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示词（按 Ctrl+Enter 或 Alt+Enter 生成）",
-    "Add a random artist to the prompt.": "随机添加一个艺术家到提示词中",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
-    "Save style": "储存为模版风格",
-    "Apply selected styles to current prompt": "将所选样式应用于当前提示",
-    "Stop processing current image and continue processing.": "停止处理当前图像并继续处理下一个",
-    "Stop processing images and return any results accumulated so far.": "停止处理图像并返回迄今为止累积的任何结果",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要应用的模版风格； 模版风格包含正向和反向提示词，并应用于两者",
-    "Do not do anything special": "什么都不做",
-    "Which algorithm to use to produce the image": "使用哪种算法生成图像",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图像，将迭代步数设置为高于 30-40 不会有正面作用",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长内补绘制",
-    "Produce an image that can be tiled.": "生成可用于平铺(tiled)的图像",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候以较小的分辨率生成初步图像、接着放大图像，然后在不更改构图的情况下改进其中的细节",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "决定算法对图像内容的影响程度。设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
-    "How many batches of images to create": "创建多少批次的图像",
-    "How many image to create in a single batch": "每批创建多少图像",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 图像应在多大程度上服从提示词 - 较低的值会产生更有创意的结果",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一个固定随机数生成器输出的值 - 以相同参数和随机种子生成的图像会得到相同的结果",
-    "Set seed to -1, which will cause a new random number to be used every time": "将随机种子设置为-1，则每次都会使用一个新的随机数",
-    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的随机种子，如果想要固定结果就会很有用",
-    "Seed of a different picture to be mixed into the generation.": "将要参与生成的另一张图的随机种子",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要产生多强烈的变化。设为 0 时，将没有效果。设为 1 时，你将获得完全产自差异随机种子的图像（ancestral 采样器除外，你只是单纯地生成了一些东西）",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "尝试生成与在指定分辨率下使用相同随机种子生成的图像相似的图片",
-    "This text is used to rotate the feature space of the imgs embs": "此文本用于旋转图集 embeddings 的特征空间",
-    "Separate values for X axis using commas.": "使用逗号分隔 X 轴的值",
-    "Separate values for Y axis using commas.": "使用逗号分隔 Y 轴的值",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "将图像写入目录（默认 - log/images）并将生成参数写入 csv 文件",
-    "Open images output directory": "打开图像输出目录",
-    "How much to blur the mask before processing, in pixels.": "处理前要对蒙版进行多强的模糊，以像素为单位",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 处理蒙版区域之前要在蒙版区域内放置什么",
-    "fill it with colors of the image": "用图像的颜色填充它",
-    "keep whatever was there originally": "保留原来的东西",
-    "fill it with latent space noise": "用潜空间的噪声填充它",
-    "fill it with latent space zeroes": "用潜空间的零填充它",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做内补绘制，缩小后粘贴到原始图像中",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
-    "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像并用作下次迭代输入的次数",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，去噪强度都会乘以该值。<1 表示减少多样性，因此你的这一组图将集中在固定的图像上。>1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。图块(Tiles)之间需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
-    "A directory on the same machine where the server is running.": "与服务器主机上的目录",
-    "Leave blank to save images to the default path.": "留空以将图像保存到默认路径",
-    "Result = A * (1 - M) + B * M": "结果 = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "结果 = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "第一个和最后一个数字必须是 1。例:'1, 2, 1'",
-    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
-    "Path to directory with input images": "带有输入图像的路径",
-    "Path to directory where to write outputs": "进行输出的路径",
-    "Input images directory": "输入图像目录",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像的文件名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果启用此选项，水印将不会添加到生成出来的图像中。警告：如果你不添加水印，你的行为可能是不符合专业操守的",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像和概览图的子目录: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
-    "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络修复低质量面部",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。留空以保持文件名文本不变",
-    "This string will be used to join split words into a single line if the option above is enabled.": "如果启用了上述选项，则此处的字符会用于将拆分的单词接合为同一行",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置名称列表，以逗号分隔，设置应转到顶部的快速访问栏，而不是通常的设置选项卡。有关设置名称，请参见 modules/shared.py。需要重新启动才能应用",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声。你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
-    "Enable Autocomplete": "开启Tag补全",
-    "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
-    "Roll three": "抽三位出來",
-    "Generate forever": "不停地生成",
-    "Cancel generate forever": "取消不停地生成"
-}
diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
new file mode 100644
index 00000000..e453f5e3
--- /dev/null
+++ b/localizations/zh_CN.json
@@ -0,0 +1,488 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "载入中...",
+    "view": "查看",
+    "api": "api",
+    "•": "•",
+    "built with gradio": "基于 Gradio 构建",
+    "Stable Diffusion checkpoint": "Stable Diffusion 模型(ckpt)",
+    "txt2img": "文生图",
+    "img2img": "图生图",
+    "Extras": "后处理",
+    "PNG Info": "PNG 信息",
+    "Checkpoint Merger": "模型(ckpt)合并工具",
+    "Train": "训练",
+    "Create aesthetic embedding": "生成美术风格 embedding",
+    "Image Browser": "图库浏览器",
+    "History": "历史记录",
+    "Settings": "设置",
+    "Prompt": "提示词",
+    "Negative prompt": "反向提示词",
+    "Run": "运行",
+    "Skip": "跳过",
+    "Interrupt": "中止",
+    "Generate": "生成",
+    "Style 1": "模版风格 1",
+    "Style 2": "模版风格 2",
+    "Label": "标签",
+    "File": "文件",
+    "Drop File Here": "拖拽文件到此",
+    "-": "-",
+    "or": "或",
+    "Click to Upload": "点击上传",
+    "Image": "图像",
+    "Check progress": "查看进度",
+    "Check progress (first)": "(首次)查看进度",
+    "Sampling Steps": "采样迭代步数",
+    "Sampling method": "采样方法",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "宽度",
+    "Height": "高度",
+    "Restore faces": "面部修复",
+    "Tiling": "可平铺(Tiling)",
+    "Highres. fix": "高分辨率修复",
+    "Firstpass width": "第一遍的宽度",
+    "Firstpass height": "第一遍的高度",
+    "Denoising strength": "去噪强度",
+    "Batch count": "批次",
+    "Batch size": "批量",
+    "CFG Scale": "提示词相关性(CFG Scale)",
+    "Seed": "随机种子",
+    "Extra": "额外参数",
+    "Variation seed": "差异随机种子",
+    "Variation strength": "差异强度",
+    "Resize seed from width": "自宽度缩放随机种子",
+    "Resize seed from height": "自高度缩放随机种子",
+    "Open for Clip Aesthetic!": "打开美术风格 Clip!",
+    "▼": "▼",
+    "Aesthetic weight": "美术风格权重",
+    "Aesthetic steps": "美术风格迭代步数",
+    "Aesthetic learning rate": "美术风格学习率",
+    "Slerp interpolation": "Slerp 插值",
+    "Aesthetic imgs embedding": "美术风格图集 embedding",
+    "None": "无",
+    "Aesthetic text for imgs": "该图集的美术风格描述",
+    "Slerp angle": "Slerp 角度",
+    "Is negative text": "是反向提示词",
+    "Script": "脚本",
+    "Embedding to Shareable PNG": "将 Embedding 转换为可分享的 PNG",
+    "Prompt matrix": "提示词矩阵",
+    "Prompts from file or textbox": "从文本框或文件载入提示词",
+    "X/Y plot": "X/Y 图表",
+    "Source embedding to convert": "用于转换的源 Embedding",
+    "Embedding token": "Embedding 的 token (关键词)",
+    "Put variable parts at start of prompt": "把变量部分放在提示词文本的开头",
+    "Show Textbox": "显示文本框",
+    "File with inputs": "含输入内容的文件",
+    "Prompts": "提示词",
+    "X type": "X轴类型",
+    "Nothing": "无",
+    "Var. seed": "差异随机种子",
+    "Var. strength": "差异强度",
+    "Steps": "迭代步数",
+    "Prompt S/R": "提示词替换",
+    "Prompt order": "提示词顺序",
+    "Sampler": "采样器",
+    "Checkpoint name": "模型(ckpt)名",
+    "Hypernetwork": "Hypernetwork",
+    "Hypernet str.": "Hypernetwork 强度",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "最小 Sigma",
+    "Sigma max": "最大 Sigma",
+    "Sigma noise": "Sigma noise",
+    "Eta": "Eta",
+    "Clip skip": "Clip 跳过",
+    "Denoising": "去噪",
+    "X values": "X轴数值",
+    "Y type": "Y轴类型",
+    "Y values": "Y轴数值",
+    "Draw legend": "在图表中包括轴标题",
+    "Include Separate Images": "包括独立的图像",
+    "Keep -1 for seeds": "保持随机种子为-1",
+    "Drop Image Here": "拖拽图像到此",
+    "Save": "保存",
+    "Send to img2img": ">> 图生图",
+    "Send to inpaint": ">> 内补绘制",
+    "Send to extras": ">> 后处理",
+    "Make Zip when Save?": "保存时生成zip压缩文件?",
+    "Textbox": "文本框",
+    "Interrogate\nCLIP": "CLIP\n反推提示词",
+    "Interrogate\nDeepBooru": "DeepBooru\n反推提示词",
+    "Inpaint": "内补绘制",
+    "Batch img2img": "批量图生图",
+    "Image for img2img": "图生图的图像",
+    "Image for inpainting with mask": "用于内补绘制蒙版内容的图像",
+    "Mask": "蒙版",
+    "Mask blur": "蒙版模糊",
+    "Mask mode": "蒙版模式",
+    "Draw mask": "绘制蒙版",
+    "Upload mask": "上传蒙版",
+    "Masking mode": "蒙版模式",
+    "Inpaint masked": "内补绘制蒙版内容",
+    "Inpaint not masked": "内补绘制非蒙版内容",
+    "Masked content": "蒙版蒙住的内容",
+    "fill": "填充",
+    "original": "原图",
+    "latent noise": "潜空间噪声",
+    "latent nothing": "潜空间数值零",
+    "Inpaint at full resolution": "以完整分辨率进行内补绘制",
+    "Inpaint at full resolution padding, pixels": "以完整分辨率进行内补绘制 - 填补像素",
+    "Process images in a directory on the same machine where the server is running.": "在服务器主机上的目录中处理图像",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "指定一个空的文件夹为输出目录而非默认的 output 文件夹为输出目录",
+    "Disabled when launched with --hide-ui-dir-config.": "启动 --hide-ui-dir-config 时禁用",
+    "Input directory": "输入目录",
+    "Output directory": "输出目录",
+    "Resize mode": "缩放模式",
+    "Just resize": "只缩放",
+    "Crop and resize": "缩放并剪裁",
+    "Resize and fill": "缩放并填充",
+    "img2img alternative test": "图生图的另一种测试",
+    "Loopback": "回送",
+    "Outpainting mk2": "外补绘制第二版",
+    "Poor man's outpainting": "效果稍差的外补绘制",
+    "SD upscale": "使用 SD 放大(SD upscale)",
+    "should be 2 or lower.": "必须小于等于2",
+    "Override `Sampling method` to Euler?(this method is built for it)": "覆写 `采样方法` 为 Euler?(这个方法就是为这样做设计的)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆写 `提示词` 为 `初始提示词`?(包括`反向提示词`)",
+    "Original prompt": "初始提示词",
+    "Original negative prompt": "初始反向提示词",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "覆写 `采样迭代步数` 为 `解码迭代步数`?",
+    "Decode steps": "解码迭代步数",
+    "Override `Denoising strength` to 1?": "覆写 `去噪强度` 为 1?",
+    "Decode CFG scale": "解码提示词相关性(CFG scale)",
+    "Randomness": "随机度",
+    "Sigma adjustment for finding noise for image": "为寻找图中噪点的 Sigma 调整",
+    "Loops": "迭代次数",
+    "Denoising strength change factor": "去噪强度的调整系数",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推荐设置：采样迭代步数：80-100，采样器：Euler a，去噪强度：0.8",
+    "Pixels to expand": "拓展的像素数",
+    "Outpainting direction": "外补绘制的方向",
+    "left": "左",
+    "right": "右",
+    "up": "上",
+    "down": "下",
+    "Fall-off exponent (lower=higher detail)": "衰减指数(越低细节越好)",
+    "Color variation": "色彩变化",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置图块尺寸(tile size)",
+    "Tile overlap": "图块重叠的像素(Tile overlap)",
+    "Upscaler": "放大算法",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "BSRGAN 4x": "BSRGAN 4x",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR_4x": "SwinIR 4x",
+    "Single Image": "单个图像",
+    "Batch Process": "批量处理",
+    "Batch from Directory": "从目录进行批量处理",
+    "Source": "来源",
+    "Show result images": "显示输出图像",
+    "Scale by": "等比缩放",
+    "Scale to": "指定尺寸缩放",
+    "Resize": "缩放",
+    "Crop to fit": "裁剪以适应",
+    "Upscaler 2": "放大算法 2",
+    "Upscaler 2 visibility": "放大算法 2 可见度",
+    "GFPGAN visibility": "GFPGAN 可见度",
+    "CodeFormer visibility": "CodeFormer 可见度",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 权重 (0 = 最大效果, 1 = 最小效果)",
+    "Open output directory": "打开输出目录",
+    "Send to txt2img": ">> 文生图",
+    "A merger of the two checkpoints will be generated in your": "合并后的模型(ckpt)会生成在你的",
+    "checkpoint": "模型(ckpt)",
+    "directory.": "目录",
+    "Primary model (A)": "主要模型 (A)",
+    "Secondary model (B)": "第二模型 (B)",
+    "Tertiary model (C)": "第三模型 (C)",
+    "Custom Name (Optional)": "自定义名称 (可选)",
+    "Multiplier (M) - set to 0 to get model A": "倍率 (M) - 设为 0 等价于模型 A",
+    "Interpolation Method": "插值方法",
+    "Weighted sum": "加权和",
+    "Add difference": "添加差分",
+    "Save as float16": "以 float16 储存",
+    "See": "查看",
+    "wiki": "wiki",
+    "for detailed explanation.": "以了解详细说明",
+    "Create embedding": "生成 embedding",
+    "Create aesthetic images embedding": "生成美术风格图集 embedding",
+    "Create hypernetwork": "生成 hypernetwork",
+    "Preprocess images": "图像预处理",
+    "Name": "名称",
+    "Initialization text": "初始化文字",
+    "Number of vectors per token": "每个 token 的向量数",
+    "Overwrite Old Embedding": "覆写旧的 Embedding",
+    "Modules": "模块",
+    "Enter hypernetwork layer structure": "输入 hypernetwork 层结构",
+    "Select activation function of hypernetwork": "选择 hypernetwork 的激活函数",
+    "linear": "linear",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "Add layer normalization": "添加层标准化",
+    "Use dropout": "采用 dropout 防止过拟合",
+    "Overwrite Old Hypernetwork": "覆写旧的 Hypernetwork",
+    "Source directory": "源目录",
+    "Destination directory": "目标目录",
+    "Existing Caption txt Action": "对已有的 txt 说明文字的行为",
+    "ignore": "无视",
+    "copy": "复制",
+    "prepend": "放前面",
+    "append": "放后面",
+    "Create flipped copies": "生成镜像副本",
+    "Split oversized images into two": "将过大的图像分为两份",
+    "Split oversized images": "分割过大的图像",
+    "Use BLIP for caption": "使用 BLIP 生成说明文字(自然语言描述)",
+    "Use deepbooru for caption": "使用 deepbooru 生成说明文字(tags)",
+    "Split image threshold": "图像分割阈值",
+    "Split image overlap ratio": "分割图像重叠的比率",
+    "Preprocess": "预处理",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "训练 embedding； 必须指定一组具有 1:1 比例图像的目录",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "训练 embedding 或者 hypernetwork； 必须指定一组具有 1:1 比例图像的目录",
+    "[wiki]": "[wiki]",
+    "Embedding": "Embedding",
+    "Embedding Learning rate": "Embedding 学习率",
+    "Hypernetwork Learning rate": "Hypernetwork 学习率",
+    "Learning rate": "学习率",
+    "Dataset directory": "数据集目录",
+    "Log directory": "日志目录",
+    "Prompt template file": "提示词模版文件",
+    "Max steps": "最大迭代步数",
+    "Save an image to log directory every N steps, 0 to disable": "每 N 步保存一个图像到日志目录，0 表示禁用",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步将 embedding 的副本保存到日志目录，0 表示禁用",
+    "Save images with embedding in PNG chunks": "保存图像并在 PNG 文件中嵌入 embedding 文件",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "进行预览时从文生图选项卡中读取参数（提示词等）",
+    "Train Hypernetwork": "训练 Hypernetwork",
+    "Train Embedding": "训练 Embedding",
+    "Create an aesthetic embedding out of any number of images": "从任意数量的图像中创建美术风格 embedding",
+    "Create images embedding": "生成图集 embedding",
+    "txt2img history": "文生图历史记录",
+    "img2img history": "图生图历史记录",
+    "extras history": "后处理历史记录",
+    "Renew Page": "刷新页面",
+    "extras": "后处理",
+    "favorites": "收藏夹",
+    "custom fold": "自定义文件夹",
+    "Load": "载入",
+    "Images directory": "图像目录",
+    "Prev batch": "上一批",
+    "Next batch": "下一批",
+    "First Page": "首页",
+    "Prev Page": "上一页",
+    "Page Index": "页数",
+    "Next Page": "下一页",
+    "End Page": "尾页",
+    "number of images to delete consecutively next": "接下来要连续删除的图像数",
+    "Delete": "删除",
+    "Generate Info": "生成信息",
+    "File Name": "文件名",
+    "Collect": "收藏",
+    "Refresh page": "刷新页面",
+    "Date to": "日期至",
+    "Number": "数量",
+    "set_index": "设置索引",
+    "Checkbox": "勾选框",
+    "Apply settings": "保存设置",
+    "Saving images/grids": "保存图像/概览图",
+    "Always save all generated images": "始终保存所有生成的图像",
+    "File format for images": "图像的文件格式",
+    "Images filename pattern": "图像文件名格式",
+    "Always save all generated image grids": "始终保存所有生成的概览图",
+    "File format for grids": "概览图的文件格式",
+    "Add extended info (seed, prompt) to filename when saving grid": "保存概览时将扩展信息（随机种子、提示词）添加到文件名",
+    "Do not save grids consisting of one picture": "只有一张图片时不要保存概览图",
+    "Prevent empty spots in grid (when set to autodetect)": "（在自动检测时）防止概览图中出现空位",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "概览行数； 使用 -1 进行自动检测，使用 0 使其与批量大小相同",
+    "Save text information about generation parameters as chunks to png files": "将有关生成参数的文本信息作为块保存到 png 文件中",
+    "Create a text file next to every image with generation parameters.": "保存图像时在每个图像旁边创建一个文本文件储存生成参数",
+    "Save a copy of image before doing face restoration.": "在进行面部修复之前保存图像副本",
+    "Quality for saved jpeg images": "保存的 jpeg 图像的质量",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 图像大于 4MB 或宽高大于 4000，则缩小并保存副本为 JPG",
+    "Use original name for output filename during batch process in extras tab": "在后处理选项卡中的批量处理过程中使用原始名称作为输出文件名",
+    "When using 'Save' button, only save a single selected image": "使用“保存”按钮时，只保存一个选定的图像",
+    "Do not add watermark to images": "不要给图像加水印",
+    "Paths for saving": "保存路径",
+    "Output directory for images; if empty, defaults to three directories below": "图像的输出目录； 如果为空，则默认为以下三个目录",
+    "Output directory for txt2img images": "文生图的输出目录",
+    "Output directory for img2img images": "图生图的输出目录",
+    "Output directory for images from extras tab": "后处理的输出目录",
+    "Output directory for grids; if empty, defaults to two directories below": "概览图的输出目录； 如果为空，则默认为以下两个目录",
+    "Output directory for txt2img grids": "文生图概览的输出目录",
+    "Output directory for img2img grids": "图生图概览的输出目录",
+    "Directory for saving images using the Save button": "使用“保存”按钮保存图像的目录",
+    "Saving to a directory": "保存到目录",
+    "Save images to a subdirectory": "将图像保存到子目录",
+    "Save grids to a subdirectory": "将概览图保存到子目录",
+    "When using \"Save\" button, save images to a subdirectory": "使用“保存”按钮时，将图像保存到子目录",
+    "Directory name pattern": "目录名称格式",
+    "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示词数量",
+    "Upscaling": "放大",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的图块尺寸(Tile size)。0 = 不分块(no tiling)",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN 的图块重叠(Tile overlap)像素。低值 = 可见接缝",
+    "Tile size for all SwinIR.": "适用所有 SwinIR 系算法的图块尺寸(Tile size)",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR 的图块重叠(Tile overlap)像素。低值 = 可见接缝",
+    "LDSR processing steps. Lower = faster": "LDSR 处理迭代步数。更低 = 更快",
+    "Upscaler for img2img": "图生图的放大算法",
+    "Upscale latent space image when doing hires. fix": "做高分辨率修复时也放大潜空间图像",
+    "Face restoration": "面部修复",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 权重参数； 0 = 最大效果； 1 = 最小效果",
+    "Move face restoration model from VRAM into RAM after processing": "面部修复处理完成后将面部修复模型从显存(VRAM)移至内存(RAM)",
+    "System": "系统",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成图像时每秒轮询显存(VRAM)使用情况的次数。设置为 0 以禁用",
+    "Always print all generation info to standard output": "始终将所有生成信息输出到 standard output (一般为控制台)",
+    "Add a second progress bar to the console that shows progress for an entire job.": "向控制台添加第二个进度条，显示整个作业的进度",
+    "Training": "训练",
+    "Unload VAE and CLIP from VRAM when training": "训练时从显存(VRAM)中取消 VAE 和 CLIP 的加载",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
+    "Filename word regex": "文件名用词的正则表达式",
+    "Filename join string": "文件名连接用字符串",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每个 epoch 中单个输入图像的重复次数； 仅用于显示 epoch 数",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步保存一个包含 loss 的 csv 到日志目录，0 表示禁用",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "缓存在内存(RAM)中的模型(ckpt)",
+    "Hypernetwork strength": "Hypernetwork 强度",
+    "Apply color correction to img2img results to match original colors.": "对图生图结果应用颜色校正以匹配原始颜色",
+    "Save a copy of image before applying color correction to img2img results": "在对图生图结果应用颜色校正之前保存图像副本",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（正常情况下更弱的去噪需要更少的迭代步数）",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。这可能会改变现有的随机种子。需要重新启动才能应用",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。可用于复现旧随机种子",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 采样器批量生成与生成单个图像时产出相同的图像",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个 token 时，通过从 n 个 token 中的最后一个逗号填补来提高一致性",
+    "Filter NSFW content": "过滤成人内容",
+    "Stop At last layers of CLIP model": "在 CLIP 模型的最后哪一层停下",
+    "Interrogate Options": "反推提示词选项",
+    "Interrogate: keep models in VRAM": "反推: 将模型保存在显存(VRAM)中",
+    "Interrogate: use artists from artists.csv": "反推: 使用 artists.csv 中的艺术家",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "反推: 在生成结果中包含与模型标签(tags)相匹配的等级（对基于生成自然语言描述的反推没有影响）",
+    "Interrogate: num_beams for BLIP": "反推: BLIP 的 num_beams",
+    "Interrogate: minimum description length (excluding artists, etc..)": "反推: 最小描述长度（不包括艺术家, 等…）",
+    "Interrogate: maximum description length": "反推: 最大描述长度",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: 文本文件中的最大行数（0 = 无限制）",
+    "Interrogate: deepbooru score threshold": "反推: deepbooru 分数阈值",
+    "Interrogate: deepbooru sort alphabetically": "反推: deepbooru 按字母顺序排序",
+    "use spaces for tags in deepbooru": "在 deepbooru 中为标签使用空格",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "在 deepbooru 中使用转义 (\\) 括号（因此它们用作文字括号而不是强调符号）",
+    "User interface": "用户界面",
+    "Show progressbar": "显示进度条",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 个采样迭代步数显示图像生成进度。设置 0 禁用",
+    "Show previews of all images generated in a batch as a grid": "以网格的形式预览所有批量生成出来的图像",
+    "Show grid in results for web": "在网页的结果中显示概览图",
+    "Do not show any images in results for web": "不在网页的结果中显示任何图像",
+    "Add model hash to generation information": "将模型的哈希值添加到生成信息",
+    "Add model name to generation information": "将模型名称添加到生成信息",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "当从文本读取生成参数到 UI（从 PNG 信息或粘贴文本）时，不要更改选定的模型(ckpt)",
+    "Font for image grids that have text": "有文字的概览图使用的字体",
+    "Enable full page image viewer": "启用整页图像查看器",
+    "Show images zoomed in by default in full page image viewer": "在整页图像查看器中默认放大显示图像",
+    "Show generation progress in window title.": "在窗口标题中显示生成进度",
+    "Quicksettings list": "快速设置列表",
+    "Localization (requires restart)": "本地化（需要重新启动）",
+    "Sampler parameters": "采样器参数",
+    "Hide samplers in user interface (requires restart)": "在用户界面中隐藏采样器（需要重新启动）",
+    "eta (noise multiplier) for DDIM": "DDIM 的 eta (噪声乘数) ",
+    "eta (noise multiplier) for ancestral samplers": "ancestral 采样器的 eta (噪声乘数)",
+    "img2img DDIM discretize": "图生图 DDIM 离散化",
+    "uniform": "均勻",
+    "quad": "二阶",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "最小(tmin) sigma",
+    "sigma noise": "sigma 噪声",
+    "Eta noise seed delta": "Eta 噪声种子偏移(noise seed delta)",
+    "Images Browser": "图库浏览器",
+    "Preload images at startup": "在启动时预载图像",
+    "Number of columns on the page": "每页列数",
+    "Number of rows on the page": "每页行数",
+    "Number of pictures displayed on each page": "每页显示的图像数量",
+    "Minimum number of pages per load": "每次加载的最小页数",
+    "Number of grids in each row": "每行显示多少格",
+    "Wildcards": "通配符",
+    "Use same seed for all images": "为所有图像使用同一个随机种子",
+    "Request browser notifications": "请求浏览器通知",
+    "Download localization template": "下载本地化模板",
+    "Reload custom script bodies (No ui updates, No restart)": "重新加载自定义脚本主体（无 ui 更新，无重启）",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重启 Gradio 及刷新组件（仅限自定义脚本、ui.py、js 和 css）",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示词（按 Ctrl+Enter 或 Alt+Enter 生成）",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示词（按 Ctrl+Enter 或 Alt+Enter 生成）",
+    "Add a random artist to the prompt.": "随机添加一个艺术家到提示词中",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
+    "Save style": "储存为模版风格",
+    "Apply selected styles to current prompt": "将所选样式应用于当前提示",
+    "Stop processing current image and continue processing.": "停止处理当前图像并继续处理下一个",
+    "Stop processing images and return any results accumulated so far.": "停止处理图像并返回迄今为止累积的任何结果",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要应用的模版风格； 模版风格包含正向和反向提示词，并应用于两者",
+    "Do not do anything special": "什么都不做",
+    "Which algorithm to use to produce the image": "使用哪种算法生成图像",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图像，将迭代步数设置为高于 30-40 不会有正面作用",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长内补绘制",
+    "Produce an image that can be tiled.": "生成可用于平铺(tiled)的图像",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候以较小的分辨率生成初步图像、接着放大图像，然后在不更改构图的情况下改进其中的细节",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "决定算法对图像内容的影响程度。设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
+    "How many batches of images to create": "创建多少批次的图像",
+    "How many image to create in a single batch": "每批创建多少图像",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 图像应在多大程度上服从提示词 - 较低的值会产生更有创意的结果",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一个固定随机数生成器输出的值 - 以相同参数和随机种子生成的图像会得到相同的结果",
+    "Set seed to -1, which will cause a new random number to be used every time": "将随机种子设置为-1，则每次都会使用一个新的随机数",
+    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的随机种子，如果想要固定结果就会很有用",
+    "Seed of a different picture to be mixed into the generation.": "将要参与生成的另一张图的随机种子",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要产生多强烈的变化。设为 0 时，将没有效果。设为 1 时，你将获得完全产自差异随机种子的图像（ancestral 采样器除外，你只是单纯地生成了一些东西）",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "尝试生成与在指定分辨率下使用相同随机种子生成的图像相似的图片",
+    "This text is used to rotate the feature space of the imgs embs": "此文本用于旋转图集 embeddings 的特征空间",
+    "Separate values for X axis using commas.": "使用逗号分隔 X 轴的值",
+    "Separate values for Y axis using commas.": "使用逗号分隔 Y 轴的值",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "将图像写入目录（默认 - log/images）并将生成参数写入 csv 文件",
+    "Open images output directory": "打开图像输出目录",
+    "How much to blur the mask before processing, in pixels.": "处理前要对蒙版进行多强的模糊，以像素为单位",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 处理蒙版区域之前要在蒙版区域内放置什么",
+    "fill it with colors of the image": "用图像的颜色填充它",
+    "keep whatever was there originally": "保留原来的东西",
+    "fill it with latent space noise": "用潜空间的噪声填充它",
+    "fill it with latent space zeroes": "用潜空间的零填充它",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做内补绘制，缩小后粘贴到原始图像中",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
+    "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像并用作下次迭代输入的次数",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，去噪强度都会乘以该值。<1 表示减少多样性，因此你的这一组图将集中在固定的图像上。>1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。图块(Tiles)之间需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
+    "A directory on the same machine where the server is running.": "与服务器主机上的目录",
+    "Leave blank to save images to the default path.": "留空以将图像保存到默认路径",
+    "Result = A * (1 - M) + B * M": "结果 = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "结果 = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "第一个和最后一个数字必须是 1。例:'1, 2, 1'",
+    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
+    "Path to directory with input images": "带有输入图像的路径",
+    "Path to directory where to write outputs": "进行输出的路径",
+    "Input images directory": "输入图像目录",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像的文件名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果启用此选项，水印将不会添加到生成出来的图像中。警告：如果你不添加水印，你的行为可能是不符合专业操守的",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像和概览图的子目录: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
+    "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络修复低质量面部",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。留空以保持文件名文本不变",
+    "This string will be used to join split words into a single line if the option above is enabled.": "如果启用了上述选项，则此处的字符会用于将拆分的单词接合为同一行",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置名称列表，以逗号分隔，设置应转到顶部的快速访问栏，而不是通常的设置选项卡。有关设置名称，请参见 modules/shared.py。需要重新启动才能应用",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声。你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
+    "Enable Autocomplete": "开启Tag补全",
+    "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
+    "Roll three": "抽三位出來",
+    "Generate forever": "不停地生成",
+    "Cancel generate forever": "取消不停地生成"
+}
-- 
cgit v1.2.1


From 99d728b5b18829c8a6b7b2d69c9b9327dd257896 Mon Sep 17 00:00:00 2001
From: Tony Beeman <beeman@gmail.com>
Date: Sun, 23 Oct 2022 23:16:47 -0700
Subject: Add Iterate Button and Improve PFF UI

---
 scripts/prompts_from_file.py | 54 ++++++++++++++++++++++++++------------------
 1 file changed, 32 insertions(+), 22 deletions(-)

diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 1266be6f..1be22960 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -1,6 +1,7 @@
 import copy
 import math
 import os
+import random
 import sys
 import traceback
 import shlex
@@ -81,32 +82,34 @@ def cmdargs(line):
     return res
 
 
+def load_prompt_file(file):
+    if (file is None):
+        lines = []
+    else:
+        lines = [x.strip() for x in file.decode('utf8', errors='ignore').split("\n")]
+
+    return None, "\n".join(lines), gr.update(lines=7)
+
 class Script(scripts.Script):
     def title(self):
         return "Prompts from file or textbox"
 
     def ui(self, is_img2img):
-        # This checkbox would look nicer as two tabs, but there are two problems:
-        # 1) There is a bug in Gradio 3.3 that prevents visibility from working on Tabs
-        # 2) Even with Gradio 3.3.1, returning a control (like Tabs) that can't be used as input
-        #    causes a AttributeError: 'Tabs' object has no attribute 'preprocess' assert,
-        #    due to the way Script assumes all controls returned can be used as inputs.
-        # Therefore, there's no good way to use grouping components right now,
-        # so we will use a checkbox! :)
-        checkbox_txt = gr.Checkbox(label="Show Textbox", value=False)
-        file = gr.File(label="File with inputs", type='bytes')
-        prompt_txt = gr.TextArea(label="Prompts")
-        checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt])
-        return [checkbox_txt, file, prompt_txt]
-
-    def on_show(self, checkbox_txt, file, prompt_txt):
-        return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ]
-
-    def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
-        if checkbox_txt:
-            lines = [x.strip() for x in prompt_txt.splitlines()]
-        else:
-            lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
+        checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False)
+
+        prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1)
+        file = gr.File(label="Upload prompt inputs", type='bytes')
+
+        file.change(fn=load_prompt_file, inputs=[file], outputs=[file, prompt_txt, prompt_txt])
+
+        # We start at one line. When the text changes, we jump to seven lines, or two lines if no \n.
+        # We don't shrink back to 1, because that causes the control to ignore [enter], and it may
+        # be unclear to the user that shift-enter is needed.
+        prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt])
+        return [checkbox_iterate, file, prompt_txt]
+
+    def run(self, p, checkbox_iterate, file, prompt_txt: str):
+        lines = [x.strip() for x in prompt_txt.splitlines()]
         lines = [x for x in lines if len(x) > 0]
 
         p.do_not_save_grid = True
@@ -134,6 +137,9 @@ class Script(scripts.Script):
             jobs.append(args)
 
         print(f"Will process {len(lines)} lines in {job_count} jobs.")
+        if (checkbox_iterate and p.seed == -1):
+            p.seed = int(random.randrange(4294967294))
+
         state.job_count = job_count
 
         images = []
@@ -146,5 +152,9 @@ class Script(scripts.Script):
 
             proc = process_images(copy_p)
             images += proc.images
+            
+            if (checkbox_iterate):
+                p.seed = p.seed + (p.batch_size * p.n_iter)
+
 
-        return Processed(p, images, p.seed, "")
+        return Processed(p, images, p.seed, "")
\ No newline at end of file
-- 
cgit v1.2.1


From dc6779f6f384a83ee2b96e60d841116bd4a878eb Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Wed, 26 Oct 2022 19:52:34 +0900
Subject: Update new strings

Translated new strings in PFF UI
---
 localizations/ko_KR.json | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index ff70f1ea..803b7fb9 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -218,6 +218,7 @@
   "Interrogate: use artists from artists.csv": "분석 : artists.csv의 작가들 사용하기",
   "Interrupt": "중단",
   "Is negative text": "네거티브 텍스트일시 체크",
+  "Iterate seed every line": "줄마다 시드 반복하기",
   "Just resize": "리사이징",
   "Keep -1 for seeds": "시드값 -1로 유지",
   "keep whatever was there originally": "이미지 원본 유지",
@@ -234,6 +235,7 @@
   "Leave blank to save images to the default path.": "기존 저장 경로에 이미지들을 저장하려면 비워두세요.",
   "left": "왼쪽",
   "linear": "linear",
+  "List of prompt inputs": "프롬프트 입력 리스트",
   "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "설정 탭이 아니라 상단의 빠른 설정 바에 위치시킬 설정 이름을 쉼표로 분리해서 입력하십시오. 설정 이름은 modules/shared.py에서 찾을 수 있습니다. 재시작이 필요합니다.",
   "LMS": "LMS",
   "LMS Karras": "LMS Karras",
@@ -464,6 +466,7 @@
   "uniform": "uniform",
   "up": "위쪽",
   "Upload mask": "마스크 업로드하기",
+  "Upload prompt inputs": "입력할 프롬프트를 업로드하십시오",
   "Upscale latent space image when doing hires. fix": "고해상도 보정 사용시 잠재 공간 이미지 업스케일하기",
   "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
   "Upscaler": "업스케일러",
-- 
cgit v1.2.1


From eba38de38d43eeb9689a8ddded8a871f0d64bd92 Mon Sep 17 00:00:00 2001
From: benlisquare <benlisquare>
Date: Wed, 26 Oct 2022 22:10:32 +1100
Subject: create new localisation JSON for zh_TW (Traditional Chinese, Taiwan
 locale)

---
 localizations/zh_TW.json | 488 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 488 insertions(+)
 create mode 100644 localizations/zh_TW.json

diff --git a/localizations/zh_TW.json b/localizations/zh_TW.json
new file mode 100644
index 00000000..43db9c31
--- /dev/null
+++ b/localizations/zh_TW.json
@@ -0,0 +1,488 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "載入中...",
+    "view": "查看",
+    "api": "api",
+    "•": "•",
+    "built with gradio": "基於 Gradio 構建",
+    "Stable Diffusion checkpoint": "Stable Diffusion 模型",
+    "txt2img": "文生圖",
+    "img2img": "圖生圖",
+    "Extras": "後處理",
+    "PNG Info": "PNG 信息",
+    "Checkpoint Merger": "模型合併工具",
+    "Train": "訓練",
+    "Create aesthetic embedding": "生成美術風格 embedding",
+    "Image Browser": "圖庫瀏覽器",
+    "History": "歷史記錄",
+    "Settings": "設定",
+    "Prompt": "提示詞",
+    "Negative prompt": "反向提示詞",
+    "Run": "運行",
+    "Skip": "跳過",
+    "Interrupt": "中止",
+    "Generate": "生成",
+    "Style 1": "模版風格 1",
+    "Style 2": "模版風格 2",
+    "Label": "標籤",
+    "File": "檔案",
+    "Drop File Here": "拖拽檔案到此",
+    "-": "-",
+    "or": "或",
+    "Click to Upload": "點擊上載",
+    "Image": "圖像",
+    "Check progress": "查看進度",
+    "Check progress (first)": "(首次)查看進度",
+    "Sampling Steps": "採樣迭代步數",
+    "Sampling method": "採樣方法",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "寬度",
+    "Height": "高度",
+    "Restore faces": "面部修復",
+    "Tiling": "可平鋪",
+    "Highres. fix": "高解析度修復",
+    "Firstpass width": "第一遍的寬度",
+    "Firstpass height": "第一遍的高度",
+    "Denoising strength": "去噪強度",
+    "Batch count": "批次",
+    "Batch size": "批量",
+    "CFG Scale": "提示詞相關性(CFG)",
+    "Seed": "隨機種子",
+    "Extra": "額外參數",
+    "Variation seed": "差異隨機種子",
+    "Variation strength": "差異強度",
+    "Resize seed from width": "自寬度縮放隨機種子",
+    "Resize seed from height": "自高度縮放隨機種子",
+    "Open for Clip Aesthetic!": "打開美術風格 Clip!",
+    "▼": "▼",
+    "Aesthetic weight": "美術風格權重",
+    "Aesthetic steps": "美術風格迭代步數",
+    "Aesthetic learning rate": "美術風格學習率",
+    "Slerp interpolation": "Slerp 插值",
+    "Aesthetic imgs embedding": "美術風格圖集 embedding",
+    "None": "無",
+    "Aesthetic text for imgs": "該圖集的美術風格描述",
+    "Slerp angle": "Slerp 角度",
+    "Is negative text": "是反向提示詞",
+    "Script": "腳本",
+    "Embedding to Shareable PNG": "將 Embedding 轉換為可分享的 PNG",
+    "Prompt matrix": "提示詞矩陣",
+    "Prompts from file or textbox": "從文本框或檔案載入提示詞",
+    "X/Y plot": "X/Y 圖表",
+    "Source embedding to convert": "用於轉換的源 Embedding",
+    "Embedding token": "Embedding 的關鍵詞",
+    "Put variable parts at start of prompt": "把變量部分放在提示詞文本的開頭",
+    "Show Textbox": "顯示文本框",
+    "File with inputs": "含輸入內容的檔案",
+    "Prompts": "提示詞",
+    "X type": "X軸類型",
+    "Nothing": "無",
+    "Var. seed": "差異隨機種子",
+    "Var. strength": "差異強度",
+    "Steps": "迭代步數",
+    "Prompt S/R": "提示詞替換",
+    "Prompt order": "提示詞順序",
+    "Sampler": "採樣器",
+    "Checkpoint name": "模型名",
+    "Hypernetwork": "Hypernetwork",
+    "Hypernet str.": "Hypernetwork 強度",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "最小 Sigma",
+    "Sigma max": "最大 Sigma",
+    "Sigma noise": "Sigma noise",
+    "Eta": "Eta",
+    "Clip skip": "Clip 跳過",
+    "Denoising": "去噪",
+    "X values": "X軸數值",
+    "Y type": "Y軸類型",
+    "Y values": "Y軸數值",
+    "Draw legend": "在圖表中包括軸標題",
+    "Include Separate Images": "包括獨立的圖像",
+    "Keep -1 for seeds": "保持隨機種子為-1",
+    "Drop Image Here": "拖拽圖像到此",
+    "Save": "保存",
+    "Send to img2img": ">> 圖生圖",
+    "Send to inpaint": ">> 內補繪製",
+    "Send to extras": ">> 後處理",
+    "Make Zip when Save?": "保存時生成ZIP壓縮檔案?",
+    "Textbox": "文本框",
+    "Interrogate\nCLIP": "CLIP\n反推提示詞",
+    "Interrogate\nDeepBooru": "DeepBooru\n反推提示詞",
+    "Inpaint": "內補繪製",
+    "Batch img2img": "批量圖生圖",
+    "Image for img2img": "圖生圖的圖像",
+    "Image for inpainting with mask": "用於內補繪製蒙版內容的圖像",
+    "Mask": "蒙版",
+    "Mask blur": "蒙版模糊",
+    "Mask mode": "蒙版模式",
+    "Draw mask": "繪製蒙版",
+    "Upload mask": "上載蒙版",
+    "Masking mode": "蒙版模式",
+    "Inpaint masked": "內補繪製蒙版內容",
+    "Inpaint not masked": "內補繪製非蒙版內容",
+    "Masked content": "蒙版蒙住的內容",
+    "fill": "填充",
+    "original": "原圖",
+    "latent noise": "潛空間噪聲",
+    "latent nothing": "潛空間數值零",
+    "Inpaint at full resolution": "以完整解析度進行內補繪製",
+    "Inpaint at full resolution padding, pixels": "以完整解析度進行內補繪製 - 填補畫素",
+    "Process images in a directory on the same machine where the server is running.": "在伺服器主機上的目錄中處理圖像",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "指定一個空的檔案夾為輸出目錄而非預設的 output 檔案夾為輸出目錄",
+    "Disabled when launched with --hide-ui-dir-config.": "啟動 --hide-ui-dir-config 時禁用",
+    "Input directory": "輸入目錄",
+    "Output directory": "輸出目錄",
+    "Resize mode": "縮放模式",
+    "Just resize": "只縮放",
+    "Crop and resize": "縮放並剪裁",
+    "Resize and fill": "縮放並填充",
+    "img2img alternative test": "圖生圖的另一種測試",
+    "Loopback": "回送",
+    "Outpainting mk2": "外補繪製第二版",
+    "Poor man's outpainting": "效果稍差的外補繪製",
+    "SD upscale": "使用 SD 放大",
+    "should be 2 or lower.": "必須小於等於2",
+    "Override `Sampling method` to Euler?(this method is built for it)": "覆寫「採樣方法」為 Euler?(這個方法就是為這樣做設計的)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆寫「提示詞」為「初始提示詞」?(包括「反向提示詞」)",
+    "Original prompt": "初始提示詞",
+    "Original negative prompt": "初始反向提示詞",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "覆寫「採樣迭代步數」為「解碼迭代步數」?",
+    "Decode steps": "解碼迭代步數",
+    "Override `Denoising strength` to 1?": "覆寫「去噪強度」為1?",
+    "Decode CFG scale": "解碼提示詞相關性(CFG)",
+    "Randomness": "隨機度",
+    "Sigma adjustment for finding noise for image": "為尋找圖中噪點的 Sigma 調整",
+    "Loops": "迭代次數",
+    "Denoising strength change factor": "去噪強度的調整係數",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推薦設定：採樣迭代步數：80-100，採樣器：Euler a，去噪強度：0.8",
+    "Pixels to expand": "拓展的畫素數",
+    "Outpainting direction": "外補繪製的方向",
+    "left": "左",
+    "right": "右",
+    "up": "上",
+    "down": "下",
+    "Fall-off exponent (lower=higher detail)": "衰減指數(越低細節越好)",
+    "Color variation": "色彩變化",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "將圖像放大到兩倍尺寸； 使用寬度和高度滑塊設定圖塊尺寸",
+    "Tile overlap": "圖塊重疊的畫素",
+    "Upscaler": "放大算法",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "BSRGAN 4x": "BSRGAN 4x",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR_4x": "SwinIR 4x",
+    "Single Image": "單個圖像",
+    "Batch Process": "批量處理",
+    "Batch from Directory": "從目錄進行批量處理",
+    "Source": "來源",
+    "Show result images": "顯示輸出圖像",
+    "Scale by": "等比縮放",
+    "Scale to": "指定尺寸縮放",
+    "Resize": "縮放",
+    "Crop to fit": "裁剪以適應",
+    "Upscaler 2": "放大算法 2",
+    "Upscaler 2 visibility": "放大算法 2 可見度",
+    "GFPGAN visibility": "GFPGAN 可見度",
+    "CodeFormer visibility": "CodeFormer 可見度",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 權重 (0 = 最大效果, 1 = 最小效果)",
+    "Open output directory": "打開輸出目錄",
+    "Send to txt2img": ">> 文生圖",
+    "A merger of the two checkpoints will be generated in your": "合併後的模型會生成在你的",
+    "checkpoint": "模型",
+    "directory.": "目錄",
+    "Primary model (A)": "主要模型 (A)",
+    "Secondary model (B)": "第二模型 (B)",
+    "Tertiary model (C)": "第三模型 (C)",
+    "Custom Name (Optional)": "自定義名稱 (可選)",
+    "Multiplier (M) - set to 0 to get model A": "倍率 (M) - 設為 0 等價於模型 A",
+    "Interpolation Method": "插值方法",
+    "Weighted sum": "加權和",
+    "Add difference": "添加差分",
+    "Save as float16": "以 float16 儲存",
+    "See": "查看",
+    "wiki": "wiki",
+    "for detailed explanation.": "以了解詳細說明",
+    "Create embedding": "生成 embedding",
+    "Create aesthetic images embedding": "生成美術風格圖集 embedding",
+    "Create hypernetwork": "生成 hypernetwork",
+    "Preprocess images": "圖像預處理",
+    "Name": "名稱",
+    "Initialization text": "初始化文字",
+    "Number of vectors per token": "每個 token 的向量數",
+    "Overwrite Old Embedding": "覆寫舊的 Embedding",
+    "Modules": "模塊",
+    "Enter hypernetwork layer structure": "輸入 hypernetwork 層結構",
+    "Select activation function of hypernetwork": "選擇 hypernetwork 的激活函數",
+    "linear": "linear",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "Add layer normalization": "添加層標準化",
+    "Use dropout": "採用 dropout 防止過擬合",
+    "Overwrite Old Hypernetwork": "覆寫舊的 Hypernetwork",
+    "Source directory": "源目錄",
+    "Destination directory": "目標目錄",
+    "Existing Caption txt Action": "對已有的TXT說明文字的行為",
+    "ignore": "無視",
+    "copy": "複製",
+    "prepend": "放前面",
+    "append": "放後面",
+    "Create flipped copies": "生成鏡像副本",
+    "Split oversized images into two": "將過大的圖像分為兩份",
+    "Split oversized images": "分割過大的圖像",
+    "Use BLIP for caption": "使用 BLIP 生成說明文字(自然語言描述)",
+    "Use deepbooru for caption": "使用 deepbooru 生成說明文字(標籤)",
+    "Split image threshold": "圖像分割閾值",
+    "Split image overlap ratio": "分割圖像重疊的比率",
+    "Preprocess": "預處理",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "訓練 embedding； 必須指定一組具有 1:1 比例圖像的目錄",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "訓練 embedding 或者 hypernetwork； 必須指定一組具有 1:1 比例圖像的目錄",
+    "[wiki]": "[wiki]",
+    "Embedding": "Embedding",
+    "Embedding Learning rate": "Embedding 學習率",
+    "Hypernetwork Learning rate": "Hypernetwork 學習率",
+    "Learning rate": "學習率",
+    "Dataset directory": "數據集目錄",
+    "Log directory": "日誌目錄",
+    "Prompt template file": "提示詞模版檔案",
+    "Max steps": "最大迭代步數",
+    "Save an image to log directory every N steps, 0 to disable": "每 N 步保存一個圖像到日誌目錄，0 表示禁用",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步將 embedding 的副本保存到日誌目錄，0 表示禁用",
+    "Save images with embedding in PNG chunks": "保存圖像並在 PNG 檔案中嵌入 embedding 檔案",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "進行預覽時從文生圖選項卡中讀取參數（提示詞等）",
+    "Train Hypernetwork": "訓練 Hypernetwork",
+    "Train Embedding": "訓練 Embedding",
+    "Create an aesthetic embedding out of any number of images": "從任意數量的圖像中創建美術風格 embedding",
+    "Create images embedding": "生成圖集 embedding",
+    "txt2img history": "文生圖歷史記錄",
+    "img2img history": "圖生圖歷史記錄",
+    "extras history": "後處理歷史記錄",
+    "Renew Page": "刷新頁面",
+    "extras": "後處理",
+    "favorites": "收藏夾",
+    "custom fold": "自定義檔案夾",
+    "Load": "載入",
+    "Images directory": "圖像目錄",
+    "Prev batch": "上一批",
+    "Next batch": "下一批",
+    "First Page": "首頁",
+    "Prev Page": "上一頁",
+    "Page Index": "頁數",
+    "Next Page": "下一頁",
+    "End Page": "尾頁",
+    "number of images to delete consecutively next": "接下來要連續刪除的圖像數",
+    "Delete": "刪除",
+    "Generate Info": "生成信息",
+    "File Name": "檔案名",
+    "Collect": "收藏",
+    "Refresh page": "刷新頁面",
+    "Date to": "日期至",
+    "Number": "數量",
+    "set_index": "設定索引",
+    "Checkbox": "核取方塊",
+    "Apply settings": "保存設定",
+    "Saving images/grids": "保存圖像/概覽圖",
+    "Always save all generated images": "始終保存所有生成的圖像",
+    "File format for images": "圖像的檔案格式",
+    "Images filename pattern": "圖像檔案名格式",
+    "Always save all generated image grids": "始終保存所有生成的概覽圖",
+    "File format for grids": "概覽圖的檔案格式",
+    "Add extended info (seed, prompt) to filename when saving grid": "保存概覽時將擴展信息（隨機種子、提示詞）添加到檔案名",
+    "Do not save grids consisting of one picture": "只有一張圖片時不要保存概覽圖",
+    "Prevent empty spots in grid (when set to autodetect)": "（在自動檢測時）防止概覽圖中出現空位",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "概覽行數； 使用 -1 進行自動檢測，使用 0 使其與批量大小相同",
+    "Save text information about generation parameters as chunks to png files": "將有關生成參數的文本信息作為塊保存到PNG檔案中",
+    "Create a text file next to every image with generation parameters.": "保存圖像時在每個圖像旁邊創建一個文本檔案儲存生成參數",
+    "Save a copy of image before doing face restoration.": "在進行面部修復之前保存圖像副本",
+    "Quality for saved jpeg images": "保存的JPEG圖像的質量",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 圖像大於 4MB 或寬高大於 4000，則縮小並保存副本為 JPG",
+    "Use original name for output filename during batch process in extras tab": "在後處理選項卡中的批量處理過程中使用原始名稱作為輸出檔案名",
+    "When using 'Save' button, only save a single selected image": "使用「保存」按鈕時，只保存一個選定的圖像",
+    "Do not add watermark to images": "不要給圖像加水印",
+    "Paths for saving": "保存路徑",
+    "Output directory for images; if empty, defaults to three directories below": "圖像的輸出目錄； 如果為空，則預設為以下三個目錄",
+    "Output directory for txt2img images": "文生圖的輸出目錄",
+    "Output directory for img2img images": "圖生圖的輸出目錄",
+    "Output directory for images from extras tab": "後處理的輸出目錄",
+    "Output directory for grids; if empty, defaults to two directories below": "概覽圖的輸出目錄； 如果為空，則預設為以下兩個目錄",
+    "Output directory for txt2img grids": "文生圖概覽的輸出目錄",
+    "Output directory for img2img grids": "圖生圖概覽的輸出目錄",
+    "Directory for saving images using the Save button": "使用「保存」按鈕保存圖像的目錄",
+    "Saving to a directory": "保存到目錄",
+    "Save images to a subdirectory": "將圖像保存到子目錄",
+    "Save grids to a subdirectory": "將概覽圖保存到子目錄",
+    "When using \"Save\" button, save images to a subdirectory": "使用「保存」按鈕時，將圖像保存到子目錄",
+    "Directory name pattern": "目錄名稱格式",
+    "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示詞數量",
+    "Upscaling": "放大",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的圖塊尺寸。0 = 不分塊",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN 的圖塊重疊畫素。低值 = 可見接縫",
+    "Tile size for all SwinIR.": "適用所有 SwinIR 系算法的圖塊尺寸",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR 的圖塊重疊畫素。低值 = 可見接縫",
+    "LDSR processing steps. Lower = faster": "LDSR 處理迭代步數。更低 = 更快",
+    "Upscaler for img2img": "圖生圖的放大算法",
+    "Upscale latent space image when doing hires. fix": "做高解析度修復時也放大潛空間圖像",
+    "Face restoration": "面部修復",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 權重參數； 0 = 最大效果； 1 = 最小效果",
+    "Move face restoration model from VRAM into RAM after processing": "面部修復處理完成後將面部修復模型從顯存(VRAM)移至內存(RAM)",
+    "System": "系統",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成圖像時每秒輪詢顯存(VRAM)使用情況的次數。設定為 0 以禁用",
+    "Always print all generation info to standard output": "始終將所有生成信息輸出到 standard output (一般為控制台)",
+    "Add a second progress bar to the console that shows progress for an entire job.": "向控制台添加第二個進度列，顯示整個作業的進度",
+    "Training": "訓練",
+    "Unload VAE and CLIP from VRAM when training": "訓練時從顯存(VRAM)中取消 VAE 和 CLIP 的加載",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "訓練時將 VAE 和 CLIP 從顯存(VRAM)移放到內存(RAM)，節省顯存(VRAM)",
+    "Filename word regex": "檔案名用詞的正則表達式",
+    "Filename join string": "檔案名連接用字符串",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每個 epoch 中單個輸入圖像的重複次數； 僅用於顯示 epoch 數",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步保存一個包含 loss 的CSV到日誌目錄，0 表示禁用",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "緩存在內存(RAM)中的模型",
+    "Hypernetwork strength": "Hypernetwork 強度",
+    "Apply color correction to img2img results to match original colors.": "對圖生圖結果應用顏色校正以匹配原始顏色",
+    "Save a copy of image before applying color correction to img2img results": "在對圖生圖結果應用顏色校正之前保存圖像副本",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在進行圖生圖的時候，確切地執行滑塊指定的迭代步數（正常情況下更弱的去噪需要更少的迭代步數）",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 採樣器中啟用量化以獲得更清晰、更清晰的結果。這可能會改變現有的隨機種子。需要重新啟動才能應用",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "強調符：使用 (文字) 使模型更關注該文本，使用 [文字] 使其減少關注",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用舊的強調符實現。可用於復現舊隨機種子",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 採樣器批量生成與生成單個圖像時產出相同的圖像",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "當使用超過 75 個 token 時，通過從 n 個 token 中的最後一個逗號填補來提高一致性",
+    "Filter NSFW content": "過濾成人內容",
+    "Stop At last layers of CLIP model": "在 CLIP 模型的最後哪一層停下",
+    "Interrogate Options": "反推提示詞選項",
+    "Interrogate: keep models in VRAM": "反推: 將模型保存在顯存(VRAM)中",
+    "Interrogate: use artists from artists.csv": "反推: 使用 artists.csv 中的藝術家",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "反推: 在生成結果中包含與模型標籤相匹配的等級（對基於生成自然語言描述的反推沒有影響）",
+    "Interrogate: num_beams for BLIP": "反推: BLIP 的 num_beams",
+    "Interrogate: minimum description length (excluding artists, etc..)": "反推: 最小描述長度（不包括藝術家, 等…）",
+    "Interrogate: maximum description length": "反推: 最大描述長度",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: 文本檔案中的最大行數（0 = 無限制）",
+    "Interrogate: deepbooru score threshold": "反推: deepbooru 分數閾值",
+    "Interrogate: deepbooru sort alphabetically": "反推: deepbooru 按字母順序排序",
+    "use spaces for tags in deepbooru": "在 deepbooru 中為標籤使用空格",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "在 deepbooru 中使用轉義 (\\) 括號（因此它們用作文字括號而不是強調符號）",
+    "User interface": "用戶界面",
+    "Show progressbar": "顯示進度列",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 個採樣迭代步數顯示圖像生成進度。設定 0 禁用",
+    "Show previews of all images generated in a batch as a grid": "以網格的形式預覽所有批量生成出來的圖像",
+    "Show grid in results for web": "在網頁的結果中顯示概覽圖",
+    "Do not show any images in results for web": "不在網頁的結果中顯示任何圖像",
+    "Add model hash to generation information": "將模型的雜湊值添加到生成信息",
+    "Add model name to generation information": "將模型名稱添加到生成信息",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "當從文本讀取生成參數到 UI（從 PNG 信息或粘貼文本）時，不要更改選定的模型",
+    "Font for image grids that have text": "有文字的概覽圖使用的字體",
+    "Enable full page image viewer": "啟用整頁圖像查看器",
+    "Show images zoomed in by default in full page image viewer": "在整頁圖像查看器中預設放大顯示圖像",
+    "Show generation progress in window title.": "在窗口標題中顯示生成進度",
+    "Quicksettings list": "快速設定列表",
+    "Localization (requires restart)": "本地化（需要重新啟動）",
+    "Sampler parameters": "採樣器參數",
+    "Hide samplers in user interface (requires restart)": "在用戶界面中隱藏採樣器（需要重新啟動）",
+    "eta (noise multiplier) for DDIM": "DDIM 的 eta (噪聲乘數) ",
+    "eta (noise multiplier) for ancestral samplers": "ancestral 採樣器的 eta (噪聲乘數)",
+    "img2img DDIM discretize": "圖生圖 DDIM 離散化",
+    "uniform": "均勻",
+    "quad": "二階",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "最小(tmin) sigma",
+    "sigma noise": "sigma 噪聲",
+    "Eta noise seed delta": "Eta 噪聲種子偏移(noise seed delta)",
+    "Images Browser": "圖庫瀏覽器",
+    "Preload images at startup": "在啟動時預載圖像",
+    "Number of columns on the page": "每頁列數",
+    "Number of rows on the page": "每頁行數",
+    "Number of pictures displayed on each page": "每頁顯示的圖像數量",
+    "Minimum number of pages per load": "每次加載的最小頁數",
+    "Number of grids in each row": "每行顯示多少格",
+    "Wildcards": "通配符",
+    "Use same seed for all images": "為所有圖像使用同一個隨機種子",
+    "Request browser notifications": "請求瀏覽器通知",
+    "Download localization template": "下載本地化模板",
+    "Reload custom script bodies (No ui updates, No restart)": "重新加載自定義腳本主體（無UI更新，無重啟）",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重啟 Gradio 及刷新組件（僅限自定義腳本、ui.py、JS 和 CSS）",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示詞（按 Ctrl+Enter 或 Alt+Enter 生成）",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示詞（按 Ctrl+Enter 或 Alt+Enter 生成）",
+    "Add a random artist to the prompt.": "隨機添加一個藝術家到提示詞中",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "從提示詞中讀取生成參數，如果提示詞為空，則讀取上一次的生成參數到用戶界面",
+    "Save style": "存儲為模板風格",
+    "Apply selected styles to current prompt": "將所選樣式應用於當前提示",
+    "Stop processing current image and continue processing.": "停止處理當前圖像並繼續處理下一個",
+    "Stop processing images and return any results accumulated so far.": "停止處理圖像並返回迄今為止累積的任何結果",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要應用的模版風格； 模版風格包含正向和反向提示詞，並應用於兩者",
+    "Do not do anything special": "什麼都不做",
+    "Which algorithm to use to produce the image": "使用哪種算法生成圖像",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有創意，可以根據迭代步數獲得完全不同的圖像，將迭代步數設定為高於 30-40 不會有正面作用",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅長內補繪製",
+    "Produce an image that can be tiled.": "生成可用於平舖的圖像",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用兩步處理的時候以較小的解析度生成初步圖像、接著放大圖像，然後在不更改構圖的情況下改進其中的細節",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "決定算法對圖像內容的影響程度。設定 0 時，什麼都不會改變，而在 1 時，你將獲得不相關的圖像。值低於 1.0 時，處理的迭代步數將少於「採樣迭代步數」滑塊指定的步數",
+    "How many batches of images to create": "創建多少批次的圖像",
+    "How many image to create in a single batch": "每批創建多少圖像",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 圖像應在多大程度上服從提示詞 - 較低的值會產生更有創意的結果",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一個固定隨機數生成器輸出的值 - 以相同參數和隨機種子生成的圖像會得到相同的結果",
+    "Set seed to -1, which will cause a new random number to be used every time": "將隨機種子設定為-1，則每次都會使用一個新的隨機數",
+    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的隨機種子，如果想要固定結果就會很有用",
+    "Seed of a different picture to be mixed into the generation.": "將要參與生成的另一張圖的隨機種子",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要產生多強烈的變化。設為 0 時，將沒有效果。設為 1 時，你將獲得完全產自差異隨機種子的圖像（ancestral 採樣器除外，你只是單純地生成了一些東西）",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "嘗試生成與在指定解析度下使用相同隨機種子生成的圖像相似的圖片",
+    "This text is used to rotate the feature space of the imgs embs": "此文本用於旋轉圖集 embeddings 的特徵空間",
+    "Separate values for X axis using commas.": "使用逗號分隔 X 軸的值",
+    "Separate values for Y axis using commas.": "使用逗號分隔 Y 軸的值",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "將圖像寫入目錄（預設 - log/images）並將生成參數寫入CSV檔案",
+    "Open images output directory": "打開圖像輸出目錄",
+    "How much to blur the mask before processing, in pixels.": "處理前要對蒙版進行多強的模糊，以畫素為單位",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 處理蒙版區域之前要在蒙版區域內放置什麼",
+    "fill it with colors of the image": "用圖像的顏色填充它",
+    "keep whatever was there originally": "保留原來的内容",
+    "fill it with latent space noise": "用潛空間的噪聲填充它",
+    "fill it with latent space zeroes": "用潛空間的零填充它",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "將蒙版區域放大到目標解析度，做內補繪製，縮小後粘貼到原始圖像中",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "將圖像大小調整為目標解析度。除非高度和寬度匹配，否則你將獲得不正確的縱橫比",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "調整圖像大小，使整個目標解析度都被圖像填充。裁剪多出來的部分",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "調整圖像大小，使整個圖像在目標解析度內。用圖像的顏色填充空白區域",
+    "How many times to repeat processing an image and using it as input for the next iteration": "重複處理圖像並用作下次迭代輸入的次數",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每個循環中，去噪強度都會乘以該值。<1 表示減少多樣性，因此你的這一組圖將集中在固定的圖像上。>1 意味著增加多樣性，因此你的這一組圖將變得越來越混亂",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大時，圖塊之間應該有多少畫素重疊。圖塊之間需要重疊才可以讓它們在合併回一張圖像時，沒有清晰可見的接縫",
+    "A directory on the same machine where the server is running.": "與伺服器主機上的目錄",
+    "Leave blank to save images to the default path.": "留空以將圖像保存到預設路徑",
+    "Result = A * (1 - M) + B * M": "結果 = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "結果 = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "第一個和最後一個數字必須是 1。例:'1, 2, 1'",
+    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "訓練應該多快。低值將需要更長的時間來訓練，高值可能無法收斂（無法產生準確的結果）以及/也許可能會破壞 embedding（如果你在訓練信息文本框中看到 Loss: nan 就會發生這種情況。如果發生這種情況，你需要從較舊的未損壞的備份手動恢復 embedding）\n\n你可以使用以下語法設定單個數值或多個學習率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步將以 0.005 的速率訓練，接著直到 1000 步為止以 1e-3 訓練，然後剩餘所有步以 1e-5 訓練",
+    "Path to directory with input images": "帶有輸入圖像的路徑",
+    "Path to directory where to write outputs": "進行輸出的路徑",
+    "Input images directory": "輸入圖像目錄",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下標籤定義如何選擇圖像的檔案名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 預設請留空",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果啟用此選項，水印將不會添加到生成出來的圖像中。警告：如果你不添加水印，你的行為可能是不符合道德操守的",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下標籤定義如何選擇圖像和概覽圖的子目錄: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 預設請留空",
+    "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神經網路修復低質量面部",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正則表達式將用於從檔案名中提取單詞，並將使用以下選項將它們接合到用於訓練的標籤文本中。留空以保持檔案名文本不變",
+    "This string will be used to join split words into a single line if the option above is enabled.": "如果啟用了上述選項，則此處的字符會用於將拆分的單詞接合為同一行",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "設定名稱列表，以逗號分隔，設定應轉到頂部的快速訪問欄，而不是通常的設定選項卡。有關設定名稱，請參見 modules/shared.py。需要重新啟動才能應用",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果這個值不為零，它將被添加到隨機種子中，並在使用帶有 Eta 的採樣器時用於初始化隨機噪聲。你可以使用它來產生更多的圖像變化，或者你可以使用它來模仿其他軟件生成的圖像，如果你知道你在做什麼",
+    "Enable Autocomplete": "開啟Tag補全",
+    "Allowed categories for random artists selection when using the Roll button": "使用抽選藝術家按鈕時將會隨機的藝術家類別",
+    "Roll three": "抽三位出來",
+    "Generate forever": "不停地生成",
+    "Cancel generate forever": "取消不停地生成"
+}
-- 
cgit v1.2.1


From 7bd8581e461623932ffbd5762ee931ee51f798db Mon Sep 17 00:00:00 2001
From: Sihan Wang <31711261+shwang95@users.noreply.github.com>
Date: Wed, 26 Oct 2022 20:32:55 +0800
Subject: Fix error caused by EXIF transpose when using custom scripts

Some custom scripts read image directly and no need to select image in UI, this will cause error.
---
 modules/img2img.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/img2img.py b/modules/img2img.py
index 9c0cf23e..86a19f37 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -80,7 +80,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         mask = None
 
     # Use the EXIF orientation of photos taken by smartphones.
-    image = ImageOps.exif_transpose(image) 
+    if image is not None:
+        image = ImageOps.exif_transpose(image) 
 
     assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
 
-- 
cgit v1.2.1


From b2e0d8ba789b345145436f6e960a3f0a896a6643 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Wed, 26 Oct 2022 09:54:26 -0300
Subject: Remove folder endpoint

---
 modules/api/api.py    | 9 ---------
 modules/api/models.py | 6 +-----
 2 files changed, 1 insertion(+), 14 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index ca289d9f..49c213ea 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -32,7 +32,6 @@ class Api:
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
-        self.app.add_api_route("/sdapi/v1/extra-folder-images", self.extras_folder_processing_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -126,14 +125,6 @@ class Api:
 
         return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
     
-    def extras_folder_processing_api(self, req:ExtrasFoldersRequest):
-        reqDict = setUpscalers(req)
-
-        with self.queue_lock:
-            result = run_extras(extras_mode=2, image=None, image_folder=None, **reqDict)
-
-        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
-
     def pnginfoapi(self):
         raise NotImplementedError
 
diff --git a/modules/api/models.py b/modules/api/models.py
index 00406368..dd122321 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -148,8 +148,4 @@ class ExtrasBatchImagesRequest(ExtrasBaseRequest):
     imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
 
 class ExtrasBatchImagesResponse(ExtraBaseResponse):
-    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
-
-class ExtrasFoldersRequest(ExtrasBaseRequest):
-    input_dir: str = Field(title="Input directory", description="Directory path from where to take the images")
-    output_dir: str = Field(title="Output directory", description="Directory path to put the processsed images into")
+    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
\ No newline at end of file
-- 
cgit v1.2.1


From 737eb28faca8be2bb996ee0930ec77d1f7ebd939 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 26 Oct 2022 14:45:33 +0100
Subject: typo: cmd_opts.embedding_dir to cmd_opts.embeddings_dir

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 4fcebe74..ff002d3e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -380,7 +380,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     embedding.cached_checksum = None
     # Before saving for the last time, change name back to base name (as opposed to the save_embedding_every step-suffixed naming convention).
     embedding.name = embedding_name
-    filename = os.path.join(shared.cmd_opts.embedding_dir, f'{embedding.name}.pt')
+    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding.name}.pt')
     embedding.save(filename)
 
     return embedding, filename
-- 
cgit v1.2.1


From fddb4883f4a408b3464076465e1b0949ebe0fc30 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Wed, 26 Oct 2022 22:33:45 +0800
Subject: prototype progress api

---
 modules/api/api.py | 89 +++++++++++++++++++++++++++++++++++++++++++++---------
 modules/shared.py  | 13 ++++++++
 2 files changed, 88 insertions(+), 14 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 6e9d6097..c038f674 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,8 +1,11 @@
+import time
+
 from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.sd_samplers import all_samplers
 from modules.extras import run_pnginfo
 import modules.shared as shared
+from modules import devices
 import uvicorn
 from fastapi import Body, APIRouter, HTTPException
 from fastapi.responses import JSONResponse
@@ -25,6 +28,37 @@ class ImageToImageResponse(BaseModel):
     parameters: Json
     info: Json
 
+class ProgressResponse(BaseModel):
+    progress: float
+    eta_relative: float
+    state: Json
+
+# copy from wrap_gradio_gpu_call of webui.py
+# because queue lock will be acquired in api handlers
+# and time start needs to be set
+# the function has been modified into two parts
+
+def before_gpu_call():
+    devices.torch_gc()
+
+    shared.state.sampling_step = 0
+    shared.state.job_count = -1
+    shared.state.job_no = 0
+    shared.state.job_timestamp = shared.state.get_job_timestamp()
+    shared.state.current_latent = None
+    shared.state.current_image = None
+    shared.state.current_image_sampling_step = 0
+    shared.state.skipped = False
+    shared.state.interrupted = False
+    shared.state.textinfo = None
+    shared.state.time_start = time.time()
+
+
+def after_gpu_call():
+    shared.state.job = ""
+    shared.state.job_count = 0
+
+    devices.torch_gc()
 
 class Api:
     def __init__(self, app, queue_lock):
@@ -33,6 +67,7 @@ class Api:
         self.queue_lock = queue_lock
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"])
 
     def __base64_to_image(self, base64_string):
         # if has a comma, deal with prefix
@@ -44,12 +79,12 @@ class Api:
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
-        
+
         if sampler_index is None:
-            raise HTTPException(status_code=404, detail="Sampler not found") 
-        
+            raise HTTPException(status_code=404, detail="Sampler not found")
+
         populate = txt2imgreq.copy(update={ # Override __init__ params
-            "sd_model": shared.sd_model, 
+            "sd_model": shared.sd_model,
             "sampler_index": sampler_index[0],
             "do_not_save_samples": True,
             "do_not_save_grid": True
@@ -57,9 +92,11 @@ class Api:
         )
         p = StableDiffusionProcessingTxt2Img(**vars(populate))
         # Override object param
+        before_gpu_call()
         with self.queue_lock:
             processed = process_images(p)
-        
+        after_gpu_call()
+
         b64images = []
         for i in processed.images:
             buffer = io.BytesIO()
@@ -67,30 +104,30 @@ class Api:
             b64images.append(base64.b64encode(buffer.getvalue()))
 
         return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.js())
-        
-        
+
+
 
     def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
         sampler_index = sampler_to_index(img2imgreq.sampler_index)
-        
+
         if sampler_index is None:
-            raise HTTPException(status_code=404, detail="Sampler not found") 
+            raise HTTPException(status_code=404, detail="Sampler not found")
 
 
         init_images = img2imgreq.init_images
         if init_images is None:
-            raise HTTPException(status_code=404, detail="Init image not found") 
+            raise HTTPException(status_code=404, detail="Init image not found")
 
         mask = img2imgreq.mask
         if mask:
             mask = self.__base64_to_image(mask)
 
-        
+
         populate = img2imgreq.copy(update={ # Override __init__ params
-            "sd_model": shared.sd_model, 
+            "sd_model": shared.sd_model,
             "sampler_index": sampler_index[0],
             "do_not_save_samples": True,
-            "do_not_save_grid": True, 
+            "do_not_save_grid": True,
             "mask": mask
             }
         )
@@ -103,9 +140,11 @@ class Api:
 
         p.init_images = imgs
         # Override object param
+        before_gpu_call()
         with self.queue_lock:
             processed = process_images(p)
-        
+        after_gpu_call()
+
         b64images = []
         for i in processed.images:
             buffer = io.BytesIO()
@@ -118,6 +157,28 @@ class Api:
 
         return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js())
 
+    def progressapi(self):
+        # copy from check_progress_call of ui.py
+
+        if shared.state.job_count == 0:
+            return ProgressResponse(progress=0, eta_relative=0, state=shared.state.js())
+
+        # avoid dividing zero
+        progress = 0.01
+
+        if shared.state.job_count > 0:
+            progress += shared.state.job_no / shared.state.job_count
+        if shared.state.sampling_steps > 0:
+            progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
+
+        time_since_start = time.time() - shared.state.time_start
+        eta = (time_since_start/progress)
+        eta_relative = eta-time_since_start
+
+        progress = min(progress, 1)
+
+        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.js())
+
     def extrasapi(self):
         raise NotImplementedError
 
diff --git a/modules/shared.py b/modules/shared.py
index 1a9b8289..00f61898 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -146,6 +146,19 @@ class State:
     def get_job_timestamp(self):
         return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?
 
+    def js(self):
+        obj = {
+            "skipped": self.skipped,
+            "interrupted": self.skipped,
+            "job": self.job,
+            "job_count": self.job_count,
+            "job_no": self.job_no,
+            "sampling_step": self.sampling_step,
+            "sampling_steps": self.sampling_steps,
+        }
+
+        return json.dumps(obj)
+
 
 state = State()
 
-- 
cgit v1.2.1


From 0dd8480281ffa3e58439a3ce059c02d9f3baa5c7 Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Wed, 26 Oct 2022 11:08:44 -0400
Subject: fix: Correct before image saved callback

---
 modules/script_callbacks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 6ea58d61..cedbe7bd 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -69,7 +69,7 @@ def ui_settings_callback():
 
 
 def before_image_saved_callback(params: ImageSaveParams):
-    for c in callbacks_image_saved:
+    for c in callbacks_before_image_saved:
         try:
             c.callback(params)
         except Exception:
-- 
cgit v1.2.1


From 57450003a9d1d530f88ecc603eb1b8a344180961 Mon Sep 17 00:00:00 2001
From: Eugenio Buffo <buffo.eugenio@gmail.com>
Date: Wed, 26 Oct 2022 17:32:35 +0200
Subject: Added localizations/it_IT.json

---
 localizations/it_IT.json | 492 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 492 insertions(+)
 create mode 100644 localizations/it_IT.json

diff --git a/localizations/it_IT.json b/localizations/it_IT.json
new file mode 100644
index 00000000..6151ed1c
--- /dev/null
+++ b/localizations/it_IT.json
@@ -0,0 +1,492 @@
+{
+  "⤡": "⤡",
+  "⊞": "⊞",
+  "×": "×",
+  "❮": "❮",
+  "❯": "❯",
+  "Loading...": "Caricamento...",
+  "view": "mostra ",
+  "api": "API",
+  "•": "•",
+  "built with gradio": "Sviluppato con Gradio",
+  "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
+  "txt2img": "txt2img",
+  "img2img": "img2img",
+  "Extras": "Extras",
+  "PNG Info": "PNG Info",
+  "Checkpoint Merger": "Checkpoint Merger",
+  "Train": "Train",
+  "Image Browser": "Galleria Immagini",
+  "Settings": "Impostazioni",
+  "Prompt": "Prompt",
+  "Negative prompt": "Prompt negativo",
+  "Run": "Esegui",
+  "Skip": "Salta",
+  "Interrupt": "Interrompi",
+  "Generate": "Genera",
+  "Style 1": "Stile 1",
+  "Style 2": "Stile 2",
+  "Label": "Etichetta",
+  "File": "File",
+  "Drop File Here": "Trascina il file qui",
+  "-": "-",
+  "or": "o",
+  "Click to Upload": "Clicca per caricare",
+  "Image": "Immagine",
+  "Check progress": "Verifica progresso",
+  "Check progress (first)": "Verifica progresso (iniziale)",
+  "Sampling Steps": "Sampling Steps",
+  "Sampling method": "Sampling method",
+  "Euler a": "Euler a",
+  "Euler": "Euler",
+  "LMS": "LMS",
+  "Heun": "Heun",
+  "DPM2": "DPM2",
+  "DPM2 a": "DPM2 a",
+  "DPM fast": "DPM fast",
+  "DPM adaptive": "DPM adaptive",
+  "LMS Karras": "LMS Karras",
+  "DPM2 Karras": "DPM2 Karras",
+  "DPM2 a Karras": "DPM2 a Karras",
+  "DDIM": "DDIM",
+  "PLMS": "PLMS",
+  "Width": "Larghezza",
+  "Height": "Altezza",
+  "Restore faces": "Ripristina volti",
+  "Tiling": "Tiling",
+  "Highres. fix": "Highres. fix",
+  "Firstpass width": "Larghezza del primo step",
+  "Firstpass height": "Altezza del primo step",
+  "Denoising strength": "Denoising strength",
+  "Batch count": "Batch count",
+  "Batch size": "Batch size",
+  "CFG Scale": "CFG Scale",
+  "Seed": "Seed",
+  "Extra": "Extra",
+  "Variation seed": "Variation seed",
+  "Variation strength": "Variation strength",
+  "Resize seed from width": "Ridimensiona seed da larghezza",
+  "Resize seed from height": "Ridimensiona seed da altezza",
+  "Script": "Script",
+  "None": "Nessuno",
+  "Prompt matrix": "Prompt matrix",
+  "Prompts from file or textbox": "Prompts da file o casella di testo",
+  "X/Y plot": "X/Y plot",
+  "Put variable parts at start of prompt": "Inserisce una parte variabile all'inizio del prompt",
+  "Iterate seed every line": "Itera seed per ogni linea",
+  "List of prompt inputs": "Lista dei prompt in input",
+  "Upload prompt inputs": "Carica prompt di input",
+  "Show Textbox": "Mostra la casella di testo",
+  "File with inputs": "File con input",
+  "Prompts": "Prompts",
+  "X type": "X type",
+  "Nothing": "Nulla",
+  "Var. seed": "Var. seed",
+  "Var. strength": "Var. strength",
+  "Steps": "Steps",
+  "Prompt order": "Prompt order",
+  "Sampler": "Sampler",
+  "Checkpoint name": "Checkpoint name",
+  "Hypernetwork": "Hypernetwork",
+  "Hypernet str.": "Hypernet str.",
+  "Sigma Churn": "Sigma Churn",
+  "Sigma min": "Sigma min",
+  "Sigma max": "Sigma max",
+  "Sigma noise": "Sigma noise",
+  "Eta": "Eta",
+  "Clip skip": "Clip skip",
+  "Denoising": "Denoising",
+  "X values": "X values",
+  "Y type": "Y type",
+  "Y values": "Y values",
+  "Draw legend": "Disegna legenda",
+  "Include Separate Images": "Includi immagini separate",
+  "Keep -1 for seeds": "Mantieni il seed a -1",
+  "Drop Image Here": "Trascina l'immagine qui",
+  "Save": "Salva",
+  "Send to img2img": "Invia a img2img",
+  "Send to inpaint": "Invia a inpaint",
+  "Send to extras": "Invia a extras",
+  "Make Zip when Save?": "Creare Zip al salvataggio?",
+  "Textbox": "Casella di testo",
+  "Interrogate\nCLIP": "Interroga\nCLIP",
+  "Inpaint": "Inpaint",
+  "Batch img2img": "Batch img2img",
+  "Image for img2img": "Immagine per img2img",
+  "Image for inpainting with mask": "Immagine per inpainting con maschera",
+  "Mask": "Mask",
+  "Mask blur": "Maschera sfocatura",
+  "Mask mode": "Modalità maschera",
+  "Draw mask": "Disegna maschera",
+  "Upload mask": "Carica maschera",
+  "Masking mode": "Modalità mascheramento",
+  "Inpaint masked": "Inpaint mascherato",
+  "Inpaint not masked": "Inpaint non mascherato",
+  "Masked content": "Maschera contenuto",
+  "fill": "riempi",
+  "original": "originale",
+  "latent noise": "latent noise",
+  "latent nothing": "latent nothing",
+  "Inpaint at full resolution": "Inpaint alla massima risoluzione",
+  "Inpaint at full resolution padding, pixels": "Inpaint alla massima risoluzione padding, pixels",
+  "Process images in a directory on the same machine where the server is running.": "Processa le immagini in una cartella nella stessa macchina in cui il server è stato lanciato.",
+  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa una cartella di output vuota per salvare le immagini normalmente invece di scrivere nella cartella di output",
+  "Input directory": "Cartella di Input",
+  "Output directory": "Cartella di Output",
+  "Resize mode": "Modalità ridimensionamento",
+  "Just resize": "Solo ridimensionamento",
+  "Crop and resize": "Taglia e Ridimensiona",
+  "Resize and fill": "Ridimensiona e Riempi",
+  "img2img alternative test": "img2img alternative test",
+  "Loopback": "Loopback",
+  "Outpainting mk2": "Outpainting mk2",
+  "Poor man's outpainting": "Poor man's outpainting",
+  "SD upscale": "SD upscale",
+  "should be 2 or lower.": "deve essere 2 o inferiore.",
+  "Override `Sampling method` to Euler?(this method is built for it)": "Ripristinare il `Sampling method` in Euler?(metodo di default)",
+  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Ripristinare il `prompt` al valore del `prompt originale`? (e `prompt negativo`)",
+  "Original prompt": "Prompt originale",
+  "Original negative prompt": "Promp negativo originale",
+  "Override `Sampling Steps` to the same value as `Decode steps`?": "Ripristinare il `Sampling Steps` al valore di `Decode steps`?",
+  "Decode steps": "Decode steps",
+  "Override `Denoising strength` to 1?": "Ripristinare `Denoising strength` a 1?",
+  "Decode CFG scale": "Decode CFG scale",
+  "Randomness": "Casualità",
+  "Sigma adjustment for finding noise for image": "Sigma adjustment for finding noise for image. ",
+  "Loops": "Loops",
+  "Denoising strength change factor": "Denoising strength change factor",
+  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Impostazioni Raccomandate: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8",
+  "Pixels to expand": "Pixels to expand",
+  "Outpainting direction": "Direzione Outpainting",
+  "left": "sinistra",
+  "right": "destra",
+  "up": "su",
+  "down": "giù",
+  "Fall-off exponent (lower=higher detail)": "Fall-off exponent (inferiore=maggiori dettagli)",
+  "Color variation": "Variazione del colore",
+  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Verrà effettuato l'upscale dell'immagine di due volte le sue dimensioni; usa gli sliders di larghezza e altezza per configurare la dimensione del tile",
+  "Tile overlap": "Sovrapposizione Tile",
+  "Upscaler": "Upscaler",
+  "Lanczos": "Lanczos",
+  "LDSR": "LDSR",
+  "SwinIR 4x": "SwinIR 4x",
+  "ScuNET GAN": "ScuNET GAN",
+  "ScuNET PSNR": "ScuNET PSNR",
+  "ESRGAN_4x": "ESRGAN_4x",
+  "Single Image": "Singola Immagine",
+  "Batch Process": "Batch Process",
+  "Batch from Directory": "Batch da Cartella",
+  "Source": "Sorgente",
+  "Show result images": "Mostra risultati",
+  "Scale by": "Scala di",
+  "Scale to": "Scala a",
+  "Resize": "Ridimensionamento",
+  "Crop to fit": "Taglia per adeguare",
+  "Upscaler 2 visibility": "Visibilità Upscaler 2",
+  "GFPGAN visibility": "Visibilità GFPGAN",
+  "CodeFormer visibility": "Visibilità CodeFormer",
+  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso CodeFormer (0 = effetto massimo, 1 = effetto minimo)",
+  "Open output directory": "Apri cartella di output",
+  "Send to txt2img": "Invia a txt2img",
+  "A merger of the two checkpoints will be generated in your": "Una fusione dei due checkpoint sarà generata nella ",
+  "checkpoint": "checkpoint",
+  "directory.": " directory.",
+  "Primary model (A)": "Primo modello (A)",
+  "Secondary model (B)": "Secondo modello (B)",
+  "Tertiary model (C)": "Terzo modello (C)",
+  "Custom Name (Optional)": "Nome Personalizzato (opzionale)",
+  "Multiplier (M) - set to 0 to get model A": "Moltiplicatore (M) - Imposta 0 per ottenere il modello A",
+  "Interpolation Method": "Metodo di Interpolazione",
+  "Weighted sum": "Somma pesata",
+  "Add difference": "Add difference",
+  "Save as float16": "Salva come float16",
+  "See": "Verifica la ",
+  "wiki": "wiki ",
+  "for detailed explanation.": "per una spiegazione dettagliata.",
+  "Create embedding": "Crea embedding",
+  "Create hypernetwork": "Crea hypernetwork",
+  "Preprocess images": "Pre-processa immagini",
+  "Name": "Nome",
+  "Initialization text": "Testo di Inizializzazione",
+  "Number of vectors per token": "Numero di vettori per token",
+  "Overwrite Old Embedding": "Sovrascrivi Vecchio Embedding",
+  "Modules": "Moduli",
+  "Enter hypernetwork layer structure": "Inserisci la struttura livelli dell'hypernetwork",
+  "Select activation function of hypernetwork": "Seleziona la funzione di attivazione dell'hypernetwork",
+  "linear": "linear",
+  "relu": "relu",
+  "leakyrelu": "leakyrelu",
+  "elu": "elu",
+  "swish": "swish",
+  "tanh": "tanh",
+  "sigmoid": "sigmoid",
+  "celu": "celu",
+  "gelu": "gelu",
+  "glu": "glu",
+  "hardshrink": "hardshrink",
+  "hardsigmoid": "hardsigmoid",
+  "hardswish": "hardswish",
+  "hardtanh": "hardtanh",
+  "logsigmoid": "logsigmoid",
+  "logsoftmax": "logsoftmax",
+  "mish": "mish",
+  "multiheadattention": "multiheadattention",
+  "prelu": "prelu",
+  "rrelu": "rrelu",
+  "relu6": "relu6",
+  "selu": "selu",
+  "silu": "silu",
+  "softmax": "softmax",
+  "softmax2d": "softmax2d",
+  "softmin": "softmin",
+  "softplus": "softplus",
+  "softshrink": "softshrink",
+  "softsign": "softsign",
+  "tanhshrink": "tanhshrink",
+  "threshold": "threshold",
+  "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleziona Inizializzazione dei pesi dei livelli. (relu-like: Kaiming, sigmoid-like: Xavier). Xavier è raccomandato",
+  "Normal": "Normal",
+  "KaimingUniform": "KaimingUniform",
+  "KaimingNormal": "KaimingNormal",
+  "XavierUniform": "XavierUniform",
+  "XavierNormal": "XavierNormal",
+  "Add layer normalization": "Aggiungi normalizzazione dei livelli",
+  "Use dropout": "Usa dropout",
+  "Overwrite Old Hypernetwork": "Sovrascrivi Vecchio Hypernetwork",
+  "Source directory": "Cartella sorgente",
+  "Destination directory": "Cartella di destinazione",
+  "Existing Caption txt Action": "Azione in caso di didascalia già presente",
+  "ignore": "ignora",
+  "copy": "copia",
+  "prepend": "anteponi",
+  "append": "apponi",
+  "Create flipped copies": "Crea copie specchiate",
+  "Split oversized images": "Dividi immagini grandi",
+  "Use BLIP for caption": "Usa BLIP per la didascalia",
+  "Use deepbooru for caption": "Usa deepbooru per la didascalia",
+  "Split image threshold": "Dividi Threshold immagine",
+  "Split image overlap ratio": "Rapporto di sovrapposizione dell'immagine",
+  "Preprocess": "Preprocessa",
+  "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images",
+  "[wiki]": "[wiki]",
+  "Embedding": "Embedding",
+  "Embedding Learning rate": "Embedding Learning rate",
+  "Hypernetwork Learning rate": "Hypernetwork Learning rate",
+  "Dataset directory": "Cartella dataset",
+  "Log directory": "Cartella log",
+  "Prompt template file": "Prompt template file",
+  "Max steps": "Max steps",
+  "Save an image to log directory every N steps, 0 to disable": "Salva un'immagine ogni N step, 0 per disabilitare",
+  "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva una copia dell'embedding nella cartella log ogni N step, 0 per disabilitare",
+  "Save images with embedding in PNG chunks": "Salva le immagini con embedding in PNG",
+  "Read parameters (prompt, etc...) from txt2img tab when making previews": "Usa i parametri (prompt, etc...) di txt2img per visualizzare le anteprime",
+  "Train Hypernetwork": "Train Hypernetwork",
+  "Train Embedding": "Train Embedding",
+  "extras": "Extra",
+  "favorites": "Preferiti",
+  "custom fold": "Cartella personalizzata",
+  "Load": "Carica",
+  "Images directory": "Cartella Immagini",
+  "Prev batch": "Batch Precedente",
+  "Next batch": "Batch Successivo",
+  "First Page": "Prima Pagina",
+  "Prev Page": "Pagina Precedente",
+  "Page Index": "Indice Pagina",
+  "Next Page": "Pagina Successiva",
+  "End Page": "Pagina Finale",
+  "number of images to delete consecutively next": "numero di immagini da eliminare consecutivamente a seguire",
+  "Delete": "Elimina",
+  "Generate Info": "Genera Info",
+  "File Name": "Nome File",
+  "Collect": "Ottieni",
+  "Refresh page": "Aggiorna Pagina",
+  "Date to": "Data fine",
+  "Number": "Numero",
+  "set_index": "set_index",
+  "Checkbox": "Checkbox",
+  "Apply settings": "Applica impostazioni",
+  "Saving images/grids": "Salvataggio immagini/griglie",
+  "Always save all generated images": "Salva sempre tutte le immagini generate",
+  "File format for images": "Formato file per le immagini",
+  "Images filename pattern": "Configura Pattern per il nome dei file immagine",
+  "Add number to filename when saving": "Aggiungi un numero al nome del file al salvataggio",
+  "Always save all generated image grids": "Salva sempre tutte le griglie delle immagini generate",
+  "File format for grids": "Formato file per le giglie",
+  "Add extended info (seed, prompt) to filename when saving grid": "Aggiungi informazioni estese (seed, prompt) al nome del file al salvataggio della griglia",
+  "Do not save grids consisting of one picture": "Non salvare griglie composte da una sola immagine",
+  "Prevent empty spots in grid (when set to autodetect)": "Previeni spazi vuoti nella griglia",
+  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
+  "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
+  "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
+  "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
+  "Quality for saved jpeg images": "Quality for saved jpeg images",
+  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
+  "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
+  "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
+  "Do not add watermark to images": "Do not add watermark to images",
+  "Paths for saving": "Paths for saving",
+  "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
+  "Output directory for txt2img images": "Output directory for txt2img images",
+  "Output directory for img2img images": "Output directory for img2img images",
+  "Output directory for images from extras tab": "Output directory for images from extras tab",
+  "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
+  "Output directory for txt2img grids": "Output directory for txt2img grids",
+  "Output directory for img2img grids": "Output directory for img2img grids",
+  "Directory for saving images using the Save button": "Directory for saving images using the Save button",
+  "Saving to a directory": "Saving to a directory",
+  "Save images to a subdirectory": "Save images to a subdirectory",
+  "Save grids to a subdirectory": "Save grids to a subdirectory",
+  "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
+  "Directory name pattern": "Directory name pattern",
+  "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
+  "Upscaling": "Upscaling",
+  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
+  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
+  "Tile size for all SwinIR.": "Tile size for all SwinIR.",
+  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
+  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
+  "Upscaler for img2img": "Upscaler for img2img",
+  "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
+  "Face restoration": "Face restoration",
+  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
+  "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
+  "System": "System",
+  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
+  "Always print all generation info to standard output": "Always print all generation info to standard output",
+  "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
+  "Training": "Training",
+  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.",
+  "Filename word regex": "Filename word regex",
+  "Filename join string": "Filename join string",
+  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
+  "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
+  "Stable Diffusion": "Stable Diffusion",
+  "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
+  "Hypernetwork strength": "Hypernetwork strength",
+  "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
+  "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
+  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
+  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
+  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
+  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
+  "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
+  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
+  "Filter NSFW content": "Filter NSFW content",
+  "Stop At last layers of CLIP model": "Stop At last layers of CLIP model",
+  "Interrogate Options": "Interrogate Options",
+  "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM",
+  "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
+  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
+  "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
+  "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
+  "Interrogate: maximum description length": "Interrogate: maximum description length",
+  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
+  "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold",
+  "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically",
+  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
+  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
+  "User interface": "User interface",
+  "Show progressbar": "Show progressbar",
+  "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
+  "Show previews of all images generated in a batch as a grid": "Show previews of all images generated in a batch as a grid",
+  "Show grid in results for web": "Show grid in results for web",
+  "Do not show any images in results for web": "Do not show any images in results for web",
+  "Add model hash to generation information": "Add model hash to generation information",
+  "Add model name to generation information": "Add model name to generation information",
+  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
+  "Font for image grids that have text": "Font for image grids that have text",
+  "Enable full page image viewer": "Enable full page image viewer",
+  "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
+  "Show generation progress in window title.": "Show generation progress in window title.",
+  "Quicksettings list": "Quicksettings list",
+  "Localization (requires restart)": "Localization (requires restart)",
+  "ar_AR": "ar_AR",
+  "es_ES": "es_ES",
+  "fr-FR": "fr-FR",
+  "it_IT": "it_IT",
+  "ja_JP": "ja_JP",
+  "ko_KR": "ko_KR",
+  "ru_RU": "ru_RU",
+  "tr_TR": "tr_TR",
+  "zh_CN": "zh_CN",
+  "Sampler parameters": "Sampler parameters",
+  "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
+  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
+  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
+  "img2img DDIM discretize": "img2img DDIM discretize",
+  "uniform": "uniform",
+  "quad": "quad",
+  "sigma churn": "sigma churn",
+  "sigma tmin": "sigma tmin",
+  "sigma noise": "sigma noise",
+  "Eta noise seed delta": "Eta noise seed delta",
+  "Images Browser": "Images Browser",
+  "Preload images at startup": "Preload images at startup",
+  "Number of columns on the page": "Number of columns on the page",
+  "Number of rows on the page": "Number of rows on the page",
+  "Minimum number of pages per load": "Minimum number of pages per load",
+  "Request browser notifications": "Request browser notifications",
+  "Download localization template": "Download localization template",
+  "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (press Ctrl+Enter or Alt+Enter to generate)",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)",
+  "Add a random artist to the prompt.": "Add a random artist to the prompt.",
+  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
+  "Save style": "Save style",
+  "Apply selected styles to current prompt": "Apply selected styles to current prompt",
+  "Stop processing current image and continue processing.": "Stop processing current image and continue processing.",
+  "Stop processing images and return any results accumulated so far.": "Stop processing images and return any results accumulated so far.",
+  "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
+  "Do not do anything special": "Do not do anything special",
+  "Which algorithm to use to produce the image": "Which algorithm to use to produce the image",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help",
+  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - best at inpainting",
+  "Produce an image that can be tiled.": "Produce an image that can be tiled.",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
+  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
+  "How many batches of images to create": "How many batches of images to create",
+  "How many image to create in a single batch": "How many image to create in a single batch",
+  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
+  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
+  "Set seed to -1, which will cause a new random number to be used every time": "Set seed to -1, which will cause a new random number to be used every time",
+  "Reuse seed from last generation, mostly useful if it was randomed": "Reuse seed from last generation, mostly useful if it was randomed",
+  "Seed of a different picture to be mixed into the generation.": "Seed of a different picture to be mixed into the generation.",
+  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution",
+  "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others",
+  "Separate values for X axis using commas.": "Separate values for X axis using commas.",
+  "Separate values for Y axis using commas.": "Separate values for Y axis using commas.",
+  "Write image to a directory (default - log/images) and generation parameters into csv file.": "Write image to a directory (default - log/images) and generation parameters into csv file.",
+  "Open images output directory": "Open images output directory",
+  "How much to blur the mask before processing, in pixels.": "How much to blur the mask before processing, in pixels.",
+  "What to put inside the masked area before processing it with Stable Diffusion.": "What to put inside the masked area before processing it with Stable Diffusion.",
+  "fill it with colors of the image": "fill it with colors of the image",
+  "keep whatever was there originally": "keep whatever was there originally",
+  "fill it with latent space noise": "fill it with latent space noise",
+  "fill it with latent space zeroes": "fill it with latent space zeroes",
+  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
+  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.",
+  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.",
+  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.",
+  "How many times to repeat processing an image and using it as input for the next iteration": "How many times to repeat processing an image and using it as input for the next iteration",
+  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.",
+  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.",
+  "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
+  "Leave blank to save images to the default path.": "Leave blank to save images to the default path.",
+  "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M",
+  "Result = A + (B - C) * M": "Result = A + (B - C) * M",
+  "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
+  "Path to directory with input images": "Path to directory with input images",
+  "Path to directory where to write outputs": "Path to directory where to write outputs",
+  "Input images directory": "Input images directory",
+  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
+  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
+  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
+  "Restore low quality faces using GFPGAN neural network": "Restore low quality faces using GFPGAN neural network",
+  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
+  "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
+  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
+  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
+  "Enable Autocomplete": "Enable Autocomplete"
+}
-- 
cgit v1.2.1


From 1a4216fabf2daabcf657a50e3b90e053c253a718 Mon Sep 17 00:00:00 2001
From: Eugenio Buffo <buffo.eugenio@gmail.com>
Date: Wed, 26 Oct 2022 19:45:36 +0200
Subject: Finalised IT localization file

---
 localizations/it_IT.json | 294 +++++++++++++++++++++++------------------------
 1 file changed, 147 insertions(+), 147 deletions(-)

diff --git a/localizations/it_IT.json b/localizations/it_IT.json
index 6151ed1c..d05035f9 100644
--- a/localizations/it_IT.json
+++ b/localizations/it_IT.json
@@ -12,7 +12,7 @@
   "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
   "txt2img": "txt2img",
   "img2img": "img2img",
-  "Extras": "Extras",
+  "Extras": "Extra",
   "PNG Info": "PNG Info",
   "Checkpoint Merger": "Checkpoint Merger",
   "Train": "Train",
@@ -106,7 +106,7 @@
   "Save": "Salva",
   "Send to img2img": "Invia a img2img",
   "Send to inpaint": "Invia a inpaint",
-  "Send to extras": "Invia a extras",
+  "Send to extras": "Invia a extra",
   "Make Zip when Save?": "Creare Zip al salvataggio?",
   "Textbox": "Casella di testo",
   "Interrogate\nCLIP": "Interroga\nCLIP",
@@ -315,91 +315,91 @@
   "Add extended info (seed, prompt) to filename when saving grid": "Aggiungi informazioni estese (seed, prompt) al nome del file al salvataggio della griglia",
   "Do not save grids consisting of one picture": "Non salvare griglie composte da una sola immagine",
   "Prevent empty spots in grid (when set to autodetect)": "Previeni spazi vuoti nella griglia",
-  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size",
-  "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files",
-  "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.",
-  "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.",
-  "Quality for saved jpeg images": "Quality for saved jpeg images",
-  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG",
-  "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab",
-  "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image",
-  "Do not add watermark to images": "Do not add watermark to images",
-  "Paths for saving": "Paths for saving",
-  "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below",
-  "Output directory for txt2img images": "Output directory for txt2img images",
-  "Output directory for img2img images": "Output directory for img2img images",
-  "Output directory for images from extras tab": "Output directory for images from extras tab",
-  "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below",
-  "Output directory for txt2img grids": "Output directory for txt2img grids",
-  "Output directory for img2img grids": "Output directory for img2img grids",
-  "Directory for saving images using the Save button": "Directory for saving images using the Save button",
-  "Saving to a directory": "Saving to a directory",
-  "Save images to a subdirectory": "Save images to a subdirectory",
-  "Save grids to a subdirectory": "Save grids to a subdirectory",
-  "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory",
-  "Directory name pattern": "Directory name pattern",
-  "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
+  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Numero righe griglia; usa -1 per trovarlo automaticamente e 0 per essere lo stesso del batch size",
+  "Save text information about generation parameters as chunks to png files": "Salva le informazioni dei parametri di generazione come chunks nei file PNG",
+  "Create a text file next to every image with generation parameters.": "Crea un file di testo per ogni immagine con i parametri di generazione.",
+  "Save a copy of image before doing face restoration.": "Salva una copia dell'immagine prima di fare la riparazione volti.",
+  "Quality for saved jpeg images": "Qualità per le immagini salvate in JPEG",
+  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l'immagine PNG image è più grande di 4MB o qualsiasi dimensione maggiore di 4000, effettua il downscale e salva come JPG",
+  "Use original name for output filename during batch process in extras tab": "Usa il nome originale del file come nome del file di output durante il 'batch process' nella tab 'extras'",
+  "When using 'Save' button, only save a single selected image": "Usando il tasto 'Save', verrà salvata solo la singola immagine selezionata",
+  "Do not add watermark to images": "Non aggiungere watermark alle immagini",
+  "Paths for saving": "Percorsi per il salvataggio",
+  "Output directory for images; if empty, defaults to three directories below": "Cartella di Output globale per immagini; se vuota, verranno usate di default le cartelle indicate sotto",
+  "Output directory for txt2img images": "Cartella di Output per txt2img",
+  "Output directory for img2img images": "Cartella di Output per img2img",
+  "Output directory for images from extras tab": "Cartella di Output per immagini dalla tab 'extra'",
+  "Output directory for grids; if empty, defaults to two directories below": "Cartella di Output globale per le griglie; se vuota, verranno usate di default le cartelle indicate sotto",
+  "Output directory for txt2img grids": "Cartella di Output per txt2img grids",
+  "Output directory for img2img grids": "Cartella di Output per img2img grids",
+  "Directory for saving images using the Save button": "Cartella di Output per il salvataggion con il tasto 'Salva'",
+  "Saving to a directory": "Salvataggio in cartelle",
+  "Save images to a subdirectory": "Salva immagini in una sottocartella",
+  "Save grids to a subdirectory": "Salva griglie in una sottocartella",
+  "When using \"Save\" button, save images to a subdirectory": "Usando il tasto \"Salva\", le immagini verranno salvate in una sottocartella",
+  "Directory name pattern": "Pattern del nome della cartella",
+  "Max prompt words for [prompt_words] pattern": "Massimo numero di parole per il pattern [prompt_words]",
   "Upscaling": "Upscaling",
-  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.",
-  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.",
-  "Tile size for all SwinIR.": "Tile size for all SwinIR.",
-  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.",
-  "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
-  "Upscaler for img2img": "Upscaler for img2img",
+  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Dimensione Tile per upscalers ESRGAN. 0 = no tiling.",
+  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sovrapposizione Tile, in pixels per upscalers ESRGAN. Valore basso = taglio visible.",
+  "Tile size for all SwinIR.": "Dimensione Tile per SwinIR.",
+  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sovrapposizione Tile, in pixels per upscalers SwinIR. Valore basso = taglio visible.",
+  "LDSR processing steps. Lower = faster": "LDSR processing steps. Basso = Veloce",
+  "Upscaler for img2img": "Upscaler per img2img",
   "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
-  "Face restoration": "Face restoration",
-  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect",
-  "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing",
-  "System": "System",
-  "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.",
-  "Always print all generation info to standard output": "Always print all generation info to standard output",
-  "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.",
+  "Face restoration": "Ripristino volti",
+  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Peso CodeFormer; 0 = effetto massimo; 1 = effetto minimo",
+  "Move face restoration model from VRAM into RAM after processing": "Sposta il modello di ripristino volti dalla VRAM alla RAM dopo averlo processato",
+  "System": "Sistema",
+  "VRAM usage polls per second during generation. Set to 0 to disable.": "Utilizzo VRAM al secondo durante la generazione. Imposta 0 per disabilitare.",
+  "Always print all generation info to standard output": "Mostra sempre tutte le informazioni della generazione nello standard output",
+  "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda progress bar alla console per mostrare il progresso complessivo della generazione.",
   "Training": "Training",
-  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.",
+  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP in RAM durante il training di hypernetwork. Risparmia VRAM.",
   "Filename word regex": "Filename word regex",
   "Filename join string": "Filename join string",
-  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
-  "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
+  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per singola immagine in input per epoch; usato solo per mostrare il numero di epoch",
+  "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salva un csv contenente il loss nella cartella log ogni N step, 0 per disabilitare",
   "Stable Diffusion": "Stable Diffusion",
-  "Checkpoints to cache in RAM": "Checkpoints to cache in RAM",
+  "Checkpoints to cache in RAM": "Checkpoints da memorizzare in RAM",
   "Hypernetwork strength": "Hypernetwork strength",
-  "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.",
-  "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results",
-  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).",
-  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.",
-  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention",
-  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.",
-  "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image",
-  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens",
-  "Filter NSFW content": "Filter NSFW content",
-  "Stop At last layers of CLIP model": "Stop At last layers of CLIP model",
-  "Interrogate Options": "Interrogate Options",
-  "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM",
-  "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv",
+  "Apply color correction to img2img results to match original colors.": "Applica color correction ai risultati img2img per uguagliare i colori originali.",
+  "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la color correction ai risultati img2img",
+  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, effettua esattamente la quantità di step indicata (normalmente se ne effettuano di meno con meno riduzione del disturbo).",
+  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Abilita la quantizzazione nei campionatori K per risultati più nitidi e puliti. Questo può cambiare seed esistenti. Richiede il riavvio per essere applicato.",
+  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Enfasi: usa (testo) per far prestare maggiore attenzione al testo indicato e [testo] per renderlo meno importante",
+  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usa la vecchia implementazione per l'enfasi. Può essere utile per riprodurre vecchi seeds.",
+  "Make K-diffusion samplers produce same images in a batch as when making a single image": "Fa sì che i K-Diffusion producano le stesse immagini in un batch come quando si effettuano una singola immagine",
+  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coerenza aggiungendo dall'ultima virgola ulteriori N token quando si usano più di 75 token.",
+  "Filter NSFW content": "Filtra contenuti NSFW",
+  "Stop At last layers of CLIP model": "Fermati all'ultimo livello del modello CLIP",
+  "Interrogate Options": "Opzioni Interrogate",
+  "Interrogate: keep models in VRAM": "Interrogate: mantieni modelli nella VRAM",
+  "Interrogate: use artists from artists.csv": "Interrogate: usa artisti dal file artists.csv",
   "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
-  "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
-  "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
-  "Interrogate: maximum description length": "Interrogate: maximum description length",
-  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
-  "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold",
-  "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically",
-  "use spaces for tags in deepbooru": "use spaces for tags in deepbooru",
-  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)",
-  "User interface": "User interface",
-  "Show progressbar": "Show progressbar",
-  "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.",
-  "Show previews of all images generated in a batch as a grid": "Show previews of all images generated in a batch as a grid",
-  "Show grid in results for web": "Show grid in results for web",
-  "Do not show any images in results for web": "Do not show any images in results for web",
-  "Add model hash to generation information": "Add model hash to generation information",
-  "Add model name to generation information": "Add model name to generation information",
-  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.",
-  "Font for image grids that have text": "Font for image grids that have text",
-  "Enable full page image viewer": "Enable full page image viewer",
-  "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer",
-  "Show generation progress in window title.": "Show generation progress in window title.",
-  "Quicksettings list": "Quicksettings list",
-  "Localization (requires restart)": "Localization (requires restart)",
+  "Interrogate: num_beams for BLIP": "Interrogate: num_beams per BLIP",
+  "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minima lunghezza della descrizione (escludendo artisti, etc..)",
+  "Interrogate: maximum description length": "Interrogate: massima lunghezza descrizione",
+  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: massimo numero di linee di testo per file (0 = Nessun limite)",
+  "Interrogate: deepbooru score threshold": "Interrogate: soglia punteggio deepbooru",
+  "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru ordina alfabeticamente",
+  "use spaces for tags in deepbooru": "usa spazi per tag su deepbooru",
+  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "effettuta l'escape (\\) delle parentesi in deepbooru (così vengono usati come parentesi e non come enfasi)",
+  "User interface": "Interfaccia Utente",
+  "Show progressbar": "Mostra barre di caricamento",
+  "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostra il progresso della generazione immagini ogni N step. Imposta 0 per disabilitare.",
+  "Show previews of all images generated in a batch as a grid": "Mostra l'anteprima di tutte le immagini di un batch come griglia",
+  "Show grid in results for web": "Mostra la griglia nei risultati per web",
+  "Do not show any images in results for web": "Non mostrare alcun risultato per web",
+  "Add model hash to generation information": "Aggiungi l'hash del modello alle informazioni di generazione",
+  "Add model name to generation information": "Aggiungi il nome del modello alle informazioni di generazione",
+  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Alla lettura dei parametri di generazione da testo ad interfaccia (da PNG o testo copiato), non cambiare il modello/checkpoint.",
+  "Font for image grids that have text": "Font per griglie di immagini con testo",
+  "Enable full page image viewer": "Abilita la visualizzazione a pagina intera",
+  "Show images zoomed in by default in full page image viewer": "Mostra le immagini zoommate di default nella visualizzazione a pagina intera",
+  "Show generation progress in window title.": "Mostra il progresso di generazione nel titolo della finestra.",
+  "Quicksettings list": "Lista imporazioni rapide",
+  "Localization (requires restart)": "Localization (richiede riavvio)",
   "ar_AR": "ar_AR",
   "es_ES": "es_ES",
   "fr-FR": "fr-FR",
@@ -409,10 +409,10 @@
   "ru_RU": "ru_RU",
   "tr_TR": "tr_TR",
   "zh_CN": "zh_CN",
-  "Sampler parameters": "Sampler parameters",
-  "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)",
-  "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM",
-  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers",
+  "Sampler parameters": "Parametri del Sampler",
+  "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (richiede riavvio)",
+  "eta (noise multiplier) for DDIM": "eta (noise multiplier) per DDIM",
+  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) per ancestral samplers",
   "img2img DDIM discretize": "img2img DDIM discretize",
   "uniform": "uniform",
   "quad": "quad",
@@ -420,73 +420,73 @@
   "sigma tmin": "sigma tmin",
   "sigma noise": "sigma noise",
   "Eta noise seed delta": "Eta noise seed delta",
-  "Images Browser": "Images Browser",
-  "Preload images at startup": "Preload images at startup",
-  "Number of columns on the page": "Number of columns on the page",
-  "Number of rows on the page": "Number of rows on the page",
-  "Minimum number of pages per load": "Minimum number of pages per load",
-  "Request browser notifications": "Request browser notifications",
-  "Download localization template": "Download localization template",
-  "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)",
-  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (press Ctrl+Enter or Alt+Enter to generate)",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)",
-  "Add a random artist to the prompt.": "Add a random artist to the prompt.",
-  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
-  "Save style": "Save style",
-  "Apply selected styles to current prompt": "Apply selected styles to current prompt",
-  "Stop processing current image and continue processing.": "Stop processing current image and continue processing.",
-  "Stop processing images and return any results accumulated so far.": "Stop processing images and return any results accumulated so far.",
-  "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both",
-  "Do not do anything special": "Do not do anything special",
-  "Which algorithm to use to produce the image": "Which algorithm to use to produce the image",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help",
-  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - best at inpainting",
-  "Produce an image that can be tiled.": "Produce an image that can be tiled.",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
+  "Images Browser": "Galleria Immagini",
+  "Preload images at startup": "Precarica immagini all'avvio",
+  "Number of columns on the page": "Numero di colonne per pagina",
+  "Number of rows on the page": "Numero di righe per pagina",
+  "Minimum number of pages per load": "Minimo numero di pagine da caricare",
+  "Request browser notifications": "Richiedi notifiche via browser",
+  "Download localization template": "Scarica template di Localization",
+  "Reload custom script bodies (No ui updates, No restart)": "Ricarica gli script custom (Nessun aggiornamento UI, Nessun riavvio)",
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Riavvio Gradio and Aggiorna i componenti (solo Script Custom, ui.py, js e css)",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (premi Ctrl+Enter o Alt+Enter per generare)",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (premi Ctrl+Enter o Alt+Enter per generare)",
+  "Add a random artist to the prompt.": "Aggiungi un'artista casuale al prompt.",
+  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leggi i parametri di generazione dal prompt o dall'ultima generazione se il prompt è vuoto nell'interfaccia utente.",
+  "Save style": "Salva stile",
+  "Apply selected styles to current prompt": "Applica lo stile selezionato al prompt corrente",
+  "Stop processing current image and continue processing.": "Smette di processare l'immagine corrente e continua a processare.",
+  "Stop processing images and return any results accumulated so far.": "Smette di processare l'immagine corrente e restituisce tutti i risultati accumulati fin'ora.",
+  "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stile da applicare; gli stili verranno applicati sia ai prompt positivi che ai prompt negativi",
+  "Do not do anything special": "Non fa nulla di speciale",
+  "Which algorithm to use to produce the image": "Algoritmo usato per produrre l'immagine",
+  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - molto creativo, può generare immagini completamente diverse in base alla quantità di step, impostare valori maggiori di 30-40 non aiuta",
+  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - il migliore su inpainting",
+  "Produce an image that can be tiled.": "Produce un'immagine che può essere usata come texture.",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Usa un processo a due step per creare parzialmente un'immagine a bassa risoluzione, eseguire l'upscale e dopo migliorare i dettagli senza cambiarne la composizione.",
   "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
-  "How many batches of images to create": "How many batches of images to create",
-  "How many image to create in a single batch": "How many image to create in a single batch",
-  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
-  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
-  "Set seed to -1, which will cause a new random number to be used every time": "Set seed to -1, which will cause a new random number to be used every time",
-  "Reuse seed from last generation, mostly useful if it was randomed": "Reuse seed from last generation, mostly useful if it was randomed",
-  "Seed of a different picture to be mixed into the generation.": "Seed of a different picture to be mixed into the generation.",
+  "How many batches of images to create": "Quanti batch di immagini creare in una sola generazione",
+  "How many image to create in a single batch": "Quante immagini creare per ogni singolo batch",
+  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - quanto l'immagine sarà conforme al prompt - valori bassi generano risultati più creativi",
+  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Un valore che determina l'output del generatore casuale di numeri - se crei un'immagine con gli stessi parametri e seed, otterrai lo stesso risultato",
+  "Set seed to -1, which will cause a new random number to be used every time": "Imposta il seed a -1, dunque verrà usato un numero diverso ogni volta",
+  "Reuse seed from last generation, mostly useful if it was randomed": "Riutilizza il seed dall'ultima generazione, utile nel caso fosse casuale",
+  "Seed of a different picture to be mixed into the generation.": "Seed di differenti generazioni, mescolati nella generazione corrente.",
   "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution",
-  "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others",
-  "Separate values for X axis using commas.": "Separate values for X axis using commas.",
-  "Separate values for Y axis using commas.": "Separate values for Y axis using commas.",
-  "Write image to a directory (default - log/images) and generation parameters into csv file.": "Write image to a directory (default - log/images) and generation parameters into csv file.",
-  "Open images output directory": "Open images output directory",
-  "How much to blur the mask before processing, in pixels.": "How much to blur the mask before processing, in pixels.",
-  "What to put inside the masked area before processing it with Stable Diffusion.": "What to put inside the masked area before processing it with Stable Diffusion.",
-  "fill it with colors of the image": "fill it with colors of the image",
-  "keep whatever was there originally": "keep whatever was there originally",
-  "fill it with latent space noise": "fill it with latent space noise",
-  "fill it with latent space zeroes": "fill it with latent space zeroes",
-  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
-  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.",
-  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.",
-  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.",
-  "How many times to repeat processing an image and using it as input for the next iteration": "How many times to repeat processing an image and using it as input for the next iteration",
-  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.",
-  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.",
-  "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.",
-  "Leave blank to save images to the default path.": "Leave blank to save images to the default path.",
-  "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M",
-  "Result = A + (B - C) * M": "Result = A + (B - C) * M",
-  "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
-  "Path to directory with input images": "Path to directory with input images",
-  "Path to directory where to write outputs": "Path to directory where to write outputs",
-  "Input images directory": "Input images directory",
-  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
-  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
+  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Prova a generare immagini simili a quelle che sarebbero state prodotte con lo stesso seed ad una specifica risoluzione",
+  "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "Separa una lista di parole usando le virgole, la prima parola verrà usata come parola chiave: lo script cercherà la parola nel prompt e la rimpiazzerà con le altre",
+  "Separate values for X axis using commas.": "Separa i valori per l'asse X usando le virgole.",
+  "Separate values for Y axis using commas.": "Separa i valori per l'asse Y usando le virgole.",
+  "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva l'immagine in una cartella (default - log/images) e i parametri di generazione in un file CSV.",
+  "Open images output directory": "Apri la cartella di output delle immagini",
+  "How much to blur the mask before processing, in pixels.": "Quanto sfocare le maschere prima di processarle, in pixel.",
+  "What to put inside the masked area before processing it with Stable Diffusion.": "Cosa inserire nell'area di mascheramento prima di processarla con Stable Diffusion.",
+  "fill it with colors of the image": "riempi con colori dell'immagine",
+  "keep whatever was there originally": "mantieni qualsiasi cosa ci fosse originariamente",
+  "fill it with latent space noise": "riempi con latent space noise",
+  "fill it with latent space zeroes": "riempi con latent space zeroes",
+  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Effettua l'Upscale della regione mascherata alla risoluzione obiettivo, effettua l'inpainting, esegue il downscale e lo incolla nell'immagine originale",
+  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Ridimensiona l'immagine alla risoluzione obiettivo. Se l'altezza e la larghezza non coincidono, il risultato avrà un'aspect ratio scorretto.",
+  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Ridimensiona l'immagine affinché l'intera risoluzione obiettivo sia riempita con l'immagine. Taglia le parti che restano fuori.",
+  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "RRidimensiona l'immagine affinché l'intera risoluzione obiettivo sia riempita con l'immagine. Riempie gli spazi vuoti con i colori dell'immagine.",
+  "How many times to repeat processing an image and using it as input for the next iteration": "Quante volte processare un immagine e usarla come input per la prossima iterazione",
+  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In modalità Loopback, per ogni loop la denoising strength sarà moltiplicata per il suo valore. <1 significa ridurre la varietà, dunque la sequenza convergerà in un'immagine più sistemata. >1 significa aumentare la varietà, quindi la sequenza sarà sempre più caotica.",
+  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Per l'upscale in SD, quanta sovrapposizione vi sarà in pixel per ogni tile. I tile si sovrapporranno in modo da essere fusi insieme in una singola immagine. Non vi sarà alcuna linea visibile.",
+  "A directory on the same machine where the server is running.": "Una cartella nella stessa macchina in cui il server è in funzione.",
+  "Leave blank to save images to the default path.": "Lascia vuoto per salvare le immagini nel percorso di default.",
+  "Result = A * (1 - M) + B * M": "Risultato = A * (1 - M) + B * M",
+  "Result = A + (B - C) * M": "Risultato = A + (B - C) * M",
+  "1st and last digit must be 1. ex:'1, 2, 1'": "Il primo e l'ulitmo carattere devono essere 1. es:'1, 2, 1'",
+  "Path to directory with input images": "Percorso della cartella con le immagini di input",
+  "Path to directory where to write outputs": "Percorso della cartella dove posizione le immagini di output",
+  "Input images directory": "Cartella immagini di input",
+  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa i seguenti tag per definire come i nomi dei file saranno scelti: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; lascia vuoto per default.",
+  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se questa opzione non è abilita, i watermark non verranno aggiunti. Attenzione: se non aggiungi i watermark, potresti star agendo in modo non etico.",
   "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
-  "Restore low quality faces using GFPGAN neural network": "Restore low quality faces using GFPGAN neural network",
-  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
-  "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.",
+  "Restore low quality faces using GFPGAN neural network": "Ripristina i volti in bassa qualità usando la rete neurale GFPGAN",
+  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Questa espressione regolare verrà usata per estrarre parole dai nomi dei file, e verranno uniti usanto le opzioni indicate sotto per il training. Lascia vuoto per mantenere il nome del file così com'è.",
+  "This string will be used to join split words into a single line if the option above is enabled.": "Questa stringa sarà usata per unire parole tagliate in una sola riga se l'opzione sopra è abilitata.",
   "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
   "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
-  "Enable Autocomplete": "Enable Autocomplete"
+  "Enable Autocomplete": "Abilita Autocompletamento"
 }
-- 
cgit v1.2.1


From 54cdd4e1f4168697f32882c0b33842e767b66884 Mon Sep 17 00:00:00 2001
From: xmodar <modar.alfadly@gmail.com>
Date: Wed, 26 Oct 2022 22:49:45 +0300
Subject: Add LTR checkpoint lists and updated Arabic localization

---
 localizations/ar_AR.json | 26 ++++++++++++++------------
 style.css                | 16 +++++++++++++---
 2 files changed, 27 insertions(+), 15 deletions(-)

diff --git a/localizations/ar_AR.json b/localizations/ar_AR.json
index 21c24e62..d330b919 100644
--- a/localizations/ar_AR.json
+++ b/localizations/ar_AR.json
@@ -71,9 +71,10 @@
   "Script": "أداة",
   "Prompt matrix": "مصفوفة طلبات",
   "Put variable parts at start of prompt": "الجزء المتغير في بداية الطلب",
-  "Prompts from file or textbox": "قائمة طلبات",
-  "Show Textbox": "أظهر مربع النص",
-  "File with inputs": "ملف نصي للطلبات",
+  "Prompts from file or textbox": " قائمة طلبات",
+  "Iterate seed every line": "غير البذرة مع كل طلب",
+  "List of prompt inputs": "قائمة الطلبات",
+  "Upload prompt inputs": "اجلب الطلبات من ملف",
   "Drop File Here": "اسقط ملف هنا",
   "-": "-",
   "or": "أو",
@@ -219,7 +220,7 @@
   "Run": "تشغيل",
   "Train": "تدريب",
   "See": "اقرأ",
-  "wiki": "الـwiki ",
+  "wiki": " الـwiki ",
   "for detailed explanation.": "لمعرفة المزيد",
   "Create embedding": "إنشاء تضمين",
   "Name": "الاسم",
@@ -231,11 +232,7 @@
   "Enter hypernetwork layer structure": "ترتيب مضاعفات عرض الطبقات",
   "1st and last digit must be 1. ex:'1, 2, 1'": "المضاعفين الأول والأخير يجب أن يكونا 1، مثال: 1, 2, 1",
   "Select activation function of hypernetwork": "دالة التنشيط",
-  "linear": "خطية (بدون)",
-  "relu": "ريلو (ReLU)",
-  "leakyrelu": "ريلو المتسربة (LeakyReLU)",
-  "elu": "إيلو (ELU)",
-  "swish": "سويش (Swish)",
+  "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "تهيئة الأوزان (استخدم Kaiming مع relu وشبيهاتها وXavier مع sigmoid وشبيهاتها)",
   "Add layer normalization": "أضف تسوية الطبقات (LayerNorm)",
   "Use dropout": "استخدم الإسقاط (Dropout)",
   "Overwrite Old Hypernetwork": "استبدل الشبكة الفائقة القديمة",
@@ -251,6 +248,11 @@
   "Split oversized images": "قسّم الصور الكبيرة",
   "Split image threshold": "حد تقسيم الصور الكبيرة",
   "Split image overlap ratio": "نسبة تداخل اقسام الصور الكبيرة",
+  "Auto focal point crop": "اقتصاص تلقائي",
+  "Focal point face weight": "تمركز الوجوه",
+  "Focal point entropy weight": "تمركز التنوع",
+  "Focal point edges weight": "تمركز الحواف",
+  "Create debug image": "احفظ نتائج التحليل أيضا",
   "Use BLIP for caption": "استخدم BLIP لتوصيف الصور",
   "Use deepbooru for caption": "استخدم deepbooru لتوصيف الصور",
   "Preprocess": "معالجة مسبقة",
@@ -328,7 +330,7 @@
   "Save grids to a subdirectory": "احفظ جداول الصور في مجلد فرعي",
   "When using \"Save\" button, save images to a subdirectory": "احفظ الصور في مجلد فرعي عند الضغط على زر الحفظ",
   "Directory name pattern": "نمط اسم المجلد",
-  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم الأوسمة التالية لحديد كيفية تسمية المجلدات الفرعية للصور والصور المركبة: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] أو اتركه فارغا إذا أردت",
+  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم الأوسمة التالية لتحديد كيفية تسمية المجلدات الفرعية للصور والصور المركبة: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] أو اتركه فارغا إذا أردت",
   "Max prompt words for [prompt_words] pattern": "أقصى عدد كلمات الطلب عند استخدام وسم [prompt_words]",
   "Upscaling": "تكبير الصور",
   "Tile size for ESRGAN upscalers. 0 = no tiling.": "حجم نافذة المكبر ESRGAN (يمكن أن يكون 0)",
@@ -341,6 +343,7 @@
   "Upscale latent space image when doing hires. fix": "عند اختيار \"أقل دقة\" قم بالتكبير في الفضاء الكامن",
   "Face restoration": "تحسين الوجوه",
   "Face restoration model": "نموذج تحسين الوجوه",
+  "Restore low quality faces using GFPGAN neural network": "استخدم نموذج GFPGAN لتحسين الوجوه",
   "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "وزن CodeFormer (يزيد التفاصيل على حساب الجودة)",
   "Move face restoration model from VRAM into RAM after processing": "احذف محسن الوجوه من ذاكرة كرت الشاشة (VRAM) بعد استخدامه",
   "System": "النظام",
@@ -396,8 +399,7 @@
   "Show generation progress in window title.": "أظهر التقدم في عنوان النافذة",
   "Quicksettings list": "قائمة الإعدادات السريعة",
   "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "قائمة مقسمة بفواصل لأسماء الإعدادات التي يجب أن تظهر في الأعلى لتسهيل الوصول إليها، انظر إلى modules/shared.py لمعرفة الأسماء، يتطلب إعادة تشغيل",
-  "Localization (requires restart)": "اللغة/language (يتطلب إعادة تشغيل/requires restart)",
-  "ar_AR": "العربية",
+  "Localization (requires restart)": "اللغة (تتطلب إعادة تشغيل)",
   "Sampler parameters": "عوامل أساليب الخطو",
   "Hide samplers in user interface (requires restart)": "اخف أساليب الخطو التالية (يتطلب إعادة تشغيل)",
   "eta (noise multiplier) for DDIM": "العامل Eta η لأسلوب الخطو DDIM",
diff --git a/style.css b/style.css
index 13a9fb07..f4d37e72 100644
--- a/style.css
+++ b/style.css
@@ -522,18 +522,28 @@ If you change anything above, you need to make sure it is RTL compliant by just
 your changes through converters like https://cssjanus.github.io/ or https://rtlcss.com/.
 Then, you will need to add the RTL counterpart only if needed in the rtl section below.*/
 @media rtl {
-    /* this part was manualy added */
+    /* this part was added manually */
     :host {
         direction: rtl;
     }
-    .output-html:has(.performance), .gr-text-input {
+    .file-preview,
+    .gr-text-input,
+    .output-html:has(.performance),
+    #txt2img_style_index > label > select,
+    #txt2img_style2_index > label > select,
+    #img2img_style_index > label > select,
+    #img2img_style2_index > label > select,
+    #setting_sd_model_checkpoint > label > select,
+    #modelmerger_primary_model_name > label > select,
+    #modelmerger_secondary_model_name > label > select,
+    #modelmerger_tertiary_model_name > label > select {
         direction: ltr;
     }
     .gr-radio, .gr-checkbox{
         margin-left: 0.25em;
     }
 
-    /* this part was automatically generated with few manual modifications */
+    /* automatically generated with few manual modifications */
     .performance .time {
         margin-right: unset;
         margin-left: 0;
-- 
cgit v1.2.1


From 3de036514138d7cdcba9729c975f1683a8e06b16 Mon Sep 17 00:00:00 2001
From: xmodar <modar.alfadly@gmail.com>
Date: Wed, 26 Oct 2022 23:56:11 +0300
Subject: Add id access to scripts list in the css

---
 modules/scripts.py |  2 +-
 style.css          | 17 ++++++-----------
 2 files changed, 7 insertions(+), 12 deletions(-)

diff --git a/modules/scripts.py b/modules/scripts.py
index 9323af3e..a7f36012 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -236,7 +236,7 @@ class ScriptRunner:
             with gr.Group():
                 create_script_ui(script, inputs, inputs_alwayson)
 
-        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
+        dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
         dropdown.save_to_config = True
         inputs[0] = dropdown
 
diff --git a/style.css b/style.css
index f4d37e72..c49954e0 100644
--- a/style.css
+++ b/style.css
@@ -526,19 +526,14 @@ Then, you will need to add the RTL counterpart only if needed in the rtl section
     :host {
         direction: rtl;
     }
-    .file-preview,
-    .gr-text-input,
-    .output-html:has(.performance),
-    #txt2img_style_index > label > select,
-    #txt2img_style2_index > label > select,
-    #img2img_style_index > label > select,
-    #img2img_style2_index > label > select,
-    #setting_sd_model_checkpoint > label > select,
-    #modelmerger_primary_model_name > label > select,
-    #modelmerger_secondary_model_name > label > select,
-    #modelmerger_tertiary_model_name > label > select {
+    select, .file-preview, .gr-text-input, .output-html:has(.performance) {
         direction: ltr;
     }
+    #script_list > label > select,
+    #x_type > label > select,
+    #y_type > label > select {
+        direction: rtl;
+    }
     .gr-radio, .gr-checkbox{
         margin-left: 0.25em;
     }
-- 
cgit v1.2.1


From bf7cbcdeef2c6b6a4b4b0e6e349bf0fe7087afd4 Mon Sep 17 00:00:00 2001
From: xmodar <modar.alfadly@gmail.com>
Date: Thu, 27 Oct 2022 01:30:09 +0300
Subject: Add Arabic localization feedback revisions

---
 localizations/ar_AR.json | 62 ++++++++++++++++++++++++------------------------
 1 file changed, 31 insertions(+), 31 deletions(-)

diff --git a/localizations/ar_AR.json b/localizations/ar_AR.json
index d330b919..7864ac03 100644
--- a/localizations/ar_AR.json
+++ b/localizations/ar_AR.json
@@ -4,7 +4,7 @@
   "view": "اعرض ",
   "api": "واجهة البرمجة",
   "built with gradio": "مبني باستخدام gradio",
-  "Stable Diffusion checkpoint": "ملف أوزان Stable Diffusion",
+  "Stable Diffusion checkpoint": "أوزان نموذج الإنتشار المسقر",
   "txt2img": "نص إلى صورة",
   "Prompt": "الطلب",
   "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "الطلب (لبدء الإنتاج Ctrl+Enter أو Alt+Enter اضغط)",
@@ -16,7 +16,7 @@
   "Apply selected styles to current prompt": "ألحق الإضافات المحددة إلى الطلب وعكسه",
   "Generate": "أنتج",
   "Skip": "تخطى",
-  "Stop processing current image and continue processing.": "تجاهل إنتاج هذة الحزمة وانتقل إلى الحزمة التالية",
+  "Stop processing current image and continue processing.": "لا تكمل خطوات هذة الحزمة وانتقل إلى الحزمة التالية",
   "Interrupt": "توقف",
   "Stop processing images and return any results accumulated so far.": "توقف عن الإنتاج واعرض ما تم إلى الآن",
   "Style 1": "الإضافة 1",
@@ -27,18 +27,18 @@
   "Sampling method": "أسلوب الخطو",
   "Which algorithm to use to produce the image": "Sampler: اسم نظام تحديد طريقة تغيير المسافات بين الخطوات",
   "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral: طريقة مبدعة يمكن أن تنتج صور مختلفة على حسب عدد الخطوات، لا تتغير بعد 30-40 خطوة",
-  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models: الأفضل في التحبير",
+  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models: الأفضل في الإنتاج الجزئي",
   "Width": "العرض",
   "Height": "الإرتفاع",
   "Restore faces": "تحسين الوجوه",
   "Tiling": "ترصيف",
   "Produce an image that can be tiled.": "أنتج صور يمكن ترصيفها بجانب بعضها كالبلاط",
-  "Highres. fix": "أقل دقة",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "أنتج صورة بدقة منخفضة ثم قم برفع الدقة فيما بعد لتسريع الإنتاج عندما تكون الدقة المطلوبة كبيرة",
+  "Highres. fix": "إصلاح الدقة العالية",
+  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "أنتج صورة بدقة منخفضة ثم قم برفع الدقة فيما بعد لمنع التشوهات التي تحصل عندما تكون الدقة المطلوبة كبيرة",
   "Firstpass width": "العرض الأولي",
   "Firstpass height": "الإرتفاع الأولي",
-  "Denoising strength": "قسط المشوار",
-  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Denoising strength: حدد مقدار جزء المشوار المراد قطعه (1: كامل المشوار ينتج صورة مختلفة تماما، 0: ثابت لا يغير الصورة)",
+  "Denoising strength": "المدى",
+  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Denoising strength: حدد مدى الإبتعاد عن الصورة (عدد الخطوات الفعلي = عدد الخطوات * المدى)",
   "Batch count": "عدد الحزم",
   "How many batches of images to create": "يتم إنتاج الصور على دفعات، كل دفعة فيها حزمة من الصور",
   "Batch size": "حجم الحزمة",
@@ -68,7 +68,7 @@
   "This text is used to rotate the feature space of the imgs embs": "لإعادة توجيه التضمين التجميلي",
   "Slerp angle": "أثر الطلب",
   "Is negative text": "الطلب عكسي",
-  "Script": "أداة",
+  "Script": "أدوات خاصة",
   "Prompt matrix": "مصفوفة طلبات",
   "Put variable parts at start of prompt": "الجزء المتغير في بداية الطلب",
   "Prompts from file or textbox": " قائمة طلبات",
@@ -98,19 +98,19 @@
   "Sigma noise": "التشويش (Snoise)",
   "Eta": "العامل Eta η",
   "Clip skip": "تخطي آخر طبقات CLIP",
-  "Denoising": "قسط المشوار",
+  "Denoising": "المدى",
   "X values": "قيم العامل الأول",
   "Separate values for X axis using commas.": "افصل القيم بفواصل (,) من اليسار إلى اليمين",
   "Y type": "العامل الثاني",
   "Y values": "قيم العامل الثاني",
   "Separate values for Y axis using commas.": "افصل القيم بفواصل (,) من الأعلى إلى الأسفل",
-  "Draw legend": "أضف الهامش",
+  "Draw legend": "أضف مفتاح التوضيح",
   "Include Separate Images": "أضف الصور منفصلة",
   "Keep -1 for seeds": "استخدم بذور عشوائية",
   "Save": "احفظ",
   "Write image to a directory (default - log/images) and generation parameters into csv file.": "احفظ الصور مع ملف العوامل بصيغة CSV",
   "Send to img2img": "أرسل لصورة إلى صورة",
-  "Send to inpaint": "أرسل للتحبير",
+  "Send to inpaint": "أرسل للإنتاج الجزئي",
   "Send to extras": "أرسل للمعالجة",
   "Open images output directory": "افتح مجلد الصور المخرجة",
   "Make Zip when Save?": "ضع النتائج في ملف مضغوط عند الحفظ",
@@ -129,19 +129,19 @@
   "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "استبدل الطلب وعكسه في الأعلى بالطلب الأصلي وعكسه التاليين",
   "Original prompt": "الطلب الأصلي",
   "Original negative prompt": "عكس الطلب الأصلي",
-  "Override `Sampling Steps` to the same value as `Decode steps`?": "استدبل عدد الخطوات بعدد الخطوات الأصلية",
+  "Override `Sampling Steps` to the same value as `Decode steps`?": "استبدل عدد الخطوات بعدد الخطوات الأصلية",
   "Decode steps": "عدد الخطوات الأصلية",
-  "Override `Denoising strength` to 1?": "اجعل قسط المشوار 1",
+  "Override `Denoising strength` to 1?": "اجعل المدى 1",
   "Decode CFG scale": "التركيز",
   "Randomness": "العشوائية",
   "Sigma adjustment for finding noise for image": "لا تسمح بتثبيت قيمة التباين",
   "Loopback": "اجترار وتكرار",
   "Loops": "عدد المرات",
   "How many times to repeat processing an image and using it as input for the next iteration": "كم مرة يتم أخذ مخرجات الإنتاج كمدخلات وإعادة الإنتاج مرة أخرى",
-  "Denoising strength change factor": "معدل تغيير قسط المشوار",
-  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "يتم ضرب قسط المشوار بهذا الرقم في كل مرة، إذا استخدمت رقم أصغر من 1 يمكن الرسو على نتيجة، وإذا استخدمت رقم أكبر من 1 تصبح النتيجة عشوائية",
+  "Denoising strength change factor": "معدل تغيير المدى",
+  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "يتم ضرب المدى بهذا الرقم في كل مرة، إذا استخدمت رقم أصغر من 1 يمكن الرسو على نتيجة، وإذا استخدمت رقم أكبر من 1 تصبح النتيجة عشوائية",
   "Outpainting mk2": "توسيع الصورة (mk2)",
-  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "يفضل استخدام: 80-100 خطوة، أسلوب Euler a، قسط المشوار 0.8",
+  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "يفضل استخدام: 80-100 خطوة، أسلوب Euler a، المدى 0.8",
   "Pixels to expand": "عدد البيكسلات",
   "Mask blur": "تنعيم القناع",
   "How much to blur the mask before processing, in pixels.": "مقدرا تنعيم القناع قبل استخدامه (يقاس بالبيكسل)",
@@ -163,18 +163,18 @@
   "fill it with latent space noise": "املأه باستخدام تشويش من الفضاء الكامن",
   "latent nothing": "تصفير كامن",
   "fill it with latent space zeroes": "استبدل مكان القناع في الفضاء الكامن بأصفار",
-  "SD upscale": "مضاعفة الدقة",
+  "SD upscale": "مضاعفة الدقة بنموذج الإنتشار المستقر",
   "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "سيتم تكبير حجم الصورة إلى الضعف، استخدم الطول والإرتفاع في الأعلى لتحديد حجم نافذة المكبر",
   "Tile overlap": "تداخل النافذة",
   "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "المكبر ينظر إلى أجزاء الصورة من خلال نافذة لتكبير المحتوى ثم ينتقل إلى الجزء المجاور، يفضل أن يكون هناك تداخل بين كل رقعة لكي لا يكون هناك اختلاف واضح بينهم",
   "Upscaler": "طريقة التكبير",
-  "Inpaint": "تحبير",
+  "Inpaint": "إنتاج جزئي",
   "Draw mask": "ارسم القناع",
   "Upload mask": "ارفع القناع",
-  "Inpaint masked": "تحبير ما بداخل القناع",
-  "Inpaint not masked": "تحبير ما حول القناع",
-  "Inpaint at full resolution": "تحبير بالدقة الكاملة",
-  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "كبر ما يراد تحبيره ثم صغر النتيجة وألصقها في مكانها",
+  "Inpaint masked": "أنتج ما بداخل القناع",
+  "Inpaint not masked": "أنتج ما حول القناع",
+  "Inpaint at full resolution": "إنتاج بالدقة الكاملة",
+  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "كبر ما يراد إعادة إنتاجه ثم صغر النتيجة وألصقها في مكانها",
   "Inpaint at full resolution padding, pixels": "عدد بيكسلات التبطين",
   "Batch img2img": "صور إلى صور",
   "Process images in a directory on the same machine where the server is running.": "حدد مسار مجلد صور موجود في جهاز الخادم",
@@ -232,12 +232,12 @@
   "Enter hypernetwork layer structure": "ترتيب مضاعفات عرض الطبقات",
   "1st and last digit must be 1. ex:'1, 2, 1'": "المضاعفين الأول والأخير يجب أن يكونا 1، مثال: 1, 2, 1",
   "Select activation function of hypernetwork": "دالة التنشيط",
-  "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "تهيئة الأوزان (استخدم Kaiming مع relu وشبيهاتها وXavier مع sigmoid وشبيهاتها)",
+  "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "تهيئة الأوزان (استخدم Kaiming مع relu وأمثالها وXavier مع sigmoid وأمثالها)",
   "Add layer normalization": "أضف تسوية الطبقات (LayerNorm)",
   "Use dropout": "استخدم الإسقاط (Dropout)",
   "Overwrite Old Hypernetwork": "استبدل الشبكة الفائقة القديمة",
   "Preprocess images": "معالجة مسبقة للصور",
-  "Source directory": "مجلد المخرجات",
+  "Source directory": "مجلد المدخلات",
   "Destination directory": "مجلد المخرجات",
   "Existing Caption txt Action": "اذا كانت الصورة لديها توصيف (طلب)",
   "ignore": "تجاهل",
@@ -252,7 +252,7 @@
   "Focal point face weight": "تمركز الوجوه",
   "Focal point entropy weight": "تمركز التنوع",
   "Focal point edges weight": "تمركز الحواف",
-  "Create debug image": "احفظ نتائج التحليل أيضا",
+  "Create debug image": "أنتج صورة كمثال",
   "Use BLIP for caption": "استخدم BLIP لتوصيف الصور",
   "Use deepbooru for caption": "استخدم deepbooru لتوصيف الصور",
   "Preprocess": "معالجة مسبقة",
@@ -266,7 +266,7 @@
   "Log directory": "مجلد السجل",
   "Path to directory where to write outputs": "مسار مجلد الصور المخرجة",
   "Prompt template file": "ملف صيغ الطلبات",
-  "Max steps": "أقصى عدد خطوات التدريب",
+  "Max steps": "أقصى عدد لخطوات التدريب",
   "Save an image to log directory every N steps, 0 to disable": "احفظ صورة في السجل بعد كل كم خطوة تدريب (لا تحفظ إذا كان 0)",
   "Save a copy of embedding to log directory every N steps, 0 to disable": "احفظ التضمين في السجل بعد كل كم خطوة تدريب (لا تحفظ إذا كان 0)",
   "Save images with embedding in PNG chunks": "احفظ التضمين بداخل ملف الصورة كعامل يمكن استخراجه من عوامل الصورة (صيغة PNG)",
@@ -340,7 +340,7 @@
   "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "تداخل نافذة المكبر SwinIR (تتضح الفواصل إذا كان قليل)",
   "LDSR processing steps. Lower = faster": "عدد خطوات مكبر LDSR (أسرع كلما قلت)",
   "Upscaler for img2img": "طريقة التكبير تحت تبويب صورة إلى صورة",
-  "Upscale latent space image when doing hires. fix": "عند اختيار \"أقل دقة\" قم بالتكبير في الفضاء الكامن",
+  "Upscale latent space image when doing hires. fix": "عند اختيار \"إصلاح الدقة العالية\" قم بالتكبير في الفضاء الكامن",
   "Face restoration": "تحسين الوجوه",
   "Face restoration model": "نموذج تحسين الوجوه",
   "Restore low quality faces using GFPGAN neural network": "استخدم نموذج GFPGAN لتحسين الوجوه",
@@ -358,12 +358,12 @@
   "This string will be used to join split words into a single line if the option above is enabled.": "سيستخدم بين كل كلمة يتم استخراجها من الخيار السابق",
   "Number of repeats for a single input image per epoch; used only for displaying epoch number": "عدد مرات تكرار الصور في كل دورة (Epoch)، يستخدم للعرض فقط",
   "Save an csv containing the loss to log directory every N steps, 0 to disable": "احفظ خسارة التدريب (Loss) في السجل بعد كل كم خطوة (لا تحفظ إذا كان 0)",
-  "Stable Diffusion": "نموذج التشعث الرزين (Stable Diffusion)",
+  "Stable Diffusion": "نموذج الإنتشار المستقر (Stable Diffusion)",
   "Checkpoints to cache in RAM": "عدد النماذج التي تترك في الذاكرة العشوائية (RAM)",
   "Hypernetwork strength": "قوة الشبكة الفائقة",
   "Apply color correction to img2img results to match original colors.": "صحح ألوان نتائج صورة إلى صورة لتشابه الصورة الأصلية",
   "Save a copy of image before applying color correction to img2img results": "احفظ نسخة من الصور المنتجة من صورة إلى صورة قبل عملية تصحيح الألوان",
-  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "استخدم بالضبط عدد الخطوات المحددة بالرغم من قسط المشوار (عدد الخطوات الإفتراضي = عدد الخطوات / قسط المشوار)",
+  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "استخدم بالضبط عدد الخطوات المحددة بالرغم عن المدى (عدد الخطوات الإفتراضي = عدد الخطوات / المدى)",
   "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "فعل التجزيء الكمي (Quantization) لأغلب أساليب الخطو (k-diffusion) للحصول على صور أنظف (يتطلب إعادة تشغيل)",
   "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "أحط أي كلمة أو عبارة في الطلب أو عكسه بأقواس () للتشديد أو [] للتيسير",
   "Use old emphasis implementation. Can be useful to reproduce old seeds.": "استخدم الطريقة القديمة للتشديد (إذا كانت لديك نتائج قديمة تريد تجربتها)",
@@ -419,6 +419,6 @@
   "Minimum number of pages per load": "أقل عدد صور يتم تحميلها كل مرة",
   "Request browser notifications": "اطلب تنبيهات المتصفح",
   "Download localization template": "حمل ملف اللغة",
-  "Reload custom script bodies (No ui updates, No restart)": "أعد تحميل الأدوات (بدون واجهة المستخدم ولا يحتاج إعادة تشغيل)",
-  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعد تحميل مكونات gradio (الأدوات الخاصة وواجهة المستخدم فقط)"
+  "Reload custom script bodies (No ui updates, No restart)": "أعد تحميل الأدوات الخاصة (بدون واجهة المستخدم ولا يحتاج إعادة تشغيل)",
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعد تشغيل gradio والأدوات الخاصة وواجهة المستخدم فقط"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From 95b8e49e5b21b58d0e3c0684be7fa55923809771 Mon Sep 17 00:00:00 2001
From: Martucci <73501718+M-art-ucci@users.noreply.github.com>
Date: Wed, 26 Oct 2022 20:04:48 -0300
Subject: Localization file for portuguese (brazil)

---
 localizations/pt_BR.json | 472 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 472 insertions(+)
 create mode 100644 localizations/pt_BR.json

diff --git a/localizations/pt_BR.json b/localizations/pt_BR.json
new file mode 100644
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+++ b/localizations/pt_BR.json
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+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "Carregando...",
+    "view": "Visualizar",
+    "api": "api",
+    "•": "•",
+    "built with gradio": "montado com gradio",
+    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "Extras",
+    "PNG Info": "PNG Info",
+    "History": "Histórico",
+    "Checkpoint Merger": "Checkpoint Merger",
+    "Train": "Treinar",
+    "Settings": "configurações",
+    "Prompt": "Prompt",
+    "Negative prompt": "Prompt negativo",
+    "Run": "Rodar",
+    "Skip": "Pular",
+    "Interrupt": "Interromper",
+    "Generate": "Gerar",
+    "Style 1": "Estilo 1",
+    "Style 2": "Estilo 2",
+    "Label": "Etiqueta",
+    "File": "Arquivo",
+    "Drop File Here": "Arraste aqui o arquivo",
+    "-": "-",
+    "or": "ou",
+    "Click to Upload": "Clique para subir",
+    "Image": "Imagem",
+    "Check progress": "Checar progresso",
+    "Check progress (first)": "Checar progresso (primeiro)",
+    "Sampling Steps": "Sampling Steps",
+    "Sampling method": "Sampling method",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Largura",
+    "Height": "Altura",
+    "Restore faces": "Refinar rosto",
+    "Tiling": "Estilo azulejo",
+    "Highres. fix": "Ajuste de alta resolução",
+    "Firstpass width": "Largura Firstpass",
+    "Firstpass height": "Altura Firstpass",
+    "Denoising strength": "Força do Denoise",
+    "Batch count": "Quantidade por leva",
+    "Batch size": "Quantidade de levas",
+    "CFG Scale": "Escala CFG",
+    "Seed": "Seed",
+    "Extra": "Extra",
+    "Variation seed": "Variação de seed",
+    "Variation strength": "Forca da variação",
+    "Resize seed from width": "Ajustar seed a partir da largura",
+    "Resize seed from height": "Ajustar seed a partir da altura",
+    "Script": "Script",
+    "None": "Nenhum",
+    "Prompt matrix": "Matriz de Prompt",
+    "Prompts from file or textbox": "Prompts de arquivo ou caixa de texto",
+    "X/Y plot": "X/Y plot",
+    "Put variable parts at start of prompt": "Colocar partes variáveis no começo do prompt",
+	"Iterate seed every line": "Iterar seed em todas as linhas",
+    "List of prompt inputs": "Lista de textos para prompt",
+    "Upload prompt inputs": "Subir textos para prompts",
+    "X type": "X type",
+    "Nothing": "Nada",
+    "Var. seed": "Var. seed",
+    "Var. strength": "Var. da força",
+    "Steps": "Steps",
+    "Prompt S/R": "Prompt S/R",
+    "Prompt order": "Ordem de Prompt",
+    "Sampler": "Sampler",
+    "Checkpoint name": "Nome do intervalo",
+    "Hypernetwork": "Hypernetwork",
+    "Hypernet str.": "Hypernet str.",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "Sigma min",
+    "Sigma max": "Sigma max",
+    "Sigma noise": "Sigma noise",
+    "Eta": "Eta",
+    "Clip skip": "Clip skip",
+    "Denoising": "Denoising",
+    "X values": "X values",
+    "Y type": "Y type",
+    "Y values": "Y values",
+    "Draw legend": "Draw legend",
+    "Include Separate Images": "Incluir Imagens Separadas",
+    "Keep -1 for seeds": "Manter em -1 para seeds",
+    "Save": "Salvar",
+    "Send to img2img": "Mandar para img2img",
+    "Send to inpaint": "Mandar para inpaint",
+    "Send to extras": "Mandar para extras",
+    "Make Zip when Save?": "Criar um Zip quando salvar?",
+    "Textbox": "Caixa de texto",
+    "Interrogate\nCLIP": "Interrogar\nCLIP",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "Leva de img2img",
+    "Image for img2img": "Imagem para img2img",
+    "Drop Image Here": "Arrastar imagem para cá",
+    "Image for inpainting with mask": "Imagem para inpainting com máscara",
+    "Mask": "Mascara",
+    "Mask blur": "Borda da máscara",
+    "Mask mode": "Modo de máscara",
+    "Draw mask": "Desenhar máscara",
+    "Upload mask": "Subir máscara",
+    "Masking mode": "Tipo de máscara",
+    "Inpaint masked": "Inpaint o que esta dentro da máscara",
+    "Inpaint not masked": "Inpaint o que esta fora da máscara",
+    "Masked content": "Conteúdo da máscara",
+    "fill": "preencher",
+    "original": "original",
+    "latent noise": "latent noise",
+    "latent nothing": "latent nothing",
+    "Inpaint at full resolution": "Inpaint em resolução máxima",
+    "Inpaint at full resolution padding, pixels": "Inpaint em resolução máxima de padding, pixels",
+    "Process images in a directory on the same machine where the server is running.": "Processar imagens no diretório da mesma maquina onde o servidor está rodando.",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usar um diretório vazio para salvar imagens, ao invés de salvá-las no diretório output.",
+    "Input directory": "Diretório de Input",
+    "Output directory": "Diretório de Output",
+    "Resize mode": "Modo de aumento",
+    "Just resize": "Apenas aumentar",
+    "Crop and resize": "Cortar e aumentar",
+    "Resize and fill": "Aumentar e preencher",
+    "img2img alternative test": "Teste alternativo de img2img",
+    "Loopback": "Loopback",
+    "Outpainting mk2": "Outpainting mk2",
+    "Poor man's outpainting": "Poor man's outpainting",
+    "SD upscale": "Aumento SD",
+    "should be 2 or lower.": "precisa ser 2 ou menor.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Sobrepor 'sampling method' para Euler? (esse método foi feito para isso)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Sobrepor 'prompt' para o mesmo valor que o 'prompt original'? (também para o 'prompt negativo')",
+    "Original prompt": "Prompt Original",
+    "Original negative prompt": "Prompt Negativo Original",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Sobrepor 'Sampling Steps' para o mesmo valor que 'Decode steps'?",
+    "Decode steps": "Decode steps",
+    "Override `Denoising strength` to 1?": "Sobrepor 'Forca do Denoise' para 1?",
+    "Decode CFG scale": "Escala CFG de Decode",
+    "Randomness": "Aleatoriedade",
+    "Sigma adjustment for finding noise for image": "Ajuste Sigma para encontrar ruído para imagem",
+    "Loops": "Loops",
+    "Denoising strength change factor": "Fator de mudança na força do Denoise",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Configurações recomendadas",
+    "Pixels to expand": "Pixels para expandir",
+    "Outpainting direction": "Direção do Outpainting",
+    "left": "esquerda",
+    "right": "direita",
+    "up": "cima",
+    "down": "baixo",
+    "Fall-off exponent (lower=higher detail)": "Exponente Fall-off exponent (menor=mais detalhes)",
+    "Color variation": "Variação de cor",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenta a imagem para duas vezes as dimensoes; ajuste a largura e altura para definir o tamanho do azulejo",
+    "Tile overlap": "sobreposicao de azulejo",
+    "Upscaler": "Ferramenta de aumento",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR 4x": "SwinIR 4x",
+    "Single Image": "Imagem Única",
+    "Batch Process": "Processo em Levas",
+    "Batch from Directory": "Levas a partir de diretório",
+    "Source": "Origem",
+    "Show result images": "Mostrar imagens resultantes",
+    "Scale by": "Aumentar proporcionalmente em",
+    "Scale to": "Aumentar proporcionalmente para",
+    "Resize": "Aumentar para",
+    "Crop to fit": "Cortar para caber",
+    "Upscaler 2 visibility": "Visibilidade da ferramenta de aumento 2",
+    "GFPGAN visibility": "Visibilidade GFPGAN",
+    "CodeFormer visibility": "Visibilidade CodeFormer",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso do CodeFormer (0 = efeito máximo, 1 = efeito mínimo)",
+    "Open output directory": "Abrir diretorio de output",
+    "Send to txt2img": "Mandar para txt2img",
+    "A merger of the two checkpoints will be generated in your": "Uma combinação de dois intervalos vai ser gerado em",
+    "checkpoint": "intervalo",
+    "directory.": "diretório.",
+    "Primary model (A)": "Modelo Primário (A)",
+    "Secondary model (B)": "Modelo Secundário (B)",
+    "Tertiary model (C)": "Modelo Terciário (C)",
+    "Custom Name (Optional)": "Nome Personalizado (Opcional)",
+    "Multiplier (M) - set to 0 to get model A": "Multiplicador (M) - definir em 0 para obter o modelo A",
+    "Interpolation Method": "Método de Interpolação",
+    "Weighted sum": "Soma de weights",
+    "Add difference": "Acrescentar diferença",
+    "Save as float16": "Salvar como float16",
+    "See": "Veja",
+    "wiki": "wiki",
+    "for detailed explanation.": "para explicações detalhadas.",
+    "Create embedding": "Criar embedding",
+    "Create hypernetwork": "Criar hypernetwork",
+    "Preprocess images": "Pré-processar imagens",
+    "Name": "Nome",
+    "Initialization text": "Texto de Inicialização",
+    "Number of vectors per token": "Número de vetores por token",
+    "Overwrite Old Embedding": "Sobrescrever Embedding Anterior",
+    "Modules": "Módulos",
+    "Enter hypernetwork layer structure": "Inserir camada estrutural de hypernetwork",
+    "Select activation function of hypernetwork": "Selecionar a função de ativação de hypernetwork",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardswish": "hardswish",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "multiheadattention": "multiheadattention",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "threshold",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Selecionar a inicialização de weiths de camada. relu-like - Kaiming, sigmoid-like - Xavier é recomendado",
+    "Normal": "Normal",
+    "KaimingUniform": "KaimingUniform",
+    "KaimingNormal": "KaimingNormal",
+    "XavierUniform": "XavierUniform",
+    "XavierNormal": "XavierNormal",
+    "Add layer normalization": "Adicionar camada de normalização",
+    "Use dropout": "Sobrescrever Hypernetwork Anterior",
+    "Overwrite Old Hypernetwork": "Sobrescrever Hypernetwork Anterior",
+    "Source directory": "Diretório de origem",
+    "Destination directory": "Diretório de destino",
+    "Existing Caption txt Action": "Ação de legenda txt já existente",
+    "ignore": "ignorar",
+    "copy": "copiar",
+    "prepend": "preceder",
+    "append": "acrescentar",
+    "Create flipped copies": "Criar cópias espelhadas",
+    "Split oversized images into two": "Dividir imagens maiores em duas",
+    "Auto focal point crop": "Ajuste de corte em ponto focal automático",
+    "Use BLIP for caption": "Usar BLIP para legenda",
+    "Use deepbooru for caption": "Usar deepbooru para legenda",
+    "Split image threshold": "Limite de separação de imagem",
+    "Split image overlap ratio": "Proporção de sobreposição da separação de imagem",
+    "Focal point face weight": "Weight de ponto focal para rosto",
+    "Focal point entropy weight": "Weight de ponto focal para entropia",
+    "Focal point edges weight": "Weight de ponto focal para bordas",
+    "Create debug image": "Criar imagem de depuração",
+    "Preprocess": "Pré-processar",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Treinar um embedding; precisa especificar um diretório com imagens de proporção 1:1",
+    "[wiki]": "[wiki]",
+    "Embedding": "Embedding",
+    "Learning rate": "Taxa de aprendizagem",
+    "Hypernetwork Learning rate": "Taxa de treinamento de Hypernetwork",
+    "Dataset directory": "Diretório de Dataset",
+    "Log directory": "Diretório de Log",
+    "Prompt template file": "Arquivo padrão de Prompt",
+    "Max steps": "Steps máximos",
+    "Save an image to log directory every N steps, 0 to disable": "Salvar uma imagem no diretório de log a cada N steps. 0 para desativar",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva uma cópia do embedding no diretório de log a cada N steps. 0 para desativar",
+    "Save images with embedding in PNG chunks": "Salva imagens com embedding em blocos de PNG",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Ler parâmetros (prompt, etc...) para a aba txt2img durante os previews",
+    "Train Hypernetwork": "Treinar Hypernetwork",
+    "Train Embedding": "Treinar Embedding",
+    "Apply settings": "Aplicar configurações",
+    "Saving images/grids": "Salvar imagens/grades",
+    "Always save all generated images": "Sempre salvar todas as imagens geradas",
+    "File format for images": "Tipo de formato das imagens salvas",
+    "Images filename pattern": "Padrão de nomeação para imagens salvas",
+    "Add number to filename when saving": "Adicionar número para o nome do arquivo quando salvar",
+    "Always save all generated image grids": "Sempre salvar todas as grades de imagens",
+    "File format for grids": "Tipo de formato das grades de imagens salvas",
+    "Add extended info (seed, prompt) to filename when saving grid": "Adicionar informações extras (seed, prompt) para os arquivos quando gerar uma grade",
+    "Do not save grids consisting of one picture": "Não salvar grades de apenas uma imagem",
+    "Prevent empty spots in grid (when set to autodetect)": "Previnir pontos vazios na grade (quando marcado para autodetectar)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Contagem de colunas da grade; -1 para autodetectar e 0 para ser igual ao valor do tamanho das levas",
+    "-1": "-1",
+    "Save text information about generation parameters as chunks to png files": "Salvar informações de parâmetros de geração como blocos png",
+    "Create a text file next to every image with generation parameters.": "Criar um arquivo de texto com informações de geração junto a cada imagem gerada.",
+    "Save a copy of image before doing face restoration.": "Salva uma cópia de cada imagem antes do refinamento facial.",
+    "Quality for saved jpeg images": "Qualidade das imagens jpeg",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se a imagem PNG for maior que 4MB ou qualquer dimensão mair que 4000, diminuir e salvar uma cópia em JPG",
+    "Use original name for output filename during batch process in extras tab": "Usar o nome original para os arquivos de output durante o processo de levas da aba Extras",
+    "When using 'Save' button, only save a single selected image": "Quando usar o botão 'Salvar', somente salvar as imagens selecionadas.",
+    "Do not add watermark to images": "Não adicionar marca dágua nas imagens",
+    "Paths for saving": "Caminho para salvamento",
+    "Output directory for images; if empty, defaults to three directories below": "Diretório de output para imagens; se deixado em branco, as imagens vao para os seguintes diretórios",
+    "Output directory for txt2img images": "Diretório de Output para imagens txt2img",
+    "Output directory for img2img images": "Diretório de Output para imagens img2img",
+    "Output directory for images from extras tab": "Diretório de Output para a aba Extras",
+    "Output directory for grids; if empty, defaults to two directories below": "Diretório de Output para grades; se vazio, vão para os diretórios seguintes",
+    "Output directory for txt2img grids": "Diretório de Output para grades de imagens txt2img",
+    "Output directory for img2img grids": "Diretório de Output para grades de imagens img2img",
+    "Directory for saving images using the Save button": "Diretório de Output para imagens salvas com o botão 'Salvar'",
+    "Saving to a directory": "Salvando para um diretório",
+    "Save images to a subdirectory": "Salvar imagens para um subdiretório",
+    "Save grids to a subdirectory": "Salvar grades de imagens para um subdiretório",
+    "When using \"Save\" button, save images to a subdirectory": "Quando usar o botão \"Salvar\", sasalvar imagens para um subdiretório",
+    "Directory name pattern": "Padrão de nomeacao de diretório",
+    "Max prompt words for [prompt_words] pattern": "Número máximo de palavras do padrão de prompt [prompt_words]",
+    "Upscaling": "Aumentando",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tamanho do azulejo para ferramenta de aumento ESRGAN. 0 = sem azulejo.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sobreposição de azulejo, em pixels, para ferramentas de aumento ESRGAN. Valores baixos = junção mais aparente.",
+    "Tile size for all SwinIR.": "Tamanho do azulejo para todo SwinIR.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sobreposição de azulejo, em pixels,  para SwinIR. Valores baixos = junção mais aparente.",
+    "LDSR processing steps. Lower = faster": "Steps de processamento LDSR. Menos = rápido",
+    "Upscaler for img2img": "Ferramenta de aumento para img2img",
+    "Upscale latent space image when doing hires. fix": "Aumento em imagem latent space quando usando a resolução de alta definição - hires. fix",
+    "Face restoration": "Refinamento de rosto",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Paramento de CodeFormer weight; 0 = efeito máximo; 1 = efeito mínimo",
+    "Move face restoration model from VRAM into RAM after processing": "Mover o processo de refinamento de rosto da VRAM da placa de vídeo para a RAM do computador depois do processamento.",
+    "System": "System",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Uso de VRAM por segundo durante gerações. Deixar em 0 para desativar.",
+    "Always print all generation info to standard output": "Sempre mostrar as informações de todas as gerações no padrão de output",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Adicionar uma segunda barra de processamento no console que mostra a progressão de todo o trabalho.",
+    "Training": "Treinando",
+    "Unload VAE and CLIP from VRAM when training": "Desativar VAE e CLIP da VRAM quando treinando",
+    "Filename word regex": "Palavra de nome de arquivo regex",
+    "Filename join string": "Nome de arquivo join string",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Número de repetições para input único de imagens por epoch; serve apenas para mostrar o número de epoch",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salvar um csv com as perdas para o diretório de log a cada N steps, 0 para desativar",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "Intervalos para manter na RAM",
+    "Hypernetwork strength": "Força da Hypernetwork",
+    "Apply color correction to img2img results to match original colors.": "Aplicar correção de cor nas imagens geradas em img2img, usando a imagem original como base. Recomendação: se marcar essa opção, marcar também a de baixo",
+    "Save a copy of image before applying color correction to img2img results": "Salvar uma cópia das imagens geradas em img2img antes de aplicar a correção de cor",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Durante gerações img2img, fazer examente o número de steps definidos na barra (normalmente você faz menos steps com denoising menor).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Ativar quantização em K samples para resultados mais nítidos e visíveis. Pode alterar seeds ja existentes. Precisa reiniciar para funcionar.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Ênfase: usar parênteses ao redor de palavras (texto de exemplo) para fazer o modelo dar mais atenção par aquela palavra ou frase, e chaves [texto de exemplo] para tirar atenção",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usar método anterior de implementação de ênfase. Útil para reproduzir seeds antigas.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Faz os samplers K-diffusion produzirem imagens iguais em levas quando criando uma única imagem",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta a coerência por padding da última vírgula dentro de n tokens quando usar mais de 75 tokens",
+    "Filter NSFW content": "Filtrar conteúdo NSFW",
+    "Stop At last layers of CLIP model": "Para na última cada do CLIP model",
+    "Interrogate Options": "Opções de Interrogaçõ",
+    "Interrogate: keep models in VRAM": "Interrogar: manter modelos na VRAM",
+    "Interrogate: use artists from artists.csv": "Interrogar: usa artistas e estilos do documento artists.csv",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogar: incluir filas de model tags combinando nos resultados(Não tem efeito na interrogação feita por legenda).",
+    "Interrogate: num_beams for BLIP": "Interrogar: num_beams para BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Interrogar: tamanho mínimo da descrição (tirando artistas, etc..)",
+    "Interrogate: maximum description length": "Interrogar: tamanho máximo da descrição",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: núumero máximo de linhas no arquivo de texto(0 = Sem limites NÃO RECOMENDADO)",
+    "Interrogate: deepbooru score threshold": "Interrogate: limite de score deepbooru",
+    "Interrogate: deepbooru sort alphabetically": "Interrogar: organizar deepbooru por ordem alfabética",
+    "use spaces for tags in deepbooru": "usar epaços para tags em deepbooru",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "espaço (\\) colchetes em deepbooru (são usados como colchetes ao invés de dar ênfase)",
+    "User interface": "Interface de Usuário",
+    "Show progressbar": "Mostrar barra de progresso",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostrar a criação de imagens a cada N sampling steps. Em 1 já dá para ver o processo de geração. Marcar como 0 para desativar.",
+    "Show previews of all images generated in a batch as a grid": "Mostrar previsualização de todas as imagens geradas em leva numa grade",
+    "Show grid in results for web": "Mostrar grade em resultados para web",
+    "Do not show any images in results for web": "Nao mostrar nenhuma imagem em resultados para web",
+    "Add model hash to generation information": "Adicionar hash do modelo para infromação de geração",
+    "Add model name to generation information": "Adicionar nome do modelo para infromação de geração",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Quando ler parâmetros de texto para a interface (de informações de PNG ou texto copiado), não alterar o modelo/intervalo selecionado.",
+    "Font for image grids that have text": "Fonte para grade de imagenss que tem texto",
+    "Enable full page image viewer": "Ativar visualizador de página inteira",
+    "Show images zoomed in by default in full page image viewer": "Mostrar imagens com zoom por definição no visualizador de página inteira",
+    "Show generation progress in window title.": "Mostrar barra de progresso no nome da janela.",
+    "Quicksettings list": "Lista de configurações rapidas",
+    "Localization (requires restart)": "Localização (precisa reiniciar)",
+    "Sampler parameters": "Parâmetros de Sampler",
+    "ar_AR": "ar_AR",
+    "es_ES": "es_ES",
+    "fr-FR": "fr-FR",
+    "ja_JP": "ja_JP",
+    "ko_KR": "ko_KR",
+    "ru_RU": "ru_RU",
+    "tr_TR": "tr_TR",
+    "zh_CN": "zh_CN",
+    "Sampler parameters": "Parâmetros de Sampler",
+    "Hide samplers in user interface (requires restart)": "Esconder samplers na interface de usuário (precisa reiniciar)",
+    "eta (noise multiplier) for DDIM": "eta (noise multiplier) para DDIM",
+    "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) para ancestral samplers",
+    "img2img DDIM discretize": "img2img DDIM discretize",
+    "uniform": "uniform",
+    "quad": "quad",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma noise",
+    "Eta noise seed delta": "Eta noise seed delta",
+    "Request browser notifications": "Pedir notificações de browser",
+    "Download localization template": "Download de template para localização",
+    "Reload custom script bodies (No ui updates, No restart)": "Recarregar scripts personalizados (Sem atualizar o UI, Sem reiniciar)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Recarregar Gradio e atualizar componentes (Scripts personalizados, ui.py, js e css)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (apertar Ctrl+Enter ou Alt+Enter para gerar)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt Negativo (apertar Ctrl+Enter ou Alt+Enter para gerar)",
+    "Add a random artist to the prompt.": "Adicionar um artista aleatório para o prompt.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Lê os parâmetros de geração do prompt ou da última geraçao, caso o prompt esteja vazio.",
+    "Save style": "Salva um estilo de prompt.",
+    "Apply selected styles to current prompt": "Aplica o estilo para o prompt atual.",
+    "Stop processing current image and continue processing.": "Pula a imagem sendo gerada e vai para a próxima.",
+    "Stop processing images and return any results accumulated so far.": "Interrompe o processo e mostra o que foi gerado até então.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Estilo para aplicar; também serve para o prompt negativo e vai preencher se usado.",
+    "Do not do anything special": "Não faz nada de especial",
+    "Which algorithm to use to produce the image": "O tipo de algoritmo para gerar imagens.",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - cria mais variações para as imagens em diferentes passos. Mais que 40 passos cancela o efeito.",
+    "Denoising Diffusion Implicit Models - Funciona melhor para inpainting.": "Denoising Diffusion Implicit Models - Funciona melhor para inpainting.",
+    "Produce an image that can be tiled.": "Cria uma imagem que emenda nela mesma pelas bordas. Testar em https://www.pycheung.com/checker/.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Cria um processo em duas etapas, com uma imagem em baixa qualidade primeiro, aumenta a imagem e refina os detalhes sem alterar a composição da imagem",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Quanto o algoritmo deve manter da imagem original. Em 0, nada muda. Em 1 o algoritmo ignora a imagem original. Valores menores que 1.0 demoram mais.",
+    "How many batches of images to create": "Quantas imagens criar em uma leva.",
+    "How many image to create in a single batch": "Quantas levas criar (multiplica o valor pelo Batch count).",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - Quanto maior o valor, mais segue o prompt e quanto menor, menor segue.",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Codigo de geração de uma imagem - criando uma imagem com os mesmos parâmetros e seed trazem o mesmo resultado.",
+    "Set seed to -1, which will cause a new random number to be used every time": "Define seed como -1, deixando o valor que vai aparecer como aleatório.",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Reutiliza o seed da última geração",
+    "Seed of a different picture to be mixed into the generation.": "Usar um seed de outra imagem para a geração.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Qual a variação a ser gerada. Em 0, não tem efeito. Em 1, gera uma imagem completa com a variação de seed, (exceto com samplers a).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Tenta gerar uma imagem similar ao que teria sido feito com a mesma seed em dimensões especifica.",
+    "Separate values for X axis using commas.": "Separa os valores para o eixo X usando vírgulas.",
+    "Separate values for Y axis using commas.": "Separa os valores para o eixo Y usando vírgulas.",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva a imagem no diretório padrão ou escolhido e cria um arquivo csv com os parâmetros da geração.",
+    "Open images output directory": "Abre o diretório de saída de imagens.",
+    "How much to blur the mask before processing, in pixels.": "Transição do contorno da máscara, em pixels.",
+    "What to put inside the masked area before processing it with Stable Diffusion.":  "O que vai dentro da máscara antes de processar.",
+	"fill it with colors of the image": "Preenche usando as cores da imagem.",
+    "keep whatever was there originally": "Mantem o que estava dentro.",
+    "fill it with latent space noise": "Preenche com ruídos.",
+    "fill it with latent space zeroes": "Preenche com zeros.",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Utiliza o processo de aumentar a imagem e depois reduz a qualidade para preencher a região. Consome mais tempo.",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Se a imagem não for quadrada, estica ela para caber nas dimensões escolhidas.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Mantém o máximo da imagem dentro das dimensões escolhidas e corta o resto.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Coloca a imagem inteira dentro das dimensões escolhidas e preenche o espaço vazio.",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "Quantas vezes vai reutilizar a imagem para o processo em loop.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Quantos pixels se sobrepõe durante o aumento da imagem, para garantir que não haverá marcas.",
+    "A directory on the same machine where the server is running.": "Um diretório na mesma máquina onde o server está rodando.",
+    "Leave blank to save images to the default path.": "Deixar em branco para salvar imagens no diretório padrão.",
+    "Result = A * (1 - M) + B * M": "Resultado = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Resultado = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "Primeiro e último dígito precisam ser 1. ex:'1, 2, 1'",
+    "Path to directory with input images": "Diretório para imagens de entrada",
+    "Path to directory where to write outputs": "Diretório para salvar imagens de saída",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa essas tags para definir como os nomes dos arquivos sao escolhidos: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; deixe em branco para manter o padrao.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se esta opção estiver marcada, as imagens não vão ter marca dágua. Aviso: se você não quer a marca dágua, você pode estar se envolvendo em comportamentos antiéticos",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa essas tags para definir como os nomes dos subdiretorios e ghrades sao escolhidos: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; deixe em branco para manter o padrao.",
+    "Restore low quality faces using GFPGAN neural network": "Restaurar baixa qualidade de rosto usando a rede neural GFPGAN",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Esta expressão regular vai retirar palavras do nome do arquivo e vai se juntar usando a opção abaixo em etiquetas usadas em treinamento. Não mexer para manter os nomes como estão.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Junta palavras separadas em uma linha só.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Lista de nomes de configuração, separados por vírgulas, para configurações que ficam na barra de acesso rápido do topo. Mais detalhes em modules/shared.py para configurações de nomes. Precisa reiniciar o webui-user.bat.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se o valor não for 0, vai ser adicionado ao seed e usado para gerar ruídos aleatórios quando o sampler tem estimativa de entrega. Isso traz mais variações de imagens ou coincide com imagens de outras aplicações (quando você sabe o que esta fazendo).",
+    "/0.0": "/0.0",
+    "queue:": "queue:",
+    "/": "/",
+    "|": "|",
+    "processing |": "processando |",
+    "localizationpt": "localizationpt"
+}
+
+
-- 
cgit v1.2.1


From c40994a7b25df472c7c1b0b5adc36e8a8c169e29 Mon Sep 17 00:00:00 2001
From: benlisquare <benlisquare>
Date: Thu, 27 Oct 2022 10:41:19 +1100
Subject: adjustments to zh_TW localisation per suggestions by dtlnor

---
 localizations/zh_TW.json | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/localizations/zh_TW.json b/localizations/zh_TW.json
index 43db9c31..43f95498 100644
--- a/localizations/zh_TW.json
+++ b/localizations/zh_TW.json
@@ -9,12 +9,12 @@
     "api": "api",
     "•": "•",
     "built with gradio": "基於 Gradio 構建",
-    "Stable Diffusion checkpoint": "Stable Diffusion 模型",
+    "Stable Diffusion checkpoint": "Stable Diffusion 模型權重存檔點",
     "txt2img": "文生圖",
     "img2img": "圖生圖",
     "Extras": "後處理",
     "PNG Info": "PNG 信息",
-    "Checkpoint Merger": "模型合併工具",
+    "Checkpoint Merger": "模型權重存檔點合併工具",
     "Train": "訓練",
     "Create aesthetic embedding": "生成美術風格 embedding",
     "Image Browser": "圖庫瀏覽器",
@@ -99,7 +99,7 @@
     "Prompt S/R": "提示詞替換",
     "Prompt order": "提示詞順序",
     "Sampler": "採樣器",
-    "Checkpoint name": "模型名",
+    "Checkpoint name": "模型權重存檔點的名稱",
     "Hypernetwork": "Hypernetwork",
     "Hypernet str.": "Hypernetwork 強度",
     "Sigma Churn": "Sigma Churn",
@@ -206,8 +206,8 @@
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 權重 (0 = 最大效果, 1 = 最小效果)",
     "Open output directory": "打開輸出目錄",
     "Send to txt2img": ">> 文生圖",
-    "A merger of the two checkpoints will be generated in your": "合併後的模型會生成在你的",
-    "checkpoint": "模型",
+    "A merger of the two checkpoints will be generated in your": "合併後的模型權重存檔點會生成在你的",
+    "checkpoint": "模型權重存檔點",
     "directory.": "目錄",
     "Primary model (A)": "主要模型 (A)",
     "Secondary model (B)": "第二模型 (B)",
@@ -357,7 +357,7 @@
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每個 epoch 中單個輸入圖像的重複次數； 僅用於顯示 epoch 數",
     "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步保存一個包含 loss 的CSV到日誌目錄，0 表示禁用",
     "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "緩存在內存(RAM)中的模型",
+    "Checkpoints to cache in RAM": "緩存在內存(RAM)中的模型權重存檔點",
     "Hypernetwork strength": "Hypernetwork 強度",
     "Apply color correction to img2img results to match original colors.": "對圖生圖結果應用顏色校正以匹配原始顏色",
     "Save a copy of image before applying color correction to img2img results": "在對圖生圖結果應用顏色校正之前保存圖像副本",
@@ -389,7 +389,7 @@
     "Do not show any images in results for web": "不在網頁的結果中顯示任何圖像",
     "Add model hash to generation information": "將模型的雜湊值添加到生成信息",
     "Add model name to generation information": "將模型名稱添加到生成信息",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "當從文本讀取生成參數到 UI（從 PNG 信息或粘貼文本）時，不要更改選定的模型",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "當從文本讀取生成參數到 UI（從 PNG 信息或粘貼文本）時，不要更改選定的模型權重存檔點",
     "Font for image grids that have text": "有文字的概覽圖使用的字體",
     "Enable full page image viewer": "啟用整頁圖像查看器",
     "Show images zoomed in by default in full page image viewer": "在整頁圖像查看器中預設放大顯示圖像",
-- 
cgit v1.2.1


From 84956d2377dbe81a3f97e47a3b695163ce0a6fcb Mon Sep 17 00:00:00 2001
From: benlisquare <116663807+benlisquare@users.noreply.github.com>
Date: Thu, 27 Oct 2022 10:42:44 +1100
Subject: Update localizations/zh_TW.json per dtlnor's suggestion

Co-authored-by: dtlnor <dtlnor@hotmail.com>
---
 localizations/zh_TW.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_TW.json b/localizations/zh_TW.json
index 43f95498..4eb25f44 100644
--- a/localizations/zh_TW.json
+++ b/localizations/zh_TW.json
@@ -280,7 +280,7 @@
     "Renew Page": "刷新頁面",
     "extras": "後處理",
     "favorites": "收藏夾",
-    "custom fold": "自定義檔案夾",
+    "custom fold": "自定義資料夾",
     "Load": "載入",
     "Images directory": "圖像目錄",
     "Prev batch": "上一批",
-- 
cgit v1.2.1


From 54e7bfd1aeb073346abf532ada4714d8a0f3b2d4 Mon Sep 17 00:00:00 2001
From: benlisquare <116663807+benlisquare@users.noreply.github.com>
Date: Thu, 27 Oct 2022 10:42:57 +1100
Subject: Update localizations/zh_TW.json per dtlnor's suggestion

Co-authored-by: dtlnor <dtlnor@hotmail.com>
---
 localizations/zh_TW.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_TW.json b/localizations/zh_TW.json
index 4eb25f44..0efc7436 100644
--- a/localizations/zh_TW.json
+++ b/localizations/zh_TW.json
@@ -144,7 +144,7 @@
     "Inpaint at full resolution": "以完整解析度進行內補繪製",
     "Inpaint at full resolution padding, pixels": "以完整解析度進行內補繪製 - 填補畫素",
     "Process images in a directory on the same machine where the server is running.": "在伺服器主機上的目錄中處理圖像",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "指定一個空的檔案夾為輸出目錄而非預設的 output 檔案夾為輸出目錄",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "指定一個空的資料夾為輸出目錄而非預設的 output 資料夾為輸出目錄",
     "Disabled when launched with --hide-ui-dir-config.": "啟動 --hide-ui-dir-config 時禁用",
     "Input directory": "輸入目錄",
     "Output directory": "輸出目錄",
-- 
cgit v1.2.1


From 2ce6b646a719efde2062e1a2c742f7e5bbd1f7b6 Mon Sep 17 00:00:00 2001
From: LunixWasTaken <Lunixwastaken@protonmail.com>
Date: Thu, 27 Oct 2022 03:03:14 +0200
Subject: Add german Localization file.

---
 localizations/de_DE.json | 419 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 419 insertions(+)
 create mode 100644 localizations/de_DE.json

diff --git a/localizations/de_DE.json b/localizations/de_DE.json
new file mode 100644
index 00000000..b52fa681
--- /dev/null
+++ b/localizations/de_DE.json
@@ -0,0 +1,419 @@
+ 
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "Lädt...",
+    "view": "zeigen",
+    "api": "api",
+    "•": "•",
+    "built with gradio": "Erstellt mit Gradio",
+    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "Extras",
+    "PNG Info": "PNG Info",
+    "Checkpoint Merger": "Checkpoint Merger",
+    "Train": "Trainieren",
+    "Settings": "Einstellungen",
+    "Prompt": "Prompt",
+    "Negative prompt": "Negativer Prompt",
+    "Run": "Ausführen",
+    "Skip": "Überspringen",
+    "Interrupt": "Abbrechen",
+    "Generate": "Generieren",
+    "Style 1": "Style 1",
+    "Style 2": "Style 2",
+    "Label": "Bezeichnung",
+    "File": "Datei",
+    "Drop File Here": "Datei hier ablegen",
+    "-": "-",
+    "o": "o",
+    "Click to Upload": "Klicken zum Hochladen",
+    "Image": "Bild",
+    "Check progress": "Fortschitt prüfen",
+    "Check progress (first)": "Fortschritt prüfen (Initial)",
+    "Sampling Steps": "Sampling Steps",
+    "Sampling method": "Sampling method",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Breite",
+    "Height": "Höhe",
+    "Restore faces": "Gesichter wiederherstellen",
+    "Tiling": "Kacheln",
+    "Highres. fix": "Highres. fix",
+    "Firstpass width": "Breite erstdurchlauf",
+    "Firstpass height": "Höhe erstdurchlauf",
+    "Denoising strength": "Denoising stärke",
+    "Batch count": "Batch anzahl",
+    "Batch size": "Batch größe",
+    "CFG Scale": "CFG Scale",
+    "Seed": "Seed",
+    "Extra": "Extra",
+    "Variation seed": "Variation seed",
+    "Variation strength": "Variation strength",
+    "Resize seed from width": "Seed von der Breite her ändern",
+    "Resize seed from height": " Seed von der Breite her ändern",
+    "Script": "Script",
+    "None": "Nichts",
+    "Prompt matrix": "Prompt matrix",
+    "Prompts from file or textbox": "Prompts von Datei oder Textbox",
+    "X/Y plot": "X/Y graph",
+    "Put variable parts at start of prompt": "Variable teile am start des Prompt setzen",
+    "Show Textbox": "Textbox Anzeigen",
+    "File with inputs": "Datei mit Inputwerten",
+    "Prompts": "Prompts",
+    "X type": "X typ",
+    "Nothing": "Nichts",
+    "Var. seed": "Var. seed",
+    "Var. strength": "Var. strength",
+    "Steps": "Steps",
+    "Prompt S/R": "Prompt S/R",
+    "Prompt order": "Prompt order",
+    "Sampler": "Sampler",
+    "Checkpoint name": "Checkpoint name",
+    "Hypernetwork": "Hypernetwork",
+    "Hypernet str.": "Hypernet str.",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "Sigma min",
+    "Sigma max": "Sigma max",
+    "Sigma noise": "Sigma noise",
+    "Eta": "Eta",
+    "Clip skip": "Clip skip",
+    "Denoising": "Denoising",
+    "X values": "X values",
+    "Y type": "Y type",
+    "Y values": "Y values",
+    "Draw legend": "Legende zeichnen",
+    "Include Separate Images": "Seperate Bilder hinzufügen",
+    "Keep -1 for seeds": "-1 als Seed behalten",
+    "Drop Image Here": "Bild hier ablegen",
+    "Save": "Speichern",
+    "Send to img2img": "Senden an img2img",
+    "Send to inpaint": "Senden an inpaint",
+    "Send to extras": "Senden an extras",
+    "Make Zip when Save?": "Zip beim speichern erstellen?",
+    "Textbox": "Textbox",
+    "Interrogate\nCLIP": "CLIP\nAbfragen",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "Batch img2img",
+    "Image for img2img": "Bild für img2img",
+    "Image for inpainting with mask": "Bild für inpainting mit maske",
+    "Mask": "Maske",
+    "Mask blur": "Masken Unschärfe",
+    "Mask mode": "Masken modus",
+    "Draw mask": "Maske zeichnen",
+    "Upload mask": "Maske Hochladen",
+    "Masking mode": "Modus zum Maskieren",
+    "Inpaint masked": "Inpaint maskiertes",
+    "Inpaint not masked": "Inpaint nicht maskiertes",
+    "Masked content": "Maskierter Inhalt",
+    "fill": "ausfüllen",
+    "original": "original",
+    "latent noise": "latent noise",
+    "latent nothing": "latent nothing",
+    "Inpaint at full resolution": "Inpaint mit voller Auflösung",
+    "Inpaint at full resolution padding, pixels": "Inpaint mit voller Auflösung Abstand, Pixels",
+    "Process images in a directory on the same machine where the server is running.": "Bilder in einem Verzeichnis auf demselben Rechner verarbeiten, auf dem der Server läuft.",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Ein leeres Ausgabeverzeichnis verwenden, um Bilder normal zu speichern, anstatt in das Ausgabeverzeichnis zu schreiben.",
+    "Input directory": "Input Verzeichnis",
+    "Output directory": "Output verzeichnis",
+    "Resize mode": "Größe anpassen - Modus",
+    "Just resize": "Nur größe anpassen",
+    "Crop and resize": "Zuschneiden und größe anpassen",
+    "Resize and fill": "Größe anpassen und ausfüllen",
+    "img2img alternative test": "img2img alternative test",
+    "Loopback": "Loopback",
+    "Outpainting mk2": "Outpainting mk2",
+    "Poor man's outpainting": "Poor man's outpainting",
+    "SD upscale": "SD upscale",
+    "should be 2 or lower.": "sollte 2 oder niedriger sein.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "`Sampling method` a Euler überschreiben? (diese methode is dafür ausgelegt)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "`prompt` zum gleichen wert wie `Originaler prompt` überschreiben? (und `Negativer prompt`)",
+    "Original prompt": "Originaler Prompt",
+    "Original negative prompt": "Originaler negativer prompt",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "`Sampling Steps` zum gleichen wert wie `Decode steps` überschreiben?",
+    "Decode steps": "Decode steps",
+    "Override `Denoising strength` to 1?": "`Denoising strength` zu 1 überschreiben?",
+    "Decode CFG scale": "Decode CFG scale",
+    "Randomness": "Zufälligkeit",
+    "Sigma adjustment for finding noise for image": "Sigma anpassungen um noise für Bilder zu finden.",
+    "Loops": "Loops",
+    "Denoising strength change factor": "Denoising strength change factor",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Empfohlene Einstellungen: Sampling Schritte: 80-100, Sampler Methode: Euler a, Denoising stärke: 0.8",
+    "Pixels to expand": "Anz. Pixel zum erweitern",
+    "Outpainting direction": "Outpainting Richtung",
+    "left": "Links",
+    "right": "Rechts",
+    "up": "Hoch",
+    "down": "Runter",
+    "Fall-off exponent (lower=higher detail)": "Fall-off exponent (weniger=mehr details)",
+    "Color variation": "Farb variationen",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Skaliert das Bild auf die doppelte Größe; Benutz die Schieberegler für Breite und Höhe, um die Kachelgröße einzustellen",
+    "Tile overlap": "Kacheln überlappungen",
+    "Upscaler": "Upscaler",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "SwinIR 4x": "SwinIR 4x",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "Single Image": "Ein Bild",
+    "Batch Process": "Massenverarbeitung",
+    "Batch from Directory": "Massenverarbeitung vom Verzeichnis",
+    "Source": "Quelle",
+    "Show result images": "Bildergebnisse zeigen",
+    "Scale by": "Skalieren von",
+    "Scale to": "Skalieren zu",
+    "Resize": "Größe anpassen",
+    "Crop to fit": "Zuschneiden damit es passt",
+    "Upscaler 2 visibility": "Upscaler 2 sichtbarkeit",
+    "GFPGAN visibility": "GFPGAN sichtbarkeit",
+    "CodeFormer visibility": "CodeFormer sichtbarkeit",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer gewicht (0 = maximaler effekt, 1 = minimalster effekt)",
+    "Open output directory": "Zielverzeichnis öffnen",
+    "Send to txt2img": "Senden an txt2img",
+    "A merger of the two checkpoints will be generated in your": "Die zusammgeführten Checkpoints werden gespeichert unter",
+    "checkpoint": "checkpoint",
+    "directory.": "Verzeichnis.",
+    "Primary model (A)": "Primäres Modell (A)",
+    "Secondary model (B)": "Sekundäres Modell (B)",
+    "Tertiary model (C)": "Tertiäres Modell (C)",
+    "Custom Name (Optional)": "Eigener Name (Optional)",
+    "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - auf 0 setzen um Modell A zu bekommen",
+    "Interpolation Method": "Interpolationsmethode",
+    "Weighted sum": "Weighted sum",
+    "Add difference": "Add difference",
+    "Save as float16": "Speichern als float16",
+    "See": "Siehe ",
+    "wiki": "wiki ",
+    "for detailed explanation.": "für eine detailierte erklärung.",
+    "Create embedding": "Embedding erstellen",
+    "Create hypernetwork": "Hypernetwork erstellen",
+    "Preprocess images": "Bilder vorbereiten",
+    "Name": "Name",
+    "Initialization text": "Initialisierungstext",
+    "Number of vectors per token": "Anzahl der vektoren pro token",
+    "Overwrite Old Embedding": "Alte Embeddings überschreiben",
+    "Modules": "Module",
+    "Enter hypernetwork layer structure": "Hypernetwork-Schichtstruktur angeben",
+    "Select activation function of hypernetwork": "Aktivierungsfunktion des Hypernetwork auswählen",
+    "linear": "linear",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "Add layer normalization": "Schicht normalisierung hinzufügen",
+    "Use dropout": "Dropout benutzen",
+    "Overwrite Old Hypernetwork": "Altes Hypernetwork überschreiben",
+    "Source directory": "Quellenverzeichnis",
+    "Destination directory": "Zielverzeichnis",
+    "Existing Caption txt Action": "Vorhandene Beschriftung der txt Aktion",
+    "ignore": "ignorieren",
+    "copy": "kopieren",
+    "prepend": "vorangestellt",
+    "append": "anhängen",
+    "Create flipped copies": "Gespiegelte Bilder erstellen",
+    "Split oversized images": "Übergroße Bilder aufteilen",
+    "Use BLIP for caption": "BLIP für Überschrift nutzen",
+    "Use deepbooru for caption": "Deepbooru für Überschrift nutzen",
+    "Split image threshold": "Bilder aufteilen Grenzwert",
+    "Split image overlap ratio": "Überschneidungsverhältnis beim Bilder aufteilen",
+    "Preprocess": "Vorbereiten",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Trainieren eines Embeddings oder ein Hypernetwork; Sie müssen ein Verzeichnis mit einem Satz von Bildern im Verhältnis 1:1 angeben",
+    "[wiki]": "[wiki]",
+    "Embedding": "Embedding",
+    "Embedding Learning rate": "Embedding Learning rate",
+    "Hypernetwork Learning rate": "Hypernetwork Learning rate",
+    "Dataset directory": "Dataset verzeichnis",
+    "Log directory": "Log verzeichnis",
+    "Prompt template file": "Vorlage Datei für Prompt",
+    "Max steps": "Max steps",
+    "Save an image to log directory every N steps, 0 to disable": "Alle N steps, ein Bild im Log Verzeichnis speichern, 0 zum deaktivieren",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Alle N steps, eine Kopie des Embedding im Log Verzeichnis speichern, 0 zum deaktivieren",
+    "Save images with embedding in PNG chunks": "Das Bild mit embedding in PNG Fragmente speichern",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Bei vorschau die Parameter (prompt, etc...) vom txt2img tab lesen",
+    "Train Hypernetwork": "Hypernetwork Trainieren",
+    "Train Embedding": "Embedding Trainieren",
+    "Apply settings": "Einstellungen anwenden",
+    "Saving images/grids": "Bilder/Raster speichern",
+    "Always save all generated images": "Grundsätzlich alle generieren Bilder speichern",
+    "File format for images": "Dateiformat für Bilder",
+    "Images filename pattern": "Dateinamen vorlage für Bilder",
+    "Add number to filename when saving": "Beim speichern, dem Dateinamen die Nummer anhängen",
+    "Always save all generated image grids": "Grundsätzlich alle generieren Raster speichern",
+    "File format for grids": "Dateiformat für Raster",
+    "Add extended info (seed, prompt) to filename when saving grid": "Speichern von Raster, erweiterte infos (seed, prompt) dem Dateinamen anhängen",
+    "Do not save grids consisting of one picture": "Raster, die nur aus einem Bild bestehen, nicht speichern",
+    "Prevent empty spots in grid (when set to autodetect)": "Lücken im Raster verhindern (falls aus autodetect gesetzt)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Raster reihen anzahl; -1 für Autodetect und 0 für die anzahl wie batch size",
+    "Save text information about generation parameters as chunks to png files": "Information zu Generierungsparameter als Fragmente in PNG dateien speichern",
+    "Create a text file next to every image with generation parameters.": "Textdatei mit Generierungsparameter seperat zu Bilddatei speichern",
+    "Save a copy of image before doing face restoration.": "Kope des Bildes vor und nach Gesichtswiederhestellung speichern",
+    "Quality for saved jpeg images": "Qualität der als JPEG gespeicherten Bilder",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Wenn PNG Bild größer als 4MB oder Dimensionen Größer als 4000, herunterskalieren und als JPG speichern.",
+    "Use original name for output filename during batch process in extras tab": "Originaler name als Output Dateinamen benutzen während Massenverarbeitung in Extras tab.",
+    "When using 'Save' button, only save a single selected image": "Beim benutzen von 'Speichern', nur das gewählte Bild speichern.",
+    "Do not add watermark to images": "Den Bildern kein Wasserzeichen hinzufügen",
+    "Paths for saving": "Pfade zum speichern",
+    "Output directory for images; if empty, defaults to three directories below": "Ausgabeverzeichnis für Bilder: wenn leer fällt zurück auf drei Verzeichnisse unterhalb",
+    "Output directory for txt2img images": "Ausgabeverzeichnis für txt2img",
+    "Output directory for img2img images": "Ausgabeverzeichnis für img2img",
+    "Output directory for images from extras tab": "Ausgabeverzeichnis für extras",
+    "Output directory for grids; if empty, defaults to two directories below": "Ausgabeverzeichnis für Raster; wenn leer fällt zurück auf zwei Verzeichnisse unterhalb.",
+    "Output directory for txt2img grids": "Ausgabeverzeichnis für  txt2img Raster",
+    "Output directory for img2img grids": "Ausgabeverzeichnis für img2img Raster",
+    "Directory for saving images using the Save button": "Ausgabeverzeichnis Bilder für 'Speichern' Button",
+    "Saving to a directory": "Speichern in ein Verzeichnis",
+    "Save images to a subdirectory": "Bilder in ein Unterverzeichnis speichern",
+    "Save grids to a subdirectory": "Raster in ein Unterverzeichnis speichern",
+    "When using \"Save\" button, save images to a subdirectory": "Beim benutzen von 'Save' Button, Bilder in Unterverzeichnis speichern",
+    "Directory name pattern": "Verzeichnisname pattern",
+    "Max prompt words for [prompt_words] pattern": "Maximale anzahl der Wörter für [prompt_words] pattern",
+    "Upscaling": "Hochskalieren",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Kachelgröße für ESRGAN upscalers. 0 = keine Kacheln.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Kacheln überlappungen, größe in pixel für ESRGAN upscalers. Niedrige Zahl = Sichtbare übergänge.",
+    "Tile size for all SwinIR.": "Kachelgröße für alle SwinIR.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Kacheln überlappungen, größe in pixel für SwinIR. Niedrige Zahl = Sichtbare übergänge.",
+    "LDSR processing steps. Lower = faster": "LDSR verarbeitungsschritte. niedriger = schneller",
+    "Upscaler for img2img": "Upscaler für img2img",
+    "Upscale latent space image when doing hires. fix": "Upscale latent space beim ausführen von hires. fix",
+    "Face restoration": "Gesichtswiederhestellung",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer gewicht parameter; 0 = maximaler effekt; 1 = minimalster effekt",
+    "Move face restoration model from VRAM into RAM after processing": "Gesichtswiederhestellungs-Modell nach Verarbeitung vom VRAM ins RAM verschieben",
+    "System": "System",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Abfragen pro Sekunde zur VRAM auslastung. 0 = deaktiviert",
+    "Always print all generation info to standard output": "Grundsätzlich alle Generierungsparameter im standard output ausgeben",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Zweite Fortschrittsleiste in der Konsole hinzufügen, die den Fortschitt für den ganzen Job anzeigt.",
+    "Training": "Training",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "VAE und CLIP während Hypernetwork-Training in RAM verschieben. Spart VRAM.",
+    "Filename word regex": "Filename word regex",
+    "Filename join string": "Filename join string",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Anzahl der wiederholungen für ein einziges Input-Bild pro epoche; nur dazu genutzt um epochen nummer anzuzeigen",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "CSV die den 'loss' alle N steps beinhaltet. 0 = deaktiviert",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "Anz. Checkpoints im RAM zu Cachen",
+    "Hypernetwork strength": "Hypernetwork stärke",
+    "Apply color correction to img2img results to match original colors.": "Farbkorrigierungen auf img2img anwenden um den Original zu gleichen.",
+    "Save a copy of image before applying color correction to img2img results": "Eine Kopie vom Bild vor der anwendung der img2img Farbkorrigierungen speichern.",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Mit img2img, führe die exakte anzahl an steps aus, die im Slider angegeben sind. (Im Normalfall würdest du mit Denoising weniger nehmen)",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "ermöglicht die Quantisierung in K-Samplern für schärfere und sauberere Ergebnisse. Dadurch können sich existierende Seeds verändern. Für die Anwendung ist ein Neustart erforderlich.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Hervorhebung: Ermöglicht (text) damit das Modell dem Text (mehr) oder [weniger] gewichtung gibt.",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Verwendet die alte Implementierung von Hervorhebungen. Kann für das benutzen von älteren Seeds nützlich sein.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Sorgt dafür, dass K-Diffusions-Sampler in einem Batch die gleichen Bilder erzeugen wie bei der Erstellung eines einzelnen Bildes.",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Erhöhung der Kohärenz durch Auffüllen ab dem letzten Komma innerhalb von N Token, wenn mehr als 75 Token verwendet werden",
+    "Filter NSFW content": "Filter NSFW inhalte",
+    "Stop At last layers of CLIP model": "Stop bei den letzten Schichten des CLIP-Modells",
+    "Interrogate Options": "Optionen abfragen",
+    "Interrogate: keep models in VRAM": "Abfragen: Modelle im VRAM behalten",
+    "Interrogate: use artists from artists.csv": "Abfragen: Künstler aus artists.csv verwenden",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Abfragen: Rangfolge der Modell-Tags in den Ergebnissen berücksichtigen (hat keine Auswirkung auf beschriftungsbasierte Abfragesysteme).",
+    "Interrogate: num_beams for BLIP": "Abfragen: num_beams für BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Abfrage: Mindestlänge der Beschreibung (ohne Künstler usw.)",
+    "Interrogate: maximum description length": "Abfragen: maximale Länge der Beschreibung",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximale Anzahl von Zeilen in der Textdatei (0 = keine Begrenzung)",
+    "Interrogate: deepbooru score threshold": "Abfrage: deepbooru schwelle",
+    "Interrogate: deepbooru sort alphabetically": "Abfrage: deepbooru alphabetisch sortieren",
+    "use spaces for tags in deepbooru": "Leerzeichen für Tags in deepbooru verwenden",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Escape-Klammern (\\) in deepbooru (damit sie als wörtliche Klammern und nicht zur Hervorhebung verwendet werden)",
+    "User interface": "Benutzerinterface",
+    "Show progressbar": "Fortschrittsleiste Anzeigen",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Zeigt den Fortschritt der Bilderstellung alle N Schritte an. 0 setzen, um zu deaktivieren.",
+    "Show previews of all images generated in a batch as a grid": "Vorschaubilder aller in einem Batch erzeugten Bilder als Raster anzeigen",
+    "Show grid in results for web": "Raster in den Ergebnissen für Web anzeigen",
+    "Do not show any images in results for web": "Keine Bilder in den Ergebnissen für das Web anzeigen",
+    "Add model hash to generation information": "Modell-Hash zu Generierungsinformationen hinzufügen",
+    "Add model name to generation information": "Modell-Name zu Generierungsinformationen hinzufügen",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Beim Einlesen von Generierungsparametern aus Text in die Benutzeroberfläche (aus PNG-Informationen oder eingefügtem Text) wird das ausgewählte Modell/der ausgewählte Checkpoint nicht geändert.",
+    "Font for image grids that have text": "Schriftart für Bildraster, die Text enthalten",
+    "Enable full page image viewer": "Ganzseitenbildbetrachter einschalten",
+    "Show images zoomed in by default in full page image viewer": "Bilder standardmäßig vergrößert im Ganzseitenbildbetrachter anzeigen",
+    "Show generation progress in window title.": "Generierungsfortschritt im Fenstertitel anzeigen.",
+    "Quicksettings list": "Quicksettings-Liste",
+    "Localization (requires restart)": "Lokalisierung (erfordert Neustart)",
+    "Sampler parameters": "Sampler parameter",
+    "Hide samplers in user interface (requires restart)": "Ausblenden von Samplern in der Benutzeroberfläche (erfordert einen Neustart)",
+    "eta (noise multiplier) for DDIM": "eta (noise multiplier) für DDIM",
+    "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) für ancestral samplers",
+    "img2img DDIM discretize": "img2img DDIM discretize",
+    "uniform": "uniform",
+    "quad": "quad",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma noise",
+    "Eta noise seed delta": "Eta noise seed delta",
+    "Request browser notifications": "Browser-Benachrichtigungen anfordern",
+    "Download localization template": "Vorlage für Lokalisierung herunterladen",
+    "Reload custom script bodies (No ui updates, No restart)": "Neu laden von benutzerdefinierten Skripten (keine Aktualisierung der Benutzeroberfläche, kein Neustart)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradio neu starten und Komponenten aktualisieren (nur Custom Scripts, ui.py, js und css)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (zum Erzeugen Strg+Eingabe oder Alt+Eingabe drücken)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (presiona Ctrl+Enter o Alt+Enter para generar)",
+    "Add a random artist to the prompt.": "Zufälligen Künstler den Prompt hinzufügen.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Import von Generierungsparameter aus dem Prompt oder aus der letzten Generierung, wenn das Prompt in der Benutzeroberfläche leer ist.",
+    "Save style": "Style speichern",
+    "Apply selected styles to current prompt": "Momentan ausgewählte styles auf den Prompt anwenden",
+    "Stop processing current image and continue processing.": "Verarbeitung des momentanen Bildes abbrechen und zum nächsten fortsetzen.",
+    "Stop processing images and return any results accumulated so far.": "Verarbeitung abbrechen und alle bisherigen Bilder ausgeben.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style, der angwendet werden soll. Styles haben sowohl negative als auch positive prompt anteile, die auf beide angewendet werden.",
+    "Do not do anything special": "Nichts besonderes machen",
+    "Which algorithm to use to produce the image": "Der zu benutzende algorithmus für die Bildgeneration",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - sehr kreativ, jeder kann unterschiedliche Bilder, abhängig von der Step/Schritt anzahl bekommen. Werte höher als 30-40 helfen nicht.",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - am besten für img2img inpainting",
+    "Produce an image that can be tiled.": "Bild erzeugen, dass gekachelt werden kann.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Verwendung eines zweistufigen Prozess, um ein Bild teilweise mit geringerer Auflösung zu erstellen, hochzuskalieren und dann die Details zu verbessern, ohne die zusammensetzung zu verändern.",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Bestimmt, wie wenig Bezug der Algorithmus zu dem Inhalt des Bildes haben soll. Bei 0 ändert sich nichts, und bei 1 erhalten Sie ein Bild ohne Bezug. Bei Werten unter 1,0 erfolgt die Verarbeitung in weniger Schritten, als der Schieberegler Sampling-Schritte angibt.",
+    "How many batches of images to create": "Wie viele Stapel von Bildern erstellt werden sollen",
+    "How many image to create in a single batch": "Wie viele Bilder in einem einzigen Stapel erstellt werden sollen",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - wie stark das Bild der Aufforderung entsprechen soll - niedrigere Werte führen zu kreativeren Ergebnissen",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Ein Wert, der die Ausgabe des Zufallszahlengenerators bestimmt: Wenn Sie ein Bild mit denselben Parametern und demselben Seed wie ein anderes Bild erstellen, erhalten Sie dasselbe Ergebnis.",
+    "Set seed to -1, which will cause a new random number to be used every time": "Seed auf -1 setzen, so dass jedes Mal eine neue Zufallszahl verwendet wird",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Wiederverwendung des Seed der letzten Generation, meist nützlich, wenn es zufällig gewählt wurde",
+    "Seed of a different picture to be mixed into the generation.": "Seed eines anderen Bildes, das bei der Erzeugung reingemischt wird.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Wie stark die Veränderung sein soll. Bei 0 gibt es keinen Effekt. Bei 1 erhalten Sie das vollständige Bild mit variations Seed (außer bei ancestral samplers,wie Euler A, wo Sie nur etwas erhalten).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Versuchen ein Bild zu erzeugen, das dem ähnelt, das mit demselben Seed bei der angegebenen Auflösung erzeugt worden wäre.",
+    "Separate values for X axis using commas.": "Trennen Sie die Werte für die X-Achse durch Kommas.",
+    "Separate values for Y axis using commas.": "Trennen Sie die Werte für die Y-Achse durch Kommas.",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Bild in ein Verzeichnis schreiben (Standard - log/images) und Generierungsparameter in eine csv-Datei.",
+    "Open images output directory": "Bildverzeichnis öffnen",
+    "How much to blur the mask before processing, in pixels.": "Wie stark die Maske vor der Verarbeitung verwischt werden soll, in Pixeln.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Was soll in den maskierten Bereich vor der Verarbeitung mit Stable Diffusion.",
+    "fill it with colors of the image": "Füllen mit den Farben des Bildes",
+    "keep whatever was there originally": "Originalen Inhalt behalten",
+    "fill it with latent space noise": "Füllen mit latent space noise",
+    "fill it with latent space zeroes": "Füllen mit latent space zeroes",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Hochskalieren des maskierten Bereichs auf die Zielauflösung, Inpainting, Zurückskalieren und Einfügen in das Originalbild.",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Die Größe des Bildes auf die gewünschte Auflösung ändern. Wenn Höhe und Breite nicht übereinstimmen, erhalten Sie ein falsches Seitenverhältnis.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Die Größe des Bildes so ändern, dass die gesamte Zielauflösung mit dem Bild ausgefüllt wird. Herausragende Teile zuschneiden.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Die Größe des Bildes so ändern, dass die gesamte Zielauflösung mit dem Bild ausgefüllt wird. Herausragende Teile mit Farben des Bildes ausfüllen.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Wie oft soll die Verarbeitung eines Bildes wiederholt werden, um es als Eingabe für die nächste Iteration zu verwenden",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "Im Loopback-Modus wird die Stärke der Rauschunterdrückung in jeder Schleife mit diesem Wert multipliziert. <1 bedeutet abnehmende Vielfalt, so dass Ihre Sequenz zu einem festen Bild konvergiert. >1 bedeutet zunehmende Vielfalt, so dass die Sequenz immer chaotischer wird..",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Wie viel Pixel sollten sich beim SD-Upscale zwischen den Kacheln überlappen? Die Kacheln überlappen sich so, dass beim Zusammenfügen zu einem Bild keine deutlich sichtbare Naht entsteht..",
+    "A directory on the same machine where the server is running.": "Ein Verzeichnis auf demselben Rechner, auf dem der Server läuft.",
+    "Leave blank to save images to the default path.": "Leer lassen, um Bilder im Standardpfad zu speichern.",
+    "Result = A * (1 - M) + B * M": "Ergebnis = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Ergebnis = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "Erste und letzte Ziffer muss 1 sein. Bspw:'1, 2, 1'",
+    "Path to directory with input images": "Pfad zum Verzeichnis mit den Eingabebildern",
+    "Path to directory where to write outputs": "Pfad zum Verzeichnis, in das die Ausgaben geschrieben werden sollen",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Verwenden Sie die folgenden Tags, um festzulegen, wie die Dateinamen für Bilder ausgewählt werden: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leer lassen, um Standardwerte zu verwenden.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Wenn diese Option aktiviert ist, wird den erstellten Bildern kein unsichtbares Wasserzeichen hinzugefügt. Achtung: Wenn Sie kein Wasserzeichen hinzufügen, verhalten Sie sich möglicherweise unethisch gegenüber sensiblen Personen.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Verwenden Sie die folgenden Tags, um festzulegen, wie Unterverzeichnisse für Bilder und Raster ausgewählt werden: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leer lassen, um Standardwerte zu verwenden.",
+    "Restore low quality faces using GFPGAN neural network": "Wiederherstellung von Gesichtern schlechter Qualität mit GFPGAN neural network",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Dieser reguläre Ausdruck wird verwendet, um Wörter aus dem Dateinamen zu extrahieren, die dann mit der unten stehenden Option zu einem Beschriftungstext für das Training zusammengefügt werden. Leer lassen, um den Text des Dateinamens so zu belassen, wie er ist.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Diese Zeichenfolge wird verwendet, um getrennte Wörter in einer einzigen Zeile zu verbinden, wenn die obige Option aktiviert ist.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Liste von Einstellungsnamen, getrennt durch Kommata, für Einstellungen, die in der Schnellzugriffsleiste oben erscheinen sollen, anstatt auf dem üblichen Einstellungs-Tab. Siehe modules/shared.py für Einstellungsnamen. Erfordert einen Neustart zur Anwendung.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Wenn dieser Wert ungleich Null ist, wird er zum Seed addiert und zur Initialisierung des RNG für Rauschen bei der Verwendung von Samplern mit Eta verwendet. Sie können dies verwenden, um noch mehr Variationen von Bildern zu erzeugen, oder Sie können dies verwenden, um Bilder von anderer Software abzugleichen, wenn Sie wissen, was Sie tun."
+}
-- 
cgit v1.2.1


From 4a4647e0dfc812783db7fa993d486b031f098ef8 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 27 Oct 2022 13:36:11 +0800
Subject: create send to buttons in one module

---
 javascript/ui.js                           |  24 +-
 modules/generation_parameters_copypaste.py |  86 +++++++-
 modules/shared.py                          |   1 +
 modules/ui.py                              | 344 +++++++++--------------------
 webui-user.bat                             |   2 +-
 5 files changed, 187 insertions(+), 270 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index cfd0dcd3..7e116465 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -45,14 +45,14 @@ function switch_to_txt2img(){
     return args_to_array(arguments);
 }
 
-function switch_to_img2img_img2img(){
+function switch_to_img2img(){
     gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
     gradioApp().getElementById('mode_img2img').querySelectorAll('button')[0].click();
 
     return args_to_array(arguments);
 }
 
-function switch_to_img2img_inpaint(){
+function switch_to_inpaint(){
     gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
     gradioApp().getElementById('mode_img2img').querySelectorAll('button')[1].click();
 
@@ -65,26 +65,6 @@ function switch_to_extras(){
     return args_to_array(arguments);
 }
 
-function extract_image_from_gallery_txt2img(gallery){
-    switch_to_txt2img()
-    return extract_image_from_gallery(gallery);
-}
-
-function extract_image_from_gallery_img2img(gallery){
-    switch_to_img2img_img2img()
-    return extract_image_from_gallery(gallery);
-}
-
-function extract_image_from_gallery_inpaint(gallery){
-    switch_to_img2img_inpaint()
-    return extract_image_from_gallery(gallery);
-}
-
-function extract_image_from_gallery_extras(gallery){
-    switch_to_extras()
-    return extract_image_from_gallery(gallery);
-}
-
 function get_tab_index(tabId){
     var res = 0
 
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index f73647da..2b80737a 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -3,13 +3,16 @@ import re
 import gradio as gr
 from modules.shared import script_path
 from modules import shared
+import tempfile
+from PIL import Image, PngImagePlugin
 
 re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
 re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
 type_of_gr_update = type(gr.update())
-
+paste_fields = {}
+bind_list = []
 
 def quote(text):
     if ',' not in str(text):
@@ -20,6 +23,81 @@ def quote(text):
     text = text.replace('"', '\\"')
     return f'"{text}"'
 
+def image_from_url_text(filedata):
+    if type(filedata) == dict and filedata["is_file"]:
+        filename = filedata["name"]
+        tempdir = os.path.normpath(tempfile.gettempdir())
+        normfn = os.path.normpath(filename)
+        assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
+
+        return Image.open(filename)
+
+    if type(filedata) == list:
+        if len(filedata) == 0:
+            return None
+
+        filedata = filedata[0]
+
+    if filedata.startswith("data:image/png;base64,"):
+        filedata = filedata[len("data:image/png;base64,"):]
+
+    filedata = base64.decodebytes(filedata.encode('utf-8'))
+    image = Image.open(io.BytesIO(filedata))
+    return image
+
+def add_paste_fields(tabname, init_img, fields):
+    paste_fields[tabname] = {"init_img":init_img, "fields": fields}
+
+def create_buttons(tabs_list):
+    buttons = {}
+    for tab in tabs_list:
+        buttons[tab] = gr.Button(f"Send to {tab}")
+    return buttons
+
+#if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab 
+def bind_buttons(buttons, send_image, send_generate_info):
+    bind_list.append([buttons, send_image, send_generate_info])
+
+def run_bind():
+    for buttons, send_image, send_generate_info in bind_list:
+        for tab in buttons:
+            button = buttons[tab]
+            if send_image and paste_fields[tab]["init_img"]:
+                if type(send_image) == gr.Gallery:
+                    button.click(
+                        fn=lambda x: image_from_url_text(x),
+                        _js="extract_image_from_gallery",
+                        inputs=[send_image],
+                        outputs=[paste_fields[tab]["init_img"]],
+                    )
+                else:
+                    button.click(
+                        fn=lambda x:x,
+                        inputs=[send_image],
+                        outputs=[paste_fields[tab]["init_img"]],
+                    )
+            
+            if send_generate_info and paste_fields[tab]["fields"] is not None:
+                paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Size-1', 'Size-2']
+                if shared.opts.send_seed:
+                    paste_field_names += ["Seed"]
+                if send_generate_info in paste_fields:                    
+                    button.click(
+                        fn=lambda *x:x,
+                        inputs=[field for field,name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
+                        outputs=[field for field,name in paste_fields[tab]["fields"] if name in paste_field_names],
+                    )
+
+                else:
+                    connect_paste(button, [(field, name) for field, name in paste_fields[tab]["fields"]  if name in paste_field_names], send_generate_info)
+
+            button.click(
+                fn=None,
+                _js=f"switch_to_{tab}",
+                inputs=None,
+                outputs=None,
+            )
+
 def parse_generation_parameters(x: str):
     """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
@@ -67,8 +145,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 
     return res
 
-
-def connect_paste(button, paste_fields, input_comp, js=None):
+def connect_paste(button, paste_fields, input_comp):
     def paste_func(prompt):
         if not prompt and not shared.cmd_opts.hide_ui_dir_config:
             filename = os.path.join(script_path, "params.txt")
@@ -106,7 +183,8 @@ def connect_paste(button, paste_fields, input_comp, js=None):
 
     button.click(
         fn=paste_func,
-        _js=js,
         inputs=[input_comp],
         outputs=[x[0] for x in paste_fields],
     )
+
+
diff --git a/modules/shared.py b/modules/shared.py
index f8b13b06..3ade2afa 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -279,6 +279,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
+    "send_seed": OptionInfo(False, "Send seed when sending prompt or image to other interface"),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
diff --git a/modules/ui.py b/modules/ui.py
index 3e5b84d2..ccba14b6 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -35,7 +35,7 @@ if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
 
 import modules.codeformer_model
-import modules.generation_parameters_copypaste
+import modules.generation_parameters_copypaste as parameters_copypaste
 import modules.gfpgan_model
 import modules.hypernetworks.ui
 import modules.ldsr_model
@@ -49,14 +49,11 @@ from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
 mimetypes.add_type('application/javascript', '.js')
-txt2img_paste_fields = []
-img2img_paste_fields = []
-init_img_components = {}
-
 
 if not cmd_opts.share and not cmd_opts.listen:
     # fix gradio phoning home
@@ -99,37 +96,11 @@ def plaintext_to_html(text):
     text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
     return text
 
-
-def image_from_url_text(filedata):
-    if type(filedata) == dict and filedata["is_file"]:
-        filename = filedata["name"]
-        tempdir = os.path.normpath(tempfile.gettempdir())
-        normfn = os.path.normpath(filename)
-        assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
-
-        return Image.open(filename)
-
-    if type(filedata) == list:
-        if len(filedata) == 0:
-            return None
-
-        filedata = filedata[0]
-
-    if filedata.startswith("data:image/png;base64,"):
-        filedata = filedata[len("data:image/png;base64,"):]
-
-    filedata = base64.decodebytes(filedata.encode('utf-8'))
-    image = Image.open(io.BytesIO(filedata))
-    return image
-
-
 def send_gradio_gallery_to_image(x):
     if len(x) == 0:
         return None
-
     return image_from_url_text(x[0])
 
-
 def save_files(js_data, images, do_make_zip, index):
     import csv
     filenames = []
@@ -193,7 +164,6 @@ def save_files(js_data, images, do_make_zip, index):
 
     return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
-
 def save_pil_to_file(pil_image, dir=None):
     use_metadata = False
     metadata = PngImagePlugin.PngInfo()
@@ -626,6 +596,83 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
     )
     return refresh_button
 
+def create_output_panel(tabname, outdir):    
+    def open_folder(f):
+        if not os.path.exists(f):
+            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
+            return
+        elif not os.path.isdir(f):
+            print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+            return
+
+        if not shared.cmd_opts.hide_ui_dir_config:
+            path = os.path.normpath(f)
+            if platform.system() == "Windows":
+                os.startfile(path)
+            elif platform.system() == "Darwin":
+                sp.Popen(["open", path])
+            else:
+                sp.Popen(["xdg-open", path])
+    
+    with gr.Column(variant='panel'):        
+            with gr.Group():                
+                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+               
+            generation_info = None
+            with gr.Column():
+                with gr.Row():
+                    if tabname != "extras":
+                        save = gr.Button('Save')
+
+                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
+                    open_folder = gr.Button(folder_symbol, elem_id=button_id)
+
+                open_folder.click(
+                    fn=lambda: open_folder(opts.outdir_samples or outdir),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                if tabname != "extras":           
+                    with gr.Row():
+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+                    with gr.Row():
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
+
+                        save.click(
+                            fn=wrap_gradio_call(save_files),
+                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
+                            inputs=[
+                                generation_info,
+                                result_gallery,
+                                do_make_zip,
+                                html_info,
+                            ],
+                            outputs=[
+                                download_files,
+                                html_info,
+                                html_info,
+                                html_info,
+                            ]
+                        )
+                else:
+                    html_info_x = gr.HTML()
+                    html_info = gr.HTML()
+                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
+                    
+            
 
 def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
@@ -676,31 +723,10 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
-            with gr.Column(variant='panel'):
-
-                with gr.Group():
-                    txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
-                    txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
-
-                with gr.Column():
-                    with gr.Row():
-                        save = gr.Button('Save')
-                        send_to_img2img = gr.Button('Send to img2img')
-                        send_to_inpaint = gr.Button('Send to inpaint')
-                        send_to_extras = gr.Button('Send to extras')
-                        button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                        open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
-
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
+     
 
+            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)  
+                
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
@@ -755,24 +781,7 @@ def create_ui(wrap_gradio_gpu_call):
                 fn=lambda x: gr_show(x),
                 inputs=[enable_hr],
                 outputs=[hr_options],
-            )
-
-            save.click(
-                fn=wrap_gradio_call(save_files),
-                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
-                inputs=[
-                    generation_info,
-                    txt2img_gallery,
-                    do_make_zip,
-                    html_info,
-                ],
-                outputs=[
-                    download_files,
-                    html_info,
-                    html_info,
-                    html_info,
-                ]
-            )
+            )            
 
             roll.click(
                 fn=roll_artist,
@@ -785,8 +794,7 @@ def create_ui(wrap_gradio_gpu_call):
                 ]
             )
 
-            global txt2img_paste_fields 
-            txt2img_paste_fields = [
+            parameters_copypaste.add_paste_fields("txt2img", None, [
                 (txt2img_prompt, "Prompt"),
                 (txt2img_negative_prompt, "Negative prompt"),
                 (steps, "Steps"),
@@ -807,7 +815,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
                 *modules.scripts.scripts_txt2img.infotext_fields
-            ]
+            ])
 
             txt2img_preview_params = [
                 txt2img_prompt,
@@ -894,30 +902,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
 
-            with gr.Column(variant='panel'):
-
-                with gr.Group():
-                    img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
-                    img2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='img2img_gallery').style(grid=4)
-
-                with gr.Column():
-                    with gr.Row():
-                        save = gr.Button('Save')
-                        img2img_send_to_img2img = gr.Button('Send to img2img')
-                        img2img_send_to_inpaint = gr.Button('Send to inpaint')
-                        img2img_send_to_extras = gr.Button('Send to extras')
-                        button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                        open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
-
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
+            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)                 
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -1004,24 +989,8 @@ def create_ui(wrap_gradio_gpu_call):
                     fn=interrogate_deepbooru,
                     inputs=[init_img],
                     outputs=[img2img_prompt],
-                )
-
-            save.click(
-                fn=wrap_gradio_call(save_files),
-                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
-                inputs=[
-                    generation_info,
-                    img2img_gallery,
-                    do_make_zip,
-                    html_info,
-                ],
-                outputs=[
-                    download_files,
-                    html_info,
-                    html_info,
-                    html_info,
-                ]
             )
+           
 
             roll.click(
                 fn=roll_artist,
@@ -1056,7 +1025,8 @@ def create_ui(wrap_gradio_gpu_call):
                     outputs=[prompt, negative_prompt, style1, style2],
                 )
 
-            global img2img_paste_fields
+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+
             img2img_paste_fields = [
                 (img2img_prompt, "Prompt"),
                 (img2img_negative_prompt, "Negative prompt"),
@@ -1075,7 +1045,9 @@ def create_ui(wrap_gradio_gpu_call):
                 (denoising_strength, "Denoising strength"),
                 *modules.scripts.scripts_img2img.infotext_fields
             ]
-            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields) 
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
+
 
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
@@ -1122,15 +1094,8 @@ def create_ui(wrap_gradio_gpu_call):
 
                 submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
 
-            with gr.Column(variant='panel'):
-                result_images = gr.Gallery(label="Result", show_label=False)
-                html_info_x = gr.HTML()
-                html_info = gr.HTML()
-                extras_send_to_img2img = gr.Button('Send to img2img')
-                extras_send_to_inpaint = gr.Button('Send to inpaint')
-                button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else ''
-                open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
 
+            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
 
         submit.click(
             fn=wrap_gradio_gpu_call(modules.extras.run_extras),
@@ -1160,23 +1125,8 @@ def create_ui(wrap_gradio_gpu_call):
                 html_info,
             ]
         )
+        parameters_copypaste.add_paste_fields("extras", extras_image, None) 
 
-        extras_send_to_img2img.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_img2img",
-            inputs=[result_images],
-            outputs=[init_img],
-        )
-
-        extras_send_to_inpaint.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_inpaint",
-            inputs=[result_images],
-            outputs=[init_img_with_mask],
-        )
-
-    global  init_img_components
-    init_img_components = {"img2img":init_img, "inpaint":init_img_with_mask, "extras":extras_image}
 
     with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
         with gr.Row().style(equal_height=False):
@@ -1187,11 +1137,10 @@ def create_ui(wrap_gradio_gpu_call):
                 html = gr.HTML()
                 generation_info = gr.Textbox(visible=False)
                 html2 = gr.HTML()
-
                 with gr.Row():
-                    pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                    pnginfo_send_to_img2img = gr.Button('Send to img2img')
-
+                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+                parameters_copypaste.bind_buttons(buttons, image, generation_info)                
+                
         image.change(
             fn=wrap_gradio_call(modules.extras.run_pnginfo),
             inputs=[image],
@@ -1475,28 +1424,6 @@ def create_ui(wrap_gradio_gpu_call):
     script_callbacks.ui_settings_callback()
     opts.reorder()
 
-    def open_folder(f):
-        if not os.path.exists(f):
-            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
-            return
-        elif not os.path.isdir(f):
-            print(f"""
-WARNING
-An open_folder request was made with an argument that is not a folder.
-This could be an error or a malicious attempt to run code on your computer.
-Requested path was: {f}
-""", file=sys.stderr)
-            return
-
-        if not shared.cmd_opts.hide_ui_dir_config:
-            path = os.path.normpath(f)
-            if platform.system() == "Windows":
-                os.startfile(path)
-            elif platform.system() == "Darwin":
-                sp.Popen(["open", path])
-            else:
-                sp.Popen(["xdg-open", path])
-
     def run_settings(*args):
         changed = 0
 
@@ -1641,6 +1568,8 @@ Requested path was: {f}
         if column is not None:
             column.__exit__()
 
+        parameters_copypaste.run_bind()
+
     interfaces = [
         (txt2img_interface, "txt2img", "txt2img"),
         (img2img_interface, "img2img", "img2img"),
@@ -1731,85 +1660,14 @@ Requested path was: {f}
                 component_dict['sd_model_checkpoint'],
             ]
         )
-        paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Seed', 'Size-1', 'Size-2']
-        txt2img_fields = [field for field,name in txt2img_paste_fields if name in paste_field_names]
-        img2img_fields = [field for field,name in img2img_paste_fields if name in paste_field_names]
-        send_to_img2img.click(
-            fn=lambda img, *args: (image_from_url_text(img),*args),
-            _js="(gallery, ...args) => [extract_image_from_gallery_img2img(gallery), ...args]",
-            inputs=[txt2img_gallery] + txt2img_fields,
-            outputs=[init_img] + img2img_fields,
-        )
-
-        send_to_inpaint.click(
-            fn=lambda x, *args: (image_from_url_text(x), *args),
-            _js="(gallery, ...args) => [extract_image_from_gallery_inpaint(gallery), ...args]",
-            inputs=[txt2img_gallery] + txt2img_fields,
-            outputs=[init_img_with_mask] + img2img_fields,
-        )
-
-        img2img_send_to_img2img.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_img2img",
-            inputs=[img2img_gallery],
-            outputs=[init_img],
-        )
-
-        img2img_send_to_inpaint.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_inpaint",
-            inputs=[img2img_gallery],
-            outputs=[init_img_with_mask],
-        )
-
-        send_to_extras.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_extras",
-            inputs=[txt2img_gallery],
-            outputs=[extras_image],
-        )
-
-        open_txt2img_folder.click(
-            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_txt2img_samples),
-            inputs=[],
-            outputs=[],
-        )
-
-        open_img2img_folder.click(
-            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_img2img_samples),
-            inputs=[],
-            outputs=[],
-        )
-
-        open_extras_folder.click(
-            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_extras_samples),
-            inputs=[],
-            outputs=[],
-        )
-
-        img2img_send_to_extras.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_extras",
-            inputs=[img2img_gallery],
-            outputs=[extras_image],
-        )
+ 
 
         settings_map = {
             'sd_hypernetwork': 'Hypernet',
             'CLIP_stop_at_last_layers': 'Clip skip',
             'sd_model_checkpoint': 'Model hash',
         }
-
-        settings_paste_fields = [
-            (component_dict[k], lambda d, k=k, v=v: apply_setting(k, d.get(v, None)))
-            for k, v in settings_map.items()
-        ]
-
-        modules.generation_parameters_copypaste.connect_paste(txt2img_paste, txt2img_paste_fields + settings_paste_fields, txt2img_prompt)
-        modules.generation_parameters_copypaste.connect_paste(img2img_paste, img2img_paste_fields + settings_paste_fields, img2img_prompt)
-
-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_txt2img')
-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_img2img_img2img')
+       
 
     ui_config_file = cmd_opts.ui_config_file
     ui_settings = {}
diff --git a/webui-user.bat b/webui-user.bat
index e5a257be..fcd0f1fb 100644
--- a/webui-user.bat
+++ b/webui-user.bat
@@ -3,6 +3,6 @@
 set PYTHON=
 set GIT=
 set VENV_DIR=
-set COMMANDLINE_ARGS=
+set COMMANDLINE_ARGS=--ui-debug-mode
 
 call webui.bat
-- 
cgit v1.2.1


From 85fcccc105aa50f1d78de559233eaa9f384608b5 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Wed, 26 Oct 2022 22:24:33 +0900
Subject: Squashed commit of fixing dropout silently

fix dropouts for future hypernetworks

add kwargs for Hypernetwork class

hypernet UI for gradio input

add recommended options

remove as options

revert adding options in ui
---
 modules/hypernetworks/hypernetwork.py | 25 +++++++++++++++++--------
 modules/ui.py                         |  4 ++--
 2 files changed, 19 insertions(+), 10 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 2997cead..dd921153 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -34,7 +34,8 @@ class HypernetworkModule(torch.nn.Module):
     }
     activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False, activate_output=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
+                 add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -60,7 +61,7 @@ class HypernetworkModule(torch.nn.Module):
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
             # Add dropout except last layer
-            if use_dropout and i < len(layer_structure) - 3:
+            if 'last_layer_dropout' in kwargs and kwargs['last_layer_dropout'] and use_dropout and i < len(layer_structure) - 2:
                 linears.append(torch.nn.Dropout(p=0.3))
 
         self.linear = torch.nn.Sequential(*linears)
@@ -126,7 +127,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -139,11 +140,14 @@ class Hypernetwork:
         self.add_layer_norm = add_layer_norm
         self.use_dropout = use_dropout
         self.activate_output = activate_output
+        self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+                                   self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+                                   self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
             )
 
     def weights(self):
@@ -172,7 +176,8 @@ class Hypernetwork:
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
         state_dict['activate_output'] = self.activate_output
-
+        state_dict['last_layer_dropout'] = self.last_layer_dropout
+        
         torch.save(state_dict, filename)
 
     def load(self, filename):
@@ -193,12 +198,16 @@ class Hypernetwork:
         self.use_dropout = state_dict.get('use_dropout', False)
         print(f"Dropout usage is set to {self.use_dropout}" )
         self.activate_output = state_dict.get('activate_output', True)
+        print(f"Activate last layer is set to {self.activate_output}")
+        self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
 
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
+                                       self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
+                                       self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
                 )
 
         self.name = state_dict.get('name', self.name)
diff --git a/modules/ui.py b/modules/ui.py
index 0a63e357..55cbe859 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1238,8 +1238,8 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Normal is default, for experiments, relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
                     overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
-- 
cgit v1.2.1


From cc56df996e95c2c82295ab7b9928da2544791220 Mon Sep 17 00:00:00 2001
From: guaneec <guaneec@users.noreply.github.com>
Date: Wed, 26 Oct 2022 23:51:51 +0800
Subject: Fix dropout logic

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index dd921153..b17598fe 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -35,7 +35,7 @@ class HypernetworkModule(torch.nn.Module):
     activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
     def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
-                 add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):
+                 add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=True):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -61,7 +61,7 @@ class HypernetworkModule(torch.nn.Module):
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
             # Add dropout except last layer
-            if 'last_layer_dropout' in kwargs and kwargs['last_layer_dropout'] and use_dropout and i < len(layer_structure) - 2:
+            if use_dropout and (i < len(layer_structure) - 3 or last_layer_dropout and i < len(layer_structure) - 2):
                 linears.append(torch.nn.Dropout(p=0.3))
 
         self.linear = torch.nn.Sequential(*linears)
-- 
cgit v1.2.1


From 300e5774e76e959e0f99deee071d4564e156917c Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 27 Oct 2022 13:42:20 +0800
Subject: create send to buttons in one module

---
 .gitignore | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.gitignore b/.gitignore
index 70660c51..26be36b3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -29,4 +29,6 @@ notification.mp3
 /textual_inversion
 .vscode
 /extensions
+/webui-user.bat
+/webui-user.sh
 
-- 
cgit v1.2.1


From ed0821de2114fe2fccbb8dabb72f4ed73e6289ca Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 27 Oct 2022 13:43:16 +0800
Subject: create send to buttons in one module

---
 webui-user.bat | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/webui-user.bat b/webui-user.bat
index fcd0f1fb..e5a257be 100644
--- a/webui-user.bat
+++ b/webui-user.bat
@@ -3,6 +3,6 @@
 set PYTHON=
 set GIT=
 set VENV_DIR=
-set COMMANDLINE_ARGS=--ui-debug-mode
+set COMMANDLINE_ARGS=
 
 call webui.bat
-- 
cgit v1.2.1


From 029d7c75436558f1e884bb127caed73caaecb83a Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Thu, 27 Oct 2022 14:44:53 +0900
Subject: Revert unresolved changes in Bias initialization

it should be zeros_ or parameterized in future properly.
---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b17598fe..25427a37 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -75,7 +75,7 @@ class HypernetworkModule(torch.nn.Module):
                     w, b = layer.weight.data, layer.bias.data
                     if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm:
                         normal_(w, mean=0.0, std=0.01)
-                        normal_(b, mean=0.0, std=0.005)
+                        normal_(b, mean=0.0, std=0)
                     elif weight_init == 'XavierUniform':
                         xavier_uniform_(w)
                         zeros_(b)
-- 
cgit v1.2.1


From 462e6ba6675bd14c0f82e465423a0eedfff82372 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Thu, 27 Oct 2022 15:40:24 +0900
Subject: Disable unavailable or duplicate options

---
 modules/hypernetworks/ui.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 2c6c0470..c2d4b51c 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -8,7 +8,8 @@ import modules.textual_inversion.textual_inversion
 from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
-keys = list(hypernetwork.HypernetworkModule.activation_dict.keys())
+not_available = ["hardswish", "multiheadattention"]
+keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
 
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
     # Remove illegal characters from name.
-- 
cgit v1.2.1


From e0cbf53f451f45ea73dafab654eb6596cbd67ec2 Mon Sep 17 00:00:00 2001
From: yfszzx <yfszzx@gmail.com>
Date: Thu, 27 Oct 2022 18:00:51 +0800
Subject: create send to buttons by extensions

---
 modules/ui.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index ccba14b6..922a2163 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1568,7 +1568,8 @@ def create_ui(wrap_gradio_gpu_call):
         if column is not None:
             column.__exit__()
 
-        parameters_copypaste.run_bind()
+
+        
 
     interfaces = [
         (txt2img_interface, "txt2img", "txt2img"),
@@ -1581,7 +1582,7 @@ def create_ui(wrap_gradio_gpu_call):
 
     interfaces += script_callbacks.ui_tabs_callback()
 
-    interfaces += [(settings_interface, "Settings", "settings")]
+    interfaces += [(settings_interface, "Settings", "settings")]       
 
     css = ""
 
@@ -1667,7 +1668,8 @@ def create_ui(wrap_gradio_gpu_call):
             'CLIP_stop_at_last_layers': 'Clip skip',
             'sd_model_checkpoint': 'Model hash',
         }
-       
+
+        parameters_copypaste.run_bind()       
 
     ui_config_file = cmd_opts.ui_config_file
     ui_settings = {}
-- 
cgit v1.2.1


From 035b875e1a3ce4a925aaba3dd55baea21e765e7f Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Thu, 27 Oct 2022 20:14:51 +0900
Subject: Edit CODEOWNERS for ko_KR.json permissions

---
 CODEOWNERS | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CODEOWNERS b/CODEOWNERS
index 935fedcf..fa7b93c7 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -1 +1,2 @@
 *       @AUTOMATIC1111
+localizations/ko_KR.json @36DB
\ No newline at end of file
-- 
cgit v1.2.1


From 9b50d9bbbfa61828c318373d5a7f0a6c1f3e273c Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Thu, 27 Oct 2022 20:16:51 +0900
Subject: Update CODEOWNERS file

---
 CODEOWNERS | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CODEOWNERS b/CODEOWNERS
index fa7b93c7..19e49bf5 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -1,2 +1,3 @@
 *       @AUTOMATIC1111
+[Localization]
 localizations/ko_KR.json @36DB
\ No newline at end of file
-- 
cgit v1.2.1


From 6a0eebfbee40c4c120c480ca229357cd3a9e69f3 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Thu, 27 Oct 2022 20:21:15 +0900
Subject: Update 2 cause I'm an idiot

---
 CODEOWNERS | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index 19e49bf5..d42e6412 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -1,3 +1,4 @@
 *       @AUTOMATIC1111
+
 [Localization]
-localizations/ko_KR.json @36DB
\ No newline at end of file
+/localizations/*   @36DB
\ No newline at end of file
-- 
cgit v1.2.1


From 34c953381192a2c0167fc1385f91b57b18a603c1 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Thu, 27 Oct 2022 20:23:33 +0900
Subject: Apparently brackets don't work, gitlab docs fooled me

---
 CODEOWNERS | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index d42e6412..12e87aae 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -1,4 +1,3 @@
 *       @AUTOMATIC1111
 
-[Localization]
-/localizations/*   @36DB
\ No newline at end of file
+/localizations/ko_KR.json   @36DB
\ No newline at end of file
-- 
cgit v1.2.1


From e64ccd18e111fcc284efe681a88c3ebafdd4c563 Mon Sep 17 00:00:00 2001
From: xmodar <modar.alfadly@gmail.com>
Date: Thu, 27 Oct 2022 14:50:55 +0300
Subject: Add minor edits to Arabic localization

---
 localizations/ar_AR.json | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/localizations/ar_AR.json b/localizations/ar_AR.json
index 7864ac03..271ebb87 100644
--- a/localizations/ar_AR.json
+++ b/localizations/ar_AR.json
@@ -252,7 +252,7 @@
   "Focal point face weight": "تمركز الوجوه",
   "Focal point entropy weight": "تمركز التنوع",
   "Focal point edges weight": "تمركز الحواف",
-  "Create debug image": "أنتج صورة كمثال",
+  "Create debug image": "احفظ نتائج التحليل أيضا",
   "Use BLIP for caption": "استخدم BLIP لتوصيف الصور",
   "Use deepbooru for caption": "استخدم deepbooru لتوصيف الصور",
   "Preprocess": "معالجة مسبقة",
@@ -267,8 +267,8 @@
   "Path to directory where to write outputs": "مسار مجلد الصور المخرجة",
   "Prompt template file": "ملف صيغ الطلبات",
   "Max steps": "أقصى عدد لخطوات التدريب",
-  "Save an image to log directory every N steps, 0 to disable": "احفظ صورة في السجل بعد كل كم خطوة تدريب (لا تحفظ إذا كان 0)",
-  "Save a copy of embedding to log directory every N steps, 0 to disable": "احفظ التضمين في السجل بعد كل كم خطوة تدريب (لا تحفظ إذا كان 0)",
+  "Save an image to log directory every N steps, 0 to disable": "احفظ صورة في السجل بعد كل كم خطوة تدريب (إذا 0 لا تحفظ)",
+  "Save a copy of embedding to log directory every N steps, 0 to disable": "احفظ التضمين في السجل بعد كل كم خطوة تدريب (إذا 0 لا تحفظ)",
   "Save images with embedding in PNG chunks": "احفظ التضمين بداخل ملف الصورة كعامل يمكن استخراجه من عوامل الصورة (صيغة PNG)",
   "Read parameters (prompt, etc...) from txt2img tab when making previews": "استخدم قيم العوامل الموجودة في تبويب نص إلى صورة لعرض نتائجهم أثناء التدريب",
   "Train Hypernetwork": "درّب الشبكة الفائقة",
@@ -357,7 +357,7 @@
   "Filename join string": "النص الفاصل للكلمات المدموجة",
   "This string will be used to join split words into a single line if the option above is enabled.": "سيستخدم بين كل كلمة يتم استخراجها من الخيار السابق",
   "Number of repeats for a single input image per epoch; used only for displaying epoch number": "عدد مرات تكرار الصور في كل دورة (Epoch)، يستخدم للعرض فقط",
-  "Save an csv containing the loss to log directory every N steps, 0 to disable": "احفظ خسارة التدريب (Loss) في السجل بعد كل كم خطوة (لا تحفظ إذا كان 0)",
+  "Save an csv containing the loss to log directory every N steps, 0 to disable": "احفظ خسارة التدريب (Loss) في السجل بعد كل كم خطوة (إذا 0 لا تحفظ)",
   "Stable Diffusion": "نموذج الإنتشار المستقر (Stable Diffusion)",
   "Checkpoints to cache in RAM": "عدد النماذج التي تترك في الذاكرة العشوائية (RAM)",
   "Hypernetwork strength": "قوة الشبكة الفائقة",
@@ -386,7 +386,7 @@
   "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "تجاهل الأقواس عند استخدام deepbooru (كي لا تعامل كأقواس التشديد)",
   "User interface": "واجهة المستخدم",
   "Show progressbar": "اظهر شريط التقدم",
-  "Show image creation progress every N sampling steps. Set 0 to disable.": "اعرض صورة بعد كل كم خطوة (لا تعرض إذا كان 0)",
+  "Show image creation progress every N sampling steps. Set 0 to disable.": "اعرض صورة بعد كل كم خطوة (إذا 0 لا تعرض)",
   "Show previews of all images generated in a batch as a grid": "اعرض كل الصور التي تم إنتاجها في حزمة كجدول",
   "Show grid in results for web": "أظهر نتائج جداول الصور",
   "Do not show any images in results for web": "لا تظهر نتائج الصور",
@@ -420,5 +420,5 @@
   "Request browser notifications": "اطلب تنبيهات المتصفح",
   "Download localization template": "حمل ملف اللغة",
   "Reload custom script bodies (No ui updates, No restart)": "أعد تحميل الأدوات الخاصة (بدون واجهة المستخدم ولا يحتاج إعادة تشغيل)",
-  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعد تشغيل gradio والأدوات الخاصة وواجهة المستخدم فقط"
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعد تشغيل gradio وتحميل الأدوات الخاصة وواجهة المستخدم"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From c4b5ca5778340b21288d84dfb8fe1d5773c886a8 Mon Sep 17 00:00:00 2001
From: Yuta Hayashibe <yuta@hayashibe.jp>
Date: Thu, 27 Oct 2022 22:00:28 +0900
Subject: Truncate too long filename

---
 modules/images.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index 7870b5b7..42363ed3 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -416,6 +416,14 @@ def get_next_sequence_number(path, basename):
     return result + 1
 
 
+def truncate_fullpath(full_path, encoding='utf-8'):
+    dir_name, full_name = os.path.split(full_path)
+    file_name, file_ext = os.path.splitext(full_name)
+    max_length = os.statvfs(dir_name).f_namemax
+    file_name_truncated = file_name.encode(encoding)[:max_length - len(file_ext)].decode(encoding, 'ignore')
+    return os.path.join(dir_name , file_name_truncated + file_ext)
+
+
 def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
     """Save an image.
 
@@ -456,7 +464,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
 
     if save_to_dirs:
         dirname = namegen.apply(opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /')
-        path = os.path.join(path, dirname)
+        path = truncate_fullpath(os.path.join(path, dirname))
 
     os.makedirs(path, exist_ok=True)
 
@@ -480,13 +488,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             fullfn = None
             for i in range(500):
                 fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
-                fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
+                fullfn = truncate_fullpath(os.path.join(path, f"{fn}{file_decoration}.{extension}"))
                 if not os.path.exists(fullfn):
                     break
         else:
-            fullfn = os.path.join(path, f"{file_decoration}.{extension}")
+            fullfn = truncate_fullpath(os.path.join(path, f"{file_decoration}.{extension}"))
     else:
-        fullfn = os.path.join(path, f"{forced_filename}.{extension}")
+        fullfn = truncate_fullpath(os.path.join(path, f"{forced_filename}.{extension}"))
 
     pnginfo = existing_info or {}
     if info is not None:
-- 
cgit v1.2.1


From 6e10078b2be8e690b5f85619b335e1dcd4fa8a3f Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Thu, 27 Oct 2022 22:21:56 +0900
Subject: Attention editing with hotkeys should work with KR now

Added the word "Prompt" in the placeholders to pass the check from edit-attention.js
---
 localizations/webui.bat | 62 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)
 create mode 100644 localizations/webui.bat

diff --git a/localizations/webui.bat b/localizations/webui.bat
new file mode 100644
index 00000000..a38a28bb
--- /dev/null
+++ b/localizations/webui.bat
@@ -0,0 +1,62 @@
+@echo off
+
+if not defined PYTHON (set PYTHON=python)
+if not defined VENV_DIR (set VENV_DIR=venv)
+
+set ERROR_REPORTING=FALSE
+
+mkdir tmp 2>NUL
+
+%PYTHON% -c "" >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :start_venv
+echo Couldn't launch python
+goto :show_stdout_stderr
+
+:start_venv
+if [%VENV_DIR%] == [-] goto :skip_venv
+
+dir %VENV_DIR%\Scripts\Python.exe >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :activate_venv
+
+for /f "delims=" %%i in ('CALL %PYTHON% -c "import sys; print(sys.executable)"') do set PYTHON_FULLNAME="%%i"
+echo Creating venv in directory %VENV_DIR% using python %PYTHON_FULLNAME%
+%PYTHON_FULLNAME% -m venv %VENV_DIR% >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :activate_venv
+echo Unable to create venv in directory %VENV_DIR%
+goto :show_stdout_stderr
+
+:activate_venv
+set PYTHON="%~dp0%VENV_DIR%\Scripts\Python.exe"
+echo venv %PYTHON%
+goto :launch
+
+:skip_venv
+
+:launch
+%PYTHON% launch.py %*
+pause
+exit /b
+
+:show_stdout_stderr
+
+echo.
+echo exit code: %errorlevel%
+
+for /f %%i in ("tmp\stdout.txt") do set size=%%~zi
+if %size% equ 0 goto :show_stderr
+echo.
+echo stdout:
+type tmp\stdout.txt
+
+:show_stderr
+for /f %%i in ("tmp\stderr.txt") do set size=%%~zi
+if %size% equ 0 goto :show_stderr
+echo.
+echo stderr:
+type tmp\stderr.txt
+
+:endofscript
+
+echo.
+echo Launch unsuccessful. Exiting.
+pause
-- 
cgit v1.2.1


From 9358a421cfa2ccd866825d8022f93a12268e7dc3 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Thu, 27 Oct 2022 22:24:05 +0900
Subject: Remove files that shouldn't be here

---
 localizations/webui.bat | 62 -------------------------------------------------
 1 file changed, 62 deletions(-)
 delete mode 100644 localizations/webui.bat

diff --git a/localizations/webui.bat b/localizations/webui.bat
deleted file mode 100644
index a38a28bb..00000000
--- a/localizations/webui.bat
+++ /dev/null
@@ -1,62 +0,0 @@
-@echo off
-
-if not defined PYTHON (set PYTHON=python)
-if not defined VENV_DIR (set VENV_DIR=venv)
-
-set ERROR_REPORTING=FALSE
-
-mkdir tmp 2>NUL
-
-%PYTHON% -c "" >tmp/stdout.txt 2>tmp/stderr.txt
-if %ERRORLEVEL% == 0 goto :start_venv
-echo Couldn't launch python
-goto :show_stdout_stderr
-
-:start_venv
-if [%VENV_DIR%] == [-] goto :skip_venv
-
-dir %VENV_DIR%\Scripts\Python.exe >tmp/stdout.txt 2>tmp/stderr.txt
-if %ERRORLEVEL% == 0 goto :activate_venv
-
-for /f "delims=" %%i in ('CALL %PYTHON% -c "import sys; print(sys.executable)"') do set PYTHON_FULLNAME="%%i"
-echo Creating venv in directory %VENV_DIR% using python %PYTHON_FULLNAME%
-%PYTHON_FULLNAME% -m venv %VENV_DIR% >tmp/stdout.txt 2>tmp/stderr.txt
-if %ERRORLEVEL% == 0 goto :activate_venv
-echo Unable to create venv in directory %VENV_DIR%
-goto :show_stdout_stderr
-
-:activate_venv
-set PYTHON="%~dp0%VENV_DIR%\Scripts\Python.exe"
-echo venv %PYTHON%
-goto :launch
-
-:skip_venv
-
-:launch
-%PYTHON% launch.py %*
-pause
-exit /b
-
-:show_stdout_stderr
-
-echo.
-echo exit code: %errorlevel%
-
-for /f %%i in ("tmp\stdout.txt") do set size=%%~zi
-if %size% equ 0 goto :show_stderr
-echo.
-echo stdout:
-type tmp\stdout.txt
-
-:show_stderr
-for /f %%i in ("tmp\stderr.txt") do set size=%%~zi
-if %size% equ 0 goto :show_stderr
-echo.
-echo stderr:
-type tmp\stderr.txt
-
-:endofscript
-
-echo.
-echo Launch unsuccessful. Exiting.
-pause
-- 
cgit v1.2.1


From a668444110743cd163474ec563b0e69025dea3d2 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Thu, 27 Oct 2022 22:24:29 +0900
Subject: Attention editing hotkey fix part 2

---
 localizations/ko_KR.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 803b7fb9..3d631066 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -263,7 +263,7 @@
   "Multiplier (M) - set to 0 to get model A": "배율 (M) - 0으로 적용하면 모델 A를 얻게 됩니다",
   "Name": "이름",
   "Negative prompt": "네거티브 프롬프트",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트(Prompt) 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
   "Next batch": "다음 묶음",
   "Next Page": "다음 페이지",
   "None": "없음",
@@ -321,7 +321,7 @@
   "Process images in a directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리의 이미지들을 처리합니다.",
   "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
   "Prompt": "프롬프트",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
+  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트(Prompt) 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
   "Prompt matrix": "프롬프트 매트릭스",
   "Prompt order": "프롬프트 순서",
   "Prompt S/R": "프롬프트 스타일 변경",
-- 
cgit v1.2.1


From 3d38416352cb715406d6a5b76462bbd6de9f7e0d Mon Sep 17 00:00:00 2001
From: Martucci <73501718+M-art-ucci@users.noreply.github.com>
Date: Thu, 27 Oct 2022 10:25:54 -0300
Subject: =?UTF-8?q?Mais=20ajustes=20de=20tradu=C3=A7=C3=A3o?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/pt_BR.json | 193 +++++++++++++++++++++++------------------------
 1 file changed, 96 insertions(+), 97 deletions(-)

diff --git a/localizations/pt_BR.json b/localizations/pt_BR.json
index a2a9be72..487432ae 100644
--- a/localizations/pt_BR.json
+++ b/localizations/pt_BR.json
@@ -8,30 +8,29 @@
     "view": "Visualizar",
     "api": "api",
     "•": "•",
-    "built with gradio": "montado com gradio",
+    "built with gradio": "Criado com gradio",
     "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
     "txt2img": "txt2img",
     "img2img": "img2img",
     "Extras": "Extras",
-    "PNG Info": "PNG Info",
-    "History": "Histórico",
-    "Checkpoint Merger": "Checkpoint Merger",
+    "PNG Info": "Informações dePNG",
+    "Checkpoint Merger": "Junção de Checkpoint",
     "Train": "Treinar",
     "Settings": "configurações",
     "Prompt": "Prompt",
     "Negative prompt": "Prompt negativo",
-    "Run": "Rodar",
+    "Run": "Executar",
     "Skip": "Pular",
     "Interrupt": "Interromper",
     "Generate": "Gerar",
     "Style 1": "Estilo 1",
     "Style 2": "Estilo 2",
-    "Label": "Etiqueta",
+    "Label": "Rótulo",
     "File": "Arquivo",
-    "Drop File Here": "Arraste aqui o arquivo",
+    "Drop File Here": "Coloque aqui o arquivo",
     "-": "-",
     "or": "ou",
-    "Click to Upload": "Clique para subir",
+    "Click to Upload": "Clique para carregar um arquivo",
     "Image": "Imagem",
     "Check progress": "Checar progresso",
     "Check progress (first)": "Checar progresso (primeiro)",
@@ -55,51 +54,52 @@
     "Restore faces": "Refinar rosto",
     "Tiling": "Estilo azulejo",
     "Highres. fix": "Ajuste de alta resolução",
-    "Firstpass width": "Largura Firstpass",
-    "Firstpass height": "Altura Firstpass",
-    "Denoising strength": "Força do Denoise",
+    "Firstpass width": "Passagem inicial de Largura",
+    "Firstpass height": "Passagem inicial de Altura",
+    "Denoising strength": "Quantidade de Denoise",
     "Batch count": "Quantidade por leva",
     "Batch size": "Quantidade de levas",
     "CFG Scale": "Escala CFG",
     "Seed": "Seed",
     "Extra": "Extra",
     "Variation seed": "Variação de seed",
-    "Variation strength": "Forca da variação",
-    "Resize seed from width": "Ajustar seed a partir da largura",
-    "Resize seed from height": "Ajustar seed a partir da altura",
+    "Variation strength": "Força da variação",
+    "Resize seed from width": "Ajustar o tamanho da seed a partir da largura",
+    "Resize seed from height": "Ajustar o tamanho daseed a partir da altura",
     "Script": "Script",
     "None": "Nenhum",
     "Prompt matrix": "Matriz de Prompt",
-    "Prompts from file or textbox": "Prompts de arquivo ou caixa de texto",
+    "Prompts from file or textbox": "Prompts a partir de arquivo ou caixa de texto",
     "X/Y plot": "X/Y plot",
     "Put variable parts at start of prompt": "Colocar partes variáveis no começo do prompt",
 	"Iterate seed every line": "Iterar seed em todas as linhas",
     "List of prompt inputs": "Lista de textos para prompt",
-    "Upload prompt inputs": "Subir textos para prompts",
-    "X type": "X type",
-    "Nothing": "Nada",
+    "Upload prompt inputs": "Carregar textos para prompts",
+    "X type": "Tipo do X",
+    "Nothing": "Nenhum",
     "Var. seed": "Var. seed",
     "Var. strength": "Var. da força",
     "Steps": "Steps",
     "Prompt S/R": "Prompt S/R",
     "Prompt order": "Ordem de Prompt",
     "Sampler": "Sampler",
-    "Checkpoint name": "Nome do intervalo",
+    "Checkpoint name": "Nome do Checkpoint",
     "Hypernetwork": "Hypernetwork",
-    "Hypernet str.": "Hypernet str.",
+    "Hypernet str.": "Força da Hypernet",
     "Sigma Churn": "Sigma Churn",
     "Sigma min": "Sigma min",
     "Sigma max": "Sigma max",
     "Sigma noise": "Sigma noise",
-    "Eta": "Eta",
-    "Clip skip": "Clip skip",
+    "Eta": "Tempo estimado",
+    "Clip skip": "Pular Clip",
     "Denoising": "Denoising",
-    "X values": "X values",
-    "Y type": "Y type",
-    "Y values": "Y values",
-    "Draw legend": "Draw legend",
+    "X values": "Valores de X",
+    "Y type": "Tipos de Y",
+    "Y values": "Valores de Y",
+    "Draw legend": "Desenhar a legenda",
     "Include Separate Images": "Incluir Imagens Separadas",
     "Keep -1 for seeds": "Manter em -1 para seeds",
+    "Drop Image Here": "Arraste a imagem aqui",
     "Save": "Salvar",
     "Send to img2img": "Mandar para img2img",
     "Send to inpaint": "Mandar para inpaint",
@@ -113,46 +113,46 @@
     "Drop Image Here": "Arrastar imagem para cá",
     "Image for inpainting with mask": "Imagem para inpainting com máscara",
     "Mask": "Mascara",
-    "Mask blur": "Borda da máscara",
+    "Mask blur": "Desfoque da máscara",
     "Mask mode": "Modo de máscara",
     "Draw mask": "Desenhar máscara",
-    "Upload mask": "Subir máscara",
+    "Upload mask": "Carregar máscara",
     "Masking mode": "Tipo de máscara",
-    "Inpaint masked": "Inpaint o que esta dentro da máscara",
-    "Inpaint not masked": "Inpaint o que esta fora da máscara",
+    "Inpaint masked": "Inpaint o que está dentro da máscara",
+    "Inpaint not masked": "Inpaint o que está fora da máscara",
     "Masked content": "Conteúdo da máscara",
     "fill": "preencher",
     "original": "original",
     "latent noise": "latent noise",
     "latent nothing": "latent nothing",
-    "Inpaint at full resolution": "Inpaint em resolução máxima",
-    "Inpaint at full resolution padding, pixels": "Inpaint em resolução máxima de padding, pixels",
+    "Inpaint at full resolution": "Inpaint em resolução total",
+    "Inpaint at full resolution padding, pixels": "Inpaint de preenchimento em resolução total, pixels",
     "Process images in a directory on the same machine where the server is running.": "Processar imagens no diretório da mesma maquina onde o servidor está rodando.",
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usar um diretório vazio para salvar imagens, ao invés de salvá-las no diretório output.",
     "Input directory": "Diretório de Input",
     "Output directory": "Diretório de Output",
-    "Resize mode": "Modo de aumento",
-    "Just resize": "Apenas aumentar",
-    "Crop and resize": "Cortar e aumentar",
-    "Resize and fill": "Aumentar e preencher",
+    "Resize mode": "Modo de ajuste de tamanho",
+    "Just resize": "Apenas ajustar",
+    "Crop and resize": "Cortar e ajustar",
+    "Resize and fill": "Ajustar e preencher",
     "img2img alternative test": "Teste alternativo de img2img",
     "Loopback": "Loopback",
     "Outpainting mk2": "Outpainting mk2",
     "Poor man's outpainting": "Poor man's outpainting",
-    "SD upscale": "Aumento SD",
-    "should be 2 or lower.": "precisa ser 2 ou menor.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Sobrepor 'sampling method' para Euler? (esse método foi feito para isso)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Sobrepor 'prompt' para o mesmo valor que o 'prompt original'? (também para o 'prompt negativo')",
-    "Original prompt": "Prompt Original",
-    "Original negative prompt": "Prompt Negativo Original",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Sobrepor 'Sampling Steps' para o mesmo valor que 'Decode steps'?",
+    "SD upscale": "Ajuste SD",
+    "should be 2 or lower.": "precisa ser 2 ou menos.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Substituir 'sampling method' para Euler? (esse método foi feito para isso)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Substituir 'prompt' para o mesmo valor que o 'prompt original'? (também para o 'prompt negativo')",
+    "Original prompt": "Prompt original",
+    "Original negative prompt": "Prompt negativo original",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Substituir 'Sampling Steps' para o mesmo valor que 'Decode steps'?",
     "Decode steps": "Decode steps",
-    "Override `Denoising strength` to 1?": "Sobrepor 'Forca do Denoise' para 1?",
+    "Override `Denoising strength` to 1?": "Substituir 'Quantidade do Denoise' para 1?",
     "Decode CFG scale": "Escala CFG de Decode",
     "Randomness": "Aleatoriedade",
     "Sigma adjustment for finding noise for image": "Ajuste Sigma para encontrar ruído para imagem",
     "Loops": "Loops",
-    "Denoising strength change factor": "Fator de mudança na força do Denoise",
+    "Denoising strength change factor": "Fator de mudança na quantidade do Denoise",
     "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Configurações recomendadas",
     "Pixels to expand": "Pixels para expandir",
     "Outpainting direction": "Direção do Outpainting",
@@ -160,17 +160,23 @@
     "right": "direita",
     "up": "cima",
     "down": "baixo",
-    "Fall-off exponent (lower=higher detail)": "Exponente Fall-off exponent (menor=mais detalhes)",
+    "Fall-off exponent (lower=higher detail)": "Exponente de queda (menor=mais detalhes)",
     "Color variation": "Variação de cor",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenta a imagem para duas vezes as dimensoes; ajuste a largura e altura para definir o tamanho do azulejo",
-    "Tile overlap": "sobreposicao de azulejo",
-    "Upscaler": "Ferramenta de aumento",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenta a imagem para duas vezes as dimensoes; ajust a largura e altura para definir o tamanho do azulejo",
+    "Tile overlap": "substituição de azulejo",
+    "Upscaler": "Ferramenta de ajuste de aumento",
     "Lanczos": "Lanczos",
     "LDSR": "LDSR",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "ScuNET GAN": "ScuNET GAN",
+    "4x_foolhardy_Remacri": "4x_foolhardy_Remacri",
+    "Put ESRGAN models here": "Coloque modelos ESRGAN aqui",
+    "R-ESRGAN General 4xV3": "R-ESRGAN General 4xV3",
+    "R-ESRGAN AnimeVideo": "R-ESRGAN AnimeVideo",
+    "R-ESRGAN 4x+": "R-ESRGAN 4x+",
+    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+    "R-ESRGAN 2x+": "R-ESRGAN 2x+",
+    "ScuNET": "ScuNET",
     "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR 4x": "SwinIR 4x",
+    "put_swinir_models_here": "put_swinir_models_here",
     "Single Image": "Imagem Única",
     "Batch Process": "Processo em Levas",
     "Batch from Directory": "Levas a partir de diretório",
@@ -186,19 +192,19 @@
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso do CodeFormer (0 = efeito máximo, 1 = efeito mínimo)",
     "Open output directory": "Abrir diretorio de output",
     "Send to txt2img": "Mandar para txt2img",
-    "A merger of the two checkpoints will be generated in your": "Uma combinação de dois intervalos vai ser gerado em",
-    "checkpoint": "intervalo",
+    "A merger of the two checkpoints will be generated in your": "Uma combinação de dois checkpoints vai ser gerada em",
+    "checkpoint": "checkpoint",
     "directory.": "diretório.",
-    "Primary model (A)": "Modelo Primário (A)",
-    "Secondary model (B)": "Modelo Secundário (B)",
-    "Tertiary model (C)": "Modelo Terciário (C)",
-    "Custom Name (Optional)": "Nome Personalizado (Opcional)",
+    "Primary model (A)": "Modelo primário (A)",
+    "Secondary model (B)": "Modelo secundário (B)",
+    "Tertiary model (C)": "Modelo terciário (C)",
+    "Custom Name (Optional)": "Nome personalizado (Opcional)",
     "Multiplier (M) - set to 0 to get model A": "Multiplicador (M) - definir em 0 para obter o modelo A",
     "Interpolation Method": "Método de Interpolação",
     "Weighted sum": "Soma de weights",
     "Add difference": "Acrescentar diferença",
     "Save as float16": "Salvar como float16",
-    "See": "Veja",
+    "See": "Ver",
     "wiki": "wiki",
     "for detailed explanation.": "para explicações detalhadas.",
     "Create embedding": "Criar embedding",
@@ -207,9 +213,9 @@
     "Name": "Nome",
     "Initialization text": "Texto de Inicialização",
     "Number of vectors per token": "Número de vetores por token",
-    "Overwrite Old Embedding": "Sobrescrever Embedding Anterior",
+    "Overwrite Old Embedding": "Substituir Embedding anterior",
     "Modules": "Módulos",
-    "Enter hypernetwork layer structure": "Inserir camada estrutural de hypernetwork",
+    "Enter hypernetwork layer structure": "Inserir na camada estrutural de hypernetwork",
     "Select activation function of hypernetwork": "Selecionar a função de ativação de hypernetwork",
     "relu": "relu",
     "leakyrelu": "leakyrelu",
@@ -241,14 +247,14 @@
     "softsign": "softsign",
     "tanhshrink": "tanhshrink",
     "threshold": "threshold",
-    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Selecionar a inicialização de weiths de camada. relu-like - Kaiming, sigmoid-like - Xavier é recomendado",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Selecionar a inicialização de pesos de camada. relu-like - Kaiming, sigmoid-like - Xavier é recomendado",
     "Normal": "Normal",
     "KaimingUniform": "KaimingUniform",
     "KaimingNormal": "KaimingNormal",
     "XavierUniform": "XavierUniform",
     "XavierNormal": "XavierNormal",
-    "Add layer normalization": "Adicionar camada de normalização",
-    "Use dropout": "Sobrescrever Hypernetwork Anterior",
+    "Add layer normalization": "Adicionar normalização de camada",
+    "Use dropout": "Usar dropout",
     "Overwrite Old Hypernetwork": "Sobrescrever Hypernetwork Anterior",
     "Source directory": "Diretório de origem",
     "Destination directory": "Diretório de destino",
@@ -262,17 +268,17 @@
     "Auto focal point crop": "Ajuste de corte em ponto focal automático",
     "Use BLIP for caption": "Usar BLIP para legenda",
     "Use deepbooru for caption": "Usar deepbooru para legenda",
-    "Split image threshold": "Limite de separação de imagem",
-    "Split image overlap ratio": "Proporção de sobreposição da separação de imagem",
-    "Focal point face weight": "Weight de ponto focal para rosto",
-    "Focal point entropy weight": "Weight de ponto focal para entropia",
-    "Focal point edges weight": "Weight de ponto focal para bordas",
+    "Split image threshold": "Limite de divisão de imagem",
+    "Split image overlap ratio": "Proporção de sobreposição da divisão de imagem",
+    "Focal point face weight": "Peso de ponto focal para rosto",
+    "Focal point entropy weight": "Peso de ponto focal para entropia",
+    "Focal point edges weight": "Peso de ponto focal para bordas",
     "Create debug image": "Criar imagem de depuração",
     "Preprocess": "Pré-processar",
     "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Treinar um embedding; precisa especificar um diretório com imagens de proporção 1:1",
     "[wiki]": "[wiki]",
     "Embedding": "Embedding",
-    "Learning rate": "Taxa de aprendizagem",
+    "Embedding Learning rate": "Taxa de aprendizagem do embedding",
     "Hypernetwork Learning rate": "Taxa de treinamento de Hypernetwork",
     "Dataset directory": "Diretório de Dataset",
     "Log directory": "Diretório de Log",
@@ -296,7 +302,6 @@
     "Do not save grids consisting of one picture": "Não salvar grades de apenas uma imagem",
     "Prevent empty spots in grid (when set to autodetect)": "Previnir pontos vazios na grade (quando marcado para autodetectar)",
     "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Contagem de colunas da grade; -1 para autodetectar e 0 para ser igual ao valor do tamanho das levas",
-    "-1": "-1",
     "Save text information about generation parameters as chunks to png files": "Salvar informações de parâmetros de geração como blocos png",
     "Create a text file next to every image with generation parameters.": "Criar um arquivo de texto com informações de geração junto a cada imagem gerada.",
     "Save a copy of image before doing face restoration.": "Salva uma cópia de cada imagem antes do refinamento facial.",
@@ -317,8 +322,8 @@
     "Saving to a directory": "Salvando para um diretório",
     "Save images to a subdirectory": "Salvar imagens para um subdiretório",
     "Save grids to a subdirectory": "Salvar grades de imagens para um subdiretório",
-    "When using \"Save\" button, save images to a subdirectory": "Quando usar o botão \"Salvar\", sasalvar imagens para um subdiretório",
-    "Directory name pattern": "Padrão de nomeacao de diretório",
+    "When using \"Save\" button, save images to a subdirectory": "Quando usar o botão \"Salvar\", salvar imagens para um subdiretório",
+    "Directory name pattern": "Padrão de nomeação de diretório",
     "Max prompt words for [prompt_words] pattern": "Número máximo de palavras do padrão de prompt [prompt_words]",
     "Upscaling": "Aumentando",
     "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tamanho do azulejo para ferramenta de aumento ESRGAN. 0 = sem azulejo.",
@@ -329,20 +334,20 @@
     "Upscaler for img2img": "Ferramenta de aumento para img2img",
     "Upscale latent space image when doing hires. fix": "Aumento em imagem latent space quando usando a resolução de alta definição - hires. fix",
     "Face restoration": "Refinamento de rosto",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Paramento de CodeFormer weight; 0 = efeito máximo; 1 = efeito mínimo",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Paramento de peso do CodeFormer; 0 = efeito máximo; 1 = efeito mínimo",
     "Move face restoration model from VRAM into RAM after processing": "Mover o processo de refinamento de rosto da VRAM da placa de vídeo para a RAM do computador depois do processamento.",
     "System": "System",
     "VRAM usage polls per second during generation. Set to 0 to disable.": "Uso de VRAM por segundo durante gerações. Deixar em 0 para desativar.",
     "Always print all generation info to standard output": "Sempre mostrar as informações de todas as gerações no padrão de output",
     "Add a second progress bar to the console that shows progress for an entire job.": "Adicionar uma segunda barra de processamento no console que mostra a progressão de todo o trabalho.",
-    "Training": "Treinando",
-    "Unload VAE and CLIP from VRAM when training": "Desativar VAE e CLIP da VRAM quando treinando",
+    "Training": "Treinamento",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Mover VAE e CLIP para a RAM quando treinando hypernetwork. Preserva VRAM.",
     "Filename word regex": "Palavra de nome de arquivo regex",
     "Filename join string": "Nome de arquivo join string",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Número de repetições para input único de imagens por epoch; serve apenas para mostrar o número de epoch",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Número de repetições para input único de imagens por época; serve apenas para mostrar o número de época",
     "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salvar um csv com as perdas para o diretório de log a cada N steps, 0 para desativar",
     "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "Intervalos para manter na RAM",
+    "Checkpoints to cache in RAM": "Checkpoints para manter no cache da RAM",
     "Hypernetwork strength": "Força da Hypernetwork",
     "Apply color correction to img2img results to match original colors.": "Aplicar correção de cor nas imagens geradas em img2img, usando a imagem original como base. Recomendação: se marcar essa opção, marcar também a de baixo",
     "Save a copy of image before applying color correction to img2img results": "Salvar uma cópia das imagens geradas em img2img antes de aplicar a correção de cor",
@@ -352,17 +357,17 @@
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usar método anterior de implementação de ênfase. Útil para reproduzir seeds antigas.",
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "Faz os samplers K-diffusion produzirem imagens iguais em levas quando criando uma única imagem",
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta a coerência por padding da última vírgula dentro de n tokens quando usar mais de 75 tokens",
-    "Filter NSFW content": "Filtrar conteúdo NSFW",
-    "Stop At last layers of CLIP model": "Para na última cada do CLIP model",
-    "Interrogate Options": "Opções de Interrogaçõ",
+    "Filter NSFW content": "Filtra conteúdos polêmicos (violência, nudez etc)",
+    "Stop At last layers of CLIP model": "Para na última camada do modelo CLIP",
+    "Interrogate Options": "Opções de Interrogação",
     "Interrogate: keep models in VRAM": "Interrogar: manter modelos na VRAM",
     "Interrogate: use artists from artists.csv": "Interrogar: usa artistas e estilos do documento artists.csv",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogar: incluir filas de model tags combinando nos resultados(Não tem efeito na interrogação feita por legenda).",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogar: incluir classificação de tags de modelo combinando nos resultados(Não tem efeito na interrogação feita por legenda).",
     "Interrogate: num_beams for BLIP": "Interrogar: num_beams para BLIP",
     "Interrogate: minimum description length (excluding artists, etc..)": "Interrogar: tamanho mínimo da descrição (tirando artistas, etc..)",
     "Interrogate: maximum description length": "Interrogar: tamanho máximo da descrição",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: núumero máximo de linhas no arquivo de texto(0 = Sem limites NÃO RECOMENDADO)",
-    "Interrogate: deepbooru score threshold": "Interrogate: limite de score deepbooru",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: número máximo de linhas no arquivo de texto(0 = Sem limites - NÃO RECOMENDADO)",
+    "Interrogate: deepbooru score threshold": "Interrogação: limite de score deepbooru",
     "Interrogate: deepbooru sort alphabetically": "Interrogar: organizar deepbooru por ordem alfabética",
     "use spaces for tags in deepbooru": "usar epaços para tags em deepbooru",
     "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "espaço (\\) colchetes em deepbooru (são usados como colchetes ao invés de dar ênfase)",
@@ -371,7 +376,7 @@
     "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostrar a criação de imagens a cada N sampling steps. Em 1 já dá para ver o processo de geração. Marcar como 0 para desativar.",
     "Show previews of all images generated in a batch as a grid": "Mostrar previsualização de todas as imagens geradas em leva numa grade",
     "Show grid in results for web": "Mostrar grade em resultados para web",
-    "Do not show any images in results for web": "Nao mostrar nenhuma imagem em resultados para web",
+    "Do not show any images in results for web": "Não mostrar nenhuma imagem em resultados para web",
     "Add model hash to generation information": "Adicionar hash do modelo para infromação de geração",
     "Add model name to generation information": "Adicionar nome do modelo para infromação de geração",
     "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Quando ler parâmetros de texto para a interface (de informações de PNG ou texto copiado), não alterar o modelo/intervalo selecionado.",
@@ -381,7 +386,6 @@
     "Show generation progress in window title.": "Mostrar barra de progresso no nome da janela.",
     "Quicksettings list": "Lista de configurações rapidas",
     "Localization (requires restart)": "Localização (precisa reiniciar)",
-    "Sampler parameters": "Parâmetros de Sampler",
     "ar_AR": "ar_AR",
     "es_ES": "es_ES",
     "fr-FR": "fr-FR",
@@ -401,10 +405,10 @@
     "sigma tmin": "sigma tmin",
     "sigma noise": "sigma noise",
     "Eta noise seed delta": "Eta noise seed delta",
-    "Request browser notifications": "Pedir notificações de browser",
-    "Download localization template": "Download de template para localização",
-    "Reload custom script bodies (No ui updates, No restart)": "Recarregar scripts personalizados (Sem atualizar o UI, Sem reiniciar)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Recarregar Gradio e atualizar componentes (Scripts personalizados, ui.py, js e css)",
+    "Request browser notifications": "Solicitar notificações do navegador",
+    "Download localization template": "Baixar arquivo modelo de localização",
+    "Reload custom script bodies (No ui updates, No restart)": "Recarregar scripts personalizados (Sem atualizar a interface, Sem reiniciar)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Reiniciar Gradio e atualizar componentes (Scripts personalizados, ui.py, js e css)",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (apertar Ctrl+Enter ou Alt+Enter para gerar)",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt Negativo (apertar Ctrl+Enter ou Alt+Enter para gerar)",
     "Add a random artist to the prompt.": "Adicionar um artista aleatório para o prompt.",
@@ -414,7 +418,7 @@
     "Stop processing current image and continue processing.": "Pula a imagem sendo gerada e vai para a próxima.",
     "Stop processing images and return any results accumulated so far.": "Interrompe o processo e mostra o que foi gerado até então.",
     "Style to apply; styles have components for both positive and negative prompts and apply to both": "Estilo para aplicar; também serve para o prompt negativo e vai preencher se usado.",
-    "Do not do anything special": "Não faz nada de especial",
+    "Do not do anything special": "Não faça nada de especial",
     "Which algorithm to use to produce the image": "O tipo de algoritmo para gerar imagens.",
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - cria mais variações para as imagens em diferentes passos. Mais que 40 passos cancela o efeito.",
     "Denoising Diffusion Implicit Models - Funciona melhor para inpainting.": "Denoising Diffusion Implicit Models - Funciona melhor para inpainting.",
@@ -444,6 +448,7 @@
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Se a imagem não for quadrada, estica ela para caber nas dimensões escolhidas.",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Mantém o máximo da imagem dentro das dimensões escolhidas e corta o resto.",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Coloca a imagem inteira dentro das dimensões escolhidas e preenche o espaço vazio.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Quantas vezes vai repetir o processamento de uma imagem e usá-la como entrada para a próxima iteração",
     "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "Quantas vezes vai reutilizar a imagem para o processo em loop.",
     "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Quantos pixels se sobrepõe durante o aumento da imagem, para garantir que não haverá marcas.",
     "A directory on the same machine where the server is running.": "Um diretório na mesma máquina onde o server está rodando.",
@@ -461,12 +466,6 @@
     "This string will be used to join split words into a single line if the option above is enabled.": "Junta palavras separadas em uma linha só.",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Lista de nomes de configuração, separados por vírgulas, para configurações que ficam na barra de acesso rápido do topo. Mais detalhes em modules/shared.py para configurações de nomes. Precisa reiniciar o webui-user.bat.",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se o valor não for 0, vai ser adicionado ao seed e usado para gerar ruídos aleatórios quando o sampler tem estimativa de entrega. Isso traz mais variações de imagens ou coincide com imagens de outras aplicações (quando você sabe o que esta fazendo).",
-    "/0.0": "/0.0",
-    "queue:": "queue:",
-    "/": "/",
-    "|": "|",
-    "processing |": "processando |",
-    "localizationpt": "localizationpt"
 }
 
 
-- 
cgit v1.2.1


From 0995e879cea8ce871489ea8e393bb0eba6edc09c Mon Sep 17 00:00:00 2001
From: Florian Horn <florianins@gmail.com>
Date: Thu, 27 Oct 2022 16:20:01 +0200
Subject: added save button and shortcut (s) to Modal View

---
 javascript/imageviewer.js | 29 +++++++++++++++++++++++++++++
 modules/ui.py             |  8 +++++---
 style.css                 | 18 ++++++++++++++++--
 3 files changed, 50 insertions(+), 5 deletions(-)

diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js
index 9e380c65..3aa3b29e 100644
--- a/javascript/imageviewer.js
+++ b/javascript/imageviewer.js
@@ -81,6 +81,25 @@ function modalImageSwitch(offset) {
     }
 }
 
+function saveImage(){
+    const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
+    const tabImg2Img = gradioApp().getElementById("tab_img2img")
+    const saveTxt2Img = "save_txt2img"
+    const saveImg2Img = "save_img2img"
+    if (tabTxt2Img.style["display"] != "none") {
+        gradioApp().getElementById(saveTxt2Img).click()
+    } else if (tabImg2Img.style["display"] != "none") {
+        gradioApp().getElementById(saveImg2Img).click()
+    } else {
+        console.error("missing implementation for saving modal of this type")
+    }
+}
+
+function modalSaveImage(event) {
+    saveImage()
+    event.stopPropagation()
+}
+
 function modalNextImage(event) {
     modalImageSwitch(1)
     event.stopPropagation()
@@ -93,6 +112,9 @@ function modalPrevImage(event) {
 
 function modalKeyHandler(event) {
     switch (event.key) {
+        case "s":
+            saveImage()
+            break;
         case "ArrowLeft":
             modalPrevImage(event)
             break;
@@ -198,6 +220,13 @@ document.addEventListener("DOMContentLoaded", function() {
     modalTileImage.title = "Preview tiling";
     modalControls.appendChild(modalTileImage)
 
+    const modalSave = document.createElement("span")
+    modalSave.className = "modalSave cursor"
+    modalSave.innerHTML = "&#x1F5AB;"
+    modalSave.addEventListener("click", modalSaveImage, true)
+    modalSave.title = "Save Image(s)"
+    modalControls.appendChild(modalSave)
+
     const modalClose = document.createElement('span')
     modalClose.className = 'modalClose cursor';
     modalClose.innerHTML = '&times;'
diff --git a/modules/ui.py b/modules/ui.py
index 0a63e357..1332e265 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -630,7 +630,7 @@ def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
-
+    
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
@@ -683,7 +683,8 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Column():
                     with gr.Row():
-                        save = gr.Button('Save')
+                        saveButtonId = 'save_txt2img'
+                        save = gr.Button('Save', elem_id=saveButtonId)
                         send_to_img2img = gr.Button('Send to img2img')
                         send_to_inpaint = gr.Button('Send to inpaint')
                         send_to_extras = gr.Button('Send to extras')
@@ -901,7 +902,8 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Column():
                     with gr.Row():
-                        save = gr.Button('Save')
+                        saveButtonId = 'save_img2img'
+                        save = gr.Button('Save', elem_id=saveButtonId)
                         img2img_send_to_img2img = gr.Button('Send to img2img')
                         img2img_send_to_inpaint = gr.Button('Send to inpaint')
                         img2img_send_to_extras = gr.Button('Send to extras')
diff --git a/style.css b/style.css
index 13a9fb07..fa357a49 100644
--- a/style.css
+++ b/style.css
@@ -314,8 +314,8 @@ input[type="range"]{
 
 .modalControls {
     display: grid;
-    grid-template-columns: 32px auto 1fr 32px;
-    grid-template-areas: "zoom tile space close";
+    grid-template-columns: 32px auto 32px 1fr 32px;
+    grid-template-areas: "zoom tile save space close";
     position: absolute;
     top: 0;
     left: 0;
@@ -333,6 +333,10 @@ input[type="range"]{
     grid-area: zoom;
 }
 
+.modalSave {
+    grid-area: save;
+}
+
 .modalTileImage {
     grid-area: tile;
 }
@@ -346,8 +350,18 @@ input[type="range"]{
   cursor: pointer;
 }
 
+.modalSave {
+    color: white;
+    font-size: 30px;
+    margin-top: 6px;
+    font-weight: bold;
+    cursor: pointer;
+}
+
 .modalClose:hover,
 .modalClose:focus,
+.modalSave:hover,
+.modalSave:focus,
 .modalZoom:hover,
 .modalZoom:focus {
   color: #999;
-- 
cgit v1.2.1


From 268159cfe3231743c554a1a9bf15d090c758f920 Mon Sep 17 00:00:00 2001
From: Florian Horn <florianins@gmail.com>
Date: Thu, 27 Oct 2022 16:32:10 +0200
Subject: fixed indentation

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 1332e265..d49b10b2 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -630,7 +630,7 @@ def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
-    
+
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
-- 
cgit v1.2.1


From bf25b51c3167426d3a340d2a3facea58813d4914 Mon Sep 17 00:00:00 2001
From: Florian Horn <florianins@gmail.com>
Date: Thu, 27 Oct 2022 16:38:55 +0200
Subject: fixed position to be in line with the other icons

---
 style.css | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/style.css b/style.css
index fa357a49..7b3ad050 100644
--- a/style.css
+++ b/style.css
@@ -314,7 +314,7 @@ input[type="range"]{
 
 .modalControls {
     display: grid;
-    grid-template-columns: 32px auto 32px 1fr 32px;
+    grid-template-columns: 32px 32px 32px 1fr 32px;
     grid-template-areas: "zoom tile save space close";
     position: absolute;
     top: 0;
@@ -352,8 +352,8 @@ input[type="range"]{
 
 .modalSave {
     color: white;
-    font-size: 30px;
-    margin-top: 6px;
+    font-size: 28px;
+    margin-top: 8px;
     font-weight: bold;
     cursor: pointer;
 }
-- 
cgit v1.2.1


From 68760a48cb537e08e58014819bb8f83dbd30e831 Mon Sep 17 00:00:00 2001
From: xmodar <modar.alfadly@gmail.com>
Date: Thu, 27 Oct 2022 17:46:00 +0300
Subject: Add forced LTR for training progress

---
 style.css | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/style.css b/style.css
index c49954e0..8dd483ef 100644
--- a/style.css
+++ b/style.css
@@ -526,7 +526,7 @@ Then, you will need to add the RTL counterpart only if needed in the rtl section
     :host {
         direction: rtl;
     }
-    select, .file-preview, .gr-text-input, .output-html:has(.performance) {
+    select, .file-preview, .gr-text-input, .output-html:has(.performance), #ti_progress {
         direction: ltr;
     }
     #script_list > label > select,
-- 
cgit v1.2.1


From a0a7024c679056dd66beb1832e52041b10143130 Mon Sep 17 00:00:00 2001
From: FlameLaw <116745066+FlameLaw@users.noreply.github.com>
Date: Fri, 28 Oct 2022 02:13:48 +0900
Subject: Fix random dataset shuffle on TI

---
 modules/textual_inversion/dataset.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 5b1c5002..8bb00d27 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -86,12 +86,12 @@ class PersonalizedBase(Dataset):
         assert len(self.dataset) > 0, "No images have been found in the dataset."
         self.length = len(self.dataset) * repeats // batch_size
 
-        self.initial_indexes = np.arange(len(self.dataset))
+        self.dataset_length = len(self.dataset)
         self.indexes = None
         self.shuffle()
 
     def shuffle(self):
-        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0]).numpy()]
+        self.indexes = np.random.permutation(self.dataset_length)
 
     def create_text(self, filename_text):
         text = random.choice(self.lines)
-- 
cgit v1.2.1


From 26a3fd2fe9314330336fb0e28d1e9ca7d2abe10e Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 27 Oct 2022 11:27:59 -0700
Subject: Highres fix works with unmasked latent. Also refactor the mask
 creation to make it more accesible.

---
 modules/processing.py | 134 ++++++++++++++++++++++++++++----------------------
 1 file changed, 76 insertions(+), 58 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index f72185ac..548eec29 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -129,6 +129,73 @@ class StableDiffusionProcessing():
         self.all_seeds = None
         self.all_subseeds = None
 
+    def txt2img_image_conditioning(self, x, width=None, height=None):
+        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
+            # Dummy zero conditioning if we're not using inpainting model.
+            # Still takes up a bit of memory, but no encoder call.
+            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+            return torch.zeros(
+                x.shape[0], 5, 1, 1, 
+                dtype=x.dtype, 
+                device=x.device
+            )
+
+        height = height or self.height
+        width = width or self.width
+
+        # The "masked-image" in this case will just be all zeros since the entire image is masked.
+        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
+
+        # Add the fake full 1s mask to the first dimension.
+        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+        image_conditioning = image_conditioning.to(x.dtype)            
+
+        return image_conditioning
+
+    def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):
+        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
+            # Dummy zero conditioning if we're not using inpainting model.
+            return torch.zeros(
+                latent_image.shape[0], 5, 1, 1,
+                dtype=latent_image.dtype,
+                device=latent_image.device
+            )
+
+        # Handle the different mask inputs
+        if image_mask is not None:
+            if torch.is_tensor(image_mask):
+                conditioning_mask = image_mask
+            else:
+                conditioning_mask = np.array(image_mask.convert("L"))
+                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+                conditioning_mask = torch.round(conditioning_mask)
+        else:
+            conditioning_mask = torch.ones(1, 1, *source_image.shape[-2:])
+
+        # Create another latent image, this time with a masked version of the original input.
+        # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
+        conditioning_mask = conditioning_mask.to(source_image.device)
+        conditioning_image = torch.lerp(
+            source_image,
+            source_image * (1.0 - conditioning_mask),
+            getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
+        )
+        
+        # Encode the new masked image using first stage of network.
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+
+        # Create the concatenated conditioning tensor to be fed to `c_concat`
+        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
+        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+        image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+        image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype)
+
+        return image_conditioning
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
@@ -571,37 +638,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-    def create_dummy_mask(self, x, width=None, height=None):
-        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            height = height or self.height
-            width = width or self.width
-
-            # The "masked-image" in this case will just be all zeros since the entire image is masked.
-            image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-            image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
-
-            # Add the fake full 1s mask to the first dimension.
-            image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-            image_conditioning = image_conditioning.to(x.dtype)
-
-        else:
-            # Dummy zero conditioning if we're not using inpainting model.
-            # Still takes up a bit of memory, but no encoder call.
-            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-            image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
-
-        return image_conditioning
-
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height))
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x, self.firstphase_width, self.firstphase_height))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
@@ -638,7 +684,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=self.create_dummy_mask(samples))
+        image_conditioning = self.img2img_image_conditioning(
+            decoded_samples, 
+            samples, 
+            decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
+        )
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)
 
         return samples
 
@@ -770,40 +821,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            if self.image_mask is not None:
-                conditioning_mask = np.array(self.image_mask.convert("L"))
-                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
-                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
-
-                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
-                conditioning_mask = torch.round(conditioning_mask)
-            else:
-                conditioning_mask = torch.ones(1, 1, *image.shape[-2:])
-
-            # Create another latent image, this time with a masked version of the original input.
-            conditioning_mask = conditioning_mask.to(image.device)
-
-            # Smoothly interpolate between the masked and unmasked latent conditioning image.
-            conditioning_image = torch.lerp(
-                image,
-                image * (1.0 - conditioning_mask),
-                getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
-            )
-            
-            conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
-
-            # Create the concatenated conditioning tensor to be fed to `c_concat`
-            conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
-            conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
-            self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
-            self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
-        else:
-            self.image_conditioning = torch.zeros(
-                self.init_latent.shape[0], 5, 1, 1,
-                dtype=self.init_latent.dtype,
-                device=self.init_latent.device
-            )
+        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
 
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
-- 
cgit v1.2.1


From a38496c1deef12f56f74f8abce2034bef8bdaccb Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 27 Oct 2022 11:31:31 -0700
Subject: Moved mask weight config to SD section

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index d47378e8..9c2fa0d4 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -267,6 +267,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
+    "inpainting_mask_weight": OptionInfo(1.0, "Strength of img2img conditioning mask for inpainting models.", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
@@ -320,7 +321,6 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
-    "inpainting_mask_weight": OptionInfo(1.0, "Blend betweeen an unmasked and masked conditioning image for inpainting models.", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
 }))
 
 
-- 
cgit v1.2.1


From b68c7c437eda2840a304539dd2acd0b0894e920c Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Thu, 27 Oct 2022 11:45:35 -0700
Subject: Updated name and hover text.

---
 javascript/hints.js | 1 +
 modules/shared.py   | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index 6ddd6aec..623bc25c 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -75,6 +75,7 @@ titles = {
     "Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.",
 
     "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.",
+    "Inpainting conditioning mask strength": "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.",
 
     "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).",
 
diff --git a/modules/shared.py b/modules/shared.py
index 9c2fa0d4..7c428d90 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -267,7 +267,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
-    "inpainting_mask_weight": OptionInfo(1.0, "Strength of img2img conditioning mask for inpainting models.", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
-- 
cgit v1.2.1


From bdc90837987ed8919dd611fd01553b0c170ded5c Mon Sep 17 00:00:00 2001
From: Roy Shilkrot <roy.shil@gmail.com>
Date: Thu, 27 Oct 2022 15:20:15 -0400
Subject: Add a barebones interrogate API

---
 launch.py             |  2 +-
 modules/api/api.py    | 25 ++++++++++++++++++++++++-
 modules/api/models.py | 13 ++++++++++++-
 webui.py              | 12 ++++++++----
 4 files changed, 45 insertions(+), 7 deletions(-)

diff --git a/launch.py b/launch.py
index 8affd410..ae79b4a4 100644
--- a/launch.py
+++ b/launch.py
@@ -198,7 +198,7 @@ def prepare_enviroment():
 def start_webui():
     print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}")
     import webui
-    webui.webui()
+    webui.webui_or_api()
 
 
 if __name__ == "__main__":
diff --git a/modules/api/api.py b/modules/api/api.py
index 6e9d6097..eabdb7b8 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,4 +1,4 @@
-from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
+from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI, InterrogateAPI
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.sd_samplers import all_samplers
 from modules.extras import run_pnginfo
@@ -25,6 +25,11 @@ class ImageToImageResponse(BaseModel):
     parameters: Json
     info: Json
 
+class InterrogateResponse(BaseModel):
+    caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
+    parameters: Json
+    info: Json
+
 
 class Api:
     def __init__(self, app, queue_lock):
@@ -33,6 +38,7 @@ class Api:
         self.queue_lock = queue_lock
         self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
 
     def __base64_to_image(self, base64_string):
         # if has a comma, deal with prefix
@@ -118,6 +124,23 @@ class Api:
 
         return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js())
 
+    def interrogateapi(self, interrogatereq: InterrogateAPI):
+        image_b64 = interrogatereq.image
+        if image_b64 is None:
+            raise HTTPException(status_code=404, detail="Image not found") 
+
+        populate = interrogatereq.copy(update={ # Override __init__ params
+            }
+        )
+
+        img = self.__base64_to_image(image_b64)
+
+        # Override object param
+        with self.queue_lock:
+            processed = shared.interrogator.interrogate(img)
+        
+        return InterrogateResponse(caption=processed, parameters=json.dumps(vars(interrogatereq)), info=None)
+
     def extrasapi(self):
         raise NotImplementedError
 
diff --git a/modules/api/models.py b/modules/api/models.py
index 079e33d9..8be64749 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -63,7 +63,12 @@ class PydanticModelGenerator:
             
                 
         self._model_name = model_name
-        self._class_data = merge_class_params(class_instance)
+
+        if class_instance is not None:
+            self._class_data = merge_class_params(class_instance)
+        else:
+            self._class_data = {}            
+
         self._model_def = [
             ModelDef(
                 field=underscore(k),
@@ -105,4 +110,10 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingImg2Img", 
     StableDiffusionProcessingImg2Img,
     [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}]
+).generate_model()
+
+InterrogateAPI = PydanticModelGenerator(
+    "Interrogate", 
+    None,
+    [{"key": "image", "type": str, "default": None}]
 ).generate_model()
\ No newline at end of file
diff --git a/webui.py b/webui.py
index ade7334b..7a4bb2a2 100644
--- a/webui.py
+++ b/webui.py
@@ -146,7 +146,9 @@ def webui():
         app.add_middleware(GZipMiddleware, minimum_size=1000)
 
         if (launch_api):
-            create_api(app)
+            print('launching API')
+            api = create_api(app)
+            api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
 
         wait_on_server(demo)
 
@@ -161,10 +163,12 @@ def webui():
         print('Restarting Gradio')
 
 
-
-task = []
-if __name__ == "__main__":
+def webui_or_api():
     if cmd_opts.nowebui:
         api_only()
     else:
         webui()
+
+task = []
+if __name__ == "__main__":
+    webui_or_api()
\ No newline at end of file
-- 
cgit v1.2.1


From 5e6344261d11bd2bce42eaa7b8425564a4867745 Mon Sep 17 00:00:00 2001
From: Martucci <73501718+M-art-ucci@users.noreply.github.com>
Date: Thu, 27 Oct 2022 17:24:20 -0300
Subject: Compromise with other PR for this fork

---
 localizations/pt_BR.json | 253 +++++++++++++++++++++++------------------------
 1 file changed, 125 insertions(+), 128 deletions(-)

diff --git a/localizations/pt_BR.json b/localizations/pt_BR.json
index 487432ae..d2e01c79 100644
--- a/localizations/pt_BR.json
+++ b/localizations/pt_BR.json
@@ -5,7 +5,7 @@
     "❮": "❮",
     "❯": "❯",
     "Loading...": "Carregando...",
-    "view": "Visualizar",
+    "view": "ver ",
     "api": "api",
     "•": "•",
     "built with gradio": "Criado com gradio",
@@ -14,9 +14,9 @@
     "img2img": "img2img",
     "Extras": "Extras",
     "PNG Info": "Informações dePNG",
-    "Checkpoint Merger": "Junção de Checkpoint",
+    "Checkpoint Merger": "Fusão de Checkpoint",
     "Train": "Treinar",
-    "Settings": "configurações",
+    "Settings": "Configurações",
     "Prompt": "Prompt",
     "Negative prompt": "Prompt negativo",
     "Run": "Executar",
@@ -34,8 +34,8 @@
     "Image": "Imagem",
     "Check progress": "Checar progresso",
     "Check progress (first)": "Checar progresso (primeiro)",
-    "Sampling Steps": "Sampling Steps",
-    "Sampling method": "Sampling method",
+    "Sampling Steps": "Passos de amostragem",
+    "Sampling method": "Método de amostragem",
     "Euler a": "Euler a",
     "Euler": "Euler",
     "LMS": "LMS",
@@ -51,35 +51,35 @@
     "PLMS": "PLMS",
     "Width": "Largura",
     "Height": "Altura",
-    "Restore faces": "Refinar rosto",
-    "Tiling": "Estilo azulejo",
+    "Restore faces": "Restaurar rostoss",
+    "Tiling": "Ladrilhar",
     "Highres. fix": "Ajuste de alta resolução",
-    "Firstpass width": "Passagem inicial de Largura",
-    "Firstpass height": "Passagem inicial de Altura",
-    "Denoising strength": "Quantidade de Denoise",
-    "Batch count": "Quantidade por leva",
-    "Batch size": "Quantidade de levas",
+    "Firstpass width": "Primeira Passagem da largura",
+    "Firstpass height": "Primeira Passagem da altura",
+    "Denoising strength": "Denoising strength",
+    "Batch count": "Quantidade por lote",
+    "Batch size": "Quantidade de lotes",
     "CFG Scale": "Escala CFG",
     "Seed": "Seed",
     "Extra": "Extra",
     "Variation seed": "Variação de seed",
     "Variation strength": "Força da variação",
-    "Resize seed from width": "Ajustar o tamanho da seed a partir da largura",
-    "Resize seed from height": "Ajustar o tamanho daseed a partir da altura",
+    "Resize seed from width": "Redimensionar a seed a partir da largura",
+    "Resize seed from height": "Redimensionar a seed a partir da altura",
     "Script": "Script",
     "None": "Nenhum",
     "Prompt matrix": "Matriz de Prompt",
     "Prompts from file or textbox": "Prompts a partir de arquivo ou caixa de texto",
     "X/Y plot": "X/Y plot",
-    "Put variable parts at start of prompt": "Colocar partes variáveis no começo do prompt",
-	"Iterate seed every line": "Iterar seed em todas as linhas",
-    "List of prompt inputs": "Lista de textos para prompt",
-    "Upload prompt inputs": "Carregar textos para prompts",
+    "Put variable parts at start of prompt": "Coloca partes variáveis no começo do prompt",
+    "Iterate seed every line": "Iterar seed a cada linha",
+    "List of prompt inputs": "Lista de entradas de texto para prompt",
+    "Upload prompt inputs": "Carregar entradas de textos para prompts",
     "X type": "Tipo do X",
     "Nothing": "Nenhum",
     "Var. seed": "Var. seed",
     "Var. strength": "Var. da força",
-    "Steps": "Steps",
+    "Steps": "Passos",
     "Prompt S/R": "Prompt S/R",
     "Prompt order": "Ordem de Prompt",
     "Sampler": "Sampler",
@@ -94,33 +94,32 @@
     "Clip skip": "Pular Clip",
     "Denoising": "Denoising",
     "X values": "Valores de X",
-    "Y type": "Tipos de Y",
+    "Y type": "Tipo de Y",
     "Y values": "Valores de Y",
     "Draw legend": "Desenhar a legenda",
     "Include Separate Images": "Incluir Imagens Separadas",
     "Keep -1 for seeds": "Manter em -1 para seeds",
-    "Drop Image Here": "Arraste a imagem aqui",
+    "Drop Image Here": "Coloque a imagem aqui",
     "Save": "Salvar",
     "Send to img2img": "Mandar para img2img",
     "Send to inpaint": "Mandar para inpaint",
     "Send to extras": "Mandar para extras",
     "Make Zip when Save?": "Criar um Zip quando salvar?",
     "Textbox": "Caixa de texto",
-    "Interrogate\nCLIP": "Interrogar\nCLIP",
+    "Interrogate\nCLIP": "Interrogatório\nCLIP",
     "Inpaint": "Inpaint",
-    "Batch img2img": "Leva de img2img",
+    "Batch img2img": "Lote img2img",
     "Image for img2img": "Imagem para img2img",
-    "Drop Image Here": "Arrastar imagem para cá",
     "Image for inpainting with mask": "Imagem para inpainting com máscara",
-    "Mask": "Mascara",
+    "Mask": "Máscara",
     "Mask blur": "Desfoque da máscara",
     "Mask mode": "Modo de máscara",
     "Draw mask": "Desenhar máscara",
     "Upload mask": "Carregar máscara",
-    "Masking mode": "Tipo de máscara",
+    "Masking mode": "Modo de máscara",
     "Inpaint masked": "Inpaint o que está dentro da máscara",
     "Inpaint not masked": "Inpaint o que está fora da máscara",
-    "Masked content": "Conteúdo da máscara",
+    "Masked content": "Conteúdo mascarado",
     "fill": "preencher",
     "original": "original",
     "latent noise": "latent noise",
@@ -129,26 +128,26 @@
     "Inpaint at full resolution padding, pixels": "Inpaint de preenchimento em resolução total, pixels",
     "Process images in a directory on the same machine where the server is running.": "Processar imagens no diretório da mesma maquina onde o servidor está rodando.",
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usar um diretório vazio para salvar imagens, ao invés de salvá-las no diretório output.",
-    "Input directory": "Diretório de Input",
-    "Output directory": "Diretório de Output",
-    "Resize mode": "Modo de ajuste de tamanho",
-    "Just resize": "Apenas ajustar",
-    "Crop and resize": "Cortar e ajustar",
-    "Resize and fill": "Ajustar e preencher",
+    "Input directory": "Diretório de entrada",
+    "Output directory": "Diretório de saída",
+    "Resize mode": "Modo de redimensionamento",
+    "Just resize": "Apenas redimensionar",
+    "Crop and resize": "Cortar e redimensionar",
+    "Resize and fill": "Redimensionar e preencher",
     "img2img alternative test": "Teste alternativo de img2img",
     "Loopback": "Loopback",
     "Outpainting mk2": "Outpainting mk2",
     "Poor man's outpainting": "Poor man's outpainting",
-    "SD upscale": "Ajuste SD",
-    "should be 2 or lower.": "precisa ser 2 ou menos.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Substituir 'sampling method' para Euler? (esse método foi feito para isso)",
+    "SD upscale": "Ampliamento SD",
+    "should be 2 or lower.": "deve ser 2 ou menos.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Substituir 'Método de amostragem' para Euler? (este método foi feito para isso)",
     "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Substituir 'prompt' para o mesmo valor que o 'prompt original'? (também para o 'prompt negativo')",
     "Original prompt": "Prompt original",
     "Original negative prompt": "Prompt negativo original",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Substituir 'Sampling Steps' para o mesmo valor que 'Decode steps'?",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Substituir 'Passos de Amostragem' para o mesmo valor que 'Decodificar Passos'?",
     "Decode steps": "Decode steps",
     "Override `Denoising strength` to 1?": "Substituir 'Quantidade do Denoise' para 1?",
-    "Decode CFG scale": "Escala CFG de Decode",
+    "Decode CFG scale": "Decodificar escala CFG",
     "Randomness": "Aleatoriedade",
     "Sigma adjustment for finding noise for image": "Ajuste Sigma para encontrar ruído para imagem",
     "Loops": "Loops",
@@ -162,9 +161,9 @@
     "down": "baixo",
     "Fall-off exponent (lower=higher detail)": "Exponente de queda (menor=mais detalhes)",
     "Color variation": "Variação de cor",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenta a imagem para duas vezes as dimensoes; ajust a largura e altura para definir o tamanho do azulejo",
-    "Tile overlap": "substituição de azulejo",
-    "Upscaler": "Ferramenta de ajuste de aumento",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Amplia a imagem em dobro; ajusta a largura e altura para definir o tamanho do ladrilho",
+    "Tile overlap": "Sobreposição de ladrilho",
+    "Upscaler": "Ampliador",
     "Lanczos": "Lanczos",
     "LDSR": "LDSR",
     "4x_foolhardy_Remacri": "4x_foolhardy_Remacri",
@@ -177,22 +176,22 @@
     "ScuNET": "ScuNET",
     "ScuNET PSNR": "ScuNET PSNR",
     "put_swinir_models_here": "put_swinir_models_here",
-    "Single Image": "Imagem Única",
-    "Batch Process": "Processo em Levas",
-    "Batch from Directory": "Levas a partir de diretório",
+    "Single Image": "Uma imagem",
+    "Batch Process": "Processo em lote",
+    "Batch from Directory": "Lote apartir de diretório",
     "Source": "Origem",
     "Show result images": "Mostrar imagens resultantes",
     "Scale by": "Aumentar proporcionalmente em",
     "Scale to": "Aumentar proporcionalmente para",
-    "Resize": "Aumentar para",
+    "Resize": "Redimensionar",
     "Crop to fit": "Cortar para caber",
-    "Upscaler 2 visibility": "Visibilidade da ferramenta de aumento 2",
+    "Upscaler 2 visibility": "Visibilidade da ferramenta de ampliação 2",
     "GFPGAN visibility": "Visibilidade GFPGAN",
     "CodeFormer visibility": "Visibilidade CodeFormer",
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso do CodeFormer (0 = efeito máximo, 1 = efeito mínimo)",
-    "Open output directory": "Abrir diretorio de output",
+    "Open output directory": "Abrir diretório de saída",
     "Send to txt2img": "Mandar para txt2img",
-    "A merger of the two checkpoints will be generated in your": "Uma combinação de dois checkpoints vai ser gerada em",
+    "A merger of the two checkpoints will be generated in your": "Uma fusão dos dois checkpoints será gerada em seu",
     "checkpoint": "checkpoint",
     "directory.": "diretório.",
     "Primary model (A)": "Modelo primário (A)",
@@ -201,21 +200,21 @@
     "Custom Name (Optional)": "Nome personalizado (Opcional)",
     "Multiplier (M) - set to 0 to get model A": "Multiplicador (M) - definir em 0 para obter o modelo A",
     "Interpolation Method": "Método de Interpolação",
-    "Weighted sum": "Soma de weights",
+    "Weighted sum": "Soma de pesos",
     "Add difference": "Acrescentar diferença",
     "Save as float16": "Salvar como float16",
     "See": "Ver",
     "wiki": "wiki",
-    "for detailed explanation.": "para explicações detalhadas.",
-    "Create embedding": "Criar embedding",
+    "for detailed explanation.": "para explicação detalhada.",
+    "Create embedding": "Criar incorporação",
     "Create hypernetwork": "Criar hypernetwork",
     "Preprocess images": "Pré-processar imagens",
     "Name": "Nome",
-    "Initialization text": "Texto de Inicialização",
+    "Initialization text": "Texto de inicialização",
     "Number of vectors per token": "Número de vetores por token",
-    "Overwrite Old Embedding": "Substituir Embedding anterior",
+    "Overwrite Old Embedding": "Substituir Incorporação anterior",
     "Modules": "Módulos",
-    "Enter hypernetwork layer structure": "Inserir na camada estrutural de hypernetwork",
+    "Enter hypernetwork layer structure": "Entrar na estrutura de camadas da hypernetwork",
     "Select activation function of hypernetwork": "Selecionar a função de ativação de hypernetwork",
     "relu": "relu",
     "leakyrelu": "leakyrelu",
@@ -261,13 +260,13 @@
     "Existing Caption txt Action": "Ação de legenda txt já existente",
     "ignore": "ignorar",
     "copy": "copiar",
-    "prepend": "preceder",
-    "append": "acrescentar",
+    "prepend": "adicionar ao início",
+    "append": "adicionar ao final",
     "Create flipped copies": "Criar cópias espelhadas",
     "Split oversized images into two": "Dividir imagens maiores em duas",
     "Auto focal point crop": "Ajuste de corte em ponto focal automático",
-    "Use BLIP for caption": "Usar BLIP para legenda",
-    "Use deepbooru for caption": "Usar deepbooru para legenda",
+    "Use BLIP for caption": "Usar BLIP para o título",
+    "Use deepbooru for caption": "Usar deepbooru para o título",
     "Split image threshold": "Limite de divisão de imagem",
     "Split image overlap ratio": "Proporção de sobreposição da divisão de imagem",
     "Focal point face weight": "Peso de ponto focal para rosto",
@@ -278,18 +277,18 @@
     "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Treinar um embedding; precisa especificar um diretório com imagens de proporção 1:1",
     "[wiki]": "[wiki]",
     "Embedding": "Embedding",
-    "Embedding Learning rate": "Taxa de aprendizagem do embedding",
-    "Hypernetwork Learning rate": "Taxa de treinamento de Hypernetwork",
+    "Embedding Learning rate": "Taxa de aprendizagem da incorporação",
+    "Hypernetwork Learning rate": "Taxa de aprendizagem de Hypernetwork",
     "Dataset directory": "Diretório de Dataset",
     "Log directory": "Diretório de Log",
     "Prompt template file": "Arquivo padrão de Prompt",
-    "Max steps": "Steps máximos",
-    "Save an image to log directory every N steps, 0 to disable": "Salvar uma imagem no diretório de log a cada N steps. 0 para desativar",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva uma cópia do embedding no diretório de log a cada N steps. 0 para desativar",
-    "Save images with embedding in PNG chunks": "Salva imagens com embedding em blocos de PNG",
+    "Max steps": "Passos máximos",
+    "Save an image to log directory every N steps, 0 to disable": "Salvar uma imagem no diretório de log a cada N passos. 0 para desativar",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva uma cópia da incorporação no diretório de log a cada N passos. 0 para desativar",
+    "Save images with embedding in PNG chunks": "Salva imagens com incorporação em segmentos de PNG",
     "Read parameters (prompt, etc...) from txt2img tab when making previews": "Ler parâmetros (prompt, etc...) para a aba txt2img durante os previews",
     "Train Hypernetwork": "Treinar Hypernetwork",
-    "Train Embedding": "Treinar Embedding",
+    "Train Embedding": "Treinar Incorporação",
     "Apply settings": "Aplicar configurações",
     "Saving images/grids": "Salvar imagens/grades",
     "Always save all generated images": "Sempre salvar todas as imagens geradas",
@@ -300,9 +299,9 @@
     "File format for grids": "Tipo de formato das grades de imagens salvas",
     "Add extended info (seed, prompt) to filename when saving grid": "Adicionar informações extras (seed, prompt) para os arquivos quando gerar uma grade",
     "Do not save grids consisting of one picture": "Não salvar grades de apenas uma imagem",
-    "Prevent empty spots in grid (when set to autodetect)": "Previnir pontos vazios na grade (quando marcado para autodetectar)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Contagem de colunas da grade; -1 para autodetectar e 0 para ser igual ao valor do tamanho das levas",
-    "Save text information about generation parameters as chunks to png files": "Salvar informações de parâmetros de geração como blocos png",
+    "Prevent empty spots in grid (when set to autodetect)": "Previnir espaços vazios na grade (quando marcado para autodetectar)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Contagem de linhas da grade; -1 para autodetectar e 0 para ser igual ao valor do tamanho das levas",
+    "Save text information about generation parameters as chunks to png files": "Salvar informações de parâmetros de geração como segmentos png",
     "Create a text file next to every image with generation parameters.": "Criar um arquivo de texto com informações de geração junto a cada imagem gerada.",
     "Save a copy of image before doing face restoration.": "Salva uma cópia de cada imagem antes do refinamento facial.",
     "Quality for saved jpeg images": "Qualidade das imagens jpeg",
@@ -310,41 +309,41 @@
     "Use original name for output filename during batch process in extras tab": "Usar o nome original para os arquivos de output durante o processo de levas da aba Extras",
     "When using 'Save' button, only save a single selected image": "Quando usar o botão 'Salvar', somente salvar as imagens selecionadas.",
     "Do not add watermark to images": "Não adicionar marca dágua nas imagens",
-    "Paths for saving": "Caminho para salvamento",
-    "Output directory for images; if empty, defaults to three directories below": "Diretório de output para imagens; se deixado em branco, as imagens vao para os seguintes diretórios",
-    "Output directory for txt2img images": "Diretório de Output para imagens txt2img",
-    "Output directory for img2img images": "Diretório de Output para imagens img2img",
-    "Output directory for images from extras tab": "Diretório de Output para a aba Extras",
-    "Output directory for grids; if empty, defaults to two directories below": "Diretório de Output para grades; se vazio, vão para os diretórios seguintes",
-    "Output directory for txt2img grids": "Diretório de Output para grades de imagens txt2img",
-    "Output directory for img2img grids": "Diretório de Output para grades de imagens img2img",
-    "Directory for saving images using the Save button": "Diretório de Output para imagens salvas com o botão 'Salvar'",
+    "Paths for saving": "Caminhos para salvar",
+    "Output directory for images; if empty, defaults to three directories below": "Diretório de saída para imagens; se deixado em branco, as imagens vao para os seguintes diretórios",
+    "Output directory for txt2img images": "Diretório de Saída para imagens txt2img",
+    "Output directory for img2img images": "Diretório de Saída para imagens img2img",
+    "Output directory for images from extras tab": "Diretório de Saída para a aba Extras",
+    "Output directory for grids; if empty, defaults to two directories below": "Diretório de Saída para grades; se vazio, vão para os diretórios seguintes",
+    "Output directory for txt2img grids": "Diretório de Saída para grades de imagens txt2img",
+    "Output directory for img2img grids": "Diretório de Saída para grades de imagens img2img",
+    "Directory for saving images using the Save button": "Diretório para imagens salvas utilizando o botão de salvar",
     "Saving to a directory": "Salvando para um diretório",
     "Save images to a subdirectory": "Salvar imagens para um subdiretório",
     "Save grids to a subdirectory": "Salvar grades de imagens para um subdiretório",
     "When using \"Save\" button, save images to a subdirectory": "Quando usar o botão \"Salvar\", salvar imagens para um subdiretório",
-    "Directory name pattern": "Padrão de nomeação de diretório",
+    "Directory name pattern": "Padrão de nome de diretório",
     "Max prompt words for [prompt_words] pattern": "Número máximo de palavras do padrão de prompt [prompt_words]",
-    "Upscaling": "Aumentando",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tamanho do azulejo para ferramenta de aumento ESRGAN. 0 = sem azulejo.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sobreposição de azulejo, em pixels, para ferramentas de aumento ESRGAN. Valores baixos = junção mais aparente.",
+    "Upscaling": "Ampliando",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tamanho do ladrilho para ampliação ESRGAN. 0 = sem ladrilho.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sobreposição de azulejo, em pixels, para amplicação  ESRGAN. Valores baixos = linhas de fusão mais aparente.",
     "Tile size for all SwinIR.": "Tamanho do azulejo para todo SwinIR.",
     "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sobreposição de azulejo, em pixels,  para SwinIR. Valores baixos = junção mais aparente.",
     "LDSR processing steps. Lower = faster": "Steps de processamento LDSR. Menos = rápido",
-    "Upscaler for img2img": "Ferramenta de aumento para img2img",
-    "Upscale latent space image when doing hires. fix": "Aumento em imagem latent space quando usando a resolução de alta definição - hires. fix",
+    "Upscaler for img2img": "Ampliação para img2img",
+    "Upscale latent space image when doing hires. fix": "Ampliar a imagem do espaço latente quando usando o ajuste de alta definição - hires. fix",
     "Face restoration": "Refinamento de rosto",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Paramento de peso do CodeFormer; 0 = efeito máximo; 1 = efeito mínimo",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Parâmento de peso do CodeFormer; 0 = efeito máximo; 1 = efeito mínimo",
     "Move face restoration model from VRAM into RAM after processing": "Mover o processo de refinamento de rosto da VRAM da placa de vídeo para a RAM do computador depois do processamento.",
-    "System": "System",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "Uso de VRAM por segundo durante gerações. Deixar em 0 para desativar.",
+    "System": "Sistema",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Levantamento de uso de VRAM por segundo durante gerações. Deixar em 0 para desativar.",
     "Always print all generation info to standard output": "Sempre mostrar as informações de todas as gerações no padrão de output",
     "Add a second progress bar to the console that shows progress for an entire job.": "Adicionar uma segunda barra de processamento no console que mostra a progressão de todo o trabalho.",
     "Training": "Treinamento",
     "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Mover VAE e CLIP para a RAM quando treinando hypernetwork. Preserva VRAM.",
     "Filename word regex": "Palavra de nome de arquivo regex",
     "Filename join string": "Nome de arquivo join string",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Número de repetições para input único de imagens por época; serve apenas para mostrar o número de época",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Número de repetições para entrada única de imagens por época; serve apenas para mostrar o número de época",
     "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salvar um csv com as perdas para o diretório de log a cada N steps, 0 para desativar",
     "Stable Diffusion": "Stable Diffusion",
     "Checkpoints to cache in RAM": "Checkpoints para manter no cache da RAM",
@@ -355,21 +354,21 @@
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Ativar quantização em K samples para resultados mais nítidos e visíveis. Pode alterar seeds ja existentes. Precisa reiniciar para funcionar.",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Ênfase: usar parênteses ao redor de palavras (texto de exemplo) para fazer o modelo dar mais atenção par aquela palavra ou frase, e chaves [texto de exemplo] para tirar atenção",
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usar método anterior de implementação de ênfase. Útil para reproduzir seeds antigas.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Faz os samplers K-diffusion produzirem imagens iguais em levas quando criando uma única imagem",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta a coerência por padding da última vírgula dentro de n tokens quando usar mais de 75 tokens",
-    "Filter NSFW content": "Filtra conteúdos polêmicos (violência, nudez etc)",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Faz as amostragens K-diffusion produzirem imagens iguais em lotes quando criando uma única imagem",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta a coerência por preenchimento apartir da ultima vírgula dentro de n tokens quando usando mais de 75 tokens",
+    "Filter NSFW content": "Filtra conteúdos inadequados(geralmente +18)",
     "Stop At last layers of CLIP model": "Para na última camada do modelo CLIP",
-    "Interrogate Options": "Opções de Interrogação",
+    "Interrogate Options": "Opções de Interrogatório",
     "Interrogate: keep models in VRAM": "Interrogar: manter modelos na VRAM",
     "Interrogate: use artists from artists.csv": "Interrogar: usa artistas e estilos do documento artists.csv",
     "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogar: incluir classificação de tags de modelo combinando nos resultados(Não tem efeito na interrogação feita por legenda).",
     "Interrogate: num_beams for BLIP": "Interrogar: num_beams para BLIP",
     "Interrogate: minimum description length (excluding artists, etc..)": "Interrogar: tamanho mínimo da descrição (tirando artistas, etc..)",
     "Interrogate: maximum description length": "Interrogar: tamanho máximo da descrição",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: número máximo de linhas no arquivo de texto(0 = Sem limites - NÃO RECOMENDADO)",
-    "Interrogate: deepbooru score threshold": "Interrogação: limite de score deepbooru",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: número máximo de linhas no arquivo de texto(0 = Sem limites)",
+    "Interrogate: deepbooru score threshold": "Interrogatório: limite de score deepbooru",
     "Interrogate: deepbooru sort alphabetically": "Interrogar: organizar deepbooru por ordem alfabética",
-    "use spaces for tags in deepbooru": "usar epaços para tags em deepbooru",
+    "use spaces for tags in deepbooru": "usar espaços para tags em deepbooru",
     "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "espaço (\\) colchetes em deepbooru (são usados como colchetes ao invés de dar ênfase)",
     "User interface": "Interface de Usuário",
     "Show progressbar": "Mostrar barra de progresso",
@@ -377,8 +376,8 @@
     "Show previews of all images generated in a batch as a grid": "Mostrar previsualização de todas as imagens geradas em leva numa grade",
     "Show grid in results for web": "Mostrar grade em resultados para web",
     "Do not show any images in results for web": "Não mostrar nenhuma imagem em resultados para web",
-    "Add model hash to generation information": "Adicionar hash do modelo para infromação de geração",
-    "Add model name to generation information": "Adicionar nome do modelo para infromação de geração",
+    "Add model hash to generation information": "Adicionar hash do modelo para informação de geração",
+    "Add model name to generation information": "Adicionar nome do modelo para informação de geração",
     "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Quando ler parâmetros de texto para a interface (de informações de PNG ou texto copiado), não alterar o modelo/intervalo selecionado.",
     "Font for image grids that have text": "Fonte para grade de imagenss que tem texto",
     "Enable full page image viewer": "Ativar visualizador de página inteira",
@@ -394,17 +393,17 @@
     "ru_RU": "ru_RU",
     "tr_TR": "tr_TR",
     "zh_CN": "zh_CN",
-    "Sampler parameters": "Parâmetros de Sampler",
-    "Hide samplers in user interface (requires restart)": "Esconder samplers na interface de usuário (precisa reiniciar)",
-    "eta (noise multiplier) for DDIM": "eta (noise multiplier) para DDIM",
-    "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) para ancestral samplers",
-    "img2img DDIM discretize": "img2img DDIM discretize",
-    "uniform": "uniform",
+    "Sampler parameters": "Parâmetros de Amostragem",
+    "Hide samplers in user interface (requires restart)": "Esconder amostragens na interface de usuário (precisa reiniciar)",
+    "eta (noise multiplier) for DDIM": "tempo estimado (multiplicador de ruído) para DDIM",
+    "eta (noise multiplier) for ancestral samplers": "tempo estimado (multiplicador de ruído) para amostragens ancestrais",
+    "img2img DDIM discretize": "Discretização de img2img DDIM",
+    "uniform": "uniforme",
     "quad": "quad",
     "sigma churn": "sigma churn",
     "sigma tmin": "sigma tmin",
     "sigma noise": "sigma noise",
-    "Eta noise seed delta": "Eta noise seed delta",
+    "Eta noise seed delta": "tempo estimado para ruído seed delta",
     "Request browser notifications": "Solicitar notificações do navegador",
     "Download localization template": "Baixar arquivo modelo de localização",
     "Reload custom script bodies (No ui updates, No restart)": "Recarregar scripts personalizados (Sem atualizar a interface, Sem reiniciar)",
@@ -422,17 +421,17 @@
     "Which algorithm to use to produce the image": "O tipo de algoritmo para gerar imagens.",
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - cria mais variações para as imagens em diferentes passos. Mais que 40 passos cancela o efeito.",
     "Denoising Diffusion Implicit Models - Funciona melhor para inpainting.": "Denoising Diffusion Implicit Models - Funciona melhor para inpainting.",
-    "Produce an image that can be tiled.": "Cria uma imagem que emenda nela mesma pelas bordas. Testar em https://www.pycheung.com/checker/.",
+    "Produce an image that can be tiled.": "Produz uma imagem que pode ser ladrilhada.",
     "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Cria um processo em duas etapas, com uma imagem em baixa qualidade primeiro, aumenta a imagem e refina os detalhes sem alterar a composição da imagem",
     "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Quanto o algoritmo deve manter da imagem original. Em 0, nada muda. Em 1 o algoritmo ignora a imagem original. Valores menores que 1.0 demoram mais.",
-    "How many batches of images to create": "Quantas imagens criar em uma leva.",
-    "How many image to create in a single batch": "Quantas levas criar (multiplica o valor pelo Batch count).",
+    "How many batches of images to create": "Quantos lotes de imagens criar",
+    "How many image to create in a single batch": "Quantas imagens criar em um único lote",
     "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - Quanto maior o valor, mais segue o prompt e quanto menor, menor segue.",
     "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Codigo de geração de uma imagem - criando uma imagem com os mesmos parâmetros e seed trazem o mesmo resultado.",
     "Set seed to -1, which will cause a new random number to be used every time": "Define seed como -1, deixando o valor que vai aparecer como aleatório.",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Reutiliza o seed da última geração",
-    "Seed of a different picture to be mixed into the generation.": "Usar um seed de outra imagem para a geração.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Qual a variação a ser gerada. Em 0, não tem efeito. Em 1, gera uma imagem completa com a variação de seed, (exceto com samplers a).",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Reutilizar a seed da última geração, útil principalmente se ela foi aleatória",
+    "Seed of a different picture to be mixed into the generation.": "Seed de uma imagem diferente é misturada na geração.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Qual a variação a ser gerada. Em 0, não tem efeito. Em 1, gera uma imagem completa com a variação de seed, (exceto com amostragens a).",
     "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Tenta gerar uma imagem similar ao que teria sido feito com a mesma seed em dimensões especifica.",
     "Separate values for X axis using commas.": "Separa os valores para o eixo X usando vírgulas.",
     "Separate values for Y axis using commas.": "Separa os valores para o eixo Y usando vírgulas.",
@@ -441,31 +440,29 @@
     "How much to blur the mask before processing, in pixels.": "Transição do contorno da máscara, em pixels.",
     "What to put inside the masked area before processing it with Stable Diffusion.":  "O que vai dentro da máscara antes de processar.",
 	"fill it with colors of the image": "Preenche usando as cores da imagem.",
-    "keep whatever was there originally": "Mantem o que estava dentro.",
-    "fill it with latent space noise": "Preenche com ruídos.",
-    "fill it with latent space zeroes": "Preenche com zeros.",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Utiliza o processo de aumentar a imagem e depois reduz a qualidade para preencher a região. Consome mais tempo.",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Se a imagem não for quadrada, estica ela para caber nas dimensões escolhidas.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Mantém o máximo da imagem dentro das dimensões escolhidas e corta o resto.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Coloca a imagem inteira dentro das dimensões escolhidas e preenche o espaço vazio.",
+    "keep whatever was there originally": "manter usando o que estava lá originalmente",
+    "fill it with latent space noise": "Preenche com ruídos do espaço latente.",
+    "fill it with latent space zeroes": "Preenche com zeros do espaço latente.",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Faz ampliação na região com máscara para atingir a resolução desejada, faz inpainting, faz downscale para voltar na resolução original e cola na imagem original",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Redimensiona a imagem para a resolução desejada. A menos que a altura e a largura correspondam, você obterá uma proporção incorreta.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Redimensiona a imagem para que toda a resolução desejada seja preenchida com a imagem. Corta as partes que ficaram pra fora.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Redimensiona a imagem para que toda a imagem esteja dentro da resolução desejada. Preenche o espaço vazio com as cores da imagem.",
     "How many times to repeat processing an image and using it as input for the next iteration": "Quantas vezes vai repetir o processamento de uma imagem e usá-la como entrada para a próxima iteração",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "Quantas vezes vai reutilizar a imagem para o processo em loop.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Quantos pixels se sobrepõe durante o aumento da imagem, para garantir que não haverá marcas.",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "No modo de loopback, em cada loop a força do denoise é multiplicado por este valor. <1 significa diminuir a variedade para que sua sequência converta em uma imagem fixa. >1 significa aumentar a variedade para que sua sequência se torne cada vez mais caótica.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Para ampliação SD, quanta sobreposição em pixels deve haver entre os ladrilhos. Os ladrilhos se sobrepõem para que, quando forem mesclados de volta em uma imagem, não haja linhas de junção claramente visível.",
     "A directory on the same machine where the server is running.": "Um diretório na mesma máquina onde o server está rodando.",
-    "Leave blank to save images to the default path.": "Deixar em branco para salvar imagens no diretório padrão.",
+    "Leave blank to save images to the default path.": "Deixar em branco para salvar imagens no caminho padrão.",
     "Result = A * (1 - M) + B * M": "Resultado = A * (1 - M) + B * M",
     "Result = A + (B - C) * M": "Resultado = A + (B - C) * M",
     "1st and last digit must be 1. ex:'1, 2, 1'": "Primeiro e último dígito precisam ser 1. ex:'1, 2, 1'",
-    "Path to directory with input images": "Diretório para imagens de entrada",
-    "Path to directory where to write outputs": "Diretório para salvar imagens de saída",
+    "Path to directory with input images": "Caminho para o diretório com imagens de entrada",
+    "Path to directory where to write outputs": "Caminho para o diretório para gravar as saídas",
     "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa essas tags para definir como os nomes dos arquivos sao escolhidos: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; deixe em branco para manter o padrao.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se esta opção estiver marcada, as imagens não vão ter marca dágua. Aviso: se você não quer a marca dágua, você pode estar se envolvendo em comportamentos antiéticos",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se esta opção estiver marcada, as imagens não vão ter marca d'água. Aviso: se você não quer a marca d'água, você pode estar se envolvendo em comportamentos antiéticos",
     "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa essas tags para definir como os nomes dos subdiretorios e ghrades sao escolhidos: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; deixe em branco para manter o padrao.",
-    "Restore low quality faces using GFPGAN neural network": "Restaurar baixa qualidade de rosto usando a rede neural GFPGAN",
+    "Restore low quality faces using GFPGAN neural network": "Restaurar rostos de baixa qualidade usando a rede neural GFPGAN",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Esta expressão regular vai retirar palavras do nome do arquivo e vai se juntar usando a opção abaixo em etiquetas usadas em treinamento. Não mexer para manter os nomes como estão.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "Junta palavras separadas em uma linha só.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Lista de nomes de configuração, separados por vírgulas, para configurações que ficam na barra de acesso rápido do topo. Mais detalhes em modules/shared.py para configurações de nomes. Precisa reiniciar o webui-user.bat.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se o valor não for 0, vai ser adicionado ao seed e usado para gerar ruídos aleatórios quando o sampler tem estimativa de entrega. Isso traz mais variações de imagens ou coincide com imagens de outras aplicações (quando você sabe o que esta fazendo).",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Esta string será usada para unir palavras divididas em uma única linha se a opção acima estiver habilitada.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Lista de nomes de configurações, separados por vírgulas, para configurações que devem ir para a barra de acesso rápido na parte superior, em vez da guia de configuração usual. Veja modules/shared.py para nomes de configuração. Necessita reinicialização para aplicar.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se este valor for diferente de zero, ele será adicionado à seed e usado para inicializar o RNG para ruídos ao usar amostragens com Tempo Estimado. Você pode usar isso para produzir ainda mais variações de imagens ou pode usar isso para combinar imagens de outro software se souber o que está fazendo."
 }
-
-
-- 
cgit v1.2.1


From b50ff4f4e4d4d6bf31e222832d3fe4cfde4703c9 Mon Sep 17 00:00:00 2001
From: Josh Watzman <github@jwatzman.org>
Date: Thu, 27 Oct 2022 21:59:16 +0100
Subject: Reduce peak memory usage when changing models

A few tweaks to reduce peak memory usage, the biggest being that if we
aren't using the checkpoint cache, we shouldn't duplicate the model
state dict just to immediately throw it away.

On my machine with 16GB of RAM, this change means I can typically change
models, whereas before it would typically OOM.
---
 modules/sd_models.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index e697bb72..203e99a8 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -170,7 +170,9 @@ def load_model_weights(model, checkpoint_info):
             print(f"Global Step: {pl_sd['global_step']}")
 
         sd = get_state_dict_from_checkpoint(pl_sd)
-        missing, extra = model.load_state_dict(sd, strict=False)
+        del pl_sd
+        model.load_state_dict(sd, strict=False)
+        del sd
 
         if shared.cmd_opts.opt_channelslast:
             model.to(memory_format=torch.channels_last)
@@ -194,9 +196,10 @@ def load_model_weights(model, checkpoint_info):
 
         model.first_stage_model.to(devices.dtype_vae)
 
-        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
-        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
-            checkpoints_loaded.popitem(last=False)  # LRU
+        if shared.opts.sd_checkpoint_cache > 0:
+            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+            while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+                checkpoints_loaded.popitem(last=False)  # LRU
     else:
         print(f"Loading weights [{sd_model_hash}] from cache")
         checkpoints_loaded.move_to_end(checkpoint_info)
-- 
cgit v1.2.1


From 4ca4900bd43ecca9069733b91ac44715b6681089 Mon Sep 17 00:00:00 2001
From: Martucci <73501718+M-art-ucci@users.noreply.github.com>
Date: Thu, 27 Oct 2022 18:04:08 -0300
Subject: Update pt_BR.json

---
 localizations/pt_BR.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/pt_BR.json b/localizations/pt_BR.json
index d2e01c79..690dd640 100644
--- a/localizations/pt_BR.json
+++ b/localizations/pt_BR.json
@@ -13,7 +13,7 @@
     "txt2img": "txt2img",
     "img2img": "img2img",
     "Extras": "Extras",
-    "PNG Info": "Informações dePNG",
+    "PNG Info": "Informações de PNG",
     "Checkpoint Merger": "Fusão de Checkpoint",
     "Train": "Treinar",
     "Settings": "Configurações",
-- 
cgit v1.2.1


From d814db1c2552f145fb28018af045d702167d3bcb Mon Sep 17 00:00:00 2001
From: Martucci <73501718+M-art-ucci@users.noreply.github.com>
Date: Thu, 27 Oct 2022 18:09:45 -0300
Subject: Update pt_BR.json

---
 localizations/pt_BR.json | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/localizations/pt_BR.json b/localizations/pt_BR.json
index 690dd640..49d71edc 100644
--- a/localizations/pt_BR.json
+++ b/localizations/pt_BR.json
@@ -51,7 +51,7 @@
     "PLMS": "PLMS",
     "Width": "Largura",
     "Height": "Altura",
-    "Restore faces": "Restaurar rostoss",
+    "Restore faces": "Restaurar rostos",
     "Tiling": "Ladrilhar",
     "Highres. fix": "Ajuste de alta resolução",
     "Firstpass width": "Primeira Passagem da largura",
@@ -191,9 +191,9 @@
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso do CodeFormer (0 = efeito máximo, 1 = efeito mínimo)",
     "Open output directory": "Abrir diretório de saída",
     "Send to txt2img": "Mandar para txt2img",
-    "A merger of the two checkpoints will be generated in your": "Uma fusão dos dois checkpoints será gerada em seu",
+    "A merger of the two checkpoints will be generated in your": "Uma fusão dos dois checkpoints será gerada em seu ",
     "checkpoint": "checkpoint",
-    "directory.": "diretório.",
+    "directory.": " diretório.",
     "Primary model (A)": "Modelo primário (A)",
     "Secondary model (B)": "Modelo secundário (B)",
     "Tertiary model (C)": "Modelo terciário (C)",
-- 
cgit v1.2.1


From 403c5dba86f0faac6746df24d16e4d08aab8c9c5 Mon Sep 17 00:00:00 2001
From: Florian Horn <florianins@gmail.com>
Date: Fri, 28 Oct 2022 00:58:18 +0200
Subject: hide save btn for other tabs than txt2img and img2img

---
 javascript/imageviewer.js | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js
index 3aa3b29e..67916536 100644
--- a/javascript/imageviewer.js
+++ b/javascript/imageviewer.js
@@ -13,6 +13,15 @@ function showModal(event) {
     }
     lb.style.display = "block";
     lb.focus()
+
+    const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
+    const tabImg2Img = gradioApp().getElementById("tab_img2img")
+    // show the save button in modal only on txt2img or img2img tabs
+    if (tabTxt2Img.style.display != "none" || tabImg2Img.style.display != "none") {
+        gradioApp().getElementById("modal_save").style.display = "inline"
+    } else {
+        gradioApp().getElementById("modal_save").style.display = "none"
+    }
     event.stopPropagation()
 }
 
@@ -86,9 +95,9 @@ function saveImage(){
     const tabImg2Img = gradioApp().getElementById("tab_img2img")
     const saveTxt2Img = "save_txt2img"
     const saveImg2Img = "save_img2img"
-    if (tabTxt2Img.style["display"] != "none") {
+    if (tabTxt2Img.style.display != "none") {
         gradioApp().getElementById(saveTxt2Img).click()
-    } else if (tabImg2Img.style["display"] != "none") {
+    } else if (tabImg2Img.style.display != "none") {
         gradioApp().getElementById(saveImg2Img).click()
     } else {
         console.error("missing implementation for saving modal of this type")
@@ -222,6 +231,7 @@ document.addEventListener("DOMContentLoaded", function() {
 
     const modalSave = document.createElement("span")
     modalSave.className = "modalSave cursor"
+    modalSave.id = "modal_save"
     modalSave.innerHTML = "&#x1F5AB;"
     modalSave.addEventListener("click", modalSaveImage, true)
     modalSave.title = "Save Image(s)"
-- 
cgit v1.2.1


From ccde874974ff8a5dd1ae40d8e9dd4166d166d646 Mon Sep 17 00:00:00 2001
From: benlisquare <benlisquare>
Date: Fri, 28 Oct 2022 13:51:54 +1100
Subject: adjustments to zh_TW localisation per suggestions by snowmeow2

---
 localizations/zh_TW.json | 270 +++++++++++++++++++++++------------------------
 1 file changed, 135 insertions(+), 135 deletions(-)

diff --git a/localizations/zh_TW.json b/localizations/zh_TW.json
index 0efc7436..724df1ac 100644
--- a/localizations/zh_TW.json
+++ b/localizations/zh_TW.json
@@ -4,8 +4,8 @@
     "×": "×",
     "❮": "❮",
     "❯": "❯",
-    "Loading...": "載入中...",
-    "view": "查看",
+    "Loading...": "載入中…",
+    "view": "檢視",
     "api": "api",
     "•": "•",
     "built with gradio": "基於 Gradio 構建",
@@ -13,7 +13,7 @@
     "txt2img": "文生圖",
     "img2img": "圖生圖",
     "Extras": "後處理",
-    "PNG Info": "PNG 信息",
+    "PNG Info": "PNG 資訊",
     "Checkpoint Merger": "模型權重存檔點合併工具",
     "Train": "訓練",
     "Create aesthetic embedding": "生成美術風格 embedding",
@@ -22,7 +22,7 @@
     "Settings": "設定",
     "Prompt": "提示詞",
     "Negative prompt": "反向提示詞",
-    "Run": "運行",
+    "Run": "執行",
     "Skip": "跳過",
     "Interrupt": "中止",
     "Generate": "生成",
@@ -30,14 +30,14 @@
     "Style 2": "模版風格 2",
     "Label": "標籤",
     "File": "檔案",
-    "Drop File Here": "拖拽檔案到此",
+    "Drop File Here": "拖曳檔案到此",
     "-": "-",
     "or": "或",
-    "Click to Upload": "點擊上載",
+    "Click to Upload": "點擊上傳",
     "Image": "圖像",
-    "Check progress": "查看進度",
-    "Check progress (first)": "(首次)查看進度",
-    "Sampling Steps": "採樣迭代步數",
+    "Check progress": "檢視進度",
+    "Check progress (first)": "（首次）檢視進度",
+    "Sampling Steps": "採樣疊代步數",
     "Sampling method": "採樣方法",
     "Euler a": "Euler a",
     "Euler": "Euler",
@@ -62,7 +62,7 @@
     "Denoising strength": "去噪強度",
     "Batch count": "批次",
     "Batch size": "批量",
-    "CFG Scale": "提示詞相關性(CFG)",
+    "CFG Scale": "提示詞相關性（CFG）",
     "Seed": "隨機種子",
     "Extra": "額外參數",
     "Variation seed": "差異隨機種子",
@@ -72,7 +72,7 @@
     "Open for Clip Aesthetic!": "打開美術風格 Clip!",
     "▼": "▼",
     "Aesthetic weight": "美術風格權重",
-    "Aesthetic steps": "美術風格迭代步數",
+    "Aesthetic steps": "美術風格疊代步數",
     "Aesthetic learning rate": "美術風格學習率",
     "Slerp interpolation": "Slerp 插值",
     "Aesthetic imgs embedding": "美術風格圖集 embedding",
@@ -80,22 +80,22 @@
     "Aesthetic text for imgs": "該圖集的美術風格描述",
     "Slerp angle": "Slerp 角度",
     "Is negative text": "是反向提示詞",
-    "Script": "腳本",
+    "Script": "指令碼",
     "Embedding to Shareable PNG": "將 Embedding 轉換為可分享的 PNG",
     "Prompt matrix": "提示詞矩陣",
-    "Prompts from file or textbox": "從文本框或檔案載入提示詞",
+    "Prompts from file or textbox": "從文字方塊或檔案載入提示詞",
     "X/Y plot": "X/Y 圖表",
     "Source embedding to convert": "用於轉換的源 Embedding",
     "Embedding token": "Embedding 的關鍵詞",
     "Put variable parts at start of prompt": "把變量部分放在提示詞文本的開頭",
-    "Show Textbox": "顯示文本框",
+    "Show Textbox": "顯示文字方塊",
     "File with inputs": "含輸入內容的檔案",
     "Prompts": "提示詞",
     "X type": "X軸類型",
     "Nothing": "無",
     "Var. seed": "差異隨機種子",
     "Var. strength": "差異強度",
-    "Steps": "迭代步數",
+    "Steps": "疊代步數",
     "Prompt S/R": "提示詞替換",
     "Prompt order": "提示詞順序",
     "Sampler": "採樣器",
@@ -115,13 +115,13 @@
     "Draw legend": "在圖表中包括軸標題",
     "Include Separate Images": "包括獨立的圖像",
     "Keep -1 for seeds": "保持隨機種子為-1",
-    "Drop Image Here": "拖拽圖像到此",
-    "Save": "保存",
+    "Drop Image Here": "拖曳圖像到此",
+    "Save": "儲存",
     "Send to img2img": ">> 圖生圖",
     "Send to inpaint": ">> 內補繪製",
     "Send to extras": ">> 後處理",
-    "Make Zip when Save?": "保存時生成ZIP壓縮檔案?",
-    "Textbox": "文本框",
+    "Make Zip when Save?": "儲存時生成ZIP壓縮檔案？",
+    "Textbox": "文字方塊",
     "Interrogate\nCLIP": "CLIP\n反推提示詞",
     "Interrogate\nDeepBooru": "DeepBooru\n反推提示詞",
     "Inpaint": "內補繪製",
@@ -132,7 +132,7 @@
     "Mask blur": "蒙版模糊",
     "Mask mode": "蒙版模式",
     "Draw mask": "繪製蒙版",
-    "Upload mask": "上載蒙版",
+    "Upload mask": "上傳蒙版",
     "Masking mode": "蒙版模式",
     "Inpaint masked": "內補繪製蒙版內容",
     "Inpaint not masked": "內補繪製非蒙版內容",
@@ -142,7 +142,7 @@
     "latent noise": "潛空間噪聲",
     "latent nothing": "潛空間數值零",
     "Inpaint at full resolution": "以完整解析度進行內補繪製",
-    "Inpaint at full resolution padding, pixels": "以完整解析度進行內補繪製 - 填補畫素",
+    "Inpaint at full resolution padding, pixels": "以完整解析度進行內補繪製 — 填補畫素",
     "Process images in a directory on the same machine where the server is running.": "在伺服器主機上的目錄中處理圖像",
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "指定一個空的資料夾為輸出目錄而非預設的 output 資料夾為輸出目錄",
     "Disabled when launched with --hide-ui-dir-config.": "啟動 --hide-ui-dir-config 時禁用",
@@ -158,30 +158,30 @@
     "Poor man's outpainting": "效果稍差的外補繪製",
     "SD upscale": "使用 SD 放大",
     "should be 2 or lower.": "必須小於等於2",
-    "Override `Sampling method` to Euler?(this method is built for it)": "覆寫「採樣方法」為 Euler?(這個方法就是為這樣做設計的)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆寫「提示詞」為「初始提示詞」?(包括「反向提示詞」)",
+    "Override `Sampling method` to Euler?(this method is built for it)": "覆寫「採樣方法」為 Euler？（這個方法就是為這樣做設計的）",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆寫「提示詞」為「初始提示詞」？（包括「反向提示詞」）",
     "Original prompt": "初始提示詞",
     "Original negative prompt": "初始反向提示詞",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "覆寫「採樣迭代步數」為「解碼迭代步數」?",
-    "Decode steps": "解碼迭代步數",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "覆寫「採樣疊代步數」為「解碼疊代步數」？",
+    "Decode steps": "解碼疊代步數",
     "Override `Denoising strength` to 1?": "覆寫「去噪強度」為1?",
-    "Decode CFG scale": "解碼提示詞相關性(CFG)",
+    "Decode CFG scale": "解碼提示詞相關性（CFG）",
     "Randomness": "隨機度",
     "Sigma adjustment for finding noise for image": "為尋找圖中噪點的 Sigma 調整",
-    "Loops": "迭代次數",
+    "Loops": "疊代次數",
     "Denoising strength change factor": "去噪強度的調整係數",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推薦設定：採樣迭代步數：80-100，採樣器：Euler a，去噪強度：0.8",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推薦設定：採樣疊代步數：80-100，採樣器：Euler a，去噪強度：0.8",
     "Pixels to expand": "拓展的畫素數",
     "Outpainting direction": "外補繪製的方向",
     "left": "左",
     "right": "右",
     "up": "上",
     "down": "下",
-    "Fall-off exponent (lower=higher detail)": "衰減指數(越低細節越好)",
+    "Fall-off exponent (lower=higher detail)": "衰減指數（越低細節越好）",
     "Color variation": "色彩變化",
     "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "將圖像放大到兩倍尺寸； 使用寬度和高度滑塊設定圖塊尺寸",
     "Tile overlap": "圖塊重疊的畫素",
-    "Upscaler": "放大算法",
+    "Upscaler": "放大演算法",
     "Lanczos": "Lanczos",
     "LDSR": "LDSR",
     "BSRGAN 4x": "BSRGAN 4x",
@@ -199,11 +199,11 @@
     "Scale to": "指定尺寸縮放",
     "Resize": "縮放",
     "Crop to fit": "裁剪以適應",
-    "Upscaler 2": "放大算法 2",
-    "Upscaler 2 visibility": "放大算法 2 可見度",
+    "Upscaler 2": "放大演算法 2",
+    "Upscaler 2 visibility": "放大演算法 2 可見度",
     "GFPGAN visibility": "GFPGAN 可見度",
     "CodeFormer visibility": "CodeFormer 可見度",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 權重 (0 = 最大效果, 1 = 最小效果)",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 權重 （0 = 最大效果， 1 = 最小效果）",
     "Open output directory": "打開輸出目錄",
     "Send to txt2img": ">> 文生圖",
     "A merger of the two checkpoints will be generated in your": "合併後的模型權重存檔點會生成在你的",
@@ -212,13 +212,13 @@
     "Primary model (A)": "主要模型 (A)",
     "Secondary model (B)": "第二模型 (B)",
     "Tertiary model (C)": "第三模型 (C)",
-    "Custom Name (Optional)": "自定義名稱 (可選)",
+    "Custom Name (Optional)": "自訂名稱 （可選）",
     "Multiplier (M) - set to 0 to get model A": "倍率 (M) - 設為 0 等價於模型 A",
     "Interpolation Method": "插值方法",
     "Weighted sum": "加權和",
-    "Add difference": "添加差分",
+    "Add difference": "加入差分",
     "Save as float16": "以 float16 儲存",
-    "See": "查看",
+    "See": "檢視",
     "wiki": "wiki",
     "for detailed explanation.": "以了解詳細說明",
     "Create embedding": "生成 embedding",
@@ -229,7 +229,7 @@
     "Initialization text": "初始化文字",
     "Number of vectors per token": "每個 token 的向量數",
     "Overwrite Old Embedding": "覆寫舊的 Embedding",
-    "Modules": "模塊",
+    "Modules": "模組",
     "Enter hypernetwork layer structure": "輸入 hypernetwork 層結構",
     "Select activation function of hypernetwork": "選擇 hypernetwork 的激活函數",
     "linear": "linear",
@@ -237,10 +237,10 @@
     "leakyrelu": "leakyrelu",
     "elu": "elu",
     "swish": "swish",
-    "Add layer normalization": "添加層標準化",
+    "Add layer normalization": "加入層標準化",
     "Use dropout": "採用 dropout 防止過擬合",
     "Overwrite Old Hypernetwork": "覆寫舊的 Hypernetwork",
-    "Source directory": "源目錄",
+    "Source directory": "來源目錄",
     "Destination directory": "目標目錄",
     "Existing Caption txt Action": "對已有的TXT說明文字的行為",
     "ignore": "無視",
@@ -250,8 +250,8 @@
     "Create flipped copies": "生成鏡像副本",
     "Split oversized images into two": "將過大的圖像分為兩份",
     "Split oversized images": "分割過大的圖像",
-    "Use BLIP for caption": "使用 BLIP 生成說明文字(自然語言描述)",
-    "Use deepbooru for caption": "使用 deepbooru 生成說明文字(標籤)",
+    "Use BLIP for caption": "使用 BLIP 生成說明文字（自然語言描述）",
+    "Use deepbooru for caption": "使用 deepbooru 生成說明文字（標籤）",
     "Split image threshold": "圖像分割閾值",
     "Split image overlap ratio": "分割圖像重疊的比率",
     "Preprocess": "預處理",
@@ -262,17 +262,17 @@
     "Embedding Learning rate": "Embedding 學習率",
     "Hypernetwork Learning rate": "Hypernetwork 學習率",
     "Learning rate": "學習率",
-    "Dataset directory": "數據集目錄",
+    "Dataset directory": "資料集目錄",
     "Log directory": "日誌目錄",
     "Prompt template file": "提示詞模版檔案",
-    "Max steps": "最大迭代步數",
-    "Save an image to log directory every N steps, 0 to disable": "每 N 步保存一個圖像到日誌目錄，0 表示禁用",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步將 embedding 的副本保存到日誌目錄，0 表示禁用",
-    "Save images with embedding in PNG chunks": "保存圖像並在 PNG 檔案中嵌入 embedding 檔案",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "進行預覽時從文生圖選項卡中讀取參數（提示詞等）",
+    "Max steps": "最大疊代步數",
+    "Save an image to log directory every N steps, 0 to disable": "每 N 步儲存一個圖像到日誌目錄，0 表示禁用",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步將 embedding 的副本儲存到日誌目錄，0 表示禁用",
+    "Save images with embedding in PNG chunks": "儲存圖像並在 PNG 檔案中嵌入 embedding 檔案",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "進行預覽時從文生圖頁籤中讀取參數（提示詞等）",
     "Train Hypernetwork": "訓練 Hypernetwork",
     "Train Embedding": "訓練 Embedding",
-    "Create an aesthetic embedding out of any number of images": "從任意數量的圖像中創建美術風格 embedding",
+    "Create an aesthetic embedding out of any number of images": "從任意數量的圖像中建立美術風格 embedding",
     "Create images embedding": "生成圖集 embedding",
     "txt2img history": "文生圖歷史記錄",
     "img2img history": "圖生圖歷史記錄",
@@ -280,7 +280,7 @@
     "Renew Page": "刷新頁面",
     "extras": "後處理",
     "favorites": "收藏夾",
-    "custom fold": "自定義資料夾",
+    "custom fold": "自訂資料夾",
     "Load": "載入",
     "Images directory": "圖像目錄",
     "Prev batch": "上一批",
@@ -292,7 +292,7 @@
     "End Page": "尾頁",
     "number of images to delete consecutively next": "接下來要連續刪除的圖像數",
     "Delete": "刪除",
-    "Generate Info": "生成信息",
+    "Generate Info": "生成資訊",
     "File Name": "檔案名",
     "Collect": "收藏",
     "Refresh page": "刷新頁面",
@@ -300,26 +300,26 @@
     "Number": "數量",
     "set_index": "設定索引",
     "Checkbox": "核取方塊",
-    "Apply settings": "保存設定",
-    "Saving images/grids": "保存圖像/概覽圖",
-    "Always save all generated images": "始終保存所有生成的圖像",
+    "Apply settings": "儲存設定",
+    "Saving images/grids": "儲存圖像／概覽圖",
+    "Always save all generated images": "始終儲存所有生成的圖像",
     "File format for images": "圖像的檔案格式",
     "Images filename pattern": "圖像檔案名格式",
-    "Always save all generated image grids": "始終保存所有生成的概覽圖",
+    "Always save all generated image grids": "始終儲存所有生成的概覽圖",
     "File format for grids": "概覽圖的檔案格式",
-    "Add extended info (seed, prompt) to filename when saving grid": "保存概覽時將擴展信息（隨機種子、提示詞）添加到檔案名",
-    "Do not save grids consisting of one picture": "只有一張圖片時不要保存概覽圖",
+    "Add extended info (seed, prompt) to filename when saving grid": "儲存概覽時將擴展資訊（隨機種子，提示詞）加入到檔案名",
+    "Do not save grids consisting of one picture": "只有一張圖片時不要儲存概覽圖",
     "Prevent empty spots in grid (when set to autodetect)": "（在自動檢測時）防止概覽圖中出現空位",
     "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "概覽行數； 使用 -1 進行自動檢測，使用 0 使其與批量大小相同",
-    "Save text information about generation parameters as chunks to png files": "將有關生成參數的文本信息作為塊保存到PNG檔案中",
-    "Create a text file next to every image with generation parameters.": "保存圖像時在每個圖像旁邊創建一個文本檔案儲存生成參數",
-    "Save a copy of image before doing face restoration.": "在進行面部修復之前保存圖像副本",
-    "Quality for saved jpeg images": "保存的JPEG圖像的質量",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 圖像大於 4MB 或寬高大於 4000，則縮小並保存副本為 JPG",
-    "Use original name for output filename during batch process in extras tab": "在後處理選項卡中的批量處理過程中使用原始名稱作為輸出檔案名",
-    "When using 'Save' button, only save a single selected image": "使用「保存」按鈕時，只保存一個選定的圖像",
-    "Do not add watermark to images": "不要給圖像加水印",
-    "Paths for saving": "保存路徑",
+    "Save text information about generation parameters as chunks to png files": "將有關生成參數的文本資訊作為塊儲存到PNG檔案中",
+    "Create a text file next to every image with generation parameters.": "儲存圖像時在每個圖像旁邊建立一個文本檔案儲存生成參數",
+    "Save a copy of image before doing face restoration.": "在進行面部修復之前儲存圖像副本",
+    "Quality for saved jpeg images": "儲存的JPEG圖像的品質",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 圖像大於 4MB 或寬高大於 4000，則縮小並儲存副本為 JPG",
+    "Use original name for output filename during batch process in extras tab": "在後處理頁籤中的批量處理過程中使用原始名稱作為輸出檔案名",
+    "When using 'Save' button, only save a single selected image": "使用「儲存」按鈕時，只儲存一個選定的圖像",
+    "Do not add watermark to images": "不要給圖像加浮水印",
+    "Paths for saving": "儲存路徑",
     "Output directory for images; if empty, defaults to three directories below": "圖像的輸出目錄； 如果為空，則預設為以下三個目錄",
     "Output directory for txt2img images": "文生圖的輸出目錄",
     "Output directory for img2img images": "圖生圖的輸出目錄",
@@ -327,119 +327,119 @@
     "Output directory for grids; if empty, defaults to two directories below": "概覽圖的輸出目錄； 如果為空，則預設為以下兩個目錄",
     "Output directory for txt2img grids": "文生圖概覽的輸出目錄",
     "Output directory for img2img grids": "圖生圖概覽的輸出目錄",
-    "Directory for saving images using the Save button": "使用「保存」按鈕保存圖像的目錄",
-    "Saving to a directory": "保存到目錄",
-    "Save images to a subdirectory": "將圖像保存到子目錄",
-    "Save grids to a subdirectory": "將概覽圖保存到子目錄",
-    "When using \"Save\" button, save images to a subdirectory": "使用「保存」按鈕時，將圖像保存到子目錄",
+    "Directory for saving images using the Save button": "使用「儲存」按鈕儲存圖像的目錄",
+    "Saving to a directory": "儲存到目錄",
+    "Save images to a subdirectory": "將圖像儲存到子目錄",
+    "Save grids to a subdirectory": "將概覽圖儲存到子目錄",
+    "When using \"Save\" button, save images to a subdirectory": "使用「儲存」按鈕時，將圖像儲存到子目錄",
     "Directory name pattern": "目錄名稱格式",
     "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示詞數量",
     "Upscaling": "放大",
     "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的圖塊尺寸。0 = 不分塊",
     "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN 的圖塊重疊畫素。低值 = 可見接縫",
-    "Tile size for all SwinIR.": "適用所有 SwinIR 系算法的圖塊尺寸",
+    "Tile size for all SwinIR.": "適用所有 SwinIR 系演算法的圖塊尺寸",
     "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR 的圖塊重疊畫素。低值 = 可見接縫",
-    "LDSR processing steps. Lower = faster": "LDSR 處理迭代步數。更低 = 更快",
-    "Upscaler for img2img": "圖生圖的放大算法",
+    "LDSR processing steps. Lower = faster": "LDSR 處理疊代步數。更低 = 更快",
+    "Upscaler for img2img": "圖生圖的放大演算法",
     "Upscale latent space image when doing hires. fix": "做高解析度修復時也放大潛空間圖像",
     "Face restoration": "面部修復",
     "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 權重參數； 0 = 最大效果； 1 = 最小效果",
-    "Move face restoration model from VRAM into RAM after processing": "面部修復處理完成後將面部修復模型從顯存(VRAM)移至內存(RAM)",
+    "Move face restoration model from VRAM into RAM after processing": "面部修復處理完成後將面部修復模型從顯存（VRAM）移至內存（RAM）",
     "System": "系統",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成圖像時每秒輪詢顯存(VRAM)使用情況的次數。設定為 0 以禁用",
-    "Always print all generation info to standard output": "始終將所有生成信息輸出到 standard output (一般為控制台)",
-    "Add a second progress bar to the console that shows progress for an entire job.": "向控制台添加第二個進度列，顯示整個作業的進度",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成圖像時每秒輪詢顯存（VRAM）使用情況的次數。設定為 0 以禁用",
+    "Always print all generation info to standard output": "始終將所有生成資訊輸出到 standard output （一般為控制台）",
+    "Add a second progress bar to the console that shows progress for an entire job.": "向控制台加入第二個進度列，顯示整個作業的進度",
     "Training": "訓練",
-    "Unload VAE and CLIP from VRAM when training": "訓練時從顯存(VRAM)中取消 VAE 和 CLIP 的加載",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "訓練時將 VAE 和 CLIP 從顯存(VRAM)移放到內存(RAM)，節省顯存(VRAM)",
+    "Unload VAE and CLIP from VRAM when training": "訓練時從顯存（VRAM）中取消 VAE 和 CLIP 的載入",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "訓練時將 VAE 和 CLIP 從顯存（VRAM）移放到內存（RAM），節省顯存（VRAM）",
     "Filename word regex": "檔案名用詞的正則表達式",
-    "Filename join string": "檔案名連接用字符串",
+    "Filename join string": "檔案名連接用字串",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每個 epoch 中單個輸入圖像的重複次數； 僅用於顯示 epoch 數",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步保存一個包含 loss 的CSV到日誌目錄，0 表示禁用",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步儲存一個包含 loss 的CSV到日誌目錄，0 表示禁用",
     "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "緩存在內存(RAM)中的模型權重存檔點",
+    "Checkpoints to cache in RAM": "快取在內存（RAM）中的模型權重存檔點",
     "Hypernetwork strength": "Hypernetwork 強度",
-    "Apply color correction to img2img results to match original colors.": "對圖生圖結果應用顏色校正以匹配原始顏色",
-    "Save a copy of image before applying color correction to img2img results": "在對圖生圖結果應用顏色校正之前保存圖像副本",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在進行圖生圖的時候，確切地執行滑塊指定的迭代步數（正常情況下更弱的去噪需要更少的迭代步數）",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 採樣器中啟用量化以獲得更清晰、更清晰的結果。這可能會改變現有的隨機種子。需要重新啟動才能應用",
+    "Apply color correction to img2img results to match original colors.": "對圖生圖結果套用顏色校正以匹配原始顏色",
+    "Save a copy of image before applying color correction to img2img results": "在對圖生圖結果套用顏色校正之前儲存圖像副本",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在進行圖生圖的時候，確切地執行滑塊指定的疊代步數（正常情況下更弱的去噪需要更少的疊代步數）",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 採樣器中啟用量化以獲得更清晰，更清晰的結果。這可能會改變現有的隨機種子。需要重新啟動才能套用",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "強調符：使用 (文字) 使模型更關注該文本，使用 [文字] 使其減少關注",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用舊的強調符實現。可用於復現舊隨機種子",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用舊的強調符實作。可用於復現舊隨機種子",
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 採樣器批量生成與生成單個圖像時產出相同的圖像",
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "當使用超過 75 個 token 時，通過從 n 個 token 中的最後一個逗號填補來提高一致性",
     "Filter NSFW content": "過濾成人內容",
     "Stop At last layers of CLIP model": "在 CLIP 模型的最後哪一層停下",
     "Interrogate Options": "反推提示詞選項",
-    "Interrogate: keep models in VRAM": "反推: 將模型保存在顯存(VRAM)中",
-    "Interrogate: use artists from artists.csv": "反推: 使用 artists.csv 中的藝術家",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "反推: 在生成結果中包含與模型標籤相匹配的等級（對基於生成自然語言描述的反推沒有影響）",
-    "Interrogate: num_beams for BLIP": "反推: BLIP 的 num_beams",
-    "Interrogate: minimum description length (excluding artists, etc..)": "反推: 最小描述長度（不包括藝術家, 等…）",
-    "Interrogate: maximum description length": "反推: 最大描述長度",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: 文本檔案中的最大行數（0 = 無限制）",
-    "Interrogate: deepbooru score threshold": "反推: deepbooru 分數閾值",
-    "Interrogate: deepbooru sort alphabetically": "反推: deepbooru 按字母順序排序",
+    "Interrogate: keep models in VRAM": "反推： 將模型儲存在顯存（VRAM）中",
+    "Interrogate: use artists from artists.csv": "反推： 使用 artists.csv 中的藝術家",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "反推： 在生成結果中包含與模型標籤相匹配的等級（對基於生成自然語言描述的反推沒有影響）",
+    "Interrogate: num_beams for BLIP": "反推： BLIP 的 num_beams",
+    "Interrogate: minimum description length (excluding artists, etc..)": "反推： 最小描述長度（不包括藝術家, 等…）",
+    "Interrogate: maximum description length": "反推： 最大描述長度",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP： 文本檔案中的最大行數（0 = 無限制）",
+    "Interrogate: deepbooru score threshold": "反推： deepbooru 分數閾值",
+    "Interrogate: deepbooru sort alphabetically": "反推： deepbooru 按字母順序排序",
     "use spaces for tags in deepbooru": "在 deepbooru 中為標籤使用空格",
     "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "在 deepbooru 中使用轉義 (\\) 括號（因此它們用作文字括號而不是強調符號）",
-    "User interface": "用戶界面",
+    "User interface": "使用者介面",
     "Show progressbar": "顯示進度列",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 個採樣迭代步數顯示圖像生成進度。設定 0 禁用",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 個採樣疊代步數顯示圖像生成進度。設定 0 禁用",
     "Show previews of all images generated in a batch as a grid": "以網格的形式預覽所有批量生成出來的圖像",
     "Show grid in results for web": "在網頁的結果中顯示概覽圖",
     "Do not show any images in results for web": "不在網頁的結果中顯示任何圖像",
-    "Add model hash to generation information": "將模型的雜湊值添加到生成信息",
-    "Add model name to generation information": "將模型名稱添加到生成信息",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "當從文本讀取生成參數到 UI（從 PNG 信息或粘貼文本）時，不要更改選定的模型權重存檔點",
+    "Add model hash to generation information": "將模型的雜湊值加入到生成資訊",
+    "Add model name to generation information": "將模型名稱加入到生成資訊",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "當從文本讀取生成參數到 UI（從 PNG 資訊或粘貼文本）時，不要更改選定的模型權重存檔點",
     "Font for image grids that have text": "有文字的概覽圖使用的字體",
-    "Enable full page image viewer": "啟用整頁圖像查看器",
-    "Show images zoomed in by default in full page image viewer": "在整頁圖像查看器中預設放大顯示圖像",
-    "Show generation progress in window title.": "在窗口標題中顯示生成進度",
+    "Enable full page image viewer": "啟用整頁圖像檢視器",
+    "Show images zoomed in by default in full page image viewer": "在整頁圖像檢視器中預設放大顯示圖像",
+    "Show generation progress in window title.": "在視窗標題中顯示生成進度",
     "Quicksettings list": "快速設定列表",
     "Localization (requires restart)": "本地化（需要重新啟動）",
     "Sampler parameters": "採樣器參數",
-    "Hide samplers in user interface (requires restart)": "在用戶界面中隱藏採樣器（需要重新啟動）",
-    "eta (noise multiplier) for DDIM": "DDIM 的 eta (噪聲乘數) ",
-    "eta (noise multiplier) for ancestral samplers": "ancestral 採樣器的 eta (噪聲乘數)",
+    "Hide samplers in user interface (requires restart)": "在使用者介面中隱藏採樣器（需要重新啟動）",
+    "eta (noise multiplier) for DDIM": "DDIM 的 eta （噪聲乘數）",
+    "eta (noise multiplier) for ancestral samplers": "ancestral 採樣器的 eta （噪聲乘數）",
     "img2img DDIM discretize": "圖生圖 DDIM 離散化",
     "uniform": "均勻",
     "quad": "二階",
     "sigma churn": "sigma churn",
     "sigma tmin": "最小(tmin) sigma",
     "sigma noise": "sigma 噪聲",
-    "Eta noise seed delta": "Eta 噪聲種子偏移(noise seed delta)",
+    "Eta noise seed delta": "Eta 噪聲種子偏移（noise seed delta）",
     "Images Browser": "圖庫瀏覽器",
     "Preload images at startup": "在啟動時預載圖像",
     "Number of columns on the page": "每頁列數",
     "Number of rows on the page": "每頁行數",
     "Number of pictures displayed on each page": "每頁顯示的圖像數量",
-    "Minimum number of pages per load": "每次加載的最小頁數",
+    "Minimum number of pages per load": "每次載入的最小頁數",
     "Number of grids in each row": "每行顯示多少格",
-    "Wildcards": "通配符",
+    "Wildcards": "萬用字元",
     "Use same seed for all images": "為所有圖像使用同一個隨機種子",
     "Request browser notifications": "請求瀏覽器通知",
     "Download localization template": "下載本地化模板",
-    "Reload custom script bodies (No ui updates, No restart)": "重新加載自定義腳本主體（無UI更新，無重啟）",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重啟 Gradio 及刷新組件（僅限自定義腳本、ui.py、JS 和 CSS）",
+    "Reload custom script bodies (No ui updates, No restart)": "重新載入自訂指令碼主體（無UI更新，無重啟）",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重啟 Gradio 及刷新組件（僅限自訂指令碼，ui.py，JS 和 CSS）",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示詞（按 Ctrl+Enter 或 Alt+Enter 生成）",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示詞（按 Ctrl+Enter 或 Alt+Enter 生成）",
-    "Add a random artist to the prompt.": "隨機添加一個藝術家到提示詞中",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "從提示詞中讀取生成參數，如果提示詞為空，則讀取上一次的生成參數到用戶界面",
+    "Add a random artist to the prompt.": "隨機加入一個藝術家到提示詞中",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "從提示詞中讀取生成參數，如果提示詞為空，則讀取上一次的生成參數到使用者介面",
     "Save style": "存儲為模板風格",
-    "Apply selected styles to current prompt": "將所選樣式應用於當前提示",
+    "Apply selected styles to current prompt": "將所選樣式套用於當前提示",
     "Stop processing current image and continue processing.": "停止處理當前圖像並繼續處理下一個",
     "Stop processing images and return any results accumulated so far.": "停止處理圖像並返回迄今為止累積的任何結果",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要應用的模版風格； 模版風格包含正向和反向提示詞，並應用於兩者",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要套用的模版風格； 模版風格包含正向和反向提示詞，並套用於兩者",
     "Do not do anything special": "什麼都不做",
-    "Which algorithm to use to produce the image": "使用哪種算法生成圖像",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有創意，可以根據迭代步數獲得完全不同的圖像，將迭代步數設定為高於 30-40 不會有正面作用",
+    "Which algorithm to use to produce the image": "使用哪種演算法生成圖像",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有創意，可以根據疊代步數獲得完全不同的圖像，將疊代步數設定為高於 30-40 不會有正面作用",
     "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅長內補繪製",
     "Produce an image that can be tiled.": "生成可用於平舖的圖像",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用兩步處理的時候以較小的解析度生成初步圖像、接著放大圖像，然後在不更改構圖的情況下改進其中的細節",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "決定算法對圖像內容的影響程度。設定 0 時，什麼都不會改變，而在 1 時，你將獲得不相關的圖像。值低於 1.0 時，處理的迭代步數將少於「採樣迭代步數」滑塊指定的步數",
-    "How many batches of images to create": "創建多少批次的圖像",
-    "How many image to create in a single batch": "每批創建多少圖像",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用兩步處理的時候以較小的解析度生成初步圖像，接著放大圖像，然後在不更改構圖的情況下改進其中的細節",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "決定演算法對圖像內容的影響程度。設定 0 時，什麼都不會改變，而在 1 時，你將獲得不相關的圖像。值低於 1.0 時，處理的疊代步數將少於「採樣疊代步數」滑塊指定的步數",
+    "How many batches of images to create": "建立多少批次的圖像",
+    "How many image to create in a single batch": "每批建立多少圖像",
     "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 圖像應在多大程度上服從提示詞 - 較低的值會產生更有創意的結果",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一個固定隨機數生成器輸出的值 - 以相同參數和隨機種子生成的圖像會得到相同的結果",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一個固定隨機數生成器輸出的值 — 以相同參數和隨機種子生成的圖像會得到相同的結果",
     "Set seed to -1, which will cause a new random number to be used every time": "將隨機種子設定為-1，則每次都會使用一個新的隨機數",
     "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的隨機種子，如果想要固定結果就會很有用",
     "Seed of a different picture to be mixed into the generation.": "將要參與生成的另一張圖的隨機種子",
@@ -448,7 +448,7 @@
     "This text is used to rotate the feature space of the imgs embs": "此文本用於旋轉圖集 embeddings 的特徵空間",
     "Separate values for X axis using commas.": "使用逗號分隔 X 軸的值",
     "Separate values for Y axis using commas.": "使用逗號分隔 Y 軸的值",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "將圖像寫入目錄（預設 - log/images）並將生成參數寫入CSV檔案",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "將圖像寫入目錄（預設 — log/images）並將生成參數寫入CSV檔案",
     "Open images output directory": "打開圖像輸出目錄",
     "How much to blur the mask before processing, in pixels.": "處理前要對蒙版進行多強的模糊，以畫素為單位",
     "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 處理蒙版區域之前要在蒙版區域內放置什麼",
@@ -460,26 +460,26 @@
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "將圖像大小調整為目標解析度。除非高度和寬度匹配，否則你將獲得不正確的縱橫比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "調整圖像大小，使整個目標解析度都被圖像填充。裁剪多出來的部分",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "調整圖像大小，使整個圖像在目標解析度內。用圖像的顏色填充空白區域",
-    "How many times to repeat processing an image and using it as input for the next iteration": "重複處理圖像並用作下次迭代輸入的次數",
+    "How many times to repeat processing an image and using it as input for the next iteration": "重複處理圖像並用作下次疊代輸入的次數",
     "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每個循環中，去噪強度都會乘以該值。<1 表示減少多樣性，因此你的這一組圖將集中在固定的圖像上。>1 意味著增加多樣性，因此你的這一組圖將變得越來越混亂",
     "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大時，圖塊之間應該有多少畫素重疊。圖塊之間需要重疊才可以讓它們在合併回一張圖像時，沒有清晰可見的接縫",
     "A directory on the same machine where the server is running.": "與伺服器主機上的目錄",
-    "Leave blank to save images to the default path.": "留空以將圖像保存到預設路徑",
+    "Leave blank to save images to the default path.": "留空以將圖像儲存到預設路徑",
     "Result = A * (1 - M) + B * M": "結果 = A * (1 - M) + B * M",
     "Result = A + (B - C) * M": "結果 = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "第一個和最後一個數字必須是 1。例:'1, 2, 1'",
-    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "訓練應該多快。低值將需要更長的時間來訓練，高值可能無法收斂（無法產生準確的結果）以及/也許可能會破壞 embedding（如果你在訓練信息文本框中看到 Loss: nan 就會發生這種情況。如果發生這種情況，你需要從較舊的未損壞的備份手動恢復 embedding）\n\n你可以使用以下語法設定單個數值或多個學習率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步將以 0.005 的速率訓練，接著直到 1000 步為止以 1e-3 訓練，然後剩餘所有步以 1e-5 訓練",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "第一個和最後一個數字必須是 1。例：'1, 2, 1'",
+    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "訓練應該多快。低值將需要更長的時間來訓練，高值可能無法收斂（無法產生準確的結果）以及／也許可能會破壞 embedding（如果你在訓練資訊文字方塊中看到 Loss: nan 就會發生這種情況。如果發生這種情況，你需要從較舊的未損壞的備份手動恢復 embedding）\n\n你可以使用以下語法設定單個數值或多個學習率：\n\n   率1:步限1, 率2:步限2, …\n\n如：   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步將以 0.005 的速率訓練，接著直到 1000 步為止以 1e-3 訓練，然後剩餘所有步以 1e-5 訓練",
     "Path to directory with input images": "帶有輸入圖像的路徑",
     "Path to directory where to write outputs": "進行輸出的路徑",
     "Input images directory": "輸入圖像目錄",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下標籤定義如何選擇圖像的檔案名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 預設請留空",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果啟用此選項，水印將不會添加到生成出來的圖像中。警告：如果你不添加水印，你的行為可能是不符合道德操守的",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下標籤定義如何選擇圖像和概覽圖的子目錄: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 預設請留空",
-    "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神經網路修復低質量面部",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下標籤定義如何選擇圖像的檔案名： [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]； 預設請留空",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果啟用此選項，浮水印將不會加入到生成出來的圖像中。警告：如果你不加入浮水印，你的行為可能是不符合道德操守的",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下標籤定義如何選擇圖像和概覽圖的子目錄： [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]； 預設請留空",
+    "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神經網路修復低品質面部",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正則表達式將用於從檔案名中提取單詞，並將使用以下選項將它們接合到用於訓練的標籤文本中。留空以保持檔案名文本不變",
-    "This string will be used to join split words into a single line if the option above is enabled.": "如果啟用了上述選項，則此處的字符會用於將拆分的單詞接合為同一行",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "設定名稱列表，以逗號分隔，設定應轉到頂部的快速訪問欄，而不是通常的設定選項卡。有關設定名稱，請參見 modules/shared.py。需要重新啟動才能應用",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果這個值不為零，它將被添加到隨機種子中，並在使用帶有 Eta 的採樣器時用於初始化隨機噪聲。你可以使用它來產生更多的圖像變化，或者你可以使用它來模仿其他軟件生成的圖像，如果你知道你在做什麼",
+    "This string will be used to join split words into a single line if the option above is enabled.": "如果啟用了上述選項，則此處的字元會用於將拆分的單詞接合為同一行",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "設定名稱列表，以逗號分隔，設定應轉到頂部的快速存取列，而不是通常的設定頁籤。有關設定名稱，請參見 modules/shared.py。需要重新啟動才能套用",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果這個值不為零，它將被加入到隨機種子中，並在使用帶有 Eta 的採樣器時用於初始化隨機噪聲。你可以使用它來產生更多的圖像變化，或者你可以使用它來模仿其他軟體生成的圖像，如果你知道你在做什麼",
     "Enable Autocomplete": "開啟Tag補全",
     "Allowed categories for random artists selection when using the Roll button": "使用抽選藝術家按鈕時將會隨機的藝術家類別",
     "Roll three": "抽三位出來",
-- 
cgit v1.2.1


From 5d5dc64064d8ca399a76fe44dbb62bdef6c4b7c4 Mon Sep 17 00:00:00 2001
From: Antonio <antonio.hernandez.uk@gmail.com>
Date: Fri, 28 Oct 2022 05:49:39 +0200
Subject: Natural sorting for dropdown checkpoint list

Example:

Before					After

11.ckpt					11.ckpt
ab.ckpt					ab.ckpt
ade_pablo_step_1000.ckpt	ade_pablo_step_500.ckpt
ade_pablo_step_500.ckpt	ade_pablo_step_1000.ckpt
ade_step_1000.ckpt		ade_step_500.ckpt
ade_step_1500.ckpt		ade_step_1000.ckpt
ade_step_2000.ckpt		ade_step_1500.ckpt
ade_step_2500.ckpt		ade_step_2000.ckpt
ade_step_3000.ckpt		ade_step_2500.ckpt
ade_step_500.ckpt			ade_step_3000.ckpt
atp_step_5500.ckpt			atp_step_5500.ckpt
model1.ckpt				model1.ckpt
model10.ckpt				model10.ckpt
model1000.ckpt			model33.ckpt
model33.ckpt				model50.ckpt
model400.ckpt			model400.ckpt
model50.ckpt				model1000.ckpt
moo44.ckpt				moo44.ckpt
v1-4-pruned-emaonly.ckpt	v1-4-pruned-emaonly.ckpt
v1-5-pruned-emaonly.ckpt	v1-5-pruned-emaonly.ckpt
v1-5-pruned.ckpt			v1-5-pruned.ckpt
v1-5-vae.ckpt				v1-5-vae.ckpt
---
 modules/sd_models.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index e697bb72..64d5ee0d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -3,6 +3,7 @@ import os.path
 import sys
 from collections import namedtuple
 import torch
+import re
 from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
@@ -35,8 +36,10 @@ def setup_model():
     list_models()
 
 
-def checkpoint_tiles():
-    return sorted([x.title for x in checkpoints_list.values()])
+def checkpoint_tiles(): 
+    convert = lambda name: int(name) if name.isdigit() else name.lower() 
+    alphanumeric_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)] 
+    return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key)
 
 
 def list_models():
-- 
cgit v1.2.1


From b2a8b263b2f09bd772f75502c5a83656580f34ec Mon Sep 17 00:00:00 2001
From: benkyoujouzu <benkyoujouzu@gmail.com>
Date: Thu, 27 Oct 2022 13:00:47 +0800
Subject: Add missing support for linear activation in hypernetwork

---
 modules/hypernetworks/hypernetwork.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8113b35b..87cf3cf3 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -25,6 +25,7 @@ from statistics import stdev, mean
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
     activation_dict = {
+        "linear": torch.nn.Identity,
         "relu": torch.nn.ReLU,
         "leakyrelu": torch.nn.LeakyReLU,
         "elu": torch.nn.ELU,
-- 
cgit v1.2.1


From 9e465c8aa5616df4c6723bee007ffd3910404f12 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Thu, 27 Oct 2022 23:03:34 -0700
Subject: Add strength to textinfo.

---
 modules/processing.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/processing.py b/modules/processing.py
index 4efba946..93066522 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -329,6 +329,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
         "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
+        "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.1


From d4a069a23cb19104b4e58a33d0d1670fadaefb7a Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Thu, 27 Oct 2022 23:16:27 -0700
Subject: Read hypernet strength from PNG info.

---
 modules/ui.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/ui.py b/modules/ui.py
index 0a63e357..62a2f4f3 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1812,6 +1812,7 @@ Requested path was: {f}
 
         settings_map = {
             'sd_hypernetwork': 'Hypernet',
+            'sd_hypernetwork_strength': 'Hypernetwork strength',
             'CLIP_stop_at_last_layers': 'Clip skip',
             'sd_model_checkpoint': 'Model hash',
         }
-- 
cgit v1.2.1


From c0677b33161f04c3ed1a7a78f4c7288fb95787b7 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Thu, 27 Oct 2022 23:31:45 -0700
Subject: Explicitly state when Hypernet is none.

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 93066522..74a0cd64 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -328,7 +328,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
+        "Hypernet": ("None" if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
         "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
-- 
cgit v1.2.1


From db5a354c489bfd1c95e0bbf9af12ab8b5d6fe170 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Fri, 28 Oct 2022 01:41:57 -0700
Subject: Always ignore "None.pt" in the hypernet directory.

---
 modules/hypernetworks/hypernetwork.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8113b35b..cd920df5 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -208,13 +208,16 @@ def list_hypernetworks(path):
     res = {}
     for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
         name = os.path.splitext(os.path.basename(filename))[0]
-        res[name] = filename
+        # Prevent a hypothetical "None.pt" from being listed.
+        if name != "None":
+            res[name] = filename
     return res
 
 
 def load_hypernetwork(filename):
     path = shared.hypernetworks.get(filename, None)
-    if path is not None:
+    # Prevent any file named "None.pt" from being loaded.
+    if path is not None and filename != "None":
         print(f"Loading hypernetwork {filename}")
         try:
             shared.loaded_hypernetwork = Hypernetwork()
-- 
cgit v1.2.1


From 9ceef81f77ecce89f0c8f412c4d849210d852e82 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Fri, 28 Oct 2022 20:48:08 +0700
Subject: Fix log off by 1

---
 modules/hypernetworks/hypernetwork.py          | 12 +++++++-----
 modules/textual_inversion/learn_schedule.py    |  2 +-
 modules/textual_inversion/textual_inversion.py | 24 ++++++++++++------------
 3 files changed, 20 insertions(+), 18 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8113b35b..a0297997 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -428,7 +428,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             optimizer.step()
 
-        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
+        steps_done = hypernetwork.step + 1
+
+        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
             raise RuntimeError("Loss diverged.")
         
         if len(previous_mean_losses) > 1:
@@ -438,9 +440,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
         pbar.set_description(dataset_loss_info)
 
-        if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
+        if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
             # Before saving, change name to match current checkpoint.
-            hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'
+            hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
             hypernetwork.save(last_saved_file)
 
@@ -449,8 +451,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             "learn_rate": scheduler.learn_rate
         })
 
-        if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
-            forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'
+        if images_dir is not None and steps_done % create_image_every == 0:
+            forced_filename = f'{hypernetwork_name}-{steps_done}'
             last_saved_image = os.path.join(images_dir, forced_filename)
 
             optimizer.zero_grad()
diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index 2062726a..3a736065 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -52,7 +52,7 @@ class LearnRateScheduler:
         self.finished = False
 
     def apply(self, optimizer, step_number):
-        if step_number <= self.end_step:
+        if step_number < self.end_step:
             return
 
         try:
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index ff002d3e..17dfb223 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -184,9 +184,8 @@ def write_loss(log_directory, filename, step, epoch_len, values):
     if shared.opts.training_write_csv_every == 0:
         return
 
-    if step % shared.opts.training_write_csv_every != 0:
+    if (step + 1) % shared.opts.training_write_csv_every != 0:
         return
-
     write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
 
     with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
@@ -196,11 +195,11 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             csv_writer.writeheader()
 
         epoch = step // epoch_len
-        epoch_step = step - epoch * epoch_len
+        epoch_step = step % epoch_len 
 
         csv_writer.writerow({
             "step": step + 1,
-            "epoch": epoch + 1,
+            "epoch": epoch,
             "epoch_step": epoch_step + 1,
             **values,
         })
@@ -282,15 +281,16 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             loss.backward()
             optimizer.step()
 
+        steps_done = embedding.step + 1
 
         epoch_num = embedding.step // len(ds)
-        epoch_step = embedding.step - (epoch_num * len(ds)) + 1
+        epoch_step = embedding.step % len(ds)
 
-        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")
+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
 
-        if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
+        if embedding_dir is not None and steps_done % save_embedding_every == 0:
             # Before saving, change name to match current checkpoint.
-            embedding.name = f'{embedding_name}-{embedding.step}'
+            embedding.name = f'{embedding_name}-{steps_done}'
             last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
             embedding.save(last_saved_file)
             embedding_yet_to_be_embedded = True
@@ -300,8 +300,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             "learn_rate": scheduler.learn_rate
         })
 
-        if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
-            forced_filename = f'{embedding_name}-{embedding.step}'
+        if images_dir is not None and steps_done % create_image_every == 0:
+            forced_filename = f'{embedding_name}-{steps_done}'
             last_saved_image = os.path.join(images_dir, forced_filename)
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
@@ -334,7 +334,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
             if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
 
-                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
+                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
 
                 info = PngImagePlugin.PngInfo()
                 data = torch.load(last_saved_file)
@@ -350,7 +350,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 checkpoint = sd_models.select_checkpoint()
                 footer_left = checkpoint.model_name
                 footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '{}v {}s'.format(vectorSize, embedding.step)
+                footer_right = '{}v {}s'.format(vectorSize, steps_done)
 
                 captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
                 captioned_image = insert_image_data_embed(captioned_image, data)
-- 
cgit v1.2.1


From 26d08193848568b06105a1ee7b76f338ebf0f0ee Mon Sep 17 00:00:00 2001
From: Chris OBryan <13701027+cobryan05@users.noreply.github.com>
Date: Fri, 28 Oct 2022 13:24:11 -0500
Subject: extras: Add option to run upscaling before face fixing

Face restoration can look much better if ran after upscaling, as it
allows the restoration to fix upscaling artifacts. This patch adds
an option to choose which order to run upscaling/face fixing in.
---
 modules/extras.py | 145 +++++++++++++++++++++++++++++++++++-------------------
 modules/ui.py     |   4 ++
 2 files changed, 99 insertions(+), 50 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 22c5a1c1..79047f3a 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -7,6 +7,10 @@ from PIL import Image
 import torch
 import tqdm
 
+from typing import Callable, List, Tuple
+from functools import partial
+from dataclasses import dataclass
+
 from modules import processing, shared, images, devices, sd_models
 from modules.shared import opts
 import modules.gfpgan_model
@@ -20,7 +24,7 @@ import gradio as gr
 cached_images = {}
 
 
-def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool ):
     devices.torch_gc()
 
     imageArr = []
@@ -56,68 +60,109 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     else:
         outpath = opts.outdir_samples or opts.outdir_extras_samples
 
-    
-    for image, image_name in zip(imageArr, imageNameArr):
-        if image is None:
-            return outputs, "Please select an input image.", ''
-        existing_pnginfo = image.info or {}
 
-        image = image.convert("RGB")
-        info = ""
+    # Extra operation definitions
+    def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
+        res = Image.fromarray(restored_img)
+
+        if gfpgan_visibility < 1.0:
+            res = Image.blend(image, res, gfpgan_visibility)
+
+        info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
+        return (res, info)
 
-        if gfpgan_visibility > 0:
-            restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
-            res = Image.fromarray(restored_img)
+    def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
+        res = Image.fromarray(restored_img)
 
-            if gfpgan_visibility < 1.0:
-                res = Image.blend(image, res, gfpgan_visibility)
+        if codeformer_visibility < 1.0:
+            res = Image.blend(image, res, codeformer_visibility)
 
-            info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
-            image = res
+        info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
+        return (res, info)
 
-        if codeformer_visibility > 0:
-            restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
-            res = Image.fromarray(restored_img)
 
-            if codeformer_visibility < 1.0:
-                res = Image.blend(image, res, codeformer_visibility)
+    def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
+        small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
+        pixels = tuple(np.array(small).flatten().tolist())
+        key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight,
+                resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
 
-            info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
-            image = res
+        c = cached_images.get(key)
+        if c is None:
+            upscaler = shared.sd_upscalers[scaler_index]
+            c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+            if mode == 1 and crop:
+                cropped = Image.new("RGB", (resize_w, resize_h))
+                cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
+                c = cropped
+            cached_images[key] = c
+        return c
 
+
+    def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        # Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text
+        nonlocal upscaling_resize
         if resize_mode == 1:
             upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
             crop_info = " (crop)" if upscaling_crop else ""
             info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
+        return (image, info)
+
+    @dataclass
+    class UpscaleParams:
+        upscaler_idx: int
+        blend_alpha: float
+
+    def run_upscalers_blend( params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        blended_result: Image.Image = None
+        for upscaler in params:
+            res = upscale(image, upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
+            info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
+            if blended_result is None:
+                blended_result = res
+            else:
+                blended_result = Image.blend(blended_result, res, upscaler.blend_alpha)
+        return (blended_result, info)
+
+    # Build a list of operations to run
+    facefix_ops: List[Callable] = []
+    if gfpgan_visibility > 0:
+        facefix_ops.append(run_gfpgan)
+    if codeformer_visibility > 0:
+        facefix_ops.append(run_codeformer)
+
+    upscale_ops: List[Callable] = []
+    if resize_mode == 1:
+        upscale_ops.append(run_prepare_crop)
+
+    if upscaling_resize != 0:
+        step_params: List[UpscaleParams] = []
+        step_params.append( UpscaleParams( upscaler_idx=extras_upscaler_1, blend_alpha=1.0 ))
+        if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
+            step_params.append( UpscaleParams( upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility ) )
+
+        upscale_ops.append( partial(run_upscalers_blend, step_params) )
+
+
+    extras_ops: List[Callable] = []
+    if upscale_first:
+        extras_ops = upscale_ops + facefix_ops
+    else:
+        extras_ops = facefix_ops + upscale_ops
+
+
+    for image, image_name in zip(imageArr, imageNameArr):
+        if image is None:
+            return outputs, "Please select an input image.", ''
+        existing_pnginfo = image.info or {}
 
-        if upscaling_resize != 1.0:
-            def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
-                small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
-                pixels = tuple(np.array(small).flatten().tolist())
-                key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight, 
-                       resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
-
-                c = cached_images.get(key)
-                if c is None:
-                    upscaler = shared.sd_upscalers[scaler_index]
-                    c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
-                    if mode == 1 and crop:
-                        cropped = Image.new("RGB", (resize_w, resize_h))
-                        cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
-                        c = cropped
-                    cached_images[key] = c
-
-                return c
-
-            info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
-            res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-
-            if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-                res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
-                res = Image.blend(res, res2, extras_upscaler_2_visibility)
-
-            image = res
+        image = image.convert("RGB")
+        info = ""
+        # Run each operation on each image
+        for op in extras_ops:
+            image, info = op(image, info)
 
         while len(cached_images) > 2:
             del cached_images[next(iter(cached_images.keys()))]
diff --git a/modules/ui.py b/modules/ui.py
index 0a63e357..16b6ac49 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1119,6 +1119,9 @@ def create_ui(wrap_gradio_gpu_call):
                     codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
                     codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
 
+                with gr.Group():
+                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
+
                 submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
 
             with gr.Column(variant='panel'):
@@ -1152,6 +1155,7 @@ def create_ui(wrap_gradio_gpu_call):
                 extras_upscaler_1,
                 extras_upscaler_2,
                 extras_upscaler_2_visibility,
+                upscale_before_face_fix,
             ],
             outputs=[
                 result_images,
-- 
cgit v1.2.1


From bde4731f1d3ddf30c46f86c9f6e71e6c0644089d Mon Sep 17 00:00:00 2001
From: Chris OBryan <13701027+cobryan05@users.noreply.github.com>
Date: Fri, 28 Oct 2022 14:30:04 -0500
Subject: extras: Rework image cache

Bit of a refactor to the image cache to make it easier to extend.
Also takes into account the entire image instead of just a cropped portion.
---
 modules/extras.py | 52 ++++++++++++++++++++++++++++++++--------------------
 1 file changed, 32 insertions(+), 20 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 79047f3a..cffe0381 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -7,7 +7,7 @@ from PIL import Image
 import torch
 import tqdm
 
-from typing import Callable, List, Tuple
+from typing import Callable, Dict, List, Tuple
 from functools import partial
 from dataclasses import dataclass
 
@@ -21,7 +21,18 @@ import piexif.helper
 import gradio as gr
 
 
-cached_images = {}
+@dataclass(frozen=True)
+class CacheKey:
+    image_hash: int
+    info_hash: int
+    args_hash: int
+
+@dataclass
+class CacheEntry:
+    image: Image.Image
+    info: str
+
+cached_images: Dict[CacheKey, CacheEntry] = {}
 
 
 def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool ):
@@ -84,22 +95,13 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
 
     def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
-        small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
-        pixels = tuple(np.array(small).flatten().tolist())
-        key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight,
-                resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
-
-        c = cached_images.get(key)
-        if c is None:
-            upscaler = shared.sd_upscalers[scaler_index]
-            c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
-            if mode == 1 and crop:
-                cropped = Image.new("RGB", (resize_w, resize_h))
-                cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
-                c = cropped
-            cached_images[key] = c
-        return c
-
+        upscaler = shared.sd_upscalers[scaler_index]
+        res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+        if mode == 1 and crop:
+            cropped = Image.new("RGB", (resize_w, resize_h))
+            cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2))
+            res = cropped
+        return res
 
     def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
         # Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text
@@ -118,8 +120,18 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     def run_upscalers_blend( params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
         blended_result: Image.Image = None
         for upscaler in params:
-            res = upscale(image, upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-            info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
+            upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
+            cache_key = CacheKey( image_hash = hash(np.array(image.getdata()).tobytes()),
+                                  info_hash = hash(info),
+                                  args_hash = hash(upscale_args) )
+            cached_entry = cached_images.get(cache_key)
+            if cached_entry is None:
+                res = upscale(image, *upscale_args)
+                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
+                cached_images[cache_key] = CacheEntry(image=res, info=info)
+            else:
+                res, info = cached_entry.image, cached_entry.info
+
             if blended_result is None:
                 blended_result = res
             else:
-- 
cgit v1.2.1


From 1f1b327959b546b5e6f995905a1699c5fe4a0c35 Mon Sep 17 00:00:00 2001
From: Chris OBryan <13701027+cobryan05@users.noreply.github.com>
Date: Fri, 28 Oct 2022 16:11:16 -0500
Subject: extras: Make image cache LRU

This changes the extras image cache into a Least-Recently-Used
cache. This allows more experimentation with different upscalers
without missing the cache.

Max cache size is increased to 5 and is cleared on source image
update.
---
 modules/extras.py | 67 +++++++++++++++++++++++++++++++------------------------
 modules/ui.py     |  5 +++++
 2 files changed, 43 insertions(+), 29 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index cffe0381..72cc6d1d 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -1,3 +1,4 @@
+from __future__ import annotations
 import math
 import os
 
@@ -7,7 +8,7 @@ from PIL import Image
 import torch
 import tqdm
 
-from typing import Callable, Dict, List, Tuple
+from typing import Callable, List, OrderedDict, Tuple
 from functools import partial
 from dataclasses import dataclass
 
@@ -21,18 +22,34 @@ import piexif.helper
 import gradio as gr
 
 
-@dataclass(frozen=True)
-class CacheKey:
-    image_hash: int
-    info_hash: int
-    args_hash: int
+class LruCache(OrderedDict):
+    @dataclass(frozen=True)
+    class Key:
+        image_hash: int
+        info_hash: int
+        args_hash: int
 
-@dataclass
-class CacheEntry:
-    image: Image.Image
-    info: str
+    @dataclass
+    class Value:
+        image: Image.Image
+        info: str
+
+    def __init__(self, max_size:int = 5, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._max_size = max_size
+
+    def get(self, key: LruCache.Key) -> LruCache.Value:
+        ret = super().get(key)
+        if ret is not None:
+            self.move_to_end(key) # Move to end of eviction list
+        return ret
+
+    def put(self, key: LruCache.Key, value: LruCache.Value) -> None:
+        self[key] = value
+        while len(self) > self._max_size:
+            self.popitem(last=False)
 
-cached_images: Dict[CacheKey, CacheEntry] = {}
+cached_images: LruCache = LruCache(max_size = 5)
 
 
 def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool ):
@@ -121,14 +138,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         blended_result: Image.Image = None
         for upscaler in params:
             upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-            cache_key = CacheKey( image_hash = hash(np.array(image.getdata()).tobytes()),
+            cache_key = LruCache.Key( image_hash = hash(np.array(image.getdata()).tobytes()),
                                   info_hash = hash(info),
-                                  args_hash = hash(upscale_args) )
+                                  args_hash = hash(upscale_args + (upscaler.blend_alpha,)) )
             cached_entry = cached_images.get(cache_key)
             if cached_entry is None:
                 res = upscale(image, *upscale_args)
                 info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
-                cached_images[cache_key] = CacheEntry(image=res, info=info)
+                cached_images.put(cache_key, LruCache.Value(image=res, info=info))
             else:
                 res, info = cached_entry.image, cached_entry.info
 
@@ -140,14 +157,11 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
     # Build a list of operations to run
     facefix_ops: List[Callable] = []
-    if gfpgan_visibility > 0:
-        facefix_ops.append(run_gfpgan)
-    if codeformer_visibility > 0:
-        facefix_ops.append(run_codeformer)
+    facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else []
+    facefix_ops += [run_codeformer] if codeformer_visibility > 0 else []
 
     upscale_ops: List[Callable] = []
-    if resize_mode == 1:
-        upscale_ops.append(run_prepare_crop)
+    upscale_ops += [run_prepare_crop] if resize_mode == 1 else []
 
     if upscaling_resize != 0:
         step_params: List[UpscaleParams] = []
@@ -157,12 +171,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         upscale_ops.append( partial(run_upscalers_blend, step_params) )
 
-
-    extras_ops: List[Callable] = []
-    if upscale_first:
-        extras_ops = upscale_ops + facefix_ops
-    else:
-        extras_ops = facefix_ops + upscale_ops
+    extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)
 
 
     for image, image_name in zip(imageArr, imageNameArr):
@@ -176,9 +185,6 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         for op in extras_ops:
             image, info = op(image, info)
 
-        while len(cached_images) > 2:
-            del cached_images[next(iter(cached_images.keys()))]
-        
         if opts.use_original_name_batch and image_name != None:
             basename = os.path.splitext(os.path.basename(image_name))[0]
         else:
@@ -198,6 +204,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
     return outputs, plaintext_to_html(info), ''
 
+def clear_cache():
+    cached_images.clear()
+
 
 def run_pnginfo(image):
     if image is None:
diff --git a/modules/ui.py b/modules/ui.py
index 16b6ac49..b7c36c55 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1178,6 +1178,11 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[init_img_with_mask],
         )
 
+        extras_image.change(
+            fn=modules.extras.clear_cache,
+            inputs=[], outputs=[]
+        )
+
     with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
-- 
cgit v1.2.1


From 5732c0282d529ef2e0591c76e16959e97240dad8 Mon Sep 17 00:00:00 2001
From: Chris OBryan <13701027+cobryan05@users.noreply.github.com>
Date: Fri, 28 Oct 2022 16:36:25 -0500
Subject: extras-tweaks: autoformat changed lines

---
 modules/extras.py | 30 +++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 72cc6d1d..50026a25 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -34,14 +34,14 @@ class LruCache(OrderedDict):
         image: Image.Image
         info: str
 
-    def __init__(self, max_size:int = 5, *args, **kwargs):
+    def __init__(self, max_size: int = 5, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self._max_size = max_size
 
     def get(self, key: LruCache.Key) -> LruCache.Value:
         ret = super().get(key)
         if ret is not None:
-            self.move_to_end(key) # Move to end of eviction list
+            self.move_to_end(key)  # Move to end of eviction list
         return ret
 
     def put(self, key: LruCache.Key, value: LruCache.Value) -> None:
@@ -49,10 +49,11 @@ class LruCache(OrderedDict):
         while len(self) > self._max_size:
             self.popitem(last=False)
 
-cached_images: LruCache = LruCache(max_size = 5)
 
+cached_images: LruCache = LruCache(max_size=5)
 
-def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool ):
+
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool):
     devices.torch_gc()
 
     imageArr = []
@@ -88,8 +89,8 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     else:
         outpath = opts.outdir_samples or opts.outdir_extras_samples
 
-
     # Extra operation definitions
+
     def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
         restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
         res = Image.fromarray(restored_img)
@@ -110,7 +111,6 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
         return (res, info)
 
-
     def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
         upscaler = shared.sd_upscalers[scaler_index]
         res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
@@ -134,13 +134,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         upscaler_idx: int
         blend_alpha: float
 
-    def run_upscalers_blend( params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+    def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
         blended_result: Image.Image = None
         for upscaler in params:
-            upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-            cache_key = LruCache.Key( image_hash = hash(np.array(image.getdata()).tobytes()),
-                                  info_hash = hash(info),
-                                  args_hash = hash(upscale_args + (upscaler.blend_alpha,)) )
+            upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,
+                            upscaling_resize_w, upscaling_resize_h, upscaling_crop)
+            cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()),
+                                     info_hash=hash(info),
+                                     args_hash=hash(upscale_args + (upscaler.blend_alpha,)))
             cached_entry = cached_images.get(cache_key)
             if cached_entry is None:
                 res = upscale(image, *upscale_args)
@@ -165,15 +166,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
     if upscaling_resize != 0:
         step_params: List[UpscaleParams] = []
-        step_params.append( UpscaleParams( upscaler_idx=extras_upscaler_1, blend_alpha=1.0 ))
+        step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_1, blend_alpha=1.0))
         if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-            step_params.append( UpscaleParams( upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility ) )
+            step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility))
 
-        upscale_ops.append( partial(run_upscalers_blend, step_params) )
+        upscale_ops.append(partial(run_upscalers_blend, step_params))
 
     extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)
 
-
     for image, image_name in zip(imageArr, imageNameArr):
         if image is None:
             return outputs, "Please select an input image.", ''
-- 
cgit v1.2.1


From d8b366146748555a18b595af400c8cb222ea0ec9 Mon Sep 17 00:00:00 2001
From: Chris OBryan <13701027+cobryan05@users.noreply.github.com>
Date: Fri, 28 Oct 2022 16:55:02 -0500
Subject: extras: upscaler blending should not be considered in cache key

---
 modules/extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/extras.py b/modules/extras.py
index 50026a25..681d8d5a 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -141,7 +141,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
                             upscaling_resize_w, upscaling_resize_h, upscaling_crop)
             cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()),
                                      info_hash=hash(info),
-                                     args_hash=hash(upscale_args + (upscaler.blend_alpha,)))
+                                     args_hash=hash(upscale_args))
             cached_entry = cached_images.get(cache_key)
             if cached_entry is None:
                 res = upscale(image, *upscale_args)
-- 
cgit v1.2.1


From 539c0f51e436beeb0ca2b8b8d52b24f4b59ad56a Mon Sep 17 00:00:00 2001
From: Yaiol <38218161+Yaiol@users.noreply.github.com>
Date: Sat, 29 Oct 2022 01:07:01 +0200
Subject: Update images.py

Filename tags [height] and [width] are wrongly referencing to process size instead of resulting image size. Making all upscale files named wrongly.
---
 modules/images.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index 7870b5b7..a0728553 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -300,8 +300,8 @@ class FilenameGenerator:
         'seed': lambda self: self.seed if self.seed is not None else '',
         'steps': lambda self:  self.p and self.p.steps,
         'cfg': lambda self: self.p and self.p.cfg_scale,
-        'width': lambda self: self.p and self.p.width,
-        'height': lambda self: self.p and self.p.height,
+        'width': lambda self: self.image.width,
+        'height': lambda self: self.image.height,
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
         'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
@@ -315,10 +315,11 @@ class FilenameGenerator:
     }
     default_time_format = '%Y%m%d%H%M%S'
 
-    def __init__(self, p, seed, prompt):
+    def __init__(self, p, seed, prompt, image):
         self.p = p
         self.seed = seed
         self.prompt = prompt
+        self.image = image
 
     def prompt_no_style(self):
         if self.p is None or self.prompt is None:
@@ -449,7 +450,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         txt_fullfn (`str` or None):
             If a text file is saved for this image, this will be its full path. Otherwise None.
     """
-    namegen = FilenameGenerator(p, seed, prompt)
+    namegen = FilenameGenerator(p, seed, prompt, image)
 
     if save_to_dirs is None:
         save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
-- 
cgit v1.2.1


From f361e804ebaa5af4a10711ece2522869fb64a4c6 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sat, 29 Oct 2022 08:36:50 +0900
Subject: Re enable linear

---
 modules/hypernetworks/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index c2d4b51c..aad09ffc 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
 not_available = ["hardswish", "multiheadattention"]
-keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
+keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
 
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
     # Remove illegal characters from name.
-- 
cgit v1.2.1


From bce5adcd6de1ad608df8d813a92e1167b7a1d3f2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 07:37:06 +0300
Subject: change default hypernet activation function to linear

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 0a63e357..2541970d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1238,7 +1238,7 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
                     new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
-- 
cgit v1.2.1


From a1e5e0d7669def010ecf31d801d6f0667bcf8061 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 08:11:03 +0300
Subject: skip filenames starting with . for img2img and extras batch modes

---
 modules/extras.py  | 2 +-
 modules/img2img.py | 2 +-
 modules/shared.py  | 5 +++++
 3 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 681d8d5a..4d51088b 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -72,7 +72,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         if input_dir == '':
             return outputs, "Please select an input directory.", ''
-        image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)]
+        image_list = shared.listfiles(input_dir)
         for img in image_list:
             try:
                 image = Image.open(img)
diff --git a/modules/img2img.py b/modules/img2img.py
index 9c0cf23e..efda26e1 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -19,7 +19,7 @@ import modules.scripts
 def process_batch(p, input_dir, output_dir, args):
     processing.fix_seed(p)
 
-    images = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
+    images = shared.listfiles(input_dir)
 
     print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
 
diff --git a/modules/shared.py b/modules/shared.py
index 7c428d90..7e634423 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -450,3 +450,8 @@ total_tqdm = TotalTQDM()
 
 mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
 mem_mon.start()
+
+
+def listfiles(dirname):
+    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
+    return [file for file in filenames if os.path.isfile(file)]
-- 
cgit v1.2.1


From 2d220afb24bd9812d5124814f670ec2a1ff5b0fe Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 08:26:12 +0300
Subject: fix open folder button not working

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 922a2163..20cc10cf 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -631,9 +631,9 @@ Requested path was: {f}
 
                     buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
                     button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                    open_folder = gr.Button(folder_symbol, elem_id=button_id)
+                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
 
-                open_folder.click(
+                open_folder_button.click(
                     fn=lambda: open_folder(opts.outdir_samples or outdir),
                     inputs=[],
                     outputs=[],
-- 
cgit v1.2.1


From a33d0a9a65189be35038be5765f2b4d1b19bab0f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 08:28:48 +0300
Subject: remove weird spaces added to ui.py over time

---
 modules/ui.py | 51 +++++++++++++++++++++++++--------------------------
 1 file changed, 25 insertions(+), 26 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 20cc10cf..46657dd4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -596,7 +596,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
     )
     return refresh_button
 
-def create_output_panel(tabname, outdir):    
+def create_output_panel(tabname, outdir):
     def open_folder(f):
         if not os.path.exists(f):
             print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
@@ -618,11 +618,11 @@ Requested path was: {f}
                 sp.Popen(["open", path])
             else:
                 sp.Popen(["xdg-open", path])
-    
-    with gr.Column(variant='panel'):        
-            with gr.Group():                
+
+    with gr.Column(variant='panel'):
+            with gr.Group():
                 result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
-               
+
             generation_info = None
             with gr.Column():
                 with gr.Row():
@@ -639,7 +639,7 @@ Requested path was: {f}
                     outputs=[],
                 )
 
-                if tabname != "extras":           
+                if tabname != "extras":
                     with gr.Row():
                         do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
 
@@ -671,8 +671,7 @@ Requested path was: {f}
                     html_info = gr.HTML()
                 parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
                 return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
-                    
-            
+
 
 def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
@@ -723,10 +722,10 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
-     
 
-            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)  
-                
+
+            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
@@ -781,7 +780,7 @@ def create_ui(wrap_gradio_gpu_call):
                 fn=lambda x: gr_show(x),
                 inputs=[enable_hr],
                 outputs=[hr_options],
-            )            
+            )
 
             roll.click(
                 fn=roll_artist,
@@ -902,7 +901,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
 
-            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)                 
+            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -990,7 +989,7 @@ def create_ui(wrap_gradio_gpu_call):
                     inputs=[init_img],
                     outputs=[img2img_prompt],
             )
-           
+
 
             roll.click(
                 fn=roll_artist,
@@ -1045,7 +1044,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (denoising_strength, "Denoising strength"),
                 *modules.scripts.scripts_img2img.infotext_fields
             ]
-            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields) 
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
             parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
 
 
@@ -1077,9 +1076,9 @@ def create_ui(wrap_gradio_gpu_call):
                                 upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
                                 upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-                
+
                 with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") 
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
                 with gr.Group():
                     extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
@@ -1125,7 +1124,7 @@ def create_ui(wrap_gradio_gpu_call):
                 html_info,
             ]
         )
-        parameters_copypaste.add_paste_fields("extras", extras_image, None) 
+        parameters_copypaste.add_paste_fields("extras", extras_image, None)
 
 
     with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
@@ -1139,14 +1138,14 @@ def create_ui(wrap_gradio_gpu_call):
                 html2 = gr.HTML()
                 with gr.Row():
                     buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-                parameters_copypaste.bind_buttons(buttons, image, generation_info)                
-                
+                parameters_copypaste.bind_buttons(buttons, image, generation_info)
+
         image.change(
             fn=wrap_gradio_call(modules.extras.run_pnginfo),
             inputs=[image],
             outputs=[html, generation_info, html2],
         )
-   
+
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
@@ -1569,7 +1568,7 @@ def create_ui(wrap_gradio_gpu_call):
             column.__exit__()
 
 
-        
+
 
     interfaces = [
         (txt2img_interface, "txt2img", "txt2img"),
@@ -1582,7 +1581,7 @@ def create_ui(wrap_gradio_gpu_call):
 
     interfaces += script_callbacks.ui_tabs_callback()
 
-    interfaces += [(settings_interface, "Settings", "settings")]       
+    interfaces += [(settings_interface, "Settings", "settings")]
 
     css = ""
 
@@ -1661,7 +1660,7 @@ def create_ui(wrap_gradio_gpu_call):
                 component_dict['sd_model_checkpoint'],
             ]
         )
- 
+
 
         settings_map = {
             'sd_hypernetwork': 'Hypernet',
@@ -1669,7 +1668,7 @@ def create_ui(wrap_gradio_gpu_call):
             'sd_model_checkpoint': 'Model hash',
         }
 
-        parameters_copypaste.run_bind()       
+        parameters_copypaste.run_bind()
 
     ui_config_file = cmd_opts.ui_config_file
     ui_settings = {}
@@ -1749,7 +1748,7 @@ def load_javascript(raw_response):
         javascript = f'<script>{jsfile.read()}</script>'
 
     scripts_list = modules.scripts.list_scripts("javascript", ".js")
-    
+
     for basedir, filename, path in scripts_list:
         with open(path, "r", encoding="utf8") as jsfile:
             javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
-- 
cgit v1.2.1


From 45ca67f35a9dd4c05fae295e54b38bbbd9ae5a84 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 08:29:50 +0300
Subject: remove repeated gitignore entries

---
 .gitignore | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/.gitignore b/.gitignore
index 26be36b3..8fa05852 100644
--- a/.gitignore
+++ b/.gitignore
@@ -29,6 +29,3 @@ notification.mp3
 /textual_inversion
 .vscode
 /extensions
-/webui-user.bat
-/webui-user.sh
-
-- 
cgit v1.2.1


From 3c207ca68483b3406faf519bde2743b578dac222 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 08:42:34 +0300
Subject: add needed imports fr new code in copypaste.py

---
 modules/generation_parameters_copypaste.py | 9 +++++++++
 modules/ui.py                              | 7 -------
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 2b80737a..224a17ea 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -1,3 +1,5 @@
+import base64
+import io
 import os
 import re
 import gradio as gr
@@ -14,6 +16,7 @@ type_of_gr_update = type(gr.update())
 paste_fields = {}
 bind_list = []
 
+
 def quote(text):
     if ',' not in str(text):
         return text
@@ -23,6 +26,7 @@ def quote(text):
     text = text.replace('"', '\\"')
     return f'"{text}"'
 
+
 def image_from_url_text(filedata):
     if type(filedata) == dict and filedata["is_file"]:
         filename = filedata["name"]
@@ -45,19 +49,23 @@ def image_from_url_text(filedata):
     image = Image.open(io.BytesIO(filedata))
     return image
 
+
 def add_paste_fields(tabname, init_img, fields):
     paste_fields[tabname] = {"init_img":init_img, "fields": fields}
 
+
 def create_buttons(tabs_list):
     buttons = {}
     for tab in tabs_list:
         buttons[tab] = gr.Button(f"Send to {tab}")
     return buttons
 
+
 #if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab 
 def bind_buttons(buttons, send_image, send_generate_info):
     bind_list.append([buttons, send_image, send_generate_info])
 
+
 def run_bind():
     for buttons, send_image, send_generate_info in bind_list:
         for tab in buttons:
@@ -98,6 +106,7 @@ def run_bind():
                 outputs=None,
             )
 
+
 def parse_generation_parameters(x: str):
     """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
diff --git a/modules/ui.py b/modules/ui.py
index 46657dd4..280910d0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1,6 +1,4 @@
-import base64
 import html
-import io
 import json
 import math
 import mimetypes
@@ -18,13 +16,8 @@ import gradio as gr
 import gradio.routes
 import gradio.utils
 import numpy as np
-import piexif
-import torch
 from PIL import Image, PngImagePlugin
 
-import gradio as gr
-import gradio.utils
-import gradio.routes
 
 from modules import sd_hijack, sd_models, localization, script_callbacks
 from modules.paths import script_path
-- 
cgit v1.2.1


From 2922d8144f677ea3c37189a2b2e3c3d3ff6d3916 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 09:01:04 +0300
Subject: make existing image browser extension not break

---
 modules/generation_parameters_copypaste.py | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 224a17ea..d590e9ee 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -51,7 +51,14 @@ def image_from_url_text(filedata):
 
 
 def add_paste_fields(tabname, init_img, fields):
-    paste_fields[tabname] = {"init_img":init_img, "fields": fields}
+    paste_fields[tabname] = {"init_img": init_img, "fields": fields}
+
+    # backwards compatibility for existing extensions
+    import modules.ui
+    if tabname == 'txt2img':
+        modules.ui.txt2img_paste_fields = fields
+    elif tabname == 'img2img':
+        modules.ui.img2img_paste_fields = fields
 
 
 def create_buttons(tabs_list):
@@ -61,7 +68,7 @@ def create_buttons(tabs_list):
     return buttons
 
 
-#if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab 
+#if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab
 def bind_buttons(buttons, send_image, send_generate_info):
     bind_list.append([buttons, send_image, send_generate_info])
 
@@ -84,12 +91,12 @@ def run_bind():
                         inputs=[send_image],
                         outputs=[paste_fields[tab]["init_img"]],
                     )
-            
+
             if send_generate_info and paste_fields[tab]["fields"] is not None:
                 paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Size-1', 'Size-2']
                 if shared.opts.send_seed:
                     paste_field_names += ["Seed"]
-                if send_generate_info in paste_fields:                    
+                if send_generate_info in paste_fields:
                     button.click(
                         fn=lambda *x:x,
                         inputs=[field for field,name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
@@ -154,7 +161,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 
     return res
 
-def connect_paste(button, paste_fields, input_comp):
+
+def connect_paste(button, paste_fields, input_comp, jsfunc=None):
     def paste_func(prompt):
         if not prompt and not shared.cmd_opts.hide_ui_dir_config:
             filename = os.path.join(script_path, "params.txt")
@@ -192,6 +200,7 @@ def connect_paste(button, paste_fields, input_comp):
 
     button.click(
         fn=paste_func,
+        _js=jsfunc,
         inputs=[input_comp],
         outputs=[x[0] for x in paste_fields],
     )
-- 
cgit v1.2.1


From 78b879b4426d3730acdf3f4737d37a2ee29cb023 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 09:02:02 +0300
Subject: delete the submodule dir (why do you keep doing this)

---
 extensions/stable-diffusion-webui-inspiration | 1 -
 1 file changed, 1 deletion(-)
 delete mode 160000 extensions/stable-diffusion-webui-inspiration

diff --git a/extensions/stable-diffusion-webui-inspiration b/extensions/stable-diffusion-webui-inspiration
deleted file mode 160000
index c4b9f5c2..00000000
--- a/extensions/stable-diffusion-webui-inspiration
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit c4b9f5c233c8619ee140b763e706edc26cae0f1d
-- 
cgit v1.2.1


From 28e6d4a54ea1fa1e34ad1ea0742ab2003ed7fa7f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 09:13:36 +0300
Subject: add element ids for save buttons for #3798

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 66b743f5..3c34eca0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -620,7 +620,7 @@ Requested path was: {f}
             with gr.Column():
                 with gr.Row():
                     if tabname != "extras":
-                        save = gr.Button('Save')
+                        save = gr.Button('Save', elem_id=f'save_{tabname}')
 
                     buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
                     button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-- 
cgit v1.2.1


From beb6fc29798d82f1b08a34cf5dd79e4ab29d4cd0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 09:57:22 +0300
Subject: move send seed option to UI section and make it false by default

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 5d1ceb85..fb84afd8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -280,7 +280,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
-    "send_seed": OptionInfo(False, "Send seed when sending prompt or image to other interface"),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
@@ -306,6 +305,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
     "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
+    "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-- 
cgit v1.2.1


From 2c4d20388425a5e40b93eef3722e42e8d375fbb4 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Sat, 29 Oct 2022 00:36:51 -0700
Subject: Revert "Explicitly state when Hypernet is none."

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 377c0978..04fdda7c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -395,7 +395,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Hypernet": ("None" if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
+        "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
         "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
-- 
cgit v1.2.1


From 35c45df28b303a05d56a13cb56d4046f08cf8c25 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 10:56:19 +0300
Subject: =?UTF-8?q?fix=20broken=20=E2=86=99=20button,=20fix=20field=20past?=
 =?UTF-8?q?e=20ignoring=20most=20of=20useful=20fields=20for=20for=20#3768?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 modules/generation_parameters_copypaste.py | 36 ++++++++++++++++++-------
 modules/ui.py                              | 43 +++++++++++-------------------
 2 files changed, 41 insertions(+), 38 deletions(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index d590e9ee..bbaad42e 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -6,7 +6,7 @@ import gradio as gr
 from modules.shared import script_path
 from modules import shared
 import tempfile
-from PIL import Image, PngImagePlugin
+from PIL import Image
 
 re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
@@ -61,6 +61,24 @@ def add_paste_fields(tabname, init_img, fields):
         modules.ui.img2img_paste_fields = fields
 
 
+def integrate_settings_paste_fields(component_dict):
+    from modules import ui
+
+    settings_map = {
+        'sd_hypernetwork': 'Hypernet',
+        'CLIP_stop_at_last_layers': 'Clip skip',
+        'sd_model_checkpoint': 'Model hash',
+    }
+    settings_paste_fields = [
+        (component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))
+        for k, v in settings_map.items()
+    ]
+
+    for tabname, info in paste_fields.items():
+        if info["fields"] is not None:
+            info["fields"] += settings_paste_fields
+
+
 def create_buttons(tabs_list):
     buttons = {}
     for tab in tabs_list:
@@ -87,24 +105,22 @@ def run_bind():
                     )
                 else:
                     button.click(
-                        fn=lambda x:x,
+                        fn=lambda x: x,
                         inputs=[send_image],
                         outputs=[paste_fields[tab]["init_img"]],
                     )
 
             if send_generate_info and paste_fields[tab]["fields"] is not None:
-                paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Size-1', 'Size-2']
-                if shared.opts.send_seed:
-                    paste_field_names += ["Seed"]
                 if send_generate_info in paste_fields:
+                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Size-1', 'Size-2'] + (["Seed"] if shared.opts.send_seed else [])
+
                     button.click(
-                        fn=lambda *x:x,
-                        inputs=[field for field,name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
-                        outputs=[field for field,name in paste_fields[tab]["fields"] if name in paste_field_names],
+                        fn=lambda *x: x,
+                        inputs=[field for field, name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
+                        outputs=[field for field, name in paste_fields[tab]["fields"] if name in paste_field_names],
                     )
-
                 else:
-                    connect_paste(button, [(field, name) for field, name in paste_fields[tab]["fields"]  if name in paste_field_names], send_generate_info)
+                    connect_paste(button, paste_fields[tab]["fields"], send_generate_info)
 
             button.click(
                 fn=None,
diff --git a/modules/ui.py b/modules/ui.py
index 3c34eca0..5055ca64 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -589,6 +589,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
     )
     return refresh_button
 
+
 def create_output_panel(tabname, outdir):
     def open_folder(f):
         if not os.path.exists(f):
@@ -716,6 +717,7 @@ def create_ui(wrap_gradio_gpu_call):
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
             txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -784,7 +786,7 @@ def create_ui(wrap_gradio_gpu_call):
                 ]
             )
 
-            parameters_copypaste.add_paste_fields("txt2img", None, [
+            txt2img_paste_fields = [
                 (txt2img_prompt, "Prompt"),
                 (txt2img_negative_prompt, "Negative prompt"),
                 (steps, "Steps"),
@@ -805,7 +807,8 @@ def create_ui(wrap_gradio_gpu_call):
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
                 *modules.scripts.scripts_txt2img.infotext_fields
-            ])
+            ]
+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
 
             txt2img_preview_params = [
                 txt2img_prompt,
@@ -893,6 +896,7 @@ def create_ui(wrap_gradio_gpu_call):
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
 
             img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
+            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -1038,7 +1042,6 @@ def create_ui(wrap_gradio_gpu_call):
             parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
             parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
 
-
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
@@ -1050,12 +1053,8 @@ def create_ui(wrap_gradio_gpu_call):
                         image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
 
                     with gr.TabItem('Batch from Directory'):
-                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs,
-                            placeholder="A directory on the same machine where the server is running."
-                        )
-                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs,
-                            placeholder="Leave blank to save images to the default path."
-                        )
+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
+                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
                         show_extras_results = gr.Checkbox(label='Show result images', value=True)
 
                 with gr.Tabs(elem_id="extras_resize_mode"):
@@ -1087,7 +1086,6 @@ def create_ui(wrap_gradio_gpu_call):
 
                 submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
 
-
             result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
 
         submit.click(
@@ -1121,7 +1119,6 @@ def create_ui(wrap_gradio_gpu_call):
         )
         parameters_copypaste.add_paste_fields("extras", extras_image, None)
 
-
         extras_image.change(
             fn=modules.extras.clear_cache,
             inputs=[], outputs=[]
@@ -1587,9 +1584,6 @@ def create_ui(wrap_gradio_gpu_call):
         if column is not None:
             column.__exit__()
 
-
-
-
     interfaces = [
         (txt2img_interface, "txt2img", "txt2img"),
         (img2img_interface, "img2img", "img2img"),
@@ -1599,10 +1593,6 @@ def create_ui(wrap_gradio_gpu_call):
         (train_interface, "Train", "ti"),
     ]
 
-    interfaces += script_callbacks.ui_tabs_callback()
-
-    interfaces += [(settings_interface, "Settings", "settings")]
-
     css = ""
 
     for cssfile in modules.scripts.list_files_with_name("style.css"):
@@ -1619,6 +1609,9 @@ def create_ui(wrap_gradio_gpu_call):
     if not cmd_opts.no_progressbar_hiding:
         css += css_hide_progressbar
 
+    interfaces += script_callbacks.ui_tabs_callback()
+    interfaces += [(settings_interface, "Settings", "settings")]
+
     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
         with gr.Row(elem_id="quicksettings"):
             for i, k, item in quicksettings_list:
@@ -1627,6 +1620,9 @@ def create_ui(wrap_gradio_gpu_call):
 
         settings_interface.gradio_ref = demo
 
+        parameters_copypaste.integrate_settings_paste_fields(component_dict)
+        parameters_copypaste.run_bind()
+
         with gr.Tabs(elem_id="tabs") as tabs:
             for interface, label, ifid in interfaces:
                 with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
@@ -1681,15 +1677,6 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
-
-        settings_map = {
-            'sd_hypernetwork': 'Hypernet',
-            'CLIP_stop_at_last_layers': 'Clip skip',
-            'sd_model_checkpoint': 'Model hash',
-        }
-
-        parameters_copypaste.run_bind()
-
     ui_config_file = cmd_opts.ui_config_file
     ui_settings = {}
     settings_count = len(ui_settings)
@@ -1708,7 +1695,7 @@ def create_ui(wrap_gradio_gpu_call):
         def apply_field(obj, field, condition=None, init_field=None):
             key = path + "/" + field
 
-            if getattr(obj,'custom_script_source',None) is not None:
+            if getattr(obj, 'custom_script_source', None) is not None:
               key = 'customscript/' + obj.custom_script_source + '/' + key
 
             if getattr(obj, 'do_not_save_to_config', False):
-- 
cgit v1.2.1


From a5f3adbdd7d9b8245f7782216ac48913660e6bb5 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sat, 29 Oct 2022 15:37:24 +0700
Subject: Allow trailing comma in learning rate

---
 modules/textual_inversion/learn_schedule.py | 33 +++++++++++++++++------------
 1 file changed, 20 insertions(+), 13 deletions(-)

diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index 3a736065..76e611b6 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -11,23 +11,30 @@ class LearnScheduleIterator:
         self.rates = []
         self.it = 0
         self.maxit = 0
-        for i, pair in enumerate(pairs):
-            tmp = pair.split(':')
-            if len(tmp) == 2:
-                step = int(tmp[1])
-                if step > cur_step:
-                    self.rates.append((float(tmp[0]), min(step, max_steps)))
-                    self.maxit += 1
-                    if step > max_steps:
+        try:
+            for i, pair in enumerate(pairs):
+                if not pair.strip():
+                    continue
+                tmp = pair.split(':')
+                if len(tmp) == 2:
+                    step = int(tmp[1])
+                    if step > cur_step:
+                        self.rates.append((float(tmp[0]), min(step, max_steps)))
+                        self.maxit += 1
+                        if step > max_steps:
+                            return
+                    elif step == -1:
+                        self.rates.append((float(tmp[0]), max_steps))
+                        self.maxit += 1
                         return
-                elif step == -1:
+                else:
                     self.rates.append((float(tmp[0]), max_steps))
                     self.maxit += 1
                     return
-            else:
-                self.rates.append((float(tmp[0]), max_steps))
-                self.maxit += 1
-                return
+            assert self.rates
+        except (ValueError, AssertionError):
+            raise Exception("Invalid learning rate schedule")
+
 
     def __iter__(self):
         return self
-- 
cgit v1.2.1


From ef4c94e1cfe66299227aa95a28c2380d21cb1600 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sat, 29 Oct 2022 15:42:51 +0700
Subject: Improve lr schedule error message

---
 modules/textual_inversion/learn_schedule.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index 76e611b6..dd0c0ad1 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -4,7 +4,7 @@ import tqdm
 class LearnScheduleIterator:
     def __init__(self, learn_rate, max_steps, cur_step=0):
         """
-        specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, 1e-5:10000 until 10000
+        specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000
         """
 
         pairs = learn_rate.split(',')
@@ -33,7 +33,7 @@ class LearnScheduleIterator:
                     return
             assert self.rates
         except (ValueError, AssertionError):
-            raise Exception("Invalid learning rate schedule")
+            raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.')
 
 
     def __iter__(self):
-- 
cgit v1.2.1


From ab27c111d06ec920791c73eea25ad9a61671852e Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sat, 29 Oct 2022 18:09:17 +0700
Subject: Add input validations before loading dataset for training

---
 modules/hypernetworks/hypernetwork.py          | 38 +++++++++++---------
 modules/textual_inversion/textual_inversion.py | 48 +++++++++++++++++++-------
 2 files changed, 58 insertions(+), 28 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 2e84583b..38f35c58 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -332,7 +332,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
-    assert hypernetwork_name, 'hypernetwork not selected'
+    save_hypernetwork_every = save_hypernetwork_every or 0
+    create_image_every = create_image_every or 0
+    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
     shared.loaded_hypernetwork = Hypernetwork()
@@ -358,39 +360,43 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     else:
         images_dir = None
 
+    hypernetwork = shared.loaded_hypernetwork
+
+    ititial_step = hypernetwork.step or 0
+    if ititial_step > steps:
+        shared.state.textinfo = f"Model has already been trained beyond specified max steps"
+        return hypernetwork, filename
+
+    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+    
+    # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
         ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
+
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
         shared.sd_model.first_stage_model.to(devices.cpu)
 
-    hypernetwork = shared.loaded_hypernetwork
-    weights = hypernetwork.weights()
-    for weight in weights:
-        weight.requires_grad = True
-
     size = len(ds.indexes)
     loss_dict = defaultdict(lambda : deque(maxlen = 1024))
     losses = torch.zeros((size,))
     previous_mean_losses = [0]
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
-
-    last_saved_file = "<none>"
-    last_saved_image = "<none>"
-    forced_filename = "<none>"
-
-    ititial_step = hypernetwork.step or 0
-    if ititial_step > steps:
-        return hypernetwork, filename
-
-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+    
+    weights = hypernetwork.weights()
+    for weight in weights:
+        weight.requires_grad = True
     # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
     optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     steps_without_grad = 0
 
+    last_saved_file = "<none>"
+    last_saved_image = "<none>"
+    forced_filename = "<none>"
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 17dfb223..44f06443 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -204,9 +204,30 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             **values,
         })
 
+def validate_train_inputs(model_name, learn_rate, batch_size, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):
+    assert model_name, f"{name} not selected"
+    assert learn_rate, "Learning rate is empty or 0"
+    assert isinstance(batch_size, int), "Batch size must be integer"
+    assert batch_size > 0, "Batch size must be positive"
+    assert data_root, "Dataset directory is empty"
+    assert os.path.isdir(data_root), "Dataset directory doesn't exist"
+    assert os.listdir(data_root), "Dataset directory is empty"
+    assert template_file, "Prompt template file is empty"
+    assert os.path.isfile(template_file), "Prompt template file doesn't exist"
+    assert steps, "Max steps is empty or 0"
+    assert isinstance(steps, int), "Max steps must be integer"
+    assert steps > 0 , "Max steps must be positive"
+    assert isinstance(save_model_every, int), "Save {name} must be integer"
+    assert save_model_every >= 0 , "Save {name} must be positive or 0"
+    assert isinstance(create_image_every, int), "Create image must be integer"
+    assert create_image_every >= 0 , "Create image must be positive or 0"
+    if save_model_every or create_image_every:
+        assert log_directory, "Log directory is empty"
 
 def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
-    assert embedding_name, 'embedding not selected'
+    save_embedding_every = save_embedding_every or 0
+    create_image_every = create_image_every or 0
+    validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
 
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
@@ -232,17 +253,27 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         os.makedirs(images_embeds_dir, exist_ok=True)
     else:
         images_embeds_dir = None
-        
+
     cond_model = shared.sd_model.cond_stage_model
 
+    hijack = sd_hijack.model_hijack
+
+    embedding = hijack.embedding_db.word_embeddings[embedding_name]
+
+    ititial_step = embedding.step or 0
+    if ititial_step > steps:
+        shared.state.textinfo = f"Model has already been trained beyond specified max steps"
+        return embedding, filename
+
+    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+
+    # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
         ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
 
-    hijack = sd_hijack.model_hijack
-
-    embedding = hijack.embedding_db.word_embeddings[embedding_name]
     embedding.vec.requires_grad = True
+    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
 
     losses = torch.zeros((32,))
 
@@ -251,13 +282,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
 
-    ititial_step = embedding.step or 0
-    if ititial_step > steps:
-        return embedding, filename
-
-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
-
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, entries in pbar:
         embedding.step = i + ititial_step
-- 
cgit v1.2.1


From 3ce2bfdf95bd5f26d0f6e250e67338ada91980d1 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sat, 29 Oct 2022 19:43:21 +0700
Subject: Add cleanup after training

---
 modules/hypernetworks/hypernetwork.py          | 201 +++++++++++++------------
 modules/textual_inversion/textual_inversion.py | 185 ++++++++++++-----------
 2 files changed, 200 insertions(+), 186 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 38f35c58..170d5ea4 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -398,110 +398,112 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     forced_filename = "<none>"
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, entries in pbar:
-        hypernetwork.step = i + ititial_step
-        if len(loss_dict) > 0:
-            previous_mean_losses = [i[-1] for i in loss_dict.values()]
-            previous_mean_loss = mean(previous_mean_losses)
-            
-        scheduler.apply(optimizer, hypernetwork.step)
-        if scheduler.finished:
-            break
-
-        if shared.state.interrupted:
-            break
-
-        with torch.autocast("cuda"):
-            c = stack_conds([entry.cond for entry in entries]).to(devices.device)
-            # c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
-            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-            loss = shared.sd_model(x, c)[0]
-            del x
-            del c
-
-            losses[hypernetwork.step % losses.shape[0]] = loss.item()
-            for entry in entries:
-                loss_dict[entry.filename].append(loss.item())
-                
-            optimizer.zero_grad()
-            weights[0].grad = None
-            loss.backward()
 
-            if weights[0].grad is None:
-                steps_without_grad += 1
+    try:
+        for i, entries in pbar:
+            hypernetwork.step = i + ititial_step
+            if len(loss_dict) > 0:
+                previous_mean_losses = [i[-1] for i in loss_dict.values()]
+                previous_mean_loss = mean(previous_mean_losses)
+                
+            scheduler.apply(optimizer, hypernetwork.step)
+            if scheduler.finished:
+                break
+
+            if shared.state.interrupted:
+                break
+
+            with torch.autocast("cuda"):
+                c = stack_conds([entry.cond for entry in entries]).to(devices.device)
+                # c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
+                x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+                loss = shared.sd_model(x, c)[0]
+                del x
+                del c
+
+                losses[hypernetwork.step % losses.shape[0]] = loss.item()
+                for entry in entries:
+                    loss_dict[entry.filename].append(loss.item())
+                    
+                optimizer.zero_grad()
+                weights[0].grad = None
+                loss.backward()
+
+                if weights[0].grad is None:
+                    steps_without_grad += 1
+                else:
+                    steps_without_grad = 0
+                assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
+
+                optimizer.step()
+
+            steps_done = hypernetwork.step + 1
+
+            if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
+                raise RuntimeError("Loss diverged.")
+            
+            if len(previous_mean_losses) > 1:
+                std = stdev(previous_mean_losses)
             else:
-                steps_without_grad = 0
-            assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
-
-            optimizer.step()
-
-        steps_done = hypernetwork.step + 1
-
-        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
-            raise RuntimeError("Loss diverged.")
-        
-        if len(previous_mean_losses) > 1:
-            std = stdev(previous_mean_losses)
-        else:
-            std = 0
-        dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
-        pbar.set_description(dataset_loss_info)
-
-        if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
-            # Before saving, change name to match current checkpoint.
-            hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
-            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
-            hypernetwork.save(last_saved_file)
-
-        textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
-            "loss": f"{previous_mean_loss:.7f}",
-            "learn_rate": scheduler.learn_rate
-        })
-
-        if images_dir is not None and steps_done % create_image_every == 0:
-            forced_filename = f'{hypernetwork_name}-{steps_done}'
-            last_saved_image = os.path.join(images_dir, forced_filename)
-
-            optimizer.zero_grad()
-            shared.sd_model.cond_stage_model.to(devices.device)
-            shared.sd_model.first_stage_model.to(devices.device)
-
-            p = processing.StableDiffusionProcessingTxt2Img(
-                sd_model=shared.sd_model,
-                do_not_save_grid=True,
-                do_not_save_samples=True,
-            )
+                std = 0
+            dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
+            pbar.set_description(dataset_loss_info)
+
+            if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
+                # Before saving, change name to match current checkpoint.
+                hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
+                last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
+                hypernetwork.save(last_saved_file)
+
+            textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
+                "loss": f"{previous_mean_loss:.7f}",
+                "learn_rate": scheduler.learn_rate
+            })
+
+            if images_dir is not None and steps_done % create_image_every == 0:
+                forced_filename = f'{hypernetwork_name}-{steps_done}'
+                last_saved_image = os.path.join(images_dir, forced_filename)
+
+                optimizer.zero_grad()
+                shared.sd_model.cond_stage_model.to(devices.device)
+                shared.sd_model.first_stage_model.to(devices.device)
+
+                p = processing.StableDiffusionProcessingTxt2Img(
+                    sd_model=shared.sd_model,
+                    do_not_save_grid=True,
+                    do_not_save_samples=True,
+                )
 
-            if preview_from_txt2img:
-                p.prompt = preview_prompt
-                p.negative_prompt = preview_negative_prompt
-                p.steps = preview_steps
-                p.sampler_index = preview_sampler_index
-                p.cfg_scale = preview_cfg_scale
-                p.seed = preview_seed
-                p.width = preview_width
-                p.height = preview_height
-            else:
-                p.prompt = entries[0].cond_text
-                p.steps = 20
+                if preview_from_txt2img:
+                    p.prompt = preview_prompt
+                    p.negative_prompt = preview_negative_prompt
+                    p.steps = preview_steps
+                    p.sampler_index = preview_sampler_index
+                    p.cfg_scale = preview_cfg_scale
+                    p.seed = preview_seed
+                    p.width = preview_width
+                    p.height = preview_height
+                else:
+                    p.prompt = entries[0].cond_text
+                    p.steps = 20
 
-            preview_text = p.prompt
+                preview_text = p.prompt
 
-            processed = processing.process_images(p)
-            image = processed.images[0] if len(processed.images)>0 else None
+                processed = processing.process_images(p)
+                image = processed.images[0] if len(processed.images)>0 else None
 
-            if unload:
-                shared.sd_model.cond_stage_model.to(devices.cpu)
-                shared.sd_model.first_stage_model.to(devices.cpu)
+                if unload:
+                    shared.sd_model.cond_stage_model.to(devices.cpu)
+                    shared.sd_model.first_stage_model.to(devices.cpu)
 
-            if image is not None:
-                shared.state.current_image = image
-                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
-                last_saved_image += f", prompt: {preview_text}"
+                if image is not None:
+                    shared.state.current_image = image
+                    last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                    last_saved_image += f", prompt: {preview_text}"
 
-        shared.state.job_no = hypernetwork.step
+            shared.state.job_no = hypernetwork.step
 
-        shared.state.textinfo = f"""
+            shared.state.textinfo = f"""
 <p>
 Loss: {previous_mean_loss:.7f}<br/>
 Step: {hypernetwork.step}<br/>
@@ -510,7 +512,14 @@ Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-        
+    finally:
+        if weights:
+            for weight in weights:
+                weight.requires_grad = False
+        if unload:
+            shared.sd_model.cond_stage_model.to(devices.device)
+            shared.sd_model.first_stage_model.to(devices.device)
+
     report_statistics(loss_dict)
     checkpoint = sd_models.select_checkpoint()
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 44f06443..fd7f0897 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -283,111 +283,113 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     embedding_yet_to_be_embedded = False
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-    for i, entries in pbar:
-        embedding.step = i + ititial_step
 
-        scheduler.apply(optimizer, embedding.step)
-        if scheduler.finished:
-            break
-
-        if shared.state.interrupted:
-            break
-
-        with torch.autocast("cuda"):
-            c = cond_model([entry.cond_text for entry in entries])
-            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-            loss = shared.sd_model(x, c)[0]
-            del x
-
-            losses[embedding.step % losses.shape[0]] = loss.item()
-
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-        steps_done = embedding.step + 1
-
-        epoch_num = embedding.step // len(ds)
-        epoch_step = embedding.step % len(ds)
-
-        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
-
-        if embedding_dir is not None and steps_done % save_embedding_every == 0:
-            # Before saving, change name to match current checkpoint.
-            embedding.name = f'{embedding_name}-{steps_done}'
-            last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
-            embedding.save(last_saved_file)
-            embedding_yet_to_be_embedded = True
-
-        write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
-            "loss": f"{losses.mean():.7f}",
-            "learn_rate": scheduler.learn_rate
-        })
-
-        if images_dir is not None and steps_done % create_image_every == 0:
-            forced_filename = f'{embedding_name}-{steps_done}'
-            last_saved_image = os.path.join(images_dir, forced_filename)
-            p = processing.StableDiffusionProcessingTxt2Img(
-                sd_model=shared.sd_model,
-                do_not_save_grid=True,
-                do_not_save_samples=True,
-                do_not_reload_embeddings=True,
-            )
-
-            if preview_from_txt2img:
-                p.prompt = preview_prompt
-                p.negative_prompt = preview_negative_prompt
-                p.steps = preview_steps
-                p.sampler_index = preview_sampler_index
-                p.cfg_scale = preview_cfg_scale
-                p.seed = preview_seed
-                p.width = preview_width
-                p.height = preview_height
-            else:
-                p.prompt = entries[0].cond_text
-                p.steps = 20
-                p.width = training_width
-                p.height = training_height
+    try:
+        for i, entries in pbar:
+            embedding.step = i + ititial_step
+
+            scheduler.apply(optimizer, embedding.step)
+            if scheduler.finished:
+                break
+
+            if shared.state.interrupted:
+                break
+
+            with torch.autocast("cuda"):
+                c = cond_model([entry.cond_text for entry in entries])
+                x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+                loss = shared.sd_model(x, c)[0]
+                del x
+
+                losses[embedding.step % losses.shape[0]] = loss.item()
+
+                optimizer.zero_grad()
+                loss.backward()
+                optimizer.step()
+
+            steps_done = embedding.step + 1
+
+            epoch_num = embedding.step // len(ds)
+            epoch_step = embedding.step % len(ds)
+
+            pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
+
+            if embedding_dir is not None and steps_done % save_embedding_every == 0:
+                # Before saving, change name to match current checkpoint.
+                embedding.name = f'{embedding_name}-{steps_done}'
+                last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
+                embedding.save(last_saved_file)
+                embedding_yet_to_be_embedded = True
+
+            write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
+                "loss": f"{losses.mean():.7f}",
+                "learn_rate": scheduler.learn_rate
+            })
+
+            if images_dir is not None and steps_done % create_image_every == 0:
+                forced_filename = f'{embedding_name}-{steps_done}'
+                last_saved_image = os.path.join(images_dir, forced_filename)
+                p = processing.StableDiffusionProcessingTxt2Img(
+                    sd_model=shared.sd_model,
+                    do_not_save_grid=True,
+                    do_not_save_samples=True,
+                    do_not_reload_embeddings=True,
+                )
+
+                if preview_from_txt2img:
+                    p.prompt = preview_prompt
+                    p.negative_prompt = preview_negative_prompt
+                    p.steps = preview_steps
+                    p.sampler_index = preview_sampler_index
+                    p.cfg_scale = preview_cfg_scale
+                    p.seed = preview_seed
+                    p.width = preview_width
+                    p.height = preview_height
+                else:
+                    p.prompt = entries[0].cond_text
+                    p.steps = 20
+                    p.width = training_width
+                    p.height = training_height
 
-            preview_text = p.prompt
+                preview_text = p.prompt
 
-            processed = processing.process_images(p)
-            image = processed.images[0]
+                processed = processing.process_images(p)
+                image = processed.images[0]
 
-            shared.state.current_image = image
+                shared.state.current_image = image
 
-            if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
+                if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
 
-                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
+                    last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
 
-                info = PngImagePlugin.PngInfo()
-                data = torch.load(last_saved_file)
-                info.add_text("sd-ti-embedding", embedding_to_b64(data))
+                    info = PngImagePlugin.PngInfo()
+                    data = torch.load(last_saved_file)
+                    info.add_text("sd-ti-embedding", embedding_to_b64(data))
 
-                title = "<{}>".format(data.get('name', '???'))
+                    title = "<{}>".format(data.get('name', '???'))
 
-                try:
-                    vectorSize = list(data['string_to_param'].values())[0].shape[0]
-                except Exception as e:
-                    vectorSize = '?'
+                    try:
+                        vectorSize = list(data['string_to_param'].values())[0].shape[0]
+                    except Exception as e:
+                        vectorSize = '?'
 
-                checkpoint = sd_models.select_checkpoint()
-                footer_left = checkpoint.model_name
-                footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '{}v {}s'.format(vectorSize, steps_done)
+                    checkpoint = sd_models.select_checkpoint()
+                    footer_left = checkpoint.model_name
+                    footer_mid = '[{}]'.format(checkpoint.hash)
+                    footer_right = '{}v {}s'.format(vectorSize, steps_done)
 
-                captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
-                captioned_image = insert_image_data_embed(captioned_image, data)
+                    captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
+                    captioned_image = insert_image_data_embed(captioned_image, data)
 
-                captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
-                embedding_yet_to_be_embedded = False
+                    captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+                    embedding_yet_to_be_embedded = False
 
-            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
-            last_saved_image += f", prompt: {preview_text}"
+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                last_saved_image += f", prompt: {preview_text}"
 
-        shared.state.job_no = embedding.step
+            shared.state.job_no = embedding.step
 
-        shared.state.textinfo = f"""
+            shared.state.textinfo = f"""
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {embedding.step}<br/>
@@ -396,6 +398,9 @@ Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
+    finally:
+        if embedding and embedding.vec is not None:
+            embedding.vec.requires_grad = False
 
     checkpoint = sd_models.select_checkpoint()
 
-- 
cgit v1.2.1


From a27d19de2eff633b6a39f9f4a5c0f2d6abb81bb5 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sat, 29 Oct 2022 19:44:05 +0700
Subject: Additional assert on dataset

---
 modules/textual_inversion/dataset.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 8bb00d27..ad726577 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -42,6 +42,8 @@ class PersonalizedBase(Dataset):
         self.lines = lines
 
         assert data_root, 'dataset directory not specified'
+        assert os.path.isdir(data_root), "Dataset directory doesn't exist"
+        assert os.listdir(data_root), "Dataset directory is empty"
 
         cond_model = shared.sd_model.cond_stage_model
 
-- 
cgit v1.2.1


From 0089fa5cebe43654e6d8a45d9b880e25826c2a74 Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Sat, 29 Oct 2022 21:09:05 +0800
Subject: =?UTF-8?q?=E6=9B=B4=E6=96=B0=20zh=5FCN.json?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/zh_CN.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index e453f5e3..b208ae25 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -482,7 +482,7 @@
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声。你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
     "Enable Autocomplete": "开启Tag补全",
     "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
-    "Roll three": "抽三位出來",
+    "Roll three": "抽三位出来",
     "Generate forever": "不停地生成",
     "Cancel generate forever": "取消不停地生成"
 }
-- 
cgit v1.2.1


From cbdb5ced767d2f82296ecf22feb262870acba6f3 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Sat, 29 Oct 2022 22:33:51 +0900
Subject: Add new translations

New settings option
New extras tab option
---
 localizations/ko_KR.json | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 3d631066..8f5f155b 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -202,6 +202,7 @@
   "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트시 패딩값(픽셀 단위)",
   "Inpaint masked": "마스크만 처리",
   "Inpaint not masked": "마스크 이외만 처리",
+  "Inpainting conditioning mask strength": "인페인팅 조절 마스크 강도",
   "Input directory": "인풋 이미지 경로",
   "Input images directory": "이미지 경로 입력",
   "Interpolation Method": "보간 방법",
@@ -276,6 +277,7 @@
   "Number of repeats for a single input image per epoch; used only for displaying epoch number": "세대(Epoch)당 단일 인풋 이미지의 반복 횟수 - 세대(Epoch) 숫자를 표시하는 데에만 사용됩니다. ",
   "Number of rows on the page": "각 페이지마다 표시할 세로줄 수",
   "Number of vectors per token": "토큰별 벡터 수",
+  "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "인페인팅 모델에만 적용됩니다. 인페인팅과 이미지→이미지에서 원본 이미지를 얼마나 마스킹 처리할지 결정하는 값입니다. 1.0은 완전히 마스킹함(기본 설정)을 의미하고, 0.0은 완전히 언마스킹된 이미지를 의미합니다. 낮은 값일수록 이미지의 전체적인 구성을 유지하는 데에 도움되겠지만, 변화량이 많을수록 불안정해집니다.",
   "Open for Clip Aesthetic!": "클립 스타일 기능을 활성화하려면 클릭!",
   "Open images output directory": "이미지 저장 경로 열기",
   "Open output directory": "저장 경로 열기",
@@ -390,6 +392,7 @@
   "Select activation function of hypernetwork": "하이퍼네트워크 활성화 함수 선택",
   "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "레이어 가중치 초기화 방식 선택 - relu류 : Kaiming 추천, sigmoid류 : Xavier 추천",
   "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "WebUI에 표시할 Real-ESRGAN 모델을 선택하십시오. (재시작 필요)",
+  "Send seed when sending prompt or image to other interface": "다른 화면으로 프롬프트나 이미지를 보낼 때 시드도 함께 보내기",
   "Send to extras": "부가기능으로 전송",
   "Send to img2img": "이미지→이미지로 전송",
   "Send to inpaint": "인페인트로 전송",
@@ -467,6 +470,7 @@
   "up": "위쪽",
   "Upload mask": "마스크 업로드하기",
   "Upload prompt inputs": "입력할 프롬프트를 업로드하십시오",
+  "Upscale Before Restoring Faces": "얼굴 보정을 진행하기 전에 업스케일링 먼저 진행하기",
   "Upscale latent space image when doing hires. fix": "고해상도 보정 사용시 잠재 공간 이미지 업스케일하기",
   "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
   "Upscaler": "업스케일러",
-- 
cgit v1.2.1


From de1dc0d279a877d5d9f512befe30a7d7e5cf3881 Mon Sep 17 00:00:00 2001
From: Martin Cairns <4314538+MartinCairnsSQL@users.noreply.github.com>
Date: Sat, 29 Oct 2022 15:23:19 +0100
Subject: Add adjust_steps_if_invalid to find next valid step for ddim uniform
 sampler

---
 modules/sd_samplers.py | 28 +++++++++++++++-------------
 1 file changed, 15 insertions(+), 13 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 3670b57d..aca014e8 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -1,5 +1,6 @@
 from collections import namedtuple
 import numpy as np
+from math import floor
 import torch
 import tqdm
 from PIL import Image
@@ -205,17 +206,22 @@ class VanillaStableDiffusionSampler:
         self.mask = p.mask if hasattr(p, 'mask') else None
         self.nmask = p.nmask if hasattr(p, 'nmask') else None
 
+
+    def adjust_steps_if_invalid(self, p, num_steps):
+        if self.config.name == 'DDIM' and p.ddim_discretize == 'uniform':
+            valid_step = 999 / (1000 // num_steps)
+            if valid_step == floor(valid_step):
+                return int(valid_step) + 1
+        
+        return num_steps
+
+
     def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
-
+        steps = self.adjust_steps_if_invalid(p, steps)
         self.initialize(p)
 
-        # existing code fails with certain step counts, like 9
-        try:
-            self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-        except Exception:
-            self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-
+        self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
         x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
 
         self.init_latent = x
@@ -239,18 +245,14 @@ class VanillaStableDiffusionSampler:
         self.last_latent = x
         self.step = 0
 
-        steps = steps or p.steps
+        steps = self.adjust_steps_if_invalid(p, steps or p.steps)
 
         # Wrap the conditioning models with additional image conditioning for inpainting model
         if image_conditioning is not None:
             conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
             unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
 
-        # existing code fails with certain step counts, like 9
-        try:
-            samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
-        except Exception:
-            samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
+        samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
 
         return samples_ddim
 
-- 
cgit v1.2.1


From af45b5a11a8a393ed404be3ce0ecac14338155c7 Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sat, 29 Oct 2022 18:26:28 +0300
Subject: Testing with API added

---
 run_tests.bat        | 15 ++++++++++
 test/__init__.py     |  0
 test/server_poll.py  | 17 +++++++++++
 test/txt2img_test.py | 80 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 4 files changed, 112 insertions(+)
 create mode 100644 run_tests.bat
 create mode 100644 test/__init__.py
 create mode 100644 test/server_poll.py
 create mode 100644 test/txt2img_test.py

diff --git a/run_tests.bat b/run_tests.bat
new file mode 100644
index 00000000..3a63f034
--- /dev/null
+++ b/run_tests.bat
@@ -0,0 +1,15 @@
+@echo off
+set ERROR_REPORTING=FALSE
+set COMMANDLINE_ARGS= --api
+echo Launching SDWebUI...
+start "SDWebUITest" webui.bat
+
+if not defined PYTHON (set PYTHON=python)
+if not defined VENV_DIR (set VENV_DIR=venv)
+set PYTHON="%~dp0%VENV_DIR%\Scripts\Python.exe"
+%PYTHON% test/server_poll.py
+for /f "tokens=2 delims=," %%a in ('tasklist /v /fo csv ^| findstr /i "SDWebUITest"') do set "$PID=%%a"
+
+taskkill /PID %$PID% >nul 2>&1
+
+pause
diff --git a/test/__init__.py b/test/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/test/server_poll.py b/test/server_poll.py
new file mode 100644
index 00000000..8c0436f8
--- /dev/null
+++ b/test/server_poll.py
@@ -0,0 +1,17 @@
+import unittest
+import requests
+import time
+
+timeout_threshold = 240
+start_time = time.time()
+while time.time()-start_time < timeout_threshold:
+    try:
+        requests.head("http://localhost:7860/")
+        break
+    except requests.exceptions.ConnectionError:
+        pass
+if time.time()-start_time < timeout_threshold:
+    suite = unittest.TestLoader().discover('', pattern='*_test.py')
+    result = unittest.TextTestRunner(verbosity=2).run(suite)
+else:
+    print("Launch unsuccessful")
diff --git a/test/txt2img_test.py b/test/txt2img_test.py
new file mode 100644
index 00000000..9484fd99
--- /dev/null
+++ b/test/txt2img_test.py
@@ -0,0 +1,80 @@
+import unittest
+import requests
+import time
+
+url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
+simple_txt2img = {
+    "enable_hr": False,
+    "denoising_strength": 0,
+    "firstphase_width": 0,
+    "firstphase_height": 0,
+    "prompt": "example prompt",
+    "styles": [
+      ""
+    ],
+    "seed": -1,
+    "subseed": -1,
+    "subseed_strength": 0,
+    "seed_resize_from_h": -1,
+    "seed_resize_from_w": -1,
+    "batch_size": 1,
+    "n_iter": 1,
+    "steps": 5,
+    "cfg_scale": 7,
+    "width": 64,
+    "height": 64,
+    "restore_faces": False,
+    "tiling": False,
+    "negative_prompt": "",
+    "eta": 0,
+    "s_churn": 0,
+    "s_tmax": 0,
+    "s_tmin": 0,
+    "s_noise": 1,
+    "sampler_index": "Euler a"
+}
+
+class TestTxt2ImgWorking(unittest.TestCase):
+    def test_txt2img_simple_performed(self):
+        self.assertEqual(requests.post(url_txt2img, json=simple_txt2img).status_code, 200)
+
+    def test_txt2img_with_negative_prompt_performed(self):
+        params = simple_txt2img.copy()
+        params["negative_prompt"] = "example negative prompt"
+        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+
+    def test_txt2img_not_square_image_performed(self):
+        params = simple_txt2img.copy()
+        params["height"] = 128
+        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+
+    def test_txt2img_with_hrfix_performed(self):
+        params = simple_txt2img.copy()
+        params["enable_hr"] = True
+        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+
+    def test_txt2img_with_restore_faces_performed(self):
+        params = simple_txt2img.copy()
+        params["restore_faces"] = True
+        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+
+    def test_txt2img_with_tiling_faces_performed(self):
+        params = simple_txt2img.copy()
+        params["tiling"] = True
+        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+
+    def test_txt2img_with_vanilla_sampler_performed(self):
+        params = simple_txt2img.copy()
+        params["sampler_index"] = "PLMS"
+        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+
+    def test_txt2img_multiple_batches_performed(self):
+        params = simple_txt2img.copy()
+        params["n_iter"] = 2
+        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+
+class TestTxt2ImgCorrectness(unittest.TestCase):
+    pass
+
+if __name__ == "__main__":
+    unittest.main()
-- 
cgit v1.2.1


From f512b0828bb3f7d586c2da8caa87506998ed9212 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Sun, 30 Oct 2022 00:45:30 +0900
Subject: Update zh_CN.json

update translation content to 35c45df28b303a05d56a13cb56d4046f08cf8c25
---
 localizations/zh_CN.json | 53 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index b208ae25..94907ada 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -91,6 +91,9 @@
     "Show Textbox": "显示文本框",
     "File with inputs": "含输入内容的文件",
     "Prompts": "提示词",
+    "Iterate seed every line": "每行输入都换一个种子",
+    "List of prompt inputs": "提示词输入列表",
+    "Upload prompt inputs": "上传提示词输入文件",
     "X type": "X轴类型",
     "Nothing": "无",
     "Var. seed": "差异随机种子",
@@ -109,6 +112,7 @@
     "Eta": "Eta",
     "Clip skip": "Clip 跳过",
     "Denoising": "去噪",
+    "Cond. Image Mask Weight": "自适应图像蒙版强度",
     "X values": "X轴数值",
     "Y type": "Y轴类型",
     "Y values": "Y轴数值",
@@ -204,6 +208,7 @@
     "GFPGAN visibility": "GFPGAN 可见度",
     "CodeFormer visibility": "CodeFormer 可见度",
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 权重 (0 = 最大效果, 1 = 最小效果)",
+    "Upscale Before Restoring Faces": "放大后再进行面部修复",
     "Open output directory": "打开输出目录",
     "Send to txt2img": ">> 文生图",
     "A merger of the two checkpoints will be generated in your": "合并后的模型(ckpt)会生成在你的",
@@ -237,6 +242,36 @@
     "leakyrelu": "leakyrelu",
     "elu": "elu",
     "swish": "swish",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "阈值",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "选择初始化层权重的方案. 类relu - Kaiming, 类sigmoid - Xavier 都是比较推荐的选项",
+    "Normal": "正态",
+    "KaimingUniform": "Kaiming均匀",
+    "KaimingNormal": "Kaiming正态",
+    "XavierUniform": "Xavier均匀",
+    "XavierNormal": "Xavier正态",
     "Add layer normalization": "添加层标准化",
     "Use dropout": "采用 dropout 防止过拟合",
     "Overwrite Old Hypernetwork": "覆写旧的 Hypernetwork",
@@ -250,10 +285,15 @@
     "Create flipped copies": "生成镜像副本",
     "Split oversized images into two": "将过大的图像分为两份",
     "Split oversized images": "分割过大的图像",
+    "Auto focal point crop": "自动焦点裁切",
     "Use BLIP for caption": "使用 BLIP 生成说明文字(自然语言描述)",
     "Use deepbooru for caption": "使用 deepbooru 生成说明文字(tags)",
     "Split image threshold": "图像分割阈值",
     "Split image overlap ratio": "分割图像重叠的比率",
+    "Focal point face weight": "焦点面部权重",
+    "Focal point entropy weight": "焦点熵权重",
+    "Focal point edges weight": "焦点线条权重",
+    "Create debug image": "生成除错图片",
     "Preprocess": "预处理",
     "Train an embedding; must specify a directory with a set of 1:1 ratio images": "训练 embedding； 必须指定一组具有 1:1 比例图像的目录",
     "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "训练 embedding 或者 hypernetwork； 必须指定一组具有 1:1 比例图像的目录",
@@ -280,9 +320,11 @@
     "Renew Page": "刷新页面",
     "extras": "后处理",
     "favorites": "收藏夹",
+    "others": "其他",
     "custom fold": "自定义文件夹",
     "Load": "载入",
     "Images directory": "图像目录",
+    "Dropdown": "下拉列表",
     "Prev batch": "上一批",
     "Next batch": "下一批",
     "First Page": "首页",
@@ -291,7 +333,12 @@
     "Next Page": "下一页",
     "End Page": "尾页",
     "number of images to delete consecutively next": "接下来要连续删除的图像数",
+    "delete next": "删除下一张",
     "Delete": "删除",
+    "sort by": "排序方式",
+    "path name": "路径名",
+    "date": "日期",
+    "keyword": "关键词",
     "Generate Info": "生成信息",
     "File Name": "文件名",
     "Collect": "收藏",
@@ -299,12 +346,15 @@
     "Date to": "日期至",
     "Number": "数量",
     "set_index": "设置索引",
+    "load_switch": "载入开关",
+    "turn_page_switch": "翻页开关",
     "Checkbox": "勾选框",
     "Apply settings": "保存设置",
     "Saving images/grids": "保存图像/概览图",
     "Always save all generated images": "始终保存所有生成的图像",
     "File format for images": "图像的文件格式",
     "Images filename pattern": "图像文件名格式",
+    "Add number to filename when saving": "储存的时候在文件名里添加数字",
     "Always save all generated image grids": "始终保存所有生成的概览图",
     "File format for grids": "概览图的文件格式",
     "Add extended info (seed, prompt) to filename when saving grid": "保存概览时将扩展信息（随机种子、提示词）添加到文件名",
@@ -359,6 +409,7 @@
     "Stable Diffusion": "Stable Diffusion",
     "Checkpoints to cache in RAM": "缓存在内存(RAM)中的模型(ckpt)",
     "Hypernetwork strength": "Hypernetwork 强度",
+    "Inpainting conditioning mask strength": "内补绘制的自适应蒙版强度",
     "Apply color correction to img2img results to match original colors.": "对图生图结果应用颜色校正以匹配原始颜色",
     "Save a copy of image before applying color correction to img2img results": "在对图生图结果应用颜色校正之前保存图像副本",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（正常情况下更弱的去噪需要更少的迭代步数）",
@@ -390,6 +441,7 @@
     "Add model hash to generation information": "将模型的哈希值添加到生成信息",
     "Add model name to generation information": "将模型名称添加到生成信息",
     "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "当从文本读取生成参数到 UI（从 PNG 信息或粘贴文本）时，不要更改选定的模型(ckpt)",
+    "Send seed when sending prompt or image to other interface": "将提示词或者图片传送到其他界面的时候同时也把随机种子传送过去",
     "Font for image grids that have text": "有文字的概览图使用的字体",
     "Enable full page image viewer": "启用整页图像查看器",
     "Show images zoomed in by default in full page image viewer": "在整页图像查看器中默认放大显示图像",
@@ -478,6 +530,7 @@
     "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络修复低质量面部",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。留空以保持文件名文本不变",
     "This string will be used to join split words into a single line if the option above is enabled.": "如果启用了上述选项，则此处的字符会用于将拆分的单词接合为同一行",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "仅适用于内补绘制专用的模型。 决定了蒙版在内补绘制以及图生图中屏蔽原图内容的强度。 1.0 表示完全屏蔽，这是默认行为。 0.0 表示完全不屏蔽。 较低的值将有助于保持图像的整体构图，但很难遇到较大的变化。",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置名称列表，以逗号分隔，设置应转到顶部的快速访问栏，而不是通常的设置选项卡。有关设置名称，请参见 modules/shared.py。需要重新启动才能应用",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声。你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
     "Enable Autocomplete": "开启Tag补全",
-- 
cgit v1.2.1


From 6515dedf57db6a46c7a85b0b7a1eee0daad90123 Mon Sep 17 00:00:00 2001
From: Mackerel <mackerel@chickatrice.com>
Date: Mon, 24 Oct 2022 10:13:25 -0400
Subject: webui.sh: no automatic git pull

---
 webui.sh | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/webui.sh b/webui.sh
index a9f85d89..cc06f344 100755
--- a/webui.sh
+++ b/webui.sh
@@ -102,15 +102,14 @@ then
     exit 1
 fi
 
-printf "\n%s\n" "${delimiter}"
-printf "Clone or update stable-diffusion-webui"
-printf "\n%s\n" "${delimiter}"
 cd "${install_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/, aborting...\e[0m" "${install_dir}"; exit 1; }
 if [[ -d "${clone_dir}" ]]
 then
     cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
-    "${GIT}" pull
 else
+    printf "\n%s\n" "${delimiter}"
+    printf "Clone stable-diffusion-webui"
+    printf "\n%s\n" "${delimiter}"
     "${GIT}" clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git "${clone_dir}"
     cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
 fi
-- 
cgit v1.2.1


From 44ab954fabb9c1273366ebdca47f8da394d61aab Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Sat, 29 Oct 2022 10:02:56 -0700
Subject: Fix latent upscale highres fix #3888

---
 modules/processing.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 548eec29..f18b7db2 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -653,6 +653,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         if opts.use_scale_latent_for_hires_fix:
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+            image_conditioning = self.txt2img_image_conditioning(samples)
 
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
@@ -674,6 +675,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
+            image_conditioning = self.img2img_image_conditioning(
+                decoded_samples, 
+                samples, 
+                decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
+            )
+
         shared.state.nextjob()
 
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
@@ -684,11 +691,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        image_conditioning = self.img2img_image_conditioning(
-            decoded_samples, 
-            samples, 
-            decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
-        )
         samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)
 
         return samples
-- 
cgit v1.2.1


From ab05a74ead9fabb45dd099990e34061c7eb02ca3 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 30 Oct 2022 00:32:02 +0700
Subject: Revert "Add cleanup after training"

This reverts commit 3ce2bfdf95bd5f26d0f6e250e67338ada91980d1.
---
 modules/hypernetworks/hypernetwork.py          | 201 ++++++++++++-------------
 modules/textual_inversion/textual_inversion.py | 185 +++++++++++------------
 2 files changed, 186 insertions(+), 200 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 170d5ea4..38f35c58 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -398,112 +398,110 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     forced_filename = "<none>"
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-
-    try:
-        for i, entries in pbar:
-            hypernetwork.step = i + ititial_step
-            if len(loss_dict) > 0:
-                previous_mean_losses = [i[-1] for i in loss_dict.values()]
-                previous_mean_loss = mean(previous_mean_losses)
-                
-            scheduler.apply(optimizer, hypernetwork.step)
-            if scheduler.finished:
-                break
-
-            if shared.state.interrupted:
-                break
-
-            with torch.autocast("cuda"):
-                c = stack_conds([entry.cond for entry in entries]).to(devices.device)
-                # c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
-                x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-                loss = shared.sd_model(x, c)[0]
-                del x
-                del c
-
-                losses[hypernetwork.step % losses.shape[0]] = loss.item()
-                for entry in entries:
-                    loss_dict[entry.filename].append(loss.item())
-                    
-                optimizer.zero_grad()
-                weights[0].grad = None
-                loss.backward()
-
-                if weights[0].grad is None:
-                    steps_without_grad += 1
-                else:
-                    steps_without_grad = 0
-                assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
-
-                optimizer.step()
-
-            steps_done = hypernetwork.step + 1
-
-            if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
-                raise RuntimeError("Loss diverged.")
+    for i, entries in pbar:
+        hypernetwork.step = i + ititial_step
+        if len(loss_dict) > 0:
+            previous_mean_losses = [i[-1] for i in loss_dict.values()]
+            previous_mean_loss = mean(previous_mean_losses)
             
-            if len(previous_mean_losses) > 1:
-                std = stdev(previous_mean_losses)
+        scheduler.apply(optimizer, hypernetwork.step)
+        if scheduler.finished:
+            break
+
+        if shared.state.interrupted:
+            break
+
+        with torch.autocast("cuda"):
+            c = stack_conds([entry.cond for entry in entries]).to(devices.device)
+            # c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
+            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+            loss = shared.sd_model(x, c)[0]
+            del x
+            del c
+
+            losses[hypernetwork.step % losses.shape[0]] = loss.item()
+            for entry in entries:
+                loss_dict[entry.filename].append(loss.item())
+                
+            optimizer.zero_grad()
+            weights[0].grad = None
+            loss.backward()
+
+            if weights[0].grad is None:
+                steps_without_grad += 1
             else:
-                std = 0
-            dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
-            pbar.set_description(dataset_loss_info)
-
-            if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
-                # Before saving, change name to match current checkpoint.
-                hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
-                last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
-                hypernetwork.save(last_saved_file)
-
-            textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
-                "loss": f"{previous_mean_loss:.7f}",
-                "learn_rate": scheduler.learn_rate
-            })
-
-            if images_dir is not None and steps_done % create_image_every == 0:
-                forced_filename = f'{hypernetwork_name}-{steps_done}'
-                last_saved_image = os.path.join(images_dir, forced_filename)
-
-                optimizer.zero_grad()
-                shared.sd_model.cond_stage_model.to(devices.device)
-                shared.sd_model.first_stage_model.to(devices.device)
-
-                p = processing.StableDiffusionProcessingTxt2Img(
-                    sd_model=shared.sd_model,
-                    do_not_save_grid=True,
-                    do_not_save_samples=True,
-                )
+                steps_without_grad = 0
+            assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
 
-                if preview_from_txt2img:
-                    p.prompt = preview_prompt
-                    p.negative_prompt = preview_negative_prompt
-                    p.steps = preview_steps
-                    p.sampler_index = preview_sampler_index
-                    p.cfg_scale = preview_cfg_scale
-                    p.seed = preview_seed
-                    p.width = preview_width
-                    p.height = preview_height
-                else:
-                    p.prompt = entries[0].cond_text
-                    p.steps = 20
+            optimizer.step()
 
-                preview_text = p.prompt
+        steps_done = hypernetwork.step + 1
 
-                processed = processing.process_images(p)
-                image = processed.images[0] if len(processed.images)>0 else None
+        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
+            raise RuntimeError("Loss diverged.")
+        
+        if len(previous_mean_losses) > 1:
+            std = stdev(previous_mean_losses)
+        else:
+            std = 0
+        dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
+        pbar.set_description(dataset_loss_info)
+
+        if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
+            # Before saving, change name to match current checkpoint.
+            hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
+            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
+            hypernetwork.save(last_saved_file)
+
+        textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
+            "loss": f"{previous_mean_loss:.7f}",
+            "learn_rate": scheduler.learn_rate
+        })
+
+        if images_dir is not None and steps_done % create_image_every == 0:
+            forced_filename = f'{hypernetwork_name}-{steps_done}'
+            last_saved_image = os.path.join(images_dir, forced_filename)
+
+            optimizer.zero_grad()
+            shared.sd_model.cond_stage_model.to(devices.device)
+            shared.sd_model.first_stage_model.to(devices.device)
 
-                if unload:
-                    shared.sd_model.cond_stage_model.to(devices.cpu)
-                    shared.sd_model.first_stage_model.to(devices.cpu)
+            p = processing.StableDiffusionProcessingTxt2Img(
+                sd_model=shared.sd_model,
+                do_not_save_grid=True,
+                do_not_save_samples=True,
+            )
 
-                if image is not None:
-                    shared.state.current_image = image
-                    last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
-                    last_saved_image += f", prompt: {preview_text}"
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entries[0].cond_text
+                p.steps = 20
+
+            preview_text = p.prompt
+
+            processed = processing.process_images(p)
+            image = processed.images[0] if len(processed.images)>0 else None
+
+            if unload:
+                shared.sd_model.cond_stage_model.to(devices.cpu)
+                shared.sd_model.first_stage_model.to(devices.cpu)
 
-            shared.state.job_no = hypernetwork.step
+            if image is not None:
+                shared.state.current_image = image
+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                last_saved_image += f", prompt: {preview_text}"
 
-            shared.state.textinfo = f"""
+        shared.state.job_no = hypernetwork.step
+
+        shared.state.textinfo = f"""
 <p>
 Loss: {previous_mean_loss:.7f}<br/>
 Step: {hypernetwork.step}<br/>
@@ -512,14 +510,7 @@ Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-    finally:
-        if weights:
-            for weight in weights:
-                weight.requires_grad = False
-        if unload:
-            shared.sd_model.cond_stage_model.to(devices.device)
-            shared.sd_model.first_stage_model.to(devices.device)
-
+        
     report_statistics(loss_dict)
     checkpoint = sd_models.select_checkpoint()
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fd7f0897..44f06443 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -283,113 +283,111 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     embedding_yet_to_be_embedded = False
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
+    for i, entries in pbar:
+        embedding.step = i + ititial_step
 
-    try:
-        for i, entries in pbar:
-            embedding.step = i + ititial_step
-
-            scheduler.apply(optimizer, embedding.step)
-            if scheduler.finished:
-                break
-
-            if shared.state.interrupted:
-                break
-
-            with torch.autocast("cuda"):
-                c = cond_model([entry.cond_text for entry in entries])
-                x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-                loss = shared.sd_model(x, c)[0]
-                del x
-
-                losses[embedding.step % losses.shape[0]] = loss.item()
-
-                optimizer.zero_grad()
-                loss.backward()
-                optimizer.step()
-
-            steps_done = embedding.step + 1
-
-            epoch_num = embedding.step // len(ds)
-            epoch_step = embedding.step % len(ds)
-
-            pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
-
-            if embedding_dir is not None and steps_done % save_embedding_every == 0:
-                # Before saving, change name to match current checkpoint.
-                embedding.name = f'{embedding_name}-{steps_done}'
-                last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
-                embedding.save(last_saved_file)
-                embedding_yet_to_be_embedded = True
-
-            write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
-                "loss": f"{losses.mean():.7f}",
-                "learn_rate": scheduler.learn_rate
-            })
-
-            if images_dir is not None and steps_done % create_image_every == 0:
-                forced_filename = f'{embedding_name}-{steps_done}'
-                last_saved_image = os.path.join(images_dir, forced_filename)
-                p = processing.StableDiffusionProcessingTxt2Img(
-                    sd_model=shared.sd_model,
-                    do_not_save_grid=True,
-                    do_not_save_samples=True,
-                    do_not_reload_embeddings=True,
-                )
-
-                if preview_from_txt2img:
-                    p.prompt = preview_prompt
-                    p.negative_prompt = preview_negative_prompt
-                    p.steps = preview_steps
-                    p.sampler_index = preview_sampler_index
-                    p.cfg_scale = preview_cfg_scale
-                    p.seed = preview_seed
-                    p.width = preview_width
-                    p.height = preview_height
-                else:
-                    p.prompt = entries[0].cond_text
-                    p.steps = 20
-                    p.width = training_width
-                    p.height = training_height
+        scheduler.apply(optimizer, embedding.step)
+        if scheduler.finished:
+            break
+
+        if shared.state.interrupted:
+            break
+
+        with torch.autocast("cuda"):
+            c = cond_model([entry.cond_text for entry in entries])
+            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+            loss = shared.sd_model(x, c)[0]
+            del x
+
+            losses[embedding.step % losses.shape[0]] = loss.item()
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        steps_done = embedding.step + 1
+
+        epoch_num = embedding.step // len(ds)
+        epoch_step = embedding.step % len(ds)
+
+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
+
+        if embedding_dir is not None and steps_done % save_embedding_every == 0:
+            # Before saving, change name to match current checkpoint.
+            embedding.name = f'{embedding_name}-{steps_done}'
+            last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
+            embedding.save(last_saved_file)
+            embedding_yet_to_be_embedded = True
+
+        write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
+            "loss": f"{losses.mean():.7f}",
+            "learn_rate": scheduler.learn_rate
+        })
+
+        if images_dir is not None and steps_done % create_image_every == 0:
+            forced_filename = f'{embedding_name}-{steps_done}'
+            last_saved_image = os.path.join(images_dir, forced_filename)
+            p = processing.StableDiffusionProcessingTxt2Img(
+                sd_model=shared.sd_model,
+                do_not_save_grid=True,
+                do_not_save_samples=True,
+                do_not_reload_embeddings=True,
+            )
+
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entries[0].cond_text
+                p.steps = 20
+                p.width = training_width
+                p.height = training_height
 
-                preview_text = p.prompt
+            preview_text = p.prompt
 
-                processed = processing.process_images(p)
-                image = processed.images[0]
+            processed = processing.process_images(p)
+            image = processed.images[0]
 
-                shared.state.current_image = image
+            shared.state.current_image = image
 
-                if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
+            if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
 
-                    last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
+                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
 
-                    info = PngImagePlugin.PngInfo()
-                    data = torch.load(last_saved_file)
-                    info.add_text("sd-ti-embedding", embedding_to_b64(data))
+                info = PngImagePlugin.PngInfo()
+                data = torch.load(last_saved_file)
+                info.add_text("sd-ti-embedding", embedding_to_b64(data))
 
-                    title = "<{}>".format(data.get('name', '???'))
+                title = "<{}>".format(data.get('name', '???'))
 
-                    try:
-                        vectorSize = list(data['string_to_param'].values())[0].shape[0]
-                    except Exception as e:
-                        vectorSize = '?'
+                try:
+                    vectorSize = list(data['string_to_param'].values())[0].shape[0]
+                except Exception as e:
+                    vectorSize = '?'
 
-                    checkpoint = sd_models.select_checkpoint()
-                    footer_left = checkpoint.model_name
-                    footer_mid = '[{}]'.format(checkpoint.hash)
-                    footer_right = '{}v {}s'.format(vectorSize, steps_done)
+                checkpoint = sd_models.select_checkpoint()
+                footer_left = checkpoint.model_name
+                footer_mid = '[{}]'.format(checkpoint.hash)
+                footer_right = '{}v {}s'.format(vectorSize, steps_done)
 
-                    captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
-                    captioned_image = insert_image_data_embed(captioned_image, data)
+                captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
+                captioned_image = insert_image_data_embed(captioned_image, data)
 
-                    captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
-                    embedding_yet_to_be_embedded = False
+                captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+                embedding_yet_to_be_embedded = False
 
-                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
-                last_saved_image += f", prompt: {preview_text}"
+            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+            last_saved_image += f", prompt: {preview_text}"
 
-            shared.state.job_no = embedding.step
+        shared.state.job_no = embedding.step
 
-            shared.state.textinfo = f"""
+        shared.state.textinfo = f"""
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {embedding.step}<br/>
@@ -398,9 +396,6 @@ Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-    finally:
-        if embedding and embedding.vec is not None:
-            embedding.vec.requires_grad = False
 
     checkpoint = sd_models.select_checkpoint()
 
-- 
cgit v1.2.1


From 6e2ce4e735db64afcd0fe637327ca4ec78335706 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Sat, 29 Oct 2022 10:35:51 -0700
Subject: Added image conditioning to latent upscale. Only comuted  if the mask
 weight is not 1.0 to avoid extra memory. Also includes some code cleanup.

---
 modules/processing.py | 29 +++++++++++------------------
 1 file changed, 11 insertions(+), 18 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index f18b7db2..ee0e9e34 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -134,11 +134,7 @@ class StableDiffusionProcessing():
             # Dummy zero conditioning if we're not using inpainting model.
             # Still takes up a bit of memory, but no encoder call.
             # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-            return torch.zeros(
-                x.shape[0], 5, 1, 1, 
-                dtype=x.dtype, 
-                device=x.device
-            )
+            return x.new_zeros(x.shape[0], 5, 1, 1)
 
         height = height or self.height
         width = width or self.width
@@ -156,11 +152,7 @@ class StableDiffusionProcessing():
     def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):
         if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
             # Dummy zero conditioning if we're not using inpainting model.
-            return torch.zeros(
-                latent_image.shape[0], 5, 1, 1,
-                dtype=latent_image.dtype,
-                device=latent_image.device
-            )
+            return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
 
         # Handle the different mask inputs
         if image_mask is not None:
@@ -174,11 +166,10 @@ class StableDiffusionProcessing():
                 # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
                 conditioning_mask = torch.round(conditioning_mask)
         else:
-            conditioning_mask = torch.ones(1, 1, *source_image.shape[-2:])
+            conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
 
         # Create another latent image, this time with a masked version of the original input.
         # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
-        conditioning_mask = conditioning_mask.to(source_image.device)
         conditioning_image = torch.lerp(
             source_image,
             source_image * (1.0 - conditioning_mask),
@@ -653,7 +644,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         if opts.use_scale_latent_for_hires_fix:
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
-            image_conditioning = self.txt2img_image_conditioning(samples)
+            
+            # Avoid making the inpainting conditioning unless necessary as 
+            # this does need some extra compute to decode / encode the image again.
+            if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
+                image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
+            else:
+                image_conditioning = self.txt2img_image_conditioning(samples)
 
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
@@ -675,11 +672,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
-            image_conditioning = self.img2img_image_conditioning(
-                decoded_samples, 
-                samples, 
-                decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
-            )
+            image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
 
         shared.state.nextjob()
 
-- 
cgit v1.2.1


From a07f054c86f33360ff620d6a3fffdee366ab2d99 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 30 Oct 2022 00:49:29 +0700
Subject: Add missing info on hypernetwork/embedding model log

Mentioned here: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/1528#discussioncomment-3991513

Also group the saving into one
---
 modules/hypernetworks/hypernetwork.py          | 31 +++++++++++++-------
 modules/textual_inversion/textual_inversion.py | 39 +++++++++++++++++---------
 2 files changed, 47 insertions(+), 23 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 38f35c58..86daf825 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -361,6 +361,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         images_dir = None
 
     hypernetwork = shared.loaded_hypernetwork
+    checkpoint = sd_models.select_checkpoint()
 
     ititial_step = hypernetwork.step or 0
     if ititial_step > steps:
@@ -449,9 +450,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
             # Before saving, change name to match current checkpoint.
-            hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
-            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
-            hypernetwork.save(last_saved_file)
+            hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
+            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
+            save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{previous_mean_loss:.7f}",
@@ -512,13 +513,23 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 """
         
     report_statistics(loss_dict)
-    checkpoint = sd_models.select_checkpoint()
 
-    hypernetwork.sd_checkpoint = checkpoint.hash
-    hypernetwork.sd_checkpoint_name = checkpoint.model_name
-    # Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention).
-    hypernetwork.name = hypernetwork_name
-    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork.name}.pt')
-    hypernetwork.save(filename)
+    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+    save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
 
     return hypernetwork, filename
+
+def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
+    old_hypernetwork_name = hypernetwork.name
+    old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None
+    old_sd_checkpoint_name = hypernetwork.sd_checkpoint_name if hasattr(hypernetwork, "sd_checkpoint_name") else None
+    try:
+        hypernetwork.sd_checkpoint = checkpoint.hash
+        hypernetwork.sd_checkpoint_name = checkpoint.model_name
+        hypernetwork.name = hypernetwork_name
+        hypernetwork.save(filename)
+    except:
+        hypernetwork.sd_checkpoint = old_sd_checkpoint
+        hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name
+        hypernetwork.name = old_hypernetwork_name
+        raise
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 44f06443..ee9917ce 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -119,7 +119,7 @@ class EmbeddingDatabase:
             vec = emb.detach().to(devices.device, dtype=torch.float32)
             embedding = Embedding(vec, name)
             embedding.step = data.get('step', None)
-            embedding.sd_checkpoint = data.get('hash', None)
+            embedding.sd_checkpoint = data.get('sd_checkpoint', None)
             embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
             self.register_embedding(embedding, shared.sd_model)
 
@@ -259,6 +259,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     hijack = sd_hijack.model_hijack
 
     embedding = hijack.embedding_db.word_embeddings[embedding_name]
+    checkpoint = sd_models.select_checkpoint()
 
     ititial_step = embedding.step or 0
     if ititial_step > steps:
@@ -314,9 +315,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
         if embedding_dir is not None and steps_done % save_embedding_every == 0:
             # Before saving, change name to match current checkpoint.
-            embedding.name = f'{embedding_name}-{steps_done}'
-            last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
-            embedding.save(last_saved_file)
+            embedding_name_every = f'{embedding_name}-{steps_done}'
+            last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
+            save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
             embedding_yet_to_be_embedded = True
 
         write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
@@ -397,14 +398,26 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
 
-    checkpoint = sd_models.select_checkpoint()
-
-    embedding.sd_checkpoint = checkpoint.hash
-    embedding.sd_checkpoint_name = checkpoint.model_name
-    embedding.cached_checksum = None
-    # Before saving for the last time, change name back to base name (as opposed to the save_embedding_every step-suffixed naming convention).
-    embedding.name = embedding_name
-    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding.name}.pt')
-    embedding.save(filename)
+    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
+    save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
 
     return embedding, filename
+
+def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True):
+    old_embedding_name = embedding.name
+    old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None
+    old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
+    old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None
+    try:
+        embedding.sd_checkpoint = checkpoint.hash
+        embedding.sd_checkpoint_name = checkpoint.model_name
+        if remove_cached_checksum:
+            embedding.cached_checksum = None
+        embedding.name = embedding_name
+        embedding.save(filename)
+    except:
+        embedding.sd_checkpoint = old_sd_checkpoint
+        embedding.sd_checkpoint_name = old_sd_checkpoint_name
+        embedding.name = old_embedding_name
+        embedding.cached_checksum = old_cached_checksum
+        raise
-- 
cgit v1.2.1


From 3d58510f214c645ce5cdb261aa47df6573b239e9 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 30 Oct 2022 00:54:59 +0700
Subject: Fix dataset still being loaded even when training will be skipped

---
 modules/hypernetworks/hypernetwork.py          | 2 +-
 modules/textual_inversion/textual_inversion.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 86daf825..07acadc9 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -364,7 +364,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     checkpoint = sd_models.select_checkpoint()
 
     ititial_step = hypernetwork.step or 0
-    if ititial_step > steps:
+    if ititial_step >= steps:
         shared.state.textinfo = f"Model has already been trained beyond specified max steps"
         return hypernetwork, filename
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index ee9917ce..e0babb46 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -262,7 +262,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     checkpoint = sd_models.select_checkpoint()
 
     ititial_step = embedding.step or 0
-    if ititial_step > steps:
+    if ititial_step >= steps:
         shared.state.textinfo = f"Model has already been trained beyond specified max steps"
         return embedding, filename
 
-- 
cgit v1.2.1


From 2f3d8172c3fe80ea64ebe5c797835ca15c2e595f Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sat, 29 Oct 2022 21:43:32 +0300
Subject: img2img test template and setUp added

---
 test/extras_test.py               |   0
 test/img2img_test.py              |  59 ++++++++++++++++++++++
 test/test_files/img2img_basic.png | Bin 0 -> 9932 bytes
 test/test_files/mask_basic.png    | Bin 0 -> 362 bytes
 test/txt2img_test.py              | 102 ++++++++++++++++++--------------------
 5 files changed, 106 insertions(+), 55 deletions(-)
 create mode 100644 test/extras_test.py
 create mode 100644 test/img2img_test.py
 create mode 100644 test/test_files/img2img_basic.png
 create mode 100644 test/test_files/mask_basic.png

diff --git a/test/extras_test.py b/test/extras_test.py
new file mode 100644
index 00000000..e69de29b
diff --git a/test/img2img_test.py b/test/img2img_test.py
new file mode 100644
index 00000000..d8ed309d
--- /dev/null
+++ b/test/img2img_test.py
@@ -0,0 +1,59 @@
+import unittest
+import requests
+from gradio.processing_utils import encode_pil_to_base64
+from PIL import Image
+
+class Img2ImgWorking(unittest.TestCase):
+    def setUp(self):
+        self.url_img2img = "http://localhost:7860/sdapi/v1/img2img"
+        self.simple_img2img = {
+            "init_images": [
+                encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))
+                ],
+            "resize_mode": 0,
+            "denoising_strength": 0.75,
+            "mask": None,
+            "mask_blur": 4,
+            "inpainting_fill": 0,
+            "inpaint_full_res": False,
+            "inpaint_full_res_padding": 0,
+            "inpainting_mask_invert": 0,
+            "prompt": "example prompt",
+            "styles": [
+                ""
+                ],
+            "seed": -1,
+            "subseed": -1,
+            "subseed_strength": 0,
+            "seed_resize_from_h": -1,
+            "seed_resize_from_w": -1,
+            "batch_size": 1,
+            "n_iter": 1,
+            "steps": 3,
+            "cfg_scale": 7,
+            "width": 64,
+            "height": 64,
+            "restore_faces": False,
+            "tiling": False,
+            "negative_prompt": "",
+            "eta": 0,
+            "s_churn": 0,
+            "s_tmax": 0,
+            "s_tmin": 0,
+            "s_noise": 1,
+            "override_settings": {},
+            "sampler_index": "Euler a",
+            "include_init_images": False
+            }
+    def test_img2img_simple_performed(self):
+        self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
+
+    def test_inpainting_masked_performed(self):
+        self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png"))
+        self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
+
+class TestImg2ImgCorrectness(unittest.TestCase):
+    pass
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file
diff --git a/test/test_files/img2img_basic.png b/test/test_files/img2img_basic.png
new file mode 100644
index 00000000..49a42048
Binary files /dev/null and b/test/test_files/img2img_basic.png differ
diff --git a/test/test_files/mask_basic.png b/test/test_files/mask_basic.png
new file mode 100644
index 00000000..0c2e9a68
Binary files /dev/null and b/test/test_files/mask_basic.png differ
diff --git a/test/txt2img_test.py b/test/txt2img_test.py
index 9484fd99..ad27686a 100644
--- a/test/txt2img_test.py
+++ b/test/txt2img_test.py
@@ -1,77 +1,69 @@
 import unittest
 import requests
-import time
-
-url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
-simple_txt2img = {
-    "enable_hr": False,
-    "denoising_strength": 0,
-    "firstphase_width": 0,
-    "firstphase_height": 0,
-    "prompt": "example prompt",
-    "styles": [
-      ""
-    ],
-    "seed": -1,
-    "subseed": -1,
-    "subseed_strength": 0,
-    "seed_resize_from_h": -1,
-    "seed_resize_from_w": -1,
-    "batch_size": 1,
-    "n_iter": 1,
-    "steps": 5,
-    "cfg_scale": 7,
-    "width": 64,
-    "height": 64,
-    "restore_faces": False,
-    "tiling": False,
-    "negative_prompt": "",
-    "eta": 0,
-    "s_churn": 0,
-    "s_tmax": 0,
-    "s_tmin": 0,
-    "s_noise": 1,
-    "sampler_index": "Euler a"
-}
 
 class TestTxt2ImgWorking(unittest.TestCase):
+    def setUp(self):
+        self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
+        self.simple_txt2img = {
+            "enable_hr": False,
+            "denoising_strength": 0,
+            "firstphase_width": 0,
+            "firstphase_height": 0,
+            "prompt": "example prompt",
+            "styles": [
+            ""
+            ],
+            "seed": -1,
+            "subseed": -1,
+            "subseed_strength": 0,
+            "seed_resize_from_h": -1,
+            "seed_resize_from_w": -1,
+            "batch_size": 1,
+            "n_iter": 1,
+            "steps": 3,
+            "cfg_scale": 7,
+            "width": 64,
+            "height": 64,
+            "restore_faces": False,
+            "tiling": False,
+            "negative_prompt": "",
+            "eta": 0,
+            "s_churn": 0,
+            "s_tmax": 0,
+            "s_tmin": 0,
+            "s_noise": 1,
+            "sampler_index": "Euler a"
+        }
     def test_txt2img_simple_performed(self):
-        self.assertEqual(requests.post(url_txt2img, json=simple_txt2img).status_code, 200)
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
     def test_txt2img_with_negative_prompt_performed(self):
-        params = simple_txt2img.copy()
-        params["negative_prompt"] = "example negative prompt"
-        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+        self.simple_txt2img["negative_prompt"] = "example negative prompt"
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
     def test_txt2img_not_square_image_performed(self):
-        params = simple_txt2img.copy()
-        params["height"] = 128
-        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+        self.simple_txt2img["height"] = 128
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
     def test_txt2img_with_hrfix_performed(self):
-        params = simple_txt2img.copy()
-        params["enable_hr"] = True
-        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+        self.simple_txt2img["enable_hr"] = True
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
     def test_txt2img_with_restore_faces_performed(self):
-        params = simple_txt2img.copy()
-        params["restore_faces"] = True
-        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+        self.simple_txt2img["restore_faces"] = True
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
     def test_txt2img_with_tiling_faces_performed(self):
-        params = simple_txt2img.copy()
-        params["tiling"] = True
-        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+        self.simple_txt2img["tiling"] = True
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
     def test_txt2img_with_vanilla_sampler_performed(self):
-        params = simple_txt2img.copy()
-        params["sampler_index"] = "PLMS"
-        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+        self.simple_txt2img["sampler_index"] = "PLMS"
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
     def test_txt2img_multiple_batches_performed(self):
-        params = simple_txt2img.copy()
-        params["n_iter"] = 2
-        self.assertEqual(requests.post(url_txt2img, json=params).status_code, 200)
+        self.simple_txt2img["n_iter"] = 2
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
 class TestTxt2ImgCorrectness(unittest.TestCase):
     pass
-- 
cgit v1.2.1


From ffc5b700c417924a77797fc5e02c219f10d801be Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sat, 29 Oct 2022 21:50:06 +0300
Subject: extras test template added

---
 test/extras_test.py  | 29 +++++++++++++++++++++++++++++
 test/img2img_test.py |  4 ++--
 2 files changed, 31 insertions(+), 2 deletions(-)

diff --git a/test/extras_test.py b/test/extras_test.py
index e69de29b..2e1764d9 100644
--- a/test/extras_test.py
+++ b/test/extras_test.py
@@ -0,0 +1,29 @@
+import unittest
+import requests
+from gradio.processing_utils import encode_pil_to_base64
+from PIL import Image
+
+class TestExtrasWorking(unittest.TestCase):
+    def setUp(self):
+        self.url_img2img = "http://localhost:7860/sdapi/v1/extra-single-image"
+        self.simple_extras = {
+            "resize_mode": 0,
+            "show_extras_results": True,
+            "gfpgan_visibility": 0,
+            "codeformer_visibility": 0,
+            "codeformer_weight": 0,
+            "upscaling_resize": 2,
+            "upscaling_resize_w": 512,
+            "upscaling_resize_h": 512,
+            "upscaling_crop": True,
+            "upscaler_1": "None",
+            "upscaler_2": "None",
+            "extras_upscaler_2_visibility": 0,
+            "image": ""
+            }
+
+class TestExtrasCorrectness(unittest.TestCase):
+    pass
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/img2img_test.py b/test/img2img_test.py
index d8ed309d..61e3e285 100644
--- a/test/img2img_test.py
+++ b/test/img2img_test.py
@@ -3,7 +3,7 @@ import requests
 from gradio.processing_utils import encode_pil_to_base64
 from PIL import Image
 
-class Img2ImgWorking(unittest.TestCase):
+class TestImg2ImgWorking(unittest.TestCase):
     def setUp(self):
         self.url_img2img = "http://localhost:7860/sdapi/v1/img2img"
         self.simple_img2img = {
@@ -56,4 +56,4 @@ class TestImg2ImgCorrectness(unittest.TestCase):
     pass
 
 if __name__ == "__main__":
-    unittest.main()
\ No newline at end of file
+    unittest.main()
-- 
cgit v1.2.1


From 4609b83cd496013a05e77c42af031d89f07785a9 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sat, 29 Oct 2022 16:09:19 -0300
Subject: Add PNG Info endpoint

---
 modules/api/api.py    | 12 +++++++++---
 modules/api/models.py |  9 ++++++++-
 2 files changed, 17 insertions(+), 4 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 49c213ea..8fcd068d 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -5,7 +5,7 @@ import modules.shared as shared
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.sd_samplers import all_samplers
-from modules.extras import run_extras
+from modules.extras import run_extras, run_pnginfo
 
 def upscaler_to_index(name: str):
     try:
@@ -32,6 +32,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
+        self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -125,8 +126,13 @@ class Api:
 
         return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
     
-    def pnginfoapi(self):
-        raise NotImplementedError
+    def pnginfoapi(self, req:PNGInfoRequest):
+        if(not req.image.strip()):
+            return PNGInfoResponse(info="")
+
+        result = run_pnginfo(decode_base64_to_image(req.image.strip()))
+
+        return PNGInfoResponse(info=result[1])
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)
diff --git a/modules/api/models.py b/modules/api/models.py
index dd122321..58e8e58b 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,4 +1,5 @@
 import inspect
+from click import prompt
 from pydantic import BaseModel, Field, create_model
 from typing import Any, Optional
 from typing_extensions import Literal
@@ -148,4 +149,10 @@ class ExtrasBatchImagesRequest(ExtrasBaseRequest):
     imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
 
 class ExtrasBatchImagesResponse(ExtraBaseResponse):
-    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
\ No newline at end of file
+    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
+
+class PNGInfoRequest(BaseModel):
+    image: str = Field(title="Image", description="The base64 encoded PNG image")
+
+class PNGInfoResponse(BaseModel):
+    info: str = Field(title="Image info", description="A string with all the info the image had")
\ No newline at end of file
-- 
cgit v1.2.1


From 83a1f44ae26cb89492064bb8be0321b14a75efe4 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Sat, 29 Oct 2022 16:10:00 -0300
Subject: Fix space

---
 modules/api/api.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 8fcd068d..d0f488ca 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -126,7 +126,7 @@ class Api:
 
         return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
     
-    def pnginfoapi(self, req:PNGInfoRequest):
+    def pnginfoapi(self, req: PNGInfoRequest):
         if(not req.image.strip()):
             return PNGInfoResponse(info="")
 
-- 
cgit v1.2.1


From 9bb6b6509aff8c1e6546d5a798ef9e9922758dc4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 22:20:02 +0300
Subject: add postprocess call for scripts

---
 modules/processing.py | 12 +++++++++---
 modules/scripts.py    | 24 +++++++++++++++++++++---
 2 files changed, 30 insertions(+), 6 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 548eec29..50343846 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -478,7 +478,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
     if p.scripts is not None:
-        p.scripts.run_alwayson_scripts(p)
+        p.scripts.process(p)
 
     infotexts = []
     output_images = []
@@ -501,7 +501,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
             subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
-            if (len(prompts) == 0):
+            if len(prompts) == 0:
                 break
 
             with devices.autocast():
@@ -590,7 +590,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     devices.torch_gc()
-    return Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+
+    res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+
+    if p.scripts is not None:
+        p.scripts.postprocess(p, res)
+
+    return res
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
diff --git a/modules/scripts.py b/modules/scripts.py
index a7f36012..96e44bfd 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -64,7 +64,16 @@ class Script:
     def process(self, p, *args):
         """
         This function is called before processing begins for AlwaysVisible scripts.
-        scripts. You can modify the processing object (p) here, inject hooks, etc.
+        You can modify the processing object (p) here, inject hooks, etc.
+        args contains all values returned by components from ui()
+        """
+
+        pass
+
+    def postprocess(self, p, processed, *args):
+        """
+        This function is called after processing ends for AlwaysVisible scripts.
+        args contains all values returned by components from ui()
         """
 
         pass
@@ -289,13 +298,22 @@ class ScriptRunner:
 
         return processed
 
-    def run_alwayson_scripts(self, p):
+    def process(self, p):
         for script in self.alwayson_scripts:
             try:
                 script_args = p.script_args[script.args_from:script.args_to]
                 script.process(p, *script_args)
             except Exception:
-                print(f"Error running alwayson script: {script.filename}", file=sys.stderr)
+                print(f"Error running process: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
+    def postprocess(self, p, processed):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.postprocess(p, processed, *script_args)
+            except Exception:
+                print(f"Error running postprocess: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
     def reload_sources(self, cache):
-- 
cgit v1.2.1


From 4cb5983c308cb8a4940b00babc2cf6fc9261692f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 22:38:50 +0300
Subject: rename french translation to be in line with others

---
 localizations/fr-FR.json | 415 -----------------------------------------------
 localizations/fr_FR.json | 415 +++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 415 insertions(+), 415 deletions(-)
 delete mode 100644 localizations/fr-FR.json
 create mode 100644 localizations/fr_FR.json

diff --git a/localizations/fr-FR.json b/localizations/fr-FR.json
deleted file mode 100644
index b33d0838..00000000
--- a/localizations/fr-FR.json
+++ /dev/null
@@ -1,415 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "Chargement...",
-    "view": "vue",
-    "api": "api",
-    "•": "•",
-    "built with gradio": "Construit avec Gradio",
-    "Stable Diffusion checkpoint": "checkpoint Stable Diffusion",
-    "txt2img": "txt2img",
-    "img2img": "img2img",
-    "Extras": "Extras",
-    "PNG Info": "Infos PNG",
-    "History": "Historique",
-    "Checkpoint Merger": "Fusion de checkpoints",
-    "Train": "Entrainer",
-    "Settings": "Paramètres",
-    "Prompt": "Requête",
-    "Negative prompt": "Requête négative",
-    "Run": "Lancer",
-    "Skip": "Passer",
-    "Interrupt": "Interrrompre",
-    "Generate": "Générer",
-    "Style 1": "Style 1",
-    "Style 2": "Style 2",
-    "Label": "Etiquette",
-    "File": "Fichier",
-    "Drop File Here": "Déposer votre fichier ici",
-    "-": "-",
-    "or": "ou",
-    "Click to Upload": "Cliquer pour uploader",
-    "Image": "Image",
-    "Check progress": "Voir l'avancement",
-    "Check progress (first)": "Voir l'avancement (1er)",
-    "Sampling Steps": "Étapes d'échantillonnage",
-    "Sampling method": "Méthode d'échantillonnage",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "Largeur",
-    "Height": "Hauteur",
-    "Restore faces": "Restaurer les visages",
-    "Tiling": "Mode Tuile",
-    "Highres. fix": "Correction haute résolution",
-    "Firstpass width": "Largeur première passe",
-    "Firstpass height": "Hauteur seconde passe",
-    "Denoising strength": "Puissance de réduction du bruit",
-    "Batch count": "Nombre de lots",
-    "Batch size": "Taille de lots",
-    "CFG Scale": "Echelle CFG",
-    "Seed": "Valeur aléatoire",
-    "Extra": "Extra",
-    "Variation seed": "Variation de la valeur aléatoire",
-    "Variation strength": "Puissance de variation",
-    "Resize seed from width": "Largeur de redimensionnement de la valeur aléatoire",
-    "Resize seed from height": "Hauteur de redimensionnement de la valeur aléatoire",
-    "Script": "Script",
-    "None": "Aucun",
-    "Prompt matrix": "Matrice de requète",
-    "Prompts from file or textbox": "Requètes depuis un fichier ou une boite de dialogue",
-    "X/Y plot": "graphe X/Y",
-    "Put variable parts at start of prompt": "Mettre les mots clés variable au début de la requête",
-    "Show Textbox": "Afficher le champs texte",
-    "File with inputs": "Fichier d'entrée",
-    "Prompts": "Requêtes",
-    "X type": "Paramètre axe X",
-    "Nothing": "Rien",
-    "Var. seed": "Valeur aléatoire variable",
-    "Var. strength": "Puissance variable",
-    "Steps": "Étapes",
-    "Prompt S/R": "Cherche et remplace dans la requête",
-    "Prompt order": "Ordre de la requête",
-    "Sampler": "Echantilloneur",
-    "Checkpoint name": "Nom du checkpoint",
-    "Hypernetwork": "Hypernetwork",
-    "Hypernet str.": "Force de l'Hypernetwork",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma min": "Sigma min.",
-    "Sigma max": "Sigma max.",
-    "Sigma noise": "Bruit Sigma",
-    "Eta": "Temps estimé",
-    "Clip skip": "Passer Clip",
-    "Denoising": "Réduction du bruit",
-    "X values": "Valeurs X",
-    "Y type": "Paramètre axe Y",
-    "Y values": "Valeurs Y",
-    "Draw legend": "Afficher la légende",
-    "Include Separate Images": "Inclure les images séparées",
-    "Keep -1 for seeds": "Conserver -1 pour la valeur aléatoire",
-    "Drop Image Here": "Déposer l'image ici",
-    "Save": "Enregistrer",
-    "Send to img2img": "Envoyer vers img2img",
-    "Send to inpaint": "Envoyer vers inpaint",
-    "Send to extras": "Envoyer vers extras",
-    "Make Zip when Save?": "Créer un zip lors de l'enregistrement?",
-    "Textbox": "Champ texte",
-    "Interrogate\nCLIP": "Interroger\nCLIP",
-    "Interrogate\nDeepBooru": "Interroger\nDeepBooru",
-    "Inpaint": "Inpaint",
-    "Batch img2img": "Lot img2img",
-    "Image for img2img": "Image pour img2img",
-    "Image for inpainting with mask": "Image pour inpainting avec masque",
-    "Mask": "Masque",
-    "Mask blur": "Flou masque",
-    "Mask mode": "Mode masque",
-    "Draw mask": "Dessiner masque",
-    "Upload mask": "Uploader masque",
-    "Masking mode": "Mode de masquage",
-    "Inpaint masked": "Inpaint masqué",
-    "Inpaint not masked": "Inpaint non masqué",
-    "Masked content": "Contenu masqué",
-    "fill": "remplir",
-    "original": "original",
-    "latent noise": "bruit latent",
-    "latent nothing": "latent vide",
-    "Inpaint at full resolution": "Inpaint en pleine résolution",
-    "Inpaint at full resolution padding, pixels": "Padding de l'inpaint en pleine résolution, en pixels",
-    "Process images in a directory on the same machine where the server is running.": "Traite les images dans un dossier sur la même machine où le serveur tourne",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Utiliser un dossier de sortie vide pour enregistrer les images normalement plutôt que d'écrire dans le dossier de sortie",
-    "Input directory": "Dossier d'entrée",
-    "Output directory": "Dossier de sortie",
-    "Resize mode": "Mode redimensionnement",
-    "Just resize": "Redimensionner uniquement",
-    "Crop and resize": "Recadrer et redimensionner",
-    "Resize and fill": "Redimensionner et remplir",
-    "img2img alternative test": "Test alternatif img2img",
-    "Loopback": "Bouclage",
-    "Outpainting mk2": "Outpainting v2",
-    "Poor man's outpainting": "Outpainting du pauvre",
-    "SD upscale": "Agrandissement SD",
-    "should be 2 or lower.": "doit être inférieur ou égal à 2",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Forcer `Méthode d'échantillonnage` à Euler ? (cette méthode est dédiée à cela)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Forcer la `requête` au contenu de la `requête d'origine` ? (de même pour la `requête négative`)",
-    "Original prompt": "Requête d'origine",
-    "Original negative prompt": "Requête négative d'origine",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Forcer le valeur d'`Étapes d'échantillonnage` à la même valeur qu'`Étapes de décodage` ?",
-    "Decode steps": "Étapes de décodage",
-    "Override `Denoising strength` to 1?": "Forcer `Puissance de réduction du bruit` à 1 ?",
-    "Decode CFG scale": "Echelle CFG de décodage",
-    "Randomness": "Aléatoire",
-    "Sigma adjustment for finding noise for image": "Ajustement Sigma lors de la recherche du bruit dans l'image",
-    "Loops": "Boucles",
-    "Denoising strength change factor": "Facteur de changement de la puissance de réduction du bruit",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Paramètres recommandés : Étapes d'échantillonnage : 80-100, Echantillonneur : Euler a, Puissance de réduction du bruit : 0.8",
-    "Pixels to expand": "Pixels à étendre",
-    "Outpainting direction": "Direction de l'outpainting",
-    "left": "gauche",
-    "right": "droite",
-    "up": "haut",
-    "down": "bas",
-    "Fall-off exponent (lower=higher detail)": "Exposant de diminution (plus petit = plus de détails)",
-    "Color variation": "Variation de couleur",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Agrandira l'image à deux fois sa taille; utilisez les glissières largeur et hauteur afin de choisir la taille de tuile",
-    "Tile overlap": "Chevauchement de tuile",
-    "Upscaler": "Agrandisseur",
-    "Lanczos": "Lanczos",
-    "LDSR": "LDSR",
-    "BSRGAN 4x": "BSRGAN 4x",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR 4x": "SwinIR 4x",
-    "Single Image": "Image unique",
-    "Batch Process": "Traitement par lot",
-    "Batch from Directory": "Lot depuis un dossier",
-    "Source": "Source",
-    "Show result images": "Montrez les images résultantes",
-    "Scale by": "Mise à l'échelle de",
-    "Scale to": "Mise à l'échelle à",
-    "Resize": "Redimensionner",
-    "Crop to fit": "Recadrer à la taille",
-    "Upscaler 2": "Agrandisseur 2",
-    "Upscaler 2 visibility": "Visibilité de l'agrandisseur 2",
-    "GFPGAN visibility": "Visibilité GFPGAN",
-    "CodeFormer visibility": "Visibilité CodeFormer",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Poids CodeFormer (0 = effet maximum, 1 = effet minimum)",
-    "Open output directory": "Ouvrir le dossier de sortie",
-    "Send to txt2img": "Envoyer vers txt2img",
-    "txt2img history": "historique txt2img",
-    "img2img history": "historique img2img",
-    "extras history": "historique extras",
-    "Renew Page": "Rafraichr la page",
-    "First Page": "Première page",
-    "Prev Page": "Page précendente",
-    "Page Index": "Index des pages",
-    "Next Page": "Page suivante",
-    "End Page": "Page de fin",
-    "number of images to delete consecutively next": "nombre d'image à supprimer consécutivement ensuite",
-    "Delete": "Supprimer",
-    "Generate Info": "Générer les informations",
-    "File Name": "Nom de fichier",
-    "set_index": "set_index",
-    "A merger of the two checkpoints will be generated in your": "Une fusion des deux checkpoints sera générée dans votre",
-    "checkpoint": "checkpoint",
-    "directory.": "dossier",
-    "Primary model (A)": "Modèle primaire (A)",
-    "Secondary model (B)": "Modèle secondaire (B)",
-    "Tertiary model (C)": "Modèle tertiaire (C)",
-    "Custom Name (Optional)": "Nom personnalisé (Optionel)",
-    "Multiplier (M) - set to 0 to get model A": "Multiplieur (M) - utiliser 0 pour le modèle A",
-    "Interpolation Method": "Méthode d'interpolation",
-    "Weighted sum": "Somme pondérée",
-    "Add difference": "Ajouter différence",
-    "Save as float16": "Enregistrer en tant que float16",
-    "See": "Voir",
-    "wiki": "wiki",
-    "for detailed explanation.": "pour une explication détaillée.",
-    "Create embedding": "Créer un embedding",
-    "Create hypernetwork": "Créer un hypernetwork",
-    "Preprocess images": "Pré-traite les images",
-    "Name": "Nom",
-    "Initialization text": "Texte d'initialisation",
-    "Number of vectors per token": "Nombre de vecteurs par jeton",
-    "Modules": "Modules",
-    "Source directory": "Dossier source",
-    "Destination directory": "Dossier destination",
-    "Create flipped copies": "Créer des copies en mirroir",
-    "Split oversized images into two": "Couper les images trop grandes en deux",
-    "Use BLIP for caption": "Utiliser BLIP pour les descriptions",
-    "Use deepbooru for caption": "Utiliser deepbooru pour les descriptions",
-    "Preprocess": "Pré-traite",
-    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Entrainer un embedding ; spécifiez un dossier contenant un ensemble d'images avec un ratio de 1:1",
-    "Embedding": "Embedding",
-    "Learning rate": "Vitesse d'apprentissage",
-    "Dataset directory": "Dossier des images d'entrée",
-    "Log directory": "Dossier de journalisation",
-    "Prompt template file": "Fichier modèle de requêtes",
-    "Max steps": "Étapes max.",
-    "Save an image to log directory every N steps, 0 to disable": "Enregistrer une image dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Enregistrer une copie de l'embedding dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
-    "Save images with embedding in PNG chunks": "Sauvegarder les images incluant l'embedding dans leur blocs PNG",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Lire les paramètres (requête, etc.) depuis l'onglet txt2img lors de la génération des previews",
-    "Train Hypernetwork": "Entrainer un Hypernetwork",
-    "Train Embedding": "Entrainer un Embedding",
-    "Apply settings": "Appliquer les paramètres",
-    "Saving images/grids": "Enregistrer les images/grilles",
-    "Always save all generated images": "Toujours enregistrer toutes les images",
-    "File format for images": "Format de fichier pour les images",
-    "Images filename pattern": "Motif pour le nom de fichier des images",
-    "Always save all generated image grids": "Toujours enregistrer toutes les grilles d'images générées",
-    "File format for grids": "Format de fichier pour les grilles",
-    "Add extended info (seed, prompt) to filename when saving grid": "Ajouter les informations étendues (valeur aléatoire, requête) aux noms de fichiers lors de l'enregistrement d'une grille",
-    "Do not save grids consisting of one picture": "Ne pas enregistrer les grilles contenant une seule image",
-    "Prevent empty spots in grid (when set to autodetect)": "Eviter les vides dans la grille (quand autodétection est choisie)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Nombre de colonnes de la grille; utilisez -1 pour autodétection et 0 pour qu'il soit égal à la taille du lot",
-    "Save text information about generation parameters as chunks to png files": "Enregistrer l'information du text des paramètres de génération en tant que blocs dans les fichiers PNG",
-    "Create a text file next to every image with generation parameters.": "Créer un fichier texte contenant les paramètres de génération à côté de chaque image",
-    "Save a copy of image before doing face restoration.": "Enregistrer une copie de l'image avant de lancer la restauration de visage",
-    "Quality for saved jpeg images": "Qualité pour les images jpeg enregistrées",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Si l'image PNG est plus grande que 4MB or l'une des ses dimensions supérieure à 4000, réduire sa taille et enregistrer une copie en JPG",
-    "Use original name for output filename during batch process in extras tab": "Utiliser un nom de fichier original pour les fichiers de sortie durant le traitement par lot dans l'onglet Extras",
-    "When using 'Save' button, only save a single selected image": "A l'utilisation du bouton `Enregistrer`, n'enregistrer que l'image séléctionnée",
-    "Do not add watermark to images": "Ne pas ajouter de filigrane aux images",
-    "Paths for saving": "Chemins pour l'enregistrement",
-    "Output directory for images; if empty, defaults to three directories below": "Dossier de sortie pour les images; si non spécifié, le chemin par défaut sera trois niveau en dessous",
-    "Output directory for txt2img images": "Dossier de sortie pour les images txt2img",
-    "Output directory for img2img images": "Dossier de sortie pour les images img2img",
-    "Output directory for images from extras tab": "Dossier de sortie pour les images de l'onglet Extras",
-    "Output directory for grids; if empty, defaults to two directories below": "Dossier de sortie pour les grilles; si non spécifié, le chemin par défaut sera deux niveau en dessous",
-    "Output directory for txt2img grids": "Dossier de sortie pour les grilles txt2img",
-    "Output directory for img2img grids": "Dossier de sortie pour les grilles img2img",
-    "Directory for saving images using the Save button": "Dossier de sauvegarde des images pour le bouton `Enregistrer`",
-    "Saving to a directory": "Enregistrer dans un dossier",
-    "Save images to a subdirectory": "Enregistrer les images dans un sous dossier",
-    "Save grids to a subdirectory": "Enregistrer les grilles dans un sous dossier",
-    "When using \"Save\" button, save images to a subdirectory": "Lors de l'utilisation du bouton \"Enregistrer\", sauvegarder les images dans un sous dossier",
-    "Directory name pattern": "Motif pour le nom des dossiers",
-    "Max prompt words for [prompt_words] pattern": "Maximum de mot pour le motif [prompt_words]",
-    "Upscaling": "Agrandissement",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Taille des tuile for les agrandisseurs ESRGAN. 0 = mode tuile désactivé.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Chevauchement des tuiles, en pixel pour l'agrandisseur ESRGAN. Valeur faible = couture visible",
-    "Tile size for all SwinIR.": "Taille de la tuile pour tous les agrandisseur SwinIR.",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Chevauchement de tuile, en pixels pour SwinIR. Valeur faible = couture visible",
-    "LDSR processing steps. Lower = faster": "Echantillon du traitement LDSR. Valeur faible =  plus rapide",
-    "Upscaler for img2img": "Agrandisseur pour img2img",
-    "Upscale latent space image when doing hires. fix": "Agrandir l'image de l'espace latent lors de la correction haute résolution",
-    "Face restoration": "Restauration de visage",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Paramètre de poids pour CodeFormer; 0 = effet maximum ； 1 = effet minimum",
-    "Move face restoration model from VRAM into RAM after processing": "Déplacer le modèle de restauration de visage de la VRAM vers la RAM après traitement",
-    "System": "Système",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "Fréquence d'interrogation par seconde pendant la génération. Mettez la valeur à 0 pour désactiver.",
-    "Always print all generation info to standard output": "Toujours afficher toutes les informations de génération dans la sortie standard",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Ajouter un seconde barre de progression dans la console montrant l'avancement pour un tâche complète.",
-    "Training": "Entrainement",
-    "Unload VAE and CLIP from VRAM when training": "Décharger VAE et CLIP de la VRAM pendant l'entrainement",
-    "Filename word regex": "Regex de mot",
-    "Filename join string": "Chaine de caractère pour lier les noms de fichier",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Nombre de répétition pour une image unique par époque; utilisé seulement pour afficher le nombre d'époques",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Enregistrer un csv contenant la perte dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "Checkpoint à mettre en cache dans la RAM",
-    "Hypernetwork strength": "Force de l'Hypernetwork",
-    "Apply color correction to img2img results to match original colors.": "Appliquer une correction de couleur aux résultats img2img afin de conserver les couleurs d'origine",
-    "Save a copy of image before applying color correction to img2img results": "Enregistrer une copie de l'image avant d'appliquer les résultats de la correction de couleur img2img",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Avec img2img, executer exactement le nombre d'étapes spécifiées par la glissière (normalement moins d'étapes sont executées quand la réduction du bruit est plus faible).",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Activer la quantisation des échantillionneurs K pour des résultats plus nets et plus propres. Cela peut modifier les valeurs aléatoires existantes. Requiert un redémarrage pour être actif.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphase : utilisez (texte) afin de forcer le modèle à porter plus d'attention au texte et [texte] afin qu'il y porte moins attention",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Utilisez l'ancienne méthode d'emphase. Peut être utile afin de reproduire d'anciennes valeurs aléatoires.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Demander aux échantillionneurs K-diffusion de produire les mêmes dans un lot que lors de la génération d'une image unique",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Améliorer la cohérence en remplissant (padding) à partir de la dernière virgule dans les X jetons quand on en utilise plus de 75",
-    "Filter NSFW content": "Filtrer le contenu +18 (NSFW)",
-    "Stop At last layers of CLIP model": "S'arrêter aux derniers niveaux du modèle CLIP",
-    "Interrogate Options": "Options d'intérrogation",
-    "Interrogate: keep models in VRAM": "Interroger : conserver les modèles en VRAM",
-    "Interrogate: use artists from artists.csv": "Interroger : utiliser les artistes dans artists.csv",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interroger : inclure la correspondance du classement des labels de modèle dans les résultats (N'a pas d'effet sur les interrogateurs basés sur des descriptions) ",
-    "Interrogate: num_beams for BLIP": "Interroger : num_beams pour BLIP",
-    "Interrogate: minimum description length (excluding artists, etc..)": "Interroger : longueur minimale de la description (excluant les artistes, etc.)",
-    "Interrogate: maximum description length": "Interroger : longueur maximale de la description",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP : nombre maximum de lignes dans le fichier texte (0 = pas de limite)",
-    "Interrogate: deepbooru score threshold": "Interroger : seuil du score deepbooru",
-    "Interrogate: deepbooru sort alphabetically": "Interroger : classement alphabétique deepbooru",
-    "use spaces for tags in deepbooru": "Utiliser des espaces pour les étiquettes dans deepbooru",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "échapper (\\) les crochets dans deepbooru (afin qu'ils puissent être utilisés littéralement et non pour mettre en emphase)",
-    "User interface": "Interface utilisateur",
-    "Show progressbar": "Afficher la barre de progression",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Afficher l'état d'avancement de la création d'image toutes les X étapes d'échantillionnage. Utiliser 0 pour désactiver.",
-    "Show grid in results for web": "Afficher la grille dans les résultats web",
-    "Do not show any images in results for web": "N'afficher aucune image dans les résultats web'",
-    "Add model hash to generation information": "Ajouter le hash du modèle dans l'information de génération",
-    "Add model name to generation information": "Ajouter le nom du modèle dans l'information de génération",
-    "Font for image grids that have text": "Police pour les grilles d'images contenant du texte",
-    "Enable full page image viewer": "Activer l'affichage des images en plein écran",
-    "Show images zoomed in by default in full page image viewer": "Afficher les images zoomées par défaut lors de l'affichage en plein écran",
-    "Show generation progress in window title.": "Afficher l'avancement de la génération dans le titre de la fenêtre.",
-    "Quicksettings list": "Liste de réglages rapides",
-    "Localization (requires restart)": "Localisation (requiert un redémarrage)",
-    "Sampler parameters": "Paramètres de l'échantillionneur",
-    "Hide samplers in user interface (requires restart)": "Cacher les échantillonneurs dans l'interface utilisateur (requiert un redémarrage)",
-    "eta (noise multiplier) for DDIM": "eta (multiplicateur de bruit) pour DDIM",
-    "eta (noise multiplier) for ancestral samplers": "eta (multiplicateur de bruit) poru les échantillionneurs de type 'ancestral'",
-    "img2img DDIM discretize": "Discrétisation DDIM pour img2img",
-    "uniform": "uniforme",
-    "quad": "quad",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "sigma tmin",
-    "sigma noise": "sigma noise",
-    "Eta noise seed delta": "Eta noise seed delta",
-    "Request browser notifications": "Demander les notifications au navigateur",
-    "Download localization template": "Télécharger le modèle de localisation",
-    "Reload custom script bodies (No ui updates, No restart)": "Recharger le contenu des scripts personnalisés (Pas de mise à jour de l'interface, Pas de redémarrage)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Redémarrer Gradio et rafraichir les composants (Scripts personnalisés, ui.py, js et css uniquement)",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Requête (Ctrl + Entrée ou Alt + Entrée pour générer)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Requête négative (Ctrl + Entrée ou Alt + Entrée pour générer)",
-    "Add a random artist to the prompt.": "Ajouter un artiste aléatoire à la requête",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Lire les paramètres de génération depuis la requête, ou depuis la dernière génération si la requête est vide dans l'interface utilisateur.",
-    "Save style": "Sauvegarder le style",
-    "Apply selected styles to current prompt": "Appliquer les styles séléctionnés à la requête actuelle",
-    "Stop processing current image and continue processing.": "Arrêter le traitement de l'image actuelle et continuer le traitement.",
-    "Stop processing images and return any results accumulated so far.": "Arrêter le traitement des images et retourne les résultats accumulés depuis le début.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style à appliquer ; les styles sont composés de requêtes positives et négatives et s'appliquent au deux",
-    "Do not do anything special": "Ne rien faire de particulier",
-    "Which algorithm to use to produce the image": "Quel algorithme utiliser pour produire l'image",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - très créatif, peut générer des images complètement différentes en fonction du nombre d'étapes, utiliser plus de 30 à 40 étapes n'améliore pas le résultat",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Modèles implicite de réduction du bruit à diffusion - utile pour l'inpainting",
-    "Produce an image that can be tiled.": "Produit une image qui peut être bouclée (tuile).",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilise un processus en deux étapes afin de créer partiellement une image dans une résolution plus faible, l'agrandir et améliorer ses détails sans modifier la composition",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Détermine à quel point l'algorithme doit respecter le contenu de l'image. A 0 rien ne changera, à 1 l'image sera entièrement différente. Avec des valeurs inférieures à 1.0 le traitement utilisera moins d'étapes que ce que la glissière Étapes d'échantillionnage spécifie. ",
-    "How many batches of images to create": "Combien de lots d'images créer",
-    "How many image to create in a single batch": "Combien d'images créer par lot",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - spécifie à quel point l'image doit se conformer à la requête - des valeurs plus faibles produisent des résultats plus créatifs",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Une valeur qui détermine la sortie du générateur de nombres aléatoires - si vous créez une image avec les mêmes paramètres et valeur aléatoire qu'une autre, le résultat sera identique",
-    "Set seed to -1, which will cause a new random number to be used every time": "Passer la valeur aléatoire à -1, cela causera qu'un nombre aléatoire différent sera utilisé à chaque fois",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Réutiliser la valeur aléatoire de la dernière génération, généralement utile uniquement si elle était randomisée",
-    "Seed of a different picture to be mixed into the generation.": "Valeur aléatoire d'une image différente à mélanger dans la génération",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Force de la variation à produire. A 0 il n'y aura pas d'effet. A 1 l'image sera composée uniquement de la valeur aléatoire variable spécifiée (à l'exception des échantillionneurs `ancestral`)",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Essayer de produire une image similaire à ce qu'elle aurait été avec la même valeur aléatoire, mais dans la résolution spécifiée",
-    "Separate values for X axis using commas.": "Séparer les valeurs pour l'axe X par des virgules",
-    "Separate values for Y axis using commas.": "Séparer les valeurs pour l'axe Y par des virgules",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Ecrire l'image dans un dossier (par défaut - log/images) et les paramètres de génération dans un fichier csv.",
-    "Open images output directory": "Ouvrir le dossier de sortie des images",
-    "How much to blur the mask before processing, in pixels.": "Quantité de flou à appliquer au masque avant traitement, en pixels",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Avec quoi remplir la zone masquée avant traitement par Stable Diffusion.",
-    "fill it with colors of the image": "remplir avec les couleurs de l'image",
-    "keep whatever was there originally": "conserver ce qui était présent à l'origine",
-    "fill it with latent space noise": "remplir avec le bruit de l'espace latent",
-    "fill it with latent space zeroes": "remplir avec des zéros dans l'espace latent",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Agrandir la région masquées à la résolution cible, exécuter l'inpainting, réduire à nouveau puis coller dans l'image originale",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Redimensionner l'image dans la résolution cible. A moins que la hauteur et la largeur coincident le ratio de l'image sera incorrect.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Redimensionner l'image afin que l'entièreté de la résolution cible soit remplie par l'image. Recadrer les parties qui dépassent.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Redimensionner l'image afin que l'entièreté de l'image soit contenue dans la résolution cible. Remplir l'espace vide avec les couleurs de l'image.",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Combien de fois répéter le traitement d'une image et l'utiliser comme entrée pour la prochaine itération",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "En mode bouclage (Loopback), à chaque tour de la boucle la force du réducteur de bruit est multipliée par cette valeur. <1 signifie réduire la variation donc votre séquence convergera vers une image fixe. >1 signifie augmenter la variation donc votre séquence deviendra de plus en plus chaotique. ",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Pour l'agrandissement SD, de combien les tuiles doivent se chevaucher, en pixels. Les tuiles se chevauchent de manière à ce qu'il n'y ait pas de couture visible une fois fusionnées en une image. ",
-    "A directory on the same machine where the server is running.": "Un dossier sur la même machine où le serveur tourne.",
-    "Leave blank to save images to the default path.": "Laisser vide pour sauvegarder les images dans le chemin par défaut.",
-    "Result = A * (1 - M) + B * M": "Résultat = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "Résultat = A + (B - C) * M",
-    "Path to directory with input images": "Chemin vers le dossier contenant les images d'entrée",
-    "Path to directory where to write outputs": "Chemin vers le dossier où écrire les sorties",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utiliser les étiquettes suivantes pour définir le nom des images : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp] ; laisser vide pour le nom par défaut.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Si cette option est activée le filigrane ne sera pas ajouté au images crées. Attention : si vous n'ajoutez pas de filigrane vous pourriez vous comporter de manière non éthique.",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utiliser les étiquettes suivantes pour définir le nom des sous dossiers pour les images et les grilles  : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp] ; laisser vide pour le nom par défaut.",
-    "Restore low quality faces using GFPGAN neural network": "Restaurer les visages de basse qualité en utilisant le réseau neuronal GFPGAN",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Cette expression régulière sera utilisée pour extraire les mots depuis le nom de fichier ; ils seront joints en utilisant l'option ci dessous en une étiquette utilisée pour l'entrainement. Laisser vide pour conserver le texte du nom de fichier tel quel.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "Cette chaine de caractères sera utilisée pour joindre les mots séparés en une ligne unique si l'option ci dessus est activée.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Liste des noms de paramètres, séparés par des virgules, pour les paramètres de la barre d'accès rapide en haut de page, plutôt que dans la page habituelle des paramètres. Voir modules/shared.py pour définir les noms. Requiert un redémarrage pour s'appliquer.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Si cette valeur est différente de zéro elle sera ajoutée à la valeur aléatoire et utilisée pour initialiser le générateur de nombres aléatoires du bruit lors de l'utilisation des échantillonneurs supportants Eta. Vous pouvez l'utiliser pour produire encore plus de variation dans les images, ou vous pouvez utiliser ceci pour faire correspondre les images avec d'autres logiciels si vous savez ce que vous faites.",
-    "Enable Autocomplete": "Activer l'autocomplétion",
-    "/0.0": "/0.0"
-}
\ No newline at end of file
diff --git a/localizations/fr_FR.json b/localizations/fr_FR.json
new file mode 100644
index 00000000..b33d0838
--- /dev/null
+++ b/localizations/fr_FR.json
@@ -0,0 +1,415 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "Chargement...",
+    "view": "vue",
+    "api": "api",
+    "•": "•",
+    "built with gradio": "Construit avec Gradio",
+    "Stable Diffusion checkpoint": "checkpoint Stable Diffusion",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "Extras",
+    "PNG Info": "Infos PNG",
+    "History": "Historique",
+    "Checkpoint Merger": "Fusion de checkpoints",
+    "Train": "Entrainer",
+    "Settings": "Paramètres",
+    "Prompt": "Requête",
+    "Negative prompt": "Requête négative",
+    "Run": "Lancer",
+    "Skip": "Passer",
+    "Interrupt": "Interrrompre",
+    "Generate": "Générer",
+    "Style 1": "Style 1",
+    "Style 2": "Style 2",
+    "Label": "Etiquette",
+    "File": "Fichier",
+    "Drop File Here": "Déposer votre fichier ici",
+    "-": "-",
+    "or": "ou",
+    "Click to Upload": "Cliquer pour uploader",
+    "Image": "Image",
+    "Check progress": "Voir l'avancement",
+    "Check progress (first)": "Voir l'avancement (1er)",
+    "Sampling Steps": "Étapes d'échantillonnage",
+    "Sampling method": "Méthode d'échantillonnage",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Largeur",
+    "Height": "Hauteur",
+    "Restore faces": "Restaurer les visages",
+    "Tiling": "Mode Tuile",
+    "Highres. fix": "Correction haute résolution",
+    "Firstpass width": "Largeur première passe",
+    "Firstpass height": "Hauteur seconde passe",
+    "Denoising strength": "Puissance de réduction du bruit",
+    "Batch count": "Nombre de lots",
+    "Batch size": "Taille de lots",
+    "CFG Scale": "Echelle CFG",
+    "Seed": "Valeur aléatoire",
+    "Extra": "Extra",
+    "Variation seed": "Variation de la valeur aléatoire",
+    "Variation strength": "Puissance de variation",
+    "Resize seed from width": "Largeur de redimensionnement de la valeur aléatoire",
+    "Resize seed from height": "Hauteur de redimensionnement de la valeur aléatoire",
+    "Script": "Script",
+    "None": "Aucun",
+    "Prompt matrix": "Matrice de requète",
+    "Prompts from file or textbox": "Requètes depuis un fichier ou une boite de dialogue",
+    "X/Y plot": "graphe X/Y",
+    "Put variable parts at start of prompt": "Mettre les mots clés variable au début de la requête",
+    "Show Textbox": "Afficher le champs texte",
+    "File with inputs": "Fichier d'entrée",
+    "Prompts": "Requêtes",
+    "X type": "Paramètre axe X",
+    "Nothing": "Rien",
+    "Var. seed": "Valeur aléatoire variable",
+    "Var. strength": "Puissance variable",
+    "Steps": "Étapes",
+    "Prompt S/R": "Cherche et remplace dans la requête",
+    "Prompt order": "Ordre de la requête",
+    "Sampler": "Echantilloneur",
+    "Checkpoint name": "Nom du checkpoint",
+    "Hypernetwork": "Hypernetwork",
+    "Hypernet str.": "Force de l'Hypernetwork",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma min": "Sigma min.",
+    "Sigma max": "Sigma max.",
+    "Sigma noise": "Bruit Sigma",
+    "Eta": "Temps estimé",
+    "Clip skip": "Passer Clip",
+    "Denoising": "Réduction du bruit",
+    "X values": "Valeurs X",
+    "Y type": "Paramètre axe Y",
+    "Y values": "Valeurs Y",
+    "Draw legend": "Afficher la légende",
+    "Include Separate Images": "Inclure les images séparées",
+    "Keep -1 for seeds": "Conserver -1 pour la valeur aléatoire",
+    "Drop Image Here": "Déposer l'image ici",
+    "Save": "Enregistrer",
+    "Send to img2img": "Envoyer vers img2img",
+    "Send to inpaint": "Envoyer vers inpaint",
+    "Send to extras": "Envoyer vers extras",
+    "Make Zip when Save?": "Créer un zip lors de l'enregistrement?",
+    "Textbox": "Champ texte",
+    "Interrogate\nCLIP": "Interroger\nCLIP",
+    "Interrogate\nDeepBooru": "Interroger\nDeepBooru",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "Lot img2img",
+    "Image for img2img": "Image pour img2img",
+    "Image for inpainting with mask": "Image pour inpainting avec masque",
+    "Mask": "Masque",
+    "Mask blur": "Flou masque",
+    "Mask mode": "Mode masque",
+    "Draw mask": "Dessiner masque",
+    "Upload mask": "Uploader masque",
+    "Masking mode": "Mode de masquage",
+    "Inpaint masked": "Inpaint masqué",
+    "Inpaint not masked": "Inpaint non masqué",
+    "Masked content": "Contenu masqué",
+    "fill": "remplir",
+    "original": "original",
+    "latent noise": "bruit latent",
+    "latent nothing": "latent vide",
+    "Inpaint at full resolution": "Inpaint en pleine résolution",
+    "Inpaint at full resolution padding, pixels": "Padding de l'inpaint en pleine résolution, en pixels",
+    "Process images in a directory on the same machine where the server is running.": "Traite les images dans un dossier sur la même machine où le serveur tourne",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Utiliser un dossier de sortie vide pour enregistrer les images normalement plutôt que d'écrire dans le dossier de sortie",
+    "Input directory": "Dossier d'entrée",
+    "Output directory": "Dossier de sortie",
+    "Resize mode": "Mode redimensionnement",
+    "Just resize": "Redimensionner uniquement",
+    "Crop and resize": "Recadrer et redimensionner",
+    "Resize and fill": "Redimensionner et remplir",
+    "img2img alternative test": "Test alternatif img2img",
+    "Loopback": "Bouclage",
+    "Outpainting mk2": "Outpainting v2",
+    "Poor man's outpainting": "Outpainting du pauvre",
+    "SD upscale": "Agrandissement SD",
+    "should be 2 or lower.": "doit être inférieur ou égal à 2",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Forcer `Méthode d'échantillonnage` à Euler ? (cette méthode est dédiée à cela)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Forcer la `requête` au contenu de la `requête d'origine` ? (de même pour la `requête négative`)",
+    "Original prompt": "Requête d'origine",
+    "Original negative prompt": "Requête négative d'origine",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Forcer le valeur d'`Étapes d'échantillonnage` à la même valeur qu'`Étapes de décodage` ?",
+    "Decode steps": "Étapes de décodage",
+    "Override `Denoising strength` to 1?": "Forcer `Puissance de réduction du bruit` à 1 ?",
+    "Decode CFG scale": "Echelle CFG de décodage",
+    "Randomness": "Aléatoire",
+    "Sigma adjustment for finding noise for image": "Ajustement Sigma lors de la recherche du bruit dans l'image",
+    "Loops": "Boucles",
+    "Denoising strength change factor": "Facteur de changement de la puissance de réduction du bruit",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Paramètres recommandés : Étapes d'échantillonnage : 80-100, Echantillonneur : Euler a, Puissance de réduction du bruit : 0.8",
+    "Pixels to expand": "Pixels à étendre",
+    "Outpainting direction": "Direction de l'outpainting",
+    "left": "gauche",
+    "right": "droite",
+    "up": "haut",
+    "down": "bas",
+    "Fall-off exponent (lower=higher detail)": "Exposant de diminution (plus petit = plus de détails)",
+    "Color variation": "Variation de couleur",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Agrandira l'image à deux fois sa taille; utilisez les glissières largeur et hauteur afin de choisir la taille de tuile",
+    "Tile overlap": "Chevauchement de tuile",
+    "Upscaler": "Agrandisseur",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "BSRGAN 4x": "BSRGAN 4x",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR 4x": "SwinIR 4x",
+    "Single Image": "Image unique",
+    "Batch Process": "Traitement par lot",
+    "Batch from Directory": "Lot depuis un dossier",
+    "Source": "Source",
+    "Show result images": "Montrez les images résultantes",
+    "Scale by": "Mise à l'échelle de",
+    "Scale to": "Mise à l'échelle à",
+    "Resize": "Redimensionner",
+    "Crop to fit": "Recadrer à la taille",
+    "Upscaler 2": "Agrandisseur 2",
+    "Upscaler 2 visibility": "Visibilité de l'agrandisseur 2",
+    "GFPGAN visibility": "Visibilité GFPGAN",
+    "CodeFormer visibility": "Visibilité CodeFormer",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Poids CodeFormer (0 = effet maximum, 1 = effet minimum)",
+    "Open output directory": "Ouvrir le dossier de sortie",
+    "Send to txt2img": "Envoyer vers txt2img",
+    "txt2img history": "historique txt2img",
+    "img2img history": "historique img2img",
+    "extras history": "historique extras",
+    "Renew Page": "Rafraichr la page",
+    "First Page": "Première page",
+    "Prev Page": "Page précendente",
+    "Page Index": "Index des pages",
+    "Next Page": "Page suivante",
+    "End Page": "Page de fin",
+    "number of images to delete consecutively next": "nombre d'image à supprimer consécutivement ensuite",
+    "Delete": "Supprimer",
+    "Generate Info": "Générer les informations",
+    "File Name": "Nom de fichier",
+    "set_index": "set_index",
+    "A merger of the two checkpoints will be generated in your": "Une fusion des deux checkpoints sera générée dans votre",
+    "checkpoint": "checkpoint",
+    "directory.": "dossier",
+    "Primary model (A)": "Modèle primaire (A)",
+    "Secondary model (B)": "Modèle secondaire (B)",
+    "Tertiary model (C)": "Modèle tertiaire (C)",
+    "Custom Name (Optional)": "Nom personnalisé (Optionel)",
+    "Multiplier (M) - set to 0 to get model A": "Multiplieur (M) - utiliser 0 pour le modèle A",
+    "Interpolation Method": "Méthode d'interpolation",
+    "Weighted sum": "Somme pondérée",
+    "Add difference": "Ajouter différence",
+    "Save as float16": "Enregistrer en tant que float16",
+    "See": "Voir",
+    "wiki": "wiki",
+    "for detailed explanation.": "pour une explication détaillée.",
+    "Create embedding": "Créer un embedding",
+    "Create hypernetwork": "Créer un hypernetwork",
+    "Preprocess images": "Pré-traite les images",
+    "Name": "Nom",
+    "Initialization text": "Texte d'initialisation",
+    "Number of vectors per token": "Nombre de vecteurs par jeton",
+    "Modules": "Modules",
+    "Source directory": "Dossier source",
+    "Destination directory": "Dossier destination",
+    "Create flipped copies": "Créer des copies en mirroir",
+    "Split oversized images into two": "Couper les images trop grandes en deux",
+    "Use BLIP for caption": "Utiliser BLIP pour les descriptions",
+    "Use deepbooru for caption": "Utiliser deepbooru pour les descriptions",
+    "Preprocess": "Pré-traite",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Entrainer un embedding ; spécifiez un dossier contenant un ensemble d'images avec un ratio de 1:1",
+    "Embedding": "Embedding",
+    "Learning rate": "Vitesse d'apprentissage",
+    "Dataset directory": "Dossier des images d'entrée",
+    "Log directory": "Dossier de journalisation",
+    "Prompt template file": "Fichier modèle de requêtes",
+    "Max steps": "Étapes max.",
+    "Save an image to log directory every N steps, 0 to disable": "Enregistrer une image dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Enregistrer une copie de l'embedding dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
+    "Save images with embedding in PNG chunks": "Sauvegarder les images incluant l'embedding dans leur blocs PNG",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Lire les paramètres (requête, etc.) depuis l'onglet txt2img lors de la génération des previews",
+    "Train Hypernetwork": "Entrainer un Hypernetwork",
+    "Train Embedding": "Entrainer un Embedding",
+    "Apply settings": "Appliquer les paramètres",
+    "Saving images/grids": "Enregistrer les images/grilles",
+    "Always save all generated images": "Toujours enregistrer toutes les images",
+    "File format for images": "Format de fichier pour les images",
+    "Images filename pattern": "Motif pour le nom de fichier des images",
+    "Always save all generated image grids": "Toujours enregistrer toutes les grilles d'images générées",
+    "File format for grids": "Format de fichier pour les grilles",
+    "Add extended info (seed, prompt) to filename when saving grid": "Ajouter les informations étendues (valeur aléatoire, requête) aux noms de fichiers lors de l'enregistrement d'une grille",
+    "Do not save grids consisting of one picture": "Ne pas enregistrer les grilles contenant une seule image",
+    "Prevent empty spots in grid (when set to autodetect)": "Eviter les vides dans la grille (quand autodétection est choisie)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Nombre de colonnes de la grille; utilisez -1 pour autodétection et 0 pour qu'il soit égal à la taille du lot",
+    "Save text information about generation parameters as chunks to png files": "Enregistrer l'information du text des paramètres de génération en tant que blocs dans les fichiers PNG",
+    "Create a text file next to every image with generation parameters.": "Créer un fichier texte contenant les paramètres de génération à côté de chaque image",
+    "Save a copy of image before doing face restoration.": "Enregistrer une copie de l'image avant de lancer la restauration de visage",
+    "Quality for saved jpeg images": "Qualité pour les images jpeg enregistrées",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Si l'image PNG est plus grande que 4MB or l'une des ses dimensions supérieure à 4000, réduire sa taille et enregistrer une copie en JPG",
+    "Use original name for output filename during batch process in extras tab": "Utiliser un nom de fichier original pour les fichiers de sortie durant le traitement par lot dans l'onglet Extras",
+    "When using 'Save' button, only save a single selected image": "A l'utilisation du bouton `Enregistrer`, n'enregistrer que l'image séléctionnée",
+    "Do not add watermark to images": "Ne pas ajouter de filigrane aux images",
+    "Paths for saving": "Chemins pour l'enregistrement",
+    "Output directory for images; if empty, defaults to three directories below": "Dossier de sortie pour les images; si non spécifié, le chemin par défaut sera trois niveau en dessous",
+    "Output directory for txt2img images": "Dossier de sortie pour les images txt2img",
+    "Output directory for img2img images": "Dossier de sortie pour les images img2img",
+    "Output directory for images from extras tab": "Dossier de sortie pour les images de l'onglet Extras",
+    "Output directory for grids; if empty, defaults to two directories below": "Dossier de sortie pour les grilles; si non spécifié, le chemin par défaut sera deux niveau en dessous",
+    "Output directory for txt2img grids": "Dossier de sortie pour les grilles txt2img",
+    "Output directory for img2img grids": "Dossier de sortie pour les grilles img2img",
+    "Directory for saving images using the Save button": "Dossier de sauvegarde des images pour le bouton `Enregistrer`",
+    "Saving to a directory": "Enregistrer dans un dossier",
+    "Save images to a subdirectory": "Enregistrer les images dans un sous dossier",
+    "Save grids to a subdirectory": "Enregistrer les grilles dans un sous dossier",
+    "When using \"Save\" button, save images to a subdirectory": "Lors de l'utilisation du bouton \"Enregistrer\", sauvegarder les images dans un sous dossier",
+    "Directory name pattern": "Motif pour le nom des dossiers",
+    "Max prompt words for [prompt_words] pattern": "Maximum de mot pour le motif [prompt_words]",
+    "Upscaling": "Agrandissement",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Taille des tuile for les agrandisseurs ESRGAN. 0 = mode tuile désactivé.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Chevauchement des tuiles, en pixel pour l'agrandisseur ESRGAN. Valeur faible = couture visible",
+    "Tile size for all SwinIR.": "Taille de la tuile pour tous les agrandisseur SwinIR.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Chevauchement de tuile, en pixels pour SwinIR. Valeur faible = couture visible",
+    "LDSR processing steps. Lower = faster": "Echantillon du traitement LDSR. Valeur faible =  plus rapide",
+    "Upscaler for img2img": "Agrandisseur pour img2img",
+    "Upscale latent space image when doing hires. fix": "Agrandir l'image de l'espace latent lors de la correction haute résolution",
+    "Face restoration": "Restauration de visage",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Paramètre de poids pour CodeFormer; 0 = effet maximum ； 1 = effet minimum",
+    "Move face restoration model from VRAM into RAM after processing": "Déplacer le modèle de restauration de visage de la VRAM vers la RAM après traitement",
+    "System": "Système",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Fréquence d'interrogation par seconde pendant la génération. Mettez la valeur à 0 pour désactiver.",
+    "Always print all generation info to standard output": "Toujours afficher toutes les informations de génération dans la sortie standard",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Ajouter un seconde barre de progression dans la console montrant l'avancement pour un tâche complète.",
+    "Training": "Entrainement",
+    "Unload VAE and CLIP from VRAM when training": "Décharger VAE et CLIP de la VRAM pendant l'entrainement",
+    "Filename word regex": "Regex de mot",
+    "Filename join string": "Chaine de caractère pour lier les noms de fichier",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Nombre de répétition pour une image unique par époque; utilisé seulement pour afficher le nombre d'époques",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Enregistrer un csv contenant la perte dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "Checkpoint à mettre en cache dans la RAM",
+    "Hypernetwork strength": "Force de l'Hypernetwork",
+    "Apply color correction to img2img results to match original colors.": "Appliquer une correction de couleur aux résultats img2img afin de conserver les couleurs d'origine",
+    "Save a copy of image before applying color correction to img2img results": "Enregistrer une copie de l'image avant d'appliquer les résultats de la correction de couleur img2img",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Avec img2img, executer exactement le nombre d'étapes spécifiées par la glissière (normalement moins d'étapes sont executées quand la réduction du bruit est plus faible).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Activer la quantisation des échantillionneurs K pour des résultats plus nets et plus propres. Cela peut modifier les valeurs aléatoires existantes. Requiert un redémarrage pour être actif.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphase : utilisez (texte) afin de forcer le modèle à porter plus d'attention au texte et [texte] afin qu'il y porte moins attention",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Utilisez l'ancienne méthode d'emphase. Peut être utile afin de reproduire d'anciennes valeurs aléatoires.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Demander aux échantillionneurs K-diffusion de produire les mêmes dans un lot que lors de la génération d'une image unique",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Améliorer la cohérence en remplissant (padding) à partir de la dernière virgule dans les X jetons quand on en utilise plus de 75",
+    "Filter NSFW content": "Filtrer le contenu +18 (NSFW)",
+    "Stop At last layers of CLIP model": "S'arrêter aux derniers niveaux du modèle CLIP",
+    "Interrogate Options": "Options d'intérrogation",
+    "Interrogate: keep models in VRAM": "Interroger : conserver les modèles en VRAM",
+    "Interrogate: use artists from artists.csv": "Interroger : utiliser les artistes dans artists.csv",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interroger : inclure la correspondance du classement des labels de modèle dans les résultats (N'a pas d'effet sur les interrogateurs basés sur des descriptions) ",
+    "Interrogate: num_beams for BLIP": "Interroger : num_beams pour BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Interroger : longueur minimale de la description (excluant les artistes, etc.)",
+    "Interrogate: maximum description length": "Interroger : longueur maximale de la description",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP : nombre maximum de lignes dans le fichier texte (0 = pas de limite)",
+    "Interrogate: deepbooru score threshold": "Interroger : seuil du score deepbooru",
+    "Interrogate: deepbooru sort alphabetically": "Interroger : classement alphabétique deepbooru",
+    "use spaces for tags in deepbooru": "Utiliser des espaces pour les étiquettes dans deepbooru",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "échapper (\\) les crochets dans deepbooru (afin qu'ils puissent être utilisés littéralement et non pour mettre en emphase)",
+    "User interface": "Interface utilisateur",
+    "Show progressbar": "Afficher la barre de progression",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Afficher l'état d'avancement de la création d'image toutes les X étapes d'échantillionnage. Utiliser 0 pour désactiver.",
+    "Show grid in results for web": "Afficher la grille dans les résultats web",
+    "Do not show any images in results for web": "N'afficher aucune image dans les résultats web'",
+    "Add model hash to generation information": "Ajouter le hash du modèle dans l'information de génération",
+    "Add model name to generation information": "Ajouter le nom du modèle dans l'information de génération",
+    "Font for image grids that have text": "Police pour les grilles d'images contenant du texte",
+    "Enable full page image viewer": "Activer l'affichage des images en plein écran",
+    "Show images zoomed in by default in full page image viewer": "Afficher les images zoomées par défaut lors de l'affichage en plein écran",
+    "Show generation progress in window title.": "Afficher l'avancement de la génération dans le titre de la fenêtre.",
+    "Quicksettings list": "Liste de réglages rapides",
+    "Localization (requires restart)": "Localisation (requiert un redémarrage)",
+    "Sampler parameters": "Paramètres de l'échantillionneur",
+    "Hide samplers in user interface (requires restart)": "Cacher les échantillonneurs dans l'interface utilisateur (requiert un redémarrage)",
+    "eta (noise multiplier) for DDIM": "eta (multiplicateur de bruit) pour DDIM",
+    "eta (noise multiplier) for ancestral samplers": "eta (multiplicateur de bruit) poru les échantillionneurs de type 'ancestral'",
+    "img2img DDIM discretize": "Discrétisation DDIM pour img2img",
+    "uniform": "uniforme",
+    "quad": "quad",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma noise",
+    "Eta noise seed delta": "Eta noise seed delta",
+    "Request browser notifications": "Demander les notifications au navigateur",
+    "Download localization template": "Télécharger le modèle de localisation",
+    "Reload custom script bodies (No ui updates, No restart)": "Recharger le contenu des scripts personnalisés (Pas de mise à jour de l'interface, Pas de redémarrage)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Redémarrer Gradio et rafraichir les composants (Scripts personnalisés, ui.py, js et css uniquement)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Requête (Ctrl + Entrée ou Alt + Entrée pour générer)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Requête négative (Ctrl + Entrée ou Alt + Entrée pour générer)",
+    "Add a random artist to the prompt.": "Ajouter un artiste aléatoire à la requête",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Lire les paramètres de génération depuis la requête, ou depuis la dernière génération si la requête est vide dans l'interface utilisateur.",
+    "Save style": "Sauvegarder le style",
+    "Apply selected styles to current prompt": "Appliquer les styles séléctionnés à la requête actuelle",
+    "Stop processing current image and continue processing.": "Arrêter le traitement de l'image actuelle et continuer le traitement.",
+    "Stop processing images and return any results accumulated so far.": "Arrêter le traitement des images et retourne les résultats accumulés depuis le début.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style à appliquer ; les styles sont composés de requêtes positives et négatives et s'appliquent au deux",
+    "Do not do anything special": "Ne rien faire de particulier",
+    "Which algorithm to use to produce the image": "Quel algorithme utiliser pour produire l'image",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - très créatif, peut générer des images complètement différentes en fonction du nombre d'étapes, utiliser plus de 30 à 40 étapes n'améliore pas le résultat",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Modèles implicite de réduction du bruit à diffusion - utile pour l'inpainting",
+    "Produce an image that can be tiled.": "Produit une image qui peut être bouclée (tuile).",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilise un processus en deux étapes afin de créer partiellement une image dans une résolution plus faible, l'agrandir et améliorer ses détails sans modifier la composition",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Détermine à quel point l'algorithme doit respecter le contenu de l'image. A 0 rien ne changera, à 1 l'image sera entièrement différente. Avec des valeurs inférieures à 1.0 le traitement utilisera moins d'étapes que ce que la glissière Étapes d'échantillionnage spécifie. ",
+    "How many batches of images to create": "Combien de lots d'images créer",
+    "How many image to create in a single batch": "Combien d'images créer par lot",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - spécifie à quel point l'image doit se conformer à la requête - des valeurs plus faibles produisent des résultats plus créatifs",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Une valeur qui détermine la sortie du générateur de nombres aléatoires - si vous créez une image avec les mêmes paramètres et valeur aléatoire qu'une autre, le résultat sera identique",
+    "Set seed to -1, which will cause a new random number to be used every time": "Passer la valeur aléatoire à -1, cela causera qu'un nombre aléatoire différent sera utilisé à chaque fois",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Réutiliser la valeur aléatoire de la dernière génération, généralement utile uniquement si elle était randomisée",
+    "Seed of a different picture to be mixed into the generation.": "Valeur aléatoire d'une image différente à mélanger dans la génération",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Force de la variation à produire. A 0 il n'y aura pas d'effet. A 1 l'image sera composée uniquement de la valeur aléatoire variable spécifiée (à l'exception des échantillionneurs `ancestral`)",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Essayer de produire une image similaire à ce qu'elle aurait été avec la même valeur aléatoire, mais dans la résolution spécifiée",
+    "Separate values for X axis using commas.": "Séparer les valeurs pour l'axe X par des virgules",
+    "Separate values for Y axis using commas.": "Séparer les valeurs pour l'axe Y par des virgules",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Ecrire l'image dans un dossier (par défaut - log/images) et les paramètres de génération dans un fichier csv.",
+    "Open images output directory": "Ouvrir le dossier de sortie des images",
+    "How much to blur the mask before processing, in pixels.": "Quantité de flou à appliquer au masque avant traitement, en pixels",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Avec quoi remplir la zone masquée avant traitement par Stable Diffusion.",
+    "fill it with colors of the image": "remplir avec les couleurs de l'image",
+    "keep whatever was there originally": "conserver ce qui était présent à l'origine",
+    "fill it with latent space noise": "remplir avec le bruit de l'espace latent",
+    "fill it with latent space zeroes": "remplir avec des zéros dans l'espace latent",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Agrandir la région masquées à la résolution cible, exécuter l'inpainting, réduire à nouveau puis coller dans l'image originale",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Redimensionner l'image dans la résolution cible. A moins que la hauteur et la largeur coincident le ratio de l'image sera incorrect.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Redimensionner l'image afin que l'entièreté de la résolution cible soit remplie par l'image. Recadrer les parties qui dépassent.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Redimensionner l'image afin que l'entièreté de l'image soit contenue dans la résolution cible. Remplir l'espace vide avec les couleurs de l'image.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Combien de fois répéter le traitement d'une image et l'utiliser comme entrée pour la prochaine itération",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "En mode bouclage (Loopback), à chaque tour de la boucle la force du réducteur de bruit est multipliée par cette valeur. <1 signifie réduire la variation donc votre séquence convergera vers une image fixe. >1 signifie augmenter la variation donc votre séquence deviendra de plus en plus chaotique. ",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Pour l'agrandissement SD, de combien les tuiles doivent se chevaucher, en pixels. Les tuiles se chevauchent de manière à ce qu'il n'y ait pas de couture visible une fois fusionnées en une image. ",
+    "A directory on the same machine where the server is running.": "Un dossier sur la même machine où le serveur tourne.",
+    "Leave blank to save images to the default path.": "Laisser vide pour sauvegarder les images dans le chemin par défaut.",
+    "Result = A * (1 - M) + B * M": "Résultat = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Résultat = A + (B - C) * M",
+    "Path to directory with input images": "Chemin vers le dossier contenant les images d'entrée",
+    "Path to directory where to write outputs": "Chemin vers le dossier où écrire les sorties",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utiliser les étiquettes suivantes pour définir le nom des images : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp] ; laisser vide pour le nom par défaut.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Si cette option est activée le filigrane ne sera pas ajouté au images crées. Attention : si vous n'ajoutez pas de filigrane vous pourriez vous comporter de manière non éthique.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utiliser les étiquettes suivantes pour définir le nom des sous dossiers pour les images et les grilles  : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp] ; laisser vide pour le nom par défaut.",
+    "Restore low quality faces using GFPGAN neural network": "Restaurer les visages de basse qualité en utilisant le réseau neuronal GFPGAN",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Cette expression régulière sera utilisée pour extraire les mots depuis le nom de fichier ; ils seront joints en utilisant l'option ci dessous en une étiquette utilisée pour l'entrainement. Laisser vide pour conserver le texte du nom de fichier tel quel.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Cette chaine de caractères sera utilisée pour joindre les mots séparés en une ligne unique si l'option ci dessus est activée.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Liste des noms de paramètres, séparés par des virgules, pour les paramètres de la barre d'accès rapide en haut de page, plutôt que dans la page habituelle des paramètres. Voir modules/shared.py pour définir les noms. Requiert un redémarrage pour s'appliquer.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Si cette valeur est différente de zéro elle sera ajoutée à la valeur aléatoire et utilisée pour initialiser le générateur de nombres aléatoires du bruit lors de l'utilisation des échantillonneurs supportants Eta. Vous pouvez l'utiliser pour produire encore plus de variation dans les images, ou vous pouvez utiliser ceci pour faire correspondre les images avec d'autres logiciels si vous savez ce que vous faites.",
+    "Enable Autocomplete": "Activer l'autocomplétion",
+    "/0.0": "/0.0"
+}
\ No newline at end of file
-- 
cgit v1.2.1


From d699720254365069866eafcdc519743664075a6d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 29 Oct 2022 22:39:10 +0300
Subject: add translators to codeowners with their respective translation files

---
 CODEOWNERS | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index 12e87aae..a48d8012 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -1,3 +1,13 @@
 *       @AUTOMATIC1111
-
-/localizations/ko_KR.json   @36DB
\ No newline at end of file
+/localizations/ar_AR.json   @xmodar @blackneoo
+/localizations/de_DE.json   @LunixWasTaken
+/localizations/es_ES.json   @innovaciones
+/localizations/fr_FR.json   @tumbly
+/localizations/it_IT.json   @EugenioBuffo
+/localizations/ja_JP.json   @yuuki76
+/localizations/ko_KR.json   @36DB
+/localizations/pt_BR.json   @M-art-ucci
+/localizations/ru_RU.json   @kabachuha
+/localizations/tr_TR.json   @camenduru
+/localizations/zh_CN.json   @dtlnor @bgluminous
+/localizations/zh_TW.json   @benlisquare
-- 
cgit v1.2.1


From f62db4d5c753bc32d2ae166606ce41f4c5fa5c43 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 03:55:43 +0800
Subject: fix progress response model

---
 modules/api/api.py    | 30 ------------------------------
 modules/api/models.py |  8 ++++----
 2 files changed, 4 insertions(+), 34 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index e93cddcb..7e8522a2 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,33 +1,3 @@
-# import time
-
-# from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
-# from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-# from modules.sd_samplers import all_samplers
-# from modules.extras import run_pnginfo
-# import modules.shared as shared
-# from modules import devices
-# import uvicorn
-# from fastapi import Body, APIRouter, HTTPException
-# from fastapi.responses import JSONResponse
-# from pydantic import BaseModel, Field, Json
-# from typing import List
-# import json
-# import io
-# import base64
-# from PIL import Image
-
-# sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
-
-# class TextToImageResponse(BaseModel):
-#     images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-#     parameters: Json
-#     info: Json
-
-# class ImageToImageResponse(BaseModel):
-#     images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-#     parameters: Json
-#     info: Json
-
 import time
 import uvicorn
 from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
diff --git a/modules/api/models.py b/modules/api/models.py
index 8d4abc39..e1762fb9 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,6 +1,6 @@
 import inspect
 from click import prompt
-from pydantic import BaseModel, Field, create_model
+from pydantic import BaseModel, Field, Json, create_model
 from typing import Any, Optional
 from typing_extensions import Literal
 from inflection import underscore
@@ -158,6 +158,6 @@ class PNGInfoResponse(BaseModel):
     info: str = Field(title="Image info", description="A string with all the info the image had")
 
 class ProgressResponse(BaseModel):
-    progress: float
-    eta_relative: float
-    state: dict
+    progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
+    eta_relative: float = Field(title="ETA in secs")
+    state: Json
-- 
cgit v1.2.1


From e9c6c2a51f972fd7cd88ea740ade4ac3d8108b67 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 04:02:56 +0800
Subject: add description for state field

---
 modules/api/models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/api/models.py b/modules/api/models.py
index e1762fb9..709ab5a6 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -160,4 +160,4 @@ class PNGInfoResponse(BaseModel):
 class ProgressResponse(BaseModel):
     progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
     eta_relative: float = Field(title="ETA in secs")
-    state: Json
+    state: Json = Field(title="State", description="The current state snapshot")
-- 
cgit v1.2.1


From 88f46a5bec610cf03641f18becbe3deda541e982 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 05:04:29 +0800
Subject: update progress response model

---
 modules/api/api.py    | 6 +++---
 modules/api/models.py | 4 ++--
 modules/shared.py     | 4 ++--
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 7e8522a2..5912d289 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -61,7 +61,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
         self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
         self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
-        self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"])
+        self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -171,7 +171,7 @@ class Api:
         # copy from check_progress_call of ui.py
 
         if shared.state.job_count == 0:
-            return ProgressResponse(progress=0, eta_relative=0, state=shared.state.js())
+            return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict())
 
         # avoid dividing zero
         progress = 0.01
@@ -187,7 +187,7 @@ class Api:
 
         progress = min(progress, 1)
 
-        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.js())
+        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict())
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)
diff --git a/modules/api/models.py b/modules/api/models.py
index 709ab5a6..0ab85ec5 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,6 +1,6 @@
 import inspect
 from click import prompt
-from pydantic import BaseModel, Field, Json, create_model
+from pydantic import BaseModel, Field, create_model
 from typing import Any, Optional
 from typing_extensions import Literal
 from inflection import underscore
@@ -160,4 +160,4 @@ class PNGInfoResponse(BaseModel):
 class ProgressResponse(BaseModel):
     progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
     eta_relative: float = Field(title="ETA in secs")
-    state: Json = Field(title="State", description="The current state snapshot")
+    state: dict = Field(title="State", description="The current state snapshot")
diff --git a/modules/shared.py b/modules/shared.py
index 0f4c035d..f7b0990c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -147,7 +147,7 @@ class State:
     def get_job_timestamp(self):
         return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?
 
-    def js(self):
+    def dict(self):
         obj = {
             "skipped": self.skipped,
             "interrupted": self.skipped,
@@ -158,7 +158,7 @@ class State:
             "sampling_steps": self.sampling_steps,
         }
 
-        return json.dumps(obj)
+        return obj
 
 
 state = State()
-- 
cgit v1.2.1


From 39f55c3c35873bc7dd9792cb2155746a1c3d4292 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Sat, 29 Oct 2022 14:13:02 -0700
Subject: Re-add explicit device move

---
 modules/processing.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/processing.py b/modules/processing.py
index ee0e9e34..d07e3db9 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -170,6 +170,7 @@ class StableDiffusionProcessing():
 
         # Create another latent image, this time with a masked version of the original input.
         # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
+        conditioning_mask = conditioning_mask.to(source_image.device).to(source_image.dtype)
         conditioning_image = torch.lerp(
             source_image,
             source_image * (1.0 - conditioning_mask),
-- 
cgit v1.2.1


From 9f104b53c425e248595e5b6481336d2a339e015e Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 05:19:17 +0800
Subject: preview current image when opts.show_progress_every_n_steps is
 enabled

---
 modules/api/api.py    | 8 ++++++--
 modules/api/models.py | 1 +
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 5912d289..e960bb7b 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,7 +1,7 @@
 import time
 import uvicorn
 from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, Depends, HTTPException
 import modules.shared as shared
 from modules import devices
 from modules.api.models import *
@@ -187,7 +187,11 @@ class Api:
 
         progress = min(progress, 1)
 
-        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict())
+        current_image = None
+        if shared.state.current_image:
+            current_image = encode_pil_to_base64(shared.state.current_image)
+
+        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)
diff --git a/modules/api/models.py b/modules/api/models.py
index 0ab85ec5..c8bc719a 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -161,3 +161,4 @@ class ProgressResponse(BaseModel):
     progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
     eta_relative: float = Field(title="ETA in secs")
     state: dict = Field(title="State", description="The current state snapshot")
+    current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
-- 
cgit v1.2.1


From 22a54b058211d7b067dce92a3c1dad8d5849da70 Mon Sep 17 00:00:00 2001
From: Strothis <mathis.andreas.stroehlein@gmail.com>
Date: Sat, 29 Oct 2022 23:43:30 +0200
Subject: Fix German Localization

---
 localizations/de_DE.json | 525 +++++++++++++++++++++++++----------------------
 1 file changed, 282 insertions(+), 243 deletions(-)

diff --git a/localizations/de_DE.json b/localizations/de_DE.json
index b52fa681..56d54b54 100644
--- a/localizations/de_DE.json
+++ b/localizations/de_DE.json
@@ -1,42 +1,41 @@
- 
 {
     "⤡": "⤡",
     "⊞": "⊞",
     "×": "×",
     "❮": "❮",
     "❯": "❯",
+    "view": "API ",
+    "api": "anzeigen",
+    "•": " • ",
+    "built with gradio": "Mit Gradio erstellt",
     "Loading...": "Lädt...",
-    "view": "zeigen",
-    "api": "api",
-    "•": "•",
-    "built with gradio": "Erstellt mit Gradio",
-    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
+    "Stable Diffusion checkpoint": "Stable Diffusion Checkpoint",
     "txt2img": "txt2img",
     "img2img": "img2img",
     "Extras": "Extras",
     "PNG Info": "PNG Info",
-    "Checkpoint Merger": "Checkpoint Merger",
+    "Checkpoint Merger": "Checkpoint Fusion",
     "Train": "Trainieren",
     "Settings": "Einstellungen",
     "Prompt": "Prompt",
-    "Negative prompt": "Negativer Prompt",
+    "Negative prompt": "Negative Prompt",
     "Run": "Ausführen",
     "Skip": "Überspringen",
     "Interrupt": "Abbrechen",
     "Generate": "Generieren",
-    "Style 1": "Style 1",
-    "Style 2": "Style 2",
+    "Style 1": "Stil 1",
+    "Style 2": "Stil 2",
     "Label": "Bezeichnung",
     "File": "Datei",
     "Drop File Here": "Datei hier ablegen",
     "-": "-",
-    "o": "o",
-    "Click to Upload": "Klicken zum Hochladen",
+    "o": "oder",
+    "Click to Upload": "Hochladen",
     "Image": "Bild",
     "Check progress": "Fortschitt prüfen",
     "Check progress (first)": "Fortschritt prüfen (Initial)",
-    "Sampling Steps": "Sampling Steps",
-    "Sampling method": "Sampling method",
+    "Sampling Steps": "Samplingschritte",
+    "Sampling method": "Samplingmethode",
     "Euler a": "Euler a",
     "Euler": "Euler",
     "LMS": "LMS",
@@ -54,37 +53,37 @@
     "Height": "Höhe",
     "Restore faces": "Gesichter wiederherstellen",
     "Tiling": "Kacheln",
-    "Highres. fix": "Highres. fix",
-    "Firstpass width": "Breite erstdurchlauf",
-    "Firstpass height": "Höhe erstdurchlauf",
-    "Denoising strength": "Denoising stärke",
-    "Batch count": "Batch anzahl",
-    "Batch size": "Batch größe",
-    "CFG Scale": "CFG Scale",
+    "Highres. fix": "Highres. Fix",
+    "Firstpass width": "Breite Erstdurchlauf",
+    "Firstpass height": "Höhe Erstdurchlauf",
+    "Denoising strength": "Denoisingstärke",
+    "Batch count": "Batchanzahl",
+    "Batch size": "Batchgröße",
+    "CFG Scale": "CFG-Skala",
     "Seed": "Seed",
     "Extra": "Extra",
-    "Variation seed": "Variation seed",
-    "Variation strength": "Variation strength",
-    "Resize seed from width": "Seed von der Breite her ändern",
-    "Resize seed from height": " Seed von der Breite her ändern",
-    "Script": "Script",
+    "Variation seed": "Variationsseed",
+    "Variation strength": "Variationsstärke",
+    "Resize seed from width": "Seed von Breite ändern",
+    "Resize seed from height": "Seed von Höhe ändern",
+    "Script": "Skript",
     "None": "Nichts",
-    "Prompt matrix": "Prompt matrix",
-    "Prompts from file or textbox": "Prompts von Datei oder Textbox",
-    "X/Y plot": "X/Y graph",
+    "Prompt matrix": "Promptmatrix",
+    "Prompts from file or textbox": "Prompts aus Datei oder Textfeld",
+    "X/Y plot": "X/Y Graf",
     "Put variable parts at start of prompt": "Variable teile am start des Prompt setzen",
-    "Show Textbox": "Textbox Anzeigen",
-    "File with inputs": "Datei mit Inputwerten",
-    "Prompts": "Prompts",
-    "X type": "X typ",
+    "Iterate seed every line": "Iterate seed every line",
+    "List of prompt inputs": "List of prompt inputs",
+    "Upload prompt inputs": "Upload prompt inputs",
+    "X type": "X-Typ",
     "Nothing": "Nichts",
     "Var. seed": "Var. seed",
     "Var. strength": "Var. strength",
-    "Steps": "Steps",
-    "Prompt S/R": "Prompt S/R",
-    "Prompt order": "Prompt order",
+    "Steps": "Schritte",
+    "Prompt S/R": "Prompt Suchen/Ersetzen",
+    "Prompt order": "Promptreihenfolge",
     "Sampler": "Sampler",
-    "Checkpoint name": "Checkpoint name",
+    "Checkpoint name": "Checkpointname",
     "Hypernetwork": "Hypernetwork",
     "Hypernet str.": "Hypernet str.",
     "Sigma Churn": "Sigma Churn",
@@ -94,76 +93,78 @@
     "Eta": "Eta",
     "Clip skip": "Clip skip",
     "Denoising": "Denoising",
-    "X values": "X values",
-    "Y type": "Y type",
-    "Y values": "Y values",
+    "Cond. Image Mask Weight": "Cond. Image Mask Weight",
+    "X values": "X-Werte",
+    "Y type": "Y-Typ",
+    "Y values": "Y-Werte",
     "Draw legend": "Legende zeichnen",
     "Include Separate Images": "Seperate Bilder hinzufügen",
     "Keep -1 for seeds": "-1 als Seed behalten",
-    "Drop Image Here": "Bild hier ablegen",
     "Save": "Speichern",
-    "Send to img2img": "Senden an img2img",
-    "Send to inpaint": "Senden an inpaint",
-    "Send to extras": "Senden an extras",
-    "Make Zip when Save?": "Zip beim speichern erstellen?",
-    "Textbox": "Textbox",
-    "Interrogate\nCLIP": "CLIP\nAbfragen",
+    "Send to img2img": "An img2img senden",
+    "Send to inpaint": "An Inpaint senden",
+    "Send to extras": "An Extras senden",
+    "Make Zip when Save?": "Zip beim Speichern erstellen?",
+    "Textbox": "Textfeld",
+    "Interrogate\nCLIP": "Interrogate\nCLIP",
+    "Interrogate\nDeepBooru": "Interrogate\nDeepBooru",
     "Inpaint": "Inpaint",
     "Batch img2img": "Batch img2img",
     "Image for img2img": "Bild für img2img",
-    "Image for inpainting with mask": "Bild für inpainting mit maske",
+    "Drop Image Here": "Bild hier ablegen",
+    "Image for inpainting with mask": "Bild für inpainting mit Maske",
     "Mask": "Maske",
-    "Mask blur": "Masken Unschärfe",
-    "Mask mode": "Masken modus",
+    "Mask blur": "Maskenunschärfe",
+    "Mask mode": "Maskenmodus",
     "Draw mask": "Maske zeichnen",
-    "Upload mask": "Maske Hochladen",
-    "Masking mode": "Modus zum Maskieren",
-    "Inpaint masked": "Inpaint maskiertes",
-    "Inpaint not masked": "Inpaint nicht maskiertes",
+    "Upload mask": "Maske hochladen",
+    "Masking mode": "Maskierungsmodus",
+    "Inpaint masked": "Maskiertes inpainten",
+    "Inpaint not masked": "Nicht maskiertes inpainten",
     "Masked content": "Maskierter Inhalt",
     "fill": "ausfüllen",
     "original": "original",
     "latent noise": "latent noise",
     "latent nothing": "latent nothing",
     "Inpaint at full resolution": "Inpaint mit voller Auflösung",
-    "Inpaint at full resolution padding, pixels": "Inpaint mit voller Auflösung Abstand, Pixels",
+    "Inpaint at full resolution padding, pixels": "Inpaint bei voller Auflösung Abstand, Pixel",
     "Process images in a directory on the same machine where the server is running.": "Bilder in einem Verzeichnis auf demselben Rechner verarbeiten, auf dem der Server läuft.",
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Ein leeres Ausgabeverzeichnis verwenden, um Bilder normal zu speichern, anstatt in das Ausgabeverzeichnis zu schreiben.",
-    "Input directory": "Input Verzeichnis",
-    "Output directory": "Output verzeichnis",
-    "Resize mode": "Größe anpassen - Modus",
-    "Just resize": "Nur größe anpassen",
-    "Crop and resize": "Zuschneiden und größe anpassen",
+    "Input directory": "Eingabeverzeichnis",
+    "Output directory": "Ausgabeverzeichnis",
+    "Resize mode": "Größenänderungsmodus",
+    "Just resize": "Nur Größe anpassen",
+    "Crop and resize": "Zuschneiden und Größe anpassen",
     "Resize and fill": "Größe anpassen und ausfüllen",
-    "img2img alternative test": "img2img alternative test",
+    "img2img alternative test": "img2img alternativer Test",
     "Loopback": "Loopback",
     "Outpainting mk2": "Outpainting mk2",
     "Poor man's outpainting": "Poor man's outpainting",
-    "SD upscale": "SD upscale",
-    "should be 2 or lower.": "sollte 2 oder niedriger sein.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "`Sampling method` a Euler überschreiben? (diese methode is dafür ausgelegt)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "`prompt` zum gleichen wert wie `Originaler prompt` überschreiben? (und `Negativer prompt`)",
-    "Original prompt": "Originaler Prompt",
-    "Original negative prompt": "Originaler negativer prompt",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "`Sampling Steps` zum gleichen wert wie `Decode steps` überschreiben?",
-    "Decode steps": "Decode steps",
-    "Override `Denoising strength` to 1?": "`Denoising strength` zu 1 überschreiben?",
-    "Decode CFG scale": "Decode CFG scale",
+    "SD upscale": "SD-Upscale",
+    "should be 2 or lower.": "Sollte 2 oder niedriger sein.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "`Samplingmethode` auf Euler setzen? (Diese Methode is dafür ausgelegt)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "`Prompt` auf denselben Wert wie `Originale Prompt` (und `Negative Prompt`) setzen?",
+    "Original prompt": "Originale Prompt",
+    "Original negative prompt": "Originale negative Prompt",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "`Samplingschritte` auf denselben Wert wie `Dekodierschritte` setzen?",
+    "Decode steps": "Dekodierschritte",
+    "Override `Denoising strength` to 1?": "`Denoisingstärke auf 1 setzen?",
+    "Decode CFG scale": "CFG-Skala dekodieren",
     "Randomness": "Zufälligkeit",
-    "Sigma adjustment for finding noise for image": "Sigma anpassungen um noise für Bilder zu finden.",
-    "Loops": "Loops",
+    "Sigma adjustment for finding noise for image": "Sigma-Anpassung für die Suche nach Noise des Bildes",
+    "Loops": "Schleifen",
     "Denoising strength change factor": "Denoising strength change factor",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Empfohlene Einstellungen: Sampling Schritte: 80-100, Sampler Methode: Euler a, Denoising stärke: 0.8",
-    "Pixels to expand": "Anz. Pixel zum erweitern",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Empfohlene Einstellungen: Samplingschritte: 80-100, Samplermethode: Euler a, Denoisingstärke: 0.8",
+    "Pixels to expand": "Pixel zum Erweitern",
     "Outpainting direction": "Outpainting Richtung",
     "left": "Links",
     "right": "Rechts",
     "up": "Hoch",
     "down": "Runter",
-    "Fall-off exponent (lower=higher detail)": "Fall-off exponent (weniger=mehr details)",
-    "Color variation": "Farb variationen",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Skaliert das Bild auf die doppelte Größe; Benutz die Schieberegler für Breite und Höhe, um die Kachelgröße einzustellen",
-    "Tile overlap": "Kacheln überlappungen",
+    "Fall-off exponent (lower=higher detail)": "Abfallexponent (niedriger=mehr Details)",
+    "Color variation": "Farbabweichung",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Skaliert das Bild auf die doppelte Größe; Benutze die Schieberegler für Breite und Höhe, um die Kachelgröße einzustellen",
+    "Tile overlap": "Kachelüberlappung",
     "Upscaler": "Upscaler",
     "Lanczos": "Lanczos",
     "LDSR": "LDSR",
@@ -171,249 +172,287 @@
     "ScuNET GAN": "ScuNET GAN",
     "ScuNET PSNR": "ScuNET PSNR",
     "ESRGAN_4x": "ESRGAN_4x",
-    "Single Image": "Ein Bild",
-    "Batch Process": "Massenverarbeitung",
-    "Batch from Directory": "Massenverarbeitung vom Verzeichnis",
+    "Single Image": "Einzelnes Bild",
+    "Batch Process": "Batchverarbeitung",
+    "Batch from Directory": "Batchverarbeitung aus Verzeichnis",
     "Source": "Quelle",
     "Show result images": "Bildergebnisse zeigen",
-    "Scale by": "Skalieren von",
+    "Scale by": "Skalieren um",
     "Scale to": "Skalieren zu",
     "Resize": "Größe anpassen",
     "Crop to fit": "Zuschneiden damit es passt",
-    "Upscaler 2 visibility": "Upscaler 2 sichtbarkeit",
-    "GFPGAN visibility": "GFPGAN sichtbarkeit",
-    "CodeFormer visibility": "CodeFormer sichtbarkeit",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer gewicht (0 = maximaler effekt, 1 = minimalster effekt)",
-    "Open output directory": "Zielverzeichnis öffnen",
-    "Send to txt2img": "Senden an txt2img",
+    "Upscaler 2 visibility": "Upscaler 2 Sichtbarkeit",
+    "GFPGAN visibility": "GFPGAN Sichtbarkeit",
+    "CodeFormer visibility": "CodeFormer Sichtbarkeit",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer Gewichtung (0 = maximale Wirkung, 1 = minimale Wirkung)",
+    "Upscale Before Restoring Faces": "Upscale Before Restoring Faces",
+    "Send to txt2img": "An txt2img senden",
     "A merger of the two checkpoints will be generated in your": "Die zusammgeführten Checkpoints werden gespeichert unter",
-    "checkpoint": "checkpoint",
+    "checkpoint": "Checkpoint",
     "directory.": "Verzeichnis.",
     "Primary model (A)": "Primäres Modell (A)",
     "Secondary model (B)": "Sekundäres Modell (B)",
     "Tertiary model (C)": "Tertiäres Modell (C)",
     "Custom Name (Optional)": "Eigener Name (Optional)",
-    "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - auf 0 setzen um Modell A zu bekommen",
+    "Multiplier (M) - set to 0 to get model A": "Multiplikator (M) - auf 0 setzen, um Modell A zu erhalten",
     "Interpolation Method": "Interpolationsmethode",
     "Weighted sum": "Weighted sum",
     "Add difference": "Add difference",
     "Save as float16": "Speichern als float16",
     "See": "Siehe ",
-    "wiki": "wiki ",
-    "for detailed explanation.": "für eine detailierte erklärung.",
+    "wiki": "Wiki ",
+    "for detailed explanation.": "für eine ausführliche Erklärung.",
     "Create embedding": "Embedding erstellen",
     "Create hypernetwork": "Hypernetwork erstellen",
     "Preprocess images": "Bilder vorbereiten",
     "Name": "Name",
     "Initialization text": "Initialisierungstext",
-    "Number of vectors per token": "Anzahl der vektoren pro token",
+    "Number of vectors per token": "Anzahl der Vektoren pro Token",
     "Overwrite Old Embedding": "Alte Embeddings überschreiben",
     "Modules": "Module",
-    "Enter hypernetwork layer structure": "Hypernetwork-Schichtstruktur angeben",
+    "Enter hypernetwork layer structure": "Hypernetwork-Ebenenstruktur angeben",
     "Select activation function of hypernetwork": "Aktivierungsfunktion des Hypernetwork auswählen",
     "linear": "linear",
     "relu": "relu",
     "leakyrelu": "leakyrelu",
     "elu": "elu",
     "swish": "swish",
-    "Add layer normalization": "Schicht normalisierung hinzufügen",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "threshold",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Auswahl der Initialisierung der Ebenengewichte. Empfohlen wird relu-like - Kaiming, sigmoid-like - Xavier",
+    "Normal": "Normal",
+    "KaimingUniform": "KaimingUniform",
+    "KaimingNormal": "KaimingNormal",
+    "XavierUniform": "XavierUniform",
+    "XavierNormal": "XavierNormal",
+    "Add layer normalization": "Ebenennormalisierung hinzufügen",
     "Use dropout": "Dropout benutzen",
     "Overwrite Old Hypernetwork": "Altes Hypernetwork überschreiben",
     "Source directory": "Quellenverzeichnis",
     "Destination directory": "Zielverzeichnis",
-    "Existing Caption txt Action": "Vorhandene Beschriftung der txt Aktion",
+    "Existing Caption txt Action": "Vorhandene Beschriftung der txt",
     "ignore": "ignorieren",
     "copy": "kopieren",
-    "prepend": "vorangestellt",
+    "prepend": "voranstellen",
     "append": "anhängen",
     "Create flipped copies": "Gespiegelte Bilder erstellen",
     "Split oversized images": "Übergroße Bilder aufteilen",
-    "Use BLIP for caption": "BLIP für Überschrift nutzen",
-    "Use deepbooru for caption": "Deepbooru für Überschrift nutzen",
-    "Split image threshold": "Bilder aufteilen Grenzwert",
-    "Split image overlap ratio": "Überschneidungsverhältnis beim Bilder aufteilen",
+    "Auto focal point crop": "Automatisch auf Fokuspunkt zuschneiden",
+    "Use BLIP for caption": "BLIP für Beschriftung nutzen",
+    "Use deepbooru for caption": "Deepbooru für Beschriftung nutzen",
+    "Split image threshold": "Schwellenwert für die Aufteilung von Bildern",
+    "Split image overlap ratio": "Überschneidungsverhältnis der Teilbilder",
+    "Focal point face weight": "Fokuspunkt Gesicht Gewicht",
+    "Focal point entropy weight": "Fokuspunkt Entropie Gewicht",
+    "Focal point edges weight": "Fokuspunkt Kanten Gewicht",
+    "Create debug image": "Testbild erstellen",
     "Preprocess": "Vorbereiten",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Trainieren eines Embeddings oder ein Hypernetwork; Sie müssen ein Verzeichnis mit einem Satz von Bildern im Verhältnis 1:1 angeben",
-    "[wiki]": "[wiki]",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Trainieren eines Embeddings oder eines Hypernetworks; Sie müssen ein Verzeichnis mit einem Satz von Bildern im Verhältnis 1:1 angeben",
+    "[wiki]": "[Wiki]",
     "Embedding": "Embedding",
-    "Embedding Learning rate": "Embedding Learning rate",
-    "Hypernetwork Learning rate": "Hypernetwork Learning rate",
-    "Dataset directory": "Dataset verzeichnis",
-    "Log directory": "Log verzeichnis",
-    "Prompt template file": "Vorlage Datei für Prompt",
-    "Max steps": "Max steps",
-    "Save an image to log directory every N steps, 0 to disable": "Alle N steps, ein Bild im Log Verzeichnis speichern, 0 zum deaktivieren",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Alle N steps, eine Kopie des Embedding im Log Verzeichnis speichern, 0 zum deaktivieren",
-    "Save images with embedding in PNG chunks": "Das Bild mit embedding in PNG Fragmente speichern",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Bei vorschau die Parameter (prompt, etc...) vom txt2img tab lesen",
+    "Embedding Learning rate": "Embedding Lernrate",
+    "Hypernetwork Learning rate": "Hypernetwork Lernrate",
+    "Dataset directory": "Datensatzverzeichnis",
+    "Log directory": "Protokollverzeichnis",
+    "Prompt template file": "Prompt-Vorlagendatei",
+    "Max steps": "Max Schritte",
+    "Save an image to log directory every N steps, 0 to disable": "Speichere alle N Schritte ein Bild im Protokollverzeichnis, 0 zum Deaktivieren",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Speichere alle N Schritte eine Embeddingkopie im Protokollverzeichnis, 0 zum Deaktivieren",
+    "Save images with embedding in PNG chunks": "Speichere Bilder mit Embeddings in PNG Chunks",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Lese Parameter (Prompt, etc...) aus dem txt2img-Tab beim Erstellen von Vorschaubildern.",
     "Train Hypernetwork": "Hypernetwork Trainieren",
     "Train Embedding": "Embedding Trainieren",
-    "Apply settings": "Einstellungen anwenden",
+    "Apply settings": "Eintellungen anwenden",
     "Saving images/grids": "Bilder/Raster speichern",
-    "Always save all generated images": "Grundsätzlich alle generieren Bilder speichern",
+    "Always save all generated images": "Immer alle generierten Bilder speichern",
     "File format for images": "Dateiformat für Bilder",
-    "Images filename pattern": "Dateinamen vorlage für Bilder",
-    "Add number to filename when saving": "Beim speichern, dem Dateinamen die Nummer anhängen",
-    "Always save all generated image grids": "Grundsätzlich alle generieren Raster speichern",
+    "Images filename pattern": "Dateinamensmuster für Bilder",
+    "Add number to filename when saving": "Beim speichern, dem Dateinamen Nummer anhängen",
+    "Always save all generated image grids": "Immer alle generierten Bildraster speichern",
     "File format for grids": "Dateiformat für Raster",
-    "Add extended info (seed, prompt) to filename when saving grid": "Speichern von Raster, erweiterte infos (seed, prompt) dem Dateinamen anhängen",
-    "Do not save grids consisting of one picture": "Raster, die nur aus einem Bild bestehen, nicht speichern",
-    "Prevent empty spots in grid (when set to autodetect)": "Lücken im Raster verhindern (falls aus autodetect gesetzt)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Raster reihen anzahl; -1 für Autodetect und 0 für die anzahl wie batch size",
-    "Save text information about generation parameters as chunks to png files": "Information zu Generierungsparameter als Fragmente in PNG dateien speichern",
-    "Create a text file next to every image with generation parameters.": "Textdatei mit Generierungsparameter seperat zu Bilddatei speichern",
-    "Save a copy of image before doing face restoration.": "Kope des Bildes vor und nach Gesichtswiederhestellung speichern",
-    "Quality for saved jpeg images": "Qualität der als JPEG gespeicherten Bilder",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Wenn PNG Bild größer als 4MB oder Dimensionen Größer als 4000, herunterskalieren und als JPG speichern.",
-    "Use original name for output filename during batch process in extras tab": "Originaler name als Output Dateinamen benutzen während Massenverarbeitung in Extras tab.",
-    "When using 'Save' button, only save a single selected image": "Beim benutzen von 'Speichern', nur das gewählte Bild speichern.",
+    "Add extended info (seed, prompt) to filename when saving grid": "Beim Speichern von Rastern zusätzliche Information (Seed, Prompt) hinzufügen",
+    "Do not save grids consisting of one picture": "Keine Raster speichern, die nur aus einem Bild bestehen",
+    "Prevent empty spots in grid (when set to autodetect)": "Lücken im Raster verhindern (falls auf Auto-Erkennung gesetzt)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Rasterreihenanzahl; -1 für Auto-Erkennung und 0 für die gleiche wie die Batchanzahl",
+    "Save text information about generation parameters as chunks to png files": "Generationsparameter als Chunks in PNG-Dateien speichern",
+    "Create a text file next to every image with generation parameters.": "Erstelle zu jedem Bild eine Textdatei, die die Generationsparameter enthält",
+    "Save a copy of image before doing face restoration.": "Vor der Gesichtswiederhestellung eine Kopie des Bildes speichern",
+    "Quality for saved jpeg images": "Qualität der JPEG-Bilder",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Wenn ein PNG-Bild größer als 4MB oder die Dimensionen größer als 4000 ist, herunterskalieren und als JPG speichern.",
+    "Use original name for output filename during batch process in extras tab": "Orginale Dateinamen als Ausgabenamen bei der Batchverarbeitung im Extras-Tab verwenden",
+    "When using 'Save' button, only save a single selected image": "Bei der Benutzung des 'Speichern'-Knopfes, nur das ausgewählte Bild speichern",
     "Do not add watermark to images": "Den Bildern kein Wasserzeichen hinzufügen",
-    "Paths for saving": "Pfade zum speichern",
-    "Output directory for images; if empty, defaults to three directories below": "Ausgabeverzeichnis für Bilder: wenn leer fällt zurück auf drei Verzeichnisse unterhalb",
-    "Output directory for txt2img images": "Ausgabeverzeichnis für txt2img",
-    "Output directory for img2img images": "Ausgabeverzeichnis für img2img",
-    "Output directory for images from extras tab": "Ausgabeverzeichnis für extras",
-    "Output directory for grids; if empty, defaults to two directories below": "Ausgabeverzeichnis für Raster; wenn leer fällt zurück auf zwei Verzeichnisse unterhalb.",
-    "Output directory for txt2img grids": "Ausgabeverzeichnis für  txt2img Raster",
+    "Paths for saving": "Pfade zum Speichern",
+    "Output directory for images; if empty, defaults to three directories below": "Ausgabeverzeichnis für Bilder; Falls leer, werden die Pfade unterhalb verwendet",
+    "Output directory for txt2img images": "Ausgabeverzeichnis für txt2img Bilder",
+    "Output directory for img2img images": "Ausgabeverzeichnis für img2img Bilder",
+    "Output directory for images from extras tab": "Ausgabeverzeichnis für Extras-Tab Bilder",
+    "Output directory for grids; if empty, defaults to two directories below": "Ausgabeverzeichnis für Raster; Falls leer, werden die Pfade unterhalb verwendet",
+    "Output directory for txt2img grids": "Ausgabeverzeichnis für txt2img Raster",
     "Output directory for img2img grids": "Ausgabeverzeichnis für img2img Raster",
-    "Directory for saving images using the Save button": "Ausgabeverzeichnis Bilder für 'Speichern' Button",
+    "Directory for saving images using the Save button": "Ausgabeverzeichnis für Bilder, die mit dem 'Speichern'-Knopf gespeichert wurden",
     "Saving to a directory": "Speichern in ein Verzeichnis",
     "Save images to a subdirectory": "Bilder in ein Unterverzeichnis speichern",
     "Save grids to a subdirectory": "Raster in ein Unterverzeichnis speichern",
-    "When using \"Save\" button, save images to a subdirectory": "Beim benutzen von 'Save' Button, Bilder in Unterverzeichnis speichern",
-    "Directory name pattern": "Verzeichnisname pattern",
-    "Max prompt words for [prompt_words] pattern": "Maximale anzahl der Wörter für [prompt_words] pattern",
-    "Upscaling": "Hochskalieren",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Kachelgröße für ESRGAN upscalers. 0 = keine Kacheln.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Kacheln überlappungen, größe in pixel für ESRGAN upscalers. Niedrige Zahl = Sichtbare übergänge.",
+    "When using \"Save\" button, save images to a subdirectory": "Bilder bei der Benutzung des 'Speichern'-Knopfes in ein Unterverzeichnis speichern",
+    "Directory name pattern": "Muster für Verzeichnisnamen",
+    "Max prompt words for [prompt_words] pattern": "Maximale Wortanzahl für [prompt_words] Muster",
+    "Upscaling": "Upscaling",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Kachelgröße für ESRGAN-Upscaler. 0 = keine Kacheln.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Kachelüberlappung in Pixeln für ESRGAN-Upscaler. Niedrige Werte = sichtbare Naht.",
     "Tile size for all SwinIR.": "Kachelgröße für alle SwinIR.",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Kacheln überlappungen, größe in pixel für SwinIR. Niedrige Zahl = Sichtbare übergänge.",
-    "LDSR processing steps. Lower = faster": "LDSR verarbeitungsschritte. niedriger = schneller",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Kachelüberlappung in Pixeln für SwinIR. Niedrige Werte = sichtbare Naht.",
+    "LDSR processing steps. Lower = faster": "LDSR-Verarbeitungsschritte. Niedriger = schneller",
     "Upscaler for img2img": "Upscaler für img2img",
-    "Upscale latent space image when doing hires. fix": "Upscale latent space beim ausführen von hires. fix",
+    "Upscale latent space image when doing hires. fix": "Bild des Latent Space upscalen, wenn Highres. Fix benutzt wird",
     "Face restoration": "Gesichtswiederhestellung",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer gewicht parameter; 0 = maximaler effekt; 1 = minimalster effekt",
-    "Move face restoration model from VRAM into RAM after processing": "Gesichtswiederhestellungs-Modell nach Verarbeitung vom VRAM ins RAM verschieben",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer Gewichtung (0 = maximale Wirkung, 1 = minimale Wirkung)",
+    "Move face restoration model from VRAM into RAM after processing": "Verschiebe Gesichtswiederhestellung-Modell nach der Verarbeitung vom VRAM in den RAM",
     "System": "System",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "Abfragen pro Sekunde zur VRAM auslastung. 0 = deaktiviert",
-    "Always print all generation info to standard output": "Grundsätzlich alle Generierungsparameter im standard output ausgeben",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Zweite Fortschrittsleiste in der Konsole hinzufügen, die den Fortschitt für den ganzen Job anzeigt.",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM-Nutzungsabfragen pro Sekunde während der Generierung. Zum Deaktivieren auf 0 setzen.",
+    "Always print all generation info to standard output": "Immer alle Generationsinformationen in der Standardausgabe ausgeben",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Der Konsole einen zweiten Fortschrittsbalken hinzufügen, der den Fortschritt eines gesamten Auftrags anzeigt.",
     "Training": "Training",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "VAE und CLIP während Hypernetwork-Training in RAM verschieben. Spart VRAM.",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "VAE und CLIP während des Hypernetwork-Trainings in den RAM verschieben. Spart VRAM.",
     "Filename word regex": "Filename word regex",
     "Filename join string": "Filename join string",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Anzahl der wiederholungen für ein einziges Input-Bild pro epoche; nur dazu genutzt um epochen nummer anzuzeigen",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "CSV die den 'loss' alle N steps beinhaltet. 0 = deaktiviert",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Anzahl der Wiederholungen für ein einzelnes Eingabebild pro Epoche; wird nur für die Anzeige der Epochennummer verwendet",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Speichere eine csv-Datei, die den Verlust enthält, im Protokollverzeichnis alle N Schritte, 0 zum Deaktivieren",
     "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "Anz. Checkpoints im RAM zu Cachen",
-    "Hypernetwork strength": "Hypernetwork stärke",
-    "Apply color correction to img2img results to match original colors.": "Farbkorrigierungen auf img2img anwenden um den Original zu gleichen.",
-    "Save a copy of image before applying color correction to img2img results": "Eine Kopie vom Bild vor der anwendung der img2img Farbkorrigierungen speichern.",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Mit img2img, führe die exakte anzahl an steps aus, die im Slider angegeben sind. (Im Normalfall würdest du mit Denoising weniger nehmen)",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "ermöglicht die Quantisierung in K-Samplern für schärfere und sauberere Ergebnisse. Dadurch können sich existierende Seeds verändern. Für die Anwendung ist ein Neustart erforderlich.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Hervorhebung: Ermöglicht (text) damit das Modell dem Text (mehr) oder [weniger] gewichtung gibt.",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Verwendet die alte Implementierung von Hervorhebungen. Kann für das benutzen von älteren Seeds nützlich sein.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Sorgt dafür, dass K-Diffusions-Sampler in einem Batch die gleichen Bilder erzeugen wie bei der Erstellung eines einzelnen Bildes.",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Erhöhung der Kohärenz durch Auffüllen ab dem letzten Komma innerhalb von N Token, wenn mehr als 75 Token verwendet werden",
-    "Filter NSFW content": "Filter NSFW inhalte",
-    "Stop At last layers of CLIP model": "Stop bei den letzten Schichten des CLIP-Modells",
-    "Interrogate Options": "Optionen abfragen",
-    "Interrogate: keep models in VRAM": "Abfragen: Modelle im VRAM behalten",
-    "Interrogate: use artists from artists.csv": "Abfragen: Künstler aus artists.csv verwenden",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Abfragen: Rangfolge der Modell-Tags in den Ergebnissen berücksichtigen (hat keine Auswirkung auf beschriftungsbasierte Abfragesysteme).",
-    "Interrogate: num_beams for BLIP": "Abfragen: num_beams für BLIP",
-    "Interrogate: minimum description length (excluding artists, etc..)": "Abfrage: Mindestlänge der Beschreibung (ohne Künstler usw.)",
-    "Interrogate: maximum description length": "Abfragen: maximale Länge der Beschreibung",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximale Anzahl von Zeilen in der Textdatei (0 = keine Begrenzung)",
-    "Interrogate: deepbooru score threshold": "Abfrage: deepbooru schwelle",
-    "Interrogate: deepbooru sort alphabetically": "Abfrage: deepbooru alphabetisch sortieren",
-    "use spaces for tags in deepbooru": "Leerzeichen für Tags in deepbooru verwenden",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Escape-Klammern (\\) in deepbooru (damit sie als wörtliche Klammern und nicht zur Hervorhebung verwendet werden)",
-    "User interface": "Benutzerinterface",
-    "Show progressbar": "Fortschrittsleiste Anzeigen",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Zeigt den Fortschritt der Bilderstellung alle N Schritte an. 0 setzen, um zu deaktivieren.",
-    "Show previews of all images generated in a batch as a grid": "Vorschaubilder aller in einem Batch erzeugten Bilder als Raster anzeigen",
-    "Show grid in results for web": "Raster in den Ergebnissen für Web anzeigen",
-    "Do not show any images in results for web": "Keine Bilder in den Ergebnissen für das Web anzeigen",
-    "Add model hash to generation information": "Modell-Hash zu Generierungsinformationen hinzufügen",
-    "Add model name to generation information": "Modell-Name zu Generierungsinformationen hinzufügen",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Beim Einlesen von Generierungsparametern aus Text in die Benutzeroberfläche (aus PNG-Informationen oder eingefügtem Text) wird das ausgewählte Modell/der ausgewählte Checkpoint nicht geändert.",
-    "Font for image grids that have text": "Schriftart für Bildraster, die Text enthalten",
-    "Enable full page image viewer": "Ganzseitenbildbetrachter einschalten",
-    "Show images zoomed in by default in full page image viewer": "Bilder standardmäßig vergrößert im Ganzseitenbildbetrachter anzeigen",
-    "Show generation progress in window title.": "Generierungsfortschritt im Fenstertitel anzeigen.",
-    "Quicksettings list": "Quicksettings-Liste",
-    "Localization (requires restart)": "Lokalisierung (erfordert Neustart)",
-    "Sampler parameters": "Sampler parameter",
-    "Hide samplers in user interface (requires restart)": "Ausblenden von Samplern in der Benutzeroberfläche (erfordert einen Neustart)",
-    "eta (noise multiplier) for DDIM": "eta (noise multiplier) für DDIM",
-    "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) für ancestral samplers",
-    "img2img DDIM discretize": "img2img DDIM discretize",
+    "Checkpoints to cache in RAM": "Checkpoints zum Zwischenspeichern im RAM",
+    "Hypernetwork strength": "Hypernetworkstärke",
+    "Inpainting conditioning mask strength": "Inpainting Stärke der Konditionierungsmaske",
+    "Apply color correction to img2img results to match original colors.": "Farbkorrektur auf die img2img-Ergebnisse anwenden, damit sie den Originalfarben entsprechen.",
+    "Save a copy of image before applying color correction to img2img results": "Vor dem Anwenden der Farbkorrektur eine Kopie des Bildes speichern",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Mit img2img, die exakte Anzahl der Schritte ausführen, die vom Schieberegler angegeben sind (normalerweise weniger bei weniger Denoising).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Aktivieren der Quantisierung in K-Samplern für schärfere und sauberere Ergebnisse. Dies kann bestehende Seeds verändern. Erfordert Neustart.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Hervorhebung: Verwenden Sie (Text), damit das Modell dem Text mehr Aufmerksamkeit schenkt, und [Text], damit es ihm weniger Aufmerksamkeit schenkt",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Verwenden der alten Implementierung von Hervorhebungen. Kann nützlich sein, um alte Seeds zu reproduzieren.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "K-Diffusions-Sampler erzeugen in einem Batch die gleichen Bilder, wie bei der Erstellung eines einzelnen Bildes",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Erhöhung der Kohärenz durch Auffüllen ab dem letzten Komma innerhalb von n Token, wenn mehr als 75 Token verwendet werden",
+    "Filter NSFW content": "NSFW-Inhalte filtern",
+    "Stop At last layers of CLIP model": "Stoppe bei den letzten Schichten des CLIP-Modells",
+    "Interrogate Options": "Interrogate Optionen",
+    "Interrogate: keep models in VRAM": "Interrogate: Modelle im VRAM behalten",
+    "Interrogate: use artists from artists.csv": "Interrogate: Künstler aus 'artists.csv' nutzen",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: Die Rangfolge von Modell-Tags in den Ergebnissen einschließen (hat keine Auswirkung auf beschriftungsbasierte Interrogator).",
+    "Interrogate: num_beams for BLIP": "Interrogate: num_beams für BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimale Beschreibungslänge (Künstler, etc.. ausgenommen)",
+    "Interrogate: maximum description length": "Interrogate: maximale Beschreibungslänge",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximale Anzahl an Zeilen in Textdatei (0 = Kein Limit)",
+    "Interrogate: deepbooru score threshold": "Interrogate: Deepbooru minimale Punkteanzahl",
+    "Interrogate: deepbooru sort alphabetically": "Interrogate: Sortiere Deepbooru alphabetisch",
+    "use spaces for tags in deepbooru": "Benutze Leerzeichen für Deepbooru-Tags",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Escape-Klammern (\\) in Deepbooru (damit sie als normale Klammern und nicht zur Hervorhebung verwendet werden)",
+    "User interface": "Benutzeroberfläche",
+    "Show progressbar": "Fortschrittsleiste anzeigen",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Zeige eine Bildvorschau alle N Samplingschritte. Zum Deaktivieren auf 0 setzen.",
+    "Show previews of all images generated in a batch as a grid": "Zeige eine Vorschau aller erzeugten Bilder in einem Batch als Raster",
+    "Show grid in results for web": "Zeige Raster in der Web-UI Vorschau",
+    "Do not show any images in results for web": "Keine Bilder in der Web-UI Vorschau zeigen",
+    "Add model hash to generation information": "Hash des Modells zu den Generationsinformationen hinzufügen",
+    "Add model name to generation information": "Name des Modells zu den Generationsinformationen hinzufügen",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Beim Einlesen von Generierungsparametern aus Text in die Benutzeroberfläche (aus PNG-Info oder eingefügtem Text) wird das ausgewählte Modell/Checkpoint nicht geändert.",
+    "Send seed when sending prompt or image to other interface": "Den Seed, beim Senden des Bildes/Prompt zu einem anderen Tab, mitsenden",
+    "Font for image grids that have text": "Schriftart für Bildraster mit Text",
+    "Enable full page image viewer": "Ganzseitenbildbetrachter aktivieren",
+    "Show images zoomed in by default in full page image viewer": "Standardmäßig Bilder im Ganzseitenbildbetrachter vergrößert anzeigen",
+    "Show generation progress in window title.": "Generationsfortschritt im Fenstertitel anzeigen.",
+    "Quicksettings list": "Schnellzugriffsleiste",
+    "Localization (requires restart)": "Lokalisierung (Erfordert Neustart)",
+    "Sampler parameters": "Samplerparameter",
+    "Hide samplers in user interface (requires restart)": "Sampler in der Benutzeroberfläche verstecken (Erfordert Neustart)",
+    "eta (noise multiplier) for DDIM": "Eta (noise Multiplikator) für DDIM",
+    "eta (noise multiplier) for ancestral samplers": "Eta (noise Multiplikator) für Ancestral Sampler",
+    "img2img DDIM discretize": "img2img DDIM diskretisieren",
     "uniform": "uniform",
     "quad": "quad",
     "sigma churn": "sigma churn",
     "sigma tmin": "sigma tmin",
     "sigma noise": "sigma noise",
     "Eta noise seed delta": "Eta noise seed delta",
-    "Request browser notifications": "Browser-Benachrichtigungen anfordern",
+    "Request browser notifications": "Browserbenachrichtigungen anfordern",
     "Download localization template": "Vorlage für Lokalisierung herunterladen",
-    "Reload custom script bodies (No ui updates, No restart)": "Neu laden von benutzerdefinierten Skripten (keine Aktualisierung der Benutzeroberfläche, kein Neustart)",
+    "Reload custom script bodies (No ui updates, No restart)": "Benutzerdefinierte Skripte neu laden (keine Aktualisierung der Benutzeroberfläche, kein Neustart)",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradio neu starten und Komponenten aktualisieren (nur Custom Scripts, ui.py, js und css)",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (zum Erzeugen Strg+Eingabe oder Alt+Eingabe drücken)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (presiona Ctrl+Enter o Alt+Enter para generar)",
-    "Add a random artist to the prompt.": "Zufälligen Künstler den Prompt hinzufügen.",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Import von Generierungsparameter aus dem Prompt oder aus der letzten Generierung, wenn das Prompt in der Benutzeroberfläche leer ist.",
-    "Save style": "Style speichern",
-    "Apply selected styles to current prompt": "Momentan ausgewählte styles auf den Prompt anwenden",
-    "Stop processing current image and continue processing.": "Verarbeitung des momentanen Bildes abbrechen und zum nächsten fortsetzen.",
-    "Stop processing images and return any results accumulated so far.": "Verarbeitung abbrechen und alle bisherigen Bilder ausgeben.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style, der angwendet werden soll. Styles haben sowohl negative als auch positive prompt anteile, die auf beide angewendet werden.",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Negative Prompt (zum Erzeugen Strg+Eingabe oder Alt+Eingabe drücken)",
+    "Add a random artist to the prompt.": "Zufälligen Künstler der Prompt hinzufügen.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Lesen der Generationsparameter aus der Prompt oder der letzten Generation (wenn Prompt leer ist) in die Benutzeroberfläche.",
+    "Save style": "Stil speichern",
+    "Apply selected styles to current prompt": "Momentan ausgewählte Stile auf die Prompt anwenden",
+    "Stop processing current image and continue processing.": "Verarbeitung des momentanen Bildes abbrechen und Verarbeitung fortsetzen.",
+    "Stop processing images and return any results accumulated so far.": "Verarbeitung abbrechen und alle bisherigen Ergebnisse ausgeben.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stil, der angwendet werden soll. Stile haben sowohl positive als auch negative Promptanteile und werden auf beide angewandt.",
     "Do not do anything special": "Nichts besonderes machen",
-    "Which algorithm to use to produce the image": "Der zu benutzende algorithmus für die Bildgeneration",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - sehr kreativ, jeder kann unterschiedliche Bilder, abhängig von der Step/Schritt anzahl bekommen. Werte höher als 30-40 helfen nicht.",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - am besten für img2img inpainting",
+    "Which algorithm to use to produce the image": "Der zu benutzende Algorithmus für die Bildgeneration",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - sehr kreativ, kann sehr unterschiedliche Bilder in Abhängigkeit von der Schrittanzahl bekommen. Werte höher als 30-40 helfen nicht.",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Modelle - am besten für inpainting",
     "Produce an image that can be tiled.": "Bild erzeugen, dass gekachelt werden kann.",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Verwendung eines zweistufigen Prozess, um ein Bild teilweise mit geringerer Auflösung zu erstellen, hochzuskalieren und dann die Details zu verbessern, ohne die zusammensetzung zu verändern.",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Bestimmt, wie wenig Bezug der Algorithmus zu dem Inhalt des Bildes haben soll. Bei 0 ändert sich nichts, und bei 1 erhalten Sie ein Bild ohne Bezug. Bei Werten unter 1,0 erfolgt die Verarbeitung in weniger Schritten, als der Schieberegler Sampling-Schritte angibt.",
-    "How many batches of images to create": "Wie viele Stapel von Bildern erstellt werden sollen",
-    "How many image to create in a single batch": "Wie viele Bilder in einem einzigen Stapel erstellt werden sollen",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - wie stark das Bild der Aufforderung entsprechen soll - niedrigere Werte führen zu kreativeren Ergebnissen",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Ein Wert, der die Ausgabe des Zufallszahlengenerators bestimmt: Wenn Sie ein Bild mit denselben Parametern und demselben Seed wie ein anderes Bild erstellen, erhalten Sie dasselbe Ergebnis.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Verwendung eines zweistufigen Prozesses, um ein Bild mit geringerer Auflösung zu erstellen, hochzuskalieren und dann die Details zu verbessern, ohne die Komposition zu verändern.",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Bestimmt, wie wenig Bezug der Algorithmus zu dem Inhalt des Bildes haben soll. Bei 0 ändert sich nichts, und bei 1 besitzt das Bild keinen Bezug. Bei Werten unter 1,0 erfolgt die Verarbeitung in weniger Schritten, als der Schieberegler angibt.",
+    "How many batches of images to create": "Wie viele Sätze von Bildern erstellt werden sollen",
+    "How many image to create in a single batch": "Wie viele Bilder in einem Batch erstellt werden sollen",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - wie stark das Bild der Prompt entsprechen soll - niedrigere Werte führen zu kreativeren Ergebnissen",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Ein Wert, der die Ausgabe des Zufallszahlengenerators bestimmt: Wenn ein Bild mit denselben Parametern und demselben Seed wie ein anderes Bild erstellt wird, erhält man dasselbe Ergebnis.",
     "Set seed to -1, which will cause a new random number to be used every time": "Seed auf -1 setzen, so dass jedes Mal eine neue Zufallszahl verwendet wird",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Wiederverwendung des Seed der letzten Generation, meist nützlich, wenn es zufällig gewählt wurde",
-    "Seed of a different picture to be mixed into the generation.": "Seed eines anderen Bildes, das bei der Erzeugung reingemischt wird.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Wie stark die Veränderung sein soll. Bei 0 gibt es keinen Effekt. Bei 1 erhalten Sie das vollständige Bild mit variations Seed (außer bei ancestral samplers,wie Euler A, wo Sie nur etwas erhalten).",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Versuchen ein Bild zu erzeugen, das dem ähnelt, das mit demselben Seed bei der angegebenen Auflösung erzeugt worden wäre.",
-    "Separate values for X axis using commas.": "Trennen Sie die Werte für die X-Achse durch Kommas.",
-    "Separate values for Y axis using commas.": "Trennen Sie die Werte für die Y-Achse durch Kommas.",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Bild in ein Verzeichnis schreiben (Standard - log/images) und Generierungsparameter in eine csv-Datei.",
-    "Open images output directory": "Bildverzeichnis öffnen",
-    "How much to blur the mask before processing, in pixels.": "Wie stark die Maske vor der Verarbeitung verwischt werden soll, in Pixeln.",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Was soll in den maskierten Bereich vor der Verarbeitung mit Stable Diffusion.",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Wiederverwendung des Seeds der letzten Generation, meist nützlich, wenn er zufällig gewählt wurde",
+    "Seed of a different picture to be mixed into the generation.": "Seed eines anderen Bildes, der bei der Erzeugung reingemischt wird.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Wie stark die Veränderung sein soll. Bei 0 gibt es keinen Effekt. Bei 1 erhält man das vollständige Bild mit dem Variationsseed (außer bei Ancestral Samplern, wie Euler A, wo man nur etwas erhält).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Versuche ein Bild zu erzeugen, das dem ähnelt, das mit dem Seed bei der angegebenen Auflösung erzeugt worden wäre.",
+    "Separate values for X axis using commas.": "Trenne die Werte für die X-Achse durch Kommas.",
+    "Separate values for Y axis using commas.": "Trenne die Werte für die Y-Achse durch Kommas.",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Bild in ein Verzeichnis (Standard - log/images) und Generationsparameter in eine csv-Datei schreiben.",
+    "Open images output directory": "Ausgabeverzeichnis öffnen",
+    "How much to blur the mask before processing, in pixels.": "Wie stark die Maske vor der Verarbeitung weichgezeichnet werden soll, in Pixeln.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Was in den maskierten Bereich vor der Verarbeitung mit Stable Diffusion soll.",
     "fill it with colors of the image": "Füllen mit den Farben des Bildes",
     "keep whatever was there originally": "Originalen Inhalt behalten",
     "fill it with latent space noise": "Füllen mit latent space noise",
-    "fill it with latent space zeroes": "Füllen mit latent space zeroes",
+    "fill it with latent space zeroes": "Füllen mit latent space Nullen",
     "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Hochskalieren des maskierten Bereichs auf die Zielauflösung, Inpainting, Zurückskalieren und Einfügen in das Originalbild.",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Die Größe des Bildes auf die gewünschte Auflösung ändern. Wenn Höhe und Breite nicht übereinstimmen, erhalten Sie ein falsches Seitenverhältnis.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Die Größe des Bildes so ändern, dass die gesamte Zielauflösung mit dem Bild ausgefüllt wird. Herausragende Teile zuschneiden.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Die Größe des Bildes so ändern, dass die gesamte Zielauflösung mit dem Bild ausgefüllt wird. Herausragende Teile mit Farben des Bildes ausfüllen.",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Wie oft soll die Verarbeitung eines Bildes wiederholt werden, um es als Eingabe für die nächste Iteration zu verwenden",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "Im Loopback-Modus wird die Stärke der Rauschunterdrückung in jeder Schleife mit diesem Wert multipliziert. <1 bedeutet abnehmende Vielfalt, so dass Ihre Sequenz zu einem festen Bild konvergiert. >1 bedeutet zunehmende Vielfalt, so dass die Sequenz immer chaotischer wird..",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Wie viel Pixel sollten sich beim SD-Upscale zwischen den Kacheln überlappen? Die Kacheln überlappen sich so, dass beim Zusammenfügen zu einem Bild keine deutlich sichtbare Naht entsteht..",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Die Größe des Bildes auf die gewünschte Auflösung ändern. Wenn Höhe und Breite nicht übereinstimmen, erhält man ein falsches Seitenverhältnis.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Die Größe des Bildes so ändern, dass die gesamte Zielauflösung mit dem Bild ausgefüllt wird. Herausragende Teile werden abgeschnitten.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Die Größe des Bildes so ändern, dass das gesamte Bild enthalten ist. Lücken werden mit Farben des Bildes ausgefüllt.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Wie oft die Verarbeitung eines Bildes wiederholt und als Eingabe für die nächste Iteration verwendet werden soll",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "Im Loopback-Modus wird die Denoisingstärke in jeder Schleife mit diesem Wert multipliziert. <1 bedeutet abnehmende Vielfalt, so dass die Sequenz zu einem festen Bild konvergiert. >1 bedeutet zunehmende Vielfalt, so dass die Sequenz immer chaotischer wird.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Wie viel Pixel sich beim SD-Upscale zwischen den Kacheln überlappen. Die Kacheln überlappen sich so, dass beim Zusammenfügen zu einem Bild keine deutlich sichtbare Naht entsteht.",
     "A directory on the same machine where the server is running.": "Ein Verzeichnis auf demselben Rechner, auf dem der Server läuft.",
     "Leave blank to save images to the default path.": "Leer lassen, um Bilder im Standardpfad zu speichern.",
     "Result = A * (1 - M) + B * M": "Ergebnis = A * (1 - M) + B * M",
     "Result = A + (B - C) * M": "Ergebnis = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "Erste und letzte Ziffer muss 1 sein. Bspw:'1, 2, 1'",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "Erste und letzte Ziffer müssen 1 sein. Bspl:'1, 2, 1'",
     "Path to directory with input images": "Pfad zum Verzeichnis mit den Eingabebildern",
-    "Path to directory where to write outputs": "Pfad zum Verzeichnis, in das die Ausgaben geschrieben werden sollen",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Verwenden Sie die folgenden Tags, um festzulegen, wie die Dateinamen für Bilder ausgewählt werden: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leer lassen, um Standardwerte zu verwenden.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Wenn diese Option aktiviert ist, wird den erstellten Bildern kein unsichtbares Wasserzeichen hinzugefügt. Achtung: Wenn Sie kein Wasserzeichen hinzufügen, verhalten Sie sich möglicherweise unethisch gegenüber sensiblen Personen.",
+    "Path to directory where to write outputs": "Pfad zum Verzeichnis, wo die Ausgaben gespeichert werden",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Verwende die folgenden Tags, um festzulegen, wie die Dateinamen für Bilder ausgewählt werden: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leer lassen, um Standardwerte zu verwenden.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Wenn diese Option aktiviert ist, wird den erstellten Bildern kein Wasserzeichen hinzugefügt. Achtung: Wenn Sie kein Wasserzeichen hinzufügen, verhalten Sie sich möglicherweise unethisch.",
     "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Verwenden Sie die folgenden Tags, um festzulegen, wie Unterverzeichnisse für Bilder und Raster ausgewählt werden: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leer lassen, um Standardwerte zu verwenden.",
-    "Restore low quality faces using GFPGAN neural network": "Wiederherstellung von Gesichtern schlechter Qualität mit GFPGAN neural network",
+    "Restore low quality faces using GFPGAN neural network": "Wiederherstellung von Gesichtern schlechter Qualität mit dem neuralen Netzwerk GFPGAN",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Dieser reguläre Ausdruck wird verwendet, um Wörter aus dem Dateinamen zu extrahieren, die dann mit der unten stehenden Option zu einem Beschriftungstext für das Training zusammengefügt werden. Leer lassen, um den Text des Dateinamens so zu belassen, wie er ist.",
     "This string will be used to join split words into a single line if the option above is enabled.": "Diese Zeichenfolge wird verwendet, um getrennte Wörter in einer einzigen Zeile zu verbinden, wenn die obige Option aktiviert ist.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Liste von Einstellungsnamen, getrennt durch Kommata, für Einstellungen, die in der Schnellzugriffsleiste oben erscheinen sollen, anstatt auf dem üblichen Einstellungs-Tab. Siehe modules/shared.py für Einstellungsnamen. Erfordert einen Neustart zur Anwendung.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Wenn dieser Wert ungleich Null ist, wird er zum Seed addiert und zur Initialisierung des RNG für Rauschen bei der Verwendung von Samplern mit Eta verwendet. Sie können dies verwenden, um noch mehr Variationen von Bildern zu erzeugen, oder Sie können dies verwenden, um Bilder von anderer Software abzugleichen, wenn Sie wissen, was Sie tun."
-}
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Gilt nur für Inpainting-Modelle. Legt fest, wie stark das Originalbild für Inpainting und img2img maskiert werden soll. 1.0 bedeutet vollständig maskiert, was das Standardverhalten ist. 0.0 bedeutet eine vollständig unmaskierte Konditionierung. Niedrigere Werte tragen dazu bei, die Gesamtkomposition des Bildes zu erhalten, sind aber bei großen Änderungen problematisch.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Liste von Einstellungsnamen, getrennt durch Kommas, für Einstellungen, die in der Schnellzugriffsleiste oben erscheinen sollen, anstatt in dem üblichen Einstellungs-Tab. Siehe modules/shared.py für Einstellungsnamen. Erfordert einen Neustart zur Anwendung.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Wenn dieser Wert ungleich Null ist, wird er zum Seed addiert und zur Initialisierung des RNG für Noise bei der Verwendung von Samplern mit Eta verwendet. Dies kann verwendet werden, um noch mehr Variationen von Bildern zu erzeugen, oder um Bilder von anderer Software zu erzeugen, wenn Sie wissen, was Sie tun."
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 66d038f6a41507af2243ff1f6618a745a092c290 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Sat, 29 Oct 2022 15:00:08 -0700
Subject: Read hypernet strength from PNG info.

---
 modules/generation_parameters_copypaste.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index bbaad42e..59c6d7da 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -66,6 +66,7 @@ def integrate_settings_paste_fields(component_dict):
 
     settings_map = {
         'sd_hypernetwork': 'Hypernet',
+        'sd_hypernetwork_strength': 'Hypernetwork strength',
         'CLIP_stop_at_last_layers': 'Clip skip',
         'sd_model_checkpoint': 'Model hash',
     }
-- 
cgit v1.2.1


From 9f4f894d74b57c3d02ebccaa59f9c22fca2b6c90 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 06:03:32 +0800
Subject: allow skip current image in progress api

---
 modules/api/api.py    | 4 ++--
 modules/api/models.py | 3 +++
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index e960bb7b..5c5b210f 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -167,7 +167,7 @@ class Api:
 
         return PNGInfoResponse(info=result[1])
 
-    def progressapi(self):
+    def progressapi(self, req: ProgressRequest = Depends()):
         # copy from check_progress_call of ui.py
 
         if shared.state.job_count == 0:
@@ -188,7 +188,7 @@ class Api:
         progress = min(progress, 1)
 
         current_image = None
-        if shared.state.current_image:
+        if shared.state.current_image and not req.skip_current_image:
             current_image = encode_pil_to_base64(shared.state.current_image)
 
         return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
diff --git a/modules/api/models.py b/modules/api/models.py
index c8bc719a..9ee42a17 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -157,6 +157,9 @@ class PNGInfoRequest(BaseModel):
 class PNGInfoResponse(BaseModel):
     info: str = Field(title="Image info", description="A string with all the info the image had")
 
+class ProgressRequest(BaseModel):
+    skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
+
 class ProgressResponse(BaseModel):
     progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
     eta_relative: float = Field(title="ETA in secs")
-- 
cgit v1.2.1


From 35e95f574a5954e9cbe95265cbcf1386715c9e87 Mon Sep 17 00:00:00 2001
From: Riccardo Giovanetti <29801031+Harvester62@users.noreply.github.com>
Date: Sun, 30 Oct 2022 00:13:13 +0200
Subject: Italian localization (extended) [Requires Feedback]

This is my first version of an alternative localization into Italian language which is a follow-up of the current localization file made by @EugenioBuffo (#3725), which I thanks, and of my discussion "Italian localization (git newbie)" (#3633) which covers the main user interface, all the current the Extensions and Scripts, with the following exceptions:

    txt2img2img (I got errors therefore I removed it from my local installation of SD Web UI)
    Parameter Sequencer (not installed locally)
    Booru tag autocompletion (not installed locally)
    Saving steps of the sampling process (not installed locally)

I do not forecast to translate the above scripts in the short period, unless I will install them locally on my machine.

I beg your pardon if I am brutally overwriting the originally submitted file but I find quite exhausting to edit and append over a thousand lines of code to the original file. If this is mandatory, then I will delete this commit and start a new one amending the original it_IT.json file.

It is for sure not perfect and there are some translations that can be improved, therefore I wish to invite @EugenioBuffo and any other Italian mother language person willing give advice and to help to review this extensive translation . I look forward read any feedback from the community and developers. Thank you.
---
 localizations/it_IT.json | 1560 +++++++++++++++++++++++++++++++---------------
 1 file changed, 1069 insertions(+), 491 deletions(-)

diff --git a/localizations/it_IT.json b/localizations/it_IT.json
index d05035f9..9752f71c 100644
--- a/localizations/it_IT.json
+++ b/localizations/it_IT.json
@@ -1,492 +1,1070 @@
 {
-  "⤡": "⤡",
-  "⊞": "⊞",
-  "×": "×",
-  "❮": "❮",
-  "❯": "❯",
-  "Loading...": "Caricamento...",
-  "view": "mostra ",
-  "api": "API",
-  "•": "•",
-  "built with gradio": "Sviluppato con Gradio",
-  "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
-  "txt2img": "txt2img",
-  "img2img": "img2img",
-  "Extras": "Extra",
-  "PNG Info": "PNG Info",
-  "Checkpoint Merger": "Checkpoint Merger",
-  "Train": "Train",
-  "Image Browser": "Galleria Immagini",
-  "Settings": "Impostazioni",
-  "Prompt": "Prompt",
-  "Negative prompt": "Prompt negativo",
-  "Run": "Esegui",
-  "Skip": "Salta",
-  "Interrupt": "Interrompi",
-  "Generate": "Genera",
-  "Style 1": "Stile 1",
-  "Style 2": "Stile 2",
-  "Label": "Etichetta",
-  "File": "File",
-  "Drop File Here": "Trascina il file qui",
-  "-": "-",
-  "or": "o",
-  "Click to Upload": "Clicca per caricare",
-  "Image": "Immagine",
-  "Check progress": "Verifica progresso",
-  "Check progress (first)": "Verifica progresso (iniziale)",
-  "Sampling Steps": "Sampling Steps",
-  "Sampling method": "Sampling method",
-  "Euler a": "Euler a",
-  "Euler": "Euler",
-  "LMS": "LMS",
-  "Heun": "Heun",
-  "DPM2": "DPM2",
-  "DPM2 a": "DPM2 a",
-  "DPM fast": "DPM fast",
-  "DPM adaptive": "DPM adaptive",
-  "LMS Karras": "LMS Karras",
-  "DPM2 Karras": "DPM2 Karras",
-  "DPM2 a Karras": "DPM2 a Karras",
-  "DDIM": "DDIM",
-  "PLMS": "PLMS",
-  "Width": "Larghezza",
-  "Height": "Altezza",
-  "Restore faces": "Ripristina volti",
-  "Tiling": "Tiling",
-  "Highres. fix": "Highres. fix",
-  "Firstpass width": "Larghezza del primo step",
-  "Firstpass height": "Altezza del primo step",
-  "Denoising strength": "Denoising strength",
-  "Batch count": "Batch count",
-  "Batch size": "Batch size",
-  "CFG Scale": "CFG Scale",
-  "Seed": "Seed",
-  "Extra": "Extra",
-  "Variation seed": "Variation seed",
-  "Variation strength": "Variation strength",
-  "Resize seed from width": "Ridimensiona seed da larghezza",
-  "Resize seed from height": "Ridimensiona seed da altezza",
-  "Script": "Script",
-  "None": "Nessuno",
-  "Prompt matrix": "Prompt matrix",
-  "Prompts from file or textbox": "Prompts da file o casella di testo",
-  "X/Y plot": "X/Y plot",
-  "Put variable parts at start of prompt": "Inserisce una parte variabile all'inizio del prompt",
-  "Iterate seed every line": "Itera seed per ogni linea",
-  "List of prompt inputs": "Lista dei prompt in input",
-  "Upload prompt inputs": "Carica prompt di input",
-  "Show Textbox": "Mostra la casella di testo",
-  "File with inputs": "File con input",
-  "Prompts": "Prompts",
-  "X type": "X type",
-  "Nothing": "Nulla",
-  "Var. seed": "Var. seed",
-  "Var. strength": "Var. strength",
-  "Steps": "Steps",
-  "Prompt order": "Prompt order",
-  "Sampler": "Sampler",
-  "Checkpoint name": "Checkpoint name",
-  "Hypernetwork": "Hypernetwork",
-  "Hypernet str.": "Hypernet str.",
-  "Sigma Churn": "Sigma Churn",
-  "Sigma min": "Sigma min",
-  "Sigma max": "Sigma max",
-  "Sigma noise": "Sigma noise",
-  "Eta": "Eta",
-  "Clip skip": "Clip skip",
-  "Denoising": "Denoising",
-  "X values": "X values",
-  "Y type": "Y type",
-  "Y values": "Y values",
-  "Draw legend": "Disegna legenda",
-  "Include Separate Images": "Includi immagini separate",
-  "Keep -1 for seeds": "Mantieni il seed a -1",
-  "Drop Image Here": "Trascina l'immagine qui",
-  "Save": "Salva",
-  "Send to img2img": "Invia a img2img",
-  "Send to inpaint": "Invia a inpaint",
-  "Send to extras": "Invia a extra",
-  "Make Zip when Save?": "Creare Zip al salvataggio?",
-  "Textbox": "Casella di testo",
-  "Interrogate\nCLIP": "Interroga\nCLIP",
-  "Inpaint": "Inpaint",
-  "Batch img2img": "Batch img2img",
-  "Image for img2img": "Immagine per img2img",
-  "Image for inpainting with mask": "Immagine per inpainting con maschera",
-  "Mask": "Mask",
-  "Mask blur": "Maschera sfocatura",
-  "Mask mode": "Modalità maschera",
-  "Draw mask": "Disegna maschera",
-  "Upload mask": "Carica maschera",
-  "Masking mode": "Modalità mascheramento",
-  "Inpaint masked": "Inpaint mascherato",
-  "Inpaint not masked": "Inpaint non mascherato",
-  "Masked content": "Maschera contenuto",
-  "fill": "riempi",
-  "original": "originale",
-  "latent noise": "latent noise",
-  "latent nothing": "latent nothing",
-  "Inpaint at full resolution": "Inpaint alla massima risoluzione",
-  "Inpaint at full resolution padding, pixels": "Inpaint alla massima risoluzione padding, pixels",
-  "Process images in a directory on the same machine where the server is running.": "Processa le immagini in una cartella nella stessa macchina in cui il server è stato lanciato.",
-  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa una cartella di output vuota per salvare le immagini normalmente invece di scrivere nella cartella di output",
-  "Input directory": "Cartella di Input",
-  "Output directory": "Cartella di Output",
-  "Resize mode": "Modalità ridimensionamento",
-  "Just resize": "Solo ridimensionamento",
-  "Crop and resize": "Taglia e Ridimensiona",
-  "Resize and fill": "Ridimensiona e Riempi",
-  "img2img alternative test": "img2img alternative test",
-  "Loopback": "Loopback",
-  "Outpainting mk2": "Outpainting mk2",
-  "Poor man's outpainting": "Poor man's outpainting",
-  "SD upscale": "SD upscale",
-  "should be 2 or lower.": "deve essere 2 o inferiore.",
-  "Override `Sampling method` to Euler?(this method is built for it)": "Ripristinare il `Sampling method` in Euler?(metodo di default)",
-  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Ripristinare il `prompt` al valore del `prompt originale`? (e `prompt negativo`)",
-  "Original prompt": "Prompt originale",
-  "Original negative prompt": "Promp negativo originale",
-  "Override `Sampling Steps` to the same value as `Decode steps`?": "Ripristinare il `Sampling Steps` al valore di `Decode steps`?",
-  "Decode steps": "Decode steps",
-  "Override `Denoising strength` to 1?": "Ripristinare `Denoising strength` a 1?",
-  "Decode CFG scale": "Decode CFG scale",
-  "Randomness": "Casualità",
-  "Sigma adjustment for finding noise for image": "Sigma adjustment for finding noise for image. ",
-  "Loops": "Loops",
-  "Denoising strength change factor": "Denoising strength change factor",
-  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Impostazioni Raccomandate: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8",
-  "Pixels to expand": "Pixels to expand",
-  "Outpainting direction": "Direzione Outpainting",
-  "left": "sinistra",
-  "right": "destra",
-  "up": "su",
-  "down": "giù",
-  "Fall-off exponent (lower=higher detail)": "Fall-off exponent (inferiore=maggiori dettagli)",
-  "Color variation": "Variazione del colore",
-  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Verrà effettuato l'upscale dell'immagine di due volte le sue dimensioni; usa gli sliders di larghezza e altezza per configurare la dimensione del tile",
-  "Tile overlap": "Sovrapposizione Tile",
-  "Upscaler": "Upscaler",
-  "Lanczos": "Lanczos",
-  "LDSR": "LDSR",
-  "SwinIR 4x": "SwinIR 4x",
-  "ScuNET GAN": "ScuNET GAN",
-  "ScuNET PSNR": "ScuNET PSNR",
-  "ESRGAN_4x": "ESRGAN_4x",
-  "Single Image": "Singola Immagine",
-  "Batch Process": "Batch Process",
-  "Batch from Directory": "Batch da Cartella",
-  "Source": "Sorgente",
-  "Show result images": "Mostra risultati",
-  "Scale by": "Scala di",
-  "Scale to": "Scala a",
-  "Resize": "Ridimensionamento",
-  "Crop to fit": "Taglia per adeguare",
-  "Upscaler 2 visibility": "Visibilità Upscaler 2",
-  "GFPGAN visibility": "Visibilità GFPGAN",
-  "CodeFormer visibility": "Visibilità CodeFormer",
-  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso CodeFormer (0 = effetto massimo, 1 = effetto minimo)",
-  "Open output directory": "Apri cartella di output",
-  "Send to txt2img": "Invia a txt2img",
-  "A merger of the two checkpoints will be generated in your": "Una fusione dei due checkpoint sarà generata nella ",
-  "checkpoint": "checkpoint",
-  "directory.": " directory.",
-  "Primary model (A)": "Primo modello (A)",
-  "Secondary model (B)": "Secondo modello (B)",
-  "Tertiary model (C)": "Terzo modello (C)",
-  "Custom Name (Optional)": "Nome Personalizzato (opzionale)",
-  "Multiplier (M) - set to 0 to get model A": "Moltiplicatore (M) - Imposta 0 per ottenere il modello A",
-  "Interpolation Method": "Metodo di Interpolazione",
-  "Weighted sum": "Somma pesata",
-  "Add difference": "Add difference",
-  "Save as float16": "Salva come float16",
-  "See": "Verifica la ",
-  "wiki": "wiki ",
-  "for detailed explanation.": "per una spiegazione dettagliata.",
-  "Create embedding": "Crea embedding",
-  "Create hypernetwork": "Crea hypernetwork",
-  "Preprocess images": "Pre-processa immagini",
-  "Name": "Nome",
-  "Initialization text": "Testo di Inizializzazione",
-  "Number of vectors per token": "Numero di vettori per token",
-  "Overwrite Old Embedding": "Sovrascrivi Vecchio Embedding",
-  "Modules": "Moduli",
-  "Enter hypernetwork layer structure": "Inserisci la struttura livelli dell'hypernetwork",
-  "Select activation function of hypernetwork": "Seleziona la funzione di attivazione dell'hypernetwork",
-  "linear": "linear",
-  "relu": "relu",
-  "leakyrelu": "leakyrelu",
-  "elu": "elu",
-  "swish": "swish",
-  "tanh": "tanh",
-  "sigmoid": "sigmoid",
-  "celu": "celu",
-  "gelu": "gelu",
-  "glu": "glu",
-  "hardshrink": "hardshrink",
-  "hardsigmoid": "hardsigmoid",
-  "hardswish": "hardswish",
-  "hardtanh": "hardtanh",
-  "logsigmoid": "logsigmoid",
-  "logsoftmax": "logsoftmax",
-  "mish": "mish",
-  "multiheadattention": "multiheadattention",
-  "prelu": "prelu",
-  "rrelu": "rrelu",
-  "relu6": "relu6",
-  "selu": "selu",
-  "silu": "silu",
-  "softmax": "softmax",
-  "softmax2d": "softmax2d",
-  "softmin": "softmin",
-  "softplus": "softplus",
-  "softshrink": "softshrink",
-  "softsign": "softsign",
-  "tanhshrink": "tanhshrink",
-  "threshold": "threshold",
-  "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleziona Inizializzazione dei pesi dei livelli. (relu-like: Kaiming, sigmoid-like: Xavier). Xavier è raccomandato",
-  "Normal": "Normal",
-  "KaimingUniform": "KaimingUniform",
-  "KaimingNormal": "KaimingNormal",
-  "XavierUniform": "XavierUniform",
-  "XavierNormal": "XavierNormal",
-  "Add layer normalization": "Aggiungi normalizzazione dei livelli",
-  "Use dropout": "Usa dropout",
-  "Overwrite Old Hypernetwork": "Sovrascrivi Vecchio Hypernetwork",
-  "Source directory": "Cartella sorgente",
-  "Destination directory": "Cartella di destinazione",
-  "Existing Caption txt Action": "Azione in caso di didascalia già presente",
-  "ignore": "ignora",
-  "copy": "copia",
-  "prepend": "anteponi",
-  "append": "apponi",
-  "Create flipped copies": "Crea copie specchiate",
-  "Split oversized images": "Dividi immagini grandi",
-  "Use BLIP for caption": "Usa BLIP per la didascalia",
-  "Use deepbooru for caption": "Usa deepbooru per la didascalia",
-  "Split image threshold": "Dividi Threshold immagine",
-  "Split image overlap ratio": "Rapporto di sovrapposizione dell'immagine",
-  "Preprocess": "Preprocessa",
-  "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images",
-  "[wiki]": "[wiki]",
-  "Embedding": "Embedding",
-  "Embedding Learning rate": "Embedding Learning rate",
-  "Hypernetwork Learning rate": "Hypernetwork Learning rate",
-  "Dataset directory": "Cartella dataset",
-  "Log directory": "Cartella log",
-  "Prompt template file": "Prompt template file",
-  "Max steps": "Max steps",
-  "Save an image to log directory every N steps, 0 to disable": "Salva un'immagine ogni N step, 0 per disabilitare",
-  "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva una copia dell'embedding nella cartella log ogni N step, 0 per disabilitare",
-  "Save images with embedding in PNG chunks": "Salva le immagini con embedding in PNG",
-  "Read parameters (prompt, etc...) from txt2img tab when making previews": "Usa i parametri (prompt, etc...) di txt2img per visualizzare le anteprime",
-  "Train Hypernetwork": "Train Hypernetwork",
-  "Train Embedding": "Train Embedding",
-  "extras": "Extra",
-  "favorites": "Preferiti",
-  "custom fold": "Cartella personalizzata",
-  "Load": "Carica",
-  "Images directory": "Cartella Immagini",
-  "Prev batch": "Batch Precedente",
-  "Next batch": "Batch Successivo",
-  "First Page": "Prima Pagina",
-  "Prev Page": "Pagina Precedente",
-  "Page Index": "Indice Pagina",
-  "Next Page": "Pagina Successiva",
-  "End Page": "Pagina Finale",
-  "number of images to delete consecutively next": "numero di immagini da eliminare consecutivamente a seguire",
-  "Delete": "Elimina",
-  "Generate Info": "Genera Info",
-  "File Name": "Nome File",
-  "Collect": "Ottieni",
-  "Refresh page": "Aggiorna Pagina",
-  "Date to": "Data fine",
-  "Number": "Numero",
-  "set_index": "set_index",
-  "Checkbox": "Checkbox",
-  "Apply settings": "Applica impostazioni",
-  "Saving images/grids": "Salvataggio immagini/griglie",
-  "Always save all generated images": "Salva sempre tutte le immagini generate",
-  "File format for images": "Formato file per le immagini",
-  "Images filename pattern": "Configura Pattern per il nome dei file immagine",
-  "Add number to filename when saving": "Aggiungi un numero al nome del file al salvataggio",
-  "Always save all generated image grids": "Salva sempre tutte le griglie delle immagini generate",
-  "File format for grids": "Formato file per le giglie",
-  "Add extended info (seed, prompt) to filename when saving grid": "Aggiungi informazioni estese (seed, prompt) al nome del file al salvataggio della griglia",
-  "Do not save grids consisting of one picture": "Non salvare griglie composte da una sola immagine",
-  "Prevent empty spots in grid (when set to autodetect)": "Previeni spazi vuoti nella griglia",
-  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Numero righe griglia; usa -1 per trovarlo automaticamente e 0 per essere lo stesso del batch size",
-  "Save text information about generation parameters as chunks to png files": "Salva le informazioni dei parametri di generazione come chunks nei file PNG",
-  "Create a text file next to every image with generation parameters.": "Crea un file di testo per ogni immagine con i parametri di generazione.",
-  "Save a copy of image before doing face restoration.": "Salva una copia dell'immagine prima di fare la riparazione volti.",
-  "Quality for saved jpeg images": "Qualità per le immagini salvate in JPEG",
-  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l'immagine PNG image è più grande di 4MB o qualsiasi dimensione maggiore di 4000, effettua il downscale e salva come JPG",
-  "Use original name for output filename during batch process in extras tab": "Usa il nome originale del file come nome del file di output durante il 'batch process' nella tab 'extras'",
-  "When using 'Save' button, only save a single selected image": "Usando il tasto 'Save', verrà salvata solo la singola immagine selezionata",
-  "Do not add watermark to images": "Non aggiungere watermark alle immagini",
-  "Paths for saving": "Percorsi per il salvataggio",
-  "Output directory for images; if empty, defaults to three directories below": "Cartella di Output globale per immagini; se vuota, verranno usate di default le cartelle indicate sotto",
-  "Output directory for txt2img images": "Cartella di Output per txt2img",
-  "Output directory for img2img images": "Cartella di Output per img2img",
-  "Output directory for images from extras tab": "Cartella di Output per immagini dalla tab 'extra'",
-  "Output directory for grids; if empty, defaults to two directories below": "Cartella di Output globale per le griglie; se vuota, verranno usate di default le cartelle indicate sotto",
-  "Output directory for txt2img grids": "Cartella di Output per txt2img grids",
-  "Output directory for img2img grids": "Cartella di Output per img2img grids",
-  "Directory for saving images using the Save button": "Cartella di Output per il salvataggion con il tasto 'Salva'",
-  "Saving to a directory": "Salvataggio in cartelle",
-  "Save images to a subdirectory": "Salva immagini in una sottocartella",
-  "Save grids to a subdirectory": "Salva griglie in una sottocartella",
-  "When using \"Save\" button, save images to a subdirectory": "Usando il tasto \"Salva\", le immagini verranno salvate in una sottocartella",
-  "Directory name pattern": "Pattern del nome della cartella",
-  "Max prompt words for [prompt_words] pattern": "Massimo numero di parole per il pattern [prompt_words]",
-  "Upscaling": "Upscaling",
-  "Tile size for ESRGAN upscalers. 0 = no tiling.": "Dimensione Tile per upscalers ESRGAN. 0 = no tiling.",
-  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sovrapposizione Tile, in pixels per upscalers ESRGAN. Valore basso = taglio visible.",
-  "Tile size for all SwinIR.": "Dimensione Tile per SwinIR.",
-  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sovrapposizione Tile, in pixels per upscalers SwinIR. Valore basso = taglio visible.",
-  "LDSR processing steps. Lower = faster": "LDSR processing steps. Basso = Veloce",
-  "Upscaler for img2img": "Upscaler per img2img",
-  "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix",
-  "Face restoration": "Ripristino volti",
-  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Peso CodeFormer; 0 = effetto massimo; 1 = effetto minimo",
-  "Move face restoration model from VRAM into RAM after processing": "Sposta il modello di ripristino volti dalla VRAM alla RAM dopo averlo processato",
-  "System": "Sistema",
-  "VRAM usage polls per second during generation. Set to 0 to disable.": "Utilizzo VRAM al secondo durante la generazione. Imposta 0 per disabilitare.",
-  "Always print all generation info to standard output": "Mostra sempre tutte le informazioni della generazione nello standard output",
-  "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda progress bar alla console per mostrare il progresso complessivo della generazione.",
-  "Training": "Training",
-  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP in RAM durante il training di hypernetwork. Risparmia VRAM.",
-  "Filename word regex": "Filename word regex",
-  "Filename join string": "Filename join string",
-  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per singola immagine in input per epoch; usato solo per mostrare il numero di epoch",
-  "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salva un csv contenente il loss nella cartella log ogni N step, 0 per disabilitare",
-  "Stable Diffusion": "Stable Diffusion",
-  "Checkpoints to cache in RAM": "Checkpoints da memorizzare in RAM",
-  "Hypernetwork strength": "Hypernetwork strength",
-  "Apply color correction to img2img results to match original colors.": "Applica color correction ai risultati img2img per uguagliare i colori originali.",
-  "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la color correction ai risultati img2img",
-  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, effettua esattamente la quantità di step indicata (normalmente se ne effettuano di meno con meno riduzione del disturbo).",
-  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Abilita la quantizzazione nei campionatori K per risultati più nitidi e puliti. Questo può cambiare seed esistenti. Richiede il riavvio per essere applicato.",
-  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Enfasi: usa (testo) per far prestare maggiore attenzione al testo indicato e [testo] per renderlo meno importante",
-  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usa la vecchia implementazione per l'enfasi. Può essere utile per riprodurre vecchi seeds.",
-  "Make K-diffusion samplers produce same images in a batch as when making a single image": "Fa sì che i K-Diffusion producano le stesse immagini in un batch come quando si effettuano una singola immagine",
-  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coerenza aggiungendo dall'ultima virgola ulteriori N token quando si usano più di 75 token.",
-  "Filter NSFW content": "Filtra contenuti NSFW",
-  "Stop At last layers of CLIP model": "Fermati all'ultimo livello del modello CLIP",
-  "Interrogate Options": "Opzioni Interrogate",
-  "Interrogate: keep models in VRAM": "Interrogate: mantieni modelli nella VRAM",
-  "Interrogate: use artists from artists.csv": "Interrogate: usa artisti dal file artists.csv",
-  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
-  "Interrogate: num_beams for BLIP": "Interrogate: num_beams per BLIP",
-  "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minima lunghezza della descrizione (escludendo artisti, etc..)",
-  "Interrogate: maximum description length": "Interrogate: massima lunghezza descrizione",
-  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: massimo numero di linee di testo per file (0 = Nessun limite)",
-  "Interrogate: deepbooru score threshold": "Interrogate: soglia punteggio deepbooru",
-  "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru ordina alfabeticamente",
-  "use spaces for tags in deepbooru": "usa spazi per tag su deepbooru",
-  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "effettuta l'escape (\\) delle parentesi in deepbooru (così vengono usati come parentesi e non come enfasi)",
-  "User interface": "Interfaccia Utente",
-  "Show progressbar": "Mostra barre di caricamento",
-  "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostra il progresso della generazione immagini ogni N step. Imposta 0 per disabilitare.",
-  "Show previews of all images generated in a batch as a grid": "Mostra l'anteprima di tutte le immagini di un batch come griglia",
-  "Show grid in results for web": "Mostra la griglia nei risultati per web",
-  "Do not show any images in results for web": "Non mostrare alcun risultato per web",
-  "Add model hash to generation information": "Aggiungi l'hash del modello alle informazioni di generazione",
-  "Add model name to generation information": "Aggiungi il nome del modello alle informazioni di generazione",
-  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Alla lettura dei parametri di generazione da testo ad interfaccia (da PNG o testo copiato), non cambiare il modello/checkpoint.",
-  "Font for image grids that have text": "Font per griglie di immagini con testo",
-  "Enable full page image viewer": "Abilita la visualizzazione a pagina intera",
-  "Show images zoomed in by default in full page image viewer": "Mostra le immagini zoommate di default nella visualizzazione a pagina intera",
-  "Show generation progress in window title.": "Mostra il progresso di generazione nel titolo della finestra.",
-  "Quicksettings list": "Lista imporazioni rapide",
-  "Localization (requires restart)": "Localization (richiede riavvio)",
-  "ar_AR": "ar_AR",
-  "es_ES": "es_ES",
-  "fr-FR": "fr-FR",
-  "it_IT": "it_IT",
-  "ja_JP": "ja_JP",
-  "ko_KR": "ko_KR",
-  "ru_RU": "ru_RU",
-  "tr_TR": "tr_TR",
-  "zh_CN": "zh_CN",
-  "Sampler parameters": "Parametri del Sampler",
-  "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (richiede riavvio)",
-  "eta (noise multiplier) for DDIM": "eta (noise multiplier) per DDIM",
-  "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) per ancestral samplers",
-  "img2img DDIM discretize": "img2img DDIM discretize",
-  "uniform": "uniform",
-  "quad": "quad",
-  "sigma churn": "sigma churn",
-  "sigma tmin": "sigma tmin",
-  "sigma noise": "sigma noise",
-  "Eta noise seed delta": "Eta noise seed delta",
-  "Images Browser": "Galleria Immagini",
-  "Preload images at startup": "Precarica immagini all'avvio",
-  "Number of columns on the page": "Numero di colonne per pagina",
-  "Number of rows on the page": "Numero di righe per pagina",
-  "Minimum number of pages per load": "Minimo numero di pagine da caricare",
-  "Request browser notifications": "Richiedi notifiche via browser",
-  "Download localization template": "Scarica template di Localization",
-  "Reload custom script bodies (No ui updates, No restart)": "Ricarica gli script custom (Nessun aggiornamento UI, Nessun riavvio)",
-  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Riavvio Gradio and Aggiorna i componenti (solo Script Custom, ui.py, js e css)",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (premi Ctrl+Enter o Alt+Enter per generare)",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (premi Ctrl+Enter o Alt+Enter per generare)",
-  "Add a random artist to the prompt.": "Aggiungi un'artista casuale al prompt.",
-  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leggi i parametri di generazione dal prompt o dall'ultima generazione se il prompt è vuoto nell'interfaccia utente.",
-  "Save style": "Salva stile",
-  "Apply selected styles to current prompt": "Applica lo stile selezionato al prompt corrente",
-  "Stop processing current image and continue processing.": "Smette di processare l'immagine corrente e continua a processare.",
-  "Stop processing images and return any results accumulated so far.": "Smette di processare l'immagine corrente e restituisce tutti i risultati accumulati fin'ora.",
-  "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stile da applicare; gli stili verranno applicati sia ai prompt positivi che ai prompt negativi",
-  "Do not do anything special": "Non fa nulla di speciale",
-  "Which algorithm to use to produce the image": "Algoritmo usato per produrre l'immagine",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - molto creativo, può generare immagini completamente diverse in base alla quantità di step, impostare valori maggiori di 30-40 non aiuta",
-  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - il migliore su inpainting",
-  "Produce an image that can be tiled.": "Produce un'immagine che può essere usata come texture.",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Usa un processo a due step per creare parzialmente un'immagine a bassa risoluzione, eseguire l'upscale e dopo migliorare i dettagli senza cambiarne la composizione.",
-  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
-  "How many batches of images to create": "Quanti batch di immagini creare in una sola generazione",
-  "How many image to create in a single batch": "Quante immagini creare per ogni singolo batch",
-  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - quanto l'immagine sarà conforme al prompt - valori bassi generano risultati più creativi",
-  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Un valore che determina l'output del generatore casuale di numeri - se crei un'immagine con gli stessi parametri e seed, otterrai lo stesso risultato",
-  "Set seed to -1, which will cause a new random number to be used every time": "Imposta il seed a -1, dunque verrà usato un numero diverso ogni volta",
-  "Reuse seed from last generation, mostly useful if it was randomed": "Riutilizza il seed dall'ultima generazione, utile nel caso fosse casuale",
-  "Seed of a different picture to be mixed into the generation.": "Seed di differenti generazioni, mescolati nella generazione corrente.",
-  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Prova a generare immagini simili a quelle che sarebbero state prodotte con lo stesso seed ad una specifica risoluzione",
-  "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "Separa una lista di parole usando le virgole, la prima parola verrà usata come parola chiave: lo script cercherà la parola nel prompt e la rimpiazzerà con le altre",
-  "Separate values for X axis using commas.": "Separa i valori per l'asse X usando le virgole.",
-  "Separate values for Y axis using commas.": "Separa i valori per l'asse Y usando le virgole.",
-  "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva l'immagine in una cartella (default - log/images) e i parametri di generazione in un file CSV.",
-  "Open images output directory": "Apri la cartella di output delle immagini",
-  "How much to blur the mask before processing, in pixels.": "Quanto sfocare le maschere prima di processarle, in pixel.",
-  "What to put inside the masked area before processing it with Stable Diffusion.": "Cosa inserire nell'area di mascheramento prima di processarla con Stable Diffusion.",
-  "fill it with colors of the image": "riempi con colori dell'immagine",
-  "keep whatever was there originally": "mantieni qualsiasi cosa ci fosse originariamente",
-  "fill it with latent space noise": "riempi con latent space noise",
-  "fill it with latent space zeroes": "riempi con latent space zeroes",
-  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Effettua l'Upscale della regione mascherata alla risoluzione obiettivo, effettua l'inpainting, esegue il downscale e lo incolla nell'immagine originale",
-  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Ridimensiona l'immagine alla risoluzione obiettivo. Se l'altezza e la larghezza non coincidono, il risultato avrà un'aspect ratio scorretto.",
-  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Ridimensiona l'immagine affinché l'intera risoluzione obiettivo sia riempita con l'immagine. Taglia le parti che restano fuori.",
-  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "RRidimensiona l'immagine affinché l'intera risoluzione obiettivo sia riempita con l'immagine. Riempie gli spazi vuoti con i colori dell'immagine.",
-  "How many times to repeat processing an image and using it as input for the next iteration": "Quante volte processare un immagine e usarla come input per la prossima iterazione",
-  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In modalità Loopback, per ogni loop la denoising strength sarà moltiplicata per il suo valore. <1 significa ridurre la varietà, dunque la sequenza convergerà in un'immagine più sistemata. >1 significa aumentare la varietà, quindi la sequenza sarà sempre più caotica.",
-  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Per l'upscale in SD, quanta sovrapposizione vi sarà in pixel per ogni tile. I tile si sovrapporranno in modo da essere fusi insieme in una singola immagine. Non vi sarà alcuna linea visibile.",
-  "A directory on the same machine where the server is running.": "Una cartella nella stessa macchina in cui il server è in funzione.",
-  "Leave blank to save images to the default path.": "Lascia vuoto per salvare le immagini nel percorso di default.",
-  "Result = A * (1 - M) + B * M": "Risultato = A * (1 - M) + B * M",
-  "Result = A + (B - C) * M": "Risultato = A + (B - C) * M",
-  "1st and last digit must be 1. ex:'1, 2, 1'": "Il primo e l'ulitmo carattere devono essere 1. es:'1, 2, 1'",
-  "Path to directory with input images": "Percorso della cartella con le immagini di input",
-  "Path to directory where to write outputs": "Percorso della cartella dove posizione le immagini di output",
-  "Input images directory": "Cartella immagini di input",
-  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa i seguenti tag per definire come i nomi dei file saranno scelti: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; lascia vuoto per default.",
-  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se questa opzione non è abilita, i watermark non verranno aggiunti. Attenzione: se non aggiungi i watermark, potresti star agendo in modo non etico.",
-  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
-  "Restore low quality faces using GFPGAN neural network": "Ripristina i volti in bassa qualità usando la rete neurale GFPGAN",
-  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Questa espressione regolare verrà usata per estrarre parole dai nomi dei file, e verranno uniti usanto le opzioni indicate sotto per il training. Lascia vuoto per mantenere il nome del file così com'è.",
-  "This string will be used to join split words into a single line if the option above is enabled.": "Questa stringa sarà usata per unire parole tagliate in una sola riga se l'opzione sopra è abilitata.",
-  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
-  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
-  "Enable Autocomplete": "Abilita Autocompletamento"
-}
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "Caricamento...",
+    "view": "mostra ",
+    "api": "API",
+    "•": " • ",
+    "built with gradio": " Sviluppato con Gradio",
+    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "Extra",
+    "PNG Info": "Info PNG",
+    "Checkpoint Merger": "Miscelatore di Checkpoint",
+    "Train": "Addestramento",
+    "Create aesthetic embedding": "Crea incorporamento estetico",
+    "Dataset Tag Editor": "Dataset Tag Editor",
+    "Deforum": "Deforum",
+    "Artists To Study": "Artisti per studiare",
+    "Image Browser": "Galleria immagini",
+    "Inspiration": "Ispirazione",
+    "Settings": "Impostazioni",
+    "Prompt": "Prompt",
+    "Negative prompt": "Prompt negativo",
+    "Run": "Esegui",
+    "Skip": "Salta",
+    "Interrupt": "Interrompi",
+    "Generate": "Genera",
+    "Style 1": "Stile 1",
+    "Style 2": "Stile 2",
+    "Label": "Etichetta",
+    "File": "File",
+    "Drop File Here": "Trascina il file qui",
+    "-": "-",
+    "or": "o",
+    "Click to Upload": "Clicca per caricare",
+    "Image": "Immagine",
+    "Check progress": "Controlla i progressi",
+    "Check progress (first)": "Controlla i progressi (primo)",
+    "Sampling Steps": "Passi di campionamento",
+    "Sampling method": "Metodo di campionamento",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Larghezza",
+    "Height": "Altezza",
+    "Restore faces": "Restaura i volti",
+    "Tiling": "Piastrellatura",
+    "Highres. fix": "Correzione alta risoluzione",
+    "Firstpass width": "Larghezza del primo passaggio",
+    "Firstpass height": "Altezza del primo passaggio",
+    "Denoising strength": "Forza del Denoising",
+    "Batch count": "Lotti di immagini",
+    "Batch size": "Immagini per lotto",
+    "CFG Scale": "Scala CFG",
+    "Seed": "Seme",
+    "Extra": "Extra",
+    "Variation seed": "Seme della variazione",
+    "Variation strength": "Forza della variazione",
+    "Resize seed from width": "Ridimensiona il seme dalla larghezza",
+    "Resize seed from height": "Ridimensiona il seme dall'altezza",
+    "Open for Clip Aesthetic!": "Apri per Estetica CLIP!",
+    "▼": "▼",
+    "Aesthetic weight": "Estetica - Peso",
+    "Aesthetic steps": "Estetica - Passi",
+    "Aesthetic learning rate": "Estetica - Tasso di apprendimento",
+    "Slerp interpolation": "Interpolazione Slerp",
+    "Aesthetic imgs embedding": "Estetica - Incorporamento di immagini",
+    "None": "Nessuno",
+    "Aesthetic text for imgs": "Estetica - Testo per le immagini",
+    "Slerp angle": "Angolo Slerp",
+    "Is negative text": "È un testo negativo",
+    "Script": "Script",
+    "Random": "Random",
+    "Advanced prompt matrix": "Matrice di prompt avanzata",
+    "Alternate Sampler Noise Schedules": "Metodi alternativi di campionamento del rumore",
+    "Asymmetric tiling": "Piastrellatura asimmetrica",
+    "Custom code": "Custom code",
+    "Dynamic Prompting v0.2": "Prompt dinamici v0.2",
+    "Embedding to Shareable PNG": "Incorporamento convertito in PNG condivisibile",
+    "Force symmetry": "Forza la simmetria",
+    "Prompts interpolation": "Interpola Prompt",
+    "Prompt matrix": "Matrice dei prompt",
+    "Prompt morph": "Metamorfosi del prompt",
+    "Prompts from file or textbox": "Prompt da file o da casella di testo",
+    "To Infinity and Beyond": "Verso l'infinito e oltre",
+    "Seed travel": "Seed travel",
+    "Shift attention": "Sposta l'attenzione",
+    "Text to Vector Graphics": "Da testo a grafica vettoriale",
+    "X/Y plot": "Grafico X/Y",
+    "X/Y/Z plot": "Grafico X/Y/Z",
+    "Create inspiration images": "Crea immagini di ispirazione",
+    "Loops": "Loops",
+    "step1 min/max": "step1 min/max",
+    "step2 min/max": "step2 min/max",
+    "cfg1 min/max": "cfg1 min/max",
+    "cfg2 min/max": "cfg2 min/max",
+    "Keep -1 for seeds": "Keep -1 for seeds",
+    "Usage: a <corgi|cat> wearing <goggles|a hat>": "Utilizzo: a <corgi|cat> wearing <goggles|a hat>",
+    "Noise Scheduler": "Programmatore del rumore",
+    "Default": "Predefinito",
+    "Karras": "Karras",
+    "Exponential": "Esponenziale",
+    "Variance Preserving": "Conservazione della Varianza",
+    "Sigma min": "Sigma min",
+    "Sigma max": "Sigma max",
+    "Sigma rho (Karras only)": "Sigma rho (Solo Karras)",
+    "Beta distribution (VP only)": "Distribuzione Beta (Solo CV)",
+    "Beta min (VP only)": "Beta min (Solo CV)",
+    "Epsilon (VP only)": "Epsilon (Solo CV)",
+    "Tile X": "Piastrella asse X",
+    "Tile Y": "Piastrella asse Y",
+    "Python code": "Codice Python",
+    "Combinatorial generation": "Generazione combinatoria",
+    "Combinations": "Combinazioni",
+    "Choose a number of terms from a list, in this case we choose two artists": "Scegli un numero di termini da un elenco, in questo caso scegliamo due artisti",
+    "{2$$artist1|artist2|artist3}": "{2$$artist1|artist2|artist3}",
+    "If $$ is not provided, then 1$$ is assumed.": "Se $$ non viene fornito, si presume 1$$.",
+    "{1-3$$artist1|artist2|artist3}": "{1-3$$artist1|artist2|artist3}",
+    "In this case, a random number of artists between 1 and 3 is chosen.": "In questo caso viene scelto un numero casuale di artisti compreso tra 1 e 3.",
+    "Wildcards": "Termini jolly",
+    "If the groups wont drop down click": "Se i gruppi non vengono visualizzati, clicca",
+    "here": "qui",
+    "to fix the issue.": "per correggere il problema.",
+    "WILDCARD_DIR: scripts/wildcards": "WILDCARD_DIR: scripts/wildcards",
+    "You can add more wildcards by creating a text file with one term per line and name is mywildcards.txt. Place it in scripts/wildcards.": "Puoi aggiungere termini jolly creando un file di testo con un termine per riga e nominandolo, per esempio, mywildcards.txt. Inseriscilo in scripts/wildcards.",
+    "__<folder>/mywildcards__": "__<folder>/mywildcards__",
+    "will then become available.": "diverrà quindi disponibile.",
+    "Source embedding to convert": "Incorporamento sorgente da convertire",
+    "Embedding token": "Token Incorporamento",
+    "Output directory": "Cartella di output",
+    "Horizontal symmetry": "Simmetria orizzontale",
+    "Vertical symmetry": "Simmetria verticale",
+    "Alt. symmetry method (blending)": "Alt. symmetry method (blending)",
+    "Apply every n steps": "Applica ogni n passi",
+    "Skip last n steps": "Salta gli ultimi n passi",
+    "Interpolation prompt": "Prompt di interpolazione",
+    "Number of images": "Numero di immagini",
+    "Make a gif": "Crea GIF",
+    "Duration of images (ms)": "Durata delle immagini (ms)",
+    "Put variable parts at start of prompt": "Inserisce le parti variabili all'inizio del prompt",
+    "Keyframe Format:": "Formato dei fotogrammi chiave:",
+    "Seed | Prompt or just Prompt": "Seme | Prompt o semplicemente Prompt",
+    "Prompt list": "Elenco dei prompt",
+    "Number of images between keyframes": "Numero di immagini tra fotogrammi chiave",
+    "Save results as video": "Salva i risultati come video",
+    "Frames per second": "Fotogrammi al secondo",
+    "Iterate seed every line": "Iterare il seme per ogni riga",
+    "List of prompt inputs": "Elenco di prompt di input",
+    "Upload prompt inputs": "Carica un file contenente i prompt di input",
+    "n": "n",
+    "Destination seed(s) (Comma separated)": "Seme/i di destinazione (separati da virgola)",
+    "Only use Random seeds (Unless comparing paths)": "Usa solo semi casuali (a meno che non si confrontino i percorsi)",
+    "Number of random seed(s)": "Numero di semi casuali",
+    "Compare paths (Separate travels from 1st seed to each destination)": "Confronta percorsi (transizioni separate dal primo seme a ciascuna destinazione)",
+    "Steps": "Passi",
+    "Loop back to initial seed": "Ritorna al seme iniziale",
+    "Bump seed (If > 0 do a Compare Paths but only one image. No video)": "Bump seed (If > 0 do a Compare Paths but only one image. No video)",
+    "Show generated images in ui": "Mostra le immagini generate nell'interfaccia utente",
+    "\"Hug the middle\" during interpolation": "\"Hug the middle\" durante l'interpolazione",
+    "Allow the default Euler a Sampling method. (Does not produce good results)": "Consenti Euler_a come metodo di campionamento predefinito. (Non produce buoni risultati)",
+    "Visual style": "Stile visivo",
+    "Illustration": "Illustrazione",
+    "Logo": "Logo",
+    "Drawing": "Disegno",
+    "Artistic": "Artistico",
+    "Tattoo": "Tatuaggio",
+    "Gothic": "Gotico",
+    "Anime": "Anime",
+    "Cartoon": "Cartoon",
+    "Sticker": "Etichetta",
+    "Gold Pendant": "Ciondolo in oro",
+    "None - prompt only": "Nessuno - solo prompt",
+    "Enable Vectorizing": "Abilita vettorizzazione",
+    "Output format": "Formato di output",
+    "svg": "svg",
+    "pdf": "pdf",
+    "White is Opaque": "Il bianco è opaco",
+    "Cut white margin from input": "Taglia il margine bianco dall'input",
+    "Keep temp images": "Conserva le immagini temporanee",
+    "Threshold": "Soglia",
+    "Transparent PNG": "PNG trasparente",
+    "Noise Tolerance": "Tolleranza al rumore",
+    "Quantize": "Quantizzare",
+    "X type": "Parametro asse X",
+    "Nothing": "Niente",
+    "Var. seed": "Seme della variazione",
+    "Var. strength": "Forza della variazione",
+    "Prompt S/R": "Cerca e Sostituisci nel Prompt",
+    "Prompt order": "In ordine di prompt",
+    "Sampler": "Campionatore",
+    "Checkpoint name": "Nome del checkpoint",
+    "Hypernetwork": "Iperrete",
+    "Hypernet str.": "Forza della Iperrete",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma noise": "Sigma noise",
+    "Eta": "ETA",
+    "Clip skip": "Salta CLIP",
+    "Denoising": "Riduzione del rumore",
+    "Cond. Image Mask Weight": "Cond. Image Mask Weight",
+    "X values": "Valori per X",
+    "Y type": "Parametro asse Y",
+    "Y values": "Valori per Y",
+    "Draw legend": "Disegna legenda",
+    "Include Separate Images": "Includi immagini separate",
+    "Z type": "Parametro asse Z",
+    "Z values": "Valori per Z",
+    "Artist or styles name list. '.txt' files with one name per line": "Elenco nomi di artisti o stili. File '.txt' con un nome per riga",
+    "Prompt words before artist or style name": "Parole chiave prima del nome dell'artista o dello stile",
+    "Prompt words after artist or style name": "Parole chiave dopo il nome dell'artista o dello stile",
+    "Negative Prompt": "Prompt negativo",
+    "Save": "Salva",
+    "Send to img2img": "Invia a img2img",
+    "Send to inpaint": "Invia a inpaint",
+    "Send to extras": "Invia a extra",
+    "Make Zip when Save?": "Crea un file ZIP quando si usa 'Salva'",
+    "Textbox": "Casella di testo",
+    "Interrogate\nCLIP": "Interroga\nCLIP",
+    "Interrogate\nDeepBooru": "Interroga\nDeepBooru",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "Lotti img2img",
+    "Image for img2img": "Immagine per img2img",
+    "Drop Image Here": "Trascina l'immagine qui",
+    "Image for inpainting with mask": "Immagine per inpainting con maschera",
+    "Mask": "Maschera",
+    "Mask blur": "Sfocatura maschera",
+    "Mask mode": "Modalità maschera",
+    "Draw mask": "Disegna maschera",
+    "Upload mask": "Carica maschera",
+    "Masking mode": "Modalità mascheratura",
+    "Inpaint masked": "Inpaint mascherato",
+    "Inpaint not masked": "Inpaint non mascherato",
+    "Masked content": "Contenuto mascherato",
+    "fill": "riempi",
+    "original": "originale",
+    "latent noise": "rumore latente",
+    "latent nothing": "latenza nulla",
+    "Inpaint at full resolution": "Inpaint alla massima risoluzione",
+    "Inpaint at full resolution padding, pixels": "Inpaint con riempimento a piena risoluzione, pixel",
+    "Process images in a directory on the same machine where the server is running.": "Elabora le immagini in una cartella sulla stessa macchina su cui è in esecuzione il server.",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa una cartella di output vuota per salvare normalmente le immagini invece di scrivere nella cartella di output.",
+    "Input directory": "Cartella di Input",
+    "Resize mode": "Modalità di ridimensionamento",
+    "Just resize": "Ridimensiona solamente",
+    "Crop and resize": "Ritaglia e ridimensiona",
+    "Resize and fill": "Ridimensiona e riempie",
+    "Advanced loopback": "Advanced loopback", 
+    "Animator v5": "Animator v5",
+    "External Image Masking": "Immagine esterna per la mascheratura",
+    "img2img alternative test": "Test alternativo per img2img",
+    "img2tiles": "img2tiles",
+    "Interpolate": "Interpolare",
+    "Loopback": "Rielaborazione ricorsiva",
+    "Loopback and Superimpose": "Rielabora ricorsivamente e sovraimponi",
+    "Outpaint Canvas Region": "Regione della tela di Outpaint",
+    "Outpainting mk2": "Outpainting mk2",
+    "Poor man's outpainting": "Poor man's outpainting",
+    "SD upscale": "Ampliamento SD",
+    "txt2mask v0.1.1": "txt2mask v0.1.1",
+    "[C] Video to video": "[C] Video to video",
+    "Videos": "Filmati",
+    "Deforum-webui (use tab extension instead!)": "Deforum-webui (usa piuttosto la scheda Deforum delle estensioni!)",
+    "Use first image colors (custom color correction)": "Use first image colors (custom color correction)",
+    "Denoising strength change factor (overridden if proportional used)": "Denoising strength change factor (overridden if proportional used)",
+    "Zoom level": "Zoom level",
+    "Direction X": "Direction X",
+    "Direction Y": "Direction Y",
+    "Denoising strength start": "Denoising strength start",
+    "Denoising strength end": "Denoising strength end",
+    "Denoising strength proportional change starting value": "Denoising strength proportional change starting value",
+    "Denoising strength proportional change ending value (0.1 = disabled)": "Denoising strength proportional change ending value (0.1 = disabled)",
+    "Saturation enhancement per image": "Saturation enhancement per image",
+    "Use sine denoising strength variation": "Use sine denoising strength variation",
+    "Phase difference": "Phase difference",
+    "Denoising strength exponentiation": "Denoising strength exponentiation",
+    "Use sine zoom variation": "Use sine zoom variation",
+    "Zoom exponentiation": "Zoom exponentiation",
+    "Use multiple prompts": "Use multiple prompts",
+    "Same seed per prompt": "Same seed per prompt",
+    "Same seed for everything": "Same seed for everything",
+    "Original init image for everything": "Original init image for everything",
+    "Multiple prompts : 1 line positive, 1 line negative, leave a blank line for no negative": "Multiple prompts : 1 line positive, 1 line negative, leave a blank line for no negative",
+    "Render these video formats:": "Renderizza in questi formati:",
+    "GIF": "GIF",
+    "MP4": "MP4",
+    "WEBM": "WEBM",
+    "Animation Parameters": "Parametri animazione",
+    "Total Animation Length (s)": "Durata totale dell'animazione (s)",
+    "Framerate": "Frequenza dei fotogrammi",
+    "Initial Parameters": "Parametri iniziali",
+    "Denoising Strength (overrides img2img slider)": "Intensità di riduzione del rumore (sovrascrive il cursore img2img)",
+    "Seed_March": "Seed_March",
+    "Smoothing_Frames": "Smoothing_Frames",
+    "Zoom Factor (scale/s)": "Fattore di ingrandimento (scala/s)",
+    "X Pixel Shift (pixels/s)": "X Pixel Shift (pixels/s)",
+    "Y Pixel Shift (pixels/s)": "Y Pixel Shift (pixels/s)",
+    "Rotation (deg/s)": "Rotazione (gradi/s)",
+    "Prompt Template, applied to each keyframe below": "Modello di prompt, applicato a ciascun fotogramma chiave qui di seguito",
+    "Positive Prompts": "Prompt positivi",
+    "Negative Prompts": "Prompt negativi",
+    "Props": "Props",
+    "Folder:": "Cartella:",
+    "Supported Keyframes:": "Fotogrammi chiave supportati:",
+    "time_s | source | video, images, img2img | path": "time_s | source | video, images, img2img | path",
+    "time_s | prompt | positive_prompts | negative_prompts": "time_s | prompt | positive_prompts | negative_prompts",
+    "time_s | template | positive_prompts | negative_prompts": "time_s | template | positive_prompts | negative_prompts",
+    "time_s | transform | zoom | x_shift | y_shift | rotation": "time_s | transform | zoom | x_shift | y_shift | rotation",
+    "time_s | seed | new_seed_int": "time_s | seed | new_seed_int",
+    "time_s | denoise | denoise_value": "time_s | denoise | denoise_value",
+    "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name": "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name",
+    "time_s | clear_text | textblock_name": "time_s | clear_text | textblock_name",
+    "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation": "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation",
+    "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation": "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation",
+    "time_s | clear_stamp | stamp_name": "time_s | clear_stamp | stamp_name",
+    "time_s | col_set": "time_s | col_set",
+    "time_s | col_clear": "time_s | col_clear",
+    "time_s | model | sd-v1-4_f16, sd-v1-5-inpainting, sd-v1-5-pruned-emaonly_fp16, wd-v1-3-float16": "time_s | model | sd-v1-4_f16, sd-v1-5-inpainting, sd-v1-5-pruned-emaonly_fp16, wd-v1-3-float16",
+    "Keyframes:": "Fotogrammi chiave:",
+    "Masking preview size": "Dimensione dell'anteprima della mascheratura",
+    "Draw new mask on every run": "Disegna una nuova maschera ad ogni esecuzione",
+    "Process non-contigious masks separately": "Elaborare le maschere non contigue separatamente",
+    "should be 2 or lower.": "dovrebbe essere 2 o inferiore.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Sovrascrivi il `Metodo di campionamento` con Eulero? (questo metodo è stato creato per questo)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Sovrascrivi `prompt` con lo stesso valore del `prompt originale`? (e `prompt negativo`)",
+    "Original prompt": "Prompt originale",
+    "Original negative prompt": "Prompt negativo originale",
+    "Override `Sampling Steps` to the same val due as `Decode steps`?": "Sovrascrivere 'Passi di campionamento' allo stesso valore di 'Passi di decodifica'?",
+    "Decode steps": "Passi di decodifica",
+    "Override `Denoising strength` to 1?": "Sostituisci 'Forza di denoising' a 1?",
+    "Decode CFG scale": "Scala CFG di decodifica",
+    "Randomness": "Casualità",
+    "Sigma adjustment for finding noise for image": "Regolazione Sigma per trovare il rumore per l'immagine",
+    "Tile size": "Dimensione piastrella",
+    "Tile overlap": "Sovrapposizione piastrella",
+    "alternate img2img imgage": "alternate img2img imgage",
+    "interpolation values": "Valori di interpolazione",
+    "Refinement loops": "Cicli di affinamento",
+    "Loopback alpha": "Trasparenza rielaborazione ricorsiva",
+    "Border alpha": "Trasparenza del bordo",
+    "Blending strides": "Passi di fusione",
+    "Reuse Seed": "Riusa il seme",
+    "One grid": "Singola griglia",
+    "Interpolate VarSeed": "Interpola il seme di variazione",
+    "Paste on mask": "Incolla sulla maschera",
+    "Inpaint all": "Inpaint tutto",
+    "Interpolate in latent": "Interpola in latenza",
+    "Denoising strength change factor": "Fattore di variazione dell'intensità di denoising",
+    "Superimpose alpha": "Sovrapporre Alpha",
+    "Show extra settings": "Mostra impostazioni aggiuntive",
+    "Reuse seed": "Riusa il seme",
+    "CFG decay factor": "Fattore di decadimento CFG",
+    "CFG target": "CFG di destinazione",
+    "Show/Hide Canvas": "Mostra/Nascondi Tela",
+    "Left start coord": "Coordinate iniziali - Sinistra",
+    "top start coord": "Coordinate iniziali - Sopra",
+    "unused": "non usato",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Impostazioni consigliate: Passi di campionamento: 80-100, Campionatore: Euler a, Intensità denoising: 0.8",
+    "Pixels to expand": "Pixel da espandere",
+    "Outpainting direction": "Direzione di Outpainting",
+    "left": "sinistra",
+    "right": "destra",
+    "up": "sopra",
+    "down": "sotto",
+    "Fall-off exponent (lower=higher detail)": "Esponente di decremento (più basso=maggior dettaglio)",
+    "Color variation": "Variazione di colore",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenterà l'immagine al doppio delle dimensioni; utilizzare i cursori di larghezza e altezza per impostare la dimensione della piastrella",
+    "Upscaler": "Ampliamento immagine",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR 4x": "SwinIR 4x",
+    "Mask prompt": "Prompt maschera",
+    "Negative mask prompt": "Prompt maschera negativa",
+    "Mask precision": "Precisione della maschera",
+    "Mask padding": "Estendi i bordi della maschera",
+    "Brush mask mode": "Modalità pennello maschera",
+    "discard": "Scarta",
+    "add": "Aggiungi",
+    "subtract": "Sottrai",
+    "Show mask in output?": "Mostra maschera in uscita?",
+    "If you like my work, please consider showing your support on": "Se ti piace il mio lavoro, per favore considera di mostrare il tuo supporto su ",
+    "Patreon": "Patreon",
+    "Input file path": "Percorso file di input",
+    "CRF (quality, less is better, x264 param)": "CRF (qualità, meno è meglio, x264 param)",
+    "FPS": "FPS",
+    "Seed step size": "Ampiezza del gradiente del seme",
+    "Seed max distance": "Distanza massima del seme",
+    "Start time": "Orario di inizio",
+    "End time": "Orario di fine",
+    "End Prompt Blend Trigger Percent": "Percentuale di innesco del mix col prompt finale",
+    "Prompt end": "Prompt finale",
+    "Smooth video": "Rendi il filmato fluido",
+    "Seconds": "Secondi",
+    "Zoom": "Zoom",
+    "Rotate": "Ruota",
+    "Degrees": "Gradi",
+    "Is the Image Tiled?": "L'immagine è piastrellata?",
+    "TranslateX": "Traslazione X",
+    "Left": "Sinistra",
+    "PercentX": "Percentuale X",
+    "TranslateY": "Traslazione Y",
+    "Up": "Sopra",
+    "PercentY": "Percentuale Y",
+    "Show generated pictures in ui": "Mostra le immagini generate nell'interfaccia utente",
+    "Deforum v0.5-webui-beta": "Deforum v0.5-webui-beta",
+    "This script is deprecated. Please use the full Deforum extension instead.": "Questo script è obsoleto. Utilizzare invece l'estensione Deforum completa.",
+    "Update instructions:": "Istruzioni per l'aggiornamento:",
+    "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md": "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md",
+    "discord.gg/deforum": "discord.gg/deforum",
+    "Single Image": "Singola immagine",
+    "Batch Process": "Elaborare a lotti",
+    "Batch from Directory": "Lotto da cartella",
+    "Source": "Sorgente",
+    "Show result images": "Mostra le immagini dei risultati",
+    "Scale by": "Scala di",
+    "Scale to": "Scala a",
+    "Resize": "Ridimensiona",
+    "Crop to fit": "Ritaglia per adattare",
+    "Upscaler 2 visibility": "Visibilità Ampliamento immagine 2",
+    "GFPGAN visibility": "Visibilità GFPGAN",
+    "CodeFormer visibility": "Visibilità CodeFormer",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso di CodeFormer (0 = effetto massimo, 1 = effetto minimo)",
+    "Upscale Before Restoring Faces": "Amplia prima di restaurare i volti",
+    "Send to txt2img": "Invia a txt2img",
+    "A merger of the two checkpoints will be generated in your": "I due checkpoint verranno fusi nella cartella dei",
+    "checkpoint": "checkpoint",
+    "directory.": ".",
+    "Primary model (A)": "Modello Primario (A)",
+    "Secondary model (B)": "Modello Secondario (B)",
+    "Tertiary model (C)": "Modello Terziario (C)",
+    "Custom Name (Optional)": "Nome personalizzato (facoltativo)",
+    "Multiplier (M) - set to 0 to get model A": "Moltiplicatore (M): impostare a 0 per ottenere il modello A",
+    "Interpolation Method": "Metodo di interpolazione",
+    "Weighted sum": "Somma pesata",
+    "Add difference": "Aggiungi differenza",
+    "Save as float16": "Salva come float16",
+    "See": "Consulta la ",
+    "wiki": "wiki",
+    "for detailed explanation.": " per una spiegazione dettagliata.",
+    "Create embedding": "Crea Incorporamento",
+    "Create hypernetwork": "Crea Iperrete",
+    "Preprocess images": "Preprocessa le immagini",
+    "Name": "Nome",
+    "Initialization text": "Testo di inizializzazione",
+    "Number of vectors per token": "Numero di vettori per token",
+    "Overwrite Old Embedding": "Sovrascrivi il vecchio incorporamento",
+    "Modules": "Moduli",
+    "Enter hypernetwork layer structure": "Immettere la struttura del livello della Iperrete",
+    "Select activation function of hypernetwork": "Selezionare la funzione di attivazione della Iperrete",
+    "linear": "linear",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "threshold",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleziona inizializzazione dei pesi dei livelli. relu-like - Kaiming, Si consiglia sigmoid-like - Xavier",
+    "Normal": "Normal",
+    "KaimingUniform": "KaimingUniform",
+    "KaimingNormal": "KaimingNormal",
+    "XavierUniform": "XavierUniform",
+    "XavierNormal": "XavierNormal",
+    "Add layer normalization": "Aggiunge la normalizzazione del livello",
+    "Use dropout": "Usa Dropout",
+    "Overwrite Old Hypernetwork": "Sovrascrive la vecchia Iperrete",
+    "Source directory": "Cartella sorgente",
+    "Destination directory": "Cartella di destinazione",
+    "Existing Caption txt Action": "Azione sul testo della didascalia esistente",
+    "ignore": "ignora",
+    "copy": "copia",
+    "prepend": "anteporre",
+    "append": "appendere",
+    "Create flipped copies": "Crea copie specchiate",
+    "Split oversized images": "Dividi immagini di grandi dimensioni",
+    "Auto focal point crop": "Ritaglio automatico al punto focale",													 
+    "Use BLIP for caption": "Usa BLIP per la didascalia",
+    "Use deepbooru for caption": "Usa deepbooru per la didascalia",
+    "Split image threshold": "Soglia di divisione dell'immagine",
+    "Split image overlap ratio": "Rapporto di sovrapposizione dell'immagine",
+    "Focal point face weight": "Peso della faccia del punto focale",
+    "Focal point entropy weight": "Peso dell'entropia del punto focale",
+    "Focal point edges weight": "Peso dei bordi del punto focalePeso dei bordi del punto focale",
+    "Create debug image": "Crea immagine di debug",
+    "Preprocess": "Preprocessa",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Addestra un Incorporamento o Iperrete; è necessario specificare una directory con un set di immagini con rapporto 1:1",
+    "[wiki]": "[wiki]",
+    "Embedding": "Incorporamento",
+    "Embedding Learning rate": "Tasso di apprendimento Incorporamento",
+    "Hypernetwork Learning rate": "Tasso di apprendimento Iperrete",
+    "Dataset directory": "Cartella del Dataset",
+    "Log directory": "Cartella del registro",
+    "Prompt template file": "File modello prompt",
+    "Max steps": "Passi massimi",
+    "Save an image to log directory every N steps, 0 to disable": "Salva un'immagine nella cartella del registro ogni N passaggi, 0 per disabilitare",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva una copia dell'incorporamento nella cartella del registro ogni N passaggi, 0 per disabilitare",
+    "Save images with embedding in PNG chunks": "Salva le immagini con l'incorporamento in blocchi PNG",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Legge i parametri (prompt, ecc...) dalla scheda txt2img durante la creazione delle anteprime",
+    "Train Hypernetwork": "Addestra Iperrete",
+    "Train Embedding": "Addestra Incorporamento",
+    "Create an aesthetic embedding out of any number of images": "Crea un'incorporamento estetico da qualsiasi numero di immagini",
+    "Create images embedding": "Crea incorporamento di immagini",
+    "-1": "-1",
+    "This extension works well with text captions in comma-separated style (such as the tags generated by DeepBooru interrogator).": "Questa estensione funziona bene con i sottotitoli di testo in stile separato da virgole (come i tag generati dall'interrogatore DeepBooru).",
+    "Save all changes": "Salva tutte le modifiche",
+    "Backup original text file (original file will be renamed like filename.000, .001, .002, ...)": "Backup del file di testo originale (il file originale verrà rinominato come nomefile.000, .001, .002, ...)",
+    "Note:": "Note:",
+    "New text file will be created if you are using filename as captions.": "Verrà creato un nuovo file di testo se si utilizza il nome del file come didascalia.",
+    "Results": "Risultati",
+    "Load": "Carica",
+    "Dataset Images": "Immagini del Dataset",
+    "Filter and Edit Tags": "Filtra e modifica i tag",
+    "Edit Caption of Selected Image": "Modifica la didascalia dell'immagine selezionata",
+    "Search tags / Filter images by tags": "Cerca tag / Filtra le immagini per tag",
+    "Search Tags": "Cerca tag",
+    "Clear all filters": "Rimuovi tutti i filtri",
+    "Sort by": "Ordina per",
+    "Alphabetical Order": "Ordine alfabetico",
+    "Frequency": "Frequenza",
+    "Sort Order": "Ordinamento",
+    "Ascending": "Ascendente",
+    "Descending": "Discendente",
+    "Filter Images by Tags": "Filtra le immagini per tag",
+    "Edit tags in filtered images": "Modifica i tag nelle immagini filtrate",
+    "Selected Tags": "Tag selezionati",
+    "Edit Tags": "Modificare i tag",
+    "Apply changes to filtered images": "Applica le modifiche alle immagini filtrate",
+    "Append additional tags to the beginning": "Aggiungi tag addizionali all'inizio",
+    "1. The selected tags are displayed in comma separated style.": "1. I tag selezionati vengono visualizzati in uno stile separato da virgole.",
+    "2. When changes are applied, all tags in each displayed images are replaced.": "2. Quando vengono applicate le modifiche, tutti i tag in ciascuna immagine visualizzata vengono sostituiti.",
+    "3. If you change some tags into blank, they will be erased.": "3. Se modifichi alcuni tag con uno spazio vuoto, verranno cancellati.",
+    "4. If you add some tags to the end, they will be appended to the end/beginning of the text file.": "4. Se aggiungi dei tag alla fine, questi verranno aggiunti alla fine/inizio del file di testo.",
+    "5. Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "5. Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\"..",
+    "ex A.": "esempio A.",
+    "Original Text = \"A, A, B, C\" Selected Tags = \"B, A\" Edit Tags = \"X, Y\"": "Testo originale = \"A, A, B, C\" Tag selezionati = \"B, A\" Modifica tag = \"X, Y\"",
+    "Result = \"Y, Y, X, C\" (B->X, A->Y)": "Risultato = \"Y, Y, X, C\" (B->X, A->Y)",
+    "ex B.": "esempio B.",
+    "Original Text = \"A, B, C\" Selected Tags = \"(nothing)\" Edit Tags = \"X, Y\"": "Testo originale = \"A, B, C\" Tag selezionati = \"(nothing)\" Modifica tag = \"X, Y\"",
+    "Result = \"A, B, C, X, Y\" (add X and Y to the end (default))": "Risultato = \"A, B, C, X, Y\" (aggiunge X e Y alla fine (predefinito))",
+    "Risultato = \"X, Y, A, B, C\" (aggiunge X e Y all'inizio (\"Aggiungi tag addizionali all'inizio\" selezionato))": "Risultato = \"X, Y, A, B, C\" (aggiunge X e Y all'inizio (\"Aggiungi tag addizionali all'inizio\" selezionato))",
+    "ex C.": "esempio C.",
+    "Original Text = \"A, B, C, D, E\" Selected Tags = \"A, B, D\" Edit Tags = \", X, \"": "Testo originale = \"A, B, C, D, E\" Tag selezionati = \"A, B, D\" Modifica tag = \", X, \"",
+    "Result = \"X, C, E\" (A->\"\", B->X, D->\"\")": "Risultato = \"X, C, E\" (A->\"\", B->X, D->\"\")",
+    "Caption of Selected Image": "Didascalia dell'immagine selezionata",
+    "Copy caption": "Copia didascalia",
+    "Edit Caption": "Modifica didascalia",
+    "Apply changes to selected image": "Applica le modifiche all'immagine selezionata",
+    "Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\".",
+    "Info and links": "Info e link",
+    "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha": "Realizzato da deforum.github.io, port per l'interfaccia web di AUTOMATIC1111 manutenuto da kabachuha",
+    "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Repository Github originale di Deforum  github.com/deforum/stable-diffusion",
+    "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui": "Questo fork è per l'interfaccia web di AUTOMATIC1111 github.com/deforum-art/deforum-for-automatic1111-webui",
+    "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Unisciti al canale Discord ufficiale di Deforum discord.gg/deforum per condividere le tue creazioni e suggerimenti",
+    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "Manuale d'uso per la versione 0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
+    "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Spiegazione della matematica dei fotogrammi chiave docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
+    "Keyframes": "Fotogrammi chiave",
+    "Prompts": "Prompt",
+    "Init": "Inizializzare",
+    "Video output": "Uscita video",
+    "Run settings": "Esegui le impostazioni",
+    "Import settings from file": "Importa impostazioni da file",
+    "Override settings": "Sostituisci le impostazioni",
+    "Custom settings file": "File delle impostazioni personalizzate",
+    "Sampling settings": "Impostazioni di campionamento",
+    "override_these_with_webui": "Sovrascrivi con Web UI",
+    "W": "L",
+    "H": "A",
+    "seed": "Seme",
+    "sampler": "Campionatore",
+    "Enable extras": "Abilita Extra",
+    "subseed": "sub seme",
+    "subseed_strength": "Intensità subseme",
+    "steps": "passi",
+    "ddim_eta": "ETA DDIM",
+    "n_batch": "Numero lotto",
+    "make_grid": "Crea griglia",
+    "grid_rows": "Righe griglia",
+    "save_settings": "Salva impostazioni",
+    "save_samples": "Salva i campioni",
+    "display_samples": "Mostra i campioni",
+    "save_sample_per_step": "Salva campioni per passo",
+    "show_sample_per_step": "Mostra campioni per passo",
+    "Batch settings": "Impostazioni lotto",
+    "batch_name": "Nome del lotto",
+    "filename_format": "Formato nome del file",
+    "seed_behavior": "Comportamento seme",
+    "iter": "Iterativo",
+    "fixed": "Fisso",
+    "random": "Casuale",
+    "schedule": "Programmato",
+    "Animation settings": "Impostazioni animazione",
+    "animation_mode": "Modalità animazione",
+    "2D": "2D",
+    "3D": "3D",
+    "Video Input": "Ingresso video",
+    "max_frames": "Fotogrammi max",
+    "border": "Bordo",
+    "replicate": "Replica",
+    "wrap": "Impacchetta",
+    "Motion parameters:": "Parametri di movimento:",
+    "2D and 3D settings": "Impostazioni 2D e 3D",
+    "angle": "Angolo",
+    "zoom": "Zoom",
+    "translation_x": "Traslazione X",
+    "translation_y": "Traslazione Y",
+    "3D settings": "Impostazioni 3D",
+    "translation_z": "Traslazione Z",
+    "rotation_3d_x": "Rotazione 3D X",
+    "rotation_3d_y": "Rotazione 3D Y",
+    "rotation_3d_z": "Rotazione 3D Z",
+    "Prespective flip — Low VRAM pseudo-3D mode:": "Inversione prospettica: modalità pseudo-3D a bassa VRAM:",
+    "flip_2d_perspective": "Inverti prospettiva 2D",
+    "perspective_flip_theta": "Inverti prospettiva theta",
+    "perspective_flip_phi": "Inverti prospettiva phi",
+    "perspective_flip_gamma": "Inverti prospettiva gamma",
+    "perspective_flip_fv": "Inverti prospettiva fv",
+    "Generation settings:": "Impostazioni di generazione:",
+    "noise_schedule": "Pianificazione del rumore",
+    "strength_schedule": "Intensità della pianificazione",
+    "contrast_schedule": "Contrasto della pianificazione",
+    "cfg_scale_schedule": "Pianificazione della scala CFG",
+    "3D Fov settings:": "Impostazioni del campo visivo 3D:",
+    "fov_schedule": "Pianificazione del campo visivo",
+    "near_schedule": "Pianificazione da vicino",
+    "far_schedule": "Pianificazione da lontano",
+    "To enable seed schedule select seed behavior — 'schedule'": "Per abilitare la pianificazione del seme, seleziona il comportamento del seme — 'programma'",
+    "seed_schedule": "Pianificazione del seme",
+    "Coherence:": "Coerenza:",
+    "color_coherence": "Coerenza del colore",
+    "Match Frame 0 HSV": "Uguaglia HSV del fotogramma 0",
+    "Match Frame 0 LAB": "Uguaglia LAB del fotogramma 0",
+    "Match Frame 0 RGB": "Uguaglia RGB del fotogramma 0",
+    "diffusion_cadence": "Cadenza di diffusione",
+    "3D Depth Warping:": "Deformazione della profondità 3D:",
+    "use_depth_warping": "Usa la deformazione della profondità",
+    "midas_weight": "Peso MIDAS",
+    "near_plane": "Piano vicino",
+    "far_plane": "Piano lontano",
+    "fov": "Campo visivo",
+    "padding_mode": "Modalità di riempimento",
+    "reflection": "Rifletti",
+    "zeros": "Zeri",
+    "sampling_mode": "Modalità di campionamento",
+    "bicubic": "bicubic",
+    "bilinear": "bilinear",
+    "nearest": "nearest",
+    "save_depth_maps": "Salva le mappe di profondità",
+    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`modalità animazione: Nessuno` si inserisce nell'elenco di *prompt*. (Modalità batch disabilitata atm, funzionano solo i prompt di animazione)",
+    "*Important change from vanilla Deforum!*": "*Importante cambiamento rispetto alla versione originale di Deforum!*",
+    "This script uses the built-in webui weighting settings.": "Questo script utilizza le impostazioni di pesatura webui integrate.",
+    "So if you want to use math functions as prompt weights,": "Quindi, se vuoi usare le funzioni matematiche come pesi dei prompt,",
+    "keep the values above zero in both parts": "mantenere i valori sopra lo zero in entrambe le parti",
+    "Negative prompt part can be specified with --neg": "La parte negativa del prompt può essere specificata con --neg",
+    "batch_prompts (disabled atm)": "Prompt in lotti (al momento è disabilitato)",
+    "animation_prompts": "Prompt animazione",
+    "Init settings": "Impostazioni iniziali",
+    "use_init": "Usa le impostazioni iniziali",
+    "from_img2img_instead_of_link": "from_img2img_instead_of_link",
+    "strength_0_no_init": "strength_0_no_init",
+    "strength": "Intensità",
+    "init_image": "Immagine di inizializzazione",
+    "use_mask": "Usa maschera",
+    "use_alpha_as_mask": "Usa alpha come maschera",
+    "invert_mask": "Inverti la maschera",
+    "overlay_mask": "Sovrapponi la maschera",
+    "mask_file": "File della maschera",
+    "mask_brightness_adjust": "Regola la luminosità della maschera",
+    "mask_overlay_blur": "Sfocatura della sovrapposizione della maschera",
+    "Video Input:": "Ingresso video:",
+    "video_init_path": "Percorso del video di inizializzazione",
+    "extract_nth_frame": "Estrai ogni ennesimo fotogramma",
+    "overwrite_extracted_frames": "Sovrascrivi i fotogrammi estratti",
+    "use_mask_video": "Usa maschera video",
+    "video_mask_path": "Percorso della maschera video",
+    "Interpolation (turned off atm)": "Interpolazione (attualmente spento)",
+    "interpolate_key_frames": "Interpola fotogrammi chiave",
+    "interpolate_x_frames": "Interpola x fotogrammi",
+    "Resume animation:": "Riprendi l'animazione:",
+    "resume_from_timestring": "Riprendi da stringa temporale",
+    "resume_timestring": "Stringa temporale",
+    "Video output settings": "Impostazioni uscita video",
+    "skip_video_for_run_all": "skip_video_for_run_all",
+    "fps": "FPS",
+    "output_format": "Formato di uscita",
+    "PIL gif": "PIL gif",
+    "FFMPEG mp4": "FFMPEG mp4",
+    "ffmpeg_location": "Percorso ffmpeg",
+    "add_soundtrack": "Aggiungi colonna sonora",
+    "soundtrack_path": "Percorso colonna sonora",
+    "use_manual_settings": "Usa impostazioni manuali",
+    "render_steps": "Passi di renderizzazione",
+    "max_video_frames": "Numero max fotogrammi video",
+    "path_name_modifier": "Modificatore del nome del percorso",
+    "x0_pred": "x0_pred",
+    "x": "x",
+    "image_path": "Percorso immagine",
+    "mp4_path": "Percorso MP4",
+    "Click here after the generation to show the video": "Clicca qui dopo la generazione per mostrare il video",
+    "Save Settings": "Salva le impostazioni",
+    "Load Settings": "Carica le impostazioni",
+    "Path relative to the webui folder." : "Percorso relativo alla cartella webui.",
+    "Save Video Settings": "Salva impostazioni video",
+    "Load Video Settings": "Carica impostazioni video",
+    "dog": "cane",
+    "house": "casa",
+    "portrait": "ritratto",
+    "spaceship": "nave spaziale",
+    "anime": "anime",
+    "cartoon": "cartoon",
+    "digipa-high-impact": "digipa-high-impact",
+    "digipa-med-impact": "digipa-med-impact",
+    "digipa-low-impact": "digipa-low-impact",
+    "fareast": "estremo oriente",
+    "fineart": "fineart",
+    "scribbles": "scarabocchi",
+    "special": "special",
+    "ukioe": "ukioe",
+    "weird": "strano",
+    "black-white": "bianco e nero",
+    "nudity": "nudità",
+    "c": "c",
+    "Get Images": "Ottieni immagini",
+    "dog-anime": "dog-anime",
+    "dog-cartoon": "dog-cartoon",
+    "dog-digipa-high-impact": "dog-digipa-high-impact",
+    "dog-digipa-med-impact": "dog-digipa-med-impact",
+    "dog-digipa-low-impact": "dog-digipa-low-impact",
+    "dog-fareast": "dog-fareast",
+    "dog-fineart": "dog-fineart",
+    "dog-scribbles": "dog-scribbles",
+    "dog-special": "dog-special",
+    "dog-ukioe": "dog-ukioe",
+    "dog-weird": "dog-weird",
+    "dog-black-white": "dog-black-white",
+    "dog-nudity": "dog-nudity",
+    "dog-c": "dog-c",
+    "dog-n": "dog-n",
+    "house-anime": "house-anime",
+    "house-cartoon": "house-cartoon",
+    "house-digipa-high-impact": "house-digipa-high-impact",
+    "house-digipa-med-impact": "house-digipa-med-impact",
+    "house-digipa-low-impact": "house-digipa-low-impact",
+    "house-fareast": "house-fareast",
+    "house-fineart": "house-fineart",
+    "house-scribbles": "house-scribbles",
+    "house-special": "house-special",
+    "house-ukioe": "house-ukioe",
+    "house-weird": "house-weird",
+    "house-black-white": "house-black-white",
+    "house-nudity": "house-nudity",
+    "house-c": "house-c",
+    "house-n": "house-n",
+    "portrait-anime": "portrait-anime",
+    "portrait-cartoon": "portrait-cartoon",
+    "portrait-digipa-high-impact": "portrait-digipa-high-impact",
+    "portrait-digipa-med-impact": "portrait-digipa-med-impact",
+    "portrait-digipa-low-impact": "portrait-digipa-low-impact",
+    "portrait-fareast": "portrait-fareast",
+    "portrait-fineart": "portrait-fineart",
+    "portrait-scribbles": "portrait-scribbles",
+    "portrait-special": "portrait-special",
+    "portrait-ukioe": "portrait-ukioe",
+    "portrait-weird": "portrait-weird",
+    "portrait-black-white": "portrait-black-white",
+    "portrait-nudity": "portrait-nudity",
+    "portrait-c": "portrait-c",
+    "portrait-n": "portrait-n",
+    "spaceship-anime": "spaceship-anime",
+    "spaceship-cartoon": "spaceship-cartoon",
+    "spaceship-digipa-high-impact": "spaceship-digipa-high-impact",
+    "spaceship-digipa-med-impact": "spaceship-digipa-med-impact",
+    "spaceship-digipa-low-impact": "spaceship-digipa-low-impact",
+    "spaceship-fareast": "spaceship-fareast",
+    "spaceship-fineart": "spaceship-fineart",
+    "spaceship-scribbles": "spaceship-scribbles",
+    "spaceship-special": "spaceship-special",
+    "spaceship-ukioe": "spaceship-ukioe",
+    "spaceship-weird": "spaceship-weird",
+    "spaceship-black-white": "spaceship-black-white",
+    "spaceship-nudity": "spaceship-nudity",
+    "spaceship-c": "spaceship-c",
+    "spaceship-n": "spaceship-n",
+    "artists to study extension by camenduru |": "Estensione 'Artisti per studiare' a cura di camenduru |",
+    "github": "Github",
+    "|": "|",
+    "twitter": "Twitter",
+    "youtube": "Youtube",
+    "hi-res images": "Immagini in alta risoluzione",
+    "All images generated with CompVis/stable-diffusion-v1-4 +": "Tutte le immagini sono state generate con CompVis/stable-diffusion-v1-4 +",
+    "artists.csv": "artists.csv",
+    "| License: Attribution 4.0 International (CC BY 4.0)": "| Licenza: Attribution 4.0 International (CC BY 4.0)",
+    "extras": "Extra",
+    "favorites": "Preferiti",
+    "others": "Altre immagini",
+    "Images directory": "Cartella immagini",
+    "Dropdown": "Elenco cartelle",
+    "First Page": "Prima pagina",
+    "Prev Page": "Pagina precedente",
+    "Page Index": "Indice pagina",
+    "Next Page": "Pagina successiva",
+    "End Page": "Ultima pagina",
+    "delete next": "Cancella successivo",
+    "Delete": "Elimina",
+    "sort by": "Ordina per",
+    "path name": "Nome percorso",
+    "date": "Data",
+    "keyword": "Parola chiave",
+    "Generate Info": "Genera Info",
+    "File Name": "Nome del file",
+    "Collect": "Aggiungi ai preferiti",
+    "Renew page": "Aggiorna la pagina",
+    "Number": "Numero",
+    "set_index": "Imposta indice",
+    "load_switch": "load_switch",
+    "turn_page_switch": "turn_page_switch",
+    "Checkbox": "Casella di controllo",
+    "Checkbox Group": "Seleziona immagini per",
+    "artists": "Artisti",
+    "flavors": "Stili",
+    "mediums": "Tecniche",
+    "movements": "Movimenti artistici",
+    "All": "Tutto",
+    "Favorites": "Preferiti",
+    "Exclude abandoned": "Escludi scartati",
+    "Abandoned": "Scartati",
+    "Key word": "Parola chiave",
+    "Get inspiration": "Ispirami",
+    "to txt2img": "Invia a txt2img",
+    "to img2img": "Invia a img2img",
+    "Don't show again": "Scarta",
+    "Move out": "Rimuovi",
+    "set button": "Pulsante imposta", 
+    "Apply settings": "Applica le impostazioni",
+    "Saving images/grids": "Salva immagini/griglie",
+    "Always save all generated images": "Salva sempre tutte le immagini generate",
+    "File format for images": "Formato del file delle immagini",
+    "Images filename pattern": "Modello del nome dei file immagine",
+    "Add number to filename when saving": "Aggiungi un numero al nome del file al salvataggio",
+    "Always save all generated image grids": "Salva sempre tutte le griglie di immagini generate",
+    "File format for grids": "Formato del file per le griglie",
+    "Add extended info (seed, prompt) to filename when saving grid": "Aggiungi informazioni estese (seme, prompt) al nome del file durante il salvataggio della griglia",
+    "Do not save grids consisting of one picture": "Non salvare le griglie composte da una sola immagine",
+    "Prevent empty spots in grid (when set to autodetect)": "Previeni spazi vuoti nella griglia (se impostato su rilevamento automatico)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Numero di righe della griglia; utilizzare -1 per il rilevamento automatico e 0 per essere uguale alla dimensione del lotto",
+    "Save text information about generation parameters as chunks to png files": "Salva le informazioni di testo dei parametri di generazione come blocchi nel file png",
+    "Create a text file next to every image with generation parameters.": "Crea un file di testo assieme a ogni immagine con i parametri di generazione.",
+    "Save a copy of image before doing face restoration.": "Salva una copia dell'immagine prima di eseguire il restauro dei volti.",
+    "Quality for saved jpeg images": "Qualità delle immagini salvate in formato JPEG",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l'immagine PNG è più grande di 4 MB o qualsiasi dimensione è maggiore di 4000, ridimensiona e salva la copia come JPG",
+    "Use original name for output filename during batch process in extras tab": "Usa il nome originale per il nome del file di output durante l'elaborazione a lotti nella scheda 'Extra'",
+    "When using 'Save' button, only save a single selected image": "Usando il pulsante 'Salva', verrà salvata solo la singola immagine selezionata",
+    "Do not add watermark to images": "Non aggiungere la filigrana alle immagini",
+    "Paths for saving": "Percorsi di salvataggio",
+    "Output directory for images; if empty, defaults to three directories below": "Cartella di output per le immagini; se vuoto, per impostazione predefinita verranno usate le cartelle seguenti",
+    "Output directory for txt2img images": "Cartella di output per le immagini txt2img",
+    "Output directory for img2img images": "Cartella di output per le immagini img2img",
+    "Output directory for images from extras tab": "Cartella di output per le immagini dalla scheda 'Extra'",
+    "Output directory for grids; if empty, defaults to two directories below": "Cartella di output per le griglie; se vuoto, per impostazione predefinita veranno usate cartelle seguenti",
+    "Output directory for txt2img grids": "Cartella di output per le griglie txt2img",
+    "Output directory for img2img grids": "Cartella di output per le griglie img2img",
+    "Directory for saving images using the Save button": "Cartella dove salvare le immagini usando il pulsante 'Salva'",
+    "Saving to a directory": "Salva in una cartella",
+    "Save images to a subdirectory": "Salva le immagini in una sotto cartella",
+    "Save grids to a subdirectory": "Salva le griglie in una sotto cartella",
+    "When using \"Save\" button, save images to a subdirectory": "Usando il pulsante \"Salva\", le immagini verranno salvate in una sotto cartella",
+    "Directory name pattern": "Modello del nome della cartella",
+    "Max prompt words for [prompt_words] pattern": "Numero massimo di parole del prompt per il modello [prompt_words]",
+    "Upscaling": "Ampliamento",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Dimensione piastrella per ampliamento ESRGAN. 0 = nessuna piastrellatura.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per gli ampliamenti ESRGAN. Valori bassi = cucitura visibile.",
+    "Tile size for all SwinIR.": "Dimensione piastrella per tutti gli SwinIR.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per SwinIR. Valori bassi = cucitura visibile.",
+    "LDSR processing steps. Lower = faster": "Fasi di elaborazione LDSR. Più basso = più veloce",
+    "Upscaler for img2img": "Metodo di ampliamento per img2img",
+    "Upscale latent space image when doing hires. fix": "Amplia l'immagine nello spazio latente durante la correzione in alta risoluzione",
+    "Face restoration": "Restauro del viso",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Peso di CodeFormer; 0 = effetto massimo; 1 = effetto minimo",
+    "Move face restoration model from VRAM into RAM after processing": "Sposta il modello di restauro facciale dalla VRAM alla RAM dopo l'elaborazione",
+    "System": "Sistema",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Verifiche al secondo sull'utilizzo della VRAM durante la generazione. Impostare a 0 per disabilitare.",
+    "Always print all generation info to standard output": "Stampa sempre tutte le informazioni di generazione sul output standard",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda barra di avanzamento alla console che mostra l'avanzamento complessivo del lavoro.",
+    "Training": "Addestramento",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP nella RAM durante l'addestramento di Iperreti. Risparmia VRAM.",
+    "Filename word regex": "Filename word regex",
+    "Filename join string": "Filename join string",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per una singola immagine di input per epoca; utilizzato solo per visualizzare il numero di epoca",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salva un file CSV contenente la perdita nella cartella di registrazione ogni N passaggi, 0 per disabilitare",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "Checkpoint da memorizzare nella RAM",
+    "Hypernetwork strength": "Forza della Iperrete",
+    "Inpainting conditioning mask strength": "Forza della maschera di condizionamento del Inpainting",
+    "Apply color correction to img2img results to match original colors.": "Applica la correzione del colore ai risultati di img2img in modo che corrispondano ai colori originali.",
+    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la correzione del colore ai risultati di img2img",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, esegue esattamente la quantità di passi specificata dalla barra di scorrimento (normalmente se ne effettuano di meno con meno riduzione del rumore).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Abilita la quantizzazione nei campionatori K per risultati più nitidi e puliti. Questo può cambiare i semi esistenti. Richiede il riavvio per applicare la modifica.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Enfasi: utilizzare (testo) per fare in modo che il modello presti maggiore attenzione al testo e [testo] per fargli prestare meno attenzione",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usa la vecchia implementazione dell'enfasi. Può essere utile per riprodurre vecchi semi.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Fa sì che i campionatori di diffusione K producano le stesse immagini in un lotto come quando si genera una singola immagine",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coerenza disattivando dall'ultima virgola all'indietro di n token quando si utilizzano più di 75 token",
+    "Filter NSFW content": "Filtra i contenuti NSFW",
+    "Stop At last layers of CLIP model": "Fermati agli ultimi livelli del modello CLIP",
+    "Interrogate Options": "Opzioni di interrogazione",
+    "Interrogate: keep models in VRAM": "Interroga: mantieni i modelli nella VRAM",
+    "Interrogate: use artists from artists.csv": "Interroga: utilizza artisti dal file artisti.csv",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interroga: include la classifica delle corrispondenze dei tag del modello nei risultati (non ha effetto sulle interrogazioni basate su didascalie).",
+    "Interrogate: num_beams for BLIP": "Interroga: num_beams per BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Interroga: lunghezza minima della descrizione (esclusi artisti, ecc..)",
+    "Interrogate: maximum description length": "Interroga: lunghezza massima della descrizione",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: numero massimo di righe nel file di testo (0 = Nessun limite)",
+    "Interrogate: deepbooru score threshold": "Interroga: soglia del punteggio deepbooru",
+    "Interrogate: deepbooru sort alphabetically": "Interroga: deepbooru ordinato alfabeticamente",
+    "use spaces for tags in deepbooru": "usa gli spazi per i tag in deepbooru",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Effettua l'escape (\\) delle parentesi in deepbooru (così vengono usate come parentesi letterali e non per enfatizzare)",
+    "User interface": "Interfaccia Utente",
+    "Show progressbar": "Mostra la barra di avanzamento",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostra l'avanzamento della generazione dell'immagine ogni N passaggi di campionamento. Impostare a 0 per disabilitare.",
+    "Show previews of all images generated in a batch as a grid": "Mostra le anteprime di tutte le immagini generate in un lotto come una griglia",
+    "Show grid in results for web": "Mostra la griglia nei risultati per il web",
+    "Do not show any images in results for web": "Non mostrare alcuna immagine nei risultati per il web",
+    "Add model hash to generation information": "Aggiungi l'hash del modello alle informazioni sulla generazione",
+    "Add model name to generation information": "Aggiungi il nome del modello alle informazioni sulla generazione",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Durante la lettura dei parametri di generazione dal testo nell'interfaccia utente (da informazioni PNG o testo incollato), non modificare il modello/checkpoint selezionato.",
+    "Send seed when sending prompt or image to other interface": "Invia il seme quando si invia un prompt o un'immagine a un'altra interfaccia",
+    "Font for image grids that have text": "Font per griglie di immagini con testo",
+    "Enable full page image viewer": "Abilita la visualizzazione delle immagini a pagina intera",
+    "Show images zoomed in by default in full page image viewer": "Mostra le immagini ingrandite per impostazione predefinita nella visualizzazione a pagina intera",
+    "Show generation progress in window title.": "Mostra l'avanzamento della generazione nel titolo della finestra.",
+    "Quicksettings list": "Elenco delle impostazioni rapide",
+    "Localization (requires restart)": "Localizzazione (richiede il riavvio)",
+    "ar_AR": "ar_AR",
+    "de_DE": "de_DE",
+    "es_ES": "es_ES",
+    "fr-FR": "fr-FR",
+    "it_IT": "it_IT",
+    "ja_JP": "ja_JP",
+    "ko_KR": "ko_KR",
+    "pt_BR": "pt_BR",
+    "ru_RU": "ru_RU",
+    "tr_TR": "tr_TR",
+    "zh_CN": "zh_CN",
+    "zh_TW": "zh_TW",
+    "Sampler parameters": "Parametri del campionatore",
+    "Hide samplers in user interface (requires restart)": "Nascondi campionatori nell'interfaccia utente (richiede il riavvio)",
+    "eta (noise multiplier) for DDIM": "ETA (moltiplicatore di rumore) per DDIM",
+    "eta (noise multiplier) for ancestral samplers": "ETA (moltiplicatore di rumore) per campionatori ancestrali",
+    "img2img DDIM discretize": "discretizzazione DDIM per img2img",
+    "uniform": "uniforme",
+    "quad": "quad",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma noise",
+    "Eta noise seed delta": "ETA del delta del seme del rumore",
+    "Aesthetic Image Scorer": "Punteggio delle immagini estetiche",
+    "Save score as EXIF or PNG Info Chunk": "Salva il punteggio come info EXIF o PNG",
+    "Save score as tag (Windows Only)": "Salva punteggio come etichetta (solo Windows)",
+    "Force CPU (Requires Custom Script Reload)": "Forza CPU (richiede il ricaricamento dello script personalizzato)",
+    "Number of columns on image gallery": "Numero di colonne nella galleria di immagini",
+    "Images Browser": "Galleria immagini",
+    "Preload images at startup": "Precarica le immagini all'avvio",
+    "Number of columns on the page": "Numero di colonne nella pagina",
+    "Number of rows on the page": "Numero di righe nella pagina",
+    "Minimum number of pages per load": "Numero minimo di pagine da caricare",
+    "Maximum number of samples, used to determine which folders to skip when continue running the create script": "Numero massimo di campioni, utilizzato per determinare quali cartelle ignorare quando si continua a eseguire lo script di creazione",
+    "Use same seed for all images": "Usa lo stesso seme per tutte le immagini",
+    "Request browser notifications": "Richiedi le notifiche del browser",
+    "Download localization template": "Scarica il modello per la localizzazione",
+    "Reload custom script bodies (No ui updates, No restart)": "Ricarica gli script personalizzati (nessun aggiornamento dell'interfaccia utente, nessun riavvio)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Riavvia Gradio e aggiorna i componenti (solo script personalizzati, ui.py, js e css)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (premi Ctrl+Invio o Alt+Invio per generare)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (premere Ctrl+Invio o Alt+Invio per generare)",
+    "Add a random artist to the prompt.": "Aggiungi un artista casuale al prompt.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leggere i parametri di generazione dal prompt o dall'ultima generazione se il prompt è vuoto ed inserirli nell'interfaccia utente.",
+    "Save style": "Salva stile",
+    "Apply selected styles to current prompt": "Applica gli stili selezionati al prompt corrente",
+    "Stop processing current image and continue processing.": "Interrompe l'elaborazione dell'immagine corrente e continua l'elaborazione.",
+    "Stop processing images and return any results accumulated so far.": "Interrompe l'elaborazione delle immagini e restituisce tutti i risultati accumulati finora.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stile da applicare; gli stili hanno componenti sia per i prompt positivi che per quelli negativi e si applicano a entrambi",
+    "Do not do anything special": "Non fa nulla di speciale",
+    "Which algorithm to use to produce the image": "Quale algoritmo utilizzare per produrre l'immagine",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - molto creativo, si può ottenere un'immagine completamente diversa a seconda del numero di passi, impostare i passi su un valore superiore a 30-40 non aiuta",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - il migliore per inpainting",
+    "Produce an image that can be tiled.": "Produce un'immagine che può essere piastrellata.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilizza un processo in due fasi per creare parzialmente un'immagine con una risoluzione inferiore, aumentare la scala e quindi migliorarne i dettagli senza modificare la composizione",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determina quanto poco l'algoritmo dovrebbe rispettare dovrebbe il contenuto dell'immagine. A 0, non cambierà nulla e a 1 otterrai un'immagine non correlata. Con valori inferiori a 1.0 l'elaborazione richiederà meno passaggi di quelli specificati dalla barra di scorrimento dei passi di campionamento.",
+    "How many batches of images to create": "Quanti lotti di immagini generare",
+    "How many image to create in a single batch": "Quante immagini generare in un singolo lotto",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - quanto fortemente l'immagine deve conformarsi al prompt: valori più bassi producono risultati più creativi",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Un valore che determina l'output del generatore di numeri casuali: se create un'immagine con gli stessi parametri e seme di un'altra immagine, otterrete lo stesso risultato",
+    "Set seed to -1, which will cause a new random number to be used every time": "Imposta il seme su -1, che farà sì che ogni volta venga utilizzato un nuovo numero casuale",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Riusa il seme dell'ultima generazione, utile soprattutto se casuale",
+    "Seed of a different picture to be mixed into the generation.": "Seme di un'immagine diversa da miscelare nella generazione.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Quanto è forte la variazione da produrre. A 0, non ci sarà alcun effetto. A 1, otterrai l'intera immagine con il seme della variazione (tranne per i campionatori ancestrali, dove otterrai solo una leggera variazione).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Prova a produrre un'immagine simile a quella che sarebbe stata prodotta con lo stesso seme alla risoluzione specificata",
+    "This text is used to rotate the feature space of the imgs embs": "Questo testo viene utilizzato per ruotare lo spazio delle funzioni delle immagini incorporate",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Quante volte ripetere l'elaborazione di un'immagine e utilizzarla come input per l'iterazione successiva",
+    "Enter one prompt per line. Blank lines will be ignored.": "Immettere un prompt per riga. Le righe vuote verranno ignorate.",
+    "Separate values for X axis using commas.": "Separare i valori per l'asse X usando le virgole.",
+    "Separate values for Y axis using commas.": "Separare i valori per l'asse Y usando le virgole.",
+    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "Separa un elenco di parole con virgole e lo script eseguirà una variazione di prompt con quelle parole per ogni loro possibile ordine",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva l'immagine/i in una cartella (predefinita - log/images) ed i parametri di generazione in un file CSV.",
+    "Open images output directory": "Apri la cartella di output delle immagini",
+    "How much to blur the mask before processing, in pixels.": "Quanto sfocare la maschera prima dell'elaborazione, in pixel.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Cosa mettere all'interno dell'area mascherata prima di elaborarla con Stable Diffusion.",
+    "fill it with colors of the image": "riempi con i colori dell'immagine",
+    "keep whatever was there originally": "conserva tutto ciò che c'era in origine",
+    "fill it with latent space noise": "riempi di rumore spaziale latente",
+    "fill it with latent space zeroes": "riempi con zeri di spazio latente",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Ingrandisce la regione mascherata per raggiungere la risoluzione, esegue la pittura, riduce la scala e incolla nell'immagine originale",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Ridimensiona l'immagine alla risoluzione di destinazione. A meno che altezza e larghezza non corrispondano, otterrai proporzioni errate.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Ridimensionare l'immagine in modo che l'intera risoluzione di destinazione sia riempita con l'immagine. Ritaglia le parti che sporgono.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Ridimensiona l'immagine in modo che l'intera immagine rientri nella risoluzione di destinazione. Riempi lo spazio vuoto con i colori dell'immagine.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Per l'upscaling SD, quanta sovrapposizione in pixel dovrebbe esserci tra le piastrelle. Le piastrelle si sovrappongono in modo che quando vengono unite nuovamente in un'immagine, non ci siano giunture chiaramente visibili.",
+    "Process an image, use it as an input, repeat.": "Elabora un'immagine, usala come input, ripeti.",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In modalità rielaborazione ricorsiva, su ogni ciclo la forza di denoising viene moltiplicata per questo valore. <1 significa varietà decrescente in modo che la sequenza converga su un'immagine fissa. >1 significa aumentare la varietà in modo che la tua sequenza diventi sempre più caotica.",
+    "A directory on the same machine where the server is running.": "Una cartella sulla stessa macchina su cui è in esecuzione il server.",
+    "Leave blank to save images to the default path.": "Lascia vuoto per salvare le immagini nel percorso predefinito.",
+    "Result = A * (1 - M) + B * M": "Risultato = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Risultato = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "La prima e l'ultima cifra devono essere 1. Es.:'1, 2, 1'",
+    "Path to directory with input images": "Percorso della cartella con immagini di input",
+    "Path to directory where to write outputs": "Percorso della cartella in cui scrivere i risultati",
+    "C:\\directory\\of\\datasets": "C:\\cartella\\del\\dataset",
+    "Input images directory": "Cartella di input delle immagini",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Usa i seguenti tag per definire come vengono scelti i nomi dei file per le immagini: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed ], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se questa opzione è abilitata, la filigrana non verrà aggiunta alle immagini create. Attenzione: se non aggiungi la filigrana, potresti comportarti in modo non etico.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utilizzare i seguenti tag per definire come vengono scelte le sottodirectory per le immagini e le griglie: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
+    "Restore low quality faces using GFPGAN neural network": "Ripristina volti di bassa qualità utilizzando la rete neurale GFPGAN",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Questa espressione regolare verrà utilizzata per estrarre le parole dal nome del file e verranno unite utilizzando l'opzione seguente nel testo dell'etichetta utilizzato per l'addestramento. Lascia vuoto per mantenere il testo del nome del file così com'è.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Questa stringa verrà utilizzata per unire le parole divise in un'unica riga se l'opzione sopra è abilitata.",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Si applica solo ai modelli di pittura. Determina con quale forza mascherare l'immagine originale per inpainting e img2img. 1.0 significa completamente mascherato, che è il comportamento predefinito. 0.0 significa un condizionamento completamente non mascherato. Valori più bassi aiuteranno a preservare la composizione generale dell'immagine, ma avranno difficoltà con grandi cambiamenti.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Elenco dei nomi delle impostazioni, separati da virgole, per le impostazioni che dovrebbero essere visualizzate nella barra di accesso rapido in alto, anziché nella normale scheda delle impostazioni. Vedi modules/shared.py per impostare i nomi. Richiede il riavvio per applicare.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se questo valore è diverso da zero, verrà aggiunto al seed e utilizzato per inizializzare il generatore di numeri casuali per il rumore quando si utilizzano campionatori con ETA. Puoi usarlo per produrre ancora più variazioni di immagini, oppure puoi usarlo per abbinare le immagini di altri software se sai cosa stai facendo."
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 4ae575853fd3855d219d28363b745a4998244d48 Mon Sep 17 00:00:00 2001
From: Martucci <73501718+M-art-ucci@users.noreply.github.com>
Date: Sat, 29 Oct 2022 19:23:05 -0300
Subject: Final commit for october (19:22) / 22

---
 localizations/pt_BR.json | 76 ++++++++++++++++++++++++------------------------
 1 file changed, 38 insertions(+), 38 deletions(-)

diff --git a/localizations/pt_BR.json b/localizations/pt_BR.json
index 49d71edc..56281105 100644
--- a/localizations/pt_BR.json
+++ b/localizations/pt_BR.json
@@ -5,10 +5,10 @@
     "❮": "❮",
     "❯": "❯",
     "Loading...": "Carregando...",
-    "view": "ver ",
+    "view": "ver",
     "api": "api",
     "•": "•",
-    "built with gradio": "Criado com gradio",
+    "built with gradio": "criado com gradio",
     "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
     "txt2img": "txt2img",
     "img2img": "img2img",
@@ -27,14 +27,14 @@
     "Style 2": "Estilo 2",
     "Label": "Rótulo",
     "File": "Arquivo",
-    "Drop File Here": "Coloque aqui o arquivo",
+    "Drop File Here": "Solte Aqui o Arquivo",
     "-": "-",
     "or": "ou",
-    "Click to Upload": "Clique para carregar um arquivo",
+    "Click to Upload": "Clique para Carregar um Arquivo",
     "Image": "Imagem",
     "Check progress": "Checar progresso",
     "Check progress (first)": "Checar progresso (primeiro)",
-    "Sampling Steps": "Passos de amostragem",
+    "Sampling Steps": "Passos de Amostragem",
     "Sampling method": "Método de amostragem",
     "Euler a": "Euler a",
     "Euler": "Euler",
@@ -68,13 +68,13 @@
     "Resize seed from height": "Redimensionar a seed a partir da altura",
     "Script": "Script",
     "None": "Nenhum",
-    "Prompt matrix": "Matriz de Prompt",
+    "Prompt matrix": "Matriz de prompt",
     "Prompts from file or textbox": "Prompts a partir de arquivo ou caixa de texto",
     "X/Y plot": "X/Y plot",
     "Put variable parts at start of prompt": "Coloca partes variáveis no começo do prompt",
     "Iterate seed every line": "Iterar seed a cada linha",
-    "List of prompt inputs": "Lista de entradas de texto para prompt",
-    "Upload prompt inputs": "Carregar entradas de textos para prompts",
+    "List of prompt inputs": "Lista de entrada de texto para prompt",
+    "Upload prompt inputs": "Carregar entrada de texto para prompt",
     "X type": "Tipo do X",
     "Nothing": "Nenhum",
     "Var. seed": "Var. seed",
@@ -99,7 +99,7 @@
     "Draw legend": "Desenhar a legenda",
     "Include Separate Images": "Incluir Imagens Separadas",
     "Keep -1 for seeds": "Manter em -1 para seeds",
-    "Drop Image Here": "Coloque a imagem aqui",
+    "Drop Image Here": "Solte a imagem aqui",
     "Save": "Salvar",
     "Send to img2img": "Mandar para img2img",
     "Send to inpaint": "Mandar para inpaint",
@@ -137,29 +137,29 @@
     "img2img alternative test": "Teste alternativo de img2img",
     "Loopback": "Loopback",
     "Outpainting mk2": "Outpainting mk2",
-    "Poor man's outpainting": "Poor man's outpainting",
+    "Poor man's outpainting": "Poor man`s outpainting",
     "SD upscale": "Ampliamento SD",
     "should be 2 or lower.": "deve ser 2 ou menos.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Substituir 'Método de amostragem' para Euler? (este método foi feito para isso)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Substituir 'prompt' para o mesmo valor que o 'prompt original'? (também para o 'prompt negativo')",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Substituir `Método de amostragem` por Euler? (este método foi feito para isso)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Substituir `prompt` para o mesmo valor que o `prompt original`? (também para o `prompt negativo`)",
     "Original prompt": "Prompt original",
     "Original negative prompt": "Prompt negativo original",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Substituir 'Passos de Amostragem' para o mesmo valor que 'Decodificar Passos'?",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Substituir `Passos de Amostragem` para o mesmo valor que `Decodificar Passos`?",
     "Decode steps": "Decode steps",
-    "Override `Denoising strength` to 1?": "Substituir 'Quantidade do Denoise' para 1?",
+    "Override `Denoising strength` to 1?": "Substituir `Quantidade do Denoise` para 1?",
     "Decode CFG scale": "Decodificar escala CFG",
     "Randomness": "Aleatoriedade",
     "Sigma adjustment for finding noise for image": "Ajuste Sigma para encontrar ruído para imagem",
     "Loops": "Loops",
     "Denoising strength change factor": "Fator de mudança na quantidade do Denoise",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Configurações recomendadas",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Configurações recomendadas: Passos de amostragem: 80-100: Euler a, força do Denoise: 0.8",
     "Pixels to expand": "Pixels para expandir",
-    "Outpainting direction": "Direção do Outpainting",
+    "Outpainting direction": "Direção do outpainting",
     "left": "esquerda",
     "right": "direita",
     "up": "cima",
     "down": "baixo",
-    "Fall-off exponent (lower=higher detail)": "Exponente de queda (menor=mais detalhes)",
+    "Fall-off exponent (lower=higher detail)": "Expoente de queda (menor=mais detalhes)",
     "Color variation": "Variação de cor",
     "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Amplia a imagem em dobro; ajusta a largura e altura para definir o tamanho do ladrilho",
     "Tile overlap": "Sobreposição de ladrilho",
@@ -191,9 +191,9 @@
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso do CodeFormer (0 = efeito máximo, 1 = efeito mínimo)",
     "Open output directory": "Abrir diretório de saída",
     "Send to txt2img": "Mandar para txt2img",
-    "A merger of the two checkpoints will be generated in your": "Uma fusão dos dois checkpoints será gerada em seu ",
+    "A merger of the two checkpoints will be generated in your": "Uma fusão dos dois checkpoints será gerada em seu",
     "checkpoint": "checkpoint",
-    "directory.": " diretório.",
+    "directory.": "diretório.",
     "Primary model (A)": "Modelo primário (A)",
     "Secondary model (B)": "Modelo secundário (B)",
     "Tertiary model (C)": "Modelo terciário (C)",
@@ -257,7 +257,7 @@
     "Overwrite Old Hypernetwork": "Sobrescrever Hypernetwork Anterior",
     "Source directory": "Diretório de origem",
     "Destination directory": "Diretório de destino",
-    "Existing Caption txt Action": "Ação de legenda txt já existente",
+    "Existing Caption txt Action": "Ação de Título txt Já Existente",
     "ignore": "ignorar",
     "copy": "copiar",
     "prepend": "adicionar ao início",
@@ -305,9 +305,9 @@
     "Create a text file next to every image with generation parameters.": "Criar um arquivo de texto com informações de geração junto a cada imagem gerada.",
     "Save a copy of image before doing face restoration.": "Salva uma cópia de cada imagem antes do refinamento facial.",
     "Quality for saved jpeg images": "Qualidade das imagens jpeg",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se a imagem PNG for maior que 4MB ou qualquer dimensão mair que 4000, diminuir e salvar uma cópia em JPG",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se a imagem PNG for maior que 4MB ou qualquer dimensão maior que 4000, diminuir e salvar uma cópia em JPG",
     "Use original name for output filename during batch process in extras tab": "Usar o nome original para os arquivos de output durante o processo de levas da aba Extras",
-    "When using 'Save' button, only save a single selected image": "Quando usar o botão 'Salvar', somente salvar as imagens selecionadas.",
+    "When using 'Save' button, only save a single selected image": "Quando usar o botão `Salvar`, somente salvar as imagens selecionadas.",
     "Do not add watermark to images": "Não adicionar marca dágua nas imagens",
     "Paths for saving": "Caminhos para salvar",
     "Output directory for images; if empty, defaults to three directories below": "Diretório de saída para imagens; se deixado em branco, as imagens vao para os seguintes diretórios",
@@ -327,7 +327,7 @@
     "Upscaling": "Ampliando",
     "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tamanho do ladrilho para ampliação ESRGAN. 0 = sem ladrilho.",
     "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sobreposição de azulejo, em pixels, para amplicação  ESRGAN. Valores baixos = linhas de fusão mais aparente.",
-    "Tile size for all SwinIR.": "Tamanho do azulejo para todo SwinIR.",
+    "Tile size for all SwinIR.": "Tamanho do ladrilho para todo SwinIR.",
     "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sobreposição de azulejo, em pixels,  para SwinIR. Valores baixos = junção mais aparente.",
     "LDSR processing steps. Lower = faster": "Steps de processamento LDSR. Menos = rápido",
     "Upscaler for img2img": "Ampliação para img2img",
@@ -348,11 +348,11 @@
     "Stable Diffusion": "Stable Diffusion",
     "Checkpoints to cache in RAM": "Checkpoints para manter no cache da RAM",
     "Hypernetwork strength": "Força da Hypernetwork",
-    "Apply color correction to img2img results to match original colors.": "Aplicar correção de cor nas imagens geradas em img2img, usando a imagem original como base. Recomendação: se marcar essa opção, marcar também a de baixo",
+    "Apply color correction to img2img results to match original colors.": "Aplicar correção de cor nas imagens geradas em img2img, usando a imagem original como base.",
     "Save a copy of image before applying color correction to img2img results": "Salvar uma cópia das imagens geradas em img2img antes de aplicar a correção de cor",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Durante gerações img2img, fazer examente o número de steps definidos na barra (normalmente você faz menos steps com denoising menor).",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Ativar quantização em K samples para resultados mais nítidos e visíveis. Pode alterar seeds ja existentes. Precisa reiniciar para funcionar.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Ênfase: usar parênteses ao redor de palavras (texto de exemplo) para fazer o modelo dar mais atenção par aquela palavra ou frase, e chaves [texto de exemplo] para tirar atenção",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Ênfase: usar parênteses ao redor de palavras (texto de exemplo) para fazer o modelo dar mais atenção para aquela palavra ou frase, e chaves [texto de exemplo] para tirar atenção",
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usar método anterior de implementação de ênfase. Útil para reproduzir seeds antigas.",
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "Faz as amostragens K-diffusion produzirem imagens iguais em lotes quando criando uma única imagem",
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta a coerência por preenchimento apartir da ultima vírgula dentro de n tokens quando usando mais de 75 tokens",
@@ -361,7 +361,7 @@
     "Interrogate Options": "Opções de Interrogatório",
     "Interrogate: keep models in VRAM": "Interrogar: manter modelos na VRAM",
     "Interrogate: use artists from artists.csv": "Interrogar: usa artistas e estilos do documento artists.csv",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogar: incluir classificação de tags de modelo combinando nos resultados(Não tem efeito na interrogação feita por legenda).",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogar: incluir classificação de tags de modelo combinando nos resultados (Não tem efeito na interrogação feita por legenda).",
     "Interrogate: num_beams for BLIP": "Interrogar: num_beams para BLIP",
     "Interrogate: minimum description length (excluding artists, etc..)": "Interrogar: tamanho mínimo da descrição (tirando artistas, etc..)",
     "Interrogate: maximum description length": "Interrogar: tamanho máximo da descrição",
@@ -370,7 +370,7 @@
     "Interrogate: deepbooru sort alphabetically": "Interrogar: organizar deepbooru por ordem alfabética",
     "use spaces for tags in deepbooru": "usar espaços para tags em deepbooru",
     "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "espaço (\\) colchetes em deepbooru (são usados como colchetes ao invés de dar ênfase)",
-    "User interface": "Interface de Usuário",
+    "User interface": "Interface de usuário",
     "Show progressbar": "Mostrar barra de progresso",
     "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostrar a criação de imagens a cada N sampling steps. Em 1 já dá para ver o processo de geração. Marcar como 0 para desativar.",
     "Show previews of all images generated in a batch as a grid": "Mostrar previsualização de todas as imagens geradas em leva numa grade",
@@ -379,7 +379,7 @@
     "Add model hash to generation information": "Adicionar hash do modelo para informação de geração",
     "Add model name to generation information": "Adicionar nome do modelo para informação de geração",
     "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Quando ler parâmetros de texto para a interface (de informações de PNG ou texto copiado), não alterar o modelo/intervalo selecionado.",
-    "Font for image grids that have text": "Fonte para grade de imagenss que tem texto",
+    "Font for image grids that have text": "Fonte para grade de imagens que têm texto",
     "Enable full page image viewer": "Ativar visualizador de página inteira",
     "Show images zoomed in by default in full page image viewer": "Mostrar imagens com zoom por definição no visualizador de página inteira",
     "Show generation progress in window title.": "Mostrar barra de progresso no nome da janela.",
@@ -443,25 +443,25 @@
     "keep whatever was there originally": "manter usando o que estava lá originalmente",
     "fill it with latent space noise": "Preenche com ruídos do espaço latente.",
     "fill it with latent space zeroes": "Preenche com zeros do espaço latente.",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Faz ampliação na região com máscara para atingir a resolução desejada, faz inpainting, faz downscale para voltar na resolução original e cola na imagem original",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Redimensiona a imagem para a resolução desejada. A menos que a altura e a largura correspondam, você obterá uma proporção incorreta.",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Faz ampliação na região com máscara para atingir a resolução desejada, faz inpainting, faz downscale para voltar à resolução original e cola na imagem original",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Redimensiona a imagem para a resolução desejada. A menos que a altura e a largura sejam iguais, você obterá uma proporção incorreta.",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Redimensiona a imagem para que toda a resolução desejada seja preenchida com a imagem. Corta as partes que ficaram pra fora.",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Redimensiona a imagem para que toda a imagem esteja dentro da resolução desejada. Preenche o espaço vazio com as cores da imagem.",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Quantas vezes vai repetir o processamento de uma imagem e usá-la como entrada para a próxima iteração",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Número de vezes que vai repetir o processamento da imagem e usar como entrada para a próxima iteração",
     "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "No modo de loopback, em cada loop a força do denoise é multiplicado por este valor. <1 significa diminuir a variedade para que sua sequência converta em uma imagem fixa. >1 significa aumentar a variedade para que sua sequência se torne cada vez mais caótica.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Para ampliação SD, quanta sobreposição em pixels deve haver entre os ladrilhos. Os ladrilhos se sobrepõem para que, quando forem mesclados de volta em uma imagem, não haja linhas de junção claramente visível.",
-    "A directory on the same machine where the server is running.": "Um diretório na mesma máquina onde o server está rodando.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Para ampliação SD, quantidade de sobreposição em pixels que deve haver entre os ladrilhos. Os ladrilhos se sobrepõem para que, quando forem mesclados de volta em uma imagem, não haja linhas de junção claramente visíveis.",
+    "A directory on the same machine where the server is running.": "Um diretório na mesma máquina onde o servidor está rodando.",
     "Leave blank to save images to the default path.": "Deixar em branco para salvar imagens no caminho padrão.",
     "Result = A * (1 - M) + B * M": "Resultado = A * (1 - M) + B * M",
     "Result = A + (B - C) * M": "Resultado = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "Primeiro e último dígito precisam ser 1. ex:'1, 2, 1'",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "Primeiro e último dígito precisam ser 1. ex:`1, 2, 1`",
     "Path to directory with input images": "Caminho para o diretório com imagens de entrada",
     "Path to directory where to write outputs": "Caminho para o diretório para gravar as saídas",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa essas tags para definir como os nomes dos arquivos sao escolhidos: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; deixe em branco para manter o padrao.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se esta opção estiver marcada, as imagens não vão ter marca d'água. Aviso: se você não quer a marca d'água, você pode estar se envolvendo em comportamentos antiéticos",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa essas tags para definir como os nomes dos subdiretorios e ghrades sao escolhidos: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; deixe em branco para manter o padrao.",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa essas tags para definir como os nomes dos arquivos sao escolhidos: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; deixe em branco para manter o padrão.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se esta opção estiver marcada, as imagens não vão ter marca d`água. Aviso: se você não quer a marca d`água, você pode estar se envolvendo em comportamentos antiéticos",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa essas tags para definir como os nomes dos subdiretorios e grades são escolhidos: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; deixe em branco para manter o padrão.",
     "Restore low quality faces using GFPGAN neural network": "Restaurar rostos de baixa qualidade usando a rede neural GFPGAN",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Esta expressão regular vai retirar palavras do nome do arquivo e vai se juntar usando a opção abaixo em etiquetas usadas em treinamento. Não mexer para manter os nomes como estão.",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Esta expressão regular vai retirar palavras do nome do arquivo e serão juntadas via regex usando a opção abaixo em etiquetas usadas em treinamento. Não mexer para manter os nomes como estão.",
     "This string will be used to join split words into a single line if the option above is enabled.": "Esta string será usada para unir palavras divididas em uma única linha se a opção acima estiver habilitada.",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Lista de nomes de configurações, separados por vírgulas, para configurações que devem ir para a barra de acesso rápido na parte superior, em vez da guia de configuração usual. Veja modules/shared.py para nomes de configuração. Necessita reinicialização para aplicar.",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se este valor for diferente de zero, ele será adicionado à seed e usado para inicializar o RNG para ruídos ao usar amostragens com Tempo Estimado. Você pode usar isso para produzir ainda mais variações de imagens ou pode usar isso para combinar imagens de outro software se souber o que está fazendo."
-- 
cgit v1.2.1


From 3d9dd6c18439b58e16badc281d5560b1a6c53934 Mon Sep 17 00:00:00 2001
From: "Modar M. Alfadly" <modar.alfadly@gmail.com>
Date: Sun, 30 Oct 2022 02:03:15 +0300
Subject: Update ar_AR.json

---
 localizations/ar_AR.json | 102 +++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 98 insertions(+), 4 deletions(-)

diff --git a/localizations/ar_AR.json b/localizations/ar_AR.json
index 271ebb87..abbbcff4 100644
--- a/localizations/ar_AR.json
+++ b/localizations/ar_AR.json
@@ -26,8 +26,21 @@
   "Sampling Steps": "عدد الخطوات",
   "Sampling method": "أسلوب الخطو",
   "Which algorithm to use to produce the image": "Sampler: اسم نظام تحديد طريقة تغيير المسافات بين الخطوات",
+  "Euler a": "Euler a",
   "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral: طريقة مبدعة يمكن أن تنتج صور مختلفة على حسب عدد الخطوات، لا تتغير بعد 30-40 خطوة",
+  "Euler": "Euler",
+  "LMS": "LMS",
+  "Heun": "Heun",
+  "DPM2": "DPM2",
+  "DPM2 a": "DPM2 a",
+  "DPM fast": "DPM fast",
+  "DPM adaptive": "DPM adaptive",
+  "LMS Karras": "LMS Karras",
+  "DPM2 Karras": "DPM2 Karras",
+  "DPM2 a Karras": "DPM2 a Karras",
+  "DDIM": "DDIM",
   "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models: الأفضل في الإنتاج الجزئي",
+  "PLMS": "PLMS",
   "Width": "العرض",
   "Height": "الإرتفاع",
   "Restore faces": "تحسين الوجوه",
@@ -58,6 +71,7 @@
   "Resize seed from height": "إرتفاع الممزوج",
   "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Seed resize from: حدد دقة صورة الممزوج (0: نفس دقة الإنتاج)",
   "Open for Clip Aesthetic!": "تضمين تجميلي",
+  "▼": "▼",
   "Aesthetic weight": "أثر التضمين",
   "Aesthetic steps": "عدد الخطوات",
   "Aesthetic learning rate": "معدل التعلم",
@@ -79,7 +93,6 @@
   "-": "-",
   "or": "أو",
   "Click to Upload": "انقر للرفع",
-  "Prompts": "قائمة الطلبات",
   "X/Y plot": "مصفوفة عوامل",
   "X type": "العامل الأول",
   "Nothing": "لا شيء",
@@ -92,6 +105,8 @@
   "Checkpoint name": "ملف الأوزان",
   "Hypernetwork": "الشبكة الفائقة",
   "Hypernet str.": "قوة الشبكة الفائقة",
+  "Inpainting conditioning mask strength": "قوة قناع الإنتاج الجزئي",
+  "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "حدد مدى صرامة قناع الإنتاج، يصبح القناع شفاف إذا قوته 0 (لا يعمل إلا مع ملفات أوزان الإنتاج الجزئي: inpainting)",
   "Sigma Churn": "العشوائية (Schurn)",
   "Sigma min": "أدنى تشويش (Stmin)",
   "Sigma max": "أقصى تشويش (Stmax)",
@@ -99,6 +114,7 @@
   "Eta": "العامل Eta η",
   "Clip skip": "تخطي آخر طبقات CLIP",
   "Denoising": "المدى",
+  "Cond. Image Mask Weight": "قوة قناع الإنتاج الجزئي",
   "X values": "قيم العامل الأول",
   "Separate values for X axis using commas.": "افصل القيم بفواصل (,) من اليسار إلى اليمين",
   "Y type": "العامل الثاني",
@@ -168,6 +184,12 @@
   "Tile overlap": "تداخل النافذة",
   "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "المكبر ينظر إلى أجزاء الصورة من خلال نافذة لتكبير المحتوى ثم ينتقل إلى الجزء المجاور، يفضل أن يكون هناك تداخل بين كل رقعة لكي لا يكون هناك اختلاف واضح بينهم",
   "Upscaler": "طريقة التكبير",
+  "Lanczos": "Lanczos",
+  "LDSR": "LDSR",
+  "ScuNET GAN": "ScuNET GAN",
+  "ScuNET PSNR": "ScuNET PSNR",
+  "ESRGAN_4x": "ESRGAN_4x",
+  "SwinIR 4x": "SwinIR 4x",
   "Inpaint": "إنتاج جزئي",
   "Draw mask": "ارسم القناع",
   "Upload mask": "ارفع القناع",
@@ -192,6 +214,7 @@
   "GFPGAN visibility": "أثر GFPGAN (محسن وجوه)",
   "CodeFormer visibility": "أثر CodeFormer (محسن وجوه)",
   "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "وزن CodeFormer (يزيد التفاصيل على حساب الجودة)",
+  "Upscale Before Restoring Faces": "كبر قبل تحسين الوجوه",
   "Scale to": "دقة محددة",
   "Crop to fit": "قص الأطراف الزائدة إذا لم تتناسب الأبعاد",
   "Batch Process": "حزمة صور",
@@ -199,7 +222,6 @@
   "A directory on the same machine where the server is running.": "مسار مجلد صور موجود في جهاز الخادم",
   "Leave blank to save images to the default path.": "اتركه فارغا لاستخدام المسار الإفتراضي",
   "Show result images": "اعرض الصور الناتجة",
-  "Open output directory": "افتح مجلد المخرجات",
   "PNG Info": "عوامل الصورة",
   "Send to txt2img": "أرسل لنص إلى صورة",
   "Checkpoint Merger": "مزج الأوزان",
@@ -232,7 +254,41 @@
   "Enter hypernetwork layer structure": "ترتيب مضاعفات عرض الطبقات",
   "1st and last digit must be 1. ex:'1, 2, 1'": "المضاعفين الأول والأخير يجب أن يكونا 1، مثال: 1, 2, 1",
   "Select activation function of hypernetwork": "دالة التنشيط",
+  "linear": "linear",
+  "relu": "relu",
+  "leakyrelu": "leakyrelu",
+  "elu": "elu",
+  "swish": "swish",
+  "tanh": "tanh",
+  "sigmoid": "sigmoid",
+  "celu": "celu",
+  "gelu": "gelu",
+  "glu": "glu",
+  "hardshrink": "hardshrink",
+  "hardsigmoid": "hardsigmoid",
+  "hardtanh": "hardtanh",
+  "logsigmoid": "logsigmoid",
+  "logsoftmax": "logsoftmax",
+  "mish": "mish",
+  "prelu": "prelu",
+  "rrelu": "rrelu",
+  "relu6": "relu6",
+  "selu": "selu",
+  "silu": "silu",
+  "softmax": "softmax",
+  "softmax2d": "softmax2d",
+  "softmin": "softmin",
+  "softplus": "softplus",
+  "softshrink": "softshrink",
+  "softsign": "softsign",
+  "tanhshrink": "tanhshrink",
+  "threshold": "threshold",
   "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "تهيئة الأوزان (استخدم Kaiming مع relu وأمثالها وXavier مع sigmoid وأمثالها)",
+  "Normal": "Normal",
+  "KaimingUniform": "KaimingUniform",
+  "KaimingNormal": "KaimingNormal",
+  "XavierUniform": "XavierUniform",
+  "XavierNormal": "XavierNormal",
   "Add layer normalization": "أضف تسوية الطبقات (LayerNorm)",
   "Use dropout": "استخدم الإسقاط (Dropout)",
   "Overwrite Old Hypernetwork": "استبدل الشبكة الفائقة القديمة",
@@ -393,6 +449,7 @@
   "Add model hash to generation information": "أضف رمز تهشير (Hash) ملف الأوزان لعوامل الإنتاج",
   "Add model name to generation information": "أضف اسم ملف الأوزان لعوامل الإنتاج",
   "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "لا تغير الأوزان المختارة عند قراءة عوامل الإنتاج من صورة أو من ملف",
+  "Send seed when sending prompt or image to other interface": "عند إرسال صورة أو طلب ألحق البذرة أيضا",
   "Font for image grids that have text": "نوع الخط في جداول الصور التي تحتوي على نصوص",
   "Enable full page image viewer": "اسمح بعرض الصور في وضع ملئ الشاشة",
   "Show images zoomed in by default in full page image viewer": "اعرض الصور مقربة عند استخدام وضع ملئ الشاشة",
@@ -400,6 +457,18 @@
   "Quicksettings list": "قائمة الإعدادات السريعة",
   "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "قائمة مقسمة بفواصل لأسماء الإعدادات التي يجب أن تظهر في الأعلى لتسهيل الوصول إليها، انظر إلى modules/shared.py لمعرفة الأسماء، يتطلب إعادة تشغيل",
   "Localization (requires restart)": "اللغة (تتطلب إعادة تشغيل)",
+  "pt_BR": "البرتغالية",
+  "zh_CN": "الصينية",
+  "ko_KR": "الكورية",
+  "fr_FR": "الفرنسية",
+  "ru_RU": "الروسية",
+  "ar_AR": "العربية",
+  "tr_TR": "التركية",
+  "it_IT": "الإيطالية",
+  "ja_JP": "اليابانية",
+  "de_DE": "الألمانية",
+  "zh_TW": "الصينية (تايوان)",
+  "es_ES": "الإسبانية",
   "Sampler parameters": "عوامل أساليب الخطو",
   "Hide samplers in user interface (requires restart)": "اخف أساليب الخطو التالية (يتطلب إعادة تشغيل)",
   "eta (noise multiplier) for DDIM": "العامل Eta η لأسلوب الخطو DDIM",
@@ -420,5 +489,30 @@
   "Request browser notifications": "اطلب تنبيهات المتصفح",
   "Download localization template": "حمل ملف اللغة",
   "Reload custom script bodies (No ui updates, No restart)": "أعد تحميل الأدوات الخاصة (بدون واجهة المستخدم ولا يحتاج إعادة تشغيل)",
-  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعد تشغيل gradio وتحميل الأدوات الخاصة وواجهة المستخدم"
-}
\ No newline at end of file
+  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعد تشغيل gradio وتحميل الأدوات الخاصة وواجهة المستخدم",
+  "⤡": "⤡",
+  "⊞": "⊞",
+  "×": "×",
+  "❮": "❮",
+  "❯": "❯",
+  "•": "•",
+  "Label": "Label",
+  "File": "File",
+  "Image": "Image",
+  "Check progress": "Check progress",
+  "Check progress (first)": "Check progress (first)",
+  "Textbox": "Textbox",
+  "Image for img2img": "Image for img2img",
+  "Image for inpainting with mask": "Image for inpainting with mask",
+  "Mask": "Mask",
+  "Mask mode": "Mask mode",
+  "Masking mode": "Masking mode",
+  "Resize mode": "Resize mode",
+  "Prev batch": "Prev batch",
+  "Next batch": "Next batch",
+  "Refresh page": "Refresh page",
+  "Date to": "Date to",
+  "Number": "Number",
+  "set_index": "set_index",
+  "Checkbox": "Checkbox"
+}
-- 
cgit v1.2.1


From 05a657dd357eaca6940c4775daa946bd33f1167d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 30 Oct 2022 07:36:56 +0300
Subject: fix broken hires fix

---
 modules/processing.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 50343846..947ce6fa 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -686,15 +686,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
+        image_conditioning = self.txt2img_image_conditioning(x)
+
         # GC now before running the next img2img to prevent running out of memory
         x = None
         devices.torch_gc()
 
-        image_conditioning = self.img2img_image_conditioning(
-            decoded_samples, 
-            samples, 
-            decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
-        )
         samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)
 
         return samples
-- 
cgit v1.2.1


From 2f125b0a97fe1d4fbd8e4c922615d2c8dfd723fb Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Sun, 30 Oct 2022 13:07:25 +0800
Subject: =?UTF-8?q?=E6=9B=B4=E6=96=B0=20zh=5FCN.json?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/zh_CN.json | 102 +++++++++++++++++++++++------------------------
 1 file changed, 51 insertions(+), 51 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 94907ada..b3d83707 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -12,16 +12,16 @@
     "Stable Diffusion checkpoint": "Stable Diffusion 模型(ckpt)",
     "txt2img": "文生图",
     "img2img": "图生图",
-    "Extras": "后处理",
-    "PNG Info": "PNG 信息",
-    "Checkpoint Merger": "模型(ckpt)合并工具",
+    "Extras": "更多",
+    "PNG Info": "图片信息",
+    "Checkpoint Merger": "模型(ckpt)合并",
     "Train": "训练",
-    "Create aesthetic embedding": "生成美术风格 embedding",
+    "Create aesthetic embedding": "生成美术风格",
     "Image Browser": "图库浏览器",
     "History": "历史记录",
     "Settings": "设置",
     "Prompt": "提示词",
-    "Negative prompt": "反向提示词",
+    "Negative prompt": "负面提示词",
     "Run": "运行",
     "Skip": "跳过",
     "Interrupt": "中止",
@@ -61,10 +61,10 @@
     "Firstpass height": "第一遍的高度",
     "Denoising strength": "去噪强度",
     "Batch count": "批次",
-    "Batch size": "批量",
+    "Batch size": "数量",
     "CFG Scale": "提示词相关性(CFG Scale)",
     "Seed": "随机种子",
-    "Extra": "额外参数",
+    "Extra": "▼",
     "Variation seed": "差异随机种子",
     "Variation strength": "差异强度",
     "Resize seed from width": "自宽度缩放随机种子",
@@ -81,7 +81,7 @@
     "Slerp angle": "Slerp 角度",
     "Is negative text": "是反向提示词",
     "Script": "脚本",
-    "Embedding to Shareable PNG": "将 Embedding 转换为可分享的 PNG",
+    "Embedding to Shareable PNG": "将 Embedding 转换为可分享的 PNG 图片文件",
     "Prompt matrix": "提示词矩阵",
     "Prompts from file or textbox": "从文本框或文件载入提示词",
     "X/Y plot": "X/Y 图表",
@@ -123,7 +123,7 @@
     "Save": "保存",
     "Send to img2img": ">> 图生图",
     "Send to inpaint": ">> 内补绘制",
-    "Send to extras": ">> 后处理",
+    "Send to extras": ">> 更多",
     "Make Zip when Save?": "保存时生成zip压缩文件?",
     "Textbox": "文本框",
     "Interrogate\nCLIP": "CLIP\n反推提示词",
@@ -153,9 +153,9 @@
     "Input directory": "输入目录",
     "Output directory": "输出目录",
     "Resize mode": "缩放模式",
-    "Just resize": "只缩放",
-    "Crop and resize": "缩放并剪裁",
-    "Resize and fill": "缩放并填充",
+    "Just resize": "拉伸",
+    "Crop and resize": "裁剪",
+    "Resize and fill": "填充",
     "img2img alternative test": "图生图的另一种测试",
     "Loopback": "回送",
     "Outpainting mk2": "外补绘制第二版",
@@ -185,7 +185,6 @@
     "Color variation": "色彩变化",
     "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置图块尺寸(tile size)",
     "Tile overlap": "图块重叠的像素(Tile overlap)",
-    "Upscaler": "放大算法",
     "Lanczos": "Lanczos",
     "LDSR": "LDSR",
     "BSRGAN 4x": "BSRGAN 4x",
@@ -203,6 +202,7 @@
     "Scale to": "指定尺寸缩放",
     "Resize": "缩放",
     "Crop to fit": "裁剪以适应",
+    "Upscaler 1": "放大算法 1",
     "Upscaler 2": "放大算法 2",
     "Upscaler 2 visibility": "放大算法 2 可见度",
     "GFPGAN visibility": "GFPGAN 可见度",
@@ -293,11 +293,11 @@
     "Focal point face weight": "焦点面部权重",
     "Focal point entropy weight": "焦点熵权重",
     "Focal point edges weight": "焦点线条权重",
-    "Create debug image": "生成除错图片",
+    "Create debug image": "生成调试(debug)图片",
     "Preprocess": "预处理",
     "Train an embedding; must specify a directory with a set of 1:1 ratio images": "训练 embedding； 必须指定一组具有 1:1 比例图像的目录",
     "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "训练 embedding 或者 hypernetwork； 必须指定一组具有 1:1 比例图像的目录",
-    "[wiki]": "[wiki]",
+    "[wiki]": "[帮助]",
     "Embedding": "Embedding",
     "Embedding Learning rate": "Embedding 学习率",
     "Hypernetwork Learning rate": "Hypernetwork 学习率",
@@ -308,7 +308,7 @@
     "Max steps": "最大迭代步数",
     "Save an image to log directory every N steps, 0 to disable": "每 N 步保存一个图像到日志目录，0 表示禁用",
     "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步将 embedding 的副本保存到日志目录，0 表示禁用",
-    "Save images with embedding in PNG chunks": "保存图像并在 PNG 文件中嵌入 embedding 文件",
+    "Save images with embedding in PNG chunks": "保存图像并在 PNG 图片文件中嵌入 embedding 文件",
     "Read parameters (prompt, etc...) from txt2img tab when making previews": "进行预览时从文生图选项卡中读取参数（提示词等）",
     "Train Hypernetwork": "训练 Hypernetwork",
     "Train Embedding": "训练 Embedding",
@@ -316,10 +316,10 @@
     "Create images embedding": "生成图集 embedding",
     "txt2img history": "文生图历史记录",
     "img2img history": "图生图历史记录",
-    "extras history": "后处理历史记录",
+    "extras history": "更多选项卡的历史记录",
     "Renew Page": "刷新页面",
-    "extras": "后处理",
-    "favorites": "收藏夹",
+    "extras": "更多",
+    "favorites": "已保存",
     "others": "其他",
     "custom fold": "自定义文件夹",
     "Load": "载入",
@@ -341,7 +341,7 @@
     "keyword": "关键词",
     "Generate Info": "生成信息",
     "File Name": "文件名",
-    "Collect": "收藏",
+    "Collect": "保存",
     "Refresh page": "刷新页面",
     "Date to": "日期至",
     "Number": "数量",
@@ -350,37 +350,37 @@
     "turn_page_switch": "翻页开关",
     "Checkbox": "勾选框",
     "Apply settings": "保存设置",
-    "Saving images/grids": "保存图像/概览图",
+    "Saving images/grids": "保存图像/宫格图",
     "Always save all generated images": "始终保存所有生成的图像",
     "File format for images": "图像的文件格式",
     "Images filename pattern": "图像文件名格式",
     "Add number to filename when saving": "储存的时候在文件名里添加数字",
-    "Always save all generated image grids": "始终保存所有生成的概览图",
-    "File format for grids": "概览图的文件格式",
-    "Add extended info (seed, prompt) to filename when saving grid": "保存概览时将扩展信息（随机种子、提示词）添加到文件名",
-    "Do not save grids consisting of one picture": "只有一张图片时不要保存概览图",
-    "Prevent empty spots in grid (when set to autodetect)": "（在自动检测时）防止概览图中出现空位",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "概览行数； 使用 -1 进行自动检测，使用 0 使其与批量大小相同",
-    "Save text information about generation parameters as chunks to png files": "将有关生成参数的文本信息作为块保存到 png 文件中",
+    "Always save all generated image grids": "始终保存所有生成的宫格图",
+    "File format for grids": "宫格图的文件格式",
+    "Add extended info (seed, prompt) to filename when saving grid": "保存宫格图时将扩展信息（随机种子、提示词）添加到文件名",
+    "Do not save grids consisting of one picture": "只有一张图片时不要保存宫格图",
+    "Prevent empty spots in grid (when set to autodetect)": "（在自动检测时）防止宫格图中出现空位",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "宫格图行数； 使用 -1 进行自动检测，使用 0 使其与批量大小相同",
+    "Save text information about generation parameters as chunks to png files": "将有关生成参数的文本信息作为块保存到 png 图片文件中",
     "Create a text file next to every image with generation parameters.": "保存图像时在每个图像旁边创建一个文本文件储存生成参数",
     "Save a copy of image before doing face restoration.": "在进行面部修复之前保存图像副本",
     "Quality for saved jpeg images": "保存的 jpeg 图像的质量",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 图像大于 4MB 或宽高大于 4000，则缩小并保存副本为 JPG",
-    "Use original name for output filename during batch process in extras tab": "在后处理选项卡中的批量处理过程中使用原始名称作为输出文件名",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 图像大于 4MB 或宽高大于 4000，则缩小并保存副本为 JPG 图片",
+    "Use original name for output filename during batch process in extras tab": "在更多选项卡中的批量处理过程中使用原始名称作为输出文件名",
     "When using 'Save' button, only save a single selected image": "使用“保存”按钮时，只保存一个选定的图像",
     "Do not add watermark to images": "不要给图像加水印",
     "Paths for saving": "保存路径",
     "Output directory for images; if empty, defaults to three directories below": "图像的输出目录； 如果为空，则默认为以下三个目录",
     "Output directory for txt2img images": "文生图的输出目录",
     "Output directory for img2img images": "图生图的输出目录",
-    "Output directory for images from extras tab": "后处理的输出目录",
-    "Output directory for grids; if empty, defaults to two directories below": "概览图的输出目录； 如果为空，则默认为以下两个目录",
-    "Output directory for txt2img grids": "文生图概览的输出目录",
-    "Output directory for img2img grids": "图生图概览的输出目录",
+    "Output directory for images from extras tab": "更多选项卡的输出目录",
+    "Output directory for grids; if empty, defaults to two directories below": "宫格图的输出目录； 如果为空，则默认为以下两个目录",
+    "Output directory for txt2img grids": "文生图宫格的输出目录",
+    "Output directory for img2img grids": "图生图宫格的输出目录",
     "Directory for saving images using the Save button": "使用“保存”按钮保存图像的目录",
     "Saving to a directory": "保存到目录",
     "Save images to a subdirectory": "将图像保存到子目录",
-    "Save grids to a subdirectory": "将概览图保存到子目录",
+    "Save grids to a subdirectory": "将宫格图保存到子目录",
     "When using \"Save\" button, save images to a subdirectory": "使用“保存”按钮时，将图像保存到子目录",
     "Directory name pattern": "目录名称格式",
     "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示词数量",
@@ -405,7 +405,7 @@
     "Filename word regex": "文件名用词的正则表达式",
     "Filename join string": "文件名连接用字符串",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每个 epoch 中单个输入图像的重复次数； 仅用于显示 epoch 数",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步保存一个包含 loss 的 csv 到日志目录，0 表示禁用",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步保存一个包含 loss 的 csv 表格到日志目录，0 表示禁用",
     "Stable Diffusion": "Stable Diffusion",
     "Checkpoints to cache in RAM": "缓存在内存(RAM)中的模型(ckpt)",
     "Hypernetwork strength": "Hypernetwork 强度",
@@ -418,7 +418,7 @@
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。可用于复现旧随机种子",
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 采样器批量生成与生成单个图像时产出相同的图像",
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个 token 时，通过从 n 个 token 中的最后一个逗号填补来提高一致性",
-    "Filter NSFW content": "过滤成人内容",
+    "Filter NSFW content": "过滤成人内容(NSFW)",
     "Stop At last layers of CLIP model": "在 CLIP 模型的最后哪一层停下",
     "Interrogate Options": "反推提示词选项",
     "Interrogate: keep models in VRAM": "反推: 将模型保存在显存(VRAM)中",
@@ -436,18 +436,18 @@
     "Show progressbar": "显示进度条",
     "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 个采样迭代步数显示图像生成进度。设置 0 禁用",
     "Show previews of all images generated in a batch as a grid": "以网格的形式预览所有批量生成出来的图像",
-    "Show grid in results for web": "在网页的结果中显示概览图",
+    "Show grid in results for web": "在网页的结果中显示宫格图",
     "Do not show any images in results for web": "不在网页的结果中显示任何图像",
     "Add model hash to generation information": "将模型的哈希值添加到生成信息",
     "Add model name to generation information": "将模型名称添加到生成信息",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "当从文本读取生成参数到 UI（从 PNG 信息或粘贴文本）时，不要更改选定的模型(ckpt)",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "从文本读取生成参数到 UI（从 PNG 图片信息或粘贴文本）时，不要更改选定的模型(ckpt)",
     "Send seed when sending prompt or image to other interface": "将提示词或者图片传送到其他界面的时候同时也把随机种子传送过去",
-    "Font for image grids that have text": "有文字的概览图使用的字体",
+    "Font for image grids that have text": "有文字的宫格图使用的字体",
     "Enable full page image viewer": "启用整页图像查看器",
     "Show images zoomed in by default in full page image viewer": "在整页图像查看器中默认放大显示图像",
     "Show generation progress in window title.": "在窗口标题中显示生成进度",
     "Quicksettings list": "快速设置列表",
-    "Localization (requires restart)": "本地化（需要重新启动）",
+    "Localization (requires restart)": "本地化翻译（需要保存设置，并重启Gradio）",
     "Sampler parameters": "采样器参数",
     "Hide samplers in user interface (requires restart)": "在用户界面中隐藏采样器（需要重新启动）",
     "eta (noise multiplier) for DDIM": "DDIM 的 eta (噪声乘数) ",
@@ -472,8 +472,8 @@
     "Download localization template": "下载本地化模板",
     "Reload custom script bodies (No ui updates, No restart)": "重新加载自定义脚本主体（无 ui 更新，无重启）",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重启 Gradio 及刷新组件（仅限自定义脚本、ui.py、js 和 css）",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示词（按 Ctrl+Enter 或 Alt+Enter 生成）",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示词（按 Ctrl+Enter 或 Alt+Enter 生成）",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nPrompt",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nNegative prompt",
     "Add a random artist to the prompt.": "随机添加一个艺术家到提示词中",
     "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
     "Save style": "储存为模版风格",
@@ -500,14 +500,14 @@
     "This text is used to rotate the feature space of the imgs embs": "此文本用于旋转图集 embeddings 的特征空间",
     "Separate values for X axis using commas.": "使用逗号分隔 X 轴的值",
     "Separate values for Y axis using commas.": "使用逗号分隔 Y 轴的值",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "将图像写入目录（默认 - log/images）并将生成参数写入 csv 文件",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "将图像写入目录（默认 - log/images）并将生成参数写入 csv 表格文件",
     "Open images output directory": "打开图像输出目录",
     "How much to blur the mask before processing, in pixels.": "处理前要对蒙版进行多强的模糊，以像素为单位",
     "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 处理蒙版区域之前要在蒙版区域内放置什么",
-    "fill it with colors of the image": "用图像的颜色填充它",
-    "keep whatever was there originally": "保留原来的东西",
-    "fill it with latent space noise": "用潜空间的噪声填充它",
-    "fill it with latent space zeroes": "用潜空间的零填充它",
+    "fill it with colors of the image": "用图像的颜色(模糊/马赛克)填充它",
+    "keep whatever was there originally": "保留原来的图像，不进行预处理",
+    "fill it with latent space noise": "用潜空间的噪声(随机彩色噪点)填充它",
+    "fill it with latent space zeroes": "用潜空间的零(灰色)填充它",
     "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做内补绘制，缩小后粘贴到原始图像中",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
@@ -526,7 +526,7 @@
     "Input images directory": "输入图像目录",
     "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像的文件名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
     "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果启用此选项，水印将不会添加到生成出来的图像中。警告：如果你不添加水印，你的行为可能是不符合专业操守的",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像和概览图的子目录: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像和宫格图的子目录: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
     "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络修复低质量面部",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。留空以保持文件名文本不变",
     "This string will be used to join split words into a single line if the option above is enabled.": "如果启用了上述选项，则此处的字符会用于将拆分的单词接合为同一行",
@@ -536,6 +536,6 @@
     "Enable Autocomplete": "开启Tag补全",
     "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
     "Roll three": "抽三位出来",
-    "Generate forever": "不停地生成",
-    "Cancel generate forever": "取消不停地生成"
+    "Generate forever": "无限生成",
+    "Cancel generate forever": "停止无限生成"
 }
-- 
cgit v1.2.1


From 59dfe0845d964868e92572c78a420b6d68c46ea4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 30 Oct 2022 08:22:44 +0300
Subject: launch tests from launch.py with --tests commandline argument

---
 .gitignore           |  2 ++
 launch.py            | 19 +++++++++++++++++++
 run_tests.bat        | 15 ---------------
 test/extras_test.py  |  6 +++---
 test/img2img_test.py | 12 ++++++------
 test/server_poll.py  | 28 +++++++++++++++-------------
 test/txt2img_test.py |  8 +++++---
 7 files changed, 50 insertions(+), 40 deletions(-)
 delete mode 100644 run_tests.bat

diff --git a/.gitignore b/.gitignore
index 8fa05852..ee53044c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -29,3 +29,5 @@ notification.mp3
 /textual_inversion
 .vscode
 /extensions
+/test/stdout.txt
+/test/stderr.txt
diff --git a/launch.py b/launch.py
index 8affd410..33f98343 100644
--- a/launch.py
+++ b/launch.py
@@ -128,10 +128,12 @@ def prepare_enviroment():
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
     sys.argv += shlex.split(commandline_args)
+    test_argv = [x for x in sys.argv if x != '--tests']
 
     sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
     sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
     sys.argv, update_check = extract_arg(sys.argv, '--update-check')
+    sys.argv, run_tests = extract_arg(sys.argv, '--tests')
     xformers = '--xformers' in sys.argv
     deepdanbooru = '--deepdanbooru' in sys.argv
     ngrok = '--ngrok' in sys.argv
@@ -194,6 +196,23 @@ def prepare_enviroment():
         print("Exiting because of --exit argument")
         exit(0)
 
+    if run_tests:
+        tests(test_argv)
+        exit(0)
+
+
+def tests(argv):
+    print(f"Launching Web UI in another process for testing with arguments: {' '.join(argv[1:])}")
+
+    with open('test/stdout.txt', "w", encoding="utf8") as stdout, open('test/stderr.txt', "w", encoding="utf8") as stderr:
+        proc = subprocess.Popen([sys.executable, *argv], stdout=stdout, stderr=stderr)
+
+    import test.server_poll
+    test.server_poll.run_tests()
+
+    print(f"Stopping Web UI process with id {proc.pid}")
+    proc.kill()
+
 
 def start_webui():
     print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}")
diff --git a/run_tests.bat b/run_tests.bat
deleted file mode 100644
index 3a63f034..00000000
--- a/run_tests.bat
+++ /dev/null
@@ -1,15 +0,0 @@
-@echo off
-set ERROR_REPORTING=FALSE
-set COMMANDLINE_ARGS= --api
-echo Launching SDWebUI...
-start "SDWebUITest" webui.bat
-
-if not defined PYTHON (set PYTHON=python)
-if not defined VENV_DIR (set VENV_DIR=venv)
-set PYTHON="%~dp0%VENV_DIR%\Scripts\Python.exe"
-%PYTHON% test/server_poll.py
-for /f "tokens=2 delims=," %%a in ('tasklist /v /fo csv ^| findstr /i "SDWebUITest"') do set "$PID=%%a"
-
-taskkill /PID %$PID% >nul 2>&1
-
-pause
diff --git a/test/extras_test.py b/test/extras_test.py
index 2e1764d9..9b8ce0f0 100644
--- a/test/extras_test.py
+++ b/test/extras_test.py
@@ -1,7 +1,5 @@
 import unittest
-import requests
-from gradio.processing_utils import encode_pil_to_base64
-from PIL import Image
+
 
 class TestExtrasWorking(unittest.TestCase):
     def setUp(self):
@@ -22,8 +20,10 @@ class TestExtrasWorking(unittest.TestCase):
             "image": ""
             }
 
+
 class TestExtrasCorrectness(unittest.TestCase):
     pass
 
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/test/img2img_test.py b/test/img2img_test.py
index 61e3e285..012a9580 100644
--- a/test/img2img_test.py
+++ b/test/img2img_test.py
@@ -3,13 +3,12 @@ import requests
 from gradio.processing_utils import encode_pil_to_base64
 from PIL import Image
 
+
 class TestImg2ImgWorking(unittest.TestCase):
     def setUp(self):
         self.url_img2img = "http://localhost:7860/sdapi/v1/img2img"
         self.simple_img2img = {
-            "init_images": [
-                encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))
-                ],
+            "init_images": [encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))],
             "resize_mode": 0,
             "denoising_strength": 0.75,
             "mask": None,
@@ -19,9 +18,7 @@ class TestImg2ImgWorking(unittest.TestCase):
             "inpaint_full_res_padding": 0,
             "inpainting_mask_invert": 0,
             "prompt": "example prompt",
-            "styles": [
-                ""
-                ],
+            "styles": [],
             "seed": -1,
             "subseed": -1,
             "subseed_strength": 0,
@@ -45,6 +42,7 @@ class TestImg2ImgWorking(unittest.TestCase):
             "sampler_index": "Euler a",
             "include_init_images": False
             }
+
     def test_img2img_simple_performed(self):
         self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
 
@@ -52,8 +50,10 @@ class TestImg2ImgWorking(unittest.TestCase):
         self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png"))
         self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
 
+
 class TestImg2ImgCorrectness(unittest.TestCase):
     pass
 
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/test/server_poll.py b/test/server_poll.py
index 8c0436f8..eeefb7eb 100644
--- a/test/server_poll.py
+++ b/test/server_poll.py
@@ -2,16 +2,18 @@ import unittest
 import requests
 import time
 
-timeout_threshold = 240
-start_time = time.time()
-while time.time()-start_time < timeout_threshold:
-    try:
-        requests.head("http://localhost:7860/")
-        break
-    except requests.exceptions.ConnectionError:
-        pass
-if time.time()-start_time < timeout_threshold:
-    suite = unittest.TestLoader().discover('', pattern='*_test.py')
-    result = unittest.TextTestRunner(verbosity=2).run(suite)
-else:
-    print("Launch unsuccessful")
+
+def run_tests():
+    timeout_threshold = 240
+    start_time = time.time()
+    while time.time()-start_time < timeout_threshold:
+        try:
+            requests.head("http://localhost:7860/")
+            break
+        except requests.exceptions.ConnectionError:
+            pass
+    if time.time()-start_time < timeout_threshold:
+        suite = unittest.TestLoader().discover('', pattern='*_test.py')
+        result = unittest.TextTestRunner(verbosity=2).run(suite)
+    else:
+        print("Launch unsuccessful")
diff --git a/test/txt2img_test.py b/test/txt2img_test.py
index ad27686a..1936e07e 100644
--- a/test/txt2img_test.py
+++ b/test/txt2img_test.py
@@ -1,6 +1,7 @@
 import unittest
 import requests
 
+
 class TestTxt2ImgWorking(unittest.TestCase):
     def setUp(self):
         self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
@@ -10,9 +11,7 @@ class TestTxt2ImgWorking(unittest.TestCase):
             "firstphase_width": 0,
             "firstphase_height": 0,
             "prompt": "example prompt",
-            "styles": [
-            ""
-            ],
+            "styles": [],
             "seed": -1,
             "subseed": -1,
             "subseed_strength": 0,
@@ -34,6 +33,7 @@ class TestTxt2ImgWorking(unittest.TestCase):
             "s_noise": 1,
             "sampler_index": "Euler a"
         }
+
     def test_txt2img_simple_performed(self):
         self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
@@ -65,8 +65,10 @@ class TestTxt2ImgWorking(unittest.TestCase):
         self.simple_txt2img["n_iter"] = 2
         self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
+
 class TestTxt2ImgCorrectness(unittest.TestCase):
     pass
 
+
 if __name__ == "__main__":
     unittest.main()
-- 
cgit v1.2.1


From 5a6e0cfba675c0f11ade7124cbeec1356c77beb2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 30 Oct 2022 08:28:36 +0300
Subject: always add --api when running tests

---
 launch.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/launch.py b/launch.py
index 33f98343..958336f2 100644
--- a/launch.py
+++ b/launch.py
@@ -202,6 +202,9 @@ def prepare_enviroment():
 
 
 def tests(argv):
+    if "--api" not in argv:
+        argv.append("--api")
+
     print(f"Launching Web UI in another process for testing with arguments: {' '.join(argv[1:])}")
 
     with open('test/stdout.txt', "w", encoding="utf8") as stdout, open('test/stderr.txt', "w", encoding="utf8") as stderr:
-- 
cgit v1.2.1


From 61836bd544fc8f4ef62f311c9d5964fbdaeb3f4c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 30 Oct 2022 08:48:53 +0300
Subject: shorten Hypernetwork strength in infotext and omit it when it's the
 default value.

---
 modules/generation_parameters_copypaste.py | 2 +-
 modules/processing.py                      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 59c6d7da..df70c728 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -66,7 +66,7 @@ def integrate_settings_paste_fields(component_dict):
 
     settings_map = {
         'sd_hypernetwork': 'Hypernet',
-        'sd_hypernetwork_strength': 'Hypernetwork strength',
+        'sd_hypernetwork_strength': 'Hypernet strength',
         'CLIP_stop_at_last_layers': 'Clip skip',
         'sd_model_checkpoint': 'Model hash',
     }
diff --git a/modules/processing.py b/modules/processing.py
index ecaa78e2..b1df4918 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -396,7 +396,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
         "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
-        "Hypernetwork strength": (None if shared.loaded_hypernetwork is None else shared.opts.sd_hypernetwork_strength),
+        "Hypernet strength": (None if shared.loaded_hypernetwork is None or shared.opts.sd_hypernetwork_strength >= 1 else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
-- 
cgit v1.2.1


From 149784202cca8612b43629c601ee27cfda64e623 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 30 Oct 2022 09:10:22 +0300
Subject: rework #3722 to not introduce duplicate code

---
 modules/api/api.py | 43 +++++++++++++------------------------------
 modules/shared.py  | 22 +++++++++++++++++++---
 webui.py           | 19 +++----------------
 3 files changed, 35 insertions(+), 49 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 5c5b210f..6c06d449 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -9,31 +9,6 @@ from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusion
 from modules.sd_samplers import all_samplers
 from modules.extras import run_extras, run_pnginfo
 
-# copy from wrap_gradio_gpu_call of webui.py
-# because queue lock will be acquired in api handlers
-# and time start needs to be set
-# the function has been modified into two parts
-
-def before_gpu_call():
-    devices.torch_gc()
-
-    shared.state.sampling_step = 0
-    shared.state.job_count = -1
-    shared.state.job_no = 0
-    shared.state.job_timestamp = shared.state.get_job_timestamp()
-    shared.state.current_latent = None
-    shared.state.current_image = None
-    shared.state.current_image_sampling_step = 0
-    shared.state.skipped = False
-    shared.state.interrupted = False
-    shared.state.textinfo = None
-    shared.state.time_start = time.time()
-
-def after_gpu_call():
-    shared.state.job = ""
-    shared.state.job_count = 0
-
-    devices.torch_gc()
 
 def upscaler_to_index(name: str):
     try:
@@ -41,8 +16,10 @@ def upscaler_to_index(name: str):
     except:
         raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
 
+
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
+
 def setUpscalers(req: dict):
     reqDict = vars(req)
     reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1)
@@ -51,6 +28,7 @@ def setUpscalers(req: dict):
     reqDict.pop('upscaler_2')
     return reqDict
 
+
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
@@ -78,10 +56,13 @@ class Api:
         )
         p = StableDiffusionProcessingTxt2Img(**vars(populate))
         # Override object param
-        before_gpu_call()
+
+        shared.state.begin()
+
         with self.queue_lock:
             processed = process_images(p)
-        after_gpu_call()
+
+        shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images))
 
@@ -119,11 +100,13 @@ class Api:
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
-        # Override object param
-        before_gpu_call()
+
+        shared.state.begin()
+
         with self.queue_lock:
             processed = process_images(p)
-        after_gpu_call()
+
+        shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images))
 
diff --git a/modules/shared.py b/modules/shared.py
index f7b0990c..e4f163c1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -144,9 +144,6 @@ class State:
         self.sampling_step = 0
         self.current_image_sampling_step = 0
 
-    def get_job_timestamp(self):
-        return datetime.datetime.now().strftime("%Y%m%d%H%M%S")  # shouldn't this return job_timestamp?
-
     def dict(self):
         obj = {
             "skipped": self.skipped,
@@ -160,6 +157,25 @@ class State:
 
         return obj
 
+    def begin(self):
+        self.sampling_step = 0
+        self.job_count = -1
+        self.job_no = 0
+        self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+        self.current_latent = None
+        self.current_image = None
+        self.current_image_sampling_step = 0
+        self.skipped = False
+        self.interrupted = False
+        self.textinfo = None
+
+        devices.torch_gc()
+
+    def end(self):
+        self.job = ""
+        self.job_count = 0
+
+        devices.torch_gc()
 
 state = State()
 
diff --git a/webui.py b/webui.py
index ade7334b..29530872 100644
--- a/webui.py
+++ b/webui.py
@@ -46,26 +46,13 @@ def wrap_queued_call(func):
 
 def wrap_gradio_gpu_call(func, extra_outputs=None):
     def f(*args, **kwargs):
-        devices.torch_gc()
-
-        shared.state.sampling_step = 0
-        shared.state.job_count = -1
-        shared.state.job_no = 0
-        shared.state.job_timestamp = shared.state.get_job_timestamp()
-        shared.state.current_latent = None
-        shared.state.current_image = None
-        shared.state.current_image_sampling_step = 0
-        shared.state.skipped = False
-        shared.state.interrupted = False
-        shared.state.textinfo = None
+
+        shared.state.begin()
 
         with queue_lock:
             res = func(*args, **kwargs)
 
-        shared.state.job = ""
-        shared.state.job_count = 0
-
-        devices.torch_gc()
+        shared.state.end()
 
         return res
 
-- 
cgit v1.2.1


From 71571e3f055237d71ba2d47756846ad1d73be00c Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Sun, 30 Oct 2022 00:35:40 -0700
Subject: Replaced master branch fix with updated fix.

---
 modules/processing.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 3dd44d3a..512c484f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -688,8 +688,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
-        image_conditioning = self.txt2img_image_conditioning(x)
-
         # GC now before running the next img2img to prevent running out of memory
         x = None
         devices.torch_gc()
-- 
cgit v1.2.1


From be27fd4690b1eb6c74da1e31c9696a0f1901fbba Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 17:01:01 +0800
Subject: fix broken progress api by previous rework

---
 modules/shared.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/shared.py b/modules/shared.py
index e4f163c1..2c7d28a5 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -4,6 +4,7 @@ import json
 import os
 import sys
 from collections import OrderedDict
+import time
 
 import gradio as gr
 import tqdm
@@ -132,6 +133,7 @@ class State:
     current_image = None
     current_image_sampling_step = 0
     textinfo = None
+    time_start = None
 
     def skip(self):
         self.skipped = True
@@ -168,6 +170,7 @@ class State:
         self.skipped = False
         self.interrupted = False
         self.textinfo = None
+        self.time_start = time.time()
 
         devices.torch_gc()
 
-- 
cgit v1.2.1


From 1a4ff2de6a835cd8cc1590bbc1a8dedb5ad37e5b Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 17:02:47 +0800
Subject: fix current image in progress api when parallel processing enabled

---
 modules/api/api.py | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 6c06d449..97497f3f 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -3,10 +3,9 @@ import uvicorn
 from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
 from fastapi import APIRouter, Depends, HTTPException
 import modules.shared as shared
-from modules import devices
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers
+from modules.sd_samplers import all_samplers, sample_to_image, samples_to_image_grid
 from modules.extras import run_extras, run_pnginfo
 
 
@@ -170,6 +169,16 @@ class Api:
 
         progress = min(progress, 1)
 
+        # copy from check_progress_call of ui.py
+
+        if shared.parallel_processing_allowed:
+            if shared.state.sampling_step - shared.state.current_image_sampling_step >= shared.opts.show_progress_every_n_steps and shared.state.current_latent is not None:
+                if shared.opts.show_progress_grid:
+                    shared.state.current_image = samples_to_image_grid(shared.state.current_latent)
+                else:
+                    shared.state.current_image = sample_to_image(shared.state.current_latent)
+                shared.state.current_image_sampling_step = shared.state.sampling_step
+
         current_image = None
         if shared.state.current_image and not req.skip_current_image:
             current_image = encode_pil_to_base64(shared.state.current_image)
-- 
cgit v1.2.1


From b5e21e3348163f4a17d4a2e6f97af2c370edc9b3 Mon Sep 17 00:00:00 2001
From: batvbs <60730393+batvbs@users.noreply.github.com>
Date: Sun, 30 Oct 2022 17:49:17 +0800
Subject: Update zh_CN.json

---
 localizations/zh_CN.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index b3d83707..d07c03a7 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -21,7 +21,7 @@
     "History": "历史记录",
     "Settings": "设置",
     "Prompt": "提示词",
-    "Negative prompt": "负面提示词",
+    "Negative prompt": "反向提示词",
     "Run": "运行",
     "Skip": "跳过",
     "Interrupt": "中止",
-- 
cgit v1.2.1


From 34c86c12b0a9d650d4e7c5be478bca34ad8ed048 Mon Sep 17 00:00:00 2001
From: Martin Cairns <4314538+MartinCairnsSQL@users.noreply.github.com>
Date: Sun, 30 Oct 2022 11:04:27 +0000
Subject: Include PLMS in adjust steps as it also can fail in the same way

---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index aca014e8..8772db56 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -208,7 +208,7 @@ class VanillaStableDiffusionSampler:
 
 
     def adjust_steps_if_invalid(self, p, num_steps):
-        if self.config.name == 'DDIM' and p.ddim_discretize == 'uniform':
+        if  (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
             valid_step = 999 / (1000 // num_steps)
             if valid_step == floor(valid_step):
                 return int(valid_step) + 1
-- 
cgit v1.2.1


From 99c4e8d65357ebd9e3eab95e7c6de4a86f125c1a Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Sun, 30 Oct 2022 19:36:01 +0800
Subject: =?UTF-8?q?=E9=95=BF=E6=96=87=E6=9C=AC=E6=B7=BB=E5=8A=A0=E9=80=97?=
 =?UTF-8?q?=E5=8F=B7?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/zh_CN.json | 50 ++++++++++++++++++++++++------------------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index d07c03a7..5472535e 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -60,8 +60,8 @@
     "Firstpass width": "第一遍的宽度",
     "Firstpass height": "第一遍的高度",
     "Denoising strength": "去噪强度",
-    "Batch count": "批次",
-    "Batch size": "数量",
+    "Batch count": "生成批次",
+    "Batch size": "每批数量",
     "CFG Scale": "提示词相关性(CFG Scale)",
     "Seed": "随机种子",
     "Extra": "▼",
@@ -224,7 +224,7 @@
     "Add difference": "添加差分",
     "Save as float16": "以 float16 储存",
     "See": "查看",
-    "wiki": "wiki",
+    "wiki": "帮助",
     "for detailed explanation.": "以了解详细说明",
     "Create embedding": "生成 embedding",
     "Create aesthetic images embedding": "生成美术风格图集 embedding",
@@ -308,8 +308,8 @@
     "Max steps": "最大迭代步数",
     "Save an image to log directory every N steps, 0 to disable": "每 N 步保存一个图像到日志目录，0 表示禁用",
     "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步将 embedding 的副本保存到日志目录，0 表示禁用",
-    "Save images with embedding in PNG chunks": "保存图像并在 PNG 图片文件中嵌入 embedding 文件",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "进行预览时从文生图选项卡中读取参数（提示词等）",
+    "Save images with embedding in PNG chunks": "保存图像，并在 PNG 图片文件中嵌入 embedding 文件",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "进行预览时，从文生图选项卡中读取参数（提示词等）",
     "Train Hypernetwork": "训练 Hypernetwork",
     "Train Embedding": "训练 Embedding",
     "Create an aesthetic embedding out of any number of images": "从任意数量的图像中创建美术风格 embedding",
@@ -357,16 +357,16 @@
     "Add number to filename when saving": "储存的时候在文件名里添加数字",
     "Always save all generated image grids": "始终保存所有生成的宫格图",
     "File format for grids": "宫格图的文件格式",
-    "Add extended info (seed, prompt) to filename when saving grid": "保存宫格图时将扩展信息（随机种子、提示词）添加到文件名",
+    "Add extended info (seed, prompt) to filename when saving grid": "保存宫格图时，将扩展信息（随机种子、提示词）添加到文件名",
     "Do not save grids consisting of one picture": "只有一张图片时不要保存宫格图",
-    "Prevent empty spots in grid (when set to autodetect)": "（在自动检测时）防止宫格图中出现空位",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "宫格图行数； 使用 -1 进行自动检测，使用 0 使其与批量大小相同",
-    "Save text information about generation parameters as chunks to png files": "将有关生成参数的文本信息作为块保存到 png 图片文件中",
-    "Create a text file next to every image with generation parameters.": "保存图像时在每个图像旁边创建一个文本文件储存生成参数",
+    "Prevent empty spots in grid (when set to autodetect)": "（启用自动检测时）防止宫格图中出现空位",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "宫格图行数； 使用 -1 进行自动检测，使用 0 使其与每批数量相同",
+    "Save text information about generation parameters as chunks to png files": "将有关生成参数的文本信息，作为块保存到 png 图片文件中",
+    "Create a text file next to every image with generation parameters.": "保存图像时，在每个图像旁边创建一个文本文件储存生成参数",
     "Save a copy of image before doing face restoration.": "在进行面部修复之前保存图像副本",
     "Quality for saved jpeg images": "保存的 jpeg 图像的质量",
     "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 图像大于 4MB 或宽高大于 4000，则缩小并保存副本为 JPG 图片",
-    "Use original name for output filename during batch process in extras tab": "在更多选项卡中的批量处理过程中使用原始名称作为输出文件名",
+    "Use original name for output filename during batch process in extras tab": "在更多选项卡中的批量处理过程中，使用原始名称作为输出文件名",
     "When using 'Save' button, only save a single selected image": "使用“保存”按钮时，只保存一个选定的图像",
     "Do not add watermark to images": "不要给图像加水印",
     "Paths for saving": "保存路径",
@@ -391,12 +391,12 @@
     "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR 的图块重叠(Tile overlap)像素。低值 = 可见接缝",
     "LDSR processing steps. Lower = faster": "LDSR 处理迭代步数。更低 = 更快",
     "Upscaler for img2img": "图生图的放大算法",
-    "Upscale latent space image when doing hires. fix": "做高分辨率修复时也放大潜空间图像",
+    "Upscale latent space image when doing hires. fix": "做高分辨率修复时，也放大潜空间图像",
     "Face restoration": "面部修复",
     "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 权重参数； 0 = 最大效果； 1 = 最小效果",
-    "Move face restoration model from VRAM into RAM after processing": "面部修复处理完成后将面部修复模型从显存(VRAM)移至内存(RAM)",
+    "Move face restoration model from VRAM into RAM after processing": "面部修复处理完成后，将面部修复模型从显存(VRAM)移至内存(RAM)",
     "System": "系统",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成图像时每秒轮询显存(VRAM)使用情况的次数。设置为 0 以禁用",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成图像时，每秒轮询显存(VRAM)使用情况的次数。设置为 0 以禁用",
     "Always print all generation info to standard output": "始终将所有生成信息输出到 standard output (一般为控制台)",
     "Add a second progress bar to the console that shows progress for an entire job.": "向控制台添加第二个进度条，显示整个作业的进度",
     "Training": "训练",
@@ -416,7 +416,7 @@
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。这可能会改变现有的随机种子。需要重新启动才能应用",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。可用于复现旧随机种子",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 采样器批量生成与生成单个图像时产出相同的图像",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 采样器 批量生成与生成单个图像时，产出相同的图像",
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个 token 时，通过从 n 个 token 中的最后一个逗号填补来提高一致性",
     "Filter NSFW content": "过滤成人内容(NSFW)",
     "Stop At last layers of CLIP model": "在 CLIP 模型的最后哪一层停下",
@@ -435,16 +435,16 @@
     "User interface": "用户界面",
     "Show progressbar": "显示进度条",
     "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 个采样迭代步数显示图像生成进度。设置 0 禁用",
-    "Show previews of all images generated in a batch as a grid": "以网格的形式预览所有批量生成出来的图像",
+    "Show previews of all images generated in a batch as a grid": "以网格的形式，预览批量生成的所有图像",
     "Show grid in results for web": "在网页的结果中显示宫格图",
     "Do not show any images in results for web": "不在网页的结果中显示任何图像",
     "Add model hash to generation information": "将模型的哈希值添加到生成信息",
     "Add model name to generation information": "将模型名称添加到生成信息",
     "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "从文本读取生成参数到 UI（从 PNG 图片信息或粘贴文本）时，不要更改选定的模型(ckpt)",
-    "Send seed when sending prompt or image to other interface": "将提示词或者图片传送到其他界面的时候同时也把随机种子传送过去",
+    "Send seed when sending prompt or image to other interface": "将提示词或者图片发送到 >> 其他界面时，把随机种子也传送过去",
     "Font for image grids that have text": "有文字的宫格图使用的字体",
     "Enable full page image viewer": "启用整页图像查看器",
-    "Show images zoomed in by default in full page image viewer": "在整页图像查看器中默认放大显示图像",
+    "Show images zoomed in by default in full page image viewer": "在整页图像查看器中，默认放大显示图像",
     "Show generation progress in window title.": "在窗口标题中显示生成进度",
     "Quicksettings list": "快速设置列表",
     "Localization (requires restart)": "本地化翻译（需要保存设置，并重启Gradio）",
@@ -460,7 +460,7 @@
     "sigma noise": "sigma 噪声",
     "Eta noise seed delta": "Eta 噪声种子偏移(noise seed delta)",
     "Images Browser": "图库浏览器",
-    "Preload images at startup": "在启动时预载图像",
+    "Preload images at startup": "在启动时预加载图像",
     "Number of columns on the page": "每页列数",
     "Number of rows on the page": "每页行数",
     "Number of pictures displayed on each page": "每页显示的图像数量",
@@ -478,15 +478,15 @@
     "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
     "Save style": "储存为模版风格",
     "Apply selected styles to current prompt": "将所选样式应用于当前提示",
-    "Stop processing current image and continue processing.": "停止处理当前图像并继续处理下一个",
-    "Stop processing images and return any results accumulated so far.": "停止处理图像并返回迄今为止累积的任何结果",
+    "Stop processing current image and continue processing.": "停止处理当前图像，并继续处理下一个",
+    "Stop processing images and return any results accumulated so far.": "停止处理图像，并返回迄今为止累积的任何结果",
     "Style to apply; styles have components for both positive and negative prompts and apply to both": "要应用的模版风格； 模版风格包含正向和反向提示词，并应用于两者",
     "Do not do anything special": "什么都不做",
     "Which algorithm to use to produce the image": "使用哪种算法生成图像",
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图像，将迭代步数设置为高于 30-40 不会有正面作用",
     "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长内补绘制",
     "Produce an image that can be tiled.": "生成可用于平铺(tiled)的图像",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候以较小的分辨率生成初步图像、接着放大图像，然后在不更改构图的情况下改进其中的细节",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候，以较小的分辨率生成初步图像、接着放大图像，然后在不更改构图的情况下改进其中的细节",
     "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "决定算法对图像内容的影响程度。设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
     "How many batches of images to create": "创建多少批次的图像",
     "How many image to create in a single batch": "每批创建多少图像",
@@ -504,10 +504,10 @@
     "Open images output directory": "打开图像输出目录",
     "How much to blur the mask before processing, in pixels.": "处理前要对蒙版进行多强的模糊，以像素为单位",
     "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 处理蒙版区域之前要在蒙版区域内放置什么",
-    "fill it with colors of the image": "用图像的颜色(模糊/马赛克)填充它",
+    "fill it with colors of the image": "用图像的颜色(≈模糊/马赛克)填充它",
     "keep whatever was there originally": "保留原来的图像，不进行预处理",
-    "fill it with latent space noise": "用潜空间的噪声(随机彩色噪点)填充它",
-    "fill it with latent space zeroes": "用潜空间的零(灰色)填充它",
+    "fill it with latent space noise": "用潜空间的噪声(≈随机彩色噪点)填充它",
+    "fill it with latent space zeroes": "用潜空间的零(≈灰色)填充它",
     "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做内补绘制，缩小后粘贴到原始图像中",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
-- 
cgit v1.2.1


From 4b8a192f680101de247dca79e48974b53bf961fe Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sat, 29 Oct 2022 16:36:43 +0900
Subject: add optimizer save option to shared.opts

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/shared.py b/modules/shared.py
index e4f163c1..065b893d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -286,6 +286,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),
+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-- 
cgit v1.2.1


From 20194fd9752a280306fb66b57b258609b0918c46 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sat, 29 Oct 2022 16:56:42 +0900
Subject: We have duplicate linear now

---
 modules/hypernetworks/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index aad09ffc..c2d4b51c 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
 not_available = ["hardswish", "multiheadattention"]
-keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
+keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
 
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
     # Remove illegal characters from name.
-- 
cgit v1.2.1


From 9d96d7d0a0aa0a966a9aefd24342345eb65952ed Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Sun, 30 Oct 2022 20:39:04 +0900
Subject: resolve conflicts

---
 modules/hypernetworks/hypernetwork.py | 44 ++++++++++++++++++++++++++++++-----
 1 file changed, 38 insertions(+), 6 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index a11e01d6..8f74cdea 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -21,6 +21,7 @@ from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_norm
 from collections import defaultdict, deque
 from statistics import stdev, mean
 
+optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
 
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
@@ -139,6 +140,8 @@ class Hypernetwork:
         self.weight_init = weight_init
         self.add_layer_norm = add_layer_norm
         self.use_dropout = use_dropout
+        self.optimizer_name = None
+        self.optimizer_state_dict = None
 
         for size in enable_sizes or []:
             self.layers[size] = (
@@ -171,6 +174,10 @@ class Hypernetwork:
         state_dict['use_dropout'] = self.use_dropout
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
+        if self.optimizer_name is not None:
+            state_dict['optimizer_name'] = self.optimizer_name
+        if self.optimizer_state_dict:
+            state_dict['optimizer_state_dict'] = self.optimizer_state_dict
 
         torch.save(state_dict, filename)
 
@@ -190,7 +197,14 @@ class Hypernetwork:
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
         print(f"Layer norm is set to {self.add_layer_norm}")
         self.use_dropout = state_dict.get('use_dropout', False)
-        print(f"Dropout usage is set to {self.use_dropout}" )
+        print(f"Dropout usage is set to {self.use_dropout}")
+        self.optimizer_name = state_dict.get('optimizer_name', 'AdamW')
+        print(f"Optimizer name is {self.optimizer_name}")
+        self.optimizer_state_dict = state_dict.get('optimizer_state_dict', None)
+        if self.optimizer_state_dict:
+            print("Loaded existing optimizer from checkpoint")
+        else:
+            print("No saved optimizer exists in checkpoint")
 
         for size, sd in state_dict.items():
             if type(size) == int:
@@ -392,8 +406,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     weights = hypernetwork.weights()
     for weight in weights:
         weight.requires_grad = True
-    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
-    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
+    # Here we use optimizer from saved HN, or we can specify as UI option.
+    if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict:
+        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
+    else:
+        print(f"Optimizer type {optimizer_name} is not defined!")
+        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
+        optimizer_name = 'AdamW'
+    if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
+        try:
+            optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
+        except RuntimeError as e:
+            print("Cannot resume from saved optimizer!")
+            print(e)
 
     steps_without_grad = 0
 
@@ -455,8 +480,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             # Before saving, change name to match current checkpoint.
             hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
+            hypernetwork.optimizer_name = optimizer_name
+            if shared.opts.save_optimizer_state:
+                hypernetwork.optimizer_state_dict = optimizer.state_dict()
             save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
-
+            hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{previous_mean_loss:.7f}",
             "learn_rate": scheduler.learn_rate
@@ -514,14 +542,18 @@ Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-        
     report_statistics(loss_dict)
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+    hypernetwork.optimizer_name = optimizer_name
+    if shared.opts.save_optimizer_state:
+        hypernetwork.optimizer_state_dict = optimizer.state_dict()
     save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
-
+    del optimizer
+    hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
     return hypernetwork, filename
 
+
 def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
     old_hypernetwork_name = hypernetwork.name
     old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None
-- 
cgit v1.2.1


From c9bb33dd43dbb9479ff1b70351df14508c89ac60 Mon Sep 17 00:00:00 2001
From: victorca25 <victorca25@users.noreply.github.com>
Date: Sun, 30 Oct 2022 12:52:50 +0100
Subject: add resrgan 8x, allow use 1x and up to 8x extra models, move BSRGAN
 model, add nearest

---
 modules/esrgan_model.py | 17 +++++++++++++----
 modules/modelloader.py  |  3 +++
 modules/ui.py           |  2 +-
 modules/upscaler.py     | 17 ++++++++++++++++-
 4 files changed, 33 insertions(+), 6 deletions(-)

diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index a13cf6ac..c61669b4 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -50,6 +50,7 @@ def mod2normal(state_dict):
 def resrgan2normal(state_dict, nb=23):
     # this code is copied from https://github.com/victorca25/iNNfer
     if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
+        re8x = 0
         crt_net = {}
         items = []
         for k, v in state_dict.items():
@@ -75,10 +76,18 @@ def resrgan2normal(state_dict, nb=23):
         crt_net['model.3.bias'] = state_dict['conv_up1.bias']
         crt_net['model.6.weight'] = state_dict['conv_up2.weight']
         crt_net['model.6.bias'] = state_dict['conv_up2.bias']
-        crt_net['model.8.weight'] = state_dict['conv_hr.weight']
-        crt_net['model.8.bias'] = state_dict['conv_hr.bias']
-        crt_net['model.10.weight'] = state_dict['conv_last.weight']
-        crt_net['model.10.bias'] = state_dict['conv_last.bias']
+
+        if 'conv_up3.weight' in state_dict:
+            # modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
+            re8x = 3
+            crt_net['model.9.weight'] = state_dict['conv_up3.weight']
+            crt_net['model.9.bias'] = state_dict['conv_up3.bias']
+
+        crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
+        crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
+        crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
+        crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
+
         state_dict = crt_net
     return state_dict
 
diff --git a/modules/modelloader.py b/modules/modelloader.py
index b0f2f33d..e4a6f8ac 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -85,6 +85,9 @@ def cleanup_models():
     src_path = os.path.join(root_path, "ESRGAN")
     dest_path = os.path.join(models_path, "ESRGAN")
     move_files(src_path, dest_path)
+    src_path = os.path.join(models_path, "BSRGAN")
+    dest_path = os.path.join(models_path, "ESRGAN")
+    move_files(src_path, dest_path, ".pth")
     src_path = os.path.join(root_path, "gfpgan")
     dest_path = os.path.join(models_path, "GFPGAN")
     move_files(src_path, dest_path)
diff --git a/modules/ui.py b/modules/ui.py
index 5055ca64..47610f5c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1059,7 +1059,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tabs(elem_id="extras_resize_mode"):
                     with gr.TabItem('Scale by'):
-                        upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
                     with gr.TabItem('Scale to'):
                         with gr.Group():
                             with gr.Row():
diff --git a/modules/upscaler.py b/modules/upscaler.py
index 6ab2fb40..83fde7ca 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -10,6 +10,7 @@ import modules.shared
 from modules import modelloader, shared
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
+NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST)
 from modules.paths import models_path
 
 
@@ -57,7 +58,7 @@ class Upscaler:
         dest_w = img.width * scale
         dest_h = img.height * scale
         for i in range(3):
-            if img.width >= dest_w and img.height >= dest_h:
+            if img.width > dest_w and img.height > dest_h:
                 break
             img = self.do_upscale(img, selected_model)
         if img.width != dest_w or img.height != dest_h:
@@ -120,3 +121,17 @@ class UpscalerLanczos(Upscaler):
         self.name = "Lanczos"
         self.scalers = [UpscalerData("Lanczos", None, self)]
 
+
+class UpscalerNearest(Upscaler):
+    scalers = []
+
+    def do_upscale(self, img, selected_model=None):
+        return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=NEAREST)
+
+    def load_model(self, _):
+        pass
+
+    def __init__(self, dirname=None):
+        super().__init__(False)
+        self.name = "Nearest"
+        self.scalers = [UpscalerData("Nearest", None, self)]
\ No newline at end of file
-- 
cgit v1.2.1


From 5d69f75e5bc5e8908cb6c590055157f8c7d4bb3b Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Sun, 30 Oct 2022 21:24:28 +0800
Subject: =?UTF-8?q?=E6=9B=B4=E6=96=B0=20zh=5FCN.json?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/zh_CN.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 5472535e..b796c625 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -524,9 +524,9 @@
     "Path to directory with input images": "带有输入图像的路径",
     "Path to directory where to write outputs": "进行输出的路径",
     "Input images directory": "输入图像目录",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像的文件名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像的文件名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
     "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果启用此选项，水印将不会添加到生成出来的图像中。警告：如果你不添加水印，你的行为可能是不符合专业操守的",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像和宫格图的子目录: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像和宫格图的子目录: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
     "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络修复低质量面部",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。留空以保持文件名文本不变",
     "This string will be used to join split words into a single line if the option above is enabled.": "如果启用了上述选项，则此处的字符会用于将拆分的单词接合为同一行",
-- 
cgit v1.2.1


From 423f22228306ae72d0480e25add9777c3c5d8fdf Mon Sep 17 00:00:00 2001
From: Maiko Tan <maiko.tan.coding@gmail.com>
Date: Sun, 30 Oct 2022 22:46:43 +0800
Subject: feat: add app started callback

---
 modules/script_callbacks.py | 15 +++++++++++++++
 webui.py                    |  3 +++
 2 files changed, 18 insertions(+)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 6ea58d61..f5509629 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -3,6 +3,8 @@ import traceback
 from collections import namedtuple
 import inspect
 
+from fastapi import FastAPI
+from gradio import Blocks
 
 def report_exception(c, job):
     print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
@@ -25,6 +27,7 @@ class ImageSaveParams:
 
 
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
+callbacks_app_started = []
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
@@ -40,6 +43,14 @@ def clear_callbacks():
     callbacks_image_saved.clear()
 
 
+def app_started_callback(demo: Blocks, app: FastAPI):
+    for c in callbacks_app_started:
+        try:
+            c.callback(demo, app)
+        except Exception:
+            report_exception(c, 'app_started_callback')
+
+
 def model_loaded_callback(sd_model):
     for c in callbacks_model_loaded:
         try:
@@ -91,6 +102,10 @@ def add_callback(callbacks, fun):
     callbacks.append(ScriptCallback(filename, fun))
 
 
+def on_app_started(callback):
+    add_callback(callbacks_app_started, callback)
+
+
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
diff --git a/webui.py b/webui.py
index 29530872..13407e42 100644
--- a/webui.py
+++ b/webui.py
@@ -23,6 +23,7 @@ import modules.sd_hijack
 import modules.sd_models
 import modules.shared as shared
 import modules.txt2img
+import modules.script_callbacks
 
 import modules.ui
 from modules import devices
@@ -135,6 +136,8 @@ def webui():
         if (launch_api):
             create_api(app)
 
+        modules.script_callbacks.app_started_callback(demo, app)
+
         wait_on_server(demo)
 
         sd_samplers.set_samplers()
-- 
cgit v1.2.1


From cb31abcf58ea1f64266e6d821937eed058c35f4d Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 30 Oct 2022 21:54:31 +0700
Subject: Settings to select VAE

---
 modules/sd_models.py |  31 +++++--------
 modules/sd_vae.py    | 121 +++++++++++++++++++++++++++++++++++++++++++++++++++
 modules/shared.py    |   8 ++--
 webui.py             |   5 +++
 4 files changed, 141 insertions(+), 24 deletions(-)
 create mode 100644 modules/sd_vae.py

diff --git a/modules/sd_models.py b/modules/sd_models.py
index f86dc3ed..91ad4b5e 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -8,7 +8,7 @@ from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader, devices, script_callbacks
+from modules import shared, modelloader, devices, script_callbacks, sd_vae
 from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
 
@@ -160,12 +160,11 @@ def get_state_dict_from_checkpoint(pl_sd):
 
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
 
-
-def load_model_weights(model, checkpoint_info):
+def load_model_weights(model, checkpoint_info, force=False):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    if checkpoint_info not in checkpoints_loaded:
+    if force or checkpoint_info not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
         pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
@@ -186,17 +185,7 @@ def load_model_weights(model, checkpoint_info):
         devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
         devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
-        vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
-
-        if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
-            vae_file = shared.cmd_opts.vae_path
-
-        if os.path.exists(vae_file):
-            print(f"Loading VAE weights from: {vae_file}")
-            vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
-            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-            model.first_stage_model.load_state_dict(vae_dict)
-
+        sd_vae.load_vae(model, checkpoint_file)
         model.first_stage_model.to(devices.dtype_vae)
 
         if shared.opts.sd_checkpoint_cache > 0:
@@ -213,7 +202,7 @@ def load_model_weights(model, checkpoint_info):
     model.sd_checkpoint_info = checkpoint_info
 
 
-def load_model(checkpoint_info=None):
+def load_model(checkpoint_info=None, force=False):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
 
@@ -234,7 +223,7 @@ def load_model(checkpoint_info=None):
 
     do_inpainting_hijack()
     sd_model = instantiate_from_config(sd_config.model)
-    load_model_weights(sd_model, checkpoint_info)
+    load_model_weights(sd_model, checkpoint_info, force=force)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
@@ -252,16 +241,16 @@ def load_model(checkpoint_info=None):
     return sd_model
 
 
-def reload_model_weights(sd_model, info=None):
+def reload_model_weights(sd_model, info=None, force=False):
     from modules import lowvram, devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
 
-    if sd_model.sd_model_checkpoint == checkpoint_info.filename:
+    if sd_model.sd_model_checkpoint == checkpoint_info.filename and not force:
         return
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         checkpoints_loaded.clear()
-        load_model(checkpoint_info)
+        load_model(checkpoint_info, force=force)
         return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -271,7 +260,7 @@ def reload_model_weights(sd_model, info=None):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_model_weights(sd_model, checkpoint_info)
+    load_model_weights(sd_model, checkpoint_info, force=force)
 
     sd_hijack.model_hijack.hijack(sd_model)
     script_callbacks.model_loaded_callback(sd_model)
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
new file mode 100644
index 00000000..82764e55
--- /dev/null
+++ b/modules/sd_vae.py
@@ -0,0 +1,121 @@
+import torch
+import os
+from collections import namedtuple
+from modules import shared, devices
+from modules.paths import models_path
+import glob
+
+model_dir = "Stable-diffusion"
+model_path = os.path.abspath(os.path.join(models_path, model_dir))
+vae_dir = "VAE"
+vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
+
+vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
+default_vae_dict = {"auto": "auto", "None": "None"}
+default_vae_list = ["auto", "None"]
+default_vae_values = [default_vae_dict[x] for x in default_vae_list]
+vae_dict = dict(default_vae_dict)
+vae_list = list(default_vae_list)
+first_load = True
+
+def get_filename(filepath):
+    return os.path.splitext(os.path.basename(filepath))[0]
+
+def refresh_vae_list(vae_path=vae_path, model_path=model_path):
+    global vae_dict, vae_list
+    res = {}
+    candidates = [
+        *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
+        *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True)
+    ]
+    if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
+        candidates.append(shared.cmd_opts.vae_path)
+    for filepath in candidates:
+        name = get_filename(filepath)
+        res[name] = filepath
+    vae_list.clear()
+    vae_list.extend(default_vae_list)
+    vae_list.extend(list(res.keys()))
+    vae_dict.clear()
+    vae_dict.update(default_vae_dict)
+    vae_dict.update(res)
+    return vae_list
+
+def load_vae(model, checkpoint_file, vae_file="auto"):
+    global first_load, vae_dict, vae_list
+    # save_settings = False
+
+    # if vae_file argument is provided, it takes priority
+    if vae_file and vae_file not in default_vae_list:
+        if not os.path.isfile(vae_file):
+            vae_file = "auto"
+            # save_settings = True
+            print("VAE provided as function argument doesn't exist")
+    # for the first load, if vae-path is provided, it takes priority and failure is reported
+    if first_load and shared.cmd_opts.vae_path is not None:
+        if os.path.isfile(shared.cmd_opts.vae_path):
+            vae_file = shared.cmd_opts.vae_path
+            # save_settings = True
+            # print("Using VAE provided as command line argument")
+        else:
+            print("VAE provided as command line argument doesn't exist")
+    # else, we load from settings
+    if vae_file == "auto" and shared.opts.sd_vae is not None:
+        # if saved VAE settings isn't recognized, fallback to auto
+        vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
+        # if VAE selected but not found, fallback to auto
+        if vae_file not in default_vae_values and not os.path.isfile(vae_file):
+            vae_file = "auto"
+            print("Selected VAE doesn't exist")
+    # vae-path cmd arg takes priority for auto
+    if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
+        if os.path.isfile(shared.cmd_opts.vae_path):
+            vae_file = shared.cmd_opts.vae_path
+            print("Using VAE provided as command line argument")
+    # if still not found, try look for ".vae.pt" beside model
+    model_path = os.path.splitext(checkpoint_file)[0]
+    if vae_file == "auto":
+        vae_file_try = model_path + ".vae.pt"
+        if os.path.isfile(vae_file_try):
+            vae_file = vae_file_try
+            print("Using VAE found beside selected model")
+    # if still not found, try look for ".vae.ckpt" beside model
+    if vae_file == "auto":
+        vae_file_try = model_path + ".vae.ckpt"
+        if os.path.isfile(vae_file_try):
+            vae_file = vae_file_try
+            print("Using VAE found beside selected model")
+    # No more fallbacks for auto
+    if vae_file == "auto":
+        vae_file = None
+    # Last check, just because
+    if vae_file and not os.path.exists(vae_file):
+        vae_file = None
+
+    if vae_file:
+        print(f"Loading VAE weights from: {vae_file}")
+        vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
+        vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
+        model.first_stage_model.load_state_dict(vae_dict_1)
+
+    # If vae used is not in dict, update it
+    # It will be removed on refresh though
+    if vae_file is not None:
+        vae_opt = get_filename(vae_file)
+        if vae_opt not in vae_dict:
+            vae_dict[vae_opt] = vae_file
+            vae_list.append(vae_opt)
+
+    """
+    # Save current VAE to VAE settings, maybe? will it work?
+    if save_settings:
+        if vae_file is None:
+            vae_opt = "None"
+
+        # shared.opts.sd_vae = vae_opt
+    """
+
+    first_load = False
+    model.first_stage_model.to(devices.dtype_vae)
diff --git a/modules/shared.py b/modules/shared.py
index e4f163c1..06440ac4 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -14,7 +14,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models, localization
+from modules import sd_samplers, sd_models, localization, sd_vae
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -295,6 +295,7 @@ options_templates.update(options_section(('training', "Training"), {
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
@@ -407,11 +408,12 @@ class Options:
         if bad_settings > 0:
             print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
 
-    def onchange(self, key, func):
+    def onchange(self, key, func, call=True):
         item = self.data_labels.get(key)
         item.onchange = func
 
-        func()
+        if call:
+            func()
 
     def dumpjson(self):
         d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
diff --git a/webui.py b/webui.py
index 29530872..27949f3d 100644
--- a/webui.py
+++ b/webui.py
@@ -21,6 +21,7 @@ import modules.paths
 import modules.scripts
 import modules.sd_hijack
 import modules.sd_models
+import modules.sd_vae
 import modules.shared as shared
 import modules.txt2img
 
@@ -74,8 +75,12 @@ def initialize():
 
     modules.scripts.load_scripts()
 
+    modules.sd_vae.refresh_vae_list()
     modules.sd_models.load_model()
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
+    # I don't know what needs to be done to only reload VAE, with all those hijacks callbacks, and lowvram, 
+    # so for now this reloads the whole model too, and no cache
+    shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model, force=True)), call=False)
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
 
-- 
cgit v1.2.1


From 2468039df2c5705039a5c14fd74f1354093bf874 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 30 Oct 2022 21:58:31 +0700
Subject: Forgot to add this folder

---
 models/VAE/Put VAE here.txt | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 models/VAE/Put VAE here.txt

diff --git a/models/VAE/Put VAE here.txt b/models/VAE/Put VAE here.txt
new file mode 100644
index 00000000..e69de29b
-- 
cgit v1.2.1


From 0ccc982f096d035ec5c9f406e83bf95311c8349a Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 31 Oct 2022 00:03:58 +0900
Subject: re-order content to match the dump

Deprecated content was moved to the bottom to keep (somehow) backwards capability.
---
 localizations/zh_CN.json | 69 ++++++++++++++++++++++++------------------------
 1 file changed, 35 insertions(+), 34 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index b796c625..80962f62 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -18,7 +18,6 @@
     "Train": "训练",
     "Create aesthetic embedding": "生成美术风格",
     "Image Browser": "图库浏览器",
-    "History": "历史记录",
     "Settings": "设置",
     "Prompt": "提示词",
     "Negative prompt": "反向提示词",
@@ -70,7 +69,6 @@
     "Resize seed from width": "自宽度缩放随机种子",
     "Resize seed from height": "自高度缩放随机种子",
     "Open for Clip Aesthetic!": "打开美术风格 Clip!",
-    "▼": "▼",
     "Aesthetic weight": "美术风格权重",
     "Aesthetic steps": "美术风格迭代步数",
     "Aesthetic learning rate": "美术风格学习率",
@@ -87,10 +85,8 @@
     "X/Y plot": "X/Y 图表",
     "Source embedding to convert": "用于转换的源 Embedding",
     "Embedding token": "Embedding 的 token (关键词)",
+    "Output directory": "输出目录",
     "Put variable parts at start of prompt": "把变量部分放在提示词文本的开头",
-    "Show Textbox": "显示文本框",
-    "File with inputs": "含输入内容的文件",
-    "Prompts": "提示词",
     "Iterate seed every line": "每行输入都换一个种子",
     "List of prompt inputs": "提示词输入列表",
     "Upload prompt inputs": "上传提示词输入文件",
@@ -119,7 +115,6 @@
     "Draw legend": "在图表中包括轴标题",
     "Include Separate Images": "包括独立的图像",
     "Keep -1 for seeds": "保持随机种子为-1",
-    "Drop Image Here": "拖拽图像到此",
     "Save": "保存",
     "Send to img2img": ">> 图生图",
     "Send to inpaint": ">> 内补绘制",
@@ -131,6 +126,7 @@
     "Inpaint": "内补绘制",
     "Batch img2img": "批量图生图",
     "Image for img2img": "图生图的图像",
+    "Drop Image Here": "拖拽图像到此",
     "Image for inpainting with mask": "用于内补绘制蒙版内容的图像",
     "Mask": "蒙版",
     "Mask blur": "蒙版模糊",
@@ -149,9 +145,7 @@
     "Inpaint at full resolution padding, pixels": "以完整分辨率进行内补绘制 - 填补像素",
     "Process images in a directory on the same machine where the server is running.": "在服务器主机上的目录中处理图像",
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "指定一个空的文件夹为输出目录而非默认的 output 文件夹为输出目录",
-    "Disabled when launched with --hide-ui-dir-config.": "启动 --hide-ui-dir-config 时禁用",
     "Input directory": "输入目录",
-    "Output directory": "输出目录",
     "Resize mode": "缩放模式",
     "Just resize": "拉伸",
     "Crop and resize": "裁剪",
@@ -185,6 +179,7 @@
     "Color variation": "色彩变化",
     "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置图块尺寸(tile size)",
     "Tile overlap": "图块重叠的像素(Tile overlap)",
+    "Upscaler": "Upscaler",
     "Lanczos": "Lanczos",
     "LDSR": "LDSR",
     "BSRGAN 4x": "BSRGAN 4x",
@@ -202,14 +197,11 @@
     "Scale to": "指定尺寸缩放",
     "Resize": "缩放",
     "Crop to fit": "裁剪以适应",
-    "Upscaler 1": "放大算法 1",
-    "Upscaler 2": "放大算法 2",
     "Upscaler 2 visibility": "放大算法 2 可见度",
     "GFPGAN visibility": "GFPGAN 可见度",
     "CodeFormer visibility": "CodeFormer 可见度",
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 权重 (0 = 最大效果, 1 = 最小效果)",
     "Upscale Before Restoring Faces": "放大后再进行面部修复",
-    "Open output directory": "打开输出目录",
     "Send to txt2img": ">> 文生图",
     "A merger of the two checkpoints will be generated in your": "合并后的模型(ckpt)会生成在你的",
     "checkpoint": "模型(ckpt)",
@@ -227,7 +219,6 @@
     "wiki": "帮助",
     "for detailed explanation.": "以了解详细说明",
     "Create embedding": "生成 embedding",
-    "Create aesthetic images embedding": "生成美术风格图集 embedding",
     "Create hypernetwork": "生成 hypernetwork",
     "Preprocess images": "图像预处理",
     "Name": "名称",
@@ -283,7 +274,6 @@
     "prepend": "放前面",
     "append": "放后面",
     "Create flipped copies": "生成镜像副本",
-    "Split oversized images into two": "将过大的图像分为两份",
     "Split oversized images": "分割过大的图像",
     "Auto focal point crop": "自动焦点裁切",
     "Use BLIP for caption": "使用 BLIP 生成说明文字(自然语言描述)",
@@ -295,13 +285,11 @@
     "Focal point edges weight": "焦点线条权重",
     "Create debug image": "生成调试(debug)图片",
     "Preprocess": "预处理",
-    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "训练 embedding； 必须指定一组具有 1:1 比例图像的目录",
     "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "训练 embedding 或者 hypernetwork； 必须指定一组具有 1:1 比例图像的目录",
     "[wiki]": "[帮助]",
     "Embedding": "Embedding",
     "Embedding Learning rate": "Embedding 学习率",
     "Hypernetwork Learning rate": "Hypernetwork 学习率",
-    "Learning rate": "学习率",
     "Dataset directory": "数据集目录",
     "Log directory": "日志目录",
     "Prompt template file": "提示词模版文件",
@@ -314,25 +302,15 @@
     "Train Embedding": "训练 Embedding",
     "Create an aesthetic embedding out of any number of images": "从任意数量的图像中创建美术风格 embedding",
     "Create images embedding": "生成图集 embedding",
-    "txt2img history": "文生图历史记录",
-    "img2img history": "图生图历史记录",
-    "extras history": "更多选项卡的历史记录",
-    "Renew Page": "刷新页面",
-    "extras": "更多",
     "favorites": "已保存",
     "others": "其他",
-    "custom fold": "自定义文件夹",
-    "Load": "载入",
     "Images directory": "图像目录",
     "Dropdown": "下拉列表",
-    "Prev batch": "上一批",
-    "Next batch": "下一批",
     "First Page": "首页",
     "Prev Page": "上一页",
     "Page Index": "页数",
     "Next Page": "下一页",
     "End Page": "尾页",
-    "number of images to delete consecutively next": "接下来要连续删除的图像数",
     "delete next": "删除下一张",
     "Delete": "删除",
     "sort by": "排序方式",
@@ -341,9 +319,8 @@
     "keyword": "关键词",
     "Generate Info": "生成信息",
     "File Name": "文件名",
-    "Collect": "保存",
-    "Refresh page": "刷新页面",
-    "Date to": "日期至",
+    "Move to favorites": "Move to favorites",
+    "Renew Page": "刷新页面",
     "Number": "数量",
     "set_index": "设置索引",
     "load_switch": "载入开关",
@@ -400,7 +377,6 @@
     "Always print all generation info to standard output": "始终将所有生成信息输出到 standard output (一般为控制台)",
     "Add a second progress bar to the console that shows progress for an entire job.": "向控制台添加第二个进度条，显示整个作业的进度",
     "Training": "训练",
-    "Unload VAE and CLIP from VRAM when training": "训练时从显存(VRAM)中取消 VAE 和 CLIP 的加载",
     "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
     "Filename word regex": "文件名用词的正则表达式",
     "Filename join string": "文件名连接用字符串",
@@ -463,9 +439,7 @@
     "Preload images at startup": "在启动时预加载图像",
     "Number of columns on the page": "每页列数",
     "Number of rows on the page": "每页行数",
-    "Number of pictures displayed on each page": "每页显示的图像数量",
     "Minimum number of pages per load": "每次加载的最小页数",
-    "Number of grids in each row": "每行显示多少格",
     "Wildcards": "通配符",
     "Use same seed for all images": "为所有图像使用同一个随机种子",
     "Request browser notifications": "请求浏览器通知",
@@ -520,7 +494,6 @@
     "Result = A * (1 - M) + B * M": "结果 = A * (1 - M) + B * M",
     "Result = A + (B - C) * M": "结果 = A + (B - C) * M",
     "1st and last digit must be 1. ex:'1, 2, 1'": "第一个和最后一个数字必须是 1。例:'1, 2, 1'",
-    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
     "Path to directory with input images": "带有输入图像的路径",
     "Path to directory where to write outputs": "进行输出的路径",
     "Input images directory": "输入图像目录",
@@ -537,5 +510,33 @@
     "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
     "Roll three": "抽三位出来",
     "Generate forever": "无限生成",
-    "Cancel generate forever": "停止无限生成"
-}
+    "Cancel generate forever": "停止无限生成",
+    "History": "历史记录",
+    "▼": "▼",
+    "Show Textbox": "显示文本框",
+    "File with inputs": "含输入内容的文件",
+    "Prompts": "提示词",
+    "Disabled when launched with --hide-ui-dir-config.": "启动 --hide-ui-dir-config 时禁用",
+    "Upscaler 1": "放大算法 1",
+    "Upscaler 2": "放大算法 2",
+    "Open output directory": "打开输出目录",
+    "Create aesthetic images embedding": "生成美术风格图集 embedding",
+    "Split oversized images into two": "将过大的图像分为两份",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "训练 embedding； 必须指定一组具有 1:1 比例图像的目录",
+    "Learning rate": "学习率",
+    "txt2img history": "文生图历史记录",
+    "img2img history": "图生图历史记录",
+    "extras history": "更多选项卡的历史记录",
+    "extras": "更多",
+    "custom fold": "自定义文件夹",
+    "Load": "载入",
+    "Prev batch": "上一批",
+    "Next batch": "下一批",
+    "number of images to delete consecutively next": "接下来要连续删除的图像数",
+    "Date to": "日期至",
+    "Refresh page": "刷新页面",
+    "Unload VAE and CLIP from VRAM when training": "训练时从显存(VRAM)中取消 VAE 和 CLIP 的加载",
+    "Number of pictures displayed on each page": "每页显示的图像数量",
+    "Number of grids in each row": "每行显示多少格",
+    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练"
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 9d7b665d3b10193f6f9bad23f7957ef2469f6eaa Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 31 Oct 2022 00:09:58 +0900
Subject: update new content

---
 localizations/zh_CN.json | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 80962f62..80e7e4cb 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -302,8 +302,8 @@
     "Train Embedding": "训练 Embedding",
     "Create an aesthetic embedding out of any number of images": "从任意数量的图像中创建美术风格 embedding",
     "Create images embedding": "生成图集 embedding",
-    "favorites": "已保存",
-    "others": "其他",
+    "Favorites": "已保存",
+    "Others": "其他",
     "Images directory": "图像目录",
     "Dropdown": "下拉列表",
     "First Page": "首页",
@@ -319,7 +319,7 @@
     "keyword": "关键词",
     "Generate Info": "生成信息",
     "File Name": "文件名",
-    "Move to favorites": "Move to favorites",
+    "Move to favorites": "移动到已保存",
     "Renew Page": "刷新页面",
     "Number": "数量",
     "set_index": "设置索引",
-- 
cgit v1.2.1


From e1b2ea6e0012ecc988385fc523d8fb50ea5d6be5 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 30 Oct 2022 22:11:45 +0700
Subject: Change VAE search order and thus priority

---
 modules/sd_vae.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 82764e55..0767b925 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -25,10 +25,10 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     global vae_dict, vae_list
     res = {}
     candidates = [
-        *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
         *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
-        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
+        *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
         *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True)
+        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
     ]
     if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
         candidates.append(shared.cmd_opts.vae_path)
-- 
cgit v1.2.1


From 32ffc324169b73ede71ad02c5540b28b1564e466 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 31 Oct 2022 00:29:43 +0900
Subject: Update zh_CN.json

putting back the sign
---
 localizations/zh_CN.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 80e7e4cb..f9371c3e 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -69,6 +69,7 @@
     "Resize seed from width": "自宽度缩放随机种子",
     "Resize seed from height": "自高度缩放随机种子",
     "Open for Clip Aesthetic!": "打开美术风格 Clip!",
+    "▼": "▼",
     "Aesthetic weight": "美术风格权重",
     "Aesthetic steps": "美术风格迭代步数",
     "Aesthetic learning rate": "美术风格学习率",
@@ -512,7 +513,6 @@
     "Generate forever": "无限生成",
     "Cancel generate forever": "停止无限生成",
     "History": "历史记录",
-    "▼": "▼",
     "Show Textbox": "显示文本框",
     "File with inputs": "含输入内容的文件",
     "Prompts": "提示词",
-- 
cgit v1.2.1


From f87dcf1ad8722674bf4c84b0f992070af223fb7d Mon Sep 17 00:00:00 2001
From: Riccardo Giovanetti <29801031+Harvester62@users.noreply.github.com>
Date: Sun, 30 Oct 2022 18:09:13 +0100
Subject: Localization Italian - Updates and additions

Updated localization with the latest version of these Scripts/Extensions:

animator v6
StylePile
Alpha Canvas

Changed the description for the script "To Infinity and Beyond" (Verso l'infinito e oltre) from 'n' to 'Esegui n volte' (Run n times)
Added localization for the 'Random' and 'Random Grid' scripts I changed both the title and the parameters Steps and CFG labels to make it clearer that the min/max order can be inverted.
Added a few missing translations and character's corrections here and there. Still a work in progress but I will slowly fix wrong thing when I find them.
---
 localizations/it_IT.json | 2210 ++++++++++++++++++++++++----------------------
 1 file changed, 1141 insertions(+), 1069 deletions(-)

diff --git a/localizations/it_IT.json b/localizations/it_IT.json
index 9752f71c..a2cb1bb2 100644
--- a/localizations/it_IT.json
+++ b/localizations/it_IT.json
@@ -1,1070 +1,1142 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "Caricamento...",
-    "view": "mostra ",
-    "api": "API",
-    "•": " • ",
-    "built with gradio": " Sviluppato con Gradio",
-    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
-    "txt2img": "txt2img",
-    "img2img": "img2img",
-    "Extras": "Extra",
-    "PNG Info": "Info PNG",
-    "Checkpoint Merger": "Miscelatore di Checkpoint",
-    "Train": "Addestramento",
-    "Create aesthetic embedding": "Crea incorporamento estetico",
-    "Dataset Tag Editor": "Dataset Tag Editor",
-    "Deforum": "Deforum",
-    "Artists To Study": "Artisti per studiare",
-    "Image Browser": "Galleria immagini",
-    "Inspiration": "Ispirazione",
-    "Settings": "Impostazioni",
-    "Prompt": "Prompt",
-    "Negative prompt": "Prompt negativo",
-    "Run": "Esegui",
-    "Skip": "Salta",
-    "Interrupt": "Interrompi",
-    "Generate": "Genera",
-    "Style 1": "Stile 1",
-    "Style 2": "Stile 2",
-    "Label": "Etichetta",
-    "File": "File",
-    "Drop File Here": "Trascina il file qui",
-    "-": "-",
-    "or": "o",
-    "Click to Upload": "Clicca per caricare",
-    "Image": "Immagine",
-    "Check progress": "Controlla i progressi",
-    "Check progress (first)": "Controlla i progressi (primo)",
-    "Sampling Steps": "Passi di campionamento",
-    "Sampling method": "Metodo di campionamento",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "Larghezza",
-    "Height": "Altezza",
-    "Restore faces": "Restaura i volti",
-    "Tiling": "Piastrellatura",
-    "Highres. fix": "Correzione alta risoluzione",
-    "Firstpass width": "Larghezza del primo passaggio",
-    "Firstpass height": "Altezza del primo passaggio",
-    "Denoising strength": "Forza del Denoising",
-    "Batch count": "Lotti di immagini",
-    "Batch size": "Immagini per lotto",
-    "CFG Scale": "Scala CFG",
-    "Seed": "Seme",
-    "Extra": "Extra",
-    "Variation seed": "Seme della variazione",
-    "Variation strength": "Forza della variazione",
-    "Resize seed from width": "Ridimensiona il seme dalla larghezza",
-    "Resize seed from height": "Ridimensiona il seme dall'altezza",
-    "Open for Clip Aesthetic!": "Apri per Estetica CLIP!",
-    "▼": "▼",
-    "Aesthetic weight": "Estetica - Peso",
-    "Aesthetic steps": "Estetica - Passi",
-    "Aesthetic learning rate": "Estetica - Tasso di apprendimento",
-    "Slerp interpolation": "Interpolazione Slerp",
-    "Aesthetic imgs embedding": "Estetica - Incorporamento di immagini",
-    "None": "Nessuno",
-    "Aesthetic text for imgs": "Estetica - Testo per le immagini",
-    "Slerp angle": "Angolo Slerp",
-    "Is negative text": "È un testo negativo",
-    "Script": "Script",
-    "Random": "Random",
-    "Advanced prompt matrix": "Matrice di prompt avanzata",
-    "Alternate Sampler Noise Schedules": "Metodi alternativi di campionamento del rumore",
-    "Asymmetric tiling": "Piastrellatura asimmetrica",
-    "Custom code": "Custom code",
-    "Dynamic Prompting v0.2": "Prompt dinamici v0.2",
-    "Embedding to Shareable PNG": "Incorporamento convertito in PNG condivisibile",
-    "Force symmetry": "Forza la simmetria",
-    "Prompts interpolation": "Interpola Prompt",
-    "Prompt matrix": "Matrice dei prompt",
-    "Prompt morph": "Metamorfosi del prompt",
-    "Prompts from file or textbox": "Prompt da file o da casella di testo",
-    "To Infinity and Beyond": "Verso l'infinito e oltre",
-    "Seed travel": "Seed travel",
-    "Shift attention": "Sposta l'attenzione",
-    "Text to Vector Graphics": "Da testo a grafica vettoriale",
-    "X/Y plot": "Grafico X/Y",
-    "X/Y/Z plot": "Grafico X/Y/Z",
-    "Create inspiration images": "Crea immagini di ispirazione",
-    "Loops": "Loops",
-    "step1 min/max": "step1 min/max",
-    "step2 min/max": "step2 min/max",
-    "cfg1 min/max": "cfg1 min/max",
-    "cfg2 min/max": "cfg2 min/max",
-    "Keep -1 for seeds": "Keep -1 for seeds",
-    "Usage: a <corgi|cat> wearing <goggles|a hat>": "Utilizzo: a <corgi|cat> wearing <goggles|a hat>",
-    "Noise Scheduler": "Programmatore del rumore",
-    "Default": "Predefinito",
-    "Karras": "Karras",
-    "Exponential": "Esponenziale",
-    "Variance Preserving": "Conservazione della Varianza",
-    "Sigma min": "Sigma min",
-    "Sigma max": "Sigma max",
-    "Sigma rho (Karras only)": "Sigma rho (Solo Karras)",
-    "Beta distribution (VP only)": "Distribuzione Beta (Solo CV)",
-    "Beta min (VP only)": "Beta min (Solo CV)",
-    "Epsilon (VP only)": "Epsilon (Solo CV)",
-    "Tile X": "Piastrella asse X",
-    "Tile Y": "Piastrella asse Y",
-    "Python code": "Codice Python",
-    "Combinatorial generation": "Generazione combinatoria",
-    "Combinations": "Combinazioni",
-    "Choose a number of terms from a list, in this case we choose two artists": "Scegli un numero di termini da un elenco, in questo caso scegliamo due artisti",
-    "{2$$artist1|artist2|artist3}": "{2$$artist1|artist2|artist3}",
-    "If $$ is not provided, then 1$$ is assumed.": "Se $$ non viene fornito, si presume 1$$.",
-    "{1-3$$artist1|artist2|artist3}": "{1-3$$artist1|artist2|artist3}",
-    "In this case, a random number of artists between 1 and 3 is chosen.": "In questo caso viene scelto un numero casuale di artisti compreso tra 1 e 3.",
-    "Wildcards": "Termini jolly",
-    "If the groups wont drop down click": "Se i gruppi non vengono visualizzati, clicca",
-    "here": "qui",
-    "to fix the issue.": "per correggere il problema.",
-    "WILDCARD_DIR: scripts/wildcards": "WILDCARD_DIR: scripts/wildcards",
-    "You can add more wildcards by creating a text file with one term per line and name is mywildcards.txt. Place it in scripts/wildcards.": "Puoi aggiungere termini jolly creando un file di testo con un termine per riga e nominandolo, per esempio, mywildcards.txt. Inseriscilo in scripts/wildcards.",
-    "__<folder>/mywildcards__": "__<folder>/mywildcards__",
-    "will then become available.": "diverrà quindi disponibile.",
-    "Source embedding to convert": "Incorporamento sorgente da convertire",
-    "Embedding token": "Token Incorporamento",
-    "Output directory": "Cartella di output",
-    "Horizontal symmetry": "Simmetria orizzontale",
-    "Vertical symmetry": "Simmetria verticale",
-    "Alt. symmetry method (blending)": "Alt. symmetry method (blending)",
-    "Apply every n steps": "Applica ogni n passi",
-    "Skip last n steps": "Salta gli ultimi n passi",
-    "Interpolation prompt": "Prompt di interpolazione",
-    "Number of images": "Numero di immagini",
-    "Make a gif": "Crea GIF",
-    "Duration of images (ms)": "Durata delle immagini (ms)",
-    "Put variable parts at start of prompt": "Inserisce le parti variabili all'inizio del prompt",
-    "Keyframe Format:": "Formato dei fotogrammi chiave:",
-    "Seed | Prompt or just Prompt": "Seme | Prompt o semplicemente Prompt",
-    "Prompt list": "Elenco dei prompt",
-    "Number of images between keyframes": "Numero di immagini tra fotogrammi chiave",
-    "Save results as video": "Salva i risultati come video",
-    "Frames per second": "Fotogrammi al secondo",
-    "Iterate seed every line": "Iterare il seme per ogni riga",
-    "List of prompt inputs": "Elenco di prompt di input",
-    "Upload prompt inputs": "Carica un file contenente i prompt di input",
-    "n": "n",
-    "Destination seed(s) (Comma separated)": "Seme/i di destinazione (separati da virgola)",
-    "Only use Random seeds (Unless comparing paths)": "Usa solo semi casuali (a meno che non si confrontino i percorsi)",
-    "Number of random seed(s)": "Numero di semi casuali",
-    "Compare paths (Separate travels from 1st seed to each destination)": "Confronta percorsi (transizioni separate dal primo seme a ciascuna destinazione)",
-    "Steps": "Passi",
-    "Loop back to initial seed": "Ritorna al seme iniziale",
-    "Bump seed (If > 0 do a Compare Paths but only one image. No video)": "Bump seed (If > 0 do a Compare Paths but only one image. No video)",
-    "Show generated images in ui": "Mostra le immagini generate nell'interfaccia utente",
-    "\"Hug the middle\" during interpolation": "\"Hug the middle\" durante l'interpolazione",
-    "Allow the default Euler a Sampling method. (Does not produce good results)": "Consenti Euler_a come metodo di campionamento predefinito. (Non produce buoni risultati)",
-    "Visual style": "Stile visivo",
-    "Illustration": "Illustrazione",
-    "Logo": "Logo",
-    "Drawing": "Disegno",
-    "Artistic": "Artistico",
-    "Tattoo": "Tatuaggio",
-    "Gothic": "Gotico",
-    "Anime": "Anime",
-    "Cartoon": "Cartoon",
-    "Sticker": "Etichetta",
-    "Gold Pendant": "Ciondolo in oro",
-    "None - prompt only": "Nessuno - solo prompt",
-    "Enable Vectorizing": "Abilita vettorizzazione",
-    "Output format": "Formato di output",
-    "svg": "svg",
-    "pdf": "pdf",
-    "White is Opaque": "Il bianco è opaco",
-    "Cut white margin from input": "Taglia il margine bianco dall'input",
-    "Keep temp images": "Conserva le immagini temporanee",
-    "Threshold": "Soglia",
-    "Transparent PNG": "PNG trasparente",
-    "Noise Tolerance": "Tolleranza al rumore",
-    "Quantize": "Quantizzare",
-    "X type": "Parametro asse X",
-    "Nothing": "Niente",
-    "Var. seed": "Seme della variazione",
-    "Var. strength": "Forza della variazione",
-    "Prompt S/R": "Cerca e Sostituisci nel Prompt",
-    "Prompt order": "In ordine di prompt",
-    "Sampler": "Campionatore",
-    "Checkpoint name": "Nome del checkpoint",
-    "Hypernetwork": "Iperrete",
-    "Hypernet str.": "Forza della Iperrete",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma noise": "Sigma noise",
-    "Eta": "ETA",
-    "Clip skip": "Salta CLIP",
-    "Denoising": "Riduzione del rumore",
-    "Cond. Image Mask Weight": "Cond. Image Mask Weight",
-    "X values": "Valori per X",
-    "Y type": "Parametro asse Y",
-    "Y values": "Valori per Y",
-    "Draw legend": "Disegna legenda",
-    "Include Separate Images": "Includi immagini separate",
-    "Z type": "Parametro asse Z",
-    "Z values": "Valori per Z",
-    "Artist or styles name list. '.txt' files with one name per line": "Elenco nomi di artisti o stili. File '.txt' con un nome per riga",
-    "Prompt words before artist or style name": "Parole chiave prima del nome dell'artista o dello stile",
-    "Prompt words after artist or style name": "Parole chiave dopo il nome dell'artista o dello stile",
-    "Negative Prompt": "Prompt negativo",
-    "Save": "Salva",
-    "Send to img2img": "Invia a img2img",
-    "Send to inpaint": "Invia a inpaint",
-    "Send to extras": "Invia a extra",
-    "Make Zip when Save?": "Crea un file ZIP quando si usa 'Salva'",
-    "Textbox": "Casella di testo",
-    "Interrogate\nCLIP": "Interroga\nCLIP",
-    "Interrogate\nDeepBooru": "Interroga\nDeepBooru",
-    "Inpaint": "Inpaint",
-    "Batch img2img": "Lotti img2img",
-    "Image for img2img": "Immagine per img2img",
-    "Drop Image Here": "Trascina l'immagine qui",
-    "Image for inpainting with mask": "Immagine per inpainting con maschera",
-    "Mask": "Maschera",
-    "Mask blur": "Sfocatura maschera",
-    "Mask mode": "Modalità maschera",
-    "Draw mask": "Disegna maschera",
-    "Upload mask": "Carica maschera",
-    "Masking mode": "Modalità mascheratura",
-    "Inpaint masked": "Inpaint mascherato",
-    "Inpaint not masked": "Inpaint non mascherato",
-    "Masked content": "Contenuto mascherato",
-    "fill": "riempi",
-    "original": "originale",
-    "latent noise": "rumore latente",
-    "latent nothing": "latenza nulla",
-    "Inpaint at full resolution": "Inpaint alla massima risoluzione",
-    "Inpaint at full resolution padding, pixels": "Inpaint con riempimento a piena risoluzione, pixel",
-    "Process images in a directory on the same machine where the server is running.": "Elabora le immagini in una cartella sulla stessa macchina su cui è in esecuzione il server.",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa una cartella di output vuota per salvare normalmente le immagini invece di scrivere nella cartella di output.",
-    "Input directory": "Cartella di Input",
-    "Resize mode": "Modalità di ridimensionamento",
-    "Just resize": "Ridimensiona solamente",
-    "Crop and resize": "Ritaglia e ridimensiona",
-    "Resize and fill": "Ridimensiona e riempie",
-    "Advanced loopback": "Advanced loopback", 
-    "Animator v5": "Animator v5",
-    "External Image Masking": "Immagine esterna per la mascheratura",
-    "img2img alternative test": "Test alternativo per img2img",
-    "img2tiles": "img2tiles",
-    "Interpolate": "Interpolare",
-    "Loopback": "Rielaborazione ricorsiva",
-    "Loopback and Superimpose": "Rielabora ricorsivamente e sovraimponi",
-    "Outpaint Canvas Region": "Regione della tela di Outpaint",
-    "Outpainting mk2": "Outpainting mk2",
-    "Poor man's outpainting": "Poor man's outpainting",
-    "SD upscale": "Ampliamento SD",
-    "txt2mask v0.1.1": "txt2mask v0.1.1",
-    "[C] Video to video": "[C] Video to video",
-    "Videos": "Filmati",
-    "Deforum-webui (use tab extension instead!)": "Deforum-webui (usa piuttosto la scheda Deforum delle estensioni!)",
-    "Use first image colors (custom color correction)": "Use first image colors (custom color correction)",
-    "Denoising strength change factor (overridden if proportional used)": "Denoising strength change factor (overridden if proportional used)",
-    "Zoom level": "Zoom level",
-    "Direction X": "Direction X",
-    "Direction Y": "Direction Y",
-    "Denoising strength start": "Denoising strength start",
-    "Denoising strength end": "Denoising strength end",
-    "Denoising strength proportional change starting value": "Denoising strength proportional change starting value",
-    "Denoising strength proportional change ending value (0.1 = disabled)": "Denoising strength proportional change ending value (0.1 = disabled)",
-    "Saturation enhancement per image": "Saturation enhancement per image",
-    "Use sine denoising strength variation": "Use sine denoising strength variation",
-    "Phase difference": "Phase difference",
-    "Denoising strength exponentiation": "Denoising strength exponentiation",
-    "Use sine zoom variation": "Use sine zoom variation",
-    "Zoom exponentiation": "Zoom exponentiation",
-    "Use multiple prompts": "Use multiple prompts",
-    "Same seed per prompt": "Same seed per prompt",
-    "Same seed for everything": "Same seed for everything",
-    "Original init image for everything": "Original init image for everything",
-    "Multiple prompts : 1 line positive, 1 line negative, leave a blank line for no negative": "Multiple prompts : 1 line positive, 1 line negative, leave a blank line for no negative",
-    "Render these video formats:": "Renderizza in questi formati:",
-    "GIF": "GIF",
-    "MP4": "MP4",
-    "WEBM": "WEBM",
-    "Animation Parameters": "Parametri animazione",
-    "Total Animation Length (s)": "Durata totale dell'animazione (s)",
-    "Framerate": "Frequenza dei fotogrammi",
-    "Initial Parameters": "Parametri iniziali",
-    "Denoising Strength (overrides img2img slider)": "Intensità di riduzione del rumore (sovrascrive il cursore img2img)",
-    "Seed_March": "Seed_March",
-    "Smoothing_Frames": "Smoothing_Frames",
-    "Zoom Factor (scale/s)": "Fattore di ingrandimento (scala/s)",
-    "X Pixel Shift (pixels/s)": "X Pixel Shift (pixels/s)",
-    "Y Pixel Shift (pixels/s)": "Y Pixel Shift (pixels/s)",
-    "Rotation (deg/s)": "Rotazione (gradi/s)",
-    "Prompt Template, applied to each keyframe below": "Modello di prompt, applicato a ciascun fotogramma chiave qui di seguito",
-    "Positive Prompts": "Prompt positivi",
-    "Negative Prompts": "Prompt negativi",
-    "Props": "Props",
-    "Folder:": "Cartella:",
-    "Supported Keyframes:": "Fotogrammi chiave supportati:",
-    "time_s | source | video, images, img2img | path": "time_s | source | video, images, img2img | path",
-    "time_s | prompt | positive_prompts | negative_prompts": "time_s | prompt | positive_prompts | negative_prompts",
-    "time_s | template | positive_prompts | negative_prompts": "time_s | template | positive_prompts | negative_prompts",
-    "time_s | transform | zoom | x_shift | y_shift | rotation": "time_s | transform | zoom | x_shift | y_shift | rotation",
-    "time_s | seed | new_seed_int": "time_s | seed | new_seed_int",
-    "time_s | denoise | denoise_value": "time_s | denoise | denoise_value",
-    "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name": "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name",
-    "time_s | clear_text | textblock_name": "time_s | clear_text | textblock_name",
-    "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation": "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation",
-    "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation": "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation",
-    "time_s | clear_stamp | stamp_name": "time_s | clear_stamp | stamp_name",
-    "time_s | col_set": "time_s | col_set",
-    "time_s | col_clear": "time_s | col_clear",
-    "time_s | model | sd-v1-4_f16, sd-v1-5-inpainting, sd-v1-5-pruned-emaonly_fp16, wd-v1-3-float16": "time_s | model | sd-v1-4_f16, sd-v1-5-inpainting, sd-v1-5-pruned-emaonly_fp16, wd-v1-3-float16",
-    "Keyframes:": "Fotogrammi chiave:",
-    "Masking preview size": "Dimensione dell'anteprima della mascheratura",
-    "Draw new mask on every run": "Disegna una nuova maschera ad ogni esecuzione",
-    "Process non-contigious masks separately": "Elaborare le maschere non contigue separatamente",
-    "should be 2 or lower.": "dovrebbe essere 2 o inferiore.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Sovrascrivi il `Metodo di campionamento` con Eulero? (questo metodo è stato creato per questo)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Sovrascrivi `prompt` con lo stesso valore del `prompt originale`? (e `prompt negativo`)",
-    "Original prompt": "Prompt originale",
-    "Original negative prompt": "Prompt negativo originale",
-    "Override `Sampling Steps` to the same val due as `Decode steps`?": "Sovrascrivere 'Passi di campionamento' allo stesso valore di 'Passi di decodifica'?",
-    "Decode steps": "Passi di decodifica",
-    "Override `Denoising strength` to 1?": "Sostituisci 'Forza di denoising' a 1?",
-    "Decode CFG scale": "Scala CFG di decodifica",
-    "Randomness": "Casualità",
-    "Sigma adjustment for finding noise for image": "Regolazione Sigma per trovare il rumore per l'immagine",
-    "Tile size": "Dimensione piastrella",
-    "Tile overlap": "Sovrapposizione piastrella",
-    "alternate img2img imgage": "alternate img2img imgage",
-    "interpolation values": "Valori di interpolazione",
-    "Refinement loops": "Cicli di affinamento",
-    "Loopback alpha": "Trasparenza rielaborazione ricorsiva",
-    "Border alpha": "Trasparenza del bordo",
-    "Blending strides": "Passi di fusione",
-    "Reuse Seed": "Riusa il seme",
-    "One grid": "Singola griglia",
-    "Interpolate VarSeed": "Interpola il seme di variazione",
-    "Paste on mask": "Incolla sulla maschera",
-    "Inpaint all": "Inpaint tutto",
-    "Interpolate in latent": "Interpola in latenza",
-    "Denoising strength change factor": "Fattore di variazione dell'intensità di denoising",
-    "Superimpose alpha": "Sovrapporre Alpha",
-    "Show extra settings": "Mostra impostazioni aggiuntive",
-    "Reuse seed": "Riusa il seme",
-    "CFG decay factor": "Fattore di decadimento CFG",
-    "CFG target": "CFG di destinazione",
-    "Show/Hide Canvas": "Mostra/Nascondi Tela",
-    "Left start coord": "Coordinate iniziali - Sinistra",
-    "top start coord": "Coordinate iniziali - Sopra",
-    "unused": "non usato",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Impostazioni consigliate: Passi di campionamento: 80-100, Campionatore: Euler a, Intensità denoising: 0.8",
-    "Pixels to expand": "Pixel da espandere",
-    "Outpainting direction": "Direzione di Outpainting",
-    "left": "sinistra",
-    "right": "destra",
-    "up": "sopra",
-    "down": "sotto",
-    "Fall-off exponent (lower=higher detail)": "Esponente di decremento (più basso=maggior dettaglio)",
-    "Color variation": "Variazione di colore",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenterà l'immagine al doppio delle dimensioni; utilizzare i cursori di larghezza e altezza per impostare la dimensione della piastrella",
-    "Upscaler": "Ampliamento immagine",
-    "Lanczos": "Lanczos",
-    "LDSR": "LDSR",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR 4x": "SwinIR 4x",
-    "Mask prompt": "Prompt maschera",
-    "Negative mask prompt": "Prompt maschera negativa",
-    "Mask precision": "Precisione della maschera",
-    "Mask padding": "Estendi i bordi della maschera",
-    "Brush mask mode": "Modalità pennello maschera",
-    "discard": "Scarta",
-    "add": "Aggiungi",
-    "subtract": "Sottrai",
-    "Show mask in output?": "Mostra maschera in uscita?",
-    "If you like my work, please consider showing your support on": "Se ti piace il mio lavoro, per favore considera di mostrare il tuo supporto su ",
-    "Patreon": "Patreon",
-    "Input file path": "Percorso file di input",
-    "CRF (quality, less is better, x264 param)": "CRF (qualità, meno è meglio, x264 param)",
-    "FPS": "FPS",
-    "Seed step size": "Ampiezza del gradiente del seme",
-    "Seed max distance": "Distanza massima del seme",
-    "Start time": "Orario di inizio",
-    "End time": "Orario di fine",
-    "End Prompt Blend Trigger Percent": "Percentuale di innesco del mix col prompt finale",
-    "Prompt end": "Prompt finale",
-    "Smooth video": "Rendi il filmato fluido",
-    "Seconds": "Secondi",
-    "Zoom": "Zoom",
-    "Rotate": "Ruota",
-    "Degrees": "Gradi",
-    "Is the Image Tiled?": "L'immagine è piastrellata?",
-    "TranslateX": "Traslazione X",
-    "Left": "Sinistra",
-    "PercentX": "Percentuale X",
-    "TranslateY": "Traslazione Y",
-    "Up": "Sopra",
-    "PercentY": "Percentuale Y",
-    "Show generated pictures in ui": "Mostra le immagini generate nell'interfaccia utente",
-    "Deforum v0.5-webui-beta": "Deforum v0.5-webui-beta",
-    "This script is deprecated. Please use the full Deforum extension instead.": "Questo script è obsoleto. Utilizzare invece l'estensione Deforum completa.",
-    "Update instructions:": "Istruzioni per l'aggiornamento:",
-    "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md": "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md",
-    "discord.gg/deforum": "discord.gg/deforum",
-    "Single Image": "Singola immagine",
-    "Batch Process": "Elaborare a lotti",
-    "Batch from Directory": "Lotto da cartella",
-    "Source": "Sorgente",
-    "Show result images": "Mostra le immagini dei risultati",
-    "Scale by": "Scala di",
-    "Scale to": "Scala a",
-    "Resize": "Ridimensiona",
-    "Crop to fit": "Ritaglia per adattare",
-    "Upscaler 2 visibility": "Visibilità Ampliamento immagine 2",
-    "GFPGAN visibility": "Visibilità GFPGAN",
-    "CodeFormer visibility": "Visibilità CodeFormer",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso di CodeFormer (0 = effetto massimo, 1 = effetto minimo)",
-    "Upscale Before Restoring Faces": "Amplia prima di restaurare i volti",
-    "Send to txt2img": "Invia a txt2img",
-    "A merger of the two checkpoints will be generated in your": "I due checkpoint verranno fusi nella cartella dei",
-    "checkpoint": "checkpoint",
-    "directory.": ".",
-    "Primary model (A)": "Modello Primario (A)",
-    "Secondary model (B)": "Modello Secondario (B)",
-    "Tertiary model (C)": "Modello Terziario (C)",
-    "Custom Name (Optional)": "Nome personalizzato (facoltativo)",
-    "Multiplier (M) - set to 0 to get model A": "Moltiplicatore (M): impostare a 0 per ottenere il modello A",
-    "Interpolation Method": "Metodo di interpolazione",
-    "Weighted sum": "Somma pesata",
-    "Add difference": "Aggiungi differenza",
-    "Save as float16": "Salva come float16",
-    "See": "Consulta la ",
-    "wiki": "wiki",
-    "for detailed explanation.": " per una spiegazione dettagliata.",
-    "Create embedding": "Crea Incorporamento",
-    "Create hypernetwork": "Crea Iperrete",
-    "Preprocess images": "Preprocessa le immagini",
-    "Name": "Nome",
-    "Initialization text": "Testo di inizializzazione",
-    "Number of vectors per token": "Numero di vettori per token",
-    "Overwrite Old Embedding": "Sovrascrivi il vecchio incorporamento",
-    "Modules": "Moduli",
-    "Enter hypernetwork layer structure": "Immettere la struttura del livello della Iperrete",
-    "Select activation function of hypernetwork": "Selezionare la funzione di attivazione della Iperrete",
-    "linear": "linear",
-    "relu": "relu",
-    "leakyrelu": "leakyrelu",
-    "elu": "elu",
-    "swish": "swish",
-    "tanh": "tanh",
-    "sigmoid": "sigmoid",
-    "celu": "celu",
-    "gelu": "gelu",
-    "glu": "glu",
-    "hardshrink": "hardshrink",
-    "hardsigmoid": "hardsigmoid",
-    "hardtanh": "hardtanh",
-    "logsigmoid": "logsigmoid",
-    "logsoftmax": "logsoftmax",
-    "mish": "mish",
-    "prelu": "prelu",
-    "rrelu": "rrelu",
-    "relu6": "relu6",
-    "selu": "selu",
-    "silu": "silu",
-    "softmax": "softmax",
-    "softmax2d": "softmax2d",
-    "softmin": "softmin",
-    "softplus": "softplus",
-    "softshrink": "softshrink",
-    "softsign": "softsign",
-    "tanhshrink": "tanhshrink",
-    "threshold": "threshold",
-    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleziona inizializzazione dei pesi dei livelli. relu-like - Kaiming, Si consiglia sigmoid-like - Xavier",
-    "Normal": "Normal",
-    "KaimingUniform": "KaimingUniform",
-    "KaimingNormal": "KaimingNormal",
-    "XavierUniform": "XavierUniform",
-    "XavierNormal": "XavierNormal",
-    "Add layer normalization": "Aggiunge la normalizzazione del livello",
-    "Use dropout": "Usa Dropout",
-    "Overwrite Old Hypernetwork": "Sovrascrive la vecchia Iperrete",
-    "Source directory": "Cartella sorgente",
-    "Destination directory": "Cartella di destinazione",
-    "Existing Caption txt Action": "Azione sul testo della didascalia esistente",
-    "ignore": "ignora",
-    "copy": "copia",
-    "prepend": "anteporre",
-    "append": "appendere",
-    "Create flipped copies": "Crea copie specchiate",
-    "Split oversized images": "Dividi immagini di grandi dimensioni",
-    "Auto focal point crop": "Ritaglio automatico al punto focale",													 
-    "Use BLIP for caption": "Usa BLIP per la didascalia",
-    "Use deepbooru for caption": "Usa deepbooru per la didascalia",
-    "Split image threshold": "Soglia di divisione dell'immagine",
-    "Split image overlap ratio": "Rapporto di sovrapposizione dell'immagine",
-    "Focal point face weight": "Peso della faccia del punto focale",
-    "Focal point entropy weight": "Peso dell'entropia del punto focale",
-    "Focal point edges weight": "Peso dei bordi del punto focalePeso dei bordi del punto focale",
-    "Create debug image": "Crea immagine di debug",
-    "Preprocess": "Preprocessa",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Addestra un Incorporamento o Iperrete; è necessario specificare una directory con un set di immagini con rapporto 1:1",
-    "[wiki]": "[wiki]",
-    "Embedding": "Incorporamento",
-    "Embedding Learning rate": "Tasso di apprendimento Incorporamento",
-    "Hypernetwork Learning rate": "Tasso di apprendimento Iperrete",
-    "Dataset directory": "Cartella del Dataset",
-    "Log directory": "Cartella del registro",
-    "Prompt template file": "File modello prompt",
-    "Max steps": "Passi massimi",
-    "Save an image to log directory every N steps, 0 to disable": "Salva un'immagine nella cartella del registro ogni N passaggi, 0 per disabilitare",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva una copia dell'incorporamento nella cartella del registro ogni N passaggi, 0 per disabilitare",
-    "Save images with embedding in PNG chunks": "Salva le immagini con l'incorporamento in blocchi PNG",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Legge i parametri (prompt, ecc...) dalla scheda txt2img durante la creazione delle anteprime",
-    "Train Hypernetwork": "Addestra Iperrete",
-    "Train Embedding": "Addestra Incorporamento",
-    "Create an aesthetic embedding out of any number of images": "Crea un'incorporamento estetico da qualsiasi numero di immagini",
-    "Create images embedding": "Crea incorporamento di immagini",
-    "-1": "-1",
-    "This extension works well with text captions in comma-separated style (such as the tags generated by DeepBooru interrogator).": "Questa estensione funziona bene con i sottotitoli di testo in stile separato da virgole (come i tag generati dall'interrogatore DeepBooru).",
-    "Save all changes": "Salva tutte le modifiche",
-    "Backup original text file (original file will be renamed like filename.000, .001, .002, ...)": "Backup del file di testo originale (il file originale verrà rinominato come nomefile.000, .001, .002, ...)",
-    "Note:": "Note:",
-    "New text file will be created if you are using filename as captions.": "Verrà creato un nuovo file di testo se si utilizza il nome del file come didascalia.",
-    "Results": "Risultati",
-    "Load": "Carica",
-    "Dataset Images": "Immagini del Dataset",
-    "Filter and Edit Tags": "Filtra e modifica i tag",
-    "Edit Caption of Selected Image": "Modifica la didascalia dell'immagine selezionata",
-    "Search tags / Filter images by tags": "Cerca tag / Filtra le immagini per tag",
-    "Search Tags": "Cerca tag",
-    "Clear all filters": "Rimuovi tutti i filtri",
-    "Sort by": "Ordina per",
-    "Alphabetical Order": "Ordine alfabetico",
-    "Frequency": "Frequenza",
-    "Sort Order": "Ordinamento",
-    "Ascending": "Ascendente",
-    "Descending": "Discendente",
-    "Filter Images by Tags": "Filtra le immagini per tag",
-    "Edit tags in filtered images": "Modifica i tag nelle immagini filtrate",
-    "Selected Tags": "Tag selezionati",
-    "Edit Tags": "Modificare i tag",
-    "Apply changes to filtered images": "Applica le modifiche alle immagini filtrate",
-    "Append additional tags to the beginning": "Aggiungi tag addizionali all'inizio",
-    "1. The selected tags are displayed in comma separated style.": "1. I tag selezionati vengono visualizzati in uno stile separato da virgole.",
-    "2. When changes are applied, all tags in each displayed images are replaced.": "2. Quando vengono applicate le modifiche, tutti i tag in ciascuna immagine visualizzata vengono sostituiti.",
-    "3. If you change some tags into blank, they will be erased.": "3. Se modifichi alcuni tag con uno spazio vuoto, verranno cancellati.",
-    "4. If you add some tags to the end, they will be appended to the end/beginning of the text file.": "4. Se aggiungi dei tag alla fine, questi verranno aggiunti alla fine/inizio del file di testo.",
-    "5. Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "5. Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\"..",
-    "ex A.": "esempio A.",
-    "Original Text = \"A, A, B, C\" Selected Tags = \"B, A\" Edit Tags = \"X, Y\"": "Testo originale = \"A, A, B, C\" Tag selezionati = \"B, A\" Modifica tag = \"X, Y\"",
-    "Result = \"Y, Y, X, C\" (B->X, A->Y)": "Risultato = \"Y, Y, X, C\" (B->X, A->Y)",
-    "ex B.": "esempio B.",
-    "Original Text = \"A, B, C\" Selected Tags = \"(nothing)\" Edit Tags = \"X, Y\"": "Testo originale = \"A, B, C\" Tag selezionati = \"(nothing)\" Modifica tag = \"X, Y\"",
-    "Result = \"A, B, C, X, Y\" (add X and Y to the end (default))": "Risultato = \"A, B, C, X, Y\" (aggiunge X e Y alla fine (predefinito))",
-    "Risultato = \"X, Y, A, B, C\" (aggiunge X e Y all'inizio (\"Aggiungi tag addizionali all'inizio\" selezionato))": "Risultato = \"X, Y, A, B, C\" (aggiunge X e Y all'inizio (\"Aggiungi tag addizionali all'inizio\" selezionato))",
-    "ex C.": "esempio C.",
-    "Original Text = \"A, B, C, D, E\" Selected Tags = \"A, B, D\" Edit Tags = \", X, \"": "Testo originale = \"A, B, C, D, E\" Tag selezionati = \"A, B, D\" Modifica tag = \", X, \"",
-    "Result = \"X, C, E\" (A->\"\", B->X, D->\"\")": "Risultato = \"X, C, E\" (A->\"\", B->X, D->\"\")",
-    "Caption of Selected Image": "Didascalia dell'immagine selezionata",
-    "Copy caption": "Copia didascalia",
-    "Edit Caption": "Modifica didascalia",
-    "Apply changes to selected image": "Applica le modifiche all'immagine selezionata",
-    "Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\".",
-    "Info and links": "Info e link",
-    "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha": "Realizzato da deforum.github.io, port per l'interfaccia web di AUTOMATIC1111 manutenuto da kabachuha",
-    "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Repository Github originale di Deforum  github.com/deforum/stable-diffusion",
-    "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui": "Questo fork è per l'interfaccia web di AUTOMATIC1111 github.com/deforum-art/deforum-for-automatic1111-webui",
-    "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Unisciti al canale Discord ufficiale di Deforum discord.gg/deforum per condividere le tue creazioni e suggerimenti",
-    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "Manuale d'uso per la versione 0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
-    "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Spiegazione della matematica dei fotogrammi chiave docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
-    "Keyframes": "Fotogrammi chiave",
-    "Prompts": "Prompt",
-    "Init": "Inizializzare",
-    "Video output": "Uscita video",
-    "Run settings": "Esegui le impostazioni",
-    "Import settings from file": "Importa impostazioni da file",
-    "Override settings": "Sostituisci le impostazioni",
-    "Custom settings file": "File delle impostazioni personalizzate",
-    "Sampling settings": "Impostazioni di campionamento",
-    "override_these_with_webui": "Sovrascrivi con Web UI",
-    "W": "L",
-    "H": "A",
-    "seed": "Seme",
-    "sampler": "Campionatore",
-    "Enable extras": "Abilita Extra",
-    "subseed": "sub seme",
-    "subseed_strength": "Intensità subseme",
-    "steps": "passi",
-    "ddim_eta": "ETA DDIM",
-    "n_batch": "Numero lotto",
-    "make_grid": "Crea griglia",
-    "grid_rows": "Righe griglia",
-    "save_settings": "Salva impostazioni",
-    "save_samples": "Salva i campioni",
-    "display_samples": "Mostra i campioni",
-    "save_sample_per_step": "Salva campioni per passo",
-    "show_sample_per_step": "Mostra campioni per passo",
-    "Batch settings": "Impostazioni lotto",
-    "batch_name": "Nome del lotto",
-    "filename_format": "Formato nome del file",
-    "seed_behavior": "Comportamento seme",
-    "iter": "Iterativo",
-    "fixed": "Fisso",
-    "random": "Casuale",
-    "schedule": "Programmato",
-    "Animation settings": "Impostazioni animazione",
-    "animation_mode": "Modalità animazione",
-    "2D": "2D",
-    "3D": "3D",
-    "Video Input": "Ingresso video",
-    "max_frames": "Fotogrammi max",
-    "border": "Bordo",
-    "replicate": "Replica",
-    "wrap": "Impacchetta",
-    "Motion parameters:": "Parametri di movimento:",
-    "2D and 3D settings": "Impostazioni 2D e 3D",
-    "angle": "Angolo",
-    "zoom": "Zoom",
-    "translation_x": "Traslazione X",
-    "translation_y": "Traslazione Y",
-    "3D settings": "Impostazioni 3D",
-    "translation_z": "Traslazione Z",
-    "rotation_3d_x": "Rotazione 3D X",
-    "rotation_3d_y": "Rotazione 3D Y",
-    "rotation_3d_z": "Rotazione 3D Z",
-    "Prespective flip — Low VRAM pseudo-3D mode:": "Inversione prospettica: modalità pseudo-3D a bassa VRAM:",
-    "flip_2d_perspective": "Inverti prospettiva 2D",
-    "perspective_flip_theta": "Inverti prospettiva theta",
-    "perspective_flip_phi": "Inverti prospettiva phi",
-    "perspective_flip_gamma": "Inverti prospettiva gamma",
-    "perspective_flip_fv": "Inverti prospettiva fv",
-    "Generation settings:": "Impostazioni di generazione:",
-    "noise_schedule": "Pianificazione del rumore",
-    "strength_schedule": "Intensità della pianificazione",
-    "contrast_schedule": "Contrasto della pianificazione",
-    "cfg_scale_schedule": "Pianificazione della scala CFG",
-    "3D Fov settings:": "Impostazioni del campo visivo 3D:",
-    "fov_schedule": "Pianificazione del campo visivo",
-    "near_schedule": "Pianificazione da vicino",
-    "far_schedule": "Pianificazione da lontano",
-    "To enable seed schedule select seed behavior — 'schedule'": "Per abilitare la pianificazione del seme, seleziona il comportamento del seme — 'programma'",
-    "seed_schedule": "Pianificazione del seme",
-    "Coherence:": "Coerenza:",
-    "color_coherence": "Coerenza del colore",
-    "Match Frame 0 HSV": "Uguaglia HSV del fotogramma 0",
-    "Match Frame 0 LAB": "Uguaglia LAB del fotogramma 0",
-    "Match Frame 0 RGB": "Uguaglia RGB del fotogramma 0",
-    "diffusion_cadence": "Cadenza di diffusione",
-    "3D Depth Warping:": "Deformazione della profondità 3D:",
-    "use_depth_warping": "Usa la deformazione della profondità",
-    "midas_weight": "Peso MIDAS",
-    "near_plane": "Piano vicino",
-    "far_plane": "Piano lontano",
-    "fov": "Campo visivo",
-    "padding_mode": "Modalità di riempimento",
-    "reflection": "Rifletti",
-    "zeros": "Zeri",
-    "sampling_mode": "Modalità di campionamento",
-    "bicubic": "bicubic",
-    "bilinear": "bilinear",
-    "nearest": "nearest",
-    "save_depth_maps": "Salva le mappe di profondità",
-    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`modalità animazione: Nessuno` si inserisce nell'elenco di *prompt*. (Modalità batch disabilitata atm, funzionano solo i prompt di animazione)",
-    "*Important change from vanilla Deforum!*": "*Importante cambiamento rispetto alla versione originale di Deforum!*",
-    "This script uses the built-in webui weighting settings.": "Questo script utilizza le impostazioni di pesatura webui integrate.",
-    "So if you want to use math functions as prompt weights,": "Quindi, se vuoi usare le funzioni matematiche come pesi dei prompt,",
-    "keep the values above zero in both parts": "mantenere i valori sopra lo zero in entrambe le parti",
-    "Negative prompt part can be specified with --neg": "La parte negativa del prompt può essere specificata con --neg",
-    "batch_prompts (disabled atm)": "Prompt in lotti (al momento è disabilitato)",
-    "animation_prompts": "Prompt animazione",
-    "Init settings": "Impostazioni iniziali",
-    "use_init": "Usa le impostazioni iniziali",
-    "from_img2img_instead_of_link": "from_img2img_instead_of_link",
-    "strength_0_no_init": "strength_0_no_init",
-    "strength": "Intensità",
-    "init_image": "Immagine di inizializzazione",
-    "use_mask": "Usa maschera",
-    "use_alpha_as_mask": "Usa alpha come maschera",
-    "invert_mask": "Inverti la maschera",
-    "overlay_mask": "Sovrapponi la maschera",
-    "mask_file": "File della maschera",
-    "mask_brightness_adjust": "Regola la luminosità della maschera",
-    "mask_overlay_blur": "Sfocatura della sovrapposizione della maschera",
-    "Video Input:": "Ingresso video:",
-    "video_init_path": "Percorso del video di inizializzazione",
-    "extract_nth_frame": "Estrai ogni ennesimo fotogramma",
-    "overwrite_extracted_frames": "Sovrascrivi i fotogrammi estratti",
-    "use_mask_video": "Usa maschera video",
-    "video_mask_path": "Percorso della maschera video",
-    "Interpolation (turned off atm)": "Interpolazione (attualmente spento)",
-    "interpolate_key_frames": "Interpola fotogrammi chiave",
-    "interpolate_x_frames": "Interpola x fotogrammi",
-    "Resume animation:": "Riprendi l'animazione:",
-    "resume_from_timestring": "Riprendi da stringa temporale",
-    "resume_timestring": "Stringa temporale",
-    "Video output settings": "Impostazioni uscita video",
-    "skip_video_for_run_all": "skip_video_for_run_all",
-    "fps": "FPS",
-    "output_format": "Formato di uscita",
-    "PIL gif": "PIL gif",
-    "FFMPEG mp4": "FFMPEG mp4",
-    "ffmpeg_location": "Percorso ffmpeg",
-    "add_soundtrack": "Aggiungi colonna sonora",
-    "soundtrack_path": "Percorso colonna sonora",
-    "use_manual_settings": "Usa impostazioni manuali",
-    "render_steps": "Passi di renderizzazione",
-    "max_video_frames": "Numero max fotogrammi video",
-    "path_name_modifier": "Modificatore del nome del percorso",
-    "x0_pred": "x0_pred",
-    "x": "x",
-    "image_path": "Percorso immagine",
-    "mp4_path": "Percorso MP4",
-    "Click here after the generation to show the video": "Clicca qui dopo la generazione per mostrare il video",
-    "Save Settings": "Salva le impostazioni",
-    "Load Settings": "Carica le impostazioni",
-    "Path relative to the webui folder." : "Percorso relativo alla cartella webui.",
-    "Save Video Settings": "Salva impostazioni video",
-    "Load Video Settings": "Carica impostazioni video",
-    "dog": "cane",
-    "house": "casa",
-    "portrait": "ritratto",
-    "spaceship": "nave spaziale",
-    "anime": "anime",
-    "cartoon": "cartoon",
-    "digipa-high-impact": "digipa-high-impact",
-    "digipa-med-impact": "digipa-med-impact",
-    "digipa-low-impact": "digipa-low-impact",
-    "fareast": "estremo oriente",
-    "fineart": "fineart",
-    "scribbles": "scarabocchi",
-    "special": "special",
-    "ukioe": "ukioe",
-    "weird": "strano",
-    "black-white": "bianco e nero",
-    "nudity": "nudità",
-    "c": "c",
-    "Get Images": "Ottieni immagini",
-    "dog-anime": "dog-anime",
-    "dog-cartoon": "dog-cartoon",
-    "dog-digipa-high-impact": "dog-digipa-high-impact",
-    "dog-digipa-med-impact": "dog-digipa-med-impact",
-    "dog-digipa-low-impact": "dog-digipa-low-impact",
-    "dog-fareast": "dog-fareast",
-    "dog-fineart": "dog-fineart",
-    "dog-scribbles": "dog-scribbles",
-    "dog-special": "dog-special",
-    "dog-ukioe": "dog-ukioe",
-    "dog-weird": "dog-weird",
-    "dog-black-white": "dog-black-white",
-    "dog-nudity": "dog-nudity",
-    "dog-c": "dog-c",
-    "dog-n": "dog-n",
-    "house-anime": "house-anime",
-    "house-cartoon": "house-cartoon",
-    "house-digipa-high-impact": "house-digipa-high-impact",
-    "house-digipa-med-impact": "house-digipa-med-impact",
-    "house-digipa-low-impact": "house-digipa-low-impact",
-    "house-fareast": "house-fareast",
-    "house-fineart": "house-fineart",
-    "house-scribbles": "house-scribbles",
-    "house-special": "house-special",
-    "house-ukioe": "house-ukioe",
-    "house-weird": "house-weird",
-    "house-black-white": "house-black-white",
-    "house-nudity": "house-nudity",
-    "house-c": "house-c",
-    "house-n": "house-n",
-    "portrait-anime": "portrait-anime",
-    "portrait-cartoon": "portrait-cartoon",
-    "portrait-digipa-high-impact": "portrait-digipa-high-impact",
-    "portrait-digipa-med-impact": "portrait-digipa-med-impact",
-    "portrait-digipa-low-impact": "portrait-digipa-low-impact",
-    "portrait-fareast": "portrait-fareast",
-    "portrait-fineart": "portrait-fineart",
-    "portrait-scribbles": "portrait-scribbles",
-    "portrait-special": "portrait-special",
-    "portrait-ukioe": "portrait-ukioe",
-    "portrait-weird": "portrait-weird",
-    "portrait-black-white": "portrait-black-white",
-    "portrait-nudity": "portrait-nudity",
-    "portrait-c": "portrait-c",
-    "portrait-n": "portrait-n",
-    "spaceship-anime": "spaceship-anime",
-    "spaceship-cartoon": "spaceship-cartoon",
-    "spaceship-digipa-high-impact": "spaceship-digipa-high-impact",
-    "spaceship-digipa-med-impact": "spaceship-digipa-med-impact",
-    "spaceship-digipa-low-impact": "spaceship-digipa-low-impact",
-    "spaceship-fareast": "spaceship-fareast",
-    "spaceship-fineart": "spaceship-fineart",
-    "spaceship-scribbles": "spaceship-scribbles",
-    "spaceship-special": "spaceship-special",
-    "spaceship-ukioe": "spaceship-ukioe",
-    "spaceship-weird": "spaceship-weird",
-    "spaceship-black-white": "spaceship-black-white",
-    "spaceship-nudity": "spaceship-nudity",
-    "spaceship-c": "spaceship-c",
-    "spaceship-n": "spaceship-n",
-    "artists to study extension by camenduru |": "Estensione 'Artisti per studiare' a cura di camenduru |",
-    "github": "Github",
-    "|": "|",
-    "twitter": "Twitter",
-    "youtube": "Youtube",
-    "hi-res images": "Immagini in alta risoluzione",
-    "All images generated with CompVis/stable-diffusion-v1-4 +": "Tutte le immagini sono state generate con CompVis/stable-diffusion-v1-4 +",
-    "artists.csv": "artists.csv",
-    "| License: Attribution 4.0 International (CC BY 4.0)": "| Licenza: Attribution 4.0 International (CC BY 4.0)",
-    "extras": "Extra",
-    "favorites": "Preferiti",
-    "others": "Altre immagini",
-    "Images directory": "Cartella immagini",
-    "Dropdown": "Elenco cartelle",
-    "First Page": "Prima pagina",
-    "Prev Page": "Pagina precedente",
-    "Page Index": "Indice pagina",
-    "Next Page": "Pagina successiva",
-    "End Page": "Ultima pagina",
-    "delete next": "Cancella successivo",
-    "Delete": "Elimina",
-    "sort by": "Ordina per",
-    "path name": "Nome percorso",
-    "date": "Data",
-    "keyword": "Parola chiave",
-    "Generate Info": "Genera Info",
-    "File Name": "Nome del file",
-    "Collect": "Aggiungi ai preferiti",
-    "Renew page": "Aggiorna la pagina",
-    "Number": "Numero",
-    "set_index": "Imposta indice",
-    "load_switch": "load_switch",
-    "turn_page_switch": "turn_page_switch",
-    "Checkbox": "Casella di controllo",
-    "Checkbox Group": "Seleziona immagini per",
-    "artists": "Artisti",
-    "flavors": "Stili",
-    "mediums": "Tecniche",
-    "movements": "Movimenti artistici",
-    "All": "Tutto",
-    "Favorites": "Preferiti",
-    "Exclude abandoned": "Escludi scartati",
-    "Abandoned": "Scartati",
-    "Key word": "Parola chiave",
-    "Get inspiration": "Ispirami",
-    "to txt2img": "Invia a txt2img",
-    "to img2img": "Invia a img2img",
-    "Don't show again": "Scarta",
-    "Move out": "Rimuovi",
-    "set button": "Pulsante imposta", 
-    "Apply settings": "Applica le impostazioni",
-    "Saving images/grids": "Salva immagini/griglie",
-    "Always save all generated images": "Salva sempre tutte le immagini generate",
-    "File format for images": "Formato del file delle immagini",
-    "Images filename pattern": "Modello del nome dei file immagine",
-    "Add number to filename when saving": "Aggiungi un numero al nome del file al salvataggio",
-    "Always save all generated image grids": "Salva sempre tutte le griglie di immagini generate",
-    "File format for grids": "Formato del file per le griglie",
-    "Add extended info (seed, prompt) to filename when saving grid": "Aggiungi informazioni estese (seme, prompt) al nome del file durante il salvataggio della griglia",
-    "Do not save grids consisting of one picture": "Non salvare le griglie composte da una sola immagine",
-    "Prevent empty spots in grid (when set to autodetect)": "Previeni spazi vuoti nella griglia (se impostato su rilevamento automatico)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Numero di righe della griglia; utilizzare -1 per il rilevamento automatico e 0 per essere uguale alla dimensione del lotto",
-    "Save text information about generation parameters as chunks to png files": "Salva le informazioni di testo dei parametri di generazione come blocchi nel file png",
-    "Create a text file next to every image with generation parameters.": "Crea un file di testo assieme a ogni immagine con i parametri di generazione.",
-    "Save a copy of image before doing face restoration.": "Salva una copia dell'immagine prima di eseguire il restauro dei volti.",
-    "Quality for saved jpeg images": "Qualità delle immagini salvate in formato JPEG",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l'immagine PNG è più grande di 4 MB o qualsiasi dimensione è maggiore di 4000, ridimensiona e salva la copia come JPG",
-    "Use original name for output filename during batch process in extras tab": "Usa il nome originale per il nome del file di output durante l'elaborazione a lotti nella scheda 'Extra'",
-    "When using 'Save' button, only save a single selected image": "Usando il pulsante 'Salva', verrà salvata solo la singola immagine selezionata",
-    "Do not add watermark to images": "Non aggiungere la filigrana alle immagini",
-    "Paths for saving": "Percorsi di salvataggio",
-    "Output directory for images; if empty, defaults to three directories below": "Cartella di output per le immagini; se vuoto, per impostazione predefinita verranno usate le cartelle seguenti",
-    "Output directory for txt2img images": "Cartella di output per le immagini txt2img",
-    "Output directory for img2img images": "Cartella di output per le immagini img2img",
-    "Output directory for images from extras tab": "Cartella di output per le immagini dalla scheda 'Extra'",
-    "Output directory for grids; if empty, defaults to two directories below": "Cartella di output per le griglie; se vuoto, per impostazione predefinita veranno usate cartelle seguenti",
-    "Output directory for txt2img grids": "Cartella di output per le griglie txt2img",
-    "Output directory for img2img grids": "Cartella di output per le griglie img2img",
-    "Directory for saving images using the Save button": "Cartella dove salvare le immagini usando il pulsante 'Salva'",
-    "Saving to a directory": "Salva in una cartella",
-    "Save images to a subdirectory": "Salva le immagini in una sotto cartella",
-    "Save grids to a subdirectory": "Salva le griglie in una sotto cartella",
-    "When using \"Save\" button, save images to a subdirectory": "Usando il pulsante \"Salva\", le immagini verranno salvate in una sotto cartella",
-    "Directory name pattern": "Modello del nome della cartella",
-    "Max prompt words for [prompt_words] pattern": "Numero massimo di parole del prompt per il modello [prompt_words]",
-    "Upscaling": "Ampliamento",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Dimensione piastrella per ampliamento ESRGAN. 0 = nessuna piastrellatura.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per gli ampliamenti ESRGAN. Valori bassi = cucitura visibile.",
-    "Tile size for all SwinIR.": "Dimensione piastrella per tutti gli SwinIR.",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per SwinIR. Valori bassi = cucitura visibile.",
-    "LDSR processing steps. Lower = faster": "Fasi di elaborazione LDSR. Più basso = più veloce",
-    "Upscaler for img2img": "Metodo di ampliamento per img2img",
-    "Upscale latent space image when doing hires. fix": "Amplia l'immagine nello spazio latente durante la correzione in alta risoluzione",
-    "Face restoration": "Restauro del viso",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Peso di CodeFormer; 0 = effetto massimo; 1 = effetto minimo",
-    "Move face restoration model from VRAM into RAM after processing": "Sposta il modello di restauro facciale dalla VRAM alla RAM dopo l'elaborazione",
-    "System": "Sistema",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "Verifiche al secondo sull'utilizzo della VRAM durante la generazione. Impostare a 0 per disabilitare.",
-    "Always print all generation info to standard output": "Stampa sempre tutte le informazioni di generazione sul output standard",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda barra di avanzamento alla console che mostra l'avanzamento complessivo del lavoro.",
-    "Training": "Addestramento",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP nella RAM durante l'addestramento di Iperreti. Risparmia VRAM.",
-    "Filename word regex": "Filename word regex",
-    "Filename join string": "Filename join string",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per una singola immagine di input per epoca; utilizzato solo per visualizzare il numero di epoca",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salva un file CSV contenente la perdita nella cartella di registrazione ogni N passaggi, 0 per disabilitare",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "Checkpoint da memorizzare nella RAM",
-    "Hypernetwork strength": "Forza della Iperrete",
-    "Inpainting conditioning mask strength": "Forza della maschera di condizionamento del Inpainting",
-    "Apply color correction to img2img results to match original colors.": "Applica la correzione del colore ai risultati di img2img in modo che corrispondano ai colori originali.",
-    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la correzione del colore ai risultati di img2img",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, esegue esattamente la quantità di passi specificata dalla barra di scorrimento (normalmente se ne effettuano di meno con meno riduzione del rumore).",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Abilita la quantizzazione nei campionatori K per risultati più nitidi e puliti. Questo può cambiare i semi esistenti. Richiede il riavvio per applicare la modifica.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Enfasi: utilizzare (testo) per fare in modo che il modello presti maggiore attenzione al testo e [testo] per fargli prestare meno attenzione",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usa la vecchia implementazione dell'enfasi. Può essere utile per riprodurre vecchi semi.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Fa sì che i campionatori di diffusione K producano le stesse immagini in un lotto come quando si genera una singola immagine",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coerenza disattivando dall'ultima virgola all'indietro di n token quando si utilizzano più di 75 token",
-    "Filter NSFW content": "Filtra i contenuti NSFW",
-    "Stop At last layers of CLIP model": "Fermati agli ultimi livelli del modello CLIP",
-    "Interrogate Options": "Opzioni di interrogazione",
-    "Interrogate: keep models in VRAM": "Interroga: mantieni i modelli nella VRAM",
-    "Interrogate: use artists from artists.csv": "Interroga: utilizza artisti dal file artisti.csv",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interroga: include la classifica delle corrispondenze dei tag del modello nei risultati (non ha effetto sulle interrogazioni basate su didascalie).",
-    "Interrogate: num_beams for BLIP": "Interroga: num_beams per BLIP",
-    "Interrogate: minimum description length (excluding artists, etc..)": "Interroga: lunghezza minima della descrizione (esclusi artisti, ecc..)",
-    "Interrogate: maximum description length": "Interroga: lunghezza massima della descrizione",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: numero massimo di righe nel file di testo (0 = Nessun limite)",
-    "Interrogate: deepbooru score threshold": "Interroga: soglia del punteggio deepbooru",
-    "Interrogate: deepbooru sort alphabetically": "Interroga: deepbooru ordinato alfabeticamente",
-    "use spaces for tags in deepbooru": "usa gli spazi per i tag in deepbooru",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Effettua l'escape (\\) delle parentesi in deepbooru (così vengono usate come parentesi letterali e non per enfatizzare)",
-    "User interface": "Interfaccia Utente",
-    "Show progressbar": "Mostra la barra di avanzamento",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostra l'avanzamento della generazione dell'immagine ogni N passaggi di campionamento. Impostare a 0 per disabilitare.",
-    "Show previews of all images generated in a batch as a grid": "Mostra le anteprime di tutte le immagini generate in un lotto come una griglia",
-    "Show grid in results for web": "Mostra la griglia nei risultati per il web",
-    "Do not show any images in results for web": "Non mostrare alcuna immagine nei risultati per il web",
-    "Add model hash to generation information": "Aggiungi l'hash del modello alle informazioni sulla generazione",
-    "Add model name to generation information": "Aggiungi il nome del modello alle informazioni sulla generazione",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Durante la lettura dei parametri di generazione dal testo nell'interfaccia utente (da informazioni PNG o testo incollato), non modificare il modello/checkpoint selezionato.",
-    "Send seed when sending prompt or image to other interface": "Invia il seme quando si invia un prompt o un'immagine a un'altra interfaccia",
-    "Font for image grids that have text": "Font per griglie di immagini con testo",
-    "Enable full page image viewer": "Abilita la visualizzazione delle immagini a pagina intera",
-    "Show images zoomed in by default in full page image viewer": "Mostra le immagini ingrandite per impostazione predefinita nella visualizzazione a pagina intera",
-    "Show generation progress in window title.": "Mostra l'avanzamento della generazione nel titolo della finestra.",
-    "Quicksettings list": "Elenco delle impostazioni rapide",
-    "Localization (requires restart)": "Localizzazione (richiede il riavvio)",
-    "ar_AR": "ar_AR",
-    "de_DE": "de_DE",
-    "es_ES": "es_ES",
-    "fr-FR": "fr-FR",
-    "it_IT": "it_IT",
-    "ja_JP": "ja_JP",
-    "ko_KR": "ko_KR",
-    "pt_BR": "pt_BR",
-    "ru_RU": "ru_RU",
-    "tr_TR": "tr_TR",
-    "zh_CN": "zh_CN",
-    "zh_TW": "zh_TW",
-    "Sampler parameters": "Parametri del campionatore",
-    "Hide samplers in user interface (requires restart)": "Nascondi campionatori nell'interfaccia utente (richiede il riavvio)",
-    "eta (noise multiplier) for DDIM": "ETA (moltiplicatore di rumore) per DDIM",
-    "eta (noise multiplier) for ancestral samplers": "ETA (moltiplicatore di rumore) per campionatori ancestrali",
-    "img2img DDIM discretize": "discretizzazione DDIM per img2img",
-    "uniform": "uniforme",
-    "quad": "quad",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "sigma tmin",
-    "sigma noise": "sigma noise",
-    "Eta noise seed delta": "ETA del delta del seme del rumore",
-    "Aesthetic Image Scorer": "Punteggio delle immagini estetiche",
-    "Save score as EXIF or PNG Info Chunk": "Salva il punteggio come info EXIF o PNG",
-    "Save score as tag (Windows Only)": "Salva punteggio come etichetta (solo Windows)",
-    "Force CPU (Requires Custom Script Reload)": "Forza CPU (richiede il ricaricamento dello script personalizzato)",
-    "Number of columns on image gallery": "Numero di colonne nella galleria di immagini",
-    "Images Browser": "Galleria immagini",
-    "Preload images at startup": "Precarica le immagini all'avvio",
-    "Number of columns on the page": "Numero di colonne nella pagina",
-    "Number of rows on the page": "Numero di righe nella pagina",
-    "Minimum number of pages per load": "Numero minimo di pagine da caricare",
-    "Maximum number of samples, used to determine which folders to skip when continue running the create script": "Numero massimo di campioni, utilizzato per determinare quali cartelle ignorare quando si continua a eseguire lo script di creazione",
-    "Use same seed for all images": "Usa lo stesso seme per tutte le immagini",
-    "Request browser notifications": "Richiedi le notifiche del browser",
-    "Download localization template": "Scarica il modello per la localizzazione",
-    "Reload custom script bodies (No ui updates, No restart)": "Ricarica gli script personalizzati (nessun aggiornamento dell'interfaccia utente, nessun riavvio)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Riavvia Gradio e aggiorna i componenti (solo script personalizzati, ui.py, js e css)",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (premi Ctrl+Invio o Alt+Invio per generare)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (premere Ctrl+Invio o Alt+Invio per generare)",
-    "Add a random artist to the prompt.": "Aggiungi un artista casuale al prompt.",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leggere i parametri di generazione dal prompt o dall'ultima generazione se il prompt è vuoto ed inserirli nell'interfaccia utente.",
-    "Save style": "Salva stile",
-    "Apply selected styles to current prompt": "Applica gli stili selezionati al prompt corrente",
-    "Stop processing current image and continue processing.": "Interrompe l'elaborazione dell'immagine corrente e continua l'elaborazione.",
-    "Stop processing images and return any results accumulated so far.": "Interrompe l'elaborazione delle immagini e restituisce tutti i risultati accumulati finora.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stile da applicare; gli stili hanno componenti sia per i prompt positivi che per quelli negativi e si applicano a entrambi",
-    "Do not do anything special": "Non fa nulla di speciale",
-    "Which algorithm to use to produce the image": "Quale algoritmo utilizzare per produrre l'immagine",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - molto creativo, si può ottenere un'immagine completamente diversa a seconda del numero di passi, impostare i passi su un valore superiore a 30-40 non aiuta",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - il migliore per inpainting",
-    "Produce an image that can be tiled.": "Produce un'immagine che può essere piastrellata.",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilizza un processo in due fasi per creare parzialmente un'immagine con una risoluzione inferiore, aumentare la scala e quindi migliorarne i dettagli senza modificare la composizione",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determina quanto poco l'algoritmo dovrebbe rispettare dovrebbe il contenuto dell'immagine. A 0, non cambierà nulla e a 1 otterrai un'immagine non correlata. Con valori inferiori a 1.0 l'elaborazione richiederà meno passaggi di quelli specificati dalla barra di scorrimento dei passi di campionamento.",
-    "How many batches of images to create": "Quanti lotti di immagini generare",
-    "How many image to create in a single batch": "Quante immagini generare in un singolo lotto",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - quanto fortemente l'immagine deve conformarsi al prompt: valori più bassi producono risultati più creativi",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Un valore che determina l'output del generatore di numeri casuali: se create un'immagine con gli stessi parametri e seme di un'altra immagine, otterrete lo stesso risultato",
-    "Set seed to -1, which will cause a new random number to be used every time": "Imposta il seme su -1, che farà sì che ogni volta venga utilizzato un nuovo numero casuale",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Riusa il seme dell'ultima generazione, utile soprattutto se casuale",
-    "Seed of a different picture to be mixed into the generation.": "Seme di un'immagine diversa da miscelare nella generazione.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Quanto è forte la variazione da produrre. A 0, non ci sarà alcun effetto. A 1, otterrai l'intera immagine con il seme della variazione (tranne per i campionatori ancestrali, dove otterrai solo una leggera variazione).",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Prova a produrre un'immagine simile a quella che sarebbe stata prodotta con lo stesso seme alla risoluzione specificata",
-    "This text is used to rotate the feature space of the imgs embs": "Questo testo viene utilizzato per ruotare lo spazio delle funzioni delle immagini incorporate",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Quante volte ripetere l'elaborazione di un'immagine e utilizzarla come input per l'iterazione successiva",
-    "Enter one prompt per line. Blank lines will be ignored.": "Immettere un prompt per riga. Le righe vuote verranno ignorate.",
-    "Separate values for X axis using commas.": "Separare i valori per l'asse X usando le virgole.",
-    "Separate values for Y axis using commas.": "Separare i valori per l'asse Y usando le virgole.",
-    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "Separa un elenco di parole con virgole e lo script eseguirà una variazione di prompt con quelle parole per ogni loro possibile ordine",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva l'immagine/i in una cartella (predefinita - log/images) ed i parametri di generazione in un file CSV.",
-    "Open images output directory": "Apri la cartella di output delle immagini",
-    "How much to blur the mask before processing, in pixels.": "Quanto sfocare la maschera prima dell'elaborazione, in pixel.",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Cosa mettere all'interno dell'area mascherata prima di elaborarla con Stable Diffusion.",
-    "fill it with colors of the image": "riempi con i colori dell'immagine",
-    "keep whatever was there originally": "conserva tutto ciò che c'era in origine",
-    "fill it with latent space noise": "riempi di rumore spaziale latente",
-    "fill it with latent space zeroes": "riempi con zeri di spazio latente",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Ingrandisce la regione mascherata per raggiungere la risoluzione, esegue la pittura, riduce la scala e incolla nell'immagine originale",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Ridimensiona l'immagine alla risoluzione di destinazione. A meno che altezza e larghezza non corrispondano, otterrai proporzioni errate.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Ridimensionare l'immagine in modo che l'intera risoluzione di destinazione sia riempita con l'immagine. Ritaglia le parti che sporgono.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Ridimensiona l'immagine in modo che l'intera immagine rientri nella risoluzione di destinazione. Riempi lo spazio vuoto con i colori dell'immagine.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Per l'upscaling SD, quanta sovrapposizione in pixel dovrebbe esserci tra le piastrelle. Le piastrelle si sovrappongono in modo che quando vengono unite nuovamente in un'immagine, non ci siano giunture chiaramente visibili.",
-    "Process an image, use it as an input, repeat.": "Elabora un'immagine, usala come input, ripeti.",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In modalità rielaborazione ricorsiva, su ogni ciclo la forza di denoising viene moltiplicata per questo valore. <1 significa varietà decrescente in modo che la sequenza converga su un'immagine fissa. >1 significa aumentare la varietà in modo che la tua sequenza diventi sempre più caotica.",
-    "A directory on the same machine where the server is running.": "Una cartella sulla stessa macchina su cui è in esecuzione il server.",
-    "Leave blank to save images to the default path.": "Lascia vuoto per salvare le immagini nel percorso predefinito.",
-    "Result = A * (1 - M) + B * M": "Risultato = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "Risultato = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "La prima e l'ultima cifra devono essere 1. Es.:'1, 2, 1'",
-    "Path to directory with input images": "Percorso della cartella con immagini di input",
-    "Path to directory where to write outputs": "Percorso della cartella in cui scrivere i risultati",
-    "C:\\directory\\of\\datasets": "C:\\cartella\\del\\dataset",
-    "Input images directory": "Cartella di input delle immagini",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Usa i seguenti tag per definire come vengono scelti i nomi dei file per le immagini: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed ], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se questa opzione è abilitata, la filigrana non verrà aggiunta alle immagini create. Attenzione: se non aggiungi la filigrana, potresti comportarti in modo non etico.",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utilizzare i seguenti tag per definire come vengono scelte le sottodirectory per le immagini e le griglie: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
-    "Restore low quality faces using GFPGAN neural network": "Ripristina volti di bassa qualità utilizzando la rete neurale GFPGAN",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Questa espressione regolare verrà utilizzata per estrarre le parole dal nome del file e verranno unite utilizzando l'opzione seguente nel testo dell'etichetta utilizzato per l'addestramento. Lascia vuoto per mantenere il testo del nome del file così com'è.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "Questa stringa verrà utilizzata per unire le parole divise in un'unica riga se l'opzione sopra è abilitata.",
-    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Si applica solo ai modelli di pittura. Determina con quale forza mascherare l'immagine originale per inpainting e img2img. 1.0 significa completamente mascherato, che è il comportamento predefinito. 0.0 significa un condizionamento completamente non mascherato. Valori più bassi aiuteranno a preservare la composizione generale dell'immagine, ma avranno difficoltà con grandi cambiamenti.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Elenco dei nomi delle impostazioni, separati da virgole, per le impostazioni che dovrebbero essere visualizzate nella barra di accesso rapido in alto, anziché nella normale scheda delle impostazioni. Vedi modules/shared.py per impostare i nomi. Richiede il riavvio per applicare.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se questo valore è diverso da zero, verrà aggiunto al seed e utilizzato per inizializzare il generatore di numeri casuali per il rumore quando si utilizzano campionatori con ETA. Puoi usarlo per produrre ancora più variazioni di immagini, oppure puoi usarlo per abbinare le immagini di altri software se sai cosa stai facendo."
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "Caricamento...",
+    "view": "mostra ",
+    "api": "API",
+    "•": " • ",
+    "built with gradio": " Sviluppato con Gradio",
+    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "Extra",
+    "PNG Info": "Info PNG",
+    "Checkpoint Merger": "Miscelatore di Checkpoint",
+    "Train": "Addestramento",
+    "Create aesthetic embedding": "Crea incorporamento estetico",
+    "Dataset Tag Editor": "Dataset Tag Editor",
+    "Deforum": "Deforum",
+    "Artists To Study": "Artisti per studiare",
+    "Image Browser": "Galleria immagini",
+    "Inspiration": "Ispirazione",
+    "Settings": "Impostazioni",
+    "Prompt": "Prompt",
+    "Negative prompt": "Prompt negativo",
+    "Run": "Esegui",
+    "Skip": "Salta",
+    "Interrupt": "Interrompi",
+    "Generate": "Genera",
+    "Style 1": "Stile 1",
+    "Style 2": "Stile 2",
+    "Label": "Etichetta",
+    "File": "File",
+    "Drop File Here": "Trascina il file qui",
+    "-": "-",
+    "or": "o",
+    "Click to Upload": "Clicca per caricare",
+    "Image": "Immagine",
+    "Check progress": "Controlla i progressi",
+    "Check progress (first)": "Controlla i progressi (primo)",
+    "Sampling Steps": "Passi di campionamento",
+    "Sampling method": "Metodo di campionamento",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Larghezza",
+    "Height": "Altezza",
+    "Restore faces": "Restaura i volti",
+    "Tiling": "Piastrellatura",
+    "Highres. fix": "Correzione alta risoluzione",
+    "Firstpass width": "Larghezza del primo passaggio",
+    "Firstpass height": "Altezza del primo passaggio",
+    "Denoising strength": "Forza del Denoising",
+    "Batch count": "Lotti di immagini",
+    "Batch size": "Immagini per lotto",
+    "CFG Scale": "Scala CFG",
+    "Seed": "Seme",
+    "Extra": "Extra",
+    "Variation seed": "Seme della variazione",
+    "Variation strength": "Forza della variazione",
+    "Resize seed from width": "Ridimensiona il seme dalla larghezza",
+    "Resize seed from height": "Ridimensiona il seme dall`altezza",
+    "Open for Clip Aesthetic!": "Apri per Estetica CLIP!",
+    "▼": "▼",
+    "Aesthetic weight": "Estetica - Peso",
+    "Aesthetic steps": "Estetica - Passi",
+    "Aesthetic learning rate": "Estetica - Tasso di apprendimento",
+    "Slerp interpolation": "Interpolazione Slerp",
+    "Aesthetic imgs embedding": "Estetica - Incorporamento di immagini",
+    "None": "Niente",
+    "Aesthetic text for imgs": "Estetica - Testo per le immagini",
+    "Slerp angle": "Angolo Slerp",
+    "Is negative text": "È un testo negativo",
+    "Script": "Script",
+    "Random grid": "Generaz. casuale (griglia)",
+    "Random": "Generaz. casuale (no griglia)",
+    "StylePile": "StylePile",
+    "Advanced prompt matrix": "Matrice di prompt avanzata",
+    "Alternate Sampler Noise Schedules": "Metodi alternativi di campionamento del rumore",
+    "Animator v6": "Animator v6",
+    "Asymmetric tiling": "Piastrellatura asimmetrica",
+    "Custom code": "Codice personalizzato",
+    "Dynamic Prompting v0.2": "Prompt dinamici v0.2",
+    "Embedding to Shareable PNG": "Incorporamento convertito in PNG condivisibile",
+    "Force symmetry": "Forza la simmetria",
+    "Prompts interpolation": "Interpola Prompt",
+    "Prompt matrix": "Matrice dei prompt",
+    "Prompt morph": "Metamorfosi del prompt",
+    "Prompts from file or textbox": "Prompt da file o da casella di testo",
+    "To Infinity and Beyond": "Verso l'infinito e oltre",
+    "Seed travel": "Interpolazione semi",
+    "Shift attention": "Sposta l`attenzione",
+    "Text to Vector Graphics": "Da testo a grafica vettoriale",
+    "X/Y plot": "Grafico X/Y",
+    "X/Y/Z plot": "Grafico X/Y/Z",
+    "Create inspiration images": "Crea immagini di ispirazione",
+    "step1 min/max": "Passi min(o max)",
+    "step2 min/max": "Passi max (o min)",
+    "step cnt": "Q.tà di Passi",
+    "cfg1 min/max": "CFG min (o max)",
+    "cfg2 min/max": "CFG max (o min)",
+    "cfg cnt": "Q.tà di CFG",
+    "Draw legend": "Disegna legenda",
+    "Include Separate Images": "Includi immagini separate",
+    "Keep -1 for seeds": "Mantieni sempre il seme a -1",
+    "x/y change": "Inverti l`ordine degli assi X/Y (Passi/CFG)",
+    "Loops": "Cicli",
+    "Focus on:": "Focus su:",
+    "No focus": "Nessun Focus",
+    "Portraits (tick Restore faces above for best results)": "Ritratti (selezionare 'Restaura volti' in alto per ottenere i migliori risultati)",
+    "Feminine and extra attractive (tick Restore faces above for best results)": "Femminile ed estremamente attraente (selezionare 'Restaura volti' per ottenere i migliori risultati)",
+    "Masculine and extra attractive (tick Restore faces above for best results)": "Maschile ed estremamente attraente (selezionare 'Restaura volti'  per ottenere i migliori risultati)",
+    "Monsters": "Mostri",
+    "Robots": "Robot",
+    "Retrofuturistic": "Retrofuturistico",
+    "Propaganda": "Propaganda",
+    "Landscapes": "Paesaggi",
+    "Hints": "Suggerimenti",
+    "Image type": "Tipo di immagine",
+    "Not set": "Non impostato",
+    "Photography": "Fotografia",
+    "Digital art": "Arte digitale",
+    "3D Rendering": "3D Rendering",
+    "Painting": "Dipinto",
+    "Sketch": "Schizzo",
+    "Classic Comics": "Fumetti classici",
+    "Modern Comics": "Fumetti moderni",
+    "Manga": "Manga",
+    "Vector art": "Arte vettoriale",
+    "Visual style": "Stile visivo",
+    "Realism": "Realismo",
+    "Photorealism": "Fotorealismo",
+    "Hyperrealism": "Iperrealismo",
+    "Surrealism": "Surrealismo",
+    "Modern Art": "Arte moderna",
+    "Fauvism": "Fauvismo",
+    "Futurism": "Futurismo",
+    "Painterly": "Pittorico",
+    "Pointillisme": "Puntinismo",
+    "Abstract": "Astratto",
+    "Pop Art": "Pop Art",
+    "Impressionist": "Impressionista",
+    "Cubism": "Cubismo",
+    "Linocut": "Linoleografia",
+    "Fantasy": "Fantasia",
+    "Colors": "Colori",
+    "Chaotic": "Caotico",
+    "Primary colors": "Colori primari",
+    "Colorful": "Colorato",
+    "Vivid": "Vivido",
+    "Muted colors": "Colori tenui",
+    "Low contrast": "Basso contrasto",
+    "Desaturated": "Desaturato",
+    "Grayscale": "Scala di grigi",
+    "Black and white": "Bianco e nero",
+    "Infrared": "Infrarosso",
+    "Complementary": "Colori complementari",
+    "Non-complementary": "Colori non complementari",
+    "View": "Visuale",
+    "Tilt shift": "Tilt shift",
+    "Wide-angle": "Angolo ampio",
+    "Portrait": "Ritratto",
+    "Macro": "Macro",
+    "Microscopic": "Microscopico",
+    "Isometric": "Isometrico",
+    "Panorama": "Panorama",
+    "Aerial photograph": "Fotografia aerea",
+    "Artist focus (not quite finished, not sure it helps)": "Focus sull'artista (non del tutto finito, non è sicuro che aiuti)",
+    "B/W Photograpy": "Fotografia B/N",
+    "Portrait photo": "Foto ritratto",
+    "Usage: a <corgi|cat> wearing <goggles|a hat>": "Utilizzo: a <corgi|cat> wearing <goggles|a hat>",
+    "Noise Scheduler": "Programmatore del rumore",
+    "Default": "Predefinito",
+    "Karras": "Karras",
+    "Exponential": "Esponenziale",
+    "Variance Preserving": "Conservazione della Varianza",
+    "Sigma min": "Sigma min",
+    "Sigma max": "Sigma max",
+    "Sigma rho (Karras only)": "Sigma rho (Solo Karras)",
+    "Beta distribution (VP only)": "Distribuzione Beta (Solo CV)",
+    "Beta min (VP only)": "Beta min (Solo CV)",
+    "Epsilon (VP only)": "Epsilon (Solo CV)",
+    "Running in txt2img mode:": "Esecuzione in modalità txt2img:",
+    "Render these video formats:": "Renderizza in questi formati:",
+    "GIF": "GIF",
+    "MP4": "MP4",
+    "WEBM": "WEBM",
+    "Animation Parameters": "Parametri animazione",
+    "Total Animation Length (s)": "Durata totale dell`animazione (s)",
+    "Framerate": "Frequenza dei fotogrammi",
+    "Smoothing_Frames": "Smoothing_Frames",
+    "Add_Noise": "Aggiungi rumore",
+    "Noise Strength": "Intensità del rumore",
+    "Initial Parameters": "Parametri iniziali",
+    "Denoising Strength (overrides img2img slider)": "Intensità di riduzione del rumore (sovrascrive il cursore img2img)",
+    "Seed_March": "Seed_March",
+    "Zoom Factor (scale/s)": "Fattore di ingrandimento (scala/s)",
+    "X Pixel Shift (pixels/s)": "Sposta i Pixel sull`asse X (pixel/s)",
+    "Y Pixel Shift (pixels/s)": "Sposta i Pixel sull`asse Y (pixel/s)",
+    "Rotation (deg/s)": "Rotazione (gradi/s)",
+    "Prompt Template, applied to each keyframe below": "Modello di prompt, applicato a ciascun fotogramma chiave qui di seguito",
+    "Positive Prompts": "Prompt positivi",
+    "Negative Prompts": "Prompt negativi",
+    "Props, Stamps": "Props, Stamps\n ",
+    "Poper_Folder:": "Cartella Poper:",
+    "Supported Keyframes:": "Fotogrammi chiave supportati:",
+    "time_s | source | video, images, img2img | path": "time_s | source | video, images, img2img | path",
+    "time_s | prompt | positive_prompts | negative_prompts": "time_s | prompt | positive_prompts | negative_prompts",
+    "time_s | template | positive_prompts | negative_prompts": "time_s | template | positive_prompts | negative_prompts",
+    "time_s | transform | zoom | x_shift | y_shift | rotation": "time_s | transform | zoom | x_shift | y_shift | rotation",
+    "time_s | seed | new_seed_int": "time_s | seed | new_seed_int",
+    "time_s | noise | added_noise_strength": "time_s | noise | added_noise_strength",
+    "time_s | denoise | denoise_value": "time_s | denoise | denoise_value",
+    "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name": "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name",
+    "time_s | clear_text | textblock_name": "time_s | clear_text | textblock_name",
+    "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation": "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation",
+    "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation": "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation",
+    "time_s | clear_stamp | stamp_name": "time_s | clear_stamp | stamp_name",
+    "time_s | col_set": "time_s | col_set",
+    "time_s | col_clear": "time_s | col_clear",
+    "time_s | model | model": "time_s | model | model",
+    "img2img_mode": "Modalità img2img",
+    "Keyframes:": "Fotogrammi chiave:",
+    "Tile X": "Piastrella asse X",
+    "Tile Y": "Piastrella asse Y",
+    "Python code": "Codice Python",
+    "Combinatorial generation": "Generazione combinatoria",
+    "Combinations": "Combinazioni",
+    "Choose a number of terms from a list, in this case we choose two artists:": "Scegli un numero di termini da un elenco, in questo caso scegliamo due artisti:",
+    "{2$$artist1|artist2|artist3}": "{2$$artist1|artist2|artist3}",
+    "If $$ is not provided, then 1$$ is assumed.\n\n            A range can be provided:": "Se $$ non viene fornito, si presume 1$$.\n\n            È possibile fornire un intervallo di valori:",
+    "{1-3$$artist1|artist2|artist3}": "{1-3$$artist1|artist2|artist3}",
+    "In this case, a random number of artists between 1 and 3 is chosen.": "In questo caso viene scelto un numero casuale di artisti compreso tra 1 e 3.",
+    "Wildcards": "Termini jolly",
+    "If the groups wont drop down click": "Se i gruppi non vengono visualizzati, clicca",
+    "here": "qui",
+    "to fix the issue.": "per correggere il problema.",
+    "WILDCARD_DIR: scripts/wildcards": "WILDCARD_DIR: scripts/wildcards",
+    "You can add more wildcards by creating a text file with one term per line and name is mywildcards.txt. Place it in scripts/wildcards.": "Puoi aggiungere termini jolly creando un file di testo con un termine per riga e nominandolo, per esempio, mywildcards.txt. Inseriscilo in scripts/wildcards.",
+    "__<folder>/mywildcards__": "__<folder>/mywildcards__",
+    "will then become available.": "diverrà quindi disponibile.",
+    "Source embedding to convert": "Incorporamento sorgente da convertire",
+    "Embedding token": "Token Incorporamento",
+    "Output directory": "Cartella di output",
+    "Horizontal symmetry": "Simmetria orizzontale",
+    "Vertical symmetry": "Simmetria verticale",
+    "Alt. symmetry method (blending)": "Metodo di simmetria alternativo (miscelazione)",
+    "Apply every n steps": "Applica ogni n passi",
+    "Skip last n steps": "Salta gli ultimi n passi",
+    "Interpolation prompt": "Prompt di interpolazione",
+    "Number of images": "Numero di immagini",
+    "Make a gif": "Crea GIF",
+    "Duration of images (ms)": "Durata delle immagini (ms)",
+    "Put variable parts at start of prompt": "Inserisce le parti variabili all`inizio del prompt",
+    "Keyframe Format:": "Formato dei fotogrammi chiave:",
+    "Seed | Prompt or just Prompt": "Seme | Prompt o semplicemente Prompt",
+    "Prompt list": "Elenco dei prompt",
+    "Number of images between keyframes": "Numero di immagini tra fotogrammi chiave",
+    "Save results as video": "Salva i risultati come video",
+    "Frames per second": "Fotogrammi al secondo",
+    "Iterate seed every line": "Iterare il seme per ogni riga",
+    "List of prompt inputs": "Elenco di prompt di input",
+    "Upload prompt inputs": "Carica un file contenente i prompt di input",
+    "n": "Esegui n volte",
+    "Destination seed(s) (Comma separated)": "Seme/i di destinazione (separati da virgola)",
+    "Only use Random seeds (Unless comparing paths)": "Usa solo semi casuali (a meno che non si confrontino i percorsi)",
+    "Number of random seed(s)": "Numero di semi casuali",
+    "Compare paths (Separate travels from 1st seed to each destination)": "Confronta percorsi (transizioni separate dal primo seme a ciascuna destinazione)",
+    "Steps": "Passi",
+    "Loop back to initial seed": "Ritorna al seme iniziale",
+    "Bump seed (If > 0 do a Compare Paths but only one image. No video)": "Modula seme (se > 0 mescola il seme iniziale con quelli di destinazione ma solo un`immagine. Nessun video)",
+    "Show generated images in ui": "Mostra le immagini generate nell`interfaccia utente",
+    "\"Hug the middle\" during interpolation": "\"Hug the middle\" durante l`interpolazione. Rende l`interpolazione un po` più veloce all`inizio e alla fine. A volte può produrre video più fluidi, il più delle volte no.",
+    "Allow the default Euler a Sampling method. (Does not produce good results)": "Consenti Euler_a come metodo di campionamento predefinito. (Non produce buoni risultati)",
+    "Illustration": "Illustrazione",
+    "Logo": "Logo",
+    "Drawing": "Disegno",
+    "Artistic": "Artistico",
+    "Tattoo": "Tatuaggio",
+    "Gothic": "Gotico",
+    "Anime": "Anime",
+    "Cartoon": "Cartoon",
+    "Sticker": "Etichetta",
+    "Gold Pendant": "Ciondolo in oro",
+    "None - prompt only": "Nessuno - solo prompt",
+    "Enable Vectorizing": "Abilita vettorizzazione",
+    "Output format": "Formato di output",
+    "svg": "svg",
+    "pdf": "pdf",
+    "White is Opaque": "Il bianco è opaco",
+    "Cut white margin from input": "Taglia il margine bianco dall`input",
+    "Keep temp images": "Conserva le immagini temporanee",
+    "Threshold": "Soglia",
+    "Transparent PNG": "PNG trasparente",
+    "Noise Tolerance": "Tolleranza al rumore",
+    "Quantize": "Quantizzare",
+    "X type": "Parametro asse X",
+    "Nothing": "Niente",
+    "Var. seed": "Seme della variazione",
+    "Var. strength": "Forza della variazione",
+    "Prompt S/R": "Cerca e Sostituisci nel Prompt",
+    "Prompt order": "In ordine di prompt",
+    "Sampler": "Campionatore",
+    "Checkpoint name": "Nome del checkpoint",
+    "Hypernetwork": "Iperrete",
+    "Hypernet str.": "Forza della Iperrete",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma noise": "Sigma noise",
+    "Eta": "ETA",
+    "Clip skip": "Salta CLIP",
+    "Denoising": "Riduzione del rumore",
+    "Cond. Image Mask Weight": "Cond. Image Mask Weight",
+    "X values": "Valori per X",
+    "Y type": "Parametro asse Y",
+    "Y values": "Valori per Y",
+    "Z type": "Parametro asse Z",
+    "Z values": "Valori per Z",
+    "Artist or styles name list. `.txt` files with one name per line": "Elenco nomi di artisti o stili. File '.txt' con un nome per riga",
+    "Prompt words before artist or style name": "Parole chiave prima del nome dell`artista o dello stile",
+    "Prompt words after artist or style name": "Parole chiave dopo il nome dell`artista o dello stile",
+    "Negative Prompt": "Prompt negativo",
+    "Save": "Salva",
+    "Send to img2img": "Invia a img2img",
+    "Send to inpaint": "Invia a inpaint",
+    "Send to extras": "Invia a extra",
+    "Make Zip when Save?": "Crea un file ZIP quando si usa `Salva`",
+    "Textbox": "Casella di testo",
+    "Interrogate\nCLIP": "Interroga\nCLIP",
+    "Interrogate\nDeepBooru": "Interroga\nDeepBooru",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "Lotti img2img",
+    "Image for img2img": "Immagine per img2img",
+    "Drop Image Here": "Trascina l`immagine qui",
+    "Image for inpainting with mask": "Immagine per inpainting con maschera",
+    "Mask": "Maschera",
+    "Mask blur": "Sfocatura maschera",
+    "Mask mode": "Modalità maschera",
+    "Draw mask": "Disegna maschera",
+    "Upload mask": "Carica maschera",
+    "Masking mode": "Modalità mascheratura",
+    "Inpaint masked": "Inpaint mascherato",
+    "Inpaint not masked": "Inpaint non mascherato",
+    "Masked content": "Contenuto mascherato",
+    "fill": "riempi",
+    "original": "originale",
+    "latent noise": "rumore latente",
+    "latent nothing": "latenza nulla",
+    "Inpaint at full resolution": "Inpaint alla massima risoluzione",
+    "Inpaint at full resolution padding, pixels": "Inpaint con riempimento a piena risoluzione, pixel",
+    "Process images in a directory on the same machine where the server is running.": "Elabora le immagini in una cartella sulla stessa macchina su cui è in esecuzione il server.",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa una cartella di output vuota per salvare normalmente le immagini invece di scrivere nella cartella di output.",
+    "Input directory": "Cartella di Input",
+    "Resize mode": "Modalità di ridimensionamento",
+    "Just resize": "Ridimensiona solamente",
+    "Crop and resize": "Ritaglia e ridimensiona",
+    "Resize and fill": "Ridimensiona e riempie",
+    "Advanced loopback": "Advanced loopback", 
+    "External Image Masking": "Immagine esterna per la mascheratura",
+    "img2img alternative test": "Test alternativo per img2img",
+    "img2tiles": "img2tiles",
+    "Interpolate": "Interpolare",
+    "Loopback": "Rielaborazione ricorsiva",
+    "Loopback and Superimpose": "Rielabora ricorsivamente e sovraimponi",
+    "Alpha Canvas": "Alpha Canvas",
+    "Outpainting mk2": "Outpainting mk2",
+    "Poor man`s outpainting": "Poor man`s outpainting",
+    "SD upscale": "Ampliamento SD",
+    "txt2mask v0.1.1": "txt2mask v0.1.1",
+    "[C] Video to video": "[C] Video to video",
+    "Videos": "Filmati",
+    "Deforum-webui (use tab extension instead!)": "Deforum-webui (usa piuttosto la scheda Deforum delle estensioni!)",
+    "Use first image colors (custom color correction)": "Usa i colori della prima immagine (correzione del colore personalizzata)",
+    "Denoising strength change factor (overridden if proportional used)": "Fattore di variazione dell`intensità di riduzione del rumore (sovrascritto se si usa proporzionale)",
+    "Zoom level": "Livello di Zoom",
+    "Direction X": "Direzione X",
+    "Direction Y": "Direzione Y",
+    "Denoising strength start": "Intensità di riduzione del rumore - Inizio",
+    "Denoising strength end": "Intensità di riduzione del rumore - Fine",
+    "Denoising strength proportional change starting value": "Intensità di riduzione del rumore - Valore iniziale della variazione proporzionale",
+    "Denoising strength proportional change ending value (0.1 = disabled)": "Intensità di riduzione del rumore - Valore finale della variazione proporzionale (0.1 = disabilitato)",
+    "Saturation enhancement per image": "Miglioramento della saturazione per ciascuna immagine",
+    "Use sine denoising strength variation": "Utilizzare la variazione sinusoidale dell`intensità di riduzione del rumore",
+    "Phase difference": "Differenza di Fase",
+    "Denoising strength exponentiation": "Esponenziazione dell`intensità di riduzione del rumore",
+    "Use sine zoom variation": "Usa la variazione sinusoidale dello zoom",
+    "Zoom exponentiation": "Esponenziazione dello Zoom",
+    "Use multiple prompts": "Usa prompt multipli",
+    "Same seed per prompt": "Stesso seme per ogni prompt",
+    "Same seed for everything": "Stesso seme per tutto",
+    "Original init image for everything": "Immagine originale di inizializzazione per tutto",
+    "Multiple prompts : 1 line positive, 1 line negative, leave a blank line for no negative": "Prompt multipli: 1 riga positivo, 1 riga negativo, lasciare una riga vuota per nessun negativo",
+    "Running in img2img mode:": "Esecuzione in modalità img2img:",
+    "Masking preview size": "Dimensione dell`anteprima della mascheratura",
+    "Draw new mask on every run": "Disegna una nuova maschera ad ogni esecuzione",
+    "Process non-contigious masks separately": "Elaborare le maschere non contigue separatamente",
+    "should be 2 or lower.": "dovrebbe essere 2 o inferiore.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Sovrascrivi il `Metodo di campionamento` con Eulero? (questo metodo è stato creato per questo)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Sovrascrivi `prompt` con lo stesso valore del `prompt originale`? (e `prompt negativo`)",
+    "Original prompt": "Prompt originale",
+    "Original negative prompt": "Prompt negativo originale",
+    "Override `Sampling Steps` to the same val due as `Decode steps`?": "Sovrascrivere `Passi di campionamento` allo stesso valore di `Passi di decodifica`?",
+    "Decode steps": "Passi di decodifica",
+    "Override `Denoising strength` to 1?": "Sostituisci `Forza di denoising` a 1?",
+    "Decode CFG scale": "Scala CFG di decodifica",
+    "Randomness": "Casualità",
+    "Sigma adjustment for finding noise for image": "Regolazione Sigma per trovare il rumore per l`immagine",
+    "Tile size": "Dimensione piastrella",
+    "Tile overlap": "Sovrapposizione piastrella",
+    "alternate img2img imgage": "alternate img2img imgage",
+    "interpolation values": "Valori di interpolazione",
+    "Refinement loops": "Cicli di affinamento",
+    "Loopback alpha": "Trasparenza rielaborazione ricorsiva",
+    "Border alpha": "Trasparenza del bordo",
+    "Blending strides": "Passi di fusione",
+    "Reuse Seed": "Riusa il seme",
+    "One grid": "Singola griglia",
+    "Interpolate VarSeed": "Interpola il seme di variazione",
+    "Paste on mask": "Incolla sulla maschera",
+    "Inpaint all": "Inpaint tutto",
+    "Interpolate in latent": "Interpola in latenza",
+    "Denoising strength change factor": "Fattore di variazione dell`intensità di denoising",
+    "Superimpose alpha": "Sovrapporre Alpha",
+    "Show extra settings": "Mostra impostazioni aggiuntive",
+    "Reuse seed": "Riusa il seme",
+    "CFG decay factor": "Fattore di decadimento CFG",
+    "CFG target": "CFG di destinazione",
+    "Show/Hide AlphaCanvas": "Mostra/Nascondi AlphaCanvas",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Impostazioni consigliate: Passi di campionamento: 80-100, Campionatore: Euler a, Intensità denoising: 0.8",
+    "Pixels to expand": "Pixel da espandere",
+    "Outpainting direction": "Direzione di Outpainting",
+    "left": "sinistra",
+    "right": "destra",
+    "up": "sopra",
+    "down": "sotto",
+    "Fall-off exponent (lower=higher detail)": "Esponente di decremento (più basso=maggior dettaglio)",
+    "Color variation": "Variazione di colore",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenterà l`immagine al doppio delle dimensioni; utilizzare i cursori di larghezza e altezza per impostare la dimensione della piastrella",
+    "Upscaler": "Ampliamento immagine",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR 4x": "SwinIR 4x",
+    "Mask prompt": "Prompt maschera",
+    "Negative mask prompt": "Prompt maschera negativa",
+    "Mask precision": "Precisione della maschera",
+    "Mask padding": "Estendi i bordi della maschera",
+    "Brush mask mode": "Modalità pennello maschera",
+    "discard": "Scarta",
+    "add": "Aggiungi",
+    "subtract": "Sottrai",
+    "Show mask in output?": "Mostra maschera in uscita?",
+    "If you like my work, please consider showing your support on": "Se ti piace il mio lavoro, per favore considera di mostrare il tuo supporto su ",
+    "Patreon": "Patreon",
+    "Input file path": "Percorso file di input",
+    "CRF (quality, less is better, x264 param)": "CRF (qualità, meno è meglio, x264 param)",
+    "FPS": "FPS",
+    "Seed step size": "Ampiezza del gradiente del seme",
+    "Seed max distance": "Distanza massima del seme",
+    "Start time": "Orario di inizio",
+    "End time": "Orario di fine",
+    "End Prompt Blend Trigger Percent": "Percentuale di innesco del mix col prompt finale",
+    "Prompt end": "Prompt finale",
+    "Smooth video": "Rendi il filmato fluido",
+    "Seconds": "Secondi",
+    "Zoom": "Zoom",
+    "Rotate": "Ruota",
+    "Degrees": "Gradi",
+    "Is the Image Tiled?": "L`immagine è piastrellata?",
+    "TranslateX": "Traslazione X",
+    "Left": "Sinistra",
+    "PercentX": "Percentuale X",
+    "TranslateY": "Traslazione Y",
+    "Up": "Sopra",
+    "PercentY": "Percentuale Y",
+    "Show generated pictures in ui": "Mostra le immagini generate nell`interfaccia utente",
+    "Deforum v0.5-webui-beta": "Deforum v0.5-webui-beta",
+    "This script is deprecated. Please use the full Deforum extension instead.": "Questo script è obsoleto. Utilizzare invece l`estensione Deforum completa.",
+    "Update instructions:": "Istruzioni per l`aggiornamento:",
+    "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md": "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md",
+    "discord.gg/deforum": "discord.gg/deforum",
+    "Single Image": "Singola immagine",
+    "Batch Process": "Elaborare a lotti",
+    "Batch from Directory": "Lotto da cartella",
+    "Source": "Sorgente",
+    "Show result images": "Mostra le immagini dei risultati",
+    "Scale by": "Scala di",
+    "Scale to": "Scala a",
+    "Resize": "Ridimensiona",
+    "Crop to fit": "Ritaglia per adattare",
+    "Upscaler 2 visibility": "Visibilità Ampliamento immagine 2",
+    "GFPGAN visibility": "Visibilità GFPGAN",
+    "CodeFormer visibility": "Visibilità CodeFormer",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso di CodeFormer (0 = effetto massimo, 1 = effetto minimo)",
+    "Upscale Before Restoring Faces": "Amplia prima di restaurare i volti",
+    "Send to txt2img": "Invia a txt2img",
+    "A merger of the two checkpoints will be generated in your": "I due checkpoint verranno fusi nella cartella dei",
+    "checkpoint": "checkpoint",
+    "directory.": ".",
+    "Primary model (A)": "Modello Primario (A)",
+    "Secondary model (B)": "Modello Secondario (B)",
+    "Tertiary model (C)": "Modello Terziario (C)",
+    "Custom Name (Optional)": "Nome personalizzato (facoltativo)",
+    "Multiplier (M) - set to 0 to get model A": "Moltiplicatore (M): impostare a 0 per ottenere il modello A",
+    "Interpolation Method": "Metodo di interpolazione",
+    "Weighted sum": "Somma pesata",
+    "Add difference": "Aggiungi differenza",
+    "Save as float16": "Salva come float16",
+    "See": "Consulta la ",
+    "wiki": "wiki",
+    "for detailed explanation.": " per una spiegazione dettagliata.",
+    "Create embedding": "Crea Incorporamento",
+    "Create hypernetwork": "Crea Iperrete",
+    "Preprocess images": "Preprocessa le immagini",
+    "Name": "Nome",
+    "Initialization text": "Testo di inizializzazione",
+    "Number of vectors per token": "Numero di vettori per token",
+    "Overwrite Old Embedding": "Sovrascrivi il vecchio incorporamento",
+    "Modules": "Moduli",
+    "Enter hypernetwork layer structure": "Immettere la struttura del livello della Iperrete",
+    "Select activation function of hypernetwork": "Selezionare la funzione di attivazione della Iperrete",
+    "linear": "linear",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "threshold",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleziona inizializzazione dei pesi dei livelli. relu-like - Kaiming, Si consiglia sigmoid-like - Xavier",
+    "Normal": "Normal",
+    "KaimingUniform": "KaimingUniform",
+    "KaimingNormal": "KaimingNormal",
+    "XavierUniform": "XavierUniform",
+    "XavierNormal": "XavierNormal",
+    "Add layer normalization": "Aggiunge la normalizzazione del livello",
+    "Use dropout": "Usa Dropout",
+    "Overwrite Old Hypernetwork": "Sovrascrive la vecchia Iperrete",
+    "Source directory": "Cartella sorgente",
+    "Destination directory": "Cartella di destinazione",
+    "Existing Caption txt Action": "Azione sul testo della didascalia esistente",
+    "ignore": "ignora",
+    "copy": "copia",
+    "prepend": "anteporre",
+    "append": "appendere",
+    "Create flipped copies": "Crea copie specchiate",
+    "Split oversized images": "Dividi immagini di grandi dimensioni",
+    "Auto focal point crop": "Ritaglio automatico al punto focale",
+    "Use BLIP for caption": "Usa BLIP per la didascalia",
+    "Use deepbooru for caption": "Usa deepbooru per la didascalia",
+    "Split image threshold": "Soglia di divisione dell`immagine",
+    "Split image overlap ratio": "Rapporto di sovrapposizione dell`immagine",
+    "Focal point face weight": "Peso della faccia del punto focale",
+    "Focal point entropy weight": "Peso dell`entropia del punto focale",
+    "Focal point edges weight": "Peso dei bordi del punto focalePeso dei bordi del punto focale",
+    "Create debug image": "Crea immagine di debug",
+    "Preprocess": "Preprocessa",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Addestra un Incorporamento o Iperrete; è necessario specificare una directory con un set di immagini con rapporto 1:1",
+    "[wiki]": "[wiki]",
+    "Embedding": "Incorporamento",
+    "Embedding Learning rate": "Tasso di apprendimento Incorporamento",
+    "Hypernetwork Learning rate": "Tasso di apprendimento Iperrete",
+    "Dataset directory": "Cartella del Dataset",
+    "Log directory": "Cartella del registro",
+    "Prompt template file": "File modello prompt",
+    "Max steps": "Passi massimi",
+    "Save an image to log directory every N steps, 0 to disable": "Salva un`immagine nella cartella del registro ogni N passaggi, 0 per disabilitare",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva una copia dell`incorporamento nella cartella del registro ogni N passaggi, 0 per disabilitare",
+    "Save images with embedding in PNG chunks": "Salva le immagini con l`incorporamento in blocchi PNG",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Legge i parametri (prompt, ecc...) dalla scheda txt2img durante la creazione delle anteprime",
+    "Train Hypernetwork": "Addestra Iperrete",
+    "Train Embedding": "Addestra Incorporamento",
+    "Create an aesthetic embedding out of any number of images": "Crea un`incorporamento estetico da qualsiasi numero di immagini",
+    "Create images embedding": "Crea incorporamento di immagini",
+    "-1": "-1",
+    "This extension works well with text captions in comma-separated style (such as the tags generated by DeepBooru interrogator).": "Questa estensione funziona bene con i sottotitoli di testo in stile separato da virgole (come i tag generati dall`interrogatore DeepBooru).",
+    "Save all changes": "Salva tutte le modifiche",
+    "Backup original text file (original file will be renamed like filename.000, .001, .002, ...)": "Backup del file di testo originale (il file originale verrà rinominato come nomefile.000, .001, .002, ...)",
+    "Note:": "Note:",
+    "New text file will be created if you are using filename as captions.": "Verrà creato un nuovo file di testo se si utilizza il nome del file come didascalia.",
+    "Results": "Risultati",
+    "Load": "Carica",
+    "Dataset Images": "Immagini del Dataset",
+    "Filter and Edit Tags": "Filtra e modifica i tag",
+    "Edit Caption of Selected Image": "Modifica la didascalia dell`immagine selezionata",
+    "Search tags / Filter images by tags": "Cerca tag / Filtra le immagini per tag",
+    "Search Tags": "Cerca tag",
+    "Clear all filters": "Rimuovi tutti i filtri",
+    "Sort by": "Ordina per",
+    "Alphabetical Order": "Ordine alfabetico",
+    "Frequency": "Frequenza",
+    "Sort Order": "Ordinamento",
+    "Ascending": "Ascendente",
+    "Descending": "Discendente",
+    "Filter Images by Tags": "Filtra le immagini per tag",
+    "Edit tags in filtered images": "Modifica i tag nelle immagini filtrate",
+    "Selected Tags": "Tag selezionati",
+    "Edit Tags": "Modificare i tag",
+    "Apply changes to filtered images": "Applica le modifiche alle immagini filtrate",
+    "Append additional tags to the beginning": "Aggiungi tag addizionali all`inizio",
+    "1. The selected tags are displayed in comma separated style.": "1. I tag selezionati vengono visualizzati in uno stile separato da virgole.",
+    "2. When changes are applied, all tags in each displayed images are replaced.": "2. Quando vengono applicate le modifiche, tutti i tag in ciascuna immagine visualizzata vengono sostituiti.",
+    "3. If you change some tags into blank, they will be erased.": "3. Se modifichi alcuni tag con uno spazio vuoto, verranno cancellati.",
+    "4. If you add some tags to the end, they will be appended to the end/beginning of the text file.": "4. Se aggiungi dei tag alla fine, questi verranno aggiunti alla fine/inizio del file di testo.",
+    "5. Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "5. Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\"..",
+    "ex A.": "esempio A.",
+    "Original Text = \"A, A, B, C\" Selected Tags = \"B, A\" Edit Tags = \"X, Y\"": "Testo originale = \"A, A, B, C\" Tag selezionati = \"B, A\" Modifica tag = \"X, Y\"",
+    "Result = \"Y, Y, X, C\" (B->X, A->Y)": "Risultato = \"Y, Y, X, C\" (B->X, A->Y)",
+    "ex B.": "esempio B.",
+    "Original Text = \"A, B, C\" Selected Tags = \"(nothing)\" Edit Tags = \"X, Y\"": "Testo originale = \"A, B, C\" Tag selezionati = \"(nothing)\" Modifica tag = \"X, Y\"",
+    "Result = \"A, B, C, X, Y\" (add X and Y to the end (default))": "Risultato = \"A, B, C, X, Y\" (aggiunge X e Y alla fine (predefinito))",
+    "Result = \"X, Y, A, B, C\" (add X and Y to the beginning (\"Append additional tags to the beginning\" checked))": "Risultato = \"X, Y, A, B, C\" (aggiunge X e Y all`inizio (\"Aggiungi tag addizionali all`inizio\" selezionato))",
+    "ex C.": "esempio C.",
+    "Original Text = \"A, B, C, D, E\" Selected Tags = \"A, B, D\" Edit Tags = \", X, \"": "Testo originale = \"A, B, C, D, E\" Tag selezionati = \"A, B, D\" Modifica tag = \", X, \"",
+    "Result = \"X, C, E\" (A->\"\", B->X, D->\"\")": "Risultato = \"X, C, E\" (A->\"\", B->X, D->\"\")",
+    "Caption of Selected Image": "Didascalia dell`immagine selezionata",
+    "Copy caption": "Copia didascalia",
+    "Edit Caption": "Modifica didascalia",
+    "Apply changes to selected image": "Applica le modifiche all`immagine selezionata",
+    "Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\".",
+    "Info and links": "Info e link",
+    "Made by deforum.github.io, port for AUTOMATIC1111`s webui maintained by kabachuha": "Realizzato da deforum.github.io, port per l`interfaccia web di AUTOMATIC1111 manutenuto da kabachuha",
+    "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Repository Github originale di Deforum  github.com/deforum/stable-diffusion",
+    "This fork for auto1111`s webui github.com/deforum-art/deforum-for-automatic1111-webui": "Questo fork è per l`interfaccia web di AUTOMATIC1111 github.com/deforum-art/deforum-for-automatic1111-webui",
+    "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Unisciti al canale Discord ufficiale di Deforum discord.gg/deforum per condividere le tue creazioni e suggerimenti",
+    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "Manuale d`uso per la versione 0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
+    "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Spiegazione della matematica dei fotogrammi chiave docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
+    "Keyframes": "Fotogrammi chiave",
+    "Prompts": "Prompt",
+    "Init": "Inizializzare",
+    "Video output": "Uscita video",
+    "Run settings": "Esegui le impostazioni",
+    "Import settings from file": "Importa impostazioni da file",
+    "Override settings": "Sostituisci le impostazioni",
+    "Custom settings file": "File delle impostazioni personalizzate",
+    "Sampling settings": "Impostazioni di campionamento",
+    "override_these_with_webui": "Sovrascrivi con Web UI",
+    "W": "L",
+    "H": "A",
+    "seed": "Seme",
+    "sampler": "Campionatore",
+    "Enable extras": "Abilita Extra",
+    "subseed": "sub seme",
+    "subseed_strength": "Intensità subseme",
+    "steps": "passi",
+    "ddim_eta": "ETA DDIM",
+    "n_batch": "Numero lotto",
+    "make_grid": "Crea griglia",
+    "grid_rows": "Righe griglia",
+    "save_settings": "Salva impostazioni",
+    "save_samples": "Salva i campioni",
+    "display_samples": "Mostra i campioni",
+    "save_sample_per_step": "Salva campioni per passo",
+    "show_sample_per_step": "Mostra campioni per passo",
+    "Batch settings": "Impostazioni lotto",
+    "batch_name": "Nome del lotto",
+    "filename_format": "Formato nome del file",
+    "seed_behavior": "Comportamento seme",
+    "iter": "Iterativo",
+    "fixed": "Fisso",
+    "random": "Casuale",
+    "schedule": "Programmato",
+    "Animation settings": "Impostazioni animazione",
+    "animation_mode": "Modalità animazione",
+    "2D": "2D",
+    "3D": "3D",
+    "Video Input": "Ingresso video",
+    "max_frames": "Fotogrammi max",
+    "border": "Bordo",
+    "replicate": "Replica",
+    "wrap": "Impacchetta",
+    "Motion parameters:": "Parametri di movimento:",
+    "2D and 3D settings": "Impostazioni 2D e 3D",
+    "angle": "Angolo",
+    "zoom": "Zoom",
+    "translation_x": "Traslazione X",
+    "translation_y": "Traslazione Y",
+    "3D settings": "Impostazioni 3D",
+    "translation_z": "Traslazione Z",
+    "rotation_3d_x": "Rotazione 3D X",
+    "rotation_3d_y": "Rotazione 3D Y",
+    "rotation_3d_z": "Rotazione 3D Z",
+    "Prespective flip — Low VRAM pseudo-3D mode:": "Inversione prospettica: modalità pseudo-3D a bassa VRAM:",
+    "flip_2d_perspective": "Inverti prospettiva 2D",
+    "perspective_flip_theta": "Inverti prospettiva theta",
+    "perspective_flip_phi": "Inverti prospettiva phi",
+    "perspective_flip_gamma": "Inverti prospettiva gamma",
+    "perspective_flip_fv": "Inverti prospettiva fv",
+    "Generation settings:": "Impostazioni di generazione:",
+    "noise_schedule": "Pianificazione del rumore",
+    "strength_schedule": "Intensità della pianificazione",
+    "contrast_schedule": "Contrasto della pianificazione",
+    "cfg_scale_schedule": "Pianificazione della scala CFG",
+    "3D Fov settings:": "Impostazioni del campo visivo 3D:",
+    "fov_schedule": "Pianificazione del campo visivo",
+    "near_schedule": "Pianificazione da vicino",
+    "far_schedule": "Pianificazione da lontano",
+    "To enable seed schedule select seed behavior — `schedule`": "Per abilitare la pianificazione del seme, seleziona il comportamento del seme — `programma`",
+    "seed_schedule": "Pianificazione del seme",
+    "Coherence:": "Coerenza:",
+    "color_coherence": "Coerenza del colore",
+    "Match Frame 0 HSV": "Uguaglia HSV del fotogramma 0",
+    "Match Frame 0 LAB": "Uguaglia LAB del fotogramma 0",
+    "Match Frame 0 RGB": "Uguaglia RGB del fotogramma 0",
+    "diffusion_cadence": "Cadenza di diffusione",
+    "3D Depth Warping:": "Deformazione della profondità 3D:",
+    "use_depth_warping": "Usa la deformazione della profondità",
+    "midas_weight": "Peso MIDAS",
+    "near_plane": "Piano vicino",
+    "far_plane": "Piano lontano",
+    "fov": "Campo visivo",
+    "padding_mode": "Modalità di riempimento",
+    "reflection": "Rifletti",
+    "zeros": "Zeri",
+    "sampling_mode": "Modalità di campionamento",
+    "bicubic": "bicubic",
+    "bilinear": "bilinear",
+    "nearest": "nearest",
+    "save_depth_maps": "Salva le mappe di profondità",
+    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`modalità animazione: Nessuno` si inserisce nell`elenco di *prompt*. (Modalità batch disabilitata atm, funzionano solo i prompt di animazione)",
+    "*Important change from vanilla Deforum!*": "*Importante cambiamento rispetto alla versione originale di Deforum!*",
+    "This script uses the built-in webui weighting settings.": "Questo script utilizza le impostazioni di pesatura webui integrate.",
+    "So if you want to use math functions as prompt weights,": "Quindi, se vuoi usare le funzioni matematiche come pesi dei prompt,",
+    "keep the values above zero in both parts": "mantenere i valori sopra lo zero in entrambe le parti",
+    "Negative prompt part can be specified with --neg": "La parte negativa del prompt può essere specificata con --neg",
+    "batch_prompts (disabled atm)": "Prompt in lotti (al momento è disabilitato)",
+    "animation_prompts": "Prompt animazione",
+    "Init settings": "Impostazioni iniziali",
+    "use_init": "Usa le impostazioni iniziali",
+    "from_img2img_instead_of_link": "from_img2img_instead_of_link",
+    "strength_0_no_init": "strength_0_no_init",
+    "strength": "Intensità",
+    "init_image": "Immagine di inizializzazione",
+    "use_mask": "Usa maschera",
+    "use_alpha_as_mask": "Usa alpha come maschera",
+    "invert_mask": "Inverti la maschera",
+    "overlay_mask": "Sovrapponi la maschera",
+    "mask_file": "File della maschera",
+    "mask_brightness_adjust": "Regola la luminosità della maschera",
+    "mask_overlay_blur": "Sfocatura della sovrapposizione della maschera",
+    "Video Input:": "Ingresso video:",
+    "video_init_path": "Percorso del video di inizializzazione",
+    "extract_nth_frame": "Estrai ogni ennesimo fotogramma",
+    "overwrite_extracted_frames": "Sovrascrivi i fotogrammi estratti",
+    "use_mask_video": "Usa maschera video",
+    "video_mask_path": "Percorso della maschera video",
+    "Interpolation (turned off atm)": "Interpolazione (attualmente spento)",
+    "interpolate_key_frames": "Interpola fotogrammi chiave",
+    "interpolate_x_frames": "Interpola x fotogrammi",
+    "Resume animation:": "Riprendi l`animazione:",
+    "resume_from_timestring": "Riprendi da stringa temporale",
+    "resume_timestring": "Stringa temporale",
+    "Video output settings": "Impostazioni uscita video",
+    "skip_video_for_run_all": "Salta il video per eseguire tutto",
+    "fps": "FPS",
+    "output_format": "Formato di uscita",
+    "PIL gif": "PIL gif",
+    "FFMPEG mp4": "FFMPEG mp4",
+    "ffmpeg_location": "Percorso ffmpeg",
+    "add_soundtrack": "Aggiungi colonna sonora",
+    "soundtrack_path": "Percorso colonna sonora",
+    "use_manual_settings": "Usa impostazioni manuali",
+    "render_steps": "Passi di renderizzazione",
+    "max_video_frames": "Numero max fotogrammi video",
+    "path_name_modifier": "Modificatore del nome del percorso",
+    "x0_pred": "x0_pred",
+    "x": "x",
+    "image_path": "Percorso immagine",
+    "mp4_path": "Percorso MP4",
+    "Click here after the generation to show the video": "Clicca qui dopo la generazione per mostrare il video",
+    "Save Settings": "Salva le impostazioni",
+    "Load Settings": "Carica le impostazioni",
+    "Path relative to the webui folder." : "Percorso relativo alla cartella webui.",
+    "Save Video Settings": "Salva impostazioni video",
+    "Load Video Settings": "Carica impostazioni video",
+    "dog": "cane",
+    "house": "casa",
+    "portrait": "ritratto",
+    "spaceship": "nave spaziale",
+    "anime": "anime",
+    "cartoon": "cartoon",
+    "digipa-high-impact": "digipa-high-impact",
+    "digipa-med-impact": "digipa-med-impact",
+    "digipa-low-impact": "digipa-low-impact",
+    "fareast": "estremo oriente",
+    "fineart": "fineart",
+    "scribbles": "scarabocchi",
+    "special": "special",
+    "ukioe": "ukioe",
+    "weird": "strano",
+    "black-white": "bianco e nero",
+    "nudity": "nudità",
+    "c": "c",
+    "Get Images": "Ottieni immagini",
+    "dog-anime": "dog-anime",
+    "dog-cartoon": "dog-cartoon",
+    "dog-digipa-high-impact": "dog-digipa-high-impact",
+    "dog-digipa-med-impact": "dog-digipa-med-impact",
+    "dog-digipa-low-impact": "dog-digipa-low-impact",
+    "dog-fareast": "dog-fareast",
+    "dog-fineart": "dog-fineart",
+    "dog-scribbles": "dog-scribbles",
+    "dog-special": "dog-special",
+    "dog-ukioe": "dog-ukioe",
+    "dog-weird": "dog-weird",
+    "dog-black-white": "dog-black-white",
+    "dog-nudity": "dog-nudity",
+    "dog-c": "dog-c",
+    "dog-n": "dog-n",
+    "house-anime": "house-anime",
+    "house-cartoon": "house-cartoon",
+    "house-digipa-high-impact": "house-digipa-high-impact",
+    "house-digipa-med-impact": "house-digipa-med-impact",
+    "house-digipa-low-impact": "house-digipa-low-impact",
+    "house-fareast": "house-fareast",
+    "house-fineart": "house-fineart",
+    "house-scribbles": "house-scribbles",
+    "house-special": "house-special",
+    "house-ukioe": "house-ukioe",
+    "house-weird": "house-weird",
+    "house-black-white": "house-black-white",
+    "house-nudity": "house-nudity",
+    "house-c": "house-c",
+    "house-n": "house-n",
+    "portrait-anime": "portrait-anime",
+    "portrait-cartoon": "portrait-cartoon",
+    "portrait-digipa-high-impact": "portrait-digipa-high-impact",
+    "portrait-digipa-med-impact": "portrait-digipa-med-impact",
+    "portrait-digipa-low-impact": "portrait-digipa-low-impact",
+    "portrait-fareast": "portrait-fareast",
+    "portrait-fineart": "portrait-fineart",
+    "portrait-scribbles": "portrait-scribbles",
+    "portrait-special": "portrait-special",
+    "portrait-ukioe": "portrait-ukioe",
+    "portrait-weird": "portrait-weird",
+    "portrait-black-white": "portrait-black-white",
+    "portrait-nudity": "portrait-nudity",
+    "portrait-c": "portrait-c",
+    "portrait-n": "portrait-n",
+    "spaceship-anime": "spaceship-anime",
+    "spaceship-cartoon": "spaceship-cartoon",
+    "spaceship-digipa-high-impact": "spaceship-digipa-high-impact",
+    "spaceship-digipa-med-impact": "spaceship-digipa-med-impact",
+    "spaceship-digipa-low-impact": "spaceship-digipa-low-impact",
+    "spaceship-fareast": "spaceship-fareast",
+    "spaceship-fineart": "spaceship-fineart",
+    "spaceship-scribbles": "spaceship-scribbles",
+    "spaceship-special": "spaceship-special",
+    "spaceship-ukioe": "spaceship-ukioe",
+    "spaceship-weird": "spaceship-weird",
+    "spaceship-black-white": "spaceship-black-white",
+    "spaceship-nudity": "spaceship-nudity",
+    "spaceship-c": "spaceship-c",
+    "spaceship-n": "spaceship-n",
+    "artists to study extension by camenduru |": "Estensione `Artisti per studiare` a cura di camenduru |",
+    "github": "Github",
+    "|": "|",
+    "twitter": "Twitter",
+    "youtube": "Youtube",
+    "hi-res images": "Immagini in alta risoluzione",
+    "All images generated with CompVis/stable-diffusion-v1-4 +": "Tutte le immagini sono state generate con CompVis/stable-diffusion-v1-4 +",
+    "artists.csv": "artists.csv",
+    "| License: Attribution 4.0 International (CC BY 4.0)": "| Licenza: Attribution 4.0 International (CC BY 4.0)",
+    "extras": "Extra",
+    "favorites": "Preferiti",
+    "others": "Altre immagini",
+    "Images directory": "Cartella immagini",
+    "Dropdown": "Elenco cartelle",
+    "First Page": "Prima pagina",
+    "Prev Page": "Pagina precedente",
+    "Page Index": "Indice pagina",
+    "Next Page": "Pagina successiva",
+    "End Page": "Ultima pagina",
+    "delete next": "Cancella successivo",
+    "Delete": "Elimina",
+    "sort by": "Ordina per",
+    "path name": "Nome percorso",
+    "date": "Data",
+    "keyword": "Parola chiave",
+    "Generate Info": "Genera Info",
+    "File Name": "Nome del file",
+    "Collect": "Aggiungi ai preferiti",
+    "Renew page": "Aggiorna la pagina",
+    "Number": "Numero",
+    "set_index": "Imposta indice",
+    "load_switch": "load_switch",
+    "turn_page_switch": "turn_page_switch",
+    "Checkbox": "Casella di controllo",						   
+    "Checkbox Group": "Seleziona immagini per",
+    "artists": "Artisti",
+    "flavors": "Stili",
+    "mediums": "Tecniche",
+    "movements": "Movimenti artistici",
+    "All": "Tutto",
+    "Favorites": "Preferiti",
+    "Exclude abandoned": "Escludi scartati",
+    "Abandoned": "Scartati",
+    "Key word": "Parola chiave",
+    "Get inspiration": "Ispirami",
+    "to txt2img": "Invia a txt2img",
+    "to img2img": "Invia a img2img",
+    "Don`t show again": "Scarta",
+    "Move out": "Rimuovi",
+    "set button": "Pulsante imposta", 
+    "Apply settings": "Applica le impostazioni",
+    "Saving images/grids": "Salva immagini/griglie",
+    "Always save all generated images": "Salva sempre tutte le immagini generate",
+    "File format for images": "Formato del file delle immagini",
+    "Images filename pattern": "Modello del nome dei file immagine",
+    "Add number to filename when saving": "Aggiungi un numero al nome del file al salvataggio",
+    "Always save all generated image grids": "Salva sempre tutte le griglie di immagini generate",
+    "File format for grids": "Formato del file per le griglie",
+    "Add extended info (seed, prompt) to filename when saving grid": "Aggiungi informazioni estese (seme, prompt) al nome del file durante il salvataggio della griglia",
+    "Do not save grids consisting of one picture": "Non salvare le griglie composte da una sola immagine",
+    "Prevent empty spots in grid (when set to autodetect)": "Previeni spazi vuoti nella griglia (se impostato su rilevamento automatico)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Numero di righe della griglia; utilizzare -1 per il rilevamento automatico e 0 per essere uguale alla dimensione del lotto",
+    "Save text information about generation parameters as chunks to png files": "Salva le informazioni di testo dei parametri di generazione come blocchi nel file png",
+    "Create a text file next to every image with generation parameters.": "Crea un file di testo assieme a ogni immagine con i parametri di generazione.",
+    "Save a copy of image before doing face restoration.": "Salva una copia dell`immagine prima di eseguire il restauro dei volti.",
+    "Quality for saved jpeg images": "Qualità delle immagini salvate in formato JPEG",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l`immagine PNG è più grande di 4 MB o qualsiasi dimensione è maggiore di 4000, ridimensiona e salva la copia come JPG",
+    "Use original name for output filename during batch process in extras tab": "Usa il nome originale per il nome del file di output durante l`elaborazione a lotti nella scheda `Extra`",
+    "When using `Save` button, only save a single selected image": "Usando il pulsante `Salva`, verrà salvata solo la singola immagine selezionata",
+    "Do not add watermark to images": "Non aggiungere la filigrana alle immagini",
+    "Paths for saving": "Percorsi di salvataggio",
+    "Output directory for images; if empty, defaults to three directories below": "Cartella di output per le immagini; se vuoto, per impostazione predefinita verranno usate le cartelle seguenti",
+    "Output directory for txt2img images": "Cartella di output per le immagini txt2img",
+    "Output directory for img2img images": "Cartella di output per le immagini img2img",
+    "Output directory for images from extras tab": "Cartella di output per le immagini dalla scheda `Extra`",
+    "Output directory for grids; if empty, defaults to two directories below": "Cartella di output per le griglie; se vuoto, per impostazione predefinita veranno usate cartelle seguenti",
+    "Output directory for txt2img grids": "Cartella di output per le griglie txt2img",
+    "Output directory for img2img grids": "Cartella di output per le griglie img2img",
+    "Directory for saving images using the Save button": "Cartella dove salvare le immagini usando il pulsante `Salva`",
+    "Saving to a directory": "Salva in una cartella",
+    "Save images to a subdirectory": "Salva le immagini in una sotto cartella",
+    "Save grids to a subdirectory": "Salva le griglie in una sotto cartella",
+    "When using \"Save\" button, save images to a subdirectory": "Usando il pulsante \"Salva\", le immagini verranno salvate in una sotto cartella",
+    "Directory name pattern": "Modello del nome della cartella",
+    "Max prompt words for [prompt_words] pattern": "Numero massimo di parole del prompt per il modello [prompt_words]",
+    "Upscaling": "Ampliamento",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Dimensione piastrella per ampliamento ESRGAN. 0 = nessuna piastrellatura.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per gli ampliamenti ESRGAN. Valori bassi = cucitura visibile.",
+    "Tile size for all SwinIR.": "Dimensione piastrella per tutti gli SwinIR.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per SwinIR. Valori bassi = cucitura visibile.",
+    "LDSR processing steps. Lower = faster": "Fasi di elaborazione LDSR. Più basso = più veloce",
+    "Upscaler for img2img": "Metodo di ampliamento per img2img",
+    "Upscale latent space image when doing hires. fix": "Amplia l`immagine nello spazio latente durante la correzione in alta risoluzione",
+    "Face restoration": "Restauro del viso",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Peso di CodeFormer; 0 = effetto massimo; 1 = effetto minimo",
+    "Move face restoration model from VRAM into RAM after processing": "Sposta il modello di restauro facciale dalla VRAM alla RAM dopo l`elaborazione",
+    "System": "Sistema",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Verifiche al secondo sull`utilizzo della VRAM durante la generazione. Impostare a 0 per disabilitare.",
+    "Always print all generation info to standard output": "Stampa sempre tutte le informazioni di generazione sul output standard",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda barra di avanzamento alla console che mostra l`avanzamento complessivo del lavoro.",
+    "Training": "Addestramento",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP nella RAM durante l`addestramento di Iperreti. Risparmia VRAM.",
+    "Filename word regex": "Filename word regex",
+    "Filename join string": "Filename join string",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per una singola immagine di input per epoca; utilizzato solo per visualizzare il numero di epoca",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salva un file CSV contenente la perdita nella cartella di registrazione ogni N passaggi, 0 per disabilitare",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "Checkpoint da memorizzare nella RAM",
+    "Hypernetwork strength": "Forza della Iperrete",
+    "Inpainting conditioning mask strength": "Forza della maschera di condizionamento del Inpainting",
+    "Apply color correction to img2img results to match original colors.": "Applica la correzione del colore ai risultati di img2img in modo che corrispondano ai colori originali.",
+    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell`immagine prima di applicare la correzione del colore ai risultati di img2img",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you`d do less with less denoising).": "Con img2img, esegue esattamente la quantità di passi specificata dalla barra di scorrimento (normalmente se ne effettuano di meno con meno riduzione del rumore).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Abilita la quantizzazione nei campionatori K per risultati più nitidi e puliti. Questo può cambiare i semi esistenti. Richiede il riavvio per applicare la modifica.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Enfasi: utilizzare (testo) per fare in modo che il modello presti maggiore attenzione al testo e [testo] per fargli prestare meno attenzione",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usa la vecchia implementazione dell`enfasi. Può essere utile per riprodurre vecchi semi.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Fa sì che i campionatori di diffusione K producano le stesse immagini in un lotto come quando si genera una singola immagine",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coerenza disattivando dall`ultima virgola all`indietro di n token quando si utilizzano più di 75 token",
+    "Filter NSFW content": "Filtra i contenuti NSFW",
+    "Stop At last layers of CLIP model": "Fermati agli ultimi livelli del modello CLIP",
+    "Interrogate Options": "Opzioni di interrogazione",
+    "Interrogate: keep models in VRAM": "Interroga: mantieni i modelli nella VRAM",
+    "Interrogate: use artists from artists.csv": "Interroga: utilizza artisti dal file artisti.csv",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interroga: include la classifica delle corrispondenze dei tag del modello nei risultati (non ha effetto sulle interrogazioni basate su didascalie).",
+    "Interrogate: num_beams for BLIP": "Interroga: num_beams per BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Interroga: lunghezza minima della descrizione (esclusi artisti, ecc..)",
+    "Interrogate: maximum description length": "Interroga: lunghezza massima della descrizione",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: numero massimo di righe nel file di testo (0 = Nessun limite)",
+    "Interrogate: deepbooru score threshold": "Interroga: soglia del punteggio deepbooru",
+    "Interrogate: deepbooru sort alphabetically": "Interroga: deepbooru ordinato alfabeticamente",
+    "use spaces for tags in deepbooru": "usa gli spazi per i tag in deepbooru",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Effettua l`escape (\\) delle parentesi in deepbooru (così vengono usate come parentesi letterali e non per enfatizzare)",
+    "User interface": "Interfaccia Utente",
+    "Show progressbar": "Mostra la barra di avanzamento",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostra l`avanzamento della generazione dell`immagine ogni N passaggi di campionamento. Impostare a 0 per disabilitare.",
+    "Show previews of all images generated in a batch as a grid": "Mostra le anteprime di tutte le immagini generate in un lotto come una griglia",
+    "Show grid in results for web": "Mostra la griglia nei risultati per il web",
+    "Do not show any images in results for web": "Non mostrare alcuna immagine nei risultati per il web",
+    "Add model hash to generation information": "Aggiungi l`hash del modello alle informazioni sulla generazione",
+    "Add model name to generation information": "Aggiungi il nome del modello alle informazioni sulla generazione",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Durante la lettura dei parametri di generazione dal testo nell`interfaccia utente (da informazioni PNG o testo incollato), non modificare il modello/checkpoint selezionato.",
+    "Send seed when sending prompt or image to other interface": "Invia il seme quando si invia un prompt o un`immagine a un`altra interfaccia",
+    "Font for image grids that have text": "Font per griglie di immagini con testo",
+    "Enable full page image viewer": "Abilita la visualizzazione delle immagini a pagina intera",
+    "Show images zoomed in by default in full page image viewer": "Mostra le immagini ingrandite per impostazione predefinita nella visualizzazione a pagina intera",
+    "Show generation progress in window title.": "Mostra l`avanzamento della generazione nel titolo della finestra.",
+    "Quicksettings list": "Elenco delle impostazioni rapide",
+    "Localization (requires restart)": "Localizzazione (richiede il riavvio)",
+    "ar_AR": "ar_AR",
+    "de_DE": "de_DE",
+    "es_ES": "es_ES",
+    "fr_FR": "fr_FR",
+    "it_IT": "it_IT",
+    "ja_JP": "ja_JP",
+    "ko_KR": "ko_KR",
+    "pt_BR": "pt_BR",
+    "ru_RU": "ru_RU",
+    "tr_TR": "tr_TR",
+    "zh_CN": "zh_CN",
+    "zh_TW": "zh_TW",
+    "Sampler parameters": "Parametri del campionatore",
+    "Hide samplers in user interface (requires restart)": "Nascondi campionatori nell`interfaccia utente (richiede il riavvio)",
+    "eta (noise multiplier) for DDIM": "ETA (moltiplicatore di rumore) per DDIM",
+    "eta (noise multiplier) for ancestral samplers": "ETA (moltiplicatore di rumore) per campionatori ancestrali",
+    "img2img DDIM discretize": "discretizzazione DDIM per img2img",
+    "uniform": "uniforme",
+    "quad": "quad",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma noise",
+    "Eta noise seed delta": "ETA del delta del seme del rumore",
+    "Aesthetic Image Scorer": "Punteggio delle immagini estetiche",
+    "Save score as EXIF or PNG Info Chunk": "Salva il punteggio come info EXIF o PNG",
+    "Save score as tag (Windows Only)": "Salva punteggio come etichetta (solo Windows)",
+    "Force CPU (Requires Custom Script Reload)": "Forza CPU (richiede il ricaricamento dello script personalizzato)",
+    "Number of columns on image gallery": "Numero di colonne nella galleria di immagini",
+    "Images Browser": "Galleria immagini",
+    "Preload images at startup": "Precarica le immagini all`avvio",
+    "Number of columns on the page": "Numero di colonne nella pagina",
+    "Number of rows on the page": "Numero di righe nella pagina",
+    "Minimum number of pages per load": "Numero minimo di pagine da caricare",
+    "Maximum number of samples, used to determine which folders to skip when continue running the create script": "Numero massimo di campioni, utilizzato per determinare quali cartelle ignorare quando si continua a eseguire lo script di creazione",
+    "Use same seed for all images": "Usa lo stesso seme per tutte le immagini",
+    "Request browser notifications": "Richiedi le notifiche del browser",
+    "Download localization template": "Scarica il modello per la localizzazione",
+    "Reload custom script bodies (No ui updates, No restart)": "Ricarica gli script personalizzati (nessun aggiornamento dell`interfaccia utente, nessun riavvio)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Riavvia Gradio e aggiorna i componenti (solo script personalizzati, ui.py, js e css)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (premi Ctrl+Invio o Alt+Invio per generare)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (premere Ctrl+Invio o Alt+Invio per generare)",
+    "Add a random artist to the prompt.": "Aggiungi un artista casuale al prompt.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leggere i parametri di generazione dal prompt o dall`ultima generazione se il prompt è vuoto ed inserirli nell`interfaccia utente.",
+    "Save style": "Salva stile",
+    "Apply selected styles to current prompt": "Applica gli stili selezionati al prompt corrente",
+    "Stop processing current image and continue processing.": "Interrompe l`elaborazione dell`immagine corrente e continua l`elaborazione.",
+    "Stop processing images and return any results accumulated so far.": "Interrompe l`elaborazione delle immagini e restituisce tutti i risultati accumulati finora.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stile da applicare; gli stili hanno componenti sia per i prompt positivi che per quelli negativi e si applicano a entrambi",
+    "Do not do anything special": "Non fa nulla di speciale",
+    "Which algorithm to use to produce the image": "Quale algoritmo utilizzare per produrre l`immagine",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - molto creativo, si può ottenere un`immagine completamente diversa a seconda del numero di passi, impostare i passi su un valore superiore a 30-40 non aiuta",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - il migliore per inpainting",
+    "Produce an image that can be tiled.": "Produce un`immagine che può essere piastrellata.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilizza un processo in due fasi per creare parzialmente un`immagine con una risoluzione inferiore, aumentare la scala e quindi migliorarne i dettagli senza modificare la composizione",
+    "Determines how little respect the algorithm should have for image`s content. At 0, nothing will change, and at 1 you`ll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determina quanto poco l`algoritmo dovrebbe rispettare dovrebbe il contenuto dell`immagine. A 0, non cambierà nulla e a 1 otterrai un`immagine non correlata. Con valori inferiori a 1.0 l`elaborazione richiederà meno passaggi di quelli specificati dalla barra di scorrimento dei passi di campionamento.",
+    "How many batches of images to create": "Quanti lotti di immagini generare",
+    "How many image to create in a single batch": "Quante immagini generare in un singolo lotto",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - quanto fortemente l`immagine deve conformarsi al prompt: valori più bassi producono risultati più creativi",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you`ll get the same result": "Un valore che determina l`output del generatore di numeri casuali: se create un`immagine con gli stessi parametri e seme di un`altra immagine, otterrete lo stesso risultato",
+    "Set seed to -1, which will cause a new random number to be used every time": "Imposta il seme su -1, che farà sì che ogni volta venga utilizzato un nuovo numero casuale",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Riusa il seme dell`ultima generazione, utile soprattutto se casuale",
+    "Seed of a different picture to be mixed into the generation.": "Seme di un`immagine diversa da miscelare nella generazione.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Quanto è forte la variazione da produrre. A 0, non ci sarà alcun effetto. A 1, otterrai l`intera immagine con il seme della variazione (tranne per i campionatori ancestrali, dove otterrai solo una leggera variazione).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Prova a produrre un`immagine simile a quella che sarebbe stata prodotta con lo stesso seme alla risoluzione specificata",
+    "This text is used to rotate the feature space of the imgs embs": "Questo testo viene utilizzato per ruotare lo spazio delle funzioni delle immagini incorporate",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Quante volte ripetere l`elaborazione di un`immagine e utilizzarla come input per l`iterazione successiva",
+	"Hello, StylePile here.\nUntil some weird bug gets fixed you will see this even if the script itself is not active. Meanwhile, some hints to take your artwork to new heights:\nUse the `Focus on` dropdown to select complex presets. Toggle selections below (with or without Focus) to affect your results. Mix and match to get some interesting results. \nAnd some general Stable Diffusion tips that will take your designs to next level:\nYou can add parenthesis to make parts of the prompt stronger. So (((cute))) kitten will make it extra cute (try it out). This is alsow important if a style is affecting your original prompt too much. Make that prompt stronger by adding parenthesis around it, like this: ((promt)).\nYou can type promts like [A|B] to sequentially use terms one after another on each step. So, like [cat|dog] will produce a hybrid catdog. And [A:B:0.4] to switch to other terms after the first one has been active for a certain percentage of steps. So [cat:dog:0.4] will build a cat 40% of the time and then start turning it into a dog. This needs more steps to work properly.": "Salve, qui è StylePile.\nFinché qualche strano bug non verrà risolto, vedrai questo testo anche se lo script non è attivo. Nel frattempo, alcuni suggerimenti per portare la tua grafica a nuovi livelli:\nUtilizza il menu a discesa `Focus on` per selezionare valori predefiniti complessi. Attiva o disattiva le selezioni seguenti (con o senza Focus) per influire sui risultati. Mescola e abbina per ottenere risultati interessanti. \nE alcuni suggerimenti generali su Stable Diffusion che porteranno i tuoi risultati a un livello superiore:\nPuoi aggiungere parentesi per aumentare l`influenza di certe parti del prompt. Quindi `(((cute))) kitten` lo renderà molto carino (fai delle prove). Questo è importante quando uno stile influisce troppo sul prompt originale. Rendi più forte quel prompt aggiungendo delle parentesi intorno ad esso, così: ((promt)).\nPuoi digitare prompt nel formato [A|B] per usare in sequenza i termini uno dopo l`altro in ogni passaggio. Quindi, come [cat|dog] produrrà un `canegatto` ibrido. E [A:B:0.4] per passare ad altri termini dopo che il primo è stato attivo per una certa percentuale di passaggi. Quindi [cat:dog:0.4] genererà un gatto il 40% dei passaggi e poi inizierà a trasformarlo in un cane. Sono richiesti più passaggi perchè funzioni correttamente.",
+    "Enter one prompt per line. Blank lines will be ignored.": "Immettere un prompt per riga. Le righe vuote verranno ignorate.",
+    "Separate values for X axis using commas.": "Separare i valori per l`asse X usando le virgole.",
+    "Separate values for Y axis using commas.": "Separare i valori per l`asse Y usando le virgole.",
+    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "Separa un elenco di parole con virgole e lo script eseguirà una variazione di prompt con quelle parole per ogni loro possibile ordine",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva l`immagine/i in una cartella (predefinita - log/images) ed i parametri di generazione in un file CSV.",
+    "Open images output directory": "Apri la cartella di output delle immagini",
+    "How much to blur the mask before processing, in pixels.": "Quanto sfocare la maschera prima dell`elaborazione, in pixel.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Cosa mettere all`interno dell`area mascherata prima di elaborarla con Stable Diffusion.",
+    "fill it with colors of the image": "riempi con i colori dell`immagine",
+    "keep whatever was there originally": "conserva tutto ciò che c`era in origine",
+    "fill it with latent space noise": "riempi di rumore spaziale latente",
+    "fill it with latent space zeroes": "riempi con zeri di spazio latente",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Ingrandisce la regione mascherata per raggiungere la risoluzione, esegue la pittura, riduce la scala e incolla nell`immagine originale",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Ridimensiona l`immagine alla risoluzione di destinazione. A meno che altezza e larghezza non corrispondano, otterrai proporzioni errate.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Ridimensionare l`immagine in modo che l`intera risoluzione di destinazione sia riempita con l`immagine. Ritaglia le parti che sporgono.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image`s colors.": "Ridimensiona l`immagine in modo che l`intera immagine rientri nella risoluzione di destinazione. Riempi lo spazio vuoto con i colori dell`immagine.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Per l`upscaling SD, quanta sovrapposizione in pixel dovrebbe esserci tra le piastrelle. Le piastrelle si sovrappongono in modo che quando vengono unite nuovamente in un`immagine, non ci siano giunture chiaramente visibili.",
+    "Process an image, use it as an input, repeat.": "Elabora un`immagine, usala come input, ripeti.",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In modalità rielaborazione ricorsiva, su ogni ciclo la forza di denoising viene moltiplicata per questo valore. <1 significa varietà decrescente in modo che la sequenza converga su un`immagine fissa. >1 significa aumentare la varietà in modo che la tua sequenza diventi sempre più caotica.",
+    "A directory on the same machine where the server is running.": "Una cartella sulla stessa macchina su cui è in esecuzione il server.",
+    "Leave blank to save images to the default path.": "Lascia vuoto per salvare le immagini nel percorso predefinito.",
+    "Result = A * (1 - M) + B * M": "Risultato = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Risultato = A + (B - C) * M",
+    "1st and last digit must be 1. ex:`1, 2, 1`": "La prima e l`ultima cifra devono essere 1. Es.:`1, 2, 1`",
+    "Path to directory with input images": "Percorso della cartella con immagini di input",
+    "Path to directory where to write outputs": "Percorso della cartella in cui scrivere i risultati",
+    "C:\\directory\\of\\datasets": "C:\\cartella\\del\\dataset",
+    "Input images directory": "Cartella di input delle immagini",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Usa i seguenti tag per definire come vengono scelti i nomi dei file per le immagini: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed ], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l`impostazione predefinita.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se questa opzione è abilitata, la filigrana non verrà aggiunta alle immagini create. Attenzione: se non aggiungi la filigrana, potresti comportarti in modo non etico.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utilizzare i seguenti tag per definire come vengono scelte le sottodirectory per le immagini e le griglie: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l`impostazione predefinita.",
+    "Restore low quality faces using GFPGAN neural network": "Ripristina volti di bassa qualità utilizzando la rete neurale GFPGAN",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Questa espressione regolare verrà utilizzata per estrarre le parole dal nome del file e verranno unite utilizzando l`opzione seguente nel testo dell`etichetta utilizzato per l`addestramento. Lascia vuoto per mantenere il testo del nome del file così com`è.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Questa stringa verrà utilizzata per unire le parole divise in un`unica riga se l`opzione sopra è abilitata.",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Si applica solo ai modelli di pittura. Determina con quale forza mascherare l`immagine originale per inpainting e img2img. 1.0 significa completamente mascherato, che è il comportamento predefinito. 0.0 significa un condizionamento completamente non mascherato. Valori più bassi aiuteranno a preservare la composizione generale dell`immagine, ma avranno difficoltà con grandi cambiamenti.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Elenco dei nomi delle impostazioni, separati da virgole, per le impostazioni che dovrebbero essere visualizzate nella barra di accesso rapido in alto, anziché nella normale scheda delle impostazioni. Vedi modules/shared.py per impostare i nomi. Richiede il riavvio per applicare.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se questo valore è diverso da zero, verrà aggiunto al seed e utilizzato per inizializzare il generatore di numeri casuali per il rumore quando si utilizzano campionatori con ETA. Puoi usarlo per produrre ancora più variazioni di immagini, oppure puoi usarlo per abbinare le immagini di altri software se sai cosa stai facendo.",
+    "Enable Autocomplete": "Abilita autocompletamento"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From d587586d3be2de061238defb8a556f03743287f6 Mon Sep 17 00:00:00 2001
From: mawr <mawr@mail.ru>
Date: Mon, 31 Oct 2022 00:14:07 +0300
Subject: Added "--clip-models-path" switch to avoid using default
 "~/.cache/clip" and enable to run under unprivileged user without homedir

---
 modules/interrogate.py | 4 ++--
 modules/shared.py      | 1 +
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 65b05d34..9769aa34 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -56,9 +56,9 @@ class InterrogateModels:
         import clip
 
         if self.running_on_cpu:
-            model, preprocess = clip.load(clip_model_name, device="cpu")
+            model, preprocess = clip.load(clip_model_name, device="cpu", download_root=shared.cmd_opts.clip_models_path)
         else:
-            model, preprocess = clip.load(clip_model_name)
+            model, preprocess = clip.load(clip_model_name, download_root=shared.cmd_opts.clip_models_path)
 
         model.eval()
         model = model.to(devices.device_interrogate)
diff --git a/modules/shared.py b/modules/shared.py
index e4f163c1..36212031 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -51,6 +51,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
 parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
 parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
 parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
+parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
 parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
-- 
cgit v1.2.1


From d9e4e4d7a09d4aee8ce249a3c8e91ce165b10fa5 Mon Sep 17 00:00:00 2001
From: random_thoughtss <random_thoughtss@proton.me>
Date: Sun, 30 Oct 2022 15:33:02 -0700
Subject: Fix non-square full resolution inpainting.

---
 modules/masking.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/masking.py b/modules/masking.py
index fd8d9241..a5c4d2da 100644
--- a/modules/masking.py
+++ b/modules/masking.py
@@ -49,7 +49,7 @@ def expand_crop_region(crop_region, processing_width, processing_height, image_w
     ratio_processing = processing_width / processing_height
 
     if ratio_crop_region > ratio_processing:
-        desired_height = (x2 - x1) * ratio_processing
+        desired_height = (x2 - x1) / ratio_processing
         desired_height_diff = int(desired_height - (y2-y1))
         y1 -= desired_height_diff//2
         y2 += desired_height_diff - desired_height_diff//2
-- 
cgit v1.2.1


From 21fba39c609859a60616420afda3b34a89e00761 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 30 Oct 2022 23:45:52 +0000
Subject: Add callbacks and param objects

---
 modules/script_callbacks.py | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 6ea58d61..a206ea59 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -24,12 +24,22 @@ class ImageSaveParams:
         """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
 
 
+class CGFDenoiserParams:
+    def __init__(self, x_in, image_cond_in, sigma_in, sampling_step, total_sampling_steps):
+        self.x_in = x_in
+        self.image_cond_in = image_cond_in
+        self.sigma_in = sigma_in
+        self.sampling_step = sampling_step
+        self.total_sampling_steps = total_sampling_steps
+
+
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
 callbacks_model_loaded = []
 callbacks_ui_tabs = []
 callbacks_ui_settings = []
 callbacks_before_image_saved = []
 callbacks_image_saved = []
+callbacks_cfg_denoiser = []
 
 
 def clear_callbacks():
@@ -84,6 +94,14 @@ def image_saved_callback(params: ImageSaveParams):
             report_exception(c, 'image_saved_callback')
 
 
+def cfg_denoiser_callback(params: CGFDenoiserParams):
+    for c in callbacks_cfg_denoiser:
+        try:
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'cfg_denoiser_callback')
+
+
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -130,3 +148,12 @@ def on_image_saved(callback):
         - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
     """
     add_callback(callbacks_image_saved, callback)
+
+
+def on_cfg_denoiser(callback):
+    """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
+    The callback is called with one argument:
+        - params: CGFDenoiserParams - parameters to be passed to the inner model and sampling state details.
+    """
+    add_callback(callbacks_cfg_denoiser, callback)
+
-- 
cgit v1.2.1


From 8906be85ac91310b37dccddc44f23631eb7a15f5 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 30 Oct 2022 23:47:08 +0000
Subject: add callback cleardown

---
 modules/script_callbacks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index a206ea59..b0b8dc47 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -48,7 +48,7 @@ def clear_callbacks():
     callbacks_ui_settings.clear()
     callbacks_before_image_saved.clear()
     callbacks_image_saved.clear()
-
+    callbacks_cfg_denoiser.clear()
 
 def model_loaded_callback(sd_model):
     for c in callbacks_model_loaded:
-- 
cgit v1.2.1


From 8ae0ea9deaa5a09d1e0aa8b2f8e97c38d71cdbda Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Sun, 30 Oct 2022 23:48:33 +0000
Subject: Add callback to sd_samplers

---
 modules/sd_samplers.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 3670b57d..30cb5c4b 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -11,6 +11,7 @@ from modules import prompt_parser, devices, processing, images
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
+from modules.script_callbacks import CGFDenoiserParams, cfg_denoiser_callback
 
 
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
@@ -278,6 +279,8 @@ class CFGDenoiser(torch.nn.Module):
         image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
 
+        cfg_denoiser_callback(CGFDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps))
+
         if tensor.shape[1] == uncond.shape[1]:
             cond_in = torch.cat([tensor, uncond])
 
-- 
cgit v1.2.1


From 081df45da47feadfb055552c0ad5c6e6ecdd9f28 Mon Sep 17 00:00:00 2001
From: Maiko Sinkyaet Tan <maiko.tan.coding@gmail.com>
Date: Mon, 31 Oct 2022 08:47:43 +0800
Subject: docs: add python doc (?)

not sure if this available...
---
 modules/script_callbacks.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index f5509629..d8aa6f00 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -103,6 +103,8 @@ def add_callback(callbacks, fun):
 
 
 def on_app_started(callback):
+    """register a function to be called when the webui started, the gradio `Block` component and
+    fastapi `FastAPI` object are passed as the arguments"""
     add_callback(callbacks_app_started, callback)
 
 
-- 
cgit v1.2.1


From adaa699e3888e1396162083d65c63cd6774cc6b0 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sun, 30 Oct 2022 18:08:40 +0800
Subject: prototype interrupt api

---
 modules/api/api.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/modules/api/api.py b/modules/api/api.py
index 6c06d449..e702c9c0 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -40,6 +40,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
         self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
         self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
+        self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -176,6 +177,11 @@ class Api:
 
         return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
 
+    def interruptapi(self):
+        shared.state.interrupt()
+
+        return {}
+
     def launch(self, server_name, port):
         self.app.include_router(self.router)
         uvicorn.run(self.app, host=server_name, port=port)
-- 
cgit v1.2.1


From 29f758afe9c09834a1cd2dac3f5dd6fbf7dfeaa7 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Mon, 31 Oct 2022 02:39:55 +0000
Subject: Extend extras image cache with upscale_first arg

---
 modules/extras.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/extras.py b/modules/extras.py
index 4d51088b..8e2ab35c 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -141,7 +141,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
                             upscaling_resize_w, upscaling_resize_h, upscaling_crop)
             cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()),
                                      info_hash=hash(info),
-                                     args_hash=hash(upscale_args))
+                                     args_hash=hash((upscale_args, upscale_first)))
             cached_entry = cached_images.get(cache_key)
             if cached_entry is None:
                 res = upscale(image, *upscale_args)
-- 
cgit v1.2.1


From 0018f3ed62927a189c1f162c11a2e7a0cef99c1b Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Mon, 31 Oct 2022 12:32:51 +0800
Subject: =?UTF-8?q?=E6=9B=B4=E6=96=B0=20zh=5FCN.json?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/zh_CN.json | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index b796c625..248245dd 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -59,11 +59,11 @@
     "Highres. fix": "高分辨率修复",
     "Firstpass width": "第一遍的宽度",
     "Firstpass height": "第一遍的高度",
-    "Denoising strength": "去噪强度",
+    "Denoising strength": "去噪强度(Denoising strength)",
     "Batch count": "生成批次",
     "Batch size": "每批数量",
     "CFG Scale": "提示词相关性(CFG Scale)",
-    "Seed": "随机种子",
+    "Seed": "随机种子(seed)",
     "Extra": "▼",
     "Variation seed": "差异随机种子",
     "Variation strength": "差异强度",
@@ -338,7 +338,7 @@
     "sort by": "排序方式",
     "path name": "路径名",
     "date": "日期",
-    "keyword": "关键词",
+    "keyword": "搜索",
     "Generate Info": "生成信息",
     "File Name": "文件名",
     "Collect": "保存",
@@ -487,7 +487,7 @@
     "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长内补绘制",
     "Produce an image that can be tiled.": "生成可用于平铺(tiled)的图像",
     "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候，以较小的分辨率生成初步图像、接着放大图像，然后在不更改构图的情况下改进其中的细节",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "决定算法对图像内容的影响程度。设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "决定算法对图像内容的影响程度。设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。\n值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
     "How many batches of images to create": "创建多少批次的图像",
     "How many image to create in a single batch": "每批创建多少图像",
     "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 图像应在多大程度上服从提示词 - 较低的值会产生更有创意的结果",
-- 
cgit v1.2.1


From 7581091ffb14936ce85549ddfee3c2417ffb3c69 Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Mon, 31 Oct 2022 12:52:03 +0800
Subject: Denoising strength

---
 localizations/zh_CN.json | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 248245dd..3463964a 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -59,7 +59,7 @@
     "Highres. fix": "高分辨率修复",
     "Firstpass width": "第一遍的宽度",
     "Firstpass height": "第一遍的高度",
-    "Denoising strength": "去噪强度(Denoising strength)",
+    "Denoising strength": "重绘幅度(Denoising strength)",
     "Batch count": "生成批次",
     "Batch size": "每批数量",
     "CFG Scale": "提示词相关性(CFG Scale)",
@@ -168,13 +168,13 @@
     "Original negative prompt": "初始反向提示词",
     "Override `Sampling Steps` to the same value as `Decode steps`?": "覆写 `采样迭代步数` 为 `解码迭代步数`?",
     "Decode steps": "解码迭代步数",
-    "Override `Denoising strength` to 1?": "覆写 `去噪强度` 为 1?",
+    "Override `Denoising strength` to 1?": "覆写 `重绘幅度` 为 1?",
     "Decode CFG scale": "解码提示词相关性(CFG scale)",
     "Randomness": "随机度",
     "Sigma adjustment for finding noise for image": "为寻找图中噪点的 Sigma 调整",
     "Loops": "迭代次数",
-    "Denoising strength change factor": "去噪强度的调整系数",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推荐设置：采样迭代步数：80-100，采样器：Euler a，去噪强度：0.8",
+    "Denoising strength change factor": "重绘幅度的调整系数",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推荐设置：采样迭代步数：80-100，采样器：Euler a，重绘幅度：0.8",
     "Pixels to expand": "拓展的像素数",
     "Outpainting direction": "外补绘制的方向",
     "left": "左",
@@ -412,7 +412,7 @@
     "Inpainting conditioning mask strength": "内补绘制的自适应蒙版强度",
     "Apply color correction to img2img results to match original colors.": "对图生图结果应用颜色校正以匹配原始颜色",
     "Save a copy of image before applying color correction to img2img results": "在对图生图结果应用颜色校正之前保存图像副本",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（正常情况下更弱的去噪需要更少的迭代步数）",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（正常情况下更弱的重绘幅度需要更少的迭代步数）",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。这可能会改变现有的随机种子。需要重新启动才能应用",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。可用于复现旧随机种子",
@@ -513,7 +513,7 @@
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
     "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像并用作下次迭代输入的次数",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，去噪强度都会乘以该值。<1 表示减少多样性，因此你的这一组图将集中在固定的图像上。>1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，重绘幅度都会乘以该值。<1 表示减少多样性，因此你的这一组图将集中在固定的图像上。>1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
     "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。图块(Tiles)之间需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
     "A directory on the same machine where the server is running.": "与服务器主机上的目录",
     "Leave blank to save images to the default path.": "留空以将图像保存到默认路径",
-- 
cgit v1.2.1


From f719b7d0128930b49d1f16ee44796e275033415b Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 31 Oct 2022 16:17:48 +0900
Subject: Update zh_CN.json

update tag complete.
---
 localizations/zh_CN.json | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index f9371c3e..3ff2c581 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -507,7 +507,9 @@
     "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "仅适用于内补绘制专用的模型。 决定了蒙版在内补绘制以及图生图中屏蔽原图内容的强度。 1.0 表示完全屏蔽，这是默认行为。 0.0 表示完全不屏蔽。 较低的值将有助于保持图像的整体构图，但很难遇到较大的变化。",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置名称列表，以逗号分隔，设置应转到顶部的快速访问栏，而不是通常的设置选项卡。有关设置名称，请参见 modules/shared.py。需要重新启动才能应用",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声。你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
+    "Autocomplete options": "自动补全选项",
     "Enable Autocomplete": "开启Tag补全",
+    "Append commas": "附加逗号",
     "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
     "Roll three": "抽三位出来",
     "Generate forever": "无限生成",
-- 
cgit v1.2.1


From b96d0c4e9ecec3c856b9b4ec795dbd0d34fcac51 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Mon, 31 Oct 2022 14:42:28 +0700
Subject: Fix typo from previous commit

---
 modules/sd_vae.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 0767b925..2ce44d5f 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -27,8 +27,8 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     candidates = [
         *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
         *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
-        *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True)
-        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True)
     ]
     if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
         candidates.append(shared.cmd_opts.vae_path)
-- 
cgit v1.2.1


From e19edbbf724cb19387600da754627933fcc6563e Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Mon, 31 Oct 2022 16:56:41 +0900
Subject: Update ko_KR.json

Added KR support for Dynamic Prompts extension  - https://github.com/mkco5162/sd-dynamic-prompts
---
 localizations/ko_KR.json | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 8f5f155b..66d4addf 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -33,10 +33,12 @@
   "Always save all generated images": "생성된 이미지 항상 저장하기",
   "api": "",
   "append": "뒤에 삽입",
+  "Append commas": "쉼표 삽입",
   "Apply color correction to img2img results to match original colors.": "이미지→이미지 결과물이 기존 색상과 일치하도록 색상 보정 적용하기",
   "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
   "Apply settings": "설정 적용하기",
   "Auto focal point crop": "초점 기준 크롭(자동 감지)",
+  "Autocomplete options": "자동완성 설정",
   "Batch count": "배치 수",
   "Batch from Directory": "저장 경로로부터 여러장 처리",
   "Batch img2img": "이미지→이미지 배치",
@@ -64,6 +66,9 @@
   "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 가중치 설정값 (0 = 최대 효과, 1 = 최소 효과)",
   "Collect": "즐겨찾기",
   "Color variation": "색깔 다양성",
+  "Combinations": "조합",
+  "Combinatorial batches": "조합 배치 수",
+  "Combinatorial generation": "조합 생성",
   "copy": "복사",
   "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
   "Create a text file next to every image with generation parameters.": "생성된 이미지마다 생성 설정값을 담은 텍스트 파일 생성하기",
@@ -150,6 +155,7 @@
   "First Page": "처음 페이지",
   "Firstpass height": "초기 세로길이",
   "Firstpass width": "초기 가로길이",
+  "Fixed seed": "시드 고정",
   "Focal point edges weight": "경계면 가중치",
   "Focal point entropy weight": "엔트로피 가중치",
   "Focal point face weight": "얼굴 가중치",
@@ -247,6 +253,7 @@
   "Loopback": "루프백",
   "Loops": "루프 수",
   "Loss:": "손실(Loss) : ",
+  "Magic prompt": "매직 프롬프트",
   "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
   "Make K-diffusion samplers produce same images in a batch as when making a single image": "K-diffusion 샘플러들이 단일 이미지를 생성하는 것처럼 배치에서도 동일한 이미지를 생성하게 하기",
   "Make Zip when Save?": "저장 시 Zip 생성하기",
-- 
cgit v1.2.1


From 726769da35970f4c100fa7edf11850f9dc059c41 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Mon, 31 Oct 2022 15:19:34 +0700
Subject: Checkpoint cache by combination key of checkpoint and vae

---
 modules/sd_models.py | 27 ++++++++++++++++-----------
 modules/sd_vae.py    |  8 +++++++-
 2 files changed, 23 insertions(+), 12 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 91ad4b5e..850f7b7b 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -160,11 +160,15 @@ def get_state_dict_from_checkpoint(pl_sd):
 
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
 
-def load_model_weights(model, checkpoint_info, force=False):
+def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    if force or checkpoint_info not in checkpoints_loaded:
+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
+
+    checkpoint_key = (checkpoint_info, vae_file)
+
+    if checkpoint_key not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
         pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
@@ -185,24 +189,25 @@ def load_model_weights(model, checkpoint_info, force=False):
         devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
         devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
-        sd_vae.load_vae(model, checkpoint_file)
+        sd_vae.load_vae(model, vae_file)
         model.first_stage_model.to(devices.dtype_vae)
 
         if shared.opts.sd_checkpoint_cache > 0:
-            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+            checkpoints_loaded[checkpoint_key] = model.state_dict().copy()
             while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
                 checkpoints_loaded.popitem(last=False)  # LRU
     else:
-        print(f"Loading weights [{sd_model_hash}] from cache")
-        checkpoints_loaded.move_to_end(checkpoint_info)
-        model.load_state_dict(checkpoints_loaded[checkpoint_info])
+        vae_name = sd_vae.get_filename(vae_file)
+        print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache")
+        checkpoints_loaded.move_to_end(checkpoint_key)
+        model.load_state_dict(checkpoints_loaded[checkpoint_key])
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
 
-def load_model(checkpoint_info=None, force=False):
+def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
 
@@ -223,7 +228,7 @@ def load_model(checkpoint_info=None, force=False):
 
     do_inpainting_hijack()
     sd_model = instantiate_from_config(sd_config.model)
-    load_model_weights(sd_model, checkpoint_info, force=force)
+    load_model_weights(sd_model, checkpoint_info)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
@@ -250,7 +255,7 @@ def reload_model_weights(sd_model, info=None, force=False):
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         checkpoints_loaded.clear()
-        load_model(checkpoint_info, force=force)
+        load_model(checkpoint_info)
         return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -260,7 +265,7 @@ def reload_model_weights(sd_model, info=None, force=False):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_model_weights(sd_model, checkpoint_info, force=force)
+    load_model_weights(sd_model, checkpoint_info)
 
     sd_hijack.model_hijack.hijack(sd_model)
     script_callbacks.model_loaded_callback(sd_model)
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 2ce44d5f..e9239326 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -43,7 +43,7 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     vae_dict.update(res)
     return vae_list
 
-def load_vae(model, checkpoint_file, vae_file="auto"):
+def resolve_vae(checkpoint_file, vae_file="auto"):
     global first_load, vae_dict, vae_list
     # save_settings = False
 
@@ -94,6 +94,12 @@ def load_vae(model, checkpoint_file, vae_file="auto"):
     if vae_file and not os.path.exists(vae_file):
         vae_file = None
 
+    return vae_file
+
+def load_vae(model, vae_file):
+    global first_load, vae_dict, vae_list
+    # save_settings = False
+
     if vae_file:
         print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
-- 
cgit v1.2.1


From 36966e3200943dbf890b5338cfa939df552d3c47 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Mon, 31 Oct 2022 15:38:58 +0700
Subject: Fix #4035

---
 modules/sd_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index f86dc3ed..a29c8c1a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -201,7 +201,7 @@ def load_model_weights(model, checkpoint_info):
 
         if shared.opts.sd_checkpoint_cache > 0:
             checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
-            while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+            while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1:
                 checkpoints_loaded.popitem(last=False)  # LRU
     else:
         print(f"Loading weights [{sd_model_hash}] from cache")
-- 
cgit v1.2.1


From bf7a699845675eefdabb9cfa40c55398976274ae Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Mon, 31 Oct 2022 16:27:27 +0700
Subject: Fix #4035 for real now

---
 modules/sd_models.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index a29c8c1a..b2dd005a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -165,6 +165,9 @@ def load_model_weights(model, checkpoint_info):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
+    if shared.opts.sd_checkpoint_cache > 0 and hasattr(model, "sd_checkpoint_info"):
+        checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
+
     if checkpoint_info not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
@@ -198,16 +201,14 @@ def load_model_weights(model, checkpoint_info):
             model.first_stage_model.load_state_dict(vae_dict)
 
         model.first_stage_model.to(devices.dtype_vae)
-
-        if shared.opts.sd_checkpoint_cache > 0:
-            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
-            while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1:
-                checkpoints_loaded.popitem(last=False)  # LRU
     else:
         print(f"Loading weights [{sd_model_hash}] from cache")
-        checkpoints_loaded.move_to_end(checkpoint_info)
         model.load_state_dict(checkpoints_loaded[checkpoint_info])
 
+    if shared.opts.sd_checkpoint_cache > 0:
+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+            checkpoints_loaded.popitem(last=False)  # LRU
+
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
-- 
cgit v1.2.1


From f1a110a593301199713233fdf543a60e70c90fc8 Mon Sep 17 00:00:00 2001
From: Riccardo Giovanetti <29801031+Harvester62@users.noreply.github.com>
Date: Mon, 31 Oct 2022 11:39:13 +0100
Subject: Italian localization - Updated a few terms

I've updated a few terms and descriptions. Waiting for review and, hopefully, approval.
---
 it_IT.json | 1143 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1143 insertions(+)
 create mode 100644 it_IT.json

diff --git a/it_IT.json b/it_IT.json
new file mode 100644
index 00000000..7e52bf33
--- /dev/null
+++ b/it_IT.json
@@ -0,0 +1,1143 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "Caricamento...",
+    "view": "mostra ",
+    "api": "API",
+    "•": " • ",
+    "built with gradio": " Sviluppato con Gradio",
+    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "Extra",
+    "PNG Info": "Info PNG",
+    "Checkpoint Merger": "Miscelatore di Checkpoint",
+    "Train": "Addestramento",
+    "Create aesthetic embedding": "Crea incorporamento estetico",
+    "Dataset Tag Editor": "Dataset Tag Editor",
+    "Deforum": "Deforum",
+    "Artists To Study": "Artisti per studiare",
+    "Image Browser": "Galleria immagini",
+    "Inspiration": "Ispirazione",
+    "Settings": "Impostazioni",
+    "Prompt": "Prompt",
+    "Negative prompt": "Prompt negativo",
+    "Run": "Esegui",
+    "Skip": "Salta",
+    "Interrupt": "Interrompi",
+    "Generate": "Genera",
+    "Style 1": "Stile 1",
+    "Style 2": "Stile 2",
+    "Label": "Etichetta",
+    "File": "File",
+    "Drop File Here": "Trascina il file qui",
+    "-": "-",
+    "or": "o",
+    "Click to Upload": "Clicca per caricare",
+    "Image": "Immagine",
+    "Check progress": "Controlla i progressi",
+    "Check progress (first)": "Controlla i progressi (primo)",
+    "Sampling Steps": "Passi di campionamento",
+    "Sampling method": "Metodo di campionamento",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Larghezza",
+    "Height": "Altezza",
+    "Restore faces": "Restaura i volti",
+    "Tiling": "Piastrellatura",
+    "Highres. fix": "Correzione alta risoluzione",
+    "Firstpass width": "Larghezza del primo passaggio",
+    "Firstpass height": "Altezza del primo passaggio",
+    "Denoising strength": "Forza del Denoising",
+    "Batch count": "Lotti di immagini",
+    "Batch size": "Immagini per lotto",
+    "CFG Scale": "Scala CFG",
+    "Seed": "Seme",
+    "Extra": "Extra",
+    "Variation seed": "Seme della variazione",
+    "Variation strength": "Forza della variazione",
+    "Resize seed from width": "Ridimensiona il seme dalla larghezza",
+    "Resize seed from height": "Ridimensiona il seme dall'altezza",
+    "Open for Clip Aesthetic!": "Apri per Estetica CLIP!",
+    "▼": "▼",
+    "Aesthetic weight": "Estetica - Peso",
+    "Aesthetic steps": "Estetica - Passi",
+    "Aesthetic learning rate": "Estetica - Tasso di apprendimento",
+    "Slerp interpolation": "Interpolazione Slerp",
+    "Aesthetic imgs embedding": "Estetica - Incorporamento di immagini",
+    "None": "Niente",
+    "Aesthetic text for imgs": "Estetica - Testo per le immagini",
+    "Slerp angle": "Angolo Slerp",
+    "Is negative text": "È un testo negativo",
+    "Script": "Script",
+    "Random grid": "Generaz. casuale (griglia)",
+    "Random": "Generaz. casuale (no griglia)",
+    "StylePile": "StylePile",
+    "Advanced prompt matrix": "Matrice di prompt avanzata",
+    "Alternate Sampler Noise Schedules": "Metodi alternativi di campionamento del rumore",
+    "Animator v6": "Animator v6",
+    "Asymmetric tiling": "Piastrellatura asimmetrica",
+    "Custom code": "Codice personalizzato",
+    "Dynamic Prompting v0.2": "Prompt dinamici v0.2",
+    "Embedding to Shareable PNG": "Incorporamento convertito in PNG condivisibile",
+    "Force symmetry": "Forza la simmetria",
+    "Prompts interpolation": "Interpola Prompt",
+    "Prompt matrix": "Matrice dei prompt",
+    "Prompt morph": "Metamorfosi del prompt",
+    "Prompts from file or textbox": "Prompt da file o da casella di testo",
+    "To Infinity and Beyond": "Verso l'infinito e oltre",
+    "Seed travel": "Interpolazione semi",
+    "Shift attention": "Sposta l'attenzione",
+    "Text to Vector Graphics": "Da testo a grafica vettoriale",
+    "X/Y plot": "Grafico X/Y",
+    "X/Y/Z plot": "Grafico X/Y/Z",
+    "Create inspiration images": "Crea immagini di ispirazione",
+    "step1 min/max": "Passi min(o max)",
+    "step2 min/max": "Passi max (o min)",
+    "step cnt": "Q.tà di Passi",
+    "cfg1 min/max": "CFG min (o max)",
+    "cfg2 min/max": "CFG max (o min)",
+    "cfg cnt": "Q.tà di CFG",
+    "Draw legend": "Disegna legenda",
+    "Include Separate Images": "Includi immagini separate",
+    "Keep -1 for seeds": "Mantieni sempre il seme a -1",
+    "x/y change": "Inverti ordine assi X/Y (Passi/CFG)",
+    "Loops": "Cicli",
+    "Focus on:": "Focus su:",
+    "No focus": "Nessun Focus",
+    "Portraits (tick Restore faces above for best results)": "Ritratti (selezionare 'Restaura volti' in alto per ottenere i migliori risultati)",
+    "Feminine and extra attractive (tick Restore faces above for best results)": "Femminile ed estremamente attraente (selezionare 'Restaura volti' per ottenere i migliori risultati)",
+    "Masculine and extra attractive (tick Restore faces above for best results)": "Maschile ed estremamente attraente (selezionare 'Restaura volti'  per ottenere i migliori risultati)",
+    "Monsters": "Mostri",
+    "Robots": "Robot",
+    "Retrofuturistic": "Retrofuturistico",
+    "Propaganda": "Propaganda",
+    "Landscapes": "Paesaggi",
+    "Hints": "Suggerimenti",
+    "Image type": "Tipo di immagine",
+    "Not set": "Non impostato",
+    "Photography": "Fotografia",
+    "Digital art": "Arte digitale",
+    "3D Rendering": "3D Rendering",
+    "Painting": "Dipinto",
+    "Sketch": "Schizzo",
+    "Classic Comics": "Fumetti classici",
+    "Modern Comics": "Fumetti moderni",
+    "Manga": "Manga",
+    "Vector art": "Arte vettoriale",
+    "Visual style": "Stile visivo",
+    "Realism": "Realismo",
+    "Photorealism": "Fotorealismo",
+    "Hyperrealism": "Iperrealismo",
+    "Surrealism": "Surrealismo",
+    "Modern Art": "Arte moderna",
+    "Fauvism": "Fauvismo",
+    "Futurism": "Futurismo",
+    "Painterly": "Pittorico",
+    "Pointillisme": "Puntinismo",
+    "Abstract": "Astratto",
+    "Pop Art": "Pop Art",
+    "Impressionist": "Impressionista",
+    "Cubism": "Cubismo",
+    "Linocut": "Linoleografia",
+    "Fantasy": "Fantasia",
+    "Colors": "Colori",
+    "Chaotic": "Caotico",
+    "Primary colors": "Colori primari",
+    "Colorful": "Colorato",
+    "Vivid": "Vivido",
+    "Muted colors": "Colori tenui",
+    "Low contrast": "Basso contrasto",
+    "Desaturated": "Desaturato",
+    "Grayscale": "Scala di grigi",
+    "Black and white": "Bianco e nero",
+    "Infrared": "Infrarosso",
+    "Complementary": "Colori complementari",
+    "Non-complementary": "Colori non complementari",
+    "View": "Visuale",
+    "Tilt shift": "Tilt shift",
+    "Wide-angle": "Angolo ampio",
+    "Portrait": "Ritratto",
+    "Macro": "Macro",
+    "Microscopic": "Microscopico",
+    "Isometric": "Isometrico",
+    "Panorama": "Panorama",
+    "Aerial photograph": "Fotografia aerea",
+    "Artist focus (not quite finished, not sure it helps)": "Focus sull'artista (non del tutto finito, non è sicuro che aiuti)",
+    "B/W Photograpy": "Fotografia B/N",
+    "Portrait photo": "Foto ritratto",
+    "Usage: a <corgi|cat> wearing <goggles|a hat>": "Utilizzo: a <corgi|cat> wearing <goggles|a hat>",
+    "Noise Scheduler": "Pianificazione del rumore",
+    "Default": "Predefinito",
+    "Karras": "Karras",
+    "Exponential": "Esponenziale",
+    "Variance Preserving": "Conservazione della Varianza",
+    "Sigma min": "Sigma min",
+    "Sigma max": "Sigma max",
+    "Sigma rho (Karras only)": "Sigma rho (Solo Karras)",
+    "Beta distribution (VP only)": "Distribuzione Beta (Solo CV)",
+    "Beta min (VP only)": "Beta min (Solo CV)",
+    "Epsilon (VP only)": "Epsilon (Solo CV)",
+    "Running in txt2img mode:": "Running in txt2img mode:",
+    "Render these video formats:": "Renderizza in questi formati:",
+    "GIF": "GIF",
+    "MP4": "MP4",
+    "WEBM": "WEBM",
+    "Animation Parameters": "Parametri animazione",
+    "Total Animation Length (s)": "Durata totale dell'animazione (s)",
+    "Framerate": "Frequenza dei fotogrammi",
+    "Smoothing_Frames": "Smoothing_Frames",
+    "Add_Noise": "Aggiungi rumore",
+    "Noise Strength": "Intensità del rumore",
+    "Initial Parameters": "Parametri iniziali",
+    "Denoising Strength (overrides img2img slider)": "Intensità di riduzione del rumore (sovrascrive il cursore img2img)",
+    "Seed_March": "Seed_March",
+    "Zoom Factor (scale/s)": "Fattore di ingrandimento (scala/s)",
+    "X Pixel Shift (pixels/s)": "Sposta i Pixel sull'asse X (pixel/s)",
+    "Y Pixel Shift (pixels/s)": "Sposta i Pixel sull'asse Y (pixel/s)",
+    "Rotation (deg/s)": "Rotazione (gradi/s)",
+    "Prompt Template, applied to each keyframe below": "Modello di prompt, applicato a ciascun fotogramma chiave qui di seguito",
+    "Positive Prompts": "Prompt positivi",
+    "Negative Prompts": "Prompt negativi",
+    "Props, Stamps": "Props, Stamps",
+    "Poper_Folder:": "Cartella Poper:",
+    "Supported Keyframes:": "Fotogrammi chiave supportati:",
+    "time_s | source | video, images, img2img | path": "time_s | source | video, images, img2img | path",
+    "time_s | prompt | positive_prompts | negative_prompts": "time_s | prompt | positive_prompts | negative_prompts",
+    "time_s | template | positive_prompts | negative_prompts": "time_s | template | positive_prompts | negative_prompts",
+    "time_s | transform | zoom | x_shift | y_shift | rotation": "time_s | transform | zoom | x_shift | y_shift | rotation",
+    "time_s | seed | new_seed_int": "time_s | seed | new_seed_int",
+    "time_s | noise | added_noise_strength": "time_s | noise | added_noise_strength",
+    "time_s | denoise | denoise_value": "time_s | denoise | denoise_value",
+    "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name": "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name",
+    "time_s | clear_text | textblock_name": "time_s | clear_text | textblock_name",
+    "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation": "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation",
+    "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation": "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation",
+    "time_s | clear_stamp | stamp_name": "time_s | clear_stamp | stamp_name",
+    "time_s | col_set": "time_s | col_set",
+    "time_s | col_clear": "time_s | col_clear",
+    "time_s | model | model": "time_s | model | model",
+    "img2img_mode": "img2img_mode",
+    "Keyframes:": "Fotogrammi chiave:",
+    "Tile X": "Piastrella asse X",
+    "Tile Y": "Piastrella asse Y",
+    "Python code": "Codice Python",
+    "Combinatorial generation": "Generazione combinatoria",
+    "Combinations": "Combinazioni",
+    "Choose a number of terms from a list, in this case we choose two artists": "Scegli un numero di termini da un elenco, in questo caso scegliamo due artisti",
+    "{2$$artist1|artist2|artist3}": "{2$$artist1|artist2|artist3}",
+    "If $$ is not provided, then 1$$ is assumed.\n\n            A range can be provided:": "Se $$ non viene fornito, si presume 1$$.\n\n            È possibile fornire un intervallo di valori:",
+    "{1-3$$artist1|artist2|artist3}": "{1-3$$artist1|artist2|artist3}",
+    "In this case, a random number of artists between 1 and 3 is chosen.": "In questo caso viene scelto un numero casuale di artisti compreso tra 1 e 3.",
+    "Wildcards": "Termini jolly",
+    "If the groups wont drop down click": "Se i gruppi non vengono visualizzati, clicca",
+    "here": "qui",
+    "to fix the issue.": "per correggere il problema.",
+    "WILDCARD_DIR: scripts/wildcards": "WILDCARD_DIR: scripts/wildcards",
+    "You can add more wildcards by creating a text file with one term per line and name is mywildcards.txt. Place it in scripts/wildcards.": "Puoi aggiungere termini jolly creando un file di testo con un termine per riga e nominandolo, per esempio, mywildcards.txt. Inseriscilo in scripts/wildcards.",
+    "__<folder>/mywildcards__": "__<folder>/mywildcards__",
+    "will then become available.": "diverrà quindi disponibile.",
+    "Source embedding to convert": "Incorporamento sorgente da convertire",
+    "Embedding token": "Token Incorporamento",
+    "Output directory": "Cartella di output",
+    "Horizontal symmetry": "Simmetria orizzontale",
+    "Vertical symmetry": "Simmetria verticale",
+    "Alt. symmetry method (blending)": "Metodo di simmetria alternativo (miscelazione)",
+    "Apply every n steps": "Applica ogni n passi",
+    "Skip last n steps": "Salta gli ultimi n passi",
+    "Interpolation prompt": "Prompt di interpolazione",
+    "Number of images": "Numero di immagini",
+    "Make a gif": "Crea GIF",
+    "Duration of images (ms)": "Durata delle immagini (ms)",
+    "Put variable parts at start of prompt": "Inserisce le parti variabili all'inizio del prompt",
+    "Keyframe Format:": "Formato dei fotogrammi chiave:",
+    "Seed | Prompt or just Prompt": "Seme | Prompt o semplicemente Prompt",
+    "Prompt list": "Elenco dei prompt",
+    "Number of images between keyframes": "Numero di immagini tra fotogrammi chiave",
+    "Save results as video": "Salva i risultati come video",
+    "Frames per second": "Fotogrammi al secondo",
+    "Iterate seed every line": "Iterare il seme per ogni riga",
+    "List of prompt inputs": "Elenco di prompt di input",
+    "Upload prompt inputs": "Carica un file contenente i prompt di input",
+    "n": "Esegui n volte",
+    "Destination seed(s) (Comma separated)": "Seme/i di destinazione (separati da virgola)",
+    "Only use Random seeds (Unless comparing paths)": "Usa solo semi casuali (a meno che non si confrontino i percorsi)",
+    "Number of random seed(s)": "Numero di semi casuali",
+    "Compare paths (Separate travels from 1st seed to each destination)": "Confronta percorsi (transizioni separate dal primo seme a ciascuna destinazione)",
+    "Steps": "Passi",
+    "Loop back to initial seed": "Ritorna al seme iniziale",
+    "Bump seed (If > 0 do a Compare Paths but only one image. No video)": "Modula seme (se > 0 mescola il seme iniziale con quelli di destinazione ma solo un'immagine. Nessun video)",
+    "Show generated images in ui": "Mostra le immagini generate nell'interfaccia utente",
+    "\"Hug the middle\" during interpolation": "\"Hug the middle\" durante l'interpolazione. Rende l'interpolazione un po' più veloce all'inizio e alla fine. A volte può produrre video più fluidi, il più delle volte no.",
+    "Allow the default Euler a Sampling method. (Does not produce good results)": "Consenti Euler_a come metodo di campionamento predefinito. (Non produce buoni risultati)",
+    "Illustration": "Illustrazione",
+    "Logo": "Logo",
+    "Drawing": "Disegno",
+    "Artistic": "Artistico",
+    "Tattoo": "Tatuaggio",
+    "Gothic": "Gotico",
+    "Anime": "Anime",
+    "Cartoon": "Cartoon",
+    "Sticker": "Etichetta",
+    "Gold Pendant": "Ciondolo in oro",
+    "None - prompt only": "Nessuno - solo prompt",
+    "Enable Vectorizing": "Abilita vettorizzazione",
+    "Output format": "Formato di output",
+    "svg": "svg",
+    "pdf": "pdf",
+    "White is Opaque": "Il bianco è opaco",
+    "Cut white margin from input": "Taglia il margine bianco dall'input",
+    "Keep temp images": "Conserva le immagini temporanee",
+    "Threshold": "Soglia",
+    "Transparent PNG": "PNG trasparente",
+    "Noise Tolerance": "Tolleranza al rumore",
+    "Quantize": "Quantizzare",
+    "X type": "Parametro asse X",
+    "Nothing": "Niente",
+    "Var. seed": "Seme della variazione",
+    "Var. strength": "Forza della variazione",
+    "Prompt S/R": "Cerca e Sostituisci nel Prompt",
+    "Prompt order": "In ordine di prompt",
+    "Sampler": "Campionatore",
+    "Checkpoint name": "Nome del checkpoint",
+    "Hypernetwork": "Iperrete",
+    "Hypernet str.": "Forza della Iperrete",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma noise": "Sigma noise",
+    "Eta": "ETA",
+    "Clip skip": "Salta CLIP",
+    "Denoising": "Riduzione del rumore",
+    "Cond. Image Mask Weight": "Cond. Image Mask Weight",
+    "X values": "Valori per X",
+    "Y type": "Parametro asse Y",
+    "Y values": "Valori per Y",
+    "Z type": "Parametro asse Z",
+    "Z values": "Valori per Z",
+    "Artist or styles name list. '.txt' files with one name per line": "Elenco nomi di artisti o stili. File '.txt' con un nome per riga",
+    "Prompt words before artist or style name": "Parole chiave prima del nome dell'artista o dello stile",
+    "Prompt words after artist or style name": "Parole chiave dopo il nome dell'artista o dello stile",
+    "Negative Prompt": "Prompt negativo",
+    "Save": "Salva",
+    "Send to img2img": "Invia a img2img",
+    "Send to inpaint": "Invia a inpaint",
+    "Send to extras": "Invia a 'Extra'",
+    "Make Zip when Save?": "Crea un file ZIP quando si usa 'Salva'",
+    "Textbox": "Casella di testo",
+    "Interrogate\nCLIP": "Interroga\nCLIP",
+    "Interrogate\nDeepBooru": "Interroga\nDeepBooru",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "img2img in lotti",
+    "Image for img2img": "Immagine per img2img",
+    "Drop Image Here": "Trascina l'immagine qui",
+    "Image for inpainting with mask": "Immagine per inpainting con maschera",
+    "Mask": "Maschera",
+    "Mask blur": "Sfocatura maschera",
+    "Mask mode": "Modalità maschera",
+    "Draw mask": "Disegna maschera",
+    "Upload mask": "Carica maschera",
+    "Masking mode": "Modalità mascheratura",
+    "Inpaint masked": "Inpaint mascherato",
+    "Inpaint not masked": "Inpaint non mascherato",
+    "Masked content": "Contenuto mascherato",
+    "fill": "riempi",
+    "original": "originale",
+    "latent noise": "rumore nello spazio latente",
+    "latent nothing": "nulla nello spazio latente",
+    "Inpaint at full resolution": "Inpaint alla massima risoluzione",
+    "Inpaint at full resolution padding, pixels": "Inpaint con riempimento a piena risoluzione, pixel",
+    "Process images in a directory on the same machine where the server is running.": "Elabora le immagini in una cartella sulla stessa macchina su cui è in esecuzione il server.",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa una cartella di output vuota per salvare normalmente le immagini invece di scrivere nella cartella di output.",
+    "Input directory": "Cartella di Input",
+    "Resize mode": "Modalità di ridimensionamento",
+    "Just resize": "Ridimensiona solamente",
+    "Crop and resize": "Ritaglia e ridimensiona",
+    "Resize and fill": "Ridimensiona e riempie",
+    "Advanced loopback": "Advanced loopback", 
+    "External Image Masking": "Immagine esterna per la mascheratura",
+    "img2img alternative test": "Test alternativo per img2img",
+    "img2tiles": "img2tiles",
+    "Interpolate": "Interpolare",
+    "Loopback": "Rielaborazione ricorsiva",
+    "Loopback and Superimpose": "Rielabora ricorsivamente e sovraimponi",
+    "Alpha Canvas": "Alpha Canvas",
+    "Outpainting mk2": "Outpainting mk2",
+    "Poor man's outpainting": "Poor man's outpainting",
+    "SD upscale": "Ampliamento SD",
+    "txt2mask v0.1.1": "txt2mask v0.1.1",
+    "[C] Video to video": "[C] Video to video",
+    "Videos": "Filmati",
+    "Deforum-webui (use tab extension instead!)": "Deforum-webui (usa piuttosto la scheda Deforum delle estensioni!)",
+    "Use first image colors (custom color correction)": "Usa i colori della prima immagine (correzione del colore personalizzata)",
+    "Denoising strength change factor (overridden if proportional used)": "Fattore di variazione dell'intensità di riduzione del rumore (sovrascritto se si usa proporzionale)",
+    "Zoom level": "Livello di Zoom",
+    "Direction X": "Direzione X",
+    "Direction Y": "Direzione Y",
+    "Denoising strength start": "Intensità di riduzione del rumore - Inizio",
+    "Denoising strength end": "Intensità di riduzione del rumore - Fine",
+    "Denoising strength proportional change starting value": "Intensità di riduzione del rumore - Valore iniziale della variazione proporzionale",
+    "Denoising strength proportional change ending value (0.1 = disabled)": "Intensità di riduzione del rumore - Valore finale della variazione proporzionale (0.1 = disabilitato)",
+    "Saturation enhancement per image": "Miglioramento della saturazione per ciascuna immagine",
+    "Use sine denoising strength variation": "Utilizzare la variazione sinusoidale dell'intensità di riduzione del rumore",
+    "Phase difference": "Differenza di Fase",
+    "Denoising strength exponentiation": "Esponenziazione dell'intensità di riduzione del rumore",
+    "Use sine zoom variation": "Usa la variazione sinusoidale dello zoom",
+    "Zoom exponentiation": "Esponeniazione dello Zoom",
+    "Use multiple prompts": "Usa prompt multipli",
+    "Same seed per prompt": "Stesso seme per ogni prompt",
+    "Same seed for everything": "Stesso seme per tutto",
+    "Original init image for everything": "Immagine originale di inizializzazione per tutto",
+    "Multiple prompts : 1 line positive, 1 line negative, leave a blank line for no negative": "Prompt multipli: 1 riga positivo, 1 riga negativo, lasciare una riga vuota per nessun negativo",
+    "Running in img2img mode:": "Esecuzione in modalità img2img:",
+    "Masking preview size": "Dimensione dell'anteprima della mascheratura",
+    "Draw new mask on every run": "Disegna una nuova maschera ad ogni esecuzione",
+    "Process non-contigious masks separately": "Elaborare le maschere non contigue separatamente",
+    "should be 2 or lower.": "dovrebbe essere 2 o inferiore.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Sovrascrivi il 'Metodo di campionamento' con Eulero? (questo metodo è stato creato per questo)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Sovrascrivi `prompt` con lo stesso valore del `prompt originale`? (e `prompt negativo`)",
+    "Original prompt": "Prompt originale",
+    "Original negative prompt": "Prompt negativo originale",
+    "Override `Sampling Steps` to the same val due as `Decode steps`?": "Sovrascrivere 'Passi di campionamento' allo stesso valore di 'Passi di decodifica'?",
+    "Decode steps": "Passi di decodifica",
+    "Override `Denoising strength` to 1?": "Sostituisci 'Forza di denoising' a 1?",
+    "Decode CFG scale": "Scala CFG di decodifica",
+    "Randomness": "Casualità",
+    "Sigma adjustment for finding noise for image": "Regolazione Sigma per trovare il rumore per l'immagine",
+    "Tile size": "Dimensione piastrella",
+    "Tile overlap": "Sovrapposizione piastrella",
+    "alternate img2img imgage": "alternate img2img imgage",
+    "interpolation values": "Valori di interpolazione",
+    "Refinement loops": "Cicli di affinamento",
+    "Loopback alpha": "Trasparenza rielaborazione ricorsiva",
+    "Border alpha": "Trasparenza del bordo",
+    "Blending strides": "Passi di fusione",
+    "Reuse Seed": "Riusa il seme",
+    "One grid": "Singola griglia",
+    "Interpolate VarSeed": "Interpola il seme di variazione",
+    "Paste on mask": "Incolla sulla maschera",
+    "Inpaint all": "Inpaint tutto",
+    "Interpolate in latent": "Interpola nello spazio latente",
+    "Denoising strength change factor": "Fattore di variazione dell'intensità di denoising",
+    "Superimpose alpha": "Sovrapporre Alpha",
+    "Show extra settings": "Mostra impostazioni aggiuntive",
+    "Reuse seed": "Riusa il seme",
+    "CFG decay factor": "Fattore di decadimento CFG",
+    "CFG target": "CFG di destinazione",
+    "Show/Hide Canvas": "Mostra/Nascondi Tela",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Impostazioni consigliate: Passi di campionamento: 80-100, Campionatore: Euler a, Intensità denoising: 0.8",
+    "Pixels to expand": "Pixel da espandere",
+    "Outpainting direction": "Direzione di Outpainting",
+    "left": "sinistra",
+    "right": "destra",
+    "up": "sopra",
+    "down": "sotto",
+    "Fall-off exponent (lower=higher detail)": "Esponente di decremento (più basso=maggior dettaglio)",
+    "Color variation": "Variazione di colore",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenterà l'immagine al doppio delle dimensioni; utilizzare i cursori di larghezza e altezza per impostare la dimensione della piastrella",
+    "Upscaler": "Ampliamento immagine",
+    "Lanczos": "Lanczos",
+    "LDSR": "LDSR",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR 4x": "SwinIR 4x",
+    "Mask prompt": "Prompt maschera",
+    "Negative mask prompt": "Prompt maschera negativa",
+    "Mask precision": "Precisione della maschera",
+    "Mask padding": "Estendi i bordi della maschera",
+    "Brush mask mode": "Modalità pennello maschera",
+    "discard": "Scarta",
+    "add": "Aggiungi",
+    "subtract": "Sottrai",
+    "Show mask in output?": "Mostra maschera in uscita?",
+    "If you like my work, please consider showing your support on": "Se ti piace il mio lavoro, per favore considera di mostrare il tuo supporto su ",
+    "Patreon": "Patreon",
+    "Input file path": "Percorso file di input",
+    "CRF (quality, less is better, x264 param)": "CRF (qualità, meno è meglio, x264 param)",
+    "FPS": "FPS",
+    "Seed step size": "Ampiezza del gradiente del seme",
+    "Seed max distance": "Distanza massima del seme",
+    "Start time": "Orario di inizio",
+    "End time": "Orario di fine",
+    "End Prompt Blend Trigger Percent": "Percentuale di innesco del mix col prompt finale",
+    "Prompt end": "Prompt finale",
+    "Smooth video": "Rendi il filmato fluido",
+    "Seconds": "Secondi",
+    "Zoom": "Zoom",
+    "Rotate": "Ruota",
+    "Degrees": "Gradi",
+    "Is the Image Tiled?": "L'immagine è piastrellata?",
+    "TranslateX": "Traslazione X",
+    "Left": "Sinistra",
+    "PercentX": "Percentuale X",
+    "TranslateY": "Traslazione Y",
+    "Up": "Sopra",
+    "PercentY": "Percentuale Y",
+    "Show generated pictures in ui": "Mostra le immagini generate nell'interfaccia utente",
+    "Deforum v0.5-webui-beta": "Deforum v0.5-webui-beta",
+    "This script is deprecated. Please use the full Deforum extension instead.": "Questo script è obsoleto. Utilizzare invece l'estensione Deforum completa.",
+    "Update instructions:": "Istruzioni per l'aggiornamento:",
+    "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md": "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md",
+    "discord.gg/deforum": "discord.gg/deforum",
+    "Single Image": "Singola immagine",
+    "Batch Process": "Elaborare a lotti",
+    "Batch from Directory": "Lotto da cartella",
+    "Source": "Sorgente",
+    "Show result images": "Mostra le immagini dei risultati",
+    "Scale by": "Scala di",
+    "Scale to": "Scala a",
+    "Resize": "Ridimensiona",
+    "Crop to fit": "Ritaglia per adattare",
+    "Upscaler 2 visibility": "Visibilità Ampliamento immagine 2",
+    "GFPGAN visibility": "Visibilità GFPGAN",
+    "CodeFormer visibility": "Visibilità CodeFormer",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso di CodeFormer (0 = effetto massimo, 1 = effetto minimo)",
+    "Upscale Before Restoring Faces": "Amplia prima di restaurare i volti",
+    "Send to txt2img": "Invia a txt2img",
+    "A merger of the two checkpoints will be generated in your": "I due checkpoint verranno fusi nella cartella dei",
+    "checkpoint": "checkpoint",
+    "directory.": ".",
+    "Primary model (A)": "Modello Primario (A)",
+    "Secondary model (B)": "Modello Secondario (B)",
+    "Tertiary model (C)": "Modello Terziario (C)",
+    "Custom Name (Optional)": "Nome personalizzato (facoltativo)",
+    "Multiplier (M) - set to 0 to get model A": "Moltiplicatore (M): impostare a 0 per ottenere il modello A",
+    "Interpolation Method": "Metodo di interpolazione",
+    "Weighted sum": "Somma pesata",
+    "Add difference": "Aggiungi differenza",
+    "Save as float16": "Salva come float16",
+    "See": "Consulta la ",
+    "wiki": "wiki",
+    "for detailed explanation.": " per una spiegazione dettagliata.",
+    "Create embedding": "Crea Incorporamento",
+    "Create hypernetwork": "Crea Iperrete",
+    "Preprocess images": "Preprocessa le immagini",
+    "Name": "Nome",
+    "Initialization text": "Testo di inizializzazione",
+    "Number of vectors per token": "Numero di vettori per token",
+    "Overwrite Old Embedding": "Sovrascrivi il vecchio incorporamento",
+    "Modules": "Moduli",
+    "Enter hypernetwork layer structure": "Immettere la struttura del livello della Iperrete",
+    "Select activation function of hypernetwork": "Selezionare la funzione di attivazione della Iperrete",
+    "linear": "linear",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "threshold",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleziona inizializzazione dei pesi dei livelli. relu-like - Kaiming, Si consiglia sigmoid-like - Xavier",
+    "Normal": "Normal",
+    "KaimingUniform": "KaimingUniform",
+    "KaimingNormal": "KaimingNormal",
+    "XavierUniform": "XavierUniform",
+    "XavierNormal": "XavierNormal",
+    "Add layer normalization": "Aggiunge la normalizzazione del livello",
+    "Use dropout": "Usa Dropout",
+    "Overwrite Old Hypernetwork": "Sovrascrive la vecchia Iperrete",
+    "Source directory": "Cartella sorgente",
+    "Destination directory": "Cartella di destinazione",
+    "Existing Caption txt Action": "Azione sul testo della didascalia esistente",
+    "ignore": "ignora",
+    "copy": "copia",
+    "prepend": "anteporre",
+    "append": "appendere",
+    "Create flipped copies": "Crea copie specchiate",
+    "Split oversized images": "Dividi immagini di grandi dimensioni",
+    "Auto focal point crop": "Ritaglio automatico al punto focale",
+    "Use BLIP for caption": "Usa BLIP per la didascalia",
+    "Use deepbooru for caption": "Usa deepbooru per la didascalia",
+    "Split image threshold": "Soglia di divisione dell'immagine",
+    "Split image overlap ratio": "Rapporto di sovrapposizione dell'immagine",
+    "Focal point face weight": "Peso della faccia del punto focale",
+    "Focal point entropy weight": "Peso dell'entropia del punto focale",
+    "Focal point edges weight": "Peso dei bordi del punto focalePeso dei bordi del punto focale",
+    "Create debug image": "Crea immagine di debug",
+    "Preprocess": "Preprocessa",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Addestra un Incorporamento o Iperrete; è necessario specificare una directory con un set di immagini con rapporto 1:1",
+    "[wiki]": "[wiki]",
+    "Embedding": "Incorporamento",
+    "Embedding Learning rate": "Tasso di apprendimento Incorporamento",
+    "Hypernetwork Learning rate": "Tasso di apprendimento Iperrete",
+    "Dataset directory": "Cartella del Dataset",
+    "Log directory": "Cartella del registro",
+    "Prompt template file": "File modello prompt",
+    "Max steps": "Passi massimi",
+    "Save an image to log directory every N steps, 0 to disable": "Salva un'immagine nella cartella del registro ogni N passaggi, 0 per disabilitare",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva una copia dell'incorporamento nella cartella del registro ogni N passaggi, 0 per disabilitare",
+    "Save images with embedding in PNG chunks": "Salva le immagini con l'incorporamento in blocchi PNG",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Legge i parametri (prompt, ecc...) dalla scheda txt2img durante la creazione delle anteprime",
+    "Train Hypernetwork": "Addestra Iperrete",
+    "Train Embedding": "Addestra Incorporamento",
+    "Create an aesthetic embedding out of any number of images": "Crea un'incorporamento estetico da qualsiasi numero di immagini",
+    "Create images embedding": "Crea incorporamento di immagini",
+    "-1": "-1",
+    "This extension works well with text captions in comma-separated style (such as the tags generated by DeepBooru interrogator).": "Questa estensione funziona bene con i sottotitoli di testo in stile separato da virgole (come i tag generati dall'interrogatore DeepBooru).",
+    "Save all changes": "Salva tutte le modifiche",
+    "Backup original text file (original file will be renamed like filename.000, .001, .002, ...)": "Backup del file di testo originale (il file originale verrà rinominato come nomefile.000, .001, .002, ...)",
+    "Note:": "Note:",
+    "New text file will be created if you are using filename as captions.": "Verrà creato un nuovo file di testo se si utilizza il nome del file come didascalia.",
+    "Results": "Risultati",
+    "Load": "Carica",
+    "Dataset Images": "Immagini del Dataset",
+    "Filter and Edit Tags": "Filtra e modifica i tag",
+    "Edit Caption of Selected Image": "Modifica la didascalia dell'immagine selezionata",
+    "Search tags / Filter images by tags": "Cerca tag / Filtra le immagini per tag",
+    "Search Tags": "Cerca tag",
+    "Clear all filters": "Rimuovi tutti i filtri",
+    "Sort by": "Ordina per",
+    "Alphabetical Order": "Ordine alfabetico",
+    "Frequency": "Frequenza",
+    "Sort Order": "Ordinamento",
+    "Ascending": "Ascendente",
+    "Descending": "Discendente",
+    "Filter Images by Tags": "Filtra le immagini per tag",
+    "Edit tags in filtered images": "Modifica i tag nelle immagini filtrate",
+    "Selected Tags": "Tag selezionati",
+    "Edit Tags": "Modificare i tag",
+    "Apply changes to filtered images": "Applica le modifiche alle immagini filtrate",
+    "Append additional tags to the beginning": "Aggiungi tag addizionali all'inizio",
+    "1. The selected tags are displayed in comma separated style.": "1. I tag selezionati vengono visualizzati in uno stile separato da virgole.",
+    "2. When changes are applied, all tags in each displayed images are replaced.": "2. Quando vengono applicate le modifiche, tutti i tag in ciascuna immagine visualizzata vengono sostituiti.",
+    "3. If you change some tags into blank, they will be erased.": "3. Se modifichi alcuni tag con uno spazio vuoto, verranno cancellati.",
+    "4. If you add some tags to the end, they will be appended to the end/beginning of the text file.": "4. Se aggiungi dei tag alla fine, questi verranno aggiunti alla fine/inizio del file di testo.",
+    "5. Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "5. Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\"..",
+    "ex A.": "esempio A.",
+    "Original Text = \"A, A, B, C\" Selected Tags = \"B, A\" Edit Tags = \"X, Y\"": "Testo originale = \"A, A, B, C\" Tag selezionati = \"B, A\" Modifica tag = \"X, Y\"",
+    "Result = \"Y, Y, X, C\" (B->X, A->Y)": "Risultato = \"Y, Y, X, C\" (B->X, A->Y)",
+    "ex B.": "esempio B.",
+    "Original Text = \"A, B, C\" Selected Tags = \"(nothing)\" Edit Tags = \"X, Y\"": "Testo originale = \"A, B, C\" Tag selezionati = \"(nothing)\" Modifica tag = \"X, Y\"",
+    "Result = \"A, B, C, X, Y\" (add X and Y to the end (default))": "Risultato = \"A, B, C, X, Y\" (aggiunge X e Y alla fine (predefinito))",
+    "Result = \"X, Y, A, B, C\" (add X and Y to the beginning (\"Append additional tags to the beginning\" checked))": "Risultato = \"X, Y, A, B, C\" (aggiunge X e Y all'inizio (\"Aggiungi tag addizionali all'inizio\" selezionato))",
+    "ex C.": "esempio C.",
+    "Original Text = \"A, B, C, D, E\" Selected Tags = \"A, B, D\" Edit Tags = \", X, \"": "Testo originale = \"A, B, C, D, E\" Tag selezionati = \"A, B, D\" Modifica tag = \", X, \"",
+    "Result = \"X, C, E\" (A->\"\", B->X, D->\"\")": "Risultato = \"X, C, E\" (A->\"\", B->X, D->\"\")",
+    "Caption of Selected Image": "Didascalia dell'immagine selezionata",
+    "Copy caption": "Copia didascalia",
+    "Edit Caption": "Modifica didascalia",
+    "Apply changes to selected image": "Applica le modifiche all'immagine selezionata",
+    "Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\".",
+    "Info and links": "Info e link",
+    "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha": "Realizzato da deforum.github.io, port per l'interfaccia web di AUTOMATIC1111 manutenuto da kabachuha",
+    "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Repository Github originale di Deforum  github.com/deforum/stable-diffusion",
+    "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui": "Questo fork è per l'interfaccia web di AUTOMATIC1111 github.com/deforum-art/deforum-for-automatic1111-webui",
+    "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Unisciti al canale Discord ufficiale di Deforum discord.gg/deforum per condividere le tue creazioni e suggerimenti",
+    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "Manuale d'uso per la versione 0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
+    "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Spiegazione della matematica dei fotogrammi chiave docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
+    "Keyframes": "Fotogrammi chiave",
+    "Prompts": "Prompt",
+    "Init": "Inizializzare",
+    "Video output": "Uscita video",
+    "Run settings": "Esegui le impostazioni",
+    "Import settings from file": "Importa impostazioni da file",
+    "Override settings": "Sostituisci le impostazioni",
+    "Custom settings file": "File delle impostazioni personalizzate",
+    "Sampling settings": "Impostazioni di campionamento",
+    "override_these_with_webui": "Sovrascrivi con Web UI",
+    "W": "L",
+    "H": "A",
+    "seed": "Seme",
+    "sampler": "Campionatore",
+    "Enable extras": "Abilita 'Extra'",
+    "subseed": "Sub seme",
+    "subseed_strength": "Intensità subseme",
+    "steps": "Passi",
+    "ddim_eta": "ETA DDIM",
+    "n_batch": "Numero lotto",
+    "make_grid": "Crea griglia",
+    "grid_rows": "Righe griglia",
+    "save_settings": "Salva impostazioni",
+    "save_samples": "Salva i campioni",
+    "display_samples": "Mostra i campioni",
+    "save_sample_per_step": "Salva campioni per passo",
+    "show_sample_per_step": "Mostra campioni per passo",
+    "Batch settings": "Impostazioni lotto",
+    "batch_name": "Nome del lotto",
+    "filename_format": "Formato nome del file",
+    "seed_behavior": "Comportamento seme",
+    "iter": "Iterativo",
+    "fixed": "Fisso",
+    "random": "Casuale",
+    "schedule": "Pianificato",
+    "Animation settings": "Impostazioni animazione",
+    "animation_mode": "Modalità animazione",
+    "2D": "2D",
+    "3D": "3D",
+    "Video Input": "Ingresso video",
+    "max_frames": "Fotogrammi max",
+    "border": "Bordo",
+    "replicate": "Replica",
+    "wrap": "Impacchetta",
+    "Motion parameters:": "Parametri di movimento:",
+    "2D and 3D settings": "Impostazioni 2D e 3D",
+    "angle": "Angolo",
+    "zoom": "Zoom",
+    "translation_x": "Traslazione X",
+    "translation_y": "Traslazione Y",
+    "3D settings": "Impostazioni 3D",
+    "translation_z": "Traslazione Z",
+    "rotation_3d_x": "Rotazione 3D X",
+    "rotation_3d_y": "Rotazione 3D Y",
+    "rotation_3d_z": "Rotazione 3D Z",
+    "Prespective flip — Low VRAM pseudo-3D mode:": "Inversione prospettica: modalità pseudo-3D a bassa VRAM:",
+    "flip_2d_perspective": "Inverti prospettiva 2D",
+    "perspective_flip_theta": "Inverti prospettiva theta",
+    "perspective_flip_phi": "Inverti prospettiva phi",
+    "perspective_flip_gamma": "Inverti prospettiva gamma",
+    "perspective_flip_fv": "Inverti prospettiva fv",
+    "Generation settings:": "Impostazioni di generazione:",
+    "noise_schedule": "Pianificazione del rumore",
+    "strength_schedule": "Intensità della pianificazione",
+    "contrast_schedule": "Contrasto della pianificazione",
+    "cfg_scale_schedule": "Pianificazione della scala CFG",
+    "3D Fov settings:": "Impostazioni del campo visivo 3D:",
+    "fov_schedule": "Pianificazione del campo visivo",
+    "near_schedule": "Pianificazione da vicino",
+    "far_schedule": "Pianificazione da lontano",
+    "To enable seed schedule select seed behavior — 'schedule'": "Per abilitare la pianificazione del seme, seleziona il comportamento del seme — 'pianifica'",
+    "seed_schedule": "Pianificazione del seme",
+    "Coherence:": "Coerenza:",
+    "color_coherence": "Coerenza del colore",
+    "Match Frame 0 HSV": "Uguaglia HSV del fotogramma 0",
+    "Match Frame 0 LAB": "Uguaglia LAB del fotogramma 0",
+    "Match Frame 0 RGB": "Uguaglia RGB del fotogramma 0",
+    "diffusion_cadence": "Cadenza di diffusione",
+    "3D Depth Warping:": "Deformazione della profondità 3D:",
+    "use_depth_warping": "Usa la deformazione della profondità",
+    "midas_weight": "Peso MIDAS",
+    "near_plane": "Piano vicino",
+    "far_plane": "Piano lontano",
+    "fov": "Campo visivo",
+    "padding_mode": "Modalità di riempimento",
+    "reflection": "Rifletti",
+    "zeros": "Zeri",
+    "sampling_mode": "Modalità di campionamento",
+    "bicubic": "bicubic",
+    "bilinear": "bilinear",
+    "nearest": "nearest",
+    "save_depth_maps": "Salva le mappe di profondità",
+    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`modalità animazione: Nessuno` si inserisce nell'elenco di *prompt*. (Modalità batch disabilitata atm, funzionano solo i prompt di animazione)",
+    "*Important change from vanilla Deforum!*": "*Importante cambiamento rispetto alla versione originale di Deforum!*",
+    "This script uses the built-in webui weighting settings.": "Questo script utilizza le impostazioni di pesatura webui integrate.",
+    "So if you want to use math functions as prompt weights,": "Quindi, se vuoi usare le funzioni matematiche come pesi dei prompt,",
+    "keep the values above zero in both parts": "mantenere i valori sopra lo zero in entrambe le parti",
+    "Negative prompt part can be specified with --neg": "La parte negativa del prompt può essere specificata con --neg",
+    "batch_prompts (disabled atm)": "Prompt in lotti (al momento è disabilitato)",
+    "animation_prompts": "Prompt animazione",
+    "Init settings": "Impostazioni iniziali",
+    "use_init": "Usa le impostazioni iniziali",
+    "from_img2img_instead_of_link": "from_img2img_instead_of_link",
+    "strength_0_no_init": "strength_0_no_init",
+    "strength": "Intensità",
+    "init_image": "Immagine di inizializzazione",
+    "use_mask": "Usa maschera",
+    "use_alpha_as_mask": "Usa alpha come maschera",
+    "invert_mask": "Inverti la maschera",
+    "overlay_mask": "Sovrapponi la maschera",
+    "mask_file": "File della maschera",
+    "mask_brightness_adjust": "Regola la luminosità della maschera",
+    "mask_overlay_blur": "Sfocatura della sovrapposizione della maschera",
+    "Video Input:": "Ingresso video:",
+    "video_init_path": "Percorso del video di inizializzazione",
+    "extract_nth_frame": "Estrai ogni ennesimo fotogramma",
+    "overwrite_extracted_frames": "Sovrascrivi i fotogrammi estratti",
+    "use_mask_video": "Usa maschera video",
+    "video_mask_path": "Percorso della maschera video",
+    "Interpolation (turned off atm)": "Interpolazione (attualmente spento)",
+    "interpolate_key_frames": "Interpola fotogrammi chiave",
+    "interpolate_x_frames": "Interpola x fotogrammi",
+    "Resume animation:": "Riprendi l'animazione:",
+    "resume_from_timestring": "Riprendi da stringa temporale",
+    "resume_timestring": "Stringa temporale",
+    "Video output settings": "Impostazioni uscita video",
+    "skip_video_for_run_all": "Salta il video per eseguire tutto",
+    "fps": "FPS",
+    "output_format": "Formato di uscita",
+    "PIL gif": "PIL gif",
+    "FFMPEG mp4": "FFMPEG mp4",
+    "ffmpeg_location": "Percorso ffmpeg",
+    "add_soundtrack": "Aggiungi colonna sonora",
+    "soundtrack_path": "Percorso colonna sonora",
+    "use_manual_settings": "Usa impostazioni manuali",
+    "render_steps": "Passi di renderizzazione",
+    "max_video_frames": "Numero max fotogrammi video",
+    "path_name_modifier": "Modificatore del nome del percorso",
+    "x0_pred": "x0_pred",
+    "x": "x",
+    "image_path": "Percorso immagine",
+    "mp4_path": "Percorso MP4",
+    "Click here after the generation to show the video": "Clicca qui dopo la generazione per mostrare il video",
+    "Save Settings": "Salva le impostazioni",
+    "Load Settings": "Carica le impostazioni",
+    "Path relative to the webui folder." : "Percorso relativo alla cartella webui.",
+    "Save Video Settings": "Salva impostazioni video",
+    "Load Video Settings": "Carica impostazioni video",
+    "dog": "cane",
+    "house": "casa",
+    "portrait": "ritratto",
+    "spaceship": "nave spaziale",
+    "anime": "anime",
+    "cartoon": "cartoon",
+    "digipa-high-impact": "digipa-high-impact",
+    "digipa-med-impact": "digipa-med-impact",
+    "digipa-low-impact": "digipa-low-impact",
+    "fareast": "estremo oriente",
+    "fineart": "fineart",
+    "scribbles": "scarabocchi",
+    "special": "special",
+    "ukioe": "ukioe",
+    "weird": "strano",
+    "black-white": "bianco e nero",
+    "nudity": "nudità",
+    "c": "c",
+    "Get Images": "Ottieni immagini",
+    "dog-anime": "dog-anime",
+    "dog-cartoon": "dog-cartoon",
+    "dog-digipa-high-impact": "dog-digipa-high-impact",
+    "dog-digipa-med-impact": "dog-digipa-med-impact",
+    "dog-digipa-low-impact": "dog-digipa-low-impact",
+    "dog-fareast": "dog-fareast",
+    "dog-fineart": "dog-fineart",
+    "dog-scribbles": "dog-scribbles",
+    "dog-special": "dog-special",
+    "dog-ukioe": "dog-ukioe",
+    "dog-weird": "dog-weird",
+    "dog-black-white": "dog-black-white",
+    "dog-nudity": "dog-nudity",
+    "dog-c": "dog-c",
+    "dog-n": "dog-n",
+    "house-anime": "house-anime",
+    "house-cartoon": "house-cartoon",
+    "house-digipa-high-impact": "house-digipa-high-impact",
+    "house-digipa-med-impact": "house-digipa-med-impact",
+    "house-digipa-low-impact": "house-digipa-low-impact",
+    "house-fareast": "house-fareast",
+    "house-fineart": "house-fineart",
+    "house-scribbles": "house-scribbles",
+    "house-special": "house-special",
+    "house-ukioe": "house-ukioe",
+    "house-weird": "house-weird",
+    "house-black-white": "house-black-white",
+    "house-nudity": "house-nudity",
+    "house-c": "house-c",
+    "house-n": "house-n",
+    "portrait-anime": "portrait-anime",
+    "portrait-cartoon": "portrait-cartoon",
+    "portrait-digipa-high-impact": "portrait-digipa-high-impact",
+    "portrait-digipa-med-impact": "portrait-digipa-med-impact",
+    "portrait-digipa-low-impact": "portrait-digipa-low-impact",
+    "portrait-fareast": "portrait-fareast",
+    "portrait-fineart": "portrait-fineart",
+    "portrait-scribbles": "portrait-scribbles",
+    "portrait-special": "portrait-special",
+    "portrait-ukioe": "portrait-ukioe",
+    "portrait-weird": "portrait-weird",
+    "portrait-black-white": "portrait-black-white",
+    "portrait-nudity": "portrait-nudity",
+    "portrait-c": "portrait-c",
+    "portrait-n": "portrait-n",
+    "spaceship-anime": "spaceship-anime",
+    "spaceship-cartoon": "spaceship-cartoon",
+    "spaceship-digipa-high-impact": "spaceship-digipa-high-impact",
+    "spaceship-digipa-med-impact": "spaceship-digipa-med-impact",
+    "spaceship-digipa-low-impact": "spaceship-digipa-low-impact",
+    "spaceship-fareast": "spaceship-fareast",
+    "spaceship-fineart": "spaceship-fineart",
+    "spaceship-scribbles": "spaceship-scribbles",
+    "spaceship-special": "spaceship-special",
+    "spaceship-ukioe": "spaceship-ukioe",
+    "spaceship-weird": "spaceship-weird",
+    "spaceship-black-white": "spaceship-black-white",
+    "spaceship-nudity": "spaceship-nudity",
+    "spaceship-c": "spaceship-c",
+    "spaceship-n": "spaceship-n",
+    "artists to study extension by camenduru |": "Estensione 'Artisti per studiare' a cura di camenduru |",
+    "github": "Github",
+    "|": "|",
+    "twitter": "Twitter",
+    "youtube": "Youtube",
+    "hi-res images": "Immagini in alta risoluzione",
+    "All images generated with CompVis/stable-diffusion-v1-4 +": "Tutte le immagini sono state generate con CompVis/stable-diffusion-v1-4 +",
+    "artists.csv": "artists.csv",
+    "| License: Attribution 4.0 International (CC BY 4.0)": "| Licenza: Attribution 4.0 International (CC BY 4.0)",
+    "Favorites": "Preferiti",
+    "Others": "Altre immagini",
+    "Images directory": "Cartella immagini",
+    "Dropdown": "Elenco cartelle",
+    "First Page": "Prima pagina",
+    "Prev Page": "Pagina precedente",
+    "Page Index": "Indice pagina",
+    "Next Page": "Pagina successiva",
+    "End Page": "Ultima pagina",
+    "delete next": "Cancella successivo",
+    "Delete": "Elimina",
+    "sort by": "Ordina per",
+    "path name": "Nome percorso",
+    "date": "Data",
+    "keyword": "Parola chiave",
+    "Generate Info": "Genera Info",
+    "File Name": "Nome del file",
+    "Move to favorites": "Aggiungi ai preferiti",
+    "Renew page": "Aggiorna la pagina",
+    "Number": "Numero",
+    "set_index": "Imposta indice",
+    "load_switch": "load_switch",
+    "turn_page_switch": "turn_page_switch",
+    "Checkbox": "Casella di controllo",						   
+    "Checkbox Group": "Seleziona immagini per",
+    "artists": "Artisti",
+    "flavors": "Stili",
+    "mediums": "Tecniche",
+    "movements": "Movimenti artistici",
+    "All": "Tutto",
+    "Exclude abandoned": "Escludi scartati",
+    "Abandoned": "Scartati",
+    "Key word": "Parola chiave",
+    "Get inspiration": "Ispirami",
+    "to txt2img": "Invia a txt2img",
+    "to img2img": "Invia a img2img",
+    "Collect": "Salva nei preferiti",
+    "Don't show again": "Scarta",
+    "Move out": "Rimuovi",
+    "set button": "Pulsante imposta", 
+    "Apply settings": "Applica le impostazioni",
+    "Saving images/grids": "Salva immagini/griglie",
+    "Always save all generated images": "Salva sempre tutte le immagini generate",
+    "File format for images": "Formato del file delle immagini",
+    "Images filename pattern": "Modello del nome dei file immagine",
+    "Add number to filename when saving": "Aggiungi un numero al nome del file al salvataggio",
+    "Always save all generated image grids": "Salva sempre tutte le griglie di immagini generate",
+    "File format for grids": "Formato del file per le griglie",
+    "Add extended info (seed, prompt) to filename when saving grid": "Aggiungi informazioni estese (seme, prompt) al nome del file durante il salvataggio della griglia",
+    "Do not save grids consisting of one picture": "Non salvare le griglie composte da una sola immagine",
+    "Prevent empty spots in grid (when set to autodetect)": "Previeni spazi vuoti nella griglia (se impostato su rilevamento automatico)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Numero di righe della griglia; utilizzare -1 per il rilevamento automatico e 0 per essere uguale alla dimensione del lotto",
+    "Save text information about generation parameters as chunks to png files": "Salva le informazioni di testo dei parametri di generazione come blocchi nel file png",
+    "Create a text file next to every image with generation parameters.": "Crea un file di testo assieme a ogni immagine con i parametri di generazione.",
+    "Save a copy of image before doing face restoration.": "Salva una copia dell'immagine prima di eseguire il restauro dei volti.",
+    "Quality for saved jpeg images": "Qualità delle immagini salvate in formato JPEG",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l'immagine PNG è più grande di 4 MB o qualsiasi dimensione è maggiore di 4000, ridimensiona e salva la copia come JPG",
+    "Use original name for output filename during batch process in extras tab": "Usa il nome originale per il nome del file di output durante l'elaborazione a lotti nella scheda 'Extra'",
+    "When using 'Save' button, only save a single selected image": "Usando il pulsante 'Salva', verrà salvata solo la singola immagine selezionata",
+    "Do not add watermark to images": "Non aggiungere la filigrana alle immagini",
+    "Paths for saving": "Percorsi di salvataggio",
+    "Output directory for images; if empty, defaults to three directories below": "Cartella di output per le immagini; se vuoto, per impostazione predefinita verranno usate le cartelle seguenti",
+    "Output directory for txt2img images": "Cartella di output per le immagini txt2img",
+    "Output directory for img2img images": "Cartella di output per le immagini img2img",
+    "Output directory for images from extras tab": "Cartella di output per le immagini dalla scheda 'Extra'",
+    "Output directory for grids; if empty, defaults to two directories below": "Cartella di output per le griglie; se vuoto, per impostazione predefinita veranno usate cartelle seguenti",
+    "Output directory for txt2img grids": "Cartella di output per le griglie txt2img",
+    "Output directory for img2img grids": "Cartella di output per le griglie img2img",
+    "Directory for saving images using the Save button": "Cartella dove salvare le immagini usando il pulsante 'Salva'",
+    "Saving to a directory": "Salva in una cartella",
+    "Save images to a subdirectory": "Salva le immagini in una sotto cartella",
+    "Save grids to a subdirectory": "Salva le griglie in una sotto cartella",
+    "When using \"Save\" button, save images to a subdirectory": "Usando il pulsante \"Salva\", le immagini verranno salvate in una sotto cartella",
+    "Directory name pattern": "Modello del nome della cartella",
+    "Max prompt words for [prompt_words] pattern": "Numero massimo di parole del prompt per il modello [prompt_words]",
+    "Upscaling": "Ampliamento",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Dimensione piastrella per ampliamento ESRGAN. 0 = nessuna piastrellatura.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per gli ampliamenti ESRGAN. Valori bassi = cucitura visibile.",
+    "Tile size for all SwinIR.": "Dimensione piastrella per tutti gli SwinIR.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per SwinIR. Valori bassi = cucitura visibile.",
+    "LDSR processing steps. Lower = faster": "Fasi di elaborazione LDSR. Più basso = più veloce",
+    "Upscaler for img2img": "Metodo di ampliamento per img2img",
+    "Upscale latent space image when doing hires. fix": "Amplia l'immagine nello spazio latente durante la correzione in alta risoluzione",
+    "Face restoration": "Restauro del viso",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Peso di CodeFormer; 0 = effetto massimo; 1 = effetto minimo",
+    "Move face restoration model from VRAM into RAM after processing": "Sposta il modello di restauro facciale dalla VRAM alla RAM dopo l'elaborazione",
+    "System": "Sistema",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Verifiche al secondo sull'utilizzo della VRAM durante la generazione. Impostare a 0 per disabilitare.",
+    "Always print all generation info to standard output": "Stampa sempre tutte le informazioni di generazione sul output standard",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda barra di avanzamento alla console che mostra l'avanzamento complessivo del lavoro.",
+    "Training": "Addestramento",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP nella RAM durante l'addestramento di Iperreti. Risparmia VRAM.",
+    "Filename word regex": "Filename word regex",
+    "Filename join string": "Filename join string",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per una singola immagine di input per epoca; utilizzato solo per visualizzare il numero di epoca",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salva un file CSV contenente la perdita nella cartella di registrazione ogni N passaggi, 0 per disabilitare",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "Checkpoint da memorizzare nella RAM",
+    "Hypernetwork strength": "Forza della Iperrete",
+    "Inpainting conditioning mask strength": "Forza della maschera di condizionamento del Inpainting",
+    "Apply color correction to img2img results to match original colors.": "Applica la correzione del colore ai risultati di img2img in modo che corrispondano ai colori originali.",
+    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la correzione del colore ai risultati di img2img",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, esegue esattamente la quantità di passi specificata dalla barra di scorrimento (normalmente se ne effettuano di meno con meno riduzione del rumore).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Abilita la quantizzazione nei campionatori K per risultati più nitidi e puliti. Questo può cambiare i semi esistenti. Richiede il riavvio per applicare la modifica.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Enfasi: utilizzare (testo) per fare in modo che il modello presti maggiore attenzione al testo e [testo] per fargli prestare meno attenzione",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usa la vecchia implementazione dell'enfasi. Può essere utile per riprodurre vecchi semi.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Fa sì che i campionatori di diffusione K producano le stesse immagini in un lotto come quando si genera una singola immagine",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coerenza disattivando dall'ultima virgola all'indietro di n token quando si utilizzano più di 75 token",
+    "Filter NSFW content": "Filtra i contenuti NSFW",
+    "Stop At last layers of CLIP model": "Fermati agli ultimi livelli del modello CLIP",
+    "Interrogate Options": "Opzioni di interrogazione",
+    "Interrogate: keep models in VRAM": "Interroga: mantieni i modelli nella VRAM",
+    "Interrogate: use artists from artists.csv": "Interroga: utilizza artisti dal file artisti.csv",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interroga: include la classifica delle corrispondenze dei tag del modello nei risultati (non ha effetto sulle interrogazioni basate su didascalie).",
+    "Interrogate: num_beams for BLIP": "Interroga: num_beams per BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Interroga: lunghezza minima della descrizione (esclusi artisti, ecc..)",
+    "Interrogate: maximum description length": "Interroga: lunghezza massima della descrizione",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: numero massimo di righe nel file di testo (0 = Nessun limite)",
+    "Interrogate: deepbooru score threshold": "Interroga: soglia del punteggio deepbooru",
+    "Interrogate: deepbooru sort alphabetically": "Interroga: deepbooru ordinato alfabeticamente",
+    "use spaces for tags in deepbooru": "usa gli spazi per i tag in deepbooru",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Effettua l'escape (\\) delle parentesi in deepbooru (così vengono usate come parentesi letterali e non per enfatizzare)",
+    "User interface": "Interfaccia Utente",
+    "Show progressbar": "Mostra la barra di avanzamento",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostra l'avanzamento della generazione dell'immagine ogni N passaggi di campionamento. Impostare a 0 per disabilitare.",
+    "Show previews of all images generated in a batch as a grid": "Mostra le anteprime di tutte le immagini generate in un lotto come una griglia",
+    "Show grid in results for web": "Mostra la griglia nei risultati per il web",
+    "Do not show any images in results for web": "Non mostrare alcuna immagine nei risultati per il web",
+    "Add model hash to generation information": "Aggiungi l'hash del modello alle informazioni sulla generazione",
+    "Add model name to generation information": "Aggiungi il nome del modello alle informazioni sulla generazione",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Durante la lettura dei parametri di generazione dal testo nell'interfaccia utente (da informazioni PNG o testo incollato), non modificare il modello/checkpoint selezionato.",
+    "Send seed when sending prompt or image to other interface": "Invia il seme quando si invia un prompt o un'immagine a un'altra interfaccia",
+    "Font for image grids that have text": "Font per griglie di immagini con testo",
+    "Enable full page image viewer": "Abilita la visualizzazione delle immagini a pagina intera",
+    "Show images zoomed in by default in full page image viewer": "Mostra le immagini ingrandite per impostazione predefinita nella visualizzazione a pagina intera",
+    "Show generation progress in window title.": "Mostra l'avanzamento della generazione nel titolo della finestra.",
+    "Quicksettings list": "Elenco delle impostazioni rapide",
+    "Localization (requires restart)": "Localizzazione (richiede il riavvio)",
+    "ar_AR": "ar_AR",
+    "de_DE": "de_DE",
+    "es_ES": "es_ES",
+    "fr_FR": "fr_FR",
+    "it_IT": "it_IT",
+    "ja_JP": "ja_JP",
+    "ko_KR": "ko_KR",
+    "pt_BR": "pt_BR",
+    "ru_RU": "ru_RU",
+    "tr_TR": "tr_TR",
+    "zh_CN": "zh_CN",
+    "zh_TW": "zh_TW",
+    "Sampler parameters": "Parametri del campionatore",
+    "Hide samplers in user interface (requires restart)": "Nascondi campionatori nell'interfaccia utente (richiede il riavvio)",
+    "eta (noise multiplier) for DDIM": "ETA (moltiplicatore di rumore) per DDIM",
+    "eta (noise multiplier) for ancestral samplers": "ETA (moltiplicatore di rumore) per campionatori ancestrali",
+    "img2img DDIM discretize": "discretizzazione DDIM per img2img",
+    "uniform": "uniforme",
+    "quad": "quad",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma noise",
+    "Eta noise seed delta": "ETA del delta del seme del rumore",
+    "Aesthetic Image Scorer": "Punteggio delle immagini estetiche",
+    "Save score as EXIF or PNG Info Chunk": "Salva il punteggio come info EXIF o PNG",
+    "Save score as tag (Windows Only)": "Salva punteggio come etichetta (solo Windows)",
+    "Force CPU (Requires Custom Script Reload)": "Forza CPU (richiede il ricaricamento dello script personalizzato)",
+    "Number of columns on image gallery": "Numero di colonne nella galleria di immagini",
+    "Images Browser": "Galleria immagini",
+    "Preload images at startup": "Precarica le immagini all'avvio",
+    "Number of columns on the page": "Numero di colonne nella pagina",
+    "Number of rows on the page": "Numero di righe nella pagina",
+    "Minimum number of pages per load": "Numero minimo di pagine da caricare",
+    "Maximum number of samples, used to determine which folders to skip when continue running the create script": "Numero massimo di campioni, utilizzato per determinare quali cartelle ignorare quando si continua a eseguire lo script di creazione",
+    "Use same seed for all images": "Usa lo stesso seme per tutte le immagini",
+    "Request browser notifications": "Richiedi le notifiche del browser",
+    "Download localization template": "Scarica il modello per la localizzazione",
+    "Reload custom script bodies (No ui updates, No restart)": "Ricarica gli script personalizzati (nessun aggiornamento dell'interfaccia utente, nessun riavvio)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Riavvia Gradio e aggiorna i componenti (solo script personalizzati, ui.py, js e css)",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (premi Ctrl+Invio o Alt+Invio per generare)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (premere Ctrl+Invio o Alt+Invio per generare)",
+    "Add a random artist to the prompt.": "Aggiungi un artista casuale al prompt.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leggere i parametri di generazione dal prompt o dall'ultima generazione se il prompt è vuoto ed inserirli nell'interfaccia utente.",
+    "Save style": "Salva stile",
+    "Apply selected styles to current prompt": "Applica gli stili selezionati al prompt corrente",
+    "Stop processing current image and continue processing.": "Interrompe l'elaborazione dell'immagine corrente e continua l'elaborazione.",
+    "Stop processing images and return any results accumulated so far.": "Interrompe l'elaborazione delle immagini e restituisce tutti i risultati accumulati finora.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stile da applicare; gli stili hanno componenti sia per i prompt positivi che per quelli negativi e si applicano a entrambi",
+    "Do not do anything special": "Non fa nulla di speciale",
+    "Which algorithm to use to produce the image": "Quale algoritmo utilizzare per produrre l'immagine",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - molto creativo, si può ottenere un'immagine completamente diversa a seconda del numero di passi, impostare i passi su un valore superiore a 30-40 non aiuta",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - il migliore per inpainting",
+    "Produce an image that can be tiled.": "Produce un'immagine che può essere piastrellata.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilizza un processo in due fasi per creare parzialmente un'immagine con una risoluzione inferiore, aumentare la scala e quindi migliorarne i dettagli senza modificare la composizione",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determina quanto poco l'algoritmo dovrebbe rispettare dovrebbe il contenuto dell'immagine. A 0, non cambierà nulla e a 1 otterrai un'immagine non correlata. Con valori inferiori a 1.0 l'elaborazione richiederà meno passaggi di quelli specificati dalla barra di scorrimento dei passi di campionamento.",
+    "How many batches of images to create": "Quanti lotti di immagini generare",
+    "How many image to create in a single batch": "Quante immagini generare in un singolo lotto",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - quanto fortemente l'immagine deve conformarsi al prompt: valori più bassi producono risultati più creativi",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Un valore che determina l'output del generatore di numeri casuali: se create un'immagine con gli stessi parametri e seme di un'altra immagine, otterrete lo stesso risultato",
+    "Set seed to -1, which will cause a new random number to be used every time": "Imposta il seme su -1, che farà sì che ogni volta venga utilizzato un nuovo numero casuale",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Riusa il seme dell'ultima generazione, utile soprattutto se casuale",
+    "Seed of a different picture to be mixed into the generation.": "Seme di un'immagine diversa da miscelare nella generazione.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Quanto è forte la variazione da produrre. A 0, non ci sarà alcun effetto. A 1, otterrai l'intera immagine con il seme della variazione (tranne per i campionatori ancestrali, dove otterrai solo una leggera variazione).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Prova a produrre un'immagine simile a quella che sarebbe stata prodotta con lo stesso seme alla risoluzione specificata",
+    "This text is used to rotate the feature space of the imgs embs": "Questo testo viene utilizzato per ruotare lo spazio delle funzioni delle immagini incorporate",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Quante volte ripetere l'elaborazione di un'immagine e utilizzarla come input per l'iterazione successiva",
+    "Hello, StylePile here.\nUntil some weird bug gets fixed you will see this even if the script itself is not active. Meanwhile, some hints to take your artwork to new heights:\nUse the 'Focus on' dropdown to select complex presets. Toggle selections below (with or without Focus) to affect your results. Mix and match to get some interesting results. \nAnd some general Stable Diffusion tips that will take your designs to next level:\nYou can add parenthesis to make parts of the prompt stronger. So (((cute))) kitten will make it extra cute (try it out). This is alsow important if a style is affecting your original prompt too much. Make that prompt stronger by adding parenthesis around it, like this: ((promt)).\nYou can type promts like [A|B] to sequentially use terms one after another on each step. So, like [cat|dog] will produce a hybrid catdog. And [A:B:0.4] to switch to other terms after the first one has been active for a certain percentage of steps. So [cat:dog:0.4] will build a cat 40% of the time and then start turning it into a dog. This needs more steps to work properly.": "Salve, qui è StylePile.\nFinché qualche strano bug non verrà risolto, vedrai questo testo anche se lo script non è attivo. Nel frattempo, alcuni suggerimenti per portare la tua grafica a nuovi livelli:\nUtilizza il menu a discesa 'Focus on' per selezionare valori predefiniti complessi. Attiva o disattiva le selezioni seguenti (con o senza Focus) per influire sui risultati. Mescola e abbina per ottenere risultati interessanti. \nE alcuni suggerimenti generali su Stable Diffusion che porteranno i tuoi risultati a un livello superiore:\nPuoi aggiungere parentesi per aumentare l'influenza di certe parti del prompt. Quindi '(((cute))) kitten' lo renderà molto carino (fai delle prove). Questo è importante quando uno stile influisce troppo sul prompt originale. Rendi più forte quel prompt aggiungendo delle parentesi intorno ad esso, così: ((promt)).\nPuoi digitare prompt nel formato [A|B] per usare in sequenza i termini uno dopo l'altro in ogni passaggio. Quindi, come [cat|dog] produrrà un 'canegatto' ibrido. E [A:B:0.4] per passare ad altri termini dopo che il primo è stato attivo per una certa percentuale di passaggi. Quindi [cat:dog:0.4] genererà un gatto il 40% dei passaggi e poi inizierà a trasformarlo in un cane. Sono richiesti più passaggi perchè funzioni correttamente.",
+    "Enter one prompt per line. Blank lines will be ignored.": "Immettere un prompt per riga. Le righe vuote verranno ignorate.",
+    "Separate values for X axis using commas.": "Separare i valori per l'asse X usando le virgole.",
+    "Separate values for Y axis using commas.": "Separare i valori per l'asse Y usando le virgole.",
+    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "Separa un elenco di parole con virgole e lo script eseguirà una variazione di prompt con quelle parole per ogni loro possibile ordine",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva l'immagine/i in una cartella (predefinita - log/images) ed i parametri di generazione in un file CSV.",
+    "Open images output directory": "Apri la cartella di output delle immagini",
+    "How much to blur the mask before processing, in pixels.": "Quanto sfocare la maschera prima dell'elaborazione, in pixel.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Cosa mettere all'interno dell'area mascherata prima di elaborarla con Stable Diffusion.",
+    "fill it with colors of the image": "riempi con i colori dell'immagine",
+    "keep whatever was there originally": "conserva tutto ciò che c'era in origine",
+    "fill it with latent space noise": "riempi di rumore spaziale latente",
+    "fill it with latent space zeroes": "riempi con zeri di spazio latente",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Ingrandisce la regione mascherata per raggiungere la risoluzione, esegue la pittura, riduce la scala e incolla nell'immagine originale",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Ridimensiona l'immagine alla risoluzione di destinazione. A meno che altezza e larghezza non corrispondano, otterrai proporzioni errate.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Ridimensionare l'immagine in modo che l'intera risoluzione di destinazione sia riempita con l'immagine. Ritaglia le parti che sporgono.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Ridimensiona l'immagine in modo che l'intera immagine rientri nella risoluzione di destinazione. Riempi lo spazio vuoto con i colori dell'immagine.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Per l'upscaling SD, quanta sovrapposizione in pixel dovrebbe esserci tra le piastrelle. Le piastrelle si sovrappongono in modo che quando vengono unite nuovamente in un'immagine, non ci siano giunture chiaramente visibili.",
+    "Process an image, use it as an input, repeat.": "Elabora un'immagine, usala come input, ripeti.",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In modalità rielaborazione ricorsiva, su ogni ciclo la forza di denoising viene moltiplicata per questo valore. <1 significa varietà decrescente in modo che la sequenza converga su un'immagine fissa. >1 significa aumentare la varietà in modo che la tua sequenza diventi sempre più caotica.",
+    "A directory on the same machine where the server is running.": "Una cartella sulla stessa macchina su cui è in esecuzione il server.",
+    "Leave blank to save images to the default path.": "Lascia vuoto per salvare le immagini nel percorso predefinito.",
+    "Result = A * (1 - M) + B * M": "Risultato = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Risultato = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "La prima e l'ultima cifra devono essere 1. Es.:'1, 2, 1'",
+    "Path to directory with input images": "Percorso della cartella con immagini di input",
+    "Path to directory where to write outputs": "Percorso della cartella in cui scrivere i risultati",
+    "C:\\directory\\of\\datasets": "C:\\cartella\\del\\dataset",
+    "Input images directory": "Cartella di input delle immagini",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Usa i seguenti tag per definire come vengono scelti i nomi dei file per le immagini: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed ], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se questa opzione è abilitata, la filigrana non verrà aggiunta alle immagini create. Attenzione: se non aggiungi la filigrana, potresti comportarti in modo non etico.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utilizzare i seguenti tag per definire come vengono scelte le sottodirectory per le immagini e le griglie: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
+    "Restore low quality faces using GFPGAN neural network": "Ripristina volti di bassa qualità utilizzando la rete neurale GFPGAN",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Questa espressione regolare verrà utilizzata per estrarre le parole dal nome del file e verranno unite utilizzando l'opzione seguente nel testo dell'etichetta utilizzato per l'addestramento. Lascia vuoto per mantenere il testo del nome del file così com'è.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Questa stringa verrà utilizzata per unire le parole divise in un'unica riga se l'opzione sopra è abilitata.",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Si applica solo ai modelli di pittura. Determina con quale forza mascherare l'immagine originale per inpainting e img2img. 1.0 significa completamente mascherato, che è il comportamento predefinito. 0.0 significa un condizionamento completamente non mascherato. Valori più bassi aiuteranno a preservare la composizione generale dell'immagine, ma avranno difficoltà con grandi cambiamenti.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Elenco dei nomi delle impostazioni, separati da virgole, per le impostazioni che dovrebbero essere visualizzate nella barra di accesso rapido in alto, anziché nella normale scheda delle impostazioni. Vedi modules/shared.py per impostare i nomi. Richiede il riavvio per applicare.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se questo valore è diverso da zero, verrà aggiunto al seed e utilizzato per inizializzare il generatore di numeri casuali per il rumore quando si utilizzano campionatori con ETA. Puoi usarlo per produrre ancora più variazioni di immagini, oppure puoi usarlo per abbinare le immagini di altri software se sai cosa stai facendo.",
+    "Autocomplete options": "Opzioni di autocompletamento",
+    "Enable Autocomplete": "Abilita autocompletamento",
+    "Append commas": "Aggiungi virgole"
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 006756f9cd6258eae418e9209cfc13f940ec53e1 Mon Sep 17 00:00:00 2001
From: Fampai <>
Date: Mon, 31 Oct 2022 07:26:08 -0400
Subject: Added TI training optimizations

option to use xattention optimizations when training
option to unload vae when training
---
 modules/shared.py                              | 3 ++-
 modules/textual_inversion/textual_inversion.py | 9 +++++++++
 modules/textual_inversion/ui.py                | 7 +++++--
 3 files changed, 16 insertions(+), 3 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index fb84afd8..4c3d0ce7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -256,11 +256,12 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),
+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
     "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
+    "training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 17dfb223..b0a1d26b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -214,6 +214,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
 
     log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
+    unload = shared.opts.unload_models_when_training
 
     if save_embedding_every > 0:
         embedding_dir = os.path.join(log_directory, "embeddings")
@@ -238,6 +239,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
         ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
+    if unload:
+        shared.sd_model.first_stage_model.to(devices.cpu)
 
     hijack = sd_hijack.model_hijack
 
@@ -303,6 +306,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         if images_dir is not None and steps_done % create_image_every == 0:
             forced_filename = f'{embedding_name}-{steps_done}'
             last_saved_image = os.path.join(images_dir, forced_filename)
+
+            shared.sd_model.first_stage_model.to(devices.device)
+
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
                 do_not_save_grid=True,
@@ -330,6 +336,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             processed = processing.process_images(p)
             image = processed.images[0]
 
+            if unload:
+                shared.sd_model.first_stage_model.to(devices.cpu)
+
             shared.state.current_image = image
 
             if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index e712284d..d679e6f4 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -25,8 +25,10 @@ def train_embedding(*args):
 
     assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
 
+    apply_optimizations = shared.opts.training_xattention_optimizations
     try:
-        sd_hijack.undo_optimizations()
+        if not apply_optimizations:
+            sd_hijack.undo_optimizations()
 
         embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
 
@@ -38,5 +40,6 @@ Embedding saved to {html.escape(filename)}
     except Exception:
         raise
     finally:
-        sd_hijack.apply_optimizations()
+        if not apply_optimizations:
+            sd_hijack.apply_optimizations()
 
-- 
cgit v1.2.1


From 49f0dd4300381a6878ea81c9d7841174080db48f Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Mon, 31 Oct 2022 19:26:38 +0800
Subject: =?UTF-8?q?=E4=BF=AE=E6=94=B9=E4=BA=89=E8=AE=AE=E9=83=A8=E5=88=86?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/zh_CN.json | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index eece36dd..6b7a34e4 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -217,7 +217,7 @@
     "Add difference": "添加差分",
     "Save as float16": "以 float16 储存",
     "See": "查看",
-    "wiki": "帮助",
+    "wiki": "wiki文档",
     "for detailed explanation.": "以了解详细说明",
     "Create embedding": "生成 embedding",
     "Create hypernetwork": "生成 hypernetwork",
@@ -303,7 +303,7 @@
     "Train Embedding": "训练 Embedding",
     "Create an aesthetic embedding out of any number of images": "从任意数量的图像中创建美术风格 embedding",
     "Create images embedding": "生成图集 embedding",
-    "Favorites": "已保存",
+    "Favorites": "收藏夹(已保存)",
     "Others": "其他",
     "Images directory": "图像目录",
     "Dropdown": "下拉列表",
@@ -320,7 +320,7 @@
     "keyword": "搜索",
     "Generate Info": "生成信息",
     "File Name": "文件名",
-    "Move to favorites": "移动到已保存",
+    "Move to favorites": "移动到收藏夹(保存)",
     "Renew Page": "刷新页面",
     "Number": "数量",
     "set_index": "设置索引",
@@ -479,10 +479,10 @@
     "Open images output directory": "打开图像输出目录",
     "How much to blur the mask before processing, in pixels.": "处理前要对蒙版进行多强的模糊，以像素为单位",
     "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 处理蒙版区域之前要在蒙版区域内放置什么",
-    "fill it with colors of the image": "用图像的颜色(≈模糊/马赛克)填充它",
+    "fill it with colors of the image": "用图像的颜色(高斯模糊)填充它",
     "keep whatever was there originally": "保留原来的图像，不进行预处理",
-    "fill it with latent space noise": "用潜空间的噪声(≈随机彩色噪点)填充它",
-    "fill it with latent space zeroes": "用潜空间的零(≈灰色)填充它",
+    "fill it with latent space noise": "用潜空间的噪声(图像维度表现为 随机彩色噪点)填充它",
+    "fill it with latent space zeroes": "用潜空间的零(图像维度表现为 无色纯灰)填充它",
     "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做内补绘制，缩小后粘贴到原始图像中",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
-- 
cgit v1.2.1


From f65bfd74eacd0b1dac9aaea6cc1cbbbd726a3760 Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Mon, 31 Oct 2022 19:28:31 +0800
Subject: outpainting

---
 localizations/zh_CN.json | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 6b7a34e4..f82e6eda 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -153,8 +153,8 @@
     "Resize and fill": "填充",
     "img2img alternative test": "图生图的另一种测试",
     "Loopback": "回送",
-    "Outpainting mk2": "外补绘制第二版",
-    "Poor man's outpainting": "效果稍差的外补绘制",
+    "Outpainting mk2": "向外绘制第二版",
+    "Poor man's outpainting": "效果稍差的向外绘制",
     "SD upscale": "使用 SD 放大(SD upscale)",
     "should be 2 or lower.": "必须小于等于2",
     "Override `Sampling method` to Euler?(this method is built for it)": "覆写 `采样方法` 为 Euler?(这个方法就是为这样做设计的)",
@@ -171,7 +171,7 @@
     "Denoising strength change factor": "重绘幅度的调整系数",
     "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推荐设置：采样迭代步数：80-100，采样器：Euler a，重绘幅度：0.8",
     "Pixels to expand": "拓展的像素数",
-    "Outpainting direction": "外补绘制的方向",
+    "Outpainting direction": "向外绘制的方向",
     "left": "左",
     "right": "右",
     "up": "上",
-- 
cgit v1.2.1


From faa3639bf1f17eedf458cfe3171d03f8bd8ddc9d Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Mon, 31 Oct 2022 20:15:36 +0800
Subject: =?UTF-8?q?=E5=B0=86=E6=97=A0=E6=B3=95=E6=9C=AC=E5=9C=B0=E5=8C=96?=
 =?UTF-8?q?=E7=9A=84=E5=86=85=E5=AE=B9=E7=A7=BB=E5=88=B0=E5=BA=95=E9=83=A8?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/zh_CN.json | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index f82e6eda..0306bbdc 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -100,7 +100,6 @@
     "Prompt order": "提示词顺序",
     "Sampler": "采样器",
     "Checkpoint name": "模型(ckpt)名",
-    "Hypernetwork": "Hypernetwork",
     "Hypernet str.": "Hypernetwork 强度",
     "Sigma Churn": "Sigma Churn",
     "Sigma min": "最小 Sigma",
@@ -198,7 +197,7 @@
     "Scale to": "指定尺寸缩放",
     "Resize": "缩放",
     "Crop to fit": "裁剪以适应",
-    "Upscaler 2 visibility": "放大算法 2 可见度",
+    "Upscaler 2 visibility": "放大算法 2 (Upscaler 2) 可见度",
     "GFPGAN visibility": "GFPGAN 可见度",
     "CodeFormer visibility": "CodeFormer 可见度",
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 权重 (0 = 最大效果, 1 = 最小效果)",
@@ -510,7 +509,6 @@
     "Autocomplete options": "自动补全选项",
     "Enable Autocomplete": "开启Tag补全",
     "Append commas": "附加逗号",
-    "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
     "Roll three": "抽三位出来",
     "Generate forever": "无限生成",
     "Cancel generate forever": "停止无限生成",
@@ -519,6 +517,7 @@
     "File with inputs": "含输入内容的文件",
     "Prompts": "提示词",
     "Disabled when launched with --hide-ui-dir-config.": "启动 --hide-ui-dir-config 时禁用",
+    "Upscaler": "放大算法",
     "Upscaler 1": "放大算法 1",
     "Upscaler 2": "放大算法 2",
     "Open output directory": "打开输出目录",
@@ -540,5 +539,10 @@
     "Unload VAE and CLIP from VRAM when training": "训练时从显存(VRAM)中取消 VAE 和 CLIP 的加载",
     "Number of pictures displayed on each page": "每页显示的图像数量",
     "Number of grids in each row": "每行显示多少格",
-    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练"
-}
\ No newline at end of file
+    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
+    "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "用垂直的管道字符 | 将提示语分成若干部分，脚本将为它们的每一个组合创建一幅图片（除了第一部分，所有的组合都会出现）",
+    "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "选择哪些Real-ESRGAN模型显示在用户界面。(需要重新启动)",
+    "Face restoration model": "面部修复模型",
+    "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
+    "Hypernetwork": "超网络(Hypernetwork)"
+}
-- 
cgit v1.2.1


From 48787dc3d17f2bb498b1ebd6bd179176157c330b Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Mon, 31 Oct 2022 20:26:52 +0800
Subject: inpaint

---
 localizations/zh_CN.json | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 0306bbdc..222858d1 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -117,32 +117,32 @@
     "Keep -1 for seeds": "保持随机种子为-1",
     "Save": "保存",
     "Send to img2img": ">> 图生图",
-    "Send to inpaint": ">> 内补绘制",
+    "Send to inpaint": ">> 局部重绘",
     "Send to extras": ">> 更多",
     "Make Zip when Save?": "保存时生成zip压缩文件?",
     "Textbox": "文本框",
     "Interrogate\nCLIP": "CLIP\n反推提示词",
     "Interrogate\nDeepBooru": "DeepBooru\n反推提示词",
-    "Inpaint": "内补绘制",
+    "Inpaint": "局部重绘",
     "Batch img2img": "批量图生图",
     "Image for img2img": "图生图的图像",
     "Drop Image Here": "拖拽图像到此",
-    "Image for inpainting with mask": "用于内补绘制蒙版内容的图像",
+    "Image for inpainting with mask": "用于提取蒙版的图像",
     "Mask": "蒙版",
     "Mask blur": "蒙版模糊",
     "Mask mode": "蒙版模式",
     "Draw mask": "绘制蒙版",
     "Upload mask": "上传蒙版",
     "Masking mode": "蒙版模式",
-    "Inpaint masked": "内补绘制蒙版内容",
-    "Inpaint not masked": "内补绘制非蒙版内容",
+    "Inpaint masked": "重绘蒙版内容",
+    "Inpaint not masked": "重绘非蒙版内容",
     "Masked content": "蒙版蒙住的内容",
     "fill": "填充",
     "original": "原图",
     "latent noise": "潜空间噪声",
     "latent nothing": "潜空间数值零",
-    "Inpaint at full resolution": "以完整分辨率进行内补绘制",
-    "Inpaint at full resolution padding, pixels": "以完整分辨率进行内补绘制 - 填补像素",
+    "Inpaint at full resolution": "以完整分辨率进行局部重绘",
+    "Inpaint at full resolution padding, pixels": "以完整分辨率进行局部重绘 - 填补像素",
     "Process images in a directory on the same machine where the server is running.": "在服务器主机上的目录中处理图像",
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "指定一个空的文件夹为输出目录而非默认的 output 文件夹为输出目录",
     "Input directory": "输入目录",
@@ -385,7 +385,7 @@
     "Stable Diffusion": "Stable Diffusion",
     "Checkpoints to cache in RAM": "缓存在内存(RAM)中的模型(ckpt)",
     "Hypernetwork strength": "Hypernetwork 强度",
-    "Inpainting conditioning mask strength": "内补绘制的自适应蒙版强度",
+    "Inpainting conditioning mask strength": "局部重绘的自适应蒙版强度",
     "Apply color correction to img2img results to match original colors.": "对图生图结果应用颜色校正以匹配原始颜色",
     "Save a copy of image before applying color correction to img2img results": "在对图生图结果应用颜色校正之前保存图像副本",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（正常情况下更弱的重绘幅度需要更少的迭代步数）",
@@ -458,7 +458,7 @@
     "Do not do anything special": "什么都不做",
     "Which algorithm to use to produce the image": "使用哪种算法生成图像",
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图像，将迭代步数设置为高于 30-40 不会有正面作用",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长内补绘制",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长局部重绘",
     "Produce an image that can be tiled.": "生成可用于平铺(tiled)的图像",
     "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候，以较小的分辨率生成初步图像、接着放大图像，然后在不更改构图的情况下改进其中的细节",
     "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "决定算法对图像内容的影响程度。设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。\n值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
@@ -482,7 +482,7 @@
     "keep whatever was there originally": "保留原来的图像，不进行预处理",
     "fill it with latent space noise": "用潜空间的噪声(图像维度表现为 随机彩色噪点)填充它",
     "fill it with latent space zeroes": "用潜空间的零(图像维度表现为 无色纯灰)填充它",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做内补绘制，缩小后粘贴到原始图像中",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做局部重绘，缩小后粘贴到原始图像中",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
@@ -503,7 +503,7 @@
     "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络修复低质量面部",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。留空以保持文件名文本不变",
     "This string will be used to join split words into a single line if the option above is enabled.": "如果启用了上述选项，则此处的字符会用于将拆分的单词接合为同一行",
-    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "仅适用于内补绘制专用的模型。 决定了蒙版在内补绘制以及图生图中屏蔽原图内容的强度。 1.0 表示完全屏蔽，这是默认行为。 0.0 表示完全不屏蔽。 较低的值将有助于保持图像的整体构图，但很难遇到较大的变化。",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "仅适用于局部重绘专用的模型。 决定了蒙版在局部重绘以及图生图中屏蔽原图内容的强度。 1.0 表示完全屏蔽，这是默认行为。 0.0 表示完全不屏蔽。 较低的值将有助于保持图像的整体构图，但很难遇到较大的变化。",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置名称列表，以逗号分隔，设置应转到顶部的快速访问栏，而不是通常的设置选项卡。有关设置名称，请参见 modules/shared.py。需要重新启动才能应用",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声。你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
     "Autocomplete options": "自动补全选项",
-- 
cgit v1.2.1


From 6cffcf6b6dfe26f6dc3b886a29c7dd86ec79aa7a Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Mon, 31 Oct 2022 20:41:18 +0800
Subject: =?UTF-8?q?=E6=9B=B4=E6=96=B0=20zh=5FCN.json?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/zh_CN.json | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 222858d1..4ffee177 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -304,6 +304,10 @@
     "Create images embedding": "生成图集 embedding",
     "Favorites": "收藏夹(已保存)",
     "Others": "其他",
+    "Move to favorites": "移动到收藏夹(保存)",
+    "favorites": "收藏夹(已保存)",
+    "others": "其他",
+    "Collect": "收藏(保存)",
     "Images directory": "图像目录",
     "Dropdown": "下拉列表",
     "First Page": "首页",
@@ -319,7 +323,6 @@
     "keyword": "搜索",
     "Generate Info": "生成信息",
     "File Name": "文件名",
-    "Move to favorites": "移动到收藏夹(保存)",
     "Renew Page": "刷新页面",
     "Number": "数量",
     "set_index": "设置索引",
@@ -539,6 +542,7 @@
     "Unload VAE and CLIP from VRAM when training": "训练时从显存(VRAM)中取消 VAE 和 CLIP 的加载",
     "Number of pictures displayed on each page": "每页显示的图像数量",
     "Number of grids in each row": "每行显示多少格",
+    "Start drawing": "开始绘制",
     "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
     "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "用垂直的管道字符 | 将提示语分成若干部分，脚本将为它们的每一个组合创建一幅图片（除了第一部分，所有的组合都会出现）",
     "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "选择哪些Real-ESRGAN模型显示在用户界面。(需要重新启动)",
-- 
cgit v1.2.1


From 3ac05d38eb9e67d4c1923f05a2c22139782289fe Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Mon, 31 Oct 2022 21:10:27 +0800
Subject: =?UTF-8?q?bug=E4=BF=AE=E5=A4=8D?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

文本太长，遮挡提示框
---
 localizations/zh_CN.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 4ffee177..beca5bba 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -142,7 +142,7 @@
     "latent noise": "潜空间噪声",
     "latent nothing": "潜空间数值零",
     "Inpaint at full resolution": "以完整分辨率进行局部重绘",
-    "Inpaint at full resolution padding, pixels": "以完整分辨率进行局部重绘 - 填补像素",
+    "Inpaint at full resolution padding, pixels": "填补像素",
     "Process images in a directory on the same machine where the server is running.": "在服务器主机上的目录中处理图像",
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "指定一个空的文件夹为输出目录而非默认的 output 文件夹为输出目录",
     "Input directory": "输入目录",
-- 
cgit v1.2.1


From 7c8c3715f552378cf81ad28f26fad92b37bd153d Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Mon, 31 Oct 2022 20:15:33 +0700
Subject: Fix VAE refresh button stretching out

From https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/3986#issuecomment-1296990601
---
 style.css | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/style.css b/style.css
index 8b2211b1..c1b190e9 100644
--- a/style.css
+++ b/style.css
@@ -491,7 +491,7 @@ input[type="range"]{
     padding: 0;
 }
 
-#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
+#refresh_sd_model_checkpoint, #refresh_sd_vae, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
     max-width: 2.5em;
     min-width: 2.5em;
     height: 2.4em;
-- 
cgit v1.2.1


From 81624f4dfb041a53a796cdca18401e2d920972ac Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Mon, 31 Oct 2022 22:25:05 +0900
Subject: Added some extension KR support

Supported extensions
https://github.com/adieyal/sd-dynamic-prompts
https://github.com/yfszzx/stable-diffusion-webui-images-browser
https://github.com/DominikDoom/a1111-sd-webui-tagcomplete
https://github.com/lilly1987/AI-WEBUI-scripts-Random
---
 localizations/ko_KR.json | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 66d4addf..09ee5c86 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -47,6 +47,7 @@
   "BSRGAN 4x": "BSRGAN 4x",
   "built with gradio": "gradio로 제작되었습니다",
   "Cancel generate forever": "반복 생성 취소",
+  "cfg cnt": "CFG 변화 횟수",
   "cfg count": "CFG 변화 횟수",
   "CFG Scale": "CFG 스케일",
   "cfg1 min/max": "CFG1 최소/최대",
@@ -83,10 +84,12 @@
   "custom fold": "커스텀 경로",
   "Custom Name (Optional)": "병합 모델 이름 (선택사항)",
   "Dataset directory": "데이터셋 경로",
+  "date": "생성 일자",
   "DDIM": "DDIM",
   "Decode CFG scale": "디코딩 CFG 스케일",
   "Decode steps": "디코딩 스텝 수",
   "Delete": "삭제",
+  "delete next": "선택한 이미지부터 시작해서 삭제할 이미지 갯수",
   "Denoising": "디노이징",
   "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
   "Denoising strength": "디노이즈 강도",
@@ -113,6 +116,7 @@
   "Draw mask": "마스크 직접 그리기",
   "Drop File Here": "파일을 끌어 놓으세요",
   "Drop Image Here": "이미지를 끌어 놓으세요",
+  "Dropdown": "드롭다운",
   "Embedding": "임베딩",
   "Embedding Learning rate": "임베딩 학습률",
   "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "강조 : (텍스트)를 이용해 모델의 텍스트에 대한 가중치를 더 강하게 주고 [텍스트]를 이용해 더 약하게 줍니다.",
@@ -139,7 +143,7 @@
   "Face restoration": "얼굴 보정",
   "Face restoration model": "얼굴 보정 모델",
   "Fall-off exponent (lower=higher detail)": "감쇠 지수 (낮을수록 디테일이 올라감)",
-  "favorites": "즐겨찾기",
+  "Favorites": "즐겨찾기",
   "File": "파일",
   "File format for grids": "그리드 이미지 파일 형식",
   "File format for images": "이미지 파일 형식",
@@ -192,6 +196,7 @@
   "Image Browser": "이미지 브라우저",
   "Image for img2img": "Image for img2img",
   "Image for inpainting with mask": "마스크로 인페인팅할 이미지",
+  "Image not found (may have been already moved)": "이미지를 찾을 수 없습니다 (이미 옮겨졌을 수 있음)",
   "Images Browser": "이미지 브라우저",
   "Images directory": "이미지 경로",
   "Images filename pattern": "이미지 파일명 패턴",
@@ -229,6 +234,7 @@
   "Just resize": "리사이징",
   "Keep -1 for seeds": "시드값 -1로 유지",
   "keep whatever was there originally": "이미지 원본 유지",
+  "keyword": "프롬프트",
   "Label": "라벨",
   "Lanczos": "Lanczos",
   "Last prompt:": "마지막 프롬프트 : ",
@@ -267,7 +273,9 @@
   "Minimum number of pages per load": "한번 불러올 때마다 불러올 최소 페이지 수",
   "Modules": "모듈",
   "Move face restoration model from VRAM into RAM after processing": "처리가 완료되면 얼굴 보정 모델을 VRAM에서 RAM으로 옮기기",
+  "Move to favorites": "즐겨찾기로 옮기기",
   "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "하이퍼네트워크 훈련 진행 시 VAE와 CLIP을 RAM으로 옮기기. VRAM이 절약됩니다.",
+  "Moved to favorites": "즐겨찾기로 옮겨짐",
   "Multiplier (M) - set to 0 to get model A": "배율 (M) - 0으로 적용하면 모델 A를 얻게 됩니다",
   "Name": "이름",
   "Negative prompt": "네거티브 프롬프트",
@@ -292,6 +300,7 @@
   "original": "원본 유지",
   "Original negative prompt": "기존 네거티브 프롬프트",
   "Original prompt": "기존 프롬프트",
+  "Others": "기타",
   "Outpainting direction": "아웃페인팅 방향",
   "Outpainting mk2": "아웃페인팅 마크 2",
   "Output directory": "이미지 저장 경로",
@@ -310,6 +319,7 @@
   "Overwrite Old Hypernetwork": "기존 하이퍼네트워크 덮어쓰기",
   "Page Index": "페이지 인덱스",
   "parameters": "설정값",
+  "path name": "경로 이름",
   "Path to directory where to write outputs": "결과물을 출력할 경로",
   "Path to directory with input images": "인풋 이미지가 있는 경로",
   "Paths for saving": "저장 경로",
@@ -431,6 +441,7 @@
   "Skip": "건너뛰기",
   "Slerp angle": "구면 선형 보간 각도",
   "Slerp interpolation": "구면 선형 보간",
+  "sort by": "정렬 기준",
   "Source": "원본",
   "Source directory": "원본 경로",
   "Split image overlap ratio": "이미지 분할 겹침 비율",
@@ -438,6 +449,7 @@
   "Split oversized images": "사이즈가 큰 이미지 분할하기",
   "Stable Diffusion": "Stable Diffusion",
   "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
+  "step cnt": "스텝 변화 횟수",
   "step count": "스텝 변화 횟수",
   "step1 min/max": "스텝1 최소/최대",
   "step2 min/max": "스텝2 최소/최대",
-- 
cgit v1.2.1


From 965ed08e31a2e401abd34662b758c13863320fa0 Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Mon, 31 Oct 2022 21:25:48 +0800
Subject: =?UTF-8?q?=E6=9B=B4=E6=96=B0=20zh=5FCN.json?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/zh_CN.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index beca5bba..b291f8a8 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -143,8 +143,8 @@
     "latent nothing": "潜空间数值零",
     "Inpaint at full resolution": "以完整分辨率进行局部重绘",
     "Inpaint at full resolution padding, pixels": "填补像素",
-    "Process images in a directory on the same machine where the server is running.": "在服务器主机上的目录中处理图像",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "指定一个空的文件夹为输出目录而非默认的 output 文件夹为输出目录",
+    "Process images in a directory on the same machine where the server is running.": "使用服务器主机上的一个目录，作为输入目录处理图像。",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "使用一个空的文件夹作为输出目录，而不是使用默认的 output 文件夹作为输出目录。",
     "Input directory": "输入目录",
     "Resize mode": "缩放模式",
     "Just resize": "拉伸",
-- 
cgit v1.2.1


From 890e68aaf75ae80d5eb2fa95b4bf1adf78b96881 Mon Sep 17 00:00:00 2001
From: Fampai <>
Date: Mon, 31 Oct 2022 10:07:12 -0400
Subject: Fixed minor bug

when unloading vae during TI training, generating images after
training will error out
---
 modules/textual_inversion/textual_inversion.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 54a734f1..0aeb0459 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -409,6 +409,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
     save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
+    shared.sd_model.first_stage_model.to(devices.device)
 
     return embedding, filename
 
-- 
cgit v1.2.1


From 910a097ae2ed78a62101951f1b87137f9e1baaea Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 31 Oct 2022 17:36:45 +0300
Subject: add initial version of the extensions tab fix broken Restart Gradio
 button

---
 javascript/extensions.js                   |  24 +++++
 modules/extensions.py                      |  83 +++++++++++++++
 modules/generation_parameters_copypaste.py |   5 +
 modules/scripts.py                         |  21 +---
 modules/shared.py                          |  10 +-
 modules/ui.py                              |  16 ++-
 modules/ui_extensions.py                   | 162 +++++++++++++++++++++++++++++
 style.css                                  |  22 +++-
 webui.py                                   |  20 ++--
 9 files changed, 333 insertions(+), 30 deletions(-)
 create mode 100644 javascript/extensions.js
 create mode 100644 modules/extensions.py
 create mode 100644 modules/ui_extensions.py

diff --git a/javascript/extensions.js b/javascript/extensions.js
new file mode 100644
index 00000000..86f5336d
--- /dev/null
+++ b/javascript/extensions.js
@@ -0,0 +1,24 @@
+
+function extensions_apply(_, _){
+    disable = []
+    update = []
+    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
+        if(x.name.startsWith("enable_") && ! x.checked)
+            disable.push(x.name.substr(7))
+
+        if(x.name.startsWith("update_") && x.checked)
+            update.push(x.name.substr(7))
+    })
+
+    restart_reload()
+
+    return [JSON.stringify(disable), JSON.stringify(update)]
+}
+
+function extensions_check(){
+    gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
+        x.innerHTML = "Loading..."
+    })
+
+    return []
+}
\ No newline at end of file
diff --git a/modules/extensions.py b/modules/extensions.py
new file mode 100644
index 00000000..8d6ae848
--- /dev/null
+++ b/modules/extensions.py
@@ -0,0 +1,83 @@
+import os
+import sys
+import traceback
+
+import git
+
+from modules import paths, shared
+
+
+extensions = []
+extensions_dir = os.path.join(paths.script_path, "extensions")
+
+
+def active():
+    return [x for x in extensions if x.enabled]
+
+
+class Extension:
+    def __init__(self, name, path, enabled=True):
+        self.name = name
+        self.path = path
+        self.enabled = enabled
+        self.status = ''
+        self.can_update = False
+
+        repo = None
+        try:
+            if os.path.exists(os.path.join(path, ".git")):
+                repo = git.Repo(path)
+        except Exception:
+            print(f"Error reading github repository info from {path}:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+        if repo is None or repo.bare:
+            self.remote = None
+        else:
+            self.remote = next(repo.remote().urls, None)
+            self.status = 'unknown'
+
+    def list_files(self, subdir, extension):
+        from modules import scripts
+
+        dirpath = os.path.join(self.path, subdir)
+        if not os.path.isdir(dirpath):
+            return []
+
+        res = []
+        for filename in sorted(os.listdir(dirpath)):
+            res.append(scripts.ScriptFile(dirpath, filename, os.path.join(dirpath, filename)))
+
+        res = [x for x in res if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
+
+        return res
+
+    def check_updates(self):
+        repo = git.Repo(self.path)
+        for fetch in repo.remote().fetch("--dry-run"):
+            if fetch.flags != fetch.HEAD_UPTODATE:
+                self.can_update = True
+                self.status = "behind"
+                return
+
+        self.can_update = False
+        self.status = "latest"
+
+    def pull(self):
+        repo = git.Repo(self.path)
+        repo.remotes.origin.pull()
+
+
+def list_extensions():
+    extensions.clear()
+
+    if not os.path.isdir(extensions_dir):
+        return
+
+    for dirname in sorted(os.listdir(extensions_dir)):
+        path = os.path.join(extensions_dir, dirname)
+        if not os.path.isdir(path):
+            continue
+
+        extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions)
+        extensions.append(extension)
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index df70c728..985ec95e 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -17,6 +17,11 @@ paste_fields = {}
 bind_list = []
 
 
+def reset():
+    paste_fields.clear()
+    bind_list.clear()
+
+
 def quote(text):
     if ',' not in str(text):
         return text
diff --git a/modules/scripts.py b/modules/scripts.py
index 96e44bfd..533db45c 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -7,7 +7,7 @@ import modules.ui as ui
 import gradio as gr
 
 from modules.processing import StableDiffusionProcessing
-from modules import shared, paths, script_callbacks
+from modules import shared, paths, script_callbacks, extensions
 
 AlwaysVisible = object()
 
@@ -107,17 +107,8 @@ def list_scripts(scriptdirname, extension):
         for filename in sorted(os.listdir(basedir)):
             scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
 
-    extdir = os.path.join(paths.script_path, "extensions")
-    if os.path.exists(extdir):
-        for dirname in sorted(os.listdir(extdir)):
-            dirpath = os.path.join(extdir, dirname)
-            scriptdirpath = os.path.join(dirpath, scriptdirname)
-
-            if not os.path.isdir(scriptdirpath):
-                continue
-
-            for filename in sorted(os.listdir(scriptdirpath)):
-                scripts_list.append(ScriptFile(dirpath, filename, os.path.join(scriptdirpath, filename)))
+    for ext in extensions.active():
+        scripts_list += ext.list_files(scriptdirname, extension)
 
     scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
 
@@ -127,11 +118,7 @@ def list_scripts(scriptdirname, extension):
 def list_files_with_name(filename):
     res = []
 
-    dirs = [paths.script_path]
-
-    extdir = os.path.join(paths.script_path, "extensions")
-    if os.path.exists(extdir):
-        dirs += [os.path.join(extdir, d) for d in sorted(os.listdir(extdir))]
+    dirs = [paths.script_path] + [ext.path for ext in extensions.active()]
 
     for dirpath in dirs:
         if not os.path.isdir(dirpath):
diff --git a/modules/shared.py b/modules/shared.py
index e4f163c1..cce87081 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -132,6 +132,7 @@ class State:
     current_image = None
     current_image_sampling_step = 0
     textinfo = None
+    need_restart = False
 
     def skip(self):
         self.skipped = True
@@ -354,6 +355,12 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
+options_templates.update(options_section((None, "Hidden options"), {
+    "disabled_extensions": OptionInfo([], "Disable those extensions"),
+}))
+
+options_templates.update()
+
 
 class Options:
     data = None
@@ -365,8 +372,9 @@ class Options:
 
     def __setattr__(self, key, value):
         if self.data is not None:
-            if key in self.data:
+            if key in self.data or key in self.data_labels:
                 self.data[key] = value
+                return
 
         return super(Options, self).__setattr__(key, value)
 
diff --git a/modules/ui.py b/modules/ui.py
index 5055ca64..2c15abb7 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -19,7 +19,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 
 
-from modules import sd_hijack, sd_models, localization, script_callbacks
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
 from modules.paths import script_path
 
 from modules.shared import opts, cmd_opts, restricted_opts
@@ -671,6 +671,7 @@ def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
+    parameters_copypaste.reset()
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
@@ -1511,8 +1512,9 @@ def create_ui(wrap_gradio_gpu_call):
         column = None
         with gr.Row(elem_id="settings").style(equal_height=False):
             for i, (k, item) in enumerate(opts.data_labels.items()):
+                section_must_be_skipped = item.section[0] is None
 
-                if previous_section != item.section:
+                if previous_section != item.section and not section_must_be_skipped:
                     if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
                         if column is not None:
                             column.__exit__()
@@ -1531,6 +1533,8 @@ def create_ui(wrap_gradio_gpu_call):
                 if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
                     quicksettings_list.append((i, k, item))
                     components.append(dummy_component)
+                elif section_must_be_skipped:
+                    components.append(dummy_component)
                 else:
                     component = create_setting_component(k)
                     component_dict[k] = component
@@ -1572,9 +1576,10 @@ def create_ui(wrap_gradio_gpu_call):
 
         def request_restart():
             shared.state.interrupt()
-            settings_interface.gradio_ref.do_restart = True
+            shared.state.need_restart = True
 
         restart_gradio.click(
+
             fn=request_restart,
             inputs=[],
             outputs=[],
@@ -1612,14 +1617,15 @@ def create_ui(wrap_gradio_gpu_call):
     interfaces += script_callbacks.ui_tabs_callback()
     interfaces += [(settings_interface, "Settings", "settings")]
 
+    extensions_interface = ui_extensions.create_ui()
+    interfaces += [(extensions_interface, "Extensions", "extensions")]
+
     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
         with gr.Row(elem_id="quicksettings"):
             for i, k, item in quicksettings_list:
                 component = create_setting_component(k, is_quicksettings=True)
                 component_dict[k] = component
 
-        settings_interface.gradio_ref = demo
-
         parameters_copypaste.integrate_settings_paste_fields(component_dict)
         parameters_copypaste.run_bind()
 
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
new file mode 100644
index 00000000..b7d747dc
--- /dev/null
+++ b/modules/ui_extensions.py
@@ -0,0 +1,162 @@
+import json
+import os.path
+import shutil
+import sys
+import time
+import traceback
+
+import git
+
+import gradio as gr
+import html
+
+from modules import extensions, shared, paths
+
+
+def apply_and_restart(disable_list, update_list):
+    disabled = json.loads(disable_list)
+    assert type(disabled) == list, f"wrong disable_list data for apply_and_restart: {disable_list}"
+
+    update = json.loads(update_list)
+    assert type(update) == list, f"wrong update_list data for apply_and_restart: {update_list}"
+
+    update = set(update)
+
+    for ext in extensions.extensions:
+        if ext.name not in update:
+            continue
+
+        try:
+            ext.pull()
+        except Exception:
+            print(f"Error pulling updates for {ext.name}:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    shared.opts.disabled_extensions = disabled
+    shared.opts.save(shared.config_filename)
+
+    shared.state.interrupt()
+    shared.state.need_restart = True
+
+
+def check_updates():
+    for ext in extensions.extensions:
+        if ext.remote is None:
+            continue
+
+        try:
+            ext.check_updates()
+        except Exception:
+            print(f"Error checking updates for {ext.name}:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    return extension_table()
+
+
+def extension_table():
+    code = f"""<!-- {time.time()} -->
+    <table id="extensions">
+        <thead>
+            <tr>
+                <th><abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr></th>
+                <th>URL</th>
+                <th><abbr title="Use checkbox to mark the extension for update; it will be updated when you click apply button">Update</abbr></th>
+            </tr>
+        </thead>
+        <tbody>
+    """
+
+    for ext in extensions.extensions:
+        if ext.can_update:
+            ext_status = f"""<label><input class="gr-check-radio gr-checkbox" name="update_{html.escape(ext.name)}" checked="checked" type="checkbox">{html.escape(ext.status)}</label>"""
+        else:
+            ext_status = ext.status
+
+        code += f"""
+            <tr>
+                <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
+                <td><a href="{html.escape(ext.remote or '')}">{html.escape(ext.remote or '')}</a></td>
+                <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
+            </tr>
+    """
+
+    code += """
+        </tbody>
+    </table>
+    """
+
+    return code
+
+
+def install_extension_from_url(dirname, url):
+    assert url, 'No URL specified'
+
+    if dirname is None or dirname == "":
+        *parts, last_part = url.split('/')
+        last_part = last_part.replace(".git", "")
+
+        dirname = last_part
+
+    target_dir = os.path.join(extensions.extensions_dir, dirname)
+    assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
+
+    assert len([x for x in extensions.extensions if x.remote == url]) == 0, 'Extension with this URL is already installed'
+
+    tmpdir = os.path.join(paths.script_path, "tmp", dirname)
+
+    try:
+        shutil.rmtree(tmpdir, True)
+
+        repo = git.Repo.clone_from(url, tmpdir)
+        repo.remote().fetch()
+
+        os.rename(tmpdir, target_dir)
+
+        extensions.list_extensions()
+        return [extension_table(), html.escape(f"Installed into {target_dir}. Use Installed tab to restart.")]
+    finally:
+        shutil.rmtree(tmpdir, True)
+
+
+def create_ui():
+    import modules.ui
+
+    with gr.Blocks(analytics_enabled=False) as ui:
+        with gr.Tabs(elem_id="tabs_extensions") as tabs:
+            with gr.TabItem("Installed"):
+                extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False)
+                extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False)
+
+                with gr.Row():
+                    apply = gr.Button(value="Apply and restart UI", variant="primary")
+                    check = gr.Button(value="Check for updates")
+
+                extensions_table = gr.HTML(lambda: extension_table())
+
+                apply.click(
+                    fn=apply_and_restart,
+                    _js="extensions_apply",
+                    inputs=[extensions_disabled_list, extensions_update_list],
+                    outputs=[],
+                )
+
+                check.click(
+                    fn=check_updates,
+                    _js="extensions_check",
+                    inputs=[],
+                    outputs=[extensions_table],
+                )
+
+            with gr.TabItem("Install from URL"):
+                install_url = gr.Text(label="URL for extension's git repository")
+                install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
+                intall_button = gr.Button(value="Install", variant="primary")
+                intall_result = gr.HTML(elem_id="extension_install_result")
+
+                intall_button.click(
+                    fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
+                    inputs=[install_dirname, install_url],
+                    outputs=[extensions_table, intall_result],
+                )
+
+    return ui
diff --git a/style.css b/style.css
index 8b2211b1..859c3933 100644
--- a/style.css
+++ b/style.css
@@ -530,6 +530,26 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
     min-height: 480px !important;
 }
 
+/* Extensions */
+
+#extensions{
+    border-collapse: collapse;
+}
+
+#extensions td, #extensions th{
+    border: 1px solid #ccc;
+    padding: 0.25em 0.5em;
+}
+
+#extensions input[type="checkbox"]{
+    margin-right: 0.5em;
+}
+
+#tab_extensions button{
+    max-width: 16em;
+}
+
+
 /* The following handles localization for right-to-left (RTL) languages like Arabic.
 The rtl media type will only be activated by the logic in javascript/localization.js.
 If you change anything above, you need to make sure it is RTL compliant by just running
@@ -607,4 +627,4 @@ Then, you will need to add the RTL counterpart only if needed in the rtl section
         right: unset;
         left: 0.5em;
     }
-}
+}
\ No newline at end of file
diff --git a/webui.py b/webui.py
index 29530872..ad2eb236 100644
--- a/webui.py
+++ b/webui.py
@@ -9,7 +9,7 @@ from fastapi.middleware.gzip import GZipMiddleware
 
 from modules.paths import script_path
 
-from modules import devices, sd_samplers, upscaler
+from modules import devices, sd_samplers, upscaler, extensions
 import modules.codeformer_model as codeformer
 import modules.extras
 import modules.face_restoration
@@ -60,6 +60,11 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
 
 def initialize():
+    extensions.list_extensions()
+    #for ext in extensions.extensions:
+    #    print(ext.name, ext.path, ext.enabled, ext.remote)
+    #exit()
+
     if cmd_opts.ui_debug_mode:
         shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
         modules.scripts.load_scripts()
@@ -92,15 +97,18 @@ def create_api(app):
     api = Api(app, queue_lock)
     return api
 
+
 def wait_on_server(demo=None):
     while 1:
         time.sleep(0.5)
-        if demo and getattr(demo, 'do_restart', False):
+        if shared.state.need_restart:
+            shared.state.need_restart = False
             time.sleep(0.5)
             demo.close()
             time.sleep(0.5)
             break
 
+
 def api_only():
     initialize()
 
@@ -132,14 +140,16 @@ def webui():
 
         app.add_middleware(GZipMiddleware, minimum_size=1000)
 
-        if (launch_api):
+        if launch_api:
             create_api(app)
 
         wait_on_server(demo)
 
         sd_samplers.set_samplers()
 
-        print('Reloading Custom Scripts')
+        print('Reloading extensions')
+        extensions.list_extensions()
+        print('Reloading custom scripts')
         modules.scripts.reload_scripts()
         print('Reloading modules: modules.ui')
         importlib.reload(modules.ui)
@@ -148,8 +158,6 @@ def webui():
         print('Restarting Gradio')
 
 
-
-task = []
 if __name__ == "__main__":
     if cmd_opts.nowebui:
         api_only()
-- 
cgit v1.2.1


From f17769cfbc3b2f455a1c3df8a91c2f55820fe4ad Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 31 Oct 2022 17:57:16 +0300
Subject: add requirements for GitPython

---
 requirements.txt          | 1 +
 requirements_versions.txt | 1 +
 2 files changed, 2 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index 75b37c4f..fff63e7d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -26,3 +26,4 @@ torchdiffeq
 kornia
 lark
 inflection
+GitPython
diff --git a/requirements_versions.txt b/requirements_versions.txt
index 72ccc5a3..41f2501f 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -23,3 +23,4 @@ torchdiffeq==0.2.3
 kornia==0.6.7
 lark==1.1.2
 inflection==0.5.1
+GitPython==3.1.27
-- 
cgit v1.2.1


From dc7425a56e7a014cbfa3b3d44ad2321e519fe378 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 31 Oct 2022 18:33:44 +0300
Subject: disable access to extension stuff for non-local servers

---
 modules/shared.py        |  5 ++++-
 modules/ui_extensions.py | 10 ++++++++++
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index cce87081..a27c654e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -40,7 +40,7 @@ parser.add_argument("--lowram", action='store_true', help="load stable diffusion
 parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
-parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
+parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
 parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
@@ -97,6 +97,9 @@ restricted_opts = {
     "outdir_save",
 }
 
+if cmd_opts.share or cmd_opts.listen:
+    cmd_opts.disable_extension_access = True
+
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
 
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index b7d747dc..e74b7d68 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -13,7 +13,13 @@ import html
 from modules import extensions, shared, paths
 
 
+def check_access():
+    assert not shared.cmd_opts.disable_extension_access, "extension access disabed because of commandline flags"
+
+
 def apply_and_restart(disable_list, update_list):
+    check_access()
+
     disabled = json.loads(disable_list)
     assert type(disabled) == list, f"wrong disable_list data for apply_and_restart: {disable_list}"
 
@@ -40,6 +46,8 @@ def apply_and_restart(disable_list, update_list):
 
 
 def check_updates():
+    check_access()
+
     for ext in extensions.extensions:
         if ext.remote is None:
             continue
@@ -89,6 +97,8 @@ def extension_table():
 
 
 def install_extension_from_url(dirname, url):
+    check_access()
+
     assert url, 'No URL specified'
 
     if dirname is None or dirname == "":
-- 
cgit v1.2.1


From 58cc03edd0fe8c7e64297bcfe51111caaafabfd7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 31 Oct 2022 18:40:47 +0300
Subject: fix scripts I broke with the extension tab changes

---
 modules/extensions.py | 2 +-
 webui.py              | 3 ---
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/modules/extensions.py b/modules/extensions.py
index 8d6ae848..897af96e 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -46,7 +46,7 @@ class Extension:
 
         res = []
         for filename in sorted(os.listdir(dirpath)):
-            res.append(scripts.ScriptFile(dirpath, filename, os.path.join(dirpath, filename)))
+            res.append(scripts.ScriptFile(self.path, filename, os.path.join(dirpath, filename)))
 
         res = [x for x in res if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
 
diff --git a/webui.py b/webui.py
index ad2eb236..6ff95dc4 100644
--- a/webui.py
+++ b/webui.py
@@ -61,9 +61,6 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
 def initialize():
     extensions.list_extensions()
-    #for ext in extensions.extensions:
-    #    print(ext.name, ext.path, ext.enabled, ext.remote)
-    #exit()
 
     if cmd_opts.ui_debug_mode:
         shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
-- 
cgit v1.2.1


From 9e22a357545c0395c81dd800c72fa18f350545ec Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 31 Oct 2022 18:45:50 +0300
Subject: fix the error with extension tab not working because of the previous
 commit

---
 modules/shared.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index a27c654e..c83fb9f5 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -97,8 +97,7 @@ restricted_opts = {
     "outdir_save",
 }
 
-if cmd_opts.share or cmd_opts.listen:
-    cmd_opts.disable_extension_access = True
+cmd_opts.disable_extension_access = cmd_opts.share or cmd_opts.listen
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
-- 
cgit v1.2.1


From df6a7ebfe8cc4da23861e3e2583693bb7808d573 Mon Sep 17 00:00:00 2001
From: Roy Shilkrot <roy.shil@gmail.com>
Date: Mon, 31 Oct 2022 11:50:33 -0400
Subject: revert things to master

---
 launch.py             | 2 +-
 modules/api/api.py    | 2 --
 modules/api/models.py | 6 +-----
 3 files changed, 2 insertions(+), 8 deletions(-)

diff --git a/launch.py b/launch.py
index fe9cef3c..958336f2 100644
--- a/launch.py
+++ b/launch.py
@@ -220,7 +220,7 @@ def tests(argv):
 def start_webui():
     print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}")
     import webui
-    webui.webui_or_api()
+    webui.webui()
 
 
 if __name__ == "__main__":
diff --git a/modules/api/api.py b/modules/api/api.py
index c510a833..6a903e4c 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -117,8 +117,6 @@ class Api:
 
         return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
 
-    def extrasapi(self):
-        raise NotImplementedError
     def extras_single_image_api(self, req: ExtrasSingleImageRequest):
         reqDict = setUpscalers(req)
 
diff --git a/modules/api/models.py b/modules/api/models.py
index 035a7179..82ab29b8 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -64,11 +64,7 @@ class PydanticModelGenerator:
 
 
         self._model_name = model_name
-
-        if class_instance is not None:
-            self._class_data = merge_class_params(class_instance)
-        else:
-            self._class_data = {}            
+        self._class_data = merge_class_params(class_instance)
 
         self._model_def = [
             ModelDef(
-- 
cgit v1.2.1


From 3f3d14afd5abd07d3843370dc1c28be299dbdbab Mon Sep 17 00:00:00 2001
From: Roy Shilkrot <roy.shil@gmail.com>
Date: Mon, 31 Oct 2022 11:51:21 -0400
Subject: nix unused thing

---
 modules/api/api.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 6a903e4c..536e3f16 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -182,10 +182,6 @@ class Api:
         if image_b64 is None:
             raise HTTPException(status_code=404, detail="Image not found") 
 
-        populate = interrogatereq.copy(update={ # Override __init__ params
-            }
-        )
-
         img = self.__base64_to_image(image_b64)
 
         # Override object param
-- 
cgit v1.2.1


From 8954a6e7064e33250f1a8befd61e3f5df92d4e23 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Tue, 1 Nov 2022 01:29:46 +0900
Subject: Add Extension Manager strings

Since it's fixed and working I'm updating the translations
---
 localizations/ko_KR.json | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 09ee5c86..8d6c5123 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -9,6 +9,7 @@
   " images in this directory. Loaded ": "개의 이미지가 이 경로에 존재합니다. ",
   " pages": "페이지로 나뉘어 표시합니다.",
   ", divided into ": "입니다. ",
+  ". Use Installed tab to restart.": "에 성공적으로 설치하였습니다. 설치된 확장기능 탭에서 UI를 재시작해주세요.",
   "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
   "[wiki]": " [위키] 참조",
   "A directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리를 선택해 주세요.",
@@ -34,6 +35,7 @@
   "api": "",
   "append": "뒤에 삽입",
   "Append commas": "쉼표 삽입",
+  "Apply and restart UI": "적용 후 UI 재시작",
   "Apply color correction to img2img results to match original colors.": "이미지→이미지 결과물이 기존 색상과 일치하도록 색상 보정 적용하기",
   "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
   "Apply settings": "설정 적용하기",
@@ -44,6 +46,7 @@
   "Batch img2img": "이미지→이미지 배치",
   "Batch Process": "이미지 여러장 처리",
   "Batch size": "배치 크기",
+  "behind": "최신 아님",
   "BSRGAN 4x": "BSRGAN 4x",
   "built with gradio": "gradio로 제작되었습니다",
   "Cancel generate forever": "반복 생성 취소",
@@ -52,6 +55,7 @@
   "CFG Scale": "CFG 스케일",
   "cfg1 min/max": "CFG1 최소/최대",
   "cfg2 min/max": "CFG2 최소/최대",
+  "Check for updates": "업데이트 확인",
   "Check progress": "진행도 체크",
   "Check progress (first)": "진행도 체크 (처음)",
   "checkpoint": " 체크포인트 ",
@@ -136,6 +140,8 @@
   "Euler a": "Euler a",
   "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
   "Existing Caption txt Action": "이미 존재하는 캡션 텍스트 처리",
+  "Extension": "확장기능",
+  "Extensions": "확장기능",
   "Extra": "고급",
   "Extras": "부가기능",
   "extras": "부가기능",
@@ -216,6 +222,10 @@
   "Inpainting conditioning mask strength": "인페인팅 조절 마스크 강도",
   "Input directory": "인풋 이미지 경로",
   "Input images directory": "이미지 경로 입력",
+  "Install": "설치",
+  "Install from URL": "URL로부터 확장기능 설치",
+  "Installed": "설치된 확장기능",
+  "Installed into ": "확장기능을 ",
   "Interpolation Method": "보간 방법",
   "Interrogate\nCLIP": "CLIP\n분석",
   "Interrogate\nDeepBooru": "DeepBooru\n분석",
@@ -242,10 +252,12 @@
   "Last saved image:": "마지막으로 저장된 이미지 : ",
   "latent noise": "잠재 노이즈",
   "latent nothing": "잠재 공백",
+  "latest": "최신 버전",
   "LDSR": "LDSR",
   "LDSR processing steps. Lower = faster": "LDSR 스텝 수. 낮은 값 = 빠른 속도",
   "leakyrelu": "leakyrelu",
   "Leave blank to save images to the default path.": "기존 저장 경로에 이미지들을 저장하려면 비워두세요.",
+  "Leave empty for auto": "자동 설정하려면 비워두십시오",
   "left": "왼쪽",
   "linear": "linear",
   "List of prompt inputs": "프롬프트 입력 리스트",
@@ -254,6 +266,7 @@
   "LMS Karras": "LMS Karras",
   "Load": "불러오기",
   "Loading...": "로딩 중...",
+  "Local directory name": "로컬 경로 이름",
   "Localization (requires restart)": "현지화 (재시작 필요)",
   "Log directory": "로그 경로",
   "Loopback": "루프백",
@@ -486,7 +499,9 @@
   "txt2img": "텍스트→이미지",
   "txt2img history": "텍스트→이미지 기록",
   "uniform": "uniform",
+  "unknown": "알수 없음",
   "up": "위쪽",
+  "Update": "업데이트",
   "Upload mask": "마스크 업로드하기",
   "Upload prompt inputs": "입력할 프롬프트를 업로드하십시오",
   "Upscale Before Restoring Faces": "얼굴 보정을 진행하기 전에 업스케일링 먼저 진행하기",
@@ -498,9 +513,12 @@
   "Upscaler 2 visibility": "업스케일러 2 가시성",
   "Upscaler for img2img": "이미지→이미지 업스케일러",
   "Upscaling": "업스케일링",
+  "URL for extension's git repository": "확장기능의 git 레포 URL",
   "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
   "Use an empty output directory to save pictures normally instead of writing to the output directory.": "저장 경로를 비워두면 기본 저장 폴더에 이미지들이 저장됩니다.",
   "Use BLIP for caption": "캡션에 BLIP 사용",
+  "Use checkbox to enable the extension; it will be enabled or disabled when you click apply button": "체크박스를 이용해 적용할 확장기능을 선택하세요. 변경사항은 적용 후 UI 재시작 버튼을 눌러야 적용됩니다.",
+  "Use checkbox to mark the extension for update; it will be updated when you click apply button": "체크박스를 이용해 업데이트할 확장기능을 선택하세요. 업데이트는 적용 후 UI 재시작 버튼을 눌러야 적용됩니다.",
   "Use deepbooru for caption": "캡션에 deepbooru 사용",
   "Use dropout": "드롭아웃 사용",
   "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지 파일명 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
-- 
cgit v1.2.1


From 8cc20f90f485b054699a2d1e96e29bbc891265ae Mon Sep 17 00:00:00 2001
From: Riccardo Giovanetti <29801031+Harvester62@users.noreply.github.com>
Date: Mon, 31 Oct 2022 23:29:43 +0100
Subject: Italian localization - Updates Extensions

Added translations of the new Extensions tab, and a few corrections to some previously translated descriptions/terms.
---
 localizations/it_IT.json | 309 ++++++++++++++++++++++++++---------------------
 1 file changed, 170 insertions(+), 139 deletions(-)

diff --git a/localizations/it_IT.json b/localizations/it_IT.json
index a2cb1bb2..db63c819 100644
--- a/localizations/it_IT.json
+++ b/localizations/it_IT.json
@@ -23,6 +23,7 @@
     "Image Browser": "Galleria immagini",
     "Inspiration": "Ispirazione",
     "Settings": "Impostazioni",
+    "Extensions": "Estensioni",
     "Prompt": "Prompt",
     "Negative prompt": "Prompt negativo",
     "Run": "Esegui",
@@ -71,8 +72,8 @@
     "Variation seed": "Seme della variazione",
     "Variation strength": "Forza della variazione",
     "Resize seed from width": "Ridimensiona il seme dalla larghezza",
-    "Resize seed from height": "Ridimensiona il seme dall`altezza",
-    "Open for Clip Aesthetic!": "Apri per Estetica CLIP!",
+    "Resize seed from height": "Ridimensiona il seme dall'altezza",
+    "Open for Clip Aesthetic!": "Apri per Gradienti Estetici (CLIP)",
     "▼": "▼",
     "Aesthetic weight": "Estetica - Peso",
     "Aesthetic steps": "Estetica - Passi",
@@ -88,6 +89,7 @@
     "Random": "Generaz. casuale (no griglia)",
     "StylePile": "StylePile",
     "Advanced prompt matrix": "Matrice di prompt avanzata",
+    "Advanced Seed Blending": "Miscelazione Semi Avanzata",
     "Alternate Sampler Noise Schedules": "Metodi alternativi di campionamento del rumore",
     "Animator v6": "Animator v6",
     "Asymmetric tiling": "Piastrellatura asimmetrica",
@@ -101,7 +103,7 @@
     "Prompts from file or textbox": "Prompt da file o da casella di testo",
     "To Infinity and Beyond": "Verso l'infinito e oltre",
     "Seed travel": "Interpolazione semi",
-    "Shift attention": "Sposta l`attenzione",
+    "Shift attention": "Sposta l'attenzione",
     "Text to Vector Graphics": "Da testo a grafica vettoriale",
     "X/Y plot": "Grafico X/Y",
     "X/Y/Z plot": "Grafico X/Y/Z",
@@ -115,7 +117,7 @@
     "Draw legend": "Disegna legenda",
     "Include Separate Images": "Includi immagini separate",
     "Keep -1 for seeds": "Mantieni sempre il seme a -1",
-    "x/y change": "Inverti l`ordine degli assi X/Y (Passi/CFG)",
+    "x/y change": "Inverti ordine assi X/Y (Passi/CFG)",
     "Loops": "Cicli",
     "Focus on:": "Focus su:",
     "No focus": "Nessun Focus",
@@ -181,7 +183,8 @@
     "B/W Photograpy": "Fotografia B/N",
     "Portrait photo": "Foto ritratto",
     "Usage: a <corgi|cat> wearing <goggles|a hat>": "Utilizzo: a <corgi|cat> wearing <goggles|a hat>",
-    "Noise Scheduler": "Programmatore del rumore",
+    "Seeds": "Semi",
+    "Noise Scheduler": "Pianificazione del rumore",
     "Default": "Predefinito",
     "Karras": "Karras",
     "Exponential": "Esponenziale",
@@ -192,13 +195,13 @@
     "Beta distribution (VP only)": "Distribuzione Beta (Solo CV)",
     "Beta min (VP only)": "Beta min (Solo CV)",
     "Epsilon (VP only)": "Epsilon (Solo CV)",
-    "Running in txt2img mode:": "Esecuzione in modalità txt2img:",
+    "Running in txt2img mode:": "Running in txt2img mode:",
     "Render these video formats:": "Renderizza in questi formati:",
     "GIF": "GIF",
     "MP4": "MP4",
     "WEBM": "WEBM",
     "Animation Parameters": "Parametri animazione",
-    "Total Animation Length (s)": "Durata totale dell`animazione (s)",
+    "Total Animation Length (s)": "Durata totale dell'animazione (s)",
     "Framerate": "Frequenza dei fotogrammi",
     "Smoothing_Frames": "Smoothing_Frames",
     "Add_Noise": "Aggiungi rumore",
@@ -207,13 +210,13 @@
     "Denoising Strength (overrides img2img slider)": "Intensità di riduzione del rumore (sovrascrive il cursore img2img)",
     "Seed_March": "Seed_March",
     "Zoom Factor (scale/s)": "Fattore di ingrandimento (scala/s)",
-    "X Pixel Shift (pixels/s)": "Sposta i Pixel sull`asse X (pixel/s)",
-    "Y Pixel Shift (pixels/s)": "Sposta i Pixel sull`asse Y (pixel/s)",
+    "X Pixel Shift (pixels/s)": "Sposta i Pixel sull'asse X (pixel/s)",
+    "Y Pixel Shift (pixels/s)": "Sposta i Pixel sull'asse Y (pixel/s)",
     "Rotation (deg/s)": "Rotazione (gradi/s)",
     "Prompt Template, applied to each keyframe below": "Modello di prompt, applicato a ciascun fotogramma chiave qui di seguito",
     "Positive Prompts": "Prompt positivi",
     "Negative Prompts": "Prompt negativi",
-    "Props, Stamps": "Props, Stamps\n ",
+    "Props, Stamps": "Props, Stamps",
     "Poper_Folder:": "Cartella Poper:",
     "Supported Keyframes:": "Fotogrammi chiave supportati:",
     "time_s | source | video, images, img2img | path": "time_s | source | video, images, img2img | path",
@@ -231,14 +234,14 @@
     "time_s | col_set": "time_s | col_set",
     "time_s | col_clear": "time_s | col_clear",
     "time_s | model | model": "time_s | model | model",
-    "img2img_mode": "Modalità img2img",
+    "img2img_mode": "img2img_mode",
     "Keyframes:": "Fotogrammi chiave:",
     "Tile X": "Piastrella asse X",
     "Tile Y": "Piastrella asse Y",
     "Python code": "Codice Python",
     "Combinatorial generation": "Generazione combinatoria",
     "Combinations": "Combinazioni",
-    "Choose a number of terms from a list, in this case we choose two artists:": "Scegli un numero di termini da un elenco, in questo caso scegliamo due artisti:",
+    "Choose a number of terms from a list, in this case we choose two artists": "Scegli un numero di termini da un elenco, in questo caso scegliamo due artisti",
     "{2$$artist1|artist2|artist3}": "{2$$artist1|artist2|artist3}",
     "If $$ is not provided, then 1$$ is assumed.\n\n            A range can be provided:": "Se $$ non viene fornito, si presume 1$$.\n\n            È possibile fornire un intervallo di valori:",
     "{1-3$$artist1|artist2|artist3}": "{1-3$$artist1|artist2|artist3}",
@@ -263,7 +266,7 @@
     "Number of images": "Numero di immagini",
     "Make a gif": "Crea GIF",
     "Duration of images (ms)": "Durata delle immagini (ms)",
-    "Put variable parts at start of prompt": "Inserisce le parti variabili all`inizio del prompt",
+    "Put variable parts at start of prompt": "Inserisce le parti variabili all'inizio del prompt",
     "Keyframe Format:": "Formato dei fotogrammi chiave:",
     "Seed | Prompt or just Prompt": "Seme | Prompt o semplicemente Prompt",
     "Prompt list": "Elenco dei prompt",
@@ -280,9 +283,9 @@
     "Compare paths (Separate travels from 1st seed to each destination)": "Confronta percorsi (transizioni separate dal primo seme a ciascuna destinazione)",
     "Steps": "Passi",
     "Loop back to initial seed": "Ritorna al seme iniziale",
-    "Bump seed (If > 0 do a Compare Paths but only one image. No video)": "Modula seme (se > 0 mescola il seme iniziale con quelli di destinazione ma solo un`immagine. Nessun video)",
-    "Show generated images in ui": "Mostra le immagini generate nell`interfaccia utente",
-    "\"Hug the middle\" during interpolation": "\"Hug the middle\" durante l`interpolazione. Rende l`interpolazione un po` più veloce all`inizio e alla fine. A volte può produrre video più fluidi, il più delle volte no.",
+    "Bump seed (If > 0 do a Compare Paths but only one image. No video)": "Modula seme (se > 0 mescola il seme iniziale con quelli di destinazione ma solo un'immagine. Nessun video)",
+    "Show generated images in ui": "Mostra le immagini generate nell'interfaccia utente",
+    "\"Hug the middle\" during interpolation": "\"Hug the middle\" durante l'interpolazione. Rende l'interpolazione un po' più veloce all'inizio e alla fine. A volte può produrre video più fluidi, il più delle volte no.",
     "Allow the default Euler a Sampling method. (Does not produce good results)": "Consenti Euler_a come metodo di campionamento predefinito. (Non produce buoni risultati)",
     "Illustration": "Illustrazione",
     "Logo": "Logo",
@@ -300,7 +303,7 @@
     "svg": "svg",
     "pdf": "pdf",
     "White is Opaque": "Il bianco è opaco",
-    "Cut white margin from input": "Taglia il margine bianco dall`input",
+    "Cut white margin from input": "Taglia il margine bianco dall'input",
     "Keep temp images": "Conserva le immagini temporanee",
     "Threshold": "Soglia",
     "Transparent PNG": "PNG trasparente",
@@ -327,22 +330,22 @@
     "Y values": "Valori per Y",
     "Z type": "Parametro asse Z",
     "Z values": "Valori per Z",
-    "Artist or styles name list. `.txt` files with one name per line": "Elenco nomi di artisti o stili. File '.txt' con un nome per riga",
-    "Prompt words before artist or style name": "Parole chiave prima del nome dell`artista o dello stile",
-    "Prompt words after artist or style name": "Parole chiave dopo il nome dell`artista o dello stile",
+    "Artist or styles name list. '.txt' files with one name per line": "Elenco nomi di artisti o stili. File '.txt' con un nome per riga",
+    "Prompt words before artist or style name": "Parole chiave prima del nome dell'artista o dello stile",
+    "Prompt words after artist or style name": "Parole chiave dopo il nome dell'artista o dello stile",
     "Negative Prompt": "Prompt negativo",
     "Save": "Salva",
     "Send to img2img": "Invia a img2img",
-    "Send to inpaint": "Invia a inpaint",
-    "Send to extras": "Invia a extra",
-    "Make Zip when Save?": "Crea un file ZIP quando si usa `Salva`",
+    "Send to inpaint": "Invia a Inpaint",
+    "Send to extras": "Invia a Extra",
+    "Make Zip when Save?": "Crea un file ZIP quando si usa 'Salva'",
     "Textbox": "Casella di testo",
     "Interrogate\nCLIP": "Interroga\nCLIP",
     "Interrogate\nDeepBooru": "Interroga\nDeepBooru",
     "Inpaint": "Inpaint",
-    "Batch img2img": "Lotti img2img",
+    "Batch img2img": "img2img in lotti",
     "Image for img2img": "Immagine per img2img",
-    "Drop Image Here": "Trascina l`immagine qui",
+    "Drop Image Here": "Trascina l'immagine qui",
     "Image for inpainting with mask": "Immagine per inpainting con maschera",
     "Mask": "Maschera",
     "Mask blur": "Sfocatura maschera",
@@ -355,8 +358,8 @@
     "Masked content": "Contenuto mascherato",
     "fill": "riempi",
     "original": "originale",
-    "latent noise": "rumore latente",
-    "latent nothing": "latenza nulla",
+    "latent noise": "rumore nello spazio latente",
+    "latent nothing": "nulla nello spazio latente",
     "Inpaint at full resolution": "Inpaint alla massima risoluzione",
     "Inpaint at full resolution padding, pixels": "Inpaint con riempimento a piena risoluzione, pixel",
     "Process images in a directory on the same machine where the server is running.": "Elabora le immagini in una cartella sulla stessa macchina su cui è in esecuzione il server.",
@@ -370,19 +373,19 @@
     "External Image Masking": "Immagine esterna per la mascheratura",
     "img2img alternative test": "Test alternativo per img2img",
     "img2tiles": "img2tiles",
-    "Interpolate": "Interpolare",
+    "Interpolate": "Interpola immagini",
     "Loopback": "Rielaborazione ricorsiva",
     "Loopback and Superimpose": "Rielabora ricorsivamente e sovraimponi",
     "Alpha Canvas": "Alpha Canvas",
     "Outpainting mk2": "Outpainting mk2",
-    "Poor man`s outpainting": "Poor man`s outpainting",
+    "Poor man's outpainting": "Poor man's outpainting",
     "SD upscale": "Ampliamento SD",
     "txt2mask v0.1.1": "txt2mask v0.1.1",
     "[C] Video to video": "[C] Video to video",
     "Videos": "Filmati",
     "Deforum-webui (use tab extension instead!)": "Deforum-webui (usa piuttosto la scheda Deforum delle estensioni!)",
     "Use first image colors (custom color correction)": "Usa i colori della prima immagine (correzione del colore personalizzata)",
-    "Denoising strength change factor (overridden if proportional used)": "Fattore di variazione dell`intensità di riduzione del rumore (sovrascritto se si usa proporzionale)",
+    "Denoising strength change factor (overridden if proportional used)": "Fattore di variazione dell'intensità di riduzione del rumore (sovrascritto se si usa proporzionale)",
     "Zoom level": "Livello di Zoom",
     "Direction X": "Direzione X",
     "Direction Y": "Direzione Y",
@@ -391,34 +394,34 @@
     "Denoising strength proportional change starting value": "Intensità di riduzione del rumore - Valore iniziale della variazione proporzionale",
     "Denoising strength proportional change ending value (0.1 = disabled)": "Intensità di riduzione del rumore - Valore finale della variazione proporzionale (0.1 = disabilitato)",
     "Saturation enhancement per image": "Miglioramento della saturazione per ciascuna immagine",
-    "Use sine denoising strength variation": "Utilizzare la variazione sinusoidale dell`intensità di riduzione del rumore",
+    "Use sine denoising strength variation": "Utilizzare la variazione sinusoidale dell'intensità di riduzione del rumore",
     "Phase difference": "Differenza di Fase",
-    "Denoising strength exponentiation": "Esponenziazione dell`intensità di riduzione del rumore",
+    "Denoising strength exponentiation": "Esponenziazione dell'intensità di riduzione del rumore",
     "Use sine zoom variation": "Usa la variazione sinusoidale dello zoom",
-    "Zoom exponentiation": "Esponenziazione dello Zoom",
+    "Zoom exponentiation": "Esponeniazione dello Zoom",
     "Use multiple prompts": "Usa prompt multipli",
     "Same seed per prompt": "Stesso seme per ogni prompt",
     "Same seed for everything": "Stesso seme per tutto",
     "Original init image for everything": "Immagine originale di inizializzazione per tutto",
     "Multiple prompts : 1 line positive, 1 line negative, leave a blank line for no negative": "Prompt multipli: 1 riga positivo, 1 riga negativo, lasciare una riga vuota per nessun negativo",
     "Running in img2img mode:": "Esecuzione in modalità img2img:",
-    "Masking preview size": "Dimensione dell`anteprima della mascheratura",
+    "Masking preview size": "Dimensione dell'anteprima della mascheratura",
     "Draw new mask on every run": "Disegna una nuova maschera ad ogni esecuzione",
     "Process non-contigious masks separately": "Elaborare le maschere non contigue separatamente",
     "should be 2 or lower.": "dovrebbe essere 2 o inferiore.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Sovrascrivi il `Metodo di campionamento` con Eulero? (questo metodo è stato creato per questo)",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Sovrascrivi il 'Metodo di campionamento' con Eulero? (questo metodo è stato creato per questo)",
     "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Sovrascrivi `prompt` con lo stesso valore del `prompt originale`? (e `prompt negativo`)",
     "Original prompt": "Prompt originale",
     "Original negative prompt": "Prompt negativo originale",
-    "Override `Sampling Steps` to the same val due as `Decode steps`?": "Sovrascrivere `Passi di campionamento` allo stesso valore di `Passi di decodifica`?",
+    "Override `Sampling Steps` to the same val due as `Decode steps`?": "Sovrascrivere 'Passi di campionamento' allo stesso valore di 'Passi di decodifica'?",
     "Decode steps": "Passi di decodifica",
-    "Override `Denoising strength` to 1?": "Sostituisci `Forza di denoising` a 1?",
+    "Override `Denoising strength` to 1?": "Sostituisci 'Forza di denoising' a 1?",
     "Decode CFG scale": "Scala CFG di decodifica",
     "Randomness": "Casualità",
-    "Sigma adjustment for finding noise for image": "Regolazione Sigma per trovare il rumore per l`immagine",
+    "Sigma adjustment for finding noise for image": "Regolazione Sigma per trovare il rumore per l'immagine",
     "Tile size": "Dimensione piastrella",
     "Tile overlap": "Sovrapposizione piastrella",
-    "alternate img2img imgage": "alternate img2img imgage",
+    "alternate img2img imgage": "Immagine alternativa per img2img",
     "interpolation values": "Valori di interpolazione",
     "Refinement loops": "Cicli di affinamento",
     "Loopback alpha": "Trasparenza rielaborazione ricorsiva",
@@ -426,17 +429,17 @@
     "Blending strides": "Passi di fusione",
     "Reuse Seed": "Riusa il seme",
     "One grid": "Singola griglia",
-    "Interpolate VarSeed": "Interpola il seme di variazione",
+    "Interpolate VarSeed": "Interpola il seme della variazione",
     "Paste on mask": "Incolla sulla maschera",
     "Inpaint all": "Inpaint tutto",
-    "Interpolate in latent": "Interpola in latenza",
-    "Denoising strength change factor": "Fattore di variazione dell`intensità di denoising",
+    "Interpolate in latent": "Interpola nello spazio latente",
+    "Denoising strength change factor": "Fattore di variazione dell'intensità di denoising",
     "Superimpose alpha": "Sovrapporre Alpha",
     "Show extra settings": "Mostra impostazioni aggiuntive",
     "Reuse seed": "Riusa il seme",
     "CFG decay factor": "Fattore di decadimento CFG",
     "CFG target": "CFG di destinazione",
-    "Show/Hide AlphaCanvas": "Mostra/Nascondi AlphaCanvas",
+    "Show/Hide Canvas": "Mostra/Nascondi Tela",
     "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Impostazioni consigliate: Passi di campionamento: 80-100, Campionatore: Euler a, Intensità denoising: 0.8",
     "Pixels to expand": "Pixel da espandere",
     "Outpainting direction": "Direzione di Outpainting",
@@ -446,7 +449,7 @@
     "down": "sotto",
     "Fall-off exponent (lower=higher detail)": "Esponente di decremento (più basso=maggior dettaglio)",
     "Color variation": "Variazione di colore",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenterà l`immagine al doppio delle dimensioni; utilizzare i cursori di larghezza e altezza per impostare la dimensione della piastrella",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenterà l'immagine al doppio delle dimensioni; utilizzare i cursori di larghezza e altezza per impostare la dimensione della piastrella",
     "Upscaler": "Ampliamento immagine",
     "Lanczos": "Lanczos",
     "LDSR": "LDSR",
@@ -479,17 +482,17 @@
     "Zoom": "Zoom",
     "Rotate": "Ruota",
     "Degrees": "Gradi",
-    "Is the Image Tiled?": "L`immagine è piastrellata?",
+    "Is the Image Tiled?": "L'immagine è piastrellata?",
     "TranslateX": "Traslazione X",
     "Left": "Sinistra",
     "PercentX": "Percentuale X",
     "TranslateY": "Traslazione Y",
     "Up": "Sopra",
     "PercentY": "Percentuale Y",
-    "Show generated pictures in ui": "Mostra le immagini generate nell`interfaccia utente",
+    "Show generated pictures in ui": "Mostra le immagini generate nell'interfaccia utente",
     "Deforum v0.5-webui-beta": "Deforum v0.5-webui-beta",
-    "This script is deprecated. Please use the full Deforum extension instead.": "Questo script è obsoleto. Utilizzare invece l`estensione Deforum completa.",
-    "Update instructions:": "Istruzioni per l`aggiornamento:",
+    "This script is deprecated. Please use the full Deforum extension instead.": "Questo script è obsoleto. Utilizzare invece l'estensione Deforum completa.",
+    "Update instructions:": "Istruzioni per l'aggiornamento:",
     "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md": "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md",
     "discord.gg/deforum": "discord.gg/deforum",
     "Single Image": "Singola immagine",
@@ -582,10 +585,10 @@
     "Auto focal point crop": "Ritaglio automatico al punto focale",
     "Use BLIP for caption": "Usa BLIP per la didascalia",
     "Use deepbooru for caption": "Usa deepbooru per la didascalia",
-    "Split image threshold": "Soglia di divisione dell`immagine",
-    "Split image overlap ratio": "Rapporto di sovrapposizione dell`immagine",
+    "Split image threshold": "Soglia di divisione dell'immagine",
+    "Split image overlap ratio": "Rapporto di sovrapposizione dell'immagine",
     "Focal point face weight": "Peso della faccia del punto focale",
-    "Focal point entropy weight": "Peso dell`entropia del punto focale",
+    "Focal point entropy weight": "Peso dell'entropia del punto focale",
     "Focal point edges weight": "Peso dei bordi del punto focalePeso dei bordi del punto focale",
     "Create debug image": "Crea immagine di debug",
     "Preprocess": "Preprocessa",
@@ -598,16 +601,16 @@
     "Log directory": "Cartella del registro",
     "Prompt template file": "File modello prompt",
     "Max steps": "Passi massimi",
-    "Save an image to log directory every N steps, 0 to disable": "Salva un`immagine nella cartella del registro ogni N passaggi, 0 per disabilitare",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva una copia dell`incorporamento nella cartella del registro ogni N passaggi, 0 per disabilitare",
-    "Save images with embedding in PNG chunks": "Salva le immagini con l`incorporamento in blocchi PNG",
+    "Save an image to log directory every N steps, 0 to disable": "Salva un'immagine nella cartella del registro ogni N passaggi, 0 per disabilitare",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva una copia dell'incorporamento nella cartella del registro ogni N passaggi, 0 per disabilitare",
+    "Save images with embedding in PNG chunks": "Salva le immagini con l'incorporamento in blocchi PNG",
     "Read parameters (prompt, etc...) from txt2img tab when making previews": "Legge i parametri (prompt, ecc...) dalla scheda txt2img durante la creazione delle anteprime",
     "Train Hypernetwork": "Addestra Iperrete",
     "Train Embedding": "Addestra Incorporamento",
-    "Create an aesthetic embedding out of any number of images": "Crea un`incorporamento estetico da qualsiasi numero di immagini",
+    "Create an aesthetic embedding out of any number of images": "Crea un'incorporamento estetico da qualsiasi numero di immagini",
     "Create images embedding": "Crea incorporamento di immagini",
     "-1": "-1",
-    "This extension works well with text captions in comma-separated style (such as the tags generated by DeepBooru interrogator).": "Questa estensione funziona bene con i sottotitoli di testo in stile separato da virgole (come i tag generati dall`interrogatore DeepBooru).",
+    "This extension works well with text captions in comma-separated style (such as the tags generated by DeepBooru interrogator).": "Questa estensione funziona bene con i sottotitoli di testo in stile separato da virgole (come i tag generati dall'interrogatore DeepBooru).",
     "Save all changes": "Salva tutte le modifiche",
     "Backup original text file (original file will be renamed like filename.000, .001, .002, ...)": "Backup del file di testo originale (il file originale verrà rinominato come nomefile.000, .001, .002, ...)",
     "Note:": "Note:",
@@ -616,7 +619,7 @@
     "Load": "Carica",
     "Dataset Images": "Immagini del Dataset",
     "Filter and Edit Tags": "Filtra e modifica i tag",
-    "Edit Caption of Selected Image": "Modifica la didascalia dell`immagine selezionata",
+    "Edit Caption of Selected Image": "Modifica la didascalia dell'immagine selezionata",
     "Search tags / Filter images by tags": "Cerca tag / Filtra le immagini per tag",
     "Search Tags": "Cerca tag",
     "Clear all filters": "Rimuovi tutti i filtri",
@@ -631,7 +634,7 @@
     "Selected Tags": "Tag selezionati",
     "Edit Tags": "Modificare i tag",
     "Apply changes to filtered images": "Applica le modifiche alle immagini filtrate",
-    "Append additional tags to the beginning": "Aggiungi tag addizionali all`inizio",
+    "Append additional tags to the beginning": "Aggiungi tag addizionali all'inizio",
     "1. The selected tags are displayed in comma separated style.": "1. I tag selezionati vengono visualizzati in uno stile separato da virgole.",
     "2. When changes are applied, all tags in each displayed images are replaced.": "2. Quando vengono applicate le modifiche, tutti i tag in ciascuna immagine visualizzata vengono sostituiti.",
     "3. If you change some tags into blank, they will be erased.": "3. Se modifichi alcuni tag con uno spazio vuoto, verranno cancellati.",
@@ -643,21 +646,21 @@
     "ex B.": "esempio B.",
     "Original Text = \"A, B, C\" Selected Tags = \"(nothing)\" Edit Tags = \"X, Y\"": "Testo originale = \"A, B, C\" Tag selezionati = \"(nothing)\" Modifica tag = \"X, Y\"",
     "Result = \"A, B, C, X, Y\" (add X and Y to the end (default))": "Risultato = \"A, B, C, X, Y\" (aggiunge X e Y alla fine (predefinito))",
-    "Result = \"X, Y, A, B, C\" (add X and Y to the beginning (\"Append additional tags to the beginning\" checked))": "Risultato = \"X, Y, A, B, C\" (aggiunge X e Y all`inizio (\"Aggiungi tag addizionali all`inizio\" selezionato))",
+    "Result = \"X, Y, A, B, C\" (add X and Y to the beginning (\"Append additional tags to the beginning\" checked))": "Risultato = \"X, Y, A, B, C\" (aggiunge X e Y all'inizio (\"Aggiungi tag addizionali all'inizio\" selezionato))",
     "ex C.": "esempio C.",
     "Original Text = \"A, B, C, D, E\" Selected Tags = \"A, B, D\" Edit Tags = \", X, \"": "Testo originale = \"A, B, C, D, E\" Tag selezionati = \"A, B, D\" Modifica tag = \", X, \"",
     "Result = \"X, C, E\" (A->\"\", B->X, D->\"\")": "Risultato = \"X, C, E\" (A->\"\", B->X, D->\"\")",
-    "Caption of Selected Image": "Didascalia dell`immagine selezionata",
+    "Caption of Selected Image": "Didascalia dell'immagine selezionata",
     "Copy caption": "Copia didascalia",
     "Edit Caption": "Modifica didascalia",
-    "Apply changes to selected image": "Applica le modifiche all`immagine selezionata",
+    "Apply changes to selected image": "Applica le modifiche all'immagine selezionata",
     "Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\".",
     "Info and links": "Info e link",
-    "Made by deforum.github.io, port for AUTOMATIC1111`s webui maintained by kabachuha": "Realizzato da deforum.github.io, port per l`interfaccia web di AUTOMATIC1111 manutenuto da kabachuha",
+    "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha": "Realizzato da deforum.github.io, port per l'interfaccia web di AUTOMATIC1111 manutenuto da kabachuha",
     "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Repository Github originale di Deforum  github.com/deforum/stable-diffusion",
-    "This fork for auto1111`s webui github.com/deforum-art/deforum-for-automatic1111-webui": "Questo fork è per l`interfaccia web di AUTOMATIC1111 github.com/deforum-art/deforum-for-automatic1111-webui",
+    "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui": "Questo fork è per l'interfaccia web di AUTOMATIC1111 github.com/deforum-art/deforum-for-automatic1111-webui",
     "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Unisciti al canale Discord ufficiale di Deforum discord.gg/deforum per condividere le tue creazioni e suggerimenti",
-    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "Manuale d`uso per la versione 0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
+    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "Manuale d'uso per la versione 0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
     "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Spiegazione della matematica dei fotogrammi chiave docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
     "Keyframes": "Fotogrammi chiave",
     "Prompts": "Prompt",
@@ -673,10 +676,10 @@
     "H": "A",
     "seed": "Seme",
     "sampler": "Campionatore",
-    "Enable extras": "Abilita Extra",
-    "subseed": "sub seme",
+    "Enable extras": "Abilita 'Extra'",
+    "subseed": "Sub seme",
     "subseed_strength": "Intensità subseme",
-    "steps": "passi",
+    "steps": "Passi",
     "ddim_eta": "ETA DDIM",
     "n_batch": "Numero lotto",
     "make_grid": "Crea griglia",
@@ -693,7 +696,7 @@
     "iter": "Iterativo",
     "fixed": "Fisso",
     "random": "Casuale",
-    "schedule": "Programmato",
+    "schedule": "Pianificato",
     "Animation settings": "Impostazioni animazione",
     "animation_mode": "Modalità animazione",
     "2D": "2D",
@@ -729,7 +732,7 @@
     "fov_schedule": "Pianificazione del campo visivo",
     "near_schedule": "Pianificazione da vicino",
     "far_schedule": "Pianificazione da lontano",
-    "To enable seed schedule select seed behavior — `schedule`": "Per abilitare la pianificazione del seme, seleziona il comportamento del seme — `programma`",
+    "To enable seed schedule select seed behavior — 'schedule'": "Per abilitare la pianificazione del seme, seleziona il comportamento del seme — 'pianifica'",
     "seed_schedule": "Pianificazione del seme",
     "Coherence:": "Coerenza:",
     "color_coherence": "Coerenza del colore",
@@ -751,7 +754,7 @@
     "bilinear": "bilinear",
     "nearest": "nearest",
     "save_depth_maps": "Salva le mappe di profondità",
-    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`modalità animazione: Nessuno` si inserisce nell`elenco di *prompt*. (Modalità batch disabilitata atm, funzionano solo i prompt di animazione)",
+    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`modalità animazione: Nessuno` si inserisce nell'elenco di *prompt*. (Modalità batch disabilitata atm, funzionano solo i prompt di animazione)",
     "*Important change from vanilla Deforum!*": "*Importante cambiamento rispetto alla versione originale di Deforum!*",
     "This script uses the built-in webui weighting settings.": "Questo script utilizza le impostazioni di pesatura webui integrate.",
     "So if you want to use math functions as prompt weights,": "Quindi, se vuoi usare le funzioni matematiche come pesi dei prompt,",
@@ -781,7 +784,7 @@
     "Interpolation (turned off atm)": "Interpolazione (attualmente spento)",
     "interpolate_key_frames": "Interpola fotogrammi chiave",
     "interpolate_x_frames": "Interpola x fotogrammi",
-    "Resume animation:": "Riprendi l`animazione:",
+    "Resume animation:": "Riprendi l'animazione:",
     "resume_from_timestring": "Riprendi da stringa temporale",
     "resume_timestring": "Stringa temporale",
     "Video output settings": "Impostazioni uscita video",
@@ -886,7 +889,7 @@
     "spaceship-nudity": "spaceship-nudity",
     "spaceship-c": "spaceship-c",
     "spaceship-n": "spaceship-n",
-    "artists to study extension by camenduru |": "Estensione `Artisti per studiare` a cura di camenduru |",
+    "artists to study extension by camenduru |": "Estensione 'Artisti per studiare' a cura di camenduru |",
     "github": "Github",
     "|": "|",
     "twitter": "Twitter",
@@ -895,9 +898,8 @@
     "All images generated with CompVis/stable-diffusion-v1-4 +": "Tutte le immagini sono state generate con CompVis/stable-diffusion-v1-4 +",
     "artists.csv": "artists.csv",
     "| License: Attribution 4.0 International (CC BY 4.0)": "| Licenza: Attribution 4.0 International (CC BY 4.0)",
-    "extras": "Extra",
-    "favorites": "Preferiti",
-    "others": "Altre immagini",
+    "Favorites": "Preferiti",
+    "Others": "Altre immagini",
     "Images directory": "Cartella immagini",
     "Dropdown": "Elenco cartelle",
     "First Page": "Prima pagina",
@@ -913,7 +915,7 @@
     "keyword": "Parola chiave",
     "Generate Info": "Genera Info",
     "File Name": "Nome del file",
-    "Collect": "Aggiungi ai preferiti",
+    "Move to favorites": "Aggiungi ai preferiti",
     "Renew page": "Aggiorna la pagina",
     "Number": "Numero",
     "set_index": "Imposta indice",
@@ -926,14 +928,14 @@
     "mediums": "Tecniche",
     "movements": "Movimenti artistici",
     "All": "Tutto",
-    "Favorites": "Preferiti",
     "Exclude abandoned": "Escludi scartati",
     "Abandoned": "Scartati",
     "Key word": "Parola chiave",
     "Get inspiration": "Ispirami",
     "to txt2img": "Invia a txt2img",
     "to img2img": "Invia a img2img",
-    "Don`t show again": "Scarta",
+    "Collect": "Salva nei preferiti",
+    "Don't show again": "Scarta",
     "Move out": "Rimuovi",
     "set button": "Pulsante imposta", 
     "Apply settings": "Applica le impostazioni",
@@ -950,21 +952,21 @@
     "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Numero di righe della griglia; utilizzare -1 per il rilevamento automatico e 0 per essere uguale alla dimensione del lotto",
     "Save text information about generation parameters as chunks to png files": "Salva le informazioni di testo dei parametri di generazione come blocchi nel file png",
     "Create a text file next to every image with generation parameters.": "Crea un file di testo assieme a ogni immagine con i parametri di generazione.",
-    "Save a copy of image before doing face restoration.": "Salva una copia dell`immagine prima di eseguire il restauro dei volti.",
+    "Save a copy of image before doing face restoration.": "Salva una copia dell'immagine prima di eseguire il restauro dei volti.",
     "Quality for saved jpeg images": "Qualità delle immagini salvate in formato JPEG",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l`immagine PNG è più grande di 4 MB o qualsiasi dimensione è maggiore di 4000, ridimensiona e salva la copia come JPG",
-    "Use original name for output filename during batch process in extras tab": "Usa il nome originale per il nome del file di output durante l`elaborazione a lotti nella scheda `Extra`",
-    "When using `Save` button, only save a single selected image": "Usando il pulsante `Salva`, verrà salvata solo la singola immagine selezionata",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l'immagine PNG è più grande di 4 MB o qualsiasi dimensione è maggiore di 4000, ridimensiona e salva la copia come JPG",
+    "Use original name for output filename during batch process in extras tab": "Usa il nome originale per il nome del file di output durante l'elaborazione a lotti nella scheda 'Extra'",
+    "When using 'Save' button, only save a single selected image": "Usando il pulsante 'Salva', verrà salvata solo la singola immagine selezionata",
     "Do not add watermark to images": "Non aggiungere la filigrana alle immagini",
     "Paths for saving": "Percorsi di salvataggio",
     "Output directory for images; if empty, defaults to three directories below": "Cartella di output per le immagini; se vuoto, per impostazione predefinita verranno usate le cartelle seguenti",
     "Output directory for txt2img images": "Cartella di output per le immagini txt2img",
     "Output directory for img2img images": "Cartella di output per le immagini img2img",
-    "Output directory for images from extras tab": "Cartella di output per le immagini dalla scheda `Extra`",
+    "Output directory for images from extras tab": "Cartella di output per le immagini dalla scheda 'Extra'",
     "Output directory for grids; if empty, defaults to two directories below": "Cartella di output per le griglie; se vuoto, per impostazione predefinita veranno usate cartelle seguenti",
     "Output directory for txt2img grids": "Cartella di output per le griglie txt2img",
     "Output directory for img2img grids": "Cartella di output per le griglie img2img",
-    "Directory for saving images using the Save button": "Cartella dove salvare le immagini usando il pulsante `Salva`",
+    "Directory for saving images using the Save button": "Cartella dove salvare le immagini usando il pulsante 'Salva'",
     "Saving to a directory": "Salva in una cartella",
     "Save images to a subdirectory": "Salva le immagini in una sotto cartella",
     "Save grids to a subdirectory": "Salva le griglie in una sotto cartella",
@@ -978,16 +980,16 @@
     "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per SwinIR. Valori bassi = cucitura visibile.",
     "LDSR processing steps. Lower = faster": "Fasi di elaborazione LDSR. Più basso = più veloce",
     "Upscaler for img2img": "Metodo di ampliamento per img2img",
-    "Upscale latent space image when doing hires. fix": "Amplia l`immagine nello spazio latente durante la correzione in alta risoluzione",
+    "Upscale latent space image when doing hires. fix": "Amplia l'immagine nello spazio latente durante la correzione in alta risoluzione",
     "Face restoration": "Restauro del viso",
     "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Peso di CodeFormer; 0 = effetto massimo; 1 = effetto minimo",
-    "Move face restoration model from VRAM into RAM after processing": "Sposta il modello di restauro facciale dalla VRAM alla RAM dopo l`elaborazione",
+    "Move face restoration model from VRAM into RAM after processing": "Sposta il modello di restauro facciale dalla VRAM alla RAM dopo l'elaborazione",
     "System": "Sistema",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "Verifiche al secondo sull`utilizzo della VRAM durante la generazione. Impostare a 0 per disabilitare.",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Verifiche al secondo sull'utilizzo della VRAM durante la generazione. Impostare a 0 per disabilitare.",
     "Always print all generation info to standard output": "Stampa sempre tutte le informazioni di generazione sul output standard",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda barra di avanzamento alla console che mostra l`avanzamento complessivo del lavoro.",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda barra di avanzamento alla console che mostra l'avanzamento complessivo del lavoro.",
     "Training": "Addestramento",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP nella RAM durante l`addestramento di Iperreti. Risparmia VRAM.",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP nella RAM durante l'addestramento di Iperreti. Risparmia VRAM.",
     "Filename word regex": "Filename word regex",
     "Filename join string": "Filename join string",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per una singola immagine di input per epoca; utilizzato solo per visualizzare il numero di epoca",
@@ -997,13 +999,13 @@
     "Hypernetwork strength": "Forza della Iperrete",
     "Inpainting conditioning mask strength": "Forza della maschera di condizionamento del Inpainting",
     "Apply color correction to img2img results to match original colors.": "Applica la correzione del colore ai risultati di img2img in modo che corrispondano ai colori originali.",
-    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell`immagine prima di applicare la correzione del colore ai risultati di img2img",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you`d do less with less denoising).": "Con img2img, esegue esattamente la quantità di passi specificata dalla barra di scorrimento (normalmente se ne effettuano di meno con meno riduzione del rumore).",
+    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la correzione del colore ai risultati di img2img",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, esegue esattamente la quantità di passi specificata dalla barra di scorrimento (normalmente se ne effettuano di meno con meno riduzione del rumore).",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Abilita la quantizzazione nei campionatori K per risultati più nitidi e puliti. Questo può cambiare i semi esistenti. Richiede il riavvio per applicare la modifica.",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Enfasi: utilizzare (testo) per fare in modo che il modello presti maggiore attenzione al testo e [testo] per fargli prestare meno attenzione",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usa la vecchia implementazione dell`enfasi. Può essere utile per riprodurre vecchi semi.",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usa la vecchia implementazione dell'enfasi. Può essere utile per riprodurre vecchi semi.",
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "Fa sì che i campionatori di diffusione K producano le stesse immagini in un lotto come quando si genera una singola immagine",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coerenza disattivando dall`ultima virgola all`indietro di n token quando si utilizzano più di 75 token",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coerenza disattivando dall'ultima virgola all'indietro di n token quando si utilizzano più di 75 token",
     "Filter NSFW content": "Filtra i contenuti NSFW",
     "Stop At last layers of CLIP model": "Fermati agli ultimi livelli del modello CLIP",
     "Interrogate Options": "Opzioni di interrogazione",
@@ -1017,21 +1019,21 @@
     "Interrogate: deepbooru score threshold": "Interroga: soglia del punteggio deepbooru",
     "Interrogate: deepbooru sort alphabetically": "Interroga: deepbooru ordinato alfabeticamente",
     "use spaces for tags in deepbooru": "usa gli spazi per i tag in deepbooru",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Effettua l`escape (\\) delle parentesi in deepbooru (così vengono usate come parentesi letterali e non per enfatizzare)",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Effettua l'escape (\\) delle parentesi in deepbooru (così vengono usate come parentesi letterali e non per enfatizzare)",
     "User interface": "Interfaccia Utente",
     "Show progressbar": "Mostra la barra di avanzamento",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostra l`avanzamento della generazione dell`immagine ogni N passaggi di campionamento. Impostare a 0 per disabilitare.",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostra l'avanzamento della generazione dell'immagine ogni N passaggi di campionamento. Impostare a 0 per disabilitare.",
     "Show previews of all images generated in a batch as a grid": "Mostra le anteprime di tutte le immagini generate in un lotto come una griglia",
     "Show grid in results for web": "Mostra la griglia nei risultati per il web",
     "Do not show any images in results for web": "Non mostrare alcuna immagine nei risultati per il web",
-    "Add model hash to generation information": "Aggiungi l`hash del modello alle informazioni sulla generazione",
+    "Add model hash to generation information": "Aggiungi l'hash del modello alle informazioni sulla generazione",
     "Add model name to generation information": "Aggiungi il nome del modello alle informazioni sulla generazione",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Durante la lettura dei parametri di generazione dal testo nell`interfaccia utente (da informazioni PNG o testo incollato), non modificare il modello/checkpoint selezionato.",
-    "Send seed when sending prompt or image to other interface": "Invia il seme quando si invia un prompt o un`immagine a un`altra interfaccia",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Durante la lettura dei parametri di generazione dal testo nell'interfaccia utente (da informazioni PNG o testo incollato), non modificare il modello/checkpoint selezionato.",
+    "Send seed when sending prompt or image to other interface": "Invia il seme quando si invia un prompt o un'immagine a un'altra interfaccia",
     "Font for image grids that have text": "Font per griglie di immagini con testo",
     "Enable full page image viewer": "Abilita la visualizzazione delle immagini a pagina intera",
     "Show images zoomed in by default in full page image viewer": "Mostra le immagini ingrandite per impostazione predefinita nella visualizzazione a pagina intera",
-    "Show generation progress in window title.": "Mostra l`avanzamento della generazione nel titolo della finestra.",
+    "Show generation progress in window title.": "Mostra l'avanzamento della generazione nel titolo della finestra.",
     "Quicksettings list": "Elenco delle impostazioni rapide",
     "Localization (requires restart)": "Localizzazione (richiede il riavvio)",
     "ar_AR": "ar_AR",
@@ -1047,7 +1049,7 @@
     "zh_CN": "zh_CN",
     "zh_TW": "zh_TW",
     "Sampler parameters": "Parametri del campionatore",
-    "Hide samplers in user interface (requires restart)": "Nascondi campionatori nell`interfaccia utente (richiede il riavvio)",
+    "Hide samplers in user interface (requires restart)": "Nascondi campionatori nell'interfaccia utente (richiede il riavvio)",
     "eta (noise multiplier) for DDIM": "ETA (moltiplicatore di rumore) per DDIM",
     "eta (noise multiplier) for ancestral samplers": "ETA (moltiplicatore di rumore) per campionatori ancestrali",
     "img2img DDIM discretize": "discretizzazione DDIM per img2img",
@@ -1063,7 +1065,7 @@
     "Force CPU (Requires Custom Script Reload)": "Forza CPU (richiede il ricaricamento dello script personalizzato)",
     "Number of columns on image gallery": "Numero di colonne nella galleria di immagini",
     "Images Browser": "Galleria immagini",
-    "Preload images at startup": "Precarica le immagini all`avvio",
+    "Preload images at startup": "Precarica le immagini all'avvio",
     "Number of columns on the page": "Numero di colonne nella pagina",
     "Number of rows on the page": "Numero di righe nella pagina",
     "Minimum number of pages per load": "Numero minimo di pagine da caricare",
@@ -1071,72 +1073,101 @@
     "Use same seed for all images": "Usa lo stesso seme per tutte le immagini",
     "Request browser notifications": "Richiedi le notifiche del browser",
     "Download localization template": "Scarica il modello per la localizzazione",
-    "Reload custom script bodies (No ui updates, No restart)": "Ricarica gli script personalizzati (nessun aggiornamento dell`interfaccia utente, nessun riavvio)",
+    "Reload custom script bodies (No ui updates, No restart)": "Ricarica gli script personalizzati (nessun aggiornamento dell'interfaccia utente, nessun riavvio)",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Riavvia Gradio e aggiorna i componenti (solo script personalizzati, ui.py, js e css)",
+    "Installed": "Installato",
+    "Install from URL": "Installa da URL",
+    "Apply and restart UI": "Applica e riavvia l'interfaccia utente",
+    "Check for updates": "Controlla aggiornamenti",
+    "Extension": "Estensione",
+    "URL": "URL",
+    "Update": "Aggiorna",
+    "aesthetic-gradients": "Gradienti Estetici (CLIP)",
+    "https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients",
+    "unknown": "sconosciuto",
+    "aesthetic-image-scorer": "Punteggio delle immagini estetiche",
+    "https://github.com/tsngo/stable-diffusion-webui-aesthetic-image-scorer": "https://github.com/tsngo/stable-diffusion-webui-aesthetic-image-scorer",
+    "dataset-tag-editor": "Dataset Tag Editor",
+    "https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor.git": "https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor.git",
+    "deforum": "Deforum",
+    "https://github.com/deforum-art/deforum-for-automatic1111-webui/": "https://github.com/deforum-art/deforum-for-automatic1111-webui/",
+    "stable-diffusion-webui-artists-to-study": "Artisti per studiare",
+    "https://github.com/camenduru/stable-diffusion-webui-artists-to-study": "https://github.com/camenduru/stable-diffusion-webui-artists-to-study",
+    "stable-diffusion-webui-images-browser": "Galleria immagini",
+    "https://github.com/yfszzx/stable-diffusion-webui-images-browser": "https://github.com/yfszzx/stable-diffusion-webui-images-browser",
+    "stable-diffusion-webui-inspiration": "Ispirazione",
+    "https://github.com/yfszzx/stable-diffusion-webui-inspiration": "https://github.com/yfszzx/stable-diffusion-webui-inspiration",
+    "tag-autocomplete": "Autocompletamento etichette",
+    "https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git": "https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git",
+    "wildcards": "Termini Jolly",
+    "https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards.git": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards.git",
+    "URL for extension's git repository": "URL del repository GIT dell'estensione",
+    "Local directory name": "Nome cartella locale",
+    "Install": "Installa",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (premi Ctrl+Invio o Alt+Invio per generare)",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (premere Ctrl+Invio o Alt+Invio per generare)",
     "Add a random artist to the prompt.": "Aggiungi un artista casuale al prompt.",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leggere i parametri di generazione dal prompt o dall`ultima generazione se il prompt è vuoto ed inserirli nell`interfaccia utente.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leggere i parametri di generazione dal prompt o dall'ultima generazione se il prompt è vuoto ed inserirli nell'interfaccia utente.",
     "Save style": "Salva stile",
     "Apply selected styles to current prompt": "Applica gli stili selezionati al prompt corrente",
-    "Stop processing current image and continue processing.": "Interrompe l`elaborazione dell`immagine corrente e continua l`elaborazione.",
-    "Stop processing images and return any results accumulated so far.": "Interrompe l`elaborazione delle immagini e restituisce tutti i risultati accumulati finora.",
+    "Stop processing current image and continue processing.": "Interrompe l'elaborazione dell'immagine corrente e continua l'elaborazione.",
+    "Stop processing images and return any results accumulated so far.": "Interrompe l'elaborazione delle immagini e restituisce tutti i risultati accumulati finora.",
     "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stile da applicare; gli stili hanno componenti sia per i prompt positivi che per quelli negativi e si applicano a entrambi",
     "Do not do anything special": "Non fa nulla di speciale",
-    "Which algorithm to use to produce the image": "Quale algoritmo utilizzare per produrre l`immagine",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - molto creativo, si può ottenere un`immagine completamente diversa a seconda del numero di passi, impostare i passi su un valore superiore a 30-40 non aiuta",
+    "Which algorithm to use to produce the image": "Quale algoritmo utilizzare per produrre l'immagine",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - molto creativo, si può ottenere un'immagine completamente diversa a seconda del numero di passi, impostare i passi su un valore superiore a 30-40 non aiuta",
     "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - il migliore per inpainting",
-    "Produce an image that can be tiled.": "Produce un`immagine che può essere piastrellata.",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilizza un processo in due fasi per creare parzialmente un`immagine con una risoluzione inferiore, aumentare la scala e quindi migliorarne i dettagli senza modificare la composizione",
-    "Determines how little respect the algorithm should have for image`s content. At 0, nothing will change, and at 1 you`ll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determina quanto poco l`algoritmo dovrebbe rispettare dovrebbe il contenuto dell`immagine. A 0, non cambierà nulla e a 1 otterrai un`immagine non correlata. Con valori inferiori a 1.0 l`elaborazione richiederà meno passaggi di quelli specificati dalla barra di scorrimento dei passi di campionamento.",
+    "Produce an image that can be tiled.": "Produce un'immagine che può essere piastrellata.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilizza un processo in due fasi per creare parzialmente un'immagine con una risoluzione inferiore, aumentare la scala e quindi migliorarne i dettagli senza modificare la composizione",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determina quanto poco l'algoritmo dovrebbe rispettare dovrebbe il contenuto dell'immagine. A 0, non cambierà nulla e a 1 otterrai un'immagine non correlata. Con valori inferiori a 1.0 l'elaborazione richiederà meno passaggi di quelli specificati dalla barra di scorrimento dei passi di campionamento.",
     "How many batches of images to create": "Quanti lotti di immagini generare",
     "How many image to create in a single batch": "Quante immagini generare in un singolo lotto",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - quanto fortemente l`immagine deve conformarsi al prompt: valori più bassi producono risultati più creativi",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you`ll get the same result": "Un valore che determina l`output del generatore di numeri casuali: se create un`immagine con gli stessi parametri e seme di un`altra immagine, otterrete lo stesso risultato",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - quanto fortemente l'immagine deve conformarsi al prompt: valori più bassi producono risultati più creativi",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Un valore che determina l'output del generatore di numeri casuali: se create un'immagine con gli stessi parametri e seme di un'altra immagine, otterrete lo stesso risultato",
     "Set seed to -1, which will cause a new random number to be used every time": "Imposta il seme su -1, che farà sì che ogni volta venga utilizzato un nuovo numero casuale",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Riusa il seme dell`ultima generazione, utile soprattutto se casuale",
-    "Seed of a different picture to be mixed into the generation.": "Seme di un`immagine diversa da miscelare nella generazione.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Quanto è forte la variazione da produrre. A 0, non ci sarà alcun effetto. A 1, otterrai l`intera immagine con il seme della variazione (tranne per i campionatori ancestrali, dove otterrai solo una leggera variazione).",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Prova a produrre un`immagine simile a quella che sarebbe stata prodotta con lo stesso seme alla risoluzione specificata",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Riusa il seme dell'ultima generazione, utile soprattutto se casuale",
+    "Seed of a different picture to be mixed into the generation.": "Seme di un'immagine diversa da miscelare nella generazione.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Quanto è forte la variazione da produrre. A 0, non ci sarà alcun effetto. A 1, otterrai l'intera immagine con il seme della variazione (tranne per i campionatori ancestrali, dove otterrai solo una leggera variazione).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Prova a produrre un'immagine simile a quella che sarebbe stata prodotta con lo stesso seme alla risoluzione specificata",
     "This text is used to rotate the feature space of the imgs embs": "Questo testo viene utilizzato per ruotare lo spazio delle funzioni delle immagini incorporate",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Quante volte ripetere l`elaborazione di un`immagine e utilizzarla come input per l`iterazione successiva",
-	"Hello, StylePile here.\nUntil some weird bug gets fixed you will see this even if the script itself is not active. Meanwhile, some hints to take your artwork to new heights:\nUse the `Focus on` dropdown to select complex presets. Toggle selections below (with or without Focus) to affect your results. Mix and match to get some interesting results. \nAnd some general Stable Diffusion tips that will take your designs to next level:\nYou can add parenthesis to make parts of the prompt stronger. So (((cute))) kitten will make it extra cute (try it out). This is alsow important if a style is affecting your original prompt too much. Make that prompt stronger by adding parenthesis around it, like this: ((promt)).\nYou can type promts like [A|B] to sequentially use terms one after another on each step. So, like [cat|dog] will produce a hybrid catdog. And [A:B:0.4] to switch to other terms after the first one has been active for a certain percentage of steps. So [cat:dog:0.4] will build a cat 40% of the time and then start turning it into a dog. This needs more steps to work properly.": "Salve, qui è StylePile.\nFinché qualche strano bug non verrà risolto, vedrai questo testo anche se lo script non è attivo. Nel frattempo, alcuni suggerimenti per portare la tua grafica a nuovi livelli:\nUtilizza il menu a discesa `Focus on` per selezionare valori predefiniti complessi. Attiva o disattiva le selezioni seguenti (con o senza Focus) per influire sui risultati. Mescola e abbina per ottenere risultati interessanti. \nE alcuni suggerimenti generali su Stable Diffusion che porteranno i tuoi risultati a un livello superiore:\nPuoi aggiungere parentesi per aumentare l`influenza di certe parti del prompt. Quindi `(((cute))) kitten` lo renderà molto carino (fai delle prove). Questo è importante quando uno stile influisce troppo sul prompt originale. Rendi più forte quel prompt aggiungendo delle parentesi intorno ad esso, così: ((promt)).\nPuoi digitare prompt nel formato [A|B] per usare in sequenza i termini uno dopo l`altro in ogni passaggio. Quindi, come [cat|dog] produrrà un `canegatto` ibrido. E [A:B:0.4] per passare ad altri termini dopo che il primo è stato attivo per una certa percentuale di passaggi. Quindi [cat:dog:0.4] genererà un gatto il 40% dei passaggi e poi inizierà a trasformarlo in un cane. Sono richiesti più passaggi perchè funzioni correttamente.",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Quante volte ripetere l'elaborazione di un'immagine e utilizzarla come input per l'iterazione successiva",
+    "Hello, StylePile here.\nUntil some weird bug gets fixed you will see this even if the script itself is not active. Meanwhile, some hints to take your artwork to new heights:\nUse the 'Focus on' dropdown to select complex presets. Toggle selections below (with or without Focus) to affect your results. Mix and match to get some interesting results. \nAnd some general Stable Diffusion tips that will take your designs to next level:\nYou can add parenthesis to make parts of the prompt stronger. So (((cute))) kitten will make it extra cute (try it out). This is alsow important if a style is affecting your original prompt too much. Make that prompt stronger by adding parenthesis around it, like this: ((promt)).\nYou can type promts like [A|B] to sequentially use terms one after another on each step. So, like [cat|dog] will produce a hybrid catdog. And [A:B:0.4] to switch to other terms after the first one has been active for a certain percentage of steps. So [cat:dog:0.4] will build a cat 40% of the time and then start turning it into a dog. This needs more steps to work properly.": "Salve, qui è StylePile.\nFinché qualche strano bug non verrà risolto, vedrai questo testo anche se lo script non è attivo. Nel frattempo, alcuni suggerimenti per portare la tua grafica a nuovi livelli:\nUtilizza il menu a discesa 'Focus on' per selezionare valori predefiniti complessi. Attiva o disattiva le selezioni seguenti (con o senza Focus) per influire sui risultati. Mescola e abbina per ottenere risultati interessanti. \nE alcuni suggerimenti generali su Stable Diffusion che porteranno i tuoi risultati a un livello superiore:\nPuoi aggiungere parentesi per aumentare l'influenza di certe parti del prompt. Quindi '(((cute))) kitten' lo renderà molto carino (fai delle prove). Questo è importante quando uno stile influisce troppo sul prompt originale. Rendi più forte quel prompt aggiungendo delle parentesi intorno ad esso, così: ((promt)).\nPuoi digitare prompt nel formato [A|B] per usare in sequenza i termini uno dopo l'altro in ogni passaggio. Quindi, come [cat|dog] produrrà un 'canegatto' ibrido. E [A:B:0.4] per passare ad altri termini dopo che il primo è stato attivo per una certa percentuale di passaggi. Quindi [cat:dog:0.4] genererà un gatto il 40% dei passaggi e poi inizierà a trasformarlo in un cane. Sono richiesti più passaggi perchè funzioni correttamente.",
     "Enter one prompt per line. Blank lines will be ignored.": "Immettere un prompt per riga. Le righe vuote verranno ignorate.",
-    "Separate values for X axis using commas.": "Separare i valori per l`asse X usando le virgole.",
-    "Separate values for Y axis using commas.": "Separare i valori per l`asse Y usando le virgole.",
+    "Separate values for X axis using commas.": "Separare i valori per l'asse X usando le virgole.",
+    "Separate values for Y axis using commas.": "Separare i valori per l'asse Y usando le virgole.",
     "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "Separa un elenco di parole con virgole e lo script eseguirà una variazione di prompt con quelle parole per ogni loro possibile ordine",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva l`immagine/i in una cartella (predefinita - log/images) ed i parametri di generazione in un file CSV.",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva l'immagine/i in una cartella (predefinita - log/images) ed i parametri di generazione in un file CSV.",
     "Open images output directory": "Apri la cartella di output delle immagini",
-    "How much to blur the mask before processing, in pixels.": "Quanto sfocare la maschera prima dell`elaborazione, in pixel.",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Cosa mettere all`interno dell`area mascherata prima di elaborarla con Stable Diffusion.",
-    "fill it with colors of the image": "riempi con i colori dell`immagine",
-    "keep whatever was there originally": "conserva tutto ciò che c`era in origine",
+    "How much to blur the mask before processing, in pixels.": "Quanto sfocare la maschera prima dell'elaborazione, in pixel.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Cosa mettere all'interno dell'area mascherata prima di elaborarla con Stable Diffusion.",
+    "fill it with colors of the image": "riempi con i colori dell'immagine",
+    "keep whatever was there originally": "conserva tutto ciò che c'era in origine",
     "fill it with latent space noise": "riempi di rumore spaziale latente",
     "fill it with latent space zeroes": "riempi con zeri di spazio latente",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Ingrandisce la regione mascherata per raggiungere la risoluzione, esegue la pittura, riduce la scala e incolla nell`immagine originale",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Ridimensiona l`immagine alla risoluzione di destinazione. A meno che altezza e larghezza non corrispondano, otterrai proporzioni errate.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Ridimensionare l`immagine in modo che l`intera risoluzione di destinazione sia riempita con l`immagine. Ritaglia le parti che sporgono.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image`s colors.": "Ridimensiona l`immagine in modo che l`intera immagine rientri nella risoluzione di destinazione. Riempi lo spazio vuoto con i colori dell`immagine.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Per l`upscaling SD, quanta sovrapposizione in pixel dovrebbe esserci tra le piastrelle. Le piastrelle si sovrappongono in modo che quando vengono unite nuovamente in un`immagine, non ci siano giunture chiaramente visibili.",
-    "Process an image, use it as an input, repeat.": "Elabora un`immagine, usala come input, ripeti.",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In modalità rielaborazione ricorsiva, su ogni ciclo la forza di denoising viene moltiplicata per questo valore. <1 significa varietà decrescente in modo che la sequenza converga su un`immagine fissa. >1 significa aumentare la varietà in modo che la tua sequenza diventi sempre più caotica.",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Ingrandisce la regione mascherata per raggiungere la risoluzione, esegue la pittura, riduce la scala e incolla nell'immagine originale",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Ridimensiona l'immagine alla risoluzione di destinazione. A meno che altezza e larghezza non corrispondano, otterrai proporzioni errate.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Ridimensionare l'immagine in modo che l'intera risoluzione di destinazione sia riempita con l'immagine. Ritaglia le parti che sporgono.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Ridimensiona l'immagine in modo che l'intera immagine rientri nella risoluzione di destinazione. Riempi lo spazio vuoto con i colori dell'immagine.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Per l'upscaling SD, quanta sovrapposizione in pixel dovrebbe esserci tra le piastrelle. Le piastrelle si sovrappongono in modo che quando vengono unite nuovamente in un'immagine, non ci siano giunture chiaramente visibili.",
+    "Process an image, use it as an input, repeat.": "Elabora un'immagine, usala come input, ripeti.",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In modalità rielaborazione ricorsiva, su ogni ciclo la forza di denoising viene moltiplicata per questo valore. <1 significa varietà decrescente in modo che la sequenza converga su un'immagine fissa. >1 significa aumentare la varietà in modo che la tua sequenza diventi sempre più caotica.",
     "A directory on the same machine where the server is running.": "Una cartella sulla stessa macchina su cui è in esecuzione il server.",
     "Leave blank to save images to the default path.": "Lascia vuoto per salvare le immagini nel percorso predefinito.",
     "Result = A * (1 - M) + B * M": "Risultato = A * (1 - M) + B * M",
     "Result = A + (B - C) * M": "Risultato = A + (B - C) * M",
-    "1st and last digit must be 1. ex:`1, 2, 1`": "La prima e l`ultima cifra devono essere 1. Es.:`1, 2, 1`",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "La prima e l'ultima cifra devono essere 1. Es.:'1, 2, 1'",
     "Path to directory with input images": "Percorso della cartella con immagini di input",
     "Path to directory where to write outputs": "Percorso della cartella in cui scrivere i risultati",
     "C:\\directory\\of\\datasets": "C:\\cartella\\del\\dataset",
     "Input images directory": "Cartella di input delle immagini",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Usa i seguenti tag per definire come vengono scelti i nomi dei file per le immagini: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed ], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l`impostazione predefinita.",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Usa i seguenti tag per definire come vengono scelti i nomi dei file per le immagini: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed ], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
     "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se questa opzione è abilitata, la filigrana non verrà aggiunta alle immagini create. Attenzione: se non aggiungi la filigrana, potresti comportarti in modo non etico.",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utilizzare i seguenti tag per definire come vengono scelte le sottodirectory per le immagini e le griglie: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l`impostazione predefinita.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utilizzare i seguenti tag per definire come vengono scelte le sottodirectory per le immagini e le griglie: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
     "Restore low quality faces using GFPGAN neural network": "Ripristina volti di bassa qualità utilizzando la rete neurale GFPGAN",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Questa espressione regolare verrà utilizzata per estrarre le parole dal nome del file e verranno unite utilizzando l`opzione seguente nel testo dell`etichetta utilizzato per l`addestramento. Lascia vuoto per mantenere il testo del nome del file così com`è.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "Questa stringa verrà utilizzata per unire le parole divise in un`unica riga se l`opzione sopra è abilitata.",
-    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Si applica solo ai modelli di pittura. Determina con quale forza mascherare l`immagine originale per inpainting e img2img. 1.0 significa completamente mascherato, che è il comportamento predefinito. 0.0 significa un condizionamento completamente non mascherato. Valori più bassi aiuteranno a preservare la composizione generale dell`immagine, ma avranno difficoltà con grandi cambiamenti.",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Questa espressione regolare verrà utilizzata per estrarre le parole dal nome del file e verranno unite utilizzando l'opzione seguente nel testo dell'etichetta utilizzato per l'addestramento. Lascia vuoto per mantenere il testo del nome del file così com'è.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Questa stringa verrà utilizzata per unire le parole divise in un'unica riga se l'opzione sopra è abilitata.",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Si applica solo ai modelli di pittura. Determina con quale forza mascherare l'immagine originale per inpainting e img2img. 1.0 significa completamente mascherato, che è il comportamento predefinito. 0.0 significa un condizionamento completamente non mascherato. Valori più bassi aiuteranno a preservare la composizione generale dell'immagine, ma avranno difficoltà con grandi cambiamenti.",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Elenco dei nomi delle impostazioni, separati da virgole, per le impostazioni che dovrebbero essere visualizzate nella barra di accesso rapido in alto, anziché nella normale scheda delle impostazioni. Vedi modules/shared.py per impostare i nomi. Richiede il riavvio per applicare.",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se questo valore è diverso da zero, verrà aggiunto al seed e utilizzato per inizializzare il generatore di numeri casuali per il rumore quando si utilizzano campionatori con ETA. Puoi usarlo per produrre ancora più variazioni di immagini, oppure puoi usarlo per abbinare le immagini di altri software se sai cosa stai facendo.",
-    "Enable Autocomplete": "Abilita autocompletamento"
+    "Leave empty for auto": "Lasciare vuoto per automatico"
 }
\ No newline at end of file
-- 
cgit v1.2.1


From 8792be50078c2e89ac2fa8ff4e4c1ca82f140d45 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Mon, 31 Oct 2022 17:29:04 -0700
Subject: Add PNG info to pngs only if option is enabled.

---
 modules/images.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index a0728553..ae705cbd 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -510,8 +510,9 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
 
     if extension.lower() == '.png':
         pnginfo_data = PngImagePlugin.PngInfo()
-        for k, v in params.pnginfo.items():
-            pnginfo_data.add_text(k, str(v))
+        if opts.enable_pnginfo:
+            for k, v in params.pnginfo.items():
+                pnginfo_data.add_text(k, str(v))
 
         image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
 
-- 
cgit v1.2.1


From 525c1edf431d9c9efc1349be8657f0301299e3bc Mon Sep 17 00:00:00 2001
From: k_sugawara <k_sugawara@radiu5.co.jp>
Date: Tue, 1 Nov 2022 09:40:54 +0900
Subject: make save dir if save dir is not exists

---
 modules/img2img.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/img2img.py b/modules/img2img.py
index efda26e1..35c5df9b 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -55,6 +55,7 @@ def process_batch(p, input_dir, output_dir, args):
                 filename = f"{left}-{n}{right}"
 
             if not save_normally:
+                os.makedirs(output_dir, exist_ok=True)
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-- 
cgit v1.2.1


From 56660f0946600da881a742afa8569fe4559cb5c6 Mon Sep 17 00:00:00 2001
From: batvbs <60730393+batvbs@users.noreply.github.com>
Date: Tue, 1 Nov 2022 11:17:30 +0800
Subject: Update zh_CN.json

---
 localizations/zh_CN.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index b291f8a8..2d70dab1 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -500,9 +500,9 @@
     "Path to directory with input images": "带有输入图像的路径",
     "Path to directory where to write outputs": "进行输出的路径",
     "Input images directory": "输入图像目录",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像的文件名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像的文件名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; 默认请留空",
     "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果启用此选项，水印将不会添加到生成出来的图像中。警告：如果你不添加水印，你的行为可能是不符合专业操守的",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像和宫格图的子目录: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 默认请留空",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像和宫格图的子目录: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; 默认请留空",
     "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络修复低质量面部",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。留空以保持文件名文本不变",
     "This string will be used to join split words into a single line if the option above is enabled.": "如果启用了上述选项，则此处的字符会用于将拆分的单词接合为同一行",
-- 
cgit v1.2.1


From 24a76340ba14c63b8523c254769c781d4b62c347 Mon Sep 17 00:00:00 2001
From: batvbs <60730393+batvbs@users.noreply.github.com>
Date: Tue, 1 Nov 2022 14:30:59 +0800
Subject: Update zh_CN.json

---
 localizations/zh_CN.json | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 2d70dab1..c23e123b 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -481,10 +481,10 @@
     "Open images output directory": "打开图像输出目录",
     "How much to blur the mask before processing, in pixels.": "处理前要对蒙版进行多强的模糊，以像素为单位",
     "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 处理蒙版区域之前要在蒙版区域内放置什么",
-    "fill it with colors of the image": "用图像的颜色(高斯模糊)填充它",
+    "fill it with colors of the image": "用图像的颜色(高强度模糊)填充它",
     "keep whatever was there originally": "保留原来的图像，不进行预处理",
-    "fill it with latent space noise": "用潜空间的噪声(图像维度表现为 随机彩色噪点)填充它",
-    "fill it with latent space zeroes": "用潜空间的零(图像维度表现为 无色纯灰)填充它",
+    "fill it with latent space noise": "用潜空间的噪声填充它",
+    "fill it with latent space zeroes": "用潜空间的零填充它",
     "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做局部重绘，缩小后粘贴到原始图像中",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
-- 
cgit v1.2.1


From bef1d0e836a337b1673a903be5d872b2d2869514 Mon Sep 17 00:00:00 2001
From: batvbs <60730393+batvbs@users.noreply.github.com>
Date: Tue, 1 Nov 2022 14:32:12 +0800
Subject: Update localizations/zh_CN.json

Co-authored-by: dtlnor <dtlnor@hotmail.com>
---
 localizations/zh_CN.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index c23e123b..a8b99591 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -544,7 +544,7 @@
     "Number of grids in each row": "每行显示多少格",
     "Start drawing": "开始绘制",
     "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
-    "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "用垂直的管道字符 | 将提示语分成若干部分，脚本将为它们的每一个组合创建一幅图片（除了第一部分，所有的组合都会出现）",
+    "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "用竖线分隔符(|)将提示词分成若干部分，脚本将为它们的每一个组合创建一幅图片（除了被分割的第一部分，所有的组合都会包含这部分）",
     "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "选择哪些Real-ESRGAN模型显示在用户界面。(需要重新启动)",
     "Face restoration model": "面部修复模型",
     "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
-- 
cgit v1.2.1


From 19b59d320c144718f2cb65b77c111f0ec82c8ccd Mon Sep 17 00:00:00 2001
From: batvbs <60730393+batvbs@users.noreply.github.com>
Date: Tue, 1 Nov 2022 14:33:20 +0800
Subject: Update localizations/zh_CN.json

Co-authored-by: dtlnor <dtlnor@hotmail.com>
---
 localizations/zh_CN.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index a8b99591..d3b572e6 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -127,7 +127,7 @@
     "Batch img2img": "批量图生图",
     "Image for img2img": "图生图的图像",
     "Drop Image Here": "拖拽图像到此",
-    "Image for inpainting with mask": "用于提取蒙版的图像",
+    "Image for inpainting with mask": "用于局部重绘并手动画蒙版的图像",
     "Mask": "蒙版",
     "Mask blur": "蒙版模糊",
     "Mask mode": "蒙版模式",
-- 
cgit v1.2.1


From 5b0f624bdc1335313258f59a37607e699e800c22 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 1 Nov 2022 09:59:00 +0300
Subject: Added Available tab to extensions UI.

---
 javascript/extensions.js |  13 +++++-
 modules/ui_extensions.py | 112 +++++++++++++++++++++++++++++++++++++++++++----
 style.css                |   9 ++--
 3 files changed, 122 insertions(+), 12 deletions(-)

diff --git a/javascript/extensions.js b/javascript/extensions.js
index 86f5336d..59179ca6 100644
--- a/javascript/extensions.js
+++ b/javascript/extensions.js
@@ -21,4 +21,15 @@ function extensions_check(){
     })
 
     return []
-}
\ No newline at end of file
+}
+
+function install_extension_from_index(button, url){
+    button.disabled = "disabled"
+    button.value = "Installing..."
+
+    textarea = gradioApp().querySelector('#extension_to_install textarea')
+    textarea.value = url
+	textarea.dispatchEvent(new Event("input", { bubbles: true }))
+
+    gradioApp().querySelector('#install_extension_button').click()
+}
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index e74b7d68..ab807722 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -13,6 +13,9 @@ import html
 from modules import extensions, shared, paths
 
 
+available_extensions = {"extensions": []}
+
+
 def check_access():
     assert not shared.cmd_opts.disable_extension_access, "extension access disabed because of commandline flags"
 
@@ -96,6 +99,14 @@ def extension_table():
     return code
 
 
+def normalize_git_url(url):
+    if url is None:
+        return ""
+
+    url = url.replace(".git", "")
+    return url
+
+
 def install_extension_from_url(dirname, url):
     check_access()
 
@@ -103,14 +114,15 @@ def install_extension_from_url(dirname, url):
 
     if dirname is None or dirname == "":
         *parts, last_part = url.split('/')
-        last_part = last_part.replace(".git", "")
+        last_part = normalize_git_url(last_part)
 
         dirname = last_part
 
     target_dir = os.path.join(extensions.extensions_dir, dirname)
     assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
 
-    assert len([x for x in extensions.extensions if x.remote == url]) == 0, 'Extension with this URL is already installed'
+    normalized_url = normalize_git_url(url)
+    assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'
 
     tmpdir = os.path.join(paths.script_path, "tmp", dirname)
 
@@ -128,18 +140,80 @@ def install_extension_from_url(dirname, url):
         shutil.rmtree(tmpdir, True)
 
 
+def install_extension_from_index(url):
+    ext_table, message = install_extension_from_url(None, url)
+
+    return refresh_available_extensions_from_data(), ext_table, message
+
+
+def refresh_available_extensions(url):
+    global available_extensions
+
+    import urllib.request
+    with urllib.request.urlopen(url) as response:
+        text = response.read()
+
+    available_extensions = json.loads(text)
+
+    return url, refresh_available_extensions_from_data(), ''
+
+
+def refresh_available_extensions_from_data():
+    extlist = available_extensions["extensions"]
+    installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
+
+    code = f"""<!-- {time.time()} -->
+    <table id="available_extensions">
+        <thead>
+            <tr>
+                <th>Extension</th>
+                <th>Description</th>
+                <th>Action</th>
+            </tr>
+        </thead>
+        <tbody>
+    """
+
+    for ext in extlist:
+        name = ext.get("name", "noname")
+        url = ext.get("url", None)
+        description = ext.get("description", "")
+
+        if url is None:
+            continue
+
+        existing = installed_extension_urls.get(normalize_git_url(url), None)
+
+        install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
+
+        code += f"""
+            <tr>
+                <td><a href="{html.escape(url)}">{html.escape(name)}</a></td>
+                <td>{html.escape(description)}</td>
+                <td>{install_code}</td>
+            </tr>
+    """
+
+    code += """
+        </tbody>
+    </table>
+    """
+
+    return code
+
+
 def create_ui():
     import modules.ui
 
     with gr.Blocks(analytics_enabled=False) as ui:
         with gr.Tabs(elem_id="tabs_extensions") as tabs:
             with gr.TabItem("Installed"):
-                extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False)
-                extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False)
 
                 with gr.Row():
                     apply = gr.Button(value="Apply and restart UI", variant="primary")
                     check = gr.Button(value="Check for updates")
+                    extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
+                    extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
 
                 extensions_table = gr.HTML(lambda: extension_table())
 
@@ -157,16 +231,38 @@ def create_ui():
                     outputs=[extensions_table],
                 )
 
+            with gr.TabItem("Available"):
+                with gr.Row():
+                    refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
+                    available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/wiki/AUTOMATIC1111/stable-diffusion-webui/Extensions-index.md", label="Extension index URL").style(container=False)
+                    extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
+                    install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
+
+                install_result = gr.HTML()
+                available_extensions_table = gr.HTML()
+
+                refresh_available_extensions_button.click(
+                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update()]),
+                    inputs=[available_extensions_index],
+                    outputs=[available_extensions_index, available_extensions_table, install_result],
+                )
+
+                install_extension_button.click(
+                    fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
+                    inputs=[extension_to_install],
+                    outputs=[available_extensions_table, extensions_table, install_result],
+                )
+
             with gr.TabItem("Install from URL"):
                 install_url = gr.Text(label="URL for extension's git repository")
                 install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
-                intall_button = gr.Button(value="Install", variant="primary")
-                intall_result = gr.HTML(elem_id="extension_install_result")
+                install_button = gr.Button(value="Install", variant="primary")
+                install_result = gr.HTML(elem_id="extension_install_result")
 
-                intall_button.click(
+                install_button.click(
                     fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
                     inputs=[install_dirname, install_url],
-                    outputs=[extensions_table, intall_result],
+                    outputs=[extensions_table, install_result],
                 )
 
     return ui
diff --git a/style.css b/style.css
index 859c3933..dfef0dc5 100644
--- a/style.css
+++ b/style.css
@@ -532,16 +532,16 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
 
 /* Extensions */
 
-#extensions{
+#tab_extensions table{
     border-collapse: collapse;
 }
 
-#extensions td, #extensions th{
+#tab_extensions table td, #tab_extensions table th{
     border: 1px solid #ccc;
     padding: 0.25em 0.5em;
 }
 
-#extensions input[type="checkbox"]{
+#tab_extensions table input[type="checkbox"]{
     margin-right: 0.5em;
 }
 
@@ -549,6 +549,9 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
     max-width: 16em;
 }
 
+#tab_extensions input[disabled="disabled"]{
+    opacity: 0.5;
+}
 
 /* The following handles localization for right-to-left (RTL) languages like Arabic.
 The rtl media type will only be activated by the logic in javascript/localization.js.
-- 
cgit v1.2.1


From af758e97fa2c4c853042f121af4e974be01e6696 Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Tue, 1 Nov 2022 04:01:49 -0300
Subject: Unload sd_model before loading the other

---
 modules/lowvram.py    | 21 +++++++++++++--------
 modules/processing.py |  3 +++
 modules/sd_hijack.py  |  4 ++++
 modules/sd_models.py  | 14 +++++++++++++-
 webui.py              |  2 +-
 5 files changed, 34 insertions(+), 10 deletions(-)

diff --git a/modules/lowvram.py b/modules/lowvram.py
index f327c3df..a4652cb1 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -38,13 +38,18 @@ def setup_for_low_vram(sd_model, use_medvram):
     # see below for register_forward_pre_hook;
     # first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
     # useless here, and we just replace those methods
-    def first_stage_model_encode_wrap(self, encoder, x):
-        send_me_to_gpu(self, None)
-        return encoder(x)
 
-    def first_stage_model_decode_wrap(self, decoder, z):
-        send_me_to_gpu(self, None)
-        return decoder(z)
+    first_stage_model = sd_model.first_stage_model
+    first_stage_model_encode = sd_model.first_stage_model.encode
+    first_stage_model_decode = sd_model.first_stage_model.decode
+
+    def first_stage_model_encode_wrap(x):
+        send_me_to_gpu(first_stage_model, None)
+        return first_stage_model_encode(x)
+
+    def first_stage_model_decode_wrap(z):
+        send_me_to_gpu(first_stage_model, None)
+        return first_stage_model_decode(z)
 
     # remove three big modules, cond, first_stage, and unet from the model and then
     # send the model to GPU. Then put modules back. the modules will be in CPU.
@@ -56,8 +61,8 @@ def setup_for_low_vram(sd_model, use_medvram):
     # register hooks for those the first two models
     sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
     sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
-    sd_model.first_stage_model.encode = lambda x, en=sd_model.first_stage_model.encode: first_stage_model_encode_wrap(sd_model.first_stage_model, en, x)
-    sd_model.first_stage_model.decode = lambda z, de=sd_model.first_stage_model.decode: first_stage_model_decode_wrap(sd_model.first_stage_model, de, z)
+    sd_model.first_stage_model.encode = first_stage_model_encode_wrap
+    sd_model.first_stage_model.decode = first_stage_model_decode_wrap
     parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
 
     if use_medvram:
diff --git a/modules/processing.py b/modules/processing.py
index b1df4918..57d3a523 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -597,6 +597,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.scripts is not None:
         p.scripts.postprocess(p, res)
 
+    p.sd_model = None
+    p.sampler = None
+
     return res
 
 
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 0f10828e..bc49d235 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -94,6 +94,10 @@ class StableDiffusionModelHijack:
         if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
             model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
 
+        self.layers = None
+        self.circular_enabled = False
+        self.clip = None
+
     def apply_circular(self, enable):
         if self.circular_enabled == enable:
             return
diff --git a/modules/sd_models.py b/modules/sd_models.py
index f86dc3ed..90007da3 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -1,6 +1,7 @@
 import collections
 import os.path
 import sys
+import gc
 from collections import namedtuple
 import torch
 import re
@@ -220,6 +221,12 @@ def load_model(checkpoint_info=None):
     if checkpoint_info.config != shared.cmd_opts.config:
         print(f"Loading config from: {checkpoint_info.config}")
 
+    if shared.sd_model:
+        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
+        shared.sd_model = None
+        gc.collect()
+        devices.torch_gc()
+
     sd_config = OmegaConf.load(checkpoint_info.config)
     
     if should_hijack_inpainting(checkpoint_info):
@@ -233,6 +240,7 @@ def load_model(checkpoint_info=None):
         checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
 
     do_inpainting_hijack()
+
     sd_model = instantiate_from_config(sd_config.model)
     load_model_weights(sd_model, checkpoint_info)
 
@@ -252,14 +260,18 @@ def load_model(checkpoint_info=None):
     return sd_model
 
 
-def reload_model_weights(sd_model, info=None):
+def reload_model_weights(sd_model=None, info=None):
     from modules import lowvram, devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
 
+    if not sd_model:
+        sd_model = shared.sd_model
+
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
+        del sd_model
         checkpoints_loaded.clear()
         load_model(checkpoint_info)
         return shared.sd_model
diff --git a/webui.py b/webui.py
index 6ff95dc4..9c393e55 100644
--- a/webui.py
+++ b/webui.py
@@ -77,7 +77,7 @@ def initialize():
     modules.scripts.load_scripts()
 
     modules.sd_models.load_model()
-    shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
+    shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
 
-- 
cgit v1.2.1


From 5029d159b3e21b643afd7ea5ba9596287ad32e36 Mon Sep 17 00:00:00 2001
From: Eugenio Buffo <58123757+EugenioBuffo@users.noreply.github.com>
Date: Tue, 1 Nov 2022 08:36:03 +0100
Subject: Deleted extra it_IT localization

Deleted it_IT localization file outside localization folder as it was not intentionally committed
---
 it_IT.json | 1143 ------------------------------------------------------------
 1 file changed, 1143 deletions(-)
 delete mode 100644 it_IT.json

diff --git a/it_IT.json b/it_IT.json
deleted file mode 100644
index 7e52bf33..00000000
--- a/it_IT.json
+++ /dev/null
@@ -1,1143 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "Caricamento...",
-    "view": "mostra ",
-    "api": "API",
-    "•": " • ",
-    "built with gradio": " Sviluppato con Gradio",
-    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
-    "txt2img": "txt2img",
-    "img2img": "img2img",
-    "Extras": "Extra",
-    "PNG Info": "Info PNG",
-    "Checkpoint Merger": "Miscelatore di Checkpoint",
-    "Train": "Addestramento",
-    "Create aesthetic embedding": "Crea incorporamento estetico",
-    "Dataset Tag Editor": "Dataset Tag Editor",
-    "Deforum": "Deforum",
-    "Artists To Study": "Artisti per studiare",
-    "Image Browser": "Galleria immagini",
-    "Inspiration": "Ispirazione",
-    "Settings": "Impostazioni",
-    "Prompt": "Prompt",
-    "Negative prompt": "Prompt negativo",
-    "Run": "Esegui",
-    "Skip": "Salta",
-    "Interrupt": "Interrompi",
-    "Generate": "Genera",
-    "Style 1": "Stile 1",
-    "Style 2": "Stile 2",
-    "Label": "Etichetta",
-    "File": "File",
-    "Drop File Here": "Trascina il file qui",
-    "-": "-",
-    "or": "o",
-    "Click to Upload": "Clicca per caricare",
-    "Image": "Immagine",
-    "Check progress": "Controlla i progressi",
-    "Check progress (first)": "Controlla i progressi (primo)",
-    "Sampling Steps": "Passi di campionamento",
-    "Sampling method": "Metodo di campionamento",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "Larghezza",
-    "Height": "Altezza",
-    "Restore faces": "Restaura i volti",
-    "Tiling": "Piastrellatura",
-    "Highres. fix": "Correzione alta risoluzione",
-    "Firstpass width": "Larghezza del primo passaggio",
-    "Firstpass height": "Altezza del primo passaggio",
-    "Denoising strength": "Forza del Denoising",
-    "Batch count": "Lotti di immagini",
-    "Batch size": "Immagini per lotto",
-    "CFG Scale": "Scala CFG",
-    "Seed": "Seme",
-    "Extra": "Extra",
-    "Variation seed": "Seme della variazione",
-    "Variation strength": "Forza della variazione",
-    "Resize seed from width": "Ridimensiona il seme dalla larghezza",
-    "Resize seed from height": "Ridimensiona il seme dall'altezza",
-    "Open for Clip Aesthetic!": "Apri per Estetica CLIP!",
-    "▼": "▼",
-    "Aesthetic weight": "Estetica - Peso",
-    "Aesthetic steps": "Estetica - Passi",
-    "Aesthetic learning rate": "Estetica - Tasso di apprendimento",
-    "Slerp interpolation": "Interpolazione Slerp",
-    "Aesthetic imgs embedding": "Estetica - Incorporamento di immagini",
-    "None": "Niente",
-    "Aesthetic text for imgs": "Estetica - Testo per le immagini",
-    "Slerp angle": "Angolo Slerp",
-    "Is negative text": "È un testo negativo",
-    "Script": "Script",
-    "Random grid": "Generaz. casuale (griglia)",
-    "Random": "Generaz. casuale (no griglia)",
-    "StylePile": "StylePile",
-    "Advanced prompt matrix": "Matrice di prompt avanzata",
-    "Alternate Sampler Noise Schedules": "Metodi alternativi di campionamento del rumore",
-    "Animator v6": "Animator v6",
-    "Asymmetric tiling": "Piastrellatura asimmetrica",
-    "Custom code": "Codice personalizzato",
-    "Dynamic Prompting v0.2": "Prompt dinamici v0.2",
-    "Embedding to Shareable PNG": "Incorporamento convertito in PNG condivisibile",
-    "Force symmetry": "Forza la simmetria",
-    "Prompts interpolation": "Interpola Prompt",
-    "Prompt matrix": "Matrice dei prompt",
-    "Prompt morph": "Metamorfosi del prompt",
-    "Prompts from file or textbox": "Prompt da file o da casella di testo",
-    "To Infinity and Beyond": "Verso l'infinito e oltre",
-    "Seed travel": "Interpolazione semi",
-    "Shift attention": "Sposta l'attenzione",
-    "Text to Vector Graphics": "Da testo a grafica vettoriale",
-    "X/Y plot": "Grafico X/Y",
-    "X/Y/Z plot": "Grafico X/Y/Z",
-    "Create inspiration images": "Crea immagini di ispirazione",
-    "step1 min/max": "Passi min(o max)",
-    "step2 min/max": "Passi max (o min)",
-    "step cnt": "Q.tà di Passi",
-    "cfg1 min/max": "CFG min (o max)",
-    "cfg2 min/max": "CFG max (o min)",
-    "cfg cnt": "Q.tà di CFG",
-    "Draw legend": "Disegna legenda",
-    "Include Separate Images": "Includi immagini separate",
-    "Keep -1 for seeds": "Mantieni sempre il seme a -1",
-    "x/y change": "Inverti ordine assi X/Y (Passi/CFG)",
-    "Loops": "Cicli",
-    "Focus on:": "Focus su:",
-    "No focus": "Nessun Focus",
-    "Portraits (tick Restore faces above for best results)": "Ritratti (selezionare 'Restaura volti' in alto per ottenere i migliori risultati)",
-    "Feminine and extra attractive (tick Restore faces above for best results)": "Femminile ed estremamente attraente (selezionare 'Restaura volti' per ottenere i migliori risultati)",
-    "Masculine and extra attractive (tick Restore faces above for best results)": "Maschile ed estremamente attraente (selezionare 'Restaura volti'  per ottenere i migliori risultati)",
-    "Monsters": "Mostri",
-    "Robots": "Robot",
-    "Retrofuturistic": "Retrofuturistico",
-    "Propaganda": "Propaganda",
-    "Landscapes": "Paesaggi",
-    "Hints": "Suggerimenti",
-    "Image type": "Tipo di immagine",
-    "Not set": "Non impostato",
-    "Photography": "Fotografia",
-    "Digital art": "Arte digitale",
-    "3D Rendering": "3D Rendering",
-    "Painting": "Dipinto",
-    "Sketch": "Schizzo",
-    "Classic Comics": "Fumetti classici",
-    "Modern Comics": "Fumetti moderni",
-    "Manga": "Manga",
-    "Vector art": "Arte vettoriale",
-    "Visual style": "Stile visivo",
-    "Realism": "Realismo",
-    "Photorealism": "Fotorealismo",
-    "Hyperrealism": "Iperrealismo",
-    "Surrealism": "Surrealismo",
-    "Modern Art": "Arte moderna",
-    "Fauvism": "Fauvismo",
-    "Futurism": "Futurismo",
-    "Painterly": "Pittorico",
-    "Pointillisme": "Puntinismo",
-    "Abstract": "Astratto",
-    "Pop Art": "Pop Art",
-    "Impressionist": "Impressionista",
-    "Cubism": "Cubismo",
-    "Linocut": "Linoleografia",
-    "Fantasy": "Fantasia",
-    "Colors": "Colori",
-    "Chaotic": "Caotico",
-    "Primary colors": "Colori primari",
-    "Colorful": "Colorato",
-    "Vivid": "Vivido",
-    "Muted colors": "Colori tenui",
-    "Low contrast": "Basso contrasto",
-    "Desaturated": "Desaturato",
-    "Grayscale": "Scala di grigi",
-    "Black and white": "Bianco e nero",
-    "Infrared": "Infrarosso",
-    "Complementary": "Colori complementari",
-    "Non-complementary": "Colori non complementari",
-    "View": "Visuale",
-    "Tilt shift": "Tilt shift",
-    "Wide-angle": "Angolo ampio",
-    "Portrait": "Ritratto",
-    "Macro": "Macro",
-    "Microscopic": "Microscopico",
-    "Isometric": "Isometrico",
-    "Panorama": "Panorama",
-    "Aerial photograph": "Fotografia aerea",
-    "Artist focus (not quite finished, not sure it helps)": "Focus sull'artista (non del tutto finito, non è sicuro che aiuti)",
-    "B/W Photograpy": "Fotografia B/N",
-    "Portrait photo": "Foto ritratto",
-    "Usage: a <corgi|cat> wearing <goggles|a hat>": "Utilizzo: a <corgi|cat> wearing <goggles|a hat>",
-    "Noise Scheduler": "Pianificazione del rumore",
-    "Default": "Predefinito",
-    "Karras": "Karras",
-    "Exponential": "Esponenziale",
-    "Variance Preserving": "Conservazione della Varianza",
-    "Sigma min": "Sigma min",
-    "Sigma max": "Sigma max",
-    "Sigma rho (Karras only)": "Sigma rho (Solo Karras)",
-    "Beta distribution (VP only)": "Distribuzione Beta (Solo CV)",
-    "Beta min (VP only)": "Beta min (Solo CV)",
-    "Epsilon (VP only)": "Epsilon (Solo CV)",
-    "Running in txt2img mode:": "Running in txt2img mode:",
-    "Render these video formats:": "Renderizza in questi formati:",
-    "GIF": "GIF",
-    "MP4": "MP4",
-    "WEBM": "WEBM",
-    "Animation Parameters": "Parametri animazione",
-    "Total Animation Length (s)": "Durata totale dell'animazione (s)",
-    "Framerate": "Frequenza dei fotogrammi",
-    "Smoothing_Frames": "Smoothing_Frames",
-    "Add_Noise": "Aggiungi rumore",
-    "Noise Strength": "Intensità del rumore",
-    "Initial Parameters": "Parametri iniziali",
-    "Denoising Strength (overrides img2img slider)": "Intensità di riduzione del rumore (sovrascrive il cursore img2img)",
-    "Seed_March": "Seed_March",
-    "Zoom Factor (scale/s)": "Fattore di ingrandimento (scala/s)",
-    "X Pixel Shift (pixels/s)": "Sposta i Pixel sull'asse X (pixel/s)",
-    "Y Pixel Shift (pixels/s)": "Sposta i Pixel sull'asse Y (pixel/s)",
-    "Rotation (deg/s)": "Rotazione (gradi/s)",
-    "Prompt Template, applied to each keyframe below": "Modello di prompt, applicato a ciascun fotogramma chiave qui di seguito",
-    "Positive Prompts": "Prompt positivi",
-    "Negative Prompts": "Prompt negativi",
-    "Props, Stamps": "Props, Stamps",
-    "Poper_Folder:": "Cartella Poper:",
-    "Supported Keyframes:": "Fotogrammi chiave supportati:",
-    "time_s | source | video, images, img2img | path": "time_s | source | video, images, img2img | path",
-    "time_s | prompt | positive_prompts | negative_prompts": "time_s | prompt | positive_prompts | negative_prompts",
-    "time_s | template | positive_prompts | negative_prompts": "time_s | template | positive_prompts | negative_prompts",
-    "time_s | transform | zoom | x_shift | y_shift | rotation": "time_s | transform | zoom | x_shift | y_shift | rotation",
-    "time_s | seed | new_seed_int": "time_s | seed | new_seed_int",
-    "time_s | noise | added_noise_strength": "time_s | noise | added_noise_strength",
-    "time_s | denoise | denoise_value": "time_s | denoise | denoise_value",
-    "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name": "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name",
-    "time_s | clear_text | textblock_name": "time_s | clear_text | textblock_name",
-    "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation": "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation",
-    "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation": "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation",
-    "time_s | clear_stamp | stamp_name": "time_s | clear_stamp | stamp_name",
-    "time_s | col_set": "time_s | col_set",
-    "time_s | col_clear": "time_s | col_clear",
-    "time_s | model | model": "time_s | model | model",
-    "img2img_mode": "img2img_mode",
-    "Keyframes:": "Fotogrammi chiave:",
-    "Tile X": "Piastrella asse X",
-    "Tile Y": "Piastrella asse Y",
-    "Python code": "Codice Python",
-    "Combinatorial generation": "Generazione combinatoria",
-    "Combinations": "Combinazioni",
-    "Choose a number of terms from a list, in this case we choose two artists": "Scegli un numero di termini da un elenco, in questo caso scegliamo due artisti",
-    "{2$$artist1|artist2|artist3}": "{2$$artist1|artist2|artist3}",
-    "If $$ is not provided, then 1$$ is assumed.\n\n            A range can be provided:": "Se $$ non viene fornito, si presume 1$$.\n\n            È possibile fornire un intervallo di valori:",
-    "{1-3$$artist1|artist2|artist3}": "{1-3$$artist1|artist2|artist3}",
-    "In this case, a random number of artists between 1 and 3 is chosen.": "In questo caso viene scelto un numero casuale di artisti compreso tra 1 e 3.",
-    "Wildcards": "Termini jolly",
-    "If the groups wont drop down click": "Se i gruppi non vengono visualizzati, clicca",
-    "here": "qui",
-    "to fix the issue.": "per correggere il problema.",
-    "WILDCARD_DIR: scripts/wildcards": "WILDCARD_DIR: scripts/wildcards",
-    "You can add more wildcards by creating a text file with one term per line and name is mywildcards.txt. Place it in scripts/wildcards.": "Puoi aggiungere termini jolly creando un file di testo con un termine per riga e nominandolo, per esempio, mywildcards.txt. Inseriscilo in scripts/wildcards.",
-    "__<folder>/mywildcards__": "__<folder>/mywildcards__",
-    "will then become available.": "diverrà quindi disponibile.",
-    "Source embedding to convert": "Incorporamento sorgente da convertire",
-    "Embedding token": "Token Incorporamento",
-    "Output directory": "Cartella di output",
-    "Horizontal symmetry": "Simmetria orizzontale",
-    "Vertical symmetry": "Simmetria verticale",
-    "Alt. symmetry method (blending)": "Metodo di simmetria alternativo (miscelazione)",
-    "Apply every n steps": "Applica ogni n passi",
-    "Skip last n steps": "Salta gli ultimi n passi",
-    "Interpolation prompt": "Prompt di interpolazione",
-    "Number of images": "Numero di immagini",
-    "Make a gif": "Crea GIF",
-    "Duration of images (ms)": "Durata delle immagini (ms)",
-    "Put variable parts at start of prompt": "Inserisce le parti variabili all'inizio del prompt",
-    "Keyframe Format:": "Formato dei fotogrammi chiave:",
-    "Seed | Prompt or just Prompt": "Seme | Prompt o semplicemente Prompt",
-    "Prompt list": "Elenco dei prompt",
-    "Number of images between keyframes": "Numero di immagini tra fotogrammi chiave",
-    "Save results as video": "Salva i risultati come video",
-    "Frames per second": "Fotogrammi al secondo",
-    "Iterate seed every line": "Iterare il seme per ogni riga",
-    "List of prompt inputs": "Elenco di prompt di input",
-    "Upload prompt inputs": "Carica un file contenente i prompt di input",
-    "n": "Esegui n volte",
-    "Destination seed(s) (Comma separated)": "Seme/i di destinazione (separati da virgola)",
-    "Only use Random seeds (Unless comparing paths)": "Usa solo semi casuali (a meno che non si confrontino i percorsi)",
-    "Number of random seed(s)": "Numero di semi casuali",
-    "Compare paths (Separate travels from 1st seed to each destination)": "Confronta percorsi (transizioni separate dal primo seme a ciascuna destinazione)",
-    "Steps": "Passi",
-    "Loop back to initial seed": "Ritorna al seme iniziale",
-    "Bump seed (If > 0 do a Compare Paths but only one image. No video)": "Modula seme (se > 0 mescola il seme iniziale con quelli di destinazione ma solo un'immagine. Nessun video)",
-    "Show generated images in ui": "Mostra le immagini generate nell'interfaccia utente",
-    "\"Hug the middle\" during interpolation": "\"Hug the middle\" durante l'interpolazione. Rende l'interpolazione un po' più veloce all'inizio e alla fine. A volte può produrre video più fluidi, il più delle volte no.",
-    "Allow the default Euler a Sampling method. (Does not produce good results)": "Consenti Euler_a come metodo di campionamento predefinito. (Non produce buoni risultati)",
-    "Illustration": "Illustrazione",
-    "Logo": "Logo",
-    "Drawing": "Disegno",
-    "Artistic": "Artistico",
-    "Tattoo": "Tatuaggio",
-    "Gothic": "Gotico",
-    "Anime": "Anime",
-    "Cartoon": "Cartoon",
-    "Sticker": "Etichetta",
-    "Gold Pendant": "Ciondolo in oro",
-    "None - prompt only": "Nessuno - solo prompt",
-    "Enable Vectorizing": "Abilita vettorizzazione",
-    "Output format": "Formato di output",
-    "svg": "svg",
-    "pdf": "pdf",
-    "White is Opaque": "Il bianco è opaco",
-    "Cut white margin from input": "Taglia il margine bianco dall'input",
-    "Keep temp images": "Conserva le immagini temporanee",
-    "Threshold": "Soglia",
-    "Transparent PNG": "PNG trasparente",
-    "Noise Tolerance": "Tolleranza al rumore",
-    "Quantize": "Quantizzare",
-    "X type": "Parametro asse X",
-    "Nothing": "Niente",
-    "Var. seed": "Seme della variazione",
-    "Var. strength": "Forza della variazione",
-    "Prompt S/R": "Cerca e Sostituisci nel Prompt",
-    "Prompt order": "In ordine di prompt",
-    "Sampler": "Campionatore",
-    "Checkpoint name": "Nome del checkpoint",
-    "Hypernetwork": "Iperrete",
-    "Hypernet str.": "Forza della Iperrete",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma noise": "Sigma noise",
-    "Eta": "ETA",
-    "Clip skip": "Salta CLIP",
-    "Denoising": "Riduzione del rumore",
-    "Cond. Image Mask Weight": "Cond. Image Mask Weight",
-    "X values": "Valori per X",
-    "Y type": "Parametro asse Y",
-    "Y values": "Valori per Y",
-    "Z type": "Parametro asse Z",
-    "Z values": "Valori per Z",
-    "Artist or styles name list. '.txt' files with one name per line": "Elenco nomi di artisti o stili. File '.txt' con un nome per riga",
-    "Prompt words before artist or style name": "Parole chiave prima del nome dell'artista o dello stile",
-    "Prompt words after artist or style name": "Parole chiave dopo il nome dell'artista o dello stile",
-    "Negative Prompt": "Prompt negativo",
-    "Save": "Salva",
-    "Send to img2img": "Invia a img2img",
-    "Send to inpaint": "Invia a inpaint",
-    "Send to extras": "Invia a 'Extra'",
-    "Make Zip when Save?": "Crea un file ZIP quando si usa 'Salva'",
-    "Textbox": "Casella di testo",
-    "Interrogate\nCLIP": "Interroga\nCLIP",
-    "Interrogate\nDeepBooru": "Interroga\nDeepBooru",
-    "Inpaint": "Inpaint",
-    "Batch img2img": "img2img in lotti",
-    "Image for img2img": "Immagine per img2img",
-    "Drop Image Here": "Trascina l'immagine qui",
-    "Image for inpainting with mask": "Immagine per inpainting con maschera",
-    "Mask": "Maschera",
-    "Mask blur": "Sfocatura maschera",
-    "Mask mode": "Modalità maschera",
-    "Draw mask": "Disegna maschera",
-    "Upload mask": "Carica maschera",
-    "Masking mode": "Modalità mascheratura",
-    "Inpaint masked": "Inpaint mascherato",
-    "Inpaint not masked": "Inpaint non mascherato",
-    "Masked content": "Contenuto mascherato",
-    "fill": "riempi",
-    "original": "originale",
-    "latent noise": "rumore nello spazio latente",
-    "latent nothing": "nulla nello spazio latente",
-    "Inpaint at full resolution": "Inpaint alla massima risoluzione",
-    "Inpaint at full resolution padding, pixels": "Inpaint con riempimento a piena risoluzione, pixel",
-    "Process images in a directory on the same machine where the server is running.": "Elabora le immagini in una cartella sulla stessa macchina su cui è in esecuzione il server.",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa una cartella di output vuota per salvare normalmente le immagini invece di scrivere nella cartella di output.",
-    "Input directory": "Cartella di Input",
-    "Resize mode": "Modalità di ridimensionamento",
-    "Just resize": "Ridimensiona solamente",
-    "Crop and resize": "Ritaglia e ridimensiona",
-    "Resize and fill": "Ridimensiona e riempie",
-    "Advanced loopback": "Advanced loopback", 
-    "External Image Masking": "Immagine esterna per la mascheratura",
-    "img2img alternative test": "Test alternativo per img2img",
-    "img2tiles": "img2tiles",
-    "Interpolate": "Interpolare",
-    "Loopback": "Rielaborazione ricorsiva",
-    "Loopback and Superimpose": "Rielabora ricorsivamente e sovraimponi",
-    "Alpha Canvas": "Alpha Canvas",
-    "Outpainting mk2": "Outpainting mk2",
-    "Poor man's outpainting": "Poor man's outpainting",
-    "SD upscale": "Ampliamento SD",
-    "txt2mask v0.1.1": "txt2mask v0.1.1",
-    "[C] Video to video": "[C] Video to video",
-    "Videos": "Filmati",
-    "Deforum-webui (use tab extension instead!)": "Deforum-webui (usa piuttosto la scheda Deforum delle estensioni!)",
-    "Use first image colors (custom color correction)": "Usa i colori della prima immagine (correzione del colore personalizzata)",
-    "Denoising strength change factor (overridden if proportional used)": "Fattore di variazione dell'intensità di riduzione del rumore (sovrascritto se si usa proporzionale)",
-    "Zoom level": "Livello di Zoom",
-    "Direction X": "Direzione X",
-    "Direction Y": "Direzione Y",
-    "Denoising strength start": "Intensità di riduzione del rumore - Inizio",
-    "Denoising strength end": "Intensità di riduzione del rumore - Fine",
-    "Denoising strength proportional change starting value": "Intensità di riduzione del rumore - Valore iniziale della variazione proporzionale",
-    "Denoising strength proportional change ending value (0.1 = disabled)": "Intensità di riduzione del rumore - Valore finale della variazione proporzionale (0.1 = disabilitato)",
-    "Saturation enhancement per image": "Miglioramento della saturazione per ciascuna immagine",
-    "Use sine denoising strength variation": "Utilizzare la variazione sinusoidale dell'intensità di riduzione del rumore",
-    "Phase difference": "Differenza di Fase",
-    "Denoising strength exponentiation": "Esponenziazione dell'intensità di riduzione del rumore",
-    "Use sine zoom variation": "Usa la variazione sinusoidale dello zoom",
-    "Zoom exponentiation": "Esponeniazione dello Zoom",
-    "Use multiple prompts": "Usa prompt multipli",
-    "Same seed per prompt": "Stesso seme per ogni prompt",
-    "Same seed for everything": "Stesso seme per tutto",
-    "Original init image for everything": "Immagine originale di inizializzazione per tutto",
-    "Multiple prompts : 1 line positive, 1 line negative, leave a blank line for no negative": "Prompt multipli: 1 riga positivo, 1 riga negativo, lasciare una riga vuota per nessun negativo",
-    "Running in img2img mode:": "Esecuzione in modalità img2img:",
-    "Masking preview size": "Dimensione dell'anteprima della mascheratura",
-    "Draw new mask on every run": "Disegna una nuova maschera ad ogni esecuzione",
-    "Process non-contigious masks separately": "Elaborare le maschere non contigue separatamente",
-    "should be 2 or lower.": "dovrebbe essere 2 o inferiore.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Sovrascrivi il 'Metodo di campionamento' con Eulero? (questo metodo è stato creato per questo)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Sovrascrivi `prompt` con lo stesso valore del `prompt originale`? (e `prompt negativo`)",
-    "Original prompt": "Prompt originale",
-    "Original negative prompt": "Prompt negativo originale",
-    "Override `Sampling Steps` to the same val due as `Decode steps`?": "Sovrascrivere 'Passi di campionamento' allo stesso valore di 'Passi di decodifica'?",
-    "Decode steps": "Passi di decodifica",
-    "Override `Denoising strength` to 1?": "Sostituisci 'Forza di denoising' a 1?",
-    "Decode CFG scale": "Scala CFG di decodifica",
-    "Randomness": "Casualità",
-    "Sigma adjustment for finding noise for image": "Regolazione Sigma per trovare il rumore per l'immagine",
-    "Tile size": "Dimensione piastrella",
-    "Tile overlap": "Sovrapposizione piastrella",
-    "alternate img2img imgage": "alternate img2img imgage",
-    "interpolation values": "Valori di interpolazione",
-    "Refinement loops": "Cicli di affinamento",
-    "Loopback alpha": "Trasparenza rielaborazione ricorsiva",
-    "Border alpha": "Trasparenza del bordo",
-    "Blending strides": "Passi di fusione",
-    "Reuse Seed": "Riusa il seme",
-    "One grid": "Singola griglia",
-    "Interpolate VarSeed": "Interpola il seme di variazione",
-    "Paste on mask": "Incolla sulla maschera",
-    "Inpaint all": "Inpaint tutto",
-    "Interpolate in latent": "Interpola nello spazio latente",
-    "Denoising strength change factor": "Fattore di variazione dell'intensità di denoising",
-    "Superimpose alpha": "Sovrapporre Alpha",
-    "Show extra settings": "Mostra impostazioni aggiuntive",
-    "Reuse seed": "Riusa il seme",
-    "CFG decay factor": "Fattore di decadimento CFG",
-    "CFG target": "CFG di destinazione",
-    "Show/Hide Canvas": "Mostra/Nascondi Tela",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Impostazioni consigliate: Passi di campionamento: 80-100, Campionatore: Euler a, Intensità denoising: 0.8",
-    "Pixels to expand": "Pixel da espandere",
-    "Outpainting direction": "Direzione di Outpainting",
-    "left": "sinistra",
-    "right": "destra",
-    "up": "sopra",
-    "down": "sotto",
-    "Fall-off exponent (lower=higher detail)": "Esponente di decremento (più basso=maggior dettaglio)",
-    "Color variation": "Variazione di colore",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenterà l'immagine al doppio delle dimensioni; utilizzare i cursori di larghezza e altezza per impostare la dimensione della piastrella",
-    "Upscaler": "Ampliamento immagine",
-    "Lanczos": "Lanczos",
-    "LDSR": "LDSR",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR 4x": "SwinIR 4x",
-    "Mask prompt": "Prompt maschera",
-    "Negative mask prompt": "Prompt maschera negativa",
-    "Mask precision": "Precisione della maschera",
-    "Mask padding": "Estendi i bordi della maschera",
-    "Brush mask mode": "Modalità pennello maschera",
-    "discard": "Scarta",
-    "add": "Aggiungi",
-    "subtract": "Sottrai",
-    "Show mask in output?": "Mostra maschera in uscita?",
-    "If you like my work, please consider showing your support on": "Se ti piace il mio lavoro, per favore considera di mostrare il tuo supporto su ",
-    "Patreon": "Patreon",
-    "Input file path": "Percorso file di input",
-    "CRF (quality, less is better, x264 param)": "CRF (qualità, meno è meglio, x264 param)",
-    "FPS": "FPS",
-    "Seed step size": "Ampiezza del gradiente del seme",
-    "Seed max distance": "Distanza massima del seme",
-    "Start time": "Orario di inizio",
-    "End time": "Orario di fine",
-    "End Prompt Blend Trigger Percent": "Percentuale di innesco del mix col prompt finale",
-    "Prompt end": "Prompt finale",
-    "Smooth video": "Rendi il filmato fluido",
-    "Seconds": "Secondi",
-    "Zoom": "Zoom",
-    "Rotate": "Ruota",
-    "Degrees": "Gradi",
-    "Is the Image Tiled?": "L'immagine è piastrellata?",
-    "TranslateX": "Traslazione X",
-    "Left": "Sinistra",
-    "PercentX": "Percentuale X",
-    "TranslateY": "Traslazione Y",
-    "Up": "Sopra",
-    "PercentY": "Percentuale Y",
-    "Show generated pictures in ui": "Mostra le immagini generate nell'interfaccia utente",
-    "Deforum v0.5-webui-beta": "Deforum v0.5-webui-beta",
-    "This script is deprecated. Please use the full Deforum extension instead.": "Questo script è obsoleto. Utilizzare invece l'estensione Deforum completa.",
-    "Update instructions:": "Istruzioni per l'aggiornamento:",
-    "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md": "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md",
-    "discord.gg/deforum": "discord.gg/deforum",
-    "Single Image": "Singola immagine",
-    "Batch Process": "Elaborare a lotti",
-    "Batch from Directory": "Lotto da cartella",
-    "Source": "Sorgente",
-    "Show result images": "Mostra le immagini dei risultati",
-    "Scale by": "Scala di",
-    "Scale to": "Scala a",
-    "Resize": "Ridimensiona",
-    "Crop to fit": "Ritaglia per adattare",
-    "Upscaler 2 visibility": "Visibilità Ampliamento immagine 2",
-    "GFPGAN visibility": "Visibilità GFPGAN",
-    "CodeFormer visibility": "Visibilità CodeFormer",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso di CodeFormer (0 = effetto massimo, 1 = effetto minimo)",
-    "Upscale Before Restoring Faces": "Amplia prima di restaurare i volti",
-    "Send to txt2img": "Invia a txt2img",
-    "A merger of the two checkpoints will be generated in your": "I due checkpoint verranno fusi nella cartella dei",
-    "checkpoint": "checkpoint",
-    "directory.": ".",
-    "Primary model (A)": "Modello Primario (A)",
-    "Secondary model (B)": "Modello Secondario (B)",
-    "Tertiary model (C)": "Modello Terziario (C)",
-    "Custom Name (Optional)": "Nome personalizzato (facoltativo)",
-    "Multiplier (M) - set to 0 to get model A": "Moltiplicatore (M): impostare a 0 per ottenere il modello A",
-    "Interpolation Method": "Metodo di interpolazione",
-    "Weighted sum": "Somma pesata",
-    "Add difference": "Aggiungi differenza",
-    "Save as float16": "Salva come float16",
-    "See": "Consulta la ",
-    "wiki": "wiki",
-    "for detailed explanation.": " per una spiegazione dettagliata.",
-    "Create embedding": "Crea Incorporamento",
-    "Create hypernetwork": "Crea Iperrete",
-    "Preprocess images": "Preprocessa le immagini",
-    "Name": "Nome",
-    "Initialization text": "Testo di inizializzazione",
-    "Number of vectors per token": "Numero di vettori per token",
-    "Overwrite Old Embedding": "Sovrascrivi il vecchio incorporamento",
-    "Modules": "Moduli",
-    "Enter hypernetwork layer structure": "Immettere la struttura del livello della Iperrete",
-    "Select activation function of hypernetwork": "Selezionare la funzione di attivazione della Iperrete",
-    "linear": "linear",
-    "relu": "relu",
-    "leakyrelu": "leakyrelu",
-    "elu": "elu",
-    "swish": "swish",
-    "tanh": "tanh",
-    "sigmoid": "sigmoid",
-    "celu": "celu",
-    "gelu": "gelu",
-    "glu": "glu",
-    "hardshrink": "hardshrink",
-    "hardsigmoid": "hardsigmoid",
-    "hardtanh": "hardtanh",
-    "logsigmoid": "logsigmoid",
-    "logsoftmax": "logsoftmax",
-    "mish": "mish",
-    "prelu": "prelu",
-    "rrelu": "rrelu",
-    "relu6": "relu6",
-    "selu": "selu",
-    "silu": "silu",
-    "softmax": "softmax",
-    "softmax2d": "softmax2d",
-    "softmin": "softmin",
-    "softplus": "softplus",
-    "softshrink": "softshrink",
-    "softsign": "softsign",
-    "tanhshrink": "tanhshrink",
-    "threshold": "threshold",
-    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleziona inizializzazione dei pesi dei livelli. relu-like - Kaiming, Si consiglia sigmoid-like - Xavier",
-    "Normal": "Normal",
-    "KaimingUniform": "KaimingUniform",
-    "KaimingNormal": "KaimingNormal",
-    "XavierUniform": "XavierUniform",
-    "XavierNormal": "XavierNormal",
-    "Add layer normalization": "Aggiunge la normalizzazione del livello",
-    "Use dropout": "Usa Dropout",
-    "Overwrite Old Hypernetwork": "Sovrascrive la vecchia Iperrete",
-    "Source directory": "Cartella sorgente",
-    "Destination directory": "Cartella di destinazione",
-    "Existing Caption txt Action": "Azione sul testo della didascalia esistente",
-    "ignore": "ignora",
-    "copy": "copia",
-    "prepend": "anteporre",
-    "append": "appendere",
-    "Create flipped copies": "Crea copie specchiate",
-    "Split oversized images": "Dividi immagini di grandi dimensioni",
-    "Auto focal point crop": "Ritaglio automatico al punto focale",
-    "Use BLIP for caption": "Usa BLIP per la didascalia",
-    "Use deepbooru for caption": "Usa deepbooru per la didascalia",
-    "Split image threshold": "Soglia di divisione dell'immagine",
-    "Split image overlap ratio": "Rapporto di sovrapposizione dell'immagine",
-    "Focal point face weight": "Peso della faccia del punto focale",
-    "Focal point entropy weight": "Peso dell'entropia del punto focale",
-    "Focal point edges weight": "Peso dei bordi del punto focalePeso dei bordi del punto focale",
-    "Create debug image": "Crea immagine di debug",
-    "Preprocess": "Preprocessa",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Addestra un Incorporamento o Iperrete; è necessario specificare una directory con un set di immagini con rapporto 1:1",
-    "[wiki]": "[wiki]",
-    "Embedding": "Incorporamento",
-    "Embedding Learning rate": "Tasso di apprendimento Incorporamento",
-    "Hypernetwork Learning rate": "Tasso di apprendimento Iperrete",
-    "Dataset directory": "Cartella del Dataset",
-    "Log directory": "Cartella del registro",
-    "Prompt template file": "File modello prompt",
-    "Max steps": "Passi massimi",
-    "Save an image to log directory every N steps, 0 to disable": "Salva un'immagine nella cartella del registro ogni N passaggi, 0 per disabilitare",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva una copia dell'incorporamento nella cartella del registro ogni N passaggi, 0 per disabilitare",
-    "Save images with embedding in PNG chunks": "Salva le immagini con l'incorporamento in blocchi PNG",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Legge i parametri (prompt, ecc...) dalla scheda txt2img durante la creazione delle anteprime",
-    "Train Hypernetwork": "Addestra Iperrete",
-    "Train Embedding": "Addestra Incorporamento",
-    "Create an aesthetic embedding out of any number of images": "Crea un'incorporamento estetico da qualsiasi numero di immagini",
-    "Create images embedding": "Crea incorporamento di immagini",
-    "-1": "-1",
-    "This extension works well with text captions in comma-separated style (such as the tags generated by DeepBooru interrogator).": "Questa estensione funziona bene con i sottotitoli di testo in stile separato da virgole (come i tag generati dall'interrogatore DeepBooru).",
-    "Save all changes": "Salva tutte le modifiche",
-    "Backup original text file (original file will be renamed like filename.000, .001, .002, ...)": "Backup del file di testo originale (il file originale verrà rinominato come nomefile.000, .001, .002, ...)",
-    "Note:": "Note:",
-    "New text file will be created if you are using filename as captions.": "Verrà creato un nuovo file di testo se si utilizza il nome del file come didascalia.",
-    "Results": "Risultati",
-    "Load": "Carica",
-    "Dataset Images": "Immagini del Dataset",
-    "Filter and Edit Tags": "Filtra e modifica i tag",
-    "Edit Caption of Selected Image": "Modifica la didascalia dell'immagine selezionata",
-    "Search tags / Filter images by tags": "Cerca tag / Filtra le immagini per tag",
-    "Search Tags": "Cerca tag",
-    "Clear all filters": "Rimuovi tutti i filtri",
-    "Sort by": "Ordina per",
-    "Alphabetical Order": "Ordine alfabetico",
-    "Frequency": "Frequenza",
-    "Sort Order": "Ordinamento",
-    "Ascending": "Ascendente",
-    "Descending": "Discendente",
-    "Filter Images by Tags": "Filtra le immagini per tag",
-    "Edit tags in filtered images": "Modifica i tag nelle immagini filtrate",
-    "Selected Tags": "Tag selezionati",
-    "Edit Tags": "Modificare i tag",
-    "Apply changes to filtered images": "Applica le modifiche alle immagini filtrate",
-    "Append additional tags to the beginning": "Aggiungi tag addizionali all'inizio",
-    "1. The selected tags are displayed in comma separated style.": "1. I tag selezionati vengono visualizzati in uno stile separato da virgole.",
-    "2. When changes are applied, all tags in each displayed images are replaced.": "2. Quando vengono applicate le modifiche, tutti i tag in ciascuna immagine visualizzata vengono sostituiti.",
-    "3. If you change some tags into blank, they will be erased.": "3. Se modifichi alcuni tag con uno spazio vuoto, verranno cancellati.",
-    "4. If you add some tags to the end, they will be appended to the end/beginning of the text file.": "4. Se aggiungi dei tag alla fine, questi verranno aggiunti alla fine/inizio del file di testo.",
-    "5. Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "5. Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\"..",
-    "ex A.": "esempio A.",
-    "Original Text = \"A, A, B, C\" Selected Tags = \"B, A\" Edit Tags = \"X, Y\"": "Testo originale = \"A, A, B, C\" Tag selezionati = \"B, A\" Modifica tag = \"X, Y\"",
-    "Result = \"Y, Y, X, C\" (B->X, A->Y)": "Risultato = \"Y, Y, X, C\" (B->X, A->Y)",
-    "ex B.": "esempio B.",
-    "Original Text = \"A, B, C\" Selected Tags = \"(nothing)\" Edit Tags = \"X, Y\"": "Testo originale = \"A, B, C\" Tag selezionati = \"(nothing)\" Modifica tag = \"X, Y\"",
-    "Result = \"A, B, C, X, Y\" (add X and Y to the end (default))": "Risultato = \"A, B, C, X, Y\" (aggiunge X e Y alla fine (predefinito))",
-    "Result = \"X, Y, A, B, C\" (add X and Y to the beginning (\"Append additional tags to the beginning\" checked))": "Risultato = \"X, Y, A, B, C\" (aggiunge X e Y all'inizio (\"Aggiungi tag addizionali all'inizio\" selezionato))",
-    "ex C.": "esempio C.",
-    "Original Text = \"A, B, C, D, E\" Selected Tags = \"A, B, D\" Edit Tags = \", X, \"": "Testo originale = \"A, B, C, D, E\" Tag selezionati = \"A, B, D\" Modifica tag = \", X, \"",
-    "Result = \"X, C, E\" (A->\"\", B->X, D->\"\")": "Risultato = \"X, C, E\" (A->\"\", B->X, D->\"\")",
-    "Caption of Selected Image": "Didascalia dell'immagine selezionata",
-    "Copy caption": "Copia didascalia",
-    "Edit Caption": "Modifica didascalia",
-    "Apply changes to selected image": "Applica le modifiche all'immagine selezionata",
-    "Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\".",
-    "Info and links": "Info e link",
-    "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha": "Realizzato da deforum.github.io, port per l'interfaccia web di AUTOMATIC1111 manutenuto da kabachuha",
-    "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Repository Github originale di Deforum  github.com/deforum/stable-diffusion",
-    "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui": "Questo fork è per l'interfaccia web di AUTOMATIC1111 github.com/deforum-art/deforum-for-automatic1111-webui",
-    "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Unisciti al canale Discord ufficiale di Deforum discord.gg/deforum per condividere le tue creazioni e suggerimenti",
-    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "Manuale d'uso per la versione 0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
-    "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Spiegazione della matematica dei fotogrammi chiave docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
-    "Keyframes": "Fotogrammi chiave",
-    "Prompts": "Prompt",
-    "Init": "Inizializzare",
-    "Video output": "Uscita video",
-    "Run settings": "Esegui le impostazioni",
-    "Import settings from file": "Importa impostazioni da file",
-    "Override settings": "Sostituisci le impostazioni",
-    "Custom settings file": "File delle impostazioni personalizzate",
-    "Sampling settings": "Impostazioni di campionamento",
-    "override_these_with_webui": "Sovrascrivi con Web UI",
-    "W": "L",
-    "H": "A",
-    "seed": "Seme",
-    "sampler": "Campionatore",
-    "Enable extras": "Abilita 'Extra'",
-    "subseed": "Sub seme",
-    "subseed_strength": "Intensità subseme",
-    "steps": "Passi",
-    "ddim_eta": "ETA DDIM",
-    "n_batch": "Numero lotto",
-    "make_grid": "Crea griglia",
-    "grid_rows": "Righe griglia",
-    "save_settings": "Salva impostazioni",
-    "save_samples": "Salva i campioni",
-    "display_samples": "Mostra i campioni",
-    "save_sample_per_step": "Salva campioni per passo",
-    "show_sample_per_step": "Mostra campioni per passo",
-    "Batch settings": "Impostazioni lotto",
-    "batch_name": "Nome del lotto",
-    "filename_format": "Formato nome del file",
-    "seed_behavior": "Comportamento seme",
-    "iter": "Iterativo",
-    "fixed": "Fisso",
-    "random": "Casuale",
-    "schedule": "Pianificato",
-    "Animation settings": "Impostazioni animazione",
-    "animation_mode": "Modalità animazione",
-    "2D": "2D",
-    "3D": "3D",
-    "Video Input": "Ingresso video",
-    "max_frames": "Fotogrammi max",
-    "border": "Bordo",
-    "replicate": "Replica",
-    "wrap": "Impacchetta",
-    "Motion parameters:": "Parametri di movimento:",
-    "2D and 3D settings": "Impostazioni 2D e 3D",
-    "angle": "Angolo",
-    "zoom": "Zoom",
-    "translation_x": "Traslazione X",
-    "translation_y": "Traslazione Y",
-    "3D settings": "Impostazioni 3D",
-    "translation_z": "Traslazione Z",
-    "rotation_3d_x": "Rotazione 3D X",
-    "rotation_3d_y": "Rotazione 3D Y",
-    "rotation_3d_z": "Rotazione 3D Z",
-    "Prespective flip — Low VRAM pseudo-3D mode:": "Inversione prospettica: modalità pseudo-3D a bassa VRAM:",
-    "flip_2d_perspective": "Inverti prospettiva 2D",
-    "perspective_flip_theta": "Inverti prospettiva theta",
-    "perspective_flip_phi": "Inverti prospettiva phi",
-    "perspective_flip_gamma": "Inverti prospettiva gamma",
-    "perspective_flip_fv": "Inverti prospettiva fv",
-    "Generation settings:": "Impostazioni di generazione:",
-    "noise_schedule": "Pianificazione del rumore",
-    "strength_schedule": "Intensità della pianificazione",
-    "contrast_schedule": "Contrasto della pianificazione",
-    "cfg_scale_schedule": "Pianificazione della scala CFG",
-    "3D Fov settings:": "Impostazioni del campo visivo 3D:",
-    "fov_schedule": "Pianificazione del campo visivo",
-    "near_schedule": "Pianificazione da vicino",
-    "far_schedule": "Pianificazione da lontano",
-    "To enable seed schedule select seed behavior — 'schedule'": "Per abilitare la pianificazione del seme, seleziona il comportamento del seme — 'pianifica'",
-    "seed_schedule": "Pianificazione del seme",
-    "Coherence:": "Coerenza:",
-    "color_coherence": "Coerenza del colore",
-    "Match Frame 0 HSV": "Uguaglia HSV del fotogramma 0",
-    "Match Frame 0 LAB": "Uguaglia LAB del fotogramma 0",
-    "Match Frame 0 RGB": "Uguaglia RGB del fotogramma 0",
-    "diffusion_cadence": "Cadenza di diffusione",
-    "3D Depth Warping:": "Deformazione della profondità 3D:",
-    "use_depth_warping": "Usa la deformazione della profondità",
-    "midas_weight": "Peso MIDAS",
-    "near_plane": "Piano vicino",
-    "far_plane": "Piano lontano",
-    "fov": "Campo visivo",
-    "padding_mode": "Modalità di riempimento",
-    "reflection": "Rifletti",
-    "zeros": "Zeri",
-    "sampling_mode": "Modalità di campionamento",
-    "bicubic": "bicubic",
-    "bilinear": "bilinear",
-    "nearest": "nearest",
-    "save_depth_maps": "Salva le mappe di profondità",
-    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`modalità animazione: Nessuno` si inserisce nell'elenco di *prompt*. (Modalità batch disabilitata atm, funzionano solo i prompt di animazione)",
-    "*Important change from vanilla Deforum!*": "*Importante cambiamento rispetto alla versione originale di Deforum!*",
-    "This script uses the built-in webui weighting settings.": "Questo script utilizza le impostazioni di pesatura webui integrate.",
-    "So if you want to use math functions as prompt weights,": "Quindi, se vuoi usare le funzioni matematiche come pesi dei prompt,",
-    "keep the values above zero in both parts": "mantenere i valori sopra lo zero in entrambe le parti",
-    "Negative prompt part can be specified with --neg": "La parte negativa del prompt può essere specificata con --neg",
-    "batch_prompts (disabled atm)": "Prompt in lotti (al momento è disabilitato)",
-    "animation_prompts": "Prompt animazione",
-    "Init settings": "Impostazioni iniziali",
-    "use_init": "Usa le impostazioni iniziali",
-    "from_img2img_instead_of_link": "from_img2img_instead_of_link",
-    "strength_0_no_init": "strength_0_no_init",
-    "strength": "Intensità",
-    "init_image": "Immagine di inizializzazione",
-    "use_mask": "Usa maschera",
-    "use_alpha_as_mask": "Usa alpha come maschera",
-    "invert_mask": "Inverti la maschera",
-    "overlay_mask": "Sovrapponi la maschera",
-    "mask_file": "File della maschera",
-    "mask_brightness_adjust": "Regola la luminosità della maschera",
-    "mask_overlay_blur": "Sfocatura della sovrapposizione della maschera",
-    "Video Input:": "Ingresso video:",
-    "video_init_path": "Percorso del video di inizializzazione",
-    "extract_nth_frame": "Estrai ogni ennesimo fotogramma",
-    "overwrite_extracted_frames": "Sovrascrivi i fotogrammi estratti",
-    "use_mask_video": "Usa maschera video",
-    "video_mask_path": "Percorso della maschera video",
-    "Interpolation (turned off atm)": "Interpolazione (attualmente spento)",
-    "interpolate_key_frames": "Interpola fotogrammi chiave",
-    "interpolate_x_frames": "Interpola x fotogrammi",
-    "Resume animation:": "Riprendi l'animazione:",
-    "resume_from_timestring": "Riprendi da stringa temporale",
-    "resume_timestring": "Stringa temporale",
-    "Video output settings": "Impostazioni uscita video",
-    "skip_video_for_run_all": "Salta il video per eseguire tutto",
-    "fps": "FPS",
-    "output_format": "Formato di uscita",
-    "PIL gif": "PIL gif",
-    "FFMPEG mp4": "FFMPEG mp4",
-    "ffmpeg_location": "Percorso ffmpeg",
-    "add_soundtrack": "Aggiungi colonna sonora",
-    "soundtrack_path": "Percorso colonna sonora",
-    "use_manual_settings": "Usa impostazioni manuali",
-    "render_steps": "Passi di renderizzazione",
-    "max_video_frames": "Numero max fotogrammi video",
-    "path_name_modifier": "Modificatore del nome del percorso",
-    "x0_pred": "x0_pred",
-    "x": "x",
-    "image_path": "Percorso immagine",
-    "mp4_path": "Percorso MP4",
-    "Click here after the generation to show the video": "Clicca qui dopo la generazione per mostrare il video",
-    "Save Settings": "Salva le impostazioni",
-    "Load Settings": "Carica le impostazioni",
-    "Path relative to the webui folder." : "Percorso relativo alla cartella webui.",
-    "Save Video Settings": "Salva impostazioni video",
-    "Load Video Settings": "Carica impostazioni video",
-    "dog": "cane",
-    "house": "casa",
-    "portrait": "ritratto",
-    "spaceship": "nave spaziale",
-    "anime": "anime",
-    "cartoon": "cartoon",
-    "digipa-high-impact": "digipa-high-impact",
-    "digipa-med-impact": "digipa-med-impact",
-    "digipa-low-impact": "digipa-low-impact",
-    "fareast": "estremo oriente",
-    "fineart": "fineart",
-    "scribbles": "scarabocchi",
-    "special": "special",
-    "ukioe": "ukioe",
-    "weird": "strano",
-    "black-white": "bianco e nero",
-    "nudity": "nudità",
-    "c": "c",
-    "Get Images": "Ottieni immagini",
-    "dog-anime": "dog-anime",
-    "dog-cartoon": "dog-cartoon",
-    "dog-digipa-high-impact": "dog-digipa-high-impact",
-    "dog-digipa-med-impact": "dog-digipa-med-impact",
-    "dog-digipa-low-impact": "dog-digipa-low-impact",
-    "dog-fareast": "dog-fareast",
-    "dog-fineart": "dog-fineart",
-    "dog-scribbles": "dog-scribbles",
-    "dog-special": "dog-special",
-    "dog-ukioe": "dog-ukioe",
-    "dog-weird": "dog-weird",
-    "dog-black-white": "dog-black-white",
-    "dog-nudity": "dog-nudity",
-    "dog-c": "dog-c",
-    "dog-n": "dog-n",
-    "house-anime": "house-anime",
-    "house-cartoon": "house-cartoon",
-    "house-digipa-high-impact": "house-digipa-high-impact",
-    "house-digipa-med-impact": "house-digipa-med-impact",
-    "house-digipa-low-impact": "house-digipa-low-impact",
-    "house-fareast": "house-fareast",
-    "house-fineart": "house-fineart",
-    "house-scribbles": "house-scribbles",
-    "house-special": "house-special",
-    "house-ukioe": "house-ukioe",
-    "house-weird": "house-weird",
-    "house-black-white": "house-black-white",
-    "house-nudity": "house-nudity",
-    "house-c": "house-c",
-    "house-n": "house-n",
-    "portrait-anime": "portrait-anime",
-    "portrait-cartoon": "portrait-cartoon",
-    "portrait-digipa-high-impact": "portrait-digipa-high-impact",
-    "portrait-digipa-med-impact": "portrait-digipa-med-impact",
-    "portrait-digipa-low-impact": "portrait-digipa-low-impact",
-    "portrait-fareast": "portrait-fareast",
-    "portrait-fineart": "portrait-fineart",
-    "portrait-scribbles": "portrait-scribbles",
-    "portrait-special": "portrait-special",
-    "portrait-ukioe": "portrait-ukioe",
-    "portrait-weird": "portrait-weird",
-    "portrait-black-white": "portrait-black-white",
-    "portrait-nudity": "portrait-nudity",
-    "portrait-c": "portrait-c",
-    "portrait-n": "portrait-n",
-    "spaceship-anime": "spaceship-anime",
-    "spaceship-cartoon": "spaceship-cartoon",
-    "spaceship-digipa-high-impact": "spaceship-digipa-high-impact",
-    "spaceship-digipa-med-impact": "spaceship-digipa-med-impact",
-    "spaceship-digipa-low-impact": "spaceship-digipa-low-impact",
-    "spaceship-fareast": "spaceship-fareast",
-    "spaceship-fineart": "spaceship-fineart",
-    "spaceship-scribbles": "spaceship-scribbles",
-    "spaceship-special": "spaceship-special",
-    "spaceship-ukioe": "spaceship-ukioe",
-    "spaceship-weird": "spaceship-weird",
-    "spaceship-black-white": "spaceship-black-white",
-    "spaceship-nudity": "spaceship-nudity",
-    "spaceship-c": "spaceship-c",
-    "spaceship-n": "spaceship-n",
-    "artists to study extension by camenduru |": "Estensione 'Artisti per studiare' a cura di camenduru |",
-    "github": "Github",
-    "|": "|",
-    "twitter": "Twitter",
-    "youtube": "Youtube",
-    "hi-res images": "Immagini in alta risoluzione",
-    "All images generated with CompVis/stable-diffusion-v1-4 +": "Tutte le immagini sono state generate con CompVis/stable-diffusion-v1-4 +",
-    "artists.csv": "artists.csv",
-    "| License: Attribution 4.0 International (CC BY 4.0)": "| Licenza: Attribution 4.0 International (CC BY 4.0)",
-    "Favorites": "Preferiti",
-    "Others": "Altre immagini",
-    "Images directory": "Cartella immagini",
-    "Dropdown": "Elenco cartelle",
-    "First Page": "Prima pagina",
-    "Prev Page": "Pagina precedente",
-    "Page Index": "Indice pagina",
-    "Next Page": "Pagina successiva",
-    "End Page": "Ultima pagina",
-    "delete next": "Cancella successivo",
-    "Delete": "Elimina",
-    "sort by": "Ordina per",
-    "path name": "Nome percorso",
-    "date": "Data",
-    "keyword": "Parola chiave",
-    "Generate Info": "Genera Info",
-    "File Name": "Nome del file",
-    "Move to favorites": "Aggiungi ai preferiti",
-    "Renew page": "Aggiorna la pagina",
-    "Number": "Numero",
-    "set_index": "Imposta indice",
-    "load_switch": "load_switch",
-    "turn_page_switch": "turn_page_switch",
-    "Checkbox": "Casella di controllo",						   
-    "Checkbox Group": "Seleziona immagini per",
-    "artists": "Artisti",
-    "flavors": "Stili",
-    "mediums": "Tecniche",
-    "movements": "Movimenti artistici",
-    "All": "Tutto",
-    "Exclude abandoned": "Escludi scartati",
-    "Abandoned": "Scartati",
-    "Key word": "Parola chiave",
-    "Get inspiration": "Ispirami",
-    "to txt2img": "Invia a txt2img",
-    "to img2img": "Invia a img2img",
-    "Collect": "Salva nei preferiti",
-    "Don't show again": "Scarta",
-    "Move out": "Rimuovi",
-    "set button": "Pulsante imposta", 
-    "Apply settings": "Applica le impostazioni",
-    "Saving images/grids": "Salva immagini/griglie",
-    "Always save all generated images": "Salva sempre tutte le immagini generate",
-    "File format for images": "Formato del file delle immagini",
-    "Images filename pattern": "Modello del nome dei file immagine",
-    "Add number to filename when saving": "Aggiungi un numero al nome del file al salvataggio",
-    "Always save all generated image grids": "Salva sempre tutte le griglie di immagini generate",
-    "File format for grids": "Formato del file per le griglie",
-    "Add extended info (seed, prompt) to filename when saving grid": "Aggiungi informazioni estese (seme, prompt) al nome del file durante il salvataggio della griglia",
-    "Do not save grids consisting of one picture": "Non salvare le griglie composte da una sola immagine",
-    "Prevent empty spots in grid (when set to autodetect)": "Previeni spazi vuoti nella griglia (se impostato su rilevamento automatico)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Numero di righe della griglia; utilizzare -1 per il rilevamento automatico e 0 per essere uguale alla dimensione del lotto",
-    "Save text information about generation parameters as chunks to png files": "Salva le informazioni di testo dei parametri di generazione come blocchi nel file png",
-    "Create a text file next to every image with generation parameters.": "Crea un file di testo assieme a ogni immagine con i parametri di generazione.",
-    "Save a copy of image before doing face restoration.": "Salva una copia dell'immagine prima di eseguire il restauro dei volti.",
-    "Quality for saved jpeg images": "Qualità delle immagini salvate in formato JPEG",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l'immagine PNG è più grande di 4 MB o qualsiasi dimensione è maggiore di 4000, ridimensiona e salva la copia come JPG",
-    "Use original name for output filename during batch process in extras tab": "Usa il nome originale per il nome del file di output durante l'elaborazione a lotti nella scheda 'Extra'",
-    "When using 'Save' button, only save a single selected image": "Usando il pulsante 'Salva', verrà salvata solo la singola immagine selezionata",
-    "Do not add watermark to images": "Non aggiungere la filigrana alle immagini",
-    "Paths for saving": "Percorsi di salvataggio",
-    "Output directory for images; if empty, defaults to three directories below": "Cartella di output per le immagini; se vuoto, per impostazione predefinita verranno usate le cartelle seguenti",
-    "Output directory for txt2img images": "Cartella di output per le immagini txt2img",
-    "Output directory for img2img images": "Cartella di output per le immagini img2img",
-    "Output directory for images from extras tab": "Cartella di output per le immagini dalla scheda 'Extra'",
-    "Output directory for grids; if empty, defaults to two directories below": "Cartella di output per le griglie; se vuoto, per impostazione predefinita veranno usate cartelle seguenti",
-    "Output directory for txt2img grids": "Cartella di output per le griglie txt2img",
-    "Output directory for img2img grids": "Cartella di output per le griglie img2img",
-    "Directory for saving images using the Save button": "Cartella dove salvare le immagini usando il pulsante 'Salva'",
-    "Saving to a directory": "Salva in una cartella",
-    "Save images to a subdirectory": "Salva le immagini in una sotto cartella",
-    "Save grids to a subdirectory": "Salva le griglie in una sotto cartella",
-    "When using \"Save\" button, save images to a subdirectory": "Usando il pulsante \"Salva\", le immagini verranno salvate in una sotto cartella",
-    "Directory name pattern": "Modello del nome della cartella",
-    "Max prompt words for [prompt_words] pattern": "Numero massimo di parole del prompt per il modello [prompt_words]",
-    "Upscaling": "Ampliamento",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Dimensione piastrella per ampliamento ESRGAN. 0 = nessuna piastrellatura.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per gli ampliamenti ESRGAN. Valori bassi = cucitura visibile.",
-    "Tile size for all SwinIR.": "Dimensione piastrella per tutti gli SwinIR.",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per SwinIR. Valori bassi = cucitura visibile.",
-    "LDSR processing steps. Lower = faster": "Fasi di elaborazione LDSR. Più basso = più veloce",
-    "Upscaler for img2img": "Metodo di ampliamento per img2img",
-    "Upscale latent space image when doing hires. fix": "Amplia l'immagine nello spazio latente durante la correzione in alta risoluzione",
-    "Face restoration": "Restauro del viso",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Peso di CodeFormer; 0 = effetto massimo; 1 = effetto minimo",
-    "Move face restoration model from VRAM into RAM after processing": "Sposta il modello di restauro facciale dalla VRAM alla RAM dopo l'elaborazione",
-    "System": "Sistema",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "Verifiche al secondo sull'utilizzo della VRAM durante la generazione. Impostare a 0 per disabilitare.",
-    "Always print all generation info to standard output": "Stampa sempre tutte le informazioni di generazione sul output standard",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda barra di avanzamento alla console che mostra l'avanzamento complessivo del lavoro.",
-    "Training": "Addestramento",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP nella RAM durante l'addestramento di Iperreti. Risparmia VRAM.",
-    "Filename word regex": "Filename word regex",
-    "Filename join string": "Filename join string",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per una singola immagine di input per epoca; utilizzato solo per visualizzare il numero di epoca",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salva un file CSV contenente la perdita nella cartella di registrazione ogni N passaggi, 0 per disabilitare",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "Checkpoint da memorizzare nella RAM",
-    "Hypernetwork strength": "Forza della Iperrete",
-    "Inpainting conditioning mask strength": "Forza della maschera di condizionamento del Inpainting",
-    "Apply color correction to img2img results to match original colors.": "Applica la correzione del colore ai risultati di img2img in modo che corrispondano ai colori originali.",
-    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la correzione del colore ai risultati di img2img",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, esegue esattamente la quantità di passi specificata dalla barra di scorrimento (normalmente se ne effettuano di meno con meno riduzione del rumore).",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Abilita la quantizzazione nei campionatori K per risultati più nitidi e puliti. Questo può cambiare i semi esistenti. Richiede il riavvio per applicare la modifica.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Enfasi: utilizzare (testo) per fare in modo che il modello presti maggiore attenzione al testo e [testo] per fargli prestare meno attenzione",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usa la vecchia implementazione dell'enfasi. Può essere utile per riprodurre vecchi semi.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Fa sì che i campionatori di diffusione K producano le stesse immagini in un lotto come quando si genera una singola immagine",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coerenza disattivando dall'ultima virgola all'indietro di n token quando si utilizzano più di 75 token",
-    "Filter NSFW content": "Filtra i contenuti NSFW",
-    "Stop At last layers of CLIP model": "Fermati agli ultimi livelli del modello CLIP",
-    "Interrogate Options": "Opzioni di interrogazione",
-    "Interrogate: keep models in VRAM": "Interroga: mantieni i modelli nella VRAM",
-    "Interrogate: use artists from artists.csv": "Interroga: utilizza artisti dal file artisti.csv",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interroga: include la classifica delle corrispondenze dei tag del modello nei risultati (non ha effetto sulle interrogazioni basate su didascalie).",
-    "Interrogate: num_beams for BLIP": "Interroga: num_beams per BLIP",
-    "Interrogate: minimum description length (excluding artists, etc..)": "Interroga: lunghezza minima della descrizione (esclusi artisti, ecc..)",
-    "Interrogate: maximum description length": "Interroga: lunghezza massima della descrizione",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: numero massimo di righe nel file di testo (0 = Nessun limite)",
-    "Interrogate: deepbooru score threshold": "Interroga: soglia del punteggio deepbooru",
-    "Interrogate: deepbooru sort alphabetically": "Interroga: deepbooru ordinato alfabeticamente",
-    "use spaces for tags in deepbooru": "usa gli spazi per i tag in deepbooru",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Effettua l'escape (\\) delle parentesi in deepbooru (così vengono usate come parentesi letterali e non per enfatizzare)",
-    "User interface": "Interfaccia Utente",
-    "Show progressbar": "Mostra la barra di avanzamento",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostra l'avanzamento della generazione dell'immagine ogni N passaggi di campionamento. Impostare a 0 per disabilitare.",
-    "Show previews of all images generated in a batch as a grid": "Mostra le anteprime di tutte le immagini generate in un lotto come una griglia",
-    "Show grid in results for web": "Mostra la griglia nei risultati per il web",
-    "Do not show any images in results for web": "Non mostrare alcuna immagine nei risultati per il web",
-    "Add model hash to generation information": "Aggiungi l'hash del modello alle informazioni sulla generazione",
-    "Add model name to generation information": "Aggiungi il nome del modello alle informazioni sulla generazione",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Durante la lettura dei parametri di generazione dal testo nell'interfaccia utente (da informazioni PNG o testo incollato), non modificare il modello/checkpoint selezionato.",
-    "Send seed when sending prompt or image to other interface": "Invia il seme quando si invia un prompt o un'immagine a un'altra interfaccia",
-    "Font for image grids that have text": "Font per griglie di immagini con testo",
-    "Enable full page image viewer": "Abilita la visualizzazione delle immagini a pagina intera",
-    "Show images zoomed in by default in full page image viewer": "Mostra le immagini ingrandite per impostazione predefinita nella visualizzazione a pagina intera",
-    "Show generation progress in window title.": "Mostra l'avanzamento della generazione nel titolo della finestra.",
-    "Quicksettings list": "Elenco delle impostazioni rapide",
-    "Localization (requires restart)": "Localizzazione (richiede il riavvio)",
-    "ar_AR": "ar_AR",
-    "de_DE": "de_DE",
-    "es_ES": "es_ES",
-    "fr_FR": "fr_FR",
-    "it_IT": "it_IT",
-    "ja_JP": "ja_JP",
-    "ko_KR": "ko_KR",
-    "pt_BR": "pt_BR",
-    "ru_RU": "ru_RU",
-    "tr_TR": "tr_TR",
-    "zh_CN": "zh_CN",
-    "zh_TW": "zh_TW",
-    "Sampler parameters": "Parametri del campionatore",
-    "Hide samplers in user interface (requires restart)": "Nascondi campionatori nell'interfaccia utente (richiede il riavvio)",
-    "eta (noise multiplier) for DDIM": "ETA (moltiplicatore di rumore) per DDIM",
-    "eta (noise multiplier) for ancestral samplers": "ETA (moltiplicatore di rumore) per campionatori ancestrali",
-    "img2img DDIM discretize": "discretizzazione DDIM per img2img",
-    "uniform": "uniforme",
-    "quad": "quad",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "sigma tmin",
-    "sigma noise": "sigma noise",
-    "Eta noise seed delta": "ETA del delta del seme del rumore",
-    "Aesthetic Image Scorer": "Punteggio delle immagini estetiche",
-    "Save score as EXIF or PNG Info Chunk": "Salva il punteggio come info EXIF o PNG",
-    "Save score as tag (Windows Only)": "Salva punteggio come etichetta (solo Windows)",
-    "Force CPU (Requires Custom Script Reload)": "Forza CPU (richiede il ricaricamento dello script personalizzato)",
-    "Number of columns on image gallery": "Numero di colonne nella galleria di immagini",
-    "Images Browser": "Galleria immagini",
-    "Preload images at startup": "Precarica le immagini all'avvio",
-    "Number of columns on the page": "Numero di colonne nella pagina",
-    "Number of rows on the page": "Numero di righe nella pagina",
-    "Minimum number of pages per load": "Numero minimo di pagine da caricare",
-    "Maximum number of samples, used to determine which folders to skip when continue running the create script": "Numero massimo di campioni, utilizzato per determinare quali cartelle ignorare quando si continua a eseguire lo script di creazione",
-    "Use same seed for all images": "Usa lo stesso seme per tutte le immagini",
-    "Request browser notifications": "Richiedi le notifiche del browser",
-    "Download localization template": "Scarica il modello per la localizzazione",
-    "Reload custom script bodies (No ui updates, No restart)": "Ricarica gli script personalizzati (nessun aggiornamento dell'interfaccia utente, nessun riavvio)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Riavvia Gradio e aggiorna i componenti (solo script personalizzati, ui.py, js e css)",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (premi Ctrl+Invio o Alt+Invio per generare)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (premere Ctrl+Invio o Alt+Invio per generare)",
-    "Add a random artist to the prompt.": "Aggiungi un artista casuale al prompt.",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leggere i parametri di generazione dal prompt o dall'ultima generazione se il prompt è vuoto ed inserirli nell'interfaccia utente.",
-    "Save style": "Salva stile",
-    "Apply selected styles to current prompt": "Applica gli stili selezionati al prompt corrente",
-    "Stop processing current image and continue processing.": "Interrompe l'elaborazione dell'immagine corrente e continua l'elaborazione.",
-    "Stop processing images and return any results accumulated so far.": "Interrompe l'elaborazione delle immagini e restituisce tutti i risultati accumulati finora.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stile da applicare; gli stili hanno componenti sia per i prompt positivi che per quelli negativi e si applicano a entrambi",
-    "Do not do anything special": "Non fa nulla di speciale",
-    "Which algorithm to use to produce the image": "Quale algoritmo utilizzare per produrre l'immagine",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - molto creativo, si può ottenere un'immagine completamente diversa a seconda del numero di passi, impostare i passi su un valore superiore a 30-40 non aiuta",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - il migliore per inpainting",
-    "Produce an image that can be tiled.": "Produce un'immagine che può essere piastrellata.",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilizza un processo in due fasi per creare parzialmente un'immagine con una risoluzione inferiore, aumentare la scala e quindi migliorarne i dettagli senza modificare la composizione",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determina quanto poco l'algoritmo dovrebbe rispettare dovrebbe il contenuto dell'immagine. A 0, non cambierà nulla e a 1 otterrai un'immagine non correlata. Con valori inferiori a 1.0 l'elaborazione richiederà meno passaggi di quelli specificati dalla barra di scorrimento dei passi di campionamento.",
-    "How many batches of images to create": "Quanti lotti di immagini generare",
-    "How many image to create in a single batch": "Quante immagini generare in un singolo lotto",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - quanto fortemente l'immagine deve conformarsi al prompt: valori più bassi producono risultati più creativi",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Un valore che determina l'output del generatore di numeri casuali: se create un'immagine con gli stessi parametri e seme di un'altra immagine, otterrete lo stesso risultato",
-    "Set seed to -1, which will cause a new random number to be used every time": "Imposta il seme su -1, che farà sì che ogni volta venga utilizzato un nuovo numero casuale",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Riusa il seme dell'ultima generazione, utile soprattutto se casuale",
-    "Seed of a different picture to be mixed into the generation.": "Seme di un'immagine diversa da miscelare nella generazione.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Quanto è forte la variazione da produrre. A 0, non ci sarà alcun effetto. A 1, otterrai l'intera immagine con il seme della variazione (tranne per i campionatori ancestrali, dove otterrai solo una leggera variazione).",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Prova a produrre un'immagine simile a quella che sarebbe stata prodotta con lo stesso seme alla risoluzione specificata",
-    "This text is used to rotate the feature space of the imgs embs": "Questo testo viene utilizzato per ruotare lo spazio delle funzioni delle immagini incorporate",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Quante volte ripetere l'elaborazione di un'immagine e utilizzarla come input per l'iterazione successiva",
-    "Hello, StylePile here.\nUntil some weird bug gets fixed you will see this even if the script itself is not active. Meanwhile, some hints to take your artwork to new heights:\nUse the 'Focus on' dropdown to select complex presets. Toggle selections below (with or without Focus) to affect your results. Mix and match to get some interesting results. \nAnd some general Stable Diffusion tips that will take your designs to next level:\nYou can add parenthesis to make parts of the prompt stronger. So (((cute))) kitten will make it extra cute (try it out). This is alsow important if a style is affecting your original prompt too much. Make that prompt stronger by adding parenthesis around it, like this: ((promt)).\nYou can type promts like [A|B] to sequentially use terms one after another on each step. So, like [cat|dog] will produce a hybrid catdog. And [A:B:0.4] to switch to other terms after the first one has been active for a certain percentage of steps. So [cat:dog:0.4] will build a cat 40% of the time and then start turning it into a dog. This needs more steps to work properly.": "Salve, qui è StylePile.\nFinché qualche strano bug non verrà risolto, vedrai questo testo anche se lo script non è attivo. Nel frattempo, alcuni suggerimenti per portare la tua grafica a nuovi livelli:\nUtilizza il menu a discesa 'Focus on' per selezionare valori predefiniti complessi. Attiva o disattiva le selezioni seguenti (con o senza Focus) per influire sui risultati. Mescola e abbina per ottenere risultati interessanti. \nE alcuni suggerimenti generali su Stable Diffusion che porteranno i tuoi risultati a un livello superiore:\nPuoi aggiungere parentesi per aumentare l'influenza di certe parti del prompt. Quindi '(((cute))) kitten' lo renderà molto carino (fai delle prove). Questo è importante quando uno stile influisce troppo sul prompt originale. Rendi più forte quel prompt aggiungendo delle parentesi intorno ad esso, così: ((promt)).\nPuoi digitare prompt nel formato [A|B] per usare in sequenza i termini uno dopo l'altro in ogni passaggio. Quindi, come [cat|dog] produrrà un 'canegatto' ibrido. E [A:B:0.4] per passare ad altri termini dopo che il primo è stato attivo per una certa percentuale di passaggi. Quindi [cat:dog:0.4] genererà un gatto il 40% dei passaggi e poi inizierà a trasformarlo in un cane. Sono richiesti più passaggi perchè funzioni correttamente.",
-    "Enter one prompt per line. Blank lines will be ignored.": "Immettere un prompt per riga. Le righe vuote verranno ignorate.",
-    "Separate values for X axis using commas.": "Separare i valori per l'asse X usando le virgole.",
-    "Separate values for Y axis using commas.": "Separare i valori per l'asse Y usando le virgole.",
-    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "Separa un elenco di parole con virgole e lo script eseguirà una variazione di prompt con quelle parole per ogni loro possibile ordine",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva l'immagine/i in una cartella (predefinita - log/images) ed i parametri di generazione in un file CSV.",
-    "Open images output directory": "Apri la cartella di output delle immagini",
-    "How much to blur the mask before processing, in pixels.": "Quanto sfocare la maschera prima dell'elaborazione, in pixel.",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Cosa mettere all'interno dell'area mascherata prima di elaborarla con Stable Diffusion.",
-    "fill it with colors of the image": "riempi con i colori dell'immagine",
-    "keep whatever was there originally": "conserva tutto ciò che c'era in origine",
-    "fill it with latent space noise": "riempi di rumore spaziale latente",
-    "fill it with latent space zeroes": "riempi con zeri di spazio latente",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Ingrandisce la regione mascherata per raggiungere la risoluzione, esegue la pittura, riduce la scala e incolla nell'immagine originale",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Ridimensiona l'immagine alla risoluzione di destinazione. A meno che altezza e larghezza non corrispondano, otterrai proporzioni errate.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Ridimensionare l'immagine in modo che l'intera risoluzione di destinazione sia riempita con l'immagine. Ritaglia le parti che sporgono.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Ridimensiona l'immagine in modo che l'intera immagine rientri nella risoluzione di destinazione. Riempi lo spazio vuoto con i colori dell'immagine.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Per l'upscaling SD, quanta sovrapposizione in pixel dovrebbe esserci tra le piastrelle. Le piastrelle si sovrappongono in modo che quando vengono unite nuovamente in un'immagine, non ci siano giunture chiaramente visibili.",
-    "Process an image, use it as an input, repeat.": "Elabora un'immagine, usala come input, ripeti.",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In modalità rielaborazione ricorsiva, su ogni ciclo la forza di denoising viene moltiplicata per questo valore. <1 significa varietà decrescente in modo che la sequenza converga su un'immagine fissa. >1 significa aumentare la varietà in modo che la tua sequenza diventi sempre più caotica.",
-    "A directory on the same machine where the server is running.": "Una cartella sulla stessa macchina su cui è in esecuzione il server.",
-    "Leave blank to save images to the default path.": "Lascia vuoto per salvare le immagini nel percorso predefinito.",
-    "Result = A * (1 - M) + B * M": "Risultato = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "Risultato = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "La prima e l'ultima cifra devono essere 1. Es.:'1, 2, 1'",
-    "Path to directory with input images": "Percorso della cartella con immagini di input",
-    "Path to directory where to write outputs": "Percorso della cartella in cui scrivere i risultati",
-    "C:\\directory\\of\\datasets": "C:\\cartella\\del\\dataset",
-    "Input images directory": "Cartella di input delle immagini",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Usa i seguenti tag per definire come vengono scelti i nomi dei file per le immagini: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed ], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se questa opzione è abilitata, la filigrana non verrà aggiunta alle immagini create. Attenzione: se non aggiungi la filigrana, potresti comportarti in modo non etico.",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utilizzare i seguenti tag per definire come vengono scelte le sottodirectory per le immagini e le griglie: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
-    "Restore low quality faces using GFPGAN neural network": "Ripristina volti di bassa qualità utilizzando la rete neurale GFPGAN",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Questa espressione regolare verrà utilizzata per estrarre le parole dal nome del file e verranno unite utilizzando l'opzione seguente nel testo dell'etichetta utilizzato per l'addestramento. Lascia vuoto per mantenere il testo del nome del file così com'è.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "Questa stringa verrà utilizzata per unire le parole divise in un'unica riga se l'opzione sopra è abilitata.",
-    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Si applica solo ai modelli di pittura. Determina con quale forza mascherare l'immagine originale per inpainting e img2img. 1.0 significa completamente mascherato, che è il comportamento predefinito. 0.0 significa un condizionamento completamente non mascherato. Valori più bassi aiuteranno a preservare la composizione generale dell'immagine, ma avranno difficoltà con grandi cambiamenti.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Elenco dei nomi delle impostazioni, separati da virgole, per le impostazioni che dovrebbero essere visualizzate nella barra di accesso rapido in alto, anziché nella normale scheda delle impostazioni. Vedi modules/shared.py per impostare i nomi. Richiede il riavvio per applicare.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se questo valore è diverso da zero, verrà aggiunto al seed e utilizzato per inizializzare il generatore di numeri casuali per il rumore quando si utilizzano campionatori con ETA. Puoi usarlo per produrre ancora più variazioni di immagini, oppure puoi usarlo per abbinare le immagini di altri software se sai cosa stai facendo.",
-    "Autocomplete options": "Opzioni di autocompletamento",
-    "Enable Autocomplete": "Abilita autocompletamento",
-    "Append commas": "Aggiungi virgole"
-}
\ No newline at end of file
-- 
cgit v1.2.1


From 2c02fc8ba6e0c8b7902bc6697ce6153fd689cd41 Mon Sep 17 00:00:00 2001
From: Eugenio Buffo <58123757+EugenioBuffo@users.noreply.github.com>
Date: Tue, 1 Nov 2022 08:43:02 +0100
Subject: Removed duplicated string from it_IT localization

Removed "Peso dei bordi del punto focale" duplicated string from "Focal point edges weight"
---
 localizations/it_IT.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/localizations/it_IT.json b/localizations/it_IT.json
index db63c819..0a216e22 100644
--- a/localizations/it_IT.json
+++ b/localizations/it_IT.json
@@ -589,7 +589,7 @@
     "Split image overlap ratio": "Rapporto di sovrapposizione dell'immagine",
     "Focal point face weight": "Peso della faccia del punto focale",
     "Focal point entropy weight": "Peso dell'entropia del punto focale",
-    "Focal point edges weight": "Peso dei bordi del punto focalePeso dei bordi del punto focale",
+    "Focal point edges weight": "Peso dei bordi del punto focale",
     "Create debug image": "Crea immagine di debug",
     "Preprocess": "Preprocessa",
     "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Addestra un Incorporamento o Iperrete; è necessario specificare una directory con un set di immagini con rapporto 1:1",
@@ -1170,4 +1170,4 @@
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Elenco dei nomi delle impostazioni, separati da virgole, per le impostazioni che dovrebbero essere visualizzate nella barra di accesso rapido in alto, anziché nella normale scheda delle impostazioni. Vedi modules/shared.py per impostare i nomi. Richiede il riavvio per applicare.",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se questo valore è diverso da zero, verrà aggiunto al seed e utilizzato per inizializzare il generatore di numeri casuali per il rumore quando si utilizzano campionatori con ETA. Puoi usarlo per produrre ancora più variazioni di immagini, oppure puoi usarlo per abbinare le immagini di altri software se sai cosa stai facendo.",
     "Leave empty for auto": "Lasciare vuoto per automatico"
-}
\ No newline at end of file
+}
-- 
cgit v1.2.1


From 6bab858095a01f3cabdcbbcf3f42c913f54f500f Mon Sep 17 00:00:00 2001
From: batvbs <60730393+batvbs@users.noreply.github.com>
Date: Tue, 1 Nov 2022 18:46:20 +0800
Subject: Update localizations/zh_CN.json

Co-authored-by: liggest <43201720+liggest@users.noreply.github.com>
---
 localizations/zh_CN.json | 1 -
 1 file changed, 1 deletion(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index d3b572e6..d6d03e0f 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -179,7 +179,6 @@
     "Color variation": "色彩变化",
     "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置图块尺寸(tile size)",
     "Tile overlap": "图块重叠的像素(Tile overlap)",
-    "Upscaler": "Upscaler",
     "Lanczos": "Lanczos",
     "LDSR": "LDSR",
     "BSRGAN 4x": "BSRGAN 4x",
-- 
cgit v1.2.1


From d35bf649456da2558cbb6f2ea16fa1606022b7e7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 1 Nov 2022 14:19:24 +0300
Subject: make launch.py run installers for extensions that have ones add some
 more classes to safety module for an extension

---
 launch.py       | 22 ++++++++++++++++++++--
 modules/safe.py |  2 +-
 2 files changed, 21 insertions(+), 3 deletions(-)

diff --git a/launch.py b/launch.py
index 958336f2..0d90d553 100644
--- a/launch.py
+++ b/launch.py
@@ -7,6 +7,7 @@ import shlex
 import platform
 
 dir_repos = "repositories"
+dir_extensions = "extensions"
 python = sys.executable
 git = os.environ.get('GIT', "git")
 index_url = os.environ.get('INDEX_URL', "")
@@ -101,9 +102,24 @@ def version_check(commit):
         else:
             print("Not a git clone, can't perform version check.")
     except Exception as e:
-        print("versipm check failed",e)
+        print("version check failed", e)
+
+
+def run_extensions_installers():
+    if not os.path.isdir(dir_extensions):
+        return
+
+    for dirname_extension in os.listdir(dir_extensions):
+        path_installer = os.path.join(dir_extensions, dirname_extension, "install.py")
+        if not os.path.isfile(path_installer):
+            continue
+
+        try:
+            print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {dirname_extension}"))
+        except Exception as e:
+            print(e, file=sys.stderr)
+
 
-        
 def prepare_enviroment():
     torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
@@ -189,6 +205,8 @@ def prepare_enviroment():
 
     run_pip(f"install -r {requirements_file}", "requirements for Web UI")
 
+    run_extensions_installers()
+
     if update_check:
         version_check(commit)
     
diff --git a/modules/safe.py b/modules/safe.py
index 399165a1..348a24fc 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -32,7 +32,7 @@ class RestrictedUnpickler(pickle.Unpickler):
             return getattr(collections, name)
         if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
             return getattr(torch._utils, name)
-        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']:
+        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage']:
             return getattr(torch, name)
         if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
             return getattr(torch.nn.modules.container, name)
-- 
cgit v1.2.1


From b85e83c3bd869c3f1ffacf8d3ff97bd9d406acff Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 1 Nov 2022 14:48:53 +0300
Subject: add PYTHONPATH for extension's install.py

---
 launch.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/launch.py b/launch.py
index 0d90d553..ff2f74ba 100644
--- a/launch.py
+++ b/launch.py
@@ -17,11 +17,11 @@ def extract_arg(args, name):
     return [x for x in args if x != name], name in args
 
 
-def run(command, desc=None, errdesc=None):
+def run(command, desc=None, errdesc=None, custom_env=None):
     if desc is not None:
         print(desc)
 
-    result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
+    result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env)
 
     if result.returncode != 0:
 
@@ -115,7 +115,10 @@ def run_extensions_installers():
             continue
 
         try:
-            print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {dirname_extension}"))
+            env = os.environ.copy()
+            env['PYTHONPATH'] = os.path.abspath(".")
+
+            print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {dirname_extension}", custom_env=env))
         except Exception as e:
             print(e, file=sys.stderr)
 
-- 
cgit v1.2.1


From 42b5c73352f7c0c89ab6c34991b368f55952cd18 Mon Sep 17 00:00:00 2001
From: laksjdjf <aikasama@hotmail.co.jp>
Date: Tue, 1 Nov 2022 22:50:47 +0900
Subject: Update requirements.txt

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index fff63e7d..a53522f3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -12,7 +12,7 @@ opencv-python
 requests
 piexif
 Pillow
-pytorch_lightning
+pytorch_lightning==1.7.7
 realesrgan
 scikit-image>=0.19
 timm==0.4.12
-- 
cgit v1.2.1


From 20c3d556f641f5314b76de819bcbca7dc3ad4b0a Mon Sep 17 00:00:00 2001
From: Riccardo Giovanetti <29801031+Harvester62@users.noreply.github.com>
Date: Tue, 1 Nov 2022 15:13:01 +0100
Subject: Italian localization - Additions and updates

Added translations for these Extensions/Scripts:

Dynamic Prompts
Alpha Canvas
Artists to study
Aesthetic Score

Added a few missing translations and corrected some others. Updated to the latest Extension management tool version.

While I was able to translate in the statistics the text "Time taken:" because the timing itself is a separate label, I couldn't do the same with the labels "Torch active/reserved: 3881/3892 MiB" and "Sys VRAM: 4859/8192 MiB (59.31%)" because the values (memory, percentage) are embedded in the labels (or perhaps I am not enough acknowledged to be sure I am doing it right simply translating the text).
---
 localizations/it_IT.json | 128 +++++++++++++++++++++++++++++++----------------
 1 file changed, 86 insertions(+), 42 deletions(-)

diff --git a/localizations/it_IT.json b/localizations/it_IT.json
index 0a216e22..b0e5a36e 100644
--- a/localizations/it_IT.json
+++ b/localizations/it_IT.json
@@ -94,7 +94,6 @@
     "Animator v6": "Animator v6",
     "Asymmetric tiling": "Piastrellatura asimmetrica",
     "Custom code": "Codice personalizzato",
-    "Dynamic Prompting v0.2": "Prompt dinamici v0.2",
     "Embedding to Shareable PNG": "Incorporamento convertito in PNG condivisibile",
     "Force symmetry": "Forza la simmetria",
     "Prompts interpolation": "Interpola Prompt",
@@ -107,6 +106,7 @@
     "Text to Vector Graphics": "Da testo a grafica vettoriale",
     "X/Y plot": "Grafico X/Y",
     "X/Y/Z plot": "Grafico X/Y/Z",
+    "Dynamic Prompting v0.13.6": "Prompt dinamici v0.13.6",
     "Create inspiration images": "Crea immagini di ispirazione",
     "step1 min/max": "Passi min(o max)",
     "step2 min/max": "Passi max (o min)",
@@ -195,7 +195,7 @@
     "Beta distribution (VP only)": "Distribuzione Beta (Solo CV)",
     "Beta min (VP only)": "Beta min (Solo CV)",
     "Epsilon (VP only)": "Epsilon (Solo CV)",
-    "Running in txt2img mode:": "Running in txt2img mode:",
+    "Running in txt2img mode:": "Esecusione in modalità txt2img:",
     "Render these video formats:": "Renderizza in questi formati:",
     "GIF": "GIF",
     "MP4": "MP4",
@@ -203,11 +203,11 @@
     "Animation Parameters": "Parametri animazione",
     "Total Animation Length (s)": "Durata totale dell'animazione (s)",
     "Framerate": "Frequenza dei fotogrammi",
-    "Smoothing_Frames": "Smoothing_Frames",
+    "Smoothing_Frames": "Fotogrammi da appianare",
     "Add_Noise": "Aggiungi rumore",
     "Noise Strength": "Intensità del rumore",
     "Initial Parameters": "Parametri iniziali",
-    "Denoising Strength (overrides img2img slider)": "Intensità di riduzione del rumore (sovrascrive il cursore img2img)",
+    "Denoising Strength": "Intensità di riduzione del rumore",
     "Seed_March": "Seed_March",
     "Zoom Factor (scale/s)": "Fattore di ingrandimento (scala/s)",
     "X Pixel Shift (pixels/s)": "Sposta i Pixel sull'asse X (pixel/s)",
@@ -216,8 +216,8 @@
     "Prompt Template, applied to each keyframe below": "Modello di prompt, applicato a ciascun fotogramma chiave qui di seguito",
     "Positive Prompts": "Prompt positivi",
     "Negative Prompts": "Prompt negativi",
-    "Props, Stamps": "Props, Stamps",
-    "Poper_Folder:": "Cartella Poper:",
+    "Props, Stamps": "Immagini Clipart da diffondere (prop), o da applicare in post elaborazione e non essere diffuse (stamp).",
+    "Poper_Folder:": "Cartella Immagini Clipart (PNG trasparenti):",
     "Supported Keyframes:": "Fotogrammi chiave supportati:",
     "time_s | source | video, images, img2img | path": "time_s | source | video, images, img2img | path",
     "time_s | prompt | positive_prompts | negative_prompts": "time_s | prompt | positive_prompts | negative_prompts",
@@ -234,26 +234,11 @@
     "time_s | col_set": "time_s | col_set",
     "time_s | col_clear": "time_s | col_clear",
     "time_s | model | model": "time_s | model | model",
-    "img2img_mode": "img2img_mode",
+    "img2img_mode": "Modalità img2img",
     "Keyframes:": "Fotogrammi chiave:",
     "Tile X": "Piastrella asse X",
     "Tile Y": "Piastrella asse Y",
     "Python code": "Codice Python",
-    "Combinatorial generation": "Generazione combinatoria",
-    "Combinations": "Combinazioni",
-    "Choose a number of terms from a list, in this case we choose two artists": "Scegli un numero di termini da un elenco, in questo caso scegliamo due artisti",
-    "{2$$artist1|artist2|artist3}": "{2$$artist1|artist2|artist3}",
-    "If $$ is not provided, then 1$$ is assumed.\n\n            A range can be provided:": "Se $$ non viene fornito, si presume 1$$.\n\n            È possibile fornire un intervallo di valori:",
-    "{1-3$$artist1|artist2|artist3}": "{1-3$$artist1|artist2|artist3}",
-    "In this case, a random number of artists between 1 and 3 is chosen.": "In questo caso viene scelto un numero casuale di artisti compreso tra 1 e 3.",
-    "Wildcards": "Termini jolly",
-    "If the groups wont drop down click": "Se i gruppi non vengono visualizzati, clicca",
-    "here": "qui",
-    "to fix the issue.": "per correggere il problema.",
-    "WILDCARD_DIR: scripts/wildcards": "WILDCARD_DIR: scripts/wildcards",
-    "You can add more wildcards by creating a text file with one term per line and name is mywildcards.txt. Place it in scripts/wildcards.": "Puoi aggiungere termini jolly creando un file di testo con un termine per riga e nominandolo, per esempio, mywildcards.txt. Inseriscilo in scripts/wildcards.",
-    "__<folder>/mywildcards__": "__<folder>/mywildcards__",
-    "will then become available.": "diverrà quindi disponibile.",
     "Source embedding to convert": "Incorporamento sorgente da convertire",
     "Embedding token": "Token Incorporamento",
     "Output directory": "Cartella di output",
@@ -324,12 +309,30 @@
     "Eta": "ETA",
     "Clip skip": "Salta CLIP",
     "Denoising": "Riduzione del rumore",
-    "Cond. Image Mask Weight": "Cond. Image Mask Weight",
+    "Cond. Image Mask Weight": "Peso maschera immagine condizionale",
     "X values": "Valori per X",
     "Y type": "Parametro asse Y",
     "Y values": "Valori per Y",
     "Z type": "Parametro asse Z",
     "Z values": "Valori per Z",
+    "Combinatorial generation": "Generazione combinatoria",
+    "Combinatorial batches": "Lotti combinatori",
+    "Magic prompt": "Prompt magico",
+    "Fixed seed": "Seme fisso",
+    "Combinations": "Combinazioni",
+    "Choose a number of terms from a list, in this case we choose two artists": "Scegli un numero di termini da un elenco, in questo caso scegliamo due artisti",
+    "{{2$artist1|artist2|artist3}}": "{{2$artist1|artist2|artist3}}",
+    "If $ is not provided, then 1$ is assumed.\n\n    A range can be provided:": "Se $ non viene fornito, si presume 1$.\n\n    È possibile fornire un intervallo di valori:",
+    "{{1-3$artist1|artist2|artist3}}": "{{1-3$artist1|artist2|artist3}}",
+    "In this case, a random number of artists between 1 and 3 is chosen.": "In questo caso viene scelto un numero casuale di artisti compreso tra 1 e 3.",
+    "Wildcards": "Termini jolly",
+    "If the groups wont drop down click": "Se i gruppi non vengono visualizzati, clicca",
+    "here": "qui",
+    "to fix the issue.": "per correggere il problema.",
+    "WILDCARD_DIR: C:\\stable-diffusion-webui\\extensions\\sd-dynamic-prompts\\wildcards": "WILDCARD_DIR: C:\\stable-diffusion-webui\\extensions\\sd-dynamic-prompts\\wildcards",
+    "You can add more wildcards by creating a text file with one term per line and name is mywildcards.txt. Place it in scripts/wildcards.": "Puoi aggiungere termini jolly creando un file di testo con un termine per riga e nominandolo, per esempio, mywildcards.txt. Inseriscilo in scripts/wildcards.",
+    "__<folder>/mywildcards__": "__<cartella>/mywildcards__",
+    "will then become available.": "diverrà quindi disponibile.",
     "Artist or styles name list. '.txt' files with one name per line": "Elenco nomi di artisti o stili. File '.txt' con un nome per riga",
     "Prompt words before artist or style name": "Parole chiave prima del nome dell'artista o dello stile",
     "Prompt words after artist or style name": "Parole chiave dopo il nome dell'artista o dello stile",
@@ -439,7 +442,7 @@
     "Reuse seed": "Riusa il seme",
     "CFG decay factor": "Fattore di decadimento CFG",
     "CFG target": "CFG di destinazione",
-    "Show/Hide Canvas": "Mostra/Nascondi Tela",
+    "Show/Hide AlphaCanvas": "Mostra/Nascondi AlphaCanvas",
     "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Impostazioni consigliate: Passi di campionamento: 80-100, Campionatore: Euler a, Intensità denoising: 0.8",
     "Pixels to expand": "Pixel da espandere",
     "Outpainting direction": "Direzione di Outpainting",
@@ -535,7 +538,7 @@
     "Modules": "Moduli",
     "Enter hypernetwork layer structure": "Immettere la struttura del livello della Iperrete",
     "Select activation function of hypernetwork": "Selezionare la funzione di attivazione della Iperrete",
-    "linear": "linear",
+    "linear": "lineare",
     "relu": "relu",
     "leakyrelu": "leakyrelu",
     "elu": "elu",
@@ -563,9 +566,9 @@
     "softshrink": "softshrink",
     "softsign": "softsign",
     "tanhshrink": "tanhshrink",
-    "threshold": "threshold",
+    "threshold": "soglia",
     "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleziona inizializzazione dei pesi dei livelli. relu-like - Kaiming, Si consiglia sigmoid-like - Xavier",
-    "Normal": "Normal",
+    "Normal": "Normale",
     "KaimingUniform": "KaimingUniform",
     "KaimingNormal": "KaimingNormal",
     "XavierUniform": "XavierUniform",
@@ -764,8 +767,8 @@
     "animation_prompts": "Prompt animazione",
     "Init settings": "Impostazioni iniziali",
     "use_init": "Usa le impostazioni iniziali",
-    "from_img2img_instead_of_link": "from_img2img_instead_of_link",
-    "strength_0_no_init": "strength_0_no_init",
+    "from_img2img_instead_of_link": "da img2img invece che da link",
+    "strength_0_no_init": "Intensità 0 nessuna inizializzazione",
     "strength": "Intensità",
     "init_image": "Immagine di inizializzazione",
     "use_mask": "Usa maschera",
@@ -791,8 +794,8 @@
     "skip_video_for_run_all": "Salta il video per eseguire tutto",
     "fps": "FPS",
     "output_format": "Formato di uscita",
-    "PIL gif": "PIL gif",
-    "FFMPEG mp4": "FFMPEG mp4",
+    "PIL gif": "PIL GIF",
+    "FFMPEG mp4": "FFMPEG MP4",
     "ffmpeg_location": "Percorso ffmpeg",
     "add_soundtrack": "Aggiungi colonna sonora",
     "soundtrack_path": "Percorso colonna sonora",
@@ -916,7 +919,7 @@
     "Generate Info": "Genera Info",
     "File Name": "Nome del file",
     "Move to favorites": "Aggiungi ai preferiti",
-    "Renew page": "Aggiorna la pagina",
+    "Renew Page": "Aggiorna la pagina",
     "Number": "Numero",
     "set_index": "Imposta indice",
     "load_switch": "load_switch",
@@ -990,8 +993,8 @@
     "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda barra di avanzamento alla console che mostra l'avanzamento complessivo del lavoro.",
     "Training": "Addestramento",
     "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP nella RAM durante l'addestramento di Iperreti. Risparmia VRAM.",
-    "Filename word regex": "Filename word regex",
-    "Filename join string": "Filename join string",
+    "Filename word regex": "Espressione regolare per estrarre parole dal nome del file",
+    "Filename join string": "Stringa per unire le parole estratte dal nome del file",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per una singola immagine di input per epoca; utilizzato solo per visualizzare il numero di epoca",
     "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salva un file CSV contenente la perdita nella cartella di registrazione ogni N passaggi, 0 per disabilitare",
     "Stable Diffusion": "Stable Diffusion",
@@ -1059,11 +1062,17 @@
     "sigma tmin": "sigma tmin",
     "sigma noise": "sigma noise",
     "Eta noise seed delta": "ETA del delta del seme del rumore",
+    "Number of columns on image gallery": "Numero di colonne nella galleria di immagini",
     "Aesthetic Image Scorer": "Punteggio delle immagini estetiche",
     "Save score as EXIF or PNG Info Chunk": "Salva il punteggio come info EXIF o PNG",
-    "Save score as tag (Windows Only)": "Salva punteggio come etichetta (solo Windows)",
+    "aesthetic_score": "Punteggio estetico",
+    "cfg_scale": "Scala CFG",
+    "sd_model_hash": "Hash del modello SD",
+    "hash": "Hash",
+    "Save tags (Windows only)": "Salva etichette (solo Windows)",
+    "Save category (Windows only)": "Salva categoria (solo Windows)",
+    "Save generation params text": "Salva testo parametri di generazione",
     "Force CPU (Requires Custom Script Reload)": "Forza CPU (richiede il ricaricamento dello script personalizzato)",
-    "Number of columns on image gallery": "Numero di colonne nella galleria di immagini",
     "Images Browser": "Galleria immagini",
     "Preload images at startup": "Precarica le immagini all'avvio",
     "Number of columns on the page": "Numero di colonne nella pagina",
@@ -1076,6 +1085,7 @@
     "Reload custom script bodies (No ui updates, No restart)": "Ricarica gli script personalizzati (nessun aggiornamento dell'interfaccia utente, nessun riavvio)",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Riavvia Gradio e aggiorna i componenti (solo script personalizzati, ui.py, js e css)",
     "Installed": "Installato",
+    "Available": "Disponibile",
     "Install from URL": "Installa da URL",
     "Apply and restart UI": "Applica e riavvia l'interfaccia utente",
     "Check for updates": "Controlla aggiornamenti",
@@ -1085,12 +1095,14 @@
     "aesthetic-gradients": "Gradienti Estetici (CLIP)",
     "https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients",
     "unknown": "sconosciuto",
-    "aesthetic-image-scorer": "Punteggio delle immagini estetiche",
-    "https://github.com/tsngo/stable-diffusion-webui-aesthetic-image-scorer": "https://github.com/tsngo/stable-diffusion-webui-aesthetic-image-scorer",
     "dataset-tag-editor": "Dataset Tag Editor",
     "https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor.git": "https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor.git",
-    "deforum": "Deforum",
-    "https://github.com/deforum-art/deforum-for-automatic1111-webui/": "https://github.com/deforum-art/deforum-for-automatic1111-webui/",
+    "deforum-for-automatic1111-webui": "Deforum",
+    "https://github.com/deforum-art/deforum-for-automatic1111-webui": "https://github.com/deforum-art/deforum-for-automatic1111-webui",
+    "sd-dynamic-prompts": "Prompt dinamici",
+    "https://github.com/adieyal/sd-dynamic-prompts": "https://github.com/adieyal/sd-dynamic-prompts",
+    "stable-diffusion-webui-aesthetic-image-scorer": "Punteggio immagini estetiche",
+    "https://github.com/tsngo/stable-diffusion-webui-aesthetic-image-scorer": "https://github.com/tsngo/stable-diffusion-webui-aesthetic-image-scorer",
     "stable-diffusion-webui-artists-to-study": "Artisti per studiare",
     "https://github.com/camenduru/stable-diffusion-webui-artists-to-study": "https://github.com/camenduru/stable-diffusion-webui-artists-to-study",
     "stable-diffusion-webui-images-browser": "Galleria immagini",
@@ -1101,6 +1113,8 @@
     "https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git": "https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git",
     "wildcards": "Termini Jolly",
     "https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards.git": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards.git",
+    "Load from:": "Carica da:",
+    "Extension index URL": "URL dell'indice delle Estensioni", 
     "URL for extension's git repository": "URL del repository GIT dell'estensione",
     "Local directory name": "Nome cartella locale",
     "Install": "Installa",
@@ -1169,5 +1183,35 @@
     "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Si applica solo ai modelli di pittura. Determina con quale forza mascherare l'immagine originale per inpainting e img2img. 1.0 significa completamente mascherato, che è il comportamento predefinito. 0.0 significa un condizionamento completamente non mascherato. Valori più bassi aiuteranno a preservare la composizione generale dell'immagine, ma avranno difficoltà con grandi cambiamenti.",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Elenco dei nomi delle impostazioni, separati da virgole, per le impostazioni che dovrebbero essere visualizzate nella barra di accesso rapido in alto, anziché nella normale scheda delle impostazioni. Vedi modules/shared.py per impostare i nomi. Richiede il riavvio per applicare.",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se questo valore è diverso da zero, verrà aggiunto al seed e utilizzato per inizializzare il generatore di numeri casuali per il rumore quando si utilizzano campionatori con ETA. Puoi usarlo per produrre ancora più variazioni di immagini, oppure puoi usarlo per abbinare le immagini di altri software se sai cosa stai facendo.",
-    "Leave empty for auto": "Lasciare vuoto per automatico"
-}
+    "Leave empty for auto": "Lasciare vuoto per automatico",
+    "Autocomplete options": "Opzioni di autocompletamento",
+    "Enable Autocomplete": "Abilita autocompletamento",
+    "Append commas": "Aggiungi virgole",
+    "AlphaCanvas": "AlphaCanvas",
+    "Close": "Chiudi",
+    "Grab Results": "Ottieni risultati",
+    "Apply Patch": "Applica Patch",
+    "Hue:0": "Hue:0",
+    "S:0": "S:0",
+    "L:0": "L:0",
+    "Load Canvas": "Carica Tela",
+    "saveCanvas": "Salva Tela",
+    "latest": "aggiornato",
+    "behind": "da aggiornare",
+    "Description": "Descrizione",
+    "Action": "Azione",
+    "Aesthetic Gradients": "Gradienti estetici",
+    "Create an embedding from one or few pictures and use it to apply their style to generated images.": "Crea un incorporamento da una o poche immagini e usalo per applicare il loro stile alle immagini generate.",
+    "Sample extension. Allows you to use __name__ syntax in your prompt to get a random line from a file named name.txt in the wildcards directory. Also see Dynamic Prompts for similar functionality.": "Estensione del campione. Consente di utilizzare la sintassi __name__ nel prompt per ottenere una riga casuale da un file denominato name.txt nella cartella dei termini jolly. Vedi anche 'Prompt dinamici' per funzionalità simili.",
+    "Dynamic Prompts": "Prompt dinamici",
+    "Implements an expressive template language for random or combinatorial prompt generation along with features to support deep wildcard directory structures.": "Implementa un  modello di linguaggio espressivo per la generazione di prompt casuale o combinatoria insieme a funzionalità per supportare cartelle strutturate contenenti termini jolly.",
+    "Image browser": "Galleria immagini",
+    "Provides an interface to browse created images in the web browser.": "Fornisce un'interfaccia  nel browser web per sfogliare le immagini create.",
+    "Randomly display the pictures of the artist's or artistic genres typical style, more pictures of this artist or genre is displayed after selecting. So you don't have to worry about how hard it is to choose the right style of art when you create.": "Visualizza in modo casuale le immagini dello stile tipico dell'artista o dei generi artistici, dopo la selezione vengono visualizzate più immagini di questo artista o genere. Così non dovete preoccuparvi della difficoltà di scegliere lo stile artistico giusto quando create.",
+    "The official port of Deforum, an extensive script for 2D and 3D animations, supporting keyframable sequences, dynamic math parameters (even inside the prompts), dynamic masking, depth estimation and warping.": "Il porting ufficiale di Deforum, uno script completo per animazioni 2D e 3D, che supporta sequenze di fotogrammi chiave, parametri matematici dinamici (anche all'interno dei prompt), mascheramento dinamico, stima della profondità e warping.",
+    "Artists to study": "Artisti per studiare",
+    "Shows a gallery of generated pictures by artists separated into categories.": "Mostra una galleria di immagini generate dagli artisti suddivise in categorie.",
+    "Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Calcola il punteggio estetico per le immagini generate utilizzando il predittore del punteggio estetico CLIP+MLP basato su Chad Scorer",
+    "Lets you edit captions in training datasets.": "Consente di modificare i sottotitoli nei set di dati di addestramento.",
+    "Time taken:": "Tempo impiegato:"
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 467cae167a3066ffa2b2a5e6f16dd42642219aba Mon Sep 17 00:00:00 2001
From: TinkTheBoush <TinkTheBoush@github.com>
Date: Tue, 1 Nov 2022 23:29:12 +0900
Subject: append_tag_shuffle

---
 modules/hypernetworks/hypernetwork.py          |  4 ++--
 modules/textual_inversion/dataset.py           | 10 ++++++++--
 modules/textual_inversion/textual_inversion.py |  4 ++--
 modules/ui.py                                  |  3 +++
 4 files changed, 15 insertions(+), 6 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index a11e01d6..7630fb81 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -331,7 +331,7 @@ def report_statistics(loss_info:dict):
 
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, shuffle_tags, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
@@ -376,7 +376,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, shuffle_tags=shuffle_tags, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index ad726577..e9d97cc1 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -24,7 +24,7 @@ class DatasetEntry:
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", shuffle_tags=True, model=None, device=None, template_file=None, include_cond=False, batch_size=1):
         re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
 
         self.placeholder_token = placeholder_token
@@ -33,6 +33,7 @@ class PersonalizedBase(Dataset):
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
+        self.shuffle_tags = shuffle_tags
 
         self.dataset = []
 
@@ -98,7 +99,12 @@ class PersonalizedBase(Dataset):
     def create_text(self, filename_text):
         text = random.choice(self.lines)
         text = text.replace("[name]", self.placeholder_token)
-        text = text.replace("[filewords]", filename_text)
+        if self.tag_shuffle:
+            tags = filename_text.split(',')
+            random.shuffle(tags)
+            text = text.replace("[filewords]", ','.join(tags))
+        else:
+            text = text.replace("[filewords]", filename_text)
         return text
 
     def __len__(self):
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e0babb46..64700e23 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -224,7 +224,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, data_root, templat
     if save_model_every or create_image_every:
         assert log_directory, "Log directory is empty"
 
-def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, shuffle_tags, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
     validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
@@ -271,7 +271,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, shuffle_tags=shuffle_tags, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
 
     embedding.vec.requires_grad = True
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
diff --git a/modules/ui.py b/modules/ui.py
index 2c15abb7..ad383979 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1267,6 +1267,7 @@ def create_ui(wrap_gradio_gpu_call):
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
                     preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
+                    shuffle_tags = gr.Checkbox(label='Shuffleing tags by "," when create texts', value=True)
 
                     with gr.Row():
                         interrupt_training = gr.Button(value="Interrupt")
@@ -1361,6 +1362,7 @@ def create_ui(wrap_gradio_gpu_call):
                 template_file,
                 save_image_with_stored_embedding,
                 preview_from_txt2img,
+                shuffle_tags,
                 *txt2img_preview_params,
             ],
             outputs=[
@@ -1385,6 +1387,7 @@ def create_ui(wrap_gradio_gpu_call):
                 save_embedding_every,
                 template_file,
                 preview_from_txt2img,
+                shuffle_tags,
                 *txt2img_preview_params,
             ],
             outputs=[
-- 
cgit v1.2.1


From 72ea78cf64a111df025e5b71e2106b8c3dad28c3 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Tue, 1 Nov 2022 23:38:19 +0900
Subject: Update zh_CN.json

include new changes
---
 localizations/zh_CN.json | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index d6d03e0f..e700a1ab 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -19,6 +19,7 @@
     "Create aesthetic embedding": "生成美术风格",
     "Image Browser": "图库浏览器",
     "Settings": "设置",
+    "Extensions": "扩展",
     "Prompt": "提示词",
     "Negative prompt": "反向提示词",
     "Run": "运行",
@@ -448,6 +449,20 @@
     "Download localization template": "下载本地化模板",
     "Reload custom script bodies (No ui updates, No restart)": "重新加载自定义脚本主体（无 ui 更新，无重启）",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重启 Gradio 及刷新组件（仅限自定义脚本、ui.py、js 和 css）",
+    "Installed": "已安装",
+    "Available": "可用",
+    "Install from URL": "从网址安装",
+    "Apply and restart UI": "应用并重启用户界面",
+    "Check for updates": "检查更新",
+    "Extension": "扩展",
+    "URL": "网址",
+    "Update": "更新",
+    "unknown": "未知",
+    "Load from:": "加载自",
+    "Extension index URL": "扩展列表链接",
+    "URL for extension's git repository": "扩展的 git 仓库链接",
+    "Local directory name": "本地路径名",
+    "Install": "安装",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nPrompt",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nNegative prompt",
     "Add a random artist to the prompt.": "随机添加一个艺术家到提示词中",
@@ -508,6 +523,7 @@
     "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "仅适用于局部重绘专用的模型。 决定了蒙版在局部重绘以及图生图中屏蔽原图内容的强度。 1.0 表示完全屏蔽，这是默认行为。 0.0 表示完全不屏蔽。 较低的值将有助于保持图像的整体构图，但很难遇到较大的变化。",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置名称列表，以逗号分隔，设置应转到顶部的快速访问栏，而不是通常的设置选项卡。有关设置名称，请参见 modules/shared.py。需要重新启动才能应用",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声。你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
+    "Leave empty for auto": "留空时自动生成",
     "Autocomplete options": "自动补全选项",
     "Enable Autocomplete": "开启Tag补全",
     "Append commas": "附加逗号",
-- 
cgit v1.2.1


From 8a62d6431d2b1b2bb92409f0403c56fa423763f4 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Tue, 1 Nov 2022 23:39:02 +0900
Subject: re-order json content

---
 localizations/zh_CN.json | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index e700a1ab..83c4ef3c 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -102,6 +102,7 @@
     "Sampler": "采样器",
     "Checkpoint name": "模型(ckpt)名",
     "Hypernet str.": "Hypernetwork 强度",
+    "Hypernetwork": "超网络(Hypernetwork)",
     "Sigma Churn": "Sigma Churn",
     "Sigma min": "最小 Sigma",
     "Sigma max": "最大 Sigma",
@@ -304,10 +305,6 @@
     "Create images embedding": "生成图集 embedding",
     "Favorites": "收藏夹(已保存)",
     "Others": "其他",
-    "Move to favorites": "移动到收藏夹(保存)",
-    "favorites": "收藏夹(已保存)",
-    "others": "其他",
-    "Collect": "收藏(保存)",
     "Images directory": "图像目录",
     "Dropdown": "下拉列表",
     "First Page": "首页",
@@ -563,5 +560,8 @@
     "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "选择哪些Real-ESRGAN模型显示在用户界面。(需要重新启动)",
     "Face restoration model": "面部修复模型",
     "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
-    "Hypernetwork": "超网络(Hypernetwork)"
+    "Move to favorites": "移动到收藏夹(保存)",
+    "favorites": "收藏夹(已保存)",
+    "others": "其他",
+    "Collect": "收藏(保存)"
 }
-- 
cgit v1.2.1


From 82ba978d8538e932dec633b61411034886720d16 Mon Sep 17 00:00:00 2001
From: batvbs <60730393+batvbs@users.noreply.github.com>
Date: Tue, 1 Nov 2022 22:47:28 +0800
Subject: Update zh_CN.json

---
 localizations/zh_CN.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index d6d03e0f..3e193024 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -431,7 +431,7 @@
     "eta (noise multiplier) for DDIM": "DDIM 的 eta (噪声乘数) ",
     "eta (noise multiplier) for ancestral samplers": "ancestral 采样器的 eta (噪声乘数)",
     "img2img DDIM discretize": "图生图 DDIM 离散化",
-    "uniform": "均勻",
+    "uniform": "均匀",
     "quad": "二阶",
     "sigma churn": "sigma churn",
     "sigma tmin": "最小(tmin) sigma",
-- 
cgit v1.2.1


From 534bcfbac83c92be685b3fbd3f427e9bef716a57 Mon Sep 17 00:00:00 2001
From: batvbs <60730393+batvbs@users.noreply.github.com>
Date: Tue, 1 Nov 2022 22:54:21 +0800
Subject: Update zh_CN.json

---
 localizations/zh_CN.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 3e193024..d8bc91cd 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -453,7 +453,7 @@
     "Add a random artist to the prompt.": "随机添加一个艺术家到提示词中",
     "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
     "Save style": "储存为模版风格",
-    "Apply selected styles to current prompt": "将所选样式应用于当前提示",
+    "Apply selected styles to current prompt": "将所选模板风格，应用于当前提示词",
     "Stop processing current image and continue processing.": "停止处理当前图像，并继续处理下一个",
     "Stop processing images and return any results accumulated so far.": "停止处理图像，并返回迄今为止累积的任何结果",
     "Style to apply; styles have components for both positive and negative prompts and apply to both": "要应用的模版风格； 模版风格包含正向和反向提示词，并应用于两者",
-- 
cgit v1.2.1


From bc607686065b8c7751d1af7c05b960378fa256de Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Tue, 1 Nov 2022 23:26:55 +0800
Subject: Enable override_settings to take effect for hypernetworks

---
 modules/processing.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 57d3a523..86d015af 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -422,13 +422,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     try:
         for k, v in p.override_settings.items():
-            opts.data[k] = v  # we don't call onchange for simplicity which makes changing model, hypernet impossible
+            opts.data[k] = v  # we don't call onchange for simplicity which makes changing model impossible
+            if k == 'sd_hypernetwork': shared.reload_hypernetworks()  # make onchange call for changing hypernet since it is relatively fast to load on-change, while SD models are not
 
         res = process_images_inner(p)
 
-    finally:
+    finally:  # restore opts to original state
         for k, v in stored_opts.items():
             opts.data[k] = v
+            if k == 'sd_hypernetwork': shared.reload_hypernetworks()
 
     return res
 
-- 
cgit v1.2.1


From e0695019acff0bda913b42ee5ccf92f50cfed2d0 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Wed, 2 Nov 2022 00:33:11 +0900
Subject: Update KR translation

More strings in the Extensions tab
---
 localizations/ko_KR.json | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 8d6c5123..d152e575 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -15,6 +15,7 @@
   "A directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리를 선택해 주세요.",
   "A merger of the two checkpoints will be generated in your": "체크포인트들이 병합된 결과물이 당신의",
   "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
+  "Action": "작업",
   "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
   "Add a second progress bar to the console that shows progress for an entire job.": "콘솔에 전체 작업의 진행도를 보여주는 2번째 프로그레스 바 추가하기",
   "Add difference": "차이점 추가",
@@ -23,6 +24,8 @@
   "Add model hash to generation information": "생성 정보에 모델 해시 추가",
   "Add model name to generation information": "생성 정보에 모델 이름 추가",
   "Add number to filename when saving": "이미지를 저장할 때 파일명에 숫자 추가하기",
+  "Aesthetic Gradients": "스타일 그라디언트",
+  "Aesthetic Image Scorer": "스타일 이미지 스코어러",
   "Aesthetic imgs embedding": "스타일 이미지 임베딩",
   "Aesthetic learning rate": "스타일 학습 수",
   "Aesthetic steps": "스타일 스텝 수",
@@ -39,8 +42,10 @@
   "Apply color correction to img2img results to match original colors.": "이미지→이미지 결과물이 기존 색상과 일치하도록 색상 보정 적용하기",
   "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
   "Apply settings": "설정 적용하기",
+  "Artists to study": "연구할만한 작가들",
   "Auto focal point crop": "초점 기준 크롭(자동 감지)",
   "Autocomplete options": "자동완성 설정",
+  "Available": "지원되는 확장기능 목록",
   "Batch count": "배치 수",
   "Batch from Directory": "저장 경로로부터 여러장 처리",
   "Batch img2img": "이미지→이미지 배치",
@@ -49,6 +54,7 @@
   "behind": "최신 아님",
   "BSRGAN 4x": "BSRGAN 4x",
   "built with gradio": "gradio로 제작되었습니다",
+  "Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Chad 스코어러를 기반으로 한 CLIP+MLP 스타일 점수 예측기를 이용해 생성된 이미지의 스타일 점수를 계산합니다.",
   "Cancel generate forever": "반복 생성 취소",
   "cfg cnt": "CFG 변화 횟수",
   "cfg count": "CFG 변화 횟수",
@@ -78,6 +84,7 @@
   "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
   "Create a text file next to every image with generation parameters.": "생성된 이미지마다 생성 설정값을 담은 텍스트 파일 생성하기",
   "Create aesthetic images embedding": "스타일 이미지 임베딩 생성하기",
+  "Create an embedding from one or few pictures and use it to apply their style to generated images.": "하나 혹은 그 이상의 이미지들로부터 임베딩을 생성해, 그 이미지들의 스타일을 다른 이미지 생성 시 적용할 수 있게 해줍니다.",
   "Create debug image": "디버그 이미지 생성",
   "Create embedding": "임베딩 생성",
   "Create flipped copies": "좌우로 뒤집은 복사본 생성",
@@ -88,6 +95,7 @@
   "custom fold": "커스텀 경로",
   "Custom Name (Optional)": "병합 모델 이름 (선택사항)",
   "Dataset directory": "데이터셋 경로",
+  "Dataset Tag Editor": "데이터셋 태그 편집기",
   "date": "생성 일자",
   "DDIM": "DDIM",
   "Decode CFG scale": "디코딩 CFG 스케일",
@@ -98,6 +106,7 @@
   "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
   "Denoising strength": "디노이즈 강도",
   "Denoising strength change factor": "디노이즈 강도 변경 배수",
+  "Description": "설명",
   "Destination directory": "결과물 저장 경로",
   "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
   "Directory for saving images using the Save button": "저장 버튼을 이용해 저장하는 이미지들의 저장 경로",
@@ -121,6 +130,7 @@
   "Drop File Here": "파일을 끌어 놓으세요",
   "Drop Image Here": "이미지를 끌어 놓으세요",
   "Dropdown": "드롭다운",
+  "Dynamic Prompts": "다이나믹 프롬프트",
   "Embedding": "임베딩",
   "Embedding Learning rate": "임베딩 학습률",
   "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "강조 : (텍스트)를 이용해 모델의 텍스트에 대한 가중치를 더 강하게 주고 [텍스트]를 이용해 더 약하게 줍니다.",
@@ -141,6 +151,7 @@
   "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
   "Existing Caption txt Action": "이미 존재하는 캡션 텍스트 처리",
   "Extension": "확장기능",
+  "Extension index URL": "확장기능 목록 URL",
   "Extensions": "확장기능",
   "Extra": "고급",
   "Extras": "부가기능",
@@ -200,6 +211,7 @@
   "ignore": "무시",
   "Image": "이미지",
   "Image Browser": "이미지 브라우저",
+  "Image browser": "이미지 브라우저",
   "Image for img2img": "Image for img2img",
   "Image for inpainting with mask": "마스크로 인페인팅할 이미지",
   "Image not found (may have been already moved)": "이미지를 찾을 수 없습니다 (이미 옮겨졌을 수 있음)",
@@ -210,6 +222,7 @@
   "img2img alternative test": "이미지→이미지 대체버전 테스트",
   "img2img DDIM discretize": "이미지→이미지 DDIM 이산화",
   "img2img history": "이미지→이미지 기록",
+  "Implements an expressive template language for random or combinatorial prompt generation along with features to support deep wildcard directory structures.": "무작위/조합 프롬프트 생성을 위한 문법과 복잡한 와일드카드 구조를 지원합니다.",
   "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "루프백 모드에서는 매 루프마다 디노이즈 강도에 이 값이 곱해집니다. 1보다 작을 경우 다양성이 낮아져 결과 이미지들이 고정된 형태로 모일 겁니다. 1보다 클 경우 다양성이 높아져 결과 이미지들이 갈수록 혼란스러워지겠죠.",
   "Include Separate Images": "분리된 이미지 포함하기",
   "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "75개보다 많은 토큰을 사용시 마지막 쉼표로부터 N개의 토큰 이내에 패딩을 추가해 통일성 증가시키기",
@@ -222,6 +235,7 @@
   "Inpainting conditioning mask strength": "인페인팅 조절 마스크 강도",
   "Input directory": "인풋 이미지 경로",
   "Input images directory": "이미지 경로 입력",
+  "Inspiration": "\"영감\"",
   "Install": "설치",
   "Install from URL": "URL로부터 확장기능 설치",
   "Installed": "설치된 확장기능",
@@ -259,12 +273,14 @@
   "Leave blank to save images to the default path.": "기존 저장 경로에 이미지들을 저장하려면 비워두세요.",
   "Leave empty for auto": "자동 설정하려면 비워두십시오",
   "left": "왼쪽",
+  "Lets you edit captions in training datasets.": "훈련에 사용되는 데이터셋의 캡션을 수정할 수 있게 해줍니다.",
   "linear": "linear",
   "List of prompt inputs": "프롬프트 입력 리스트",
   "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "설정 탭이 아니라 상단의 빠른 설정 바에 위치시킬 설정 이름을 쉼표로 분리해서 입력하십시오. 설정 이름은 modules/shared.py에서 찾을 수 있습니다. 재시작이 필요합니다.",
   "LMS": "LMS",
   "LMS Karras": "LMS Karras",
   "Load": "불러오기",
+  "Load from:": "URL로부터 불러오기",
   "Loading...": "로딩 중...",
   "Local directory name": "로컬 경로 이름",
   "Localization (requires restart)": "현지화 (재시작 필요)",
@@ -360,6 +376,7 @@
   "Prompt template file": "프롬프트 템플릿 파일 경로",
   "Prompts": "프롬프트",
   "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
+  "Provides an interface to browse created images in the web browser.": "생성된 이미지를 브라우저 내에서 볼 수 있는 인터페이스를 추가합니다.",
   "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
   "quad": "quad",
   "Quality for saved jpeg images": "저장된 jpeg 이미지들의 품질",
@@ -367,11 +384,13 @@
   "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
   "Random": "랜덤",
   "Random grid": "랜덤 그리드",
+  "Randomly display the pictures of the artist's or artistic genres typical style, more pictures of this artist or genre is displayed after selecting. So you don't have to worry about how hard it is to choose the right style of art when you create.": "특정 작가 또는 스타일의 이미지들 중 하나를 무작위로 보여줍니다. 선택 후 선택한 작가 또는 스타일의 이미지들이 더 나타나게 됩니다. 고르기 어려워도 걱정하실 필요 없어요!",
   "Randomness": "랜덤성",
   "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
   "Read parameters (prompt, etc...) from txt2img tab when making previews": "프리뷰 이미지 생성 시 텍스트→이미지 탭에서 설정값(프롬프트 등) 읽어오기",
   "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "추천 설정값 - 샘플링 스텝 수 : 80-100 , 샘플러 : Euler a, 디노이즈 강도 : 0.8",
   "Reload custom script bodies (No ui updates, No restart)": "커스텀 스크립트 리로드하기(UI 업데이트 없음, 재시작 없음)",
+  "Reloading...": "재시작 중...",
   "relu": "relu",
   "Renew Page": "Renew Page",
   "Request browser notifications": "브라우저 알림 권한 요청",
@@ -391,6 +410,7 @@
   "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
   "right": "오른쪽",
   "Run": "가동",
+  "Sample extension. Allows you to use __name__ syntax in your prompt to get a random line from a file named name.txt in the wildcards directory. Also see Dynamic Prompts for similar functionality.": "샘플 확장기능입니다. __이름__형식의 문법을 사용해 와일드카드 경로 내의 이름.txt파일로부터 무작위 프롬프트를 적용할 수 있게 해줍니다. 유사한 확장기능으로 다이나믹 프롬프트가 있습니다.",
   "Sampler": "샘플러",
   "Sampler parameters": "샘플러 설정값",
   "Sampling method": "샘플링 방법",
@@ -442,6 +462,7 @@
   "Show progressbar": "프로그레스 바 보이기",
   "Show result images": "이미지 결과 보이기",
   "Show Textbox": "텍스트박스 보이기",
+  "Shows a gallery of generated pictures by artists separated into categories.": "생성된 이미지들을 작가별로 분류해 보여줍니다. 원본 - https://artiststostudy.pages.dev",
   "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
   "Sigma Churn": "시그마 섞기",
   "sigma churn": "시그마 섞기",
@@ -479,6 +500,7 @@
   "System": "시스템",
   "Tertiary model (C)": "3차 모델 (C)",
   "Textbox": "텍스트박스",
+  "The official port of Deforum, an extensive script for 2D and 3D animations, supporting keyframable sequences, dynamic math parameters (even inside the prompts), dynamic masking, depth estimation and warping.": "Deforum의 공식 포팅 버전입니다. 2D와 3D 애니메이션, 키프레임 시퀀스, 수학적 매개변수, 다이나믹 마스킹 등을 지원합니다.",
   "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "이 정규표현식은 파일명으로부터 단어를 추출하는 데 사용됩니다. 추출된 단어들은 하단의 설정을 이용해 라벨 텍스트로 변환되어 훈련에 사용됩니다. 파일명 텍스트를 유지하려면 비워두십시오.",
   "This string will be used to join split words into a single line if the option above is enabled.": "이 문자열은 상단 설정이 활성화되어있을 때 분리된 단어들을 한 줄로 합치는 데 사용됩니다.",
   "This text is used to rotate the feature space of the imgs embs": "이 텍스트는 이미지 임베딩의 특징 공간을 회전하는 데 사용됩니다.",
-- 
cgit v1.2.1


From 198a1ffcfc963a3d74674fad560e87dbebf7949f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 1 Nov 2022 19:13:59 +0300
Subject: fix API returning extra stuff in base64 encoded iamges for #3972

---
 modules/api/api.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index e702c9c0..bb87d795 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,6 +1,8 @@
+import base64
+import io
 import time
 import uvicorn
-from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
+from gradio.processing_utils import decode_base64_to_file, decode_base64_to_image
 from fastapi import APIRouter, Depends, HTTPException
 import modules.shared as shared
 from modules import devices
@@ -29,6 +31,12 @@ def setUpscalers(req: dict):
     return reqDict
 
 
+def encode_pil_to_base64(image):
+    buffer = io.BytesIO()
+    image.save(buffer, format="png")
+    return base64.b64encode(buffer.getvalue())
+
+
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
-- 
cgit v1.2.1


From 9e28f09735406955f5f46b09890fa243fc81e2b0 Mon Sep 17 00:00:00 2001
From: dhwz <dhwz@gmx.net>
Date: Tue, 1 Nov 2022 17:39:09 +0100
Subject: Update de_DE.json

---
 localizations/de_DE.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/localizations/de_DE.json b/localizations/de_DE.json
index 56d54b54..5e254446 100644
--- a/localizations/de_DE.json
+++ b/localizations/de_DE.json
@@ -70,7 +70,7 @@
     "None": "Nichts",
     "Prompt matrix": "Promptmatrix",
     "Prompts from file or textbox": "Prompts aus Datei oder Textfeld",
-    "X/Y plot": "X/Y Graf",
+    "X/Y plot": "X/Y Graph",
     "Put variable parts at start of prompt": "Variable teile am start des Prompt setzen",
     "Iterate seed every line": "Iterate seed every line",
     "List of prompt inputs": "List of prompt inputs",
@@ -455,4 +455,4 @@
     "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Gilt nur für Inpainting-Modelle. Legt fest, wie stark das Originalbild für Inpainting und img2img maskiert werden soll. 1.0 bedeutet vollständig maskiert, was das Standardverhalten ist. 0.0 bedeutet eine vollständig unmaskierte Konditionierung. Niedrigere Werte tragen dazu bei, die Gesamtkomposition des Bildes zu erhalten, sind aber bei großen Änderungen problematisch.",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Liste von Einstellungsnamen, getrennt durch Kommas, für Einstellungen, die in der Schnellzugriffsleiste oben erscheinen sollen, anstatt in dem üblichen Einstellungs-Tab. Siehe modules/shared.py für Einstellungsnamen. Erfordert einen Neustart zur Anwendung.",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Wenn dieser Wert ungleich Null ist, wird er zum Seed addiert und zur Initialisierung des RNG für Noise bei der Verwendung von Samplern mit Eta verwendet. Dies kann verwendet werden, um noch mehr Variationen von Bildern zu erzeugen, oder um Bilder von anderer Software zu erzeugen, wenn Sie wissen, was Sie tun."
-}
\ No newline at end of file
+}
-- 
cgit v1.2.1


From 401350cd59555439570ba5bc95f0ac5698e372e4 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Tue, 1 Nov 2022 14:03:56 -0500
Subject: clear on the client-side again

---
 javascript/ui.js | 31 +++++++++++++++++++++----------
 modules/ui.py    |  9 +--------
 2 files changed, 22 insertions(+), 18 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index be47ec64..00e80fd6 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -142,22 +142,33 @@ function selected_tab_id() {
 
 }
 
-function clear_prompt(_, _prompt_neg, confirmed, _token_counter) {
-confirmed = false
+function clear_prompt() {
 
 if(confirm("Delete prompt?")) {
-    confirmed = true
-} else {
-return [_, _prompt_neg, confirmed, _token_counter]
-}
 
-    if(selected_tab_id() == "tab_txt2img") {
-        update_token_counter("img2img_token_button")
+    let pos_prompt = gradioApp().querySelector("#txt2img_prompt > label > textarea");
+    let neg_prompt = gradioApp().querySelector("#txt2img_neg_prompt > label > textarea");
+
+    if (selected_tab_id() == "tab_txt2img") {
     } else {
-        update_token_counter("txt2img_token_button")
+        pos_prompt = gradioApp().querySelector("#img2img_prompt > label > textarea");
+        neg_prompt = gradioApp().querySelector("#img2img_neg_prompt > label > textarea");
     }
 
-    return [_, _prompt_neg, confirmed, _token_counter]
+    pos_prompt.value = ""
+    neg_prompt.value = ""
+
+    //update prompt values on server-side
+    pos_prompt.dispatchEvent(
+        new Event("input", {bubbles: true})
+    )
+    neg_prompt.dispatchEvent(
+        new Event("input", {bubbles: true})
+    )
+
+    return true
+} else return false
+
 }
 
 
diff --git a/modules/ui.py b/modules/ui.py
index 447722cd..f43e79ab 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -400,19 +400,12 @@ def create_seed_inputs():
     return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
 
 
-def clear_prompt(prompt, _prompt_neg, confirmed, _token_counter):
-    """Given confirmation from a user on the client-side, go ahead with clearing prompt"""
-    if confirmed:
-        return ["", "", confirmed, update_token_counter("", 1)]
-    else:
-        return [prompt, _prompt_neg, confirmed, _token_counter]
-
 
 def connect_clear_prompt(button, prompt, prompt_neg, _dummy_confirmed, token_counter):
     """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
     button.click(
         _js="clear_prompt",
-        fn=clear_prompt,
+        fn=None,
         inputs=[prompt, prompt_neg, _dummy_confirmed, token_counter],
         outputs=[prompt, prompt_neg, _dummy_confirmed, token_counter],
     )
-- 
cgit v1.2.1


From 1dd5d6bafad7575f347056a29636cbab71c1c468 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Tue, 1 Nov 2022 14:33:55 -0500
Subject: clean py func defs

---
 modules/ui.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index f43e79ab..8a1f3887 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -401,13 +401,13 @@ def create_seed_inputs():
 
 
 
-def connect_clear_prompt(button, prompt, prompt_neg, _dummy_confirmed, token_counter):
+def connect_clear_prompt(button):
     """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
     button.click(
         _js="clear_prompt",
         fn=None,
-        inputs=[prompt, prompt_neg, _dummy_confirmed, token_counter],
-        outputs=[prompt, prompt_neg, _dummy_confirmed, token_counter],
+        inputs=[],
+        outputs=[],
     )
 
 
@@ -746,7 +746,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            connect_clear_prompt(clear_prompt_button, txt2img_prompt, txt2img_negative_prompt, dummy_component, token_counter)
+            connect_clear_prompt(clear_prompt_button)
 
             txt2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
@@ -929,7 +929,7 @@ def create_ui(wrap_gradio_gpu_call):
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            connect_clear_prompt(clear_prompt_button, img2img_prompt, img2img_negative_prompt, dummy_component, token_counter)
+            connect_clear_prompt(clear_prompt_button)
 
             img2img_prompt_img.change(
                 fn=modules.images.image_data,
-- 
cgit v1.2.1


From 86d35526a13a0e2432ab71d1d40b191615d3e343 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Tue, 1 Nov 2022 14:53:40 -0500
Subject: make line evil again

---
 modules/ui.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 8a1f3887..bd67c1bd 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -692,9 +692,7 @@ def create_ui(wrap_gradio_gpu_call):
     parameters_copypaste.reset()
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,\
-        txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter,\
-        token_button, clear_prompt_button = create_toprow(is_img2img=False)
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button, clear_prompt_button = create_toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -850,9 +848,7 @@ def create_ui(wrap_gradio_gpu_call):
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit,\
-        img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,\
-        token_counter, token_button, clear_prompt_button = create_toprow(is_img2img=True)
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button, clear_prompt_button = create_toprow(is_img2img=True)
 
 
         with gr.Row(elem_id='img2img_progress_row'):
-- 
cgit v1.2.1


From cffc240a7327ae60671ff533469fc4ed4bf605de Mon Sep 17 00:00:00 2001
From: Nerogar <nerogar@arcor.de>
Date: Sun, 23 Oct 2022 14:05:25 +0200
Subject: fixed textual inversion training with inpainting models

---
 modules/textual_inversion/textual_inversion.py | 27 +++++++++++++++++++++++++-
 1 file changed, 26 insertions(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 0aeb0459..2630c7c9 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -224,6 +224,26 @@ def validate_train_inputs(model_name, learn_rate, batch_size, data_root, templat
     if save_model_every or create_image_every:
         assert log_directory, "Log directory is empty"
 
+def create_dummy_mask(x, width=None, height=None):
+    if shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}:
+
+        # The "masked-image" in this case will just be all zeros since the entire image is masked.
+        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+        image_conditioning = shared.sd_model.get_first_stage_encoding(shared.sd_model.encode_first_stage(image_conditioning))
+
+        # Add the fake full 1s mask to the first dimension.
+        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+        image_conditioning = image_conditioning.to(x.dtype)
+
+    else:
+        # Dummy zero conditioning if we're not using inpainting model.
+        # Still takes up a bit of memory, but no encoder call.
+        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+        image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+
+    return image_conditioning
+
+
 def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
@@ -286,6 +306,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
 
+    img_c = None
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
     for i, entries in pbar:
         embedding.step = i + ititial_step
@@ -299,8 +320,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
         with torch.autocast("cuda"):
             c = cond_model([entry.cond_text for entry in entries])
+            if img_c is None:
+                img_c = create_dummy_mask(c, training_width, training_height)
+
             x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-            loss = shared.sd_model(x, c)[0]
+            cond = {"c_concat": [img_c], "c_crossattn": [c]}
+            loss = shared.sd_model(x, cond)[0]
             del x
 
             losses[embedding.step % losses.shape[0]] = loss.item()
-- 
cgit v1.2.1


From 7b652c5df7f9167fdedfb13b4eed75caacd2e6a0 Mon Sep 17 00:00:00 2001
From: Eugenio Buffo <58123757+EugenioBuffo@users.noreply.github.com>
Date: Wed, 2 Nov 2022 00:07:37 +0100
Subject: Fixed misspelled word on it_IT

---
 localizations/it_IT.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/localizations/it_IT.json b/localizations/it_IT.json
index b0e5a36e..49489f40 100644
--- a/localizations/it_IT.json
+++ b/localizations/it_IT.json
@@ -195,7 +195,7 @@
     "Beta distribution (VP only)": "Distribuzione Beta (Solo CV)",
     "Beta min (VP only)": "Beta min (Solo CV)",
     "Epsilon (VP only)": "Epsilon (Solo CV)",
-    "Running in txt2img mode:": "Esecusione in modalità txt2img:",
+    "Running in txt2img mode:": "Esecuzione in modalità txt2img:",
     "Render these video formats:": "Renderizza in questi formati:",
     "GIF": "GIF",
     "MP4": "MP4",
@@ -1214,4 +1214,4 @@
     "Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Calcola il punteggio estetico per le immagini generate utilizzando il predittore del punteggio estetico CLIP+MLP basato su Chad Scorer",
     "Lets you edit captions in training datasets.": "Consente di modificare i sottotitoli nei set di dati di addestramento.",
     "Time taken:": "Tempo impiegato:"
-}
\ No newline at end of file
+}
-- 
cgit v1.2.1


From cd88e21dc5d5cfdfbd408454acd259b7db9d0ec8 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 2 Nov 2022 00:34:58 +0000
Subject: Class Name typo and add descriptions to fields.

---
 modules/script_callbacks.py | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index b0b8dc47..ff40b056 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -24,13 +24,22 @@ class ImageSaveParams:
         """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
 
 
-class CGFDenoiserParams:
-    def __init__(self, x_in, image_cond_in, sigma_in, sampling_step, total_sampling_steps):
-        self.x_in = x_in
-        self.image_cond_in = image_cond_in
-        self.sigma_in = sigma_in
+class CFGDenoiserParams:
+    def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):
+        self.x = x
+        """Latent image representation in the process of being denoised"""
+        
+        self.image_cond = image_cond
+        """Conditioning image"""
+        
+        self.sigma = sigma
+        """Current sigma noise step value"""
+        
         self.sampling_step = sampling_step
+        """Current Sampling step number"""
+        
         self.total_sampling_steps = total_sampling_steps
+        """Total number of sampling steps planned"""
 
 
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
@@ -94,7 +103,7 @@ def image_saved_callback(params: ImageSaveParams):
             report_exception(c, 'image_saved_callback')
 
 
-def cfg_denoiser_callback(params: CGFDenoiserParams):
+def cfg_denoiser_callback(params: CFGDenoiserParams):
     for c in callbacks_cfg_denoiser:
         try:
             c.callback(params)
@@ -153,7 +162,7 @@ def on_image_saved(callback):
 def on_cfg_denoiser(callback):
     """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
     The callback is called with one argument:
-        - params: CGFDenoiserParams - parameters to be passed to the inner model and sampling state details.
+        - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
     """
     add_callback(callbacks_cfg_denoiser, callback)
 
-- 
cgit v1.2.1


From 5b6bedf6f2ebacb7f1f5809af8e26a6a1af16e2a Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 2 Nov 2022 00:38:17 +0000
Subject: Update class name and assign back to vars

---
 modules/sd_samplers.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 30cb5c4b..ebc0d896 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -11,7 +11,7 @@ from modules import prompt_parser, devices, processing, images
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
-from modules.script_callbacks import CGFDenoiserParams, cfg_denoiser_callback
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
 
 
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
@@ -279,7 +279,11 @@ class CFGDenoiser(torch.nn.Module):
         image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
         sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
 
-        cfg_denoiser_callback(CGFDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps))
+        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
+        cfg_denoiser_callback(denoiser_params)
+        x_in = denoiser_params.x
+        image_cond_in = denoiser_params.image_cond
+        sigma_in = denoiser_params.sigma
 
         if tensor.shape[1] == uncond.shape[1]:
             cond_in = torch.cat([tensor, uncond])
-- 
cgit v1.2.1


From c9148b2312b36fee8727f5233da9dbe32aa1f58c Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Tue, 1 Nov 2022 21:56:47 -0300
Subject: Release processing resources after it finishes

---
 modules/img2img.py    | 2 ++
 modules/processing.py | 7 ++++---
 modules/txt2img.py    | 2 ++
 3 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/modules/img2img.py b/modules/img2img.py
index 35c5df9b..fac010aa 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -137,6 +137,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         if processed is None:
             processed = process_images(p)
 
+    p.close()
+
     shared.total_tqdm.clear()
 
     generation_info_js = processed.js()
diff --git a/modules/processing.py b/modules/processing.py
index 57d3a523..b541ee2b 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -202,6 +202,10 @@ class StableDiffusionProcessing():
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         raise NotImplementedError()
 
+    def close(self):
+        self.sd_model = None
+        self.sampler = None
+
 
 class Processed:
     def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
@@ -597,9 +601,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.scripts is not None:
         p.scripts.postprocess(p, res)
 
-    p.sd_model = None
-    p.sampler = None
-
     return res
 
 
diff --git a/modules/txt2img.py b/modules/txt2img.py
index c9d5a090..8e4e8677 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -47,6 +47,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     if processed is None:
         processed = process_images(p)
 
+    p.close()
+
     shared.total_tqdm.clear()
 
     generation_info_js = processed.js()
-- 
cgit v1.2.1


From ba13643bdd0a6a00f9a71dcfc47616c74962a58c Mon Sep 17 00:00:00 2001
From: Martucci <73501718+M-art-ucci@users.noreply.github.com>
Date: Tue, 1 Nov 2022 22:44:01 -0300
Subject: Update for extensions tab and other minor fizes
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

First commit of november.
Extensions tab localized and other minor translations fixes.

To other portuguese users: se você tiver sugestões de melhores traduções conforme uso, favor entrar em contato ou enviar PRs referentes a localização.
Também seria bem-vindo lusófonos de outros países, caso queiram discutir uma convergência entre diferentes variações do portguês.
---
 localizations/pt_BR.json | 53 ++++++++++++++++++++++++++++++++----------------
 1 file changed, 35 insertions(+), 18 deletions(-)

diff --git a/localizations/pt_BR.json b/localizations/pt_BR.json
index 56281105..d869170a 100644
--- a/localizations/pt_BR.json
+++ b/localizations/pt_BR.json
@@ -17,6 +17,7 @@
     "Checkpoint Merger": "Fusão de Checkpoint",
     "Train": "Treinar",
     "Settings": "Configurações",
+    "Extensions": "Extensions",
     "Prompt": "Prompt",
     "Negative prompt": "Prompt negativo",
     "Run": "Executar",
@@ -93,13 +94,13 @@
     "Eta": "Tempo estimado",
     "Clip skip": "Pular Clip",
     "Denoising": "Denoising",
+    "Cond. Image Mask Weight": "Peso da Máscara Condicional de Imagem",
     "X values": "Valores de X",
     "Y type": "Tipo de Y",
     "Y values": "Valores de Y",
     "Draw legend": "Desenhar a legenda",
     "Include Separate Images": "Incluir Imagens Separadas",
     "Keep -1 for seeds": "Manter em -1 para seeds",
-    "Drop Image Here": "Solte a imagem aqui",
     "Save": "Salvar",
     "Send to img2img": "Mandar para img2img",
     "Send to inpaint": "Mandar para inpaint",
@@ -110,6 +111,7 @@
     "Inpaint": "Inpaint",
     "Batch img2img": "Lote img2img",
     "Image for img2img": "Imagem para img2img",
+    "Drop Image Here": "Solte a imagem aqui",
     "Image for inpainting with mask": "Imagem para inpainting com máscara",
     "Mask": "Máscara",
     "Mask blur": "Desfoque da máscara",
@@ -166,16 +168,10 @@
     "Upscaler": "Ampliador",
     "Lanczos": "Lanczos",
     "LDSR": "LDSR",
-    "4x_foolhardy_Remacri": "4x_foolhardy_Remacri",
-    "Put ESRGAN models here": "Coloque modelos ESRGAN aqui",
-    "R-ESRGAN General 4xV3": "R-ESRGAN General 4xV3",
-    "R-ESRGAN AnimeVideo": "R-ESRGAN AnimeVideo",
-    "R-ESRGAN 4x+": "R-ESRGAN 4x+",
-    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
-    "R-ESRGAN 2x+": "R-ESRGAN 2x+",
-    "ScuNET": "ScuNET",
+    "ESRGAN_4x": "ESRGAN_4x",
+    "ScuNET GAN": "ScuNET GAN",
     "ScuNET PSNR": "ScuNET PSNR",
-    "put_swinir_models_here": "put_swinir_models_here",
+    "SwinIR 4x": "SwinIR 4x",
     "Single Image": "Uma imagem",
     "Batch Process": "Processo em lote",
     "Batch from Directory": "Lote apartir de diretório",
@@ -189,7 +185,7 @@
     "GFPGAN visibility": "Visibilidade GFPGAN",
     "CodeFormer visibility": "Visibilidade CodeFormer",
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso do CodeFormer (0 = efeito máximo, 1 = efeito mínimo)",
-    "Open output directory": "Abrir diretório de saída",
+    "Upscale Before Restoring Faces": "Ampliar Antes de Refinar Rostos",
     "Send to txt2img": "Mandar para txt2img",
     "A merger of the two checkpoints will be generated in your": "Uma fusão dos dois checkpoints será gerada em seu",
     "checkpoint": "checkpoint",
@@ -216,6 +212,7 @@
     "Modules": "Módulos",
     "Enter hypernetwork layer structure": "Entrar na estrutura de camadas da hypernetwork",
     "Select activation function of hypernetwork": "Selecionar a função de ativação de hypernetwork",
+    "linear": "linear",
     "relu": "relu",
     "leakyrelu": "leakyrelu",
     "elu": "elu",
@@ -227,12 +224,10 @@
     "glu": "glu",
     "hardshrink": "hardshrink",
     "hardsigmoid": "hardsigmoid",
-    "hardswish": "hardswish",
     "hardtanh": "hardtanh",
     "logsigmoid": "logsigmoid",
     "logsoftmax": "logsoftmax",
     "mish": "mish",
-    "multiheadattention": "multiheadattention",
     "prelu": "prelu",
     "rrelu": "rrelu",
     "relu6": "relu6",
@@ -274,9 +269,9 @@
     "Focal point edges weight": "Peso de ponto focal para bordas",
     "Create debug image": "Criar imagem de depuração",
     "Preprocess": "Pré-processar",
-    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Treinar um embedding; precisa especificar um diretório com imagens de proporção 1:1",
+    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Treinar uma incorporação; precisa especificar um diretório com imagens de proporção 1:1",
     "[wiki]": "[wiki]",
-    "Embedding": "Embedding",
+    "Embedding": "Incorporação",
     "Embedding Learning rate": "Taxa de aprendizagem da incorporação",
     "Hypernetwork Learning rate": "Taxa de aprendizagem de Hypernetwork",
     "Dataset directory": "Diretório de Dataset",
@@ -345,9 +340,11 @@
     "Filename join string": "Nome de arquivo join string",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Número de repetições para entrada única de imagens por época; serve apenas para mostrar o número de época",
     "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salvar um csv com as perdas para o diretório de log a cada N steps, 0 para desativar",
+    "Use cross attention optimizations while training": "Usar otimizações de atenção cruzada enquanto treinando",
     "Stable Diffusion": "Stable Diffusion",
     "Checkpoints to cache in RAM": "Checkpoints para manter no cache da RAM",
     "Hypernetwork strength": "Força da Hypernetwork",
+     "Inpainting conditioning mask strength": "Força do inpaint para máscaras condicioniais",
     "Apply color correction to img2img results to match original colors.": "Aplicar correção de cor nas imagens geradas em img2img, usando a imagem original como base.",
     "Save a copy of image before applying color correction to img2img results": "Salvar uma cópia das imagens geradas em img2img antes de aplicar a correção de cor",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Durante gerações img2img, fazer examente o número de steps definidos na barra (normalmente você faz menos steps com denoising menor).",
@@ -379,6 +376,7 @@
     "Add model hash to generation information": "Adicionar hash do modelo para informação de geração",
     "Add model name to generation information": "Adicionar nome do modelo para informação de geração",
     "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Quando ler parâmetros de texto para a interface (de informações de PNG ou texto copiado), não alterar o modelo/intervalo selecionado.",
+    "Send seed when sending prompt or image to other interface": "Enviar seed quando enviar prompt ou imagem para outra interface",
     "Font for image grids that have text": "Fonte para grade de imagens que têm texto",
     "Enable full page image viewer": "Ativar visualizador de página inteira",
     "Show images zoomed in by default in full page image viewer": "Mostrar imagens com zoom por definição no visualizador de página inteira",
@@ -386,13 +384,17 @@
     "Quicksettings list": "Lista de configurações rapidas",
     "Localization (requires restart)": "Localização (precisa reiniciar)",
     "ar_AR": "ar_AR",
+    "de_DE": "de_DE",
     "es_ES": "es_ES",
-    "fr-FR": "fr-FR",
+    "fr_FR": "fr_FR",
+    "it_IT": "it_IT",
     "ja_JP": "ja_JP",
     "ko_KR": "ko_KR",
+    "pt_BR": "pt_BR",
     "ru_RU": "ru_RU",
     "tr_TR": "tr_TR",
     "zh_CN": "zh_CN",
+    "zh_TW": "zh_TW",
     "Sampler parameters": "Parâmetros de Amostragem",
     "Hide samplers in user interface (requires restart)": "Esconder amostragens na interface de usuário (precisa reiniciar)",
     "eta (noise multiplier) for DDIM": "tempo estimado (multiplicador de ruído) para DDIM",
@@ -408,6 +410,19 @@
     "Download localization template": "Baixar arquivo modelo de localização",
     "Reload custom script bodies (No ui updates, No restart)": "Recarregar scripts personalizados (Sem atualizar a interface, Sem reiniciar)",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Reiniciar Gradio e atualizar componentes (Scripts personalizados, ui.py, js e css)",
+    "Installed": "Instalado",
+    "Available": "Disponível",
+    "Install from URL": "Instalado de URL",
+    "Apply and restart UI": "Apicar e reiniciar a interface",
+    "Check for updates": "Procurar por atualizações",
+    "Extension": "Extensão",
+    "URL": "URL",
+    "Update": "Atualização",
+    "Load from:": "Carregar de:",
+    "Extension index URL": "Índice de extensão URL",
+    "URL for extension's git repository": "URL para repositório git da extensão",
+    "Local directory name": "Nome do diretório local",
+    "Install": "Instalar",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (apertar Ctrl+Enter ou Alt+Enter para gerar)",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt Negativo (apertar Ctrl+Enter ou Alt+Enter para gerar)",
     "Add a random artist to the prompt.": "Adicionar um artista aleatório para o prompt.",
@@ -420,7 +435,7 @@
     "Do not do anything special": "Não faça nada de especial",
     "Which algorithm to use to produce the image": "O tipo de algoritmo para gerar imagens.",
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - cria mais variações para as imagens em diferentes passos. Mais que 40 passos cancela o efeito.",
-    "Denoising Diffusion Implicit Models - Funciona melhor para inpainting.": "Denoising Diffusion Implicit Models - Funciona melhor para inpainting.",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - Funciona melhor para inpainting.",
     "Produce an image that can be tiled.": "Produz uma imagem que pode ser ladrilhada.",
     "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Cria um processo em duas etapas, com uma imagem em baixa qualidade primeiro, aumenta a imagem e refina os detalhes sem alterar a composição da imagem",
     "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Quanto o algoritmo deve manter da imagem original. Em 0, nada muda. Em 1 o algoritmo ignora a imagem original. Valores menores que 1.0 demoram mais.",
@@ -438,7 +453,7 @@
     "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva a imagem no diretório padrão ou escolhido e cria um arquivo csv com os parâmetros da geração.",
     "Open images output directory": "Abre o diretório de saída de imagens.",
     "How much to blur the mask before processing, in pixels.": "Transição do contorno da máscara, em pixels.",
-    "What to put inside the masked area before processing it with Stable Diffusion.":  "O que vai dentro da máscara antes de processar.",
+    "What to put inside the masked area before processing it with Stable Diffusion.":  "O que vai dentro da máscara antes de processá-la com Stable Diffusion.",
 	"fill it with colors of the image": "Preenche usando as cores da imagem.",
     "keep whatever was there originally": "manter usando o que estava lá originalmente",
     "fill it with latent space noise": "Preenche com ruídos do espaço latente.",
@@ -463,6 +478,8 @@
     "Restore low quality faces using GFPGAN neural network": "Restaurar rostos de baixa qualidade usando a rede neural GFPGAN",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Esta expressão regular vai retirar palavras do nome do arquivo e serão juntadas via regex usando a opção abaixo em etiquetas usadas em treinamento. Não mexer para manter os nomes como estão.",
     "This string will be used to join split words into a single line if the option above is enabled.": "Esta string será usada para unir palavras divididas em uma única linha se a opção acima estiver habilitada.",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Aplicável somente para modelos de inpaint. Determina quanto deve mascarar da imagem original para inpaint e img2img. 1.0 significa totalmente mascarado, que é o comportamento padrão. 0.0 significa uma condição totalmente não mascarada. Valores baixos ajudam a preservar a composição geral da imagem, mas vai encontrar dificuldades com grandes mudanças.",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Lista de nomes de configurações, separados por vírgulas, para configurações que devem ir para a barra de acesso rápido na parte superior, em vez da guia de configuração usual. Veja modules/shared.py para nomes de configuração. Necessita reinicialização para aplicar.",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se este valor for diferente de zero, ele será adicionado à seed e usado para inicializar o RNG para ruídos ao usar amostragens com Tempo Estimado. Você pode usar isso para produzir ainda mais variações de imagens ou pode usar isso para combinar imagens de outro software se souber o que está fazendo."
+    "Leave empty for auto": "Deixar desmarcado para automático"
 }
-- 
cgit v1.2.1


From 315bd7c9e8a20a28fa7fd1ddd5fddbf3b5a9b41c Mon Sep 17 00:00:00 2001
From: Keith Dreibelbis <keith@crittercism.com>
Date: Tue, 1 Nov 2022 19:45:35 -0700
Subject: prompts_from_file: allow random seeds to be preserved for the list of
 prompts

---
 scripts/prompts_from_file.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 1be22960..8d4911ae 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -96,6 +96,7 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):
         checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False)
+        checkbox_iterate_batch = gr.Checkbox(label="Preserve random seed across lines (for use with \"Generate Forever\")", value=False)
 
         prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1)
         file = gr.File(label="Upload prompt inputs", type='bytes')
@@ -106,9 +107,9 @@ class Script(scripts.Script):
         # We don't shrink back to 1, because that causes the control to ignore [enter], and it may
         # be unclear to the user that shift-enter is needed.
         prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt])
-        return [checkbox_iterate, file, prompt_txt]
+        return [checkbox_iterate, checkbox_iterate_batch, file, prompt_txt]
 
-    def run(self, p, checkbox_iterate, file, prompt_txt: str):
+    def run(self, p, checkbox_iterate, checkbox_iterate_batch, file, prompt_txt: str):
         lines = [x.strip() for x in prompt_txt.splitlines()]
         lines = [x for x in lines if len(x) > 0]
 
@@ -137,7 +138,7 @@ class Script(scripts.Script):
             jobs.append(args)
 
         print(f"Will process {len(lines)} lines in {job_count} jobs.")
-        if (checkbox_iterate and p.seed == -1):
+        if ((checkbox_iterate or checkbox_iterate_batch) and p.seed == -1):
             p.seed = int(random.randrange(4294967294))
 
         state.job_count = job_count
-- 
cgit v1.2.1


From 55688c48806f9383f3a56f6b9a0ab8fbf205edd2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 2 Nov 2022 07:02:45 +0300
Subject: rename the seed option from #4146

---
 scripts/prompts_from_file.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 8d4911ae..d187cd9c 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -96,7 +96,7 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):
         checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False)
-        checkbox_iterate_batch = gr.Checkbox(label="Preserve random seed across lines (for use with \"Generate Forever\")", value=False)
+        checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False)
 
         prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1)
         file = gr.File(label="Upload prompt inputs", type='bytes')
@@ -138,7 +138,7 @@ class Script(scripts.Script):
             jobs.append(args)
 
         print(f"Will process {len(lines)} lines in {job_count} jobs.")
-        if ((checkbox_iterate or checkbox_iterate_batch) and p.seed == -1):
+        if (checkbox_iterate or checkbox_iterate_batch) and p.seed == -1:
             p.seed = int(random.randrange(4294967294))
 
         state.job_count = job_count
@@ -154,7 +154,7 @@ class Script(scripts.Script):
             proc = process_images(copy_p)
             images += proc.images
             
-            if (checkbox_iterate):
+            if checkbox_iterate:
                 p.seed = p.seed + (p.batch_size * p.n_iter)
 
 
-- 
cgit v1.2.1


From 5510c282b1f1974005790066b5e444f74a5178fb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 2 Nov 2022 07:26:31 +0300
Subject: fix for extensions' javascript not loading

---
 modules/ui.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 2c15abb7..a94f46ea 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -671,6 +671,8 @@ def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
+    reload_javascript()
+
     parameters_copypaste.reset()
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
@@ -1782,4 +1784,3 @@ def load_javascript(raw_response):
 
 
 reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse)
-reload_javascript()
-- 
cgit v1.2.1


From 056f06d3738c267b1014e6e8e1ef5bd97af1fb45 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Wed, 2 Nov 2022 12:51:46 +0700
Subject: Reload VAE without reloading sd checkpoint

---
 modules/sd_models.py | 15 ++++----
 modules/sd_vae.py    | 97 ++++++++++++++++++++++++++++++++++++++++++++++++----
 webui.py             |  4 +--
 3 files changed, 98 insertions(+), 18 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6ab85b65..883639d1 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -159,15 +159,13 @@ def get_state_dict_from_checkpoint(pl_sd):
     return pl_sd
 
 
-vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
-
 def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
     vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
 
-    checkpoint_key = (checkpoint_info, vae_file)
+    checkpoint_key = checkpoint_info
 
     if checkpoint_key not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
@@ -190,13 +188,12 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
         devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
-        sd_vae.load_vae(model, vae_file)
-        model.first_stage_model.to(devices.dtype_vae)
-
         if shared.opts.sd_checkpoint_cache > 0:
+            # if PR #4035 were to get merged, restore base VAE first before caching
             checkpoints_loaded[checkpoint_key] = model.state_dict().copy()
             while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
                 checkpoints_loaded.popitem(last=False)  # LRU
+
     else:
         vae_name = sd_vae.get_filename(vae_file)
         print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache")
@@ -207,6 +204,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    sd_vae.load_vae(model, vae_file)
+
 
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
@@ -254,14 +253,14 @@ def load_model(checkpoint_info=None):
     return sd_model
 
 
-def reload_model_weights(sd_model=None, info=None, force=False):
+def reload_model_weights(sd_model=None, info=None):
     from modules import lowvram, devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
  
     if not sd_model:
         sd_model = shared.sd_model
 
-    if sd_model.sd_model_checkpoint == checkpoint_info.filename and not force:
+    if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index e9239326..78e14e8a 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -1,26 +1,65 @@
 import torch
 import os
 from collections import namedtuple
-from modules import shared, devices
+from modules import shared, devices, script_callbacks
 from modules.paths import models_path
 import glob
 
+
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
 vae_dir = "VAE"
 vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
 
+
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
+
+
 default_vae_dict = {"auto": "auto", "None": "None"}
 default_vae_list = ["auto", "None"]
+
+
 default_vae_values = [default_vae_dict[x] for x in default_vae_list]
 vae_dict = dict(default_vae_dict)
 vae_list = list(default_vae_list)
 first_load = True
 
+
+base_vae = None
+loaded_vae_file = None
+checkpoint_info = None
+
+
+def get_base_vae(model):
+    if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
+        return base_vae
+    return None
+
+
+def store_base_vae(model):
+    global base_vae, checkpoint_info
+    if checkpoint_info != model.sd_checkpoint_info:
+        base_vae = model.first_stage_model.state_dict().copy()
+        checkpoint_info = model.sd_checkpoint_info
+
+
+def delete_base_vae():
+    global base_vae, checkpoint_info
+    base_vae = None
+    checkpoint_info = None
+
+
+def restore_base_vae(model):
+    global base_vae, checkpoint_info
+    if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
+        load_vae_dict(model, base_vae)
+    delete_base_vae()
+
+
 def get_filename(filepath):
     return os.path.splitext(os.path.basename(filepath))[0]
 
+
 def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     global vae_dict, vae_list
     res = {}
@@ -43,6 +82,7 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     vae_dict.update(res)
     return vae_list
 
+
 def resolve_vae(checkpoint_file, vae_file="auto"):
     global first_load, vae_dict, vae_list
     # save_settings = False
@@ -96,24 +136,26 @@ def resolve_vae(checkpoint_file, vae_file="auto"):
 
     return vae_file
 
-def load_vae(model, vae_file):
-    global first_load, vae_dict, vae_list
+
+def load_vae(model, vae_file=None):
+    global first_load, vae_dict, vae_list, loaded_vae_file
     # save_settings = False
 
     if vae_file:
         print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
         vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-        model.first_stage_model.load_state_dict(vae_dict_1)
+        load_vae_dict(model, vae_dict_1)
 
-    # If vae used is not in dict, update it
-    # It will be removed on refresh though
-    if vae_file is not None:
+        # If vae used is not in dict, update it
+        # It will be removed on refresh though
         vae_opt = get_filename(vae_file)
         if vae_opt not in vae_dict:
             vae_dict[vae_opt] = vae_file
             vae_list.append(vae_opt)
 
+    loaded_vae_file = vae_file
+
     """
     # Save current VAE to VAE settings, maybe? will it work?
     if save_settings:
@@ -124,4 +166,45 @@ def load_vae(model, vae_file):
     """
 
     first_load = False
+
+
+# don't call this from outside
+def load_vae_dict(model, vae_dict_1=None):
+    if vae_dict_1:
+        store_base_vae(model)
+        model.first_stage_model.load_state_dict(vae_dict_1)
+    else:
+        restore_base_vae()
     model.first_stage_model.to(devices.dtype_vae)
+
+
+def reload_vae_weights(sd_model=None, vae_file="auto"):
+    from modules import lowvram, devices, sd_hijack
+
+    if not sd_model:
+        sd_model = shared.sd_model
+
+    checkpoint_info = sd_model.sd_checkpoint_info
+    checkpoint_file = checkpoint_info.filename
+    vae_file = resolve_vae(checkpoint_file, vae_file=vae_file)
+
+    if loaded_vae_file == vae_file:
+        return
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.send_everything_to_cpu()
+    else:
+        sd_model.to(devices.cpu)
+
+    sd_hijack.model_hijack.undo_hijack(sd_model)
+
+    load_vae(sd_model, vae_file)
+
+    sd_hijack.model_hijack.hijack(sd_model)
+    script_callbacks.model_loaded_callback(sd_model)
+
+    if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+        sd_model.to(devices.device)
+
+    print(f"VAE Weights loaded.")
+    return sd_model
diff --git a/webui.py b/webui.py
index 7cb4691b..034777a2 100644
--- a/webui.py
+++ b/webui.py
@@ -81,9 +81,7 @@ def initialize():
     modules.sd_vae.refresh_vae_list()
     modules.sd_models.load_model()
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
-    # I don't know what needs to be done to only reload VAE, with all those hijacks callbacks, and lowvram, 
-    # so for now this reloads the whole model too
-    shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(force=True)), call=False)
+    shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
 
-- 
cgit v1.2.1


From 95c6308ccd2e075d1fb804f5b98a4f0b07b87b7d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 2 Nov 2022 09:47:53 +0300
Subject: switch to gradio 3.8

---
 javascript/progressbar.js | 30 ++++++++++++++++++++++++------
 modules/ui.py             |  5 ++---
 requirements.txt          |  2 +-
 requirements_versions.txt |  2 +-
 style.css                 | 10 ++++++++++
 5 files changed, 38 insertions(+), 11 deletions(-)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 7a05726e..671fde34 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -3,8 +3,21 @@ global_progressbars = {}
 galleries = {}
 galleryObservers = {}
 
+// this tracks laumnches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running
+timeoutIds = {}
+
 function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
-    var progressbar = gradioApp().getElementById(id_progressbar)
+    // gradio 3.8's enlightened approach allows them to create two nested div elements inside each other with same id
+    // every time you use gr.HTML(elem_id='xxx'), so we handle this here
+    var progressbar = gradioApp().querySelector("#"+id_progressbar+" #"+id_progressbar)
+    var progressbarParent
+    if(progressbar){
+        progressbarParent = gradioApp().querySelector("#"+id_progressbar)
+    } else{
+        progressbar = gradioApp().getElementById(id_progressbar)
+        progressbarParent = null
+    }
+
     var skip = id_skip ? gradioApp().getElementById(id_skip) : null
     var interrupt = gradioApp().getElementById(id_interrupt)
     
@@ -26,18 +39,26 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
 	    global_progressbars[id_progressbar] = progressbar
 
         var mutationObserver = new MutationObserver(function(m){
+            if(timeoutIds[id_part]) return;
+
             preview = gradioApp().getElementById(id_preview)
             gallery = gradioApp().getElementById(id_gallery)
 
             if(preview != null && gallery != null){
                 preview.style.width = gallery.clientWidth + "px"
                 preview.style.height = gallery.clientHeight + "px"
+                if(progressbarParent) progressbar.style.width = progressbarParent.clientWidth + "px"
 
 				//only watch gallery if there is a generation process going on
                 check_gallery(id_gallery);
 
                 var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
-                if(!progressDiv){
+                if(progressDiv){
+                    timeoutIds[id_part] = window.setTimeout(function() {
+                        timeoutIds[id_part] = null
+                        requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt)
+                    }, 500)
+                } else{
                     if (skip) {
                         skip.style.display = "none"
                     }
@@ -47,13 +68,10 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
                     if (galleryObservers[id_gallery]) {
                         galleryObservers[id_gallery].disconnect();
                         galleries[id_gallery] = null;
-                    }    
+                    }
                 }
-
-
             }
 
-            window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500)
         });
         mutationObserver.observe( progressbar, { childList:true, subtree:true })
 	}
diff --git a/modules/ui.py b/modules/ui.py
index a94f46ea..45cd8c3f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1572,8 +1572,7 @@ def create_ui(wrap_gradio_gpu_call):
         reload_script_bodies.click(
             fn=reload_scripts,
             inputs=[],
-            outputs=[],
-            _js='function(){}'
+            outputs=[]
         )
 
         def request_restart():
@@ -1585,7 +1584,7 @@ def create_ui(wrap_gradio_gpu_call):
             fn=request_restart,
             inputs=[],
             outputs=[],
-            _js='function(){restart_reload()}'
+            _js='restart_reload'
         )
 
         if column is not None:
diff --git a/requirements.txt b/requirements.txt
index a53522f3..79e8b7c6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,7 +4,7 @@ fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
-gradio==3.5
+gradio==3.8
 invisible-watermark
 numpy
 omegaconf
diff --git a/requirements_versions.txt b/requirements_versions.txt
index 41f2501f..7bd16712 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -2,7 +2,7 @@ transformers==4.19.2
 diffusers==0.3.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.5
+gradio==3.8
 numpy==1.23.3
 Pillow==9.2.0
 realesrgan==0.3.0
diff --git a/style.css b/style.css
index dfef0dc5..e53079e0 100644
--- a/style.css
+++ b/style.css
@@ -260,6 +260,16 @@ input[type="range"]{
 #txt2img_negative_prompt, #img2img_negative_prompt{
 }
 
+/* gradio 3.8 adds opacity to progressbar which makes it blink; disable it here */
+.transition.opacity-20 {
+  opacity: 1 !important;
+}
+
+/* more gradio's garbage cleanup */
+.min-h-\[4rem\] {
+  min-height: unset !important;
+}
+
 #txt2img_progressbar, #img2img_progressbar, #ti_progressbar{
   position: absolute;
   z-index: 1000;
-- 
cgit v1.2.1


From dd2108fdac2ebf943d4ac3563a49202222b88acf Mon Sep 17 00:00:00 2001
From: Maiko Tan <maiko.tan.coding@gmail.com>
Date: Wed, 2 Nov 2022 15:04:35 +0800
Subject: fix: should invoke callback as well in api only mode

---
 modules/script_callbacks.py | 3 ++-
 webui.py                    | 2 ++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index da88635b..c28e220e 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -2,6 +2,7 @@ import sys
 import traceback
 from collections import namedtuple
 import inspect
+from typing import Optional
 
 from fastapi import FastAPI
 from gradio import Blocks
@@ -62,7 +63,7 @@ def clear_callbacks():
     callbacks_image_saved.clear()
     callbacks_cfg_denoiser.clear()
 
-def app_started_callback(demo: Blocks, app: FastAPI):
+def app_started_callback(demo: Optional[Blocks], app: FastAPI):
     for c in callbacks_app_started:
         try:
             c.callback(demo, app)
diff --git a/webui.py b/webui.py
index 84e5c1fd..dc4223dc 100644
--- a/webui.py
+++ b/webui.py
@@ -114,6 +114,8 @@ def api_only():
     app.add_middleware(GZipMiddleware, minimum_size=1000)
     api = create_api(app)
 
+    modules.script_callbacks.app_started_callback(None, app)
+
     api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
 
 
-- 
cgit v1.2.1


From a5409a6e4bc3eaa9757a7505d4564ad8e0d899ea Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Wed, 2 Nov 2022 14:37:22 +0700
Subject: Save VAE provided by cmd_opts.vae_path

---
 modules/sd_vae.py | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 78e14e8a..71e7a6e6 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -78,27 +78,24 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     vae_list.extend(default_vae_list)
     vae_list.extend(list(res.keys()))
     vae_dict.clear()
-    vae_dict.update(default_vae_dict)
     vae_dict.update(res)
+    vae_dict.update(default_vae_dict)
     return vae_list
 
 
 def resolve_vae(checkpoint_file, vae_file="auto"):
     global first_load, vae_dict, vae_list
-    # save_settings = False
 
-    # if vae_file argument is provided, it takes priority
+    # if vae_file argument is provided, it takes priority, but not saved
     if vae_file and vae_file not in default_vae_list:
         if not os.path.isfile(vae_file):
             vae_file = "auto"
-            # save_settings = True
             print("VAE provided as function argument doesn't exist")
-    # for the first load, if vae-path is provided, it takes priority and failure is reported
+    # for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
     if first_load and shared.cmd_opts.vae_path is not None:
         if os.path.isfile(shared.cmd_opts.vae_path):
             vae_file = shared.cmd_opts.vae_path
-            # save_settings = True
-            # print("Using VAE provided as command line argument")
+            shared.opts.data['sd_vae'] = get_filename(vae_file)
         else:
             print("VAE provided as command line argument doesn't exist")
     # else, we load from settings
-- 
cgit v1.2.1


From b421c5ee605f087d543d7e5e98856da81184d1c7 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Wed, 2 Nov 2022 17:16:47 +0900
Subject: Update ko_KR.json

New options in scripts
---
 localizations/ko_KR.json | 1 +
 1 file changed, 1 insertion(+)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index d152e575..7bb15ea6 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -547,6 +547,7 @@
   "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지와 그리드의 하위 디렉토리명의 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
   "Use old emphasis implementation. Can be useful to reproduce old seeds.": "옛 방식의 강조 구현을 사용합니다. 옛 시드를 재현하는 데 효과적일 수 있습니다.",
   "Use original name for output filename during batch process in extras tab": "부가기능 탭에서 이미지를 여러장 처리 시 결과물 파일명에 기존 파일명 사용하기",
+  "Use same random seed for all lines": "모든 줄에 동일한 시드 사용",
   "Use same seed for each image": "각 이미지에 동일한 시드 사용",
   "use spaces for tags in deepbooru": "deepbooru에서 태그에 공백 사용",
   "User interface": "사용자 인터페이스",
-- 
cgit v1.2.1


From 4a8cf01f6f7f072cc9c67d6b31662384b212dd9c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 2 Nov 2022 12:12:32 +0300
Subject: remove duplicate code from #3970

---
 modules/api/api.py | 10 +---------
 modules/shared.py  | 14 ++++++++++++++
 modules/ui.py      | 10 +---------
 3 files changed, 16 insertions(+), 18 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index b3d85e46..71c9c160 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -178,15 +178,7 @@ class Api:
 
         progress = min(progress, 1)
 
-        # copy from check_progress_call of ui.py
-
-        if shared.parallel_processing_allowed:
-            if shared.state.sampling_step - shared.state.current_image_sampling_step >= shared.opts.show_progress_every_n_steps and shared.state.current_latent is not None:
-                if shared.opts.show_progress_grid:
-                    shared.state.current_image = samples_to_image_grid(shared.state.current_latent)
-                else:
-                    shared.state.current_image = sample_to_image(shared.state.current_latent)
-                shared.state.current_image_sampling_step = shared.state.sampling_step
+        shared.state.set_current_image()
 
         current_image = None
         if shared.state.current_image and not req.skip_current_image:
diff --git a/modules/shared.py b/modules/shared.py
index 04aaa648..e65f6080 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -184,6 +184,20 @@ class State:
 
         devices.torch_gc()
 
+    """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
+    def set_current_image(self):
+        if not parallel_processing_allowed:
+            return
+
+        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and self.current_latent is not None:
+            if opts.show_progress_grid:
+                self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
+            else:
+                self.current_image = sd_samplers.sample_to_image(self.current_latent)
+
+            self.current_image_sampling_step = self.sampling_step
+
+
 state = State()
 
 artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
diff --git a/modules/ui.py b/modules/ui.py
index 45cd8c3f..784439ba 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -277,15 +277,7 @@ def check_progress_call(id_part):
     preview_visibility = gr_show(False)
 
     if opts.show_progress_every_n_steps > 0:
-        if shared.parallel_processing_allowed:
-
-            if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None:
-                if opts.show_progress_grid:
-                    shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent)
-                else:
-                    shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)
-                shared.state.current_image_sampling_step = shared.state.sampling_step
-
+        shared.state.set_current_image()
         image = shared.state.current_image
 
         if image is None:
-- 
cgit v1.2.1


From 9c67408004ed132637d10321bf44565f82055fd2 Mon Sep 17 00:00:00 2001
From: timntorres <116157310+timntorres@users.noreply.github.com>
Date: Wed, 2 Nov 2022 02:18:21 -0700
Subject: Allow saving "before-highres-fix. (#4150)

* Save image/s before doing highres fix.
---
 modules/processing.py  | 17 +++++++++++++++--
 modules/sd_samplers.py |  5 ++---
 modules/shared.py      |  1 +
 3 files changed, 18 insertions(+), 5 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index b541ee2b..2dcf4879 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -521,7 +521,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
             with devices.autocast():
-                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+                # Only Txt2Img needs an extra argument, n, when saving intermediate images pre highres fix. 
+                if isinstance(p, StableDiffusionProcessingTxt2Img):
+                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, n=n)
+                else:
+                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
 
             samples_ddim = samples_ddim.to(devices.dtype_vae)
             x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
@@ -649,7 +653,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, n=0):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
@@ -685,6 +689,15 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
+        # Save a copy of the image/s before doing highres fix, if applicable.
+        if opts.save and not self.do_not_save_samples and opts.save_images_before_highres_fix:
+            for i in range(self.batch_size):
+                # This batch's ith image.
+                img = sd_samplers.sample_to_image(samples, i)
+                # Index that accounts for both batch size and batch count.
+                ind = i + self.batch_size*n
+                images.save_image(img, self.outpath_samples, "", self.all_seeds[ind], self.all_prompts[ind], opts.samples_format, suffix=f"-before-highres-fix")
+
         shared.state.nextjob()
 
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 44d4c189..d7fa89a0 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -93,9 +93,8 @@ def single_sample_to_image(sample):
     return Image.fromarray(x_sample)
 
 
-def sample_to_image(samples):
-    return single_sample_to_image(samples[0])
-
+def sample_to_image(samples, index=0):
+    return single_sample_to_image(samples[index])
 
 def samples_to_image_grid(samples):
     return images.image_grid([single_sample_to_image(sample) for sample in samples])
diff --git a/modules/shared.py b/modules/shared.py
index e65f6080..ce991424 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -255,6 +255,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
     "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
     "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
+    "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
     "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
     "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
 
-- 
cgit v1.2.1


From eb5e82c7ddf5e72fa13b83bd1f12d3a07a4de1a4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 2 Nov 2022 12:45:03 +0300
Subject: do not unnecessarily run VAE one more time when saving intermediate
 image with hires fix

---
 modules/processing.py  | 39 ++++++++++++++++++++-------------------
 modules/sd_samplers.py |  1 +
 modules/shared.py      |  2 +-
 scripts/img2imgalt.py  |  3 +--
 4 files changed, 23 insertions(+), 22 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 2dcf4879..3a364b5f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -199,7 +199,7 @@ class StableDiffusionProcessing():
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         raise NotImplementedError()
 
     def close(self):
@@ -521,11 +521,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
             with devices.autocast():
-                # Only Txt2Img needs an extra argument, n, when saving intermediate images pre highres fix. 
-                if isinstance(p, StableDiffusionProcessingTxt2Img):
-                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, n=n)
-                else:
-                    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
 
             samples_ddim = samples_ddim.to(devices.dtype_vae)
             x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
@@ -653,7 +649,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, n=0):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
@@ -666,9 +662,21 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
+        """saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images"""
+        def save_intermediate(image, index):
+            if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
+                return
+
+            if not isinstance(image, Image.Image):
+                image = sd_samplers.sample_to_image(image, index)
+
+            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
+
         if opts.use_scale_latent_for_hires_fix:
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
 
+            for i in range(samples.shape[0]):
+                save_intermediate(samples, i)
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
@@ -678,6 +686,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                 x_sample = x_sample.astype(np.uint8)
                 image = Image.fromarray(x_sample)
+
+                save_intermediate(image, i)
+
                 image = images.resize_image(0, image, self.width, self.height)
                 image = np.array(image).astype(np.float32) / 255.0
                 image = np.moveaxis(image, 2, 0)
@@ -689,15 +700,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
-        # Save a copy of the image/s before doing highres fix, if applicable.
-        if opts.save and not self.do_not_save_samples and opts.save_images_before_highres_fix:
-            for i in range(self.batch_size):
-                # This batch's ith image.
-                img = sd_samplers.sample_to_image(samples, i)
-                # Index that accounts for both batch size and batch count.
-                ind = i + self.batch_size*n
-                images.save_image(img, self.outpath_samples, "", self.all_seeds[ind], self.all_prompts[ind], opts.samples_format, suffix=f"-before-highres-fix")
-
         shared.state.nextjob()
 
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
@@ -844,8 +846,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
         self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
 
-
-    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
@@ -856,4 +857,4 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         del x
         devices.torch_gc()
 
-        return samples
\ No newline at end of file
+        return samples
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d7fa89a0..c7c414ef 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -96,6 +96,7 @@ def single_sample_to_image(sample):
 def sample_to_image(samples, index=0):
     return single_sample_to_image(samples[index])
 
+
 def samples_to_image_grid(samples):
     return images.image_grid([single_sample_to_image(sample) for sample in samples])
 
diff --git a/modules/shared.py b/modules/shared.py
index ce991424..01f47e38 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -256,6 +256,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
     "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
     "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
+    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
     "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
 
@@ -322,7 +323,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
-    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index 88abc093..964b75c7 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -166,8 +166,7 @@ class Script(scripts.Script):
         if override_strength:
             p.denoising_strength = 1.0
 
-
-        def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+        def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
             lat = (p.init_latent.cpu().numpy() * 10).astype(int)
 
             same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \
-- 
cgit v1.2.1


From f2a5cbe6f55592c4c5527b8e0bf99ea8d658f057 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 2 Nov 2022 14:41:29 +0300
Subject: fix #3986 breaking --no-half-vae

---
 modules/sd_models.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 883639d1..5075fadb 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -183,11 +183,20 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
             model.to(memory_format=torch.channels_last)
 
         if not shared.cmd_opts.no_half:
+            vae = model.first_stage_model
+
+            # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
+            if shared.cmd_opts.no_half_vae:
+                model.first_stage_model = None
+
             model.half()
+            model.first_stage_model = vae
 
         devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
         devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
+        model.first_stage_model.to(devices.dtype_vae)
+
         if shared.opts.sd_checkpoint_cache > 0:
             # if PR #4035 were to get merged, restore base VAE first before caching
             checkpoints_loaded[checkpoint_key] = model.state_dict().copy()
-- 
cgit v1.2.1


From 13cbc1622e78d818b77292b6ae589f45f3040e94 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Wed, 2 Nov 2022 21:00:24 +0900
Subject: Update ko_KR.json

Fix some changed setting strings and added new ones
---
 localizations/ko_KR.json | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 7bb15ea6..874771f9 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -43,6 +43,7 @@
   "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
   "Apply settings": "설정 적용하기",
   "Artists to study": "연구할만한 작가들",
+  "auto": "자동",
   "Auto focal point crop": "초점 기준 크롭(자동 감지)",
   "Autocomplete options": "자동완성 설정",
   "Available": "지원되는 확장기능 목록",
@@ -303,7 +304,7 @@
   "Modules": "모듈",
   "Move face restoration model from VRAM into RAM after processing": "처리가 완료되면 얼굴 보정 모델을 VRAM에서 RAM으로 옮기기",
   "Move to favorites": "즐겨찾기로 옮기기",
-  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "하이퍼네트워크 훈련 진행 시 VAE와 CLIP을 RAM으로 옮기기. VRAM이 절약됩니다.",
+  "Move VAE and CLIP to RAM when training if possible. Saves VRAM.": "훈련 진행 시 가능하면 VAE와 CLIP을 RAM으로 옮기기. VRAM이 절약됩니다.",
   "Moved to favorites": "즐겨찾기로 옮겨짐",
   "Multiplier (M) - set to 0 to get model A": "배율 (M) - 0으로 적용하면 모델 A를 얻게 됩니다",
   "Name": "이름",
@@ -418,6 +419,7 @@
   "Save": "저장",
   "Save a copy of embedding to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 임베딩을 저장합니다, 비활성화하려면 0으로 설정하십시오.",
   "Save a copy of image before applying color correction to img2img results": "이미지→이미지 결과물에 색상 보정을 진행하기 전 이미지의 복사본을 저장하기",
+  "Save a copy of image before applying highres fix.": "고해상도 보정을 진행하기 전 이미지의 복사본을 저장하기",
   "Save a copy of image before doing face restoration.": "얼굴 보정을 진행하기 전 이미지의 복사본을 저장하기",
   "Save an csv containing the loss to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 손실(Loss)을 포함하는 csv 파일을 저장합니다, 비활성화하려면 0으로 설정하십시오.",
   "Save an image to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 이미지를 저장합니다, 비활성화하려면 0으로 설정하십시오.",
@@ -541,6 +543,7 @@
   "Use BLIP for caption": "캡션에 BLIP 사용",
   "Use checkbox to enable the extension; it will be enabled or disabled when you click apply button": "체크박스를 이용해 적용할 확장기능을 선택하세요. 변경사항은 적용 후 UI 재시작 버튼을 눌러야 적용됩니다.",
   "Use checkbox to mark the extension for update; it will be updated when you click apply button": "체크박스를 이용해 업데이트할 확장기능을 선택하세요. 업데이트는 적용 후 UI 재시작 버튼을 눌러야 적용됩니다.",
+  "Use cross attention optimizations while training": "훈련 진행 시 크로스 어텐션 최적화 사용",
   "Use deepbooru for caption": "캡션에 deepbooru 사용",
   "Use dropout": "드롭아웃 사용",
   "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지 파일명 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
-- 
cgit v1.2.1


From 3178c35224467893cf8dcedb1028c59c6c23db58 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Wed, 2 Nov 2022 22:16:32 +0900
Subject: resolve conflicts

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 065b893d..959937d7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -285,7 +285,7 @@ options_templates.update(options_section(('system', "System"), {
 }))
 
 options_templates.update(options_section(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),
+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
-- 
cgit v1.2.1


From 9b5f85ac83f864310fe19c9deab6670bad695b0d Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Wed, 2 Nov 2022 22:18:04 +0900
Subject: first revert

---
 modules/shared.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 959937d7..7e8c552b 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -286,7 +286,6 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
-    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-- 
cgit v1.2.1


From 7ea5956ad5fa925f92116e8a3bf78d7f6517b654 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Wed, 2 Nov 2022 22:18:55 +0900
Subject: now add

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/shared.py b/modules/shared.py
index d8e99f85..7ecb40d8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -309,6 +309,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-- 
cgit v1.2.1


From e21fcd72fcf147904a1df060226c4df12acf251e Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Wed, 2 Nov 2022 22:37:45 +0800
Subject: add back png info in image api

---
 modules/api/api.py | 21 +++++++++++++++++----
 1 file changed, 17 insertions(+), 4 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 71c9c160..ceaf08b0 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -7,8 +7,9 @@ from fastapi import APIRouter, Depends, HTTPException
 import modules.shared as shared
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers, sample_to_image, samples_to_image_grid
+from modules.sd_samplers import all_samplers
 from modules.extras import run_extras, run_pnginfo
+from PIL import PngImagePlugin
 
 
 def upscaler_to_index(name: str):
@@ -31,9 +32,21 @@ def setUpscalers(req: dict):
 
 
 def encode_pil_to_base64(image):
-    buffer = io.BytesIO()
-    image.save(buffer, format="png")
-    return base64.b64encode(buffer.getvalue())
+    with io.BytesIO() as output_bytes:
+
+        # Copy any text-only metadata
+        use_metadata = False
+        metadata = PngImagePlugin.PngInfo()
+        for key, value in image.info.items():
+            if isinstance(key, str) and isinstance(value, str):
+                metadata.add_text(key, value)
+                use_metadata = True
+
+        image.save(
+            output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
+        )
+        bytes_data = output_bytes.getvalue()
+    return base64.b64encode(bytes_data)
 
 
 class Api:
-- 
cgit v1.2.1


From d624cb82a7c65a1ea04e4b6e23f0164a3ba25e25 Mon Sep 17 00:00:00 2001
From: Ikko Ashimine <eltociear@gmail.com>
Date: Thu, 3 Nov 2022 01:05:00 +0900
Subject: Fix typo in ui.js

interation -> interaction
---
 javascript/ui.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 7e116465..0308dce3 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -1,4 +1,4 @@
-// various functions for interation with ui.py not large enough to warrant putting them in separate files
+// various functions for interaction with ui.py not large enough to warrant putting them in separate files
 
 function set_theme(theme){
     gradioURL = window.location.href
-- 
cgit v1.2.1


From a9e979977a8e3999b01b6a086bb1332ab7ab308b Mon Sep 17 00:00:00 2001
From: Artem Zagidulin <p0chta@yandex.ru>
Date: Wed, 2 Nov 2022 19:05:01 +0300
Subject: process_one

---
 modules/processing.py |  3 +++
 modules/scripts.py    | 16 ++++++++++++++++
 2 files changed, 19 insertions(+)

diff --git a/modules/processing.py b/modules/processing.py
index 3a364b5f..72a2ee4e 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -509,6 +509,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if len(prompts) == 0:
                 break
 
+            if p.scripts is not None:
+                p.scripts.process_one(p)
+
             with devices.autocast():
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
diff --git a/modules/scripts.py b/modules/scripts.py
index 533db45c..9f82efea 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -70,6 +70,13 @@ class Script:
 
         pass
 
+    def process_one(self, p, *args):
+        """
+        Same as process(), but called for every iteration
+        """
+
+        pass
+
     def postprocess(self, p, processed, *args):
         """
         This function is called after processing ends for AlwaysVisible scripts.
@@ -294,6 +301,15 @@ class ScriptRunner:
                 print(f"Error running process: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
+    def process_one(self, p):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.process_one(p, *script_args)
+            except Exception:
+                print(f"Error running process_one: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
     def postprocess(self, p, processed):
         for script in self.alwayson_scripts:
             try:
-- 
cgit v1.2.1


From f1b6ac64e451036fb4dfabe66d79488c56c06776 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Kyu=E2=99=A5?= <3ad4gum@gmail.com>
Date: Wed, 2 Nov 2022 17:24:42 +0100
Subject: Added option to preview Created images on batch completion.

---
 modules/shared.py | 25 ++++++++++++++++---------
 modules/ui.py     |  2 +-
 2 files changed, 17 insertions(+), 10 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index d8e99f85..d4cf32a4 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -146,6 +146,9 @@ class State:
         self.interrupted = True
 
     def nextjob(self):
+        if opts.show_progress_every_n_steps == -1: 
+            self.do_set_current_image()
+            
         self.job_no += 1
         self.sampling_step = 0
         self.current_image_sampling_step = 0
@@ -186,17 +189,21 @@ class State:
 
     """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
     def set_current_image(self):
+        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0:
+            self.do_set_current_image()
+
+    def do_set_current_image(self):
         if not parallel_processing_allowed:
             return
+        if self.current_latent is None:
+            return
+            
+        if opts.show_progress_grid:
+            self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
+        else:
+            self.current_image = sd_samplers.sample_to_image(self.current_latent)
 
-        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and self.current_latent is not None:
-            if opts.show_progress_grid:
-                self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
-            else:
-                self.current_image = sd_samplers.sample_to_image(self.current_latent)
-
-            self.current_image_sampling_step = self.sampling_step
-
+        self.current_image_sampling_step = self.sampling_step
 
 state = State()
 
@@ -351,7 +358,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
-    "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
+    "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
     "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
diff --git a/modules/ui.py b/modules/ui.py
index 2609857e..29de1e10 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -276,7 +276,7 @@ def check_progress_call(id_part):
     image = gr_show(False)
     preview_visibility = gr_show(False)
 
-    if opts.show_progress_every_n_steps > 0:
+    if opts.show_progress_every_n_steps != 0:
         shared.state.set_current_image()
         image = shared.state.current_image
 
-- 
cgit v1.2.1


From c07f1d0d7821f85b9ce1419992c118963d605bd7 Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Wed, 2 Nov 2022 16:59:10 +0000
Subject: Convert callbacks into a private map, add utility functions for
 removing callbacks

---
 modules/script_callbacks.py | 68 +++++++++++++++++++++++++++------------------
 1 file changed, 41 insertions(+), 27 deletions(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index c28e220e..4a7fb944 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -46,25 +46,23 @@ class CFGDenoiserParams:
 
 
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
-callbacks_app_started = []
-callbacks_model_loaded = []
-callbacks_ui_tabs = []
-callbacks_ui_settings = []
-callbacks_before_image_saved = []
-callbacks_image_saved = []
-callbacks_cfg_denoiser = []
+__callback_map = dict(
+    callbacks_app_started=[],
+    callbacks_model_loaded=[],
+    callbacks_ui_tabs=[],
+    callbacks_ui_settings=[],
+    callbacks_before_image_saved=[],
+    callbacks_image_saved=[],
+    callbacks_cfg_denoiser=[]
+)
 
 
 def clear_callbacks():
-    callbacks_model_loaded.clear()
-    callbacks_ui_tabs.clear()
-    callbacks_ui_settings.clear()
-    callbacks_before_image_saved.clear()
-    callbacks_image_saved.clear()
-    callbacks_cfg_denoiser.clear()
+    for callback_list in __callback_map.values():
+        callback_list.clear()
 
 def app_started_callback(demo: Optional[Blocks], app: FastAPI):
-    for c in callbacks_app_started:
+    for c in __callback_map['callbacks_app_started']:
         try:
             c.callback(demo, app)
         except Exception:
@@ -72,7 +70,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI):
 
 
 def model_loaded_callback(sd_model):
-    for c in callbacks_model_loaded:
+    for c in __callback_map['callbacks_model_loaded']:
         try:
             c.callback(sd_model)
         except Exception:
@@ -82,7 +80,7 @@ def model_loaded_callback(sd_model):
 def ui_tabs_callback():
     res = []
     
-    for c in callbacks_ui_tabs:
+    for c in __callback_map['callbacks_ui_tabs']:
         try:
             res += c.callback() or []
         except Exception:
@@ -92,7 +90,7 @@ def ui_tabs_callback():
 
 
 def ui_settings_callback():
-    for c in callbacks_ui_settings:
+    for c in __callback_map['callbacks_ui_settings']:
         try:
             c.callback()
         except Exception:
@@ -100,7 +98,7 @@ def ui_settings_callback():
 
 
 def before_image_saved_callback(params: ImageSaveParams):
-    for c in callbacks_before_image_saved:
+    for c in __callback_map['callbacks_before_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -108,7 +106,7 @@ def before_image_saved_callback(params: ImageSaveParams):
 
 
 def image_saved_callback(params: ImageSaveParams):
-    for c in callbacks_image_saved:
+    for c in __callback_map['callbacks_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -116,7 +114,7 @@ def image_saved_callback(params: ImageSaveParams):
 
 
 def cfg_denoiser_callback(params: CFGDenoiserParams):
-    for c in callbacks_cfg_denoiser:
+    for c in __callback_map['callbacks_cfg_denoiser']:
         try:
             c.callback(params)
         except Exception:
@@ -129,17 +127,33 @@ def add_callback(callbacks, fun):
 
     callbacks.append(ScriptCallback(filename, fun))
 
+    
+def remove_current_script_callbacks():
+    stack = [x for x in inspect.stack() if x.filename != __file__]
+    filename = stack[0].filename if len(stack) > 0 else 'unknown file'
+    if filename == 'unknown file':
+        return
+    for callback_list in __callback_map.values():
+        for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
+            callback_list.remove(callback_to_remove)
+
+
+def remove_callbacks_for_function(callback_func):
+    for callback_list in __callback_map.values():
+        for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
+            callback_list.remove(callback_to_remove)
+
 
 def on_app_started(callback):
     """register a function to be called when the webui started, the gradio `Block` component and
     fastapi `FastAPI` object are passed as the arguments"""
-    add_callback(callbacks_app_started, callback)
+    add_callback(__callback_map['callbacks_app_started'], callback)
 
 
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
-    add_callback(callbacks_model_loaded, callback)
+    add_callback(__callback_map['callbacks_model_loaded'], callback)
 
 
 def on_ui_tabs(callback):
@@ -152,13 +166,13 @@ def on_ui_tabs(callback):
     title is tab text displayed to user in the UI
     elem_id is HTML id for the tab
     """
-    add_callback(callbacks_ui_tabs, callback)
+    add_callback(__callback_map['callbacks_ui_tabs'], callback)
 
 
 def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
-    add_callback(callbacks_ui_settings, callback)
+    add_callback(__callback_map['callbacks_ui_settings'], callback)
 
 
 def on_before_image_saved(callback):
@@ -166,7 +180,7 @@ def on_before_image_saved(callback):
     The callback is called with one argument:
         - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
     """
-    add_callback(callbacks_before_image_saved, callback)
+    add_callback(__callback_map['callbacks_before_image_saved'], callback)
 
 
 def on_image_saved(callback):
@@ -174,7 +188,7 @@ def on_image_saved(callback):
     The callback is called with one argument:
         - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
     """
-    add_callback(callbacks_image_saved, callback)
+    add_callback(__callback_map['callbacks_image_saved'], callback)
 
 
 def on_cfg_denoiser(callback):
@@ -182,5 +196,5 @@ def on_cfg_denoiser(callback):
     The callback is called with one argument:
         - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
     """
-    add_callback(callbacks_cfg_denoiser, callback)
+    add_callback(__callback_map['callbacks_cfg_denoiser'], callback)
 
-- 
cgit v1.2.1


From 1c9db534bd4ca7facb2b99cc3ad5bbbedf5e539e Mon Sep 17 00:00:00 2001
From: Martucci <73501718+M-art-ucci@users.noreply.github.com>
Date: Wed, 2 Nov 2022 14:35:37 -0300
Subject: pt_BR minor issue with lat comma

 and a few translation tweaks
---
 localizations/pt_BR.json | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/localizations/pt_BR.json b/localizations/pt_BR.json
index d869170a..c8ef1b11 100644
--- a/localizations/pt_BR.json
+++ b/localizations/pt_BR.json
@@ -17,7 +17,7 @@
     "Checkpoint Merger": "Fusão de Checkpoint",
     "Train": "Treinar",
     "Settings": "Configurações",
-    "Extensions": "Extensions",
+    "Extensions": "Extensões",
     "Prompt": "Prompt",
     "Negative prompt": "Prompt negativo",
     "Run": "Executar",
@@ -57,7 +57,7 @@
     "Highres. fix": "Ajuste de alta resolução",
     "Firstpass width": "Primeira Passagem da largura",
     "Firstpass height": "Primeira Passagem da altura",
-    "Denoising strength": "Denoising strength",
+    "Denoising strength": "Força do denoise",
     "Batch count": "Quantidade por lote",
     "Batch size": "Quantidade de lotes",
     "CFG Scale": "Escala CFG",
@@ -480,6 +480,6 @@
     "This string will be used to join split words into a single line if the option above is enabled.": "Esta string será usada para unir palavras divididas em uma única linha se a opção acima estiver habilitada.",
     "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Aplicável somente para modelos de inpaint. Determina quanto deve mascarar da imagem original para inpaint e img2img. 1.0 significa totalmente mascarado, que é o comportamento padrão. 0.0 significa uma condição totalmente não mascarada. Valores baixos ajudam a preservar a composição geral da imagem, mas vai encontrar dificuldades com grandes mudanças.",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Lista de nomes de configurações, separados por vírgulas, para configurações que devem ir para a barra de acesso rápido na parte superior, em vez da guia de configuração usual. Veja modules/shared.py para nomes de configuração. Necessita reinicialização para aplicar.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se este valor for diferente de zero, ele será adicionado à seed e usado para inicializar o RNG para ruídos ao usar amostragens com Tempo Estimado. Você pode usar isso para produzir ainda mais variações de imagens ou pode usar isso para combinar imagens de outro software se souber o que está fazendo."
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se este valor for diferente de zero, ele será adicionado à seed e usado para inicializar o RNG para ruídos ao usar amostragens com Tempo Estimado. Você pode usar isso para produzir ainda mais variações de imagens ou pode usar isso para combinar imagens de outro software se souber o que está fazendo.",
     "Leave empty for auto": "Deixar desmarcado para automático"
 }
-- 
cgit v1.2.1


From de64146ad2fc2030a4cd3545676f9e18c93b8b18 Mon Sep 17 00:00:00 2001
From: Artem Zagidulin <p0chta@yandex.ru>
Date: Wed, 2 Nov 2022 21:30:50 +0300
Subject: add number of itter

---
 modules/processing.py | 2 +-
 modules/scripts.py    | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 72a2ee4e..17f4a5ec 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -510,7 +510,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 break
 
             if p.scripts is not None:
-                p.scripts.process_one(p)
+                p.scripts.process_one(p, n)
 
             with devices.autocast():
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
diff --git a/modules/scripts.py b/modules/scripts.py
index 9f82efea..7aa0d56a 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -70,7 +70,7 @@ class Script:
 
         pass
 
-    def process_one(self, p, *args):
+    def process_one(self, p, n, *args):
         """
         Same as process(), but called for every iteration
         """
@@ -301,11 +301,11 @@ class ScriptRunner:
                 print(f"Error running process: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
-    def process_one(self, p):
+    def process_one(self, p, n):
         for script in self.alwayson_scripts:
             try:
                 script_args = p.script_args[script.args_from:script.args_to]
-                script.process_one(p, *script_args)
+                script.process_one(p, n, *script_args)
             except Exception:
                 print(f"Error running process_one: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
-- 
cgit v1.2.1


From 32d95c1129127ed7b8d5f03b464943fb4ace4342 Mon Sep 17 00:00:00 2001
From: Riccardo Giovanetti <29801031+Harvester62@users.noreply.github.com>
Date: Wed, 2 Nov 2022 22:42:40 +0100
Subject: Italian localization - Additions and Updates

Updated localization with the latest version of these Scripts/Extensions:

unprompted (new script)
img2tiles
random
random grid

Some new options in the Extras and Settings have been translated too.
---
 localizations/it_IT.json | 32 ++++++++++++++++++++++++--------
 1 file changed, 24 insertions(+), 8 deletions(-)

diff --git a/localizations/it_IT.json b/localizations/it_IT.json
index 49489f40..de599a85 100644
--- a/localizations/it_IT.json
+++ b/localizations/it_IT.json
@@ -104,6 +104,7 @@
     "Seed travel": "Interpolazione semi",
     "Shift attention": "Sposta l'attenzione",
     "Text to Vector Graphics": "Da testo a grafica vettoriale",
+    "Unprompted": "Unprompted",
     "X/Y plot": "Grafico X/Y",
     "X/Y/Z plot": "Grafico X/Y/Z",
     "Dynamic Prompting v0.13.6": "Prompt dinamici v0.13.6",
@@ -195,7 +196,7 @@
     "Beta distribution (VP only)": "Distribuzione Beta (Solo CV)",
     "Beta min (VP only)": "Beta min (Solo CV)",
     "Epsilon (VP only)": "Epsilon (Solo CV)",
-    "Running in txt2img mode:": "Esecuzione in modalità txt2img:",
+    "Running in txt2img mode:": "Esecusione in modalità txt2img:",
     "Render these video formats:": "Renderizza in questi formati:",
     "GIF": "GIF",
     "MP4": "MP4",
@@ -259,6 +260,7 @@
     "Save results as video": "Salva i risultati come video",
     "Frames per second": "Fotogrammi al secondo",
     "Iterate seed every line": "Iterare il seme per ogni riga",
+    "Use same random seed for all lines": "Usa lo stesso seme casuale per tutte le righe",
     "List of prompt inputs": "Elenco di prompt di input",
     "Upload prompt inputs": "Carica un file contenente i prompt di input",
     "n": "Esegui n volte",
@@ -294,6 +296,13 @@
     "Transparent PNG": "PNG trasparente",
     "Noise Tolerance": "Tolleranza al rumore",
     "Quantize": "Quantizzare",
+    "Dry Run": "Esecuzione a vuoto (Debug)",
+    "NEW!": "NUOVO!",
+    "Premium Fantasy Card Template": "Premium Fantasy Card Template",
+    "is now available.": "è ora disponibile.",
+    "Generate a wide variety of creatures and characters in the style of a fantasy card game. Perfect for heroes, animals, monsters, and even crazy hybrids.": "Genera un'ampia varietà di creature e personaggi nello stile di un gioco di carte fantasy. Perfetto per eroi, animali, mostri e persino ibridi incredibili.",
+    "Learn More ➜": "Per saperne di più ➜",
+    "Purchases help fund the continued development of Unprompted. Thank you for your support!": "Gli acquisti aiutano a finanziare il continuo sviluppo di Unprompted. Grazie per il vostro sostegno!",
     "X type": "Parametro asse X",
     "Nothing": "Niente",
     "Var. seed": "Seme della variazione",
@@ -424,6 +433,7 @@
     "Sigma adjustment for finding noise for image": "Regolazione Sigma per trovare il rumore per l'immagine",
     "Tile size": "Dimensione piastrella",
     "Tile overlap": "Sovrapposizione piastrella",
+    "New seed for each tile": "Nuovo seme per ogni piastrella",
     "alternate img2img imgage": "Immagine alternativa per img2img",
     "interpolation values": "Valori di interpolazione",
     "Refinement loops": "Cicli di affinamento",
@@ -455,8 +465,9 @@
     "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenterà l'immagine al doppio delle dimensioni; utilizzare i cursori di larghezza e altezza per impostare la dimensione della piastrella",
     "Upscaler": "Ampliamento immagine",
     "Lanczos": "Lanczos",
+    "Nearest": "Nearest",
     "LDSR": "LDSR",
-    "ESRGAN_4x": "ESRGAN_4x",
+    "BSRGAN": "BSRGAN",
     "ScuNET GAN": "ScuNET GAN",
     "ScuNET PSNR": "ScuNET PSNR",
     "SwinIR 4x": "SwinIR 4x",
@@ -808,6 +819,7 @@
     "image_path": "Percorso immagine",
     "mp4_path": "Percorso MP4",
     "Click here after the generation to show the video": "Clicca qui dopo la generazione per mostrare il video",
+    "NOTE: If the 'Generate' button doesn't work, go in Settings and click 'Restart Gradio and Refresh...'.": "NOTA: se il pulsante 'Genera' non funziona, vai in Impostazioni e fai clic su 'Riavvia Gradio e Aggiorna...'.",
     "Save Settings": "Salva le impostazioni",
     "Load Settings": "Carica le impostazioni",
     "Path relative to the webui folder." : "Percorso relativo alla cartella webui.",
@@ -922,8 +934,8 @@
     "Renew Page": "Aggiorna la pagina",
     "Number": "Numero",
     "set_index": "Imposta indice",
-    "load_switch": "load_switch",
-    "turn_page_switch": "turn_page_switch",
+    "load_switch": "Carica",
+    "turn_page_switch": "Volta pagina",
     "Checkbox": "Casella di controllo",						   
     "Checkbox Group": "Seleziona immagini per",
     "artists": "Artisti",
@@ -956,6 +968,8 @@
     "Save text information about generation parameters as chunks to png files": "Salva le informazioni di testo dei parametri di generazione come blocchi nel file png",
     "Create a text file next to every image with generation parameters.": "Crea un file di testo assieme a ogni immagine con i parametri di generazione.",
     "Save a copy of image before doing face restoration.": "Salva una copia dell'immagine prima di eseguire il restauro dei volti.",
+    "Save a copy of image before applying highres fix.": "Salva una copia dell'immagine prima di applicare la correzione ad alta risoluzione.",
+    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la correzione del colore ai risultati di img2img",
     "Quality for saved jpeg images": "Qualità delle immagini salvate in formato JPEG",
     "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l'immagine PNG è più grande di 4 MB o qualsiasi dimensione è maggiore di 4000, ridimensiona e salva la copia come JPG",
     "Use original name for output filename during batch process in extras tab": "Usa il nome originale per il nome del file di output durante l'elaborazione a lotti nella scheda 'Extra'",
@@ -997,12 +1011,14 @@
     "Filename join string": "Stringa per unire le parole estratte dal nome del file",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per una singola immagine di input per epoca; utilizzato solo per visualizzare il numero di epoca",
     "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salva un file CSV contenente la perdita nella cartella di registrazione ogni N passaggi, 0 per disabilitare",
+    "Use cross attention optimizations while training": "Usa le ottimizzazioni di controllo dell'attenzione incrociato durante l'allenamento",
     "Stable Diffusion": "Stable Diffusion",
     "Checkpoints to cache in RAM": "Checkpoint da memorizzare nella RAM",
+    "SD VAE": "SD VAE",
+    "auto": "auto",
     "Hypernetwork strength": "Forza della Iperrete",
     "Inpainting conditioning mask strength": "Forza della maschera di condizionamento del Inpainting",
     "Apply color correction to img2img results to match original colors.": "Applica la correzione del colore ai risultati di img2img in modo che corrispondano ai colori originali.",
-    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la correzione del colore ai risultati di img2img",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, esegue esattamente la quantità di passi specificata dalla barra di scorrimento (normalmente se ne effettuano di meno con meno riduzione del rumore).",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Abilita la quantizzazione nei campionatori K per risultati più nitidi e puliti. Questo può cambiare i semi esistenti. Richiede il riavvio per applicare la modifica.",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Enfasi: utilizzare (testo) per fare in modo che il modello presti maggiore attenzione al testo e [testo] per fargli prestare meno attenzione",
@@ -1194,8 +1210,8 @@
     "Hue:0": "Hue:0",
     "S:0": "S:0",
     "L:0": "L:0",
-    "Load Canvas": "Carica Tela",
-    "saveCanvas": "Salva Tela",
+    "Load Canvas": "Carica Canvas",
+    "Save Canvas": "Salva Canvas",
     "latest": "aggiornato",
     "behind": "da aggiornare",
     "Description": "Descrizione",
@@ -1214,4 +1230,4 @@
     "Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Calcola il punteggio estetico per le immagini generate utilizzando il predittore del punteggio estetico CLIP+MLP basato su Chad Scorer",
     "Lets you edit captions in training datasets.": "Consente di modificare i sottotitoli nei set di dati di addestramento.",
     "Time taken:": "Tempo impiegato:"
-}
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 2ac25ea64f31fd0e7dea35d27a52f3646618c3b6 Mon Sep 17 00:00:00 2001
From: digburn <digburned@gmail.com>
Date: Wed, 2 Nov 2022 21:52:23 +0000
Subject: fix: Add required parameter to API extras route

---
 modules/api/models.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/api/models.py b/modules/api/models.py
index 9ee42a17..9069c0ac 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -131,6 +131,7 @@ class ExtrasBaseRequest(BaseModel):
     upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
+    upscale_first: bool = Field(default=True, title="Upscale first", description="Should the upscaler run before restoring faces?")
 
 class ExtraBaseResponse(BaseModel):
     html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
-- 
cgit v1.2.1


From b2c08283bc92a62a66117923b0b13b9b968b6870 Mon Sep 17 00:00:00 2001
From: innovaciones <sonygarcia99@gmail.com>
Date: Wed, 2 Nov 2022 16:14:44 -0600
Subject: New strings, some tweaks and fixes

---
 localizations/es_ES.json | 422 ++++++++++++++++++++++++++++++++++++++---------
 1 file changed, 348 insertions(+), 74 deletions(-)

diff --git a/localizations/es_ES.json b/localizations/es_ES.json
index d9ca4ef7..9ba66c5b 100644
--- a/localizations/es_ES.json
+++ b/localizations/es_ES.json
@@ -5,18 +5,21 @@
     "❮": "❮",
     "❯": "❯",
     "Loading...": "Cargando...",
-    "view": "mostrar",
-    "api": "api",
-    "•": "•",
-    "built with gradio": "Construido con Gradio",
+    "view": "Mostrar ",
+    "api": "API",
+    "•": " • ",
+    "Construido con Gradio": "Construido con Gradio",
     "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
     "txt2img": "txt2img",
     "img2img": "img2img",
     "Extras": "Extras",
-    "PNG Info": "PNG Info",
-    "Checkpoint Merger": "Checkpoint Merger",
-    "Train": "Train",
+    "PNG Info": "Info PNG",
+    "Checkpoint Merger": "Fusionar Checkpoints",
+    "Train": "Entrenar",
+    "Deforum": "Deforum",
+    "Image Browser": "Navegador de Imágenes",
     "Settings": "Ajustes",
+    "Extensions": "Extensiones",
     "Prompt": "Prompt",
     "Negative prompt": "Prompt negativo",
     "Run": "Ejecutar",
@@ -27,15 +30,15 @@
     "Style 2": "Estilo 2",
     "Label": "Etiqueta",
     "File": "Archivo",
-    "Drop File Here": "Suelta el Archivo Aquí",
+    "Coloque el archivo aquí": "Suelta el archivo aquí",
     "-": "-",
     "o": "o",
-    "Click to Upload": "Click para Cargar",
+    "Haga click para cargar": "Haz click para cargar",
     "Image": "Imagen",
     "Check progress": "Comprobar progreso",
     "Check progress (first)": "Comprobar progreso (inicial)",
     "Sampling Steps": "Sampling Steps",
-    "Sampling method": "Sampling method",
+    "Sampling method": "Método de Sampling",
     "Euler a": "Euler a",
     "Euler": "Euler",
     "LMS": "LMS",
@@ -57,33 +60,34 @@
     "Firstpass width": "Ancho original",
     "Firstpass height": "Alto original",
     "Denoising strength": "Denoising strength",
-    "Batch count": "Batch count",
-    "Batch size": "Batch size",
-    "CFG Scale": "CFG Scale",
+    "Batch count": "Cantidad del Batch",
+    "Batch size": "Tamaño del Batch",
+    "CFG Scale": "Escala CFG",
     "Seed": "Seed",
     "Extra": "Extra",
-    "Variation seed": "Variation seed",
-    "Variation strength": "Variation strength",
+    "Variation seed": "Seed de variación",
+    "Variation strength": "Fuerza de variación",
     "Resize seed from width": "Redimensionar seed del ancho",
     "Resize seed from height": "Redimensionar seed del alto",
     "Script": "Script",
     "None": "Ninguno",
-    "Prompt matrix": "Prompt matrix",
+    "Prompt matrix": "Prompt en matriz",
     "Prompts from file or textbox": "Prompts desde archivo o campo de texto",
-    "X/Y plot": "X/Y plot",
+    "X/Y plot": "Tabla X/Y",
     "Put variable parts at start of prompt": "Poner partes variables al inicio del prompt",
-    "Show Textbox": "Mostrar Campo de texto",
-    "File with inputs": "Archivo con inputs",
-    "Prompts": "Prompts",
-    "X type": "X type",
+    "Iterate seed every line": "Repetir seed en cada línea",
+    "Use same random seed for all lines": "Utiliza el mismo seed aleatorio para todas las líneas",
+    "List of prompt inputs": "Listado de prompts",
+    "Upload prompt inputs": "Cargar archivo de prompts",
+    "X type": "X",
     "Nothing": "Nada",
     "Var. seed": "Var. seed",
-    "Var. strength": "Var. strength",
+    "Var. strength": "Var. fuerza",
     "Steps": "Steps",
     "Prompt S/R": "Prompt S/R",
     "Prompt order": "Prompt order",
     "Sampler": "Sampler",
-    "Checkpoint name": "Checkpoint name",
+    "Checkpoint name": "Nombre Checkpoint",
     "Hypernetwork": "Hypernetwork",
     "Hypernet str.": "Hypernet str.",
     "Sigma Churn": "Sigma Churn",
@@ -93,13 +97,13 @@
     "Eta": "Eta",
     "Clip skip": "Clip skip",
     "Denoising": "Denoising",
-    "X values": "X values",
-    "Y type": "Y type",
-    "Y values": "Y values",
+    "Cond. Image Mask Weight": "Cond. Image Mask Weight",
+    "X values": "Valores X",
+    "Y type": "Y",
+    "Y values": "Valores Y",
     "Draw legend": "Agregar leyenda",
     "Include Separate Images": "Incluir Imágenes Separadas",
     "Keep -1 for seeds": "Mantener -1 para seeds",
-    "Drop Image Here": "Suelta la Imagen Aquí",
     "Save": "Guardar",
     "Send to img2img": "Enviar a img2img",
     "Send to inpaint": "Enviar a inpaint",
@@ -110,22 +114,23 @@
     "Inpaint": "Inpaint",
     "Batch img2img": "Batch img2img",
     "Image for img2img": "Imagen para img2img",
-    "Image for inpainting with mask": "Imagen para inpainting con mask",
-    "Mask": "Mask",
-    "Mask blur": "Mask blur",
-    "Mask mode": "Mask mode",
-    "Draw mask": "Dibujar mask",
-    "Upload mask": "Cargar mask",
-    "Masking mode": "Masking mode",
-    "Inpaint masked": "Inpaint masked",
-    "Inpaint not masked": "Inpaint not masked",
-    "Masked content": "Masked content",
-    "fill": "fill",
+    "Coloque la imagen aquí": "Suelta la imagen aquí",
+    "Image for inpainting with mask": "Imagen para inpainting con máscara",
+    "Mask": "Máscara",
+    "Mask blur": "Difuminar máscara",
+    "Mask mode": "Modo máscara",
+    "Draw mask": "Dibujar máscara",
+    "Upload mask": "Cargar máscara",
+    "Masking mode": "Modo de enmascarado",
+    "Inpaint masked": "Inpaint con enmascarado",
+    "Inpaint not masked": "Inpaint sin enmascarado",
+    "Masked content": "Contenido enmascarado",
+    "fill": "rellenar",
     "original": "original",
     "latent noise": "latent noise",
     "latent nothing": "latent nothing",
     "Inpaint at full resolution": "Inpaint a resolución completa",
-    "Inpaint at full resolution padding, pixels": "Inpaint a resolución completa padding, pixeles",
+    "Inpaint at full resolution padding, pixels": "Inpaint a resolución completa con relleno, en pixeles",
     "Process images in a directory on the same machine where the server is running.": "Procesa imágenes en un directorio en la misma máquina donde se ejecuta el servidor.",
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa un directorio de salida vacío para guardar imágenes normalmente en lugar de escribir en el directorio de salida.",
     "Input directory": "Directorio de entrada",
@@ -139,15 +144,16 @@
     "Outpainting mk2": "Outpainting mk2",
     "Poor man's outpainting": "Poor man's outpainting",
     "SD upscale": "SD upscale",
+    "Deforum-webui (use tab extension instead!)": "Deforum-webui (utiliza la extensión en su lugar!)",
     "should be 2 or lower.": "debe ser 2 o menos.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Anular `Sampling method` a Euler? (este método está diseñado para ello)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Anular `prompt` al mismo valor `prompt original`? (y `prompt negativo`)",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Invalidar `Sampling method` a Euler? (este método está diseñado para ello)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Invalidar `prompt` al mismo valor `prompt original`? (y `prompt negativo`)",
     "Original prompt": "Prompt original",
     "Original negative prompt": "Promp negativo original",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Anular `Sampling Steps` al mismo valor de `Decode steps`?",
+    "Override `Sampling Steps` to the same value as `Decode steps`?": "Invalidar `Sampling Steps` al mismo valor de `Decode steps`?",
     "Decode steps": "Decode steps",
-    "Override `Denoising strength` to 1?": "Anular `Denoising strength` a 1?",
-    "Decode CFG scale": "Decode CFG scale",
+    "Override `Denoising strength` to 1?": "Invalidar `Denoising strength` a 1?",
+    "Decode CFG scale": "Decodificar escala CFG",
     "Randomness": "Aleatoriedad",
     "Sigma adjustment for finding noise for image": "Ajuste Sigma para encontrar ruido para la imagen.",
     "Loops": "Loops",
@@ -165,29 +171,35 @@
     "Tile overlap": "Solapar mosaicos",
     "Upscaler": "Upscaler",
     "Lanczos": "Lanczos",
+    "Nearest": "Nearest",
     "LDSR": "LDSR",
-    "SwinIR 4x": "SwinIR 4x",
+    "ESRGAN_4x": "ESRGAN_4x",
     "ScuNET GAN": "ScuNET GAN",
     "ScuNET PSNR": "ScuNET PSNR",
-    "ESRGAN_4x": "ESRGAN_4x",
+    "SwinIR 4x": "SwinIR 4x",
+    "Deforum v0.5-webui-beta": "Deforum v0.5-webui-beta",
+    "This script is deprecated. Please use the full Deforum extension instead.": "Este script está obsoleto. Utiliza la extensión completa de Deforum en su lugar.",
+    "Update instructions:": "Instrucciones para actualizar:",
+    "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md": "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md",
+    "discord.gg/deforum": "discord.gg/deforum",
     "Single Image": "Imagen Única",
     "Batch Process": "Batch Process",
     "Batch from Directory": "Batch desde Directorio",
     "Source": "Origen",
-    "Show result images": "Mostrar resultados de imágenes",
+    "Show result images": "Mostrar imágenes generadas",
     "Scale by": "Escalar por",
     "Scale to": "Escalar a",
     "Resize": "Redimensionar",
     "Crop to fit": "Recortar para ajustar",
-    "Upscaler 2 visibility": "Upscaler 2 visibilidad",
-    "GFPGAN visibility": "GFPGAN visibilidad",
-    "CodeFormer visibility": "CodeFormer visibilidad",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer peso (0 = efecto máximo, 1 = efecto mínimo)",
-    "Open output directory": "Abrir directorio de salida",
+    "Upscaler 2 visibility": "Visibilidad Upscaler 2",
+    "GFPGAN visibility": "Visibilidad GFPGAN",
+    "CodeFormer visibility": "Visibilidad CodeFormer",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Influencia CodeFormer (0 = efecto máximo, 1 = efecto mínimo)",
+    "Upscale Before Restoring Faces": "Escalar antes de Restaurar Rostros",
     "Send to txt2img": "Enviar a txt2img",
-    "A merger of the two checkpoints will be generated in your": "Se generará una fusión de los dos checkpoints en su",
-    "checkpoint": "checkpoint",
-    "directory.": "directorio.",
+    "A merger of the two checkpoints will be generated in your": "Se generará una fusión de los dos checkpoints en tu ",
+    "checkpoint": "directorio ",
+    "directory.": "de modelos.",
     "Primary model (A)": "Modelo primario (A)",
     "Secondary model (B)": "Modelo secundario (B)",
     "Tertiary model (C)": "Modelo terciario (C)",
@@ -215,6 +227,36 @@
     "leakyrelu": "leakyrelu",
     "elu": "elu",
     "swish": "swish",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "threshold",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleccionar inicialización de modelos de capa. relu-like - Kaiming, sigmoid-like - Xavier es el recomendado",
+    "Normal": "Normal",
+    "KaimingUniform": "KaimingUniform",
+    "KaimingNormal": "KaimingNormal",
+    "XavierUniform": "XavierUniform",
+    "XavierNormal": "XavierNormal",
     "Add layer normalization": "Agregar normalización de capa",
     "Use dropout": "Usar dropout",
     "Overwrite Old Hypernetwork": "Sobrescribir Hypernetwork Anterior",
@@ -226,11 +268,16 @@
     "prepend": "anteponer",
     "append": "añadir",
     "Create flipped copies": "Crear copias volteadas",
-    "Split oversized images": "Dividir imágenes grandes",
-    "Use BLIP for caption": "Usar BLIP para subtítulo",
-    "Use deepbooru for caption": "Usar deepbooru para subtítulo",
-    "Split image threshold": "Threshold imagen dividida",
-    "Split image overlap ratio": "Overlap ratio imagen dividida",
+    "Split oversized images": "Dividir imágenes muy grandes",
+    "Auto focal point crop": "Recorte de punto focal automático",
+    "Use BLIP for caption": "Usar BLIP para leyenda",
+    "Use deepbooru for caption": "Usar deepbooru para leyenda",
+    "Split image threshold": "Umbral en imagen dividida",
+    "Split image overlap ratio": "Relación de superposición en imagen dividida",
+    "Focal point face weight": "Peso de la cara del punto focal",
+    "Focal point entropy weight": "Focal point entropy weight",
+    "Focal point edges weight": "Focal point edges weight",
+    "Create debug image": "Crear imagen de depuración",
     "Preprocess": "Preproceso",
     "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Entrenar un embedding o Hypernetwork; debes especificar un directorio con un conjunto de imágenes con una proporción de 1:1 ",
     "[wiki]": "[wiki]",
@@ -245,13 +292,194 @@
     "Save a copy of embedding to log directory every N steps, 0 to disable": "Guarda una copia de embedding en el directorio log cada N pasos, 0 para deshabilitar",
     "Save images with embedding in PNG chunks": "Guarda imágenes con embedding en fragmentos PNG",
     "Read parameters (prompt, etc...) from txt2img tab when making previews": "Leer parámetros (prompt, etc...) desde la pestaña txt2img al hacer vistas previas",
-    "Train Hypernetwork": "Train Hypernetwork",
-    "Train Embedding": "Train Embedding",
+    "Train Hypernetwork": "Entrenar Hypernetwork",
+    "Train Embedding": "Entrenar Embedding",
+    "Info and links": "Información y enlaces",
+    "▼": "▼",
+    "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha": "Desarrolado por deforum.github.io, port para AUTOMATIC1111's webui mantenido por kabachuha",
+    "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Deforum Github repo  github.com/deforum/stable-diffusion",
+    "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui": "Fork para auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui",
+    "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Únete al Discord oficial Deforum discord.gg/deforum para compartir tus creaciones y sugerencias",
+    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "Guía de usuario v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
+    "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Explicación de fotogramas matemáticos docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
+    "Keyframes": "Keyframes",
+    "Prompts": "Prompts",
+    "Init": "Init",
+    "Video output": "Salida de vídeo",
+    "Run settings": "Ejecutar ajustes",
+    "Import settings from file": "Importar ajustes desde archivo",
+    "Override settings": "Invalidar ajustes",
+    "Custom settings file": "Archivo de ajustes personalizados",
+    "Sampling settings": "Ajustes de sampling",
+    "override_these_with_webui": "override_these_with_webui",
+    "W": "Ancho",
+    "H": "Alto",
+    "seed": "seed",
+    "sampler": "sampler",
+    "Enable extras": "Activar extras",
+    "subseed": "subseed",
+    "subseed_strength": "subseed_strength",
+    "steps": "steps",
+    "ddim_eta": "ddim_eta",
+    "n_batch": "n_batch",
+    "make_grid": "make_grid",
+    "grid_rows": "grid_rows",
+    "save_settings": "save_settings",
+    "save_samples": "save_samples",
+    "display_samples": "display_samples",
+    "save_sample_per_step": "save_sample_per_step",
+    "show_sample_per_step": "show_sample_per_step",
+    "Batch settings": "Ajustes de batch",
+    "batch_name": "batch_name",
+    "filename_format": "filename_format",
+    "seed_behavior": "seed_behavior",
+    "iter": "iter",
+    "fixed": "fixed",
+    "random": "random",
+    "schedule": "schedule",
+    "Animation settings": "Ajustes de animación",
+    "animation_mode": "animation_mode",
+    "2D": "2D",
+    "3D": "3D",
+    "Video Input": "Entrada de Video",
+    "max_frames": "max_frames",
+    "border": "border",
+    "replicate": "replicate",
+    "wrap": "wrap",
+    "Motion parameters:": "Parámetros de movimiento:",
+    "2D and 3D settings": "Ajustes 2D y 3D",
+    "angle": "angle",
+    "zoom": "zoom",
+    "translation_x": "translation_x",
+    "translation_y": "translation_y",
+    "3D settings": "Ajustes 3D",
+    "translation_z": "translation_z",
+    "rotation_3d_x": "rotation_3d_x",
+    "rotation_3d_y": "rotation_3d_y",
+    "rotation_3d_z": "rotation_3d_z",
+    "Prespective flip — Low VRAM pseudo-3D mode:": "Prespective flip — Modo Low VRAM pseudo-3D:",
+    "flip_2d_perspective": "flip_2d_perspective",
+    "perspective_flip_theta": "perspective_flip_theta",
+    "perspective_flip_phi": "perspective_flip_phi",
+    "perspective_flip_gamma": "perspective_flip_gamma",
+    "perspective_flip_fv": "perspective_flip_fv",
+    "Generation settings:": "Ajustes de generación:",
+    "noise_schedule": "noise_schedule",
+    "strength_schedule": "strength_schedule",
+    "contrast_schedule": "contrast_schedule",
+    "cfg_scale_schedule": "cfg_scale_schedule",
+    "3D Fov settings:": "Ajustes 3D Fov:",
+    "fov_schedule": "fov_schedule",
+    "near_schedule": "near_schedule",
+    "far_schedule": "far_schedule",
+    "To enable seed schedule select seed behavior — 'schedule'": "Para habilitar el seed schedule, selecciona el comportamiento del seed — 'schedule'",
+    "seed_schedule": "seed_schedule",
+    "Coherence:": "Coherencia:",
+    "color_coherence": "color_coherence",
+    "Match Frame 0 HSV": "Match Frame 0 HSV",
+    "Match Frame 0 LAB": "Match Frame 0 LAB",
+    "Match Frame 0 RGB": "Match Frame 0 RGB",
+    "diffusion_cadence": "diffusion_cadence",
+    "3D Depth Warping:": "3D Depth Warping:",
+    "use_depth_warping": "use_depth_warping",
+    "midas_weight": "midas_weight",
+    "near_plane": "near_plane",
+    "far_plane": "far_plane",
+    "fov": "fov",
+    "padding_mode": "padding_mode",
+    "reflection": "reflection",
+    "zeros": "zeros",
+    "sampling_mode": "sampling_mode",
+    "bicubic": "bicubic",
+    "bilinear": "bilinear",
+    "nearest": "nearest",
+    "save_depth_maps": "save_depth_maps",
+    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`animation_mode: None` batches en lista de *prompts*. (Modo batch deshabilitado por el momento, solamente animation_prompts esta funcionando)",
+    "*Important change from vanilla Deforum!*": "*Cambios importantes en Deforum!*",
+    "This script uses the built-in webui weighting settings.": "Este script utiliza la configuración de pesos integrados.",
+    "So if you want to use math functions as prompt weights,": "Entonces, si deseas usar funciones matemáticas con pesos en los prompts,",
+    "keep the values above zero in both parts": "manten los valores por encima de cero en ambas partes",
+    "Negative prompt part can be specified with --neg": "La parte de prompt negativo se puede especificar utilizando --neg",
+    "batch_prompts (disabled atm)": "batch_prompts (deshabilitado por el momento)",
+    "animation_prompts": "animation_prompts",
+    "Init settings": "Ajustes Init",
+    "use_init": "use_init",
+    "from_img2img_instead_of_link": "from_img2img_instead_of_link",
+    "strength_0_no_init": "strength_0_no_init",
+    "strength": "strength",
+    "init_image": "init_image",
+    "use_mask": "use_mask",
+    "use_alpha_as_mask": "use_alpha_as_mask",
+    "invert_mask": "invert_mask",
+    "overlay_mask": "overlay_mask",
+    "mask_file": "mask_file",
+    "mask_brightness_adjust": "mask_brightness_adjust",
+    "mask_overlay_blur": "mask_overlay_blur",
+    "Video Input:": "Entrada de Video:",
+    "video_init_path": "video_init_path",
+    "extract_nth_frame": "extract_nth_frame",
+    "overwrite_extracted_frames": "overwrite_extracted_frames",
+    "use_mask_video": "use_mask_video",
+    "video_mask_path": "video_mask_path",
+    "Interpolation (turned off atm)": "Interpolación (apagado por el momento)",
+    "interpolate_key_frames": "interpolate_key_frames",
+    "interpolate_x_frames": "interpolate_x_frames",
+    "Resume animation:": "Reanudar animación:",
+    "resume_from_timestring": "resume_from_timestring",
+    "resume_timestring": "resume_timestring",
+    "Video output settings": "Ajustes video de salida",
+    "skip_video_for_run_all": "skip_video_for_run_all",
+    "fps": "fps",
+    "output_format": "output_format",
+    "PIL gif": "PIL gif",
+    "FFMPEG mp4": "FFMPEG mp4",
+    "ffmpeg_location": "ffmpeg_location",
+    "add_soundtrack": "add_soundtrack",
+    "soundtrack_path": "soundtrack_path",
+    "use_manual_settings": "use_manual_settings",
+    "render_steps": "render_steps",
+    "max_video_frames": "max_video_frames",
+    "path_name_modifier": "path_name_modifier",
+    "x0_pred": "x0_pred",
+    "x": "x",
+    "image_path": "image_path",
+    "mp4_path": "mp4_path",
+    "Click here after the generation to show the video": "Haz click aquí después de la generación para mostrar el video",
+    "NOTE: If the 'Generate' button doesn't work, go in Settings and click 'Restart Gradio and Refresh...'.": "NOTA: Si el botón 'Generar' no funciona, ve a los Ajustes y presiona 'Reinciar Gradio y Refrescar...'.",
+    "Save Settings": "Guardar Ajustes",
+    "Load Settings": "Cargar Ajustes",
+    "Path relative to the webui folder": "Ruta relativa al folder principal",
+    "Save Video Settings": "Guardar Ajustes de Video",
+    "Load Video Settings": "Cargar Ajustes de Video",
+    "Favorites": "Favoritos",
+    "Others": "Otros",
+    "Images directory": "Directorio de Imágenes",
+    "Dropdown": "Menú desplegable",
+    "First Page": "Primera Página",
+    "Prev Page": "Página Anterior",
+    "Page Index": "Índice de Página",
+    "Next Page": "Página Siguiente",
+    "End Page": "Última Página",
+    "delete next": "eliminar siguiente",
+    "Delete": "Eliminar",
+    "sort by": "ordenar por",
+    "path name": "nombre de ruta",
+    "date": "fecha",
+    "keyword": "palabra clave",
+    "Generate Info": "Generar Info",
+    "File Name": "Nombre de Archivo",
+    "Move to favorites": "Mover a favoritos",
+    "Renew Page": "Recargar Página",
+    "Number": "Número",
+    "set_index": "set_index",
+    "load_switch": "load_switch",
+    "turn_page_switch": "turn_page_switch",
+    "Checkbox": "Checkbox",
     "Apply settings": "Aplicar ajustes",
     "Saving images/grids": "Guardar imágenes/grids",
     "Always save all generated images": "Siempre guardar imágenes generadas",
     "File format for images": "Formato de archivo para imágenes",
-    "Images filename pattern": "Patrón nombre archivo imágenes",
+    "Images filename pattern": "Patrón en nombre archivo imágenes",
     "Add number to filename when saving": "Agregar número al nombre de archivo al guardar",
     "Always save all generated image grids": "Siempre guardar grids de imágenes generadas",
     "File format for grids": "Formato de archivo para grids",
@@ -262,6 +490,8 @@
     "Save text information about generation parameters as chunks to png files": "Guardar información de texto sobre parámetros de generación como fragmentos en archivos png",
     "Create a text file next to every image with generation parameters.": "Crear un archivo de texto junto a cada imagen con parámetros de generación.",
     "Save a copy of image before doing face restoration.": "Guardar una copia de la imagen antes de restaurar rostro.",
+    "Save a copy of image before applying highres fix.": "Guardar una copia de la imagen antes de aplicar highres fix.",
+    "Save a copy of image before applying color correction to img2img results": "Guarda una copia de la imagen antes de aplicar la corrección de color a los resultados de img2img",
     "Quality for saved jpeg images": "Calidad para imágenes jpeg guardadas",
     "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Si la imagen PNG es más grande de 4 MB o cualquier dimensión es más grande que 4000, reduce la escala y guarda la copia como JPG",
     "Use original name for output filename during batch process in extras tab": "Use el nombre original para el nombre del archivo de salida durante el batch process en la pestaña extras",
@@ -291,23 +521,26 @@
     "Upscaler for img2img": "Upscaler para img2img",
     "Upscale latent space image when doing hires. fix": "Upscale latent space al aplicar hires. fix",
     "Face restoration": "Restauración de rostro",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer parámetro peso; 0 = máximo efecto; 1 = mínimo efecto",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Parámetro influencia CodeFormer; 0 = máximo efecto; 1 = mínimo efecto",
     "Move face restoration model from VRAM into RAM after processing": "Mover modelo de restauración de rostro del VRAM al RAM después de procesar",
     "System": "Sistema",
     "VRAM usage polls per second during generation. Set to 0 to disable.": "Sondeos de uso de VRAM por segundo durante la generación. Establecer en 0 para deshabilitar.",
     "Always print all generation info to standard output": "Imprime siempre toda la información de generación en la salida estándar",
     "Add a second progress bar to the console that shows progress for an entire job.": "Agrega una segunda barra de progreso a la consola que muestra el progreso de un trabajo completo.",
     "Training": "Entrenamiento",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Mover VAE y CLIP al RAM al entrenar un hypernetwork. Ahorra VRAM.",
+    "Move VAE and CLIP to RAM when training if possible. Saves VRAM.": "Mover VAE y CLIP al RAM al entrenar cuando sea posible. Ahorra VRAM.",
     "Filename word regex": "Filename word regex",
     "Filename join string": "Filename join string",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Número de repeticiones para una sola imagen de entrada por epoch; utilizado solo para mostrar el número epoch",
     "Save an csv containing the loss to log directory every N steps, 0 to disable": "Guarda un csv que contenga la pérdida en el directorio log cada N pasos, 0 para deshabilitar",
+    "Use cross attention optimizations while training": "Utiliza optimizaciones de atención cruzada durante el entrenamiento",
     "Stable Diffusion": "Stable Diffusion",
     "Checkpoints to cache in RAM": "Checkpoints al cache en RAM",
+    "SD VAE": "SD VAE",
+    "auto": "auto",
     "Hypernetwork strength": "Hypernetwork strength",
+    "Inpainting conditioning mask strength": "Fuerza de la máscara en acondicionamiento Inpainting",
     "Apply color correction to img2img results to match original colors.": "Aplica la corrección de color a los resultados de img2img para que coincidan con los colores originales.",
-    "Save a copy of image before applying color correction to img2img results": "Guarda una copia de la imagen antes de aplicar la corrección de color a los resultados de img2img",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, hace exactamente la cantidad de pasos que especifica el slider (normalmente haría menos con menos eliminación de ruido).",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Habilita la cuantificación en K samplers para obtener resultados más nítidos y limpios. Esto puede cambiar los seeds existentes. Requiere reiniciar para aplicar.",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Énfasis: utiliza (texto) para que el modelo preste más atención al texto y [texto] para que preste menos atención",
@@ -324,7 +557,7 @@
     "Interrogate: minimum description length (excluding artists, etc..)": "Interrogar: longitud mínima de la descripción (excluyendo artistas, etc.)",
     "Interrogate: maximum description length": "Interrogar: longitud máxima de la descripción",
     "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: número máximo de líneas en el archivo de texto (0 = Sin límite)",
-    "Interrogate: deepbooru score threshold": "Interrogar: deepbooru score threshold",
+    "Interrogate: deepbooru score threshold": "Interrogar: deepbooru umbral de puntuación",
     "Interrogate: deepbooru sort alphabetically": "Interrogar: deepbooru ordenar alfabéticamente",
     "use spaces for tags in deepbooru": "usar espacios para etiquetas en deepbooru",
     "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "corchetes de escape (\\) en deepbooru (por lo que se usan como corchetes literales y no para enfatizar)",
@@ -337,13 +570,26 @@
     "Add model hash to generation information": "Agregar hash de modelo a la información de generación",
     "Add model name to generation information": "Agregar nombre de modelo a la información de generación",
     "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Al leer los parámetros de generación del texto en la interfaz de usuario (desde PNG Info o texto pegado), no cambia el modelo/checkpoint seleccionado.",
+    "Send seed when sending prompt or image to other interface": "Enviar seed cuando se envíe el prompt o imagen a otra interfase",
     "Font for image grids that have text": "Tipografía para grids de imágenes que tienen texto",
     "Enable full page image viewer": "Habilitar visor de imágenes de página completa",
     "Show images zoomed in by default in full page image viewer": "Mostrar imágenes ampliadas de forma predeterminada en el visor de imágenes de página completa",
     "Show generation progress in window title.": "Muestra el progreso de la generación en el título de la ventana del navegador.",
     "Quicksettings list": "Lista de ajustes rápidos",
     "Localization (requires restart)": "Traducción (requiere reiniciar)",
-    "Sampler parameters": "Parámetros sampler",
+    "fr_FR": "fr_FR",
+    "tr_TR": "tr_TR",
+    "it_IT": "it_IT",
+    "de_DE": "de_DE",
+    "ru_RU": "ru_RU",
+    "ja_JP": "ja_JP",
+    "es_ES": "es_ES",
+    "ko_KR": "ko_KR",
+    "zh_TW": "zh_TW",
+    "zh_CN": "zh_CN",
+    "ar_AR": "ar_AR",
+    "pt_BR": "pt_BR",
+    "Sampler parameters": "Parámetros del sampler",
     "Hide samplers in user interface (requires restart)": "Ocultar samplers en interfaz de usuario (requiere reiniciar)",
     "eta (noise multiplier) for DDIM": "eta (noise multiplier) para DDIM",
     "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) para ancestral samplers",
@@ -354,10 +600,35 @@
     "sigma tmin": "sigma tmin",
     "sigma noise": "sigma noise",
     "Eta noise seed delta": "Eta noise seed delta",
+    "Images Browser": "Navegador de Imágenes",
+    "Preload images at startup": "Precargar imágenes al iniciar",
+    "Number of columns on the page": "Número de columnas en la página",
+    "Number of rows on the page": "Número de filas en la página",
+    "Minimum number of pages per load": "Número mínimo de páginas por carga",
     "Request browser notifications": "Solicitar notificaciones del navegador",
     "Download localization template": "Descargar plantilla de traducción",
     "Reload custom script bodies (No ui updates, No restart)": "Recargar custom script bodies (Sin actualizar UI, Sin reiniciar)",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Reiniciar Gradio y Refrescar componentes (Custom Scripts, ui.py, js y css)",
+    "Installed": "Instaladas",
+    "Available": "Disponibles",
+    "Install from URL": "Instalar desde URL",
+    "Apply and restart UI": "Aplicar y reiniciar UI",
+    "Check for updates": "Buscar actualizaciones",
+    "Extension": "Extensión",
+    "URL": "URL",
+    "Update": "Actualizar",
+    "deforum-for-automatic1111-webui": "deforum-for-automatic1111-webui",
+    "https://github.com/deforum-art/deforum-for-automatic1111-webui": "https://github.com/deforum-art/deforum-for-automatic1111-webui",
+    "unknown": "desconocido",
+    "stable-diffusion-webui-images-browser": "stable-diffusion-webui-images-browser",
+    "https://github.com/yfszzx/stable-diffusion-webui-images-browser": "https://github.com/yfszzx/stable-diffusion-webui-images-browser",
+    "Load from:": "Cargar desde:",
+    "Extension index URL": "URL índice de extensiones",
+    "URL for extension's git repository": "URL repositorio git de extensión",
+    "Local directory name": "Nombre directorio local",
+    "Install": "Instalar",
+    "Ver": "Ver",
+    "Entrenar un embedding o Hypernetwork; debes especificar un directorio con un conjunto de imágenes con una proporción de 1:1": "Entrenar un embedding o Hypernetwork; debes especificar un directorio con un conjunto de imágenes con una proporción de 1:1",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (presiona Ctrl+Enter o Alt+Enter para generar)",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (presiona Ctrl+Enter o Alt+Enter para generar)",
     "Add a random artist to the prompt.": "Agregar un artista aleatorio al prompt.",
@@ -387,16 +658,16 @@
     "Separate values for Y axis using commas.": "Separar valores para Y usando comas.",
     "Write image to a directory (default - log/images) and generation parameters into csv file.": "Escribe la imagen en un directorio (predeterminado: log/images) y los parámetros de generación en el archivo csv.",
     "Open images output directory": "Abrir directorio de imágenes",
-    "How much to blur the mask before processing, in pixels.": "Cuánto blur al mask antes de procesar, en píxeles.",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Qué poner dentro del área con mask antes de procesarlo con Stable Diffusion.",
+    "How much to blur the mask before processing, in pixels.": "Cuánto difuminado a la máscara antes de procesarla, en píxeles.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Qué poner dentro del área enmascarada antes de procesarla con Stable Diffusion.",
     "fill it with colors of the image": "rellenarlo con los colores de la imagen",
     "keep whatever was there originally": "mantener lo que estaba allí originalmente",
     "fill it with latent space noise": "rellenarlo con latent space noise",
     "fill it with latent space zeroes": "rellenarlo con latent space zeroes",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Upscale la región con mask a la resolución objetivo, vuelve a pintar, reduce la escala hacia atrás y pégala en la imagen original",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Cambia el tamaño de la imagen a la resolución destino. A menos que la altura y el ancho coincidan, obtendrás una relación de aspecto incorrecta.",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Escalar la región con máscara a la resolución objetivo, vuelve a pintar, reduce la escala hacia atrás y pégala en la imagen original",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Cambia el tamaño de la imagen a la resolución objetivo. A menos que la altura y el ancho coincidan, obtendrás una relación de aspecto incorrecta.",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Cambia el tamaño de la imagen para que la totalidad de la resolución destino se llene con la imagen. Recorta las partes que sobresalen.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Cambia el tamaño de la imagen para que la totalidad de la imagen esté dentro de la resolución de destino. Rellena el espacio vacío con los colores de la imagen.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Cambia el tamaño de la imagen para que la totalidad de la imagen esté dentro de la resolución objetivo. Rellena el espacio vacío con los colores de la imagen.",
     "How many times to repeat processing an image and using it as input for the next iteration": "Cuántas veces repetir el procesamiento de una imagen y usarla como entrada para la próxima iteración",
     "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "En modo loopback, en cada bucle, la fuerza de eliminación de ruido se multiplica por este valor. <1 significa variedad decreciente, por lo que su secuencia convergerá en una imagen fija. >1 significa aumentar la variedad, por lo que su secuencia se volverá cada vez más caótica.",
     "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Para SD upscale, cuánta superposición en píxeles debe haber entre mosaicos. Los mosaicos se superponen de modo que cuando se fusionan nuevamente en una imagen, no hay una unión claramente visible.",
@@ -407,12 +678,15 @@
     "1st and last digit must be 1. ex:'1, 2, 1'": "Primer y último dígito debe ser 1. ej:'1, 2, 1'",
     "Path to directory with input images": "Ruta al directorio con imágenes de entrada",
     "Path to directory where to write outputs": "Ruta al directorio donde escribir salidas",
+    "Input images directory": "Directorio de imágenes de entrada",
     "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa las siguientes etiquetas para definir cómo se eligen los nombres de archivo para las imágenes: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; dejar vacío para utilizar predeterminados.",
     "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Si esta opción está habilitada, el watermark no se agregará a las imágenes creadas. Advertencia: si no agregas un watermark, es posible que te estés comportando de manera poco ética.",
     "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa las siguiente etiquetas para definir cómo los subdirectorios para imágenes y grids son seleccionados: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; dejar vacío para utilizar predeterminados.",
     "Restore low quality faces using GFPGAN neural network": "Restaurar rostros de baja calidad utilizando GFPGAN neural network",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Esta expresión regular se usará para extraer palabras del nombre de archivo y se unirán usando la opción a continuación en el texto de la etiqueta que se usa para el entrenamiento. Dejar vacío para mantener el texto del nombre de archivo tal como está.",
     "This string will be used to join split words into a single line if the option above is enabled.": "Esta cadena se usará para unir palabras divididas en una sola línea si la opción anterior está habilitada.",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Solo se aplica a modelos inpainting. Determina con qué fuerza enmascarar la imagen original para inpainting en img2img. 1.0 significa totalmente enmascarado, que es el comportamiento predeterminado. 0.0 significa un condicionamiento totalmente desenmascarado. Los valores más bajos ayudarán a preservar la composición general de la imagen, pero tendrán problemas con los grandes cambios.",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Lista de nombres de configuración, separados por comas, para configuraciones que deben ir a la barra de acceso rápido en la parte superior, en lugar de la pestaña de configuración habitual. Ver modules/shared.py para configurar los nombres. Requiere reiniciar para aplicar.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Si este valor no es cero, se agregará al seed y se usará para inicializar RNG para ruidos cuando se usan samplers con Eta. Puedes usar esto para producir aún más variaciones de imágenes, o puedes usar esto para hacer coincidir imágenes de otro software si sabes lo que estás haciendo."
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Si este valor no es cero, se agregará al seed y se usará para inicializar RNG para ruidos cuando se usan samplers con Eta. Puedes usar esto para producir aún más variaciones de imágenes, o puedes usar esto para hacer coincidir imágenes de otro software si sabes lo que estás haciendo.",
+    "Leave empty for auto": "Dejar vacío para automático"
 }
-- 
cgit v1.2.1


From 17315499eab7b3e5d6f36f0de5ef3bfc09230d46 Mon Sep 17 00:00:00 2001
From: Riccardo Giovanetti <29801031+Harvester62@users.noreply.github.com>
Date: Wed, 2 Nov 2022 23:15:17 +0100
Subject: Italian localization - Additions and Updates (fix typos)

Updated localization with the latest version of these Scripts/Extensions:

unprompted (new)
img2tiles
random
random grid

Some new options in the Extras and Settings have been translated too.

P.S.: I fixed a couple of typos. By mistake I uploaded this file also in the main branch of my fork and didn't know how to revert the commit. Sorry for the mess.
---
 it_IT.json | 1233 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1233 insertions(+)
 create mode 100644 it_IT.json

diff --git a/it_IT.json b/it_IT.json
new file mode 100644
index 00000000..a78fd574
--- /dev/null
+++ b/it_IT.json
@@ -0,0 +1,1233 @@
+{
+    "⤡": "⤡",
+    "⊞": "⊞",
+    "×": "×",
+    "❮": "❮",
+    "❯": "❯",
+    "Loading...": "Caricamento...",
+    "view": "mostra ",
+    "api": "API",
+    "•": " • ",
+    "built with gradio": " Sviluppato con Gradio",
+    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
+    "txt2img": "txt2img",
+    "img2img": "img2img",
+    "Extras": "Extra",
+    "PNG Info": "Info PNG",
+    "Checkpoint Merger": "Miscelatore di Checkpoint",
+    "Train": "Addestramento",
+    "Create aesthetic embedding": "Crea incorporamento estetico",
+    "Dataset Tag Editor": "Dataset Tag Editor",
+    "Deforum": "Deforum",
+    "Artists To Study": "Artisti per studiare",
+    "Image Browser": "Galleria immagini",
+    "Inspiration": "Ispirazione",
+    "Settings": "Impostazioni",
+    "Extensions": "Estensioni",
+    "Prompt": "Prompt",
+    "Negative prompt": "Prompt negativo",
+    "Run": "Esegui",
+    "Skip": "Salta",
+    "Interrupt": "Interrompi",
+    "Generate": "Genera",
+    "Style 1": "Stile 1",
+    "Style 2": "Stile 2",
+    "Label": "Etichetta",
+    "File": "File",
+    "Drop File Here": "Trascina il file qui",
+    "-": "-",
+    "or": "o",
+    "Click to Upload": "Clicca per caricare",
+    "Image": "Immagine",
+    "Check progress": "Controlla i progressi",
+    "Check progress (first)": "Controlla i progressi (primo)",
+    "Sampling Steps": "Passi di campionamento",
+    "Sampling method": "Metodo di campionamento",
+    "Euler a": "Euler a",
+    "Euler": "Euler",
+    "LMS": "LMS",
+    "Heun": "Heun",
+    "DPM2": "DPM2",
+    "DPM2 a": "DPM2 a",
+    "DPM fast": "DPM fast",
+    "DPM adaptive": "DPM adaptive",
+    "LMS Karras": "LMS Karras",
+    "DPM2 Karras": "DPM2 Karras",
+    "DPM2 a Karras": "DPM2 a Karras",
+    "DDIM": "DDIM",
+    "PLMS": "PLMS",
+    "Width": "Larghezza",
+    "Height": "Altezza",
+    "Restore faces": "Restaura i volti",
+    "Tiling": "Piastrellatura",
+    "Highres. fix": "Correzione alta risoluzione",
+    "Firstpass width": "Larghezza del primo passaggio",
+    "Firstpass height": "Altezza del primo passaggio",
+    "Denoising strength": "Forza del Denoising",
+    "Batch count": "Lotti di immagini",
+    "Batch size": "Immagini per lotto",
+    "CFG Scale": "Scala CFG",
+    "Seed": "Seme",
+    "Extra": "Extra",
+    "Variation seed": "Seme della variazione",
+    "Variation strength": "Forza della variazione",
+    "Resize seed from width": "Ridimensiona il seme dalla larghezza",
+    "Resize seed from height": "Ridimensiona il seme dall'altezza",
+    "Open for Clip Aesthetic!": "Apri per Gradienti Estetici (CLIP)",
+    "▼": "▼",
+    "Aesthetic weight": "Estetica - Peso",
+    "Aesthetic steps": "Estetica - Passi",
+    "Aesthetic learning rate": "Estetica - Tasso di apprendimento",
+    "Slerp interpolation": "Interpolazione Slerp",
+    "Aesthetic imgs embedding": "Estetica - Incorporamento di immagini",
+    "None": "Niente",
+    "Aesthetic text for imgs": "Estetica - Testo per le immagini",
+    "Slerp angle": "Angolo Slerp",
+    "Is negative text": "È un testo negativo",
+    "Script": "Script",
+    "Random grid": "Generaz. casuale (griglia)",
+    "Random": "Generaz. casuale (no griglia)",
+    "StylePile": "StylePile",
+    "Advanced prompt matrix": "Matrice di prompt avanzata",
+    "Advanced Seed Blending": "Miscelazione Semi Avanzata",
+    "Alternate Sampler Noise Schedules": "Metodi alternativi di campionamento del rumore",
+    "Animator v6": "Animator v6",
+    "Asymmetric tiling": "Piastrellatura asimmetrica",
+    "Custom code": "Codice personalizzato",
+    "Embedding to Shareable PNG": "Incorporamento convertito in PNG condivisibile",
+    "Force symmetry": "Forza la simmetria",
+    "Prompts interpolation": "Interpola Prompt",
+    "Prompt matrix": "Matrice dei prompt",
+    "Prompt morph": "Metamorfosi del prompt",
+    "Prompts from file or textbox": "Prompt da file o da casella di testo",
+    "To Infinity and Beyond": "Verso l'infinito e oltre",
+    "Seed travel": "Interpolazione semi",
+    "Shift attention": "Sposta l'attenzione",
+    "Text to Vector Graphics": "Da testo a grafica vettoriale",
+    "Unprompted": "Unprompted",
+    "X/Y plot": "Grafico X/Y",
+    "X/Y/Z plot": "Grafico X/Y/Z",
+    "Dynamic Prompting v0.13.6": "Prompt dinamici v0.13.6",
+    "Create inspiration images": "Crea immagini di ispirazione",
+    "step1 min/max": "Passi min(o max)",
+    "step2 min/max": "Passi max (o min)",
+    "step cnt": "Q.tà di Passi",
+    "cfg1 min/max": "CFG min (o max)",
+    "cfg2 min/max": "CFG max (o min)",
+    "cfg cnt": "Q.tà di CFG",
+    "Draw legend": "Disegna legenda",
+    "Include Separate Images": "Includi immagini separate",
+    "Keep -1 for seeds": "Mantieni sempre il seme a -1",
+    "x/y change": "Inverti ordine assi X/Y (Passi/CFG)",
+    "Loops": "Cicli",
+    "Focus on:": "Focus su:",
+    "No focus": "Nessun Focus",
+    "Portraits (tick Restore faces above for best results)": "Ritratti (selezionare 'Restaura volti' in alto per ottenere i migliori risultati)",
+    "Feminine and extra attractive (tick Restore faces above for best results)": "Femminile ed estremamente attraente (selezionare 'Restaura volti' per ottenere i migliori risultati)",
+    "Masculine and extra attractive (tick Restore faces above for best results)": "Maschile ed estremamente attraente (selezionare 'Restaura volti'  per ottenere i migliori risultati)",
+    "Monsters": "Mostri",
+    "Robots": "Robot",
+    "Retrofuturistic": "Retrofuturistico",
+    "Propaganda": "Propaganda",
+    "Landscapes": "Paesaggi",
+    "Hints": "Suggerimenti",
+    "Image type": "Tipo di immagine",
+    "Not set": "Non impostato",
+    "Photography": "Fotografia",
+    "Digital art": "Arte digitale",
+    "3D Rendering": "3D Rendering",
+    "Painting": "Dipinto",
+    "Sketch": "Schizzo",
+    "Classic Comics": "Fumetti classici",
+    "Modern Comics": "Fumetti moderni",
+    "Manga": "Manga",
+    "Vector art": "Arte vettoriale",
+    "Visual style": "Stile visivo",
+    "Realism": "Realismo",
+    "Photorealism": "Fotorealismo",
+    "Hyperrealism": "Iperrealismo",
+    "Surrealism": "Surrealismo",
+    "Modern Art": "Arte moderna",
+    "Fauvism": "Fauvismo",
+    "Futurism": "Futurismo",
+    "Painterly": "Pittorico",
+    "Pointillisme": "Puntinismo",
+    "Abstract": "Astratto",
+    "Pop Art": "Pop Art",
+    "Impressionist": "Impressionista",
+    "Cubism": "Cubismo",
+    "Linocut": "Linoleografia",
+    "Fantasy": "Fantasia",
+    "Colors": "Colori",
+    "Chaotic": "Caotico",
+    "Primary colors": "Colori primari",
+    "Colorful": "Colorato",
+    "Vivid": "Vivido",
+    "Muted colors": "Colori tenui",
+    "Low contrast": "Basso contrasto",
+    "Desaturated": "Desaturato",
+    "Grayscale": "Scala di grigi",
+    "Black and white": "Bianco e nero",
+    "Infrared": "Infrarosso",
+    "Complementary": "Colori complementari",
+    "Non-complementary": "Colori non complementari",
+    "View": "Visuale",
+    "Tilt shift": "Tilt shift",
+    "Wide-angle": "Angolo ampio",
+    "Portrait": "Ritratto",
+    "Macro": "Macro",
+    "Microscopic": "Microscopico",
+    "Isometric": "Isometrico",
+    "Panorama": "Panorama",
+    "Aerial photograph": "Fotografia aerea",
+    "Artist focus (not quite finished, not sure it helps)": "Focus sull'artista (non del tutto finito, non è sicuro che aiuti)",
+    "B/W Photograpy": "Fotografia B/N",
+    "Portrait photo": "Foto ritratto",
+    "Usage: a <corgi|cat> wearing <goggles|a hat>": "Utilizzo: a <corgi|cat> wearing <goggles|a hat>",
+    "Seeds": "Semi",
+    "Noise Scheduler": "Pianificazione del rumore",
+    "Default": "Predefinito",
+    "Karras": "Karras",
+    "Exponential": "Esponenziale",
+    "Variance Preserving": "Conservazione della Varianza",
+    "Sigma min": "Sigma min",
+    "Sigma max": "Sigma max",
+    "Sigma rho (Karras only)": "Sigma rho (Solo Karras)",
+    "Beta distribution (VP only)": "Distribuzione Beta (Solo CV)",
+    "Beta min (VP only)": "Beta min (Solo CV)",
+    "Epsilon (VP only)": "Epsilon (Solo CV)",
+    "Running in txt2img mode:": "Esecuzione in modalità txt2img:",
+    "Render these video formats:": "Renderizza in questi formati:",
+    "GIF": "GIF",
+    "MP4": "MP4",
+    "WEBM": "WEBM",
+    "Animation Parameters": "Parametri animazione",
+    "Total Animation Length (s)": "Durata totale dell'animazione (s)",
+    "Framerate": "Frequenza dei fotogrammi",
+    "Smoothing_Frames": "Fotogrammi da appianare",
+    "Add_Noise": "Aggiungi rumore",
+    "Noise Strength": "Intensità del rumore",
+    "Initial Parameters": "Parametri iniziali",
+    "Denoising Strength": "Intensità di riduzione del rumore",
+    "Seed_March": "Seed_March",
+    "Zoom Factor (scale/s)": "Fattore di ingrandimento (scala/s)",
+    "X Pixel Shift (pixels/s)": "Sposta i Pixel sull'asse X (pixel/s)",
+    "Y Pixel Shift (pixels/s)": "Sposta i Pixel sull'asse Y (pixel/s)",
+    "Rotation (deg/s)": "Rotazione (gradi/s)",
+    "Prompt Template, applied to each keyframe below": "Modello di prompt, applicato a ciascun fotogramma chiave qui di seguito",
+    "Positive Prompts": "Prompt positivi",
+    "Negative Prompts": "Prompt negativi",
+    "Props, Stamps": "Immagini Clipart da diffondere (prop), o da applicare in post elaborazione e non essere diffuse (stamp).",
+    "Poper_Folder:": "Cartella Immagini Clipart (PNG trasparenti):",
+    "Supported Keyframes:": "Fotogrammi chiave supportati:",
+    "time_s | source | video, images, img2img | path": "time_s | source | video, images, img2img | path",
+    "time_s | prompt | positive_prompts | negative_prompts": "time_s | prompt | positive_prompts | negative_prompts",
+    "time_s | template | positive_prompts | negative_prompts": "time_s | template | positive_prompts | negative_prompts",
+    "time_s | transform | zoom | x_shift | y_shift | rotation": "time_s | transform | zoom | x_shift | y_shift | rotation",
+    "time_s | seed | new_seed_int": "time_s | seed | new_seed_int",
+    "time_s | noise | added_noise_strength": "time_s | noise | added_noise_strength",
+    "time_s | denoise | denoise_value": "time_s | denoise | denoise_value",
+    "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name": "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name",
+    "time_s | clear_text | textblock_name": "time_s | clear_text | textblock_name",
+    "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation": "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation",
+    "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation": "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation",
+    "time_s | clear_stamp | stamp_name": "time_s | clear_stamp | stamp_name",
+    "time_s | col_set": "time_s | col_set",
+    "time_s | col_clear": "time_s | col_clear",
+    "time_s | model | model": "time_s | model | model",
+    "img2img_mode": "Modalità img2img",
+    "Keyframes:": "Fotogrammi chiave:",
+    "Tile X": "Piastrella asse X",
+    "Tile Y": "Piastrella asse Y",
+    "Python code": "Codice Python",
+    "Source embedding to convert": "Incorporamento sorgente da convertire",
+    "Embedding token": "Token Incorporamento",
+    "Output directory": "Cartella di output",
+    "Horizontal symmetry": "Simmetria orizzontale",
+    "Vertical symmetry": "Simmetria verticale",
+    "Alt. symmetry method (blending)": "Metodo di simmetria alternativo (miscelazione)",
+    "Apply every n steps": "Applica ogni n passi",
+    "Skip last n steps": "Salta gli ultimi n passi",
+    "Interpolation prompt": "Prompt di interpolazione",
+    "Number of images": "Numero di immagini",
+    "Make a gif": "Crea GIF",
+    "Duration of images (ms)": "Durata delle immagini (ms)",
+    "Put variable parts at start of prompt": "Inserisce le parti variabili all'inizio del prompt",
+    "Keyframe Format:": "Formato dei fotogrammi chiave:",
+    "Seed | Prompt or just Prompt": "Seme | Prompt o semplicemente Prompt",
+    "Prompt list": "Elenco dei prompt",
+    "Number of images between keyframes": "Numero di immagini tra fotogrammi chiave",
+    "Save results as video": "Salva i risultati come video",
+    "Frames per second": "Fotogrammi al secondo",
+    "Iterate seed every line": "Iterare il seme per ogni riga",
+    "Use same random seed for all lines": "Usa lo stesso seme casuale per tutte le righe",
+    "List of prompt inputs": "Elenco di prompt di input",
+    "Upload prompt inputs": "Carica un file contenente i prompt di input",
+    "n": "Esegui n volte",
+    "Destination seed(s) (Comma separated)": "Seme/i di destinazione (separati da virgola)",
+    "Only use Random seeds (Unless comparing paths)": "Usa solo semi casuali (a meno che non si confrontino i percorsi)",
+    "Number of random seed(s)": "Numero di semi casuali",
+    "Compare paths (Separate travels from 1st seed to each destination)": "Confronta percorsi (transizioni separate dal primo seme a ciascuna destinazione)",
+    "Steps": "Passi",
+    "Loop back to initial seed": "Ritorna al seme iniziale",
+    "Bump seed (If > 0 do a Compare Paths but only one image. No video)": "Modula seme (se > 0 mescola il seme iniziale con quelli di destinazione ma solo un'immagine. Nessun video)",
+    "Show generated images in ui": "Mostra le immagini generate nell'interfaccia utente",
+    "\"Hug the middle\" during interpolation": "\"Hug the middle\" durante l'interpolazione. Rende l'interpolazione un po' più veloce all'inizio e alla fine. A volte può produrre video più fluidi, il più delle volte no.",
+    "Allow the default Euler a Sampling method. (Does not produce good results)": "Consenti Euler_a come metodo di campionamento predefinito. (Non produce buoni risultati)",
+    "Illustration": "Illustrazione",
+    "Logo": "Logo",
+    "Drawing": "Disegno",
+    "Artistic": "Artistico",
+    "Tattoo": "Tatuaggio",
+    "Gothic": "Gotico",
+    "Anime": "Anime",
+    "Cartoon": "Cartoon",
+    "Sticker": "Etichetta",
+    "Gold Pendant": "Ciondolo in oro",
+    "None - prompt only": "Nessuno - solo prompt",
+    "Enable Vectorizing": "Abilita vettorizzazione",
+    "Output format": "Formato di output",
+    "svg": "svg",
+    "pdf": "pdf",
+    "White is Opaque": "Il bianco è opaco",
+    "Cut white margin from input": "Taglia il margine bianco dall'input",
+    "Keep temp images": "Conserva le immagini temporanee",
+    "Threshold": "Soglia",
+    "Transparent PNG": "PNG trasparente",
+    "Noise Tolerance": "Tolleranza al rumore",
+    "Quantize": "Quantizzare",
+    "Dry Run": "Esecuzione a vuoto (Debug)",
+    "NEW!": "NUOVO!",
+    "Premium Fantasy Card Template": "Premium Fantasy Card Template",
+    "is now available.": "è ora disponibile.",
+    "Generate a wide variety of creatures and characters in the style of a fantasy card game. Perfect for heroes, animals, monsters, and even crazy hybrids.": "Genera un'ampia varietà di creature e personaggi nello stile di un gioco di carte fantasy. Perfetto per eroi, animali, mostri e persino ibridi incredibili.",
+    "Learn More ➜": "Per saperne di più ➜",
+    "Purchases help fund the continued development of Unprompted. Thank you for your support!": "Gli acquisti aiutano a finanziare il continuo sviluppo di Unprompted. Grazie per il vostro sostegno!",
+    "X type": "Parametro asse X",
+    "Nothing": "Niente",
+    "Var. seed": "Seme della variazione",
+    "Var. strength": "Forza della variazione",
+    "Prompt S/R": "Cerca e Sostituisci nel Prompt",
+    "Prompt order": "In ordine di prompt",
+    "Sampler": "Campionatore",
+    "Checkpoint name": "Nome del checkpoint",
+    "Hypernetwork": "Iperrete",
+    "Hypernet str.": "Forza della Iperrete",
+    "Sigma Churn": "Sigma Churn",
+    "Sigma noise": "Sigma noise",
+    "Eta": "ETA",
+    "Clip skip": "Salta CLIP",
+    "Denoising": "Riduzione del rumore",
+    "Cond. Image Mask Weight": "Peso maschera immagine condizionale",
+    "X values": "Valori per X",
+    "Y type": "Parametro asse Y",
+    "Y values": "Valori per Y",
+    "Z type": "Parametro asse Z",
+    "Z values": "Valori per Z",
+    "Combinatorial generation": "Generazione combinatoria",
+    "Combinatorial batches": "Lotti combinatori",
+    "Magic prompt": "Prompt magico",
+    "Fixed seed": "Seme fisso",
+    "Combinations": "Combinazioni",
+    "Choose a number of terms from a list, in this case we choose two artists": "Scegli un numero di termini da un elenco, in questo caso scegliamo due artisti",
+    "{{2$artist1|artist2|artist3}}": "{{2$artist1|artist2|artist3}}",
+    "If $ is not provided, then 1$ is assumed.\n\n    A range can be provided:": "Se $ non viene fornito, si presume 1$.\n\n    È possibile fornire un intervallo di valori:",
+    "{{1-3$artist1|artist2|artist3}}": "{{1-3$artist1|artist2|artist3}}",
+    "In this case, a random number of artists between 1 and 3 is chosen.": "In questo caso viene scelto un numero casuale di artisti compreso tra 1 e 3.",
+    "Wildcards": "Termini jolly",
+    "If the groups wont drop down click": "Se i gruppi non vengono visualizzati, clicca",
+    "here": "qui",
+    "to fix the issue.": "per correggere il problema.",
+    "WILDCARD_DIR: C:\\stable-diffusion-webui\\extensions\\sd-dynamic-prompts\\wildcards": "WILDCARD_DIR: C:\\stable-diffusion-webui\\extensions\\sd-dynamic-prompts\\wildcards",
+    "You can add more wildcards by creating a text file with one term per line and name is mywildcards.txt. Place it in scripts/wildcards.": "Puoi aggiungere termini jolly creando un file di testo con un termine per riga e nominandolo, per esempio, mywildcards.txt. Inseriscilo in scripts/wildcards.",
+    "__<folder>/mywildcards__": "__<cartella>/mywildcards__",
+    "will then become available.": "diverrà quindi disponibile.",
+    "Artist or styles name list. '.txt' files with one name per line": "Elenco nomi di artisti o stili. File '.txt' con un nome per riga",
+    "Prompt words before artist or style name": "Parole chiave prima del nome dell'artista o dello stile",
+    "Prompt words after artist or style name": "Parole chiave dopo il nome dell'artista o dello stile",
+    "Negative Prompt": "Prompt negativo",
+    "Save": "Salva",
+    "Send to img2img": "Invia a img2img",
+    "Send to inpaint": "Invia a Inpaint",
+    "Send to extras": "Invia a Extra",
+    "Make Zip when Save?": "Crea un file ZIP quando si usa 'Salva'",
+    "Textbox": "Casella di testo",
+    "Interrogate\nCLIP": "Interroga\nCLIP",
+    "Interrogate\nDeepBooru": "Interroga\nDeepBooru",
+    "Inpaint": "Inpaint",
+    "Batch img2img": "img2img in lotti",
+    "Image for img2img": "Immagine per img2img",
+    "Drop Image Here": "Trascina l'immagine qui",
+    "Image for inpainting with mask": "Immagine per inpainting con maschera",
+    "Mask": "Maschera",
+    "Mask blur": "Sfocatura maschera",
+    "Mask mode": "Modalità maschera",
+    "Draw mask": "Disegna maschera",
+    "Upload mask": "Carica maschera",
+    "Masking mode": "Modalità mascheratura",
+    "Inpaint masked": "Inpaint mascherato",
+    "Inpaint not masked": "Inpaint non mascherato",
+    "Masked content": "Contenuto mascherato",
+    "fill": "riempi",
+    "original": "originale",
+    "latent noise": "rumore nello spazio latente",
+    "latent nothing": "nulla nello spazio latente",
+    "Inpaint at full resolution": "Inpaint alla massima risoluzione",
+    "Inpaint at full resolution padding, pixels": "Inpaint con riempimento a piena risoluzione, pixel",
+    "Process images in a directory on the same machine where the server is running.": "Elabora le immagini in una cartella sulla stessa macchina su cui è in esecuzione il server.",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa una cartella di output vuota per salvare normalmente le immagini invece di scrivere nella cartella di output.",
+    "Input directory": "Cartella di Input",
+    "Resize mode": "Modalità di ridimensionamento",
+    "Just resize": "Ridimensiona solamente",
+    "Crop and resize": "Ritaglia e ridimensiona",
+    "Resize and fill": "Ridimensiona e riempie",
+    "Advanced loopback": "Advanced loopback", 
+    "External Image Masking": "Immagine esterna per la mascheratura",
+    "img2img alternative test": "Test alternativo per img2img",
+    "img2tiles": "img2tiles",
+    "Interpolate": "Interpola immagini",
+    "Loopback": "Rielaborazione ricorsiva",
+    "Loopback and Superimpose": "Rielabora ricorsivamente e sovraimponi",
+    "Alpha Canvas": "Alpha Canvas",
+    "Outpainting mk2": "Outpainting mk2",
+    "Poor man's outpainting": "Poor man's outpainting",
+    "SD upscale": "Ampliamento SD",
+    "txt2mask v0.1.1": "txt2mask v0.1.1",
+    "[C] Video to video": "[C] Video to video",
+    "Videos": "Filmati",
+    "Deforum-webui (use tab extension instead!)": "Deforum-webui (usa piuttosto la scheda Deforum delle estensioni!)",
+    "Use first image colors (custom color correction)": "Usa i colori della prima immagine (correzione del colore personalizzata)",
+    "Denoising strength change factor (overridden if proportional used)": "Fattore di variazione dell'intensità di riduzione del rumore (sovrascritto se si usa proporzionale)",
+    "Zoom level": "Livello di Zoom",
+    "Direction X": "Direzione X",
+    "Direction Y": "Direzione Y",
+    "Denoising strength start": "Intensità di riduzione del rumore - Inizio",
+    "Denoising strength end": "Intensità di riduzione del rumore - Fine",
+    "Denoising strength proportional change starting value": "Intensità di riduzione del rumore - Valore iniziale della variazione proporzionale",
+    "Denoising strength proportional change ending value (0.1 = disabled)": "Intensità di riduzione del rumore - Valore finale della variazione proporzionale (0.1 = disabilitato)",
+    "Saturation enhancement per image": "Miglioramento della saturazione per ciascuna immagine",
+    "Use sine denoising strength variation": "Utilizzare la variazione sinusoidale dell'intensità di riduzione del rumore",
+    "Phase difference": "Differenza di Fase",
+    "Denoising strength exponentiation": "Esponenziazione dell'intensità di riduzione del rumore",
+    "Use sine zoom variation": "Usa la variazione sinusoidale dello zoom",
+    "Zoom exponentiation": "Esponeniazione dello Zoom",
+    "Use multiple prompts": "Usa prompt multipli",
+    "Same seed per prompt": "Stesso seme per ogni prompt",
+    "Same seed for everything": "Stesso seme per tutto",
+    "Original init image for everything": "Immagine originale di inizializzazione per tutto",
+    "Multiple prompts : 1 line positive, 1 line negative, leave a blank line for no negative": "Prompt multipli: 1 riga positivo, 1 riga negativo, lasciare una riga vuota per nessun negativo",
+    "Running in img2img mode:": "Esecuzione in modalità img2img:",
+    "Masking preview size": "Dimensione dell'anteprima della mascheratura",
+    "Draw new mask on every run": "Disegna una nuova maschera ad ogni esecuzione",
+    "Process non-contigious masks separately": "Elaborare le maschere non contigue separatamente",
+    "should be 2 or lower.": "dovrebbe essere 2 o inferiore.",
+    "Override `Sampling method` to Euler?(this method is built for it)": "Sovrascrivi il 'Metodo di campionamento' con Eulero? (questo metodo è stato creato per questo)",
+    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Sovrascrivi `prompt` con lo stesso valore del `prompt originale`? (e `prompt negativo`)",
+    "Original prompt": "Prompt originale",
+    "Original negative prompt": "Prompt negativo originale",
+    "Override `Sampling Steps` to the same val due as `Decode steps`?": "Sovrascrivere 'Passi di campionamento' allo stesso valore di 'Passi di decodifica'?",
+    "Decode steps": "Passi di decodifica",
+    "Override `Denoising strength` to 1?": "Sostituisci 'Forza di denoising' a 1?",
+    "Decode CFG scale": "Scala CFG di decodifica",
+    "Randomness": "Casualità",
+    "Sigma adjustment for finding noise for image": "Regolazione Sigma per trovare il rumore per l'immagine",
+    "Tile size": "Dimensione piastrella",
+    "Tile overlap": "Sovrapposizione piastrella",
+    "New seed for each tile": "Nuovo seme per ogni piastrella",
+    "alternate img2img imgage": "Immagine alternativa per img2img",
+    "interpolation values": "Valori di interpolazione",
+    "Refinement loops": "Cicli di affinamento",
+    "Loopback alpha": "Trasparenza rielaborazione ricorsiva",
+    "Border alpha": "Trasparenza del bordo",
+    "Blending strides": "Passi di fusione",
+    "Reuse Seed": "Riusa il seme",
+    "One grid": "Singola griglia",
+    "Interpolate VarSeed": "Interpola il seme della variazione",
+    "Paste on mask": "Incolla sulla maschera",
+    "Inpaint all": "Inpaint tutto",
+    "Interpolate in latent": "Interpola nello spazio latente",
+    "Denoising strength change factor": "Fattore di variazione dell'intensità di denoising",
+    "Superimpose alpha": "Sovrapporre Alpha",
+    "Show extra settings": "Mostra impostazioni aggiuntive",
+    "Reuse seed": "Riusa il seme",
+    "CFG decay factor": "Fattore di decadimento CFG",
+    "CFG target": "CFG di destinazione",
+    "Show/Hide AlphaCanvas": "Mostra/Nascondi AlphaCanvas",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Impostazioni consigliate: Passi di campionamento: 80-100, Campionatore: Euler a, Intensità denoising: 0.8",
+    "Pixels to expand": "Pixel da espandere",
+    "Outpainting direction": "Direzione di Outpainting",
+    "left": "sinistra",
+    "right": "destra",
+    "up": "sopra",
+    "down": "sotto",
+    "Fall-off exponent (lower=higher detail)": "Esponente di decremento (più basso=maggior dettaglio)",
+    "Color variation": "Variazione di colore",
+    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenterà l'immagine al doppio delle dimensioni; utilizzare i cursori di larghezza e altezza per impostare la dimensione della piastrella",
+    "Upscaler": "Ampliamento immagine",
+    "Lanczos": "Lanczos",
+    "Nearest": "Nearest",
+    "LDSR": "LDSR",
+    "BSRGAN": "BSRGAN",
+    "ScuNET GAN": "ScuNET GAN",
+    "ScuNET PSNR": "ScuNET PSNR",
+    "SwinIR 4x": "SwinIR 4x",
+    "Mask prompt": "Prompt maschera",
+    "Negative mask prompt": "Prompt maschera negativa",
+    "Mask precision": "Precisione della maschera",
+    "Mask padding": "Estendi i bordi della maschera",
+    "Brush mask mode": "Modalità pennello maschera",
+    "discard": "Scarta",
+    "add": "Aggiungi",
+    "subtract": "Sottrai",
+    "Show mask in output?": "Mostra maschera in uscita?",
+    "If you like my work, please consider showing your support on": "Se ti piace il mio lavoro, per favore considera di mostrare il tuo supporto su ",
+    "Patreon": "Patreon",
+    "Input file path": "Percorso file di input",
+    "CRF (quality, less is better, x264 param)": "CRF (qualità, meno è meglio, x264 param)",
+    "FPS": "FPS",
+    "Seed step size": "Ampiezza del gradiente del seme",
+    "Seed max distance": "Distanza massima del seme",
+    "Start time": "Orario di inizio",
+    "End time": "Orario di fine",
+    "End Prompt Blend Trigger Percent": "Percentuale di innesco del mix col prompt finale",
+    "Prompt end": "Prompt finale",
+    "Smooth video": "Rendi il filmato fluido",
+    "Seconds": "Secondi",
+    "Zoom": "Zoom",
+    "Rotate": "Ruota",
+    "Degrees": "Gradi",
+    "Is the Image Tiled?": "L'immagine è piastrellata?",
+    "TranslateX": "Traslazione X",
+    "Left": "Sinistra",
+    "PercentX": "Percentuale X",
+    "TranslateY": "Traslazione Y",
+    "Up": "Sopra",
+    "PercentY": "Percentuale Y",
+    "Show generated pictures in ui": "Mostra le immagini generate nell'interfaccia utente",
+    "Deforum v0.5-webui-beta": "Deforum v0.5-webui-beta",
+    "This script is deprecated. Please use the full Deforum extension instead.": "Questo script è obsoleto. Utilizzare invece l'estensione Deforum completa.",
+    "Update instructions:": "Istruzioni per l'aggiornamento:",
+    "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md": "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md",
+    "discord.gg/deforum": "discord.gg/deforum",
+    "Single Image": "Singola immagine",
+    "Batch Process": "Elaborare a lotti",
+    "Batch from Directory": "Lotto da cartella",
+    "Source": "Sorgente",
+    "Show result images": "Mostra le immagini dei risultati",
+    "Scale by": "Scala di",
+    "Scale to": "Scala a",
+    "Resize": "Ridimensiona",
+    "Crop to fit": "Ritaglia per adattare",
+    "Upscaler 2 visibility": "Visibilità Ampliamento immagine 2",
+    "GFPGAN visibility": "Visibilità GFPGAN",
+    "CodeFormer visibility": "Visibilità CodeFormer",
+    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso di CodeFormer (0 = effetto massimo, 1 = effetto minimo)",
+    "Upscale Before Restoring Faces": "Amplia prima di restaurare i volti",
+    "Send to txt2img": "Invia a txt2img",
+    "A merger of the two checkpoints will be generated in your": "I due checkpoint verranno fusi nella cartella dei",
+    "checkpoint": "checkpoint",
+    "directory.": ".",
+    "Primary model (A)": "Modello Primario (A)",
+    "Secondary model (B)": "Modello Secondario (B)",
+    "Tertiary model (C)": "Modello Terziario (C)",
+    "Custom Name (Optional)": "Nome personalizzato (facoltativo)",
+    "Multiplier (M) - set to 0 to get model A": "Moltiplicatore (M): impostare a 0 per ottenere il modello A",
+    "Interpolation Method": "Metodo di interpolazione",
+    "Weighted sum": "Somma pesata",
+    "Add difference": "Aggiungi differenza",
+    "Save as float16": "Salva come float16",
+    "See": "Consulta la ",
+    "wiki": "wiki",
+    "for detailed explanation.": " per una spiegazione dettagliata.",
+    "Create embedding": "Crea Incorporamento",
+    "Create hypernetwork": "Crea Iperrete",
+    "Preprocess images": "Preprocessa le immagini",
+    "Name": "Nome",
+    "Initialization text": "Testo di inizializzazione",
+    "Number of vectors per token": "Numero di vettori per token",
+    "Overwrite Old Embedding": "Sovrascrivi il vecchio incorporamento",
+    "Modules": "Moduli",
+    "Enter hypernetwork layer structure": "Immettere la struttura del livello della Iperrete",
+    "Select activation function of hypernetwork": "Selezionare la funzione di attivazione della Iperrete",
+    "linear": "lineare",
+    "relu": "relu",
+    "leakyrelu": "leakyrelu",
+    "elu": "elu",
+    "swish": "swish",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "soglia",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleziona inizializzazione dei pesi dei livelli. relu-like - Kaiming, Si consiglia sigmoid-like - Xavier",
+    "Normal": "Normale",
+    "KaimingUniform": "KaimingUniform",
+    "KaimingNormal": "KaimingNormal",
+    "XavierUniform": "XavierUniform",
+    "XavierNormal": "XavierNormal",
+    "Add layer normalization": "Aggiunge la normalizzazione del livello",
+    "Use dropout": "Usa Dropout",
+    "Overwrite Old Hypernetwork": "Sovrascrive la vecchia Iperrete",
+    "Source directory": "Cartella sorgente",
+    "Destination directory": "Cartella di destinazione",
+    "Existing Caption txt Action": "Azione sul testo della didascalia esistente",
+    "ignore": "ignora",
+    "copy": "copia",
+    "prepend": "anteporre",
+    "append": "appendere",
+    "Create flipped copies": "Crea copie specchiate",
+    "Split oversized images": "Dividi immagini di grandi dimensioni",
+    "Auto focal point crop": "Ritaglio automatico al punto focale",
+    "Use BLIP for caption": "Usa BLIP per la didascalia",
+    "Use deepbooru for caption": "Usa deepbooru per la didascalia",
+    "Split image threshold": "Soglia di divisione dell'immagine",
+    "Split image overlap ratio": "Rapporto di sovrapposizione dell'immagine",
+    "Focal point face weight": "Peso della faccia del punto focale",
+    "Focal point entropy weight": "Peso dell'entropia del punto focale",
+    "Focal point edges weight": "Peso dei bordi del punto focale",
+    "Create debug image": "Crea immagine di debug",
+    "Preprocess": "Preprocessa",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Addestra un Incorporamento o Iperrete; è necessario specificare una directory con un set di immagini con rapporto 1:1",
+    "[wiki]": "[wiki]",
+    "Embedding": "Incorporamento",
+    "Embedding Learning rate": "Tasso di apprendimento Incorporamento",
+    "Hypernetwork Learning rate": "Tasso di apprendimento Iperrete",
+    "Dataset directory": "Cartella del Dataset",
+    "Log directory": "Cartella del registro",
+    "Prompt template file": "File modello prompt",
+    "Max steps": "Passi massimi",
+    "Save an image to log directory every N steps, 0 to disable": "Salva un'immagine nella cartella del registro ogni N passaggi, 0 per disabilitare",
+    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva una copia dell'incorporamento nella cartella del registro ogni N passaggi, 0 per disabilitare",
+    "Save images with embedding in PNG chunks": "Salva le immagini con l'incorporamento in blocchi PNG",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Legge i parametri (prompt, ecc...) dalla scheda txt2img durante la creazione delle anteprime",
+    "Train Hypernetwork": "Addestra Iperrete",
+    "Train Embedding": "Addestra Incorporamento",
+    "Create an aesthetic embedding out of any number of images": "Crea un'incorporamento estetico da qualsiasi numero di immagini",
+    "Create images embedding": "Crea incorporamento di immagini",
+    "-1": "-1",
+    "This extension works well with text captions in comma-separated style (such as the tags generated by DeepBooru interrogator).": "Questa estensione funziona bene con i sottotitoli di testo in stile separato da virgole (come i tag generati dall'interrogatore DeepBooru).",
+    "Save all changes": "Salva tutte le modifiche",
+    "Backup original text file (original file will be renamed like filename.000, .001, .002, ...)": "Backup del file di testo originale (il file originale verrà rinominato come nomefile.000, .001, .002, ...)",
+    "Note:": "Note:",
+    "New text file will be created if you are using filename as captions.": "Verrà creato un nuovo file di testo se si utilizza il nome del file come didascalia.",
+    "Results": "Risultati",
+    "Load": "Carica",
+    "Dataset Images": "Immagini del Dataset",
+    "Filter and Edit Tags": "Filtra e modifica i tag",
+    "Edit Caption of Selected Image": "Modifica la didascalia dell'immagine selezionata",
+    "Search tags / Filter images by tags": "Cerca tag / Filtra le immagini per tag",
+    "Search Tags": "Cerca tag",
+    "Clear all filters": "Rimuovi tutti i filtri",
+    "Sort by": "Ordina per",
+    "Alphabetical Order": "Ordine alfabetico",
+    "Frequency": "Frequenza",
+    "Sort Order": "Ordinamento",
+    "Ascending": "Ascendente",
+    "Descending": "Discendente",
+    "Filter Images by Tags": "Filtra le immagini per tag",
+    "Edit tags in filtered images": "Modifica i tag nelle immagini filtrate",
+    "Selected Tags": "Tag selezionati",
+    "Edit Tags": "Modificare i tag",
+    "Apply changes to filtered images": "Applica le modifiche alle immagini filtrate",
+    "Append additional tags to the beginning": "Aggiungi tag addizionali all'inizio",
+    "1. The selected tags are displayed in comma separated style.": "1. I tag selezionati vengono visualizzati in uno stile separato da virgole.",
+    "2. When changes are applied, all tags in each displayed images are replaced.": "2. Quando vengono applicate le modifiche, tutti i tag in ciascuna immagine visualizzata vengono sostituiti.",
+    "3. If you change some tags into blank, they will be erased.": "3. Se modifichi alcuni tag con uno spazio vuoto, verranno cancellati.",
+    "4. If you add some tags to the end, they will be appended to the end/beginning of the text file.": "4. Se aggiungi dei tag alla fine, questi verranno aggiunti alla fine/inizio del file di testo.",
+    "5. Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "5. Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\"..",
+    "ex A.": "esempio A.",
+    "Original Text = \"A, A, B, C\" Selected Tags = \"B, A\" Edit Tags = \"X, Y\"": "Testo originale = \"A, A, B, C\" Tag selezionati = \"B, A\" Modifica tag = \"X, Y\"",
+    "Result = \"Y, Y, X, C\" (B->X, A->Y)": "Risultato = \"Y, Y, X, C\" (B->X, A->Y)",
+    "ex B.": "esempio B.",
+    "Original Text = \"A, B, C\" Selected Tags = \"(nothing)\" Edit Tags = \"X, Y\"": "Testo originale = \"A, B, C\" Tag selezionati = \"(nothing)\" Modifica tag = \"X, Y\"",
+    "Result = \"A, B, C, X, Y\" (add X and Y to the end (default))": "Risultato = \"A, B, C, X, Y\" (aggiunge X e Y alla fine (predefinito))",
+    "Result = \"X, Y, A, B, C\" (add X and Y to the beginning (\"Append additional tags to the beginning\" checked))": "Risultato = \"X, Y, A, B, C\" (aggiunge X e Y all'inizio (\"Aggiungi tag addizionali all'inizio\" selezionato))",
+    "ex C.": "esempio C.",
+    "Original Text = \"A, B, C, D, E\" Selected Tags = \"A, B, D\" Edit Tags = \", X, \"": "Testo originale = \"A, B, C, D, E\" Tag selezionati = \"A, B, D\" Modifica tag = \", X, \"",
+    "Result = \"X, C, E\" (A->\"\", B->X, D->\"\")": "Risultato = \"X, C, E\" (A->\"\", B->X, D->\"\")",
+    "Caption of Selected Image": "Didascalia dell'immagine selezionata",
+    "Copy caption": "Copia didascalia",
+    "Edit Caption": "Modifica didascalia",
+    "Apply changes to selected image": "Applica le modifiche all'immagine selezionata",
+    "Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\".",
+    "Info and links": "Info e link",
+    "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha": "Realizzato da deforum.github.io, port per l'interfaccia web di AUTOMATIC1111 manutenuto da kabachuha",
+    "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Repository Github originale di Deforum  github.com/deforum/stable-diffusion",
+    "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui": "Questo fork è per l'interfaccia web di AUTOMATIC1111 github.com/deforum-art/deforum-for-automatic1111-webui",
+    "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Unisciti al canale Discord ufficiale di Deforum discord.gg/deforum per condividere le tue creazioni e suggerimenti",
+    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "Manuale d'uso per la versione 0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
+    "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Spiegazione della matematica dei fotogrammi chiave docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
+    "Keyframes": "Fotogrammi chiave",
+    "Prompts": "Prompt",
+    "Init": "Inizializzare",
+    "Video output": "Uscita video",
+    "Run settings": "Esegui le impostazioni",
+    "Import settings from file": "Importa impostazioni da file",
+    "Override settings": "Sostituisci le impostazioni",
+    "Custom settings file": "File delle impostazioni personalizzate",
+    "Sampling settings": "Impostazioni di campionamento",
+    "override_these_with_webui": "Sovrascrivi con Web UI",
+    "W": "L",
+    "H": "A",
+    "seed": "Seme",
+    "sampler": "Campionatore",
+    "Enable extras": "Abilita 'Extra'",
+    "subseed": "Sub seme",
+    "subseed_strength": "Intensità subseme",
+    "steps": "Passi",
+    "ddim_eta": "ETA DDIM",
+    "n_batch": "Numero lotto",
+    "make_grid": "Crea griglia",
+    "grid_rows": "Righe griglia",
+    "save_settings": "Salva impostazioni",
+    "save_samples": "Salva i campioni",
+    "display_samples": "Mostra i campioni",
+    "save_sample_per_step": "Salva campioni per passo",
+    "show_sample_per_step": "Mostra campioni per passo",
+    "Batch settings": "Impostazioni lotto",
+    "batch_name": "Nome del lotto",
+    "filename_format": "Formato nome del file",
+    "seed_behavior": "Comportamento seme",
+    "iter": "Iterativo",
+    "fixed": "Fisso",
+    "random": "Casuale",
+    "schedule": "Pianificato",
+    "Animation settings": "Impostazioni animazione",
+    "animation_mode": "Modalità animazione",
+    "2D": "2D",
+    "3D": "3D",
+    "Video Input": "Ingresso video",
+    "max_frames": "Fotogrammi max",
+    "border": "Bordo",
+    "replicate": "Replica",
+    "wrap": "Impacchetta",
+    "Motion parameters:": "Parametri di movimento:",
+    "2D and 3D settings": "Impostazioni 2D e 3D",
+    "angle": "Angolo",
+    "zoom": "Zoom",
+    "translation_x": "Traslazione X",
+    "translation_y": "Traslazione Y",
+    "3D settings": "Impostazioni 3D",
+    "translation_z": "Traslazione Z",
+    "rotation_3d_x": "Rotazione 3D X",
+    "rotation_3d_y": "Rotazione 3D Y",
+    "rotation_3d_z": "Rotazione 3D Z",
+    "Prespective flip — Low VRAM pseudo-3D mode:": "Inversione prospettica: modalità pseudo-3D a bassa VRAM:",
+    "flip_2d_perspective": "Inverti prospettiva 2D",
+    "perspective_flip_theta": "Inverti prospettiva theta",
+    "perspective_flip_phi": "Inverti prospettiva phi",
+    "perspective_flip_gamma": "Inverti prospettiva gamma",
+    "perspective_flip_fv": "Inverti prospettiva fv",
+    "Generation settings:": "Impostazioni di generazione:",
+    "noise_schedule": "Pianificazione del rumore",
+    "strength_schedule": "Intensità della pianificazione",
+    "contrast_schedule": "Contrasto della pianificazione",
+    "cfg_scale_schedule": "Pianificazione della scala CFG",
+    "3D Fov settings:": "Impostazioni del campo visivo 3D:",
+    "fov_schedule": "Pianificazione del campo visivo",
+    "near_schedule": "Pianificazione da vicino",
+    "far_schedule": "Pianificazione da lontano",
+    "To enable seed schedule select seed behavior — 'schedule'": "Per abilitare la pianificazione del seme, seleziona il comportamento del seme — 'pianifica'",
+    "seed_schedule": "Pianificazione del seme",
+    "Coherence:": "Coerenza:",
+    "color_coherence": "Coerenza del colore",
+    "Match Frame 0 HSV": "Uguaglia HSV del fotogramma 0",
+    "Match Frame 0 LAB": "Uguaglia LAB del fotogramma 0",
+    "Match Frame 0 RGB": "Uguaglia RGB del fotogramma 0",
+    "diffusion_cadence": "Cadenza di diffusione",
+    "3D Depth Warping:": "Deformazione della profondità 3D:",
+    "use_depth_warping": "Usa la deformazione della profondità",
+    "midas_weight": "Peso MIDAS",
+    "near_plane": "Piano vicino",
+    "far_plane": "Piano lontano",
+    "fov": "Campo visivo",
+    "padding_mode": "Modalità di riempimento",
+    "reflection": "Rifletti",
+    "zeros": "Zeri",
+    "sampling_mode": "Modalità di campionamento",
+    "bicubic": "bicubic",
+    "bilinear": "bilinear",
+    "nearest": "nearest",
+    "save_depth_maps": "Salva le mappe di profondità",
+    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`modalità animazione: Nessuno` si inserisce nell'elenco di *prompt*. (Modalità batch disabilitata atm, funzionano solo i prompt di animazione)",
+    "*Important change from vanilla Deforum!*": "*Importante cambiamento rispetto alla versione originale di Deforum!*",
+    "This script uses the built-in webui weighting settings.": "Questo script utilizza le impostazioni di pesatura webui integrate.",
+    "So if you want to use math functions as prompt weights,": "Quindi, se vuoi usare le funzioni matematiche come pesi dei prompt,",
+    "keep the values above zero in both parts": "mantenere i valori sopra lo zero in entrambe le parti",
+    "Negative prompt part can be specified with --neg": "La parte negativa del prompt può essere specificata con --neg",
+    "batch_prompts (disabled atm)": "Prompt in lotti (al momento è disabilitato)",
+    "animation_prompts": "Prompt animazione",
+    "Init settings": "Impostazioni iniziali",
+    "use_init": "Usa le impostazioni iniziali",
+    "from_img2img_instead_of_link": "da img2img invece che da link",
+    "strength_0_no_init": "Intensità 0 nessuna inizializzazione",
+    "strength": "Intensità",
+    "init_image": "Immagine di inizializzazione",
+    "use_mask": "Usa maschera",
+    "use_alpha_as_mask": "Usa alpha come maschera",
+    "invert_mask": "Inverti la maschera",
+    "overlay_mask": "Sovrapponi la maschera",
+    "mask_file": "File della maschera",
+    "mask_brightness_adjust": "Regola la luminosità della maschera",
+    "mask_overlay_blur": "Sfocatura della sovrapposizione della maschera",
+    "Video Input:": "Ingresso video:",
+    "video_init_path": "Percorso del video di inizializzazione",
+    "extract_nth_frame": "Estrai ogni ennesimo fotogramma",
+    "overwrite_extracted_frames": "Sovrascrivi i fotogrammi estratti",
+    "use_mask_video": "Usa maschera video",
+    "video_mask_path": "Percorso della maschera video",
+    "Interpolation (turned off atm)": "Interpolazione (attualmente spento)",
+    "interpolate_key_frames": "Interpola fotogrammi chiave",
+    "interpolate_x_frames": "Interpola x fotogrammi",
+    "Resume animation:": "Riprendi l'animazione:",
+    "resume_from_timestring": "Riprendi da stringa temporale",
+    "resume_timestring": "Stringa temporale",
+    "Video output settings": "Impostazioni uscita video",
+    "skip_video_for_run_all": "Salta il video per eseguire tutto",
+    "fps": "FPS",
+    "output_format": "Formato di uscita",
+    "PIL gif": "PIL GIF",
+    "FFMPEG mp4": "FFMPEG MP4",
+    "ffmpeg_location": "Percorso ffmpeg",
+    "add_soundtrack": "Aggiungi colonna sonora",
+    "soundtrack_path": "Percorso colonna sonora",
+    "use_manual_settings": "Usa impostazioni manuali",
+    "render_steps": "Passi di renderizzazione",
+    "max_video_frames": "Numero max fotogrammi video",
+    "path_name_modifier": "Modificatore del nome del percorso",
+    "x0_pred": "x0_pred",
+    "x": "x",
+    "image_path": "Percorso immagine",
+    "mp4_path": "Percorso MP4",
+    "Click here after the generation to show the video": "Clicca qui dopo la generazione per mostrare il video",
+    "NOTE: If the 'Generate' button doesn't work, go in Settings and click 'Restart Gradio and Refresh...'.": "NOTA: se il pulsante 'Genera' non funziona, vai in Impostazioni e fai clic su 'Riavvia Gradio e Aggiorna...'.",
+    "Save Settings": "Salva le impostazioni",
+    "Load Settings": "Carica le impostazioni",
+    "Path relative to the webui folder." : "Percorso relativo alla cartella webui.",
+    "Save Video Settings": "Salva impostazioni video",
+    "Load Video Settings": "Carica impostazioni video",
+    "dog": "cane",
+    "house": "casa",
+    "portrait": "ritratto",
+    "spaceship": "nave spaziale",
+    "anime": "anime",
+    "cartoon": "cartoon",
+    "digipa-high-impact": "digipa-high-impact",
+    "digipa-med-impact": "digipa-med-impact",
+    "digipa-low-impact": "digipa-low-impact",
+    "fareast": "estremo oriente",
+    "fineart": "fineart",
+    "scribbles": "scarabocchi",
+    "special": "special",
+    "ukioe": "ukioe",
+    "weird": "strano",
+    "black-white": "bianco e nero",
+    "nudity": "nudità",
+    "c": "c",
+    "Get Images": "Ottieni immagini",
+    "dog-anime": "dog-anime",
+    "dog-cartoon": "dog-cartoon",
+    "dog-digipa-high-impact": "dog-digipa-high-impact",
+    "dog-digipa-med-impact": "dog-digipa-med-impact",
+    "dog-digipa-low-impact": "dog-digipa-low-impact",
+    "dog-fareast": "dog-fareast",
+    "dog-fineart": "dog-fineart",
+    "dog-scribbles": "dog-scribbles",
+    "dog-special": "dog-special",
+    "dog-ukioe": "dog-ukioe",
+    "dog-weird": "dog-weird",
+    "dog-black-white": "dog-black-white",
+    "dog-nudity": "dog-nudity",
+    "dog-c": "dog-c",
+    "dog-n": "dog-n",
+    "house-anime": "house-anime",
+    "house-cartoon": "house-cartoon",
+    "house-digipa-high-impact": "house-digipa-high-impact",
+    "house-digipa-med-impact": "house-digipa-med-impact",
+    "house-digipa-low-impact": "house-digipa-low-impact",
+    "house-fareast": "house-fareast",
+    "house-fineart": "house-fineart",
+    "house-scribbles": "house-scribbles",
+    "house-special": "house-special",
+    "house-ukioe": "house-ukioe",
+    "house-weird": "house-weird",
+    "house-black-white": "house-black-white",
+    "house-nudity": "house-nudity",
+    "house-c": "house-c",
+    "house-n": "house-n",
+    "portrait-anime": "portrait-anime",
+    "portrait-cartoon": "portrait-cartoon",
+    "portrait-digipa-high-impact": "portrait-digipa-high-impact",
+    "portrait-digipa-med-impact": "portrait-digipa-med-impact",
+    "portrait-digipa-low-impact": "portrait-digipa-low-impact",
+    "portrait-fareast": "portrait-fareast",
+    "portrait-fineart": "portrait-fineart",
+    "portrait-scribbles": "portrait-scribbles",
+    "portrait-special": "portrait-special",
+    "portrait-ukioe": "portrait-ukioe",
+    "portrait-weird": "portrait-weird",
+    "portrait-black-white": "portrait-black-white",
+    "portrait-nudity": "portrait-nudity",
+    "portrait-c": "portrait-c",
+    "portrait-n": "portrait-n",
+    "spaceship-anime": "spaceship-anime",
+    "spaceship-cartoon": "spaceship-cartoon",
+    "spaceship-digipa-high-impact": "spaceship-digipa-high-impact",
+    "spaceship-digipa-med-impact": "spaceship-digipa-med-impact",
+    "spaceship-digipa-low-impact": "spaceship-digipa-low-impact",
+    "spaceship-fareast": "spaceship-fareast",
+    "spaceship-fineart": "spaceship-fineart",
+    "spaceship-scribbles": "spaceship-scribbles",
+    "spaceship-special": "spaceship-special",
+    "spaceship-ukioe": "spaceship-ukioe",
+    "spaceship-weird": "spaceship-weird",
+    "spaceship-black-white": "spaceship-black-white",
+    "spaceship-nudity": "spaceship-nudity",
+    "spaceship-c": "spaceship-c",
+    "spaceship-n": "spaceship-n",
+    "artists to study extension by camenduru |": "Estensione 'Artisti per studiare' a cura di camenduru |",
+    "github": "Github",
+    "|": "|",
+    "twitter": "Twitter",
+    "youtube": "Youtube",
+    "hi-res images": "Immagini in alta risoluzione",
+    "All images generated with CompVis/stable-diffusion-v1-4 +": "Tutte le immagini sono state generate con CompVis/stable-diffusion-v1-4 +",
+    "artists.csv": "artists.csv",
+    "| License: Attribution 4.0 International (CC BY 4.0)": "| Licenza: Attribution 4.0 International (CC BY 4.0)",
+    "Favorites": "Preferiti",
+    "Others": "Altre immagini",
+    "Images directory": "Cartella immagini",
+    "Dropdown": "Elenco cartelle",
+    "First Page": "Prima pagina",
+    "Prev Page": "Pagina precedente",
+    "Page Index": "Indice pagina",
+    "Next Page": "Pagina successiva",
+    "End Page": "Ultima pagina",
+    "delete next": "Cancella successivo",
+    "Delete": "Elimina",
+    "sort by": "Ordina per",
+    "path name": "Nome percorso",
+    "date": "Data",
+    "keyword": "Parola chiave",
+    "Generate Info": "Genera Info",
+    "File Name": "Nome del file",
+    "Move to favorites": "Aggiungi ai preferiti",
+    "Renew Page": "Aggiorna la pagina",
+    "Number": "Numero",
+    "set_index": "Imposta indice",
+    "load_switch": "Carica",
+    "turn_page_switch": "Volta pagina",
+    "Checkbox": "Casella di controllo",						   
+    "Checkbox Group": "Seleziona immagini per",
+    "artists": "Artisti",
+    "flavors": "Stili",
+    "mediums": "Tecniche",
+    "movements": "Movimenti artistici",
+    "All": "Tutto",
+    "Exclude abandoned": "Escludi scartati",
+    "Abandoned": "Scartati",
+    "Key word": "Parola chiave",
+    "Get inspiration": "Ispirami",
+    "to txt2img": "Invia a txt2img",
+    "to img2img": "Invia a img2img",
+    "Collect": "Salva nei preferiti",
+    "Don't show again": "Scarta",
+    "Move out": "Rimuovi",
+    "set button": "Pulsante imposta", 
+    "Apply settings": "Applica le impostazioni",
+    "Saving images/grids": "Salva immagini/griglie",
+    "Always save all generated images": "Salva sempre tutte le immagini generate",
+    "File format for images": "Formato del file delle immagini",
+    "Images filename pattern": "Modello del nome dei file immagine",
+    "Add number to filename when saving": "Aggiungi un numero al nome del file al salvataggio",
+    "Always save all generated image grids": "Salva sempre tutte le griglie di immagini generate",
+    "File format for grids": "Formato del file per le griglie",
+    "Add extended info (seed, prompt) to filename when saving grid": "Aggiungi informazioni estese (seme, prompt) al nome del file durante il salvataggio della griglia",
+    "Do not save grids consisting of one picture": "Non salvare le griglie composte da una sola immagine",
+    "Prevent empty spots in grid (when set to autodetect)": "Previeni spazi vuoti nella griglia (se impostato su rilevamento automatico)",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Numero di righe della griglia; utilizzare -1 per il rilevamento automatico e 0 per essere uguale alla dimensione del lotto",
+    "Save text information about generation parameters as chunks to png files": "Salva le informazioni di testo dei parametri di generazione come blocchi nel file png",
+    "Create a text file next to every image with generation parameters.": "Crea un file di testo assieme a ogni immagine con i parametri di generazione.",
+    "Save a copy of image before doing face restoration.": "Salva una copia dell'immagine prima di eseguire il restauro dei volti.",
+    "Save a copy of image before applying highres fix.": "Salva una copia dell'immagine prima di applicare la correzione ad alta risoluzione.",
+    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la correzione del colore ai risultati di img2img",
+    "Quality for saved jpeg images": "Qualità delle immagini salvate in formato JPEG",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l'immagine PNG è più grande di 4 MB o qualsiasi dimensione è maggiore di 4000, ridimensiona e salva la copia come JPG",
+    "Use original name for output filename during batch process in extras tab": "Usa il nome originale per il nome del file di output durante l'elaborazione a lotti nella scheda 'Extra'",
+    "When using 'Save' button, only save a single selected image": "Usando il pulsante 'Salva', verrà salvata solo la singola immagine selezionata",
+    "Do not add watermark to images": "Non aggiungere la filigrana alle immagini",
+    "Paths for saving": "Percorsi di salvataggio",
+    "Output directory for images; if empty, defaults to three directories below": "Cartella di output per le immagini; se vuoto, per impostazione predefinita verranno usate le cartelle seguenti",
+    "Output directory for txt2img images": "Cartella di output per le immagini txt2img",
+    "Output directory for img2img images": "Cartella di output per le immagini img2img",
+    "Output directory for images from extras tab": "Cartella di output per le immagini dalla scheda 'Extra'",
+    "Output directory for grids; if empty, defaults to two directories below": "Cartella di output per le griglie; se vuoto, per impostazione predefinita veranno usate cartelle seguenti",
+    "Output directory for txt2img grids": "Cartella di output per le griglie txt2img",
+    "Output directory for img2img grids": "Cartella di output per le griglie img2img",
+    "Directory for saving images using the Save button": "Cartella dove salvare le immagini usando il pulsante 'Salva'",
+    "Saving to a directory": "Salva in una cartella",
+    "Save images to a subdirectory": "Salva le immagini in una sotto cartella",
+    "Save grids to a subdirectory": "Salva le griglie in una sotto cartella",
+    "When using \"Save\" button, save images to a subdirectory": "Usando il pulsante \"Salva\", le immagini verranno salvate in una sotto cartella",
+    "Directory name pattern": "Modello del nome della cartella",
+    "Max prompt words for [prompt_words] pattern": "Numero massimo di parole del prompt per il modello [prompt_words]",
+    "Upscaling": "Ampliamento",
+    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Dimensione piastrella per ampliamento ESRGAN. 0 = nessuna piastrellatura.",
+    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per gli ampliamenti ESRGAN. Valori bassi = cucitura visibile.",
+    "Tile size for all SwinIR.": "Dimensione piastrella per tutti gli SwinIR.",
+    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per SwinIR. Valori bassi = cucitura visibile.",
+    "LDSR processing steps. Lower = faster": "Fasi di elaborazione LDSR. Più basso = più veloce",
+    "Upscaler for img2img": "Metodo di ampliamento per img2img",
+    "Upscale latent space image when doing hires. fix": "Amplia l'immagine nello spazio latente durante la correzione in alta risoluzione",
+    "Face restoration": "Restauro del viso",
+    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Peso di CodeFormer; 0 = effetto massimo; 1 = effetto minimo",
+    "Move face restoration model from VRAM into RAM after processing": "Sposta il modello di restauro facciale dalla VRAM alla RAM dopo l'elaborazione",
+    "System": "Sistema",
+    "VRAM usage polls per second during generation. Set to 0 to disable.": "Verifiche al secondo sull'utilizzo della VRAM durante la generazione. Impostare a 0 per disabilitare.",
+    "Always print all generation info to standard output": "Stampa sempre tutte le informazioni di generazione sul output standard",
+    "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda barra di avanzamento alla console che mostra l'avanzamento complessivo del lavoro.",
+    "Training": "Addestramento",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP nella RAM durante l'addestramento di Iperreti. Risparmia VRAM.",
+    "Filename word regex": "Espressione regolare per estrarre parole dal nome del file",
+    "Filename join string": "Stringa per unire le parole estratte dal nome del file",
+    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per una singola immagine di input per epoca; utilizzato solo per visualizzare il numero di epoca",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salva un file CSV contenente la perdita nella cartella di registrazione ogni N passaggi, 0 per disabilitare",
+    "Use cross attention optimizations while training": "Usa le ottimizzazioni di controllo dell'attenzione incrociato durante l'addestramento",
+    "Stable Diffusion": "Stable Diffusion",
+    "Checkpoints to cache in RAM": "Checkpoint da memorizzare nella RAM",
+    "SD VAE": "SD VAE",
+    "auto": "auto",
+    "Hypernetwork strength": "Forza della Iperrete",
+    "Inpainting conditioning mask strength": "Forza della maschera di condizionamento del Inpainting",
+    "Apply color correction to img2img results to match original colors.": "Applica la correzione del colore ai risultati di img2img in modo che corrispondano ai colori originali.",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, esegue esattamente la quantità di passi specificata dalla barra di scorrimento (normalmente se ne effettuano di meno con meno riduzione del rumore).",
+    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Abilita la quantizzazione nei campionatori K per risultati più nitidi e puliti. Questo può cambiare i semi esistenti. Richiede il riavvio per applicare la modifica.",
+    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Enfasi: utilizzare (testo) per fare in modo che il modello presti maggiore attenzione al testo e [testo] per fargli prestare meno attenzione",
+    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usa la vecchia implementazione dell'enfasi. Può essere utile per riprodurre vecchi semi.",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Fa sì che i campionatori di diffusione K producano le stesse immagini in un lotto come quando si genera una singola immagine",
+    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coerenza disattivando dall'ultima virgola all'indietro di n token quando si utilizzano più di 75 token",
+    "Filter NSFW content": "Filtra i contenuti NSFW",
+    "Stop At last layers of CLIP model": "Fermati agli ultimi livelli del modello CLIP",
+    "Interrogate Options": "Opzioni di interrogazione",
+    "Interrogate: keep models in VRAM": "Interroga: mantieni i modelli nella VRAM",
+    "Interrogate: use artists from artists.csv": "Interroga: utilizza artisti dal file artisti.csv",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interroga: include la classifica delle corrispondenze dei tag del modello nei risultati (non ha effetto sulle interrogazioni basate su didascalie).",
+    "Interrogate: num_beams for BLIP": "Interroga: num_beams per BLIP",
+    "Interrogate: minimum description length (excluding artists, etc..)": "Interroga: lunghezza minima della descrizione (esclusi artisti, ecc..)",
+    "Interrogate: maximum description length": "Interroga: lunghezza massima della descrizione",
+    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: numero massimo di righe nel file di testo (0 = Nessun limite)",
+    "Interrogate: deepbooru score threshold": "Interroga: soglia del punteggio deepbooru",
+    "Interrogate: deepbooru sort alphabetically": "Interroga: deepbooru ordinato alfabeticamente",
+    "use spaces for tags in deepbooru": "usa gli spazi per i tag in deepbooru",
+    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Effettua l'escape (\\) delle parentesi in deepbooru (così vengono usate come parentesi letterali e non per enfatizzare)",
+    "User interface": "Interfaccia Utente",
+    "Show progressbar": "Mostra la barra di avanzamento",
+    "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostra l'avanzamento della generazione dell'immagine ogni N passaggi di campionamento. Impostare a 0 per disabilitare.",
+    "Show previews of all images generated in a batch as a grid": "Mostra le anteprime di tutte le immagini generate in un lotto come una griglia",
+    "Show grid in results for web": "Mostra la griglia nei risultati per il web",
+    "Do not show any images in results for web": "Non mostrare alcuna immagine nei risultati per il web",
+    "Add model hash to generation information": "Aggiungi l'hash del modello alle informazioni sulla generazione",
+    "Add model name to generation information": "Aggiungi il nome del modello alle informazioni sulla generazione",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Durante la lettura dei parametri di generazione dal testo nell'interfaccia utente (da informazioni PNG o testo incollato), non modificare il modello/checkpoint selezionato.",
+    "Send seed when sending prompt or image to other interface": "Invia il seme quando si invia un prompt o un'immagine a un'altra interfaccia",
+    "Font for image grids that have text": "Font per griglie di immagini con testo",
+    "Enable full page image viewer": "Abilita la visualizzazione delle immagini a pagina intera",
+    "Show images zoomed in by default in full page image viewer": "Mostra le immagini ingrandite per impostazione predefinita nella visualizzazione a pagina intera",
+    "Show generation progress in window title.": "Mostra l'avanzamento della generazione nel titolo della finestra.",
+    "Quicksettings list": "Elenco delle impostazioni rapide",
+    "Localization (requires restart)": "Localizzazione (richiede il riavvio)",
+    "ar_AR": "ar_AR",
+    "de_DE": "de_DE",
+    "es_ES": "es_ES",
+    "fr_FR": "fr_FR",
+    "it_IT": "it_IT",
+    "ja_JP": "ja_JP",
+    "ko_KR": "ko_KR",
+    "pt_BR": "pt_BR",
+    "ru_RU": "ru_RU",
+    "tr_TR": "tr_TR",
+    "zh_CN": "zh_CN",
+    "zh_TW": "zh_TW",
+    "Sampler parameters": "Parametri del campionatore",
+    "Hide samplers in user interface (requires restart)": "Nascondi campionatori nell'interfaccia utente (richiede il riavvio)",
+    "eta (noise multiplier) for DDIM": "ETA (moltiplicatore di rumore) per DDIM",
+    "eta (noise multiplier) for ancestral samplers": "ETA (moltiplicatore di rumore) per campionatori ancestrali",
+    "img2img DDIM discretize": "discretizzazione DDIM per img2img",
+    "uniform": "uniforme",
+    "quad": "quad",
+    "sigma churn": "sigma churn",
+    "sigma tmin": "sigma tmin",
+    "sigma noise": "sigma noise",
+    "Eta noise seed delta": "ETA del delta del seme del rumore",
+    "Number of columns on image gallery": "Numero di colonne nella galleria di immagini",
+    "Aesthetic Image Scorer": "Punteggio delle immagini estetiche",
+    "Save score as EXIF or PNG Info Chunk": "Salva il punteggio come info EXIF o PNG",
+    "aesthetic_score": "Punteggio estetico",
+    "cfg_scale": "Scala CFG",
+    "sd_model_hash": "Hash del modello SD",
+    "hash": "Hash",
+    "Save tags (Windows only)": "Salva etichette (solo Windows)",
+    "Save category (Windows only)": "Salva categoria (solo Windows)",
+    "Save generation params text": "Salva testo parametri di generazione",
+    "Force CPU (Requires Custom Script Reload)": "Forza CPU (richiede il ricaricamento dello script personalizzato)",
+    "Images Browser": "Galleria immagini",
+    "Preload images at startup": "Precarica le immagini all'avvio",
+    "Number of columns on the page": "Numero di colonne nella pagina",
+    "Number of rows on the page": "Numero di righe nella pagina",
+    "Minimum number of pages per load": "Numero minimo di pagine da caricare",
+    "Maximum number of samples, used to determine which folders to skip when continue running the create script": "Numero massimo di campioni, utilizzato per determinare quali cartelle ignorare quando si continua a eseguire lo script di creazione",
+    "Use same seed for all images": "Usa lo stesso seme per tutte le immagini",
+    "Request browser notifications": "Richiedi le notifiche del browser",
+    "Download localization template": "Scarica il modello per la localizzazione",
+    "Reload custom script bodies (No ui updates, No restart)": "Ricarica gli script personalizzati (nessun aggiornamento dell'interfaccia utente, nessun riavvio)",
+    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Riavvia Gradio e aggiorna i componenti (solo script personalizzati, ui.py, js e css)",
+    "Installed": "Installato",
+    "Available": "Disponibile",
+    "Install from URL": "Installa da URL",
+    "Apply and restart UI": "Applica e riavvia l'interfaccia utente",
+    "Check for updates": "Controlla aggiornamenti",
+    "Extension": "Estensione",
+    "URL": "URL",
+    "Update": "Aggiorna",
+    "aesthetic-gradients": "Gradienti Estetici (CLIP)",
+    "https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients",
+    "unknown": "sconosciuto",
+    "dataset-tag-editor": "Dataset Tag Editor",
+    "https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor.git": "https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor.git",
+    "deforum-for-automatic1111-webui": "Deforum",
+    "https://github.com/deforum-art/deforum-for-automatic1111-webui": "https://github.com/deforum-art/deforum-for-automatic1111-webui",
+    "sd-dynamic-prompts": "Prompt dinamici",
+    "https://github.com/adieyal/sd-dynamic-prompts": "https://github.com/adieyal/sd-dynamic-prompts",
+    "stable-diffusion-webui-aesthetic-image-scorer": "Punteggio immagini estetiche",
+    "https://github.com/tsngo/stable-diffusion-webui-aesthetic-image-scorer": "https://github.com/tsngo/stable-diffusion-webui-aesthetic-image-scorer",
+    "stable-diffusion-webui-artists-to-study": "Artisti per studiare",
+    "https://github.com/camenduru/stable-diffusion-webui-artists-to-study": "https://github.com/camenduru/stable-diffusion-webui-artists-to-study",
+    "stable-diffusion-webui-images-browser": "Galleria immagini",
+    "https://github.com/yfszzx/stable-diffusion-webui-images-browser": "https://github.com/yfszzx/stable-diffusion-webui-images-browser",
+    "stable-diffusion-webui-inspiration": "Ispirazione",
+    "https://github.com/yfszzx/stable-diffusion-webui-inspiration": "https://github.com/yfszzx/stable-diffusion-webui-inspiration",
+    "tag-autocomplete": "Autocompletamento etichette",
+    "https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git": "https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git",
+    "wildcards": "Termini Jolly",
+    "https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards.git": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards.git",
+    "Load from:": "Carica da:",
+    "Extension index URL": "URL dell'indice delle Estensioni", 
+    "URL for extension's git repository": "URL del repository GIT dell'estensione",
+    "Local directory name": "Nome cartella locale",
+    "Install": "Installa",
+    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (premi Ctrl+Invio o Alt+Invio per generare)",
+    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (premere Ctrl+Invio o Alt+Invio per generare)",
+    "Add a random artist to the prompt.": "Aggiungi un artista casuale al prompt.",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leggere i parametri di generazione dal prompt o dall'ultima generazione se il prompt è vuoto ed inserirli nell'interfaccia utente.",
+    "Save style": "Salva stile",
+    "Apply selected styles to current prompt": "Applica gli stili selezionati al prompt corrente",
+    "Stop processing current image and continue processing.": "Interrompe l'elaborazione dell'immagine corrente e continua l'elaborazione.",
+    "Stop processing images and return any results accumulated so far.": "Interrompe l'elaborazione delle immagini e restituisce tutti i risultati accumulati finora.",
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stile da applicare; gli stili hanno componenti sia per i prompt positivi che per quelli negativi e si applicano a entrambi",
+    "Do not do anything special": "Non fa nulla di speciale",
+    "Which algorithm to use to produce the image": "Quale algoritmo utilizzare per produrre l'immagine",
+    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - molto creativo, si può ottenere un'immagine completamente diversa a seconda del numero di passi, impostare i passi su un valore superiore a 30-40 non aiuta",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - il migliore per inpainting",
+    "Produce an image that can be tiled.": "Produce un'immagine che può essere piastrellata.",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilizza un processo in due fasi per creare parzialmente un'immagine con una risoluzione inferiore, aumentare la scala e quindi migliorarne i dettagli senza modificare la composizione",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determina quanto poco l'algoritmo dovrebbe rispettare dovrebbe il contenuto dell'immagine. A 0, non cambierà nulla e a 1 otterrai un'immagine non correlata. Con valori inferiori a 1.0 l'elaborazione richiederà meno passaggi di quelli specificati dalla barra di scorrimento dei passi di campionamento.",
+    "How many batches of images to create": "Quanti lotti di immagini generare",
+    "How many image to create in a single batch": "Quante immagini generare in un singolo lotto",
+    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - quanto fortemente l'immagine deve conformarsi al prompt: valori più bassi producono risultati più creativi",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Un valore che determina l'output del generatore di numeri casuali: se create un'immagine con gli stessi parametri e seme di un'altra immagine, otterrete lo stesso risultato",
+    "Set seed to -1, which will cause a new random number to be used every time": "Imposta il seme su -1, che farà sì che ogni volta venga utilizzato un nuovo numero casuale",
+    "Reuse seed from last generation, mostly useful if it was randomed": "Riusa il seme dell'ultima generazione, utile soprattutto se casuale",
+    "Seed of a different picture to be mixed into the generation.": "Seme di un'immagine diversa da miscelare nella generazione.",
+    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Quanto è forte la variazione da produrre. A 0, non ci sarà alcun effetto. A 1, otterrai l'intera immagine con il seme della variazione (tranne per i campionatori ancestrali, dove otterrai solo una leggera variazione).",
+    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Prova a produrre un'immagine simile a quella che sarebbe stata prodotta con lo stesso seme alla risoluzione specificata",
+    "This text is used to rotate the feature space of the imgs embs": "Questo testo viene utilizzato per ruotare lo spazio delle funzioni delle immagini incorporate",
+    "How many times to repeat processing an image and using it as input for the next iteration": "Quante volte ripetere l'elaborazione di un'immagine e utilizzarla come input per l'iterazione successiva",
+    "Hello, StylePile here.\nUntil some weird bug gets fixed you will see this even if the script itself is not active. Meanwhile, some hints to take your artwork to new heights:\nUse the 'Focus on' dropdown to select complex presets. Toggle selections below (with or without Focus) to affect your results. Mix and match to get some interesting results. \nAnd some general Stable Diffusion tips that will take your designs to next level:\nYou can add parenthesis to make parts of the prompt stronger. So (((cute))) kitten will make it extra cute (try it out). This is alsow important if a style is affecting your original prompt too much. Make that prompt stronger by adding parenthesis around it, like this: ((promt)).\nYou can type promts like [A|B] to sequentially use terms one after another on each step. So, like [cat|dog] will produce a hybrid catdog. And [A:B:0.4] to switch to other terms after the first one has been active for a certain percentage of steps. So [cat:dog:0.4] will build a cat 40% of the time and then start turning it into a dog. This needs more steps to work properly.": "Salve, qui è StylePile.\nFinché qualche strano bug non verrà risolto, vedrai questo testo anche se lo script non è attivo. Nel frattempo, alcuni suggerimenti per portare la tua grafica a nuovi livelli:\nUtilizza il menu a discesa 'Focus on' per selezionare valori predefiniti complessi. Attiva o disattiva le selezioni seguenti (con o senza Focus) per influire sui risultati. Mescola e abbina per ottenere risultati interessanti. \nE alcuni suggerimenti generali su Stable Diffusion che porteranno i tuoi risultati a un livello superiore:\nPuoi aggiungere parentesi per aumentare l'influenza di certe parti del prompt. Quindi '(((cute))) kitten' lo renderà molto carino (fai delle prove). Questo è importante quando uno stile influisce troppo sul prompt originale. Rendi più forte quel prompt aggiungendo delle parentesi intorno ad esso, così: ((promt)).\nPuoi digitare prompt nel formato [A|B] per usare in sequenza i termini uno dopo l'altro in ogni passaggio. Quindi, come [cat|dog] produrrà un 'canegatto' ibrido. E [A:B:0.4] per passare ad altri termini dopo che il primo è stato attivo per una certa percentuale di passaggi. Quindi [cat:dog:0.4] genererà un gatto il 40% dei passaggi e poi inizierà a trasformarlo in un cane. Sono richiesti più passaggi perchè funzioni correttamente.",
+    "Enter one prompt per line. Blank lines will be ignored.": "Immettere un prompt per riga. Le righe vuote verranno ignorate.",
+    "Separate values for X axis using commas.": "Separare i valori per l'asse X usando le virgole.",
+    "Separate values for Y axis using commas.": "Separare i valori per l'asse Y usando le virgole.",
+    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "Separa un elenco di parole con virgole e lo script eseguirà una variazione di prompt con quelle parole per ogni loro possibile ordine",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva l'immagine/i in una cartella (predefinita - log/images) ed i parametri di generazione in un file CSV.",
+    "Open images output directory": "Apri la cartella di output delle immagini",
+    "How much to blur the mask before processing, in pixels.": "Quanto sfocare la maschera prima dell'elaborazione, in pixel.",
+    "What to put inside the masked area before processing it with Stable Diffusion.": "Cosa mettere all'interno dell'area mascherata prima di elaborarla con Stable Diffusion.",
+    "fill it with colors of the image": "riempi con i colori dell'immagine",
+    "keep whatever was there originally": "conserva tutto ciò che c'era in origine",
+    "fill it with latent space noise": "riempi di rumore spaziale latente",
+    "fill it with latent space zeroes": "riempi con zeri di spazio latente",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Ingrandisce la regione mascherata per raggiungere la risoluzione, esegue la pittura, riduce la scala e incolla nell'immagine originale",
+    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Ridimensiona l'immagine alla risoluzione di destinazione. A meno che altezza e larghezza non corrispondano, otterrai proporzioni errate.",
+    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Ridimensionare l'immagine in modo che l'intera risoluzione di destinazione sia riempita con l'immagine. Ritaglia le parti che sporgono.",
+    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Ridimensiona l'immagine in modo che l'intera immagine rientri nella risoluzione di destinazione. Riempi lo spazio vuoto con i colori dell'immagine.",
+    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Per l'upscaling SD, quanta sovrapposizione in pixel dovrebbe esserci tra le piastrelle. Le piastrelle si sovrappongono in modo che quando vengono unite nuovamente in un'immagine, non ci siano giunture chiaramente visibili.",
+    "Process an image, use it as an input, repeat.": "Elabora un'immagine, usala come input, ripeti.",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In modalità rielaborazione ricorsiva, su ogni ciclo la forza di denoising viene moltiplicata per questo valore. <1 significa varietà decrescente in modo che la sequenza converga su un'immagine fissa. >1 significa aumentare la varietà in modo che la tua sequenza diventi sempre più caotica.",
+    "A directory on the same machine where the server is running.": "Una cartella sulla stessa macchina su cui è in esecuzione il server.",
+    "Leave blank to save images to the default path.": "Lascia vuoto per salvare le immagini nel percorso predefinito.",
+    "Result = A * (1 - M) + B * M": "Risultato = A * (1 - M) + B * M",
+    "Result = A + (B - C) * M": "Risultato = A + (B - C) * M",
+    "1st and last digit must be 1. ex:'1, 2, 1'": "La prima e l'ultima cifra devono essere 1. Es.:'1, 2, 1'",
+    "Path to directory with input images": "Percorso della cartella con immagini di input",
+    "Path to directory where to write outputs": "Percorso della cartella in cui scrivere i risultati",
+    "C:\\directory\\of\\datasets": "C:\\cartella\\del\\dataset",
+    "Input images directory": "Cartella di input delle immagini",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Usa i seguenti tag per definire come vengono scelti i nomi dei file per le immagini: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed ], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
+    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se questa opzione è abilitata, la filigrana non verrà aggiunta alle immagini create. Attenzione: se non aggiungi la filigrana, potresti comportarti in modo non etico.",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utilizzare i seguenti tag per definire come vengono scelte le sottodirectory per le immagini e le griglie: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
+    "Restore low quality faces using GFPGAN neural network": "Ripristina volti di bassa qualità utilizzando la rete neurale GFPGAN",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Questa espressione regolare verrà utilizzata per estrarre le parole dal nome del file e verranno unite utilizzando l'opzione seguente nel testo dell'etichetta utilizzato per l'addestramento. Lascia vuoto per mantenere il testo del nome del file così com'è.",
+    "This string will be used to join split words into a single line if the option above is enabled.": "Questa stringa verrà utilizzata per unire le parole divise in un'unica riga se l'opzione sopra è abilitata.",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Si applica solo ai modelli di pittura. Determina con quale forza mascherare l'immagine originale per inpainting e img2img. 1.0 significa completamente mascherato, che è il comportamento predefinito. 0.0 significa un condizionamento completamente non mascherato. Valori più bassi aiuteranno a preservare la composizione generale dell'immagine, ma avranno difficoltà con grandi cambiamenti.",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Elenco dei nomi delle impostazioni, separati da virgole, per le impostazioni che dovrebbero essere visualizzate nella barra di accesso rapido in alto, anziché nella normale scheda delle impostazioni. Vedi modules/shared.py per impostare i nomi. Richiede il riavvio per applicare.",
+    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se questo valore è diverso da zero, verrà aggiunto al seed e utilizzato per inizializzare il generatore di numeri casuali per il rumore quando si utilizzano campionatori con ETA. Puoi usarlo per produrre ancora più variazioni di immagini, oppure puoi usarlo per abbinare le immagini di altri software se sai cosa stai facendo.",
+    "Leave empty for auto": "Lasciare vuoto per automatico",
+    "Autocomplete options": "Opzioni di autocompletamento",
+    "Enable Autocomplete": "Abilita autocompletamento",
+    "Append commas": "Aggiungi virgole",
+    "AlphaCanvas": "AlphaCanvas",
+    "Close": "Chiudi",
+    "Grab Results": "Ottieni risultati",
+    "Apply Patch": "Applica Patch",
+    "Hue:0": "Hue:0",
+    "S:0": "S:0",
+    "L:0": "L:0",
+    "Load Canvas": "Carica Canvas",
+    "Save Canvas": "Salva Canvas",
+    "latest": "aggiornato",
+    "behind": "da aggiornare",
+    "Description": "Descrizione",
+    "Action": "Azione",
+    "Aesthetic Gradients": "Gradienti estetici",
+    "Create an embedding from one or few pictures and use it to apply their style to generated images.": "Crea un incorporamento da una o poche immagini e usalo per applicare il loro stile alle immagini generate.",
+    "Sample extension. Allows you to use __name__ syntax in your prompt to get a random line from a file named name.txt in the wildcards directory. Also see Dynamic Prompts for similar functionality.": "Estensione del campione. Consente di utilizzare la sintassi __name__ nel prompt per ottenere una riga casuale da un file denominato name.txt nella cartella dei termini jolly. Vedi anche 'Prompt dinamici' per funzionalità simili.",
+    "Dynamic Prompts": "Prompt dinamici",
+    "Implements an expressive template language for random or combinatorial prompt generation along with features to support deep wildcard directory structures.": "Implementa un  modello di linguaggio espressivo per la generazione di prompt casuale o combinatoria insieme a funzionalità per supportare cartelle strutturate contenenti termini jolly.",
+    "Image browser": "Galleria immagini",
+    "Provides an interface to browse created images in the web browser.": "Fornisce un'interfaccia  nel browser web per sfogliare le immagini create.",
+    "Randomly display the pictures of the artist's or artistic genres typical style, more pictures of this artist or genre is displayed after selecting. So you don't have to worry about how hard it is to choose the right style of art when you create.": "Visualizza in modo casuale le immagini dello stile tipico dell'artista o dei generi artistici, dopo la selezione vengono visualizzate più immagini di questo artista o genere. Così non dovete preoccuparvi della difficoltà di scegliere lo stile artistico giusto quando create.",
+    "The official port of Deforum, an extensive script for 2D and 3D animations, supporting keyframable sequences, dynamic math parameters (even inside the prompts), dynamic masking, depth estimation and warping.": "Il porting ufficiale di Deforum, uno script completo per animazioni 2D e 3D, che supporta sequenze di fotogrammi chiave, parametri matematici dinamici (anche all'interno dei prompt), mascheramento dinamico, stima della profondità e warping.",
+    "Artists to study": "Artisti per studiare",
+    "Shows a gallery of generated pictures by artists separated into categories.": "Mostra una galleria di immagini generate dagli artisti suddivise in categorie.",
+    "Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Calcola il punteggio estetico per le immagini generate utilizzando il predittore del punteggio estetico CLIP+MLP basato su Chad Scorer",
+    "Lets you edit captions in training datasets.": "Consente di modificare i sottotitoli nei set di dati di addestramento.",
+    "Time taken:": "Tempo impiegato:"
+}
\ No newline at end of file
-- 
cgit v1.2.1


From de11709479431df6e5b78afd83c70dac7bc8c2e6 Mon Sep 17 00:00:00 2001
From: batvbs <60730393+batvbs@users.noreply.github.com>
Date: Thu, 3 Nov 2022 07:34:23 +0800
Subject: Inpaint at full resolution

---
 localizations/zh_CN.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index d8bc91cd..f91bd02a 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -141,7 +141,7 @@
     "original": "原图",
     "latent noise": "潜空间噪声",
     "latent nothing": "潜空间数值零",
-    "Inpaint at full resolution": "以完整分辨率进行局部重绘",
+    "Inpaint at full resolution": "全分辨率局部重绘",
     "Inpaint at full resolution padding, pixels": "填补像素",
     "Process images in a directory on the same machine where the server is running.": "使用服务器主机上的一个目录，作为输入目录处理图像。",
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "使用一个空的文件夹作为输出目录，而不是使用默认的 output 文件夹作为输出目录。",
@@ -484,7 +484,7 @@
     "keep whatever was there originally": "保留原来的图像，不进行预处理",
     "fill it with latent space noise": "用潜空间的噪声填充它",
     "fill it with latent space zeroes": "用潜空间的零填充它",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做局部重绘，缩小后粘贴到原始图像中",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做局部重绘，缩小后粘贴到原始图像中。请注意，填补像素 仅对 全分辨率局部重绘 生效。",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
-- 
cgit v1.2.1


From 29c43935fb110d2a329b31ec9bc7bbe344e7a8e2 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Thu, 3 Nov 2022 11:17:44 +0900
Subject: unify translation style

---
 localizations/zh_CN.json | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 04e63c79..7f8f4ba3 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -37,8 +37,8 @@
     "Image": "图像",
     "Check progress": "查看进度",
     "Check progress (first)": "(首次)查看进度",
-    "Sampling Steps": "采样迭代步数",
-    "Sampling method": "采样方法",
+    "Sampling Steps": "采样迭代步数 (Steps)",
+    "Sampling method": "采样方法 (Sampler)",
     "Euler a": "Euler a",
     "Euler": "Euler",
     "LMS": "LMS",
@@ -101,8 +101,8 @@
     "Prompt order": "提示词顺序",
     "Sampler": "采样器",
     "Checkpoint name": "模型(ckpt)名",
-    "Hypernet str.": "Hypernetwork 强度",
     "Hypernetwork": "超网络(Hypernetwork)",
+    "Hypernet str.": "超网络(Hypernetwork) 强度",
     "Sigma Churn": "Sigma Churn",
     "Sigma min": "最小 Sigma",
     "Sigma max": "最大 Sigma",
@@ -260,10 +260,10 @@
     "threshold": "阈值",
     "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "选择初始化层权重的方案. 类relu - Kaiming, 类sigmoid - Xavier 都是比较推荐的选项",
     "Normal": "正态",
-    "KaimingUniform": "Kaiming均匀",
-    "KaimingNormal": "Kaiming正态",
-    "XavierUniform": "Xavier均匀",
-    "XavierNormal": "Xavier正态",
+    "KaimingUniform": "Kaiming 均匀",
+    "KaimingNormal": "Kaiming 正态",
+    "XavierUniform": "Xavier 均匀",
+    "XavierNormal": "Xavier 正态",
     "Add layer normalization": "添加层标准化",
     "Use dropout": "采用 dropout 防止过拟合",
     "Overwrite Old Hypernetwork": "覆写旧的 Hypernetwork",
@@ -395,7 +395,7 @@
     "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 采样器 批量生成与生成单个图像时，产出相同的图像",
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个 token 时，通过从 n 个 token 中的最后一个逗号填补来提高一致性",
     "Filter NSFW content": "过滤成人内容(NSFW)",
-    "Stop At last layers of CLIP model": "在 CLIP 模型的最后哪一层停下",
+    "Stop At last layers of CLIP model": "在 CLIP 模型的最后哪一层停下 (Clip skip)",
     "Interrogate Options": "反推提示词选项",
     "Interrogate: keep models in VRAM": "反推: 将模型保存在显存(VRAM)中",
     "Interrogate: use artists from artists.csv": "反推: 使用 artists.csv 中的艺术家",
@@ -416,7 +416,7 @@
     "Do not show any images in results for web": "不在网页的结果中显示任何图像",
     "Add model hash to generation information": "将模型的哈希值添加到生成信息",
     "Add model name to generation information": "将模型名称添加到生成信息",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "从文本读取生成参数到 UI（从 PNG 图片信息或粘贴文本）时，不要更改选定的模型(ckpt)",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "从文本读取生成参数到用户界面（从 PNG 图片信息或粘贴文本）时，不要更改选定的模型(ckpt)",
     "Send seed when sending prompt or image to other interface": "将提示词或者图片发送到 >> 其他界面时，把随机种子也传送过去",
     "Font for image grids that have text": "有文字的宫格图使用的字体",
     "Enable full page image viewer": "启用整页图像查看器",
@@ -434,7 +434,7 @@
     "sigma churn": "sigma churn",
     "sigma tmin": "最小(tmin) sigma",
     "sigma noise": "sigma 噪声",
-    "Eta noise seed delta": "Eta 噪声种子偏移(noise seed delta)",
+    "Eta noise seed delta": "Eta 噪声种子偏移(ENSD - Eta noise seed delta)",
     "Images Browser": "图库浏览器",
     "Preload images at startup": "在启动时预加载图像",
     "Number of columns on the page": "每页列数",
@@ -444,7 +444,7 @@
     "Use same seed for all images": "为所有图像使用同一个随机种子",
     "Request browser notifications": "请求浏览器通知",
     "Download localization template": "下载本地化模板",
-    "Reload custom script bodies (No ui updates, No restart)": "重新加载自定义脚本主体（无 ui 更新，无重启）",
+    "Reload custom script bodies (No ui updates, No restart)": "重新加载自定义脚本主体（无用户界面更新，无重启）",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重启 Gradio 及刷新组件（仅限自定义脚本、ui.py、js 和 css）",
     "Installed": "已安装",
     "Available": "可用",
@@ -557,7 +557,7 @@
     "Start drawing": "开始绘制",
     "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
     "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "用竖线分隔符(|)将提示词分成若干部分，脚本将为它们的每一个组合创建一幅图片（除了被分割的第一部分，所有的组合都会包含这部分）",
-    "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "选择哪些Real-ESRGAN模型显示在用户界面。(需要重新启动)",
+    "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "选择哪些 Real-ESRGAN 模型显示在网页用户界面。(需要重新启动)",
     "Face restoration model": "面部修复模型",
     "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
     "Move to favorites": "移动到收藏夹(保存)",
-- 
cgit v1.2.1


From 313e14de04d9955c6ad077341feceb0fc7f2f1d3 Mon Sep 17 00:00:00 2001
From: Chris OBryan <13701027+cobryan05@users.noreply.github.com>
Date: Wed, 2 Nov 2022 21:37:43 -0500
Subject: extras - skip unnecessary second hash of image

There is no need to re-hash the input image each iteration of the loop.
This also reverts PR #4026 as it was determined the cache hits it avoids
were actually valid.
---
 modules/extras.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 8e2ab35c..71b93a06 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -136,12 +136,13 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
     def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
         blended_result: Image.Image = None
+        image_hash: str = hash(np.array(image.getdata()).tobytes())
         for upscaler in params:
             upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,
                             upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-            cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()),
+            cache_key = LruCache.Key(image_hash=image_hash,
                                      info_hash=hash(info),
-                                     args_hash=hash((upscale_args, upscale_first)))
+                                     args_hash=hash(upscale_args))
             cached_entry = cached_images.get(cache_key)
             if cached_entry is None:
                 res = upscale(image, *upscale_args)
-- 
cgit v1.2.1


From dcf73cf779b8c72e022082a7178254b3e3ec110c Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Thu, 3 Nov 2022 11:45:24 +0900
Subject: Update zh_CN.json

- re-order some element
- update new content
---
 localizations/zh_CN.json | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 7f8f4ba3..30373160 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -90,6 +90,7 @@
     "Output directory": "输出目录",
     "Put variable parts at start of prompt": "把变量部分放在提示词文本的开头",
     "Iterate seed every line": "每行输入都换一个种子",
+    "Use same random seed for all lines": "每行输入都使用同一个随机种子",
     "List of prompt inputs": "提示词输入列表",
     "Upload prompt inputs": "上传提示词输入文件",
     "X type": "X轴类型",
@@ -320,6 +321,7 @@
     "keyword": "搜索",
     "Generate Info": "生成信息",
     "File Name": "文件名",
+    "Move to favorites": "移动到收藏夹(保存)",
     "Renew Page": "刷新页面",
     "Number": "数量",
     "set_index": "设置索引",
@@ -341,6 +343,8 @@
     "Save text information about generation parameters as chunks to png files": "将有关生成参数的文本信息，作为块保存到 png 图片文件中",
     "Create a text file next to every image with generation parameters.": "保存图像时，在每个图像旁边创建一个文本文件储存生成参数",
     "Save a copy of image before doing face restoration.": "在进行面部修复之前保存图像副本",
+    "Save a copy of image before applying highres fix.": "在做高分辨率修复之前保存初始图像副本",
+    "Save a copy of image before applying color correction to img2img results": "在对图生图结果应用颜色校正之前保存图像副本",
     "Quality for saved jpeg images": "保存的 jpeg 图像的质量",
     "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 图像大于 4MB 或宽高大于 4000，则缩小并保存副本为 JPG 图片",
     "Use original name for output filename during batch process in extras tab": "在更多选项卡中的批量处理过程中，使用原始名称作为输出文件名",
@@ -377,17 +381,19 @@
     "Always print all generation info to standard output": "始终将所有生成信息输出到 standard output (一般为控制台)",
     "Add a second progress bar to the console that shows progress for an entire job.": "向控制台添加第二个进度条，显示整个作业的进度",
     "Training": "训练",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
+    "Move VAE and CLIP to RAM when training if possible. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)如果可行的话，节省显存(VRAM)",
     "Filename word regex": "文件名用词的正则表达式",
     "Filename join string": "文件名连接用字符串",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每个 epoch 中单个输入图像的重复次数； 仅用于显示 epoch 数",
     "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步保存一个包含 loss 的 csv 表格到日志目录，0 表示禁用",
+    "Use cross attention optimizations while training": "训练时开启 cross attention 优化",
     "Stable Diffusion": "Stable Diffusion",
     "Checkpoints to cache in RAM": "缓存在内存(RAM)中的模型(ckpt)",
+    "SD VAE": "模型的 VAE (SD VAE)",
+    "auto": "自动",
     "Hypernetwork strength": "Hypernetwork 强度",
     "Inpainting conditioning mask strength": "局部重绘的自适应蒙版强度",
     "Apply color correction to img2img results to match original colors.": "对图生图结果应用颜色校正以匹配原始颜色",
-    "Save a copy of image before applying color correction to img2img results": "在对图生图结果应用颜色校正之前保存图像副本",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（正常情况下更弱的重绘幅度需要更少的迭代步数）",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。这可能会改变现有的随机种子。需要重新启动才能应用",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
@@ -560,8 +566,9 @@
     "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "选择哪些 Real-ESRGAN 模型显示在网页用户界面。(需要重新启动)",
     "Face restoration model": "面部修复模型",
     "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
-    "Move to favorites": "移动到收藏夹(保存)",
     "favorites": "收藏夹(已保存)",
     "others": "其他",
-    "Collect": "收藏(保存)"
+    "Collect": "收藏(保存)",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
+    
 }
-- 
cgit v1.2.1


From 53e72e15f0916eb63a5e231ef0e749e9c2d0e531 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Thu, 3 Nov 2022 12:27:32 +0900
Subject: polish translation content

---
 localizations/zh_CN.json | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 30373160..eee27793 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -111,7 +111,7 @@
     "Eta": "Eta",
     "Clip skip": "Clip 跳过",
     "Denoising": "去噪",
-    "Cond. Image Mask Weight": "自适应图像蒙版强度",
+    "Cond. Image Mask Weight": "图像调节屏蔽度",
     "X values": "X轴数值",
     "Y type": "Y轴类型",
     "Y values": "Y轴数值",
@@ -392,7 +392,7 @@
     "SD VAE": "模型的 VAE (SD VAE)",
     "auto": "自动",
     "Hypernetwork strength": "Hypernetwork 强度",
-    "Inpainting conditioning mask strength": "局部重绘的自适应蒙版强度",
+    "Inpainting conditioning mask strength": "局部重绘时图像调节的蒙版屏蔽强度",
     "Apply color correction to img2img results to match original colors.": "对图生图结果应用颜色校正以匹配原始颜色",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（正常情况下更弱的重绘幅度需要更少的迭代步数）",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。这可能会改变现有的随机种子。需要重新启动才能应用",
@@ -523,7 +523,7 @@
     "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络修复低质量面部",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。留空以保持文件名文本不变",
     "This string will be used to join split words into a single line if the option above is enabled.": "如果启用了上述选项，则此处的字符会用于将拆分的单词接合为同一行",
-    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "仅适用于局部重绘专用的模型。 决定了蒙版在局部重绘以及图生图中屏蔽原图内容的强度。 1.0 表示完全屏蔽，这是默认行为。 0.0 表示完全不屏蔽。 较低的值将有助于保持图像的整体构图，但很难遇到较大的变化。",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "仅适用于局部重绘专用的模型（模型后缀为 inpainting.ckpt 的模型）。 决定了蒙版在局部重绘以及图生图中屏蔽原图内容的强度。 1.0 表示完全屏蔽原图，这是默认行为。 0.0 表示完全不屏蔽让原图进行图像调节。 较低的值将有助于保持原图的整体构图，但很难遇到较大的变化。",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置名称列表，以逗号分隔，设置应转到顶部的快速访问栏，而不是通常的设置选项卡。有关设置名称，请参见 modules/shared.py。需要重新启动才能应用",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声。你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
     "Leave empty for auto": "留空时自动生成",
@@ -569,6 +569,5 @@
     "favorites": "收藏夹(已保存)",
     "others": "其他",
     "Collect": "收藏(保存)",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
-    
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)"    
 }
-- 
cgit v1.2.1


From 7a2e36b583ef9eaefa44322e16faff6f9f1af169 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Thu, 3 Nov 2022 00:51:22 -0300
Subject: Add config and lists endpoints

---
 modules/api/api.py    | 97 ++++++++++++++++++++++++++++++++++++++++++++++++---
 modules/api/models.py | 70 +++++++++++++++++++++++++++++++++++--
 2 files changed, 159 insertions(+), 8 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 71c9c160..ed2dce5d 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -2,14 +2,17 @@ import base64
 import io
 import time
 import uvicorn
-from gradio.processing_utils import decode_base64_to_file, decode_base64_to_image
-from fastapi import APIRouter, Depends, HTTPException
+from threading import Lock
+from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
+from fastapi import APIRouter, Depends, FastAPI, HTTPException
 import modules.shared as shared
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers, sample_to_image, samples_to_image_grid
+from modules.sd_samplers import all_samplers
 from modules.extras import run_extras, run_pnginfo
-
+from modules.sd_models import checkpoints_list
+from modules.realesrgan_model import get_realesrgan_models
+from typing import List
 
 def upscaler_to_index(name: str):
     try:
@@ -37,7 +40,7 @@ def encode_pil_to_base64(image):
 
 
 class Api:
-    def __init__(self, app, queue_lock):
+    def __init__(self, app: FastAPI, queue_lock: Lock):
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
@@ -48,6 +51,19 @@ class Api:
         self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
         self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
         self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
+        self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
+        self.app.add_api_route("/sdapi/v1/info", self.get_info, methods=["GET"])
+        self.app.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
+        self.app.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
+        self.app.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
+        self.app.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
+        self.app.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
+        self.app.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
+        self.app.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
+        self.app.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
+        self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -190,6 +206,77 @@ class Api:
         shared.state.interrupt()
 
         return {}
+        
+    def get_config(self):
+        options = {}
+        for key in shared.opts.data.keys():
+            metadata = shared.opts.data_labels.get(key)
+            if(metadata is not None):
+                options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)})
+            else:
+                options.update({key: shared.opts.data.get(key, None)})
+        
+        return options
+        
+    def set_config(self, req: OptionsModel):
+        reqDict = vars(req)
+        for o in reqDict:
+            setattr(shared.opts, o, reqDict[o])
+
+        shared.opts.save(shared.config_filename)
+        return
+
+    def get_cmd_flags(self):
+        return vars(shared.cmd_opts)
+
+    def get_info(self):
+
+        return {
+            "hypernetworks": [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks],
+            "face_restorers": [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers],
+            "realesrgan_models":[{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)],
+            "promp_styles":[shared.prompt_styles.styles[k] for k in shared.prompt_styles.styles],
+            "artists_categories": shared.artist_db.cats,
+            # "artists": [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
+        }
+
+    def get_samplers(self):
+        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers]
+
+    def get_upscalers(self):
+        upscalers = []
+        
+        for upscaler in shared.sd_upscalers:
+            u = upscaler.scaler
+            upscalers.append({"name":u.name, "model_name":u.model_name, "model_path":u.model_path, "model_url":u.model_url})
+        
+        return upscalers
+        
+    def get_sd_models(self):
+        return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": x.config} for x in checkpoints_list.values()]
+
+    def get_hypernetworks(self):
+        return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
+
+    def get_face_restorers(self):
+        return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers]
+
+    def get_realesrgan_models(self):
+        return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
+    
+    def get_promp_styles(self):
+        styleList = []
+        for k in shared.prompt_styles.styles:
+            style = shared.prompt_styles.styles[k] 
+            styleList.append({"name":style[0], "prompt": style[1], "negative_prompr": style[2]})
+
+        return styleList
+
+    def get_artists_categories(self):
+        return shared.artist_db.cats
+
+    def get_artists(self):
+        return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)
diff --git a/modules/api/models.py b/modules/api/models.py
index 9ee42a17..b54b188a 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,11 +1,10 @@
 import inspect
-from click import prompt
 from pydantic import BaseModel, Field, create_model
-from typing import Any, Optional
+from typing import Any, Optional, Union
 from typing_extensions import Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
-from modules.shared import sd_upscalers
+from modules.shared import sd_upscalers, opts, parser
 
 API_NOT_ALLOWED = [
     "self",
@@ -165,3 +164,68 @@ class ProgressResponse(BaseModel):
     eta_relative: float = Field(title="ETA in secs")
     state: dict = Field(title="State", description="The current state snapshot")
     current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
+
+fields = {}
+for key, value in opts.data.items():
+    metadata = opts.data_labels.get(key)
+    optType = opts.typemap.get(type(value), type(value))
+
+    if (metadata is not None):
+        fields.update({key: (Optional[optType], Field(
+            default=metadata.default ,description=metadata.label))})
+    else:
+        fields.update({key: (Optional[optType], Field())})
+
+OptionsModel = create_model("Options", **fields)
+
+flags = {}
+_options = vars(parser)['_option_string_actions']
+for key in _options:
+    if(_options[key].dest != 'help'):
+        flag = _options[key]
+        _type = str 
+        if(_options[key].default != None): _type = type(_options[key].default) 
+        flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
+
+FlagsModel = create_model("Flags", **flags)
+
+class SamplerItem(BaseModel):
+    name: str = Field(title="Name")
+    aliases: list[str]  = Field(title="Aliases")
+    options: dict[str, str] = Field(title="Options")
+
+class UpscalerItem(BaseModel):
+    name: str = Field(title="Name")
+    model_name: str | None = Field(title="Model Name")
+    model_path: str | None = Field(title="Path")
+    model_url: str | None = Field(title="URL")
+
+class SDModelItem(BaseModel):
+    title: str = Field(title="Title")
+    model_name: str = Field(title="Model Name")
+    hash: str = Field(title="Hash")
+    filename: str = Field(title="Filename")
+    config: str = Field(title="Config file")
+
+class HypernetworkItem(BaseModel):
+    name: str = Field(title="Name")
+    path: str | None = Field(title="Path")
+
+class FaceRestorerItem(BaseModel):
+    name: str = Field(title="Name")
+    cmd_dir: str | None = Field(title="Path")
+
+class RealesrganItem(BaseModel):
+    name: str = Field(title="Name")
+    path: str | None = Field(title="Path")
+    scale: int | None = Field(title="Scale")
+
+class PromptStyleItem(BaseModel):
+    name: str = Field(title="Name")
+    prompt: str | None = Field(title="Prompt")
+    negative_prompt: str | None = Field(title="Negative Prompt")
+
+class ArtistItem(BaseModel):
+    name: str = Field(title="Name")
+    score: float = Field(title="Score")
+    category: str = Field(title="Category")
\ No newline at end of file
-- 
cgit v1.2.1


From 743fffa3d6c2e9e6bb5f48093a4c88f3b53e001d Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Thu, 3 Nov 2022 00:52:01 -0300
Subject: Remove unused endpoint

---
 modules/api/api.py | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index ed2dce5d..a49f3755 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -54,7 +54,6 @@ class Api:
         self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
         self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
-        self.app.add_api_route("/sdapi/v1/info", self.get_info, methods=["GET"])
         self.app.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
         self.app.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
         self.app.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
@@ -229,17 +228,6 @@ class Api:
     def get_cmd_flags(self):
         return vars(shared.cmd_opts)
 
-    def get_info(self):
-
-        return {
-            "hypernetworks": [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks],
-            "face_restorers": [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers],
-            "realesrgan_models":[{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)],
-            "promp_styles":[shared.prompt_styles.styles[k] for k in shared.prompt_styles.styles],
-            "artists_categories": shared.artist_db.cats,
-            # "artists": [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
-        }
-
     def get_samplers(self):
         return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers]
 
-- 
cgit v1.2.1


From e33d6cbddd08870e348d10a58af41fb677a39fd6 Mon Sep 17 00:00:00 2001
From: Ju1-js <40339350+Ju1-js@users.noreply.github.com>
Date: Wed, 2 Nov 2022 21:04:49 -0700
Subject: Make extension manager Remote links open a new tab

---
 modules/ui_extensions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index ab807722..a81de9a7 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -86,7 +86,7 @@ def extension_table():
         code += f"""
             <tr>
                 <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
-                <td><a href="{html.escape(ext.remote or '')}">{html.escape(ext.remote or '')}</a></td>
+                <td><a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape(ext.remote or '')}</a></td>
                 <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
             </tr>
     """
-- 
cgit v1.2.1


From 792b72fd6b6686e77cea09781852528fb15f40b4 Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Thu, 3 Nov 2022 13:03:08 +0800
Subject: =?UTF-8?q?=E6=9B=B4=E6=96=B0=20zh=5FCN.json?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/zh_CN.json | 23 +++++++++++++++++++++--
 1 file changed, 21 insertions(+), 2 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index f91bd02a..4a06941e 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -484,7 +484,7 @@
     "keep whatever was there originally": "保留原来的图像，不进行预处理",
     "fill it with latent space noise": "用潜空间的噪声填充它",
     "fill it with latent space zeroes": "用潜空间的零填充它",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做局部重绘，缩小后粘贴到原始图像中。请注意，填补像素 仅对 全分辨率局部重绘 生效。",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做局部重绘，缩小后粘贴到原始图像中。\n请注意，填补像素 仅对 全分辨率局部重绘 生效。",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
@@ -508,6 +508,7 @@
     "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "仅适用于局部重绘专用的模型。 决定了蒙版在局部重绘以及图生图中屏蔽原图内容的强度。 1.0 表示完全屏蔽，这是默认行为。 0.0 表示完全不屏蔽。 较低的值将有助于保持图像的整体构图，但很难遇到较大的变化。",
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置名称列表，以逗号分隔，设置应转到顶部的快速访问栏，而不是通常的设置选项卡。有关设置名称，请参见 modules/shared.py。需要重新启动才能应用",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声。你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
+
     "Autocomplete options": "自动补全选项",
     "Enable Autocomplete": "开启Tag补全",
     "Append commas": "附加逗号",
@@ -541,11 +542,29 @@
     "Unload VAE and CLIP from VRAM when training": "训练时从显存(VRAM)中取消 VAE 和 CLIP 的加载",
     "Number of pictures displayed on each page": "每页显示的图像数量",
     "Number of grids in each row": "每行显示多少格",
+
     "Start drawing": "开始绘制",
     "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
     "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "用竖线分隔符(|)将提示词分成若干部分，脚本将为它们的每一个组合创建一幅图片（除了被分割的第一部分，所有的组合都会包含这部分）",
     "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "选择哪些Real-ESRGAN模型显示在用户界面。(需要重新启动)",
     "Face restoration model": "面部修复模型",
     "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
-    "Hypernetwork": "超网络(Hypernetwork)"
+    "Hypernetwork": "超网络(Hypernetwork)",
+    "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "迭代改进生成的图像多少次；更高的值需要更长的时间；非常低的值会产生不好的结果",
+    "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt": "在图像上画一个蒙版，脚本会根据提示重新生成蒙版区域的内容",
+    "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back": "正常提升图像，将结果分割成瓦片，用img2img改进每个瓦片，将整个图像合并回来",
+    "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "创建一个网格，图像将有不同的参数。使用下面的输入来指定哪些参数将由列和行共享",
+    "Run Python code. Advanced user only. Must run program with --allow-code for this to work": "运行Python代码。仅限高级用户。必须用 --allow-code 来运行程序，这样才能工作。",
+    "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "用逗号隔开一个单词列表，第一个单词将被用作关键词：脚本将在提示中搜索这个单词，并用其他单词替换它。",
+    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "用逗号分开一个单词列表，脚本将用这些单词的每一个可能的顺序制作一个变体的提示。",
+    "Reconstruct prompt from existing image and put it into the prompt field.": "从现有的图像中重构提示，并将其放入提示字段。",
+    "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle": "在[prompt_words]选项中设置要使用的最大字数；注意：如果字数太长，可能会超过系统可处理的文件路径的最大长度",
+    "Process an image, use it as an input, repeat.": "处理一张图片，将其作为输入，重复。",
+    "Insert selected styles into prompt fields": "在提示字段中插入选定的样式",
+    "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.": "将当前的提示语保存为样式。如果你在文本中添加标记{prompt}，当你将来使用该样式时，该样式会将其作为你的提示的占位符。",
+    "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.": "在制作图像之前从检查点加载权重。你可以使用哈希值或文件名的一部分（如设置中所示）作为检查点名称。建议与Y轴一起使用以减少切换。",
+    "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "Torch active: 在生成过程中，Torch使用的显存(VRAM)峰值，不包括缓存的数据。\nTorch reserved: Torch分配的显存(VRAM)的峰值量，包括所有活动和缓存数据。\nSys VRAM: 所有应用程序的显存(VRAM)分配的峰值量 / GPU的总显存(VRAM)（峰值利用率%）。",
+    "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "缩放潜在空间中的图像。另一种方法是，从潜在表示中产生完整的图像，提高其比例，然后再将其移回潜在空间。",
+
+    "----": "----"
 }
-- 
cgit v1.2.1


From fb1374791bf4b4c9b49de5378f29b12fdabcac97 Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Thu, 3 Nov 2022 13:08:11 +0800
Subject: Fix --nowebui argument being ineffective

---
 launch.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/launch.py b/launch.py
index 958336f2..b061bce6 100644
--- a/launch.py
+++ b/launch.py
@@ -217,12 +217,15 @@ def tests(argv):
     proc.kill()
 
 
-def start_webui():
-    print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}")
+def start():
+    print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {' '.join(sys.argv[1:])}")
     import webui
-    webui.webui()
+    if '--nowebui' in sys.argv:
+        webui.api_only()
+    else:
+        webui.webui()
 
 
 if __name__ == "__main__":
     prepare_enviroment()
-    start_webui()
+    start()
-- 
cgit v1.2.1


From 0b143c1163a96b193a4e8512be9c5831c661a50d Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Thu, 3 Nov 2022 14:30:53 +0900
Subject: Separate .optim file from model

---
 modules/hypernetworks/hypernetwork.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8f74cdea..63c25de8 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -161,6 +161,7 @@ class Hypernetwork:
 
     def save(self, filename):
         state_dict = {}
+        optimizer_saved_dict = {}
 
         for k, v in self.layers.items():
             state_dict[k] = (v[0].state_dict(), v[1].state_dict())
@@ -175,9 +176,10 @@ class Hypernetwork:
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
         if self.optimizer_name is not None:
-            state_dict['optimizer_name'] = self.optimizer_name
+            optimizer_saved_dict['optimizer_name'] = self.optimizer_name
         if self.optimizer_state_dict:
-            state_dict['optimizer_state_dict'] = self.optimizer_state_dict
+            optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
+            torch.save(optimizer_saved_dict, filename + '.optim')
 
         torch.save(state_dict, filename)
 
@@ -198,9 +200,11 @@ class Hypernetwork:
         print(f"Layer norm is set to {self.add_layer_norm}")
         self.use_dropout = state_dict.get('use_dropout', False)
         print(f"Dropout usage is set to {self.use_dropout}")
-        self.optimizer_name = state_dict.get('optimizer_name', 'AdamW')
+
+        optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
+        self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
         print(f"Optimizer name is {self.optimizer_name}")
-        self.optimizer_state_dict = state_dict.get('optimizer_state_dict', None)
+        self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
         if self.optimizer_state_dict:
             print("Loaded existing optimizer from checkpoint")
         else:
-- 
cgit v1.2.1


From 1764ac3c8bc482bd575987850e96630d9115e51a Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Thu, 3 Nov 2022 14:49:26 +0900
Subject: use hash to check valid optim

---
 modules/hypernetworks/hypernetwork.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 63c25de8..4230b8cf 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -177,12 +177,13 @@ class Hypernetwork:
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
         if self.optimizer_name is not None:
             optimizer_saved_dict['optimizer_name'] = self.optimizer_name
+
+        torch.save(state_dict, filename)
         if self.optimizer_state_dict:
+            optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
             optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
             torch.save(optimizer_saved_dict, filename + '.optim')
 
-        torch.save(state_dict, filename)
-
     def load(self, filename):
         self.filename = filename
         if self.name is None:
@@ -204,7 +205,10 @@ class Hypernetwork:
         optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
         self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
         print(f"Optimizer name is {self.optimizer_name}")
-        self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
+        if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
+            self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
+        else:
+            self.optimizer_state_dict = None
         if self.optimizer_state_dict:
             print("Loaded existing optimizer from checkpoint")
         else:
@@ -229,7 +233,7 @@ def list_hypernetworks(path):
         name = os.path.splitext(os.path.basename(filename))[0]
         # Prevent a hypothetical "None.pt" from being listed.
         if name != "None":
-            res[name] = filename
+            res[name + f"({sd_models.model_hash(filename)})"] = filename
     return res
 
 
@@ -375,6 +379,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     else:
         hypernetwork_dir = None
 
+    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
     if create_image_every > 0:
         images_dir = os.path.join(log_directory, "images")
         os.makedirs(images_dir, exist_ok=True)
-- 
cgit v1.2.1


From fd785eab48e3e5b4762d46e0c537e60492cbee20 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Thu, 3 Nov 2022 15:51:33 +0900
Subject: polish translation content

---
 localizations/zh_CN.json | 34 +++++++++++++++++-----------------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 94ebe1b6..6ca50fbd 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -146,8 +146,8 @@
     "latent nothing": "潜空间数值零",
     "Inpaint at full resolution": "全分辨率局部重绘",
     "Inpaint at full resolution padding, pixels": "填补像素",
-    "Process images in a directory on the same machine where the server is running.": "使用服务器主机上的一个目录，作为输入目录处理图像。",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "使用一个空的文件夹作为输出目录，而不是使用默认的 output 文件夹作为输出目录。",
+    "Process images in a directory on the same machine where the server is running.": "使用服务器主机上的一个目录，作为输入目录处理图像",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "使用一个空的文件夹作为输出目录，而不是使用默认的 output 文件夹作为输出目录",
     "Input directory": "输入目录",
     "Resize mode": "缩放模式",
     "Just resize": "拉伸",
@@ -502,7 +502,7 @@
     "keep whatever was there originally": "保留原来的图像，不进行预处理",
     "fill it with latent space noise": "用潜空间的噪声填充它",
     "fill it with latent space zeroes": "用潜空间的零填充它",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做局部重绘，缩小后粘贴到原始图像中。\n请注意，填补像素 仅对 全分辨率局部重绘 生效。",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做局部重绘，缩小后粘贴到原始图像中。\n请注意，填补像素 仅对 全分辨率局部重绘 生效",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
@@ -523,8 +523,8 @@
     "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络修复低质量面部",
     "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。留空以保持文件名文本不变",
     "This string will be used to join split words into a single line if the option above is enabled.": "如果启用了上述选项，则此处的字符会用于将拆分的单词接合为同一行",
-    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "仅适用于局部重绘专用的模型（模型后缀为 inpainting.ckpt 的模型）。 决定了蒙版在局部重绘以及图生图中屏蔽原图内容的强度。 1.0 表示完全屏蔽原图，这是默认行为。 0.0 表示完全不屏蔽让原图进行图像调节。 较低的值将有助于保持原图的整体构图，但很难遇到较大的变化。",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置名称列表，以逗号分隔，设置应转到顶部的快速访问栏，而不是通常的设置选项卡。有关设置名称，请参见 modules/shared.py。需要重新启动才能应用",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "仅适用于局部重绘专用的模型（模型后缀为 inpainting.ckpt 的模型）。决定了蒙版在局部重绘以及图生图中屏蔽原图内容的强度。 1.0 表示完全屏蔽原图，这是默认行为。0.0 表示完全不屏蔽让原图进行图像调节。较低的值将有助于保持原图的整体构图，但很难遇到较大的变化",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置项名称的列表，以逗号分隔，该设置会移动到顶部的快速访问栏，而不是默认的设置选项卡。有关设置名称，请参见 modules/shared.py。需要重新启动才能应用",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声。你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
     "Leave empty for auto": "留空时自动生成",
     "Autocomplete options": "自动补全选项",
@@ -573,19 +573,19 @@
     "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
     "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "迭代改进生成的图像多少次；更高的值需要更长的时间；非常低的值会产生不好的结果",
     "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt": "在图像上画一个蒙版，脚本会根据提示重新生成蒙版区域的内容",
-    "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back": "正常提升图像，将结果分割成瓦片，用img2img改进每个瓦片，将整个图像合并回来",
+    "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back": "正常放大图像，将结果分割成图块（tiles），用图生图改进每个图块（tiles），最后将整个图像合并回来",
     "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "创建一个网格，图像将有不同的参数。使用下面的输入来指定哪些参数将由列和行共享",
-    "Run Python code. Advanced user only. Must run program with --allow-code for this to work": "运行Python代码。仅限高级用户。必须用 --allow-code 来运行程序，这样才能工作。",
-    "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "用逗号隔开一个单词列表，第一个单词将被用作关键词：脚本将在提示中搜索这个单词，并用其他单词替换它。",
-    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "用逗号分开一个单词列表，脚本将用这些单词的每一个可能的顺序制作一个变体的提示。",
-    "Reconstruct prompt from existing image and put it into the prompt field.": "从现有的图像中重构提示，并将其放入提示字段。",
-    "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle": "在[prompt_words]选项中设置要使用的最大字数；注意：如果字数太长，可能会超过系统可处理的文件路径的最大长度",
-    "Process an image, use it as an input, repeat.": "处理一张图片，将其作为输入，重复。",
-    "Insert selected styles into prompt fields": "在提示字段中插入选定的样式",
-    "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.": "将当前的提示语保存为样式。如果你在文本中添加标记{prompt}，当你将来使用该样式时，该样式会将其作为你的提示的占位符。",
-    "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.": "在制作图像之前从检查点加载权重。你可以使用哈希值或文件名的一部分（如设置中所示）作为检查点名称。建议与Y轴一起使用以减少切换。",
-    "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "Torch active: 在生成过程中，Torch使用的显存(VRAM)峰值，不包括缓存的数据。\nTorch reserved: Torch分配的显存(VRAM)的峰值量，包括所有活动和缓存数据。\nSys VRAM: 所有应用程序的显存(VRAM)分配的峰值量 / GPU的总显存(VRAM)（峰值利用率%）。",
-    "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "缩放潜在空间中的图像。另一种方法是，从潜在表示中产生完整的图像，提高其比例，然后再将其移回潜在空间。",
+    "Run Python code. Advanced user only. Must run program with --allow-code for this to work": "运行 Python 代码。仅限老手使用。必须以 --allow-code 来开启程序，才能使其运行",
+    "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "以逗号分割的单词列表，第一个单词将被用作关键词：脚本将在提示词中搜索这个单词，并用其他单词替换它",
+    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "以逗号分割的单词列表，脚本会排列出这些单词的所有排列方式，并加入提示词各生成一次",
+    "Reconstruct prompt from existing image and put it into the prompt field.": "从现有的图像中重构出提示词，并将其放入提示词的输入文本框",
+    "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle": "设置在[prompt_words]选项中要使用的最大字数；注意：如果字数太长，可能会超过系统可处理的文件路径的最大长度",
+    "Process an image, use it as an input, repeat.": "处理一张图像，将其作为输入，并重复",
+    "Insert selected styles into prompt fields": "在提示词中插入选定的模版风格",
+    "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.": "将当前的提示词保存为模版风格。如果你在文本中添加{prompt}标记，那么将来你使用该模版风格时，你现有的提示词会替换模版风格中的{prompt}",
+    "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.": "在生成图像之前从模型(ckpt)中加载权重。你可以使用哈希值或文件名的一部分（如设置中所示）作为模型(ckpt)名称。建议用在Y轴上以减少过程中模型的切换",
+    "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "Torch active: 在生成过程中，Torch使用的显存(VRAM)峰值，不包括缓存的数据。\nTorch reserved: Torch 分配的显存(VRAM)的峰值量，包括所有活动和缓存数据。\nSys VRAM: 所有应用程序分配的显存(VRAM)的峰值量 / GPU 的总显存(VRAM)（峰值利用率%）",
+    "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "放大潜空间中的图像。而另一种方法是，从潜变量表达中直接解码并生成完整的图像，接着放大它，然后再将其编码回潜空间",
 
     "----": "----"
 }
-- 
cgit v1.2.1


From 5bbef814adbe1aa827fb1c312c6e8183dae99d7c Mon Sep 17 00:00:00 2001
From: batvbs <60730393+batvbs@users.noreply.github.com>
Date: Thu, 3 Nov 2022 16:47:37 +0800
Subject: Update localizations/zh_CN.json

Co-authored-by: dtlnor <dtlnor@hotmail.com>
---
 localizations/zh_CN.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 6ca50fbd..7abd793d 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -145,7 +145,7 @@
     "latent noise": "潜空间噪声",
     "latent nothing": "潜空间数值零",
     "Inpaint at full resolution": "全分辨率局部重绘",
-    "Inpaint at full resolution padding, pixels": "填补像素",
+    "Inpaint at full resolution padding, pixels": "预留像素",
     "Process images in a directory on the same machine where the server is running.": "使用服务器主机上的一个目录，作为输入目录处理图像",
     "Use an empty output directory to save pictures normally instead of writing to the output directory.": "使用一个空的文件夹作为输出目录，而不是使用默认的 output 文件夹作为输出目录",
     "Input directory": "输入目录",
-- 
cgit v1.2.1


From 8db85d597e2d930e338250052e76c9bc1d68a8ef Mon Sep 17 00:00:00 2001
From: batvbs <60730393+batvbs@users.noreply.github.com>
Date: Thu, 3 Nov 2022 16:47:45 +0800
Subject: Update localizations/zh_CN.json

Co-authored-by: dtlnor <dtlnor@hotmail.com>
---
 localizations/zh_CN.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 7abd793d..cb70b094 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -502,7 +502,7 @@
     "keep whatever was there originally": "保留原来的图像，不进行预处理",
     "fill it with latent space noise": "用潜空间的噪声填充它",
     "fill it with latent space zeroes": "用潜空间的零填充它",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域放大到目标分辨率，做局部重绘，缩小后粘贴到原始图像中。\n请注意，填补像素 仅对 全分辨率局部重绘 生效",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域（包括预留像素长度的缓冲区域）放大到目标分辨率，进行局部重绘。\n然后缩小并粘贴回原始图像中",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
-- 
cgit v1.2.1


From 7e5f1562ecb08557c2997bdc4fd26a77e41b0e25 Mon Sep 17 00:00:00 2001
From: byzod <byzod@users.noreply.github.com>
Date: Thu, 3 Nov 2022 18:54:25 +0800
Subject: Update edit-attention.js

Fix https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/3904
(Some sort of a workaround, the best way is to add unique id or class name to those prompt boxes)
---
 javascript/edit-attention.js | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js
index c0d29a74..b947cbec 100644
--- a/javascript/edit-attention.js
+++ b/javascript/edit-attention.js
@@ -1,7 +1,6 @@
 addEventListener('keydown', (event) => {
 	let target = event.originalTarget || event.composedPath()[0];
-	if (!target.hasAttribute("placeholder")) return;
-	if (!target.placeholder.toLowerCase().includes("prompt")) return;
+	if (!target.matches("#toprow textarea.gr-text-input[placeholder]")) return;
 	if (! (event.metaKey || event.ctrlKey)) return;
 
 
-- 
cgit v1.2.1


From 70714be4301d9873c7f90eab65c71aa06182ee5b Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Thu, 3 Nov 2022 19:28:25 +0800
Subject: =?UTF-8?q?=E5=B0=86=E6=97=A0=E6=B3=95=E6=9C=AC=E5=9C=B0=E5=8C=96?=
 =?UTF-8?q?=E7=9A=84=E5=86=85=E5=AE=B9=E7=A7=BB=E5=88=B0=E5=BA=95=E9=83=A8?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/zh_CN.json | 70 ++++++++++++++++++++++++++----------------------
 1 file changed, 38 insertions(+), 32 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index cb70b094..6d6720c9 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -70,7 +70,6 @@
     "Resize seed from width": "自宽度缩放随机种子",
     "Resize seed from height": "自高度缩放随机种子",
     "Open for Clip Aesthetic!": "打开美术风格 Clip!",
-    "▼": "▼",
     "Aesthetic weight": "美术风格权重",
     "Aesthetic steps": "美术风格迭代步数",
     "Aesthetic learning rate": "美术风格学习率",
@@ -468,10 +467,6 @@
     "Install": "安装",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nPrompt",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nNegative prompt",
-    "Add a random artist to the prompt.": "随机添加一个艺术家到提示词中",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
-    "Save style": "储存为模版风格",
-    "Apply selected styles to current prompt": "将所选模板风格，应用于当前提示词",
     "Stop processing current image and continue processing.": "停止处理当前图像，并继续处理下一个",
     "Stop processing images and return any results accumulated so far.": "停止处理图像，并返回迄今为止累积的任何结果",
     "Style to apply; styles have components for both positive and negative prompts and apply to both": "要应用的模版风格； 模版风格包含正向和反向提示词，并应用于两者",
@@ -533,14 +528,47 @@
     "Roll three": "抽三位出来",
     "Generate forever": "无限生成",
     "Cancel generate forever": "停止无限生成",
+    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
+    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
+    "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "迭代改进生成的图像多少次；更高的值需要更长的时间；非常低的值会产生不好的结果",
+    "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt": "在图像上画一个蒙版，脚本会根据提示重新生成蒙版区域的内容",
+    "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back": "正常放大图像，将结果分割成图块（tiles），用图生图改进每个图块（tiles），最后将整个图像合并回来",
+    "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "创建一个网格，图像将有不同的参数。使用下面的输入来指定哪些参数将由列和行共享",
+    "Run Python code. Advanced user only. Must run program with --allow-code for this to work": "运行 Python 代码。仅限老手使用。必须以 --allow-code 来开启程序，才能使其运行",
+    "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "以逗号分割的单词列表，第一个单词将被用作关键词：脚本将在提示词中搜索这个单词，并用其他单词替换它",
+    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "以逗号分割的单词列表，脚本会排列出这些单词的所有排列方式，并加入提示词各生成一次",
+    "Reconstruct prompt from existing image and put it into the prompt field.": "从现有的图像中重构出提示词，并将其放入提示词的输入文本框",
+    "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle": "设置在[prompt_words]选项中要使用的最大字数；注意：如果字数太长，可能会超过系统可处理的文件路径的最大长度",
+    "Process an image, use it as an input, repeat.": "处理一张图像，将其作为输入，并重复",
+    "Insert selected styles into prompt fields": "在提示词中插入选定的模版风格",
+    "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.": "将当前的提示词保存为模版风格。如果你在文本中添加{prompt}标记，那么将来你使用该模版风格时，你现有的提示词会替换模版风格中的{prompt}",
+    "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.": "在生成图像之前从模型(ckpt)中加载权重。你可以使用哈希值或文件名的一部分（如设置中所示）作为模型(ckpt)名称。建议用在Y轴上以减少过程中模型的切换",
+    "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "Torch active: 在生成过程中，Torch使用的显存(VRAM)峰值，不包括缓存的数据。\nTorch reserved: Torch 分配的显存(VRAM)的峰值量，包括所有活动和缓存数据。\nSys VRAM: 所有应用程序分配的显存(VRAM)的峰值量 / GPU 的总显存(VRAM)（峰值利用率%）",
+    "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "放大潜空间中的图像。而另一种方法是，从潜变量表达中直接解码并生成完整的图像，接着放大它，然后再将其编码回潜空间",
+    "Upscaler": "放大算法",
+    "Start drawing": "开始绘制",
+    
+
+    "----无效----": "----以下内容无法被翻译，Bug----",
+    "Add a random artist to the prompt.": "随机添加一个艺术家到提示词中",
+    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
+    "Save style": "储存为模版风格",
+    "Apply selected styles to current prompt": "将所选模板风格，应用于当前提示词",
+    "Upscaler 1": "放大算法 1",
+    "Upscaler 2": "放大算法 2",
+    "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "用竖线分隔符(|)将提示词分成若干部分，脚本将为它们的每一个组合创建一幅图片（除了被分割的第一部分，所有的组合都会包含这部分）",
+    "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "选择哪些 Real-ESRGAN 模型显示在网页用户界面。(需要重新启动)",
+    "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
+    "Face restoration model": "面部修复模型",
+
+
+    "----已移除----": "----以下内容在webui新版本已移除----",
+    "▼": "▼",
     "History": "历史记录",
     "Show Textbox": "显示文本框",
     "File with inputs": "含输入内容的文件",
     "Prompts": "提示词",
     "Disabled when launched with --hide-ui-dir-config.": "启动 --hide-ui-dir-config 时禁用",
-    "Upscaler": "放大算法",
-    "Upscaler 1": "放大算法 1",
-    "Upscaler 2": "放大算法 2",
     "Open output directory": "打开输出目录",
     "Create aesthetic images embedding": "生成美术风格图集 embedding",
     "Split oversized images into two": "将过大的图像分为两份",
@@ -560,32 +588,10 @@
     "Unload VAE and CLIP from VRAM when training": "训练时从显存(VRAM)中取消 VAE 和 CLIP 的加载",
     "Number of pictures displayed on each page": "每页显示的图像数量",
     "Number of grids in each row": "每行显示多少格",
-
-    "Start drawing": "开始绘制",
-    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
-    "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "用竖线分隔符(|)将提示词分成若干部分，脚本将为它们的每一个组合创建一幅图片（除了被分割的第一部分，所有的组合都会包含这部分）",
-    "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "选择哪些 Real-ESRGAN 模型显示在网页用户界面。(需要重新启动)",
-    "Face restoration model": "面部修复模型",
-    "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
     "favorites": "收藏夹(已保存)",
     "others": "其他",
     "Collect": "收藏(保存)",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
-    "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "迭代改进生成的图像多少次；更高的值需要更长的时间；非常低的值会产生不好的结果",
-    "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt": "在图像上画一个蒙版，脚本会根据提示重新生成蒙版区域的内容",
-    "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back": "正常放大图像，将结果分割成图块（tiles），用图生图改进每个图块（tiles），最后将整个图像合并回来",
-    "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "创建一个网格，图像将有不同的参数。使用下面的输入来指定哪些参数将由列和行共享",
-    "Run Python code. Advanced user only. Must run program with --allow-code for this to work": "运行 Python 代码。仅限老手使用。必须以 --allow-code 来开启程序，才能使其运行",
-    "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "以逗号分割的单词列表，第一个单词将被用作关键词：脚本将在提示词中搜索这个单词，并用其他单词替换它",
-    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "以逗号分割的单词列表，脚本会排列出这些单词的所有排列方式，并加入提示词各生成一次",
-    "Reconstruct prompt from existing image and put it into the prompt field.": "从现有的图像中重构出提示词，并将其放入提示词的输入文本框",
-    "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle": "设置在[prompt_words]选项中要使用的最大字数；注意：如果字数太长，可能会超过系统可处理的文件路径的最大长度",
-    "Process an image, use it as an input, repeat.": "处理一张图像，将其作为输入，并重复",
-    "Insert selected styles into prompt fields": "在提示词中插入选定的模版风格",
-    "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.": "将当前的提示词保存为模版风格。如果你在文本中添加{prompt}标记，那么将来你使用该模版风格时，你现有的提示词会替换模版风格中的{prompt}",
-    "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.": "在生成图像之前从模型(ckpt)中加载权重。你可以使用哈希值或文件名的一部分（如设置中所示）作为模型(ckpt)名称。建议用在Y轴上以减少过程中模型的切换",
-    "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "Torch active: 在生成过程中，Torch使用的显存(VRAM)峰值，不包括缓存的数据。\nTorch reserved: Torch 分配的显存(VRAM)的峰值量，包括所有活动和缓存数据。\nSys VRAM: 所有应用程序分配的显存(VRAM)的峰值量 / GPU 的总显存(VRAM)（峰值利用率%）",
-    "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "放大潜空间中的图像。而另一种方法是，从潜变量表达中直接解码并生成完整的图像，接着放大它，然后再将其编码回潜空间",
 
-    "----": "----"
+
+    "--------": "--------"
 }
-- 
cgit v1.2.1


From 8b6a9035d56fb4ce03f74e133246838f70383663 Mon Sep 17 00:00:00 2001
From: Eugenio Buffo <58123757+EugenioBuffo@users.noreply.github.com>
Date: Thu, 3 Nov 2022 13:26:59 +0100
Subject: Delete it_IT.json

---
 it_IT.json | 1233 ------------------------------------------------------------
 1 file changed, 1233 deletions(-)
 delete mode 100644 it_IT.json

diff --git a/it_IT.json b/it_IT.json
deleted file mode 100644
index a78fd574..00000000
--- a/it_IT.json
+++ /dev/null
@@ -1,1233 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "Caricamento...",
-    "view": "mostra ",
-    "api": "API",
-    "•": " • ",
-    "built with gradio": " Sviluppato con Gradio",
-    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
-    "txt2img": "txt2img",
-    "img2img": "img2img",
-    "Extras": "Extra",
-    "PNG Info": "Info PNG",
-    "Checkpoint Merger": "Miscelatore di Checkpoint",
-    "Train": "Addestramento",
-    "Create aesthetic embedding": "Crea incorporamento estetico",
-    "Dataset Tag Editor": "Dataset Tag Editor",
-    "Deforum": "Deforum",
-    "Artists To Study": "Artisti per studiare",
-    "Image Browser": "Galleria immagini",
-    "Inspiration": "Ispirazione",
-    "Settings": "Impostazioni",
-    "Extensions": "Estensioni",
-    "Prompt": "Prompt",
-    "Negative prompt": "Prompt negativo",
-    "Run": "Esegui",
-    "Skip": "Salta",
-    "Interrupt": "Interrompi",
-    "Generate": "Genera",
-    "Style 1": "Stile 1",
-    "Style 2": "Stile 2",
-    "Label": "Etichetta",
-    "File": "File",
-    "Drop File Here": "Trascina il file qui",
-    "-": "-",
-    "or": "o",
-    "Click to Upload": "Clicca per caricare",
-    "Image": "Immagine",
-    "Check progress": "Controlla i progressi",
-    "Check progress (first)": "Controlla i progressi (primo)",
-    "Sampling Steps": "Passi di campionamento",
-    "Sampling method": "Metodo di campionamento",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "Larghezza",
-    "Height": "Altezza",
-    "Restore faces": "Restaura i volti",
-    "Tiling": "Piastrellatura",
-    "Highres. fix": "Correzione alta risoluzione",
-    "Firstpass width": "Larghezza del primo passaggio",
-    "Firstpass height": "Altezza del primo passaggio",
-    "Denoising strength": "Forza del Denoising",
-    "Batch count": "Lotti di immagini",
-    "Batch size": "Immagini per lotto",
-    "CFG Scale": "Scala CFG",
-    "Seed": "Seme",
-    "Extra": "Extra",
-    "Variation seed": "Seme della variazione",
-    "Variation strength": "Forza della variazione",
-    "Resize seed from width": "Ridimensiona il seme dalla larghezza",
-    "Resize seed from height": "Ridimensiona il seme dall'altezza",
-    "Open for Clip Aesthetic!": "Apri per Gradienti Estetici (CLIP)",
-    "▼": "▼",
-    "Aesthetic weight": "Estetica - Peso",
-    "Aesthetic steps": "Estetica - Passi",
-    "Aesthetic learning rate": "Estetica - Tasso di apprendimento",
-    "Slerp interpolation": "Interpolazione Slerp",
-    "Aesthetic imgs embedding": "Estetica - Incorporamento di immagini",
-    "None": "Niente",
-    "Aesthetic text for imgs": "Estetica - Testo per le immagini",
-    "Slerp angle": "Angolo Slerp",
-    "Is negative text": "È un testo negativo",
-    "Script": "Script",
-    "Random grid": "Generaz. casuale (griglia)",
-    "Random": "Generaz. casuale (no griglia)",
-    "StylePile": "StylePile",
-    "Advanced prompt matrix": "Matrice di prompt avanzata",
-    "Advanced Seed Blending": "Miscelazione Semi Avanzata",
-    "Alternate Sampler Noise Schedules": "Metodi alternativi di campionamento del rumore",
-    "Animator v6": "Animator v6",
-    "Asymmetric tiling": "Piastrellatura asimmetrica",
-    "Custom code": "Codice personalizzato",
-    "Embedding to Shareable PNG": "Incorporamento convertito in PNG condivisibile",
-    "Force symmetry": "Forza la simmetria",
-    "Prompts interpolation": "Interpola Prompt",
-    "Prompt matrix": "Matrice dei prompt",
-    "Prompt morph": "Metamorfosi del prompt",
-    "Prompts from file or textbox": "Prompt da file o da casella di testo",
-    "To Infinity and Beyond": "Verso l'infinito e oltre",
-    "Seed travel": "Interpolazione semi",
-    "Shift attention": "Sposta l'attenzione",
-    "Text to Vector Graphics": "Da testo a grafica vettoriale",
-    "Unprompted": "Unprompted",
-    "X/Y plot": "Grafico X/Y",
-    "X/Y/Z plot": "Grafico X/Y/Z",
-    "Dynamic Prompting v0.13.6": "Prompt dinamici v0.13.6",
-    "Create inspiration images": "Crea immagini di ispirazione",
-    "step1 min/max": "Passi min(o max)",
-    "step2 min/max": "Passi max (o min)",
-    "step cnt": "Q.tà di Passi",
-    "cfg1 min/max": "CFG min (o max)",
-    "cfg2 min/max": "CFG max (o min)",
-    "cfg cnt": "Q.tà di CFG",
-    "Draw legend": "Disegna legenda",
-    "Include Separate Images": "Includi immagini separate",
-    "Keep -1 for seeds": "Mantieni sempre il seme a -1",
-    "x/y change": "Inverti ordine assi X/Y (Passi/CFG)",
-    "Loops": "Cicli",
-    "Focus on:": "Focus su:",
-    "No focus": "Nessun Focus",
-    "Portraits (tick Restore faces above for best results)": "Ritratti (selezionare 'Restaura volti' in alto per ottenere i migliori risultati)",
-    "Feminine and extra attractive (tick Restore faces above for best results)": "Femminile ed estremamente attraente (selezionare 'Restaura volti' per ottenere i migliori risultati)",
-    "Masculine and extra attractive (tick Restore faces above for best results)": "Maschile ed estremamente attraente (selezionare 'Restaura volti'  per ottenere i migliori risultati)",
-    "Monsters": "Mostri",
-    "Robots": "Robot",
-    "Retrofuturistic": "Retrofuturistico",
-    "Propaganda": "Propaganda",
-    "Landscapes": "Paesaggi",
-    "Hints": "Suggerimenti",
-    "Image type": "Tipo di immagine",
-    "Not set": "Non impostato",
-    "Photography": "Fotografia",
-    "Digital art": "Arte digitale",
-    "3D Rendering": "3D Rendering",
-    "Painting": "Dipinto",
-    "Sketch": "Schizzo",
-    "Classic Comics": "Fumetti classici",
-    "Modern Comics": "Fumetti moderni",
-    "Manga": "Manga",
-    "Vector art": "Arte vettoriale",
-    "Visual style": "Stile visivo",
-    "Realism": "Realismo",
-    "Photorealism": "Fotorealismo",
-    "Hyperrealism": "Iperrealismo",
-    "Surrealism": "Surrealismo",
-    "Modern Art": "Arte moderna",
-    "Fauvism": "Fauvismo",
-    "Futurism": "Futurismo",
-    "Painterly": "Pittorico",
-    "Pointillisme": "Puntinismo",
-    "Abstract": "Astratto",
-    "Pop Art": "Pop Art",
-    "Impressionist": "Impressionista",
-    "Cubism": "Cubismo",
-    "Linocut": "Linoleografia",
-    "Fantasy": "Fantasia",
-    "Colors": "Colori",
-    "Chaotic": "Caotico",
-    "Primary colors": "Colori primari",
-    "Colorful": "Colorato",
-    "Vivid": "Vivido",
-    "Muted colors": "Colori tenui",
-    "Low contrast": "Basso contrasto",
-    "Desaturated": "Desaturato",
-    "Grayscale": "Scala di grigi",
-    "Black and white": "Bianco e nero",
-    "Infrared": "Infrarosso",
-    "Complementary": "Colori complementari",
-    "Non-complementary": "Colori non complementari",
-    "View": "Visuale",
-    "Tilt shift": "Tilt shift",
-    "Wide-angle": "Angolo ampio",
-    "Portrait": "Ritratto",
-    "Macro": "Macro",
-    "Microscopic": "Microscopico",
-    "Isometric": "Isometrico",
-    "Panorama": "Panorama",
-    "Aerial photograph": "Fotografia aerea",
-    "Artist focus (not quite finished, not sure it helps)": "Focus sull'artista (non del tutto finito, non è sicuro che aiuti)",
-    "B/W Photograpy": "Fotografia B/N",
-    "Portrait photo": "Foto ritratto",
-    "Usage: a <corgi|cat> wearing <goggles|a hat>": "Utilizzo: a <corgi|cat> wearing <goggles|a hat>",
-    "Seeds": "Semi",
-    "Noise Scheduler": "Pianificazione del rumore",
-    "Default": "Predefinito",
-    "Karras": "Karras",
-    "Exponential": "Esponenziale",
-    "Variance Preserving": "Conservazione della Varianza",
-    "Sigma min": "Sigma min",
-    "Sigma max": "Sigma max",
-    "Sigma rho (Karras only)": "Sigma rho (Solo Karras)",
-    "Beta distribution (VP only)": "Distribuzione Beta (Solo CV)",
-    "Beta min (VP only)": "Beta min (Solo CV)",
-    "Epsilon (VP only)": "Epsilon (Solo CV)",
-    "Running in txt2img mode:": "Esecuzione in modalità txt2img:",
-    "Render these video formats:": "Renderizza in questi formati:",
-    "GIF": "GIF",
-    "MP4": "MP4",
-    "WEBM": "WEBM",
-    "Animation Parameters": "Parametri animazione",
-    "Total Animation Length (s)": "Durata totale dell'animazione (s)",
-    "Framerate": "Frequenza dei fotogrammi",
-    "Smoothing_Frames": "Fotogrammi da appianare",
-    "Add_Noise": "Aggiungi rumore",
-    "Noise Strength": "Intensità del rumore",
-    "Initial Parameters": "Parametri iniziali",
-    "Denoising Strength": "Intensità di riduzione del rumore",
-    "Seed_March": "Seed_March",
-    "Zoom Factor (scale/s)": "Fattore di ingrandimento (scala/s)",
-    "X Pixel Shift (pixels/s)": "Sposta i Pixel sull'asse X (pixel/s)",
-    "Y Pixel Shift (pixels/s)": "Sposta i Pixel sull'asse Y (pixel/s)",
-    "Rotation (deg/s)": "Rotazione (gradi/s)",
-    "Prompt Template, applied to each keyframe below": "Modello di prompt, applicato a ciascun fotogramma chiave qui di seguito",
-    "Positive Prompts": "Prompt positivi",
-    "Negative Prompts": "Prompt negativi",
-    "Props, Stamps": "Immagini Clipart da diffondere (prop), o da applicare in post elaborazione e non essere diffuse (stamp).",
-    "Poper_Folder:": "Cartella Immagini Clipart (PNG trasparenti):",
-    "Supported Keyframes:": "Fotogrammi chiave supportati:",
-    "time_s | source | video, images, img2img | path": "time_s | source | video, images, img2img | path",
-    "time_s | prompt | positive_prompts | negative_prompts": "time_s | prompt | positive_prompts | negative_prompts",
-    "time_s | template | positive_prompts | negative_prompts": "time_s | template | positive_prompts | negative_prompts",
-    "time_s | transform | zoom | x_shift | y_shift | rotation": "time_s | transform | zoom | x_shift | y_shift | rotation",
-    "time_s | seed | new_seed_int": "time_s | seed | new_seed_int",
-    "time_s | noise | added_noise_strength": "time_s | noise | added_noise_strength",
-    "time_s | denoise | denoise_value": "time_s | denoise | denoise_value",
-    "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name": "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name",
-    "time_s | clear_text | textblock_name": "time_s | clear_text | textblock_name",
-    "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation": "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation",
-    "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation": "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation",
-    "time_s | clear_stamp | stamp_name": "time_s | clear_stamp | stamp_name",
-    "time_s | col_set": "time_s | col_set",
-    "time_s | col_clear": "time_s | col_clear",
-    "time_s | model | model": "time_s | model | model",
-    "img2img_mode": "Modalità img2img",
-    "Keyframes:": "Fotogrammi chiave:",
-    "Tile X": "Piastrella asse X",
-    "Tile Y": "Piastrella asse Y",
-    "Python code": "Codice Python",
-    "Source embedding to convert": "Incorporamento sorgente da convertire",
-    "Embedding token": "Token Incorporamento",
-    "Output directory": "Cartella di output",
-    "Horizontal symmetry": "Simmetria orizzontale",
-    "Vertical symmetry": "Simmetria verticale",
-    "Alt. symmetry method (blending)": "Metodo di simmetria alternativo (miscelazione)",
-    "Apply every n steps": "Applica ogni n passi",
-    "Skip last n steps": "Salta gli ultimi n passi",
-    "Interpolation prompt": "Prompt di interpolazione",
-    "Number of images": "Numero di immagini",
-    "Make a gif": "Crea GIF",
-    "Duration of images (ms)": "Durata delle immagini (ms)",
-    "Put variable parts at start of prompt": "Inserisce le parti variabili all'inizio del prompt",
-    "Keyframe Format:": "Formato dei fotogrammi chiave:",
-    "Seed | Prompt or just Prompt": "Seme | Prompt o semplicemente Prompt",
-    "Prompt list": "Elenco dei prompt",
-    "Number of images between keyframes": "Numero di immagini tra fotogrammi chiave",
-    "Save results as video": "Salva i risultati come video",
-    "Frames per second": "Fotogrammi al secondo",
-    "Iterate seed every line": "Iterare il seme per ogni riga",
-    "Use same random seed for all lines": "Usa lo stesso seme casuale per tutte le righe",
-    "List of prompt inputs": "Elenco di prompt di input",
-    "Upload prompt inputs": "Carica un file contenente i prompt di input",
-    "n": "Esegui n volte",
-    "Destination seed(s) (Comma separated)": "Seme/i di destinazione (separati da virgola)",
-    "Only use Random seeds (Unless comparing paths)": "Usa solo semi casuali (a meno che non si confrontino i percorsi)",
-    "Number of random seed(s)": "Numero di semi casuali",
-    "Compare paths (Separate travels from 1st seed to each destination)": "Confronta percorsi (transizioni separate dal primo seme a ciascuna destinazione)",
-    "Steps": "Passi",
-    "Loop back to initial seed": "Ritorna al seme iniziale",
-    "Bump seed (If > 0 do a Compare Paths but only one image. No video)": "Modula seme (se > 0 mescola il seme iniziale con quelli di destinazione ma solo un'immagine. Nessun video)",
-    "Show generated images in ui": "Mostra le immagini generate nell'interfaccia utente",
-    "\"Hug the middle\" during interpolation": "\"Hug the middle\" durante l'interpolazione. Rende l'interpolazione un po' più veloce all'inizio e alla fine. A volte può produrre video più fluidi, il più delle volte no.",
-    "Allow the default Euler a Sampling method. (Does not produce good results)": "Consenti Euler_a come metodo di campionamento predefinito. (Non produce buoni risultati)",
-    "Illustration": "Illustrazione",
-    "Logo": "Logo",
-    "Drawing": "Disegno",
-    "Artistic": "Artistico",
-    "Tattoo": "Tatuaggio",
-    "Gothic": "Gotico",
-    "Anime": "Anime",
-    "Cartoon": "Cartoon",
-    "Sticker": "Etichetta",
-    "Gold Pendant": "Ciondolo in oro",
-    "None - prompt only": "Nessuno - solo prompt",
-    "Enable Vectorizing": "Abilita vettorizzazione",
-    "Output format": "Formato di output",
-    "svg": "svg",
-    "pdf": "pdf",
-    "White is Opaque": "Il bianco è opaco",
-    "Cut white margin from input": "Taglia il margine bianco dall'input",
-    "Keep temp images": "Conserva le immagini temporanee",
-    "Threshold": "Soglia",
-    "Transparent PNG": "PNG trasparente",
-    "Noise Tolerance": "Tolleranza al rumore",
-    "Quantize": "Quantizzare",
-    "Dry Run": "Esecuzione a vuoto (Debug)",
-    "NEW!": "NUOVO!",
-    "Premium Fantasy Card Template": "Premium Fantasy Card Template",
-    "is now available.": "è ora disponibile.",
-    "Generate a wide variety of creatures and characters in the style of a fantasy card game. Perfect for heroes, animals, monsters, and even crazy hybrids.": "Genera un'ampia varietà di creature e personaggi nello stile di un gioco di carte fantasy. Perfetto per eroi, animali, mostri e persino ibridi incredibili.",
-    "Learn More ➜": "Per saperne di più ➜",
-    "Purchases help fund the continued development of Unprompted. Thank you for your support!": "Gli acquisti aiutano a finanziare il continuo sviluppo di Unprompted. Grazie per il vostro sostegno!",
-    "X type": "Parametro asse X",
-    "Nothing": "Niente",
-    "Var. seed": "Seme della variazione",
-    "Var. strength": "Forza della variazione",
-    "Prompt S/R": "Cerca e Sostituisci nel Prompt",
-    "Prompt order": "In ordine di prompt",
-    "Sampler": "Campionatore",
-    "Checkpoint name": "Nome del checkpoint",
-    "Hypernetwork": "Iperrete",
-    "Hypernet str.": "Forza della Iperrete",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma noise": "Sigma noise",
-    "Eta": "ETA",
-    "Clip skip": "Salta CLIP",
-    "Denoising": "Riduzione del rumore",
-    "Cond. Image Mask Weight": "Peso maschera immagine condizionale",
-    "X values": "Valori per X",
-    "Y type": "Parametro asse Y",
-    "Y values": "Valori per Y",
-    "Z type": "Parametro asse Z",
-    "Z values": "Valori per Z",
-    "Combinatorial generation": "Generazione combinatoria",
-    "Combinatorial batches": "Lotti combinatori",
-    "Magic prompt": "Prompt magico",
-    "Fixed seed": "Seme fisso",
-    "Combinations": "Combinazioni",
-    "Choose a number of terms from a list, in this case we choose two artists": "Scegli un numero di termini da un elenco, in questo caso scegliamo due artisti",
-    "{{2$artist1|artist2|artist3}}": "{{2$artist1|artist2|artist3}}",
-    "If $ is not provided, then 1$ is assumed.\n\n    A range can be provided:": "Se $ non viene fornito, si presume 1$.\n\n    È possibile fornire un intervallo di valori:",
-    "{{1-3$artist1|artist2|artist3}}": "{{1-3$artist1|artist2|artist3}}",
-    "In this case, a random number of artists between 1 and 3 is chosen.": "In questo caso viene scelto un numero casuale di artisti compreso tra 1 e 3.",
-    "Wildcards": "Termini jolly",
-    "If the groups wont drop down click": "Se i gruppi non vengono visualizzati, clicca",
-    "here": "qui",
-    "to fix the issue.": "per correggere il problema.",
-    "WILDCARD_DIR: C:\\stable-diffusion-webui\\extensions\\sd-dynamic-prompts\\wildcards": "WILDCARD_DIR: C:\\stable-diffusion-webui\\extensions\\sd-dynamic-prompts\\wildcards",
-    "You can add more wildcards by creating a text file with one term per line and name is mywildcards.txt. Place it in scripts/wildcards.": "Puoi aggiungere termini jolly creando un file di testo con un termine per riga e nominandolo, per esempio, mywildcards.txt. Inseriscilo in scripts/wildcards.",
-    "__<folder>/mywildcards__": "__<cartella>/mywildcards__",
-    "will then become available.": "diverrà quindi disponibile.",
-    "Artist or styles name list. '.txt' files with one name per line": "Elenco nomi di artisti o stili. File '.txt' con un nome per riga",
-    "Prompt words before artist or style name": "Parole chiave prima del nome dell'artista o dello stile",
-    "Prompt words after artist or style name": "Parole chiave dopo il nome dell'artista o dello stile",
-    "Negative Prompt": "Prompt negativo",
-    "Save": "Salva",
-    "Send to img2img": "Invia a img2img",
-    "Send to inpaint": "Invia a Inpaint",
-    "Send to extras": "Invia a Extra",
-    "Make Zip when Save?": "Crea un file ZIP quando si usa 'Salva'",
-    "Textbox": "Casella di testo",
-    "Interrogate\nCLIP": "Interroga\nCLIP",
-    "Interrogate\nDeepBooru": "Interroga\nDeepBooru",
-    "Inpaint": "Inpaint",
-    "Batch img2img": "img2img in lotti",
-    "Image for img2img": "Immagine per img2img",
-    "Drop Image Here": "Trascina l'immagine qui",
-    "Image for inpainting with mask": "Immagine per inpainting con maschera",
-    "Mask": "Maschera",
-    "Mask blur": "Sfocatura maschera",
-    "Mask mode": "Modalità maschera",
-    "Draw mask": "Disegna maschera",
-    "Upload mask": "Carica maschera",
-    "Masking mode": "Modalità mascheratura",
-    "Inpaint masked": "Inpaint mascherato",
-    "Inpaint not masked": "Inpaint non mascherato",
-    "Masked content": "Contenuto mascherato",
-    "fill": "riempi",
-    "original": "originale",
-    "latent noise": "rumore nello spazio latente",
-    "latent nothing": "nulla nello spazio latente",
-    "Inpaint at full resolution": "Inpaint alla massima risoluzione",
-    "Inpaint at full resolution padding, pixels": "Inpaint con riempimento a piena risoluzione, pixel",
-    "Process images in a directory on the same machine where the server is running.": "Elabora le immagini in una cartella sulla stessa macchina su cui è in esecuzione il server.",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa una cartella di output vuota per salvare normalmente le immagini invece di scrivere nella cartella di output.",
-    "Input directory": "Cartella di Input",
-    "Resize mode": "Modalità di ridimensionamento",
-    "Just resize": "Ridimensiona solamente",
-    "Crop and resize": "Ritaglia e ridimensiona",
-    "Resize and fill": "Ridimensiona e riempie",
-    "Advanced loopback": "Advanced loopback", 
-    "External Image Masking": "Immagine esterna per la mascheratura",
-    "img2img alternative test": "Test alternativo per img2img",
-    "img2tiles": "img2tiles",
-    "Interpolate": "Interpola immagini",
-    "Loopback": "Rielaborazione ricorsiva",
-    "Loopback and Superimpose": "Rielabora ricorsivamente e sovraimponi",
-    "Alpha Canvas": "Alpha Canvas",
-    "Outpainting mk2": "Outpainting mk2",
-    "Poor man's outpainting": "Poor man's outpainting",
-    "SD upscale": "Ampliamento SD",
-    "txt2mask v0.1.1": "txt2mask v0.1.1",
-    "[C] Video to video": "[C] Video to video",
-    "Videos": "Filmati",
-    "Deforum-webui (use tab extension instead!)": "Deforum-webui (usa piuttosto la scheda Deforum delle estensioni!)",
-    "Use first image colors (custom color correction)": "Usa i colori della prima immagine (correzione del colore personalizzata)",
-    "Denoising strength change factor (overridden if proportional used)": "Fattore di variazione dell'intensità di riduzione del rumore (sovrascritto se si usa proporzionale)",
-    "Zoom level": "Livello di Zoom",
-    "Direction X": "Direzione X",
-    "Direction Y": "Direzione Y",
-    "Denoising strength start": "Intensità di riduzione del rumore - Inizio",
-    "Denoising strength end": "Intensità di riduzione del rumore - Fine",
-    "Denoising strength proportional change starting value": "Intensità di riduzione del rumore - Valore iniziale della variazione proporzionale",
-    "Denoising strength proportional change ending value (0.1 = disabled)": "Intensità di riduzione del rumore - Valore finale della variazione proporzionale (0.1 = disabilitato)",
-    "Saturation enhancement per image": "Miglioramento della saturazione per ciascuna immagine",
-    "Use sine denoising strength variation": "Utilizzare la variazione sinusoidale dell'intensità di riduzione del rumore",
-    "Phase difference": "Differenza di Fase",
-    "Denoising strength exponentiation": "Esponenziazione dell'intensità di riduzione del rumore",
-    "Use sine zoom variation": "Usa la variazione sinusoidale dello zoom",
-    "Zoom exponentiation": "Esponeniazione dello Zoom",
-    "Use multiple prompts": "Usa prompt multipli",
-    "Same seed per prompt": "Stesso seme per ogni prompt",
-    "Same seed for everything": "Stesso seme per tutto",
-    "Original init image for everything": "Immagine originale di inizializzazione per tutto",
-    "Multiple prompts : 1 line positive, 1 line negative, leave a blank line for no negative": "Prompt multipli: 1 riga positivo, 1 riga negativo, lasciare una riga vuota per nessun negativo",
-    "Running in img2img mode:": "Esecuzione in modalità img2img:",
-    "Masking preview size": "Dimensione dell'anteprima della mascheratura",
-    "Draw new mask on every run": "Disegna una nuova maschera ad ogni esecuzione",
-    "Process non-contigious masks separately": "Elaborare le maschere non contigue separatamente",
-    "should be 2 or lower.": "dovrebbe essere 2 o inferiore.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Sovrascrivi il 'Metodo di campionamento' con Eulero? (questo metodo è stato creato per questo)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Sovrascrivi `prompt` con lo stesso valore del `prompt originale`? (e `prompt negativo`)",
-    "Original prompt": "Prompt originale",
-    "Original negative prompt": "Prompt negativo originale",
-    "Override `Sampling Steps` to the same val due as `Decode steps`?": "Sovrascrivere 'Passi di campionamento' allo stesso valore di 'Passi di decodifica'?",
-    "Decode steps": "Passi di decodifica",
-    "Override `Denoising strength` to 1?": "Sostituisci 'Forza di denoising' a 1?",
-    "Decode CFG scale": "Scala CFG di decodifica",
-    "Randomness": "Casualità",
-    "Sigma adjustment for finding noise for image": "Regolazione Sigma per trovare il rumore per l'immagine",
-    "Tile size": "Dimensione piastrella",
-    "Tile overlap": "Sovrapposizione piastrella",
-    "New seed for each tile": "Nuovo seme per ogni piastrella",
-    "alternate img2img imgage": "Immagine alternativa per img2img",
-    "interpolation values": "Valori di interpolazione",
-    "Refinement loops": "Cicli di affinamento",
-    "Loopback alpha": "Trasparenza rielaborazione ricorsiva",
-    "Border alpha": "Trasparenza del bordo",
-    "Blending strides": "Passi di fusione",
-    "Reuse Seed": "Riusa il seme",
-    "One grid": "Singola griglia",
-    "Interpolate VarSeed": "Interpola il seme della variazione",
-    "Paste on mask": "Incolla sulla maschera",
-    "Inpaint all": "Inpaint tutto",
-    "Interpolate in latent": "Interpola nello spazio latente",
-    "Denoising strength change factor": "Fattore di variazione dell'intensità di denoising",
-    "Superimpose alpha": "Sovrapporre Alpha",
-    "Show extra settings": "Mostra impostazioni aggiuntive",
-    "Reuse seed": "Riusa il seme",
-    "CFG decay factor": "Fattore di decadimento CFG",
-    "CFG target": "CFG di destinazione",
-    "Show/Hide AlphaCanvas": "Mostra/Nascondi AlphaCanvas",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Impostazioni consigliate: Passi di campionamento: 80-100, Campionatore: Euler a, Intensità denoising: 0.8",
-    "Pixels to expand": "Pixel da espandere",
-    "Outpainting direction": "Direzione di Outpainting",
-    "left": "sinistra",
-    "right": "destra",
-    "up": "sopra",
-    "down": "sotto",
-    "Fall-off exponent (lower=higher detail)": "Esponente di decremento (più basso=maggior dettaglio)",
-    "Color variation": "Variazione di colore",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenterà l'immagine al doppio delle dimensioni; utilizzare i cursori di larghezza e altezza per impostare la dimensione della piastrella",
-    "Upscaler": "Ampliamento immagine",
-    "Lanczos": "Lanczos",
-    "Nearest": "Nearest",
-    "LDSR": "LDSR",
-    "BSRGAN": "BSRGAN",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR 4x": "SwinIR 4x",
-    "Mask prompt": "Prompt maschera",
-    "Negative mask prompt": "Prompt maschera negativa",
-    "Mask precision": "Precisione della maschera",
-    "Mask padding": "Estendi i bordi della maschera",
-    "Brush mask mode": "Modalità pennello maschera",
-    "discard": "Scarta",
-    "add": "Aggiungi",
-    "subtract": "Sottrai",
-    "Show mask in output?": "Mostra maschera in uscita?",
-    "If you like my work, please consider showing your support on": "Se ti piace il mio lavoro, per favore considera di mostrare il tuo supporto su ",
-    "Patreon": "Patreon",
-    "Input file path": "Percorso file di input",
-    "CRF (quality, less is better, x264 param)": "CRF (qualità, meno è meglio, x264 param)",
-    "FPS": "FPS",
-    "Seed step size": "Ampiezza del gradiente del seme",
-    "Seed max distance": "Distanza massima del seme",
-    "Start time": "Orario di inizio",
-    "End time": "Orario di fine",
-    "End Prompt Blend Trigger Percent": "Percentuale di innesco del mix col prompt finale",
-    "Prompt end": "Prompt finale",
-    "Smooth video": "Rendi il filmato fluido",
-    "Seconds": "Secondi",
-    "Zoom": "Zoom",
-    "Rotate": "Ruota",
-    "Degrees": "Gradi",
-    "Is the Image Tiled?": "L'immagine è piastrellata?",
-    "TranslateX": "Traslazione X",
-    "Left": "Sinistra",
-    "PercentX": "Percentuale X",
-    "TranslateY": "Traslazione Y",
-    "Up": "Sopra",
-    "PercentY": "Percentuale Y",
-    "Show generated pictures in ui": "Mostra le immagini generate nell'interfaccia utente",
-    "Deforum v0.5-webui-beta": "Deforum v0.5-webui-beta",
-    "This script is deprecated. Please use the full Deforum extension instead.": "Questo script è obsoleto. Utilizzare invece l'estensione Deforum completa.",
-    "Update instructions:": "Istruzioni per l'aggiornamento:",
-    "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md": "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md",
-    "discord.gg/deforum": "discord.gg/deforum",
-    "Single Image": "Singola immagine",
-    "Batch Process": "Elaborare a lotti",
-    "Batch from Directory": "Lotto da cartella",
-    "Source": "Sorgente",
-    "Show result images": "Mostra le immagini dei risultati",
-    "Scale by": "Scala di",
-    "Scale to": "Scala a",
-    "Resize": "Ridimensiona",
-    "Crop to fit": "Ritaglia per adattare",
-    "Upscaler 2 visibility": "Visibilità Ampliamento immagine 2",
-    "GFPGAN visibility": "Visibilità GFPGAN",
-    "CodeFormer visibility": "Visibilità CodeFormer",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso di CodeFormer (0 = effetto massimo, 1 = effetto minimo)",
-    "Upscale Before Restoring Faces": "Amplia prima di restaurare i volti",
-    "Send to txt2img": "Invia a txt2img",
-    "A merger of the two checkpoints will be generated in your": "I due checkpoint verranno fusi nella cartella dei",
-    "checkpoint": "checkpoint",
-    "directory.": ".",
-    "Primary model (A)": "Modello Primario (A)",
-    "Secondary model (B)": "Modello Secondario (B)",
-    "Tertiary model (C)": "Modello Terziario (C)",
-    "Custom Name (Optional)": "Nome personalizzato (facoltativo)",
-    "Multiplier (M) - set to 0 to get model A": "Moltiplicatore (M): impostare a 0 per ottenere il modello A",
-    "Interpolation Method": "Metodo di interpolazione",
-    "Weighted sum": "Somma pesata",
-    "Add difference": "Aggiungi differenza",
-    "Save as float16": "Salva come float16",
-    "See": "Consulta la ",
-    "wiki": "wiki",
-    "for detailed explanation.": " per una spiegazione dettagliata.",
-    "Create embedding": "Crea Incorporamento",
-    "Create hypernetwork": "Crea Iperrete",
-    "Preprocess images": "Preprocessa le immagini",
-    "Name": "Nome",
-    "Initialization text": "Testo di inizializzazione",
-    "Number of vectors per token": "Numero di vettori per token",
-    "Overwrite Old Embedding": "Sovrascrivi il vecchio incorporamento",
-    "Modules": "Moduli",
-    "Enter hypernetwork layer structure": "Immettere la struttura del livello della Iperrete",
-    "Select activation function of hypernetwork": "Selezionare la funzione di attivazione della Iperrete",
-    "linear": "lineare",
-    "relu": "relu",
-    "leakyrelu": "leakyrelu",
-    "elu": "elu",
-    "swish": "swish",
-    "tanh": "tanh",
-    "sigmoid": "sigmoid",
-    "celu": "celu",
-    "gelu": "gelu",
-    "glu": "glu",
-    "hardshrink": "hardshrink",
-    "hardsigmoid": "hardsigmoid",
-    "hardtanh": "hardtanh",
-    "logsigmoid": "logsigmoid",
-    "logsoftmax": "logsoftmax",
-    "mish": "mish",
-    "prelu": "prelu",
-    "rrelu": "rrelu",
-    "relu6": "relu6",
-    "selu": "selu",
-    "silu": "silu",
-    "softmax": "softmax",
-    "softmax2d": "softmax2d",
-    "softmin": "softmin",
-    "softplus": "softplus",
-    "softshrink": "softshrink",
-    "softsign": "softsign",
-    "tanhshrink": "tanhshrink",
-    "threshold": "soglia",
-    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleziona inizializzazione dei pesi dei livelli. relu-like - Kaiming, Si consiglia sigmoid-like - Xavier",
-    "Normal": "Normale",
-    "KaimingUniform": "KaimingUniform",
-    "KaimingNormal": "KaimingNormal",
-    "XavierUniform": "XavierUniform",
-    "XavierNormal": "XavierNormal",
-    "Add layer normalization": "Aggiunge la normalizzazione del livello",
-    "Use dropout": "Usa Dropout",
-    "Overwrite Old Hypernetwork": "Sovrascrive la vecchia Iperrete",
-    "Source directory": "Cartella sorgente",
-    "Destination directory": "Cartella di destinazione",
-    "Existing Caption txt Action": "Azione sul testo della didascalia esistente",
-    "ignore": "ignora",
-    "copy": "copia",
-    "prepend": "anteporre",
-    "append": "appendere",
-    "Create flipped copies": "Crea copie specchiate",
-    "Split oversized images": "Dividi immagini di grandi dimensioni",
-    "Auto focal point crop": "Ritaglio automatico al punto focale",
-    "Use BLIP for caption": "Usa BLIP per la didascalia",
-    "Use deepbooru for caption": "Usa deepbooru per la didascalia",
-    "Split image threshold": "Soglia di divisione dell'immagine",
-    "Split image overlap ratio": "Rapporto di sovrapposizione dell'immagine",
-    "Focal point face weight": "Peso della faccia del punto focale",
-    "Focal point entropy weight": "Peso dell'entropia del punto focale",
-    "Focal point edges weight": "Peso dei bordi del punto focale",
-    "Create debug image": "Crea immagine di debug",
-    "Preprocess": "Preprocessa",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Addestra un Incorporamento o Iperrete; è necessario specificare una directory con un set di immagini con rapporto 1:1",
-    "[wiki]": "[wiki]",
-    "Embedding": "Incorporamento",
-    "Embedding Learning rate": "Tasso di apprendimento Incorporamento",
-    "Hypernetwork Learning rate": "Tasso di apprendimento Iperrete",
-    "Dataset directory": "Cartella del Dataset",
-    "Log directory": "Cartella del registro",
-    "Prompt template file": "File modello prompt",
-    "Max steps": "Passi massimi",
-    "Save an image to log directory every N steps, 0 to disable": "Salva un'immagine nella cartella del registro ogni N passaggi, 0 per disabilitare",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva una copia dell'incorporamento nella cartella del registro ogni N passaggi, 0 per disabilitare",
-    "Save images with embedding in PNG chunks": "Salva le immagini con l'incorporamento in blocchi PNG",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Legge i parametri (prompt, ecc...) dalla scheda txt2img durante la creazione delle anteprime",
-    "Train Hypernetwork": "Addestra Iperrete",
-    "Train Embedding": "Addestra Incorporamento",
-    "Create an aesthetic embedding out of any number of images": "Crea un'incorporamento estetico da qualsiasi numero di immagini",
-    "Create images embedding": "Crea incorporamento di immagini",
-    "-1": "-1",
-    "This extension works well with text captions in comma-separated style (such as the tags generated by DeepBooru interrogator).": "Questa estensione funziona bene con i sottotitoli di testo in stile separato da virgole (come i tag generati dall'interrogatore DeepBooru).",
-    "Save all changes": "Salva tutte le modifiche",
-    "Backup original text file (original file will be renamed like filename.000, .001, .002, ...)": "Backup del file di testo originale (il file originale verrà rinominato come nomefile.000, .001, .002, ...)",
-    "Note:": "Note:",
-    "New text file will be created if you are using filename as captions.": "Verrà creato un nuovo file di testo se si utilizza il nome del file come didascalia.",
-    "Results": "Risultati",
-    "Load": "Carica",
-    "Dataset Images": "Immagini del Dataset",
-    "Filter and Edit Tags": "Filtra e modifica i tag",
-    "Edit Caption of Selected Image": "Modifica la didascalia dell'immagine selezionata",
-    "Search tags / Filter images by tags": "Cerca tag / Filtra le immagini per tag",
-    "Search Tags": "Cerca tag",
-    "Clear all filters": "Rimuovi tutti i filtri",
-    "Sort by": "Ordina per",
-    "Alphabetical Order": "Ordine alfabetico",
-    "Frequency": "Frequenza",
-    "Sort Order": "Ordinamento",
-    "Ascending": "Ascendente",
-    "Descending": "Discendente",
-    "Filter Images by Tags": "Filtra le immagini per tag",
-    "Edit tags in filtered images": "Modifica i tag nelle immagini filtrate",
-    "Selected Tags": "Tag selezionati",
-    "Edit Tags": "Modificare i tag",
-    "Apply changes to filtered images": "Applica le modifiche alle immagini filtrate",
-    "Append additional tags to the beginning": "Aggiungi tag addizionali all'inizio",
-    "1. The selected tags are displayed in comma separated style.": "1. I tag selezionati vengono visualizzati in uno stile separato da virgole.",
-    "2. When changes are applied, all tags in each displayed images are replaced.": "2. Quando vengono applicate le modifiche, tutti i tag in ciascuna immagine visualizzata vengono sostituiti.",
-    "3. If you change some tags into blank, they will be erased.": "3. Se modifichi alcuni tag con uno spazio vuoto, verranno cancellati.",
-    "4. If you add some tags to the end, they will be appended to the end/beginning of the text file.": "4. Se aggiungi dei tag alla fine, questi verranno aggiunti alla fine/inizio del file di testo.",
-    "5. Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "5. Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\"..",
-    "ex A.": "esempio A.",
-    "Original Text = \"A, A, B, C\" Selected Tags = \"B, A\" Edit Tags = \"X, Y\"": "Testo originale = \"A, A, B, C\" Tag selezionati = \"B, A\" Modifica tag = \"X, Y\"",
-    "Result = \"Y, Y, X, C\" (B->X, A->Y)": "Risultato = \"Y, Y, X, C\" (B->X, A->Y)",
-    "ex B.": "esempio B.",
-    "Original Text = \"A, B, C\" Selected Tags = \"(nothing)\" Edit Tags = \"X, Y\"": "Testo originale = \"A, B, C\" Tag selezionati = \"(nothing)\" Modifica tag = \"X, Y\"",
-    "Result = \"A, B, C, X, Y\" (add X and Y to the end (default))": "Risultato = \"A, B, C, X, Y\" (aggiunge X e Y alla fine (predefinito))",
-    "Result = \"X, Y, A, B, C\" (add X and Y to the beginning (\"Append additional tags to the beginning\" checked))": "Risultato = \"X, Y, A, B, C\" (aggiunge X e Y all'inizio (\"Aggiungi tag addizionali all'inizio\" selezionato))",
-    "ex C.": "esempio C.",
-    "Original Text = \"A, B, C, D, E\" Selected Tags = \"A, B, D\" Edit Tags = \", X, \"": "Testo originale = \"A, B, C, D, E\" Tag selezionati = \"A, B, D\" Modifica tag = \", X, \"",
-    "Result = \"X, C, E\" (A->\"\", B->X, D->\"\")": "Risultato = \"X, C, E\" (A->\"\", B->X, D->\"\")",
-    "Caption of Selected Image": "Didascalia dell'immagine selezionata",
-    "Copy caption": "Copia didascalia",
-    "Edit Caption": "Modifica didascalia",
-    "Apply changes to selected image": "Applica le modifiche all'immagine selezionata",
-    "Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\".",
-    "Info and links": "Info e link",
-    "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha": "Realizzato da deforum.github.io, port per l'interfaccia web di AUTOMATIC1111 manutenuto da kabachuha",
-    "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Repository Github originale di Deforum  github.com/deforum/stable-diffusion",
-    "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui": "Questo fork è per l'interfaccia web di AUTOMATIC1111 github.com/deforum-art/deforum-for-automatic1111-webui",
-    "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Unisciti al canale Discord ufficiale di Deforum discord.gg/deforum per condividere le tue creazioni e suggerimenti",
-    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "Manuale d'uso per la versione 0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
-    "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Spiegazione della matematica dei fotogrammi chiave docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
-    "Keyframes": "Fotogrammi chiave",
-    "Prompts": "Prompt",
-    "Init": "Inizializzare",
-    "Video output": "Uscita video",
-    "Run settings": "Esegui le impostazioni",
-    "Import settings from file": "Importa impostazioni da file",
-    "Override settings": "Sostituisci le impostazioni",
-    "Custom settings file": "File delle impostazioni personalizzate",
-    "Sampling settings": "Impostazioni di campionamento",
-    "override_these_with_webui": "Sovrascrivi con Web UI",
-    "W": "L",
-    "H": "A",
-    "seed": "Seme",
-    "sampler": "Campionatore",
-    "Enable extras": "Abilita 'Extra'",
-    "subseed": "Sub seme",
-    "subseed_strength": "Intensità subseme",
-    "steps": "Passi",
-    "ddim_eta": "ETA DDIM",
-    "n_batch": "Numero lotto",
-    "make_grid": "Crea griglia",
-    "grid_rows": "Righe griglia",
-    "save_settings": "Salva impostazioni",
-    "save_samples": "Salva i campioni",
-    "display_samples": "Mostra i campioni",
-    "save_sample_per_step": "Salva campioni per passo",
-    "show_sample_per_step": "Mostra campioni per passo",
-    "Batch settings": "Impostazioni lotto",
-    "batch_name": "Nome del lotto",
-    "filename_format": "Formato nome del file",
-    "seed_behavior": "Comportamento seme",
-    "iter": "Iterativo",
-    "fixed": "Fisso",
-    "random": "Casuale",
-    "schedule": "Pianificato",
-    "Animation settings": "Impostazioni animazione",
-    "animation_mode": "Modalità animazione",
-    "2D": "2D",
-    "3D": "3D",
-    "Video Input": "Ingresso video",
-    "max_frames": "Fotogrammi max",
-    "border": "Bordo",
-    "replicate": "Replica",
-    "wrap": "Impacchetta",
-    "Motion parameters:": "Parametri di movimento:",
-    "2D and 3D settings": "Impostazioni 2D e 3D",
-    "angle": "Angolo",
-    "zoom": "Zoom",
-    "translation_x": "Traslazione X",
-    "translation_y": "Traslazione Y",
-    "3D settings": "Impostazioni 3D",
-    "translation_z": "Traslazione Z",
-    "rotation_3d_x": "Rotazione 3D X",
-    "rotation_3d_y": "Rotazione 3D Y",
-    "rotation_3d_z": "Rotazione 3D Z",
-    "Prespective flip — Low VRAM pseudo-3D mode:": "Inversione prospettica: modalità pseudo-3D a bassa VRAM:",
-    "flip_2d_perspective": "Inverti prospettiva 2D",
-    "perspective_flip_theta": "Inverti prospettiva theta",
-    "perspective_flip_phi": "Inverti prospettiva phi",
-    "perspective_flip_gamma": "Inverti prospettiva gamma",
-    "perspective_flip_fv": "Inverti prospettiva fv",
-    "Generation settings:": "Impostazioni di generazione:",
-    "noise_schedule": "Pianificazione del rumore",
-    "strength_schedule": "Intensità della pianificazione",
-    "contrast_schedule": "Contrasto della pianificazione",
-    "cfg_scale_schedule": "Pianificazione della scala CFG",
-    "3D Fov settings:": "Impostazioni del campo visivo 3D:",
-    "fov_schedule": "Pianificazione del campo visivo",
-    "near_schedule": "Pianificazione da vicino",
-    "far_schedule": "Pianificazione da lontano",
-    "To enable seed schedule select seed behavior — 'schedule'": "Per abilitare la pianificazione del seme, seleziona il comportamento del seme — 'pianifica'",
-    "seed_schedule": "Pianificazione del seme",
-    "Coherence:": "Coerenza:",
-    "color_coherence": "Coerenza del colore",
-    "Match Frame 0 HSV": "Uguaglia HSV del fotogramma 0",
-    "Match Frame 0 LAB": "Uguaglia LAB del fotogramma 0",
-    "Match Frame 0 RGB": "Uguaglia RGB del fotogramma 0",
-    "diffusion_cadence": "Cadenza di diffusione",
-    "3D Depth Warping:": "Deformazione della profondità 3D:",
-    "use_depth_warping": "Usa la deformazione della profondità",
-    "midas_weight": "Peso MIDAS",
-    "near_plane": "Piano vicino",
-    "far_plane": "Piano lontano",
-    "fov": "Campo visivo",
-    "padding_mode": "Modalità di riempimento",
-    "reflection": "Rifletti",
-    "zeros": "Zeri",
-    "sampling_mode": "Modalità di campionamento",
-    "bicubic": "bicubic",
-    "bilinear": "bilinear",
-    "nearest": "nearest",
-    "save_depth_maps": "Salva le mappe di profondità",
-    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`modalità animazione: Nessuno` si inserisce nell'elenco di *prompt*. (Modalità batch disabilitata atm, funzionano solo i prompt di animazione)",
-    "*Important change from vanilla Deforum!*": "*Importante cambiamento rispetto alla versione originale di Deforum!*",
-    "This script uses the built-in webui weighting settings.": "Questo script utilizza le impostazioni di pesatura webui integrate.",
-    "So if you want to use math functions as prompt weights,": "Quindi, se vuoi usare le funzioni matematiche come pesi dei prompt,",
-    "keep the values above zero in both parts": "mantenere i valori sopra lo zero in entrambe le parti",
-    "Negative prompt part can be specified with --neg": "La parte negativa del prompt può essere specificata con --neg",
-    "batch_prompts (disabled atm)": "Prompt in lotti (al momento è disabilitato)",
-    "animation_prompts": "Prompt animazione",
-    "Init settings": "Impostazioni iniziali",
-    "use_init": "Usa le impostazioni iniziali",
-    "from_img2img_instead_of_link": "da img2img invece che da link",
-    "strength_0_no_init": "Intensità 0 nessuna inizializzazione",
-    "strength": "Intensità",
-    "init_image": "Immagine di inizializzazione",
-    "use_mask": "Usa maschera",
-    "use_alpha_as_mask": "Usa alpha come maschera",
-    "invert_mask": "Inverti la maschera",
-    "overlay_mask": "Sovrapponi la maschera",
-    "mask_file": "File della maschera",
-    "mask_brightness_adjust": "Regola la luminosità della maschera",
-    "mask_overlay_blur": "Sfocatura della sovrapposizione della maschera",
-    "Video Input:": "Ingresso video:",
-    "video_init_path": "Percorso del video di inizializzazione",
-    "extract_nth_frame": "Estrai ogni ennesimo fotogramma",
-    "overwrite_extracted_frames": "Sovrascrivi i fotogrammi estratti",
-    "use_mask_video": "Usa maschera video",
-    "video_mask_path": "Percorso della maschera video",
-    "Interpolation (turned off atm)": "Interpolazione (attualmente spento)",
-    "interpolate_key_frames": "Interpola fotogrammi chiave",
-    "interpolate_x_frames": "Interpola x fotogrammi",
-    "Resume animation:": "Riprendi l'animazione:",
-    "resume_from_timestring": "Riprendi da stringa temporale",
-    "resume_timestring": "Stringa temporale",
-    "Video output settings": "Impostazioni uscita video",
-    "skip_video_for_run_all": "Salta il video per eseguire tutto",
-    "fps": "FPS",
-    "output_format": "Formato di uscita",
-    "PIL gif": "PIL GIF",
-    "FFMPEG mp4": "FFMPEG MP4",
-    "ffmpeg_location": "Percorso ffmpeg",
-    "add_soundtrack": "Aggiungi colonna sonora",
-    "soundtrack_path": "Percorso colonna sonora",
-    "use_manual_settings": "Usa impostazioni manuali",
-    "render_steps": "Passi di renderizzazione",
-    "max_video_frames": "Numero max fotogrammi video",
-    "path_name_modifier": "Modificatore del nome del percorso",
-    "x0_pred": "x0_pred",
-    "x": "x",
-    "image_path": "Percorso immagine",
-    "mp4_path": "Percorso MP4",
-    "Click here after the generation to show the video": "Clicca qui dopo la generazione per mostrare il video",
-    "NOTE: If the 'Generate' button doesn't work, go in Settings and click 'Restart Gradio and Refresh...'.": "NOTA: se il pulsante 'Genera' non funziona, vai in Impostazioni e fai clic su 'Riavvia Gradio e Aggiorna...'.",
-    "Save Settings": "Salva le impostazioni",
-    "Load Settings": "Carica le impostazioni",
-    "Path relative to the webui folder." : "Percorso relativo alla cartella webui.",
-    "Save Video Settings": "Salva impostazioni video",
-    "Load Video Settings": "Carica impostazioni video",
-    "dog": "cane",
-    "house": "casa",
-    "portrait": "ritratto",
-    "spaceship": "nave spaziale",
-    "anime": "anime",
-    "cartoon": "cartoon",
-    "digipa-high-impact": "digipa-high-impact",
-    "digipa-med-impact": "digipa-med-impact",
-    "digipa-low-impact": "digipa-low-impact",
-    "fareast": "estremo oriente",
-    "fineart": "fineart",
-    "scribbles": "scarabocchi",
-    "special": "special",
-    "ukioe": "ukioe",
-    "weird": "strano",
-    "black-white": "bianco e nero",
-    "nudity": "nudità",
-    "c": "c",
-    "Get Images": "Ottieni immagini",
-    "dog-anime": "dog-anime",
-    "dog-cartoon": "dog-cartoon",
-    "dog-digipa-high-impact": "dog-digipa-high-impact",
-    "dog-digipa-med-impact": "dog-digipa-med-impact",
-    "dog-digipa-low-impact": "dog-digipa-low-impact",
-    "dog-fareast": "dog-fareast",
-    "dog-fineart": "dog-fineart",
-    "dog-scribbles": "dog-scribbles",
-    "dog-special": "dog-special",
-    "dog-ukioe": "dog-ukioe",
-    "dog-weird": "dog-weird",
-    "dog-black-white": "dog-black-white",
-    "dog-nudity": "dog-nudity",
-    "dog-c": "dog-c",
-    "dog-n": "dog-n",
-    "house-anime": "house-anime",
-    "house-cartoon": "house-cartoon",
-    "house-digipa-high-impact": "house-digipa-high-impact",
-    "house-digipa-med-impact": "house-digipa-med-impact",
-    "house-digipa-low-impact": "house-digipa-low-impact",
-    "house-fareast": "house-fareast",
-    "house-fineart": "house-fineart",
-    "house-scribbles": "house-scribbles",
-    "house-special": "house-special",
-    "house-ukioe": "house-ukioe",
-    "house-weird": "house-weird",
-    "house-black-white": "house-black-white",
-    "house-nudity": "house-nudity",
-    "house-c": "house-c",
-    "house-n": "house-n",
-    "portrait-anime": "portrait-anime",
-    "portrait-cartoon": "portrait-cartoon",
-    "portrait-digipa-high-impact": "portrait-digipa-high-impact",
-    "portrait-digipa-med-impact": "portrait-digipa-med-impact",
-    "portrait-digipa-low-impact": "portrait-digipa-low-impact",
-    "portrait-fareast": "portrait-fareast",
-    "portrait-fineart": "portrait-fineart",
-    "portrait-scribbles": "portrait-scribbles",
-    "portrait-special": "portrait-special",
-    "portrait-ukioe": "portrait-ukioe",
-    "portrait-weird": "portrait-weird",
-    "portrait-black-white": "portrait-black-white",
-    "portrait-nudity": "portrait-nudity",
-    "portrait-c": "portrait-c",
-    "portrait-n": "portrait-n",
-    "spaceship-anime": "spaceship-anime",
-    "spaceship-cartoon": "spaceship-cartoon",
-    "spaceship-digipa-high-impact": "spaceship-digipa-high-impact",
-    "spaceship-digipa-med-impact": "spaceship-digipa-med-impact",
-    "spaceship-digipa-low-impact": "spaceship-digipa-low-impact",
-    "spaceship-fareast": "spaceship-fareast",
-    "spaceship-fineart": "spaceship-fineart",
-    "spaceship-scribbles": "spaceship-scribbles",
-    "spaceship-special": "spaceship-special",
-    "spaceship-ukioe": "spaceship-ukioe",
-    "spaceship-weird": "spaceship-weird",
-    "spaceship-black-white": "spaceship-black-white",
-    "spaceship-nudity": "spaceship-nudity",
-    "spaceship-c": "spaceship-c",
-    "spaceship-n": "spaceship-n",
-    "artists to study extension by camenduru |": "Estensione 'Artisti per studiare' a cura di camenduru |",
-    "github": "Github",
-    "|": "|",
-    "twitter": "Twitter",
-    "youtube": "Youtube",
-    "hi-res images": "Immagini in alta risoluzione",
-    "All images generated with CompVis/stable-diffusion-v1-4 +": "Tutte le immagini sono state generate con CompVis/stable-diffusion-v1-4 +",
-    "artists.csv": "artists.csv",
-    "| License: Attribution 4.0 International (CC BY 4.0)": "| Licenza: Attribution 4.0 International (CC BY 4.0)",
-    "Favorites": "Preferiti",
-    "Others": "Altre immagini",
-    "Images directory": "Cartella immagini",
-    "Dropdown": "Elenco cartelle",
-    "First Page": "Prima pagina",
-    "Prev Page": "Pagina precedente",
-    "Page Index": "Indice pagina",
-    "Next Page": "Pagina successiva",
-    "End Page": "Ultima pagina",
-    "delete next": "Cancella successivo",
-    "Delete": "Elimina",
-    "sort by": "Ordina per",
-    "path name": "Nome percorso",
-    "date": "Data",
-    "keyword": "Parola chiave",
-    "Generate Info": "Genera Info",
-    "File Name": "Nome del file",
-    "Move to favorites": "Aggiungi ai preferiti",
-    "Renew Page": "Aggiorna la pagina",
-    "Number": "Numero",
-    "set_index": "Imposta indice",
-    "load_switch": "Carica",
-    "turn_page_switch": "Volta pagina",
-    "Checkbox": "Casella di controllo",						   
-    "Checkbox Group": "Seleziona immagini per",
-    "artists": "Artisti",
-    "flavors": "Stili",
-    "mediums": "Tecniche",
-    "movements": "Movimenti artistici",
-    "All": "Tutto",
-    "Exclude abandoned": "Escludi scartati",
-    "Abandoned": "Scartati",
-    "Key word": "Parola chiave",
-    "Get inspiration": "Ispirami",
-    "to txt2img": "Invia a txt2img",
-    "to img2img": "Invia a img2img",
-    "Collect": "Salva nei preferiti",
-    "Don't show again": "Scarta",
-    "Move out": "Rimuovi",
-    "set button": "Pulsante imposta", 
-    "Apply settings": "Applica le impostazioni",
-    "Saving images/grids": "Salva immagini/griglie",
-    "Always save all generated images": "Salva sempre tutte le immagini generate",
-    "File format for images": "Formato del file delle immagini",
-    "Images filename pattern": "Modello del nome dei file immagine",
-    "Add number to filename when saving": "Aggiungi un numero al nome del file al salvataggio",
-    "Always save all generated image grids": "Salva sempre tutte le griglie di immagini generate",
-    "File format for grids": "Formato del file per le griglie",
-    "Add extended info (seed, prompt) to filename when saving grid": "Aggiungi informazioni estese (seme, prompt) al nome del file durante il salvataggio della griglia",
-    "Do not save grids consisting of one picture": "Non salvare le griglie composte da una sola immagine",
-    "Prevent empty spots in grid (when set to autodetect)": "Previeni spazi vuoti nella griglia (se impostato su rilevamento automatico)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Numero di righe della griglia; utilizzare -1 per il rilevamento automatico e 0 per essere uguale alla dimensione del lotto",
-    "Save text information about generation parameters as chunks to png files": "Salva le informazioni di testo dei parametri di generazione come blocchi nel file png",
-    "Create a text file next to every image with generation parameters.": "Crea un file di testo assieme a ogni immagine con i parametri di generazione.",
-    "Save a copy of image before doing face restoration.": "Salva una copia dell'immagine prima di eseguire il restauro dei volti.",
-    "Save a copy of image before applying highres fix.": "Salva una copia dell'immagine prima di applicare la correzione ad alta risoluzione.",
-    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la correzione del colore ai risultati di img2img",
-    "Quality for saved jpeg images": "Qualità delle immagini salvate in formato JPEG",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l'immagine PNG è più grande di 4 MB o qualsiasi dimensione è maggiore di 4000, ridimensiona e salva la copia come JPG",
-    "Use original name for output filename during batch process in extras tab": "Usa il nome originale per il nome del file di output durante l'elaborazione a lotti nella scheda 'Extra'",
-    "When using 'Save' button, only save a single selected image": "Usando il pulsante 'Salva', verrà salvata solo la singola immagine selezionata",
-    "Do not add watermark to images": "Non aggiungere la filigrana alle immagini",
-    "Paths for saving": "Percorsi di salvataggio",
-    "Output directory for images; if empty, defaults to three directories below": "Cartella di output per le immagini; se vuoto, per impostazione predefinita verranno usate le cartelle seguenti",
-    "Output directory for txt2img images": "Cartella di output per le immagini txt2img",
-    "Output directory for img2img images": "Cartella di output per le immagini img2img",
-    "Output directory for images from extras tab": "Cartella di output per le immagini dalla scheda 'Extra'",
-    "Output directory for grids; if empty, defaults to two directories below": "Cartella di output per le griglie; se vuoto, per impostazione predefinita veranno usate cartelle seguenti",
-    "Output directory for txt2img grids": "Cartella di output per le griglie txt2img",
-    "Output directory for img2img grids": "Cartella di output per le griglie img2img",
-    "Directory for saving images using the Save button": "Cartella dove salvare le immagini usando il pulsante 'Salva'",
-    "Saving to a directory": "Salva in una cartella",
-    "Save images to a subdirectory": "Salva le immagini in una sotto cartella",
-    "Save grids to a subdirectory": "Salva le griglie in una sotto cartella",
-    "When using \"Save\" button, save images to a subdirectory": "Usando il pulsante \"Salva\", le immagini verranno salvate in una sotto cartella",
-    "Directory name pattern": "Modello del nome della cartella",
-    "Max prompt words for [prompt_words] pattern": "Numero massimo di parole del prompt per il modello [prompt_words]",
-    "Upscaling": "Ampliamento",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Dimensione piastrella per ampliamento ESRGAN. 0 = nessuna piastrellatura.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per gli ampliamenti ESRGAN. Valori bassi = cucitura visibile.",
-    "Tile size for all SwinIR.": "Dimensione piastrella per tutti gli SwinIR.",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per SwinIR. Valori bassi = cucitura visibile.",
-    "LDSR processing steps. Lower = faster": "Fasi di elaborazione LDSR. Più basso = più veloce",
-    "Upscaler for img2img": "Metodo di ampliamento per img2img",
-    "Upscale latent space image when doing hires. fix": "Amplia l'immagine nello spazio latente durante la correzione in alta risoluzione",
-    "Face restoration": "Restauro del viso",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Peso di CodeFormer; 0 = effetto massimo; 1 = effetto minimo",
-    "Move face restoration model from VRAM into RAM after processing": "Sposta il modello di restauro facciale dalla VRAM alla RAM dopo l'elaborazione",
-    "System": "Sistema",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "Verifiche al secondo sull'utilizzo della VRAM durante la generazione. Impostare a 0 per disabilitare.",
-    "Always print all generation info to standard output": "Stampa sempre tutte le informazioni di generazione sul output standard",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda barra di avanzamento alla console che mostra l'avanzamento complessivo del lavoro.",
-    "Training": "Addestramento",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP nella RAM durante l'addestramento di Iperreti. Risparmia VRAM.",
-    "Filename word regex": "Espressione regolare per estrarre parole dal nome del file",
-    "Filename join string": "Stringa per unire le parole estratte dal nome del file",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per una singola immagine di input per epoca; utilizzato solo per visualizzare il numero di epoca",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salva un file CSV contenente la perdita nella cartella di registrazione ogni N passaggi, 0 per disabilitare",
-    "Use cross attention optimizations while training": "Usa le ottimizzazioni di controllo dell'attenzione incrociato durante l'addestramento",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "Checkpoint da memorizzare nella RAM",
-    "SD VAE": "SD VAE",
-    "auto": "auto",
-    "Hypernetwork strength": "Forza della Iperrete",
-    "Inpainting conditioning mask strength": "Forza della maschera di condizionamento del Inpainting",
-    "Apply color correction to img2img results to match original colors.": "Applica la correzione del colore ai risultati di img2img in modo che corrispondano ai colori originali.",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, esegue esattamente la quantità di passi specificata dalla barra di scorrimento (normalmente se ne effettuano di meno con meno riduzione del rumore).",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Abilita la quantizzazione nei campionatori K per risultati più nitidi e puliti. Questo può cambiare i semi esistenti. Richiede il riavvio per applicare la modifica.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Enfasi: utilizzare (testo) per fare in modo che il modello presti maggiore attenzione al testo e [testo] per fargli prestare meno attenzione",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usa la vecchia implementazione dell'enfasi. Può essere utile per riprodurre vecchi semi.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Fa sì che i campionatori di diffusione K producano le stesse immagini in un lotto come quando si genera una singola immagine",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coerenza disattivando dall'ultima virgola all'indietro di n token quando si utilizzano più di 75 token",
-    "Filter NSFW content": "Filtra i contenuti NSFW",
-    "Stop At last layers of CLIP model": "Fermati agli ultimi livelli del modello CLIP",
-    "Interrogate Options": "Opzioni di interrogazione",
-    "Interrogate: keep models in VRAM": "Interroga: mantieni i modelli nella VRAM",
-    "Interrogate: use artists from artists.csv": "Interroga: utilizza artisti dal file artisti.csv",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interroga: include la classifica delle corrispondenze dei tag del modello nei risultati (non ha effetto sulle interrogazioni basate su didascalie).",
-    "Interrogate: num_beams for BLIP": "Interroga: num_beams per BLIP",
-    "Interrogate: minimum description length (excluding artists, etc..)": "Interroga: lunghezza minima della descrizione (esclusi artisti, ecc..)",
-    "Interrogate: maximum description length": "Interroga: lunghezza massima della descrizione",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: numero massimo di righe nel file di testo (0 = Nessun limite)",
-    "Interrogate: deepbooru score threshold": "Interroga: soglia del punteggio deepbooru",
-    "Interrogate: deepbooru sort alphabetically": "Interroga: deepbooru ordinato alfabeticamente",
-    "use spaces for tags in deepbooru": "usa gli spazi per i tag in deepbooru",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Effettua l'escape (\\) delle parentesi in deepbooru (così vengono usate come parentesi letterali e non per enfatizzare)",
-    "User interface": "Interfaccia Utente",
-    "Show progressbar": "Mostra la barra di avanzamento",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostra l'avanzamento della generazione dell'immagine ogni N passaggi di campionamento. Impostare a 0 per disabilitare.",
-    "Show previews of all images generated in a batch as a grid": "Mostra le anteprime di tutte le immagini generate in un lotto come una griglia",
-    "Show grid in results for web": "Mostra la griglia nei risultati per il web",
-    "Do not show any images in results for web": "Non mostrare alcuna immagine nei risultati per il web",
-    "Add model hash to generation information": "Aggiungi l'hash del modello alle informazioni sulla generazione",
-    "Add model name to generation information": "Aggiungi il nome del modello alle informazioni sulla generazione",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Durante la lettura dei parametri di generazione dal testo nell'interfaccia utente (da informazioni PNG o testo incollato), non modificare il modello/checkpoint selezionato.",
-    "Send seed when sending prompt or image to other interface": "Invia il seme quando si invia un prompt o un'immagine a un'altra interfaccia",
-    "Font for image grids that have text": "Font per griglie di immagini con testo",
-    "Enable full page image viewer": "Abilita la visualizzazione delle immagini a pagina intera",
-    "Show images zoomed in by default in full page image viewer": "Mostra le immagini ingrandite per impostazione predefinita nella visualizzazione a pagina intera",
-    "Show generation progress in window title.": "Mostra l'avanzamento della generazione nel titolo della finestra.",
-    "Quicksettings list": "Elenco delle impostazioni rapide",
-    "Localization (requires restart)": "Localizzazione (richiede il riavvio)",
-    "ar_AR": "ar_AR",
-    "de_DE": "de_DE",
-    "es_ES": "es_ES",
-    "fr_FR": "fr_FR",
-    "it_IT": "it_IT",
-    "ja_JP": "ja_JP",
-    "ko_KR": "ko_KR",
-    "pt_BR": "pt_BR",
-    "ru_RU": "ru_RU",
-    "tr_TR": "tr_TR",
-    "zh_CN": "zh_CN",
-    "zh_TW": "zh_TW",
-    "Sampler parameters": "Parametri del campionatore",
-    "Hide samplers in user interface (requires restart)": "Nascondi campionatori nell'interfaccia utente (richiede il riavvio)",
-    "eta (noise multiplier) for DDIM": "ETA (moltiplicatore di rumore) per DDIM",
-    "eta (noise multiplier) for ancestral samplers": "ETA (moltiplicatore di rumore) per campionatori ancestrali",
-    "img2img DDIM discretize": "discretizzazione DDIM per img2img",
-    "uniform": "uniforme",
-    "quad": "quad",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "sigma tmin",
-    "sigma noise": "sigma noise",
-    "Eta noise seed delta": "ETA del delta del seme del rumore",
-    "Number of columns on image gallery": "Numero di colonne nella galleria di immagini",
-    "Aesthetic Image Scorer": "Punteggio delle immagini estetiche",
-    "Save score as EXIF or PNG Info Chunk": "Salva il punteggio come info EXIF o PNG",
-    "aesthetic_score": "Punteggio estetico",
-    "cfg_scale": "Scala CFG",
-    "sd_model_hash": "Hash del modello SD",
-    "hash": "Hash",
-    "Save tags (Windows only)": "Salva etichette (solo Windows)",
-    "Save category (Windows only)": "Salva categoria (solo Windows)",
-    "Save generation params text": "Salva testo parametri di generazione",
-    "Force CPU (Requires Custom Script Reload)": "Forza CPU (richiede il ricaricamento dello script personalizzato)",
-    "Images Browser": "Galleria immagini",
-    "Preload images at startup": "Precarica le immagini all'avvio",
-    "Number of columns on the page": "Numero di colonne nella pagina",
-    "Number of rows on the page": "Numero di righe nella pagina",
-    "Minimum number of pages per load": "Numero minimo di pagine da caricare",
-    "Maximum number of samples, used to determine which folders to skip when continue running the create script": "Numero massimo di campioni, utilizzato per determinare quali cartelle ignorare quando si continua a eseguire lo script di creazione",
-    "Use same seed for all images": "Usa lo stesso seme per tutte le immagini",
-    "Request browser notifications": "Richiedi le notifiche del browser",
-    "Download localization template": "Scarica il modello per la localizzazione",
-    "Reload custom script bodies (No ui updates, No restart)": "Ricarica gli script personalizzati (nessun aggiornamento dell'interfaccia utente, nessun riavvio)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Riavvia Gradio e aggiorna i componenti (solo script personalizzati, ui.py, js e css)",
-    "Installed": "Installato",
-    "Available": "Disponibile",
-    "Install from URL": "Installa da URL",
-    "Apply and restart UI": "Applica e riavvia l'interfaccia utente",
-    "Check for updates": "Controlla aggiornamenti",
-    "Extension": "Estensione",
-    "URL": "URL",
-    "Update": "Aggiorna",
-    "aesthetic-gradients": "Gradienti Estetici (CLIP)",
-    "https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients",
-    "unknown": "sconosciuto",
-    "dataset-tag-editor": "Dataset Tag Editor",
-    "https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor.git": "https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor.git",
-    "deforum-for-automatic1111-webui": "Deforum",
-    "https://github.com/deforum-art/deforum-for-automatic1111-webui": "https://github.com/deforum-art/deforum-for-automatic1111-webui",
-    "sd-dynamic-prompts": "Prompt dinamici",
-    "https://github.com/adieyal/sd-dynamic-prompts": "https://github.com/adieyal/sd-dynamic-prompts",
-    "stable-diffusion-webui-aesthetic-image-scorer": "Punteggio immagini estetiche",
-    "https://github.com/tsngo/stable-diffusion-webui-aesthetic-image-scorer": "https://github.com/tsngo/stable-diffusion-webui-aesthetic-image-scorer",
-    "stable-diffusion-webui-artists-to-study": "Artisti per studiare",
-    "https://github.com/camenduru/stable-diffusion-webui-artists-to-study": "https://github.com/camenduru/stable-diffusion-webui-artists-to-study",
-    "stable-diffusion-webui-images-browser": "Galleria immagini",
-    "https://github.com/yfszzx/stable-diffusion-webui-images-browser": "https://github.com/yfszzx/stable-diffusion-webui-images-browser",
-    "stable-diffusion-webui-inspiration": "Ispirazione",
-    "https://github.com/yfszzx/stable-diffusion-webui-inspiration": "https://github.com/yfszzx/stable-diffusion-webui-inspiration",
-    "tag-autocomplete": "Autocompletamento etichette",
-    "https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git": "https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git",
-    "wildcards": "Termini Jolly",
-    "https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards.git": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards.git",
-    "Load from:": "Carica da:",
-    "Extension index URL": "URL dell'indice delle Estensioni", 
-    "URL for extension's git repository": "URL del repository GIT dell'estensione",
-    "Local directory name": "Nome cartella locale",
-    "Install": "Installa",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (premi Ctrl+Invio o Alt+Invio per generare)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (premere Ctrl+Invio o Alt+Invio per generare)",
-    "Add a random artist to the prompt.": "Aggiungi un artista casuale al prompt.",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leggere i parametri di generazione dal prompt o dall'ultima generazione se il prompt è vuoto ed inserirli nell'interfaccia utente.",
-    "Save style": "Salva stile",
-    "Apply selected styles to current prompt": "Applica gli stili selezionati al prompt corrente",
-    "Stop processing current image and continue processing.": "Interrompe l'elaborazione dell'immagine corrente e continua l'elaborazione.",
-    "Stop processing images and return any results accumulated so far.": "Interrompe l'elaborazione delle immagini e restituisce tutti i risultati accumulati finora.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stile da applicare; gli stili hanno componenti sia per i prompt positivi che per quelli negativi e si applicano a entrambi",
-    "Do not do anything special": "Non fa nulla di speciale",
-    "Which algorithm to use to produce the image": "Quale algoritmo utilizzare per produrre l'immagine",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - molto creativo, si può ottenere un'immagine completamente diversa a seconda del numero di passi, impostare i passi su un valore superiore a 30-40 non aiuta",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - il migliore per inpainting",
-    "Produce an image that can be tiled.": "Produce un'immagine che può essere piastrellata.",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilizza un processo in due fasi per creare parzialmente un'immagine con una risoluzione inferiore, aumentare la scala e quindi migliorarne i dettagli senza modificare la composizione",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determina quanto poco l'algoritmo dovrebbe rispettare dovrebbe il contenuto dell'immagine. A 0, non cambierà nulla e a 1 otterrai un'immagine non correlata. Con valori inferiori a 1.0 l'elaborazione richiederà meno passaggi di quelli specificati dalla barra di scorrimento dei passi di campionamento.",
-    "How many batches of images to create": "Quanti lotti di immagini generare",
-    "How many image to create in a single batch": "Quante immagini generare in un singolo lotto",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - quanto fortemente l'immagine deve conformarsi al prompt: valori più bassi producono risultati più creativi",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Un valore che determina l'output del generatore di numeri casuali: se create un'immagine con gli stessi parametri e seme di un'altra immagine, otterrete lo stesso risultato",
-    "Set seed to -1, which will cause a new random number to be used every time": "Imposta il seme su -1, che farà sì che ogni volta venga utilizzato un nuovo numero casuale",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Riusa il seme dell'ultima generazione, utile soprattutto se casuale",
-    "Seed of a different picture to be mixed into the generation.": "Seme di un'immagine diversa da miscelare nella generazione.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Quanto è forte la variazione da produrre. A 0, non ci sarà alcun effetto. A 1, otterrai l'intera immagine con il seme della variazione (tranne per i campionatori ancestrali, dove otterrai solo una leggera variazione).",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Prova a produrre un'immagine simile a quella che sarebbe stata prodotta con lo stesso seme alla risoluzione specificata",
-    "This text is used to rotate the feature space of the imgs embs": "Questo testo viene utilizzato per ruotare lo spazio delle funzioni delle immagini incorporate",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Quante volte ripetere l'elaborazione di un'immagine e utilizzarla come input per l'iterazione successiva",
-    "Hello, StylePile here.\nUntil some weird bug gets fixed you will see this even if the script itself is not active. Meanwhile, some hints to take your artwork to new heights:\nUse the 'Focus on' dropdown to select complex presets. Toggle selections below (with or without Focus) to affect your results. Mix and match to get some interesting results. \nAnd some general Stable Diffusion tips that will take your designs to next level:\nYou can add parenthesis to make parts of the prompt stronger. So (((cute))) kitten will make it extra cute (try it out). This is alsow important if a style is affecting your original prompt too much. Make that prompt stronger by adding parenthesis around it, like this: ((promt)).\nYou can type promts like [A|B] to sequentially use terms one after another on each step. So, like [cat|dog] will produce a hybrid catdog. And [A:B:0.4] to switch to other terms after the first one has been active for a certain percentage of steps. So [cat:dog:0.4] will build a cat 40% of the time and then start turning it into a dog. This needs more steps to work properly.": "Salve, qui è StylePile.\nFinché qualche strano bug non verrà risolto, vedrai questo testo anche se lo script non è attivo. Nel frattempo, alcuni suggerimenti per portare la tua grafica a nuovi livelli:\nUtilizza il menu a discesa 'Focus on' per selezionare valori predefiniti complessi. Attiva o disattiva le selezioni seguenti (con o senza Focus) per influire sui risultati. Mescola e abbina per ottenere risultati interessanti. \nE alcuni suggerimenti generali su Stable Diffusion che porteranno i tuoi risultati a un livello superiore:\nPuoi aggiungere parentesi per aumentare l'influenza di certe parti del prompt. Quindi '(((cute))) kitten' lo renderà molto carino (fai delle prove). Questo è importante quando uno stile influisce troppo sul prompt originale. Rendi più forte quel prompt aggiungendo delle parentesi intorno ad esso, così: ((promt)).\nPuoi digitare prompt nel formato [A|B] per usare in sequenza i termini uno dopo l'altro in ogni passaggio. Quindi, come [cat|dog] produrrà un 'canegatto' ibrido. E [A:B:0.4] per passare ad altri termini dopo che il primo è stato attivo per una certa percentuale di passaggi. Quindi [cat:dog:0.4] genererà un gatto il 40% dei passaggi e poi inizierà a trasformarlo in un cane. Sono richiesti più passaggi perchè funzioni correttamente.",
-    "Enter one prompt per line. Blank lines will be ignored.": "Immettere un prompt per riga. Le righe vuote verranno ignorate.",
-    "Separate values for X axis using commas.": "Separare i valori per l'asse X usando le virgole.",
-    "Separate values for Y axis using commas.": "Separare i valori per l'asse Y usando le virgole.",
-    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "Separa un elenco di parole con virgole e lo script eseguirà una variazione di prompt con quelle parole per ogni loro possibile ordine",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva l'immagine/i in una cartella (predefinita - log/images) ed i parametri di generazione in un file CSV.",
-    "Open images output directory": "Apri la cartella di output delle immagini",
-    "How much to blur the mask before processing, in pixels.": "Quanto sfocare la maschera prima dell'elaborazione, in pixel.",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Cosa mettere all'interno dell'area mascherata prima di elaborarla con Stable Diffusion.",
-    "fill it with colors of the image": "riempi con i colori dell'immagine",
-    "keep whatever was there originally": "conserva tutto ciò che c'era in origine",
-    "fill it with latent space noise": "riempi di rumore spaziale latente",
-    "fill it with latent space zeroes": "riempi con zeri di spazio latente",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Ingrandisce la regione mascherata per raggiungere la risoluzione, esegue la pittura, riduce la scala e incolla nell'immagine originale",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Ridimensiona l'immagine alla risoluzione di destinazione. A meno che altezza e larghezza non corrispondano, otterrai proporzioni errate.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Ridimensionare l'immagine in modo che l'intera risoluzione di destinazione sia riempita con l'immagine. Ritaglia le parti che sporgono.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Ridimensiona l'immagine in modo che l'intera immagine rientri nella risoluzione di destinazione. Riempi lo spazio vuoto con i colori dell'immagine.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Per l'upscaling SD, quanta sovrapposizione in pixel dovrebbe esserci tra le piastrelle. Le piastrelle si sovrappongono in modo che quando vengono unite nuovamente in un'immagine, non ci siano giunture chiaramente visibili.",
-    "Process an image, use it as an input, repeat.": "Elabora un'immagine, usala come input, ripeti.",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In modalità rielaborazione ricorsiva, su ogni ciclo la forza di denoising viene moltiplicata per questo valore. <1 significa varietà decrescente in modo che la sequenza converga su un'immagine fissa. >1 significa aumentare la varietà in modo che la tua sequenza diventi sempre più caotica.",
-    "A directory on the same machine where the server is running.": "Una cartella sulla stessa macchina su cui è in esecuzione il server.",
-    "Leave blank to save images to the default path.": "Lascia vuoto per salvare le immagini nel percorso predefinito.",
-    "Result = A * (1 - M) + B * M": "Risultato = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "Risultato = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "La prima e l'ultima cifra devono essere 1. Es.:'1, 2, 1'",
-    "Path to directory with input images": "Percorso della cartella con immagini di input",
-    "Path to directory where to write outputs": "Percorso della cartella in cui scrivere i risultati",
-    "C:\\directory\\of\\datasets": "C:\\cartella\\del\\dataset",
-    "Input images directory": "Cartella di input delle immagini",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Usa i seguenti tag per definire come vengono scelti i nomi dei file per le immagini: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed ], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se questa opzione è abilitata, la filigrana non verrà aggiunta alle immagini create. Attenzione: se non aggiungi la filigrana, potresti comportarti in modo non etico.",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utilizzare i seguenti tag per definire come vengono scelte le sottodirectory per le immagini e le griglie: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
-    "Restore low quality faces using GFPGAN neural network": "Ripristina volti di bassa qualità utilizzando la rete neurale GFPGAN",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Questa espressione regolare verrà utilizzata per estrarre le parole dal nome del file e verranno unite utilizzando l'opzione seguente nel testo dell'etichetta utilizzato per l'addestramento. Lascia vuoto per mantenere il testo del nome del file così com'è.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "Questa stringa verrà utilizzata per unire le parole divise in un'unica riga se l'opzione sopra è abilitata.",
-    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Si applica solo ai modelli di pittura. Determina con quale forza mascherare l'immagine originale per inpainting e img2img. 1.0 significa completamente mascherato, che è il comportamento predefinito. 0.0 significa un condizionamento completamente non mascherato. Valori più bassi aiuteranno a preservare la composizione generale dell'immagine, ma avranno difficoltà con grandi cambiamenti.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Elenco dei nomi delle impostazioni, separati da virgole, per le impostazioni che dovrebbero essere visualizzate nella barra di accesso rapido in alto, anziché nella normale scheda delle impostazioni. Vedi modules/shared.py per impostare i nomi. Richiede il riavvio per applicare.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se questo valore è diverso da zero, verrà aggiunto al seed e utilizzato per inizializzare il generatore di numeri casuali per il rumore quando si utilizzano campionatori con ETA. Puoi usarlo per produrre ancora più variazioni di immagini, oppure puoi usarlo per abbinare le immagini di altri software se sai cosa stai facendo.",
-    "Leave empty for auto": "Lasciare vuoto per automatico",
-    "Autocomplete options": "Opzioni di autocompletamento",
-    "Enable Autocomplete": "Abilita autocompletamento",
-    "Append commas": "Aggiungi virgole",
-    "AlphaCanvas": "AlphaCanvas",
-    "Close": "Chiudi",
-    "Grab Results": "Ottieni risultati",
-    "Apply Patch": "Applica Patch",
-    "Hue:0": "Hue:0",
-    "S:0": "S:0",
-    "L:0": "L:0",
-    "Load Canvas": "Carica Canvas",
-    "Save Canvas": "Salva Canvas",
-    "latest": "aggiornato",
-    "behind": "da aggiornare",
-    "Description": "Descrizione",
-    "Action": "Azione",
-    "Aesthetic Gradients": "Gradienti estetici",
-    "Create an embedding from one or few pictures and use it to apply their style to generated images.": "Crea un incorporamento da una o poche immagini e usalo per applicare il loro stile alle immagini generate.",
-    "Sample extension. Allows you to use __name__ syntax in your prompt to get a random line from a file named name.txt in the wildcards directory. Also see Dynamic Prompts for similar functionality.": "Estensione del campione. Consente di utilizzare la sintassi __name__ nel prompt per ottenere una riga casuale da un file denominato name.txt nella cartella dei termini jolly. Vedi anche 'Prompt dinamici' per funzionalità simili.",
-    "Dynamic Prompts": "Prompt dinamici",
-    "Implements an expressive template language for random or combinatorial prompt generation along with features to support deep wildcard directory structures.": "Implementa un  modello di linguaggio espressivo per la generazione di prompt casuale o combinatoria insieme a funzionalità per supportare cartelle strutturate contenenti termini jolly.",
-    "Image browser": "Galleria immagini",
-    "Provides an interface to browse created images in the web browser.": "Fornisce un'interfaccia  nel browser web per sfogliare le immagini create.",
-    "Randomly display the pictures of the artist's or artistic genres typical style, more pictures of this artist or genre is displayed after selecting. So you don't have to worry about how hard it is to choose the right style of art when you create.": "Visualizza in modo casuale le immagini dello stile tipico dell'artista o dei generi artistici, dopo la selezione vengono visualizzate più immagini di questo artista o genere. Così non dovete preoccuparvi della difficoltà di scegliere lo stile artistico giusto quando create.",
-    "The official port of Deforum, an extensive script for 2D and 3D animations, supporting keyframable sequences, dynamic math parameters (even inside the prompts), dynamic masking, depth estimation and warping.": "Il porting ufficiale di Deforum, uno script completo per animazioni 2D e 3D, che supporta sequenze di fotogrammi chiave, parametri matematici dinamici (anche all'interno dei prompt), mascheramento dinamico, stima della profondità e warping.",
-    "Artists to study": "Artisti per studiare",
-    "Shows a gallery of generated pictures by artists separated into categories.": "Mostra una galleria di immagini generate dagli artisti suddivise in categorie.",
-    "Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Calcola il punteggio estetico per le immagini generate utilizzando il predittore del punteggio estetico CLIP+MLP basato su Chad Scorer",
-    "Lets you edit captions in training datasets.": "Consente di modificare i sottotitoli nei set di dati di addestramento.",
-    "Time taken:": "Tempo impiegato:"
-}
\ No newline at end of file
-- 
cgit v1.2.1


From b81fad071d08fad9ea9fab87b77e22b7f04b4062 Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Thu, 3 Nov 2022 20:28:06 +0800
Subject: =?UTF-8?q?=20=E9=83=A8=E5=88=86=E6=97=A0=E6=B3=95=E6=9C=AC?=
 =?UTF-8?q?=E5=9C=B0=E5=8C=96=E5=86=85=E5=AE=B9=E7=9A=84=E6=9B=BF=E4=BB=A3?=
 =?UTF-8?q?=E6=96=B9=E6=A1=88?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/zh_CN.json | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 6d6720c9..bacf905a 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -198,7 +198,6 @@
     "Scale to": "指定尺寸缩放",
     "Resize": "缩放",
     "Crop to fit": "裁剪以适应",
-    "Upscaler 2 visibility": "放大算法 2 (Upscaler 2) 可见度",
     "GFPGAN visibility": "GFPGAN 可见度",
     "CodeFormer visibility": "CodeFormer 可见度",
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 权重 (0 = 最大效果, 1 = 最小效果)",
@@ -469,7 +468,6 @@
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nNegative prompt",
     "Stop processing current image and continue processing.": "停止处理当前图像，并继续处理下一个",
     "Stop processing images and return any results accumulated so far.": "停止处理图像，并返回迄今为止累积的任何结果",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要应用的模版风格； 模版风格包含正向和反向提示词，并应用于两者",
     "Do not do anything special": "什么都不做",
     "Which algorithm to use to produce the image": "使用哪种算法生成图像",
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图像，将迭代步数设置为高于 30-40 不会有正面作用",
@@ -480,9 +478,6 @@
     "How many batches of images to create": "创建多少批次的图像",
     "How many image to create in a single batch": "每批创建多少图像",
     "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 图像应在多大程度上服从提示词 - 较低的值会产生更有创意的结果",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一个固定随机数生成器输出的值 - 以相同参数和随机种子生成的图像会得到相同的结果",
-    "Set seed to -1, which will cause a new random number to be used every time": "将随机种子设置为-1，则每次都会使用一个新的随机数",
-    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的随机种子，如果想要固定结果就会很有用",
     "Seed of a different picture to be mixed into the generation.": "将要参与生成的另一张图的随机种子",
     "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要产生多强烈的变化。设为 0 时，将没有效果。设为 1 时，你将获得完全产自差异随机种子的图像（ancestral 采样器除外，你只是单纯地生成了一些东西）",
     "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "尝试生成与在指定分辨率下使用相同随机种子生成的图像相似的图片",
@@ -490,7 +485,6 @@
     "Separate values for X axis using commas.": "使用逗号分隔 X 轴的值",
     "Separate values for Y axis using commas.": "使用逗号分隔 Y 轴的值",
     "Write image to a directory (default - log/images) and generation parameters into csv file.": "将图像写入目录（默认 - log/images）并将生成参数写入 csv 表格文件",
-    "Open images output directory": "打开图像输出目录",
     "How much to blur the mask before processing, in pixels.": "处理前要对蒙版进行多强的模糊，以像素为单位",
     "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 处理蒙版区域之前要在蒙版区域内放置什么",
     "fill it with colors of the image": "用图像的颜色(高强度模糊)填充它",
@@ -547,13 +541,16 @@
     "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "放大潜空间中的图像。而另一种方法是，从潜变量表达中直接解码并生成完整的图像，接着放大它，然后再将其编码回潜空间",
     "Upscaler": "放大算法",
     "Start drawing": "开始绘制",
-    
+
 
     "----无效----": "----以下内容无法被翻译，Bug----",
     "Add a random artist to the prompt.": "随机添加一个艺术家到提示词中",
     "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
     "Save style": "储存为模版风格",
     "Apply selected styles to current prompt": "将所选模板风格，应用于当前提示词",
+    "Set seed to -1, which will cause a new random number to be used every time": "将随机种子设置为-1，则每次都会使用一个新的随机数",
+    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的随机种子，如果想要固定结果就会很有用",
+    "Open images output directory": "打开图像输出目录",
     "Upscaler 1": "放大算法 1",
     "Upscaler 2": "放大算法 2",
     "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "用竖线分隔符(|)将提示词分成若干部分，脚本将为它们的每一个组合创建一幅图片（除了被分割的第一部分，所有的组合都会包含这部分）",
@@ -561,6 +558,10 @@
     "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
     "Face restoration model": "面部修复模型",
 
+    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要使用的模版风格； 模版风格包含正向和反向提示词，并应用于两者\n\ud83c\udfa8    随机添加一个艺术家到提示词中\n \u2199\ufe0f     从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面\n\ud83d\udcbe    将当前的提示词保存为模版风格(保存在styles.csv)\n\ud83d\udccb    将所选模板风格，应用于当前提示词\n如果你在文本中添加{prompt}标记，并保存为模版风格\n那么将来你使用该模版风格时，你现有的提示词会替换模版风格中的{prompt}",
+    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一个固定随机数生成器输出的值 - 以相同参数和随机种子生成的图像会得到相同的结果\n\ud83c\udfb2    将随机种子设置为-1，则每次都会使用一个新的随机数\n\u267b\ufe0f    重用上一次使用的随机种子，如果想要固定输出结果就会很有用",
+    "Upscaler 2 visibility": "放大算法 2 (Upscaler 2) 可见度",
+
 
     "----已移除----": "----以下内容在webui新版本已移除----",
     "▼": "▼",
-- 
cgit v1.2.1


From 8bc003c9bb7ba3f90a4d075196a2231915a3fa06 Mon Sep 17 00:00:00 2001
From: Eugenio Buffo <58123757+EugenioBuffo@users.noreply.github.com>
Date: Thu, 3 Nov 2022 13:28:56 +0100
Subject: Fixed misspelled word

---
 localizations/it_IT.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/localizations/it_IT.json b/localizations/it_IT.json
index de599a85..83d0ccce 100644
--- a/localizations/it_IT.json
+++ b/localizations/it_IT.json
@@ -196,7 +196,7 @@
     "Beta distribution (VP only)": "Distribuzione Beta (Solo CV)",
     "Beta min (VP only)": "Beta min (Solo CV)",
     "Epsilon (VP only)": "Epsilon (Solo CV)",
-    "Running in txt2img mode:": "Esecusione in modalità txt2img:",
+    "Running in txt2img mode:": "Esecuzione in modalità txt2img:",
     "Render these video formats:": "Renderizza in questi formati:",
     "GIF": "GIF",
     "MP4": "MP4",
@@ -1230,4 +1230,4 @@
     "Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Calcola il punteggio estetico per le immagini generate utilizzando il predittore del punteggio estetico CLIP+MLP basato su Chad Scorer",
     "Lets you edit captions in training datasets.": "Consente di modificare i sottotitoli nei set di dati di addestramento.",
     "Time taken:": "Tempo impiegato:"
-}
\ No newline at end of file
+}
-- 
cgit v1.2.1


From 17bd3f4ea730436599849eddbaa78e2879b793d2 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <brunoseoaneamarillo@gmail.com>
Date: Thu, 3 Nov 2022 10:08:18 -0300
Subject: Add tests

---
 test/utils_test.py | 63 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 63 insertions(+)
 create mode 100644 test/utils_test.py

diff --git a/test/utils_test.py b/test/utils_test.py
new file mode 100644
index 00000000..65d3d177
--- /dev/null
+++ b/test/utils_test.py
@@ -0,0 +1,63 @@
+import unittest
+import requests
+
+class UtilsTests(unittest.TestCase):
+  def setUp(self):
+    self.url_options = "http://localhost:7860/sdapi/v1/options"
+    self.url_cmd_flags = "http://localhost:7860/sdapi/v1/cmd-flags"
+    self.url_samplers = "http://localhost:7860/sdapi/v1/samplers"
+    self.url_upscalers = "http://localhost:7860/sdapi/v1/upscalers"
+    self.url_sd_models = "http://localhost:7860/sdapi/v1/sd-models"
+    self.url_hypernetworks = "http://localhost:7860/sdapi/v1/hypernetworks"
+    self.url_face_restorers = "http://localhost:7860/sdapi/v1/face-restorers"
+    self.url_realesrgan_models = "http://localhost:7860/sdapi/v1/realesrgan-models"
+    self.url_prompt_styles = "http://localhost:7860/sdapi/v1/prompt-styles"
+    self.url_artist_categories = "http://localhost:7860/sdapi/v1/artist-categories"
+    self.url_artists = "http://localhost:7860/sdapi/v1/artists"
+
+  def test_options_get(self):
+    self.assertEqual(requests.get(self.url_options).status_code, 200)
+
+  def test_options_write(self):
+    response = requests.get(self.url_options)
+    self.assertEqual(response.status_code, 200)
+    
+    pre_value = response.json()["send_seed"]
+
+    self.assertEqual(requests.post(self.url_options, json={"send_seed":not pre_value}).status_code, 200)
+
+    response = requests.get(self.url_options)
+    self.assertEqual(response.status_code, 200)
+    self.assertEqual(response.json()["send_seed"], not pre_value)
+
+    requests.post(self.url_options, json={"send_seed": pre_value})
+
+  def test_cmd_flags(self):
+    self.assertEqual(requests.get(self.url_cmd_flags).status_code, 200)
+
+  def test_samplers(self):
+    self.assertEqual(requests.get(self.url_samplers).status_code, 200)
+
+  def test_upscalers(self):
+    self.assertEqual(requests.get(self.url_upscalers).status_code, 200)
+
+  def test_sd_models(self):
+    self.assertEqual(requests.get(self.url_sd_models).status_code, 200)
+
+  def test_hypernetworks(self):
+    self.assertEqual(requests.get(self.url_hypernetworks).status_code, 200)
+
+  def test_face_restorers(self):
+    self.assertEqual(requests.get(self.url_face_restorers).status_code, 200)
+  
+  def test_realesrgan_models(self):
+    self.assertEqual(requests.get(self.url_realesrgan_models).status_code, 200)
+  
+  def test_prompt_styles(self):
+    self.assertEqual(requests.get(self.url_prompt_styles).status_code, 200)
+  
+  def test_artist_categories(self):
+    self.assertEqual(requests.get(self.url_artist_categories).status_code, 200)
+
+  def test_artists(self):
+    self.assertEqual(requests.get(self.url_artists).status_code, 200)
\ No newline at end of file
-- 
cgit v1.2.1


From 3bf8da465946126efbe66becb31c055ee7f70ea6 Mon Sep 17 00:00:00 2001
From: batvbs <batvbs@qq.com>
Date: Thu, 3 Nov 2022 21:58:46 +0800
Subject: =?UTF-8?q?=E6=9B=B4=E6=96=B0=20zh=5FCN.json?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 localizations/zh_CN.json | 20 +++++++++++++++++---
 1 file changed, 17 insertions(+), 3 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index bacf905a..8a00c11c 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -444,13 +444,11 @@
     "Number of columns on the page": "每页列数",
     "Number of rows on the page": "每页行数",
     "Minimum number of pages per load": "每次加载的最小页数",
-    "Wildcards": "通配符",
     "Use same seed for all images": "为所有图像使用同一个随机种子",
     "Request browser notifications": "请求浏览器通知",
     "Download localization template": "下载本地化模板",
     "Reload custom script bodies (No ui updates, No restart)": "重新加载自定义脚本主体（无用户界面更新，无重启）",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重启 Gradio 及刷新组件（仅限自定义脚本、ui.py、js 和 css）",
-    "Installed": "已安装",
     "Available": "可用",
     "Install from URL": "从网址安装",
     "Apply and restart UI": "应用并重启用户界面",
@@ -463,7 +461,6 @@
     "Extension index URL": "扩展列表链接",
     "URL for extension's git repository": "扩展的 git 仓库链接",
     "Local directory name": "本地路径名",
-    "Install": "安装",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nPrompt",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nNegative prompt",
     "Stop processing current image and continue processing.": "停止处理当前图像，并继续处理下一个",
@@ -541,6 +538,20 @@
     "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "放大潜空间中的图像。而另一种方法是，从潜变量表达中直接解码并生成完整的图像，接着放大它，然后再将其编码回潜空间",
     "Upscaler": "放大算法",
     "Start drawing": "开始绘制",
+    "Description": "描述",
+    "Action": "行动",
+    "Aesthetic Gradients": "美术风格",
+    "aesthetic-gradients": "美术风格",
+    "Wildcards": "通配符",
+    "stable-diffusion-webui-wildcards": "通配符",
+    "Dynamic Prompts": "动态提示",
+    "Image browser": "图库浏览器",
+    "images-browser": "图库浏览器",
+    "Inspiration": "灵感",
+    "Deforum": "Deforum",
+    "Artists to study": "艺术家图库",
+    "Aesthetic Image Scorer": "美术风格评分",
+    "Dataset Tag Editor": "数据集标签编辑器",
 
 
     "----无效----": "----以下内容无法被翻译，Bug----",
@@ -557,6 +568,9 @@
     "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "选择哪些 Real-ESRGAN 模型显示在网页用户界面。(需要重新启动)",
     "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
     "Face restoration model": "面部修复模型",
+    "Install": "安装",
+    "Installing...": "安装中...",
+    "Installed": "已安装",
 
     "Style to apply; styles have components for both positive and negative prompts and apply to both": "要使用的模版风格； 模版风格包含正向和反向提示词，并应用于两者\n\ud83c\udfa8    随机添加一个艺术家到提示词中\n \u2199\ufe0f     从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面\n\ud83d\udcbe    将当前的提示词保存为模版风格(保存在styles.csv)\n\ud83d\udccb    将所选模板风格，应用于当前提示词\n如果你在文本中添加{prompt}标记，并保存为模版风格\n那么将来你使用该模版风格时，你现有的提示词会替换模版风格中的{prompt}",
     "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一个固定随机数生成器输出的值 - 以相同参数和随机种子生成的图像会得到相同的结果\n\ud83c\udfb2    将随机种子设置为-1，则每次都会使用一个新的随机数\n\u267b\ufe0f    重用上一次使用的随机种子，如果想要固定输出结果就会很有用",
-- 
cgit v1.2.1


From 8b913ea03a21d49278609b20e77183559372817c Mon Sep 17 00:00:00 2001
From: benlisquare <benlisquare>
Date: Fri, 4 Nov 2022 04:30:29 +1100
Subject: Update Traditional Chinese (zh_TW) localisation JSON

---
 localizations/zh_TW.json | 280 ++++++++++++++++++++++++++++++++---------------
 1 file changed, 194 insertions(+), 86 deletions(-)

diff --git a/localizations/zh_TW.json b/localizations/zh_TW.json
index 724df1ac..7467db88 100644
--- a/localizations/zh_TW.json
+++ b/localizations/zh_TW.json
@@ -12,14 +12,15 @@
     "Stable Diffusion checkpoint": "Stable Diffusion 模型權重存檔點",
     "txt2img": "文生圖",
     "img2img": "圖生圖",
-    "Extras": "後處理",
-    "PNG Info": "PNG 資訊",
-    "Checkpoint Merger": "模型權重存檔點合併工具",
+    "Extras": "更多",
+    "PNG Info": "圖片資訊",
+    "Checkpoint Merger": "模型權重存檔點合併",
     "Train": "訓練",
-    "Create aesthetic embedding": "生成美術風格 embedding",
+    "Create aesthetic embedding": "生成美術風格",
     "Image Browser": "圖庫瀏覽器",
     "History": "歷史記錄",
     "Settings": "設定",
+    "Extensions": "擴充",
     "Prompt": "提示詞",
     "Negative prompt": "反向提示詞",
     "Run": "執行",
@@ -28,7 +29,7 @@
     "Generate": "生成",
     "Style 1": "模版風格 1",
     "Style 2": "模版風格 2",
-    "Label": "標籤",
+    "Label": "標記",
     "File": "檔案",
     "Drop File Here": "拖曳檔案到此",
     "-": "-",
@@ -59,12 +60,12 @@
     "Highres. fix": "高解析度修復",
     "Firstpass width": "第一遍的寬度",
     "Firstpass height": "第一遍的高度",
-    "Denoising strength": "去噪強度",
-    "Batch count": "批次",
-    "Batch size": "批量",
+    "Denoising strength": "重繪幅度",
+    "Batch count": "生成批次",
+    "Batch size": "每批數量",
     "CFG Scale": "提示詞相關性（CFG）",
     "Seed": "隨機種子",
-    "Extra": "額外參數",
+    "Extra": "▼",
     "Variation seed": "差異隨機種子",
     "Variation strength": "差異強度",
     "Resize seed from width": "自寬度縮放隨機種子",
@@ -81,7 +82,7 @@
     "Slerp angle": "Slerp 角度",
     "Is negative text": "是反向提示詞",
     "Script": "指令碼",
-    "Embedding to Shareable PNG": "將 Embedding 轉換為可分享的 PNG",
+    "Embedding to Shareable PNG": "將 Embedding 轉換為可分享的 PNG 圖片檔案",
     "Prompt matrix": "提示詞矩陣",
     "Prompts from file or textbox": "從文字方塊或檔案載入提示詞",
     "X/Y plot": "X/Y 圖表",
@@ -91,6 +92,10 @@
     "Show Textbox": "顯示文字方塊",
     "File with inputs": "含輸入內容的檔案",
     "Prompts": "提示詞",
+    "Iterate seed every line": "每行輸入都換一個種子",
+    "Use same random seed for all lines": "每行輸入都使用同一個隨機種子",
+    "List of prompt inputs": "提示詞輸入列表",
+    "Upload prompt inputs": "上傳提示詞輸入檔案",
     "X type": "X軸類型",
     "Nothing": "無",
     "Var. seed": "差異隨機種子",
@@ -100,8 +105,8 @@
     "Prompt order": "提示詞順序",
     "Sampler": "採樣器",
     "Checkpoint name": "模型權重存檔點的名稱",
-    "Hypernetwork": "Hypernetwork",
-    "Hypernet str.": "Hypernetwork 強度",
+    "Hypernetwork": "超網路",
+    "Hypernet str.": "超網路強度",
     "Sigma Churn": "Sigma Churn",
     "Sigma min": "最小 Sigma",
     "Sigma max": "最大 Sigma",
@@ -109,6 +114,7 @@
     "Eta": "Eta",
     "Clip skip": "Clip 跳過",
     "Denoising": "去噪",
+    "Cond. Image Mask Weight": "圖像調節屏蔽度",
     "X values": "X軸數值",
     "Y type": "Y軸類型",
     "Y values": "Y軸數值",
@@ -118,44 +124,44 @@
     "Drop Image Here": "拖曳圖像到此",
     "Save": "儲存",
     "Send to img2img": ">> 圖生圖",
-    "Send to inpaint": ">> 內補繪製",
-    "Send to extras": ">> 後處理",
+    "Send to inpaint": ">> 局部重繪",
+    "Send to extras": ">> 更多",
     "Make Zip when Save?": "儲存時生成ZIP壓縮檔案？",
     "Textbox": "文字方塊",
     "Interrogate\nCLIP": "CLIP\n反推提示詞",
     "Interrogate\nDeepBooru": "DeepBooru\n反推提示詞",
-    "Inpaint": "內補繪製",
+    "Inpaint": "局部重繪",
     "Batch img2img": "批量圖生圖",
     "Image for img2img": "圖生圖的圖像",
-    "Image for inpainting with mask": "用於內補繪製蒙版內容的圖像",
+    "Image for inpainting with mask": "用於局部重繪並手動畫蒙版的圖像",
     "Mask": "蒙版",
     "Mask blur": "蒙版模糊",
     "Mask mode": "蒙版模式",
     "Draw mask": "繪製蒙版",
     "Upload mask": "上傳蒙版",
     "Masking mode": "蒙版模式",
-    "Inpaint masked": "內補繪製蒙版內容",
-    "Inpaint not masked": "內補繪製非蒙版內容",
+    "Inpaint masked": "重繪蒙版內容",
+    "Inpaint not masked": "重繪非蒙版內容",
     "Masked content": "蒙版蒙住的內容",
     "fill": "填充",
     "original": "原圖",
     "latent noise": "潛空間噪聲",
     "latent nothing": "潛空間數值零",
-    "Inpaint at full resolution": "以完整解析度進行內補繪製",
-    "Inpaint at full resolution padding, pixels": "以完整解析度進行內補繪製 — 填補畫素",
-    "Process images in a directory on the same machine where the server is running.": "在伺服器主機上的目錄中處理圖像",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "指定一個空的資料夾為輸出目錄而非預設的 output 資料夾為輸出目錄",
+    "Inpaint at full resolution": "全解析度局部重繪",
+    "Inpaint at full resolution padding, pixels": "預留畫素",
+    "Process images in a directory on the same machine where the server is running.": "使用伺服器主機上的一個目錄，作為輸入目錄處理圖像",
+    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "使用一個空的資料夾作為輸出目錄，而不是使用預設的 output 資料夾作為輸出目錄",
     "Disabled when launched with --hide-ui-dir-config.": "啟動 --hide-ui-dir-config 時禁用",
     "Input directory": "輸入目錄",
     "Output directory": "輸出目錄",
     "Resize mode": "縮放模式",
-    "Just resize": "只縮放",
-    "Crop and resize": "縮放並剪裁",
-    "Resize and fill": "縮放並填充",
+    "Just resize": "拉伸",
+    "Crop and resize": "裁剪",
+    "Resize and fill": "填充",
     "img2img alternative test": "圖生圖的另一種測試",
     "Loopback": "回送",
-    "Outpainting mk2": "外補繪製第二版",
-    "Poor man's outpainting": "效果稍差的外補繪製",
+    "Outpainting mk2": "向外繪製第二版",
+    "Poor man's outpainting": "效果稍差的向外繪製",
     "SD upscale": "使用 SD 放大",
     "should be 2 or lower.": "必須小於等於2",
     "Override `Sampling method` to Euler?(this method is built for it)": "覆寫「採樣方法」為 Euler？（這個方法就是為這樣做設計的）",
@@ -164,15 +170,15 @@
     "Original negative prompt": "初始反向提示詞",
     "Override `Sampling Steps` to the same value as `Decode steps`?": "覆寫「採樣疊代步數」為「解碼疊代步數」？",
     "Decode steps": "解碼疊代步數",
-    "Override `Denoising strength` to 1?": "覆寫「去噪強度」為1?",
+    "Override `Denoising strength` to 1?": "覆寫「重繪幅度」為1?",
     "Decode CFG scale": "解碼提示詞相關性（CFG）",
     "Randomness": "隨機度",
     "Sigma adjustment for finding noise for image": "為尋找圖中噪點的 Sigma 調整",
     "Loops": "疊代次數",
-    "Denoising strength change factor": "去噪強度的調整係數",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推薦設定：採樣疊代步數：80-100，採樣器：Euler a，去噪強度：0.8",
+    "Denoising strength change factor": "重繪幅度的調整係數",
+    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推薦設定：採樣疊代步數：80-100，採樣器：Euler a，重繪幅度：0.8",
     "Pixels to expand": "拓展的畫素數",
-    "Outpainting direction": "外補繪製的方向",
+    "Outpainting direction": "向外繪製的方向",
     "left": "左",
     "right": "右",
     "up": "上",
@@ -205,6 +211,7 @@
     "CodeFormer visibility": "CodeFormer 可見度",
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 權重 （0 = 最大效果， 1 = 最小效果）",
     "Open output directory": "打開輸出目錄",
+    "Upscale Before Restoring Faces": "放大後再進行面部修復",
     "Send to txt2img": ">> 文生圖",
     "A merger of the two checkpoints will be generated in your": "合併後的模型權重存檔點會生成在你的",
     "checkpoint": "模型權重存檔點",
@@ -219,7 +226,7 @@
     "Add difference": "加入差分",
     "Save as float16": "以 float16 儲存",
     "See": "檢視",
-    "wiki": "wiki",
+    "wiki": "wiki文件",
     "for detailed explanation.": "以了解詳細說明",
     "Create embedding": "生成 embedding",
     "Create aesthetic images embedding": "生成美術風格圖集 embedding",
@@ -237,6 +244,36 @@
     "leakyrelu": "leakyrelu",
     "elu": "elu",
     "swish": "swish",
+    "tanh": "tanh",
+    "sigmoid": "sigmoid",
+    "celu": "celu",
+    "gelu": "gelu",
+    "glu": "glu",
+    "hardshrink": "hardshrink",
+    "hardsigmoid": "hardsigmoid",
+    "hardtanh": "hardtanh",
+    "logsigmoid": "logsigmoid",
+    "logsoftmax": "logsoftmax",
+    "mish": "mish",
+    "prelu": "prelu",
+    "rrelu": "rrelu",
+    "relu6": "relu6",
+    "selu": "selu",
+    "silu": "silu",
+    "softmax": "softmax",
+    "softmax2d": "softmax2d",
+    "softmin": "softmin",
+    "softplus": "softplus",
+    "softshrink": "softshrink",
+    "softsign": "softsign",
+    "tanhshrink": "tanhshrink",
+    "threshold": "閾值",
+    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "挑選初始化層權重的方案. 類relu - Kaiming, 類sigmoid - Xavier 都是比較推薦的選項",
+    "Normal": "正態",
+    "KaimingUniform": "Kaiming 均勻",
+    "KaimingNormal": "Kaiming 正態",
+    "XavierUniform": "Xavier 均勻",
+    "XavierNormal": "Xavier 正態",
     "Add layer normalization": "加入層標準化",
     "Use dropout": "採用 dropout 防止過擬合",
     "Overwrite Old Hypernetwork": "覆寫舊的 Hypernetwork",
@@ -250,10 +287,15 @@
     "Create flipped copies": "生成鏡像副本",
     "Split oversized images into two": "將過大的圖像分為兩份",
     "Split oversized images": "分割過大的圖像",
+    "Auto focal point crop": "自動焦點裁切",
     "Use BLIP for caption": "使用 BLIP 生成說明文字（自然語言描述）",
-    "Use deepbooru for caption": "使用 deepbooru 生成說明文字（標籤）",
+    "Use deepbooru for caption": "使用 deepbooru 生成說明文字（標記）",
     "Split image threshold": "圖像分割閾值",
     "Split image overlap ratio": "分割圖像重疊的比率",
+    "Focal point face weight": "焦點面部權重",
+    "Focal point entropy weight": "焦點熵權重",
+    "Focal point edges weight": "焦點線條權重",
+    "Create debug image": "生成除錯圖片",
     "Preprocess": "預處理",
     "Train an embedding; must specify a directory with a set of 1:1 ratio images": "訓練 embedding； 必須指定一組具有 1:1 比例圖像的目錄",
     "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "訓練 embedding 或者 hypernetwork； 必須指定一組具有 1:1 比例圖像的目錄",
@@ -268,8 +310,8 @@
     "Max steps": "最大疊代步數",
     "Save an image to log directory every N steps, 0 to disable": "每 N 步儲存一個圖像到日誌目錄，0 表示禁用",
     "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步將 embedding 的副本儲存到日誌目錄，0 表示禁用",
-    "Save images with embedding in PNG chunks": "儲存圖像並在 PNG 檔案中嵌入 embedding 檔案",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "進行預覽時從文生圖頁籤中讀取參數（提示詞等）",
+    "Save images with embedding in PNG chunks": "儲存圖像，並在 PNG 圖片檔案中嵌入 embedding 檔案",
+    "Read parameters (prompt, etc...) from txt2img tab when making previews": "進行預覽時，從文生圖頁籤中讀取參數（提示詞等）",
     "Train Hypernetwork": "訓練 Hypernetwork",
     "Train Embedding": "訓練 Embedding",
     "Create an aesthetic embedding out of any number of images": "從任意數量的圖像中建立美術風格 embedding",
@@ -277,60 +319,74 @@
     "txt2img history": "文生圖歷史記錄",
     "img2img history": "圖生圖歷史記錄",
     "extras history": "後處理歷史記錄",
-    "Renew Page": "刷新頁面",
     "extras": "後處理",
     "favorites": "收藏夾",
+    "Favorites": "收藏夾",
+    "Others": "其他",
     "custom fold": "自訂資料夾",
     "Load": "載入",
     "Images directory": "圖像目錄",
     "Prev batch": "上一批",
     "Next batch": "下一批",
+    "Dropdown": "下拉式清單",
     "First Page": "首頁",
     "Prev Page": "上一頁",
     "Page Index": "頁數",
     "Next Page": "下一頁",
     "End Page": "尾頁",
     "number of images to delete consecutively next": "接下來要連續刪除的圖像數",
+    "delete next": "刪除下一張",
     "Delete": "刪除",
+    "sort by": "排序方式",
+    "path name": "路徑名",
+    "date": "日期",
+    "keyword": "搜尋",
     "Generate Info": "生成資訊",
     "File Name": "檔案名",
     "Collect": "收藏",
     "Refresh page": "刷新頁面",
     "Date to": "日期至",
+    "Move to favorites": "移動到收藏夾",
+    "Renew Page": "刷新頁面",
     "Number": "數量",
     "set_index": "設定索引",
+    "load_switch": "載入開關",
+    "turn_page_switch": "翻頁開關",
     "Checkbox": "核取方塊",
     "Apply settings": "儲存設定",
-    "Saving images/grids": "儲存圖像／概覽圖",
+    "Saving images/grids": "儲存圖像／宮格圖",
     "Always save all generated images": "始終儲存所有生成的圖像",
     "File format for images": "圖像的檔案格式",
     "Images filename pattern": "圖像檔案名格式",
-    "Always save all generated image grids": "始終儲存所有生成的概覽圖",
-    "File format for grids": "概覽圖的檔案格式",
-    "Add extended info (seed, prompt) to filename when saving grid": "儲存概覽時將擴展資訊（隨機種子，提示詞）加入到檔案名",
-    "Do not save grids consisting of one picture": "只有一張圖片時不要儲存概覽圖",
-    "Prevent empty spots in grid (when set to autodetect)": "（在自動檢測時）防止概覽圖中出現空位",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "概覽行數； 使用 -1 進行自動檢測，使用 0 使其與批量大小相同",
-    "Save text information about generation parameters as chunks to png files": "將有關生成參數的文本資訊作為塊儲存到PNG檔案中",
-    "Create a text file next to every image with generation parameters.": "儲存圖像時在每個圖像旁邊建立一個文本檔案儲存生成參數",
+    "Add number to filename when saving": "儲存的時候在檔案名里加入數字",
+    "Always save all generated image grids": "始終儲存所有生成的宮格圖",
+    "File format for grids": "宮格圖的檔案格式",
+    "Add extended info (seed, prompt) to filename when saving grid": "儲存宮格圖時，將擴展資訊（隨機種子，提示詞）加入到檔案名",
+    "Do not save grids consisting of one picture": "只有一張圖片時不要儲存宮格圖",
+    "Prevent empty spots in grid (when set to autodetect)": "（啟用自動偵測時）防止宮格圖中出現空位",
+    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "宮格圖行數； 使用 -1 進行自動檢測，使用 0 使其與每批數量相同",
+    "Save text information about generation parameters as chunks to png files": "將有關生成參數的文本資訊，作為塊儲存到PNG圖片檔案中",
+    "Create a text file next to every image with generation parameters.": "儲存圖像時，在每個圖像旁邊建立一個文本檔案儲存生成參數",
     "Save a copy of image before doing face restoration.": "在進行面部修復之前儲存圖像副本",
+    "Save a copy of image before applying highres fix.": "在做高解析度修復之前儲存初始圖像副本",
+    "Save a copy of image before applying color correction to img2img results": "在對圖生圖結果套用顏色校正之前儲存圖像副本",
     "Quality for saved jpeg images": "儲存的JPEG圖像的品質",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 圖像大於 4MB 或寬高大於 4000，則縮小並儲存副本為 JPG",
-    "Use original name for output filename during batch process in extras tab": "在後處理頁籤中的批量處理過程中使用原始名稱作為輸出檔案名",
+    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 圖像大於 4MB 或寬高大於 4000，則縮小並儲存副本為 JPG 圖片",
+    "Use original name for output filename during batch process in extras tab": "在更多頁籤中的批量處理過程中，使用原始名稱作為輸出檔案名",
     "When using 'Save' button, only save a single selected image": "使用「儲存」按鈕時，只儲存一個選定的圖像",
     "Do not add watermark to images": "不要給圖像加浮水印",
     "Paths for saving": "儲存路徑",
     "Output directory for images; if empty, defaults to three directories below": "圖像的輸出目錄； 如果為空，則預設為以下三個目錄",
     "Output directory for txt2img images": "文生圖的輸出目錄",
     "Output directory for img2img images": "圖生圖的輸出目錄",
-    "Output directory for images from extras tab": "後處理的輸出目錄",
-    "Output directory for grids; if empty, defaults to two directories below": "概覽圖的輸出目錄； 如果為空，則預設為以下兩個目錄",
-    "Output directory for txt2img grids": "文生圖概覽的輸出目錄",
-    "Output directory for img2img grids": "圖生圖概覽的輸出目錄",
+    "Output directory for images from extras tab": "更多頁籤的輸出目錄",
+    "Output directory for grids; if empty, defaults to two directories below": "宮格圖的輸出目錄； 如果為空，則預設為以下兩個目錄",
+    "Output directory for txt2img grids": "文生圖宮格的輸出目錄",
+    "Output directory for img2img grids": "圖生圖宮格的輸出目錄",
     "Directory for saving images using the Save button": "使用「儲存」按鈕儲存圖像的目錄",
     "Saving to a directory": "儲存到目錄",
     "Save images to a subdirectory": "將圖像儲存到子目錄",
-    "Save grids to a subdirectory": "將概覽圖儲存到子目錄",
+    "Save grids to a subdirectory": "將宮格圖儲存到子目錄",
     "When using \"Save\" button, save images to a subdirectory": "使用「儲存」按鈕時，將圖像儲存到子目錄",
     "Directory name pattern": "目錄名稱格式",
     "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示詞數量",
@@ -341,10 +397,10 @@
     "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR 的圖塊重疊畫素。低值 = 可見接縫",
     "LDSR processing steps. Lower = faster": "LDSR 處理疊代步數。更低 = 更快",
     "Upscaler for img2img": "圖生圖的放大演算法",
-    "Upscale latent space image when doing hires. fix": "做高解析度修復時也放大潛空間圖像",
+    "Upscale latent space image when doing hires. fix": "做高解析度修復時，也放大潛空間圖像",
     "Face restoration": "面部修復",
     "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 權重參數； 0 = 最大效果； 1 = 最小效果",
-    "Move face restoration model from VRAM into RAM after processing": "面部修復處理完成後將面部修復模型從顯存（VRAM）移至內存（RAM）",
+    "Move face restoration model from VRAM into RAM after processing": "面部修復處理完成後，將面部修復模型從顯存（VRAM）移至內存（RAM）",
     "System": "系統",
     "VRAM usage polls per second during generation. Set to 0 to disable.": "生成圖像時每秒輪詢顯存（VRAM）使用情況的次數。設定為 0 以禁用",
     "Always print all generation info to standard output": "始終將所有生成資訊輸出到 standard output （一般為控制台）",
@@ -355,47 +411,51 @@
     "Filename word regex": "檔案名用詞的正則表達式",
     "Filename join string": "檔案名連接用字串",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每個 epoch 中單個輸入圖像的重複次數； 僅用於顯示 epoch 數",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步儲存一個包含 loss 的CSV到日誌目錄，0 表示禁用",
+    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步儲存一個包含 loss 的CSV表格到日誌目錄，0 表示禁用",
+    "Use cross attention optimizations while training": "訓練時開啟 cross attention 最佳化",
     "Stable Diffusion": "Stable Diffusion",
     "Checkpoints to cache in RAM": "快取在內存（RAM）中的模型權重存檔點",
+    "SD VAE": "模型的VAE",
+    "auto": "自動",
     "Hypernetwork strength": "Hypernetwork 強度",
+    "Inpainting conditioning mask strength": "局部重繪時圖像調節的蒙版屏蔽強度",
     "Apply color correction to img2img results to match original colors.": "對圖生圖結果套用顏色校正以匹配原始顏色",
-    "Save a copy of image before applying color correction to img2img results": "在對圖生圖結果套用顏色校正之前儲存圖像副本",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在進行圖生圖的時候，確切地執行滑塊指定的疊代步數（正常情況下更弱的去噪需要更少的疊代步數）",
+    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在進行圖生圖的時候，確切地執行滑塊指定的疊代步數（正常情況下更弱的重繪幅度需要更少的疊代步數）",
     "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 採樣器中啟用量化以獲得更清晰，更清晰的結果。這可能會改變現有的隨機種子。需要重新啟動才能套用",
     "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "強調符：使用 (文字) 使模型更關注該文本，使用 [文字] 使其減少關注",
     "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用舊的強調符實作。可用於復現舊隨機種子",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 採樣器批量生成與生成單個圖像時產出相同的圖像",
+    "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 採樣器批量生成與生成單個圖像時，產出相同的圖像",
     "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "當使用超過 75 個 token 時，通過從 n 個 token 中的最後一個逗號填補來提高一致性",
     "Filter NSFW content": "過濾成人內容",
     "Stop At last layers of CLIP model": "在 CLIP 模型的最後哪一層停下",
     "Interrogate Options": "反推提示詞選項",
     "Interrogate: keep models in VRAM": "反推： 將模型儲存在顯存（VRAM）中",
     "Interrogate: use artists from artists.csv": "反推： 使用 artists.csv 中的藝術家",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "反推： 在生成結果中包含與模型標籤相匹配的等級（對基於生成自然語言描述的反推沒有影響）",
+    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "反推： 在生成結果中包含與模型標記相匹配的等級（對基於生成自然語言描述的反推沒有影響）",
     "Interrogate: num_beams for BLIP": "反推： BLIP 的 num_beams",
-    "Interrogate: minimum description length (excluding artists, etc..)": "反推： 最小描述長度（不包括藝術家, 等…）",
+    "Interrogate: minimum description length (excluding artists, etc..)": "反推： 最小描述長度（不包括藝術家，等…）",
     "Interrogate: maximum description length": "反推： 最大描述長度",
     "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP： 文本檔案中的最大行數（0 = 無限制）",
     "Interrogate: deepbooru score threshold": "反推： deepbooru 分數閾值",
     "Interrogate: deepbooru sort alphabetically": "反推： deepbooru 按字母順序排序",
-    "use spaces for tags in deepbooru": "在 deepbooru 中為標籤使用空格",
+    "use spaces for tags in deepbooru": "在 deepbooru 中為標記使用空格",
     "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "在 deepbooru 中使用轉義 (\\) 括號（因此它們用作文字括號而不是強調符號）",
     "User interface": "使用者介面",
     "Show progressbar": "顯示進度列",
     "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 個採樣疊代步數顯示圖像生成進度。設定 0 禁用",
-    "Show previews of all images generated in a batch as a grid": "以網格的形式預覽所有批量生成出來的圖像",
-    "Show grid in results for web": "在網頁的結果中顯示概覽圖",
+    "Show previews of all images generated in a batch as a grid": "以網格的形式，預覽批量生成的所有圖像",
+    "Show grid in results for web": "在網頁的結果中顯示宮格圖",
     "Do not show any images in results for web": "不在網頁的結果中顯示任何圖像",
     "Add model hash to generation information": "將模型的雜湊值加入到生成資訊",
     "Add model name to generation information": "將模型名稱加入到生成資訊",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "當從文本讀取生成參數到 UI（從 PNG 資訊或粘貼文本）時，不要更改選定的模型權重存檔點",
-    "Font for image grids that have text": "有文字的概覽圖使用的字體",
+    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "從文本讀取生成參數到使用者介面（從 PNG 圖片資訊或粘貼文本）時，不要更改選定的模型權重存檔點",
+    "Send seed when sending prompt or image to other interface": "將提示詞或者圖片發送到 >> 其他界面時，把隨機種子也傳送過去",
+    "Font for image grids that have text": "有文字的宮格圖使用的字體",
     "Enable full page image viewer": "啟用整頁圖像檢視器",
-    "Show images zoomed in by default in full page image viewer": "在整頁圖像檢視器中預設放大顯示圖像",
+    "Show images zoomed in by default in full page image viewer": "在整頁圖像檢視器中，預設放大顯示圖像",
     "Show generation progress in window title.": "在視窗標題中顯示生成進度",
     "Quicksettings list": "快速設定列表",
-    "Localization (requires restart)": "本地化（需要重新啟動）",
+    "Localization (requires restart)": "本地化翻譯（需要儲存設定，並重啟Gradio）",
     "Sampler parameters": "採樣器參數",
     "Hide samplers in user interface (requires restart)": "在使用者介面中隱藏採樣器（需要重新啟動）",
     "eta (noise multiplier) for DDIM": "DDIM 的 eta （噪聲乘數）",
@@ -406,9 +466,9 @@
     "sigma churn": "sigma churn",
     "sigma tmin": "最小(tmin) sigma",
     "sigma noise": "sigma 噪聲",
-    "Eta noise seed delta": "Eta 噪聲種子偏移（noise seed delta）",
+    "Eta noise seed delta": "Eta 噪聲種子偏移（ENSD）",
     "Images Browser": "圖庫瀏覽器",
-    "Preload images at startup": "在啟動時預載圖像",
+    "Preload images at startup": "在啟動時預加載圖像",
     "Number of columns on the page": "每頁列數",
     "Number of rows on the page": "每頁行數",
     "Number of pictures displayed on each page": "每頁顯示的圖像數量",
@@ -418,24 +478,36 @@
     "Use same seed for all images": "為所有圖像使用同一個隨機種子",
     "Request browser notifications": "請求瀏覽器通知",
     "Download localization template": "下載本地化模板",
-    "Reload custom script bodies (No ui updates, No restart)": "重新載入自訂指令碼主體（無UI更新，無重啟）",
+    "Reload custom script bodies (No ui updates, No restart)": "重新載入自訂指令碼主體（無使用者介面更新，無重啟）",
     "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重啟 Gradio 及刷新組件（僅限自訂指令碼，ui.py，JS 和 CSS）",
+    "Available": "可用",
+    "Install from URL": "從網址安裝",
+    "Apply and restart UI": "應用並重啟使用者介面",
+    "Check for updates": "檢查更新",
+    "Extension": "擴充",
+    "URL": "網址",
+    "Update": "更新",
+    "unknown": "未知",
+    "Load from:": "載入自",
+    "Extension index URL": "擴充清單連結",
+    "URL for extension's git repository": "擴充的 git 倉庫連結",
+    "Local directory name": "本地路徑名",
     "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示詞（按 Ctrl+Enter 或 Alt+Enter 生成）",
     "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示詞（按 Ctrl+Enter 或 Alt+Enter 生成）",
     "Add a random artist to the prompt.": "隨機加入一個藝術家到提示詞中",
     "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "從提示詞中讀取生成參數，如果提示詞為空，則讀取上一次的生成參數到使用者介面",
     "Save style": "存儲為模板風格",
     "Apply selected styles to current prompt": "將所選樣式套用於當前提示",
-    "Stop processing current image and continue processing.": "停止處理當前圖像並繼續處理下一個",
-    "Stop processing images and return any results accumulated so far.": "停止處理圖像並返回迄今為止累積的任何結果",
+    "Stop processing current image and continue processing.": "停止處理當前圖像，並繼續處理下一個",
+    "Stop processing images and return any results accumulated so far.": "停止處理圖像，並返回迄今為止累積的任何結果",
     "Style to apply; styles have components for both positive and negative prompts and apply to both": "要套用的模版風格； 模版風格包含正向和反向提示詞，並套用於兩者",
     "Do not do anything special": "什麼都不做",
     "Which algorithm to use to produce the image": "使用哪種演算法生成圖像",
     "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有創意，可以根據疊代步數獲得完全不同的圖像，將疊代步數設定為高於 30-40 不會有正面作用",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅長內補繪製",
+    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅長局部重繪",
     "Produce an image that can be tiled.": "生成可用於平舖的圖像",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用兩步處理的時候以較小的解析度生成初步圖像，接著放大圖像，然後在不更改構圖的情況下改進其中的細節",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "決定演算法對圖像內容的影響程度。設定 0 時，什麼都不會改變，而在 1 時，你將獲得不相關的圖像。值低於 1.0 時，處理的疊代步數將少於「採樣疊代步數」滑塊指定的步數",
+    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用兩步處理的時候，以較小的解析度生成初步圖像，接著放大圖像，然後在不更改構圖的情況下改進其中的細節",
+    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "決定演算法對圖像內容的影響程度。設定 0 時，什麼都不會改變，而在 1 時，你將獲得不相關的圖像。\n值低於 1.0 時，處理的疊代步數將少於「採樣疊代步數」滑塊指定的步數",
     "How many batches of images to create": "建立多少批次的圖像",
     "How many image to create in a single batch": "每批建立多少圖像",
     "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 圖像應在多大程度上服從提示詞 - 較低的值會產生更有創意的結果",
@@ -448,20 +520,20 @@
     "This text is used to rotate the feature space of the imgs embs": "此文本用於旋轉圖集 embeddings 的特徵空間",
     "Separate values for X axis using commas.": "使用逗號分隔 X 軸的值",
     "Separate values for Y axis using commas.": "使用逗號分隔 Y 軸的值",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "將圖像寫入目錄（預設 — log/images）並將生成參數寫入CSV檔案",
+    "Write image to a directory (default - log/images) and generation parameters into csv file.": "將圖像寫入目錄（預設 — log/images）並將生成參數寫入CSV表格檔案",
     "Open images output directory": "打開圖像輸出目錄",
     "How much to blur the mask before processing, in pixels.": "處理前要對蒙版進行多強的模糊，以畫素為單位",
     "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 處理蒙版區域之前要在蒙版區域內放置什麼",
-    "fill it with colors of the image": "用圖像的顏色填充它",
-    "keep whatever was there originally": "保留原來的内容",
+    "fill it with colors of the image": "用圖像的顏色（高強度模糊）填充它",
+    "keep whatever was there originally": "保留原來的圖像，不進行預處理",
     "fill it with latent space noise": "用潛空間的噪聲填充它",
     "fill it with latent space zeroes": "用潛空間的零填充它",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "將蒙版區域放大到目標解析度，做內補繪製，縮小後粘貼到原始圖像中",
+    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "將蒙版區域（包括預留畫素長度的緩衝區域）放大到目標解析度，進行局部重繪。\n然後縮小並粘貼回原始圖像中",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "將圖像大小調整為目標解析度。除非高度和寬度匹配，否則你將獲得不正確的縱橫比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "調整圖像大小，使整個目標解析度都被圖像填充。裁剪多出來的部分",
     "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "調整圖像大小，使整個圖像在目標解析度內。用圖像的顏色填充空白區域",
     "How many times to repeat processing an image and using it as input for the next iteration": "重複處理圖像並用作下次疊代輸入的次數",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每個循環中，去噪強度都會乘以該值。<1 表示減少多樣性，因此你的這一組圖將集中在固定的圖像上。>1 意味著增加多樣性，因此你的這一組圖將變得越來越混亂",
+    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每個循環中，重繪幅度都會乘以該值。<1 表示減少多樣性，因此你的這一組圖將集中在固定的圖像上。>1 意味著增加多樣性，因此你的這一組圖將變得越來越混亂",
     "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大時，圖塊之間應該有多少畫素重疊。圖塊之間需要重疊才可以讓它們在合併回一張圖像時，沒有清晰可見的接縫",
     "A directory on the same machine where the server is running.": "與伺服器主機上的目錄",
     "Leave blank to save images to the default path.": "留空以將圖像儲存到預設路徑",
@@ -472,17 +544,53 @@
     "Path to directory with input images": "帶有輸入圖像的路徑",
     "Path to directory where to write outputs": "進行輸出的路徑",
     "Input images directory": "輸入圖像目錄",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下標籤定義如何選擇圖像的檔案名： [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]； 預設請留空",
+    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下標記定義如何選擇圖像的檔案名： [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]； 預設請留空",
     "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果啟用此選項，浮水印將不會加入到生成出來的圖像中。警告：如果你不加入浮水印，你的行為可能是不符合道德操守的",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "使用以下標籤定義如何選擇圖像和概覽圖的子目錄： [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]； 預設請留空",
+    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下標記定義如何選擇圖像和宮格圖的子目錄： [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]； 預設請留空",
     "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神經網路修復低品質面部",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正則表達式將用於從檔案名中提取單詞，並將使用以下選項將它們接合到用於訓練的標籤文本中。留空以保持檔案名文本不變",
+    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正則表達式將用於從檔案名中提取單詞，並將使用以下選項將它們接合到用於訓練的標記文本中。留空以保持檔案名文本不變",
     "This string will be used to join split words into a single line if the option above is enabled.": "如果啟用了上述選項，則此處的字元會用於將拆分的單詞接合為同一行",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "設定名稱列表，以逗號分隔，設定應轉到頂部的快速存取列，而不是通常的設定頁籤。有關設定名稱，請參見 modules/shared.py。需要重新啟動才能套用",
+    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "僅適用於局部重繪專用的模型（模型後綴為 inpainting.ckpt 的模型）。決定了蒙版在局部重繪以及圖生圖中屏蔽原圖內容的強度。 1.0 表示完全屏蔽原圖，這是預設行為。 0.0 表示完全不屏蔽讓原圖進行圖像調節。較低的值將有助於保持原圖的整體構圖，但很難遇到較大的變化",
+    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "設定項名稱列表，以逗號分隔，該設定會移動到頂部的快速存取列，而不是預設的設定頁籤。有關設定名稱，請參見 modules/shared.py。需要重新啟動才能套用",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果這個值不為零，它將被加入到隨機種子中，並在使用帶有 Eta 的採樣器時用於初始化隨機噪聲。你可以使用它來產生更多的圖像變化，或者你可以使用它來模仿其他軟體生成的圖像，如果你知道你在做什麼",
+    "Leave empty for auto": "留空時自動生成",
+    "Autocomplete options": "自動補全選項",
     "Enable Autocomplete": "開啟Tag補全",
+    "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "選擇哪些 Real-ESRGAN 模型顯示在網頁使用者介面。（需要重新啟動）",
     "Allowed categories for random artists selection when using the Roll button": "使用抽選藝術家按鈕時將會隨機的藝術家類別",
+    "Append commas": "附加逗號",
     "Roll three": "抽三位出來",
-    "Generate forever": "不停地生成",
-    "Cancel generate forever": "取消不停地生成"
+    "Generate forever": "無限生成",
+    "Cancel generate forever": "停止無限生成",
+    "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "疊代改進生成的圖像多少次；更高的值需要更長的時間；非常低的值會產生不好的結果",
+    "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt": "在圖像上畫一個蒙版，指令碼會根據提示重新生成蒙版區域的內容",
+    "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back": "正常放大圖像，將結果分割成圖塊，用圖生圖改進每個圖塊，最後將整個圖像合併回來",
+    "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "創建一個網格，圖像將有不同的參數。使用下面的輸入來指定哪些參數將由列和行共享",
+    "Run Python code. Advanced user only. Must run program with --allow-code for this to work": "執行 Python 程式碼。僅限老手使用。必須以 --allow-code 來開啟程式，才能使其執行",
+    "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "以逗號分割的單詞列表，第一個單詞將被用作關鍵詞：指令碼將在提示詞中搜尋這個單詞，並用其他單詞替換它",
+    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "以逗號分割的單詞列表，指令碼會排列出這些單詞的所有排列方式，並加入提示詞各生成一次",
+    "Reconstruct prompt from existing image and put it into the prompt field.": "從現有的圖像中重構出提示詞，並將其放入提示詞的輸入文字方塊",
+    "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle": "設定在[prompt_words]選項中要使用的最大字數；注意：如果字數太長，可能會超過系統可處理的檔案路徑的最大長度",
+    "Process an image, use it as an input, repeat.": "處理一張圖像，將其作為輸入，並重複",
+    "Insert selected styles into prompt fields": "在提示詞中插入選定的模版風格",
+    "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.": "將當前的提示詞儲存為模版風格。如果你在文本中加入{prompt}標記，那麼將來你使用該模版風格時，你現有的提示詞會替換模版風格中的{prompt}",
+    "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.": "在生成圖像之前從模型權重存檔點中載入權重。你可以使用哈希值或檔案名的一部分（如設定中所示）作為模型權重存檔點名稱。建議用在Y軸上以減少過程中模型的切換",
+    "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "Torch active: 在生成過程中，Torch使用的顯存(VRAM)峰值，不包括快取的數據。\nTorch reserved: Torch 分配的顯存(VRAM)的峰值量，包括所有活動和快取數據。\nSys VRAM: 所有應用程式分配的顯存(VRAM)的峰值量 / GPU 的總顯存(VRAM)（峰值利用率%）",
+    "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "放大潛空間中的圖像。而另一種方法是，從潛變量表達中直接解碼並生成完整的圖像，接著放大它，然後再將其編碼回潛空間",
+    "Start drawing": "開始繪製",
+    "Description": "描述",
+    "Action": "行動",
+    "Aesthetic Gradients": "美術風格",
+    "aesthetic-gradients": "美術風格",
+    "stable-diffusion-webui-wildcards": "萬用字元",
+    "Dynamic Prompts": "動態提示",
+    "images-browser": "圖庫瀏覽器",
+    "Inspiration": "靈感",
+    "Deforum": "Deforum",
+    "Artists to study": "藝術家圖庫",
+    "Aesthetic Image Scorer": "美術風格評分",
+    "Dataset Tag Editor": "數據集標記編輯器",
+    "Install": "安裝",
+    "Installing...": "安裝中…",
+    "Installed": "已安裝"
 }
-- 
cgit v1.2.1


From 86b7fc6e5ed56327fa12b444ca2444b13eb98aa8 Mon Sep 17 00:00:00 2001
From: thesved <2893181+thesved@users.noreply.github.com>
Date: Thu, 3 Nov 2022 19:44:47 +0100
Subject: Make DDIM and PLMS work on Mac OS

Fix register_buffer error on Mac OS
---
 modules/sd_hijack_inpainting.py | 19 ++++++++++++++++++-
 1 file changed, 18 insertions(+), 1 deletion(-)

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index fd92a335..202b42cf 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -1,4 +1,5 @@
 import torch
+import modules.devices as devices
 
 from einops import repeat
 from omegaconf import ListConfig
@@ -314,6 +315,20 @@ class LatentInpaintDiffusion(LatentDiffusion):
         self.masked_image_key = masked_image_key
         assert self.masked_image_key in concat_keys
         self.concat_keys = concat_keys
+            
+
+# =================================================================================================
+# Fix register buffer bug for Mac OS, Viktor Tabori, viktor.doklist.com/start-here
+# =================================================================================================
+def register_buffer(self, name, attr):
+    if type(attr) == torch.Tensor:
+        optimal_type = devices.get_optimal_device()
+        if attr.device != optimal_type:
+           if getattr(torch, 'has_mps', False):
+               attr = attr.to(device="mps", dtype=torch.float32)
+           else:
+               attr = attr.to(optimal_type)
+    setattr(self, name, attr)
 
 
 def should_hijack_inpainting(checkpoint_info):
@@ -326,6 +341,8 @@ def do_inpainting_hijack():
 
     ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
     ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
+    ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer
 
     ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
-    ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
\ No newline at end of file
+    ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
+    ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer
-- 
cgit v1.2.1


From 459e05c2bdaf9e4e9babc49875eaf11510a898d1 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Fri, 4 Nov 2022 07:25:12 +0900
Subject: Update zh_CN.json

- update new content
- polish some translation
---
 localizations/zh_CN.json | 42 +++++++++++++++++++++++++++---------------
 1 file changed, 27 insertions(+), 15 deletions(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 8a00c11c..56c8980e 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -7,7 +7,7 @@
     "Loading...": "载入中...",
     "view": "查看",
     "api": "api",
-    "•": "•",
+    "•": " • ",
     "built with gradio": "基于 Gradio 构建",
     "Stable Diffusion checkpoint": "Stable Diffusion 模型(ckpt)",
     "txt2img": "文生图",
@@ -69,15 +69,15 @@
     "Variation strength": "差异强度",
     "Resize seed from width": "自宽度缩放随机种子",
     "Resize seed from height": "自高度缩放随机种子",
-    "Open for Clip Aesthetic!": "打开美术风格 Clip!",
+    "Open for Clip Aesthetic!": "打开以调整 Clip 的美术风格！",
     "Aesthetic weight": "美术风格权重",
     "Aesthetic steps": "美术风格迭代步数",
     "Aesthetic learning rate": "美术风格学习率",
-    "Slerp interpolation": "Slerp 插值",
+    "Slerp interpolation": "球面线性插值",
     "Aesthetic imgs embedding": "美术风格图集 embedding",
     "None": "无",
     "Aesthetic text for imgs": "该图集的美术风格描述",
-    "Slerp angle": "Slerp 角度",
+    "Slerp angle": "球面线性插值角度",
     "Is negative text": "是反向提示词",
     "Script": "脚本",
     "Embedding to Shareable PNG": "将 Embedding 转换为可分享的 PNG 图片文件",
@@ -181,7 +181,9 @@
     "Color variation": "色彩变化",
     "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置图块尺寸(tile size)",
     "Tile overlap": "图块重叠的像素(Tile overlap)",
+    "Upscaler": "放大算法",
     "Lanczos": "Lanczos",
+    "Nearest": "最邻近(整数缩放)",
     "LDSR": "LDSR",
     "BSRGAN 4x": "BSRGAN 4x",
     "ESRGAN_4x": "ESRGAN_4x",
@@ -198,6 +200,7 @@
     "Scale to": "指定尺寸缩放",
     "Resize": "缩放",
     "Crop to fit": "裁剪以适应",
+    "Upscaler 2 visibility": "放大算法 2 (Upscaler 2) 可见度",
     "GFPGAN visibility": "GFPGAN 可见度",
     "CodeFormer visibility": "CodeFormer 可见度",
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 权重 (0 = 最大效果, 1 = 最小效果)",
@@ -286,7 +289,7 @@
     "Create debug image": "生成调试(debug)图片",
     "Preprocess": "预处理",
     "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "训练 embedding 或者 hypernetwork； 必须指定一组具有 1:1 比例图像的目录",
-    "[wiki]": "[帮助]",
+    "[wiki]": "[wiki文档]",
     "Embedding": "Embedding",
     "Embedding Learning rate": "Embedding 学习率",
     "Hypernetwork Learning rate": "Hypernetwork 学习率",
@@ -456,7 +459,17 @@
     "Extension": "扩展",
     "URL": "网址",
     "Update": "更新",
+    "a1111-sd-webui-tagcomplete": "Tag自动补全",
     "unknown": "未知",
+    "deforum-for-automatic1111-webui": "Deforum",
+    "sd-dynamic-prompting": "动态提示词",
+    "stable-diffusion-webui-aesthetic-gradients": "美术风格梯度",
+    "stable-diffusion-webui-aesthetic-image-scorer": "美术风格评分",
+    "stable-diffusion-webui-artists-to-study": "艺术家图库",
+    "stable-diffusion-webui-dataset-tag-editor": "数据集标签编辑器",
+    "stable-diffusion-webui-images-browser": "图库浏览器",
+    "stable-diffusion-webui-inspiration": "灵感",
+    "stable-diffusion-webui-wildcards": "通配符",
     "Load from:": "加载自",
     "Extension index URL": "扩展列表链接",
     "URL for extension's git repository": "扩展的 git 仓库链接",
@@ -486,8 +499,8 @@
     "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 处理蒙版区域之前要在蒙版区域内放置什么",
     "fill it with colors of the image": "用图像的颜色(高强度模糊)填充它",
     "keep whatever was there originally": "保留原来的图像，不进行预处理",
-    "fill it with latent space noise": "用潜空间的噪声填充它",
-    "fill it with latent space zeroes": "用潜空间的零填充它",
+    "fill it with latent space noise": "于潜空间填充噪声",
+    "fill it with latent space zeroes": "于潜空间填零",
     "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域（包括预留像素长度的缓冲区域）放大到目标分辨率，进行局部重绘。\n然后缩小并粘贴回原始图像中",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
@@ -516,6 +529,8 @@
     "Autocomplete options": "自动补全选项",
     "Enable Autocomplete": "开启Tag补全",
     "Append commas": "附加逗号",
+    "latest": "最新",
+    "behind": "落后",
     "Roll three": "抽三位出来",
     "Generate forever": "无限生成",
     "Cancel generate forever": "停止无限生成",
@@ -536,15 +551,13 @@
     "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.": "在生成图像之前从模型(ckpt)中加载权重。你可以使用哈希值或文件名的一部分（如设置中所示）作为模型(ckpt)名称。建议用在Y轴上以减少过程中模型的切换",
     "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "Torch active: 在生成过程中，Torch使用的显存(VRAM)峰值，不包括缓存的数据。\nTorch reserved: Torch 分配的显存(VRAM)的峰值量，包括所有活动和缓存数据。\nSys VRAM: 所有应用程序分配的显存(VRAM)的峰值量 / GPU 的总显存(VRAM)（峰值利用率%）",
     "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "放大潜空间中的图像。而另一种方法是，从潜变量表达中直接解码并生成完整的图像，接着放大它，然后再将其编码回潜空间",
-    "Upscaler": "放大算法",
     "Start drawing": "开始绘制",
     "Description": "描述",
     "Action": "行动",
-    "Aesthetic Gradients": "美术风格",
-    "aesthetic-gradients": "美术风格",
+    "Aesthetic Gradients": "美术风格梯度",
+    "aesthetic-gradients": "美术风格梯度",
     "Wildcards": "通配符",
-    "stable-diffusion-webui-wildcards": "通配符",
-    "Dynamic Prompts": "动态提示",
+    "Dynamic Prompts": "动态提示词",
     "Image browser": "图库浏览器",
     "images-browser": "图库浏览器",
     "Inspiration": "灵感",
@@ -554,7 +567,7 @@
     "Dataset Tag Editor": "数据集标签编辑器",
 
 
-    "----无效----": "----以下内容无法被翻译，Bug----",
+    "----not work----": "----以下内容无法被翻译，Bug----",
     "Add a random artist to the prompt.": "随机添加一个艺术家到提示词中",
     "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
     "Save style": "储存为模版风格",
@@ -574,10 +587,9 @@
 
     "Style to apply; styles have components for both positive and negative prompts and apply to both": "要使用的模版风格； 模版风格包含正向和反向提示词，并应用于两者\n\ud83c\udfa8    随机添加一个艺术家到提示词中\n \u2199\ufe0f     从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面\n\ud83d\udcbe    将当前的提示词保存为模版风格(保存在styles.csv)\n\ud83d\udccb    将所选模板风格，应用于当前提示词\n如果你在文本中添加{prompt}标记，并保存为模版风格\n那么将来你使用该模版风格时，你现有的提示词会替换模版风格中的{prompt}",
     "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一个固定随机数生成器输出的值 - 以相同参数和随机种子生成的图像会得到相同的结果\n\ud83c\udfb2    将随机种子设置为-1，则每次都会使用一个新的随机数\n\u267b\ufe0f    重用上一次使用的随机种子，如果想要固定输出结果就会很有用",
-    "Upscaler 2 visibility": "放大算法 2 (Upscaler 2) 可见度",
 
 
-    "----已移除----": "----以下内容在webui新版本已移除----",
+    "----deprecated----": "----以下内容在webui新版本已移除----",
     "▼": "▼",
     "History": "历史记录",
     "Show Textbox": "显示文本框",
-- 
cgit v1.2.1


From b2c48091db394c2b7d375a33f18d90c924cd4363 Mon Sep 17 00:00:00 2001
From: Gur <sevenflash@yandex.ru>
Date: Fri, 4 Nov 2022 06:55:03 +0800
Subject: fixed api compatibility with python 3.8

---
 modules/api/models.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/modules/api/models.py b/modules/api/models.py
index 9ee42a17..29a934ba 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -6,6 +6,7 @@ from typing_extensions import Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 from modules.shared import sd_upscalers
+from typing import List
 
 API_NOT_ALLOWED = [
     "self",
@@ -109,12 +110,12 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
 ).generate_model()
 
 class TextToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: dict
     info: str
 
 class ImageToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: dict
     info: str
 
@@ -146,10 +147,10 @@ class FileData(BaseModel):
     name: str = Field(title="File name")
 
 class ExtrasBatchImagesRequest(ExtrasBaseRequest):
-    imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
+    imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
 
 class ExtrasBatchImagesResponse(ExtraBaseResponse):
-    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
+    images: List[str] = Field(title="Images", description="The generated images in base64 format.")
 
 class PNGInfoRequest(BaseModel):
     image: str = Field(title="Image", description="The base64 encoded PNG image")
-- 
cgit v1.2.1


From 8eb64dab3e9e40531f6a3fa606a1c23a62987249 Mon Sep 17 00:00:00 2001
From: digburn <115176097+digburn@users.noreply.github.com>
Date: Fri, 4 Nov 2022 00:35:18 +0000
Subject: fix: correct default val of upscale_first to False

---
 modules/api/models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/api/models.py b/modules/api/models.py
index 9069c0ac..68fb45c6 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -131,7 +131,7 @@ class ExtrasBaseRequest(BaseModel):
     upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
-    upscale_first: bool = Field(default=True, title="Upscale first", description="Should the upscaler run before restoring faces?")
+    upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?")
 
 class ExtraBaseResponse(BaseModel):
     html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
-- 
cgit v1.2.1


From f59855dce3dbcceb676da5e1f6d2c9fdbc344198 Mon Sep 17 00:00:00 2001
From: benlisquare <116663807+benlisquare@users.noreply.github.com>
Date: Fri, 4 Nov 2022 11:42:53 +1100
Subject: Apply missing translations to Traditional Chinese (zh_TW)
 localisation JSON

---
 localizations/zh_TW.json | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/localizations/zh_TW.json b/localizations/zh_TW.json
index 7467db88..4e6dac44 100644
--- a/localizations/zh_TW.json
+++ b/localizations/zh_TW.json
@@ -408,6 +408,7 @@
     "Training": "訓練",
     "Unload VAE and CLIP from VRAM when training": "訓練時從顯存（VRAM）中取消 VAE 和 CLIP 的載入",
     "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "訓練時將 VAE 和 CLIP 從顯存（VRAM）移放到內存（RAM），節省顯存（VRAM）",
+    "Move VAE and CLIP to RAM when training if possible. Saves VRAM.": "訓練時將 VAE 和 CLIP 從顯存（VRAM）移放到內存（RAM）如果可行的話，節省顯存（VRAM）",
     "Filename word regex": "檔案名用詞的正則表達式",
     "Filename join string": "檔案名連接用字串",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每個 epoch 中單個輸入圖像的重複次數； 僅用於顯示 epoch 數",
@@ -590,6 +591,7 @@
     "Artists to study": "藝術家圖庫",
     "Aesthetic Image Scorer": "美術風格評分",
     "Dataset Tag Editor": "數據集標記編輯器",
+    "Face restoration model": "面部修復模型",
     "Install": "安裝",
     "Installing...": "安裝中…",
     "Installed": "已安裝"
-- 
cgit v1.2.1


From 3780ad3ad837dd406da39eebd5d91009b5a58445 Mon Sep 17 00:00:00 2001
From: digburn <digburned@gmail.com>
Date: Fri, 4 Nov 2022 00:40:21 +0000
Subject: fix: loading models without vae from cache

---
 modules/sd_models.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5075fadb..ae427a5c 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -204,8 +204,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
                 checkpoints_loaded.popitem(last=False)  # LRU
 
     else:
-        vae_name = sd_vae.get_filename(vae_file)
-        print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache")
+        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
+        vae_message = f" with {vae_name} VAE" if vae_name else ""
+        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
         checkpoints_loaded.move_to_end(checkpoint_key)
         model.load_state_dict(checkpoints_loaded[checkpoint_key])
 
-- 
cgit v1.2.1


From e533ff61c1baa4ad047f9c8dc05c17b64ee89ddf Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Thu, 3 Nov 2022 22:28:22 -0700
Subject: Lift extras generate button a la #4246.

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 2609857e..6461002a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1052,6 +1052,8 @@ def create_ui(wrap_gradio_gpu_call):
                         extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
                         show_extras_results = gr.Checkbox(label='Show result images', value=True)
 
+                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
                 with gr.Tabs(elem_id="extras_resize_mode"):
                     with gr.TabItem('Scale by'):
                         upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
@@ -1079,8 +1081,6 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
 
-                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
             result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
 
         submit.click(
-- 
cgit v1.2.1


From 4dd898b8c15e342f817d3fb1c8dc9f2d5d111022 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 08:38:11 +0300
Subject: do not mess with components' visibility for scripts; instead create
 group components and show/hide those; this will break scripts that create
 invisible components and rely on UI but the earlier i make this change the
 better

---
 modules/scripts.py               | 34 ++++++++++++++++++----------------
 scripts/custom_code.py           |  2 +-
 scripts/outpainting_mk_2.py      |  2 +-
 scripts/poor_mans_outpainting.py |  4 ++--
 scripts/prompts_from_file.py     | 10 +++++-----
 scripts/sd_upscale.py            |  4 ++--
 scripts/xy_grid.py               |  8 ++++----
 7 files changed, 33 insertions(+), 31 deletions(-)

diff --git a/modules/scripts.py b/modules/scripts.py
index 533db45c..28ce07f4 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -18,6 +18,9 @@ class Script:
     args_to = None
     alwayson = False
 
+    """A gr.Group component that has all script's UI inside it"""
+    group = None
+
     infotext_fields = None
     """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
     parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
@@ -218,8 +221,6 @@ class ScriptRunner:
 
             for control in controls:
                 control.custom_script_source = os.path.basename(script.filename)
-                if not script.alwayson:
-                    control.visible = False
 
             if script.infotext_fields is not None:
                 self.infotext_fields += script.infotext_fields
@@ -229,40 +230,41 @@ class ScriptRunner:
             script.args_to = len(inputs)
 
         for script in self.alwayson_scripts:
-            with gr.Group():
+            with gr.Group() as group:
                 create_script_ui(script, inputs, inputs_alwayson)
 
+            script.group = group
+
         dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
         dropdown.save_to_config = True
         inputs[0] = dropdown
 
         for script in self.selectable_scripts:
-            create_script_ui(script, inputs, inputs_alwayson)
+            with gr.Group(visible=False) as group:
+                create_script_ui(script, inputs, inputs_alwayson)
+
+            script.group = group
 
         def select_script(script_index):
-            if 0 < script_index <= len(self.selectable_scripts):
-                script = self.selectable_scripts[script_index-1]
-                args_from = script.args_from
-                args_to = script.args_to
-            else:
-                args_from = 0
-                args_to = 0
+            selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None
 
-            return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)]
+            return [gr.update(visible=selected_script == s) for s in self.selectable_scripts]
 
         def init_field(title):
+            """called when an initial value is set from ui-config.json to show script's UI components"""
+
             if title == 'None':
                 return
+
             script_index = self.titles.index(title)
-            script = self.selectable_scripts[script_index]
-            for i in range(script.args_from, script.args_to):
-                inputs[i].visible = True
+            self.selectable_scripts[script_index].group.visible = True
 
         dropdown.init_field = init_field
+
         dropdown.change(
             fn=select_script,
             inputs=[dropdown],
-            outputs=inputs
+            outputs=[script.group for script in self.selectable_scripts]
         )
 
         return inputs
diff --git a/scripts/custom_code.py b/scripts/custom_code.py
index a9b10c09..22e7b77a 100644
--- a/scripts/custom_code.py
+++ b/scripts/custom_code.py
@@ -14,7 +14,7 @@ class Script(scripts.Script):
         return cmd_opts.allow_code
 
     def ui(self, is_img2img):
-        code = gr.Textbox(label="Python code", visible=False, lines=1)
+        code = gr.Textbox(label="Python code", lines=1)
 
         return [code]
 
diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py
index 2afd4aa5..cf71cb92 100644
--- a/scripts/outpainting_mk_2.py
+++ b/scripts/outpainting_mk_2.py
@@ -132,7 +132,7 @@ class Script(scripts.Script):
         info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8</p>")
 
         pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128)
-        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, visible=False)
+        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8)
         direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'])
         noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0)
         color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05)
diff --git a/scripts/poor_mans_outpainting.py b/scripts/poor_mans_outpainting.py
index b0469110..ea45beb0 100644
--- a/scripts/poor_mans_outpainting.py
+++ b/scripts/poor_mans_outpainting.py
@@ -22,8 +22,8 @@ class Script(scripts.Script):
             return None
 
         pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128)
-        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, visible=False)
-        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", visible=False)
+        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index")
         direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'])
 
         return [pixels, mask_blur, inpainting_fill, direction]
diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index d187cd9c..3388bc77 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -83,13 +83,14 @@ def cmdargs(line):
 
 
 def load_prompt_file(file):
-    if (file is None):
+    if file is None:
         lines = []
     else:
         lines = [x.strip() for x in file.decode('utf8', errors='ignore').split("\n")]
 
     return None, "\n".join(lines), gr.update(lines=7)
 
+
 class Script(scripts.Script):
     def title(self):
         return "Prompts from file or textbox"
@@ -107,9 +108,9 @@ class Script(scripts.Script):
         # We don't shrink back to 1, because that causes the control to ignore [enter], and it may
         # be unclear to the user that shift-enter is needed.
         prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt])
-        return [checkbox_iterate, checkbox_iterate_batch, file, prompt_txt]
+        return [checkbox_iterate, checkbox_iterate_batch, prompt_txt]
 
-    def run(self, p, checkbox_iterate, checkbox_iterate_batch, file, prompt_txt: str):
+    def run(self, p, checkbox_iterate, checkbox_iterate_batch, prompt_txt: str):
         lines = [x.strip() for x in prompt_txt.splitlines()]
         lines = [x for x in lines if len(x) > 0]
 
@@ -157,5 +158,4 @@ class Script(scripts.Script):
             if checkbox_iterate:
                 p.seed = p.seed + (p.batch_size * p.n_iter)
 
-
-        return Processed(p, images, p.seed, "")
\ No newline at end of file
+        return Processed(p, images, p.seed, "")
diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
index cb37ff7e..01074291 100644
--- a/scripts/sd_upscale.py
+++ b/scripts/sd_upscale.py
@@ -18,8 +18,8 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):
         info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image to twice the dimensions; use width and height sliders to set tile size</p>")
-        overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, visible=False)
-        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index", visible=False)
+        overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)
+        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
         return [info, overlap, upscaler_index]
 
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index f5255786..417ed0d4 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -263,12 +263,12 @@ class Script(scripts.Script):
         current_axis_options = [x for x in axis_options if type(x) == AxisOption or type(x) == AxisOptionImg2Img and is_img2img]
 
         with gr.Row():
-            x_type = gr.Dropdown(label="X type", choices=[x.label for x in current_axis_options], value=current_axis_options[1].label, visible=False, type="index", elem_id="x_type")
-            x_values = gr.Textbox(label="X values", visible=False, lines=1)
+            x_type = gr.Dropdown(label="X type", choices=[x.label for x in current_axis_options], value=current_axis_options[1].label, type="index", elem_id="x_type")
+            x_values = gr.Textbox(label="X values", lines=1)
 
         with gr.Row():
-            y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, visible=False, type="index", elem_id="y_type")
-            y_values = gr.Textbox(label="Y values", visible=False, lines=1)
+            y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, type="index", elem_id="y_type")
+            y_values = gr.Textbox(label="Y values", lines=1)
         
         draw_legend = gr.Checkbox(label='Draw legend', value=True)
         include_lone_images = gr.Checkbox(label='Include Separate Images', value=False)
-- 
cgit v1.2.1


From f2b69709eaff88fc3a2bd49585556ec0883bf5ea Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 09:42:25 +0300
Subject: move option access checking to options class out of various places
 scattered through code

---
 modules/processing.py |  4 ++--
 modules/shared.py     | 11 +++++++++++
 modules/ui.py         | 20 +++++---------------
 3 files changed, 18 insertions(+), 17 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 2168208c..a46e592d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -418,13 +418,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     try:
         for k, v in p.override_settings.items():
-            opts.data[k] = v  # we don't call onchange for simplicity which makes changing model, hypernet impossible
+            setattr(opts, k, v) # we don't call onchange for simplicity which makes changing model, hypernet impossible
 
         res = process_images_inner(p)
 
     finally:
         for k, v in stored_opts.items():
-            opts.data[k] = v
+            setattr(opts, k, v)
 
     return res
 
diff --git a/modules/shared.py b/modules/shared.py
index d8e99f85..024c771a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -396,6 +396,15 @@ class Options:
     def __setattr__(self, key, value):
         if self.data is not None:
             if key in self.data or key in self.data_labels:
+                assert not cmd_opts.freeze_settings, "changing settings is disabled"
+
+                comp_args = opts.data_labels[key].component_args
+                if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
+                    raise RuntimeError(f"not possible to set {key} because it is restricted")
+
+                if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+                    raise RuntimeError(f"not possible to set {key} because it is restricted")
+
                 self.data[key] = value
                 return
 
@@ -412,6 +421,8 @@ class Options:
         return super(Options, self).__getattribute__(item)
 
     def save(self, filename):
+        assert not cmd_opts.freeze_settings, "saving settings is disabled"
+
         with open(filename, "w", encoding="utf8") as file:
             json.dump(self.data, file, indent=4)
 
diff --git a/modules/ui.py b/modules/ui.py
index b2b1c854..633b56ef 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1438,8 +1438,6 @@ def create_ui(wrap_gradio_gpu_call):
     def run_settings(*args):
         changed = 0
 
-        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
-
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
             if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
                 return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
@@ -1448,15 +1446,9 @@ def create_ui(wrap_gradio_gpu_call):
             if comp == dummy_component:
                 continue
 
-            comp_args = opts.data_labels[key].component_args
-            if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
-                continue
-
-            if cmd_opts.hide_ui_dir_config and key in restricted_opts:
-                continue
-
             oldval = opts.data.get(key, None)
-            opts.data[key] = value
+
+            setattr(opts, key, value)
 
             if oldval != value:
                 if opts.data_labels[key].onchange is not None:
@@ -1469,17 +1461,15 @@ def create_ui(wrap_gradio_gpu_call):
         return f'{changed} settings changed.', opts.dumpjson()
 
     def run_settings_single(value, key):
-        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
-
         if not opts.same_type(value, opts.data_labels[key].default):
             return gr.update(visible=True), opts.dumpjson()
 
         oldval = opts.data.get(key, None)
-        if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+        try:
+            setattr(opts, key, value)
+        except Exception:
             return gr.update(value=oldval), opts.dumpjson()
 
-        opts.data[key] = value
-
         if oldval != value:
             if opts.data_labels[key].onchange is not None:
                 opts.data_labels[key].onchange()
-- 
cgit v1.2.1


From 0abb39f461baa343ae7c23abffb261e57c3168d4 Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Fri, 4 Nov 2022 15:47:19 +0900
Subject: resolve conflict - first revert

---
 modules/hypernetworks/hypernetwork.py | 123 ++++++++++++++--------------------
 1 file changed, 52 insertions(+), 71 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 4230b8cf..674fcedd 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -21,7 +21,6 @@ from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_norm
 from collections import defaultdict, deque
 from statistics import stdev, mean
 
-optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
 
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
@@ -34,9 +33,12 @@ class HypernetworkModule(torch.nn.Module):
         "tanh": torch.nn.Tanh,
         "sigmoid": torch.nn.Sigmoid,
     }
-    activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
+    activation_dict.update(
+        {cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if
+         inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
+                 add_layer_norm=False, use_dropout=False):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -47,7 +49,7 @@ class HypernetworkModule(torch.nn.Module):
         for i in range(len(layer_structure) - 1):
 
             # Add a fully-connected layer
-            linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
+            linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i + 1])))
 
             # Add an activation func
             if activation_func == "linear" or activation_func is None:
@@ -59,7 +61,7 @@ class HypernetworkModule(torch.nn.Module):
 
             # Add layer normalization
             if add_layer_norm:
-                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
+                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i + 1])))
 
             # Add dropout expect last layer
             if use_dropout and i < len(layer_structure) - 3:
@@ -128,7 +130,8 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None,
+                 add_layer_norm=False, use_dropout=False):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -140,13 +143,13 @@ class Hypernetwork:
         self.weight_init = weight_init
         self.add_layer_norm = add_layer_norm
         self.use_dropout = use_dropout
-        self.optimizer_name = None
-        self.optimizer_state_dict = None
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+                                   self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+                                   self.add_layer_norm, self.use_dropout),
             )
 
     def weights(self):
@@ -161,7 +164,6 @@ class Hypernetwork:
 
     def save(self, filename):
         state_dict = {}
-        optimizer_saved_dict = {}
 
         for k, v in self.layers.items():
             state_dict[k] = (v[0].state_dict(), v[1].state_dict())
@@ -175,14 +177,8 @@ class Hypernetwork:
         state_dict['use_dropout'] = self.use_dropout
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
-        if self.optimizer_name is not None:
-            optimizer_saved_dict['optimizer_name'] = self.optimizer_name
 
         torch.save(state_dict, filename)
-        if self.optimizer_state_dict:
-            optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
-            optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
-            torch.save(optimizer_saved_dict, filename + '.optim')
 
     def load(self, filename):
         self.filename = filename
@@ -202,23 +198,13 @@ class Hypernetwork:
         self.use_dropout = state_dict.get('use_dropout', False)
         print(f"Dropout usage is set to {self.use_dropout}")
 
-        optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
-        self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
-        print(f"Optimizer name is {self.optimizer_name}")
-        if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
-            self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
-        else:
-            self.optimizer_state_dict = None
-        if self.optimizer_state_dict:
-            print("Loaded existing optimizer from checkpoint")
-        else:
-            print("No saved optimizer exists in checkpoint")
-
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
+                                       self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
+                                       self.add_layer_norm, self.use_dropout),
                 )
 
         self.name = state_dict.get('name', self.name)
@@ -233,7 +219,7 @@ def list_hypernetworks(path):
         name = os.path.splitext(os.path.basename(filename))[0]
         # Prevent a hypothetical "None.pt" from being listed.
         if name != "None":
-            res[name + f"({sd_models.model_hash(filename)})"] = filename
+            res[name] = filename
     return res
 
 
@@ -330,7 +316,7 @@ def statistics(data):
         std = 0
     else:
         std = stdev(data)
-    total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"
+    total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std / (len(data) ** 0.5):.3f})"
     recent_data = data[-32:]
     if len(recent_data) < 2:
         std = 0
@@ -340,7 +326,7 @@ def statistics(data):
     return total_information, recent_information
 
 
-def report_statistics(loss_info:dict):
+def report_statistics(loss_info: dict):
     keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
     for key in keys:
         try:
@@ -352,14 +338,18 @@ def report_statistics(loss_info:dict):
             print(e)
 
 
-
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width,
+                       training_height, steps, create_image_every, save_hypernetwork_every, template_file,
+                       preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps,
+                       preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
     save_hypernetwork_every = save_hypernetwork_every or 0
     create_image_every = create_image_every or 0
-    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
+    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps,
+                                            save_hypernetwork_every, create_image_every, log_directory,
+                                            name="hypernetwork")
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
     shared.loaded_hypernetwork = Hypernetwork()
@@ -379,7 +369,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     else:
         hypernetwork_dir = None
 
-    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
     if create_image_every > 0:
         images_dir = os.path.join(log_directory, "images")
         os.makedirs(images_dir, exist_ok=True)
@@ -395,39 +384,34 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    
+
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width,
+                                                                height=training_height,
+                                                                repeats=shared.opts.training_image_repeats_per_epoch,
+                                                                placeholder_token=hypernetwork_name,
+                                                                model=shared.sd_model, device=devices.device,
+                                                                template_file=template_file, include_cond=True,
+                                                                batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     size = len(ds.indexes)
-    loss_dict = defaultdict(lambda : deque(maxlen = 1024))
+    loss_dict = defaultdict(lambda: deque(maxlen=1024))
     losses = torch.zeros((size,))
     previous_mean_losses = [0]
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
-    
+
     weights = hypernetwork.weights()
     for weight in weights:
         weight.requires_grad = True
-    # Here we use optimizer from saved HN, or we can specify as UI option.
-    if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict:
-        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
-    else:
-        print(f"Optimizer type {optimizer_name} is not defined!")
-        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
-        optimizer_name = 'AdamW'
-    if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
-        try:
-            optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
-        except RuntimeError as e:
-            print("Cannot resume from saved optimizer!")
-            print(e)
+    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
+    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     steps_without_grad = 0
 
@@ -441,7 +425,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         if len(loss_dict) > 0:
             previous_mean_losses = [i[-1] for i in loss_dict.values()]
             previous_mean_loss = mean(previous_mean_losses)
-            
+
         scheduler.apply(optimizer, hypernetwork.step)
         if scheduler.finished:
             break
@@ -460,7 +444,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
             for entry in entries:
                 loss_dict[entry.filename].append(loss.item())
-                
+
             optimizer.zero_grad()
             weights[0].grad = None
             loss.backward()
@@ -475,9 +459,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         steps_done = hypernetwork.step + 1
 
-        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
+        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
             raise RuntimeError("Loss diverged.")
-        
+
         if len(previous_mean_losses) > 1:
             std = stdev(previous_mean_losses)
         else:
@@ -489,11 +473,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             # Before saving, change name to match current checkpoint.
             hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
-            hypernetwork.optimizer_name = optimizer_name
-            if shared.opts.save_optimizer_state:
-                hypernetwork.optimizer_state_dict = optimizer.state_dict()
             save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
-            hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
+
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{previous_mean_loss:.7f}",
             "learn_rate": scheduler.learn_rate
@@ -529,7 +510,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             preview_text = p.prompt
 
             processed = processing.process_images(p)
-            image = processed.images[0] if len(processed.images)>0 else None
+            image = processed.images[0] if len(processed.images) > 0 else None
 
             if unload:
                 shared.sd_model.cond_stage_model.to(devices.cpu)
@@ -537,7 +518,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             if image is not None:
                 shared.state.current_image = image
-                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt,
+                                                                     shared.opts.samples_format, processed.infotexts[0],
+                                                                     p=p, forced_filename=forced_filename,
+                                                                     save_to_dirs=False)
                 last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = hypernetwork.step
@@ -551,15 +535,12 @@ Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
+
     report_statistics(loss_dict)
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
-    hypernetwork.optimizer_name = optimizer_name
-    if shared.opts.save_optimizer_state:
-        hypernetwork.optimizer_state_dict = optimizer.state_dict()
     save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
-    del optimizer
-    hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
+
     return hypernetwork, filename
 
 
@@ -576,4 +557,4 @@ def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
         hypernetwork.sd_checkpoint = old_sd_checkpoint
         hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name
         hypernetwork.name = old_hypernetwork_name
-        raise
+        raise
\ No newline at end of file
-- 
cgit v1.2.1


From 0d07cbfa15d34294a4fa22d74359cdd6fe2f799c Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Fri, 4 Nov 2022 15:50:54 +0900
Subject: I blame code autocomplete

---
 modules/hypernetworks/hypernetwork.py | 76 +++++++++++++----------------------
 1 file changed, 27 insertions(+), 49 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 674fcedd..a11e01d6 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -33,12 +33,9 @@ class HypernetworkModule(torch.nn.Module):
         "tanh": torch.nn.Tanh,
         "sigmoid": torch.nn.Sigmoid,
     }
-    activation_dict.update(
-        {cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if
-         inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
+    activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
-                 add_layer_norm=False, use_dropout=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -49,7 +46,7 @@ class HypernetworkModule(torch.nn.Module):
         for i in range(len(layer_structure) - 1):
 
             # Add a fully-connected layer
-            linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i + 1])))
+            linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
 
             # Add an activation func
             if activation_func == "linear" or activation_func is None:
@@ -61,7 +58,7 @@ class HypernetworkModule(torch.nn.Module):
 
             # Add layer normalization
             if add_layer_norm:
-                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i + 1])))
+                linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
             # Add dropout expect last layer
             if use_dropout and i < len(layer_structure) - 3:
@@ -130,8 +127,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None,
-                 add_layer_norm=False, use_dropout=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -146,10 +142,8 @@ class Hypernetwork:
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
-                                   self.add_layer_norm, self.use_dropout),
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
-                                   self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
             )
 
     def weights(self):
@@ -196,15 +190,13 @@ class Hypernetwork:
         self.add_layer_norm = state_dict.get('is_layer_norm', False)
         print(f"Layer norm is set to {self.add_layer_norm}")
         self.use_dropout = state_dict.get('use_dropout', False)
-        print(f"Dropout usage is set to {self.use_dropout}")
+        print(f"Dropout usage is set to {self.use_dropout}" )
 
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
-                                       self.add_layer_norm, self.use_dropout),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
-                                       self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
                 )
 
         self.name = state_dict.get('name', self.name)
@@ -316,7 +308,7 @@ def statistics(data):
         std = 0
     else:
         std = stdev(data)
-    total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std / (len(data) ** 0.5):.3f})"
+    total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"
     recent_data = data[-32:]
     if len(recent_data) < 2:
         std = 0
@@ -326,7 +318,7 @@ def statistics(data):
     return total_information, recent_information
 
 
-def report_statistics(loss_info: dict):
+def report_statistics(loss_info:dict):
     keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
     for key in keys:
         try:
@@ -338,18 +330,14 @@ def report_statistics(loss_info: dict):
             print(e)
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width,
-                       training_height, steps, create_image_every, save_hypernetwork_every, template_file,
-                       preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps,
-                       preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
     save_hypernetwork_every = save_hypernetwork_every or 0
     create_image_every = create_image_every or 0
-    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps,
-                                            save_hypernetwork_every, create_image_every, log_directory,
-                                            name="hypernetwork")
+    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
     shared.loaded_hypernetwork = Hypernetwork()
@@ -384,29 +372,23 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-
+    
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width,
-                                                                height=training_height,
-                                                                repeats=shared.opts.training_image_repeats_per_epoch,
-                                                                placeholder_token=hypernetwork_name,
-                                                                model=shared.sd_model, device=devices.device,
-                                                                template_file=template_file, include_cond=True,
-                                                                batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     size = len(ds.indexes)
-    loss_dict = defaultdict(lambda: deque(maxlen=1024))
+    loss_dict = defaultdict(lambda : deque(maxlen = 1024))
     losses = torch.zeros((size,))
     previous_mean_losses = [0]
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
-
+    
     weights = hypernetwork.weights()
     for weight in weights:
         weight.requires_grad = True
@@ -425,7 +407,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         if len(loss_dict) > 0:
             previous_mean_losses = [i[-1] for i in loss_dict.values()]
             previous_mean_loss = mean(previous_mean_losses)
-
+            
         scheduler.apply(optimizer, hypernetwork.step)
         if scheduler.finished:
             break
@@ -444,7 +426,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
             for entry in entries:
                 loss_dict[entry.filename].append(loss.item())
-
+                
             optimizer.zero_grad()
             weights[0].grad = None
             loss.backward()
@@ -459,9 +441,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         steps_done = hypernetwork.step + 1
 
-        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
+        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
             raise RuntimeError("Loss diverged.")
-
+        
         if len(previous_mean_losses) > 1:
             std = stdev(previous_mean_losses)
         else:
@@ -510,7 +492,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             preview_text = p.prompt
 
             processed = processing.process_images(p)
-            image = processed.images[0] if len(processed.images) > 0 else None
+            image = processed.images[0] if len(processed.images)>0 else None
 
             if unload:
                 shared.sd_model.cond_stage_model.to(devices.cpu)
@@ -518,10 +500,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             if image is not None:
                 shared.state.current_image = image
-                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt,
-                                                                     shared.opts.samples_format, processed.infotexts[0],
-                                                                     p=p, forced_filename=forced_filename,
-                                                                     save_to_dirs=False)
+                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
                 last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = hypernetwork.step
@@ -535,7 +514,7 @@ Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-
+        
     report_statistics(loss_dict)
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
@@ -543,7 +522,6 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     return hypernetwork, filename
 
-
 def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
     old_hypernetwork_name = hypernetwork.name
     old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None
@@ -557,4 +535,4 @@ def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
         hypernetwork.sd_checkpoint = old_sd_checkpoint
         hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name
         hypernetwork.name = old_hypernetwork_name
-        raise
\ No newline at end of file
+        raise
-- 
cgit v1.2.1


From 283249d2390f0f3a1c8a55d5d9aa551e3e9b2f9c Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Fri, 4 Nov 2022 15:57:17 +0900
Subject: apply

---
 modules/hypernetworks/hypernetwork.py | 54 +++++++++++++++++++++++++++++++----
 1 file changed, 49 insertions(+), 5 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 6e1a10cf..de8688a9 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -22,6 +22,8 @@ from collections import defaultdict, deque
 from statistics import stdev, mean
 
 
+optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
+
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
     activation_dict = {
@@ -142,6 +144,8 @@ class Hypernetwork:
         self.use_dropout = use_dropout
         self.activate_output = activate_output
         self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True
+        self.optimizer_name = None
+        self.optimizer_state_dict = None
 
         for size in enable_sizes or []:
             self.layers[size] = (
@@ -163,6 +167,7 @@ class Hypernetwork:
 
     def save(self, filename):
         state_dict = {}
+        optimizer_saved_dict = {}
 
         for k, v in self.layers.items():
             state_dict[k] = (v[0].state_dict(), v[1].state_dict())
@@ -178,8 +183,15 @@ class Hypernetwork:
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
         state_dict['activate_output'] = self.activate_output
         state_dict['last_layer_dropout'] = self.last_layer_dropout
-        
+
+        if self.optimizer_name is not None:
+            optimizer_saved_dict['optimizer_name'] = self.optimizer_name
+
         torch.save(state_dict, filename)
+        if self.optimizer_state_dict:
+            optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
+            optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
+            torch.save(optimizer_saved_dict, filename + '.optim')
 
     def load(self, filename):
         self.filename = filename
@@ -202,6 +214,18 @@ class Hypernetwork:
         print(f"Activate last layer is set to {self.activate_output}")
         self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
 
+        optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
+        self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
+        print(f"Optimizer name is {self.optimizer_name}")
+        if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
+            self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
+        else:
+            self.optimizer_state_dict = None
+        if self.optimizer_state_dict:
+            print("Loaded existing optimizer from checkpoint")
+        else:
+            print("No saved optimizer exists in checkpoint")
+
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
@@ -223,7 +247,7 @@ def list_hypernetworks(path):
         name = os.path.splitext(os.path.basename(filename))[0]
         # Prevent a hypothetical "None.pt" from being listed.
         if name != "None":
-            res[name] = filename
+            res[name + f"({sd_models.model_hash(filename)})"] = filename
     return res
 
 
@@ -369,6 +393,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     else:
         hypernetwork_dir = None
 
+    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
     if create_image_every > 0:
         images_dir = os.path.join(log_directory, "images")
         os.makedirs(images_dir, exist_ok=True)
@@ -404,8 +429,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     weights = hypernetwork.weights()
     for weight in weights:
         weight.requires_grad = True
-    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
-    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
+    # Here we use optimizer from saved HN, or we can specify as UI option.
+    if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict:
+        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
+    else:
+        print(f"Optimizer type {optimizer_name} is not defined!")
+        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
+        optimizer_name = 'AdamW'
+    if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
+        try:
+            optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
+        except RuntimeError as e:
+            print("Cannot resume from saved optimizer!")
+            print(e)
 
     steps_without_grad = 0
 
@@ -467,7 +503,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             # Before saving, change name to match current checkpoint.
             hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
+            hypernetwork.optimizer_name = optimizer_name
+            if shared.opts.save_optimizer_state:
+                hypernetwork.optimizer_state_dict = optimizer.state_dict()
             save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
+            hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
             "loss": f"{previous_mean_loss:.7f}",
@@ -530,8 +570,12 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     report_statistics(loss_dict)
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+    hypernetwork.optimizer_name = optimizer_name
+    if shared.opts.save_optimizer_state:
+        hypernetwork.optimizer_state_dict = optimizer.state_dict()
     save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
-
+    del optimizer
+    hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
     return hypernetwork, filename
 
 def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
-- 
cgit v1.2.1


From f5d394214d6ee74a682d0a1016bcbebc4b43c13a Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Fri, 4 Nov 2022 16:04:03 +0900
Subject: split before declaring file name

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index de8688a9..9b6a3e62 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -382,6 +382,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     shared.state.textinfo = "Initializing hypernetwork training..."
     shared.state.job_count = steps
 
+    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
 
     log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
@@ -393,7 +394,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     else:
         hypernetwork_dir = None
 
-    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
     if create_image_every > 0:
         images_dir = os.path.join(log_directory, "images")
         os.makedirs(images_dir, exist_ok=True)
-- 
cgit v1.2.1


From 5f0117154382eb0e2547c72630256681673e353b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 10:07:29 +0300
Subject: shut down gradio's "everything allowed" CORS policy; I checked the
 main functionality to work with this, but if this breaks some exotic
 workflow, I'm sorry.

---
 README.md | 7 ++++---
 webui.py  | 6 ++++++
 2 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 55c050d5..33508f31 100644
--- a/README.md
+++ b/README.md
@@ -155,14 +155,15 @@ The documentation was moved from this README over to the project's [wiki](https:
 - Swin2SR - https://github.com/mv-lab/swin2sr
 - LDSR - https://github.com/Hafiidz/latent-diffusion
 - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
-- Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
-- InvokeAI, lstein - Cross Attention layer optimization - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
-- Rinon Gal - Textual Inversion - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
+- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
+- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
+- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
 - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
 - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
 - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
 - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
 - xformers - https://github.com/facebookresearch/xformers
 - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
+- Security advice - RyotaK
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)
diff --git a/webui.py b/webui.py
index 3b21c071..81df09dd 100644
--- a/webui.py
+++ b/webui.py
@@ -141,6 +141,12 @@ def webui():
         # after initial launch, disable --autolaunch for subsequent restarts
         cmd_opts.autolaunch = False
 
+        # gradio uses a very open CORS policy via app.user_middleware, which makes it possible for
+        # an attacker to trick the user into opening a malicious HTML page, which makes a request to the
+        # running web ui and do whatever the attcker wants, including installing an extension and
+        # runnnig its code. We disable this here. Suggested by RyotaK.
+        app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
+
         app.add_middleware(GZipMiddleware, minimum_size=1000)
 
         if launch_api:
-- 
cgit v1.2.1


From 1ca0bcd3a7003dd2c1324de7d97fd2a6fc5ddc53 Mon Sep 17 00:00:00 2001
From: aria1th <35677394+aria1th@users.noreply.github.com>
Date: Fri, 4 Nov 2022 16:09:19 +0900
Subject: only save if option is enabled

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 9b6a3e62..b1f308e2 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -188,7 +188,7 @@ class Hypernetwork:
             optimizer_saved_dict['optimizer_name'] = self.optimizer_name
 
         torch.save(state_dict, filename)
-        if self.optimizer_state_dict:
+        if shared.opts.save_optimizer_state and self.optimizer_state_dict:
             optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
             optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
             torch.save(optimizer_saved_dict, filename + '.optim')
-- 
cgit v1.2.1


From ccf1a15412ef6b518f9f54cc26a0ee5edf458108 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 10:16:19 +0300
Subject: add an option to enable installing extensions with --listen or
 --share

---
 modules/shared.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 024c771a..0a39cdf2 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -44,6 +44,7 @@ parser.add_argument("--precision", type=str, help="evaluate at this precision",
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
 parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
+parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
@@ -99,7 +100,7 @@ restricted_opts = {
     "outdir_save",
 }
 
-cmd_opts.disable_extension_access = cmd_opts.share or cmd_opts.listen
+cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen) and not cmd_opts.enable_insecure_extension_access
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
-- 
cgit v1.2.1


From 321e13ca176b256177c4a752d1f2bbee79b5532e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 10:35:30 +0300
Subject: produce a readable error message when setting an option fails on the
 settings screen

---
 modules/ui.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 633b56ef..3ac7540c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1439,8 +1439,7 @@ def create_ui(wrap_gradio_gpu_call):
         changed = 0
 
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
-                return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
+            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
 
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
             if comp == dummy_component:
@@ -1458,7 +1457,7 @@ def create_ui(wrap_gradio_gpu_call):
 
         opts.save(shared.config_filename)
 
-        return f'{changed} settings changed.', opts.dumpjson()
+        return opts.dumpjson(), f'{changed} settings changed.'
 
     def run_settings_single(value, key):
         if not opts.same_type(value, opts.data_labels[key].default):
@@ -1622,9 +1621,9 @@ def create_ui(wrap_gradio_gpu_call):
 
         text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
         settings_submit.click(
-            fn=run_settings,
+            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
             inputs=components,
-            outputs=[result, text_settings],
+            outputs=[text_settings, result],
         )
 
         for i, k, item in quicksettings_list:
-- 
cgit v1.2.1


From f674c488d9701e577e2aaf25e331fb44ada4f1ef Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 10:45:34 +0300
Subject: bugfix: save image for hires fix BEFORE upscaling latent space

---
 modules/processing.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index a46e592d..7a2fc218 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -665,17 +665,17 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
 
         if opts.use_scale_latent_for_hires_fix:
+            for i in range(samples.shape[0]):
+                save_intermediate(samples, i)
+
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
-            
+
             # Avoid making the inpainting conditioning unless necessary as 
             # this does need some extra compute to decode / encode the image again.
             if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
                 image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
             else:
                 image_conditioning = self.txt2img_image_conditioning(samples)
-
-            for i in range(samples.shape[0]):
-                save_intermediate(samples, i)
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
-- 
cgit v1.2.1


From 7278897982bfb640ee95f144c97ed25fb3f77ea3 Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Fri, 4 Nov 2022 17:12:28 +0900
Subject: Update shared.py

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 4d6e1c8b..6e7a02e0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -309,7 +309,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
-    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state with checkpoints. This will cause file size to increase VERY much."),
+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-- 
cgit v1.2.1


From 99043f33606d3057f83ea52a403e10cd29d1f7e7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 11:20:42 +0300
Subject: fix one of previous merges breaking the program

---
 modules/sd_models.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 63e07a12..34c57bfa 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -167,6 +167,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         sd_vae.restore_base_vae(model)
         checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
 
+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
+
     if checkpoint_info not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-- 
cgit v1.2.1


From eeb07330131012c0294afb79165b90270679b9c7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 11:21:40 +0300
Subject: change process_one virtual function for script to process_batch, add
 extra args and docs

---
 modules/processing.py |  2 +-
 modules/scripts.py    | 16 +++++++++++-----
 2 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index e20d8fc4..03c9143d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -502,7 +502,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 break
 
             if p.scripts is not None:
-                p.scripts.process_one(p, n)
+                p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
 
             with devices.autocast():
                 uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
diff --git a/modules/scripts.py b/modules/scripts.py
index 75e47cd2..366c90d7 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -73,9 +73,15 @@ class Script:
 
         pass
 
-    def process_one(self, p, n, *args):
+    def process_batch(self, p, *args, **kwargs):
         """
-        Same as process(), but called for every iteration
+        Same as process(), but called for every batch.
+
+        **kwargs will have those items:
+          - batch_number - index of current batch, from 0 to number of batches-1
+          - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
+          - seeds - list of seeds for current batch
+          - subseeds - list of subseeds for current batch
         """
 
         pass
@@ -303,13 +309,13 @@ class ScriptRunner:
                 print(f"Error running process: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
-    def process_one(self, p, n):
+    def process_batch(self, p, **kwargs):
         for script in self.alwayson_scripts:
             try:
                 script_args = p.script_args[script.args_from:script.args_to]
-                script.process_one(p, n, *script_args)
+                script.process_batch(p, *script_args, **kwargs)
             except Exception:
-                print(f"Error running process_one: {script.filename}", file=sys.stderr)
+                print(f"Error running process_batch: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
     def postprocess(self, p, processed):
-- 
cgit v1.2.1


From 39541d7725bc42f456a604b07c50aba503a5a09a Mon Sep 17 00:00:00 2001
From: Fampai <>
Date: Fri, 4 Nov 2022 04:50:22 -0400
Subject: Fixes race condition in training when VAE is unloaded

set_current_image can attempt to use the VAE when it is unloaded to
the CPU while training
---
 modules/hypernetworks/hypernetwork.py          | 4 ++++
 modules/textual_inversion/textual_inversion.py | 5 +++++
 2 files changed, 9 insertions(+)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 6e1a10cf..fcb96059 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -390,7 +390,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     with torch.autocast("cuda"):
         ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
+    old_parallel_processing_allowed = shared.parallel_processing_allowed
+
     if unload:
+        shared.parallel_processing_allowed = False
         shared.sd_model.cond_stage_model.to(devices.cpu)
         shared.sd_model.first_stage_model.to(devices.cpu)
 
@@ -531,6 +534,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
     save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
+    shared.parallel_processing_allowed = old_parallel_processing_allowed
 
     return hypernetwork, filename
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 0aeb0459..55892c57 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -273,7 +273,11 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
         ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
+
+    old_parallel_processing_allowed = shared.parallel_processing_allowed
+
     if unload:
+        shared.parallel_processing_allowed = False
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     embedding.vec.requires_grad = True
@@ -410,6 +414,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
     save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
     shared.sd_model.first_stage_model.to(devices.device)
+    shared.parallel_processing_allowed = old_parallel_processing_allowed
 
     return embedding, filename
 
-- 
cgit v1.2.1


From db50a9ab6cac5d66513e828db96e4270a33f7803 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Fri, 4 Nov 2022 18:12:27 +0900
Subject: Update ko_KR.json

Added new strings/Revamped edited strings
---
 localizations/ko_KR.json | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 874771f9..29e10075 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -24,6 +24,7 @@
   "Add model hash to generation information": "생성 정보에 모델 해시 추가",
   "Add model name to generation information": "생성 정보에 모델 이름 추가",
   "Add number to filename when saving": "이미지를 저장할 때 파일명에 숫자 추가하기",
+  "Adds a tab to the webui that allows the user to automatically extract keyframes from video, and manually extract 512x512 crops of those frames for use in model training.": "WebUI에 비디오로부터 자동으로 키프레임을 추출하고, 그 키프레임으로부터 모델 훈련에 사용될 512x512 이미지를 잘라낼 수 있는 탭을 추가합니다.",
   "Aesthetic Gradients": "스타일 그라디언트",
   "Aesthetic Image Scorer": "스타일 이미지 스코어러",
   "Aesthetic imgs embedding": "스타일 이미지 임베딩",
@@ -260,6 +261,7 @@
   "Keep -1 for seeds": "시드값 -1로 유지",
   "keep whatever was there originally": "이미지 원본 유지",
   "keyword": "프롬프트",
+  "Krita Plugin.": "Kirta 플러그인입니다.",
   "Label": "라벨",
   "Lanczos": "Lanczos",
   "Last prompt:": "마지막 프롬프트 : ",
@@ -441,8 +443,8 @@
   "See": "자세한 설명은",
   "Seed": "시드",
   "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
-  "Select activation function of hypernetwork": "하이퍼네트워크 활성화 함수 선택",
-  "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "레이어 가중치 초기화 방식 선택 - relu류 : Kaiming 추천, sigmoid류 : Xavier 추천",
+  "Select activation function of hypernetwork. Recommended : Swish / Linear(none)": "하이퍼네트워크 활성화 함수 선택 - 추천 : Swish / Linear(None)",
+  "Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise": "레이어 가중치 초기화 방식 선택 - relu류 : Kaiming 추천, sigmoid류 : Xavier 추천, 그 외 : Normal",
   "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "WebUI에 표시할 Real-ESRGAN 모델을 선택하십시오. (재시작 필요)",
   "Send seed when sending prompt or image to other interface": "다른 화면으로 프롬프트나 이미지를 보낼 때 시드도 함께 보내기",
   "Send to extras": "부가기능으로 전송",
@@ -458,7 +460,7 @@
   "should be 2 or lower.": "이 2 이하여야 합니다.",
   "Show generation progress in window title.": "창 타이틀에 생성 진행도 보여주기",
   "Show grid in results for web": "웹에서 결과창에 그리드 보여주기",
-  "Show image creation progress every N sampling steps. Set 0 to disable.": "N번째 샘플링 스텝마다 이미지 생성 과정 보이기 - 비활성화하려면 0으로 설정",
+  "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.": "N번째 샘플링 스텝마다 이미지 생성 과정 보이기 - 비활성화하려면 0으로 설정, 배치 생성 완료시 보이려면 -1로 설정",
   "Show images zoomed in by default in full page image viewer": "전체 페이지 이미지 뷰어에서 기본값으로 이미지 확대해서 보여주기",
   "Show previews of all images generated in a batch as a grid": "배치에서 생성된 모든 이미지의 미리보기를 그리드 형식으로 보여주기",
   "Show progressbar": "프로그레스 바 보이기",
@@ -520,6 +522,7 @@
   "Train Embedding": "임베딩 훈련",
   "Train Hypernetwork": "하이퍼네트워크 훈련",
   "Training": "훈련",
+  "training-picker": "훈련용 선택기",
   "txt2img": "텍스트→이미지",
   "txt2img history": "텍스트→이미지 기록",
   "uniform": "uniform",
-- 
cgit v1.2.1


From 5359fd30f2c4f6a3d9c42a6b837f8188d459a1ec Mon Sep 17 00:00:00 2001
From: Sihan Wang <31711261+shwang95@users.noreply.github.com>
Date: Fri, 4 Nov 2022 17:52:46 +0800
Subject: Rename confusing translation
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

"Denoising strength" in UI was translated as "重绘幅度" while "denoising" in the X/Y plot is translated as "去噪", totally confusing.
---
 localizations/zh_CN.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
index 56c8980e..ff785fc0 100644
--- a/localizations/zh_CN.json
+++ b/localizations/zh_CN.json
@@ -109,7 +109,7 @@
     "Sigma noise": "Sigma noise",
     "Eta": "Eta",
     "Clip skip": "Clip 跳过",
-    "Denoising": "去噪",
+    "Denoising": "重绘幅度",
     "Cond. Image Mask Weight": "图像调节屏蔽度",
     "X values": "X轴数值",
     "Y type": "Y轴类型",
-- 
cgit v1.2.1


From 821e2b883dbb42a187bc37379175cd55b7cd7e81 Mon Sep 17 00:00:00 2001
From: TinkTheBoush <TinkTheBoush@github.com>
Date: Fri, 4 Nov 2022 19:39:03 +0900
Subject: change option position to Training setting

---
 modules/hypernetworks/hypernetwork.py          | 4 ++--
 modules/shared.py                              | 1 +
 modules/textual_inversion/dataset.py           | 5 ++---
 modules/textual_inversion/textual_inversion.py | 4 ++--
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7630fb81..a11e01d6 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -331,7 +331,7 @@ def report_statistics(loss_info:dict):
 
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, shuffle_tags, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
@@ -376,7 +376,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, shuffle_tags=shuffle_tags, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
diff --git a/modules/shared.py b/modules/shared.py
index 1ccb269a..e1d9bdf1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -290,6 +290,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
+    "shuffle_tags": OptionInfo(False, "Shuffleing tags by "," when create texts."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index e9d97cc1..df278dc2 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -24,7 +24,7 @@ class DatasetEntry:
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", shuffle_tags=True, model=None, device=None, template_file=None, include_cond=False, batch_size=1):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):
         re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
 
         self.placeholder_token = placeholder_token
@@ -33,7 +33,6 @@ class PersonalizedBase(Dataset):
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
-        self.shuffle_tags = shuffle_tags
 
         self.dataset = []
 
@@ -99,7 +98,7 @@ class PersonalizedBase(Dataset):
     def create_text(self, filename_text):
         text = random.choice(self.lines)
         text = text.replace("[name]", self.placeholder_token)
-        if self.tag_shuffle:
+        if shared.opts.shuffle_tags:
             tags = filename_text.split(',')
             random.shuffle(tags)
             text = text.replace("[filewords]", ','.join(tags))
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 82dde931..0aeb0459 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -224,7 +224,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, data_root, templat
     if save_model_every or create_image_every:
         assert log_directory, "Log directory is empty"
 
-def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, shuffle_tags, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
     validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
@@ -272,7 +272,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, shuffle_tags=shuffle_tags, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
     if unload:
         shared.sd_model.first_stage_model.to(devices.cpu)
 
-- 
cgit v1.2.1


From 45b65e87e0ef64b3e457f7d20c62d591cdcd0e7b Mon Sep 17 00:00:00 2001
From: TinkTheBoush <TinkTheBoush@github.com>
Date: Fri, 4 Nov 2022 19:48:28 +0900
Subject: remove ui option

---
 modules/ui.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 6f3836c6..45cd8c3f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1269,7 +1269,6 @@ def create_ui(wrap_gradio_gpu_call):
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
                     preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
-                    shuffle_tags = gr.Checkbox(label='Shuffleing tags by "," when create texts', value=True)
 
                     with gr.Row():
                         interrupt_training = gr.Button(value="Interrupt")
@@ -1364,7 +1363,6 @@ def create_ui(wrap_gradio_gpu_call):
                 template_file,
                 save_image_with_stored_embedding,
                 preview_from_txt2img,
-                shuffle_tags,
                 *txt2img_preview_params,
             ],
             outputs=[
@@ -1389,7 +1387,6 @@ def create_ui(wrap_gradio_gpu_call):
                 save_embedding_every,
                 template_file,
                 preview_from_txt2img,
-                shuffle_tags,
                 *txt2img_preview_params,
             ],
             outputs=[
-- 
cgit v1.2.1


From fd62727893f9face287b0a9620251afaa38a627d Mon Sep 17 00:00:00 2001
From: Isaac Poulton <flagredomega@gmail.com>
Date: Fri, 4 Nov 2022 18:34:35 +0700
Subject: Sort hypernetworks

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 6e1a10cf..f1f04a70 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -224,7 +224,7 @@ def list_hypernetworks(path):
         # Prevent a hypothetical "None.pt" from being listed.
         if name != "None":
             res[name] = filename
-    return res
+    return dict(sorted(res.items()))
 
 
 def load_hypernetwork(filename):
-- 
cgit v1.2.1


From c3cd0d7a86f35a5bfc58fdc3ecfaf203c0aee06f Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 4 Nov 2022 12:19:16 +0000
Subject: Should be one underscore for module privates not two

---
 modules/script_callbacks.py | 37 ++++++++++++++++++-------------------
 1 file changed, 18 insertions(+), 19 deletions(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 4a7fb944..83da7ca4 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -46,7 +46,7 @@ class CFGDenoiserParams:
 
 
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
-__callback_map = dict(
+_callback_map = dict(
     callbacks_app_started=[],
     callbacks_model_loaded=[],
     callbacks_ui_tabs=[],
@@ -58,11 +58,11 @@ __callback_map = dict(
 
 
 def clear_callbacks():
-    for callback_list in __callback_map.values():
+    for callback_list in _callback_map.values():
         callback_list.clear()
 
 def app_started_callback(demo: Optional[Blocks], app: FastAPI):
-    for c in __callback_map['callbacks_app_started']:
+    for c in _callback_map['callbacks_app_started']:
         try:
             c.callback(demo, app)
         except Exception:
@@ -70,7 +70,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI):
 
 
 def model_loaded_callback(sd_model):
-    for c in __callback_map['callbacks_model_loaded']:
+    for c in _callback_map['callbacks_model_loaded']:
         try:
             c.callback(sd_model)
         except Exception:
@@ -80,7 +80,7 @@ def model_loaded_callback(sd_model):
 def ui_tabs_callback():
     res = []
     
-    for c in __callback_map['callbacks_ui_tabs']:
+    for c in _callback_map['callbacks_ui_tabs']:
         try:
             res += c.callback() or []
         except Exception:
@@ -90,7 +90,7 @@ def ui_tabs_callback():
 
 
 def ui_settings_callback():
-    for c in __callback_map['callbacks_ui_settings']:
+    for c in _callback_map['callbacks_ui_settings']:
         try:
             c.callback()
         except Exception:
@@ -98,7 +98,7 @@ def ui_settings_callback():
 
 
 def before_image_saved_callback(params: ImageSaveParams):
-    for c in __callback_map['callbacks_before_image_saved']:
+    for c in _callback_map['callbacks_before_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -106,7 +106,7 @@ def before_image_saved_callback(params: ImageSaveParams):
 
 
 def image_saved_callback(params: ImageSaveParams):
-    for c in __callback_map['callbacks_image_saved']:
+    for c in _callback_map['callbacks_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -114,7 +114,7 @@ def image_saved_callback(params: ImageSaveParams):
 
 
 def cfg_denoiser_callback(params: CFGDenoiserParams):
-    for c in __callback_map['callbacks_cfg_denoiser']:
+    for c in _callback_map['callbacks_cfg_denoiser']:
         try:
             c.callback(params)
         except Exception:
@@ -133,13 +133,13 @@ def remove_current_script_callbacks():
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
     if filename == 'unknown file':
         return
-    for callback_list in __callback_map.values():
+    for callback_list in _callback_map.values():
         for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
             callback_list.remove(callback_to_remove)
 
 
 def remove_callbacks_for_function(callback_func):
-    for callback_list in __callback_map.values():
+    for callback_list in _callback_map.values():
         for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
             callback_list.remove(callback_to_remove)
 
@@ -147,13 +147,13 @@ def remove_callbacks_for_function(callback_func):
 def on_app_started(callback):
     """register a function to be called when the webui started, the gradio `Block` component and
     fastapi `FastAPI` object are passed as the arguments"""
-    add_callback(__callback_map['callbacks_app_started'], callback)
+    add_callback(_callback_map['callbacks_app_started'], callback)
 
 
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
-    add_callback(__callback_map['callbacks_model_loaded'], callback)
+    add_callback(_callback_map['callbacks_model_loaded'], callback)
 
 
 def on_ui_tabs(callback):
@@ -166,13 +166,13 @@ def on_ui_tabs(callback):
     title is tab text displayed to user in the UI
     elem_id is HTML id for the tab
     """
-    add_callback(__callback_map['callbacks_ui_tabs'], callback)
+    add_callback(_callback_map['callbacks_ui_tabs'], callback)
 
 
 def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
-    add_callback(__callback_map['callbacks_ui_settings'], callback)
+    add_callback(_callback_map['callbacks_ui_settings'], callback)
 
 
 def on_before_image_saved(callback):
@@ -180,7 +180,7 @@ def on_before_image_saved(callback):
     The callback is called with one argument:
         - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
     """
-    add_callback(__callback_map['callbacks_before_image_saved'], callback)
+    add_callback(_callback_map['callbacks_before_image_saved'], callback)
 
 
 def on_image_saved(callback):
@@ -188,7 +188,7 @@ def on_image_saved(callback):
     The callback is called with one argument:
         - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
     """
-    add_callback(__callback_map['callbacks_image_saved'], callback)
+    add_callback(_callback_map['callbacks_image_saved'], callback)
 
 
 def on_cfg_denoiser(callback):
@@ -196,5 +196,4 @@ def on_cfg_denoiser(callback):
     The callback is called with one argument:
         - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
     """
-    add_callback(__callback_map['callbacks_cfg_denoiser'], callback)
-
+    add_callback(_callback_map['callbacks_cfg_denoiser'], callback)
-- 
cgit v1.2.1


From f316280ad3634a2343b086a6de0bfcd473e18599 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 16:48:40 +0300
Subject: fix the error that prevents from setting some options

---
 modules/shared.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index a9e28b9c..962115f6 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -406,7 +406,8 @@ class Options:
             if key in self.data or key in self.data_labels:
                 assert not cmd_opts.freeze_settings, "changing settings is disabled"
 
-                comp_args = opts.data_labels[key].component_args
+                info = opts.data_labels.get(key, None)
+                comp_args = info.component_args if info else None
                 if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
                     raise RuntimeError(f"not possible to set {key} because it is restricted")
 
-- 
cgit v1.2.1


From 116bcf730ade8d3ac5d76d04c5887b6bba000970 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 16:48:46 +0300
Subject: disable setting options via API until it is fixed by the author

---
 modules/api/api.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/modules/api/api.py b/modules/api/api.py
index a49f3755..8a7ab2f5 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -218,6 +218,10 @@ class Api:
         return options
         
     def set_config(self, req: OptionsModel):
+        # currently req has all options fields even if you send a dict like { "send_seed": false }, which means it will
+        # overwrite all options with default values.
+        raise RuntimeError('Setting options via API is not supported')
+
         reqDict = vars(req)
         for o in reqDict:
             setattr(shared.opts, o, reqDict[o])
-- 
cgit v1.2.1


From 08feb4c364e8b2aed929fd7d22dfa21a93d78b2c Mon Sep 17 00:00:00 2001
From: Isaac Poulton <flagredomega@gmail.com>
Date: Fri, 4 Nov 2022 20:53:11 +0700
Subject: Sort straight out of the glob

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index f1f04a70..a441ab10 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -219,12 +219,12 @@ class Hypernetwork:
 
 def list_hypernetworks(path):
     res = {}
-    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
+    for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
         name = os.path.splitext(os.path.basename(filename))[0]
         # Prevent a hypothetical "None.pt" from being listed.
         if name != "None":
             res[name] = filename
-    return dict(sorted(res.items()))
+    return res
 
 
 def load_hypernetwork(filename):
-- 
cgit v1.2.1


From 4a73d433b19741cacebac82987f49a15bea9913e Mon Sep 17 00:00:00 2001
From: benlisquare <116663807+benlisquare@users.noreply.github.com>
Date: Sat, 5 Nov 2022 02:48:36 +1100
Subject: General fixes to Traditional Chinese (zh_TW) localisation JSON

---
 localizations/zh_TW.json | 54 +++++++++++++++++++++++++++++-------------------
 1 file changed, 33 insertions(+), 21 deletions(-)

diff --git a/localizations/zh_TW.json b/localizations/zh_TW.json
index 4e6dac44..b818936b 100644
--- a/localizations/zh_TW.json
+++ b/localizations/zh_TW.json
@@ -7,7 +7,7 @@
     "Loading...": "載入中…",
     "view": "檢視",
     "api": "api",
-    "•": "•",
+    "•": " • ",
     "built with gradio": "基於 Gradio 構建",
     "Stable Diffusion checkpoint": "Stable Diffusion 模型權重存檔點",
     "txt2img": "文生圖",
@@ -70,12 +70,12 @@
     "Variation strength": "差異強度",
     "Resize seed from width": "自寬度縮放隨機種子",
     "Resize seed from height": "自高度縮放隨機種子",
-    "Open for Clip Aesthetic!": "打開美術風格 Clip!",
+    "Open for Clip Aesthetic!": "打開以調整 Clip 的美術風格！",
     "▼": "▼",
     "Aesthetic weight": "美術風格權重",
     "Aesthetic steps": "美術風格疊代步數",
     "Aesthetic learning rate": "美術風格學習率",
-    "Slerp interpolation": "Slerp 插值",
+    "Slerp interpolation": "球麵線性插值角度",
     "Aesthetic imgs embedding": "美術風格圖集 embedding",
     "None": "無",
     "Aesthetic text for imgs": "該圖集的美術風格描述",
@@ -105,15 +105,15 @@
     "Prompt order": "提示詞順序",
     "Sampler": "採樣器",
     "Checkpoint name": "模型權重存檔點的名稱",
-    "Hypernetwork": "超網路",
-    "Hypernet str.": "超網路強度",
+    "Hypernetwork": "超網路（Hypernetwork）",
+    "Hypernet str.": "超網路（Hypernetwork）強度",
     "Sigma Churn": "Sigma Churn",
     "Sigma min": "最小 Sigma",
     "Sigma max": "最大 Sigma",
     "Sigma noise": "Sigma noise",
     "Eta": "Eta",
     "Clip skip": "Clip 跳過",
-    "Denoising": "去噪",
+    "Denoising": "重繪幅度",
     "Cond. Image Mask Weight": "圖像調節屏蔽度",
     "X values": "X軸數值",
     "Y type": "Y軸類型",
@@ -189,6 +189,7 @@
     "Tile overlap": "圖塊重疊的畫素",
     "Upscaler": "放大演算法",
     "Lanczos": "Lanczos",
+    "Nearest": "最鄰近（整數縮放）",
     "LDSR": "LDSR",
     "BSRGAN 4x": "BSRGAN 4x",
     "ESRGAN_4x": "ESRGAN_4x",
@@ -230,15 +231,15 @@
     "for detailed explanation.": "以了解詳細說明",
     "Create embedding": "生成 embedding",
     "Create aesthetic images embedding": "生成美術風格圖集 embedding",
-    "Create hypernetwork": "生成 hypernetwork",
+    "Create hypernetwork": "生成超網路（Hypernetwork）",
     "Preprocess images": "圖像預處理",
     "Name": "名稱",
     "Initialization text": "初始化文字",
     "Number of vectors per token": "每個 token 的向量數",
     "Overwrite Old Embedding": "覆寫舊的 Embedding",
     "Modules": "模組",
-    "Enter hypernetwork layer structure": "輸入 hypernetwork 層結構",
-    "Select activation function of hypernetwork": "選擇 hypernetwork 的激活函數",
+    "Enter hypernetwork layer structure": "輸入超網路（Hypernetwork）層結構",
+    "Select activation function of hypernetwork": "選擇超網路（Hypernetwork）的激活函數",
     "linear": "linear",
     "relu": "relu",
     "leakyrelu": "leakyrelu",
@@ -276,7 +277,7 @@
     "XavierNormal": "Xavier 正態",
     "Add layer normalization": "加入層標準化",
     "Use dropout": "採用 dropout 防止過擬合",
-    "Overwrite Old Hypernetwork": "覆寫舊的 Hypernetwork",
+    "Overwrite Old Hypernetwork": "覆寫舊的超網路（Hypernetwork）",
     "Source directory": "來源目錄",
     "Destination directory": "目標目錄",
     "Existing Caption txt Action": "對已有的TXT說明文字的行為",
@@ -298,11 +299,11 @@
     "Create debug image": "生成除錯圖片",
     "Preprocess": "預處理",
     "Train an embedding; must specify a directory with a set of 1:1 ratio images": "訓練 embedding； 必須指定一組具有 1:1 比例圖像的目錄",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "訓練 embedding 或者 hypernetwork； 必須指定一組具有 1:1 比例圖像的目錄",
-    "[wiki]": "[wiki]",
+    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "訓練 embedding 或者超網路（Hypernetwork）； 必須指定一組具有 1:1 比例圖像的目錄",
+    "[wiki]": "[wiki文件]",
     "Embedding": "Embedding",
     "Embedding Learning rate": "Embedding 學習率",
-    "Hypernetwork Learning rate": "Hypernetwork 學習率",
+    "Hypernetwork Learning rate": "超網路（Hypernetwork）學習率",
     "Learning rate": "學習率",
     "Dataset directory": "資料集目錄",
     "Log directory": "日誌目錄",
@@ -312,7 +313,7 @@
     "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步將 embedding 的副本儲存到日誌目錄，0 表示禁用",
     "Save images with embedding in PNG chunks": "儲存圖像，並在 PNG 圖片檔案中嵌入 embedding 檔案",
     "Read parameters (prompt, etc...) from txt2img tab when making previews": "進行預覽時，從文生圖頁籤中讀取參數（提示詞等）",
-    "Train Hypernetwork": "訓練 Hypernetwork",
+    "Train Hypernetwork": "訓練超網路（Hypernetwork）",
     "Train Embedding": "訓練 Embedding",
     "Create an aesthetic embedding out of any number of images": "從任意數量的圖像中建立美術風格 embedding",
     "Create images embedding": "生成圖集 embedding",
@@ -418,7 +419,7 @@
     "Checkpoints to cache in RAM": "快取在內存（RAM）中的模型權重存檔點",
     "SD VAE": "模型的VAE",
     "auto": "自動",
-    "Hypernetwork strength": "Hypernetwork 強度",
+    "Hypernetwork strength": "超網路（Hypernetwork）強度",
     "Inpainting conditioning mask strength": "局部重繪時圖像調節的蒙版屏蔽強度",
     "Apply color correction to img2img results to match original colors.": "對圖生圖結果套用顏色校正以匹配原始顏色",
     "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在進行圖生圖的時候，確切地執行滑塊指定的疊代步數（正常情況下更弱的重繪幅度需要更少的疊代步數）",
@@ -488,7 +489,17 @@
     "Extension": "擴充",
     "URL": "網址",
     "Update": "更新",
+    "a1111-sd-webui-tagcomplete": "標記自動補全",
     "unknown": "未知",
+    "deforum-for-automatic1111-webui": "Deforum",
+    "sd-dynamic-prompting": "動態提示詞",
+    "stable-diffusion-webui-aesthetic-gradients": "美術風格梯度",
+    "stable-diffusion-webui-aesthetic-image-scorer": "美術風格評等",
+    "stable-diffusion-webui-artists-to-study": "藝術家圖庫",
+    "stable-diffusion-webui-dataset-tag-editor": "資料集標記編輯器",
+    "stable-diffusion-webui-images-browser": "圖庫瀏覽器",
+    "stable-diffusion-webui-inspiration": "靈感",
+    "stable-diffusion-webui-wildcards": "萬用字元",
     "Load from:": "載入自",
     "Extension index URL": "擴充清單連結",
     "URL for extension's git repository": "擴充的 git 倉庫連結",
@@ -527,8 +538,8 @@
     "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 處理蒙版區域之前要在蒙版區域內放置什麼",
     "fill it with colors of the image": "用圖像的顏色（高強度模糊）填充它",
     "keep whatever was there originally": "保留原來的圖像，不進行預處理",
-    "fill it with latent space noise": "用潛空間的噪聲填充它",
-    "fill it with latent space zeroes": "用潛空間的零填充它",
+    "fill it with latent space noise": "於潛空間填充噪聲",
+    "fill it with latent space zeroes": "於潛空間填零",
     "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "將蒙版區域（包括預留畫素長度的緩衝區域）放大到目標解析度，進行局部重繪。\n然後縮小並粘貼回原始圖像中",
     "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "將圖像大小調整為目標解析度。除非高度和寬度匹配，否則你將獲得不正確的縱橫比",
     "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "調整圖像大小，使整個目標解析度都被圖像填充。裁剪多出來的部分",
@@ -560,6 +571,8 @@
     "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "選擇哪些 Real-ESRGAN 模型顯示在網頁使用者介面。（需要重新啟動）",
     "Allowed categories for random artists selection when using the Roll button": "使用抽選藝術家按鈕時將會隨機的藝術家類別",
     "Append commas": "附加逗號",
+    "latest": "最新",
+    "behind": "落後",
     "Roll three": "抽三位出來",
     "Generate forever": "無限生成",
     "Cancel generate forever": "停止無限生成",
@@ -581,10 +594,9 @@
     "Start drawing": "開始繪製",
     "Description": "描述",
     "Action": "行動",
-    "Aesthetic Gradients": "美術風格",
-    "aesthetic-gradients": "美術風格",
-    "stable-diffusion-webui-wildcards": "萬用字元",
-    "Dynamic Prompts": "動態提示",
+    "Aesthetic Gradients": "美術風格梯度",
+    "aesthetic-gradients": "美術風格梯度",
+    "Dynamic Prompts": "動態提示詞",
     "images-browser": "圖庫瀏覽器",
     "Inspiration": "靈感",
     "Deforum": "Deforum",
-- 
cgit v1.2.1


From 5844ef8a9a165e0f456a4658bda830282cf5a55e Mon Sep 17 00:00:00 2001
From: DepFA <35278260+dfaker@users.noreply.github.com>
Date: Fri, 4 Nov 2022 16:02:25 +0000
Subject: remove private underscore indicator

---
 modules/script_callbacks.py | 36 ++++++++++++++++++------------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 83da7ca4..74dfb880 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -46,7 +46,7 @@ class CFGDenoiserParams:
 
 
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
-_callback_map = dict(
+callback_map = dict(
     callbacks_app_started=[],
     callbacks_model_loaded=[],
     callbacks_ui_tabs=[],
@@ -58,11 +58,11 @@ _callback_map = dict(
 
 
 def clear_callbacks():
-    for callback_list in _callback_map.values():
+    for callback_list in callback_map.values():
         callback_list.clear()
 
 def app_started_callback(demo: Optional[Blocks], app: FastAPI):
-    for c in _callback_map['callbacks_app_started']:
+    for c in callback_map['callbacks_app_started']:
         try:
             c.callback(demo, app)
         except Exception:
@@ -70,7 +70,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI):
 
 
 def model_loaded_callback(sd_model):
-    for c in _callback_map['callbacks_model_loaded']:
+    for c in callback_map['callbacks_model_loaded']:
         try:
             c.callback(sd_model)
         except Exception:
@@ -80,7 +80,7 @@ def model_loaded_callback(sd_model):
 def ui_tabs_callback():
     res = []
     
-    for c in _callback_map['callbacks_ui_tabs']:
+    for c in callback_map['callbacks_ui_tabs']:
         try:
             res += c.callback() or []
         except Exception:
@@ -90,7 +90,7 @@ def ui_tabs_callback():
 
 
 def ui_settings_callback():
-    for c in _callback_map['callbacks_ui_settings']:
+    for c in callback_map['callbacks_ui_settings']:
         try:
             c.callback()
         except Exception:
@@ -98,7 +98,7 @@ def ui_settings_callback():
 
 
 def before_image_saved_callback(params: ImageSaveParams):
-    for c in _callback_map['callbacks_before_image_saved']:
+    for c in callback_map['callbacks_before_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -106,7 +106,7 @@ def before_image_saved_callback(params: ImageSaveParams):
 
 
 def image_saved_callback(params: ImageSaveParams):
-    for c in _callback_map['callbacks_image_saved']:
+    for c in callback_map['callbacks_image_saved']:
         try:
             c.callback(params)
         except Exception:
@@ -114,7 +114,7 @@ def image_saved_callback(params: ImageSaveParams):
 
 
 def cfg_denoiser_callback(params: CFGDenoiserParams):
-    for c in _callback_map['callbacks_cfg_denoiser']:
+    for c in callback_map['callbacks_cfg_denoiser']:
         try:
             c.callback(params)
         except Exception:
@@ -133,13 +133,13 @@ def remove_current_script_callbacks():
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
     if filename == 'unknown file':
         return
-    for callback_list in _callback_map.values():
+    for callback_list in callback_map.values():
         for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
             callback_list.remove(callback_to_remove)
 
 
 def remove_callbacks_for_function(callback_func):
-    for callback_list in _callback_map.values():
+    for callback_list in callback_map.values():
         for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
             callback_list.remove(callback_to_remove)
 
@@ -147,13 +147,13 @@ def remove_callbacks_for_function(callback_func):
 def on_app_started(callback):
     """register a function to be called when the webui started, the gradio `Block` component and
     fastapi `FastAPI` object are passed as the arguments"""
-    add_callback(_callback_map['callbacks_app_started'], callback)
+    add_callback(callback_map['callbacks_app_started'], callback)
 
 
 def on_model_loaded(callback):
     """register a function to be called when the stable diffusion model is created; the model is
     passed as an argument"""
-    add_callback(_callback_map['callbacks_model_loaded'], callback)
+    add_callback(callback_map['callbacks_model_loaded'], callback)
 
 
 def on_ui_tabs(callback):
@@ -166,13 +166,13 @@ def on_ui_tabs(callback):
     title is tab text displayed to user in the UI
     elem_id is HTML id for the tab
     """
-    add_callback(_callback_map['callbacks_ui_tabs'], callback)
+    add_callback(callback_map['callbacks_ui_tabs'], callback)
 
 
 def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
-    add_callback(_callback_map['callbacks_ui_settings'], callback)
+    add_callback(callback_map['callbacks_ui_settings'], callback)
 
 
 def on_before_image_saved(callback):
@@ -180,7 +180,7 @@ def on_before_image_saved(callback):
     The callback is called with one argument:
         - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
     """
-    add_callback(_callback_map['callbacks_before_image_saved'], callback)
+    add_callback(callback_map['callbacks_before_image_saved'], callback)
 
 
 def on_image_saved(callback):
@@ -188,7 +188,7 @@ def on_image_saved(callback):
     The callback is called with one argument:
         - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
     """
-    add_callback(_callback_map['callbacks_image_saved'], callback)
+    add_callback(callback_map['callbacks_image_saved'], callback)
 
 
 def on_cfg_denoiser(callback):
@@ -196,4 +196,4 @@ def on_cfg_denoiser(callback):
     The callback is called with one argument:
         - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
     """
-    add_callback(_callback_map['callbacks_cfg_denoiser'], callback)
+    add_callback(callback_map['callbacks_cfg_denoiser'], callback)
-- 
cgit v1.2.1


From d469186fb051aa8ae0c868d1b1e3e11b0c846312 Mon Sep 17 00:00:00 2001
From: benlisquare <116663807+benlisquare@users.noreply.github.com>
Date: Sat, 5 Nov 2022 03:42:50 +1100
Subject: Minor fix to Traditional Chinese (zh_TW) JSON

---
 localizations/zh_TW.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/localizations/zh_TW.json b/localizations/zh_TW.json
index b818936b..04bde864 100644
--- a/localizations/zh_TW.json
+++ b/localizations/zh_TW.json
@@ -75,7 +75,7 @@
     "Aesthetic weight": "美術風格權重",
     "Aesthetic steps": "美術風格疊代步數",
     "Aesthetic learning rate": "美術風格學習率",
-    "Slerp interpolation": "球麵線性插值角度",
+    "Slerp interpolation": "球面線性插值角度",
     "Aesthetic imgs embedding": "美術風格圖集 embedding",
     "None": "無",
     "Aesthetic text for imgs": "該圖集的美術風格描述",
-- 
cgit v1.2.1


From 0d7e01d9950e013784c4b77c05aa7583ea69edc8 Mon Sep 17 00:00:00 2001
From: innovaciones <sonygarcia99@gmail.com>
Date: Fri, 4 Nov 2022 12:14:32 -0600
Subject: Open extensions links in new tab

Fixed for "Available" tab
---
 modules/ui_extensions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index a81de9a7..8e0d41d5 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -188,7 +188,7 @@ def refresh_available_extensions_from_data():
 
         code += f"""
             <tr>
-                <td><a href="{html.escape(url)}">{html.escape(name)}</a></td>
+                <td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a></td>
                 <td>{html.escape(description)}</td>
                 <td>{install_code}</td>
             </tr>
-- 
cgit v1.2.1


From b8435e632f7ba0da12a2c8e9c788dda519279d24 Mon Sep 17 00:00:00 2001
From: evshiron <evshiron@gmail.com>
Date: Sat, 5 Nov 2022 02:36:47 +0800
Subject: add --cors-allow-origins cmd opt

---
 modules/shared.py | 7 ++++---
 webui.py          | 9 +++++++++
 2 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index a9e28b9c..e83cbcdf 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -86,6 +86,7 @@ parser.add_argument("--nowebui", action='store_true', help="use api=True to laun
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
 parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
+parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origins", default=None)
 
 cmd_opts = parser.parse_args()
 restricted_opts = {
@@ -147,9 +148,9 @@ class State:
         self.interrupted = True
 
     def nextjob(self):
-        if opts.show_progress_every_n_steps == -1: 
+        if opts.show_progress_every_n_steps == -1:
             self.do_set_current_image()
-            
+
         self.job_no += 1
         self.sampling_step = 0
         self.current_image_sampling_step = 0
@@ -198,7 +199,7 @@ class State:
             return
         if self.current_latent is None:
             return
-            
+
         if opts.show_progress_grid:
             self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
         else:
diff --git a/webui.py b/webui.py
index 81df09dd..3788af0b 100644
--- a/webui.py
+++ b/webui.py
@@ -5,6 +5,7 @@ import importlib
 import signal
 import threading
 from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
 
 from modules.paths import script_path
@@ -93,6 +94,11 @@ def initialize():
     signal.signal(signal.SIGINT, sigint_handler)
 
 
+def setup_cors(app):
+    if cmd_opts.cors_allow_origins:
+        app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_methods=['*'])
+
+
 def create_api(app):
     from modules.api.api import Api
     api = Api(app, queue_lock)
@@ -114,6 +120,7 @@ def api_only():
     initialize()
 
     app = FastAPI()
+    setup_cors(app)
     app.add_middleware(GZipMiddleware, minimum_size=1000)
     api = create_api(app)
 
@@ -147,6 +154,8 @@ def webui():
         # runnnig its code. We disable this here. Suggested by RyotaK.
         app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
 
+        setup_cors(app)
+
         app.add_middleware(GZipMiddleware, minimum_size=1000)
 
         if launch_api:
-- 
cgit v1.2.1


From 467d8b967b5d1b1984ab113bec3fff217736e7ac Mon Sep 17 00:00:00 2001
From: AngelBottomless <35677394+aria1th@users.noreply.github.com>
Date: Sat, 5 Nov 2022 04:24:42 +0900
Subject: Fix errors from commit f2b697 with --hide-ui-dir-config

https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/f2b69709eaff88fc3a2bd49585556ec0883bf5ea
---
 modules/ui.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 4c2829af..76ca9b07 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1446,17 +1446,19 @@ def create_ui(wrap_gradio_gpu_call):
                 continue
 
             oldval = opts.data.get(key, None)
-
-            setattr(opts, key, value)
-
+            try:
+                setattr(opts, key, value)
+            except RuntimeError:
+                continue
             if oldval != value:
                 if opts.data_labels[key].onchange is not None:
                     opts.data_labels[key].onchange()
 
                 changed += 1
-
-        opts.save(shared.config_filename)
-
+        try:
+            opts.save(shared.config_filename)
+        except RuntimeError:
+            return opts.dumpjson(), f'{changed} settings changed without save.'
         return opts.dumpjson(), f'{changed} settings changed.'
 
     def run_settings_single(value, key):
-- 
cgit v1.2.1


From 30b1bcc64e67ad50c5d3af3a6fe1bd1e9553f34e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 4 Nov 2022 22:56:18 +0300
Subject: fix upscale loop erroneously applied multiple times

---
 modules/upscaler.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/modules/upscaler.py b/modules/upscaler.py
index 83fde7ca..c4e6e6bd 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -57,10 +57,18 @@ class Upscaler:
         self.scale = scale
         dest_w = img.width * scale
         dest_h = img.height * scale
+
         for i in range(3):
-            if img.width > dest_w and img.height > dest_h:
-                break
+            shape = (img.width, img.height)
+
             img = self.do_upscale(img, selected_model)
+
+            if shape == (img.width, img.height):
+                break
+
+            if img.width >= dest_w and img.height >= dest_h:
+                break
+
         if img.width != dest_w or img.height != dest_h:
             img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS)
 
-- 
cgit v1.2.1


From c0f7dbda3361daaa3e315e747fe5bebb75ea55d0 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Fri, 4 Nov 2022 23:01:58 +0000
Subject: Update k-diffusion to release 0.0.10

---
 launch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index 2a51f20e..5fa11560 100644
--- a/launch.py
+++ b/launch.py
@@ -142,7 +142,7 @@ def prepare_enviroment():
 
     stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
     taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
-    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
+    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "60e5042ca0da89c14d1dd59d73883280f8fce991")
     codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
-- 
cgit v1.2.1


From 6008c0773ea575353f9b87da8a58454e20cc7857 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Fri, 4 Nov 2022 23:03:05 +0000
Subject: Add support for new DPM-Solver++ samplers

---
 modules/sd_samplers.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index c7c414ef..7ece6556 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -29,6 +29,10 @@ samplers_k_diffusion = [
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
     ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
+    ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM-Solver++(2S) Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
 ]
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.1


From f92dc505a013af9e385c7edbdf97539be62503d6 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Fri, 4 Nov 2022 23:12:48 +0000
Subject: Fix name

---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 7ece6556..b28a2e4c 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -31,7 +31,7 @@ samplers_k_diffusion = [
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
     ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
     ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
-    ('DPM-Solver++(2S) Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM-Solver++(2S) a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
     ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
 ]
 
-- 
cgit v1.2.1


From 1b6c2fc749e12f12bbee4705e65f217d23fa9072 Mon Sep 17 00:00:00 2001
From: hentailord85ez <112723046+hentailord85ez@users.noreply.github.com>
Date: Fri, 4 Nov 2022 23:28:13 +0000
Subject: Reorder samplers

---
 modules/sd_samplers.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b28a2e4c..1e88f7ee 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -24,13 +24,13 @@ samplers_k_diffusion = [
     ('Heun', 'sample_heun', ['k_heun'], {}),
     ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
+    ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
     ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
-    ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
-    ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM-Solver++(2S) a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
     ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
 ]
-- 
cgit v1.2.1


From ebce0c57c78a3f22178e3a38938d19ec0dfb703d Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Sat, 5 Nov 2022 11:38:24 +0800
Subject: Use typing.Optional instead of | to add support for Python 3.9 and
 below.

---
 modules/api/models.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/modules/api/models.py b/modules/api/models.py
index 2ae75f43..a44c5ddd 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,6 +1,6 @@
 import inspect
 from pydantic import BaseModel, Field, create_model
-from typing import Any, Optional, Union
+from typing import Any, Optional
 from typing_extensions import Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
@@ -185,22 +185,22 @@ _options = vars(parser)['_option_string_actions']
 for key in _options:
     if(_options[key].dest != 'help'):
         flag = _options[key]
-        _type = str 
-        if(_options[key].default != None): _type = type(_options[key].default) 
+        _type = str
+        if _options[key].default is not None: _type = type(_options[key].default)
         flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
 
 FlagsModel = create_model("Flags", **flags)
 
 class SamplerItem(BaseModel):
     name: str = Field(title="Name")
-    aliases: list[str]  = Field(title="Aliases")
+    aliases: list[str] = Field(title="Aliases")
     options: dict[str, str] = Field(title="Options")
 
 class UpscalerItem(BaseModel):
     name: str = Field(title="Name")
-    model_name: str | None = Field(title="Model Name")
-    model_path: str | None = Field(title="Path")
-    model_url: str | None = Field(title="URL")
+    model_name: Optional[str] = Field(title="Model Name")
+    model_path: Optional[str] = Field(title="Path")
+    model_url: Optional[str] = Field(title="URL")
 
 class SDModelItem(BaseModel):
     title: str = Field(title="Title")
@@ -211,21 +211,21 @@ class SDModelItem(BaseModel):
 
 class HypernetworkItem(BaseModel):
     name: str = Field(title="Name")
-    path: str | None = Field(title="Path")
+    path: Optional[str] = Field(title="Path")
 
 class FaceRestorerItem(BaseModel):
     name: str = Field(title="Name")
-    cmd_dir: str | None = Field(title="Path")
+    cmd_dir: Optional[str] = Field(title="Path")
 
 class RealesrganItem(BaseModel):
     name: str = Field(title="Name")
-    path: str | None = Field(title="Path")
-    scale: int | None = Field(title="Scale")
+    path: Optional[str] = Field(title="Path")
+    scale: Optional[int] = Field(title="Scale")
 
 class PromptStyleItem(BaseModel):
     name: str = Field(title="Name")
-    prompt: str | None = Field(title="Prompt")
-    negative_prompt: str | None = Field(title="Negative Prompt")
+    prompt: Optional[str] = Field(title="Prompt")
+    negative_prompt: Optional[str] = Field(title="Negative Prompt")
 
 class ArtistItem(BaseModel):
     name: str = Field(title="Name")
-- 
cgit v1.2.1


From e9a5562b9b27a1a4f9c282637b111cefd9727a41 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Sat, 5 Nov 2022 04:06:51 -0500
Subject: add support for tls (gradio tls options)

---
 modules/shared.py |  3 +++
 webui.py          | 22 ++++++++++++++++++++--
 2 files changed, 23 insertions(+), 2 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 962115f6..7a20c3af 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -86,6 +86,9 @@ parser.add_argument("--nowebui", action='store_true', help="use api=True to laun
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
 parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
+parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
+parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
+parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
 
 cmd_opts = parser.parse_args()
 restricted_opts = {
diff --git a/webui.py b/webui.py
index 81df09dd..d366f4ca 100644
--- a/webui.py
+++ b/webui.py
@@ -34,7 +34,7 @@ from modules.shared import cmd_opts
 import modules.hypernetworks.hypernetwork
 
 queue_lock = threading.Lock()
-
+server_name = "0.0.0.0" if cmd_opts.listen else cmd_opts.server_name
 
 def wrap_queued_call(func):
     def f(*args, **kwargs):
@@ -85,6 +85,22 @@ def initialize():
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
 
+    if cmd_opts.tls_keyfile is not None and cmd_opts.tls_keyfile is not None:
+
+        try:
+            if not os.path.exists(cmd_opts.tls_keyfile):
+                print("Invalid path to TLS keyfile given")
+            if not os.path.exists(cmd_opts.tls_certfile):
+                print(f"Invalid path to TLS certfile: '{cmd_opts.tls_certfile}'")
+        except TypeError:
+            cmd_opts.tls_keyfile = cmd_opts.tls_certfile = None
+            print(f"path: '{cmd_opts.tls_keyfile}' {type(cmd_opts.tls_keyfile)}")
+            print(f"path: '{cmd_opts.tls_certfile}' {type(cmd_opts.tls_certfile)}")
+            print("TLS setup invalid, running webui without TLS")
+        else:
+            print("Running with TLS")
+
+
     # make the program just exit at ctrl+c without waiting for anything
     def sigint_handler(sig, frame):
         print(f'Interrupted with signal {sig} in {frame}')
@@ -131,8 +147,10 @@ def webui():
 
         app, local_url, share_url = demo.launch(
             share=cmd_opts.share,
-            server_name="0.0.0.0" if cmd_opts.listen else None,
+            server_name=server_name,
             server_port=cmd_opts.port,
+            ssl_keyfile=cmd_opts.tls_keyfile,
+            ssl_certfile=cmd_opts.tls_certfile,
             debug=cmd_opts.gradio_debug,
             auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
             inbrowser=cmd_opts.autolaunch,
-- 
cgit v1.2.1


From a02bad570ef7718436369bb4e4aa5b8e0f1f5689 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Sat, 5 Nov 2022 04:14:21 -0500
Subject: rm dbg

---
 webui.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/webui.py b/webui.py
index d366f4ca..222dbeee 100644
--- a/webui.py
+++ b/webui.py
@@ -94,8 +94,6 @@ def initialize():
                 print(f"Invalid path to TLS certfile: '{cmd_opts.tls_certfile}'")
         except TypeError:
             cmd_opts.tls_keyfile = cmd_opts.tls_certfile = None
-            print(f"path: '{cmd_opts.tls_keyfile}' {type(cmd_opts.tls_keyfile)}")
-            print(f"path: '{cmd_opts.tls_certfile}' {type(cmd_opts.tls_certfile)}")
             print("TLS setup invalid, running webui without TLS")
         else:
             print("Running with TLS")
-- 
cgit v1.2.1


From 03b08c4a6b0609f24ec789d40100529b92ef0612 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 5 Nov 2022 15:04:48 +0300
Subject: do not die when an extension's repo has no remote

---
 modules/extensions.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/extensions.py b/modules/extensions.py
index 897af96e..8e0977fd 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -34,8 +34,11 @@ class Extension:
         if repo is None or repo.bare:
             self.remote = None
         else:
-            self.remote = next(repo.remote().urls, None)
-            self.status = 'unknown'
+            try:
+                self.remote = next(repo.remote().urls, None)
+                self.status = 'unknown'
+            except Exception:
+                self.remote = None
 
     def list_files(self, subdir, extension):
         from modules import scripts
-- 
cgit v1.2.1


From a170e3d22231e145f42bb878a76ae5f76fdca230 Mon Sep 17 00:00:00 2001
From: Evgeniy <github@printerror.xyz>
Date: Sat, 5 Nov 2022 17:06:56 +0300
Subject: Python 3.8 typing compatibility

Solves problems with

```Traceback (most recent call last):
  File "webui.py", line 201, in <module>
    webui()
  File "webui.py", line 178, in webui
    create_api(app)
  File "webui.py", line 117, in create_api
    from modules.api.api import Api
  File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\api.py", line 9, in <module>
    from modules.api.models import *
  File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\models.py", line 194, in <module>
    class SamplerItem(BaseModel):
  File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\models.py", line 196, in SamplerItem
    aliases: list[str] = Field(title="Aliases")
TypeError: 'type' object is not subscriptable```

and

```Traceback (most recent call last):
  File "webui.py", line 201, in <module>
    webui()
  File "webui.py", line 178, in webui
    create_api(app)
  File "webui.py", line 117, in create_api
    from modules.api.api import Api
  File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\api.py", line 9, in <module>
    from modules.api.models import *
  File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\models.py", line 194, in <module>
    class SamplerItem(BaseModel):
  File "H:\AIart\stable-diffusion\stable-diffusion-webui\modules\api\models.py", line 197, in SamplerItem
    options: dict[str, str] = Field(title="Options")
TypeError: 'type' object is not subscriptable```
---
 modules/api/models.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/api/models.py b/modules/api/models.py
index a44c5ddd..f89da1ff 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -5,7 +5,7 @@ from typing_extensions import Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 from modules.shared import sd_upscalers, opts, parser
-from typing import List
+from typing import Dict, List
 
 API_NOT_ALLOWED = [
     "self",
@@ -193,8 +193,8 @@ FlagsModel = create_model("Flags", **flags)
 
 class SamplerItem(BaseModel):
     name: str = Field(title="Name")
-    aliases: list[str] = Field(title="Aliases")
-    options: dict[str, str] = Field(title="Options")
+    aliases: List[str] = Field(title="Aliases")
+    options: Dict[str, str] = Field(title="Options")
 
 class UpscalerItem(BaseModel):
     name: str = Field(title="Name")
@@ -230,4 +230,4 @@ class PromptStyleItem(BaseModel):
 class ArtistItem(BaseModel):
     name: str = Field(title="Name")
     score: float = Field(title="Score")
-    category: str = Field(title="Category")
\ No newline at end of file
+    category: str = Field(title="Category")
-- 
cgit v1.2.1


From 62e3d71aa778928d63cab81d9d8cde33e55bebb3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 5 Nov 2022 17:09:42 +0300
Subject: rework the code to not use the walrus operator because colab's 3.7
 does not support it

---
 modules/hypernetworks/hypernetwork.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 5ceed6ee..7f182712 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -429,13 +429,16 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     weights = hypernetwork.weights()
     for weight in weights:
         weight.requires_grad = True
+
     # Here we use optimizer from saved HN, or we can specify as UI option.
-    if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict:
+    if hypernetwork.optimizer_name in optimizer_dict:
         optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
+        optimizer_name = hypernetwork.optimizer_name
     else:
-        print(f"Optimizer type {optimizer_name} is not defined!")
+        print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
         optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
         optimizer_name = 'AdamW'
+
     if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
         try:
             optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
-- 
cgit v1.2.1


From 159475e072f2ed3db8235aab9c3fa18640b93b80 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 5 Nov 2022 18:32:22 +0300
Subject: tweak names a bit for new samplers

---
 modules/sd_samplers.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 1e88f7ee..783992d2 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -24,15 +24,15 @@ samplers_k_diffusion = [
     ('Heun', 'sample_heun', ['k_heun'], {}),
     ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
-    ('DPM-Solver++(2S) a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
-    ('DPM-Solver++(2M)', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
     ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
-    ('DPM-Solver++(2S) a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
-    ('DPM-Solver++(2M) Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
 ]
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.1


From 29f48b7803dd0890cb5328fa290ab12045706316 Mon Sep 17 00:00:00 2001
From: Dynamic <bradje@naver.com>
Date: Sun, 6 Nov 2022 00:37:37 +0900
Subject: Update ko_KR.json

New setting option and some additional extension index strings
---
 localizations/ko_KR.json | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
index 29e10075..cf302aaf 100644
--- a/localizations/ko_KR.json
+++ b/localizations/ko_KR.json
@@ -16,6 +16,7 @@
   "A merger of the two checkpoints will be generated in your": "체크포인트들이 병합된 결과물이 당신의",
   "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
   "Action": "작업",
+  "Add a button to convert the prompts used in NovelAI for use in the WebUI. In addition, add a button that allows you to recall a previously used prompt.": "NovelAI에서 사용되는 프롬프트를 WebUI에서 사용할 수 있게 변환하는 버튼을 추가합니다. 덤으로 이전에 사용한 프롬프트를 불러오는 버튼도 추가됩니다.",
   "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
   "Add a second progress bar to the console that shows progress for an entire job.": "콘솔에 전체 작업의 진행도를 보여주는 2번째 프로그레스 바 추가하기",
   "Add difference": "차이점 추가",
@@ -24,6 +25,7 @@
   "Add model hash to generation information": "생성 정보에 모델 해시 추가",
   "Add model name to generation information": "생성 정보에 모델 이름 추가",
   "Add number to filename when saving": "이미지를 저장할 때 파일명에 숫자 추가하기",
+  "Adds a tab that lets you preview how CLIP model would tokenize your text.": "CLIP 모델이 텍스트를 어떻게 토큰화할지 미리 보여주는 탭을 추가합니다.",
   "Adds a tab to the webui that allows the user to automatically extract keyframes from video, and manually extract 512x512 crops of those frames for use in model training.": "WebUI에 비디오로부터 자동으로 키프레임을 추출하고, 그 키프레임으로부터 모델 훈련에 사용될 512x512 이미지를 잘라낼 수 있는 탭을 추가합니다.",
   "Aesthetic Gradients": "스타일 그라디언트",
   "Aesthetic Image Scorer": "스타일 이미지 스코어러",
@@ -33,6 +35,7 @@
   "Aesthetic text for imgs": "스타일 텍스트",
   "Aesthetic weight": "스타일 가중치",
   "Allowed categories for random artists selection when using the Roll button": "랜덤 버튼을 눌러 무작위 작가를 선택할 때 허용된 카테고리",
+  "Allows you to include various shortcodes in your prompts. You can pull text from files, set up your own variables, process text through conditional functions, and so much more - it's like wildcards on steroids.": "프롬프트에 다양한 숏코드를 추가할 수 있게 해줍니다. 파일로부터 텍스트 추출, 변수 설정, 조건 함수로 텍스트 처리 등등 - 스테로이드를 맞은 와일드카드라 할 수 있죠.",
   "Always print all generation info to standard output": "기본 아웃풋에 모든 생성 정보 항상 출력하기",
   "Always save all generated image grids": "생성된 이미지 그리드 항상 저장하기",
   "Always save all generated images": "생성된 이미지 항상 저장하기",
@@ -54,6 +57,7 @@
   "Batch Process": "이미지 여러장 처리",
   "Batch size": "배치 크기",
   "behind": "최신 아님",
+  "Booru tag autocompletion": "Booru 태그 자동완성",
   "BSRGAN 4x": "BSRGAN 4x",
   "built with gradio": "gradio로 제작되었습니다",
   "Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Chad 스코어러를 기반으로 한 CLIP+MLP 스타일 점수 예측기를 이용해 생성된 이미지의 스타일 점수를 계산합니다.",
@@ -114,6 +118,7 @@
   "Directory for saving images using the Save button": "저장 버튼을 이용해 저장하는 이미지들의 저장 경로",
   "Directory name pattern": "디렉토리명 패턴",
   "directory.": "저장 경로에 저장됩니다.",
+  "Displays autocompletion hints for tags from image booru boards such as Danbooru. Uses local tag CSV files and includes a config for customization.": "Danbooru 같은 이미지 booru 보드의 태그에 대한 자동완성 힌트를 보여줍니다. 로컬 환경에 저장된 CSV 파일을 사용하고 조정 가능한 설정 파일이 포함되어 있습니다.",
   "Do not add watermark to images": "이미지에 워터마크 추가하지 않기",
   "Do not do anything special": "아무것도 하지 않기",
   "Do not save grids consisting of one picture": "이미지가 1개뿐인 그리드는 저장하지 않기",
@@ -317,6 +322,7 @@
   "None": "없음",
   "Nothing": "없음",
   "Nothing found in the image.": "Nothing found in the image.",
+  "novelai-2-local-prompt": "NovelAI 프롬프트 변환기",
   "Number of columns on the page": "각 페이지마다 표시할 가로줄 수",
   "Number of grids in each row": "각 세로줄마다 표시될 그리드 수",
   "number of images to delete consecutively next": "연속적으로 삭제할 이미지 수",
@@ -431,6 +437,7 @@
   "Save images with embedding in PNG chunks": "PNG 청크로 이미지에 임베딩을 포함시켜 저장",
   "Save style": "스타일 저장",
   "Save text information about generation parameters as chunks to png files": "이미지 생성 설정값을 PNG 청크에 텍스트로 저장",
+  "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file.": "옵티마이저 상태를 별개의 *.optim 파일로 저장하기. 하이퍼네트워크 파일과 일치하는 optim 파일로부터 훈련을 재개할 수 있습니다.",
   "Saving images/grids": "이미지/그리드 저장",
   "Saving to a directory": "디렉토리에 저장",
   "Scale by": "스케일링 배수 지정",
@@ -515,6 +522,7 @@
   "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 업스케일러들의 타일 사이즈. 0 = 타일링 없음.",
   "Tiling": "타일링",
   "Time taken:": "소요 시간 : ",
+  "tokenizer": "토크나이저",
   "Torch active/reserved:": "활성화/예약된 Torch 양 : ",
   "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "활성화된 Torch : 생성 도중 캐시된 데이터를 포함해 사용된 VRAM의 최대량\n예약된 Torch : 활성화되고 캐시된 모든 데이터를 포함해 Torch에게 할당된 VRAM의 최대량\n시스템 VRAM : 모든 어플리케이션에 할당된 VRAM 최대량 / 총 GPU VRAM (최고 이용도%)",
   "Train": "훈련",
-- 
cgit v1.2.1


From 0b028c84ab8a8edcf9648622ed3fe4b8ca013334 Mon Sep 17 00:00:00 2001
From: Riccardo Giovanetti <29801031+Harvester62@users.noreply.github.com>
Date: Sat, 5 Nov 2022 18:12:06 +0100
Subject: Italian localization - Additions and updates

Updated localization with the latest version of these Scripts/Extensions:

SD-Chad - Stable Diffusion Aesthetic Scorer (added)
AlphaCanvas
StylePile
Alternate Sampler Noise Schedules
SD Latent Mirroring (new)
SD Dynamic Prompts
Dataset Tag Editor
Depth Maps (new)
Improved prompt matrix (new)

New Extensions:

Auto-sd-paint-ext (new)
training-picker (new)
Unprompted
NovelAI-2-local-prompt (new)
tokenizer (new)

Hope there aren't too many mistakes or wrong translations, in case let me know.
---
 localizations/it_IT.json | 514 ++++++++++++++++++++++++++++++++++++++---------
 1 file changed, 423 insertions(+), 91 deletions(-)

diff --git a/localizations/it_IT.json b/localizations/it_IT.json
index 83d0ccce..22a6f546 100644
--- a/localizations/it_IT.json
+++ b/localizations/it_IT.json
@@ -17,11 +17,14 @@
     "Checkpoint Merger": "Miscelatore di Checkpoint",
     "Train": "Addestramento",
     "Create aesthetic embedding": "Crea incorporamento estetico",
+    "auto-sd-paint-ext Guide/Panel": "KRITA Plugin Guida",
     "Dataset Tag Editor": "Dataset Tag Editor",
     "Deforum": "Deforum",
     "Artists To Study": "Artisti per studiare",
     "Image Browser": "Galleria immagini",
     "Inspiration": "Ispirazione",
+    "Tokenizer": "Tokenizzatore",
+    "Training Picker": "Training Picker",
     "Settings": "Impostazioni",
     "Extensions": "Estensioni",
     "Prompt": "Prompt",
@@ -49,11 +52,15 @@
     "Heun": "Heun",
     "DPM2": "DPM2",
     "DPM2 a": "DPM2 a",
+    "DPM++ 2S a": "DPM++ 2S a",
+    "DPM++ 2M": "DPM++ 2M",
     "DPM fast": "DPM fast",
     "DPM adaptive": "DPM adaptive",
     "LMS Karras": "LMS Karras",
     "DPM2 Karras": "DPM2 Karras",
     "DPM2 a Karras": "DPM2 a Karras",
+    "DPM++ 2S a Karras": "DPM++ 2S a Karras",
+    "DPM++ 2M Karras": "DPM++ 2M Karras",
     "DDIM": "DDIM",
     "PLMS": "PLMS",
     "Width": "Larghezza",
@@ -84,18 +91,30 @@
     "Aesthetic text for imgs": "Estetica - Testo per le immagini",
     "Slerp angle": "Angolo Slerp",
     "Is negative text": "È un testo negativo",
+    "Unprompted": "Unprompted",
+    "Dry Run": "Esecuzione a vuoto (Debug)",
+    "NEW!": "NUOVO!",
+    "Premium Fantasy Card Template": "Premium Fantasy Card Template",
+    "is now available.": "è ora disponibile.",
+    "Generate a wide variety of creatures and characters in the style of a fantasy card game. Perfect for heroes, animals, monsters, and even crazy hybrids.": "Genera un'ampia varietà di creature e personaggi nello stile di un gioco di carte fantasy. Perfetto per eroi, animali, mostri e persino ibridi incredibili.",
+    "Learn More ➜": "Per saperne di più ➜",
+    "Purchases help fund the continued development of Unprompted. Thank you for your support!": "Gli acquisti aiutano a finanziare il continuo sviluppo di Unprompted. Grazie per il vostro sostegno!",
+    "Dismiss": "Rimuovi",
     "Script": "Script",
     "Random grid": "Generaz. casuale (griglia)",
     "Random": "Generaz. casuale (no griglia)",
     "StylePile": "StylePile",
-    "Advanced prompt matrix": "Matrice di prompt avanzata",
     "Advanced Seed Blending": "Miscelazione Semi Avanzata",
     "Alternate Sampler Noise Schedules": "Metodi alternativi di campionamento del rumore",
     "Animator v6": "Animator v6",
     "Asymmetric tiling": "Piastrellatura asimmetrica",
+    "SD Chad - Stable Diffusion Aesthetic Scorer": "SD Chad - Assegna Punteggio Estetica",
     "Custom code": "Codice personalizzato",
-    "Embedding to Shareable PNG": "Incorporamento convertito in PNG condivisibile",
+    "DepthMap v0.1.0": "DepthMap v0.1.0",
+    "Embedding to Shareable PNG": "Converte Incorporamento in PNG condivisibile",
     "Force symmetry": "Forza la simmetria",
+    "Improved prompt matrix": "Improved prompt matrix",
+    "Latent Mirroring": "Rispecchia e capovolge l'immagine latente",
     "Prompts interpolation": "Interpola Prompt",
     "Prompt matrix": "Matrice dei prompt",
     "Prompt morph": "Metamorfosi del prompt",
@@ -104,7 +123,6 @@
     "Seed travel": "Interpolazione semi",
     "Shift attention": "Sposta l'attenzione",
     "Text to Vector Graphics": "Da testo a grafica vettoriale",
-    "Unprompted": "Unprompted",
     "X/Y plot": "Grafico X/Y",
     "X/Y/Z plot": "Grafico X/Y/Z",
     "Dynamic Prompting v0.13.6": "Prompt dinamici v0.13.6",
@@ -120,70 +138,186 @@
     "Keep -1 for seeds": "Mantieni sempre il seme a -1",
     "x/y change": "Inverti ordine assi X/Y (Passi/CFG)",
     "Loops": "Cicli",
-    "Focus on:": "Focus su:",
-    "No focus": "Nessun Focus",
-    "Portraits (tick Restore faces above for best results)": "Ritratti (selezionare 'Restaura volti' in alto per ottenere i migliori risultati)",
-    "Feminine and extra attractive (tick Restore faces above for best results)": "Femminile ed estremamente attraente (selezionare 'Restaura volti' per ottenere i migliori risultati)",
-    "Masculine and extra attractive (tick Restore faces above for best results)": "Maschile ed estremamente attraente (selezionare 'Restaura volti'  per ottenere i migliori risultati)",
-    "Monsters": "Mostri",
-    "Robots": "Robot",
-    "Retrofuturistic": "Retrofuturistico",
-    "Propaganda": "Propaganda",
-    "Landscapes": "Paesaggi",
-    "Hints": "Suggerimenti",
+    "Set Batch count to Sequential prompt count": "Imposta il lotto di immagini sul numero di prompt sequenziali",
+    "Sequential prompts [X]": "Prompt sequenziali [X]",
+    "Random prompts [R]": "Prompt randomici [R]",
+    "Image type strength": "Intensità del tipo di immagine",
     "Image type": "Tipo di immagine",
     "Not set": "Non impostato",
     "Photography": "Fotografia",
     "Digital art": "Arte digitale",
-    "3D Rendering": "3D Rendering",
+    "3D Rendering": "Rendering 3D",
     "Painting": "Dipinto",
-    "Sketch": "Schizzo",
-    "Classic Comics": "Fumetti classici",
-    "Modern Comics": "Fumetti moderni",
-    "Manga": "Manga",
-    "Vector art": "Arte vettoriale",
-    "Visual style": "Stile visivo",
-    "Realism": "Realismo",
-    "Photorealism": "Fotorealismo",
-    "Hyperrealism": "Iperrealismo",
-    "Surrealism": "Surrealismo",
-    "Modern Art": "Arte moderna",
-    "Fauvism": "Fauvismo",
-    "Futurism": "Futurismo",
-    "Painterly": "Pittorico",
-    "Pointillisme": "Puntinismo",
-    "Abstract": "Astratto",
-    "Pop Art": "Pop Art",
-    "Impressionist": "Impressionista",
-    "Cubism": "Cubismo",
-    "Linocut": "Linoleografia",
-    "Fantasy": "Fantasia",
+    "Vector Art": "Arte vettoriale",
+    "Mood": "Stato d'animo",
+    "Amusing": "Amusing",
+    "Angry": "Angry",
+    "Cosy": "Cosy",
+    "Depressing": "Depressing",
+    "Disgusting": "Disgusting",
+    "Embarrassing": "Embarrassing",
+    "Energetic": "Energetic",
+    "Evil": "Evil",
+    "Fearful": "Fearful",
+    "Frightening": "Frightening",
+    "Grim": "Grim",
+    "Guilty": "Guilty",
+    "Happy": "Happy",
+    "Hopeful": "Hopeful",
+    "Hopeless": "Hopeless",
+    "Lonely": "Lonely",
+    "Lustful": "Lustful",
+    "Peaceful": "Peaceful",
+    "Proud": "Proud",
+    "Relieving": "Relieving",
+    "Romantic": "Romantic",
+    "Sad": "Sad",
+    "Satisfying": "Satisfying",
+    "Shameful": "Shameful",
+    "Surprising": "Surprising",
     "Colors": "Colori",
-    "Chaotic": "Caotico",
-    "Primary colors": "Colori primari",
-    "Colorful": "Colorato",
+    "Primary Colors": "Primary Colors",
     "Vivid": "Vivido",
-    "Muted colors": "Colori tenui",
-    "Low contrast": "Basso contrasto",
-    "Desaturated": "Desaturato",
-    "Grayscale": "Scala di grigi",
-    "Black and white": "Bianco e nero",
-    "Infrared": "Infrarosso",
-    "Complementary": "Colori complementari",
-    "Non-complementary": "Colori non complementari",
+    "Pastel Colors": "Pastel Colors",
+    "Muted Colors": "Muted Colors",
+    "Grayscale": "Grayscale",
+    "Black and white": "Black and white",
+    "Infrared": "Infrared",
     "View": "Visuale",
-    "Tilt shift": "Tilt shift",
-    "Wide-angle": "Angolo ampio",
+    "Symmetrical": "Symmetrical",
+    "Tilt-shift": "Tilt-shift",
+    "Long shot angle": "Long shot angle",
+    "Medium shot angle": "Medium shot angle",
+    "Wide shot angle": "Wide shot angle",
     "Portrait": "Ritratto",
+    "Extreme close-up angle": "Extreme close-up angle",
     "Macro": "Macro",
     "Microscopic": "Microscopico",
     "Isometric": "Isometrico",
     "Panorama": "Panorama",
-    "Aerial photograph": "Fotografia aerea",
-    "Artist focus (not quite finished, not sure it helps)": "Focus sull'artista (non del tutto finito, non è sicuro che aiuti)",
-    "B/W Photograpy": "Fotografia B/N",
-    "Portrait photo": "Foto ritratto",
-    "Usage: a <corgi|cat> wearing <goggles|a hat>": "Utilizzo: a <corgi|cat> wearing <goggles|a hat>",
+    "Fisheye lens": "Fisheye lens",
+    "Overhead-angle": "Overhead-angle",
+    "Birds eye view": "Birds eye view",
+    "Focus on (unfinished)": "Focus su (incompleto)",
+    "No focus": "Nessun focus",
+    "Portraits": "Portraits",
+    "Feminine portrait": "Ritratto femminile",
+    "Masculine portrait": "Ritratto maschile",
+    "WaiFusion": "WaiFusion",
+    "Horrible Monsters": "Horrible Monsters",
+    "Robots": "Robot",
+    "Retrofuturism": "Retrofuturism",
+    "Propaganda": "Propaganda",
+    "Landscapes": "Paesaggi",
+    "Visual style strength": "Forza dello stile visivo",
+    "Visual style": "Stile visivo",
+    "Abstract": "Abstract",
+    "Acidwave": "Acidwave",
+    "Acrylic": "Acrylic",
+    "Aestheticism": "Aestheticism",
+    "Art Deco": "Art Deco",
+    "Nouveau": "Nouveau",
+    "Ashcan School Style": "Ashcan School Style",
+    "Avant-garde": "Avant-garde",
+    "Ballpoint Pen": "Ballpoint Pen",
+    "Baroque": "Baroque",
+    "Classicism": "Classicism",
+    "CoBrA": "CoBrA",
+    "Colored Pencil": "Colored Pencil",
+    "Constructivism": "Constructivism",
+    "Cubism": "Cubism",
+    "Dreamcore": "Dreamcore",
+    "Drip": "Drip",
+    "Encaustic": "Encaustic",
+    "Expressionism": "Expressionism",
+    "Fauvism": "Fauvism",
+    "Finger Painting": "Finger Painting",
+    "Futurism": "Futurism",
+    "Gouache": "Gouache",
+    "Hot Wax": "Hot Wax",
+    "Impressionism": "Impressionism",
+    "Ink Wash": "Ink Wash",
+    "Japanese": "Japanese",
+    "Korean": "Korean",
+    "Line Art": "Line Art",
+    "Linocut": "Linocut",
+    "Lowpoly": "Lowpoly",
+    "Manga": "Manga",
+    "Marker": "Marker",
+    "Modern Comics": "Fumetti moderni",
+    "Mural": "Mural",
+    "Neoclassicism": "Neoclassicism",
+    "Oil Painting": "Oil Painting",
+    "Pastel": "Pastel",
+    "Pencil": "Pencil",
+    "Photorealism": "Photorealism",
+    "Pixel Art": "Pixel Art",
+    "Pop Art": "Pop Art",
+    "Pop Surrealism": "Pop Surrealism",
+    "Psychedelic": "Psychedelic",
+    "Realism": "Realism",
+    "Renaissance": "Renaissance",
+    "Rococo": "Rococo",
+    "Sprite Artwork": "Sprite Artwork",
+    "Street Art": "Street Art",
+    "Suprematism": "Suprematism",
+    "Vaporwave": "Vaporwave",
+    "Vintage Comics": "Vintage Comics",
+    "Watercolor": "Watercolor",
+    "Artist influence": "Influenza dell'Artista",
+    "Artist": "Artista",
+    "Examples, Help and Tips": "Esempi, aiuto e suggerimenti",
+    "Style examples": "Esempi di Stile",
+    "Artist examples": "Esempi di Artisti",
+    "Mood examples": "Esempi di stato d'animo",
+    "Help": "Aiuto",
+    "Hello, StylePile here": "Ciao, questo è StylePile",
+    "Introduction": "Introduzione",
+    "is a mix and match system for adding elements to prompts that affect the style of the result. Hence the name. By default, these elements are placed in a specific order and given strength values. Which means the result sort-of evolves. I have generated thousands of images for each main": "è un sistema mescola e abbina per aggiungere elementi ai prompt che influiscono sullo stile del risultato. Da qui il nome. Per impostazione predefinita, questi elementi vengono inseriti in un ordine specifico e vengono assegnati valori di forza. Il che significa che il risultato si evolve. Ho generato migliaia di immagini per ogni principale",
+    "and tweaked the keywords to attempt giving expected results most of the time. Certainly, your suggestions for improvements are very welcome.": "e ottimizzato le parole chiave per tentare di fornire i risultati attesi per la maggior parte del tempo. Certamente, i tuoi suggerimenti per miglioramenti sono molto graditi.",
+    "Base workflow": "Flusso di lavoro di base",
+    "For example, if you select the": "Ad esempio, se si seleziona il",
+    "image type, then almost all results will look like Paintings. Selecting": "tipo di immagine, quindi quasi tutti i risultati sembreranno Dipinti. Selezione",
+    "will have a certain influence on the overall look in some way (if it's something humanoid it may show emotion, but also colors and overall feel may change). Setting": "avrà una certa influenza sull'aspetto generale in qualche modo (se è qualcosa di umanoide può mostrare emozioni, ma anche i colori e la sensazione generale possono cambiare). Ambientazione",
+    "will change the general tonality of the result. And setting": "cambierà la tonalità generale del risultato. E ambientazione",
+    "will attempt to change how the subject is viewed. Attempt, because view appears to be the least reliable keyword. These elements are placed in order of influence and supported by certain strength values. These basic settings produce very quick results close to the general look you want.": "tenterà di cambiare la modalità di visualizzazione dell'oggetto. Tentativo, perché view sembra essere la parola chiave meno affidabile. Questi elementi sono posti in ordine di influenza e supportati da determinati valori di forza. Queste impostazioni di base producono risultati molto rapidi vicini all'aspetto generale desiderato.",
+    "Moving on, adding a": "Andando avanti, aggiungendo a",
+    "will combine with": "si combinerà con",
+    "to influence how the result generally looks. These styles are based on classic and modern Painting/Art/design movements (which I picked after hours and thousands of samples of testing) and can have a strong influence on the end result. Either it will be more realistic or artistic, or look like a comic book etc. In general, this is a really strong element for getting the look you want. Its influence can be adjusted with the slider above. Experiment with the values, keeping in mind that anything above 1.5 will start becoming a mess. In a similar way, but more focused, you can select an": "per influenzare l'aspetto generale del risultato. Questi stili si basano su movimenti Pittura/Arte/design classici e moderni (che ho scelto dopo ore e migliaia di campioni di test) e possono avere una forte influenza sul risultato finale. O sarà più realistico o artistico, o assomiglierà a un fumetto, ecc. In generale, questo è un elemento davvero forte per ottenere l'aspetto che desideri. La sua influenza può essere regolata con il cursore in alto. Sperimenta con i valori, tenendo presente che qualsiasi cosa al di sopra di 1,5 inizierà a diventare un pasticcio. In modo simile, ma più mirato, puoi selezionare un",
+    "and, of course, that will have a very visible effect on the result as well. Currently there are 135 artists, 55 art styles and 25 emotions available for selection and represented with preview images.": "e, naturalmente, anche questo avrà un effetto molto visibile sul risultato. Attualmente ci sono 135 artisti, 55 stili artistici e 25 emozioni disponibili per la selezione e rappresentate con immagini di anteprima.",
+    "Strength of these settings has been preset at 1.3, as that appears to be the golden ratio for getting good results. Sometimes very low settings have an interesting result as well. You can, and should, freely mix and match these settings to get different results. Classic Painting styles affected or affecting 3D look quite interesting. Photography can look cool with some of the brighter, more artistic styles etc. Sometimes raising CFG scale to 15,20 or more also helps to REALLY push the style onto the image.": "La forza di queste impostazioni è stata preimpostata a 1,3, poiché sembra essere il rapporto aureo per ottenere buoni risultati. A volte anche impostazioni molto basse hanno un risultato interessante. Puoi, e dovresti, mescolare e abbinare liberamente queste impostazioni per ottenere risultati diversi. Gli stili di pittura classica influenzati o influenzati dal 3D sembrano piuttosto interessanti. La fotografia può sembrare interessante con alcuni degli stili più luminosi, più artistici, ecc. A volte aumentare la scala CFG a 15,20 o più aiuta anche a spingere VERAMENTE lo stile sull'immagine.",
+    "Advanced workflow": "Flusso di lavoro avanzato",
+    "StylePile can overtake the generation process, allowing you to generate a large amount of different results with very little extra work. There are two types of variables you can use: [X] and [R]. When you add an [X] to your prompt, it sequentially takes values from the": "StylePile può prendere il controllo del processo di generazione, consentendoti di generare una grande quantità di risultati diversi con pochissimo lavoro extra. Ci sono due tipi di variabili che puoi usare: [X] e [R]. Quando aggiungi una [X] al tuo prompt, prende in sequenza i valori da",
+    "Sequential prompts": "Prompt sequenziali",
+    "text area. You can have dozens of lines there and they will be processed in sequence. When you add [R] to the prompt a value from the": "area di testo. Puoi avere dozzine di righe lì e verranno elaborate in sequenza. Quando si aggiunge [R] al prompt un valore da",
+    "Random prompts": "Prompt casuali",
+    "text area will be inserted in its place. By combining these a huge variety in prompts is very easy to do.": "l'area di testo verrà inserita al suo posto. Combinando questi un'enorme varietà di prompt è molto facile da fare.",
+    "When using this,": "Quando si utilizza questo,",
+    "will move through the prompts and": "si sposterà attraverso i prompt e",
+    "will set how many copies with the given prompt to make. If the seed is not random, it will increase with each batch size step. Any random elements will still be picked randomly.": "imposterà quante copie fare con il prompt dato. Se il seme non è casuale, aumenterà ad ogni passaggio della dimensione del batch. Eventuali elementi casuali verranno comunque selezionati casualmente.",
+    "Tips and tricks": "Suggerimenti e trucchi",
+    "If you add your own Artist, I would recommend having \"by Artist\" in front of their name. Depending on Artist's popularity (or lack thereof) this appears to have a very tangible influence on the result.": "Se aggiungi il tuo artista, ti consiglio di avere \"per artista\" davanti al suo nome. A seconda della popolarità dell'artista (o della sua mancanza) questo sembra avere un'influenza molto tangibile sul risultato.",
+    "Parenthesis can be added to make pArts of the prompt stronger. So": "È possibile aggiungere parentesi per rendere più forti parti del prompt. Così",
+    "((cute kitten))": "((cute kitten))",
+    "will make it extra cute (try it out). This is also important if a style is affecting your original prompt too much. Make that prompt stronger by adding parenthesis around it, like this:": "lo renderà molto carino (provalo). Questo è importante anche se uno stile influisce troppo sul prompt originale. Rendi più forte quel prompt aggiungendo parentesi attorno ad esso, in questo modo:",
+    "((promt))": "((promt))",
+    ". A strength modifier value can also be used, like this": ". È anche possibile utilizzare un valore modificatore di forza, in questo modo",
+    "(prompt:1.1)": "(prompt:1.1)",
+    ". To save some typing you can select the line you want to make stronger and use": ". Per salvare un po' di digitazione puoi selezionare la linea che vuoi rendere più forte e utilizzare",
+    "Ctrl+Shift+Arrow keys up": "Ctrl+Shift+Freccia su",
+    "to add these parenthesis and change the value. As you can see by default values on most sliders, 1.3 seems like a good stArting point if you want to see some impact.": "per aggiungere queste parentesi e modificare il valore. Come puoi vedere per i valori predefiniti sulla maggior parte dei cursori, 1.3 sembra un buon punto di partenza se vuoi vedere un certo impatto.",
+    "Prompts can be split like": "I prompt possono essere divisi come",
+    "[A|B]": "[A|B]",
+    "to sequentially use terms, one after another on each step. For example": "per utilizzare in sequenza i termini, uno dopo l'altro in ogni passaggio. Per esempio",
+    "[cat|dog]": "[cat|dog]",
+    "will produce a hybrid catdog.": "produrrà un cane-gatto ibrido.",
+    "Using": "Usando",
+    "[A:B:0.4]": "[A:B:0.4]",
+    "will switch to other terms after the first one has been active for a certain percentage of steps. So": "passerà ad altri termini dopo che il primo è stato attivo per una certa percentuale di passaggi. Così",
+    "[cat:dog:0.4]": "[cat:dog:0.4]",
+    "will build a cat 40% of the time and then stArt turning it into a dog. Usually this needs more steps to work properly.": "costruirà un gatto il 40% delle volte e poi inizierà a trasformarlo in un cane. Di solito questo richiede più passaggi per funzionare correttamente.",
+    "In conclusion": "In conclusione",
+    "I made this because manually changing keywords, looking up possible styles, etc was a pain. It is meant as a fun tool to explore possibilities and make learning Stable Diffusion easier. If you have some ideas or, better yet, would like to contribute in some way*, just visit https://github.com/some9000/StylePile\n*Hey, if you have a 12Gb graphics card just laying around I'm happy to take it (:": "L'ho fatto perché cambiare manualmente le parole chiave, cercare possibili stili, ecc. era una seccatura. È inteso come uno strumento divertente per esplorare le possibilità e rendere più facile l'apprendimento della Diffusione stabile. Se hai qualche idea o, meglio ancora, vorresti contribuire in qualche modo*, visita pure https://github.com/some9000/StylePile\n*Hey, se hai una scheda grafica da 12 Gb in giro, sono felice di prenderla (:",
     "Seeds": "Semi",
     "Noise Scheduler": "Pianificazione del rumore",
     "Default": "Predefinito",
@@ -217,7 +351,7 @@
     "Prompt Template, applied to each keyframe below": "Modello di prompt, applicato a ciascun fotogramma chiave qui di seguito",
     "Positive Prompts": "Prompt positivi",
     "Negative Prompts": "Prompt negativi",
-    "Props, Stamps": "Immagini Clipart da diffondere (prop), o da applicare in post elaborazione e non essere diffuse (stamp).",
+    "Props, Stamps": "Immagini Clipart da diffondere (prop), o da applicare in post elaborazione e non essere diffuse (stamp)",
     "Poper_Folder:": "Cartella Immagini Clipart (PNG trasparenti):",
     "Supported Keyframes:": "Fotogrammi chiave supportati:",
     "time_s | source | video, images, img2img | path": "time_s | source | video, images, img2img | path",
@@ -239,7 +373,22 @@
     "Keyframes:": "Fotogrammi chiave:",
     "Tile X": "Piastrella asse X",
     "Tile Y": "Piastrella asse Y",
+    "Number of images to generate": "Numero di immagini da generare",
     "Python code": "Codice Python",
+    "Model": "Modello",
+    "dpt_large": "Depth large",
+    "dpt_hybrid": "Depth hybrid",
+    "midas_v21": "MIDAS v21",
+    "midas_v21_small": "MIDAS v21 small",
+    "Compute on": "Calcola",
+    "GPU": "GPU",
+    "CPU": "CPU",
+    "Save DepthMap": "Salva DepthMap",
+    "Show DepthMap": "Mostra DepthMap",
+    "Combine into one image.": "Combina in un'unica immagine.",
+    "Combine axis": "Combina gli assi",
+    "Vertical": "Verticale",
+    "Horizontal": "Orizzontale",
     "Source embedding to convert": "Incorporamento sorgente da convertire",
     "Embedding token": "Token Incorporamento",
     "Output directory": "Cartella di output",
@@ -248,6 +397,16 @@
     "Alt. symmetry method (blending)": "Metodo di simmetria alternativo (miscelazione)",
     "Apply every n steps": "Applica ogni n passi",
     "Skip last n steps": "Salta gli ultimi n passi",
+    "Usage: a <corgi|cat> wearing <goggles|a hat>": "Utilizzo: a <corgi|cat> wearing <goggles|a hat>",
+    "Mirror application mode": "modalità di applicazione specchiatura",
+    "Alternate Steps": "Passaggi alternati",
+    "Blend Average": "Miscelazione media",
+    "Mirror style": "Stile specchiatura",
+    "Vertical Mirroring": "Specchiatura verticale",
+    "Horizontal Mirroring": "Specchiatura orizzontale",
+    "90 Degree Rotation": "Rotazione 90 gradi",
+    "180 Degree Rotation": "Rotazione 180 gradi",
+    "Maximum steps fraction to mirror at": "Frazione di passi massima su cui eseguire la specchiatura",
     "Interpolation prompt": "Prompt di interpolazione",
     "Number of images": "Numero di immagini",
     "Make a gif": "Crea GIF",
@@ -263,7 +422,7 @@
     "Use same random seed for all lines": "Usa lo stesso seme casuale per tutte le righe",
     "List of prompt inputs": "Elenco di prompt di input",
     "Upload prompt inputs": "Carica un file contenente i prompt di input",
-    "n": "Esegui n volte",
+    "n": "n",
     "Destination seed(s) (Comma separated)": "Seme/i di destinazione (separati da virgola)",
     "Only use Random seeds (Unless comparing paths)": "Usa solo semi casuali (a meno che non si confrontino i percorsi)",
     "Number of random seed(s)": "Numero di semi casuali",
@@ -296,13 +455,6 @@
     "Transparent PNG": "PNG trasparente",
     "Noise Tolerance": "Tolleranza al rumore",
     "Quantize": "Quantizzare",
-    "Dry Run": "Esecuzione a vuoto (Debug)",
-    "NEW!": "NUOVO!",
-    "Premium Fantasy Card Template": "Premium Fantasy Card Template",
-    "is now available.": "è ora disponibile.",
-    "Generate a wide variety of creatures and characters in the style of a fantasy card game. Perfect for heroes, animals, monsters, and even crazy hybrids.": "Genera un'ampia varietà di creature e personaggi nello stile di un gioco di carte fantasy. Perfetto per eroi, animali, mostri e persino ibridi incredibili.",
-    "Learn More ➜": "Per saperne di più ➜",
-    "Purchases help fund the continued development of Unprompted. Thank you for your support!": "Gli acquisti aiutano a finanziare il continuo sviluppo di Unprompted. Grazie per il vostro sostegno!",
     "X type": "Parametro asse X",
     "Nothing": "Niente",
     "Var. seed": "Seme della variazione",
@@ -347,8 +499,8 @@
     "Prompt words after artist or style name": "Parole chiave dopo il nome dell'artista o dello stile",
     "Negative Prompt": "Prompt negativo",
     "Save": "Salva",
-    "Send to img2img": "Invia a img2img",
-    "Send to inpaint": "Invia a Inpaint",
+    "Send to img2img": "Invia a\nimg2img",
+    "Send to inpaint": "Invia a\nInpaint",
     "Send to extras": "Invia a Extra",
     "Make Zip when Save?": "Crea un file ZIP quando si usa 'Salva'",
     "Textbox": "Casella di testo",
@@ -381,7 +533,7 @@
     "Just resize": "Ridimensiona solamente",
     "Crop and resize": "Ritaglia e ridimensiona",
     "Resize and fill": "Ridimensiona e riempie",
-    "Advanced loopback": "Advanced loopback", 
+    "Advanced loopback": "Elaborazione ricorsiva avanzata", 
     "External Image Masking": "Immagine esterna per la mascheratura",
     "img2img alternative test": "Test alternativo per img2img",
     "img2tiles": "img2tiles",
@@ -408,9 +560,9 @@
     "Saturation enhancement per image": "Miglioramento della saturazione per ciascuna immagine",
     "Use sine denoising strength variation": "Utilizzare la variazione sinusoidale dell'intensità di riduzione del rumore",
     "Phase difference": "Differenza di Fase",
-    "Denoising strength exponentiation": "Esponenziazione dell'intensità di riduzione del rumore",
+    "Denoising strength exponentiation": "Esponente intensità denoising",
     "Use sine zoom variation": "Usa la variazione sinusoidale dello zoom",
-    "Zoom exponentiation": "Esponeniazione dello Zoom",
+    "Zoom exponentiation": "Esponente Zoom",
     "Use multiple prompts": "Usa prompt multipli",
     "Same seed per prompt": "Stesso seme per ogni prompt",
     "Same seed for everything": "Stesso seme per tutto",
@@ -460,7 +612,7 @@
     "right": "destra",
     "up": "sopra",
     "down": "sotto",
-    "Fall-off exponent (lower=higher detail)": "Esponente di decremento (più basso=maggior dettaglio)",
+    "Fall-off exponent (lower=higher detail)": "Esponente di decremento (più basso = maggior dettaglio)",
     "Color variation": "Variazione di colore",
     "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenterà l'immagine al doppio delle dimensioni; utilizzare i cursori di larghezza e altezza per impostare la dimensione della piastrella",
     "Upscaler": "Ampliamento immagine",
@@ -523,7 +675,7 @@
     "CodeFormer visibility": "Visibilità CodeFormer",
     "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso di CodeFormer (0 = effetto massimo, 1 = effetto minimo)",
     "Upscale Before Restoring Faces": "Amplia prima di restaurare i volti",
-    "Send to txt2img": "Invia a txt2img",
+    "Send to txt2img": "Invia a\ntxt2img",
     "A merger of the two checkpoints will be generated in your": "I due checkpoint verranno fusi nella cartella dei",
     "checkpoint": "checkpoint",
     "directory.": ".",
@@ -623,20 +775,75 @@
     "Train Embedding": "Addestra Incorporamento",
     "Create an aesthetic embedding out of any number of images": "Crea un'incorporamento estetico da qualsiasi numero di immagini",
     "Create images embedding": "Crea incorporamento di immagini",
+    "Generate Krita Plugin Symlink Command": "Genera comando per creare collegamento simbolico del plugin Krita",
+    "Launch Krita.": "Avvia Krita.",
+    "On the menubar, go to": "Sulla barra dei menu, vai a",
+    "Settings > Manage Resources...": "Settings > Manage Resources...",
+    "In the window that appears, click": "Nella finestra che appare, fai clic su",
+    "Open Resource Folder": "Open Resource Folder",
+    "In the file explorer that appears, look for a folder called": "In Esplora file, una volta aperto, cerca una cartella chiamata",
+    "pykrita": "pykrita",
+    "or create it.": "o creala.",
+    "Enter the": "Entra nella",
+    "folder and copy the folder location from the address bar.": "cartella e copia il percorso della cartella dalla barra degli indirizzi.",
+    "Paste the folder location below.": "Incolla il percorso della cartella qui di seguito.",
+    "Pykrita Folder Location": "Posizione della cartella Pykrita",
+    "Search for \"Command Prompt\" in the Start Menu, right-click and click \"Run as Administrator...\", paste the follow commands and hit Enter:": "Cerca \"Prompt dei comandi\" nel menu Start, fai clic con il pulsante destro del mouse e fai clic su \"Esegui come amministratore...\", incolla i seguenti comandi e premi Invio:",
+    "mklink /j \"<path_to_pykrita>\\krita_diff\" \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff\"\nmklink \"<path_to_pykrita>\\krita_diff.desktop\" \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff.desktop\"": "mklink /j \"<path_to_pykrita>\\krita_diff\" \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff\"\nmklink \"<path_to_pykrita>\\krita_diff.desktop\" \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff.desktop\"",
+    "Linux command:": "comando Linux:",
+    "ln -s \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff\" \"<path_to_pykrita>/krita_diff\"\nln -s \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff.desktop\" \"<path_to_pykrita>/krita_diff.desktop\"": "ln -s \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff\" \"<path_to_pykrita>/krita_diff\"\nln -s \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff.desktop\" \"<path_to_pykrita>/krita_diff.desktop\"",
+    "NOTE": "NOTE",
+    ": Symlinks will break if you move or rename the repository or any\nof its parent folders or otherwise change the path such that the symlink\nbecomes invalid. In which case, repeat the above steps with the new": ": I collegamenti simbolici si interrompono se si sposta o si rinomina il repository o una qualsiasi\n delle sue cartelle principali o si modifica in altro modo il percorso in modo tale che il collegamento simbolico\ndiventi non valido. In tal caso, ripetere i passaggi precedenti con il nuovo ",
+    "folder location and (auto-detected) repository location.": "percorso della cartella e percorso del repository (rilevato automaticamente).",
+    ": Ensure": ": Assicurarsi che",
+    "webui-user.bat": "webui-user.bat",
+    "/": "/",
+    "webui-user.sh": "webui-user.sh",
+    "contains": "contenga",
+    "--api": "--api",
+    "in": "in",
+    "COMMANDLINE_ARGS": "COMMANDLINE_ARGS",
+    "!": "!",
+    "Enabling the Krita Plugin": "Abilitazione del plugin Krita",
+    "Restart Krita.": "Riavvia Krita.",
+    "Settings > Configure Krita...": "Settings > Configure Krita...",
+    "On the left sidebar, go to": "Nella barra laterale sinistra, vai a",
+    "Python Plugin Manager": "Python Plugin Manager",
+    "Look for": "Cerca",
+    "Stable Diffusion Plugin": "Stable Diffusion Plugin",
+    "and tick the checkbox.": "e spunta la casella.",
+    "Restart Krita again for changes to take effect.": "Riavvia Krita per rendere effettive le modifiche.",
+    "The": "Il",
+    "SD Plugin": "SD Plugin",
+    "docked window should appear on the left of the Krita window. If it does not, look on the menubar under": "dovrebbe apparire come finestra ancorata a sinistra della finestra di Krita. In caso contrario, guarda nella barra dei menu sotto",
+    "Settings > Dockers": "Settings > Dockers",
+    "for": "per",
+    "Next Steps": "Prossimi passi",
+    "Troubleshooting": "Risoluzione dei problemi",
+    "Update Guide": "Guida all'aggiornamento",
+    "Usage Guide": "Guida all'uso",
+    "TODO: Control/status panel": "TODO: pannello di controllo/stato",
     "-1": "-1",
     "This extension works well with text captions in comma-separated style (such as the tags generated by DeepBooru interrogator).": "Questa estensione funziona bene con i sottotitoli di testo in stile separato da virgole (come i tag generati dall'interrogatore DeepBooru).",
     "Save all changes": "Salva tutte le modifiche",
     "Backup original text file (original file will be renamed like filename.000, .001, .002, ...)": "Backup del file di testo originale (il file originale verrà rinominato come nomefile.000, .001, .002, ...)",
     "Note:": "Note:",
-    "New text file will be created if you are using filename as captions.": "Verrà creato un nuovo file di testo se si utilizza il nome del file come didascalia.",
+    "New text file will be created if you are using filename as captions.": " Verrà creato un nuovo file di testo se si utilizza il nome del file come didascalia.",
     "Results": "Risultati",
     "Load": "Carica",
+    "Load from subdirectories": "Carica da sotto cartella",
     "Dataset Images": "Immagini del Dataset",
+    "Displayed Images : 0 / 0 total": "Immagini mostrate : 0 / 0 totali",
+    "Current Tag Filter :": "Filtro Tag corrente :",
+    "Current Selection Filter : 0 images": "Filtro di selezione corrente : 0 immagini",
+    "Selected Image :": "Immagine selezionata :",
     "Filter and Edit Tags": "Filtra e modifica i tag",
+    "Filter by Selection": "Filtra per selezione",
     "Edit Caption of Selected Image": "Modifica la didascalia dell'immagine selezionata",
     "Search tags / Filter images by tags": "Cerca tag / Filtra le immagini per tag",
     "Search Tags": "Cerca tag",
-    "Clear all filters": "Rimuovi tutti i filtri",
+    "Clear tag filters": "Cancella i filtri dei tag",
+    "Clear ALL filters": "Rimuovi tutti i filtri",
     "Sort by": "Ordina per",
     "Alphabetical Order": "Ordine alfabetico",
     "Frequency": "Frequenza",
@@ -664,6 +871,13 @@
     "ex C.": "esempio C.",
     "Original Text = \"A, B, C, D, E\" Selected Tags = \"A, B, D\" Edit Tags = \", X, \"": "Testo originale = \"A, B, C, D, E\" Tag selezionati = \"A, B, D\" Modifica tag = \", X, \"",
     "Result = \"X, C, E\" (A->\"\", B->X, D->\"\")": "Risultato = \"X, C, E\" (A->\"\", B->X, D->\"\")",
+    "Select images from the left gallery.": "Seleziona le immagini dalla galleria di sinistra.",
+    "Add selection [Enter]": "Aggiungi selezione [Invio]",
+    "Filter Images": "Filtra le immagini",
+    "Remove selection [Delete]": "Rimuovi selezione [Canc]",
+    "Invert selection": "Inverti selezione",
+    "Clear selection": "Cancella selezione",
+    "Apply selection filter": "Applica il filtro di selezione",
     "Caption of Selected Image": "Didascalia dell'immagine selezionata",
     "Copy caption": "Copia didascalia",
     "Edit Caption": "Modifica didascalia",
@@ -768,7 +982,7 @@
     "bilinear": "bilinear",
     "nearest": "nearest",
     "save_depth_maps": "Salva le mappe di profondità",
-    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`modalità animazione: Nessuno` si inserisce nell'elenco di *prompt*. (Modalità batch disabilitata atm, funzionano solo i prompt di animazione)",
+    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`modalità animazione: Nessuno` si inserisce nell'elenco di *prompt*. (Modalità batch al momento disabilitata, funzionano solo i prompt di animazione)",
     "*Important change from vanilla Deforum!*": "*Importante cambiamento rispetto alla versione originale di Deforum!*",
     "This script uses the built-in webui weighting settings.": "Questo script utilizza le impostazioni di pesatura webui integrate.",
     "So if you want to use math functions as prompt weights,": "Quindi, se vuoi usare le funzioni matematiche come pesi dei prompt,",
@@ -783,12 +997,16 @@
     "strength": "Intensità",
     "init_image": "Immagine di inizializzazione",
     "use_mask": "Usa maschera",
-    "use_alpha_as_mask": "Usa alpha come maschera",
+    "use_alpha_as_mask": "Usa Alpha come maschera",
     "invert_mask": "Inverti la maschera",
     "overlay_mask": "Sovrapponi la maschera",
     "mask_file": "File della maschera",
+    "mask_contrast_adjust": "regolazione contrasto maschera",
     "mask_brightness_adjust": "Regola la luminosità della maschera",
     "mask_overlay_blur": "Sfocatura della sovrapposizione della maschera",
+    "mask_fill": "riempimento maschera",
+    "full_res_mask": "maschera a piena risoluzione",
+    "full_res_mask_padding": "riempimento maschera a piena risoluzione",
     "Video Input:": "Ingresso video:",
     "video_init_path": "Percorso del video di inizializzazione",
     "extract_nth_frame": "Estrai ogni ennesimo fotogramma",
@@ -819,6 +1037,7 @@
     "image_path": "Percorso immagine",
     "mp4_path": "Percorso MP4",
     "Click here after the generation to show the video": "Clicca qui dopo la generazione per mostrare il video",
+    "Close the video": "Chiudi il video",
     "NOTE: If the 'Generate' button doesn't work, go in Settings and click 'Restart Gradio and Refresh...'.": "NOTA: se il pulsante 'Genera' non funziona, vai in Impostazioni e fai clic su 'Riavvia Gradio e Aggiorna...'.",
     "Save Settings": "Salva le impostazioni",
     "Load Settings": "Carica le impostazioni",
@@ -829,7 +1048,6 @@
     "house": "casa",
     "portrait": "ritratto",
     "spaceship": "nave spaziale",
-    "anime": "anime",
     "cartoon": "cartoon",
     "digipa-high-impact": "digipa-high-impact",
     "digipa-med-impact": "digipa-med-impact",
@@ -936,8 +1154,8 @@
     "set_index": "Imposta indice",
     "load_switch": "Carica",
     "turn_page_switch": "Volta pagina",
-    "Checkbox": "Casella di controllo",						   
-    "Checkbox Group": "Seleziona immagini per",
+    "Checkbox": "Selezione",						   
+    "Checkbox Group": "Seleziona per",
     "artists": "Artisti",
     "flavors": "Stili",
     "mediums": "Tecniche",
@@ -947,12 +1165,24 @@
     "Abandoned": "Scartati",
     "Key word": "Parola chiave",
     "Get inspiration": "Ispirami",
-    "to txt2img": "Invia a txt2img",
-    "to img2img": "Invia a img2img",
+    "to txt2img": "Invia a\ntxt2img",
+    "to img2img": "Invia a\nimg2img",
     "Collect": "Salva nei preferiti",
     "Don't show again": "Scarta",
     "Move out": "Rimuovi",
-    "set button": "Pulsante imposta", 
+    "set button": "Pulsante imposta",
+    "Before your text is sent to the neural network, it gets turned into numbers in a process called tokenization. These tokens are how the neural network reads and interprets text. Thanks to our great friends at Shousetsu愛 for inspiration for this feature.": "Prima che il tuo testo venga inviato alla rete neurale, verrà trasformato in numeri in un processo chiamato tokenizzazione. Questi token sono il modo in cui la rete neurale legge e interpreta il testo. Grazie ai nostri grandi amici di Shousetsu愛 per l'ispirazione per questa funzione.",
+    "Tokenize": "Tokenizzare",
+    "Text": "Testo",
+    "Tokens": "Token",
+    "Video to extract frames from:": "Video da cui estrarre fotogrammi:",
+    "Only extract keyframes (recommended)": "Estrae solo fotogrammi chiave (consigliato)",
+    "Extract Frames": "Estrai fotogrammi",
+    "Extracted Frame Set": "Set di fotogrammi estratti",
+    "Resize crops to 512x512": "Ridimensiona i ritagli a 512x512",
+    "Save crops to:": "Salva ritagli in:",
+    "<": "<",
+    ">": ">",
     "Apply settings": "Applica le impostazioni",
     "Saving images/grids": "Salva immagini/griglie",
     "Always save all generated images": "Salva sempre tutte le immagini generate",
@@ -1007,6 +1237,7 @@
     "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda barra di avanzamento alla console che mostra l'avanzamento complessivo del lavoro.",
     "Training": "Addestramento",
     "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP nella RAM durante l'addestramento di Iperreti. Risparmia VRAM.",
+    "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file.": "Salva lo stato dell'ottimizzatore come file *.optim separato. L'addestramento può essere ripreso con lo stesso HN e il file optim corrispondente.",
     "Filename word regex": "Espressione regolare per estrarre parole dal nome del file",
     "Filename join string": "Stringa per unire le parole estratte dal nome del file",
     "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per una singola immagine di input per epoca; utilizzato solo per visualizzare il numero di epoca",
@@ -1111,10 +1342,14 @@
     "aesthetic-gradients": "Gradienti Estetici (CLIP)",
     "https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients",
     "unknown": "sconosciuto",
+    "auto-sd-paint-ext": "KRITA Plugin",
+    "https://github.com/Interpause/auto-sd-paint-ext": "https://github.com/Interpause/auto-sd-paint-ext",
     "dataset-tag-editor": "Dataset Tag Editor",
     "https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor.git": "https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor.git",
     "deforum-for-automatic1111-webui": "Deforum",
     "https://github.com/deforum-art/deforum-for-automatic1111-webui": "https://github.com/deforum-art/deforum-for-automatic1111-webui",
+    "novelai-2-local-prompt": "Converti NovelAI",
+    "https://github.com/animerl/novelai-2-local-prompt": "https://github.com/animerl/novelai-2-local-prompt",
     "sd-dynamic-prompts": "Prompt dinamici",
     "https://github.com/adieyal/sd-dynamic-prompts": "https://github.com/adieyal/sd-dynamic-prompts",
     "stable-diffusion-webui-aesthetic-image-scorer": "Punteggio immagini estetiche",
@@ -1125,8 +1360,14 @@
     "https://github.com/yfszzx/stable-diffusion-webui-images-browser": "https://github.com/yfszzx/stable-diffusion-webui-images-browser",
     "stable-diffusion-webui-inspiration": "Ispirazione",
     "https://github.com/yfszzx/stable-diffusion-webui-inspiration": "https://github.com/yfszzx/stable-diffusion-webui-inspiration",
+    "stable-diffusion-webui-tokenizer": "Tokenizzatore",
+    "https://github.com/AUTOMATIC1111/stable-diffusion-webui-tokenizer.git": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-tokenizer.git",
     "tag-autocomplete": "Autocompletamento etichette",
     "https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git": "https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git",
+    "training-picker": "Selezionatore per l'addestramento",
+    "https://github.com/Maurdekye/training-picker.git": "https://github.com/Maurdekye/training-picker.git",
+    "unprompted": "Unprompted",
+    "https://github.com/ThereforeGames/unprompted": "https://github.com/ThereforeGames/unprompted",
     "wildcards": "Termini Jolly",
     "https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards.git": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards.git",
     "Load from:": "Carica da:",
@@ -1161,7 +1402,8 @@
     "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Prova a produrre un'immagine simile a quella che sarebbe stata prodotta con lo stesso seme alla risoluzione specificata",
     "This text is used to rotate the feature space of the imgs embs": "Questo testo viene utilizzato per ruotare lo spazio delle funzioni delle immagini incorporate",
     "How many times to repeat processing an image and using it as input for the next iteration": "Quante volte ripetere l'elaborazione di un'immagine e utilizzarla come input per l'iterazione successiva",
-    "Hello, StylePile here.\nUntil some weird bug gets fixed you will see this even if the script itself is not active. Meanwhile, some hints to take your artwork to new heights:\nUse the 'Focus on' dropdown to select complex presets. Toggle selections below (with or without Focus) to affect your results. Mix and match to get some interesting results. \nAnd some general Stable Diffusion tips that will take your designs to next level:\nYou can add parenthesis to make parts of the prompt stronger. So (((cute))) kitten will make it extra cute (try it out). This is alsow important if a style is affecting your original prompt too much. Make that prompt stronger by adding parenthesis around it, like this: ((promt)).\nYou can type promts like [A|B] to sequentially use terms one after another on each step. So, like [cat|dog] will produce a hybrid catdog. And [A:B:0.4] to switch to other terms after the first one has been active for a certain percentage of steps. So [cat:dog:0.4] will build a cat 40% of the time and then start turning it into a dog. This needs more steps to work properly.": "Salve, qui è StylePile.\nFinché qualche strano bug non verrà risolto, vedrai questo testo anche se lo script non è attivo. Nel frattempo, alcuni suggerimenti per portare la tua grafica a nuovi livelli:\nUtilizza il menu a discesa 'Focus on' per selezionare valori predefiniti complessi. Attiva o disattiva le selezioni seguenti (con o senza Focus) per influire sui risultati. Mescola e abbina per ottenere risultati interessanti. \nE alcuni suggerimenti generali su Stable Diffusion che porteranno i tuoi risultati a un livello superiore:\nPuoi aggiungere parentesi per aumentare l'influenza di certe parti del prompt. Quindi '(((cute))) kitten' lo renderà molto carino (fai delle prove). Questo è importante quando uno stile influisce troppo sul prompt originale. Rendi più forte quel prompt aggiungendo delle parentesi intorno ad esso, così: ((promt)).\nPuoi digitare prompt nel formato [A|B] per usare in sequenza i termini uno dopo l'altro in ogni passaggio. Quindi, come [cat|dog] produrrà un 'canegatto' ibrido. E [A:B:0.4] per passare ad altri termini dopo che il primo è stato attivo per una certa percentuale di passaggi. Quindi [cat:dog:0.4] genererà un gatto il 40% dei passaggi e poi inizierà a trasformarlo in un cane. Sono richiesti più passaggi perchè funzioni correttamente.",
+    "Insert [X] anywhere in main prompt to sequentially insert values from here.": "Inserisci [X] ovunque nel prompt principale per inserire in sequenza i valori da qui.",
+    "Insert [R] anywhere in main prompt to randomly insert values from here.": "Inserisci [R] ovunque nel prompt principale per inserire i valori in modo casuale da qui.",
     "Enter one prompt per line. Blank lines will be ignored.": "Immettere un prompt per riga. Le righe vuote verranno ignorate.",
     "Separate values for X axis using commas.": "Separare i valori per l'asse X usando le virgole.",
     "Separate values for Y axis using commas.": "Separare i valori per l'asse Y usando le virgole.",
@@ -1188,8 +1430,10 @@
     "1st and last digit must be 1. ex:'1, 2, 1'": "La prima e l'ultima cifra devono essere 1. Es.:'1, 2, 1'",
     "Path to directory with input images": "Percorso della cartella con immagini di input",
     "Path to directory where to write outputs": "Percorso della cartella in cui scrivere i risultati",
+    "C:\\\\...\\pykrita": "C:\\\\...\\pykrita",
     "C:\\directory\\of\\datasets": "C:\\cartella\\del\\dataset",
     "Input images directory": "Cartella di input delle immagini",
+    "Prompt for tokenization": "Prompt for tokenization",
     "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Usa i seguenti tag per definire come vengono scelti i nomi dei file per le immagini: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed ], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
     "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se questa opzione è abilitata, la filigrana non verrà aggiunta alle immagini create. Attenzione: se non aggiungi la filigrana, potresti comportarti in modo non etico.",
     "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utilizzare i seguenti tag per definire come vengono scelte le sottodirectory per le immagini e le griglie: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
@@ -1200,24 +1444,103 @@
     "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Elenco dei nomi delle impostazioni, separati da virgole, per le impostazioni che dovrebbero essere visualizzate nella barra di accesso rapido in alto, anziché nella normale scheda delle impostazioni. Vedi modules/shared.py per impostare i nomi. Richiede il riavvio per applicare.",
     "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se questo valore è diverso da zero, verrà aggiunto al seed e utilizzato per inizializzare il generatore di numeri casuali per il rumore quando si utilizzano campionatori con ETA. Puoi usarlo per produrre ancora più variazioni di immagini, oppure puoi usarlo per abbinare le immagini di altri software se sai cosa stai facendo.",
     "Leave empty for auto": "Lasciare vuoto per automatico",
+    "NAIConvert": "Converti\nNovelAI",
+    "History": "Crononolgia",
+    "specify a custom settings file and ignore settings displayed in the interface": "specificare un file di impostazioni personalizzate e ignorare le impostazioni visualizzate nell'interfaccia",
+    "the path to a custom settings file": "il percorso di un file delle impostazioni personalizzate",
+    "The width of the output images, in pixels (must be a multiple of 64)": "La larghezza delle immagini di output, in pixel (deve essere un multiplo di 64)",
+    "The height of the output images, in pixels (must be a multiple of 64)": "L'altezza delle immagini di output, in pixel (deve essere un multiplo di 64)",
+    "enable additional seed settings": "abilitare impostazioni seme aggiuntive",
+    "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "Quante volte migliorare l'immagine generata in modo iterativo; valori più alti richiedono più tempo; valori molto bassi possono produrre risultati negativi",
+    "output images will be placed in a folder with this name, inside of the img2img output folder": "le immagini di output verranno collocate in una cartella con questo nome, all'interno della cartella di output img2img",
+    "specify the format of the filename for output images": "specificare il formato del nome file per le immagini di output",
+    "defines the seed behavior that is used for animations": "definisce il comportamento del seme utilizzato per le animazioni",
+    "the seed value will increment by 1 for each subsequent frame of the animation": "il valore del seme aumenterà di 1 per ogni fotogramma successivo dell'animazione",
+    "selects the type of animation": "seleziona il tipo di animazione",
+    "only 2D motion parameters will be used, but this mode uses the least amount of VRAM. You can optionally enable flip_2d_perspective to enable some psuedo-3d animation parameters while in 2D mode.": "verranno utilizzati solo i parametri di movimento 2D, ma questa modalità utilizza la quantità minima di VRAM. Puoi facoltativamente abilitare flip_2d_perspective per abilitare alcuni parametri di animazione psuedo-3d mentre sei in modalità 2D.",
+    "the maximum number of output images to be created": "il numero massimo di immagini di output da creare",
+    "controls handling method of pixels to be generated when the image is smaller than the frame.": "controlla il metodo di gestione dei pixel da generare quando l'immagine è più piccola del fotogramma.",
+    "repeats the edge of the pixels, and extends them. Animations with quick motion may yield lines where this border function was attempting to populate pixels into the empty space created.": "ripete il bordo dei pixel e li estende. Le animazioni con movimento rapido possono produrre linee in cui questa funzione di bordo stava tentando di popolare i pixel nello spazio vuoto creato.",
+    "2D operator to rotate canvas clockwise/anticlockwise in degrees per frame": "Operatore 2D per ruotare la tela in senso orario/antiorario in gradi per fotogramma",
+    "2D operator that scales the canvas size, multiplicatively. [static = 1.0]": "Operatore 2D che ridimensiona le dimensioni della tela, in modo moltiplicativo. [statico = 1.0]",
+    "2D & 3D operator to move canvas left/right in pixels per frame": "Operatore 2D e 3D per spostare la tela a sinistra/destra in pixel per fotogramma",
+    "2D & 3D operator to move canvas up/down in pixels per frame": "Operatore 2D e 3D per spostare la tela su/giù in pixel per fotogramma",
+    "3D operator to move canvas towards/away from view [speed set by FOV]": "Operatore 3D per spostare la tela da/verso la visuale [velocità impostata da FOV]",
+    "3D operator to tilt canvas up/down in degrees per frame": "Operatore 3D per inclinare la tela su/giù in gradi per fotogramma",
+    "3D operator to pan canvas left/right in degrees per frame": "Operatore 3D per eseguire una panoramica della tela a sinistra/destra in gradi per fotogramma",
+    "3D operator to roll canvas clockwise/anticlockwise": "Operatore 3D per ruotare la tela in senso orario/antiorario",
+    "enables 2D mode functions to simulate faux 3D movement": "abilita le funzioni della modalità 2D per simulare falsi movimenti 3D",
+    "the roll effect angle": "l'angolo dell'effetto di rollio",
+    "the tilt effect angle": "l'angolo dell'effetto di inclinazione",
+    "the pan effect angle": "l'angolo dell'effetto panoramica",
+    "the 2D vanishing point of perspective (recommended range 30-160)": "il punto di fuga prospettico 2D (intervallo consigliato 30-160)",
+    "amount of graininess to add per frame for diffusion diversity": "quantità di granulosità da aggiungere per fotogramma per la diversità di diffusione",
+    "amount of presence of previous frame to influence next frame, also controls steps in the following formula [steps - (strength_schedule * steps)]": "quantità di presenza del fotogramma precedente per influenzare il fotogramma successivo, controlla anche i passaggi nella seguente formula [passi - (forza pianificazione * steps)]",
+    "adjusts the overall contrast per frame [default neutral at 1.0]": "regola il contrasto generale per fotogramma [neutro predefinito a 1.0]",
+    "how closely the image should conform to the prompt. Lower values produce more creative results. (recommended range 5-15)": "quanto l'immagine deve essere conforme al prompt. Valori più bassi producono risultati più creativi. (intervallo consigliato 5-15)",
+    "adjusts the scale at which the canvas is moved in 3D by the translation_z value. [maximum range -180 to +180, with 0 being undefined. Values closer to 180 will make the image have less depth, while values closer to 0 will allow more depth]": "regola la scala alla quale la tela viene spostata in 3D in base al valore traslazione_z. [intervallo massimo da -180 a +180, con 0 non definito. Valori più vicini a 180 renderanno l'immagine meno profonda, mentre valori più vicini a 0 consentiranno una maggiore profondità]",
+    "allows you to specify seeds at a specific schedule, if seed_behavior is set to schedule.": "ti consente di specificare i semi in base a una pianificazione specifica, se seed_behavior è impostato su pianificazione.",
+    "The color coherence will attempt to sample the overall pixel color information, and trend those values analyzed in the first frame to be applied to future frames.": "La coerenza del colore tenterà di campionare le informazioni complessive sul colore dei pixel e tendere quei valori analizzati nel primo fotogramma da applicare ai fotogrammi futuri.",
+    "LAB is a more linear approach to mimic human perception of color space - a good default setting for most users.": "LAB è un approccio più lineare per imitare la percezione umana dello spazio colore, una buona impostazione predefinita per la maggior parte degli utenti.",
+    "The default setting of 1 will cause every frame to receive diffusion in the sequence of image outputs. A setting of 2 will only diffuse on every other frame, yet motion will still be in effect. The output of images during the cadence sequence will be automatically blended, additively and saved to the specified drive. This may improve the illusion of coherence in some workflows as the content and context of an image will not change or diffuse during frames that were skipped. Higher values of 4-8 cadence will skip over a larger amount of frames and only diffuse the “Nth” frame as set by the diffusion_cadence value. This may produce more continuity in an animation, at the cost of little opportunity to add more diffused content. In extreme examples, motion within a frame will fail to produce diverse prompt context, and the space will be filled with lines or approximations of content - resulting in unexpected animation patterns and artifacts. Video Input & Interpolation modes are not affected by diffusion_cadence.": "L'impostazione predefinita di 1 farà sì che ogni fotogramma riceva la diffusione nella sequenza di output delle immagini. Un'impostazione di 2 si diffonderà solo su ogni altro fotogramma, ma il movimento sarà comunque attivo. L'output delle immagini durante la sequenza di cadenza verrà automaticamente miscelato, additivato e salvato nell'unità specificata. Ciò può migliorare l'illusione di coerenza in alcuni flussi di lavoro poiché il contenuto e il contesto di un'immagine non cambiano o si diffondono durante i fotogrammi che sono stati ignorati. Valori più alti di 4-8 cadenza salteranno una quantità maggiore di fotogrammi e diffonderanno solo il fotogramma 'Nth' come impostato dal valore di diffusione_cadenza. Ciò può produrre più continuità in un'animazione, a costo di poche opportunità di aggiungere contenuti più diffusi. In esempi estremi, il movimento all'interno di un fotogramma non riuscirà a produrre un contesto di richiesta diverso e lo spazio sarà riempito con linee o approssimazioni di contenuto, risultando in modelli di animazione e artefatti inaspettati. Le modalità di ingresso video e interpolazione non sono influenzate da diffusion_cadence.",
+    "enables instructions to warp an image dynamically in 3D mode only.": "abilita le istruzioni per deformare un'immagine dinamicamente solo in modalità 3D.",
+    "sets a midpoint at which a depthmap is to be drawn: range [-1 to +1]": "imposta un punto medio in cui deve essere disegnata una mappa di profondità: range [da -1 a +1]",
+    "instructs the handling of pixels outside the field of view as they come into the scene.": "indica la gestione dei pixel al di fuori del campo visivo quando entrano nella scena.",
+    "choose from Bicubic, Bilinear or Nearest modes. (Recommended: Bicubic)": "scegliere tra le modalità Bicubico, Bilineare o Più vicino. (Consigliato: Bicubico)",
+    "will output a greyscale depth map image alongside the output images.": "produrrà un'immagine della mappa di profondità in scala di grigi insieme alle immagini di output.",
+    "Diffuse the first frame based on an image, similar to img2img.": "Diffondi il primo fotogramma in base a un'immagine, simile a img2img.",
+    "Set the strength to 0 automatically when no init image is used": "Imposta automaticamente l'intensità su 0 quando non viene utilizzata alcuna immagine iniziale",
+    "Controls the strength of the diffusion on the init image. 0 = disabled": "Controlla l'intensità della diffusione sull'immagine iniziale 0 = disabilitato",
+    "the path to your init image": "il percorso alla tua immagine iniziale",
+    "Use a grayscale image as a mask on your init image. Whiter areas of the mask are areas that change more.": "Usa un'immagine in scala di grigi come maschera sull'immagine iniziale. Le aree più bianche della maschera sono aree che cambiano di più.",
+    "use the alpha channel of the init image as the mask": "usa il canale alfa dell'immagine iniziale come maschera",
+    "Inverts the colors of the mask": "Inverte i colori della maschera",
+    "Overlay the masked image at the end of the generation so it does not get degraded by encoding and decoding": "Sovrapponi l'immagine mascherata alla fine della generazione in modo che non venga degradata dalla codifica e dalla decodifica",
+    "the path to your mask image": "il percorso per l'immagine della maschera",
+    "adjust the brightness of the mask. Should be a positive number, with 1.0 meaning no adjustment.": "regola la luminosità della maschera. Dovrebbe essere un numero positivo, con 1.0 che significa nessuna correzione.",
+    "Blur edges of final overlay mask, if used. Minimum = 0 (no blur)": "Sfoca i bordi della maschera di sovrapposizione finale, se utilizzata. Minimo = 0 (nessuna sfocatura)",
+    "the directory at which your video file is located for Video Input mode only.": "la cartella in cui si trova il file video per la modalità solo Ingresso video.",
+    "during the run sequence, only frames specified by this value will be extracted, saved, and diffused upon. A value of 1 indicates that every frame is to be accounted for. Values of 2 will use every other frame for the sequence. Higher values will skip that number of frames respectively.": "durante la sequenza di esecuzione, verranno estratti, salvati e diffusi solo i frame specificati da questo valore. Un valore di 1 indica che deve essere contabilizzato ogni frame. I valori di 2 utilizzeranno ogni altro fotogramma per la sequenza. Valori più alti salteranno rispettivamente quel numero di fotogrammi.",
+    "when enabled, will re-extract video frames each run. When using video_input mode, the run will be instructed to write video frames to the drive. If you’ve already populated the frames needed, uncheck this box to skip past redundant extraction, and immediately start the render. If you have not extracted frames, you must run at least once with this box checked to write the necessary frames.": "quando abilitato, estrarrà nuovamente i fotogrammi video ad ogni esecuzione. Quando si utilizza la modalità Ingresso video, all'esecuzione verrà richiesto di scrivere fotogrammi video sull'unità. Se avete già popolato i fotogrammi necessari, deselezionate questa casella per saltare l'estrazione ridondante e avviare immediatamente il rendering. Se non sono stati estratti i frame, è necessario eseguire almeno una volta l'operazione con questa casella selezionata per scrivere i fotogrammi necessari.",
+    "video_input mode only, enables the extraction and use of a separate video file intended for use as a mask. White areas of the extracted video frames will not be affected by diffusion, while black areas will be fully effected. Lighter/darker areas are affected dynamically.": "in modalità solo video Input, consente l'estrazione e l'utilizzo di un file video separato destinato all'uso come maschera. Le aree bianche dei fotogrammi video estratti non saranno interessate dalla diffusione, mentre le aree nere saranno interamente interessate. Le aree più chiare/scure sono interessate in modo dinamico.",
+    "the directory in which your mask video is located.": "la cartella in cui si trova il video della maschera.",
+    "selects whether to ignore prompt schedule or _x_frames.": "seleziona se ignorare la pianificazione dei prompt o _x_frames.",
+    "the number of frames to transition thru between prompts (when interpolate_key_frames = true, then the numbers in front of the animation prompts will dynamically guide the images based on their value. If set to false, will ignore the prompt numbers and force interpole_x_frames value regardless of prompt number)": "il numero di fotogrammi per la transizione tra i prompt (quando interpola fotogrammi chiave = true, i numeri davanti ai prompt dell'animazione guideranno dinamicamente le immagini in base al loro valore. Se impostato su false, ignorerà i numeri dei prompt e forzerà il valore di interpola x fotogrammi indipendentemente da numero di richiesta)",
+    "instructs the run to start from a specified point": "avvia l'esecuzione da un punto specificato",
+    "the required timestamp to reference when resuming. Currently only available in 2D & 3D mode, the timestamp is saved as the settings .txt file name as well as images produced during your previous run. The format follows: yyyymmddhhmmss - a timestamp of when the run was started to diffuse.": "l'orario a cui fare riferimento quando si riprende. Attualmente disponibile solo in modalità 2D e 3D, l'orario viene salvato come nome del file .txt delle impostazioni e come immagini prodotte durante la corsa precedente. Il formato segue: yyyymmddhhmmss - l'orario di quando è stata avviata la diffusione dell'esecuzione.",
+    "when checked, do not output a video": "se selezionato, non produce un video",
+    "The frames per second that the video will run at": "I fotogrammi al secondo a cui verrà eseguito il video",
+    "select the type of video file to output": "selezionare il tipo di file video da produrre",
+    "create an animated GIF": "crea una GIF animata",
+    "the path to where ffmpeg is located": "il percorso in cui si trova ffmpeg",
+    "when this box is checked, and FFMPEG mp4 is selected as the output format, an audio file will be multiplexed with the video.": "quando questa casella è selezionata e FFMPEG mp4 è selezionato come formato di output, un file audio verrà multiplexato con il video.",
+    "the path to an audio file to accompany the video": "tIl percorso di un file audio per accompagnare il video",
+    "when this is unchecked, the video will automatically be created in the same output folder as the images. Check this box to specify different settings for the creation of the video, specified by the following options": "quando questo è deselezionato, il video verrà creato automaticamente nella stessa cartella di output delle immagini. Selezionare questa casella per specificare impostazioni diverse per la creazione del video, specificate dalle seguenti opzioni",
+    "render each step of diffusion as a separate frame": "renderizzare ogni fase di diffusione come fotogramma separato",
+    "the maximum number of frames to include in the video, when use_manual_settings is checked": "il numero massimo di fotogrammi da includere nel video, quando è selezionato 'Usa impostazioni manuali'",
+    "the location of images to create the video from, when use_manual_settings is checked": "la posizione delle immagini da cui creare il video, quando è selezionato 'Usa impostazioni manuali'",
+    "the output location of the mp4 file, when use_manual_settings is checked": "il percorso di output del file mp4, quando è selezionato 'Usa impostazioni manuali'",
     "Autocomplete options": "Opzioni di autocompletamento",
     "Enable Autocomplete": "Abilita autocompletamento",
     "Append commas": "Aggiungi virgole",
+    "Error": "Errore",
+    "F": "F",
+    "Time taken:": "Tempo impiegato:",
+    "latest": "aggiornato",
+    "behind": "da aggiornare",
     "AlphaCanvas": "AlphaCanvas",
-    "Close": "Chiudi",
-    "Grab Results": "Ottieni risultati",
-    "Apply Patch": "Applica Patch",
     "Hue:0": "Hue:0",
     "S:0": "S:0",
     "L:0": "L:0",
     "Load Canvas": "Carica Canvas",
     "Save Canvas": "Salva Canvas",
-    "latest": "aggiornato",
-    "behind": "da aggiornare",
+    "Close": "Chiudi",
+    "Grab Results": "Ottieni risultati",
+    "Apply Patch": "Applica Patch",
     "Description": "Descrizione",
     "Action": "Azione",
     "Aesthetic Gradients": "Gradienti estetici",
-    "Create an embedding from one or few pictures and use it to apply their style to generated images.": "Crea un incorporamento da una o poche immagini e usalo per applicare il loro stile alle immagini generate.",
+    "Create an embedding from one or few pictures and use it to apply their style to generated images.": "Crea un incorporamento da una o poche immagini e lo usa per applicare il loro stile alle immagini generate.",
     "Sample extension. Allows you to use __name__ syntax in your prompt to get a random line from a file named name.txt in the wildcards directory. Also see Dynamic Prompts for similar functionality.": "Estensione del campione. Consente di utilizzare la sintassi __name__ nel prompt per ottenere una riga casuale da un file denominato name.txt nella cartella dei termini jolly. Vedi anche 'Prompt dinamici' per funzionalità simili.",
     "Dynamic Prompts": "Prompt dinamici",
     "Implements an expressive template language for random or combinatorial prompt generation along with features to support deep wildcard directory structures.": "Implementa un  modello di linguaggio espressivo per la generazione di prompt casuale o combinatoria insieme a funzionalità per supportare cartelle strutturate contenenti termini jolly.",
@@ -1226,8 +1549,17 @@
     "Randomly display the pictures of the artist's or artistic genres typical style, more pictures of this artist or genre is displayed after selecting. So you don't have to worry about how hard it is to choose the right style of art when you create.": "Visualizza in modo casuale le immagini dello stile tipico dell'artista o dei generi artistici, dopo la selezione vengono visualizzate più immagini di questo artista o genere. Così non dovete preoccuparvi della difficoltà di scegliere lo stile artistico giusto quando create.",
     "The official port of Deforum, an extensive script for 2D and 3D animations, supporting keyframable sequences, dynamic math parameters (even inside the prompts), dynamic masking, depth estimation and warping.": "Il porting ufficiale di Deforum, uno script completo per animazioni 2D e 3D, che supporta sequenze di fotogrammi chiave, parametri matematici dinamici (anche all'interno dei prompt), mascheramento dinamico, stima della profondità e warping.",
     "Artists to study": "Artisti per studiare",
-    "Shows a gallery of generated pictures by artists separated into categories.": "Mostra una galleria di immagini generate dagli artisti suddivise in categorie.",
+    "Shows a gallery of generated pictures by artists separated into categories.": "Mostra una galleria di immagini generate in base agli artisti e suddivise in categorie.",
     "Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Calcola il punteggio estetico per le immagini generate utilizzando il predittore del punteggio estetico CLIP+MLP basato su Chad Scorer",
-    "Lets you edit captions in training datasets.": "Consente di modificare i sottotitoli nei set di dati di addestramento.",
-    "Time taken:": "Tempo impiegato:"
-}
+    "Lets you edit captions in training datasets.": "Consente di modificare le didascalie nei set di dati di addestramento.",
+    "Krita Plugin.": "Krita Plugin.",
+    "Adds a tab to the webui that allows the user to automatically extract keyframes from video, and manually extract 512x512 crops of those frames for use in model training.": "Aggiunge una scheda all'interfaccia Web che consente all'utente di estrarre automaticamente i fotogrammi chiave dal video ed estrarre manualmente ritagli 512x512 di quei fotogrammi da utilizzare nell'addestramento del modello.",
+    "Allows you to include various shortcodes in your prompts. You can pull text from files, set up your own variables, process text through conditional functions, and so much more - it's like wildcards on steroids..": "Consente di includere vari codici brevi nei prompt. Puoi estrarre il testo dai file, impostare le tue variabili, elaborare il testo tramite funzioni condizionali e molto altro ancora: è come i termini jolly ma potenziato.",
+    "Booru tag autocompletion": "Completamento automatico del tag Booru",
+    "Displays autocompletion hints for tags from image booru boards such as Danbooru. Uses local tag CSV files and includes a config for customization.": "Visualizza suggerimenti per il completamento automatico dei tag dalle schede di immagini Booru, come Danbooru. Utilizza file CSV di tag locali e include una configurazione per la personalizzazione.",
+    "Add a button to convert the prompts used in NovelAI for use in the WebUI. In addition, add a button that allows you to recall a previously used prompt.": "Aggiunge un pulsante per convertire i prompt utilizzati in NovelAI per l'uso in Stable Diffusion Web UI. Inoltre, aggiunge un pulsante che permette di richiamare un prompt utilizzato in precedenza.",
+    "tokenizer": "Tokenizzatore",
+    "Adds a tab that lets you preview how CLIP model would tokenize your text.": "Aggiunge una scheda che consente di visualizzare in anteprima il modo in cui il modello CLIP tokenizzerebbe il vostro testo.",						 				 
+    "parameters": "Parametri",
+    "The directory does not exist": "La cartella non esiste"			  
+}
\ No newline at end of file
-- 
cgit v1.2.1


From 99b05addb1c98169d78957f13efef308aef0af94 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <breyxxi@gmail.com>
Date: Sat, 5 Nov 2022 18:46:47 -0300
Subject: Fix options endpoint not showing the full list of options

---
 modules/api/models.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/api/models.py b/modules/api/models.py
index f89da1ff..0ea62155 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -168,9 +168,9 @@ class ProgressResponse(BaseModel):
     current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
 
 fields = {}
-for key, value in opts.data.items():
-    metadata = opts.data_labels.get(key)
-    optType = opts.typemap.get(type(value), type(value))
+for key, metadata in opts.data_labels.items():
+    value = opts.data.get(key)
+    optType = opts.typemap.get(type(metadata.default), type(value))
 
     if (metadata is not None):
         fields.update({key: (Optional[optType], Field(
-- 
cgit v1.2.1


From 0ebf66b575f008a027097946eb2f6845feffd010 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <breyxxi@gmail.com>
Date: Sat, 5 Nov 2022 18:58:19 -0300
Subject: Fix set config endpoint

---
 modules/api/api.py | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 112000b8..a924c83a 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -230,14 +230,10 @@ class Api:
 
         return options
 
-    def set_config(self, req: OptionsModel):
-        # currently req has all options fields even if you send a dict like { "send_seed": false }, which means it will
-        # overwrite all options with default values.
-        raise RuntimeError('Setting options via API is not supported')
-
-        reqDict = vars(req)
-        for o in reqDict:
-            setattr(shared.opts, o, reqDict[o])
+    def set_config(self, req: Dict[str, Any]):
+       
+        for o in req:
+            setattr(shared.opts, o, req[o])
 
         shared.opts.save(shared.config_filename)
         return
-- 
cgit v1.2.1


From 3c72055c22425dcde0739b5246e3501f4a3ec794 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <breyxxi@gmail.com>
Date: Sat, 5 Nov 2022 19:05:15 -0300
Subject: Add skip endpoint

---
 modules/api/api.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/modules/api/api.py b/modules/api/api.py
index a924c83a..c7ceb787 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -64,6 +64,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
         self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
         self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
+        self.app.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
         self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
@@ -219,6 +220,11 @@ class Api:
 
         return {}
 
+    def skip(self):
+        shared.state.skip()
+
+        return
+
     def get_config(self):
         options = {}
         for key in shared.opts.data.keys():
-- 
cgit v1.2.1


From 7f63980e479c7ffaec907fb659b5024e96eb72e7 Mon Sep 17 00:00:00 2001
From: Bruno Seoane <breyxxi@gmail.com>
Date: Sat, 5 Nov 2022 19:09:13 -0300
Subject: Remove unnecesary return

---
 modules/api/api.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index c7ceb787..33e6c6dc 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -223,8 +223,6 @@ class Api:
     def skip(self):
         shared.state.skip()
 
-        return
-
     def get_config(self):
         options = {}
         for key in shared.opts.data.keys():
-- 
cgit v1.2.1


From 4796db85b5315ea74c4f795b5d85384c8f521a3f Mon Sep 17 00:00:00 2001
From: byzod <byzod@users.noreply.github.com>
Date: Sun, 6 Nov 2022 10:12:57 +0800
Subject: ignores file format settings for grids

---
 scripts/prompt_matrix.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/prompt_matrix.py b/scripts/prompt_matrix.py
index e49c9b20..fd314b55 100644
--- a/scripts/prompt_matrix.py
+++ b/scripts/prompt_matrix.py
@@ -82,6 +82,6 @@ class Script(scripts.Script):
         processed.images.insert(0, grid)
 
         if opts.grid_save:
-            images.save_image(processed.images[0], p.outpath_grids, "prompt_matrix", prompt=original_prompt, seed=processed.seed, grid=True, p=p)
+            images.save_image(processed.images[0], p.outpath_grids, "prompt_matrix", extension=opts.grid_format, prompt=original_prompt, seed=processed.seed, grid=True, p=p)
 
         return processed
-- 
cgit v1.2.1


From 9cc48fee4859908deefbb917b9521dc8aa43a89e Mon Sep 17 00:00:00 2001
From: byzod <byzod@users.noreply.github.com>
Date: Sun, 6 Nov 2022 10:15:02 +0800
Subject: fix scripts ignores file format settings for grids

---
 scripts/xy_grid.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 417ed0d4..45a78db2 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -393,6 +393,6 @@ class Script(scripts.Script):
             )
 
         if opts.grid_save:
-            images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed, grid=True, p=p)
+            images.save_image(processed.images[0], p.outpath_grids, "xy_grid", extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
 
         return processed
-- 
cgit v1.2.1


From 6603f63b7b8af39ab815091460c5c2a12d3f253e Mon Sep 17 00:00:00 2001
From: Han Lin <hotdogee@gmail.com>
Date: Sun, 6 Nov 2022 11:08:20 +0800
Subject: Fixes LDSR upscaler producing black bars

---
 modules/ldsr_model_arch.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
index 14db5076..90e0a2f0 100644
--- a/modules/ldsr_model_arch.py
+++ b/modules/ldsr_model_arch.py
@@ -101,8 +101,8 @@ class LDSR:
         down_sample_rate = target_scale / 4
         wd = width_og * down_sample_rate
         hd = height_og * down_sample_rate
-        width_downsampled_pre = int(wd)
-        height_downsampled_pre = int(hd)
+        width_downsampled_pre = int(np.ceil(wd))
+        height_downsampled_pre = int(np.ceil(hd))
 
         if down_sample_rate != 1:
             print(
@@ -110,7 +110,12 @@ class LDSR:
             im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
         else:
             print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
-        logs = self.run(model["model"], im_og, diffusion_steps, eta)
+        
+        # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
+        pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
+        im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
+        
+        logs = self.run(model["model"], im_padded, diffusion_steps, eta)
 
         sample = logs["sample"]
         sample = sample.detach().cpu()
@@ -120,6 +125,9 @@ class LDSR:
         sample = np.transpose(sample, (0, 2, 3, 1))
         a = Image.fromarray(sample[0])
 
+        # remove padding
+        a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
+
         del model
         gc.collect()
         torch.cuda.empty_cache()
-- 
cgit v1.2.1


From a2a1a2f7270a865175f64475229838a8d64509ea Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 6 Nov 2022 09:02:25 +0300
Subject: add ability to create extensions that add localizations

---
 javascript/ui.js        | 2 ++
 modules/localization.py | 6 ++++++
 modules/scripts.py      | 1 -
 modules/shared.py       | 2 --
 modules/ui.py           | 3 +--
 webui.py                | 9 +++++----
 6 files changed, 14 insertions(+), 9 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 7e116465..95cfd106 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -208,4 +208,6 @@ function update_token_counter(button_id) {
 function restart_reload(){
     document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
     setTimeout(function(){location.reload()},2000)
+
+    return []
 }
diff --git a/modules/localization.py b/modules/localization.py
index b1810cda..f6a6f2fb 100644
--- a/modules/localization.py
+++ b/modules/localization.py
@@ -3,6 +3,7 @@ import os
 import sys
 import traceback
 
+
 localizations = {}
 
 
@@ -16,6 +17,11 @@ def list_localizations(dirname):
 
         localizations[fn] = os.path.join(dirname, file)
 
+    from modules import scripts
+    for file in scripts.list_scripts("localizations", ".json"):
+        fn, ext = os.path.splitext(file.filename)
+        localizations[fn] = file.path
+
 
 def localization_js(current_localization_name):
     fn = localizations.get(current_localization_name, None)
diff --git a/modules/scripts.py b/modules/scripts.py
index 366c90d7..637b2329 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -3,7 +3,6 @@ import sys
 import traceback
 from collections import namedtuple
 
-import modules.ui as ui
 import gradio as gr
 
 from modules.processing import StableDiffusionProcessing
diff --git a/modules/shared.py b/modules/shared.py
index 70b998ff..e8bacd3c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -221,8 +221,6 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
 
-localization.list_localizations(cmd_opts.localizations_dir)
-
 
 def realesrgan_models_names():
     import modules.realesrgan_model
diff --git a/modules/ui.py b/modules/ui.py
index 76ca9b07..23643c22 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1563,11 +1563,10 @@ def create_ui(wrap_gradio_gpu_call):
             shared.state.need_restart = True
 
         restart_gradio.click(
-
             fn=request_restart,
+            _js='restart_reload',
             inputs=[],
             outputs=[],
-            _js='restart_reload'
         )
 
         if column is not None:
diff --git a/webui.py b/webui.py
index a5a520f0..4342a962 100644
--- a/webui.py
+++ b/webui.py
@@ -10,7 +10,7 @@ from fastapi.middleware.gzip import GZipMiddleware
 
 from modules.paths import script_path
 
-from modules import devices, sd_samplers, upscaler, extensions
+from modules import devices, sd_samplers, upscaler, extensions, localization
 import modules.codeformer_model as codeformer
 import modules.extras
 import modules.face_restoration
@@ -28,9 +28,7 @@ import modules.txt2img
 import modules.script_callbacks
 
 import modules.ui
-from modules import devices
 from modules import modelloader
-from modules.paths import script_path
 from modules.shared import cmd_opts
 import modules.hypernetworks.hypernetwork
 
@@ -64,6 +62,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
 def initialize():
     extensions.list_extensions()
+    localization.list_localizations(cmd_opts.localizations_dir)
 
     if cmd_opts.ui_debug_mode:
         shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
@@ -99,7 +98,6 @@ def initialize():
         else:
             print("Running with TLS")
 
-
     # make the program just exit at ctrl+c without waiting for anything
     def sigint_handler(sig, frame):
         print(f'Interrupted with signal {sig} in {frame}')
@@ -185,6 +183,9 @@ def webui():
 
         print('Reloading extensions')
         extensions.list_extensions()
+
+        localization.list_localizations(cmd_opts.localizations_dir)
+
         print('Reloading custom scripts')
         modules.scripts.reload_scripts()
         print('Reloading modules: modules.ui')
-- 
cgit v1.2.1


From e5b4e3f820cd09e751f1d168ab05d606d078a0d9 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 6 Nov 2022 10:12:53 +0300
Subject: add tags to extensions, and ability to filter out tags list changed
 Settings keys in UI do not print VRAM/etc stats everywhere but in calls that
 use GPU

---
 modules/ui.py            | 25 ++++++++++++----------
 modules/ui_extensions.py | 55 ++++++++++++++++++++++++++++++++++++++----------
 style.css                |  5 +++++
 webui.py                 |  2 +-
 4 files changed, 64 insertions(+), 23 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 23643c22..c946ad59 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -174,9 +174,9 @@ def save_pil_to_file(pil_image, dir=None):
 gr.processing_utils.save_pil_to_file = save_pil_to_file
 
 
-def wrap_gradio_call(func, extra_outputs=None):
+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
     def f(*args, extra_outputs_array=extra_outputs, **kwargs):
-        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled
+        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
         if run_memmon:
             shared.mem_mon.monitor()
         t = time.perf_counter()
@@ -203,11 +203,18 @@ def wrap_gradio_call(func, extra_outputs=None):
 
             res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
 
+        shared.state.skipped = False
+        shared.state.interrupted = False
+        shared.state.job_count = 0
+
+        if not add_stats:
+            return tuple(res)
+
         elapsed = time.perf_counter() - t
         elapsed_m = int(elapsed // 60)
         elapsed_s = elapsed % 60
         elapsed_text = f"{elapsed_s:.2f}s"
-        if (elapsed_m > 0):
+        if elapsed_m > 0:
             elapsed_text = f"{elapsed_m}m "+elapsed_text
 
         if run_memmon:
@@ -225,10 +232,6 @@ def wrap_gradio_call(func, extra_outputs=None):
         # last item is always HTML
         res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
 
-        shared.state.skipped = False
-        shared.state.interrupted = False
-        shared.state.job_count = 0
-
         return tuple(res)
 
     return f
@@ -1436,7 +1439,7 @@ def create_ui(wrap_gradio_gpu_call):
     opts.reorder()
 
     def run_settings(*args):
-        changed = 0
+        changed = []
 
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
             assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
@@ -1454,12 +1457,12 @@ def create_ui(wrap_gradio_gpu_call):
                 if opts.data_labels[key].onchange is not None:
                     opts.data_labels[key].onchange()
 
-                changed += 1
+                changed.append(key)
         try:
             opts.save(shared.config_filename)
         except RuntimeError:
-            return opts.dumpjson(), f'{changed} settings changed without save.'
-        return opts.dumpjson(), f'{changed} settings changed.'
+            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
 
     def run_settings_single(value, key):
         if not opts.same_type(value, opts.data_labels[key].default):
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 8e0d41d5..02ab9643 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -140,13 +140,15 @@ def install_extension_from_url(dirname, url):
         shutil.rmtree(tmpdir, True)
 
 
-def install_extension_from_index(url):
+def install_extension_from_index(url, hide_tags):
     ext_table, message = install_extension_from_url(None, url)
 
-    return refresh_available_extensions_from_data(), ext_table, message
+    code, _ = refresh_available_extensions_from_data(hide_tags)
 
+    return code, ext_table, message
 
-def refresh_available_extensions(url):
+
+def refresh_available_extensions(url, hide_tags):
     global available_extensions
 
     import urllib.request
@@ -155,13 +157,25 @@ def refresh_available_extensions(url):
 
     available_extensions = json.loads(text)
 
-    return url, refresh_available_extensions_from_data(), ''
+    code, tags = refresh_available_extensions_from_data(hide_tags)
+
+    return url, code, gr.CheckboxGroup.update(choices=tags), ''
+
+
+def refresh_available_extensions_for_tags(hide_tags):
+    code, _ = refresh_available_extensions_from_data(hide_tags)
 
+    return code, ''
 
-def refresh_available_extensions_from_data():
+
+def refresh_available_extensions_from_data(hide_tags):
     extlist = available_extensions["extensions"]
     installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
 
+    tags = available_extensions.get("tags", {})
+    tags_to_hide = set(hide_tags)
+    hidden = 0
+
     code = f"""<!-- {time.time()} -->
     <table id="available_extensions">
         <thead>
@@ -178,17 +192,24 @@ def refresh_available_extensions_from_data():
         name = ext.get("name", "noname")
         url = ext.get("url", None)
         description = ext.get("description", "")
+        extension_tags = ext.get("tags", [])
 
         if url is None:
             continue
 
+        if len([x for x in extension_tags if x in tags_to_hide]) > 0:
+            hidden += 1
+            continue
+
         existing = installed_extension_urls.get(normalize_git_url(url), None)
 
         install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
 
+        tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])
+
         code += f"""
             <tr>
-                <td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a></td>
+                <td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a><br />{tags_text}</td>
                 <td>{html.escape(description)}</td>
                 <td>{install_code}</td>
             </tr>
@@ -199,7 +220,10 @@ def refresh_available_extensions_from_data():
     </table>
     """
 
-    return code
+    if hidden > 0:
+        code += f"<p>Extension hidden: {hidden}</p>"
+
+    return code, list(tags)
 
 
 def create_ui():
@@ -238,21 +262,30 @@ def create_ui():
                     extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
                     install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
 
+                with gr.Row():
+                    hide_tags = gr.CheckboxGroup(value=["ads", "localization"], label="Hide extensions with tags", choices=["script", "ads", "localization"])
+
                 install_result = gr.HTML()
                 available_extensions_table = gr.HTML()
 
                 refresh_available_extensions_button.click(
-                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update()]),
-                    inputs=[available_extensions_index],
-                    outputs=[available_extensions_index, available_extensions_table, install_result],
+                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),
+                    inputs=[available_extensions_index, hide_tags],
+                    outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result],
                 )
 
                 install_extension_button.click(
                     fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
-                    inputs=[extension_to_install],
+                    inputs=[extension_to_install, hide_tags],
                     outputs=[available_extensions_table, extensions_table, install_result],
                 )
 
+                hide_tags.change(
+                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
+                    inputs=[hide_tags],
+                    outputs=[available_extensions_table, install_result]
+                )
+
             with gr.TabItem("Install from URL"):
                 install_url = gr.Text(label="URL for extension's git repository")
                 install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
diff --git a/style.css b/style.css
index a0382a8c..e2b71f25 100644
--- a/style.css
+++ b/style.css
@@ -563,6 +563,11 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
     opacity: 0.5;
 }
 
+.extension-tag{
+    font-weight: bold;
+    font-size: 95%;
+}
+
 /* The following handles localization for right-to-left (RTL) languages like Arabic.
 The rtl media type will only be activated by the logic in javascript/localization.js.
 If you change anything above, you need to make sure it is RTL compliant by just running
diff --git a/webui.py b/webui.py
index 4342a962..f4f1d74d 100644
--- a/webui.py
+++ b/webui.py
@@ -57,7 +57,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
         return res
 
-    return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
+    return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True)
 
 
 def initialize():
-- 
cgit v1.2.1


From 9cd1a66648b4c19136687100f9705d442f31e7f9 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 6 Nov 2022 10:37:08 +0300
Subject: remove localization people from CODEOWNERS add a note

---
 CODEOWNERS | 23 +++++++++++------------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index a48d8012..7438c9bc 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -1,13 +1,12 @@
 *       @AUTOMATIC1111
-/localizations/ar_AR.json   @xmodar @blackneoo
-/localizations/de_DE.json   @LunixWasTaken
-/localizations/es_ES.json   @innovaciones
-/localizations/fr_FR.json   @tumbly
-/localizations/it_IT.json   @EugenioBuffo
-/localizations/ja_JP.json   @yuuki76
-/localizations/ko_KR.json   @36DB
-/localizations/pt_BR.json   @M-art-ucci
-/localizations/ru_RU.json   @kabachuha
-/localizations/tr_TR.json   @camenduru
-/localizations/zh_CN.json   @dtlnor @bgluminous
-/localizations/zh_TW.json   @benlisquare
+
+# if you were managing a localization and were removed from this file, this is because
+# the intended way to do localizations now is via extensions. See:
+# https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Developing-extensions
+# Make a repo with your localization and since you are still listed as a collaborator
+# you can add it to the wiki page yourself. This change is because some people complained
+# the git commit log is cluttered with things unrelated to almost everyone and
+# because I believe this is the best overall for the project to handle localizations almost
+# entirely without my oversight.
+
+
-- 
cgit v1.2.1


From 6e4de5b4422dfc0d45063b2c8c78b19f00321615 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 6 Nov 2022 11:20:23 +0300
Subject: add load_with_extra function for modules to load checkpoints with
 extended whitelist

---
 modules/safe.py | 40 +++++++++++++++++++++++++++++++++++++---
 1 file changed, 37 insertions(+), 3 deletions(-)

diff --git a/modules/safe.py b/modules/safe.py
index 348a24fc..a9209e38 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -23,11 +23,18 @@ def encode(*args):
 
 
 class RestrictedUnpickler(pickle.Unpickler):
+    extra_handler = None
+
     def persistent_load(self, saved_id):
         assert saved_id[0] == 'storage'
         return TypedStorage()
 
     def find_class(self, module, name):
+        if self.extra_handler is not None:
+            res = self.extra_handler(module, name)
+            if res is not None:
+                return res
+
         if module == 'collections' and name == 'OrderedDict':
             return getattr(collections, name)
         if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
@@ -52,7 +59,7 @@ class RestrictedUnpickler(pickle.Unpickler):
             return set
 
         # Forbid everything else.
-        raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")
+        raise Exception(f"global '{module}/{name}' is forbidden")
 
 
 allowed_zip_names = ["archive/data.pkl", "archive/version"]
@@ -69,7 +76,7 @@ def check_zip_filenames(filename, names):
         raise Exception(f"bad file inside {filename}: {name}")
 
 
-def check_pt(filename):
+def check_pt(filename, extra_handler):
     try:
 
         # new pytorch format is a zip file
@@ -78,6 +85,7 @@ def check_pt(filename):
 
             with z.open('archive/data.pkl') as file:
                 unpickler = RestrictedUnpickler(file)
+                unpickler.extra_handler = extra_handler
                 unpickler.load()
 
     except zipfile.BadZipfile:
@@ -85,16 +93,42 @@ def check_pt(filename):
         # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
         with open(filename, "rb") as file:
             unpickler = RestrictedUnpickler(file)
+            unpickler.extra_handler = extra_handler
             for i in range(5):
                 unpickler.load()
 
 
 def load(filename, *args, **kwargs):
+    return load_with_extra(filename, *args, **kwargs)
+
+
+def load_with_extra(filename, extra_handler=None, *args, **kwargs):
+    """
+    this functon is intended to be used by extensions that want to load models with
+    some extra classes in them that the usual unpickler would find suspicious.
+
+    Use the extra_handler argument to specify a function that takes module and field name as text,
+    and returns that field's value:
+
+    ```python
+    def extra(module, name):
+        if module == 'collections' and name == 'OrderedDict':
+            return collections.OrderedDict
+
+        return None
+
+    safe.load_with_extra('model.pt', extra_handler=extra)
+    ```
+
+    The alternative to this is just to use safe.unsafe_torch_load('model.pt'), which as the name implies is
+    definitely unsafe.
+    """
+
     from modules import shared
 
     try:
         if not shared.cmd_opts.disable_safe_unpickle:
-            check_pt(filename)
+            check_pt(filename, extra_handler)
 
     except pickle.UnpicklingError:
         print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
-- 
cgit v1.2.1


From 55ca04095845b41bf66333b3b7343e3ea0babed1 Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Sun, 6 Nov 2022 16:31:44 +0800
Subject: Resolve conflict

---
 modules/processing.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 86d015af..db35983b 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -422,14 +422,14 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     try:
         for k, v in p.override_settings.items():
-            opts.data[k] = v  # we don't call onchange for simplicity which makes changing model impossible
+            setattr(opts, k, v)  # we don't call onchange for simplicity which makes changing model impossible
             if k == 'sd_hypernetwork': shared.reload_hypernetworks()  # make onchange call for changing hypernet since it is relatively fast to load on-change, while SD models are not
 
         res = process_images_inner(p)
 
     finally:  # restore opts to original state
         for k, v in stored_opts.items():
-            opts.data[k] = v
+            setattr(opts, k, v)
             if k == 'sd_hypernetwork': shared.reload_hypernetworks()
 
     return res
-- 
cgit v1.2.1


From 32c0eab89538ba3900bf499291720f80ae4b43e5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 6 Nov 2022 14:39:41 +0300
Subject: load all settings in one call instead of one by one when the page
 loads

---
 modules/ui.py | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index c946ad59..34c31ef1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1141,7 +1141,7 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[html, generation_info, html2],
         )
 
-    with gr.Blocks() as modelmerger_interface:
+    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
                 gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
@@ -1161,7 +1161,7 @@ def create_ui(wrap_gradio_gpu_call):
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
 
-    with gr.Blocks() as train_interface:
+    with gr.Blocks(analytics_enabled=False) as train_interface:
         with gr.Row().style(equal_height=False):
             gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
 
@@ -1420,15 +1420,14 @@ def create_ui(wrap_gradio_gpu_call):
 
         if info.refresh is not None:
             if is_quicksettings:
-                res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
+                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
                 create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
             else:
                 with gr.Row(variant="compact"):
-                    res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
+                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
                     create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
         else:
-            res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
-
+            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
 
         return res
 
@@ -1639,6 +1638,17 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[component, text_settings],
             )
 
+        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
+
+        def get_settings_values():
+            return [getattr(opts, key) for key in component_keys]
+
+        demo.load(
+            fn=get_settings_values,
+            inputs=[],
+            outputs=[component_dict[k] for k in component_keys],
+        )
+
         def modelmerger(*args):
             try:
                 results = modules.extras.run_modelmerger(*args)
-- 
cgit v1.2.1


From 67c8e11be74180be19341aebbd6a246c37a79fbb Mon Sep 17 00:00:00 2001
From: snowmeow2 <snowmeow2@example.com>
Date: Mon, 7 Nov 2022 02:32:06 +0800
Subject: Adding DeepDanbooru to the interrogation API

---
 modules/api/api.py    | 16 ++++++++++++++--
 modules/api/models.py |  1 +
 2 files changed, 15 insertions(+), 2 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 688469ad..596a6616 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -15,6 +15,9 @@ from modules.sd_models import checkpoints_list
 from modules.realesrgan_model import get_realesrgan_models
 from typing import List
 
+if shared.cmd_opts.deepdanbooru:
+    from modules.deepbooru import get_deepbooru_tags
+
 def upscaler_to_index(name: str):
     try:
         return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
@@ -220,11 +223,20 @@ class Api:
         if image_b64 is None:
             raise HTTPException(status_code=404, detail="Image not found") 
 
-        img = self.__base64_to_image(image_b64)
+        img = decode_base64_to_image(image_b64)
+        img = img.convert('RGB')
 
         # Override object param
         with self.queue_lock:
-            processed = shared.interrogator.interrogate(img)
+            if interrogatereq.model == "clip":
+                processed = shared.interrogator.interrogate(img)
+            elif interrogatereq.model == "deepdanbooru":
+                if shared.cmd_opts.deepdanbooru:
+                    processed = get_deepbooru_tags(img)
+                else:
+                    raise HTTPException(status_code=404, detail="Model not found. Add --deepdanbooru when launching for using the model.")
+            else:
+                raise HTTPException(status_code=404, detail="Model not found")
         
         return InterrogateResponse(caption=processed)
 
diff --git a/modules/api/models.py b/modules/api/models.py
index 34dbfa16..f9cd929e 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -170,6 +170,7 @@ class ProgressResponse(BaseModel):
 
 class InterrogateRequest(BaseModel):
     image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+    model: str = Field(default="clip", title="Model", description="The interrogate model used.")
 
 class InterrogateResponse(BaseModel):
     caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
-- 
cgit v1.2.1


From 804d9fb83d0c63ca3acd36378707ce47b8f12599 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 6 Nov 2022 23:22:36 +0300
Subject: bump gradio to 3.9

---
 requirements.txt          | 2 +-
 requirements_versions.txt | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 79e8b7c6..0fba0b23 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,7 +4,7 @@ fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
-gradio==3.8
+gradio==3.9
 invisible-watermark
 numpy
 omegaconf
diff --git a/requirements_versions.txt b/requirements_versions.txt
index 7bd16712..f7059f20 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -2,7 +2,7 @@ transformers==4.19.2
 diffusers==0.3.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.8
+gradio==3.9
 numpy==1.23.3
 Pillow==9.2.0
 realesrgan==0.3.0
-- 
cgit v1.2.1


From cd6c55c1ab14fcab15329cde599cf79e8d555657 Mon Sep 17 00:00:00 2001
From: pepe10-gpu <pepe.dannyboy@gmail.com>
Date: Sun, 6 Nov 2022 17:05:51 -0800
Subject: 16xx card fix

cudnn
---
 modules/devices.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/devices.py b/modules/devices.py
index 7511e1dc..858bf399 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -39,10 +39,13 @@ def torch_gc():
 
 def enable_tf32():
     if torch.cuda.is_available():
+        torch.backends.cudnn.benchmark = True
+        torch.backends.cudnn.enabled = True
         torch.backends.cuda.matmul.allow_tf32 = True
         torch.backends.cudnn.allow_tf32 = True
 
 
+
 errors.run(enable_tf32, "Enabling TF32")
 
 device = device_interrogate = device_gfpgan = device_swinir = device_esrgan = device_scunet = device_codeformer = None
-- 
cgit v1.2.1


From a258fd60dbe2d68325339405a2aa72816d06d2fd Mon Sep 17 00:00:00 2001
From: Keavon Chambers <keavon@keavon.com>
Date: Mon, 7 Nov 2022 00:13:58 -0800
Subject: Add CORS-allow policy launch argument using regex

---
 modules/shared.py | 7 ++++---
 webui.py          | 6 +++++-
 2 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index e8bacd3c..55de286d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -81,12 +81,13 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
 parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
-parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
+parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
+parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
 parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
-parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origins", default=None)
+parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None)
+parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
 parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
 parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
diff --git a/webui.py b/webui.py
index f4f1d74d..066d94f7 100644
--- a/webui.py
+++ b/webui.py
@@ -107,8 +107,12 @@ def initialize():
 
 
 def setup_cors(app):
-    if cmd_opts.cors_allow_origins:
+    if cmd_opts.cors_allow_origins and cmd_opts.cors_allow_origins_regex:
+        app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_origin_regex=cmd_opts.cors_allow_origins_regex, allow_methods=['*'])
+    elif cmd_opts.cors_allow_origins:
         app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_methods=['*'])
+    elif cmd_opts.cors_allow_origins_regex:
+        app.add_middleware(CORSMiddleware, allow_origin_regex=cmd_opts.cors_allow_origins_regex, allow_methods=['*'])
 
 
 def create_api(app):
-- 
cgit v1.2.1


From 9ed4a126bd6421f91bf4a9bdd348b6aef0a378c6 Mon Sep 17 00:00:00 2001
From: kavorite <kavorite@gmail.com>
Date: Mon, 7 Nov 2022 19:58:49 -0500
Subject: add gradio-inpaint-tool; color-sketch

---
 modules/img2img.py | 19 +++++++++++++------
 modules/shared.py  |  1 +
 modules/ui.py      | 11 ++++++++++-
 3 files changed, 24 insertions(+), 7 deletions(-)

diff --git a/modules/img2img.py b/modules/img2img.py
index be9f3653..00c6f827 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -59,18 +59,25 @@ def process_batch(p, input_dir, output_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_with_mask_orig, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
     is_inpaint = mode == 1
     is_batch = mode == 2
 
     if is_inpaint:
         # Drawn mask
         if mask_mode == 0:
-            image = init_img_with_mask['image']
-            mask = init_img_with_mask['mask']
-            alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
-            mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
-            image = image.convert('RGB')
+            image = init_img_with_mask
+            is_mask_sketch = isinstance(image, dict)
+            if is_mask_sketch:  
+                # Sketch: mask iff. not transparent
+                image, mask = image["image"], image["mask"]
+                mask = np.array(mask)[..., -1] > 0
+            else:
+                # Color-sketch: mask iff. painted over
+                orig = init_img_with_mask_orig or image
+                mask = np.any(np.array(image) != np.array(orig), axis=-1)
+            mask = Image.fromarray(mask.astype(np.uint8) * 255, "L")
+            image = image.convert("RGB")
         # Uploaded mask
         else:
             image = init_img_inpaint
diff --git a/modules/shared.py b/modules/shared.py
index d8e99f85..325e37d9 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -71,6 +71,7 @@ parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui
 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
 parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor")
+parser.add_argument("--gradio-inpaint-tool", type=str, choices=["sketch", "color-sketch"], default="sketch", help="gradio inpainting editor: can be either sketch to only blur/noise the input, or color-sketch to paint over it")
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
diff --git a/modules/ui.py b/modules/ui.py
index 2609857e..db323e9c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -840,8 +840,17 @@ def create_ui(wrap_gradio_gpu_call):
                         init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
 
                     with gr.TabItem('Inpaint', id='inpaint'):
-                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
+                        init_img_with_mask_orig = gr.State(None)
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_inpaint_tool, image_mode="RGBA").style(height=480)
 
+                        def update_orig(image, state):
+                            if image is not None:
+                                same_size = state is not None and state.size == image.size
+                                has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+                                edited = same_size and has_exact_match
+                                return image if not edited or state is None else state
+
+                        init_img_with_mask.change(update_orig, [init_img_with_mask, init_img_with_mask_orig], init_img_with_mask_orig)
                         init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
                         init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
 
-- 
cgit v1.2.1


From 29eff4a194d22f0f0e7a7a976d746a71a4193cf5 Mon Sep 17 00:00:00 2001
From: pepe10-gpu <pepe.dannyboy@gmail.com>
Date: Mon, 7 Nov 2022 18:06:48 -0800
Subject: terrible hack

---
 modules/devices.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 858bf399..4c63f465 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -39,8 +39,15 @@ def torch_gc():
 
 def enable_tf32():
     if torch.cuda.is_available():
-        torch.backends.cudnn.benchmark = True
-        torch.backends.cudnn.enabled = True
+        #TODO: make this better; find a way to check if it is a turing card
+        turing = ["1630","1650","1660","Quadro RTX 3000","Quadro RTX 4000","Quadro RTX 4000","Quadro RTX 5000","Quadro RTX 5000","Quadro RTX 6000","Quadro RTX 6000","Quadro RTX 8000","Quadro RTX T400","Quadro RTX T400","Quadro RTX T600","Quadro RTX T1000","Quadro RTX T1000","2060","2070","2080","Titan RTX","Tesla T4","MX450","MX550"]
+        for devid in range(0,torch.cuda.device_count()):
+            for i in turing:
+                if i in torch.cuda.get_device_name(devid):
+                    shd = True
+        if shd:
+            torch.backends.cudnn.benchmark = True
+            torch.backends.cudnn.enabled = True
         torch.backends.cuda.matmul.allow_tf32 = True
         torch.backends.cudnn.allow_tf32 = True
 
-- 
cgit v1.2.1


From c5334fc56b3d44976425da2e6d0a303ae96836a1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 8 Nov 2022 08:35:01 +0300
Subject: fix javascript duplication bug after pressing the restart UI button

---
 modules/ui.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 34c31ef1..67cf1d6a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1752,7 +1752,7 @@ def create_ui(wrap_gradio_gpu_call):
     return demo
 
 
-def load_javascript(raw_response):
+def reload_javascript():
     with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
         javascript = f'<script>{jsfile.read()}</script>'
 
@@ -1768,7 +1768,7 @@ def load_javascript(raw_response):
     javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
 
     def template_response(*args, **kwargs):
-        res = raw_response(*args, **kwargs)
+        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
         res.body = res.body.replace(
             b'</head>', f'{javascript}</head>'.encode("utf8"))
         res.init_headers()
@@ -1777,4 +1777,5 @@ def load_javascript(raw_response):
     gradio.routes.templates.TemplateResponse = template_response
 
 
-reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse)
+if not hasattr(shared, 'GradioTemplateResponseOriginal'):
+    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
-- 
cgit v1.2.1


From 8011be33c36eb7aa9e9498fc714614034e07f67a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 8 Nov 2022 08:37:05 +0300
Subject: move functions out of main body for image preprocessing for easier
 hijacking

---
 modules/textual_inversion/preprocess.py | 162 ++++++++++++++++++--------------
 1 file changed, 93 insertions(+), 69 deletions(-)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index e13b1894..488aa5b5 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -35,6 +35,84 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
             deepbooru.release_process()
 
 
+def listfiles(dirname):
+    return os.listdir(dirname)
+
+
+class PreprocessParams:
+    src = None
+    dstdir = None
+    subindex = 0
+    flip = False
+    process_caption = False
+    process_caption_deepbooru = False
+    preprocess_txt_action = None
+
+
+def save_pic_with_caption(image, index, params: PreprocessParams, existing_caption=None):
+    caption = ""
+
+    if params.process_caption:
+        caption += shared.interrogator.generate_caption(image)
+
+    if params.process_caption_deepbooru:
+        if len(caption) > 0:
+            caption += ", "
+        caption += deepbooru.get_tags_from_process(image)
+
+    filename_part = params.src
+    filename_part = os.path.splitext(filename_part)[0]
+    filename_part = os.path.basename(filename_part)
+
+    basename = f"{index:05}-{params.subindex}-{filename_part}"
+    image.save(os.path.join(params.dstdir, f"{basename}.png"))
+
+    if params.preprocess_txt_action == 'prepend' and existing_caption:
+        caption = existing_caption + ' ' + caption
+    elif params.preprocess_txt_action == 'append' and existing_caption:
+        caption = caption + ' ' + existing_caption
+    elif params.preprocess_txt_action == 'copy' and existing_caption:
+        caption = existing_caption
+
+    caption = caption.strip()
+
+    if len(caption) > 0:
+        with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:
+            file.write(caption)
+
+    params.subindex += 1
+
+
+def save_pic(image, index, params, existing_caption=None):
+    save_pic_with_caption(image, index, params, existing_caption=existing_caption)
+
+    if params.flip:
+        save_pic_with_caption(ImageOps.mirror(image), index, params, existing_caption=existing_caption)
+
+
+def split_pic(image, inverse_xy, width, height, overlap_ratio):
+    if inverse_xy:
+        from_w, from_h = image.height, image.width
+        to_w, to_h = height, width
+    else:
+        from_w, from_h = image.width, image.height
+        to_w, to_h = width, height
+    h = from_h * to_w // from_w
+    if inverse_xy:
+        image = image.resize((h, to_w))
+    else:
+        image = image.resize((to_w, h))
+
+    split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
+    y_step = (h - to_h) / (split_count - 1)
+    for i in range(split_count):
+        y = int(y_step * i)
+        if inverse_xy:
+            splitted = image.crop((y, 0, y + to_h, to_w))
+        else:
+            splitted = image.crop((0, y, to_w, y + to_h))
+        yield splitted
+
 
 def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):
     width = process_width
@@ -48,82 +126,28 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
 
     os.makedirs(dst, exist_ok=True)
 
-    files = os.listdir(src)
+    files = listfiles(src)
 
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
 
-    def save_pic_with_caption(image, index, existing_caption=None):
-        caption = ""
-
-        if process_caption:
-            caption += shared.interrogator.generate_caption(image)
-
-        if process_caption_deepbooru:
-            if len(caption) > 0:
-                caption += ", "
-            caption += deepbooru.get_tags_from_process(image)
-
-        filename_part = filename
-        filename_part = os.path.splitext(filename_part)[0]
-        filename_part = os.path.basename(filename_part)
-
-        basename = f"{index:05}-{subindex[0]}-{filename_part}"
-        image.save(os.path.join(dst, f"{basename}.png"))
-
-        if preprocess_txt_action == 'prepend' and existing_caption:
-            caption = existing_caption + ' ' + caption
-        elif preprocess_txt_action == 'append' and existing_caption:
-            caption = caption + ' ' + existing_caption
-        elif preprocess_txt_action == 'copy' and existing_caption:
-            caption = existing_caption
-
-        caption = caption.strip()
-        
-        if len(caption) > 0:
-            with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:
-                file.write(caption)
-
-        subindex[0] += 1
-
-    def save_pic(image, index, existing_caption=None):
-        save_pic_with_caption(image, index, existing_caption=existing_caption)
-
-        if process_flip:
-            save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption)
-
-    def split_pic(image, inverse_xy):
-        if inverse_xy:
-            from_w, from_h = image.height, image.width
-            to_w, to_h = height, width
-        else:
-            from_w, from_h = image.width, image.height
-            to_w, to_h = width, height
-        h = from_h * to_w // from_w
-        if inverse_xy:
-            image = image.resize((h, to_w))
-        else:
-            image = image.resize((to_w, h))
-
-        split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
-        y_step = (h - to_h) / (split_count - 1)
-        for i in range(split_count):
-            y = int(y_step * i)
-            if inverse_xy:
-                splitted = image.crop((y, 0, y + to_h, to_w))
-            else:
-                splitted = image.crop((0, y, to_w, y + to_h))
-            yield splitted
-
+    params = PreprocessParams()
+    params.dstdir = dst
+    params.flip = process_flip
+    params.process_caption = process_caption
+    params.process_caption_deepbooru = process_caption_deepbooru
+    params.preprocess_txt_action = preprocess_txt_action
 
     for index, imagefile in enumerate(tqdm.tqdm(files)):
-        subindex = [0]
+        params.subindex = 0
         filename = os.path.join(src, imagefile)
         try:
             img = Image.open(filename).convert("RGB")
         except Exception:
             continue
 
+        params.src = filename
+
         existing_caption = None
         existing_caption_filename = os.path.splitext(filename)[0] + '.txt'
         if os.path.exists(existing_caption_filename):
@@ -143,8 +167,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
         process_default_resize = True
 
         if process_split and ratio < 1.0 and ratio <= split_threshold:
-            for splitted in split_pic(img, inverse_xy):
-                save_pic(splitted, index, existing_caption=existing_caption)
+            for splitted in split_pic(img, inverse_xy, width, height, overlap_ratio):
+                save_pic(splitted, index, params, existing_caption=existing_caption)
             process_default_resize = False
 
         if process_focal_crop and img.height != img.width:
@@ -165,11 +189,11 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
                 dnn_model_path = dnn_model_path,
             )
             for focal in autocrop.crop_image(img, autocrop_settings):
-                save_pic(focal, index, existing_caption=existing_caption)
+                save_pic(focal, index, params, existing_caption=existing_caption)
             process_default_resize = False
 
         if process_default_resize:
             img = images.resize_image(1, img, width, height)
-            save_pic(img, index, existing_caption=existing_caption)
+            save_pic(img, index, params, existing_caption=existing_caption)
 
-        shared.state.nextjob()
\ No newline at end of file
+        shared.state.nextjob()
-- 
cgit v1.2.1


From 1610b3258458025025e9c4faae57d290e4519745 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 8 Nov 2022 08:38:10 +0300
Subject: add callback for creating a tab in train UI

---
 modules/script_callbacks.py | 27 +++++++++++++++++++++++++--
 modules/ui.py               |  4 ++++
 2 files changed, 29 insertions(+), 2 deletions(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 74dfb880..f19e164c 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -7,6 +7,7 @@ from typing import Optional
 from fastapi import FastAPI
 from gradio import Blocks
 
+
 def report_exception(c, job):
     print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
     print(traceback.format_exc(), file=sys.stderr)
@@ -45,15 +46,21 @@ class CFGDenoiserParams:
         """Total number of sampling steps planned"""
 
 
+class UiTrainTabParams:
+    def __init__(self, txt2img_preview_params):
+        self.txt2img_preview_params = txt2img_preview_params
+
+
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
 callback_map = dict(
     callbacks_app_started=[],
     callbacks_model_loaded=[],
     callbacks_ui_tabs=[],
+    callbacks_ui_train_tabs=[],
     callbacks_ui_settings=[],
     callbacks_before_image_saved=[],
     callbacks_image_saved=[],
-    callbacks_cfg_denoiser=[]
+    callbacks_cfg_denoiser=[],
 )
 
 
@@ -61,6 +68,7 @@ def clear_callbacks():
     for callback_list in callback_map.values():
         callback_list.clear()
 
+
 def app_started_callback(demo: Optional[Blocks], app: FastAPI):
     for c in callback_map['callbacks_app_started']:
         try:
@@ -79,7 +87,7 @@ def model_loaded_callback(sd_model):
 
 def ui_tabs_callback():
     res = []
-    
+
     for c in callback_map['callbacks_ui_tabs']:
         try:
             res += c.callback() or []
@@ -89,6 +97,14 @@ def ui_tabs_callback():
     return res
 
 
+def ui_train_tabs_callback(params: UiTrainTabParams):
+    for c in callback_map['callbacks_ui_train_tabs']:
+        try:
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'callbacks_ui_train_tabs')
+
+
 def ui_settings_callback():
     for c in callback_map['callbacks_ui_settings']:
         try:
@@ -169,6 +185,13 @@ def on_ui_tabs(callback):
     add_callback(callback_map['callbacks_ui_tabs'], callback)
 
 
+def on_ui_train_tabs(callback):
+    """register a function to be called when the UI is creating new tabs for the train tab.
+    Create your new tabs with gr.Tab.
+    """
+    add_callback(callback_map['callbacks_ui_train_tabs'], callback)
+
+
 def on_ui_settings(callback):
     """register a function to be called before UI settings are populated; add your settings
     by using shared.opts.add_option(shared.OptionInfo(...)) """
diff --git a/modules/ui.py b/modules/ui.py
index 67cf1d6a..7ea1177f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1270,6 +1270,10 @@ def create_ui(wrap_gradio_gpu_call):
                         train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
                         train_embedding = gr.Button(value="Train Embedding", variant='primary')
 
+                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+                script_callbacks.ui_train_tabs_callback(params)
+
             with gr.Column():
                 progressbar = gr.HTML(elem_id="ti_progressbar")
                 ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-- 
cgit v1.2.1


From ac085628540d0ec6a988fad93f5b8f2154209571 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 8 Nov 2022 10:01:06 +0300
Subject: Remove old localizations from the main repo. Where are they now?
 Here:
 https://github.com/AUTOMATIC1111/stable-diffusion-webui-old-localizations

---
 localizations/ar_AR.json |  518 ---------------
 localizations/de_DE.json |  458 --------------
 localizations/es_ES.json |  692 --------------------
 localizations/fr_FR.json |  415 ------------
 localizations/it_IT.json | 1565 ----------------------------------------------
 localizations/ja_JP.json |  482 --------------
 localizations/ko_KR.json |  592 ------------------
 localizations/pt_BR.json |  485 --------------
 localizations/ru_RU.json |  475 --------------
 localizations/tr_TR.json |  423 -------------
 localizations/zh_CN.json |  624 ------------------
 localizations/zh_TW.json |  610 ------------------
 12 files changed, 7339 deletions(-)
 delete mode 100644 localizations/ar_AR.json
 delete mode 100644 localizations/de_DE.json
 delete mode 100644 localizations/es_ES.json
 delete mode 100644 localizations/fr_FR.json
 delete mode 100644 localizations/it_IT.json
 delete mode 100644 localizations/ja_JP.json
 delete mode 100644 localizations/ko_KR.json
 delete mode 100644 localizations/pt_BR.json
 delete mode 100644 localizations/ru_RU.json
 delete mode 100644 localizations/tr_TR.json
 delete mode 100644 localizations/zh_CN.json
 delete mode 100644 localizations/zh_TW.json

diff --git a/localizations/ar_AR.json b/localizations/ar_AR.json
deleted file mode 100644
index abbbcff4..00000000
--- a/localizations/ar_AR.json
+++ /dev/null
@@ -1,518 +0,0 @@
-{
-  "rtl": true,
-  "Loading...": "لحظة...",
-  "view": "اعرض ",
-  "api": "واجهة البرمجة",
-  "built with gradio": "مبني باستخدام gradio",
-  "Stable Diffusion checkpoint": "أوزان نموذج الإنتشار المسقر",
-  "txt2img": "نص إلى صورة",
-  "Prompt": "الطلب",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "الطلب (لبدء الإنتاج Ctrl+Enter أو Alt+Enter اضغط)",
-  "Negative prompt": "عكس الطلب",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "عكس الطلب (لبدء الإنتاج Ctrl+Enter أو Alt+Enter اضغط)",
-  "Add a random artist to the prompt.": "أضف فنان عشوائي للطلب",
-  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "اقرأ عوامل الإنتاج من الطلب أو من الإنتاج السابق إذا كان الطلب فارغا",
-  "Save style": "احتفظ بالطلب وعكسه كإضافة",
-  "Apply selected styles to current prompt": "ألحق الإضافات المحددة إلى الطلب وعكسه",
-  "Generate": "أنتج",
-  "Skip": "تخطى",
-  "Stop processing current image and continue processing.": "لا تكمل خطوات هذة الحزمة وانتقل إلى الحزمة التالية",
-  "Interrupt": "توقف",
-  "Stop processing images and return any results accumulated so far.": "توقف عن الإنتاج واعرض ما تم إلى الآن",
-  "Style 1": "الإضافة 1",
-  "Style to apply; styles have components for both positive and negative prompts and apply to both": "الإضافات (styles) عبارة عن كلمات تتكرر كثيرا يتم إلحاقها بالطلب وعكسه عند الرغبة",
-  "Style 2": "الإضافة 2",
-  "Do not do anything special": "لا يغير شيئا",
-  "Sampling Steps": "عدد الخطوات",
-  "Sampling method": "أسلوب الخطو",
-  "Which algorithm to use to produce the image": "Sampler: اسم نظام تحديد طريقة تغيير المسافات بين الخطوات",
-  "Euler a": "Euler a",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral: طريقة مبدعة يمكن أن تنتج صور مختلفة على حسب عدد الخطوات، لا تتغير بعد 30-40 خطوة",
-  "Euler": "Euler",
-  "LMS": "LMS",
-  "Heun": "Heun",
-  "DPM2": "DPM2",
-  "DPM2 a": "DPM2 a",
-  "DPM fast": "DPM fast",
-  "DPM adaptive": "DPM adaptive",
-  "LMS Karras": "LMS Karras",
-  "DPM2 Karras": "DPM2 Karras",
-  "DPM2 a Karras": "DPM2 a Karras",
-  "DDIM": "DDIM",
-  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models: الأفضل في الإنتاج الجزئي",
-  "PLMS": "PLMS",
-  "Width": "العرض",
-  "Height": "الإرتفاع",
-  "Restore faces": "تحسين الوجوه",
-  "Tiling": "ترصيف",
-  "Produce an image that can be tiled.": "أنتج صور يمكن ترصيفها بجانب بعضها كالبلاط",
-  "Highres. fix": "إصلاح الدقة العالية",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "أنتج صورة بدقة منخفضة ثم قم برفع الدقة فيما بعد لمنع التشوهات التي تحصل عندما تكون الدقة المطلوبة كبيرة",
-  "Firstpass width": "العرض الأولي",
-  "Firstpass height": "الإرتفاع الأولي",
-  "Denoising strength": "المدى",
-  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Denoising strength: حدد مدى الإبتعاد عن الصورة (عدد الخطوات الفعلي = عدد الخطوات * المدى)",
-  "Batch count": "عدد الحزم",
-  "How many batches of images to create": "يتم إنتاج الصور على دفعات، كل دفعة فيها حزمة من الصور",
-  "Batch size": "حجم الحزمة",
-  "How many image to create in a single batch": "Batch size: إنتاج حزمة صور أسرع من إنتاجهم فرادى، حدد عدد الصور في كل حزمة",
-  "CFG Scale": "التركيز",
-  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "CFG scale: يحدد مقدار التركيز على تلبية الطلب وتجنب عكسه، كلما زاد قل الإبداع",
-  "Seed": "البذرة",
-  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Seed: رقم طبيعي عشوائي يسمح بإعادة إنتاج نفس الصورة إذا توافقت قيم العوامل الأخرى",
-  "Set seed to -1, which will cause a new random number to be used every time": "استخدم بذرة جديدة في كل مرة (نرمز لهذا الخيار بجعل قيمة البذرة 1-)",
-  "Reuse seed from last generation, mostly useful if it was randomed": "أعد استخدام البذرة من الإنتاج السابق",
-  "Extra": "مزج",
-  "Variation seed": "بذرة الممزوج",
-  "Seed of a different picture to be mixed into the generation.": "Variation seed: بذرة صورة أخرى ليتم مزجها مع الصورة الحالية",
-  "Variation strength": "أثر الممزوج",
-  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Variation seed strength: مقدار أثر الصورة المدمجة على النتيجة النهائية (0: لا أثر، 1: أثر كامل ما عدا عند استخدام أسلوب خطو سلفي Ancestral)",
-  "Resize seed from width": "عرض الممزوج",
-  "Resize seed from height": "إرتفاع الممزوج",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Seed resize from: حدد دقة صورة الممزوج (0: نفس دقة الإنتاج)",
-  "Open for Clip Aesthetic!": "تضمين تجميلي",
-  "▼": "▼",
-  "Aesthetic weight": "أثر التضمين",
-  "Aesthetic steps": "عدد الخطوات",
-  "Aesthetic learning rate": "معدل التعلم",
-  "Slerp interpolation": "امزج بطريقة كروية",
-  "Aesthetic imgs embedding": "التضمين",
-  "None": "بدون",
-  "Aesthetic text for imgs": "الطلب (اختياري)",
-  "This text is used to rotate the feature space of the imgs embs": "لإعادة توجيه التضمين التجميلي",
-  "Slerp angle": "أثر الطلب",
-  "Is negative text": "الطلب عكسي",
-  "Script": "أدوات خاصة",
-  "Prompt matrix": "مصفوفة طلبات",
-  "Put variable parts at start of prompt": "الجزء المتغير في بداية الطلب",
-  "Prompts from file or textbox": " قائمة طلبات",
-  "Iterate seed every line": "غير البذرة مع كل طلب",
-  "List of prompt inputs": "قائمة الطلبات",
-  "Upload prompt inputs": "اجلب الطلبات من ملف",
-  "Drop File Here": "اسقط ملف هنا",
-  "-": "-",
-  "or": "أو",
-  "Click to Upload": "انقر للرفع",
-  "X/Y plot": "مصفوفة عوامل",
-  "X type": "العامل الأول",
-  "Nothing": "لا شيء",
-  "Var. seed": "بذرة الممزوج",
-  "Var. strength": "أثر الممزوج",
-  "Steps": "عدد الخطوات",
-  "Prompt S/R": "كلمات بديلة",
-  "Prompt order": "ترتيب الكلمات",
-  "Sampler": "أسلوب الخطو",
-  "Checkpoint name": "ملف الأوزان",
-  "Hypernetwork": "الشبكة الفائقة",
-  "Hypernet str.": "قوة الشبكة الفائقة",
-  "Inpainting conditioning mask strength": "قوة قناع الإنتاج الجزئي",
-  "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "حدد مدى صرامة قناع الإنتاج، يصبح القناع شفاف إذا قوته 0 (لا يعمل إلا مع ملفات أوزان الإنتاج الجزئي: inpainting)",
-  "Sigma Churn": "العشوائية (Schurn)",
-  "Sigma min": "أدنى تشويش (Stmin)",
-  "Sigma max": "أقصى تشويش (Stmax)",
-  "Sigma noise": "التشويش (Snoise)",
-  "Eta": "العامل Eta η",
-  "Clip skip": "تخطي آخر طبقات CLIP",
-  "Denoising": "المدى",
-  "Cond. Image Mask Weight": "قوة قناع الإنتاج الجزئي",
-  "X values": "قيم العامل الأول",
-  "Separate values for X axis using commas.": "افصل القيم بفواصل (,) من اليسار إلى اليمين",
-  "Y type": "العامل الثاني",
-  "Y values": "قيم العامل الثاني",
-  "Separate values for Y axis using commas.": "افصل القيم بفواصل (,) من الأعلى إلى الأسفل",
-  "Draw legend": "أضف مفتاح التوضيح",
-  "Include Separate Images": "أضف الصور منفصلة",
-  "Keep -1 for seeds": "استخدم بذور عشوائية",
-  "Save": "احفظ",
-  "Write image to a directory (default - log/images) and generation parameters into csv file.": "احفظ الصور مع ملف العوامل بصيغة CSV",
-  "Send to img2img": "أرسل لصورة إلى صورة",
-  "Send to inpaint": "أرسل للإنتاج الجزئي",
-  "Send to extras": "أرسل للمعالجة",
-  "Open images output directory": "افتح مجلد الصور المخرجة",
-  "Make Zip when Save?": "ضع النتائج في ملف مضغوط عند الحفظ",
-  "img2img": "صورة إلى صورة",
-  "Interrogate\nCLIP": "استجواب\nCLIP",
-  "Drop Image Here": "اسقط صورة هنا",
-  "Just resize": "تغيير الحجم فقط",
-  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "غير حجم الصورة بدون مراعات اتزان الأبعاد",
-  "Crop and resize": "تغيير الحجم وقص الأطراف",
-  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "غير حجم الصورة واقتص الأطراف الزائدة",
-  "Resize and fill": "تغيير الحجم وتبطين الأطراف",
-  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "غير حجم الصورة واملأ الأطراف الزائدة بألوان من الصورة",
-  "img2img alternative test": "استجواب الصورة (تجريبي)",
-  "should be 2 or lower.": "يفترض أن يكون 2 أو أقل",
-  "Override `Sampling method` to Euler?(this method is built for it)": "استخدم أسلوب خطو Euler (مستحسن)",
-  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "استبدل الطلب وعكسه في الأعلى بالطلب الأصلي وعكسه التاليين",
-  "Original prompt": "الطلب الأصلي",
-  "Original negative prompt": "عكس الطلب الأصلي",
-  "Override `Sampling Steps` to the same value as `Decode steps`?": "استبدل عدد الخطوات بعدد الخطوات الأصلية",
-  "Decode steps": "عدد الخطوات الأصلية",
-  "Override `Denoising strength` to 1?": "اجعل المدى 1",
-  "Decode CFG scale": "التركيز",
-  "Randomness": "العشوائية",
-  "Sigma adjustment for finding noise for image": "لا تسمح بتثبيت قيمة التباين",
-  "Loopback": "اجترار وتكرار",
-  "Loops": "عدد المرات",
-  "How many times to repeat processing an image and using it as input for the next iteration": "كم مرة يتم أخذ مخرجات الإنتاج كمدخلات وإعادة الإنتاج مرة أخرى",
-  "Denoising strength change factor": "معدل تغيير المدى",
-  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "يتم ضرب المدى بهذا الرقم في كل مرة، إذا استخدمت رقم أصغر من 1 يمكن الرسو على نتيجة، وإذا استخدمت رقم أكبر من 1 تصبح النتيجة عشوائية",
-  "Outpainting mk2": "توسيع الصورة (mk2)",
-  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "يفضل استخدام: 80-100 خطوة، أسلوب Euler a، المدى 0.8",
-  "Pixels to expand": "عدد البيكسلات",
-  "Mask blur": "تنعيم القناع",
-  "How much to blur the mask before processing, in pixels.": "مقدرا تنعيم القناع قبل استخدامه (يقاس بالبيكسل)",
-  "Outpainting direction": "اتجاه توسيع الصورة",
-  "left": "يسار",
-  "right": "يمين",
-  "up": "فوق",
-  "down": "تحت",
-  "Fall-off exponent (lower=higher detail)": "قوة السقوط (كلما قلت زادت التفاصيل)",
-  "Color variation": "تنوع الألوان",
-  "Poor man's outpainting": "توسيع الصورة (بدائي)",
-  "Masked content": "محتويات القناع",
-  "What to put inside the masked area before processing it with Stable Diffusion.": "ما يوضع مكان الفراغ في الصورة الذي نريد إنتاج محتوياته",
-  "fill": "ألوان",
-  "fill it with colors of the image": "املأ باستخدام ألوان مأخوذة من باقي الصورة",
-  "original": "بدون تغيير",
-  "keep whatever was there originally": "أبق محتويات ما تحت القناع كما هي",
-  "latent noise": "تشويش كامن",
-  "fill it with latent space noise": "املأه باستخدام تشويش من الفضاء الكامن",
-  "latent nothing": "تصفير كامن",
-  "fill it with latent space zeroes": "استبدل مكان القناع في الفضاء الكامن بأصفار",
-  "SD upscale": "مضاعفة الدقة بنموذج الإنتشار المستقر",
-  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "سيتم تكبير حجم الصورة إلى الضعف، استخدم الطول والإرتفاع في الأعلى لتحديد حجم نافذة المكبر",
-  "Tile overlap": "تداخل النافذة",
-  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "المكبر ينظر إلى أجزاء الصورة من خلال نافذة لتكبير المحتوى ثم ينتقل إلى الجزء المجاور، يفضل أن يكون هناك تداخل بين كل رقعة لكي لا يكون هناك اختلاف واضح بينهم",
-  "Upscaler": "طريقة التكبير",
-  "Lanczos": "Lanczos",
-  "LDSR": "LDSR",
-  "ScuNET GAN": "ScuNET GAN",
-  "ScuNET PSNR": "ScuNET PSNR",
-  "ESRGAN_4x": "ESRGAN_4x",
-  "SwinIR 4x": "SwinIR 4x",
-  "Inpaint": "إنتاج جزئي",
-  "Draw mask": "ارسم القناع",
-  "Upload mask": "ارفع القناع",
-  "Inpaint masked": "أنتج ما بداخل القناع",
-  "Inpaint not masked": "أنتج ما حول القناع",
-  "Inpaint at full resolution": "إنتاج بالدقة الكاملة",
-  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "كبر ما يراد إعادة إنتاجه ثم صغر النتيجة وألصقها في مكانها",
-  "Inpaint at full resolution padding, pixels": "عدد بيكسلات التبطين",
-  "Batch img2img": "صور إلى صور",
-  "Process images in a directory on the same machine where the server is running.": "حدد مسار مجلد صور موجود في جهاز الخادم",
-  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "يمكنك أيضا تحديد مجلد حفظ النتائج (غير الإفتراضي)",
-  "Input directory": "مجلد المدخلات",
-  "Output directory": "مجلد المخرجات",
-  "Extras": "معالجة",
-  "Single Image": "صورة واحدة",
-  "Source": "المصدر",
-  "Scale by": "مضاعفة الدقة",
-  "Resize": "تغيير الحجم",
-  "Upscaler 1": "المكبر الأول",
-  "Upscaler 2": "المكبر الثاني",
-  "Upscaler 2 visibility": "أثر المكبر الثاني",
-  "GFPGAN visibility": "أثر GFPGAN (محسن وجوه)",
-  "CodeFormer visibility": "أثر CodeFormer (محسن وجوه)",
-  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "وزن CodeFormer (يزيد التفاصيل على حساب الجودة)",
-  "Upscale Before Restoring Faces": "كبر قبل تحسين الوجوه",
-  "Scale to": "دقة محددة",
-  "Crop to fit": "قص الأطراف الزائدة إذا لم تتناسب الأبعاد",
-  "Batch Process": "حزمة صور",
-  "Batch from Directory": "حزمة من مجلد",
-  "A directory on the same machine where the server is running.": "مسار مجلد صور موجود في جهاز الخادم",
-  "Leave blank to save images to the default path.": "اتركه فارغا لاستخدام المسار الإفتراضي",
-  "Show result images": "اعرض الصور الناتجة",
-  "PNG Info": "عوامل الصورة",
-  "Send to txt2img": "أرسل لنص إلى صورة",
-  "Checkpoint Merger": "مزج الأوزان",
-  "A merger of the two checkpoints will be generated in your": "سيتم مزج الأوزان التالية وحفظ الأوزان المدجمة مع ",
-  "checkpoint": "الأوزان",
-  "directory.": " مجلد.",
-  "Primary model (A)": "الأوزان الأولى (A)",
-  "Secondary model (B)": "الأوزان الثانية (B)",
-  "Tertiary model (C)": "الأوزان الثالثة (C)",
-  "Custom Name (Optional)": "الاسم الجديد (اختياري)",
-  "Multiplier (M) - set to 0 to get model A": "العامل M: مسافة الإبتعاد عن الأوزان الأولى A",
-  "Interpolation Method": "طريقة المزج",
-  "Weighted sum": "خطية",
-  "Result = A * (1 - M) + B * M": "النتيجة = A * (1 - M) + B * M",
-  "Add difference": "جمع الفرق",
-  "Result = A + (B - C) * M": "النتيجة = A + (B - C) * M",
-  "Save as float16": "احفظ بدقة float16",
-  "Run": "تشغيل",
-  "Train": "تدريب",
-  "See": "اقرأ",
-  "wiki": " الـwiki ",
-  "for detailed explanation.": "لمعرفة المزيد",
-  "Create embedding": "إنشاء تضمين",
-  "Name": "الاسم",
-  "Initialization text": "النص المبدأي",
-  "Number of vectors per token": "عدد المتجهات لكل وحدة لغوية",
-  "Overwrite Old Embedding": "استبدل التضمين القديم",
-  "Create hypernetwork": "إنشاء شبكة فائقة",
-  "Modules": "الأجزاء",
-  "Enter hypernetwork layer structure": "ترتيب مضاعفات عرض الطبقات",
-  "1st and last digit must be 1. ex:'1, 2, 1'": "المضاعفين الأول والأخير يجب أن يكونا 1، مثال: 1, 2, 1",
-  "Select activation function of hypernetwork": "دالة التنشيط",
-  "linear": "linear",
-  "relu": "relu",
-  "leakyrelu": "leakyrelu",
-  "elu": "elu",
-  "swish": "swish",
-  "tanh": "tanh",
-  "sigmoid": "sigmoid",
-  "celu": "celu",
-  "gelu": "gelu",
-  "glu": "glu",
-  "hardshrink": "hardshrink",
-  "hardsigmoid": "hardsigmoid",
-  "hardtanh": "hardtanh",
-  "logsigmoid": "logsigmoid",
-  "logsoftmax": "logsoftmax",
-  "mish": "mish",
-  "prelu": "prelu",
-  "rrelu": "rrelu",
-  "relu6": "relu6",
-  "selu": "selu",
-  "silu": "silu",
-  "softmax": "softmax",
-  "softmax2d": "softmax2d",
-  "softmin": "softmin",
-  "softplus": "softplus",
-  "softshrink": "softshrink",
-  "softsign": "softsign",
-  "tanhshrink": "tanhshrink",
-  "threshold": "threshold",
-  "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "تهيئة الأوزان (استخدم Kaiming مع relu وأمثالها وXavier مع sigmoid وأمثالها)",
-  "Normal": "Normal",
-  "KaimingUniform": "KaimingUniform",
-  "KaimingNormal": "KaimingNormal",
-  "XavierUniform": "XavierUniform",
-  "XavierNormal": "XavierNormal",
-  "Add layer normalization": "أضف تسوية الطبقات (LayerNorm)",
-  "Use dropout": "استخدم الإسقاط (Dropout)",
-  "Overwrite Old Hypernetwork": "استبدل الشبكة الفائقة القديمة",
-  "Preprocess images": "معالجة مسبقة للصور",
-  "Source directory": "مجلد المدخلات",
-  "Destination directory": "مجلد المخرجات",
-  "Existing Caption txt Action": "اذا كانت الصورة لديها توصيف (طلب)",
-  "ignore": "تجاهل",
-  "copy": "انسخ",
-  "prepend": "أسبق",
-  "append": "ألحق",
-  "Create flipped copies": "انشئ نسخ معكوسة للصور",
-  "Split oversized images": "قسّم الصور الكبيرة",
-  "Split image threshold": "حد تقسيم الصور الكبيرة",
-  "Split image overlap ratio": "نسبة تداخل اقسام الصور الكبيرة",
-  "Auto focal point crop": "اقتصاص تلقائي",
-  "Focal point face weight": "تمركز الوجوه",
-  "Focal point entropy weight": "تمركز التنوع",
-  "Focal point edges weight": "تمركز الحواف",
-  "Create debug image": "احفظ نتائج التحليل أيضا",
-  "Use BLIP for caption": "استخدم BLIP لتوصيف الصور",
-  "Use deepbooru for caption": "استخدم deepbooru لتوصيف الصور",
-  "Preprocess": "معالجة مسبقة",
-  "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "درب التضمين أو الشبكة الفائقة: يجب تحديد مجلد يحتوي صور مربعة فقط ",
-  "[wiki]": "[wiki]",
-  "Embedding": "التضمين",
-  "Embedding Learning rate": "معدل تعلم التضمين",
-  "Hypernetwork Learning rate": "معدل تعلم الشبكة الفائقة",
-  "Dataset directory": "مجلد مجموعة البيانات",
-  "Path to directory with input images": "مسار مجلد الصور المدخلة",
-  "Log directory": "مجلد السجل",
-  "Path to directory where to write outputs": "مسار مجلد الصور المخرجة",
-  "Prompt template file": "ملف صيغ الطلبات",
-  "Max steps": "أقصى عدد لخطوات التدريب",
-  "Save an image to log directory every N steps, 0 to disable": "احفظ صورة في السجل بعد كل كم خطوة تدريب (إذا 0 لا تحفظ)",
-  "Save a copy of embedding to log directory every N steps, 0 to disable": "احفظ التضمين في السجل بعد كل كم خطوة تدريب (إذا 0 لا تحفظ)",
-  "Save images with embedding in PNG chunks": "احفظ التضمين بداخل ملف الصورة كعامل يمكن استخراجه من عوامل الصورة (صيغة PNG)",
-  "Read parameters (prompt, etc...) from txt2img tab when making previews": "استخدم قيم العوامل الموجودة في تبويب نص إلى صورة لعرض نتائجهم أثناء التدريب",
-  "Train Hypernetwork": "درّب الشبكة الفائقة",
-  "Train Embedding": "درّب التضمين",
-  "Create aesthetic embedding": "تضمين تجميلي",
-  "Create an aesthetic embedding out of any number of images": "انشئ تضمين تجميلي يعبر عن مجموعة من الصور",
-  "Create images embedding": "انشئ التضمين التجميلي",
-  "Image Browser": "معرض الصور",
-  "Load": "حمّل",
-  "Images directory": "مجلد الصور",
-  "First Page": "الصفحة الأولى",
-  "Prev Page": "الصفحة السابقة",
-  "Page Index": "رقم الصفحة",
-  "Next Page": "الصفحة التالية",
-  "End Page": "الصفحة الأخيرة",
-  "number of images to delete consecutively next": "عدد الصور المتتالية للحذف",
-  "Delete": "احذف",
-  "Generate Info": "معلومات عامة",
-  "File Name": "اسم الملف",
-  "Collect": "اجمع",
-  "extras": "معالجة",
-  "favorites": "المفضلة",
-  "custom fold": "مجلد آخر",
-  "Input images directory": "مجلد الصور المدخلة",
-  "Settings": "إعدادات",
-  "Apply settings": "طبق الإعدادت",
-  "Saving images/grids": "حفظ الصور وجداولها",
-  "Always save all generated images": "احفظ كل الصور المنتجة",
-  "File format for images": "صيغة ملفات الصور",
-  "Images filename pattern": "نمط تسمية الصور",
-  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم الأوسمة التالية لتحديد كيفية تسمية الصور: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] أو اتركه فارغا إذا أردت",
-  "Add number to filename when saving": "دائما أضف رقم على اسم الملف",
-  "Always save all generated image grids": "احفظ جداول الصور دائما",
-  "File format for grids": "صيغة ملفات جداول الصور",
-  "Add extended info (seed, prompt) to filename when saving grid": "أضف عوامل الإنتاج (مثل الطلب والبذرة) لأسماء جداول الصور",
-  "Do not save grids consisting of one picture": "لا تحفظ جدول الصور عند إنتاج صورة واحدة فقط",
-  "Prevent empty spots in grid (when set to autodetect)": "في الوضع التلقائي امنع الفراغات في جداول الصور",
-  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "عدد صفوف جداول الصور (1-: تلقائي، 0: نفس حجم الحزمة)",
-  "Save text information about generation parameters as chunks to png files": "احفظ عوامل الإنتاج داخل ملفات الصور (صيغة PNG)",
-  "Create a text file next to every image with generation parameters.": "انشئ ملف نصي يحتوي على عوامل الإنتاج بجانب كل صورة",
-  "Save a copy of image before doing face restoration.": "احفظ نسخة من الصورة قبل تحسين الوجوه",
-  "Quality for saved jpeg images": "جودة حفظ صور JPEG",
-  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "إذا كان حجم ملف صورة PNG أكبر من 4MB أو أحد أبعاد الصورة أكبر من 4000 بيكسل، صغر الصورة واحفظها بصيغة JPEG",
-  "Use original name for output filename during batch process in extras tab": "استخدم الإسم الأصلي للصور عند معالجتهم في حزم تحت تبويب معالجة",
-  "When using 'Save' button, only save a single selected image": "احفظ صورة واحدة فقط عند الضغط على حفظ",
-  "Do not add watermark to images": "لا تضف علامة مائية على الصور",
-  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "الصور المنتجة لن تحتوي على علامة مائية عند تفعيل هذا الخيار، تحذير: ربما قد يدل هذا على أنك تتصرف بشكل غير أخلاقي",
-  "Paths for saving": "اماكن الحفظ",
-  "Output directory for images; if empty, defaults to three directories below": "مسار حفظ الصور المخرجة؛ يمكن أن يترك فارغا",
-  "Output directory for txt2img images": "مسار الصور المخرجة من تبويب نص إلى صورة",
-  "Output directory for img2img images": "مسار الصور المخرجة من تبويب صورة إلى صورة",
-  "Output directory for images from extras tab": "مسار الصور المخرجة من تبويب معالجة",
-  "Output directory for grids; if empty, defaults to two directories below": "مسار حفظ جداول الصور المخرجة؛ يمكن أن يترك فارغا",
-  "Output directory for txt2img grids": "مسار جداول الصور المخرجة من تبويب نص إلى صورة",
-  "Output directory for img2img grids": "مسار جداول الصور المخرجة من تبويب صورة إلى صورة",
-  "Directory for saving images using the Save button": "مسار حفظ الصور عند الضغط على زر الحفظ",
-  "Saving to a directory": "مجلدات الحفظ",
-  "Save images to a subdirectory": "احفظ الصور في مجلد فرعي",
-  "Save grids to a subdirectory": "احفظ جداول الصور في مجلد فرعي",
-  "When using \"Save\" button, save images to a subdirectory": "احفظ الصور في مجلد فرعي عند الضغط على زر الحفظ",
-  "Directory name pattern": "نمط اسم المجلد",
-  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "استخدم الأوسمة التالية لتحديد كيفية تسمية المجلدات الفرعية للصور والصور المركبة: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp] أو اتركه فارغا إذا أردت",
-  "Max prompt words for [prompt_words] pattern": "أقصى عدد كلمات الطلب عند استخدام وسم [prompt_words]",
-  "Upscaling": "تكبير الصور",
-  "Tile size for ESRGAN upscalers. 0 = no tiling.": "حجم نافذة المكبر ESRGAN (يمكن أن يكون 0)",
-  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "تداخل نافذة المكبر ESRGAN (تتضح الفواصل إذا كان قليل)",
-  "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "حدد ما تريد عرضه كخيار لمكبر من نوع Real-ESRGAN يمكن استخدامه (يتطلب إعادة تشغيل)",
-  "Tile size for all SwinIR.": "حجم نافذة المكبر SwinIR",
-  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "تداخل نافذة المكبر SwinIR (تتضح الفواصل إذا كان قليل)",
-  "LDSR processing steps. Lower = faster": "عدد خطوات مكبر LDSR (أسرع كلما قلت)",
-  "Upscaler for img2img": "طريقة التكبير تحت تبويب صورة إلى صورة",
-  "Upscale latent space image when doing hires. fix": "عند اختيار \"إصلاح الدقة العالية\" قم بالتكبير في الفضاء الكامن",
-  "Face restoration": "تحسين الوجوه",
-  "Face restoration model": "نموذج تحسين الوجوه",
-  "Restore low quality faces using GFPGAN neural network": "استخدم نموذج GFPGAN لتحسين الوجوه",
-  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "وزن CodeFormer (يزيد التفاصيل على حساب الجودة)",
-  "Move face restoration model from VRAM into RAM after processing": "احذف محسن الوجوه من ذاكرة كرت الشاشة (VRAM) بعد استخدامه",
-  "System": "النظام",
-  "VRAM usage polls per second during generation. Set to 0 to disable.": "عدد مرات التحقق من ذاكرة كرت الشاشة المستخدمة (VRAM) في الثانية",
-  "Always print all generation info to standard output": "اطبع دائما كل عوامل الإنتاج",
-  "Add a second progress bar to the console that shows progress for an entire job.": "أضف شريط تقدم ثاني لعملية الإنتاج الكاملة",
-  "Training": "التدريب",
-  "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "قلل استخدام ذاكرة كرت الشاشة (VRAM) عند تدريب الشبكة الفائقة بالتخلص من CLIP وVAE",
-  "Filename word regex": "التعبير النمطي (RegEx) لكلمات اسم الملف",
-  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "استخدم هذا التعبير لاستخراج كلمات من أسماء الملفات ليتم دمجها بالخيار التالي لتستخدم في التدريب، اتركه فارغا لتستخدم اسم الملف كما هو",
-  "Filename join string": "النص الفاصل للكلمات المدموجة",
-  "This string will be used to join split words into a single line if the option above is enabled.": "سيستخدم بين كل كلمة يتم استخراجها من الخيار السابق",
-  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "عدد مرات تكرار الصور في كل دورة (Epoch)، يستخدم للعرض فقط",
-  "Save an csv containing the loss to log directory every N steps, 0 to disable": "احفظ خسارة التدريب (Loss) في السجل بعد كل كم خطوة (إذا 0 لا تحفظ)",
-  "Stable Diffusion": "نموذج الإنتشار المستقر (Stable Diffusion)",
-  "Checkpoints to cache in RAM": "عدد النماذج التي تترك في الذاكرة العشوائية (RAM)",
-  "Hypernetwork strength": "قوة الشبكة الفائقة",
-  "Apply color correction to img2img results to match original colors.": "صحح ألوان نتائج صورة إلى صورة لتشابه الصورة الأصلية",
-  "Save a copy of image before applying color correction to img2img results": "احفظ نسخة من الصور المنتجة من صورة إلى صورة قبل عملية تصحيح الألوان",
-  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "استخدم بالضبط عدد الخطوات المحددة بالرغم عن المدى (عدد الخطوات الإفتراضي = عدد الخطوات / المدى)",
-  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "فعل التجزيء الكمي (Quantization) لأغلب أساليب الخطو (k-diffusion) للحصول على صور أنظف (يتطلب إعادة تشغيل)",
-  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "أحط أي كلمة أو عبارة في الطلب أو عكسه بأقواس () للتشديد أو [] للتيسير",
-  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "استخدم الطريقة القديمة للتشديد (إذا كانت لديك نتائج قديمة تريد تجربتها)",
-  "Make K-diffusion samplers produce same images in a batch as when making a single image": "عدم تأثير تحزيم الصور على البذور عند استخدام أساليب خطو (k-diffusion)",
-  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "عند استخدام طلب طويل أكبر من 75 وحدة لغوية، افصل الطلب عند أخر فاصلة قبل كم كلمة",
-  "Filter NSFW content": "انتق المحتوى النظيف فقط",
-  "Stop At last layers of CLIP model": "قف عند آخر كم طبقة لنموذج CLIP",
-  "Allowed categories for random artists selection when using the Roll button": "اختر اهتمامات الفنانين المسموح بإضافتهم للطلب",
-  "Interrogate Options": "خيارات الاستجواب",
-  "Interrogate: keep models in VRAM": "ابق نموذج الاستجواب في ذاكرة كرت الشاشة (VRAM)",
-  "Interrogate: use artists from artists.csv": "استخدم قائمة الفنانين في الاستجواب من ملف artists.csv",
-  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "أدرج رتب الشعارات عند الاستجواب (لا تعمل مع جميع النماذج)",
-  "Interrogate: num_beams for BLIP": "عدد أشعة الاستجواب لنموذج BLIP",
-  "Interrogate: minimum description length (excluding artists, etc..)": "أقل عدد كلمات للتوصيف (لا يتضمن الفنانين وغيرهم)",
-  "Interrogate: maximum description length": "أكثر عدد كلمات للتوصيف",
-  "CLIP: maximum number of lines in text file (0 = No limit)": "أكثر عدد أسطر لملف نصي عند الاستجواب باستخدام CLIP (0 = بدون حد)",
-  "Interrogate: deepbooru score threshold": "حد درجة الاستجواب باستخدام deepbooru",
-  "Interrogate: deepbooru sort alphabetically": "رتب نتائج توصيف deepbooru أبجديا",
-  "use spaces for tags in deepbooru": "افصل شعارات deepbooru بمسافات",
-  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "تجاهل الأقواس عند استخدام deepbooru (كي لا تعامل كأقواس التشديد)",
-  "User interface": "واجهة المستخدم",
-  "Show progressbar": "اظهر شريط التقدم",
-  "Show image creation progress every N sampling steps. Set 0 to disable.": "اعرض صورة بعد كل كم خطوة (إذا 0 لا تعرض)",
-  "Show previews of all images generated in a batch as a grid": "اعرض كل الصور التي تم إنتاجها في حزمة كجدول",
-  "Show grid in results for web": "أظهر نتائج جداول الصور",
-  "Do not show any images in results for web": "لا تظهر نتائج الصور",
-  "Add model hash to generation information": "أضف رمز تهشير (Hash) ملف الأوزان لعوامل الإنتاج",
-  "Add model name to generation information": "أضف اسم ملف الأوزان لعوامل الإنتاج",
-  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "لا تغير الأوزان المختارة عند قراءة عوامل الإنتاج من صورة أو من ملف",
-  "Send seed when sending prompt or image to other interface": "عند إرسال صورة أو طلب ألحق البذرة أيضا",
-  "Font for image grids that have text": "نوع الخط في جداول الصور التي تحتوي على نصوص",
-  "Enable full page image viewer": "اسمح بعرض الصور في وضع ملئ الشاشة",
-  "Show images zoomed in by default in full page image viewer": "اعرض الصور مقربة عند استخدام وضع ملئ الشاشة",
-  "Show generation progress in window title.": "أظهر التقدم في عنوان النافذة",
-  "Quicksettings list": "قائمة الإعدادات السريعة",
-  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "قائمة مقسمة بفواصل لأسماء الإعدادات التي يجب أن تظهر في الأعلى لتسهيل الوصول إليها، انظر إلى modules/shared.py لمعرفة الأسماء، يتطلب إعادة تشغيل",
-  "Localization (requires restart)": "اللغة (تتطلب إعادة تشغيل)",
-  "pt_BR": "البرتغالية",
-  "zh_CN": "الصينية",
-  "ko_KR": "الكورية",
-  "fr_FR": "الفرنسية",
-  "ru_RU": "الروسية",
-  "ar_AR": "العربية",
-  "tr_TR": "التركية",
-  "it_IT": "الإيطالية",
-  "ja_JP": "اليابانية",
-  "de_DE": "الألمانية",
-  "zh_TW": "الصينية (تايوان)",
-  "es_ES": "الإسبانية",
-  "Sampler parameters": "عوامل أساليب الخطو",
-  "Hide samplers in user interface (requires restart)": "اخف أساليب الخطو التالية (يتطلب إعادة تشغيل)",
-  "eta (noise multiplier) for DDIM": "العامل Eta η لأسلوب الخطو DDIM",
-  "eta (noise multiplier) for ancestral samplers": "العامل Eta η لأساليب الخطو السلفية (Ancestral)",
-  "img2img DDIM discretize": "طريقة التقطيع (Discretization) لأسلوب الخطو DDIM في وضع صورة إلى صورة",
-  "uniform": "خطية",
-  "quad": "تربيعية",
-  "sigma churn": "العشوائية (Schurn)",
-  "sigma tmin": "أدنى تشويش (Stmin)",
-  "sigma noise": "التشويش (Snoise)",
-  "Eta noise seed delta": "إزاحة بذرة أساليب الخطو التي تستعمل العامل Eta η",
-  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "هذا الرقم سيتم إضافته إلى البذرة عند استخدام أحد أساليب الخطو التي تستعمل العامل Eta η، يفيد هذا الخيار في مشابهة نتائج بعض البرامج الأخرى التي تستعمله",
-  "Images Browser": "معرض الصور",
-  "Preload images at startup": "حمل الصور عند بدء التشغيل",
-  "Number of columns on the page": "عدد الأعمدة في كل صفحة",
-  "Number of rows on the page": "عدد الصفوف في كل صفحة",
-  "Minimum number of pages per load": "أقل عدد صور يتم تحميلها كل مرة",
-  "Request browser notifications": "اطلب تنبيهات المتصفح",
-  "Download localization template": "حمل ملف اللغة",
-  "Reload custom script bodies (No ui updates, No restart)": "أعد تحميل الأدوات الخاصة (بدون واجهة المستخدم ولا يحتاج إعادة تشغيل)",
-  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "أعد تشغيل gradio وتحميل الأدوات الخاصة وواجهة المستخدم",
-  "⤡": "⤡",
-  "⊞": "⊞",
-  "×": "×",
-  "❮": "❮",
-  "❯": "❯",
-  "•": "•",
-  "Label": "Label",
-  "File": "File",
-  "Image": "Image",
-  "Check progress": "Check progress",
-  "Check progress (first)": "Check progress (first)",
-  "Textbox": "Textbox",
-  "Image for img2img": "Image for img2img",
-  "Image for inpainting with mask": "Image for inpainting with mask",
-  "Mask": "Mask",
-  "Mask mode": "Mask mode",
-  "Masking mode": "Masking mode",
-  "Resize mode": "Resize mode",
-  "Prev batch": "Prev batch",
-  "Next batch": "Next batch",
-  "Refresh page": "Refresh page",
-  "Date to": "Date to",
-  "Number": "Number",
-  "set_index": "set_index",
-  "Checkbox": "Checkbox"
-}
diff --git a/localizations/de_DE.json b/localizations/de_DE.json
deleted file mode 100644
index 5e254446..00000000
--- a/localizations/de_DE.json
+++ /dev/null
@@ -1,458 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "view": "API ",
-    "api": "anzeigen",
-    "•": " • ",
-    "built with gradio": "Mit Gradio erstellt",
-    "Loading...": "Lädt...",
-    "Stable Diffusion checkpoint": "Stable Diffusion Checkpoint",
-    "txt2img": "txt2img",
-    "img2img": "img2img",
-    "Extras": "Extras",
-    "PNG Info": "PNG Info",
-    "Checkpoint Merger": "Checkpoint Fusion",
-    "Train": "Trainieren",
-    "Settings": "Einstellungen",
-    "Prompt": "Prompt",
-    "Negative prompt": "Negative Prompt",
-    "Run": "Ausführen",
-    "Skip": "Überspringen",
-    "Interrupt": "Abbrechen",
-    "Generate": "Generieren",
-    "Style 1": "Stil 1",
-    "Style 2": "Stil 2",
-    "Label": "Bezeichnung",
-    "File": "Datei",
-    "Drop File Here": "Datei hier ablegen",
-    "-": "-",
-    "o": "oder",
-    "Click to Upload": "Hochladen",
-    "Image": "Bild",
-    "Check progress": "Fortschitt prüfen",
-    "Check progress (first)": "Fortschritt prüfen (Initial)",
-    "Sampling Steps": "Samplingschritte",
-    "Sampling method": "Samplingmethode",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "Breite",
-    "Height": "Höhe",
-    "Restore faces": "Gesichter wiederherstellen",
-    "Tiling": "Kacheln",
-    "Highres. fix": "Highres. Fix",
-    "Firstpass width": "Breite Erstdurchlauf",
-    "Firstpass height": "Höhe Erstdurchlauf",
-    "Denoising strength": "Denoisingstärke",
-    "Batch count": "Batchanzahl",
-    "Batch size": "Batchgröße",
-    "CFG Scale": "CFG-Skala",
-    "Seed": "Seed",
-    "Extra": "Extra",
-    "Variation seed": "Variationsseed",
-    "Variation strength": "Variationsstärke",
-    "Resize seed from width": "Seed von Breite ändern",
-    "Resize seed from height": "Seed von Höhe ändern",
-    "Script": "Skript",
-    "None": "Nichts",
-    "Prompt matrix": "Promptmatrix",
-    "Prompts from file or textbox": "Prompts aus Datei oder Textfeld",
-    "X/Y plot": "X/Y Graph",
-    "Put variable parts at start of prompt": "Variable teile am start des Prompt setzen",
-    "Iterate seed every line": "Iterate seed every line",
-    "List of prompt inputs": "List of prompt inputs",
-    "Upload prompt inputs": "Upload prompt inputs",
-    "X type": "X-Typ",
-    "Nothing": "Nichts",
-    "Var. seed": "Var. seed",
-    "Var. strength": "Var. strength",
-    "Steps": "Schritte",
-    "Prompt S/R": "Prompt Suchen/Ersetzen",
-    "Prompt order": "Promptreihenfolge",
-    "Sampler": "Sampler",
-    "Checkpoint name": "Checkpointname",
-    "Hypernetwork": "Hypernetwork",
-    "Hypernet str.": "Hypernet str.",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma min": "Sigma min",
-    "Sigma max": "Sigma max",
-    "Sigma noise": "Sigma noise",
-    "Eta": "Eta",
-    "Clip skip": "Clip skip",
-    "Denoising": "Denoising",
-    "Cond. Image Mask Weight": "Cond. Image Mask Weight",
-    "X values": "X-Werte",
-    "Y type": "Y-Typ",
-    "Y values": "Y-Werte",
-    "Draw legend": "Legende zeichnen",
-    "Include Separate Images": "Seperate Bilder hinzufügen",
-    "Keep -1 for seeds": "-1 als Seed behalten",
-    "Save": "Speichern",
-    "Send to img2img": "An img2img senden",
-    "Send to inpaint": "An Inpaint senden",
-    "Send to extras": "An Extras senden",
-    "Make Zip when Save?": "Zip beim Speichern erstellen?",
-    "Textbox": "Textfeld",
-    "Interrogate\nCLIP": "Interrogate\nCLIP",
-    "Interrogate\nDeepBooru": "Interrogate\nDeepBooru",
-    "Inpaint": "Inpaint",
-    "Batch img2img": "Batch img2img",
-    "Image for img2img": "Bild für img2img",
-    "Drop Image Here": "Bild hier ablegen",
-    "Image for inpainting with mask": "Bild für inpainting mit Maske",
-    "Mask": "Maske",
-    "Mask blur": "Maskenunschärfe",
-    "Mask mode": "Maskenmodus",
-    "Draw mask": "Maske zeichnen",
-    "Upload mask": "Maske hochladen",
-    "Masking mode": "Maskierungsmodus",
-    "Inpaint masked": "Maskiertes inpainten",
-    "Inpaint not masked": "Nicht maskiertes inpainten",
-    "Masked content": "Maskierter Inhalt",
-    "fill": "ausfüllen",
-    "original": "original",
-    "latent noise": "latent noise",
-    "latent nothing": "latent nothing",
-    "Inpaint at full resolution": "Inpaint mit voller Auflösung",
-    "Inpaint at full resolution padding, pixels": "Inpaint bei voller Auflösung Abstand, Pixel",
-    "Process images in a directory on the same machine where the server is running.": "Bilder in einem Verzeichnis auf demselben Rechner verarbeiten, auf dem der Server läuft.",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Ein leeres Ausgabeverzeichnis verwenden, um Bilder normal zu speichern, anstatt in das Ausgabeverzeichnis zu schreiben.",
-    "Input directory": "Eingabeverzeichnis",
-    "Output directory": "Ausgabeverzeichnis",
-    "Resize mode": "Größenänderungsmodus",
-    "Just resize": "Nur Größe anpassen",
-    "Crop and resize": "Zuschneiden und Größe anpassen",
-    "Resize and fill": "Größe anpassen und ausfüllen",
-    "img2img alternative test": "img2img alternativer Test",
-    "Loopback": "Loopback",
-    "Outpainting mk2": "Outpainting mk2",
-    "Poor man's outpainting": "Poor man's outpainting",
-    "SD upscale": "SD-Upscale",
-    "should be 2 or lower.": "Sollte 2 oder niedriger sein.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "`Samplingmethode` auf Euler setzen? (Diese Methode is dafür ausgelegt)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "`Prompt` auf denselben Wert wie `Originale Prompt` (und `Negative Prompt`) setzen?",
-    "Original prompt": "Originale Prompt",
-    "Original negative prompt": "Originale negative Prompt",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "`Samplingschritte` auf denselben Wert wie `Dekodierschritte` setzen?",
-    "Decode steps": "Dekodierschritte",
-    "Override `Denoising strength` to 1?": "`Denoisingstärke auf 1 setzen?",
-    "Decode CFG scale": "CFG-Skala dekodieren",
-    "Randomness": "Zufälligkeit",
-    "Sigma adjustment for finding noise for image": "Sigma-Anpassung für die Suche nach Noise des Bildes",
-    "Loops": "Schleifen",
-    "Denoising strength change factor": "Denoising strength change factor",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Empfohlene Einstellungen: Samplingschritte: 80-100, Samplermethode: Euler a, Denoisingstärke: 0.8",
-    "Pixels to expand": "Pixel zum Erweitern",
-    "Outpainting direction": "Outpainting Richtung",
-    "left": "Links",
-    "right": "Rechts",
-    "up": "Hoch",
-    "down": "Runter",
-    "Fall-off exponent (lower=higher detail)": "Abfallexponent (niedriger=mehr Details)",
-    "Color variation": "Farbabweichung",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Skaliert das Bild auf die doppelte Größe; Benutze die Schieberegler für Breite und Höhe, um die Kachelgröße einzustellen",
-    "Tile overlap": "Kachelüberlappung",
-    "Upscaler": "Upscaler",
-    "Lanczos": "Lanczos",
-    "LDSR": "LDSR",
-    "SwinIR 4x": "SwinIR 4x",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "Single Image": "Einzelnes Bild",
-    "Batch Process": "Batchverarbeitung",
-    "Batch from Directory": "Batchverarbeitung aus Verzeichnis",
-    "Source": "Quelle",
-    "Show result images": "Bildergebnisse zeigen",
-    "Scale by": "Skalieren um",
-    "Scale to": "Skalieren zu",
-    "Resize": "Größe anpassen",
-    "Crop to fit": "Zuschneiden damit es passt",
-    "Upscaler 2 visibility": "Upscaler 2 Sichtbarkeit",
-    "GFPGAN visibility": "GFPGAN Sichtbarkeit",
-    "CodeFormer visibility": "CodeFormer Sichtbarkeit",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer Gewichtung (0 = maximale Wirkung, 1 = minimale Wirkung)",
-    "Upscale Before Restoring Faces": "Upscale Before Restoring Faces",
-    "Send to txt2img": "An txt2img senden",
-    "A merger of the two checkpoints will be generated in your": "Die zusammgeführten Checkpoints werden gespeichert unter",
-    "checkpoint": "Checkpoint",
-    "directory.": "Verzeichnis.",
-    "Primary model (A)": "Primäres Modell (A)",
-    "Secondary model (B)": "Sekundäres Modell (B)",
-    "Tertiary model (C)": "Tertiäres Modell (C)",
-    "Custom Name (Optional)": "Eigener Name (Optional)",
-    "Multiplier (M) - set to 0 to get model A": "Multiplikator (M) - auf 0 setzen, um Modell A zu erhalten",
-    "Interpolation Method": "Interpolationsmethode",
-    "Weighted sum": "Weighted sum",
-    "Add difference": "Add difference",
-    "Save as float16": "Speichern als float16",
-    "See": "Siehe ",
-    "wiki": "Wiki ",
-    "for detailed explanation.": "für eine ausführliche Erklärung.",
-    "Create embedding": "Embedding erstellen",
-    "Create hypernetwork": "Hypernetwork erstellen",
-    "Preprocess images": "Bilder vorbereiten",
-    "Name": "Name",
-    "Initialization text": "Initialisierungstext",
-    "Number of vectors per token": "Anzahl der Vektoren pro Token",
-    "Overwrite Old Embedding": "Alte Embeddings überschreiben",
-    "Modules": "Module",
-    "Enter hypernetwork layer structure": "Hypernetwork-Ebenenstruktur angeben",
-    "Select activation function of hypernetwork": "Aktivierungsfunktion des Hypernetwork auswählen",
-    "linear": "linear",
-    "relu": "relu",
-    "leakyrelu": "leakyrelu",
-    "elu": "elu",
-    "swish": "swish",
-    "tanh": "tanh",
-    "sigmoid": "sigmoid",
-    "celu": "celu",
-    "gelu": "gelu",
-    "glu": "glu",
-    "hardshrink": "hardshrink",
-    "hardsigmoid": "hardsigmoid",
-    "hardtanh": "hardtanh",
-    "logsigmoid": "logsigmoid",
-    "logsoftmax": "logsoftmax",
-    "mish": "mish",
-    "prelu": "prelu",
-    "rrelu": "rrelu",
-    "relu6": "relu6",
-    "selu": "selu",
-    "silu": "silu",
-    "softmax": "softmax",
-    "softmax2d": "softmax2d",
-    "softmin": "softmin",
-    "softplus": "softplus",
-    "softshrink": "softshrink",
-    "softsign": "softsign",
-    "tanhshrink": "tanhshrink",
-    "threshold": "threshold",
-    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Auswahl der Initialisierung der Ebenengewichte. Empfohlen wird relu-like - Kaiming, sigmoid-like - Xavier",
-    "Normal": "Normal",
-    "KaimingUniform": "KaimingUniform",
-    "KaimingNormal": "KaimingNormal",
-    "XavierUniform": "XavierUniform",
-    "XavierNormal": "XavierNormal",
-    "Add layer normalization": "Ebenennormalisierung hinzufügen",
-    "Use dropout": "Dropout benutzen",
-    "Overwrite Old Hypernetwork": "Altes Hypernetwork überschreiben",
-    "Source directory": "Quellenverzeichnis",
-    "Destination directory": "Zielverzeichnis",
-    "Existing Caption txt Action": "Vorhandene Beschriftung der txt",
-    "ignore": "ignorieren",
-    "copy": "kopieren",
-    "prepend": "voranstellen",
-    "append": "anhängen",
-    "Create flipped copies": "Gespiegelte Bilder erstellen",
-    "Split oversized images": "Übergroße Bilder aufteilen",
-    "Auto focal point crop": "Automatisch auf Fokuspunkt zuschneiden",
-    "Use BLIP for caption": "BLIP für Beschriftung nutzen",
-    "Use deepbooru for caption": "Deepbooru für Beschriftung nutzen",
-    "Split image threshold": "Schwellenwert für die Aufteilung von Bildern",
-    "Split image overlap ratio": "Überschneidungsverhältnis der Teilbilder",
-    "Focal point face weight": "Fokuspunkt Gesicht Gewicht",
-    "Focal point entropy weight": "Fokuspunkt Entropie Gewicht",
-    "Focal point edges weight": "Fokuspunkt Kanten Gewicht",
-    "Create debug image": "Testbild erstellen",
-    "Preprocess": "Vorbereiten",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Trainieren eines Embeddings oder eines Hypernetworks; Sie müssen ein Verzeichnis mit einem Satz von Bildern im Verhältnis 1:1 angeben",
-    "[wiki]": "[Wiki]",
-    "Embedding": "Embedding",
-    "Embedding Learning rate": "Embedding Lernrate",
-    "Hypernetwork Learning rate": "Hypernetwork Lernrate",
-    "Dataset directory": "Datensatzverzeichnis",
-    "Log directory": "Protokollverzeichnis",
-    "Prompt template file": "Prompt-Vorlagendatei",
-    "Max steps": "Max Schritte",
-    "Save an image to log directory every N steps, 0 to disable": "Speichere alle N Schritte ein Bild im Protokollverzeichnis, 0 zum Deaktivieren",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Speichere alle N Schritte eine Embeddingkopie im Protokollverzeichnis, 0 zum Deaktivieren",
-    "Save images with embedding in PNG chunks": "Speichere Bilder mit Embeddings in PNG Chunks",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Lese Parameter (Prompt, etc...) aus dem txt2img-Tab beim Erstellen von Vorschaubildern.",
-    "Train Hypernetwork": "Hypernetwork Trainieren",
-    "Train Embedding": "Embedding Trainieren",
-    "Apply settings": "Eintellungen anwenden",
-    "Saving images/grids": "Bilder/Raster speichern",
-    "Always save all generated images": "Immer alle generierten Bilder speichern",
-    "File format for images": "Dateiformat für Bilder",
-    "Images filename pattern": "Dateinamensmuster für Bilder",
-    "Add number to filename when saving": "Beim speichern, dem Dateinamen Nummer anhängen",
-    "Always save all generated image grids": "Immer alle generierten Bildraster speichern",
-    "File format for grids": "Dateiformat für Raster",
-    "Add extended info (seed, prompt) to filename when saving grid": "Beim Speichern von Rastern zusätzliche Information (Seed, Prompt) hinzufügen",
-    "Do not save grids consisting of one picture": "Keine Raster speichern, die nur aus einem Bild bestehen",
-    "Prevent empty spots in grid (when set to autodetect)": "Lücken im Raster verhindern (falls auf Auto-Erkennung gesetzt)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Rasterreihenanzahl; -1 für Auto-Erkennung und 0 für die gleiche wie die Batchanzahl",
-    "Save text information about generation parameters as chunks to png files": "Generationsparameter als Chunks in PNG-Dateien speichern",
-    "Create a text file next to every image with generation parameters.": "Erstelle zu jedem Bild eine Textdatei, die die Generationsparameter enthält",
-    "Save a copy of image before doing face restoration.": "Vor der Gesichtswiederhestellung eine Kopie des Bildes speichern",
-    "Quality for saved jpeg images": "Qualität der JPEG-Bilder",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Wenn ein PNG-Bild größer als 4MB oder die Dimensionen größer als 4000 ist, herunterskalieren und als JPG speichern.",
-    "Use original name for output filename during batch process in extras tab": "Orginale Dateinamen als Ausgabenamen bei der Batchverarbeitung im Extras-Tab verwenden",
-    "When using 'Save' button, only save a single selected image": "Bei der Benutzung des 'Speichern'-Knopfes, nur das ausgewählte Bild speichern",
-    "Do not add watermark to images": "Den Bildern kein Wasserzeichen hinzufügen",
-    "Paths for saving": "Pfade zum Speichern",
-    "Output directory for images; if empty, defaults to three directories below": "Ausgabeverzeichnis für Bilder; Falls leer, werden die Pfade unterhalb verwendet",
-    "Output directory for txt2img images": "Ausgabeverzeichnis für txt2img Bilder",
-    "Output directory for img2img images": "Ausgabeverzeichnis für img2img Bilder",
-    "Output directory for images from extras tab": "Ausgabeverzeichnis für Extras-Tab Bilder",
-    "Output directory for grids; if empty, defaults to two directories below": "Ausgabeverzeichnis für Raster; Falls leer, werden die Pfade unterhalb verwendet",
-    "Output directory for txt2img grids": "Ausgabeverzeichnis für txt2img Raster",
-    "Output directory for img2img grids": "Ausgabeverzeichnis für img2img Raster",
-    "Directory for saving images using the Save button": "Ausgabeverzeichnis für Bilder, die mit dem 'Speichern'-Knopf gespeichert wurden",
-    "Saving to a directory": "Speichern in ein Verzeichnis",
-    "Save images to a subdirectory": "Bilder in ein Unterverzeichnis speichern",
-    "Save grids to a subdirectory": "Raster in ein Unterverzeichnis speichern",
-    "When using \"Save\" button, save images to a subdirectory": "Bilder bei der Benutzung des 'Speichern'-Knopfes in ein Unterverzeichnis speichern",
-    "Directory name pattern": "Muster für Verzeichnisnamen",
-    "Max prompt words for [prompt_words] pattern": "Maximale Wortanzahl für [prompt_words] Muster",
-    "Upscaling": "Upscaling",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Kachelgröße für ESRGAN-Upscaler. 0 = keine Kacheln.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Kachelüberlappung in Pixeln für ESRGAN-Upscaler. Niedrige Werte = sichtbare Naht.",
-    "Tile size for all SwinIR.": "Kachelgröße für alle SwinIR.",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Kachelüberlappung in Pixeln für SwinIR. Niedrige Werte = sichtbare Naht.",
-    "LDSR processing steps. Lower = faster": "LDSR-Verarbeitungsschritte. Niedriger = schneller",
-    "Upscaler for img2img": "Upscaler für img2img",
-    "Upscale latent space image when doing hires. fix": "Bild des Latent Space upscalen, wenn Highres. Fix benutzt wird",
-    "Face restoration": "Gesichtswiederhestellung",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer Gewichtung (0 = maximale Wirkung, 1 = minimale Wirkung)",
-    "Move face restoration model from VRAM into RAM after processing": "Verschiebe Gesichtswiederhestellung-Modell nach der Verarbeitung vom VRAM in den RAM",
-    "System": "System",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM-Nutzungsabfragen pro Sekunde während der Generierung. Zum Deaktivieren auf 0 setzen.",
-    "Always print all generation info to standard output": "Immer alle Generationsinformationen in der Standardausgabe ausgeben",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Der Konsole einen zweiten Fortschrittsbalken hinzufügen, der den Fortschritt eines gesamten Auftrags anzeigt.",
-    "Training": "Training",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "VAE und CLIP während des Hypernetwork-Trainings in den RAM verschieben. Spart VRAM.",
-    "Filename word regex": "Filename word regex",
-    "Filename join string": "Filename join string",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Anzahl der Wiederholungen für ein einzelnes Eingabebild pro Epoche; wird nur für die Anzeige der Epochennummer verwendet",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Speichere eine csv-Datei, die den Verlust enthält, im Protokollverzeichnis alle N Schritte, 0 zum Deaktivieren",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "Checkpoints zum Zwischenspeichern im RAM",
-    "Hypernetwork strength": "Hypernetworkstärke",
-    "Inpainting conditioning mask strength": "Inpainting Stärke der Konditionierungsmaske",
-    "Apply color correction to img2img results to match original colors.": "Farbkorrektur auf die img2img-Ergebnisse anwenden, damit sie den Originalfarben entsprechen.",
-    "Save a copy of image before applying color correction to img2img results": "Vor dem Anwenden der Farbkorrektur eine Kopie des Bildes speichern",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Mit img2img, die exakte Anzahl der Schritte ausführen, die vom Schieberegler angegeben sind (normalerweise weniger bei weniger Denoising).",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Aktivieren der Quantisierung in K-Samplern für schärfere und sauberere Ergebnisse. Dies kann bestehende Seeds verändern. Erfordert Neustart.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Hervorhebung: Verwenden Sie (Text), damit das Modell dem Text mehr Aufmerksamkeit schenkt, und [Text], damit es ihm weniger Aufmerksamkeit schenkt",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Verwenden der alten Implementierung von Hervorhebungen. Kann nützlich sein, um alte Seeds zu reproduzieren.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "K-Diffusions-Sampler erzeugen in einem Batch die gleichen Bilder, wie bei der Erstellung eines einzelnen Bildes",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Erhöhung der Kohärenz durch Auffüllen ab dem letzten Komma innerhalb von n Token, wenn mehr als 75 Token verwendet werden",
-    "Filter NSFW content": "NSFW-Inhalte filtern",
-    "Stop At last layers of CLIP model": "Stoppe bei den letzten Schichten des CLIP-Modells",
-    "Interrogate Options": "Interrogate Optionen",
-    "Interrogate: keep models in VRAM": "Interrogate: Modelle im VRAM behalten",
-    "Interrogate: use artists from artists.csv": "Interrogate: Künstler aus 'artists.csv' nutzen",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: Die Rangfolge von Modell-Tags in den Ergebnissen einschließen (hat keine Auswirkung auf beschriftungsbasierte Interrogator).",
-    "Interrogate: num_beams for BLIP": "Interrogate: num_beams für BLIP",
-    "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimale Beschreibungslänge (Künstler, etc.. ausgenommen)",
-    "Interrogate: maximum description length": "Interrogate: maximale Beschreibungslänge",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximale Anzahl an Zeilen in Textdatei (0 = Kein Limit)",
-    "Interrogate: deepbooru score threshold": "Interrogate: Deepbooru minimale Punkteanzahl",
-    "Interrogate: deepbooru sort alphabetically": "Interrogate: Sortiere Deepbooru alphabetisch",
-    "use spaces for tags in deepbooru": "Benutze Leerzeichen für Deepbooru-Tags",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Escape-Klammern (\\) in Deepbooru (damit sie als normale Klammern und nicht zur Hervorhebung verwendet werden)",
-    "User interface": "Benutzeroberfläche",
-    "Show progressbar": "Fortschrittsleiste anzeigen",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Zeige eine Bildvorschau alle N Samplingschritte. Zum Deaktivieren auf 0 setzen.",
-    "Show previews of all images generated in a batch as a grid": "Zeige eine Vorschau aller erzeugten Bilder in einem Batch als Raster",
-    "Show grid in results for web": "Zeige Raster in der Web-UI Vorschau",
-    "Do not show any images in results for web": "Keine Bilder in der Web-UI Vorschau zeigen",
-    "Add model hash to generation information": "Hash des Modells zu den Generationsinformationen hinzufügen",
-    "Add model name to generation information": "Name des Modells zu den Generationsinformationen hinzufügen",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Beim Einlesen von Generierungsparametern aus Text in die Benutzeroberfläche (aus PNG-Info oder eingefügtem Text) wird das ausgewählte Modell/Checkpoint nicht geändert.",
-    "Send seed when sending prompt or image to other interface": "Den Seed, beim Senden des Bildes/Prompt zu einem anderen Tab, mitsenden",
-    "Font for image grids that have text": "Schriftart für Bildraster mit Text",
-    "Enable full page image viewer": "Ganzseitenbildbetrachter aktivieren",
-    "Show images zoomed in by default in full page image viewer": "Standardmäßig Bilder im Ganzseitenbildbetrachter vergrößert anzeigen",
-    "Show generation progress in window title.": "Generationsfortschritt im Fenstertitel anzeigen.",
-    "Quicksettings list": "Schnellzugriffsleiste",
-    "Localization (requires restart)": "Lokalisierung (Erfordert Neustart)",
-    "Sampler parameters": "Samplerparameter",
-    "Hide samplers in user interface (requires restart)": "Sampler in der Benutzeroberfläche verstecken (Erfordert Neustart)",
-    "eta (noise multiplier) for DDIM": "Eta (noise Multiplikator) für DDIM",
-    "eta (noise multiplier) for ancestral samplers": "Eta (noise Multiplikator) für Ancestral Sampler",
-    "img2img DDIM discretize": "img2img DDIM diskretisieren",
-    "uniform": "uniform",
-    "quad": "quad",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "sigma tmin",
-    "sigma noise": "sigma noise",
-    "Eta noise seed delta": "Eta noise seed delta",
-    "Request browser notifications": "Browserbenachrichtigungen anfordern",
-    "Download localization template": "Vorlage für Lokalisierung herunterladen",
-    "Reload custom script bodies (No ui updates, No restart)": "Benutzerdefinierte Skripte neu laden (keine Aktualisierung der Benutzeroberfläche, kein Neustart)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradio neu starten und Komponenten aktualisieren (nur Custom Scripts, ui.py, js und css)",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (zum Erzeugen Strg+Eingabe oder Alt+Eingabe drücken)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Negative Prompt (zum Erzeugen Strg+Eingabe oder Alt+Eingabe drücken)",
-    "Add a random artist to the prompt.": "Zufälligen Künstler der Prompt hinzufügen.",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Lesen der Generationsparameter aus der Prompt oder der letzten Generation (wenn Prompt leer ist) in die Benutzeroberfläche.",
-    "Save style": "Stil speichern",
-    "Apply selected styles to current prompt": "Momentan ausgewählte Stile auf die Prompt anwenden",
-    "Stop processing current image and continue processing.": "Verarbeitung des momentanen Bildes abbrechen und Verarbeitung fortsetzen.",
-    "Stop processing images and return any results accumulated so far.": "Verarbeitung abbrechen und alle bisherigen Ergebnisse ausgeben.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stil, der angwendet werden soll. Stile haben sowohl positive als auch negative Promptanteile und werden auf beide angewandt.",
-    "Do not do anything special": "Nichts besonderes machen",
-    "Which algorithm to use to produce the image": "Der zu benutzende Algorithmus für die Bildgeneration",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - sehr kreativ, kann sehr unterschiedliche Bilder in Abhängigkeit von der Schrittanzahl bekommen. Werte höher als 30-40 helfen nicht.",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Modelle - am besten für inpainting",
-    "Produce an image that can be tiled.": "Bild erzeugen, dass gekachelt werden kann.",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Verwendung eines zweistufigen Prozesses, um ein Bild mit geringerer Auflösung zu erstellen, hochzuskalieren und dann die Details zu verbessern, ohne die Komposition zu verändern.",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Bestimmt, wie wenig Bezug der Algorithmus zu dem Inhalt des Bildes haben soll. Bei 0 ändert sich nichts, und bei 1 besitzt das Bild keinen Bezug. Bei Werten unter 1,0 erfolgt die Verarbeitung in weniger Schritten, als der Schieberegler angibt.",
-    "How many batches of images to create": "Wie viele Sätze von Bildern erstellt werden sollen",
-    "How many image to create in a single batch": "Wie viele Bilder in einem Batch erstellt werden sollen",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - wie stark das Bild der Prompt entsprechen soll - niedrigere Werte führen zu kreativeren Ergebnissen",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Ein Wert, der die Ausgabe des Zufallszahlengenerators bestimmt: Wenn ein Bild mit denselben Parametern und demselben Seed wie ein anderes Bild erstellt wird, erhält man dasselbe Ergebnis.",
-    "Set seed to -1, which will cause a new random number to be used every time": "Seed auf -1 setzen, so dass jedes Mal eine neue Zufallszahl verwendet wird",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Wiederverwendung des Seeds der letzten Generation, meist nützlich, wenn er zufällig gewählt wurde",
-    "Seed of a different picture to be mixed into the generation.": "Seed eines anderen Bildes, der bei der Erzeugung reingemischt wird.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Wie stark die Veränderung sein soll. Bei 0 gibt es keinen Effekt. Bei 1 erhält man das vollständige Bild mit dem Variationsseed (außer bei Ancestral Samplern, wie Euler A, wo man nur etwas erhält).",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Versuche ein Bild zu erzeugen, das dem ähnelt, das mit dem Seed bei der angegebenen Auflösung erzeugt worden wäre.",
-    "Separate values for X axis using commas.": "Trenne die Werte für die X-Achse durch Kommas.",
-    "Separate values for Y axis using commas.": "Trenne die Werte für die Y-Achse durch Kommas.",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Bild in ein Verzeichnis (Standard - log/images) und Generationsparameter in eine csv-Datei schreiben.",
-    "Open images output directory": "Ausgabeverzeichnis öffnen",
-    "How much to blur the mask before processing, in pixels.": "Wie stark die Maske vor der Verarbeitung weichgezeichnet werden soll, in Pixeln.",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Was in den maskierten Bereich vor der Verarbeitung mit Stable Diffusion soll.",
-    "fill it with colors of the image": "Füllen mit den Farben des Bildes",
-    "keep whatever was there originally": "Originalen Inhalt behalten",
-    "fill it with latent space noise": "Füllen mit latent space noise",
-    "fill it with latent space zeroes": "Füllen mit latent space Nullen",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Hochskalieren des maskierten Bereichs auf die Zielauflösung, Inpainting, Zurückskalieren und Einfügen in das Originalbild.",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Die Größe des Bildes auf die gewünschte Auflösung ändern. Wenn Höhe und Breite nicht übereinstimmen, erhält man ein falsches Seitenverhältnis.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Die Größe des Bildes so ändern, dass die gesamte Zielauflösung mit dem Bild ausgefüllt wird. Herausragende Teile werden abgeschnitten.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Die Größe des Bildes so ändern, dass das gesamte Bild enthalten ist. Lücken werden mit Farben des Bildes ausgefüllt.",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Wie oft die Verarbeitung eines Bildes wiederholt und als Eingabe für die nächste Iteration verwendet werden soll",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "Im Loopback-Modus wird die Denoisingstärke in jeder Schleife mit diesem Wert multipliziert. <1 bedeutet abnehmende Vielfalt, so dass die Sequenz zu einem festen Bild konvergiert. >1 bedeutet zunehmende Vielfalt, so dass die Sequenz immer chaotischer wird.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Wie viel Pixel sich beim SD-Upscale zwischen den Kacheln überlappen. Die Kacheln überlappen sich so, dass beim Zusammenfügen zu einem Bild keine deutlich sichtbare Naht entsteht.",
-    "A directory on the same machine where the server is running.": "Ein Verzeichnis auf demselben Rechner, auf dem der Server läuft.",
-    "Leave blank to save images to the default path.": "Leer lassen, um Bilder im Standardpfad zu speichern.",
-    "Result = A * (1 - M) + B * M": "Ergebnis = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "Ergebnis = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "Erste und letzte Ziffer müssen 1 sein. Bspl:'1, 2, 1'",
-    "Path to directory with input images": "Pfad zum Verzeichnis mit den Eingabebildern",
-    "Path to directory where to write outputs": "Pfad zum Verzeichnis, wo die Ausgaben gespeichert werden",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Verwende die folgenden Tags, um festzulegen, wie die Dateinamen für Bilder ausgewählt werden: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leer lassen, um Standardwerte zu verwenden.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Wenn diese Option aktiviert ist, wird den erstellten Bildern kein Wasserzeichen hinzugefügt. Achtung: Wenn Sie kein Wasserzeichen hinzufügen, verhalten Sie sich möglicherweise unethisch.",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Verwenden Sie die folgenden Tags, um festzulegen, wie Unterverzeichnisse für Bilder und Raster ausgewählt werden: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leer lassen, um Standardwerte zu verwenden.",
-    "Restore low quality faces using GFPGAN neural network": "Wiederherstellung von Gesichtern schlechter Qualität mit dem neuralen Netzwerk GFPGAN",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Dieser reguläre Ausdruck wird verwendet, um Wörter aus dem Dateinamen zu extrahieren, die dann mit der unten stehenden Option zu einem Beschriftungstext für das Training zusammengefügt werden. Leer lassen, um den Text des Dateinamens so zu belassen, wie er ist.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "Diese Zeichenfolge wird verwendet, um getrennte Wörter in einer einzigen Zeile zu verbinden, wenn die obige Option aktiviert ist.",
-    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Gilt nur für Inpainting-Modelle. Legt fest, wie stark das Originalbild für Inpainting und img2img maskiert werden soll. 1.0 bedeutet vollständig maskiert, was das Standardverhalten ist. 0.0 bedeutet eine vollständig unmaskierte Konditionierung. Niedrigere Werte tragen dazu bei, die Gesamtkomposition des Bildes zu erhalten, sind aber bei großen Änderungen problematisch.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Liste von Einstellungsnamen, getrennt durch Kommas, für Einstellungen, die in der Schnellzugriffsleiste oben erscheinen sollen, anstatt in dem üblichen Einstellungs-Tab. Siehe modules/shared.py für Einstellungsnamen. Erfordert einen Neustart zur Anwendung.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Wenn dieser Wert ungleich Null ist, wird er zum Seed addiert und zur Initialisierung des RNG für Noise bei der Verwendung von Samplern mit Eta verwendet. Dies kann verwendet werden, um noch mehr Variationen von Bildern zu erzeugen, oder um Bilder von anderer Software zu erzeugen, wenn Sie wissen, was Sie tun."
-}
diff --git a/localizations/es_ES.json b/localizations/es_ES.json
deleted file mode 100644
index 9ba66c5b..00000000
--- a/localizations/es_ES.json
+++ /dev/null
@@ -1,692 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "Cargando...",
-    "view": "Mostrar ",
-    "api": "API",
-    "•": " • ",
-    "Construido con Gradio": "Construido con Gradio",
-    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
-    "txt2img": "txt2img",
-    "img2img": "img2img",
-    "Extras": "Extras",
-    "PNG Info": "Info PNG",
-    "Checkpoint Merger": "Fusionar Checkpoints",
-    "Train": "Entrenar",
-    "Deforum": "Deforum",
-    "Image Browser": "Navegador de Imágenes",
-    "Settings": "Ajustes",
-    "Extensions": "Extensiones",
-    "Prompt": "Prompt",
-    "Negative prompt": "Prompt negativo",
-    "Run": "Ejecutar",
-    "Skip": "Saltar",
-    "Interrupt": "Interrumpir",
-    "Generate": "Generar",
-    "Style 1": "Estilo 1",
-    "Style 2": "Estilo 2",
-    "Label": "Etiqueta",
-    "File": "Archivo",
-    "Coloque el archivo aquí": "Suelta el archivo aquí",
-    "-": "-",
-    "o": "o",
-    "Haga click para cargar": "Haz click para cargar",
-    "Image": "Imagen",
-    "Check progress": "Comprobar progreso",
-    "Check progress (first)": "Comprobar progreso (inicial)",
-    "Sampling Steps": "Sampling Steps",
-    "Sampling method": "Método de Sampling",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "Ancho",
-    "Height": "Alto",
-    "Restore faces": "Restaurar rostros",
-    "Tiling": "Mosaico",
-    "Highres. fix": "Highres. fix",
-    "Firstpass width": "Ancho original",
-    "Firstpass height": "Alto original",
-    "Denoising strength": "Denoising strength",
-    "Batch count": "Cantidad del Batch",
-    "Batch size": "Tamaño del Batch",
-    "CFG Scale": "Escala CFG",
-    "Seed": "Seed",
-    "Extra": "Extra",
-    "Variation seed": "Seed de variación",
-    "Variation strength": "Fuerza de variación",
-    "Resize seed from width": "Redimensionar seed del ancho",
-    "Resize seed from height": "Redimensionar seed del alto",
-    "Script": "Script",
-    "None": "Ninguno",
-    "Prompt matrix": "Prompt en matriz",
-    "Prompts from file or textbox": "Prompts desde archivo o campo de texto",
-    "X/Y plot": "Tabla X/Y",
-    "Put variable parts at start of prompt": "Poner partes variables al inicio del prompt",
-    "Iterate seed every line": "Repetir seed en cada línea",
-    "Use same random seed for all lines": "Utiliza el mismo seed aleatorio para todas las líneas",
-    "List of prompt inputs": "Listado de prompts",
-    "Upload prompt inputs": "Cargar archivo de prompts",
-    "X type": "X",
-    "Nothing": "Nada",
-    "Var. seed": "Var. seed",
-    "Var. strength": "Var. fuerza",
-    "Steps": "Steps",
-    "Prompt S/R": "Prompt S/R",
-    "Prompt order": "Prompt order",
-    "Sampler": "Sampler",
-    "Checkpoint name": "Nombre Checkpoint",
-    "Hypernetwork": "Hypernetwork",
-    "Hypernet str.": "Hypernet str.",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma min": "Sigma min",
-    "Sigma max": "Sigma max",
-    "Sigma noise": "Sigma noise",
-    "Eta": "Eta",
-    "Clip skip": "Clip skip",
-    "Denoising": "Denoising",
-    "Cond. Image Mask Weight": "Cond. Image Mask Weight",
-    "X values": "Valores X",
-    "Y type": "Y",
-    "Y values": "Valores Y",
-    "Draw legend": "Agregar leyenda",
-    "Include Separate Images": "Incluir Imágenes Separadas",
-    "Keep -1 for seeds": "Mantener -1 para seeds",
-    "Save": "Guardar",
-    "Send to img2img": "Enviar a img2img",
-    "Send to inpaint": "Enviar a inpaint",
-    "Send to extras": "Enviar a extras",
-    "Make Zip when Save?": "Crear Zip al Guardar?",
-    "Textbox": "Campo de texto",
-    "Interrogate\nCLIP": "Interrogar\nCLIP",
-    "Inpaint": "Inpaint",
-    "Batch img2img": "Batch img2img",
-    "Image for img2img": "Imagen para img2img",
-    "Coloque la imagen aquí": "Suelta la imagen aquí",
-    "Image for inpainting with mask": "Imagen para inpainting con máscara",
-    "Mask": "Máscara",
-    "Mask blur": "Difuminar máscara",
-    "Mask mode": "Modo máscara",
-    "Draw mask": "Dibujar máscara",
-    "Upload mask": "Cargar máscara",
-    "Masking mode": "Modo de enmascarado",
-    "Inpaint masked": "Inpaint con enmascarado",
-    "Inpaint not masked": "Inpaint sin enmascarado",
-    "Masked content": "Contenido enmascarado",
-    "fill": "rellenar",
-    "original": "original",
-    "latent noise": "latent noise",
-    "latent nothing": "latent nothing",
-    "Inpaint at full resolution": "Inpaint a resolución completa",
-    "Inpaint at full resolution padding, pixels": "Inpaint a resolución completa con relleno, en pixeles",
-    "Process images in a directory on the same machine where the server is running.": "Procesa imágenes en un directorio en la misma máquina donde se ejecuta el servidor.",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa un directorio de salida vacío para guardar imágenes normalmente en lugar de escribir en el directorio de salida.",
-    "Input directory": "Directorio de entrada",
-    "Output directory": "Directorio de salida",
-    "Resize mode": "Modo de cambio de tamaño",
-    "Just resize": "Solo redimensionar",
-    "Crop and resize": "Recortar y redimensionar",
-    "Resize and fill": "Redimensionar y rellenar",
-    "img2img alternative test": "img2img alternative test",
-    "Loopback": "Loopback",
-    "Outpainting mk2": "Outpainting mk2",
-    "Poor man's outpainting": "Poor man's outpainting",
-    "SD upscale": "SD upscale",
-    "Deforum-webui (use tab extension instead!)": "Deforum-webui (utiliza la extensión en su lugar!)",
-    "should be 2 or lower.": "debe ser 2 o menos.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Invalidar `Sampling method` a Euler? (este método está diseñado para ello)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Invalidar `prompt` al mismo valor `prompt original`? (y `prompt negativo`)",
-    "Original prompt": "Prompt original",
-    "Original negative prompt": "Promp negativo original",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Invalidar `Sampling Steps` al mismo valor de `Decode steps`?",
-    "Decode steps": "Decode steps",
-    "Override `Denoising strength` to 1?": "Invalidar `Denoising strength` a 1?",
-    "Decode CFG scale": "Decodificar escala CFG",
-    "Randomness": "Aleatoriedad",
-    "Sigma adjustment for finding noise for image": "Ajuste Sigma para encontrar ruido para la imagen.",
-    "Loops": "Loops",
-    "Denoising strength change factor": "Denoising strength change factor",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Ajustes recomendados: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8",
-    "Pixels to expand": "Píxeles para expandir",
-    "Outpainting direction": "Dirección Outpainting",
-    "left": "izquierda",
-    "right": "derecha",
-    "up": "arriba",
-    "down": "abajo",
-    "Fall-off exponent (lower=higher detail)": "Fall-off exponent (inferior=mayor detalle)",
-    "Color variation": "Variación de color",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Mejorará la imagen al doble de las dimensiones; usa los controles deslizantes de ancho y alto para establecer el tamaño del mosaico",
-    "Tile overlap": "Solapar mosaicos",
-    "Upscaler": "Upscaler",
-    "Lanczos": "Lanczos",
-    "Nearest": "Nearest",
-    "LDSR": "LDSR",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR 4x": "SwinIR 4x",
-    "Deforum v0.5-webui-beta": "Deforum v0.5-webui-beta",
-    "This script is deprecated. Please use the full Deforum extension instead.": "Este script está obsoleto. Utiliza la extensión completa de Deforum en su lugar.",
-    "Update instructions:": "Instrucciones para actualizar:",
-    "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md": "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md",
-    "discord.gg/deforum": "discord.gg/deforum",
-    "Single Image": "Imagen Única",
-    "Batch Process": "Batch Process",
-    "Batch from Directory": "Batch desde Directorio",
-    "Source": "Origen",
-    "Show result images": "Mostrar imágenes generadas",
-    "Scale by": "Escalar por",
-    "Scale to": "Escalar a",
-    "Resize": "Redimensionar",
-    "Crop to fit": "Recortar para ajustar",
-    "Upscaler 2 visibility": "Visibilidad Upscaler 2",
-    "GFPGAN visibility": "Visibilidad GFPGAN",
-    "CodeFormer visibility": "Visibilidad CodeFormer",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Influencia CodeFormer (0 = efecto máximo, 1 = efecto mínimo)",
-    "Upscale Before Restoring Faces": "Escalar antes de Restaurar Rostros",
-    "Send to txt2img": "Enviar a txt2img",
-    "A merger of the two checkpoints will be generated in your": "Se generará una fusión de los dos checkpoints en tu ",
-    "checkpoint": "directorio ",
-    "directory.": "de modelos.",
-    "Primary model (A)": "Modelo primario (A)",
-    "Secondary model (B)": "Modelo secundario (B)",
-    "Tertiary model (C)": "Modelo terciario (C)",
-    "Custom Name (Optional)": "Nombre personalizado (Opcional)",
-    "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - establecer en 0 para obtener el modelo A",
-    "Interpolation Method": "Método de interpolación",
-    "Weighted sum": "Weighted sum",
-    "Add difference": "Add difference",
-    "Save as float16": "Guardar como float16",
-    "See": "Ver ",
-    "wiki": "wiki ",
-    "for detailed explanation.": "para una explicación detallada.",
-    "Create embedding": "Crear embedding",
-    "Create hypernetwork": "Crear hypernetwork",
-    "Preprocess images": "Preprocesar imágenes",
-    "Name": "Nombre",
-    "Initialization text": "Texto de inicialización",
-    "Number of vectors per token": "Número de vectores por token",
-    "Overwrite Old Embedding": "Sobrescribir Embedding Anterior",
-    "Modules": "Módulos",
-    "Enter hypernetwork layer structure": "Ingresa la estructura de capa del hypernetwork",
-    "Select activation function of hypernetwork": "Selecciona la función de activación del hypernetwork",
-    "linear": "linear",
-    "relu": "relu",
-    "leakyrelu": "leakyrelu",
-    "elu": "elu",
-    "swish": "swish",
-    "tanh": "tanh",
-    "sigmoid": "sigmoid",
-    "celu": "celu",
-    "gelu": "gelu",
-    "glu": "glu",
-    "hardshrink": "hardshrink",
-    "hardsigmoid": "hardsigmoid",
-    "hardtanh": "hardtanh",
-    "logsigmoid": "logsigmoid",
-    "logsoftmax": "logsoftmax",
-    "mish": "mish",
-    "prelu": "prelu",
-    "rrelu": "rrelu",
-    "relu6": "relu6",
-    "selu": "selu",
-    "silu": "silu",
-    "softmax": "softmax",
-    "softmax2d": "softmax2d",
-    "softmin": "softmin",
-    "softplus": "softplus",
-    "softshrink": "softshrink",
-    "softsign": "softsign",
-    "tanhshrink": "tanhshrink",
-    "threshold": "threshold",
-    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleccionar inicialización de modelos de capa. relu-like - Kaiming, sigmoid-like - Xavier es el recomendado",
-    "Normal": "Normal",
-    "KaimingUniform": "KaimingUniform",
-    "KaimingNormal": "KaimingNormal",
-    "XavierUniform": "XavierUniform",
-    "XavierNormal": "XavierNormal",
-    "Add layer normalization": "Agregar normalización de capa",
-    "Use dropout": "Usar dropout",
-    "Overwrite Old Hypernetwork": "Sobrescribir Hypernetwork Anterior",
-    "Source directory": "Directorio de origen",
-    "Destination directory": "Directorio de salida",
-    "Existing Caption txt Action": "Existing Caption txt Action",
-    "ignore": "ignorar",
-    "copy": "copiar",
-    "prepend": "anteponer",
-    "append": "añadir",
-    "Create flipped copies": "Crear copias volteadas",
-    "Split oversized images": "Dividir imágenes muy grandes",
-    "Auto focal point crop": "Recorte de punto focal automático",
-    "Use BLIP for caption": "Usar BLIP para leyenda",
-    "Use deepbooru for caption": "Usar deepbooru para leyenda",
-    "Split image threshold": "Umbral en imagen dividida",
-    "Split image overlap ratio": "Relación de superposición en imagen dividida",
-    "Focal point face weight": "Peso de la cara del punto focal",
-    "Focal point entropy weight": "Focal point entropy weight",
-    "Focal point edges weight": "Focal point edges weight",
-    "Create debug image": "Crear imagen de depuración",
-    "Preprocess": "Preproceso",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Entrenar un embedding o Hypernetwork; debes especificar un directorio con un conjunto de imágenes con una proporción de 1:1 ",
-    "[wiki]": "[wiki]",
-    "Embedding": "Embedding",
-    "Embedding Learning rate": "Embedding Learning rate",
-    "Hypernetwork Learning rate": "Hypernetwork Learning rate",
-    "Dataset directory": "Directorio dataset",
-    "Log directory": "Directorio log",
-    "Prompt template file": "Prompt archivos plantilla",
-    "Max steps": "Max steps",
-    "Save an image to log directory every N steps, 0 to disable": "Guarda una imagen en el directorio log cada N pasos, 0 para deshabilitar",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Guarda una copia de embedding en el directorio log cada N pasos, 0 para deshabilitar",
-    "Save images with embedding in PNG chunks": "Guarda imágenes con embedding en fragmentos PNG",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Leer parámetros (prompt, etc...) desde la pestaña txt2img al hacer vistas previas",
-    "Train Hypernetwork": "Entrenar Hypernetwork",
-    "Train Embedding": "Entrenar Embedding",
-    "Info and links": "Información y enlaces",
-    "▼": "▼",
-    "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha": "Desarrolado por deforum.github.io, port para AUTOMATIC1111's webui mantenido por kabachuha",
-    "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Deforum Github repo  github.com/deforum/stable-diffusion",
-    "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui": "Fork para auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui",
-    "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Únete al Discord oficial Deforum discord.gg/deforum para compartir tus creaciones y sugerencias",
-    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "Guía de usuario v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
-    "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Explicación de fotogramas matemáticos docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
-    "Keyframes": "Keyframes",
-    "Prompts": "Prompts",
-    "Init": "Init",
-    "Video output": "Salida de vídeo",
-    "Run settings": "Ejecutar ajustes",
-    "Import settings from file": "Importar ajustes desde archivo",
-    "Override settings": "Invalidar ajustes",
-    "Custom settings file": "Archivo de ajustes personalizados",
-    "Sampling settings": "Ajustes de sampling",
-    "override_these_with_webui": "override_these_with_webui",
-    "W": "Ancho",
-    "H": "Alto",
-    "seed": "seed",
-    "sampler": "sampler",
-    "Enable extras": "Activar extras",
-    "subseed": "subseed",
-    "subseed_strength": "subseed_strength",
-    "steps": "steps",
-    "ddim_eta": "ddim_eta",
-    "n_batch": "n_batch",
-    "make_grid": "make_grid",
-    "grid_rows": "grid_rows",
-    "save_settings": "save_settings",
-    "save_samples": "save_samples",
-    "display_samples": "display_samples",
-    "save_sample_per_step": "save_sample_per_step",
-    "show_sample_per_step": "show_sample_per_step",
-    "Batch settings": "Ajustes de batch",
-    "batch_name": "batch_name",
-    "filename_format": "filename_format",
-    "seed_behavior": "seed_behavior",
-    "iter": "iter",
-    "fixed": "fixed",
-    "random": "random",
-    "schedule": "schedule",
-    "Animation settings": "Ajustes de animación",
-    "animation_mode": "animation_mode",
-    "2D": "2D",
-    "3D": "3D",
-    "Video Input": "Entrada de Video",
-    "max_frames": "max_frames",
-    "border": "border",
-    "replicate": "replicate",
-    "wrap": "wrap",
-    "Motion parameters:": "Parámetros de movimiento:",
-    "2D and 3D settings": "Ajustes 2D y 3D",
-    "angle": "angle",
-    "zoom": "zoom",
-    "translation_x": "translation_x",
-    "translation_y": "translation_y",
-    "3D settings": "Ajustes 3D",
-    "translation_z": "translation_z",
-    "rotation_3d_x": "rotation_3d_x",
-    "rotation_3d_y": "rotation_3d_y",
-    "rotation_3d_z": "rotation_3d_z",
-    "Prespective flip — Low VRAM pseudo-3D mode:": "Prespective flip — Modo Low VRAM pseudo-3D:",
-    "flip_2d_perspective": "flip_2d_perspective",
-    "perspective_flip_theta": "perspective_flip_theta",
-    "perspective_flip_phi": "perspective_flip_phi",
-    "perspective_flip_gamma": "perspective_flip_gamma",
-    "perspective_flip_fv": "perspective_flip_fv",
-    "Generation settings:": "Ajustes de generación:",
-    "noise_schedule": "noise_schedule",
-    "strength_schedule": "strength_schedule",
-    "contrast_schedule": "contrast_schedule",
-    "cfg_scale_schedule": "cfg_scale_schedule",
-    "3D Fov settings:": "Ajustes 3D Fov:",
-    "fov_schedule": "fov_schedule",
-    "near_schedule": "near_schedule",
-    "far_schedule": "far_schedule",
-    "To enable seed schedule select seed behavior — 'schedule'": "Para habilitar el seed schedule, selecciona el comportamiento del seed — 'schedule'",
-    "seed_schedule": "seed_schedule",
-    "Coherence:": "Coherencia:",
-    "color_coherence": "color_coherence",
-    "Match Frame 0 HSV": "Match Frame 0 HSV",
-    "Match Frame 0 LAB": "Match Frame 0 LAB",
-    "Match Frame 0 RGB": "Match Frame 0 RGB",
-    "diffusion_cadence": "diffusion_cadence",
-    "3D Depth Warping:": "3D Depth Warping:",
-    "use_depth_warping": "use_depth_warping",
-    "midas_weight": "midas_weight",
-    "near_plane": "near_plane",
-    "far_plane": "far_plane",
-    "fov": "fov",
-    "padding_mode": "padding_mode",
-    "reflection": "reflection",
-    "zeros": "zeros",
-    "sampling_mode": "sampling_mode",
-    "bicubic": "bicubic",
-    "bilinear": "bilinear",
-    "nearest": "nearest",
-    "save_depth_maps": "save_depth_maps",
-    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`animation_mode: None` batches en lista de *prompts*. (Modo batch deshabilitado por el momento, solamente animation_prompts esta funcionando)",
-    "*Important change from vanilla Deforum!*": "*Cambios importantes en Deforum!*",
-    "This script uses the built-in webui weighting settings.": "Este script utiliza la configuración de pesos integrados.",
-    "So if you want to use math functions as prompt weights,": "Entonces, si deseas usar funciones matemáticas con pesos en los prompts,",
-    "keep the values above zero in both parts": "manten los valores por encima de cero en ambas partes",
-    "Negative prompt part can be specified with --neg": "La parte de prompt negativo se puede especificar utilizando --neg",
-    "batch_prompts (disabled atm)": "batch_prompts (deshabilitado por el momento)",
-    "animation_prompts": "animation_prompts",
-    "Init settings": "Ajustes Init",
-    "use_init": "use_init",
-    "from_img2img_instead_of_link": "from_img2img_instead_of_link",
-    "strength_0_no_init": "strength_0_no_init",
-    "strength": "strength",
-    "init_image": "init_image",
-    "use_mask": "use_mask",
-    "use_alpha_as_mask": "use_alpha_as_mask",
-    "invert_mask": "invert_mask",
-    "overlay_mask": "overlay_mask",
-    "mask_file": "mask_file",
-    "mask_brightness_adjust": "mask_brightness_adjust",
-    "mask_overlay_blur": "mask_overlay_blur",
-    "Video Input:": "Entrada de Video:",
-    "video_init_path": "video_init_path",
-    "extract_nth_frame": "extract_nth_frame",
-    "overwrite_extracted_frames": "overwrite_extracted_frames",
-    "use_mask_video": "use_mask_video",
-    "video_mask_path": "video_mask_path",
-    "Interpolation (turned off atm)": "Interpolación (apagado por el momento)",
-    "interpolate_key_frames": "interpolate_key_frames",
-    "interpolate_x_frames": "interpolate_x_frames",
-    "Resume animation:": "Reanudar animación:",
-    "resume_from_timestring": "resume_from_timestring",
-    "resume_timestring": "resume_timestring",
-    "Video output settings": "Ajustes video de salida",
-    "skip_video_for_run_all": "skip_video_for_run_all",
-    "fps": "fps",
-    "output_format": "output_format",
-    "PIL gif": "PIL gif",
-    "FFMPEG mp4": "FFMPEG mp4",
-    "ffmpeg_location": "ffmpeg_location",
-    "add_soundtrack": "add_soundtrack",
-    "soundtrack_path": "soundtrack_path",
-    "use_manual_settings": "use_manual_settings",
-    "render_steps": "render_steps",
-    "max_video_frames": "max_video_frames",
-    "path_name_modifier": "path_name_modifier",
-    "x0_pred": "x0_pred",
-    "x": "x",
-    "image_path": "image_path",
-    "mp4_path": "mp4_path",
-    "Click here after the generation to show the video": "Haz click aquí después de la generación para mostrar el video",
-    "NOTE: If the 'Generate' button doesn't work, go in Settings and click 'Restart Gradio and Refresh...'.": "NOTA: Si el botón 'Generar' no funciona, ve a los Ajustes y presiona 'Reinciar Gradio y Refrescar...'.",
-    "Save Settings": "Guardar Ajustes",
-    "Load Settings": "Cargar Ajustes",
-    "Path relative to the webui folder": "Ruta relativa al folder principal",
-    "Save Video Settings": "Guardar Ajustes de Video",
-    "Load Video Settings": "Cargar Ajustes de Video",
-    "Favorites": "Favoritos",
-    "Others": "Otros",
-    "Images directory": "Directorio de Imágenes",
-    "Dropdown": "Menú desplegable",
-    "First Page": "Primera Página",
-    "Prev Page": "Página Anterior",
-    "Page Index": "Índice de Página",
-    "Next Page": "Página Siguiente",
-    "End Page": "Última Página",
-    "delete next": "eliminar siguiente",
-    "Delete": "Eliminar",
-    "sort by": "ordenar por",
-    "path name": "nombre de ruta",
-    "date": "fecha",
-    "keyword": "palabra clave",
-    "Generate Info": "Generar Info",
-    "File Name": "Nombre de Archivo",
-    "Move to favorites": "Mover a favoritos",
-    "Renew Page": "Recargar Página",
-    "Number": "Número",
-    "set_index": "set_index",
-    "load_switch": "load_switch",
-    "turn_page_switch": "turn_page_switch",
-    "Checkbox": "Checkbox",
-    "Apply settings": "Aplicar ajustes",
-    "Saving images/grids": "Guardar imágenes/grids",
-    "Always save all generated images": "Siempre guardar imágenes generadas",
-    "File format for images": "Formato de archivo para imágenes",
-    "Images filename pattern": "Patrón en nombre archivo imágenes",
-    "Add number to filename when saving": "Agregar número al nombre de archivo al guardar",
-    "Always save all generated image grids": "Siempre guardar grids de imágenes generadas",
-    "File format for grids": "Formato de archivo para grids",
-    "Add extended info (seed, prompt) to filename when saving grid": "Agregar información extendida (seed, prompt) al nombre del archivo al guardar grid",
-    "Do not save grids consisting of one picture": "No guardar grids que consisten en una imagen",
-    "Prevent empty spots in grid (when set to autodetect)": "Evitar espacios vacíos en grids (cuando se establece detección automática)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Recuento de filas de grids; usar -1 para la detección automática y 0 para que sea igual al batch size",
-    "Save text information about generation parameters as chunks to png files": "Guardar información de texto sobre parámetros de generación como fragmentos en archivos png",
-    "Create a text file next to every image with generation parameters.": "Crear un archivo de texto junto a cada imagen con parámetros de generación.",
-    "Save a copy of image before doing face restoration.": "Guardar una copia de la imagen antes de restaurar rostro.",
-    "Save a copy of image before applying highres fix.": "Guardar una copia de la imagen antes de aplicar highres fix.",
-    "Save a copy of image before applying color correction to img2img results": "Guarda una copia de la imagen antes de aplicar la corrección de color a los resultados de img2img",
-    "Quality for saved jpeg images": "Calidad para imágenes jpeg guardadas",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Si la imagen PNG es más grande de 4 MB o cualquier dimensión es más grande que 4000, reduce la escala y guarda la copia como JPG",
-    "Use original name for output filename during batch process in extras tab": "Use el nombre original para el nombre del archivo de salida durante el batch process en la pestaña extras",
-    "When using 'Save' button, only save a single selected image": "Al usar el botón 'Guardar', solo guarda una sola imagen seleccionada",
-    "Do not add watermark to images": "No agregar watermark a las imágenes",
-    "Paths for saving": "Directorios para guardar",
-    "Output directory for images; if empty, defaults to three directories below": "Directorio de imágenes; si está vacío, se utilizan los siguientes 3 directorios",
-    "Output directory for txt2img images": "Directorio para guardar imágenes de txt2img",
-    "Output directory for img2img images": "Directorio para guardar imágenes de img2img",
-    "Output directory for images from extras tab": "Directorio para guardar imágenes de extras",
-    "Output directory for grids; if empty, defaults to two directories below": "Directorio de grids; si está vacío, se utilizan los siguientes 2 directorios",
-    "Output directory for txt2img grids": "Directorio para guardar txt2img grids",
-    "Output directory for img2img grids": "Directorio para guardar img2img grids",
-    "Directory for saving images using the Save button": "Directorio para guardar imágenes usando el botón Guardar",
-    "Saving to a directory": "Guardando a un directorio",
-    "Save images to a subdirectory": "Guardar imágenes a un subdirectorio",
-    "Save grids to a subdirectory": "Guardar grids a un subdirectorio",
-    "When using \"Save\" button, save images to a subdirectory": "Al usar el botón \"Guardar\", guarda las imágenes en un subdirectorio",
-    "Directory name pattern": "Patrón nombre directorio",
-    "Max prompt words for [prompt_words] pattern": "Máximo de palabras en prompt [prompt_words] para patrón",
-    "Upscaling": "Upscaling",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tamaño mosaico para ESRGAN upscalers. 0 = sin mosaico.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Solapar mosaico, en pixeles para ESRGAN upscalers. Valores bajos = unión visible.",
-    "Tile size for all SwinIR.": "Tamaño mosaico para SwinIR.",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Solapar mosaico, en pixeles para SwinIR. Valores bajos = unión visible.",
-    "LDSR processing steps. Lower = faster": "LDSR processing steps. Más bajo = rápido",
-    "Upscaler for img2img": "Upscaler para img2img",
-    "Upscale latent space image when doing hires. fix": "Upscale latent space al aplicar hires. fix",
-    "Face restoration": "Restauración de rostro",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Parámetro influencia CodeFormer; 0 = máximo efecto; 1 = mínimo efecto",
-    "Move face restoration model from VRAM into RAM after processing": "Mover modelo de restauración de rostro del VRAM al RAM después de procesar",
-    "System": "Sistema",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "Sondeos de uso de VRAM por segundo durante la generación. Establecer en 0 para deshabilitar.",
-    "Always print all generation info to standard output": "Imprime siempre toda la información de generación en la salida estándar",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Agrega una segunda barra de progreso a la consola que muestra el progreso de un trabajo completo.",
-    "Training": "Entrenamiento",
-    "Move VAE and CLIP to RAM when training if possible. Saves VRAM.": "Mover VAE y CLIP al RAM al entrenar cuando sea posible. Ahorra VRAM.",
-    "Filename word regex": "Filename word regex",
-    "Filename join string": "Filename join string",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Número de repeticiones para una sola imagen de entrada por epoch; utilizado solo para mostrar el número epoch",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Guarda un csv que contenga la pérdida en el directorio log cada N pasos, 0 para deshabilitar",
-    "Use cross attention optimizations while training": "Utiliza optimizaciones de atención cruzada durante el entrenamiento",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "Checkpoints al cache en RAM",
-    "SD VAE": "SD VAE",
-    "auto": "auto",
-    "Hypernetwork strength": "Hypernetwork strength",
-    "Inpainting conditioning mask strength": "Fuerza de la máscara en acondicionamiento Inpainting",
-    "Apply color correction to img2img results to match original colors.": "Aplica la corrección de color a los resultados de img2img para que coincidan con los colores originales.",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, hace exactamente la cantidad de pasos que especifica el slider (normalmente haría menos con menos eliminación de ruido).",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Habilita la cuantificación en K samplers para obtener resultados más nítidos y limpios. Esto puede cambiar los seeds existentes. Requiere reiniciar para aplicar.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Énfasis: utiliza (texto) para que el modelo preste más atención al texto y [texto] para que preste menos atención",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Utiliza la implementación de énfasis antiguo. Puede ser útil para reproducir seeds anteriores.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Hace que los K-diffusion samplers produzcan las mismas imágenes en un lote que cuando se crea una sola imagen",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coherencia rellenando desde la última coma dentro de n tokens cuando se use más de 75 tokens",
-    "Filter NSFW content": "Filtrar contenido NSFW",
-    "Stop At last layers of CLIP model": "Detener en las últimas capas del modelo CLIP",
-    "Interrogate Options": "Opciones de Interrogar",
-    "Interrogate: keep models in VRAM": "Interrogar: mantener modelos en VRAM",
-    "Interrogate: use artists from artists.csv": "Interrogar: utilizar artistas de artists.csv",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogar: incluir rangos de coincidencias de etiquetas de modelos en los resultados (No tiene efecto en los interrogadores basados en subtítulos).",
-    "Interrogate: num_beams for BLIP": "Interrogar: num_beams para BLIP",
-    "Interrogate: minimum description length (excluding artists, etc..)": "Interrogar: longitud mínima de la descripción (excluyendo artistas, etc.)",
-    "Interrogate: maximum description length": "Interrogar: longitud máxima de la descripción",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: número máximo de líneas en el archivo de texto (0 = Sin límite)",
-    "Interrogate: deepbooru score threshold": "Interrogar: deepbooru umbral de puntuación",
-    "Interrogate: deepbooru sort alphabetically": "Interrogar: deepbooru ordenar alfabéticamente",
-    "use spaces for tags in deepbooru": "usar espacios para etiquetas en deepbooru",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "corchetes de escape (\\) en deepbooru (por lo que se usan como corchetes literales y no para enfatizar)",
-    "User interface": "Interfaz de usuario",
-    "Show progressbar": "Mostrar barra de progreso",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Muestra el progreso de creación de la imagen cada N sampling steps. Establecer 0 para deshabilitar.",
-    "Show previews of all images generated in a batch as a grid": "Mostrar vistas previas de todas las imágenes generadas en un batch como un grid",
-    "Show grid in results for web": "Mostrar grids en resultados para web",
-    "Do not show any images in results for web": "No mostrar ninguna imagen en los resultados para web",
-    "Add model hash to generation information": "Agregar hash de modelo a la información de generación",
-    "Add model name to generation information": "Agregar nombre de modelo a la información de generación",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Al leer los parámetros de generación del texto en la interfaz de usuario (desde PNG Info o texto pegado), no cambia el modelo/checkpoint seleccionado.",
-    "Send seed when sending prompt or image to other interface": "Enviar seed cuando se envíe el prompt o imagen a otra interfase",
-    "Font for image grids that have text": "Tipografía para grids de imágenes que tienen texto",
-    "Enable full page image viewer": "Habilitar visor de imágenes de página completa",
-    "Show images zoomed in by default in full page image viewer": "Mostrar imágenes ampliadas de forma predeterminada en el visor de imágenes de página completa",
-    "Show generation progress in window title.": "Muestra el progreso de la generación en el título de la ventana del navegador.",
-    "Quicksettings list": "Lista de ajustes rápidos",
-    "Localization (requires restart)": "Traducción (requiere reiniciar)",
-    "fr_FR": "fr_FR",
-    "tr_TR": "tr_TR",
-    "it_IT": "it_IT",
-    "de_DE": "de_DE",
-    "ru_RU": "ru_RU",
-    "ja_JP": "ja_JP",
-    "es_ES": "es_ES",
-    "ko_KR": "ko_KR",
-    "zh_TW": "zh_TW",
-    "zh_CN": "zh_CN",
-    "ar_AR": "ar_AR",
-    "pt_BR": "pt_BR",
-    "Sampler parameters": "Parámetros del sampler",
-    "Hide samplers in user interface (requires restart)": "Ocultar samplers en interfaz de usuario (requiere reiniciar)",
-    "eta (noise multiplier) for DDIM": "eta (noise multiplier) para DDIM",
-    "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) para ancestral samplers",
-    "img2img DDIM discretize": "img2img DDIM discretize",
-    "uniform": "uniform",
-    "quad": "quad",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "sigma tmin",
-    "sigma noise": "sigma noise",
-    "Eta noise seed delta": "Eta noise seed delta",
-    "Images Browser": "Navegador de Imágenes",
-    "Preload images at startup": "Precargar imágenes al iniciar",
-    "Number of columns on the page": "Número de columnas en la página",
-    "Number of rows on the page": "Número de filas en la página",
-    "Minimum number of pages per load": "Número mínimo de páginas por carga",
-    "Request browser notifications": "Solicitar notificaciones del navegador",
-    "Download localization template": "Descargar plantilla de traducción",
-    "Reload custom script bodies (No ui updates, No restart)": "Recargar custom script bodies (Sin actualizar UI, Sin reiniciar)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Reiniciar Gradio y Refrescar componentes (Custom Scripts, ui.py, js y css)",
-    "Installed": "Instaladas",
-    "Available": "Disponibles",
-    "Install from URL": "Instalar desde URL",
-    "Apply and restart UI": "Aplicar y reiniciar UI",
-    "Check for updates": "Buscar actualizaciones",
-    "Extension": "Extensión",
-    "URL": "URL",
-    "Update": "Actualizar",
-    "deforum-for-automatic1111-webui": "deforum-for-automatic1111-webui",
-    "https://github.com/deforum-art/deforum-for-automatic1111-webui": "https://github.com/deforum-art/deforum-for-automatic1111-webui",
-    "unknown": "desconocido",
-    "stable-diffusion-webui-images-browser": "stable-diffusion-webui-images-browser",
-    "https://github.com/yfszzx/stable-diffusion-webui-images-browser": "https://github.com/yfszzx/stable-diffusion-webui-images-browser",
-    "Load from:": "Cargar desde:",
-    "Extension index URL": "URL índice de extensiones",
-    "URL for extension's git repository": "URL repositorio git de extensión",
-    "Local directory name": "Nombre directorio local",
-    "Install": "Instalar",
-    "Ver": "Ver",
-    "Entrenar un embedding o Hypernetwork; debes especificar un directorio con un conjunto de imágenes con una proporción de 1:1": "Entrenar un embedding o Hypernetwork; debes especificar un directorio con un conjunto de imágenes con una proporción de 1:1",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (presiona Ctrl+Enter o Alt+Enter para generar)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (presiona Ctrl+Enter o Alt+Enter para generar)",
-    "Add a random artist to the prompt.": "Agregar un artista aleatorio al prompt.",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leer los parámetros de generación del prompt o de la última generación si el prompt está vacío en la interfaz de usuario.",
-    "Save style": "Guardar estilo",
-    "Apply selected styles to current prompt": "Aplicar estilos seleccionados al prompt",
-    "Stop processing current image and continue processing.": "Dejar de procesar la imagen actual y continuar procesando.",
-    "Stop processing images and return any results accumulated so far.": "Dejar de procesar imágenes y devuelva los resultados acumulados hasta el momento.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Estilo a aplicar; los estilos tienen componentes tanto para prompts positivos como negativos y se aplican a ambos",
-    "Do not do anything special": "No hacer nada especial",
-    "Which algorithm to use to produce the image": "Qué algoritmo usar para producir la imagen",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - muy creativo, cada uno puede obtener una imagen completamente diferente dependiendo del conteo de steps, configurar los steps a más de 30-40 no ayuda",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - funcionan mejor para inpainting",
-    "Produce an image that can be tiled.": "Produce una imagen que puede usarse como mosaico.",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Usa un proceso de dos pasos para crear parcialmente una imagen con una resolución más pequeña, mejora la escala y luego mejora los detalles sin cambiar la composición",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determina qué tan poco respeto debe tener el algoritmo por el contenido de la imagen. En 0, nada cambiará y en 1 obtendrá una imagen no relacionada. Con valores por debajo de 1.0, el procesamiento tomará menos steps de los que especifica el slider de Sampling Steps.",
-    "How many batches of images to create": "Cuantos batches de imágenes para crear",
-    "How many image to create in a single batch": "Cuantas imágenes para crear en un solo batch",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - con qué fuerza debe ajustarse la imagen al prompt: los valores más bajos producen resultados más creativos",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Un valor que determina la salida del generador de números aleatorios: si creas una imagen con los mismos parámetros y el seed que otra imagen, obtendrás el mismo resultado",
-    "Set seed to -1, which will cause a new random number to be used every time": "Establece el seed a -1, lo que hará que se use un nuevo número aleatorio cada vez",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Reutilice el seed de la última generación, muy útil si fue aleatoria",
-    "Seed of a different picture to be mixed into the generation.": "Seed de una imagen diferente para ser mezclada en la generación.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Qué fuerte de una variación para producir. En 0, no habrá ningún efecto. En 1, obtendrá la imagen completa con variation seed (excepto para ancestral samplers, donde solo obtendrás algo).",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Intenta producir una imagen similar a la que se habría producido con el mismo seed a la resolución especificada",
-    "Separate values for X axis using commas.": "Separar valores para X usando comas.",
-    "Separate values for Y axis using commas.": "Separar valores para Y usando comas.",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Escribe la imagen en un directorio (predeterminado: log/images) y los parámetros de generación en el archivo csv.",
-    "Open images output directory": "Abrir directorio de imágenes",
-    "How much to blur the mask before processing, in pixels.": "Cuánto difuminado a la máscara antes de procesarla, en píxeles.",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Qué poner dentro del área enmascarada antes de procesarla con Stable Diffusion.",
-    "fill it with colors of the image": "rellenarlo con los colores de la imagen",
-    "keep whatever was there originally": "mantener lo que estaba allí originalmente",
-    "fill it with latent space noise": "rellenarlo con latent space noise",
-    "fill it with latent space zeroes": "rellenarlo con latent space zeroes",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Escalar la región con máscara a la resolución objetivo, vuelve a pintar, reduce la escala hacia atrás y pégala en la imagen original",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Cambia el tamaño de la imagen a la resolución objetivo. A menos que la altura y el ancho coincidan, obtendrás una relación de aspecto incorrecta.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Cambia el tamaño de la imagen para que la totalidad de la resolución destino se llene con la imagen. Recorta las partes que sobresalen.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Cambia el tamaño de la imagen para que la totalidad de la imagen esté dentro de la resolución objetivo. Rellena el espacio vacío con los colores de la imagen.",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Cuántas veces repetir el procesamiento de una imagen y usarla como entrada para la próxima iteración",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "En modo loopback, en cada bucle, la fuerza de eliminación de ruido se multiplica por este valor. <1 significa variedad decreciente, por lo que su secuencia convergerá en una imagen fija. >1 significa aumentar la variedad, por lo que su secuencia se volverá cada vez más caótica.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Para SD upscale, cuánta superposición en píxeles debe haber entre mosaicos. Los mosaicos se superponen de modo que cuando se fusionan nuevamente en una imagen, no hay una unión claramente visible.",
-    "A directory on the same machine where the server is running.": "Un directorio en la misma máquina donde se ejecuta el servidor.",
-    "Leave blank to save images to the default path.": "Déjalo en blanco para guardar las imágenes en la ruta predeterminada.",
-    "Result = A * (1 - M) + B * M": "Resultado = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "Resultado = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "Primer y último dígito debe ser 1. ej:'1, 2, 1'",
-    "Path to directory with input images": "Ruta al directorio con imágenes de entrada",
-    "Path to directory where to write outputs": "Ruta al directorio donde escribir salidas",
-    "Input images directory": "Directorio de imágenes de entrada",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa las siguientes etiquetas para definir cómo se eligen los nombres de archivo para las imágenes: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; dejar vacío para utilizar predeterminados.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Si esta opción está habilitada, el watermark no se agregará a las imágenes creadas. Advertencia: si no agregas un watermark, es posible que te estés comportando de manera poco ética.",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa las siguiente etiquetas para definir cómo los subdirectorios para imágenes y grids son seleccionados: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; dejar vacío para utilizar predeterminados.",
-    "Restore low quality faces using GFPGAN neural network": "Restaurar rostros de baja calidad utilizando GFPGAN neural network",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Esta expresión regular se usará para extraer palabras del nombre de archivo y se unirán usando la opción a continuación en el texto de la etiqueta que se usa para el entrenamiento. Dejar vacío para mantener el texto del nombre de archivo tal como está.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "Esta cadena se usará para unir palabras divididas en una sola línea si la opción anterior está habilitada.",
-    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Solo se aplica a modelos inpainting. Determina con qué fuerza enmascarar la imagen original para inpainting en img2img. 1.0 significa totalmente enmascarado, que es el comportamiento predeterminado. 0.0 significa un condicionamiento totalmente desenmascarado. Los valores más bajos ayudarán a preservar la composición general de la imagen, pero tendrán problemas con los grandes cambios.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Lista de nombres de configuración, separados por comas, para configuraciones que deben ir a la barra de acceso rápido en la parte superior, en lugar de la pestaña de configuración habitual. Ver modules/shared.py para configurar los nombres. Requiere reiniciar para aplicar.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Si este valor no es cero, se agregará al seed y se usará para inicializar RNG para ruidos cuando se usan samplers con Eta. Puedes usar esto para producir aún más variaciones de imágenes, o puedes usar esto para hacer coincidir imágenes de otro software si sabes lo que estás haciendo.",
-    "Leave empty for auto": "Dejar vacío para automático"
-}
diff --git a/localizations/fr_FR.json b/localizations/fr_FR.json
deleted file mode 100644
index b33d0838..00000000
--- a/localizations/fr_FR.json
+++ /dev/null
@@ -1,415 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "Chargement...",
-    "view": "vue",
-    "api": "api",
-    "•": "•",
-    "built with gradio": "Construit avec Gradio",
-    "Stable Diffusion checkpoint": "checkpoint Stable Diffusion",
-    "txt2img": "txt2img",
-    "img2img": "img2img",
-    "Extras": "Extras",
-    "PNG Info": "Infos PNG",
-    "History": "Historique",
-    "Checkpoint Merger": "Fusion de checkpoints",
-    "Train": "Entrainer",
-    "Settings": "Paramètres",
-    "Prompt": "Requête",
-    "Negative prompt": "Requête négative",
-    "Run": "Lancer",
-    "Skip": "Passer",
-    "Interrupt": "Interrrompre",
-    "Generate": "Générer",
-    "Style 1": "Style 1",
-    "Style 2": "Style 2",
-    "Label": "Etiquette",
-    "File": "Fichier",
-    "Drop File Here": "Déposer votre fichier ici",
-    "-": "-",
-    "or": "ou",
-    "Click to Upload": "Cliquer pour uploader",
-    "Image": "Image",
-    "Check progress": "Voir l'avancement",
-    "Check progress (first)": "Voir l'avancement (1er)",
-    "Sampling Steps": "Étapes d'échantillonnage",
-    "Sampling method": "Méthode d'échantillonnage",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "Largeur",
-    "Height": "Hauteur",
-    "Restore faces": "Restaurer les visages",
-    "Tiling": "Mode Tuile",
-    "Highres. fix": "Correction haute résolution",
-    "Firstpass width": "Largeur première passe",
-    "Firstpass height": "Hauteur seconde passe",
-    "Denoising strength": "Puissance de réduction du bruit",
-    "Batch count": "Nombre de lots",
-    "Batch size": "Taille de lots",
-    "CFG Scale": "Echelle CFG",
-    "Seed": "Valeur aléatoire",
-    "Extra": "Extra",
-    "Variation seed": "Variation de la valeur aléatoire",
-    "Variation strength": "Puissance de variation",
-    "Resize seed from width": "Largeur de redimensionnement de la valeur aléatoire",
-    "Resize seed from height": "Hauteur de redimensionnement de la valeur aléatoire",
-    "Script": "Script",
-    "None": "Aucun",
-    "Prompt matrix": "Matrice de requète",
-    "Prompts from file or textbox": "Requètes depuis un fichier ou une boite de dialogue",
-    "X/Y plot": "graphe X/Y",
-    "Put variable parts at start of prompt": "Mettre les mots clés variable au début de la requête",
-    "Show Textbox": "Afficher le champs texte",
-    "File with inputs": "Fichier d'entrée",
-    "Prompts": "Requêtes",
-    "X type": "Paramètre axe X",
-    "Nothing": "Rien",
-    "Var. seed": "Valeur aléatoire variable",
-    "Var. strength": "Puissance variable",
-    "Steps": "Étapes",
-    "Prompt S/R": "Cherche et remplace dans la requête",
-    "Prompt order": "Ordre de la requête",
-    "Sampler": "Echantilloneur",
-    "Checkpoint name": "Nom du checkpoint",
-    "Hypernetwork": "Hypernetwork",
-    "Hypernet str.": "Force de l'Hypernetwork",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma min": "Sigma min.",
-    "Sigma max": "Sigma max.",
-    "Sigma noise": "Bruit Sigma",
-    "Eta": "Temps estimé",
-    "Clip skip": "Passer Clip",
-    "Denoising": "Réduction du bruit",
-    "X values": "Valeurs X",
-    "Y type": "Paramètre axe Y",
-    "Y values": "Valeurs Y",
-    "Draw legend": "Afficher la légende",
-    "Include Separate Images": "Inclure les images séparées",
-    "Keep -1 for seeds": "Conserver -1 pour la valeur aléatoire",
-    "Drop Image Here": "Déposer l'image ici",
-    "Save": "Enregistrer",
-    "Send to img2img": "Envoyer vers img2img",
-    "Send to inpaint": "Envoyer vers inpaint",
-    "Send to extras": "Envoyer vers extras",
-    "Make Zip when Save?": "Créer un zip lors de l'enregistrement?",
-    "Textbox": "Champ texte",
-    "Interrogate\nCLIP": "Interroger\nCLIP",
-    "Interrogate\nDeepBooru": "Interroger\nDeepBooru",
-    "Inpaint": "Inpaint",
-    "Batch img2img": "Lot img2img",
-    "Image for img2img": "Image pour img2img",
-    "Image for inpainting with mask": "Image pour inpainting avec masque",
-    "Mask": "Masque",
-    "Mask blur": "Flou masque",
-    "Mask mode": "Mode masque",
-    "Draw mask": "Dessiner masque",
-    "Upload mask": "Uploader masque",
-    "Masking mode": "Mode de masquage",
-    "Inpaint masked": "Inpaint masqué",
-    "Inpaint not masked": "Inpaint non masqué",
-    "Masked content": "Contenu masqué",
-    "fill": "remplir",
-    "original": "original",
-    "latent noise": "bruit latent",
-    "latent nothing": "latent vide",
-    "Inpaint at full resolution": "Inpaint en pleine résolution",
-    "Inpaint at full resolution padding, pixels": "Padding de l'inpaint en pleine résolution, en pixels",
-    "Process images in a directory on the same machine where the server is running.": "Traite les images dans un dossier sur la même machine où le serveur tourne",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Utiliser un dossier de sortie vide pour enregistrer les images normalement plutôt que d'écrire dans le dossier de sortie",
-    "Input directory": "Dossier d'entrée",
-    "Output directory": "Dossier de sortie",
-    "Resize mode": "Mode redimensionnement",
-    "Just resize": "Redimensionner uniquement",
-    "Crop and resize": "Recadrer et redimensionner",
-    "Resize and fill": "Redimensionner et remplir",
-    "img2img alternative test": "Test alternatif img2img",
-    "Loopback": "Bouclage",
-    "Outpainting mk2": "Outpainting v2",
-    "Poor man's outpainting": "Outpainting du pauvre",
-    "SD upscale": "Agrandissement SD",
-    "should be 2 or lower.": "doit être inférieur ou égal à 2",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Forcer `Méthode d'échantillonnage` à Euler ? (cette méthode est dédiée à cela)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Forcer la `requête` au contenu de la `requête d'origine` ? (de même pour la `requête négative`)",
-    "Original prompt": "Requête d'origine",
-    "Original negative prompt": "Requête négative d'origine",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Forcer le valeur d'`Étapes d'échantillonnage` à la même valeur qu'`Étapes de décodage` ?",
-    "Decode steps": "Étapes de décodage",
-    "Override `Denoising strength` to 1?": "Forcer `Puissance de réduction du bruit` à 1 ?",
-    "Decode CFG scale": "Echelle CFG de décodage",
-    "Randomness": "Aléatoire",
-    "Sigma adjustment for finding noise for image": "Ajustement Sigma lors de la recherche du bruit dans l'image",
-    "Loops": "Boucles",
-    "Denoising strength change factor": "Facteur de changement de la puissance de réduction du bruit",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Paramètres recommandés : Étapes d'échantillonnage : 80-100, Echantillonneur : Euler a, Puissance de réduction du bruit : 0.8",
-    "Pixels to expand": "Pixels à étendre",
-    "Outpainting direction": "Direction de l'outpainting",
-    "left": "gauche",
-    "right": "droite",
-    "up": "haut",
-    "down": "bas",
-    "Fall-off exponent (lower=higher detail)": "Exposant de diminution (plus petit = plus de détails)",
-    "Color variation": "Variation de couleur",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Agrandira l'image à deux fois sa taille; utilisez les glissières largeur et hauteur afin de choisir la taille de tuile",
-    "Tile overlap": "Chevauchement de tuile",
-    "Upscaler": "Agrandisseur",
-    "Lanczos": "Lanczos",
-    "LDSR": "LDSR",
-    "BSRGAN 4x": "BSRGAN 4x",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR 4x": "SwinIR 4x",
-    "Single Image": "Image unique",
-    "Batch Process": "Traitement par lot",
-    "Batch from Directory": "Lot depuis un dossier",
-    "Source": "Source",
-    "Show result images": "Montrez les images résultantes",
-    "Scale by": "Mise à l'échelle de",
-    "Scale to": "Mise à l'échelle à",
-    "Resize": "Redimensionner",
-    "Crop to fit": "Recadrer à la taille",
-    "Upscaler 2": "Agrandisseur 2",
-    "Upscaler 2 visibility": "Visibilité de l'agrandisseur 2",
-    "GFPGAN visibility": "Visibilité GFPGAN",
-    "CodeFormer visibility": "Visibilité CodeFormer",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Poids CodeFormer (0 = effet maximum, 1 = effet minimum)",
-    "Open output directory": "Ouvrir le dossier de sortie",
-    "Send to txt2img": "Envoyer vers txt2img",
-    "txt2img history": "historique txt2img",
-    "img2img history": "historique img2img",
-    "extras history": "historique extras",
-    "Renew Page": "Rafraichr la page",
-    "First Page": "Première page",
-    "Prev Page": "Page précendente",
-    "Page Index": "Index des pages",
-    "Next Page": "Page suivante",
-    "End Page": "Page de fin",
-    "number of images to delete consecutively next": "nombre d'image à supprimer consécutivement ensuite",
-    "Delete": "Supprimer",
-    "Generate Info": "Générer les informations",
-    "File Name": "Nom de fichier",
-    "set_index": "set_index",
-    "A merger of the two checkpoints will be generated in your": "Une fusion des deux checkpoints sera générée dans votre",
-    "checkpoint": "checkpoint",
-    "directory.": "dossier",
-    "Primary model (A)": "Modèle primaire (A)",
-    "Secondary model (B)": "Modèle secondaire (B)",
-    "Tertiary model (C)": "Modèle tertiaire (C)",
-    "Custom Name (Optional)": "Nom personnalisé (Optionel)",
-    "Multiplier (M) - set to 0 to get model A": "Multiplieur (M) - utiliser 0 pour le modèle A",
-    "Interpolation Method": "Méthode d'interpolation",
-    "Weighted sum": "Somme pondérée",
-    "Add difference": "Ajouter différence",
-    "Save as float16": "Enregistrer en tant que float16",
-    "See": "Voir",
-    "wiki": "wiki",
-    "for detailed explanation.": "pour une explication détaillée.",
-    "Create embedding": "Créer un embedding",
-    "Create hypernetwork": "Créer un hypernetwork",
-    "Preprocess images": "Pré-traite les images",
-    "Name": "Nom",
-    "Initialization text": "Texte d'initialisation",
-    "Number of vectors per token": "Nombre de vecteurs par jeton",
-    "Modules": "Modules",
-    "Source directory": "Dossier source",
-    "Destination directory": "Dossier destination",
-    "Create flipped copies": "Créer des copies en mirroir",
-    "Split oversized images into two": "Couper les images trop grandes en deux",
-    "Use BLIP for caption": "Utiliser BLIP pour les descriptions",
-    "Use deepbooru for caption": "Utiliser deepbooru pour les descriptions",
-    "Preprocess": "Pré-traite",
-    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Entrainer un embedding ; spécifiez un dossier contenant un ensemble d'images avec un ratio de 1:1",
-    "Embedding": "Embedding",
-    "Learning rate": "Vitesse d'apprentissage",
-    "Dataset directory": "Dossier des images d'entrée",
-    "Log directory": "Dossier de journalisation",
-    "Prompt template file": "Fichier modèle de requêtes",
-    "Max steps": "Étapes max.",
-    "Save an image to log directory every N steps, 0 to disable": "Enregistrer une image dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Enregistrer une copie de l'embedding dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
-    "Save images with embedding in PNG chunks": "Sauvegarder les images incluant l'embedding dans leur blocs PNG",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Lire les paramètres (requête, etc.) depuis l'onglet txt2img lors de la génération des previews",
-    "Train Hypernetwork": "Entrainer un Hypernetwork",
-    "Train Embedding": "Entrainer un Embedding",
-    "Apply settings": "Appliquer les paramètres",
-    "Saving images/grids": "Enregistrer les images/grilles",
-    "Always save all generated images": "Toujours enregistrer toutes les images",
-    "File format for images": "Format de fichier pour les images",
-    "Images filename pattern": "Motif pour le nom de fichier des images",
-    "Always save all generated image grids": "Toujours enregistrer toutes les grilles d'images générées",
-    "File format for grids": "Format de fichier pour les grilles",
-    "Add extended info (seed, prompt) to filename when saving grid": "Ajouter les informations étendues (valeur aléatoire, requête) aux noms de fichiers lors de l'enregistrement d'une grille",
-    "Do not save grids consisting of one picture": "Ne pas enregistrer les grilles contenant une seule image",
-    "Prevent empty spots in grid (when set to autodetect)": "Eviter les vides dans la grille (quand autodétection est choisie)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Nombre de colonnes de la grille; utilisez -1 pour autodétection et 0 pour qu'il soit égal à la taille du lot",
-    "Save text information about generation parameters as chunks to png files": "Enregistrer l'information du text des paramètres de génération en tant que blocs dans les fichiers PNG",
-    "Create a text file next to every image with generation parameters.": "Créer un fichier texte contenant les paramètres de génération à côté de chaque image",
-    "Save a copy of image before doing face restoration.": "Enregistrer une copie de l'image avant de lancer la restauration de visage",
-    "Quality for saved jpeg images": "Qualité pour les images jpeg enregistrées",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Si l'image PNG est plus grande que 4MB or l'une des ses dimensions supérieure à 4000, réduire sa taille et enregistrer une copie en JPG",
-    "Use original name for output filename during batch process in extras tab": "Utiliser un nom de fichier original pour les fichiers de sortie durant le traitement par lot dans l'onglet Extras",
-    "When using 'Save' button, only save a single selected image": "A l'utilisation du bouton `Enregistrer`, n'enregistrer que l'image séléctionnée",
-    "Do not add watermark to images": "Ne pas ajouter de filigrane aux images",
-    "Paths for saving": "Chemins pour l'enregistrement",
-    "Output directory for images; if empty, defaults to three directories below": "Dossier de sortie pour les images; si non spécifié, le chemin par défaut sera trois niveau en dessous",
-    "Output directory for txt2img images": "Dossier de sortie pour les images txt2img",
-    "Output directory for img2img images": "Dossier de sortie pour les images img2img",
-    "Output directory for images from extras tab": "Dossier de sortie pour les images de l'onglet Extras",
-    "Output directory for grids; if empty, defaults to two directories below": "Dossier de sortie pour les grilles; si non spécifié, le chemin par défaut sera deux niveau en dessous",
-    "Output directory for txt2img grids": "Dossier de sortie pour les grilles txt2img",
-    "Output directory for img2img grids": "Dossier de sortie pour les grilles img2img",
-    "Directory for saving images using the Save button": "Dossier de sauvegarde des images pour le bouton `Enregistrer`",
-    "Saving to a directory": "Enregistrer dans un dossier",
-    "Save images to a subdirectory": "Enregistrer les images dans un sous dossier",
-    "Save grids to a subdirectory": "Enregistrer les grilles dans un sous dossier",
-    "When using \"Save\" button, save images to a subdirectory": "Lors de l'utilisation du bouton \"Enregistrer\", sauvegarder les images dans un sous dossier",
-    "Directory name pattern": "Motif pour le nom des dossiers",
-    "Max prompt words for [prompt_words] pattern": "Maximum de mot pour le motif [prompt_words]",
-    "Upscaling": "Agrandissement",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Taille des tuile for les agrandisseurs ESRGAN. 0 = mode tuile désactivé.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Chevauchement des tuiles, en pixel pour l'agrandisseur ESRGAN. Valeur faible = couture visible",
-    "Tile size for all SwinIR.": "Taille de la tuile pour tous les agrandisseur SwinIR.",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Chevauchement de tuile, en pixels pour SwinIR. Valeur faible = couture visible",
-    "LDSR processing steps. Lower = faster": "Echantillon du traitement LDSR. Valeur faible =  plus rapide",
-    "Upscaler for img2img": "Agrandisseur pour img2img",
-    "Upscale latent space image when doing hires. fix": "Agrandir l'image de l'espace latent lors de la correction haute résolution",
-    "Face restoration": "Restauration de visage",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Paramètre de poids pour CodeFormer; 0 = effet maximum ； 1 = effet minimum",
-    "Move face restoration model from VRAM into RAM after processing": "Déplacer le modèle de restauration de visage de la VRAM vers la RAM après traitement",
-    "System": "Système",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "Fréquence d'interrogation par seconde pendant la génération. Mettez la valeur à 0 pour désactiver.",
-    "Always print all generation info to standard output": "Toujours afficher toutes les informations de génération dans la sortie standard",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Ajouter un seconde barre de progression dans la console montrant l'avancement pour un tâche complète.",
-    "Training": "Entrainement",
-    "Unload VAE and CLIP from VRAM when training": "Décharger VAE et CLIP de la VRAM pendant l'entrainement",
-    "Filename word regex": "Regex de mot",
-    "Filename join string": "Chaine de caractère pour lier les noms de fichier",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Nombre de répétition pour une image unique par époque; utilisé seulement pour afficher le nombre d'époques",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Enregistrer un csv contenant la perte dans le dossier de journalisation toutes les N étapes, 0 pour désactiver",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "Checkpoint à mettre en cache dans la RAM",
-    "Hypernetwork strength": "Force de l'Hypernetwork",
-    "Apply color correction to img2img results to match original colors.": "Appliquer une correction de couleur aux résultats img2img afin de conserver les couleurs d'origine",
-    "Save a copy of image before applying color correction to img2img results": "Enregistrer une copie de l'image avant d'appliquer les résultats de la correction de couleur img2img",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Avec img2img, executer exactement le nombre d'étapes spécifiées par la glissière (normalement moins d'étapes sont executées quand la réduction du bruit est plus faible).",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Activer la quantisation des échantillionneurs K pour des résultats plus nets et plus propres. Cela peut modifier les valeurs aléatoires existantes. Requiert un redémarrage pour être actif.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphase : utilisez (texte) afin de forcer le modèle à porter plus d'attention au texte et [texte] afin qu'il y porte moins attention",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Utilisez l'ancienne méthode d'emphase. Peut être utile afin de reproduire d'anciennes valeurs aléatoires.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Demander aux échantillionneurs K-diffusion de produire les mêmes dans un lot que lors de la génération d'une image unique",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Améliorer la cohérence en remplissant (padding) à partir de la dernière virgule dans les X jetons quand on en utilise plus de 75",
-    "Filter NSFW content": "Filtrer le contenu +18 (NSFW)",
-    "Stop At last layers of CLIP model": "S'arrêter aux derniers niveaux du modèle CLIP",
-    "Interrogate Options": "Options d'intérrogation",
-    "Interrogate: keep models in VRAM": "Interroger : conserver les modèles en VRAM",
-    "Interrogate: use artists from artists.csv": "Interroger : utiliser les artistes dans artists.csv",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interroger : inclure la correspondance du classement des labels de modèle dans les résultats (N'a pas d'effet sur les interrogateurs basés sur des descriptions) ",
-    "Interrogate: num_beams for BLIP": "Interroger : num_beams pour BLIP",
-    "Interrogate: minimum description length (excluding artists, etc..)": "Interroger : longueur minimale de la description (excluant les artistes, etc.)",
-    "Interrogate: maximum description length": "Interroger : longueur maximale de la description",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP : nombre maximum de lignes dans le fichier texte (0 = pas de limite)",
-    "Interrogate: deepbooru score threshold": "Interroger : seuil du score deepbooru",
-    "Interrogate: deepbooru sort alphabetically": "Interroger : classement alphabétique deepbooru",
-    "use spaces for tags in deepbooru": "Utiliser des espaces pour les étiquettes dans deepbooru",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "échapper (\\) les crochets dans deepbooru (afin qu'ils puissent être utilisés littéralement et non pour mettre en emphase)",
-    "User interface": "Interface utilisateur",
-    "Show progressbar": "Afficher la barre de progression",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Afficher l'état d'avancement de la création d'image toutes les X étapes d'échantillionnage. Utiliser 0 pour désactiver.",
-    "Show grid in results for web": "Afficher la grille dans les résultats web",
-    "Do not show any images in results for web": "N'afficher aucune image dans les résultats web'",
-    "Add model hash to generation information": "Ajouter le hash du modèle dans l'information de génération",
-    "Add model name to generation information": "Ajouter le nom du modèle dans l'information de génération",
-    "Font for image grids that have text": "Police pour les grilles d'images contenant du texte",
-    "Enable full page image viewer": "Activer l'affichage des images en plein écran",
-    "Show images zoomed in by default in full page image viewer": "Afficher les images zoomées par défaut lors de l'affichage en plein écran",
-    "Show generation progress in window title.": "Afficher l'avancement de la génération dans le titre de la fenêtre.",
-    "Quicksettings list": "Liste de réglages rapides",
-    "Localization (requires restart)": "Localisation (requiert un redémarrage)",
-    "Sampler parameters": "Paramètres de l'échantillionneur",
-    "Hide samplers in user interface (requires restart)": "Cacher les échantillonneurs dans l'interface utilisateur (requiert un redémarrage)",
-    "eta (noise multiplier) for DDIM": "eta (multiplicateur de bruit) pour DDIM",
-    "eta (noise multiplier) for ancestral samplers": "eta (multiplicateur de bruit) poru les échantillionneurs de type 'ancestral'",
-    "img2img DDIM discretize": "Discrétisation DDIM pour img2img",
-    "uniform": "uniforme",
-    "quad": "quad",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "sigma tmin",
-    "sigma noise": "sigma noise",
-    "Eta noise seed delta": "Eta noise seed delta",
-    "Request browser notifications": "Demander les notifications au navigateur",
-    "Download localization template": "Télécharger le modèle de localisation",
-    "Reload custom script bodies (No ui updates, No restart)": "Recharger le contenu des scripts personnalisés (Pas de mise à jour de l'interface, Pas de redémarrage)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Redémarrer Gradio et rafraichir les composants (Scripts personnalisés, ui.py, js et css uniquement)",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Requête (Ctrl + Entrée ou Alt + Entrée pour générer)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Requête négative (Ctrl + Entrée ou Alt + Entrée pour générer)",
-    "Add a random artist to the prompt.": "Ajouter un artiste aléatoire à la requête",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Lire les paramètres de génération depuis la requête, ou depuis la dernière génération si la requête est vide dans l'interface utilisateur.",
-    "Save style": "Sauvegarder le style",
-    "Apply selected styles to current prompt": "Appliquer les styles séléctionnés à la requête actuelle",
-    "Stop processing current image and continue processing.": "Arrêter le traitement de l'image actuelle et continuer le traitement.",
-    "Stop processing images and return any results accumulated so far.": "Arrêter le traitement des images et retourne les résultats accumulés depuis le début.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style à appliquer ; les styles sont composés de requêtes positives et négatives et s'appliquent au deux",
-    "Do not do anything special": "Ne rien faire de particulier",
-    "Which algorithm to use to produce the image": "Quel algorithme utiliser pour produire l'image",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - très créatif, peut générer des images complètement différentes en fonction du nombre d'étapes, utiliser plus de 30 à 40 étapes n'améliore pas le résultat",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Modèles implicite de réduction du bruit à diffusion - utile pour l'inpainting",
-    "Produce an image that can be tiled.": "Produit une image qui peut être bouclée (tuile).",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilise un processus en deux étapes afin de créer partiellement une image dans une résolution plus faible, l'agrandir et améliorer ses détails sans modifier la composition",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Détermine à quel point l'algorithme doit respecter le contenu de l'image. A 0 rien ne changera, à 1 l'image sera entièrement différente. Avec des valeurs inférieures à 1.0 le traitement utilisera moins d'étapes que ce que la glissière Étapes d'échantillionnage spécifie. ",
-    "How many batches of images to create": "Combien de lots d'images créer",
-    "How many image to create in a single batch": "Combien d'images créer par lot",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - spécifie à quel point l'image doit se conformer à la requête - des valeurs plus faibles produisent des résultats plus créatifs",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Une valeur qui détermine la sortie du générateur de nombres aléatoires - si vous créez une image avec les mêmes paramètres et valeur aléatoire qu'une autre, le résultat sera identique",
-    "Set seed to -1, which will cause a new random number to be used every time": "Passer la valeur aléatoire à -1, cela causera qu'un nombre aléatoire différent sera utilisé à chaque fois",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Réutiliser la valeur aléatoire de la dernière génération, généralement utile uniquement si elle était randomisée",
-    "Seed of a different picture to be mixed into the generation.": "Valeur aléatoire d'une image différente à mélanger dans la génération",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Force de la variation à produire. A 0 il n'y aura pas d'effet. A 1 l'image sera composée uniquement de la valeur aléatoire variable spécifiée (à l'exception des échantillionneurs `ancestral`)",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Essayer de produire une image similaire à ce qu'elle aurait été avec la même valeur aléatoire, mais dans la résolution spécifiée",
-    "Separate values for X axis using commas.": "Séparer les valeurs pour l'axe X par des virgules",
-    "Separate values for Y axis using commas.": "Séparer les valeurs pour l'axe Y par des virgules",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Ecrire l'image dans un dossier (par défaut - log/images) et les paramètres de génération dans un fichier csv.",
-    "Open images output directory": "Ouvrir le dossier de sortie des images",
-    "How much to blur the mask before processing, in pixels.": "Quantité de flou à appliquer au masque avant traitement, en pixels",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Avec quoi remplir la zone masquée avant traitement par Stable Diffusion.",
-    "fill it with colors of the image": "remplir avec les couleurs de l'image",
-    "keep whatever was there originally": "conserver ce qui était présent à l'origine",
-    "fill it with latent space noise": "remplir avec le bruit de l'espace latent",
-    "fill it with latent space zeroes": "remplir avec des zéros dans l'espace latent",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Agrandir la région masquées à la résolution cible, exécuter l'inpainting, réduire à nouveau puis coller dans l'image originale",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Redimensionner l'image dans la résolution cible. A moins que la hauteur et la largeur coincident le ratio de l'image sera incorrect.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Redimensionner l'image afin que l'entièreté de la résolution cible soit remplie par l'image. Recadrer les parties qui dépassent.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Redimensionner l'image afin que l'entièreté de l'image soit contenue dans la résolution cible. Remplir l'espace vide avec les couleurs de l'image.",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Combien de fois répéter le traitement d'une image et l'utiliser comme entrée pour la prochaine itération",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "En mode bouclage (Loopback), à chaque tour de la boucle la force du réducteur de bruit est multipliée par cette valeur. <1 signifie réduire la variation donc votre séquence convergera vers une image fixe. >1 signifie augmenter la variation donc votre séquence deviendra de plus en plus chaotique. ",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Pour l'agrandissement SD, de combien les tuiles doivent se chevaucher, en pixels. Les tuiles se chevauchent de manière à ce qu'il n'y ait pas de couture visible une fois fusionnées en une image. ",
-    "A directory on the same machine where the server is running.": "Un dossier sur la même machine où le serveur tourne.",
-    "Leave blank to save images to the default path.": "Laisser vide pour sauvegarder les images dans le chemin par défaut.",
-    "Result = A * (1 - M) + B * M": "Résultat = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "Résultat = A + (B - C) * M",
-    "Path to directory with input images": "Chemin vers le dossier contenant les images d'entrée",
-    "Path to directory where to write outputs": "Chemin vers le dossier où écrire les sorties",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utiliser les étiquettes suivantes pour définir le nom des images : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp] ; laisser vide pour le nom par défaut.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Si cette option est activée le filigrane ne sera pas ajouté au images crées. Attention : si vous n'ajoutez pas de filigrane vous pourriez vous comporter de manière non éthique.",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utiliser les étiquettes suivantes pour définir le nom des sous dossiers pour les images et les grilles  : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp] ; laisser vide pour le nom par défaut.",
-    "Restore low quality faces using GFPGAN neural network": "Restaurer les visages de basse qualité en utilisant le réseau neuronal GFPGAN",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Cette expression régulière sera utilisée pour extraire les mots depuis le nom de fichier ; ils seront joints en utilisant l'option ci dessous en une étiquette utilisée pour l'entrainement. Laisser vide pour conserver le texte du nom de fichier tel quel.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "Cette chaine de caractères sera utilisée pour joindre les mots séparés en une ligne unique si l'option ci dessus est activée.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Liste des noms de paramètres, séparés par des virgules, pour les paramètres de la barre d'accès rapide en haut de page, plutôt que dans la page habituelle des paramètres. Voir modules/shared.py pour définir les noms. Requiert un redémarrage pour s'appliquer.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Si cette valeur est différente de zéro elle sera ajoutée à la valeur aléatoire et utilisée pour initialiser le générateur de nombres aléatoires du bruit lors de l'utilisation des échantillonneurs supportants Eta. Vous pouvez l'utiliser pour produire encore plus de variation dans les images, ou vous pouvez utiliser ceci pour faire correspondre les images avec d'autres logiciels si vous savez ce que vous faites.",
-    "Enable Autocomplete": "Activer l'autocomplétion",
-    "/0.0": "/0.0"
-}
\ No newline at end of file
diff --git a/localizations/it_IT.json b/localizations/it_IT.json
deleted file mode 100644
index 22a6f546..00000000
--- a/localizations/it_IT.json
+++ /dev/null
@@ -1,1565 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "Caricamento...",
-    "view": "mostra ",
-    "api": "API",
-    "•": " • ",
-    "built with gradio": " Sviluppato con Gradio",
-    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
-    "txt2img": "txt2img",
-    "img2img": "img2img",
-    "Extras": "Extra",
-    "PNG Info": "Info PNG",
-    "Checkpoint Merger": "Miscelatore di Checkpoint",
-    "Train": "Addestramento",
-    "Create aesthetic embedding": "Crea incorporamento estetico",
-    "auto-sd-paint-ext Guide/Panel": "KRITA Plugin Guida",
-    "Dataset Tag Editor": "Dataset Tag Editor",
-    "Deforum": "Deforum",
-    "Artists To Study": "Artisti per studiare",
-    "Image Browser": "Galleria immagini",
-    "Inspiration": "Ispirazione",
-    "Tokenizer": "Tokenizzatore",
-    "Training Picker": "Training Picker",
-    "Settings": "Impostazioni",
-    "Extensions": "Estensioni",
-    "Prompt": "Prompt",
-    "Negative prompt": "Prompt negativo",
-    "Run": "Esegui",
-    "Skip": "Salta",
-    "Interrupt": "Interrompi",
-    "Generate": "Genera",
-    "Style 1": "Stile 1",
-    "Style 2": "Stile 2",
-    "Label": "Etichetta",
-    "File": "File",
-    "Drop File Here": "Trascina il file qui",
-    "-": "-",
-    "or": "o",
-    "Click to Upload": "Clicca per caricare",
-    "Image": "Immagine",
-    "Check progress": "Controlla i progressi",
-    "Check progress (first)": "Controlla i progressi (primo)",
-    "Sampling Steps": "Passi di campionamento",
-    "Sampling method": "Metodo di campionamento",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM++ 2S a": "DPM++ 2S a",
-    "DPM++ 2M": "DPM++ 2M",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DPM++ 2S a Karras": "DPM++ 2S a Karras",
-    "DPM++ 2M Karras": "DPM++ 2M Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "Larghezza",
-    "Height": "Altezza",
-    "Restore faces": "Restaura i volti",
-    "Tiling": "Piastrellatura",
-    "Highres. fix": "Correzione alta risoluzione",
-    "Firstpass width": "Larghezza del primo passaggio",
-    "Firstpass height": "Altezza del primo passaggio",
-    "Denoising strength": "Forza del Denoising",
-    "Batch count": "Lotti di immagini",
-    "Batch size": "Immagini per lotto",
-    "CFG Scale": "Scala CFG",
-    "Seed": "Seme",
-    "Extra": "Extra",
-    "Variation seed": "Seme della variazione",
-    "Variation strength": "Forza della variazione",
-    "Resize seed from width": "Ridimensiona il seme dalla larghezza",
-    "Resize seed from height": "Ridimensiona il seme dall'altezza",
-    "Open for Clip Aesthetic!": "Apri per Gradienti Estetici (CLIP)",
-    "▼": "▼",
-    "Aesthetic weight": "Estetica - Peso",
-    "Aesthetic steps": "Estetica - Passi",
-    "Aesthetic learning rate": "Estetica - Tasso di apprendimento",
-    "Slerp interpolation": "Interpolazione Slerp",
-    "Aesthetic imgs embedding": "Estetica - Incorporamento di immagini",
-    "None": "Niente",
-    "Aesthetic text for imgs": "Estetica - Testo per le immagini",
-    "Slerp angle": "Angolo Slerp",
-    "Is negative text": "È un testo negativo",
-    "Unprompted": "Unprompted",
-    "Dry Run": "Esecuzione a vuoto (Debug)",
-    "NEW!": "NUOVO!",
-    "Premium Fantasy Card Template": "Premium Fantasy Card Template",
-    "is now available.": "è ora disponibile.",
-    "Generate a wide variety of creatures and characters in the style of a fantasy card game. Perfect for heroes, animals, monsters, and even crazy hybrids.": "Genera un'ampia varietà di creature e personaggi nello stile di un gioco di carte fantasy. Perfetto per eroi, animali, mostri e persino ibridi incredibili.",
-    "Learn More ➜": "Per saperne di più ➜",
-    "Purchases help fund the continued development of Unprompted. Thank you for your support!": "Gli acquisti aiutano a finanziare il continuo sviluppo di Unprompted. Grazie per il vostro sostegno!",
-    "Dismiss": "Rimuovi",
-    "Script": "Script",
-    "Random grid": "Generaz. casuale (griglia)",
-    "Random": "Generaz. casuale (no griglia)",
-    "StylePile": "StylePile",
-    "Advanced Seed Blending": "Miscelazione Semi Avanzata",
-    "Alternate Sampler Noise Schedules": "Metodi alternativi di campionamento del rumore",
-    "Animator v6": "Animator v6",
-    "Asymmetric tiling": "Piastrellatura asimmetrica",
-    "SD Chad - Stable Diffusion Aesthetic Scorer": "SD Chad - Assegna Punteggio Estetica",
-    "Custom code": "Codice personalizzato",
-    "DepthMap v0.1.0": "DepthMap v0.1.0",
-    "Embedding to Shareable PNG": "Converte Incorporamento in PNG condivisibile",
-    "Force symmetry": "Forza la simmetria",
-    "Improved prompt matrix": "Improved prompt matrix",
-    "Latent Mirroring": "Rispecchia e capovolge l'immagine latente",
-    "Prompts interpolation": "Interpola Prompt",
-    "Prompt matrix": "Matrice dei prompt",
-    "Prompt morph": "Metamorfosi del prompt",
-    "Prompts from file or textbox": "Prompt da file o da casella di testo",
-    "To Infinity and Beyond": "Verso l'infinito e oltre",
-    "Seed travel": "Interpolazione semi",
-    "Shift attention": "Sposta l'attenzione",
-    "Text to Vector Graphics": "Da testo a grafica vettoriale",
-    "X/Y plot": "Grafico X/Y",
-    "X/Y/Z plot": "Grafico X/Y/Z",
-    "Dynamic Prompting v0.13.6": "Prompt dinamici v0.13.6",
-    "Create inspiration images": "Crea immagini di ispirazione",
-    "step1 min/max": "Passi min(o max)",
-    "step2 min/max": "Passi max (o min)",
-    "step cnt": "Q.tà di Passi",
-    "cfg1 min/max": "CFG min (o max)",
-    "cfg2 min/max": "CFG max (o min)",
-    "cfg cnt": "Q.tà di CFG",
-    "Draw legend": "Disegna legenda",
-    "Include Separate Images": "Includi immagini separate",
-    "Keep -1 for seeds": "Mantieni sempre il seme a -1",
-    "x/y change": "Inverti ordine assi X/Y (Passi/CFG)",
-    "Loops": "Cicli",
-    "Set Batch count to Sequential prompt count": "Imposta il lotto di immagini sul numero di prompt sequenziali",
-    "Sequential prompts [X]": "Prompt sequenziali [X]",
-    "Random prompts [R]": "Prompt randomici [R]",
-    "Image type strength": "Intensità del tipo di immagine",
-    "Image type": "Tipo di immagine",
-    "Not set": "Non impostato",
-    "Photography": "Fotografia",
-    "Digital art": "Arte digitale",
-    "3D Rendering": "Rendering 3D",
-    "Painting": "Dipinto",
-    "Vector Art": "Arte vettoriale",
-    "Mood": "Stato d'animo",
-    "Amusing": "Amusing",
-    "Angry": "Angry",
-    "Cosy": "Cosy",
-    "Depressing": "Depressing",
-    "Disgusting": "Disgusting",
-    "Embarrassing": "Embarrassing",
-    "Energetic": "Energetic",
-    "Evil": "Evil",
-    "Fearful": "Fearful",
-    "Frightening": "Frightening",
-    "Grim": "Grim",
-    "Guilty": "Guilty",
-    "Happy": "Happy",
-    "Hopeful": "Hopeful",
-    "Hopeless": "Hopeless",
-    "Lonely": "Lonely",
-    "Lustful": "Lustful",
-    "Peaceful": "Peaceful",
-    "Proud": "Proud",
-    "Relieving": "Relieving",
-    "Romantic": "Romantic",
-    "Sad": "Sad",
-    "Satisfying": "Satisfying",
-    "Shameful": "Shameful",
-    "Surprising": "Surprising",
-    "Colors": "Colori",
-    "Primary Colors": "Primary Colors",
-    "Vivid": "Vivido",
-    "Pastel Colors": "Pastel Colors",
-    "Muted Colors": "Muted Colors",
-    "Grayscale": "Grayscale",
-    "Black and white": "Black and white",
-    "Infrared": "Infrared",
-    "View": "Visuale",
-    "Symmetrical": "Symmetrical",
-    "Tilt-shift": "Tilt-shift",
-    "Long shot angle": "Long shot angle",
-    "Medium shot angle": "Medium shot angle",
-    "Wide shot angle": "Wide shot angle",
-    "Portrait": "Ritratto",
-    "Extreme close-up angle": "Extreme close-up angle",
-    "Macro": "Macro",
-    "Microscopic": "Microscopico",
-    "Isometric": "Isometrico",
-    "Panorama": "Panorama",
-    "Fisheye lens": "Fisheye lens",
-    "Overhead-angle": "Overhead-angle",
-    "Birds eye view": "Birds eye view",
-    "Focus on (unfinished)": "Focus su (incompleto)",
-    "No focus": "Nessun focus",
-    "Portraits": "Portraits",
-    "Feminine portrait": "Ritratto femminile",
-    "Masculine portrait": "Ritratto maschile",
-    "WaiFusion": "WaiFusion",
-    "Horrible Monsters": "Horrible Monsters",
-    "Robots": "Robot",
-    "Retrofuturism": "Retrofuturism",
-    "Propaganda": "Propaganda",
-    "Landscapes": "Paesaggi",
-    "Visual style strength": "Forza dello stile visivo",
-    "Visual style": "Stile visivo",
-    "Abstract": "Abstract",
-    "Acidwave": "Acidwave",
-    "Acrylic": "Acrylic",
-    "Aestheticism": "Aestheticism",
-    "Art Deco": "Art Deco",
-    "Nouveau": "Nouveau",
-    "Ashcan School Style": "Ashcan School Style",
-    "Avant-garde": "Avant-garde",
-    "Ballpoint Pen": "Ballpoint Pen",
-    "Baroque": "Baroque",
-    "Classicism": "Classicism",
-    "CoBrA": "CoBrA",
-    "Colored Pencil": "Colored Pencil",
-    "Constructivism": "Constructivism",
-    "Cubism": "Cubism",
-    "Dreamcore": "Dreamcore",
-    "Drip": "Drip",
-    "Encaustic": "Encaustic",
-    "Expressionism": "Expressionism",
-    "Fauvism": "Fauvism",
-    "Finger Painting": "Finger Painting",
-    "Futurism": "Futurism",
-    "Gouache": "Gouache",
-    "Hot Wax": "Hot Wax",
-    "Impressionism": "Impressionism",
-    "Ink Wash": "Ink Wash",
-    "Japanese": "Japanese",
-    "Korean": "Korean",
-    "Line Art": "Line Art",
-    "Linocut": "Linocut",
-    "Lowpoly": "Lowpoly",
-    "Manga": "Manga",
-    "Marker": "Marker",
-    "Modern Comics": "Fumetti moderni",
-    "Mural": "Mural",
-    "Neoclassicism": "Neoclassicism",
-    "Oil Painting": "Oil Painting",
-    "Pastel": "Pastel",
-    "Pencil": "Pencil",
-    "Photorealism": "Photorealism",
-    "Pixel Art": "Pixel Art",
-    "Pop Art": "Pop Art",
-    "Pop Surrealism": "Pop Surrealism",
-    "Psychedelic": "Psychedelic",
-    "Realism": "Realism",
-    "Renaissance": "Renaissance",
-    "Rococo": "Rococo",
-    "Sprite Artwork": "Sprite Artwork",
-    "Street Art": "Street Art",
-    "Suprematism": "Suprematism",
-    "Vaporwave": "Vaporwave",
-    "Vintage Comics": "Vintage Comics",
-    "Watercolor": "Watercolor",
-    "Artist influence": "Influenza dell'Artista",
-    "Artist": "Artista",
-    "Examples, Help and Tips": "Esempi, aiuto e suggerimenti",
-    "Style examples": "Esempi di Stile",
-    "Artist examples": "Esempi di Artisti",
-    "Mood examples": "Esempi di stato d'animo",
-    "Help": "Aiuto",
-    "Hello, StylePile here": "Ciao, questo è StylePile",
-    "Introduction": "Introduzione",
-    "is a mix and match system for adding elements to prompts that affect the style of the result. Hence the name. By default, these elements are placed in a specific order and given strength values. Which means the result sort-of evolves. I have generated thousands of images for each main": "è un sistema mescola e abbina per aggiungere elementi ai prompt che influiscono sullo stile del risultato. Da qui il nome. Per impostazione predefinita, questi elementi vengono inseriti in un ordine specifico e vengono assegnati valori di forza. Il che significa che il risultato si evolve. Ho generato migliaia di immagini per ogni principale",
-    "and tweaked the keywords to attempt giving expected results most of the time. Certainly, your suggestions for improvements are very welcome.": "e ottimizzato le parole chiave per tentare di fornire i risultati attesi per la maggior parte del tempo. Certamente, i tuoi suggerimenti per miglioramenti sono molto graditi.",
-    "Base workflow": "Flusso di lavoro di base",
-    "For example, if you select the": "Ad esempio, se si seleziona il",
-    "image type, then almost all results will look like Paintings. Selecting": "tipo di immagine, quindi quasi tutti i risultati sembreranno Dipinti. Selezione",
-    "will have a certain influence on the overall look in some way (if it's something humanoid it may show emotion, but also colors and overall feel may change). Setting": "avrà una certa influenza sull'aspetto generale in qualche modo (se è qualcosa di umanoide può mostrare emozioni, ma anche i colori e la sensazione generale possono cambiare). Ambientazione",
-    "will change the general tonality of the result. And setting": "cambierà la tonalità generale del risultato. E ambientazione",
-    "will attempt to change how the subject is viewed. Attempt, because view appears to be the least reliable keyword. These elements are placed in order of influence and supported by certain strength values. These basic settings produce very quick results close to the general look you want.": "tenterà di cambiare la modalità di visualizzazione dell'oggetto. Tentativo, perché view sembra essere la parola chiave meno affidabile. Questi elementi sono posti in ordine di influenza e supportati da determinati valori di forza. Queste impostazioni di base producono risultati molto rapidi vicini all'aspetto generale desiderato.",
-    "Moving on, adding a": "Andando avanti, aggiungendo a",
-    "will combine with": "si combinerà con",
-    "to influence how the result generally looks. These styles are based on classic and modern Painting/Art/design movements (which I picked after hours and thousands of samples of testing) and can have a strong influence on the end result. Either it will be more realistic or artistic, or look like a comic book etc. In general, this is a really strong element for getting the look you want. Its influence can be adjusted with the slider above. Experiment with the values, keeping in mind that anything above 1.5 will start becoming a mess. In a similar way, but more focused, you can select an": "per influenzare l'aspetto generale del risultato. Questi stili si basano su movimenti Pittura/Arte/design classici e moderni (che ho scelto dopo ore e migliaia di campioni di test) e possono avere una forte influenza sul risultato finale. O sarà più realistico o artistico, o assomiglierà a un fumetto, ecc. In generale, questo è un elemento davvero forte per ottenere l'aspetto che desideri. La sua influenza può essere regolata con il cursore in alto. Sperimenta con i valori, tenendo presente che qualsiasi cosa al di sopra di 1,5 inizierà a diventare un pasticcio. In modo simile, ma più mirato, puoi selezionare un",
-    "and, of course, that will have a very visible effect on the result as well. Currently there are 135 artists, 55 art styles and 25 emotions available for selection and represented with preview images.": "e, naturalmente, anche questo avrà un effetto molto visibile sul risultato. Attualmente ci sono 135 artisti, 55 stili artistici e 25 emozioni disponibili per la selezione e rappresentate con immagini di anteprima.",
-    "Strength of these settings has been preset at 1.3, as that appears to be the golden ratio for getting good results. Sometimes very low settings have an interesting result as well. You can, and should, freely mix and match these settings to get different results. Classic Painting styles affected or affecting 3D look quite interesting. Photography can look cool with some of the brighter, more artistic styles etc. Sometimes raising CFG scale to 15,20 or more also helps to REALLY push the style onto the image.": "La forza di queste impostazioni è stata preimpostata a 1,3, poiché sembra essere il rapporto aureo per ottenere buoni risultati. A volte anche impostazioni molto basse hanno un risultato interessante. Puoi, e dovresti, mescolare e abbinare liberamente queste impostazioni per ottenere risultati diversi. Gli stili di pittura classica influenzati o influenzati dal 3D sembrano piuttosto interessanti. La fotografia può sembrare interessante con alcuni degli stili più luminosi, più artistici, ecc. A volte aumentare la scala CFG a 15,20 o più aiuta anche a spingere VERAMENTE lo stile sull'immagine.",
-    "Advanced workflow": "Flusso di lavoro avanzato",
-    "StylePile can overtake the generation process, allowing you to generate a large amount of different results with very little extra work. There are two types of variables you can use: [X] and [R]. When you add an [X] to your prompt, it sequentially takes values from the": "StylePile può prendere il controllo del processo di generazione, consentendoti di generare una grande quantità di risultati diversi con pochissimo lavoro extra. Ci sono due tipi di variabili che puoi usare: [X] e [R]. Quando aggiungi una [X] al tuo prompt, prende in sequenza i valori da",
-    "Sequential prompts": "Prompt sequenziali",
-    "text area. You can have dozens of lines there and they will be processed in sequence. When you add [R] to the prompt a value from the": "area di testo. Puoi avere dozzine di righe lì e verranno elaborate in sequenza. Quando si aggiunge [R] al prompt un valore da",
-    "Random prompts": "Prompt casuali",
-    "text area will be inserted in its place. By combining these a huge variety in prompts is very easy to do.": "l'area di testo verrà inserita al suo posto. Combinando questi un'enorme varietà di prompt è molto facile da fare.",
-    "When using this,": "Quando si utilizza questo,",
-    "will move through the prompts and": "si sposterà attraverso i prompt e",
-    "will set how many copies with the given prompt to make. If the seed is not random, it will increase with each batch size step. Any random elements will still be picked randomly.": "imposterà quante copie fare con il prompt dato. Se il seme non è casuale, aumenterà ad ogni passaggio della dimensione del batch. Eventuali elementi casuali verranno comunque selezionati casualmente.",
-    "Tips and tricks": "Suggerimenti e trucchi",
-    "If you add your own Artist, I would recommend having \"by Artist\" in front of their name. Depending on Artist's popularity (or lack thereof) this appears to have a very tangible influence on the result.": "Se aggiungi il tuo artista, ti consiglio di avere \"per artista\" davanti al suo nome. A seconda della popolarità dell'artista (o della sua mancanza) questo sembra avere un'influenza molto tangibile sul risultato.",
-    "Parenthesis can be added to make pArts of the prompt stronger. So": "È possibile aggiungere parentesi per rendere più forti parti del prompt. Così",
-    "((cute kitten))": "((cute kitten))",
-    "will make it extra cute (try it out). This is also important if a style is affecting your original prompt too much. Make that prompt stronger by adding parenthesis around it, like this:": "lo renderà molto carino (provalo). Questo è importante anche se uno stile influisce troppo sul prompt originale. Rendi più forte quel prompt aggiungendo parentesi attorno ad esso, in questo modo:",
-    "((promt))": "((promt))",
-    ". A strength modifier value can also be used, like this": ". È anche possibile utilizzare un valore modificatore di forza, in questo modo",
-    "(prompt:1.1)": "(prompt:1.1)",
-    ". To save some typing you can select the line you want to make stronger and use": ". Per salvare un po' di digitazione puoi selezionare la linea che vuoi rendere più forte e utilizzare",
-    "Ctrl+Shift+Arrow keys up": "Ctrl+Shift+Freccia su",
-    "to add these parenthesis and change the value. As you can see by default values on most sliders, 1.3 seems like a good stArting point if you want to see some impact.": "per aggiungere queste parentesi e modificare il valore. Come puoi vedere per i valori predefiniti sulla maggior parte dei cursori, 1.3 sembra un buon punto di partenza se vuoi vedere un certo impatto.",
-    "Prompts can be split like": "I prompt possono essere divisi come",
-    "[A|B]": "[A|B]",
-    "to sequentially use terms, one after another on each step. For example": "per utilizzare in sequenza i termini, uno dopo l'altro in ogni passaggio. Per esempio",
-    "[cat|dog]": "[cat|dog]",
-    "will produce a hybrid catdog.": "produrrà un cane-gatto ibrido.",
-    "Using": "Usando",
-    "[A:B:0.4]": "[A:B:0.4]",
-    "will switch to other terms after the first one has been active for a certain percentage of steps. So": "passerà ad altri termini dopo che il primo è stato attivo per una certa percentuale di passaggi. Così",
-    "[cat:dog:0.4]": "[cat:dog:0.4]",
-    "will build a cat 40% of the time and then stArt turning it into a dog. Usually this needs more steps to work properly.": "costruirà un gatto il 40% delle volte e poi inizierà a trasformarlo in un cane. Di solito questo richiede più passaggi per funzionare correttamente.",
-    "In conclusion": "In conclusione",
-    "I made this because manually changing keywords, looking up possible styles, etc was a pain. It is meant as a fun tool to explore possibilities and make learning Stable Diffusion easier. If you have some ideas or, better yet, would like to contribute in some way*, just visit https://github.com/some9000/StylePile\n*Hey, if you have a 12Gb graphics card just laying around I'm happy to take it (:": "L'ho fatto perché cambiare manualmente le parole chiave, cercare possibili stili, ecc. era una seccatura. È inteso come uno strumento divertente per esplorare le possibilità e rendere più facile l'apprendimento della Diffusione stabile. Se hai qualche idea o, meglio ancora, vorresti contribuire in qualche modo*, visita pure https://github.com/some9000/StylePile\n*Hey, se hai una scheda grafica da 12 Gb in giro, sono felice di prenderla (:",
-    "Seeds": "Semi",
-    "Noise Scheduler": "Pianificazione del rumore",
-    "Default": "Predefinito",
-    "Karras": "Karras",
-    "Exponential": "Esponenziale",
-    "Variance Preserving": "Conservazione della Varianza",
-    "Sigma min": "Sigma min",
-    "Sigma max": "Sigma max",
-    "Sigma rho (Karras only)": "Sigma rho (Solo Karras)",
-    "Beta distribution (VP only)": "Distribuzione Beta (Solo CV)",
-    "Beta min (VP only)": "Beta min (Solo CV)",
-    "Epsilon (VP only)": "Epsilon (Solo CV)",
-    "Running in txt2img mode:": "Esecuzione in modalità txt2img:",
-    "Render these video formats:": "Renderizza in questi formati:",
-    "GIF": "GIF",
-    "MP4": "MP4",
-    "WEBM": "WEBM",
-    "Animation Parameters": "Parametri animazione",
-    "Total Animation Length (s)": "Durata totale dell'animazione (s)",
-    "Framerate": "Frequenza dei fotogrammi",
-    "Smoothing_Frames": "Fotogrammi da appianare",
-    "Add_Noise": "Aggiungi rumore",
-    "Noise Strength": "Intensità del rumore",
-    "Initial Parameters": "Parametri iniziali",
-    "Denoising Strength": "Intensità di riduzione del rumore",
-    "Seed_March": "Seed_March",
-    "Zoom Factor (scale/s)": "Fattore di ingrandimento (scala/s)",
-    "X Pixel Shift (pixels/s)": "Sposta i Pixel sull'asse X (pixel/s)",
-    "Y Pixel Shift (pixels/s)": "Sposta i Pixel sull'asse Y (pixel/s)",
-    "Rotation (deg/s)": "Rotazione (gradi/s)",
-    "Prompt Template, applied to each keyframe below": "Modello di prompt, applicato a ciascun fotogramma chiave qui di seguito",
-    "Positive Prompts": "Prompt positivi",
-    "Negative Prompts": "Prompt negativi",
-    "Props, Stamps": "Immagini Clipart da diffondere (prop), o da applicare in post elaborazione e non essere diffuse (stamp)",
-    "Poper_Folder:": "Cartella Immagini Clipart (PNG trasparenti):",
-    "Supported Keyframes:": "Fotogrammi chiave supportati:",
-    "time_s | source | video, images, img2img | path": "time_s | source | video, images, img2img | path",
-    "time_s | prompt | positive_prompts | negative_prompts": "time_s | prompt | positive_prompts | negative_prompts",
-    "time_s | template | positive_prompts | negative_prompts": "time_s | template | positive_prompts | negative_prompts",
-    "time_s | transform | zoom | x_shift | y_shift | rotation": "time_s | transform | zoom | x_shift | y_shift | rotation",
-    "time_s | seed | new_seed_int": "time_s | seed | new_seed_int",
-    "time_s | noise | added_noise_strength": "time_s | noise | added_noise_strength",
-    "time_s | denoise | denoise_value": "time_s | denoise | denoise_value",
-    "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name": "time_s | set_text | textblock_name | text_prompt | x | y | w | h | fore_color | back_color | font_name",
-    "time_s | clear_text | textblock_name": "time_s | clear_text | textblock_name",
-    "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation": "time_s | prop | prop_name | prop_filename | x pos | y pos | scale | rotation",
-    "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation": "time_s | set_stamp | stamp_name | stamp_filename | x pos | y pos | scale | rotation",
-    "time_s | clear_stamp | stamp_name": "time_s | clear_stamp | stamp_name",
-    "time_s | col_set": "time_s | col_set",
-    "time_s | col_clear": "time_s | col_clear",
-    "time_s | model | model": "time_s | model | model",
-    "img2img_mode": "Modalità img2img",
-    "Keyframes:": "Fotogrammi chiave:",
-    "Tile X": "Piastrella asse X",
-    "Tile Y": "Piastrella asse Y",
-    "Number of images to generate": "Numero di immagini da generare",
-    "Python code": "Codice Python",
-    "Model": "Modello",
-    "dpt_large": "Depth large",
-    "dpt_hybrid": "Depth hybrid",
-    "midas_v21": "MIDAS v21",
-    "midas_v21_small": "MIDAS v21 small",
-    "Compute on": "Calcola",
-    "GPU": "GPU",
-    "CPU": "CPU",
-    "Save DepthMap": "Salva DepthMap",
-    "Show DepthMap": "Mostra DepthMap",
-    "Combine into one image.": "Combina in un'unica immagine.",
-    "Combine axis": "Combina gli assi",
-    "Vertical": "Verticale",
-    "Horizontal": "Orizzontale",
-    "Source embedding to convert": "Incorporamento sorgente da convertire",
-    "Embedding token": "Token Incorporamento",
-    "Output directory": "Cartella di output",
-    "Horizontal symmetry": "Simmetria orizzontale",
-    "Vertical symmetry": "Simmetria verticale",
-    "Alt. symmetry method (blending)": "Metodo di simmetria alternativo (miscelazione)",
-    "Apply every n steps": "Applica ogni n passi",
-    "Skip last n steps": "Salta gli ultimi n passi",
-    "Usage: a <corgi|cat> wearing <goggles|a hat>": "Utilizzo: a <corgi|cat> wearing <goggles|a hat>",
-    "Mirror application mode": "modalità di applicazione specchiatura",
-    "Alternate Steps": "Passaggi alternati",
-    "Blend Average": "Miscelazione media",
-    "Mirror style": "Stile specchiatura",
-    "Vertical Mirroring": "Specchiatura verticale",
-    "Horizontal Mirroring": "Specchiatura orizzontale",
-    "90 Degree Rotation": "Rotazione 90 gradi",
-    "180 Degree Rotation": "Rotazione 180 gradi",
-    "Maximum steps fraction to mirror at": "Frazione di passi massima su cui eseguire la specchiatura",
-    "Interpolation prompt": "Prompt di interpolazione",
-    "Number of images": "Numero di immagini",
-    "Make a gif": "Crea GIF",
-    "Duration of images (ms)": "Durata delle immagini (ms)",
-    "Put variable parts at start of prompt": "Inserisce le parti variabili all'inizio del prompt",
-    "Keyframe Format:": "Formato dei fotogrammi chiave:",
-    "Seed | Prompt or just Prompt": "Seme | Prompt o semplicemente Prompt",
-    "Prompt list": "Elenco dei prompt",
-    "Number of images between keyframes": "Numero di immagini tra fotogrammi chiave",
-    "Save results as video": "Salva i risultati come video",
-    "Frames per second": "Fotogrammi al secondo",
-    "Iterate seed every line": "Iterare il seme per ogni riga",
-    "Use same random seed for all lines": "Usa lo stesso seme casuale per tutte le righe",
-    "List of prompt inputs": "Elenco di prompt di input",
-    "Upload prompt inputs": "Carica un file contenente i prompt di input",
-    "n": "n",
-    "Destination seed(s) (Comma separated)": "Seme/i di destinazione (separati da virgola)",
-    "Only use Random seeds (Unless comparing paths)": "Usa solo semi casuali (a meno che non si confrontino i percorsi)",
-    "Number of random seed(s)": "Numero di semi casuali",
-    "Compare paths (Separate travels from 1st seed to each destination)": "Confronta percorsi (transizioni separate dal primo seme a ciascuna destinazione)",
-    "Steps": "Passi",
-    "Loop back to initial seed": "Ritorna al seme iniziale",
-    "Bump seed (If > 0 do a Compare Paths but only one image. No video)": "Modula seme (se > 0 mescola il seme iniziale con quelli di destinazione ma solo un'immagine. Nessun video)",
-    "Show generated images in ui": "Mostra le immagini generate nell'interfaccia utente",
-    "\"Hug the middle\" during interpolation": "\"Hug the middle\" durante l'interpolazione. Rende l'interpolazione un po' più veloce all'inizio e alla fine. A volte può produrre video più fluidi, il più delle volte no.",
-    "Allow the default Euler a Sampling method. (Does not produce good results)": "Consenti Euler_a come metodo di campionamento predefinito. (Non produce buoni risultati)",
-    "Illustration": "Illustrazione",
-    "Logo": "Logo",
-    "Drawing": "Disegno",
-    "Artistic": "Artistico",
-    "Tattoo": "Tatuaggio",
-    "Gothic": "Gotico",
-    "Anime": "Anime",
-    "Cartoon": "Cartoon",
-    "Sticker": "Etichetta",
-    "Gold Pendant": "Ciondolo in oro",
-    "None - prompt only": "Nessuno - solo prompt",
-    "Enable Vectorizing": "Abilita vettorizzazione",
-    "Output format": "Formato di output",
-    "svg": "svg",
-    "pdf": "pdf",
-    "White is Opaque": "Il bianco è opaco",
-    "Cut white margin from input": "Taglia il margine bianco dall'input",
-    "Keep temp images": "Conserva le immagini temporanee",
-    "Threshold": "Soglia",
-    "Transparent PNG": "PNG trasparente",
-    "Noise Tolerance": "Tolleranza al rumore",
-    "Quantize": "Quantizzare",
-    "X type": "Parametro asse X",
-    "Nothing": "Niente",
-    "Var. seed": "Seme della variazione",
-    "Var. strength": "Forza della variazione",
-    "Prompt S/R": "Cerca e Sostituisci nel Prompt",
-    "Prompt order": "In ordine di prompt",
-    "Sampler": "Campionatore",
-    "Checkpoint name": "Nome del checkpoint",
-    "Hypernetwork": "Iperrete",
-    "Hypernet str.": "Forza della Iperrete",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma noise": "Sigma noise",
-    "Eta": "ETA",
-    "Clip skip": "Salta CLIP",
-    "Denoising": "Riduzione del rumore",
-    "Cond. Image Mask Weight": "Peso maschera immagine condizionale",
-    "X values": "Valori per X",
-    "Y type": "Parametro asse Y",
-    "Y values": "Valori per Y",
-    "Z type": "Parametro asse Z",
-    "Z values": "Valori per Z",
-    "Combinatorial generation": "Generazione combinatoria",
-    "Combinatorial batches": "Lotti combinatori",
-    "Magic prompt": "Prompt magico",
-    "Fixed seed": "Seme fisso",
-    "Combinations": "Combinazioni",
-    "Choose a number of terms from a list, in this case we choose two artists": "Scegli un numero di termini da un elenco, in questo caso scegliamo due artisti",
-    "{{2$artist1|artist2|artist3}}": "{{2$artist1|artist2|artist3}}",
-    "If $ is not provided, then 1$ is assumed.\n\n    A range can be provided:": "Se $ non viene fornito, si presume 1$.\n\n    È possibile fornire un intervallo di valori:",
-    "{{1-3$artist1|artist2|artist3}}": "{{1-3$artist1|artist2|artist3}}",
-    "In this case, a random number of artists between 1 and 3 is chosen.": "In questo caso viene scelto un numero casuale di artisti compreso tra 1 e 3.",
-    "Wildcards": "Termini jolly",
-    "If the groups wont drop down click": "Se i gruppi non vengono visualizzati, clicca",
-    "here": "qui",
-    "to fix the issue.": "per correggere il problema.",
-    "WILDCARD_DIR: C:\\stable-diffusion-webui\\extensions\\sd-dynamic-prompts\\wildcards": "WILDCARD_DIR: C:\\stable-diffusion-webui\\extensions\\sd-dynamic-prompts\\wildcards",
-    "You can add more wildcards by creating a text file with one term per line and name is mywildcards.txt. Place it in scripts/wildcards.": "Puoi aggiungere termini jolly creando un file di testo con un termine per riga e nominandolo, per esempio, mywildcards.txt. Inseriscilo in scripts/wildcards.",
-    "__<folder>/mywildcards__": "__<cartella>/mywildcards__",
-    "will then become available.": "diverrà quindi disponibile.",
-    "Artist or styles name list. '.txt' files with one name per line": "Elenco nomi di artisti o stili. File '.txt' con un nome per riga",
-    "Prompt words before artist or style name": "Parole chiave prima del nome dell'artista o dello stile",
-    "Prompt words after artist or style name": "Parole chiave dopo il nome dell'artista o dello stile",
-    "Negative Prompt": "Prompt negativo",
-    "Save": "Salva",
-    "Send to img2img": "Invia a\nimg2img",
-    "Send to inpaint": "Invia a\nInpaint",
-    "Send to extras": "Invia a Extra",
-    "Make Zip when Save?": "Crea un file ZIP quando si usa 'Salva'",
-    "Textbox": "Casella di testo",
-    "Interrogate\nCLIP": "Interroga\nCLIP",
-    "Interrogate\nDeepBooru": "Interroga\nDeepBooru",
-    "Inpaint": "Inpaint",
-    "Batch img2img": "img2img in lotti",
-    "Image for img2img": "Immagine per img2img",
-    "Drop Image Here": "Trascina l'immagine qui",
-    "Image for inpainting with mask": "Immagine per inpainting con maschera",
-    "Mask": "Maschera",
-    "Mask blur": "Sfocatura maschera",
-    "Mask mode": "Modalità maschera",
-    "Draw mask": "Disegna maschera",
-    "Upload mask": "Carica maschera",
-    "Masking mode": "Modalità mascheratura",
-    "Inpaint masked": "Inpaint mascherato",
-    "Inpaint not masked": "Inpaint non mascherato",
-    "Masked content": "Contenuto mascherato",
-    "fill": "riempi",
-    "original": "originale",
-    "latent noise": "rumore nello spazio latente",
-    "latent nothing": "nulla nello spazio latente",
-    "Inpaint at full resolution": "Inpaint alla massima risoluzione",
-    "Inpaint at full resolution padding, pixels": "Inpaint con riempimento a piena risoluzione, pixel",
-    "Process images in a directory on the same machine where the server is running.": "Elabora le immagini in una cartella sulla stessa macchina su cui è in esecuzione il server.",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usa una cartella di output vuota per salvare normalmente le immagini invece di scrivere nella cartella di output.",
-    "Input directory": "Cartella di Input",
-    "Resize mode": "Modalità di ridimensionamento",
-    "Just resize": "Ridimensiona solamente",
-    "Crop and resize": "Ritaglia e ridimensiona",
-    "Resize and fill": "Ridimensiona e riempie",
-    "Advanced loopback": "Elaborazione ricorsiva avanzata", 
-    "External Image Masking": "Immagine esterna per la mascheratura",
-    "img2img alternative test": "Test alternativo per img2img",
-    "img2tiles": "img2tiles",
-    "Interpolate": "Interpola immagini",
-    "Loopback": "Rielaborazione ricorsiva",
-    "Loopback and Superimpose": "Rielabora ricorsivamente e sovraimponi",
-    "Alpha Canvas": "Alpha Canvas",
-    "Outpainting mk2": "Outpainting mk2",
-    "Poor man's outpainting": "Poor man's outpainting",
-    "SD upscale": "Ampliamento SD",
-    "txt2mask v0.1.1": "txt2mask v0.1.1",
-    "[C] Video to video": "[C] Video to video",
-    "Videos": "Filmati",
-    "Deforum-webui (use tab extension instead!)": "Deforum-webui (usa piuttosto la scheda Deforum delle estensioni!)",
-    "Use first image colors (custom color correction)": "Usa i colori della prima immagine (correzione del colore personalizzata)",
-    "Denoising strength change factor (overridden if proportional used)": "Fattore di variazione dell'intensità di riduzione del rumore (sovrascritto se si usa proporzionale)",
-    "Zoom level": "Livello di Zoom",
-    "Direction X": "Direzione X",
-    "Direction Y": "Direzione Y",
-    "Denoising strength start": "Intensità di riduzione del rumore - Inizio",
-    "Denoising strength end": "Intensità di riduzione del rumore - Fine",
-    "Denoising strength proportional change starting value": "Intensità di riduzione del rumore - Valore iniziale della variazione proporzionale",
-    "Denoising strength proportional change ending value (0.1 = disabled)": "Intensità di riduzione del rumore - Valore finale della variazione proporzionale (0.1 = disabilitato)",
-    "Saturation enhancement per image": "Miglioramento della saturazione per ciascuna immagine",
-    "Use sine denoising strength variation": "Utilizzare la variazione sinusoidale dell'intensità di riduzione del rumore",
-    "Phase difference": "Differenza di Fase",
-    "Denoising strength exponentiation": "Esponente intensità denoising",
-    "Use sine zoom variation": "Usa la variazione sinusoidale dello zoom",
-    "Zoom exponentiation": "Esponente Zoom",
-    "Use multiple prompts": "Usa prompt multipli",
-    "Same seed per prompt": "Stesso seme per ogni prompt",
-    "Same seed for everything": "Stesso seme per tutto",
-    "Original init image for everything": "Immagine originale di inizializzazione per tutto",
-    "Multiple prompts : 1 line positive, 1 line negative, leave a blank line for no negative": "Prompt multipli: 1 riga positivo, 1 riga negativo, lasciare una riga vuota per nessun negativo",
-    "Running in img2img mode:": "Esecuzione in modalità img2img:",
-    "Masking preview size": "Dimensione dell'anteprima della mascheratura",
-    "Draw new mask on every run": "Disegna una nuova maschera ad ogni esecuzione",
-    "Process non-contigious masks separately": "Elaborare le maschere non contigue separatamente",
-    "should be 2 or lower.": "dovrebbe essere 2 o inferiore.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Sovrascrivi il 'Metodo di campionamento' con Eulero? (questo metodo è stato creato per questo)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Sovrascrivi `prompt` con lo stesso valore del `prompt originale`? (e `prompt negativo`)",
-    "Original prompt": "Prompt originale",
-    "Original negative prompt": "Prompt negativo originale",
-    "Override `Sampling Steps` to the same val due as `Decode steps`?": "Sovrascrivere 'Passi di campionamento' allo stesso valore di 'Passi di decodifica'?",
-    "Decode steps": "Passi di decodifica",
-    "Override `Denoising strength` to 1?": "Sostituisci 'Forza di denoising' a 1?",
-    "Decode CFG scale": "Scala CFG di decodifica",
-    "Randomness": "Casualità",
-    "Sigma adjustment for finding noise for image": "Regolazione Sigma per trovare il rumore per l'immagine",
-    "Tile size": "Dimensione piastrella",
-    "Tile overlap": "Sovrapposizione piastrella",
-    "New seed for each tile": "Nuovo seme per ogni piastrella",
-    "alternate img2img imgage": "Immagine alternativa per img2img",
-    "interpolation values": "Valori di interpolazione",
-    "Refinement loops": "Cicli di affinamento",
-    "Loopback alpha": "Trasparenza rielaborazione ricorsiva",
-    "Border alpha": "Trasparenza del bordo",
-    "Blending strides": "Passi di fusione",
-    "Reuse Seed": "Riusa il seme",
-    "One grid": "Singola griglia",
-    "Interpolate VarSeed": "Interpola il seme della variazione",
-    "Paste on mask": "Incolla sulla maschera",
-    "Inpaint all": "Inpaint tutto",
-    "Interpolate in latent": "Interpola nello spazio latente",
-    "Denoising strength change factor": "Fattore di variazione dell'intensità di denoising",
-    "Superimpose alpha": "Sovrapporre Alpha",
-    "Show extra settings": "Mostra impostazioni aggiuntive",
-    "Reuse seed": "Riusa il seme",
-    "CFG decay factor": "Fattore di decadimento CFG",
-    "CFG target": "CFG di destinazione",
-    "Show/Hide AlphaCanvas": "Mostra/Nascondi AlphaCanvas",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Impostazioni consigliate: Passi di campionamento: 80-100, Campionatore: Euler a, Intensità denoising: 0.8",
-    "Pixels to expand": "Pixel da espandere",
-    "Outpainting direction": "Direzione di Outpainting",
-    "left": "sinistra",
-    "right": "destra",
-    "up": "sopra",
-    "down": "sotto",
-    "Fall-off exponent (lower=higher detail)": "Esponente di decremento (più basso = maggior dettaglio)",
-    "Color variation": "Variazione di colore",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Aumenterà l'immagine al doppio delle dimensioni; utilizzare i cursori di larghezza e altezza per impostare la dimensione della piastrella",
-    "Upscaler": "Ampliamento immagine",
-    "Lanczos": "Lanczos",
-    "Nearest": "Nearest",
-    "LDSR": "LDSR",
-    "BSRGAN": "BSRGAN",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR 4x": "SwinIR 4x",
-    "Mask prompt": "Prompt maschera",
-    "Negative mask prompt": "Prompt maschera negativa",
-    "Mask precision": "Precisione della maschera",
-    "Mask padding": "Estendi i bordi della maschera",
-    "Brush mask mode": "Modalità pennello maschera",
-    "discard": "Scarta",
-    "add": "Aggiungi",
-    "subtract": "Sottrai",
-    "Show mask in output?": "Mostra maschera in uscita?",
-    "If you like my work, please consider showing your support on": "Se ti piace il mio lavoro, per favore considera di mostrare il tuo supporto su ",
-    "Patreon": "Patreon",
-    "Input file path": "Percorso file di input",
-    "CRF (quality, less is better, x264 param)": "CRF (qualità, meno è meglio, x264 param)",
-    "FPS": "FPS",
-    "Seed step size": "Ampiezza del gradiente del seme",
-    "Seed max distance": "Distanza massima del seme",
-    "Start time": "Orario di inizio",
-    "End time": "Orario di fine",
-    "End Prompt Blend Trigger Percent": "Percentuale di innesco del mix col prompt finale",
-    "Prompt end": "Prompt finale",
-    "Smooth video": "Rendi il filmato fluido",
-    "Seconds": "Secondi",
-    "Zoom": "Zoom",
-    "Rotate": "Ruota",
-    "Degrees": "Gradi",
-    "Is the Image Tiled?": "L'immagine è piastrellata?",
-    "TranslateX": "Traslazione X",
-    "Left": "Sinistra",
-    "PercentX": "Percentuale X",
-    "TranslateY": "Traslazione Y",
-    "Up": "Sopra",
-    "PercentY": "Percentuale Y",
-    "Show generated pictures in ui": "Mostra le immagini generate nell'interfaccia utente",
-    "Deforum v0.5-webui-beta": "Deforum v0.5-webui-beta",
-    "This script is deprecated. Please use the full Deforum extension instead.": "Questo script è obsoleto. Utilizzare invece l'estensione Deforum completa.",
-    "Update instructions:": "Istruzioni per l'aggiornamento:",
-    "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md": "github.com/deforum-art/deforum-for-automatic1111-webui/blob/automatic1111-webui/README.md",
-    "discord.gg/deforum": "discord.gg/deforum",
-    "Single Image": "Singola immagine",
-    "Batch Process": "Elaborare a lotti",
-    "Batch from Directory": "Lotto da cartella",
-    "Source": "Sorgente",
-    "Show result images": "Mostra le immagini dei risultati",
-    "Scale by": "Scala di",
-    "Scale to": "Scala a",
-    "Resize": "Ridimensiona",
-    "Crop to fit": "Ritaglia per adattare",
-    "Upscaler 2 visibility": "Visibilità Ampliamento immagine 2",
-    "GFPGAN visibility": "Visibilità GFPGAN",
-    "CodeFormer visibility": "Visibilità CodeFormer",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso di CodeFormer (0 = effetto massimo, 1 = effetto minimo)",
-    "Upscale Before Restoring Faces": "Amplia prima di restaurare i volti",
-    "Send to txt2img": "Invia a\ntxt2img",
-    "A merger of the two checkpoints will be generated in your": "I due checkpoint verranno fusi nella cartella dei",
-    "checkpoint": "checkpoint",
-    "directory.": ".",
-    "Primary model (A)": "Modello Primario (A)",
-    "Secondary model (B)": "Modello Secondario (B)",
-    "Tertiary model (C)": "Modello Terziario (C)",
-    "Custom Name (Optional)": "Nome personalizzato (facoltativo)",
-    "Multiplier (M) - set to 0 to get model A": "Moltiplicatore (M): impostare a 0 per ottenere il modello A",
-    "Interpolation Method": "Metodo di interpolazione",
-    "Weighted sum": "Somma pesata",
-    "Add difference": "Aggiungi differenza",
-    "Save as float16": "Salva come float16",
-    "See": "Consulta la ",
-    "wiki": "wiki",
-    "for detailed explanation.": " per una spiegazione dettagliata.",
-    "Create embedding": "Crea Incorporamento",
-    "Create hypernetwork": "Crea Iperrete",
-    "Preprocess images": "Preprocessa le immagini",
-    "Name": "Nome",
-    "Initialization text": "Testo di inizializzazione",
-    "Number of vectors per token": "Numero di vettori per token",
-    "Overwrite Old Embedding": "Sovrascrivi il vecchio incorporamento",
-    "Modules": "Moduli",
-    "Enter hypernetwork layer structure": "Immettere la struttura del livello della Iperrete",
-    "Select activation function of hypernetwork": "Selezionare la funzione di attivazione della Iperrete",
-    "linear": "lineare",
-    "relu": "relu",
-    "leakyrelu": "leakyrelu",
-    "elu": "elu",
-    "swish": "swish",
-    "tanh": "tanh",
-    "sigmoid": "sigmoid",
-    "celu": "celu",
-    "gelu": "gelu",
-    "glu": "glu",
-    "hardshrink": "hardshrink",
-    "hardsigmoid": "hardsigmoid",
-    "hardtanh": "hardtanh",
-    "logsigmoid": "logsigmoid",
-    "logsoftmax": "logsoftmax",
-    "mish": "mish",
-    "prelu": "prelu",
-    "rrelu": "rrelu",
-    "relu6": "relu6",
-    "selu": "selu",
-    "silu": "silu",
-    "softmax": "softmax",
-    "softmax2d": "softmax2d",
-    "softmin": "softmin",
-    "softplus": "softplus",
-    "softshrink": "softshrink",
-    "softsign": "softsign",
-    "tanhshrink": "tanhshrink",
-    "threshold": "soglia",
-    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Seleziona inizializzazione dei pesi dei livelli. relu-like - Kaiming, Si consiglia sigmoid-like - Xavier",
-    "Normal": "Normale",
-    "KaimingUniform": "KaimingUniform",
-    "KaimingNormal": "KaimingNormal",
-    "XavierUniform": "XavierUniform",
-    "XavierNormal": "XavierNormal",
-    "Add layer normalization": "Aggiunge la normalizzazione del livello",
-    "Use dropout": "Usa Dropout",
-    "Overwrite Old Hypernetwork": "Sovrascrive la vecchia Iperrete",
-    "Source directory": "Cartella sorgente",
-    "Destination directory": "Cartella di destinazione",
-    "Existing Caption txt Action": "Azione sul testo della didascalia esistente",
-    "ignore": "ignora",
-    "copy": "copia",
-    "prepend": "anteporre",
-    "append": "appendere",
-    "Create flipped copies": "Crea copie specchiate",
-    "Split oversized images": "Dividi immagini di grandi dimensioni",
-    "Auto focal point crop": "Ritaglio automatico al punto focale",
-    "Use BLIP for caption": "Usa BLIP per la didascalia",
-    "Use deepbooru for caption": "Usa deepbooru per la didascalia",
-    "Split image threshold": "Soglia di divisione dell'immagine",
-    "Split image overlap ratio": "Rapporto di sovrapposizione dell'immagine",
-    "Focal point face weight": "Peso della faccia del punto focale",
-    "Focal point entropy weight": "Peso dell'entropia del punto focale",
-    "Focal point edges weight": "Peso dei bordi del punto focale",
-    "Create debug image": "Crea immagine di debug",
-    "Preprocess": "Preprocessa",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Addestra un Incorporamento o Iperrete; è necessario specificare una directory con un set di immagini con rapporto 1:1",
-    "[wiki]": "[wiki]",
-    "Embedding": "Incorporamento",
-    "Embedding Learning rate": "Tasso di apprendimento Incorporamento",
-    "Hypernetwork Learning rate": "Tasso di apprendimento Iperrete",
-    "Dataset directory": "Cartella del Dataset",
-    "Log directory": "Cartella del registro",
-    "Prompt template file": "File modello prompt",
-    "Max steps": "Passi massimi",
-    "Save an image to log directory every N steps, 0 to disable": "Salva un'immagine nella cartella del registro ogni N passaggi, 0 per disabilitare",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva una copia dell'incorporamento nella cartella del registro ogni N passaggi, 0 per disabilitare",
-    "Save images with embedding in PNG chunks": "Salva le immagini con l'incorporamento in blocchi PNG",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Legge i parametri (prompt, ecc...) dalla scheda txt2img durante la creazione delle anteprime",
-    "Train Hypernetwork": "Addestra Iperrete",
-    "Train Embedding": "Addestra Incorporamento",
-    "Create an aesthetic embedding out of any number of images": "Crea un'incorporamento estetico da qualsiasi numero di immagini",
-    "Create images embedding": "Crea incorporamento di immagini",
-    "Generate Krita Plugin Symlink Command": "Genera comando per creare collegamento simbolico del plugin Krita",
-    "Launch Krita.": "Avvia Krita.",
-    "On the menubar, go to": "Sulla barra dei menu, vai a",
-    "Settings > Manage Resources...": "Settings > Manage Resources...",
-    "In the window that appears, click": "Nella finestra che appare, fai clic su",
-    "Open Resource Folder": "Open Resource Folder",
-    "In the file explorer that appears, look for a folder called": "In Esplora file, una volta aperto, cerca una cartella chiamata",
-    "pykrita": "pykrita",
-    "or create it.": "o creala.",
-    "Enter the": "Entra nella",
-    "folder and copy the folder location from the address bar.": "cartella e copia il percorso della cartella dalla barra degli indirizzi.",
-    "Paste the folder location below.": "Incolla il percorso della cartella qui di seguito.",
-    "Pykrita Folder Location": "Posizione della cartella Pykrita",
-    "Search for \"Command Prompt\" in the Start Menu, right-click and click \"Run as Administrator...\", paste the follow commands and hit Enter:": "Cerca \"Prompt dei comandi\" nel menu Start, fai clic con il pulsante destro del mouse e fai clic su \"Esegui come amministratore...\", incolla i seguenti comandi e premi Invio:",
-    "mklink /j \"<path_to_pykrita>\\krita_diff\" \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff\"\nmklink \"<path_to_pykrita>\\krita_diff.desktop\" \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff.desktop\"": "mklink /j \"<path_to_pykrita>\\krita_diff\" \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff\"\nmklink \"<path_to_pykrita>\\krita_diff.desktop\" \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff.desktop\"",
-    "Linux command:": "comando Linux:",
-    "ln -s \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff\" \"<path_to_pykrita>/krita_diff\"\nln -s \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff.desktop\" \"<path_to_pykrita>/krita_diff.desktop\"": "ln -s \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff\" \"<path_to_pykrita>/krita_diff\"\nln -s \"C:\\stable-diffusion-webui\\extensions\\auto-sd-paint-ext\\frontends\\krita\\krita_diff.desktop\" \"<path_to_pykrita>/krita_diff.desktop\"",
-    "NOTE": "NOTE",
-    ": Symlinks will break if you move or rename the repository or any\nof its parent folders or otherwise change the path such that the symlink\nbecomes invalid. In which case, repeat the above steps with the new": ": I collegamenti simbolici si interrompono se si sposta o si rinomina il repository o una qualsiasi\n delle sue cartelle principali o si modifica in altro modo il percorso in modo tale che il collegamento simbolico\ndiventi non valido. In tal caso, ripetere i passaggi precedenti con il nuovo ",
-    "folder location and (auto-detected) repository location.": "percorso della cartella e percorso del repository (rilevato automaticamente).",
-    ": Ensure": ": Assicurarsi che",
-    "webui-user.bat": "webui-user.bat",
-    "/": "/",
-    "webui-user.sh": "webui-user.sh",
-    "contains": "contenga",
-    "--api": "--api",
-    "in": "in",
-    "COMMANDLINE_ARGS": "COMMANDLINE_ARGS",
-    "!": "!",
-    "Enabling the Krita Plugin": "Abilitazione del plugin Krita",
-    "Restart Krita.": "Riavvia Krita.",
-    "Settings > Configure Krita...": "Settings > Configure Krita...",
-    "On the left sidebar, go to": "Nella barra laterale sinistra, vai a",
-    "Python Plugin Manager": "Python Plugin Manager",
-    "Look for": "Cerca",
-    "Stable Diffusion Plugin": "Stable Diffusion Plugin",
-    "and tick the checkbox.": "e spunta la casella.",
-    "Restart Krita again for changes to take effect.": "Riavvia Krita per rendere effettive le modifiche.",
-    "The": "Il",
-    "SD Plugin": "SD Plugin",
-    "docked window should appear on the left of the Krita window. If it does not, look on the menubar under": "dovrebbe apparire come finestra ancorata a sinistra della finestra di Krita. In caso contrario, guarda nella barra dei menu sotto",
-    "Settings > Dockers": "Settings > Dockers",
-    "for": "per",
-    "Next Steps": "Prossimi passi",
-    "Troubleshooting": "Risoluzione dei problemi",
-    "Update Guide": "Guida all'aggiornamento",
-    "Usage Guide": "Guida all'uso",
-    "TODO: Control/status panel": "TODO: pannello di controllo/stato",
-    "-1": "-1",
-    "This extension works well with text captions in comma-separated style (such as the tags generated by DeepBooru interrogator).": "Questa estensione funziona bene con i sottotitoli di testo in stile separato da virgole (come i tag generati dall'interrogatore DeepBooru).",
-    "Save all changes": "Salva tutte le modifiche",
-    "Backup original text file (original file will be renamed like filename.000, .001, .002, ...)": "Backup del file di testo originale (il file originale verrà rinominato come nomefile.000, .001, .002, ...)",
-    "Note:": "Note:",
-    "New text file will be created if you are using filename as captions.": " Verrà creato un nuovo file di testo se si utilizza il nome del file come didascalia.",
-    "Results": "Risultati",
-    "Load": "Carica",
-    "Load from subdirectories": "Carica da sotto cartella",
-    "Dataset Images": "Immagini del Dataset",
-    "Displayed Images : 0 / 0 total": "Immagini mostrate : 0 / 0 totali",
-    "Current Tag Filter :": "Filtro Tag corrente :",
-    "Current Selection Filter : 0 images": "Filtro di selezione corrente : 0 immagini",
-    "Selected Image :": "Immagine selezionata :",
-    "Filter and Edit Tags": "Filtra e modifica i tag",
-    "Filter by Selection": "Filtra per selezione",
-    "Edit Caption of Selected Image": "Modifica la didascalia dell'immagine selezionata",
-    "Search tags / Filter images by tags": "Cerca tag / Filtra le immagini per tag",
-    "Search Tags": "Cerca tag",
-    "Clear tag filters": "Cancella i filtri dei tag",
-    "Clear ALL filters": "Rimuovi tutti i filtri",
-    "Sort by": "Ordina per",
-    "Alphabetical Order": "Ordine alfabetico",
-    "Frequency": "Frequenza",
-    "Sort Order": "Ordinamento",
-    "Ascending": "Ascendente",
-    "Descending": "Discendente",
-    "Filter Images by Tags": "Filtra le immagini per tag",
-    "Edit tags in filtered images": "Modifica i tag nelle immagini filtrate",
-    "Selected Tags": "Tag selezionati",
-    "Edit Tags": "Modificare i tag",
-    "Apply changes to filtered images": "Applica le modifiche alle immagini filtrate",
-    "Append additional tags to the beginning": "Aggiungi tag addizionali all'inizio",
-    "1. The selected tags are displayed in comma separated style.": "1. I tag selezionati vengono visualizzati in uno stile separato da virgole.",
-    "2. When changes are applied, all tags in each displayed images are replaced.": "2. Quando vengono applicate le modifiche, tutti i tag in ciascuna immagine visualizzata vengono sostituiti.",
-    "3. If you change some tags into blank, they will be erased.": "3. Se modifichi alcuni tag con uno spazio vuoto, verranno cancellati.",
-    "4. If you add some tags to the end, they will be appended to the end/beginning of the text file.": "4. Se aggiungi dei tag alla fine, questi verranno aggiunti alla fine/inizio del file di testo.",
-    "5. Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "5. Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\"..",
-    "ex A.": "esempio A.",
-    "Original Text = \"A, A, B, C\" Selected Tags = \"B, A\" Edit Tags = \"X, Y\"": "Testo originale = \"A, A, B, C\" Tag selezionati = \"B, A\" Modifica tag = \"X, Y\"",
-    "Result = \"Y, Y, X, C\" (B->X, A->Y)": "Risultato = \"Y, Y, X, C\" (B->X, A->Y)",
-    "ex B.": "esempio B.",
-    "Original Text = \"A, B, C\" Selected Tags = \"(nothing)\" Edit Tags = \"X, Y\"": "Testo originale = \"A, B, C\" Tag selezionati = \"(nothing)\" Modifica tag = \"X, Y\"",
-    "Result = \"A, B, C, X, Y\" (add X and Y to the end (default))": "Risultato = \"A, B, C, X, Y\" (aggiunge X e Y alla fine (predefinito))",
-    "Result = \"X, Y, A, B, C\" (add X and Y to the beginning (\"Append additional tags to the beginning\" checked))": "Risultato = \"X, Y, A, B, C\" (aggiunge X e Y all'inizio (\"Aggiungi tag addizionali all'inizio\" selezionato))",
-    "ex C.": "esempio C.",
-    "Original Text = \"A, B, C, D, E\" Selected Tags = \"A, B, D\" Edit Tags = \", X, \"": "Testo originale = \"A, B, C, D, E\" Tag selezionati = \"A, B, D\" Modifica tag = \", X, \"",
-    "Result = \"X, C, E\" (A->\"\", B->X, D->\"\")": "Risultato = \"X, C, E\" (A->\"\", B->X, D->\"\")",
-    "Select images from the left gallery.": "Seleziona le immagini dalla galleria di sinistra.",
-    "Add selection [Enter]": "Aggiungi selezione [Invio]",
-    "Filter Images": "Filtra le immagini",
-    "Remove selection [Delete]": "Rimuovi selezione [Canc]",
-    "Invert selection": "Inverti selezione",
-    "Clear selection": "Cancella selezione",
-    "Apply selection filter": "Applica il filtro di selezione",
-    "Caption of Selected Image": "Didascalia dell'immagine selezionata",
-    "Copy caption": "Copia didascalia",
-    "Edit Caption": "Modifica didascalia",
-    "Apply changes to selected image": "Applica le modifiche all'immagine selezionata",
-    "Changes are not applied to the text files until the \"Save all changes\" button is pressed.": "Le modifiche non vengono applicate ai file di testo finché non viene premuto il pulsante \"Salva tutte le modifiche\".",
-    "Info and links": "Info e link",
-    "Made by deforum.github.io, port for AUTOMATIC1111's webui maintained by kabachuha": "Realizzato da deforum.github.io, port per l'interfaccia web di AUTOMATIC1111 manutenuto da kabachuha",
-    "Original Deforum Github repo  github.com/deforum/stable-diffusion": "Repository Github originale di Deforum  github.com/deforum/stable-diffusion",
-    "This fork for auto1111's webui github.com/deforum-art/deforum-for-automatic1111-webui": "Questo fork è per l'interfaccia web di AUTOMATIC1111 github.com/deforum-art/deforum-for-automatic1111-webui",
-    "Join the official Deforum Discord discord.gg/deforum to share your creations and suggestions": "Unisciti al canale Discord ufficiale di Deforum discord.gg/deforum per condividere le tue creazioni e suggerimenti",
-    "User guide for v0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit": "Manuale d'uso per la versione 0.5 docs.google.com/document/d/1pEobUknMFMkn8F5TMsv8qRzamXX_75BShMMXV8IFslI/edit",
-    "Math keyframing explanation docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing": "Spiegazione della matematica dei fotogrammi chiave docs.google.com/document/d/1pfW1PwbDIuW0cv-dnuyYj1UzPqe23BlSLTJsqazffXM/edit?usp=sharing",
-    "Keyframes": "Fotogrammi chiave",
-    "Prompts": "Prompt",
-    "Init": "Inizializzare",
-    "Video output": "Uscita video",
-    "Run settings": "Esegui le impostazioni",
-    "Import settings from file": "Importa impostazioni da file",
-    "Override settings": "Sostituisci le impostazioni",
-    "Custom settings file": "File delle impostazioni personalizzate",
-    "Sampling settings": "Impostazioni di campionamento",
-    "override_these_with_webui": "Sovrascrivi con Web UI",
-    "W": "L",
-    "H": "A",
-    "seed": "Seme",
-    "sampler": "Campionatore",
-    "Enable extras": "Abilita 'Extra'",
-    "subseed": "Sub seme",
-    "subseed_strength": "Intensità subseme",
-    "steps": "Passi",
-    "ddim_eta": "ETA DDIM",
-    "n_batch": "Numero lotto",
-    "make_grid": "Crea griglia",
-    "grid_rows": "Righe griglia",
-    "save_settings": "Salva impostazioni",
-    "save_samples": "Salva i campioni",
-    "display_samples": "Mostra i campioni",
-    "save_sample_per_step": "Salva campioni per passo",
-    "show_sample_per_step": "Mostra campioni per passo",
-    "Batch settings": "Impostazioni lotto",
-    "batch_name": "Nome del lotto",
-    "filename_format": "Formato nome del file",
-    "seed_behavior": "Comportamento seme",
-    "iter": "Iterativo",
-    "fixed": "Fisso",
-    "random": "Casuale",
-    "schedule": "Pianificato",
-    "Animation settings": "Impostazioni animazione",
-    "animation_mode": "Modalità animazione",
-    "2D": "2D",
-    "3D": "3D",
-    "Video Input": "Ingresso video",
-    "max_frames": "Fotogrammi max",
-    "border": "Bordo",
-    "replicate": "Replica",
-    "wrap": "Impacchetta",
-    "Motion parameters:": "Parametri di movimento:",
-    "2D and 3D settings": "Impostazioni 2D e 3D",
-    "angle": "Angolo",
-    "zoom": "Zoom",
-    "translation_x": "Traslazione X",
-    "translation_y": "Traslazione Y",
-    "3D settings": "Impostazioni 3D",
-    "translation_z": "Traslazione Z",
-    "rotation_3d_x": "Rotazione 3D X",
-    "rotation_3d_y": "Rotazione 3D Y",
-    "rotation_3d_z": "Rotazione 3D Z",
-    "Prespective flip — Low VRAM pseudo-3D mode:": "Inversione prospettica: modalità pseudo-3D a bassa VRAM:",
-    "flip_2d_perspective": "Inverti prospettiva 2D",
-    "perspective_flip_theta": "Inverti prospettiva theta",
-    "perspective_flip_phi": "Inverti prospettiva phi",
-    "perspective_flip_gamma": "Inverti prospettiva gamma",
-    "perspective_flip_fv": "Inverti prospettiva fv",
-    "Generation settings:": "Impostazioni di generazione:",
-    "noise_schedule": "Pianificazione del rumore",
-    "strength_schedule": "Intensità della pianificazione",
-    "contrast_schedule": "Contrasto della pianificazione",
-    "cfg_scale_schedule": "Pianificazione della scala CFG",
-    "3D Fov settings:": "Impostazioni del campo visivo 3D:",
-    "fov_schedule": "Pianificazione del campo visivo",
-    "near_schedule": "Pianificazione da vicino",
-    "far_schedule": "Pianificazione da lontano",
-    "To enable seed schedule select seed behavior — 'schedule'": "Per abilitare la pianificazione del seme, seleziona il comportamento del seme — 'pianifica'",
-    "seed_schedule": "Pianificazione del seme",
-    "Coherence:": "Coerenza:",
-    "color_coherence": "Coerenza del colore",
-    "Match Frame 0 HSV": "Uguaglia HSV del fotogramma 0",
-    "Match Frame 0 LAB": "Uguaglia LAB del fotogramma 0",
-    "Match Frame 0 RGB": "Uguaglia RGB del fotogramma 0",
-    "diffusion_cadence": "Cadenza di diffusione",
-    "3D Depth Warping:": "Deformazione della profondità 3D:",
-    "use_depth_warping": "Usa la deformazione della profondità",
-    "midas_weight": "Peso MIDAS",
-    "near_plane": "Piano vicino",
-    "far_plane": "Piano lontano",
-    "fov": "Campo visivo",
-    "padding_mode": "Modalità di riempimento",
-    "reflection": "Rifletti",
-    "zeros": "Zeri",
-    "sampling_mode": "Modalità di campionamento",
-    "bicubic": "bicubic",
-    "bilinear": "bilinear",
-    "nearest": "nearest",
-    "save_depth_maps": "Salva le mappe di profondità",
-    "`animation_mode: None` batches on list of *prompts*. (Batch mode disabled atm, only animation_prompts are working)": "`modalità animazione: Nessuno` si inserisce nell'elenco di *prompt*. (Modalità batch al momento disabilitata, funzionano solo i prompt di animazione)",
-    "*Important change from vanilla Deforum!*": "*Importante cambiamento rispetto alla versione originale di Deforum!*",
-    "This script uses the built-in webui weighting settings.": "Questo script utilizza le impostazioni di pesatura webui integrate.",
-    "So if you want to use math functions as prompt weights,": "Quindi, se vuoi usare le funzioni matematiche come pesi dei prompt,",
-    "keep the values above zero in both parts": "mantenere i valori sopra lo zero in entrambe le parti",
-    "Negative prompt part can be specified with --neg": "La parte negativa del prompt può essere specificata con --neg",
-    "batch_prompts (disabled atm)": "Prompt in lotti (al momento è disabilitato)",
-    "animation_prompts": "Prompt animazione",
-    "Init settings": "Impostazioni iniziali",
-    "use_init": "Usa le impostazioni iniziali",
-    "from_img2img_instead_of_link": "da img2img invece che da link",
-    "strength_0_no_init": "Intensità 0 nessuna inizializzazione",
-    "strength": "Intensità",
-    "init_image": "Immagine di inizializzazione",
-    "use_mask": "Usa maschera",
-    "use_alpha_as_mask": "Usa Alpha come maschera",
-    "invert_mask": "Inverti la maschera",
-    "overlay_mask": "Sovrapponi la maschera",
-    "mask_file": "File della maschera",
-    "mask_contrast_adjust": "regolazione contrasto maschera",
-    "mask_brightness_adjust": "Regola la luminosità della maschera",
-    "mask_overlay_blur": "Sfocatura della sovrapposizione della maschera",
-    "mask_fill": "riempimento maschera",
-    "full_res_mask": "maschera a piena risoluzione",
-    "full_res_mask_padding": "riempimento maschera a piena risoluzione",
-    "Video Input:": "Ingresso video:",
-    "video_init_path": "Percorso del video di inizializzazione",
-    "extract_nth_frame": "Estrai ogni ennesimo fotogramma",
-    "overwrite_extracted_frames": "Sovrascrivi i fotogrammi estratti",
-    "use_mask_video": "Usa maschera video",
-    "video_mask_path": "Percorso della maschera video",
-    "Interpolation (turned off atm)": "Interpolazione (attualmente spento)",
-    "interpolate_key_frames": "Interpola fotogrammi chiave",
-    "interpolate_x_frames": "Interpola x fotogrammi",
-    "Resume animation:": "Riprendi l'animazione:",
-    "resume_from_timestring": "Riprendi da stringa temporale",
-    "resume_timestring": "Stringa temporale",
-    "Video output settings": "Impostazioni uscita video",
-    "skip_video_for_run_all": "Salta il video per eseguire tutto",
-    "fps": "FPS",
-    "output_format": "Formato di uscita",
-    "PIL gif": "PIL GIF",
-    "FFMPEG mp4": "FFMPEG MP4",
-    "ffmpeg_location": "Percorso ffmpeg",
-    "add_soundtrack": "Aggiungi colonna sonora",
-    "soundtrack_path": "Percorso colonna sonora",
-    "use_manual_settings": "Usa impostazioni manuali",
-    "render_steps": "Passi di renderizzazione",
-    "max_video_frames": "Numero max fotogrammi video",
-    "path_name_modifier": "Modificatore del nome del percorso",
-    "x0_pred": "x0_pred",
-    "x": "x",
-    "image_path": "Percorso immagine",
-    "mp4_path": "Percorso MP4",
-    "Click here after the generation to show the video": "Clicca qui dopo la generazione per mostrare il video",
-    "Close the video": "Chiudi il video",
-    "NOTE: If the 'Generate' button doesn't work, go in Settings and click 'Restart Gradio and Refresh...'.": "NOTA: se il pulsante 'Genera' non funziona, vai in Impostazioni e fai clic su 'Riavvia Gradio e Aggiorna...'.",
-    "Save Settings": "Salva le impostazioni",
-    "Load Settings": "Carica le impostazioni",
-    "Path relative to the webui folder." : "Percorso relativo alla cartella webui.",
-    "Save Video Settings": "Salva impostazioni video",
-    "Load Video Settings": "Carica impostazioni video",
-    "dog": "cane",
-    "house": "casa",
-    "portrait": "ritratto",
-    "spaceship": "nave spaziale",
-    "cartoon": "cartoon",
-    "digipa-high-impact": "digipa-high-impact",
-    "digipa-med-impact": "digipa-med-impact",
-    "digipa-low-impact": "digipa-low-impact",
-    "fareast": "estremo oriente",
-    "fineart": "fineart",
-    "scribbles": "scarabocchi",
-    "special": "special",
-    "ukioe": "ukioe",
-    "weird": "strano",
-    "black-white": "bianco e nero",
-    "nudity": "nudità",
-    "c": "c",
-    "Get Images": "Ottieni immagini",
-    "dog-anime": "dog-anime",
-    "dog-cartoon": "dog-cartoon",
-    "dog-digipa-high-impact": "dog-digipa-high-impact",
-    "dog-digipa-med-impact": "dog-digipa-med-impact",
-    "dog-digipa-low-impact": "dog-digipa-low-impact",
-    "dog-fareast": "dog-fareast",
-    "dog-fineart": "dog-fineart",
-    "dog-scribbles": "dog-scribbles",
-    "dog-special": "dog-special",
-    "dog-ukioe": "dog-ukioe",
-    "dog-weird": "dog-weird",
-    "dog-black-white": "dog-black-white",
-    "dog-nudity": "dog-nudity",
-    "dog-c": "dog-c",
-    "dog-n": "dog-n",
-    "house-anime": "house-anime",
-    "house-cartoon": "house-cartoon",
-    "house-digipa-high-impact": "house-digipa-high-impact",
-    "house-digipa-med-impact": "house-digipa-med-impact",
-    "house-digipa-low-impact": "house-digipa-low-impact",
-    "house-fareast": "house-fareast",
-    "house-fineart": "house-fineart",
-    "house-scribbles": "house-scribbles",
-    "house-special": "house-special",
-    "house-ukioe": "house-ukioe",
-    "house-weird": "house-weird",
-    "house-black-white": "house-black-white",
-    "house-nudity": "house-nudity",
-    "house-c": "house-c",
-    "house-n": "house-n",
-    "portrait-anime": "portrait-anime",
-    "portrait-cartoon": "portrait-cartoon",
-    "portrait-digipa-high-impact": "portrait-digipa-high-impact",
-    "portrait-digipa-med-impact": "portrait-digipa-med-impact",
-    "portrait-digipa-low-impact": "portrait-digipa-low-impact",
-    "portrait-fareast": "portrait-fareast",
-    "portrait-fineart": "portrait-fineart",
-    "portrait-scribbles": "portrait-scribbles",
-    "portrait-special": "portrait-special",
-    "portrait-ukioe": "portrait-ukioe",
-    "portrait-weird": "portrait-weird",
-    "portrait-black-white": "portrait-black-white",
-    "portrait-nudity": "portrait-nudity",
-    "portrait-c": "portrait-c",
-    "portrait-n": "portrait-n",
-    "spaceship-anime": "spaceship-anime",
-    "spaceship-cartoon": "spaceship-cartoon",
-    "spaceship-digipa-high-impact": "spaceship-digipa-high-impact",
-    "spaceship-digipa-med-impact": "spaceship-digipa-med-impact",
-    "spaceship-digipa-low-impact": "spaceship-digipa-low-impact",
-    "spaceship-fareast": "spaceship-fareast",
-    "spaceship-fineart": "spaceship-fineart",
-    "spaceship-scribbles": "spaceship-scribbles",
-    "spaceship-special": "spaceship-special",
-    "spaceship-ukioe": "spaceship-ukioe",
-    "spaceship-weird": "spaceship-weird",
-    "spaceship-black-white": "spaceship-black-white",
-    "spaceship-nudity": "spaceship-nudity",
-    "spaceship-c": "spaceship-c",
-    "spaceship-n": "spaceship-n",
-    "artists to study extension by camenduru |": "Estensione 'Artisti per studiare' a cura di camenduru |",
-    "github": "Github",
-    "|": "|",
-    "twitter": "Twitter",
-    "youtube": "Youtube",
-    "hi-res images": "Immagini in alta risoluzione",
-    "All images generated with CompVis/stable-diffusion-v1-4 +": "Tutte le immagini sono state generate con CompVis/stable-diffusion-v1-4 +",
-    "artists.csv": "artists.csv",
-    "| License: Attribution 4.0 International (CC BY 4.0)": "| Licenza: Attribution 4.0 International (CC BY 4.0)",
-    "Favorites": "Preferiti",
-    "Others": "Altre immagini",
-    "Images directory": "Cartella immagini",
-    "Dropdown": "Elenco cartelle",
-    "First Page": "Prima pagina",
-    "Prev Page": "Pagina precedente",
-    "Page Index": "Indice pagina",
-    "Next Page": "Pagina successiva",
-    "End Page": "Ultima pagina",
-    "delete next": "Cancella successivo",
-    "Delete": "Elimina",
-    "sort by": "Ordina per",
-    "path name": "Nome percorso",
-    "date": "Data",
-    "keyword": "Parola chiave",
-    "Generate Info": "Genera Info",
-    "File Name": "Nome del file",
-    "Move to favorites": "Aggiungi ai preferiti",
-    "Renew Page": "Aggiorna la pagina",
-    "Number": "Numero",
-    "set_index": "Imposta indice",
-    "load_switch": "Carica",
-    "turn_page_switch": "Volta pagina",
-    "Checkbox": "Selezione",						   
-    "Checkbox Group": "Seleziona per",
-    "artists": "Artisti",
-    "flavors": "Stili",
-    "mediums": "Tecniche",
-    "movements": "Movimenti artistici",
-    "All": "Tutto",
-    "Exclude abandoned": "Escludi scartati",
-    "Abandoned": "Scartati",
-    "Key word": "Parola chiave",
-    "Get inspiration": "Ispirami",
-    "to txt2img": "Invia a\ntxt2img",
-    "to img2img": "Invia a\nimg2img",
-    "Collect": "Salva nei preferiti",
-    "Don't show again": "Scarta",
-    "Move out": "Rimuovi",
-    "set button": "Pulsante imposta",
-    "Before your text is sent to the neural network, it gets turned into numbers in a process called tokenization. These tokens are how the neural network reads and interprets text. Thanks to our great friends at Shousetsu愛 for inspiration for this feature.": "Prima che il tuo testo venga inviato alla rete neurale, verrà trasformato in numeri in un processo chiamato tokenizzazione. Questi token sono il modo in cui la rete neurale legge e interpreta il testo. Grazie ai nostri grandi amici di Shousetsu愛 per l'ispirazione per questa funzione.",
-    "Tokenize": "Tokenizzare",
-    "Text": "Testo",
-    "Tokens": "Token",
-    "Video to extract frames from:": "Video da cui estrarre fotogrammi:",
-    "Only extract keyframes (recommended)": "Estrae solo fotogrammi chiave (consigliato)",
-    "Extract Frames": "Estrai fotogrammi",
-    "Extracted Frame Set": "Set di fotogrammi estratti",
-    "Resize crops to 512x512": "Ridimensiona i ritagli a 512x512",
-    "Save crops to:": "Salva ritagli in:",
-    "<": "<",
-    ">": ">",
-    "Apply settings": "Applica le impostazioni",
-    "Saving images/grids": "Salva immagini/griglie",
-    "Always save all generated images": "Salva sempre tutte le immagini generate",
-    "File format for images": "Formato del file delle immagini",
-    "Images filename pattern": "Modello del nome dei file immagine",
-    "Add number to filename when saving": "Aggiungi un numero al nome del file al salvataggio",
-    "Always save all generated image grids": "Salva sempre tutte le griglie di immagini generate",
-    "File format for grids": "Formato del file per le griglie",
-    "Add extended info (seed, prompt) to filename when saving grid": "Aggiungi informazioni estese (seme, prompt) al nome del file durante il salvataggio della griglia",
-    "Do not save grids consisting of one picture": "Non salvare le griglie composte da una sola immagine",
-    "Prevent empty spots in grid (when set to autodetect)": "Previeni spazi vuoti nella griglia (se impostato su rilevamento automatico)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Numero di righe della griglia; utilizzare -1 per il rilevamento automatico e 0 per essere uguale alla dimensione del lotto",
-    "Save text information about generation parameters as chunks to png files": "Salva le informazioni di testo dei parametri di generazione come blocchi nel file png",
-    "Create a text file next to every image with generation parameters.": "Crea un file di testo assieme a ogni immagine con i parametri di generazione.",
-    "Save a copy of image before doing face restoration.": "Salva una copia dell'immagine prima di eseguire il restauro dei volti.",
-    "Save a copy of image before applying highres fix.": "Salva una copia dell'immagine prima di applicare la correzione ad alta risoluzione.",
-    "Save a copy of image before applying color correction to img2img results": "Salva una copia dell'immagine prima di applicare la correzione del colore ai risultati di img2img",
-    "Quality for saved jpeg images": "Qualità delle immagini salvate in formato JPEG",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se l'immagine PNG è più grande di 4 MB o qualsiasi dimensione è maggiore di 4000, ridimensiona e salva la copia come JPG",
-    "Use original name for output filename during batch process in extras tab": "Usa il nome originale per il nome del file di output durante l'elaborazione a lotti nella scheda 'Extra'",
-    "When using 'Save' button, only save a single selected image": "Usando il pulsante 'Salva', verrà salvata solo la singola immagine selezionata",
-    "Do not add watermark to images": "Non aggiungere la filigrana alle immagini",
-    "Paths for saving": "Percorsi di salvataggio",
-    "Output directory for images; if empty, defaults to three directories below": "Cartella di output per le immagini; se vuoto, per impostazione predefinita verranno usate le cartelle seguenti",
-    "Output directory for txt2img images": "Cartella di output per le immagini txt2img",
-    "Output directory for img2img images": "Cartella di output per le immagini img2img",
-    "Output directory for images from extras tab": "Cartella di output per le immagini dalla scheda 'Extra'",
-    "Output directory for grids; if empty, defaults to two directories below": "Cartella di output per le griglie; se vuoto, per impostazione predefinita veranno usate cartelle seguenti",
-    "Output directory for txt2img grids": "Cartella di output per le griglie txt2img",
-    "Output directory for img2img grids": "Cartella di output per le griglie img2img",
-    "Directory for saving images using the Save button": "Cartella dove salvare le immagini usando il pulsante 'Salva'",
-    "Saving to a directory": "Salva in una cartella",
-    "Save images to a subdirectory": "Salva le immagini in una sotto cartella",
-    "Save grids to a subdirectory": "Salva le griglie in una sotto cartella",
-    "When using \"Save\" button, save images to a subdirectory": "Usando il pulsante \"Salva\", le immagini verranno salvate in una sotto cartella",
-    "Directory name pattern": "Modello del nome della cartella",
-    "Max prompt words for [prompt_words] pattern": "Numero massimo di parole del prompt per il modello [prompt_words]",
-    "Upscaling": "Ampliamento",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Dimensione piastrella per ampliamento ESRGAN. 0 = nessuna piastrellatura.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per gli ampliamenti ESRGAN. Valori bassi = cucitura visibile.",
-    "Tile size for all SwinIR.": "Dimensione piastrella per tutti gli SwinIR.",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sovrapposizione delle piastrelle, in pixel per SwinIR. Valori bassi = cucitura visibile.",
-    "LDSR processing steps. Lower = faster": "Fasi di elaborazione LDSR. Più basso = più veloce",
-    "Upscaler for img2img": "Metodo di ampliamento per img2img",
-    "Upscale latent space image when doing hires. fix": "Amplia l'immagine nello spazio latente durante la correzione in alta risoluzione",
-    "Face restoration": "Restauro del viso",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Peso di CodeFormer; 0 = effetto massimo; 1 = effetto minimo",
-    "Move face restoration model from VRAM into RAM after processing": "Sposta il modello di restauro facciale dalla VRAM alla RAM dopo l'elaborazione",
-    "System": "Sistema",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "Verifiche al secondo sull'utilizzo della VRAM durante la generazione. Impostare a 0 per disabilitare.",
-    "Always print all generation info to standard output": "Stampa sempre tutte le informazioni di generazione sul output standard",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Aggiungi una seconda barra di avanzamento alla console che mostra l'avanzamento complessivo del lavoro.",
-    "Training": "Addestramento",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Sposta VAE e CLIP nella RAM durante l'addestramento di Iperreti. Risparmia VRAM.",
-    "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file.": "Salva lo stato dell'ottimizzatore come file *.optim separato. L'addestramento può essere ripreso con lo stesso HN e il file optim corrispondente.",
-    "Filename word regex": "Espressione regolare per estrarre parole dal nome del file",
-    "Filename join string": "Stringa per unire le parole estratte dal nome del file",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Numero di ripetizioni per una singola immagine di input per epoca; utilizzato solo per visualizzare il numero di epoca",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salva un file CSV contenente la perdita nella cartella di registrazione ogni N passaggi, 0 per disabilitare",
-    "Use cross attention optimizations while training": "Usa le ottimizzazioni di controllo dell'attenzione incrociato durante l'allenamento",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "Checkpoint da memorizzare nella RAM",
-    "SD VAE": "SD VAE",
-    "auto": "auto",
-    "Hypernetwork strength": "Forza della Iperrete",
-    "Inpainting conditioning mask strength": "Forza della maschera di condizionamento del Inpainting",
-    "Apply color correction to img2img results to match original colors.": "Applica la correzione del colore ai risultati di img2img in modo che corrispondano ai colori originali.",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Con img2img, esegue esattamente la quantità di passi specificata dalla barra di scorrimento (normalmente se ne effettuano di meno con meno riduzione del rumore).",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Abilita la quantizzazione nei campionatori K per risultati più nitidi e puliti. Questo può cambiare i semi esistenti. Richiede il riavvio per applicare la modifica.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Enfasi: utilizzare (testo) per fare in modo che il modello presti maggiore attenzione al testo e [testo] per fargli prestare meno attenzione",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usa la vecchia implementazione dell'enfasi. Può essere utile per riprodurre vecchi semi.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Fa sì che i campionatori di diffusione K producano le stesse immagini in un lotto come quando si genera una singola immagine",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta la coerenza disattivando dall'ultima virgola all'indietro di n token quando si utilizzano più di 75 token",
-    "Filter NSFW content": "Filtra i contenuti NSFW",
-    "Stop At last layers of CLIP model": "Fermati agli ultimi livelli del modello CLIP",
-    "Interrogate Options": "Opzioni di interrogazione",
-    "Interrogate: keep models in VRAM": "Interroga: mantieni i modelli nella VRAM",
-    "Interrogate: use artists from artists.csv": "Interroga: utilizza artisti dal file artisti.csv",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interroga: include la classifica delle corrispondenze dei tag del modello nei risultati (non ha effetto sulle interrogazioni basate su didascalie).",
-    "Interrogate: num_beams for BLIP": "Interroga: num_beams per BLIP",
-    "Interrogate: minimum description length (excluding artists, etc..)": "Interroga: lunghezza minima della descrizione (esclusi artisti, ecc..)",
-    "Interrogate: maximum description length": "Interroga: lunghezza massima della descrizione",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: numero massimo di righe nel file di testo (0 = Nessun limite)",
-    "Interrogate: deepbooru score threshold": "Interroga: soglia del punteggio deepbooru",
-    "Interrogate: deepbooru sort alphabetically": "Interroga: deepbooru ordinato alfabeticamente",
-    "use spaces for tags in deepbooru": "usa gli spazi per i tag in deepbooru",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Effettua l'escape (\\) delle parentesi in deepbooru (così vengono usate come parentesi letterali e non per enfatizzare)",
-    "User interface": "Interfaccia Utente",
-    "Show progressbar": "Mostra la barra di avanzamento",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostra l'avanzamento della generazione dell'immagine ogni N passaggi di campionamento. Impostare a 0 per disabilitare.",
-    "Show previews of all images generated in a batch as a grid": "Mostra le anteprime di tutte le immagini generate in un lotto come una griglia",
-    "Show grid in results for web": "Mostra la griglia nei risultati per il web",
-    "Do not show any images in results for web": "Non mostrare alcuna immagine nei risultati per il web",
-    "Add model hash to generation information": "Aggiungi l'hash del modello alle informazioni sulla generazione",
-    "Add model name to generation information": "Aggiungi il nome del modello alle informazioni sulla generazione",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Durante la lettura dei parametri di generazione dal testo nell'interfaccia utente (da informazioni PNG o testo incollato), non modificare il modello/checkpoint selezionato.",
-    "Send seed when sending prompt or image to other interface": "Invia il seme quando si invia un prompt o un'immagine a un'altra interfaccia",
-    "Font for image grids that have text": "Font per griglie di immagini con testo",
-    "Enable full page image viewer": "Abilita la visualizzazione delle immagini a pagina intera",
-    "Show images zoomed in by default in full page image viewer": "Mostra le immagini ingrandite per impostazione predefinita nella visualizzazione a pagina intera",
-    "Show generation progress in window title.": "Mostra l'avanzamento della generazione nel titolo della finestra.",
-    "Quicksettings list": "Elenco delle impostazioni rapide",
-    "Localization (requires restart)": "Localizzazione (richiede il riavvio)",
-    "ar_AR": "ar_AR",
-    "de_DE": "de_DE",
-    "es_ES": "es_ES",
-    "fr_FR": "fr_FR",
-    "it_IT": "it_IT",
-    "ja_JP": "ja_JP",
-    "ko_KR": "ko_KR",
-    "pt_BR": "pt_BR",
-    "ru_RU": "ru_RU",
-    "tr_TR": "tr_TR",
-    "zh_CN": "zh_CN",
-    "zh_TW": "zh_TW",
-    "Sampler parameters": "Parametri del campionatore",
-    "Hide samplers in user interface (requires restart)": "Nascondi campionatori nell'interfaccia utente (richiede il riavvio)",
-    "eta (noise multiplier) for DDIM": "ETA (moltiplicatore di rumore) per DDIM",
-    "eta (noise multiplier) for ancestral samplers": "ETA (moltiplicatore di rumore) per campionatori ancestrali",
-    "img2img DDIM discretize": "discretizzazione DDIM per img2img",
-    "uniform": "uniforme",
-    "quad": "quad",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "sigma tmin",
-    "sigma noise": "sigma noise",
-    "Eta noise seed delta": "ETA del delta del seme del rumore",
-    "Number of columns on image gallery": "Numero di colonne nella galleria di immagini",
-    "Aesthetic Image Scorer": "Punteggio delle immagini estetiche",
-    "Save score as EXIF or PNG Info Chunk": "Salva il punteggio come info EXIF o PNG",
-    "aesthetic_score": "Punteggio estetico",
-    "cfg_scale": "Scala CFG",
-    "sd_model_hash": "Hash del modello SD",
-    "hash": "Hash",
-    "Save tags (Windows only)": "Salva etichette (solo Windows)",
-    "Save category (Windows only)": "Salva categoria (solo Windows)",
-    "Save generation params text": "Salva testo parametri di generazione",
-    "Force CPU (Requires Custom Script Reload)": "Forza CPU (richiede il ricaricamento dello script personalizzato)",
-    "Images Browser": "Galleria immagini",
-    "Preload images at startup": "Precarica le immagini all'avvio",
-    "Number of columns on the page": "Numero di colonne nella pagina",
-    "Number of rows on the page": "Numero di righe nella pagina",
-    "Minimum number of pages per load": "Numero minimo di pagine da caricare",
-    "Maximum number of samples, used to determine which folders to skip when continue running the create script": "Numero massimo di campioni, utilizzato per determinare quali cartelle ignorare quando si continua a eseguire lo script di creazione",
-    "Use same seed for all images": "Usa lo stesso seme per tutte le immagini",
-    "Request browser notifications": "Richiedi le notifiche del browser",
-    "Download localization template": "Scarica il modello per la localizzazione",
-    "Reload custom script bodies (No ui updates, No restart)": "Ricarica gli script personalizzati (nessun aggiornamento dell'interfaccia utente, nessun riavvio)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Riavvia Gradio e aggiorna i componenti (solo script personalizzati, ui.py, js e css)",
-    "Installed": "Installato",
-    "Available": "Disponibile",
-    "Install from URL": "Installa da URL",
-    "Apply and restart UI": "Applica e riavvia l'interfaccia utente",
-    "Check for updates": "Controlla aggiornamenti",
-    "Extension": "Estensione",
-    "URL": "URL",
-    "Update": "Aggiorna",
-    "aesthetic-gradients": "Gradienti Estetici (CLIP)",
-    "https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients",
-    "unknown": "sconosciuto",
-    "auto-sd-paint-ext": "KRITA Plugin",
-    "https://github.com/Interpause/auto-sd-paint-ext": "https://github.com/Interpause/auto-sd-paint-ext",
-    "dataset-tag-editor": "Dataset Tag Editor",
-    "https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor.git": "https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor.git",
-    "deforum-for-automatic1111-webui": "Deforum",
-    "https://github.com/deforum-art/deforum-for-automatic1111-webui": "https://github.com/deforum-art/deforum-for-automatic1111-webui",
-    "novelai-2-local-prompt": "Converti NovelAI",
-    "https://github.com/animerl/novelai-2-local-prompt": "https://github.com/animerl/novelai-2-local-prompt",
-    "sd-dynamic-prompts": "Prompt dinamici",
-    "https://github.com/adieyal/sd-dynamic-prompts": "https://github.com/adieyal/sd-dynamic-prompts",
-    "stable-diffusion-webui-aesthetic-image-scorer": "Punteggio immagini estetiche",
-    "https://github.com/tsngo/stable-diffusion-webui-aesthetic-image-scorer": "https://github.com/tsngo/stable-diffusion-webui-aesthetic-image-scorer",
-    "stable-diffusion-webui-artists-to-study": "Artisti per studiare",
-    "https://github.com/camenduru/stable-diffusion-webui-artists-to-study": "https://github.com/camenduru/stable-diffusion-webui-artists-to-study",
-    "stable-diffusion-webui-images-browser": "Galleria immagini",
-    "https://github.com/yfszzx/stable-diffusion-webui-images-browser": "https://github.com/yfszzx/stable-diffusion-webui-images-browser",
-    "stable-diffusion-webui-inspiration": "Ispirazione",
-    "https://github.com/yfszzx/stable-diffusion-webui-inspiration": "https://github.com/yfszzx/stable-diffusion-webui-inspiration",
-    "stable-diffusion-webui-tokenizer": "Tokenizzatore",
-    "https://github.com/AUTOMATIC1111/stable-diffusion-webui-tokenizer.git": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-tokenizer.git",
-    "tag-autocomplete": "Autocompletamento etichette",
-    "https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git": "https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git",
-    "training-picker": "Selezionatore per l'addestramento",
-    "https://github.com/Maurdekye/training-picker.git": "https://github.com/Maurdekye/training-picker.git",
-    "unprompted": "Unprompted",
-    "https://github.com/ThereforeGames/unprompted": "https://github.com/ThereforeGames/unprompted",
-    "wildcards": "Termini Jolly",
-    "https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards.git": "https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards.git",
-    "Load from:": "Carica da:",
-    "Extension index URL": "URL dell'indice delle Estensioni", 
-    "URL for extension's git repository": "URL del repository GIT dell'estensione",
-    "Local directory name": "Nome cartella locale",
-    "Install": "Installa",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (premi Ctrl+Invio o Alt+Invio per generare)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt negativo (premere Ctrl+Invio o Alt+Invio per generare)",
-    "Add a random artist to the prompt.": "Aggiungi un artista casuale al prompt.",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Leggere i parametri di generazione dal prompt o dall'ultima generazione se il prompt è vuoto ed inserirli nell'interfaccia utente.",
-    "Save style": "Salva stile",
-    "Apply selected styles to current prompt": "Applica gli stili selezionati al prompt corrente",
-    "Stop processing current image and continue processing.": "Interrompe l'elaborazione dell'immagine corrente e continua l'elaborazione.",
-    "Stop processing images and return any results accumulated so far.": "Interrompe l'elaborazione delle immagini e restituisce tutti i risultati accumulati finora.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Stile da applicare; gli stili hanno componenti sia per i prompt positivi che per quelli negativi e si applicano a entrambi",
-    "Do not do anything special": "Non fa nulla di speciale",
-    "Which algorithm to use to produce the image": "Quale algoritmo utilizzare per produrre l'immagine",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - molto creativo, si può ottenere un'immagine completamente diversa a seconda del numero di passi, impostare i passi su un valore superiore a 30-40 non aiuta",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - il migliore per inpainting",
-    "Produce an image that can be tiled.": "Produce un'immagine che può essere piastrellata.",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Utilizza un processo in due fasi per creare parzialmente un'immagine con una risoluzione inferiore, aumentare la scala e quindi migliorarne i dettagli senza modificare la composizione",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determina quanto poco l'algoritmo dovrebbe rispettare dovrebbe il contenuto dell'immagine. A 0, non cambierà nulla e a 1 otterrai un'immagine non correlata. Con valori inferiori a 1.0 l'elaborazione richiederà meno passaggi di quelli specificati dalla barra di scorrimento dei passi di campionamento.",
-    "How many batches of images to create": "Quanti lotti di immagini generare",
-    "How many image to create in a single batch": "Quante immagini generare in un singolo lotto",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - quanto fortemente l'immagine deve conformarsi al prompt: valori più bassi producono risultati più creativi",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Un valore che determina l'output del generatore di numeri casuali: se create un'immagine con gli stessi parametri e seme di un'altra immagine, otterrete lo stesso risultato",
-    "Set seed to -1, which will cause a new random number to be used every time": "Imposta il seme su -1, che farà sì che ogni volta venga utilizzato un nuovo numero casuale",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Riusa il seme dell'ultima generazione, utile soprattutto se casuale",
-    "Seed of a different picture to be mixed into the generation.": "Seme di un'immagine diversa da miscelare nella generazione.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Quanto è forte la variazione da produrre. A 0, non ci sarà alcun effetto. A 1, otterrai l'intera immagine con il seme della variazione (tranne per i campionatori ancestrali, dove otterrai solo una leggera variazione).",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Prova a produrre un'immagine simile a quella che sarebbe stata prodotta con lo stesso seme alla risoluzione specificata",
-    "This text is used to rotate the feature space of the imgs embs": "Questo testo viene utilizzato per ruotare lo spazio delle funzioni delle immagini incorporate",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Quante volte ripetere l'elaborazione di un'immagine e utilizzarla come input per l'iterazione successiva",
-    "Insert [X] anywhere in main prompt to sequentially insert values from here.": "Inserisci [X] ovunque nel prompt principale per inserire in sequenza i valori da qui.",
-    "Insert [R] anywhere in main prompt to randomly insert values from here.": "Inserisci [R] ovunque nel prompt principale per inserire i valori in modo casuale da qui.",
-    "Enter one prompt per line. Blank lines will be ignored.": "Immettere un prompt per riga. Le righe vuote verranno ignorate.",
-    "Separate values for X axis using commas.": "Separare i valori per l'asse X usando le virgole.",
-    "Separate values for Y axis using commas.": "Separare i valori per l'asse Y usando le virgole.",
-    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "Separa un elenco di parole con virgole e lo script eseguirà una variazione di prompt con quelle parole per ogni loro possibile ordine",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva l'immagine/i in una cartella (predefinita - log/images) ed i parametri di generazione in un file CSV.",
-    "Open images output directory": "Apri la cartella di output delle immagini",
-    "How much to blur the mask before processing, in pixels.": "Quanto sfocare la maschera prima dell'elaborazione, in pixel.",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Cosa mettere all'interno dell'area mascherata prima di elaborarla con Stable Diffusion.",
-    "fill it with colors of the image": "riempi con i colori dell'immagine",
-    "keep whatever was there originally": "conserva tutto ciò che c'era in origine",
-    "fill it with latent space noise": "riempi di rumore spaziale latente",
-    "fill it with latent space zeroes": "riempi con zeri di spazio latente",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Ingrandisce la regione mascherata per raggiungere la risoluzione, esegue la pittura, riduce la scala e incolla nell'immagine originale",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Ridimensiona l'immagine alla risoluzione di destinazione. A meno che altezza e larghezza non corrispondano, otterrai proporzioni errate.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Ridimensionare l'immagine in modo che l'intera risoluzione di destinazione sia riempita con l'immagine. Ritaglia le parti che sporgono.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Ridimensiona l'immagine in modo che l'intera immagine rientri nella risoluzione di destinazione. Riempi lo spazio vuoto con i colori dell'immagine.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Per l'upscaling SD, quanta sovrapposizione in pixel dovrebbe esserci tra le piastrelle. Le piastrelle si sovrappongono in modo che quando vengono unite nuovamente in un'immagine, non ci siano giunture chiaramente visibili.",
-    "Process an image, use it as an input, repeat.": "Elabora un'immagine, usala come input, ripeti.",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In modalità rielaborazione ricorsiva, su ogni ciclo la forza di denoising viene moltiplicata per questo valore. <1 significa varietà decrescente in modo che la sequenza converga su un'immagine fissa. >1 significa aumentare la varietà in modo che la tua sequenza diventi sempre più caotica.",
-    "A directory on the same machine where the server is running.": "Una cartella sulla stessa macchina su cui è in esecuzione il server.",
-    "Leave blank to save images to the default path.": "Lascia vuoto per salvare le immagini nel percorso predefinito.",
-    "Result = A * (1 - M) + B * M": "Risultato = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "Risultato = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "La prima e l'ultima cifra devono essere 1. Es.:'1, 2, 1'",
-    "Path to directory with input images": "Percorso della cartella con immagini di input",
-    "Path to directory where to write outputs": "Percorso della cartella in cui scrivere i risultati",
-    "C:\\\\...\\pykrita": "C:\\\\...\\pykrita",
-    "C:\\directory\\of\\datasets": "C:\\cartella\\del\\dataset",
-    "Input images directory": "Cartella di input delle immagini",
-    "Prompt for tokenization": "Prompt for tokenization",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Usa i seguenti tag per definire come vengono scelti i nomi dei file per le immagini: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed ], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se questa opzione è abilitata, la filigrana non verrà aggiunta alle immagini create. Attenzione: se non aggiungi la filigrana, potresti comportarti in modo non etico.",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Utilizzare i seguenti tag per definire come vengono scelte le sottodirectory per le immagini e le griglie: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; lasciare vuoto per usare l'impostazione predefinita.",
-    "Restore low quality faces using GFPGAN neural network": "Ripristina volti di bassa qualità utilizzando la rete neurale GFPGAN",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Questa espressione regolare verrà utilizzata per estrarre le parole dal nome del file e verranno unite utilizzando l'opzione seguente nel testo dell'etichetta utilizzato per l'addestramento. Lascia vuoto per mantenere il testo del nome del file così com'è.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "Questa stringa verrà utilizzata per unire le parole divise in un'unica riga se l'opzione sopra è abilitata.",
-    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Si applica solo ai modelli di pittura. Determina con quale forza mascherare l'immagine originale per inpainting e img2img. 1.0 significa completamente mascherato, che è il comportamento predefinito. 0.0 significa un condizionamento completamente non mascherato. Valori più bassi aiuteranno a preservare la composizione generale dell'immagine, ma avranno difficoltà con grandi cambiamenti.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Elenco dei nomi delle impostazioni, separati da virgole, per le impostazioni che dovrebbero essere visualizzate nella barra di accesso rapido in alto, anziché nella normale scheda delle impostazioni. Vedi modules/shared.py per impostare i nomi. Richiede il riavvio per applicare.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se questo valore è diverso da zero, verrà aggiunto al seed e utilizzato per inizializzare il generatore di numeri casuali per il rumore quando si utilizzano campionatori con ETA. Puoi usarlo per produrre ancora più variazioni di immagini, oppure puoi usarlo per abbinare le immagini di altri software se sai cosa stai facendo.",
-    "Leave empty for auto": "Lasciare vuoto per automatico",
-    "NAIConvert": "Converti\nNovelAI",
-    "History": "Crononolgia",
-    "specify a custom settings file and ignore settings displayed in the interface": "specificare un file di impostazioni personalizzate e ignorare le impostazioni visualizzate nell'interfaccia",
-    "the path to a custom settings file": "il percorso di un file delle impostazioni personalizzate",
-    "The width of the output images, in pixels (must be a multiple of 64)": "La larghezza delle immagini di output, in pixel (deve essere un multiplo di 64)",
-    "The height of the output images, in pixels (must be a multiple of 64)": "L'altezza delle immagini di output, in pixel (deve essere un multiplo di 64)",
-    "enable additional seed settings": "abilitare impostazioni seme aggiuntive",
-    "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "Quante volte migliorare l'immagine generata in modo iterativo; valori più alti richiedono più tempo; valori molto bassi possono produrre risultati negativi",
-    "output images will be placed in a folder with this name, inside of the img2img output folder": "le immagini di output verranno collocate in una cartella con questo nome, all'interno della cartella di output img2img",
-    "specify the format of the filename for output images": "specificare il formato del nome file per le immagini di output",
-    "defines the seed behavior that is used for animations": "definisce il comportamento del seme utilizzato per le animazioni",
-    "the seed value will increment by 1 for each subsequent frame of the animation": "il valore del seme aumenterà di 1 per ogni fotogramma successivo dell'animazione",
-    "selects the type of animation": "seleziona il tipo di animazione",
-    "only 2D motion parameters will be used, but this mode uses the least amount of VRAM. You can optionally enable flip_2d_perspective to enable some psuedo-3d animation parameters while in 2D mode.": "verranno utilizzati solo i parametri di movimento 2D, ma questa modalità utilizza la quantità minima di VRAM. Puoi facoltativamente abilitare flip_2d_perspective per abilitare alcuni parametri di animazione psuedo-3d mentre sei in modalità 2D.",
-    "the maximum number of output images to be created": "il numero massimo di immagini di output da creare",
-    "controls handling method of pixels to be generated when the image is smaller than the frame.": "controlla il metodo di gestione dei pixel da generare quando l'immagine è più piccola del fotogramma.",
-    "repeats the edge of the pixels, and extends them. Animations with quick motion may yield lines where this border function was attempting to populate pixels into the empty space created.": "ripete il bordo dei pixel e li estende. Le animazioni con movimento rapido possono produrre linee in cui questa funzione di bordo stava tentando di popolare i pixel nello spazio vuoto creato.",
-    "2D operator to rotate canvas clockwise/anticlockwise in degrees per frame": "Operatore 2D per ruotare la tela in senso orario/antiorario in gradi per fotogramma",
-    "2D operator that scales the canvas size, multiplicatively. [static = 1.0]": "Operatore 2D che ridimensiona le dimensioni della tela, in modo moltiplicativo. [statico = 1.0]",
-    "2D & 3D operator to move canvas left/right in pixels per frame": "Operatore 2D e 3D per spostare la tela a sinistra/destra in pixel per fotogramma",
-    "2D & 3D operator to move canvas up/down in pixels per frame": "Operatore 2D e 3D per spostare la tela su/giù in pixel per fotogramma",
-    "3D operator to move canvas towards/away from view [speed set by FOV]": "Operatore 3D per spostare la tela da/verso la visuale [velocità impostata da FOV]",
-    "3D operator to tilt canvas up/down in degrees per frame": "Operatore 3D per inclinare la tela su/giù in gradi per fotogramma",
-    "3D operator to pan canvas left/right in degrees per frame": "Operatore 3D per eseguire una panoramica della tela a sinistra/destra in gradi per fotogramma",
-    "3D operator to roll canvas clockwise/anticlockwise": "Operatore 3D per ruotare la tela in senso orario/antiorario",
-    "enables 2D mode functions to simulate faux 3D movement": "abilita le funzioni della modalità 2D per simulare falsi movimenti 3D",
-    "the roll effect angle": "l'angolo dell'effetto di rollio",
-    "the tilt effect angle": "l'angolo dell'effetto di inclinazione",
-    "the pan effect angle": "l'angolo dell'effetto panoramica",
-    "the 2D vanishing point of perspective (recommended range 30-160)": "il punto di fuga prospettico 2D (intervallo consigliato 30-160)",
-    "amount of graininess to add per frame for diffusion diversity": "quantità di granulosità da aggiungere per fotogramma per la diversità di diffusione",
-    "amount of presence of previous frame to influence next frame, also controls steps in the following formula [steps - (strength_schedule * steps)]": "quantità di presenza del fotogramma precedente per influenzare il fotogramma successivo, controlla anche i passaggi nella seguente formula [passi - (forza pianificazione * steps)]",
-    "adjusts the overall contrast per frame [default neutral at 1.0]": "regola il contrasto generale per fotogramma [neutro predefinito a 1.0]",
-    "how closely the image should conform to the prompt. Lower values produce more creative results. (recommended range 5-15)": "quanto l'immagine deve essere conforme al prompt. Valori più bassi producono risultati più creativi. (intervallo consigliato 5-15)",
-    "adjusts the scale at which the canvas is moved in 3D by the translation_z value. [maximum range -180 to +180, with 0 being undefined. Values closer to 180 will make the image have less depth, while values closer to 0 will allow more depth]": "regola la scala alla quale la tela viene spostata in 3D in base al valore traslazione_z. [intervallo massimo da -180 a +180, con 0 non definito. Valori più vicini a 180 renderanno l'immagine meno profonda, mentre valori più vicini a 0 consentiranno una maggiore profondità]",
-    "allows you to specify seeds at a specific schedule, if seed_behavior is set to schedule.": "ti consente di specificare i semi in base a una pianificazione specifica, se seed_behavior è impostato su pianificazione.",
-    "The color coherence will attempt to sample the overall pixel color information, and trend those values analyzed in the first frame to be applied to future frames.": "La coerenza del colore tenterà di campionare le informazioni complessive sul colore dei pixel e tendere quei valori analizzati nel primo fotogramma da applicare ai fotogrammi futuri.",
-    "LAB is a more linear approach to mimic human perception of color space - a good default setting for most users.": "LAB è un approccio più lineare per imitare la percezione umana dello spazio colore, una buona impostazione predefinita per la maggior parte degli utenti.",
-    "The default setting of 1 will cause every frame to receive diffusion in the sequence of image outputs. A setting of 2 will only diffuse on every other frame, yet motion will still be in effect. The output of images during the cadence sequence will be automatically blended, additively and saved to the specified drive. This may improve the illusion of coherence in some workflows as the content and context of an image will not change or diffuse during frames that were skipped. Higher values of 4-8 cadence will skip over a larger amount of frames and only diffuse the “Nth” frame as set by the diffusion_cadence value. This may produce more continuity in an animation, at the cost of little opportunity to add more diffused content. In extreme examples, motion within a frame will fail to produce diverse prompt context, and the space will be filled with lines or approximations of content - resulting in unexpected animation patterns and artifacts. Video Input & Interpolation modes are not affected by diffusion_cadence.": "L'impostazione predefinita di 1 farà sì che ogni fotogramma riceva la diffusione nella sequenza di output delle immagini. Un'impostazione di 2 si diffonderà solo su ogni altro fotogramma, ma il movimento sarà comunque attivo. L'output delle immagini durante la sequenza di cadenza verrà automaticamente miscelato, additivato e salvato nell'unità specificata. Ciò può migliorare l'illusione di coerenza in alcuni flussi di lavoro poiché il contenuto e il contesto di un'immagine non cambiano o si diffondono durante i fotogrammi che sono stati ignorati. Valori più alti di 4-8 cadenza salteranno una quantità maggiore di fotogrammi e diffonderanno solo il fotogramma 'Nth' come impostato dal valore di diffusione_cadenza. Ciò può produrre più continuità in un'animazione, a costo di poche opportunità di aggiungere contenuti più diffusi. In esempi estremi, il movimento all'interno di un fotogramma non riuscirà a produrre un contesto di richiesta diverso e lo spazio sarà riempito con linee o approssimazioni di contenuto, risultando in modelli di animazione e artefatti inaspettati. Le modalità di ingresso video e interpolazione non sono influenzate da diffusion_cadence.",
-    "enables instructions to warp an image dynamically in 3D mode only.": "abilita le istruzioni per deformare un'immagine dinamicamente solo in modalità 3D.",
-    "sets a midpoint at which a depthmap is to be drawn: range [-1 to +1]": "imposta un punto medio in cui deve essere disegnata una mappa di profondità: range [da -1 a +1]",
-    "instructs the handling of pixels outside the field of view as they come into the scene.": "indica la gestione dei pixel al di fuori del campo visivo quando entrano nella scena.",
-    "choose from Bicubic, Bilinear or Nearest modes. (Recommended: Bicubic)": "scegliere tra le modalità Bicubico, Bilineare o Più vicino. (Consigliato: Bicubico)",
-    "will output a greyscale depth map image alongside the output images.": "produrrà un'immagine della mappa di profondità in scala di grigi insieme alle immagini di output.",
-    "Diffuse the first frame based on an image, similar to img2img.": "Diffondi il primo fotogramma in base a un'immagine, simile a img2img.",
-    "Set the strength to 0 automatically when no init image is used": "Imposta automaticamente l'intensità su 0 quando non viene utilizzata alcuna immagine iniziale",
-    "Controls the strength of the diffusion on the init image. 0 = disabled": "Controlla l'intensità della diffusione sull'immagine iniziale 0 = disabilitato",
-    "the path to your init image": "il percorso alla tua immagine iniziale",
-    "Use a grayscale image as a mask on your init image. Whiter areas of the mask are areas that change more.": "Usa un'immagine in scala di grigi come maschera sull'immagine iniziale. Le aree più bianche della maschera sono aree che cambiano di più.",
-    "use the alpha channel of the init image as the mask": "usa il canale alfa dell'immagine iniziale come maschera",
-    "Inverts the colors of the mask": "Inverte i colori della maschera",
-    "Overlay the masked image at the end of the generation so it does not get degraded by encoding and decoding": "Sovrapponi l'immagine mascherata alla fine della generazione in modo che non venga degradata dalla codifica e dalla decodifica",
-    "the path to your mask image": "il percorso per l'immagine della maschera",
-    "adjust the brightness of the mask. Should be a positive number, with 1.0 meaning no adjustment.": "regola la luminosità della maschera. Dovrebbe essere un numero positivo, con 1.0 che significa nessuna correzione.",
-    "Blur edges of final overlay mask, if used. Minimum = 0 (no blur)": "Sfoca i bordi della maschera di sovrapposizione finale, se utilizzata. Minimo = 0 (nessuna sfocatura)",
-    "the directory at which your video file is located for Video Input mode only.": "la cartella in cui si trova il file video per la modalità solo Ingresso video.",
-    "during the run sequence, only frames specified by this value will be extracted, saved, and diffused upon. A value of 1 indicates that every frame is to be accounted for. Values of 2 will use every other frame for the sequence. Higher values will skip that number of frames respectively.": "durante la sequenza di esecuzione, verranno estratti, salvati e diffusi solo i frame specificati da questo valore. Un valore di 1 indica che deve essere contabilizzato ogni frame. I valori di 2 utilizzeranno ogni altro fotogramma per la sequenza. Valori più alti salteranno rispettivamente quel numero di fotogrammi.",
-    "when enabled, will re-extract video frames each run. When using video_input mode, the run will be instructed to write video frames to the drive. If you’ve already populated the frames needed, uncheck this box to skip past redundant extraction, and immediately start the render. If you have not extracted frames, you must run at least once with this box checked to write the necessary frames.": "quando abilitato, estrarrà nuovamente i fotogrammi video ad ogni esecuzione. Quando si utilizza la modalità Ingresso video, all'esecuzione verrà richiesto di scrivere fotogrammi video sull'unità. Se avete già popolato i fotogrammi necessari, deselezionate questa casella per saltare l'estrazione ridondante e avviare immediatamente il rendering. Se non sono stati estratti i frame, è necessario eseguire almeno una volta l'operazione con questa casella selezionata per scrivere i fotogrammi necessari.",
-    "video_input mode only, enables the extraction and use of a separate video file intended for use as a mask. White areas of the extracted video frames will not be affected by diffusion, while black areas will be fully effected. Lighter/darker areas are affected dynamically.": "in modalità solo video Input, consente l'estrazione e l'utilizzo di un file video separato destinato all'uso come maschera. Le aree bianche dei fotogrammi video estratti non saranno interessate dalla diffusione, mentre le aree nere saranno interamente interessate. Le aree più chiare/scure sono interessate in modo dinamico.",
-    "the directory in which your mask video is located.": "la cartella in cui si trova il video della maschera.",
-    "selects whether to ignore prompt schedule or _x_frames.": "seleziona se ignorare la pianificazione dei prompt o _x_frames.",
-    "the number of frames to transition thru between prompts (when interpolate_key_frames = true, then the numbers in front of the animation prompts will dynamically guide the images based on their value. If set to false, will ignore the prompt numbers and force interpole_x_frames value regardless of prompt number)": "il numero di fotogrammi per la transizione tra i prompt (quando interpola fotogrammi chiave = true, i numeri davanti ai prompt dell'animazione guideranno dinamicamente le immagini in base al loro valore. Se impostato su false, ignorerà i numeri dei prompt e forzerà il valore di interpola x fotogrammi indipendentemente da numero di richiesta)",
-    "instructs the run to start from a specified point": "avvia l'esecuzione da un punto specificato",
-    "the required timestamp to reference when resuming. Currently only available in 2D & 3D mode, the timestamp is saved as the settings .txt file name as well as images produced during your previous run. The format follows: yyyymmddhhmmss - a timestamp of when the run was started to diffuse.": "l'orario a cui fare riferimento quando si riprende. Attualmente disponibile solo in modalità 2D e 3D, l'orario viene salvato come nome del file .txt delle impostazioni e come immagini prodotte durante la corsa precedente. Il formato segue: yyyymmddhhmmss - l'orario di quando è stata avviata la diffusione dell'esecuzione.",
-    "when checked, do not output a video": "se selezionato, non produce un video",
-    "The frames per second that the video will run at": "I fotogrammi al secondo a cui verrà eseguito il video",
-    "select the type of video file to output": "selezionare il tipo di file video da produrre",
-    "create an animated GIF": "crea una GIF animata",
-    "the path to where ffmpeg is located": "il percorso in cui si trova ffmpeg",
-    "when this box is checked, and FFMPEG mp4 is selected as the output format, an audio file will be multiplexed with the video.": "quando questa casella è selezionata e FFMPEG mp4 è selezionato come formato di output, un file audio verrà multiplexato con il video.",
-    "the path to an audio file to accompany the video": "tIl percorso di un file audio per accompagnare il video",
-    "when this is unchecked, the video will automatically be created in the same output folder as the images. Check this box to specify different settings for the creation of the video, specified by the following options": "quando questo è deselezionato, il video verrà creato automaticamente nella stessa cartella di output delle immagini. Selezionare questa casella per specificare impostazioni diverse per la creazione del video, specificate dalle seguenti opzioni",
-    "render each step of diffusion as a separate frame": "renderizzare ogni fase di diffusione come fotogramma separato",
-    "the maximum number of frames to include in the video, when use_manual_settings is checked": "il numero massimo di fotogrammi da includere nel video, quando è selezionato 'Usa impostazioni manuali'",
-    "the location of images to create the video from, when use_manual_settings is checked": "la posizione delle immagini da cui creare il video, quando è selezionato 'Usa impostazioni manuali'",
-    "the output location of the mp4 file, when use_manual_settings is checked": "il percorso di output del file mp4, quando è selezionato 'Usa impostazioni manuali'",
-    "Autocomplete options": "Opzioni di autocompletamento",
-    "Enable Autocomplete": "Abilita autocompletamento",
-    "Append commas": "Aggiungi virgole",
-    "Error": "Errore",
-    "F": "F",
-    "Time taken:": "Tempo impiegato:",
-    "latest": "aggiornato",
-    "behind": "da aggiornare",
-    "AlphaCanvas": "AlphaCanvas",
-    "Hue:0": "Hue:0",
-    "S:0": "S:0",
-    "L:0": "L:0",
-    "Load Canvas": "Carica Canvas",
-    "Save Canvas": "Salva Canvas",
-    "Close": "Chiudi",
-    "Grab Results": "Ottieni risultati",
-    "Apply Patch": "Applica Patch",
-    "Description": "Descrizione",
-    "Action": "Azione",
-    "Aesthetic Gradients": "Gradienti estetici",
-    "Create an embedding from one or few pictures and use it to apply their style to generated images.": "Crea un incorporamento da una o poche immagini e lo usa per applicare il loro stile alle immagini generate.",
-    "Sample extension. Allows you to use __name__ syntax in your prompt to get a random line from a file named name.txt in the wildcards directory. Also see Dynamic Prompts for similar functionality.": "Estensione del campione. Consente di utilizzare la sintassi __name__ nel prompt per ottenere una riga casuale da un file denominato name.txt nella cartella dei termini jolly. Vedi anche 'Prompt dinamici' per funzionalità simili.",
-    "Dynamic Prompts": "Prompt dinamici",
-    "Implements an expressive template language for random or combinatorial prompt generation along with features to support deep wildcard directory structures.": "Implementa un  modello di linguaggio espressivo per la generazione di prompt casuale o combinatoria insieme a funzionalità per supportare cartelle strutturate contenenti termini jolly.",
-    "Image browser": "Galleria immagini",
-    "Provides an interface to browse created images in the web browser.": "Fornisce un'interfaccia  nel browser web per sfogliare le immagini create.",
-    "Randomly display the pictures of the artist's or artistic genres typical style, more pictures of this artist or genre is displayed after selecting. So you don't have to worry about how hard it is to choose the right style of art when you create.": "Visualizza in modo casuale le immagini dello stile tipico dell'artista o dei generi artistici, dopo la selezione vengono visualizzate più immagini di questo artista o genere. Così non dovete preoccuparvi della difficoltà di scegliere lo stile artistico giusto quando create.",
-    "The official port of Deforum, an extensive script for 2D and 3D animations, supporting keyframable sequences, dynamic math parameters (even inside the prompts), dynamic masking, depth estimation and warping.": "Il porting ufficiale di Deforum, uno script completo per animazioni 2D e 3D, che supporta sequenze di fotogrammi chiave, parametri matematici dinamici (anche all'interno dei prompt), mascheramento dinamico, stima della profondità e warping.",
-    "Artists to study": "Artisti per studiare",
-    "Shows a gallery of generated pictures by artists separated into categories.": "Mostra una galleria di immagini generate in base agli artisti e suddivise in categorie.",
-    "Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Calcola il punteggio estetico per le immagini generate utilizzando il predittore del punteggio estetico CLIP+MLP basato su Chad Scorer",
-    "Lets you edit captions in training datasets.": "Consente di modificare le didascalie nei set di dati di addestramento.",
-    "Krita Plugin.": "Krita Plugin.",
-    "Adds a tab to the webui that allows the user to automatically extract keyframes from video, and manually extract 512x512 crops of those frames for use in model training.": "Aggiunge una scheda all'interfaccia Web che consente all'utente di estrarre automaticamente i fotogrammi chiave dal video ed estrarre manualmente ritagli 512x512 di quei fotogrammi da utilizzare nell'addestramento del modello.",
-    "Allows you to include various shortcodes in your prompts. You can pull text from files, set up your own variables, process text through conditional functions, and so much more - it's like wildcards on steroids..": "Consente di includere vari codici brevi nei prompt. Puoi estrarre il testo dai file, impostare le tue variabili, elaborare il testo tramite funzioni condizionali e molto altro ancora: è come i termini jolly ma potenziato.",
-    "Booru tag autocompletion": "Completamento automatico del tag Booru",
-    "Displays autocompletion hints for tags from image booru boards such as Danbooru. Uses local tag CSV files and includes a config for customization.": "Visualizza suggerimenti per il completamento automatico dei tag dalle schede di immagini Booru, come Danbooru. Utilizza file CSV di tag locali e include una configurazione per la personalizzazione.",
-    "Add a button to convert the prompts used in NovelAI for use in the WebUI. In addition, add a button that allows you to recall a previously used prompt.": "Aggiunge un pulsante per convertire i prompt utilizzati in NovelAI per l'uso in Stable Diffusion Web UI. Inoltre, aggiunge un pulsante che permette di richiamare un prompt utilizzato in precedenza.",
-    "tokenizer": "Tokenizzatore",
-    "Adds a tab that lets you preview how CLIP model would tokenize your text.": "Aggiunge una scheda che consente di visualizzare in anteprima il modo in cui il modello CLIP tokenizzerebbe il vostro testo.",						 				 
-    "parameters": "Parametri",
-    "The directory does not exist": "La cartella non esiste"			  
-}
\ No newline at end of file
diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json
deleted file mode 100644
index a6cc2477..00000000
--- a/localizations/ja_JP.json
+++ /dev/null
@@ -1,482 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "読み込み中...",
-    "view": "view",
-    "api": "api",
-    "•": "•",
-    "gradioで作ろう": "gradioで作ろう",
-    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
-    "Stop At last layers of CLIP model": "最後から何層目でCLIPを止めるか",
-    "txt2img": "txt2img",
-    "img2img": "img2img",
-    "Extras": "その他",
-    "PNG Info": "PNG内の情報を表示",
-    "Image Browser": "画像閲覧",
-    "Checkpoint Merger": "Checkpointの統合",
-    "Train": "学習",
-    "Create aesthetic embedding": "aesthetic embeddingを作る",
-    "Settings": "設定",
-    "Prompt": "プロンプト",
-    "Negative prompt": "ネガティブ プロンプト",
-    "Run": "実行",
-    "Skip": "スキップ",
-    "Interrupt": "中断",
-    "Generate": "生成！",
-    "Style 1": "スタイル 1",
-    "Style 2": "スタイル 2",
-    "Label": "ラベル",
-    "File": "ファイル",
-    "ここにファイルをドロップ": "ここにファイルをドロップ",
-    "-": "-",
-    "または": "または",
-    "クリックしてアップロード": "クリックしてアップロード",
-    "Image": "画像",
-    "Check progress": "Check progress",
-    "Check progress (first)": "Check progress (first)",
-    "Sampling Steps": "サンプリング回数",
-    "Sampling method": "サンプリングアルゴリズム",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "幅",
-    "Height": "高さ",
-    "Restore faces": "顔修復",
-    "Tiling": "テクスチャ生成モード",
-    "Highres. fix": "高解像度 fix(マウスオーバーで詳細)",
-    "Firstpass width": "Firstpass width",
-    "Firstpass height": "Firstpass height",
-    "Denoising strength": "ノイズ除去強度",
-    "Batch count": "バッチ生成回数",
-    "Batch size": "バッチあたり生成枚数",
-    "CFG Scale": "CFG Scale",
-    "Seed": "シード値",
-    "Extra": "その他",
-    "Variation seed": "Variation シード値",
-    "Variation strength": "Variation 強度",
-    "Resize seed from width": "Resize seed from width",
-    "Resize seed from height": "Resize seed from height",
-    "Open for Clip Aesthetic!": "Open for Clip Aesthetic!",
-    "▼": "▼",
-    "Aesthetic weight": "Aesthetic weight",
-    "Aesthetic steps": "Aesthetic steps",
-    "Aesthetic learning rate": "Aesthetic learning rate",
-    "Slerp interpolation": "Slerp interpolation",
-    "Aesthetic imgs embedding": "Aesthetic imgs embedding",
-    "None": "なし",
-    "Aesthetic text for imgs": "Aesthetic text for imgs",
-    "Slerp angle": "Slerp angle",
-    "Is negative text": "Is negative text",
-    "Script": "スクリプト",
-    "Prompt matrix": "Prompt matrix",
-    "Prompts from file or textbox": "Prompts from file or textbox",
-    "Save steps of the sampling process to files": "Save steps of the sampling process to files",
-    "X/Y plot": "X/Y plot",
-    "Put variable parts at start of prompt": "Put variable parts at start of prompt",
-    "Show Textbox": "Show Textbox",
-    "File with inputs": "File with inputs",
-    "Prompts": "プロンプト",
-    "Save images to path": "Save images to path",
-    "X type": "X軸の種類",
-    "Nothing": "なし",
-    "Var. seed": "Var. seed",
-    "Var. strength": "Var. 強度",
-    "Steps": "ステップ数",
-    "Prompt S/R": "Prompt S/R",
-    "Prompt order": "Prompt order",
-    "Sampler": "サンプラー",
-    "Checkpoint name": "Checkpoint名",
-    "Hypernetwork": "Hypernetwork",
-    "Hypernet str.": "Hypernetの強度",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma min": "Sigma min",
-    "Sigma max": "Sigma max",
-    "Sigma noise": "Sigma noise",
-    "Eta": "Eta",
-    "Clip skip": "Clip skip",
-    "Denoising": "Denoising",
-    "X values": "Xの値",
-    "Y type": "Y軸の種類",
-    "Y values": "Yの値",
-    "Draw legend": "凡例を描画",
-    "Include Separate Images": "Include Separate Images",
-    "Keep -1 for seeds": "シード値を-1で固定",
-    "ここに画像をドロップ": "ここに画像をドロップ",
-    "Save": "保存",
-    "Send to img2img": "img2imgに送る",
-    "Send to inpaint": "描き直しに送る",
-    "Send to extras": "その他タブに送る",
-    "Make Zip when Save?": "保存するときZipも同時に作る",
-    "Textbox": "Textbox",
-    "Interrogate\nCLIP": "Interrogate\nCLIP",
-    "Interrogate\nDeepBooru": "Interrogate\nDeepBooru",
-    "Inpaint": "描き直し(Inpaint)",
-    "Batch img2img": "Batch img2img",
-    "Image for img2img": "Image for img2img",
-    "Image for inpainting with mask": "Image for inpainting with mask",
-    "Mask": "マスク",
-    "Mask blur": "マスクぼかし",
-    "Mask mode": "マスクモード",
-    "Draw mask": "マスクをかける",
-    "Upload mask": "マスクをアップロードする",
-    "Masking mode": "マスキング方法",
-    "Inpaint masked": "マスクされた場所を描き直す",
-    "Inpaint not masked": "マスクされていない場所を描き直す",
-    "Masked content": "マスクされたコンテンツ",
-    "fill": "埋める",
-    "original": "オリジナル",
-    "latent noise": "潜在空間でのノイズ",
-    "latent nothing": "潜在空間での無",
-    "Inpaint at full resolution": "フル解像度で描き直す",
-    "Inpaint at full resolution padding, pixels": "フル解像度で描き直す際のパディング数。px単位。",
-    "Process images in a directory on the same machine where the server is running.": "サーバーが稼働しているマシンと同じフォルダにある画像を処理します",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "\"出力フォルダ\"を空にすると、通常の画像と同様に保存されます。",
-    "Input directory": "入力フォルダ",
-    "Output directory": "出力フォルダ",
-    "Resize mode": "リサイズモード",
-    "Just resize": "リサイズのみ",
-    "Crop and resize": "切り取ってからリサイズ",
-    "Resize and fill": "リサイズして埋める",
-    "img2img alternative test": "img2img alternative test",
-    "Loopback": "ループバック",
-    "Outpainting mk2": "Outpainting mk2",
-    "Poor man's outpainting": "Poor man's outpainting",
-    "SD upscale": "SD アップスケール",
-    "[C] Video to video": "[C] Video to video",
-    "should be 2 or lower.": "2以下にすること",
-    "Override `Sampling method` to Euler?(this method is built for it)": "サンプリングアルゴリズムをEulerに上書きする(そうすることを前提に設計されています)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "プロンプトをオリジナルプロンプトと同じ値に上書きする(ネガティブプロンプトも同様)",
-    "Original prompt": "オリジナルのプロンプト",
-    "Original negative prompt": "オリジナルのネガティブプロンプト",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "サンプリング数をデコードステップ数と同じ値に上書きする",
-    "Decode steps": "デコードステップ数",
-    "Override `Denoising strength` to 1?": "ノイズ除去強度を1に上書きする",
-    "Decode CFG scale": "Decode CFG scale",
-    "Randomness": "ランダム性",
-    "Sigma adjustment for finding noise for image": "Sigma adjustment for finding noise for image",
-    "Loops": "ループ数",
-    "Denoising strength change factor": "Denoising strength change factor",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推奨設定: サンプリング回数: 80-100, サンプリングアルゴリズム: Euler a, ノイズ除去強度: 0.8",
-    "Pixels to expand": "Pixels to expand",
-    "Outpainting direction": "Outpainting direction",
-    "left": "左",
-    "right": "右",
-    "up": "上",
-    "down": "下",
-    "Fall-off exponent (lower=higher detail)": "Fall-off exponent (lower=higher detail)",
-    "Color variation": "Color variation",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "画像を2倍の大きさにアップスケールします。幅と高さのスライダーでタイルの大きさを設定します。",
-    "Tile overlap": "Tile overlap",
-    "Upscaler": "アップスケーラー",
-    "Lanczos": "Lanczos",
-    "LDSR": "LDSR",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "R-ESRGAN General 4xV3": "R-ESRGAN General 4xV3",
-    "R-ESRGAN General WDN 4xV3": "R-ESRGAN General WDN 4xV3",
-    "R-ESRGAN AnimeVideo": "R-ESRGAN AnimeVideo",
-    "R-ESRGAN 4x+": "R-ESRGAN 4x+",
-    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
-    "R-ESRGAN 2x+": "R-ESRGAN 2x+",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR 4x": "SwinIR 4x",
-    "Input file path": "Input file path",
-    "CRF (quality, less is better, x264 param)": "CRF (quality, less is better, x264 param)",
-    "FPS": "FPS",
-    "Seed step size": "Seed step size",
-    "Seed max distance": "Seed max distance",
-    "Start time": "Start time",
-    "End time": "End time",
-    "Single Image": "単一画像",
-    "Batch Process": "バッチ処理",
-    "Batch from Directory": "フォルダからバッチ処理",
-    "Source": "入力",
-    "Show result images": "出力画像を表示",
-    "Scale by": "倍率指定",
-    "Scale to": "解像度指定",
-    "Resize": "倍率",
-    "Crop to fit": "合うように切り抜き",
-    "Upscaler 2 visibility": "Upscaler 2 visibility",
-    "GFPGAN visibility": "GFPGAN visibility",
-    "CodeFormer visibility": "CodeFormer visibility",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormerの重み (注:0で最大、1で最小)",
-    "Open output directory": "出力フォルダを開く",
-    "Send to txt2img": "txt2imgに送る",
-    "extras": "その他タブ",
-    "favorites": "お気に入り",
-    "Load": "読み込み",
-    "Images directory": "フォルダ",
-    "Prev batch": "前の batch",
-    "Next batch": "次の batch",
-    "First Page": "最初のぺージへ",
-    "Prev Page": "前ページへ",
-    "Page Index": "ページ番号",
-    "Next Page": "次ページへ",
-    "End Page": "最後のページへ",
-    "number of images to delete consecutively next": "次の削除で一度に削除する画像数",
-    "Delete": "削除",
-    "Generate Info": "生成情報",
-    "File Name": "ファイル名",
-    "Collect": "保存(お気に入り)",
-    "Refresh page": "ページを更新",
-    "Date to": "Date to",
-    "Number": "Number",
-    "set_index": "set_index",
-    "Checkbox": "Checkbox",
-    "A merger of the two checkpoints will be generated in your": "統合されたチェックポイントはあなたの",
-    "checkpoint": "checkpoint",
-    "directory.": "フォルダに保存されます.",
-    "Primary model (A)": "1つめのmodel (A)",
-    "Secondary model (B)": "2つめのmodel (B)",
-    "Tertiary model (C)": "3つめのmodel (C)",
-    "Custom Name (Optional)": "Custom Name (任意)",
-    "Multiplier (M) - set to 0 to get model A": "Multiplier (M) 0にすると完全にmodel Aとなります (ツールチップ参照)",
-    "Interpolation Method": "混合(Interpolation)方式",
-    "Weighted sum": "加重平均",
-    "Add difference": "差を加える",
-    "Save as float16": "float16で保存",
-    "See": "詳細な説明については",
-    "wiki": "wiki",
-    "for detailed explanation.": "を見てください。",
-    "Create embedding": "Embeddingを作る",
-    "Create hypernetwork": "Hypernetworkを作る",
-    "Preprocess images": "画像の前処理",
-    "Name": "名称",
-    "Initialization text": "Initialization text",
-    "Number of vectors per token": "Number of vectors per token",
-    "Overwrite Old Embedding": "古いEmbeddingを上書き",
-    "Modules": "モジュール",
-    "Enter hypernetwork layer structure": "Hypernetworkのレイヤー構造を入力",
-    "Select activation function of hypernetwork": "Hypernetworkの活性化関数",
-    "linear": "linear",
-    "relu": "relu",
-    "leakyrelu": "leakyrelu",
-    "elu": "elu",
-    "swish": "swish",
-    "Add layer normalization": "Add layer normalization",
-    "Use dropout": "Use dropout",
-    "Overwrite Old Hypernetwork": "古いHypernetworkを上書きする",
-    "Source directory": "入力フォルダ",
-    "Destination directory": "出力フォルダ",
-    "Existing Caption txt Action": "既存のキャプションの取り扱い",
-    "ignore": "無視する",
-    "copy": "コピーする",
-    "prepend": "先頭に加える",
-    "append": "末尾に加える",
-    "Create flipped copies": "反転画像を生成する",
-    "Split oversized images": "大きすぎる画像を分割する",
-    "Use BLIP for caption": "BLIPで説明をつける",
-    "Use deepbooru for caption": "deepbooruで説明をつける",
-    "Split image threshold": "分割する大きさの閾値",
-    "Split image overlap ratio": "Split image overlap ratio",
-    "Preprocess": "前処理開始",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "EmbeddingまたはHypernetworkを学習します。1:1の比率の画像セットを含むフォルダを指定する必要があります。",
-    "[wiki]": "[wiki]",
-    "Embedding": "Embedding",
-    "Embedding Learning rate": "Embeddingの学習率(Learning rate)",
-    "Hypernetwork Learning rate": "Hypernetworkの学習率(Learning rate)",
-    "Dataset directory": "データセットフォルダ",
-    "Log directory": "ログフォルダ",
-    "Prompt template file": "プロンプトのテンプレートファイル",
-    "Max steps": "最大ステップ数",
-    "Save an image to log directory every N steps, 0 to disable": "指定したステップ数ごとに画像を生成し、ログに保存する。0で無効化。",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "指定したステップ数ごとにEmbeddingのコピーをログに保存する。0で無効化。",
-    "Save images with embedding in PNG chunks": "保存する画像にembeddingを埋め込む",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "プレビューの作成にtxt2imgタブから読み込んだパラメータ(プロンプトなど)を使う",
-    "Train Hypernetwork": "Hypernetworkの学習を開始",
-    "Train Embedding": "Embeddingの学習を開始",
-    "Create an aesthetic embedding out of any number of images": "Create an aesthetic embedding out of any number of images",
-    "Create images embedding": "Create images embedding",
-    "Apply settings": "設定を適用",
-    "Saving images/grids": "画像/グリッドの保存",
-    "Always save all generated images": "生成された画像をすべて保存する",
-    "File format for images": "画像ファイルの保存形式",
-    "Images filename pattern": "ファイル名のパターン",
-    "Always save all generated image grids": "グリッド画像を常に保存する",
-    "File format for grids": "グリッド画像の保存形式",
-    "Add extended info (seed, prompt) to filename when saving grid": "保存するグリッド画像のファイル名に追加情報(シード値、プロンプト)を加える",
-    "Do not save grids consisting of one picture": "1画像からなるグリッド画像は保存しない",
-    "Prevent empty spots in grid (when set to autodetect)": "(自動設定のとき)グリッドに空隙が生じるのを防ぐ",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "グリッドの列数; -1で自動設定、0でバッチ生成回数と同じにする",
-    "Save text information about generation parameters as chunks to png files": "生成に関するパラメーターをPNG画像に含める",
-    "Create a text file next to every image with generation parameters.": "保存する画像とともに生成パラメータをテキストファイルで保存する",
-    "Save a copy of image before doing face restoration.": "顔修復を行う前にコピーを保存しておく。",
-    "Quality for saved jpeg images": "JPG保存時の画質",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "PNG画像が4MBを超えるか、どちらか1辺の長さが4000を超えたなら、ダウンスケールしてコピーを別にJPGで保存する",
-    "Use original name for output filename during batch process in extras tab": "その他タブでバッチ処理をする際、元のファイル名を出力ファイル名に使う",
-    "When using 'Save' button, only save a single selected image": "\"保存\"ボタンを使うとき、単一の選択された画像のみを保存する",
-    "Do not add watermark to images": "電子透かしを画像に追加しない",
-    "Paths for saving": "保存する場所",
-    "Output directory for images; if empty, defaults to three directories below": "画像の保存先フォルダ(下項目のデフォルト値になります)",
-    "Output directory for txt2img images": "txt2imgで作った画像の保存先フォルダ",
-    "Output directory for img2img images": "img2imgで作った画像の保存先フォルダ",
-    "Output directory for images from extras tab": "その他タブで作った画像の保存先フォルダ",
-    "Output directory for grids; if empty, defaults to two directories below": "画像の保存先フォルダ(下項目のデフォルト値になります)",
-    "Output directory for txt2img grids": "txt2imgで作ったグリッドの保存先フォルダ",
-    "Output directory for img2img grids": "img2imgで作ったグリッドの保存先フォルダ",
-    "Directory for saving images using the Save button": "保存ボタンを押したときの画像の保存先フォルダ",
-    "Saving to a directory": "フォルダについて",
-    "Save images to a subdirectory": "画像をサブフォルダに保存する",
-    "Save grids to a subdirectory": "グリッドをサブフォルダに保存する",
-    "When using \"Save\" button, save images to a subdirectory": "保存ボタンを押した時、画像をサブフォルダに保存する",
-    "Directory name pattern": "フォルダ名のパターン",
-    "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern",
-    "Upscaling": "アップスケール",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGANのタイルサイズ。0とするとタイルしない。",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGANのタイルの重複部分のピクセル数。少なくするとつなぎ目が見えやすくなる。",
-    "Tile size for all SwinIR.": "SwinIRのタイルサイズ",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIRのタイルの重複部分のピクセル数。少なくするとつなぎ目が見えやすくなる。",
-    "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster",
-    "Upscaler for img2img": "img2imgで使うアップスケーラー",
-    "Upscale latent space image when doing hires. fix": "高解像度 fix時に潜在空間(latent space)の画像をアップスケールする",
-    "Face restoration": "顔修復",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormerの重みパラメーター;0が最大で1が最小",
-    "Move face restoration model from VRAM into RAM after processing": "処理終了後、顔修復モデルをVRAMからRAMへと移動する",
-    "System": "システム設定",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成中のVRAM使用率の取得間隔。0にすると取得しない。",
-    "Always print all generation info to standard output": "常にすべての生成に関する情報を標準出力(stdout)に出力する",
-    "Add a second progress bar to the console that shows progress for an entire job.": "ジョブ全体の進捗をコンソールに表示する2つ目のプログレスバーを追加する",
-    "Training": "学習",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "hypernetworkの学習をするとき、VAEとCLIPをRAMへ退避する。VRAMが節約できます。",
-    "Filename word regex": "ファイル名の正規表現(学習用)",
-    "Filename join string": "ファイル名の結合子",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "RAMにキャッシュするCheckpoint数",
-    "Hypernetwork strength": "Hypernetwork strength",
-    "Apply color correction to img2img results to match original colors.": "元画像に合わせてimg2imgの結果を色補正する",
-    "Save a copy of image before applying color correction to img2img results": "色補正をする前の画像も保存する",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "img2imgでスライダーで指定されたステップ数を正確に実行する（通常は、ノイズ除去を少なくするためにより少ないステップ数で実行します）。",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "より良い結果を得るために、Kサンプラーで量子化を有効にします。これにより既存のシードが変更される可能性があります。適用するには再起動が必要です。",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "強調: (text)とするとモデルはtextをより強く扱い、[text]とするとモデルはtextをより弱く扱います。",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "古い強調の実装を使う。古い生成物を再現するのに使えます。",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "K-diffusionサンプラーによるバッチ生成時に、単一画像生成時と同じ画像を生成する",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "75トークン以上を使用する場合、nトークン内の最後のカンマからパディングして一貫性を高める",
-    "Filter NSFW content": "NSFW(≒R-18)なコンテンツを検閲する",
-    "Interrogate Options": "Interrogate 設定",
-    "Interrogate: keep models in VRAM": "Interrogate: モデルをVRAMに保持する",
-    "Interrogate: use artists from artists.csv": "Interrogate: artists.csvにある芸術家などの名称を利用する",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).",
-    "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP",
-    "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)",
-    "Interrogate: maximum description length": "Interrogate: maximum description length",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)",
-    "Interrogate: deepbooru score threshold": "Interrogate: deepbooruで拾う単語のスコア閾値",
-    "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooruで単語をアルファベット順に並べる",
-    "use spaces for tags in deepbooru": "deepbooruのタグでスペースを使う",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "deepbooruで括弧をエスケープする(\\) (強調を示す()ではなく、文字通りの()であることをモデルに示すため)",
-    "User interface": "UI設定",
-    "Show progressbar": "プログレスバーを表示",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "指定したステップ数ごとに画像の生成過程を表示する。0で無効化。",
-    "Show previews of all images generated in a batch as a grid": "Show previews of all images generated in a batch as a grid",
-    "Show grid in results for web": "WebUI上でグリッド表示",
-    "Do not show any images in results for web": "WebUI上で一切画像を表示しない",
-    "Add model hash to generation information": "モデルのハッシュ値を生成情報に追加",
-    "Add model name to generation information": "モデルの名称を生成情報に追加",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "テキストからUIに生成パラメータを読み込む場合(PNG情報または貼り付けられたテキストから)、選択されたモデル/チェックポイントは変更しない。",
-    "Font for image grids that have text": "画像グリッド内のテキストフォント",
-    "Enable full page image viewer": "フルページの画像ビューワーを有効化",
-    "Show images zoomed in by default in full page image viewer": "フルページ画像ビューアでデフォルトで画像を拡大して表示する",
-    "Show generation progress in window title.": "ウィンドウのタイトルで生成の進捗を表示",
-    "Quicksettings list": "クイック設定",
-    "Localization (requires restart)": "言語 (プログラムの再起動が必要)",
-    "ja_JP": "ja_JP",
-    "ru_RU": "ru_RU",
-    "Sampler parameters": "サンプラー parameters",
-    "Hide samplers in user interface (requires restart)": "使わないサンプリングアルゴリズムを隠す (再起動が必要)",
-    "eta (noise multiplier) for DDIM": "DDIMで用いるeta (noise multiplier)",
-    "eta (noise multiplier) for ancestral samplers": "ancestral サンプラーで用いるeta (noise multiplier)",
-    "img2img DDIM discretize": "img2img DDIM discretize",
-    "uniform": "uniform",
-    "quad": "quad",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "sigma tmin",
-    "sigma noise": "sigma noise",
-    "Eta noise seed delta": "Eta noise seed delta",
-    "Images Browser": "画像閲覧",
-    "Preload images at startup": "起動時に画像を読み込んでおく",
-    "Number of pictures displayed on each page": "各ページに表示される画像の枚数",
-    "Minimum number of pages per load": "Minimum number of pages per load",
-    "Number of grids in each row": "Number of grids in each row",
-    "Request browser notifications": "ブラウザ通知の許可を要求する",
-    "Download localization template": "ローカライゼーション用のテンプレートをダウンロードする",
-    "Reload custom script bodies (No ui updates, No restart)": "カスタムスクリプトを再読み込み (UIは変更されず、再起動もしません。)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradioを再起動してコンポーネントをリフレッシュする (Custom Scripts, ui.py, js, cssのみ影響を受ける)",
-    "Audio": "音声",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "プロンプト (Ctrl+Enter か Alt+Enter を押して生成)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "ネガティブ プロンプト (Ctrl+Enter か Alt+Enter を押して生成)",
-    "Add a random artist to the prompt.": "芸術家などの名称をプロンプトに追加",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "プロンプトから生成パラメータを読み込むか、プロンプトが空の場合は最後の生成パラメータをユーザーインターフェースに読み込む。",
-    "Save style": "スタイルを保存する",
-    "Apply selected styles to current prompt": "現在のプロンプトに選択したスタイルを適用する",
-    "Stop processing current image and continue processing.": "現在の処理を中断し、その後の処理は続ける",
-    "Stop processing images and return any results accumulated so far.": "処理を中断し、それまでに出来た結果を表示する",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "適用するスタイル。スタイルは、ポジティブプロンプトとネガティブプロンプトの両方のコンポーネントを持ち、両方に適用される。",
-    "Do not do anything special": "特別なことをなにもしない",
-    "Which algorithm to use to produce the image": "どのアルゴリズムを使って生成するか",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常に独創的で、ステップ数によって全く異なる画像が得られる、ステップ数を30～40より高く設定しても効果がない。",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 描き直しには最適",
-    "Produce an image that can be tiled.": "タイルとして扱える画像を生成する",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "2ステップで、まず部分的に小さい解像度で画像を作成し、その後アップスケールすることで、構図を変えずにディテールが改善されます。",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "アルゴリズムが画像の内容をどの程度参考にするかを決定します。0 にすると何も変わりませんし、 1 にすると全く無関係な画像になります。1.0未満の値ではスライダーで指定したサンプリングステップ数よりも少ないステップ数で処理が行われます。",
-    "How many batches of images to create": "バッチ処理を何回行うか",
-    "How many image to create in a single batch": "1回のバッチ処理で何枚の画像を生成するか",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 生成する画像がどの程度プロンプトに沿ったものになるか。 - 低い値の方がよりクリエイティブな結果を生み出します。",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "乱数発生器の出力を決定する値。同じパラメータとシードで画像を作成すれば、同じ結果が得られます。",
-    "Set seed to -1, which will cause a new random number to be used every time": "シード値を-1に設定。つまり、毎回ランダムに生成します。",
-    "Reuse seed from last generation, mostly useful if it was randomed": "前回生成時のシード値を読み出す。(ランダム生成時に便利)",
-    "Seed of a different picture to be mixed into the generation.": "生成時に混合されることになる画像のシード値",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Variationの強度。0の場合、何の効果もありません。1では、バリエーションシードで完全な画像を得ることができます（Ancestalなアルゴリズム以外では、何か(?)を得るだけです）。",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "同じシードで指定された解像度の似た画像を生成することを試みる。",
-    "This text is used to rotate the feature space of the imgs embs": "This text is used to rotate the feature space of the imgs embs",
-    "Separate values for X axis using commas.": "X軸に用いる値をカンマ(,)で区切って入力してください。",
-    "Separate values for Y axis using commas.": "Y軸に用いる値をカンマ(,)で区切って入力してください。",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "画像はフォルダ(デフォルト:log/images)に、生成パラメータはcsvファイルに書き出します。",
-    "Open images output directory": "画像の出力フォルダを開く",
-    "How much to blur the mask before processing, in pixels.": "処理前にどれだけマスクをぼかすか。px単位。",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Stable Diffusionにわたす前にマスクされたエリアに何を書き込むか",
-    "fill it with colors of the image": "元画像の色で埋める",
-    "keep whatever was there originally": "もともとあったものをそのままにする",
-    "fill it with latent space noise": "潜在空間(latent space)におけるノイズで埋める",
-    "fill it with latent space zeroes": "潜在空間(latent space)における0で埋める",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "マスクされた領域をターゲット解像度にアップスケールし、インペイントを行い、元の解像度にダウンスケールして元の画像に貼り付けます。",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "画像をターゲット解像度にリサイズします。高さと幅が一致しない場合、アスペクト比が正しくなくなります。",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "対象の解像度に画像をフィットさせます。はみ出た部分は切り取られます。",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "画像をリサイズして、ターゲット解像度の中に収まるようにします。空白部分は画像の色で埋めます。",
-    "How many times to repeat processing an image and using it as input for the next iteration": "何回画像処理を繰り返し、次の反復処理の入力として使用するか",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "ループバックモードにおいて、各ループでのノイズ除去の強度はこの値によって乗算されます。1より小さければ変化が小さくなっていって、生成される画像は1つの画像に収束します。1より大きいとどんどん変化が大きくなるので、生成される画像はよりカオスになります。",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SDアップスケールで、どれだけタイル間の重なりを確保するか(px単位)。タイルの一部を重複させることで、1枚の画像にした時明らかな継ぎ目がなくなります。",
-    "A directory on the same machine where the server is running.": "サーバーが稼働しているのと同じマシンのあるフォルダ",
-    "Leave blank to save images to the default path.": "空欄でデフォルトの場所へ画像を保存",
-    "Input images directory": "Input images directory",
-    "Result = A * (1 - M) + B * M": "出力されるモデル = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "出力されるモデル = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "最初と最後の数字は1でなければなりません。 例:'1, 2, 1'",
-    "Path to directory with input images": "入力ファイルのあるフォルダの場所",
-    "Path to directory where to write outputs": "出力を書き込むフォルダの場所",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "以下のタグを用いてファイル名パターンを決められます: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 空白でデフォルト設定。",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "このオプションを有効にすると、作成された画像にウォーターマークが追加されなくなります。警告:ウォーターマークを追加しない場合、非倫理的な行動とみなされる場合があります。",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "以下のタグを用いてサブフォルダのフォルダ名パターンを決められます: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; 空白でデフォルト設定",
-    "Restore low quality faces using GFPGAN neural network": "GFPGANを用いて低クオリティーな顔画像を修復",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "この正規表現を使ってファイル名から単語を抽出し、以下のオプションで結合して学習用のラベルテキストにします。ファイル名のテキストをそのまま使用する場合は、空白にしてください。",
-    "This string will be used to join split words into a single line if the option above is enabled.": "この文字列は、上記のオプションが有効な場合に、分割された単語を1行に結合するために使用されます。",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "上部のクイックアクセスバーに置く設定の設定名をカンマで区切って入力。設定名については modules/shared.py を参照してください。適用するには再起動が必要です。",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "この値が0以外の場合、シードに追加され、Etaでサンプラーを使用する際のノイズ用の乱数生成器を初期化するのに使用されます。これを利用して、さらにバリエーション豊かな画像を作成したり、他のソフトの画像に合わせたりすることができます。",
-    "NAIConvert": "NAIから変換",
-    "History": "履歴",
-    "Enable Autocomplete": "自動補完を有効化"
-}
\ No newline at end of file
diff --git a/localizations/ko_KR.json b/localizations/ko_KR.json
deleted file mode 100644
index cf302aaf..00000000
--- a/localizations/ko_KR.json
+++ /dev/null
@@ -1,592 +0,0 @@
-{
-  "×": "×",
-  "•": "•",
-  "⊞": "⊞",
-  "❮": "❮",
-  "❯": "❯",
-  "⤡": "⤡",
-  " images during ": "개의 이미지를 불러왔고, 생성 기간은 ",
-  " images in this directory. Loaded ": "개의 이미지가 이 경로에 존재합니다. ",
-  " pages": "페이지로 나뉘어 표시합니다.",
-  ", divided into ": "입니다. ",
-  ". Use Installed tab to restart.": "에 성공적으로 설치하였습니다. 설치된 확장기능 탭에서 UI를 재시작해주세요.",
-  "1st and last digit must be 1. ex:'1, 2, 1'": "1st and last digit must be 1. ex:'1, 2, 1'",
-  "[wiki]": " [위키] 참조",
-  "A directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리를 선택해 주세요.",
-  "A merger of the two checkpoints will be generated in your": "체크포인트들이 병합된 결과물이 당신의",
-  "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
-  "Action": "작업",
-  "Add a button to convert the prompts used in NovelAI for use in the WebUI. In addition, add a button that allows you to recall a previously used prompt.": "NovelAI에서 사용되는 프롬프트를 WebUI에서 사용할 수 있게 변환하는 버튼을 추가합니다. 덤으로 이전에 사용한 프롬프트를 불러오는 버튼도 추가됩니다.",
-  "Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
-  "Add a second progress bar to the console that shows progress for an entire job.": "콘솔에 전체 작업의 진행도를 보여주는 2번째 프로그레스 바 추가하기",
-  "Add difference": "차이점 추가",
-  "Add extended info (seed, prompt) to filename when saving grid": "그리드 저장 시 파일명에 추가 정보(시드, 프롬프트) 기입",
-  "Add layer normalization": "레이어 정규화(normalization) 추가",
-  "Add model hash to generation information": "생성 정보에 모델 해시 추가",
-  "Add model name to generation information": "생성 정보에 모델 이름 추가",
-  "Add number to filename when saving": "이미지를 저장할 때 파일명에 숫자 추가하기",
-  "Adds a tab that lets you preview how CLIP model would tokenize your text.": "CLIP 모델이 텍스트를 어떻게 토큰화할지 미리 보여주는 탭을 추가합니다.",
-  "Adds a tab to the webui that allows the user to automatically extract keyframes from video, and manually extract 512x512 crops of those frames for use in model training.": "WebUI에 비디오로부터 자동으로 키프레임을 추출하고, 그 키프레임으로부터 모델 훈련에 사용될 512x512 이미지를 잘라낼 수 있는 탭을 추가합니다.",
-  "Aesthetic Gradients": "스타일 그라디언트",
-  "Aesthetic Image Scorer": "스타일 이미지 스코어러",
-  "Aesthetic imgs embedding": "스타일 이미지 임베딩",
-  "Aesthetic learning rate": "스타일 학습 수",
-  "Aesthetic steps": "스타일 스텝 수",
-  "Aesthetic text for imgs": "스타일 텍스트",
-  "Aesthetic weight": "스타일 가중치",
-  "Allowed categories for random artists selection when using the Roll button": "랜덤 버튼을 눌러 무작위 작가를 선택할 때 허용된 카테고리",
-  "Allows you to include various shortcodes in your prompts. You can pull text from files, set up your own variables, process text through conditional functions, and so much more - it's like wildcards on steroids.": "프롬프트에 다양한 숏코드를 추가할 수 있게 해줍니다. 파일로부터 텍스트 추출, 변수 설정, 조건 함수로 텍스트 처리 등등 - 스테로이드를 맞은 와일드카드라 할 수 있죠.",
-  "Always print all generation info to standard output": "기본 아웃풋에 모든 생성 정보 항상 출력하기",
-  "Always save all generated image grids": "생성된 이미지 그리드 항상 저장하기",
-  "Always save all generated images": "생성된 이미지 항상 저장하기",
-  "api": "",
-  "append": "뒤에 삽입",
-  "Append commas": "쉼표 삽입",
-  "Apply and restart UI": "적용 후 UI 재시작",
-  "Apply color correction to img2img results to match original colors.": "이미지→이미지 결과물이 기존 색상과 일치하도록 색상 보정 적용하기",
-  "Apply selected styles to current prompt": "현재 프롬프트에 선택된 스타일 적용",
-  "Apply settings": "설정 적용하기",
-  "Artists to study": "연구할만한 작가들",
-  "auto": "자동",
-  "Auto focal point crop": "초점 기준 크롭(자동 감지)",
-  "Autocomplete options": "자동완성 설정",
-  "Available": "지원되는 확장기능 목록",
-  "Batch count": "배치 수",
-  "Batch from Directory": "저장 경로로부터 여러장 처리",
-  "Batch img2img": "이미지→이미지 배치",
-  "Batch Process": "이미지 여러장 처리",
-  "Batch size": "배치 크기",
-  "behind": "최신 아님",
-  "Booru tag autocompletion": "Booru 태그 자동완성",
-  "BSRGAN 4x": "BSRGAN 4x",
-  "built with gradio": "gradio로 제작되었습니다",
-  "Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Chad 스코어러를 기반으로 한 CLIP+MLP 스타일 점수 예측기를 이용해 생성된 이미지의 스타일 점수를 계산합니다.",
-  "Cancel generate forever": "반복 생성 취소",
-  "cfg cnt": "CFG 변화 횟수",
-  "cfg count": "CFG 변화 횟수",
-  "CFG Scale": "CFG 스케일",
-  "cfg1 min/max": "CFG1 최소/최대",
-  "cfg2 min/max": "CFG2 최소/최대",
-  "Check for updates": "업데이트 확인",
-  "Check progress": "진행도 체크",
-  "Check progress (first)": "진행도 체크 (처음)",
-  "checkpoint": " 체크포인트 ",
-  "Checkpoint Merger": "체크포인트 병합",
-  "Checkpoint name": "체크포인트 이름",
-  "Checkpoints to cache in RAM": "RAM에 캐싱할 체크포인트 수",
-  "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 이미지가 주어진 프롬프트를 얼마나 따를지를 정해주는 수치 - 낮은 값일수록 더 창의적인 결과물이 나옴",
-  "Click to Upload": "클릭해서 업로드하기",
-  "Clip skip": "클립 건너뛰기",
-  "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP : 텍스트 파일 최대 라인 수 (0 = 제한 없음)",
-  "CodeFormer visibility": "CodeFormer 가시성",
-  "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 가중치 (0 = 최대 효과, 1 = 최소 효과)",
-  "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 가중치 설정값 (0 = 최대 효과, 1 = 최소 효과)",
-  "Collect": "즐겨찾기",
-  "Color variation": "색깔 다양성",
-  "Combinations": "조합",
-  "Combinatorial batches": "조합 배치 수",
-  "Combinatorial generation": "조합 생성",
-  "copy": "복사",
-  "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "서로 다른 설정값으로 생성된 이미지의 그리드를 만듭니다. 아래의 설정으로 가로/세로에 어떤 설정값을 적용할지 선택하세요.",
-  "Create a text file next to every image with generation parameters.": "생성된 이미지마다 생성 설정값을 담은 텍스트 파일 생성하기",
-  "Create aesthetic images embedding": "스타일 이미지 임베딩 생성하기",
-  "Create an embedding from one or few pictures and use it to apply their style to generated images.": "하나 혹은 그 이상의 이미지들로부터 임베딩을 생성해, 그 이미지들의 스타일을 다른 이미지 생성 시 적용할 수 있게 해줍니다.",
-  "Create debug image": "디버그 이미지 생성",
-  "Create embedding": "임베딩 생성",
-  "Create flipped copies": "좌우로 뒤집은 복사본 생성",
-  "Create hypernetwork": "하이퍼네트워크 생성",
-  "Create images embedding": "이미지 임베딩 생성하기",
-  "Crop and resize": "잘라낸 후 리사이징",
-  "Crop to fit": "잘라내서 맞추기",
-  "custom fold": "커스텀 경로",
-  "Custom Name (Optional)": "병합 모델 이름 (선택사항)",
-  "Dataset directory": "데이터셋 경로",
-  "Dataset Tag Editor": "데이터셋 태그 편집기",
-  "date": "생성 일자",
-  "DDIM": "DDIM",
-  "Decode CFG scale": "디코딩 CFG 스케일",
-  "Decode steps": "디코딩 스텝 수",
-  "Delete": "삭제",
-  "delete next": "선택한 이미지부터 시작해서 삭제할 이미지 갯수",
-  "Denoising": "디노이징",
-  "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 인페이팅에 뛰어남",
-  "Denoising strength": "디노이즈 강도",
-  "Denoising strength change factor": "디노이즈 강도 변경 배수",
-  "Description": "설명",
-  "Destination directory": "결과물 저장 경로",
-  "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "알고리즘이 얼마나 원본 이미지를 반영할지를 결정하는 수치입니다. 0일 경우 아무것도 바뀌지 않고, 1일 경우 원본 이미지와 전혀 관련없는 결과물을 얻게 됩니다. 1.0 아래의 값일 경우, 설정된 샘플링 스텝 수보다 적은 스텝 수를 거치게 됩니다.",
-  "Directory for saving images using the Save button": "저장 버튼을 이용해 저장하는 이미지들의 저장 경로",
-  "Directory name pattern": "디렉토리명 패턴",
-  "directory.": "저장 경로에 저장됩니다.",
-  "Displays autocompletion hints for tags from image booru boards such as Danbooru. Uses local tag CSV files and includes a config for customization.": "Danbooru 같은 이미지 booru 보드의 태그에 대한 자동완성 힌트를 보여줍니다. 로컬 환경에 저장된 CSV 파일을 사용하고 조정 가능한 설정 파일이 포함되어 있습니다.",
-  "Do not add watermark to images": "이미지에 워터마크 추가하지 않기",
-  "Do not do anything special": "아무것도 하지 않기",
-  "Do not save grids consisting of one picture": "이미지가 1개뿐인 그리드는 저장하지 않기",
-  "Do not show any images in results for web": "웹에서 결과창에 아무 이미지도 보여주지 않기",
-  "down": "아래쪽",
-  "Download": "다운로드",
-  "Download localization template": "현지화 템플릿 다운로드",
-  "DPM adaptive": "DPM adaptive",
-  "DPM fast": "DPM fast",
-  "DPM2": "DPM2",
-  "DPM2 a": "DPM2 a",
-  "DPM2 a Karras": "DPM2 a Karras",
-  "DPM2 Karras": "DPM2 Karras",
-  "Draw legend": "범례 그리기",
-  "Draw mask": "마스크 직접 그리기",
-  "Drop File Here": "파일을 끌어 놓으세요",
-  "Drop Image Here": "이미지를 끌어 놓으세요",
-  "Dropdown": "드롭다운",
-  "Dynamic Prompts": "다이나믹 프롬프트",
-  "Embedding": "임베딩",
-  "Embedding Learning rate": "임베딩 학습률",
-  "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "강조 : (텍스트)를 이용해 모델의 텍스트에 대한 가중치를 더 강하게 주고 [텍스트]를 이용해 더 약하게 줍니다.",
-  "Enable Autocomplete": "태그 자동완성 사용",
-  "Enable full page image viewer": "전체 페이지 이미지 뷰어 활성화",
-  "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "더 예리하고 깔끔한 결과물을 위해 K 샘플러들에 양자화를 적용합니다. 존재하는 시드가 변경될 수 있습니다. 재시작이 필요합니다.",
-  "End Page": "마지막 페이지",
-  "Enter hypernetwork layer structure": "하이퍼네트워크 레이어 구조 입력",
-  "Error": "오류",
-  "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "deepbooru에서 괄호를 역슬래시(\\)로 이스케이프 처리하기(가중치 강조가 아니라 실제 괄호로 사용되게 하기 위해)",
-  "ESRGAN_4x": "ESRGAN_4x",
-  "Eta": "Eta",
-  "eta (noise multiplier) for ancestral samplers": "ancestral 샘플러를 위한 eta(노이즈 배수)값",
-  "eta (noise multiplier) for DDIM": "DDIM을 위한 eta(노이즈 배수)값",
-  "Eta noise seed delta": "Eta 노이즈 시드 변화",
-  "Euler": "Euler",
-  "Euler a": "Euler a",
-  "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 매우 창의적, 스텝 수에 따라 완전히 다른 결과물이 나올 수 있음. 30~40보다 높은 스텝 수는 효과가 미미함",
-  "Existing Caption txt Action": "이미 존재하는 캡션 텍스트 처리",
-  "Extension": "확장기능",
-  "Extension index URL": "확장기능 목록 URL",
-  "Extensions": "확장기능",
-  "Extra": "고급",
-  "Extras": "부가기능",
-  "extras": "부가기능",
-  "extras history": "부가기능 기록",
-  "Face restoration": "얼굴 보정",
-  "Face restoration model": "얼굴 보정 모델",
-  "Fall-off exponent (lower=higher detail)": "감쇠 지수 (낮을수록 디테일이 올라감)",
-  "Favorites": "즐겨찾기",
-  "File": "파일",
-  "File format for grids": "그리드 이미지 파일 형식",
-  "File format for images": "이미지 파일 형식",
-  "File Name": "파일 이름",
-  "File with inputs": "설정값 파일",
-  "Filename join string": "파일명 병합 문자열",
-  "Filename word regex": "파일명 정규표현식",
-  "fill": "채우기",
-  "fill it with colors of the image": "이미지의 색상으로 채우기",
-  "fill it with latent space noise": "잠재 공간 노이즈로 채우기",
-  "fill it with latent space zeroes": "잠재 공간의 0값으로 채우기",
-  "Filter NSFW content": "성인 컨텐츠 필터링하기",
-  "First Page": "처음 페이지",
-  "Firstpass height": "초기 세로길이",
-  "Firstpass width": "초기 가로길이",
-  "Fixed seed": "시드 고정",
-  "Focal point edges weight": "경계면 가중치",
-  "Focal point entropy weight": "엔트로피 가중치",
-  "Focal point face weight": "얼굴 가중치",
-  "Font for image grids that have text": "텍스트가 존재하는 그리드 이미지의 폰트",
-  "for detailed explanation.": "를 참조하십시오.",
-  "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD 업스케일링에서 타일 간 몇 픽셀을 겹치게 할지 결정하는 설정값입니다. 타일들이 다시 한 이미지로 합쳐질 때, 눈에 띄는 이음매가 없도록 서로 겹치게 됩니다.",
-  "Generate": "생성",
-  "Generate forever": "반복 생성",
-  "Generate Info": "생성 정보",
-  "GFPGAN visibility": "GFPGAN 가시성",
-  "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "그리드 세로줄 수 : -1로 설정 시 자동 감지/0으로 설정 시 배치 크기와 동일",
-  "Height": "세로",
-  "Heun": "Heun",
-  "hide": "api 숨기기",
-  "Hide samplers in user interface (requires restart)": "사용자 인터페이스에서 숨길 샘플러 선택(재시작 필요)",
-  "Highres. fix": "고해상도 보정",
-  "History": "기록",
-  "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "훈련이 얼마나 빨리 이루어질지 정하는 값입니다. 값이 낮을수록 훈련 시간이 길어지고, 높은 값일수록 정확한 결과를 내는 데 실패하고 임베딩을 망가뜨릴 수 있습니다(임베딩이 망가진 경우에는 훈련 정보 텍스트박스에 손실(Loss) : nan 이라고 출력되게 됩니다. 이 경우에는 망가지지 않은 이전 백업본을 불러와야 합니다).\n\n학습률은 하나의 값으로 설정할 수도 있고, 다음 문법을 사용해 여러 값을 사용할 수도 있습니다 :\n\n학습률_1:최대 스텝수_1, 학습률_2:최대 스텝수_2, ...\n\n예 : 0.005:100, 1e-3:1000, 1e-5\n\n예의 설정값은 첫 100스텝동안 0.005의 학습률로, 그 이후 1000스텝까지는 1e-3으로, 남은 스텝은 1e-5로 훈련하게 됩니다.",
-  "How many batches of images to create": "생성할 이미지 배치 수",
-  "How many image to create in a single batch": "한 배치당 이미지 수",
-  "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "생성된 이미지를 향상할 횟수; 매우 낮은 값은 만족스럽지 못한 결과물을 출력할 수 있음",
-  "How many times to repeat processing an image and using it as input for the next iteration": "이미지를 생성 후 원본으로 몇 번 반복해서 사용할지 결정하는 값",
-  "How much to blur the mask before processing, in pixels.": "이미지 생성 전 마스크를 얼마나 블러처리할지 결정하는 값. 픽셀 단위",
-  "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "바리에이션을 얼마나 줄지 정하는 수치 - 0일 경우 아무것도 바뀌지 않고, 1일 경우 바리에이션 시드로부터 생성된 이미지를 얻게 됩니다. (Ancestral 샘플러 제외 - 이 경우에는 좀 다른 무언가를 얻게 됩니다)",
-  "Hypernet str.": "하이퍼네트워크 강도",
-  "Hypernetwork": "하이퍼네트워크",
-  "Hypernetwork Learning rate": "하이퍼네트워크 학습률",
-  "Hypernetwork strength": "하이퍼네트워크 강도",
-  "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "PNG 이미지가 4MB보다 크거나 가로 또는 세로길이가 4000보다 클 경우, 다운스케일 후 JPG로 복사본 저장하기",
-  "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "이 옵션이 활성화되면 생성된 이미지에 워터마크가 추가되지 않습니다. 경고 : 워터마크를 추가하지 않는다면, 비윤리적인 행동을 하는 중일지도 모릅니다.",
-  "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "이 값이 0이 아니라면, 시드에 해당 값이 더해지고, Eta가 있는 샘플러를 사용할 때 노이즈의 RNG 조정을 위해 해당 값이 사용됩니다. 이 설정으로 더 다양한 이미지를 생성하거나, 잘 알고 계시다면 특정 소프트웨어의 결과값을 재현할 수도 있습니다.",
-  "ignore": "무시",
-  "Image": "이미지",
-  "Image Browser": "이미지 브라우저",
-  "Image browser": "이미지 브라우저",
-  "Image for img2img": "Image for img2img",
-  "Image for inpainting with mask": "마스크로 인페인팅할 이미지",
-  "Image not found (may have been already moved)": "이미지를 찾을 수 없습니다 (이미 옮겨졌을 수 있음)",
-  "Images Browser": "이미지 브라우저",
-  "Images directory": "이미지 경로",
-  "Images filename pattern": "이미지 파일명 패턴",
-  "img2img": "이미지→이미지",
-  "img2img alternative test": "이미지→이미지 대체버전 테스트",
-  "img2img DDIM discretize": "이미지→이미지 DDIM 이산화",
-  "img2img history": "이미지→이미지 기록",
-  "Implements an expressive template language for random or combinatorial prompt generation along with features to support deep wildcard directory structures.": "무작위/조합 프롬프트 생성을 위한 문법과 복잡한 와일드카드 구조를 지원합니다.",
-  "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "루프백 모드에서는 매 루프마다 디노이즈 강도에 이 값이 곱해집니다. 1보다 작을 경우 다양성이 낮아져 결과 이미지들이 고정된 형태로 모일 겁니다. 1보다 클 경우 다양성이 높아져 결과 이미지들이 갈수록 혼란스러워지겠죠.",
-  "Include Separate Images": "분리된 이미지 포함하기",
-  "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "75개보다 많은 토큰을 사용시 마지막 쉼표로부터 N개의 토큰 이내에 패딩을 추가해 통일성 증가시키기",
-  "Initialization text": "초기화 텍스트",
-  "Inpaint": "인페인트",
-  "Inpaint at full resolution": "전체 해상도로 인페인트하기",
-  "Inpaint at full resolution padding, pixels": "전체 해상도로 인페인트시 패딩값(픽셀 단위)",
-  "Inpaint masked": "마스크만 처리",
-  "Inpaint not masked": "마스크 이외만 처리",
-  "Inpainting conditioning mask strength": "인페인팅 조절 마스크 강도",
-  "Input directory": "인풋 이미지 경로",
-  "Input images directory": "이미지 경로 입력",
-  "Inspiration": "\"영감\"",
-  "Install": "설치",
-  "Install from URL": "URL로부터 확장기능 설치",
-  "Installed": "설치된 확장기능",
-  "Installed into ": "확장기능을 ",
-  "Interpolation Method": "보간 방법",
-  "Interrogate\nCLIP": "CLIP\n분석",
-  "Interrogate\nDeepBooru": "DeepBooru\n분석",
-  "Interrogate Options": "분석 설정",
-  "Interrogate: deepbooru score threshold": "분석 : deepbooru 점수 임계값",
-  "Interrogate: deepbooru sort alphabetically": "분석 : deepbooru 알파벳 순서로 정렬하기",
-  "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "분석 : 결과물에 모델 태그의 랭크 포함하기 (캡션 바탕의 분석기에는 효과 없음)",
-  "Interrogate: keep models in VRAM": "분석 : VRAM에 모델 유지하기",
-  "Interrogate: maximum description length": "분석 : 설명 최대 길이",
-  "Interrogate: minimum description length (excluding artists, etc..)": "분석 : 설명 최소 길이(작가 등등..제외)",
-  "Interrogate: num_beams for BLIP": "분석 : BLIP의 num_beams값",
-  "Interrogate: use artists from artists.csv": "분석 : artists.csv의 작가들 사용하기",
-  "Interrupt": "중단",
-  "Is negative text": "네거티브 텍스트일시 체크",
-  "Iterate seed every line": "줄마다 시드 반복하기",
-  "Just resize": "리사이징",
-  "Keep -1 for seeds": "시드값 -1로 유지",
-  "keep whatever was there originally": "이미지 원본 유지",
-  "keyword": "프롬프트",
-  "Krita Plugin.": "Kirta 플러그인입니다.",
-  "Label": "라벨",
-  "Lanczos": "Lanczos",
-  "Last prompt:": "마지막 프롬프트 : ",
-  "Last saved hypernetwork:": "마지막으로 저장된 하이퍼네트워크 : ",
-  "Last saved image:": "마지막으로 저장된 이미지 : ",
-  "latent noise": "잠재 노이즈",
-  "latent nothing": "잠재 공백",
-  "latest": "최신 버전",
-  "LDSR": "LDSR",
-  "LDSR processing steps. Lower = faster": "LDSR 스텝 수. 낮은 값 = 빠른 속도",
-  "leakyrelu": "leakyrelu",
-  "Leave blank to save images to the default path.": "기존 저장 경로에 이미지들을 저장하려면 비워두세요.",
-  "Leave empty for auto": "자동 설정하려면 비워두십시오",
-  "left": "왼쪽",
-  "Lets you edit captions in training datasets.": "훈련에 사용되는 데이터셋의 캡션을 수정할 수 있게 해줍니다.",
-  "linear": "linear",
-  "List of prompt inputs": "프롬프트 입력 리스트",
-  "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "설정 탭이 아니라 상단의 빠른 설정 바에 위치시킬 설정 이름을 쉼표로 분리해서 입력하십시오. 설정 이름은 modules/shared.py에서 찾을 수 있습니다. 재시작이 필요합니다.",
-  "LMS": "LMS",
-  "LMS Karras": "LMS Karras",
-  "Load": "불러오기",
-  "Load from:": "URL로부터 불러오기",
-  "Loading...": "로딩 중...",
-  "Local directory name": "로컬 경로 이름",
-  "Localization (requires restart)": "현지화 (재시작 필요)",
-  "Log directory": "로그 경로",
-  "Loopback": "루프백",
-  "Loops": "루프 수",
-  "Loss:": "손실(Loss) : ",
-  "Magic prompt": "매직 프롬프트",
-  "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "동일한 시드 값으로 생성되었을 이미지를 주어진 해상도로 최대한 유사하게 재현합니다.",
-  "Make K-diffusion samplers produce same images in a batch as when making a single image": "K-diffusion 샘플러들이 단일 이미지를 생성하는 것처럼 배치에서도 동일한 이미지를 생성하게 하기",
-  "Make Zip when Save?": "저장 시 Zip 생성하기",
-  "Mask": "마스크",
-  "Mask blur": "마스크 블러",
-  "Mask mode": "마스크 모드",
-  "Masked content": "마스크된 부분",
-  "Masking mode": "마스킹 모드",
-  "Max prompt words for [prompt_words] pattern": "[prompt_words] 패턴의 최대 프롬프트 단어 수",
-  "Max steps": "최대 스텝 수",
-  "Minimum number of pages per load": "한번 불러올 때마다 불러올 최소 페이지 수",
-  "Modules": "모듈",
-  "Move face restoration model from VRAM into RAM after processing": "처리가 완료되면 얼굴 보정 모델을 VRAM에서 RAM으로 옮기기",
-  "Move to favorites": "즐겨찾기로 옮기기",
-  "Move VAE and CLIP to RAM when training if possible. Saves VRAM.": "훈련 진행 시 가능하면 VAE와 CLIP을 RAM으로 옮기기. VRAM이 절약됩니다.",
-  "Moved to favorites": "즐겨찾기로 옮겨짐",
-  "Multiplier (M) - set to 0 to get model A": "배율 (M) - 0으로 적용하면 모델 A를 얻게 됩니다",
-  "Name": "이름",
-  "Negative prompt": "네거티브 프롬프트",
-  "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "네거티브 프롬프트(Prompt) 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Next batch": "다음 묶음",
-  "Next Page": "다음 페이지",
-  "None": "없음",
-  "Nothing": "없음",
-  "Nothing found in the image.": "Nothing found in the image.",
-  "novelai-2-local-prompt": "NovelAI 프롬프트 변환기",
-  "Number of columns on the page": "각 페이지마다 표시할 가로줄 수",
-  "Number of grids in each row": "각 세로줄마다 표시될 그리드 수",
-  "number of images to delete consecutively next": "연속적으로 삭제할 이미지 수",
-  "Number of pictures displayed on each page": "각 페이지에 표시될 이미지 수",
-  "Number of repeats for a single input image per epoch; used only for displaying epoch number": "세대(Epoch)당 단일 인풋 이미지의 반복 횟수 - 세대(Epoch) 숫자를 표시하는 데에만 사용됩니다. ",
-  "Number of rows on the page": "각 페이지마다 표시할 세로줄 수",
-  "Number of vectors per token": "토큰별 벡터 수",
-  "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "인페인팅 모델에만 적용됩니다. 인페인팅과 이미지→이미지에서 원본 이미지를 얼마나 마스킹 처리할지 결정하는 값입니다. 1.0은 완전히 마스킹함(기본 설정)을 의미하고, 0.0은 완전히 언마스킹된 이미지를 의미합니다. 낮은 값일수록 이미지의 전체적인 구성을 유지하는 데에 도움되겠지만, 변화량이 많을수록 불안정해집니다.",
-  "Open for Clip Aesthetic!": "클립 스타일 기능을 활성화하려면 클릭!",
-  "Open images output directory": "이미지 저장 경로 열기",
-  "Open output directory": "저장 경로 열기",
-  "or": "또는",
-  "original": "원본 유지",
-  "Original negative prompt": "기존 네거티브 프롬프트",
-  "Original prompt": "기존 프롬프트",
-  "Others": "기타",
-  "Outpainting direction": "아웃페인팅 방향",
-  "Outpainting mk2": "아웃페인팅 마크 2",
-  "Output directory": "이미지 저장 경로",
-  "Output directory for grids; if empty, defaults to two directories below": "그리드 이미지 저장 경로 - 비워둘 시 하단의 2가지 기본 경로로 설정됨",
-  "Output directory for images from extras tab": "부가기능 탭 저장 경로",
-  "Output directory for images; if empty, defaults to three directories below": "이미지 저장 경로 - 비워둘 시 하단의 3가지 기본 경로로 설정됨",
-  "Output directory for img2img grids": "이미지→이미지 그리드 저장 경로",
-  "Output directory for img2img images": "이미지→이미지 저장 경로",
-  "Output directory for txt2img grids": "텍스트→이미지 그리드 저장 경로",
-  "Output directory for txt2img images": "텍스트→이미지 저장 경로",
-  "Override `Denoising strength` to 1?": "디노이즈 강도를 1로 적용할까요?",
-  "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "프롬프트 값을 기존 프롬프트와 동일하게 적용할까요?(네거티브 프롬프트 포함)",
-  "Override `Sampling method` to Euler?(this method is built for it)": "샘플링 방법을 Euler로 적용할까요?(이 기능은 해당 샘플러를 위해 만들어져 있습니다)",
-  "Override `Sampling Steps` to the same value as `Decode steps`?": "샘플링 스텝 수를 디코딩 스텝 수와 동일하게 적용할까요?",
-  "Overwrite Old Embedding": "기존 임베딩 덮어쓰기",
-  "Overwrite Old Hypernetwork": "기존 하이퍼네트워크 덮어쓰기",
-  "Page Index": "페이지 인덱스",
-  "parameters": "설정값",
-  "path name": "경로 이름",
-  "Path to directory where to write outputs": "결과물을 출력할 경로",
-  "Path to directory with input images": "인풋 이미지가 있는 경로",
-  "Paths for saving": "저장 경로",
-  "Pixels to expand": "확장할 픽셀 수",
-  "PLMS": "PLMS",
-  "PNG Info": "PNG 정보",
-  "Poor man's outpainting": "가난뱅이의 아웃페인팅",
-  "Preload images at startup": "WebUI 가동 시 이미지 프리로드하기",
-  "Preparing dataset from": "준비된 데이터셋 경로 : ",
-  "prepend": "앞에 삽입",
-  "Preprocess": "전처리",
-  "Preprocess images": "이미지 전처리",
-  "Prev batch": "이전 묶음",
-  "Prev Page": "이전 페이지",
-  "Prevent empty spots in grid (when set to autodetect)": "(자동 감지 사용시)그리드에 빈칸이 생기는 것 방지하기",
-  "Primary model (A)": "주 모델 (A)",
-  "Process an image, use it as an input, repeat.": "이미지를 생성하고, 생성한 이미지를 다시 원본으로 사용하는 과정을 반복합니다.",
-  "Process images in a directory on the same machine where the server is running.": "WebUI 서버가 돌아가고 있는 디바이스에 존재하는 디렉토리의 이미지들을 처리합니다.",
-  "Produce an image that can be tiled.": "타일링 가능한 이미지를 생성합니다.",
-  "Prompt": "프롬프트",
-  "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "프롬프트(Prompt) 입력(Ctrl+Enter나 Alt+Enter로 생성 시작)",
-  "Prompt matrix": "프롬프트 매트릭스",
-  "Prompt order": "프롬프트 순서",
-  "Prompt S/R": "프롬프트 스타일 변경",
-  "Prompt template file": "프롬프트 템플릿 파일 경로",
-  "Prompts": "프롬프트",
-  "Prompts from file or textbox": "파일이나 텍스트박스로부터 프롬프트 불러오기",
-  "Provides an interface to browse created images in the web browser.": "생성된 이미지를 브라우저 내에서 볼 수 있는 인터페이스를 추가합니다.",
-  "Put variable parts at start of prompt": "변경되는 프롬프트를 앞에 위치시키기",
-  "quad": "quad",
-  "Quality for saved jpeg images": "저장된 jpeg 이미지들의 품질",
-  "Quicksettings list": "빠른 설정 리스트",
-  "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
-  "Random": "랜덤",
-  "Random grid": "랜덤 그리드",
-  "Randomly display the pictures of the artist's or artistic genres typical style, more pictures of this artist or genre is displayed after selecting. So you don't have to worry about how hard it is to choose the right style of art when you create.": "특정 작가 또는 스타일의 이미지들 중 하나를 무작위로 보여줍니다. 선택 후 선택한 작가 또는 스타일의 이미지들이 더 나타나게 됩니다. 고르기 어려워도 걱정하실 필요 없어요!",
-  "Randomness": "랜덤성",
-  "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "클립보드에 복사된 정보로부터 설정값 읽어오기/프롬프트창이 비어있을경우 제일 최근 설정값 불러오기",
-  "Read parameters (prompt, etc...) from txt2img tab when making previews": "프리뷰 이미지 생성 시 텍스트→이미지 탭에서 설정값(프롬프트 등) 읽어오기",
-  "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "추천 설정값 - 샘플링 스텝 수 : 80-100 , 샘플러 : Euler a, 디노이즈 강도 : 0.8",
-  "Reload custom script bodies (No ui updates, No restart)": "커스텀 스크립트 리로드하기(UI 업데이트 없음, 재시작 없음)",
-  "Reloading...": "재시작 중...",
-  "relu": "relu",
-  "Renew Page": "Renew Page",
-  "Request browser notifications": "브라우저 알림 권한 요청",
-  "Resize": "리사이징 배수",
-  "Resize and fill": "리사이징 후 채우기",
-  "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "설정된 해상도로 이미지 리사이징을 진행합니다. 원본과 가로/세로 길이가 일치하지 않을 경우, 부정확한 화면비의 이미지를 얻게 됩니다.",
-  "Resize mode": "리사이징 모드",
-  "Resize seed from height": "시드 리사이징 가로길이",
-  "Resize seed from width": "시드 리사이징 세로길이",
-  "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "이미지 전체가 설정된 해상도 내부에 들어가게 리사이징을 진행합니다. 빈 공간은 이미지의 색상으로 채웁니다.",
-  "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "설정된 해상도 전체가 이미지로 가득차게 리사이징을 진행합니다. 튀어나오는 부분은 잘라냅니다.",
-  "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradio를 재시작하고 컴포넌트 새로고침하기 (커스텀 스크립트, ui.py, js, css만 해당됨)",
-  "Restore faces": "얼굴 보정",
-  "Restore low quality faces using GFPGAN neural network": "GFPGAN 신경망을 이용해 저품질의 얼굴을 보정합니다.",
-  "Result = A * (1 - M) + B * M": "결과물 = A * (1 - M) + B * M",
-  "Result = A + (B - C) * M": "결과물 = A + (B - C) * M",
-  "Reuse seed from last generation, mostly useful if it was randomed": "이전 생성에서 사용된 시드를 불러옵니다. 랜덤하게 생성했을 시 도움됨",
-  "right": "오른쪽",
-  "Run": "가동",
-  "Sample extension. Allows you to use __name__ syntax in your prompt to get a random line from a file named name.txt in the wildcards directory. Also see Dynamic Prompts for similar functionality.": "샘플 확장기능입니다. __이름__형식의 문법을 사용해 와일드카드 경로 내의 이름.txt파일로부터 무작위 프롬프트를 적용할 수 있게 해줍니다. 유사한 확장기능으로 다이나믹 프롬프트가 있습니다.",
-  "Sampler": "샘플러",
-  "Sampler parameters": "샘플러 설정값",
-  "Sampling method": "샘플링 방법",
-  "Sampling Steps": "샘플링 스텝 수",
-  "Save": "저장",
-  "Save a copy of embedding to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 임베딩을 저장합니다, 비활성화하려면 0으로 설정하십시오.",
-  "Save a copy of image before applying color correction to img2img results": "이미지→이미지 결과물에 색상 보정을 진행하기 전 이미지의 복사본을 저장하기",
-  "Save a copy of image before applying highres fix.": "고해상도 보정을 진행하기 전 이미지의 복사본을 저장하기",
-  "Save a copy of image before doing face restoration.": "얼굴 보정을 진행하기 전 이미지의 복사본을 저장하기",
-  "Save an csv containing the loss to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 손실(Loss)을 포함하는 csv 파일을 저장합니다, 비활성화하려면 0으로 설정하십시오.",
-  "Save an image to log directory every N steps, 0 to disable": "N스텝마다 로그 경로에 이미지를 저장합니다, 비활성화하려면 0으로 설정하십시오.",
-  "Save as float16": "float16으로 저장",
-  "Save grids to a subdirectory": "그리드 이미지를 하위 디렉토리에 저장하기",
-  "Save images to a subdirectory": "이미지를 하위 디렉토리에 저장하기",
-  "Save images with embedding in PNG chunks": "PNG 청크로 이미지에 임베딩을 포함시켜 저장",
-  "Save style": "스타일 저장",
-  "Save text information about generation parameters as chunks to png files": "이미지 생성 설정값을 PNG 청크에 텍스트로 저장",
-  "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file.": "옵티마이저 상태를 별개의 *.optim 파일로 저장하기. 하이퍼네트워크 파일과 일치하는 optim 파일로부터 훈련을 재개할 수 있습니다.",
-  "Saving images/grids": "이미지/그리드 저장",
-  "Saving to a directory": "디렉토리에 저장",
-  "Scale by": "스케일링 배수 지정",
-  "Scale to": "스케일링 사이즈 지정",
-  "Script": "스크립트",
-  "ScuNET GAN": "ScuNET GAN",
-  "ScuNET PSNR": "ScuNET PSNR",
-  "SD upscale": "SD 업스케일링",
-  "Secondary model (B)": "2차 모델 (B)",
-  "See": "자세한 설명은",
-  "Seed": "시드",
-  "Seed of a different picture to be mixed into the generation.": "결과물에 섞일 다른 그림의 시드",
-  "Select activation function of hypernetwork. Recommended : Swish / Linear(none)": "하이퍼네트워크 활성화 함수 선택 - 추천 : Swish / Linear(None)",
-  "Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise": "레이어 가중치 초기화 방식 선택 - relu류 : Kaiming 추천, sigmoid류 : Xavier 추천, 그 외 : Normal",
-  "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "WebUI에 표시할 Real-ESRGAN 모델을 선택하십시오. (재시작 필요)",
-  "Send seed when sending prompt or image to other interface": "다른 화면으로 프롬프트나 이미지를 보낼 때 시드도 함께 보내기",
-  "Send to extras": "부가기능으로 전송",
-  "Send to img2img": "이미지→이미지로 전송",
-  "Send to inpaint": "인페인트로 전송",
-  "Send to txt2img": "텍스트→이미지로 전송",
-  "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "(|)를 이용해 프롬프트를 분리할 시 첫 프롬프트를 제외하고 모든 프롬프트의 조합마다 이미지를 생성합니다. 첫 프롬프트는 모든 조합에 포함되게 됩니다.",
-  "Separate values for X axis using commas.": "쉼표로 X축에 적용할 값 분리",
-  "Separate values for Y axis using commas.": "쉼표로 Y축에 적용할 값 분리",
-  "Set seed to -1, which will cause a new random number to be used every time": "시드를 -1로 적용 - 매번 랜덤한 시드가 적용되게 됩니다.",
-  "set_index": "set_index",
-  "Settings": "설정",
-  "should be 2 or lower.": "이 2 이하여야 합니다.",
-  "Show generation progress in window title.": "창 타이틀에 생성 진행도 보여주기",
-  "Show grid in results for web": "웹에서 결과창에 그리드 보여주기",
-  "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.": "N번째 샘플링 스텝마다 이미지 생성 과정 보이기 - 비활성화하려면 0으로 설정, 배치 생성 완료시 보이려면 -1로 설정",
-  "Show images zoomed in by default in full page image viewer": "전체 페이지 이미지 뷰어에서 기본값으로 이미지 확대해서 보여주기",
-  "Show previews of all images generated in a batch as a grid": "배치에서 생성된 모든 이미지의 미리보기를 그리드 형식으로 보여주기",
-  "Show progressbar": "프로그레스 바 보이기",
-  "Show result images": "이미지 결과 보이기",
-  "Show Textbox": "텍스트박스 보이기",
-  "Shows a gallery of generated pictures by artists separated into categories.": "생성된 이미지들을 작가별로 분류해 보여줍니다. 원본 - https://artiststostudy.pages.dev",
-  "Sigma adjustment for finding noise for image": "이미지 노이즈를 찾기 위해 시그마 조정",
-  "Sigma Churn": "시그마 섞기",
-  "sigma churn": "시그마 섞기",
-  "Sigma max": "시그마 최댓값",
-  "Sigma min": "시그마 최솟값",
-  "Sigma noise": "시그마 노이즈",
-  "sigma noise": "시그마 노이즈",
-  "sigma tmin": "시그마 tmin",
-  "Single Image": "단일 이미지",
-  "Skip": "건너뛰기",
-  "Slerp angle": "구면 선형 보간 각도",
-  "Slerp interpolation": "구면 선형 보간",
-  "sort by": "정렬 기준",
-  "Source": "원본",
-  "Source directory": "원본 경로",
-  "Split image overlap ratio": "이미지 분할 겹침 비율",
-  "Split image threshold": "이미지 분할 임계값",
-  "Split oversized images": "사이즈가 큰 이미지 분할하기",
-  "Stable Diffusion": "Stable Diffusion",
-  "Stable Diffusion checkpoint": "Stable Diffusion 체크포인트",
-  "step cnt": "스텝 변화 횟수",
-  "step count": "스텝 변화 횟수",
-  "step1 min/max": "스텝1 최소/최대",
-  "step2 min/max": "스텝2 최소/최대",
-  "Step:": "Step:",
-  "Steps": "스텝 수",
-  "Stop At last layers of CLIP model": "CLIP 모델의 n번째 레이어에서 멈추기",
-  "Stop processing current image and continue processing.": "현재 진행중인 이미지 생성을 중단하고 작업을 계속하기",
-  "Stop processing images and return any results accumulated so far.": "이미지 생성을 중단하고 지금까지 진행된 결과물 출력",
-  "Style 1": "스타일 1",
-  "Style 2": "스타일 2",
-  "Style to apply; styles have components for both positive and negative prompts and apply to both": "적용할 스타일 - 스타일은 긍정/부정 프롬프트 모두에 대한 설정값을 가지고 있고 양쪽 모두에 적용 가능합니다.",
-  "SwinIR 4x": "SwinIR 4x",
-  "Sys VRAM:": "시스템 VRAM : ",
-  "System": "시스템",
-  "Tertiary model (C)": "3차 모델 (C)",
-  "Textbox": "텍스트박스",
-  "The official port of Deforum, an extensive script for 2D and 3D animations, supporting keyframable sequences, dynamic math parameters (even inside the prompts), dynamic masking, depth estimation and warping.": "Deforum의 공식 포팅 버전입니다. 2D와 3D 애니메이션, 키프레임 시퀀스, 수학적 매개변수, 다이나믹 마스킹 등을 지원합니다.",
-  "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "이 정규표현식은 파일명으로부터 단어를 추출하는 데 사용됩니다. 추출된 단어들은 하단의 설정을 이용해 라벨 텍스트로 변환되어 훈련에 사용됩니다. 파일명 텍스트를 유지하려면 비워두십시오.",
-  "This string will be used to join split words into a single line if the option above is enabled.": "이 문자열은 상단 설정이 활성화되어있을 때 분리된 단어들을 한 줄로 합치는 데 사용됩니다.",
-  "This text is used to rotate the feature space of the imgs embs": "이 텍스트는 이미지 임베딩의 특징 공간을 회전하는 데 사용됩니다.",
-  "Tile overlap": "타일 겹침",
-  "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN 업스케일러들의 타일 중첩 수치, 픽셀 단위. 낮은 값 = 눈에 띄는 이음매.",
-  "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR의 타일 중첩 수치, 픽셀 단위. 낮은 값 = 눈에 띄는 이음매.",
-  "Tile size for all SwinIR.": "SwinIR의 타일 사이즈.",
-  "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 업스케일러들의 타일 사이즈. 0 = 타일링 없음.",
-  "Tiling": "타일링",
-  "Time taken:": "소요 시간 : ",
-  "tokenizer": "토크나이저",
-  "Torch active/reserved:": "활성화/예약된 Torch 양 : ",
-  "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "활성화된 Torch : 생성 도중 캐시된 데이터를 포함해 사용된 VRAM의 최대량\n예약된 Torch : 활성화되고 캐시된 모든 데이터를 포함해 Torch에게 할당된 VRAM의 최대량\n시스템 VRAM : 모든 어플리케이션에 할당된 VRAM 최대량 / 총 GPU VRAM (최고 이용도%)",
-  "Train": "훈련",
-  "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "임베딩이나 하이퍼네트워크를 훈련시킵니다. 1:1 비율의 이미지가 있는 경로를 지정해야 합니다.",
-  "Train Embedding": "임베딩 훈련",
-  "Train Hypernetwork": "하이퍼네트워크 훈련",
-  "Training": "훈련",
-  "training-picker": "훈련용 선택기",
-  "txt2img": "텍스트→이미지",
-  "txt2img history": "텍스트→이미지 기록",
-  "uniform": "uniform",
-  "unknown": "알수 없음",
-  "up": "위쪽",
-  "Update": "업데이트",
-  "Upload mask": "마스크 업로드하기",
-  "Upload prompt inputs": "입력할 프롬프트를 업로드하십시오",
-  "Upscale Before Restoring Faces": "얼굴 보정을 진행하기 전에 업스케일링 먼저 진행하기",
-  "Upscale latent space image when doing hires. fix": "고해상도 보정 사용시 잠재 공간 이미지 업스케일하기",
-  "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "마스크된 부분을 설정된 해상도로 업스케일하고, 인페인팅을 진행한 뒤, 다시 다운스케일 후 원본 이미지에 붙여넣습니다.",
-  "Upscaler": "업스케일러",
-  "Upscaler 1": "업스케일러 1",
-  "Upscaler 2": "업스케일러 2",
-  "Upscaler 2 visibility": "업스케일러 2 가시성",
-  "Upscaler for img2img": "이미지→이미지 업스케일러",
-  "Upscaling": "업스케일링",
-  "URL for extension's git repository": "확장기능의 git 레포 URL",
-  "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "저해상도 이미지를 1차적으로 생성 후 업스케일을 진행하여, 이미지의 전체적인 구성을 바꾸지 않고 세부적인 디테일을 향상시킵니다.",
-  "Use an empty output directory to save pictures normally instead of writing to the output directory.": "저장 경로를 비워두면 기본 저장 폴더에 이미지들이 저장됩니다.",
-  "Use BLIP for caption": "캡션에 BLIP 사용",
-  "Use checkbox to enable the extension; it will be enabled or disabled when you click apply button": "체크박스를 이용해 적용할 확장기능을 선택하세요. 변경사항은 적용 후 UI 재시작 버튼을 눌러야 적용됩니다.",
-  "Use checkbox to mark the extension for update; it will be updated when you click apply button": "체크박스를 이용해 업데이트할 확장기능을 선택하세요. 업데이트는 적용 후 UI 재시작 버튼을 눌러야 적용됩니다.",
-  "Use cross attention optimizations while training": "훈련 진행 시 크로스 어텐션 최적화 사용",
-  "Use deepbooru for caption": "캡션에 deepbooru 사용",
-  "Use dropout": "드롭아웃 사용",
-  "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지 파일명 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
-  "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "다음 태그들을 사용해 이미지와 그리드의 하위 디렉토리명의 형식을 결정하세요 : [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]. 비워두면 기본값으로 설정됩니다.",
-  "Use old emphasis implementation. Can be useful to reproduce old seeds.": "옛 방식의 강조 구현을 사용합니다. 옛 시드를 재현하는 데 효과적일 수 있습니다.",
-  "Use original name for output filename during batch process in extras tab": "부가기능 탭에서 이미지를 여러장 처리 시 결과물 파일명에 기존 파일명 사용하기",
-  "Use same random seed for all lines": "모든 줄에 동일한 시드 사용",
-  "Use same seed for each image": "각 이미지에 동일한 시드 사용",
-  "use spaces for tags in deepbooru": "deepbooru에서 태그에 공백 사용",
-  "User interface": "사용자 인터페이스",
-  "Var. seed": "바리에이션 시드",
-  "Var. strength": "바리에이션 강도",
-  "Variation seed": "바리에이션 시드",
-  "Variation strength": "바리에이션 강도",
-  "view": "api 보이기",
-  "VRAM usage polls per second during generation. Set to 0 to disable.": "생성 도중 초당 VRAM 사용량 폴링 수. 비활성화하려면 0으로 설정하십시오.",
-  "Weighted sum": "가중 합",
-  "What to put inside the masked area before processing it with Stable Diffusion.": "Stable Diffusion으로 이미지를 생성하기 전 마스크된 부분에 무엇을 채울지 결정하는 설정값",
-  "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "PNG 정보나 붙여넣은 텍스트로부터 생성 설정값을 읽어올 때, 선택된 모델/체크포인트는 변경하지 않기.",
-  "When using \"Save\" button, save images to a subdirectory": "저장 버튼 사용시, 이미지를 하위 디렉토리에 저장하기",
-  "When using 'Save' button, only save a single selected image": "저장 버튼 사용시, 선택된 이미지 1개만 저장하기",
-  "Which algorithm to use to produce the image": "이미지를 생성할 때 사용할 알고리즘",
-  "Width": "가로",
-  "wiki": " 위키",
-  "Wildcards": "와일드카드",
-  "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "이미지를 설정된 사이즈의 2배로 업스케일합니다. 상단의 가로와 세로 슬라이더를 이용해 타일 사이즈를 지정하세요.",
-  "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "이미지→이미지 진행 시, 슬라이더로 설정한 스텝 수를 정확히 실행하기 (일반적으로 디노이즈 강도가 낮을수록 실제 설정된 스텝 수보다 적게 진행됨)",
-  "Write image to a directory (default - log/images) and generation parameters into csv file.": "이미지를 경로에 저장하고, 설정값들을 csv 파일로 저장합니다. (기본 경로 - log/images)",
-  "X type": "X축",
-  "X values": "X 설정값",
-  "x/y change": "X/Y축 변경",
-  "X/Y plot": "X/Y 플롯",
-  "Y type": "Y축",
-  "Y values": "Y 설정값"
-}
\ No newline at end of file
diff --git a/localizations/pt_BR.json b/localizations/pt_BR.json
deleted file mode 100644
index c8ef1b11..00000000
--- a/localizations/pt_BR.json
+++ /dev/null
@@ -1,485 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "Carregando...",
-    "view": "ver",
-    "api": "api",
-    "•": "•",
-    "built with gradio": "criado com gradio",
-    "Stable Diffusion checkpoint": "Stable Diffusion checkpoint",
-    "txt2img": "txt2img",
-    "img2img": "img2img",
-    "Extras": "Extras",
-    "PNG Info": "Informações de PNG",
-    "Checkpoint Merger": "Fusão de Checkpoint",
-    "Train": "Treinar",
-    "Settings": "Configurações",
-    "Extensions": "Extensões",
-    "Prompt": "Prompt",
-    "Negative prompt": "Prompt negativo",
-    "Run": "Executar",
-    "Skip": "Pular",
-    "Interrupt": "Interromper",
-    "Generate": "Gerar",
-    "Style 1": "Estilo 1",
-    "Style 2": "Estilo 2",
-    "Label": "Rótulo",
-    "File": "Arquivo",
-    "Drop File Here": "Solte Aqui o Arquivo",
-    "-": "-",
-    "or": "ou",
-    "Click to Upload": "Clique para Carregar um Arquivo",
-    "Image": "Imagem",
-    "Check progress": "Checar progresso",
-    "Check progress (first)": "Checar progresso (primeiro)",
-    "Sampling Steps": "Passos de Amostragem",
-    "Sampling method": "Método de amostragem",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "Largura",
-    "Height": "Altura",
-    "Restore faces": "Restaurar rostos",
-    "Tiling": "Ladrilhar",
-    "Highres. fix": "Ajuste de alta resolução",
-    "Firstpass width": "Primeira Passagem da largura",
-    "Firstpass height": "Primeira Passagem da altura",
-    "Denoising strength": "Força do denoise",
-    "Batch count": "Quantidade por lote",
-    "Batch size": "Quantidade de lotes",
-    "CFG Scale": "Escala CFG",
-    "Seed": "Seed",
-    "Extra": "Extra",
-    "Variation seed": "Variação de seed",
-    "Variation strength": "Força da variação",
-    "Resize seed from width": "Redimensionar a seed a partir da largura",
-    "Resize seed from height": "Redimensionar a seed a partir da altura",
-    "Script": "Script",
-    "None": "Nenhum",
-    "Prompt matrix": "Matriz de prompt",
-    "Prompts from file or textbox": "Prompts a partir de arquivo ou caixa de texto",
-    "X/Y plot": "X/Y plot",
-    "Put variable parts at start of prompt": "Coloca partes variáveis no começo do prompt",
-    "Iterate seed every line": "Iterar seed a cada linha",
-    "List of prompt inputs": "Lista de entrada de texto para prompt",
-    "Upload prompt inputs": "Carregar entrada de texto para prompt",
-    "X type": "Tipo do X",
-    "Nothing": "Nenhum",
-    "Var. seed": "Var. seed",
-    "Var. strength": "Var. da força",
-    "Steps": "Passos",
-    "Prompt S/R": "Prompt S/R",
-    "Prompt order": "Ordem de Prompt",
-    "Sampler": "Sampler",
-    "Checkpoint name": "Nome do Checkpoint",
-    "Hypernetwork": "Hypernetwork",
-    "Hypernet str.": "Força da Hypernet",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma min": "Sigma min",
-    "Sigma max": "Sigma max",
-    "Sigma noise": "Sigma noise",
-    "Eta": "Tempo estimado",
-    "Clip skip": "Pular Clip",
-    "Denoising": "Denoising",
-    "Cond. Image Mask Weight": "Peso da Máscara Condicional de Imagem",
-    "X values": "Valores de X",
-    "Y type": "Tipo de Y",
-    "Y values": "Valores de Y",
-    "Draw legend": "Desenhar a legenda",
-    "Include Separate Images": "Incluir Imagens Separadas",
-    "Keep -1 for seeds": "Manter em -1 para seeds",
-    "Save": "Salvar",
-    "Send to img2img": "Mandar para img2img",
-    "Send to inpaint": "Mandar para inpaint",
-    "Send to extras": "Mandar para extras",
-    "Make Zip when Save?": "Criar um Zip quando salvar?",
-    "Textbox": "Caixa de texto",
-    "Interrogate\nCLIP": "Interrogatório\nCLIP",
-    "Inpaint": "Inpaint",
-    "Batch img2img": "Lote img2img",
-    "Image for img2img": "Imagem para img2img",
-    "Drop Image Here": "Solte a imagem aqui",
-    "Image for inpainting with mask": "Imagem para inpainting com máscara",
-    "Mask": "Máscara",
-    "Mask blur": "Desfoque da máscara",
-    "Mask mode": "Modo de máscara",
-    "Draw mask": "Desenhar máscara",
-    "Upload mask": "Carregar máscara",
-    "Masking mode": "Modo de máscara",
-    "Inpaint masked": "Inpaint o que está dentro da máscara",
-    "Inpaint not masked": "Inpaint o que está fora da máscara",
-    "Masked content": "Conteúdo mascarado",
-    "fill": "preencher",
-    "original": "original",
-    "latent noise": "latent noise",
-    "latent nothing": "latent nothing",
-    "Inpaint at full resolution": "Inpaint em resolução total",
-    "Inpaint at full resolution padding, pixels": "Inpaint de preenchimento em resolução total, pixels",
-    "Process images in a directory on the same machine where the server is running.": "Processar imagens no diretório da mesma maquina onde o servidor está rodando.",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Usar um diretório vazio para salvar imagens, ao invés de salvá-las no diretório output.",
-    "Input directory": "Diretório de entrada",
-    "Output directory": "Diretório de saída",
-    "Resize mode": "Modo de redimensionamento",
-    "Just resize": "Apenas redimensionar",
-    "Crop and resize": "Cortar e redimensionar",
-    "Resize and fill": "Redimensionar e preencher",
-    "img2img alternative test": "Teste alternativo de img2img",
-    "Loopback": "Loopback",
-    "Outpainting mk2": "Outpainting mk2",
-    "Poor man's outpainting": "Poor man`s outpainting",
-    "SD upscale": "Ampliamento SD",
-    "should be 2 or lower.": "deve ser 2 ou menos.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Substituir `Método de amostragem` por Euler? (este método foi feito para isso)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Substituir `prompt` para o mesmo valor que o `prompt original`? (também para o `prompt negativo`)",
-    "Original prompt": "Prompt original",
-    "Original negative prompt": "Prompt negativo original",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Substituir `Passos de Amostragem` para o mesmo valor que `Decodificar Passos`?",
-    "Decode steps": "Decode steps",
-    "Override `Denoising strength` to 1?": "Substituir `Quantidade do Denoise` para 1?",
-    "Decode CFG scale": "Decodificar escala CFG",
-    "Randomness": "Aleatoriedade",
-    "Sigma adjustment for finding noise for image": "Ajuste Sigma para encontrar ruído para imagem",
-    "Loops": "Loops",
-    "Denoising strength change factor": "Fator de mudança na quantidade do Denoise",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Configurações recomendadas: Passos de amostragem: 80-100: Euler a, força do Denoise: 0.8",
-    "Pixels to expand": "Pixels para expandir",
-    "Outpainting direction": "Direção do outpainting",
-    "left": "esquerda",
-    "right": "direita",
-    "up": "cima",
-    "down": "baixo",
-    "Fall-off exponent (lower=higher detail)": "Expoente de queda (menor=mais detalhes)",
-    "Color variation": "Variação de cor",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Amplia a imagem em dobro; ajusta a largura e altura para definir o tamanho do ladrilho",
-    "Tile overlap": "Sobreposição de ladrilho",
-    "Upscaler": "Ampliador",
-    "Lanczos": "Lanczos",
-    "LDSR": "LDSR",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR 4x": "SwinIR 4x",
-    "Single Image": "Uma imagem",
-    "Batch Process": "Processo em lote",
-    "Batch from Directory": "Lote apartir de diretório",
-    "Source": "Origem",
-    "Show result images": "Mostrar imagens resultantes",
-    "Scale by": "Aumentar proporcionalmente em",
-    "Scale to": "Aumentar proporcionalmente para",
-    "Resize": "Redimensionar",
-    "Crop to fit": "Cortar para caber",
-    "Upscaler 2 visibility": "Visibilidade da ferramenta de ampliação 2",
-    "GFPGAN visibility": "Visibilidade GFPGAN",
-    "CodeFormer visibility": "Visibilidade CodeFormer",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Peso do CodeFormer (0 = efeito máximo, 1 = efeito mínimo)",
-    "Upscale Before Restoring Faces": "Ampliar Antes de Refinar Rostos",
-    "Send to txt2img": "Mandar para txt2img",
-    "A merger of the two checkpoints will be generated in your": "Uma fusão dos dois checkpoints será gerada em seu",
-    "checkpoint": "checkpoint",
-    "directory.": "diretório.",
-    "Primary model (A)": "Modelo primário (A)",
-    "Secondary model (B)": "Modelo secundário (B)",
-    "Tertiary model (C)": "Modelo terciário (C)",
-    "Custom Name (Optional)": "Nome personalizado (Opcional)",
-    "Multiplier (M) - set to 0 to get model A": "Multiplicador (M) - definir em 0 para obter o modelo A",
-    "Interpolation Method": "Método de Interpolação",
-    "Weighted sum": "Soma de pesos",
-    "Add difference": "Acrescentar diferença",
-    "Save as float16": "Salvar como float16",
-    "See": "Ver",
-    "wiki": "wiki",
-    "for detailed explanation.": "para explicação detalhada.",
-    "Create embedding": "Criar incorporação",
-    "Create hypernetwork": "Criar hypernetwork",
-    "Preprocess images": "Pré-processar imagens",
-    "Name": "Nome",
-    "Initialization text": "Texto de inicialização",
-    "Number of vectors per token": "Número de vetores por token",
-    "Overwrite Old Embedding": "Substituir Incorporação anterior",
-    "Modules": "Módulos",
-    "Enter hypernetwork layer structure": "Entrar na estrutura de camadas da hypernetwork",
-    "Select activation function of hypernetwork": "Selecionar a função de ativação de hypernetwork",
-    "linear": "linear",
-    "relu": "relu",
-    "leakyrelu": "leakyrelu",
-    "elu": "elu",
-    "swish": "swish",
-    "tanh": "tanh",
-    "sigmoid": "sigmoid",
-    "celu": "celu",
-    "gelu": "gelu",
-    "glu": "glu",
-    "hardshrink": "hardshrink",
-    "hardsigmoid": "hardsigmoid",
-    "hardtanh": "hardtanh",
-    "logsigmoid": "logsigmoid",
-    "logsoftmax": "logsoftmax",
-    "mish": "mish",
-    "prelu": "prelu",
-    "rrelu": "rrelu",
-    "relu6": "relu6",
-    "selu": "selu",
-    "silu": "silu",
-    "softmax": "softmax",
-    "softmax2d": "softmax2d",
-    "softmin": "softmin",
-    "softplus": "softplus",
-    "softshrink": "softshrink",
-    "softsign": "softsign",
-    "tanhshrink": "tanhshrink",
-    "threshold": "threshold",
-    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "Selecionar a inicialização de pesos de camada. relu-like - Kaiming, sigmoid-like - Xavier é recomendado",
-    "Normal": "Normal",
-    "KaimingUniform": "KaimingUniform",
-    "KaimingNormal": "KaimingNormal",
-    "XavierUniform": "XavierUniform",
-    "XavierNormal": "XavierNormal",
-    "Add layer normalization": "Adicionar normalização de camada",
-    "Use dropout": "Usar dropout",
-    "Overwrite Old Hypernetwork": "Sobrescrever Hypernetwork Anterior",
-    "Source directory": "Diretório de origem",
-    "Destination directory": "Diretório de destino",
-    "Existing Caption txt Action": "Ação de Título txt Já Existente",
-    "ignore": "ignorar",
-    "copy": "copiar",
-    "prepend": "adicionar ao início",
-    "append": "adicionar ao final",
-    "Create flipped copies": "Criar cópias espelhadas",
-    "Split oversized images into two": "Dividir imagens maiores em duas",
-    "Auto focal point crop": "Ajuste de corte em ponto focal automático",
-    "Use BLIP for caption": "Usar BLIP para o título",
-    "Use deepbooru for caption": "Usar deepbooru para o título",
-    "Split image threshold": "Limite de divisão de imagem",
-    "Split image overlap ratio": "Proporção de sobreposição da divisão de imagem",
-    "Focal point face weight": "Peso de ponto focal para rosto",
-    "Focal point entropy weight": "Peso de ponto focal para entropia",
-    "Focal point edges weight": "Peso de ponto focal para bordas",
-    "Create debug image": "Criar imagem de depuração",
-    "Preprocess": "Pré-processar",
-    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Treinar uma incorporação; precisa especificar um diretório com imagens de proporção 1:1",
-    "[wiki]": "[wiki]",
-    "Embedding": "Incorporação",
-    "Embedding Learning rate": "Taxa de aprendizagem da incorporação",
-    "Hypernetwork Learning rate": "Taxa de aprendizagem de Hypernetwork",
-    "Dataset directory": "Diretório de Dataset",
-    "Log directory": "Diretório de Log",
-    "Prompt template file": "Arquivo padrão de Prompt",
-    "Max steps": "Passos máximos",
-    "Save an image to log directory every N steps, 0 to disable": "Salvar uma imagem no diretório de log a cada N passos. 0 para desativar",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Salva uma cópia da incorporação no diretório de log a cada N passos. 0 para desativar",
-    "Save images with embedding in PNG chunks": "Salva imagens com incorporação em segmentos de PNG",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Ler parâmetros (prompt, etc...) para a aba txt2img durante os previews",
-    "Train Hypernetwork": "Treinar Hypernetwork",
-    "Train Embedding": "Treinar Incorporação",
-    "Apply settings": "Aplicar configurações",
-    "Saving images/grids": "Salvar imagens/grades",
-    "Always save all generated images": "Sempre salvar todas as imagens geradas",
-    "File format for images": "Tipo de formato das imagens salvas",
-    "Images filename pattern": "Padrão de nomeação para imagens salvas",
-    "Add number to filename when saving": "Adicionar número para o nome do arquivo quando salvar",
-    "Always save all generated image grids": "Sempre salvar todas as grades de imagens",
-    "File format for grids": "Tipo de formato das grades de imagens salvas",
-    "Add extended info (seed, prompt) to filename when saving grid": "Adicionar informações extras (seed, prompt) para os arquivos quando gerar uma grade",
-    "Do not save grids consisting of one picture": "Não salvar grades de apenas uma imagem",
-    "Prevent empty spots in grid (when set to autodetect)": "Previnir espaços vazios na grade (quando marcado para autodetectar)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Contagem de linhas da grade; -1 para autodetectar e 0 para ser igual ao valor do tamanho das levas",
-    "Save text information about generation parameters as chunks to png files": "Salvar informações de parâmetros de geração como segmentos png",
-    "Create a text file next to every image with generation parameters.": "Criar um arquivo de texto com informações de geração junto a cada imagem gerada.",
-    "Save a copy of image before doing face restoration.": "Salva uma cópia de cada imagem antes do refinamento facial.",
-    "Quality for saved jpeg images": "Qualidade das imagens jpeg",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Se a imagem PNG for maior que 4MB ou qualquer dimensão maior que 4000, diminuir e salvar uma cópia em JPG",
-    "Use original name for output filename during batch process in extras tab": "Usar o nome original para os arquivos de output durante o processo de levas da aba Extras",
-    "When using 'Save' button, only save a single selected image": "Quando usar o botão `Salvar`, somente salvar as imagens selecionadas.",
-    "Do not add watermark to images": "Não adicionar marca dágua nas imagens",
-    "Paths for saving": "Caminhos para salvar",
-    "Output directory for images; if empty, defaults to three directories below": "Diretório de saída para imagens; se deixado em branco, as imagens vao para os seguintes diretórios",
-    "Output directory for txt2img images": "Diretório de Saída para imagens txt2img",
-    "Output directory for img2img images": "Diretório de Saída para imagens img2img",
-    "Output directory for images from extras tab": "Diretório de Saída para a aba Extras",
-    "Output directory for grids; if empty, defaults to two directories below": "Diretório de Saída para grades; se vazio, vão para os diretórios seguintes",
-    "Output directory for txt2img grids": "Diretório de Saída para grades de imagens txt2img",
-    "Output directory for img2img grids": "Diretório de Saída para grades de imagens img2img",
-    "Directory for saving images using the Save button": "Diretório para imagens salvas utilizando o botão de salvar",
-    "Saving to a directory": "Salvando para um diretório",
-    "Save images to a subdirectory": "Salvar imagens para um subdiretório",
-    "Save grids to a subdirectory": "Salvar grades de imagens para um subdiretório",
-    "When using \"Save\" button, save images to a subdirectory": "Quando usar o botão \"Salvar\", salvar imagens para um subdiretório",
-    "Directory name pattern": "Padrão de nome de diretório",
-    "Max prompt words for [prompt_words] pattern": "Número máximo de palavras do padrão de prompt [prompt_words]",
-    "Upscaling": "Ampliando",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tamanho do ladrilho para ampliação ESRGAN. 0 = sem ladrilho.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Sobreposição de azulejo, em pixels, para amplicação  ESRGAN. Valores baixos = linhas de fusão mais aparente.",
-    "Tile size for all SwinIR.": "Tamanho do ladrilho para todo SwinIR.",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Sobreposição de azulejo, em pixels,  para SwinIR. Valores baixos = junção mais aparente.",
-    "LDSR processing steps. Lower = faster": "Steps de processamento LDSR. Menos = rápido",
-    "Upscaler for img2img": "Ampliação para img2img",
-    "Upscale latent space image when doing hires. fix": "Ampliar a imagem do espaço latente quando usando o ajuste de alta definição - hires. fix",
-    "Face restoration": "Refinamento de rosto",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Parâmento de peso do CodeFormer; 0 = efeito máximo; 1 = efeito mínimo",
-    "Move face restoration model from VRAM into RAM after processing": "Mover o processo de refinamento de rosto da VRAM da placa de vídeo para a RAM do computador depois do processamento.",
-    "System": "Sistema",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "Levantamento de uso de VRAM por segundo durante gerações. Deixar em 0 para desativar.",
-    "Always print all generation info to standard output": "Sempre mostrar as informações de todas as gerações no padrão de output",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Adicionar uma segunda barra de processamento no console que mostra a progressão de todo o trabalho.",
-    "Training": "Treinamento",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Mover VAE e CLIP para a RAM quando treinando hypernetwork. Preserva VRAM.",
-    "Filename word regex": "Palavra de nome de arquivo regex",
-    "Filename join string": "Nome de arquivo join string",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Número de repetições para entrada única de imagens por época; serve apenas para mostrar o número de época",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Salvar um csv com as perdas para o diretório de log a cada N steps, 0 para desativar",
-    "Use cross attention optimizations while training": "Usar otimizações de atenção cruzada enquanto treinando",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "Checkpoints para manter no cache da RAM",
-    "Hypernetwork strength": "Força da Hypernetwork",
-     "Inpainting conditioning mask strength": "Força do inpaint para máscaras condicioniais",
-    "Apply color correction to img2img results to match original colors.": "Aplicar correção de cor nas imagens geradas em img2img, usando a imagem original como base.",
-    "Save a copy of image before applying color correction to img2img results": "Salvar uma cópia das imagens geradas em img2img antes de aplicar a correção de cor",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "Durante gerações img2img, fazer examente o número de steps definidos na barra (normalmente você faz menos steps com denoising menor).",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Ativar quantização em K samples para resultados mais nítidos e visíveis. Pode alterar seeds ja existentes. Precisa reiniciar para funcionar.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Ênfase: usar parênteses ao redor de palavras (texto de exemplo) para fazer o modelo dar mais atenção para aquela palavra ou frase, e chaves [texto de exemplo] para tirar atenção",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Usar método anterior de implementação de ênfase. Útil para reproduzir seeds antigas.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Faz as amostragens K-diffusion produzirem imagens iguais em lotes quando criando uma única imagem",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Aumenta a coerência por preenchimento apartir da ultima vírgula dentro de n tokens quando usando mais de 75 tokens",
-    "Filter NSFW content": "Filtra conteúdos inadequados(geralmente +18)",
-    "Stop At last layers of CLIP model": "Para na última camada do modelo CLIP",
-    "Interrogate Options": "Opções de Interrogatório",
-    "Interrogate: keep models in VRAM": "Interrogar: manter modelos na VRAM",
-    "Interrogate: use artists from artists.csv": "Interrogar: usa artistas e estilos do documento artists.csv",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogar: incluir classificação de tags de modelo combinando nos resultados (Não tem efeito na interrogação feita por legenda).",
-    "Interrogate: num_beams for BLIP": "Interrogar: num_beams para BLIP",
-    "Interrogate: minimum description length (excluding artists, etc..)": "Interrogar: tamanho mínimo da descrição (tirando artistas, etc..)",
-    "Interrogate: maximum description length": "Interrogar: tamanho máximo da descrição",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: número máximo de linhas no arquivo de texto(0 = Sem limites)",
-    "Interrogate: deepbooru score threshold": "Interrogatório: limite de score deepbooru",
-    "Interrogate: deepbooru sort alphabetically": "Interrogar: organizar deepbooru por ordem alfabética",
-    "use spaces for tags in deepbooru": "usar espaços para tags em deepbooru",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "espaço (\\) colchetes em deepbooru (são usados como colchetes ao invés de dar ênfase)",
-    "User interface": "Interface de usuário",
-    "Show progressbar": "Mostrar barra de progresso",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Mostrar a criação de imagens a cada N sampling steps. Em 1 já dá para ver o processo de geração. Marcar como 0 para desativar.",
-    "Show previews of all images generated in a batch as a grid": "Mostrar previsualização de todas as imagens geradas em leva numa grade",
-    "Show grid in results for web": "Mostrar grade em resultados para web",
-    "Do not show any images in results for web": "Não mostrar nenhuma imagem em resultados para web",
-    "Add model hash to generation information": "Adicionar hash do modelo para informação de geração",
-    "Add model name to generation information": "Adicionar nome do modelo para informação de geração",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Quando ler parâmetros de texto para a interface (de informações de PNG ou texto copiado), não alterar o modelo/intervalo selecionado.",
-    "Send seed when sending prompt or image to other interface": "Enviar seed quando enviar prompt ou imagem para outra interface",
-    "Font for image grids that have text": "Fonte para grade de imagens que têm texto",
-    "Enable full page image viewer": "Ativar visualizador de página inteira",
-    "Show images zoomed in by default in full page image viewer": "Mostrar imagens com zoom por definição no visualizador de página inteira",
-    "Show generation progress in window title.": "Mostrar barra de progresso no nome da janela.",
-    "Quicksettings list": "Lista de configurações rapidas",
-    "Localization (requires restart)": "Localização (precisa reiniciar)",
-    "ar_AR": "ar_AR",
-    "de_DE": "de_DE",
-    "es_ES": "es_ES",
-    "fr_FR": "fr_FR",
-    "it_IT": "it_IT",
-    "ja_JP": "ja_JP",
-    "ko_KR": "ko_KR",
-    "pt_BR": "pt_BR",
-    "ru_RU": "ru_RU",
-    "tr_TR": "tr_TR",
-    "zh_CN": "zh_CN",
-    "zh_TW": "zh_TW",
-    "Sampler parameters": "Parâmetros de Amostragem",
-    "Hide samplers in user interface (requires restart)": "Esconder amostragens na interface de usuário (precisa reiniciar)",
-    "eta (noise multiplier) for DDIM": "tempo estimado (multiplicador de ruído) para DDIM",
-    "eta (noise multiplier) for ancestral samplers": "tempo estimado (multiplicador de ruído) para amostragens ancestrais",
-    "img2img DDIM discretize": "Discretização de img2img DDIM",
-    "uniform": "uniforme",
-    "quad": "quad",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "sigma tmin",
-    "sigma noise": "sigma noise",
-    "Eta noise seed delta": "tempo estimado para ruído seed delta",
-    "Request browser notifications": "Solicitar notificações do navegador",
-    "Download localization template": "Baixar arquivo modelo de localização",
-    "Reload custom script bodies (No ui updates, No restart)": "Recarregar scripts personalizados (Sem atualizar a interface, Sem reiniciar)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Reiniciar Gradio e atualizar componentes (Scripts personalizados, ui.py, js e css)",
-    "Installed": "Instalado",
-    "Available": "Disponível",
-    "Install from URL": "Instalado de URL",
-    "Apply and restart UI": "Apicar e reiniciar a interface",
-    "Check for updates": "Procurar por atualizações",
-    "Extension": "Extensão",
-    "URL": "URL",
-    "Update": "Atualização",
-    "Load from:": "Carregar de:",
-    "Extension index URL": "Índice de extensão URL",
-    "URL for extension's git repository": "URL para repositório git da extensão",
-    "Local directory name": "Nome do diretório local",
-    "Install": "Instalar",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt (apertar Ctrl+Enter ou Alt+Enter para gerar)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Prompt Negativo (apertar Ctrl+Enter ou Alt+Enter para gerar)",
-    "Add a random artist to the prompt.": "Adicionar um artista aleatório para o prompt.",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Lê os parâmetros de geração do prompt ou da última geraçao, caso o prompt esteja vazio.",
-    "Save style": "Salva um estilo de prompt.",
-    "Apply selected styles to current prompt": "Aplica o estilo para o prompt atual.",
-    "Stop processing current image and continue processing.": "Pula a imagem sendo gerada e vai para a próxima.",
-    "Stop processing images and return any results accumulated so far.": "Interrompe o processo e mostra o que foi gerado até então.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Estilo para aplicar; também serve para o prompt negativo e vai preencher se usado.",
-    "Do not do anything special": "Não faça nada de especial",
-    "Which algorithm to use to produce the image": "O tipo de algoritmo para gerar imagens.",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - cria mais variações para as imagens em diferentes passos. Mais que 40 passos cancela o efeito.",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - Funciona melhor para inpainting.",
-    "Produce an image that can be tiled.": "Produz uma imagem que pode ser ladrilhada.",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Cria um processo em duas etapas, com uma imagem em baixa qualidade primeiro, aumenta a imagem e refina os detalhes sem alterar a composição da imagem",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Quanto o algoritmo deve manter da imagem original. Em 0, nada muda. Em 1 o algoritmo ignora a imagem original. Valores menores que 1.0 demoram mais.",
-    "How many batches of images to create": "Quantos lotes de imagens criar",
-    "How many image to create in a single batch": "Quantas imagens criar em um único lote",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - Quanto maior o valor, mais segue o prompt e quanto menor, menor segue.",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Codigo de geração de uma imagem - criando uma imagem com os mesmos parâmetros e seed trazem o mesmo resultado.",
-    "Set seed to -1, which will cause a new random number to be used every time": "Define seed como -1, deixando o valor que vai aparecer como aleatório.",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Reutilizar a seed da última geração, útil principalmente se ela foi aleatória",
-    "Seed of a different picture to be mixed into the generation.": "Seed de uma imagem diferente é misturada na geração.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Qual a variação a ser gerada. Em 0, não tem efeito. Em 1, gera uma imagem completa com a variação de seed, (exceto com amostragens a).",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Tenta gerar uma imagem similar ao que teria sido feito com a mesma seed em dimensões especifica.",
-    "Separate values for X axis using commas.": "Separa os valores para o eixo X usando vírgulas.",
-    "Separate values for Y axis using commas.": "Separa os valores para o eixo Y usando vírgulas.",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Salva a imagem no diretório padrão ou escolhido e cria um arquivo csv com os parâmetros da geração.",
-    "Open images output directory": "Abre o diretório de saída de imagens.",
-    "How much to blur the mask before processing, in pixels.": "Transição do contorno da máscara, em pixels.",
-    "What to put inside the masked area before processing it with Stable Diffusion.":  "O que vai dentro da máscara antes de processá-la com Stable Diffusion.",
-	"fill it with colors of the image": "Preenche usando as cores da imagem.",
-    "keep whatever was there originally": "manter usando o que estava lá originalmente",
-    "fill it with latent space noise": "Preenche com ruídos do espaço latente.",
-    "fill it with latent space zeroes": "Preenche com zeros do espaço latente.",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Faz ampliação na região com máscara para atingir a resolução desejada, faz inpainting, faz downscale para voltar à resolução original e cola na imagem original",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Redimensiona a imagem para a resolução desejada. A menos que a altura e a largura sejam iguais, você obterá uma proporção incorreta.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Redimensiona a imagem para que toda a resolução desejada seja preenchida com a imagem. Corta as partes que ficaram pra fora.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Redimensiona a imagem para que toda a imagem esteja dentro da resolução desejada. Preenche o espaço vazio com as cores da imagem.",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Número de vezes que vai repetir o processamento da imagem e usar como entrada para a próxima iteração",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "No modo de loopback, em cada loop a força do denoise é multiplicado por este valor. <1 significa diminuir a variedade para que sua sequência converta em uma imagem fixa. >1 significa aumentar a variedade para que sua sequência se torne cada vez mais caótica.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Para ampliação SD, quantidade de sobreposição em pixels que deve haver entre os ladrilhos. Os ladrilhos se sobrepõem para que, quando forem mesclados de volta em uma imagem, não haja linhas de junção claramente visíveis.",
-    "A directory on the same machine where the server is running.": "Um diretório na mesma máquina onde o servidor está rodando.",
-    "Leave blank to save images to the default path.": "Deixar em branco para salvar imagens no caminho padrão.",
-    "Result = A * (1 - M) + B * M": "Resultado = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "Resultado = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "Primeiro e último dígito precisam ser 1. ex:`1, 2, 1`",
-    "Path to directory with input images": "Caminho para o diretório com imagens de entrada",
-    "Path to directory where to write outputs": "Caminho para o diretório para gravar as saídas",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa essas tags para definir como os nomes dos arquivos sao escolhidos: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; deixe em branco para manter o padrão.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Se esta opção estiver marcada, as imagens não vão ter marca d`água. Aviso: se você não quer a marca d`água, você pode estar se envolvendo em comportamentos antiéticos",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Usa essas tags para definir como os nomes dos subdiretorios e grades são escolhidos: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; deixe em branco para manter o padrão.",
-    "Restore low quality faces using GFPGAN neural network": "Restaurar rostos de baixa qualidade usando a rede neural GFPGAN",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Esta expressão regular vai retirar palavras do nome do arquivo e serão juntadas via regex usando a opção abaixo em etiquetas usadas em treinamento. Não mexer para manter os nomes como estão.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "Esta string será usada para unir palavras divididas em uma única linha se a opção acima estiver habilitada.",
-    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "Aplicável somente para modelos de inpaint. Determina quanto deve mascarar da imagem original para inpaint e img2img. 1.0 significa totalmente mascarado, que é o comportamento padrão. 0.0 significa uma condição totalmente não mascarada. Valores baixos ajudam a preservar a composição geral da imagem, mas vai encontrar dificuldades com grandes mudanças.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Lista de nomes de configurações, separados por vírgulas, para configurações que devem ir para a barra de acesso rápido na parte superior, em vez da guia de configuração usual. Veja modules/shared.py para nomes de configuração. Necessita reinicialização para aplicar.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Se este valor for diferente de zero, ele será adicionado à seed e usado para inicializar o RNG para ruídos ao usar amostragens com Tempo Estimado. Você pode usar isso para produzir ainda mais variações de imagens ou pode usar isso para combinar imagens de outro software se souber o que está fazendo.",
-    "Leave empty for auto": "Deixar desmarcado para automático"
-}
diff --git a/localizations/ru_RU.json b/localizations/ru_RU.json
deleted file mode 100644
index 664d36ea..00000000
--- a/localizations/ru_RU.json
+++ /dev/null
@@ -1,475 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "Загрузка...",
-    "view": "просмотр",
-    "api": "api",
-    "•": "•",
-    "built with gradio": "На основе Gradio",
-    "Stable Diffusion checkpoint": "Веса Stable Diffusion",
-    "txt2img": "текст-в-рисунок",
-    "img2img": "рисунок-в-рисунок",
-    "Extras": "Дополнения",
-    "PNG Info": "Информация о PNG",
-    "Image Browser": "Просмотр изображений",
-    "History": "Журнал",
-    "Checkpoint Merger": "Слияние весов",
-    "Train": "Обучение",
-    "Create aesthetic embedding": "Создать эмбеддинг эстетики",
-    "Settings": "Настройки",
-    "Prompt": "Запрос",
-    "Negative prompt": "Исключающий запрос",
-    "Run": "Запустить",
-    "Skip": "Пропустить",
-    "Interrupt": "Прервать",
-    "Generate": "Создать",
-    "Style 1": "Стиль 1",
-    "Style 2": "Стиль 2",
-    "Label": "Метка",
-    "File": "Файл",
-    "Drop File Here": "Перетащите файл сюда",
-    "-": "-",
-    "or": "или",
-    "Click to Upload": "Нажмите, чтобы загрузить",
-    "Image": "Рисунок",
-    "Check progress": "Узнать состояние",
-    "Check progress (first)": "Узнать состояние первого",
-    "Sampling Steps": "Шагов семплера",
-    "Sampling method": "Метод семплирования",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "Ширина",
-    "Height": "Высота",
-    "Restore faces": "Восстановить лица",
-    "Tiling": "Замощение",
-    "Highres. fix": "HD-режим",
-    "Firstpass width": "Ширина первого прохода",
-    "Firstpass height": "Высота первого прохода",
-    "Denoising strength": "Сила шумоподавления",
-    "Batch count": "Рисунков подряд",
-    "Batch size": "Рисунков параллельно",
-    "CFG Scale": "Близость к запросу",
-    "Seed": "Семя",
-    "Extra": "Дополнения",
-    "Variation seed": "Вариация семени",
-    "Variation strength": "Вариация шумоподавления",
-    "Resize seed from width": "Поправка в семя от ширины",
-    "Resize seed from height": "Поправка в семя от высоты",
-    "Open for Clip Aesthetic!": "Clip-эстетика!",
-    "▼": "▼",
-    "Aesthetic weight": "Вес эстетики",
-    "Aesthetic steps": "Шагов эстетики",
-    "Aesthetic learning rate": "Скорость обучения эстетики",
-    "Slerp interpolation": "Slerp-интерполяция",
-    "Aesthetic imgs embedding": "Рисунки - эмбеддинги эстетики",
-    "None": "Ничего",
-    "Aesthetic text for imgs": "Имя эстетики рисунков",
-    "Slerp angle": "Угол slerp",
-    "Is negative text": "Это текст для исключения",
-    "Script": "Скрипт",
-    "Prompt matrix": "Матрица запросов",
-    "Prompts from file or textbox": "Запросы из файла или текста",
-    "X/Y plot": "X/Y-график",
-    "Put variable parts at start of prompt": "Переменное начало запроса",
-    "Show Textbox": "Показать текстовый ввод",
-    "File with inputs": "Файл входа",
-    "Prompts": "Запросы",
-    "X type": "Ось X",
-    "Nothing": "Ничего",
-    "Var. seed": "Вариация семени",
-    "Var. strength": "Вариация силы",
-    "Steps": "Число шагов",
-    "Prompt S/R": "Вариация запроса",
-    "Prompt order": "Порядок запросов",
-    "Sampler": "Семплер",
-    "Checkpoint name": "Имя файла весов",
-    "Hypernetwork": "Гиперсеть",
-    "Hypernet str.": "Строка гиперсети",
-    "Sigma Churn": "Возмущение сигмы",
-    "Sigma min": "Мин. сигма",
-    "Sigma max": "Макс. сигма",
-    "Sigma noise": "Сигма-шум",
-    "Eta": "Расчётное время",
-    "Clip skip": "Пропустить Clip",
-    "Denoising": "Шумоподавление",
-    "X values": "Значения X",
-    "Y type": "Тип Y",
-    "Y values": "Значения Y",
-    "Draw legend": "Легенда графика",
-    "Include Separate Images": "Включить отдельные рисунки",
-    "Keep -1 for seeds": "-1 для семени",
-    "Drop Image Here": "Перетащите рисунок сюда",
-    "Save": "Сохранить",
-    "Send to img2img": "В рисунок-в-рисунок",
-    "Send to inpaint": "В режим врисовывания",
-    "Send to extras": "В дополнения",
-    "Make Zip when Save?": "Создать zip при сохранении?",
-    "Textbox": "Текст",
-    "Interrogate\nCLIP": "Распознавание\nCLIP",
-    "Interrogate\nDeepBooru": "Распознавание\nDeepBooru",
-    "Inpaint": "врисовать",
-    "Batch img2img": "рисунок-в-рисунок (набор)",
-    "Image for img2img": "рисунок-в-рисунок (вход)",
-    "Image for inpainting with mask": "врисовать (вход с трафаретом)",
-    "Mask": "Трафарет",
-    "Mask blur": "Размытие трафарета",
-    "Mask mode": "Режим трафарета",
-    "Draw mask": "Нарисовать трафарет",
-    "Upload mask": "Загрузить трафарет",
-    "Masking mode": "Режим трафарета",
-    "Inpaint masked": "Внутри трафарета",
-    "Inpaint not masked": "Вне трафарета",
-    "Masked content": "Под трафаретом",
-    "fill": "залить",
-    "original": "сохранить",
-    "latent noise": "латентный шум",
-    "latent nothing": "латентная пустота",
-    "Inpaint at full resolution": "Врисовать при полном разрешении",
-    "Inpaint at full resolution padding, pixels": "Врисовать с достройкой до полного разрешения, в пикселях",
-    "Process images in a directory on the same machine where the server is running.": "Обрабатывать рисунки на том же компьютере, где сервер",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Использовать пустую папку вместо того, чтобы выводить в output",
-    "Disabled when launched with --hide-ui-dir-config.": "Выключено при запуске с  --hide-ui-dir-config",
-    "Input directory": "Папка входа",
-    "Output directory": "Папка выхода",
-    "Resize mode": "Масштабирование",
-    "Just resize": "Только сжать",
-    "Crop and resize": "Сжать и обрезать",
-    "Resize and fill": "Сжать и залить",
-    "img2img alternative test": "рисунок-в-рисунок (альтернатива)",
-    "Loopback": "Прокручивание",
-    "Outpainting mk2": "Обрисовыватель mk2",
-    "Poor man's outpainting": "Хоть какой-то обрисовыватель",
-    "SD upscale": "SD-апскейл",
-    "should be 2 or lower.": "должно быть меньше равно 2",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Сменить метод семплирования на метод Эйлера?(скрипт строился с его учётом)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Сменить `запрос` на `изначальный запрос`?(и `запрос-исключение`)",
-    "Original prompt": "Изначальный запрос",
-    "Original negative prompt": "Изначальный запрос-исключение",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Сменить число шагов на число шагов декодирования?",
-    "Decode steps": "Шагов декодирования",
-    "Override `Denoising strength` to 1?": "Сменить силу шумоподавления на 1?",
-    "Decode CFG scale": "Близость к запросу декодирования",
-    "Randomness": "Случайность",
-    "Sigma adjustment for finding noise for image": "Поправка к сигме подбора шума для рисунка",
-    "Loops": "Циклов",
-    "Denoising strength change factor": "Множитель силы шумоподавления",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Рекоммендуемые настройки： Число шагов：80-100，Метод：Euler a，Шумоподавление：0.8",
-    "Pixels to expand": "Пикселов расширить",
-    "Outpainting direction": "Направление обрисовывания",
-    "left": "влево",
-    "right": "вправо",
-    "up": "вверх",
-    "down": "вниз",
-    "Fall-off exponent (lower=higher detail)": "Степень затухания （меньше=больше деталей）",
-    "Color variation": "Вариация цвета",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Расширит рисунок дважды； ползунки ширины и высоты устанавливают размеры плиток",
-    "Tile overlap": "Перекрытие плиток",
-    "Upscaler": "Апскейлер",
-    "Lanczos": "Lanczos",
-    "LDSR": "LDSR",
-    "BSRGAN 4x": "BSRGAN 4x",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR_4x": "SwinIR 4x",
-    "Single Image": "Один рисунок",
-    "Batch Process": "Набор рисунков",
-    "Batch from Directory": "Рисунки из папки",
-    "Source": "Вход",
-    "Show result images": "Показать результаты",
-    "Scale by": "Увеличить в",
-    "Scale to": "Увеличить до",
-    "Resize": "Масштабировать",
-    "Crop to fit": "Обрезать до рамки",
-    "Upscaler 2": "Апскейлер 2",
-    "Upscaler 2 visibility": "Видимость Апскейлера 2",
-    "GFPGAN visibility": "Видимость GFPGAN",
-    "CodeFormer visibility": "Видимость CodeFormer",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Вес CodeFormer (0 = максимальное действие, 1 = минимальное)",
-    "Open output directory": "Открыть папку выхода",
-    "Send to txt2img": "В текст-в-рисунок",
-    "txt2img history": "журнал текста-в-рисунок",
-    "img2img history": "журнал рисунка-в-рисунок",
-    "extras history": "журнал дополнений",
-    "Renew Page": "Обновить страницу",
-    "extras": "дополнения",
-    "favorites": "избранное",
-    "Load": "Загрузить",
-    "Images directory": "Папка с рисунками",
-    "Prev batch": "Пред. набор",
-    "Next batch": "След. набор",
-    "First Page": "Первая страница",
-    "Prev Page": "Пред. страница",
-    "Page Index": "Список страниц",
-    "Next Page": "След. страница",
-    "End Page": "Конец страницы",
-    "number of images to delete consecutively next": "сколько рисунков удалить подряд",
-    "Delete": "Удалить",
-    "Generate Info": "Сведения о генерации",
-    "File Name": "Имя файла",
-    "Collect": "Накопить",
-    "Refresh page": "Обновить страницу",
-    "Date to": "Дата",
-    "Number": "Число",
-    "set_index": "индекс",
-    "Checkbox": "Галочка",
-    "A merger of the two checkpoints will be generated in your": "Слияние весов будет создано, где хранятся",
-    "checkpoint": "ckpt",
-    "directory.": "веса",
-    "Primary model (A)": "Первичная модель (A)",
-    "Secondary model (B)": "Вторичная модель (B)",
-    "Tertiary model (C)": "Третичная модель (C)",
-    "Custom Name (Optional)": "Произвольное имя (необязательно)",
-    "Multiplier (M) - set to 0 to get model A": "Множитель (M) - 0 даст модель A",
-    "Interpolation Method": "Метод интерполяции",
-    "Weighted sum": "Взвешенная сумма",
-    "Add difference": "Сумма разностей",
-    "Save as float16": "Сохранить как float16",
-    "See": "См.",
-    "wiki": "вики",
-    "for detailed explanation.": "для подробных объяснений.",
-    "Create embedding": "Создать эмбеддинг",
-    "Create aesthetic images embedding": "Создать эмбеддинг эстетики по рисункам",
-    "Create hypernetwork": "Создать гиперсеть",
-    "Preprocess images": "Предобработать рисунки",
-    "Name": "Имя",
-    "Initialization text": "Соответствующий текст",
-    "Number of vectors per token": "Векторов на токен",
-    "Overwrite Old Embedding": "Перезаписать эмбеддинг",
-    "Source directory": "Исходная папка",
-    "Modules": "Модули",
-    "Enter hypernetwork layer structure": "Структура слоёв гиперсети",
-    "Add layer normalization": "Добавить нормализацию слоёв",
-    "Overwrite Old Hypernetwork": "Перезаписать гиперсеть",
-    "Select activation function of hypernetwork": "Функция активации гиперсети",
-    "linear": "линейная",
-    "relu": "relu",
-    "leakyrelu": "leakyrelu",
-    "Destination directory": "Папка назначения",
-    "Existing Caption txt Action": "Что делать с предыдущим текстом",
-    "ignore": "игнорировать",
-    "copy": "копировать",
-    "prepend": "в начало",
-    "append": "в конец",
-    "Create flipped copies": "Создать отражённые копии",
-    "Split oversized images into two": "Поделить слишком большие рисунки пополам",
-    "Split oversized images": "Поделить слишком большие рисунки",
-    "Use BLIP for caption": "Использовать BLIP для названий",
-    "Use deepbooru for caption": "Использовать deepbooru для тегов",
-    "Split image threshold": "Порог разделения рисунков",
-    "Split image overlap ratio": "Пропорции разделения рисунков",
-    "Preprocess": "Предобработка",
-    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Обучить эмбеддинг； укажите папку рисунков с пропорциями 1:1",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Обучить эмбеддинг или гиперсеть； укажите папку рисунков с пропорциями 1:1",
-    "[wiki]": "[вики]",
-    "Embedding": "Эмбеддинг",
-    "Embedding Learning rate": "Скорость обучения эмбеддинга",
-    "Hypernetwork Learning rate": "Скорость обучения гиперсети",
-    "Learning rate": "Скорость обучения",
-    "Dataset directory": "Папка датасета",
-    "Log directory": "Папка журнала",
-    "Prompt template file": "Файл шаблона запроса",
-    "Max steps": "Макс. шагов",
-    "Save an image to log directory every N steps, 0 to disable": "Сохранять рисунок каждые N шагов, 0 чтобы отключить",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Сохранять эмбеддинг каждые N шагов, 0 чтобы отключить",
-    "Save images with embedding in PNG chunks": "Сохранить рисунок с эмбеддингом в виде PNG-фрагментов",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Считать параметры (запрос и т.д.) из вкладки текст-в-рисунок для предпросмотра",
-    "Train Hypernetwork": "Обучить гиперсеть",
-    "Train Embedding": "Обучить эмбеддинг",
-    "Create an aesthetic embedding out of any number of images": "Создать эмбеддинг эстетики по любому числу рисунков",
-    "Create images embedding": "Создать эмбеддинг рисунков",
-    "Apply settings": "Применить настройки",
-    "Saving images/grids": "Сохранение рисунков/таблиц",
-    "Always save all generated images": "Всегда сохранять созданные рисунки",
-    "File format for images": "Формат файла рисунков",
-    "Images filename pattern": "Формат имени файлов рисунков",
-    "Always save all generated image grids": "Всегда сохранять созданные таблицы",
-    "File format for grids": "Формат файла таблиц",
-    "Add extended info (seed, prompt) to filename when saving grid": "Вставлять доп. сведения (семя, запрос) в имя файла таблиц",
-    "Do not save grids consisting of one picture": "Не сохранять таблицы из одного рисунка",
-    "Prevent empty spots in grid (when set to autodetect)": "Не допускать пустоты в таблицах （автообнаружение）",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Число строк таблицы； -1, чтобы автоматически, 0 — размер набора",
-    "Save text information about generation parameters as chunks to png files": "Встроить сведения о генерации в файлы png",
-    "Create a text file next to every image with generation parameters.": "Создать текстовый файл для каждого рисунка с параметрами генерации",
-    "Save a copy of image before doing face restoration.": "Сохранить копию перед восстановлением лиц",
-    "Quality for saved jpeg images": "Качество jpeg-рисунков",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Если размер PNG больше 4МБ или рисунок шире 4000 пикселей, пересжать в JPEG",
-    "Use original name for output filename during batch process in extras tab": "Использовать исходное имя выходного файла для обработки набора во вкладке дополнений",
-    "When using 'Save' button, only save a single selected image": "Сохранять только один рисунок при нажатии кнопки Сохранить",
-    "Do not add watermark to images": "Не добавлять водяной знак",
-    "Paths for saving": "Папки сохранений",
-    "Output directory for images; if empty, defaults to three directories below": "Папка выхода рисунков; если пусто, использует те, что ниже",
-    "Output directory for txt2img images": "Папка выхода текста-в-рисунок",
-    "Output directory for img2img images": "Папка выхода рисунка-в-рисунок",
-    "Output directory for images from extras tab": "Папка выхода для дополнений",
-    "Output directory for grids; if empty, defaults to two directories below": "Папка выхода таблиц; если пусто, использует папки выше",
-    "Output directory for txt2img grids": "Папка выхода текста-в-рисунок",
-    "Output directory for img2img grids": "Папка выхода рисунка-в-рисунок",
-    "Directory for saving images using the Save button": "Папка выхода для кнопки Сохранить",
-    "Saving to a directory": "Сохранить в папку",
-    "Save images to a subdirectory": "Сохранить рисунки в подпапку",
-    "Save grids to a subdirectory": "Сохранить таблицы в подпапку",
-    "When using \"Save\" button, save images to a subdirectory": "При нажатии кнопки Сохранить, сложить рисунки в подпапку",
-    "Directory name pattern": "Шаблон имени папки",
-    "Max prompt words for [prompt_words] pattern": "Макс. число слов для шаблона [prompt_words]",
-    "Upscaling": "Апскейл",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "Размер плитки для ESRGAN. 0 = нет замощения",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Наложение плиток ESRGAN, в пикселях. Меньше = выделеннее шов",
-    "Tile size for all SwinIR.": "Размер плиток SwinIR",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Наложение плиток SwinIR, в пикселях. Меньше = выделеннее шов",
-    "LDSR processing steps. Lower = faster": "Число шагов LDSR. Меньше = быстрее",
-    "Upscaler for img2img": "Апскейлер рисунка-в-рисунок",
-    "Upscale latent space image when doing hires. fix": "Апскейлить образ латентного пространства для HD-режима",
-    "Face restoration": "Восстановление лиц",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Вес CodeFormer； 0 = максимальное действие； 1 = минимальное",
-    "Move face restoration model from VRAM into RAM after processing": "Переместить модель восстановления лиц из ВОЗУ в ОЗУ после обработки",
-    "System": "Система",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "Сколько раз в секунду следить за потреблением ВОЗУ. 0, чтобы отключить",
-    "Always print all generation info to standard output": "Выводить все сведения о генерации в стандартный вывод",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Вторая шкала прогресса для всей задачи",
-    "Training": "Обучение",
-    "Unload VAE and CLIP from VRAM when training": "Убрать VAE и CLIP из ВОЗУ на время обучения",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Переместить VAE и CLIP в ОЗУ на время обучения гиперсети. Сохраняет ВОЗУ",
-    "Filename word regex": "Regex имени файла",
-    "Filename join string": "Дополнить к имени файла",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Число повторов для каждого рисунка за эпоху; используется только, чтобы отобразить число эпохи",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Сохранять csv с параметром loss в папку журнала каждые N шагов, 0 - отключить",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "Удерживать веса в ОЗУ",
-    "Hypernetwork strength": "Сила гиперсети",
-    "Apply color correction to img2img results to match original colors.": "Цветокоррекция вывода рисунка-в-рисунок, сохраняющая исходные цвета",
-    "Save a copy of image before applying color correction to img2img results": "Сохранить копию рисунка перед цветокоррекцией",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "В режиме рисунок-в-рисунок сделать ровно указанное ползунком число шагов (обычно шумоподавление их уменьшает)",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Включить квантование К-семплерах для более резких и чистых результатов. Может потребовать поменять семя. Требует перезапуска.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Скобки: (понятие) - больше внимания к тексту, [понятие] - меньше внимания к тексту",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Включить старую обработку скобок. Может потребоваться, чтобы воспроизвести старые семена.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "Заставить семплеры K-diffusion производить тот же самый рисунок в наборе, как и в единичной генерации",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Увеличить связность, достраивая запрос от последней запятой до n токенов, когда используется свыше 75 токенов",
-    "Filter NSFW content": "Фильтровать небезопасный контент",
-    "Stop At last layers of CLIP model": "Остановиться на последних слоях модели CLIP",
-    "Interrogate Options": "Опции распознавания",
-    "Interrogate: keep models in VRAM": "Распознавание: хранить модели в ВОЗУ",
-    "Interrogate: use artists from artists.csv": "Распознавание: использовать художников из artists.csv",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Распознавание: включить ранжирование совпавших тегов в результате (не работает для распознавателей-создателей заголовков)",
-    "Interrogate: num_beams for BLIP": "Распознавание: num_beams для BLIP",
-    "Interrogate: minimum description length (excluding artists, etc..)": "Распознавание: минимальная длина описания (исключая художников и т.п.)",
-    "Interrogate: maximum description length": "Распознавание: максимальная длина описания",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: максимальное число строк в текстовом файле (0 = без ограничений)",
-    "Interrogate: deepbooru score threshold": "Распознавание: ограничение счёта deepbooru",
-    "Interrogate: deepbooru sort alphabetically": "Распознавание: сортировать deepbooru по алфавиту",
-    "use spaces for tags in deepbooru": "Пробелы для тегов deepbooru",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Использовать скобки в deepbooru как обычные скобки, а не для усиления",
-    "User interface": "Пользовательский интерфейс",
-    "Show progressbar": "Шкала прогресса",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Показывать процесс созданния рисунка каждые N шагов. 0 - отключить",
-    "Show grid in results for web": "Показать таблицу в выводе браузера",
-    "Do not show any images in results for web": "Не показывать выходные рисунки в браузере",
-    "Add model hash to generation information": "Добавить хеш весов к параметрам генерации",
-    "Add model name to generation information": "Добавить имя весов к параметрам генерации",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "При считывании параметров генерации из текста в интерфейс, не менять выбранную модель/веса.",
-    "Font for image grids that have text": "Шрифт для таблиц, содержащих текст",
-    "Enable full page image viewer": "Включить полноэкранный просмотр картинок",
-    "Show images zoomed in by default in full page image viewer": "По умолчанию увеличивать картинки в полноэкранном просмотре",
-    "Show generation progress in window title.": "Отображать прогресс в имени вкладки",
-    "Quicksettings list": "Список быстрых настроек",
-    "Localization (requires restart)": "Перевод （требует перезапуск）",
-    "Sampler parameters": "Параметры семплера",
-    "Hide samplers in user interface (requires restart)": "Убрать семплеры из интерфейса (требует перезапуск)",
-    "eta (noise multiplier) for DDIM": "eta (множитель шума) DDIM",
-    "eta (noise multiplier) for ancestral samplers": "eta (множитель шума) для ancestral-семплеров",
-    "img2img DDIM discretize": "дискретизация DDIM для рисунка-в-рисунок",
-    "uniform": "однородная",
-    "quad": "квадратичная",
-    "sigma churn": "сигма-вариация",
-    "sigma tmin": "сигма-tmin",
-    "sigma noise": "сигма-шум",
-    "Eta noise seed delta": "Eta (дельта шума семени)",
-    "Images Browser": "Просмотр изображений",
-    "Preload images at startup": "Предзагружать рисунки во время запуска",
-    "Number of pictures displayed on each page": "Число рисунков на каждой странице",
-    "Minimum number of pages per load": "Мин. число загружаемых страниц",
-    "Number of grids in each row": "Число таблиц в каждой строке",
-    "Request browser notifications": "Запросить уведомления браузера",
-    "Download localization template": "Загрузить щаблон перевода",
-    "Reload custom script bodies (No ui updates, No restart)": "Перезагрузить пользовательские скрипты (не требует обновления интерфейса и перезапуска)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Перезагрузить Gradio и обновить компоненты (только пользовательские скрипты, ui.py, js и css)",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Запрос （нажмите Ctrl+Enter или Alt+Enter для генерации）",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Запрос-исключение （нажмите Ctrl+Enter или Alt+Enter для генерации）",
-    "Add a random artist to the prompt.": "Добавить случайного художника к запросу",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Считать параметры генерации из запроса или из предыдущей генерации в пользовательский интерфейс, если пусто",
-    "Save style": "Сохранить стиль",
-    "Apply selected styles to current prompt": "Применить выбранные стили к текущему промпту",
-    "Stop processing current image and continue processing.": "Прекратить обрабатывать текущий рисунок, но продолжить работу",
-    "Stop processing images and return any results accumulated so far.": "Прекратить обрабатку рисунков и вернуть всё, что успели сделать.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Стиль к применению; стили содержат как запрос, так и исключение, и применяют их оба",
-    "Do not do anything special": "Не делать ничего особенного",
-    "Which algorithm to use to produce the image": "Какой алгоритм использовать для того, чтобы произвести рисунок",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - очень творческий, в зависимости от числа шагов может привести совершенно к различным результатам, выше 30-40 лучше не ставить",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit модели - лучше всего для обрисовки",
-    "Produce an image that can be tiled.": "Сделать из рисунка непрерывную обёртку",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Применить двушаговый процесс, чтобы создать рисунок на меньшем разрешении, апскейлнуть, а затем улучшить детали без смены композиции",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Определяет, насколько сильно алгоритм будет опираться на содержание изображения. 0 - не меняет ничего, 1 - совсем не связанный выход. Меньше 1.0 процесс использует меньше шагов, чем указано их ползунком.",
-    "How many batches of images to create": "Сколько создать наборов из картинок",
-    "How many image to create in a single batch": "Сколько картинок создать в каждом наборе",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale: насколько сильно изображение должно соответсвтовать запросу — меньшие значения приведут к более свободным итогам",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Значение, которое определяет выход генератора случайных чисел — если вы создадите рисунок с теми же параметрами и семенем, как у другого изображения, вы получите тот же результат",
-    "Set seed to -1, which will cause a new random number to be used every time": "Установить семя в -1, что вызовет каждый раз случайное число",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Использовать семя предыдущей генерации, обычно полезно, если оно было случайным",
-    "Seed of a different picture to be mixed into the generation.": "Семя с другого рисунка, подмешенного в генерацию.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Насколько сильную вариацию произвести. При 0м значении действия не будет. Для 1 вы получите полноценный рисунок с семенем вариации (кроме ancestral-семплеров, где вы просто что-то получите).",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Попытаться воспроизвести изображение, похожее на то, чтобы получилось с тем же семенем на выбранном разрешении",
-    "This text is used to rotate the feature space of the imgs embs": "Этот текст используется, чтобы произвести вращение пространства признаков из эмбеддинга рисунков",
-    "Separate values for X axis using commas.": "Отдельные значения оси X через запятую.",
-    "Separate values for Y axis using commas.": "Отдельные значения оси Y через запятую.",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Записать изображение в папку (по-умолчанию - log/images), а параметры генерации - в csv файл",
-    "Open images output directory": "Открыть папку сохранения изображений",
-    "How much to blur the mask before processing, in pixels.": "Насколько пикселей размыть трафарет перед обработкой",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Что поместить в область под трафаретом перед обработкой Stable Diffusion",
-    "fill it with colors of the image": "залить цветами изображения",
-    "keep whatever was there originally": "сохранить то, что было до этого",
-    "fill it with latent space noise": "залить латентным шумом",
-    "fill it with latent space zeroes": "залить латентными нулями",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "апскейл до нужного разрешения, врисовка, сжатие до начального размера и вставка в исходный рисунок",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Масшабировать изображение до нужного разрешения. Если только высота и ширина не совпадают, вы получите неверное соотношение сторон.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Масштабировать изображение так, чтобы им заполнялось всё выбранное выходное разрешение. Обрезать выступающие части",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Масштабировать изображение так, всё изображение помещалось в выбранное выходное разрешение. Заполнить пустое место цветами изображения.",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Сколько раз повторить обработку изображения и использовать её как вход для следующией итерации",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "В режиме прокрутки, для каждого цикла сила шумоподавления умножается на это значение. <1 уменьшает вариации так, чтобы последовательность сошлась на какой-то одной картинке. >1 увеличивает вариации, так что ваша последовательность станет всё более и более сумбурной.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Для SD-апскейла, как много перекрытия в пикселях должно быть между плитками. Плитки перекрываются таким образом, чтобы они могли сойтись обратно в единое изображение, без видимого шва.",
-    "A directory on the same machine where the server is running.": "Папка на той же машине, где запущен сервер",
-    "Leave blank to save images to the default path.": "Оставьте пустым, чтобы сохранить рисунки в папку по-умолчанию",
-    "Result = A * (1 - M) + B * M": "Выход = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "Выход = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "1я и последняя цифры должны быть 1. напр.'1, 2, 1'",
-    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "как быстро будет происходить обучение. Меньшие значения увеличат время обучения, но высокие могут нарушить сходимость модели (не будет создавать должные результаты) и/или сломать эмбеддинг. (Это случилось, если вы видете Loss: nan в текстовом окне вывода обучения. В этом случае вам придётся восстанавливать эмбеддинг вручную из старой, не повреждённой резервной копии).\n\nВы также можете указать единичное значение или последовательность из нескольких, используя следующий синтаксис:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nБудет обучаться со скоростью 0.005 первые 100 шагов, затем 1e-3 до 1000 шагов, после 1e-5 для всех оставшихся шагов.",
-    "Path to directory with input images": "Путь к папке со входными изображениями",
-    "Path to directory where to write outputs": "Путь к папке, в которую записывать результаты",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Используйте следующие теги, чтобы определить, как подбираются названия файлов для изображений: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; если пусто, используется значение по-умолчанию",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Когда эта опция включена, на созданные изображения не будет добавляться водяной знак. Предупреждение: не добавляя водяной знак, вы, вероятно, ведёте себя аморально.",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Используйте следующие теги, чтобы определить, как подбираются названия подпапок для рисунков и табоиц: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; если пусто, используется значение по-умолчанию",
-    "Restore low quality faces using GFPGAN neural network": "Восстановить низкокачественные лица, используя нейросеть GFPGAN",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Это регулярное выражение будет использовано, чтобы извлечь слова из имени файла, и они будут соединены с текстом в метке ниже как вход во время обучения. Оставьте пустым, чтобы сохранить имя файла как есть",
-    "This string will be used to join split words into a single line if the option above is enabled.": "Эта строка будет использована, чтобы объединить разделённые слова в одну строку, если включена опция выше.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Список имён настроек, разделённый запятыми, предназначенных для быстрого доступа через панель наверху, а не через привычную вкладку настроек. Для применения требует перезапуска.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Если это значение не нулевое, оно будет добавлено к семени и использовано для инициалицации ГСЧ шума семплеров с параметром Eta. Вы можете использовать это, чтобы произвести ещё больше вариаций рисунков, либо же для того, чтобы подойти близко к результатам других программ, если знаете, что делаете.",
-    "Enable Autocomplete": "Включить автодополнение",
-    "Allowed categories for random artists selection when using the Roll button": "Разрешённые категории художников для случайного выбора при использовании кнопки + три",
-    "Roll three": "+ три",
-    "Generate forever": "Непрерывная генерация",
-    "Cancel generate forever": "Отключить непрерывную генерацию"
-}
diff --git a/localizations/tr_TR.json b/localizations/tr_TR.json
deleted file mode 100644
index 74cdf056..00000000
--- a/localizations/tr_TR.json
+++ /dev/null
@@ -1,423 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "Yükleniyor...",
-    "view": "arayüz",
-    "api": "",
-    "•": "-",
-    "built with gradio": "gradio ile inşa edildi",
-    "Stable Diffusion checkpoint": "Kararlı Difüzyon kontrol noktası",
-    "txt2img": "txt2img",
-    "img2img": "img2img",
-    "Extras": "Ekstralar",
-    "PNG Info": "PNG Bilgisi",
-    "Checkpoint Merger": "Checkpoint Birleştir",
-    "Train": "Eğitim",
-    "Settings": "Ayarlar",
-    "Prompt": "İstem",
-    "Negative prompt": "Negatif istem",
-    "Run": "Koşmak",
-    "Skip": "Atla",
-    "Interrupt": "Durdur",
-    "Generate": "Oluştur",
-    "Style 1": "Stil 1",
-    "Style 2": "Stil 2",
-    "Label": "Etiket",
-    "File": "Dosya",
-    "Drop File Here": "Dosyayı Buraya Bırakın",
-    "-": "-",
-    "or": "veya",
-    "Click to Upload": "Yüklemek için Tıklayınız",
-    "Image": "Resim",
-    "Check progress": "İlerlemeyi kontrol edin",
-    "Check progress (first)": "Önce ilerlemeyi kontrol edin",
-    "Sampling Steps": "Örnekleme Adımları",
-    "Sampling method": "Örnekleme yöntemi",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM hızlı",
-    "DPM adaptive": "DPM uyarlanabilir",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "Genişlik",
-    "Height": "Yükseklik",
-    "Restore faces": "Yüzleri düzeltme",
-    "Tiling": "Döşeme Oluştur",
-    "Highres. fix": "Highres. düzeltme",
-    "Firstpass width": "İlk geçiş genişliği",
-    "Firstpass height": "İlk geçiş yüksekliği",
-    "Denoising strength": "Gürültü arındırma gücü",
-    "Batch count": "Grup sayısı",
-    "Batch size": "Grup büyüklüğü",
-    "CFG Scale": "CFG Ölçeği",
-    "Seed": "Tohum",
-    "Extra": "Ekstra",
-    "Variation seed": "Varyasyon tohumu",
-    "Variation strength": "Varyasyon gücü",
-    "Resize seed from width": "Tohumu genişlik ile yeniden boyutlandırma",
-    "Resize seed from height": "Tohumu yükseklik ile yeniden boyutlandırma",
-    "Script": "Scriptler",
-    "None": "Hiçbiri",
-    "Prompt matrix": "İstem matrisi",
-    "Prompts from file or textbox": "Dosyadan veya metin kutusundan istemler",
-    "X/Y plot": "X/Y grafiği",
-    "Put variable parts at start of prompt": "Değişken parçaları komut isteminin başına koyun",
-    "Show Textbox": "Metin Kutusunu Göster",
-    "File with inputs": "Girdileri içeren dosya",
-    "Prompts": "İpuçları",
-    "X type": "X tipi",
-    "Nothing": "Hiçbir şey",
-    "Var. seed": "Var. tohum",
-    "Var. strength": "Var. güç",
-    "Steps": "Adımlar",
-    "Prompt S/R": "İstem S/R",
-    "Prompt order": "İstem sırası",
-    "Sampler": "Örnekleyici",
-    "Checkpoint name": "Kontrol noktası adı",
-    "Hypernetwork": "Hipernetwork",
-    "Hypernet str.": "Hypernet str.",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma min": "Sigma dakika",
-    "Sigma max": "Sigma maksimum",
-    "Sigma noise": "Sigma gürültüsü",
-    "Eta": "Eta",
-    "Clip skip": "Klip atlama",
-    "Denoising": "Denoising",
-    "X values": "X değerleri",
-    "Y type": "Y tipi",
-    "Y values": "Y değerleri",
-    "Draw legend": "Gösterge çizin",
-    "Include Separate Images": "Ayrı Görseller Ekleyin",
-    "Keep -1 for seeds": "Tohumlar için -1'i saklayın",
-    "Drop Image Here": "Resmi Buraya Bırakın",
-    "Save": "Kaydet",
-    "Send to img2img": "img2img'ye gönder",
-    "Send to inpaint": "Inpaint'e gönder",
-    "Send to extras": "Ekstralara gönder",
-    "Make Zip when Save?": "Kaydederken Zip Yap?",
-    "Textbox": "Metin Kutusu",
-    "Interrogate\nCLIP": "Sorgula\nCLIP",
-    "Inpaint": "Inpaint",
-    "Batch img2img": "Toplu img2img",
-    "Image for img2img": "img2img için resim",
-    "Image for inpainting with mask": "Maske ile inpainting için görüntü",
-    "Mask": "Maske",
-    "Mask blur": "Maske bulanıklığı",
-    "Mask mode": "Maske modu",
-    "Draw mask": "Maske çizin",
-    "Upload mask": "Maske yükle",
-    "Masking mode": "Maskeleme modu",
-    "Inpaint masked": "Maskeli inpaint",
-    "Inpaint not masked": "Boya maskelenmemiş",
-    "Masked content": "Maskelenmiş içerik",
-    "fill": "doldurun",
-    "original": "orijinal",
-    "latent noise": "gizli gürültü",
-    "latent nothing": "gizli hiçbir şey",
-    "Inpaint at full resolution": "Tam çözünürlükte inpaint",
-    "Inpaint at full resolution padding, pixels": "Tam çözünürlükte inpaint dolgu, piksel",
-    "Process images in a directory on the same machine where the server is running.": "Görüntüleri sunucunun çalıştığı makinedeki bir dizinde işleyin.",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Resimleri çıktı dizinine yazmak yerine normal şekilde kaydetmek için boş bir çıktı dizini kullanın.",
-    "Input directory": "Girdi dizini",
-    "Output directory": "Çıktı dizini",
-    "Resize mode": "Yeniden boyutlandırma modu",
-    "Just resize": "Sadece yeniden boyutlandır",
-    "Crop and resize": "Kırpma ve yeniden boyutlandırma",
-    "Resize and fill": "Yeniden boyutlandırın ve doldurun",
-    "img2img alternative test": "img2img alternatif test",
-    "Loopback": "Geri Döngü",
-    "Outpainting mk2": "Outpainting mk2",
-    "Poor man's outpainting": "Zavallı adamın dış boyaması",
-    "SD upscale": "SD lüks",
-    "should be 2 or lower.": "2 veya daha düşük olmalıdır.",
-    "Override `Sampling method` to Euler?(this method is built for it)": "Euler için `Örnekleme yöntemini` geçersiz kılın (bu yöntem bunun için oluşturulmuştur)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Prompt` değerini `orijinal prompt` ile aynı değere geçersiz kılma (ve `negatif prompt`)",
-    "Original prompt": "Orijinal bilgi istemi",
-    "Original negative prompt": "Orijinal negatif istem",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "Örnekleme Adımlarını `Kod çözme adımları` ile aynı değere mi geçersiz kılıyorsunuz?",
-    "Decode steps": "Kod çözme adımları",
-    "Override `Denoising strength` to 1?": "`Denoising strength` değerini 1 olarak geçersiz kıl?",
-    "Decode CFG scale": "CFG ölçeğinin kodunu çöz",
-    "Randomness": "Rastgelelik",
-    "Sigma adjustment for finding noise for image": "Görüntü için gürültü bulmaya yönelik Sigma ayarı",
-    "Loops": "Döngüler",
-    "Denoising strength change factor": "Denoising gücü değişim faktörü",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Önerilen ayarlar: Örnekleme Adımları: 80-100, Örnekleyici: Euler a, Denoising gücü: 0.8",
-    "Pixels to expand": "Genişletilecek pikseller",
-    "Outpainting direction": "Dış boyama yönü",
-    "left": "sol",
-    "right": "doğru",
-    "up": "yukarı",
-    "down": "aşağı",
-    "Fall-off exponent (lower=higher detail)": "Düşme üssü (düşük=daha yüksek detay)",
-    "Color variation": "Renk çeşitliliği",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Görüntüyü boyutlarının iki katına yükseltir; döşeme boyutunu ayarlamak için genişlik ve yükseklik kaydırıcılarını kullanın",
-    "Tile overlap": "Karo örtüşmesi",
-    "Upscaler": "Upscaler",
-    "Lanczos": "Lanczos",
-    "LDSR": "LDSR",
-    "SwinIR 4x": "SwinIR 4x",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "Single Image": "Tek Resim",
-    "Batch Process": "Toplu İşlem",
-    "Batch from Directory": "Dizinden Toplu İş",
-    "Source": "Kaynak",
-    "Show result images": "Sonuç resimlerini göster",
-    "Scale by": "Ölçek tarafından",
-    "Scale to": "Ölçeklendir",
-    "Resize": "Yeniden Boyutlandır",
-    "Crop to fit": "Sığdırmak için kırpın",
-    "Upscaler 2 visibility": "Upscaler 2 görünürlüğü",
-    "GFPGAN visibility": "GFPGAN görünürlüğü",
-    "CodeFormer visibility": "CodeFormer görünürlüğü",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer ağırlığı (0 = maksimum etki, 1 = minimum etki)",
-    "Open output directory": "Çıktı dizinini aç",
-    "Send to txt2img": "txt2img'ye gönder",
-    "A merger of the two checkpoints will be generated in your": "İki kontrol noktasının bir birleşimi sizin kontrol noktanızda oluşturulacaktır.",
-    "checkpoint": "kontrol noktası",
-    "directory.": "dizin.",
-    "Primary model (A)": "Birincil model (A)",
-    "Secondary model (B)": "İkincil model (B)",
-    "Tertiary model (C)": "Üçüncü model (C)",
-    "Custom Name (Optional)": "Özel Ad (İsteğe Bağlı)",
-    "Multiplier (M) - set to 0 to get model A": "Çarpan (M) - A modelini elde etmek için 0'a ayarlayın",
-    "Interpolation Method": "İnterpolasyon Yöntemi",
-    "Weighted sum": "Ağırlıklı toplam",
-    "Add difference": "Farklılık ekleyin",
-    "Save as float16": "float16 olarak kaydet",
-    "See": "Bkz. ",
-    "wiki": "wiki",
-    "for detailed explanation.": " ayrıntılı açıklama için.",
-    "Create embedding": "Yerleştirme oluşturma",
-    "Create hypernetwork": "Hipernet oluşturun",
-    "Preprocess images": "Görüntüleri ön işleme",
-    "Name": "İsim",
-    "Initialization text": "Başlatma metni",
-    "Number of vectors per token": "Belirteç başına vektör sayısı",
-    "Overwrite Old Embedding": "Eski Yerleştirmenin Üzerine Yaz",
-    "Modules": "Modüller",
-    "Enter hypernetwork layer structure": "Hipernetwork katman yapısına girin",
-    "Select activation function of hypernetwork": "Hipernetwork'ün aktivasyon fonksiyonunu seçin",
-    "linear": "doğrusal",
-    "relu": "relu",
-    "leakyrelu": "leakyrelu",
-    "elu": "elu",
-    "swish": "swish",
-    "Add layer normalization": "Katman normalizasyonu ekleyin",
-    "Use dropout": "Bırakmayı kullanın",
-    "Overwrite Old Hypernetwork": "Eski Hipernetwork'ün Üzerine Yazma",
-    "Source directory": "Kaynak dizini",
-    "Destination directory": "Hedef dizini",
-    "Existing Caption txt Action": "Mevcut Başlık txt Eylem",
-    "ignore": "görmezden gel",
-    "copy": "kopya",
-    "prepend": "prepend",
-    "append": "ekle",
-    "Create flipped copies": "Ters çevrilmiş kopyalar oluşturun",
-    "Split oversized images": "Büyük boyutlu görüntüleri bölme",
-    "Use BLIP for caption": "Başlık için BLIP kullanın",
-    "Use deepbooru for caption": "Başlık için deepbooru kullanın",
-    "Split image threshold": "Bölünmüş görüntü eşiği",
-    "Split image overlap ratio": "Bölünmüş görüntü örtüşme oranı",
-    "Preprocess": "Ön işlem",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Bir gömme veya Hipernetwork eğitin; 1:1 oranlı görüntülerin bulunduğu bir dizin belirtmelisiniz",
-    "[wiki]": "[wiki]",
-    "Embedding": "Yerleştirme",
-    "Embedding Learning rate": "Gömme Öğrenme oranı",
-    "Hypernetwork Learning rate": "Hypernetwork Öğrenme oranı",
-    "Dataset directory": "Veri seti dizini",
-    "Log directory": "Günlük dizini",
-    "Prompt template file": "Komut istemi şablon dosyası",
-    "Max steps": "Maksimum adım",
-    "Save an image to log directory every N steps, 0 to disable": "Her N adımda bir görüntüyü günlük dizinine kaydet, 0 devre dışı bırakmak için",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "Katıştırmanın bir kopyasını her N adımda bir günlük dizinine kaydedin, devre dışı bırakmak için 0",
-    "Save images with embedding in PNG chunks": "Görüntüleri PNG parçalarına yerleştirerek kaydedin",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "Önizleme yaparken txt2img sekmesinden parametreleri (istem, vb...) okuma",
-    "Train Hypernetwork": "Tren Hipernetwork",
-    "Train Embedding": "Tren Gömme",
-    "Apply settings": "Ayarları uygula",
-    "Saving images/grids": "Görüntüleri/gridleri kaydetme",
-    "Always save all generated images": "Oluşturulan tüm görüntüleri her zaman kaydedin",
-    "File format for images": "Görüntüler için dosya formatı",
-    "Images filename pattern": "Görüntü dosya adı deseni",
-    "Add number to filename when saving": "Kaydederken dosya adına numara ekle",
-    "Always save all generated image grids": "Oluşturulan tüm görüntü ızgaralarını her zaman kaydedin",
-    "File format for grids": "Izgaralar için dosya formatı",
-    "Add extended info (seed, prompt) to filename when saving grid": "Izgarayı kaydederken dosya adına genişletilmiş bilgi (tohum, istem) ekleyin",
-    "Do not save grids consisting of one picture": "Tek resimden oluşan ızgaraları kaydetmeyin",
-    "Prevent empty spots in grid (when set to autodetect)": "Izgaradaki boş noktaları önleme (otomatik algılamaya ayarlandığında)",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Izgara satır sayısı; otomatik algılama için -1, yığın boyutuyla aynı olması için 0 kullanın",
-    "Save text information about generation parameters as chunks to png files": "Üretim parametreleri hakkındaki metin bilgilerini png dosyalarına parçalar halinde kaydedin",
-    "Create a text file next to every image with generation parameters.": "Oluşturma parametreleri ile her görüntünün yanında bir metin dosyası oluşturun.",
-    "Save a copy of image before doing face restoration.": "Yüz restorasyonu yapmadan önce görüntünün bir kopyasını kaydedin.",
-    "Quality for saved jpeg images": "Kaydedilen jpeg görüntüleri için kalite",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "PNG görüntüsü 4MB'den büyükse veya herhangi bir boyut 4000'den büyükse, ölçeği küçültün ve kopyayı JPG olarak kaydedin",
-    "Use original name for output filename during batch process in extras tab": "Ekstralar sekmesinde toplu işlem sırasında çıktı dosya adı için orijinal adı kullan",
-    "When using 'Save' button, only save a single selected image": "'Kaydet' düğmesini kullanırken, yalnızca seçilen tek bir resmi kaydedin",
-    "Do not add watermark to images": "Görüntülere filigran eklemeyin",
-    "Paths for saving": "Tasarruf için yollar",
-    "Output directory for images; if empty, defaults to three directories below": "Görüntüler için çıktı dizini; boşsa, varsayılan olarak aşağıdaki üç dizine gider",
-    "Output directory for txt2img images": "txt2img görüntüleri için çıktı dizini",
-    "Output directory for img2img images": "img2img görüntüleri için çıktı dizini",
-    "Output directory for images from extras tab": "Ekstralar sekmesindeki görüntüler için çıktı dizini",
-    "Output directory for grids; if empty, defaults to two directories below": "Izgaralar için çıktı dizini; boşsa, varsayılan olarak aşağıdaki iki dizine gider",
-    "Output directory for txt2img grids": "txt2img ızgaraları için çıktı dizini",
-    "Output directory for img2img grids": "img2img ızgaraları için çıktı dizini",
-    "Directory for saving images using the Save button": "Kaydet düğmesini kullanarak görüntüleri kaydetmek için dizin",
-    "Saving to a directory": "Bir dizine kaydetme",
-    "Save images to a subdirectory": "Görüntüleri bir alt dizine kaydetme",
-    "Save grids to a subdirectory": "Izgaraları bir alt dizine kaydetme",
-    "When using \"Save\" button, save images to a subdirectory": "\"Kaydet\" düğmesini kullanırken, görüntüleri bir alt dizine kaydedin",
-    "Directory name pattern": "Dizin adı kalıbı",
-    "Max prompt words for [prompt_words] pattern": "prompt_words] kalıbı için maksimum istem sözcükleri",
-    "Upscaling": "Yükseltme",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN yükselticileri için döşeme boyutu. 0 = döşeme yok.",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN yükselticileri için piksel cinsinden döşeme örtüşmesi. Düşük değerler = görünür bağlantı hattı.",
-    "Tile size for all SwinIR.": "Tüm SwinIR için döşeme boyutu.",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR için piksel cinsinden döşeme örtüşmesi. Düşük değerler = görünür dikiş.",
-    "LDSR processing steps. Lower = faster": "LDSR işleme adımları. Düşük = daha hızlı",
-    "Upscaler for img2img": "img2img için üst ölçekleyici",
-    "Upscale latent space image when doing hires. fix": "İşe alım yaparken gizli alan görüntüsünü yükselt. düzelt",
-    "Face restoration": "Yüz restorasyonu",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer ağırlık parametresi; 0 = maksimum etki; 1 = minimum etki",
-    "Move face restoration model from VRAM into RAM after processing": "İşlemden sonra yüz restorasyon modelini VRAM'den RAM'e taşıma",
-    "System": "Sistem",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "Üretim sırasında saniye başına VRAM kullanım yoklamaları. Devre dışı bırakmak için 0 olarak ayarlayın.",
-    "Always print all generation info to standard output": "Tüm üretim bilgilerini her zaman standart çıktıya yazdır",
-    "Add a second progress bar to the console that shows progress for an entire job.": "Konsola tüm iş için ilerlemeyi gösteren ikinci bir ilerleme çubuğu ekleyin.",
-    "Training": "Eğitim",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Hiperneti eğitirken VAE ve CLIP'i RAM'e taşıyın. VRAM'den tasarruf sağlar.",
-    "Filename word regex": "Dosya adı kelime regex",
-    "Filename join string": "Dosya adı birleştirme dizesi",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Epok başına tek bir girdi görüntüsü için tekrar sayısı; yalnızca epok numarasını görüntülemek için kullanılır",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "Her N adımda bir günlük dizinine kaybı içeren bir csv kaydedin, devre dışı bırakmak için 0",
-    "Stable Diffusion": "Kararlı Difüzyon",
-    "Checkpoints to cache in RAM": "RAM'de önbelleğe alınacak kontrol noktaları",
-    "Hypernetwork strength": "Hipernetwork gücü",
-    "Apply color correction to img2img results to match original colors.": "Orijinal renklerle eşleştirmek için img2img sonuçlarına renk düzeltmesi uygulayın.",
-    "Save a copy of image before applying color correction to img2img results": "img2img sonuçlarına renk düzeltmesi uygulamadan önce görüntünün bir kopyasını kaydedin",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "img2img ile, kaydırıcının belirttiği adım miktarını tam olarak yapın (normalde daha az denoising ile daha az yaparsınız).",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Daha keskin ve temiz sonuçlar için K örnekleyicilerinde nicelemeyi etkinleştirin. Bu, mevcut tohumları değiştirebilir. Uygulamak için yeniden başlatma gerektirir.",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Vurgu: modelin metne daha fazla dikkat etmesini sağlamak için (metin) ve daha az dikkat etmesini sağlamak için [metin] kullanın",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Eski vurgu uygulamasını kullanın. Eski tohumları yeniden üretmek faydalı olabilir.",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "K-difüzyon örnekleyicilerinin tek bir görüntü oluştururken olduğu gibi toplu halde aynı görüntüleri üretmesini sağlayın",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "75'ten fazla belirteç kullanıldığında n belirteç içindeki son virgülden itibaren dolgu yaparak tutarlılığı artırın",
-    "Filter NSFW content": "NSFW içeriği filtreleme",
-    "Stop At last layers of CLIP model": "Durdur CLIP modelinin son katmanlarında",
-    "Interrogate Options": "Sorgulama Seçenekleri",
-    "Interrogate: keep models in VRAM": "Sorgula: modelleri VRAM'de tut",
-    "Interrogate: use artists from artists.csv": "Sorgula: artists.csv dosyasındaki sanatçıları kullan",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: sonuçlara eşleşen model etiketlerinin sıralarını dahil et (Başlık tabanlı sorgulayıcılar üzerinde etkisi yoktur).",
-    "Interrogate: num_beams for BLIP": "Sorgula: BLIP için num_beams",
-    "Interrogate: minimum description length (excluding artists, etc..)": "Sorgula: minimum açıklama uzunluğu (sanatçılar vb. hariç)",
-    "Interrogate: maximum description length": "Sorgula: maksimum açıklama uzunluğu",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: metin dosyasındaki maksimum satır sayısı (0 = Sınır yok)",
-    "Interrogate: deepbooru score threshold": "Sorgula: deepbooru puan eşiği",
-    "Interrogate: deepbooru sort alphabetically": "Sorgula: deepbooru alfabetik olarak sırala",
-    "use spaces for tags in deepbooru": "deepbooru'da etiketler için boşluk kullanın",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "deepbooru'da kaçış (\\) parantezleri (böylece vurgu için değil, gerçek parantez olarak kullanılırlar)",
-    "User interface": "Kullanıcı arayüzü",
-    "Show progressbar": "İlerleme çubuğunu göster",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "Her N örnekleme adımında görüntü oluşturma ilerlemesini gösterir. Devre dışı bırakmak için 0 olarak ayarlayın.",
-    "Show previews of all images generated in a batch as a grid": "Bir toplu işte oluşturulan tüm görüntülerin önizlemelerini ızgara olarak göster",
-    "Show grid in results for web": "Web için sonuçlarda ızgarayı göster",
-    "Do not show any images in results for web": "Web için sonuçlarda herhangi bir resim gösterme",
-    "Add model hash to generation information": "Üretim bilgilerine model karması ekleyin",
-    "Add model name to generation information": "Üretim bilgilerine model adı ekleme",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "Üretim parametrelerini metinden kullanıcı arayüzüne okurken (PNG bilgisinden veya yapıştırılan metinden), seçilen modeli/denetim noktasını değiştirmeyin.",
-    "Font for image grids that have text": "Metin içeren görüntü ızgaraları için yazı tipi",
-    "Enable full page image viewer": "Tam sayfa resim görüntüleyiciyi etkinleştir",
-    "Show images zoomed in by default in full page image viewer": "Tam sayfa resim görüntüleyicide resimleri varsayılan olarak yakınlaştırılmış olarak gösterme",
-    "Show generation progress in window title.": "Pencere başlığında üretim ilerlemesini göster.",
-    "Quicksettings list": "Hızlı ayarlar listesi",
-    "Localization (requires restart)": "Yerelleştirme (yeniden başlatma gerektirir)",
-    "ko_KR": "ko_KR",
-    "ru_RU": "ru_RU",
-    "es_ES": "es_ES",
-    "ja_JP": "ja_JP",
-    "ar_AR": "ar_AR",
-    "Sampler parameters": "Örnekleyici parametreleri",
-    "Hide samplers in user interface (requires restart)": "Kullanıcı arayüzünde örnekleyicileri gizle (yeniden başlatma gerektirir)",
-    "eta (noise multiplier) for DDIM": "DDIM için eta (gürültü çarpanı)",
-    "eta (noise multiplier) for ancestral samplers": "eta örnekleyiciler için eta (gürültü çarpanı)",
-    "img2img DDIM discretize": "img2img DDIM discretize",
-    "uniform": "üniforma",
-    "quad": "dörtlü",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "sigma tmin",
-    "sigma noise": "sigma gürültüsü",
-    "Eta noise seed delta": "Eta gürültü tohum deltası",
-    "Request browser notifications": "Tarayıcı bildirimleri isteyin",
-    "Download localization template": "Dil şablonunu indirin",
-    "Reload custom script bodies (No ui updates, No restart)": "Kişisel komut dosyası gövdelerini yeniden yükle (Kullanıcı arayüzü güncellemesi yok, yeniden başlatma yok)",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradio'yu yeniden başlatın ve bileşenleri yenileyin (yalnızca Özel Komut Dosyaları, ui.py, js ve css)",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "İstem (oluşturmak için Ctrl+Enter veya Alt+Enter tuşlarına basın)",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Negatif istem (oluşturmak için Ctrl+Enter veya Alt+Enter tuşlarına basın)",
-    "Add a random artist to the prompt.": "Komut istemine rastgele bir sanatçı ekleyin.",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Kullanıcı arayüzüne istemden veya istem boşsa son üretimden üretim parametrelerini okuyun.",
-    "Save style": "Stil kaydet",
-    "Apply selected styles to current prompt": "Seçilen stilleri geçerli komut istemine uygulama",
-    "Stop processing current image and continue processing.": "Geçerli görüntüyü işlemeyi durdurun ve işlemeye devam edin.",
-    "Stop processing images and return any results accumulated so far.": "Görüntüleri işlemeyi durdurun ve o ana kadar biriken tüm sonuçları döndürün.",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "Uygulanacak stil; stillerin hem pozitif hem de negatif istemler için bileşenleri vardır ve her ikisine de uygulanır",
-    "Do not do anything special": "Özel bir şey yapmayın",
-    "Which algorithm to use to produce the image": "Görüntüyü üretmek için hangi algoritmanın kullanılacağı",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - çok yaratıcı, adım sayısına bağlı olarak her biri tamamen farklı bir resim elde edebilir, adımları 30-40'tan daha yükseğe ayarlamak yardımcı olmaz",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Difüzyon Örtük Modelleri - en iyi inpainting",
-    "Produce an image that can be tiled.": "Döşenebilen bir görüntü üretin.",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Bir görüntüyü kısmen daha düşük çözünürlükte oluşturmak, büyütmek ve ardından kompozisyonu değiştirmeden ayrıntıları iyileştirmek için iki adımlı bir işlem kullanın",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Algoritmanın resmin içeriğine ne kadar az saygı göstermesi gerektiğini belirler. 0'da hiçbir şey değişmez ve 1'de ilgisiz bir görüntü elde edersiniz. 1,0'ın altındaki değerlerde işleme, Örnekleme Adımları kaydırıcısının belirttiğinden daha az adım atacaktır.",
-    "How many batches of images to create": "Kaç görüntü grubu oluşturulacağı",
-    "How many image to create in a single batch": "Tek bir partide kaç görüntü oluşturulacağı",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Sınıflandırıcı Serbest Rehberlik Ölçeği - görüntünün istemle ne kadar uyumlu olması gerektiği - düşük değerler daha yaratıcı sonuçlar üretir",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Rastgele sayı üretecinin çıktısını belirleyen bir değer - başka bir resimle aynı parametrelere ve tohuma sahip bir resim oluşturursanız, aynı sonucu alırsınız",
-    "Set seed to -1, which will cause a new random number to be used every time": "Tohum değerini -1 olarak ayarlayın, bu her seferinde yeni bir rastgele sayı kullanılmasına neden olacaktır",
-    "Reuse seed from last generation, mostly useful if it was randomed": "Son nesilden tohumu yeniden kullanın, çoğunlukla rastgele ise kullanışlıdır",
-    "Seed of a different picture to be mixed into the generation.": "Nesle karıştırılacak farklı bir resmin tohumu.",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Ne kadar güçlü bir varyasyon üretileceği. 0'da hiçbir etki olmayacaktır. 1'de, varyasyon tohumu ile tam bir resim elde edersiniz (sadece bir şey alacağınız atasal örnekleyiciler hariç).",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Belirtilen çözünürlükte aynı tohumla üretilecek olana benzer bir resim üretme girişiminde bulunun",
-    "Separate values for X axis using commas.": "X ekseni için değerleri virgül kullanarak ayırın.",
-    "Separate values for Y axis using commas.": "Y ekseni için değerleri virgül kullanarak ayırın.",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "Görüntüyü bir dizine (varsayılan - log/images) ve üretim parametrelerini csv dosyasına yazın.",
-    "Open images output directory": "Görüntü çıktı dizinini açın",
-    "How much to blur the mask before processing, in pixels.": "İşlemeden önce maskenin piksel cinsinden ne kadar bulanıklaştırılacağı.",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "Kararlı Difüzyon ile işlemeden önce maskelenmiş alanın içine ne konulacağı.",
-    "fill it with colors of the image": "Görüntünün renkleriyle doldurun",
-    "keep whatever was there originally": "başlangıçta orada ne varsa saklayın",
-    "fill it with latent space noise": "gizli alan gürültüsü ile doldurun",
-    "fill it with latent space zeroes": "gizli uzay sıfırları ile doldurun",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Maskelenmiş bölgeyi hedef çözünürlüğe yükseltme, inpainting yapma, ölçeği küçültme ve orijinal görüntüye yapıştırma",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Görüntüyü hedef çözünürlüğe göre yeniden boyutlandırın. Yükseklik ve genişlik eşleşmediği sürece, yanlış en boy oranı elde edersiniz.",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Görüntüyü, hedef çözünürlüğün tamamı görüntüyle dolacak şekilde yeniden boyutlandırın. Dışarıda kalan kısımları kırpın.",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Görüntünün tamamı hedef çözünürlüğün içinde olacak şekilde görüntüyü yeniden boyutlandırın. Boş alanı görüntünün renkleriyle doldurun.",
-    "How many times to repeat processing an image and using it as input for the next iteration": "Bir görüntüyü işlemeyi kaç kez tekrarlamak ve bir sonraki yineleme için girdi olarak kullanmak",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "Geri döngü modunda, her döngüde denoising gücü bu değerle çarpılır. <1 çeşitliliğin azalması anlamına gelir, böylece diziniz sabit bir resme yakınsayacaktır. >1'den büyük olması çeşitliliğin artması anlamına gelir, böylece sekansınız gittikçe daha kaotik hale gelecektir.",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "SD yükseltme için karolar arasında piksel olarak ne kadar örtüşme olmalıdır. Döşemeler, tekrar tek bir resimde birleştirildiklerinde açıkça görülebilen bir dikiş olmayacak şekilde üst üste biner.",
-    "A directory on the same machine where the server is running.": "Sunucunun çalıştığı makinedeki bir dizin.",
-    "Leave blank to save images to the default path.": "Görüntüleri varsayılan yola kaydetmek için boş bırakın.",
-    "Result = A * (1 - M) + B * M": "Sonuç = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "Sonuç = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "1. ve son rakam 1 olmalıdır. örn:'1, 2, 1'",
-    "Path to directory with input images": "Girdi resimlerinin bulunduğu dizinin yolu",
-    "Path to directory where to write outputs": "Çıktıların yazılacağı dizinin yolu",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Görüntülerin dosya adlarının nasıl seçileceğini tanımlamak için aşağıdaki etiketleri kullanın: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; varsayılan için boş bırakın.",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Bu seçenek etkinleştirilirse, oluşturulan görüntülere filigran eklenmeyecektir. Uyarı: filigran eklemezseniz, etik olmayan bir şekilde davranıyor olabilirsiniz.",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "Görüntüler ve ızgaralar için alt dizinlerin nasıl seçileceğini tanımlamak için aşağıdaki etiketleri kullanın: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; varsayılan için boş bırakın.",
-    "Restore low quality faces using GFPGAN neural network": "GFPGAN sinir ağını kullanarak düşük kaliteli yüzleri geri yükleme",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Bu düzenli ifade, dosya adından sözcükleri ayıklamak için kullanılır ve bunlar aşağıdaki seçenek kullanılarak eğitim için kullanılan etiket metnine birleştirilir. Dosya adı metnini olduğu gibi tutmak için boş bırakın.",
-    "This string will be used to join split words into a single line if the option above is enabled.": "Bu dize, yukarıdaki seçenek etkinleştirilirse bölünmüş kelimeleri tek bir satırda birleştirmek için kullanılacaktır.",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Normal ayar sekmesi yerine üstteki hızlı erişim çubuğuna gitmesi gereken ayarlar için virgülle ayrılmış ayar adlarının listesi. Ayar adları için modules/shared.py dosyasına bakın. Uygulanması için yeniden başlatma gerekir.",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Bu değer sıfır değilse, tohuma eklenecek ve Eta ile örnekleyiciler kullanılırken gürültüler için RNG'yi başlatmak için kullanılacaktır. Bunu daha fazla görüntü çeşitliliği üretmek için kullanabilir veya ne yaptığınızı biliyorsanız diğer yazılımların görüntülerini eşleştirmek için kullanabilirsiniz.."
-}
diff --git a/localizations/zh_CN.json b/localizations/zh_CN.json
deleted file mode 100644
index ff785fc0..00000000
--- a/localizations/zh_CN.json
+++ /dev/null
@@ -1,624 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "载入中...",
-    "view": "查看",
-    "api": "api",
-    "•": " • ",
-    "built with gradio": "基于 Gradio 构建",
-    "Stable Diffusion checkpoint": "Stable Diffusion 模型(ckpt)",
-    "txt2img": "文生图",
-    "img2img": "图生图",
-    "Extras": "更多",
-    "PNG Info": "图片信息",
-    "Checkpoint Merger": "模型(ckpt)合并",
-    "Train": "训练",
-    "Create aesthetic embedding": "生成美术风格",
-    "Image Browser": "图库浏览器",
-    "Settings": "设置",
-    "Extensions": "扩展",
-    "Prompt": "提示词",
-    "Negative prompt": "反向提示词",
-    "Run": "运行",
-    "Skip": "跳过",
-    "Interrupt": "中止",
-    "Generate": "生成",
-    "Style 1": "模版风格 1",
-    "Style 2": "模版风格 2",
-    "Label": "标签",
-    "File": "文件",
-    "Drop File Here": "拖拽文件到此",
-    "-": "-",
-    "or": "或",
-    "Click to Upload": "点击上传",
-    "Image": "图像",
-    "Check progress": "查看进度",
-    "Check progress (first)": "(首次)查看进度",
-    "Sampling Steps": "采样迭代步数 (Steps)",
-    "Sampling method": "采样方法 (Sampler)",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "宽度",
-    "Height": "高度",
-    "Restore faces": "面部修复",
-    "Tiling": "可平铺(Tiling)",
-    "Highres. fix": "高分辨率修复",
-    "Firstpass width": "第一遍的宽度",
-    "Firstpass height": "第一遍的高度",
-    "Denoising strength": "重绘幅度(Denoising strength)",
-    "Batch count": "生成批次",
-    "Batch size": "每批数量",
-    "CFG Scale": "提示词相关性(CFG Scale)",
-    "Seed": "随机种子(seed)",
-    "Extra": "▼",
-    "Variation seed": "差异随机种子",
-    "Variation strength": "差异强度",
-    "Resize seed from width": "自宽度缩放随机种子",
-    "Resize seed from height": "自高度缩放随机种子",
-    "Open for Clip Aesthetic!": "打开以调整 Clip 的美术风格！",
-    "Aesthetic weight": "美术风格权重",
-    "Aesthetic steps": "美术风格迭代步数",
-    "Aesthetic learning rate": "美术风格学习率",
-    "Slerp interpolation": "球面线性插值",
-    "Aesthetic imgs embedding": "美术风格图集 embedding",
-    "None": "无",
-    "Aesthetic text for imgs": "该图集的美术风格描述",
-    "Slerp angle": "球面线性插值角度",
-    "Is negative text": "是反向提示词",
-    "Script": "脚本",
-    "Embedding to Shareable PNG": "将 Embedding 转换为可分享的 PNG 图片文件",
-    "Prompt matrix": "提示词矩阵",
-    "Prompts from file or textbox": "从文本框或文件载入提示词",
-    "X/Y plot": "X/Y 图表",
-    "Source embedding to convert": "用于转换的源 Embedding",
-    "Embedding token": "Embedding 的 token (关键词)",
-    "Output directory": "输出目录",
-    "Put variable parts at start of prompt": "把变量部分放在提示词文本的开头",
-    "Iterate seed every line": "每行输入都换一个种子",
-    "Use same random seed for all lines": "每行输入都使用同一个随机种子",
-    "List of prompt inputs": "提示词输入列表",
-    "Upload prompt inputs": "上传提示词输入文件",
-    "X type": "X轴类型",
-    "Nothing": "无",
-    "Var. seed": "差异随机种子",
-    "Var. strength": "差异强度",
-    "Steps": "迭代步数",
-    "Prompt S/R": "提示词替换",
-    "Prompt order": "提示词顺序",
-    "Sampler": "采样器",
-    "Checkpoint name": "模型(ckpt)名",
-    "Hypernetwork": "超网络(Hypernetwork)",
-    "Hypernet str.": "超网络(Hypernetwork) 强度",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma min": "最小 Sigma",
-    "Sigma max": "最大 Sigma",
-    "Sigma noise": "Sigma noise",
-    "Eta": "Eta",
-    "Clip skip": "Clip 跳过",
-    "Denoising": "重绘幅度",
-    "Cond. Image Mask Weight": "图像调节屏蔽度",
-    "X values": "X轴数值",
-    "Y type": "Y轴类型",
-    "Y values": "Y轴数值",
-    "Draw legend": "在图表中包括轴标题",
-    "Include Separate Images": "包括独立的图像",
-    "Keep -1 for seeds": "保持随机种子为-1",
-    "Save": "保存",
-    "Send to img2img": ">> 图生图",
-    "Send to inpaint": ">> 局部重绘",
-    "Send to extras": ">> 更多",
-    "Make Zip when Save?": "保存时生成zip压缩文件?",
-    "Textbox": "文本框",
-    "Interrogate\nCLIP": "CLIP\n反推提示词",
-    "Interrogate\nDeepBooru": "DeepBooru\n反推提示词",
-    "Inpaint": "局部重绘",
-    "Batch img2img": "批量图生图",
-    "Image for img2img": "图生图的图像",
-    "Drop Image Here": "拖拽图像到此",
-    "Image for inpainting with mask": "用于局部重绘并手动画蒙版的图像",
-    "Mask": "蒙版",
-    "Mask blur": "蒙版模糊",
-    "Mask mode": "蒙版模式",
-    "Draw mask": "绘制蒙版",
-    "Upload mask": "上传蒙版",
-    "Masking mode": "蒙版模式",
-    "Inpaint masked": "重绘蒙版内容",
-    "Inpaint not masked": "重绘非蒙版内容",
-    "Masked content": "蒙版蒙住的内容",
-    "fill": "填充",
-    "original": "原图",
-    "latent noise": "潜空间噪声",
-    "latent nothing": "潜空间数值零",
-    "Inpaint at full resolution": "全分辨率局部重绘",
-    "Inpaint at full resolution padding, pixels": "预留像素",
-    "Process images in a directory on the same machine where the server is running.": "使用服务器主机上的一个目录，作为输入目录处理图像",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "使用一个空的文件夹作为输出目录，而不是使用默认的 output 文件夹作为输出目录",
-    "Input directory": "输入目录",
-    "Resize mode": "缩放模式",
-    "Just resize": "拉伸",
-    "Crop and resize": "裁剪",
-    "Resize and fill": "填充",
-    "img2img alternative test": "图生图的另一种测试",
-    "Loopback": "回送",
-    "Outpainting mk2": "向外绘制第二版",
-    "Poor man's outpainting": "效果稍差的向外绘制",
-    "SD upscale": "使用 SD 放大(SD upscale)",
-    "should be 2 or lower.": "必须小于等于2",
-    "Override `Sampling method` to Euler?(this method is built for it)": "覆写 `采样方法` 为 Euler?(这个方法就是为这样做设计的)",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆写 `提示词` 为 `初始提示词`?(包括`反向提示词`)",
-    "Original prompt": "初始提示词",
-    "Original negative prompt": "初始反向提示词",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "覆写 `采样迭代步数` 为 `解码迭代步数`?",
-    "Decode steps": "解码迭代步数",
-    "Override `Denoising strength` to 1?": "覆写 `重绘幅度` 为 1?",
-    "Decode CFG scale": "解码提示词相关性(CFG scale)",
-    "Randomness": "随机度",
-    "Sigma adjustment for finding noise for image": "为寻找图中噪点的 Sigma 调整",
-    "Loops": "迭代次数",
-    "Denoising strength change factor": "重绘幅度的调整系数",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推荐设置：采样迭代步数：80-100，采样器：Euler a，重绘幅度：0.8",
-    "Pixels to expand": "拓展的像素数",
-    "Outpainting direction": "向外绘制的方向",
-    "left": "左",
-    "right": "右",
-    "up": "上",
-    "down": "下",
-    "Fall-off exponent (lower=higher detail)": "衰减指数(越低细节越好)",
-    "Color variation": "色彩变化",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "将图像放大到两倍尺寸； 使用宽度和高度滑块设置图块尺寸(tile size)",
-    "Tile overlap": "图块重叠的像素(Tile overlap)",
-    "Upscaler": "放大算法",
-    "Lanczos": "Lanczos",
-    "Nearest": "最邻近(整数缩放)",
-    "LDSR": "LDSR",
-    "BSRGAN 4x": "BSRGAN 4x",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR_4x": "SwinIR 4x",
-    "Single Image": "单个图像",
-    "Batch Process": "批量处理",
-    "Batch from Directory": "从目录进行批量处理",
-    "Source": "来源",
-    "Show result images": "显示输出图像",
-    "Scale by": "等比缩放",
-    "Scale to": "指定尺寸缩放",
-    "Resize": "缩放",
-    "Crop to fit": "裁剪以适应",
-    "Upscaler 2 visibility": "放大算法 2 (Upscaler 2) 可见度",
-    "GFPGAN visibility": "GFPGAN 可见度",
-    "CodeFormer visibility": "CodeFormer 可见度",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 权重 (0 = 最大效果, 1 = 最小效果)",
-    "Upscale Before Restoring Faces": "放大后再进行面部修复",
-    "Send to txt2img": ">> 文生图",
-    "A merger of the two checkpoints will be generated in your": "合并后的模型(ckpt)会生成在你的",
-    "checkpoint": "模型(ckpt)",
-    "directory.": "目录",
-    "Primary model (A)": "主要模型 (A)",
-    "Secondary model (B)": "第二模型 (B)",
-    "Tertiary model (C)": "第三模型 (C)",
-    "Custom Name (Optional)": "自定义名称 (可选)",
-    "Multiplier (M) - set to 0 to get model A": "倍率 (M) - 设为 0 等价于模型 A",
-    "Interpolation Method": "插值方法",
-    "Weighted sum": "加权和",
-    "Add difference": "添加差分",
-    "Save as float16": "以 float16 储存",
-    "See": "查看",
-    "wiki": "wiki文档",
-    "for detailed explanation.": "以了解详细说明",
-    "Create embedding": "生成 embedding",
-    "Create hypernetwork": "生成 hypernetwork",
-    "Preprocess images": "图像预处理",
-    "Name": "名称",
-    "Initialization text": "初始化文字",
-    "Number of vectors per token": "每个 token 的向量数",
-    "Overwrite Old Embedding": "覆写旧的 Embedding",
-    "Modules": "模块",
-    "Enter hypernetwork layer structure": "输入 hypernetwork 层结构",
-    "Select activation function of hypernetwork": "选择 hypernetwork 的激活函数",
-    "linear": "linear",
-    "relu": "relu",
-    "leakyrelu": "leakyrelu",
-    "elu": "elu",
-    "swish": "swish",
-    "tanh": "tanh",
-    "sigmoid": "sigmoid",
-    "celu": "celu",
-    "gelu": "gelu",
-    "glu": "glu",
-    "hardshrink": "hardshrink",
-    "hardsigmoid": "hardsigmoid",
-    "hardtanh": "hardtanh",
-    "logsigmoid": "logsigmoid",
-    "logsoftmax": "logsoftmax",
-    "mish": "mish",
-    "prelu": "prelu",
-    "rrelu": "rrelu",
-    "relu6": "relu6",
-    "selu": "selu",
-    "silu": "silu",
-    "softmax": "softmax",
-    "softmax2d": "softmax2d",
-    "softmin": "softmin",
-    "softplus": "softplus",
-    "softshrink": "softshrink",
-    "softsign": "softsign",
-    "tanhshrink": "tanhshrink",
-    "threshold": "阈值",
-    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "选择初始化层权重的方案. 类relu - Kaiming, 类sigmoid - Xavier 都是比较推荐的选项",
-    "Normal": "正态",
-    "KaimingUniform": "Kaiming 均匀",
-    "KaimingNormal": "Kaiming 正态",
-    "XavierUniform": "Xavier 均匀",
-    "XavierNormal": "Xavier 正态",
-    "Add layer normalization": "添加层标准化",
-    "Use dropout": "采用 dropout 防止过拟合",
-    "Overwrite Old Hypernetwork": "覆写旧的 Hypernetwork",
-    "Source directory": "源目录",
-    "Destination directory": "目标目录",
-    "Existing Caption txt Action": "对已有的 txt 说明文字的行为",
-    "ignore": "无视",
-    "copy": "复制",
-    "prepend": "放前面",
-    "append": "放后面",
-    "Create flipped copies": "生成镜像副本",
-    "Split oversized images": "分割过大的图像",
-    "Auto focal point crop": "自动焦点裁切",
-    "Use BLIP for caption": "使用 BLIP 生成说明文字(自然语言描述)",
-    "Use deepbooru for caption": "使用 deepbooru 生成说明文字(tags)",
-    "Split image threshold": "图像分割阈值",
-    "Split image overlap ratio": "分割图像重叠的比率",
-    "Focal point face weight": "焦点面部权重",
-    "Focal point entropy weight": "焦点熵权重",
-    "Focal point edges weight": "焦点线条权重",
-    "Create debug image": "生成调试(debug)图片",
-    "Preprocess": "预处理",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "训练 embedding 或者 hypernetwork； 必须指定一组具有 1:1 比例图像的目录",
-    "[wiki]": "[wiki文档]",
-    "Embedding": "Embedding",
-    "Embedding Learning rate": "Embedding 学习率",
-    "Hypernetwork Learning rate": "Hypernetwork 学习率",
-    "Dataset directory": "数据集目录",
-    "Log directory": "日志目录",
-    "Prompt template file": "提示词模版文件",
-    "Max steps": "最大迭代步数",
-    "Save an image to log directory every N steps, 0 to disable": "每 N 步保存一个图像到日志目录，0 表示禁用",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步将 embedding 的副本保存到日志目录，0 表示禁用",
-    "Save images with embedding in PNG chunks": "保存图像，并在 PNG 图片文件中嵌入 embedding 文件",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "进行预览时，从文生图选项卡中读取参数（提示词等）",
-    "Train Hypernetwork": "训练 Hypernetwork",
-    "Train Embedding": "训练 Embedding",
-    "Create an aesthetic embedding out of any number of images": "从任意数量的图像中创建美术风格 embedding",
-    "Create images embedding": "生成图集 embedding",
-    "Favorites": "收藏夹(已保存)",
-    "Others": "其他",
-    "Images directory": "图像目录",
-    "Dropdown": "下拉列表",
-    "First Page": "首页",
-    "Prev Page": "上一页",
-    "Page Index": "页数",
-    "Next Page": "下一页",
-    "End Page": "尾页",
-    "delete next": "删除下一张",
-    "Delete": "删除",
-    "sort by": "排序方式",
-    "path name": "路径名",
-    "date": "日期",
-    "keyword": "搜索",
-    "Generate Info": "生成信息",
-    "File Name": "文件名",
-    "Move to favorites": "移动到收藏夹(保存)",
-    "Renew Page": "刷新页面",
-    "Number": "数量",
-    "set_index": "设置索引",
-    "load_switch": "载入开关",
-    "turn_page_switch": "翻页开关",
-    "Checkbox": "勾选框",
-    "Apply settings": "保存设置",
-    "Saving images/grids": "保存图像/宫格图",
-    "Always save all generated images": "始终保存所有生成的图像",
-    "File format for images": "图像的文件格式",
-    "Images filename pattern": "图像文件名格式",
-    "Add number to filename when saving": "储存的时候在文件名里添加数字",
-    "Always save all generated image grids": "始终保存所有生成的宫格图",
-    "File format for grids": "宫格图的文件格式",
-    "Add extended info (seed, prompt) to filename when saving grid": "保存宫格图时，将扩展信息（随机种子、提示词）添加到文件名",
-    "Do not save grids consisting of one picture": "只有一张图片时不要保存宫格图",
-    "Prevent empty spots in grid (when set to autodetect)": "（启用自动检测时）防止宫格图中出现空位",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "宫格图行数； 使用 -1 进行自动检测，使用 0 使其与每批数量相同",
-    "Save text information about generation parameters as chunks to png files": "将有关生成参数的文本信息，作为块保存到 png 图片文件中",
-    "Create a text file next to every image with generation parameters.": "保存图像时，在每个图像旁边创建一个文本文件储存生成参数",
-    "Save a copy of image before doing face restoration.": "在进行面部修复之前保存图像副本",
-    "Save a copy of image before applying highres fix.": "在做高分辨率修复之前保存初始图像副本",
-    "Save a copy of image before applying color correction to img2img results": "在对图生图结果应用颜色校正之前保存图像副本",
-    "Quality for saved jpeg images": "保存的 jpeg 图像的质量",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 图像大于 4MB 或宽高大于 4000，则缩小并保存副本为 JPG 图片",
-    "Use original name for output filename during batch process in extras tab": "在更多选项卡中的批量处理过程中，使用原始名称作为输出文件名",
-    "When using 'Save' button, only save a single selected image": "使用“保存”按钮时，只保存一个选定的图像",
-    "Do not add watermark to images": "不要给图像加水印",
-    "Paths for saving": "保存路径",
-    "Output directory for images; if empty, defaults to three directories below": "图像的输出目录； 如果为空，则默认为以下三个目录",
-    "Output directory for txt2img images": "文生图的输出目录",
-    "Output directory for img2img images": "图生图的输出目录",
-    "Output directory for images from extras tab": "更多选项卡的输出目录",
-    "Output directory for grids; if empty, defaults to two directories below": "宫格图的输出目录； 如果为空，则默认为以下两个目录",
-    "Output directory for txt2img grids": "文生图宫格的输出目录",
-    "Output directory for img2img grids": "图生图宫格的输出目录",
-    "Directory for saving images using the Save button": "使用“保存”按钮保存图像的目录",
-    "Saving to a directory": "保存到目录",
-    "Save images to a subdirectory": "将图像保存到子目录",
-    "Save grids to a subdirectory": "将宫格图保存到子目录",
-    "When using \"Save\" button, save images to a subdirectory": "使用“保存”按钮时，将图像保存到子目录",
-    "Directory name pattern": "目录名称格式",
-    "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示词数量",
-    "Upscaling": "放大",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的图块尺寸(Tile size)。0 = 不分块(no tiling)",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN 的图块重叠(Tile overlap)像素。低值 = 可见接缝",
-    "Tile size for all SwinIR.": "适用所有 SwinIR 系算法的图块尺寸(Tile size)",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR 的图块重叠(Tile overlap)像素。低值 = 可见接缝",
-    "LDSR processing steps. Lower = faster": "LDSR 处理迭代步数。更低 = 更快",
-    "Upscaler for img2img": "图生图的放大算法",
-    "Upscale latent space image when doing hires. fix": "做高分辨率修复时，也放大潜空间图像",
-    "Face restoration": "面部修复",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 权重参数； 0 = 最大效果； 1 = 最小效果",
-    "Move face restoration model from VRAM into RAM after processing": "面部修复处理完成后，将面部修复模型从显存(VRAM)移至内存(RAM)",
-    "System": "系统",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成图像时，每秒轮询显存(VRAM)使用情况的次数。设置为 0 以禁用",
-    "Always print all generation info to standard output": "始终将所有生成信息输出到 standard output (一般为控制台)",
-    "Add a second progress bar to the console that shows progress for an entire job.": "向控制台添加第二个进度条，显示整个作业的进度",
-    "Training": "训练",
-    "Move VAE and CLIP to RAM when training if possible. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)如果可行的话，节省显存(VRAM)",
-    "Filename word regex": "文件名用词的正则表达式",
-    "Filename join string": "文件名连接用字符串",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每个 epoch 中单个输入图像的重复次数； 仅用于显示 epoch 数",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步保存一个包含 loss 的 csv 表格到日志目录，0 表示禁用",
-    "Use cross attention optimizations while training": "训练时开启 cross attention 优化",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "缓存在内存(RAM)中的模型(ckpt)",
-    "SD VAE": "模型的 VAE (SD VAE)",
-    "auto": "自动",
-    "Hypernetwork strength": "Hypernetwork 强度",
-    "Inpainting conditioning mask strength": "局部重绘时图像调节的蒙版屏蔽强度",
-    "Apply color correction to img2img results to match original colors.": "对图生图结果应用颜色校正以匹配原始颜色",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在进行图生图的时候，确切地执行滑块指定的迭代步数（正常情况下更弱的重绘幅度需要更少的迭代步数）",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 采样器中启用量化以获得更清晰、更清晰的结果。这可能会改变现有的随机种子。需要重新启动才能应用",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "强调符：使用 (文字) 使模型更关注该文本，使用 [文字] 使其减少关注",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用旧的强调符实现。可用于复现旧随机种子",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 采样器 批量生成与生成单个图像时，产出相同的图像",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "当使用超过 75 个 token 时，通过从 n 个 token 中的最后一个逗号填补来提高一致性",
-    "Filter NSFW content": "过滤成人内容(NSFW)",
-    "Stop At last layers of CLIP model": "在 CLIP 模型的最后哪一层停下 (Clip skip)",
-    "Interrogate Options": "反推提示词选项",
-    "Interrogate: keep models in VRAM": "反推: 将模型保存在显存(VRAM)中",
-    "Interrogate: use artists from artists.csv": "反推: 使用 artists.csv 中的艺术家",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "反推: 在生成结果中包含与模型标签(tags)相匹配的等级（对基于生成自然语言描述的反推没有影响）",
-    "Interrogate: num_beams for BLIP": "反推: BLIP 的 num_beams",
-    "Interrogate: minimum description length (excluding artists, etc..)": "反推: 最小描述长度（不包括艺术家, 等…）",
-    "Interrogate: maximum description length": "反推: 最大描述长度",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: 文本文件中的最大行数（0 = 无限制）",
-    "Interrogate: deepbooru score threshold": "反推: deepbooru 分数阈值",
-    "Interrogate: deepbooru sort alphabetically": "反推: deepbooru 按字母顺序排序",
-    "use spaces for tags in deepbooru": "在 deepbooru 中为标签使用空格",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "在 deepbooru 中使用转义 (\\) 括号（因此它们用作文字括号而不是强调符号）",
-    "User interface": "用户界面",
-    "Show progressbar": "显示进度条",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 个采样迭代步数显示图像生成进度。设置 0 禁用",
-    "Show previews of all images generated in a batch as a grid": "以网格的形式，预览批量生成的所有图像",
-    "Show grid in results for web": "在网页的结果中显示宫格图",
-    "Do not show any images in results for web": "不在网页的结果中显示任何图像",
-    "Add model hash to generation information": "将模型的哈希值添加到生成信息",
-    "Add model name to generation information": "将模型名称添加到生成信息",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "从文本读取生成参数到用户界面（从 PNG 图片信息或粘贴文本）时，不要更改选定的模型(ckpt)",
-    "Send seed when sending prompt or image to other interface": "将提示词或者图片发送到 >> 其他界面时，把随机种子也传送过去",
-    "Font for image grids that have text": "有文字的宫格图使用的字体",
-    "Enable full page image viewer": "启用整页图像查看器",
-    "Show images zoomed in by default in full page image viewer": "在整页图像查看器中，默认放大显示图像",
-    "Show generation progress in window title.": "在窗口标题中显示生成进度",
-    "Quicksettings list": "快速设置列表",
-    "Localization (requires restart)": "本地化翻译（需要保存设置，并重启Gradio）",
-    "Sampler parameters": "采样器参数",
-    "Hide samplers in user interface (requires restart)": "在用户界面中隐藏采样器（需要重新启动）",
-    "eta (noise multiplier) for DDIM": "DDIM 的 eta (噪声乘数) ",
-    "eta (noise multiplier) for ancestral samplers": "ancestral 采样器的 eta (噪声乘数)",
-    "img2img DDIM discretize": "图生图 DDIM 离散化",
-    "uniform": "均匀",
-    "quad": "二阶",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "最小(tmin) sigma",
-    "sigma noise": "sigma 噪声",
-    "Eta noise seed delta": "Eta 噪声种子偏移(ENSD - Eta noise seed delta)",
-    "Images Browser": "图库浏览器",
-    "Preload images at startup": "在启动时预加载图像",
-    "Number of columns on the page": "每页列数",
-    "Number of rows on the page": "每页行数",
-    "Minimum number of pages per load": "每次加载的最小页数",
-    "Use same seed for all images": "为所有图像使用同一个随机种子",
-    "Request browser notifications": "请求浏览器通知",
-    "Download localization template": "下载本地化模板",
-    "Reload custom script bodies (No ui updates, No restart)": "重新加载自定义脚本主体（无用户界面更新，无重启）",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重启 Gradio 及刷新组件（仅限自定义脚本、ui.py、js 和 css）",
-    "Available": "可用",
-    "Install from URL": "从网址安装",
-    "Apply and restart UI": "应用并重启用户界面",
-    "Check for updates": "检查更新",
-    "Extension": "扩展",
-    "URL": "网址",
-    "Update": "更新",
-    "a1111-sd-webui-tagcomplete": "Tag自动补全",
-    "unknown": "未知",
-    "deforum-for-automatic1111-webui": "Deforum",
-    "sd-dynamic-prompting": "动态提示词",
-    "stable-diffusion-webui-aesthetic-gradients": "美术风格梯度",
-    "stable-diffusion-webui-aesthetic-image-scorer": "美术风格评分",
-    "stable-diffusion-webui-artists-to-study": "艺术家图库",
-    "stable-diffusion-webui-dataset-tag-editor": "数据集标签编辑器",
-    "stable-diffusion-webui-images-browser": "图库浏览器",
-    "stable-diffusion-webui-inspiration": "灵感",
-    "stable-diffusion-webui-wildcards": "通配符",
-    "Load from:": "加载自",
-    "Extension index URL": "扩展列表链接",
-    "URL for extension's git repository": "扩展的 git 仓库链接",
-    "Local directory name": "本地路径名",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nPrompt",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示词（按 Ctrl+Enter 或 Alt+Enter 生成）\nNegative prompt",
-    "Stop processing current image and continue processing.": "停止处理当前图像，并继续处理下一个",
-    "Stop processing images and return any results accumulated so far.": "停止处理图像，并返回迄今为止累积的任何结果",
-    "Do not do anything special": "什么都不做",
-    "Which algorithm to use to produce the image": "使用哪种算法生成图像",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有创意，可以根据迭代步数获得完全不同的图像，将迭代步数设置为高于 30-40 不会有正面作用",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅长局部重绘",
-    "Produce an image that can be tiled.": "生成可用于平铺(tiled)的图像",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用两步处理的时候，以较小的分辨率生成初步图像、接着放大图像，然后在不更改构图的情况下改进其中的细节",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "决定算法对图像内容的影响程度。设置 0 时，什么都不会改变，而在 1 时，你将获得不相关的图像。\n值低于 1.0 时，处理的迭代步数将少于“采样迭代步数”滑块指定的步数",
-    "How many batches of images to create": "创建多少批次的图像",
-    "How many image to create in a single batch": "每批创建多少图像",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 图像应在多大程度上服从提示词 - 较低的值会产生更有创意的结果",
-    "Seed of a different picture to be mixed into the generation.": "将要参与生成的另一张图的随机种子",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要产生多强烈的变化。设为 0 时，将没有效果。设为 1 时，你将获得完全产自差异随机种子的图像（ancestral 采样器除外，你只是单纯地生成了一些东西）",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "尝试生成与在指定分辨率下使用相同随机种子生成的图像相似的图片",
-    "This text is used to rotate the feature space of the imgs embs": "此文本用于旋转图集 embeddings 的特征空间",
-    "Separate values for X axis using commas.": "使用逗号分隔 X 轴的值",
-    "Separate values for Y axis using commas.": "使用逗号分隔 Y 轴的值",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "将图像写入目录（默认 - log/images）并将生成参数写入 csv 表格文件",
-    "How much to blur the mask before processing, in pixels.": "处理前要对蒙版进行多强的模糊，以像素为单位",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 处理蒙版区域之前要在蒙版区域内放置什么",
-    "fill it with colors of the image": "用图像的颜色(高强度模糊)填充它",
-    "keep whatever was there originally": "保留原来的图像，不进行预处理",
-    "fill it with latent space noise": "于潜空间填充噪声",
-    "fill it with latent space zeroes": "于潜空间填零",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "将蒙版区域（包括预留像素长度的缓冲区域）放大到目标分辨率，进行局部重绘。\n然后缩小并粘贴回原始图像中",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "将图像大小调整为目标分辨率。除非高度和宽度匹配，否则你将获得不正确的纵横比",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "调整图像大小，使整个目标分辨率都被图像填充。裁剪多出来的部分",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "调整图像大小，使整个图像在目标分辨率内。用图像的颜色填充空白区域",
-    "How many times to repeat processing an image and using it as input for the next iteration": "重复处理图像并用作下次迭代输入的次数",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每个循环中，重绘幅度都会乘以该值。<1 表示减少多样性，因此你的这一组图将集中在固定的图像上。>1 意味着增加多样性，因此你的这一组图将变得越来越混乱",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大(SD upscale)时，图块(Tiles)之间应该有多少像素重叠。图块(Tiles)之间需要重叠才可以让它们在合并回一张图像时，没有清晰可见的接缝",
-    "A directory on the same machine where the server is running.": "与服务器主机上的目录",
-    "Leave blank to save images to the default path.": "留空以将图像保存到默认路径",
-    "Result = A * (1 - M) + B * M": "结果 = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "结果 = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "第一个和最后一个数字必须是 1。例:'1, 2, 1'",
-    "Path to directory with input images": "带有输入图像的路径",
-    "Path to directory where to write outputs": "进行输出的路径",
-    "Input images directory": "输入图像目录",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像的文件名: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; 默认请留空",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果启用此选项，水印将不会添加到生成出来的图像中。警告：如果你不添加水印，你的行为可能是不符合专业操守的",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下标签定义如何选择图像和宫格图的子目录: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; 默认请留空",
-    "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神经网络修复低质量面部",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正则表达式将用于从文件名中提取单词，并将使用以下选项将它们接合到用于训练的标签文本中。留空以保持文件名文本不变",
-    "This string will be used to join split words into a single line if the option above is enabled.": "如果启用了上述选项，则此处的字符会用于将拆分的单词接合为同一行",
-    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "仅适用于局部重绘专用的模型（模型后缀为 inpainting.ckpt 的模型）。决定了蒙版在局部重绘以及图生图中屏蔽原图内容的强度。 1.0 表示完全屏蔽原图，这是默认行为。0.0 表示完全不屏蔽让原图进行图像调节。较低的值将有助于保持原图的整体构图，但很难遇到较大的变化",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "设置项名称的列表，以逗号分隔，该设置会移动到顶部的快速访问栏，而不是默认的设置选项卡。有关设置名称，请参见 modules/shared.py。需要重新启动才能应用",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果这个值不为零，它将被添加到随机种子中，并在使用带有 Eta 的采样器时用于初始化随机噪声。你可以使用它来产生更多的图像变化，或者你可以使用它来模仿其他软件生成的图像，如果你知道你在做什么",
-    "Leave empty for auto": "留空时自动生成",
-    "Autocomplete options": "自动补全选项",
-    "Enable Autocomplete": "开启Tag补全",
-    "Append commas": "附加逗号",
-    "latest": "最新",
-    "behind": "落后",
-    "Roll three": "抽三位出来",
-    "Generate forever": "无限生成",
-    "Cancel generate forever": "停止无限生成",
-    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "训练应该多快。低值将需要更长的时间来训练，高值可能无法收敛（无法产生准确的结果）以及/也许可能会破坏 embedding（如果你在训练信息文本框中看到 Loss: nan 就会发生这种情况。如果发生这种情况，你需要从较旧的未损坏的备份手动恢复 embedding）\n\n你可以使用以下语法设置单个数值或多个学习率：\n\n   率1:步限1, 率2:步限2, ...\n\n如:   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步将以 0.005 的速率训练，接着直到 1000 步为止以 1e-3 训练，然后剩余所有步以 1e-5 训练",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "训练时将 VAE 和 CLIP 从显存(VRAM)移放到内存(RAM)，节省显存(VRAM)",
-    "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "迭代改进生成的图像多少次；更高的值需要更长的时间；非常低的值会产生不好的结果",
-    "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt": "在图像上画一个蒙版，脚本会根据提示重新生成蒙版区域的内容",
-    "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back": "正常放大图像，将结果分割成图块（tiles），用图生图改进每个图块（tiles），最后将整个图像合并回来",
-    "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "创建一个网格，图像将有不同的参数。使用下面的输入来指定哪些参数将由列和行共享",
-    "Run Python code. Advanced user only. Must run program with --allow-code for this to work": "运行 Python 代码。仅限老手使用。必须以 --allow-code 来开启程序，才能使其运行",
-    "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "以逗号分割的单词列表，第一个单词将被用作关键词：脚本将在提示词中搜索这个单词，并用其他单词替换它",
-    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "以逗号分割的单词列表，脚本会排列出这些单词的所有排列方式，并加入提示词各生成一次",
-    "Reconstruct prompt from existing image and put it into the prompt field.": "从现有的图像中重构出提示词，并将其放入提示词的输入文本框",
-    "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle": "设置在[prompt_words]选项中要使用的最大字数；注意：如果字数太长，可能会超过系统可处理的文件路径的最大长度",
-    "Process an image, use it as an input, repeat.": "处理一张图像，将其作为输入，并重复",
-    "Insert selected styles into prompt fields": "在提示词中插入选定的模版风格",
-    "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.": "将当前的提示词保存为模版风格。如果你在文本中添加{prompt}标记，那么将来你使用该模版风格时，你现有的提示词会替换模版风格中的{prompt}",
-    "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.": "在生成图像之前从模型(ckpt)中加载权重。你可以使用哈希值或文件名的一部分（如设置中所示）作为模型(ckpt)名称。建议用在Y轴上以减少过程中模型的切换",
-    "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "Torch active: 在生成过程中，Torch使用的显存(VRAM)峰值，不包括缓存的数据。\nTorch reserved: Torch 分配的显存(VRAM)的峰值量，包括所有活动和缓存数据。\nSys VRAM: 所有应用程序分配的显存(VRAM)的峰值量 / GPU 的总显存(VRAM)（峰值利用率%）",
-    "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "放大潜空间中的图像。而另一种方法是，从潜变量表达中直接解码并生成完整的图像，接着放大它，然后再将其编码回潜空间",
-    "Start drawing": "开始绘制",
-    "Description": "描述",
-    "Action": "行动",
-    "Aesthetic Gradients": "美术风格梯度",
-    "aesthetic-gradients": "美术风格梯度",
-    "Wildcards": "通配符",
-    "Dynamic Prompts": "动态提示词",
-    "Image browser": "图库浏览器",
-    "images-browser": "图库浏览器",
-    "Inspiration": "灵感",
-    "Deforum": "Deforum",
-    "Artists to study": "艺术家图库",
-    "Aesthetic Image Scorer": "美术风格评分",
-    "Dataset Tag Editor": "数据集标签编辑器",
-
-
-    "----not work----": "----以下内容无法被翻译，Bug----",
-    "Add a random artist to the prompt.": "随机添加一个艺术家到提示词中",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面",
-    "Save style": "储存为模版风格",
-    "Apply selected styles to current prompt": "将所选模板风格，应用于当前提示词",
-    "Set seed to -1, which will cause a new random number to be used every time": "将随机种子设置为-1，则每次都会使用一个新的随机数",
-    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的随机种子，如果想要固定结果就会很有用",
-    "Open images output directory": "打开图像输出目录",
-    "Upscaler 1": "放大算法 1",
-    "Upscaler 2": "放大算法 2",
-    "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)": "用竖线分隔符(|)将提示词分成若干部分，脚本将为它们的每一个组合创建一幅图片（除了被分割的第一部分，所有的组合都会包含这部分）",
-    "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "选择哪些 Real-ESRGAN 模型显示在网页用户界面。(需要重新启动)",
-    "Allowed categories for random artists selection when using the Roll button": "使用抽选艺术家按钮时将会随机的艺术家类别",
-    "Face restoration model": "面部修复模型",
-    "Install": "安装",
-    "Installing...": "安装中...",
-    "Installed": "已安装",
-
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要使用的模版风格； 模版风格包含正向和反向提示词，并应用于两者\n\ud83c\udfa8    随机添加一个艺术家到提示词中\n \u2199\ufe0f     从提示词中读取生成参数，如果提示词为空，则读取上一次的生成参数到用户界面\n\ud83d\udcbe    将当前的提示词保存为模版风格(保存在styles.csv)\n\ud83d\udccb    将所选模板风格，应用于当前提示词\n如果你在文本中添加{prompt}标记，并保存为模版风格\n那么将来你使用该模版风格时，你现有的提示词会替换模版风格中的{prompt}",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一个固定随机数生成器输出的值 - 以相同参数和随机种子生成的图像会得到相同的结果\n\ud83c\udfb2    将随机种子设置为-1，则每次都会使用一个新的随机数\n\u267b\ufe0f    重用上一次使用的随机种子，如果想要固定输出结果就会很有用",
-
-
-    "----deprecated----": "----以下内容在webui新版本已移除----",
-    "▼": "▼",
-    "History": "历史记录",
-    "Show Textbox": "显示文本框",
-    "File with inputs": "含输入内容的文件",
-    "Prompts": "提示词",
-    "Disabled when launched with --hide-ui-dir-config.": "启动 --hide-ui-dir-config 时禁用",
-    "Open output directory": "打开输出目录",
-    "Create aesthetic images embedding": "生成美术风格图集 embedding",
-    "Split oversized images into two": "将过大的图像分为两份",
-    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "训练 embedding； 必须指定一组具有 1:1 比例图像的目录",
-    "Learning rate": "学习率",
-    "txt2img history": "文生图历史记录",
-    "img2img history": "图生图历史记录",
-    "extras history": "更多选项卡的历史记录",
-    "extras": "更多",
-    "custom fold": "自定义文件夹",
-    "Load": "载入",
-    "Prev batch": "上一批",
-    "Next batch": "下一批",
-    "number of images to delete consecutively next": "接下来要连续删除的图像数",
-    "Date to": "日期至",
-    "Refresh page": "刷新页面",
-    "Unload VAE and CLIP from VRAM when training": "训练时从显存(VRAM)中取消 VAE 和 CLIP 的加载",
-    "Number of pictures displayed on each page": "每页显示的图像数量",
-    "Number of grids in each row": "每行显示多少格",
-    "favorites": "收藏夹(已保存)",
-    "others": "其他",
-    "Collect": "收藏(保存)",
-
-
-    "--------": "--------"
-}
diff --git a/localizations/zh_TW.json b/localizations/zh_TW.json
deleted file mode 100644
index 04bde864..00000000
--- a/localizations/zh_TW.json
+++ /dev/null
@@ -1,610 +0,0 @@
-{
-    "⤡": "⤡",
-    "⊞": "⊞",
-    "×": "×",
-    "❮": "❮",
-    "❯": "❯",
-    "Loading...": "載入中…",
-    "view": "檢視",
-    "api": "api",
-    "•": " • ",
-    "built with gradio": "基於 Gradio 構建",
-    "Stable Diffusion checkpoint": "Stable Diffusion 模型權重存檔點",
-    "txt2img": "文生圖",
-    "img2img": "圖生圖",
-    "Extras": "更多",
-    "PNG Info": "圖片資訊",
-    "Checkpoint Merger": "模型權重存檔點合併",
-    "Train": "訓練",
-    "Create aesthetic embedding": "生成美術風格",
-    "Image Browser": "圖庫瀏覽器",
-    "History": "歷史記錄",
-    "Settings": "設定",
-    "Extensions": "擴充",
-    "Prompt": "提示詞",
-    "Negative prompt": "反向提示詞",
-    "Run": "執行",
-    "Skip": "跳過",
-    "Interrupt": "中止",
-    "Generate": "生成",
-    "Style 1": "模版風格 1",
-    "Style 2": "模版風格 2",
-    "Label": "標記",
-    "File": "檔案",
-    "Drop File Here": "拖曳檔案到此",
-    "-": "-",
-    "or": "或",
-    "Click to Upload": "點擊上傳",
-    "Image": "圖像",
-    "Check progress": "檢視進度",
-    "Check progress (first)": "（首次）檢視進度",
-    "Sampling Steps": "採樣疊代步數",
-    "Sampling method": "採樣方法",
-    "Euler a": "Euler a",
-    "Euler": "Euler",
-    "LMS": "LMS",
-    "Heun": "Heun",
-    "DPM2": "DPM2",
-    "DPM2 a": "DPM2 a",
-    "DPM fast": "DPM fast",
-    "DPM adaptive": "DPM adaptive",
-    "LMS Karras": "LMS Karras",
-    "DPM2 Karras": "DPM2 Karras",
-    "DPM2 a Karras": "DPM2 a Karras",
-    "DDIM": "DDIM",
-    "PLMS": "PLMS",
-    "Width": "寬度",
-    "Height": "高度",
-    "Restore faces": "面部修復",
-    "Tiling": "可平鋪",
-    "Highres. fix": "高解析度修復",
-    "Firstpass width": "第一遍的寬度",
-    "Firstpass height": "第一遍的高度",
-    "Denoising strength": "重繪幅度",
-    "Batch count": "生成批次",
-    "Batch size": "每批數量",
-    "CFG Scale": "提示詞相關性（CFG）",
-    "Seed": "隨機種子",
-    "Extra": "▼",
-    "Variation seed": "差異隨機種子",
-    "Variation strength": "差異強度",
-    "Resize seed from width": "自寬度縮放隨機種子",
-    "Resize seed from height": "自高度縮放隨機種子",
-    "Open for Clip Aesthetic!": "打開以調整 Clip 的美術風格！",
-    "▼": "▼",
-    "Aesthetic weight": "美術風格權重",
-    "Aesthetic steps": "美術風格疊代步數",
-    "Aesthetic learning rate": "美術風格學習率",
-    "Slerp interpolation": "球面線性插值角度",
-    "Aesthetic imgs embedding": "美術風格圖集 embedding",
-    "None": "無",
-    "Aesthetic text for imgs": "該圖集的美術風格描述",
-    "Slerp angle": "Slerp 角度",
-    "Is negative text": "是反向提示詞",
-    "Script": "指令碼",
-    "Embedding to Shareable PNG": "將 Embedding 轉換為可分享的 PNG 圖片檔案",
-    "Prompt matrix": "提示詞矩陣",
-    "Prompts from file or textbox": "從文字方塊或檔案載入提示詞",
-    "X/Y plot": "X/Y 圖表",
-    "Source embedding to convert": "用於轉換的源 Embedding",
-    "Embedding token": "Embedding 的關鍵詞",
-    "Put variable parts at start of prompt": "把變量部分放在提示詞文本的開頭",
-    "Show Textbox": "顯示文字方塊",
-    "File with inputs": "含輸入內容的檔案",
-    "Prompts": "提示詞",
-    "Iterate seed every line": "每行輸入都換一個種子",
-    "Use same random seed for all lines": "每行輸入都使用同一個隨機種子",
-    "List of prompt inputs": "提示詞輸入列表",
-    "Upload prompt inputs": "上傳提示詞輸入檔案",
-    "X type": "X軸類型",
-    "Nothing": "無",
-    "Var. seed": "差異隨機種子",
-    "Var. strength": "差異強度",
-    "Steps": "疊代步數",
-    "Prompt S/R": "提示詞替換",
-    "Prompt order": "提示詞順序",
-    "Sampler": "採樣器",
-    "Checkpoint name": "模型權重存檔點的名稱",
-    "Hypernetwork": "超網路（Hypernetwork）",
-    "Hypernet str.": "超網路（Hypernetwork）強度",
-    "Sigma Churn": "Sigma Churn",
-    "Sigma min": "最小 Sigma",
-    "Sigma max": "最大 Sigma",
-    "Sigma noise": "Sigma noise",
-    "Eta": "Eta",
-    "Clip skip": "Clip 跳過",
-    "Denoising": "重繪幅度",
-    "Cond. Image Mask Weight": "圖像調節屏蔽度",
-    "X values": "X軸數值",
-    "Y type": "Y軸類型",
-    "Y values": "Y軸數值",
-    "Draw legend": "在圖表中包括軸標題",
-    "Include Separate Images": "包括獨立的圖像",
-    "Keep -1 for seeds": "保持隨機種子為-1",
-    "Drop Image Here": "拖曳圖像到此",
-    "Save": "儲存",
-    "Send to img2img": ">> 圖生圖",
-    "Send to inpaint": ">> 局部重繪",
-    "Send to extras": ">> 更多",
-    "Make Zip when Save?": "儲存時生成ZIP壓縮檔案？",
-    "Textbox": "文字方塊",
-    "Interrogate\nCLIP": "CLIP\n反推提示詞",
-    "Interrogate\nDeepBooru": "DeepBooru\n反推提示詞",
-    "Inpaint": "局部重繪",
-    "Batch img2img": "批量圖生圖",
-    "Image for img2img": "圖生圖的圖像",
-    "Image for inpainting with mask": "用於局部重繪並手動畫蒙版的圖像",
-    "Mask": "蒙版",
-    "Mask blur": "蒙版模糊",
-    "Mask mode": "蒙版模式",
-    "Draw mask": "繪製蒙版",
-    "Upload mask": "上傳蒙版",
-    "Masking mode": "蒙版模式",
-    "Inpaint masked": "重繪蒙版內容",
-    "Inpaint not masked": "重繪非蒙版內容",
-    "Masked content": "蒙版蒙住的內容",
-    "fill": "填充",
-    "original": "原圖",
-    "latent noise": "潛空間噪聲",
-    "latent nothing": "潛空間數值零",
-    "Inpaint at full resolution": "全解析度局部重繪",
-    "Inpaint at full resolution padding, pixels": "預留畫素",
-    "Process images in a directory on the same machine where the server is running.": "使用伺服器主機上的一個目錄，作為輸入目錄處理圖像",
-    "Use an empty output directory to save pictures normally instead of writing to the output directory.": "使用一個空的資料夾作為輸出目錄，而不是使用預設的 output 資料夾作為輸出目錄",
-    "Disabled when launched with --hide-ui-dir-config.": "啟動 --hide-ui-dir-config 時禁用",
-    "Input directory": "輸入目錄",
-    "Output directory": "輸出目錄",
-    "Resize mode": "縮放模式",
-    "Just resize": "拉伸",
-    "Crop and resize": "裁剪",
-    "Resize and fill": "填充",
-    "img2img alternative test": "圖生圖的另一種測試",
-    "Loopback": "回送",
-    "Outpainting mk2": "向外繪製第二版",
-    "Poor man's outpainting": "效果稍差的向外繪製",
-    "SD upscale": "使用 SD 放大",
-    "should be 2 or lower.": "必須小於等於2",
-    "Override `Sampling method` to Euler?(this method is built for it)": "覆寫「採樣方法」為 Euler？（這個方法就是為這樣做設計的）",
-    "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "覆寫「提示詞」為「初始提示詞」？（包括「反向提示詞」）",
-    "Original prompt": "初始提示詞",
-    "Original negative prompt": "初始反向提示詞",
-    "Override `Sampling Steps` to the same value as `Decode steps`?": "覆寫「採樣疊代步數」為「解碼疊代步數」？",
-    "Decode steps": "解碼疊代步數",
-    "Override `Denoising strength` to 1?": "覆寫「重繪幅度」為1?",
-    "Decode CFG scale": "解碼提示詞相關性（CFG）",
-    "Randomness": "隨機度",
-    "Sigma adjustment for finding noise for image": "為尋找圖中噪點的 Sigma 調整",
-    "Loops": "疊代次數",
-    "Denoising strength change factor": "重繪幅度的調整係數",
-    "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "推薦設定：採樣疊代步數：80-100，採樣器：Euler a，重繪幅度：0.8",
-    "Pixels to expand": "拓展的畫素數",
-    "Outpainting direction": "向外繪製的方向",
-    "left": "左",
-    "right": "右",
-    "up": "上",
-    "down": "下",
-    "Fall-off exponent (lower=higher detail)": "衰減指數（越低細節越好）",
-    "Color variation": "色彩變化",
-    "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "將圖像放大到兩倍尺寸； 使用寬度和高度滑塊設定圖塊尺寸",
-    "Tile overlap": "圖塊重疊的畫素",
-    "Upscaler": "放大演算法",
-    "Lanczos": "Lanczos",
-    "Nearest": "最鄰近（整數縮放）",
-    "LDSR": "LDSR",
-    "BSRGAN 4x": "BSRGAN 4x",
-    "ESRGAN_4x": "ESRGAN_4x",
-    "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B",
-    "ScuNET GAN": "ScuNET GAN",
-    "ScuNET PSNR": "ScuNET PSNR",
-    "SwinIR_4x": "SwinIR 4x",
-    "Single Image": "單個圖像",
-    "Batch Process": "批量處理",
-    "Batch from Directory": "從目錄進行批量處理",
-    "Source": "來源",
-    "Show result images": "顯示輸出圖像",
-    "Scale by": "等比縮放",
-    "Scale to": "指定尺寸縮放",
-    "Resize": "縮放",
-    "Crop to fit": "裁剪以適應",
-    "Upscaler 2": "放大演算法 2",
-    "Upscaler 2 visibility": "放大演算法 2 可見度",
-    "GFPGAN visibility": "GFPGAN 可見度",
-    "CodeFormer visibility": "CodeFormer 可見度",
-    "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer 權重 （0 = 最大效果， 1 = 最小效果）",
-    "Open output directory": "打開輸出目錄",
-    "Upscale Before Restoring Faces": "放大後再進行面部修復",
-    "Send to txt2img": ">> 文生圖",
-    "A merger of the two checkpoints will be generated in your": "合併後的模型權重存檔點會生成在你的",
-    "checkpoint": "模型權重存檔點",
-    "directory.": "目錄",
-    "Primary model (A)": "主要模型 (A)",
-    "Secondary model (B)": "第二模型 (B)",
-    "Tertiary model (C)": "第三模型 (C)",
-    "Custom Name (Optional)": "自訂名稱 （可選）",
-    "Multiplier (M) - set to 0 to get model A": "倍率 (M) - 設為 0 等價於模型 A",
-    "Interpolation Method": "插值方法",
-    "Weighted sum": "加權和",
-    "Add difference": "加入差分",
-    "Save as float16": "以 float16 儲存",
-    "See": "檢視",
-    "wiki": "wiki文件",
-    "for detailed explanation.": "以了解詳細說明",
-    "Create embedding": "生成 embedding",
-    "Create aesthetic images embedding": "生成美術風格圖集 embedding",
-    "Create hypernetwork": "生成超網路（Hypernetwork）",
-    "Preprocess images": "圖像預處理",
-    "Name": "名稱",
-    "Initialization text": "初始化文字",
-    "Number of vectors per token": "每個 token 的向量數",
-    "Overwrite Old Embedding": "覆寫舊的 Embedding",
-    "Modules": "模組",
-    "Enter hypernetwork layer structure": "輸入超網路（Hypernetwork）層結構",
-    "Select activation function of hypernetwork": "選擇超網路（Hypernetwork）的激活函數",
-    "linear": "linear",
-    "relu": "relu",
-    "leakyrelu": "leakyrelu",
-    "elu": "elu",
-    "swish": "swish",
-    "tanh": "tanh",
-    "sigmoid": "sigmoid",
-    "celu": "celu",
-    "gelu": "gelu",
-    "glu": "glu",
-    "hardshrink": "hardshrink",
-    "hardsigmoid": "hardsigmoid",
-    "hardtanh": "hardtanh",
-    "logsigmoid": "logsigmoid",
-    "logsoftmax": "logsoftmax",
-    "mish": "mish",
-    "prelu": "prelu",
-    "rrelu": "rrelu",
-    "relu6": "relu6",
-    "selu": "selu",
-    "silu": "silu",
-    "softmax": "softmax",
-    "softmax2d": "softmax2d",
-    "softmin": "softmin",
-    "softplus": "softplus",
-    "softshrink": "softshrink",
-    "softsign": "softsign",
-    "tanhshrink": "tanhshrink",
-    "threshold": "閾值",
-    "Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended": "挑選初始化層權重的方案. 類relu - Kaiming, 類sigmoid - Xavier 都是比較推薦的選項",
-    "Normal": "正態",
-    "KaimingUniform": "Kaiming 均勻",
-    "KaimingNormal": "Kaiming 正態",
-    "XavierUniform": "Xavier 均勻",
-    "XavierNormal": "Xavier 正態",
-    "Add layer normalization": "加入層標準化",
-    "Use dropout": "採用 dropout 防止過擬合",
-    "Overwrite Old Hypernetwork": "覆寫舊的超網路（Hypernetwork）",
-    "Source directory": "來源目錄",
-    "Destination directory": "目標目錄",
-    "Existing Caption txt Action": "對已有的TXT說明文字的行為",
-    "ignore": "無視",
-    "copy": "複製",
-    "prepend": "放前面",
-    "append": "放後面",
-    "Create flipped copies": "生成鏡像副本",
-    "Split oversized images into two": "將過大的圖像分為兩份",
-    "Split oversized images": "分割過大的圖像",
-    "Auto focal point crop": "自動焦點裁切",
-    "Use BLIP for caption": "使用 BLIP 生成說明文字（自然語言描述）",
-    "Use deepbooru for caption": "使用 deepbooru 生成說明文字（標記）",
-    "Split image threshold": "圖像分割閾值",
-    "Split image overlap ratio": "分割圖像重疊的比率",
-    "Focal point face weight": "焦點面部權重",
-    "Focal point entropy weight": "焦點熵權重",
-    "Focal point edges weight": "焦點線條權重",
-    "Create debug image": "生成除錯圖片",
-    "Preprocess": "預處理",
-    "Train an embedding; must specify a directory with a set of 1:1 ratio images": "訓練 embedding； 必須指定一組具有 1:1 比例圖像的目錄",
-    "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "訓練 embedding 或者超網路（Hypernetwork）； 必須指定一組具有 1:1 比例圖像的目錄",
-    "[wiki]": "[wiki文件]",
-    "Embedding": "Embedding",
-    "Embedding Learning rate": "Embedding 學習率",
-    "Hypernetwork Learning rate": "超網路（Hypernetwork）學習率",
-    "Learning rate": "學習率",
-    "Dataset directory": "資料集目錄",
-    "Log directory": "日誌目錄",
-    "Prompt template file": "提示詞模版檔案",
-    "Max steps": "最大疊代步數",
-    "Save an image to log directory every N steps, 0 to disable": "每 N 步儲存一個圖像到日誌目錄，0 表示禁用",
-    "Save a copy of embedding to log directory every N steps, 0 to disable": "每 N 步將 embedding 的副本儲存到日誌目錄，0 表示禁用",
-    "Save images with embedding in PNG chunks": "儲存圖像，並在 PNG 圖片檔案中嵌入 embedding 檔案",
-    "Read parameters (prompt, etc...) from txt2img tab when making previews": "進行預覽時，從文生圖頁籤中讀取參數（提示詞等）",
-    "Train Hypernetwork": "訓練超網路（Hypernetwork）",
-    "Train Embedding": "訓練 Embedding",
-    "Create an aesthetic embedding out of any number of images": "從任意數量的圖像中建立美術風格 embedding",
-    "Create images embedding": "生成圖集 embedding",
-    "txt2img history": "文生圖歷史記錄",
-    "img2img history": "圖生圖歷史記錄",
-    "extras history": "後處理歷史記錄",
-    "extras": "後處理",
-    "favorites": "收藏夾",
-    "Favorites": "收藏夾",
-    "Others": "其他",
-    "custom fold": "自訂資料夾",
-    "Load": "載入",
-    "Images directory": "圖像目錄",
-    "Prev batch": "上一批",
-    "Next batch": "下一批",
-    "Dropdown": "下拉式清單",
-    "First Page": "首頁",
-    "Prev Page": "上一頁",
-    "Page Index": "頁數",
-    "Next Page": "下一頁",
-    "End Page": "尾頁",
-    "number of images to delete consecutively next": "接下來要連續刪除的圖像數",
-    "delete next": "刪除下一張",
-    "Delete": "刪除",
-    "sort by": "排序方式",
-    "path name": "路徑名",
-    "date": "日期",
-    "keyword": "搜尋",
-    "Generate Info": "生成資訊",
-    "File Name": "檔案名",
-    "Collect": "收藏",
-    "Refresh page": "刷新頁面",
-    "Date to": "日期至",
-    "Move to favorites": "移動到收藏夾",
-    "Renew Page": "刷新頁面",
-    "Number": "數量",
-    "set_index": "設定索引",
-    "load_switch": "載入開關",
-    "turn_page_switch": "翻頁開關",
-    "Checkbox": "核取方塊",
-    "Apply settings": "儲存設定",
-    "Saving images/grids": "儲存圖像／宮格圖",
-    "Always save all generated images": "始終儲存所有生成的圖像",
-    "File format for images": "圖像的檔案格式",
-    "Images filename pattern": "圖像檔案名格式",
-    "Add number to filename when saving": "儲存的時候在檔案名里加入數字",
-    "Always save all generated image grids": "始終儲存所有生成的宮格圖",
-    "File format for grids": "宮格圖的檔案格式",
-    "Add extended info (seed, prompt) to filename when saving grid": "儲存宮格圖時，將擴展資訊（隨機種子，提示詞）加入到檔案名",
-    "Do not save grids consisting of one picture": "只有一張圖片時不要儲存宮格圖",
-    "Prevent empty spots in grid (when set to autodetect)": "（啟用自動偵測時）防止宮格圖中出現空位",
-    "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "宮格圖行數； 使用 -1 進行自動檢測，使用 0 使其與每批數量相同",
-    "Save text information about generation parameters as chunks to png files": "將有關生成參數的文本資訊，作為塊儲存到PNG圖片檔案中",
-    "Create a text file next to every image with generation parameters.": "儲存圖像時，在每個圖像旁邊建立一個文本檔案儲存生成參數",
-    "Save a copy of image before doing face restoration.": "在進行面部修復之前儲存圖像副本",
-    "Save a copy of image before applying highres fix.": "在做高解析度修復之前儲存初始圖像副本",
-    "Save a copy of image before applying color correction to img2img results": "在對圖生圖結果套用顏色校正之前儲存圖像副本",
-    "Quality for saved jpeg images": "儲存的JPEG圖像的品質",
-    "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "如果 PNG 圖像大於 4MB 或寬高大於 4000，則縮小並儲存副本為 JPG 圖片",
-    "Use original name for output filename during batch process in extras tab": "在更多頁籤中的批量處理過程中，使用原始名稱作為輸出檔案名",
-    "When using 'Save' button, only save a single selected image": "使用「儲存」按鈕時，只儲存一個選定的圖像",
-    "Do not add watermark to images": "不要給圖像加浮水印",
-    "Paths for saving": "儲存路徑",
-    "Output directory for images; if empty, defaults to three directories below": "圖像的輸出目錄； 如果為空，則預設為以下三個目錄",
-    "Output directory for txt2img images": "文生圖的輸出目錄",
-    "Output directory for img2img images": "圖生圖的輸出目錄",
-    "Output directory for images from extras tab": "更多頁籤的輸出目錄",
-    "Output directory for grids; if empty, defaults to two directories below": "宮格圖的輸出目錄； 如果為空，則預設為以下兩個目錄",
-    "Output directory for txt2img grids": "文生圖宮格的輸出目錄",
-    "Output directory for img2img grids": "圖生圖宮格的輸出目錄",
-    "Directory for saving images using the Save button": "使用「儲存」按鈕儲存圖像的目錄",
-    "Saving to a directory": "儲存到目錄",
-    "Save images to a subdirectory": "將圖像儲存到子目錄",
-    "Save grids to a subdirectory": "將宮格圖儲存到子目錄",
-    "When using \"Save\" button, save images to a subdirectory": "使用「儲存」按鈕時，將圖像儲存到子目錄",
-    "Directory name pattern": "目錄名稱格式",
-    "Max prompt words for [prompt_words] pattern": "[prompt_words] 格式的最大提示詞數量",
-    "Upscaling": "放大",
-    "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 的圖塊尺寸。0 = 不分塊",
-    "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGAN 的圖塊重疊畫素。低值 = 可見接縫",
-    "Tile size for all SwinIR.": "適用所有 SwinIR 系演算法的圖塊尺寸",
-    "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIR 的圖塊重疊畫素。低值 = 可見接縫",
-    "LDSR processing steps. Lower = faster": "LDSR 處理疊代步數。更低 = 更快",
-    "Upscaler for img2img": "圖生圖的放大演算法",
-    "Upscale latent space image when doing hires. fix": "做高解析度修復時，也放大潛空間圖像",
-    "Face restoration": "面部修復",
-    "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer 權重參數； 0 = 最大效果； 1 = 最小效果",
-    "Move face restoration model from VRAM into RAM after processing": "面部修復處理完成後，將面部修復模型從顯存（VRAM）移至內存（RAM）",
-    "System": "系統",
-    "VRAM usage polls per second during generation. Set to 0 to disable.": "生成圖像時每秒輪詢顯存（VRAM）使用情況的次數。設定為 0 以禁用",
-    "Always print all generation info to standard output": "始終將所有生成資訊輸出到 standard output （一般為控制台）",
-    "Add a second progress bar to the console that shows progress for an entire job.": "向控制台加入第二個進度列，顯示整個作業的進度",
-    "Training": "訓練",
-    "Unload VAE and CLIP from VRAM when training": "訓練時從顯存（VRAM）中取消 VAE 和 CLIP 的載入",
-    "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "訓練時將 VAE 和 CLIP 從顯存（VRAM）移放到內存（RAM），節省顯存（VRAM）",
-    "Move VAE and CLIP to RAM when training if possible. Saves VRAM.": "訓練時將 VAE 和 CLIP 從顯存（VRAM）移放到內存（RAM）如果可行的話，節省顯存（VRAM）",
-    "Filename word regex": "檔案名用詞的正則表達式",
-    "Filename join string": "檔案名連接用字串",
-    "Number of repeats for a single input image per epoch; used only for displaying epoch number": "每個 epoch 中單個輸入圖像的重複次數； 僅用於顯示 epoch 數",
-    "Save an csv containing the loss to log directory every N steps, 0 to disable": "每 N 步儲存一個包含 loss 的CSV表格到日誌目錄，0 表示禁用",
-    "Use cross attention optimizations while training": "訓練時開啟 cross attention 最佳化",
-    "Stable Diffusion": "Stable Diffusion",
-    "Checkpoints to cache in RAM": "快取在內存（RAM）中的模型權重存檔點",
-    "SD VAE": "模型的VAE",
-    "auto": "自動",
-    "Hypernetwork strength": "超網路（Hypernetwork）強度",
-    "Inpainting conditioning mask strength": "局部重繪時圖像調節的蒙版屏蔽強度",
-    "Apply color correction to img2img results to match original colors.": "對圖生圖結果套用顏色校正以匹配原始顏色",
-    "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "在進行圖生圖的時候，確切地執行滑塊指定的疊代步數（正常情況下更弱的重繪幅度需要更少的疊代步數）",
-    "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "在 K 採樣器中啟用量化以獲得更清晰，更清晰的結果。這可能會改變現有的隨機種子。需要重新啟動才能套用",
-    "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "強調符：使用 (文字) 使模型更關注該文本，使用 [文字] 使其減少關注",
-    "Use old emphasis implementation. Can be useful to reproduce old seeds.": "使用舊的強調符實作。可用於復現舊隨機種子",
-    "Make K-diffusion samplers produce same images in a batch as when making a single image": "使 K-diffusion 採樣器批量生成與生成單個圖像時，產出相同的圖像",
-    "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "當使用超過 75 個 token 時，通過從 n 個 token 中的最後一個逗號填補來提高一致性",
-    "Filter NSFW content": "過濾成人內容",
-    "Stop At last layers of CLIP model": "在 CLIP 模型的最後哪一層停下",
-    "Interrogate Options": "反推提示詞選項",
-    "Interrogate: keep models in VRAM": "反推： 將模型儲存在顯存（VRAM）中",
-    "Interrogate: use artists from artists.csv": "反推： 使用 artists.csv 中的藝術家",
-    "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "反推： 在生成結果中包含與模型標記相匹配的等級（對基於生成自然語言描述的反推沒有影響）",
-    "Interrogate: num_beams for BLIP": "反推： BLIP 的 num_beams",
-    "Interrogate: minimum description length (excluding artists, etc..)": "反推： 最小描述長度（不包括藝術家，等…）",
-    "Interrogate: maximum description length": "反推： 最大描述長度",
-    "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP： 文本檔案中的最大行數（0 = 無限制）",
-    "Interrogate: deepbooru score threshold": "反推： deepbooru 分數閾值",
-    "Interrogate: deepbooru sort alphabetically": "反推： deepbooru 按字母順序排序",
-    "use spaces for tags in deepbooru": "在 deepbooru 中為標記使用空格",
-    "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "在 deepbooru 中使用轉義 (\\) 括號（因此它們用作文字括號而不是強調符號）",
-    "User interface": "使用者介面",
-    "Show progressbar": "顯示進度列",
-    "Show image creation progress every N sampling steps. Set 0 to disable.": "每 N 個採樣疊代步數顯示圖像生成進度。設定 0 禁用",
-    "Show previews of all images generated in a batch as a grid": "以網格的形式，預覽批量生成的所有圖像",
-    "Show grid in results for web": "在網頁的結果中顯示宮格圖",
-    "Do not show any images in results for web": "不在網頁的結果中顯示任何圖像",
-    "Add model hash to generation information": "將模型的雜湊值加入到生成資訊",
-    "Add model name to generation information": "將模型名稱加入到生成資訊",
-    "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "從文本讀取生成參數到使用者介面（從 PNG 圖片資訊或粘貼文本）時，不要更改選定的模型權重存檔點",
-    "Send seed when sending prompt or image to other interface": "將提示詞或者圖片發送到 >> 其他界面時，把隨機種子也傳送過去",
-    "Font for image grids that have text": "有文字的宮格圖使用的字體",
-    "Enable full page image viewer": "啟用整頁圖像檢視器",
-    "Show images zoomed in by default in full page image viewer": "在整頁圖像檢視器中，預設放大顯示圖像",
-    "Show generation progress in window title.": "在視窗標題中顯示生成進度",
-    "Quicksettings list": "快速設定列表",
-    "Localization (requires restart)": "本地化翻譯（需要儲存設定，並重啟Gradio）",
-    "Sampler parameters": "採樣器參數",
-    "Hide samplers in user interface (requires restart)": "在使用者介面中隱藏採樣器（需要重新啟動）",
-    "eta (noise multiplier) for DDIM": "DDIM 的 eta （噪聲乘數）",
-    "eta (noise multiplier) for ancestral samplers": "ancestral 採樣器的 eta （噪聲乘數）",
-    "img2img DDIM discretize": "圖生圖 DDIM 離散化",
-    "uniform": "均勻",
-    "quad": "二階",
-    "sigma churn": "sigma churn",
-    "sigma tmin": "最小(tmin) sigma",
-    "sigma noise": "sigma 噪聲",
-    "Eta noise seed delta": "Eta 噪聲種子偏移（ENSD）",
-    "Images Browser": "圖庫瀏覽器",
-    "Preload images at startup": "在啟動時預加載圖像",
-    "Number of columns on the page": "每頁列數",
-    "Number of rows on the page": "每頁行數",
-    "Number of pictures displayed on each page": "每頁顯示的圖像數量",
-    "Minimum number of pages per load": "每次載入的最小頁數",
-    "Number of grids in each row": "每行顯示多少格",
-    "Wildcards": "萬用字元",
-    "Use same seed for all images": "為所有圖像使用同一個隨機種子",
-    "Request browser notifications": "請求瀏覽器通知",
-    "Download localization template": "下載本地化模板",
-    "Reload custom script bodies (No ui updates, No restart)": "重新載入自訂指令碼主體（無使用者介面更新，無重啟）",
-    "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "重啟 Gradio 及刷新組件（僅限自訂指令碼，ui.py，JS 和 CSS）",
-    "Available": "可用",
-    "Install from URL": "從網址安裝",
-    "Apply and restart UI": "應用並重啟使用者介面",
-    "Check for updates": "檢查更新",
-    "Extension": "擴充",
-    "URL": "網址",
-    "Update": "更新",
-    "a1111-sd-webui-tagcomplete": "標記自動補全",
-    "unknown": "未知",
-    "deforum-for-automatic1111-webui": "Deforum",
-    "sd-dynamic-prompting": "動態提示詞",
-    "stable-diffusion-webui-aesthetic-gradients": "美術風格梯度",
-    "stable-diffusion-webui-aesthetic-image-scorer": "美術風格評等",
-    "stable-diffusion-webui-artists-to-study": "藝術家圖庫",
-    "stable-diffusion-webui-dataset-tag-editor": "資料集標記編輯器",
-    "stable-diffusion-webui-images-browser": "圖庫瀏覽器",
-    "stable-diffusion-webui-inspiration": "靈感",
-    "stable-diffusion-webui-wildcards": "萬用字元",
-    "Load from:": "載入自",
-    "Extension index URL": "擴充清單連結",
-    "URL for extension's git repository": "擴充的 git 倉庫連結",
-    "Local directory name": "本地路徑名",
-    "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "提示詞（按 Ctrl+Enter 或 Alt+Enter 生成）",
-    "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "反向提示詞（按 Ctrl+Enter 或 Alt+Enter 生成）",
-    "Add a random artist to the prompt.": "隨機加入一個藝術家到提示詞中",
-    "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "從提示詞中讀取生成參數，如果提示詞為空，則讀取上一次的生成參數到使用者介面",
-    "Save style": "存儲為模板風格",
-    "Apply selected styles to current prompt": "將所選樣式套用於當前提示",
-    "Stop processing current image and continue processing.": "停止處理當前圖像，並繼續處理下一個",
-    "Stop processing images and return any results accumulated so far.": "停止處理圖像，並返回迄今為止累積的任何結果",
-    "Style to apply; styles have components for both positive and negative prompts and apply to both": "要套用的模版風格； 模版風格包含正向和反向提示詞，並套用於兩者",
-    "Do not do anything special": "什麼都不做",
-    "Which algorithm to use to produce the image": "使用哪種演算法生成圖像",
-    "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常有創意，可以根據疊代步數獲得完全不同的圖像，將疊代步數設定為高於 30-40 不會有正面作用",
-    "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit models - 最擅長局部重繪",
-    "Produce an image that can be tiled.": "生成可用於平舖的圖像",
-    "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "使用兩步處理的時候，以較小的解析度生成初步圖像，接著放大圖像，然後在不更改構圖的情況下改進其中的細節",
-    "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "決定演算法對圖像內容的影響程度。設定 0 時，什麼都不會改變，而在 1 時，你將獲得不相關的圖像。\n值低於 1.0 時，處理的疊代步數將少於「採樣疊代步數」滑塊指定的步數",
-    "How many batches of images to create": "建立多少批次的圖像",
-    "How many image to create in a single batch": "每批建立多少圖像",
-    "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - 圖像應在多大程度上服從提示詞 - 較低的值會產生更有創意的結果",
-    "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "一個固定隨機數生成器輸出的值 — 以相同參數和隨機種子生成的圖像會得到相同的結果",
-    "Set seed to -1, which will cause a new random number to be used every time": "將隨機種子設定為-1，則每次都會使用一個新的隨機數",
-    "Reuse seed from last generation, mostly useful if it was randomed": "重用上一次使用的隨機種子，如果想要固定結果就會很有用",
-    "Seed of a different picture to be mixed into the generation.": "將要參與生成的另一張圖的隨機種子",
-    "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "想要產生多強烈的變化。設為 0 時，將沒有效果。設為 1 時，你將獲得完全產自差異隨機種子的圖像（ancestral 採樣器除外，你只是單純地生成了一些東西）",
-    "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "嘗試生成與在指定解析度下使用相同隨機種子生成的圖像相似的圖片",
-    "This text is used to rotate the feature space of the imgs embs": "此文本用於旋轉圖集 embeddings 的特徵空間",
-    "Separate values for X axis using commas.": "使用逗號分隔 X 軸的值",
-    "Separate values for Y axis using commas.": "使用逗號分隔 Y 軸的值",
-    "Write image to a directory (default - log/images) and generation parameters into csv file.": "將圖像寫入目錄（預設 — log/images）並將生成參數寫入CSV表格檔案",
-    "Open images output directory": "打開圖像輸出目錄",
-    "How much to blur the mask before processing, in pixels.": "處理前要對蒙版進行多強的模糊，以畫素為單位",
-    "What to put inside the masked area before processing it with Stable Diffusion.": "在使用 Stable Diffusion 處理蒙版區域之前要在蒙版區域內放置什麼",
-    "fill it with colors of the image": "用圖像的顏色（高強度模糊）填充它",
-    "keep whatever was there originally": "保留原來的圖像，不進行預處理",
-    "fill it with latent space noise": "於潛空間填充噪聲",
-    "fill it with latent space zeroes": "於潛空間填零",
-    "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "將蒙版區域（包括預留畫素長度的緩衝區域）放大到目標解析度，進行局部重繪。\n然後縮小並粘貼回原始圖像中",
-    "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "將圖像大小調整為目標解析度。除非高度和寬度匹配，否則你將獲得不正確的縱橫比",
-    "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "調整圖像大小，使整個目標解析度都被圖像填充。裁剪多出來的部分",
-    "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "調整圖像大小，使整個圖像在目標解析度內。用圖像的顏色填充空白區域",
-    "How many times to repeat processing an image and using it as input for the next iteration": "重複處理圖像並用作下次疊代輸入的次數",
-    "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "在回送模式下，在每個循環中，重繪幅度都會乘以該值。<1 表示減少多樣性，因此你的這一組圖將集中在固定的圖像上。>1 意味著增加多樣性，因此你的這一組圖將變得越來越混亂",
-    "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "使用 SD 放大時，圖塊之間應該有多少畫素重疊。圖塊之間需要重疊才可以讓它們在合併回一張圖像時，沒有清晰可見的接縫",
-    "A directory on the same machine where the server is running.": "與伺服器主機上的目錄",
-    "Leave blank to save images to the default path.": "留空以將圖像儲存到預設路徑",
-    "Result = A * (1 - M) + B * M": "結果 = A * (1 - M) + B * M",
-    "Result = A + (B - C) * M": "結果 = A + (B - C) * M",
-    "1st and last digit must be 1. ex:'1, 2, 1'": "第一個和最後一個數字必須是 1。例：'1, 2, 1'",
-    "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "訓練應該多快。低值將需要更長的時間來訓練，高值可能無法收斂（無法產生準確的結果）以及／也許可能會破壞 embedding（如果你在訓練資訊文字方塊中看到 Loss: nan 就會發生這種情況。如果發生這種情況，你需要從較舊的未損壞的備份手動恢復 embedding）\n\n你可以使用以下語法設定單個數值或多個學習率：\n\n   率1:步限1, 率2:步限2, …\n\n如：   0.005:100, 1e-3:1000, 1e-5\n\n即前 100 步將以 0.005 的速率訓練，接著直到 1000 步為止以 1e-3 訓練，然後剩餘所有步以 1e-5 訓練",
-    "Path to directory with input images": "帶有輸入圖像的路徑",
-    "Path to directory where to write outputs": "進行輸出的路徑",
-    "Input images directory": "輸入圖像目錄",
-    "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下標記定義如何選擇圖像的檔案名： [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]； 預設請留空",
-    "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "如果啟用此選項，浮水印將不會加入到生成出來的圖像中。警告：如果你不加入浮水印，你的行為可能是不符合道德操守的",
-    "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.": "使用以下標記定義如何選擇圖像和宮格圖的子目錄： [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]； 預設請留空",
-    "Restore low quality faces using GFPGAN neural network": "使用 GFPGAN 神經網路修復低品質面部",
-    "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "此正則表達式將用於從檔案名中提取單詞，並將使用以下選項將它們接合到用於訓練的標記文本中。留空以保持檔案名文本不變",
-    "This string will be used to join split words into a single line if the option above is enabled.": "如果啟用了上述選項，則此處的字元會用於將拆分的單詞接合為同一行",
-    "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.": "僅適用於局部重繪專用的模型（模型後綴為 inpainting.ckpt 的模型）。決定了蒙版在局部重繪以及圖生圖中屏蔽原圖內容的強度。 1.0 表示完全屏蔽原圖，這是預設行為。 0.0 表示完全不屏蔽讓原圖進行圖像調節。較低的值將有助於保持原圖的整體構圖，但很難遇到較大的變化",
-    "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "設定項名稱列表，以逗號分隔，該設定會移動到頂部的快速存取列，而不是預設的設定頁籤。有關設定名稱，請參見 modules/shared.py。需要重新啟動才能套用",
-    "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "如果這個值不為零，它將被加入到隨機種子中，並在使用帶有 Eta 的採樣器時用於初始化隨機噪聲。你可以使用它來產生更多的圖像變化，或者你可以使用它來模仿其他軟體生成的圖像，如果你知道你在做什麼",
-    "Leave empty for auto": "留空時自動生成",
-    "Autocomplete options": "自動補全選項",
-    "Enable Autocomplete": "開啟Tag補全",
-    "Select which Real-ESRGAN models to show in the web UI. (Requires restart)": "選擇哪些 Real-ESRGAN 模型顯示在網頁使用者介面。（需要重新啟動）",
-    "Allowed categories for random artists selection when using the Roll button": "使用抽選藝術家按鈕時將會隨機的藝術家類別",
-    "Append commas": "附加逗號",
-    "latest": "最新",
-    "behind": "落後",
-    "Roll three": "抽三位出來",
-    "Generate forever": "無限生成",
-    "Cancel generate forever": "停止無限生成",
-    "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results": "疊代改進生成的圖像多少次；更高的值需要更長的時間；非常低的值會產生不好的結果",
-    "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt": "在圖像上畫一個蒙版，指令碼會根據提示重新生成蒙版區域的內容",
-    "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back": "正常放大圖像，將結果分割成圖塊，用圖生圖改進每個圖塊，最後將整個圖像合併回來",
-    "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows": "創建一個網格，圖像將有不同的參數。使用下面的輸入來指定哪些參數將由列和行共享",
-    "Run Python code. Advanced user only. Must run program with --allow-code for this to work": "執行 Python 程式碼。僅限老手使用。必須以 --allow-code 來開啟程式，才能使其執行",
-    "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others": "以逗號分割的單詞列表，第一個單詞將被用作關鍵詞：指令碼將在提示詞中搜尋這個單詞，並用其他單詞替換它",
-    "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order": "以逗號分割的單詞列表，指令碼會排列出這些單詞的所有排列方式，並加入提示詞各生成一次",
-    "Reconstruct prompt from existing image and put it into the prompt field.": "從現有的圖像中重構出提示詞，並將其放入提示詞的輸入文字方塊",
-    "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle": "設定在[prompt_words]選項中要使用的最大字數；注意：如果字數太長，可能會超過系統可處理的檔案路徑的最大長度",
-    "Process an image, use it as an input, repeat.": "處理一張圖像，將其作為輸入，並重複",
-    "Insert selected styles into prompt fields": "在提示詞中插入選定的模版風格",
-    "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.": "將當前的提示詞儲存為模版風格。如果你在文本中加入{prompt}標記，那麼將來你使用該模版風格時，你現有的提示詞會替換模版風格中的{prompt}",
-    "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.": "在生成圖像之前從模型權重存檔點中載入權重。你可以使用哈希值或檔案名的一部分（如設定中所示）作為模型權重存檔點名稱。建議用在Y軸上以減少過程中模型的切換",
-    "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "Torch active: 在生成過程中，Torch使用的顯存(VRAM)峰值，不包括快取的數據。\nTorch reserved: Torch 分配的顯存(VRAM)的峰值量，包括所有活動和快取數據。\nSys VRAM: 所有應用程式分配的顯存(VRAM)的峰值量 / GPU 的總顯存(VRAM)（峰值利用率%）",
-    "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.": "放大潛空間中的圖像。而另一種方法是，從潛變量表達中直接解碼並生成完整的圖像，接著放大它，然後再將其編碼回潛空間",
-    "Start drawing": "開始繪製",
-    "Description": "描述",
-    "Action": "行動",
-    "Aesthetic Gradients": "美術風格梯度",
-    "aesthetic-gradients": "美術風格梯度",
-    "Dynamic Prompts": "動態提示詞",
-    "images-browser": "圖庫瀏覽器",
-    "Inspiration": "靈感",
-    "Deforum": "Deforum",
-    "Artists to study": "藝術家圖庫",
-    "Aesthetic Image Scorer": "美術風格評分",
-    "Dataset Tag Editor": "數據集標記編輯器",
-    "Face restoration model": "面部修復模型",
-    "Install": "安裝",
-    "Installing...": "安裝中…",
-    "Installed": "已安裝"
-}
-- 
cgit v1.2.1


From c34542a48376e4972de955aab00ffc8359f7d792 Mon Sep 17 00:00:00 2001
From: kavorite <kavorite@gmail.com>
Date: Tue, 8 Nov 2022 03:25:59 -0500
Subject: add new color-sketch state to img2img invocation

---
 modules/ui.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/ui.py b/modules/ui.py
index db323e9c..29954f2a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -941,6 +941,7 @@ def create_ui(wrap_gradio_gpu_call):
                     img2img_prompt_style2,
                     init_img,
                     init_img_with_mask,
+                    init_img_with_mask_orig,
                     init_img_inpaint,
                     init_mask_inpaint,
                     mask_mode,
-- 
cgit v1.2.1


From cfcadeae9a61e1aff32960864f90299412c86d5c Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Tue, 8 Nov 2022 10:03:56 -0600
Subject: Add option to preload extensions

By creating a file called "preload.py" in an extension folder and declaring a preload(parser) method, we can add extra command-line args for an extension.
---
 modules/extensions.py | 23 ++++++++++++++++++++++-
 modules/shared.py     |  5 ++++-
 2 files changed, 26 insertions(+), 2 deletions(-)

diff --git a/modules/extensions.py b/modules/extensions.py
index 8e0977fd..544f3580 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -1,12 +1,12 @@
 import os
 import sys
 import traceback
+from importlib.machinery import SourceFileLoader
 
 import git
 
 from modules import paths, shared
 
-
 extensions = []
 extensions_dir = os.path.join(paths.script_path, "extensions")
 
@@ -84,3 +84,24 @@ def list_extensions():
 
         extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions)
         extensions.append(extension)
+
+
+def preload_extensions(parser):
+    if not os.path.isdir(extensions_dir):
+        return
+
+    for dirname in sorted(os.listdir(extensions_dir)):
+        path = os.path.join(extensions_dir, dirname)
+        if not os.path.isdir(path):
+            continue
+        for file in os.listdir(path):
+            if "preload.py" in file:
+                full_file = os.path.join(path, file)
+                print(f"Got preload file: {full_file}")
+
+                try:
+                    ext = SourceFileLoader("preload", full_file).load_module()
+                    parser = ext.preload(parser)
+                except Exception as e:
+                    print(f"Exception preloading script: {e}")
+    return parser
\ No newline at end of file
diff --git a/modules/shared.py b/modules/shared.py
index e8bacd3c..222ad4fb 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -15,7 +15,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models, localization, sd_vae
+from modules import sd_samplers, sd_models, localization, sd_vae, extensions
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -91,7 +91,10 @@ parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requ
 parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
 
+extensions.preload_extensions(parser)
+
 cmd_opts = parser.parse_args()
+
 restricted_opts = {
     "samples_filename_pattern",
     "directories_filename_pattern",
-- 
cgit v1.2.1


From 62e9fec3df8518da3a2c35fa090bb54946c856b2 Mon Sep 17 00:00:00 2001
From: pepe10-gpu <pepe.dannyboy@gmail.com>
Date: Tue, 8 Nov 2022 15:19:09 -0800
Subject: actual better fix

thanks C43H66N12O12S2
---
 modules/devices.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 4c63f465..058a5e00 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -39,12 +39,9 @@ def torch_gc():
 
 def enable_tf32():
     if torch.cuda.is_available():
-        #TODO: make this better; find a way to check if it is a turing card
-        turing = ["1630","1650","1660","Quadro RTX 3000","Quadro RTX 4000","Quadro RTX 4000","Quadro RTX 5000","Quadro RTX 5000","Quadro RTX 6000","Quadro RTX 6000","Quadro RTX 8000","Quadro RTX T400","Quadro RTX T400","Quadro RTX T600","Quadro RTX T1000","Quadro RTX T1000","2060","2070","2080","Titan RTX","Tesla T4","MX450","MX550"]
         for devid in range(0,torch.cuda.device_count()):
-            for i in turing:
-                if i in torch.cuda.get_device_name(devid):
-                    shd = True
+            if torch.cuda.get_device_capability(devid) == (7, 5):
+                shd = True
         if shd:
             torch.backends.cudnn.benchmark = True
             torch.backends.cudnn.enabled = True
-- 
cgit v1.2.1


From 59bb1d36ea69db449cfe23be4988ab4f6711bf4b Mon Sep 17 00:00:00 2001
From: kavorite <kavorite@gmail.com>
Date: Tue, 8 Nov 2022 22:06:29 -0500
Subject: blur mask with color-sketch + add paint transparency slider

---
 modules/img2img.py | 21 +++++++++++++--------
 modules/ui.py      |  3 +++
 2 files changed, 16 insertions(+), 8 deletions(-)

diff --git a/modules/img2img.py b/modules/img2img.py
index 00c6f827..644297da 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -4,7 +4,7 @@ import sys
 import traceback
 
 import numpy as np
-from PIL import Image, ImageOps, ImageChops
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance
 
 from modules import devices
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
@@ -40,7 +40,7 @@ def process_batch(p, input_dir, output_dir, args):
 
         img = Image.open(image)
         # Use the EXIF orientation of photos taken by smartphones.
-        img = ImageOps.exif_transpose(img) 
+        img = ImageOps.exif_transpose(img)
         p.init_images = [img] * p.batch_size
 
         proc = modules.scripts.scripts_img2img.run(p, *args)
@@ -59,7 +59,7 @@ def process_batch(p, input_dir, output_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_with_mask_orig, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_with_mask_orig, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
     is_inpaint = mode == 1
     is_batch = mode == 2
 
@@ -68,15 +68,20 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         if mask_mode == 0:
             image = init_img_with_mask
             is_mask_sketch = isinstance(image, dict)
-            if is_mask_sketch:  
+            is_mask_paint = not is_mask_sketch
+            if is_mask_sketch:
                 # Sketch: mask iff. not transparent
                 image, mask = image["image"], image["mask"]
-                mask = np.array(mask)[..., -1] > 0
+                pred = np.array(mask)[..., -1] > 0
             else:
                 # Color-sketch: mask iff. painted over
                 orig = init_img_with_mask_orig or image
-                mask = np.any(np.array(image) != np.array(orig), axis=-1)
-            mask = Image.fromarray(mask.astype(np.uint8) * 255, "L")
+                pred = np.any(np.array(image) != np.array(orig), axis=-1)
+            mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
+            if is_mask_paint:
+                mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
+                blur = ImageFilter.GaussianBlur(mask_blur)
+                image = Image.composite(image.filter(blur), orig, mask.filter(blur))
             image = image.convert("RGB")
         # Uploaded mask
         else:
@@ -89,7 +94,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
 
     # Use the EXIF orientation of photos taken by smartphones.
     if image is not None:
-        image = ImageOps.exif_transpose(image) 
+        image = ImageOps.exif_transpose(image)
 
     assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
 
diff --git a/modules/ui.py b/modules/ui.py
index 29954f2a..16982abf 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -854,6 +854,8 @@ def create_ui(wrap_gradio_gpu_call):
                         init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
                         init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
 
+                        show_mask_alpha = cmd_opts.gradio_inpaint_tool == "color-sketch"
+                        mask_alpha = gr.Slider(label="Mask transparency", interactive=show_mask_alpha, visible=show_mask_alpha)
                         mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
 
                         with gr.Row():
@@ -948,6 +950,7 @@ def create_ui(wrap_gradio_gpu_call):
                     steps,
                     sampler_index,
                     mask_blur,
+                    mask_alpha,
                     inpainting_fill,
                     restore_faces,
                     tiling,
-- 
cgit v1.2.1


From 3b51d239ac9201228c6032fc109111e347e8e6b0 Mon Sep 17 00:00:00 2001
From: cluder <1590330+cluder@users.noreply.github.com>
Date: Wed, 9 Nov 2022 04:54:21 +0100
Subject: - do not use ckpt cache, if disabled - cache model after is has been
 loaded from file

---
 modules/sd_models.py | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 34c57bfa..720c2a96 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -163,13 +163,21 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    if shared.opts.sd_checkpoint_cache > 0 and hasattr(model, "sd_checkpoint_info"):
+    cache_enabled = shared.opts.sd_checkpoint_cache > 0
+
+    if cache_enabled:
         sd_vae.restore_base_vae(model)
-        checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
 
     vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
 
-    if checkpoint_info not in checkpoints_loaded:
+    if cache_enabled and checkpoint_info in checkpoints_loaded:
+        # use checkpoint cache
+        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
+        vae_message = f" with {vae_name} VAE" if vae_name else ""
+        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
+        model.load_state_dict(checkpoints_loaded[checkpoint_info])
+    else:
+        # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
         pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
@@ -180,6 +188,10 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         del pl_sd
         model.load_state_dict(sd, strict=False)
         del sd
+        
+        if cache_enabled:
+            # cache newly loaded model
+            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
 
         if shared.cmd_opts.opt_channelslast:
             model.to(memory_format=torch.channels_last)
@@ -199,13 +211,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
 
         model.first_stage_model.to(devices.dtype_vae)
 
-    else:
-        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
-        vae_message = f" with {vae_name} VAE" if vae_name else ""
-        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
-        model.load_state_dict(checkpoints_loaded[checkpoint_info])
-
-    if shared.opts.sd_checkpoint_cache > 0:
+    # clean up cache if limit is reached
+    if cache_enabled:
         while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
             checkpoints_loaded.popitem(last=False)  # LRU
 
-- 
cgit v1.2.1


From eebf49592ad2c0933e58b06a098b92e48d47e4fe Mon Sep 17 00:00:00 2001
From: cluder <1590330+cluder@users.noreply.github.com>
Date: Wed, 9 Nov 2022 07:17:09 +0100
Subject: restore #4035 behavior

- if checkpoint cache is set to 1, keep 2 models in cache (current +1 more)
---
 modules/sd_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 720c2a96..80addf03 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -213,7 +213,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
 
     # clean up cache if limit is reached
     if cache_enabled:
-        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1: # we need to count the current model
             checkpoints_loaded.popitem(last=False)  # LRU
 
     model.sd_model_hash = sd_model_hash
-- 
cgit v1.2.1


From 0a54bd3395e30bf7772ad6f11d69c5c437a1ab73 Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Wed, 9 Nov 2022 11:15:17 -0600
Subject: Optional Accelerate Launch

---
 requirements.txt          |  1 +
 requirements_versions.txt |  1 +
 webui-user.bat            |  1 +
 webui-user.sh             |  3 +++
 webui.bat                 | 13 +++++++++++++
 webui.sh                  | 16 ++++++++++++----
 6 files changed, 31 insertions(+), 4 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 0fba0b23..762db4f3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,4 @@
+accelerate
 basicsr
 diffusers
 fairscale==0.4.4
diff --git a/requirements_versions.txt b/requirements_versions.txt
index f7059f20..662ca684 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -1,5 +1,6 @@
 transformers==4.19.2
 diffusers==0.3.0
+accelerate==0.12.0
 basicsr==1.4.2
 gfpgan==1.3.8
 gradio==3.9
diff --git a/webui-user.bat b/webui-user.bat
index e5a257be..0c120111 100644
--- a/webui-user.bat
+++ b/webui-user.bat
@@ -4,5 +4,6 @@ set PYTHON=
 set GIT=
 set VENV_DIR=
 set COMMANDLINE_ARGS=
+set ACCELERATE=
 
 call webui.bat
diff --git a/webui-user.sh b/webui-user.sh
index 30646f5c..16e42759 100644
--- a/webui-user.sh
+++ b/webui-user.sh
@@ -40,4 +40,7 @@ export COMMANDLINE_ARGS=""
 #export CODEFORMER_COMMIT_HASH=""
 #export BLIP_COMMIT_HASH=""
 
+# Uncomment to enable accelerated launch
+#export ACCELERATE="True"
+
 ###########################################
diff --git a/webui.bat b/webui.bat
index a38a28bb..2a066d60 100644
--- a/webui.bat
+++ b/webui.bat
@@ -28,15 +28,28 @@ goto :show_stdout_stderr
 :activate_venv
 set PYTHON="%~dp0%VENV_DIR%\Scripts\Python.exe"
 echo venv %PYTHON%
+if [%ACCELERATE%] == ["True"] goto :accelerate
 goto :launch
 
 :skip_venv
 
+:accelerate
+echo "Checking for accelerate"
+dir %VENV_DIR%\Scripts\accelerate.exe >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :accelerate_launch
+
 :launch
 %PYTHON% launch.py %*
 pause
 exit /b
 
+:accelerate_launch
+echo "Accelerating2"
+set ACCELERATE="%~dp0%VENV_DIR%\Scripts\accelerate.exe"
+%ACCELERATE% launch --num_cpu_threads_per_process=6 launch.py
+pause
+exit /b
+
 :show_stdout_stderr
 
 echo.
diff --git a/webui.sh b/webui.sh
index cc06f344..d89b344b 100755
--- a/webui.sh
+++ b/webui.sh
@@ -134,7 +134,15 @@ else
     exit 1
 fi
 
-printf "\n%s\n" "${delimiter}"
-printf "Launching launch.py..."
-printf "\n%s\n" "${delimiter}"
-"${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
+if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ]
+then
+    printf "\n%s\n" "${delimiter}"
+    printf "Accelerating launch.py..."
+    printf "\n%s\n" "${delimiter}"
+    accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@"
+else
+    printf "\n%s\n" "${delimiter}"
+    printf "Launching launch.py..."
+    printf "\n%s\n" "${delimiter}"
+    "${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
+fi
-- 
cgit v1.2.1


From 81f2575df91a50e4aa9ca816e02e3f77342eedc8 Mon Sep 17 00:00:00 2001
From: Liam <liamthekerr@gmail.com>
Date: Wed, 9 Nov 2022 15:24:31 -0500
Subject: updating the displayed generation info when user clicks images in the
 gallery. feature request 4415

---
 javascript/ui.js             | 10 +++++++++-
 modules/ui.py                | 20 ++++++++++++++++++++
 scripts/prompt_matrix.py     |  2 ++
 scripts/prompts_from_file.py |  6 +++++-
 4 files changed, 36 insertions(+), 2 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 95cfd106..443d1642 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -179,9 +179,17 @@ onUiUpdate(function(){
 		img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
 		img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
 	}
+	if (!txt2img_gallery) {
+		txt2img_gallery = gradioApp().querySelector('#txt2img_gallery')
+		txt2img_gallery?.addEventListener('click', () => gradioApp().getElementById("txt2img_generation_info_button").click());
+	}
+	if (!img2img_gallery) {
+		img2img_gallery = gradioApp().querySelector('#img2img_gallery')
+		img2img_gallery?.addEventListener('click', () => gradioApp().getElementById("img2img_generation_info_button").click());
+	}
 })
 
-let txt2img_textarea, img2img_textarea = undefined;
+let txt2img_textarea, img2img_textarea, txt2img_gallery, img2img_gallery = undefined;
 let wait_time = 800
 let token_timeout;
 
diff --git a/modules/ui.py b/modules/ui.py
index 7ea1177f..756499d1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -566,6 +566,17 @@ def apply_setting(key, value):
     return value
 
 
+def update_generation_info(args):
+    generation_info, html_info, img_index = args
+    try:
+        generation_info = json.loads(generation_info)
+        return plaintext_to_html(generation_info["infotexts"][img_index])
+    except Exception:
+        pass
+    # if the json parse or anything else fails, just return the old html_info
+    return html_info
+
+
 def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
     def refresh():
         refresh_method()
@@ -638,6 +649,15 @@ Requested path was: {f}
                     with gr.Group():
                         html_info = gr.HTML()
                         generation_info = gr.Textbox(visible=False)
+                        if tabname == 'txt2img' or tabname == 'img2img':
+                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
+                            generation_info_button.click(
+                                fn=update_generation_info,
+                                _js="(x, y) => [x, y, selected_gallery_index()]",
+                                inputs=[generation_info, html_info],
+                                outputs=[html_info],
+                                preprocess=False
+                            )
 
                         save.click(
                             fn=wrap_gradio_call(save_files),
diff --git a/scripts/prompt_matrix.py b/scripts/prompt_matrix.py
index e49c9b20..4d1e152d 100644
--- a/scripts/prompt_matrix.py
+++ b/scripts/prompt_matrix.py
@@ -80,6 +80,8 @@ class Script(scripts.Script):
         grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2))
         grid = images.draw_prompt_matrix(grid, p.width, p.height, prompt_matrix_parts)
         processed.images.insert(0, grid)
+        processed.index_of_first_image = 1
+        processed.infotexts.insert(0, processed.infotexts[0])
 
         if opts.grid_save:
             images.save_image(processed.images[0], p.outpath_grids, "prompt_matrix", prompt=original_prompt, seed=processed.seed, grid=True, p=p)
diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 3388bc77..32fe6bdb 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -145,6 +145,8 @@ class Script(scripts.Script):
         state.job_count = job_count
 
         images = []
+        all_prompts = []
+        infotexts = []
         for n, args in enumerate(jobs):
             state.job = f"{state.job_no + 1} out of {state.job_count}"
 
@@ -157,5 +159,7 @@ class Script(scripts.Script):
             
             if checkbox_iterate:
                 p.seed = p.seed + (p.batch_size * p.n_iter)
+            all_prompts += proc.all_prompts
+            infotexts += proc.infotexts
 
-        return Processed(p, images, p.seed, "")
+        return Processed(p, images, p.seed, "", all_prompts=all_prompts, infotexts=infotexts)
-- 
cgit v1.2.1


From 893191cab24cc3511135495d6d2c8d81f5ec63a3 Mon Sep 17 00:00:00 2001
From: Tong Zeng <tong.zeng@geely.com>
Date: Thu, 10 Nov 2022 10:34:03 +0800
Subject: fix a bug in list_files_with_name

---
 modules/scripts.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/scripts.py b/modules/scripts.py
index 637b2329..22d8908b 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -140,7 +140,7 @@ def list_files_with_name(filename):
             continue
 
         path = os.path.join(dirpath, filename)
-        if os.path.isfile(filename):
+        if os.path.isfile(path):
             res.append(path)
 
     return res
-- 
cgit v1.2.1


From 2505f39e28177452a92426f3b60d8edbe6ed1b14 Mon Sep 17 00:00:00 2001
From: JingShing <JingShingLai@gmail.com>
Date: Thu, 10 Nov 2022 20:39:20 +0800
Subject: Add username and password in ngrok.

---
 modules/ngrok.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/modules/ngrok.py b/modules/ngrok.py
index 5c5f349a..e506accb 100644
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -1,14 +1,22 @@
 from pyngrok import ngrok, conf, exception
 
-
 def connect(token, port, region):
     if token == None:
         token = 'None'
+    else:
+        if ':' in token:
+            # token = authtoken:username:password
+            account = token.split(':')[1] + ':' + token.split(':')[-1]
+            token = token.split(':')[0]
+
     config = conf.PyngrokConfig(
         auth_token=token, region=region
     )
     try:
-        public_url = ngrok.connect(port, pyngrok_config=config).public_url
+        if account:
+            public_url = ngrok.connect(port, pyngrok_config=config, auth=account).public_url
+        else:
+            public_url = ngrok.connect(port, pyngrok_config=config).public_url
     except exception.PyngrokNgrokError:
         print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
-- 
cgit v1.2.1


From 1a01191e27545e9dae5255d59c920b6da5b236f4 Mon Sep 17 00:00:00 2001
From: JingShing <JingShingLai@gmail.com>
Date: Thu, 10 Nov 2022 20:42:41 +0800
Subject: Add username and password in ngrok.

---
 modules/ngrok.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/modules/ngrok.py b/modules/ngrok.py
index e506accb..10d2179f 100644
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -1,6 +1,7 @@
 from pyngrok import ngrok, conf, exception
 
 def connect(token, port, region):
+    account = None
     if token == None:
         token = 'None'
     else:
@@ -13,10 +14,10 @@ def connect(token, port, region):
         auth_token=token, region=region
     )
     try:
-        if account:
-            public_url = ngrok.connect(port, pyngrok_config=config, auth=account).public_url
-        else:
+        if account == None:
             public_url = ngrok.connect(port, pyngrok_config=config).public_url
+        else:
+            public_url = ngrok.connect(port, pyngrok_config=config, auth=account).public_url
     except exception.PyngrokNgrokError:
         print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
-- 
cgit v1.2.1


From ac6fd2a5d91ca1ee36d721ef483b101597d3ee25 Mon Sep 17 00:00:00 2001
From: d8ahazard <d8ahazard@gmail.com>
Date: Thu, 10 Nov 2022 09:39:43 -0600
Subject: Fix accelerate check when spaces in path

---
 webui.bat | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/webui.bat b/webui.bat
index 2a066d60..d4d626e2 100644
--- a/webui.bat
+++ b/webui.bat
@@ -35,8 +35,8 @@ goto :launch
 
 :accelerate
 echo "Checking for accelerate"
-dir %VENV_DIR%\Scripts\accelerate.exe >tmp/stdout.txt 2>tmp/stderr.txt
-if %ERRORLEVEL% == 0 goto :accelerate_launch
+set ACCELERATE="%~dp0%VENV_DIR%\Scripts\accelerate.exe"
+if EXIST %ACCELERATE% goto :accelerate_launch
 
 :launch
 %PYTHON% launch.py %*
@@ -44,8 +44,7 @@ pause
 exit /b
 
 :accelerate_launch
-echo "Accelerating2"
-set ACCELERATE="%~dp0%VENV_DIR%\Scripts\accelerate.exe"
+echo "Accelerating"
 %ACCELERATE% launch --num_cpu_threads_per_process=6 launch.py
 pause
 exit /b
-- 
cgit v1.2.1


From b98740129c435f04a060369bd071fc4bafe021f5 Mon Sep 17 00:00:00 2001
From: Liam <liamthekerr@gmail.com>
Date: Thu, 10 Nov 2022 13:07:41 -0500
Subject: added event listener for the image gallery modal; moved js to
 separate file

---
 javascript/generationParams.js | 33 +++++++++++++++++++++++++++++++++
 javascript/ui.js               | 10 +---------
 modules/ui.py                  |  2 ++
 3 files changed, 36 insertions(+), 9 deletions(-)
 create mode 100644 javascript/generationParams.js

diff --git a/javascript/generationParams.js b/javascript/generationParams.js
new file mode 100644
index 00000000..95f05093
--- /dev/null
+++ b/javascript/generationParams.js
@@ -0,0 +1,33 @@
+// attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes
+
+let txt2img_gallery, img2img_gallery, modal = undefined;
+onUiUpdate(function(){
+	if (!txt2img_gallery) {
+		txt2img_gallery = attachGalleryListeners("txt2img")
+	}
+	if (!img2img_gallery) {
+		img2img_gallery = attachGalleryListeners("img2img")
+	}
+	if (!modal) {
+		modal = gradioApp().getElementById('lightboxModal')
+		modalObserver.observe(modal,  { attributes : true, attributeFilter : ['style'] });
+	}
+});
+
+let modalObserver = new MutationObserver(function(mutations) {
+	mutations.forEach(function(mutationRecord) {
+		let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText
+		if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img')
+			gradioApp().getElementById(selectedTab+"_generation_info_button").click()
+	});
+});
+
+function attachGalleryListeners(tab_name) {
+	gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
+	gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click());
+	gallery?.addEventListener('keydown', (e) => {
+		if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow
+			gradioApp().getElementById(tab_name+"_generation_info_button").click()
+	});
+	return gallery;
+}
diff --git a/javascript/ui.js b/javascript/ui.js
index 443d1642..95cfd106 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -179,17 +179,9 @@ onUiUpdate(function(){
 		img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
 		img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
 	}
-	if (!txt2img_gallery) {
-		txt2img_gallery = gradioApp().querySelector('#txt2img_gallery')
-		txt2img_gallery?.addEventListener('click', () => gradioApp().getElementById("txt2img_generation_info_button").click());
-	}
-	if (!img2img_gallery) {
-		img2img_gallery = gradioApp().querySelector('#img2img_gallery')
-		img2img_gallery?.addEventListener('click', () => gradioApp().getElementById("img2img_generation_info_button").click());
-	}
 })
 
-let txt2img_textarea, img2img_textarea, txt2img_gallery, img2img_gallery = undefined;
+let txt2img_textarea, img2img_textarea = undefined;
 let wait_time = 800
 let token_timeout;
 
diff --git a/modules/ui.py b/modules/ui.py
index 756499d1..5dce7f3b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -570,6 +570,8 @@ def update_generation_info(args):
     generation_info, html_info, img_index = args
     try:
         generation_info = json.loads(generation_info)
+        if img_index < 0 or img_index >= len(generation_info["infotexts"]):
+            return html_info
         return plaintext_to_html(generation_info["infotexts"][img_index])
     except Exception:
         pass
-- 
cgit v1.2.1


From 6f8a807fe4eb41f6eb355c80fe96cd60b8e8a5a9 Mon Sep 17 00:00:00 2001
From: KyuSeok Jung <wjdrbtjr495@gmail.com>
Date: Fri, 11 Nov 2022 09:22:49 +0900
Subject: Update shared.py

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 89f4d5ee..82da5ce0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -321,7 +321,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
-    "shuffle_tags": OptionInfo(False, "Shuffleing tags by "," when create texts."),
+    "shuffle_tags": OptionInfo(False, "Shuffleing tags by ',' when create texts."),
     "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
-- 
cgit v1.2.1


From 13a2f1dca32980339e1fb4d1995cde428db798c5 Mon Sep 17 00:00:00 2001
From: KyuSeok Jung <wjdrbtjr495@gmail.com>
Date: Fri, 11 Nov 2022 10:29:55 +0900
Subject: adding tag drop out option

---
 modules/textual_inversion/dataset.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index df278dc2..a95c7835 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -98,12 +98,12 @@ class PersonalizedBase(Dataset):
     def create_text(self, filename_text):
         text = random.choice(self.lines)
         text = text.replace("[name]", self.placeholder_token)
+        tags = filename_text.split(',')
+        if shared.opt.tag_drop_out != 0:
+            tags = [t for t in tags if random.random() > shared.opt.tag_drop_out]
         if shared.opts.shuffle_tags:
-            tags = filename_text.split(',')
             random.shuffle(tags)
-            text = text.replace("[filewords]", ','.join(tags))
-        else:
-            text = text.replace("[filewords]", filename_text)
+        text = text.replace("[filewords]", ','.join(tags))
         return text
 
     def __len__(self):
-- 
cgit v1.2.1


From 0959907f87314cbee8a80036ec8ae24c65888f7f Mon Sep 17 00:00:00 2001
From: KyuSeok Jung <wjdrbtjr495@gmail.com>
Date: Fri, 11 Nov 2022 10:31:14 +0900
Subject: adding tag dropout option

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/shared.py b/modules/shared.py
index 82da5ce0..f2ea3baa 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -322,6 +322,7 @@ options_templates.update(options_section(('system', "System"), {
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "shuffle_tags": OptionInfo(False, "Shuffleing tags by ',' when create texts."),
+    "tag_drop_out": OptionInfo(0, "Dropout tags when create texts", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.1}),
     "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
-- 
cgit v1.2.1


From b19af67d29356f97fea5cccfdfa12583f605243f Mon Sep 17 00:00:00 2001
From: KyuSeok Jung <wjdrbtjr495@gmail.com>
Date: Fri, 11 Nov 2022 10:54:19 +0900
Subject: Update dataset.py

---
 modules/textual_inversion/dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index a95c7835..e2cb8428 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -99,7 +99,7 @@ class PersonalizedBase(Dataset):
         text = random.choice(self.lines)
         text = text.replace("[name]", self.placeholder_token)
         tags = filename_text.split(',')
-        if shared.opt.tag_drop_out != 0:
+        if shared.opts.tag_drop_out != 0:
             tags = [t for t in tags if random.random() > shared.opt.tag_drop_out]
         if shared.opts.shuffle_tags:
             random.shuffle(tags)
-- 
cgit v1.2.1


From c556d34523e8764bd66bf6a7bf97d06add420020 Mon Sep 17 00:00:00 2001
From: NoCrypt <57245077+NoCrypt@users.noreply.github.com>
Date: Fri, 11 Nov 2022 08:54:51 +0700
Subject: Forcing HTTPS instead of HTTP for ngrok

For security reason.
---
 modules/ngrok.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ngrok.py b/modules/ngrok.py
index 5c5f349a..25c53af8 100644
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -8,7 +8,7 @@ def connect(token, port, region):
         auth_token=token, region=region
     )
     try:
-        public_url = ngrok.connect(port, pyngrok_config=config).public_url
+        public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
     except exception.PyngrokNgrokError:
         print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
-- 
cgit v1.2.1


From a1e271207dfc3e89b1286ba41d96b459f210c4b2 Mon Sep 17 00:00:00 2001
From: KyuSeok Jung <wjdrbtjr495@gmail.com>
Date: Fri, 11 Nov 2022 10:56:53 +0900
Subject: Update dataset.py

---
 modules/textual_inversion/dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index e2cb8428..eb75c376 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -100,7 +100,7 @@ class PersonalizedBase(Dataset):
         text = text.replace("[name]", self.placeholder_token)
         tags = filename_text.split(',')
         if shared.opts.tag_drop_out != 0:
-            tags = [t for t in tags if random.random() > shared.opt.tag_drop_out]
+            tags = [t for t in tags if random.random() > shared.opts.tag_drop_out]
         if shared.opts.shuffle_tags:
             random.shuffle(tags)
         text = text.replace("[filewords]", ','.join(tags))
-- 
cgit v1.2.1


From 7ba3923d5b494b7756d0b12f33acb3716d830b9a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 11 Nov 2022 18:20:18 +0300
Subject: move DDIM/PLMS fix for OSX out of the file with inpainting code.

---
 modules/sd_hijack.py            | 23 +++++++++++++++++++++++
 modules/sd_hijack_inpainting.py | 18 +-----------------
 2 files changed, 24 insertions(+), 17 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index bc49d235..75b2d22d 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -14,6 +14,8 @@ from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
+import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
 
 attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
@@ -406,3 +408,24 @@ def add_circular_option_to_conv_2d():
 
 
 model_hijack = StableDiffusionModelHijack()
+
+
+def register_buffer(self, name, attr):
+    """
+    Fix register buffer bug for Mac OS.
+    """
+
+    if type(attr) == torch.Tensor:
+        if attr.device != devices.device:
+
+            # would this not break cuda when torch adds has_mps() to main version?
+            if getattr(torch, 'has_mps', False):
+                attr = attr.to(device="mps", dtype=torch.float32)
+            else:
+                attr = attr.to(devices.device)
+
+    setattr(self, name, attr)
+
+
+ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer
+ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 202b42cf..46714a4f 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -1,5 +1,4 @@
 import torch
-import modules.devices as devices
 
 from einops import repeat
 from omegaconf import ListConfig
@@ -315,20 +314,6 @@ class LatentInpaintDiffusion(LatentDiffusion):
         self.masked_image_key = masked_image_key
         assert self.masked_image_key in concat_keys
         self.concat_keys = concat_keys
-            
-
-# =================================================================================================
-# Fix register buffer bug for Mac OS, Viktor Tabori, viktor.doklist.com/start-here
-# =================================================================================================
-def register_buffer(self, name, attr):
-    if type(attr) == torch.Tensor:
-        optimal_type = devices.get_optimal_device()
-        if attr.device != optimal_type:
-           if getattr(torch, 'has_mps', False):
-               attr = attr.to(device="mps", dtype=torch.float32)
-           else:
-               attr = attr.to(optimal_type)
-    setattr(self, name, attr)
 
 
 def should_hijack_inpainting(checkpoint_info):
@@ -341,8 +326,7 @@ def do_inpainting_hijack():
 
     ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
     ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
-    ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer
 
     ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
     ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
-    ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer
+
-- 
cgit v1.2.1


From 76ab31e18898d4c2aacb9725cfbe25b230bff974 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=BA=90=E6=96=87=E9=9B=A8?=
 <41315874+fumiama@users.noreply.github.com>
Date: Sat, 12 Nov 2022 11:02:40 +0800
Subject: Fix wrong mps selection below MasOS 12.3

---
 modules/devices.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 7511e1dc..9a3d29d7 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -3,8 +3,15 @@ import contextlib
 import torch
 from modules import errors
 
-# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
-has_mps = getattr(torch, 'has_mps', False)
+# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
+# check `getattr` and try it for compatibility
+def has_mps() -> bool:
+    if getattr(torch, 'has_mps', False): return False
+    try:
+        torch.zeros(1).to(torch.device("mps"))
+        return True
+    except Exception:
+        return False
 
 cpu = torch.device("cpu")
 
@@ -25,7 +32,7 @@ def get_optimal_device():
         else:
             return torch.device("cuda")
 
-    if has_mps:
+    if has_mps():
         return torch.device("mps")
 
     return cpu
-- 
cgit v1.2.1


From 1130d5df669911a5c67696be90bccca3ecf5f487 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=BA=90=E6=96=87=E9=9B=A8?=
 <41315874+fumiama@users.noreply.github.com>
Date: Sat, 12 Nov 2022 11:09:28 +0800
Subject: Update devices.py

---
 modules/devices.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/devices.py b/modules/devices.py
index 9a3d29d7..bd3e4ffb 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -6,7 +6,7 @@ from modules import errors
 # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
 # check `getattr` and try it for compatibility
 def has_mps() -> bool:
-    if getattr(torch, 'has_mps', False): return False
+    if not getattr(torch, 'has_mps', False): return False
     try:
         torch.zeros(1).to(torch.device("mps"))
         return True
-- 
cgit v1.2.1


From c62d17aee36b5f4ca24f9cfa7bf6d7aca0c923f8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 12 Nov 2022 10:00:22 +0300
Subject: use the new devices.has_mps() function in register_buffer for
 DDIM/PLMS fix for OSX

---
 modules/sd_hijack.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 75b2d22d..97979d05 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -418,8 +418,7 @@ def register_buffer(self, name, attr):
     if type(attr) == torch.Tensor:
         if attr.device != devices.device:
 
-            # would this not break cuda when torch adds has_mps() to main version?
-            if getattr(torch, 'has_mps', False):
+            if devices.has_mps():
                 attr = attr.to(device="mps", dtype=torch.float32)
             else:
                 attr = attr.to(devices.device)
-- 
cgit v1.2.1


From 0ab0a50f9ae14bd7ce7ec518323ebd31c7971155 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 12 Nov 2022 10:00:49 +0300
Subject: change formatting to match the main program in devices.py

---
 modules/devices.py | 21 ++++++++++++++++-----
 1 file changed, 16 insertions(+), 5 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index bd3e4ffb..67165bf6 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -3,23 +3,27 @@ import contextlib
 import torch
 from modules import errors
 
+
 # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
 # check `getattr` and try it for compatibility
 def has_mps() -> bool:
-    if not getattr(torch, 'has_mps', False): return False
+    if not getattr(torch, 'has_mps', False):
+        return False
     try:
         torch.zeros(1).to(torch.device("mps"))
         return True
     except Exception:
         return False
 
-cpu = torch.device("cpu")
 
 def extract_device_id(args, name):
     for x in range(len(args)):
-        if name in args[x]: return args[x+1]
+        if name in args[x]:
+            return args[x + 1]
+
     return None
 
+
 def get_optimal_device():
     if torch.cuda.is_available():
         from modules import shared
@@ -52,10 +56,12 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
+cpu = torch.device("cpu")
 device = device_interrogate = device_gfpgan = device_swinir = device_esrgan = device_scunet = device_codeformer = None
 dtype = torch.float16
 dtype_vae = torch.float16
 
+
 def randn(seed, shape):
     # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
     if device.type == 'mps':
@@ -89,6 +95,11 @@ def autocast(disable=False):
 
     return torch.autocast("cuda")
 
+
 # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
-def mps_contiguous(input_tensor, device): return input_tensor.contiguous() if device.type == 'mps' else input_tensor
-def mps_contiguous_to(input_tensor, device): return mps_contiguous(input_tensor, device).to(device)
+def mps_contiguous(input_tensor, device):
+    return input_tensor.contiguous() if device.type == 'mps' else input_tensor
+
+
+def mps_contiguous_to(input_tensor, device):
+    return mps_contiguous(input_tensor, device).to(device)
-- 
cgit v1.2.1


From a1a376331c9ecbbee77b86daeaba44587cc56557 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 12 Nov 2022 10:56:06 +0300
Subject: make existing script loading and new preload code use same code for
 loading modules limit extension preload scripts to just one file named
 preload.py

---
 modules/extensions.py     | 21 ---------------------
 modules/script_loading.py | 34 ++++++++++++++++++++++++++++++++++
 modules/scripts.py        | 46 +++++++++++++++++-----------------------------
 modules/shared.py         |  5 ++---
 4 files changed, 53 insertions(+), 53 deletions(-)
 create mode 100644 modules/script_loading.py

diff --git a/modules/extensions.py b/modules/extensions.py
index 544f3580..94ce479a 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -1,7 +1,6 @@
 import os
 import sys
 import traceback
-from importlib.machinery import SourceFileLoader
 
 import git
 
@@ -85,23 +84,3 @@ def list_extensions():
         extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions)
         extensions.append(extension)
 
-
-def preload_extensions(parser):
-    if not os.path.isdir(extensions_dir):
-        return
-
-    for dirname in sorted(os.listdir(extensions_dir)):
-        path = os.path.join(extensions_dir, dirname)
-        if not os.path.isdir(path):
-            continue
-        for file in os.listdir(path):
-            if "preload.py" in file:
-                full_file = os.path.join(path, file)
-                print(f"Got preload file: {full_file}")
-
-                try:
-                    ext = SourceFileLoader("preload", full_file).load_module()
-                    parser = ext.preload(parser)
-                except Exception as e:
-                    print(f"Exception preloading script: {e}")
-    return parser
\ No newline at end of file
diff --git a/modules/script_loading.py b/modules/script_loading.py
new file mode 100644
index 00000000..f93f0951
--- /dev/null
+++ b/modules/script_loading.py
@@ -0,0 +1,34 @@
+import os
+import sys
+import traceback
+from types import ModuleType
+
+
+def load_module(path):
+    with open(path, "r", encoding="utf8") as file:
+        text = file.read()
+
+    compiled = compile(text, path, 'exec')
+    module = ModuleType(os.path.basename(path))
+    exec(compiled, module.__dict__)
+
+    return module
+
+
+def preload_extensions(extensions_dir, parser):
+    if not os.path.isdir(extensions_dir):
+        return
+
+    for dirname in sorted(os.listdir(extensions_dir)):
+        preload_script = os.path.join(extensions_dir, dirname, "preload.py")
+        if not os.path.isfile(preload_script):
+            continue
+
+        try:
+            module = load_module(preload_script)
+            if hasattr(module, 'preload'):
+                module.preload(parser)
+
+        except Exception:
+            print(f"Error running preload() for {preload_script}", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
diff --git a/modules/scripts.py b/modules/scripts.py
index 22d8908b..986b1914 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -6,7 +6,7 @@ from collections import namedtuple
 import gradio as gr
 
 from modules.processing import StableDiffusionProcessing
-from modules import shared, paths, script_callbacks, extensions
+from modules import shared, paths, script_callbacks, extensions, script_loading
 
 AlwaysVisible = object()
 
@@ -161,13 +161,7 @@ def load_scripts():
                 sys.path = [scriptfile.basedir] + sys.path
             current_basedir = scriptfile.basedir
 
-            with open(scriptfile.path, "r", encoding="utf8") as file:
-                text = file.read()
-
-            from types import ModuleType
-            compiled = compile(text, scriptfile.path, 'exec')
-            module = ModuleType(scriptfile.filename)
-            exec(compiled, module.__dict__)
+            module = script_loading.load_module(scriptfile.path)
 
             for key, script_class in module.__dict__.items():
                 if type(script_class) == type and issubclass(script_class, Script):
@@ -328,27 +322,21 @@ class ScriptRunner:
 
     def reload_sources(self, cache):
         for si, script in list(enumerate(self.scripts)):
-            with open(script.filename, "r", encoding="utf8") as file:
-                args_from = script.args_from
-                args_to = script.args_to
-                filename = script.filename
-                text = file.read()
-
-                from types import ModuleType
-
-                module = cache.get(filename, None)
-                if module is None:
-                    compiled = compile(text, filename, 'exec')
-                    module = ModuleType(script.filename)
-                    exec(compiled, module.__dict__)
-                    cache[filename] = module
-
-                for key, script_class in module.__dict__.items():
-                    if type(script_class) == type and issubclass(script_class, Script):
-                        self.scripts[si] = script_class()
-                        self.scripts[si].filename = filename
-                        self.scripts[si].args_from = args_from
-                        self.scripts[si].args_to = args_to
+            args_from = script.args_from
+            args_to = script.args_to
+            filename = script.filename
+
+            module = cache.get(filename, None)
+            if module is None:
+                module = script_loading.load_module(script.filename)
+                cache[filename] = module
+
+            for key, script_class in module.__dict__.items():
+                if type(script_class) == type and issubclass(script_class, Script):
+                    self.scripts[si] = script_class()
+                    self.scripts[si].filename = filename
+                    self.scripts[si].args_from = args_from
+                    self.scripts[si].args_to = args_to
 
 
 scripts_txt2img = ScriptRunner()
diff --git a/modules/shared.py b/modules/shared.py
index 17132e42..6936cbe0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -3,7 +3,6 @@ import datetime
 import json
 import os
 import sys
-from collections import OrderedDict
 import time
 
 import gradio as gr
@@ -15,7 +14,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models, localization, sd_vae, extensions
+from modules import sd_samplers, sd_models, localization, sd_vae, extensions, script_loading
 from modules.hypernetworks import hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
@@ -91,7 +90,7 @@ parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requ
 parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
 
-extensions.preload_extensions(parser)
+script_loading.preload_extensions(extensions.extensions_dir, parser)
 
 cmd_opts = parser.parse_args()
 
-- 
cgit v1.2.1


From 98947d173e3f1667eba29c904f681047dea9de90 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 12 Nov 2022 11:11:47 +0300
Subject: run installers for newly installed extensions

---
 launch.py                | 26 +++++++++++++++-----------
 modules/ui_extensions.py |  3 +++
 2 files changed, 18 insertions(+), 11 deletions(-)

diff --git a/launch.py b/launch.py
index 5fa11560..8e65676d 100644
--- a/launch.py
+++ b/launch.py
@@ -105,22 +105,26 @@ def version_check(commit):
         print("version check failed", e)
 
 
+def run_extension_installer(extension_dir):
+    path_installer = os.path.join(extension_dir, "install.py")
+    if not os.path.isfile(path_installer):
+        return
+
+    try:
+        env = os.environ.copy()
+        env['PYTHONPATH'] = os.path.abspath(".")
+
+        print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
+    except Exception as e:
+        print(e, file=sys.stderr)
+
+
 def run_extensions_installers():
     if not os.path.isdir(dir_extensions):
         return
 
     for dirname_extension in os.listdir(dir_extensions):
-        path_installer = os.path.join(dir_extensions, dirname_extension, "install.py")
-        if not os.path.isfile(path_installer):
-            continue
-
-        try:
-            env = os.environ.copy()
-            env['PYTHONPATH'] = os.path.abspath(".")
-
-            print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {dirname_extension}", custom_env=env))
-        except Exception as e:
-            print(e, file=sys.stderr)
+        run_extension_installer(os.path.join(dir_extensions, dirname_extension))
 
 
 def prepare_enviroment():
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 02ab9643..6671cb60 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -134,6 +134,9 @@ def install_extension_from_url(dirname, url):
 
         os.rename(tmpdir, target_dir)
 
+        import launch
+        launch.run_extension_installer(target_dir)
+
         extensions.list_extensions()
         return [extension_table(), html.escape(f"Installed into {target_dir}. Use Installed tab to restart.")]
     finally:
-- 
cgit v1.2.1


From f4a488f585c09b420dc05199240e68f8fb74337f Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 7 Nov 2022 20:12:31 -0500
Subject: Set device for facelib/facexlib and gfpgan

* FaceXLib/FaceLib doesn't pass the device argument to RetinaFace but instead chooses one itself and sets it to a global - in order to use a device other than its internally chosen default it is necessary to manually replace the default value
* The GFPGAN constructor needs the device argument to work with MPS or a CUDA device ID that differs from the default
---
 modules/codeformer_model.py | 3 +++
 modules/gfpgan_model.py     | 4 +++-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
index e6d9fa4f..ab40d842 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -36,6 +36,7 @@ def setup_model(dirname):
         from basicsr.utils.download_util import load_file_from_url
         from basicsr.utils import imwrite, img2tensor, tensor2img
         from facelib.utils.face_restoration_helper import FaceRestoreHelper
+        from facelib.detection.retinaface import retinaface
         from modules.shared import cmd_opts
 
         net_class = CodeFormer
@@ -65,6 +66,8 @@ def setup_model(dirname):
                 net.load_state_dict(checkpoint)
                 net.eval()
 
+                if hasattr(retinaface, 'device'):
+                    retinaface.device = devices.device_codeformer
                 face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
 
                 self.net = net
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index a9452dce..1e2dbc32 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -36,7 +36,9 @@ def gfpgann():
     else:
         print("Unable to load gfpgan model!")
         return None
-    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
+    if hasattr(facexlib.detection.retinaface, 'device'):
+        facexlib.detection.retinaface.device = devices.device_gfpgan
+    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
     loaded_gfpgan_model = model
 
     return model
-- 
cgit v1.2.1


From 007f4f7314eabd9cc3a2b0d11889de49ad3c682a Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sat, 12 Nov 2022 15:12:15 +0300
Subject: Tests cleaned up

---
 launch.py                |   5 ++++-
 test/server_poll.py      |   7 ++++---
 test/test_files/empty.pt | Bin 0 -> 431 bytes
 test/txt2img_test.py     |   4 +++-
 test/utils_test.py       |  18 +++++++++---------
 5 files changed, 20 insertions(+), 14 deletions(-)
 create mode 100644 test/test_files/empty.pt

diff --git a/launch.py b/launch.py
index 8e65676d..6822a01d 100644
--- a/launch.py
+++ b/launch.py
@@ -229,6 +229,9 @@ def prepare_enviroment():
 def tests(argv):
     if "--api" not in argv:
         argv.append("--api")
+    if "--ckpt" not in argv:
+        argv.append("--ckpt")
+        argv.append("./test/test_files/empty.pt")
 
     print(f"Launching Web UI in another process for testing with arguments: {' '.join(argv[1:])}")
 
@@ -236,7 +239,7 @@ def tests(argv):
         proc = subprocess.Popen([sys.executable, *argv], stdout=stdout, stderr=stderr)
 
     import test.server_poll
-    test.server_poll.run_tests()
+    test.server_poll.run_tests(proc)
 
     print(f"Stopping Web UI process with id {proc.pid}")
     proc.kill()
diff --git a/test/server_poll.py b/test/server_poll.py
index eeefb7eb..8e63b450 100644
--- a/test/server_poll.py
+++ b/test/server_poll.py
@@ -3,7 +3,7 @@ import requests
 import time
 
 
-def run_tests():
+def run_tests(proc):
     timeout_threshold = 240
     start_time = time.time()
     while time.time()-start_time < timeout_threshold:
@@ -11,8 +11,9 @@ def run_tests():
             requests.head("http://localhost:7860/")
             break
         except requests.exceptions.ConnectionError:
-            pass
-    if time.time()-start_time < timeout_threshold:
+            if proc.poll() is not None:
+                break
+    if proc.poll() is None:
         suite = unittest.TestLoader().discover('', pattern='*_test.py')
         result = unittest.TextTestRunner(verbosity=2).run(suite)
     else:
diff --git a/test/test_files/empty.pt b/test/test_files/empty.pt
new file mode 100644
index 00000000..c6ac59eb
Binary files /dev/null and b/test/test_files/empty.pt differ
diff --git a/test/txt2img_test.py b/test/txt2img_test.py
index 1936e07e..ce752085 100644
--- a/test/txt2img_test.py
+++ b/test/txt2img_test.py
@@ -53,13 +53,15 @@ class TestTxt2ImgWorking(unittest.TestCase):
         self.simple_txt2img["restore_faces"] = True
         self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
-    def test_txt2img_with_tiling_faces_performed(self):
+    def test_txt2img_with_tiling_performed(self):
         self.simple_txt2img["tiling"] = True
         self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
     def test_txt2img_with_vanilla_sampler_performed(self):
         self.simple_txt2img["sampler_index"] = "PLMS"
         self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+        self.simple_txt2img["sampler_index"] = "DDIM"
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
     def test_txt2img_multiple_batches_performed(self):
         self.simple_txt2img["n_iter"] = 2
diff --git a/test/utils_test.py b/test/utils_test.py
index 65d3d177..be9e6bf8 100644
--- a/test/utils_test.py
+++ b/test/utils_test.py
@@ -18,19 +18,19 @@ class UtilsTests(unittest.TestCase):
   def test_options_get(self):
     self.assertEqual(requests.get(self.url_options).status_code, 200)
 
-  def test_options_write(self):
-    response = requests.get(self.url_options)
-    self.assertEqual(response.status_code, 200)
+  # def test_options_write(self):
+  #   response = requests.get(self.url_options)
+  #   self.assertEqual(response.status_code, 200)
     
-    pre_value = response.json()["send_seed"]
+  #   pre_value = response.json()["send_seed"]
 
-    self.assertEqual(requests.post(self.url_options, json={"send_seed":not pre_value}).status_code, 200)
+  #   self.assertEqual(requests.post(self.url_options, json={"send_seed":not pre_value}).status_code, 200)
 
-    response = requests.get(self.url_options)
-    self.assertEqual(response.status_code, 200)
-    self.assertEqual(response.json()["send_seed"], not pre_value)
+  #   response = requests.get(self.url_options)
+  #   self.assertEqual(response.status_code, 200)
+  #   self.assertEqual(response.json()["send_seed"], not pre_value)
 
-    requests.post(self.url_options, json={"send_seed": pre_value})
+  #   requests.post(self.url_options, json={"send_seed": pre_value})
 
   def test_cmd_flags(self):
     self.assertEqual(requests.get(self.url_cmd_flags).status_code, 200)
-- 
cgit v1.2.1


From d671d1d45dfab61292ed788fd7778a33a82212ee Mon Sep 17 00:00:00 2001
From: Mrau Hu <mrauhu@yandex.ru>
Date: Sat, 12 Nov 2022 21:44:42 +0300
Subject: Fix: `error: Your local changes to the following files would be
 overwritten by merge` when run `pull()` method, because WSL2 Docker set 755
 file permissions instead of 644, this results to the error.

Updated `Extension` class: replaced `pull()` with `fetch_and_reset_hard()` method.

Updated `apply_and_restart()` function: replaced `ext.pull()` with `ext.fetch_and_reset_hard()` function.
---
 modules/extensions.py    | 7 +++++--
 modules/ui_extensions.py | 4 ++--
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/modules/extensions.py b/modules/extensions.py
index 94ce479a..db9c4200 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -65,9 +65,12 @@ class Extension:
         self.can_update = False
         self.status = "latest"
 
-    def pull(self):
+    def fetch_and_reset_hard(self):
         repo = git.Repo(self.path)
-        repo.remotes.origin.pull()
+        # Fix: `error: Your local changes to the following files would be overwritten by merge`,
+        # because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
+        repo.git.fetch('--all')
+        repo.git.reset('--hard', 'origin')
 
 
 def list_extensions():
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 6671cb60..030f011e 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -36,9 +36,9 @@ def apply_and_restart(disable_list, update_list):
             continue
 
         try:
-            ext.pull()
+            ext.fetch_and_reset_hard()
         except Exception:
-            print(f"Error pulling updates for {ext.name}:", file=sys.stderr)
+            print(f"Error getting updates for {ext.name}:", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
     shared.opts.disabled_extensions = disabled
-- 
cgit v1.2.1


From d20dbe47e06de7f6c0e65242a04c9bb1410ef7cb Mon Sep 17 00:00:00 2001
From: Xu Cuijie <975114697@qq.com>
Date: Sun, 13 Nov 2022 10:31:03 +0800
Subject: fix the model name error of Real-ESRGAN in the opts default value

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 6936cbe0..c46c29f7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -299,7 +299,7 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
 options_templates.update(options_section(('upscaling', "Upscaling"), {
     "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
-    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN x4+", "R-ESRGAN x4+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
+    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
     "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
-- 
cgit v1.2.1


From 084cf043900bb2c5503155ee2a24a52cc1db3eca Mon Sep 17 00:00:00 2001
From: Anas Abou Allaban <aabouallaban@pm.me>
Date: Sat, 12 Nov 2022 22:41:22 -0500
Subject: Fix env var names

---
 launch.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/launch.py b/launch.py
index 8e65676d..0f84b5d1 100644
--- a/launch.py
+++ b/launch.py
@@ -139,9 +139,9 @@ def prepare_enviroment():
     xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
 
     stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/CompVis/stable-diffusion.git")
-    taming_transformers_repo = os.environ.get('TAMING_REANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
+    taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
     k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
-    codeformer_repo = os.environ.get('CODEFORMET_REPO', 'https://github.com/sczhou/CodeFormer.git')
+    codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
     blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
 
     stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
-- 
cgit v1.2.1


From 6fa891b934ba854efa87315baffc4ff458ab2539 Mon Sep 17 00:00:00 2001
From: KEV <go2008to@gmail.com>
Date: Mon, 14 Nov 2022 00:25:38 +1000
Subject: Add 'Inpainting strength' to the 'generation_params' dictionary of
 'infotext' which is saved into the 'params.txt' or png chunks.

Value appears only if 'Denoising strength' appears too.
---
 modules/processing.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/processing.py b/modules/processing.py
index 03c9143d..01d7cbdc 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -399,6 +399,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
+        "Inpainting strength": (None if getattr(p, 'denoising_strength', None) is None else shared.opts.inpainting_mask_weight),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
-- 
cgit v1.2.1


From 671c0e42b4167f4b7ff93e3b96922bf130c12718 Mon Sep 17 00:00:00 2001
From: Ryan Voots <simcop2387@simcop2387.info>
Date: Sun, 13 Nov 2022 13:39:41 -0500
Subject: Fix docker tmp/ and extensions/ handling for docker.  might also work
 for symlinks

---
 modules/ui_extensions.py | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 6671cb60..95b63f24 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -9,6 +9,8 @@ import git
 
 import gradio as gr
 import html
+import shutil
+import errno
 
 from modules import extensions, shared, paths
 
@@ -132,7 +134,18 @@ def install_extension_from_url(dirname, url):
         repo = git.Repo.clone_from(url, tmpdir)
         repo.remote().fetch()
 
-        os.rename(tmpdir, target_dir)
+        try:
+            os.rename(tmpdir, target_dir)
+        except OSError as err:
+            # TODO what does this do on windows? I think it'll be a different error code but I don't have a system to check it
+            # Shouldn't cause any new issues at least but we probably want to handle it there too.
+            if err.errno == errno.EXDEV:
+                # Cross device link, typical in docker or when tmp/ and extensions/ are on different file systems
+                # Since we can't use a rename, do the slower but more versitile shutil.move()
+                shutil.move(tmpdir, target_dir)
+            else:
+                # Something else, not enough free space, permissions, etc.  rethrow it so that it gets handled.
+                raise(err)
 
         import launch
         launch.run_extension_installer(target_dir)
-- 
cgit v1.2.1


From 9a1aff645a4bea745145c57c96950fbd3fcca27c Mon Sep 17 00:00:00 2001
From: parasi <kingofint22@gmail.com>
Date: Sun, 13 Nov 2022 13:44:27 -0600
Subject: resolve [name] after resolving [filewords] in training

---
 modules/textual_inversion/dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index eb75c376..06f271f9 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -97,13 +97,13 @@ class PersonalizedBase(Dataset):
 
     def create_text(self, filename_text):
         text = random.choice(self.lines)
-        text = text.replace("[name]", self.placeholder_token)
         tags = filename_text.split(',')
         if shared.opts.tag_drop_out != 0:
             tags = [t for t in tags if random.random() > shared.opts.tag_drop_out]
         if shared.opts.shuffle_tags:
             random.shuffle(tags)
         text = text.replace("[filewords]", ','.join(tags))
+        text = text.replace("[name]", self.placeholder_token)
         return text
 
     def __len__(self):
-- 
cgit v1.2.1


From 40ae95d53218b3b8f12fca50b5e4e98a1e50af4b Mon Sep 17 00:00:00 2001
From: KEV <go2008to@gmail.com>
Date: Mon, 14 Nov 2022 18:05:59 +1000
Subject: Fix retrieving value for 'x/y plot' script.

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 01d7cbdc..2fc9fe13 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -399,7 +399,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
-        "Inpainting strength": (None if getattr(p, 'denoising_strength', None) is None else shared.opts.inpainting_mask_weight),
+        "Inpainting strength": (None if getattr(p, 'denoising_strength', None) is None else getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)),
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
-- 
cgit v1.2.1


From 93d6c0209ae55632b72751cf82740e32a0cd81bc Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Mon, 14 Nov 2022 13:39:22 +0300
Subject: Tests separated for github-actions CI

---
 .github/workflows/run_tests.yaml       | 26 ++++++++++++
 launch.py                              | 36 ++++++++++------
 test/advanced_features/__init__.py     |  0
 test/advanced_features/extras_test.py  | 29 +++++++++++++
 test/advanced_features/txt2img_test.py | 47 +++++++++++++++++++++
 test/basic_features/__init__.py        |  0
 test/basic_features/img2img_test.py    | 55 ++++++++++++++++++++++++
 test/basic_features/txt2img_test.py    | 72 ++++++++++++++++++++++++++++++++
 test/basic_features/utils_test.py      | 67 ++++++++++++++++++++++++++++++
 test/extras_test.py                    | 29 -------------
 test/img2img_test.py                   | 59 --------------------------
 test/server_poll.py                    |  6 ++-
 test/txt2img_test.py                   | 76 ----------------------------------
 test/utils_test.py                     | 63 ----------------------------
 14 files changed, 324 insertions(+), 241 deletions(-)
 create mode 100644 .github/workflows/run_tests.yaml
 create mode 100644 test/advanced_features/__init__.py
 create mode 100644 test/advanced_features/extras_test.py
 create mode 100644 test/advanced_features/txt2img_test.py
 create mode 100644 test/basic_features/__init__.py
 create mode 100644 test/basic_features/img2img_test.py
 create mode 100644 test/basic_features/txt2img_test.py
 create mode 100644 test/basic_features/utils_test.py
 delete mode 100644 test/extras_test.py
 delete mode 100644 test/img2img_test.py
 delete mode 100644 test/txt2img_test.py
 delete mode 100644 test/utils_test.py

diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml
new file mode 100644
index 00000000..a56a8110
--- /dev/null
+++ b/.github/workflows/run_tests.yaml
@@ -0,0 +1,26 @@
+name: Run tests on CPU with empty model
+
+on:
+  - push
+  - pull_request
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v3
+        with:
+          python-version: 3.10.6
+      - uses: actions/cache@v2
+        with:
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-
+      - name: Run tests
+        run: |
+          export COMMANDLINE_ARGS="--tests basic_features --no-half --disable-opt-split-attention --use-cpu all"
+          python launch.py
diff --git a/launch.py b/launch.py
index 6822a01d..d0f502c2 100644
--- a/launch.py
+++ b/launch.py
@@ -17,6 +17,19 @@ def extract_arg(args, name):
     return [x for x in args if x != name], name in args
 
 
+def extract_opt(args, name):
+    opt = None
+    is_present = False
+    if name in args:
+        is_present = True
+        idx = args.index(name)
+        del args[idx]
+        if idx < len(args) and args[idx][0] != "-":
+            opt = args[idx]
+            del args[idx]
+    return args, is_present, opt
+
+
 def run(command, desc=None, errdesc=None, custom_env=None):
     if desc is not None:
         print(desc)
@@ -151,12 +164,11 @@ def prepare_enviroment():
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
     sys.argv += shlex.split(commandline_args)
-    test_argv = [x for x in sys.argv if x != '--tests']
 
     sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
     sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
     sys.argv, update_check = extract_arg(sys.argv, '--update-check')
-    sys.argv, run_tests = extract_arg(sys.argv, '--tests')
+    sys.argv, run_tests, test_dir = extract_opt(sys.argv, '--tests')
     xformers = '--xformers' in sys.argv
     deepdanbooru = '--deepdanbooru' in sys.argv
     ngrok = '--ngrok' in sys.argv
@@ -222,24 +234,24 @@ def prepare_enviroment():
         exit(0)
 
     if run_tests:
-        tests(test_argv)
+        tests(test_dir)
         exit(0)
 
 
-def tests(argv):
-    if "--api" not in argv:
-        argv.append("--api")
-    if "--ckpt" not in argv:
-        argv.append("--ckpt")
-        argv.append("./test/test_files/empty.pt")
+def tests(test_dir):
+    if "--api" not in sys.argv:
+        sys.argv.append("--api")
+    if "--ckpt" not in sys.argv:
+        sys.argv.append("--ckpt")
+        sys.argv.append("./test/test_files/empty.pt")
 
-    print(f"Launching Web UI in another process for testing with arguments: {' '.join(argv[1:])}")
+    print(f"Launching Web UI in another process for testing with arguments: {' '.join(sys.argv[1:])}")
 
     with open('test/stdout.txt', "w", encoding="utf8") as stdout, open('test/stderr.txt', "w", encoding="utf8") as stderr:
-        proc = subprocess.Popen([sys.executable, *argv], stdout=stdout, stderr=stderr)
+        proc = subprocess.Popen([sys.executable, *sys.argv], stdout=stdout, stderr=stderr)
 
     import test.server_poll
-    test.server_poll.run_tests(proc)
+    test.server_poll.run_tests(proc, test_dir)
 
     print(f"Stopping Web UI process with id {proc.pid}")
     proc.kill()
diff --git a/test/advanced_features/__init__.py b/test/advanced_features/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/test/advanced_features/extras_test.py b/test/advanced_features/extras_test.py
new file mode 100644
index 00000000..8763f8ed
--- /dev/null
+++ b/test/advanced_features/extras_test.py
@@ -0,0 +1,29 @@
+import unittest
+
+
+class TestExtrasWorking(unittest.TestCase):
+    def setUp(self):
+        self.url_img2img = "http://localhost:7860/sdapi/v1/extra-single-image"
+        self.simple_extras = {
+            "resize_mode": 0,
+            "show_extras_results": True,
+            "gfpgan_visibility": 0,
+            "codeformer_visibility": 0,
+            "codeformer_weight": 0,
+            "upscaling_resize": 2,
+            "upscaling_resize_w": 128,
+            "upscaling_resize_h": 128,
+            "upscaling_crop": True,
+            "upscaler_1": "None",
+            "upscaler_2": "None",
+            "extras_upscaler_2_visibility": 0,
+            "image": ""
+            }
+
+
+class TestExtrasCorrectness(unittest.TestCase):
+    pass
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/advanced_features/txt2img_test.py b/test/advanced_features/txt2img_test.py
new file mode 100644
index 00000000..36ed7b9a
--- /dev/null
+++ b/test/advanced_features/txt2img_test.py
@@ -0,0 +1,47 @@
+import unittest
+import requests
+
+
+class TestTxt2ImgWorking(unittest.TestCase):
+    def setUp(self):
+        self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
+        self.simple_txt2img = {
+            "enable_hr": False,
+            "denoising_strength": 0,
+            "firstphase_width": 0,
+            "firstphase_height": 0,
+            "prompt": "example prompt",
+            "styles": [],
+            "seed": -1,
+            "subseed": -1,
+            "subseed_strength": 0,
+            "seed_resize_from_h": -1,
+            "seed_resize_from_w": -1,
+            "batch_size": 1,
+            "n_iter": 1,
+            "steps": 3,
+            "cfg_scale": 7,
+            "width": 64,
+            "height": 64,
+            "restore_faces": False,
+            "tiling": False,
+            "negative_prompt": "",
+            "eta": 0,
+            "s_churn": 0,
+            "s_tmax": 0,
+            "s_tmin": 0,
+            "s_noise": 1,
+            "sampler_index": "Euler a"
+        }
+
+    def test_txt2img_with_restore_faces_performed(self):
+        self.simple_txt2img["restore_faces"] = True
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+
+class TestTxt2ImgCorrectness(unittest.TestCase):
+    pass
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/basic_features/__init__.py b/test/basic_features/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/test/basic_features/img2img_test.py b/test/basic_features/img2img_test.py
new file mode 100644
index 00000000..0a9c1e8a
--- /dev/null
+++ b/test/basic_features/img2img_test.py
@@ -0,0 +1,55 @@
+import unittest
+import requests
+from gradio.processing_utils import encode_pil_to_base64
+from PIL import Image
+
+
+class TestImg2ImgWorking(unittest.TestCase):
+    def setUp(self):
+        self.url_img2img = "http://localhost:7860/sdapi/v1/img2img"
+        self.simple_img2img = {
+            "init_images": [encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))],
+            "resize_mode": 0,
+            "denoising_strength": 0.75,
+            "mask": None,
+            "mask_blur": 4,
+            "inpainting_fill": 0,
+            "inpaint_full_res": False,
+            "inpaint_full_res_padding": 0,
+            "inpainting_mask_invert": 0,
+            "prompt": "example prompt",
+            "styles": [],
+            "seed": -1,
+            "subseed": -1,
+            "subseed_strength": 0,
+            "seed_resize_from_h": -1,
+            "seed_resize_from_w": -1,
+            "batch_size": 1,
+            "n_iter": 1,
+            "steps": 3,
+            "cfg_scale": 7,
+            "width": 64,
+            "height": 64,
+            "restore_faces": False,
+            "tiling": False,
+            "negative_prompt": "",
+            "eta": 0,
+            "s_churn": 0,
+            "s_tmax": 0,
+            "s_tmin": 0,
+            "s_noise": 1,
+            "override_settings": {},
+            "sampler_index": "Euler a",
+            "include_init_images": False
+            }
+
+    def test_img2img_simple_performed(self):
+        self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
+
+    def test_inpainting_masked_performed(self):
+        self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png"))
+        self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/basic_features/txt2img_test.py b/test/basic_features/txt2img_test.py
new file mode 100644
index 00000000..fe4af999
--- /dev/null
+++ b/test/basic_features/txt2img_test.py
@@ -0,0 +1,72 @@
+import unittest
+import requests
+
+
+class TestTxt2ImgWorking(unittest.TestCase):
+    def setUp(self):
+        self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
+        self.simple_txt2img = {
+            "enable_hr": False,
+            "denoising_strength": 0,
+            "firstphase_width": 0,
+            "firstphase_height": 0,
+            "prompt": "example prompt",
+            "styles": [],
+            "seed": -1,
+            "subseed": -1,
+            "subseed_strength": 0,
+            "seed_resize_from_h": -1,
+            "seed_resize_from_w": -1,
+            "batch_size": 1,
+            "n_iter": 1,
+            "steps": 3,
+            "cfg_scale": 7,
+            "width": 64,
+            "height": 64,
+            "restore_faces": False,
+            "tiling": False,
+            "negative_prompt": "",
+            "eta": 0,
+            "s_churn": 0,
+            "s_tmax": 0,
+            "s_tmin": 0,
+            "s_noise": 1,
+            "sampler_index": "Euler a"
+        }
+
+    def test_txt2img_simple_performed(self):
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_with_negative_prompt_performed(self):
+        self.simple_txt2img["negative_prompt"] = "example negative prompt"
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_not_square_image_performed(self):
+        self.simple_txt2img["height"] = 128
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_with_hrfix_performed(self):
+        self.simple_txt2img["enable_hr"] = True
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_with_restore_faces_performed(self):
+        self.simple_txt2img["restore_faces"] = True
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_with_tiling_performed(self):
+        self.simple_txt2img["tiling"] = True
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_with_vanilla_sampler_performed(self):
+        self.simple_txt2img["sampler_index"] = "PLMS"
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+        self.simple_txt2img["sampler_index"] = "DDIM"
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+    def test_txt2img_multiple_batches_performed(self):
+        self.simple_txt2img["n_iter"] = 2
+        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/basic_features/utils_test.py b/test/basic_features/utils_test.py
new file mode 100644
index 00000000..9706db8b
--- /dev/null
+++ b/test/basic_features/utils_test.py
@@ -0,0 +1,67 @@
+import unittest
+import requests
+
+class UtilsTests(unittest.TestCase):
+  def setUp(self):
+    self.url_options = "http://localhost:7860/sdapi/v1/options"
+    self.url_cmd_flags = "http://localhost:7860/sdapi/v1/cmd-flags"
+    self.url_samplers = "http://localhost:7860/sdapi/v1/samplers"
+    self.url_upscalers = "http://localhost:7860/sdapi/v1/upscalers"
+    self.url_sd_models = "http://localhost:7860/sdapi/v1/sd-models"
+    self.url_hypernetworks = "http://localhost:7860/sdapi/v1/hypernetworks"
+    self.url_face_restorers = "http://localhost:7860/sdapi/v1/face-restorers"
+    self.url_realesrgan_models = "http://localhost:7860/sdapi/v1/realesrgan-models"
+    self.url_prompt_styles = "http://localhost:7860/sdapi/v1/prompt-styles"
+    self.url_artist_categories = "http://localhost:7860/sdapi/v1/artist-categories"
+    self.url_artists = "http://localhost:7860/sdapi/v1/artists"
+
+  def test_options_get(self):
+    self.assertEqual(requests.get(self.url_options).status_code, 200)
+
+  # def test_options_write(self):
+  #   response = requests.get(self.url_options)
+  #   self.assertEqual(response.status_code, 200)
+    
+  #   pre_value = response.json()["send_seed"]
+
+  #   self.assertEqual(requests.post(self.url_options, json={"send_seed":not pre_value}).status_code, 200)
+
+  #   response = requests.get(self.url_options)
+  #   self.assertEqual(response.status_code, 200)
+  #   self.assertEqual(response.json()["send_seed"], not pre_value)
+
+  #   requests.post(self.url_options, json={"send_seed": pre_value})
+
+  def test_cmd_flags(self):
+    self.assertEqual(requests.get(self.url_cmd_flags).status_code, 200)
+
+  def test_samplers(self):
+    self.assertEqual(requests.get(self.url_samplers).status_code, 200)
+
+  def test_upscalers(self):
+    self.assertEqual(requests.get(self.url_upscalers).status_code, 200)
+
+  def test_sd_models(self):
+    self.assertEqual(requests.get(self.url_sd_models).status_code, 200)
+
+  def test_hypernetworks(self):
+    self.assertEqual(requests.get(self.url_hypernetworks).status_code, 200)
+
+  def test_face_restorers(self):
+    self.assertEqual(requests.get(self.url_face_restorers).status_code, 200)
+  
+  def test_realesrgan_models(self):
+    self.assertEqual(requests.get(self.url_realesrgan_models).status_code, 200)
+  
+  def test_prompt_styles(self):
+    self.assertEqual(requests.get(self.url_prompt_styles).status_code, 200)
+  
+  def test_artist_categories(self):
+    self.assertEqual(requests.get(self.url_artist_categories).status_code, 200)
+
+  def test_artists(self):
+    self.assertEqual(requests.get(self.url_artists).status_code, 200)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/extras_test.py b/test/extras_test.py
deleted file mode 100644
index 9b8ce0f0..00000000
--- a/test/extras_test.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import unittest
-
-
-class TestExtrasWorking(unittest.TestCase):
-    def setUp(self):
-        self.url_img2img = "http://localhost:7860/sdapi/v1/extra-single-image"
-        self.simple_extras = {
-            "resize_mode": 0,
-            "show_extras_results": True,
-            "gfpgan_visibility": 0,
-            "codeformer_visibility": 0,
-            "codeformer_weight": 0,
-            "upscaling_resize": 2,
-            "upscaling_resize_w": 512,
-            "upscaling_resize_h": 512,
-            "upscaling_crop": True,
-            "upscaler_1": "None",
-            "upscaler_2": "None",
-            "extras_upscaler_2_visibility": 0,
-            "image": ""
-            }
-
-
-class TestExtrasCorrectness(unittest.TestCase):
-    pass
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/test/img2img_test.py b/test/img2img_test.py
deleted file mode 100644
index 012a9580..00000000
--- a/test/img2img_test.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import unittest
-import requests
-from gradio.processing_utils import encode_pil_to_base64
-from PIL import Image
-
-
-class TestImg2ImgWorking(unittest.TestCase):
-    def setUp(self):
-        self.url_img2img = "http://localhost:7860/sdapi/v1/img2img"
-        self.simple_img2img = {
-            "init_images": [encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))],
-            "resize_mode": 0,
-            "denoising_strength": 0.75,
-            "mask": None,
-            "mask_blur": 4,
-            "inpainting_fill": 0,
-            "inpaint_full_res": False,
-            "inpaint_full_res_padding": 0,
-            "inpainting_mask_invert": 0,
-            "prompt": "example prompt",
-            "styles": [],
-            "seed": -1,
-            "subseed": -1,
-            "subseed_strength": 0,
-            "seed_resize_from_h": -1,
-            "seed_resize_from_w": -1,
-            "batch_size": 1,
-            "n_iter": 1,
-            "steps": 3,
-            "cfg_scale": 7,
-            "width": 64,
-            "height": 64,
-            "restore_faces": False,
-            "tiling": False,
-            "negative_prompt": "",
-            "eta": 0,
-            "s_churn": 0,
-            "s_tmax": 0,
-            "s_tmin": 0,
-            "s_noise": 1,
-            "override_settings": {},
-            "sampler_index": "Euler a",
-            "include_init_images": False
-            }
-
-    def test_img2img_simple_performed(self):
-        self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
-
-    def test_inpainting_masked_performed(self):
-        self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png"))
-        self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
-
-
-class TestImg2ImgCorrectness(unittest.TestCase):
-    pass
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/test/server_poll.py b/test/server_poll.py
index 8e63b450..c71e906a 100644
--- a/test/server_poll.py
+++ b/test/server_poll.py
@@ -3,7 +3,7 @@ import requests
 import time
 
 
-def run_tests(proc):
+def run_tests(proc, test_dir):
     timeout_threshold = 240
     start_time = time.time()
     while time.time()-start_time < timeout_threshold:
@@ -14,7 +14,9 @@ def run_tests(proc):
             if proc.poll() is not None:
                 break
     if proc.poll() is None:
-        suite = unittest.TestLoader().discover('', pattern='*_test.py')
+        if test_dir is None:
+            test_dir = ""
+        suite = unittest.TestLoader().discover(test_dir, pattern="*_test.py", top_level_dir="test")
         result = unittest.TextTestRunner(verbosity=2).run(suite)
     else:
         print("Launch unsuccessful")
diff --git a/test/txt2img_test.py b/test/txt2img_test.py
deleted file mode 100644
index ce752085..00000000
--- a/test/txt2img_test.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import unittest
-import requests
-
-
-class TestTxt2ImgWorking(unittest.TestCase):
-    def setUp(self):
-        self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
-        self.simple_txt2img = {
-            "enable_hr": False,
-            "denoising_strength": 0,
-            "firstphase_width": 0,
-            "firstphase_height": 0,
-            "prompt": "example prompt",
-            "styles": [],
-            "seed": -1,
-            "subseed": -1,
-            "subseed_strength": 0,
-            "seed_resize_from_h": -1,
-            "seed_resize_from_w": -1,
-            "batch_size": 1,
-            "n_iter": 1,
-            "steps": 3,
-            "cfg_scale": 7,
-            "width": 64,
-            "height": 64,
-            "restore_faces": False,
-            "tiling": False,
-            "negative_prompt": "",
-            "eta": 0,
-            "s_churn": 0,
-            "s_tmax": 0,
-            "s_tmin": 0,
-            "s_noise": 1,
-            "sampler_index": "Euler a"
-        }
-
-    def test_txt2img_simple_performed(self):
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_with_negative_prompt_performed(self):
-        self.simple_txt2img["negative_prompt"] = "example negative prompt"
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_not_square_image_performed(self):
-        self.simple_txt2img["height"] = 128
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_with_hrfix_performed(self):
-        self.simple_txt2img["enable_hr"] = True
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_with_restore_faces_performed(self):
-        self.simple_txt2img["restore_faces"] = True
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_with_tiling_performed(self):
-        self.simple_txt2img["tiling"] = True
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_with_vanilla_sampler_performed(self):
-        self.simple_txt2img["sampler_index"] = "PLMS"
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-        self.simple_txt2img["sampler_index"] = "DDIM"
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_multiple_batches_performed(self):
-        self.simple_txt2img["n_iter"] = 2
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-
-class TestTxt2ImgCorrectness(unittest.TestCase):
-    pass
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/test/utils_test.py b/test/utils_test.py
deleted file mode 100644
index be9e6bf8..00000000
--- a/test/utils_test.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import unittest
-import requests
-
-class UtilsTests(unittest.TestCase):
-  def setUp(self):
-    self.url_options = "http://localhost:7860/sdapi/v1/options"
-    self.url_cmd_flags = "http://localhost:7860/sdapi/v1/cmd-flags"
-    self.url_samplers = "http://localhost:7860/sdapi/v1/samplers"
-    self.url_upscalers = "http://localhost:7860/sdapi/v1/upscalers"
-    self.url_sd_models = "http://localhost:7860/sdapi/v1/sd-models"
-    self.url_hypernetworks = "http://localhost:7860/sdapi/v1/hypernetworks"
-    self.url_face_restorers = "http://localhost:7860/sdapi/v1/face-restorers"
-    self.url_realesrgan_models = "http://localhost:7860/sdapi/v1/realesrgan-models"
-    self.url_prompt_styles = "http://localhost:7860/sdapi/v1/prompt-styles"
-    self.url_artist_categories = "http://localhost:7860/sdapi/v1/artist-categories"
-    self.url_artists = "http://localhost:7860/sdapi/v1/artists"
-
-  def test_options_get(self):
-    self.assertEqual(requests.get(self.url_options).status_code, 200)
-
-  # def test_options_write(self):
-  #   response = requests.get(self.url_options)
-  #   self.assertEqual(response.status_code, 200)
-    
-  #   pre_value = response.json()["send_seed"]
-
-  #   self.assertEqual(requests.post(self.url_options, json={"send_seed":not pre_value}).status_code, 200)
-
-  #   response = requests.get(self.url_options)
-  #   self.assertEqual(response.status_code, 200)
-  #   self.assertEqual(response.json()["send_seed"], not pre_value)
-
-  #   requests.post(self.url_options, json={"send_seed": pre_value})
-
-  def test_cmd_flags(self):
-    self.assertEqual(requests.get(self.url_cmd_flags).status_code, 200)
-
-  def test_samplers(self):
-    self.assertEqual(requests.get(self.url_samplers).status_code, 200)
-
-  def test_upscalers(self):
-    self.assertEqual(requests.get(self.url_upscalers).status_code, 200)
-
-  def test_sd_models(self):
-    self.assertEqual(requests.get(self.url_sd_models).status_code, 200)
-
-  def test_hypernetworks(self):
-    self.assertEqual(requests.get(self.url_hypernetworks).status_code, 200)
-
-  def test_face_restorers(self):
-    self.assertEqual(requests.get(self.url_face_restorers).status_code, 200)
-  
-  def test_realesrgan_models(self):
-    self.assertEqual(requests.get(self.url_realesrgan_models).status_code, 200)
-  
-  def test_prompt_styles(self):
-    self.assertEqual(requests.get(self.url_prompt_styles).status_code, 200)
-  
-  def test_artist_categories(self):
-    self.assertEqual(requests.get(self.url_artist_categories).status_code, 200)
-
-  def test_artists(self):
-    self.assertEqual(requests.get(self.url_artists).status_code, 200)
\ No newline at end of file
-- 
cgit v1.2.1


From 3ffc1c6ceee169fac767a956fd0d4f153b005dbf Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Mon, 14 Nov 2022 13:45:21 +0300
Subject: skip cuda test

---
 .github/workflows/run_tests.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml
index a56a8110..f3048681 100644
--- a/.github/workflows/run_tests.yaml
+++ b/.github/workflows/run_tests.yaml
@@ -22,5 +22,5 @@ jobs:
             ${{ runner.os }}-pip-
       - name: Run tests
         run: |
-          export COMMANDLINE_ARGS="--tests basic_features --no-half --disable-opt-split-attention --use-cpu all"
+          export COMMANDLINE_ARGS="--tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test"
           python launch.py
-- 
cgit v1.2.1


From 0646040667b59526ac8346d53efd14dc0e75b01e Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Mon, 14 Nov 2022 14:36:07 +0300
Subject: Propagate test error and try it without localhost

---
 launch.py                              |  7 ++++---
 test/advanced_features/extras_test.py  |  2 +-
 test/advanced_features/txt2img_test.py |  2 +-
 test/basic_features/img2img_test.py    |  2 +-
 test/basic_features/txt2img_test.py    |  2 +-
 test/basic_features/utils_test.py      | 22 +++++++++++-----------
 test/server_poll.py                    |  4 +++-
 7 files changed, 22 insertions(+), 19 deletions(-)

diff --git a/launch.py b/launch.py
index d0f502c2..8ed1dffc 100644
--- a/launch.py
+++ b/launch.py
@@ -234,8 +234,8 @@ def prepare_enviroment():
         exit(0)
 
     if run_tests:
-        tests(test_dir)
-        exit(0)
+        exitcode = tests(test_dir)
+        exit(exitcode)
 
 
 def tests(test_dir):
@@ -251,10 +251,11 @@ def tests(test_dir):
         proc = subprocess.Popen([sys.executable, *sys.argv], stdout=stdout, stderr=stderr)
 
     import test.server_poll
-    test.server_poll.run_tests(proc, test_dir)
+    exitcode = test.server_poll.run_tests(proc, test_dir)
 
     print(f"Stopping Web UI process with id {proc.pid}")
     proc.kill()
+    return exitcode
 
 
 def start():
diff --git a/test/advanced_features/extras_test.py b/test/advanced_features/extras_test.py
index 8763f8ed..abdd5aa2 100644
--- a/test/advanced_features/extras_test.py
+++ b/test/advanced_features/extras_test.py
@@ -3,7 +3,7 @@ import unittest
 
 class TestExtrasWorking(unittest.TestCase):
     def setUp(self):
-        self.url_img2img = "http://localhost:7860/sdapi/v1/extra-single-image"
+        self.url_img2img = "http://127.0.0.1:7860/sdapi/v1/extra-single-image"
         self.simple_extras = {
             "resize_mode": 0,
             "show_extras_results": True,
diff --git a/test/advanced_features/txt2img_test.py b/test/advanced_features/txt2img_test.py
index 36ed7b9a..6ab5a242 100644
--- a/test/advanced_features/txt2img_test.py
+++ b/test/advanced_features/txt2img_test.py
@@ -4,7 +4,7 @@ import requests
 
 class TestTxt2ImgWorking(unittest.TestCase):
     def setUp(self):
-        self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
+        self.url_txt2img = "http://127.0.0.1:7860/sdapi/v1/txt2img"
         self.simple_txt2img = {
             "enable_hr": False,
             "denoising_strength": 0,
diff --git a/test/basic_features/img2img_test.py b/test/basic_features/img2img_test.py
index 0a9c1e8a..b9a34113 100644
--- a/test/basic_features/img2img_test.py
+++ b/test/basic_features/img2img_test.py
@@ -6,7 +6,7 @@ from PIL import Image
 
 class TestImg2ImgWorking(unittest.TestCase):
     def setUp(self):
-        self.url_img2img = "http://localhost:7860/sdapi/v1/img2img"
+        self.url_img2img = "http://127.0.0.1:7860/sdapi/v1/img2img"
         self.simple_img2img = {
             "init_images": [encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))],
             "resize_mode": 0,
diff --git a/test/basic_features/txt2img_test.py b/test/basic_features/txt2img_test.py
index fe4af999..0be675d9 100644
--- a/test/basic_features/txt2img_test.py
+++ b/test/basic_features/txt2img_test.py
@@ -4,7 +4,7 @@ import requests
 
 class TestTxt2ImgWorking(unittest.TestCase):
     def setUp(self):
-        self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
+        self.url_txt2img = "http://127.0.0.1:7860/sdapi/v1/txt2img"
         self.simple_txt2img = {
             "enable_hr": False,
             "denoising_strength": 0,
diff --git a/test/basic_features/utils_test.py b/test/basic_features/utils_test.py
index 9706db8b..592f7619 100644
--- a/test/basic_features/utils_test.py
+++ b/test/basic_features/utils_test.py
@@ -3,17 +3,17 @@ import requests
 
 class UtilsTests(unittest.TestCase):
   def setUp(self):
-    self.url_options = "http://localhost:7860/sdapi/v1/options"
-    self.url_cmd_flags = "http://localhost:7860/sdapi/v1/cmd-flags"
-    self.url_samplers = "http://localhost:7860/sdapi/v1/samplers"
-    self.url_upscalers = "http://localhost:7860/sdapi/v1/upscalers"
-    self.url_sd_models = "http://localhost:7860/sdapi/v1/sd-models"
-    self.url_hypernetworks = "http://localhost:7860/sdapi/v1/hypernetworks"
-    self.url_face_restorers = "http://localhost:7860/sdapi/v1/face-restorers"
-    self.url_realesrgan_models = "http://localhost:7860/sdapi/v1/realesrgan-models"
-    self.url_prompt_styles = "http://localhost:7860/sdapi/v1/prompt-styles"
-    self.url_artist_categories = "http://localhost:7860/sdapi/v1/artist-categories"
-    self.url_artists = "http://localhost:7860/sdapi/v1/artists"
+    self.url_options = "http://127.0.0.1:7860/sdapi/v1/options"
+    self.url_cmd_flags = "http://127.0.0.1:7860/sdapi/v1/cmd-flags"
+    self.url_samplers = "http://127.0.0.1:7860/sdapi/v1/samplers"
+    self.url_upscalers = "http://127.0.0.1:7860/sdapi/v1/upscalers"
+    self.url_sd_models = "http://127.0.0.1:7860/sdapi/v1/sd-models"
+    self.url_hypernetworks = "http://127.0.0.1:7860/sdapi/v1/hypernetworks"
+    self.url_face_restorers = "http://127.0.0.1:7860/sdapi/v1/face-restorers"
+    self.url_realesrgan_models = "http://127.0.0.1:7860/sdapi/v1/realesrgan-models"
+    self.url_prompt_styles = "http://127.0.0.1:7860/sdapi/v1/prompt-styles"
+    self.url_artist_categories = "http://127.0.0.1:7860/sdapi/v1/artist-categories"
+    self.url_artists = "http://127.0.0.1:7860/sdapi/v1/artists"
 
   def test_options_get(self):
     self.assertEqual(requests.get(self.url_options).status_code, 200)
diff --git a/test/server_poll.py b/test/server_poll.py
index c71e906a..028fd476 100644
--- a/test/server_poll.py
+++ b/test/server_poll.py
@@ -8,7 +8,7 @@ def run_tests(proc, test_dir):
     start_time = time.time()
     while time.time()-start_time < timeout_threshold:
         try:
-            requests.head("http://localhost:7860/")
+            requests.head("http://127.0.0.1:7860/")
             break
         except requests.exceptions.ConnectionError:
             if proc.poll() is not None:
@@ -18,5 +18,7 @@ def run_tests(proc, test_dir):
             test_dir = ""
         suite = unittest.TestLoader().discover(test_dir, pattern="*_test.py", top_level_dir="test")
         result = unittest.TextTestRunner(verbosity=2).run(suite)
+        return len(result.failures)
     else:
         print("Launch unsuccessful")
+        return 1
-- 
cgit v1.2.1


From 7416ac8d3cadcc6a53bbcc41e1cd184fa1587afd Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Mon, 14 Nov 2022 14:55:39 +0300
Subject: Use localhost with 80 port, count errors as well

---
 test/advanced_features/extras_test.py  |  2 +-
 test/advanced_features/txt2img_test.py |  2 +-
 test/basic_features/img2img_test.py    |  2 +-
 test/basic_features/txt2img_test.py    |  2 +-
 test/basic_features/utils_test.py      | 22 +++++++++++-----------
 test/server_poll.py                    |  4 ++--
 6 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/test/advanced_features/extras_test.py b/test/advanced_features/extras_test.py
index abdd5aa2..4b8ae25a 100644
--- a/test/advanced_features/extras_test.py
+++ b/test/advanced_features/extras_test.py
@@ -3,7 +3,7 @@ import unittest
 
 class TestExtrasWorking(unittest.TestCase):
     def setUp(self):
-        self.url_img2img = "http://127.0.0.1:7860/sdapi/v1/extra-single-image"
+        self.url_img2img = "http://localhost:80/sdapi/v1/extra-single-image"
         self.simple_extras = {
             "resize_mode": 0,
             "show_extras_results": True,
diff --git a/test/advanced_features/txt2img_test.py b/test/advanced_features/txt2img_test.py
index 6ab5a242..e6c3531a 100644
--- a/test/advanced_features/txt2img_test.py
+++ b/test/advanced_features/txt2img_test.py
@@ -4,7 +4,7 @@ import requests
 
 class TestTxt2ImgWorking(unittest.TestCase):
     def setUp(self):
-        self.url_txt2img = "http://127.0.0.1:7860/sdapi/v1/txt2img"
+        self.url_txt2img = "http://localhost:80/sdapi/v1/txt2img"
         self.simple_txt2img = {
             "enable_hr": False,
             "denoising_strength": 0,
diff --git a/test/basic_features/img2img_test.py b/test/basic_features/img2img_test.py
index b9a34113..c4c9a90f 100644
--- a/test/basic_features/img2img_test.py
+++ b/test/basic_features/img2img_test.py
@@ -6,7 +6,7 @@ from PIL import Image
 
 class TestImg2ImgWorking(unittest.TestCase):
     def setUp(self):
-        self.url_img2img = "http://127.0.0.1:7860/sdapi/v1/img2img"
+        self.url_img2img = "http://localhost:80/sdapi/v1/img2img"
         self.simple_img2img = {
             "init_images": [encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))],
             "resize_mode": 0,
diff --git a/test/basic_features/txt2img_test.py b/test/basic_features/txt2img_test.py
index 0be675d9..4f1fc77d 100644
--- a/test/basic_features/txt2img_test.py
+++ b/test/basic_features/txt2img_test.py
@@ -4,7 +4,7 @@ import requests
 
 class TestTxt2ImgWorking(unittest.TestCase):
     def setUp(self):
-        self.url_txt2img = "http://127.0.0.1:7860/sdapi/v1/txt2img"
+        self.url_txt2img = "http://localhost:80/sdapi/v1/txt2img"
         self.simple_txt2img = {
             "enable_hr": False,
             "denoising_strength": 0,
diff --git a/test/basic_features/utils_test.py b/test/basic_features/utils_test.py
index 592f7619..fdb72b9a 100644
--- a/test/basic_features/utils_test.py
+++ b/test/basic_features/utils_test.py
@@ -3,17 +3,17 @@ import requests
 
 class UtilsTests(unittest.TestCase):
   def setUp(self):
-    self.url_options = "http://127.0.0.1:7860/sdapi/v1/options"
-    self.url_cmd_flags = "http://127.0.0.1:7860/sdapi/v1/cmd-flags"
-    self.url_samplers = "http://127.0.0.1:7860/sdapi/v1/samplers"
-    self.url_upscalers = "http://127.0.0.1:7860/sdapi/v1/upscalers"
-    self.url_sd_models = "http://127.0.0.1:7860/sdapi/v1/sd-models"
-    self.url_hypernetworks = "http://127.0.0.1:7860/sdapi/v1/hypernetworks"
-    self.url_face_restorers = "http://127.0.0.1:7860/sdapi/v1/face-restorers"
-    self.url_realesrgan_models = "http://127.0.0.1:7860/sdapi/v1/realesrgan-models"
-    self.url_prompt_styles = "http://127.0.0.1:7860/sdapi/v1/prompt-styles"
-    self.url_artist_categories = "http://127.0.0.1:7860/sdapi/v1/artist-categories"
-    self.url_artists = "http://127.0.0.1:7860/sdapi/v1/artists"
+    self.url_options = "http://localhost:80/sdapi/v1/options"
+    self.url_cmd_flags = "http://localhost:80/sdapi/v1/cmd-flags"
+    self.url_samplers = "http://localhost:80/sdapi/v1/samplers"
+    self.url_upscalers = "http://localhost:80/sdapi/v1/upscalers"
+    self.url_sd_models = "http://localhost:80/sdapi/v1/sd-models"
+    self.url_hypernetworks = "http://localhost:80/sdapi/v1/hypernetworks"
+    self.url_face_restorers = "http://localhost:80/sdapi/v1/face-restorers"
+    self.url_realesrgan_models = "http://localhost:80/sdapi/v1/realesrgan-models"
+    self.url_prompt_styles = "http://localhost:80/sdapi/v1/prompt-styles"
+    self.url_artist_categories = "http://localhost:80/sdapi/v1/artist-categories"
+    self.url_artists = "http://localhost:80/sdapi/v1/artists"
 
   def test_options_get(self):
     self.assertEqual(requests.get(self.url_options).status_code, 200)
diff --git a/test/server_poll.py b/test/server_poll.py
index 028fd476..e4462b6c 100644
--- a/test/server_poll.py
+++ b/test/server_poll.py
@@ -8,7 +8,7 @@ def run_tests(proc, test_dir):
     start_time = time.time()
     while time.time()-start_time < timeout_threshold:
         try:
-            requests.head("http://127.0.0.1:7860/")
+            requests.head("http://localhost:80/")
             break
         except requests.exceptions.ConnectionError:
             if proc.poll() is not None:
@@ -18,7 +18,7 @@ def run_tests(proc, test_dir):
             test_dir = ""
         suite = unittest.TestLoader().discover(test_dir, pattern="*_test.py", top_level_dir="test")
         result = unittest.TextTestRunner(verbosity=2).run(suite)
-        return len(result.failures)
+        return len(result.failures) + len(result.errors)
     else:
         print("Launch unsuccessful")
         return 1
-- 
cgit v1.2.1


From 5808241dd76983212ab8e27b07f72866671f0b2d Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Mon, 14 Nov 2022 15:14:52 +0300
Subject: Use 80 port on launch

---
 .github/workflows/run_tests.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml
index f3048681..223a31b9 100644
--- a/.github/workflows/run_tests.yaml
+++ b/.github/workflows/run_tests.yaml
@@ -22,5 +22,5 @@ jobs:
             ${{ runner.os }}-pip-
       - name: Run tests
         run: |
-          export COMMANDLINE_ARGS="--tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test"
+          export COMMANDLINE_ARGS="--tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test --port 80"
           python launch.py
-- 
cgit v1.2.1


From 9e4f68acad4697fdf10004eb85d3f6f769c2c70b Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Mon, 14 Nov 2022 18:40:15 +0300
Subject: Stop exporting cl args and upload stdout and stderr as artifacts

---
 .github/workflows/run_tests.yaml       | 11 +++++++++--
 launch.py                              |  2 ++
 test/advanced_features/extras_test.py  |  2 +-
 test/advanced_features/txt2img_test.py |  2 +-
 test/basic_features/img2img_test.py    |  2 +-
 test/basic_features/txt2img_test.py    |  2 +-
 test/basic_features/utils_test.py      | 22 +++++++++++-----------
 test/server_poll.py                    |  2 +-
 8 files changed, 27 insertions(+), 18 deletions(-)

diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml
index 223a31b9..558b0c61 100644
--- a/.github/workflows/run_tests.yaml
+++ b/.github/workflows/run_tests.yaml
@@ -22,5 +22,12 @@ jobs:
             ${{ runner.os }}-pip-
       - name: Run tests
         run: |
-          export COMMANDLINE_ARGS="--tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test --port 80"
-          python launch.py
+          python launch.py --tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
+      - name: Upload main app stdout-stderr
+        uses: actions/upload-artifact@v3
+        if: always()
+        with:
+          name: stdout-stderr
+          path: |
+            ./test/stdout.txt
+            ./test/stderr.txt
diff --git a/launch.py b/launch.py
index 8ed1dffc..ed21d0e6 100644
--- a/launch.py
+++ b/launch.py
@@ -244,6 +244,8 @@ def tests(test_dir):
     if "--ckpt" not in sys.argv:
         sys.argv.append("--ckpt")
         sys.argv.append("./test/test_files/empty.pt")
+    if "--skip-torch-cuda-test" not in sys.argv:
+        sys.argv.append("--skip-torch-cuda-test")
 
     print(f"Launching Web UI in another process for testing with arguments: {' '.join(sys.argv[1:])}")
 
diff --git a/test/advanced_features/extras_test.py b/test/advanced_features/extras_test.py
index 4b8ae25a..8763f8ed 100644
--- a/test/advanced_features/extras_test.py
+++ b/test/advanced_features/extras_test.py
@@ -3,7 +3,7 @@ import unittest
 
 class TestExtrasWorking(unittest.TestCase):
     def setUp(self):
-        self.url_img2img = "http://localhost:80/sdapi/v1/extra-single-image"
+        self.url_img2img = "http://localhost:7860/sdapi/v1/extra-single-image"
         self.simple_extras = {
             "resize_mode": 0,
             "show_extras_results": True,
diff --git a/test/advanced_features/txt2img_test.py b/test/advanced_features/txt2img_test.py
index e6c3531a..36ed7b9a 100644
--- a/test/advanced_features/txt2img_test.py
+++ b/test/advanced_features/txt2img_test.py
@@ -4,7 +4,7 @@ import requests
 
 class TestTxt2ImgWorking(unittest.TestCase):
     def setUp(self):
-        self.url_txt2img = "http://localhost:80/sdapi/v1/txt2img"
+        self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
         self.simple_txt2img = {
             "enable_hr": False,
             "denoising_strength": 0,
diff --git a/test/basic_features/img2img_test.py b/test/basic_features/img2img_test.py
index c4c9a90f..0a9c1e8a 100644
--- a/test/basic_features/img2img_test.py
+++ b/test/basic_features/img2img_test.py
@@ -6,7 +6,7 @@ from PIL import Image
 
 class TestImg2ImgWorking(unittest.TestCase):
     def setUp(self):
-        self.url_img2img = "http://localhost:80/sdapi/v1/img2img"
+        self.url_img2img = "http://localhost:7860/sdapi/v1/img2img"
         self.simple_img2img = {
             "init_images": [encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))],
             "resize_mode": 0,
diff --git a/test/basic_features/txt2img_test.py b/test/basic_features/txt2img_test.py
index 4f1fc77d..fe4af999 100644
--- a/test/basic_features/txt2img_test.py
+++ b/test/basic_features/txt2img_test.py
@@ -4,7 +4,7 @@ import requests
 
 class TestTxt2ImgWorking(unittest.TestCase):
     def setUp(self):
-        self.url_txt2img = "http://localhost:80/sdapi/v1/txt2img"
+        self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
         self.simple_txt2img = {
             "enable_hr": False,
             "denoising_strength": 0,
diff --git a/test/basic_features/utils_test.py b/test/basic_features/utils_test.py
index fdb72b9a..9706db8b 100644
--- a/test/basic_features/utils_test.py
+++ b/test/basic_features/utils_test.py
@@ -3,17 +3,17 @@ import requests
 
 class UtilsTests(unittest.TestCase):
   def setUp(self):
-    self.url_options = "http://localhost:80/sdapi/v1/options"
-    self.url_cmd_flags = "http://localhost:80/sdapi/v1/cmd-flags"
-    self.url_samplers = "http://localhost:80/sdapi/v1/samplers"
-    self.url_upscalers = "http://localhost:80/sdapi/v1/upscalers"
-    self.url_sd_models = "http://localhost:80/sdapi/v1/sd-models"
-    self.url_hypernetworks = "http://localhost:80/sdapi/v1/hypernetworks"
-    self.url_face_restorers = "http://localhost:80/sdapi/v1/face-restorers"
-    self.url_realesrgan_models = "http://localhost:80/sdapi/v1/realesrgan-models"
-    self.url_prompt_styles = "http://localhost:80/sdapi/v1/prompt-styles"
-    self.url_artist_categories = "http://localhost:80/sdapi/v1/artist-categories"
-    self.url_artists = "http://localhost:80/sdapi/v1/artists"
+    self.url_options = "http://localhost:7860/sdapi/v1/options"
+    self.url_cmd_flags = "http://localhost:7860/sdapi/v1/cmd-flags"
+    self.url_samplers = "http://localhost:7860/sdapi/v1/samplers"
+    self.url_upscalers = "http://localhost:7860/sdapi/v1/upscalers"
+    self.url_sd_models = "http://localhost:7860/sdapi/v1/sd-models"
+    self.url_hypernetworks = "http://localhost:7860/sdapi/v1/hypernetworks"
+    self.url_face_restorers = "http://localhost:7860/sdapi/v1/face-restorers"
+    self.url_realesrgan_models = "http://localhost:7860/sdapi/v1/realesrgan-models"
+    self.url_prompt_styles = "http://localhost:7860/sdapi/v1/prompt-styles"
+    self.url_artist_categories = "http://localhost:7860/sdapi/v1/artist-categories"
+    self.url_artists = "http://localhost:7860/sdapi/v1/artists"
 
   def test_options_get(self):
     self.assertEqual(requests.get(self.url_options).status_code, 200)
diff --git a/test/server_poll.py b/test/server_poll.py
index e4462b6c..d4df697b 100644
--- a/test/server_poll.py
+++ b/test/server_poll.py
@@ -8,7 +8,7 @@ def run_tests(proc, test_dir):
     start_time = time.time()
     while time.time()-start_time < timeout_threshold:
         try:
-            requests.head("http://localhost:80/")
+            requests.head("http://localhost:7860/")
             break
         except requests.exceptions.ConnectionError:
             if proc.poll() is not None:
-- 
cgit v1.2.1


From 4a35c3744c68be470e6caf72689322ed58f90aac Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Mon, 14 Nov 2022 18:57:14 +0300
Subject: remove test requiring codeformers

---
 test/basic_features/txt2img_test.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/test/basic_features/txt2img_test.py b/test/basic_features/txt2img_test.py
index fe4af999..1c2674b2 100644
--- a/test/basic_features/txt2img_test.py
+++ b/test/basic_features/txt2img_test.py
@@ -49,10 +49,6 @@ class TestTxt2ImgWorking(unittest.TestCase):
         self.simple_txt2img["enable_hr"] = True
         self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
 
-    def test_txt2img_with_restore_faces_performed(self):
-        self.simple_txt2img["restore_faces"] = True
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
     def test_txt2img_with_tiling_performed(self):
         self.simple_txt2img["tiling"] = True
         self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-- 
cgit v1.2.1


From a07107900019c8f18ed0f8d07e39435ee3189e79 Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Mon, 14 Nov 2022 19:22:06 +0300
Subject: Use empty model as CLIP weights

---
 .github/workflows/run_tests.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml
index 558b0c61..4aeeab9c 100644
--- a/.github/workflows/run_tests.yaml
+++ b/.github/workflows/run_tests.yaml
@@ -22,7 +22,7 @@ jobs:
             ${{ runner.os }}-pip-
       - name: Run tests
         run: |
-          python launch.py --tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
+          python launch.py --tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test --clip-models-path ./test/test_files/empty.pt
       - name: Upload main app stdout-stderr
         uses: actions/upload-artifact@v3
         if: always()
-- 
cgit v1.2.1


From 3405acc6a4dcef2b73782a04924a9a12422e54f0 Mon Sep 17 00:00:00 2001
From: papuSpartan <mcgpapu@gmail.com>
Date: Mon, 14 Nov 2022 14:07:13 -0600
Subject: Give --server-name priority over --listen and add check for
 --server-name in addition to --share and --listen

---
 modules/shared.py | 2 +-
 webui.py          | 5 ++++-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 6936cbe0..c628b580 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -106,7 +106,7 @@ restricted_opts = {
     "outdir_save",
 }
 
-cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen) and not cmd_opts.enable_insecure_extension_access
+cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
diff --git a/webui.py b/webui.py
index f4f1d74d..fc776669 100644
--- a/webui.py
+++ b/webui.py
@@ -33,7 +33,10 @@ from modules.shared import cmd_opts
 import modules.hypernetworks.hypernetwork
 
 queue_lock = threading.Lock()
-server_name = "0.0.0.0" if cmd_opts.listen else cmd_opts.server_name
+if cmd_opts.server_name:
+    server_name = cmd_opts.server_name
+else:
+    server_name = "0.0.0.0" if cmd_opts.listen else None
 
 def wrap_queued_call(func):
     def f(*args, **kwargs):
-- 
cgit v1.2.1


From 8f2ff861d31972d12de278075ea9c0c0deef99de Mon Sep 17 00:00:00 2001
From: Maiko Sinkyaet Tan <maiko.tan.coding@gmail.com>
Date: Tue, 15 Nov 2022 16:12:34 +0800
Subject: feat: add http basic authentication for api

---
 modules/api/api.py | 61 ++++++++++++++++++++++++++++++++++++------------------
 modules/shared.py  |  1 +
 2 files changed, 42 insertions(+), 20 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 596a6616..6bb01603 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -5,6 +5,9 @@ import uvicorn
 from threading import Lock
 from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
 from fastapi import APIRouter, Depends, FastAPI, HTTPException
+from fastapi.security import HTTPBasic, HTTPBasicCredentials
+from secrets import compare_digest
+
 import modules.shared as shared
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@@ -57,29 +60,47 @@ def encode_pil_to_base64(image):
 
 class Api:
     def __init__(self, app: FastAPI, queue_lock: Lock):
+        if shared.cmd_opts.api_auth:
+            self.credenticals = dict()
+            for auth in shared.cmd_opts.api_auth.split(","):
+                user, password = auth.split(":")
+                self.credenticals[user] = password
+
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
-        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
-        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
-        self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
-        self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
-        self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
-        self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
-        self.app.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
-        self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
-        self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
-        self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
-        self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
-        self.app.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
-        self.app.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
-        self.app.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
-        self.app.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
-        self.app.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
-        self.app.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
-        self.app.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
-        self.app.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
-        self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
+        self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
+        self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
+        self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
+        self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
+        self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
+        self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
+        self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
+        self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
+        self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
+        self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
+        self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
+        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
+        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
+        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
+        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
+        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
+        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
+        self.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
+        self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
+        self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
+
+    def add_api_route(self, path: str, endpoint, **kwargs):
+        if shared.cmd_opts.api_auth:
+            return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
+        return self.app.add_api_route(path, endpoint, **kwargs)
+
+    def auth(self, credenticals: HTTPBasicCredentials = Depends(HTTPBasic())):
+        if credenticals.username in self.credenticals:
+            if compare_digest(credenticals.password, self.credenticals[credenticals.username]):
+                return True
+
+        raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
diff --git a/modules/shared.py b/modules/shared.py
index 6936cbe0..62d526fd 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -81,6 +81,7 @@ parser.add_argument("--enable-console-prompts", action='store_true', help="print
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
+parser.add_argument("--api-auth", type=str, help='Set authentication for api like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-- 
cgit v1.2.1


From 72b52fbb77360f848cfa296b0c79d2bc0a1060f2 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Wed, 16 Nov 2022 13:08:03 +0900
Subject: add css override

---
 modules/ui.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/ui.py b/modules/ui.py
index 5dce7f3b..5e2a992f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -69,8 +69,11 @@ sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
 css_hide_progressbar = """
 .wrap .m-12 svg { display:none!important; }
 .wrap .m-12::before { content:"Loading..." }
+.wrap .z-20 svg { display:none!important; }
+.wrap .z-20::before { content:"Loading..." }
 .progress-bar { display:none!important; }
 .meta-text { display:none!important; }
+.meta-text-center { display:none!important; }
 """
 
 # Using constants for these since the variation selector isn't visible.
-- 
cgit v1.2.1


From 9bbe1e3c2e54f64283bb333ebb648d8f40f5d4ee Mon Sep 17 00:00:00 2001
From: Llewellyn Pritchard <xacc.ide@gmail.com>
Date: Wed, 16 Nov 2022 19:19:00 +0200
Subject: Fix unbounded prompt growth scripts that loop

---
 modules/processing.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/modules/processing.py b/modules/processing.py
index 03c9143d..2fd12288 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -450,6 +450,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     modules.sd_hijack.model_hijack.clear_comments()
 
     comments = {}
+    prompt_tmp = p.prompt
+    negative_prompt_tmp = p.negative_prompt
 
     shared.prompt_styles.apply_styles(p)
 
@@ -596,6 +598,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.scripts is not None:
         p.scripts.postprocess(p, res)
 
+    p.prompt = prompt_tmp
+    p.negative_prompt = negative_prompt_tmp
+
     return res
 
 
-- 
cgit v1.2.1


From abfa22c16fb3d9b1ed8d049c7b68e94d1cca5b82 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 7 Nov 2022 19:25:43 -0500
Subject: Revert "MPS Upscalers Fix"

This reverts commit 768b95394a8500da639b947508f78296524f1836.
---
 modules/devices.py      | 9 ---------
 modules/esrgan_model.py | 2 +-
 modules/scunet_model.py | 3 ++-
 modules/swinir_model.py | 2 +-
 4 files changed, 4 insertions(+), 12 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 67165bf6..a87d0d4c 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -94,12 +94,3 @@ def autocast(disable=False):
         return contextlib.nullcontext()
 
     return torch.autocast("cuda")
-
-
-# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
-def mps_contiguous(input_tensor, device):
-    return input_tensor.contiguous() if device.type == 'mps' else input_tensor
-
-
-def mps_contiguous_to(input_tensor, device):
-    return mps_contiguous(input_tensor, device).to(device)
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index c61669b4..9a9c38f1 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -199,7 +199,7 @@ def upscale_without_tiling(model, img):
     img = img[:, :, ::-1]
     img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
     img = torch.from_numpy(img).float()
-    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_esrgan)
+    img = img.unsqueeze(0).to(devices.device_esrgan)
     with torch.no_grad():
         output = model(img)
     output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/scunet_model.py b/modules/scunet_model.py
index 59532274..36a996bf 100644
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@@ -54,8 +54,9 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         img = img[:, :, ::-1]
         img = np.moveaxis(img, 2, 0) / 255
         img = torch.from_numpy(img).float()
-        img = devices.mps_contiguous_to(img.unsqueeze(0), device)
+        img = img.unsqueeze(0).to(device)
 
+        img = img.to(device)
         with torch.no_grad():
             output = model(img)
         output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index 4253b66d..facd262d 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -111,7 +111,7 @@ def upscale(
     img = img[:, :, ::-1]
     img = np.moveaxis(img, 2, 0) / 255
     img = torch.from_numpy(img).float()
-    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_swinir)
+    img = img.unsqueeze(0).to(devices.device_swinir)
     with torch.no_grad(), precision_scope("cuda"):
         _, _, h_old, w_old = img.size()
         h_pad = (h_old // window_size + 1) * window_size - h_old
-- 
cgit v1.2.1


From a5106a7cdc24153332e4eb1d28e66ea1d7f1ef79 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 7 Nov 2022 19:44:27 -0500
Subject: Remove extra .to(device)

---
 modules/scunet_model.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/scunet_model.py b/modules/scunet_model.py
index 36a996bf..52360241 100644
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@@ -56,7 +56,6 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         img = torch.from_numpy(img).float()
         img = img.unsqueeze(0).to(device)
 
-        img = img.to(device)
         with torch.no_grad():
             output = model(img)
         output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
-- 
cgit v1.2.1


From c8c40c8a643f2d20e3475e4d9ae7aae6d36c7e85 Mon Sep 17 00:00:00 2001
From: space-nuko <24979496+space-nuko@users.noreply.github.com>
Date: Thu, 17 Nov 2022 18:03:57 -0800
Subject: Add interrupt button to preprocessing

---
 modules/textual_inversion/ui.py |  2 +-
 modules/ui.py                   | 10 +++++++++-
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index d679e6f4..35c4feef 100644
--- a/modules/textual_inversion/ui.py
+++ b/modules/textual_inversion/ui.py
@@ -18,7 +18,7 @@ def create_embedding(name, initialization_text, nvpt, overwrite_old):
 def preprocess(*args):
     modules.textual_inversion.preprocess.preprocess(*args)
 
-    return "Preprocessing finished.", ""
+    return f"Preprocessing {'interrupted' if shared.state.interrupted else 'finished'}.", ""
 
 
 def train_embedding(*args):
diff --git a/modules/ui.py b/modules/ui.py
index 5dce7f3b..88e3c827 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1249,7 +1249,9 @@ def create_ui(wrap_gradio_gpu_call):
                             gr.HTML(value="")
 
                         with gr.Column():
-                            run_preprocess = gr.Button(value="Preprocess", variant='primary')
+                            with gr.Row():
+                                interrupt_preprocessing = gr.Button("Interrupt")
+                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                     process_split.change(
                         fn=lambda show: gr_show(show),
@@ -1422,6 +1424,12 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[],
         )
 
+        interrupt_preprocessing.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
     def create_setting_component(key, is_quicksettings=False):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
-- 
cgit v1.2.1


From 17e44328204a09653bb89eea18b7b489cc118703 Mon Sep 17 00:00:00 2001
From: killfrenzy96 <evan298@gmail.com>
Date: Fri, 18 Nov 2022 21:22:55 +1100
Subject: cleanly undo circular hijack #4818

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 97979d05..eaedac13 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -96,8 +96,8 @@ class StableDiffusionModelHijack:
         if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
             model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
 
+        self.apply_circular(False)
         self.layers = None
-        self.circular_enabled = False
         self.clip = None
 
     def apply_circular(self, enable):
-- 
cgit v1.2.1


From 694611cbd8151533cf18e01a8ead32771ea12ff3 Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sat, 19 Nov 2022 00:56:08 +0300
Subject: Apply suggestions from code review

Use last version of setup-python action
Remove unnecesarry multicommand from run
Remove current directory from artifact paths

Co-authored-by: Margen67 <Margen67@users.noreply.github.com>
---
 .github/workflows/run_tests.yaml | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml
index 4aeeab9c..6c6f15a8 100644
--- a/.github/workflows/run_tests.yaml
+++ b/.github/workflows/run_tests.yaml
@@ -11,7 +11,7 @@ jobs:
       - name: Checkout Code
         uses: actions/checkout@v3
       - name: Set up Python 3.10
-        uses: actions/setup-python@v3
+        uses: actions/setup-python@v4
         with:
           python-version: 3.10.6
       - uses: actions/cache@v2
@@ -21,13 +21,12 @@ jobs:
           restore-keys: |
             ${{ runner.os }}-pip-
       - name: Run tests
-        run: |
-          python launch.py --tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test --clip-models-path ./test/test_files/empty.pt
+        run: python launch.py --tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test --clip-models-path ./test/test_files/empty.pt
       - name: Upload main app stdout-stderr
         uses: actions/upload-artifact@v3
         if: always()
         with:
           name: stdout-stderr
           path: |
-            ./test/stdout.txt
-            ./test/stderr.txt
+            test/stdout.txt
+            test/stderr.txt
-- 
cgit v1.2.1


From 8ab4927452b04dcd30847eaf92ea7a9f3b9c74e1 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Wed, 2 Nov 2022 22:54:09 +0700
Subject: Fix model wasn't restored even when choosing "None"

---
 modules/sd_vae.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 71e7a6e6..7a79239f 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -50,8 +50,8 @@ def delete_base_vae():
 
 
 def restore_base_vae(model):
-    global base_vae, checkpoint_info
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
+        print("Restoring base VAE")
         load_vae_dict(model, base_vae)
     delete_base_vae()
 
@@ -143,6 +143,7 @@ def load_vae(model, vae_file=None):
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
         vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
         load_vae_dict(model, vae_dict_1)
+        store_base_vae(model)
 
         # If vae used is not in dict, update it
         # It will be removed on refresh though
@@ -150,6 +151,9 @@ def load_vae(model, vae_file=None):
         if vae_opt not in vae_dict:
             vae_dict[vae_opt] = vae_file
             vae_list.append(vae_opt)
+            # shared.opts.data['sd_vae'] = vae_opt
+    else:
+        restore_base_vae(model)
 
     loaded_vae_file = vae_file
 
@@ -166,12 +170,8 @@ def load_vae(model, vae_file=None):
 
 
 # don't call this from outside
-def load_vae_dict(model, vae_dict_1=None):
-    if vae_dict_1:
-        store_base_vae(model)
-        model.first_stage_model.load_state_dict(vae_dict_1)
-    else:
-        restore_base_vae()
+def load_vae_dict(model, vae_dict_1):
+    model.first_stage_model.load_state_dict(vae_dict_1)
     model.first_stage_model.to(devices.dtype_vae)
 
 
-- 
cgit v1.2.1


From abc1e79a5da24a1ea0f4bceedcdf225f32010aa8 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Thu, 3 Nov 2022 11:10:53 +0700
Subject: Fix base VAE caching was done after loading VAE, also add safeguard

---
 modules/sd_models.py |  1 +
 modules/sd_vae.py    | 19 ++++++++-----------
 2 files changed, 9 insertions(+), 11 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 80addf03..e4dba62c 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -220,6 +220,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    sd_vae.clear_loaded_vae()
     sd_vae.load_vae(model, vae_file)
 
 
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 7a79239f..dd69a5e6 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -15,7 +15,7 @@ vae_path = os.path.abspath(os.path.join(models_path, vae_dir))
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
 
 
-default_vae_dict = {"auto": "auto", "None": "None"}
+default_vae_dict = {"auto": "auto", "None": None, None: None}
 default_vae_list = ["auto", "None"]
 
 
@@ -39,6 +39,7 @@ def get_base_vae(model):
 def store_base_vae(model):
     global base_vae, checkpoint_info
     if checkpoint_info != model.sd_checkpoint_info:
+        assert not loaded_vae_file, "Trying to store non-base VAE!"
         base_vae = model.first_stage_model.state_dict().copy()
         checkpoint_info = model.sd_checkpoint_info
 
@@ -50,9 +51,11 @@ def delete_base_vae():
 
 
 def restore_base_vae(model):
+    global loaded_vae_file
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
         print("Restoring base VAE")
         load_vae_dict(model, base_vae)
+        loaded_vae_file = None
     delete_base_vae()
 
 
@@ -140,10 +143,10 @@ def load_vae(model, vae_file=None):
 
     if vae_file:
         print(f"Loading VAE weights from: {vae_file}")
+        store_base_vae(model)
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
         vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
         load_vae_dict(model, vae_dict_1)
-        store_base_vae(model)
 
         # If vae used is not in dict, update it
         # It will be removed on refresh though
@@ -157,15 +160,6 @@ def load_vae(model, vae_file=None):
 
     loaded_vae_file = vae_file
 
-    """
-    # Save current VAE to VAE settings, maybe? will it work?
-    if save_settings:
-        if vae_file is None:
-            vae_opt = "None"
-
-        # shared.opts.sd_vae = vae_opt
-    """
-
     first_load = False
 
 
@@ -174,6 +168,9 @@ def load_vae_dict(model, vae_dict_1):
     model.first_stage_model.load_state_dict(vae_dict_1)
     model.first_stage_model.to(devices.dtype_vae)
 
+def clear_loaded_vae():
+    global loaded_vae_file
+    loaded_vae_file = None
 
 def reload_vae_weights(sd_model=None, vae_file="auto"):
     from modules import lowvram, devices, sd_hijack
-- 
cgit v1.2.1


From c7be83bf0240498d9382e2afeaa3f0677d26c7f6 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 13 Nov 2022 11:11:14 +0700
Subject: Misc

Misc
---
 modules/sd_models.py | 1 +
 modules/sd_vae.py    | 3 +--
 modules/shared.py    | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index e4dba62c..cd7fe37a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -220,6 +220,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    sd_vae.delete_base_vae()
     sd_vae.clear_loaded_vae()
     sd_vae.load_vae(model, vae_file)
 
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index dd69a5e6..13bf3d31 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -154,8 +154,7 @@ def load_vae(model, vae_file=None):
         if vae_opt not in vae_dict:
             vae_dict[vae_opt] = vae_file
             vae_list.append(vae_opt)
-            # shared.opts.data['sd_vae'] = vae_opt
-    else:
+    elif loaded_vae_file:
         restore_base_vae(model)
 
     loaded_vae_file = vae_file
diff --git a/modules/shared.py b/modules/shared.py
index 17132e42..a9daf800 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -335,7 +335,7 @@ options_templates.update(options_section(('training', "Training"), {
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
+    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-- 
cgit v1.2.1


From 9fdc343dcaee70f1a0ff15c0cc668dbd487abc61 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Thu, 17 Nov 2022 18:04:10 +0700
Subject: Fix model caching requiring deepcopy

---
 modules/sd_vae.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 13bf3d31..5b4709b5 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -4,6 +4,7 @@ from collections import namedtuple
 from modules import shared, devices, script_callbacks
 from modules.paths import models_path
 import glob
+from copy import deepcopy
 
 
 model_dir = "Stable-diffusion"
@@ -40,7 +41,7 @@ def store_base_vae(model):
     global base_vae, checkpoint_info
     if checkpoint_info != model.sd_checkpoint_info:
         assert not loaded_vae_file, "Trying to store non-base VAE!"
-        base_vae = model.first_stage_model.state_dict().copy()
+        base_vae = deepcopy(model.first_stage_model.state_dict())
         checkpoint_info = model.sd_checkpoint_info
 
 
-- 
cgit v1.2.1


From 028b67b6357b5a00ccbd6ea72d2f244a6664162b Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sat, 19 Nov 2022 01:27:54 +0700
Subject: Use underscore naming for "private" functions in sd_vae

---
 modules/sd_vae.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 5b4709b5..d82a7bad 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -55,7 +55,7 @@ def restore_base_vae(model):
     global loaded_vae_file
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
         print("Restoring base VAE")
-        load_vae_dict(model, base_vae)
+        _load_vae_dict(model, base_vae)
         loaded_vae_file = None
     delete_base_vae()
 
@@ -147,7 +147,7 @@ def load_vae(model, vae_file=None):
         store_base_vae(model)
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
         vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-        load_vae_dict(model, vae_dict_1)
+        _load_vae_dict(model, vae_dict_1)
 
         # If vae used is not in dict, update it
         # It will be removed on refresh though
@@ -164,7 +164,7 @@ def load_vae(model, vae_file=None):
 
 
 # don't call this from outside
-def load_vae_dict(model, vae_dict_1):
+def _load_vae_dict(model, vae_dict_1):
     model.first_stage_model.load_state_dict(vae_dict_1)
     model.first_stage_model.to(devices.dtype_vae)
 
-- 
cgit v1.2.1


From 0663706d4405b4f76ce653097f4f8989ee8b8684 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Thu, 3 Nov 2022 13:47:03 +0700
Subject: Option to use selected VAE as default fallback instead of primary
 option

---
 modules/sd_vae.py | 25 ++++++++++++++++---------
 modules/shared.py |  1 +
 webui.py          |  1 +
 3 files changed, 18 insertions(+), 9 deletions(-)

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 71e7a6e6..0b5f0213 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -83,7 +83,19 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     return vae_list
 
 
-def resolve_vae(checkpoint_file, vae_file="auto"):
+def get_vae_from_settings(vae_file="auto"):
+    # else, we load from settings, if not set to be default
+    if vae_file == "auto" and shared.opts.sd_vae is not None:
+        # if saved VAE settings isn't recognized, fallback to auto
+        vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
+        # if VAE selected but not found, fallback to auto
+        if vae_file not in default_vae_values and not os.path.isfile(vae_file):
+            vae_file = "auto"
+            print("Selected VAE doesn't exist")
+    return vae_file
+
+
+def resolve_vae(checkpoint_file=None, vae_file="auto"):
     global first_load, vae_dict, vae_list
 
     # if vae_file argument is provided, it takes priority, but not saved
@@ -98,14 +110,9 @@ def resolve_vae(checkpoint_file, vae_file="auto"):
             shared.opts.data['sd_vae'] = get_filename(vae_file)
         else:
             print("VAE provided as command line argument doesn't exist")
-    # else, we load from settings
-    if vae_file == "auto" and shared.opts.sd_vae is not None:
-        # if saved VAE settings isn't recognized, fallback to auto
-        vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
-        # if VAE selected but not found, fallback to auto
-        if vae_file not in default_vae_values and not os.path.isfile(vae_file):
-            vae_file = "auto"
-            print("Selected VAE doesn't exist")
+    # fallback to selector in settings, if vae selector not set to act as default fallback
+    if not shared.opts.sd_vae_as_default:
+        vae_file = get_vae_from_settings(vae_file)
     # vae-path cmd arg takes priority for auto
     if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
         if os.path.isfile(shared.cmd_opts.vae_path):
diff --git a/modules/shared.py b/modules/shared.py
index 17132e42..b84767f0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -336,6 +336,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
+    "sd_vae_as_default": OptionInfo(False, "Use selected VAE as default fallback instead"),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
diff --git a/webui.py b/webui.py
index f4f1d74d..2cd3bae9 100644
--- a/webui.py
+++ b/webui.py
@@ -82,6 +82,7 @@ def initialize():
     modules.sd_models.load_model()
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
     shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
+    shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
 
-- 
cgit v1.2.1


From 2c5ca706a7e624d268545ba3318ba230b7b33477 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 13 Nov 2022 10:55:47 +0700
Subject: Remove no longer necessary parts and add vae_file safeguard

---
 modules/sd_models.py | 10 ++--------
 modules/sd_vae.py    |  1 +
 2 files changed, 3 insertions(+), 8 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 80addf03..c59151e0 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -165,16 +165,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
 
     cache_enabled = shared.opts.sd_checkpoint_cache > 0
 
-    if cache_enabled:
-        sd_vae.restore_base_vae(model)
-
-    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
-
     if cache_enabled and checkpoint_info in checkpoints_loaded:
         # use checkpoint cache
-        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
-        vae_message = f" with {vae_name} VAE" if vae_name else ""
-        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
+        print(f"Loading weights [{sd_model_hash}] from cache")
         model.load_state_dict(checkpoints_loaded[checkpoint_info])
     else:
         # load from file
@@ -220,6 +213,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
     sd_vae.load_vae(model, vae_file)
 
 
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 71e7a6e6..8bdb2c17 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -139,6 +139,7 @@ def load_vae(model, vae_file=None):
     # save_settings = False
 
     if vae_file:
+        assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
         print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
         vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-- 
cgit v1.2.1


From 271fd2d700a59e80d9dc9f23ad3ef08c988e8b24 Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 13 Nov 2022 10:58:15 +0700
Subject: More verbose messages

---
 modules/sd_vae.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 8bdb2c17..fa8de905 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -89,15 +89,15 @@ def resolve_vae(checkpoint_file, vae_file="auto"):
     # if vae_file argument is provided, it takes priority, but not saved
     if vae_file and vae_file not in default_vae_list:
         if not os.path.isfile(vae_file):
+            print(f"VAE provided as function argument doesn't exist: {vae_file}")
             vae_file = "auto"
-            print("VAE provided as function argument doesn't exist")
     # for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
     if first_load and shared.cmd_opts.vae_path is not None:
         if os.path.isfile(shared.cmd_opts.vae_path):
             vae_file = shared.cmd_opts.vae_path
             shared.opts.data['sd_vae'] = get_filename(vae_file)
         else:
-            print("VAE provided as command line argument doesn't exist")
+            print(f"VAE provided as command line argument doesn't exist: {vae_file}")
     # else, we load from settings
     if vae_file == "auto" and shared.opts.sd_vae is not None:
         # if saved VAE settings isn't recognized, fallback to auto
@@ -105,25 +105,25 @@ def resolve_vae(checkpoint_file, vae_file="auto"):
         # if VAE selected but not found, fallback to auto
         if vae_file not in default_vae_values and not os.path.isfile(vae_file):
             vae_file = "auto"
-            print("Selected VAE doesn't exist")
+            print(f"Selected VAE doesn't exist: {vae_file}")
     # vae-path cmd arg takes priority for auto
     if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
         if os.path.isfile(shared.cmd_opts.vae_path):
             vae_file = shared.cmd_opts.vae_path
-            print("Using VAE provided as command line argument")
+            print(f"Using VAE provided as command line argument: {vae_file}")
     # if still not found, try look for ".vae.pt" beside model
     model_path = os.path.splitext(checkpoint_file)[0]
     if vae_file == "auto":
         vae_file_try = model_path + ".vae.pt"
         if os.path.isfile(vae_file_try):
             vae_file = vae_file_try
-            print("Using VAE found beside selected model")
+            print(f"Using VAE found similar to selected model: {vae_file}")
     # if still not found, try look for ".vae.ckpt" beside model
     if vae_file == "auto":
         vae_file_try = model_path + ".vae.ckpt"
         if os.path.isfile(vae_file_try):
             vae_file = vae_file_try
-            print("Using VAE found beside selected model")
+            print(f"Using VAE found similar to selected model: {vae_file}")
     # No more fallbacks for auto
     if vae_file == "auto":
         vae_file = None
-- 
cgit v1.2.1


From c8f7b5cdd73969d3d5027ceb71cbbd83d557702b Mon Sep 17 00:00:00 2001
From: Muhammad Rizqi Nur <rizqinur2010@gmail.com>
Date: Sun, 13 Nov 2022 11:11:14 +0700
Subject: Misc

Misc
---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 17132e42..a9daf800 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -335,7 +335,7 @@ options_templates.update(options_section(('training', "Training"), {
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
+    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-- 
cgit v1.2.1


From d9fd4525a5d684100997130cc4132736bab1e4d9 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 11:09:44 +0300
Subject: change text for sd_vae_as_default that makes more sense to me

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 5528ab15..1c42641d 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -335,7 +335,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
-    "sd_vae_as_default": OptionInfo(False, "Use selected VAE as default fallback instead"),
+    "sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-- 
cgit v1.2.1


From cdc8020d13c5eef099c609b0a911ccf3568afc0d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 12:01:51 +0300
Subject: change StableDiffusionProcessing to internally use sampler name
 instead of sampler index

---
 modules/api/api.py                             | 26 ++++++++---------------
 modules/hypernetworks/hypernetwork.py          |  4 ++--
 modules/images.py                              |  2 +-
 modules/img2img.py                             |  4 ++--
 modules/processing.py                          | 29 +++++++++++---------------
 modules/sd_samplers.py                         | 13 +++++++++---
 modules/textual_inversion/textual_inversion.py |  4 ++--
 modules/txt2img.py                             |  3 ++-
 modules/ui.py                                  |  2 +-
 scripts/img2imgalt.py                          |  4 ++--
 scripts/xy_grid.py                             | 12 +++++------
 11 files changed, 49 insertions(+), 54 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 596a6616..0eccccbb 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -6,9 +6,9 @@ from threading import Lock
 from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
 from fastapi import APIRouter, Depends, FastAPI, HTTPException
 import modules.shared as shared
+from modules import sd_samplers
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers
 from modules.extras import run_extras, run_pnginfo
 from PIL import PngImagePlugin
 from modules.sd_models import checkpoints_list
@@ -25,8 +25,12 @@ def upscaler_to_index(name: str):
         raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
 
 
-sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
+def validate_sampler_name(name):
+    config = sd_samplers.all_samplers_map.get(name, None)
+    if config is None:
+        raise HTTPException(status_code=404, detail="Sampler not found")
 
+    return name
 
 def setUpscalers(req: dict):
     reqDict = vars(req)
@@ -82,14 +86,9 @@ class Api:
         self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
-        sampler_index = sampler_to_index(txt2imgreq.sampler_index)
-
-        if sampler_index is None:
-            raise HTTPException(status_code=404, detail="Sampler not found")
-
         populate = txt2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
-            "sampler_index": sampler_index[0],
+            "sampler_name": validate_sampler_name(txt2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True
             }
@@ -109,12 +108,6 @@ class Api:
         return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
 
     def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
-        sampler_index = sampler_to_index(img2imgreq.sampler_index)
-
-        if sampler_index is None:
-            raise HTTPException(status_code=404, detail="Sampler not found")
-
-
         init_images = img2imgreq.init_images
         if init_images is None:
             raise HTTPException(status_code=404, detail="Init image not found")
@@ -123,10 +116,9 @@ class Api:
         if mask:
             mask = decode_base64_to_image(mask)
 
-
         populate = img2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
-            "sampler_index": sampler_index[0],
+            "sampler_name": validate_sampler_name(img2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True,
             "mask": mask
@@ -272,7 +264,7 @@ class Api:
         return vars(shared.cmd_opts)
 
     def get_samplers(self):
-        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers]
+        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
 
     def get_upscalers(self):
         upscalers = []
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7f182712..fbb87dd1 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -12,7 +12,7 @@ import torch
 import tqdm
 from einops import rearrange, repeat
 from ldm.util import default
-from modules import devices, processing, sd_models, shared
+from modules import devices, processing, sd_models, shared, sd_samplers
 from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
@@ -535,7 +535,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
                 p.prompt = preview_prompt
                 p.negative_prompt = preview_negative_prompt
                 p.steps = preview_steps
-                p.sampler_index = preview_sampler_index
+                p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
                 p.cfg_scale = preview_cfg_scale
                 p.seed = preview_seed
                 p.width = preview_width
diff --git a/modules/images.py b/modules/images.py
index ae705cbd..26d5b7a9 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -303,7 +303,7 @@ class FilenameGenerator:
         'width': lambda self: self.image.width,
         'height': lambda self: self.image.height,
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
-        'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),
+        'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
         'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
         'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
diff --git a/modules/img2img.py b/modules/img2img.py
index be9f3653..9fc5b693 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -6,7 +6,7 @@ import traceback
 import numpy as np
 from PIL import Image, ImageOps, ImageChops
 
-from modules import devices
+from modules import devices, sd_samplers
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
 import modules.shared as shared
@@ -99,7 +99,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         seed_resize_from_h=seed_resize_from_h,
         seed_resize_from_w=seed_resize_from_w,
         seed_enable_extras=seed_enable_extras,
-        sampler_index=sampler_index,
+        sampler_index=sd_samplers.samplers_for_img2img[sampler_index].name,
         batch_size=batch_size,
         n_iter=n_iter,
         steps=steps,
diff --git a/modules/processing.py b/modules/processing.py
index 03c9143d..be2edf48 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -2,6 +2,7 @@ import json
 import math
 import os
 import sys
+import warnings
 
 import torch
 import numpy as np
@@ -66,19 +67,15 @@ def apply_overlay(image, paste_loc, index, overlays):
 
     return image
 
-def get_correct_sampler(p):
-    if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
-        return sd_samplers.samplers
-    elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
-        return sd_samplers.samplers_for_img2img
-    elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI):
-        return sd_samplers.samplers
 
 class StableDiffusionProcessing():
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
     """
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_index: int = 0, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, sampler_index: int = None):
+        if sampler_index is not None:
+            warnings.warn("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name")
+
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -91,7 +88,7 @@ class StableDiffusionProcessing():
         self.subseed_strength: float = subseed_strength
         self.seed_resize_from_h: int = seed_resize_from_h
         self.seed_resize_from_w: int = seed_resize_from_w
-        self.sampler_index: int = sampler_index
+        self.sampler_name: str = sampler_name
         self.batch_size: int = batch_size
         self.n_iter: int = n_iter
         self.steps: int = steps
@@ -210,8 +207,7 @@ class Processed:
         self.info = info
         self.width = p.width
         self.height = p.height
-        self.sampler_index = p.sampler_index
-        self.sampler = sd_samplers.samplers[p.sampler_index].name
+        self.sampler_name = p.sampler_name
         self.cfg_scale = p.cfg_scale
         self.steps = p.steps
         self.batch_size = p.batch_size
@@ -256,8 +252,7 @@ class Processed:
             "subseed_strength": self.subseed_strength,
             "width": self.width,
             "height": self.height,
-            "sampler_index": self.sampler_index,
-            "sampler": self.sampler,
+            "sampler_name": self.sampler_name,
             "cfg_scale": self.cfg_scale,
             "steps": self.steps,
             "batch_size": self.batch_size,
@@ -384,7 +379,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params = {
         "Steps": p.steps,
-        "Sampler": get_correct_sampler(p)[p.sampler_index].name,
+        "Sampler": p.sampler_name,
         "CFG scale": p.cfg_scale,
         "Seed": all_seeds[index],
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
@@ -645,7 +640,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
+        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
@@ -706,7 +701,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         shared.state.nextjob()
 
-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
+        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
@@ -743,7 +738,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.image_conditioning = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
+        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
         crop_region = None
 
         if self.image_mask is not None:
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 783992d2..4fe67854 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -46,16 +46,23 @@ all_samplers = [
     SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
     SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
 ]
+all_samplers_map = {x.name: x for x in all_samplers}
 
 samplers = []
 samplers_for_img2img = []
 
 
-def create_sampler_with_index(list_of_configs, index, model):
-    config = list_of_configs[index]
+def create_sampler(name, model):
+    if name is not None:
+        config = all_samplers_map.get(name, None)
+    else:
+        config = all_samplers[0]
+
+    assert config is not None, f'bad sampler name: {name}'
+
     sampler = config.constructor(model)
     sampler.config = config
-    
+
     return sampler
 
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 0aeb0459..5e4d8688 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -10,7 +10,7 @@ import csv
 
 from PIL import Image, PngImagePlugin
 
-from modules import shared, devices, sd_hijack, processing, sd_models, images
+from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -345,7 +345,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 p.prompt = preview_prompt
                 p.negative_prompt = preview_negative_prompt
                 p.steps = preview_steps
-                p.sampler_index = preview_sampler_index
+                p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
                 p.cfg_scale = preview_cfg_scale
                 p.seed = preview_seed
                 p.width = preview_width
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 8e4e8677..c8f81176 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -1,4 +1,5 @@
 import modules.scripts
+from modules import sd_samplers
 from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
     StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, cmd_opts
@@ -21,7 +22,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         seed_resize_from_h=seed_resize_from_h,
         seed_resize_from_w=seed_resize_from_w,
         seed_enable_extras=seed_enable_extras,
-        sampler_index=sampler_index,
+        sampler_name=sd_samplers.samplers[sampler_index].name,
         batch_size=batch_size,
         n_iter=n_iter,
         steps=steps,
diff --git a/modules/ui.py b/modules/ui.py
index 5dce7f3b..2d488741 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -142,7 +142,7 @@ def save_files(js_data, images, do_make_zip, index):
                 filenames.append(os.path.basename(txt_fullfn))
                 fullfns.append(txt_fullfn)
 
-        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
 
     # Make Zip
     if do_make_zip:
diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py
index 964b75c7..1229f61b 100644
--- a/scripts/img2imgalt.py
+++ b/scripts/img2imgalt.py
@@ -157,7 +157,7 @@ class Script(scripts.Script):
     def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
         # Override
         if override_sampler:
-            p.sampler_index = [sampler.name for sampler in sd_samplers.samplers].index("Euler")
+            p.sampler_name = "Euler"
         if override_prompt:
             p.prompt = original_prompt
             p.negative_prompt = original_negative_prompt
@@ -191,7 +191,7 @@ class Script(scripts.Script):
             
             combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
             
-            sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)
+            sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
 
             sigmas = sampler.model_wrap.get_sigmas(p.steps)
             
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 417ed0d4..b0b9d84d 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -10,9 +10,9 @@ import numpy as np
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import images
+from modules import images, sd_samplers
 from modules.hypernetworks import hypernetwork
-from modules.processing import process_images, Processed, get_correct_sampler, StableDiffusionProcessingTxt2Img
+from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.sd_samplers
@@ -60,9 +60,9 @@ def apply_order(p, x, xs):
     p.prompt = prompt_tmp + p.prompt
     
 
-def build_samplers_dict(p):
+def build_samplers_dict():
     samplers_dict = {}
-    for i, sampler in enumerate(get_correct_sampler(p)):
+    for i, sampler in enumerate(sd_samplers.all_samplers):
         samplers_dict[sampler.name.lower()] = i
         for alias in sampler.aliases:
             samplers_dict[alias.lower()] = i
@@ -70,7 +70,7 @@ def build_samplers_dict(p):
 
 
 def apply_sampler(p, x, xs):
-    sampler_index = build_samplers_dict(p).get(x.lower(), None)
+    sampler_index = build_samplers_dict().get(x.lower(), None)
     if sampler_index is None:
         raise RuntimeError(f"Unknown sampler: {x}")
 
@@ -78,7 +78,7 @@ def apply_sampler(p, x, xs):
 
 
 def confirm_samplers(p, xs):
-    samplers_dict = build_samplers_dict(p)
+    samplers_dict = build_samplers_dict()
     for x in xs:
         if x.lower() not in samplers_dict.keys():
             raise RuntimeError(f"Unknown sampler: {x}")
-- 
cgit v1.2.1


From 0d702930b068ca8da8eb0117613053a480d9439e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 12:47:52 +0300
Subject: renamed Inpainting strength infotext to Conditional mask weight, made
 it only appear if using inpainting model, made it possible to read the
 setting from it using the blue arrow button

---
 modules/generation_parameters_copypaste.py | 1 +
 modules/processing.py                      | 9 ++++++++-
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 985ec95e..1408ea05 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -73,6 +73,7 @@ def integrate_settings_paste_fields(component_dict):
         'sd_hypernetwork': 'Hypernet',
         'sd_hypernetwork_strength': 'Hypernet strength',
         'CLIP_stop_at_last_layers': 'Clip skip',
+        'inpainting_mask_weight': 'Conditional mask weight',
         'sd_model_checkpoint': 'Model hash',
     }
     settings_paste_fields = [
diff --git a/modules/processing.py b/modules/processing.py
index fb30aa81..def95846 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -113,6 +113,7 @@ class StableDiffusionProcessing():
         self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
         self.s_noise = s_noise or opts.s_noise
         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
+        self.is_using_inpainting_conditioning = False
 
         if not seed_enable_extras:
             self.subseed = -1
@@ -133,6 +134,8 @@ class StableDiffusionProcessing():
             # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
             return x.new_zeros(x.shape[0], 5, 1, 1)
 
+        self.is_using_inpainting_conditioning = True
+
         height = height or self.height
         width = width or self.width
 
@@ -151,6 +154,8 @@ class StableDiffusionProcessing():
             # Dummy zero conditioning if we're not using inpainting model.
             return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
 
+        self.is_using_inpainting_conditioning = True
+
         # Handle the different mask inputs
         if image_mask is not None:
             if torch.is_tensor(image_mask):
@@ -234,6 +239,7 @@ class Processed:
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
         self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
         self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
+        self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
 
         self.all_prompts = all_prompts or [self.prompt]
         self.all_seeds = all_seeds or [self.seed]
@@ -268,6 +274,7 @@ class Processed:
             "styles": self.styles,
             "job_timestamp": self.job_timestamp,
             "clip_skip": self.clip_skip,
+            "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
         }
 
         return json.dumps(obj)
@@ -394,7 +401,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
-        "Inpainting strength": (None if getattr(p, 'denoising_strength', None) is None else getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)),
+        "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
-- 
cgit v1.2.1


From 617c5b486f42aa73062ee7699ee1147eb995c899 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 13:23:25 +0300
Subject: make it possible for StableDiffusionProcessing to accept multiple
 different negative prompts in a batch

---
 modules/processing.py | 46 ++++++++++++++++++++++++----------------------
 modules/styles.py     | 11 -----------
 2 files changed, 24 insertions(+), 33 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 604d822a..bc7e5311 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -124,6 +124,7 @@ class StableDiffusionProcessing():
         self.scripts = None
         self.script_args = None
         self.all_prompts = None
+        self.all_negative_prompts = None
         self.all_seeds = None
         self.all_subseeds = None
 
@@ -202,7 +203,7 @@ class StableDiffusionProcessing():
 
 
 class Processed:
-    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
+    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
         self.images = images_list
         self.prompt = p.prompt
         self.negative_prompt = p.negative_prompt
@@ -241,16 +242,18 @@ class Processed:
         self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
         self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
 
-        self.all_prompts = all_prompts or [self.prompt]
-        self.all_seeds = all_seeds or [self.seed]
-        self.all_subseeds = all_subseeds or [self.subseed]
+        self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
+        self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
+        self.all_seeds = all_seeds or p.all_seeds or [self.seed]
+        self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
         self.infotexts = infotexts or [info]
 
     def js(self):
         obj = {
-            "prompt": self.prompt,
+            "prompt": self.all_prompts[0],
             "all_prompts": self.all_prompts,
-            "negative_prompt": self.negative_prompt,
+            "negative_prompt": self.all_negative_prompts[0],
+            "all_negative_prompts": self.all_negative_prompts,
             "seed": self.seed,
             "all_seeds": self.all_seeds,
             "subseed": self.subseed,
@@ -411,7 +414,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
-    negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
+    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[0] if  p.all_negative_prompts[0] else ""
 
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
@@ -440,10 +443,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     else:
         assert p.prompt is not None
 
-    with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
-        processed = Processed(p, [], p.seed, "")
-        file.write(processed.infotext(p, 0))
-
     devices.torch_gc()
 
     seed = get_fixed_seed(p.seed)
@@ -453,15 +452,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     modules.sd_hijack.model_hijack.clear_comments()
 
     comments = {}
-    prompt_tmp = p.prompt
-    negative_prompt_tmp = p.negative_prompt
-
-    shared.prompt_styles.apply_styles(p)
 
     if type(p.prompt) == list:
-        p.all_prompts = p.prompt
+        p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
+    else:
+        p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]
+
+    if type(p.negative_prompt) == list:
+        p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
     else:
-        p.all_prompts = p.batch_size * p.n_iter * [p.prompt]
+        p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
 
     if type(seed) == list:
         p.all_seeds = seed
@@ -476,6 +476,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     def infotext(iteration=0, position_in_batch=0):
         return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
 
+    with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
+        processed = Processed(p, [], p.seed, "")
+        file.write(processed.infotext(p, 0))
+
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
@@ -500,6 +504,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 break
 
             prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+            negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
             seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
             subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
@@ -510,7 +515,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
 
             with devices.autocast():
-                uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
+                uc = prompt_parser.get_learned_conditioning(shared.sd_model, negative_prompts, p.steps)
                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
 
             if len(model_hijack.comments) > 0:
@@ -596,14 +601,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     devices.torch_gc()
 
-    res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+    res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
 
     if p.scripts is not None:
         p.scripts.postprocess(p, res)
 
-    p.prompt = prompt_tmp
-    p.negative_prompt = negative_prompt_tmp
-
     return res
 
 
diff --git a/modules/styles.py b/modules/styles.py
index 3bf5c5b6..ce6e71ca 100644
--- a/modules/styles.py
+++ b/modules/styles.py
@@ -65,17 +65,6 @@ class StyleDatabase:
     def apply_negative_styles_to_prompt(self, prompt, styles):
         return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])
 
-    def apply_styles(self, p: StableDiffusionProcessing) -> None:
-        if isinstance(p.prompt, list):
-            p.prompt = [self.apply_styles_to_prompt(prompt, p.styles) for prompt in p.prompt]
-        else:
-            p.prompt = self.apply_styles_to_prompt(p.prompt, p.styles)
-
-        if isinstance(p.negative_prompt, list):
-            p.negative_prompt = [self.apply_negative_styles_to_prompt(prompt, p.styles) for prompt in p.negative_prompt]
-        else:
-            p.negative_prompt = self.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)
-
     def save_styles(self, path: str) -> None:
         # Write to temporary file first, so we don't nuke the file if something goes wrong
         fd, temp_path = tempfile.mkstemp(".csv")
-- 
cgit v1.2.1


From 413c077969d35bc90a8b3218ab0db7e35c8c46f2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 13:47:37 +0300
Subject: prevent StableDiffusionProcessingImg2Img changing image_mask field as
 an alternative solution to #4765

---
 modules/processing.py | 33 ++++++++++++++++-----------------
 1 file changed, 16 insertions(+), 17 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index bc7e5311..accb31d1 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -740,7 +740,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.denoising_strength: float = denoising_strength
         self.init_latent = None
         self.image_mask = mask
-        #self.image_unblurred_mask = None
         self.latent_mask = None
         self.mask_for_overlay = None
         self.mask_blur = mask_blur
@@ -756,36 +755,36 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
         crop_region = None
 
-        if self.image_mask is not None:
-            self.image_mask = self.image_mask.convert('L')
+        image_mask = self.image_mask
 
-            if self.inpainting_mask_invert:
-                self.image_mask = ImageOps.invert(self.image_mask)
+        if image_mask is not None:
+            image_mask = image_mask.convert('L')
 
-            #self.image_unblurred_mask = self.image_mask
+            if self.inpainting_mask_invert:
+                image_mask = ImageOps.invert(image_mask)
 
             if self.mask_blur > 0:
-                self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
+                image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
 
             if self.inpaint_full_res:
-                self.mask_for_overlay = self.image_mask
-                mask = self.image_mask.convert('L')
+                self.mask_for_overlay = image_mask
+                mask = image_mask.convert('L')
                 crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
                 crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
                 x1, y1, x2, y2 = crop_region
 
                 mask = mask.crop(crop_region)
-                self.image_mask = images.resize_image(2, mask, self.width, self.height)
+                image_mask = images.resize_image(2, mask, self.width, self.height)
                 self.paste_to = (x1, y1, x2-x1, y2-y1)
             else:
-                self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
-                np_mask = np.array(self.image_mask)
+                image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
+                np_mask = np.array(image_mask)
                 np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
                 self.mask_for_overlay = Image.fromarray(np_mask)
 
             self.overlay_images = []
 
-        latent_mask = self.latent_mask if self.latent_mask is not None else self.image_mask
+        latent_mask = self.latent_mask if self.latent_mask is not None else image_mask
 
         add_color_corrections = opts.img2img_color_correction and self.color_corrections is None
         if add_color_corrections:
@@ -797,7 +796,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             if crop_region is None:
                 image = images.resize_image(self.resize_mode, image, self.width, self.height)
 
-            if self.image_mask is not None:
+            if image_mask is not None:
                 image_masked = Image.new('RGBa', (image.width, image.height))
                 image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
 
@@ -807,7 +806,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
                 image = image.crop(crop_region)
                 image = images.resize_image(2, image, self.width, self.height)
 
-            if self.image_mask is not None:
+            if image_mask is not None:
                 if self.inpainting_fill != 1:
                     image = masking.fill(image, latent_mask)
 
@@ -839,7 +838,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
         self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
 
-        if self.image_mask is not None:
+        if image_mask is not None:
             init_mask = latent_mask
             latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
             latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
@@ -856,7 +855,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
+        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-- 
cgit v1.2.1


From 14dfede8ddbf6b82bb290d5c4292d52b6b68e3ca Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sat, 19 Nov 2022 14:15:10 +0300
Subject: Minor fixes

Remove unused test completely
Change job name
Don't use empty.pt as CLIP weights - it wont work.
Use latest version of actions/cache
---
 .github/workflows/run_tests.yaml  |  9 ++++-----
 test/basic_features/utils_test.py | 14 --------------
 2 files changed, 4 insertions(+), 19 deletions(-)

diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml
index 6c6f15a8..49dc92bd 100644
--- a/.github/workflows/run_tests.yaml
+++ b/.github/workflows/run_tests.yaml
@@ -1,4 +1,4 @@
-name: Run tests on CPU with empty model
+name: Run basic features tests on CPU with empty SD model
 
 on:
   - push
@@ -14,14 +14,13 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.10.6
-      - uses: actions/cache@v2
+      - uses: actions/cache@v3
         with:
           path: ~/.cache/pip
           key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
-          restore-keys: |
-            ${{ runner.os }}-pip-
+          restore-keys: ${{ runner.os }}-pip-
       - name: Run tests
-        run: python launch.py --tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test --clip-models-path ./test/test_files/empty.pt
+        run: python launch.py --tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
       - name: Upload main app stdout-stderr
         uses: actions/upload-artifact@v3
         if: always()
diff --git a/test/basic_features/utils_test.py b/test/basic_features/utils_test.py
index 9706db8b..765470c9 100644
--- a/test/basic_features/utils_test.py
+++ b/test/basic_features/utils_test.py
@@ -18,20 +18,6 @@ class UtilsTests(unittest.TestCase):
   def test_options_get(self):
     self.assertEqual(requests.get(self.url_options).status_code, 200)
 
-  # def test_options_write(self):
-  #   response = requests.get(self.url_options)
-  #   self.assertEqual(response.status_code, 200)
-    
-  #   pre_value = response.json()["send_seed"]
-
-  #   self.assertEqual(requests.post(self.url_options, json={"send_seed":not pre_value}).status_code, 200)
-
-  #   response = requests.get(self.url_options)
-  #   self.assertEqual(response.status_code, 200)
-  #   self.assertEqual(response.json()["send_seed"], not pre_value)
-
-  #   requests.post(self.url_options, json={"send_seed": pre_value})
-
   def test_cmd_flags(self):
     self.assertEqual(requests.get(self.url_cmd_flags).status_code, 200)
 
-- 
cgit v1.2.1


From 5a6387e189dc365c47a7979b9040d5b6fdd7ba43 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 15:15:24 +0300
Subject: make it possible to change models etc by editing options using API

---
 modules/api/api.py |  7 +++----
 modules/shared.py  | 17 +++++++++++++++++
 modules/ui.py      | 22 ++++------------------
 3 files changed, 24 insertions(+), 22 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index bf700ed0..1e324d8d 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -253,9 +253,8 @@ class Api:
         return options
 
     def set_config(self, req: Dict[str, Any]):
-       
-        for o in req:
-            setattr(shared.opts, o, req[o])
+        for k, v in req.items():
+            shared.opts.set(k, v)
 
         shared.opts.save(shared.config_filename)
         return
@@ -264,7 +263,7 @@ class Api:
         return vars(shared.cmd_opts)
 
     def get_samplers(self):
-        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
+        return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
 
     def get_upscalers(self):
         upscalers = []
diff --git a/modules/shared.py b/modules/shared.py
index 84567c8e..58f53e54 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -437,6 +437,23 @@ class Options:
 
         return super(Options, self).__getattribute__(item)
 
+    def set(self, key, value):
+        """sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
+
+        oldval = self.data.get(key, None)
+        if oldval == value:
+            return False
+
+        try:
+            setattr(self, key, value)
+        except RuntimeError:
+            return False
+
+        if self.data_labels[key].onchange is not None:
+            self.data_labels[key].onchange()
+
+        return True
+
     def save(self, filename):
         assert not cmd_opts.freeze_settings, "saving settings is disabled"
 
diff --git a/modules/ui.py b/modules/ui.py
index 5dd97754..bb090c62 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1484,16 +1484,9 @@ def create_ui(wrap_gradio_gpu_call):
             if comp == dummy_component:
                 continue
 
-            oldval = opts.data.get(key, None)
-            try:
-                setattr(opts, key, value)
-            except RuntimeError:
-                continue
-            if oldval != value:
-                if opts.data_labels[key].onchange is not None:
-                    opts.data_labels[key].onchange()
-
+            if opts.set(key, value):
                 changed.append(key)
+
         try:
             opts.save(shared.config_filename)
         except RuntimeError:
@@ -1504,15 +1497,8 @@ def create_ui(wrap_gradio_gpu_call):
         if not opts.same_type(value, opts.data_labels[key].default):
             return gr.update(visible=True), opts.dumpjson()
 
-        oldval = opts.data.get(key, None)
-        try:
-            setattr(opts, key, value)
-        except Exception:
-            return gr.update(value=oldval), opts.dumpjson()
-
-        if oldval != value:
-            if opts.data_labels[key].onchange is not None:
-                opts.data_labels[key].onchange()
+        if not opts.set(key, value):
+            return gr.update(value=getattr(opts, key)), opts.dumpjson()
 
         opts.save(shared.config_filename)
 
-- 
cgit v1.2.1


From 3596af07493ab7981ef92074f979eeee8fa624c4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 19:10:17 +0300
Subject: Add API for scripts to add elements anywhere in UI.

---
 modules/script_callbacks.py | 35 +++++++++++++++++++++++
 modules/scripts.py          | 69 +++++++++++++++++++++++++++++++++++++++++++--
 modules/ui.py               | 12 ++++++--
 3 files changed, 111 insertions(+), 5 deletions(-)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index f19e164c..8e22f875 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -61,6 +61,8 @@ callback_map = dict(
     callbacks_before_image_saved=[],
     callbacks_image_saved=[],
     callbacks_cfg_denoiser=[],
+    callbacks_before_component=[],
+    callbacks_after_component=[],
 )
 
 
@@ -137,6 +139,22 @@ def cfg_denoiser_callback(params: CFGDenoiserParams):
             report_exception(c, 'cfg_denoiser_callback')
 
 
+def before_component_callback(component, **kwargs):
+    for c in callback_map['callbacks_before_component']:
+        try:
+            c.callback(component, **kwargs)
+        except Exception:
+            report_exception(c, 'before_component_callback')
+
+
+def after_component_callback(component, **kwargs):
+    for c in callback_map['callbacks_after_component']:
+        try:
+            c.callback(component, **kwargs)
+        except Exception:
+            report_exception(c, 'after_component_callback')
+
+
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -220,3 +238,20 @@ def on_cfg_denoiser(callback):
         - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
     """
     add_callback(callback_map['callbacks_cfg_denoiser'], callback)
+
+
+def on_before_component(callback):
+    """register a function to be called before a component is created.
+    The callback is called with arguments:
+        - component - gradio component that is about to be created.
+        - **kwargs - args to gradio.components.IOComponent.__init__ function
+
+    Use elem_id/label fields of kwargs to figure out which component it is.
+    This can be useful to inject your own components somewhere in the middle of vanilla UI.
+    """
+    add_callback(callback_map['callbacks_before_component'], callback)
+
+
+def on_after_component(callback):
+    """register a function to be called after a component is created. See on_before_component for more."""
+    add_callback(callback_map['callbacks_after_component'], callback)
diff --git a/modules/scripts.py b/modules/scripts.py
index 986b1914..b934d881 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -17,6 +17,9 @@ class Script:
     args_to = None
     alwayson = False
 
+    is_txt2img = False
+    is_img2img = False
+
     """A gr.Group component that has all script's UI inside it"""
     group = None
 
@@ -93,6 +96,23 @@ class Script:
 
         pass
 
+    def before_component(self, component, **kwargs):
+        """
+        Called before a component is created.
+        Use elem_id/label fields of kwargs to figure out which component it is.
+        This can be useful to inject your own components somewhere in the middle of vanilla UI.
+        You can return created components in the ui() function to add them to the list of arguments for your processing functions
+        """
+
+        pass
+
+    def after_component(self, component, **kwargs):
+        """
+        Called after a component is created. Same as above.
+        """
+
+        pass
+
     def describe(self):
         """unused"""
         return ""
@@ -195,12 +215,18 @@ class ScriptRunner:
         self.titles = []
         self.infotext_fields = []
 
-    def setup_ui(self, is_img2img):
+    def initialize_scripts(self, is_img2img):
+        self.scripts.clear()
+        self.alwayson_scripts.clear()
+        self.selectable_scripts.clear()
+
         for script_class, path, basedir in scripts_data:
             script = script_class()
             script.filename = path
+            script.is_txt2img = not is_img2img
+            script.is_img2img = is_img2img
 
-            visibility = script.show(is_img2img)
+            visibility = script.show(script.is_img2img)
 
             if visibility == AlwaysVisible:
                 self.scripts.append(script)
@@ -211,6 +237,7 @@ class ScriptRunner:
                 self.scripts.append(script)
                 self.selectable_scripts.append(script)
 
+    def setup_ui(self):
         self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
 
         inputs = [None]
@@ -220,7 +247,7 @@ class ScriptRunner:
             script.args_from = len(inputs)
             script.args_to = len(inputs)
 
-            controls = wrap_call(script.ui, script.filename, "ui", is_img2img)
+            controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
 
             if controls is None:
                 return
@@ -320,6 +347,22 @@ class ScriptRunner:
                 print(f"Error running postprocess: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
+    def before_component(self, component, **kwargs):
+        for script in self.scripts:
+            try:
+                script.before_component(component, **kwargs)
+            except Exception:
+                print(f"Error running before_component: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
+    def after_component(self, component, **kwargs):
+        for script in self.scripts:
+            try:
+                script.after_component(component, **kwargs)
+            except Exception:
+                print(f"Error running after_component: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
     def reload_sources(self, cache):
         for si, script in list(enumerate(self.scripts)):
             args_from = script.args_from
@@ -341,6 +384,7 @@ class ScriptRunner:
 
 scripts_txt2img = ScriptRunner()
 scripts_img2img = ScriptRunner()
+scripts_current: ScriptRunner = None
 
 
 def reload_script_body_only():
@@ -357,3 +401,22 @@ def reload_scripts():
     scripts_txt2img = ScriptRunner()
     scripts_img2img = ScriptRunner()
 
+
+def IOComponent_init(self, *args, **kwargs):
+    if scripts_current is not None:
+        scripts_current.before_component(self, **kwargs)
+
+    script_callbacks.before_component_callback(self, **kwargs)
+
+    res = original_IOComponent_init(self, *args, **kwargs)
+
+    script_callbacks.after_component_callback(self, **kwargs)
+
+    if scripts_current is not None:
+        scripts_current.after_component(self, **kwargs)
+
+    return res
+
+
+original_IOComponent_init = gr.components.IOComponent.__init__
+gr.components.IOComponent.__init__ = IOComponent_init
diff --git a/modules/ui.py b/modules/ui.py
index bb090c62..a5953fce 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -695,6 +695,9 @@ def create_ui(wrap_gradio_gpu_call):
 
     parameters_copypaste.reset()
 
+    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
+    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
@@ -737,7 +740,7 @@ def create_ui(wrap_gradio_gpu_call):
                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
 
                 with gr.Group():
-                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
+                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
 
             txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
             parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
@@ -846,6 +849,9 @@ def create_ui(wrap_gradio_gpu_call):
 
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
+    modules.scripts.scripts_current = modules.scripts.scripts_img2img
+    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
 
@@ -916,7 +922,7 @@ def create_ui(wrap_gradio_gpu_call):
                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
 
                 with gr.Group():
-                    custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
+                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()
 
             img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
             parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
@@ -1065,6 +1071,8 @@ def create_ui(wrap_gradio_gpu_call):
             parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
             parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
 
+    modules.scripts.scripts_current = None
+
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
-- 
cgit v1.2.1


From 47a44c7e421b98ca07e92dbf88769b04c9e28f86 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 19 Nov 2022 21:06:58 +0300
Subject: revert change to webui-user.bat

---
 webui-user.bat | 1 -
 1 file changed, 1 deletion(-)

diff --git a/webui-user.bat b/webui-user.bat
index 0c120111..e5a257be 100644
--- a/webui-user.bat
+++ b/webui-user.bat
@@ -4,6 +4,5 @@ set PYTHON=
 set GIT=
 set VENV_DIR=
 set COMMANDLINE_ARGS=
-set ACCELERATE=
 
 call webui.bat
-- 
cgit v1.2.1


From ac7ecd2d847bf4e3a9503db0f2a291e32b82302c Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Sat, 19 Nov 2022 14:49:22 -0500
Subject: Label and load SD .safetensors model files

---
 README.md              |  1 +
 modules/modelloader.py |  1 +
 modules/sd_models.py   | 24 ++++++++++++++++--------
 requirements.txt       |  1 +
 4 files changed, 19 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 33508f31..ba9f3952 100644
--- a/README.md
+++ b/README.md
@@ -84,6 +84,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - API
 - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. 
 - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
+- Can use safetensors to safely load model files without python pickle
 
 ## Where are Aesthetic Gradients?!?!
 Aesthetic Gradients are now an extension. You can install it using git:
diff --git a/modules/modelloader.py b/modules/modelloader.py
index e4a6f8ac..7d2f0ade 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -82,6 +82,7 @@ def cleanup_models():
     src_path = models_path
     dest_path = os.path.join(models_path, "Stable-diffusion")
     move_files(src_path, dest_path, ".ckpt")
+    move_files(src_path, dest_path, ".safetensors")
     src_path = os.path.join(root_path, "ESRGAN")
     dest_path = os.path.join(models_path, "ESRGAN")
     move_files(src_path, dest_path)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index c59151e0..4ccdf30b 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,6 +4,7 @@ import sys
 import gc
 from collections import namedtuple
 import torch
+from safetensors.torch import load_file
 import re
 from omegaconf import OmegaConf
 
@@ -16,9 +17,10 @@ from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inp
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config', 'exttype'])
 checkpoints_list = {}
 checkpoints_loaded = collections.OrderedDict()
+checkpoint_types = {'.ckpt':'pickle','.safetensors':'safetensors'}
 
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -45,7 +47,7 @@ def checkpoint_tiles():
 
 def list_models():
     checkpoints_list.clear()
-    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"])
+    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt",".safetensors"])
 
     def modeltitle(path, shorthash):
         abspath = os.path.abspath(path)
@@ -60,15 +62,15 @@ def list_models():
         if name.startswith("\\") or name.startswith("/"):
             name = name[1:]
 
-        shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
+        shortname, ext = os.path.splitext(name.replace("/", "_").replace("\\", "_"))
 
-        return f'{name} [{shorthash}]', shortname
+        return f'{name} [{checkpoint_types[ext]}] [{shorthash}]', shortname
 
     cmd_ckpt = shared.cmd_opts.ckpt
     if os.path.exists(cmd_ckpt):
         h = model_hash(cmd_ckpt)
         title, short_model_name = modeltitle(cmd_ckpt, h)
-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config, '')
         shared.opts.data['sd_model_checkpoint'] = title
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
@@ -76,12 +78,12 @@ def list_models():
         h = model_hash(filename)
         title, short_model_name = modeltitle(filename, h)
 
-        basename, _ = os.path.splitext(filename)
+        basename, ext = os.path.splitext(filename)
         config = basename + ".yaml"
         if not os.path.exists(config):
             config = shared.cmd_opts.config
 
-        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
+        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config, ext)
 
 
 def get_closet_checkpoint_match(searchString):
@@ -173,7 +175,13 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-        pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+        if(checkpoint_types[checkpoint_info.exttype] == 'safetensors'):
+            # safely load weights
+            # TODO: safetensors supports zero copy fast load to gpu, see issue #684
+            pl_sd = load_file(checkpoint_file, device=shared.weight_load_location)
+        else:
+            pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+
         if "global_step" in pl_sd:
             print(f"Global Step: {pl_sd['global_step']}")
 
diff --git a/requirements.txt b/requirements.txt
index 762db4f3..f7de9f70 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -28,3 +28,4 @@ kornia
 lark
 inflection
 GitPython
+safetensors
-- 
cgit v1.2.1


From bd68e35de3b7cf7547ed97d8bdf60147402133cc Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Sun, 20 Nov 2022 12:35:26 +0900
Subject: Gradient accumulation, autocast fix, new latent sampling method, etc

---
 modules/hypernetworks/hypernetwork.py          | 269 +++++++++++----------
 modules/sd_hijack.py                           |   9 +-
 modules/sd_hijack_checkpoint.py                |  10 +
 modules/shared.py                              |   3 +-
 modules/textual_inversion/dataset.py           | 134 +++++++----
 modules/textual_inversion/textual_inversion.py | 320 ++++++++++++++-----------
 modules/ui.py                                  |  16 +-
 7 files changed, 448 insertions(+), 313 deletions(-)
 create mode 100644 modules/sd_hijack_checkpoint.py

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index fbb87dd1..3d3301b0 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -367,13 +367,13 @@ def report_statistics(loss_info:dict):
 
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
     save_hypernetwork_every = save_hypernetwork_every or 0
     create_image_every = create_image_every or 0
-    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
+    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
     shared.loaded_hypernetwork = Hypernetwork()
@@ -403,28 +403,24 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     hypernetwork = shared.loaded_hypernetwork
     checkpoint = sd_models.select_checkpoint()
 
-    ititial_step = hypernetwork.step or 0
-    if ititial_step >= steps:
+    initial_step = hypernetwork.step or 0
+    if initial_step >= steps:
         shared.state.textinfo = f"Model has already been trained beyond specified max steps"
         return hypernetwork, filename
 
-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    
+    scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
+
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
-    with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
+
+    pin_memory = shared.opts.pin_memory
+
+    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
+    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, batch_size=ds.batch_size, pin_memory=pin_memory)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
         shared.sd_model.first_stage_model.to(devices.cpu)
-
-    size = len(ds.indexes)
-    loss_dict = defaultdict(lambda : deque(maxlen = 1024))
-    losses = torch.zeros((size,))
-    previous_mean_losses = [0]
-    previous_mean_loss = 0
-    print("Mean loss of {} elements".format(size))
     
     weights = hypernetwork.weights()
     for weight in weights:
@@ -436,8 +432,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         optimizer_name = hypernetwork.optimizer_name
     else:
         print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
-        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
-        optimizer_name = 'AdamW'
+    optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
+    optimizer_name = 'AdamW'
 
     if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
         try:
@@ -446,131 +442,155 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             print("Cannot resume from saved optimizer!")
             print(e)
 
+    scaler = torch.cuda.amp.GradScaler()
+    
+    batch_size = ds.batch_size
+    gradient_step = ds.gradient_step
+    # n steps = batch_size * gradient_step * n image processed
+    steps_per_epoch = len(ds) // batch_size // gradient_step
+    max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step
+    loss_step = 0
+    _loss_step = 0 #internal
+    # size = len(ds.indexes)
+    # loss_dict = defaultdict(lambda : deque(maxlen = 1024))
+    # losses = torch.zeros((size,))
+    # previous_mean_losses = [0]
+    # previous_mean_loss = 0
+    # print("Mean loss of {} elements".format(size))
+
     steps_without_grad = 0
 
     last_saved_file = "<none>"
     last_saved_image = "<none>"
     forced_filename = "<none>"
 
-    pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, entries in pbar:
-        hypernetwork.step = i + ititial_step
-        if len(loss_dict) > 0:
-            previous_mean_losses = [i[-1] for i in loss_dict.values()]
-            previous_mean_loss = mean(previous_mean_losses)
-            
-        scheduler.apply(optimizer, hypernetwork.step)
-        if scheduler.finished:
-            break
-
-        if shared.state.interrupted:
-            break
-
-        with torch.autocast("cuda"):
-            c = stack_conds([entry.cond for entry in entries]).to(devices.device)
-            # c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
-            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-            loss = shared.sd_model(x, c)[0]
-            del x
-            del c
-
-            losses[hypernetwork.step % losses.shape[0]] = loss.item()
-            for entry in entries:
-                loss_dict[entry.filename].append(loss.item())
+    pbar = tqdm.tqdm(total=steps - initial_step)
+    try:
+        for i in range((steps-initial_step) * gradient_step):
+            if scheduler.finished:
+                break
+            if shared.state.interrupted:
+                break
+            for j, batch in enumerate(dl):
+                # works as a drop_last=True for gradient accumulation
+                if j == max_steps_per_epoch:
+                    break
+                scheduler.apply(optimizer, hypernetwork.step)
+                if scheduler.finished:
+                    break
+                if shared.state.interrupted:
+                    break
+
+                with torch.autocast("cuda"):
+                    x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
+                    if tag_drop_out != 0 or shuffle_tags:
+                        shared.sd_model.cond_stage_model.to(devices.device)
+                        c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory)
+                        shared.sd_model.cond_stage_model.to(devices.cpu)
+                    else:
+                        c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)
+                    loss = shared.sd_model(x, c)[0] / gradient_step
+                    del x
+                    del c
+
+                    _loss_step += loss.item()
+                scaler.scale(loss).backward()
+                # go back until we reach gradient accumulation steps
+                if (j + 1) % gradient_step != 0:
+                    continue
+                # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.7f}")
+                # scaler.unscale_(optimizer)
+                # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.15f}")
+                # torch.nn.utils.clip_grad_norm_(weights, max_norm=1.0)
+                # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.15f}")
+                scaler.step(optimizer)
+                scaler.update()
+                hypernetwork.step += 1
+                pbar.update()
+                optimizer.zero_grad(set_to_none=True)
+                loss_step = _loss_step
+                _loss_step = 0
+
+                steps_done = hypernetwork.step + 1
                 
-            optimizer.zero_grad()
-            weights[0].grad = None
-            loss.backward()
-
-            if weights[0].grad is None:
-                steps_without_grad += 1
-            else:
-                steps_without_grad = 0
-            assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
-
-            optimizer.step()
-
-        steps_done = hypernetwork.step + 1
-
-        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
-            raise RuntimeError("Loss diverged.")
-        
-        if len(previous_mean_losses) > 1:
-            std = stdev(previous_mean_losses)
-        else:
-            std = 0
-        dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
-        pbar.set_description(dataset_loss_info)
-
-        if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
-            # Before saving, change name to match current checkpoint.
-            hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
-            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
-            hypernetwork.optimizer_name = optimizer_name
-            if shared.opts.save_optimizer_state:
-                hypernetwork.optimizer_state_dict = optimizer.state_dict()
-            save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
-            hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
-
-        textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
-            "loss": f"{previous_mean_loss:.7f}",
-            "learn_rate": scheduler.learn_rate
-        })
-
-        if images_dir is not None and steps_done % create_image_every == 0:
-            forced_filename = f'{hypernetwork_name}-{steps_done}'
-            last_saved_image = os.path.join(images_dir, forced_filename)
-
-            optimizer.zero_grad()
-            shared.sd_model.cond_stage_model.to(devices.device)
-            shared.sd_model.first_stage_model.to(devices.device)
-
-            p = processing.StableDiffusionProcessingTxt2Img(
-                sd_model=shared.sd_model,
-                do_not_save_grid=True,
-                do_not_save_samples=True,
-            )
-
-            if preview_from_txt2img:
-                p.prompt = preview_prompt
-                p.negative_prompt = preview_negative_prompt
-                p.steps = preview_steps
-                p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
-                p.cfg_scale = preview_cfg_scale
-                p.seed = preview_seed
-                p.width = preview_width
-                p.height = preview_height
-            else:
-                p.prompt = entries[0].cond_text
-                p.steps = 20
+                epoch_num = hypernetwork.step // steps_per_epoch
+                epoch_step = hypernetwork.step % steps_per_epoch
+
+                pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
+                if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
+                    # Before saving, change name to match current checkpoint.
+                    hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
+                    last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
+                    hypernetwork.optimizer_name = optimizer_name
+                    if shared.opts.save_optimizer_state:
+                        hypernetwork.optimizer_state_dict = optimizer.state_dict()
+                    save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
+                    hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
+
+                textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, steps_per_epoch, {
+                    "loss": f"{loss_step:.7f}",
+                    "learn_rate": scheduler.learn_rate
+                })
+
+                if images_dir is not None and steps_done % create_image_every == 0:
+                    forced_filename = f'{hypernetwork_name}-{steps_done}'
+                    last_saved_image = os.path.join(images_dir, forced_filename)
+
+                    shared.sd_model.cond_stage_model.to(devices.device)
+                    shared.sd_model.first_stage_model.to(devices.device)
+
+                    p = processing.StableDiffusionProcessingTxt2Img(
+                        sd_model=shared.sd_model,
+                        do_not_save_grid=True,
+                        do_not_save_samples=True,
+                    )
+
+                    if preview_from_txt2img:
+                        p.prompt = preview_prompt
+                        p.negative_prompt = preview_negative_prompt
+                        p.steps = preview_steps
+                        p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
+                        p.cfg_scale = preview_cfg_scale
+                        p.seed = preview_seed
+                        p.width = preview_width
+                        p.height = preview_height
+                    else:
+                        p.prompt = batch.cond_text[0]
+                        p.steps = 20
+                        p.width = training_width
+                        p.height = training_height
 
-            preview_text = p.prompt
+                    preview_text = p.prompt
 
-            processed = processing.process_images(p)
-            image = processed.images[0] if len(processed.images)>0 else None
+                    processed = processing.process_images(p)
+                    image = processed.images[0] if len(processed.images) > 0 else None
 
-            if unload:
-                shared.sd_model.cond_stage_model.to(devices.cpu)
-                shared.sd_model.first_stage_model.to(devices.cpu)
+                    if unload:
+                        shared.sd_model.cond_stage_model.to(devices.cpu)
+                        shared.sd_model.first_stage_model.to(devices.cpu)
 
-            if image is not None:
-                shared.state.current_image = image
-                last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
-                last_saved_image += f", prompt: {preview_text}"
+                    if image is not None:
+                        shared.state.current_image = image
+                        last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                        last_saved_image += f", prompt: {preview_text}"
 
-        shared.state.job_no = hypernetwork.step
+                shared.state.job_no = hypernetwork.step
 
-        shared.state.textinfo = f"""
+                shared.state.textinfo = f"""
 <p>
-Loss: {previous_mean_loss:.7f}<br/>
+Loss: {loss_step:.7f}<br/>
 Step: {hypernetwork.step}<br/>
-Last prompt: {html.escape(entries[0].cond_text)}<br/>
+Last prompt: {html.escape(batch.cond_text[0])}<br/>
 Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-        
-    report_statistics(loss_dict)
+    except Exception:
+        print(traceback.format_exc(), file=sys.stderr)
+    finally:
+        pbar.leave = False
+        pbar.close()
+        #report_statistics(loss_dict)
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
     hypernetwork.optimizer_name = optimizer_name
@@ -579,6 +599,9 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
     del optimizer
     hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.
+    shared.sd_model.cond_stage_model.to(devices.device)
+    shared.sd_model.first_stage_model.to(devices.device)
+
     return hypernetwork, filename
 
 def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index eaedac13..29c8b561 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -8,7 +8,7 @@ from torch import einsum
 from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
-from modules import prompt_parser, devices, sd_hijack_optimizations, shared
+from modules import prompt_parser, devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.shared import opts, device, cmd_opts
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
@@ -59,6 +59,10 @@ def undo_optimizations():
 def get_target_prompt_token_count(token_count):
     return math.ceil(max(token_count, 1) / 75) * 75
 
+def fix_checkpoint():
+    ldm.modules.attention.BasicTransformerBlock.forward = sd_hijack_checkpoint.BasicTransformerBlock_forward
+    ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = sd_hijack_checkpoint.ResBlock_forward
+    ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = sd_hijack_checkpoint.AttentionBlock_forward
 
 class StableDiffusionModelHijack:
     fixes = None
@@ -78,6 +82,7 @@ class StableDiffusionModelHijack:
         self.clip = m.cond_stage_model
 
         apply_optimizations()
+        fix_checkpoint()
 
         def flatten(el):
             flattened = [flatten(children) for children in el.children()]
@@ -303,7 +308,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
         else:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
-
+        
         self.hijack.comments += hijack_comments
 
         if len(used_custom_terms) > 0:
diff --git a/modules/sd_hijack_checkpoint.py b/modules/sd_hijack_checkpoint.py
new file mode 100644
index 00000000..5712972f
--- /dev/null
+++ b/modules/sd_hijack_checkpoint.py
@@ -0,0 +1,10 @@
+from torch.utils.checkpoint import checkpoint
+
+def BasicTransformerBlock_forward(self, x, context=None):
+    return checkpoint(self._forward, x, context)
+
+def AttentionBlock_forward(self, x):
+    return checkpoint(self._forward, x)
+
+def ResBlock_forward(self, x, emb):
+    return checkpoint(self._forward, x, emb)
\ No newline at end of file
diff --git a/modules/shared.py b/modules/shared.py
index a4457305..3704ce23 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -322,8 +322,7 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
-    "shuffle_tags": OptionInfo(False, "Shuffleing tags by ',' when create texts."),
-    "tag_drop_out": OptionInfo(0, "Dropout tags when create texts", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.1}),
+    "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
     "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index eb75c376..d594b49d 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -3,7 +3,7 @@ import numpy as np
 import PIL
 import torch
 from PIL import Image
-from torch.utils.data import Dataset
+from torch.utils.data import Dataset, DataLoader
 from torchvision import transforms
 
 import random
@@ -11,25 +11,28 @@ import tqdm
 from modules import devices, shared
 import re
 
+from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+
 re_numbers_at_start = re.compile(r"^[-\d]+\s*")
 
 
 class DatasetEntry:
-    def __init__(self, filename=None, latent=None, filename_text=None):
+    def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None):
         self.filename = filename
-        self.latent = latent
         self.filename_text = filename_text
-        self.cond = None
-        self.cond_text = None
+        self.latent_dist = latent_dist
+        self.latent_sample = latent_sample
+        self.cond = cond
+        self.cond_text = cond_text
+        self.pixel_values = pixel_values
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'):        
         re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
-
+        
         self.placeholder_token = placeholder_token
 
-        self.batch_size = batch_size
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
@@ -45,11 +48,16 @@ class PersonalizedBase(Dataset):
         assert os.path.isdir(data_root), "Dataset directory doesn't exist"
         assert os.listdir(data_root), "Dataset directory is empty"
 
-        cond_model = shared.sd_model.cond_stage_model
-
         self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
+
+        
+        self.shuffle_tags = shuffle_tags
+        self.tag_drop_out = tag_drop_out
+
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
+            if shared.state.interrupted:
+                raise Exception("inturrupted")
             try:
                 image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
             except Exception:
@@ -71,37 +79,58 @@ class PersonalizedBase(Dataset):
             npimage = np.array(image).astype(np.uint8)
             npimage = (npimage / 127.5 - 1.0).astype(np.float32)
 
-            torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32)
-            torchdata = torch.moveaxis(torchdata, 2, 0)
-
-            init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
-            init_latent = init_latent.to(devices.cpu)
-
-            entry = DatasetEntry(filename=path, filename_text=filename_text, latent=init_latent)
-
-            if include_cond:
+            torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
+            latent_sample = None
+
+            with torch.autocast("cuda"):
+                latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
+
+            if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):
+                latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
+                latent_sampling_method = "once"
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
+            elif latent_sampling_method == "deterministic":
+                # Works only for DiagonalGaussianDistribution
+                latent_dist.std = 0
+                latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
+            elif latent_sampling_method == "random":
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)
+
+            if not (self.tag_drop_out != 0 or self.shuffle_tags):
                 entry.cond_text = self.create_text(filename_text)
-                entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
 
-            self.dataset.append(entry)
-
-        assert len(self.dataset) > 0, "No images have been found in the dataset."
-        self.length = len(self.dataset) * repeats // batch_size
+            if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
+                with torch.autocast("cuda"):
+                    entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
+            # elif not include_cond:
+            #     _, _, _, _, hijack_fixes, token_count = cond_model.process_text([entry.cond_text])
+            #     max_n = token_count // 75
+            #     index_list = [ [] for _ in range(max_n + 1) ]
+            #     for n, (z, _) in hijack_fixes[0]:
+            #         index_list[n].append(z)
+            #     with torch.autocast("cuda"):
+            #         entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
+            #     entry.emb_index = index_list
 
-        self.dataset_length = len(self.dataset)
-        self.indexes = None
-        self.shuffle()
+            self.dataset.append(entry)
+            del torchdata
+            del latent_dist
+            del latent_sample
 
-    def shuffle(self):
-        self.indexes = np.random.permutation(self.dataset_length)
+        self.length = len(self.dataset)
+        assert self.length > 0, "No images have been found in the dataset."
+        self.batch_size = min(batch_size, self.length)
+        self.gradient_step = min(gradient_step, self.length // self.batch_size)
+        self.latent_sampling_method = latent_sampling_method
 
     def create_text(self, filename_text):
         text = random.choice(self.lines)
         text = text.replace("[name]", self.placeholder_token)
         tags = filename_text.split(',')
-        if shared.opts.tag_drop_out != 0:
-            tags = [t for t in tags if random.random() > shared.opts.tag_drop_out]
-        if shared.opts.shuffle_tags:
+        if self.tag_drop_out != 0:
+            tags = [t for t in tags if random.random() > self.tag_drop_out]
+        if self.shuffle_tags:
             random.shuffle(tags)
         text = text.replace("[filewords]", ','.join(tags))
         return text
@@ -110,19 +139,28 @@ class PersonalizedBase(Dataset):
         return self.length
 
     def __getitem__(self, i):
-        res = []
-
-        for j in range(self.batch_size):
-            position = i * self.batch_size + j
-            if position % len(self.indexes) == 0:
-                self.shuffle()
-
-            index = self.indexes[position % len(self.indexes)]
-            entry = self.dataset[index]
-
-            if entry.cond is None:
-                entry.cond_text = self.create_text(entry.filename_text)
-
-            res.append(entry)
-
-        return res
+        entry = self.dataset[i]
+        if self.tag_drop_out != 0 or self.shuffle_tags:
+            entry.cond_text = self.create_text(entry.filename_text)
+        if self.latent_sampling_method == "random":
+            entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist)
+        return entry
+
+class PersonalizedDataLoader(DataLoader):
+    def __init__(self, *args, **kwargs):
+        super(PersonalizedDataLoader, self).__init__(shuffle=True, drop_last=True, *args, **kwargs)
+        self.collate_fn = collate_wrapper
+        
+
+class BatchLoader:
+    def __init__(self, data):
+        self.cond_text = [entry.cond_text for entry in data]
+        self.cond = [entry.cond for entry in data]
+        self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
+
+    def pin_memory(self):
+        self.latent_sample = self.latent_sample.pin_memory()
+        return self
+
+def collate_wrapper(batch):
+    return BatchLoader(batch)
\ No newline at end of file
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 5e4d8688..1d5e3a32 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -184,7 +184,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
     if shared.opts.training_write_csv_every == 0:
         return
 
-    if (step + 1) % shared.opts.training_write_csv_every != 0:
+    if step % shared.opts.training_write_csv_every != 0:
         return
     write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
 
@@ -194,21 +194,23 @@ def write_loss(log_directory, filename, step, epoch_len, values):
         if write_csv_header:
             csv_writer.writeheader()
 
-        epoch = step // epoch_len
-        epoch_step = step % epoch_len 
+        epoch = (step - 1) // epoch_len
+        epoch_step = (step - 1) % epoch_len 
 
         csv_writer.writerow({
-            "step": step + 1,
+            "step": step,
             "epoch": epoch,
-            "epoch_step": epoch_step + 1,
+            "epoch_step": epoch_step,
             **values,
         })
 
-def validate_train_inputs(model_name, learn_rate, batch_size, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):
+def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):
     assert model_name, f"{name} not selected"
     assert learn_rate, "Learning rate is empty or 0"
     assert isinstance(batch_size, int), "Batch size must be integer"
     assert batch_size > 0, "Batch size must be positive"
+    assert isinstance(gradient_step, int), "Gradient accumulation step must be integer"
+    assert gradient_step > 0, "Gradient accumulation step must be positive"
     assert data_root, "Dataset directory is empty"
     assert os.path.isdir(data_root), "Dataset directory doesn't exist"
     assert os.listdir(data_root), "Dataset directory is empty"
@@ -224,10 +226,10 @@ def validate_train_inputs(model_name, learn_rate, batch_size, data_root, templat
     if save_model_every or create_image_every:
         assert log_directory, "Log directory is empty"
 
-def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
-    validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
+    validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
 
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
@@ -255,161 +257,205 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     else:
         images_embeds_dir = None
 
-    cond_model = shared.sd_model.cond_stage_model
-
     hijack = sd_hijack.model_hijack
 
     embedding = hijack.embedding_db.word_embeddings[embedding_name]
     checkpoint = sd_models.select_checkpoint()
 
-    ititial_step = embedding.step or 0
-    if ititial_step >= steps:
+    initial_step = embedding.step or 0
+    if initial_step >= steps:
         shared.state.textinfo = f"Model has already been trained beyond specified max steps"
         return embedding, filename
+    scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
 
-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-
-    # dataset loading may take a while, so input validations and early returns should be done before this
+   # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
-    with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
+    
+    pin_memory = shared.opts.pin_memory
+    
+    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
+
+    latent_sampling_method = ds.latent_sampling_method
+
+    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, batch_size=ds.batch_size, pin_memory=False)
+
     if unload:
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     embedding.vec.requires_grad = True
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
+    scaler = torch.cuda.amp.GradScaler()
 
-    losses = torch.zeros((32,))
+    batch_size = ds.batch_size
+    gradient_step = ds.gradient_step
+    # n steps = batch_size * gradient_step * n image processed
+    steps_per_epoch = len(ds) // batch_size // gradient_step
+    max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step
+    loss_step = 0
+    _loss_step = 0 #internal
 
+    
     last_saved_file = "<none>"
     last_saved_image = "<none>"
     forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
-
-    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-    for i, entries in pbar:
-        embedding.step = i + ititial_step
-
-        scheduler.apply(optimizer, embedding.step)
-        if scheduler.finished:
-            break
-
-        if shared.state.interrupted:
-            break
-
-        with torch.autocast("cuda"):
-            c = cond_model([entry.cond_text for entry in entries])
-            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-            loss = shared.sd_model(x, c)[0]
-            del x
-
-            losses[embedding.step % losses.shape[0]] = loss.item()
-
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-        steps_done = embedding.step + 1
-
-        epoch_num = embedding.step // len(ds)
-        epoch_step = embedding.step % len(ds)
-
-        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
-
-        if embedding_dir is not None and steps_done % save_embedding_every == 0:
-            # Before saving, change name to match current checkpoint.
-            embedding_name_every = f'{embedding_name}-{steps_done}'
-            last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
-            save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
-            embedding_yet_to_be_embedded = True
-
-        write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
-            "loss": f"{losses.mean():.7f}",
-            "learn_rate": scheduler.learn_rate
-        })
-
-        if images_dir is not None and steps_done % create_image_every == 0:
-            forced_filename = f'{embedding_name}-{steps_done}'
-            last_saved_image = os.path.join(images_dir, forced_filename)
-
-            shared.sd_model.first_stage_model.to(devices.device)
-
-            p = processing.StableDiffusionProcessingTxt2Img(
-                sd_model=shared.sd_model,
-                do_not_save_grid=True,
-                do_not_save_samples=True,
-                do_not_reload_embeddings=True,
-            )
-
-            if preview_from_txt2img:
-                p.prompt = preview_prompt
-                p.negative_prompt = preview_negative_prompt
-                p.steps = preview_steps
-                p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
-                p.cfg_scale = preview_cfg_scale
-                p.seed = preview_seed
-                p.width = preview_width
-                p.height = preview_height
-            else:
-                p.prompt = entries[0].cond_text
-                p.steps = 20
-                p.width = training_width
-                p.height = training_height
-
-            preview_text = p.prompt
-
-            processed = processing.process_images(p)
-            image = processed.images[0]
-
-            if unload:
-                shared.sd_model.first_stage_model.to(devices.cpu)
-
-            shared.state.current_image = image
-
-            if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
-
-                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
-
-                info = PngImagePlugin.PngInfo()
-                data = torch.load(last_saved_file)
-                info.add_text("sd-ti-embedding", embedding_to_b64(data))
-
-                title = "<{}>".format(data.get('name', '???'))
-
-                try:
-                    vectorSize = list(data['string_to_param'].values())[0].shape[0]
-                except Exception as e:
-                    vectorSize = '?'
-
-                checkpoint = sd_models.select_checkpoint()
-                footer_left = checkpoint.model_name
-                footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '{}v {}s'.format(vectorSize, steps_done)
-
-                captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
-                captioned_image = insert_image_data_embed(captioned_image, data)
-
-                captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
-                embedding_yet_to_be_embedded = False
-
-            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
-            last_saved_image += f", prompt: {preview_text}"
-
-        shared.state.job_no = embedding.step
-
-        shared.state.textinfo = f"""
+    
+    pbar = tqdm.tqdm(total=steps - initial_step)
+    try:
+        for i in range((steps-initial_step) * gradient_step):
+            if scheduler.finished:
+                break
+            if shared.state.interrupted:
+                break
+            for j, batch in enumerate(dl):
+                # works as a drop_last=True for gradient accumulation
+                if j == max_steps_per_epoch:
+                    break
+                scheduler.apply(optimizer, embedding.step)
+                if scheduler.finished:
+                    break
+                if shared.state.interrupted:
+                    break
+
+                with torch.autocast("cuda"):
+                    # c = stack_conds(batch.cond).to(devices.device)
+                    # mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)
+                    # print(mask)
+                    # c[:, 1:1+embedding.vec.shape[0]] = embedding.vec.to(devices.device, non_blocking=pin_memory)
+                    x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
+                    c = shared.sd_model.cond_stage_model(batch.cond_text)
+                    loss = shared.sd_model(x, c)[0] / gradient_step
+                    del x
+                    
+                    _loss_step += loss.item()
+                scaler.scale(loss).backward()
+                
+                # go back until we reach gradient accumulation steps
+                if (j + 1) % gradient_step != 0:
+                    continue
+                #print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
+                #scaler.unscale_(optimizer)
+                #print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
+                #torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=1.0)
+                #print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
+                scaler.step(optimizer)
+                scaler.update()
+                embedding.step += 1
+                pbar.update()
+                optimizer.zero_grad(set_to_none=True)
+                loss_step = _loss_step
+                _loss_step = 0
+
+                steps_done = embedding.step + 1
+
+                epoch_num = embedding.step // steps_per_epoch
+                epoch_step = embedding.step % steps_per_epoch
+
+                pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
+                if embedding_dir is not None and steps_done % save_embedding_every == 0:
+                    # Before saving, change name to match current checkpoint.
+                    embedding_name_every = f'{embedding_name}-{steps_done}'
+                    last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
+                    #if shared.opts.save_optimizer_state:
+                        #embedding.optimizer_state_dict = optimizer.state_dict()
+                    save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
+                    embedding_yet_to_be_embedded = True
+
+                write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, steps_per_epoch, {
+                    "loss": f"{loss_step:.7f}",
+                    "learn_rate": scheduler.learn_rate
+                })
+
+                if images_dir is not None and steps_done % create_image_every == 0:
+                    forced_filename = f'{embedding_name}-{steps_done}'
+                    last_saved_image = os.path.join(images_dir, forced_filename)
+
+                    shared.sd_model.first_stage_model.to(devices.device)
+
+                    p = processing.StableDiffusionProcessingTxt2Img(
+                        sd_model=shared.sd_model,
+                        do_not_save_grid=True,
+                        do_not_save_samples=True,
+                        do_not_reload_embeddings=True,
+                    )
+
+                    if preview_from_txt2img:
+                        p.prompt = preview_prompt
+                        p.negative_prompt = preview_negative_prompt
+                        p.steps = preview_steps
+                        p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
+                        p.cfg_scale = preview_cfg_scale
+                        p.seed = preview_seed
+                        p.width = preview_width
+                        p.height = preview_height
+                    else:
+                        p.prompt = batch.cond_text[0]
+                        p.steps = 20
+                        p.width = training_width
+                        p.height = training_height
+
+                    preview_text = p.prompt
+
+                    processed = processing.process_images(p)
+                    image = processed.images[0] if len(processed.images) > 0 else None
+
+                    if unload:
+                        shared.sd_model.first_stage_model.to(devices.cpu)
+
+                    if image is not None:
+                        shared.state.current_image = image
+                        last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                        last_saved_image += f", prompt: {preview_text}"
+
+                    if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
+
+                        last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
+
+                        info = PngImagePlugin.PngInfo()
+                        data = torch.load(last_saved_file)
+                        info.add_text("sd-ti-embedding", embedding_to_b64(data))
+
+                        title = "<{}>".format(data.get('name', '???'))
+
+                        try:
+                            vectorSize = list(data['string_to_param'].values())[0].shape[0]
+                        except Exception as e:
+                            vectorSize = '?'
+
+                        checkpoint = sd_models.select_checkpoint()
+                        footer_left = checkpoint.model_name
+                        footer_mid = '[{}]'.format(checkpoint.hash)
+                        footer_right = '{}v {}s'.format(vectorSize, steps_done)
+
+                        captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
+                        captioned_image = insert_image_data_embed(captioned_image, data)
+
+                        captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+                        embedding_yet_to_be_embedded = False
+
+                    last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+                    last_saved_image += f", prompt: {preview_text}"
+
+                shared.state.job_no = embedding.step
+
+                shared.state.textinfo = f"""
 <p>
-Loss: {losses.mean():.7f}<br/>
+Loss: {loss_step:.7f}<br/>
 Step: {embedding.step}<br/>
-Last prompt: {html.escape(entries[0].cond_text)}<br/>
+Last prompt: {html.escape(batch.cond_text[0])}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
-
-    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
-    save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
-    shared.sd_model.first_stage_model.to(devices.device)
+        filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
+        save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
+    except Exception:
+        print(traceback.format_exc(), file=sys.stderr)
+        pass
+    finally:
+        pbar.leave = False
+        pbar.close()
+        shared.sd_model.first_stage_model.to(devices.device)
 
     return embedding, filename
 
diff --git a/modules/ui.py b/modules/ui.py
index a5953fce..9d2a1cbf 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1262,7 +1262,7 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             with gr.Row():
                                 interrupt_preprocessing = gr.Button("Interrupt")
-                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
+                            run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                     process_split.change(
                         fn=lambda show: gr_show(show),
@@ -1289,6 +1289,7 @@ def create_ui(wrap_gradio_gpu_call):
                         hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
 
                     batch_size = gr.Number(label='Batch size', value=1, precision=0)
+                    gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
@@ -1299,6 +1300,11 @@ def create_ui(wrap_gradio_gpu_call):
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
                     preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
+                    with gr.Row():
+                        shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False)
+                        tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0)
+                    with gr.Row():
+                        latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
 
                     with gr.Row():
                         interrupt_training = gr.Button(value="Interrupt")
@@ -1387,11 +1393,15 @@ def create_ui(wrap_gradio_gpu_call):
                 train_embedding_name,
                 embedding_learn_rate,
                 batch_size,
+                gradient_step,
                 dataset_directory,
                 log_directory,
                 training_width,
                 training_height,
                 steps,
+                shuffle_tags,
+                tag_drop_out,
+                latent_sampling_method,
                 create_image_every,
                 save_embedding_every,
                 template_file,
@@ -1412,11 +1422,15 @@ def create_ui(wrap_gradio_gpu_call):
                 train_hypernetwork_name,
                 hypernetwork_learn_rate,
                 batch_size,
+                gradient_step,
                 dataset_directory,
                 log_directory,
                 training_width,
                 training_height,
                 steps,
+                shuffle_tags,
+                tag_drop_out,
+                latent_sampling_method,
                 create_image_every,
                 save_embedding_every,
                 template_file,
-- 
cgit v1.2.1


From a4a5735d0a80218e59f8a6e8401726f7209a6a8d Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Sun, 20 Nov 2022 12:38:18 +0900
Subject: remove unnecessary comment

---
 modules/textual_inversion/dataset.py | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index d594b49d..1dd53b85 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -103,15 +103,6 @@ class PersonalizedBase(Dataset):
             if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
                 with torch.autocast("cuda"):
                     entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
-            # elif not include_cond:
-            #     _, _, _, _, hijack_fixes, token_count = cond_model.process_text([entry.cond_text])
-            #     max_n = token_count // 75
-            #     index_list = [ [] for _ in range(max_n + 1) ]
-            #     for n, (z, _) in hijack_fixes[0]:
-            #         index_list[n].append(z)
-            #     with torch.autocast("cuda"):
-            #         entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
-            #     entry.emb_index = index_list
 
             self.dataset.append(entry)
             del torchdata
-- 
cgit v1.2.1


From 2d22d72cdaaf2b78b2986b841d478c11ac855dd2 Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Sun, 20 Nov 2022 16:14:27 +0900
Subject: fix random sampling with pin_memory

---
 modules/textual_inversion/dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 1dd53b85..110c0e09 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -134,7 +134,7 @@ class PersonalizedBase(Dataset):
         if self.tag_drop_out != 0 or self.shuffle_tags:
             entry.cond_text = self.create_text(entry.filename_text)
         if self.latent_sampling_method == "random":
-            entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist)
+            entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist).to(devices.cpu)
         return entry
 
 class PersonalizedDataLoader(DataLoader):
-- 
cgit v1.2.1


From c81d440d876dfd2ab3560410f37442ef56fc6632 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 20 Nov 2022 16:39:20 +0300
Subject: moved deepdanbooru to pure pytorch implementation

---
 README.md                               |   2 +-
 launch.py                               |   5 -
 modules/api/api.py                      |  10 +-
 modules/deepbooru.py                    | 258 +++++-------
 modules/deepbooru_model.py              | 676 ++++++++++++++++++++++++++++++++
 modules/shared.py                       |   2 +-
 modules/textual_inversion/preprocess.py |  12 +-
 modules/ui.py                           |   7 +-
 8 files changed, 777 insertions(+), 195 deletions(-)
 create mode 100644 modules/deepbooru_model.py

diff --git a/README.md b/README.md
index 33508f31..5f5ab3aa 100644
--- a/README.md
+++ b/README.md
@@ -70,7 +70,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
      - separate prompts using uppercase `AND`
      - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
-- DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
+- DeepDanbooru integration, creates danbooru style tags for anime prompts
 - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
 - via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
 - Generate forever option
diff --git a/launch.py b/launch.py
index 0f84b5d1..d2f1055c 100644
--- a/launch.py
+++ b/launch.py
@@ -134,7 +134,6 @@ def prepare_enviroment():
 
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
     clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
-    deepdanbooru_package = os.environ.get('DEEPDANBOORU_PACKAGE', "git+https://github.com/KichangKim/DeepDanbooru.git@d91a2963bf87c6a770d74894667e9ffa9f6de7ff")
 
     xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
 
@@ -158,7 +157,6 @@ def prepare_enviroment():
     sys.argv, update_check = extract_arg(sys.argv, '--update-check')
     sys.argv, run_tests = extract_arg(sys.argv, '--tests')
     xformers = '--xformers' in sys.argv
-    deepdanbooru = '--deepdanbooru' in sys.argv
     ngrok = '--ngrok' in sys.argv
 
     try:
@@ -193,9 +191,6 @@ def prepare_enviroment():
         elif platform.system() == "Linux":
             run_pip("install xformers", "xformers")
 
-    if not is_installed("deepdanbooru") and deepdanbooru:
-        run_pip(f"install {deepdanbooru_package}#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
-
     if not is_installed("pyngrok") and ngrok:
         run_pip("install pyngrok", "ngrok")
 
diff --git a/modules/api/api.py b/modules/api/api.py
index 79b2c818..7a567be3 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -9,7 +9,7 @@ from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers
+from modules import sd_samplers, deepbooru
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.extras import run_extras, run_pnginfo
@@ -18,9 +18,6 @@ from modules.sd_models import checkpoints_list
 from modules.realesrgan_model import get_realesrgan_models
 from typing import List
 
-if shared.cmd_opts.deepdanbooru:
-    from modules.deepbooru import get_deepbooru_tags
-
 def upscaler_to_index(name: str):
     try:
         return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
@@ -245,10 +242,7 @@ class Api:
             if interrogatereq.model == "clip":
                 processed = shared.interrogator.interrogate(img)
             elif interrogatereq.model == "deepdanbooru":
-                if shared.cmd_opts.deepdanbooru:
-                    processed = get_deepbooru_tags(img)
-                else:
-                    raise HTTPException(status_code=404, detail="Model not found. Add --deepdanbooru when launching for using the model.")
+                processed = deepbooru.model.tag(img)
             else:
                 raise HTTPException(status_code=404, detail="Model not found")
         
diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 8bbc90a4..b9066d81 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -1,173 +1,97 @@
-import os.path
-from concurrent.futures import ProcessPoolExecutor
-import multiprocessing
-import time
+import os
 import re
 
+import torch
+from PIL import Image
+import numpy as np
+
+from modules import modelloader, paths, deepbooru_model, devices, images, shared
+
 re_special = re.compile(r'([\\()])')
 
-def get_deepbooru_tags(pil_image):
-    """
-    This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
-    """
-    from modules import shared  # prevents circular reference
-
-    try:
-        create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts())
-        return get_tags_from_process(pil_image)
-    finally:
-        release_process()
-
-
-OPT_INCLUDE_RANKS = "include_ranks"
-def create_deepbooru_opts():
-    from modules import shared
-
-    return {
-        "use_spaces": shared.opts.deepbooru_use_spaces,
-        "use_escape": shared.opts.deepbooru_escape,
-        "alpha_sort": shared.opts.deepbooru_sort_alpha,
-        OPT_INCLUDE_RANKS: shared.opts.interrogate_return_ranks,
-    }
-
-
-def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts):
-    model, tags = get_deepbooru_tags_model()
-    while True: # while process is running, keep monitoring queue for new image
-        pil_image = queue.get()
-        if pil_image == "QUIT":
-            break
-        else:
-            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts)
-
-
-def create_deepbooru_process(threshold, deepbooru_opts):
-    """
-    Creates deepbooru process.  A queue is created to send images into the process.  This enables multiple images
-    to be processed in a row without reloading the model or creating a new process.  To return the data, a shared
-    dictionary is created to hold the tags created.  To wait for tags to be returned, a value of -1 is assigned
-    to the dictionary and the method adding the image to the queue should wait for this value to be updated with
-    the tags.
-    """
-    from modules import shared  # prevents circular reference
-    context = multiprocessing.get_context("spawn")
-    shared.deepbooru_process_manager = context.Manager()
-    shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
-    shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
-    shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process = context.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
-    shared.deepbooru_process.start()
-
-
-def get_tags_from_process(image):
-    from modules import shared
-
-    shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process_queue.put(image)
-    while shared.deepbooru_process_return["value"] == -1:
-        time.sleep(0.2)
-    caption = shared.deepbooru_process_return["value"]
-    shared.deepbooru_process_return["value"] = -1
-
-    return caption
-
-
-def release_process():
-    """
-    Stops the deepbooru process to return used memory
-    """
-    from modules import shared  # prevents circular reference
-    shared.deepbooru_process_queue.put("QUIT")
-    shared.deepbooru_process.join()
-    shared.deepbooru_process_queue = None
-    shared.deepbooru_process = None
-    shared.deepbooru_process_return = None
-    shared.deepbooru_process_manager = None
-
-def get_deepbooru_tags_model():
-    import deepdanbooru as dd
-    import tensorflow as tf
-    import numpy as np
-    this_folder = os.path.dirname(__file__)
-    model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
-    if not os.path.exists(os.path.join(model_path, 'project.json')):
-        # there is no point importing these every time
-        import zipfile
-        from basicsr.utils.download_util import load_file_from_url
-        load_file_from_url(
-            r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
-            model_path)
-        with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
-            zip_ref.extractall(model_path)
-        os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
-
-    tags = dd.project.load_tags_from_project(model_path)
-    model = dd.project.load_model_from_project(
-        model_path, compile_model=False
-    )
-    return model, tags
-
-
-def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts):
-    import deepdanbooru as dd
-    import tensorflow as tf
-    import numpy as np
-
-    alpha_sort = deepbooru_opts['alpha_sort']
-    use_spaces = deepbooru_opts['use_spaces']
-    use_escape = deepbooru_opts['use_escape']
-    include_ranks = deepbooru_opts['include_ranks']
-
-    width = model.input_shape[2]
-    height = model.input_shape[1]
-    image = np.array(pil_image)
-    image = tf.image.resize(
-        image,
-        size=(height, width),
-        method=tf.image.ResizeMethod.AREA,
-        preserve_aspect_ratio=True,
-    )
-    image = image.numpy()  # EagerTensor to np.array
-    image = dd.image.transform_and_pad_image(image, width, height)
-    image = image / 255.0
-    image_shape = image.shape
-    image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
-
-    y = model.predict(image)[0]
-
-    result_dict = {}
-
-    for i, tag in enumerate(tags):
-        result_dict[tag] = y[i]
-
-    unsorted_tags_in_theshold = []
-    result_tags_print = []
-    for tag in tags:
-        if result_dict[tag] >= threshold:
+
+class DeepDanbooru:
+    def __init__(self):
+        self.model = None
+
+    def load(self):
+        if self.model is not None:
+            return
+
+        files = modelloader.load_models(
+            model_path=os.path.join(paths.models_path, "torch_deepdanbooru"),
+            model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt',
+            ext_filter=".pt",
+            download_name='model-resnet_custom_v3.pt',
+        )
+
+        self.model = deepbooru_model.DeepDanbooruModel()
+        self.model.load_state_dict(torch.load(files[0], map_location="cpu"))
+
+        self.model.eval()
+        self.model.to(devices.cpu, devices.dtype)
+
+    def start(self):
+        self.load()
+        self.model.to(devices.device)
+
+    def stop(self):
+        if not shared.opts.interrogate_keep_models_in_memory:
+            self.model.to(devices.cpu)
+            devices.torch_gc()
+
+    def tag(self, pil_image):
+        self.start()
+        res = self.tag_multi(pil_image)
+        self.stop()
+
+        return res
+
+    def tag_multi(self, pil_image, force_disable_ranks=False):
+        threshold = shared.opts.interrogate_deepbooru_score_threshold
+        use_spaces = shared.opts.deepbooru_use_spaces
+        use_escape = shared.opts.deepbooru_escape
+        alpha_sort = shared.opts.deepbooru_sort_alpha
+        include_ranks = shared.opts.interrogate_return_ranks and not force_disable_ranks
+
+        pic = images.resize_image(2, pil_image.convert("RGB"), 512, 512)
+        a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
+
+        with torch.no_grad(), devices.autocast():
+            x = torch.from_numpy(a).cuda()
+            y = self.model(x)[0].detach().cpu().numpy()
+
+        probability_dict = {}
+
+        for tag, probability in zip(self.model.tags, y):
+            if probability < threshold:
+                continue
+
             if tag.startswith("rating:"):
                 continue
-            unsorted_tags_in_theshold.append((result_dict[tag], tag))
-            result_tags_print.append(f'{result_dict[tag]} {tag}')
-
-    # sort tags
-    result_tags_out = []
-    sort_ndx = 0
-    if alpha_sort:
-        sort_ndx = 1
-
-    # sort by reverse by likelihood and normal for alpha, and format tag text as requested
-    unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
-    for weight, tag in unsorted_tags_in_theshold:
-        tag_outformat = tag
-        if use_spaces:
-            tag_outformat = tag_outformat.replace('_', ' ')
-        if use_escape:
-            tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
-        if include_ranks:
-            tag_outformat = f"({tag_outformat}:{weight:.3f})"
-
-        result_tags_out.append(tag_outformat)
-
-    print('\n'.join(sorted(result_tags_print, reverse=True)))
-
-    return ', '.join(result_tags_out)
+
+            probability_dict[tag] = probability
+
+        if alpha_sort:
+            tags = sorted(probability_dict)
+        else:
+            tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])]
+
+        res = []
+
+        for tag in tags:
+            probability = probability_dict[tag]
+            tag_outformat = tag
+            if use_spaces:
+                tag_outformat = tag_outformat.replace('_', ' ')
+            if use_escape:
+                tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
+            if include_ranks:
+                tag_outformat = f"({tag_outformat}:{probability:.3f})"
+
+            res.append(tag_outformat)
+
+        return ", ".join(res)
+
+
+model = DeepDanbooru()
diff --git a/modules/deepbooru_model.py b/modules/deepbooru_model.py
new file mode 100644
index 00000000..edd40c81
--- /dev/null
+++ b/modules/deepbooru_model.py
@@ -0,0 +1,676 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
+
+
+class DeepDanbooruModel(nn.Module):
+    def __init__(self):
+        super(DeepDanbooruModel, self).__init__()
+
+        self.tags = []
+
+        self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2))
+        self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2))
+        self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+        self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64)
+        self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
+        self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+        self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
+        self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
+        self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+        self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
+        self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
+        self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+        self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2))
+        self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128)
+        self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2))
+        self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+        self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+        self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+        self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2))
+        self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256)
+        self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
+        self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
+        self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2))
+        self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+        self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+        self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+        self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2))
+        self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512)
+        self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2))
+        self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
+        self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
+        self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
+        self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
+        self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
+        self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
+        self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
+        self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2))
+        self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024)
+        self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2))
+        self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
+        self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
+        self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
+        self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
+        self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
+        self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
+        self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
+        self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False)
+
+    def forward(self, *inputs):
+        t_358, = inputs
+        t_359 = t_358.permute(*[0, 3, 1, 2])
+        t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)
+        t_360 = self.n_Conv_0(t_359_padded)
+        t_361 = F.relu(t_360)
+        t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))
+        t_362 = self.n_MaxPool_0(t_361)
+        t_363 = self.n_Conv_1(t_362)
+        t_364 = self.n_Conv_2(t_362)
+        t_365 = F.relu(t_364)
+        t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0)
+        t_366 = self.n_Conv_3(t_365_padded)
+        t_367 = F.relu(t_366)
+        t_368 = self.n_Conv_4(t_367)
+        t_369 = torch.add(t_368, t_363)
+        t_370 = F.relu(t_369)
+        t_371 = self.n_Conv_5(t_370)
+        t_372 = F.relu(t_371)
+        t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0)
+        t_373 = self.n_Conv_6(t_372_padded)
+        t_374 = F.relu(t_373)
+        t_375 = self.n_Conv_7(t_374)
+        t_376 = torch.add(t_375, t_370)
+        t_377 = F.relu(t_376)
+        t_378 = self.n_Conv_8(t_377)
+        t_379 = F.relu(t_378)
+        t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0)
+        t_380 = self.n_Conv_9(t_379_padded)
+        t_381 = F.relu(t_380)
+        t_382 = self.n_Conv_10(t_381)
+        t_383 = torch.add(t_382, t_377)
+        t_384 = F.relu(t_383)
+        t_385 = self.n_Conv_11(t_384)
+        t_386 = self.n_Conv_12(t_384)
+        t_387 = F.relu(t_386)
+        t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0)
+        t_388 = self.n_Conv_13(t_387_padded)
+        t_389 = F.relu(t_388)
+        t_390 = self.n_Conv_14(t_389)
+        t_391 = torch.add(t_390, t_385)
+        t_392 = F.relu(t_391)
+        t_393 = self.n_Conv_15(t_392)
+        t_394 = F.relu(t_393)
+        t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0)
+        t_395 = self.n_Conv_16(t_394_padded)
+        t_396 = F.relu(t_395)
+        t_397 = self.n_Conv_17(t_396)
+        t_398 = torch.add(t_397, t_392)
+        t_399 = F.relu(t_398)
+        t_400 = self.n_Conv_18(t_399)
+        t_401 = F.relu(t_400)
+        t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0)
+        t_402 = self.n_Conv_19(t_401_padded)
+        t_403 = F.relu(t_402)
+        t_404 = self.n_Conv_20(t_403)
+        t_405 = torch.add(t_404, t_399)
+        t_406 = F.relu(t_405)
+        t_407 = self.n_Conv_21(t_406)
+        t_408 = F.relu(t_407)
+        t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0)
+        t_409 = self.n_Conv_22(t_408_padded)
+        t_410 = F.relu(t_409)
+        t_411 = self.n_Conv_23(t_410)
+        t_412 = torch.add(t_411, t_406)
+        t_413 = F.relu(t_412)
+        t_414 = self.n_Conv_24(t_413)
+        t_415 = F.relu(t_414)
+        t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0)
+        t_416 = self.n_Conv_25(t_415_padded)
+        t_417 = F.relu(t_416)
+        t_418 = self.n_Conv_26(t_417)
+        t_419 = torch.add(t_418, t_413)
+        t_420 = F.relu(t_419)
+        t_421 = self.n_Conv_27(t_420)
+        t_422 = F.relu(t_421)
+        t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0)
+        t_423 = self.n_Conv_28(t_422_padded)
+        t_424 = F.relu(t_423)
+        t_425 = self.n_Conv_29(t_424)
+        t_426 = torch.add(t_425, t_420)
+        t_427 = F.relu(t_426)
+        t_428 = self.n_Conv_30(t_427)
+        t_429 = F.relu(t_428)
+        t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0)
+        t_430 = self.n_Conv_31(t_429_padded)
+        t_431 = F.relu(t_430)
+        t_432 = self.n_Conv_32(t_431)
+        t_433 = torch.add(t_432, t_427)
+        t_434 = F.relu(t_433)
+        t_435 = self.n_Conv_33(t_434)
+        t_436 = F.relu(t_435)
+        t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0)
+        t_437 = self.n_Conv_34(t_436_padded)
+        t_438 = F.relu(t_437)
+        t_439 = self.n_Conv_35(t_438)
+        t_440 = torch.add(t_439, t_434)
+        t_441 = F.relu(t_440)
+        t_442 = self.n_Conv_36(t_441)
+        t_443 = self.n_Conv_37(t_441)
+        t_444 = F.relu(t_443)
+        t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0)
+        t_445 = self.n_Conv_38(t_444_padded)
+        t_446 = F.relu(t_445)
+        t_447 = self.n_Conv_39(t_446)
+        t_448 = torch.add(t_447, t_442)
+        t_449 = F.relu(t_448)
+        t_450 = self.n_Conv_40(t_449)
+        t_451 = F.relu(t_450)
+        t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0)
+        t_452 = self.n_Conv_41(t_451_padded)
+        t_453 = F.relu(t_452)
+        t_454 = self.n_Conv_42(t_453)
+        t_455 = torch.add(t_454, t_449)
+        t_456 = F.relu(t_455)
+        t_457 = self.n_Conv_43(t_456)
+        t_458 = F.relu(t_457)
+        t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0)
+        t_459 = self.n_Conv_44(t_458_padded)
+        t_460 = F.relu(t_459)
+        t_461 = self.n_Conv_45(t_460)
+        t_462 = torch.add(t_461, t_456)
+        t_463 = F.relu(t_462)
+        t_464 = self.n_Conv_46(t_463)
+        t_465 = F.relu(t_464)
+        t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0)
+        t_466 = self.n_Conv_47(t_465_padded)
+        t_467 = F.relu(t_466)
+        t_468 = self.n_Conv_48(t_467)
+        t_469 = torch.add(t_468, t_463)
+        t_470 = F.relu(t_469)
+        t_471 = self.n_Conv_49(t_470)
+        t_472 = F.relu(t_471)
+        t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0)
+        t_473 = self.n_Conv_50(t_472_padded)
+        t_474 = F.relu(t_473)
+        t_475 = self.n_Conv_51(t_474)
+        t_476 = torch.add(t_475, t_470)
+        t_477 = F.relu(t_476)
+        t_478 = self.n_Conv_52(t_477)
+        t_479 = F.relu(t_478)
+        t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0)
+        t_480 = self.n_Conv_53(t_479_padded)
+        t_481 = F.relu(t_480)
+        t_482 = self.n_Conv_54(t_481)
+        t_483 = torch.add(t_482, t_477)
+        t_484 = F.relu(t_483)
+        t_485 = self.n_Conv_55(t_484)
+        t_486 = F.relu(t_485)
+        t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0)
+        t_487 = self.n_Conv_56(t_486_padded)
+        t_488 = F.relu(t_487)
+        t_489 = self.n_Conv_57(t_488)
+        t_490 = torch.add(t_489, t_484)
+        t_491 = F.relu(t_490)
+        t_492 = self.n_Conv_58(t_491)
+        t_493 = F.relu(t_492)
+        t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0)
+        t_494 = self.n_Conv_59(t_493_padded)
+        t_495 = F.relu(t_494)
+        t_496 = self.n_Conv_60(t_495)
+        t_497 = torch.add(t_496, t_491)
+        t_498 = F.relu(t_497)
+        t_499 = self.n_Conv_61(t_498)
+        t_500 = F.relu(t_499)
+        t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0)
+        t_501 = self.n_Conv_62(t_500_padded)
+        t_502 = F.relu(t_501)
+        t_503 = self.n_Conv_63(t_502)
+        t_504 = torch.add(t_503, t_498)
+        t_505 = F.relu(t_504)
+        t_506 = self.n_Conv_64(t_505)
+        t_507 = F.relu(t_506)
+        t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0)
+        t_508 = self.n_Conv_65(t_507_padded)
+        t_509 = F.relu(t_508)
+        t_510 = self.n_Conv_66(t_509)
+        t_511 = torch.add(t_510, t_505)
+        t_512 = F.relu(t_511)
+        t_513 = self.n_Conv_67(t_512)
+        t_514 = F.relu(t_513)
+        t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0)
+        t_515 = self.n_Conv_68(t_514_padded)
+        t_516 = F.relu(t_515)
+        t_517 = self.n_Conv_69(t_516)
+        t_518 = torch.add(t_517, t_512)
+        t_519 = F.relu(t_518)
+        t_520 = self.n_Conv_70(t_519)
+        t_521 = F.relu(t_520)
+        t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0)
+        t_522 = self.n_Conv_71(t_521_padded)
+        t_523 = F.relu(t_522)
+        t_524 = self.n_Conv_72(t_523)
+        t_525 = torch.add(t_524, t_519)
+        t_526 = F.relu(t_525)
+        t_527 = self.n_Conv_73(t_526)
+        t_528 = F.relu(t_527)
+        t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0)
+        t_529 = self.n_Conv_74(t_528_padded)
+        t_530 = F.relu(t_529)
+        t_531 = self.n_Conv_75(t_530)
+        t_532 = torch.add(t_531, t_526)
+        t_533 = F.relu(t_532)
+        t_534 = self.n_Conv_76(t_533)
+        t_535 = F.relu(t_534)
+        t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0)
+        t_536 = self.n_Conv_77(t_535_padded)
+        t_537 = F.relu(t_536)
+        t_538 = self.n_Conv_78(t_537)
+        t_539 = torch.add(t_538, t_533)
+        t_540 = F.relu(t_539)
+        t_541 = self.n_Conv_79(t_540)
+        t_542 = F.relu(t_541)
+        t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0)
+        t_543 = self.n_Conv_80(t_542_padded)
+        t_544 = F.relu(t_543)
+        t_545 = self.n_Conv_81(t_544)
+        t_546 = torch.add(t_545, t_540)
+        t_547 = F.relu(t_546)
+        t_548 = self.n_Conv_82(t_547)
+        t_549 = F.relu(t_548)
+        t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0)
+        t_550 = self.n_Conv_83(t_549_padded)
+        t_551 = F.relu(t_550)
+        t_552 = self.n_Conv_84(t_551)
+        t_553 = torch.add(t_552, t_547)
+        t_554 = F.relu(t_553)
+        t_555 = self.n_Conv_85(t_554)
+        t_556 = F.relu(t_555)
+        t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0)
+        t_557 = self.n_Conv_86(t_556_padded)
+        t_558 = F.relu(t_557)
+        t_559 = self.n_Conv_87(t_558)
+        t_560 = torch.add(t_559, t_554)
+        t_561 = F.relu(t_560)
+        t_562 = self.n_Conv_88(t_561)
+        t_563 = F.relu(t_562)
+        t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0)
+        t_564 = self.n_Conv_89(t_563_padded)
+        t_565 = F.relu(t_564)
+        t_566 = self.n_Conv_90(t_565)
+        t_567 = torch.add(t_566, t_561)
+        t_568 = F.relu(t_567)
+        t_569 = self.n_Conv_91(t_568)
+        t_570 = F.relu(t_569)
+        t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0)
+        t_571 = self.n_Conv_92(t_570_padded)
+        t_572 = F.relu(t_571)
+        t_573 = self.n_Conv_93(t_572)
+        t_574 = torch.add(t_573, t_568)
+        t_575 = F.relu(t_574)
+        t_576 = self.n_Conv_94(t_575)
+        t_577 = F.relu(t_576)
+        t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0)
+        t_578 = self.n_Conv_95(t_577_padded)
+        t_579 = F.relu(t_578)
+        t_580 = self.n_Conv_96(t_579)
+        t_581 = torch.add(t_580, t_575)
+        t_582 = F.relu(t_581)
+        t_583 = self.n_Conv_97(t_582)
+        t_584 = F.relu(t_583)
+        t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0)
+        t_585 = self.n_Conv_98(t_584_padded)
+        t_586 = F.relu(t_585)
+        t_587 = self.n_Conv_99(t_586)
+        t_588 = self.n_Conv_100(t_582)
+        t_589 = torch.add(t_587, t_588)
+        t_590 = F.relu(t_589)
+        t_591 = self.n_Conv_101(t_590)
+        t_592 = F.relu(t_591)
+        t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0)
+        t_593 = self.n_Conv_102(t_592_padded)
+        t_594 = F.relu(t_593)
+        t_595 = self.n_Conv_103(t_594)
+        t_596 = torch.add(t_595, t_590)
+        t_597 = F.relu(t_596)
+        t_598 = self.n_Conv_104(t_597)
+        t_599 = F.relu(t_598)
+        t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0)
+        t_600 = self.n_Conv_105(t_599_padded)
+        t_601 = F.relu(t_600)
+        t_602 = self.n_Conv_106(t_601)
+        t_603 = torch.add(t_602, t_597)
+        t_604 = F.relu(t_603)
+        t_605 = self.n_Conv_107(t_604)
+        t_606 = F.relu(t_605)
+        t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0)
+        t_607 = self.n_Conv_108(t_606_padded)
+        t_608 = F.relu(t_607)
+        t_609 = self.n_Conv_109(t_608)
+        t_610 = torch.add(t_609, t_604)
+        t_611 = F.relu(t_610)
+        t_612 = self.n_Conv_110(t_611)
+        t_613 = F.relu(t_612)
+        t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0)
+        t_614 = self.n_Conv_111(t_613_padded)
+        t_615 = F.relu(t_614)
+        t_616 = self.n_Conv_112(t_615)
+        t_617 = torch.add(t_616, t_611)
+        t_618 = F.relu(t_617)
+        t_619 = self.n_Conv_113(t_618)
+        t_620 = F.relu(t_619)
+        t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0)
+        t_621 = self.n_Conv_114(t_620_padded)
+        t_622 = F.relu(t_621)
+        t_623 = self.n_Conv_115(t_622)
+        t_624 = torch.add(t_623, t_618)
+        t_625 = F.relu(t_624)
+        t_626 = self.n_Conv_116(t_625)
+        t_627 = F.relu(t_626)
+        t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0)
+        t_628 = self.n_Conv_117(t_627_padded)
+        t_629 = F.relu(t_628)
+        t_630 = self.n_Conv_118(t_629)
+        t_631 = torch.add(t_630, t_625)
+        t_632 = F.relu(t_631)
+        t_633 = self.n_Conv_119(t_632)
+        t_634 = F.relu(t_633)
+        t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0)
+        t_635 = self.n_Conv_120(t_634_padded)
+        t_636 = F.relu(t_635)
+        t_637 = self.n_Conv_121(t_636)
+        t_638 = torch.add(t_637, t_632)
+        t_639 = F.relu(t_638)
+        t_640 = self.n_Conv_122(t_639)
+        t_641 = F.relu(t_640)
+        t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0)
+        t_642 = self.n_Conv_123(t_641_padded)
+        t_643 = F.relu(t_642)
+        t_644 = self.n_Conv_124(t_643)
+        t_645 = torch.add(t_644, t_639)
+        t_646 = F.relu(t_645)
+        t_647 = self.n_Conv_125(t_646)
+        t_648 = F.relu(t_647)
+        t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0)
+        t_649 = self.n_Conv_126(t_648_padded)
+        t_650 = F.relu(t_649)
+        t_651 = self.n_Conv_127(t_650)
+        t_652 = torch.add(t_651, t_646)
+        t_653 = F.relu(t_652)
+        t_654 = self.n_Conv_128(t_653)
+        t_655 = F.relu(t_654)
+        t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0)
+        t_656 = self.n_Conv_129(t_655_padded)
+        t_657 = F.relu(t_656)
+        t_658 = self.n_Conv_130(t_657)
+        t_659 = torch.add(t_658, t_653)
+        t_660 = F.relu(t_659)
+        t_661 = self.n_Conv_131(t_660)
+        t_662 = F.relu(t_661)
+        t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0)
+        t_663 = self.n_Conv_132(t_662_padded)
+        t_664 = F.relu(t_663)
+        t_665 = self.n_Conv_133(t_664)
+        t_666 = torch.add(t_665, t_660)
+        t_667 = F.relu(t_666)
+        t_668 = self.n_Conv_134(t_667)
+        t_669 = F.relu(t_668)
+        t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0)
+        t_670 = self.n_Conv_135(t_669_padded)
+        t_671 = F.relu(t_670)
+        t_672 = self.n_Conv_136(t_671)
+        t_673 = torch.add(t_672, t_667)
+        t_674 = F.relu(t_673)
+        t_675 = self.n_Conv_137(t_674)
+        t_676 = F.relu(t_675)
+        t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0)
+        t_677 = self.n_Conv_138(t_676_padded)
+        t_678 = F.relu(t_677)
+        t_679 = self.n_Conv_139(t_678)
+        t_680 = torch.add(t_679, t_674)
+        t_681 = F.relu(t_680)
+        t_682 = self.n_Conv_140(t_681)
+        t_683 = F.relu(t_682)
+        t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0)
+        t_684 = self.n_Conv_141(t_683_padded)
+        t_685 = F.relu(t_684)
+        t_686 = self.n_Conv_142(t_685)
+        t_687 = torch.add(t_686, t_681)
+        t_688 = F.relu(t_687)
+        t_689 = self.n_Conv_143(t_688)
+        t_690 = F.relu(t_689)
+        t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0)
+        t_691 = self.n_Conv_144(t_690_padded)
+        t_692 = F.relu(t_691)
+        t_693 = self.n_Conv_145(t_692)
+        t_694 = torch.add(t_693, t_688)
+        t_695 = F.relu(t_694)
+        t_696 = self.n_Conv_146(t_695)
+        t_697 = F.relu(t_696)
+        t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0)
+        t_698 = self.n_Conv_147(t_697_padded)
+        t_699 = F.relu(t_698)
+        t_700 = self.n_Conv_148(t_699)
+        t_701 = torch.add(t_700, t_695)
+        t_702 = F.relu(t_701)
+        t_703 = self.n_Conv_149(t_702)
+        t_704 = F.relu(t_703)
+        t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0)
+        t_705 = self.n_Conv_150(t_704_padded)
+        t_706 = F.relu(t_705)
+        t_707 = self.n_Conv_151(t_706)
+        t_708 = torch.add(t_707, t_702)
+        t_709 = F.relu(t_708)
+        t_710 = self.n_Conv_152(t_709)
+        t_711 = F.relu(t_710)
+        t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0)
+        t_712 = self.n_Conv_153(t_711_padded)
+        t_713 = F.relu(t_712)
+        t_714 = self.n_Conv_154(t_713)
+        t_715 = torch.add(t_714, t_709)
+        t_716 = F.relu(t_715)
+        t_717 = self.n_Conv_155(t_716)
+        t_718 = F.relu(t_717)
+        t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0)
+        t_719 = self.n_Conv_156(t_718_padded)
+        t_720 = F.relu(t_719)
+        t_721 = self.n_Conv_157(t_720)
+        t_722 = torch.add(t_721, t_716)
+        t_723 = F.relu(t_722)
+        t_724 = self.n_Conv_158(t_723)
+        t_725 = self.n_Conv_159(t_723)
+        t_726 = F.relu(t_725)
+        t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0)
+        t_727 = self.n_Conv_160(t_726_padded)
+        t_728 = F.relu(t_727)
+        t_729 = self.n_Conv_161(t_728)
+        t_730 = torch.add(t_729, t_724)
+        t_731 = F.relu(t_730)
+        t_732 = self.n_Conv_162(t_731)
+        t_733 = F.relu(t_732)
+        t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0)
+        t_734 = self.n_Conv_163(t_733_padded)
+        t_735 = F.relu(t_734)
+        t_736 = self.n_Conv_164(t_735)
+        t_737 = torch.add(t_736, t_731)
+        t_738 = F.relu(t_737)
+        t_739 = self.n_Conv_165(t_738)
+        t_740 = F.relu(t_739)
+        t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0)
+        t_741 = self.n_Conv_166(t_740_padded)
+        t_742 = F.relu(t_741)
+        t_743 = self.n_Conv_167(t_742)
+        t_744 = torch.add(t_743, t_738)
+        t_745 = F.relu(t_744)
+        t_746 = self.n_Conv_168(t_745)
+        t_747 = self.n_Conv_169(t_745)
+        t_748 = F.relu(t_747)
+        t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0)
+        t_749 = self.n_Conv_170(t_748_padded)
+        t_750 = F.relu(t_749)
+        t_751 = self.n_Conv_171(t_750)
+        t_752 = torch.add(t_751, t_746)
+        t_753 = F.relu(t_752)
+        t_754 = self.n_Conv_172(t_753)
+        t_755 = F.relu(t_754)
+        t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0)
+        t_756 = self.n_Conv_173(t_755_padded)
+        t_757 = F.relu(t_756)
+        t_758 = self.n_Conv_174(t_757)
+        t_759 = torch.add(t_758, t_753)
+        t_760 = F.relu(t_759)
+        t_761 = self.n_Conv_175(t_760)
+        t_762 = F.relu(t_761)
+        t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0)
+        t_763 = self.n_Conv_176(t_762_padded)
+        t_764 = F.relu(t_763)
+        t_765 = self.n_Conv_177(t_764)
+        t_766 = torch.add(t_765, t_760)
+        t_767 = F.relu(t_766)
+        t_768 = self.n_Conv_178(t_767)
+        t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:])
+        t_770 = torch.squeeze(t_769, 3)
+        t_770 = torch.squeeze(t_770, 2)
+        t_771 = torch.sigmoid(t_770)
+        return t_771
+
+    def load_state_dict(self, state_dict, **kwargs):
+        self.tags = state_dict.get('tags', [])
+
+        super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'})
+
diff --git a/modules/shared.py b/modules/shared.py
index a4457305..c93ae2a3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -55,7 +55,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
 parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
-parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
+parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
 parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 488aa5b5..56b9b2eb 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -6,12 +6,10 @@ import sys
 import tqdm
 import time
 
-from modules import shared, images
+from modules import shared, images, deepbooru
 from modules.paths import models_path
 from modules.shared import opts, cmd_opts
 from modules.textual_inversion import autocrop
-if cmd_opts.deepdanbooru:
-    import modules.deepbooru as deepbooru
 
 
 def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):
@@ -20,9 +18,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
             shared.interrogator.load()
 
         if process_caption_deepbooru:
-            db_opts = deepbooru.create_deepbooru_opts()
-            db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
-            deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
+            deepbooru.model.start()
 
         preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug)
 
@@ -32,7 +28,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
             shared.interrogator.send_blip_to_ram()
 
         if process_caption_deepbooru:
-            deepbooru.release_process()
+            deepbooru.model.stop()
 
 
 def listfiles(dirname):
@@ -58,7 +54,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
     if params.process_caption_deepbooru:
         if len(caption) > 0:
             caption += ", "
-        caption += deepbooru.get_tags_from_process(image)
+        caption += deepbooru.model.tag_multi(image)
 
     filename_part = params.src
     filename_part = os.path.splitext(filename_part)[0]
diff --git a/modules/ui.py b/modules/ui.py
index a5953fce..e6da1b2a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -19,14 +19,11 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 
 
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru
 from modules.paths import script_path
 
 from modules.shared import opts, cmd_opts, restricted_opts
 
-if cmd_opts.deepdanbooru:
-    from modules.deepbooru import get_deepbooru_tags
-
 import modules.codeformer_model
 import modules.generation_parameters_copypaste as parameters_copypaste
 import modules.gfpgan_model
@@ -352,7 +349,7 @@ def interrogate(image):
 
 
 def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image)
+    prompt = deepbooru.model.tag(image)
     return gr_show(True) if prompt is None else prompt
 
 
-- 
cgit v1.2.1


From 471189743a5f3dd7a5c822e63aad28f950abbd94 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jonas=20B=C3=B6er?= <webmaster@morgil.de>
Date: Sun, 20 Nov 2022 15:57:43 +0100
Subject: Move progress info to beginning of title

because who has so few tabs open that they can see the end of a tab name?
---
 javascript/progressbar.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 671fde34..43d1d1ce 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -23,7 +23,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
     
     if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){
         if(progressbar.innerText){
-            let newtitle = 'Stable Diffusion - ' + progressbar.innerText
+            let newtitle = '[' + progressbar.innerText.trim() + '] Stable Diffusion';
             if(document.title != newtitle){
                 document.title =  newtitle;          
             }
-- 
cgit v1.2.1


From 637815632f9f362c9959e53139d37e88ea9ace6f Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Sun, 20 Nov 2022 13:36:05 -0500
Subject: Generalize SD torch load/save to implement safetensor merging compat

---
 modules/extras.py    |   15 +-
 modules/sd_models.py |   25 +-
 modules/ui.py        | 3626 +++++++++++++++++++++++++-------------------------
 3 files changed, 1840 insertions(+), 1826 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 71b93a06..820427de 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -249,7 +249,7 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
+def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, save_as_safetensors, custom_name):
     def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
@@ -264,16 +264,16 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
     teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
 
     print(f"Loading {primary_model_info.filename}...")
-    primary_model = torch.load(primary_model_info.filename, map_location='cpu')
+    primary_model = sd_models.torch_load(primary_model_info.filename, primary_model_info, map_override='cpu')
     theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
 
     print(f"Loading {secondary_model_info.filename}...")
-    secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
+    secondary_model = sd_models.torch_load(secondary_model_info.filename, primary_model_info, map_override='cpu')
     theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
 
     if teritary_model_info is not None:
         print(f"Loading {teritary_model_info.filename}...")
-        teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
+        teritary_model = sd_models.torch_load(teritary_model_info.filename, teritary_model_info, map_override='cpu')
         theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
     else:
         teritary_model = None
@@ -314,12 +314,13 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
 
-    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
-    filename = filename if custom_name == '' else (custom_name + '.ckpt')
+    output_exttype = '.safetensors' if save_as_safetensors else '.ckpt'
+    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged' + output_exttype
+    filename = filename if custom_name == '' else (custom_name + output_exttype)
     output_modelname = os.path.join(ckpt_dir, filename)
 
     print(f"Saving to {output_modelname}...")
-    torch.save(primary_model, output_modelname)
+    sd_models.torch_save(primary_model, output_modelname)
 
     sd_models.list_models()
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 4ccdf30b..2f8c2c48 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,7 +4,7 @@ import sys
 import gc
 from collections import namedtuple
 import torch
-from safetensors.torch import load_file
+from safetensors.torch import load_file, save_file
 import re
 from omegaconf import OmegaConf
 
@@ -143,6 +143,22 @@ def transform_checkpoint_dict_key(k):
 
     return k
 
+def torch_load(model_filename, model_info, map_override=None):
+    map_override=shared.weight_load_location if not map_override else map_override
+    if(checkpoint_types[model_info.exttype] == 'safetensors'):
+        # safely load weights
+        # TODO: safetensors supports zero copy fast load to gpu, see issue #684
+        return load_file(model_filename, device=map_override)
+    else:
+        return torch.load(model_filename, map_location=map_override)
+
+def torch_save(model, output_filename):
+    basename, exttype = os.path.splitext(output_filename)
+    if(checkpoint_types[exttype] == 'safetensors'):
+        # [=====  >] Reticulating brines...
+        save_file(model, output_filename, metadata={"format": "pt"})
+    else:
+        torch.save(model, output_filename)
 
 def get_state_dict_from_checkpoint(pl_sd):
     if "state_dict" in pl_sd:
@@ -175,12 +191,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-        if(checkpoint_types[checkpoint_info.exttype] == 'safetensors'):
-            # safely load weights
-            # TODO: safetensors supports zero copy fast load to gpu, see issue #684
-            pl_sd = load_file(checkpoint_file, device=shared.weight_load_location)
-        else:
-            pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+        pl_sd = torch_load(checkpoint_file, checkpoint_info)
 
         if "global_step" in pl_sd:
             print(f"Global Step: {pl_sd['global_step']}")
diff --git a/modules/ui.py b/modules/ui.py
index a5953fce..a2b06aae 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1,1812 +1,1814 @@
-import html
-import json
-import math
-import mimetypes
-import os
-import platform
-import random
-import subprocess as sp
-import sys
-import tempfile
-import time
-import traceback
-from functools import partial, reduce
-
-import gradio as gr
-import gradio.routes
-import gradio.utils
-import numpy as np
-from PIL import Image, PngImagePlugin
-
-
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
-from modules.paths import script_path
-
-from modules.shared import opts, cmd_opts, restricted_opts
-
-if cmd_opts.deepdanbooru:
-    from modules.deepbooru import get_deepbooru_tags
-
-import modules.codeformer_model
-import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.ldsr_model
-import modules.scripts
-import modules.shared as shared
-import modules.styles
-import modules.textual_inversion.ui
-from modules import prompt_parser
-from modules.images import save_image
-from modules.sd_hijack import model_hijack
-from modules.sd_samplers import samplers, samplers_for_img2img
-import modules.textual_inversion.ui
-import modules.hypernetworks.ui
-from modules.generation_parameters_copypaste import image_from_url_text
-
-# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
-mimetypes.init()
-mimetypes.add_type('application/javascript', '.js')
-
-if not cmd_opts.share and not cmd_opts.listen:
-    # fix gradio phoning home
-    gradio.utils.version_check = lambda: None
-    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
-
-if cmd_opts.ngrok != None:
-    import modules.ngrok as ngrok
-    print('ngrok authtoken detected, trying to connect...')
-    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
-
-
-def gr_show(visible=True):
-    return {"visible": visible, "__type__": "update"}
-
-
-sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
-sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
-
-css_hide_progressbar = """
-.wrap .m-12 svg { display:none!important; }
-.wrap .m-12::before { content:"Loading..." }
-.wrap .z-20 svg { display:none!important; }
-.wrap .z-20::before { content:"Loading..." }
-.progress-bar { display:none!important; }
-.meta-text { display:none!important; }
-.meta-text-center { display:none!important; }
-"""
-
-# Using constants for these since the variation selector isn't visible.
-# Important that they exactly match script.js for tooltip to work.
-random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
-reuse_symbol = '\u267b\ufe0f'  # ♻️
-art_symbol = '\U0001f3a8'  # 🎨
-paste_symbol = '\u2199\ufe0f'  # ↙
-folder_symbol = '\U0001f4c2'  # 📂
-refresh_symbol = '\U0001f504'  # 🔄
-save_style_symbol = '\U0001f4be'  # 💾
-apply_style_symbol = '\U0001f4cb'  # 📋
-
-
-def plaintext_to_html(text):
-    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
-    return text
-
-def send_gradio_gallery_to_image(x):
-    if len(x) == 0:
-        return None
-    return image_from_url_text(x[0])
-
-def save_files(js_data, images, do_make_zip, index):
-    import csv
-    filenames = []
-    fullfns = []
-
-    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
-    class MyObject:
-        def __init__(self, d=None):
-            if d is not None:
-                for key, value in d.items():
-                    setattr(self, key, value)
-
-    data = json.loads(js_data)
-
-    p = MyObject(data)
-    path = opts.outdir_save
-    save_to_dirs = opts.use_save_to_dirs_for_ui
-    extension: str = opts.samples_format
-    start_index = 0
-
-    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
-
-        images = [images[index]]
-        start_index = index
-
-    os.makedirs(opts.outdir_save, exist_ok=True)
-
-    with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
-        at_start = file.tell() == 0
-        writer = csv.writer(file)
-        if at_start:
-            writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
-
-        for image_index, filedata in enumerate(images, start_index):
-            image = image_from_url_text(filedata)
-
-            is_grid = image_index < p.index_of_first_image
-            i = 0 if is_grid else (image_index - p.index_of_first_image)
-
-            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
-
-            filename = os.path.relpath(fullfn, path)
-            filenames.append(filename)
-            fullfns.append(fullfn)
-            if txt_fullfn:
-                filenames.append(os.path.basename(txt_fullfn))
-                fullfns.append(txt_fullfn)
-
-        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
-
-    # Make Zip
-    if do_make_zip:
-        zip_filepath = os.path.join(path, "images.zip")
-
-        from zipfile import ZipFile
-        with ZipFile(zip_filepath, "w") as zip_file:
-            for i in range(len(fullfns)):
-                with open(fullfns[i], mode="rb") as f:
-                    zip_file.writestr(filenames[i], f.read())
-        fullfns.insert(0, zip_filepath)
-
-    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
-
-def save_pil_to_file(pil_image, dir=None):
-    use_metadata = False
-    metadata = PngImagePlugin.PngInfo()
-    for key, value in pil_image.info.items():
-        if isinstance(key, str) and isinstance(value, str):
-            metadata.add_text(key, value)
-            use_metadata = True
-
-    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
-    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
-    return file_obj
-
-
-# override save to file function so that it also writes PNG info
-gr.processing_utils.save_pil_to_file = save_pil_to_file
-
-
-def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
-    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
-        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
-        if run_memmon:
-            shared.mem_mon.monitor()
-        t = time.perf_counter()
-
-        try:
-            res = list(func(*args, **kwargs))
-        except Exception as e:
-            # When printing out our debug argument list, do not print out more than a MB of text
-            max_debug_str_len = 131072 # (1024*1024)/8
-
-            print("Error completing request", file=sys.stderr)
-            argStr = f"Arguments: {str(args)} {str(kwargs)}"
-            print(argStr[:max_debug_str_len], file=sys.stderr)
-            if len(argStr) > max_debug_str_len:
-                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
-
-            print(traceback.format_exc(), file=sys.stderr)
-
-            shared.state.job = ""
-            shared.state.job_count = 0
-
-            if extra_outputs_array is None:
-                extra_outputs_array = [None, '']
-
-            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
-
-        shared.state.skipped = False
-        shared.state.interrupted = False
-        shared.state.job_count = 0
-
-        if not add_stats:
-            return tuple(res)
-
-        elapsed = time.perf_counter() - t
-        elapsed_m = int(elapsed // 60)
-        elapsed_s = elapsed % 60
-        elapsed_text = f"{elapsed_s:.2f}s"
-        if elapsed_m > 0:
-            elapsed_text = f"{elapsed_m}m "+elapsed_text
-
-        if run_memmon:
-            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
-            active_peak = mem_stats['active_peak']
-            reserved_peak = mem_stats['reserved_peak']
-            sys_peak = mem_stats['system_peak']
-            sys_total = mem_stats['total']
-            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
-
-            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
-        else:
-            vram_html = ''
-
-        # last item is always HTML
-        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
-
-        return tuple(res)
-
-    return f
-
-
-def calc_time_left(progress, threshold, label, force_display):
-    if progress == 0:
-        return ""
-    else:
-        time_since_start = time.time() - shared.state.time_start
-        eta = (time_since_start/progress)
-        eta_relative = eta-time_since_start
-        if (eta_relative > threshold and progress > 0.02) or force_display:
-            if eta_relative > 3600:
-                return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
-            elif eta_relative > 60:
-                return label + time.strftime('%M:%S',  time.gmtime(eta_relative))
-            else:
-                return label + time.strftime('%Ss',  time.gmtime(eta_relative))
-        else:
-            return ""
-
-
-def check_progress_call(id_part):
-    if shared.state.job_count == 0:
-        return "", gr_show(False), gr_show(False), gr_show(False)
-
-    progress = 0
-
-    if shared.state.job_count > 0:
-        progress += shared.state.job_no / shared.state.job_count
-    if shared.state.sampling_steps > 0:
-        progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
-
-    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
-    if time_left != "":
-        shared.state.time_left_force_display = True
-
-    progress = min(progress, 1)
-
-    progressbar = ""
-    if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
-
-    image = gr_show(False)
-    preview_visibility = gr_show(False)
-
-    if opts.show_progress_every_n_steps != 0:
-        shared.state.set_current_image()
-        image = shared.state.current_image
-
-        if image is None:
-            image = gr.update(value=None)
-        else:
-            preview_visibility = gr_show(True)
-
-    if shared.state.textinfo is not None:
-        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
-    else:
-        textinfo_result = gr_show(False)
-
-    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
-
-
-def check_progress_call_initial(id_part):
-    shared.state.job_count = -1
-    shared.state.current_latent = None
-    shared.state.current_image = None
-    shared.state.textinfo = None
-    shared.state.time_start = time.time()
-    shared.state.time_left_force_display = False
-
-    return check_progress_call(id_part)
-
-
-def roll_artist(prompt):
-    allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
-    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
-
-    return prompt + ", " + artist.name if prompt != '' else artist.name
-
-
-def visit(x, func, path=""):
-    if hasattr(x, 'children'):
-        for c in x.children:
-            visit(c, func, path)
-    elif x.label is not None:
-        func(path + "/" + str(x.label), x)
-
-
-def add_style(name: str, prompt: str, negative_prompt: str):
-    if name is None:
-        return [gr_show() for x in range(4)]
-
-    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
-    shared.prompt_styles.styles[style.name] = style
-    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
-    # reserialize all styles every time we save them
-    shared.prompt_styles.save_styles(shared.styles_filename)
-
-    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(4)]
-
-
-def apply_styles(prompt, prompt_neg, style1_name, style2_name):
-    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
-    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
-
-    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
-
-
-def interrogate(image):
-    prompt = shared.interrogator.interrogate(image)
-
-    return gr_show(True) if prompt is None else prompt
-
-
-def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image)
-    return gr_show(True) if prompt is None else prompt
-
-
-def create_seed_inputs():
-    with gr.Row():
-        with gr.Box():
-            with gr.Row(elem_id='seed_row'):
-                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
-                seed.style(container=False)
-                random_seed = gr.Button(random_symbol, elem_id='random_seed')
-                reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
-
-        with gr.Box(elem_id='subseed_show_box'):
-            seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
-
-    # Components to show/hide based on the 'Extra' checkbox
-    seed_extras = []
-
-    with gr.Row(visible=False) as seed_extra_row_1:
-        seed_extras.append(seed_extra_row_1)
-        with gr.Box():
-            with gr.Row(elem_id='subseed_row'):
-                subseed = gr.Number(label='Variation seed', value=-1)
-                subseed.style(container=False)
-                random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
-                reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
-        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
-
-    with gr.Row(visible=False) as seed_extra_row_2:
-        seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0)
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
-
-    random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
-    random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
-
-    def change_visibility(show):
-        return {comp: gr_show(show) for comp in seed_extras}
-
-    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
-
-    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
-
-
-def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
-    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
-        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
-        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
-    def copy_seed(gen_info_string: str, index):
-        res = -1
-
-        try:
-            gen_info = json.loads(gen_info_string)
-            index -= gen_info.get('index_of_first_image', 0)
-
-            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
-                all_subseeds = gen_info.get('all_subseeds', [-1])
-                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
-            else:
-                all_seeds = gen_info.get('all_seeds', [-1])
-                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
-
-        except json.decoder.JSONDecodeError as e:
-            if gen_info_string != '':
-                print("Error parsing JSON generation info:", file=sys.stderr)
-                print(gen_info_string, file=sys.stderr)
-
-        return [res, gr_show(False)]
-
-    reuse_seed.click(
-        fn=copy_seed,
-        _js="(x, y) => [x, selected_gallery_index()]",
-        show_progress=False,
-        inputs=[generation_info, dummy_component],
-        outputs=[seed, dummy_component]
-    )
-
-
-def update_token_counter(text, steps):
-    try:
-        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
-        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
-
-    except Exception:
-        # a parsing error can happen here during typing, and we don't want to bother the user with
-        # messages related to it in console
-        prompt_schedules = [[[steps, text]]]
-
-    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
-    prompts = [prompt_text for step, prompt_text in flat_prompts]
-    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
-    style_class = ' class="red"' if (token_count > max_length) else ""
-    return f"<span {style_class}>{token_count}/{max_length}</span>"
-
-
-def create_toprow(is_img2img):
-    id_part = "img2img" if is_img2img else "txt2img"
-
-    with gr.Row(elem_id="toprow"):
-        with gr.Column(scale=6):
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
-                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
-                        )
-
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
-                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
-                        )
-
-        with gr.Column(scale=1, elem_id="roll_col"):
-            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
-            paste = gr.Button(value=paste_symbol, elem_id="paste")
-            save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
-            prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-
-            token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
-            token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-
-        button_interrogate = None
-        button_deepbooru = None
-        if is_img2img:
-            with gr.Column(scale=1, elem_id="interrogate_col"):
-                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-
-                if cmd_opts.deepdanbooru:
-                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-
-        with gr.Column(scale=1):
-            with gr.Row():
-                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
-                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
-                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
-
-                skip.click(
-                    fn=lambda: shared.state.skip(),
-                    inputs=[],
-                    outputs=[],
-                )
-
-                interrupt.click(
-                    fn=lambda: shared.state.interrupt(),
-                    inputs=[],
-                    outputs=[],
-                )
-
-            with gr.Row():
-                with gr.Column(scale=1, elem_id="style_pos_col"):
-                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
-                    prompt_style.save_to_config = True
-
-                with gr.Column(scale=1, elem_id="style_neg_col"):
-                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
-                    prompt_style2.save_to_config = True
-
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
-
-
-def setup_progressbar(progressbar, preview, id_part, textinfo=None):
-    if textinfo is None:
-        textinfo = gr.HTML(visible=False)
-
-    check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
-    check_progress.click(
-        fn=lambda: check_progress_call(id_part),
-        show_progress=False,
-        inputs=[],
-        outputs=[progressbar, preview, preview, textinfo],
-    )
-
-    check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
-    check_progress_initial.click(
-        fn=lambda: check_progress_call_initial(id_part),
-        show_progress=False,
-        inputs=[],
-        outputs=[progressbar, preview, preview, textinfo],
-    )
-
-
-def apply_setting(key, value):
-    if value is None:
-        return gr.update()
-
-    if shared.cmd_opts.freeze_settings:
-        return gr.update()
-
-    # dont allow model to be swapped when model hash exists in prompt
-    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
-        return gr.update()
-
-    if key == "sd_model_checkpoint":
-        ckpt_info = sd_models.get_closet_checkpoint_match(value)
-
-        if ckpt_info is not None:
-            value = ckpt_info.title
-        else:
-            return gr.update()
-
-    comp_args = opts.data_labels[key].component_args
-    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
-        return
-
-    valtype = type(opts.data_labels[key].default)
-    oldval = opts.data[key]
-    opts.data[key] = valtype(value) if valtype != type(None) else value
-    if oldval != value and opts.data_labels[key].onchange is not None:
-        opts.data_labels[key].onchange()
-
-    opts.save(shared.config_filename)
-    return value
-
-
-def update_generation_info(args):
-    generation_info, html_info, img_index = args
-    try:
-        generation_info = json.loads(generation_info)
-        if img_index < 0 or img_index >= len(generation_info["infotexts"]):
-            return html_info
-        return plaintext_to_html(generation_info["infotexts"][img_index])
-    except Exception:
-        pass
-    # if the json parse or anything else fails, just return the old html_info
-    return html_info
-
-
-def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
-    def refresh():
-        refresh_method()
-        args = refreshed_args() if callable(refreshed_args) else refreshed_args
-
-        for k, v in args.items():
-            setattr(refresh_component, k, v)
-
-        return gr.update(**(args or {}))
-
-    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
-    refresh_button.click(
-        fn=refresh,
-        inputs=[],
-        outputs=[refresh_component]
-    )
-    return refresh_button
-
-
-def create_output_panel(tabname, outdir):
-    def open_folder(f):
-        if not os.path.exists(f):
-            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
-            return
-        elif not os.path.isdir(f):
-            print(f"""
-WARNING
-An open_folder request was made with an argument that is not a folder.
-This could be an error or a malicious attempt to run code on your computer.
-Requested path was: {f}
-""", file=sys.stderr)
-            return
-
-        if not shared.cmd_opts.hide_ui_dir_config:
-            path = os.path.normpath(f)
-            if platform.system() == "Windows":
-                os.startfile(path)
-            elif platform.system() == "Darwin":
-                sp.Popen(["open", path])
-            else:
-                sp.Popen(["xdg-open", path])
-
-    with gr.Column(variant='panel'):
-            with gr.Group():
-                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
-
-            generation_info = None
-            with gr.Column():
-                with gr.Row():
-                    if tabname != "extras":
-                        save = gr.Button('Save', elem_id=f'save_{tabname}')
-
-                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
-                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
-
-                open_folder_button.click(
-                    fn=lambda: open_folder(opts.outdir_samples or outdir),
-                    inputs=[],
-                    outputs=[],
-                )
-
-                if tabname != "extras":
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
-                        if tabname == 'txt2img' or tabname == 'img2img':
-                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
-                            generation_info_button.click(
-                                fn=update_generation_info,
-                                _js="(x, y) => [x, y, selected_gallery_index()]",
-                                inputs=[generation_info, html_info],
-                                outputs=[html_info],
-                                preprocess=False
-                            )
-
-                        save.click(
-                            fn=wrap_gradio_call(save_files),
-                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
-                            inputs=[
-                                generation_info,
-                                result_gallery,
-                                do_make_zip,
-                                html_info,
-                            ],
-                            outputs=[
-                                download_files,
-                                html_info,
-                                html_info,
-                                html_info,
-                            ]
-                        )
-                else:
-                    html_info_x = gr.HTML()
-                    html_info = gr.HTML()
-                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
-                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
-
-
-def create_ui(wrap_gradio_gpu_call):
-    import modules.img2img
-    import modules.txt2img
-
-    reload_javascript()
-
-    parameters_copypaste.reset()
-
-    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
-    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
-
-    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
-        dummy_component = gr.Label(visible=False)
-        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
-
-        with gr.Row(elem_id='txt2img_progress_row'):
-            with gr.Column(scale=1):
-                pass
-
-            with gr.Column(scale=1):
-                progressbar = gr.HTML(elem_id="txt2img_progressbar")
-                txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
-                setup_progressbar(progressbar, txt2img_preview, 'txt2img')
-
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
-
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-
-                with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
-                    enable_hr = gr.Checkbox(label='Highres. fix', value=False)
-
-                with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
-
-                with gr.Row(equal_height=True):
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
-                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
-                with gr.Group():
-                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
-
-            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
-            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
-
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
-            txt2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
-                _js="submit",
-                inputs=[
-                    txt2img_prompt,
-                    txt2img_negative_prompt,
-                    txt2img_prompt_style,
-                    txt2img_prompt_style2,
-                    steps,
-                    sampler_index,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    height,
-                    width,
-                    enable_hr,
-                    denoising_strength,
-                    firstphase_width,
-                    firstphase_height,
-                ] + custom_inputs,
-
-                outputs=[
-                    txt2img_gallery,
-                    generation_info,
-                    html_info
-                ],
-                show_progress=False,
-            )
-
-            txt2img_prompt.submit(**txt2img_args)
-            submit.click(**txt2img_args)
-
-            txt_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    txt_prompt_img
-                ],
-                outputs=[
-                    txt2img_prompt,
-                    txt_prompt_img
-                ]
-            )
-
-            enable_hr.change(
-                fn=lambda x: gr_show(x),
-                inputs=[enable_hr],
-                outputs=[hr_options],
-            )
-
-            roll.click(
-                fn=roll_artist,
-                _js="update_txt2img_tokens",
-                inputs=[
-                    txt2img_prompt,
-                ],
-                outputs=[
-                    txt2img_prompt,
-                ]
-            )
-
-            txt2img_paste_fields = [
-                (txt2img_prompt, "Prompt"),
-                (txt2img_negative_prompt, "Negative prompt"),
-                (steps, "Steps"),
-                (sampler_index, "Sampler"),
-                (restore_faces, "Face restoration"),
-                (cfg_scale, "CFG scale"),
-                (seed, "Seed"),
-                (width, "Size-1"),
-                (height, "Size-2"),
-                (batch_size, "Batch size"),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
-                (denoising_strength, "Denoising strength"),
-                (enable_hr, lambda d: "Denoising strength" in d),
-                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
-                (firstphase_width, "First pass size-1"),
-                (firstphase_height, "First pass size-2"),
-                *modules.scripts.scripts_txt2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
-
-            txt2img_preview_params = [
-                txt2img_prompt,
-                txt2img_negative_prompt,
-                steps,
-                sampler_index,
-                cfg_scale,
-                seed,
-                width,
-                height,
-            ]
-
-            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-
-    modules.scripts.scripts_current = modules.scripts.scripts_img2img
-    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
-
-    with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
-
-        with gr.Row(elem_id='img2img_progress_row'):
-            img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
-
-            with gr.Column(scale=1):
-                pass
-
-            with gr.Column(scale=1):
-                progressbar = gr.HTML(elem_id="img2img_progressbar")
-                img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
-                setup_progressbar(progressbar, img2img_preview, 'img2img')
-
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-
-                with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
-                    with gr.TabItem('img2img', id='img2img'):
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
-
-                    with gr.TabItem('Inpaint', id='inpaint'):
-                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
-
-                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
-                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
-
-                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
-
-                        with gr.Row():
-                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
-                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
-
-                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
-
-                        with gr.Row():
-                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
-                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
-
-                    with gr.TabItem('Batch img2img', id='batch'):
-                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
-                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
-                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
-                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
-
-                with gr.Row():
-                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
-
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
-
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
-
-                with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
-
-                with gr.Row():
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
-                with gr.Group():
-                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
-                with gr.Group():
-                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()
-
-            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
-            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
-
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
-            img2img_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    img2img_prompt_img
-                ],
-                outputs=[
-                    img2img_prompt,
-                    img2img_prompt_img
-                ]
-            )
-
-            mask_mode.change(
-                lambda mode, img: {
-                    init_img_with_mask: gr_show(mode == 0),
-                    init_img_inpaint: gr_show(mode == 1),
-                    init_mask_inpaint: gr_show(mode == 1),
-                },
-                inputs=[mask_mode, init_img_with_mask],
-                outputs=[
-                    init_img_with_mask,
-                    init_img_inpaint,
-                    init_mask_inpaint,
-                ],
-            )
-
-            img2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
-                _js="submit_img2img",
-                inputs=[
-                    dummy_component,
-                    img2img_prompt,
-                    img2img_negative_prompt,
-                    img2img_prompt_style,
-                    img2img_prompt_style2,
-                    init_img,
-                    init_img_with_mask,
-                    init_img_inpaint,
-                    init_mask_inpaint,
-                    mask_mode,
-                    steps,
-                    sampler_index,
-                    mask_blur,
-                    inpainting_fill,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    denoising_strength,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    height,
-                    width,
-                    resize_mode,
-                    inpaint_full_res,
-                    inpaint_full_res_padding,
-                    inpainting_mask_invert,
-                    img2img_batch_input_dir,
-                    img2img_batch_output_dir,
-                ] + custom_inputs,
-                outputs=[
-                    img2img_gallery,
-                    generation_info,
-                    html_info
-                ],
-                show_progress=False,
-            )
-
-            img2img_prompt.submit(**img2img_args)
-            submit.click(**img2img_args)
-
-            img2img_interrogate.click(
-                fn=interrogate,
-                inputs=[init_img],
-                outputs=[img2img_prompt],
-            )
-
-            if cmd_opts.deepdanbooru:
-                img2img_deepbooru.click(
-                    fn=interrogate_deepbooru,
-                    inputs=[init_img],
-                    outputs=[img2img_prompt],
-            )
-
-
-            roll.click(
-                fn=roll_artist,
-                _js="update_img2img_tokens",
-                inputs=[
-                    img2img_prompt,
-                ],
-                outputs=[
-                    img2img_prompt,
-                ]
-            )
-
-            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
-            style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
-            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
-
-            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
-                button.click(
-                    fn=add_style,
-                    _js="ask_for_style_name",
-                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
-                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
-                    inputs=[dummy_component, prompt, negative_prompt],
-                    outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
-                )
-
-            for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
-                button.click(
-                    fn=apply_styles,
-                    _js=js_func,
-                    inputs=[prompt, negative_prompt, style1, style2],
-                    outputs=[prompt, negative_prompt, style1, style2],
-                )
-
-            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
-
-            img2img_paste_fields = [
-                (img2img_prompt, "Prompt"),
-                (img2img_negative_prompt, "Negative prompt"),
-                (steps, "Steps"),
-                (sampler_index, "Sampler"),
-                (restore_faces, "Face restoration"),
-                (cfg_scale, "CFG scale"),
-                (seed, "Seed"),
-                (width, "Size-1"),
-                (height, "Size-2"),
-                (batch_size, "Batch size"),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
-                (denoising_strength, "Denoising strength"),
-                *modules.scripts.scripts_img2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
-            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
-
-    modules.scripts.scripts_current = None
-
-    with gr.Blocks(analytics_enabled=False) as extras_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                with gr.Tabs(elem_id="mode_extras"):
-                    with gr.TabItem('Single Image'):
-                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
-
-                    with gr.TabItem('Batch Process'):
-                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
-
-                    with gr.TabItem('Batch from Directory'):
-                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
-                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
-                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
-
-                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
-                with gr.Tabs(elem_id="extras_resize_mode"):
-                    with gr.TabItem('Scale by'):
-                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
-                    with gr.TabItem('Scale to'):
-                        with gr.Group():
-                            with gr.Row():
-                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
-                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
-                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-
-                with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-
-                with gr.Group():
-                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
-
-                with gr.Group():
-                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
-
-                with gr.Group():
-                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
-                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
-
-                with gr.Group():
-                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
-
-            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
-
-        submit.click(
-            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
-            _js="get_extras_tab_index",
-            inputs=[
-                dummy_component,
-                dummy_component,
-                extras_image,
-                image_batch,
-                extras_batch_input_dir,
-                extras_batch_output_dir,
-                show_extras_results,
-                gfpgan_visibility,
-                codeformer_visibility,
-                codeformer_weight,
-                upscaling_resize,
-                upscaling_resize_w,
-                upscaling_resize_h,
-                upscaling_crop,
-                extras_upscaler_1,
-                extras_upscaler_2,
-                extras_upscaler_2_visibility,
-                upscale_before_face_fix,
-            ],
-            outputs=[
-                result_images,
-                html_info_x,
-                html_info,
-            ]
-        )
-        parameters_copypaste.add_paste_fields("extras", extras_image, None)
-
-        extras_image.change(
-            fn=modules.extras.clear_cache,
-            inputs=[], outputs=[]
-        )
-
-    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
-
-            with gr.Column(variant='panel'):
-                html = gr.HTML()
-                generation_info = gr.Textbox(visible=False)
-                html2 = gr.HTML()
-                with gr.Row():
-                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-                parameters_copypaste.bind_buttons(buttons, image, generation_info)
-
-        image.change(
-            fn=wrap_gradio_call(modules.extras.run_pnginfo),
-            inputs=[image],
-            outputs=[html, generation_info, html2],
-        )
-
-    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
-
-                with gr.Row():
-                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
-                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
-                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
-                custom_name = gr.Textbox(label="Custom Name (Optional)")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
-                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
-                save_as_half = gr.Checkbox(value=False, label="Save as float16")
-                modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
-
-            with gr.Column(variant='panel'):
-                submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
-
-    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
-
-    with gr.Blocks(analytics_enabled=False) as train_interface:
-        with gr.Row().style(equal_height=False):
-            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
-
-        with gr.Row().style(equal_height=False):
-            with gr.Tabs(elem_id="train_tabs"):
-
-                with gr.Tab(label="Create embedding"):
-                    new_embedding_name = gr.Textbox(label="Name")
-                    initialization_text = gr.Textbox(label="Initialization text", value="*")
-                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
-                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_embedding = gr.Button(value="Create embedding", variant='primary')
-
-                with gr.Tab(label="Create hypernetwork"):
-                    new_hypernetwork_name = gr.Textbox(label="Name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
-                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
-                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
-                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
-                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
-
-                with gr.Tab(label="Preprocess images"):
-                    process_src = gr.Textbox(label='Source directory')
-                    process_dst = gr.Textbox(label='Destination directory')
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
-
-                    with gr.Row():
-                        process_flip = gr.Checkbox(label='Create flipped copies')
-                        process_split = gr.Checkbox(label='Split oversized images')
-                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')
-                        process_caption = gr.Checkbox(label='Use BLIP for caption')
-                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
-
-                    with gr.Row(visible=False) as process_split_extra_row:
-                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
-
-                    with gr.Row(visible=False) as process_focal_crop_row:
-                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            with gr.Row():
-                                interrupt_preprocessing = gr.Button("Interrupt")
-                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
-
-                    process_split.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_split],
-                        outputs=[process_split_extra_row],
-                    )
-
-                    process_focal_crop.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_focal_crop],
-                        outputs=[process_focal_crop_row],
-                    )
-
-                with gr.Tab(label="Train"):
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
-                    with gr.Row():
-                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
-                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
-                    with gr.Row():
-                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
-                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
-                    with gr.Row():
-                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
-                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
-
-                    batch_size = gr.Number(label='Batch size', value=1, precision=0)
-                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
-                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
-                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-                    steps = gr.Number(label='Max steps', value=100000, precision=0)
-                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
-                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
-
-                    with gr.Row():
-                        interrupt_training = gr.Button(value="Interrupt")
-                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
-                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
-
-                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
-
-                script_callbacks.ui_train_tabs_callback(params)
-
-            with gr.Column():
-                progressbar = gr.HTML(elem_id="ti_progressbar")
-                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-
-                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
-                ti_preview = gr.Image(elem_id='ti_preview', visible=False)
-                ti_progress = gr.HTML(elem_id="ti_progress", value="")
-                ti_outcome = gr.HTML(elem_id="ti_error", value="")
-                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
-
-        create_embedding.click(
-            fn=modules.textual_inversion.ui.create_embedding,
-            inputs=[
-                new_embedding_name,
-                initialization_text,
-                nvpt,
-                overwrite_old_embedding,
-            ],
-            outputs=[
-                train_embedding_name,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        create_hypernetwork.click(
-            fn=modules.hypernetworks.ui.create_hypernetwork,
-            inputs=[
-                new_hypernetwork_name,
-                new_hypernetwork_sizes,
-                overwrite_old_hypernetwork,
-                new_hypernetwork_layer_structure,
-                new_hypernetwork_activation_func,
-                new_hypernetwork_initialization_option,
-                new_hypernetwork_add_layer_norm,
-                new_hypernetwork_use_dropout
-            ],
-            outputs=[
-                train_hypernetwork_name,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        run_preprocess.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                process_src,
-                process_dst,
-                process_width,
-                process_height,
-                preprocess_txt_action,
-                process_flip,
-                process_split,
-                process_caption,
-                process_caption_deepbooru,
-                process_split_threshold,
-                process_overlap_ratio,
-                process_focal_crop,
-                process_focal_crop_face_weight,
-                process_focal_crop_entropy_weight,
-                process_focal_crop_edges_weight,
-                process_focal_crop_debug,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ],
-        )
-
-        train_embedding.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                train_embedding_name,
-                embedding_learn_rate,
-                batch_size,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                steps,
-                create_image_every,
-                save_embedding_every,
-                template_file,
-                save_image_with_stored_embedding,
-                preview_from_txt2img,
-                *txt2img_preview_params,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        train_hypernetwork.click(
-            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                train_hypernetwork_name,
-                hypernetwork_learn_rate,
-                batch_size,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                steps,
-                create_image_every,
-                save_embedding_every,
-                template_file,
-                preview_from_txt2img,
-                *txt2img_preview_params,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        interrupt_training.click(
-            fn=lambda: shared.state.interrupt(),
-            inputs=[],
-            outputs=[],
-        )
-
-        interrupt_preprocessing.click(
-            fn=lambda: shared.state.interrupt(),
-            inputs=[],
-            outputs=[],
-        )
-
-    def create_setting_component(key, is_quicksettings=False):
-        def fun():
-            return opts.data[key] if key in opts.data else opts.data_labels[key].default
-
-        info = opts.data_labels[key]
-        t = type(info.default)
-
-        args = info.component_args() if callable(info.component_args) else info.component_args
-
-        if info.component is not None:
-            comp = info.component
-        elif t == str:
-            comp = gr.Textbox
-        elif t == int:
-            comp = gr.Number
-        elif t == bool:
-            comp = gr.Checkbox
-        else:
-            raise Exception(f'bad options item type: {str(t)} for key {key}')
-
-        elem_id = "setting_"+key
-
-        if info.refresh is not None:
-            if is_quicksettings:
-                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
-            else:
-                with gr.Row(variant="compact"):
-                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                    create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
-        else:
-            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-
-        return res
-
-    components = []
-    component_dict = {}
-
-    script_callbacks.ui_settings_callback()
-    opts.reorder()
-
-    def run_settings(*args):
-        changed = []
-
-        for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
-
-        for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            if comp == dummy_component:
-                continue
-
-            if opts.set(key, value):
-                changed.append(key)
-
-        try:
-            opts.save(shared.config_filename)
-        except RuntimeError:
-            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
-        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
-
-    def run_settings_single(value, key):
-        if not opts.same_type(value, opts.data_labels[key].default):
-            return gr.update(visible=True), opts.dumpjson()
-
-        if not opts.set(key, value):
-            return gr.update(value=getattr(opts, key)), opts.dumpjson()
-
-        opts.save(shared.config_filename)
-
-        return gr.update(value=value), opts.dumpjson()
-
-    with gr.Blocks(analytics_enabled=False) as settings_interface:
-        settings_submit = gr.Button(value="Apply settings", variant='primary')
-        result = gr.HTML()
-
-        settings_cols = 3
-        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
-
-        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
-        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
-
-        quicksettings_list = []
-
-        cols_displayed = 0
-        items_displayed = 0
-        previous_section = None
-        column = None
-        with gr.Row(elem_id="settings").style(equal_height=False):
-            for i, (k, item) in enumerate(opts.data_labels.items()):
-                section_must_be_skipped = item.section[0] is None
-
-                if previous_section != item.section and not section_must_be_skipped:
-                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
-                        if column is not None:
-                            column.__exit__()
-
-                        column = gr.Column(variant='panel')
-                        column.__enter__()
-
-                        items_displayed = 0
-                        cols_displayed += 1
-
-                    previous_section = item.section
-
-                    elem_id, text = item.section
-                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
-
-                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
-                    quicksettings_list.append((i, k, item))
-                    components.append(dummy_component)
-                elif section_must_be_skipped:
-                    components.append(dummy_component)
-                else:
-                    component = create_setting_component(k)
-                    component_dict[k] = component
-                    components.append(component)
-                    items_displayed += 1
-
-        with gr.Row():
-            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
-            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
-
-        with gr.Row():
-            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
-            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
-
-        request_notifications.click(
-            fn=lambda: None,
-            inputs=[],
-            outputs=[],
-            _js='function(){}'
-        )
-
-        download_localization.click(
-            fn=lambda: None,
-            inputs=[],
-            outputs=[],
-            _js='download_localization'
-        )
-
-        def reload_scripts():
-            modules.scripts.reload_script_body_only()
-            reload_javascript()  # need to refresh the html page
-
-        reload_script_bodies.click(
-            fn=reload_scripts,
-            inputs=[],
-            outputs=[]
-        )
-
-        def request_restart():
-            shared.state.interrupt()
-            shared.state.need_restart = True
-
-        restart_gradio.click(
-            fn=request_restart,
-            _js='restart_reload',
-            inputs=[],
-            outputs=[],
-        )
-
-        if column is not None:
-            column.__exit__()
-
-    interfaces = [
-        (txt2img_interface, "txt2img", "txt2img"),
-        (img2img_interface, "img2img", "img2img"),
-        (extras_interface, "Extras", "extras"),
-        (pnginfo_interface, "PNG Info", "pnginfo"),
-        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
-        (train_interface, "Train", "ti"),
-    ]
-
-    css = ""
-
-    for cssfile in modules.scripts.list_files_with_name("style.css"):
-        if not os.path.isfile(cssfile):
-            continue
-
-        with open(cssfile, "r", encoding="utf8") as file:
-            css += file.read() + "\n"
-
-    if os.path.exists(os.path.join(script_path, "user.css")):
-        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
-            css += file.read() + "\n"
-
-    if not cmd_opts.no_progressbar_hiding:
-        css += css_hide_progressbar
-
-    interfaces += script_callbacks.ui_tabs_callback()
-    interfaces += [(settings_interface, "Settings", "settings")]
-
-    extensions_interface = ui_extensions.create_ui()
-    interfaces += [(extensions_interface, "Extensions", "extensions")]
-
-    with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
-        with gr.Row(elem_id="quicksettings"):
-            for i, k, item in quicksettings_list:
-                component = create_setting_component(k, is_quicksettings=True)
-                component_dict[k] = component
-
-        parameters_copypaste.integrate_settings_paste_fields(component_dict)
-        parameters_copypaste.run_bind()
-
-        with gr.Tabs(elem_id="tabs") as tabs:
-            for interface, label, ifid in interfaces:
-                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
-                    interface.render()
-
-        if os.path.exists(os.path.join(script_path, "notification.mp3")):
-            audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
-
-        text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
-        settings_submit.click(
-            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
-            inputs=components,
-            outputs=[text_settings, result],
-        )
-
-        for i, k, item in quicksettings_list:
-            component = component_dict[k]
-
-            component.change(
-                fn=lambda value, k=k: run_settings_single(value, key=k),
-                inputs=[component],
-                outputs=[component, text_settings],
-            )
-
-        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
-
-        def get_settings_values():
-            return [getattr(opts, key) for key in component_keys]
-
-        demo.load(
-            fn=get_settings_values,
-            inputs=[],
-            outputs=[component_dict[k] for k in component_keys],
-        )
-
-        def modelmerger(*args):
-            try:
-                results = modules.extras.run_modelmerger(*args)
-            except Exception as e:
-                print("Error loading/saving model file:", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-                modules.sd_models.list_models()  # to remove the potentially missing models from the list
-                return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
-            return results
-
-        modelmerger_merge.click(
-            fn=modelmerger,
-            inputs=[
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                interp_method,
-                interp_amount,
-                save_as_half,
-                custom_name,
-            ],
-            outputs=[
-                submit_result,
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                component_dict['sd_model_checkpoint'],
-            ]
-        )
-
-    ui_config_file = cmd_opts.ui_config_file
-    ui_settings = {}
-    settings_count = len(ui_settings)
-    error_loading = False
-
-    try:
-        if os.path.exists(ui_config_file):
-            with open(ui_config_file, "r", encoding="utf8") as file:
-                ui_settings = json.load(file)
-    except Exception:
-        error_loading = True
-        print("Error loading settings:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-
-    def loadsave(path, x):
-        def apply_field(obj, field, condition=None, init_field=None):
-            key = path + "/" + field
-
-            if getattr(obj, 'custom_script_source', None) is not None:
-              key = 'customscript/' + obj.custom_script_source + '/' + key
-
-            if getattr(obj, 'do_not_save_to_config', False):
-                return
-
-            saved_value = ui_settings.get(key, None)
-            if saved_value is None:
-                ui_settings[key] = getattr(obj, field)
-            elif condition and not condition(saved_value):
-                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
-            else:
-                setattr(obj, field, saved_value)
-                if init_field is not None:
-                    init_field(saved_value)
-
-        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
-            apply_field(x, 'visible')
-
-        if type(x) == gr.Slider:
-            apply_field(x, 'value')
-            apply_field(x, 'minimum')
-            apply_field(x, 'maximum')
-            apply_field(x, 'step')
-
-        if type(x) == gr.Radio:
-            apply_field(x, 'value', lambda val: val in x.choices)
-
-        if type(x) == gr.Checkbox:
-            apply_field(x, 'value')
-
-        if type(x) == gr.Textbox:
-            apply_field(x, 'value')
-
-        if type(x) == gr.Number:
-            apply_field(x, 'value')
-
-        # Since there are many dropdowns that shouldn't be saved,
-        # we only mark dropdowns that should be saved.
-        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
-            apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
-            apply_field(x, 'visible')
-
-    visit(txt2img_interface, loadsave, "txt2img")
-    visit(img2img_interface, loadsave, "img2img")
-    visit(extras_interface, loadsave, "extras")
-    visit(modelmerger_interface, loadsave, "modelmerger")
-
-    if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
-        with open(ui_config_file, "w", encoding="utf8") as file:
-            json.dump(ui_settings, file, indent=4)
-
-    return demo
-
-
-def reload_javascript():
-    with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
-        javascript = f'<script>{jsfile.read()}</script>'
-
-    scripts_list = modules.scripts.list_scripts("javascript", ".js")
-
-    for basedir, filename, path in scripts_list:
-        with open(path, "r", encoding="utf8") as jsfile:
-            javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
-
-    if cmd_opts.theme is not None:
-        javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
-
-    javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
-
-    def template_response(*args, **kwargs):
-        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
-        res.body = res.body.replace(
-            b'</head>', f'{javascript}</head>'.encode("utf8"))
-        res.init_headers()
-        return res
-
-    gradio.routes.templates.TemplateResponse = template_response
-
-
-if not hasattr(shared, 'GradioTemplateResponseOriginal'):
-    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
+import html
+import json
+import math
+import mimetypes
+import os
+import platform
+import random
+import subprocess as sp
+import sys
+import tempfile
+import time
+import traceback
+from functools import partial, reduce
+
+import gradio as gr
+import gradio.routes
+import gradio.utils
+import numpy as np
+from PIL import Image, PngImagePlugin
+
+
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
+from modules.paths import script_path
+
+from modules.shared import opts, cmd_opts, restricted_opts
+
+if cmd_opts.deepdanbooru:
+    from modules.deepbooru import get_deepbooru_tags
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.ldsr_model
+import modules.scripts
+import modules.shared as shared
+import modules.styles
+import modules.textual_inversion.ui
+from modules import prompt_parser
+from modules.images import save_image
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers import samplers, samplers_for_img2img
+import modules.textual_inversion.ui
+import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
+
+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
+mimetypes.init()
+mimetypes.add_type('application/javascript', '.js')
+
+if not cmd_opts.share and not cmd_opts.listen:
+    # fix gradio phoning home
+    gradio.utils.version_check = lambda: None
+    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
+
+if cmd_opts.ngrok != None:
+    import modules.ngrok as ngrok
+    print('ngrok authtoken detected, trying to connect...')
+    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
+
+
+def gr_show(visible=True):
+    return {"visible": visible, "__type__": "update"}
+
+
+sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
+sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
+
+css_hide_progressbar = """
+.wrap .m-12 svg { display:none!important; }
+.wrap .m-12::before { content:"Loading..." }
+.wrap .z-20 svg { display:none!important; }
+.wrap .z-20::before { content:"Loading..." }
+.progress-bar { display:none!important; }
+.meta-text { display:none!important; }
+.meta-text-center { display:none!important; }
+"""
+
+# Using constants for these since the variation selector isn't visible.
+# Important that they exactly match script.js for tooltip to work.
+random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
+reuse_symbol = '\u267b\ufe0f'  # ♻️
+art_symbol = '\U0001f3a8'  # 🎨
+paste_symbol = '\u2199\ufe0f'  # ↙
+folder_symbol = '\U0001f4c2'  # 📂
+refresh_symbol = '\U0001f504'  # 🔄
+save_style_symbol = '\U0001f4be'  # 💾
+apply_style_symbol = '\U0001f4cb'  # 📋
+
+
+def plaintext_to_html(text):
+    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
+    return text
+
+def send_gradio_gallery_to_image(x):
+    if len(x) == 0:
+        return None
+    return image_from_url_text(x[0])
+
+def save_files(js_data, images, do_make_zip, index):
+    import csv
+    filenames = []
+    fullfns = []
+
+    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
+    class MyObject:
+        def __init__(self, d=None):
+            if d is not None:
+                for key, value in d.items():
+                    setattr(self, key, value)
+
+    data = json.loads(js_data)
+
+    p = MyObject(data)
+    path = opts.outdir_save
+    save_to_dirs = opts.use_save_to_dirs_for_ui
+    extension: str = opts.samples_format
+    start_index = 0
+
+    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
+
+        images = [images[index]]
+        start_index = index
+
+    os.makedirs(opts.outdir_save, exist_ok=True)
+
+    with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
+        at_start = file.tell() == 0
+        writer = csv.writer(file)
+        if at_start:
+            writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
+
+        for image_index, filedata in enumerate(images, start_index):
+            image = image_from_url_text(filedata)
+
+            is_grid = image_index < p.index_of_first_image
+            i = 0 if is_grid else (image_index - p.index_of_first_image)
+
+            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+
+            filename = os.path.relpath(fullfn, path)
+            filenames.append(filename)
+            fullfns.append(fullfn)
+            if txt_fullfn:
+                filenames.append(os.path.basename(txt_fullfn))
+                fullfns.append(txt_fullfn)
+
+        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+
+    # Make Zip
+    if do_make_zip:
+        zip_filepath = os.path.join(path, "images.zip")
+
+        from zipfile import ZipFile
+        with ZipFile(zip_filepath, "w") as zip_file:
+            for i in range(len(fullfns)):
+                with open(fullfns[i], mode="rb") as f:
+                    zip_file.writestr(filenames[i], f.read())
+        fullfns.insert(0, zip_filepath)
+
+    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+
+def save_pil_to_file(pil_image, dir=None):
+    use_metadata = False
+    metadata = PngImagePlugin.PngInfo()
+    for key, value in pil_image.info.items():
+        if isinstance(key, str) and isinstance(value, str):
+            metadata.add_text(key, value)
+            use_metadata = True
+
+    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
+    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
+    return file_obj
+
+
+# override save to file function so that it also writes PNG info
+gr.processing_utils.save_pil_to_file = save_pil_to_file
+
+
+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
+        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
+        if run_memmon:
+            shared.mem_mon.monitor()
+        t = time.perf_counter()
+
+        try:
+            res = list(func(*args, **kwargs))
+        except Exception as e:
+            # When printing out our debug argument list, do not print out more than a MB of text
+            max_debug_str_len = 131072 # (1024*1024)/8
+
+            print("Error completing request", file=sys.stderr)
+            argStr = f"Arguments: {str(args)} {str(kwargs)}"
+            print(argStr[:max_debug_str_len], file=sys.stderr)
+            if len(argStr) > max_debug_str_len:
+                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
+
+            print(traceback.format_exc(), file=sys.stderr)
+
+            shared.state.job = ""
+            shared.state.job_count = 0
+
+            if extra_outputs_array is None:
+                extra_outputs_array = [None, '']
+
+            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
+
+        shared.state.skipped = False
+        shared.state.interrupted = False
+        shared.state.job_count = 0
+
+        if not add_stats:
+            return tuple(res)
+
+        elapsed = time.perf_counter() - t
+        elapsed_m = int(elapsed // 60)
+        elapsed_s = elapsed % 60
+        elapsed_text = f"{elapsed_s:.2f}s"
+        if elapsed_m > 0:
+            elapsed_text = f"{elapsed_m}m "+elapsed_text
+
+        if run_memmon:
+            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
+            active_peak = mem_stats['active_peak']
+            reserved_peak = mem_stats['reserved_peak']
+            sys_peak = mem_stats['system_peak']
+            sys_total = mem_stats['total']
+            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
+
+            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
+        else:
+            vram_html = ''
+
+        # last item is always HTML
+        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
+
+        return tuple(res)
+
+    return f
+
+
+def calc_time_left(progress, threshold, label, force_display):
+    if progress == 0:
+        return ""
+    else:
+        time_since_start = time.time() - shared.state.time_start
+        eta = (time_since_start/progress)
+        eta_relative = eta-time_since_start
+        if (eta_relative > threshold and progress > 0.02) or force_display:
+            if eta_relative > 3600:
+                return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
+            elif eta_relative > 60:
+                return label + time.strftime('%M:%S',  time.gmtime(eta_relative))
+            else:
+                return label + time.strftime('%Ss',  time.gmtime(eta_relative))
+        else:
+            return ""
+
+
+def check_progress_call(id_part):
+    if shared.state.job_count == 0:
+        return "", gr_show(False), gr_show(False), gr_show(False)
+
+    progress = 0
+
+    if shared.state.job_count > 0:
+        progress += shared.state.job_no / shared.state.job_count
+    if shared.state.sampling_steps > 0:
+        progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
+
+    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
+    if time_left != "":
+        shared.state.time_left_force_display = True
+
+    progress = min(progress, 1)
+
+    progressbar = ""
+    if opts.show_progressbar:
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
+
+    image = gr_show(False)
+    preview_visibility = gr_show(False)
+
+    if opts.show_progress_every_n_steps != 0:
+        shared.state.set_current_image()
+        image = shared.state.current_image
+
+        if image is None:
+            image = gr.update(value=None)
+        else:
+            preview_visibility = gr_show(True)
+
+    if shared.state.textinfo is not None:
+        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
+    else:
+        textinfo_result = gr_show(False)
+
+    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
+
+
+def check_progress_call_initial(id_part):
+    shared.state.job_count = -1
+    shared.state.current_latent = None
+    shared.state.current_image = None
+    shared.state.textinfo = None
+    shared.state.time_start = time.time()
+    shared.state.time_left_force_display = False
+
+    return check_progress_call(id_part)
+
+
+def roll_artist(prompt):
+    allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
+    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
+
+    return prompt + ", " + artist.name if prompt != '' else artist.name
+
+
+def visit(x, func, path=""):
+    if hasattr(x, 'children'):
+        for c in x.children:
+            visit(c, func, path)
+    elif x.label is not None:
+        func(path + "/" + str(x.label), x)
+
+
+def add_style(name: str, prompt: str, negative_prompt: str):
+    if name is None:
+        return [gr_show() for x in range(4)]
+
+    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
+    shared.prompt_styles.styles[style.name] = style
+    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
+    # reserialize all styles every time we save them
+    shared.prompt_styles.save_styles(shared.styles_filename)
+
+    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(4)]
+
+
+def apply_styles(prompt, prompt_neg, style1_name, style2_name):
+    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
+    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
+
+    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
+
+
+def interrogate(image):
+    prompt = shared.interrogator.interrogate(image)
+
+    return gr_show(True) if prompt is None else prompt
+
+
+def interrogate_deepbooru(image):
+    prompt = get_deepbooru_tags(image)
+    return gr_show(True) if prompt is None else prompt
+
+
+def create_seed_inputs():
+    with gr.Row():
+        with gr.Box():
+            with gr.Row(elem_id='seed_row'):
+                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
+                seed.style(container=False)
+                random_seed = gr.Button(random_symbol, elem_id='random_seed')
+                reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
+
+        with gr.Box(elem_id='subseed_show_box'):
+            seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
+
+    # Components to show/hide based on the 'Extra' checkbox
+    seed_extras = []
+
+    with gr.Row(visible=False) as seed_extra_row_1:
+        seed_extras.append(seed_extra_row_1)
+        with gr.Box():
+            with gr.Row(elem_id='subseed_row'):
+                subseed = gr.Number(label='Variation seed', value=-1)
+                subseed.style(container=False)
+                random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
+                reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
+        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
+
+    with gr.Row(visible=False) as seed_extra_row_2:
+        seed_extras.append(seed_extra_row_2)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0)
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
+
+    random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
+    random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
+
+    def change_visibility(show):
+        return {comp: gr_show(show) for comp in seed_extras}
+
+    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
+
+    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
+
+
+def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
+    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
+        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
+        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
+    def copy_seed(gen_info_string: str, index):
+        res = -1
+
+        try:
+            gen_info = json.loads(gen_info_string)
+            index -= gen_info.get('index_of_first_image', 0)
+
+            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+                all_subseeds = gen_info.get('all_subseeds', [-1])
+                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
+            else:
+                all_seeds = gen_info.get('all_seeds', [-1])
+                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
+
+        except json.decoder.JSONDecodeError as e:
+            if gen_info_string != '':
+                print("Error parsing JSON generation info:", file=sys.stderr)
+                print(gen_info_string, file=sys.stderr)
+
+        return [res, gr_show(False)]
+
+    reuse_seed.click(
+        fn=copy_seed,
+        _js="(x, y) => [x, selected_gallery_index()]",
+        show_progress=False,
+        inputs=[generation_info, dummy_component],
+        outputs=[seed, dummy_component]
+    )
+
+
+def update_token_counter(text, steps):
+    try:
+        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
+
+    except Exception:
+        # a parsing error can happen here during typing, and we don't want to bother the user with
+        # messages related to it in console
+        prompt_schedules = [[[steps, text]]]
+
+    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
+    prompts = [prompt_text for step, prompt_text in flat_prompts]
+    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
+    style_class = ' class="red"' if (token_count > max_length) else ""
+    return f"<span {style_class}>{token_count}/{max_length}</span>"
+
+
+def create_toprow(is_img2img):
+    id_part = "img2img" if is_img2img else "txt2img"
+
+    with gr.Row(elem_id="toprow"):
+        with gr.Column(scale=6):
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
+                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
+                        )
+
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
+                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
+                        )
+
+        with gr.Column(scale=1, elem_id="roll_col"):
+            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
+            paste = gr.Button(value=paste_symbol, elem_id="paste")
+            save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
+            prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
+
+            token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
+            token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+
+        button_interrogate = None
+        button_deepbooru = None
+        if is_img2img:
+            with gr.Column(scale=1, elem_id="interrogate_col"):
+                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+
+                if cmd_opts.deepdanbooru:
+                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+
+        with gr.Column(scale=1):
+            with gr.Row():
+                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
+                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
+                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
+
+                skip.click(
+                    fn=lambda: shared.state.skip(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                interrupt.click(
+                    fn=lambda: shared.state.interrupt(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+            with gr.Row():
+                with gr.Column(scale=1, elem_id="style_pos_col"):
+                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style.save_to_config = True
+
+                with gr.Column(scale=1, elem_id="style_neg_col"):
+                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style2.save_to_config = True
+
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
+
+
+def setup_progressbar(progressbar, preview, id_part, textinfo=None):
+    if textinfo is None:
+        textinfo = gr.HTML(visible=False)
+
+    check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
+    check_progress.click(
+        fn=lambda: check_progress_call(id_part),
+        show_progress=False,
+        inputs=[],
+        outputs=[progressbar, preview, preview, textinfo],
+    )
+
+    check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
+    check_progress_initial.click(
+        fn=lambda: check_progress_call_initial(id_part),
+        show_progress=False,
+        inputs=[],
+        outputs=[progressbar, preview, preview, textinfo],
+    )
+
+
+def apply_setting(key, value):
+    if value is None:
+        return gr.update()
+
+    if shared.cmd_opts.freeze_settings:
+        return gr.update()
+
+    # dont allow model to be swapped when model hash exists in prompt
+    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+        return gr.update()
+
+    if key == "sd_model_checkpoint":
+        ckpt_info = sd_models.get_closet_checkpoint_match(value)
+
+        if ckpt_info is not None:
+            value = ckpt_info.title
+        else:
+            return gr.update()
+
+    comp_args = opts.data_labels[key].component_args
+    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
+        return
+
+    valtype = type(opts.data_labels[key].default)
+    oldval = opts.data[key]
+    opts.data[key] = valtype(value) if valtype != type(None) else value
+    if oldval != value and opts.data_labels[key].onchange is not None:
+        opts.data_labels[key].onchange()
+
+    opts.save(shared.config_filename)
+    return value
+
+
+def update_generation_info(args):
+    generation_info, html_info, img_index = args
+    try:
+        generation_info = json.loads(generation_info)
+        if img_index < 0 or img_index >= len(generation_info["infotexts"]):
+            return html_info
+        return plaintext_to_html(generation_info["infotexts"][img_index])
+    except Exception:
+        pass
+    # if the json parse or anything else fails, just return the old html_info
+    return html_info
+
+
+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+    def refresh():
+        refresh_method()
+        args = refreshed_args() if callable(refreshed_args) else refreshed_args
+
+        for k, v in args.items():
+            setattr(refresh_component, k, v)
+
+        return gr.update(**(args or {}))
+
+    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
+    refresh_button.click(
+        fn=refresh,
+        inputs=[],
+        outputs=[refresh_component]
+    )
+    return refresh_button
+
+
+def create_output_panel(tabname, outdir):
+    def open_folder(f):
+        if not os.path.exists(f):
+            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
+            return
+        elif not os.path.isdir(f):
+            print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+            return
+
+        if not shared.cmd_opts.hide_ui_dir_config:
+            path = os.path.normpath(f)
+            if platform.system() == "Windows":
+                os.startfile(path)
+            elif platform.system() == "Darwin":
+                sp.Popen(["open", path])
+            else:
+                sp.Popen(["xdg-open", path])
+
+    with gr.Column(variant='panel'):
+            with gr.Group():
+                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+
+            generation_info = None
+            with gr.Column():
+                with gr.Row():
+                    if tabname != "extras":
+                        save = gr.Button('Save', elem_id=f'save_{tabname}')
+
+                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
+                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
+
+                open_folder_button.click(
+                    fn=lambda: open_folder(opts.outdir_samples or outdir),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                if tabname != "extras":
+                    with gr.Row():
+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+                    with gr.Row():
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
+                        if tabname == 'txt2img' or tabname == 'img2img':
+                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
+                            generation_info_button.click(
+                                fn=update_generation_info,
+                                _js="(x, y) => [x, y, selected_gallery_index()]",
+                                inputs=[generation_info, html_info],
+                                outputs=[html_info],
+                                preprocess=False
+                            )
+
+                        save.click(
+                            fn=wrap_gradio_call(save_files),
+                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
+                            inputs=[
+                                generation_info,
+                                result_gallery,
+                                do_make_zip,
+                                html_info,
+                            ],
+                            outputs=[
+                                download_files,
+                                html_info,
+                                html_info,
+                                html_info,
+                            ]
+                        )
+                else:
+                    html_info_x = gr.HTML()
+                    html_info = gr.HTML()
+                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
+
+
+def create_ui(wrap_gradio_gpu_call):
+    import modules.img2img
+    import modules.txt2img
+
+    reload_javascript()
+
+    parameters_copypaste.reset()
+
+    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
+    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+
+    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
+        dummy_component = gr.Label(visible=False)
+        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
+
+        with gr.Row(elem_id='txt2img_progress_row'):
+            with gr.Column(scale=1):
+                pass
+
+            with gr.Column(scale=1):
+                progressbar = gr.HTML(elem_id="txt2img_progressbar")
+                txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
+                setup_progressbar(progressbar, txt2img_preview, 'txt2img')
+
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+                sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
+
+                with gr.Group():
+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+
+                with gr.Row():
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
+                    tiling = gr.Checkbox(label='Tiling', value=False)
+                    enable_hr = gr.Checkbox(label='Highres. fix', value=False)
+
+                with gr.Row(visible=False) as hr_options:
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
+
+                with gr.Row(equal_height=True):
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+
+                with gr.Group():
+                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+
+            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            txt2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
+                _js="submit",
+                inputs=[
+                    txt2img_prompt,
+                    txt2img_negative_prompt,
+                    txt2img_prompt_style,
+                    txt2img_prompt_style2,
+                    steps,
+                    sampler_index,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    height,
+                    width,
+                    enable_hr,
+                    denoising_strength,
+                    firstphase_width,
+                    firstphase_height,
+                ] + custom_inputs,
+
+                outputs=[
+                    txt2img_gallery,
+                    generation_info,
+                    html_info
+                ],
+                show_progress=False,
+            )
+
+            txt2img_prompt.submit(**txt2img_args)
+            submit.click(**txt2img_args)
+
+            txt_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    txt_prompt_img
+                ],
+                outputs=[
+                    txt2img_prompt,
+                    txt_prompt_img
+                ]
+            )
+
+            enable_hr.change(
+                fn=lambda x: gr_show(x),
+                inputs=[enable_hr],
+                outputs=[hr_options],
+            )
+
+            roll.click(
+                fn=roll_artist,
+                _js="update_txt2img_tokens",
+                inputs=[
+                    txt2img_prompt,
+                ],
+                outputs=[
+                    txt2img_prompt,
+                ]
+            )
+
+            txt2img_paste_fields = [
+                (txt2img_prompt, "Prompt"),
+                (txt2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (denoising_strength, "Denoising strength"),
+                (enable_hr, lambda d: "Denoising strength" in d),
+                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
+                (firstphase_width, "First pass size-1"),
+                (firstphase_height, "First pass size-2"),
+                *modules.scripts.scripts_txt2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
+
+            txt2img_preview_params = [
+                txt2img_prompt,
+                txt2img_negative_prompt,
+                steps,
+                sampler_index,
+                cfg_scale,
+                seed,
+                width,
+                height,
+            ]
+
+            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
+
+    modules.scripts.scripts_current = modules.scripts.scripts_img2img
+    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+
+    with gr.Blocks(analytics_enabled=False) as img2img_interface:
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
+
+        with gr.Row(elem_id='img2img_progress_row'):
+            img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
+
+            with gr.Column(scale=1):
+                pass
+
+            with gr.Column(scale=1):
+                progressbar = gr.HTML(elem_id="img2img_progressbar")
+                img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
+                setup_progressbar(progressbar, img2img_preview, 'img2img')
+
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+
+                with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
+                    with gr.TabItem('img2img', id='img2img'):
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
+
+                    with gr.TabItem('Inpaint', id='inpaint'):
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
+
+                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
+                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
+
+                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+
+                        with gr.Row():
+                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
+                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
+
+                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
+
+                        with gr.Row():
+                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
+                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
+
+                    with gr.TabItem('Batch img2img', id='batch'):
+                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
+                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
+                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
+
+                with gr.Row():
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
+
+                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+                sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
+
+                with gr.Group():
+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
+
+                with gr.Row():
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
+                    tiling = gr.Checkbox(label='Tiling', value=False)
+
+                with gr.Row():
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+                with gr.Group():
+                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
+
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+
+                with gr.Group():
+                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+
+            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
+            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            img2img_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    img2img_prompt_img
+                ],
+                outputs=[
+                    img2img_prompt,
+                    img2img_prompt_img
+                ]
+            )
+
+            mask_mode.change(
+                lambda mode, img: {
+                    init_img_with_mask: gr_show(mode == 0),
+                    init_img_inpaint: gr_show(mode == 1),
+                    init_mask_inpaint: gr_show(mode == 1),
+                },
+                inputs=[mask_mode, init_img_with_mask],
+                outputs=[
+                    init_img_with_mask,
+                    init_img_inpaint,
+                    init_mask_inpaint,
+                ],
+            )
+
+            img2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
+                _js="submit_img2img",
+                inputs=[
+                    dummy_component,
+                    img2img_prompt,
+                    img2img_negative_prompt,
+                    img2img_prompt_style,
+                    img2img_prompt_style2,
+                    init_img,
+                    init_img_with_mask,
+                    init_img_inpaint,
+                    init_mask_inpaint,
+                    mask_mode,
+                    steps,
+                    sampler_index,
+                    mask_blur,
+                    inpainting_fill,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    denoising_strength,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    height,
+                    width,
+                    resize_mode,
+                    inpaint_full_res,
+                    inpaint_full_res_padding,
+                    inpainting_mask_invert,
+                    img2img_batch_input_dir,
+                    img2img_batch_output_dir,
+                ] + custom_inputs,
+                outputs=[
+                    img2img_gallery,
+                    generation_info,
+                    html_info
+                ],
+                show_progress=False,
+            )
+
+            img2img_prompt.submit(**img2img_args)
+            submit.click(**img2img_args)
+
+            img2img_interrogate.click(
+                fn=interrogate,
+                inputs=[init_img],
+                outputs=[img2img_prompt],
+            )
+
+            if cmd_opts.deepdanbooru:
+                img2img_deepbooru.click(
+                    fn=interrogate_deepbooru,
+                    inputs=[init_img],
+                    outputs=[img2img_prompt],
+            )
+
+
+            roll.click(
+                fn=roll_artist,
+                _js="update_img2img_tokens",
+                inputs=[
+                    img2img_prompt,
+                ],
+                outputs=[
+                    img2img_prompt,
+                ]
+            )
+
+            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
+            style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
+            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
+
+            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
+                button.click(
+                    fn=add_style,
+                    _js="ask_for_style_name",
+                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
+                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
+                    inputs=[dummy_component, prompt, negative_prompt],
+                    outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
+                )
+
+            for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
+                button.click(
+                    fn=apply_styles,
+                    _js=js_func,
+                    inputs=[prompt, negative_prompt, style1, style2],
+                    outputs=[prompt, negative_prompt, style1, style2],
+                )
+
+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+
+            img2img_paste_fields = [
+                (img2img_prompt, "Prompt"),
+                (img2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (denoising_strength, "Denoising strength"),
+                *modules.scripts.scripts_img2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
+
+    modules.scripts.scripts_current = None
+
+    with gr.Blocks(analytics_enabled=False) as extras_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                with gr.Tabs(elem_id="mode_extras"):
+                    with gr.TabItem('Single Image'):
+                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
+
+                    with gr.TabItem('Batch Process'):
+                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
+
+                    with gr.TabItem('Batch from Directory'):
+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
+                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
+                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
+
+                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
+                with gr.Tabs(elem_id="extras_resize_mode"):
+                    with gr.TabItem('Scale by'):
+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
+                    with gr.TabItem('Scale to'):
+                        with gr.Group():
+                            with gr.Row():
+                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
+                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
+                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
+
+                with gr.Group():
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+
+                with gr.Group():
+                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
+
+                with gr.Group():
+                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
+
+                with gr.Group():
+                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
+                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
+
+                with gr.Group():
+                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
+
+            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
+
+        submit.click(
+            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
+            _js="get_extras_tab_index",
+            inputs=[
+                dummy_component,
+                dummy_component,
+                extras_image,
+                image_batch,
+                extras_batch_input_dir,
+                extras_batch_output_dir,
+                show_extras_results,
+                gfpgan_visibility,
+                codeformer_visibility,
+                codeformer_weight,
+                upscaling_resize,
+                upscaling_resize_w,
+                upscaling_resize_h,
+                upscaling_crop,
+                extras_upscaler_1,
+                extras_upscaler_2,
+                extras_upscaler_2_visibility,
+                upscale_before_face_fix,
+            ],
+            outputs=[
+                result_images,
+                html_info_x,
+                html_info,
+            ]
+        )
+        parameters_copypaste.add_paste_fields("extras", extras_image, None)
+
+        extras_image.change(
+            fn=modules.extras.clear_cache,
+            inputs=[], outputs=[]
+        )
+
+    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
+
+            with gr.Column(variant='panel'):
+                html = gr.HTML()
+                generation_info = gr.Textbox(visible=False)
+                html2 = gr.HTML()
+                with gr.Row():
+                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+                parameters_copypaste.bind_buttons(buttons, image, generation_info)
+
+        image.change(
+            fn=wrap_gradio_call(modules.extras.run_pnginfo),
+            inputs=[image],
+            outputs=[html, generation_info, html2],
+        )
+
+    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
+
+                with gr.Row():
+                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+                custom_name = gr.Textbox(label="Custom Name (Optional)")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
+                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
+                save_as_half = gr.Checkbox(value=False, label="Save as float16")
+                save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")
+                modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
+
+            with gr.Column(variant='panel'):
+                submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
+
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+    with gr.Blocks(analytics_enabled=False) as train_interface:
+        with gr.Row().style(equal_height=False):
+            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+
+        with gr.Row().style(equal_height=False):
+            with gr.Tabs(elem_id="train_tabs"):
+
+                with gr.Tab(label="Create embedding"):
+                    new_embedding_name = gr.Textbox(label="Name")
+                    initialization_text = gr.Textbox(label="Initialization text", value="*")
+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_embedding = gr.Button(value="Create embedding", variant='primary')
+
+                with gr.Tab(label="Create hypernetwork"):
+                    new_hypernetwork_name = gr.Textbox(label="Name")
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
+                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
+                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
+                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
+
+                with gr.Tab(label="Preprocess images"):
+                    process_src = gr.Textbox(label='Source directory')
+                    process_dst = gr.Textbox(label='Destination directory')
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
+
+                    with gr.Row():
+                        process_flip = gr.Checkbox(label='Create flipped copies')
+                        process_split = gr.Checkbox(label='Split oversized images')
+                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')
+                        process_caption = gr.Checkbox(label='Use BLIP for caption')
+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
+
+                    with gr.Row(visible=False) as process_split_extra_row:
+                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
+
+                    with gr.Row(visible=False) as process_focal_crop_row:
+                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            with gr.Row():
+                                interrupt_preprocessing = gr.Button("Interrupt")
+                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
+
+                    process_split.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_split],
+                        outputs=[process_split_extra_row],
+                    )
+
+                    process_focal_crop.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_focal_crop],
+                        outputs=[process_focal_crop_row],
+                    )
+
+                with gr.Tab(label="Train"):
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
+                    with gr.Row():
+                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+                    with gr.Row():
+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
+                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
+                    with gr.Row():
+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
+
+                    batch_size = gr.Number(label='Batch size', value=1, precision=0)
+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
+                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    steps = gr.Number(label='Max steps', value=100000, precision=0)
+                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
+                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
+
+                    with gr.Row():
+                        interrupt_training = gr.Button(value="Interrupt")
+                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
+                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
+
+                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+                script_callbacks.ui_train_tabs_callback(params)
+
+            with gr.Column():
+                progressbar = gr.HTML(elem_id="ti_progressbar")
+                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
+
+                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
+                ti_preview = gr.Image(elem_id='ti_preview', visible=False)
+                ti_progress = gr.HTML(elem_id="ti_progress", value="")
+                ti_outcome = gr.HTML(elem_id="ti_error", value="")
+                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
+
+        create_embedding.click(
+            fn=modules.textual_inversion.ui.create_embedding,
+            inputs=[
+                new_embedding_name,
+                initialization_text,
+                nvpt,
+                overwrite_old_embedding,
+            ],
+            outputs=[
+                train_embedding_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        create_hypernetwork.click(
+            fn=modules.hypernetworks.ui.create_hypernetwork,
+            inputs=[
+                new_hypernetwork_name,
+                new_hypernetwork_sizes,
+                overwrite_old_hypernetwork,
+                new_hypernetwork_layer_structure,
+                new_hypernetwork_activation_func,
+                new_hypernetwork_initialization_option,
+                new_hypernetwork_add_layer_norm,
+                new_hypernetwork_use_dropout
+            ],
+            outputs=[
+                train_hypernetwork_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        run_preprocess.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                process_src,
+                process_dst,
+                process_width,
+                process_height,
+                preprocess_txt_action,
+                process_flip,
+                process_split,
+                process_caption,
+                process_caption_deepbooru,
+                process_split_threshold,
+                process_overlap_ratio,
+                process_focal_crop,
+                process_focal_crop_face_weight,
+                process_focal_crop_entropy_weight,
+                process_focal_crop_edges_weight,
+                process_focal_crop_debug,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ],
+        )
+
+        train_embedding.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                train_embedding_name,
+                embedding_learn_rate,
+                batch_size,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                steps,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                save_image_with_stored_embedding,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        train_hypernetwork.click(
+            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                train_hypernetwork_name,
+                hypernetwork_learn_rate,
+                batch_size,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                steps,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        interrupt_training.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+        interrupt_preprocessing.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+    def create_setting_component(key, is_quicksettings=False):
+        def fun():
+            return opts.data[key] if key in opts.data else opts.data_labels[key].default
+
+        info = opts.data_labels[key]
+        t = type(info.default)
+
+        args = info.component_args() if callable(info.component_args) else info.component_args
+
+        if info.component is not None:
+            comp = info.component
+        elif t == str:
+            comp = gr.Textbox
+        elif t == int:
+            comp = gr.Number
+        elif t == bool:
+            comp = gr.Checkbox
+        else:
+            raise Exception(f'bad options item type: {str(t)} for key {key}')
+
+        elem_id = "setting_"+key
+
+        if info.refresh is not None:
+            if is_quicksettings:
+                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+                create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+            else:
+                with gr.Row(variant="compact"):
+                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+                    create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+        else:
+            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+
+        return res
+
+    components = []
+    component_dict = {}
+
+    script_callbacks.ui_settings_callback()
+    opts.reorder()
+
+    def run_settings(*args):
+        changed = []
+
+        for key, value, comp in zip(opts.data_labels.keys(), args, components):
+            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
+
+        for key, value, comp in zip(opts.data_labels.keys(), args, components):
+            if comp == dummy_component:
+                continue
+
+            if opts.set(key, value):
+                changed.append(key)
+
+        try:
+            opts.save(shared.config_filename)
+        except RuntimeError:
+            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
+
+    def run_settings_single(value, key):
+        if not opts.same_type(value, opts.data_labels[key].default):
+            return gr.update(visible=True), opts.dumpjson()
+
+        if not opts.set(key, value):
+            return gr.update(value=getattr(opts, key)), opts.dumpjson()
+
+        opts.save(shared.config_filename)
+
+        return gr.update(value=value), opts.dumpjson()
+
+    with gr.Blocks(analytics_enabled=False) as settings_interface:
+        settings_submit = gr.Button(value="Apply settings", variant='primary')
+        result = gr.HTML()
+
+        settings_cols = 3
+        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
+
+        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
+        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
+
+        quicksettings_list = []
+
+        cols_displayed = 0
+        items_displayed = 0
+        previous_section = None
+        column = None
+        with gr.Row(elem_id="settings").style(equal_height=False):
+            for i, (k, item) in enumerate(opts.data_labels.items()):
+                section_must_be_skipped = item.section[0] is None
+
+                if previous_section != item.section and not section_must_be_skipped:
+                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
+                        if column is not None:
+                            column.__exit__()
+
+                        column = gr.Column(variant='panel')
+                        column.__enter__()
+
+                        items_displayed = 0
+                        cols_displayed += 1
+
+                    previous_section = item.section
+
+                    elem_id, text = item.section
+                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
+
+                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
+                    quicksettings_list.append((i, k, item))
+                    components.append(dummy_component)
+                elif section_must_be_skipped:
+                    components.append(dummy_component)
+                else:
+                    component = create_setting_component(k)
+                    component_dict[k] = component
+                    components.append(component)
+                    items_displayed += 1
+
+        with gr.Row():
+            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+
+        with gr.Row():
+            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
+            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
+
+        request_notifications.click(
+            fn=lambda: None,
+            inputs=[],
+            outputs=[],
+            _js='function(){}'
+        )
+
+        download_localization.click(
+            fn=lambda: None,
+            inputs=[],
+            outputs=[],
+            _js='download_localization'
+        )
+
+        def reload_scripts():
+            modules.scripts.reload_script_body_only()
+            reload_javascript()  # need to refresh the html page
+
+        reload_script_bodies.click(
+            fn=reload_scripts,
+            inputs=[],
+            outputs=[]
+        )
+
+        def request_restart():
+            shared.state.interrupt()
+            shared.state.need_restart = True
+
+        restart_gradio.click(
+            fn=request_restart,
+            _js='restart_reload',
+            inputs=[],
+            outputs=[],
+        )
+
+        if column is not None:
+            column.__exit__()
+
+    interfaces = [
+        (txt2img_interface, "txt2img", "txt2img"),
+        (img2img_interface, "img2img", "img2img"),
+        (extras_interface, "Extras", "extras"),
+        (pnginfo_interface, "PNG Info", "pnginfo"),
+        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+        (train_interface, "Train", "ti"),
+    ]
+
+    css = ""
+
+    for cssfile in modules.scripts.list_files_with_name("style.css"):
+        if not os.path.isfile(cssfile):
+            continue
+
+        with open(cssfile, "r", encoding="utf8") as file:
+            css += file.read() + "\n"
+
+    if os.path.exists(os.path.join(script_path, "user.css")):
+        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
+            css += file.read() + "\n"
+
+    if not cmd_opts.no_progressbar_hiding:
+        css += css_hide_progressbar
+
+    interfaces += script_callbacks.ui_tabs_callback()
+    interfaces += [(settings_interface, "Settings", "settings")]
+
+    extensions_interface = ui_extensions.create_ui()
+    interfaces += [(extensions_interface, "Extensions", "extensions")]
+
+    with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
+        with gr.Row(elem_id="quicksettings"):
+            for i, k, item in quicksettings_list:
+                component = create_setting_component(k, is_quicksettings=True)
+                component_dict[k] = component
+
+        parameters_copypaste.integrate_settings_paste_fields(component_dict)
+        parameters_copypaste.run_bind()
+
+        with gr.Tabs(elem_id="tabs") as tabs:
+            for interface, label, ifid in interfaces:
+                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
+                    interface.render()
+
+        if os.path.exists(os.path.join(script_path, "notification.mp3")):
+            audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+
+        text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
+        settings_submit.click(
+            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
+            inputs=components,
+            outputs=[text_settings, result],
+        )
+
+        for i, k, item in quicksettings_list:
+            component = component_dict[k]
+
+            component.change(
+                fn=lambda value, k=k: run_settings_single(value, key=k),
+                inputs=[component],
+                outputs=[component, text_settings],
+            )
+
+        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
+
+        def get_settings_values():
+            return [getattr(opts, key) for key in component_keys]
+
+        demo.load(
+            fn=get_settings_values,
+            inputs=[],
+            outputs=[component_dict[k] for k in component_keys],
+        )
+
+        def modelmerger(*args):
+            try:
+                results = modules.extras.run_modelmerger(*args)
+            except Exception as e:
+                print("Error loading/saving model file:", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+                modules.sd_models.list_models()  # to remove the potentially missing models from the list
+                return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
+            return results
+
+        modelmerger_merge.click(
+            fn=modelmerger,
+            inputs=[
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                interp_method,
+                interp_amount,
+                save_as_half,
+                save_as_safetensors,
+                custom_name,
+            ],
+            outputs=[
+                submit_result,
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                component_dict['sd_model_checkpoint'],
+            ]
+        )
+
+    ui_config_file = cmd_opts.ui_config_file
+    ui_settings = {}
+    settings_count = len(ui_settings)
+    error_loading = False
+
+    try:
+        if os.path.exists(ui_config_file):
+            with open(ui_config_file, "r", encoding="utf8") as file:
+                ui_settings = json.load(file)
+    except Exception:
+        error_loading = True
+        print("Error loading settings:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
+    def loadsave(path, x):
+        def apply_field(obj, field, condition=None, init_field=None):
+            key = path + "/" + field
+
+            if getattr(obj, 'custom_script_source', None) is not None:
+              key = 'customscript/' + obj.custom_script_source + '/' + key
+
+            if getattr(obj, 'do_not_save_to_config', False):
+                return
+
+            saved_value = ui_settings.get(key, None)
+            if saved_value is None:
+                ui_settings[key] = getattr(obj, field)
+            elif condition and not condition(saved_value):
+                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
+            else:
+                setattr(obj, field, saved_value)
+                if init_field is not None:
+                    init_field(saved_value)
+
+        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
+            apply_field(x, 'visible')
+
+        if type(x) == gr.Slider:
+            apply_field(x, 'value')
+            apply_field(x, 'minimum')
+            apply_field(x, 'maximum')
+            apply_field(x, 'step')
+
+        if type(x) == gr.Radio:
+            apply_field(x, 'value', lambda val: val in x.choices)
+
+        if type(x) == gr.Checkbox:
+            apply_field(x, 'value')
+
+        if type(x) == gr.Textbox:
+            apply_field(x, 'value')
+
+        if type(x) == gr.Number:
+            apply_field(x, 'value')
+
+        # Since there are many dropdowns that shouldn't be saved,
+        # we only mark dropdowns that should be saved.
+        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
+            apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
+            apply_field(x, 'visible')
+
+    visit(txt2img_interface, loadsave, "txt2img")
+    visit(img2img_interface, loadsave, "img2img")
+    visit(extras_interface, loadsave, "extras")
+    visit(modelmerger_interface, loadsave, "modelmerger")
+
+    if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
+        with open(ui_config_file, "w", encoding="utf8") as file:
+            json.dump(ui_settings, file, indent=4)
+
+    return demo
+
+
+def reload_javascript():
+    with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
+        javascript = f'<script>{jsfile.read()}</script>'
+
+    scripts_list = modules.scripts.list_scripts("javascript", ".js")
+
+    for basedir, filename, path in scripts_list:
+        with open(path, "r", encoding="utf8") as jsfile:
+            javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
+
+    if cmd_opts.theme is not None:
+        javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
+
+    javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
+
+    def template_response(*args, **kwargs):
+        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
+        res.body = res.body.replace(
+            b'</head>', f'{javascript}</head>'.encode("utf8"))
+        res.init_headers()
+        return res
+
+    gradio.routes.templates.TemplateResponse = template_response
+
+
+if not hasattr(shared, 'GradioTemplateResponseOriginal'):
+    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
-- 
cgit v1.2.1


From 927d24ef82f0eedebabb908c43829388f413d73c Mon Sep 17 00:00:00 2001
From: Liam <liamthekerr@gmail.com>
Date: Sun, 20 Nov 2022 13:52:18 -0500
Subject: made selected_gallery_index query selectors more restrictive

---
 javascript/ui.js | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 95cfd106..2ca66d79 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -8,8 +8,8 @@ function set_theme(theme){
 }
 
 function selected_gallery_index(){
-    var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item')
-    var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2')
+    var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item')
+    var button = gradioApp().querySelector('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item.\\!ring-2')
 
     var result = -1
     buttons.forEach(function(v, i){ if(v==button) { result = i } })
-- 
cgit v1.2.1


From 5b57f61ba47f8b11d19a5b46e7fb5a52458abae5 Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Mon, 21 Nov 2022 10:15:46 +0900
Subject: fix pin_memory with different latent sampling method

---
 modules/hypernetworks/hypernetwork.py          |  5 ++++-
 modules/textual_inversion/dataset.py           | 23 +++++++++++++++++++----
 modules/textual_inversion/textual_inversion.py |  7 +------
 3 files changed, 24 insertions(+), 11 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 3d3301b0..0128419b 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -416,7 +416,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
     pin_memory = shared.opts.pin_memory
 
     ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
-    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, batch_size=ds.batch_size, pin_memory=pin_memory)
+    
+    latent_sampling_method = ds.latent_sampling_method
+
+    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 110c0e09..f470324a 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -138,9 +138,12 @@ class PersonalizedBase(Dataset):
         return entry
 
 class PersonalizedDataLoader(DataLoader):
-    def __init__(self, *args, **kwargs):
-        super(PersonalizedDataLoader, self).__init__(shuffle=True, drop_last=True, *args, **kwargs)
-        self.collate_fn = collate_wrapper
+    def __init__(self, dataset, latent_sampling_method="once", batch_size=1, pin_memory=False):
+        super(PersonalizedDataLoader, self).__init__(dataset, shuffle=True, drop_last=True, batch_size=batch_size, pin_memory=pin_memory)
+        if latent_sampling_method == "random":
+            self.collate_fn = collate_wrapper_random
+        else:
+            self.collate_fn = collate_wrapper
         
 
 class BatchLoader:
@@ -148,10 +151,22 @@ class BatchLoader:
         self.cond_text = [entry.cond_text for entry in data]
         self.cond = [entry.cond for entry in data]
         self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
+        #self.emb_index = [entry.emb_index for entry in data]
+        #print(self.latent_sample.device)
 
     def pin_memory(self):
         self.latent_sample = self.latent_sample.pin_memory()
         return self
 
 def collate_wrapper(batch):
-    return BatchLoader(batch)
\ No newline at end of file
+    return BatchLoader(batch)
+
+class BatchLoaderRandom(BatchLoader):
+    def __init__(self, data):
+        super().__init__(data)
+
+    def pin_memory(self):
+        return self
+
+def collate_wrapper_random(batch):
+    return BatchLoaderRandom(batch)
\ No newline at end of file
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 1d5e3a32..3036e48a 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -277,7 +277,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 
     latent_sampling_method = ds.latent_sampling_method
 
-    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, batch_size=ds.batch_size, pin_memory=False)
+    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)
 
     if unload:
         shared.sd_model.first_stage_model.to(devices.cpu)
@@ -333,11 +333,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                 # go back until we reach gradient accumulation steps
                 if (j + 1) % gradient_step != 0:
                     continue
-                #print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
-                #scaler.unscale_(optimizer)
-                #print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
-                #torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=1.0)
-                #print(f"grad:{embedding.vec.grad.detach().cpu().abs().mean().item():.7f}")
                 scaler.step(optimizer)
                 scaler.update()
                 embedding.step += 1
-- 
cgit v1.2.1


From 9ae30b34508770c941f4d09187c0d8e82bb009c8 Mon Sep 17 00:00:00 2001
From: dtlnor <dtlnor@hotmail.com>
Date: Mon, 21 Nov 2022 12:53:55 +0900
Subject: remove cmd args requirement for deepdanbooru

---
 modules/ui.py | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index e6da1b2a..aba13926 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -478,9 +478,7 @@ def create_toprow(is_img2img):
         if is_img2img:
             with gr.Column(scale=1, elem_id="interrogate_col"):
                 button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-
-                if cmd_opts.deepdanbooru:
-                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
 
         with gr.Column(scale=1):
             with gr.Row():
@@ -1004,11 +1002,10 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[img2img_prompt],
             )
 
-            if cmd_opts.deepdanbooru:
-                img2img_deepbooru.click(
-                    fn=interrogate_deepbooru,
-                    inputs=[init_img],
-                    outputs=[img2img_prompt],
+            img2img_deepbooru.click(
+                fn=interrogate_deepbooru,
+                inputs=[init_img],
+                outputs=[img2img_prompt],
             )
 
 
@@ -1240,7 +1237,7 @@ def create_ui(wrap_gradio_gpu_call):
                         process_split = gr.Checkbox(label='Split oversized images')
                         process_focal_crop = gr.Checkbox(label='Auto focal point crop')
                         process_caption = gr.Checkbox(label='Use BLIP for caption')
-                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True)
 
                     with gr.Row(visible=False) as process_split_extra_row:
                         process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-- 
cgit v1.2.1


From e247b7400a592c0a19c197cd080aeec38ee02b68 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Thu, 17 Nov 2022 03:52:17 -0500
Subject: Add fixes for PyTorch 1.12.1

Fix typo "MasOS" -> "macOS"

If MPS is available and PyTorch is an earlier version than 1.13:
* Monkey patch torch.Tensor.to to ensure all tensors sent to MPS are contiguous
* Monkey patch torch.nn.functional.layer_norm to ensure input tensor is contiguous (required for this program to work with MPS on unmodified PyTorch 1.12.1)
---
 modules/devices.py | 28 +++++++++++++++++++++++++++-
 1 file changed, 27 insertions(+), 1 deletion(-)

diff --git a/modules/devices.py b/modules/devices.py
index a87d0d4c..6e8277e5 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -2,9 +2,10 @@ import sys, os, shlex
 import contextlib
 import torch
 from modules import errors
+from packaging import version
 
 
-# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
+# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
 # check `getattr` and try it for compatibility
 def has_mps() -> bool:
     if not getattr(torch, 'has_mps', False):
@@ -94,3 +95,28 @@ def autocast(disable=False):
         return contextlib.nullcontext()
 
     return torch.autocast("cuda")
+
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
+orig_tensor_to = torch.Tensor.to
+def tensor_to_fix(self, *args, **kwargs):
+    if self.device.type != 'mps' and \
+       ((len(args) > 0 and isinstance(args[0], torch.device) and args[0].type == 'mps') or \
+       (isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')):
+        self = self.contiguous()
+    return orig_tensor_to(self, *args, **kwargs)
+
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/80800 
+orig_layer_norm = torch.nn.functional.layer_norm
+def layer_norm_fix(*args, **kwargs):
+    if len(args) > 0 and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps':
+        args = list(args)
+        args[0] = args[0].contiguous()
+    return orig_layer_norm(*args, **kwargs)
+
+
+# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
+if has_mps() and version.parse(torch.__version__) < version.parse("1.13"):
+    torch.Tensor.to = tensor_to_fix
+    torch.nn.functional.layer_norm = layer_norm_fix
-- 
cgit v1.2.1


From 563ea3f6ff66e0eba44033163d24e42297465a47 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 21 Nov 2022 02:56:00 -0500
Subject: Change .cuda() to .to(devices.device)

---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index b9066d81..31ec7e17 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -58,7 +58,7 @@ class DeepDanbooru:
         a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
 
         with torch.no_grad(), devices.autocast():
-            x = torch.from_numpy(a).cuda()
+            x = torch.from_numpy(a).to(devices.device)
             y = self.model(x)[0].detach().cpu().numpy()
 
         probability_dict = {}
-- 
cgit v1.2.1


From 0efffbb407a9d07eae6850374099775385ce176c Mon Sep 17 00:00:00 2001
From: Nicolas Patry <patry.nicolas@protonmail.com>
Date: Mon, 21 Nov 2022 14:04:25 +0100
Subject: Supporting `*.safetensors` format.

If a model file exists with extension `.safetensors` then we can load it
more safely than with PyTorch weights.
---
 modules/sd_models.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 80addf03..0164cc1b 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -45,7 +45,7 @@ def checkpoint_tiles():
 
 def list_models():
     checkpoints_list.clear()
-    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"])
+    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"])
 
     def modeltitle(path, shorthash):
         abspath = os.path.abspath(path)
@@ -180,7 +180,14 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-        pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+        if checkpoint_file.endswith(".safetensors"):
+            try:
+                from safetensors.torch import load_file
+            except ImportError as e:
+                raise ImportError(f"The model is in safetensors format and it is not installed, use `pip install safetensors`: {e}")
+            pl_sd = load_file(checkpoint_file, device=shared.weight_load_location)
+        else:
+            pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
         if "global_step" in pl_sd:
             print(f"Global Step: {pl_sd['global_step']}")
 
-- 
cgit v1.2.1


From 162fef394f8d80b54df7ede9e3b7ba65da23d3c5 Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Mon, 21 Nov 2022 10:50:57 -0500
Subject: Patch line ui endings

---
 modules/ui.py | 3628 ++++++++++++++++++++++++++++-----------------------------
 1 file changed, 1814 insertions(+), 1814 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index a2b06aae..54d3293a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1,1814 +1,1814 @@
-import html
-import json
-import math
-import mimetypes
-import os
-import platform
-import random
-import subprocess as sp
-import sys
-import tempfile
-import time
-import traceback
-from functools import partial, reduce
-
-import gradio as gr
-import gradio.routes
-import gradio.utils
-import numpy as np
-from PIL import Image, PngImagePlugin
-
-
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
-from modules.paths import script_path
-
-from modules.shared import opts, cmd_opts, restricted_opts
-
-if cmd_opts.deepdanbooru:
-    from modules.deepbooru import get_deepbooru_tags
-
-import modules.codeformer_model
-import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.ldsr_model
-import modules.scripts
-import modules.shared as shared
-import modules.styles
-import modules.textual_inversion.ui
-from modules import prompt_parser
-from modules.images import save_image
-from modules.sd_hijack import model_hijack
-from modules.sd_samplers import samplers, samplers_for_img2img
-import modules.textual_inversion.ui
-import modules.hypernetworks.ui
-from modules.generation_parameters_copypaste import image_from_url_text
-
-# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
-mimetypes.init()
-mimetypes.add_type('application/javascript', '.js')
-
-if not cmd_opts.share and not cmd_opts.listen:
-    # fix gradio phoning home
-    gradio.utils.version_check = lambda: None
-    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
-
-if cmd_opts.ngrok != None:
-    import modules.ngrok as ngrok
-    print('ngrok authtoken detected, trying to connect...')
-    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
-
-
-def gr_show(visible=True):
-    return {"visible": visible, "__type__": "update"}
-
-
-sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
-sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
-
-css_hide_progressbar = """
-.wrap .m-12 svg { display:none!important; }
-.wrap .m-12::before { content:"Loading..." }
-.wrap .z-20 svg { display:none!important; }
-.wrap .z-20::before { content:"Loading..." }
-.progress-bar { display:none!important; }
-.meta-text { display:none!important; }
-.meta-text-center { display:none!important; }
-"""
-
-# Using constants for these since the variation selector isn't visible.
-# Important that they exactly match script.js for tooltip to work.
-random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
-reuse_symbol = '\u267b\ufe0f'  # ♻️
-art_symbol = '\U0001f3a8'  # 🎨
-paste_symbol = '\u2199\ufe0f'  # ↙
-folder_symbol = '\U0001f4c2'  # 📂
-refresh_symbol = '\U0001f504'  # 🔄
-save_style_symbol = '\U0001f4be'  # 💾
-apply_style_symbol = '\U0001f4cb'  # 📋
-
-
-def plaintext_to_html(text):
-    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
-    return text
-
-def send_gradio_gallery_to_image(x):
-    if len(x) == 0:
-        return None
-    return image_from_url_text(x[0])
-
-def save_files(js_data, images, do_make_zip, index):
-    import csv
-    filenames = []
-    fullfns = []
-
-    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
-    class MyObject:
-        def __init__(self, d=None):
-            if d is not None:
-                for key, value in d.items():
-                    setattr(self, key, value)
-
-    data = json.loads(js_data)
-
-    p = MyObject(data)
-    path = opts.outdir_save
-    save_to_dirs = opts.use_save_to_dirs_for_ui
-    extension: str = opts.samples_format
-    start_index = 0
-
-    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
-
-        images = [images[index]]
-        start_index = index
-
-    os.makedirs(opts.outdir_save, exist_ok=True)
-
-    with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
-        at_start = file.tell() == 0
-        writer = csv.writer(file)
-        if at_start:
-            writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
-
-        for image_index, filedata in enumerate(images, start_index):
-            image = image_from_url_text(filedata)
-
-            is_grid = image_index < p.index_of_first_image
-            i = 0 if is_grid else (image_index - p.index_of_first_image)
-
-            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
-
-            filename = os.path.relpath(fullfn, path)
-            filenames.append(filename)
-            fullfns.append(fullfn)
-            if txt_fullfn:
-                filenames.append(os.path.basename(txt_fullfn))
-                fullfns.append(txt_fullfn)
-
-        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
-
-    # Make Zip
-    if do_make_zip:
-        zip_filepath = os.path.join(path, "images.zip")
-
-        from zipfile import ZipFile
-        with ZipFile(zip_filepath, "w") as zip_file:
-            for i in range(len(fullfns)):
-                with open(fullfns[i], mode="rb") as f:
-                    zip_file.writestr(filenames[i], f.read())
-        fullfns.insert(0, zip_filepath)
-
-    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
-
-def save_pil_to_file(pil_image, dir=None):
-    use_metadata = False
-    metadata = PngImagePlugin.PngInfo()
-    for key, value in pil_image.info.items():
-        if isinstance(key, str) and isinstance(value, str):
-            metadata.add_text(key, value)
-            use_metadata = True
-
-    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
-    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
-    return file_obj
-
-
-# override save to file function so that it also writes PNG info
-gr.processing_utils.save_pil_to_file = save_pil_to_file
-
-
-def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
-    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
-        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
-        if run_memmon:
-            shared.mem_mon.monitor()
-        t = time.perf_counter()
-
-        try:
-            res = list(func(*args, **kwargs))
-        except Exception as e:
-            # When printing out our debug argument list, do not print out more than a MB of text
-            max_debug_str_len = 131072 # (1024*1024)/8
-
-            print("Error completing request", file=sys.stderr)
-            argStr = f"Arguments: {str(args)} {str(kwargs)}"
-            print(argStr[:max_debug_str_len], file=sys.stderr)
-            if len(argStr) > max_debug_str_len:
-                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
-
-            print(traceback.format_exc(), file=sys.stderr)
-
-            shared.state.job = ""
-            shared.state.job_count = 0
-
-            if extra_outputs_array is None:
-                extra_outputs_array = [None, '']
-
-            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
-
-        shared.state.skipped = False
-        shared.state.interrupted = False
-        shared.state.job_count = 0
-
-        if not add_stats:
-            return tuple(res)
-
-        elapsed = time.perf_counter() - t
-        elapsed_m = int(elapsed // 60)
-        elapsed_s = elapsed % 60
-        elapsed_text = f"{elapsed_s:.2f}s"
-        if elapsed_m > 0:
-            elapsed_text = f"{elapsed_m}m "+elapsed_text
-
-        if run_memmon:
-            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
-            active_peak = mem_stats['active_peak']
-            reserved_peak = mem_stats['reserved_peak']
-            sys_peak = mem_stats['system_peak']
-            sys_total = mem_stats['total']
-            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
-
-            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
-        else:
-            vram_html = ''
-
-        # last item is always HTML
-        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
-
-        return tuple(res)
-
-    return f
-
-
-def calc_time_left(progress, threshold, label, force_display):
-    if progress == 0:
-        return ""
-    else:
-        time_since_start = time.time() - shared.state.time_start
-        eta = (time_since_start/progress)
-        eta_relative = eta-time_since_start
-        if (eta_relative > threshold and progress > 0.02) or force_display:
-            if eta_relative > 3600:
-                return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
-            elif eta_relative > 60:
-                return label + time.strftime('%M:%S',  time.gmtime(eta_relative))
-            else:
-                return label + time.strftime('%Ss',  time.gmtime(eta_relative))
-        else:
-            return ""
-
-
-def check_progress_call(id_part):
-    if shared.state.job_count == 0:
-        return "", gr_show(False), gr_show(False), gr_show(False)
-
-    progress = 0
-
-    if shared.state.job_count > 0:
-        progress += shared.state.job_no / shared.state.job_count
-    if shared.state.sampling_steps > 0:
-        progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
-
-    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
-    if time_left != "":
-        shared.state.time_left_force_display = True
-
-    progress = min(progress, 1)
-
-    progressbar = ""
-    if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
-
-    image = gr_show(False)
-    preview_visibility = gr_show(False)
-
-    if opts.show_progress_every_n_steps != 0:
-        shared.state.set_current_image()
-        image = shared.state.current_image
-
-        if image is None:
-            image = gr.update(value=None)
-        else:
-            preview_visibility = gr_show(True)
-
-    if shared.state.textinfo is not None:
-        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
-    else:
-        textinfo_result = gr_show(False)
-
-    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
-
-
-def check_progress_call_initial(id_part):
-    shared.state.job_count = -1
-    shared.state.current_latent = None
-    shared.state.current_image = None
-    shared.state.textinfo = None
-    shared.state.time_start = time.time()
-    shared.state.time_left_force_display = False
-
-    return check_progress_call(id_part)
-
-
-def roll_artist(prompt):
-    allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
-    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
-
-    return prompt + ", " + artist.name if prompt != '' else artist.name
-
-
-def visit(x, func, path=""):
-    if hasattr(x, 'children'):
-        for c in x.children:
-            visit(c, func, path)
-    elif x.label is not None:
-        func(path + "/" + str(x.label), x)
-
-
-def add_style(name: str, prompt: str, negative_prompt: str):
-    if name is None:
-        return [gr_show() for x in range(4)]
-
-    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
-    shared.prompt_styles.styles[style.name] = style
-    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
-    # reserialize all styles every time we save them
-    shared.prompt_styles.save_styles(shared.styles_filename)
-
-    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(4)]
-
-
-def apply_styles(prompt, prompt_neg, style1_name, style2_name):
-    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
-    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
-
-    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
-
-
-def interrogate(image):
-    prompt = shared.interrogator.interrogate(image)
-
-    return gr_show(True) if prompt is None else prompt
-
-
-def interrogate_deepbooru(image):
-    prompt = get_deepbooru_tags(image)
-    return gr_show(True) if prompt is None else prompt
-
-
-def create_seed_inputs():
-    with gr.Row():
-        with gr.Box():
-            with gr.Row(elem_id='seed_row'):
-                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
-                seed.style(container=False)
-                random_seed = gr.Button(random_symbol, elem_id='random_seed')
-                reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
-
-        with gr.Box(elem_id='subseed_show_box'):
-            seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
-
-    # Components to show/hide based on the 'Extra' checkbox
-    seed_extras = []
-
-    with gr.Row(visible=False) as seed_extra_row_1:
-        seed_extras.append(seed_extra_row_1)
-        with gr.Box():
-            with gr.Row(elem_id='subseed_row'):
-                subseed = gr.Number(label='Variation seed', value=-1)
-                subseed.style(container=False)
-                random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
-                reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
-        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
-
-    with gr.Row(visible=False) as seed_extra_row_2:
-        seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0)
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
-
-    random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
-    random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
-
-    def change_visibility(show):
-        return {comp: gr_show(show) for comp in seed_extras}
-
-    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
-
-    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
-
-
-def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
-    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
-        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
-        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
-    def copy_seed(gen_info_string: str, index):
-        res = -1
-
-        try:
-            gen_info = json.loads(gen_info_string)
-            index -= gen_info.get('index_of_first_image', 0)
-
-            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
-                all_subseeds = gen_info.get('all_subseeds', [-1])
-                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
-            else:
-                all_seeds = gen_info.get('all_seeds', [-1])
-                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
-
-        except json.decoder.JSONDecodeError as e:
-            if gen_info_string != '':
-                print("Error parsing JSON generation info:", file=sys.stderr)
-                print(gen_info_string, file=sys.stderr)
-
-        return [res, gr_show(False)]
-
-    reuse_seed.click(
-        fn=copy_seed,
-        _js="(x, y) => [x, selected_gallery_index()]",
-        show_progress=False,
-        inputs=[generation_info, dummy_component],
-        outputs=[seed, dummy_component]
-    )
-
-
-def update_token_counter(text, steps):
-    try:
-        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
-        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
-
-    except Exception:
-        # a parsing error can happen here during typing, and we don't want to bother the user with
-        # messages related to it in console
-        prompt_schedules = [[[steps, text]]]
-
-    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
-    prompts = [prompt_text for step, prompt_text in flat_prompts]
-    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
-    style_class = ' class="red"' if (token_count > max_length) else ""
-    return f"<span {style_class}>{token_count}/{max_length}</span>"
-
-
-def create_toprow(is_img2img):
-    id_part = "img2img" if is_img2img else "txt2img"
-
-    with gr.Row(elem_id="toprow"):
-        with gr.Column(scale=6):
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
-                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
-                        )
-
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
-                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
-                        )
-
-        with gr.Column(scale=1, elem_id="roll_col"):
-            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
-            paste = gr.Button(value=paste_symbol, elem_id="paste")
-            save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
-            prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-
-            token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
-            token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-
-        button_interrogate = None
-        button_deepbooru = None
-        if is_img2img:
-            with gr.Column(scale=1, elem_id="interrogate_col"):
-                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-
-                if cmd_opts.deepdanbooru:
-                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-
-        with gr.Column(scale=1):
-            with gr.Row():
-                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
-                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
-                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
-
-                skip.click(
-                    fn=lambda: shared.state.skip(),
-                    inputs=[],
-                    outputs=[],
-                )
-
-                interrupt.click(
-                    fn=lambda: shared.state.interrupt(),
-                    inputs=[],
-                    outputs=[],
-                )
-
-            with gr.Row():
-                with gr.Column(scale=1, elem_id="style_pos_col"):
-                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
-                    prompt_style.save_to_config = True
-
-                with gr.Column(scale=1, elem_id="style_neg_col"):
-                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
-                    prompt_style2.save_to_config = True
-
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
-
-
-def setup_progressbar(progressbar, preview, id_part, textinfo=None):
-    if textinfo is None:
-        textinfo = gr.HTML(visible=False)
-
-    check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
-    check_progress.click(
-        fn=lambda: check_progress_call(id_part),
-        show_progress=False,
-        inputs=[],
-        outputs=[progressbar, preview, preview, textinfo],
-    )
-
-    check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
-    check_progress_initial.click(
-        fn=lambda: check_progress_call_initial(id_part),
-        show_progress=False,
-        inputs=[],
-        outputs=[progressbar, preview, preview, textinfo],
-    )
-
-
-def apply_setting(key, value):
-    if value is None:
-        return gr.update()
-
-    if shared.cmd_opts.freeze_settings:
-        return gr.update()
-
-    # dont allow model to be swapped when model hash exists in prompt
-    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
-        return gr.update()
-
-    if key == "sd_model_checkpoint":
-        ckpt_info = sd_models.get_closet_checkpoint_match(value)
-
-        if ckpt_info is not None:
-            value = ckpt_info.title
-        else:
-            return gr.update()
-
-    comp_args = opts.data_labels[key].component_args
-    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
-        return
-
-    valtype = type(opts.data_labels[key].default)
-    oldval = opts.data[key]
-    opts.data[key] = valtype(value) if valtype != type(None) else value
-    if oldval != value and opts.data_labels[key].onchange is not None:
-        opts.data_labels[key].onchange()
-
-    opts.save(shared.config_filename)
-    return value
-
-
-def update_generation_info(args):
-    generation_info, html_info, img_index = args
-    try:
-        generation_info = json.loads(generation_info)
-        if img_index < 0 or img_index >= len(generation_info["infotexts"]):
-            return html_info
-        return plaintext_to_html(generation_info["infotexts"][img_index])
-    except Exception:
-        pass
-    # if the json parse or anything else fails, just return the old html_info
-    return html_info
-
-
-def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
-    def refresh():
-        refresh_method()
-        args = refreshed_args() if callable(refreshed_args) else refreshed_args
-
-        for k, v in args.items():
-            setattr(refresh_component, k, v)
-
-        return gr.update(**(args or {}))
-
-    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
-    refresh_button.click(
-        fn=refresh,
-        inputs=[],
-        outputs=[refresh_component]
-    )
-    return refresh_button
-
-
-def create_output_panel(tabname, outdir):
-    def open_folder(f):
-        if not os.path.exists(f):
-            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
-            return
-        elif not os.path.isdir(f):
-            print(f"""
-WARNING
-An open_folder request was made with an argument that is not a folder.
-This could be an error or a malicious attempt to run code on your computer.
-Requested path was: {f}
-""", file=sys.stderr)
-            return
-
-        if not shared.cmd_opts.hide_ui_dir_config:
-            path = os.path.normpath(f)
-            if platform.system() == "Windows":
-                os.startfile(path)
-            elif platform.system() == "Darwin":
-                sp.Popen(["open", path])
-            else:
-                sp.Popen(["xdg-open", path])
-
-    with gr.Column(variant='panel'):
-            with gr.Group():
-                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
-
-            generation_info = None
-            with gr.Column():
-                with gr.Row():
-                    if tabname != "extras":
-                        save = gr.Button('Save', elem_id=f'save_{tabname}')
-
-                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
-                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
-
-                open_folder_button.click(
-                    fn=lambda: open_folder(opts.outdir_samples or outdir),
-                    inputs=[],
-                    outputs=[],
-                )
-
-                if tabname != "extras":
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
-                        if tabname == 'txt2img' or tabname == 'img2img':
-                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
-                            generation_info_button.click(
-                                fn=update_generation_info,
-                                _js="(x, y) => [x, y, selected_gallery_index()]",
-                                inputs=[generation_info, html_info],
-                                outputs=[html_info],
-                                preprocess=False
-                            )
-
-                        save.click(
-                            fn=wrap_gradio_call(save_files),
-                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
-                            inputs=[
-                                generation_info,
-                                result_gallery,
-                                do_make_zip,
-                                html_info,
-                            ],
-                            outputs=[
-                                download_files,
-                                html_info,
-                                html_info,
-                                html_info,
-                            ]
-                        )
-                else:
-                    html_info_x = gr.HTML()
-                    html_info = gr.HTML()
-                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
-                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
-
-
-def create_ui(wrap_gradio_gpu_call):
-    import modules.img2img
-    import modules.txt2img
-
-    reload_javascript()
-
-    parameters_copypaste.reset()
-
-    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
-    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
-
-    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
-        dummy_component = gr.Label(visible=False)
-        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
-
-        with gr.Row(elem_id='txt2img_progress_row'):
-            with gr.Column(scale=1):
-                pass
-
-            with gr.Column(scale=1):
-                progressbar = gr.HTML(elem_id="txt2img_progressbar")
-                txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
-                setup_progressbar(progressbar, txt2img_preview, 'txt2img')
-
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
-
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-
-                with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
-                    enable_hr = gr.Checkbox(label='Highres. fix', value=False)
-
-                with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
-
-                with gr.Row(equal_height=True):
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
-                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
-                with gr.Group():
-                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
-
-            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
-            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
-
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
-            txt2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
-                _js="submit",
-                inputs=[
-                    txt2img_prompt,
-                    txt2img_negative_prompt,
-                    txt2img_prompt_style,
-                    txt2img_prompt_style2,
-                    steps,
-                    sampler_index,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    height,
-                    width,
-                    enable_hr,
-                    denoising_strength,
-                    firstphase_width,
-                    firstphase_height,
-                ] + custom_inputs,
-
-                outputs=[
-                    txt2img_gallery,
-                    generation_info,
-                    html_info
-                ],
-                show_progress=False,
-            )
-
-            txt2img_prompt.submit(**txt2img_args)
-            submit.click(**txt2img_args)
-
-            txt_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    txt_prompt_img
-                ],
-                outputs=[
-                    txt2img_prompt,
-                    txt_prompt_img
-                ]
-            )
-
-            enable_hr.change(
-                fn=lambda x: gr_show(x),
-                inputs=[enable_hr],
-                outputs=[hr_options],
-            )
-
-            roll.click(
-                fn=roll_artist,
-                _js="update_txt2img_tokens",
-                inputs=[
-                    txt2img_prompt,
-                ],
-                outputs=[
-                    txt2img_prompt,
-                ]
-            )
-
-            txt2img_paste_fields = [
-                (txt2img_prompt, "Prompt"),
-                (txt2img_negative_prompt, "Negative prompt"),
-                (steps, "Steps"),
-                (sampler_index, "Sampler"),
-                (restore_faces, "Face restoration"),
-                (cfg_scale, "CFG scale"),
-                (seed, "Seed"),
-                (width, "Size-1"),
-                (height, "Size-2"),
-                (batch_size, "Batch size"),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
-                (denoising_strength, "Denoising strength"),
-                (enable_hr, lambda d: "Denoising strength" in d),
-                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
-                (firstphase_width, "First pass size-1"),
-                (firstphase_height, "First pass size-2"),
-                *modules.scripts.scripts_txt2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
-
-            txt2img_preview_params = [
-                txt2img_prompt,
-                txt2img_negative_prompt,
-                steps,
-                sampler_index,
-                cfg_scale,
-                seed,
-                width,
-                height,
-            ]
-
-            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
-
-    modules.scripts.scripts_current = modules.scripts.scripts_img2img
-    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
-
-    with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
-
-        with gr.Row(elem_id='img2img_progress_row'):
-            img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
-
-            with gr.Column(scale=1):
-                pass
-
-            with gr.Column(scale=1):
-                progressbar = gr.HTML(elem_id="img2img_progressbar")
-                img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
-                setup_progressbar(progressbar, img2img_preview, 'img2img')
-
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-
-                with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
-                    with gr.TabItem('img2img', id='img2img'):
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
-
-                    with gr.TabItem('Inpaint', id='inpaint'):
-                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
-
-                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
-                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
-
-                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
-
-                        with gr.Row():
-                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
-                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
-
-                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
-
-                        with gr.Row():
-                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
-                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
-
-                    with gr.TabItem('Batch img2img', id='batch'):
-                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
-                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
-                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
-                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
-
-                with gr.Row():
-                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
-
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
-
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
-
-                with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
-
-                with gr.Row():
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
-
-                with gr.Group():
-                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
-
-                with gr.Group():
-                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()
-
-            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
-            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
-
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
-            img2img_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    img2img_prompt_img
-                ],
-                outputs=[
-                    img2img_prompt,
-                    img2img_prompt_img
-                ]
-            )
-
-            mask_mode.change(
-                lambda mode, img: {
-                    init_img_with_mask: gr_show(mode == 0),
-                    init_img_inpaint: gr_show(mode == 1),
-                    init_mask_inpaint: gr_show(mode == 1),
-                },
-                inputs=[mask_mode, init_img_with_mask],
-                outputs=[
-                    init_img_with_mask,
-                    init_img_inpaint,
-                    init_mask_inpaint,
-                ],
-            )
-
-            img2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
-                _js="submit_img2img",
-                inputs=[
-                    dummy_component,
-                    img2img_prompt,
-                    img2img_negative_prompt,
-                    img2img_prompt_style,
-                    img2img_prompt_style2,
-                    init_img,
-                    init_img_with_mask,
-                    init_img_inpaint,
-                    init_mask_inpaint,
-                    mask_mode,
-                    steps,
-                    sampler_index,
-                    mask_blur,
-                    inpainting_fill,
-                    restore_faces,
-                    tiling,
-                    batch_count,
-                    batch_size,
-                    cfg_scale,
-                    denoising_strength,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
-                    height,
-                    width,
-                    resize_mode,
-                    inpaint_full_res,
-                    inpaint_full_res_padding,
-                    inpainting_mask_invert,
-                    img2img_batch_input_dir,
-                    img2img_batch_output_dir,
-                ] + custom_inputs,
-                outputs=[
-                    img2img_gallery,
-                    generation_info,
-                    html_info
-                ],
-                show_progress=False,
-            )
-
-            img2img_prompt.submit(**img2img_args)
-            submit.click(**img2img_args)
-
-            img2img_interrogate.click(
-                fn=interrogate,
-                inputs=[init_img],
-                outputs=[img2img_prompt],
-            )
-
-            if cmd_opts.deepdanbooru:
-                img2img_deepbooru.click(
-                    fn=interrogate_deepbooru,
-                    inputs=[init_img],
-                    outputs=[img2img_prompt],
-            )
-
-
-            roll.click(
-                fn=roll_artist,
-                _js="update_img2img_tokens",
-                inputs=[
-                    img2img_prompt,
-                ],
-                outputs=[
-                    img2img_prompt,
-                ]
-            )
-
-            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
-            style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
-            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
-
-            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
-                button.click(
-                    fn=add_style,
-                    _js="ask_for_style_name",
-                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
-                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
-                    inputs=[dummy_component, prompt, negative_prompt],
-                    outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
-                )
-
-            for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
-                button.click(
-                    fn=apply_styles,
-                    _js=js_func,
-                    inputs=[prompt, negative_prompt, style1, style2],
-                    outputs=[prompt, negative_prompt, style1, style2],
-                )
-
-            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
-
-            img2img_paste_fields = [
-                (img2img_prompt, "Prompt"),
-                (img2img_negative_prompt, "Negative prompt"),
-                (steps, "Steps"),
-                (sampler_index, "Sampler"),
-                (restore_faces, "Face restoration"),
-                (cfg_scale, "CFG scale"),
-                (seed, "Seed"),
-                (width, "Size-1"),
-                (height, "Size-2"),
-                (batch_size, "Batch size"),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
-                (denoising_strength, "Denoising strength"),
-                *modules.scripts.scripts_img2img.infotext_fields
-            ]
-            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
-            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
-
-    modules.scripts.scripts_current = None
-
-    with gr.Blocks(analytics_enabled=False) as extras_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                with gr.Tabs(elem_id="mode_extras"):
-                    with gr.TabItem('Single Image'):
-                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
-
-                    with gr.TabItem('Batch Process'):
-                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
-
-                    with gr.TabItem('Batch from Directory'):
-                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
-                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
-                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
-
-                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
-                with gr.Tabs(elem_id="extras_resize_mode"):
-                    with gr.TabItem('Scale by'):
-                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
-                    with gr.TabItem('Scale to'):
-                        with gr.Group():
-                            with gr.Row():
-                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
-                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
-                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-
-                with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-
-                with gr.Group():
-                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
-
-                with gr.Group():
-                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
-
-                with gr.Group():
-                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
-                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
-
-                with gr.Group():
-                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
-
-            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
-
-        submit.click(
-            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
-            _js="get_extras_tab_index",
-            inputs=[
-                dummy_component,
-                dummy_component,
-                extras_image,
-                image_batch,
-                extras_batch_input_dir,
-                extras_batch_output_dir,
-                show_extras_results,
-                gfpgan_visibility,
-                codeformer_visibility,
-                codeformer_weight,
-                upscaling_resize,
-                upscaling_resize_w,
-                upscaling_resize_h,
-                upscaling_crop,
-                extras_upscaler_1,
-                extras_upscaler_2,
-                extras_upscaler_2_visibility,
-                upscale_before_face_fix,
-            ],
-            outputs=[
-                result_images,
-                html_info_x,
-                html_info,
-            ]
-        )
-        parameters_copypaste.add_paste_fields("extras", extras_image, None)
-
-        extras_image.change(
-            fn=modules.extras.clear_cache,
-            inputs=[], outputs=[]
-        )
-
-    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
-
-            with gr.Column(variant='panel'):
-                html = gr.HTML()
-                generation_info = gr.Textbox(visible=False)
-                html2 = gr.HTML()
-                with gr.Row():
-                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-                parameters_copypaste.bind_buttons(buttons, image, generation_info)
-
-        image.change(
-            fn=wrap_gradio_call(modules.extras.run_pnginfo),
-            inputs=[image],
-            outputs=[html, generation_info, html2],
-        )
-
-    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
-
-                with gr.Row():
-                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
-                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
-                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
-                custom_name = gr.Textbox(label="Custom Name (Optional)")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
-                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
-                save_as_half = gr.Checkbox(value=False, label="Save as float16")
-                save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")
-                modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
-
-            with gr.Column(variant='panel'):
-                submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
-
-    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
-
-    with gr.Blocks(analytics_enabled=False) as train_interface:
-        with gr.Row().style(equal_height=False):
-            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
-
-        with gr.Row().style(equal_height=False):
-            with gr.Tabs(elem_id="train_tabs"):
-
-                with gr.Tab(label="Create embedding"):
-                    new_embedding_name = gr.Textbox(label="Name")
-                    initialization_text = gr.Textbox(label="Initialization text", value="*")
-                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
-                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_embedding = gr.Button(value="Create embedding", variant='primary')
-
-                with gr.Tab(label="Create hypernetwork"):
-                    new_hypernetwork_name = gr.Textbox(label="Name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
-                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
-                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
-                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
-                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
-
-                with gr.Tab(label="Preprocess images"):
-                    process_src = gr.Textbox(label='Source directory')
-                    process_dst = gr.Textbox(label='Destination directory')
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
-
-                    with gr.Row():
-                        process_flip = gr.Checkbox(label='Create flipped copies')
-                        process_split = gr.Checkbox(label='Split oversized images')
-                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')
-                        process_caption = gr.Checkbox(label='Use BLIP for caption')
-                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
-
-                    with gr.Row(visible=False) as process_split_extra_row:
-                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
-
-                    with gr.Row(visible=False) as process_focal_crop_row:
-                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')
-
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            with gr.Row():
-                                interrupt_preprocessing = gr.Button("Interrupt")
-                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
-
-                    process_split.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_split],
-                        outputs=[process_split_extra_row],
-                    )
-
-                    process_focal_crop.change(
-                        fn=lambda show: gr_show(show),
-                        inputs=[process_focal_crop],
-                        outputs=[process_focal_crop_row],
-                    )
-
-                with gr.Tab(label="Train"):
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
-                    with gr.Row():
-                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
-                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
-                    with gr.Row():
-                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
-                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
-                    with gr.Row():
-                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
-                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
-
-                    batch_size = gr.Number(label='Batch size', value=1, precision=0)
-                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
-                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
-                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
-                    steps = gr.Number(label='Max steps', value=100000, precision=0)
-                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
-                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
-
-                    with gr.Row():
-                        interrupt_training = gr.Button(value="Interrupt")
-                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
-                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
-
-                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
-
-                script_callbacks.ui_train_tabs_callback(params)
-
-            with gr.Column():
-                progressbar = gr.HTML(elem_id="ti_progressbar")
-                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-
-                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
-                ti_preview = gr.Image(elem_id='ti_preview', visible=False)
-                ti_progress = gr.HTML(elem_id="ti_progress", value="")
-                ti_outcome = gr.HTML(elem_id="ti_error", value="")
-                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
-
-        create_embedding.click(
-            fn=modules.textual_inversion.ui.create_embedding,
-            inputs=[
-                new_embedding_name,
-                initialization_text,
-                nvpt,
-                overwrite_old_embedding,
-            ],
-            outputs=[
-                train_embedding_name,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        create_hypernetwork.click(
-            fn=modules.hypernetworks.ui.create_hypernetwork,
-            inputs=[
-                new_hypernetwork_name,
-                new_hypernetwork_sizes,
-                overwrite_old_hypernetwork,
-                new_hypernetwork_layer_structure,
-                new_hypernetwork_activation_func,
-                new_hypernetwork_initialization_option,
-                new_hypernetwork_add_layer_norm,
-                new_hypernetwork_use_dropout
-            ],
-            outputs=[
-                train_hypernetwork_name,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        run_preprocess.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                process_src,
-                process_dst,
-                process_width,
-                process_height,
-                preprocess_txt_action,
-                process_flip,
-                process_split,
-                process_caption,
-                process_caption_deepbooru,
-                process_split_threshold,
-                process_overlap_ratio,
-                process_focal_crop,
-                process_focal_crop_face_weight,
-                process_focal_crop_entropy_weight,
-                process_focal_crop_edges_weight,
-                process_focal_crop_debug,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ],
-        )
-
-        train_embedding.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                train_embedding_name,
-                embedding_learn_rate,
-                batch_size,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                steps,
-                create_image_every,
-                save_embedding_every,
-                template_file,
-                save_image_with_stored_embedding,
-                preview_from_txt2img,
-                *txt2img_preview_params,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        train_hypernetwork.click(
-            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                train_hypernetwork_name,
-                hypernetwork_learn_rate,
-                batch_size,
-                dataset_directory,
-                log_directory,
-                training_width,
-                training_height,
-                steps,
-                create_image_every,
-                save_embedding_every,
-                template_file,
-                preview_from_txt2img,
-                *txt2img_preview_params,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        interrupt_training.click(
-            fn=lambda: shared.state.interrupt(),
-            inputs=[],
-            outputs=[],
-        )
-
-        interrupt_preprocessing.click(
-            fn=lambda: shared.state.interrupt(),
-            inputs=[],
-            outputs=[],
-        )
-
-    def create_setting_component(key, is_quicksettings=False):
-        def fun():
-            return opts.data[key] if key in opts.data else opts.data_labels[key].default
-
-        info = opts.data_labels[key]
-        t = type(info.default)
-
-        args = info.component_args() if callable(info.component_args) else info.component_args
-
-        if info.component is not None:
-            comp = info.component
-        elif t == str:
-            comp = gr.Textbox
-        elif t == int:
-            comp = gr.Number
-        elif t == bool:
-            comp = gr.Checkbox
-        else:
-            raise Exception(f'bad options item type: {str(t)} for key {key}')
-
-        elem_id = "setting_"+key
-
-        if info.refresh is not None:
-            if is_quicksettings:
-                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
-            else:
-                with gr.Row(variant="compact"):
-                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-                    create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
-        else:
-            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
-
-        return res
-
-    components = []
-    component_dict = {}
-
-    script_callbacks.ui_settings_callback()
-    opts.reorder()
-
-    def run_settings(*args):
-        changed = []
-
-        for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
-
-        for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            if comp == dummy_component:
-                continue
-
-            if opts.set(key, value):
-                changed.append(key)
-
-        try:
-            opts.save(shared.config_filename)
-        except RuntimeError:
-            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
-        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
-
-    def run_settings_single(value, key):
-        if not opts.same_type(value, opts.data_labels[key].default):
-            return gr.update(visible=True), opts.dumpjson()
-
-        if not opts.set(key, value):
-            return gr.update(value=getattr(opts, key)), opts.dumpjson()
-
-        opts.save(shared.config_filename)
-
-        return gr.update(value=value), opts.dumpjson()
-
-    with gr.Blocks(analytics_enabled=False) as settings_interface:
-        settings_submit = gr.Button(value="Apply settings", variant='primary')
-        result = gr.HTML()
-
-        settings_cols = 3
-        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
-
-        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
-        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
-
-        quicksettings_list = []
-
-        cols_displayed = 0
-        items_displayed = 0
-        previous_section = None
-        column = None
-        with gr.Row(elem_id="settings").style(equal_height=False):
-            for i, (k, item) in enumerate(opts.data_labels.items()):
-                section_must_be_skipped = item.section[0] is None
-
-                if previous_section != item.section and not section_must_be_skipped:
-                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
-                        if column is not None:
-                            column.__exit__()
-
-                        column = gr.Column(variant='panel')
-                        column.__enter__()
-
-                        items_displayed = 0
-                        cols_displayed += 1
-
-                    previous_section = item.section
-
-                    elem_id, text = item.section
-                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
-
-                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
-                    quicksettings_list.append((i, k, item))
-                    components.append(dummy_component)
-                elif section_must_be_skipped:
-                    components.append(dummy_component)
-                else:
-                    component = create_setting_component(k)
-                    component_dict[k] = component
-                    components.append(component)
-                    items_displayed += 1
-
-        with gr.Row():
-            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
-            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
-
-        with gr.Row():
-            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
-            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
-
-        request_notifications.click(
-            fn=lambda: None,
-            inputs=[],
-            outputs=[],
-            _js='function(){}'
-        )
-
-        download_localization.click(
-            fn=lambda: None,
-            inputs=[],
-            outputs=[],
-            _js='download_localization'
-        )
-
-        def reload_scripts():
-            modules.scripts.reload_script_body_only()
-            reload_javascript()  # need to refresh the html page
-
-        reload_script_bodies.click(
-            fn=reload_scripts,
-            inputs=[],
-            outputs=[]
-        )
-
-        def request_restart():
-            shared.state.interrupt()
-            shared.state.need_restart = True
-
-        restart_gradio.click(
-            fn=request_restart,
-            _js='restart_reload',
-            inputs=[],
-            outputs=[],
-        )
-
-        if column is not None:
-            column.__exit__()
-
-    interfaces = [
-        (txt2img_interface, "txt2img", "txt2img"),
-        (img2img_interface, "img2img", "img2img"),
-        (extras_interface, "Extras", "extras"),
-        (pnginfo_interface, "PNG Info", "pnginfo"),
-        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
-        (train_interface, "Train", "ti"),
-    ]
-
-    css = ""
-
-    for cssfile in modules.scripts.list_files_with_name("style.css"):
-        if not os.path.isfile(cssfile):
-            continue
-
-        with open(cssfile, "r", encoding="utf8") as file:
-            css += file.read() + "\n"
-
-    if os.path.exists(os.path.join(script_path, "user.css")):
-        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
-            css += file.read() + "\n"
-
-    if not cmd_opts.no_progressbar_hiding:
-        css += css_hide_progressbar
-
-    interfaces += script_callbacks.ui_tabs_callback()
-    interfaces += [(settings_interface, "Settings", "settings")]
-
-    extensions_interface = ui_extensions.create_ui()
-    interfaces += [(extensions_interface, "Extensions", "extensions")]
-
-    with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
-        with gr.Row(elem_id="quicksettings"):
-            for i, k, item in quicksettings_list:
-                component = create_setting_component(k, is_quicksettings=True)
-                component_dict[k] = component
-
-        parameters_copypaste.integrate_settings_paste_fields(component_dict)
-        parameters_copypaste.run_bind()
-
-        with gr.Tabs(elem_id="tabs") as tabs:
-            for interface, label, ifid in interfaces:
-                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
-                    interface.render()
-
-        if os.path.exists(os.path.join(script_path, "notification.mp3")):
-            audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
-
-        text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
-        settings_submit.click(
-            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
-            inputs=components,
-            outputs=[text_settings, result],
-        )
-
-        for i, k, item in quicksettings_list:
-            component = component_dict[k]
-
-            component.change(
-                fn=lambda value, k=k: run_settings_single(value, key=k),
-                inputs=[component],
-                outputs=[component, text_settings],
-            )
-
-        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
-
-        def get_settings_values():
-            return [getattr(opts, key) for key in component_keys]
-
-        demo.load(
-            fn=get_settings_values,
-            inputs=[],
-            outputs=[component_dict[k] for k in component_keys],
-        )
-
-        def modelmerger(*args):
-            try:
-                results = modules.extras.run_modelmerger(*args)
-            except Exception as e:
-                print("Error loading/saving model file:", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-                modules.sd_models.list_models()  # to remove the potentially missing models from the list
-                return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
-            return results
-
-        modelmerger_merge.click(
-            fn=modelmerger,
-            inputs=[
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                interp_method,
-                interp_amount,
-                save_as_half,
-                save_as_safetensors,
-                custom_name,
-            ],
-            outputs=[
-                submit_result,
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                component_dict['sd_model_checkpoint'],
-            ]
-        )
-
-    ui_config_file = cmd_opts.ui_config_file
-    ui_settings = {}
-    settings_count = len(ui_settings)
-    error_loading = False
-
-    try:
-        if os.path.exists(ui_config_file):
-            with open(ui_config_file, "r", encoding="utf8") as file:
-                ui_settings = json.load(file)
-    except Exception:
-        error_loading = True
-        print("Error loading settings:", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-
-    def loadsave(path, x):
-        def apply_field(obj, field, condition=None, init_field=None):
-            key = path + "/" + field
-
-            if getattr(obj, 'custom_script_source', None) is not None:
-              key = 'customscript/' + obj.custom_script_source + '/' + key
-
-            if getattr(obj, 'do_not_save_to_config', False):
-                return
-
-            saved_value = ui_settings.get(key, None)
-            if saved_value is None:
-                ui_settings[key] = getattr(obj, field)
-            elif condition and not condition(saved_value):
-                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
-            else:
-                setattr(obj, field, saved_value)
-                if init_field is not None:
-                    init_field(saved_value)
-
-        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
-            apply_field(x, 'visible')
-
-        if type(x) == gr.Slider:
-            apply_field(x, 'value')
-            apply_field(x, 'minimum')
-            apply_field(x, 'maximum')
-            apply_field(x, 'step')
-
-        if type(x) == gr.Radio:
-            apply_field(x, 'value', lambda val: val in x.choices)
-
-        if type(x) == gr.Checkbox:
-            apply_field(x, 'value')
-
-        if type(x) == gr.Textbox:
-            apply_field(x, 'value')
-
-        if type(x) == gr.Number:
-            apply_field(x, 'value')
-
-        # Since there are many dropdowns that shouldn't be saved,
-        # we only mark dropdowns that should be saved.
-        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
-            apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
-            apply_field(x, 'visible')
-
-    visit(txt2img_interface, loadsave, "txt2img")
-    visit(img2img_interface, loadsave, "img2img")
-    visit(extras_interface, loadsave, "extras")
-    visit(modelmerger_interface, loadsave, "modelmerger")
-
-    if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
-        with open(ui_config_file, "w", encoding="utf8") as file:
-            json.dump(ui_settings, file, indent=4)
-
-    return demo
-
-
-def reload_javascript():
-    with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
-        javascript = f'<script>{jsfile.read()}</script>'
-
-    scripts_list = modules.scripts.list_scripts("javascript", ".js")
-
-    for basedir, filename, path in scripts_list:
-        with open(path, "r", encoding="utf8") as jsfile:
-            javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
-
-    if cmd_opts.theme is not None:
-        javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
-
-    javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
-
-    def template_response(*args, **kwargs):
-        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
-        res.body = res.body.replace(
-            b'</head>', f'{javascript}</head>'.encode("utf8"))
-        res.init_headers()
-        return res
-
-    gradio.routes.templates.TemplateResponse = template_response
-
-
-if not hasattr(shared, 'GradioTemplateResponseOriginal'):
-    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
+import html
+import json
+import math
+import mimetypes
+import os
+import platform
+import random
+import subprocess as sp
+import sys
+import tempfile
+import time
+import traceback
+from functools import partial, reduce
+
+import gradio as gr
+import gradio.routes
+import gradio.utils
+import numpy as np
+from PIL import Image, PngImagePlugin
+
+
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
+from modules.paths import script_path
+
+from modules.shared import opts, cmd_opts, restricted_opts
+
+if cmd_opts.deepdanbooru:
+    from modules.deepbooru import get_deepbooru_tags
+
+import modules.codeformer_model
+import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.gfpgan_model
+import modules.hypernetworks.ui
+import modules.ldsr_model
+import modules.scripts
+import modules.shared as shared
+import modules.styles
+import modules.textual_inversion.ui
+from modules import prompt_parser
+from modules.images import save_image
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers import samplers, samplers_for_img2img
+import modules.textual_inversion.ui
+import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
+
+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
+mimetypes.init()
+mimetypes.add_type('application/javascript', '.js')
+
+if not cmd_opts.share and not cmd_opts.listen:
+    # fix gradio phoning home
+    gradio.utils.version_check = lambda: None
+    gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
+
+if cmd_opts.ngrok != None:
+    import modules.ngrok as ngrok
+    print('ngrok authtoken detected, trying to connect...')
+    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
+
+
+def gr_show(visible=True):
+    return {"visible": visible, "__type__": "update"}
+
+
+sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
+sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
+
+css_hide_progressbar = """
+.wrap .m-12 svg { display:none!important; }
+.wrap .m-12::before { content:"Loading..." }
+.wrap .z-20 svg { display:none!important; }
+.wrap .z-20::before { content:"Loading..." }
+.progress-bar { display:none!important; }
+.meta-text { display:none!important; }
+.meta-text-center { display:none!important; }
+"""
+
+# Using constants for these since the variation selector isn't visible.
+# Important that they exactly match script.js for tooltip to work.
+random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
+reuse_symbol = '\u267b\ufe0f'  # ♻️
+art_symbol = '\U0001f3a8'  # 🎨
+paste_symbol = '\u2199\ufe0f'  # ↙
+folder_symbol = '\U0001f4c2'  # 📂
+refresh_symbol = '\U0001f504'  # 🔄
+save_style_symbol = '\U0001f4be'  # 💾
+apply_style_symbol = '\U0001f4cb'  # 📋
+
+
+def plaintext_to_html(text):
+    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
+    return text
+
+def send_gradio_gallery_to_image(x):
+    if len(x) == 0:
+        return None
+    return image_from_url_text(x[0])
+
+def save_files(js_data, images, do_make_zip, index):
+    import csv
+    filenames = []
+    fullfns = []
+
+    #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
+    class MyObject:
+        def __init__(self, d=None):
+            if d is not None:
+                for key, value in d.items():
+                    setattr(self, key, value)
+
+    data = json.loads(js_data)
+
+    p = MyObject(data)
+    path = opts.outdir_save
+    save_to_dirs = opts.use_save_to_dirs_for_ui
+    extension: str = opts.samples_format
+    start_index = 0
+
+    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
+
+        images = [images[index]]
+        start_index = index
+
+    os.makedirs(opts.outdir_save, exist_ok=True)
+
+    with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
+        at_start = file.tell() == 0
+        writer = csv.writer(file)
+        if at_start:
+            writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
+
+        for image_index, filedata in enumerate(images, start_index):
+            image = image_from_url_text(filedata)
+
+            is_grid = image_index < p.index_of_first_image
+            i = 0 if is_grid else (image_index - p.index_of_first_image)
+
+            fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+
+            filename = os.path.relpath(fullfn, path)
+            filenames.append(filename)
+            fullfns.append(fullfn)
+            if txt_fullfn:
+                filenames.append(os.path.basename(txt_fullfn))
+                fullfns.append(txt_fullfn)
+
+        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+
+    # Make Zip
+    if do_make_zip:
+        zip_filepath = os.path.join(path, "images.zip")
+
+        from zipfile import ZipFile
+        with ZipFile(zip_filepath, "w") as zip_file:
+            for i in range(len(fullfns)):
+                with open(fullfns[i], mode="rb") as f:
+                    zip_file.writestr(filenames[i], f.read())
+        fullfns.insert(0, zip_filepath)
+
+    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+
+def save_pil_to_file(pil_image, dir=None):
+    use_metadata = False
+    metadata = PngImagePlugin.PngInfo()
+    for key, value in pil_image.info.items():
+        if isinstance(key, str) and isinstance(value, str):
+            metadata.add_text(key, value)
+            use_metadata = True
+
+    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
+    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
+    return file_obj
+
+
+# override save to file function so that it also writes PNG info
+gr.processing_utils.save_pil_to_file = save_pil_to_file
+
+
+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
+        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
+        if run_memmon:
+            shared.mem_mon.monitor()
+        t = time.perf_counter()
+
+        try:
+            res = list(func(*args, **kwargs))
+        except Exception as e:
+            # When printing out our debug argument list, do not print out more than a MB of text
+            max_debug_str_len = 131072 # (1024*1024)/8
+
+            print("Error completing request", file=sys.stderr)
+            argStr = f"Arguments: {str(args)} {str(kwargs)}"
+            print(argStr[:max_debug_str_len], file=sys.stderr)
+            if len(argStr) > max_debug_str_len:
+                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
+
+            print(traceback.format_exc(), file=sys.stderr)
+
+            shared.state.job = ""
+            shared.state.job_count = 0
+
+            if extra_outputs_array is None:
+                extra_outputs_array = [None, '']
+
+            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
+
+        shared.state.skipped = False
+        shared.state.interrupted = False
+        shared.state.job_count = 0
+
+        if not add_stats:
+            return tuple(res)
+
+        elapsed = time.perf_counter() - t
+        elapsed_m = int(elapsed // 60)
+        elapsed_s = elapsed % 60
+        elapsed_text = f"{elapsed_s:.2f}s"
+        if elapsed_m > 0:
+            elapsed_text = f"{elapsed_m}m "+elapsed_text
+
+        if run_memmon:
+            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
+            active_peak = mem_stats['active_peak']
+            reserved_peak = mem_stats['reserved_peak']
+            sys_peak = mem_stats['system_peak']
+            sys_total = mem_stats['total']
+            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
+
+            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
+        else:
+            vram_html = ''
+
+        # last item is always HTML
+        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
+
+        return tuple(res)
+
+    return f
+
+
+def calc_time_left(progress, threshold, label, force_display):
+    if progress == 0:
+        return ""
+    else:
+        time_since_start = time.time() - shared.state.time_start
+        eta = (time_since_start/progress)
+        eta_relative = eta-time_since_start
+        if (eta_relative > threshold and progress > 0.02) or force_display:
+            if eta_relative > 3600:
+                return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
+            elif eta_relative > 60:
+                return label + time.strftime('%M:%S',  time.gmtime(eta_relative))
+            else:
+                return label + time.strftime('%Ss',  time.gmtime(eta_relative))
+        else:
+            return ""
+
+
+def check_progress_call(id_part):
+    if shared.state.job_count == 0:
+        return "", gr_show(False), gr_show(False), gr_show(False)
+
+    progress = 0
+
+    if shared.state.job_count > 0:
+        progress += shared.state.job_no / shared.state.job_count
+    if shared.state.sampling_steps > 0:
+        progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
+
+    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display )
+    if time_left != "":
+        shared.state.time_left_force_display = True
+
+    progress = min(progress, 1)
+
+    progressbar = ""
+    if opts.show_progressbar:
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}</div></div>"""
+
+    image = gr_show(False)
+    preview_visibility = gr_show(False)
+
+    if opts.show_progress_every_n_steps != 0:
+        shared.state.set_current_image()
+        image = shared.state.current_image
+
+        if image is None:
+            image = gr.update(value=None)
+        else:
+            preview_visibility = gr_show(True)
+
+    if shared.state.textinfo is not None:
+        textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
+    else:
+        textinfo_result = gr_show(False)
+
+    return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
+
+
+def check_progress_call_initial(id_part):
+    shared.state.job_count = -1
+    shared.state.current_latent = None
+    shared.state.current_image = None
+    shared.state.textinfo = None
+    shared.state.time_start = time.time()
+    shared.state.time_left_force_display = False
+
+    return check_progress_call(id_part)
+
+
+def roll_artist(prompt):
+    allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
+    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
+
+    return prompt + ", " + artist.name if prompt != '' else artist.name
+
+
+def visit(x, func, path=""):
+    if hasattr(x, 'children'):
+        for c in x.children:
+            visit(c, func, path)
+    elif x.label is not None:
+        func(path + "/" + str(x.label), x)
+
+
+def add_style(name: str, prompt: str, negative_prompt: str):
+    if name is None:
+        return [gr_show() for x in range(4)]
+
+    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
+    shared.prompt_styles.styles[style.name] = style
+    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
+    # reserialize all styles every time we save them
+    shared.prompt_styles.save_styles(shared.styles_filename)
+
+    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(4)]
+
+
+def apply_styles(prompt, prompt_neg, style1_name, style2_name):
+    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
+    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
+
+    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
+
+
+def interrogate(image):
+    prompt = shared.interrogator.interrogate(image)
+
+    return gr_show(True) if prompt is None else prompt
+
+
+def interrogate_deepbooru(image):
+    prompt = get_deepbooru_tags(image)
+    return gr_show(True) if prompt is None else prompt
+
+
+def create_seed_inputs():
+    with gr.Row():
+        with gr.Box():
+            with gr.Row(elem_id='seed_row'):
+                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
+                seed.style(container=False)
+                random_seed = gr.Button(random_symbol, elem_id='random_seed')
+                reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
+
+        with gr.Box(elem_id='subseed_show_box'):
+            seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
+
+    # Components to show/hide based on the 'Extra' checkbox
+    seed_extras = []
+
+    with gr.Row(visible=False) as seed_extra_row_1:
+        seed_extras.append(seed_extra_row_1)
+        with gr.Box():
+            with gr.Row(elem_id='subseed_row'):
+                subseed = gr.Number(label='Variation seed', value=-1)
+                subseed.style(container=False)
+                random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
+                reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
+        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
+
+    with gr.Row(visible=False) as seed_extra_row_2:
+        seed_extras.append(seed_extra_row_2)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0)
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
+
+    random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
+    random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
+
+    def change_visibility(show):
+        return {comp: gr_show(show) for comp in seed_extras}
+
+    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
+
+    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
+
+
+def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
+    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
+        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
+        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
+    def copy_seed(gen_info_string: str, index):
+        res = -1
+
+        try:
+            gen_info = json.loads(gen_info_string)
+            index -= gen_info.get('index_of_first_image', 0)
+
+            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+                all_subseeds = gen_info.get('all_subseeds', [-1])
+                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
+            else:
+                all_seeds = gen_info.get('all_seeds', [-1])
+                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
+
+        except json.decoder.JSONDecodeError as e:
+            if gen_info_string != '':
+                print("Error parsing JSON generation info:", file=sys.stderr)
+                print(gen_info_string, file=sys.stderr)
+
+        return [res, gr_show(False)]
+
+    reuse_seed.click(
+        fn=copy_seed,
+        _js="(x, y) => [x, selected_gallery_index()]",
+        show_progress=False,
+        inputs=[generation_info, dummy_component],
+        outputs=[seed, dummy_component]
+    )
+
+
+def update_token_counter(text, steps):
+    try:
+        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
+
+    except Exception:
+        # a parsing error can happen here during typing, and we don't want to bother the user with
+        # messages related to it in console
+        prompt_schedules = [[[steps, text]]]
+
+    flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
+    prompts = [prompt_text for step, prompt_text in flat_prompts]
+    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
+    style_class = ' class="red"' if (token_count > max_length) else ""
+    return f"<span {style_class}>{token_count}/{max_length}</span>"
+
+
+def create_toprow(is_img2img):
+    id_part = "img2img" if is_img2img else "txt2img"
+
+    with gr.Row(elem_id="toprow"):
+        with gr.Column(scale=6):
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
+                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
+                        )
+
+            with gr.Row():
+                with gr.Column(scale=80):
+                    with gr.Row():
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
+                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
+                        )
+
+        with gr.Column(scale=1, elem_id="roll_col"):
+            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
+            paste = gr.Button(value=paste_symbol, elem_id="paste")
+            save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
+            prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
+
+            token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
+            token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+
+        button_interrogate = None
+        button_deepbooru = None
+        if is_img2img:
+            with gr.Column(scale=1, elem_id="interrogate_col"):
+                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+
+                if cmd_opts.deepdanbooru:
+                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+
+        with gr.Column(scale=1):
+            with gr.Row():
+                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
+                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
+                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
+
+                skip.click(
+                    fn=lambda: shared.state.skip(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                interrupt.click(
+                    fn=lambda: shared.state.interrupt(),
+                    inputs=[],
+                    outputs=[],
+                )
+
+            with gr.Row():
+                with gr.Column(scale=1, elem_id="style_pos_col"):
+                    prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style.save_to_config = True
+
+                with gr.Column(scale=1, elem_id="style_neg_col"):
+                    prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style2.save_to_config = True
+
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
+
+
+def setup_progressbar(progressbar, preview, id_part, textinfo=None):
+    if textinfo is None:
+        textinfo = gr.HTML(visible=False)
+
+    check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
+    check_progress.click(
+        fn=lambda: check_progress_call(id_part),
+        show_progress=False,
+        inputs=[],
+        outputs=[progressbar, preview, preview, textinfo],
+    )
+
+    check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
+    check_progress_initial.click(
+        fn=lambda: check_progress_call_initial(id_part),
+        show_progress=False,
+        inputs=[],
+        outputs=[progressbar, preview, preview, textinfo],
+    )
+
+
+def apply_setting(key, value):
+    if value is None:
+        return gr.update()
+
+    if shared.cmd_opts.freeze_settings:
+        return gr.update()
+
+    # dont allow model to be swapped when model hash exists in prompt
+    if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+        return gr.update()
+
+    if key == "sd_model_checkpoint":
+        ckpt_info = sd_models.get_closet_checkpoint_match(value)
+
+        if ckpt_info is not None:
+            value = ckpt_info.title
+        else:
+            return gr.update()
+
+    comp_args = opts.data_labels[key].component_args
+    if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
+        return
+
+    valtype = type(opts.data_labels[key].default)
+    oldval = opts.data[key]
+    opts.data[key] = valtype(value) if valtype != type(None) else value
+    if oldval != value and opts.data_labels[key].onchange is not None:
+        opts.data_labels[key].onchange()
+
+    opts.save(shared.config_filename)
+    return value
+
+
+def update_generation_info(args):
+    generation_info, html_info, img_index = args
+    try:
+        generation_info = json.loads(generation_info)
+        if img_index < 0 or img_index >= len(generation_info["infotexts"]):
+            return html_info
+        return plaintext_to_html(generation_info["infotexts"][img_index])
+    except Exception:
+        pass
+    # if the json parse or anything else fails, just return the old html_info
+    return html_info
+
+
+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+    def refresh():
+        refresh_method()
+        args = refreshed_args() if callable(refreshed_args) else refreshed_args
+
+        for k, v in args.items():
+            setattr(refresh_component, k, v)
+
+        return gr.update(**(args or {}))
+
+    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
+    refresh_button.click(
+        fn=refresh,
+        inputs=[],
+        outputs=[refresh_component]
+    )
+    return refresh_button
+
+
+def create_output_panel(tabname, outdir):
+    def open_folder(f):
+        if not os.path.exists(f):
+            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
+            return
+        elif not os.path.isdir(f):
+            print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+            return
+
+        if not shared.cmd_opts.hide_ui_dir_config:
+            path = os.path.normpath(f)
+            if platform.system() == "Windows":
+                os.startfile(path)
+            elif platform.system() == "Darwin":
+                sp.Popen(["open", path])
+            else:
+                sp.Popen(["xdg-open", path])
+
+    with gr.Column(variant='panel'):
+            with gr.Group():
+                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+
+            generation_info = None
+            with gr.Column():
+                with gr.Row():
+                    if tabname != "extras":
+                        save = gr.Button('Save', elem_id=f'save_{tabname}')
+
+                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
+                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
+
+                open_folder_button.click(
+                    fn=lambda: open_folder(opts.outdir_samples or outdir),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                if tabname != "extras":
+                    with gr.Row():
+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+                    with gr.Row():
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
+                        if tabname == 'txt2img' or tabname == 'img2img':
+                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
+                            generation_info_button.click(
+                                fn=update_generation_info,
+                                _js="(x, y) => [x, y, selected_gallery_index()]",
+                                inputs=[generation_info, html_info],
+                                outputs=[html_info],
+                                preprocess=False
+                            )
+
+                        save.click(
+                            fn=wrap_gradio_call(save_files),
+                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
+                            inputs=[
+                                generation_info,
+                                result_gallery,
+                                do_make_zip,
+                                html_info,
+                            ],
+                            outputs=[
+                                download_files,
+                                html_info,
+                                html_info,
+                                html_info,
+                            ]
+                        )
+                else:
+                    html_info_x = gr.HTML()
+                    html_info = gr.HTML()
+                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
+
+
+def create_ui(wrap_gradio_gpu_call):
+    import modules.img2img
+    import modules.txt2img
+
+    reload_javascript()
+
+    parameters_copypaste.reset()
+
+    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
+    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+
+    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
+        dummy_component = gr.Label(visible=False)
+        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
+
+        with gr.Row(elem_id='txt2img_progress_row'):
+            with gr.Column(scale=1):
+                pass
+
+            with gr.Column(scale=1):
+                progressbar = gr.HTML(elem_id="txt2img_progressbar")
+                txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
+                setup_progressbar(progressbar, txt2img_preview, 'txt2img')
+
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+                sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
+
+                with gr.Group():
+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+
+                with gr.Row():
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
+                    tiling = gr.Checkbox(label='Tiling', value=False)
+                    enable_hr = gr.Checkbox(label='Highres. fix', value=False)
+
+                with gr.Row(visible=False) as hr_options:
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
+
+                with gr.Row(equal_height=True):
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+
+                with gr.Group():
+                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+
+            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            txt2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
+                _js="submit",
+                inputs=[
+                    txt2img_prompt,
+                    txt2img_negative_prompt,
+                    txt2img_prompt_style,
+                    txt2img_prompt_style2,
+                    steps,
+                    sampler_index,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    height,
+                    width,
+                    enable_hr,
+                    denoising_strength,
+                    firstphase_width,
+                    firstphase_height,
+                ] + custom_inputs,
+
+                outputs=[
+                    txt2img_gallery,
+                    generation_info,
+                    html_info
+                ],
+                show_progress=False,
+            )
+
+            txt2img_prompt.submit(**txt2img_args)
+            submit.click(**txt2img_args)
+
+            txt_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    txt_prompt_img
+                ],
+                outputs=[
+                    txt2img_prompt,
+                    txt_prompt_img
+                ]
+            )
+
+            enable_hr.change(
+                fn=lambda x: gr_show(x),
+                inputs=[enable_hr],
+                outputs=[hr_options],
+            )
+
+            roll.click(
+                fn=roll_artist,
+                _js="update_txt2img_tokens",
+                inputs=[
+                    txt2img_prompt,
+                ],
+                outputs=[
+                    txt2img_prompt,
+                ]
+            )
+
+            txt2img_paste_fields = [
+                (txt2img_prompt, "Prompt"),
+                (txt2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (denoising_strength, "Denoising strength"),
+                (enable_hr, lambda d: "Denoising strength" in d),
+                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
+                (firstphase_width, "First pass size-1"),
+                (firstphase_height, "First pass size-2"),
+                *modules.scripts.scripts_txt2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
+
+            txt2img_preview_params = [
+                txt2img_prompt,
+                txt2img_negative_prompt,
+                steps,
+                sampler_index,
+                cfg_scale,
+                seed,
+                width,
+                height,
+            ]
+
+            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
+
+    modules.scripts.scripts_current = modules.scripts.scripts_img2img
+    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+
+    with gr.Blocks(analytics_enabled=False) as img2img_interface:
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
+
+        with gr.Row(elem_id='img2img_progress_row'):
+            img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
+
+            with gr.Column(scale=1):
+                pass
+
+            with gr.Column(scale=1):
+                progressbar = gr.HTML(elem_id="img2img_progressbar")
+                img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
+                setup_progressbar(progressbar, img2img_preview, 'img2img')
+
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+
+                with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
+                    with gr.TabItem('img2img', id='img2img'):
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
+
+                    with gr.TabItem('Inpaint', id='inpaint'):
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask",  show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
+
+                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
+                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
+
+                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+
+                        with gr.Row():
+                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
+                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
+
+                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
+
+                        with gr.Row():
+                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
+                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
+
+                    with gr.TabItem('Batch img2img', id='batch'):
+                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+                        gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
+                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
+                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
+
+                with gr.Row():
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
+
+                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
+                sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
+
+                with gr.Group():
+                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
+                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
+
+                with gr.Row():
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
+                    tiling = gr.Checkbox(label='Tiling', value=False)
+
+                with gr.Row():
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+
+                with gr.Group():
+                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
+
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+
+                with gr.Group():
+                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+
+            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
+            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
+
+            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
+            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
+
+            img2img_prompt_img.change(
+                fn=modules.images.image_data,
+                inputs=[
+                    img2img_prompt_img
+                ],
+                outputs=[
+                    img2img_prompt,
+                    img2img_prompt_img
+                ]
+            )
+
+            mask_mode.change(
+                lambda mode, img: {
+                    init_img_with_mask: gr_show(mode == 0),
+                    init_img_inpaint: gr_show(mode == 1),
+                    init_mask_inpaint: gr_show(mode == 1),
+                },
+                inputs=[mask_mode, init_img_with_mask],
+                outputs=[
+                    init_img_with_mask,
+                    init_img_inpaint,
+                    init_mask_inpaint,
+                ],
+            )
+
+            img2img_args = dict(
+                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
+                _js="submit_img2img",
+                inputs=[
+                    dummy_component,
+                    img2img_prompt,
+                    img2img_negative_prompt,
+                    img2img_prompt_style,
+                    img2img_prompt_style2,
+                    init_img,
+                    init_img_with_mask,
+                    init_img_inpaint,
+                    init_mask_inpaint,
+                    mask_mode,
+                    steps,
+                    sampler_index,
+                    mask_blur,
+                    inpainting_fill,
+                    restore_faces,
+                    tiling,
+                    batch_count,
+                    batch_size,
+                    cfg_scale,
+                    denoising_strength,
+                    seed,
+                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    height,
+                    width,
+                    resize_mode,
+                    inpaint_full_res,
+                    inpaint_full_res_padding,
+                    inpainting_mask_invert,
+                    img2img_batch_input_dir,
+                    img2img_batch_output_dir,
+                ] + custom_inputs,
+                outputs=[
+                    img2img_gallery,
+                    generation_info,
+                    html_info
+                ],
+                show_progress=False,
+            )
+
+            img2img_prompt.submit(**img2img_args)
+            submit.click(**img2img_args)
+
+            img2img_interrogate.click(
+                fn=interrogate,
+                inputs=[init_img],
+                outputs=[img2img_prompt],
+            )
+
+            if cmd_opts.deepdanbooru:
+                img2img_deepbooru.click(
+                    fn=interrogate_deepbooru,
+                    inputs=[init_img],
+                    outputs=[img2img_prompt],
+            )
+
+
+            roll.click(
+                fn=roll_artist,
+                _js="update_img2img_tokens",
+                inputs=[
+                    img2img_prompt,
+                ],
+                outputs=[
+                    img2img_prompt,
+                ]
+            )
+
+            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
+            style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
+            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
+
+            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
+                button.click(
+                    fn=add_style,
+                    _js="ask_for_style_name",
+                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
+                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
+                    inputs=[dummy_component, prompt, negative_prompt],
+                    outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
+                )
+
+            for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
+                button.click(
+                    fn=apply_styles,
+                    _js=js_func,
+                    inputs=[prompt, negative_prompt, style1, style2],
+                    outputs=[prompt, negative_prompt, style1, style2],
+                )
+
+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+
+            img2img_paste_fields = [
+                (img2img_prompt, "Prompt"),
+                (img2img_negative_prompt, "Negative prompt"),
+                (steps, "Steps"),
+                (sampler_index, "Sampler"),
+                (restore_faces, "Face restoration"),
+                (cfg_scale, "CFG scale"),
+                (seed, "Seed"),
+                (width, "Size-1"),
+                (height, "Size-2"),
+                (batch_size, "Batch size"),
+                (subseed, "Variation seed"),
+                (subseed_strength, "Variation seed strength"),
+                (seed_resize_from_w, "Seed resize from-1"),
+                (seed_resize_from_h, "Seed resize from-2"),
+                (denoising_strength, "Denoising strength"),
+                *modules.scripts.scripts_img2img.infotext_fields
+            ]
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
+
+    modules.scripts.scripts_current = None
+
+    with gr.Blocks(analytics_enabled=False) as extras_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                with gr.Tabs(elem_id="mode_extras"):
+                    with gr.TabItem('Single Image'):
+                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
+
+                    with gr.TabItem('Batch Process'):
+                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
+
+                    with gr.TabItem('Batch from Directory'):
+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
+                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
+                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
+
+                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
+                with gr.Tabs(elem_id="extras_resize_mode"):
+                    with gr.TabItem('Scale by'):
+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
+                    with gr.TabItem('Scale to'):
+                        with gr.Group():
+                            with gr.Row():
+                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
+                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
+                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
+
+                with gr.Group():
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+
+                with gr.Group():
+                    extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
+
+                with gr.Group():
+                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
+
+                with gr.Group():
+                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
+                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
+
+                with gr.Group():
+                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
+
+            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
+
+        submit.click(
+            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
+            _js="get_extras_tab_index",
+            inputs=[
+                dummy_component,
+                dummy_component,
+                extras_image,
+                image_batch,
+                extras_batch_input_dir,
+                extras_batch_output_dir,
+                show_extras_results,
+                gfpgan_visibility,
+                codeformer_visibility,
+                codeformer_weight,
+                upscaling_resize,
+                upscaling_resize_w,
+                upscaling_resize_h,
+                upscaling_crop,
+                extras_upscaler_1,
+                extras_upscaler_2,
+                extras_upscaler_2_visibility,
+                upscale_before_face_fix,
+            ],
+            outputs=[
+                result_images,
+                html_info_x,
+                html_info,
+            ]
+        )
+        parameters_copypaste.add_paste_fields("extras", extras_image, None)
+
+        extras_image.change(
+            fn=modules.extras.clear_cache,
+            inputs=[], outputs=[]
+        )
+
+    with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
+
+            with gr.Column(variant='panel'):
+                html = gr.HTML()
+                generation_info = gr.Textbox(visible=False)
+                html2 = gr.HTML()
+                with gr.Row():
+                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+                parameters_copypaste.bind_buttons(buttons, image, generation_info)
+
+        image.change(
+            fn=wrap_gradio_call(modules.extras.run_pnginfo),
+            inputs=[image],
+            outputs=[html, generation_info, html2],
+        )
+
+    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+        with gr.Row().style(equal_height=False):
+            with gr.Column(variant='panel'):
+                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
+
+                with gr.Row():
+                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+                custom_name = gr.Textbox(label="Custom Name (Optional)")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
+                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
+                save_as_half = gr.Checkbox(value=False, label="Save as float16")
+                save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")
+                modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
+
+            with gr.Column(variant='panel'):
+                submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
+
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+    with gr.Blocks(analytics_enabled=False) as train_interface:
+        with gr.Row().style(equal_height=False):
+            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+
+        with gr.Row().style(equal_height=False):
+            with gr.Tabs(elem_id="train_tabs"):
+
+                with gr.Tab(label="Create embedding"):
+                    new_embedding_name = gr.Textbox(label="Name")
+                    initialization_text = gr.Textbox(label="Initialization text", value="*")
+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_embedding = gr.Button(value="Create embedding", variant='primary')
+
+                with gr.Tab(label="Create hypernetwork"):
+                    new_hypernetwork_name = gr.Textbox(label="Name")
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
+                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
+                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
+                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
+
+                with gr.Tab(label="Preprocess images"):
+                    process_src = gr.Textbox(label='Source directory')
+                    process_dst = gr.Textbox(label='Destination directory')
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
+
+                    with gr.Row():
+                        process_flip = gr.Checkbox(label='Create flipped copies')
+                        process_split = gr.Checkbox(label='Split oversized images')
+                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')
+                        process_caption = gr.Checkbox(label='Use BLIP for caption')
+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
+
+                    with gr.Row(visible=False) as process_split_extra_row:
+                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
+
+                    with gr.Row(visible=False) as process_focal_crop_row:
+                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
+                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')
+
+                    with gr.Row():
+                        with gr.Column(scale=3):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            with gr.Row():
+                                interrupt_preprocessing = gr.Button("Interrupt")
+                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
+
+                    process_split.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_split],
+                        outputs=[process_split_extra_row],
+                    )
+
+                    process_focal_crop.change(
+                        fn=lambda show: gr_show(show),
+                        inputs=[process_focal_crop],
+                        outputs=[process_focal_crop_row],
+                    )
+
+                with gr.Tab(label="Train"):
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
+                    with gr.Row():
+                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+                    with gr.Row():
+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
+                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
+                    with gr.Row():
+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
+
+                    batch_size = gr.Number(label='Batch size', value=1, precision=0)
+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
+                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    steps = gr.Number(label='Max steps', value=100000, precision=0)
+                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
+                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
+
+                    with gr.Row():
+                        interrupt_training = gr.Button(value="Interrupt")
+                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
+                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
+
+                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+                script_callbacks.ui_train_tabs_callback(params)
+
+            with gr.Column():
+                progressbar = gr.HTML(elem_id="ti_progressbar")
+                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
+
+                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
+                ti_preview = gr.Image(elem_id='ti_preview', visible=False)
+                ti_progress = gr.HTML(elem_id="ti_progress", value="")
+                ti_outcome = gr.HTML(elem_id="ti_error", value="")
+                setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
+
+        create_embedding.click(
+            fn=modules.textual_inversion.ui.create_embedding,
+            inputs=[
+                new_embedding_name,
+                initialization_text,
+                nvpt,
+                overwrite_old_embedding,
+            ],
+            outputs=[
+                train_embedding_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        create_hypernetwork.click(
+            fn=modules.hypernetworks.ui.create_hypernetwork,
+            inputs=[
+                new_hypernetwork_name,
+                new_hypernetwork_sizes,
+                overwrite_old_hypernetwork,
+                new_hypernetwork_layer_structure,
+                new_hypernetwork_activation_func,
+                new_hypernetwork_initialization_option,
+                new_hypernetwork_add_layer_norm,
+                new_hypernetwork_use_dropout
+            ],
+            outputs=[
+                train_hypernetwork_name,
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        run_preprocess.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                process_src,
+                process_dst,
+                process_width,
+                process_height,
+                preprocess_txt_action,
+                process_flip,
+                process_split,
+                process_caption,
+                process_caption_deepbooru,
+                process_split_threshold,
+                process_overlap_ratio,
+                process_focal_crop,
+                process_focal_crop_face_weight,
+                process_focal_crop_entropy_weight,
+                process_focal_crop_edges_weight,
+                process_focal_crop_debug,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ],
+        )
+
+        train_embedding.click(
+            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                train_embedding_name,
+                embedding_learn_rate,
+                batch_size,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                steps,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                save_image_with_stored_embedding,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        train_hypernetwork.click(
+            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+            _js="start_training_textual_inversion",
+            inputs=[
+                train_hypernetwork_name,
+                hypernetwork_learn_rate,
+                batch_size,
+                dataset_directory,
+                log_directory,
+                training_width,
+                training_height,
+                steps,
+                create_image_every,
+                save_embedding_every,
+                template_file,
+                preview_from_txt2img,
+                *txt2img_preview_params,
+            ],
+            outputs=[
+                ti_output,
+                ti_outcome,
+            ]
+        )
+
+        interrupt_training.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+        interrupt_preprocessing.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
+    def create_setting_component(key, is_quicksettings=False):
+        def fun():
+            return opts.data[key] if key in opts.data else opts.data_labels[key].default
+
+        info = opts.data_labels[key]
+        t = type(info.default)
+
+        args = info.component_args() if callable(info.component_args) else info.component_args
+
+        if info.component is not None:
+            comp = info.component
+        elif t == str:
+            comp = gr.Textbox
+        elif t == int:
+            comp = gr.Number
+        elif t == bool:
+            comp = gr.Checkbox
+        else:
+            raise Exception(f'bad options item type: {str(t)} for key {key}')
+
+        elem_id = "setting_"+key
+
+        if info.refresh is not None:
+            if is_quicksettings:
+                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+                create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+            else:
+                with gr.Row(variant="compact"):
+                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+                    create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
+        else:
+            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+
+        return res
+
+    components = []
+    component_dict = {}
+
+    script_callbacks.ui_settings_callback()
+    opts.reorder()
+
+    def run_settings(*args):
+        changed = []
+
+        for key, value, comp in zip(opts.data_labels.keys(), args, components):
+            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
+
+        for key, value, comp in zip(opts.data_labels.keys(), args, components):
+            if comp == dummy_component:
+                continue
+
+            if opts.set(key, value):
+                changed.append(key)
+
+        try:
+            opts.save(shared.config_filename)
+        except RuntimeError:
+            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
+
+    def run_settings_single(value, key):
+        if not opts.same_type(value, opts.data_labels[key].default):
+            return gr.update(visible=True), opts.dumpjson()
+
+        if not opts.set(key, value):
+            return gr.update(value=getattr(opts, key)), opts.dumpjson()
+
+        opts.save(shared.config_filename)
+
+        return gr.update(value=value), opts.dumpjson()
+
+    with gr.Blocks(analytics_enabled=False) as settings_interface:
+        settings_submit = gr.Button(value="Apply settings", variant='primary')
+        result = gr.HTML()
+
+        settings_cols = 3
+        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
+
+        quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
+        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
+
+        quicksettings_list = []
+
+        cols_displayed = 0
+        items_displayed = 0
+        previous_section = None
+        column = None
+        with gr.Row(elem_id="settings").style(equal_height=False):
+            for i, (k, item) in enumerate(opts.data_labels.items()):
+                section_must_be_skipped = item.section[0] is None
+
+                if previous_section != item.section and not section_must_be_skipped:
+                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
+                        if column is not None:
+                            column.__exit__()
+
+                        column = gr.Column(variant='panel')
+                        column.__enter__()
+
+                        items_displayed = 0
+                        cols_displayed += 1
+
+                    previous_section = item.section
+
+                    elem_id, text = item.section
+                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
+
+                if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
+                    quicksettings_list.append((i, k, item))
+                    components.append(dummy_component)
+                elif section_must_be_skipped:
+                    components.append(dummy_component)
+                else:
+                    component = create_setting_component(k)
+                    component_dict[k] = component
+                    components.append(component)
+                    items_displayed += 1
+
+        with gr.Row():
+            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+
+        with gr.Row():
+            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
+            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
+
+        request_notifications.click(
+            fn=lambda: None,
+            inputs=[],
+            outputs=[],
+            _js='function(){}'
+        )
+
+        download_localization.click(
+            fn=lambda: None,
+            inputs=[],
+            outputs=[],
+            _js='download_localization'
+        )
+
+        def reload_scripts():
+            modules.scripts.reload_script_body_only()
+            reload_javascript()  # need to refresh the html page
+
+        reload_script_bodies.click(
+            fn=reload_scripts,
+            inputs=[],
+            outputs=[]
+        )
+
+        def request_restart():
+            shared.state.interrupt()
+            shared.state.need_restart = True
+
+        restart_gradio.click(
+            fn=request_restart,
+            _js='restart_reload',
+            inputs=[],
+            outputs=[],
+        )
+
+        if column is not None:
+            column.__exit__()
+
+    interfaces = [
+        (txt2img_interface, "txt2img", "txt2img"),
+        (img2img_interface, "img2img", "img2img"),
+        (extras_interface, "Extras", "extras"),
+        (pnginfo_interface, "PNG Info", "pnginfo"),
+        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+        (train_interface, "Train", "ti"),
+    ]
+
+    css = ""
+
+    for cssfile in modules.scripts.list_files_with_name("style.css"):
+        if not os.path.isfile(cssfile):
+            continue
+
+        with open(cssfile, "r", encoding="utf8") as file:
+            css += file.read() + "\n"
+
+    if os.path.exists(os.path.join(script_path, "user.css")):
+        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
+            css += file.read() + "\n"
+
+    if not cmd_opts.no_progressbar_hiding:
+        css += css_hide_progressbar
+
+    interfaces += script_callbacks.ui_tabs_callback()
+    interfaces += [(settings_interface, "Settings", "settings")]
+
+    extensions_interface = ui_extensions.create_ui()
+    interfaces += [(extensions_interface, "Extensions", "extensions")]
+
+    with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
+        with gr.Row(elem_id="quicksettings"):
+            for i, k, item in quicksettings_list:
+                component = create_setting_component(k, is_quicksettings=True)
+                component_dict[k] = component
+
+        parameters_copypaste.integrate_settings_paste_fields(component_dict)
+        parameters_copypaste.run_bind()
+
+        with gr.Tabs(elem_id="tabs") as tabs:
+            for interface, label, ifid in interfaces:
+                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
+                    interface.render()
+
+        if os.path.exists(os.path.join(script_path, "notification.mp3")):
+            audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+
+        text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
+        settings_submit.click(
+            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
+            inputs=components,
+            outputs=[text_settings, result],
+        )
+
+        for i, k, item in quicksettings_list:
+            component = component_dict[k]
+
+            component.change(
+                fn=lambda value, k=k: run_settings_single(value, key=k),
+                inputs=[component],
+                outputs=[component, text_settings],
+            )
+
+        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]
+
+        def get_settings_values():
+            return [getattr(opts, key) for key in component_keys]
+
+        demo.load(
+            fn=get_settings_values,
+            inputs=[],
+            outputs=[component_dict[k] for k in component_keys],
+        )
+
+        def modelmerger(*args):
+            try:
+                results = modules.extras.run_modelmerger(*args)
+            except Exception as e:
+                print("Error loading/saving model file:", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+                modules.sd_models.list_models()  # to remove the potentially missing models from the list
+                return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
+            return results
+
+        modelmerger_merge.click(
+            fn=modelmerger,
+            inputs=[
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                interp_method,
+                interp_amount,
+                save_as_half,
+                save_as_safetensors,
+                custom_name,
+            ],
+            outputs=[
+                submit_result,
+                primary_model_name,
+                secondary_model_name,
+                tertiary_model_name,
+                component_dict['sd_model_checkpoint'],
+            ]
+        )
+
+    ui_config_file = cmd_opts.ui_config_file
+    ui_settings = {}
+    settings_count = len(ui_settings)
+    error_loading = False
+
+    try:
+        if os.path.exists(ui_config_file):
+            with open(ui_config_file, "r", encoding="utf8") as file:
+                ui_settings = json.load(file)
+    except Exception:
+        error_loading = True
+        print("Error loading settings:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
+    def loadsave(path, x):
+        def apply_field(obj, field, condition=None, init_field=None):
+            key = path + "/" + field
+
+            if getattr(obj, 'custom_script_source', None) is not None:
+              key = 'customscript/' + obj.custom_script_source + '/' + key
+
+            if getattr(obj, 'do_not_save_to_config', False):
+                return
+
+            saved_value = ui_settings.get(key, None)
+            if saved_value is None:
+                ui_settings[key] = getattr(obj, field)
+            elif condition and not condition(saved_value):
+                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
+            else:
+                setattr(obj, field, saved_value)
+                if init_field is not None:
+                    init_field(saved_value)
+
+        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
+            apply_field(x, 'visible')
+
+        if type(x) == gr.Slider:
+            apply_field(x, 'value')
+            apply_field(x, 'minimum')
+            apply_field(x, 'maximum')
+            apply_field(x, 'step')
+
+        if type(x) == gr.Radio:
+            apply_field(x, 'value', lambda val: val in x.choices)
+
+        if type(x) == gr.Checkbox:
+            apply_field(x, 'value')
+
+        if type(x) == gr.Textbox:
+            apply_field(x, 'value')
+
+        if type(x) == gr.Number:
+            apply_field(x, 'value')
+
+        # Since there are many dropdowns that shouldn't be saved,
+        # we only mark dropdowns that should be saved.
+        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
+            apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
+            apply_field(x, 'visible')
+
+    visit(txt2img_interface, loadsave, "txt2img")
+    visit(img2img_interface, loadsave, "img2img")
+    visit(extras_interface, loadsave, "extras")
+    visit(modelmerger_interface, loadsave, "modelmerger")
+
+    if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
+        with open(ui_config_file, "w", encoding="utf8") as file:
+            json.dump(ui_settings, file, indent=4)
+
+    return demo
+
+
+def reload_javascript():
+    with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
+        javascript = f'<script>{jsfile.read()}</script>'
+
+    scripts_list = modules.scripts.list_scripts("javascript", ".js")
+
+    for basedir, filename, path in scripts_list:
+        with open(path, "r", encoding="utf8") as jsfile:
+            javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
+
+    if cmd_opts.theme is not None:
+        javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
+
+    javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
+
+    def template_response(*args, **kwargs):
+        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
+        res.body = res.body.replace(
+            b'</head>', f'{javascript}</head>'.encode("utf8"))
+        res.init_headers()
+        return res
+
+    gradio.routes.templates.TemplateResponse = template_response
+
+
+if not hasattr(shared, 'GradioTemplateResponseOriginal'):
+    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
-- 
cgit v1.2.1


From e134b74ce95773789f69d158d23e93b7fe9295dc Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Mon, 21 Nov 2022 10:58:57 -0500
Subject: Ignore safetensor files

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index ee53044c..21fa26a7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
 __pycache__
 *.ckpt
+*.safetensors
 *.pth
 /ESRGAN/*
 /SwinIR/*
-- 
cgit v1.2.1


From 210cb4c128afdd65fa998229a97d0694154983ea Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Mon, 21 Nov 2022 16:40:18 -0500
Subject: Use GPU for loading safetensors, disable export

---
 modules/sd_models.py | 5 +++--
 modules/ui.py        | 3 ++-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2f8c2c48..2bbb3bf5 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -147,8 +147,9 @@ def torch_load(model_filename, model_info, map_override=None):
     map_override=shared.weight_load_location if not map_override else map_override
     if(checkpoint_types[model_info.exttype] == 'safetensors'):
         # safely load weights
-        # TODO: safetensors supports zero copy fast load to gpu, see issue #684
-        return load_file(model_filename, device=map_override)
+        # TODO: safetensors supports zero copy fast load to gpu, see issue #684.  
+        # GPU only for now, see https://github.com/huggingface/safetensors/issues/95
+        return load_file(model_filename, device='cuda')
     else:
         return torch.load(model_filename, map_location=map_override)
 
diff --git a/modules/ui.py b/modules/ui.py
index 54d3293a..c376a59d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1187,7 +1187,8 @@ def create_ui(wrap_gradio_gpu_call):
                 interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
                 interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
                 save_as_half = gr.Checkbox(value=False, label="Save as float16")
-                save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format")
+                # invisible until feature can be verified
+                save_as_safetensors = gr.Checkbox(value=False, label="Save as safetensors format", visible=False)
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
 
             with gr.Column(variant='panel'):
-- 
cgit v1.2.1


From 745f1e8f8008ea4906b0f5eb8b8f71d205fedf9e Mon Sep 17 00:00:00 2001
From: "Tiago F. Santos" <tiagofsantos81@sapo.pt>
Date: Tue, 22 Nov 2022 12:48:25 +0000
Subject: [CLIP interrogator] use local file, if available

---
 modules/interrogate.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 9769aa34..1a9c758e 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -14,6 +14,7 @@ import modules.shared as shared
 from modules import devices, paths, lowvram
 
 blip_image_eval_size = 384
+blip_model_local = os.path.join('models', 'Interrogator', 'BLIP_model.pth')
 blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
 clip_model_name = 'ViT-L/14'
 
@@ -47,7 +48,13 @@ class InterrogateModels:
     def load_blip_model(self):
         import models.blip
 
-        blip_model = models.blip.blip_decoder(pretrained=blip_model_url, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
+        if not os.path.isfile(blip_model_local):
+            print("Downloading BLIP...")
+            import requests as req
+            open(blip_model_local, 'wb').write(req.get(blip_model_url, allow_redirects=True).content)
+            print("BLIP downloaded to", blip_model_local + '.')
+
+        blip_model = models.blip.blip_decoder(pretrained=blip_model_local, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
         blip_model.eval()
 
         return blip_model
-- 
cgit v1.2.1


From c27a973c82c374f47fb279c1b9b8de7288fd729d Mon Sep 17 00:00:00 2001
From: Rogerooo <hugo.lopesmatias@gmail.com>
Date: Tue, 22 Nov 2022 14:02:59 +0000
Subject: fix null negative_prompt on get requests

Small typo that causes a bug when returning negative prompts from the get request.
---
 modules/api/api.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 7a567be3..08e03c13 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -305,7 +305,7 @@ class Api:
         styleList = []
         for k in shared.prompt_styles.styles:
             style = shared.prompt_styles.styles[k]
-            styleList.append({"name":style[0], "prompt": style[1], "negative_prompr": style[2]})
+            styleList.append({"name":style[0], "prompt": style[1], "negative_prompt": style[2]})
 
         return styleList
 
-- 
cgit v1.2.1


From 6ecf72b6f72220849d3dcdc27a28bf07bacc73f4 Mon Sep 17 00:00:00 2001
From: uservar <63248296+uservar@users.noreply.github.com>
Date: Tue, 22 Nov 2022 14:24:10 +0000
Subject: Update k-diffusion to Release 0.0.11

---
 launch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index d2f1055c..497ea32b 100644
--- a/launch.py
+++ b/launch.py
@@ -145,7 +145,7 @@ def prepare_enviroment():
 
     stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
     taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
-    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "60e5042ca0da89c14d1dd59d73883280f8fce991")
+    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "b325595b8a776d483f6935dfa7b45f01c27039e4)
     codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
-- 
cgit v1.2.1


From 0a01f5089127f1ab86625036526082f544344a10 Mon Sep 17 00:00:00 2001
From: uservar <63248296+uservar@users.noreply.github.com>
Date: Tue, 22 Nov 2022 14:24:50 +0000
Subject: Add DPM++ SDE sampler

---
 modules/sd_samplers.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 4fe67854..80e91d62 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -26,6 +26,7 @@ samplers_k_diffusion = [
     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
     ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
     ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
@@ -33,6 +34,7 @@ samplers_k_diffusion = [
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
     ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
     ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
 ]
 
 samplers_data_k_diffusion = [
-- 
cgit v1.2.1


From 3c3c46be5f04480fee2a7afea77242f101eea00a Mon Sep 17 00:00:00 2001
From: uservar <63248296+uservar@users.noreply.github.com>
Date: Tue, 22 Nov 2022 14:25:39 +0000
Subject: Update requirements.txt

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 762db4f3..e4e5ec64 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -28,3 +28,4 @@ kornia
 lark
 inflection
 GitPython
+torchsde
-- 
cgit v1.2.1


From 47ce73fbbf9fb361b1e12031934cefd81f3b2364 Mon Sep 17 00:00:00 2001
From: uservar <63248296+uservar@users.noreply.github.com>
Date: Tue, 22 Nov 2022 14:26:09 +0000
Subject: Update requirements_versions.txt

---
 requirements_versions.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements_versions.txt b/requirements_versions.txt
index 662ca684..8d557fe3 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -25,3 +25,4 @@ kornia==0.6.7
 lark==1.1.2
 inflection==0.5.1
 GitPython==3.1.27
+torchsde==0.2.5
-- 
cgit v1.2.1


From 45fd785436068f3b1c09fb7bc575118b6059fc7b Mon Sep 17 00:00:00 2001
From: uservar <63248296+uservar@users.noreply.github.com>
Date: Tue, 22 Nov 2022 14:52:16 +0000
Subject: Update launch.py

---
 launch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index 497ea32b..3608c103 100644
--- a/launch.py
+++ b/launch.py
@@ -145,7 +145,7 @@ def prepare_enviroment():
 
     stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
     taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
-    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "b325595b8a776d483f6935dfa7b45f01c27039e4)
+    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "b325595b8a776d483f6935dfa7b45f01c27039e4")
     codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
-- 
cgit v1.2.1


From ac90cf38c6b55d57d37923aa1fe86c7374e32d0b Mon Sep 17 00:00:00 2001
From: Tim Patton <38817597+pattontim@users.noreply.github.com>
Date: Tue, 22 Nov 2022 10:13:07 -0500
Subject: safetensors optional for now

---
 modules/sd_models.py | 9 ++++++++-
 requirements.txt     | 1 -
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2bbb3bf5..75f7ab09 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -4,7 +4,6 @@ import sys
 import gc
 from collections import namedtuple
 import torch
-from safetensors.torch import load_file, save_file
 import re
 from omegaconf import OmegaConf
 
@@ -149,6 +148,10 @@ def torch_load(model_filename, model_info, map_override=None):
         # safely load weights
         # TODO: safetensors supports zero copy fast load to gpu, see issue #684.  
         # GPU only for now, see https://github.com/huggingface/safetensors/issues/95
+        try:
+            from safetensors.torch import load_file
+        except ImportError as e:
+            raise ImportError(f"The model is in safetensors format and it is not installed, use `pip install safetensors`: {e}")
         return load_file(model_filename, device='cuda')
     else:
         return torch.load(model_filename, map_location=map_override)
@@ -157,6 +160,10 @@ def torch_save(model, output_filename):
     basename, exttype = os.path.splitext(output_filename)
     if(checkpoint_types[exttype] == 'safetensors'):
         # [=====  >] Reticulating brines...
+        try:
+            from safetensors.torch import save_file
+        except ImportError as e:
+            raise ImportError(f"Export as safetensors selected, yet it is not installed, use `pip install safetensors`: {e}")
         save_file(model, output_filename, metadata={"format": "pt"})
     else:
         torch.save(model, output_filename)
diff --git a/requirements.txt b/requirements.txt
index f7de9f70..762db4f3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -28,4 +28,3 @@ kornia
 lark
 inflection
 GitPython
-safetensors
-- 
cgit v1.2.1


From 89d8ecff09b426ddc89eb5b432825f8f4c218051 Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Wed, 23 Nov 2022 02:49:01 +0900
Subject: small fixes

---
 modules/hypernetworks/hypernetwork.py          | 6 +++---
 modules/textual_inversion/textual_inversion.py | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 0128419b..4541af18 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -435,8 +435,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
         optimizer_name = hypernetwork.optimizer_name
     else:
         print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
-    optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
-    optimizer_name = 'AdamW'
+        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
+        optimizer_name = 'AdamW'
 
     if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
         try:
@@ -582,7 +582,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
                 shared.state.textinfo = f"""
 <p>
 Loss: {loss_step:.7f}<br/>
-Step: {hypernetwork.step}<br/>
+Step: {steps_done}<br/>
 Last prompt: {html.escape(batch.cond_text[0])}<br/>
 Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 3036e48a..fee08e33 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -436,7 +436,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                 shared.state.textinfo = f"""
 <p>
 Loss: {loss_step:.7f}<br/>
-Step: {embedding.step}<br/>
+Step: {steps_done}<br/>
 Last prompt: {html.escape(batch.cond_text[0])}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
-- 
cgit v1.2.1


From 75b67eebf21f72f5b693926476d9c3b12471f0d6 Mon Sep 17 00:00:00 2001
From: Sena <34237511+sena-nana@users.noreply.github.com>
Date: Wed, 23 Nov 2022 17:43:58 +0800
Subject: Fix bare base64 not accept

---
 modules/api/api.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 7a567be3..648bd6a8 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -3,6 +3,7 @@ import io
 import time
 import uvicorn
 from threading import Lock
+from io import BytesIO
 from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
 from fastapi import APIRouter, Depends, FastAPI, HTTPException
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
@@ -13,7 +14,7 @@ from modules import sd_samplers, deepbooru
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.extras import run_extras, run_pnginfo
-from PIL import PngImagePlugin
+from PIL import PngImagePlugin,Image
 from modules.sd_models import checkpoints_list
 from modules.realesrgan_model import get_realesrgan_models
 from typing import List
@@ -133,7 +134,10 @@ class Api:
 
         mask = img2imgreq.mask
         if mask:
-            mask = decode_base64_to_image(mask)
+            if mask.startswith("data:image/"):
+                mask = decode_base64_to_image(mask)
+            else:
+                mask = Image.open(BytesIO(base64.b64decode(mask)))
 
         populate = img2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
@@ -147,7 +151,10 @@ class Api:
 
         imgs = []
         for img in init_images:
-            img = decode_base64_to_image(img)
+            if img.startswith("data:image/"):
+                img = decode_base64_to_image(img)
+            else:
+                img = Image.open(BytesIO(base64.b64decode(img)))
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
-- 
cgit v1.2.1


From adb6cb7619989cbc7a271cc6c2ae27bb936c43d9 Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Wed, 23 Nov 2022 18:11:24 +0800
Subject: Patch UNet Forward to support resolutions that are not multiples of
 64 Also modifed the UI to no longer step in 64

---
 modules/sd_hijack.py               |  2 ++
 modules/sd_hijack_optimizations.py | 31 +++++++++++++++++++++++++++++++
 modules/ui.py                      | 24 ++++++++++++------------
 3 files changed, 45 insertions(+), 12 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index eaedac13..6141f705 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -16,6 +16,7 @@ import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
+import ldm.modules.diffusionmodules.openaimodel
 
 attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
@@ -26,6 +27,7 @@ def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
+    ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_hijack_optimizations.patched_unet_forward
 
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 98123fbf..8cd4c954 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -5,6 +5,7 @@ import importlib
 
 import torch
 from torch import einsum
+import torch.nn.functional as F
 
 from ldm.util import default
 from einops import rearrange
@@ -12,6 +13,8 @@ from einops import rearrange
 from modules import shared
 from modules.hypernetworks import hypernetwork
 
+from ldm.modules.diffusionmodules.util import timestep_embedding
+
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
     try:
@@ -310,3 +313,31 @@ def xformers_attnblock_forward(self, x):
         return x + out
     except NotImplementedError:
         return cross_attention_attnblock_forward(self, x)
+
+def patched_unet_forward(self, x, timesteps=None, context=None, y=None,**kwargs):
+    assert (y is not None) == (
+        self.num_classes is not None
+    ), "must specify y if and only if the model is class-conditional"
+    hs = []
+    t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+    emb = self.time_embed(t_emb)
+
+    if self.num_classes is not None:
+        assert y.shape == (x.shape[0],)
+        emb = emb + self.label_emb(y)
+
+    h = x.type(self.dtype)
+    for module in self.input_blocks:
+        h = module(h, emb, context)
+        hs.append(h)
+    h = self.middle_block(h, emb, context)
+    for module in self.output_blocks:
+        if h.shape[-2:] != hs[-1].shape[-2:]:
+            h = F.interpolate(h, hs[-1].shape[-2:], mode="nearest")
+        h = torch.cat([h, hs.pop()], dim=1)
+        h = module(h, emb, context)
+    h = h.type(x.dtype)
+    if self.predict_codebook_ids:
+        return self.id_predictor(h)
+    else:
+        return self.out(h)
diff --git a/modules/ui.py b/modules/ui.py
index e6da1b2a..85e531af 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -380,8 +380,8 @@ def create_seed_inputs():
 
     with gr.Row(visible=False) as seed_extra_row_2:
         seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0)
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=1, label="Resize seed from width", value=0)
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=1, label="Resize seed from height", value=0)
 
     random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
     random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
@@ -715,8 +715,8 @@ def create_ui(wrap_gradio_gpu_call):
                 sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512)
+                    height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512)
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
@@ -724,8 +724,8 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=1, label="Firstpass width", value=0)
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=1, label="Firstpass height", value=0)
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
                 with gr.Row(equal_height=True):
@@ -901,8 +901,8 @@ def create_ui(wrap_gradio_gpu_call):
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height")
+                    width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512, elem_id="img2img_width")
+                    height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512, elem_id="img2img_height")
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
@@ -1231,8 +1231,8 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Tab(label="Preprocess images"):
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512)
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512)
                     preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
 
                     with gr.Row():
@@ -1289,8 +1289,8 @@ def create_ui(wrap_gradio_gpu_call):
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512)
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-- 
cgit v1.2.1


From d2c97fc3fe5857d6fba9ad1695ed3ac6ec455ca9 Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Wed, 23 Nov 2022 20:00:00 +0900
Subject: fix dropout, implement train/eval mode

---
 modules/hypernetworks/hypernetwork.py | 24 ++++++++++++++++++------
 1 file changed, 18 insertions(+), 6 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 4541af18..9388959f 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -154,16 +154,28 @@ class Hypernetwork:
                 HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
                                    self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
             )
+        self.eval_mode()
 
     def weights(self):
         res = []
+        for k, layers in self.layers.items():
+            for layer in layers:
+                res += layer.parameters()
+        return res
 
+    def train_mode(self):
         for k, layers in self.layers.items():
             for layer in layers:
                 layer.train()
-                res += layer.trainables()
+                for param in layer.parameters():
+                    param.requires_grad = True
 
-        return res
+    def eval_mode(self):
+        for k, layers in self.layers.items():
+            for layer in layers:
+                layer.eval()
+                for param in layer.parameters():
+                    param.requires_grad = False
 
     def save(self, filename):
         state_dict = {}
@@ -426,8 +438,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
         shared.sd_model.first_stage_model.to(devices.cpu)
     
     weights = hypernetwork.weights()
-    for weight in weights:
-        weight.requires_grad = True
+    hypernetwork.train_mode()
 
     # Here we use optimizer from saved HN, or we can specify as UI option.
     if hypernetwork.optimizer_name in optimizer_dict:
@@ -538,7 +549,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
                 if images_dir is not None and steps_done % create_image_every == 0:
                     forced_filename = f'{hypernetwork_name}-{steps_done}'
                     last_saved_image = os.path.join(images_dir, forced_filename)
-
+                    hypernetwork.eval_mode()
                     shared.sd_model.cond_stage_model.to(devices.device)
                     shared.sd_model.first_stage_model.to(devices.device)
 
@@ -571,7 +582,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
                     if unload:
                         shared.sd_model.cond_stage_model.to(devices.cpu)
                         shared.sd_model.first_stage_model.to(devices.cpu)
-
+                    hypernetwork.train_mode()
                     if image is not None:
                         shared.state.current_image = image
                         last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
@@ -593,6 +604,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     finally:
         pbar.leave = False
         pbar.close()
+        hypernetwork.eval_mode()
         #report_statistics(loss_dict)
 
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
-- 
cgit v1.2.1


From 1bd57cc9791e2e742f72a3d74d589f2c289e8e92 Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Wed, 23 Nov 2022 20:21:52 +0900
Subject: last_layer_dropout default to False

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 9388959f..8466887f 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -38,7 +38,7 @@ class HypernetworkModule(torch.nn.Module):
     activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
     def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
-                 add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=True):
+                 add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=False):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
-- 
cgit v1.2.1


From 6001684be3e7b023346326b9dfc771219b8fe47e Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Wed, 23 Nov 2022 06:35:44 -0800
Subject: add model_name pattern for saving

---
 javascript/hints.js | 4 ++--
 modules/images.py   | 1 +
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index 623bc25c..ac417ff6 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -62,8 +62,8 @@ titles = {
 
     "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",
 
-    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
-    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
+    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
+    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
     "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
 
     "Loopback": "Process an image, use it as an input, repeat.",
diff --git a/modules/images.py b/modules/images.py
index 26d5b7a9..420828b0 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -305,6 +305,7 @@ class FilenameGenerator:
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
         'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
+        'model_name': lambda self: shared.sd_model.sd_checkpoint_info.model_name,
         'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
         'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
         'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
-- 
cgit v1.2.1


From ffcbbcf385eb847ced957510ab726291a8b20207 Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Wed, 23 Nov 2022 06:44:20 -0800
Subject: add filename santization

Probably redundant, considering the model name *is* a filename, but I suppose better safe than sorry.
---
 modules/images.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/images.py b/modules/images.py
index 420828b0..8fa96b16 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -305,7 +305,7 @@ class FilenameGenerator:
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
         'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
-        'model_name': lambda self: shared.sd_model.sd_checkpoint_info.model_name,
+        'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.model_name, replace_spaces=False),
         'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
         'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
         'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
-- 
cgit v1.2.1


From 904121fecc0a1f11db76a73ca8649fb21e05ac5b Mon Sep 17 00:00:00 2001
From: Nandaka <nchek2000@gmail.com>
Date: Thu, 24 Nov 2022 02:39:09 +0000
Subject: Support NAI exif for PNG Info

---
 modules/extras.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/modules/extras.py b/modules/extras.py
index 71b93a06..af4cd97d 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -233,6 +233,20 @@ def run_pnginfo(image):
 
     geninfo = items.get('parameters', geninfo)
 
+    # nai prompt
+    if "Software" in items.keys() and items["Software"] == "NovelAI":
+        import json
+        json_info = json.loads(items["Comment"])
+        geninfo = f'{items["Description"]}\r\nNegative prompt: {json_info["uc"]}\r\n'
+        sampler = "Euler a"
+        if json_info["sampler"] == "k_euler_ancestral":
+            sampler = "Euler a"
+        elif json_info["sampler"] == "k_euler":
+            sampler = "Euler"
+        model_hash = '925997e9'  # assuming this is the correct model hash
+        # not sure with noise and strength parameter
+        geninfo += f'Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Model hash: {model_hash}'  # , Denoising strength: {json_info["noise"]}'
+
     info = ''
     for key, text in items.items():
         info += f"""
-- 
cgit v1.2.1


From fcd75bd8740855e0c7bc80c0e8a4e1033b76d007 Mon Sep 17 00:00:00 2001
From: Sena <34237511+sena-nana@users.noreply.github.com>
Date: Thu, 24 Nov 2022 13:10:40 +0800
Subject: Fix other apis

---
 modules/api/api.py | 16 +++++++---------
 1 file changed, 7 insertions(+), 9 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 648bd6a8..efcedbba 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -4,7 +4,7 @@ import time
 import uvicorn
 from threading import Lock
 from io import BytesIO
-from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
+from gradio.processing_utils import decode_base64_to_file
 from fastapi import APIRouter, Depends, FastAPI, HTTPException
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from secrets import compare_digest
@@ -41,6 +41,10 @@ def setUpscalers(req: dict):
     reqDict.pop('upscaler_2')
     return reqDict
 
+def decode_base64_to_image(encoding):
+    if encoding.startswith("data:image/"):
+        encoding = encoding.split(";")[1].split(",")[1]
+    return Image.open(BytesIO(base64.b64decode(encoding)))
 
 def encode_pil_to_base64(image):
     with io.BytesIO() as output_bytes:
@@ -134,10 +138,7 @@ class Api:
 
         mask = img2imgreq.mask
         if mask:
-            if mask.startswith("data:image/"):
-                mask = decode_base64_to_image(mask)
-            else:
-                mask = Image.open(BytesIO(base64.b64decode(mask)))
+            mask = decode_base64_to_image(mask)
 
         populate = img2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
@@ -151,10 +152,7 @@ class Api:
 
         imgs = []
         for img in init_images:
-            if img.startswith("data:image/"):
-                img = decode_base64_to_image(img)
-            else:
-                img = Image.open(BytesIO(base64.b64decode(img)))
+            img = decode_base64_to_image(img)
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
-- 
cgit v1.2.1


From a2ae5a655518b150a34b95d7afecc87a43280406 Mon Sep 17 00:00:00 2001
From: "Tiago F. Santos" <tiagofsantos81@sapo.pt>
Date: Thu, 24 Nov 2022 13:04:45 +0000
Subject: [interrogator] mkdir check

---
 modules/interrogate.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 1a9c758e..f177a5a8 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -14,7 +14,8 @@ import modules.shared as shared
 from modules import devices, paths, lowvram
 
 blip_image_eval_size = 384
-blip_model_local = os.path.join('models', 'Interrogator', 'BLIP_model.pth')
+blip_local_dir = os.path.join('models', 'Interrogator')
+blip_local_file = os.path.join(blip_local_dir, 'model_base_caption_capfilt_large.pth')
 blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
 clip_model_name = 'ViT-L/14'
 
@@ -48,13 +49,16 @@ class InterrogateModels:
     def load_blip_model(self):
         import models.blip
 
-        if not os.path.isfile(blip_model_local):
+        if not os.path.isfile(blip_local_file):
+            if not os.path.isdir(blip_local_dir):
+                os.mkdir(blip_local_dir)
+
             print("Downloading BLIP...")
-            import requests as req
-            open(blip_model_local, 'wb').write(req.get(blip_model_url, allow_redirects=True).content)
-            print("BLIP downloaded to", blip_model_local + '.')
+            from requests import get as reqget
+            open(blip_local_file, 'wb').write(reqget(blip_model_url, allow_redirects=True).content)
+            print("BLIP downloaded to", blip_local_file + '.')
 
-        blip_model = models.blip.blip_decoder(pretrained=blip_model_local, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
+        blip_model = models.blip.blip_decoder(pretrained=blip_local_file, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
         blip_model.eval()
 
         return blip_model
-- 
cgit v1.2.1


From c833d5bfaae05de41d8e795aba5b15822673ef04 Mon Sep 17 00:00:00 2001
From: Jay Smith <jayvsmith@gmail.com>
Date: Fri, 25 Nov 2022 20:12:23 -0600
Subject: fixes #3449 - VRAM leak when switching to/from inpainting model

---
 modules/sd_samplers.py | 33 +++++++++++++++------------------
 1 file changed, 15 insertions(+), 18 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 4fe67854..44112f99 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -1,4 +1,4 @@
-from collections import namedtuple
+from collections import namedtuple, deque
 import numpy as np
 from math import floor
 import torch
@@ -335,18 +335,28 @@ class CFGDenoiser(torch.nn.Module):
 
 
 class TorchHijack:
-    def __init__(self, kdiff_sampler):
-        self.kdiff_sampler = kdiff_sampler
+    def __init__(self, sampler_noises):
+        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
+        # implementation.
+        self.sampler_noises = deque(sampler_noises)
 
     def __getattr__(self, item):
         if item == 'randn_like':
-            return self.kdiff_sampler.randn_like
+            return self.randn_like
 
         if hasattr(torch, item):
             return getattr(torch, item)
 
         raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
 
+    def randn_like(self, x):
+        if self.sampler_noises:
+            noise = self.sampler_noises.popleft()
+            if noise.shape == x.shape:
+                return noise
+
+        return torch.randn_like(x)
+
 
 class KDiffusionSampler:
     def __init__(self, funcname, sd_model):
@@ -356,7 +366,6 @@ class KDiffusionSampler:
         self.extra_params = sampler_extra_params.get(funcname, [])
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
         self.sampler_noises = None
-        self.sampler_noise_index = 0
         self.stop_at = None
         self.eta = None
         self.default_eta = 1.0
@@ -389,26 +398,14 @@ class KDiffusionSampler:
     def number_of_needed_noises(self, p):
         return p.steps
 
-    def randn_like(self, x):
-        noise = self.sampler_noises[self.sampler_noise_index] if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises) else None
-
-        if noise is not None and x.shape == noise.shape:
-            res = noise
-        else:
-            res = torch.randn_like(x)
-
-        self.sampler_noise_index += 1
-        return res
-
     def initialize(self, p):
         self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
         self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
         self.model_wrap.step = 0
-        self.sampler_noise_index = 0
         self.eta = p.eta or opts.eta_ancestral
 
         if self.sampler_noises is not None:
-            k_diffusion.sampling.torch = TorchHijack(self)
+            k_diffusion.sampling.torch = TorchHijack(self.sampler_noises)
 
         extra_params_kwargs = {}
         for param_name in self.extra_params:
-- 
cgit v1.2.1


From ce6911158b5b2f9cf79b405a1f368f875492044d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 26 Nov 2022 16:10:46 +0300
Subject: Add support Stable Diffusion 2.0

---
 README.md                                      |  21 +-
 launch.py                                      |  12 +-
 modules/paths.py                               |   2 +-
 modules/sd_hijack.py                           | 297 +++---------------------
 modules/sd_hijack_clip.py                      | 301 +++++++++++++++++++++++++
 modules/sd_hijack_inpainting.py                |  20 +-
 modules/sd_hijack_open_clip.py                 |  37 +++
 modules/sd_samplers.py                         |  14 +-
 modules/shared.py                              |  34 ++-
 modules/textual_inversion/textual_inversion.py |   7 +-
 modules/ui.py                                  |  13 +-
 requirements.txt                               |   1 +
 requirements_versions.txt                      |   1 +
 v1-inference.yaml                              |  70 ++++++
 webui.py                                       |   5 +-
 15 files changed, 504 insertions(+), 331 deletions(-)
 create mode 100644 modules/sd_hijack_clip.py
 create mode 100644 modules/sd_hijack_open_clip.py
 create mode 100644 v1-inference.yaml

diff --git a/README.md b/README.md
index 5f5ab3aa..8a4ffade 100644
--- a/README.md
+++ b/README.md
@@ -84,26 +84,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - API
 - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. 
 - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
-
-## Where are Aesthetic Gradients?!?!
-Aesthetic Gradients are now an extension. You can install it using git:
-
-```commandline
-git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients extensions/aesthetic-gradients
-```
-
-After running this command, make sure that you have `aesthetic-gradients` dir in webui's `extensions` directory and restart
-the UI. The interface for Aesthetic Gradients should appear exactly the same as it was.
-
-## Where is History/Image browser?!?!
-Image browser is now an extension. You can install it using git:
-
-```commandline
-git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser extensions/images-browser
-```
-
-After running this command, make sure that you have `images-browser` dir in webui's `extensions` directory and restart
-the UI. The interface for Image browser should appear exactly the same as it was.
+- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
diff --git a/launch.py b/launch.py
index d2f1055c..b1626cb5 100644
--- a/launch.py
+++ b/launch.py
@@ -134,18 +134,19 @@ def prepare_enviroment():
 
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
     clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
+    openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
 
     xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
 
-    stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/CompVis/stable-diffusion.git")
+    stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
     taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
     k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
     codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
     blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
 
-    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
+    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "47b6b607fdd31875c9279cd2f4f16b92e4ea958e")
     taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
-    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "60e5042ca0da89c14d1dd59d73883280f8fce991")
+    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "5b3af030dd83e0297272d861c19477735d0317ec")
     codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
@@ -179,6 +180,9 @@ def prepare_enviroment():
     if not is_installed("clip"):
         run_pip(f"install {clip_package}", "clip")
 
+    if not is_installed("open_clip"):
+        run_pip(f"install {openclip_package}", "open_clip")
+
     if (not is_installed("xformers") or reinstall_xformers) and xformers:
         if platform.system() == "Windows":
             if platform.python_version().startswith("3.10"):
@@ -196,7 +200,7 @@ def prepare_enviroment():
 
     os.makedirs(dir_repos, exist_ok=True)
 
-    git_clone(stable_diffusion_repo, repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
+    git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
     git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
     git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
     git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
diff --git a/modules/paths.py b/modules/paths.py
index 1e7a2fbc..4dd03a35 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -9,7 +9,7 @@ sys.path.insert(0, script_path)
 
 # search for directory of stable diffusion in following places
 sd_path = None
-possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion'), '.', os.path.dirname(script_path)]
+possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion-stability-ai'), '.', os.path.dirname(script_path)]
 for possible_sd_path in possible_sd_paths:
     if os.path.exists(os.path.join(possible_sd_path, 'ldm/models/diffusion/ddpm.py')):
         sd_path = os.path.abspath(possible_sd_path)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index eaedac13..d5243fd3 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -9,18 +9,29 @@ from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared
-from modules.shared import opts, device, cmd_opts
+from modules.shared import cmd_opts
+from modules import sd_hijack_clip, sd_hijack_open_clip
+
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
+import ldm.modules.encoders.modules
 
 attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
 diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
 
+# new memory efficient cross attention blocks do not support hypernets and we already
+# have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention
+ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention
+ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
+
+# silence new console spam from SD2
+ldm.modules.attention.print = lambda *args: None
+ldm.modules.diffusionmodules.model.print = lambda *args: None
 
 def apply_optimizations():
     undo_optimizations()
@@ -49,16 +60,11 @@ def apply_optimizations():
 
 
 def undo_optimizations():
-    from modules.hypernetworks import hypernetwork
-
-    ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
+    ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward  # this stops hypernets from working
     ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
 
-def get_target_prompt_token_count(token_count):
-    return math.ceil(max(token_count, 1) / 75) * 75
-
 
 class StableDiffusionModelHijack:
     fixes = None
@@ -70,10 +76,13 @@ class StableDiffusionModelHijack:
     embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
 
     def hijack(self, m):
-        model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
-
-        model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
-        m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+        if type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
+            model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+            model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
+            m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+        elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder:
+            m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
+            m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
 
         self.clip = m.cond_stage_model
 
@@ -89,12 +98,15 @@ class StableDiffusionModelHijack:
         self.layers = flatten(m)
 
     def undo_hijack(self, m):
-        if type(m.cond_stage_model) == FrozenCLIPEmbedderWithCustomWords:
+        if type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped
 
-        model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
-        if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
-            model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
+            model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+            if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
+                model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
+        elif type(m.cond_stage_model) == sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords:
+            m.cond_stage_model.wrapped.model.token_embedding = m.cond_stage_model.wrapped.model.token_embedding.wrapped
+            m.cond_stage_model = m.cond_stage_model.wrapped
 
         self.apply_circular(False)
         self.layers = None
@@ -114,261 +126,8 @@ class StableDiffusionModelHijack:
 
     def tokenize(self, text):
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
-        return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
-
-
-class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
-    def __init__(self, wrapped, hijack):
-        super().__init__()
-        self.wrapped = wrapped
-        self.hijack: StableDiffusionModelHijack = hijack
-        self.tokenizer = wrapped.tokenizer
-        self.token_mults = {}
-
-        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
-
-        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
-        for text, ident in tokens_with_parens:
-            mult = 1.0
-            for c in text:
-                if c == '[':
-                    mult /= 1.1
-                if c == ']':
-                    mult *= 1.1
-                if c == '(':
-                    mult *= 1.1
-                if c == ')':
-                    mult /= 1.1
-
-            if mult != 1.0:
-                self.token_mults[ident] = mult
-
-    def tokenize_line(self, line, used_custom_terms, hijack_comments):
-        id_end = self.wrapped.tokenizer.eos_token_id
-
-        if opts.enable_emphasis:
-            parsed = prompt_parser.parse_prompt_attention(line)
-        else:
-            parsed = [[line, 1.0]]
-
-        tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"]
-
-        fixes = []
-        remade_tokens = []
-        multipliers = []
-        last_comma = -1
-
-        for tokens, (text, weight) in zip(tokenized, parsed):
-            i = 0
-            while i < len(tokens):
-                token = tokens[i]
-
-                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
-
-                if token == self.comma_token:
-                    last_comma = len(remade_tokens)
-                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
-                    last_comma += 1
-                    reloc_tokens = remade_tokens[last_comma:]
-                    reloc_mults = multipliers[last_comma:]
-
-                    remade_tokens = remade_tokens[:last_comma]
-                    length = len(remade_tokens)
-
-                    rem = int(math.ceil(length / 75)) * 75 - length
-                    remade_tokens += [id_end] * rem + reloc_tokens
-                    multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
-
-                if embedding is None:
-                    remade_tokens.append(token)
-                    multipliers.append(weight)
-                    i += 1
-                else:
-                    emb_len = int(embedding.vec.shape[0])
-                    iteration = len(remade_tokens) // 75
-                    if (len(remade_tokens) + emb_len) // 75 != iteration:
-                        rem = (75 * (iteration + 1) - len(remade_tokens))
-                        remade_tokens += [id_end] * rem
-                        multipliers += [1.0] * rem
-                        iteration += 1
-                    fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
-                    remade_tokens += [0] * emb_len
-                    multipliers += [weight] * emb_len
-                    used_custom_terms.append((embedding.name, embedding.checksum()))
-                    i += embedding_length_in_tokens
-
-        token_count = len(remade_tokens)
-        prompt_target_length = get_target_prompt_token_count(token_count)
-        tokens_to_add = prompt_target_length - len(remade_tokens)
-
-        remade_tokens = remade_tokens + [id_end] * tokens_to_add
-        multipliers = multipliers + [1.0] * tokens_to_add
-
-        return remade_tokens, fixes, multipliers, token_count
-
-    def process_text(self, texts):
-        used_custom_terms = []
-        remade_batch_tokens = []
-        hijack_comments = []
-        hijack_fixes = []
-        token_count = 0
-
-        cache = {}
-        batch_multipliers = []
-        for line in texts:
-            if line in cache:
-                remade_tokens, fixes, multipliers = cache[line]
-            else:
-                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
-                token_count = max(current_token_count, token_count)
-
-                cache[line] = (remade_tokens, fixes, multipliers)
-
-            remade_batch_tokens.append(remade_tokens)
-            hijack_fixes.append(fixes)
-            batch_multipliers.append(multipliers)
-
-        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
-
-    def process_text_old(self, text):
-        id_start = self.wrapped.tokenizer.bos_token_id
-        id_end = self.wrapped.tokenizer.eos_token_id
-        maxlen = self.wrapped.max_length  # you get to stay at 77
-        used_custom_terms = []
-        remade_batch_tokens = []
-        overflowing_words = []
-        hijack_comments = []
-        hijack_fixes = []
-        token_count = 0
-
-        cache = {}
-        batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
-        batch_multipliers = []
-        for tokens in batch_tokens:
-            tuple_tokens = tuple(tokens)
-
-            if tuple_tokens in cache:
-                remade_tokens, fixes, multipliers = cache[tuple_tokens]
-            else:
-                fixes = []
-                remade_tokens = []
-                multipliers = []
-                mult = 1.0
-
-                i = 0
-                while i < len(tokens):
-                    token = tokens[i]
-
-                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
-
-                    mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
-                    if mult_change is not None:
-                        mult *= mult_change
-                        i += 1
-                    elif embedding is None:
-                        remade_tokens.append(token)
-                        multipliers.append(mult)
-                        i += 1
-                    else:
-                        emb_len = int(embedding.vec.shape[0])
-                        fixes.append((len(remade_tokens), embedding))
-                        remade_tokens += [0] * emb_len
-                        multipliers += [mult] * emb_len
-                        used_custom_terms.append((embedding.name, embedding.checksum()))
-                        i += embedding_length_in_tokens
-
-                if len(remade_tokens) > maxlen - 2:
-                    vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
-                    ovf = remade_tokens[maxlen - 2:]
-                    overflowing_words = [vocab.get(int(x), "") for x in ovf]
-                    overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
-                    hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
-
-                token_count = len(remade_tokens)
-                remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
-                remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
-                cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
-
-            multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
-            multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
-
-            remade_batch_tokens.append(remade_tokens)
-            hijack_fixes.append(fixes)
-            batch_multipliers.append(multipliers)
-        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
-
-    def forward(self, text):
-        use_old = opts.use_old_emphasis_implementation
-        if use_old:
-            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
-        else:
-            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
-
-        self.hijack.comments += hijack_comments
-
-        if len(used_custom_terms) > 0:
-            self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
-
-        if use_old:
-            self.hijack.fixes = hijack_fixes
-            return self.process_tokens(remade_batch_tokens, batch_multipliers)
-
-        z = None
-        i = 0
-        while max(map(len, remade_batch_tokens)) != 0:
-            rem_tokens = [x[75:] for x in remade_batch_tokens]
-            rem_multipliers = [x[75:] for x in batch_multipliers]
-
-            self.hijack.fixes = []
-            for unfiltered in hijack_fixes:
-                fixes = []
-                for fix in unfiltered:
-                    if fix[0] == i:
-                        fixes.append(fix[1])
-                self.hijack.fixes.append(fixes)
-
-            tokens = []
-            multipliers = []
-            for j in range(len(remade_batch_tokens)):
-                if len(remade_batch_tokens[j]) > 0:
-                    tokens.append(remade_batch_tokens[j][:75])
-                    multipliers.append(batch_multipliers[j][:75])
-                else:
-                    tokens.append([self.wrapped.tokenizer.eos_token_id] * 75)
-                    multipliers.append([1.0] * 75)
-
-            z1 = self.process_tokens(tokens, multipliers)
-            z = z1 if z is None else torch.cat((z, z1), axis=-2)
-
-            remade_batch_tokens = rem_tokens
-            batch_multipliers = rem_multipliers
-            i += 1
-
-        return z
-
-    def process_tokens(self, remade_batch_tokens, batch_multipliers):
-        if not opts.use_old_emphasis_implementation:
-            remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
-            batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
-
-        tokens = torch.asarray(remade_batch_tokens).to(device)
-        outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
-
-        if opts.CLIP_stop_at_last_layers > 1:
-            z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
-            z = self.wrapped.transformer.text_model.final_layer_norm(z)
-        else:
-            z = outputs.last_hidden_state
-
-        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
-        batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
-        batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
-        original_mean = z.mean()
-        z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
-        new_mean = z.mean()
-        z *= original_mean / new_mean
+        return remade_batch_tokens[0], token_count, sd_hijack_clip.get_target_prompt_token_count(token_count)
 
-        return z
 
 
 class EmbeddingsWithFixes(torch.nn.Module):
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
new file mode 100644
index 00000000..b451d1cf
--- /dev/null
+++ b/modules/sd_hijack_clip.py
@@ -0,0 +1,301 @@
+import math
+
+import torch
+
+from modules import prompt_parser, devices
+from modules.shared import opts
+
+
+def get_target_prompt_token_count(token_count):
+    return math.ceil(max(token_count, 1) / 75) * 75
+
+
+class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
+    def __init__(self, wrapped, hijack):
+        super().__init__()
+        self.wrapped = wrapped
+        self.hijack = hijack
+
+    def tokenize(self, texts):
+        raise NotImplementedError
+
+    def encode_with_transformers(self, tokens):
+        raise NotImplementedError
+
+    def encode_embedding_init_text(self, init_text, nvpt):
+        raise NotImplementedError
+
+    def tokenize_line(self, line, used_custom_terms, hijack_comments):
+        if opts.enable_emphasis:
+            parsed = prompt_parser.parse_prompt_attention(line)
+        else:
+            parsed = [[line, 1.0]]
+
+        tokenized = self.tokenize([text for text, _ in parsed])
+
+        fixes = []
+        remade_tokens = []
+        multipliers = []
+        last_comma = -1
+
+        for tokens, (text, weight) in zip(tokenized, parsed):
+            i = 0
+            while i < len(tokens):
+                token = tokens[i]
+
+                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+
+                if token == self.comma_token:
+                    last_comma = len(remade_tokens)
+                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
+                    last_comma += 1
+                    reloc_tokens = remade_tokens[last_comma:]
+                    reloc_mults = multipliers[last_comma:]
+
+                    remade_tokens = remade_tokens[:last_comma]
+                    length = len(remade_tokens)
+
+                    rem = int(math.ceil(length / 75)) * 75 - length
+                    remade_tokens += [self.id_end] * rem + reloc_tokens
+                    multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
+
+                if embedding is None:
+                    remade_tokens.append(token)
+                    multipliers.append(weight)
+                    i += 1
+                else:
+                    emb_len = int(embedding.vec.shape[0])
+                    iteration = len(remade_tokens) // 75
+                    if (len(remade_tokens) + emb_len) // 75 != iteration:
+                        rem = (75 * (iteration + 1) - len(remade_tokens))
+                        remade_tokens += [self.id_end] * rem
+                        multipliers += [1.0] * rem
+                        iteration += 1
+                    fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
+                    remade_tokens += [0] * emb_len
+                    multipliers += [weight] * emb_len
+                    used_custom_terms.append((embedding.name, embedding.checksum()))
+                    i += embedding_length_in_tokens
+
+        token_count = len(remade_tokens)
+        prompt_target_length = get_target_prompt_token_count(token_count)
+        tokens_to_add = prompt_target_length - len(remade_tokens)
+
+        remade_tokens = remade_tokens + [self.id_end] * tokens_to_add
+        multipliers = multipliers + [1.0] * tokens_to_add
+
+        return remade_tokens, fixes, multipliers, token_count
+
+    def process_text(self, texts):
+        used_custom_terms = []
+        remade_batch_tokens = []
+        hijack_comments = []
+        hijack_fixes = []
+        token_count = 0
+
+        cache = {}
+        batch_multipliers = []
+        for line in texts:
+            if line in cache:
+                remade_tokens, fixes, multipliers = cache[line]
+            else:
+                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
+                token_count = max(current_token_count, token_count)
+
+                cache[line] = (remade_tokens, fixes, multipliers)
+
+            remade_batch_tokens.append(remade_tokens)
+            hijack_fixes.append(fixes)
+            batch_multipliers.append(multipliers)
+
+        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
+
+    def process_text_old(self, texts):
+        id_start = self.id_start
+        id_end = self.id_end
+        maxlen = self.wrapped.max_length  # you get to stay at 77
+        used_custom_terms = []
+        remade_batch_tokens = []
+        hijack_comments = []
+        hijack_fixes = []
+        token_count = 0
+
+        cache = {}
+        batch_tokens = self.tokenize(texts)
+        batch_multipliers = []
+        for tokens in batch_tokens:
+            tuple_tokens = tuple(tokens)
+
+            if tuple_tokens in cache:
+                remade_tokens, fixes, multipliers = cache[tuple_tokens]
+            else:
+                fixes = []
+                remade_tokens = []
+                multipliers = []
+                mult = 1.0
+
+                i = 0
+                while i < len(tokens):
+                    token = tokens[i]
+
+                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+
+                    mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
+                    if mult_change is not None:
+                        mult *= mult_change
+                        i += 1
+                    elif embedding is None:
+                        remade_tokens.append(token)
+                        multipliers.append(mult)
+                        i += 1
+                    else:
+                        emb_len = int(embedding.vec.shape[0])
+                        fixes.append((len(remade_tokens), embedding))
+                        remade_tokens += [0] * emb_len
+                        multipliers += [mult] * emb_len
+                        used_custom_terms.append((embedding.name, embedding.checksum()))
+                        i += embedding_length_in_tokens
+
+                if len(remade_tokens) > maxlen - 2:
+                    vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
+                    ovf = remade_tokens[maxlen - 2:]
+                    overflowing_words = [vocab.get(int(x), "") for x in ovf]
+                    overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
+                    hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
+
+                token_count = len(remade_tokens)
+                remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
+                remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
+                cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
+
+            multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
+            multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
+
+            remade_batch_tokens.append(remade_tokens)
+            hijack_fixes.append(fixes)
+            batch_multipliers.append(multipliers)
+        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
+
+    def forward(self, text):
+        use_old = opts.use_old_emphasis_implementation
+        if use_old:
+            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
+        else:
+            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
+
+        self.hijack.comments += hijack_comments
+
+        if len(used_custom_terms) > 0:
+            self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
+
+        if use_old:
+            self.hijack.fixes = hijack_fixes
+            return self.process_tokens(remade_batch_tokens, batch_multipliers)
+
+        z = None
+        i = 0
+        while max(map(len, remade_batch_tokens)) != 0:
+            rem_tokens = [x[75:] for x in remade_batch_tokens]
+            rem_multipliers = [x[75:] for x in batch_multipliers]
+
+            self.hijack.fixes = []
+            for unfiltered in hijack_fixes:
+                fixes = []
+                for fix in unfiltered:
+                    if fix[0] == i:
+                        fixes.append(fix[1])
+                self.hijack.fixes.append(fixes)
+
+            tokens = []
+            multipliers = []
+            for j in range(len(remade_batch_tokens)):
+                if len(remade_batch_tokens[j]) > 0:
+                    tokens.append(remade_batch_tokens[j][:75])
+                    multipliers.append(batch_multipliers[j][:75])
+                else:
+                    tokens.append([self.id_end] * 75)
+                    multipliers.append([1.0] * 75)
+
+            z1 = self.process_tokens(tokens, multipliers)
+            z = z1 if z is None else torch.cat((z, z1), axis=-2)
+
+            remade_batch_tokens = rem_tokens
+            batch_multipliers = rem_multipliers
+            i += 1
+
+        return z
+
+    def process_tokens(self, remade_batch_tokens, batch_multipliers):
+        if not opts.use_old_emphasis_implementation:
+            remade_batch_tokens = [[self.id_start] + x[:75] + [self.id_end] for x in remade_batch_tokens]
+            batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
+
+        tokens = torch.asarray(remade_batch_tokens).to(devices.device)
+
+        if self.id_end != self.id_pad:
+            for batch_pos in range(len(remade_batch_tokens)):
+                index = remade_batch_tokens[batch_pos].index(self.id_end)
+                tokens[batch_pos, index+1:tokens.shape[1]] = self.id_pad
+
+        z = self.encode_with_transformers(tokens)
+
+        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
+        batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
+        batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(devices.device)
+        original_mean = z.mean()
+        z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
+        new_mean = z.mean()
+        z *= original_mean / new_mean
+
+        return z
+
+
+class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
+    def __init__(self, wrapped, hijack):
+        super().__init__(wrapped, hijack)
+        self.tokenizer = wrapped.tokenizer
+        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
+
+        self.token_mults = {}
+        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
+        for text, ident in tokens_with_parens:
+            mult = 1.0
+            for c in text:
+                if c == '[':
+                    mult /= 1.1
+                if c == ']':
+                    mult *= 1.1
+                if c == '(':
+                    mult *= 1.1
+                if c == ')':
+                    mult /= 1.1
+
+            if mult != 1.0:
+                self.token_mults[ident] = mult
+
+        self.id_start = self.wrapped.tokenizer.bos_token_id
+        self.id_end = self.wrapped.tokenizer.eos_token_id
+        self.id_pad = self.id_end
+
+    def tokenize(self, texts):
+        tokenized = self.wrapped.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
+
+        return tokenized
+
+    def encode_with_transformers(self, tokens):
+        outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
+
+        if opts.CLIP_stop_at_last_layers > 1:
+            z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
+            z = self.wrapped.transformer.text_model.final_layer_norm(z)
+        else:
+            z = outputs.last_hidden_state
+
+        return z
+
+    def encode_embedding_init_text(self, init_text, nvpt):
+        embedding_layer = self.wrapped.transformer.text_model.embeddings
+        ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
+        embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
+
+        return embedded
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 46714a4f..938f9a58 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -199,8 +199,8 @@ def sample_plms(self,
 
 @torch.no_grad()
 def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                    unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
+                  temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                  unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None):
     b, *_, device = *x.shape, x.device
 
     def get_model_output(x, t):
@@ -249,6 +249,8 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
         pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
         if quantize_denoised:
             pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+        if dynamic_threshold is not None:
+            pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
         # direction pointing to x_t
         dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
         noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
@@ -321,12 +323,16 @@ def should_hijack_inpainting(checkpoint_info):
 
 
 def do_inpainting_hijack():
-    ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
+    # most of this stuff seems to no longer be needed because it is already included into SD2.0
+    # LatentInpaintDiffusion remains because SD2.0's LatentInpaintDiffusion can't be loaded without specifying a checkpoint
+    # p_sample_plms is needed because PLMS can't work with dicts as conditionings
+    # this file should be cleaned up later if weverything tuens out to work fine
+
+    # ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
     ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
 
-    ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
-    ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
+    # ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
+    # ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
 
     ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
-    ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
-
+    # ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
diff --git a/modules/sd_hijack_open_clip.py b/modules/sd_hijack_open_clip.py
new file mode 100644
index 00000000..f733e852
--- /dev/null
+++ b/modules/sd_hijack_open_clip.py
@@ -0,0 +1,37 @@
+import open_clip.tokenizer
+import torch
+
+from modules import sd_hijack_clip, devices
+from modules.shared import opts
+
+tokenizer = open_clip.tokenizer._tokenizer
+
+
+class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
+    def __init__(self, wrapped, hijack):
+        super().__init__(wrapped, hijack)
+
+        self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
+        self.id_start = tokenizer.encoder["<start_of_text>"]
+        self.id_end = tokenizer.encoder["<end_of_text>"]
+        self.id_pad = 0
+
+    def tokenize(self, texts):
+        assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
+
+        tokenized = [tokenizer.encode(text) for text in texts]
+
+        return tokenized
+
+    def encode_with_transformers(self, tokens):
+        # set self.wrapped.layer_idx here according to opts.CLIP_stop_at_last_layers
+        z = self.wrapped.encode_with_transformer(tokens)
+
+        return z
+
+    def encode_embedding_init_text(self, init_text, nvpt):
+        ids = tokenizer.encode(init_text)
+        ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
+        embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
+
+        return embedded
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 4fe67854..4edd8c60 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -127,7 +127,8 @@ class InterruptedException(BaseException):
 class VanillaStableDiffusionSampler:
     def __init__(self, constructor, sd_model):
         self.sampler = constructor(sd_model)
-        self.orig_p_sample_ddim = self.sampler.p_sample_ddim if hasattr(self.sampler, 'p_sample_ddim') else self.sampler.p_sample_plms
+        self.is_plms = hasattr(self.sampler, 'p_sample_plms')
+        self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim
         self.mask = None
         self.nmask = None
         self.init_latent = None
@@ -218,7 +219,6 @@ class VanillaStableDiffusionSampler:
         self.mask = p.mask if hasattr(p, 'mask') else None
         self.nmask = p.nmask if hasattr(p, 'nmask') else None
 
-
     def adjust_steps_if_invalid(self, p, num_steps):
         if  (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
             valid_step = 999 / (1000 // num_steps)
@@ -227,7 +227,6 @@ class VanillaStableDiffusionSampler:
         
         return num_steps
 
-
     def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
         steps, t_enc = setup_img2img_steps(p, steps)
         steps = self.adjust_steps_if_invalid(p, steps)
@@ -260,9 +259,10 @@ class VanillaStableDiffusionSampler:
         steps = self.adjust_steps_if_invalid(p, steps or p.steps)
 
         # Wrap the conditioning models with additional image conditioning for inpainting model
+        # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
         if image_conditioning is not None:
-            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
-            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+            conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
+            unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
 
         samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
 
@@ -350,7 +350,9 @@ class TorchHijack:
 
 class KDiffusionSampler:
     def __init__(self, funcname, sd_model):
-        self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
+        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
+
+        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
         self.funcname = funcname
         self.func = getattr(k_diffusion.sampling, self.funcname)
         self.extra_params = sampler_extra_params.get(funcname, [])
diff --git a/modules/shared.py b/modules/shared.py
index c93ae2a3..8fb1387a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -11,17 +11,15 @@ import tqdm
 import modules.artists
 import modules.interrogate
 import modules.memmon
-import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers, sd_models, localization, sd_vae, extensions, script_loading
-from modules.hypernetworks import hypernetwork
+from modules import localization, sd_vae, extensions, script_loading
 from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
 parser = argparse.ArgumentParser()
-parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
+parser.add_argument("--config", type=str, default=os.path.join(script_path, "v1-inference.yaml"), help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
 parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
@@ -121,10 +119,12 @@ xformers_available = False
 config_filename = cmd_opts.ui_settings_file
 
 os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
-hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
+hypernetworks = {}
 loaded_hypernetwork = None
 
+
 def reload_hypernetworks():
+    from modules.hypernetworks import hypernetwork
     global hypernetworks
 
     hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
@@ -206,10 +206,11 @@ class State:
         if self.current_latent is None:
             return
 
+        import modules.sd_samplers
         if opts.show_progress_grid:
-            self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
+            self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent)
         else:
-            self.current_image = sd_samplers.sample_to_image(self.current_latent)
+            self.current_image = modules.sd_samplers.sample_to_image(self.current_latent)
 
         self.current_image_sampling_step = self.sampling_step
 
@@ -248,6 +249,21 @@ def options_section(section_identifier, options_dict):
     return options_dict
 
 
+def list_checkpoint_tiles():
+    import modules.sd_models
+    return modules.sd_models.checkpoint_tiles()
+
+
+def refresh_checkpoints():
+    import modules.sd_models
+    return modules.sd_models.list_models()
+
+
+def list_samplers():
+    import modules.sd_samplers
+    return modules.sd_samplers.all_samplers
+
+
 hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
 
 options_templates = {}
@@ -333,7 +349,7 @@ options_templates.update(options_section(('training', "Training"), {
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
     "sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
@@ -385,7 +401,7 @@ options_templates.update(options_section(('ui', "User interface"), {
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-    "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in sd_samplers.all_samplers]}),
+    "hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
     "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 5e4d8688..a273e663 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -64,7 +64,8 @@ class EmbeddingDatabase:
 
         self.word_embeddings[embedding.name] = embedding
 
-        ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0]
+        # TODO changing between clip and open clip changes tokenization, which will cause embeddings to stop working
+        ids = model.cond_stage_model.tokenize([embedding.name])[0]
 
         first_id = ids[0]
         if first_id not in self.ids_lookup:
@@ -155,13 +156,11 @@ class EmbeddingDatabase:
 
 def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
     cond_model = shared.sd_model.cond_stage_model
-    embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
 
     with devices.autocast():
         cond_model([""])  # will send cond model to GPU if lowvram/medvram is active
 
-    ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
-    embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
+    embedded = cond_model.encode_embedding_init_text(init_text, num_vectors_per_token)
     vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
 
     for i in range(num_vectors_per_token):
diff --git a/modules/ui.py b/modules/ui.py
index e6da1b2a..e5cb69d0 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -478,9 +478,7 @@ def create_toprow(is_img2img):
         if is_img2img:
             with gr.Column(scale=1, elem_id="interrogate_col"):
                 button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-
-                if cmd_opts.deepdanbooru:
-                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
 
         with gr.Column(scale=1):
             with gr.Row():
@@ -1004,11 +1002,10 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[img2img_prompt],
             )
 
-            if cmd_opts.deepdanbooru:
-                img2img_deepbooru.click(
-                    fn=interrogate_deepbooru,
-                    inputs=[init_img],
-                    outputs=[img2img_prompt],
+            img2img_deepbooru.click(
+                fn=interrogate_deepbooru,
+                inputs=[init_img],
+                outputs=[img2img_prompt],
             )
 
 
diff --git a/requirements.txt b/requirements.txt
index 762db4f3..e4e5ec64 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -28,3 +28,4 @@ kornia
 lark
 inflection
 GitPython
+torchsde
diff --git a/requirements_versions.txt b/requirements_versions.txt
index 662ca684..8d557fe3 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -25,3 +25,4 @@ kornia==0.6.7
 lark==1.1.2
 inflection==0.5.1
 GitPython==3.1.27
+torchsde==0.2.5
diff --git a/v1-inference.yaml b/v1-inference.yaml
new file mode 100644
index 00000000..d4effe56
--- /dev/null
+++ b/v1-inference.yaml
@@ -0,0 +1,70 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
diff --git a/webui.py b/webui.py
index c5e5fe75..23215d1e 100644
--- a/webui.py
+++ b/webui.py
@@ -10,7 +10,7 @@ from fastapi.middleware.gzip import GZipMiddleware
 
 from modules.paths import script_path
 
-from modules import devices, sd_samplers, upscaler, extensions, localization
+from modules import shared, devices, sd_samplers, upscaler, extensions, localization
 import modules.codeformer_model as codeformer
 import modules.extras
 import modules.face_restoration
@@ -23,7 +23,6 @@ import modules.scripts
 import modules.sd_hijack
 import modules.sd_models
 import modules.sd_vae
-import modules.shared as shared
 import modules.txt2img
 import modules.script_callbacks
 
@@ -86,7 +85,7 @@ def initialize():
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
     shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
     shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
-    shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
+    shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: shared.reload_hypernetworks()))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
 
     if cmd_opts.tls_keyfile is not None and cmd_opts.tls_keyfile is not None:
-- 
cgit v1.2.1


From 1123f52cadf8d86c006177791b3191e5b8388b5a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 26 Nov 2022 16:37:37 +0300
Subject: add 1024 module for hypernets for the new open clip

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index e5cb69d0..16f262c4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1210,7 +1210,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "1024", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
                     new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
                     new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
-- 
cgit v1.2.1


From 64c7b7975cedeb2aaa1a9c8eb4a479fc575843f8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 26 Nov 2022 16:45:57 +0300
Subject: restore hypernetworks to seemingly working state

---
 modules/sd_hijack.py | 3 ++-
 modules/ui.py        | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index d5243fd3..64655eb1 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -9,6 +9,7 @@ from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared
+from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
 from modules import sd_hijack_clip, sd_hijack_open_clip
 
@@ -60,7 +61,7 @@ def apply_optimizations():
 
 
 def undo_optimizations():
-    ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward  # this stops hypernets from working
+    ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
     ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
diff --git a/modules/ui.py b/modules/ui.py
index 16f262c4..c8b8fecd 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1210,7 +1210,7 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "1024", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
                     new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
                     new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
-- 
cgit v1.2.1


From 755df94b2aa62eabd96f900e0dd7ddc83c2f692c Mon Sep 17 00:00:00 2001
From: flamelaw <flamelaw.com3d2@gmail.com>
Date: Sun, 27 Nov 2022 00:35:44 +0900
Subject: set TI AdamW default weight decay to 0

---
 modules/textual_inversion/textual_inversion.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fee08e33..b9b1394f 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -283,7 +283,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     embedding.vec.requires_grad = True
-    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
+    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)
     scaler = torch.cuda.amp.GradScaler()
 
     batch_size = ds.batch_size
-- 
cgit v1.2.1


From b5050ad2071644f7b4c99660dc66a8a95136102f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 26 Nov 2022 20:52:16 +0300
Subject: make SD2 compatible with --medvram setting

---
 modules/lowvram.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/modules/lowvram.py b/modules/lowvram.py
index a4652cb1..aa464a95 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -51,6 +51,10 @@ def setup_for_low_vram(sd_model, use_medvram):
         send_me_to_gpu(first_stage_model, None)
         return first_stage_model_decode(z)
 
+    # for SD1, cond_stage_model is CLIP and its NN is in the tranformer frield, but for SD2, it's open clip, and it's in model field
+    if hasattr(sd_model.cond_stage_model, 'model'):
+        sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
+
     # remove three big modules, cond, first_stage, and unet from the model and then
     # send the model to GPU. Then put modules back. the modules will be in CPU.
     stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model
@@ -65,6 +69,10 @@ def setup_for_low_vram(sd_model, use_medvram):
     sd_model.first_stage_model.decode = first_stage_model_decode_wrap
     parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
 
+    if hasattr(sd_model.cond_stage_model, 'model'):
+        sd_model.cond_stage_model.model = sd_model.cond_stage_model.transformer
+        del sd_model.cond_stage_model.transformer
+
     if use_medvram:
         sd_model.model.register_forward_pre_hook(send_me_to_gpu)
     else:
-- 
cgit v1.2.1


From 1e506657e1cb732a5f0e567ba2585fba2bbb1327 Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Sat, 26 Nov 2022 13:28:44 -0500
Subject: no-half support for SD 2.0

---
 modules/sd_models.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index c59151e0..0e0bd79e 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -244,6 +244,9 @@ def load_model(checkpoint_info=None):
 
     do_inpainting_hijack()
 
+    if shared.cmd_opts.no_half:
+        sd_config.model.params.unet_config.params.use_fp16 = False
+
     sd_model = instantiate_from_config(sd_config.model)
     load_model_weights(sd_model, checkpoint_info)
 
-- 
cgit v1.2.1


From c67c40f983997594f76b2312f92c3761e8d83715 Mon Sep 17 00:00:00 2001
From: Matthew McGoogan <mlmcgoogan@gmail.com>
Date: Sat, 26 Nov 2022 23:25:16 +0000
Subject: torch.cuda.empty_cache() defaults to cuda:0 device unless explicitly
 set otherwise first. Updating torch_gc() to use the device set by --device-id
 if specified to avoid OOM edge cases on multi-GPU systems.

---
 modules/devices.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 67165bf6..93d82bbc 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -44,8 +44,18 @@ def get_optimal_device():
 
 def torch_gc():
     if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-        torch.cuda.ipc_collect()
+        from modules import shared
+
+        device_id = shared.cmd_opts.device_id
+        
+        if device_id is not None:
+            cuda_device = f"cuda:{device_id}"
+        else:
+            cuda_device = "cuda"
+        
+        with torch.cuda.device(cuda_device):
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
 
 
 def enable_tf32():
-- 
cgit v1.2.1


From b006382784a2f0887317bb60ea49d19b50a5dc7e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 11:52:53 +0300
Subject: serve images from where they are saved instead of a temporary
 directory add an option to choose a different temporary directory in the UI
 add an option to cleanup the selected temporary directory at startup

---
 modules/images.py     |  2 ++
 modules/shared.py     |  7 ++++++
 modules/ui.py         | 16 -------------
 modules/ui_tempdir.py | 62 +++++++++++++++++++++++++++++++++++++++++++++++++++
 webui.py              | 16 ++++++++-----
 5 files changed, 82 insertions(+), 21 deletions(-)
 create mode 100644 modules/ui_tempdir.py

diff --git a/modules/images.py b/modules/images.py
index 26d5b7a9..8737ccff 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -524,6 +524,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     else:
         image.save(fullfn, quality=opts.jpeg_quality)
 
+    image.already_saved_as = fullfn
+
     target_side_length = 4000
     oversize = image.width > target_side_length or image.height > target_side_length
     if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024):
diff --git a/modules/shared.py b/modules/shared.py
index 8fb1387a..af975f54 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -16,6 +16,9 @@ import modules.devices as devices
 from modules import localization, sd_vae, extensions, script_loading
 from modules.paths import models_path, script_path, sd_path
 
+
+demo = None
+
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
 parser = argparse.ArgumentParser()
@@ -292,6 +295,10 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
     "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
+
+    "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
+    "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
+
 }))
 
 options_templates.update(options_section(('saving-paths', "Paths for saving"), {
diff --git a/modules/ui.py b/modules/ui.py
index c8b8fecd..ea925c40 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -157,22 +157,6 @@ def save_files(js_data, images, do_make_zip, index):
 
     return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
-def save_pil_to_file(pil_image, dir=None):
-    use_metadata = False
-    metadata = PngImagePlugin.PngInfo()
-    for key, value in pil_image.info.items():
-        if isinstance(key, str) and isinstance(value, str):
-            metadata.add_text(key, value)
-            use_metadata = True
-
-    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
-    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
-    return file_obj
-
-
-# override save to file function so that it also writes PNG info
-gr.processing_utils.save_pil_to_file = save_pil_to_file
-
 
 def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
     def f(*args, extra_outputs_array=extra_outputs, **kwargs):
diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py
new file mode 100644
index 00000000..9c6d3a9d
--- /dev/null
+++ b/modules/ui_tempdir.py
@@ -0,0 +1,62 @@
+import os
+import tempfile
+from collections import namedtuple
+
+import gradio as gr
+
+from PIL import PngImagePlugin
+
+from modules import shared
+
+
+Savedfile = namedtuple("Savedfile", ["name"])
+
+
+def save_pil_to_file(pil_image, dir=None):
+    already_saved_as = getattr(pil_image, 'already_saved_as', None)
+    if already_saved_as:
+        shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(os.path.dirname(already_saved_as))}
+        file_obj = Savedfile(already_saved_as)
+        return file_obj
+
+    if shared.opts.temp_dir != "":
+        dir = shared.opts.temp_dir
+
+    use_metadata = False
+    metadata = PngImagePlugin.PngInfo()
+    for key, value in pil_image.info.items():
+        if isinstance(key, str) and isinstance(value, str):
+            metadata.add_text(key, value)
+            use_metadata = True
+
+    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
+    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
+    return file_obj
+
+
+# override save to file function so that it also writes PNG info
+gr.processing_utils.save_pil_to_file = save_pil_to_file
+
+
+def on_tmpdir_changed():
+    if shared.opts.temp_dir == "" or shared.demo is None:
+        return
+
+    os.makedirs(shared.opts.temp_dir, exist_ok=True)
+
+    shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(shared.opts.temp_dir)}
+
+
+def cleanup_tmpdr():
+    temp_dir = shared.opts.temp_dir
+    if temp_dir == "" or not os.path.isdir(temp_dir):
+        return
+
+    for root, dirs, files in os.walk(temp_dir, topdown=False):
+        for name in files:
+            _, extension = os.path.splitext(name)
+            if extension != ".png":
+                continue
+
+            filename = os.path.join(root, name)
+            os.remove(filename)
diff --git a/webui.py b/webui.py
index 23215d1e..6b79dc55 100644
--- a/webui.py
+++ b/webui.py
@@ -10,7 +10,7 @@ from fastapi.middleware.gzip import GZipMiddleware
 
 from modules.paths import script_path
 
-from modules import shared, devices, sd_samplers, upscaler, extensions, localization
+from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir
 import modules.codeformer_model as codeformer
 import modules.extras
 import modules.face_restoration
@@ -31,12 +31,14 @@ from modules import modelloader
 from modules.shared import cmd_opts
 import modules.hypernetworks.hypernetwork
 
+
 queue_lock = threading.Lock()
 if cmd_opts.server_name:
     server_name = cmd_opts.server_name
 else:
     server_name = "0.0.0.0" if cmd_opts.listen else None
 
+
 def wrap_queued_call(func):
     def f(*args, **kwargs):
         with queue_lock:
@@ -87,6 +89,7 @@ def initialize():
     shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: shared.reload_hypernetworks()))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
+    shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
 
     if cmd_opts.tls_keyfile is not None and cmd_opts.tls_keyfile is not None:
 
@@ -149,9 +152,12 @@ def webui():
     initialize()
 
     while 1:
-        demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
+        if shared.opts.clean_temp_dir_at_start:
+            ui_tempdir.cleanup_tmpdr()
+
+        shared.demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
 
-        app, local_url, share_url = demo.launch(
+        app, local_url, share_url = shared.demo.launch(
             share=cmd_opts.share,
             server_name=server_name,
             server_port=cmd_opts.port,
@@ -178,9 +184,9 @@ def webui():
         if launch_api:
             create_api(app)
 
-        modules.script_callbacks.app_started_callback(demo, app)
+        modules.script_callbacks.app_started_callback(shared.demo, app)
 
-        wait_on_server(demo)
+        wait_on_server(shared.demo)
 
         sd_samplers.set_samplers()
 
-- 
cgit v1.2.1


From 5b2c316890b7b8af95f0d0334d1fd34b9a687b99 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 13:08:54 +0300
Subject: eliminate duplicated code from #5095

---
 modules/devices.py | 30 +++++++++++-------------------
 1 file changed, 11 insertions(+), 19 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 93d82bbc..dd50fe24 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -24,17 +24,18 @@ def extract_device_id(args, name):
     return None
 
 
-def get_optimal_device():
-    if torch.cuda.is_available():
-        from modules import shared
+def get_cuda_device_string():
+    from modules import shared
+
+    if shared.cmd_opts.device_id is not None:
+        return f"cuda:{shared.cmd_opts.device_id}"
 
-        device_id = shared.cmd_opts.device_id
+    return "cuda"
 
-        if device_id is not None:
-            cuda_device = f"cuda:{device_id}"
-            return torch.device(cuda_device)
-        else:
-            return torch.device("cuda")
+
+def get_optimal_device():
+    if torch.cuda.is_available():
+        return torch.device(get_cuda_device_string())
 
     if has_mps():
         return torch.device("mps")
@@ -44,16 +45,7 @@ def get_optimal_device():
 
 def torch_gc():
     if torch.cuda.is_available():
-        from modules import shared
-
-        device_id = shared.cmd_opts.device_id
-        
-        if device_id is not None:
-            cuda_device = f"cuda:{device_id}"
-        else:
-            cuda_device = "cuda"
-        
-        with torch.cuda.device(cuda_device):
+        with torch.cuda.device(get_cuda_device_string()):
             torch.cuda.empty_cache()
             torch.cuda.ipc_collect()
 
-- 
cgit v1.2.1


From 40ca34b837b5068ec35b8d5681bae32cf28f5816 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 13:17:39 +0300
Subject: fix for broken sampler selection in img2img and xy plot #4860 #4909

---
 modules/img2img.py    |  2 +-
 modules/processing.py |  2 +-
 scripts/xy_grid.py    | 14 +++++++-------
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/modules/img2img.py b/modules/img2img.py
index 9fc5b693..7e58994a 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -99,7 +99,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         seed_resize_from_h=seed_resize_from_h,
         seed_resize_from_w=seed_resize_from_w,
         seed_enable_extras=seed_enable_extras,
-        sampler_index=sd_samplers.samplers_for_img2img[sampler_index].name,
+        sampler_name=sd_samplers.samplers_for_img2img[sampler_index].name,
         batch_size=batch_size,
         n_iter=n_iter,
         steps=steps,
diff --git a/modules/processing.py b/modules/processing.py
index c310df6a..edceb532 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -74,7 +74,7 @@ class StableDiffusionProcessing():
     """
     def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, sampler_index: int = None):
         if sampler_index is not None:
-            warnings.warn("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name")
+            print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index b0b9d84d..2517c47d 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -62,25 +62,25 @@ def apply_order(p, x, xs):
 
 def build_samplers_dict():
     samplers_dict = {}
-    for i, sampler in enumerate(sd_samplers.all_samplers):
-        samplers_dict[sampler.name.lower()] = i
+    for sampler in sd_samplers.all_samplers:
+        samplers_dict[sampler.name.lower()] = sampler.name
         for alias in sampler.aliases:
-            samplers_dict[alias.lower()] = i
+            samplers_dict[alias.lower()] = sampler.name
     return samplers_dict
 
 
 def apply_sampler(p, x, xs):
-    sampler_index = build_samplers_dict().get(x.lower(), None)
-    if sampler_index is None:
+    sampler_name = build_samplers_dict().get(x.lower(), None)
+    if sampler_name is None:
         raise RuntimeError(f"Unknown sampler: {x}")
 
-    p.sampler_index = sampler_index
+    p.sampler_name = sampler_name
 
 
 def confirm_samplers(p, xs):
     samplers_dict = build_samplers_dict()
     for x in xs:
-        if x.lower() not in samplers_dict.keys():
+        if x.lower() not in samplers_dict:
             raise RuntimeError(f"Unknown sampler: {x}")
 
 
-- 
cgit v1.2.1


From 10923f9b3a10a9af20429e51242614e259fbd434 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 13:43:10 +0300
Subject: calculate dictionary for sampler names only once

---
 modules/sd_samplers.py |  7 +++++++
 scripts/xy_grid.py     | 14 ++------------
 2 files changed, 9 insertions(+), 12 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 43ce34eb..6f8ccf1d 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -52,6 +52,7 @@ all_samplers_map = {x.name: x for x in all_samplers}
 
 samplers = []
 samplers_for_img2img = []
+samplers_map = {}
 
 
 def create_sampler(name, model):
@@ -77,6 +78,12 @@ def set_samplers():
     samplers = [x for x in all_samplers if x.name not in hidden]
     samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
 
+    samplers_map.clear()
+    for sampler in all_samplers:
+        samplers_map[sampler.name.lower()] = sampler.name
+        for alias in sampler.aliases:
+            samplers_map[alias.lower()] = sampler.name
+
 
 set_samplers()
 
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 2517c47d..0f27deda 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -58,19 +58,10 @@ def apply_order(p, x, xs):
         prompt_tmp += part
         prompt_tmp += x[idx]
     p.prompt = prompt_tmp + p.prompt
-    
-
-def build_samplers_dict():
-    samplers_dict = {}
-    for sampler in sd_samplers.all_samplers:
-        samplers_dict[sampler.name.lower()] = sampler.name
-        for alias in sampler.aliases:
-            samplers_dict[alias.lower()] = sampler.name
-    return samplers_dict
 
 
 def apply_sampler(p, x, xs):
-    sampler_name = build_samplers_dict().get(x.lower(), None)
+    sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
     if sampler_name is None:
         raise RuntimeError(f"Unknown sampler: {x}")
 
@@ -78,9 +69,8 @@ def apply_sampler(p, x, xs):
 
 
 def confirm_samplers(p, xs):
-    samplers_dict = build_samplers_dict()
     for x in xs:
-        if x.lower() not in samplers_dict:
+        if x.lower() not in sd_samplers.samplers_map:
             raise RuntimeError(f"Unknown sampler: {x}")
 
 
-- 
cgit v1.2.1


From 6074175faa751dde933aa8e15cd687ca4e4b4a23 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 14:46:40 +0300
Subject: add safetensors to requirements

---
 modules/sd_models.py      | 11 +++++------
 requirements.txt          |  1 +
 requirements_versions.txt |  1 +
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ae36841a..77236480 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -5,6 +5,7 @@ import gc
 from collections import namedtuple
 import torch
 import re
+import safetensors.torch
 from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
@@ -173,14 +174,12 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-        if checkpoint_file.endswith(".safetensors"):
-            try:
-                from safetensors.torch import load_file
-            except ImportError as e:
-                raise ImportError(f"The model is in safetensors format and it is not installed, use `pip install safetensors`: {e}")
-            pl_sd = load_file(checkpoint_file, device=shared.weight_load_location)
+        _, extension = os.path.splitext(checkpoint_file)
+        if extension.lower() == ".safetensors":
+            pl_sd = safetensors.torch.load_file(checkpoint_file, device=shared.weight_load_location)
         else:
             pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+
         if "global_step" in pl_sd:
             print(f"Global Step: {pl_sd['global_step']}")
 
diff --git a/requirements.txt b/requirements.txt
index e4e5ec64..5f3d9623 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -29,3 +29,4 @@ lark
 inflection
 GitPython
 torchsde
+safetensors
diff --git a/requirements_versions.txt b/requirements_versions.txt
index 8d557fe3..035fa82f 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -26,3 +26,4 @@ lark==1.1.2
 inflection==0.5.1
 GitPython==3.1.27
 torchsde==0.2.5
+safetensors==0.2.5
-- 
cgit v1.2.1


From dac9b6f15de5e675053d9490a20e0457dcd1a23e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 15:51:29 +0300
Subject: add safetensors support for model merging #4869

---
 modules/extras.py    | 26 ++++++++++++++------------
 modules/sd_models.py | 26 +++++++++++++++-----------
 modules/ui.py        |  7 ++++++-
 3 files changed, 35 insertions(+), 24 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 71b93a06..3d65d90a 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -20,6 +20,7 @@ import modules.codeformer_model
 import piexif
 import piexif.helper
 import gradio as gr
+import safetensors.torch
 
 
 class LruCache(OrderedDict):
@@ -249,7 +250,7 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
+def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
     def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
@@ -264,19 +265,15 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
     teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
 
     print(f"Loading {primary_model_info.filename}...")
-    primary_model = torch.load(primary_model_info.filename, map_location='cpu')
-    theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
+    theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
 
     print(f"Loading {secondary_model_info.filename}...")
-    secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
-    theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
+    theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
 
     if teritary_model_info is not None:
         print(f"Loading {teritary_model_info.filename}...")
-        teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
-        theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
+        theta_2 = sd_models.read_state_dict(teritary_model_info.filename, map_location='cpu')
     else:
-        teritary_model = None
         theta_2 = None
 
     theta_funcs = {
@@ -295,7 +292,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
                     theta_1[key] = theta_func1(theta_1[key], t2)
                 else:
                     theta_1[key] = torch.zeros_like(theta_1[key])
-    del theta_2, teritary_model
+    del theta_2
 
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
@@ -314,12 +311,17 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
 
-    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
-    filename = filename if custom_name == '' else (custom_name + '.ckpt')
+    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.' + checkpoint_format
+    filename = filename if custom_name == '' else (custom_name + '.' + checkpoint_format)
     output_modelname = os.path.join(ckpt_dir, filename)
 
     print(f"Saving to {output_modelname}...")
-    torch.save(primary_model, output_modelname)
+
+    _, extension = os.path.splitext(output_modelname)
+    if extension.lower() == ".safetensors":
+        safetensors.torch.save_file(theta_0, output_modelname, metadata={"format": "pt"})
+    else:
+        torch.save(theta_0, output_modelname)
 
     sd_models.list_models()
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 77236480..a1ea5611 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -160,6 +160,20 @@ def get_state_dict_from_checkpoint(pl_sd):
     return pl_sd
 
 
+def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
+    _, extension = os.path.splitext(checkpoint_file)
+    if extension.lower() == ".safetensors":
+        pl_sd = safetensors.torch.load_file(checkpoint_file, device=map_location or shared.weight_load_location)
+    else:
+        pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
+
+    if print_global_state and "global_step" in pl_sd:
+        print(f"Global Step: {pl_sd['global_step']}")
+
+    sd = get_state_dict_from_checkpoint(pl_sd)
+    return sd
+
+
 def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
@@ -174,17 +188,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-        _, extension = os.path.splitext(checkpoint_file)
-        if extension.lower() == ".safetensors":
-            pl_sd = safetensors.torch.load_file(checkpoint_file, device=shared.weight_load_location)
-        else:
-            pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
-
-        if "global_step" in pl_sd:
-            print(f"Global Step: {pl_sd['global_step']}")
-
-        sd = get_state_dict_from_checkpoint(pl_sd)
-        del pl_sd
+        sd = read_state_dict(checkpoint_file)
         model.load_state_dict(sd, strict=False)
         del sd
         
diff --git a/modules/ui.py b/modules/ui.py
index de2b5544..aa13978d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1164,7 +1164,11 @@ def create_ui(wrap_gradio_gpu_call):
                 custom_name = gr.Textbox(label="Custom Name (Optional)")
                 interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
                 interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
-                save_as_half = gr.Checkbox(value=False, label="Save as float16")
+
+                with gr.Row():
+                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format")
+                    save_as_half = gr.Checkbox(value=False, label="Save as float16")
+
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
 
             with gr.Column(variant='panel'):
@@ -1692,6 +1696,7 @@ def create_ui(wrap_gradio_gpu_call):
                 interp_amount,
                 save_as_half,
                 custom_name,
+                checkpoint_format,
             ],
             outputs=[
                 submit_result,
-- 
cgit v1.2.1


From 3cf93de24f90247af33ab9cf743a6eb45308d668 Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Sun, 27 Nov 2022 21:12:37 +0800
Subject: Fix sampler_name for API requests are being ignored

---
 modules/api/api.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index efcedbba..53980551 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -112,7 +112,7 @@ class Api:
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         populate = txt2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
-            "sampler_name": validate_sampler_name(txt2imgreq.sampler_index),
+            "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True
             }
@@ -142,7 +142,7 @@ class Api:
 
         populate = img2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
-            "sampler_name": validate_sampler_name(img2imgreq.sampler_index),
+            "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True,
             "mask": mask
-- 
cgit v1.2.1


From 06ada734c7f85e5e6e2e6ae78fb873be0222bfd5 Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Sun, 27 Nov 2022 21:19:47 +0800
Subject: Prevent warning on sampler_index if sampler_name is being used

---
 modules/api/api.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/modules/api/api.py b/modules/api/api.py
index 53980551..2f450fc4 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -117,6 +117,8 @@ class Api:
             "do_not_save_grid": True
             }
         )
+        if populate.sampler_name:
+            populate.sampler_index = None  # prevent a warning later on
         p = StableDiffusionProcessingTxt2Img(**vars(populate))
         # Override object param
 
@@ -148,6 +150,8 @@ class Api:
             "mask": mask
             }
         )
+        if populate.sampler_name:
+            populate.sampler_index = None  # prevent a warning later on
         p = StableDiffusionProcessingImg2Img(**vars(populate))
 
         imgs = []
-- 
cgit v1.2.1


From 185ab3cbd1330f6dbc87e5dd72cf115b3edfd153 Mon Sep 17 00:00:00 2001
From: cat <cat@wolfgirl.org>
Date: Sat, 26 Nov 2022 20:07:51 +0500
Subject: Atomically rename saved image to avoid race condition with other
 processes.

---
 modules/images.py | 51 +++++++++++++++++++++++++++++----------------------
 1 file changed, 29 insertions(+), 22 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index b968d6a6..51b1ff9c 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -500,30 +500,39 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     image = params.image
     fullfn = params.filename
     info = params.pnginfo.get(pnginfo_section_name, None)
-    fullfn_without_extension, extension = os.path.splitext(params.filename)
 
-    def exif_bytes():
-        return piexif.dump({
-            "Exif": {
-                piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(info or "", encoding="unicode")
-            },
-        })
+    def _atomically_save_image(image_to_save, filename_without_extension, extension):
+        # save image with .tmp extension to avoid race condition when another process detects new image in the directory
+        temp_file_path = filename_without_extension + ".tmp"
+        image_format = Image.registered_extensions()[extension]
 
-    if extension.lower() == '.png':
-        pnginfo_data = PngImagePlugin.PngInfo()
-        if opts.enable_pnginfo:
-            for k, v in params.pnginfo.items():
-                pnginfo_data.add_text(k, str(v))
+        if extension.lower() == '.png':
+            pnginfo_data = PngImagePlugin.PngInfo()
+            if opts.enable_pnginfo:
+                for k, v in params.pnginfo.items():
+                    pnginfo_data.add_text(k, str(v))
 
-        image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
+            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
 
-    elif extension.lower() in (".jpg", ".jpeg", ".webp"):
-        image.save(fullfn, quality=opts.jpeg_quality)
+        elif extension.lower() in (".jpg", ".jpeg", ".webp"):
+            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
 
-        if opts.enable_pnginfo and info is not None:
-            piexif.insert(exif_bytes(), fullfn)
-    else:
-        image.save(fullfn, quality=opts.jpeg_quality)
+            if opts.enable_pnginfo and info is not None:
+                exif_bytes = piexif.dump({
+                    "Exif": {
+                        piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(info or "", encoding="unicode")
+                    },
+                })
+
+                piexif.insert(exif_bytes, temp_file_path)
+        else:
+            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
+
+        # atomically rename the file with correct extension
+        os.replace(temp_file_path, filename_without_extension + extension)
+
+    fullfn_without_extension, extension = os.path.splitext(params.filename)
+    _atomically_save_image(image, fullfn_without_extension, extension)
 
     image.already_saved_as = fullfn
 
@@ -537,9 +546,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         elif oversize:
             image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
 
-        image.save(fullfn_without_extension + ".jpg", quality=opts.jpeg_quality)
-        if opts.enable_pnginfo and info is not None:
-            piexif.insert(exif_bytes(), fullfn_without_extension + ".jpg")
+        _atomically_save_image(image, fullfn_without_extension, ".jpg")
 
     if opts.save_txt and info is not None:
         txt_fullfn = f"{fullfn_without_extension}.txt"
-- 
cgit v1.2.1


From 506d529d19f135f57e142371271f84d4971b456f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 16:28:32 +0300
Subject: rework #5012 to also work for pictures dragged into the prompt and
 also add Clip skip + ENSD to parameters

---
 modules/extras.py                          | 40 ++++--------------------------
 modules/generation_parameters_copypaste.py |  1 +
 modules/images.py                          | 38 +++++++++++++++++++++++++++-
 modules/sd_samplers.py                     |  2 +-
 4 files changed, 44 insertions(+), 37 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 0057bf9c..6021a024 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -1,6 +1,8 @@
 from __future__ import annotations
 import math
 import os
+import sys
+import traceback
 
 import numpy as np
 from PIL import Image
@@ -12,7 +14,7 @@ from typing import Callable, List, OrderedDict, Tuple
 from functools import partial
 from dataclasses import dataclass
 
-from modules import processing, shared, images, devices, sd_models
+from modules import processing, shared, images, devices, sd_models, sd_samplers
 from modules.shared import opts
 import modules.gfpgan_model
 from modules.ui import plaintext_to_html
@@ -22,7 +24,6 @@ import piexif.helper
 import gradio as gr
 import safetensors.torch
 
-
 class LruCache(OrderedDict):
     @dataclass(frozen=True)
     class Key:
@@ -214,39 +215,8 @@ def run_pnginfo(image):
     if image is None:
         return '', '', ''
 
-    items = image.info
-    geninfo = ''
-
-    if "exif" in image.info:
-        exif = piexif.load(image.info["exif"])
-        exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
-        try:
-            exif_comment = piexif.helper.UserComment.load(exif_comment)
-        except ValueError:
-            exif_comment = exif_comment.decode('utf8', errors="ignore")
-
-        items['exif comment'] = exif_comment
-        geninfo = exif_comment
-
-        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
-                      'loop', 'background', 'timestamp', 'duration']:
-            items.pop(field, None)
-
-    geninfo = items.get('parameters', geninfo)
-
-    # nai prompt
-    if "Software" in items.keys() and items["Software"] == "NovelAI":
-        import json
-        json_info = json.loads(items["Comment"])
-        geninfo = f'{items["Description"]}\r\nNegative prompt: {json_info["uc"]}\r\n'
-        sampler = "Euler a"
-        if json_info["sampler"] == "k_euler_ancestral":
-            sampler = "Euler a"
-        elif json_info["sampler"] == "k_euler":
-            sampler = "Euler"
-        model_hash = '925997e9'  # assuming this is the correct model hash
-        # not sure with noise and strength parameter
-        geninfo += f'Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Model hash: {model_hash}'  # , Denoising strength: {json_info["noise"]}'
+    geninfo, items = images.read_info_from_image(image)
+    items = {**{'parameters': geninfo}, **items}
 
     info = ''
     for key, text in items.items():
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 1408ea05..0973c695 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -75,6 +75,7 @@ def integrate_settings_paste_fields(component_dict):
         'CLIP_stop_at_last_layers': 'Clip skip',
         'inpainting_mask_weight': 'Conditional mask weight',
         'sd_model_checkpoint': 'Model hash',
+        'eta_noise_seed_delta': 'ENSD',
     }
     settings_paste_fields = [
         (component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))
diff --git a/modules/images.py b/modules/images.py
index b968d6a6..08a72e67 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -15,6 +15,7 @@ import piexif.helper
 from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
 from fonts.ttf import Roboto
 import string
+import json
 
 from modules import sd_samplers, shared, script_callbacks
 from modules.shared import opts, cmd_opts
@@ -553,10 +554,45 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     return fullfn, txt_fullfn
 
 
+def read_info_from_image(image):
+    items = image.info or {}
+
+    geninfo = items.pop('parameters', None)
+
+    if "exif" in items:
+        exif = piexif.load(items["exif"])
+        exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
+        try:
+            exif_comment = piexif.helper.UserComment.load(exif_comment)
+        except ValueError:
+            exif_comment = exif_comment.decode('utf8', errors="ignore")
+
+        items['exif comment'] = exif_comment
+        geninfo = exif_comment
+
+        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
+                      'loop', 'background', 'timestamp', 'duration']:
+            items.pop(field, None)
+
+    if items.get("Software", None) == "NovelAI":
+        try:
+            json_info = json.loads(items["Comment"])
+            sampler = sd_samplers.samplers_map.get(json_info["sampler"], "Euler a")
+
+            geninfo = f"""{items["Description"]}
+Negative prompt: {json_info["uc"]}
+Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
+        except Exception:
+            print(f"Error parsing NovelAI iamge generation parameters:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    return geninfo, items
+
+
 def image_data(data):
     try:
         image = Image.open(io.BytesIO(data))
-        textinfo = image.text["parameters"]
+        textinfo, _ = read_info_from_image(image)
         return textinfo, None
     except Exception:
         pass
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 2ca17d8b..5fefb227 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -18,7 +18,7 @@ from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
 
 samplers_k_diffusion = [
-    ('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}),
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
     ('Euler', 'sample_euler', ['k_euler'], {}),
     ('LMS', 'sample_lms', ['k_lms'], {}),
     ('Heun', 'sample_heun', ['k_heun'], {}),
-- 
cgit v1.2.1


From 8c13f3a2a56b2ab8d6ff0bc5b2ffe0313e74f323 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 16:35:35 +0300
Subject: cherrypick from #4971

---
 modules/ui.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/ui.py b/modules/ui.py
index aa13978d..446bee40 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1044,6 +1044,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (seed_resize_from_w, "Seed resize from-1"),
                 (seed_resize_from_h, "Seed resize from-2"),
                 (denoising_strength, "Denoising strength"),
+                (mask_blur, "Mask blur"),
                 *modules.scripts.scripts_img2img.infotext_fields
             ]
             parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
-- 
cgit v1.2.1


From 89d8804768d4479d72d24ebbbd2bd00650ef5dc7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 18:48:08 +0300
Subject: only run install.py for enabled extensions

---
 launch.py | 27 ++++++++++++++++++++++++---
 1 file changed, 24 insertions(+), 3 deletions(-)

diff --git a/launch.py b/launch.py
index c426e673..ad9ddd5a 100644
--- a/launch.py
+++ b/launch.py
@@ -5,6 +5,8 @@ import sys
 import importlib.util
 import shlex
 import platform
+import argparse
+import json
 
 dir_repos = "repositories"
 dir_extensions = "extensions"
@@ -132,11 +134,26 @@ def run_extension_installer(extension_dir):
         print(e, file=sys.stderr)
 
 
-def run_extensions_installers():
+def list_extensions(settings_file):
+    settings = {}
+
+    try:
+        if os.path.isfile(settings_file):
+            with open(settings_file, "r", encoding="utf8") as file:
+                settings = json.load(file)
+    except Exception as e:
+        print(e, file=sys.stderr)
+
+    disabled_extensions = set(settings.get('disabled_extensions', []))
+
+    return [x for x in os.listdir(dir_extensions) if x not in disabled_extensions]
+
+
+def run_extensions_installers(settings_file):
     if not os.path.isdir(dir_extensions):
         return
 
-    for dirname_extension in os.listdir(dir_extensions):
+    for dirname_extension in list_extensions(settings_file):
         run_extension_installer(os.path.join(dir_extensions, dirname_extension))
 
 
@@ -165,6 +182,10 @@ def prepare_enviroment():
 
     sys.argv += shlex.split(commandline_args)
 
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default='config.json')
+    args, _ = parser.parse_known_args(sys.argv)
+
     sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
     sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
     sys.argv, update_check = extract_arg(sys.argv, '--update-check')
@@ -223,7 +244,7 @@ def prepare_enviroment():
 
     run_pip(f"install -r {requirements_file}", "requirements for Web UI")
 
-    run_extensions_installers()
+    run_extensions_installers(settings_file=args.ui_settings_file)
 
     if update_check:
         version_check(commit)
-- 
cgit v1.2.1


From 9146a5884cbdf67c019685950f7ad0b3f7bd9230 Mon Sep 17 00:00:00 2001
From: uservar <63248296+uservar@users.noreply.github.com>
Date: Sun, 27 Nov 2022 19:11:50 +0000
Subject: Better should hijack inpainting detection

---
 modules/sd_hijack_inpainting.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 938f9a58..5dcbbed9 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -1,3 +1,4 @@
+import os
 import torch
 
 from einops import repeat
@@ -319,7 +320,9 @@ class LatentInpaintDiffusion(LatentDiffusion):
 
 
 def should_hijack_inpainting(checkpoint_info):
-    return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml")
+    ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
+    cfg_basename = os.path.basename(checkpoint_info.config).lower()
+    return "inpainting" in ckpt_basename and not "inpainting" in cfg_basename
 
 
 def do_inpainting_hijack():
-- 
cgit v1.2.1


From aa12dfada05a1f5bef558f24f3a318a1c293a01f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 23:04:42 +0300
Subject: fix the bug that makes it impossible to send images to other tabs

---
 modules/generation_parameters_copypaste.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 0973c695..01980dca 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -2,6 +2,8 @@ import base64
 import io
 import os
 import re
+from pathlib import Path
+
 import gradio as gr
 from modules.shared import script_path
 from modules import shared
@@ -35,9 +37,8 @@ def quote(text):
 def image_from_url_text(filedata):
     if type(filedata) == dict and filedata["is_file"]:
         filename = filedata["name"]
-        tempdir = os.path.normpath(tempfile.gettempdir())
-        normfn = os.path.normpath(filename)
-        assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
+        is_in_right_dir = any(Path(temp_dir).resolve() in Path(filename).resolve().parents for temp_dir in shared.demo.temp_dirs)
+        assert is_in_right_dir, 'trying to open image file outside of allowed directories'
 
         return Image.open(filename)
 
-- 
cgit v1.2.1


From bb11bee22ab02aa2fb5b96baa9be8103fff19e6a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 27 Nov 2022 23:14:13 +0300
Subject: if image on disk was deleted between being generated and request
 being completed, do use temporary dir to store it for the browser

---
 modules/ui_tempdir.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py
index 9c6d3a9d..07210d14 100644
--- a/modules/ui_tempdir.py
+++ b/modules/ui_tempdir.py
@@ -14,7 +14,7 @@ Savedfile = namedtuple("Savedfile", ["name"])
 
 def save_pil_to_file(pil_image, dir=None):
     already_saved_as = getattr(pil_image, 'already_saved_as', None)
-    if already_saved_as:
+    if already_saved_as and os.path.isfile(already_saved_as):
         shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(os.path.dirname(already_saved_as))}
         file_obj = Savedfile(already_saved_as)
         return file_obj
-- 
cgit v1.2.1


From 0376da180c81a11880a2587903d69d85541051e7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 28 Nov 2022 08:39:59 +0300
Subject: make it possible to save nai model using safetensors

---
 modules/sd_models.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index a1ea5611..283cf1cd 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -144,8 +144,8 @@ def transform_checkpoint_dict_key(k):
 
 
 def get_state_dict_from_checkpoint(pl_sd):
-    if "state_dict" in pl_sd:
-        pl_sd = pl_sd["state_dict"]
+    pl_sd = pl_sd.pop("state_dict", pl_sd)
+    pl_sd.pop("state_dict", None)
 
     sd = {}
     for k, v in pl_sd.items():
-- 
cgit v1.2.1


From 0b5dcb3d7ce397ad38312dbfc70febe7bb42dcc3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 28 Nov 2022 09:00:10 +0300
Subject: fix an error that happens when you type into prompt while switching
 model, put queue stuff into separate file

---
 modules/call_queue.py | 98 +++++++++++++++++++++++++++++++++++++++++++++++++++
 modules/ui.py         | 67 ++---------------------------------
 webui.py              | 30 ++--------------
 3 files changed, 104 insertions(+), 91 deletions(-)
 create mode 100644 modules/call_queue.py

diff --git a/modules/call_queue.py b/modules/call_queue.py
new file mode 100644
index 00000000..4cd49533
--- /dev/null
+++ b/modules/call_queue.py
@@ -0,0 +1,98 @@
+import html
+import sys
+import threading
+import traceback
+import time
+
+from modules import shared
+
+queue_lock = threading.Lock()
+
+
+def wrap_queued_call(func):
+    def f(*args, **kwargs):
+        with queue_lock:
+            res = func(*args, **kwargs)
+
+        return res
+
+    return f
+
+
+def wrap_gradio_gpu_call(func, extra_outputs=None):
+    def f(*args, **kwargs):
+
+        shared.state.begin()
+
+        with queue_lock:
+            res = func(*args, **kwargs)
+
+        shared.state.end()
+
+        return res
+
+    return wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True)
+
+
+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
+        run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
+        if run_memmon:
+            shared.mem_mon.monitor()
+        t = time.perf_counter()
+
+        try:
+            res = list(func(*args, **kwargs))
+        except Exception as e:
+            # When printing out our debug argument list, do not print out more than a MB of text
+            max_debug_str_len = 131072 # (1024*1024)/8
+
+            print("Error completing request", file=sys.stderr)
+            argStr = f"Arguments: {str(args)} {str(kwargs)}"
+            print(argStr[:max_debug_str_len], file=sys.stderr)
+            if len(argStr) > max_debug_str_len:
+                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
+
+            print(traceback.format_exc(), file=sys.stderr)
+
+            shared.state.job = ""
+            shared.state.job_count = 0
+
+            if extra_outputs_array is None:
+                extra_outputs_array = [None, '']
+
+            res = extra_outputs_array + [f"<div class='error'>{html.escape(type(e).__name__+': '+str(e))}</div>"]
+
+        shared.state.skipped = False
+        shared.state.interrupted = False
+        shared.state.job_count = 0
+
+        if not add_stats:
+            return tuple(res)
+
+        elapsed = time.perf_counter() - t
+        elapsed_m = int(elapsed // 60)
+        elapsed_s = elapsed % 60
+        elapsed_text = f"{elapsed_s:.2f}s"
+        if elapsed_m > 0:
+            elapsed_text = f"{elapsed_m}m "+elapsed_text
+
+        if run_memmon:
+            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
+            active_peak = mem_stats['active_peak']
+            reserved_peak = mem_stats['reserved_peak']
+            sys_peak = mem_stats['system_peak']
+            sys_total = mem_stats['total']
+            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
+
+            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
+        else:
+            vram_html = ''
+
+        # last item is always HTML
+        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
+
+        return tuple(res)
+
+    return f
+
diff --git a/modules/ui.py b/modules/ui.py
index 446bee40..00809361 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -17,7 +17,7 @@ import gradio.routes
 import gradio.utils
 import numpy as np
 from PIL import Image, PngImagePlugin
-
+from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
 from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru
 from modules.paths import script_path
@@ -158,67 +158,6 @@ def save_files(js_data, images, do_make_zip, index):
     return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
 
-def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
-    def f(*args, extra_outputs_array=extra_outputs, **kwargs):
-        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
-        if run_memmon:
-            shared.mem_mon.monitor()
-        t = time.perf_counter()
-
-        try:
-            res = list(func(*args, **kwargs))
-        except Exception as e:
-            # When printing out our debug argument list, do not print out more than a MB of text
-            max_debug_str_len = 131072 # (1024*1024)/8
-
-            print("Error completing request", file=sys.stderr)
-            argStr = f"Arguments: {str(args)} {str(kwargs)}"
-            print(argStr[:max_debug_str_len], file=sys.stderr)
-            if len(argStr) > max_debug_str_len:
-                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
-
-            print(traceback.format_exc(), file=sys.stderr)
-
-            shared.state.job = ""
-            shared.state.job_count = 0
-
-            if extra_outputs_array is None:
-                extra_outputs_array = [None, '']
-
-            res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
-
-        shared.state.skipped = False
-        shared.state.interrupted = False
-        shared.state.job_count = 0
-
-        if not add_stats:
-            return tuple(res)
-
-        elapsed = time.perf_counter() - t
-        elapsed_m = int(elapsed // 60)
-        elapsed_s = elapsed % 60
-        elapsed_text = f"{elapsed_s:.2f}s"
-        if elapsed_m > 0:
-            elapsed_text = f"{elapsed_m}m "+elapsed_text
-
-        if run_memmon:
-            mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
-            active_peak = mem_stats['active_peak']
-            reserved_peak = mem_stats['reserved_peak']
-            sys_peak = mem_stats['system_peak']
-            sys_total = mem_stats['total']
-            sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
-
-            vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
-        else:
-            vram_html = ''
-
-        # last item is always HTML
-        res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
-
-        return tuple(res)
-
-    return f
 
 
 def calc_time_left(progress, threshold, label, force_display):
@@ -666,7 +605,7 @@ Requested path was: {f}
                 return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
 
 
-def create_ui(wrap_gradio_gpu_call):
+def create_ui():
     import modules.img2img
     import modules.txt2img
 
@@ -826,7 +765,7 @@ def create_ui(wrap_gradio_gpu_call):
                 height,
             ]
 
-            token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
+            token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     modules.scripts.scripts_current = modules.scripts.scripts_img2img
     modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
diff --git a/webui.py b/webui.py
index 7a56bde8..16e7ec1a 100644
--- a/webui.py
+++ b/webui.py
@@ -8,6 +8,7 @@ from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
 
+from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call
 from modules.paths import script_path
 
 from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir
@@ -32,38 +33,12 @@ from modules.shared import cmd_opts
 import modules.hypernetworks.hypernetwork
 
 
-queue_lock = threading.Lock()
 if cmd_opts.server_name:
     server_name = cmd_opts.server_name
 else:
     server_name = "0.0.0.0" if cmd_opts.listen else None
 
 
-def wrap_queued_call(func):
-    def f(*args, **kwargs):
-        with queue_lock:
-            res = func(*args, **kwargs)
-
-        return res
-
-    return f
-
-
-def wrap_gradio_gpu_call(func, extra_outputs=None):
-    def f(*args, **kwargs):
-
-        shared.state.begin()
-
-        with queue_lock:
-            res = func(*args, **kwargs)
-
-        shared.state.end()
-
-        return res
-
-    return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True)
-
-
 def initialize():
     extensions.list_extensions()
     localization.list_localizations(cmd_opts.localizations_dir)
@@ -159,7 +134,7 @@ def webui():
         if shared.opts.clean_temp_dir_at_start:
             ui_tempdir.cleanup_tmpdr()
 
-        shared.demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
+        shared.demo = modules.ui.create_ui()
 
         app, local_url, share_url = shared.demo.launch(
             share=cmd_opts.share,
@@ -189,6 +164,7 @@ def webui():
             create_api(app)
 
         modules.script_callbacks.app_started_callback(shared.demo, app)
+        modules.script_callbacks.app_started_callback(shared.demo, app)
 
         wait_on_server(shared.demo)
 
-- 
cgit v1.2.1


From 67efee33a6c65e58b3f6c788993d0e68a33e4fd0 Mon Sep 17 00:00:00 2001
From: klimaleksus <klimaleksus@gmail.com>
Date: Mon, 28 Nov 2022 16:29:43 +0500
Subject: Make VAE step sequential to prevent VRAM spikes

---
 modules/processing.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index edceb532..fd995b8a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -530,8 +530,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             with devices.autocast():
                 samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
 
-            samples_ddim = samples_ddim.to(devices.dtype_vae)
-            x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
+            x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
+            x_samples_ddim = torch.stack(x_samples_ddim).float()
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
             del samples_ddim
-- 
cgit v1.2.1


From 0202547696a2c8cbe91efbeb30a7b2c689fbe276 Mon Sep 17 00:00:00 2001
From: kaneda2004 <emmgee@gmail.com>
Date: Mon, 28 Nov 2022 12:24:53 -0800
Subject: Update SD Upscaler to include user selectable Scale Factor

---
 scripts/sd_upscale.py | 30 ++++++++++++++++++------------
 1 file changed, 18 insertions(+), 12 deletions(-)

diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
index 01074291..4370abe1 100644
--- a/scripts/sd_upscale.py
+++ b/scripts/sd_upscale.py
@@ -17,13 +17,16 @@ class Script(scripts.Script):
         return is_img2img
 
     def ui(self, is_img2img):
-        info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image to twice the dimensions; use width and height sliders to set tile size</p>")
+        info = gr.HTML(
+            "<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
         overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)
-        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+        scale_factor = gr.Slider(minimum=0, maximum=4, step=1, label='Scale Factor', value=2)
+        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers],
+                                  value=shared.sd_upscalers[0].name, type="index")
 
-        return [info, overlap, upscaler_index]
+        return [info, overlap, upscaler_index, scale_factor]
 
-    def run(self, p, _, overlap, upscaler_index):
+    def run(self, p, _, overlap, upscaler_index, scale_factor):
         processing.fix_seed(p)
         upscaler = shared.sd_upscalers[upscaler_index]
 
@@ -34,9 +37,9 @@ class Script(scripts.Script):
         seed = p.seed
 
         init_img = p.init_images[0]
-        
-        if(upscaler.name != "None"): 
-            img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)
+
+        if (upscaler.name != "None"):
+            img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)
         else:
             img = init_img
 
@@ -59,7 +62,8 @@ class Script(scripts.Script):
         batch_count = math.ceil(len(work) / batch_size)
         state.job_count = batch_count * upscale_count
 
-        print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")
+        print(
+            f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")
 
         result_images = []
         for n in range(upscale_count):
@@ -69,7 +73,7 @@ class Script(scripts.Script):
             work_results = []
             for i in range(batch_count):
                 p.batch_size = batch_size
-                p.init_images = work[i*batch_size:(i+1)*batch_size]
+                p.init_images = work[i * batch_size:(i + 1) * batch_size]
 
                 state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}"
                 processed = processing.process_images(p)
@@ -83,15 +87,17 @@ class Script(scripts.Script):
             image_index = 0
             for y, h, row in grid.tiles:
                 for tiledata in row:
-                    tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
+                    tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (
+                    p.width, p.height))
                     image_index += 1
 
             combined_image = images.combine_grid(grid)
             result_images.append(combined_image)
 
             if opts.samples_save:
-                images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
+                images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format,
+                                  info=initial_info, p=p)
 
         processed = Processed(p, result_images, seed, initial_info)
 
-        return processed
+        return processed
\ No newline at end of file
-- 
cgit v1.2.1


From 91226829f8cef93fd3ec5ae979ec2e694f741a27 Mon Sep 17 00:00:00 2001
From: kaneda2004 <emmgee@gmail.com>
Date: Mon, 28 Nov 2022 12:28:22 -0800
Subject: Update SD Upscaler to include user selectable Scale Factor

---
 scripts/sd_upscale.py | 17 ++++++-----------
 1 file changed, 6 insertions(+), 11 deletions(-)

diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
index 4370abe1..dff54f0a 100644
--- a/scripts/sd_upscale.py
+++ b/scripts/sd_upscale.py
@@ -17,12 +17,10 @@ class Script(scripts.Script):
         return is_img2img
 
     def ui(self, is_img2img):
-        info = gr.HTML(
-            "<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
+        info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
         overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)
         scale_factor = gr.Slider(minimum=0, maximum=4, step=1, label='Scale Factor', value=2)
-        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers],
-                                  value=shared.sd_upscalers[0].name, type="index")
+        upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
         return [info, overlap, upscaler_index, scale_factor]
 
@@ -62,8 +60,7 @@ class Script(scripts.Script):
         batch_count = math.ceil(len(work) / batch_size)
         state.job_count = batch_count * upscale_count
 
-        print(
-            f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")
+        print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")
 
         result_images = []
         for n in range(upscale_count):
@@ -87,17 +84,15 @@ class Script(scripts.Script):
             image_index = 0
             for y, h, row in grid.tiles:
                 for tiledata in row:
-                    tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (
-                    p.width, p.height))
+                    tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
                     image_index += 1
 
             combined_image = images.combine_grid(grid)
             result_images.append(combined_image)
 
             if opts.samples_save:
-                images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format,
-                                  info=initial_info, p=p)
+                images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
 
         processed = Processed(p, result_images, seed, initial_info)
 
-        return processed
\ No newline at end of file
+        return processed
-- 
cgit v1.2.1


From 950d9c739ea523652c653e32c09a37c64074c38f Mon Sep 17 00:00:00 2001
From: kaneda2004 <emmgee@gmail.com>
Date: Mon, 28 Nov 2022 12:29:49 -0800
Subject: Update SD Upscaler to include user selectable Scale Factor

---
 scripts/sd_upscale.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
index dff54f0a..28bd96b3 100644
--- a/scripts/sd_upscale.py
+++ b/scripts/sd_upscale.py
@@ -19,7 +19,7 @@ class Script(scripts.Script):
     def ui(self, is_img2img):
         info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
         overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)
-        scale_factor = gr.Slider(minimum=0, maximum=4, step=1, label='Scale Factor', value=2)
+        scale_factor = gr.Slider(minimum=1, maximum=4, step=1, label='Scale Factor', value=2)
         upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
         return [info, overlap, upscaler_index, scale_factor]
-- 
cgit v1.2.1


From 98ca437edfbf71dd956d67d37f2136b12d13be0d Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Sat, 12 Nov 2022 02:17:55 -0500
Subject: Refactor and instead check if mps is being used, not availability

---
 modules/sd_hijack.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index b824b5bf..ce583950 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -182,11 +182,7 @@ def register_buffer(self, name, attr):
 
     if type(attr) == torch.Tensor:
         if attr.device != devices.device:
-
-            if devices.has_mps():
-                attr = attr.to(device="mps", dtype=torch.float32)
-            else:
-                attr = attr.to(devices.device)
+            attr = attr.to(device=devices.device, dtype=(torch.float32 if devices.device.type == 'mps' else None))
 
     setattr(self, name, attr)
 
-- 
cgit v1.2.1


From 75c4511e6b81ae8fb0dbd932043e8eb35cd09f72 Mon Sep 17 00:00:00 2001
From: zhaohu xing <920232796@qq.com>
Date: Tue, 29 Nov 2022 10:28:41 +0800
Subject: add AltDiffusion to webui

Signed-off-by: zhaohu xing <920232796@qq.com>
---
 configs/altdiffusion/ad-inference.yaml        |   72 ++
 configs/stable-diffusion/v1-inference.yaml    |   71 ++
 ldm/data/__init__.py                          |    0
 ldm/data/base.py                              |   23 +
 ldm/data/imagenet.py                          |  394 +++++++
 ldm/data/lsun.py                              |   92 ++
 ldm/lr_scheduler.py                           |   98 ++
 ldm/models/autoencoder.py                     |  443 ++++++++
 ldm/models/diffusion/__init__.py              |    0
 ldm/models/diffusion/classifier.py            |  267 +++++
 ldm/models/diffusion/ddim.py                  |  241 +++++
 ldm/models/diffusion/ddpm.py                  | 1445 +++++++++++++++++++++++++
 ldm/models/diffusion/dpm_solver/__init__.py   |    1 +
 ldm/models/diffusion/dpm_solver/dpm_solver.py | 1184 ++++++++++++++++++++
 ldm/models/diffusion/dpm_solver/sampler.py    |   82 ++
 ldm/models/diffusion/plms.py                  |  236 ++++
 ldm/modules/attention.py                      |  261 +++++
 ldm/modules/diffusionmodules/__init__.py      |    0
 ldm/modules/diffusionmodules/model.py         |  835 ++++++++++++++
 ldm/modules/diffusionmodules/openaimodel.py   |  961 ++++++++++++++++
 ldm/modules/diffusionmodules/util.py          |  267 +++++
 ldm/modules/distributions/__init__.py         |    0
 ldm/modules/distributions/distributions.py    |   92 ++
 ldm/modules/ema.py                            |   76 ++
 ldm/modules/encoders/__init__.py              |    0
 ldm/modules/encoders/modules.py               |  234 ++++
 ldm/modules/encoders/xlmr.py                  |  137 +++
 ldm/modules/image_degradation/__init__.py     |    2 +
 ldm/modules/image_degradation/bsrgan.py       |  730 +++++++++++++
 ldm/modules/image_degradation/bsrgan_light.py |  650 +++++++++++
 ldm/modules/image_degradation/utils/test.png  |  Bin 0 -> 441072 bytes
 ldm/modules/image_degradation/utils_image.py  |  916 ++++++++++++++++
 ldm/modules/losses/__init__.py                |    1 +
 ldm/modules/losses/contperceptual.py          |  111 ++
 ldm/modules/losses/vqperceptual.py            |  167 +++
 ldm/modules/x_transformer.py                  |  641 +++++++++++
 ldm/util.py                                   |  203 ++++
 modules/devices.py                            |    4 +-
 modules/sd_hijack.py                          |   23 +-
 modules/shared.py                             |    6 +-
 40 files changed, 10957 insertions(+), 9 deletions(-)
 create mode 100644 configs/altdiffusion/ad-inference.yaml
 create mode 100644 configs/stable-diffusion/v1-inference.yaml
 create mode 100644 ldm/data/__init__.py
 create mode 100644 ldm/data/base.py
 create mode 100644 ldm/data/imagenet.py
 create mode 100644 ldm/data/lsun.py
 create mode 100644 ldm/lr_scheduler.py
 create mode 100644 ldm/models/autoencoder.py
 create mode 100644 ldm/models/diffusion/__init__.py
 create mode 100644 ldm/models/diffusion/classifier.py
 create mode 100644 ldm/models/diffusion/ddim.py
 create mode 100644 ldm/models/diffusion/ddpm.py
 create mode 100644 ldm/models/diffusion/dpm_solver/__init__.py
 create mode 100644 ldm/models/diffusion/dpm_solver/dpm_solver.py
 create mode 100644 ldm/models/diffusion/dpm_solver/sampler.py
 create mode 100644 ldm/models/diffusion/plms.py
 create mode 100644 ldm/modules/attention.py
 create mode 100644 ldm/modules/diffusionmodules/__init__.py
 create mode 100644 ldm/modules/diffusionmodules/model.py
 create mode 100644 ldm/modules/diffusionmodules/openaimodel.py
 create mode 100644 ldm/modules/diffusionmodules/util.py
 create mode 100644 ldm/modules/distributions/__init__.py
 create mode 100644 ldm/modules/distributions/distributions.py
 create mode 100644 ldm/modules/ema.py
 create mode 100644 ldm/modules/encoders/__init__.py
 create mode 100644 ldm/modules/encoders/modules.py
 create mode 100644 ldm/modules/encoders/xlmr.py
 create mode 100644 ldm/modules/image_degradation/__init__.py
 create mode 100644 ldm/modules/image_degradation/bsrgan.py
 create mode 100644 ldm/modules/image_degradation/bsrgan_light.py
 create mode 100644 ldm/modules/image_degradation/utils/test.png
 create mode 100644 ldm/modules/image_degradation/utils_image.py
 create mode 100644 ldm/modules/losses/__init__.py
 create mode 100644 ldm/modules/losses/contperceptual.py
 create mode 100644 ldm/modules/losses/vqperceptual.py
 create mode 100644 ldm/modules/x_transformer.py
 create mode 100644 ldm/util.py

diff --git a/configs/altdiffusion/ad-inference.yaml b/configs/altdiffusion/ad-inference.yaml
new file mode 100644
index 00000000..1b11b63e
--- /dev/null
+++ b/configs/altdiffusion/ad-inference.yaml
@@ -0,0 +1,72 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.xlmr.BertSeriesModelWithTransformation
+      params:
+        name: "XLMR-Large"
\ No newline at end of file
diff --git a/configs/stable-diffusion/v1-inference.yaml b/configs/stable-diffusion/v1-inference.yaml
new file mode 100644
index 00000000..2e6ef0f2
--- /dev/null
+++ b/configs/stable-diffusion/v1-inference.yaml
@@ -0,0 +1,71 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      # target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+      target: altclip.model.AltCLIPEmbedder
\ No newline at end of file
diff --git a/ldm/data/__init__.py b/ldm/data/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ldm/data/base.py b/ldm/data/base.py
new file mode 100644
index 00000000..b196c2f7
--- /dev/null
+++ b/ldm/data/base.py
@@ -0,0 +1,23 @@
+from abc import abstractmethod
+from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset
+
+
+class Txt2ImgIterableBaseDataset(IterableDataset):
+    '''
+    Define an interface to make the IterableDatasets for text2img data chainable
+    '''
+    def __init__(self, num_records=0, valid_ids=None, size=256):
+        super().__init__()
+        self.num_records = num_records
+        self.valid_ids = valid_ids
+        self.sample_ids = valid_ids
+        self.size = size
+
+        print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.')
+
+    def __len__(self):
+        return self.num_records
+
+    @abstractmethod
+    def __iter__(self):
+        pass
\ No newline at end of file
diff --git a/ldm/data/imagenet.py b/ldm/data/imagenet.py
new file mode 100644
index 00000000..1c473f9c
--- /dev/null
+++ b/ldm/data/imagenet.py
@@ -0,0 +1,394 @@
+import os, yaml, pickle, shutil, tarfile, glob
+import cv2
+import albumentations
+import PIL
+import numpy as np
+import torchvision.transforms.functional as TF
+from omegaconf import OmegaConf
+from functools import partial
+from PIL import Image
+from tqdm import tqdm
+from torch.utils.data import Dataset, Subset
+
+import taming.data.utils as tdu
+from taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve
+from taming.data.imagenet import ImagePaths
+
+from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light
+
+
+def synset2idx(path_to_yaml="data/index_synset.yaml"):
+    with open(path_to_yaml) as f:
+        di2s = yaml.load(f)
+    return dict((v,k) for k,v in di2s.items())
+
+
+class ImageNetBase(Dataset):
+    def __init__(self, config=None):
+        self.config = config or OmegaConf.create()
+        if not type(self.config)==dict:
+            self.config = OmegaConf.to_container(self.config)
+        self.keep_orig_class_label = self.config.get("keep_orig_class_label", False)
+        self.process_images = True  # if False we skip loading & processing images and self.data contains filepaths
+        self._prepare()
+        self._prepare_synset_to_human()
+        self._prepare_idx_to_synset()
+        self._prepare_human_to_integer_label()
+        self._load()
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, i):
+        return self.data[i]
+
+    def _prepare(self):
+        raise NotImplementedError()
+
+    def _filter_relpaths(self, relpaths):
+        ignore = set([
+            "n06596364_9591.JPEG",
+        ])
+        relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore]
+        if "sub_indices" in self.config:
+            indices = str_to_indices(self.config["sub_indices"])
+            synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn)  # returns a list of strings
+            self.synset2idx = synset2idx(path_to_yaml=self.idx2syn)
+            files = []
+            for rpath in relpaths:
+                syn = rpath.split("/")[0]
+                if syn in synsets:
+                    files.append(rpath)
+            return files
+        else:
+            return relpaths
+
+    def _prepare_synset_to_human(self):
+        SIZE = 2655750
+        URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1"
+        self.human_dict = os.path.join(self.root, "synset_human.txt")
+        if (not os.path.exists(self.human_dict) or
+                not os.path.getsize(self.human_dict)==SIZE):
+            download(URL, self.human_dict)
+
+    def _prepare_idx_to_synset(self):
+        URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1"
+        self.idx2syn = os.path.join(self.root, "index_synset.yaml")
+        if (not os.path.exists(self.idx2syn)):
+            download(URL, self.idx2syn)
+
+    def _prepare_human_to_integer_label(self):
+        URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1"
+        self.human2integer = os.path.join(self.root, "imagenet1000_clsidx_to_labels.txt")
+        if (not os.path.exists(self.human2integer)):
+            download(URL, self.human2integer)
+        with open(self.human2integer, "r") as f:
+            lines = f.read().splitlines()
+            assert len(lines) == 1000
+            self.human2integer_dict = dict()
+            for line in lines:
+                value, key = line.split(":")
+                self.human2integer_dict[key] = int(value)
+
+    def _load(self):
+        with open(self.txt_filelist, "r") as f:
+            self.relpaths = f.read().splitlines()
+            l1 = len(self.relpaths)
+            self.relpaths = self._filter_relpaths(self.relpaths)
+            print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths)))
+
+        self.synsets = [p.split("/")[0] for p in self.relpaths]
+        self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths]
+
+        unique_synsets = np.unique(self.synsets)
+        class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets))
+        if not self.keep_orig_class_label:
+            self.class_labels = [class_dict[s] for s in self.synsets]
+        else:
+            self.class_labels = [self.synset2idx[s] for s in self.synsets]
+
+        with open(self.human_dict, "r") as f:
+            human_dict = f.read().splitlines()
+            human_dict = dict(line.split(maxsplit=1) for line in human_dict)
+
+        self.human_labels = [human_dict[s] for s in self.synsets]
+
+        labels = {
+            "relpath": np.array(self.relpaths),
+            "synsets": np.array(self.synsets),
+            "class_label": np.array(self.class_labels),
+            "human_label": np.array(self.human_labels),
+        }
+
+        if self.process_images:
+            self.size = retrieve(self.config, "size", default=256)
+            self.data = ImagePaths(self.abspaths,
+                                   labels=labels,
+                                   size=self.size,
+                                   random_crop=self.random_crop,
+                                   )
+        else:
+            self.data = self.abspaths
+
+
+class ImageNetTrain(ImageNetBase):
+    NAME = "ILSVRC2012_train"
+    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
+    AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2"
+    FILES = [
+        "ILSVRC2012_img_train.tar",
+    ]
+    SIZES = [
+        147897477120,
+    ]
+
+    def __init__(self, process_images=True, data_root=None, **kwargs):
+        self.process_images = process_images
+        self.data_root = data_root
+        super().__init__(**kwargs)
+
+    def _prepare(self):
+        if self.data_root:
+            self.root = os.path.join(self.data_root, self.NAME)
+        else:
+            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
+            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
+
+        self.datadir = os.path.join(self.root, "data")
+        self.txt_filelist = os.path.join(self.root, "filelist.txt")
+        self.expected_length = 1281167
+        self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop",
+                                    default=True)
+        if not tdu.is_prepared(self.root):
+            # prep
+            print("Preparing dataset {} in {}".format(self.NAME, self.root))
+
+            datadir = self.datadir
+            if not os.path.exists(datadir):
+                path = os.path.join(self.root, self.FILES[0])
+                if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
+                    import academictorrents as at
+                    atpath = at.get(self.AT_HASH, datastore=self.root)
+                    assert atpath == path
+
+                print("Extracting {} to {}".format(path, datadir))
+                os.makedirs(datadir, exist_ok=True)
+                with tarfile.open(path, "r:") as tar:
+                    tar.extractall(path=datadir)
+
+                print("Extracting sub-tars.")
+                subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar")))
+                for subpath in tqdm(subpaths):
+                    subdir = subpath[:-len(".tar")]
+                    os.makedirs(subdir, exist_ok=True)
+                    with tarfile.open(subpath, "r:") as tar:
+                        tar.extractall(path=subdir)
+
+            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
+            filelist = [os.path.relpath(p, start=datadir) for p in filelist]
+            filelist = sorted(filelist)
+            filelist = "\n".join(filelist)+"\n"
+            with open(self.txt_filelist, "w") as f:
+                f.write(filelist)
+
+            tdu.mark_prepared(self.root)
+
+
+class ImageNetValidation(ImageNetBase):
+    NAME = "ILSVRC2012_validation"
+    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
+    AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5"
+    VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1"
+    FILES = [
+        "ILSVRC2012_img_val.tar",
+        "validation_synset.txt",
+    ]
+    SIZES = [
+        6744924160,
+        1950000,
+    ]
+
+    def __init__(self, process_images=True, data_root=None, **kwargs):
+        self.data_root = data_root
+        self.process_images = process_images
+        super().__init__(**kwargs)
+
+    def _prepare(self):
+        if self.data_root:
+            self.root = os.path.join(self.data_root, self.NAME)
+        else:
+            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
+            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
+        self.datadir = os.path.join(self.root, "data")
+        self.txt_filelist = os.path.join(self.root, "filelist.txt")
+        self.expected_length = 50000
+        self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop",
+                                    default=False)
+        if not tdu.is_prepared(self.root):
+            # prep
+            print("Preparing dataset {} in {}".format(self.NAME, self.root))
+
+            datadir = self.datadir
+            if not os.path.exists(datadir):
+                path = os.path.join(self.root, self.FILES[0])
+                if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
+                    import academictorrents as at
+                    atpath = at.get(self.AT_HASH, datastore=self.root)
+                    assert atpath == path
+
+                print("Extracting {} to {}".format(path, datadir))
+                os.makedirs(datadir, exist_ok=True)
+                with tarfile.open(path, "r:") as tar:
+                    tar.extractall(path=datadir)
+
+                vspath = os.path.join(self.root, self.FILES[1])
+                if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]:
+                    download(self.VS_URL, vspath)
+
+                with open(vspath, "r") as f:
+                    synset_dict = f.read().splitlines()
+                    synset_dict = dict(line.split() for line in synset_dict)
+
+                print("Reorganizing into synset folders")
+                synsets = np.unique(list(synset_dict.values()))
+                for s in synsets:
+                    os.makedirs(os.path.join(datadir, s), exist_ok=True)
+                for k, v in synset_dict.items():
+                    src = os.path.join(datadir, k)
+                    dst = os.path.join(datadir, v)
+                    shutil.move(src, dst)
+
+            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
+            filelist = [os.path.relpath(p, start=datadir) for p in filelist]
+            filelist = sorted(filelist)
+            filelist = "\n".join(filelist)+"\n"
+            with open(self.txt_filelist, "w") as f:
+                f.write(filelist)
+
+            tdu.mark_prepared(self.root)
+
+
+
+class ImageNetSR(Dataset):
+    def __init__(self, size=None,
+                 degradation=None, downscale_f=4, min_crop_f=0.5, max_crop_f=1.,
+                 random_crop=True):
+        """
+        Imagenet Superresolution Dataloader
+        Performs following ops in order:
+        1.  crops a crop of size s from image either as random or center crop
+        2.  resizes crop to size with cv2.area_interpolation
+        3.  degrades resized crop with degradation_fn
+
+        :param size: resizing to size after cropping
+        :param degradation: degradation_fn, e.g. cv_bicubic or bsrgan_light
+        :param downscale_f: Low Resolution Downsample factor
+        :param min_crop_f: determines crop size s,
+          where s = c * min_img_side_len with c sampled from interval (min_crop_f, max_crop_f)
+        :param max_crop_f: ""
+        :param data_root:
+        :param random_crop:
+        """
+        self.base = self.get_base()
+        assert size
+        assert (size / downscale_f).is_integer()
+        self.size = size
+        self.LR_size = int(size / downscale_f)
+        self.min_crop_f = min_crop_f
+        self.max_crop_f = max_crop_f
+        assert(max_crop_f <= 1.)
+        self.center_crop = not random_crop
+
+        self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA)
+
+        self.pil_interpolation = False # gets reset later if incase interp_op is from pillow
+
+        if degradation == "bsrgan":
+            self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f)
+
+        elif degradation == "bsrgan_light":
+            self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f)
+
+        else:
+            interpolation_fn = {
+            "cv_nearest": cv2.INTER_NEAREST,
+            "cv_bilinear": cv2.INTER_LINEAR,
+            "cv_bicubic": cv2.INTER_CUBIC,
+            "cv_area": cv2.INTER_AREA,
+            "cv_lanczos": cv2.INTER_LANCZOS4,
+            "pil_nearest": PIL.Image.NEAREST,
+            "pil_bilinear": PIL.Image.BILINEAR,
+            "pil_bicubic": PIL.Image.BICUBIC,
+            "pil_box": PIL.Image.BOX,
+            "pil_hamming": PIL.Image.HAMMING,
+            "pil_lanczos": PIL.Image.LANCZOS,
+            }[degradation]
+
+            self.pil_interpolation = degradation.startswith("pil_")
+
+            if self.pil_interpolation:
+                self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn)
+
+            else:
+                self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size,
+                                                                          interpolation=interpolation_fn)
+
+    def __len__(self):
+        return len(self.base)
+
+    def __getitem__(self, i):
+        example = self.base[i]
+        image = Image.open(example["file_path_"])
+
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+
+        image = np.array(image).astype(np.uint8)
+
+        min_side_len = min(image.shape[:2])
+        crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None)
+        crop_side_len = int(crop_side_len)
+
+        if self.center_crop:
+            self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len)
+
+        else:
+            self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len)
+
+        image = self.cropper(image=image)["image"]
+        image = self.image_rescaler(image=image)["image"]
+
+        if self.pil_interpolation:
+            image_pil = PIL.Image.fromarray(image)
+            LR_image = self.degradation_process(image_pil)
+            LR_image = np.array(LR_image).astype(np.uint8)
+
+        else:
+            LR_image = self.degradation_process(image=image)["image"]
+
+        example["image"] = (image/127.5 - 1.0).astype(np.float32)
+        example["LR_image"] = (LR_image/127.5 - 1.0).astype(np.float32)
+
+        return example
+
+
+class ImageNetSRTrain(ImageNetSR):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def get_base(self):
+        with open("data/imagenet_train_hr_indices.p", "rb") as f:
+            indices = pickle.load(f)
+        dset = ImageNetTrain(process_images=False,)
+        return Subset(dset, indices)
+
+
+class ImageNetSRValidation(ImageNetSR):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def get_base(self):
+        with open("data/imagenet_val_hr_indices.p", "rb") as f:
+            indices = pickle.load(f)
+        dset = ImageNetValidation(process_images=False,)
+        return Subset(dset, indices)
diff --git a/ldm/data/lsun.py b/ldm/data/lsun.py
new file mode 100644
index 00000000..6256e457
--- /dev/null
+++ b/ldm/data/lsun.py
@@ -0,0 +1,92 @@
+import os
+import numpy as np
+import PIL
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+
+class LSUNBase(Dataset):
+    def __init__(self,
+                 txt_file,
+                 data_root,
+                 size=None,
+                 interpolation="bicubic",
+                 flip_p=0.5
+                 ):
+        self.data_paths = txt_file
+        self.data_root = data_root
+        with open(self.data_paths, "r") as f:
+            self.image_paths = f.read().splitlines()
+        self._length = len(self.image_paths)
+        self.labels = {
+            "relative_file_path_": [l for l in self.image_paths],
+            "file_path_": [os.path.join(self.data_root, l)
+                           for l in self.image_paths],
+        }
+
+        self.size = size
+        self.interpolation = {"linear": PIL.Image.LINEAR,
+                              "bilinear": PIL.Image.BILINEAR,
+                              "bicubic": PIL.Image.BICUBIC,
+                              "lanczos": PIL.Image.LANCZOS,
+                              }[interpolation]
+        self.flip = transforms.RandomHorizontalFlip(p=flip_p)
+
+    def __len__(self):
+        return self._length
+
+    def __getitem__(self, i):
+        example = dict((k, self.labels[k][i]) for k in self.labels)
+        image = Image.open(example["file_path_"])
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+
+        # default to score-sde preprocessing
+        img = np.array(image).astype(np.uint8)
+        crop = min(img.shape[0], img.shape[1])
+        h, w, = img.shape[0], img.shape[1]
+        img = img[(h - crop) // 2:(h + crop) // 2,
+              (w - crop) // 2:(w + crop) // 2]
+
+        image = Image.fromarray(img)
+        if self.size is not None:
+            image = image.resize((self.size, self.size), resample=self.interpolation)
+
+        image = self.flip(image)
+        image = np.array(image).astype(np.uint8)
+        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
+        return example
+
+
+class LSUNChurchesTrain(LSUNBase):
+    def __init__(self, **kwargs):
+        super().__init__(txt_file="data/lsun/church_outdoor_train.txt", data_root="data/lsun/churches", **kwargs)
+
+
+class LSUNChurchesValidation(LSUNBase):
+    def __init__(self, flip_p=0., **kwargs):
+        super().__init__(txt_file="data/lsun/church_outdoor_val.txt", data_root="data/lsun/churches",
+                         flip_p=flip_p, **kwargs)
+
+
+class LSUNBedroomsTrain(LSUNBase):
+    def __init__(self, **kwargs):
+        super().__init__(txt_file="data/lsun/bedrooms_train.txt", data_root="data/lsun/bedrooms", **kwargs)
+
+
+class LSUNBedroomsValidation(LSUNBase):
+    def __init__(self, flip_p=0.0, **kwargs):
+        super().__init__(txt_file="data/lsun/bedrooms_val.txt", data_root="data/lsun/bedrooms",
+                         flip_p=flip_p, **kwargs)
+
+
+class LSUNCatsTrain(LSUNBase):
+    def __init__(self, **kwargs):
+        super().__init__(txt_file="data/lsun/cat_train.txt", data_root="data/lsun/cats", **kwargs)
+
+
+class LSUNCatsValidation(LSUNBase):
+    def __init__(self, flip_p=0., **kwargs):
+        super().__init__(txt_file="data/lsun/cat_val.txt", data_root="data/lsun/cats",
+                         flip_p=flip_p, **kwargs)
diff --git a/ldm/lr_scheduler.py b/ldm/lr_scheduler.py
new file mode 100644
index 00000000..be39da9c
--- /dev/null
+++ b/ldm/lr_scheduler.py
@@ -0,0 +1,98 @@
+import numpy as np
+
+
+class LambdaWarmUpCosineScheduler:
+    """
+    note: use with a base_lr of 1.0
+    """
+    def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
+        self.lr_warm_up_steps = warm_up_steps
+        self.lr_start = lr_start
+        self.lr_min = lr_min
+        self.lr_max = lr_max
+        self.lr_max_decay_steps = max_decay_steps
+        self.last_lr = 0.
+        self.verbosity_interval = verbosity_interval
+
+    def schedule(self, n, **kwargs):
+        if self.verbosity_interval > 0:
+            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
+        if n < self.lr_warm_up_steps:
+            lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
+            self.last_lr = lr
+            return lr
+        else:
+            t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
+            t = min(t, 1.0)
+            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
+                    1 + np.cos(t * np.pi))
+            self.last_lr = lr
+            return lr
+
+    def __call__(self, n, **kwargs):
+        return self.schedule(n,**kwargs)
+
+
+class LambdaWarmUpCosineScheduler2:
+    """
+    supports repeated iterations, configurable via lists
+    note: use with a base_lr of 1.0.
+    """
+    def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
+        assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
+        self.lr_warm_up_steps = warm_up_steps
+        self.f_start = f_start
+        self.f_min = f_min
+        self.f_max = f_max
+        self.cycle_lengths = cycle_lengths
+        self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
+        self.last_f = 0.
+        self.verbosity_interval = verbosity_interval
+
+    def find_in_interval(self, n):
+        interval = 0
+        for cl in self.cum_cycles[1:]:
+            if n <= cl:
+                return interval
+            interval += 1
+
+    def schedule(self, n, **kwargs):
+        cycle = self.find_in_interval(n)
+        n = n - self.cum_cycles[cycle]
+        if self.verbosity_interval > 0:
+            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
+                                                       f"current cycle {cycle}")
+        if n < self.lr_warm_up_steps[cycle]:
+            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
+            self.last_f = f
+            return f
+        else:
+            t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
+            t = min(t, 1.0)
+            f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
+                    1 + np.cos(t * np.pi))
+            self.last_f = f
+            return f
+
+    def __call__(self, n, **kwargs):
+        return self.schedule(n, **kwargs)
+
+
+class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
+
+    def schedule(self, n, **kwargs):
+        cycle = self.find_in_interval(n)
+        n = n - self.cum_cycles[cycle]
+        if self.verbosity_interval > 0:
+            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
+                                                       f"current cycle {cycle}")
+
+        if n < self.lr_warm_up_steps[cycle]:
+            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
+            self.last_f = f
+            return f
+        else:
+            f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])
+            self.last_f = f
+            return f
+
diff --git a/ldm/models/autoencoder.py b/ldm/models/autoencoder.py
new file mode 100644
index 00000000..6a9c4f45
--- /dev/null
+++ b/ldm/models/autoencoder.py
@@ -0,0 +1,443 @@
+import torch
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from contextlib import contextmanager
+
+from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
+
+from ldm.modules.diffusionmodules.model import Encoder, Decoder
+from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+
+from ldm.util import instantiate_from_config
+
+
+class VQModel(pl.LightningModule):
+    def __init__(self,
+                 ddconfig,
+                 lossconfig,
+                 n_embed,
+                 embed_dim,
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 image_key="image",
+                 colorize_nlabels=None,
+                 monitor=None,
+                 batch_resize_range=None,
+                 scheduler_config=None,
+                 lr_g_factor=1.0,
+                 remap=None,
+                 sane_index_shape=False, # tell vector quantizer to return indices as bhw
+                 use_ema=False
+                 ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.n_embed = n_embed
+        self.image_key = image_key
+        self.encoder = Encoder(**ddconfig)
+        self.decoder = Decoder(**ddconfig)
+        self.loss = instantiate_from_config(lossconfig)
+        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
+                                        remap=remap,
+                                        sane_index_shape=sane_index_shape)
+        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        if colorize_nlabels is not None:
+            assert type(colorize_nlabels)==int
+            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+        if monitor is not None:
+            self.monitor = monitor
+        self.batch_resize_range = batch_resize_range
+        if self.batch_resize_range is not None:
+            print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
+
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+        self.scheduler_config = scheduler_config
+        self.lr_g_factor = lr_g_factor
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.parameters())
+            self.model_ema.copy_to(self)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def init_from_ckpt(self, path, ignore_keys=list()):
+        sd = torch.load(path, map_location="cpu")["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        missing, unexpected = self.load_state_dict(sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+            print(f"Unexpected Keys: {unexpected}")
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self)
+
+    def encode(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        quant, emb_loss, info = self.quantize(h)
+        return quant, emb_loss, info
+
+    def encode_to_prequant(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        return h
+
+    def decode(self, quant):
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+    def decode_code(self, code_b):
+        quant_b = self.quantize.embed_code(code_b)
+        dec = self.decode(quant_b)
+        return dec
+
+    def forward(self, input, return_pred_indices=False):
+        quant, diff, (_,_,ind) = self.encode(input)
+        dec = self.decode(quant)
+        if return_pred_indices:
+            return dec, diff, ind
+        return dec, diff
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+        if self.batch_resize_range is not None:
+            lower_size = self.batch_resize_range[0]
+            upper_size = self.batch_resize_range[1]
+            if self.global_step <= 4:
+                # do the first few batches with max size to avoid later oom
+                new_resize = upper_size
+            else:
+                new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
+            if new_resize != x.shape[2]:
+                x = F.interpolate(x, size=new_resize, mode="bicubic")
+            x = x.detach()
+        return x
+
+    def training_step(self, batch, batch_idx, optimizer_idx):
+        # https://github.com/pytorch/pytorch/issues/37142
+        # try not to fool the heuristics
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss, ind = self(x, return_pred_indices=True)
+
+        if optimizer_idx == 0:
+            # autoencode
+            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train",
+                                            predicted_indices=ind)
+
+            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return aeloss
+
+        if optimizer_idx == 1:
+            # discriminator
+            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train")
+            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return discloss
+
+    def validation_step(self, batch, batch_idx):
+        log_dict = self._validation_step(batch, batch_idx)
+        with self.ema_scope():
+            log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
+        return log_dict
+
+    def _validation_step(self, batch, batch_idx, suffix=""):
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss, ind = self(x, return_pred_indices=True)
+        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
+                                        self.global_step,
+                                        last_layer=self.get_last_layer(),
+                                        split="val"+suffix,
+                                        predicted_indices=ind
+                                        )
+
+        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
+                                            self.global_step,
+                                            last_layer=self.get_last_layer(),
+                                            split="val"+suffix,
+                                            predicted_indices=ind
+                                            )
+        rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
+        self.log(f"val{suffix}/rec_loss", rec_loss,
+                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+        self.log(f"val{suffix}/aeloss", aeloss,
+                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+        if version.parse(pl.__version__) >= version.parse('1.4.0'):
+            del log_dict_ae[f"val{suffix}/rec_loss"]
+        self.log_dict(log_dict_ae)
+        self.log_dict(log_dict_disc)
+        return self.log_dict
+
+    def configure_optimizers(self):
+        lr_d = self.learning_rate
+        lr_g = self.lr_g_factor*self.learning_rate
+        print("lr_d", lr_d)
+        print("lr_g", lr_g)
+        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
+                                  list(self.decoder.parameters())+
+                                  list(self.quantize.parameters())+
+                                  list(self.quant_conv.parameters())+
+                                  list(self.post_quant_conv.parameters()),
+                                  lr=lr_g, betas=(0.5, 0.9))
+        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+                                    lr=lr_d, betas=(0.5, 0.9))
+
+        if self.scheduler_config is not None:
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                },
+                {
+                    'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                },
+            ]
+            return [opt_ae, opt_disc], scheduler
+        return [opt_ae, opt_disc], []
+
+    def get_last_layer(self):
+        return self.decoder.conv_out.weight
+
+    def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.image_key)
+        x = x.to(self.device)
+        if only_inputs:
+            log["inputs"] = x
+            return log
+        xrec, _ = self(x)
+        if x.shape[1] > 3:
+            # colorize with random projection
+            assert xrec.shape[1] > 3
+            x = self.to_rgb(x)
+            xrec = self.to_rgb(xrec)
+        log["inputs"] = x
+        log["reconstructions"] = xrec
+        if plot_ema:
+            with self.ema_scope():
+                xrec_ema, _ = self(x)
+                if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
+                log["reconstructions_ema"] = xrec_ema
+        return log
+
+    def to_rgb(self, x):
+        assert self.image_key == "segmentation"
+        if not hasattr(self, "colorize"):
+            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+        x = F.conv2d(x, weight=self.colorize)
+        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+        return x
+
+
+class VQModelInterface(VQModel):
+    def __init__(self, embed_dim, *args, **kwargs):
+        super().__init__(embed_dim=embed_dim, *args, **kwargs)
+        self.embed_dim = embed_dim
+
+    def encode(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        return h
+
+    def decode(self, h, force_not_quantize=False):
+        # also go through quantization layer
+        if not force_not_quantize:
+            quant, emb_loss, info = self.quantize(h)
+        else:
+            quant = h
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+
+class AutoencoderKL(pl.LightningModule):
+    def __init__(self,
+                 ddconfig,
+                 lossconfig,
+                 embed_dim,
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 image_key="image",
+                 colorize_nlabels=None,
+                 monitor=None,
+                 ):
+        super().__init__()
+        self.image_key = image_key
+        self.encoder = Encoder(**ddconfig)
+        self.decoder = Decoder(**ddconfig)
+        self.loss = instantiate_from_config(lossconfig)
+        assert ddconfig["double_z"]
+        self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        self.embed_dim = embed_dim
+        if colorize_nlabels is not None:
+            assert type(colorize_nlabels)==int
+            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+        if monitor is not None:
+            self.monitor = monitor
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+
+    def init_from_ckpt(self, path, ignore_keys=list()):
+        sd = torch.load(path, map_location="cpu")["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        self.load_state_dict(sd, strict=False)
+        print(f"Restored from {path}")
+
+    def encode(self, x):
+        h = self.encoder(x)
+        moments = self.quant_conv(h)
+        posterior = DiagonalGaussianDistribution(moments)
+        return posterior
+
+    def decode(self, z):
+        z = self.post_quant_conv(z)
+        dec = self.decoder(z)
+        return dec
+
+    def forward(self, input, sample_posterior=True):
+        posterior = self.encode(input)
+        if sample_posterior:
+            z = posterior.sample()
+        else:
+            z = posterior.mode()
+        dec = self.decode(z)
+        return dec, posterior
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+        return x
+
+    def training_step(self, batch, batch_idx, optimizer_idx):
+        inputs = self.get_input(batch, self.image_key)
+        reconstructions, posterior = self(inputs)
+
+        if optimizer_idx == 0:
+            # train encoder+decoder+logvar
+            aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train")
+            self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+            return aeloss
+
+        if optimizer_idx == 1:
+            # train the discriminator
+            discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+                                                last_layer=self.get_last_layer(), split="train")
+
+            self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+            return discloss
+
+    def validation_step(self, batch, batch_idx):
+        inputs = self.get_input(batch, self.image_key)
+        reconstructions, posterior = self(inputs)
+        aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
+                                        last_layer=self.get_last_layer(), split="val")
+
+        discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
+                                            last_layer=self.get_last_layer(), split="val")
+
+        self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
+        self.log_dict(log_dict_ae)
+        self.log_dict(log_dict_disc)
+        return self.log_dict
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
+                                  list(self.decoder.parameters())+
+                                  list(self.quant_conv.parameters())+
+                                  list(self.post_quant_conv.parameters()),
+                                  lr=lr, betas=(0.5, 0.9))
+        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+                                    lr=lr, betas=(0.5, 0.9))
+        return [opt_ae, opt_disc], []
+
+    def get_last_layer(self):
+        return self.decoder.conv_out.weight
+
+    @torch.no_grad()
+    def log_images(self, batch, only_inputs=False, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.image_key)
+        x = x.to(self.device)
+        if not only_inputs:
+            xrec, posterior = self(x)
+            if x.shape[1] > 3:
+                # colorize with random projection
+                assert xrec.shape[1] > 3
+                x = self.to_rgb(x)
+                xrec = self.to_rgb(xrec)
+            log["samples"] = self.decode(torch.randn_like(posterior.sample()))
+            log["reconstructions"] = xrec
+        log["inputs"] = x
+        return log
+
+    def to_rgb(self, x):
+        assert self.image_key == "segmentation"
+        if not hasattr(self, "colorize"):
+            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+        x = F.conv2d(x, weight=self.colorize)
+        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+        return x
+
+
+class IdentityFirstStage(torch.nn.Module):
+    def __init__(self, *args, vq_interface=False, **kwargs):
+        self.vq_interface = vq_interface  # TODO: Should be true by default but check to not break older stuff
+        super().__init__()
+
+    def encode(self, x, *args, **kwargs):
+        return x
+
+    def decode(self, x, *args, **kwargs):
+        return x
+
+    def quantize(self, x, *args, **kwargs):
+        if self.vq_interface:
+            return x, None, [None, None, None]
+        return x
+
+    def forward(self, x, *args, **kwargs):
+        return x
diff --git a/ldm/models/diffusion/__init__.py b/ldm/models/diffusion/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ldm/models/diffusion/classifier.py b/ldm/models/diffusion/classifier.py
new file mode 100644
index 00000000..67e98b9d
--- /dev/null
+++ b/ldm/models/diffusion/classifier.py
@@ -0,0 +1,267 @@
+import os
+import torch
+import pytorch_lightning as pl
+from omegaconf import OmegaConf
+from torch.nn import functional as F
+from torch.optim import AdamW
+from torch.optim.lr_scheduler import LambdaLR
+from copy import deepcopy
+from einops import rearrange
+from glob import glob
+from natsort import natsorted
+
+from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
+from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
+
+__models__ = {
+    'class_label': EncoderUNetModel,
+    'segmentation': UNetModel
+}
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+class NoisyLatentImageClassifier(pl.LightningModule):
+
+    def __init__(self,
+                 diffusion_path,
+                 num_classes,
+                 ckpt_path=None,
+                 pool='attention',
+                 label_key=None,
+                 diffusion_ckpt_path=None,
+                 scheduler_config=None,
+                 weight_decay=1.e-2,
+                 log_steps=10,
+                 monitor='val/loss',
+                 *args,
+                 **kwargs):
+        super().__init__(*args, **kwargs)
+        self.num_classes = num_classes
+        # get latest config of diffusion model
+        diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
+        self.diffusion_config = OmegaConf.load(diffusion_config).model
+        self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
+        self.load_diffusion()
+
+        self.monitor = monitor
+        self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
+        self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
+        self.log_steps = log_steps
+
+        self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
+            else self.diffusion_model.cond_stage_key
+
+        assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
+
+        if self.label_key not in __models__:
+            raise NotImplementedError()
+
+        self.load_classifier(ckpt_path, pool)
+
+        self.scheduler_config = scheduler_config
+        self.use_scheduler = self.scheduler_config is not None
+        self.weight_decay = weight_decay
+
+    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+        sd = torch.load(path, map_location="cpu")
+        if "state_dict" in list(sd.keys()):
+            sd = sd["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+            sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+        if len(unexpected) > 0:
+            print(f"Unexpected Keys: {unexpected}")
+
+    def load_diffusion(self):
+        model = instantiate_from_config(self.diffusion_config)
+        self.diffusion_model = model.eval()
+        self.diffusion_model.train = disabled_train
+        for param in self.diffusion_model.parameters():
+            param.requires_grad = False
+
+    def load_classifier(self, ckpt_path, pool):
+        model_config = deepcopy(self.diffusion_config.params.unet_config.params)
+        model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
+        model_config.out_channels = self.num_classes
+        if self.label_key == 'class_label':
+            model_config.pool = pool
+
+        self.model = __models__[self.label_key](**model_config)
+        if ckpt_path is not None:
+            print('#####################################################################')
+            print(f'load from ckpt "{ckpt_path}"')
+            print('#####################################################################')
+            self.init_from_ckpt(ckpt_path)
+
+    @torch.no_grad()
+    def get_x_noisy(self, x, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x))
+        continuous_sqrt_alpha_cumprod = None
+        if self.diffusion_model.use_continuous_noise:
+            continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
+            # todo: make sure t+1 is correct here
+
+        return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
+                                             continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
+
+    def forward(self, x_noisy, t, *args, **kwargs):
+        return self.model(x_noisy, t)
+
+    @torch.no_grad()
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = rearrange(x, 'b h w c -> b c h w')
+        x = x.to(memory_format=torch.contiguous_format).float()
+        return x
+
+    @torch.no_grad()
+    def get_conditioning(self, batch, k=None):
+        if k is None:
+            k = self.label_key
+        assert k is not None, 'Needs to provide label key'
+
+        targets = batch[k].to(self.device)
+
+        if self.label_key == 'segmentation':
+            targets = rearrange(targets, 'b h w c -> b c h w')
+            for down in range(self.numd):
+                h, w = targets.shape[-2:]
+                targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
+
+            # targets = rearrange(targets,'b c h w -> b h w c')
+
+        return targets
+
+    def compute_top_k(self, logits, labels, k, reduction="mean"):
+        _, top_ks = torch.topk(logits, k, dim=1)
+        if reduction == "mean":
+            return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
+        elif reduction == "none":
+            return (top_ks == labels[:, None]).float().sum(dim=-1)
+
+    def on_train_epoch_start(self):
+        # save some memory
+        self.diffusion_model.model.to('cpu')
+
+    @torch.no_grad()
+    def write_logs(self, loss, logits, targets):
+        log_prefix = 'train' if self.training else 'val'
+        log = {}
+        log[f"{log_prefix}/loss"] = loss.mean()
+        log[f"{log_prefix}/acc@1"] = self.compute_top_k(
+            logits, targets, k=1, reduction="mean"
+        )
+        log[f"{log_prefix}/acc@5"] = self.compute_top_k(
+            logits, targets, k=5, reduction="mean"
+        )
+
+        self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
+        self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
+        self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
+        lr = self.optimizers().param_groups[0]['lr']
+        self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
+
+    def shared_step(self, batch, t=None):
+        x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
+        targets = self.get_conditioning(batch)
+        if targets.dim() == 4:
+            targets = targets.argmax(dim=1)
+        if t is None:
+            t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
+        else:
+            t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
+        x_noisy = self.get_x_noisy(x, t)
+        logits = self(x_noisy, t)
+
+        loss = F.cross_entropy(logits, targets, reduction='none')
+
+        self.write_logs(loss.detach(), logits.detach(), targets.detach())
+
+        loss = loss.mean()
+        return loss, logits, x_noisy, targets
+
+    def training_step(self, batch, batch_idx):
+        loss, *_ = self.shared_step(batch)
+        return loss
+
+    def reset_noise_accs(self):
+        self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
+                          range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
+
+    def on_validation_start(self):
+        self.reset_noise_accs()
+
+    @torch.no_grad()
+    def validation_step(self, batch, batch_idx):
+        loss, *_ = self.shared_step(batch)
+
+        for t in self.noisy_acc:
+            _, logits, _, targets = self.shared_step(batch, t)
+            self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
+            self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
+
+        return loss
+
+    def configure_optimizers(self):
+        optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
+
+        if self.use_scheduler:
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                }]
+            return [optimizer], scheduler
+
+        return optimizer
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, *args, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.diffusion_model.first_stage_key)
+        log['inputs'] = x
+
+        y = self.get_conditioning(batch)
+
+        if self.label_key == 'class_label':
+            y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
+            log['labels'] = y
+
+        if ismap(y):
+            log['labels'] = self.diffusion_model.to_rgb(y)
+
+            for step in range(self.log_steps):
+                current_time = step * self.log_time_interval
+
+                _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)
+
+                log[f'inputs@t{current_time}'] = x_noisy
+
+                pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
+                pred = rearrange(pred, 'b h w c -> b c h w')
+
+                log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
+
+        for key in log:
+            log[key] = log[key][:N]
+
+        return log
diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
new file mode 100644
index 00000000..fb31215d
--- /dev/null
+++ b/ldm/models/diffusion/ddim.py
@@ -0,0 +1,241 @@
+"""SAMPLING ONLY."""
+
+import torch
+import numpy as np
+from tqdm import tqdm
+from functools import partial
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
+    extract_into_tensor
+
+
+class DDIMSampler(object):
+    def __init__(self, model, schedule="linear", **kwargs):
+        super().__init__()
+        self.model = model
+        self.ddpm_num_timesteps = model.num_timesteps
+        self.schedule = schedule
+
+    def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device("cuda"):
+                attr = attr.to(torch.device("cuda"))
+        setattr(self, name, attr)
+
+    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+        alphas_cumprod = self.model.alphas_cumprod
+        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+        self.register_buffer('betas', to_torch(self.model.betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+        # ddim sampling parameters
+        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+                                                                                   ddim_timesteps=self.ddim_timesteps,
+                                                                                   eta=ddim_eta,verbose=verbose)
+        self.register_buffer('ddim_sigmas', ddim_sigmas)
+        self.register_buffer('ddim_alphas', ddim_alphas)
+        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None,
+               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               **kwargs
+               ):
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+        # sampling
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+        print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+
+        samples, intermediates = self.ddim_sampling(conditioning, size,
+                                                    callback=callback,
+                                                    img_callback=img_callback,
+                                                    quantize_denoised=quantize_x0,
+                                                    mask=mask, x0=x0,
+                                                    ddim_use_original_steps=False,
+                                                    noise_dropout=noise_dropout,
+                                                    temperature=temperature,
+                                                    score_corrector=score_corrector,
+                                                    corrector_kwargs=corrector_kwargs,
+                                                    x_T=x_T,
+                                                    log_every_t=log_every_t,
+                                                    unconditional_guidance_scale=unconditional_guidance_scale,
+                                                    unconditional_conditioning=unconditional_conditioning,
+                                                    )
+        return samples, intermediates
+
+    @torch.no_grad()
+    def ddim_sampling(self, cond, shape,
+                      x_T=None, ddim_use_original_steps=False,
+                      callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, log_every_t=100,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None,):
+        device = self.model.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        if timesteps is None:
+            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+        elif timesteps is not None and not ddim_use_original_steps:
+            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+            timesteps = self.ddim_timesteps[:subset_end]
+
+        intermediates = {'x_inter': [img], 'pred_x0': [img]}
+        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
+        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+        print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+        iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
+
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
+                img = img_orig * mask + (1. - mask) * img
+
+            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+                                      quantize_denoised=quantize_denoised, temperature=temperature,
+                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
+                                      corrector_kwargs=corrector_kwargs,
+                                      unconditional_guidance_scale=unconditional_guidance_scale,
+                                      unconditional_conditioning=unconditional_conditioning)
+            img, pred_x0 = outs
+            if callback: callback(i)
+            if img_callback: img_callback(pred_x0, i)
+
+            if index % log_every_t == 0 or index == total_steps - 1:
+                intermediates['x_inter'].append(img)
+                intermediates['pred_x0'].append(pred_x0)
+
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None):
+        b, *_, device = *x.shape, x.device
+
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            e_t = self.model.apply_model(x, t, c)
+        else:
+            x_in = torch.cat([x] * 2)
+            t_in = torch.cat([t] * 2)
+            c_in = torch.cat([unconditional_conditioning, c])
+            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+        if score_corrector is not None:
+            assert self.model.parameterization == "eps"
+            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+        # select parameters corresponding to the currently considered timestep
+        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+        # current prediction for x_0
+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        if quantize_denoised:
+            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+        # direction pointing to x_t
+        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+        return x_prev, pred_x0
+
+    @torch.no_grad()
+    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
+        # fast, but does not allow for exact reconstruction
+        # t serves as an index to gather the correct alphas
+        if use_original_steps:
+            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
+            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
+        else:
+            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
+            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
+
+        if noise is None:
+            noise = torch.randn_like(x0)
+        return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
+                extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
+
+    @torch.no_grad()
+    def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
+               use_original_steps=False):
+
+        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+        timesteps = timesteps[:t_start]
+
+        time_range = np.flip(timesteps)
+        total_steps = timesteps.shape[0]
+        print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+        iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
+        x_dec = x_latent
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
+            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
+                                          unconditional_guidance_scale=unconditional_guidance_scale,
+                                          unconditional_conditioning=unconditional_conditioning)
+        return x_dec
\ No newline at end of file
diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py
new file mode 100644
index 00000000..bbedd04c
--- /dev/null
+++ b/ldm/models/diffusion/ddpm.py
@@ -0,0 +1,1445 @@
+"""
+wild mixture of
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
+https://github.com/CompVis/taming-transformers
+-- merci
+"""
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager
+from functools import partial
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+
+
+__conditioning_keys__ = {'concat': 'c_concat',
+                         'crossattn': 'c_crossattn',
+                         'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+    return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+class DDPM(pl.LightningModule):
+    # classic DDPM with Gaussian diffusion, in image space
+    def __init__(self,
+                 unet_config,
+                 timesteps=1000,
+                 beta_schedule="linear",
+                 loss_type="l2",
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 load_only_unet=False,
+                 monitor="val/loss",
+                 use_ema=True,
+                 first_stage_key="image",
+                 image_size=256,
+                 channels=3,
+                 log_every_t=100,
+                 clip_denoised=True,
+                 linear_start=1e-4,
+                 linear_end=2e-2,
+                 cosine_s=8e-3,
+                 given_betas=None,
+                 original_elbo_weight=0.,
+                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+                 l_simple_weight=1.,
+                 conditioning_key=None,
+                 parameterization="eps",  # all assuming fixed variance schedules
+                 scheduler_config=None,
+                 use_positional_encodings=False,
+                 learn_logvar=False,
+                 logvar_init=0.,
+                 ):
+        super().__init__()
+        assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
+        self.parameterization = parameterization
+        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+        self.cond_stage_model = None
+        self.clip_denoised = clip_denoised
+        self.log_every_t = log_every_t
+        self.first_stage_key = first_stage_key
+        self.image_size = image_size  # try conv?
+        self.channels = channels
+        self.use_positional_encodings = use_positional_encodings
+        self.model = DiffusionWrapper(unet_config, conditioning_key)
+        count_params(self.model, verbose=True)
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self.model)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        self.use_scheduler = scheduler_config is not None
+        if self.use_scheduler:
+            self.scheduler_config = scheduler_config
+
+        self.v_posterior = v_posterior
+        self.original_elbo_weight = original_elbo_weight
+        self.l_simple_weight = l_simple_weight
+
+        if monitor is not None:
+            self.monitor = monitor
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+
+        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+        self.loss_type = loss_type
+
+        self.learn_logvar = learn_logvar
+        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+        if self.learn_logvar:
+            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+
+
+    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        if exists(given_betas):
+            betas = given_betas
+        else:
+            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+                                       cosine_s=cosine_s)
+        alphas = 1. - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.linear_start = linear_start
+        self.linear_end = linear_end
+        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        self.register_buffer('betas', to_torch(betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+                    1. - alphas_cumprod) + self.v_posterior * betas
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.register_buffer('posterior_variance', to_torch(posterior_variance))
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+        self.register_buffer('posterior_mean_coef1', to_torch(
+            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+        self.register_buffer('posterior_mean_coef2', to_torch(
+            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+        if self.parameterization == "eps":
+            lvlb_weights = self.betas ** 2 / (
+                        2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+        elif self.parameterization == "x0":
+            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+        else:
+            raise NotImplementedError("mu not supported")
+        # TODO how to choose this term
+        lvlb_weights[0] = lvlb_weights[1]
+        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+        assert not torch.isnan(self.lvlb_weights).all()
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.model.parameters())
+            self.model_ema.copy_to(self.model)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.model.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+        sd = torch.load(path, map_location="cpu")
+        if "state_dict" in list(sd.keys()):
+            sd = sd["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+            sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+        if len(unexpected) > 0:
+            print(f"Unexpected Keys: {unexpected}")
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def predict_start_from_noise(self, x_t, t, noise):
+        return (
+                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+        )
+
+    def q_posterior(self, x_start, x_t, t):
+        posterior_mean = (
+                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, x, t, clip_denoised: bool):
+        model_out = self.model(x, t)
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+        b, *_, device = *x.shape, x.device
+        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+        noise = noise_like(x.shape, device, repeat_noise)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def p_sample_loop(self, shape, return_intermediates=False):
+        device = self.betas.device
+        b = shape[0]
+        img = torch.randn(shape, device=device)
+        intermediates = [img]
+        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+                                clip_denoised=self.clip_denoised)
+            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+                intermediates.append(img)
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, batch_size=16, return_intermediates=False):
+        image_size = self.image_size
+        channels = self.channels
+        return self.p_sample_loop((batch_size, channels, image_size, image_size),
+                                  return_intermediates=return_intermediates)
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+    def get_loss(self, pred, target, mean=True):
+        if self.loss_type == 'l1':
+            loss = (target - pred).abs()
+            if mean:
+                loss = loss.mean()
+        elif self.loss_type == 'l2':
+            if mean:
+                loss = torch.nn.functional.mse_loss(target, pred)
+            else:
+                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+        else:
+            raise NotImplementedError("unknown loss type '{loss_type}'")
+
+        return loss
+
+    def p_losses(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_out = self.model(x_noisy, t)
+
+        loss_dict = {}
+        if self.parameterization == "eps":
+            target = noise
+        elif self.parameterization == "x0":
+            target = x_start
+        else:
+            raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
+
+        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
+
+        log_prefix = 'train' if self.training else 'val'
+
+        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
+        loss_simple = loss.mean() * self.l_simple_weight
+
+        loss_vlb = (self.lvlb_weights[t] * loss).mean()
+        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
+
+        loss = loss_simple + self.original_elbo_weight * loss_vlb
+
+        loss_dict.update({f'{log_prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def forward(self, x, *args, **kwargs):
+        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        return self.p_losses(x, t, *args, **kwargs)
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = rearrange(x, 'b h w c -> b c h w')
+        x = x.to(memory_format=torch.contiguous_format).float()
+        return x
+
+    def shared_step(self, batch):
+        x = self.get_input(batch, self.first_stage_key)
+        loss, loss_dict = self(x)
+        return loss, loss_dict
+
+    def training_step(self, batch, batch_idx):
+        loss, loss_dict = self.shared_step(batch)
+
+        self.log_dict(loss_dict, prog_bar=True,
+                      logger=True, on_step=True, on_epoch=True)
+
+        self.log("global_step", self.global_step,
+                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        if self.use_scheduler:
+            lr = self.optimizers().param_groups[0]['lr']
+            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        return loss
+
+    @torch.no_grad()
+    def validation_step(self, batch, batch_idx):
+        _, loss_dict_no_ema = self.shared_step(batch)
+        with self.ema_scope():
+            _, loss_dict_ema = self.shared_step(batch)
+            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self.model)
+
+    def _get_rows_from_list(self, samples):
+        n_imgs_per_row = len(samples)
+        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.first_stage_key)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        x = x.to(self.device)[:N]
+        log["inputs"] = x
+
+        # get diffusion row
+        diffusion_row = list()
+        x_start = x[:n_row]
+
+        for t in range(self.num_timesteps):
+            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                t = t.to(self.device).long()
+                noise = torch.randn_like(x_start)
+                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+                diffusion_row.append(x_noisy)
+
+        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+        if sample:
+            # get denoise row
+            with self.ema_scope("Plotting"):
+                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+            log["samples"] = samples
+            log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.learn_logvar:
+            params = params + [self.logvar]
+        opt = torch.optim.AdamW(params, lr=lr)
+        return opt
+
+
+class LatentDiffusion(DDPM):
+    """main class"""
+    def __init__(self,
+                 first_stage_config,
+                 cond_stage_config,
+                 num_timesteps_cond=None,
+                 cond_stage_key="image",
+                 cond_stage_trainable=False,
+                 concat_mode=True,
+                 cond_stage_forward=None,
+                 conditioning_key=None,
+                 scale_factor=1.0,
+                 scale_by_std=False,
+                 *args, **kwargs):
+        self.num_timesteps_cond = default(num_timesteps_cond, 1)
+        self.scale_by_std = scale_by_std
+        assert self.num_timesteps_cond <= kwargs['timesteps']
+        # for backwards compatibility after implementation of DiffusionWrapper
+        if conditioning_key is None:
+            conditioning_key = 'concat' if concat_mode else 'crossattn'
+        if cond_stage_config == '__is_unconditional__':
+            conditioning_key = None
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        ignore_keys = kwargs.pop("ignore_keys", [])
+        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+        self.concat_mode = concat_mode
+        self.cond_stage_trainable = cond_stage_trainable
+        self.cond_stage_key = cond_stage_key
+        try:
+            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+        except:
+            self.num_downs = 0
+        if not scale_by_std:
+            self.scale_factor = scale_factor
+        else:
+            self.register_buffer('scale_factor', torch.tensor(scale_factor))
+        self.instantiate_first_stage(first_stage_config)
+        self.instantiate_cond_stage(cond_stage_config)
+        self.cond_stage_forward = cond_stage_forward
+        self.clip_denoised = False
+        self.bbox_tokenizer = None  
+
+        self.restarted_from_ckpt = False
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys)
+            self.restarted_from_ckpt = True
+
+    def make_cond_schedule(self, ):
+        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+        self.cond_ids[:self.num_timesteps_cond] = ids
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+        # only for very first batch
+        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+            # set rescale weight to 1./std of encodings
+            print("### USING STD-RESCALING ###")
+            x = super().get_input(batch, self.first_stage_key)
+            x = x.to(self.device)
+            encoder_posterior = self.encode_first_stage(x)
+            z = self.get_first_stage_encoding(encoder_posterior).detach()
+            del self.scale_factor
+            self.register_buffer('scale_factor', 1. / z.flatten().std())
+            print(f"setting self.scale_factor to {self.scale_factor}")
+            print("### USING STD-RESCALING ###")
+
+    def register_schedule(self,
+                          given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+        self.shorten_cond_schedule = self.num_timesteps_cond > 1
+        if self.shorten_cond_schedule:
+            self.make_cond_schedule()
+
+    def instantiate_first_stage(self, config):
+        model = instantiate_from_config(config)
+        self.first_stage_model = model.eval()
+        self.first_stage_model.train = disabled_train
+        for param in self.first_stage_model.parameters():
+            param.requires_grad = False
+
+    def instantiate_cond_stage(self, config):
+        if not self.cond_stage_trainable:
+            if config == "__is_first_stage__":
+                print("Using first stage also as cond stage.")
+                self.cond_stage_model = self.first_stage_model
+            elif config == "__is_unconditional__":
+                print(f"Training {self.__class__.__name__} as an unconditional model.")
+                self.cond_stage_model = None
+                # self.be_unconditional = True
+            else:
+                model = instantiate_from_config(config)
+                self.cond_stage_model = model.eval()
+                self.cond_stage_model.train = disabled_train
+                for param in self.cond_stage_model.parameters():
+                    param.requires_grad = False
+        else:
+            assert config != '__is_first_stage__'
+            assert config != '__is_unconditional__'
+            model = instantiate_from_config(config)
+            self.cond_stage_model = model
+
+    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+        denoise_row = []
+        for zd in tqdm(samples, desc=desc):
+            denoise_row.append(self.decode_first_stage(zd.to(self.device),
+                                                            force_not_quantize=force_no_decoder_quantization))
+        n_imgs_per_row = len(denoise_row)
+        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
+        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    def get_first_stage_encoding(self, encoder_posterior):
+        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+            z = encoder_posterior.sample()
+        elif isinstance(encoder_posterior, torch.Tensor):
+            z = encoder_posterior
+        else:
+            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+        return self.scale_factor * z
+
+    def get_learned_conditioning(self, c):
+        if self.cond_stage_forward is None:
+            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+                c = self.cond_stage_model.encode(c)
+                if isinstance(c, DiagonalGaussianDistribution):
+                    c = c.mode()
+            else:
+                c = self.cond_stage_model(c)
+        else:
+            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+        return c
+
+    def meshgrid(self, h, w):
+        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+        arr = torch.cat([y, x], dim=-1)
+        return arr
+
+    def delta_border(self, h, w):
+        """
+        :param h: height
+        :param w: width
+        :return: normalized distance to image border,
+         wtith min distance = 0 at border and max dist = 0.5 at image center
+        """
+        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+        arr = self.meshgrid(h, w) / lower_right_corner
+        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+        return edge_dist
+
+    def get_weighting(self, h, w, Ly, Lx, device):
+        weighting = self.delta_border(h, w)
+        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+                               self.split_input_params["clip_max_weight"], )
+        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+        if self.split_input_params["tie_braker"]:
+            L_weighting = self.delta_border(Ly, Lx)
+            L_weighting = torch.clip(L_weighting,
+                                     self.split_input_params["clip_min_tie_weight"],
+                                     self.split_input_params["clip_max_tie_weight"])
+
+            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+            weighting = weighting * L_weighting
+        return weighting
+
+    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
+        """
+        :param x: img of size (bs, c, h, w)
+        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+        """
+        bs, nc, h, w = x.shape
+
+        # number of crops in image
+        Ly = (h - kernel_size[0]) // stride[0] + 1
+        Lx = (w - kernel_size[1]) // stride[1] + 1
+
+        if uf == 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+        elif uf > 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+                                dilation=1, padding=0,
+                                stride=(stride[0] * uf, stride[1] * uf))
+            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+        elif df > 1 and uf == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+                                dilation=1, padding=0,
+                                stride=(stride[0] // df, stride[1] // df))
+            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+        else:
+            raise NotImplementedError
+
+        return fold, unfold, normalization, weighting
+
+    @torch.no_grad()
+    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+                  cond_key=None, return_original_cond=False, bs=None):
+        x = super().get_input(batch, k)
+        if bs is not None:
+            x = x[:bs]
+        x = x.to(self.device)
+        encoder_posterior = self.encode_first_stage(x)
+        z = self.get_first_stage_encoding(encoder_posterior).detach()
+
+        if self.model.conditioning_key is not None:
+            if cond_key is None:
+                cond_key = self.cond_stage_key
+            if cond_key != self.first_stage_key:
+                if cond_key in ['caption', 'coordinates_bbox']:
+                    xc = batch[cond_key]
+                elif cond_key == 'class_label':
+                    xc = batch
+                else:
+                    xc = super().get_input(batch, cond_key).to(self.device)
+            else:
+                xc = x
+            if not self.cond_stage_trainable or force_c_encode:
+                if isinstance(xc, dict) or isinstance(xc, list):
+                    # import pudb; pudb.set_trace()
+                    c = self.get_learned_conditioning(xc)
+                else:
+                    c = self.get_learned_conditioning(xc.to(self.device))
+            else:
+                c = xc
+            if bs is not None:
+                c = c[:bs]
+
+            if self.use_positional_encodings:
+                pos_x, pos_y = self.compute_latent_shifts(batch)
+                ckey = __conditioning_keys__[self.model.conditioning_key]
+                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
+
+        else:
+            c = None
+            xc = None
+            if self.use_positional_encodings:
+                pos_x, pos_y = self.compute_latent_shifts(batch)
+                c = {'pos_x': pos_x, 'pos_y': pos_y}
+        out = [z, c]
+        if return_first_stage_outputs:
+            xrec = self.decode_first_stage(z)
+            out.extend([x, xrec])
+        if return_original_cond:
+            out.append(xc)
+        return out
+
+    @torch.no_grad()
+    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+        if predict_cids:
+            if z.dim() == 4:
+                z = torch.argmax(z.exp(), dim=1).long()
+            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+            z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+        z = 1. / self.scale_factor * z
+
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                uf = self.split_input_params["vqf"]
+                bs, nc, h, w = z.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+                z = unfold(z)  # (bn, nc * prod(**ks), L)
+                # 1. Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                # 2. apply model loop over last dim
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+                                                                 force_not_quantize=predict_cids or force_not_quantize)
+                                   for i in range(z.shape[-1])]
+                else:
+
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+                                   for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
+                o = o * weighting
+                # Reverse 1. reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
+                return decoded
+            else:
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+                else:
+                    return self.first_stage_model.decode(z)
+
+        else:
+            if isinstance(self.first_stage_model, VQModelInterface):
+                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+            else:
+                return self.first_stage_model.decode(z)
+
+    # same as above but without decorator
+    def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+        if predict_cids:
+            if z.dim() == 4:
+                z = torch.argmax(z.exp(), dim=1).long()
+            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+            z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+        z = 1. / self.scale_factor * z
+
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                uf = self.split_input_params["vqf"]
+                bs, nc, h, w = z.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+                z = unfold(z)  # (bn, nc * prod(**ks), L)
+                # 1. Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                # 2. apply model loop over last dim
+                if isinstance(self.first_stage_model, VQModelInterface):  
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+                                                                 force_not_quantize=predict_cids or force_not_quantize)
+                                   for i in range(z.shape[-1])]
+                else:
+
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+                                   for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
+                o = o * weighting
+                # Reverse 1. reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
+                return decoded
+            else:
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+                else:
+                    return self.first_stage_model.decode(z)
+
+        else:
+            if isinstance(self.first_stage_model, VQModelInterface):
+                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+            else:
+                return self.first_stage_model.decode(z)
+
+    @torch.no_grad()
+    def encode_first_stage(self, x):
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                df = self.split_input_params["vqf"]
+                self.split_input_params['original_image_size'] = x.shape[-2:]
+                bs, nc, h, w = x.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
+                z = unfold(x)  # (bn, nc * prod(**ks), L)
+                # Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
+                               for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)
+                o = o * weighting
+
+                # Reverse reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization
+                return decoded
+
+            else:
+                return self.first_stage_model.encode(x)
+        else:
+            return self.first_stage_model.encode(x)
+
+    def shared_step(self, batch, **kwargs):
+        x, c = self.get_input(batch, self.first_stage_key)
+        loss = self(x, c)
+        return loss
+
+    def forward(self, x, c, *args, **kwargs):
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        if self.model.conditioning_key is not None:
+            assert c is not None
+            if self.cond_stage_trainable:
+                c = self.get_learned_conditioning(c)
+            if self.shorten_cond_schedule:  # TODO: drop this option
+                tc = self.cond_ids[t].to(self.device)
+                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+        return self.p_losses(x, c, t, *args, **kwargs)
+
+    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
+        def rescale_bbox(bbox):
+            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
+            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
+            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
+            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
+            return x0, y0, w, h
+
+        return [rescale_bbox(b) for b in bboxes]
+
+    def apply_model(self, x_noisy, t, cond, return_ids=False):
+
+        if isinstance(cond, dict):
+            # hybrid case, cond is exptected to be a dict
+            pass
+        else:
+            if not isinstance(cond, list):
+                cond = [cond]
+            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+            cond = {key: cond}
+
+        if hasattr(self, "split_input_params"):
+            assert len(cond) == 1  # todo can only deal with one conditioning atm
+            assert not return_ids  
+            ks = self.split_input_params["ks"]  # eg. (128, 128)
+            stride = self.split_input_params["stride"]  # eg. (64, 64)
+
+            h, w = x_noisy.shape[-2:]
+
+            fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
+
+            z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
+            # Reshape to img shape
+            z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+            z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
+
+            if self.cond_stage_key in ["image", "LR_image", "segmentation",
+                                       'bbox_img'] and self.model.conditioning_key:  # todo check for completeness
+                c_key = next(iter(cond.keys()))  # get key
+                c = next(iter(cond.values()))  # get value
+                assert (len(c) == 1)  # todo extend to list with more than one elem
+                c = c[0]  # get element
+
+                c = unfold(c)
+                c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
+
+            elif self.cond_stage_key == 'coordinates_bbox':
+                assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
+
+                # assuming padding of unfold is always 0 and its dilation is always 1
+                n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
+                full_img_h, full_img_w = self.split_input_params['original_image_size']
+                # as we are operating on latents, we need the factor from the original image size to the
+                # spatial latent size to properly rescale the crops for regenerating the bbox annotations
+                num_downs = self.first_stage_model.encoder.num_resolutions - 1
+                rescale_latent = 2 ** (num_downs)
+
+                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
+                # need to rescale the tl patch coordinates to be in between (0,1)
+                tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
+                                         rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
+                                        for patch_nr in range(z.shape[-1])]
+
+                # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
+                patch_limits = [(x_tl, y_tl,
+                                 rescale_latent * ks[0] / full_img_w,
+                                 rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
+                # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
+
+                # tokenize crop coordinates for the bounding boxes of the respective patches
+                patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
+                                      for bbox in patch_limits]  # list of length l with tensors of shape (1, 2)
+                print(patch_limits_tknzd[0].shape)
+                # cut tknzd crop position from conditioning
+                assert isinstance(cond, dict), 'cond must be dict to be fed into model'
+                cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
+                print(cut_cond.shape)
+
+                adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
+                adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
+                print(adapted_cond.shape)
+                adapted_cond = self.get_learned_conditioning(adapted_cond)
+                print(adapted_cond.shape)
+                adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
+                print(adapted_cond.shape)
+
+                cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
+
+            else:
+                cond_list = [cond for i in range(z.shape[-1])]  # Todo make this more efficient
+
+            # apply model by loop over crops
+            output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
+            assert not isinstance(output_list[0],
+                                  tuple)  # todo cant deal with multiple model outputs check this never happens
+
+            o = torch.stack(output_list, axis=-1)
+            o = o * weighting
+            # Reverse reshape to img shape
+            o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+            # stitch crops together
+            x_recon = fold(o) / normalization
+
+        else:
+            x_recon = self.model(x_noisy, t, **cond)
+
+        if isinstance(x_recon, tuple) and not return_ids:
+            return x_recon[0]
+        else:
+            return x_recon
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def _prior_bpd(self, x_start):
+        """
+        Get the prior KL term for the variational lower-bound, measured in
+        bits-per-dim.
+        This term can't be optimized, as it only depends on the encoder.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :return: a batch of [N] KL values (in bits), one per batch element.
+        """
+        batch_size = x_start.shape[0]
+        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+        return mean_flat(kl_prior) / np.log(2.0)
+
+    def p_losses(self, x_start, cond, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_output = self.apply_model(x_noisy, t, cond)
+
+        loss_dict = {}
+        prefix = 'train' if self.training else 'val'
+
+        if self.parameterization == "x0":
+            target = x_start
+        elif self.parameterization == "eps":
+            target = noise
+        else:
+            raise NotImplementedError()
+
+        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+        logvar_t = self.logvar[t].to(self.device)
+        loss = loss_simple / torch.exp(logvar_t) + logvar_t
+        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+        if self.learn_logvar:
+            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+            loss_dict.update({'logvar': self.logvar.data.mean()})
+
+        loss = self.l_simple_weight * loss.mean()
+
+        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+        loss += (self.original_elbo_weight * loss_vlb)
+        loss_dict.update({f'{prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+                        return_x0=False, score_corrector=None, corrector_kwargs=None):
+        t_in = t
+        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+        if score_corrector is not None:
+            assert self.parameterization == "eps"
+            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+        if return_codebook_ids:
+            model_out, logits = model_out
+
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        else:
+            raise NotImplementedError()
+
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+        if quantize_denoised:
+            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        if return_codebook_ids:
+            return model_mean, posterior_variance, posterior_log_variance, logits
+        elif return_x0:
+            return model_mean, posterior_variance, posterior_log_variance, x_recon
+        else:
+            return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+        b, *_, device = *x.shape, x.device
+        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+                                       return_codebook_ids=return_codebook_ids,
+                                       quantize_denoised=quantize_denoised,
+                                       return_x0=return_x0,
+                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+        if return_codebook_ids:
+            raise DeprecationWarning("Support dropped.")
+            model_mean, _, model_log_variance, logits = outputs
+        elif return_x0:
+            model_mean, _, model_log_variance, x0 = outputs
+        else:
+            model_mean, _, model_log_variance = outputs
+
+        noise = noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+        if return_codebook_ids:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+        if return_x0:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+        else:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+                              log_every_t=None):
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        timesteps = self.num_timesteps
+        if batch_size is not None:
+            b = batch_size if batch_size is not None else shape[0]
+            shape = [batch_size] + list(shape)
+        else:
+            b = batch_size = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=self.device)
+        else:
+            img = x_T
+        intermediates = []
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+                        total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+        if type(temperature) == float:
+            temperature = [temperature] * timesteps
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img, x0_partial = self.p_sample(img, cond, ts,
+                                            clip_denoised=self.clip_denoised,
+                                            quantize_denoised=quantize_denoised, return_x0=True,
+                                            temperature=temperature[i], noise_dropout=noise_dropout,
+                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(x0_partial)
+            if callback: callback(i)
+            if img_callback: img_callback(img, i)
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_loop(self, cond, shape, return_intermediates=False,
+                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, start_T=None,
+                      log_every_t=None):
+
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        device = self.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        intermediates = [img]
+        if timesteps is None:
+            timesteps = self.num_timesteps
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+
+        if mask is not None:
+            assert x0 is not None
+            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img = self.p_sample(img, cond, ts,
+                                clip_denoised=self.clip_denoised,
+                                quantize_denoised=quantize_denoised)
+            if mask is not None:
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(img)
+            if callback: callback(i)
+            if img_callback: img_callback(img, i)
+
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+               verbose=True, timesteps=None, quantize_denoised=False,
+               mask=None, x0=None, shape=None,**kwargs):
+        if shape is None:
+            shape = (batch_size, self.channels, self.image_size, self.image_size)
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+        return self.p_sample_loop(cond,
+                                  shape,
+                                  return_intermediates=return_intermediates, x_T=x_T,
+                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+                                  mask=mask, x0=x0)
+
+    @torch.no_grad()
+    def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
+
+        if ddim:
+            ddim_sampler = DDIMSampler(self)
+            shape = (self.channels, self.image_size, self.image_size)
+            samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
+                                                        shape,cond,verbose=False,**kwargs)
+
+        else:
+            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+                                                 return_intermediates=True,**kwargs)
+
+        return samples, intermediates
+
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+                   plot_diffusion_rows=True, **kwargs):
+
+        use_ddim = ddim_steps is not None
+
+        log = dict()
+        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+                                           return_first_stage_outputs=True,
+                                           force_c_encode=True,
+                                           return_original_cond=True,
+                                           bs=N)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        log["inputs"] = x
+        log["reconstruction"] = xrec
+        if self.model.conditioning_key is not None:
+            if hasattr(self.cond_stage_model, "decode"):
+                xc = self.cond_stage_model.decode(c)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ["caption"]:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
+                log["conditioning"] = xc
+            elif self.cond_stage_key == 'class_label':
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
+                log['conditioning'] = xc
+            elif isimage(xc):
+                log["conditioning"] = xc
+            if ismap(xc):
+                log["original_conditioning"] = self.to_rgb(xc)
+
+        if plot_diffusion_rows:
+            # get diffusion row
+            diffusion_row = list()
+            z_start = z[:n_row]
+            for t in range(self.num_timesteps):
+                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                    t = t.to(self.device).long()
+                    noise = torch.randn_like(z_start)
+                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+                    diffusion_row.append(self.decode_first_stage(z_noisy))
+
+            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
+            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+            log["diffusion_row"] = diffusion_grid
+
+        if sample:
+            # get denoise row
+            with self.ema_scope("Plotting"):
+                samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+                                                         ddim_steps=ddim_steps,eta=ddim_eta)
+                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+            x_samples = self.decode_first_stage(samples)
+            log["samples"] = x_samples
+            if plot_denoise_rows:
+                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+                log["denoise_row"] = denoise_grid
+
+            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+                    self.first_stage_model, IdentityFirstStage):
+                # also display when quantizing x0 while sampling
+                with self.ema_scope("Plotting Quantized Denoised"):
+                    samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+                                                             ddim_steps=ddim_steps,eta=ddim_eta,
+                                                             quantize_denoised=True)
+                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+                    #                                      quantize_denoised=True)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_x0_quantized"] = x_samples
+
+            if inpaint:
+                # make a simple center square
+                b, h, w = z.shape[0], z.shape[2], z.shape[3]
+                mask = torch.ones(N, h, w).to(self.device)
+                # zeros will be filled in
+                mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+                mask = mask[:, None, ...]
+                with self.ema_scope("Plotting Inpaint"):
+
+                    samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
+                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_inpainting"] = x_samples
+                log["mask"] = mask
+
+                # outpaint
+                with self.ema_scope("Plotting Outpaint"):
+                    samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
+                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_outpainting"] = x_samples
+
+        if plot_progressive_rows:
+            with self.ema_scope("Plotting Progressives"):
+                img, progressives = self.progressive_denoising(c,
+                                                               shape=(self.channels, self.image_size, self.image_size),
+                                                               batch_size=N)
+            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+            log["progressive_row"] = prog_row
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.cond_stage_trainable:
+            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+            params = params + list(self.cond_stage_model.parameters())
+        if self.learn_logvar:
+            print('Diffusion model optimizing logvar')
+            params.append(self.logvar)
+        opt = torch.optim.AdamW(params, lr=lr)
+        if self.use_scheduler:
+            assert 'target' in self.scheduler_config
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                }]
+            return [opt], scheduler
+        return opt
+
+    @torch.no_grad()
+    def to_rgb(self, x):
+        x = x.float()
+        if not hasattr(self, "colorize"):
+            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+        x = nn.functional.conv2d(x, weight=self.colorize)
+        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+        return x
+
+
+class DiffusionWrapper(pl.LightningModule):
+    def __init__(self, diff_model_config, conditioning_key):
+        super().__init__()
+        self.diffusion_model = instantiate_from_config(diff_model_config)
+        self.conditioning_key = conditioning_key
+        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
+
+    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
+        if self.conditioning_key is None:
+            out = self.diffusion_model(x, t)
+        elif self.conditioning_key == 'concat':
+            xc = torch.cat([x] + c_concat, dim=1)
+            out = self.diffusion_model(xc, t)
+        elif self.conditioning_key == 'crossattn':
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(x, t, context=cc)
+        elif self.conditioning_key == 'hybrid':
+            xc = torch.cat([x] + c_concat, dim=1)
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(xc, t, context=cc)
+        elif self.conditioning_key == 'adm':
+            cc = c_crossattn[0]
+            out = self.diffusion_model(x, t, y=cc)
+        else:
+            raise NotImplementedError()
+
+        return out
+
+
+class Layout2ImgDiffusion(LatentDiffusion):
+    # TODO: move all layout-specific hacks to this class
+    def __init__(self, cond_stage_key, *args, **kwargs):
+        assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
+        super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
+
+    def log_images(self, batch, N=8, *args, **kwargs):
+        logs = super().log_images(batch=batch, N=N, *args, **kwargs)
+
+        key = 'train' if self.training else 'validation'
+        dset = self.trainer.datamodule.datasets[key]
+        mapper = dset.conditional_builders[self.cond_stage_key]
+
+        bbox_imgs = []
+        map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
+        for tknzd_bbox in batch[self.cond_stage_key][:N]:
+            bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
+            bbox_imgs.append(bboximg)
+
+        cond_img = torch.stack(bbox_imgs, dim=0)
+        logs['bbox_image'] = cond_img
+        return logs
diff --git a/ldm/models/diffusion/dpm_solver/__init__.py b/ldm/models/diffusion/dpm_solver/__init__.py
new file mode 100644
index 00000000..7427f38c
--- /dev/null
+++ b/ldm/models/diffusion/dpm_solver/__init__.py
@@ -0,0 +1 @@
+from .sampler import DPMSolverSampler
\ No newline at end of file
diff --git a/ldm/models/diffusion/dpm_solver/dpm_solver.py b/ldm/models/diffusion/dpm_solver/dpm_solver.py
new file mode 100644
index 00000000..bdb64e0c
--- /dev/null
+++ b/ldm/models/diffusion/dpm_solver/dpm_solver.py
@@ -0,0 +1,1184 @@
+import torch
+import torch.nn.functional as F
+import math
+
+
+class NoiseScheduleVP:
+    def __init__(
+            self,
+            schedule='discrete',
+            betas=None,
+            alphas_cumprod=None,
+            continuous_beta_0=0.1,
+            continuous_beta_1=20.,
+        ):
+        """Create a wrapper class for the forward SDE (VP type).
+
+        ***
+        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
+                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
+        ***
+
+        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
+        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
+        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
+
+            log_alpha_t = self.marginal_log_mean_coeff(t)
+            sigma_t = self.marginal_std(t)
+            lambda_t = self.marginal_lambda(t)
+
+        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
+
+            t = self.inverse_lambda(lambda_t)
+
+        ===============================================================
+
+        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
+
+        1. For discrete-time DPMs:
+
+            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
+                t_i = (i + 1) / N
+            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
+            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
+
+            Args:
+                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
+                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
+
+            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
+
+            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
+                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
+                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
+                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
+                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
+                and
+                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
+
+
+        2. For continuous-time DPMs:
+
+            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
+            schedule are the default settings in DDPM and improved-DDPM:
+
+            Args:
+                beta_min: A `float` number. The smallest beta for the linear schedule.
+                beta_max: A `float` number. The largest beta for the linear schedule.
+                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
+                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
+                T: A `float` number. The ending time of the forward process.
+
+        ===============================================================
+
+        Args:
+            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
+                    'linear' or 'cosine' for continuous-time DPMs.
+        Returns:
+            A wrapper object of the forward SDE (VP type).
+        
+        ===============================================================
+
+        Example:
+
+        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
+        >>> ns = NoiseScheduleVP('discrete', betas=betas)
+
+        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
+        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+
+        # For continuous-time DPMs (VPSDE), linear schedule:
+        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
+
+        """
+
+        if schedule not in ['discrete', 'linear', 'cosine']:
+            raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
+
+        self.schedule = schedule
+        if schedule == 'discrete':
+            if betas is not None:
+                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
+            else:
+                assert alphas_cumprod is not None
+                log_alphas = 0.5 * torch.log(alphas_cumprod)
+            self.total_N = len(log_alphas)
+            self.T = 1.
+            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
+            self.log_alpha_array = log_alphas.reshape((1, -1,))
+        else:
+            self.total_N = 1000
+            self.beta_0 = continuous_beta_0
+            self.beta_1 = continuous_beta_1
+            self.cosine_s = 0.008
+            self.cosine_beta_max = 999.
+            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
+            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
+            self.schedule = schedule
+            if schedule == 'cosine':
+                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
+                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
+                self.T = 0.9946
+            else:
+                self.T = 1.
+
+    def marginal_log_mean_coeff(self, t):
+        """
+        Compute log(alpha_t) of a given continuous-time label t in [0, T].
+        """
+        if self.schedule == 'discrete':
+            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
+        elif self.schedule == 'linear':
+            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
+        elif self.schedule == 'cosine':
+            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
+            log_alpha_t =  log_alpha_fn(t) - self.cosine_log_alpha_0
+            return log_alpha_t
+
+    def marginal_alpha(self, t):
+        """
+        Compute alpha_t of a given continuous-time label t in [0, T].
+        """
+        return torch.exp(self.marginal_log_mean_coeff(t))
+
+    def marginal_std(self, t):
+        """
+        Compute sigma_t of a given continuous-time label t in [0, T].
+        """
+        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
+
+    def marginal_lambda(self, t):
+        """
+        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
+        """
+        log_mean_coeff = self.marginal_log_mean_coeff(t)
+        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
+        return log_mean_coeff - log_std
+
+    def inverse_lambda(self, lamb):
+        """
+        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
+        """
+        if self.schedule == 'linear':
+            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+            Delta = self.beta_0**2 + tmp
+            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
+        elif self.schedule == 'discrete':
+            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
+            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))
+            return t.reshape((-1,))
+        else:
+            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
+            t = t_fn(log_alpha)
+            return t
+
+
+def model_wrapper(
+    model,
+    noise_schedule,
+    model_type="noise",
+    model_kwargs={},
+    guidance_type="uncond",
+    condition=None,
+    unconditional_condition=None,
+    guidance_scale=1.,
+    classifier_fn=None,
+    classifier_kwargs={},
+):
+    """Create a wrapper function for the noise prediction model.
+
+    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
+    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
+
+    We support four types of the diffusion model by setting `model_type`:
+
+        1. "noise": noise prediction model. (Trained by predicting noise).
+
+        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
+
+        3. "v": velocity prediction model. (Trained by predicting the velocity).
+            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
+
+            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
+                arXiv preprint arXiv:2202.00512 (2022).
+            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
+                arXiv preprint arXiv:2210.02303 (2022).
+    
+        4. "score": marginal score function. (Trained by denoising score matching).
+            Note that the score function and the noise prediction model follows a simple relationship:
+            ```
+                noise(x_t, t) = -sigma_t * score(x_t, t)
+            ```
+
+    We support three types of guided sampling by DPMs by setting `guidance_type`:
+        1. "uncond": unconditional sampling by DPMs.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+            ``
+
+        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+            `` 
+
+            The input `classifier_fn` has the following format:
+            ``
+                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
+            ``
+
+            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
+                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
+
+        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
+            `` 
+            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
+
+            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
+                arXiv preprint arXiv:2207.12598 (2022).
+        
+
+    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
+    or continuous-time labels (i.e. epsilon to T).
+
+    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
+    ``
+        def model_fn(x, t_continuous) -> noise:
+            t_input = get_model_input_time(t_continuous)
+            return noise_pred(model, x, t_input, **model_kwargs)         
+    ``
+    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
+
+    ===============================================================
+
+    Args:
+        model: A diffusion model with the corresponding format described above.
+        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+        model_type: A `str`. The parameterization type of the diffusion model.
+                    "noise" or "x_start" or "v" or "score".
+        model_kwargs: A `dict`. A dict for the other inputs of the model function.
+        guidance_type: A `str`. The type of the guidance for sampling.
+                    "uncond" or "classifier" or "classifier-free".
+        condition: A pytorch tensor. The condition for the guided sampling.
+                    Only used for "classifier" or "classifier-free" guidance type.
+        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
+                    Only used for "classifier-free" guidance type.
+        guidance_scale: A `float`. The scale for the guided sampling.
+        classifier_fn: A classifier function. Only used for the classifier guidance.
+        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
+    Returns:
+        A noise prediction model that accepts the noised data and the continuous time as the inputs.
+    """
+
+    def get_model_input_time(t_continuous):
+        """
+        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
+        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
+        For continuous-time DPMs, we just use `t_continuous`.
+        """
+        if noise_schedule.schedule == 'discrete':
+            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
+        else:
+            return t_continuous
+
+    def noise_pred_fn(x, t_continuous, cond=None):
+        if t_continuous.reshape((-1,)).shape[0] == 1:
+            t_continuous = t_continuous.expand((x.shape[0]))
+        t_input = get_model_input_time(t_continuous)
+        if cond is None:
+            output = model(x, t_input, **model_kwargs)
+        else:
+            output = model(x, t_input, cond, **model_kwargs)
+        if model_type == "noise":
+            return output
+        elif model_type == "x_start":
+            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
+        elif model_type == "v":
+            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
+        elif model_type == "score":
+            sigma_t = noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return -expand_dims(sigma_t, dims) * output
+
+    def cond_grad_fn(x, t_input):
+        """
+        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
+        """
+        with torch.enable_grad():
+            x_in = x.detach().requires_grad_(True)
+            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
+            return torch.autograd.grad(log_prob.sum(), x_in)[0]
+
+    def model_fn(x, t_continuous):
+        """
+        The noise predicition model function that is used for DPM-Solver.
+        """
+        if t_continuous.reshape((-1,)).shape[0] == 1:
+            t_continuous = t_continuous.expand((x.shape[0]))
+        if guidance_type == "uncond":
+            return noise_pred_fn(x, t_continuous)
+        elif guidance_type == "classifier":
+            assert classifier_fn is not None
+            t_input = get_model_input_time(t_continuous)
+            cond_grad = cond_grad_fn(x, t_input)
+            sigma_t = noise_schedule.marginal_std(t_continuous)
+            noise = noise_pred_fn(x, t_continuous)
+            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
+        elif guidance_type == "classifier-free":
+            if guidance_scale == 1. or unconditional_condition is None:
+                return noise_pred_fn(x, t_continuous, cond=condition)
+            else:
+                x_in = torch.cat([x] * 2)
+                t_in = torch.cat([t_continuous] * 2)
+                c_in = torch.cat([unconditional_condition, condition])
+                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
+                return noise_uncond + guidance_scale * (noise - noise_uncond)
+
+    assert model_type in ["noise", "x_start", "v"]
+    assert guidance_type in ["uncond", "classifier", "classifier-free"]
+    return model_fn
+
+
+class DPM_Solver:
+    def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
+        """Construct a DPM-Solver. 
+
+        We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
+        If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
+        If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
+            In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
+            The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
+
+        Args:
+            model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
+                ``
+                def model_fn(x, t_continuous):
+                    return noise
+                ``
+            noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+            predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
+            thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
+            max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
+        
+        [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
+        """
+        self.model = model_fn
+        self.noise_schedule = noise_schedule
+        self.predict_x0 = predict_x0
+        self.thresholding = thresholding
+        self.max_val = max_val
+
+    def noise_prediction_fn(self, x, t):
+        """
+        Return the noise prediction model.
+        """
+        return self.model(x, t)
+
+    def data_prediction_fn(self, x, t):
+        """
+        Return the data prediction model (with thresholding).
+        """
+        noise = self.noise_prediction_fn(x, t)
+        dims = x.dim()
+        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
+        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
+        if self.thresholding:
+            p = 0.995   # A hyperparameter in the paper of "Imagen" [1].
+            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
+            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
+            x0 = torch.clamp(x0, -s, s) / s
+        return x0
+
+    def model_fn(self, x, t):
+        """
+        Convert the model to the noise prediction model or the data prediction model. 
+        """
+        if self.predict_x0:
+            return self.data_prediction_fn(x, t)
+        else:
+            return self.noise_prediction_fn(x, t)
+
+    def get_time_steps(self, skip_type, t_T, t_0, N, device):
+        """Compute the intermediate time steps for sampling.
+
+        Args:
+            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
+                - 'logSNR': uniform logSNR for the time steps.
+                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
+                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
+            t_T: A `float`. The starting time of the sampling (default is T).
+            t_0: A `float`. The ending time of the sampling (default is epsilon).
+            N: A `int`. The total number of the spacing of the time steps.
+            device: A torch device.
+        Returns:
+            A pytorch tensor of the time steps, with the shape (N + 1,).
+        """
+        if skip_type == 'logSNR':
+            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
+            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
+            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
+            return self.noise_schedule.inverse_lambda(logSNR_steps)
+        elif skip_type == 'time_uniform':
+            return torch.linspace(t_T, t_0, N + 1).to(device)
+        elif skip_type == 'time_quadratic':
+            t_order = 2
+            t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
+            return t
+        else:
+            raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
+
+    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
+        """
+        Get the order of each step for sampling by the singlestep DPM-Solver.
+
+        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
+        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
+            - If order == 1:
+                We take `steps` of DPM-Solver-1 (i.e. DDIM).
+            - If order == 2:
+                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
+                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
+                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
+            - If order == 3:
+                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
+                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
+                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
+                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
+
+        ============================================
+        Args:
+            order: A `int`. The max order for the solver (2 or 3).
+            steps: A `int`. The total number of function evaluations (NFE).
+            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
+                - 'logSNR': uniform logSNR for the time steps.
+                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
+                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
+            t_T: A `float`. The starting time of the sampling (default is T).
+            t_0: A `float`. The ending time of the sampling (default is epsilon).
+            device: A torch device.
+        Returns:
+            orders: A list of the solver order of each step.
+        """
+        if order == 3:
+            K = steps // 3 + 1
+            if steps % 3 == 0:
+                orders = [3,] * (K - 2) + [2, 1]
+            elif steps % 3 == 1:
+                orders = [3,] * (K - 1) + [1]
+            else:
+                orders = [3,] * (K - 1) + [2]
+        elif order == 2:
+            if steps % 2 == 0:
+                K = steps // 2
+                orders = [2,] * K
+            else:
+                K = steps // 2 + 1
+                orders = [2,] * (K - 1) + [1]
+        elif order == 1:
+            K = 1
+            orders = [1,] * steps
+        else:
+            raise ValueError("'order' must be '1' or '2' or '3'.")
+        if skip_type == 'logSNR':
+            # To reproduce the results in DPM-Solver paper
+            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
+        else:
+            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders)).to(device)]
+        return timesteps_outer, orders
+
+    def denoise_to_zero_fn(self, x, s):
+        """
+        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. 
+        """
+        return self.data_prediction_fn(x, s)
+
+    def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
+        """
+        DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
+
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            model_s: A pytorch tensor. The model function evaluated at time `s`.
+                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+            return_intermediate: A `bool`. If true, also return the model value at time `s`.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        ns = self.noise_schedule
+        dims = x.dim()
+        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+        h = lambda_t - lambda_s
+        log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
+        sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
+        alpha_t = torch.exp(log_alpha_t)
+
+        if self.predict_x0:
+            phi_1 = torch.expm1(-h)
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            x_t = (
+                expand_dims(sigma_t / sigma_s, dims) * x
+                - expand_dims(alpha_t * phi_1, dims) * model_s
+            )
+            if return_intermediate:
+                return x_t, {'model_s': model_s}
+            else:
+                return x_t
+        else:
+            phi_1 = torch.expm1(h)
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            x_t = (
+                expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                - expand_dims(sigma_t * phi_1, dims) * model_s
+            )
+            if return_intermediate:
+                return x_t, {'model_s': model_s}
+            else:
+                return x_t
+
+    def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False, solver_type='dpm_solver'):
+        """
+        Singlestep solver DPM-Solver-2 from time `s` to time `t`.
+
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            r1: A `float`. The hyperparameter of the second-order solver.
+            model_s: A pytorch tensor. The model function evaluated at time `s`.
+                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+            return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if solver_type not in ['dpm_solver', 'taylor']:
+            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+        if r1 is None:
+            r1 = 0.5
+        ns = self.noise_schedule
+        dims = x.dim()
+        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+        h = lambda_t - lambda_s
+        lambda_s1 = lambda_s + r1 * h
+        s1 = ns.inverse_lambda(lambda_s1)
+        log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(t)
+        sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
+        alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
+
+        if self.predict_x0:
+            phi_11 = torch.expm1(-r1 * h)
+            phi_1 = torch.expm1(-h)
+
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            x_s1 = (
+                expand_dims(sigma_s1 / sigma_s, dims) * x
+                - expand_dims(alpha_s1 * phi_11, dims) * model_s
+            )
+            model_s1 = self.model_fn(x_s1, s1)
+            if solver_type == 'dpm_solver':
+                x_t = (
+                    expand_dims(sigma_t / sigma_s, dims) * x
+                    - expand_dims(alpha_t * phi_1, dims) * model_s
+                    - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
+                )
+            elif solver_type == 'taylor':
+                x_t = (
+                    expand_dims(sigma_t / sigma_s, dims) * x
+                    - expand_dims(alpha_t * phi_1, dims) * model_s
+                    + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (model_s1 - model_s)
+                )
+        else:
+            phi_11 = torch.expm1(r1 * h)
+            phi_1 = torch.expm1(h)
+
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            x_s1 = (
+                expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
+                - expand_dims(sigma_s1 * phi_11, dims) * model_s
+            )
+            model_s1 = self.model_fn(x_s1, s1)
+            if solver_type == 'dpm_solver':
+                x_t = (
+                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                    - expand_dims(sigma_t * phi_1, dims) * model_s
+                    - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
+                )
+            elif solver_type == 'taylor':
+                x_t = (
+                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                    - expand_dims(sigma_t * phi_1, dims) * model_s
+                    - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
+                )
+        if return_intermediate:
+            return x_t, {'model_s': model_s, 'model_s1': model_s1}
+        else:
+            return x_t
+
+    def singlestep_dpm_solver_third_update(self, x, s, t, r1=1./3., r2=2./3., model_s=None, model_s1=None, return_intermediate=False, solver_type='dpm_solver'):
+        """
+        Singlestep solver DPM-Solver-3 from time `s` to time `t`.
+
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            r1: A `float`. The hyperparameter of the third-order solver.
+            r2: A `float`. The hyperparameter of the third-order solver.
+            model_s: A pytorch tensor. The model function evaluated at time `s`.
+                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+            model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
+                If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
+            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if solver_type not in ['dpm_solver', 'taylor']:
+            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+        if r1 is None:
+            r1 = 1. / 3.
+        if r2 is None:
+            r2 = 2. / 3.
+        ns = self.noise_schedule
+        dims = x.dim()
+        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+        h = lambda_t - lambda_s
+        lambda_s1 = lambda_s + r1 * h
+        lambda_s2 = lambda_s + r2 * h
+        s1 = ns.inverse_lambda(lambda_s1)
+        s2 = ns.inverse_lambda(lambda_s2)
+        log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
+        sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(s2), ns.marginal_std(t)
+        alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
+
+        if self.predict_x0:
+            phi_11 = torch.expm1(-r1 * h)
+            phi_12 = torch.expm1(-r2 * h)
+            phi_1 = torch.expm1(-h)
+            phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
+            phi_2 = phi_1 / h + 1.
+            phi_3 = phi_2 / h - 0.5
+
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            if model_s1 is None:
+                x_s1 = (
+                    expand_dims(sigma_s1 / sigma_s, dims) * x
+                    - expand_dims(alpha_s1 * phi_11, dims) * model_s
+                )
+                model_s1 = self.model_fn(x_s1, s1)
+            x_s2 = (
+                expand_dims(sigma_s2 / sigma_s, dims) * x
+                - expand_dims(alpha_s2 * phi_12, dims) * model_s
+                + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
+            )
+            model_s2 = self.model_fn(x_s2, s2)
+            if solver_type == 'dpm_solver':
+                x_t = (
+                    expand_dims(sigma_t / sigma_s, dims) * x
+                    - expand_dims(alpha_t * phi_1, dims) * model_s
+                    + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
+                )
+            elif solver_type == 'taylor':
+                D1_0 = (1. / r1) * (model_s1 - model_s)
+                D1_1 = (1. / r2) * (model_s2 - model_s)
+                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
+                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
+                x_t = (
+                    expand_dims(sigma_t / sigma_s, dims) * x
+                    - expand_dims(alpha_t * phi_1, dims) * model_s
+                    + expand_dims(alpha_t * phi_2, dims) * D1
+                    - expand_dims(alpha_t * phi_3, dims) * D2
+                )
+        else:
+            phi_11 = torch.expm1(r1 * h)
+            phi_12 = torch.expm1(r2 * h)
+            phi_1 = torch.expm1(h)
+            phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
+            phi_2 = phi_1 / h - 1.
+            phi_3 = phi_2 / h - 0.5
+
+            if model_s is None:
+                model_s = self.model_fn(x, s)
+            if model_s1 is None:
+                x_s1 = (
+                    expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
+                    - expand_dims(sigma_s1 * phi_11, dims) * model_s
+                )
+                model_s1 = self.model_fn(x_s1, s1)
+            x_s2 = (
+                expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
+                - expand_dims(sigma_s2 * phi_12, dims) * model_s
+                - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
+            )
+            model_s2 = self.model_fn(x_s2, s2)
+            if solver_type == 'dpm_solver':
+                x_t = (
+                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                    - expand_dims(sigma_t * phi_1, dims) * model_s
+                    - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
+                )
+            elif solver_type == 'taylor':
+                D1_0 = (1. / r1) * (model_s1 - model_s)
+                D1_1 = (1. / r2) * (model_s2 - model_s)
+                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
+                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
+                x_t = (
+                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+                    - expand_dims(sigma_t * phi_1, dims) * model_s
+                    - expand_dims(sigma_t * phi_2, dims) * D1
+                    - expand_dims(sigma_t * phi_3, dims) * D2
+                )
+
+        if return_intermediate:
+            return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
+        else:
+            return x_t
+
+    def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
+        """
+        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
+
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            model_prev_list: A list of pytorch tensor. The previous computed model values.
+            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if solver_type not in ['dpm_solver', 'taylor']:
+            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+        ns = self.noise_schedule
+        dims = x.dim()
+        model_prev_1, model_prev_0 = model_prev_list
+        t_prev_1, t_prev_0 = t_prev_list
+        lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
+        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+        alpha_t = torch.exp(log_alpha_t)
+
+        h_0 = lambda_prev_0 - lambda_prev_1
+        h = lambda_t - lambda_prev_0
+        r0 = h_0 / h
+        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
+        if self.predict_x0:
+            if solver_type == 'dpm_solver':
+                x_t = (
+                    expand_dims(sigma_t / sigma_prev_0, dims) * x
+                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+                    - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
+                )
+            elif solver_type == 'taylor':
+                x_t = (
+                    expand_dims(sigma_t / sigma_prev_0, dims) * x
+                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+                    + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
+                )
+        else:
+            if solver_type == 'dpm_solver':
+                x_t = (
+                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+                    - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
+                )
+            elif solver_type == 'taylor':
+                x_t = (
+                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+                    - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
+                )
+        return x_t
+
+    def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
+        """
+        Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
+
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            model_prev_list: A list of pytorch tensor. The previous computed model values.
+            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        ns = self.noise_schedule
+        dims = x.dim()
+        model_prev_2, model_prev_1, model_prev_0 = model_prev_list
+        t_prev_2, t_prev_1, t_prev_0 = t_prev_list
+        lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
+        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+        alpha_t = torch.exp(log_alpha_t)
+
+        h_1 = lambda_prev_1 - lambda_prev_2
+        h_0 = lambda_prev_0 - lambda_prev_1
+        h = lambda_t - lambda_prev_0
+        r0, r1 = h_0 / h, h_1 / h
+        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
+        D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
+        D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
+        D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
+        if self.predict_x0:
+            x_t = (
+                expand_dims(sigma_t / sigma_prev_0, dims) * x
+                - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+                + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
+                - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h**2 - 0.5), dims) * D2
+            )
+        else:
+            x_t = (
+                expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+                - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+                - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
+                - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h**2 - 0.5), dims) * D2
+            )
+        return x_t
+
+    def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None, r2=None):
+        """
+        Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
+
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
+            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+            r1: A `float`. The hyperparameter of the second-order or third-order solver.
+            r2: A `float`. The hyperparameter of the third-order solver.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if order == 1:
+            return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
+        elif order == 2:
+            return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1)
+        elif order == 3:
+            return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1, r2=r2)
+        else:
+            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
+
+    def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
+        """
+        Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
+
+        Args:
+            x: A pytorch tensor. The initial value at time `s`.
+            model_prev_list: A list of pytorch tensor. The previous computed model values.
+            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_t: A pytorch tensor. The approximated solution at time `t`.
+        """
+        if order == 1:
+            return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
+        elif order == 2:
+            return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
+        elif order == 3:
+            return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
+        else:
+            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
+
+    def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5, solver_type='dpm_solver'):
+        """
+        The adaptive step size solver based on singlestep DPM-Solver.
+
+        Args:
+            x: A pytorch tensor. The initial value at time `t_T`.
+            order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
+            t_T: A `float`. The starting time of the sampling (default is T).
+            t_0: A `float`. The ending time of the sampling (default is epsilon).
+            h_init: A `float`. The initial step size (for logSNR).
+            atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
+            rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
+            theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
+            t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the 
+                current time and `t_0` is less than `t_err`. The default setting is 1e-5.
+            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+        Returns:
+            x_0: A pytorch tensor. The approximated solution at time `t_0`.
+
+        [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
+        """
+        ns = self.noise_schedule
+        s = t_T * torch.ones((x.shape[0],)).to(x)
+        lambda_s = ns.marginal_lambda(s)
+        lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
+        h = h_init * torch.ones_like(s).to(x)
+        x_prev = x
+        nfe = 0
+        if order == 2:
+            r1 = 0.5
+            lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
+            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, solver_type=solver_type, **kwargs)
+        elif order == 3:
+            r1, r2 = 1. / 3., 2. / 3.
+            lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, return_intermediate=True, solver_type=solver_type)
+            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2, solver_type=solver_type, **kwargs)
+        else:
+            raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
+        while torch.abs((s - t_0)).mean() > t_err:
+            t = ns.inverse_lambda(lambda_s + h)
+            x_lower, lower_noise_kwargs = lower_update(x, s, t)
+            x_higher = higher_update(x, s, t, **lower_noise_kwargs)
+            delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
+            norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
+            E = norm_fn((x_higher - x_lower) / delta).max()
+            if torch.all(E <= 1.):
+                x = x_higher
+                s = t
+                x_prev = x_lower
+                lambda_s = ns.marginal_lambda(s)
+            h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
+            nfe += order
+        print('adaptive solver nfe', nfe)
+        return x
+
+    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
+        method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
+        atol=0.0078, rtol=0.05,
+    ):
+        """
+        Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
+
+        =====================================================
+
+        We support the following algorithms for both noise prediction model and data prediction model:
+            - 'singlestep':
+                Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver. 
+                We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
+                The total number of function evaluations (NFE) == `steps`.
+                Given a fixed NFE == `steps`, the sampling procedure is:
+                    - If `order` == 1:
+                        - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
+                    - If `order` == 2:
+                        - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
+                        - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
+                        - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
+                    - If `order` == 3:
+                        - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
+                        - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
+                        - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
+                        - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
+            - 'multistep':
+                Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
+                We initialize the first `order` values by lower order multistep solvers.
+                Given a fixed NFE == `steps`, the sampling procedure is:
+                    Denote K = steps.
+                    - If `order` == 1:
+                        - We use K steps of DPM-Solver-1 (i.e. DDIM).
+                    - If `order` == 2:
+                        - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
+                    - If `order` == 3:
+                        - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
+            - 'singlestep_fixed':
+                Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
+                We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
+            - 'adaptive':
+                Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
+                We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
+                You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
+                (NFE) and the sample quality.
+                    - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
+                    - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
+
+        =====================================================
+
+        Some advices for choosing the algorithm:
+            - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
+                Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
+                e.g.
+                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
+                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
+                            skip_type='time_uniform', method='singlestep')
+            - For **guided sampling with large guidance scale** by DPMs:
+                Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
+                e.g.
+                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
+                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
+                            skip_type='time_uniform', method='multistep')
+
+        We support three types of `skip_type`:
+            - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
+            - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
+            - 'time_quadratic': quadratic time for the time steps.
+
+        =====================================================
+        Args:
+            x: A pytorch tensor. The initial value at time `t_start`
+                e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
+            steps: A `int`. The total number of function evaluations (NFE).
+            t_start: A `float`. The starting time of the sampling.
+                If `T` is None, we use self.noise_schedule.T (default is 1.0).
+            t_end: A `float`. The ending time of the sampling.
+                If `t_end` is None, we use 1. / self.noise_schedule.total_N.
+                e.g. if total_N == 1000, we have `t_end` == 1e-3.
+                For discrete-time DPMs:
+                    - We recommend `t_end` == 1. / self.noise_schedule.total_N.
+                For continuous-time DPMs:
+                    - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
+            order: A `int`. The order of DPM-Solver.
+            skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
+            method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
+            denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
+                Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
+
+                This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
+                score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
+                for diffusion models sampling by diffusion SDEs for low-resolutional images
+                (such as CIFAR-10). However, we observed that such trick does not matter for
+                high-resolutional images. As it needs an additional NFE, we do not recommend
+                it for high-resolutional images.
+            lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
+                Only valid for `method=multistep` and `steps < 15`. We empirically find that
+                this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
+                (especially for steps <= 10). So we recommend to set it to be `True`.
+            solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
+            atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
+            rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
+        Returns:
+            x_end: A pytorch tensor. The approximated solution at time `t_end`.
+
+        """
+        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
+        t_T = self.noise_schedule.T if t_start is None else t_start
+        device = x.device
+        if method == 'adaptive':
+            with torch.no_grad():
+                x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol, solver_type=solver_type)
+        elif method == 'multistep':
+            assert steps >= order
+            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
+            assert timesteps.shape[0] - 1 == steps
+            with torch.no_grad():
+                vec_t = timesteps[0].expand((x.shape[0]))
+                model_prev_list = [self.model_fn(x, vec_t)]
+                t_prev_list = [vec_t]
+                # Init the first `order` values by lower order multistep DPM-Solver.
+                for init_order in range(1, order):
+                    vec_t = timesteps[init_order].expand(x.shape[0])
+                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order, solver_type=solver_type)
+                    model_prev_list.append(self.model_fn(x, vec_t))
+                    t_prev_list.append(vec_t)
+                # Compute the remaining values by `order`-th order multistep DPM-Solver.
+                for step in range(order, steps + 1):
+                    vec_t = timesteps[step].expand(x.shape[0])
+                    if lower_order_final and steps < 15:
+                        step_order = min(order, steps + 1 - step)
+                    else:
+                        step_order = order
+                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order, solver_type=solver_type)
+                    for i in range(order - 1):
+                        t_prev_list[i] = t_prev_list[i + 1]
+                        model_prev_list[i] = model_prev_list[i + 1]
+                    t_prev_list[-1] = vec_t
+                    # We do not need to evaluate the final model value.
+                    if step < steps:
+                        model_prev_list[-1] = self.model_fn(x, vec_t)
+        elif method in ['singlestep', 'singlestep_fixed']:
+            if method == 'singlestep':
+                timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order, skip_type=skip_type, t_T=t_T, t_0=t_0, device=device)
+            elif method == 'singlestep_fixed':
+                K = steps // order
+                orders = [order,] * K
+                timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
+            for i, order in enumerate(orders):
+                t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
+                timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(), N=order, device=device)
+                lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
+                vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
+                h = lambda_inner[-1] - lambda_inner[0]
+                r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
+                r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
+                x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
+        if denoise_to_zero:
+            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
+        return x
+
+
+
+#############################################################
+# other utility functions
+#############################################################
+
+def interpolate_fn(x, xp, yp):
+    """
+    A piecewise linear function y = f(x), using xp and yp as keypoints.
+    We implement f(x) in a differentiable way (i.e. applicable for autograd).
+    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
+
+    Args:
+        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
+        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
+        yp: PyTorch tensor with shape [C, K].
+    Returns:
+        The function values f(x), with shape [N, C].
+    """
+    N, K = x.shape[0], xp.shape[1]
+    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
+    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
+    x_idx = torch.argmin(x_indices, dim=2)
+    cand_start_idx = x_idx - 1
+    start_idx = torch.where(
+        torch.eq(x_idx, 0),
+        torch.tensor(1, device=x.device),
+        torch.where(
+            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+        ),
+    )
+    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
+    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
+    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
+    start_idx2 = torch.where(
+        torch.eq(x_idx, 0),
+        torch.tensor(0, device=x.device),
+        torch.where(
+            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+        ),
+    )
+    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
+    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
+    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
+    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
+    return cand
+
+
+def expand_dims(v, dims):
+    """
+    Expand the tensor `v` to the dim `dims`.
+
+    Args:
+        `v`: a PyTorch tensor with shape [N].
+        `dim`: a `int`.
+    Returns:
+        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
+    """
+    return v[(...,) + (None,)*(dims - 1)]
\ No newline at end of file
diff --git a/ldm/models/diffusion/dpm_solver/sampler.py b/ldm/models/diffusion/dpm_solver/sampler.py
new file mode 100644
index 00000000..2c42d6f9
--- /dev/null
+++ b/ldm/models/diffusion/dpm_solver/sampler.py
@@ -0,0 +1,82 @@
+"""SAMPLING ONLY."""
+
+import torch
+
+from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
+
+
+class DPMSolverSampler(object):
+    def __init__(self, model, **kwargs):
+        super().__init__()
+        self.model = model
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
+        self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
+
+    def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device("cuda"):
+                attr = attr.to(torch.device("cuda"))
+        setattr(self, name, attr)
+
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None,
+               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               **kwargs
+               ):
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        # sampling
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+
+        # print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
+
+        device = self.model.betas.device
+        if x_T is None:
+            img = torch.randn(size, device=device)
+        else:
+            img = x_T
+
+        ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
+
+        model_fn = model_wrapper(
+            lambda x, t, c: self.model.apply_model(x, t, c),
+            ns,
+            model_type="noise",
+            guidance_type="classifier-free",
+            condition=conditioning,
+            unconditional_condition=unconditional_conditioning,
+            guidance_scale=unconditional_guidance_scale,
+        )
+
+        dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
+        x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
+
+        return x.to(device), None
diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py
new file mode 100644
index 00000000..78eeb100
--- /dev/null
+++ b/ldm/models/diffusion/plms.py
@@ -0,0 +1,236 @@
+"""SAMPLING ONLY."""
+
+import torch
+import numpy as np
+from tqdm import tqdm
+from functools import partial
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
+
+
+class PLMSSampler(object):
+    def __init__(self, model, schedule="linear", **kwargs):
+        super().__init__()
+        self.model = model
+        self.ddpm_num_timesteps = model.num_timesteps
+        self.schedule = schedule
+
+    def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device("cuda"):
+                attr = attr.to(torch.device("cuda"))
+        setattr(self, name, attr)
+
+    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+        if ddim_eta != 0:
+            raise ValueError('ddim_eta must be 0 for PLMS')
+        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+        alphas_cumprod = self.model.alphas_cumprod
+        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+        self.register_buffer('betas', to_torch(self.model.betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+        # ddim sampling parameters
+        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+                                                                                   ddim_timesteps=self.ddim_timesteps,
+                                                                                   eta=ddim_eta,verbose=verbose)
+        self.register_buffer('ddim_sigmas', ddim_sigmas)
+        self.register_buffer('ddim_alphas', ddim_alphas)
+        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None,
+               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               **kwargs
+               ):
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+        # sampling
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+        print(f'Data shape for PLMS sampling is {size}')
+
+        samples, intermediates = self.plms_sampling(conditioning, size,
+                                                    callback=callback,
+                                                    img_callback=img_callback,
+                                                    quantize_denoised=quantize_x0,
+                                                    mask=mask, x0=x0,
+                                                    ddim_use_original_steps=False,
+                                                    noise_dropout=noise_dropout,
+                                                    temperature=temperature,
+                                                    score_corrector=score_corrector,
+                                                    corrector_kwargs=corrector_kwargs,
+                                                    x_T=x_T,
+                                                    log_every_t=log_every_t,
+                                                    unconditional_guidance_scale=unconditional_guidance_scale,
+                                                    unconditional_conditioning=unconditional_conditioning,
+                                                    )
+        return samples, intermediates
+
+    @torch.no_grad()
+    def plms_sampling(self, cond, shape,
+                      x_T=None, ddim_use_original_steps=False,
+                      callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, log_every_t=100,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None,):
+        device = self.model.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        if timesteps is None:
+            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+        elif timesteps is not None and not ddim_use_original_steps:
+            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+            timesteps = self.ddim_timesteps[:subset_end]
+
+        intermediates = {'x_inter': [img], 'pred_x0': [img]}
+        time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
+        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+        print(f"Running PLMS Sampling with {total_steps} timesteps")
+
+        iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
+        old_eps = []
+
+        for i, step in enumerate(iterator):
+            index = total_steps - i - 1
+            ts = torch.full((b,), step, device=device, dtype=torch.long)
+            ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
+
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
+                img = img_orig * mask + (1. - mask) * img
+
+            outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+                                      quantize_denoised=quantize_denoised, temperature=temperature,
+                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
+                                      corrector_kwargs=corrector_kwargs,
+                                      unconditional_guidance_scale=unconditional_guidance_scale,
+                                      unconditional_conditioning=unconditional_conditioning,
+                                      old_eps=old_eps, t_next=ts_next)
+            img, pred_x0, e_t = outs
+            old_eps.append(e_t)
+            if len(old_eps) >= 4:
+                old_eps.pop(0)
+            if callback: callback(i)
+            if img_callback: img_callback(pred_x0, i)
+
+            if index % log_every_t == 0 or index == total_steps - 1:
+                intermediates['x_inter'].append(img)
+                intermediates['pred_x0'].append(pred_x0)
+
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
+        b, *_, device = *x.shape, x.device
+
+        def get_model_output(x, t):
+            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+                e_t = self.model.apply_model(x, t, c)
+            else:
+                x_in = torch.cat([x] * 2)
+                t_in = torch.cat([t] * 2)
+                c_in = torch.cat([unconditional_conditioning, c])
+                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+            if score_corrector is not None:
+                assert self.model.parameterization == "eps"
+                e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+            return e_t
+
+        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+        def get_x_prev_and_pred_x0(e_t, index):
+            # select parameters corresponding to the currently considered timestep
+            a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+            sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+            sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+            # current prediction for x_0
+            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+            if quantize_denoised:
+                pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+            # direction pointing to x_t
+            dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+            if noise_dropout > 0.:
+                noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+            return x_prev, pred_x0
+
+        e_t = get_model_output(x, t)
+        if len(old_eps) == 0:
+            # Pseudo Improved Euler (2nd order)
+            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+            e_t_next = get_model_output(x_prev, t_next)
+            e_t_prime = (e_t + e_t_next) / 2
+        elif len(old_eps) == 1:
+            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (3 * e_t - old_eps[-1]) / 2
+        elif len(old_eps) == 2:
+            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+        elif len(old_eps) >= 3:
+            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+        return x_prev, pred_x0, e_t
diff --git a/ldm/modules/attention.py b/ldm/modules/attention.py
new file mode 100644
index 00000000..f4eff39c
--- /dev/null
+++ b/ldm/modules/attention.py
@@ -0,0 +1,261 @@
+from inspect import isfunction
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from einops import rearrange, repeat
+
+from ldm.modules.diffusionmodules.util import checkpoint
+
+
+def exists(val):
+    return val is not None
+
+
+def uniq(arr):
+    return{el: True for el in arr}.keys()
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def max_neg_value(t):
+    return -torch.finfo(t.dtype).max
+
+
+def init_(tensor):
+    dim = tensor.shape[-1]
+    std = 1 / math.sqrt(dim)
+    tensor.uniform_(-std, std)
+    return tensor
+
+
+# feedforward
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = nn.Sequential(
+            nn.Linear(dim, inner_dim),
+            nn.GELU()
+        ) if not glu else GEGLU(dim, inner_dim)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            nn.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def Normalize(in_channels):
+    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+class LinearAttention(nn.Module):
+    def __init__(self, dim, heads=4, dim_head=32):
+        super().__init__()
+        self.heads = heads
+        hidden_dim = dim_head * heads
+        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
+        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+        qkv = self.to_qkv(x)
+        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
+        k = k.softmax(dim=-1)  
+        context = torch.einsum('bhdn,bhen->bhde', k, v)
+        out = torch.einsum('bhde,bhdn->bhen', context, q)
+        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
+        return self.to_out(out)
+
+
+class SpatialSelfAttention(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.k = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.v = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.proj_out = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=1,
+                                        stride=1,
+                                        padding=0)
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b,c,h,w = q.shape
+        q = rearrange(q, 'b c h w -> b (h w) c')
+        k = rearrange(k, 'b c h w -> b c (h w)')
+        w_ = torch.einsum('bij,bjk->bik', q, k)
+
+        w_ = w_ * (int(c)**(-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = rearrange(v, 'b c h w -> b c (h w)')
+        w_ = rearrange(w_, 'b i j -> b j i')
+        h_ = torch.einsum('bij,bjk->bik', v, w_)
+        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
+        h_ = self.proj_out(h_)
+
+        return x+h_
+
+
+class CrossAttention(nn.Module):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.scale = dim_head ** -0.5
+        self.heads = heads
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, query_dim),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x, context=None, mask=None):
+        h = self.heads
+
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+        if exists(mask):
+            mask = rearrange(mask, 'b ... -> b (...)')
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = repeat(mask, 'b j -> (b h) () j', h=h)
+            sim.masked_fill_(~mask, max_neg_value)
+
+        # attention, what we cannot get enough of
+        attn = sim.softmax(dim=-1)
+
+        out = einsum('b i j, b j d -> b i d', attn, v)
+        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+        return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
+        super().__init__()
+        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout)  # is a self-attention
+        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
+                                    heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.norm3 = nn.LayerNorm(dim)
+        self.checkpoint = checkpoint
+
+    def forward(self, x, context=None):
+        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
+
+    def _forward(self, x, context=None):
+        x = self.attn1(self.norm1(x)) + x
+        x = self.attn2(self.norm2(x), context=context) + x
+        x = self.ff(self.norm3(x)) + x
+        return x
+
+
+class SpatialTransformer(nn.Module):
+    """
+    Transformer block for image-like data.
+    First, project the input (aka embedding)
+    and reshape to b, t, d.
+    Then apply standard transformer action.
+    Finally, reshape to image
+    """
+    def __init__(self, in_channels, n_heads, d_head,
+                 depth=1, dropout=0., context_dim=None):
+        super().__init__()
+        self.in_channels = in_channels
+        inner_dim = n_heads * d_head
+        self.norm = Normalize(in_channels)
+
+        self.proj_in = nn.Conv2d(in_channels,
+                                 inner_dim,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+
+        self.transformer_blocks = nn.ModuleList(
+            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
+                for d in range(depth)]
+        )
+
+        self.proj_out = zero_module(nn.Conv2d(inner_dim,
+                                              in_channels,
+                                              kernel_size=1,
+                                              stride=1,
+                                              padding=0))
+
+    def forward(self, x, context=None):
+        # note: if no context is given, cross-attention defaults to self-attention
+        b, c, h, w = x.shape
+        x_in = x
+        x = self.norm(x)
+        x = self.proj_in(x)
+        x = rearrange(x, 'b c h w -> b (h w) c')
+        for block in self.transformer_blocks:
+            x = block(x, context=context)
+        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
+        x = self.proj_out(x)
+        return x + x_in
\ No newline at end of file
diff --git a/ldm/modules/diffusionmodules/__init__.py b/ldm/modules/diffusionmodules/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ldm/modules/diffusionmodules/model.py b/ldm/modules/diffusionmodules/model.py
new file mode 100644
index 00000000..533e589a
--- /dev/null
+++ b/ldm/modules/diffusionmodules/model.py
@@ -0,0 +1,835 @@
+# pytorch_diffusion + derived encoder decoder
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import rearrange
+
+from ldm.util import instantiate_from_config
+from ldm.modules.attention import LinearAttention
+
+
+def get_timestep_embedding(timesteps, embedding_dim):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models:
+    From Fairseq.
+    Build sinusoidal embeddings.
+    This matches the implementation in tensor2tensor, but differs slightly
+    from the description in Section 3.5 of "Attention Is All You Need".
+    """
+    assert len(timesteps.shape) == 1
+
+    half_dim = embedding_dim // 2
+    emb = math.log(10000) / (half_dim - 1)
+    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
+    emb = emb.to(device=timesteps.device)
+    emb = timesteps.float()[:, None] * emb[None, :]
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if embedding_dim % 2 == 1:  # zero pad
+        emb = torch.nn.functional.pad(emb, (0,1,0,0))
+    return emb
+
+
+def nonlinearity(x):
+    # swish
+    return x*torch.sigmoid(x)
+
+
+def Normalize(in_channels, num_groups=32):
+    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            self.conv = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, x):
+        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        if self.with_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=3,
+                                        stride=2,
+                                        padding=0)
+
+    def forward(self, x):
+        if self.with_conv:
+            pad = (0,1,0,1)
+            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+            x = self.conv(x)
+        else:
+            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+        return x
+
+
+class ResnetBlock(nn.Module):
+    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
+                 dropout, temb_channels=512):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.use_conv_shortcut = conv_shortcut
+
+        self.norm1 = Normalize(in_channels)
+        self.conv1 = torch.nn.Conv2d(in_channels,
+                                     out_channels,
+                                     kernel_size=3,
+                                     stride=1,
+                                     padding=1)
+        if temb_channels > 0:
+            self.temb_proj = torch.nn.Linear(temb_channels,
+                                             out_channels)
+        self.norm2 = Normalize(out_channels)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = torch.nn.Conv2d(out_channels,
+                                     out_channels,
+                                     kernel_size=3,
+                                     stride=1,
+                                     padding=1)
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = torch.nn.Conv2d(in_channels,
+                                                     out_channels,
+                                                     kernel_size=3,
+                                                     stride=1,
+                                                     padding=1)
+            else:
+                self.nin_shortcut = torch.nn.Conv2d(in_channels,
+                                                    out_channels,
+                                                    kernel_size=1,
+                                                    stride=1,
+                                                    padding=0)
+
+    def forward(self, x, temb):
+        h = x
+        h = self.norm1(h)
+        h = nonlinearity(h)
+        h = self.conv1(h)
+
+        if temb is not None:
+            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
+
+        h = self.norm2(h)
+        h = nonlinearity(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                x = self.conv_shortcut(x)
+            else:
+                x = self.nin_shortcut(x)
+
+        return x+h
+
+
+class LinAttnBlock(LinearAttention):
+    """to match AttnBlock usage"""
+    def __init__(self, in_channels):
+        super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.k = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.v = torch.nn.Conv2d(in_channels,
+                                 in_channels,
+                                 kernel_size=1,
+                                 stride=1,
+                                 padding=0)
+        self.proj_out = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=1,
+                                        stride=1,
+                                        padding=0)
+
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b,c,h,w = q.shape
+        q = q.reshape(b,c,h*w)
+        q = q.permute(0,2,1)   # b,hw,c
+        k = k.reshape(b,c,h*w) # b,c,hw
+        w_ = torch.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+        w_ = w_ * (int(c)**(-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = v.reshape(b,c,h*w)
+        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
+        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+        h_ = h_.reshape(b,c,h,w)
+
+        h_ = self.proj_out(h_)
+
+        return x+h_
+
+
+def make_attn(in_channels, attn_type="vanilla"):
+    assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
+    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
+    if attn_type == "vanilla":
+        return AttnBlock(in_channels)
+    elif attn_type == "none":
+        return nn.Identity(in_channels)
+    else:
+        return LinAttnBlock(in_channels)
+
+
+class Model(nn.Module):
+    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+                 resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
+        super().__init__()
+        if use_linear_attn: attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = self.ch*4
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+
+        self.use_timestep = use_timestep
+        if self.use_timestep:
+            # timestep embedding
+            self.temb = nn.Module()
+            self.temb.dense = nn.ModuleList([
+                torch.nn.Linear(self.ch,
+                                self.temb_ch),
+                torch.nn.Linear(self.temb_ch,
+                                self.temb_ch),
+            ])
+
+        # downsampling
+        self.conv_in = torch.nn.Conv2d(in_channels,
+                                       self.ch,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1)
+
+        curr_res = resolution
+        in_ch_mult = (1,)+tuple(ch_mult)
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch*in_ch_mult[i_level]
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions-1:
+                down.downsample = Downsample(block_in, resamp_with_conv)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch*ch_mult[i_level]
+            skip_in = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks+1):
+                if i_block == self.num_res_blocks:
+                    skip_in = ch*in_ch_mult[i_level]
+                block.append(ResnetBlock(in_channels=block_in+skip_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv)
+                curr_res = curr_res * 2
+            self.up.insert(0, up) # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        out_ch,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, x, t=None, context=None):
+        #assert x.shape[2] == x.shape[3] == self.resolution
+        if context is not None:
+            # assume aligned context, cat along channel axis
+            x = torch.cat((x, context), dim=1)
+        if self.use_timestep:
+            # timestep embedding
+            assert t is not None
+            temb = get_timestep_embedding(t, self.ch)
+            temb = self.temb.dense[0](temb)
+            temb = nonlinearity(temb)
+            temb = self.temb.dense[1](temb)
+        else:
+            temb = None
+
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1], temb)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions-1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks+1):
+                h = self.up[i_level].block[i_block](
+                    torch.cat([h, hs.pop()], dim=1), temb)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+    def get_last_layer(self):
+        return self.conv_out.weight
+
+
+class Encoder(nn.Module):
+    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+                 resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
+                 **ignore_kwargs):
+        super().__init__()
+        if use_linear_attn: attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+
+        # downsampling
+        self.conv_in = torch.nn.Conv2d(in_channels,
+                                       self.ch,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1)
+
+        curr_res = resolution
+        in_ch_mult = (1,)+tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch*in_ch_mult[i_level]
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions-1:
+                down.downsample = Downsample(block_in, resamp_with_conv)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        2*z_channels if double_z else z_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, x):
+        # timestep embedding
+        temb = None
+
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1], temb)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions-1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+class Decoder(nn.Module):
+    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
+                 attn_type="vanilla", **ignorekwargs):
+        super().__init__()
+        if use_linear_attn: attn_type = "linear"
+        self.ch = ch
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.give_pre_end = give_pre_end
+        self.tanh_out = tanh_out
+
+        # compute in_ch_mult, block_in and curr_res at lowest res
+        in_ch_mult = (1,)+tuple(ch_mult)
+        block_in = ch*ch_mult[self.num_resolutions-1]
+        curr_res = resolution // 2**(self.num_resolutions-1)
+        self.z_shape = (1,z_channels,curr_res,curr_res)
+        print("Working with z of shape {} = {} dimensions.".format(
+            self.z_shape, np.prod(self.z_shape)))
+
+        # z to block_in
+        self.conv_in = torch.nn.Conv2d(z_channels,
+                                       block_in,
+                                       kernel_size=3,
+                                       stride=1,
+                                       padding=1)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in,
+                                       out_channels=block_in,
+                                       temb_channels=self.temb_ch,
+                                       dropout=dropout)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch*ch_mult[i_level]
+            for i_block in range(self.num_res_blocks+1):
+                block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+                if curr_res in attn_resolutions:
+                    attn.append(make_attn(block_in, attn_type=attn_type))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in, resamp_with_conv)
+                curr_res = curr_res * 2
+            self.up.insert(0, up) # prepend to get consistent order
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        out_ch,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, z):
+        #assert z.shape[1:] == self.z_shape[1:]
+        self.last_z_shape = z.shape
+
+        # timestep embedding
+        temb = None
+
+        # z to block_in
+        h = self.conv_in(z)
+
+        # middle
+        h = self.mid.block_1(h, temb)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks+1):
+                h = self.up[i_level].block[i_block](h, temb)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        if self.give_pre_end:
+            return h
+
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        if self.tanh_out:
+            h = torch.tanh(h)
+        return h
+
+
+class SimpleDecoder(nn.Module):
+    def __init__(self, in_channels, out_channels, *args, **kwargs):
+        super().__init__()
+        self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
+                                     ResnetBlock(in_channels=in_channels,
+                                                 out_channels=2 * in_channels,
+                                                 temb_channels=0, dropout=0.0),
+                                     ResnetBlock(in_channels=2 * in_channels,
+                                                out_channels=4 * in_channels,
+                                                temb_channels=0, dropout=0.0),
+                                     ResnetBlock(in_channels=4 * in_channels,
+                                                out_channels=2 * in_channels,
+                                                temb_channels=0, dropout=0.0),
+                                     nn.Conv2d(2*in_channels, in_channels, 1),
+                                     Upsample(in_channels, with_conv=True)])
+        # end
+        self.norm_out = Normalize(in_channels)
+        self.conv_out = torch.nn.Conv2d(in_channels,
+                                        out_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, x):
+        for i, layer in enumerate(self.model):
+            if i in [1,2,3]:
+                x = layer(x, None)
+            else:
+                x = layer(x)
+
+        h = self.norm_out(x)
+        h = nonlinearity(h)
+        x = self.conv_out(h)
+        return x
+
+
+class UpsampleDecoder(nn.Module):
+    def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
+                 ch_mult=(2,2), dropout=0.0):
+        super().__init__()
+        # upsampling
+        self.temb_ch = 0
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        block_in = in_channels
+        curr_res = resolution // 2 ** (self.num_resolutions - 1)
+        self.res_blocks = nn.ModuleList()
+        self.upsample_blocks = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            res_block = []
+            block_out = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks + 1):
+                res_block.append(ResnetBlock(in_channels=block_in,
+                                         out_channels=block_out,
+                                         temb_channels=self.temb_ch,
+                                         dropout=dropout))
+                block_in = block_out
+            self.res_blocks.append(nn.ModuleList(res_block))
+            if i_level != self.num_resolutions - 1:
+                self.upsample_blocks.append(Upsample(block_in, True))
+                curr_res = curr_res * 2
+
+        # end
+        self.norm_out = Normalize(block_in)
+        self.conv_out = torch.nn.Conv2d(block_in,
+                                        out_channels,
+                                        kernel_size=3,
+                                        stride=1,
+                                        padding=1)
+
+    def forward(self, x):
+        # upsampling
+        h = x
+        for k, i_level in enumerate(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.res_blocks[i_level][i_block](h, None)
+            if i_level != self.num_resolutions - 1:
+                h = self.upsample_blocks[k](h)
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+class LatentRescaler(nn.Module):
+    def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
+        super().__init__()
+        # residual block, interpolate, residual block
+        self.factor = factor
+        self.conv_in = nn.Conv2d(in_channels,
+                                 mid_channels,
+                                 kernel_size=3,
+                                 stride=1,
+                                 padding=1)
+        self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+                                                     out_channels=mid_channels,
+                                                     temb_channels=0,
+                                                     dropout=0.0) for _ in range(depth)])
+        self.attn = AttnBlock(mid_channels)
+        self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+                                                     out_channels=mid_channels,
+                                                     temb_channels=0,
+                                                     dropout=0.0) for _ in range(depth)])
+
+        self.conv_out = nn.Conv2d(mid_channels,
+                                  out_channels,
+                                  kernel_size=1,
+                                  )
+
+    def forward(self, x):
+        x = self.conv_in(x)
+        for block in self.res_block1:
+            x = block(x, None)
+        x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
+        x = self.attn(x)
+        for block in self.res_block2:
+            x = block(x, None)
+        x = self.conv_out(x)
+        return x
+
+
+class MergedRescaleEncoder(nn.Module):
+    def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
+                 attn_resolutions, dropout=0.0, resamp_with_conv=True,
+                 ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
+        super().__init__()
+        intermediate_chn = ch * ch_mult[-1]
+        self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
+                               z_channels=intermediate_chn, double_z=False, resolution=resolution,
+                               attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
+                               out_ch=None)
+        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
+                                       mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.rescaler(x)
+        return x
+
+
+class MergedRescaleDecoder(nn.Module):
+    def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
+                 dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
+        super().__init__()
+        tmp_chn = z_channels*ch_mult[-1]
+        self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
+                               resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
+                               ch_mult=ch_mult, resolution=resolution, ch=ch)
+        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
+                                       out_channels=tmp_chn, depth=rescale_module_depth)
+
+    def forward(self, x):
+        x = self.rescaler(x)
+        x = self.decoder(x)
+        return x
+
+
+class Upsampler(nn.Module):
+    def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
+        super().__init__()
+        assert out_size >= in_size
+        num_blocks = int(np.log2(out_size//in_size))+1
+        factor_up = 1.+ (out_size % in_size)
+        print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
+        self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
+                                       out_channels=in_channels)
+        self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
+                               attn_resolutions=[], in_channels=None, ch=in_channels,
+                               ch_mult=[ch_mult for _ in range(num_blocks)])
+
+    def forward(self, x):
+        x = self.rescaler(x)
+        x = self.decoder(x)
+        return x
+
+
+class Resize(nn.Module):
+    def __init__(self, in_channels=None, learned=False, mode="bilinear"):
+        super().__init__()
+        self.with_conv = learned
+        self.mode = mode
+        if self.with_conv:
+            print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
+            raise NotImplementedError()
+            assert in_channels is not None
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv = torch.nn.Conv2d(in_channels,
+                                        in_channels,
+                                        kernel_size=4,
+                                        stride=2,
+                                        padding=1)
+
+    def forward(self, x, scale_factor=1.0):
+        if scale_factor==1.0:
+            return x
+        else:
+            x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
+        return x
+
+class FirstStagePostProcessor(nn.Module):
+
+    def __init__(self, ch_mult:list, in_channels,
+                 pretrained_model:nn.Module=None,
+                 reshape=False,
+                 n_channels=None,
+                 dropout=0.,
+                 pretrained_config=None):
+        super().__init__()
+        if pretrained_config is None:
+            assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
+            self.pretrained_model = pretrained_model
+        else:
+            assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
+            self.instantiate_pretrained(pretrained_config)
+
+        self.do_reshape = reshape
+
+        if n_channels is None:
+            n_channels = self.pretrained_model.encoder.ch
+
+        self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
+        self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
+                            stride=1,padding=1)
+
+        blocks = []
+        downs = []
+        ch_in = n_channels
+        for m in ch_mult:
+            blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
+            ch_in = m * n_channels
+            downs.append(Downsample(ch_in, with_conv=False))
+
+        self.model = nn.ModuleList(blocks)
+        self.downsampler = nn.ModuleList(downs)
+
+
+    def instantiate_pretrained(self, config):
+        model = instantiate_from_config(config)
+        self.pretrained_model = model.eval()
+        # self.pretrained_model.train = False
+        for param in self.pretrained_model.parameters():
+            param.requires_grad = False
+
+
+    @torch.no_grad()
+    def encode_with_pretrained(self,x):
+        c = self.pretrained_model.encode(x)
+        if isinstance(c, DiagonalGaussianDistribution):
+            c = c.mode()
+        return  c
+
+    def forward(self,x):
+        z_fs = self.encode_with_pretrained(x)
+        z = self.proj_norm(z_fs)
+        z = self.proj(z)
+        z = nonlinearity(z)
+
+        for submodel, downmodel in zip(self.model,self.downsampler):
+            z = submodel(z,temb=None)
+            z = downmodel(z)
+
+        if self.do_reshape:
+            z = rearrange(z,'b c h w -> b (h w) c')
+        return z
+
diff --git a/ldm/modules/diffusionmodules/openaimodel.py b/ldm/modules/diffusionmodules/openaimodel.py
new file mode 100644
index 00000000..fcf95d1e
--- /dev/null
+++ b/ldm/modules/diffusionmodules/openaimodel.py
@@ -0,0 +1,961 @@
+from abc import abstractmethod
+from functools import partial
+import math
+from typing import Iterable
+
+import numpy as np
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ldm.modules.diffusionmodules.util import (
+    checkpoint,
+    conv_nd,
+    linear,
+    avg_pool_nd,
+    zero_module,
+    normalization,
+    timestep_embedding,
+)
+from ldm.modules.attention import SpatialTransformer
+
+
+# dummy replace
+def convert_module_to_f16(x):
+    pass
+
+def convert_module_to_f32(x):
+    pass
+
+
+## go
+class AttentionPool2d(nn.Module):
+    """
+    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
+    """
+
+    def __init__(
+        self,
+        spacial_dim: int,
+        embed_dim: int,
+        num_heads_channels: int,
+        output_dim: int = None,
+    ):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
+        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
+        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
+        self.num_heads = embed_dim // num_heads_channels
+        self.attention = QKVAttention(self.num_heads)
+
+    def forward(self, x):
+        b, c, *_spatial = x.shape
+        x = x.reshape(b, c, -1)  # NC(HW)
+        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
+        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
+        x = self.qkv_proj(x)
+        x = self.attention(x)
+        x = self.c_proj(x)
+        return x[:, :, 0]
+
+
+class TimestepBlock(nn.Module):
+    """
+    Any module where forward() takes timestep embeddings as a second argument.
+    """
+
+    @abstractmethod
+    def forward(self, x, emb):
+        """
+        Apply the module to `x` given `emb` timestep embeddings.
+        """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+    """
+    A sequential module that passes timestep embeddings to the children that
+    support it as an extra input.
+    """
+
+    def forward(self, x, emb, context=None):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, emb)
+            elif isinstance(layer, SpatialTransformer):
+                x = layer(x, context)
+            else:
+                x = layer(x)
+        return x
+
+
+class Upsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(
+                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+            )
+        else:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+class TransposedUpsample(nn.Module):
+    'Learned 2x upsampling without padding'
+    def __init__(self, channels, out_channels=None, ks=5):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+
+        self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
+
+    def forward(self,x):
+        return self.up(x)
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 downsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        stride = 2 if dims != 3 else (1, 2, 2)
+        if use_conv:
+            self.op = conv_nd(
+                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
+            )
+        else:
+            assert self.channels == self.out_channels
+            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        return self.op(x)
+
+
+class ResBlock(TimestepBlock):
+    """
+    A residual block that can optionally change the number of channels.
+    :param channels: the number of input channels.
+    :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout.
+    :param out_channels: if specified, the number of out channels.
+    :param use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the
+        channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param use_checkpoint: if True, use gradient checkpointing on this module.
+    :param up: if True, use this block for upsampling.
+    :param down: if True, use this block for downsampling.
+    """
+
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        use_conv=False,
+        use_scale_shift_norm=False,
+        dims=2,
+        use_checkpoint=False,
+        up=False,
+        down=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_checkpoint = use_checkpoint
+        self.use_scale_shift_norm = use_scale_shift_norm
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            conv_nd(dims, channels, self.out_channels, 3, padding=1),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims)
+            self.x_upd = Upsample(channels, False, dims)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims)
+            self.x_upd = Downsample(channels, False, dims)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = conv_nd(
+                dims, channels, self.out_channels, 3, padding=1
+            )
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    def forward(self, x, emb):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        return checkpoint(
+            self._forward, (x, emb), self.parameters(), self.use_checkpoint
+        )
+
+
+    def _forward(self, x, emb):
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        emb_out = self.emb_layers(emb).type(h.dtype)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = th.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        use_checkpoint=False,
+        use_new_attention_order=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.use_checkpoint = use_checkpoint
+        self.norm = normalization(channels)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        if use_new_attention_order:
+            # split qkv before split heads
+            self.attention = QKVAttention(self.num_heads)
+        else:
+            # split heads before split qkv
+            self.attention = QKVAttentionLegacy(self.num_heads)
+
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    def forward(self, x):
+        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
+        #return pt_checkpoint(self._forward, x)  # pytorch
+
+    def _forward(self, x):
+        b, c, *spatial = x.shape
+        x = x.reshape(b, c, -1)
+        qkv = self.qkv(self.norm(x))
+        h = self.attention(qkv)
+        h = self.proj_out(h)
+        return (x + h).reshape(b, c, *spatial)
+
+
+def count_flops_attn(model, _x, y):
+    """
+    A counter for the `thop` package to count the operations in an
+    attention operation.
+    Meant to be used like:
+        macs, params = thop.profile(
+            model,
+            inputs=(inputs, timestamps),
+            custom_ops={QKVAttention: QKVAttention.count_flops},
+        )
+    """
+    b, c, *spatial = y[0].shape
+    num_spatial = int(np.prod(spatial))
+    # We perform two matmuls with the same number of ops.
+    # The first computes the weight matrix, the second computes
+    # the combination of the value vectors.
+    matmul_ops = 2 * b * (num_spatial ** 2) * c
+    model.total_ops += th.DoubleTensor([matmul_ops])
+
+
+class QKVAttentionLegacy(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts", q * scale, k * scale
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v)
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class QKVAttention(nn.Module):
+    """
+    A module which performs QKV attention and splits in a different order.
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.chunk(3, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts",
+            (q * scale).view(bs * self.n_heads, ch, length),
+            (k * scale).view(bs * self.n_heads, ch, length),
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+
+class UNetModel(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+    :param in_channels: channels in the input Tensor.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param num_res_blocks: number of residual blocks per downsample.
+    :param attention_resolutions: a collection of downsample rates at which
+        attention will take place. May be a set, list, or tuple.
+        For example, if this contains 4, then at 4x downsampling, attention
+        will be used.
+    :param dropout: the dropout probability.
+    :param channel_mult: channel multiplier for each level of the UNet.
+    :param conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param num_classes: if specified (as an int), then this model will be
+        class-conditional with `num_classes` classes.
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+    :param num_heads: the number of attention heads in each attention layer.
+    :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    :param use_new_attention_order: use a different attention pattern for potentially
+                                    increased efficiency.
+    """
+
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        num_classes=None,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=-1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+        use_spatial_transformer=False,    # custom transformer support
+        transformer_depth=1,              # custom transformer support
+        context_dim=None,                 # custom transformer support
+        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
+        legacy=True,
+    ):
+        super().__init__()
+        if use_spatial_transformer:
+            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+        if context_dim is not None:
+            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+            from omegaconf.listconfig import ListConfig
+            if type(context_dim) == ListConfig:
+                context_dim = list(context_dim)
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        if num_heads == -1:
+            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+        if num_head_channels == -1:
+            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+        self.image_size = image_size
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.num_classes = num_classes
+        self.use_checkpoint = use_checkpoint
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+        self.predict_codebook_ids = n_embed is not None
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        if self.num_classes is not None:
+            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        #num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads,
+                            num_head_channels=dim_head,
+                            use_new_attention_order=use_new_attention_order,
+                        ) if not use_spatial_transformer else SpatialTransformer(
+                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+        if legacy:
+            #num_heads = 1
+            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=dim_head,
+                use_new_attention_order=use_new_attention_order,
+            ) if not use_spatial_transformer else SpatialTransformer(
+                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
+                        ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(num_res_blocks + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=model_channels * mult,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = model_channels * mult
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        #num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads_upsample,
+                            num_head_channels=dim_head,
+                            use_new_attention_order=use_new_attention_order,
+                        ) if not use_spatial_transformer else SpatialTransformer(
+                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
+                        )
+                    )
+                if level and i == num_res_blocks:
+                    out_ch = ch
+                    layers.append(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        self.out = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+        )
+        if self.predict_codebook_ids:
+            self.id_predictor = nn.Sequential(
+            normalization(ch),
+            conv_nd(dims, model_channels, n_embed, 1),
+            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
+        )
+
+    def convert_to_fp16(self):
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+        self.output_blocks.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self):
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+        self.output_blocks.apply(convert_module_to_f32)
+
+    def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
+        """
+        Apply the model to an input batch.
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param context: conditioning plugged in via crossattn
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        assert (y is not None) == (
+            self.num_classes is not None
+        ), "must specify y if and only if the model is class-conditional"
+        hs = []
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+        emb = self.time_embed(t_emb)
+
+        if self.num_classes is not None:
+            assert y.shape == (x.shape[0],)
+            emb = emb + self.label_emb(y)
+
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb, context)
+            hs.append(h)
+        h = self.middle_block(h, emb, context)
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb, context)
+        h = h.type(x.dtype)
+        if self.predict_codebook_ids:
+            return self.id_predictor(h)
+        else:
+            return self.out(h)
+
+
+class EncoderUNetModel(nn.Module):
+    """
+    The half UNet model with attention and timestep embedding.
+    For usage, see UNet.
+    """
+
+    def __init__(
+        self,
+        image_size,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        use_new_attention_order=False,
+        pool="adaptive",
+        *args,
+        **kwargs
+    ):
+        super().__init__()
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.use_checkpoint = use_checkpoint
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads,
+                            num_head_channels=num_head_channels,
+                            use_new_attention_order=use_new_attention_order,
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=num_head_channels,
+                use_new_attention_order=use_new_attention_order,
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+        self.pool = pool
+        if pool == "adaptive":
+            self.out = nn.Sequential(
+                normalization(ch),
+                nn.SiLU(),
+                nn.AdaptiveAvgPool2d((1, 1)),
+                zero_module(conv_nd(dims, ch, out_channels, 1)),
+                nn.Flatten(),
+            )
+        elif pool == "attention":
+            assert num_head_channels != -1
+            self.out = nn.Sequential(
+                normalization(ch),
+                nn.SiLU(),
+                AttentionPool2d(
+                    (image_size // ds), ch, num_head_channels, out_channels
+                ),
+            )
+        elif pool == "spatial":
+            self.out = nn.Sequential(
+                nn.Linear(self._feature_size, 2048),
+                nn.ReLU(),
+                nn.Linear(2048, self.out_channels),
+            )
+        elif pool == "spatial_v2":
+            self.out = nn.Sequential(
+                nn.Linear(self._feature_size, 2048),
+                normalization(2048),
+                nn.SiLU(),
+                nn.Linear(2048, self.out_channels),
+            )
+        else:
+            raise NotImplementedError(f"Unexpected {pool} pooling")
+
+    def convert_to_fp16(self):
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self):
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+
+    def forward(self, x, timesteps):
+        """
+        Apply the model to an input batch.
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :return: an [N x K] Tensor of outputs.
+        """
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+        results = []
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb)
+            if self.pool.startswith("spatial"):
+                results.append(h.type(x.dtype).mean(dim=(2, 3)))
+        h = self.middle_block(h, emb)
+        if self.pool.startswith("spatial"):
+            results.append(h.type(x.dtype).mean(dim=(2, 3)))
+            h = th.cat(results, axis=-1)
+            return self.out(h)
+        else:
+            h = h.type(x.dtype)
+            return self.out(h)
+
diff --git a/ldm/modules/diffusionmodules/util.py b/ldm/modules/diffusionmodules/util.py
new file mode 100644
index 00000000..a952e6c4
--- /dev/null
+++ b/ldm/modules/diffusionmodules/util.py
@@ -0,0 +1,267 @@
+# adopted from
+# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+# and
+# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+# and
+# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
+#
+# thanks!
+
+
+import os
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import repeat
+
+from ldm.util import instantiate_from_config
+
+
+def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+    if schedule == "linear":
+        betas = (
+                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
+        )
+
+    elif schedule == "cosine":
+        timesteps = (
+                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+        )
+        alphas = timesteps / (1 + cosine_s) * np.pi / 2
+        alphas = torch.cos(alphas).pow(2)
+        alphas = alphas / alphas[0]
+        betas = 1 - alphas[1:] / alphas[:-1]
+        betas = np.clip(betas, a_min=0, a_max=0.999)
+
+    elif schedule == "sqrt_linear":
+        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
+    elif schedule == "sqrt":
+        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
+    else:
+        raise ValueError(f"schedule '{schedule}' unknown.")
+    return betas.numpy()
+
+
+def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
+    if ddim_discr_method == 'uniform':
+        c = num_ddpm_timesteps // num_ddim_timesteps
+        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
+    elif ddim_discr_method == 'quad':
+        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
+    else:
+        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
+
+    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
+    # add one to get the final alpha values right (the ones from first scale to data during sampling)
+    steps_out = ddim_timesteps + 1
+    if verbose:
+        print(f'Selected timesteps for ddim sampler: {steps_out}')
+    return steps_out
+
+
+def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
+    # select alphas for computing the variance schedule
+    alphas = alphacums[ddim_timesteps]
+    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
+
+    # according the the formula provided in https://arxiv.org/abs/2010.02502
+    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
+    if verbose:
+        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
+        print(f'For the chosen value of eta, which is {eta}, '
+              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
+    return sigmas, alphas, alphas_prev
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function,
+    which defines the cumulative product of (1-beta) over time from t = [0,1].
+    :param num_diffusion_timesteps: the number of betas to produce.
+    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that
+                      part of the diffusion process.
+    :param max_beta: the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+    """
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return np.array(betas)
+
+
+def extract_into_tensor(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def checkpoint(func, inputs, params, flag):
+    """
+    Evaluate a function without caching intermediate activations, allowing for
+    reduced memory at the expense of extra compute in the backward pass.
+    :param func: the function to evaluate.
+    :param inputs: the argument sequence to pass to `func`.
+    :param params: a sequence of parameters `func` depends on but does not
+                   explicitly take as arguments.
+    :param flag: if False, disable gradient checkpointing.
+    """
+    if flag:
+        args = tuple(inputs) + tuple(params)
+        return CheckpointFunction.apply(func, len(inputs), *args)
+    else:
+        return func(*inputs)
+
+
+class CheckpointFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, run_function, length, *args):
+        ctx.run_function = run_function
+        ctx.input_tensors = list(args[:length])
+        ctx.input_params = list(args[length:])
+
+        with torch.no_grad():
+            output_tensors = ctx.run_function(*ctx.input_tensors)
+        return output_tensors
+
+    @staticmethod
+    def backward(ctx, *output_grads):
+        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+        with torch.enable_grad():
+            # Fixes a bug where the first op in run_function modifies the
+            # Tensor storage in place, which is not allowed for detach()'d
+            # Tensors.
+            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+            output_tensors = ctx.run_function(*shallow_copies)
+        input_grads = torch.autograd.grad(
+            output_tensors,
+            ctx.input_tensors + ctx.input_params,
+            output_grads,
+            allow_unused=True,
+        )
+        del ctx.input_tensors
+        del ctx.input_params
+        del output_tensors
+        return (None, None) + input_grads
+
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+    """
+    Create sinusoidal timestep embeddings.
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an [N x dim] Tensor of positional embeddings.
+    """
+    if not repeat_only:
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+        ).to(device=timesteps.device)
+        args = timesteps[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    else:
+        embedding = repeat(timesteps, 'b -> b d', d=dim)
+    return embedding
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def scale_module(module, scale):
+    """
+    Scale the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def normalization(channels):
+    """
+    Make a standard normalization layer.
+    :param channels: number of input channels.
+    :return: an nn.Module for normalization.
+    """
+    return GroupNorm32(32, channels)
+
+
+# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
+class SiLU(nn.Module):
+    def forward(self, x):
+        return x * torch.sigmoid(x)
+
+
+class GroupNorm32(nn.GroupNorm):
+    def forward(self, x):
+        return super().forward(x.float()).type(x.dtype)
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+    """
+    Create a linear module.
+    """
+    return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class HybridConditioner(nn.Module):
+
+    def __init__(self, c_concat_config, c_crossattn_config):
+        super().__init__()
+        self.concat_conditioner = instantiate_from_config(c_concat_config)
+        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
+
+    def forward(self, c_concat, c_crossattn):
+        c_concat = self.concat_conditioner(c_concat)
+        c_crossattn = self.crossattn_conditioner(c_crossattn)
+        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
+
+
+def noise_like(shape, device, repeat=False):
+    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+    noise = lambda: torch.randn(shape, device=device)
+    return repeat_noise() if repeat else noise()
\ No newline at end of file
diff --git a/ldm/modules/distributions/__init__.py b/ldm/modules/distributions/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ldm/modules/distributions/distributions.py b/ldm/modules/distributions/distributions.py
new file mode 100644
index 00000000..f2b8ef90
--- /dev/null
+++ b/ldm/modules/distributions/distributions.py
@@ -0,0 +1,92 @@
+import torch
+import numpy as np
+
+
+class AbstractDistribution:
+    def sample(self):
+        raise NotImplementedError()
+
+    def mode(self):
+        raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+    def __init__(self, value):
+        self.value = value
+
+    def sample(self):
+        return self.value
+
+    def mode(self):
+        return self.value
+
+
+class DiagonalGaussianDistribution(object):
+    def __init__(self, parameters, deterministic=False):
+        self.parameters = parameters
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+
+    def sample(self):
+        x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
+        return x
+
+    def kl(self, other=None):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        else:
+            if other is None:
+                return 0.5 * torch.sum(torch.pow(self.mean, 2)
+                                       + self.var - 1.0 - self.logvar,
+                                       dim=[1, 2, 3])
+            else:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean - other.mean, 2) / other.var
+                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
+                    dim=[1, 2, 3])
+
+    def nll(self, sample, dims=[1,2,3]):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims)
+
+    def mode(self):
+        return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+    Compute the KL divergence between two gaussians.
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, torch.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for torch.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (
+        -1.0
+        + logvar2
+        - logvar1
+        + torch.exp(logvar1 - logvar2)
+        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+    )
diff --git a/ldm/modules/ema.py b/ldm/modules/ema.py
new file mode 100644
index 00000000..c8c75af4
--- /dev/null
+++ b/ldm/modules/ema.py
@@ -0,0 +1,76 @@
+import torch
+from torch import nn
+
+
+class LitEma(nn.Module):
+    def __init__(self, model, decay=0.9999, use_num_upates=True):
+        super().__init__()
+        if decay < 0.0 or decay > 1.0:
+            raise ValueError('Decay must be between 0 and 1')
+
+        self.m_name2s_name = {}
+        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
+        self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
+                             else torch.tensor(-1,dtype=torch.int))
+
+        for name, p in model.named_parameters():
+            if p.requires_grad:
+                #remove as '.'-character is not allowed in buffers
+                s_name = name.replace('.','')
+                self.m_name2s_name.update({name:s_name})
+                self.register_buffer(s_name,p.clone().detach().data)
+
+        self.collected_params = []
+
+    def forward(self,model):
+        decay = self.decay
+
+        if self.num_updates >= 0:
+            self.num_updates += 1
+            decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
+
+        one_minus_decay = 1.0 - decay
+
+        with torch.no_grad():
+            m_param = dict(model.named_parameters())
+            shadow_params = dict(self.named_buffers())
+
+            for key in m_param:
+                if m_param[key].requires_grad:
+                    sname = self.m_name2s_name[key]
+                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
+                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
+                else:
+                    assert not key in self.m_name2s_name
+
+    def copy_to(self, model):
+        m_param = dict(model.named_parameters())
+        shadow_params = dict(self.named_buffers())
+        for key in m_param:
+            if m_param[key].requires_grad:
+                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+            else:
+                assert not key in self.m_name2s_name
+
+    def store(self, parameters):
+        """
+        Save the current parameters for restoring later.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            temporarily stored.
+        """
+        self.collected_params = [param.clone() for param in parameters]
+
+    def restore(self, parameters):
+        """
+        Restore the parameters stored with the `store` method.
+        Useful to validate the model with EMA parameters without affecting the
+        original optimization process. Store the parameters before the
+        `copy_to` method. After validation (or model saving), use this to
+        restore the former parameters.
+        Args:
+          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            updated with the stored parameters.
+        """
+        for c_param, param in zip(self.collected_params, parameters):
+            param.data.copy_(c_param.data)
diff --git a/ldm/modules/encoders/__init__.py b/ldm/modules/encoders/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
new file mode 100644
index 00000000..ededbe43
--- /dev/null
+++ b/ldm/modules/encoders/modules.py
@@ -0,0 +1,234 @@
+import torch
+import torch.nn as nn
+from functools import partial
+import clip
+from einops import rearrange, repeat
+from transformers import CLIPTokenizer, CLIPTextModel
+import kornia
+
+from ldm.modules.x_transformer import Encoder, TransformerWrapper  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
+
+
+class AbstractEncoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def encode(self, *args, **kwargs):
+        raise NotImplementedError
+
+
+
+class ClassEmbedder(nn.Module):
+    def __init__(self, embed_dim, n_classes=1000, key='class'):
+        super().__init__()
+        self.key = key
+        self.embedding = nn.Embedding(n_classes, embed_dim)
+
+    def forward(self, batch, key=None):
+        if key is None:
+            key = self.key
+        # this is for use in crossattn
+        c = batch[key][:, None]
+        c = self.embedding(c)
+        return c
+
+
+class TransformerEmbedder(AbstractEncoder):
+    """Some transformer encoder layers"""
+    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
+        super().__init__()
+        self.device = device
+        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
+                                              attn_layers=Encoder(dim=n_embed, depth=n_layer))
+
+    def forward(self, tokens):
+        tokens = tokens.to(self.device)  # meh
+        z = self.transformer(tokens, return_embeddings=True)
+        return z
+
+    def encode(self, x):
+        return self(x)
+
+
+class BERTTokenizer(AbstractEncoder):
+    """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
+    def __init__(self, device="cuda", vq_interface=True, max_length=77):
+        super().__init__()
+        from transformers import BertTokenizerFast  # TODO: add to reuquirements
+        self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
+        self.device = device
+        self.vq_interface = vq_interface
+        self.max_length = max_length
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        tokens = batch_encoding["input_ids"].to(self.device)
+        return tokens
+
+    @torch.no_grad()
+    def encode(self, text):
+        tokens = self(text)
+        if not self.vq_interface:
+            return tokens
+        return None, None, [None, None, tokens]
+
+    def decode(self, text):
+        return text
+
+
+class BERTEmbedder(AbstractEncoder):
+    """Uses the BERT tokenizr model and add some transformer encoder layers"""
+    def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
+                 device="cuda",use_tokenizer=True, embedding_dropout=0.0):
+        super().__init__()
+        self.use_tknz_fn = use_tokenizer
+        if self.use_tknz_fn:
+            self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
+        self.device = device
+        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
+                                              attn_layers=Encoder(dim=n_embed, depth=n_layer),
+                                              emb_dropout=embedding_dropout)
+
+    def forward(self, text):
+        if self.use_tknz_fn:
+            tokens = self.tknz_fn(text)#.to(self.device)
+        else:
+            tokens = text
+        z = self.transformer(tokens, return_embeddings=True)
+        return z
+
+    def encode(self, text):
+        # output of length 77
+        return self(text)
+
+
+class SpatialRescaler(nn.Module):
+    def __init__(self,
+                 n_stages=1,
+                 method='bilinear',
+                 multiplier=0.5,
+                 in_channels=3,
+                 out_channels=None,
+                 bias=False):
+        super().__init__()
+        self.n_stages = n_stages
+        assert self.n_stages >= 0
+        assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
+        self.multiplier = multiplier
+        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
+        self.remap_output = out_channels is not None
+        if self.remap_output:
+            print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
+            self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
+
+    def forward(self,x):
+        for stage in range(self.n_stages):
+            x = self.interpolator(x, scale_factor=self.multiplier)
+
+
+        if self.remap_output:
+            x = self.channel_mapper(x)
+        return x
+
+    def encode(self, x):
+        return self(x)
+
+class FrozenCLIPEmbedder(AbstractEncoder):
+    """Uses the CLIP transformer encoder for text (from Hugging Face)"""
+    def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
+        super().__init__()
+        self.tokenizer = CLIPTokenizer.from_pretrained(version)
+        self.transformer = CLIPTextModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length
+        self.freeze()
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.transformer(input_ids=tokens)
+
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenCLIPTextEmbedder(nn.Module):
+    """
+    Uses the CLIP transformer encoder for text.
+    """
+    def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True):
+        super().__init__()
+        self.model, _ = clip.load(version, jit=False, device="cpu")
+        self.device = device
+        self.max_length = max_length
+        self.n_repeat = n_repeat
+        self.normalize = normalize
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        tokens = clip.tokenize(text).to(self.device)
+        z = self.model.encode_text(tokens)
+        if self.normalize:
+            z = z / torch.linalg.norm(z, dim=1, keepdim=True)
+        return z
+
+    def encode(self, text):
+        z = self(text)
+        if z.ndim==2:
+            z = z[:, None, :]
+        z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
+        return z
+
+
+class FrozenClipImageEmbedder(nn.Module):
+    """
+        Uses the CLIP image encoder.
+        """
+    def __init__(
+            self,
+            model,
+            jit=False,
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+            antialias=False,
+        ):
+        super().__init__()
+        self.model, _ = clip.load(name=model, device=device, jit=jit)
+
+        self.antialias = antialias
+
+        self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
+        self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
+
+    def preprocess(self, x):
+        # normalize to [0,1]
+        x = kornia.geometry.resize(x, (224, 224),
+                                   interpolation='bicubic',align_corners=True,
+                                   antialias=self.antialias)
+        x = (x + 1.) / 2.
+        # renormalize according to clip
+        x = kornia.enhance.normalize(x, self.mean, self.std)
+        return x
+
+    def forward(self, x):
+        # x is assumed to be in range [-1,1]
+        return self.model.encode_image(self.preprocess(x))
+
+
+if __name__ == "__main__":
+    from ldm.util import count_params
+    model = FrozenCLIPEmbedder()
+    count_params(model, verbose=True)
\ No newline at end of file
diff --git a/ldm/modules/encoders/xlmr.py b/ldm/modules/encoders/xlmr.py
new file mode 100644
index 00000000..beab3fdf
--- /dev/null
+++ b/ldm/modules/encoders/xlmr.py
@@ -0,0 +1,137 @@
+from transformers import BertPreTrainedModel,BertModel,BertConfig
+import torch.nn as nn
+import torch
+from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
+from transformers import XLMRobertaModel,XLMRobertaTokenizer
+from typing import Optional
+
+class BertSeriesConfig(BertConfig):
+    def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
+
+        super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs)
+        self.project_dim = project_dim
+        self.pooler_fn = pooler_fn
+        self.learn_encoder = learn_encoder
+
+class RobertaSeriesConfig(XLMRobertaConfig):
+    def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs):
+        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+        self.project_dim = project_dim
+        self.pooler_fn = pooler_fn
+        self.learn_encoder = learn_encoder
+
+
+class BertSeriesModelWithTransformation(BertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+    config_class = BertSeriesConfig
+
+    def __init__(self, config=None, **kargs):
+        # modify initialization for autoloading 
+        if config is None:
+            config = XLMRobertaConfig()
+            config.attention_probs_dropout_prob= 0.1
+            config.bos_token_id=0
+            config.eos_token_id=2
+            config.hidden_act='gelu'
+            config.hidden_dropout_prob=0.1
+            config.hidden_size=1024
+            config.initializer_range=0.02
+            config.intermediate_size=4096
+            config.layer_norm_eps=1e-05
+            config.max_position_embeddings=514
+
+            config.num_attention_heads=16
+            config.num_hidden_layers=24
+            config.output_past=True
+            config.pad_token_id=1
+            config.position_embedding_type= "absolute"
+
+            config.type_vocab_size= 1
+            config.use_cache=True
+            config.vocab_size= 250002
+            config.project_dim = 768
+            config.learn_encoder = False
+        super().__init__(config)
+        self.roberta = XLMRobertaModel(config)
+        self.transformation = nn.Linear(config.hidden_size,config.project_dim)
+        self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
+        self.pooler = lambda x: x[:,0]
+        self.post_init()
+
+    def encode(self,c):
+        device = next(self.parameters()).device
+        text = self.tokenizer(c,
+                        truncation=True,
+                        max_length=77,
+                        return_length=False,
+                        return_overflowing_tokens=False,
+                        padding="max_length",
+                        return_tensors="pt")
+        text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
+        text["attention_mask"] = torch.tensor(
+            text['attention_mask']).to(device)
+        features = self(**text)
+        return features['projection_state'] 
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) :
+        r"""
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+        outputs = self.roberta(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=True,
+            return_dict=return_dict,
+        )
+
+        # last module outputs
+        sequence_output = outputs[0]
+
+
+        # project every module
+        sequence_output_ln = self.pre_LN(sequence_output)
+
+        # pooler
+        pooler_output = self.pooler(sequence_output_ln)
+        pooler_output = self.transformation(pooler_output)
+        projection_state = self.transformation(outputs.last_hidden_state)
+
+        return {
+            'pooler_output':pooler_output,
+            'last_hidden_state':outputs.last_hidden_state,
+            'hidden_states':outputs.hidden_states,
+            'attentions':outputs.attentions,
+            'projection_state':projection_state,
+            'sequence_out': sequence_output
+        }
+
+
+class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
+    base_model_prefix = 'roberta'
+    config_class= RobertaSeriesConfig
\ No newline at end of file
diff --git a/ldm/modules/image_degradation/__init__.py b/ldm/modules/image_degradation/__init__.py
new file mode 100644
index 00000000..7836cada
--- /dev/null
+++ b/ldm/modules/image_degradation/__init__.py
@@ -0,0 +1,2 @@
+from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
+from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
diff --git a/ldm/modules/image_degradation/bsrgan.py b/ldm/modules/image_degradation/bsrgan.py
new file mode 100644
index 00000000..32ef5616
--- /dev/null
+++ b/ldm/modules/image_degradation/bsrgan.py
@@ -0,0 +1,730 @@
+# -*- coding: utf-8 -*-
+"""
+# --------------------------------------------
+# Super-Resolution
+# --------------------------------------------
+#
+# Kai Zhang (cskaizhang@gmail.com)
+# https://github.com/cszn
+# From 2019/03--2021/08
+# --------------------------------------------
+"""
+
+import numpy as np
+import cv2
+import torch
+
+from functools import partial
+import random
+from scipy import ndimage
+import scipy
+import scipy.stats as ss
+from scipy.interpolate import interp2d
+from scipy.linalg import orth
+import albumentations
+
+import ldm.modules.image_degradation.utils_image as util
+
+
+def modcrop_np(img, sf):
+    '''
+    Args:
+        img: numpy image, WxH or WxHxC
+        sf: scale factor
+    Return:
+        cropped image
+    '''
+    w, h = img.shape[:2]
+    im = np.copy(img)
+    return im[:w - w % sf, :h - h % sf, ...]
+
+
+"""
+# --------------------------------------------
+# anisotropic Gaussian kernels
+# --------------------------------------------
+"""
+
+
+def analytic_kernel(k):
+    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
+    k_size = k.shape[0]
+    # Calculate the big kernels size
+    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
+    # Loop over the small kernel to fill the big one
+    for r in range(k_size):
+        for c in range(k_size):
+            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
+    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
+    crop = k_size // 2
+    cropped_big_k = big_k[crop:-crop, crop:-crop]
+    # Normalize to 1
+    return cropped_big_k / cropped_big_k.sum()
+
+
+def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
+    """ generate an anisotropic Gaussian kernel
+    Args:
+        ksize : e.g., 15, kernel size
+        theta : [0,  pi], rotation angle range
+        l1    : [0.1,50], scaling of eigenvalues
+        l2    : [0.1,l1], scaling of eigenvalues
+        If l1 = l2, will get an isotropic Gaussian kernel.
+    Returns:
+        k     : kernel
+    """
+
+    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
+    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
+    D = np.array([[l1, 0], [0, l2]])
+    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
+    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
+
+    return k
+
+
+def gm_blur_kernel(mean, cov, size=15):
+    center = size / 2.0 + 0.5
+    k = np.zeros([size, size])
+    for y in range(size):
+        for x in range(size):
+            cy = y - center + 1
+            cx = x - center + 1
+            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
+
+    k = k / np.sum(k)
+    return k
+
+
+def shift_pixel(x, sf, upper_left=True):
+    """shift pixel for super-resolution with different scale factors
+    Args:
+        x: WxHxC or WxH
+        sf: scale factor
+        upper_left: shift direction
+    """
+    h, w = x.shape[:2]
+    shift = (sf - 1) * 0.5
+    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
+    if upper_left:
+        x1 = xv + shift
+        y1 = yv + shift
+    else:
+        x1 = xv - shift
+        y1 = yv - shift
+
+    x1 = np.clip(x1, 0, w - 1)
+    y1 = np.clip(y1, 0, h - 1)
+
+    if x.ndim == 2:
+        x = interp2d(xv, yv, x)(x1, y1)
+    if x.ndim == 3:
+        for i in range(x.shape[-1]):
+            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
+
+    return x
+
+
+def blur(x, k):
+    '''
+    x: image, NxcxHxW
+    k: kernel, Nx1xhxw
+    '''
+    n, c = x.shape[:2]
+    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
+    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
+    k = k.repeat(1, c, 1, 1)
+    k = k.view(-1, 1, k.shape[2], k.shape[3])
+    x = x.view(1, -1, x.shape[2], x.shape[3])
+    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
+    x = x.view(n, c, x.shape[2], x.shape[3])
+
+    return x
+
+
+def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
+    """"
+    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
+    # Kai Zhang
+    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
+    # max_var = 2.5 * sf
+    """
+    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
+    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
+    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
+    theta = np.random.rand() * np.pi  # random theta
+    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
+
+    # Set COV matrix using Lambdas and Theta
+    LAMBDA = np.diag([lambda_1, lambda_2])
+    Q = np.array([[np.cos(theta), -np.sin(theta)],
+                  [np.sin(theta), np.cos(theta)]])
+    SIGMA = Q @ LAMBDA @ Q.T
+    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
+
+    # Set expectation position (shifting kernel for aligned image)
+    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
+    MU = MU[None, None, :, None]
+
+    # Create meshgrid for Gaussian
+    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
+    Z = np.stack([X, Y], 2)[:, :, :, None]
+
+    # Calcualte Gaussian for every pixel of the kernel
+    ZZ = Z - MU
+    ZZ_t = ZZ.transpose(0, 1, 3, 2)
+    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
+
+    # shift the kernel so it will be centered
+    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
+
+    # Normalize the kernel and return
+    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
+    kernel = raw_kernel / np.sum(raw_kernel)
+    return kernel
+
+
+def fspecial_gaussian(hsize, sigma):
+    hsize = [hsize, hsize]
+    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
+    std = sigma
+    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
+    arg = -(x * x + y * y) / (2 * std * std)
+    h = np.exp(arg)
+    h[h < scipy.finfo(float).eps * h.max()] = 0
+    sumh = h.sum()
+    if sumh != 0:
+        h = h / sumh
+    return h
+
+
+def fspecial_laplacian(alpha):
+    alpha = max([0, min([alpha, 1])])
+    h1 = alpha / (alpha + 1)
+    h2 = (1 - alpha) / (alpha + 1)
+    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
+    h = np.array(h)
+    return h
+
+
+def fspecial(filter_type, *args, **kwargs):
+    '''
+    python code from:
+    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
+    '''
+    if filter_type == 'gaussian':
+        return fspecial_gaussian(*args, **kwargs)
+    if filter_type == 'laplacian':
+        return fspecial_laplacian(*args, **kwargs)
+
+
+"""
+# --------------------------------------------
+# degradation models
+# --------------------------------------------
+"""
+
+
+def bicubic_degradation(x, sf=3):
+    '''
+    Args:
+        x: HxWxC image, [0, 1]
+        sf: down-scale factor
+    Return:
+        bicubicly downsampled LR image
+    '''
+    x = util.imresize_np(x, scale=1 / sf)
+    return x
+
+
+def srmd_degradation(x, k, sf=3):
+    ''' blur + bicubic downsampling
+    Args:
+        x: HxWxC image, [0, 1]
+        k: hxw, double
+        sf: down-scale factor
+    Return:
+        downsampled LR image
+    Reference:
+        @inproceedings{zhang2018learning,
+          title={Learning a single convolutional super-resolution network for multiple degradations},
+          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+          pages={3262--3271},
+          year={2018}
+        }
+    '''
+    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
+    x = bicubic_degradation(x, sf=sf)
+    return x
+
+
+def dpsr_degradation(x, k, sf=3):
+    ''' bicubic downsampling + blur
+    Args:
+        x: HxWxC image, [0, 1]
+        k: hxw, double
+        sf: down-scale factor
+    Return:
+        downsampled LR image
+    Reference:
+        @inproceedings{zhang2019deep,
+          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
+          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+          pages={1671--1681},
+          year={2019}
+        }
+    '''
+    x = bicubic_degradation(x, sf=sf)
+    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+    return x
+
+
+def classical_degradation(x, k, sf=3):
+    ''' blur + downsampling
+    Args:
+        x: HxWxC image, [0, 1]/[0, 255]
+        k: hxw, double
+        sf: down-scale factor
+    Return:
+        downsampled LR image
+    '''
+    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
+    st = 0
+    return x[st::sf, st::sf, ...]
+
+
+def add_sharpening(img, weight=0.5, radius=50, threshold=10):
+    """USM sharpening. borrowed from real-ESRGAN
+    Input image: I; Blurry image: B.
+    1. K = I + weight * (I - B)
+    2. Mask = 1 if abs(I - B) > threshold, else: 0
+    3. Blur mask:
+    4. Out = Mask * K + (1 - Mask) * I
+    Args:
+        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
+        weight (float): Sharp weight. Default: 1.
+        radius (float): Kernel size of Gaussian blur. Default: 50.
+        threshold (int):
+    """
+    if radius % 2 == 0:
+        radius += 1
+    blur = cv2.GaussianBlur(img, (radius, radius), 0)
+    residual = img - blur
+    mask = np.abs(residual) * 255 > threshold
+    mask = mask.astype('float32')
+    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
+
+    K = img + weight * residual
+    K = np.clip(K, 0, 1)
+    return soft_mask * K + (1 - soft_mask) * img
+
+
+def add_blur(img, sf=4):
+    wd2 = 4.0 + sf
+    wd = 2.0 + 0.2 * sf
+    if random.random() < 0.5:
+        l1 = wd2 * random.random()
+        l2 = wd2 * random.random()
+        k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
+    else:
+        k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
+    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
+
+    return img
+
+
+def add_resize(img, sf=4):
+    rnum = np.random.rand()
+    if rnum > 0.8:  # up
+        sf1 = random.uniform(1, 2)
+    elif rnum < 0.7:  # down
+        sf1 = random.uniform(0.5 / sf, 1)
+    else:
+        sf1 = 1.0
+    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
+    img = np.clip(img, 0.0, 1.0)
+
+    return img
+
+
+# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+#     noise_level = random.randint(noise_level1, noise_level2)
+#     rnum = np.random.rand()
+#     if rnum > 0.6:  # add color Gaussian noise
+#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+#     elif rnum < 0.4:  # add grayscale Gaussian noise
+#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+#     else:  # add  noise
+#         L = noise_level2 / 255.
+#         D = np.diag(np.random.rand(3))
+#         U = orth(np.random.rand(3, 3))
+#         conv = np.dot(np.dot(np.transpose(U), D), U)
+#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+#     img = np.clip(img, 0.0, 1.0)
+#     return img
+
+def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+    noise_level = random.randint(noise_level1, noise_level2)
+    rnum = np.random.rand()
+    if rnum > 0.6:  # add color Gaussian noise
+        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+    elif rnum < 0.4:  # add grayscale Gaussian noise
+        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+    else:  # add  noise
+        L = noise_level2 / 255.
+        D = np.diag(np.random.rand(3))
+        U = orth(np.random.rand(3, 3))
+        conv = np.dot(np.dot(np.transpose(U), D), U)
+        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+    img = np.clip(img, 0.0, 1.0)
+    return img
+
+
+def add_speckle_noise(img, noise_level1=2, noise_level2=25):
+    noise_level = random.randint(noise_level1, noise_level2)
+    img = np.clip(img, 0.0, 1.0)
+    rnum = random.random()
+    if rnum > 0.6:
+        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+    elif rnum < 0.4:
+        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+    else:
+        L = noise_level2 / 255.
+        D = np.diag(np.random.rand(3))
+        U = orth(np.random.rand(3, 3))
+        conv = np.dot(np.dot(np.transpose(U), D), U)
+        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+    img = np.clip(img, 0.0, 1.0)
+    return img
+
+
+def add_Poisson_noise(img):
+    img = np.clip((img * 255.0).round(), 0, 255) / 255.
+    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
+    if random.random() < 0.5:
+        img = np.random.poisson(img * vals).astype(np.float32) / vals
+    else:
+        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
+        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
+        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
+        img += noise_gray[:, :, np.newaxis]
+    img = np.clip(img, 0.0, 1.0)
+    return img
+
+
+def add_JPEG_noise(img):
+    quality_factor = random.randint(30, 95)
+    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
+    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
+    img = cv2.imdecode(encimg, 1)
+    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
+    return img
+
+
+def random_crop(lq, hq, sf=4, lq_patchsize=64):
+    h, w = lq.shape[:2]
+    rnd_h = random.randint(0, h - lq_patchsize)
+    rnd_w = random.randint(0, w - lq_patchsize)
+    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
+
+    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
+    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
+    return lq, hq
+
+
+def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
+    """
+    This is the degradation model of BSRGAN from the paper
+    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+    ----------
+    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
+    sf: scale factor
+    isp_model: camera ISP model
+    Returns
+    -------
+    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+    """
+    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+    sf_ori = sf
+
+    h1, w1 = img.shape[:2]
+    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    h, w = img.shape[:2]
+
+    if h < lq_patchsize * sf or w < lq_patchsize * sf:
+        raise ValueError(f'img size ({h1}X{w1}) is too small!')
+
+    hq = img.copy()
+
+    if sf == 4 and random.random() < scale2_prob:  # downsample1
+        if np.random.rand() < 0.5:
+            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
+                             interpolation=random.choice([1, 2, 3]))
+        else:
+            img = util.imresize_np(img, 1 / 2, True)
+        img = np.clip(img, 0.0, 1.0)
+        sf = 2
+
+    shuffle_order = random.sample(range(7), 7)
+    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+    if idx1 > idx2:  # keep downsample3 last
+        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+    for i in shuffle_order:
+
+        if i == 0:
+            img = add_blur(img, sf=sf)
+
+        elif i == 1:
+            img = add_blur(img, sf=sf)
+
+        elif i == 2:
+            a, b = img.shape[1], img.shape[0]
+            # downsample2
+            if random.random() < 0.75:
+                sf1 = random.uniform(1, 2 * sf)
+                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
+                                 interpolation=random.choice([1, 2, 3]))
+            else:
+                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+                k_shifted = shift_pixel(k, sf)
+                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
+                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
+                img = img[0::sf, 0::sf, ...]  # nearest downsampling
+            img = np.clip(img, 0.0, 1.0)
+
+        elif i == 3:
+            # downsample3
+            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+            img = np.clip(img, 0.0, 1.0)
+
+        elif i == 4:
+            # add Gaussian noise
+            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
+
+        elif i == 5:
+            # add JPEG noise
+            if random.random() < jpeg_prob:
+                img = add_JPEG_noise(img)
+
+        elif i == 6:
+            # add processed camera sensor noise
+            if random.random() < isp_prob and isp_model is not None:
+                with torch.no_grad():
+                    img, hq = isp_model.forward(img.copy(), hq)
+
+    # add final JPEG compression noise
+    img = add_JPEG_noise(img)
+
+    # random crop
+    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
+
+    return img, hq
+
+
+# todo no isp_model?
+def degradation_bsrgan_variant(image, sf=4, isp_model=None):
+    """
+    This is the degradation model of BSRGAN from the paper
+    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+    ----------
+    sf: scale factor
+    isp_model: camera ISP model
+    Returns
+    -------
+    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+    """
+    image = util.uint2single(image)
+    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+    sf_ori = sf
+
+    h1, w1 = image.shape[:2]
+    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    h, w = image.shape[:2]
+
+    hq = image.copy()
+
+    if sf == 4 and random.random() < scale2_prob:  # downsample1
+        if np.random.rand() < 0.5:
+            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
+                               interpolation=random.choice([1, 2, 3]))
+        else:
+            image = util.imresize_np(image, 1 / 2, True)
+        image = np.clip(image, 0.0, 1.0)
+        sf = 2
+
+    shuffle_order = random.sample(range(7), 7)
+    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+    if idx1 > idx2:  # keep downsample3 last
+        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+    for i in shuffle_order:
+
+        if i == 0:
+            image = add_blur(image, sf=sf)
+
+        elif i == 1:
+            image = add_blur(image, sf=sf)
+
+        elif i == 2:
+            a, b = image.shape[1], image.shape[0]
+            # downsample2
+            if random.random() < 0.75:
+                sf1 = random.uniform(1, 2 * sf)
+                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
+                                   interpolation=random.choice([1, 2, 3]))
+            else:
+                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+                k_shifted = shift_pixel(k, sf)
+                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
+                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
+                image = image[0::sf, 0::sf, ...]  # nearest downsampling
+            image = np.clip(image, 0.0, 1.0)
+
+        elif i == 3:
+            # downsample3
+            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+            image = np.clip(image, 0.0, 1.0)
+
+        elif i == 4:
+            # add Gaussian noise
+            image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
+
+        elif i == 5:
+            # add JPEG noise
+            if random.random() < jpeg_prob:
+                image = add_JPEG_noise(image)
+
+        # elif i == 6:
+        #     # add processed camera sensor noise
+        #     if random.random() < isp_prob and isp_model is not None:
+        #         with torch.no_grad():
+        #             img, hq = isp_model.forward(img.copy(), hq)
+
+    # add final JPEG compression noise
+    image = add_JPEG_noise(image)
+    image = util.single2uint(image)
+    example = {"image":image}
+    return example
+
+
+# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
+def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
+    """
+    This is an extended degradation model by combining
+    the degradation models of BSRGAN and Real-ESRGAN
+    ----------
+    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
+    sf: scale factor
+    use_shuffle: the degradation shuffle
+    use_sharp: sharpening the img
+    Returns
+    -------
+    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+    """
+
+    h1, w1 = img.shape[:2]
+    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    h, w = img.shape[:2]
+
+    if h < lq_patchsize * sf or w < lq_patchsize * sf:
+        raise ValueError(f'img size ({h1}X{w1}) is too small!')
+
+    if use_sharp:
+        img = add_sharpening(img)
+    hq = img.copy()
+
+    if random.random() < shuffle_prob:
+        shuffle_order = random.sample(range(13), 13)
+    else:
+        shuffle_order = list(range(13))
+        # local shuffle for noise, JPEG is always the last one
+        shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
+        shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
+
+    poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
+
+    for i in shuffle_order:
+        if i == 0:
+            img = add_blur(img, sf=sf)
+        elif i == 1:
+            img = add_resize(img, sf=sf)
+        elif i == 2:
+            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
+        elif i == 3:
+            if random.random() < poisson_prob:
+                img = add_Poisson_noise(img)
+        elif i == 4:
+            if random.random() < speckle_prob:
+                img = add_speckle_noise(img)
+        elif i == 5:
+            if random.random() < isp_prob and isp_model is not None:
+                with torch.no_grad():
+                    img, hq = isp_model.forward(img.copy(), hq)
+        elif i == 6:
+            img = add_JPEG_noise(img)
+        elif i == 7:
+            img = add_blur(img, sf=sf)
+        elif i == 8:
+            img = add_resize(img, sf=sf)
+        elif i == 9:
+            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
+        elif i == 10:
+            if random.random() < poisson_prob:
+                img = add_Poisson_noise(img)
+        elif i == 11:
+            if random.random() < speckle_prob:
+                img = add_speckle_noise(img)
+        elif i == 12:
+            if random.random() < isp_prob and isp_model is not None:
+                with torch.no_grad():
+                    img, hq = isp_model.forward(img.copy(), hq)
+        else:
+            print('check the shuffle!')
+
+    # resize to desired size
+    img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
+                     interpolation=random.choice([1, 2, 3]))
+
+    # add final JPEG compression noise
+    img = add_JPEG_noise(img)
+
+    # random crop
+    img, hq = random_crop(img, hq, sf, lq_patchsize)
+
+    return img, hq
+
+
+if __name__ == '__main__':
+	print("hey")
+	img = util.imread_uint('utils/test.png', 3)
+	print(img)
+	img = util.uint2single(img)
+	print(img)
+	img = img[:448, :448]
+	h = img.shape[0] // 4
+	print("resizing to", h)
+	sf = 4
+	deg_fn = partial(degradation_bsrgan_variant, sf=sf)
+	for i in range(20):
+		print(i)
+		img_lq = deg_fn(img)
+		print(img_lq)
+		img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
+		print(img_lq.shape)
+		print("bicubic", img_lq_bicubic.shape)
+		print(img_hq.shape)
+		lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+		                        interpolation=0)
+		lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+		                        interpolation=0)
+		img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
+		util.imsave(img_concat, str(i) + '.png')
+
+
diff --git a/ldm/modules/image_degradation/bsrgan_light.py b/ldm/modules/image_degradation/bsrgan_light.py
new file mode 100644
index 00000000..9e1f8239
--- /dev/null
+++ b/ldm/modules/image_degradation/bsrgan_light.py
@@ -0,0 +1,650 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import cv2
+import torch
+
+from functools import partial
+import random
+from scipy import ndimage
+import scipy
+import scipy.stats as ss
+from scipy.interpolate import interp2d
+from scipy.linalg import orth
+import albumentations
+
+import ldm.modules.image_degradation.utils_image as util
+
+"""
+# --------------------------------------------
+# Super-Resolution
+# --------------------------------------------
+#
+# Kai Zhang (cskaizhang@gmail.com)
+# https://github.com/cszn
+# From 2019/03--2021/08
+# --------------------------------------------
+"""
+
+
+def modcrop_np(img, sf):
+    '''
+    Args:
+        img: numpy image, WxH or WxHxC
+        sf: scale factor
+    Return:
+        cropped image
+    '''
+    w, h = img.shape[:2]
+    im = np.copy(img)
+    return im[:w - w % sf, :h - h % sf, ...]
+
+
+"""
+# --------------------------------------------
+# anisotropic Gaussian kernels
+# --------------------------------------------
+"""
+
+
+def analytic_kernel(k):
+    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
+    k_size = k.shape[0]
+    # Calculate the big kernels size
+    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
+    # Loop over the small kernel to fill the big one
+    for r in range(k_size):
+        for c in range(k_size):
+            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
+    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
+    crop = k_size // 2
+    cropped_big_k = big_k[crop:-crop, crop:-crop]
+    # Normalize to 1
+    return cropped_big_k / cropped_big_k.sum()
+
+
+def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
+    """ generate an anisotropic Gaussian kernel
+    Args:
+        ksize : e.g., 15, kernel size
+        theta : [0,  pi], rotation angle range
+        l1    : [0.1,50], scaling of eigenvalues
+        l2    : [0.1,l1], scaling of eigenvalues
+        If l1 = l2, will get an isotropic Gaussian kernel.
+    Returns:
+        k     : kernel
+    """
+
+    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
+    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
+    D = np.array([[l1, 0], [0, l2]])
+    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
+    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
+
+    return k
+
+
+def gm_blur_kernel(mean, cov, size=15):
+    center = size / 2.0 + 0.5
+    k = np.zeros([size, size])
+    for y in range(size):
+        for x in range(size):
+            cy = y - center + 1
+            cx = x - center + 1
+            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
+
+    k = k / np.sum(k)
+    return k
+
+
+def shift_pixel(x, sf, upper_left=True):
+    """shift pixel for super-resolution with different scale factors
+    Args:
+        x: WxHxC or WxH
+        sf: scale factor
+        upper_left: shift direction
+    """
+    h, w = x.shape[:2]
+    shift = (sf - 1) * 0.5
+    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
+    if upper_left:
+        x1 = xv + shift
+        y1 = yv + shift
+    else:
+        x1 = xv - shift
+        y1 = yv - shift
+
+    x1 = np.clip(x1, 0, w - 1)
+    y1 = np.clip(y1, 0, h - 1)
+
+    if x.ndim == 2:
+        x = interp2d(xv, yv, x)(x1, y1)
+    if x.ndim == 3:
+        for i in range(x.shape[-1]):
+            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
+
+    return x
+
+
+def blur(x, k):
+    '''
+    x: image, NxcxHxW
+    k: kernel, Nx1xhxw
+    '''
+    n, c = x.shape[:2]
+    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
+    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
+    k = k.repeat(1, c, 1, 1)
+    k = k.view(-1, 1, k.shape[2], k.shape[3])
+    x = x.view(1, -1, x.shape[2], x.shape[3])
+    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
+    x = x.view(n, c, x.shape[2], x.shape[3])
+
+    return x
+
+
+def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
+    """"
+    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
+    # Kai Zhang
+    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
+    # max_var = 2.5 * sf
+    """
+    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
+    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
+    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
+    theta = np.random.rand() * np.pi  # random theta
+    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
+
+    # Set COV matrix using Lambdas and Theta
+    LAMBDA = np.diag([lambda_1, lambda_2])
+    Q = np.array([[np.cos(theta), -np.sin(theta)],
+                  [np.sin(theta), np.cos(theta)]])
+    SIGMA = Q @ LAMBDA @ Q.T
+    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
+
+    # Set expectation position (shifting kernel for aligned image)
+    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
+    MU = MU[None, None, :, None]
+
+    # Create meshgrid for Gaussian
+    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
+    Z = np.stack([X, Y], 2)[:, :, :, None]
+
+    # Calcualte Gaussian for every pixel of the kernel
+    ZZ = Z - MU
+    ZZ_t = ZZ.transpose(0, 1, 3, 2)
+    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
+
+    # shift the kernel so it will be centered
+    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
+
+    # Normalize the kernel and return
+    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
+    kernel = raw_kernel / np.sum(raw_kernel)
+    return kernel
+
+
+def fspecial_gaussian(hsize, sigma):
+    hsize = [hsize, hsize]
+    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
+    std = sigma
+    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
+    arg = -(x * x + y * y) / (2 * std * std)
+    h = np.exp(arg)
+    h[h < scipy.finfo(float).eps * h.max()] = 0
+    sumh = h.sum()
+    if sumh != 0:
+        h = h / sumh
+    return h
+
+
+def fspecial_laplacian(alpha):
+    alpha = max([0, min([alpha, 1])])
+    h1 = alpha / (alpha + 1)
+    h2 = (1 - alpha) / (alpha + 1)
+    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
+    h = np.array(h)
+    return h
+
+
+def fspecial(filter_type, *args, **kwargs):
+    '''
+    python code from:
+    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
+    '''
+    if filter_type == 'gaussian':
+        return fspecial_gaussian(*args, **kwargs)
+    if filter_type == 'laplacian':
+        return fspecial_laplacian(*args, **kwargs)
+
+
+"""
+# --------------------------------------------
+# degradation models
+# --------------------------------------------
+"""
+
+
+def bicubic_degradation(x, sf=3):
+    '''
+    Args:
+        x: HxWxC image, [0, 1]
+        sf: down-scale factor
+    Return:
+        bicubicly downsampled LR image
+    '''
+    x = util.imresize_np(x, scale=1 / sf)
+    return x
+
+
+def srmd_degradation(x, k, sf=3):
+    ''' blur + bicubic downsampling
+    Args:
+        x: HxWxC image, [0, 1]
+        k: hxw, double
+        sf: down-scale factor
+    Return:
+        downsampled LR image
+    Reference:
+        @inproceedings{zhang2018learning,
+          title={Learning a single convolutional super-resolution network for multiple degradations},
+          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+          pages={3262--3271},
+          year={2018}
+        }
+    '''
+    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
+    x = bicubic_degradation(x, sf=sf)
+    return x
+
+
+def dpsr_degradation(x, k, sf=3):
+    ''' bicubic downsampling + blur
+    Args:
+        x: HxWxC image, [0, 1]
+        k: hxw, double
+        sf: down-scale factor
+    Return:
+        downsampled LR image
+    Reference:
+        @inproceedings{zhang2019deep,
+          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
+          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+          pages={1671--1681},
+          year={2019}
+        }
+    '''
+    x = bicubic_degradation(x, sf=sf)
+    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+    return x
+
+
+def classical_degradation(x, k, sf=3):
+    ''' blur + downsampling
+    Args:
+        x: HxWxC image, [0, 1]/[0, 255]
+        k: hxw, double
+        sf: down-scale factor
+    Return:
+        downsampled LR image
+    '''
+    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
+    st = 0
+    return x[st::sf, st::sf, ...]
+
+
+def add_sharpening(img, weight=0.5, radius=50, threshold=10):
+    """USM sharpening. borrowed from real-ESRGAN
+    Input image: I; Blurry image: B.
+    1. K = I + weight * (I - B)
+    2. Mask = 1 if abs(I - B) > threshold, else: 0
+    3. Blur mask:
+    4. Out = Mask * K + (1 - Mask) * I
+    Args:
+        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
+        weight (float): Sharp weight. Default: 1.
+        radius (float): Kernel size of Gaussian blur. Default: 50.
+        threshold (int):
+    """
+    if radius % 2 == 0:
+        radius += 1
+    blur = cv2.GaussianBlur(img, (radius, radius), 0)
+    residual = img - blur
+    mask = np.abs(residual) * 255 > threshold
+    mask = mask.astype('float32')
+    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
+
+    K = img + weight * residual
+    K = np.clip(K, 0, 1)
+    return soft_mask * K + (1 - soft_mask) * img
+
+
+def add_blur(img, sf=4):
+    wd2 = 4.0 + sf
+    wd = 2.0 + 0.2 * sf
+
+    wd2 = wd2/4
+    wd = wd/4
+
+    if random.random() < 0.5:
+        l1 = wd2 * random.random()
+        l2 = wd2 * random.random()
+        k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
+    else:
+        k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
+    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
+
+    return img
+
+
+def add_resize(img, sf=4):
+    rnum = np.random.rand()
+    if rnum > 0.8:  # up
+        sf1 = random.uniform(1, 2)
+    elif rnum < 0.7:  # down
+        sf1 = random.uniform(0.5 / sf, 1)
+    else:
+        sf1 = 1.0
+    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
+    img = np.clip(img, 0.0, 1.0)
+
+    return img
+
+
+# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+#     noise_level = random.randint(noise_level1, noise_level2)
+#     rnum = np.random.rand()
+#     if rnum > 0.6:  # add color Gaussian noise
+#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+#     elif rnum < 0.4:  # add grayscale Gaussian noise
+#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+#     else:  # add  noise
+#         L = noise_level2 / 255.
+#         D = np.diag(np.random.rand(3))
+#         U = orth(np.random.rand(3, 3))
+#         conv = np.dot(np.dot(np.transpose(U), D), U)
+#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+#     img = np.clip(img, 0.0, 1.0)
+#     return img
+
+def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+    noise_level = random.randint(noise_level1, noise_level2)
+    rnum = np.random.rand()
+    if rnum > 0.6:  # add color Gaussian noise
+        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+    elif rnum < 0.4:  # add grayscale Gaussian noise
+        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+    else:  # add  noise
+        L = noise_level2 / 255.
+        D = np.diag(np.random.rand(3))
+        U = orth(np.random.rand(3, 3))
+        conv = np.dot(np.dot(np.transpose(U), D), U)
+        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+    img = np.clip(img, 0.0, 1.0)
+    return img
+
+
+def add_speckle_noise(img, noise_level1=2, noise_level2=25):
+    noise_level = random.randint(noise_level1, noise_level2)
+    img = np.clip(img, 0.0, 1.0)
+    rnum = random.random()
+    if rnum > 0.6:
+        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+    elif rnum < 0.4:
+        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+    else:
+        L = noise_level2 / 255.
+        D = np.diag(np.random.rand(3))
+        U = orth(np.random.rand(3, 3))
+        conv = np.dot(np.dot(np.transpose(U), D), U)
+        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+    img = np.clip(img, 0.0, 1.0)
+    return img
+
+
+def add_Poisson_noise(img):
+    img = np.clip((img * 255.0).round(), 0, 255) / 255.
+    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
+    if random.random() < 0.5:
+        img = np.random.poisson(img * vals).astype(np.float32) / vals
+    else:
+        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
+        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
+        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
+        img += noise_gray[:, :, np.newaxis]
+    img = np.clip(img, 0.0, 1.0)
+    return img
+
+
+def add_JPEG_noise(img):
+    quality_factor = random.randint(80, 95)
+    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
+    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
+    img = cv2.imdecode(encimg, 1)
+    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
+    return img
+
+
+def random_crop(lq, hq, sf=4, lq_patchsize=64):
+    h, w = lq.shape[:2]
+    rnd_h = random.randint(0, h - lq_patchsize)
+    rnd_w = random.randint(0, w - lq_patchsize)
+    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
+
+    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
+    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
+    return lq, hq
+
+
+def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
+    """
+    This is the degradation model of BSRGAN from the paper
+    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+    ----------
+    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
+    sf: scale factor
+    isp_model: camera ISP model
+    Returns
+    -------
+    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+    """
+    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+    sf_ori = sf
+
+    h1, w1 = img.shape[:2]
+    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    h, w = img.shape[:2]
+
+    if h < lq_patchsize * sf or w < lq_patchsize * sf:
+        raise ValueError(f'img size ({h1}X{w1}) is too small!')
+
+    hq = img.copy()
+
+    if sf == 4 and random.random() < scale2_prob:  # downsample1
+        if np.random.rand() < 0.5:
+            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
+                             interpolation=random.choice([1, 2, 3]))
+        else:
+            img = util.imresize_np(img, 1 / 2, True)
+        img = np.clip(img, 0.0, 1.0)
+        sf = 2
+
+    shuffle_order = random.sample(range(7), 7)
+    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+    if idx1 > idx2:  # keep downsample3 last
+        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+    for i in shuffle_order:
+
+        if i == 0:
+            img = add_blur(img, sf=sf)
+
+        elif i == 1:
+            img = add_blur(img, sf=sf)
+
+        elif i == 2:
+            a, b = img.shape[1], img.shape[0]
+            # downsample2
+            if random.random() < 0.75:
+                sf1 = random.uniform(1, 2 * sf)
+                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
+                                 interpolation=random.choice([1, 2, 3]))
+            else:
+                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+                k_shifted = shift_pixel(k, sf)
+                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
+                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
+                img = img[0::sf, 0::sf, ...]  # nearest downsampling
+            img = np.clip(img, 0.0, 1.0)
+
+        elif i == 3:
+            # downsample3
+            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+            img = np.clip(img, 0.0, 1.0)
+
+        elif i == 4:
+            # add Gaussian noise
+            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
+
+        elif i == 5:
+            # add JPEG noise
+            if random.random() < jpeg_prob:
+                img = add_JPEG_noise(img)
+
+        elif i == 6:
+            # add processed camera sensor noise
+            if random.random() < isp_prob and isp_model is not None:
+                with torch.no_grad():
+                    img, hq = isp_model.forward(img.copy(), hq)
+
+    # add final JPEG compression noise
+    img = add_JPEG_noise(img)
+
+    # random crop
+    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
+
+    return img, hq
+
+
+# todo no isp_model?
+def degradation_bsrgan_variant(image, sf=4, isp_model=None):
+    """
+    This is the degradation model of BSRGAN from the paper
+    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+    ----------
+    sf: scale factor
+    isp_model: camera ISP model
+    Returns
+    -------
+    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+    """
+    image = util.uint2single(image)
+    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+    sf_ori = sf
+
+    h1, w1 = image.shape[:2]
+    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    h, w = image.shape[:2]
+
+    hq = image.copy()
+
+    if sf == 4 and random.random() < scale2_prob:  # downsample1
+        if np.random.rand() < 0.5:
+            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
+                               interpolation=random.choice([1, 2, 3]))
+        else:
+            image = util.imresize_np(image, 1 / 2, True)
+        image = np.clip(image, 0.0, 1.0)
+        sf = 2
+
+    shuffle_order = random.sample(range(7), 7)
+    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+    if idx1 > idx2:  # keep downsample3 last
+        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+    for i in shuffle_order:
+
+        if i == 0:
+            image = add_blur(image, sf=sf)
+
+        # elif i == 1:
+        #     image = add_blur(image, sf=sf)
+
+        if i == 0:
+            pass
+
+        elif i == 2:
+            a, b = image.shape[1], image.shape[0]
+            # downsample2
+            if random.random() < 0.8:
+                sf1 = random.uniform(1, 2 * sf)
+                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
+                                   interpolation=random.choice([1, 2, 3]))
+            else:
+                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+                k_shifted = shift_pixel(k, sf)
+                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
+                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
+                image = image[0::sf, 0::sf, ...]  # nearest downsampling
+
+            image = np.clip(image, 0.0, 1.0)
+
+        elif i == 3:
+            # downsample3
+            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+            image = np.clip(image, 0.0, 1.0)
+
+        elif i == 4:
+            # add Gaussian noise
+            image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
+
+        elif i == 5:
+            # add JPEG noise
+            if random.random() < jpeg_prob:
+                image = add_JPEG_noise(image)
+        #
+        # elif i == 6:
+        #     # add processed camera sensor noise
+        #     if random.random() < isp_prob and isp_model is not None:
+        #         with torch.no_grad():
+        #             img, hq = isp_model.forward(img.copy(), hq)
+
+    # add final JPEG compression noise
+    image = add_JPEG_noise(image)
+    image = util.single2uint(image)
+    example = {"image": image}
+    return example
+
+
+
+
+if __name__ == '__main__':
+    print("hey")
+    img = util.imread_uint('utils/test.png', 3)
+    img = img[:448, :448]
+    h = img.shape[0] // 4
+    print("resizing to", h)
+    sf = 4
+    deg_fn = partial(degradation_bsrgan_variant, sf=sf)
+    for i in range(20):
+        print(i)
+        img_hq = img
+        img_lq = deg_fn(img)["image"]
+        img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
+        print(img_lq)
+        img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
+        print(img_lq.shape)
+        print("bicubic", img_lq_bicubic.shape)
+        print(img_hq.shape)
+        lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+                                interpolation=0)
+        lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
+                                        (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+                                        interpolation=0)
+        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
+        util.imsave(img_concat, str(i) + '.png')
diff --git a/ldm/modules/image_degradation/utils/test.png b/ldm/modules/image_degradation/utils/test.png
new file mode 100644
index 00000000..4249b43d
Binary files /dev/null and b/ldm/modules/image_degradation/utils/test.png differ
diff --git a/ldm/modules/image_degradation/utils_image.py b/ldm/modules/image_degradation/utils_image.py
new file mode 100644
index 00000000..0175f155
--- /dev/null
+++ b/ldm/modules/image_degradation/utils_image.py
@@ -0,0 +1,916 @@
+import os
+import math
+import random
+import numpy as np
+import torch
+import cv2
+from torchvision.utils import make_grid
+from datetime import datetime
+#import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
+
+
+os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+
+
+'''
+# --------------------------------------------
+# Kai Zhang (github: https://github.com/cszn)
+# 03/Mar/2019
+# --------------------------------------------
+# https://github.com/twhui/SRGAN-pyTorch
+# https://github.com/xinntao/BasicSR
+# --------------------------------------------
+'''
+
+
+IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
+
+
+def is_image_file(filename):
+    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
+
+
+def get_timestamp():
+    return datetime.now().strftime('%y%m%d-%H%M%S')
+
+
+def imshow(x, title=None, cbar=False, figsize=None):
+    plt.figure(figsize=figsize)
+    plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
+    if title:
+        plt.title(title)
+    if cbar:
+        plt.colorbar()
+    plt.show()
+
+
+def surf(Z, cmap='rainbow', figsize=None):
+    plt.figure(figsize=figsize)
+    ax3 = plt.axes(projection='3d')
+
+    w, h = Z.shape[:2]
+    xx = np.arange(0,w,1)
+    yy = np.arange(0,h,1)
+    X, Y = np.meshgrid(xx, yy)
+    ax3.plot_surface(X,Y,Z,cmap=cmap)
+    #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
+    plt.show()
+
+
+'''
+# --------------------------------------------
+# get image pathes
+# --------------------------------------------
+'''
+
+
+def get_image_paths(dataroot):
+    paths = None  # return None if dataroot is None
+    if dataroot is not None:
+        paths = sorted(_get_paths_from_images(dataroot))
+    return paths
+
+
+def _get_paths_from_images(path):
+    assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
+    images = []
+    for dirpath, _, fnames in sorted(os.walk(path)):
+        for fname in sorted(fnames):
+            if is_image_file(fname):
+                img_path = os.path.join(dirpath, fname)
+                images.append(img_path)
+    assert images, '{:s} has no valid image file'.format(path)
+    return images
+
+
+'''
+# --------------------------------------------
+# split large images into small images 
+# --------------------------------------------
+'''
+
+
+def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
+    w, h = img.shape[:2]
+    patches = []
+    if w > p_max and h > p_max:
+        w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
+        h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
+        w1.append(w-p_size)
+        h1.append(h-p_size)
+#        print(w1)
+#        print(h1)
+        for i in w1:
+            for j in h1:
+                patches.append(img[i:i+p_size, j:j+p_size,:])
+    else:
+        patches.append(img)
+
+    return patches
+
+
+def imssave(imgs, img_path):
+    """
+    imgs: list, N images of size WxHxC
+    """
+    img_name, ext = os.path.splitext(os.path.basename(img_path))
+
+    for i, img in enumerate(imgs):
+        if img.ndim == 3:
+            img = img[:, :, [2, 1, 0]]
+        new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
+        cv2.imwrite(new_path, img)
+
+
+def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
+    """
+    split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
+    and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
+    will be splitted.
+    Args:
+        original_dataroot:
+        taget_dataroot:
+        p_size: size of small images
+        p_overlap: patch size in training is a good choice
+        p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
+    """
+    paths = get_image_paths(original_dataroot)
+    for img_path in paths:
+        # img_name, ext = os.path.splitext(os.path.basename(img_path))
+        img = imread_uint(img_path, n_channels=n_channels)
+        patches = patches_from_image(img, p_size, p_overlap, p_max)
+        imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
+        #if original_dataroot == taget_dataroot:
+        #del img_path
+
+'''
+# --------------------------------------------
+# makedir
+# --------------------------------------------
+'''
+
+
+def mkdir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+
+def mkdirs(paths):
+    if isinstance(paths, str):
+        mkdir(paths)
+    else:
+        for path in paths:
+            mkdir(path)
+
+
+def mkdir_and_rename(path):
+    if os.path.exists(path):
+        new_name = path + '_archived_' + get_timestamp()
+        print('Path already exists. Rename it to [{:s}]'.format(new_name))
+        os.rename(path, new_name)
+    os.makedirs(path)
+
+
+'''
+# --------------------------------------------
+# read image from path
+# opencv is fast, but read BGR numpy image
+# --------------------------------------------
+'''
+
+
+# --------------------------------------------
+# get uint8 image of size HxWxn_channles (RGB)
+# --------------------------------------------
+def imread_uint(path, n_channels=3):
+    #  input: path
+    # output: HxWx3(RGB or GGG), or HxWx1 (G)
+    if n_channels == 1:
+        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
+        img = np.expand_dims(img, axis=2)  # HxWx1
+    elif n_channels == 3:
+        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
+        if img.ndim == 2:
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
+        else:
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
+    return img
+
+
+# --------------------------------------------
+# matlab's imwrite
+# --------------------------------------------
+def imsave(img, img_path):
+    img = np.squeeze(img)
+    if img.ndim == 3:
+        img = img[:, :, [2, 1, 0]]
+    cv2.imwrite(img_path, img)
+
+def imwrite(img, img_path):
+    img = np.squeeze(img)
+    if img.ndim == 3:
+        img = img[:, :, [2, 1, 0]]
+    cv2.imwrite(img_path, img)
+
+
+
+# --------------------------------------------
+# get single image of size HxWxn_channles (BGR)
+# --------------------------------------------
+def read_img(path):
+    # read image by cv2
+    # return: Numpy float32, HWC, BGR, [0,1]
+    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
+    img = img.astype(np.float32) / 255.
+    if img.ndim == 2:
+        img = np.expand_dims(img, axis=2)
+    # some images have 4 channels
+    if img.shape[2] > 3:
+        img = img[:, :, :3]
+    return img
+
+
+'''
+# --------------------------------------------
+# image format conversion
+# --------------------------------------------
+# numpy(single) <--->  numpy(unit)
+# numpy(single) <--->  tensor
+# numpy(unit)   <--->  tensor
+# --------------------------------------------
+'''
+
+
+# --------------------------------------------
+# numpy(single) [0, 1] <--->  numpy(unit)
+# --------------------------------------------
+
+
+def uint2single(img):
+
+    return np.float32(img/255.)
+
+
+def single2uint(img):
+
+    return np.uint8((img.clip(0, 1)*255.).round())
+
+
+def uint162single(img):
+
+    return np.float32(img/65535.)
+
+
+def single2uint16(img):
+
+    return np.uint16((img.clip(0, 1)*65535.).round())
+
+
+# --------------------------------------------
+# numpy(unit) (HxWxC or HxW) <--->  tensor
+# --------------------------------------------
+
+
+# convert uint to 4-dimensional torch tensor
+def uint2tensor4(img):
+    if img.ndim == 2:
+        img = np.expand_dims(img, axis=2)
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
+
+
+# convert uint to 3-dimensional torch tensor
+def uint2tensor3(img):
+    if img.ndim == 2:
+        img = np.expand_dims(img, axis=2)
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
+
+
+# convert 2/3/4-dimensional torch tensor to uint
+def tensor2uint(img):
+    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
+    if img.ndim == 3:
+        img = np.transpose(img, (1, 2, 0))
+    return np.uint8((img*255.0).round())
+
+
+# --------------------------------------------
+# numpy(single) (HxWxC) <--->  tensor
+# --------------------------------------------
+
+
+# convert single (HxWxC) to 3-dimensional torch tensor
+def single2tensor3(img):
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
+
+
+# convert single (HxWxC) to 4-dimensional torch tensor
+def single2tensor4(img):
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
+
+
+# convert torch tensor to single
+def tensor2single(img):
+    img = img.data.squeeze().float().cpu().numpy()
+    if img.ndim == 3:
+        img = np.transpose(img, (1, 2, 0))
+
+    return img
+
+# convert torch tensor to single
+def tensor2single3(img):
+    img = img.data.squeeze().float().cpu().numpy()
+    if img.ndim == 3:
+        img = np.transpose(img, (1, 2, 0))
+    elif img.ndim == 2:
+        img = np.expand_dims(img, axis=2)
+    return img
+
+
+def single2tensor5(img):
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
+
+
+def single32tensor5(img):
+    return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
+
+
+def single42tensor4(img):
+    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
+
+
+# from skimage.io import imread, imsave
+def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
+    '''
+    Converts a torch Tensor into an image Numpy array of BGR channel order
+    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
+    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
+    '''
+    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
+    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
+    n_dim = tensor.dim()
+    if n_dim == 4:
+        n_img = len(tensor)
+        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
+        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
+    elif n_dim == 3:
+        img_np = tensor.numpy()
+        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
+    elif n_dim == 2:
+        img_np = tensor.numpy()
+    else:
+        raise TypeError(
+            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
+    if out_type == np.uint8:
+        img_np = (img_np * 255.0).round()
+        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
+    return img_np.astype(out_type)
+
+
+'''
+# --------------------------------------------
+# Augmentation, flipe and/or rotate
+# --------------------------------------------
+# The following two are enough.
+# (1) augmet_img: numpy image of WxHxC or WxH
+# (2) augment_img_tensor4: tensor image 1xCxWxH
+# --------------------------------------------
+'''
+
+
+def augment_img(img, mode=0):
+    '''Kai Zhang (github: https://github.com/cszn)
+    '''
+    if mode == 0:
+        return img
+    elif mode == 1:
+        return np.flipud(np.rot90(img))
+    elif mode == 2:
+        return np.flipud(img)
+    elif mode == 3:
+        return np.rot90(img, k=3)
+    elif mode == 4:
+        return np.flipud(np.rot90(img, k=2))
+    elif mode == 5:
+        return np.rot90(img)
+    elif mode == 6:
+        return np.rot90(img, k=2)
+    elif mode == 7:
+        return np.flipud(np.rot90(img, k=3))
+
+
+def augment_img_tensor4(img, mode=0):
+    '''Kai Zhang (github: https://github.com/cszn)
+    '''
+    if mode == 0:
+        return img
+    elif mode == 1:
+        return img.rot90(1, [2, 3]).flip([2])
+    elif mode == 2:
+        return img.flip([2])
+    elif mode == 3:
+        return img.rot90(3, [2, 3])
+    elif mode == 4:
+        return img.rot90(2, [2, 3]).flip([2])
+    elif mode == 5:
+        return img.rot90(1, [2, 3])
+    elif mode == 6:
+        return img.rot90(2, [2, 3])
+    elif mode == 7:
+        return img.rot90(3, [2, 3]).flip([2])
+
+
+def augment_img_tensor(img, mode=0):
+    '''Kai Zhang (github: https://github.com/cszn)
+    '''
+    img_size = img.size()
+    img_np = img.data.cpu().numpy()
+    if len(img_size) == 3:
+        img_np = np.transpose(img_np, (1, 2, 0))
+    elif len(img_size) == 4:
+        img_np = np.transpose(img_np, (2, 3, 1, 0))
+    img_np = augment_img(img_np, mode=mode)
+    img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
+    if len(img_size) == 3:
+        img_tensor = img_tensor.permute(2, 0, 1)
+    elif len(img_size) == 4:
+        img_tensor = img_tensor.permute(3, 2, 0, 1)
+
+    return img_tensor.type_as(img)
+
+
+def augment_img_np3(img, mode=0):
+    if mode == 0:
+        return img
+    elif mode == 1:
+        return img.transpose(1, 0, 2)
+    elif mode == 2:
+        return img[::-1, :, :]
+    elif mode == 3:
+        img = img[::-1, :, :]
+        img = img.transpose(1, 0, 2)
+        return img
+    elif mode == 4:
+        return img[:, ::-1, :]
+    elif mode == 5:
+        img = img[:, ::-1, :]
+        img = img.transpose(1, 0, 2)
+        return img
+    elif mode == 6:
+        img = img[:, ::-1, :]
+        img = img[::-1, :, :]
+        return img
+    elif mode == 7:
+        img = img[:, ::-1, :]
+        img = img[::-1, :, :]
+        img = img.transpose(1, 0, 2)
+        return img
+
+
+def augment_imgs(img_list, hflip=True, rot=True):
+    # horizontal flip OR rotate
+    hflip = hflip and random.random() < 0.5
+    vflip = rot and random.random() < 0.5
+    rot90 = rot and random.random() < 0.5
+
+    def _augment(img):
+        if hflip:
+            img = img[:, ::-1, :]
+        if vflip:
+            img = img[::-1, :, :]
+        if rot90:
+            img = img.transpose(1, 0, 2)
+        return img
+
+    return [_augment(img) for img in img_list]
+
+
+'''
+# --------------------------------------------
+# modcrop and shave
+# --------------------------------------------
+'''
+
+
+def modcrop(img_in, scale):
+    # img_in: Numpy, HWC or HW
+    img = np.copy(img_in)
+    if img.ndim == 2:
+        H, W = img.shape
+        H_r, W_r = H % scale, W % scale
+        img = img[:H - H_r, :W - W_r]
+    elif img.ndim == 3:
+        H, W, C = img.shape
+        H_r, W_r = H % scale, W % scale
+        img = img[:H - H_r, :W - W_r, :]
+    else:
+        raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
+    return img
+
+
+def shave(img_in, border=0):
+    # img_in: Numpy, HWC or HW
+    img = np.copy(img_in)
+    h, w = img.shape[:2]
+    img = img[border:h-border, border:w-border]
+    return img
+
+
+'''
+# --------------------------------------------
+# image processing process on numpy image
+# channel_convert(in_c, tar_type, img_list):
+# rgb2ycbcr(img, only_y=True):
+# bgr2ycbcr(img, only_y=True):
+# ycbcr2rgb(img):
+# --------------------------------------------
+'''
+
+
+def rgb2ycbcr(img, only_y=True):
+    '''same as matlab rgb2ycbcr
+    only_y: only return Y channel
+    Input:
+        uint8, [0, 255]
+        float, [0, 1]
+    '''
+    in_img_type = img.dtype
+    img.astype(np.float32)
+    if in_img_type != np.uint8:
+        img *= 255.
+    # convert
+    if only_y:
+        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
+    else:
+        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
+                              [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
+    if in_img_type == np.uint8:
+        rlt = rlt.round()
+    else:
+        rlt /= 255.
+    return rlt.astype(in_img_type)
+
+
+def ycbcr2rgb(img):
+    '''same as matlab ycbcr2rgb
+    Input:
+        uint8, [0, 255]
+        float, [0, 1]
+    '''
+    in_img_type = img.dtype
+    img.astype(np.float32)
+    if in_img_type != np.uint8:
+        img *= 255.
+    # convert
+    rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
+                          [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
+    if in_img_type == np.uint8:
+        rlt = rlt.round()
+    else:
+        rlt /= 255.
+    return rlt.astype(in_img_type)
+
+
+def bgr2ycbcr(img, only_y=True):
+    '''bgr version of rgb2ycbcr
+    only_y: only return Y channel
+    Input:
+        uint8, [0, 255]
+        float, [0, 1]
+    '''
+    in_img_type = img.dtype
+    img.astype(np.float32)
+    if in_img_type != np.uint8:
+        img *= 255.
+    # convert
+    if only_y:
+        rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
+    else:
+        rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
+                              [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
+    if in_img_type == np.uint8:
+        rlt = rlt.round()
+    else:
+        rlt /= 255.
+    return rlt.astype(in_img_type)
+
+
+def channel_convert(in_c, tar_type, img_list):
+    # conversion among BGR, gray and y
+    if in_c == 3 and tar_type == 'gray':  # BGR to gray
+        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
+        return [np.expand_dims(img, axis=2) for img in gray_list]
+    elif in_c == 3 and tar_type == 'y':  # BGR to y
+        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
+        return [np.expand_dims(img, axis=2) for img in y_list]
+    elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
+        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
+    else:
+        return img_list
+
+
+'''
+# --------------------------------------------
+# metric, PSNR and SSIM
+# --------------------------------------------
+'''
+
+
+# --------------------------------------------
+# PSNR
+# --------------------------------------------
+def calculate_psnr(img1, img2, border=0):
+    # img1 and img2 have range [0, 255]
+    #img1 = img1.squeeze()
+    #img2 = img2.squeeze()
+    if not img1.shape == img2.shape:
+        raise ValueError('Input images must have the same dimensions.')
+    h, w = img1.shape[:2]
+    img1 = img1[border:h-border, border:w-border]
+    img2 = img2[border:h-border, border:w-border]
+
+    img1 = img1.astype(np.float64)
+    img2 = img2.astype(np.float64)
+    mse = np.mean((img1 - img2)**2)
+    if mse == 0:
+        return float('inf')
+    return 20 * math.log10(255.0 / math.sqrt(mse))
+
+
+# --------------------------------------------
+# SSIM
+# --------------------------------------------
+def calculate_ssim(img1, img2, border=0):
+    '''calculate SSIM
+    the same outputs as MATLAB's
+    img1, img2: [0, 255]
+    '''
+    #img1 = img1.squeeze()
+    #img2 = img2.squeeze()
+    if not img1.shape == img2.shape:
+        raise ValueError('Input images must have the same dimensions.')
+    h, w = img1.shape[:2]
+    img1 = img1[border:h-border, border:w-border]
+    img2 = img2[border:h-border, border:w-border]
+
+    if img1.ndim == 2:
+        return ssim(img1, img2)
+    elif img1.ndim == 3:
+        if img1.shape[2] == 3:
+            ssims = []
+            for i in range(3):
+                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
+            return np.array(ssims).mean()
+        elif img1.shape[2] == 1:
+            return ssim(np.squeeze(img1), np.squeeze(img2))
+    else:
+        raise ValueError('Wrong input image dimensions.')
+
+
+def ssim(img1, img2):
+    C1 = (0.01 * 255)**2
+    C2 = (0.03 * 255)**2
+
+    img1 = img1.astype(np.float64)
+    img2 = img2.astype(np.float64)
+    kernel = cv2.getGaussianKernel(11, 1.5)
+    window = np.outer(kernel, kernel.transpose())
+
+    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
+    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
+    mu1_sq = mu1**2
+    mu2_sq = mu2**2
+    mu1_mu2 = mu1 * mu2
+    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
+    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
+    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
+
+    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
+                                                            (sigma1_sq + sigma2_sq + C2))
+    return ssim_map.mean()
+
+
+'''
+# --------------------------------------------
+# matlab's bicubic imresize (numpy and torch) [0, 1]
+# --------------------------------------------
+'''
+
+
+# matlab 'imresize' function, now only support 'bicubic'
+def cubic(x):
+    absx = torch.abs(x)
+    absx2 = absx**2
+    absx3 = absx**3
+    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
+        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
+
+
+def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
+    if (scale < 1) and (antialiasing):
+        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
+        kernel_width = kernel_width / scale
+
+    # Output-space coordinates
+    x = torch.linspace(1, out_length, out_length)
+
+    # Input-space coordinates. Calculate the inverse mapping such that 0.5
+    # in output space maps to 0.5 in input space, and 0.5+scale in output
+    # space maps to 1.5 in input space.
+    u = x / scale + 0.5 * (1 - 1 / scale)
+
+    # What is the left-most pixel that can be involved in the computation?
+    left = torch.floor(u - kernel_width / 2)
+
+    # What is the maximum number of pixels that can be involved in the
+    # computation?  Note: it's OK to use an extra pixel here; if the
+    # corresponding weights are all zero, it will be eliminated at the end
+    # of this function.
+    P = math.ceil(kernel_width) + 2
+
+    # The indices of the input pixels involved in computing the k-th output
+    # pixel are in row k of the indices matrix.
+    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
+        1, P).expand(out_length, P)
+
+    # The weights used to compute the k-th output pixel are in row k of the
+    # weights matrix.
+    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
+    # apply cubic kernel
+    if (scale < 1) and (antialiasing):
+        weights = scale * cubic(distance_to_center * scale)
+    else:
+        weights = cubic(distance_to_center)
+    # Normalize the weights matrix so that each row sums to 1.
+    weights_sum = torch.sum(weights, 1).view(out_length, 1)
+    weights = weights / weights_sum.expand(out_length, P)
+
+    # If a column in weights is all zero, get rid of it. only consider the first and last column.
+    weights_zero_tmp = torch.sum((weights == 0), 0)
+    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
+        indices = indices.narrow(1, 1, P - 2)
+        weights = weights.narrow(1, 1, P - 2)
+    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
+        indices = indices.narrow(1, 0, P - 2)
+        weights = weights.narrow(1, 0, P - 2)
+    weights = weights.contiguous()
+    indices = indices.contiguous()
+    sym_len_s = -indices.min() + 1
+    sym_len_e = indices.max() - in_length
+    indices = indices + sym_len_s - 1
+    return weights, indices, int(sym_len_s), int(sym_len_e)
+
+
+# --------------------------------------------
+# imresize for tensor image [0, 1]
+# --------------------------------------------
+def imresize(img, scale, antialiasing=True):
+    # Now the scale should be the same for H and W
+    # input: img: pytorch tensor, CHW or HW [0,1]
+    # output: CHW or HW [0,1] w/o round
+    need_squeeze = True if img.dim() == 2 else False
+    if need_squeeze:
+        img.unsqueeze_(0)
+    in_C, in_H, in_W = img.size()
+    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
+    kernel_width = 4
+    kernel = 'cubic'
+
+    # Return the desired dimension order for performing the resize.  The
+    # strategy is to perform the resize first along the dimension with the
+    # smallest scale factor.
+    # Now we do not support this.
+
+    # get weights and indices
+    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
+        in_H, out_H, scale, kernel, kernel_width, antialiasing)
+    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
+        in_W, out_W, scale, kernel, kernel_width, antialiasing)
+    # process H dimension
+    # symmetric copying
+    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
+    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
+
+    sym_patch = img[:, :sym_len_Hs, :]
+    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(1, inv_idx)
+    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
+
+    sym_patch = img[:, -sym_len_He:, :]
+    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(1, inv_idx)
+    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
+
+    out_1 = torch.FloatTensor(in_C, out_H, in_W)
+    kernel_width = weights_H.size(1)
+    for i in range(out_H):
+        idx = int(indices_H[i][0])
+        for j in range(out_C):
+            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
+
+    # process W dimension
+    # symmetric copying
+    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
+    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
+
+    sym_patch = out_1[:, :, :sym_len_Ws]
+    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(2, inv_idx)
+    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
+
+    sym_patch = out_1[:, :, -sym_len_We:]
+    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(2, inv_idx)
+    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
+
+    out_2 = torch.FloatTensor(in_C, out_H, out_W)
+    kernel_width = weights_W.size(1)
+    for i in range(out_W):
+        idx = int(indices_W[i][0])
+        for j in range(out_C):
+            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
+    if need_squeeze:
+        out_2.squeeze_()
+    return out_2
+
+
+# --------------------------------------------
+# imresize for numpy image [0, 1]
+# --------------------------------------------
+def imresize_np(img, scale, antialiasing=True):
+    # Now the scale should be the same for H and W
+    # input: img: Numpy, HWC or HW [0,1]
+    # output: HWC or HW [0,1] w/o round
+    img = torch.from_numpy(img)
+    need_squeeze = True if img.dim() == 2 else False
+    if need_squeeze:
+        img.unsqueeze_(2)
+
+    in_H, in_W, in_C = img.size()
+    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
+    kernel_width = 4
+    kernel = 'cubic'
+
+    # Return the desired dimension order for performing the resize.  The
+    # strategy is to perform the resize first along the dimension with the
+    # smallest scale factor.
+    # Now we do not support this.
+
+    # get weights and indices
+    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
+        in_H, out_H, scale, kernel, kernel_width, antialiasing)
+    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
+        in_W, out_W, scale, kernel, kernel_width, antialiasing)
+    # process H dimension
+    # symmetric copying
+    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
+    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
+
+    sym_patch = img[:sym_len_Hs, :, :]
+    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(0, inv_idx)
+    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
+
+    sym_patch = img[-sym_len_He:, :, :]
+    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(0, inv_idx)
+    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
+
+    out_1 = torch.FloatTensor(out_H, in_W, in_C)
+    kernel_width = weights_H.size(1)
+    for i in range(out_H):
+        idx = int(indices_H[i][0])
+        for j in range(out_C):
+            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
+
+    # process W dimension
+    # symmetric copying
+    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
+    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
+
+    sym_patch = out_1[:, :sym_len_Ws, :]
+    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(1, inv_idx)
+    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
+
+    sym_patch = out_1[:, -sym_len_We:, :]
+    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+    sym_patch_inv = sym_patch.index_select(1, inv_idx)
+    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
+
+    out_2 = torch.FloatTensor(out_H, out_W, in_C)
+    kernel_width = weights_W.size(1)
+    for i in range(out_W):
+        idx = int(indices_W[i][0])
+        for j in range(out_C):
+            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
+    if need_squeeze:
+        out_2.squeeze_()
+
+    return out_2.numpy()
+
+
+if __name__ == '__main__':
+    print('---')
+#    img = imread_uint('test.bmp', 3)
+#    img = uint2single(img)
+#    img_bicubic = imresize_np(img, 1/4)
\ No newline at end of file
diff --git a/ldm/modules/losses/__init__.py b/ldm/modules/losses/__init__.py
new file mode 100644
index 00000000..876d7c5b
--- /dev/null
+++ b/ldm/modules/losses/__init__.py
@@ -0,0 +1 @@
+from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator
\ No newline at end of file
diff --git a/ldm/modules/losses/contperceptual.py b/ldm/modules/losses/contperceptual.py
new file mode 100644
index 00000000..672c1e32
--- /dev/null
+++ b/ldm/modules/losses/contperceptual.py
@@ -0,0 +1,111 @@
+import torch
+import torch.nn as nn
+
+from taming.modules.losses.vqperceptual import *  # TODO: taming dependency yes/no?
+
+
+class LPIPSWithDiscriminator(nn.Module):
+    def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
+                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
+                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
+                 disc_loss="hinge"):
+
+        super().__init__()
+        assert disc_loss in ["hinge", "vanilla"]
+        self.kl_weight = kl_weight
+        self.pixel_weight = pixelloss_weight
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+        # output log variance
+        self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
+
+        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
+                                                 n_layers=disc_num_layers,
+                                                 use_actnorm=use_actnorm
+                                                 ).apply(weights_init)
+        self.discriminator_iter_start = disc_start
+        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
+        self.disc_factor = disc_factor
+        self.discriminator_weight = disc_weight
+        self.disc_conditional = disc_conditional
+
+    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
+        if last_layer is not None:
+            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
+            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+        else:
+            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
+            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
+
+        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
+        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
+        d_weight = d_weight * self.discriminator_weight
+        return d_weight
+
+    def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
+                global_step, last_layer=None, cond=None, split="train",
+                weights=None):
+        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
+        if self.perceptual_weight > 0:
+            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
+            rec_loss = rec_loss + self.perceptual_weight * p_loss
+
+        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
+        weighted_nll_loss = nll_loss
+        if weights is not None:
+            weighted_nll_loss = weights*nll_loss
+        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
+        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+        kl_loss = posteriors.kl()
+        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
+
+        # now the GAN part
+        if optimizer_idx == 0:
+            # generator update
+            if cond is None:
+                assert not self.disc_conditional
+                logits_fake = self.discriminator(reconstructions.contiguous())
+            else:
+                assert self.disc_conditional
+                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
+            g_loss = -torch.mean(logits_fake)
+
+            if self.disc_factor > 0.0:
+                try:
+                    d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
+                except RuntimeError:
+                    assert not self.training
+                    d_weight = torch.tensor(0.0)
+            else:
+                d_weight = torch.tensor(0.0)
+
+            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
+            loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss
+
+            log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(),
+                   "{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(),
+                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
+                   "{}/d_weight".format(split): d_weight.detach(),
+                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
+                   "{}/g_loss".format(split): g_loss.detach().mean(),
+                   }
+            return loss, log
+
+        if optimizer_idx == 1:
+            # second pass for discriminator update
+            if cond is None:
+                logits_real = self.discriminator(inputs.contiguous().detach())
+                logits_fake = self.discriminator(reconstructions.contiguous().detach())
+            else:
+                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
+                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
+
+            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
+            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
+
+            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
+                   "{}/logits_real".format(split): logits_real.detach().mean(),
+                   "{}/logits_fake".format(split): logits_fake.detach().mean()
+                   }
+            return d_loss, log
+
diff --git a/ldm/modules/losses/vqperceptual.py b/ldm/modules/losses/vqperceptual.py
new file mode 100644
index 00000000..f6998176
--- /dev/null
+++ b/ldm/modules/losses/vqperceptual.py
@@ -0,0 +1,167 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+from einops import repeat
+
+from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
+from taming.modules.losses.lpips import LPIPS
+from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
+
+
+def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
+    assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
+    loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3])
+    loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3])
+    loss_real = (weights * loss_real).sum() / weights.sum()
+    loss_fake = (weights * loss_fake).sum() / weights.sum()
+    d_loss = 0.5 * (loss_real + loss_fake)
+    return d_loss
+
+def adopt_weight(weight, global_step, threshold=0, value=0.):
+    if global_step < threshold:
+        weight = value
+    return weight
+
+
+def measure_perplexity(predicted_indices, n_embed):
+    # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
+    # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
+    encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
+    avg_probs = encodings.mean(0)
+    perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
+    cluster_use = torch.sum(avg_probs > 0)
+    return perplexity, cluster_use
+
+def l1(x, y):
+    return torch.abs(x-y)
+
+
+def l2(x, y):
+    return torch.pow((x-y), 2)
+
+
+class VQLPIPSWithDiscriminator(nn.Module):
+    def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
+                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
+                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
+                 disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips",
+                 pixel_loss="l1"):
+        super().__init__()
+        assert disc_loss in ["hinge", "vanilla"]
+        assert perceptual_loss in ["lpips", "clips", "dists"]
+        assert pixel_loss in ["l1", "l2"]
+        self.codebook_weight = codebook_weight
+        self.pixel_weight = pixelloss_weight
+        if perceptual_loss == "lpips":
+            print(f"{self.__class__.__name__}: Running with LPIPS.")
+            self.perceptual_loss = LPIPS().eval()
+        else:
+            raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<")
+        self.perceptual_weight = perceptual_weight
+
+        if pixel_loss == "l1":
+            self.pixel_loss = l1
+        else:
+            self.pixel_loss = l2
+
+        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
+                                                 n_layers=disc_num_layers,
+                                                 use_actnorm=use_actnorm,
+                                                 ndf=disc_ndf
+                                                 ).apply(weights_init)
+        self.discriminator_iter_start = disc_start
+        if disc_loss == "hinge":
+            self.disc_loss = hinge_d_loss
+        elif disc_loss == "vanilla":
+            self.disc_loss = vanilla_d_loss
+        else:
+            raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
+        print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
+        self.disc_factor = disc_factor
+        self.discriminator_weight = disc_weight
+        self.disc_conditional = disc_conditional
+        self.n_classes = n_classes
+
+    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
+        if last_layer is not None:
+            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
+            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+        else:
+            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
+            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
+
+        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
+        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
+        d_weight = d_weight * self.discriminator_weight
+        return d_weight
+
+    def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
+                global_step, last_layer=None, cond=None, split="train", predicted_indices=None):
+        if not exists(codebook_loss):
+            codebook_loss = torch.tensor([0.]).to(inputs.device)
+        #rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
+        rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous())
+        if self.perceptual_weight > 0:
+            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
+            rec_loss = rec_loss + self.perceptual_weight * p_loss
+        else:
+            p_loss = torch.tensor([0.0])
+
+        nll_loss = rec_loss
+        #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+        nll_loss = torch.mean(nll_loss)
+
+        # now the GAN part
+        if optimizer_idx == 0:
+            # generator update
+            if cond is None:
+                assert not self.disc_conditional
+                logits_fake = self.discriminator(reconstructions.contiguous())
+            else:
+                assert self.disc_conditional
+                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
+            g_loss = -torch.mean(logits_fake)
+
+            try:
+                d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
+            except RuntimeError:
+                assert not self.training
+                d_weight = torch.tensor(0.0)
+
+            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
+            loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
+
+            log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
+                   "{}/quant_loss".format(split): codebook_loss.detach().mean(),
+                   "{}/nll_loss".format(split): nll_loss.detach().mean(),
+                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
+                   "{}/p_loss".format(split): p_loss.detach().mean(),
+                   "{}/d_weight".format(split): d_weight.detach(),
+                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
+                   "{}/g_loss".format(split): g_loss.detach().mean(),
+                   }
+            if predicted_indices is not None:
+                assert self.n_classes is not None
+                with torch.no_grad():
+                    perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)
+                log[f"{split}/perplexity"] = perplexity
+                log[f"{split}/cluster_usage"] = cluster_usage
+            return loss, log
+
+        if optimizer_idx == 1:
+            # second pass for discriminator update
+            if cond is None:
+                logits_real = self.discriminator(inputs.contiguous().detach())
+                logits_fake = self.discriminator(reconstructions.contiguous().detach())
+            else:
+                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
+                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
+
+            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
+            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
+
+            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
+                   "{}/logits_real".format(split): logits_real.detach().mean(),
+                   "{}/logits_fake".format(split): logits_fake.detach().mean()
+                   }
+            return d_loss, log
diff --git a/ldm/modules/x_transformer.py b/ldm/modules/x_transformer.py
new file mode 100644
index 00000000..5fc15bf9
--- /dev/null
+++ b/ldm/modules/x_transformer.py
@@ -0,0 +1,641 @@
+"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+from functools import partial
+from inspect import isfunction
+from collections import namedtuple
+from einops import rearrange, repeat, reduce
+
+# constants
+
+DEFAULT_DIM_HEAD = 64
+
+Intermediates = namedtuple('Intermediates', [
+    'pre_softmax_attn',
+    'post_softmax_attn'
+])
+
+LayerIntermediates = namedtuple('Intermediates', [
+    'hiddens',
+    'attn_intermediates'
+])
+
+
+class AbsolutePositionalEmbedding(nn.Module):
+    def __init__(self, dim, max_seq_len):
+        super().__init__()
+        self.emb = nn.Embedding(max_seq_len, dim)
+        self.init_()
+
+    def init_(self):
+        nn.init.normal_(self.emb.weight, std=0.02)
+
+    def forward(self, x):
+        n = torch.arange(x.shape[1], device=x.device)
+        return self.emb(n)[None, :, :]
+
+
+class FixedPositionalEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', inv_freq)
+
+    def forward(self, x, seq_dim=1, offset=0):
+        t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
+        sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
+        emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
+        return emb[None, :, :]
+
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def always(val):
+    def inner(*args, **kwargs):
+        return val
+    return inner
+
+
+def not_equals(val):
+    def inner(x):
+        return x != val
+    return inner
+
+
+def equals(val):
+    def inner(x):
+        return x == val
+    return inner
+
+
+def max_neg_value(tensor):
+    return -torch.finfo(tensor.dtype).max
+
+
+# keyword argument helpers
+
+def pick_and_pop(keys, d):
+    values = list(map(lambda key: d.pop(key), keys))
+    return dict(zip(keys, values))
+
+
+def group_dict_by_key(cond, d):
+    return_val = [dict(), dict()]
+    for key in d.keys():
+        match = bool(cond(key))
+        ind = int(not match)
+        return_val[ind][key] = d[key]
+    return (*return_val,)
+
+
+def string_begins_with(prefix, str):
+    return str.startswith(prefix)
+
+
+def group_by_key_prefix(prefix, d):
+    return group_dict_by_key(partial(string_begins_with, prefix), d)
+
+
+def groupby_prefix_and_trim(prefix, d):
+    kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
+    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
+    return kwargs_without_prefix, kwargs
+
+
+# classes
+class Scale(nn.Module):
+    def __init__(self, value, fn):
+        super().__init__()
+        self.value = value
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        x, *rest = self.fn(x, **kwargs)
+        return (x * self.value, *rest)
+
+
+class Rezero(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+        self.g = nn.Parameter(torch.zeros(1))
+
+    def forward(self, x, **kwargs):
+        x, *rest = self.fn(x, **kwargs)
+        return (x * self.g, *rest)
+
+
+class ScaleNorm(nn.Module):
+    def __init__(self, dim, eps=1e-5):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(1))
+
+    def forward(self, x):
+        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
+        return x / norm.clamp(min=self.eps) * self.g
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-8):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
+        return x / norm.clamp(min=self.eps) * self.g
+
+
+class Residual(nn.Module):
+    def forward(self, x, residual):
+        return x + residual
+
+
+class GRUGating(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.gru = nn.GRUCell(dim, dim)
+
+    def forward(self, x, residual):
+        gated_output = self.gru(
+            rearrange(x, 'b n d -> (b n) d'),
+            rearrange(residual, 'b n d -> (b n) d')
+        )
+
+        return gated_output.reshape_as(x)
+
+
+# feedforward
+
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = nn.Sequential(
+            nn.Linear(dim, inner_dim),
+            nn.GELU()
+        ) if not glu else GEGLU(dim, inner_dim)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            nn.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+# attention.
+class Attention(nn.Module):
+    def __init__(
+            self,
+            dim,
+            dim_head=DEFAULT_DIM_HEAD,
+            heads=8,
+            causal=False,
+            mask=None,
+            talking_heads=False,
+            sparse_topk=None,
+            use_entmax15=False,
+            num_mem_kv=0,
+            dropout=0.,
+            on_attn=False
+    ):
+        super().__init__()
+        if use_entmax15:
+            raise NotImplementedError("Check out entmax activation instead of softmax activation!")
+        self.scale = dim_head ** -0.5
+        self.heads = heads
+        self.causal = causal
+        self.mask = mask
+
+        inner_dim = dim_head * heads
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(dim, inner_dim, bias=False)
+        self.dropout = nn.Dropout(dropout)
+
+        # talking heads
+        self.talking_heads = talking_heads
+        if talking_heads:
+            self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
+            self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
+
+        # explicit topk sparse attention
+        self.sparse_topk = sparse_topk
+
+        # entmax
+        #self.attn_fn = entmax15 if use_entmax15 else F.softmax
+        self.attn_fn = F.softmax
+
+        # add memory key / values
+        self.num_mem_kv = num_mem_kv
+        if num_mem_kv > 0:
+            self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
+            self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
+
+        # attention on attention
+        self.attn_on_attn = on_attn
+        self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
+
+    def forward(
+            self,
+            x,
+            context=None,
+            mask=None,
+            context_mask=None,
+            rel_pos=None,
+            sinusoidal_emb=None,
+            prev_attn=None,
+            mem=None
+    ):
+        b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
+        kv_input = default(context, x)
+
+        q_input = x
+        k_input = kv_input
+        v_input = kv_input
+
+        if exists(mem):
+            k_input = torch.cat((mem, k_input), dim=-2)
+            v_input = torch.cat((mem, v_input), dim=-2)
+
+        if exists(sinusoidal_emb):
+            # in shortformer, the query would start at a position offset depending on the past cached memory
+            offset = k_input.shape[-2] - q_input.shape[-2]
+            q_input = q_input + sinusoidal_emb(q_input, offset=offset)
+            k_input = k_input + sinusoidal_emb(k_input)
+
+        q = self.to_q(q_input)
+        k = self.to_k(k_input)
+        v = self.to_v(v_input)
+
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
+
+        input_mask = None
+        if any(map(exists, (mask, context_mask))):
+            q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
+            k_mask = q_mask if not exists(context) else context_mask
+            k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
+            q_mask = rearrange(q_mask, 'b i -> b () i ()')
+            k_mask = rearrange(k_mask, 'b j -> b () () j')
+            input_mask = q_mask * k_mask
+
+        if self.num_mem_kv > 0:
+            mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
+            k = torch.cat((mem_k, k), dim=-2)
+            v = torch.cat((mem_v, v), dim=-2)
+            if exists(input_mask):
+                input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
+
+        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
+        mask_value = max_neg_value(dots)
+
+        if exists(prev_attn):
+            dots = dots + prev_attn
+
+        pre_softmax_attn = dots
+
+        if talking_heads:
+            dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
+
+        if exists(rel_pos):
+            dots = rel_pos(dots)
+
+        if exists(input_mask):
+            dots.masked_fill_(~input_mask, mask_value)
+            del input_mask
+
+        if self.causal:
+            i, j = dots.shape[-2:]
+            r = torch.arange(i, device=device)
+            mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
+            mask = F.pad(mask, (j - i, 0), value=False)
+            dots.masked_fill_(mask, mask_value)
+            del mask
+
+        if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
+            top, _ = dots.topk(self.sparse_topk, dim=-1)
+            vk = top[..., -1].unsqueeze(-1).expand_as(dots)
+            mask = dots < vk
+            dots.masked_fill_(mask, mask_value)
+            del mask
+
+        attn = self.attn_fn(dots, dim=-1)
+        post_softmax_attn = attn
+
+        attn = self.dropout(attn)
+
+        if talking_heads:
+            attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
+
+        out = einsum('b h i j, b h j d -> b h i d', attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+
+        intermediates = Intermediates(
+            pre_softmax_attn=pre_softmax_attn,
+            post_softmax_attn=post_softmax_attn
+        )
+
+        return self.to_out(out), intermediates
+
+
+class AttentionLayers(nn.Module):
+    def __init__(
+            self,
+            dim,
+            depth,
+            heads=8,
+            causal=False,
+            cross_attend=False,
+            only_cross=False,
+            use_scalenorm=False,
+            use_rmsnorm=False,
+            use_rezero=False,
+            rel_pos_num_buckets=32,
+            rel_pos_max_distance=128,
+            position_infused_attn=False,
+            custom_layers=None,
+            sandwich_coef=None,
+            par_ratio=None,
+            residual_attn=False,
+            cross_residual_attn=False,
+            macaron=False,
+            pre_norm=True,
+            gate_residual=False,
+            **kwargs
+    ):
+        super().__init__()
+        ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
+        attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
+
+        dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
+
+        self.dim = dim
+        self.depth = depth
+        self.layers = nn.ModuleList([])
+
+        self.has_pos_emb = position_infused_attn
+        self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
+        self.rotary_pos_emb = always(None)
+
+        assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
+        self.rel_pos = None
+
+        self.pre_norm = pre_norm
+
+        self.residual_attn = residual_attn
+        self.cross_residual_attn = cross_residual_attn
+
+        norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
+        norm_class = RMSNorm if use_rmsnorm else norm_class
+        norm_fn = partial(norm_class, dim)
+
+        norm_fn = nn.Identity if use_rezero else norm_fn
+        branch_fn = Rezero if use_rezero else None
+
+        if cross_attend and not only_cross:
+            default_block = ('a', 'c', 'f')
+        elif cross_attend and only_cross:
+            default_block = ('c', 'f')
+        else:
+            default_block = ('a', 'f')
+
+        if macaron:
+            default_block = ('f',) + default_block
+
+        if exists(custom_layers):
+            layer_types = custom_layers
+        elif exists(par_ratio):
+            par_depth = depth * len(default_block)
+            assert 1 < par_ratio <= par_depth, 'par ratio out of range'
+            default_block = tuple(filter(not_equals('f'), default_block))
+            par_attn = par_depth // par_ratio
+            depth_cut = par_depth * 2 // 3  # 2 / 3 attention layer cutoff suggested by PAR paper
+            par_width = (depth_cut + depth_cut // par_attn) // par_attn
+            assert len(default_block) <= par_width, 'default block is too large for par_ratio'
+            par_block = default_block + ('f',) * (par_width - len(default_block))
+            par_head = par_block * par_attn
+            layer_types = par_head + ('f',) * (par_depth - len(par_head))
+        elif exists(sandwich_coef):
+            assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
+            layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
+        else:
+            layer_types = default_block * depth
+
+        self.layer_types = layer_types
+        self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
+
+        for layer_type in self.layer_types:
+            if layer_type == 'a':
+                layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
+            elif layer_type == 'c':
+                layer = Attention(dim, heads=heads, **attn_kwargs)
+            elif layer_type == 'f':
+                layer = FeedForward(dim, **ff_kwargs)
+                layer = layer if not macaron else Scale(0.5, layer)
+            else:
+                raise Exception(f'invalid layer type {layer_type}')
+
+            if isinstance(layer, Attention) and exists(branch_fn):
+                layer = branch_fn(layer)
+
+            if gate_residual:
+                residual_fn = GRUGating(dim)
+            else:
+                residual_fn = Residual()
+
+            self.layers.append(nn.ModuleList([
+                norm_fn(),
+                layer,
+                residual_fn
+            ]))
+
+    def forward(
+            self,
+            x,
+            context=None,
+            mask=None,
+            context_mask=None,
+            mems=None,
+            return_hiddens=False
+    ):
+        hiddens = []
+        intermediates = []
+        prev_attn = None
+        prev_cross_attn = None
+
+        mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
+
+        for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
+            is_last = ind == (len(self.layers) - 1)
+
+            if layer_type == 'a':
+                hiddens.append(x)
+                layer_mem = mems.pop(0)
+
+            residual = x
+
+            if self.pre_norm:
+                x = norm(x)
+
+            if layer_type == 'a':
+                out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
+                                   prev_attn=prev_attn, mem=layer_mem)
+            elif layer_type == 'c':
+                out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
+            elif layer_type == 'f':
+                out = block(x)
+
+            x = residual_fn(out, residual)
+
+            if layer_type in ('a', 'c'):
+                intermediates.append(inter)
+
+            if layer_type == 'a' and self.residual_attn:
+                prev_attn = inter.pre_softmax_attn
+            elif layer_type == 'c' and self.cross_residual_attn:
+                prev_cross_attn = inter.pre_softmax_attn
+
+            if not self.pre_norm and not is_last:
+                x = norm(x)
+
+        if return_hiddens:
+            intermediates = LayerIntermediates(
+                hiddens=hiddens,
+                attn_intermediates=intermediates
+            )
+
+            return x, intermediates
+
+        return x
+
+
+class Encoder(AttentionLayers):
+    def __init__(self, **kwargs):
+        assert 'causal' not in kwargs, 'cannot set causality on encoder'
+        super().__init__(causal=False, **kwargs)
+
+
+
+class TransformerWrapper(nn.Module):
+    def __init__(
+            self,
+            *,
+            num_tokens,
+            max_seq_len,
+            attn_layers,
+            emb_dim=None,
+            max_mem_len=0.,
+            emb_dropout=0.,
+            num_memory_tokens=None,
+            tie_embedding=False,
+            use_pos_emb=True
+    ):
+        super().__init__()
+        assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
+
+        dim = attn_layers.dim
+        emb_dim = default(emb_dim, dim)
+
+        self.max_seq_len = max_seq_len
+        self.max_mem_len = max_mem_len
+        self.num_tokens = num_tokens
+
+        self.token_emb = nn.Embedding(num_tokens, emb_dim)
+        self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
+                    use_pos_emb and not attn_layers.has_pos_emb) else always(0)
+        self.emb_dropout = nn.Dropout(emb_dropout)
+
+        self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
+        self.attn_layers = attn_layers
+        self.norm = nn.LayerNorm(dim)
+
+        self.init_()
+
+        self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
+
+        # memory tokens (like [cls]) from Memory Transformers paper
+        num_memory_tokens = default(num_memory_tokens, 0)
+        self.num_memory_tokens = num_memory_tokens
+        if num_memory_tokens > 0:
+            self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
+
+            # let funnel encoder know number of memory tokens, if specified
+            if hasattr(attn_layers, 'num_memory_tokens'):
+                attn_layers.num_memory_tokens = num_memory_tokens
+
+    def init_(self):
+        nn.init.normal_(self.token_emb.weight, std=0.02)
+
+    def forward(
+            self,
+            x,
+            return_embeddings=False,
+            mask=None,
+            return_mems=False,
+            return_attn=False,
+            mems=None,
+            **kwargs
+    ):
+        b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
+        x = self.token_emb(x)
+        x += self.pos_emb(x)
+        x = self.emb_dropout(x)
+
+        x = self.project_emb(x)
+
+        if num_mem > 0:
+            mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
+            x = torch.cat((mem, x), dim=1)
+
+            # auto-handle masking after appending memory tokens
+            if exists(mask):
+                mask = F.pad(mask, (num_mem, 0), value=True)
+
+        x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
+        x = self.norm(x)
+
+        mem, x = x[:, :num_mem], x[:, num_mem:]
+
+        out = self.to_logits(x) if not return_embeddings else x
+
+        if return_mems:
+            hiddens = intermediates.hiddens
+            new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
+            new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
+            return out, new_mems
+
+        if return_attn:
+            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
+            return out, attn_maps
+
+        return out
+
diff --git a/ldm/util.py b/ldm/util.py
new file mode 100644
index 00000000..8ba38853
--- /dev/null
+++ b/ldm/util.py
@@ -0,0 +1,203 @@
+import importlib
+
+import torch
+import numpy as np
+from collections import abc
+from einops import rearrange
+from functools import partial
+
+import multiprocessing as mp
+from threading import Thread
+from queue import Queue
+
+from inspect import isfunction
+from PIL import Image, ImageDraw, ImageFont
+
+
+def log_txt_as_img(wh, xc, size=10):
+    # wh a tuple of (width, height)
+    # xc a list of captions to plot
+    b = len(xc)
+    txts = list()
+    for bi in range(b):
+        txt = Image.new("RGB", wh, color="white")
+        draw = ImageDraw.Draw(txt)
+        font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
+        nc = int(40 * (wh[0] / 256))
+        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
+
+        try:
+            draw.text((0, 0), lines, fill="black", font=font)
+        except UnicodeEncodeError:
+            print("Cant encode string for logging. Skipping.")
+
+        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
+        txts.append(txt)
+    txts = np.stack(txts)
+    txts = torch.tensor(txts)
+    return txts
+
+
+def ismap(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] > 3)
+
+
+def isimage(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
+
+
+def exists(x):
+    return x is not None
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def mean_flat(tensor):
+    """
+    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def count_params(model, verbose=False):
+    total_params = sum(p.numel() for p in model.parameters())
+    if verbose:
+        print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
+    return total_params
+
+
+def instantiate_from_config(config):
+    if not "target" in config:
+        if config == '__is_first_stage__':
+            return None
+        elif config == "__is_unconditional__":
+            return None
+        raise KeyError("Expected key `target` to instantiate.")
+    return get_obj_from_str(config["target"])(**config.get("params", dict()))
+
+
+def get_obj_from_str(string, reload=False):
+    module, cls = string.rsplit(".", 1)
+    if reload:
+        module_imp = importlib.import_module(module)
+        importlib.reload(module_imp)
+    return getattr(importlib.import_module(module, package=None), cls)
+
+
+def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
+    # create dummy dataset instance
+
+    # run prefetching
+    if idx_to_fn:
+        res = func(data, worker_id=idx)
+    else:
+        res = func(data)
+    Q.put([idx, res])
+    Q.put("Done")
+
+
+def parallel_data_prefetch(
+        func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False
+):
+    # if target_data_type not in ["ndarray", "list"]:
+    #     raise ValueError(
+    #         "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
+    #     )
+    if isinstance(data, np.ndarray) and target_data_type == "list":
+        raise ValueError("list expected but function got ndarray.")
+    elif isinstance(data, abc.Iterable):
+        if isinstance(data, dict):
+            print(
+                f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
+            )
+            data = list(data.values())
+        if target_data_type == "ndarray":
+            data = np.asarray(data)
+        else:
+            data = list(data)
+    else:
+        raise TypeError(
+            f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
+        )
+
+    if cpu_intensive:
+        Q = mp.Queue(1000)
+        proc = mp.Process
+    else:
+        Q = Queue(1000)
+        proc = Thread
+    # spawn processes
+    if target_data_type == "ndarray":
+        arguments = [
+            [func, Q, part, i, use_worker_id]
+            for i, part in enumerate(np.array_split(data, n_proc))
+        ]
+    else:
+        step = (
+            int(len(data) / n_proc + 1)
+            if len(data) % n_proc != 0
+            else int(len(data) / n_proc)
+        )
+        arguments = [
+            [func, Q, part, i, use_worker_id]
+            for i, part in enumerate(
+                [data[i: i + step] for i in range(0, len(data), step)]
+            )
+        ]
+    processes = []
+    for i in range(n_proc):
+        p = proc(target=_do_parallel_data_prefetch, args=arguments[i])
+        processes += [p]
+
+    # start processes
+    print(f"Start prefetching...")
+    import time
+
+    start = time.time()
+    gather_res = [[] for _ in range(n_proc)]
+    try:
+        for p in processes:
+            p.start()
+
+        k = 0
+        while k < n_proc:
+            # get result
+            res = Q.get()
+            if res == "Done":
+                k += 1
+            else:
+                gather_res[res[0]] = res[1]
+
+    except Exception as e:
+        print("Exception: ", e)
+        for p in processes:
+            p.terminate()
+
+        raise e
+    finally:
+        for p in processes:
+            p.join()
+        print(f"Prefetching complete. [{time.time() - start} sec.]")
+
+    if target_data_type == 'ndarray':
+        if not isinstance(gather_res[0], np.ndarray):
+            return np.concatenate([np.asarray(r) for r in gather_res], axis=0)
+
+        # order outputs
+        return np.concatenate(gather_res, axis=0)
+    elif target_data_type == 'list':
+        out = []
+        for r in gather_res:
+            out.extend(r)
+        return out
+    else:
+        return gather_res
diff --git a/modules/devices.py b/modules/devices.py
index 67165bf6..f30b6ebc 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -36,8 +36,8 @@ def get_optimal_device():
         else:
             return torch.device("cuda")
 
-    if has_mps():
-        return torch.device("mps")
+    # if has_mps():
+    #     return torch.device("mps")
 
     return cpu
 
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index eaedac13..26280fe4 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -70,14 +70,19 @@ class StableDiffusionModelHijack:
     embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
 
     def hijack(self, m):
-        model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+       
+        if shared.text_model_name == "XLMR-Large":
+            model_embeddings = m.cond_stage_model.roberta.embeddings
+            model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
+        else :
+            model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+            model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embeddings, self)
 
-        model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
         m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
 
         self.clip = m.cond_stage_model
 
-        apply_optimizations()
+        # apply_optimizations()
 
         def flatten(el):
             flattened = [flatten(children) for children in el.children()]
@@ -125,8 +130,11 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         self.tokenizer = wrapped.tokenizer
         self.token_mults = {}
 
-        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
-
+        try:
+            self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
+        except:
+            self.comma_token = None
+           
         tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
         for text, ident in tokens_with_parens:
             mult = 1.0
@@ -298,6 +306,9 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
 
     def forward(self, text):
+        if shared.text_model_name == "XLMR-Large":
+            return self.wrapped.encode(text)
+        
         use_old = opts.use_old_emphasis_implementation
         if use_old:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
@@ -359,7 +370,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             z = self.wrapped.transformer.text_model.final_layer_norm(z)
         else:
             z = outputs.last_hidden_state
-
+        
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
         batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
diff --git a/modules/shared.py b/modules/shared.py
index c93ae2a3..9941d2f4 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -21,7 +21,7 @@ from modules.paths import models_path, script_path, sd_path
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
 parser = argparse.ArgumentParser()
-parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
+parser.add_argument("--config", type=str, default="configs/altdiffusion/ad-inference.yaml", help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
 parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
@@ -106,6 +106,10 @@ restricted_opts = {
     "outdir_txt2img_grids",
     "outdir_save",
 }
+from omegaconf import OmegaConf
+config = OmegaConf.load(f"{cmd_opts.config}")
+# XLMR-Large
+text_model_name = config.model.params.cond_stage_config.params.name
 
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
-- 
cgit v1.2.1


From ee3f5ea3eeb31f1ed72e2f0cbed2c00a782497d8 Mon Sep 17 00:00:00 2001
From: zhaohu xing <920232796@qq.com>
Date: Tue, 29 Nov 2022 10:30:19 +0800
Subject: delete old config file

Signed-off-by: zhaohu xing <920232796@qq.com>
---
 configs/stable-diffusion/v1-inference.yaml | 71 ------------------------------
 1 file changed, 71 deletions(-)
 delete mode 100644 configs/stable-diffusion/v1-inference.yaml

diff --git a/configs/stable-diffusion/v1-inference.yaml b/configs/stable-diffusion/v1-inference.yaml
deleted file mode 100644
index 2e6ef0f2..00000000
--- a/configs/stable-diffusion/v1-inference.yaml
+++ /dev/null
@@ -1,71 +0,0 @@
-model:
-  base_learning_rate: 1.0e-04
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false   # Note: different from the one we trained before
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [ 10000 ]
-        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
-        f_start: [ 1.e-6 ]
-        f_max: [ 1. ]
-        f_min: [ 1. ]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_heads: 8
-        use_spatial_transformer: True
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: True
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      # target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
-      target: altclip.model.AltCLIPEmbedder
\ No newline at end of file
-- 
cgit v1.2.1


From 9a8678f61eff172811498a682c171399b7216e12 Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Tue, 29 Nov 2022 11:11:29 +0800
Subject: Support changing checkpoint and vae through override_settings

---
 modules/processing.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index edceb532..a5c72e3d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -20,6 +20,8 @@ import modules.shared as shared
 import modules.face_restoration
 import modules.images as images
 import modules.styles
+import modules.sd_models as sd_models
+import modules.sd_vae as sd_vae
 import logging
 
 
@@ -424,8 +426,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     try:
         for k, v in p.override_settings.items():
-            setattr(opts, k, v)  # we don't call onchange for simplicity which makes changing model impossible
-            if k == 'sd_hypernetwork': shared.reload_hypernetworks()  # make onchange call for changing hypernet since it is relatively fast to load on-change, while SD models are not
+            setattr(opts, k, v)
+            if k == 'sd_hypernetwork': shared.reload_hypernetworks()  # make onchange call for changing hypernet
+            if k == 'sd_model_checkpoint': sd_models.reload_model_weights()  # make onchange call for changing SD model
+            if k == 'sd_vae': sd_vae.reload_vae_weights()  # make onchange call for changing VAE
 
         res = process_images_inner(p)
 
@@ -433,6 +437,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         for k, v in stored_opts.items():
             setattr(opts, k, v)
             if k == 'sd_hypernetwork': shared.reload_hypernetworks()
+            if k == 'sd_model_checkpoint': sd_models.reload_model_weights()
+            if k == 'sd_vae': sd_vae.reload_vae_weights()
 
     return res
 
-- 
cgit v1.2.1


From 241cbc4d2fed71d33e5dc62c3d61b63a54e9d790 Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Tue, 29 Nov 2022 17:38:16 +0000
Subject: Hijack VQModelInterface back to AutoEncoder

---
 modules/sd_hijack_autoencoder.py | 282 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 282 insertions(+)
 create mode 100644 modules/sd_hijack_autoencoder.py

diff --git a/modules/sd_hijack_autoencoder.py b/modules/sd_hijack_autoencoder.py
new file mode 100644
index 00000000..ffa72f90
--- /dev/null
+++ b/modules/sd_hijack_autoencoder.py
@@ -0,0 +1,282 @@
+import torch
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from contextlib import contextmanager
+from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
+from ldm.modules.diffusionmodules.model import Encoder, Decoder
+from ldm.util import instantiate_from_config
+
+import ldm.models.autoencoder
+
+class VQModel(pl.LightningModule):
+    def __init__(self,
+                 ddconfig,
+                 lossconfig,
+                 n_embed,
+                 embed_dim,
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 image_key="image",
+                 colorize_nlabels=None,
+                 monitor=None,
+                 batch_resize_range=None,
+                 scheduler_config=None,
+                 lr_g_factor=1.0,
+                 remap=None,
+                 sane_index_shape=False, # tell vector quantizer to return indices as bhw
+                 use_ema=False
+                 ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.n_embed = n_embed
+        self.image_key = image_key
+        self.encoder = Encoder(**ddconfig)
+        self.decoder = Decoder(**ddconfig)
+        self.loss = instantiate_from_config(lossconfig)
+        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
+                                        remap=remap,
+                                        sane_index_shape=sane_index_shape)
+        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        if colorize_nlabels is not None:
+            assert type(colorize_nlabels)==int
+            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+        if monitor is not None:
+            self.monitor = monitor
+        self.batch_resize_range = batch_resize_range
+        if self.batch_resize_range is not None:
+            print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
+
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+        self.scheduler_config = scheduler_config
+        self.lr_g_factor = lr_g_factor
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.parameters())
+            self.model_ema.copy_to(self)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def init_from_ckpt(self, path, ignore_keys=list()):
+        sd = torch.load(path, map_location="cpu")["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        missing, unexpected = self.load_state_dict(sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+            print(f"Unexpected Keys: {unexpected}")
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self)
+
+    def encode(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        quant, emb_loss, info = self.quantize(h)
+        return quant, emb_loss, info
+
+    def encode_to_prequant(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        return h
+
+    def decode(self, quant):
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+    def decode_code(self, code_b):
+        quant_b = self.quantize.embed_code(code_b)
+        dec = self.decode(quant_b)
+        return dec
+
+    def forward(self, input, return_pred_indices=False):
+        quant, diff, (_,_,ind) = self.encode(input)
+        dec = self.decode(quant)
+        if return_pred_indices:
+            return dec, diff, ind
+        return dec, diff
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+        if self.batch_resize_range is not None:
+            lower_size = self.batch_resize_range[0]
+            upper_size = self.batch_resize_range[1]
+            if self.global_step <= 4:
+                # do the first few batches with max size to avoid later oom
+                new_resize = upper_size
+            else:
+                new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
+            if new_resize != x.shape[2]:
+                x = F.interpolate(x, size=new_resize, mode="bicubic")
+            x = x.detach()
+        return x
+
+    def training_step(self, batch, batch_idx, optimizer_idx):
+        # https://github.com/pytorch/pytorch/issues/37142
+        # try not to fool the heuristics
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss, ind = self(x, return_pred_indices=True)
+
+        if optimizer_idx == 0:
+            # autoencode
+            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train",
+                                            predicted_indices=ind)
+
+            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return aeloss
+
+        if optimizer_idx == 1:
+            # discriminator
+            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train")
+            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return discloss
+
+    def validation_step(self, batch, batch_idx):
+        log_dict = self._validation_step(batch, batch_idx)
+        with self.ema_scope():
+            log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
+        return log_dict
+
+    def _validation_step(self, batch, batch_idx, suffix=""):
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss, ind = self(x, return_pred_indices=True)
+        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
+                                        self.global_step,
+                                        last_layer=self.get_last_layer(),
+                                        split="val"+suffix,
+                                        predicted_indices=ind
+                                        )
+
+        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
+                                            self.global_step,
+                                            last_layer=self.get_last_layer(),
+                                            split="val"+suffix,
+                                            predicted_indices=ind
+                                            )
+        rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
+        self.log(f"val{suffix}/rec_loss", rec_loss,
+                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+        self.log(f"val{suffix}/aeloss", aeloss,
+                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+        if version.parse(pl.__version__) >= version.parse('1.4.0'):
+            del log_dict_ae[f"val{suffix}/rec_loss"]
+        self.log_dict(log_dict_ae)
+        self.log_dict(log_dict_disc)
+        return self.log_dict
+
+    def configure_optimizers(self):
+        lr_d = self.learning_rate
+        lr_g = self.lr_g_factor*self.learning_rate
+        print("lr_d", lr_d)
+        print("lr_g", lr_g)
+        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
+                                  list(self.decoder.parameters())+
+                                  list(self.quantize.parameters())+
+                                  list(self.quant_conv.parameters())+
+                                  list(self.post_quant_conv.parameters()),
+                                  lr=lr_g, betas=(0.5, 0.9))
+        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+                                    lr=lr_d, betas=(0.5, 0.9))
+
+        if self.scheduler_config is not None:
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                },
+                {
+                    'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                },
+            ]
+            return [opt_ae, opt_disc], scheduler
+        return [opt_ae, opt_disc], []
+
+    def get_last_layer(self):
+        return self.decoder.conv_out.weight
+
+    def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.image_key)
+        x = x.to(self.device)
+        if only_inputs:
+            log["inputs"] = x
+            return log
+        xrec, _ = self(x)
+        if x.shape[1] > 3:
+            # colorize with random projection
+            assert xrec.shape[1] > 3
+            x = self.to_rgb(x)
+            xrec = self.to_rgb(xrec)
+        log["inputs"] = x
+        log["reconstructions"] = xrec
+        if plot_ema:
+            with self.ema_scope():
+                xrec_ema, _ = self(x)
+                if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
+                log["reconstructions_ema"] = xrec_ema
+        return log
+
+    def to_rgb(self, x):
+        assert self.image_key == "segmentation"
+        if not hasattr(self, "colorize"):
+            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+        x = F.conv2d(x, weight=self.colorize)
+        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+        return x
+
+
+class VQModelInterface(VQModel):
+    def __init__(self, embed_dim, *args, **kwargs):
+        super().__init__(embed_dim=embed_dim, *args, **kwargs)
+        self.embed_dim = embed_dim
+
+    def encode(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        return h
+
+    def decode(self, h, force_not_quantize=False):
+        # also go through quantization layer
+        if not force_not_quantize:
+            quant, emb_loss, info = self.quantize(h)
+        else:
+            quant = h
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+setattr(ldm.models.autoencoder, "VQModel", VQModel)
+setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)
-- 
cgit v1.2.1


From 36c3613d16c523e43ec4dedbcbe9a3b93ad7d139 Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Tue, 29 Nov 2022 17:40:02 +0000
Subject: Add autoencoder to sd_hijack

---
 modules/sd_hijack.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index b824b5bf..26f9b951 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -11,7 +11,7 @@ import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.hypernetworks import hypernetwork
 from modules.shared import opts, device, cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_autoencoder
 
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
-- 
cgit v1.2.1


From 7193814cf7b052fe52596cab89c5f0fd95823950 Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Tue, 29 Nov 2022 19:22:53 +0000
Subject: Added purpose of this hijack to comments

---
 modules/sd_hijack_autoencoder.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/modules/sd_hijack_autoencoder.py b/modules/sd_hijack_autoencoder.py
index ffa72f90..8e03c7f8 100644
--- a/modules/sd_hijack_autoencoder.py
+++ b/modules/sd_hijack_autoencoder.py
@@ -1,3 +1,7 @@
+# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
+# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
+# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
+
 import torch
 import pytorch_lightning as pl
 import torch.nn.functional as F
-- 
cgit v1.2.1


From c4067c562698740d12ba01260627445f70f5bc24 Mon Sep 17 00:00:00 2001
From: Suffocate <70031311+lolsuffocate@users.noreply.github.com>
Date: Wed, 30 Nov 2022 01:11:01 +0000
Subject: Restore scroll position on page if giving active element focus
 changes it

---
 javascript/progressbar.js | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index 43d1d1ce..d58737c4 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -92,14 +92,26 @@ function check_gallery(id_gallery){
             if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
                 // automatically re-open previously selected index (if exists)
                 activeElement = gradioApp().activeElement;
+                let scrollX = window.scrollX;
+                let scrollY = window.scrollY;
 
                 galleryButtons[prevSelectedIndex].click();
                 showGalleryImage();
 
+                // When the gallery button is clicked, it gains focus and scrolls itself into view
+                // We need to scroll back to the previous position
+                setTimeout(function (){
+                    window.scrollTo(scrollX, scrollY);
+                }, 50);
+
                 if(activeElement){
                     // i fought this for about an hour; i don't know why the focus is lost or why this helps recover it
-                    // if somenoe has a better solution please by all means
-                    setTimeout(function() { activeElement.focus() }, 1);
+                    // if someone has a better solution please by all means
+                    setTimeout(function (){
+                        activeElement.focus({
+                            preventScroll: true // Refocus the element that was focused before the gallery was opened without scrolling to it
+                        })
+                    }, 1);
                 }
             }
         })
-- 
cgit v1.2.1


From 52cc83d36b7663a77b79fd2258d2ca871af73e55 Mon Sep 17 00:00:00 2001
From: zhaohu xing <920232796@qq.com>
Date: Wed, 30 Nov 2022 14:56:12 +0800
Subject: fix bugs

Signed-off-by: zhaohu xing <920232796@qq.com>
---
 configs/altdiffusion/ad-inference.yaml        |    2 +-
 launch.py                                     |   10 +-
 ldm/data/__init__.py                          |    0
 ldm/data/base.py                              |   23 -
 ldm/data/imagenet.py                          |  394 -------
 ldm/data/lsun.py                              |   92 --
 ldm/lr_scheduler.py                           |   98 --
 ldm/models/autoencoder.py                     |  443 --------
 ldm/models/diffusion/__init__.py              |    0
 ldm/models/diffusion/classifier.py            |  267 -----
 ldm/models/diffusion/ddim.py                  |  241 -----
 ldm/models/diffusion/ddpm.py                  | 1445 -------------------------
 ldm/models/diffusion/dpm_solver/__init__.py   |    1 -
 ldm/models/diffusion/dpm_solver/dpm_solver.py | 1184 --------------------
 ldm/models/diffusion/dpm_solver/sampler.py    |   82 --
 ldm/models/diffusion/plms.py                  |  236 ----
 ldm/modules/attention.py                      |  261 -----
 ldm/modules/diffusionmodules/__init__.py      |    0
 ldm/modules/diffusionmodules/model.py         |  835 --------------
 ldm/modules/diffusionmodules/openaimodel.py   |  961 ----------------
 ldm/modules/diffusionmodules/util.py          |  267 -----
 ldm/modules/distributions/__init__.py         |    0
 ldm/modules/distributions/distributions.py    |   92 --
 ldm/modules/ema.py                            |   76 --
 ldm/modules/encoders/__init__.py              |    0
 ldm/modules/encoders/modules.py               |  234 ----
 ldm/modules/encoders/xlmr.py                  |  137 ---
 ldm/modules/image_degradation/__init__.py     |    2 -
 ldm/modules/image_degradation/bsrgan.py       |  730 -------------
 ldm/modules/image_degradation/bsrgan_light.py |  650 -----------
 ldm/modules/image_degradation/utils/test.png  |  Bin 441072 -> 0 bytes
 ldm/modules/image_degradation/utils_image.py  |  916 ----------------
 ldm/modules/losses/__init__.py                |    1 -
 ldm/modules/losses/contperceptual.py          |  111 --
 ldm/modules/losses/vqperceptual.py            |  167 ---
 ldm/modules/x_transformer.py                  |  641 -----------
 ldm/util.py                                   |  203 ----
 modules/sd_hijack.py                          |   15 +-
 modules/sd_hijack_clip.py                     |   10 +-
 modules/xlmr.py                               |  137 +++
 40 files changed, 159 insertions(+), 10805 deletions(-)
 delete mode 100644 ldm/data/__init__.py
 delete mode 100644 ldm/data/base.py
 delete mode 100644 ldm/data/imagenet.py
 delete mode 100644 ldm/data/lsun.py
 delete mode 100644 ldm/lr_scheduler.py
 delete mode 100644 ldm/models/autoencoder.py
 delete mode 100644 ldm/models/diffusion/__init__.py
 delete mode 100644 ldm/models/diffusion/classifier.py
 delete mode 100644 ldm/models/diffusion/ddim.py
 delete mode 100644 ldm/models/diffusion/ddpm.py
 delete mode 100644 ldm/models/diffusion/dpm_solver/__init__.py
 delete mode 100644 ldm/models/diffusion/dpm_solver/dpm_solver.py
 delete mode 100644 ldm/models/diffusion/dpm_solver/sampler.py
 delete mode 100644 ldm/models/diffusion/plms.py
 delete mode 100644 ldm/modules/attention.py
 delete mode 100644 ldm/modules/diffusionmodules/__init__.py
 delete mode 100644 ldm/modules/diffusionmodules/model.py
 delete mode 100644 ldm/modules/diffusionmodules/openaimodel.py
 delete mode 100644 ldm/modules/diffusionmodules/util.py
 delete mode 100644 ldm/modules/distributions/__init__.py
 delete mode 100644 ldm/modules/distributions/distributions.py
 delete mode 100644 ldm/modules/ema.py
 delete mode 100644 ldm/modules/encoders/__init__.py
 delete mode 100644 ldm/modules/encoders/modules.py
 delete mode 100644 ldm/modules/encoders/xlmr.py
 delete mode 100644 ldm/modules/image_degradation/__init__.py
 delete mode 100644 ldm/modules/image_degradation/bsrgan.py
 delete mode 100644 ldm/modules/image_degradation/bsrgan_light.py
 delete mode 100644 ldm/modules/image_degradation/utils/test.png
 delete mode 100644 ldm/modules/image_degradation/utils_image.py
 delete mode 100644 ldm/modules/losses/__init__.py
 delete mode 100644 ldm/modules/losses/contperceptual.py
 delete mode 100644 ldm/modules/losses/vqperceptual.py
 delete mode 100644 ldm/modules/x_transformer.py
 delete mode 100644 ldm/util.py
 create mode 100644 modules/xlmr.py

diff --git a/configs/altdiffusion/ad-inference.yaml b/configs/altdiffusion/ad-inference.yaml
index 1b11b63e..cfbee72d 100644
--- a/configs/altdiffusion/ad-inference.yaml
+++ b/configs/altdiffusion/ad-inference.yaml
@@ -67,6 +67,6 @@ model:
           target: torch.nn.Identity
 
     cond_stage_config:
-      target: ldm.modules.encoders.xlmr.BertSeriesModelWithTransformation
+      target: modules.xlmr.BertSeriesModelWithTransformation
       params:
         name: "XLMR-Large"
\ No newline at end of file
diff --git a/launch.py b/launch.py
index ad9ddd5a..3f4dc870 100644
--- a/launch.py
+++ b/launch.py
@@ -233,11 +233,11 @@ def prepare_enviroment():
 
     os.makedirs(dir_repos, exist_ok=True)
 
-    git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
-    git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
-    git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
-    git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
-    git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
+    git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", )
+    git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", )
+    git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", )
+    git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", )
+    git_clone(blip_repo, repo_dir('BLIP'), "BLIP", )
 
     if not is_installed("lpips"):
         run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
diff --git a/ldm/data/__init__.py b/ldm/data/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/ldm/data/base.py b/ldm/data/base.py
deleted file mode 100644
index b196c2f7..00000000
--- a/ldm/data/base.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from abc import abstractmethod
-from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset
-
-
-class Txt2ImgIterableBaseDataset(IterableDataset):
-    '''
-    Define an interface to make the IterableDatasets for text2img data chainable
-    '''
-    def __init__(self, num_records=0, valid_ids=None, size=256):
-        super().__init__()
-        self.num_records = num_records
-        self.valid_ids = valid_ids
-        self.sample_ids = valid_ids
-        self.size = size
-
-        print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.')
-
-    def __len__(self):
-        return self.num_records
-
-    @abstractmethod
-    def __iter__(self):
-        pass
\ No newline at end of file
diff --git a/ldm/data/imagenet.py b/ldm/data/imagenet.py
deleted file mode 100644
index 1c473f9c..00000000
--- a/ldm/data/imagenet.py
+++ /dev/null
@@ -1,394 +0,0 @@
-import os, yaml, pickle, shutil, tarfile, glob
-import cv2
-import albumentations
-import PIL
-import numpy as np
-import torchvision.transforms.functional as TF
-from omegaconf import OmegaConf
-from functools import partial
-from PIL import Image
-from tqdm import tqdm
-from torch.utils.data import Dataset, Subset
-
-import taming.data.utils as tdu
-from taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve
-from taming.data.imagenet import ImagePaths
-
-from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light
-
-
-def synset2idx(path_to_yaml="data/index_synset.yaml"):
-    with open(path_to_yaml) as f:
-        di2s = yaml.load(f)
-    return dict((v,k) for k,v in di2s.items())
-
-
-class ImageNetBase(Dataset):
-    def __init__(self, config=None):
-        self.config = config or OmegaConf.create()
-        if not type(self.config)==dict:
-            self.config = OmegaConf.to_container(self.config)
-        self.keep_orig_class_label = self.config.get("keep_orig_class_label", False)
-        self.process_images = True  # if False we skip loading & processing images and self.data contains filepaths
-        self._prepare()
-        self._prepare_synset_to_human()
-        self._prepare_idx_to_synset()
-        self._prepare_human_to_integer_label()
-        self._load()
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        return self.data[i]
-
-    def _prepare(self):
-        raise NotImplementedError()
-
-    def _filter_relpaths(self, relpaths):
-        ignore = set([
-            "n06596364_9591.JPEG",
-        ])
-        relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore]
-        if "sub_indices" in self.config:
-            indices = str_to_indices(self.config["sub_indices"])
-            synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn)  # returns a list of strings
-            self.synset2idx = synset2idx(path_to_yaml=self.idx2syn)
-            files = []
-            for rpath in relpaths:
-                syn = rpath.split("/")[0]
-                if syn in synsets:
-                    files.append(rpath)
-            return files
-        else:
-            return relpaths
-
-    def _prepare_synset_to_human(self):
-        SIZE = 2655750
-        URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1"
-        self.human_dict = os.path.join(self.root, "synset_human.txt")
-        if (not os.path.exists(self.human_dict) or
-                not os.path.getsize(self.human_dict)==SIZE):
-            download(URL, self.human_dict)
-
-    def _prepare_idx_to_synset(self):
-        URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1"
-        self.idx2syn = os.path.join(self.root, "index_synset.yaml")
-        if (not os.path.exists(self.idx2syn)):
-            download(URL, self.idx2syn)
-
-    def _prepare_human_to_integer_label(self):
-        URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1"
-        self.human2integer = os.path.join(self.root, "imagenet1000_clsidx_to_labels.txt")
-        if (not os.path.exists(self.human2integer)):
-            download(URL, self.human2integer)
-        with open(self.human2integer, "r") as f:
-            lines = f.read().splitlines()
-            assert len(lines) == 1000
-            self.human2integer_dict = dict()
-            for line in lines:
-                value, key = line.split(":")
-                self.human2integer_dict[key] = int(value)
-
-    def _load(self):
-        with open(self.txt_filelist, "r") as f:
-            self.relpaths = f.read().splitlines()
-            l1 = len(self.relpaths)
-            self.relpaths = self._filter_relpaths(self.relpaths)
-            print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths)))
-
-        self.synsets = [p.split("/")[0] for p in self.relpaths]
-        self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths]
-
-        unique_synsets = np.unique(self.synsets)
-        class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets))
-        if not self.keep_orig_class_label:
-            self.class_labels = [class_dict[s] for s in self.synsets]
-        else:
-            self.class_labels = [self.synset2idx[s] for s in self.synsets]
-
-        with open(self.human_dict, "r") as f:
-            human_dict = f.read().splitlines()
-            human_dict = dict(line.split(maxsplit=1) for line in human_dict)
-
-        self.human_labels = [human_dict[s] for s in self.synsets]
-
-        labels = {
-            "relpath": np.array(self.relpaths),
-            "synsets": np.array(self.synsets),
-            "class_label": np.array(self.class_labels),
-            "human_label": np.array(self.human_labels),
-        }
-
-        if self.process_images:
-            self.size = retrieve(self.config, "size", default=256)
-            self.data = ImagePaths(self.abspaths,
-                                   labels=labels,
-                                   size=self.size,
-                                   random_crop=self.random_crop,
-                                   )
-        else:
-            self.data = self.abspaths
-
-
-class ImageNetTrain(ImageNetBase):
-    NAME = "ILSVRC2012_train"
-    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
-    AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2"
-    FILES = [
-        "ILSVRC2012_img_train.tar",
-    ]
-    SIZES = [
-        147897477120,
-    ]
-
-    def __init__(self, process_images=True, data_root=None, **kwargs):
-        self.process_images = process_images
-        self.data_root = data_root
-        super().__init__(**kwargs)
-
-    def _prepare(self):
-        if self.data_root:
-            self.root = os.path.join(self.data_root, self.NAME)
-        else:
-            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
-            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
-
-        self.datadir = os.path.join(self.root, "data")
-        self.txt_filelist = os.path.join(self.root, "filelist.txt")
-        self.expected_length = 1281167
-        self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop",
-                                    default=True)
-        if not tdu.is_prepared(self.root):
-            # prep
-            print("Preparing dataset {} in {}".format(self.NAME, self.root))
-
-            datadir = self.datadir
-            if not os.path.exists(datadir):
-                path = os.path.join(self.root, self.FILES[0])
-                if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
-                    import academictorrents as at
-                    atpath = at.get(self.AT_HASH, datastore=self.root)
-                    assert atpath == path
-
-                print("Extracting {} to {}".format(path, datadir))
-                os.makedirs(datadir, exist_ok=True)
-                with tarfile.open(path, "r:") as tar:
-                    tar.extractall(path=datadir)
-
-                print("Extracting sub-tars.")
-                subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar")))
-                for subpath in tqdm(subpaths):
-                    subdir = subpath[:-len(".tar")]
-                    os.makedirs(subdir, exist_ok=True)
-                    with tarfile.open(subpath, "r:") as tar:
-                        tar.extractall(path=subdir)
-
-            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
-            filelist = [os.path.relpath(p, start=datadir) for p in filelist]
-            filelist = sorted(filelist)
-            filelist = "\n".join(filelist)+"\n"
-            with open(self.txt_filelist, "w") as f:
-                f.write(filelist)
-
-            tdu.mark_prepared(self.root)
-
-
-class ImageNetValidation(ImageNetBase):
-    NAME = "ILSVRC2012_validation"
-    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
-    AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5"
-    VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1"
-    FILES = [
-        "ILSVRC2012_img_val.tar",
-        "validation_synset.txt",
-    ]
-    SIZES = [
-        6744924160,
-        1950000,
-    ]
-
-    def __init__(self, process_images=True, data_root=None, **kwargs):
-        self.data_root = data_root
-        self.process_images = process_images
-        super().__init__(**kwargs)
-
-    def _prepare(self):
-        if self.data_root:
-            self.root = os.path.join(self.data_root, self.NAME)
-        else:
-            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
-            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
-        self.datadir = os.path.join(self.root, "data")
-        self.txt_filelist = os.path.join(self.root, "filelist.txt")
-        self.expected_length = 50000
-        self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop",
-                                    default=False)
-        if not tdu.is_prepared(self.root):
-            # prep
-            print("Preparing dataset {} in {}".format(self.NAME, self.root))
-
-            datadir = self.datadir
-            if not os.path.exists(datadir):
-                path = os.path.join(self.root, self.FILES[0])
-                if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
-                    import academictorrents as at
-                    atpath = at.get(self.AT_HASH, datastore=self.root)
-                    assert atpath == path
-
-                print("Extracting {} to {}".format(path, datadir))
-                os.makedirs(datadir, exist_ok=True)
-                with tarfile.open(path, "r:") as tar:
-                    tar.extractall(path=datadir)
-
-                vspath = os.path.join(self.root, self.FILES[1])
-                if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]:
-                    download(self.VS_URL, vspath)
-
-                with open(vspath, "r") as f:
-                    synset_dict = f.read().splitlines()
-                    synset_dict = dict(line.split() for line in synset_dict)
-
-                print("Reorganizing into synset folders")
-                synsets = np.unique(list(synset_dict.values()))
-                for s in synsets:
-                    os.makedirs(os.path.join(datadir, s), exist_ok=True)
-                for k, v in synset_dict.items():
-                    src = os.path.join(datadir, k)
-                    dst = os.path.join(datadir, v)
-                    shutil.move(src, dst)
-
-            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
-            filelist = [os.path.relpath(p, start=datadir) for p in filelist]
-            filelist = sorted(filelist)
-            filelist = "\n".join(filelist)+"\n"
-            with open(self.txt_filelist, "w") as f:
-                f.write(filelist)
-
-            tdu.mark_prepared(self.root)
-
-
-
-class ImageNetSR(Dataset):
-    def __init__(self, size=None,
-                 degradation=None, downscale_f=4, min_crop_f=0.5, max_crop_f=1.,
-                 random_crop=True):
-        """
-        Imagenet Superresolution Dataloader
-        Performs following ops in order:
-        1.  crops a crop of size s from image either as random or center crop
-        2.  resizes crop to size with cv2.area_interpolation
-        3.  degrades resized crop with degradation_fn
-
-        :param size: resizing to size after cropping
-        :param degradation: degradation_fn, e.g. cv_bicubic or bsrgan_light
-        :param downscale_f: Low Resolution Downsample factor
-        :param min_crop_f: determines crop size s,
-          where s = c * min_img_side_len with c sampled from interval (min_crop_f, max_crop_f)
-        :param max_crop_f: ""
-        :param data_root:
-        :param random_crop:
-        """
-        self.base = self.get_base()
-        assert size
-        assert (size / downscale_f).is_integer()
-        self.size = size
-        self.LR_size = int(size / downscale_f)
-        self.min_crop_f = min_crop_f
-        self.max_crop_f = max_crop_f
-        assert(max_crop_f <= 1.)
-        self.center_crop = not random_crop
-
-        self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA)
-
-        self.pil_interpolation = False # gets reset later if incase interp_op is from pillow
-
-        if degradation == "bsrgan":
-            self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f)
-
-        elif degradation == "bsrgan_light":
-            self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f)
-
-        else:
-            interpolation_fn = {
-            "cv_nearest": cv2.INTER_NEAREST,
-            "cv_bilinear": cv2.INTER_LINEAR,
-            "cv_bicubic": cv2.INTER_CUBIC,
-            "cv_area": cv2.INTER_AREA,
-            "cv_lanczos": cv2.INTER_LANCZOS4,
-            "pil_nearest": PIL.Image.NEAREST,
-            "pil_bilinear": PIL.Image.BILINEAR,
-            "pil_bicubic": PIL.Image.BICUBIC,
-            "pil_box": PIL.Image.BOX,
-            "pil_hamming": PIL.Image.HAMMING,
-            "pil_lanczos": PIL.Image.LANCZOS,
-            }[degradation]
-
-            self.pil_interpolation = degradation.startswith("pil_")
-
-            if self.pil_interpolation:
-                self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn)
-
-            else:
-                self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size,
-                                                                          interpolation=interpolation_fn)
-
-    def __len__(self):
-        return len(self.base)
-
-    def __getitem__(self, i):
-        example = self.base[i]
-        image = Image.open(example["file_path_"])
-
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-
-        image = np.array(image).astype(np.uint8)
-
-        min_side_len = min(image.shape[:2])
-        crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None)
-        crop_side_len = int(crop_side_len)
-
-        if self.center_crop:
-            self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len)
-
-        else:
-            self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len)
-
-        image = self.cropper(image=image)["image"]
-        image = self.image_rescaler(image=image)["image"]
-
-        if self.pil_interpolation:
-            image_pil = PIL.Image.fromarray(image)
-            LR_image = self.degradation_process(image_pil)
-            LR_image = np.array(LR_image).astype(np.uint8)
-
-        else:
-            LR_image = self.degradation_process(image=image)["image"]
-
-        example["image"] = (image/127.5 - 1.0).astype(np.float32)
-        example["LR_image"] = (LR_image/127.5 - 1.0).astype(np.float32)
-
-        return example
-
-
-class ImageNetSRTrain(ImageNetSR):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    def get_base(self):
-        with open("data/imagenet_train_hr_indices.p", "rb") as f:
-            indices = pickle.load(f)
-        dset = ImageNetTrain(process_images=False,)
-        return Subset(dset, indices)
-
-
-class ImageNetSRValidation(ImageNetSR):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    def get_base(self):
-        with open("data/imagenet_val_hr_indices.p", "rb") as f:
-            indices = pickle.load(f)
-        dset = ImageNetValidation(process_images=False,)
-        return Subset(dset, indices)
diff --git a/ldm/data/lsun.py b/ldm/data/lsun.py
deleted file mode 100644
index 6256e457..00000000
--- a/ldm/data/lsun.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import os
-import numpy as np
-import PIL
-from PIL import Image
-from torch.utils.data import Dataset
-from torchvision import transforms
-
-
-class LSUNBase(Dataset):
-    def __init__(self,
-                 txt_file,
-                 data_root,
-                 size=None,
-                 interpolation="bicubic",
-                 flip_p=0.5
-                 ):
-        self.data_paths = txt_file
-        self.data_root = data_root
-        with open(self.data_paths, "r") as f:
-            self.image_paths = f.read().splitlines()
-        self._length = len(self.image_paths)
-        self.labels = {
-            "relative_file_path_": [l for l in self.image_paths],
-            "file_path_": [os.path.join(self.data_root, l)
-                           for l in self.image_paths],
-        }
-
-        self.size = size
-        self.interpolation = {"linear": PIL.Image.LINEAR,
-                              "bilinear": PIL.Image.BILINEAR,
-                              "bicubic": PIL.Image.BICUBIC,
-                              "lanczos": PIL.Image.LANCZOS,
-                              }[interpolation]
-        self.flip = transforms.RandomHorizontalFlip(p=flip_p)
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, i):
-        example = dict((k, self.labels[k][i]) for k in self.labels)
-        image = Image.open(example["file_path_"])
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-
-        # default to score-sde preprocessing
-        img = np.array(image).astype(np.uint8)
-        crop = min(img.shape[0], img.shape[1])
-        h, w, = img.shape[0], img.shape[1]
-        img = img[(h - crop) // 2:(h + crop) // 2,
-              (w - crop) // 2:(w + crop) // 2]
-
-        image = Image.fromarray(img)
-        if self.size is not None:
-            image = image.resize((self.size, self.size), resample=self.interpolation)
-
-        image = self.flip(image)
-        image = np.array(image).astype(np.uint8)
-        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
-        return example
-
-
-class LSUNChurchesTrain(LSUNBase):
-    def __init__(self, **kwargs):
-        super().__init__(txt_file="data/lsun/church_outdoor_train.txt", data_root="data/lsun/churches", **kwargs)
-
-
-class LSUNChurchesValidation(LSUNBase):
-    def __init__(self, flip_p=0., **kwargs):
-        super().__init__(txt_file="data/lsun/church_outdoor_val.txt", data_root="data/lsun/churches",
-                         flip_p=flip_p, **kwargs)
-
-
-class LSUNBedroomsTrain(LSUNBase):
-    def __init__(self, **kwargs):
-        super().__init__(txt_file="data/lsun/bedrooms_train.txt", data_root="data/lsun/bedrooms", **kwargs)
-
-
-class LSUNBedroomsValidation(LSUNBase):
-    def __init__(self, flip_p=0.0, **kwargs):
-        super().__init__(txt_file="data/lsun/bedrooms_val.txt", data_root="data/lsun/bedrooms",
-                         flip_p=flip_p, **kwargs)
-
-
-class LSUNCatsTrain(LSUNBase):
-    def __init__(self, **kwargs):
-        super().__init__(txt_file="data/lsun/cat_train.txt", data_root="data/lsun/cats", **kwargs)
-
-
-class LSUNCatsValidation(LSUNBase):
-    def __init__(self, flip_p=0., **kwargs):
-        super().__init__(txt_file="data/lsun/cat_val.txt", data_root="data/lsun/cats",
-                         flip_p=flip_p, **kwargs)
diff --git a/ldm/lr_scheduler.py b/ldm/lr_scheduler.py
deleted file mode 100644
index be39da9c..00000000
--- a/ldm/lr_scheduler.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import numpy as np
-
-
-class LambdaWarmUpCosineScheduler:
-    """
-    note: use with a base_lr of 1.0
-    """
-    def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
-        self.lr_warm_up_steps = warm_up_steps
-        self.lr_start = lr_start
-        self.lr_min = lr_min
-        self.lr_max = lr_max
-        self.lr_max_decay_steps = max_decay_steps
-        self.last_lr = 0.
-        self.verbosity_interval = verbosity_interval
-
-    def schedule(self, n, **kwargs):
-        if self.verbosity_interval > 0:
-            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
-        if n < self.lr_warm_up_steps:
-            lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
-            self.last_lr = lr
-            return lr
-        else:
-            t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
-            t = min(t, 1.0)
-            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
-                    1 + np.cos(t * np.pi))
-            self.last_lr = lr
-            return lr
-
-    def __call__(self, n, **kwargs):
-        return self.schedule(n,**kwargs)
-
-
-class LambdaWarmUpCosineScheduler2:
-    """
-    supports repeated iterations, configurable via lists
-    note: use with a base_lr of 1.0.
-    """
-    def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
-        assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
-        self.lr_warm_up_steps = warm_up_steps
-        self.f_start = f_start
-        self.f_min = f_min
-        self.f_max = f_max
-        self.cycle_lengths = cycle_lengths
-        self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
-        self.last_f = 0.
-        self.verbosity_interval = verbosity_interval
-
-    def find_in_interval(self, n):
-        interval = 0
-        for cl in self.cum_cycles[1:]:
-            if n <= cl:
-                return interval
-            interval += 1
-
-    def schedule(self, n, **kwargs):
-        cycle = self.find_in_interval(n)
-        n = n - self.cum_cycles[cycle]
-        if self.verbosity_interval > 0:
-            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
-                                                       f"current cycle {cycle}")
-        if n < self.lr_warm_up_steps[cycle]:
-            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
-            self.last_f = f
-            return f
-        else:
-            t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
-            t = min(t, 1.0)
-            f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
-                    1 + np.cos(t * np.pi))
-            self.last_f = f
-            return f
-
-    def __call__(self, n, **kwargs):
-        return self.schedule(n, **kwargs)
-
-
-class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
-
-    def schedule(self, n, **kwargs):
-        cycle = self.find_in_interval(n)
-        n = n - self.cum_cycles[cycle]
-        if self.verbosity_interval > 0:
-            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
-                                                       f"current cycle {cycle}")
-
-        if n < self.lr_warm_up_steps[cycle]:
-            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
-            self.last_f = f
-            return f
-        else:
-            f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])
-            self.last_f = f
-            return f
-
diff --git a/ldm/models/autoencoder.py b/ldm/models/autoencoder.py
deleted file mode 100644
index 6a9c4f45..00000000
--- a/ldm/models/autoencoder.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import torch
-import pytorch_lightning as pl
-import torch.nn.functional as F
-from contextlib import contextmanager
-
-from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
-
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-
-from ldm.util import instantiate_from_config
-
-
-class VQModel(pl.LightningModule):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 n_embed,
-                 embed_dim,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None,
-                 monitor=None,
-                 batch_resize_range=None,
-                 scheduler_config=None,
-                 lr_g_factor=1.0,
-                 remap=None,
-                 sane_index_shape=False, # tell vector quantizer to return indices as bhw
-                 use_ema=False
-                 ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.n_embed = n_embed
-        self.image_key = image_key
-        self.encoder = Encoder(**ddconfig)
-        self.decoder = Decoder(**ddconfig)
-        self.loss = instantiate_from_config(lossconfig)
-        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
-                                        remap=remap,
-                                        sane_index_shape=sane_index_shape)
-        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
-        if colorize_nlabels is not None:
-            assert type(colorize_nlabels)==int
-            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
-        if monitor is not None:
-            self.monitor = monitor
-        self.batch_resize_range = batch_resize_range
-        if self.batch_resize_range is not None:
-            print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
-
-        self.use_ema = use_ema
-        if self.use_ema:
-            self.model_ema = LitEma(self)
-            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-        self.scheduler_config = scheduler_config
-        self.lr_g_factor = lr_g_factor
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.parameters())
-            self.model_ema.copy_to(self)
-            if context is not None:
-                print(f"{context}: Switched to EMA weights")
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.parameters())
-                if context is not None:
-                    print(f"{context}: Restored training weights")
-
-    def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location="cpu")["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-            print(f"Unexpected Keys: {unexpected}")
-
-    def on_train_batch_end(self, *args, **kwargs):
-        if self.use_ema:
-            self.model_ema(self)
-
-    def encode(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        quant, emb_loss, info = self.quantize(h)
-        return quant, emb_loss, info
-
-    def encode_to_prequant(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        return h
-
-    def decode(self, quant):
-        quant = self.post_quant_conv(quant)
-        dec = self.decoder(quant)
-        return dec
-
-    def decode_code(self, code_b):
-        quant_b = self.quantize.embed_code(code_b)
-        dec = self.decode(quant_b)
-        return dec
-
-    def forward(self, input, return_pred_indices=False):
-        quant, diff, (_,_,ind) = self.encode(input)
-        dec = self.decode(quant)
-        if return_pred_indices:
-            return dec, diff, ind
-        return dec, diff
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
-        if self.batch_resize_range is not None:
-            lower_size = self.batch_resize_range[0]
-            upper_size = self.batch_resize_range[1]
-            if self.global_step <= 4:
-                # do the first few batches with max size to avoid later oom
-                new_resize = upper_size
-            else:
-                new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
-            if new_resize != x.shape[2]:
-                x = F.interpolate(x, size=new_resize, mode="bicubic")
-            x = x.detach()
-        return x
-
-    def training_step(self, batch, batch_idx, optimizer_idx):
-        # https://github.com/pytorch/pytorch/issues/37142
-        # try not to fool the heuristics
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss, ind = self(x, return_pred_indices=True)
-
-        if optimizer_idx == 0:
-            # autoencode
-            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train",
-                                            predicted_indices=ind)
-
-            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return aeloss
-
-        if optimizer_idx == 1:
-            # discriminator
-            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return discloss
-
-    def validation_step(self, batch, batch_idx):
-        log_dict = self._validation_step(batch, batch_idx)
-        with self.ema_scope():
-            log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
-        return log_dict
-
-    def _validation_step(self, batch, batch_idx, suffix=""):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss, ind = self(x, return_pred_indices=True)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
-                                        self.global_step,
-                                        last_layer=self.get_last_layer(),
-                                        split="val"+suffix,
-                                        predicted_indices=ind
-                                        )
-
-        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
-                                            self.global_step,
-                                            last_layer=self.get_last_layer(),
-                                            split="val"+suffix,
-                                            predicted_indices=ind
-                                            )
-        rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
-        self.log(f"val{suffix}/rec_loss", rec_loss,
-                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
-        self.log(f"val{suffix}/aeloss", aeloss,
-                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
-        if version.parse(pl.__version__) >= version.parse('1.4.0'):
-            del log_dict_ae[f"val{suffix}/rec_loss"]
-        self.log_dict(log_dict_ae)
-        self.log_dict(log_dict_disc)
-        return self.log_dict
-
-    def configure_optimizers(self):
-        lr_d = self.learning_rate
-        lr_g = self.lr_g_factor*self.learning_rate
-        print("lr_d", lr_d)
-        print("lr_g", lr_g)
-        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quantize.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=lr_g, betas=(0.5, 0.9))
-        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
-                                    lr=lr_d, betas=(0.5, 0.9))
-
-        if self.scheduler_config is not None:
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                },
-                {
-                    'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                },
-            ]
-            return [opt_ae, opt_disc], scheduler
-        return [opt_ae, opt_disc], []
-
-    def get_last_layer(self):
-        return self.decoder.conv_out.weight
-
-    def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.image_key)
-        x = x.to(self.device)
-        if only_inputs:
-            log["inputs"] = x
-            return log
-        xrec, _ = self(x)
-        if x.shape[1] > 3:
-            # colorize with random projection
-            assert xrec.shape[1] > 3
-            x = self.to_rgb(x)
-            xrec = self.to_rgb(xrec)
-        log["inputs"] = x
-        log["reconstructions"] = xrec
-        if plot_ema:
-            with self.ema_scope():
-                xrec_ema, _ = self(x)
-                if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
-                log["reconstructions_ema"] = xrec_ema
-        return log
-
-    def to_rgb(self, x):
-        assert self.image_key == "segmentation"
-        if not hasattr(self, "colorize"):
-            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
-        x = F.conv2d(x, weight=self.colorize)
-        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
-        return x
-
-
-class VQModelInterface(VQModel):
-    def __init__(self, embed_dim, *args, **kwargs):
-        super().__init__(embed_dim=embed_dim, *args, **kwargs)
-        self.embed_dim = embed_dim
-
-    def encode(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        return h
-
-    def decode(self, h, force_not_quantize=False):
-        # also go through quantization layer
-        if not force_not_quantize:
-            quant, emb_loss, info = self.quantize(h)
-        else:
-            quant = h
-        quant = self.post_quant_conv(quant)
-        dec = self.decoder(quant)
-        return dec
-
-
-class AutoencoderKL(pl.LightningModule):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 embed_dim,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None,
-                 monitor=None,
-                 ):
-        super().__init__()
-        self.image_key = image_key
-        self.encoder = Encoder(**ddconfig)
-        self.decoder = Decoder(**ddconfig)
-        self.loss = instantiate_from_config(lossconfig)
-        assert ddconfig["double_z"]
-        self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
-        self.embed_dim = embed_dim
-        if colorize_nlabels is not None:
-            assert type(colorize_nlabels)==int
-            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
-        if monitor is not None:
-            self.monitor = monitor
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-
-    def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location="cpu")["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        self.load_state_dict(sd, strict=False)
-        print(f"Restored from {path}")
-
-    def encode(self, x):
-        h = self.encoder(x)
-        moments = self.quant_conv(h)
-        posterior = DiagonalGaussianDistribution(moments)
-        return posterior
-
-    def decode(self, z):
-        z = self.post_quant_conv(z)
-        dec = self.decoder(z)
-        return dec
-
-    def forward(self, input, sample_posterior=True):
-        posterior = self.encode(input)
-        if sample_posterior:
-            z = posterior.sample()
-        else:
-            z = posterior.mode()
-        dec = self.decode(z)
-        return dec, posterior
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
-        return x
-
-    def training_step(self, batch, batch_idx, optimizer_idx):
-        inputs = self.get_input(batch, self.image_key)
-        reconstructions, posterior = self(inputs)
-
-        if optimizer_idx == 0:
-            # train encoder+decoder+logvar
-            aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-            self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
-            return aeloss
-
-        if optimizer_idx == 1:
-            # train the discriminator
-            discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
-                                                last_layer=self.get_last_layer(), split="train")
-
-            self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
-            return discloss
-
-    def validation_step(self, batch, batch_idx):
-        inputs = self.get_input(batch, self.image_key)
-        reconstructions, posterior = self(inputs)
-        aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
-                                        last_layer=self.get_last_layer(), split="val")
-
-        discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
-                                            last_layer=self.get_last_layer(), split="val")
-
-        self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
-        self.log_dict(log_dict_ae)
-        self.log_dict(log_dict_disc)
-        return self.log_dict
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=lr, betas=(0.5, 0.9))
-        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
-                                    lr=lr, betas=(0.5, 0.9))
-        return [opt_ae, opt_disc], []
-
-    def get_last_layer(self):
-        return self.decoder.conv_out.weight
-
-    @torch.no_grad()
-    def log_images(self, batch, only_inputs=False, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.image_key)
-        x = x.to(self.device)
-        if not only_inputs:
-            xrec, posterior = self(x)
-            if x.shape[1] > 3:
-                # colorize with random projection
-                assert xrec.shape[1] > 3
-                x = self.to_rgb(x)
-                xrec = self.to_rgb(xrec)
-            log["samples"] = self.decode(torch.randn_like(posterior.sample()))
-            log["reconstructions"] = xrec
-        log["inputs"] = x
-        return log
-
-    def to_rgb(self, x):
-        assert self.image_key == "segmentation"
-        if not hasattr(self, "colorize"):
-            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
-        x = F.conv2d(x, weight=self.colorize)
-        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
-        return x
-
-
-class IdentityFirstStage(torch.nn.Module):
-    def __init__(self, *args, vq_interface=False, **kwargs):
-        self.vq_interface = vq_interface  # TODO: Should be true by default but check to not break older stuff
-        super().__init__()
-
-    def encode(self, x, *args, **kwargs):
-        return x
-
-    def decode(self, x, *args, **kwargs):
-        return x
-
-    def quantize(self, x, *args, **kwargs):
-        if self.vq_interface:
-            return x, None, [None, None, None]
-        return x
-
-    def forward(self, x, *args, **kwargs):
-        return x
diff --git a/ldm/models/diffusion/__init__.py b/ldm/models/diffusion/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/ldm/models/diffusion/classifier.py b/ldm/models/diffusion/classifier.py
deleted file mode 100644
index 67e98b9d..00000000
--- a/ldm/models/diffusion/classifier.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import os
-import torch
-import pytorch_lightning as pl
-from omegaconf import OmegaConf
-from torch.nn import functional as F
-from torch.optim import AdamW
-from torch.optim.lr_scheduler import LambdaLR
-from copy import deepcopy
-from einops import rearrange
-from glob import glob
-from natsort import natsorted
-
-from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
-from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
-
-__models__ = {
-    'class_label': EncoderUNetModel,
-    'segmentation': UNetModel
-}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-class NoisyLatentImageClassifier(pl.LightningModule):
-
-    def __init__(self,
-                 diffusion_path,
-                 num_classes,
-                 ckpt_path=None,
-                 pool='attention',
-                 label_key=None,
-                 diffusion_ckpt_path=None,
-                 scheduler_config=None,
-                 weight_decay=1.e-2,
-                 log_steps=10,
-                 monitor='val/loss',
-                 *args,
-                 **kwargs):
-        super().__init__(*args, **kwargs)
-        self.num_classes = num_classes
-        # get latest config of diffusion model
-        diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
-        self.diffusion_config = OmegaConf.load(diffusion_config).model
-        self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
-        self.load_diffusion()
-
-        self.monitor = monitor
-        self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
-        self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
-        self.log_steps = log_steps
-
-        self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
-            else self.diffusion_model.cond_stage_key
-
-        assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
-
-        if self.label_key not in __models__:
-            raise NotImplementedError()
-
-        self.load_classifier(ckpt_path, pool)
-
-        self.scheduler_config = scheduler_config
-        self.use_scheduler = self.scheduler_config is not None
-        self.weight_decay = weight_decay
-
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
-
-    def load_diffusion(self):
-        model = instantiate_from_config(self.diffusion_config)
-        self.diffusion_model = model.eval()
-        self.diffusion_model.train = disabled_train
-        for param in self.diffusion_model.parameters():
-            param.requires_grad = False
-
-    def load_classifier(self, ckpt_path, pool):
-        model_config = deepcopy(self.diffusion_config.params.unet_config.params)
-        model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
-        model_config.out_channels = self.num_classes
-        if self.label_key == 'class_label':
-            model_config.pool = pool
-
-        self.model = __models__[self.label_key](**model_config)
-        if ckpt_path is not None:
-            print('#####################################################################')
-            print(f'load from ckpt "{ckpt_path}"')
-            print('#####################################################################')
-            self.init_from_ckpt(ckpt_path)
-
-    @torch.no_grad()
-    def get_x_noisy(self, x, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x))
-        continuous_sqrt_alpha_cumprod = None
-        if self.diffusion_model.use_continuous_noise:
-            continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
-            # todo: make sure t+1 is correct here
-
-        return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
-                                             continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
-
-    def forward(self, x_noisy, t, *args, **kwargs):
-        return self.model(x_noisy, t)
-
-    @torch.no_grad()
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = rearrange(x, 'b h w c -> b c h w')
-        x = x.to(memory_format=torch.contiguous_format).float()
-        return x
-
-    @torch.no_grad()
-    def get_conditioning(self, batch, k=None):
-        if k is None:
-            k = self.label_key
-        assert k is not None, 'Needs to provide label key'
-
-        targets = batch[k].to(self.device)
-
-        if self.label_key == 'segmentation':
-            targets = rearrange(targets, 'b h w c -> b c h w')
-            for down in range(self.numd):
-                h, w = targets.shape[-2:]
-                targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
-
-            # targets = rearrange(targets,'b c h w -> b h w c')
-
-        return targets
-
-    def compute_top_k(self, logits, labels, k, reduction="mean"):
-        _, top_ks = torch.topk(logits, k, dim=1)
-        if reduction == "mean":
-            return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
-        elif reduction == "none":
-            return (top_ks == labels[:, None]).float().sum(dim=-1)
-
-    def on_train_epoch_start(self):
-        # save some memory
-        self.diffusion_model.model.to('cpu')
-
-    @torch.no_grad()
-    def write_logs(self, loss, logits, targets):
-        log_prefix = 'train' if self.training else 'val'
-        log = {}
-        log[f"{log_prefix}/loss"] = loss.mean()
-        log[f"{log_prefix}/acc@1"] = self.compute_top_k(
-            logits, targets, k=1, reduction="mean"
-        )
-        log[f"{log_prefix}/acc@5"] = self.compute_top_k(
-            logits, targets, k=5, reduction="mean"
-        )
-
-        self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
-        self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
-        self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
-        lr = self.optimizers().param_groups[0]['lr']
-        self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
-
-    def shared_step(self, batch, t=None):
-        x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
-        targets = self.get_conditioning(batch)
-        if targets.dim() == 4:
-            targets = targets.argmax(dim=1)
-        if t is None:
-            t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
-        else:
-            t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
-        x_noisy = self.get_x_noisy(x, t)
-        logits = self(x_noisy, t)
-
-        loss = F.cross_entropy(logits, targets, reduction='none')
-
-        self.write_logs(loss.detach(), logits.detach(), targets.detach())
-
-        loss = loss.mean()
-        return loss, logits, x_noisy, targets
-
-    def training_step(self, batch, batch_idx):
-        loss, *_ = self.shared_step(batch)
-        return loss
-
-    def reset_noise_accs(self):
-        self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
-                          range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
-
-    def on_validation_start(self):
-        self.reset_noise_accs()
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        loss, *_ = self.shared_step(batch)
-
-        for t in self.noisy_acc:
-            _, logits, _, targets = self.shared_step(batch, t)
-            self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
-            self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
-
-        return loss
-
-    def configure_optimizers(self):
-        optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
-
-        if self.use_scheduler:
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [optimizer], scheduler
-
-        return optimizer
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, *args, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.diffusion_model.first_stage_key)
-        log['inputs'] = x
-
-        y = self.get_conditioning(batch)
-
-        if self.label_key == 'class_label':
-            y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
-            log['labels'] = y
-
-        if ismap(y):
-            log['labels'] = self.diffusion_model.to_rgb(y)
-
-            for step in range(self.log_steps):
-                current_time = step * self.log_time_interval
-
-                _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)
-
-                log[f'inputs@t{current_time}'] = x_noisy
-
-                pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
-                pred = rearrange(pred, 'b h w c -> b c h w')
-
-                log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
-
-        for key in log:
-            log[key] = log[key][:N]
-
-        return log
diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
deleted file mode 100644
index fb31215d..00000000
--- a/ldm/models/diffusion/ddim.py
+++ /dev/null
@@ -1,241 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from functools import partial
-
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
-    extract_into_tensor
-
-
-class DDIMSampler(object):
-    def __init__(self, model, schedule="linear", **kwargs):
-        super().__init__()
-        self.model = model
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != torch.device("cuda"):
-                attr = attr.to(torch.device("cuda"))
-        setattr(self, name, attr)
-
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
-        alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
-        self.register_buffer('betas', to_torch(self.model.betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-        print(f'Data shape for DDIM sampling is {size}, eta {eta}')
-
-        samples, intermediates = self.ddim_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    )
-        return samples, intermediates
-
-    @torch.no_grad()
-    def ddim_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,):
-        device = self.model.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
-            timesteps = self.ddim_timesteps[:subset_end]
-
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        print(f"Running DDIM Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
-
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
-                img = img_orig * mask + (1. - mask) * img
-
-            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning)
-            img, pred_x0 = outs
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None):
-        b, *_, device = *x.shape, x.device
-
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-            e_t = self.model.apply_model(x, t, c)
-        else:
-            x_in = torch.cat([x] * 2)
-            t_in = torch.cat([t] * 2)
-            c_in = torch.cat([unconditional_conditioning, c])
-            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-        if score_corrector is not None:
-            assert self.model.parameterization == "eps"
-            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-        # select parameters corresponding to the currently considered timestep
-        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-        # current prediction for x_0
-        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        if quantize_denoised:
-            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-        # direction pointing to x_t
-        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-        return x_prev, pred_x0
-
-    @torch.no_grad()
-    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
-        # fast, but does not allow for exact reconstruction
-        # t serves as an index to gather the correct alphas
-        if use_original_steps:
-            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
-            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
-        else:
-            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
-            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
-
-        if noise is None:
-            noise = torch.randn_like(x0)
-        return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
-                extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
-
-    @torch.no_grad()
-    def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
-               use_original_steps=False):
-
-        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
-        timesteps = timesteps[:t_start]
-
-        time_range = np.flip(timesteps)
-        total_steps = timesteps.shape[0]
-        print(f"Running DDIM Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
-        x_dec = x_latent
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
-            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
-                                          unconditional_guidance_scale=unconditional_guidance_scale,
-                                          unconditional_conditioning=unconditional_conditioning)
-        return x_dec
\ No newline at end of file
diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py
deleted file mode 100644
index bbedd04c..00000000
--- a/ldm/models/diffusion/ddpm.py
+++ /dev/null
@@ -1,1445 +0,0 @@
-"""
-wild mixture of
-https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
-https://github.com/CompVis/taming-transformers
--- merci
-"""
-
-import torch
-import torch.nn as nn
-import numpy as np
-import pytorch_lightning as pl
-from torch.optim.lr_scheduler import LambdaLR
-from einops import rearrange, repeat
-from contextlib import contextmanager
-from functools import partial
-from tqdm import tqdm
-from torchvision.utils import make_grid
-from pytorch_lightning.utilities.distributed import rank_zero_only
-
-from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
-from ldm.modules.ema import LitEma
-from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
-from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
-from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
-from ldm.models.diffusion.ddim import DDIMSampler
-
-
-__conditioning_keys__ = {'concat': 'c_concat',
-                         'crossattn': 'c_crossattn',
-                         'adm': 'y'}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def uniform_on_device(r1, r2, shape, device):
-    return (r1 - r2) * torch.rand(*shape, device=device) + r2
-
-
-class DDPM(pl.LightningModule):
-    # classic DDPM with Gaussian diffusion, in image space
-    def __init__(self,
-                 unet_config,
-                 timesteps=1000,
-                 beta_schedule="linear",
-                 loss_type="l2",
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 load_only_unet=False,
-                 monitor="val/loss",
-                 use_ema=True,
-                 first_stage_key="image",
-                 image_size=256,
-                 channels=3,
-                 log_every_t=100,
-                 clip_denoised=True,
-                 linear_start=1e-4,
-                 linear_end=2e-2,
-                 cosine_s=8e-3,
-                 given_betas=None,
-                 original_elbo_weight=0.,
-                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
-                 l_simple_weight=1.,
-                 conditioning_key=None,
-                 parameterization="eps",  # all assuming fixed variance schedules
-                 scheduler_config=None,
-                 use_positional_encodings=False,
-                 learn_logvar=False,
-                 logvar_init=0.,
-                 ):
-        super().__init__()
-        assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
-        self.parameterization = parameterization
-        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
-        self.cond_stage_model = None
-        self.clip_denoised = clip_denoised
-        self.log_every_t = log_every_t
-        self.first_stage_key = first_stage_key
-        self.image_size = image_size  # try conv?
-        self.channels = channels
-        self.use_positional_encodings = use_positional_encodings
-        self.model = DiffusionWrapper(unet_config, conditioning_key)
-        count_params(self.model, verbose=True)
-        self.use_ema = use_ema
-        if self.use_ema:
-            self.model_ema = LitEma(self.model)
-            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-        self.use_scheduler = scheduler_config is not None
-        if self.use_scheduler:
-            self.scheduler_config = scheduler_config
-
-        self.v_posterior = v_posterior
-        self.original_elbo_weight = original_elbo_weight
-        self.l_simple_weight = l_simple_weight
-
-        if monitor is not None:
-            self.monitor = monitor
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
-
-        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
-                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
-
-        self.loss_type = loss_type
-
-        self.learn_logvar = learn_logvar
-        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
-        if self.learn_logvar:
-            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
-
-
-    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        if exists(given_betas):
-            betas = given_betas
-        else:
-            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
-                                       cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
-                    1. - alphas_cumprod) + self.v_posterior * betas
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer('posterior_variance', to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
-        self.register_buffer('posterior_mean_coef1', to_torch(
-            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
-        self.register_buffer('posterior_mean_coef2', to_torch(
-            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
-
-        if self.parameterization == "eps":
-            lvlb_weights = self.betas ** 2 / (
-                        2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
-        elif self.parameterization == "x0":
-            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
-        else:
-            raise NotImplementedError("mu not supported")
-        # TODO how to choose this term
-        lvlb_weights[0] = lvlb_weights[1]
-        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
-        assert not torch.isnan(self.lvlb_weights).all()
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.model.parameters())
-            self.model_ema.copy_to(self.model)
-            if context is not None:
-                print(f"{context}: Switched to EMA weights")
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.model.parameters())
-                if context is not None:
-                    print(f"{context}: Restored training weights")
-
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
-
-    def q_mean_variance(self, x_start, t):
-        """
-        Get the distribution q(x_t | x_0).
-        :param x_start: the [N x C x ...] tensor of noiseless inputs.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
-        """
-        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
-        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
-                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, x, t, clip_denoised: bool):
-        model_out = self.model(x, t)
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
-        b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
-        noise = noise_like(x.shape, device, repeat_noise)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def p_sample_loop(self, shape, return_intermediates=False):
-        device = self.betas.device
-        b = shape[0]
-        img = torch.randn(shape, device=device)
-        intermediates = [img]
-        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
-            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
-                                clip_denoised=self.clip_denoised)
-            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
-                intermediates.append(img)
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, batch_size=16, return_intermediates=False):
-        image_size = self.image_size
-        channels = self.channels
-        return self.p_sample_loop((batch_size, channels, image_size, image_size),
-                                  return_intermediates=return_intermediates)
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
-
-    def get_loss(self, pred, target, mean=True):
-        if self.loss_type == 'l1':
-            loss = (target - pred).abs()
-            if mean:
-                loss = loss.mean()
-        elif self.loss_type == 'l2':
-            if mean:
-                loss = torch.nn.functional.mse_loss(target, pred)
-            else:
-                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
-        else:
-            raise NotImplementedError("unknown loss type '{loss_type}'")
-
-        return loss
-
-    def p_losses(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_out = self.model(x_noisy, t)
-
-        loss_dict = {}
-        if self.parameterization == "eps":
-            target = noise
-        elif self.parameterization == "x0":
-            target = x_start
-        else:
-            raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
-
-        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
-
-        log_prefix = 'train' if self.training else 'val'
-
-        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
-        loss_simple = loss.mean() * self.l_simple_weight
-
-        loss_vlb = (self.lvlb_weights[t] * loss).mean()
-        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
-
-        loss = loss_simple + self.original_elbo_weight * loss_vlb
-
-        loss_dict.update({f'{log_prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def forward(self, x, *args, **kwargs):
-        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
-        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        return self.p_losses(x, t, *args, **kwargs)
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = rearrange(x, 'b h w c -> b c h w')
-        x = x.to(memory_format=torch.contiguous_format).float()
-        return x
-
-    def shared_step(self, batch):
-        x = self.get_input(batch, self.first_stage_key)
-        loss, loss_dict = self(x)
-        return loss, loss_dict
-
-    def training_step(self, batch, batch_idx):
-        loss, loss_dict = self.shared_step(batch)
-
-        self.log_dict(loss_dict, prog_bar=True,
-                      logger=True, on_step=True, on_epoch=True)
-
-        self.log("global_step", self.global_step,
-                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        if self.use_scheduler:
-            lr = self.optimizers().param_groups[0]['lr']
-            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        return loss
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        _, loss_dict_no_ema = self.shared_step(batch)
-        with self.ema_scope():
-            _, loss_dict_ema = self.shared_step(batch)
-            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
-        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-
-    def on_train_batch_end(self, *args, **kwargs):
-        if self.use_ema:
-            self.model_ema(self.model)
-
-    def _get_rows_from_list(self, samples):
-        n_imgs_per_row = len(samples)
-        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.first_stage_key)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        x = x.to(self.device)[:N]
-        log["inputs"] = x
-
-        # get diffusion row
-        diffusion_row = list()
-        x_start = x[:n_row]
-
-        for t in range(self.num_timesteps):
-            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                t = t.to(self.device).long()
-                noise = torch.randn_like(x_start)
-                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-                diffusion_row.append(x_noisy)
-
-        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
-
-        if sample:
-            # get denoise row
-            with self.ema_scope("Plotting"):
-                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
-
-            log["samples"] = samples
-            log["denoise_row"] = self._get_rows_from_list(denoise_row)
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.learn_logvar:
-            params = params + [self.logvar]
-        opt = torch.optim.AdamW(params, lr=lr)
-        return opt
-
-
-class LatentDiffusion(DDPM):
-    """main class"""
-    def __init__(self,
-                 first_stage_config,
-                 cond_stage_config,
-                 num_timesteps_cond=None,
-                 cond_stage_key="image",
-                 cond_stage_trainable=False,
-                 concat_mode=True,
-                 cond_stage_forward=None,
-                 conditioning_key=None,
-                 scale_factor=1.0,
-                 scale_by_std=False,
-                 *args, **kwargs):
-        self.num_timesteps_cond = default(num_timesteps_cond, 1)
-        self.scale_by_std = scale_by_std
-        assert self.num_timesteps_cond <= kwargs['timesteps']
-        # for backwards compatibility after implementation of DiffusionWrapper
-        if conditioning_key is None:
-            conditioning_key = 'concat' if concat_mode else 'crossattn'
-        if cond_stage_config == '__is_unconditional__':
-            conditioning_key = None
-        ckpt_path = kwargs.pop("ckpt_path", None)
-        ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
-        self.concat_mode = concat_mode
-        self.cond_stage_trainable = cond_stage_trainable
-        self.cond_stage_key = cond_stage_key
-        try:
-            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
-            self.num_downs = 0
-        if not scale_by_std:
-            self.scale_factor = scale_factor
-        else:
-            self.register_buffer('scale_factor', torch.tensor(scale_factor))
-        self.instantiate_first_stage(first_stage_config)
-        self.instantiate_cond_stage(cond_stage_config)
-        self.cond_stage_forward = cond_stage_forward
-        self.clip_denoised = False
-        self.bbox_tokenizer = None  
-
-        self.restarted_from_ckpt = False
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys)
-            self.restarted_from_ckpt = True
-
-    def make_cond_schedule(self, ):
-        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
-        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
-        self.cond_ids[:self.num_timesteps_cond] = ids
-
-    @rank_zero_only
-    @torch.no_grad()
-    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
-        # only for very first batch
-        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
-            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
-            # set rescale weight to 1./std of encodings
-            print("### USING STD-RESCALING ###")
-            x = super().get_input(batch, self.first_stage_key)
-            x = x.to(self.device)
-            encoder_posterior = self.encode_first_stage(x)
-            z = self.get_first_stage_encoding(encoder_posterior).detach()
-            del self.scale_factor
-            self.register_buffer('scale_factor', 1. / z.flatten().std())
-            print(f"setting self.scale_factor to {self.scale_factor}")
-            print("### USING STD-RESCALING ###")
-
-    def register_schedule(self,
-                          given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
-
-        self.shorten_cond_schedule = self.num_timesteps_cond > 1
-        if self.shorten_cond_schedule:
-            self.make_cond_schedule()
-
-    def instantiate_first_stage(self, config):
-        model = instantiate_from_config(config)
-        self.first_stage_model = model.eval()
-        self.first_stage_model.train = disabled_train
-        for param in self.first_stage_model.parameters():
-            param.requires_grad = False
-
-    def instantiate_cond_stage(self, config):
-        if not self.cond_stage_trainable:
-            if config == "__is_first_stage__":
-                print("Using first stage also as cond stage.")
-                self.cond_stage_model = self.first_stage_model
-            elif config == "__is_unconditional__":
-                print(f"Training {self.__class__.__name__} as an unconditional model.")
-                self.cond_stage_model = None
-                # self.be_unconditional = True
-            else:
-                model = instantiate_from_config(config)
-                self.cond_stage_model = model.eval()
-                self.cond_stage_model.train = disabled_train
-                for param in self.cond_stage_model.parameters():
-                    param.requires_grad = False
-        else:
-            assert config != '__is_first_stage__'
-            assert config != '__is_unconditional__'
-            model = instantiate_from_config(config)
-            self.cond_stage_model = model
-
-    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
-        denoise_row = []
-        for zd in tqdm(samples, desc=desc):
-            denoise_row.append(self.decode_first_stage(zd.to(self.device),
-                                                            force_not_quantize=force_no_decoder_quantization))
-        n_imgs_per_row = len(denoise_row)
-        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
-        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    def get_first_stage_encoding(self, encoder_posterior):
-        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
-            z = encoder_posterior.sample()
-        elif isinstance(encoder_posterior, torch.Tensor):
-            z = encoder_posterior
-        else:
-            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
-        return self.scale_factor * z
-
-    def get_learned_conditioning(self, c):
-        if self.cond_stage_forward is None:
-            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
-                c = self.cond_stage_model.encode(c)
-                if isinstance(c, DiagonalGaussianDistribution):
-                    c = c.mode()
-            else:
-                c = self.cond_stage_model(c)
-        else:
-            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
-            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
-        return c
-
-    def meshgrid(self, h, w):
-        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
-        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
-
-        arr = torch.cat([y, x], dim=-1)
-        return arr
-
-    def delta_border(self, h, w):
-        """
-        :param h: height
-        :param w: width
-        :return: normalized distance to image border,
-         wtith min distance = 0 at border and max dist = 0.5 at image center
-        """
-        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
-        arr = self.meshgrid(h, w) / lower_right_corner
-        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
-        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
-        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
-        return edge_dist
-
-    def get_weighting(self, h, w, Ly, Lx, device):
-        weighting = self.delta_border(h, w)
-        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
-                               self.split_input_params["clip_max_weight"], )
-        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
-
-        if self.split_input_params["tie_braker"]:
-            L_weighting = self.delta_border(Ly, Lx)
-            L_weighting = torch.clip(L_weighting,
-                                     self.split_input_params["clip_min_tie_weight"],
-                                     self.split_input_params["clip_max_tie_weight"])
-
-            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
-            weighting = weighting * L_weighting
-        return weighting
-
-    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
-        """
-        :param x: img of size (bs, c, h, w)
-        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
-        """
-        bs, nc, h, w = x.shape
-
-        # number of crops in image
-        Ly = (h - kernel_size[0]) // stride[0] + 1
-        Lx = (w - kernel_size[1]) // stride[1] + 1
-
-        if uf == 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
-
-            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
-
-        elif uf > 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
-                                dilation=1, padding=0,
-                                stride=(stride[0] * uf, stride[1] * uf))
-            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
-
-        elif df > 1 and uf == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
-                                dilation=1, padding=0,
-                                stride=(stride[0] // df, stride[1] // df))
-            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
-
-        else:
-            raise NotImplementedError
-
-        return fold, unfold, normalization, weighting
-
-    @torch.no_grad()
-    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
-                  cond_key=None, return_original_cond=False, bs=None):
-        x = super().get_input(batch, k)
-        if bs is not None:
-            x = x[:bs]
-        x = x.to(self.device)
-        encoder_posterior = self.encode_first_stage(x)
-        z = self.get_first_stage_encoding(encoder_posterior).detach()
-
-        if self.model.conditioning_key is not None:
-            if cond_key is None:
-                cond_key = self.cond_stage_key
-            if cond_key != self.first_stage_key:
-                if cond_key in ['caption', 'coordinates_bbox']:
-                    xc = batch[cond_key]
-                elif cond_key == 'class_label':
-                    xc = batch
-                else:
-                    xc = super().get_input(batch, cond_key).to(self.device)
-            else:
-                xc = x
-            if not self.cond_stage_trainable or force_c_encode:
-                if isinstance(xc, dict) or isinstance(xc, list):
-                    # import pudb; pudb.set_trace()
-                    c = self.get_learned_conditioning(xc)
-                else:
-                    c = self.get_learned_conditioning(xc.to(self.device))
-            else:
-                c = xc
-            if bs is not None:
-                c = c[:bs]
-
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                ckey = __conditioning_keys__[self.model.conditioning_key]
-                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
-
-        else:
-            c = None
-            xc = None
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                c = {'pos_x': pos_x, 'pos_y': pos_y}
-        out = [z, c]
-        if return_first_stage_outputs:
-            xrec = self.decode_first_stage(z)
-            out.extend([x, xrec])
-        if return_original_cond:
-            out.append(xc)
-        return out
-
-    @torch.no_grad()
-    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                uf = self.split_input_params["vqf"]
-                bs, nc, h, w = z.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
-                z = unfold(z)  # (bn, nc * prod(**ks), L)
-                # 1. Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
-                                                                 force_not_quantize=predict_cids or force_not_quantize)
-                                   for i in range(z.shape[-1])]
-                else:
-
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
-                                   for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
-                o = o * weighting
-                # Reverse 1. reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
-                return decoded
-            else:
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-                else:
-                    return self.first_stage_model.decode(z)
-
-        else:
-            if isinstance(self.first_stage_model, VQModelInterface):
-                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-            else:
-                return self.first_stage_model.decode(z)
-
-    # same as above but without decorator
-    def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                uf = self.split_input_params["vqf"]
-                bs, nc, h, w = z.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
-
-                z = unfold(z)  # (bn, nc * prod(**ks), L)
-                # 1. Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):  
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
-                                                                 force_not_quantize=predict_cids or force_not_quantize)
-                                   for i in range(z.shape[-1])]
-                else:
-
-                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
-                                   for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
-                o = o * weighting
-                # Reverse 1. reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
-                return decoded
-            else:
-                if isinstance(self.first_stage_model, VQModelInterface):
-                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-                else:
-                    return self.first_stage_model.decode(z)
-
-        else:
-            if isinstance(self.first_stage_model, VQModelInterface):
-                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
-            else:
-                return self.first_stage_model.decode(z)
-
-    @torch.no_grad()
-    def encode_first_stage(self, x):
-        if hasattr(self, "split_input_params"):
-            if self.split_input_params["patch_distributed_vq"]:
-                ks = self.split_input_params["ks"]  # eg. (128, 128)
-                stride = self.split_input_params["stride"]  # eg. (64, 64)
-                df = self.split_input_params["vqf"]
-                self.split_input_params['original_image_size'] = x.shape[-2:]
-                bs, nc, h, w = x.shape
-                if ks[0] > h or ks[1] > w:
-                    ks = (min(ks[0], h), min(ks[1], w))
-                    print("reducing Kernel")
-
-                if stride[0] > h or stride[1] > w:
-                    stride = (min(stride[0], h), min(stride[1], w))
-                    print("reducing stride")
-
-                fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
-                z = unfold(x)  # (bn, nc * prod(**ks), L)
-                # Reshape to img shape
-                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
-                               for i in range(z.shape[-1])]
-
-                o = torch.stack(output_list, axis=-1)
-                o = o * weighting
-
-                # Reverse reshape to img shape
-                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-                # stitch crops together
-                decoded = fold(o)
-                decoded = decoded / normalization
-                return decoded
-
-            else:
-                return self.first_stage_model.encode(x)
-        else:
-            return self.first_stage_model.encode(x)
-
-    def shared_step(self, batch, **kwargs):
-        x, c = self.get_input(batch, self.first_stage_key)
-        loss = self(x, c)
-        return loss
-
-    def forward(self, x, c, *args, **kwargs):
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        if self.model.conditioning_key is not None:
-            assert c is not None
-            if self.cond_stage_trainable:
-                c = self.get_learned_conditioning(c)
-            if self.shorten_cond_schedule:  # TODO: drop this option
-                tc = self.cond_ids[t].to(self.device)
-                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
-        return self.p_losses(x, c, t, *args, **kwargs)
-
-    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
-        def rescale_bbox(bbox):
-            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
-            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
-            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
-            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
-            return x0, y0, w, h
-
-        return [rescale_bbox(b) for b in bboxes]
-
-    def apply_model(self, x_noisy, t, cond, return_ids=False):
-
-        if isinstance(cond, dict):
-            # hybrid case, cond is exptected to be a dict
-            pass
-        else:
-            if not isinstance(cond, list):
-                cond = [cond]
-            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
-            cond = {key: cond}
-
-        if hasattr(self, "split_input_params"):
-            assert len(cond) == 1  # todo can only deal with one conditioning atm
-            assert not return_ids  
-            ks = self.split_input_params["ks"]  # eg. (128, 128)
-            stride = self.split_input_params["stride"]  # eg. (64, 64)
-
-            h, w = x_noisy.shape[-2:]
-
-            fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
-
-            z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
-            # Reshape to img shape
-            z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-            z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
-
-            if self.cond_stage_key in ["image", "LR_image", "segmentation",
-                                       'bbox_img'] and self.model.conditioning_key:  # todo check for completeness
-                c_key = next(iter(cond.keys()))  # get key
-                c = next(iter(cond.values()))  # get value
-                assert (len(c) == 1)  # todo extend to list with more than one elem
-                c = c[0]  # get element
-
-                c = unfold(c)
-                c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
-
-                cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
-
-            elif self.cond_stage_key == 'coordinates_bbox':
-                assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
-
-                # assuming padding of unfold is always 0 and its dilation is always 1
-                n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
-                full_img_h, full_img_w = self.split_input_params['original_image_size']
-                # as we are operating on latents, we need the factor from the original image size to the
-                # spatial latent size to properly rescale the crops for regenerating the bbox annotations
-                num_downs = self.first_stage_model.encoder.num_resolutions - 1
-                rescale_latent = 2 ** (num_downs)
-
-                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
-                # need to rescale the tl patch coordinates to be in between (0,1)
-                tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
-                                         rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
-                                        for patch_nr in range(z.shape[-1])]
-
-                # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
-                patch_limits = [(x_tl, y_tl,
-                                 rescale_latent * ks[0] / full_img_w,
-                                 rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
-                # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
-
-                # tokenize crop coordinates for the bounding boxes of the respective patches
-                patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
-                                      for bbox in patch_limits]  # list of length l with tensors of shape (1, 2)
-                print(patch_limits_tknzd[0].shape)
-                # cut tknzd crop position from conditioning
-                assert isinstance(cond, dict), 'cond must be dict to be fed into model'
-                cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
-                print(cut_cond.shape)
-
-                adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
-                adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
-                print(adapted_cond.shape)
-                adapted_cond = self.get_learned_conditioning(adapted_cond)
-                print(adapted_cond.shape)
-                adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
-                print(adapted_cond.shape)
-
-                cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
-
-            else:
-                cond_list = [cond for i in range(z.shape[-1])]  # Todo make this more efficient
-
-            # apply model by loop over crops
-            output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
-            assert not isinstance(output_list[0],
-                                  tuple)  # todo cant deal with multiple model outputs check this never happens
-
-            o = torch.stack(output_list, axis=-1)
-            o = o * weighting
-            # Reverse reshape to img shape
-            o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
-            # stitch crops together
-            x_recon = fold(o) / normalization
-
-        else:
-            x_recon = self.model(x_noisy, t, **cond)
-
-        if isinstance(x_recon, tuple) and not return_ids:
-            return x_recon[0]
-        else:
-            return x_recon
-
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
-               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    def _prior_bpd(self, x_start):
-        """
-        Get the prior KL term for the variational lower-bound, measured in
-        bits-per-dim.
-        This term can't be optimized, as it only depends on the encoder.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :return: a batch of [N] KL values (in bits), one per batch element.
-        """
-        batch_size = x_start.shape[0]
-        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
-        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
-        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
-        return mean_flat(kl_prior) / np.log(2.0)
-
-    def p_losses(self, x_start, cond, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_output = self.apply_model(x_noisy, t, cond)
-
-        loss_dict = {}
-        prefix = 'train' if self.training else 'val'
-
-        if self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "eps":
-            target = noise
-        else:
-            raise NotImplementedError()
-
-        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
-        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
-
-        logvar_t = self.logvar[t].to(self.device)
-        loss = loss_simple / torch.exp(logvar_t) + logvar_t
-        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
-        if self.learn_logvar:
-            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
-            loss_dict.update({'logvar': self.logvar.data.mean()})
-
-        loss = self.l_simple_weight * loss.mean()
-
-        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
-        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
-        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
-        loss += (self.original_elbo_weight * loss_vlb)
-        loss_dict.update({f'{prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
-                        return_x0=False, score_corrector=None, corrector_kwargs=None):
-        t_in = t
-        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
-
-        if score_corrector is not None:
-            assert self.parameterization == "eps"
-            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
-
-        if return_codebook_ids:
-            model_out, logits = model_out
-
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        else:
-            raise NotImplementedError()
-
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-        if quantize_denoised:
-            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        if return_codebook_ids:
-            return model_mean, posterior_variance, posterior_log_variance, logits
-        elif return_x0:
-            return model_mean, posterior_variance, posterior_log_variance, x_recon
-        else:
-            return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
-                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
-                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
-        b, *_, device = *x.shape, x.device
-        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
-                                       return_codebook_ids=return_codebook_ids,
-                                       quantize_denoised=quantize_denoised,
-                                       return_x0=return_x0,
-                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-        if return_codebook_ids:
-            raise DeprecationWarning("Support dropped.")
-            model_mean, _, model_log_variance, logits = outputs
-        elif return_x0:
-            model_mean, _, model_log_variance, x0 = outputs
-        else:
-            model_mean, _, model_log_variance = outputs
-
-        noise = noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-
-        if return_codebook_ids:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
-        if return_x0:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
-        else:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
-                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
-                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
-                              log_every_t=None):
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        timesteps = self.num_timesteps
-        if batch_size is not None:
-            b = batch_size if batch_size is not None else shape[0]
-            shape = [batch_size] + list(shape)
-        else:
-            b = batch_size = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=self.device)
-        else:
-            img = x_T
-        intermediates = []
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
-                        total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-        if type(temperature) == float:
-            temperature = [temperature] * timesteps
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img, x0_partial = self.p_sample(img, cond, ts,
-                                            clip_denoised=self.clip_denoised,
-                                            quantize_denoised=quantize_denoised, return_x0=True,
-                                            temperature=temperature[i], noise_dropout=noise_dropout,
-                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(x0_partial)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_loop(self, cond, shape, return_intermediates=False,
-                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, start_T=None,
-                      log_every_t=None):
-
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        device = self.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        intermediates = [img]
-        if timesteps is None:
-            timesteps = self.num_timesteps
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-
-        if mask is not None:
-            assert x0 is not None
-            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img = self.p_sample(img, cond, ts,
-                                clip_denoised=self.clip_denoised,
-                                quantize_denoised=quantize_denoised)
-            if mask is not None:
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(img)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
-               verbose=True, timesteps=None, quantize_denoised=False,
-               mask=None, x0=None, shape=None,**kwargs):
-        if shape is None:
-            shape = (batch_size, self.channels, self.image_size, self.image_size)
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-        return self.p_sample_loop(cond,
-                                  shape,
-                                  return_intermediates=return_intermediates, x_T=x_T,
-                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
-                                  mask=mask, x0=x0)
-
-    @torch.no_grad()
-    def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
-
-        if ddim:
-            ddim_sampler = DDIMSampler(self)
-            shape = (self.channels, self.image_size, self.image_size)
-            samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
-                                                        shape,cond,verbose=False,**kwargs)
-
-        else:
-            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
-                                                 return_intermediates=True,**kwargs)
-
-        return samples, intermediates
-
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=True, **kwargs):
-
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
-                                           return_first_stage_outputs=True,
-                                           force_c_encode=True,
-                                           return_original_cond=True,
-                                           bs=N)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):
-                xc = self.cond_stage_model.decode(c)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ["caption"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
-                log["conditioning"] = xc
-            elif self.cond_stage_key == 'class_label':
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
-                log['conditioning'] = xc
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            with self.ema_scope("Plotting"):
-                samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
-                                                         ddim_steps=ddim_steps,eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
-                    self.first_stage_model, IdentityFirstStage):
-                # also display when quantizing x0 while sampling
-                with self.ema_scope("Plotting Quantized Denoised"):
-                    samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
-                                                             ddim_steps=ddim_steps,eta=ddim_eta,
-                                                             quantize_denoised=True)
-                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
-                    #                                      quantize_denoised=True)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_x0_quantized"] = x_samples
-
-            if inpaint:
-                # make a simple center square
-                b, h, w = z.shape[0], z.shape[2], z.shape[3]
-                mask = torch.ones(N, h, w).to(self.device)
-                # zeros will be filled in
-                mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
-                mask = mask[:, None, ...]
-                with self.ema_scope("Plotting Inpaint"):
-
-                    samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
-                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_inpainting"] = x_samples
-                log["mask"] = mask
-
-                # outpaint
-                with self.ema_scope("Plotting Outpaint"):
-                    samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
-                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_outpainting"] = x_samples
-
-        if plot_progressive_rows:
-            with self.ema_scope("Plotting Progressives"):
-                img, progressives = self.progressive_denoising(c,
-                                                               shape=(self.channels, self.image_size, self.image_size),
-                                                               batch_size=N)
-            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
-            log["progressive_row"] = prog_row
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.cond_stage_trainable:
-            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
-            params = params + list(self.cond_stage_model.parameters())
-        if self.learn_logvar:
-            print('Diffusion model optimizing logvar')
-            params.append(self.logvar)
-        opt = torch.optim.AdamW(params, lr=lr)
-        if self.use_scheduler:
-            assert 'target' in self.scheduler_config
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [opt], scheduler
-        return opt
-
-    @torch.no_grad()
-    def to_rgb(self, x):
-        x = x.float()
-        if not hasattr(self, "colorize"):
-            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
-        x = nn.functional.conv2d(x, weight=self.colorize)
-        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
-        return x
-
-
-class DiffusionWrapper(pl.LightningModule):
-    def __init__(self, diff_model_config, conditioning_key):
-        super().__init__()
-        self.diffusion_model = instantiate_from_config(diff_model_config)
-        self.conditioning_key = conditioning_key
-        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
-
-    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
-        if self.conditioning_key is None:
-            out = self.diffusion_model(x, t)
-        elif self.conditioning_key == 'concat':
-            xc = torch.cat([x] + c_concat, dim=1)
-            out = self.diffusion_model(xc, t)
-        elif self.conditioning_key == 'crossattn':
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(x, t, context=cc)
-        elif self.conditioning_key == 'hybrid':
-            xc = torch.cat([x] + c_concat, dim=1)
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(xc, t, context=cc)
-        elif self.conditioning_key == 'adm':
-            cc = c_crossattn[0]
-            out = self.diffusion_model(x, t, y=cc)
-        else:
-            raise NotImplementedError()
-
-        return out
-
-
-class Layout2ImgDiffusion(LatentDiffusion):
-    # TODO: move all layout-specific hacks to this class
-    def __init__(self, cond_stage_key, *args, **kwargs):
-        assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
-        super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
-
-    def log_images(self, batch, N=8, *args, **kwargs):
-        logs = super().log_images(batch=batch, N=N, *args, **kwargs)
-
-        key = 'train' if self.training else 'validation'
-        dset = self.trainer.datamodule.datasets[key]
-        mapper = dset.conditional_builders[self.cond_stage_key]
-
-        bbox_imgs = []
-        map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
-        for tknzd_bbox in batch[self.cond_stage_key][:N]:
-            bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
-            bbox_imgs.append(bboximg)
-
-        cond_img = torch.stack(bbox_imgs, dim=0)
-        logs['bbox_image'] = cond_img
-        return logs
diff --git a/ldm/models/diffusion/dpm_solver/__init__.py b/ldm/models/diffusion/dpm_solver/__init__.py
deleted file mode 100644
index 7427f38c..00000000
--- a/ldm/models/diffusion/dpm_solver/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .sampler import DPMSolverSampler
\ No newline at end of file
diff --git a/ldm/models/diffusion/dpm_solver/dpm_solver.py b/ldm/models/diffusion/dpm_solver/dpm_solver.py
deleted file mode 100644
index bdb64e0c..00000000
--- a/ldm/models/diffusion/dpm_solver/dpm_solver.py
+++ /dev/null
@@ -1,1184 +0,0 @@
-import torch
-import torch.nn.functional as F
-import math
-
-
-class NoiseScheduleVP:
-    def __init__(
-            self,
-            schedule='discrete',
-            betas=None,
-            alphas_cumprod=None,
-            continuous_beta_0=0.1,
-            continuous_beta_1=20.,
-        ):
-        """Create a wrapper class for the forward SDE (VP type).
-
-        ***
-        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
-                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
-        ***
-
-        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
-        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
-        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
-
-            log_alpha_t = self.marginal_log_mean_coeff(t)
-            sigma_t = self.marginal_std(t)
-            lambda_t = self.marginal_lambda(t)
-
-        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
-
-            t = self.inverse_lambda(lambda_t)
-
-        ===============================================================
-
-        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
-
-        1. For discrete-time DPMs:
-
-            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
-                t_i = (i + 1) / N
-            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
-            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
-
-            Args:
-                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
-                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
-
-            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
-
-            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
-                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
-                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
-                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
-                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
-                and
-                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
-
-
-        2. For continuous-time DPMs:
-
-            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
-            schedule are the default settings in DDPM and improved-DDPM:
-
-            Args:
-                beta_min: A `float` number. The smallest beta for the linear schedule.
-                beta_max: A `float` number. The largest beta for the linear schedule.
-                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
-                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
-                T: A `float` number. The ending time of the forward process.
-
-        ===============================================================
-
-        Args:
-            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
-                    'linear' or 'cosine' for continuous-time DPMs.
-        Returns:
-            A wrapper object of the forward SDE (VP type).
-        
-        ===============================================================
-
-        Example:
-
-        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', betas=betas)
-
-        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
-
-        # For continuous-time DPMs (VPSDE), linear schedule:
-        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
-
-        """
-
-        if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
-
-        self.schedule = schedule
-        if schedule == 'discrete':
-            if betas is not None:
-                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
-            else:
-                assert alphas_cumprod is not None
-                log_alphas = 0.5 * torch.log(alphas_cumprod)
-            self.total_N = len(log_alphas)
-            self.T = 1.
-            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
-            self.log_alpha_array = log_alphas.reshape((1, -1,))
-        else:
-            self.total_N = 1000
-            self.beta_0 = continuous_beta_0
-            self.beta_1 = continuous_beta_1
-            self.cosine_s = 0.008
-            self.cosine_beta_max = 999.
-            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
-            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
-            self.schedule = schedule
-            if schedule == 'cosine':
-                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
-                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
-                self.T = 0.9946
-            else:
-                self.T = 1.
-
-    def marginal_log_mean_coeff(self, t):
-        """
-        Compute log(alpha_t) of a given continuous-time label t in [0, T].
-        """
-        if self.schedule == 'discrete':
-            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
-        elif self.schedule == 'linear':
-            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
-        elif self.schedule == 'cosine':
-            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
-            log_alpha_t =  log_alpha_fn(t) - self.cosine_log_alpha_0
-            return log_alpha_t
-
-    def marginal_alpha(self, t):
-        """
-        Compute alpha_t of a given continuous-time label t in [0, T].
-        """
-        return torch.exp(self.marginal_log_mean_coeff(t))
-
-    def marginal_std(self, t):
-        """
-        Compute sigma_t of a given continuous-time label t in [0, T].
-        """
-        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
-
-    def marginal_lambda(self, t):
-        """
-        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
-        """
-        log_mean_coeff = self.marginal_log_mean_coeff(t)
-        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
-        return log_mean_coeff - log_std
-
-    def inverse_lambda(self, lamb):
-        """
-        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
-        """
-        if self.schedule == 'linear':
-            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            Delta = self.beta_0**2 + tmp
-            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
-        elif self.schedule == 'discrete':
-            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
-            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))
-            return t.reshape((-1,))
-        else:
-            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
-            t = t_fn(log_alpha)
-            return t
-
-
-def model_wrapper(
-    model,
-    noise_schedule,
-    model_type="noise",
-    model_kwargs={},
-    guidance_type="uncond",
-    condition=None,
-    unconditional_condition=None,
-    guidance_scale=1.,
-    classifier_fn=None,
-    classifier_kwargs={},
-):
-    """Create a wrapper function for the noise prediction model.
-
-    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
-    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
-
-    We support four types of the diffusion model by setting `model_type`:
-
-        1. "noise": noise prediction model. (Trained by predicting noise).
-
-        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
-
-        3. "v": velocity prediction model. (Trained by predicting the velocity).
-            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
-
-            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
-                arXiv preprint arXiv:2202.00512 (2022).
-            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
-                arXiv preprint arXiv:2210.02303 (2022).
-    
-        4. "score": marginal score function. (Trained by denoising score matching).
-            Note that the score function and the noise prediction model follows a simple relationship:
-            ```
-                noise(x_t, t) = -sigma_t * score(x_t, t)
-            ```
-
-    We support three types of guided sampling by DPMs by setting `guidance_type`:
-        1. "uncond": unconditional sampling by DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-
-        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            `` 
-
-            The input `classifier_fn` has the following format:
-            ``
-                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
-            ``
-
-            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
-                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
-
-        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
-            `` 
-            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
-
-            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
-                arXiv preprint arXiv:2207.12598 (2022).
-        
-
-    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
-    or continuous-time labels (i.e. epsilon to T).
-
-    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
-    ``
-        def model_fn(x, t_continuous) -> noise:
-            t_input = get_model_input_time(t_continuous)
-            return noise_pred(model, x, t_input, **model_kwargs)         
-    ``
-    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
-
-    ===============================================================
-
-    Args:
-        model: A diffusion model with the corresponding format described above.
-        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-        model_type: A `str`. The parameterization type of the diffusion model.
-                    "noise" or "x_start" or "v" or "score".
-        model_kwargs: A `dict`. A dict for the other inputs of the model function.
-        guidance_type: A `str`. The type of the guidance for sampling.
-                    "uncond" or "classifier" or "classifier-free".
-        condition: A pytorch tensor. The condition for the guided sampling.
-                    Only used for "classifier" or "classifier-free" guidance type.
-        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
-                    Only used for "classifier-free" guidance type.
-        guidance_scale: A `float`. The scale for the guided sampling.
-        classifier_fn: A classifier function. Only used for the classifier guidance.
-        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
-    Returns:
-        A noise prediction model that accepts the noised data and the continuous time as the inputs.
-    """
-
-    def get_model_input_time(t_continuous):
-        """
-        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
-        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
-        For continuous-time DPMs, we just use `t_continuous`.
-        """
-        if noise_schedule.schedule == 'discrete':
-            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
-        else:
-            return t_continuous
-
-    def noise_pred_fn(x, t_continuous, cond=None):
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        t_input = get_model_input_time(t_continuous)
-        if cond is None:
-            output = model(x, t_input, **model_kwargs)
-        else:
-            output = model(x, t_input, cond, **model_kwargs)
-        if model_type == "noise":
-            return output
-        elif model_type == "x_start":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
-        elif model_type == "v":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
-        elif model_type == "score":
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return -expand_dims(sigma_t, dims) * output
-
-    def cond_grad_fn(x, t_input):
-        """
-        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
-        """
-        with torch.enable_grad():
-            x_in = x.detach().requires_grad_(True)
-            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
-            return torch.autograd.grad(log_prob.sum(), x_in)[0]
-
-    def model_fn(x, t_continuous):
-        """
-        The noise predicition model function that is used for DPM-Solver.
-        """
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        if guidance_type == "uncond":
-            return noise_pred_fn(x, t_continuous)
-        elif guidance_type == "classifier":
-            assert classifier_fn is not None
-            t_input = get_model_input_time(t_continuous)
-            cond_grad = cond_grad_fn(x, t_input)
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            noise = noise_pred_fn(x, t_continuous)
-            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
-        elif guidance_type == "classifier-free":
-            if guidance_scale == 1. or unconditional_condition is None:
-                return noise_pred_fn(x, t_continuous, cond=condition)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t_continuous] * 2)
-                c_in = torch.cat([unconditional_condition, condition])
-                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
-                return noise_uncond + guidance_scale * (noise - noise_uncond)
-
-    assert model_type in ["noise", "x_start", "v"]
-    assert guidance_type in ["uncond", "classifier", "classifier-free"]
-    return model_fn
-
-
-class DPM_Solver:
-    def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
-        """Construct a DPM-Solver. 
-
-        We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
-        If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
-        If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
-            In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
-            The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
-
-        Args:
-            model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
-                ``
-                def model_fn(x, t_continuous):
-                    return noise
-                ``
-            noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-            predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
-            thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
-            max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
-        
-        [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
-        """
-        self.model = model_fn
-        self.noise_schedule = noise_schedule
-        self.predict_x0 = predict_x0
-        self.thresholding = thresholding
-        self.max_val = max_val
-
-    def noise_prediction_fn(self, x, t):
-        """
-        Return the noise prediction model.
-        """
-        return self.model(x, t)
-
-    def data_prediction_fn(self, x, t):
-        """
-        Return the data prediction model (with thresholding).
-        """
-        noise = self.noise_prediction_fn(x, t)
-        dims = x.dim()
-        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
-        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
-        if self.thresholding:
-            p = 0.995   # A hyperparameter in the paper of "Imagen" [1].
-            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
-            x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def model_fn(self, x, t):
-        """
-        Convert the model to the noise prediction model or the data prediction model. 
-        """
-        if self.predict_x0:
-            return self.data_prediction_fn(x, t)
-        else:
-            return self.noise_prediction_fn(x, t)
-
-    def get_time_steps(self, skip_type, t_T, t_0, N, device):
-        """Compute the intermediate time steps for sampling.
-
-        Args:
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            N: A `int`. The total number of the spacing of the time steps.
-            device: A torch device.
-        Returns:
-            A pytorch tensor of the time steps, with the shape (N + 1,).
-        """
-        if skip_type == 'logSNR':
-            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
-            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
-            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
-            return self.noise_schedule.inverse_lambda(logSNR_steps)
-        elif skip_type == 'time_uniform':
-            return torch.linspace(t_T, t_0, N + 1).to(device)
-        elif skip_type == 'time_quadratic':
-            t_order = 2
-            t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
-            return t
-        else:
-            raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
-
-    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
-        """
-        Get the order of each step for sampling by the singlestep DPM-Solver.
-
-        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
-        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
-            - If order == 1:
-                We take `steps` of DPM-Solver-1 (i.e. DDIM).
-            - If order == 2:
-                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
-                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
-                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
-            - If order == 3:
-                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
-
-        ============================================
-        Args:
-            order: A `int`. The max order for the solver (2 or 3).
-            steps: A `int`. The total number of function evaluations (NFE).
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            device: A torch device.
-        Returns:
-            orders: A list of the solver order of each step.
-        """
-        if order == 3:
-            K = steps // 3 + 1
-            if steps % 3 == 0:
-                orders = [3,] * (K - 2) + [2, 1]
-            elif steps % 3 == 1:
-                orders = [3,] * (K - 1) + [1]
-            else:
-                orders = [3,] * (K - 1) + [2]
-        elif order == 2:
-            if steps % 2 == 0:
-                K = steps // 2
-                orders = [2,] * K
-            else:
-                K = steps // 2 + 1
-                orders = [2,] * (K - 1) + [1]
-        elif order == 1:
-            K = 1
-            orders = [1,] * steps
-        else:
-            raise ValueError("'order' must be '1' or '2' or '3'.")
-        if skip_type == 'logSNR':
-            # To reproduce the results in DPM-Solver paper
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
-        else:
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders)).to(device)]
-        return timesteps_outer, orders
-
-    def denoise_to_zero_fn(self, x, s):
-        """
-        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. 
-        """
-        return self.data_prediction_fn(x, s)
-
-    def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
-        """
-        DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_1 = torch.expm1(-h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                expand_dims(sigma_t / sigma_s, dims) * x
-                - expand_dims(alpha_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-        else:
-            phi_1 = torch.expm1(h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                - expand_dims(sigma_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-
-    def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False, solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-2 from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the second-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 0.5
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
-        alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_1 = torch.expm1(-h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                expand_dims(sigma_s1 / sigma_s, dims) * x
-                - expand_dims(alpha_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-                    - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-                    + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (model_s1 - model_s)
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_1 = torch.expm1(h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                - expand_dims(sigma_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-                    - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-                    - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
-                )
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1}
-        else:
-            return x_t
-
-    def singlestep_dpm_solver_third_update(self, x, s, t, r1=1./3., r2=2./3., model_s=None, model_s1=None, return_intermediate=False, solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-3 from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
-                If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 1. / 3.
-        if r2 is None:
-            r2 = 2. / 3.
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        lambda_s2 = lambda_s + r2 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        s2 = ns.inverse_lambda(lambda_s2)
-        log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(s2), ns.marginal_std(t)
-        alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_12 = torch.expm1(-r2 * h)
-            phi_1 = torch.expm1(-h)
-            phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
-            phi_2 = phi_1 / h + 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                    expand_dims(sigma_s1 / sigma_s, dims) * x
-                    - expand_dims(alpha_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                expand_dims(sigma_s2 / sigma_s, dims) * x
-                - expand_dims(alpha_s2 * phi_12, dims) * model_s
-                + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-                    + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-                    + expand_dims(alpha_t * phi_2, dims) * D1
-                    - expand_dims(alpha_t * phi_3, dims) * D2
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_12 = torch.expm1(r2 * h)
-            phi_1 = torch.expm1(h)
-            phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
-            phi_2 = phi_1 / h - 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                    expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                    - expand_dims(sigma_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
-                - expand_dims(sigma_s2 * phi_12, dims) * model_s
-                - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-                    - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-                    - expand_dims(sigma_t * phi_2, dims) * D1
-                    - expand_dims(sigma_t * phi_3, dims) * D2
-                )
-
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
-        else:
-            return x_t
-
-    def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
-        """
-        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_1, model_prev_0 = model_prev_list
-        t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0 = h_0 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        if self.predict_x0:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(sigma_t / sigma_prev_0, dims) * x
-                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                    - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                    expand_dims(sigma_t / sigma_prev_0, dims) * x
-                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                    + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
-                )
-        else:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                    - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                    - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
-                )
-        return x_t
-
-    def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
-        """
-        Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_2, model_prev_1, model_prev_0 = model_prev_list
-        t_prev_2, t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_1 = lambda_prev_1 - lambda_prev_2
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0, r1 = h_0 / h, h_1 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
-        D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
-        D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
-        if self.predict_x0:
-            x_t = (
-                expand_dims(sigma_t / sigma_prev_0, dims) * x
-                - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
-                - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h**2 - 0.5), dims) * D2
-            )
-        else:
-            x_t = (
-                expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
-                - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h**2 - 0.5), dims) * D2
-            )
-        return x_t
-
-    def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None, r2=None):
-        """
-        Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-            r1: A `float`. The hyperparameter of the second-order or third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
-        elif order == 2:
-            return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1)
-        elif order == 3:
-            return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1, r2=r2)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
-        """
-        Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
-        elif order == 2:
-            return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        elif order == 3:
-            return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5, solver_type='dpm_solver'):
-        """
-        The adaptive step size solver based on singlestep DPM-Solver.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `t_T`.
-            order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            h_init: A `float`. The initial step size (for logSNR).
-            atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
-            rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
-            theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
-            t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the 
-                current time and `t_0` is less than `t_err`. The default setting is 1e-5.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_0: A pytorch tensor. The approximated solution at time `t_0`.
-
-        [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
-        """
-        ns = self.noise_schedule
-        s = t_T * torch.ones((x.shape[0],)).to(x)
-        lambda_s = ns.marginal_lambda(s)
-        lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
-        h = h_init * torch.ones_like(s).to(x)
-        x_prev = x
-        nfe = 0
-        if order == 2:
-            r1 = 0.5
-            lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, solver_type=solver_type, **kwargs)
-        elif order == 3:
-            r1, r2 = 1. / 3., 2. / 3.
-            lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, return_intermediate=True, solver_type=solver_type)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2, solver_type=solver_type, **kwargs)
-        else:
-            raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
-        while torch.abs((s - t_0)).mean() > t_err:
-            t = ns.inverse_lambda(lambda_s + h)
-            x_lower, lower_noise_kwargs = lower_update(x, s, t)
-            x_higher = higher_update(x, s, t, **lower_noise_kwargs)
-            delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
-            norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
-            E = norm_fn((x_higher - x_lower) / delta).max()
-            if torch.all(E <= 1.):
-                x = x_higher
-                s = t
-                x_prev = x_lower
-                lambda_s = ns.marginal_lambda(s)
-            h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
-            nfe += order
-        print('adaptive solver nfe', nfe)
-        return x
-
-    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
-        method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
-        atol=0.0078, rtol=0.05,
-    ):
-        """
-        Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
-
-        =====================================================
-
-        We support the following algorithms for both noise prediction model and data prediction model:
-            - 'singlestep':
-                Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver. 
-                We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
-                The total number of function evaluations (NFE) == `steps`.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    - If `order` == 1:
-                        - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
-                        - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
-                        - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                    - If `order` == 3:
-                        - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                        - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
-            - 'multistep':
-                Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
-                We initialize the first `order` values by lower order multistep solvers.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    Denote K = steps.
-                    - If `order` == 1:
-                        - We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
-                    - If `order` == 3:
-                        - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
-            - 'singlestep_fixed':
-                Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
-                We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
-            - 'adaptive':
-                Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
-                We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
-                You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
-                (NFE) and the sample quality.
-                    - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
-                    - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
-
-        =====================================================
-
-        Some advices for choosing the algorithm:
-            - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
-                Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
-                            skip_type='time_uniform', method='singlestep')
-            - For **guided sampling with large guidance scale** by DPMs:
-                Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
-                            skip_type='time_uniform', method='multistep')
-
-        We support three types of `skip_type`:
-            - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
-            - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
-            - 'time_quadratic': quadratic time for the time steps.
-
-        =====================================================
-        Args:
-            x: A pytorch tensor. The initial value at time `t_start`
-                e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
-            steps: A `int`. The total number of function evaluations (NFE).
-            t_start: A `float`. The starting time of the sampling.
-                If `T` is None, we use self.noise_schedule.T (default is 1.0).
-            t_end: A `float`. The ending time of the sampling.
-                If `t_end` is None, we use 1. / self.noise_schedule.total_N.
-                e.g. if total_N == 1000, we have `t_end` == 1e-3.
-                For discrete-time DPMs:
-                    - We recommend `t_end` == 1. / self.noise_schedule.total_N.
-                For continuous-time DPMs:
-                    - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
-            order: A `int`. The order of DPM-Solver.
-            skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
-            method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
-            denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
-                Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
-
-                This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
-                score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
-                for diffusion models sampling by diffusion SDEs for low-resolutional images
-                (such as CIFAR-10). However, we observed that such trick does not matter for
-                high-resolutional images. As it needs an additional NFE, we do not recommend
-                it for high-resolutional images.
-            lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
-                Only valid for `method=multistep` and `steps < 15`. We empirically find that
-                this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
-                (especially for steps <= 10). So we recommend to set it to be `True`.
-            solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
-            atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-            rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-        Returns:
-            x_end: A pytorch tensor. The approximated solution at time `t_end`.
-
-        """
-        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
-        t_T = self.noise_schedule.T if t_start is None else t_start
-        device = x.device
-        if method == 'adaptive':
-            with torch.no_grad():
-                x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol, solver_type=solver_type)
-        elif method == 'multistep':
-            assert steps >= order
-            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
-            assert timesteps.shape[0] - 1 == steps
-            with torch.no_grad():
-                vec_t = timesteps[0].expand((x.shape[0]))
-                model_prev_list = [self.model_fn(x, vec_t)]
-                t_prev_list = [vec_t]
-                # Init the first `order` values by lower order multistep DPM-Solver.
-                for init_order in range(1, order):
-                    vec_t = timesteps[init_order].expand(x.shape[0])
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order, solver_type=solver_type)
-                    model_prev_list.append(self.model_fn(x, vec_t))
-                    t_prev_list.append(vec_t)
-                # Compute the remaining values by `order`-th order multistep DPM-Solver.
-                for step in range(order, steps + 1):
-                    vec_t = timesteps[step].expand(x.shape[0])
-                    if lower_order_final and steps < 15:
-                        step_order = min(order, steps + 1 - step)
-                    else:
-                        step_order = order
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order, solver_type=solver_type)
-                    for i in range(order - 1):
-                        t_prev_list[i] = t_prev_list[i + 1]
-                        model_prev_list[i] = model_prev_list[i + 1]
-                    t_prev_list[-1] = vec_t
-                    # We do not need to evaluate the final model value.
-                    if step < steps:
-                        model_prev_list[-1] = self.model_fn(x, vec_t)
-        elif method in ['singlestep', 'singlestep_fixed']:
-            if method == 'singlestep':
-                timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order, skip_type=skip_type, t_T=t_T, t_0=t_0, device=device)
-            elif method == 'singlestep_fixed':
-                K = steps // order
-                orders = [order,] * K
-                timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
-            for i, order in enumerate(orders):
-                t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
-                timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(), N=order, device=device)
-                lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
-                vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
-                h = lambda_inner[-1] - lambda_inner[0]
-                r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
-                r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
-                x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
-        if denoise_to_zero:
-            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
-        return x
-
-
-
-#############################################################
-# other utility functions
-#############################################################
-
-def interpolate_fn(x, xp, yp):
-    """
-    A piecewise linear function y = f(x), using xp and yp as keypoints.
-    We implement f(x) in a differentiable way (i.e. applicable for autograd).
-    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
-
-    Args:
-        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
-        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
-        yp: PyTorch tensor with shape [C, K].
-    Returns:
-        The function values f(x), with shape [N, C].
-    """
-    N, K = x.shape[0], xp.shape[1]
-    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
-    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
-    x_idx = torch.argmin(x_indices, dim=2)
-    cand_start_idx = x_idx - 1
-    start_idx = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(1, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
-    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
-    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
-    start_idx2 = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(0, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
-    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
-    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
-    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
-    return cand
-
-
-def expand_dims(v, dims):
-    """
-    Expand the tensor `v` to the dim `dims`.
-
-    Args:
-        `v`: a PyTorch tensor with shape [N].
-        `dim`: a `int`.
-    Returns:
-        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
-    """
-    return v[(...,) + (None,)*(dims - 1)]
\ No newline at end of file
diff --git a/ldm/models/diffusion/dpm_solver/sampler.py b/ldm/models/diffusion/dpm_solver/sampler.py
deleted file mode 100644
index 2c42d6f9..00000000
--- a/ldm/models/diffusion/dpm_solver/sampler.py
+++ /dev/null
@@ -1,82 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-
-from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
-
-
-class DPMSolverSampler(object):
-    def __init__(self, model, **kwargs):
-        super().__init__()
-        self.model = model
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
-        self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != torch.device("cuda"):
-                attr = attr.to(torch.device("cuda"))
-        setattr(self, name, attr)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-
-        # print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
-
-        device = self.model.betas.device
-        if x_T is None:
-            img = torch.randn(size, device=device)
-        else:
-            img = x_T
-
-        ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
-
-        model_fn = model_wrapper(
-            lambda x, t, c: self.model.apply_model(x, t, c),
-            ns,
-            model_type="noise",
-            guidance_type="classifier-free",
-            condition=conditioning,
-            unconditional_condition=unconditional_conditioning,
-            guidance_scale=unconditional_guidance_scale,
-        )
-
-        dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
-        x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
-
-        return x.to(device), None
diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py
deleted file mode 100644
index 78eeb100..00000000
--- a/ldm/models/diffusion/plms.py
+++ /dev/null
@@ -1,236 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from functools import partial
-
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
-
-
-class PLMSSampler(object):
-    def __init__(self, model, schedule="linear", **kwargs):
-        super().__init__()
-        self.model = model
-        self.ddpm_num_timesteps = model.num_timesteps
-        self.schedule = schedule
-
-    def register_buffer(self, name, attr):
-        if type(attr) == torch.Tensor:
-            if attr.device != torch.device("cuda"):
-                attr = attr.to(torch.device("cuda"))
-        setattr(self, name, attr)
-
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        if ddim_eta != 0:
-            raise ValueError('ddim_eta must be 0 for PLMS')
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
-        alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
-        self.register_buffer('betas', to_torch(self.model.betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
-        # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
-        self.register_buffer('ddim_sigmas', ddim_sigmas)
-        self.register_buffer('ddim_alphas', ddim_alphas)
-        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
-        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-    @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
-        if conditioning is not None:
-            if isinstance(conditioning, dict):
-                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
-                if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-            else:
-                if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-        # sampling
-        C, H, W = shape
-        size = (batch_size, C, H, W)
-        print(f'Data shape for PLMS sampling is {size}')
-
-        samples, intermediates = self.plms_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    )
-        return samples, intermediates
-
-    @torch.no_grad()
-    def plms_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,):
-        device = self.model.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
-        elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
-            timesteps = self.ddim_timesteps[:subset_end]
-
-        intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        print(f"Running PLMS Sampling with {total_steps} timesteps")
-
-        iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
-        old_eps = []
-
-        for i, step in enumerate(iterator):
-            index = total_steps - i - 1
-            ts = torch.full((b,), step, device=device, dtype=torch.long)
-            ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
-
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
-                img = img_orig * mask + (1. - mask) * img
-
-            outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning,
-                                      old_eps=old_eps, t_next=ts_next)
-            img, pred_x0, e_t = outs
-            old_eps.append(e_t)
-            if len(old_eps) >= 4:
-                old_eps.pop(0)
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
-
-            if index % log_every_t == 0 or index == total_steps - 1:
-                intermediates['x_inter'].append(img)
-                intermediates['pred_x0'].append(pred_x0)
-
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
-        b, *_, device = *x.shape, x.device
-
-        def get_model_output(x, t):
-            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-                e_t = self.model.apply_model(x, t, c)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t] * 2)
-                c_in = torch.cat([unconditional_conditioning, c])
-                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-            if score_corrector is not None:
-                assert self.model.parameterization == "eps"
-                e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-            return e_t
-
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-
-        def get_x_prev_and_pred_x0(e_t, index):
-            # select parameters corresponding to the currently considered timestep
-            a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-            sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-            sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-            # current prediction for x_0
-            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-            if quantize_denoised:
-                pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-            # direction pointing to x_t
-            dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-            if noise_dropout > 0.:
-                noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-            return x_prev, pred_x0
-
-        e_t = get_model_output(x, t)
-        if len(old_eps) == 0:
-            # Pseudo Improved Euler (2nd order)
-            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
-            e_t_next = get_model_output(x_prev, t_next)
-            e_t_prime = (e_t + e_t_next) / 2
-        elif len(old_eps) == 1:
-            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (3 * e_t - old_eps[-1]) / 2
-        elif len(old_eps) == 2:
-            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
-        elif len(old_eps) >= 3:
-            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
-
-        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
-
-        return x_prev, pred_x0, e_t
diff --git a/ldm/modules/attention.py b/ldm/modules/attention.py
deleted file mode 100644
index f4eff39c..00000000
--- a/ldm/modules/attention.py
+++ /dev/null
@@ -1,261 +0,0 @@
-from inspect import isfunction
-import math
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-from einops import rearrange, repeat
-
-from ldm.modules.diffusionmodules.util import checkpoint
-
-
-def exists(val):
-    return val is not None
-
-
-def uniq(arr):
-    return{el: True for el in arr}.keys()
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def max_neg_value(t):
-    return -torch.finfo(t.dtype).max
-
-
-def init_(tensor):
-    dim = tensor.shape[-1]
-    std = 1 / math.sqrt(dim)
-    tensor.uniform_(-std, std)
-    return tensor
-
-
-# feedforward
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def Normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class LinearAttention(nn.Module):
-    def __init__(self, dim, heads=4, dim_head=32):
-        super().__init__()
-        self.heads = heads
-        hidden_dim = dim_head * heads
-        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
-        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-        qkv = self.to_qkv(x)
-        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
-        k = k.softmax(dim=-1)  
-        context = torch.einsum('bhdn,bhen->bhde', k, v)
-        out = torch.einsum('bhde,bhdn->bhen', context, q)
-        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
-        return self.to_out(out)
-
-
-class SpatialSelfAttention(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = rearrange(q, 'b c h w -> b (h w) c')
-        k = rearrange(k, 'b c h w -> b c (h w)')
-        w_ = torch.einsum('bij,bjk->bik', q, k)
-
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = rearrange(v, 'b c h w -> b c (h w)')
-        w_ = rearrange(w_, 'b i j -> b j i')
-        h_ = torch.einsum('bij,bjk->bik', v, w_)
-        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
-        super().__init__()
-        inner_dim = dim_head * heads
-        context_dim = default(context_dim, query_dim)
-
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
-        self.to_out = nn.Sequential(
-            nn.Linear(inner_dim, query_dim),
-            nn.Dropout(dropout)
-        )
-
-    def forward(self, x, context=None, mask=None):
-        h = self.heads
-
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-
-        if exists(mask):
-            mask = rearrange(mask, 'b ... -> b (...)')
-            max_neg_value = -torch.finfo(sim.dtype).max
-            mask = repeat(mask, 'b j -> (b h) () j', h=h)
-            sim.masked_fill_(~mask, max_neg_value)
-
-        # attention, what we cannot get enough of
-        attn = sim.softmax(dim=-1)
-
-        out = einsum('b i j, b j d -> b i d', attn, v)
-        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-        return self.to_out(out)
-
-
-class BasicTransformerBlock(nn.Module):
-    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
-        super().__init__()
-        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout)  # is a self-attention
-        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
-        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
-                                    heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
-        self.norm1 = nn.LayerNorm(dim)
-        self.norm2 = nn.LayerNorm(dim)
-        self.norm3 = nn.LayerNorm(dim)
-        self.checkpoint = checkpoint
-
-    def forward(self, x, context=None):
-        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
-
-    def _forward(self, x, context=None):
-        x = self.attn1(self.norm1(x)) + x
-        x = self.attn2(self.norm2(x), context=context) + x
-        x = self.ff(self.norm3(x)) + x
-        return x
-
-
-class SpatialTransformer(nn.Module):
-    """
-    Transformer block for image-like data.
-    First, project the input (aka embedding)
-    and reshape to b, t, d.
-    Then apply standard transformer action.
-    Finally, reshape to image
-    """
-    def __init__(self, in_channels, n_heads, d_head,
-                 depth=1, dropout=0., context_dim=None):
-        super().__init__()
-        self.in_channels = in_channels
-        inner_dim = n_heads * d_head
-        self.norm = Normalize(in_channels)
-
-        self.proj_in = nn.Conv2d(in_channels,
-                                 inner_dim,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-
-        self.transformer_blocks = nn.ModuleList(
-            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
-                for d in range(depth)]
-        )
-
-        self.proj_out = zero_module(nn.Conv2d(inner_dim,
-                                              in_channels,
-                                              kernel_size=1,
-                                              stride=1,
-                                              padding=0))
-
-    def forward(self, x, context=None):
-        # note: if no context is given, cross-attention defaults to self-attention
-        b, c, h, w = x.shape
-        x_in = x
-        x = self.norm(x)
-        x = self.proj_in(x)
-        x = rearrange(x, 'b c h w -> b (h w) c')
-        for block in self.transformer_blocks:
-            x = block(x, context=context)
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
-        x = self.proj_out(x)
-        return x + x_in
\ No newline at end of file
diff --git a/ldm/modules/diffusionmodules/__init__.py b/ldm/modules/diffusionmodules/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/ldm/modules/diffusionmodules/model.py b/ldm/modules/diffusionmodules/model.py
deleted file mode 100644
index 533e589a..00000000
--- a/ldm/modules/diffusionmodules/model.py
+++ /dev/null
@@ -1,835 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import rearrange
-
-from ldm.util import instantiate_from_config
-from ldm.modules.attention import LinearAttention
-
-
-def get_timestep_embedding(timesteps, embedding_dim):
-    """
-    This matches the implementation in Denoising Diffusion Probabilistic Models:
-    From Fairseq.
-    Build sinusoidal embeddings.
-    This matches the implementation in tensor2tensor, but differs slightly
-    from the description in Section 3.5 of "Attention Is All You Need".
-    """
-    assert len(timesteps.shape) == 1
-
-    half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
-    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
-    emb = emb.to(device=timesteps.device)
-    emb = timesteps.float()[:, None] * emb[None, :]
-    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
-    if embedding_dim % 2 == 1:  # zero pad
-        emb = torch.nn.functional.pad(emb, (0,1,0,0))
-    return emb
-
-
-def nonlinearity(x):
-    # swish
-    return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels, num_groups=32):
-    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=2,
-                                        padding=0)
-
-    def forward(self, x):
-        if self.with_conv:
-            pad = (0,1,0,1)
-            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-            x = self.conv(x)
-        else:
-            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout, temb_channels=512):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-
-        self.norm1 = Normalize(in_channels)
-        self.conv1 = torch.nn.Conv2d(in_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if temb_channels > 0:
-            self.temb_proj = torch.nn.Linear(temb_channels,
-                                             out_channels)
-        self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(out_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(in_channels,
-                                                     out_channels,
-                                                     kernel_size=3,
-                                                     stride=1,
-                                                     padding=1)
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(in_channels,
-                                                    out_channels,
-                                                    kernel_size=1,
-                                                    stride=1,
-                                                    padding=0)
-
-    def forward(self, x, temb):
-        h = x
-        h = self.norm1(h)
-        h = nonlinearity(h)
-        h = self.conv1(h)
-
-        if temb is not None:
-            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
-        h = self.norm2(h)
-        h = nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x+h
-
-
-class LinAttnBlock(LinearAttention):
-    """to match AttnBlock usage"""
-    def __init__(self, in_channels):
-        super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = q.reshape(b,c,h*w)
-        q = q.permute(0,2,1)   # b,hw,c
-        k = k.reshape(b,c,h*w) # b,c,hw
-        w_ = torch.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b,c,h*w)
-        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
-        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-        h_ = h_.reshape(b,c,h,w)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-def make_attn(in_channels, attn_type="vanilla"):
-    assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
-    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
-    if attn_type == "vanilla":
-        return AttnBlock(in_channels)
-    elif attn_type == "none":
-        return nn.Identity(in_channels)
-    else:
-        return LinAttnBlock(in_channels)
-
-
-class Model(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = self.ch*4
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        self.use_timestep = use_timestep
-        if self.use_timestep:
-            # timestep embedding
-            self.temb = nn.Module()
-            self.temb.dense = nn.ModuleList([
-                torch.nn.Linear(self.ch,
-                                self.temb_ch),
-                torch.nn.Linear(self.temb_ch,
-                                self.temb_ch),
-            ])
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            skip_in = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                if i_block == self.num_res_blocks:
-                    skip_in = ch*in_ch_mult[i_level]
-                block.append(ResnetBlock(in_channels=block_in+skip_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x, t=None, context=None):
-        #assert x.shape[2] == x.shape[3] == self.resolution
-        if context is not None:
-            # assume aligned context, cat along channel axis
-            x = torch.cat((x, context), dim=1)
-        if self.use_timestep:
-            # timestep embedding
-            assert t is not None
-            temb = get_timestep_embedding(t, self.ch)
-            temb = self.temb.dense[0](temb)
-            temb = nonlinearity(temb)
-            temb = self.temb.dense[1](temb)
-        else:
-            temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](
-                    torch.cat([h, hs.pop()], dim=1), temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-    def get_last_layer(self):
-        return self.conv_out.weight
-
-
-class Encoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
-                 **ignore_kwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.in_ch_mult = in_ch_mult
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        2*z_channels if double_z else z_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # timestep embedding
-        temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class Decoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
-                 attn_type="vanilla", **ignorekwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-        self.tanh_out = tanh_out
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        in_ch_mult = (1,)+tuple(ch_mult)
-        block_in = ch*ch_mult[self.num_resolutions-1]
-        curr_res = resolution // 2**(self.num_resolutions-1)
-        self.z_shape = (1,z_channels,curr_res,curr_res)
-        print("Working with z of shape {} = {} dimensions.".format(
-            self.z_shape, np.prod(self.z_shape)))
-
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(z_channels,
-                                       block_in,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, z):
-        #assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h, temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        if self.tanh_out:
-            h = torch.tanh(h)
-        return h
-
-
-class SimpleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, *args, **kwargs):
-        super().__init__()
-        self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
-                                     ResnetBlock(in_channels=in_channels,
-                                                 out_channels=2 * in_channels,
-                                                 temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=2 * in_channels,
-                                                out_channels=4 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=4 * in_channels,
-                                                out_channels=2 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     nn.Conv2d(2*in_channels, in_channels, 1),
-                                     Upsample(in_channels, with_conv=True)])
-        # end
-        self.norm_out = Normalize(in_channels)
-        self.conv_out = torch.nn.Conv2d(in_channels,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        for i, layer in enumerate(self.model):
-            if i in [1,2,3]:
-                x = layer(x, None)
-            else:
-                x = layer(x)
-
-        h = self.norm_out(x)
-        h = nonlinearity(h)
-        x = self.conv_out(h)
-        return x
-
-
-class UpsampleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
-                 ch_mult=(2,2), dropout=0.0):
-        super().__init__()
-        # upsampling
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        block_in = in_channels
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.res_blocks = nn.ModuleList()
-        self.upsample_blocks = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            res_block = []
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                res_block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-            self.res_blocks.append(nn.ModuleList(res_block))
-            if i_level != self.num_resolutions - 1:
-                self.upsample_blocks.append(Upsample(block_in, True))
-                curr_res = curr_res * 2
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # upsampling
-        h = x
-        for k, i_level in enumerate(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.res_blocks[i_level][i_block](h, None)
-            if i_level != self.num_resolutions - 1:
-                h = self.upsample_blocks[k](h)
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class LatentRescaler(nn.Module):
-    def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
-        super().__init__()
-        # residual block, interpolate, residual block
-        self.factor = factor
-        self.conv_in = nn.Conv2d(in_channels,
-                                 mid_channels,
-                                 kernel_size=3,
-                                 stride=1,
-                                 padding=1)
-        self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-        self.attn = AttnBlock(mid_channels)
-        self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-
-        self.conv_out = nn.Conv2d(mid_channels,
-                                  out_channels,
-                                  kernel_size=1,
-                                  )
-
-    def forward(self, x):
-        x = self.conv_in(x)
-        for block in self.res_block1:
-            x = block(x, None)
-        x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
-        x = self.attn(x)
-        for block in self.res_block2:
-            x = block(x, None)
-        x = self.conv_out(x)
-        return x
-
-
-class MergedRescaleEncoder(nn.Module):
-    def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True,
-                 ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        intermediate_chn = ch * ch_mult[-1]
-        self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
-                               z_channels=intermediate_chn, double_z=False, resolution=resolution,
-                               attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
-                               out_ch=None)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
-                                       mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.encoder(x)
-        x = self.rescaler(x)
-        return x
-
-
-class MergedRescaleDecoder(nn.Module):
-    def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
-                 dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        tmp_chn = z_channels*ch_mult[-1]
-        self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
-                               resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
-                               ch_mult=ch_mult, resolution=resolution, ch=ch)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
-                                       out_channels=tmp_chn, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Upsampler(nn.Module):
-    def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
-        super().__init__()
-        assert out_size >= in_size
-        num_blocks = int(np.log2(out_size//in_size))+1
-        factor_up = 1.+ (out_size % in_size)
-        print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
-        self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
-                                       out_channels=in_channels)
-        self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
-                               attn_resolutions=[], in_channels=None, ch=in_channels,
-                               ch_mult=[ch_mult for _ in range(num_blocks)])
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Resize(nn.Module):
-    def __init__(self, in_channels=None, learned=False, mode="bilinear"):
-        super().__init__()
-        self.with_conv = learned
-        self.mode = mode
-        if self.with_conv:
-            print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
-            raise NotImplementedError()
-            assert in_channels is not None
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=4,
-                                        stride=2,
-                                        padding=1)
-
-    def forward(self, x, scale_factor=1.0):
-        if scale_factor==1.0:
-            return x
-        else:
-            x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
-        return x
-
-class FirstStagePostProcessor(nn.Module):
-
-    def __init__(self, ch_mult:list, in_channels,
-                 pretrained_model:nn.Module=None,
-                 reshape=False,
-                 n_channels=None,
-                 dropout=0.,
-                 pretrained_config=None):
-        super().__init__()
-        if pretrained_config is None:
-            assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
-            self.pretrained_model = pretrained_model
-        else:
-            assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
-            self.instantiate_pretrained(pretrained_config)
-
-        self.do_reshape = reshape
-
-        if n_channels is None:
-            n_channels = self.pretrained_model.encoder.ch
-
-        self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
-        self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
-                            stride=1,padding=1)
-
-        blocks = []
-        downs = []
-        ch_in = n_channels
-        for m in ch_mult:
-            blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
-            ch_in = m * n_channels
-            downs.append(Downsample(ch_in, with_conv=False))
-
-        self.model = nn.ModuleList(blocks)
-        self.downsampler = nn.ModuleList(downs)
-
-
-    def instantiate_pretrained(self, config):
-        model = instantiate_from_config(config)
-        self.pretrained_model = model.eval()
-        # self.pretrained_model.train = False
-        for param in self.pretrained_model.parameters():
-            param.requires_grad = False
-
-
-    @torch.no_grad()
-    def encode_with_pretrained(self,x):
-        c = self.pretrained_model.encode(x)
-        if isinstance(c, DiagonalGaussianDistribution):
-            c = c.mode()
-        return  c
-
-    def forward(self,x):
-        z_fs = self.encode_with_pretrained(x)
-        z = self.proj_norm(z_fs)
-        z = self.proj(z)
-        z = nonlinearity(z)
-
-        for submodel, downmodel in zip(self.model,self.downsampler):
-            z = submodel(z,temb=None)
-            z = downmodel(z)
-
-        if self.do_reshape:
-            z = rearrange(z,'b c h w -> b (h w) c')
-        return z
-
diff --git a/ldm/modules/diffusionmodules/openaimodel.py b/ldm/modules/diffusionmodules/openaimodel.py
deleted file mode 100644
index fcf95d1e..00000000
--- a/ldm/modules/diffusionmodules/openaimodel.py
+++ /dev/null
@@ -1,961 +0,0 @@
-from abc import abstractmethod
-from functools import partial
-import math
-from typing import Iterable
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ldm.modules.diffusionmodules.util import (
-    checkpoint,
-    conv_nd,
-    linear,
-    avg_pool_nd,
-    zero_module,
-    normalization,
-    timestep_embedding,
-)
-from ldm.modules.attention import SpatialTransformer
-
-
-# dummy replace
-def convert_module_to_f16(x):
-    pass
-
-def convert_module_to_f32(x):
-    pass
-
-
-## go
-class AttentionPool2d(nn.Module):
-    """
-    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
-    """
-
-    def __init__(
-        self,
-        spacial_dim: int,
-        embed_dim: int,
-        num_heads_channels: int,
-        output_dim: int = None,
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
-        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
-        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
-        self.num_heads = embed_dim // num_heads_channels
-        self.attention = QKVAttention(self.num_heads)
-
-    def forward(self, x):
-        b, c, *_spatial = x.shape
-        x = x.reshape(b, c, -1)  # NC(HW)
-        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
-        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
-        x = self.qkv_proj(x)
-        x = self.attention(x)
-        x = self.c_proj(x)
-        return x[:, :, 0]
-
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
-    """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
-    """
-
-    def forward(self, x, emb, context=None):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb)
-            elif isinstance(layer, SpatialTransformer):
-                x = layer(x, context)
-            else:
-                x = layer(x)
-        return x
-
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
-            )
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-class TransposedUpsample(nn.Module):
-    'Learned 2x upsampling without padding'
-    def __init__(self, channels, out_channels=None, ks=5):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-
-        self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
-
-    def forward(self,x):
-        return self.up(x)
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels),
-            nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.emb_layers = nn.Sequential(
-            nn.SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels),
-            nn.SiLU(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        return checkpoint(
-            self._forward, (x, emb), self.parameters(), self.use_checkpoint
-        )
-
-
-    def _forward(self, x, emb):
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        emb_out = self.emb_layers(emb).type(h.dtype)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            scale, shift = th.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
-    """
-    An attention block that allows spatial positions to attend to each other.
-    Originally ported from here, but adapted to the N-d case.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
-    """
-
-    def __init__(
-        self,
-        channels,
-        num_heads=1,
-        num_head_channels=-1,
-        use_checkpoint=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        if num_head_channels == -1:
-            self.num_heads = num_heads
-        else:
-            assert (
-                channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
-            self.num_heads = channels // num_head_channels
-        self.use_checkpoint = use_checkpoint
-        self.norm = normalization(channels)
-        self.qkv = conv_nd(1, channels, channels * 3, 1)
-        if use_new_attention_order:
-            # split qkv before split heads
-            self.attention = QKVAttention(self.num_heads)
-        else:
-            # split heads before split qkv
-            self.attention = QKVAttentionLegacy(self.num_heads)
-
-        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
-    def forward(self, x):
-        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
-        #return pt_checkpoint(self._forward, x)  # pytorch
-
-    def _forward(self, x):
-        b, c, *spatial = x.shape
-        x = x.reshape(b, c, -1)
-        qkv = self.qkv(self.norm(x))
-        h = self.attention(qkv)
-        h = self.proj_out(h)
-        return (x + h).reshape(b, c, *spatial)
-
-
-def count_flops_attn(model, _x, y):
-    """
-    A counter for the `thop` package to count the operations in an
-    attention operation.
-    Meant to be used like:
-        macs, params = thop.profile(
-            model,
-            inputs=(inputs, timestamps),
-            custom_ops={QKVAttention: QKVAttention.count_flops},
-        )
-    """
-    b, c, *spatial = y[0].shape
-    num_spatial = int(np.prod(spatial))
-    # We perform two matmuls with the same number of ops.
-    # The first computes the weight matrix, the second computes
-    # the combination of the value vectors.
-    matmul_ops = 2 * b * (num_spatial ** 2) * c
-    model.total_ops += th.DoubleTensor([matmul_ops])
-
-
-class QKVAttentionLegacy(nn.Module):
-    """
-    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts", q * scale, k * scale
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v)
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class QKVAttention(nn.Module):
-    """
-    A module which performs QKV attention and splits in a different order.
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.chunk(3, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts",
-            (q * scale).view(bs * self.n_heads, ch, length),
-            (k * scale).view(bs * self.n_heads, ch, length),
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
-                                    increased efficiency.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=-1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        use_spatial_transformer=False,    # custom transformer support
-        transformer_depth=1,              # custom transformer support
-        context_dim=None,                 # custom transformer support
-        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
-        legacy=True,
-    ):
-        super().__init__()
-        if use_spatial_transformer:
-            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
-
-        if context_dim is not None:
-            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
-            from omegaconf.listconfig import ListConfig
-            if type(context_dim) == ListConfig:
-                context_dim = list(context_dim)
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        if num_heads == -1:
-            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
-
-        if num_head_channels == -1:
-            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
-
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.predict_codebook_ids = n_embed is not None
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        if self.num_classes is not None:
-            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
-                )
-            ]
-        )
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=dim_head,
-                            use_new_attention_order=use_new_attention_order,
-                        ) if not use_spatial_transformer else SpatialTransformer(
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        if num_head_channels == -1:
-            dim_head = ch // num_heads
-        else:
-            num_heads = ch // num_head_channels
-            dim_head = num_head_channels
-        if legacy:
-            #num_heads = 1
-            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=dim_head,
-                use_new_attention_order=use_new_attention_order,
-            ) if not use_spatial_transformer else SpatialTransformer(
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
-                        ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(num_res_blocks + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    ResBlock(
-                        ch + ich,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=model_channels * mult,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = model_channels * mult
-                if ds in attention_resolutions:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads_upsample,
-                            num_head_channels=dim_head,
-                            use_new_attention_order=use_new_attention_order,
-                        ) if not use_spatial_transformer else SpatialTransformer(
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
-                        )
-                    )
-                if level and i == num_res_blocks:
-                    out_ch = ch
-                    layers.append(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            normalization(ch),
-            nn.SiLU(),
-            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
-        )
-        if self.predict_codebook_ids:
-            self.id_predictor = nn.Sequential(
-            normalization(ch),
-            conv_nd(dims, model_channels, n_embed, 1),
-            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
-        )
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param context: conditioning plugged in via crossattn
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-        hs = []
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-        emb = self.time_embed(t_emb)
-
-        if self.num_classes is not None:
-            assert y.shape == (x.shape[0],)
-            emb = emb + self.label_emb(y)
-
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb, context)
-            hs.append(h)
-        h = self.middle_block(h, emb, context)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, context)
-        h = h.type(x.dtype)
-        if self.predict_codebook_ids:
-            return self.id_predictor(h)
-        else:
-            return self.out(h)
-
-
-class EncoderUNetModel(nn.Module):
-    """
-    The half UNet model with attention and timestep embedding.
-    For usage, see UNet.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        pool="adaptive",
-        *args,
-        **kwargs
-    ):
-        super().__init__()
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
-                )
-            ]
-        )
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = mult * model_channels
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=num_head_channels,
-                use_new_attention_order=use_new_attention_order,
-            ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-        self.pool = pool
-        if pool == "adaptive":
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                nn.AdaptiveAvgPool2d((1, 1)),
-                zero_module(conv_nd(dims, ch, out_channels, 1)),
-                nn.Flatten(),
-            )
-        elif pool == "attention":
-            assert num_head_channels != -1
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                AttentionPool2d(
-                    (image_size // ds), ch, num_head_channels, out_channels
-                ),
-            )
-        elif pool == "spatial":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                nn.ReLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        elif pool == "spatial_v2":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                normalization(2048),
-                nn.SiLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        else:
-            raise NotImplementedError(f"Unexpected {pool} pooling")
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps):
-        """
-        Apply the model to an input batch.
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :return: an [N x K] Tensor of outputs.
-        """
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
-        results = []
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb)
-            if self.pool.startswith("spatial"):
-                results.append(h.type(x.dtype).mean(dim=(2, 3)))
-        h = self.middle_block(h, emb)
-        if self.pool.startswith("spatial"):
-            results.append(h.type(x.dtype).mean(dim=(2, 3)))
-            h = th.cat(results, axis=-1)
-            return self.out(h)
-        else:
-            h = h.type(x.dtype)
-            return self.out(h)
-
diff --git a/ldm/modules/diffusionmodules/util.py b/ldm/modules/diffusionmodules/util.py
deleted file mode 100644
index a952e6c4..00000000
--- a/ldm/modules/diffusionmodules/util.py
+++ /dev/null
@@ -1,267 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-
-
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-
-from ldm.util import instantiate_from_config
-
-
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-    if schedule == "linear":
-        betas = (
-                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
-        )
-
-    elif schedule == "cosine":
-        timesteps = (
-                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
-        )
-        alphas = timesteps / (1 + cosine_s) * np.pi / 2
-        alphas = torch.cos(alphas).pow(2)
-        alphas = alphas / alphas[0]
-        betas = 1 - alphas[1:] / alphas[:-1]
-        betas = np.clip(betas, a_min=0, a_max=0.999)
-
-    elif schedule == "sqrt_linear":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
-    elif schedule == "sqrt":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
-    else:
-        raise ValueError(f"schedule '{schedule}' unknown.")
-    return betas.numpy()
-
-
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
-    if ddim_discr_method == 'uniform':
-        c = num_ddpm_timesteps // num_ddim_timesteps
-        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
-    elif ddim_discr_method == 'quad':
-        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
-    else:
-        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-
-    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
-    # add one to get the final alpha values right (the ones from first scale to data during sampling)
-    steps_out = ddim_timesteps + 1
-    if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
-    return steps_out
-
-
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
-    # select alphas for computing the variance schedule
-    alphas = alphacums[ddim_timesteps]
-    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-
-    # according the the formula provided in https://arxiv.org/abs/2010.02502
-    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
-    if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
-              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
-    return sigmas, alphas, alphas_prev
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-
-
-def extract_into_tensor(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def checkpoint(func, inputs, params, flag):
-    """
-    Evaluate a function without caching intermediate activations, allowing for
-    reduced memory at the expense of extra compute in the backward pass.
-    :param func: the function to evaluate.
-    :param inputs: the argument sequence to pass to `func`.
-    :param params: a sequence of parameters `func` depends on but does not
-                   explicitly take as arguments.
-    :param flag: if False, disable gradient checkpointing.
-    """
-    if flag:
-        args = tuple(inputs) + tuple(params)
-        return CheckpointFunction.apply(func, len(inputs), *args)
-    else:
-        return func(*inputs)
-
-
-class CheckpointFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, run_function, length, *args):
-        ctx.run_function = run_function
-        ctx.input_tensors = list(args[:length])
-        ctx.input_params = list(args[length:])
-
-        with torch.no_grad():
-            output_tensors = ctx.run_function(*ctx.input_tensors)
-        return output_tensors
-
-    @staticmethod
-    def backward(ctx, *output_grads):
-        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
-        with torch.enable_grad():
-            # Fixes a bug where the first op in run_function modifies the
-            # Tensor storage in place, which is not allowed for detach()'d
-            # Tensors.
-            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
-            output_tensors = ctx.run_function(*shallow_copies)
-        input_grads = torch.autograd.grad(
-            output_tensors,
-            ctx.input_tensors + ctx.input_params,
-            output_grads,
-            allow_unused=True,
-        )
-        del ctx.input_tensors
-        del ctx.input_params
-        del output_tensors
-        return (None, None) + input_grads
-
-
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
-    """
-    Create sinusoidal timestep embeddings.
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    if not repeat_only:
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-        ).to(device=timesteps.device)
-        args = timesteps[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    else:
-        embedding = repeat(timesteps, 'b -> b d', d=dim)
-    return embedding
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-
-
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def normalization(channels):
-    """
-    Make a standard normalization layer.
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(32, channels)
-
-
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
-    def forward(self, x):
-        return x * torch.sigmoid(x)
-
-
-class GroupNorm32(nn.GroupNorm):
-    def forward(self, x):
-        return super().forward(x.float()).type(x.dtype)
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class HybridConditioner(nn.Module):
-
-    def __init__(self, c_concat_config, c_crossattn_config):
-        super().__init__()
-        self.concat_conditioner = instantiate_from_config(c_concat_config)
-        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-
-    def forward(self, c_concat, c_crossattn):
-        c_concat = self.concat_conditioner(c_concat)
-        c_crossattn = self.crossattn_conditioner(c_crossattn)
-        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-
-
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
\ No newline at end of file
diff --git a/ldm/modules/distributions/__init__.py b/ldm/modules/distributions/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/ldm/modules/distributions/distributions.py b/ldm/modules/distributions/distributions.py
deleted file mode 100644
index f2b8ef90..00000000
--- a/ldm/modules/distributions/distributions.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import torch
-import numpy as np
-
-
-class AbstractDistribution:
-    def sample(self):
-        raise NotImplementedError()
-
-    def mode(self):
-        raise NotImplementedError()
-
-
-class DiracDistribution(AbstractDistribution):
-    def __init__(self, value):
-        self.value = value
-
-    def sample(self):
-        return self.value
-
-    def mode(self):
-        return self.value
-
-
-class DiagonalGaussianDistribution(object):
-    def __init__(self, parameters, deterministic=False):
-        self.parameters = parameters
-        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
-        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
-        self.deterministic = deterministic
-        self.std = torch.exp(0.5 * self.logvar)
-        self.var = torch.exp(self.logvar)
-        if self.deterministic:
-            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
-
-    def sample(self):
-        x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
-        return x
-
-    def kl(self, other=None):
-        if self.deterministic:
-            return torch.Tensor([0.])
-        else:
-            if other is None:
-                return 0.5 * torch.sum(torch.pow(self.mean, 2)
-                                       + self.var - 1.0 - self.logvar,
-                                       dim=[1, 2, 3])
-            else:
-                return 0.5 * torch.sum(
-                    torch.pow(self.mean - other.mean, 2) / other.var
-                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
-                    dim=[1, 2, 3])
-
-    def nll(self, sample, dims=[1,2,3]):
-        if self.deterministic:
-            return torch.Tensor([0.])
-        logtwopi = np.log(2.0 * np.pi)
-        return 0.5 * torch.sum(
-            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
-            dim=dims)
-
-    def mode(self):
-        return self.mean
-
-
-def normal_kl(mean1, logvar1, mean2, logvar2):
-    """
-    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
-    Compute the KL divergence between two gaussians.
-    Shapes are automatically broadcasted, so batches can be compared to
-    scalars, among other use cases.
-    """
-    tensor = None
-    for obj in (mean1, logvar1, mean2, logvar2):
-        if isinstance(obj, torch.Tensor):
-            tensor = obj
-            break
-    assert tensor is not None, "at least one argument must be a Tensor"
-
-    # Force variances to be Tensors. Broadcasting helps convert scalars to
-    # Tensors, but it does not work for torch.exp().
-    logvar1, logvar2 = [
-        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
-        for x in (logvar1, logvar2)
-    ]
-
-    return 0.5 * (
-        -1.0
-        + logvar2
-        - logvar1
-        + torch.exp(logvar1 - logvar2)
-        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
-    )
diff --git a/ldm/modules/ema.py b/ldm/modules/ema.py
deleted file mode 100644
index c8c75af4..00000000
--- a/ldm/modules/ema.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import torch
-from torch import nn
-
-
-class LitEma(nn.Module):
-    def __init__(self, model, decay=0.9999, use_num_upates=True):
-        super().__init__()
-        if decay < 0.0 or decay > 1.0:
-            raise ValueError('Decay must be between 0 and 1')
-
-        self.m_name2s_name = {}
-        self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
-        self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
-                             else torch.tensor(-1,dtype=torch.int))
-
-        for name, p in model.named_parameters():
-            if p.requires_grad:
-                #remove as '.'-character is not allowed in buffers
-                s_name = name.replace('.','')
-                self.m_name2s_name.update({name:s_name})
-                self.register_buffer(s_name,p.clone().detach().data)
-
-        self.collected_params = []
-
-    def forward(self,model):
-        decay = self.decay
-
-        if self.num_updates >= 0:
-            self.num_updates += 1
-            decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
-
-        one_minus_decay = 1.0 - decay
-
-        with torch.no_grad():
-            m_param = dict(model.named_parameters())
-            shadow_params = dict(self.named_buffers())
-
-            for key in m_param:
-                if m_param[key].requires_grad:
-                    sname = self.m_name2s_name[key]
-                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
-                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
-                else:
-                    assert not key in self.m_name2s_name
-
-    def copy_to(self, model):
-        m_param = dict(model.named_parameters())
-        shadow_params = dict(self.named_buffers())
-        for key in m_param:
-            if m_param[key].requires_grad:
-                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
-            else:
-                assert not key in self.m_name2s_name
-
-    def store(self, parameters):
-        """
-        Save the current parameters for restoring later.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            temporarily stored.
-        """
-        self.collected_params = [param.clone() for param in parameters]
-
-    def restore(self, parameters):
-        """
-        Restore the parameters stored with the `store` method.
-        Useful to validate the model with EMA parameters without affecting the
-        original optimization process. Store the parameters before the
-        `copy_to` method. After validation (or model saving), use this to
-        restore the former parameters.
-        Args:
-          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-            updated with the stored parameters.
-        """
-        for c_param, param in zip(self.collected_params, parameters):
-            param.data.copy_(c_param.data)
diff --git a/ldm/modules/encoders/__init__.py b/ldm/modules/encoders/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
deleted file mode 100644
index ededbe43..00000000
--- a/ldm/modules/encoders/modules.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import torch
-import torch.nn as nn
-from functools import partial
-import clip
-from einops import rearrange, repeat
-from transformers import CLIPTokenizer, CLIPTextModel
-import kornia
-
-from ldm.modules.x_transformer import Encoder, TransformerWrapper  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
-
-
-class AbstractEncoder(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def encode(self, *args, **kwargs):
-        raise NotImplementedError
-
-
-
-class ClassEmbedder(nn.Module):
-    def __init__(self, embed_dim, n_classes=1000, key='class'):
-        super().__init__()
-        self.key = key
-        self.embedding = nn.Embedding(n_classes, embed_dim)
-
-    def forward(self, batch, key=None):
-        if key is None:
-            key = self.key
-        # this is for use in crossattn
-        c = batch[key][:, None]
-        c = self.embedding(c)
-        return c
-
-
-class TransformerEmbedder(AbstractEncoder):
-    """Some transformer encoder layers"""
-    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
-        super().__init__()
-        self.device = device
-        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
-                                              attn_layers=Encoder(dim=n_embed, depth=n_layer))
-
-    def forward(self, tokens):
-        tokens = tokens.to(self.device)  # meh
-        z = self.transformer(tokens, return_embeddings=True)
-        return z
-
-    def encode(self, x):
-        return self(x)
-
-
-class BERTTokenizer(AbstractEncoder):
-    """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
-    def __init__(self, device="cuda", vq_interface=True, max_length=77):
-        super().__init__()
-        from transformers import BertTokenizerFast  # TODO: add to reuquirements
-        self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
-        self.device = device
-        self.vq_interface = vq_interface
-        self.max_length = max_length
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        return tokens
-
-    @torch.no_grad()
-    def encode(self, text):
-        tokens = self(text)
-        if not self.vq_interface:
-            return tokens
-        return None, None, [None, None, tokens]
-
-    def decode(self, text):
-        return text
-
-
-class BERTEmbedder(AbstractEncoder):
-    """Uses the BERT tokenizr model and add some transformer encoder layers"""
-    def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
-                 device="cuda",use_tokenizer=True, embedding_dropout=0.0):
-        super().__init__()
-        self.use_tknz_fn = use_tokenizer
-        if self.use_tknz_fn:
-            self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
-        self.device = device
-        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
-                                              attn_layers=Encoder(dim=n_embed, depth=n_layer),
-                                              emb_dropout=embedding_dropout)
-
-    def forward(self, text):
-        if self.use_tknz_fn:
-            tokens = self.tknz_fn(text)#.to(self.device)
-        else:
-            tokens = text
-        z = self.transformer(tokens, return_embeddings=True)
-        return z
-
-    def encode(self, text):
-        # output of length 77
-        return self(text)
-
-
-class SpatialRescaler(nn.Module):
-    def __init__(self,
-                 n_stages=1,
-                 method='bilinear',
-                 multiplier=0.5,
-                 in_channels=3,
-                 out_channels=None,
-                 bias=False):
-        super().__init__()
-        self.n_stages = n_stages
-        assert self.n_stages >= 0
-        assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
-        self.multiplier = multiplier
-        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
-        self.remap_output = out_channels is not None
-        if self.remap_output:
-            print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
-            self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
-
-    def forward(self,x):
-        for stage in range(self.n_stages):
-            x = self.interpolator(x, scale_factor=self.multiplier)
-
-
-        if self.remap_output:
-            x = self.channel_mapper(x)
-        return x
-
-    def encode(self, x):
-        return self(x)
-
-class FrozenCLIPEmbedder(AbstractEncoder):
-    """Uses the CLIP transformer encoder for text (from Hugging Face)"""
-    def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
-        super().__init__()
-        self.tokenizer = CLIPTokenizer.from_pretrained(version)
-        self.transformer = CLIPTextModel.from_pretrained(version)
-        self.device = device
-        self.max_length = max_length
-        self.freeze()
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
-        outputs = self.transformer(input_ids=tokens)
-
-        z = outputs.last_hidden_state
-        return z
-
-    def encode(self, text):
-        return self(text)
-
-
-class FrozenCLIPTextEmbedder(nn.Module):
-    """
-    Uses the CLIP transformer encoder for text.
-    """
-    def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True):
-        super().__init__()
-        self.model, _ = clip.load(version, jit=False, device="cpu")
-        self.device = device
-        self.max_length = max_length
-        self.n_repeat = n_repeat
-        self.normalize = normalize
-
-    def freeze(self):
-        self.model = self.model.eval()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, text):
-        tokens = clip.tokenize(text).to(self.device)
-        z = self.model.encode_text(tokens)
-        if self.normalize:
-            z = z / torch.linalg.norm(z, dim=1, keepdim=True)
-        return z
-
-    def encode(self, text):
-        z = self(text)
-        if z.ndim==2:
-            z = z[:, None, :]
-        z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
-        return z
-
-
-class FrozenClipImageEmbedder(nn.Module):
-    """
-        Uses the CLIP image encoder.
-        """
-    def __init__(
-            self,
-            model,
-            jit=False,
-            device='cuda' if torch.cuda.is_available() else 'cpu',
-            antialias=False,
-        ):
-        super().__init__()
-        self.model, _ = clip.load(name=model, device=device, jit=jit)
-
-        self.antialias = antialias
-
-        self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
-        self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
-
-    def preprocess(self, x):
-        # normalize to [0,1]
-        x = kornia.geometry.resize(x, (224, 224),
-                                   interpolation='bicubic',align_corners=True,
-                                   antialias=self.antialias)
-        x = (x + 1.) / 2.
-        # renormalize according to clip
-        x = kornia.enhance.normalize(x, self.mean, self.std)
-        return x
-
-    def forward(self, x):
-        # x is assumed to be in range [-1,1]
-        return self.model.encode_image(self.preprocess(x))
-
-
-if __name__ == "__main__":
-    from ldm.util import count_params
-    model = FrozenCLIPEmbedder()
-    count_params(model, verbose=True)
\ No newline at end of file
diff --git a/ldm/modules/encoders/xlmr.py b/ldm/modules/encoders/xlmr.py
deleted file mode 100644
index beab3fdf..00000000
--- a/ldm/modules/encoders/xlmr.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from transformers import BertPreTrainedModel,BertModel,BertConfig
-import torch.nn as nn
-import torch
-from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
-from transformers import XLMRobertaModel,XLMRobertaTokenizer
-from typing import Optional
-
-class BertSeriesConfig(BertConfig):
-    def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
-
-        super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs)
-        self.project_dim = project_dim
-        self.pooler_fn = pooler_fn
-        self.learn_encoder = learn_encoder
-
-class RobertaSeriesConfig(XLMRobertaConfig):
-    def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs):
-        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
-        self.project_dim = project_dim
-        self.pooler_fn = pooler_fn
-        self.learn_encoder = learn_encoder
-
-
-class BertSeriesModelWithTransformation(BertPreTrainedModel):
-
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
-    config_class = BertSeriesConfig
-
-    def __init__(self, config=None, **kargs):
-        # modify initialization for autoloading 
-        if config is None:
-            config = XLMRobertaConfig()
-            config.attention_probs_dropout_prob= 0.1
-            config.bos_token_id=0
-            config.eos_token_id=2
-            config.hidden_act='gelu'
-            config.hidden_dropout_prob=0.1
-            config.hidden_size=1024
-            config.initializer_range=0.02
-            config.intermediate_size=4096
-            config.layer_norm_eps=1e-05
-            config.max_position_embeddings=514
-
-            config.num_attention_heads=16
-            config.num_hidden_layers=24
-            config.output_past=True
-            config.pad_token_id=1
-            config.position_embedding_type= "absolute"
-
-            config.type_vocab_size= 1
-            config.use_cache=True
-            config.vocab_size= 250002
-            config.project_dim = 768
-            config.learn_encoder = False
-        super().__init__(config)
-        self.roberta = XLMRobertaModel(config)
-        self.transformation = nn.Linear(config.hidden_size,config.project_dim)
-        self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
-        self.pooler = lambda x: x[:,0]
-        self.post_init()
-
-    def encode(self,c):
-        device = next(self.parameters()).device
-        text = self.tokenizer(c,
-                        truncation=True,
-                        max_length=77,
-                        return_length=False,
-                        return_overflowing_tokens=False,
-                        padding="max_length",
-                        return_tensors="pt")
-        text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
-        text["attention_mask"] = torch.tensor(
-            text['attention_mask']).to(device)
-        features = self(**text)
-        return features['projection_state'] 
-
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-    ) :
-        r"""
-        """
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-
-        outputs = self.roberta(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            encoder_hidden_states=encoder_hidden_states,
-            encoder_attention_mask=encoder_attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=True,
-            return_dict=return_dict,
-        )
-
-        # last module outputs
-        sequence_output = outputs[0]
-
-
-        # project every module
-        sequence_output_ln = self.pre_LN(sequence_output)
-
-        # pooler
-        pooler_output = self.pooler(sequence_output_ln)
-        pooler_output = self.transformation(pooler_output)
-        projection_state = self.transformation(outputs.last_hidden_state)
-
-        return {
-            'pooler_output':pooler_output,
-            'last_hidden_state':outputs.last_hidden_state,
-            'hidden_states':outputs.hidden_states,
-            'attentions':outputs.attentions,
-            'projection_state':projection_state,
-            'sequence_out': sequence_output
-        }
-
-
-class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
-    base_model_prefix = 'roberta'
-    config_class= RobertaSeriesConfig
\ No newline at end of file
diff --git a/ldm/modules/image_degradation/__init__.py b/ldm/modules/image_degradation/__init__.py
deleted file mode 100644
index 7836cada..00000000
--- a/ldm/modules/image_degradation/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
-from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
diff --git a/ldm/modules/image_degradation/bsrgan.py b/ldm/modules/image_degradation/bsrgan.py
deleted file mode 100644
index 32ef5616..00000000
--- a/ldm/modules/image_degradation/bsrgan.py
+++ /dev/null
@@ -1,730 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang (cskaizhang@gmail.com)
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-
-def modcrop_np(img, sf):
-    '''
-    Args:
-        img: numpy image, WxH or WxHxC
-        sf: scale factor
-    Return:
-        cropped image
-    '''
-    w, h = img.shape[:2]
-    im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
-    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
-    k_size = k.shape[0]
-    # Calculate the big kernels size
-    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
-    # Loop over the small kernel to fill the big one
-    for r in range(k_size):
-        for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
-    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
-    crop = k_size // 2
-    cropped_big_k = big_k[crop:-crop, crop:-crop]
-    # Normalize to 1
-    return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
-    Args:
-        ksize : e.g., 15, kernel size
-        theta : [0,  pi], rotation angle range
-        l1    : [0.1,50], scaling of eigenvalues
-        l2    : [0.1,l1], scaling of eigenvalues
-        If l1 = l2, will get an isotropic Gaussian kernel.
-    Returns:
-        k     : kernel
-    """
-
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
-    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
-    D = np.array([[l1, 0], [0, l2]])
-    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
-    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
-    return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
-    center = size / 2.0 + 0.5
-    k = np.zeros([size, size])
-    for y in range(size):
-        for x in range(size):
-            cy = y - center + 1
-            cx = x - center + 1
-            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
-    k = k / np.sum(k)
-    return k
-
-
-def shift_pixel(x, sf, upper_left=True):
-    """shift pixel for super-resolution with different scale factors
-    Args:
-        x: WxHxC or WxH
-        sf: scale factor
-        upper_left: shift direction
-    """
-    h, w = x.shape[:2]
-    shift = (sf - 1) * 0.5
-    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
-    if upper_left:
-        x1 = xv + shift
-        y1 = yv + shift
-    else:
-        x1 = xv - shift
-        y1 = yv - shift
-
-    x1 = np.clip(x1, 0, w - 1)
-    y1 = np.clip(y1, 0, h - 1)
-
-    if x.ndim == 2:
-        x = interp2d(xv, yv, x)(x1, y1)
-    if x.ndim == 3:
-        for i in range(x.shape[-1]):
-            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
-    return x
-
-
-def blur(x, k):
-    '''
-    x: image, NxcxHxW
-    k: kernel, Nx1xhxw
-    '''
-    n, c = x.shape[:2]
-    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
-    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
-    k = k.repeat(1, c, 1, 1)
-    k = k.view(-1, 1, k.shape[2], k.shape[3])
-    x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
-    x = x.view(n, c, x.shape[2], x.shape[3])
-
-    return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
-    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
-    # Kai Zhang
-    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
-    # max_var = 2.5 * sf
-    """
-    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
-    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
-    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
-    theta = np.random.rand() * np.pi  # random theta
-    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
-    # Set COV matrix using Lambdas and Theta
-    LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
-    SIGMA = Q @ LAMBDA @ Q.T
-    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
-    # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
-    MU = MU[None, None, :, None]
-
-    # Create meshgrid for Gaussian
-    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
-    Z = np.stack([X, Y], 2)[:, :, :, None]
-
-    # Calcualte Gaussian for every pixel of the kernel
-    ZZ = Z - MU
-    ZZ_t = ZZ.transpose(0, 1, 3, 2)
-    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
-    # shift the kernel so it will be centered
-    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
-    # Normalize the kernel and return
-    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
-    kernel = raw_kernel / np.sum(raw_kernel)
-    return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
-    arg = -(x * x + y * y) / (2 * std * std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h / sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha, 1])])
-    h1 = alpha / (alpha + 1)
-    h2 = (1 - alpha) / (alpha + 1)
-    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
-    '''
-    Args:
-        x: HxWxC image, [0, 1]
-        sf: down-scale factor
-    Return:
-        bicubicly downsampled LR image
-    '''
-    x = util.imresize_np(x, scale=1 / sf)
-    return x
-
-
-def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2018learning,
-          title={Learning a single convolutional super-resolution network for multiple degradations},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={3262--3271},
-          year={2018}
-        }
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
-    x = bicubic_degradation(x, sf=sf)
-    return x
-
-
-def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2019deep,
-          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={1671--1681},
-          year={2019}
-        }
-    '''
-    x = bicubic_degradation(x, sf=sf)
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    return x
-
-
-def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
-    Args:
-        x: HxWxC image, [0, 1]/[0, 255]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
-    st = 0
-    return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
-    """USM sharpening. borrowed from real-ESRGAN
-    Input image: I; Blurry image: B.
-    1. K = I + weight * (I - B)
-    2. Mask = 1 if abs(I - B) > threshold, else: 0
-    3. Blur mask:
-    4. Out = Mask * K + (1 - Mask) * I
-    Args:
-        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
-        weight (float): Sharp weight. Default: 1.
-        radius (float): Kernel size of Gaussian blur. Default: 50.
-        threshold (int):
-    """
-    if radius % 2 == 0:
-        radius += 1
-    blur = cv2.GaussianBlur(img, (radius, radius), 0)
-    residual = img - blur
-    mask = np.abs(residual) * 255 > threshold
-    mask = mask.astype('float32')
-    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
-    K = img + weight * residual
-    K = np.clip(K, 0, 1)
-    return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
-    wd2 = 4.0 + sf
-    wd = 2.0 + 0.2 * sf
-    if random.random() < 0.5:
-        l1 = wd2 * random.random()
-        l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
-    else:
-        k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
-    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
-    return img
-
-
-def add_resize(img, sf=4):
-    rnum = np.random.rand()
-    if rnum > 0.8:  # up
-        sf1 = random.uniform(1, 2)
-    elif rnum < 0.7:  # down
-        sf1 = random.uniform(0.5 / sf, 1)
-    else:
-        sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
-    img = np.clip(img, 0.0, 1.0)
-
-    return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-#     noise_level = random.randint(noise_level1, noise_level2)
-#     rnum = np.random.rand()
-#     if rnum > 0.6:  # add color Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-#     elif rnum < 0.4:  # add grayscale Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-#     else:  # add  noise
-#         L = noise_level2 / 255.
-#         D = np.diag(np.random.rand(3))
-#         U = orth(np.random.rand(3, 3))
-#         conv = np.dot(np.dot(np.transpose(U), D), U)
-#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-#     img = np.clip(img, 0.0, 1.0)
-#     return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    rnum = np.random.rand()
-    if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:  # add  noise
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    img = np.clip(img, 0.0, 1.0)
-    rnum = random.random()
-    if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
-    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
-    if random.random() < 0.5:
-        img = np.random.poisson(img * vals).astype(np.float32) / vals
-    else:
-        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        img += noise_gray[:, :, np.newaxis]
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_JPEG_noise(img):
-    quality_factor = random.randint(30, 95)
-    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
-    img = cv2.imdecode(encimg, 1)
-    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
-    return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
-    h, w = lq.shape[:2]
-    rnd_h = random.randint(0, h - lq_patchsize)
-    rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
-    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
-    return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    hq = img.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
-        else:
-            img = util.imresize_np(img, 1 / 2, True)
-        img = np.clip(img, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            img = add_blur(img, sf=sf)
-
-        elif i == 1:
-            img = add_blur(img, sf=sf)
-
-        elif i == 2:
-            a, b = img.shape[1], img.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                img = img[0::sf, 0::sf, ...]  # nearest downsampling
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                img = add_JPEG_noise(img)
-
-        elif i == 6:
-            # add processed camera sensor noise
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
-    return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = image.shape[:2]
-
-    hq = image.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
-        else:
-            image = util.imresize_np(image, 1 / 2, True)
-        image = np.clip(image, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            image = add_blur(image, sf=sf)
-
-        elif i == 1:
-            image = add_blur(image, sf=sf)
-
-        elif i == 2:
-            a, b = image.shape[1], image.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                image = image[0::sf, 0::sf, ...]  # nearest downsampling
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                image = add_JPEG_noise(image)
-
-        # elif i == 6:
-        #     # add processed camera sensor noise
-        #     if random.random() < isp_prob and isp_model is not None:
-        #         with torch.no_grad():
-        #             img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    image = add_JPEG_noise(image)
-    image = util.single2uint(image)
-    example = {"image":image}
-    return example
-
-
-# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
-def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
-    """
-    This is an extended degradation model by combining
-    the degradation models of BSRGAN and Real-ESRGAN
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    use_shuffle: the degradation shuffle
-    use_sharp: sharpening the img
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    if use_sharp:
-        img = add_sharpening(img)
-    hq = img.copy()
-
-    if random.random() < shuffle_prob:
-        shuffle_order = random.sample(range(13), 13)
-    else:
-        shuffle_order = list(range(13))
-        # local shuffle for noise, JPEG is always the last one
-        shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
-        shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
-
-    poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
-
-    for i in shuffle_order:
-        if i == 0:
-            img = add_blur(img, sf=sf)
-        elif i == 1:
-            img = add_resize(img, sf=sf)
-        elif i == 2:
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-        elif i == 3:
-            if random.random() < poisson_prob:
-                img = add_Poisson_noise(img)
-        elif i == 4:
-            if random.random() < speckle_prob:
-                img = add_speckle_noise(img)
-        elif i == 5:
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-        elif i == 6:
-            img = add_JPEG_noise(img)
-        elif i == 7:
-            img = add_blur(img, sf=sf)
-        elif i == 8:
-            img = add_resize(img, sf=sf)
-        elif i == 9:
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
-        elif i == 10:
-            if random.random() < poisson_prob:
-                img = add_Poisson_noise(img)
-        elif i == 11:
-            if random.random() < speckle_prob:
-                img = add_speckle_noise(img)
-        elif i == 12:
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-        else:
-            print('check the shuffle!')
-
-    # resize to desired size
-    img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
-                     interpolation=random.choice([1, 2, 3]))
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf, lq_patchsize)
-
-    return img, hq
-
-
-if __name__ == '__main__':
-	print("hey")
-	img = util.imread_uint('utils/test.png', 3)
-	print(img)
-	img = util.uint2single(img)
-	print(img)
-	img = img[:448, :448]
-	h = img.shape[0] // 4
-	print("resizing to", h)
-	sf = 4
-	deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-	for i in range(20):
-		print(i)
-		img_lq = deg_fn(img)
-		print(img_lq)
-		img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
-		print(img_lq.shape)
-		print("bicubic", img_lq_bicubic.shape)
-		print(img_hq.shape)
-		lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-		util.imsave(img_concat, str(i) + '.png')
-
-
diff --git a/ldm/modules/image_degradation/bsrgan_light.py b/ldm/modules/image_degradation/bsrgan_light.py
deleted file mode 100644
index 9e1f8239..00000000
--- a/ldm/modules/image_degradation/bsrgan_light.py
+++ /dev/null
@@ -1,650 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang (cskaizhang@gmail.com)
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-
-def modcrop_np(img, sf):
-    '''
-    Args:
-        img: numpy image, WxH or WxHxC
-        sf: scale factor
-    Return:
-        cropped image
-    '''
-    w, h = img.shape[:2]
-    im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
-    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
-    k_size = k.shape[0]
-    # Calculate the big kernels size
-    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
-    # Loop over the small kernel to fill the big one
-    for r in range(k_size):
-        for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
-    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
-    crop = k_size // 2
-    cropped_big_k = big_k[crop:-crop, crop:-crop]
-    # Normalize to 1
-    return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
-    Args:
-        ksize : e.g., 15, kernel size
-        theta : [0,  pi], rotation angle range
-        l1    : [0.1,50], scaling of eigenvalues
-        l2    : [0.1,l1], scaling of eigenvalues
-        If l1 = l2, will get an isotropic Gaussian kernel.
-    Returns:
-        k     : kernel
-    """
-
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
-    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
-    D = np.array([[l1, 0], [0, l2]])
-    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
-    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
-    return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
-    center = size / 2.0 + 0.5
-    k = np.zeros([size, size])
-    for y in range(size):
-        for x in range(size):
-            cy = y - center + 1
-            cx = x - center + 1
-            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
-    k = k / np.sum(k)
-    return k
-
-
-def shift_pixel(x, sf, upper_left=True):
-    """shift pixel for super-resolution with different scale factors
-    Args:
-        x: WxHxC or WxH
-        sf: scale factor
-        upper_left: shift direction
-    """
-    h, w = x.shape[:2]
-    shift = (sf - 1) * 0.5
-    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
-    if upper_left:
-        x1 = xv + shift
-        y1 = yv + shift
-    else:
-        x1 = xv - shift
-        y1 = yv - shift
-
-    x1 = np.clip(x1, 0, w - 1)
-    y1 = np.clip(y1, 0, h - 1)
-
-    if x.ndim == 2:
-        x = interp2d(xv, yv, x)(x1, y1)
-    if x.ndim == 3:
-        for i in range(x.shape[-1]):
-            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
-    return x
-
-
-def blur(x, k):
-    '''
-    x: image, NxcxHxW
-    k: kernel, Nx1xhxw
-    '''
-    n, c = x.shape[:2]
-    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
-    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
-    k = k.repeat(1, c, 1, 1)
-    k = k.view(-1, 1, k.shape[2], k.shape[3])
-    x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
-    x = x.view(n, c, x.shape[2], x.shape[3])
-
-    return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
-    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
-    # Kai Zhang
-    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
-    # max_var = 2.5 * sf
-    """
-    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
-    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
-    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
-    theta = np.random.rand() * np.pi  # random theta
-    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
-    # Set COV matrix using Lambdas and Theta
-    LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
-    SIGMA = Q @ LAMBDA @ Q.T
-    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
-    # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
-    MU = MU[None, None, :, None]
-
-    # Create meshgrid for Gaussian
-    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
-    Z = np.stack([X, Y], 2)[:, :, :, None]
-
-    # Calcualte Gaussian for every pixel of the kernel
-    ZZ = Z - MU
-    ZZ_t = ZZ.transpose(0, 1, 3, 2)
-    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
-    # shift the kernel so it will be centered
-    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
-    # Normalize the kernel and return
-    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
-    kernel = raw_kernel / np.sum(raw_kernel)
-    return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
-    arg = -(x * x + y * y) / (2 * std * std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h / sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha, 1])])
-    h1 = alpha / (alpha + 1)
-    h2 = (1 - alpha) / (alpha + 1)
-    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
-    '''
-    Args:
-        x: HxWxC image, [0, 1]
-        sf: down-scale factor
-    Return:
-        bicubicly downsampled LR image
-    '''
-    x = util.imresize_np(x, scale=1 / sf)
-    return x
-
-
-def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2018learning,
-          title={Learning a single convolutional super-resolution network for multiple degradations},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={3262--3271},
-          year={2018}
-        }
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
-    x = bicubic_degradation(x, sf=sf)
-    return x
-
-
-def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2019deep,
-          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={1671--1681},
-          year={2019}
-        }
-    '''
-    x = bicubic_degradation(x, sf=sf)
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    return x
-
-
-def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
-    Args:
-        x: HxWxC image, [0, 1]/[0, 255]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
-    st = 0
-    return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
-    """USM sharpening. borrowed from real-ESRGAN
-    Input image: I; Blurry image: B.
-    1. K = I + weight * (I - B)
-    2. Mask = 1 if abs(I - B) > threshold, else: 0
-    3. Blur mask:
-    4. Out = Mask * K + (1 - Mask) * I
-    Args:
-        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
-        weight (float): Sharp weight. Default: 1.
-        radius (float): Kernel size of Gaussian blur. Default: 50.
-        threshold (int):
-    """
-    if radius % 2 == 0:
-        radius += 1
-    blur = cv2.GaussianBlur(img, (radius, radius), 0)
-    residual = img - blur
-    mask = np.abs(residual) * 255 > threshold
-    mask = mask.astype('float32')
-    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
-    K = img + weight * residual
-    K = np.clip(K, 0, 1)
-    return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
-    wd2 = 4.0 + sf
-    wd = 2.0 + 0.2 * sf
-
-    wd2 = wd2/4
-    wd = wd/4
-
-    if random.random() < 0.5:
-        l1 = wd2 * random.random()
-        l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
-    else:
-        k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
-    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
-    return img
-
-
-def add_resize(img, sf=4):
-    rnum = np.random.rand()
-    if rnum > 0.8:  # up
-        sf1 = random.uniform(1, 2)
-    elif rnum < 0.7:  # down
-        sf1 = random.uniform(0.5 / sf, 1)
-    else:
-        sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
-    img = np.clip(img, 0.0, 1.0)
-
-    return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-#     noise_level = random.randint(noise_level1, noise_level2)
-#     rnum = np.random.rand()
-#     if rnum > 0.6:  # add color Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-#     elif rnum < 0.4:  # add grayscale Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-#     else:  # add  noise
-#         L = noise_level2 / 255.
-#         D = np.diag(np.random.rand(3))
-#         U = orth(np.random.rand(3, 3))
-#         conv = np.dot(np.dot(np.transpose(U), D), U)
-#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-#     img = np.clip(img, 0.0, 1.0)
-#     return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    rnum = np.random.rand()
-    if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:  # add  noise
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    img = np.clip(img, 0.0, 1.0)
-    rnum = random.random()
-    if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
-    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
-    if random.random() < 0.5:
-        img = np.random.poisson(img * vals).astype(np.float32) / vals
-    else:
-        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        img += noise_gray[:, :, np.newaxis]
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_JPEG_noise(img):
-    quality_factor = random.randint(80, 95)
-    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
-    img = cv2.imdecode(encimg, 1)
-    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
-    return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
-    h, w = lq.shape[:2]
-    rnd_h = random.randint(0, h - lq_patchsize)
-    rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
-    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
-    return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    hq = img.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
-        else:
-            img = util.imresize_np(img, 1 / 2, True)
-        img = np.clip(img, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            img = add_blur(img, sf=sf)
-
-        elif i == 1:
-            img = add_blur(img, sf=sf)
-
-        elif i == 2:
-            a, b = img.shape[1], img.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                img = img[0::sf, 0::sf, ...]  # nearest downsampling
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                img = add_JPEG_noise(img)
-
-        elif i == 6:
-            # add processed camera sensor noise
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
-    return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = image.shape[:2]
-
-    hq = image.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
-        else:
-            image = util.imresize_np(image, 1 / 2, True)
-        image = np.clip(image, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            image = add_blur(image, sf=sf)
-
-        # elif i == 1:
-        #     image = add_blur(image, sf=sf)
-
-        if i == 0:
-            pass
-
-        elif i == 2:
-            a, b = image.shape[1], image.shape[0]
-            # downsample2
-            if random.random() < 0.8:
-                sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                image = image[0::sf, 0::sf, ...]  # nearest downsampling
-
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                image = add_JPEG_noise(image)
-        #
-        # elif i == 6:
-        #     # add processed camera sensor noise
-        #     if random.random() < isp_prob and isp_model is not None:
-        #         with torch.no_grad():
-        #             img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    image = add_JPEG_noise(image)
-    image = util.single2uint(image)
-    example = {"image": image}
-    return example
-
-
-
-
-if __name__ == '__main__':
-    print("hey")
-    img = util.imread_uint('utils/test.png', 3)
-    img = img[:448, :448]
-    h = img.shape[0] // 4
-    print("resizing to", h)
-    sf = 4
-    deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-    for i in range(20):
-        print(i)
-        img_hq = img
-        img_lq = deg_fn(img)["image"]
-        img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
-        print(img_lq)
-        img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
-        print(img_lq.shape)
-        print("bicubic", img_lq_bicubic.shape)
-        print(img_hq.shape)
-        lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                interpolation=0)
-        lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
-                                        (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                        interpolation=0)
-        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-        util.imsave(img_concat, str(i) + '.png')
diff --git a/ldm/modules/image_degradation/utils/test.png b/ldm/modules/image_degradation/utils/test.png
deleted file mode 100644
index 4249b43d..00000000
Binary files a/ldm/modules/image_degradation/utils/test.png and /dev/null differ
diff --git a/ldm/modules/image_degradation/utils_image.py b/ldm/modules/image_degradation/utils_image.py
deleted file mode 100644
index 0175f155..00000000
--- a/ldm/modules/image_degradation/utils_image.py
+++ /dev/null
@@ -1,916 +0,0 @@
-import os
-import math
-import random
-import numpy as np
-import torch
-import cv2
-from torchvision.utils import make_grid
-from datetime import datetime
-#import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
-
-
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-
-
-'''
-# --------------------------------------------
-# Kai Zhang (github: https://github.com/cszn)
-# 03/Mar/2019
-# --------------------------------------------
-# https://github.com/twhui/SRGAN-pyTorch
-# https://github.com/xinntao/BasicSR
-# --------------------------------------------
-'''
-
-
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def get_timestamp():
-    return datetime.now().strftime('%y%m%d-%H%M%S')
-
-
-def imshow(x, title=None, cbar=False, figsize=None):
-    plt.figure(figsize=figsize)
-    plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
-    if title:
-        plt.title(title)
-    if cbar:
-        plt.colorbar()
-    plt.show()
-
-
-def surf(Z, cmap='rainbow', figsize=None):
-    plt.figure(figsize=figsize)
-    ax3 = plt.axes(projection='3d')
-
-    w, h = Z.shape[:2]
-    xx = np.arange(0,w,1)
-    yy = np.arange(0,h,1)
-    X, Y = np.meshgrid(xx, yy)
-    ax3.plot_surface(X,Y,Z,cmap=cmap)
-    #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
-    plt.show()
-
-
-'''
-# --------------------------------------------
-# get image pathes
-# --------------------------------------------
-'''
-
-
-def get_image_paths(dataroot):
-    paths = None  # return None if dataroot is None
-    if dataroot is not None:
-        paths = sorted(_get_paths_from_images(dataroot))
-    return paths
-
-
-def _get_paths_from_images(path):
-    assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
-    images = []
-    for dirpath, _, fnames in sorted(os.walk(path)):
-        for fname in sorted(fnames):
-            if is_image_file(fname):
-                img_path = os.path.join(dirpath, fname)
-                images.append(img_path)
-    assert images, '{:s} has no valid image file'.format(path)
-    return images
-
-
-'''
-# --------------------------------------------
-# split large images into small images 
-# --------------------------------------------
-'''
-
-
-def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
-    w, h = img.shape[:2]
-    patches = []
-    if w > p_max and h > p_max:
-        w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
-        h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
-        w1.append(w-p_size)
-        h1.append(h-p_size)
-#        print(w1)
-#        print(h1)
-        for i in w1:
-            for j in h1:
-                patches.append(img[i:i+p_size, j:j+p_size,:])
-    else:
-        patches.append(img)
-
-    return patches
-
-
-def imssave(imgs, img_path):
-    """
-    imgs: list, N images of size WxHxC
-    """
-    img_name, ext = os.path.splitext(os.path.basename(img_path))
-
-    for i, img in enumerate(imgs):
-        if img.ndim == 3:
-            img = img[:, :, [2, 1, 0]]
-        new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
-        cv2.imwrite(new_path, img)
-
-
-def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
-    """
-    split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
-    and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
-    will be splitted.
-    Args:
-        original_dataroot:
-        taget_dataroot:
-        p_size: size of small images
-        p_overlap: patch size in training is a good choice
-        p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
-    """
-    paths = get_image_paths(original_dataroot)
-    for img_path in paths:
-        # img_name, ext = os.path.splitext(os.path.basename(img_path))
-        img = imread_uint(img_path, n_channels=n_channels)
-        patches = patches_from_image(img, p_size, p_overlap, p_max)
-        imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
-        #if original_dataroot == taget_dataroot:
-        #del img_path
-
-'''
-# --------------------------------------------
-# makedir
-# --------------------------------------------
-'''
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-
-def mkdirs(paths):
-    if isinstance(paths, str):
-        mkdir(paths)
-    else:
-        for path in paths:
-            mkdir(path)
-
-
-def mkdir_and_rename(path):
-    if os.path.exists(path):
-        new_name = path + '_archived_' + get_timestamp()
-        print('Path already exists. Rename it to [{:s}]'.format(new_name))
-        os.rename(path, new_name)
-    os.makedirs(path)
-
-
-'''
-# --------------------------------------------
-# read image from path
-# opencv is fast, but read BGR numpy image
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# get uint8 image of size HxWxn_channles (RGB)
-# --------------------------------------------
-def imread_uint(path, n_channels=3):
-    #  input: path
-    # output: HxWx3(RGB or GGG), or HxWx1 (G)
-    if n_channels == 1:
-        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
-        img = np.expand_dims(img, axis=2)  # HxWx1
-    elif n_channels == 3:
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
-        if img.ndim == 2:
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
-        else:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
-    return img
-
-
-# --------------------------------------------
-# matlab's imwrite
-# --------------------------------------------
-def imsave(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-def imwrite(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-
-
-# --------------------------------------------
-# get single image of size HxWxn_channles (BGR)
-# --------------------------------------------
-def read_img(path):
-    # read image by cv2
-    # return: Numpy float32, HWC, BGR, [0,1]
-    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
-    img = img.astype(np.float32) / 255.
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    # some images have 4 channels
-    if img.shape[2] > 3:
-        img = img[:, :, :3]
-    return img
-
-
-'''
-# --------------------------------------------
-# image format conversion
-# --------------------------------------------
-# numpy(single) <--->  numpy(unit)
-# numpy(single) <--->  tensor
-# numpy(unit)   <--->  tensor
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# numpy(single) [0, 1] <--->  numpy(unit)
-# --------------------------------------------
-
-
-def uint2single(img):
-
-    return np.float32(img/255.)
-
-
-def single2uint(img):
-
-    return np.uint8((img.clip(0, 1)*255.).round())
-
-
-def uint162single(img):
-
-    return np.float32(img/65535.)
-
-
-def single2uint16(img):
-
-    return np.uint16((img.clip(0, 1)*65535.).round())
-
-
-# --------------------------------------------
-# numpy(unit) (HxWxC or HxW) <--->  tensor
-# --------------------------------------------
-
-
-# convert uint to 4-dimensional torch tensor
-def uint2tensor4(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
-
-
-# convert uint to 3-dimensional torch tensor
-def uint2tensor3(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
-
-
-# convert 2/3/4-dimensional torch tensor to uint
-def tensor2uint(img):
-    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    return np.uint8((img*255.0).round())
-
-
-# --------------------------------------------
-# numpy(single) (HxWxC) <--->  tensor
-# --------------------------------------------
-
-
-# convert single (HxWxC) to 3-dimensional torch tensor
-def single2tensor3(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
-
-
-# convert single (HxWxC) to 4-dimensional torch tensor
-def single2tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
-
-
-# convert torch tensor to single
-def tensor2single(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-
-    return img
-
-# convert torch tensor to single
-def tensor2single3(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    elif img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return img
-
-
-def single2tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
-
-
-def single32tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
-
-
-def single42tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
-
-
-# from skimage.io import imread, imsave
-def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
-    '''
-    Converts a torch Tensor into an image Numpy array of BGR channel order
-    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
-    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
-    '''
-    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
-    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
-    n_dim = tensor.dim()
-    if n_dim == 4:
-        n_img = len(tensor)
-        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 3:
-        img_np = tensor.numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 2:
-        img_np = tensor.numpy()
-    else:
-        raise TypeError(
-            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
-    if out_type == np.uint8:
-        img_np = (img_np * 255.0).round()
-        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
-    return img_np.astype(out_type)
-
-
-'''
-# --------------------------------------------
-# Augmentation, flipe and/or rotate
-# --------------------------------------------
-# The following two are enough.
-# (1) augmet_img: numpy image of WxHxC or WxH
-# (2) augment_img_tensor4: tensor image 1xCxWxH
-# --------------------------------------------
-'''
-
-
-def augment_img(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return np.flipud(np.rot90(img))
-    elif mode == 2:
-        return np.flipud(img)
-    elif mode == 3:
-        return np.rot90(img, k=3)
-    elif mode == 4:
-        return np.flipud(np.rot90(img, k=2))
-    elif mode == 5:
-        return np.rot90(img)
-    elif mode == 6:
-        return np.rot90(img, k=2)
-    elif mode == 7:
-        return np.flipud(np.rot90(img, k=3))
-
-
-def augment_img_tensor4(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.rot90(1, [2, 3]).flip([2])
-    elif mode == 2:
-        return img.flip([2])
-    elif mode == 3:
-        return img.rot90(3, [2, 3])
-    elif mode == 4:
-        return img.rot90(2, [2, 3]).flip([2])
-    elif mode == 5:
-        return img.rot90(1, [2, 3])
-    elif mode == 6:
-        return img.rot90(2, [2, 3])
-    elif mode == 7:
-        return img.rot90(3, [2, 3]).flip([2])
-
-
-def augment_img_tensor(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    img_size = img.size()
-    img_np = img.data.cpu().numpy()
-    if len(img_size) == 3:
-        img_np = np.transpose(img_np, (1, 2, 0))
-    elif len(img_size) == 4:
-        img_np = np.transpose(img_np, (2, 3, 1, 0))
-    img_np = augment_img(img_np, mode=mode)
-    img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
-    if len(img_size) == 3:
-        img_tensor = img_tensor.permute(2, 0, 1)
-    elif len(img_size) == 4:
-        img_tensor = img_tensor.permute(3, 2, 0, 1)
-
-    return img_tensor.type_as(img)
-
-
-def augment_img_np3(img, mode=0):
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.transpose(1, 0, 2)
-    elif mode == 2:
-        return img[::-1, :, :]
-    elif mode == 3:
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 4:
-        return img[:, ::-1, :]
-    elif mode == 5:
-        img = img[:, ::-1, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 6:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        return img
-    elif mode == 7:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-
-
-def augment_imgs(img_list, hflip=True, rot=True):
-    # horizontal flip OR rotate
-    hflip = hflip and random.random() < 0.5
-    vflip = rot and random.random() < 0.5
-    rot90 = rot and random.random() < 0.5
-
-    def _augment(img):
-        if hflip:
-            img = img[:, ::-1, :]
-        if vflip:
-            img = img[::-1, :, :]
-        if rot90:
-            img = img.transpose(1, 0, 2)
-        return img
-
-    return [_augment(img) for img in img_list]
-
-
-'''
-# --------------------------------------------
-# modcrop and shave
-# --------------------------------------------
-'''
-
-
-def modcrop(img_in, scale):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    if img.ndim == 2:
-        H, W = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r]
-    elif img.ndim == 3:
-        H, W, C = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r, :]
-    else:
-        raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
-    return img
-
-
-def shave(img_in, border=0):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    h, w = img.shape[:2]
-    img = img[border:h-border, border:w-border]
-    return img
-
-
-'''
-# --------------------------------------------
-# image processing process on numpy image
-# channel_convert(in_c, tar_type, img_list):
-# rgb2ycbcr(img, only_y=True):
-# bgr2ycbcr(img, only_y=True):
-# ycbcr2rgb(img):
-# --------------------------------------------
-'''
-
-
-def rgb2ycbcr(img, only_y=True):
-    '''same as matlab rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
-                              [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def ycbcr2rgb(img):
-    '''same as matlab ycbcr2rgb
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
-                          [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def bgr2ycbcr(img, only_y=True):
-    '''bgr version of rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
-                              [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def channel_convert(in_c, tar_type, img_list):
-    # conversion among BGR, gray and y
-    if in_c == 3 and tar_type == 'gray':  # BGR to gray
-        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in gray_list]
-    elif in_c == 3 and tar_type == 'y':  # BGR to y
-        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in y_list]
-    elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
-        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
-    else:
-        return img_list
-
-
-'''
-# --------------------------------------------
-# metric, PSNR and SSIM
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# PSNR
-# --------------------------------------------
-def calculate_psnr(img1, img2, border=0):
-    # img1 and img2 have range [0, 255]
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    mse = np.mean((img1 - img2)**2)
-    if mse == 0:
-        return float('inf')
-    return 20 * math.log10(255.0 / math.sqrt(mse))
-
-
-# --------------------------------------------
-# SSIM
-# --------------------------------------------
-def calculate_ssim(img1, img2, border=0):
-    '''calculate SSIM
-    the same outputs as MATLAB's
-    img1, img2: [0, 255]
-    '''
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    if img1.ndim == 2:
-        return ssim(img1, img2)
-    elif img1.ndim == 3:
-        if img1.shape[2] == 3:
-            ssims = []
-            for i in range(3):
-                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
-            return np.array(ssims).mean()
-        elif img1.shape[2] == 1:
-            return ssim(np.squeeze(img1), np.squeeze(img2))
-    else:
-        raise ValueError('Wrong input image dimensions.')
-
-
-def ssim(img1, img2):
-    C1 = (0.01 * 255)**2
-    C2 = (0.03 * 255)**2
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
-    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1**2
-    mu2_sq = mu2**2
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
-    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
-                                                            (sigma1_sq + sigma2_sq + C2))
-    return ssim_map.mean()
-
-
-'''
-# --------------------------------------------
-# matlab's bicubic imresize (numpy and torch) [0, 1]
-# --------------------------------------------
-'''
-
-
-# matlab 'imresize' function, now only support 'bicubic'
-def cubic(x):
-    absx = torch.abs(x)
-    absx2 = absx**2
-    absx3 = absx**3
-    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
-        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
-    if (scale < 1) and (antialiasing):
-        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
-        kernel_width = kernel_width / scale
-
-    # Output-space coordinates
-    x = torch.linspace(1, out_length, out_length)
-
-    # Input-space coordinates. Calculate the inverse mapping such that 0.5
-    # in output space maps to 0.5 in input space, and 0.5+scale in output
-    # space maps to 1.5 in input space.
-    u = x / scale + 0.5 * (1 - 1 / scale)
-
-    # What is the left-most pixel that can be involved in the computation?
-    left = torch.floor(u - kernel_width / 2)
-
-    # What is the maximum number of pixels that can be involved in the
-    # computation?  Note: it's OK to use an extra pixel here; if the
-    # corresponding weights are all zero, it will be eliminated at the end
-    # of this function.
-    P = math.ceil(kernel_width) + 2
-
-    # The indices of the input pixels involved in computing the k-th output
-    # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
-        1, P).expand(out_length, P)
-
-    # The weights used to compute the k-th output pixel are in row k of the
-    # weights matrix.
-    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
-    # apply cubic kernel
-    if (scale < 1) and (antialiasing):
-        weights = scale * cubic(distance_to_center * scale)
-    else:
-        weights = cubic(distance_to_center)
-    # Normalize the weights matrix so that each row sums to 1.
-    weights_sum = torch.sum(weights, 1).view(out_length, 1)
-    weights = weights / weights_sum.expand(out_length, P)
-
-    # If a column in weights is all zero, get rid of it. only consider the first and last column.
-    weights_zero_tmp = torch.sum((weights == 0), 0)
-    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 1, P - 2)
-        weights = weights.narrow(1, 1, P - 2)
-    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 0, P - 2)
-        weights = weights.narrow(1, 0, P - 2)
-    weights = weights.contiguous()
-    indices = indices.contiguous()
-    sym_len_s = -indices.min() + 1
-    sym_len_e = indices.max() - in_length
-    indices = indices + sym_len_s - 1
-    return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-# --------------------------------------------
-# imresize for tensor image [0, 1]
-# --------------------------------------------
-def imresize(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: pytorch tensor, CHW or HW [0,1]
-    # output: CHW or HW [0,1] w/o round
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(0)
-    in_C, in_H, in_W = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
-    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:, :sym_len_Hs, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[:, -sym_len_He:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(in_C, out_H, in_W)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
-    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :, :sym_len_Ws]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, :, -sym_len_We:]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(in_C, out_H, out_W)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-    return out_2
-
-
-# --------------------------------------------
-# imresize for numpy image [0, 1]
-# --------------------------------------------
-def imresize_np(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: Numpy, HWC or HW [0,1]
-    # output: HWC or HW [0,1] w/o round
-    img = torch.from_numpy(img)
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(2)
-
-    in_H, in_W, in_C = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
-    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:sym_len_Hs, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[-sym_len_He:, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(out_H, in_W, in_C)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
-    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :sym_len_Ws, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, -sym_len_We:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(out_H, out_W, in_C)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-
-    return out_2.numpy()
-
-
-if __name__ == '__main__':
-    print('---')
-#    img = imread_uint('test.bmp', 3)
-#    img = uint2single(img)
-#    img_bicubic = imresize_np(img, 1/4)
\ No newline at end of file
diff --git a/ldm/modules/losses/__init__.py b/ldm/modules/losses/__init__.py
deleted file mode 100644
index 876d7c5b..00000000
--- a/ldm/modules/losses/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator
\ No newline at end of file
diff --git a/ldm/modules/losses/contperceptual.py b/ldm/modules/losses/contperceptual.py
deleted file mode 100644
index 672c1e32..00000000
--- a/ldm/modules/losses/contperceptual.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import torch
-import torch.nn as nn
-
-from taming.modules.losses.vqperceptual import *  # TODO: taming dependency yes/no?
-
-
-class LPIPSWithDiscriminator(nn.Module):
-    def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
-                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
-                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
-                 disc_loss="hinge"):
-
-        super().__init__()
-        assert disc_loss in ["hinge", "vanilla"]
-        self.kl_weight = kl_weight
-        self.pixel_weight = pixelloss_weight
-        self.perceptual_loss = LPIPS().eval()
-        self.perceptual_weight = perceptual_weight
-        # output log variance
-        self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
-
-        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
-                                                 n_layers=disc_num_layers,
-                                                 use_actnorm=use_actnorm
-                                                 ).apply(weights_init)
-        self.discriminator_iter_start = disc_start
-        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
-        self.disc_factor = disc_factor
-        self.discriminator_weight = disc_weight
-        self.disc_conditional = disc_conditional
-
-    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
-        if last_layer is not None:
-            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
-        else:
-            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
-
-        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
-        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
-        d_weight = d_weight * self.discriminator_weight
-        return d_weight
-
-    def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
-                global_step, last_layer=None, cond=None, split="train",
-                weights=None):
-        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
-        if self.perceptual_weight > 0:
-            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
-            rec_loss = rec_loss + self.perceptual_weight * p_loss
-
-        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
-        weighted_nll_loss = nll_loss
-        if weights is not None:
-            weighted_nll_loss = weights*nll_loss
-        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
-        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
-        kl_loss = posteriors.kl()
-        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
-
-        # now the GAN part
-        if optimizer_idx == 0:
-            # generator update
-            if cond is None:
-                assert not self.disc_conditional
-                logits_fake = self.discriminator(reconstructions.contiguous())
-            else:
-                assert self.disc_conditional
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
-            g_loss = -torch.mean(logits_fake)
-
-            if self.disc_factor > 0.0:
-                try:
-                    d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
-                except RuntimeError:
-                    assert not self.training
-                    d_weight = torch.tensor(0.0)
-            else:
-                d_weight = torch.tensor(0.0)
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss
-
-            log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(),
-                   "{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(),
-                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
-                   "{}/d_weight".format(split): d_weight.detach(),
-                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
-                   "{}/g_loss".format(split): g_loss.detach().mean(),
-                   }
-            return loss, log
-
-        if optimizer_idx == 1:
-            # second pass for discriminator update
-            if cond is None:
-                logits_real = self.discriminator(inputs.contiguous().detach())
-                logits_fake = self.discriminator(reconstructions.contiguous().detach())
-            else:
-                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
-
-            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
-                   "{}/logits_real".format(split): logits_real.detach().mean(),
-                   "{}/logits_fake".format(split): logits_fake.detach().mean()
-                   }
-            return d_loss, log
-
diff --git a/ldm/modules/losses/vqperceptual.py b/ldm/modules/losses/vqperceptual.py
deleted file mode 100644
index f6998176..00000000
--- a/ldm/modules/losses/vqperceptual.py
+++ /dev/null
@@ -1,167 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-from einops import repeat
-
-from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
-from taming.modules.losses.lpips import LPIPS
-from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
-
-
-def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
-    assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
-    loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3])
-    loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3])
-    loss_real = (weights * loss_real).sum() / weights.sum()
-    loss_fake = (weights * loss_fake).sum() / weights.sum()
-    d_loss = 0.5 * (loss_real + loss_fake)
-    return d_loss
-
-def adopt_weight(weight, global_step, threshold=0, value=0.):
-    if global_step < threshold:
-        weight = value
-    return weight
-
-
-def measure_perplexity(predicted_indices, n_embed):
-    # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
-    # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
-    encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
-    avg_probs = encodings.mean(0)
-    perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
-    cluster_use = torch.sum(avg_probs > 0)
-    return perplexity, cluster_use
-
-def l1(x, y):
-    return torch.abs(x-y)
-
-
-def l2(x, y):
-    return torch.pow((x-y), 2)
-
-
-class VQLPIPSWithDiscriminator(nn.Module):
-    def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
-                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
-                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
-                 disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips",
-                 pixel_loss="l1"):
-        super().__init__()
-        assert disc_loss in ["hinge", "vanilla"]
-        assert perceptual_loss in ["lpips", "clips", "dists"]
-        assert pixel_loss in ["l1", "l2"]
-        self.codebook_weight = codebook_weight
-        self.pixel_weight = pixelloss_weight
-        if perceptual_loss == "lpips":
-            print(f"{self.__class__.__name__}: Running with LPIPS.")
-            self.perceptual_loss = LPIPS().eval()
-        else:
-            raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<")
-        self.perceptual_weight = perceptual_weight
-
-        if pixel_loss == "l1":
-            self.pixel_loss = l1
-        else:
-            self.pixel_loss = l2
-
-        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
-                                                 n_layers=disc_num_layers,
-                                                 use_actnorm=use_actnorm,
-                                                 ndf=disc_ndf
-                                                 ).apply(weights_init)
-        self.discriminator_iter_start = disc_start
-        if disc_loss == "hinge":
-            self.disc_loss = hinge_d_loss
-        elif disc_loss == "vanilla":
-            self.disc_loss = vanilla_d_loss
-        else:
-            raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
-        print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
-        self.disc_factor = disc_factor
-        self.discriminator_weight = disc_weight
-        self.disc_conditional = disc_conditional
-        self.n_classes = n_classes
-
-    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
-        if last_layer is not None:
-            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
-        else:
-            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
-
-        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
-        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
-        d_weight = d_weight * self.discriminator_weight
-        return d_weight
-
-    def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
-                global_step, last_layer=None, cond=None, split="train", predicted_indices=None):
-        if not exists(codebook_loss):
-            codebook_loss = torch.tensor([0.]).to(inputs.device)
-        #rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
-        rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous())
-        if self.perceptual_weight > 0:
-            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
-            rec_loss = rec_loss + self.perceptual_weight * p_loss
-        else:
-            p_loss = torch.tensor([0.0])
-
-        nll_loss = rec_loss
-        #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
-        nll_loss = torch.mean(nll_loss)
-
-        # now the GAN part
-        if optimizer_idx == 0:
-            # generator update
-            if cond is None:
-                assert not self.disc_conditional
-                logits_fake = self.discriminator(reconstructions.contiguous())
-            else:
-                assert self.disc_conditional
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
-            g_loss = -torch.mean(logits_fake)
-
-            try:
-                d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
-            except RuntimeError:
-                assert not self.training
-                d_weight = torch.tensor(0.0)
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
-
-            log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
-                   "{}/quant_loss".format(split): codebook_loss.detach().mean(),
-                   "{}/nll_loss".format(split): nll_loss.detach().mean(),
-                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
-                   "{}/p_loss".format(split): p_loss.detach().mean(),
-                   "{}/d_weight".format(split): d_weight.detach(),
-                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
-                   "{}/g_loss".format(split): g_loss.detach().mean(),
-                   }
-            if predicted_indices is not None:
-                assert self.n_classes is not None
-                with torch.no_grad():
-                    perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)
-                log[f"{split}/perplexity"] = perplexity
-                log[f"{split}/cluster_usage"] = cluster_usage
-            return loss, log
-
-        if optimizer_idx == 1:
-            # second pass for discriminator update
-            if cond is None:
-                logits_real = self.discriminator(inputs.contiguous().detach())
-                logits_fake = self.discriminator(reconstructions.contiguous().detach())
-            else:
-                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
-
-            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
-                   "{}/logits_real".format(split): logits_real.detach().mean(),
-                   "{}/logits_fake".format(split): logits_fake.detach().mean()
-                   }
-            return d_loss, log
diff --git a/ldm/modules/x_transformer.py b/ldm/modules/x_transformer.py
deleted file mode 100644
index 5fc15bf9..00000000
--- a/ldm/modules/x_transformer.py
+++ /dev/null
@@ -1,641 +0,0 @@
-"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
-import torch
-from torch import nn, einsum
-import torch.nn.functional as F
-from functools import partial
-from inspect import isfunction
-from collections import namedtuple
-from einops import rearrange, repeat, reduce
-
-# constants
-
-DEFAULT_DIM_HEAD = 64
-
-Intermediates = namedtuple('Intermediates', [
-    'pre_softmax_attn',
-    'post_softmax_attn'
-])
-
-LayerIntermediates = namedtuple('Intermediates', [
-    'hiddens',
-    'attn_intermediates'
-])
-
-
-class AbsolutePositionalEmbedding(nn.Module):
-    def __init__(self, dim, max_seq_len):
-        super().__init__()
-        self.emb = nn.Embedding(max_seq_len, dim)
-        self.init_()
-
-    def init_(self):
-        nn.init.normal_(self.emb.weight, std=0.02)
-
-    def forward(self, x):
-        n = torch.arange(x.shape[1], device=x.device)
-        return self.emb(n)[None, :, :]
-
-
-class FixedPositionalEmbedding(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
-        self.register_buffer('inv_freq', inv_freq)
-
-    def forward(self, x, seq_dim=1, offset=0):
-        t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
-        sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
-        emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
-        return emb[None, :, :]
-
-
-# helpers
-
-def exists(val):
-    return val is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def always(val):
-    def inner(*args, **kwargs):
-        return val
-    return inner
-
-
-def not_equals(val):
-    def inner(x):
-        return x != val
-    return inner
-
-
-def equals(val):
-    def inner(x):
-        return x == val
-    return inner
-
-
-def max_neg_value(tensor):
-    return -torch.finfo(tensor.dtype).max
-
-
-# keyword argument helpers
-
-def pick_and_pop(keys, d):
-    values = list(map(lambda key: d.pop(key), keys))
-    return dict(zip(keys, values))
-
-
-def group_dict_by_key(cond, d):
-    return_val = [dict(), dict()]
-    for key in d.keys():
-        match = bool(cond(key))
-        ind = int(not match)
-        return_val[ind][key] = d[key]
-    return (*return_val,)
-
-
-def string_begins_with(prefix, str):
-    return str.startswith(prefix)
-
-
-def group_by_key_prefix(prefix, d):
-    return group_dict_by_key(partial(string_begins_with, prefix), d)
-
-
-def groupby_prefix_and_trim(prefix, d):
-    kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
-    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
-    return kwargs_without_prefix, kwargs
-
-
-# classes
-class Scale(nn.Module):
-    def __init__(self, value, fn):
-        super().__init__()
-        self.value = value
-        self.fn = fn
-
-    def forward(self, x, **kwargs):
-        x, *rest = self.fn(x, **kwargs)
-        return (x * self.value, *rest)
-
-
-class Rezero(nn.Module):
-    def __init__(self, fn):
-        super().__init__()
-        self.fn = fn
-        self.g = nn.Parameter(torch.zeros(1))
-
-    def forward(self, x, **kwargs):
-        x, *rest = self.fn(x, **kwargs)
-        return (x * self.g, *rest)
-
-
-class ScaleNorm(nn.Module):
-    def __init__(self, dim, eps=1e-5):
-        super().__init__()
-        self.scale = dim ** -0.5
-        self.eps = eps
-        self.g = nn.Parameter(torch.ones(1))
-
-    def forward(self, x):
-        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
-        return x / norm.clamp(min=self.eps) * self.g
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim, eps=1e-8):
-        super().__init__()
-        self.scale = dim ** -0.5
-        self.eps = eps
-        self.g = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x):
-        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
-        return x / norm.clamp(min=self.eps) * self.g
-
-
-class Residual(nn.Module):
-    def forward(self, x, residual):
-        return x + residual
-
-
-class GRUGating(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.gru = nn.GRUCell(dim, dim)
-
-    def forward(self, x, residual):
-        gated_output = self.gru(
-            rearrange(x, 'b n d -> (b n) d'),
-            rearrange(residual, 'b n d -> (b n) d')
-        )
-
-        return gated_output.reshape_as(x)
-
-
-# feedforward
-
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-# attention.
-class Attention(nn.Module):
-    def __init__(
-            self,
-            dim,
-            dim_head=DEFAULT_DIM_HEAD,
-            heads=8,
-            causal=False,
-            mask=None,
-            talking_heads=False,
-            sparse_topk=None,
-            use_entmax15=False,
-            num_mem_kv=0,
-            dropout=0.,
-            on_attn=False
-    ):
-        super().__init__()
-        if use_entmax15:
-            raise NotImplementedError("Check out entmax activation instead of softmax activation!")
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-        self.causal = causal
-        self.mask = mask
-
-        inner_dim = dim_head * heads
-
-        self.to_q = nn.Linear(dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(dim, inner_dim, bias=False)
-        self.dropout = nn.Dropout(dropout)
-
-        # talking heads
-        self.talking_heads = talking_heads
-        if talking_heads:
-            self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
-            self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
-
-        # explicit topk sparse attention
-        self.sparse_topk = sparse_topk
-
-        # entmax
-        #self.attn_fn = entmax15 if use_entmax15 else F.softmax
-        self.attn_fn = F.softmax
-
-        # add memory key / values
-        self.num_mem_kv = num_mem_kv
-        if num_mem_kv > 0:
-            self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
-            self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
-
-        # attention on attention
-        self.attn_on_attn = on_attn
-        self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
-
-    def forward(
-            self,
-            x,
-            context=None,
-            mask=None,
-            context_mask=None,
-            rel_pos=None,
-            sinusoidal_emb=None,
-            prev_attn=None,
-            mem=None
-    ):
-        b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
-        kv_input = default(context, x)
-
-        q_input = x
-        k_input = kv_input
-        v_input = kv_input
-
-        if exists(mem):
-            k_input = torch.cat((mem, k_input), dim=-2)
-            v_input = torch.cat((mem, v_input), dim=-2)
-
-        if exists(sinusoidal_emb):
-            # in shortformer, the query would start at a position offset depending on the past cached memory
-            offset = k_input.shape[-2] - q_input.shape[-2]
-            q_input = q_input + sinusoidal_emb(q_input, offset=offset)
-            k_input = k_input + sinusoidal_emb(k_input)
-
-        q = self.to_q(q_input)
-        k = self.to_k(k_input)
-        v = self.to_v(v_input)
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
-
-        input_mask = None
-        if any(map(exists, (mask, context_mask))):
-            q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
-            k_mask = q_mask if not exists(context) else context_mask
-            k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
-            q_mask = rearrange(q_mask, 'b i -> b () i ()')
-            k_mask = rearrange(k_mask, 'b j -> b () () j')
-            input_mask = q_mask * k_mask
-
-        if self.num_mem_kv > 0:
-            mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
-            k = torch.cat((mem_k, k), dim=-2)
-            v = torch.cat((mem_v, v), dim=-2)
-            if exists(input_mask):
-                input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
-
-        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
-        mask_value = max_neg_value(dots)
-
-        if exists(prev_attn):
-            dots = dots + prev_attn
-
-        pre_softmax_attn = dots
-
-        if talking_heads:
-            dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
-
-        if exists(rel_pos):
-            dots = rel_pos(dots)
-
-        if exists(input_mask):
-            dots.masked_fill_(~input_mask, mask_value)
-            del input_mask
-
-        if self.causal:
-            i, j = dots.shape[-2:]
-            r = torch.arange(i, device=device)
-            mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
-            mask = F.pad(mask, (j - i, 0), value=False)
-            dots.masked_fill_(mask, mask_value)
-            del mask
-
-        if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
-            top, _ = dots.topk(self.sparse_topk, dim=-1)
-            vk = top[..., -1].unsqueeze(-1).expand_as(dots)
-            mask = dots < vk
-            dots.masked_fill_(mask, mask_value)
-            del mask
-
-        attn = self.attn_fn(dots, dim=-1)
-        post_softmax_attn = attn
-
-        attn = self.dropout(attn)
-
-        if talking_heads:
-            attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
-
-        out = einsum('b h i j, b h j d -> b h i d', attn, v)
-        out = rearrange(out, 'b h n d -> b n (h d)')
-
-        intermediates = Intermediates(
-            pre_softmax_attn=pre_softmax_attn,
-            post_softmax_attn=post_softmax_attn
-        )
-
-        return self.to_out(out), intermediates
-
-
-class AttentionLayers(nn.Module):
-    def __init__(
-            self,
-            dim,
-            depth,
-            heads=8,
-            causal=False,
-            cross_attend=False,
-            only_cross=False,
-            use_scalenorm=False,
-            use_rmsnorm=False,
-            use_rezero=False,
-            rel_pos_num_buckets=32,
-            rel_pos_max_distance=128,
-            position_infused_attn=False,
-            custom_layers=None,
-            sandwich_coef=None,
-            par_ratio=None,
-            residual_attn=False,
-            cross_residual_attn=False,
-            macaron=False,
-            pre_norm=True,
-            gate_residual=False,
-            **kwargs
-    ):
-        super().__init__()
-        ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
-        attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
-
-        dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
-
-        self.dim = dim
-        self.depth = depth
-        self.layers = nn.ModuleList([])
-
-        self.has_pos_emb = position_infused_attn
-        self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
-        self.rotary_pos_emb = always(None)
-
-        assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
-        self.rel_pos = None
-
-        self.pre_norm = pre_norm
-
-        self.residual_attn = residual_attn
-        self.cross_residual_attn = cross_residual_attn
-
-        norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
-        norm_class = RMSNorm if use_rmsnorm else norm_class
-        norm_fn = partial(norm_class, dim)
-
-        norm_fn = nn.Identity if use_rezero else norm_fn
-        branch_fn = Rezero if use_rezero else None
-
-        if cross_attend and not only_cross:
-            default_block = ('a', 'c', 'f')
-        elif cross_attend and only_cross:
-            default_block = ('c', 'f')
-        else:
-            default_block = ('a', 'f')
-
-        if macaron:
-            default_block = ('f',) + default_block
-
-        if exists(custom_layers):
-            layer_types = custom_layers
-        elif exists(par_ratio):
-            par_depth = depth * len(default_block)
-            assert 1 < par_ratio <= par_depth, 'par ratio out of range'
-            default_block = tuple(filter(not_equals('f'), default_block))
-            par_attn = par_depth // par_ratio
-            depth_cut = par_depth * 2 // 3  # 2 / 3 attention layer cutoff suggested by PAR paper
-            par_width = (depth_cut + depth_cut // par_attn) // par_attn
-            assert len(default_block) <= par_width, 'default block is too large for par_ratio'
-            par_block = default_block + ('f',) * (par_width - len(default_block))
-            par_head = par_block * par_attn
-            layer_types = par_head + ('f',) * (par_depth - len(par_head))
-        elif exists(sandwich_coef):
-            assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
-            layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
-        else:
-            layer_types = default_block * depth
-
-        self.layer_types = layer_types
-        self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
-
-        for layer_type in self.layer_types:
-            if layer_type == 'a':
-                layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
-            elif layer_type == 'c':
-                layer = Attention(dim, heads=heads, **attn_kwargs)
-            elif layer_type == 'f':
-                layer = FeedForward(dim, **ff_kwargs)
-                layer = layer if not macaron else Scale(0.5, layer)
-            else:
-                raise Exception(f'invalid layer type {layer_type}')
-
-            if isinstance(layer, Attention) and exists(branch_fn):
-                layer = branch_fn(layer)
-
-            if gate_residual:
-                residual_fn = GRUGating(dim)
-            else:
-                residual_fn = Residual()
-
-            self.layers.append(nn.ModuleList([
-                norm_fn(),
-                layer,
-                residual_fn
-            ]))
-
-    def forward(
-            self,
-            x,
-            context=None,
-            mask=None,
-            context_mask=None,
-            mems=None,
-            return_hiddens=False
-    ):
-        hiddens = []
-        intermediates = []
-        prev_attn = None
-        prev_cross_attn = None
-
-        mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
-
-        for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
-            is_last = ind == (len(self.layers) - 1)
-
-            if layer_type == 'a':
-                hiddens.append(x)
-                layer_mem = mems.pop(0)
-
-            residual = x
-
-            if self.pre_norm:
-                x = norm(x)
-
-            if layer_type == 'a':
-                out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
-                                   prev_attn=prev_attn, mem=layer_mem)
-            elif layer_type == 'c':
-                out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
-            elif layer_type == 'f':
-                out = block(x)
-
-            x = residual_fn(out, residual)
-
-            if layer_type in ('a', 'c'):
-                intermediates.append(inter)
-
-            if layer_type == 'a' and self.residual_attn:
-                prev_attn = inter.pre_softmax_attn
-            elif layer_type == 'c' and self.cross_residual_attn:
-                prev_cross_attn = inter.pre_softmax_attn
-
-            if not self.pre_norm and not is_last:
-                x = norm(x)
-
-        if return_hiddens:
-            intermediates = LayerIntermediates(
-                hiddens=hiddens,
-                attn_intermediates=intermediates
-            )
-
-            return x, intermediates
-
-        return x
-
-
-class Encoder(AttentionLayers):
-    def __init__(self, **kwargs):
-        assert 'causal' not in kwargs, 'cannot set causality on encoder'
-        super().__init__(causal=False, **kwargs)
-
-
-
-class TransformerWrapper(nn.Module):
-    def __init__(
-            self,
-            *,
-            num_tokens,
-            max_seq_len,
-            attn_layers,
-            emb_dim=None,
-            max_mem_len=0.,
-            emb_dropout=0.,
-            num_memory_tokens=None,
-            tie_embedding=False,
-            use_pos_emb=True
-    ):
-        super().__init__()
-        assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
-
-        dim = attn_layers.dim
-        emb_dim = default(emb_dim, dim)
-
-        self.max_seq_len = max_seq_len
-        self.max_mem_len = max_mem_len
-        self.num_tokens = num_tokens
-
-        self.token_emb = nn.Embedding(num_tokens, emb_dim)
-        self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
-                    use_pos_emb and not attn_layers.has_pos_emb) else always(0)
-        self.emb_dropout = nn.Dropout(emb_dropout)
-
-        self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
-        self.attn_layers = attn_layers
-        self.norm = nn.LayerNorm(dim)
-
-        self.init_()
-
-        self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
-
-        # memory tokens (like [cls]) from Memory Transformers paper
-        num_memory_tokens = default(num_memory_tokens, 0)
-        self.num_memory_tokens = num_memory_tokens
-        if num_memory_tokens > 0:
-            self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
-
-            # let funnel encoder know number of memory tokens, if specified
-            if hasattr(attn_layers, 'num_memory_tokens'):
-                attn_layers.num_memory_tokens = num_memory_tokens
-
-    def init_(self):
-        nn.init.normal_(self.token_emb.weight, std=0.02)
-
-    def forward(
-            self,
-            x,
-            return_embeddings=False,
-            mask=None,
-            return_mems=False,
-            return_attn=False,
-            mems=None,
-            **kwargs
-    ):
-        b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
-        x = self.token_emb(x)
-        x += self.pos_emb(x)
-        x = self.emb_dropout(x)
-
-        x = self.project_emb(x)
-
-        if num_mem > 0:
-            mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
-            x = torch.cat((mem, x), dim=1)
-
-            # auto-handle masking after appending memory tokens
-            if exists(mask):
-                mask = F.pad(mask, (num_mem, 0), value=True)
-
-        x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
-        x = self.norm(x)
-
-        mem, x = x[:, :num_mem], x[:, num_mem:]
-
-        out = self.to_logits(x) if not return_embeddings else x
-
-        if return_mems:
-            hiddens = intermediates.hiddens
-            new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
-            new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
-            return out, new_mems
-
-        if return_attn:
-            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
-            return out, attn_maps
-
-        return out
-
diff --git a/ldm/util.py b/ldm/util.py
deleted file mode 100644
index 8ba38853..00000000
--- a/ldm/util.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import importlib
-
-import torch
-import numpy as np
-from collections import abc
-from einops import rearrange
-from functools import partial
-
-import multiprocessing as mp
-from threading import Thread
-from queue import Queue
-
-from inspect import isfunction
-from PIL import Image, ImageDraw, ImageFont
-
-
-def log_txt_as_img(wh, xc, size=10):
-    # wh a tuple of (width, height)
-    # xc a list of captions to plot
-    b = len(xc)
-    txts = list()
-    for bi in range(b):
-        txt = Image.new("RGB", wh, color="white")
-        draw = ImageDraw.Draw(txt)
-        font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
-        nc = int(40 * (wh[0] / 256))
-        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
-
-        try:
-            draw.text((0, 0), lines, fill="black", font=font)
-        except UnicodeEncodeError:
-            print("Cant encode string for logging. Skipping.")
-
-        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
-        txts.append(txt)
-    txts = np.stack(txts)
-    txts = torch.tensor(txts)
-    return txts
-
-
-def ismap(x):
-    if not isinstance(x, torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] > 3)
-
-
-def isimage(x):
-    if not isinstance(x, torch.Tensor):
-        return False
-    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
-
-
-def exists(x):
-    return x is not None
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def mean_flat(tensor):
-    """
-    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def count_params(model, verbose=False):
-    total_params = sum(p.numel() for p in model.parameters())
-    if verbose:
-        print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
-    return total_params
-
-
-def instantiate_from_config(config):
-    if not "target" in config:
-        if config == '__is_first_stage__':
-            return None
-        elif config == "__is_unconditional__":
-            return None
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"])(**config.get("params", dict()))
-
-
-def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
-
-
-def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
-    # create dummy dataset instance
-
-    # run prefetching
-    if idx_to_fn:
-        res = func(data, worker_id=idx)
-    else:
-        res = func(data)
-    Q.put([idx, res])
-    Q.put("Done")
-
-
-def parallel_data_prefetch(
-        func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False
-):
-    # if target_data_type not in ["ndarray", "list"]:
-    #     raise ValueError(
-    #         "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
-    #     )
-    if isinstance(data, np.ndarray) and target_data_type == "list":
-        raise ValueError("list expected but function got ndarray.")
-    elif isinstance(data, abc.Iterable):
-        if isinstance(data, dict):
-            print(
-                f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
-            )
-            data = list(data.values())
-        if target_data_type == "ndarray":
-            data = np.asarray(data)
-        else:
-            data = list(data)
-    else:
-        raise TypeError(
-            f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
-        )
-
-    if cpu_intensive:
-        Q = mp.Queue(1000)
-        proc = mp.Process
-    else:
-        Q = Queue(1000)
-        proc = Thread
-    # spawn processes
-    if target_data_type == "ndarray":
-        arguments = [
-            [func, Q, part, i, use_worker_id]
-            for i, part in enumerate(np.array_split(data, n_proc))
-        ]
-    else:
-        step = (
-            int(len(data) / n_proc + 1)
-            if len(data) % n_proc != 0
-            else int(len(data) / n_proc)
-        )
-        arguments = [
-            [func, Q, part, i, use_worker_id]
-            for i, part in enumerate(
-                [data[i: i + step] for i in range(0, len(data), step)]
-            )
-        ]
-    processes = []
-    for i in range(n_proc):
-        p = proc(target=_do_parallel_data_prefetch, args=arguments[i])
-        processes += [p]
-
-    # start processes
-    print(f"Start prefetching...")
-    import time
-
-    start = time.time()
-    gather_res = [[] for _ in range(n_proc)]
-    try:
-        for p in processes:
-            p.start()
-
-        k = 0
-        while k < n_proc:
-            # get result
-            res = Q.get()
-            if res == "Done":
-                k += 1
-            else:
-                gather_res[res[0]] = res[1]
-
-    except Exception as e:
-        print("Exception: ", e)
-        for p in processes:
-            p.terminate()
-
-        raise e
-    finally:
-        for p in processes:
-            p.join()
-        print(f"Prefetching complete. [{time.time() - start} sec.]")
-
-    if target_data_type == 'ndarray':
-        if not isinstance(gather_res[0], np.ndarray):
-            return np.concatenate([np.asarray(r) for r in gather_res], axis=0)
-
-        # order outputs
-        return np.concatenate(gather_res, axis=0)
-    elif target_data_type == 'list':
-        out = []
-        for r in gather_res:
-            out.extend(r)
-        return out
-    else:
-        return gather_res
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 3ec3f98a..edb8b420 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -28,7 +28,7 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 # new memory efficient cross attention blocks do not support hypernets and we already
 # have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention
 ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention
-ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
+# ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
 
 # silence new console spam from SD2
 ldm.modules.attention.print = lambda *args: None
@@ -82,7 +82,12 @@ class StableDiffusionModelHijack:
 
     def hijack(self, m):
 
-        if type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
+        if shared.text_model_name == "XLMR-Large":
+            model_embeddings = m.cond_stage_model.roberta.embeddings
+            model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
+            m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+            
+        elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
             model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
             model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
             m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
@@ -91,11 +96,7 @@ class StableDiffusionModelHijack:
             m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
             m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
             apply_optimizations()
-        elif shared.text_model_name == "XLMR-Large":
-            model_embeddings = m.cond_stage_model.roberta.embeddings
-            model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
-            m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
-            
+        
         self.clip = m.cond_stage_model
         
         fix_checkpoint()
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index b451d1cf..9ea6e1ce 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -4,7 +4,7 @@ import torch
 
 from modules import prompt_parser, devices
 from modules.shared import opts
-
+import modules.shared as shared
 
 def get_target_prompt_token_count(token_count):
     return math.ceil(max(token_count, 1) / 75) * 75
@@ -177,6 +177,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
 
     def forward(self, text):
+        if shared.text_model_name == "XLMR-Large":
+            return self.wrapped.encode(text)
+
         use_old = opts.use_old_emphasis_implementation
         if use_old:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
@@ -254,7 +257,10 @@ class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
     def __init__(self, wrapped, hijack):
         super().__init__(wrapped, hijack)
         self.tokenizer = wrapped.tokenizer
-        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
+        if shared.text_model_name == "XLMR-Large":
+            self.comma_token = None 
+        else :
+            self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
 
         self.token_mults = {}
         tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
diff --git a/modules/xlmr.py b/modules/xlmr.py
new file mode 100644
index 00000000..beab3fdf
--- /dev/null
+++ b/modules/xlmr.py
@@ -0,0 +1,137 @@
+from transformers import BertPreTrainedModel,BertModel,BertConfig
+import torch.nn as nn
+import torch
+from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
+from transformers import XLMRobertaModel,XLMRobertaTokenizer
+from typing import Optional
+
+class BertSeriesConfig(BertConfig):
+    def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
+
+        super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs)
+        self.project_dim = project_dim
+        self.pooler_fn = pooler_fn
+        self.learn_encoder = learn_encoder
+
+class RobertaSeriesConfig(XLMRobertaConfig):
+    def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs):
+        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+        self.project_dim = project_dim
+        self.pooler_fn = pooler_fn
+        self.learn_encoder = learn_encoder
+
+
+class BertSeriesModelWithTransformation(BertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+    config_class = BertSeriesConfig
+
+    def __init__(self, config=None, **kargs):
+        # modify initialization for autoloading 
+        if config is None:
+            config = XLMRobertaConfig()
+            config.attention_probs_dropout_prob= 0.1
+            config.bos_token_id=0
+            config.eos_token_id=2
+            config.hidden_act='gelu'
+            config.hidden_dropout_prob=0.1
+            config.hidden_size=1024
+            config.initializer_range=0.02
+            config.intermediate_size=4096
+            config.layer_norm_eps=1e-05
+            config.max_position_embeddings=514
+
+            config.num_attention_heads=16
+            config.num_hidden_layers=24
+            config.output_past=True
+            config.pad_token_id=1
+            config.position_embedding_type= "absolute"
+
+            config.type_vocab_size= 1
+            config.use_cache=True
+            config.vocab_size= 250002
+            config.project_dim = 768
+            config.learn_encoder = False
+        super().__init__(config)
+        self.roberta = XLMRobertaModel(config)
+        self.transformation = nn.Linear(config.hidden_size,config.project_dim)
+        self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
+        self.pooler = lambda x: x[:,0]
+        self.post_init()
+
+    def encode(self,c):
+        device = next(self.parameters()).device
+        text = self.tokenizer(c,
+                        truncation=True,
+                        max_length=77,
+                        return_length=False,
+                        return_overflowing_tokens=False,
+                        padding="max_length",
+                        return_tensors="pt")
+        text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
+        text["attention_mask"] = torch.tensor(
+            text['attention_mask']).to(device)
+        features = self(**text)
+        return features['projection_state'] 
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) :
+        r"""
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+        outputs = self.roberta(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=True,
+            return_dict=return_dict,
+        )
+
+        # last module outputs
+        sequence_output = outputs[0]
+
+
+        # project every module
+        sequence_output_ln = self.pre_LN(sequence_output)
+
+        # pooler
+        pooler_output = self.pooler(sequence_output_ln)
+        pooler_output = self.transformation(pooler_output)
+        projection_state = self.transformation(outputs.last_hidden_state)
+
+        return {
+            'pooler_output':pooler_output,
+            'last_hidden_state':outputs.last_hidden_state,
+            'hidden_states':outputs.hidden_states,
+            'attentions':outputs.attentions,
+            'projection_state':projection_state,
+            'sequence_out': sequence_output
+        }
+
+
+class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
+    base_model_prefix = 'roberta'
+    config_class= RobertaSeriesConfig
\ No newline at end of file
-- 
cgit v1.2.1


From 3a724e91a23a715987e3034f31d8c8f5d416bd3a Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Wed, 30 Nov 2022 20:52:32 +0800
Subject: Change to steps of 8

---
 modules/ui.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 36dd754e..acf99bda 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -380,8 +380,8 @@ def create_seed_inputs():
 
     with gr.Row(visible=False) as seed_extra_row_2:
         seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=1, label="Resize seed from width", value=0)
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=1, label="Resize seed from height", value=0)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0)
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0)
 
     random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
     random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
@@ -713,8 +713,8 @@ def create_ui(wrap_gradio_gpu_call):
                 sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512)
+                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
+                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
@@ -722,8 +722,8 @@ def create_ui(wrap_gradio_gpu_call):
                     enable_hr = gr.Checkbox(label='Highres. fix', value=False)
 
                 with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=1, label="Firstpass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=1, label="Firstpass height", value=0)
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass width", value=0)
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass height", value=0)
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
 
                 with gr.Row(equal_height=True):
@@ -899,8 +899,8 @@ def create_ui(wrap_gradio_gpu_call):
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512, elem_id="img2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512, elem_id="img2img_height")
+                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
 
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
@@ -1228,8 +1228,8 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Tab(label="Preprocess images"):
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512)
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512)
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
                     preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
 
                     with gr.Row():
@@ -1286,8 +1286,8 @@ def create_ui(wrap_gradio_gpu_call):
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=1, label="Width", value=512)
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=1, label="Height", value=512)
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-- 
cgit v1.2.1


From a44994e2c926fc1f8479281e5b1e08d7fe9db2bb Mon Sep 17 00:00:00 2001
From: Adi Eyal <adi@clearforest.io>
Date: Wed, 30 Nov 2022 15:23:53 +0200
Subject: Fixed incorrect negative prompt text in infotext

Previously only the first negative prompt in all_negative_prompts was
being used for infotext. This fixes that by selecting the index-th
negative prompt
---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index edceb532..0a73ccbb 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -414,7 +414,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
-    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[0] if  p.all_negative_prompts[0] else ""
+    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if  p.all_negative_prompts[index] else ""
 
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
-- 
cgit v1.2.1


From 21effd629d0fdfdbbff2b20a9f4a3767e7e8bd33 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 28 Nov 2022 21:24:06 -0500
Subject: Add workaround for using MPS with torchsde

---
 modules/sd_samplers.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 5fefb227..8b11f569 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -6,6 +6,7 @@ import tqdm
 from PIL import Image
 import inspect
 import k_diffusion.sampling
+import torchsde._brownian.brownian_interval
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
 from modules import prompt_parser, devices, processing, images
@@ -367,6 +368,19 @@ class TorchHijack:
         return torch.randn_like(x)
 
 
+# MPS fix for randn in torchsde
+def torchsde_randn(size, dtype, device, seed):
+    if device.type == 'mps':
+        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+    else:
+        generator = torch.Generator(device).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=device, generator=generator)
+
+
+torchsde._brownian.brownian_interval._randn = torchsde_randn
+
+
 class KDiffusionSampler:
     def __init__(self, funcname, sd_model):
         denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
-- 
cgit v1.2.1


From 4d5f1691dda971ec7b461dd880426300fd54ccee Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 28 Nov 2022 21:36:35 -0500
Subject: Use devices.autocast instead of torch.autocast

---
 modules/hypernetworks/hypernetwork.py          | 2 +-
 modules/interrogate.py                         | 3 +--
 modules/swinir_model.py                        | 6 +-----
 modules/textual_inversion/dataset.py           | 4 ++--
 modules/textual_inversion/textual_inversion.py | 2 +-
 5 files changed, 6 insertions(+), 11 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8466887f..eb5ae372 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -495,7 +495,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
                 if shared.state.interrupted:
                     break
 
-                with torch.autocast("cuda"):
+                with devices.autocast():
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
                     if tag_drop_out != 0 or shuffle_tags:
                         shared.sd_model.cond_stage_model.to(devices.device)
diff --git a/modules/interrogate.py b/modules/interrogate.py
index 9769aa34..40c6b082 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -148,8 +148,7 @@ class InterrogateModels:
 
             clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
 
-            precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
-            with torch.no_grad(), precision_scope("cuda"):
+            with torch.no_grad(), devices.autocast():
                 image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
 
                 image_features /= image_features.norm(dim=-1, keepdim=True)
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index facd262d..483eabd4 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -13,10 +13,6 @@ from modules.swinir_model_arch import SwinIR as net
 from modules.swinir_model_arch_v2 import Swin2SR as net2
 from modules.upscaler import Upscaler, UpscalerData
 
-precision_scope = (
-    torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
-)
-
 
 class UpscalerSwinIR(Upscaler):
     def __init__(self, dirname):
@@ -112,7 +108,7 @@ def upscale(
     img = np.moveaxis(img, 2, 0) / 255
     img = torch.from_numpy(img).float()
     img = img.unsqueeze(0).to(devices.device_swinir)
-    with torch.no_grad(), precision_scope("cuda"):
+    with torch.no_grad(), devices.autocast():
         _, _, h_old, w_old = img.size()
         h_pad = (h_old // window_size + 1) * window_size - h_old
         w_pad = (w_old // window_size + 1) * window_size - w_old
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index e5725f33..2dc64c3c 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -82,7 +82,7 @@ class PersonalizedBase(Dataset):
             torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
             latent_sample = None
 
-            with torch.autocast("cuda"):
+            with devices.autocast():
                 latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
 
             if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):
@@ -101,7 +101,7 @@ class PersonalizedBase(Dataset):
                 entry.cond_text = self.create_text(filename_text)
 
             if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
-                with torch.autocast("cuda"):
+                with devices.autocast():
                     entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
 
             self.dataset.append(entry)
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 4eb75cb5..daf8d1b8 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -316,7 +316,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                 if shared.state.interrupted:
                     break
 
-                with torch.autocast("cuda"):
+                with devices.autocast():
                     # c = stack_conds(batch.cond).to(devices.device)
                     # mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)
                     # print(mask)
-- 
cgit v1.2.1


From 0fddb4a1c06a6e2122add7eee3b001a6d473baee Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Wed, 30 Nov 2022 08:02:39 -0500
Subject: Rework MPS randn fix, add randn_like fix

torch.manual_seed() already sets a CPU generator, so there is no reason to create a CPU generator manually. torch.randn_like also needs a MPS fix for k-diffusion, but a torch hijack with randn_like already exists so it can also be used for that.
---
 modules/devices.py     | 15 +++------------
 modules/sd_samplers.py |  8 +++++---
 2 files changed, 8 insertions(+), 15 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index f00079c6..046460fa 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -66,24 +66,15 @@ dtype_vae = torch.float16
 
 
 def randn(seed, shape):
-    # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
-    if device.type == 'mps':
-        generator = torch.Generator(device=cpu)
-        generator.manual_seed(seed)
-        noise = torch.randn(shape, generator=generator, device=cpu).to(device)
-        return noise
-
     torch.manual_seed(seed)
+    if device.type == 'mps':
+        return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 
 
 def randn_without_seed(shape):
-    # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
     if device.type == 'mps':
-        generator = torch.Generator(device=cpu)
-        noise = torch.randn(shape, generator=generator, device=cpu).to(device)
-        return noise
-
+        return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 
 
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 8b11f569..4c123d3b 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -365,7 +365,10 @@ class TorchHijack:
             if noise.shape == x.shape:
                 return noise
 
-        return torch.randn_like(x)
+        if x.device.type == 'mps':
+            return torch.randn_like(x, device=devices.cpu).to(x.device)
+        else:
+            return torch.randn_like(x)
 
 
 # MPS fix for randn in torchsde
@@ -429,8 +432,7 @@ class KDiffusionSampler:
         self.model_wrap.step = 0
         self.eta = p.eta or opts.eta_ancestral
 
-        if self.sampler_noises is not None:
-            k_diffusion.sampling.torch = TorchHijack(self.sampler_noises)
+        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
 
         extra_params_kwargs = {}
         for param_name in self.extra_params:
-- 
cgit v1.2.1


From 79953e9b8b0d04868b719452a5b250c450f757b6 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Thu, 1 Dec 2022 03:38:13 -0500
Subject: Add support for macOS (Darwin) in launch.py

---
 launch.py     | 19 ++++++++++++++++++-
 webui-user.sh |  2 +-
 2 files changed, 19 insertions(+), 2 deletions(-)

diff --git a/launch.py b/launch.py
index ad9ddd5a..48314264 100644
--- a/launch.py
+++ b/launch.py
@@ -193,6 +193,20 @@ def prepare_enviroment():
     xformers = '--xformers' in sys.argv
     ngrok = '--ngrok' in sys.argv
 
+    if platform.system() == 'Darwin':
+        os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+        torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1 torchvision==0.13.1")
+        k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/brkirch/k-diffusion.git')
+        k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "51c9778f269cedb55a4d88c79c0246d35bdadb71")
+        if os.environ.get('COMMANDLINE_ARGS') == None:
+            if '--use-cpu' in sys.argv:
+                idx = sys.argv.index('--use-cpu')
+                if idx < len(sys.argv) and sys.argv[idx+1][0] != '-':
+                    sys.argv.insert(idx+1, 'interrogate')
+            else:
+                sys.argv += ['--use-cpu', 'interrogate']
+            sys.argv.append('--no-half')
+
     try:
         commit = run(f"{git} rev-parse HEAD").strip()
     except Exception:
@@ -204,7 +218,7 @@ def prepare_enviroment():
     if not is_installed("torch") or not is_installed("torchvision"):
         run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
 
-    if not skip_torch_cuda_test:
+    if not skip_torch_cuda_test and platform.system() != 'Darwin':
         run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
 
     if not is_installed("gfpgan"):
@@ -231,6 +245,9 @@ def prepare_enviroment():
     if not is_installed("pyngrok") and ngrok:
         run_pip("install pyngrok", "ngrok")
 
+    if platform.system() == 'Darwin' and not is_installed("psutil"):
+        run_pip("install psutil", "psutil")
+
     os.makedirs(dir_repos, exist_ok=True)
 
     git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
diff --git a/webui-user.sh b/webui-user.sh
index 16e42759..bfa53cb7 100644
--- a/webui-user.sh
+++ b/webui-user.sh
@@ -10,7 +10,7 @@
 #clone_dir="stable-diffusion-webui"
 
 # Commandline arguments for webui.py, for example: export COMMANDLINE_ARGS="--medvram --opt-split-attention"
-export COMMANDLINE_ARGS=""
+#export COMMANDLINE_ARGS=""
 
 # python3 executable
 #python_cmd="python3"
-- 
cgit v1.2.1


From bef36597cc46671eeb2041b0343bd5e183883eb7 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Thu, 1 Dec 2022 04:04:14 -0500
Subject: Fix run as root flag

Even though -f enables running webui.sh as root, the -f flag will also be passed to launch.py, causing it to exit with a usage message. This adds a line to launch.py to remove the -f flag if present.

In addition to the above, all the letters in the command line arguments after each '-' were being processed for 'f' and "illegal option" was displayed for each letter that didn't match. Instead, this commit silences those errors and stops processing if the first flag doesn't start with '-f'.
---
 launch.py | 1 +
 webui.sh  | 3 ++-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index 48314264..1661b569 100644
--- a/launch.py
+++ b/launch.py
@@ -186,6 +186,7 @@ def prepare_enviroment():
     parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default='config.json')
     args, _ = parser.parse_known_args(sys.argv)
 
+    sys.argv, _ = extract_arg(sys.argv, '-f')
     sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
     sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
     sys.argv, update_check = extract_arg(sys.argv, '--update-check')
diff --git a/webui.sh b/webui.sh
index 6d4f0992..5f48741f 100755
--- a/webui.sh
+++ b/webui.sh
@@ -51,10 +51,11 @@ fi
 can_run_as_root=0
 
 # read any command line flags to the webui.sh script
-while getopts "f" flag
+while getopts "f" flag > /dev/null 2>&1
 do
     case ${flag} in
         f) can_run_as_root=1;;
+        *) break;;
     esac
 done
 
-- 
cgit v1.2.1


From be2e6de94a5d40bff6d65497fd5ebc275b389f3f Mon Sep 17 00:00:00 2001
From: space-nuko <24979496+space-nuko@users.noreply.github.com>
Date: Thu, 1 Dec 2022 11:34:16 -0800
Subject: Fix clip skip of 1 not being restored from prompts

---
 modules/generation_parameters_copypaste.py | 4 ++++
 modules/shared.py                          | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 01980dca..44fe1a6c 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -184,6 +184,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         else:
             res[k] = v
 
+    # Missing CLIP skip means it was set to 1 (the default)
+    if "Clip skip" not in res:
+        res["Clip skip"] = "1"
+
     return res
 
 
diff --git a/modules/shared.py b/modules/shared.py
index c36ee211..b4ecc7ca 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -371,7 +371,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
-    'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
+    'CLIP_stop_at_last_layers': OptionInfo(1, "Stop at last layers of CLIP model (CLIP skip)", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-- 
cgit v1.2.1


From 9c86fb8cace6d8ac0843e0ddad0ba5ae7f3148c9 Mon Sep 17 00:00:00 2001
From: zhaohu xing <920232796@qq.com>
Date: Fri, 2 Dec 2022 16:08:46 +0800
Subject: fix bug

Signed-off-by: zhaohu xing <920232796@qq.com>
---
 modules/shared.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 1408dee3..ac7678c3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -111,7 +111,11 @@ restricted_opts = {
 from omegaconf import OmegaConf
 config = OmegaConf.load(f"{cmd_opts.config}")
 # XLMR-Large
-text_model_name = config.model.params.cond_stage_config.params.name
+try:
+    text_model_name = config.model.params.cond_stage_config.params.name
+
+except :
+    text_model_name = "stable_diffusion"
 
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
-- 
cgit v1.2.1


From e46147786914484b422899ee7154ae1685d96ae5 Mon Sep 17 00:00:00 2001
From: SmirkingFace <116507648+smirkingface@users.noreply.github.com>
Date: Fri, 2 Dec 2022 11:12:13 +0100
Subject: Fixed safe.py for pytorch 1.13 ckpt files

---
 modules/safe.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/modules/safe.py b/modules/safe.py
index a9209e38..10460ad0 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -62,14 +62,12 @@ class RestrictedUnpickler(pickle.Unpickler):
         raise Exception(f"global '{module}/{name}' is forbidden")
 
 
-allowed_zip_names = ["archive/data.pkl", "archive/version"]
-allowed_zip_names_re = re.compile(r"^archive/data/\d+$")
-
+# Regular expression that accepts 'dirname/version', 'dirname/data.pkl', and 'dirname/data/<number>'
+allowed_zip_names_re = re.compile(r"^([^/]+)/((data/\d+)|version|(data\.pkl))$")
+data_pkl_re = re.compile(r"^([^/]+)/data\.pkl$")
 
 def check_zip_filenames(filename, names):
     for name in names:
-        if name in allowed_zip_names:
-            continue
         if allowed_zip_names_re.match(name):
             continue
 
@@ -82,8 +80,14 @@ def check_pt(filename, extra_handler):
         # new pytorch format is a zip file
         with zipfile.ZipFile(filename) as z:
             check_zip_filenames(filename, z.namelist())
-
-            with z.open('archive/data.pkl') as file:
+            
+            # find filename of data.pkl in zip file: '<directory name>/data.pkl'
+            data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)]
+            if len(data_pkl_filenames) == 0:
+                raise Exception(f"data.pkl not found in {filename}")
+            if len(data_pkl_filenames) > 1:
+                raise Exception(f"Multiple data.pkl found in {filename}")
+            with z.open(data_pkl_filenames[0]) as file:
                 unpickler = RestrictedUnpickler(file)
                 unpickler.extra_handler = extra_handler
                 unpickler.load()
-- 
cgit v1.2.1


From 99b19b1a8f5d25ac43e6a031d7423e541ed31b0e Mon Sep 17 00:00:00 2001
From: jcowens <jeremy7235@gmail.com>
Date: Fri, 2 Dec 2022 02:53:26 -0800
Subject: fix typo

---
 modules/ui_extensions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 030f011e..42667941 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -17,7 +17,7 @@ available_extensions = {"extensions": []}
 
 
 def check_access():
-    assert not shared.cmd_opts.disable_extension_access, "extension access disabed because of commandline flags"
+    assert not shared.cmd_opts.disable_extension_access, "extension access disabled because of command line flags"
 
 
 def apply_and_restart(disable_list, update_list):
-- 
cgit v1.2.1


From da698ca92ed79b9104a62f34291d9b842c433a1b Mon Sep 17 00:00:00 2001
From: SmirkingFace <116507648+smirkingface@users.noreply.github.com>
Date: Fri, 2 Dec 2022 13:47:02 +0100
Subject: Fixed AttributeError where openaimodel is not found

---
 modules/sd_hijack.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index b824b5bf..eef6efd2 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -17,6 +17,7 @@ from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
+import ldm.modules.diffusionmodules.openaimodel
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
 import ldm.modules.encoders.modules
-- 
cgit v1.2.1


From 119a945ef7569128eb7d6772468ffc5567c2e161 Mon Sep 17 00:00:00 2001
From: PhytoEpidemic <64293310+PhytoEpidemic@users.noreply.github.com>
Date: Fri, 2 Dec 2022 12:16:29 -0600
Subject: Fix divide by 0 error

Fix of the edge case 0 weight that occasionally will pop up in some specific situations. This was crashing the script.
---
 modules/textual_inversion/autocrop.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 9859974a..68e1103c 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -276,8 +276,8 @@ def poi_average(pois, settings):
         weight += poi.weight
         x += poi.x * poi.weight
         y += poi.y * poi.weight
-    avg_x = round(x / weight)
-    avg_y = round(y / weight)
+    avg_x = round(weight and x / weight)
+    avg_y = round(weight and y / weight)
 
     return PointOfInterest(avg_x, avg_y)
 
@@ -338,4 +338,4 @@ class Settings:
     self.face_points_weight = face_points_weight
     self.annotate_image = annotate_image
     self.destop_view_image = False
-    self.dnn_model_path = dnn_model_path
\ No newline at end of file
+    self.dnn_model_path = dnn_model_path
-- 
cgit v1.2.1


From b2f17dd367c5758e406dd22b78ad7456dac1957a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 09:15:24 +0300
Subject: prevent include_init_images from being passed to
 StableDiffusionProcessingImg2Img in API #4989

---
 modules/api/api.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 1de3f98f..54ee7cb0 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -152,7 +152,10 @@ class Api:
         )
         if populate.sampler_name:
             populate.sampler_index = None  # prevent a warning later on
-        p = StableDiffusionProcessingImg2Img(**vars(populate))
+
+        args = vars(populate)
+        args.pop('include_init_images', None)  # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
+        p = StableDiffusionProcessingImg2Img(**args)
 
         imgs = []
         for img in init_images:
@@ -170,7 +173,7 @@ class Api:
 
         b64images = list(map(encode_pil_to_base64, processed.images))
 
-        if (not img2imgreq.include_init_images):
+        if not img2imgreq.include_init_images:
             img2imgreq.init_images = None
             img2imgreq.mask = None
 
-- 
cgit v1.2.1


From c7af672186ec09a514f0e78aa21155264e56c130 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 09:41:39 +0300
Subject: more simple config option name plus mouseover hint for clip skip

---
 javascript/hints.js | 2 ++
 modules/shared.py   | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index ac417ff6..57db35be 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -94,6 +94,8 @@ titles = {
     "Add difference": "Result = A + (B - C) * M",
 
     "Learning rate": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
+
+    "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc."
 }
 
 
diff --git a/modules/shared.py b/modules/shared.py
index b4ecc7ca..42ec4120 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -371,7 +371,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
-    'CLIP_stop_at_last_layers': OptionInfo(1, "Stop at last layers of CLIP model (CLIP skip)", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
+    'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
-- 
cgit v1.2.1


From b7ef99634c3f862e835527f8d11c4996eb4f4eec Mon Sep 17 00:00:00 2001
From: Hsiang-Cheng Yang <rick68@users.noreply.github.com>
Date: Sat, 3 Dec 2022 17:35:17 +0800
Subject: Update launch.py

fix a typo
---
 launch.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/launch.py b/launch.py
index ad9ddd5a..90b5d99a 100644
--- a/launch.py
+++ b/launch.py
@@ -157,7 +157,7 @@ def run_extensions_installers(settings_file):
         run_extension_installer(os.path.join(dir_extensions, dirname_extension))
 
 
-def prepare_enviroment():
+def prepare_environment():
     torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
     commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
@@ -290,5 +290,5 @@ def start():
 
 
 if __name__ == "__main__":
-    prepare_enviroment()
+    prepare_environment()
     start()
-- 
cgit v1.2.1


From 5ec8981df46ff6e678c09dd2c1bf4d873ac22a46 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Sat, 3 Dec 2022 02:28:53 -0500
Subject: Revert most launch.py changes, add mac user script

Adds an addition file to read environment variables from when the webui.sh is run from macOS.
---
 launch.py          | 19 +------------------
 webui-macos-env.sh | 13 +++++++++++++
 webui.sh           |  8 ++++++++
 3 files changed, 22 insertions(+), 18 deletions(-)
 create mode 100644 webui-macos-env.sh

diff --git a/launch.py b/launch.py
index 1661b569..0e1bbaf2 100644
--- a/launch.py
+++ b/launch.py
@@ -194,20 +194,6 @@ def prepare_enviroment():
     xformers = '--xformers' in sys.argv
     ngrok = '--ngrok' in sys.argv
 
-    if platform.system() == 'Darwin':
-        os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-        torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1 torchvision==0.13.1")
-        k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/brkirch/k-diffusion.git')
-        k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "51c9778f269cedb55a4d88c79c0246d35bdadb71")
-        if os.environ.get('COMMANDLINE_ARGS') == None:
-            if '--use-cpu' in sys.argv:
-                idx = sys.argv.index('--use-cpu')
-                if idx < len(sys.argv) and sys.argv[idx+1][0] != '-':
-                    sys.argv.insert(idx+1, 'interrogate')
-            else:
-                sys.argv += ['--use-cpu', 'interrogate']
-            sys.argv.append('--no-half')
-
     try:
         commit = run(f"{git} rev-parse HEAD").strip()
     except Exception:
@@ -219,7 +205,7 @@ def prepare_enviroment():
     if not is_installed("torch") or not is_installed("torchvision"):
         run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
 
-    if not skip_torch_cuda_test and platform.system() != 'Darwin':
+    if not skip_torch_cuda_test:
         run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
 
     if not is_installed("gfpgan"):
@@ -246,9 +232,6 @@ def prepare_enviroment():
     if not is_installed("pyngrok") and ngrok:
         run_pip("install pyngrok", "ngrok")
 
-    if platform.system() == 'Darwin' and not is_installed("psutil"):
-        run_pip("install psutil", "psutil")
-
     os.makedirs(dir_repos, exist_ok=True)
 
     git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
diff --git a/webui-macos-env.sh b/webui-macos-env.sh
new file mode 100644
index 00000000..68d1f754
--- /dev/null
+++ b/webui-macos-env.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+####################################################################
+#                          macOS defaults                          #
+# Please modify webui-user.sh to change these instead of this file #
+####################################################################
+
+export COMMANDLINE_ARGS="--skip-torch-cuda-test --no-half --use-cpu interrogate"
+export TORCH_COMMAND="pip install torch==1.12.1 torchvision==0.13.1"
+export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git"
+export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71"
+export PYTORCH_ENABLE_MPS_FALLBACK=1
+
+####################################################################
diff --git a/webui.sh b/webui.sh
index 5f48741f..683c97d3 100755
--- a/webui.sh
+++ b/webui.sh
@@ -4,6 +4,14 @@
 # change the variables in webui-user.sh instead #
 #################################################
 
+# If run from macOS, load defaults from webui-macos-env.sh
+if [[ "$OSTYPE" == "darwin"* ]]; then
+    if [[ -f webui-macos-env.sh ]]
+        then
+        source ./webui-macos-env.sh
+    fi
+fi
+
 # Read variables from webui-user.sh
 # shellcheck source=/dev/null
 if [[ -f webui-user.sh ]]
-- 
cgit v1.2.1


From 2651267e3af5886b8b6b1dc3023f2507f7079118 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 15:57:52 +0300
Subject: fix #4407 breaking UI entirely for card other than ones related to
 the PR

---
 modules/devices.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/modules/devices.py b/modules/devices.py
index 1325569c..547ea46c 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -53,12 +53,10 @@ def torch_gc():
 
 def enable_tf32():
     if torch.cuda.is_available():
-        for devid in range(0,torch.cuda.device_count()):
-            if torch.cuda.get_device_capability(devid) == (7, 5):
-                shd = True
-        if shd:
+        if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]):
             torch.backends.cudnn.benchmark = True
             torch.backends.cudnn.enabled = True
+
         torch.backends.cuda.matmul.allow_tf32 = True
         torch.backends.cudnn.allow_tf32 = True
 
-- 
cgit v1.2.1


From 46b0d230e7c13e247eabb22e1103ce512e7ed6b1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 16:01:23 +0300
Subject: add comment for #4407 and remove seemingly unnecessary cudnn.enabled

---
 modules/devices.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/devices.py b/modules/devices.py
index 547ea46c..d6a76844 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -53,9 +53,11 @@ def torch_gc():
 
 def enable_tf32():
     if torch.cuda.is_available():
+
+        # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
+        # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
         if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]):
             torch.backends.cudnn.benchmark = True
-            torch.backends.cudnn.enabled = True
 
         torch.backends.cuda.matmul.allow_tf32 = True
         torch.backends.cudnn.allow_tf32 = True
-- 
cgit v1.2.1


From cf3e844d1d31d64f3234a0fbdfcac91cc5834657 Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Sat, 3 Dec 2022 18:05:47 +0300
Subject: add noise strength parameter similar to NAI

---
 modules/processing.py | 1 +
 modules/shared.py     | 1 +
 2 files changed, 2 insertions(+)

diff --git a/modules/processing.py b/modules/processing.py
index 3d2c4dc9..b9cb6d32 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -861,6 +861,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        x = x*shared.opts.initial_noise_multiplier
 
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
 
diff --git a/modules/shared.py b/modules/shared.py
index 8202d8e5..4182e2ac 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -364,6 +364,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "initial_noise_multiplier": OptionInfo(1.0, "Multiply initial noise by this factor, may result in less or more detailed img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01 }),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
-- 
cgit v1.2.1


From b6e5edd74657e3fd1fbd04f341b7a84625d4aa7a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 18:06:33 +0300
Subject: add built-in extension system add support for adding upscalers in
 extensions move LDSR, ScuNET and SwinIR to built-in extensions

---
 extensions-builtin/LDSR/ldsr_model_arch.py        |  230 +++++
 extensions-builtin/LDSR/preload.py                |    6 +
 extensions-builtin/LDSR/scripts/ldsr_model.py     |   63 ++
 extensions-builtin/ScuNET/preload.py              |    6 +
 extensions-builtin/ScuNET/scripts/scunet_model.py |   87 ++
 extensions-builtin/ScuNET/scunet_model_arch.py    |  265 ++++++
 extensions-builtin/SwinIR/preload.py              |    6 +
 extensions-builtin/SwinIR/scripts/swinir_model.py |  168 ++++
 extensions-builtin/SwinIR/swinir_model_arch.py    |  867 ++++++++++++++++++
 extensions-builtin/SwinIR/swinir_model_arch_v2.py | 1017 +++++++++++++++++++++
 modules/devices.py                                |   11 +-
 modules/extensions.py                             |   22 +-
 modules/ldsr_model.py                             |   54 --
 modules/ldsr_model_arch.py                        |  230 -----
 modules/modelloader.py                            |   20 +-
 modules/scunet_model.py                           |   87 --
 modules/scunet_model_arch.py                      |  265 ------
 modules/shared.py                                 |   13 +-
 modules/swinir_model.py                           |  157 ----
 modules/swinir_model_arch.py                      |  867 ------------------
 modules/swinir_model_arch_v2.py                   | 1017 ---------------------
 modules/ui.py                                     |    1 -
 modules/ui_extensions.py                          |    8 +-
 webui.py                                          |    5 +-
 24 files changed, 2761 insertions(+), 2711 deletions(-)
 create mode 100644 extensions-builtin/LDSR/ldsr_model_arch.py
 create mode 100644 extensions-builtin/LDSR/preload.py
 create mode 100644 extensions-builtin/LDSR/scripts/ldsr_model.py
 create mode 100644 extensions-builtin/ScuNET/preload.py
 create mode 100644 extensions-builtin/ScuNET/scripts/scunet_model.py
 create mode 100644 extensions-builtin/ScuNET/scunet_model_arch.py
 create mode 100644 extensions-builtin/SwinIR/preload.py
 create mode 100644 extensions-builtin/SwinIR/scripts/swinir_model.py
 create mode 100644 extensions-builtin/SwinIR/swinir_model_arch.py
 create mode 100644 extensions-builtin/SwinIR/swinir_model_arch_v2.py
 delete mode 100644 modules/ldsr_model.py
 delete mode 100644 modules/ldsr_model_arch.py
 delete mode 100644 modules/scunet_model.py
 delete mode 100644 modules/scunet_model_arch.py
 delete mode 100644 modules/swinir_model.py
 delete mode 100644 modules/swinir_model_arch.py
 delete mode 100644 modules/swinir_model_arch_v2.py

diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py
new file mode 100644
index 00000000..90e0a2f0
--- /dev/null
+++ b/extensions-builtin/LDSR/ldsr_model_arch.py
@@ -0,0 +1,230 @@
+import gc
+import time
+import warnings
+
+import numpy as np
+import torch
+import torchvision
+from PIL import Image
+from einops import rearrange, repeat
+from omegaconf import OmegaConf
+
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.util import instantiate_from_config, ismap
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+# Create LDSR Class
+class LDSR:
+    def load_model_from_config(self, half_attention):
+        print(f"Loading model from {self.modelPath}")
+        pl_sd = torch.load(self.modelPath, map_location="cpu")
+        sd = pl_sd["state_dict"]
+        config = OmegaConf.load(self.yamlPath)
+        model = instantiate_from_config(config.model)
+        model.load_state_dict(sd, strict=False)
+        model.cuda()
+        if half_attention:
+            model = model.half()
+
+        model.eval()
+        return {"model": model}
+
+    def __init__(self, model_path, yaml_path):
+        self.modelPath = model_path
+        self.yamlPath = yaml_path
+
+    @staticmethod
+    def run(model, selected_path, custom_steps, eta):
+        example = get_cond(selected_path)
+
+        n_runs = 1
+        guider = None
+        ckwargs = None
+        ddim_use_x0_pred = False
+        temperature = 1.
+        eta = eta
+        custom_shape = None
+
+        height, width = example["image"].shape[1:3]
+        split_input = height >= 128 and width >= 128
+
+        if split_input:
+            ks = 128
+            stride = 64
+            vqf = 4  #
+            model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
+                                        "vqf": vqf,
+                                        "patch_distributed_vq": True,
+                                        "tie_braker": False,
+                                        "clip_max_weight": 0.5,
+                                        "clip_min_weight": 0.01,
+                                        "clip_max_tie_weight": 0.5,
+                                        "clip_min_tie_weight": 0.01}
+        else:
+            if hasattr(model, "split_input_params"):
+                delattr(model, "split_input_params")
+
+        x_t = None
+        logs = None
+        for n in range(n_runs):
+            if custom_shape is not None:
+                x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
+                x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
+
+            logs = make_convolutional_sample(example, model,
+                                             custom_steps=custom_steps,
+                                             eta=eta, quantize_x0=False,
+                                             custom_shape=custom_shape,
+                                             temperature=temperature, noise_dropout=0.,
+                                             corrector=guider, corrector_kwargs=ckwargs, x_T=x_t,
+                                             ddim_use_x0_pred=ddim_use_x0_pred
+                                             )
+        return logs
+
+    def super_resolution(self, image, steps=100, target_scale=2, half_attention=False):
+        model = self.load_model_from_config(half_attention)
+
+        # Run settings
+        diffusion_steps = int(steps)
+        eta = 1.0
+
+        down_sample_method = 'Lanczos'
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        im_og = image
+        width_og, height_og = im_og.size
+        # If we can adjust the max upscale size, then the 4 below should be our variable
+        down_sample_rate = target_scale / 4
+        wd = width_og * down_sample_rate
+        hd = height_og * down_sample_rate
+        width_downsampled_pre = int(np.ceil(wd))
+        height_downsampled_pre = int(np.ceil(hd))
+
+        if down_sample_rate != 1:
+            print(
+                f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
+            im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
+        else:
+            print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
+        
+        # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
+        pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
+        im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
+        
+        logs = self.run(model["model"], im_padded, diffusion_steps, eta)
+
+        sample = logs["sample"]
+        sample = sample.detach().cpu()
+        sample = torch.clamp(sample, -1., 1.)
+        sample = (sample + 1.) / 2. * 255
+        sample = sample.numpy().astype(np.uint8)
+        sample = np.transpose(sample, (0, 2, 3, 1))
+        a = Image.fromarray(sample[0])
+
+        # remove padding
+        a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
+
+        del model
+        gc.collect()
+        torch.cuda.empty_cache()
+        return a
+
+
+def get_cond(selected_path):
+    example = dict()
+    up_f = 4
+    c = selected_path.convert('RGB')
+    c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
+    c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
+                                                    antialias=True)
+    c_up = rearrange(c_up, '1 c h w -> 1 h w c')
+    c = rearrange(c, '1 c h w -> 1 h w c')
+    c = 2. * c - 1.
+
+    c = c.to(torch.device("cuda"))
+    example["LR_image"] = c
+    example["image"] = c_up
+
+    return example
+
+
+@torch.no_grad()
+def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
+                    mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
+                    corrector_kwargs=None, x_t=None
+                    ):
+    ddim = DDIMSampler(model)
+    bs = shape[0]
+    shape = shape[1:]
+    print(f"Sampling with eta = {eta}; steps: {steps}")
+    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
+                                         normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
+                                         mask=mask, x0=x0, temperature=temperature, verbose=False,
+                                         score_corrector=score_corrector,
+                                         corrector_kwargs=corrector_kwargs, x_t=x_t)
+
+    return samples, intermediates
+
+
+@torch.no_grad()
+def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
+                              corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
+    log = dict()
+
+    z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
+                                        return_first_stage_outputs=True,
+                                        force_c_encode=not (hasattr(model, 'split_input_params')
+                                                            and model.cond_stage_key == 'coordinates_bbox'),
+                                        return_original_cond=True)
+
+    if custom_shape is not None:
+        z = torch.randn(custom_shape)
+        print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
+
+    z0 = None
+
+    log["input"] = x
+    log["reconstruction"] = xrec
+
+    if ismap(xc):
+        log["original_conditioning"] = model.to_rgb(xc)
+        if hasattr(model, 'cond_stage_key'):
+            log[model.cond_stage_key] = model.to_rgb(xc)
+
+    else:
+        log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
+        if model.cond_stage_model:
+            log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
+            if model.cond_stage_key == 'class_label':
+                log[model.cond_stage_key] = xc[model.cond_stage_key]
+
+    with model.ema_scope("Plotting"):
+        t0 = time.time()
+
+        sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
+                                                eta=eta,
+                                                quantize_x0=quantize_x0, mask=None, x0=z0,
+                                                temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
+                                                x_t=x_T)
+        t1 = time.time()
+
+        if ddim_use_x0_pred:
+            sample = intermediates['pred_x0'][-1]
+
+    x_sample = model.decode_first_stage(sample)
+
+    try:
+        x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
+        log["sample_noquant"] = x_sample_noquant
+        log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
+    except:
+        pass
+
+    log["sample"] = x_sample
+    log["time"] = t1 - t0
+
+    return log
diff --git a/extensions-builtin/LDSR/preload.py b/extensions-builtin/LDSR/preload.py
new file mode 100644
index 00000000..d746007c
--- /dev/null
+++ b/extensions-builtin/LDSR/preload.py
@@ -0,0 +1,6 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+    parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(paths.models_path, 'LDSR'))
diff --git a/extensions-builtin/LDSR/scripts/ldsr_model.py b/extensions-builtin/LDSR/scripts/ldsr_model.py
new file mode 100644
index 00000000..841ecba0
--- /dev/null
+++ b/extensions-builtin/LDSR/scripts/ldsr_model.py
@@ -0,0 +1,63 @@
+import os
+import sys
+import traceback
+
+from basicsr.utils.download_util import load_file_from_url
+
+from modules.upscaler import Upscaler, UpscalerData
+from ldsr_model_arch import LDSR
+from modules import shared, script_callbacks
+
+
+class UpscalerLDSR(Upscaler):
+    def __init__(self, user_path):
+        self.name = "LDSR"
+        self.user_path = user_path
+        self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
+        self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
+        super().__init__()
+        scaler_data = UpscalerData("LDSR", None, self)
+        self.scalers = [scaler_data]
+
+    def load_model(self, path: str):
+        # Remove incorrect project.yaml file if too big
+        yaml_path = os.path.join(self.model_path, "project.yaml")
+        old_model_path = os.path.join(self.model_path, "model.pth")
+        new_model_path = os.path.join(self.model_path, "model.ckpt")
+        if os.path.exists(yaml_path):
+            statinfo = os.stat(yaml_path)
+            if statinfo.st_size >= 10485760:
+                print("Removing invalid LDSR YAML file.")
+                os.remove(yaml_path)
+        if os.path.exists(old_model_path):
+            print("Renaming model from model.pth to model.ckpt")
+            os.rename(old_model_path, new_model_path)
+        model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
+                                   file_name="model.ckpt", progress=True)
+        yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
+                                  file_name="project.yaml", progress=True)
+
+        try:
+            return LDSR(model, yaml)
+
+        except Exception:
+            print("Error importing LDSR:", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+        return None
+
+    def do_upscale(self, img, path):
+        ldsr = self.load_model(path)
+        if ldsr is None:
+            print("NO LDSR!")
+            return img
+        ddim_steps = shared.opts.ldsr_steps
+        return ldsr.super_resolution(img, ddim_steps, self.scale)
+
+
+def on_ui_settings():
+    import gradio as gr
+
+    shared.opts.add_option("ldsr_steps", shared.OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}, section=('upscaling', "Upscaling")))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)
diff --git a/extensions-builtin/ScuNET/preload.py b/extensions-builtin/ScuNET/preload.py
new file mode 100644
index 00000000..f12c5b90
--- /dev/null
+++ b/extensions-builtin/ScuNET/preload.py
@@ -0,0 +1,6 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+    parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(paths.models_path, 'ScuNET'))
diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py
new file mode 100644
index 00000000..e0fbf3a3
--- /dev/null
+++ b/extensions-builtin/ScuNET/scripts/scunet_model.py
@@ -0,0 +1,87 @@
+import os.path
+import sys
+import traceback
+
+import PIL.Image
+import numpy as np
+import torch
+from basicsr.utils.download_util import load_file_from_url
+
+import modules.upscaler
+from modules import devices, modelloader
+from scunet_model_arch import SCUNet as net
+
+
+class UpscalerScuNET(modules.upscaler.Upscaler):
+    def __init__(self, dirname):
+        self.name = "ScuNET"
+        self.model_name = "ScuNET GAN"
+        self.model_name2 = "ScuNET PSNR"
+        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
+        self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
+        self.user_path = dirname
+        super().__init__()
+        model_paths = self.find_models(ext_filter=[".pth"])
+        scalers = []
+        add_model2 = True
+        for file in model_paths:
+            if "http" in file:
+                name = self.model_name
+            else:
+                name = modelloader.friendly_name(file)
+            if name == self.model_name2 or file == self.model_url2:
+                add_model2 = False
+            try:
+                scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
+                scalers.append(scaler_data)
+            except Exception:
+                print(f"Error loading ScuNET model: {file}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+        if add_model2:
+            scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
+            scalers.append(scaler_data2)
+        self.scalers = scalers
+
+    def do_upscale(self, img: PIL.Image, selected_file):
+        torch.cuda.empty_cache()
+
+        model = self.load_model(selected_file)
+        if model is None:
+            return img
+
+        device = devices.get_device_for('scunet')
+        img = np.array(img)
+        img = img[:, :, ::-1]
+        img = np.moveaxis(img, 2, 0) / 255
+        img = torch.from_numpy(img).float()
+        img = img.unsqueeze(0).to(device)
+
+        with torch.no_grad():
+            output = model(img)
+        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
+        output = 255. * np.moveaxis(output, 0, 2)
+        output = output.astype(np.uint8)
+        output = output[:, :, ::-1]
+        torch.cuda.empty_cache()
+        return PIL.Image.fromarray(output, 'RGB')
+
+    def load_model(self, path: str):
+        device = devices.get_device_for('scunet')
+        if "http" in path:
+            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
+                                          progress=True)
+        else:
+            filename = path
+        if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
+            print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
+            return None
+
+        model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
+        model.load_state_dict(torch.load(filename), strict=True)
+        model.eval()
+        for k, v in model.named_parameters():
+            v.requires_grad = False
+        model = model.to(device)
+
+        return model
+
diff --git a/extensions-builtin/ScuNET/scunet_model_arch.py b/extensions-builtin/ScuNET/scunet_model_arch.py
new file mode 100644
index 00000000..43ca8d36
--- /dev/null
+++ b/extensions-builtin/ScuNET/scunet_model_arch.py
@@ -0,0 +1,265 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+from timm.models.layers import trunc_normal_, DropPath
+
+
+class WMSA(nn.Module):
+    """ Self-attention module in Swin Transformer
+    """
+
+    def __init__(self, input_dim, output_dim, head_dim, window_size, type):
+        super(WMSA, self).__init__()
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.head_dim = head_dim
+        self.scale = self.head_dim ** -0.5
+        self.n_heads = input_dim // head_dim
+        self.window_size = window_size
+        self.type = type
+        self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
+
+        self.relative_position_params = nn.Parameter(
+            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
+
+        self.linear = nn.Linear(self.input_dim, self.output_dim)
+
+        trunc_normal_(self.relative_position_params, std=.02)
+        self.relative_position_params = torch.nn.Parameter(
+            self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
+                                                                                                                 2).transpose(
+                0, 1))
+
+    def generate_mask(self, h, w, p, shift):
+        """ generating the mask of SW-MSA
+        Args:
+            shift: shift parameters in CyclicShift.
+        Returns:
+            attn_mask: should be (1 1 w p p),
+        """
+        # supporting square.
+        attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
+        if self.type == 'W':
+            return attn_mask
+
+        s = p - shift
+        attn_mask[-1, :, :s, :, s:, :] = True
+        attn_mask[-1, :, s:, :, :s, :] = True
+        attn_mask[:, -1, :, :s, :, s:] = True
+        attn_mask[:, -1, :, s:, :, :s] = True
+        attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
+        return attn_mask
+
+    def forward(self, x):
+        """ Forward pass of Window Multi-head Self-attention module.
+        Args:
+            x: input tensor with shape of [b h w c];
+            attn_mask: attention mask, fill -inf where the value is True;
+        Returns:
+            output: tensor shape [b h w c]
+        """
+        if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
+        x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
+        h_windows = x.size(1)
+        w_windows = x.size(2)
+        # square validation
+        # assert h_windows == w_windows
+
+        x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
+        qkv = self.embedding_layer(x)
+        q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
+        sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
+        # Adding learnable relative embedding
+        sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
+        # Using Attn Mask to distinguish different subwindows.
+        if self.type != 'W':
+            attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
+            sim = sim.masked_fill_(attn_mask, float("-inf"))
+
+        probs = nn.functional.softmax(sim, dim=-1)
+        output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
+        output = rearrange(output, 'h b w p c -> b w p (h c)')
+        output = self.linear(output)
+        output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
+
+        if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
+                                                 dims=(1, 2))
+        return output
+
+    def relative_embedding(self):
+        cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
+        relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
+        # negative is allowed
+        return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
+
+
+class Block(nn.Module):
+    def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+        """ SwinTransformer Block
+        """
+        super(Block, self).__init__()
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        assert type in ['W', 'SW']
+        self.type = type
+        if input_resolution <= window_size:
+            self.type = 'W'
+
+        self.ln1 = nn.LayerNorm(input_dim)
+        self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.ln2 = nn.LayerNorm(input_dim)
+        self.mlp = nn.Sequential(
+            nn.Linear(input_dim, 4 * input_dim),
+            nn.GELU(),
+            nn.Linear(4 * input_dim, output_dim),
+        )
+
+    def forward(self, x):
+        x = x + self.drop_path(self.msa(self.ln1(x)))
+        x = x + self.drop_path(self.mlp(self.ln2(x)))
+        return x
+
+
+class ConvTransBlock(nn.Module):
+    def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+        """ SwinTransformer and Conv Block
+        """
+        super(ConvTransBlock, self).__init__()
+        self.conv_dim = conv_dim
+        self.trans_dim = trans_dim
+        self.head_dim = head_dim
+        self.window_size = window_size
+        self.drop_path = drop_path
+        self.type = type
+        self.input_resolution = input_resolution
+
+        assert self.type in ['W', 'SW']
+        if self.input_resolution <= self.window_size:
+            self.type = 'W'
+
+        self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
+                                 self.type, self.input_resolution)
+        self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+        self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+
+        self.conv_block = nn.Sequential(
+            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
+            nn.ReLU(True),
+            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
+        )
+
+    def forward(self, x):
+        conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
+        conv_x = self.conv_block(conv_x) + conv_x
+        trans_x = Rearrange('b c h w -> b h w c')(trans_x)
+        trans_x = self.trans_block(trans_x)
+        trans_x = Rearrange('b h w c -> b c h w')(trans_x)
+        res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
+        x = x + res
+
+        return x
+
+
+class SCUNet(nn.Module):
+    # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
+    def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
+        super(SCUNet, self).__init__()
+        if config is None:
+            config = [2, 2, 2, 2, 2, 2, 2]
+        self.config = config
+        self.dim = dim
+        self.head_dim = 32
+        self.window_size = 8
+
+        # drop path rate for each layer
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
+
+        self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
+
+        begin = 0
+        self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution)
+                        for i in range(config[0])] + \
+                       [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[0]
+        self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution // 2)
+                        for i in range(config[1])] + \
+                       [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[1]
+        self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                       'W' if not i % 2 else 'SW', input_resolution // 4)
+                        for i in range(config[2])] + \
+                       [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
+
+        begin += config[2]
+        self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                      'W' if not i % 2 else 'SW', input_resolution // 8)
+                       for i in range(config[3])]
+
+        begin += config[3]
+        self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution // 4)
+                      for i in range(config[4])]
+
+        begin += config[4]
+        self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution // 2)
+                      for i in range(config[5])]
+
+        begin += config[5]
+        self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
+                     [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+                                     'W' if not i % 2 else 'SW', input_resolution)
+                      for i in range(config[6])]
+
+        self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
+
+        self.m_head = nn.Sequential(*self.m_head)
+        self.m_down1 = nn.Sequential(*self.m_down1)
+        self.m_down2 = nn.Sequential(*self.m_down2)
+        self.m_down3 = nn.Sequential(*self.m_down3)
+        self.m_body = nn.Sequential(*self.m_body)
+        self.m_up3 = nn.Sequential(*self.m_up3)
+        self.m_up2 = nn.Sequential(*self.m_up2)
+        self.m_up1 = nn.Sequential(*self.m_up1)
+        self.m_tail = nn.Sequential(*self.m_tail)
+        # self.apply(self._init_weights)
+
+    def forward(self, x0):
+
+        h, w = x0.size()[-2:]
+        paddingBottom = int(np.ceil(h / 64) * 64 - h)
+        paddingRight = int(np.ceil(w / 64) * 64 - w)
+        x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
+
+        x1 = self.m_head(x0)
+        x2 = self.m_down1(x1)
+        x3 = self.m_down2(x2)
+        x4 = self.m_down3(x3)
+        x = self.m_body(x4)
+        x = self.m_up3(x + x4)
+        x = self.m_up2(x + x3)
+        x = self.m_up1(x + x2)
+        x = self.m_tail(x + x1)
+
+        x = x[..., :h, :w]
+
+        return x
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
\ No newline at end of file
diff --git a/extensions-builtin/SwinIR/preload.py b/extensions-builtin/SwinIR/preload.py
new file mode 100644
index 00000000..567e44bc
--- /dev/null
+++ b/extensions-builtin/SwinIR/preload.py
@@ -0,0 +1,6 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+    parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(paths.models_path, 'SwinIR'))
diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py
new file mode 100644
index 00000000..782769e2
--- /dev/null
+++ b/extensions-builtin/SwinIR/scripts/swinir_model.py
@@ -0,0 +1,168 @@
+import contextlib
+import os
+
+import numpy as np
+import torch
+from PIL import Image
+from basicsr.utils.download_util import load_file_from_url
+from tqdm import tqdm
+
+from modules import modelloader, devices, script_callbacks, shared
+from modules.shared import cmd_opts, opts
+from swinir_model_arch import SwinIR as net
+from swinir_model_arch_v2 import Swin2SR as net2
+from modules.upscaler import Upscaler, UpscalerData
+
+
+device_swinir = devices.get_device_for('swinir')
+
+
+class UpscalerSwinIR(Upscaler):
+    def __init__(self, dirname):
+        self.name = "SwinIR"
+        self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
+                         "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
+                         "-L_x4_GAN.pth "
+        self.model_name = "SwinIR 4x"
+        self.user_path = dirname
+        super().__init__()
+        scalers = []
+        model_files = self.find_models(ext_filter=[".pt", ".pth"])
+        for model in model_files:
+            if "http" in model:
+                name = self.model_name
+            else:
+                name = modelloader.friendly_name(model)
+            model_data = UpscalerData(name, model, self)
+            scalers.append(model_data)
+        self.scalers = scalers
+
+    def do_upscale(self, img, model_file):
+        model = self.load_model(model_file)
+        if model is None:
+            return img
+        model = model.to(device_swinir, dtype=devices.dtype)
+        img = upscale(img, model)
+        try:
+            torch.cuda.empty_cache()
+        except:
+            pass
+        return img
+
+    def load_model(self, path, scale=4):
+        if "http" in path:
+            dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
+            filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True)
+        else:
+            filename = path
+        if filename is None or not os.path.exists(filename):
+            return None
+        if filename.endswith(".v2.pth"):
+            model = net2(
+            upscale=scale,
+            in_chans=3,
+            img_size=64,
+            window_size=8,
+            img_range=1.0,
+            depths=[6, 6, 6, 6, 6, 6],
+            embed_dim=180,
+            num_heads=[6, 6, 6, 6, 6, 6],
+            mlp_ratio=2,
+            upsampler="nearest+conv",
+            resi_connection="1conv",
+            )
+            params = None
+        else:
+            model = net(
+                upscale=scale,
+                in_chans=3,
+                img_size=64,
+                window_size=8,
+                img_range=1.0,
+                depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
+                embed_dim=240,
+                num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
+                mlp_ratio=2,
+                upsampler="nearest+conv",
+                resi_connection="3conv",
+            )
+            params = "params_ema"
+
+        pretrained_model = torch.load(filename)
+        if params is not None:
+            model.load_state_dict(pretrained_model[params], strict=True)
+        else:
+            model.load_state_dict(pretrained_model, strict=True)
+        return model
+
+
+def upscale(
+        img,
+        model,
+        tile=opts.SWIN_tile,
+        tile_overlap=opts.SWIN_tile_overlap,
+        window_size=8,
+        scale=4,
+):
+    img = np.array(img)
+    img = img[:, :, ::-1]
+    img = np.moveaxis(img, 2, 0) / 255
+    img = torch.from_numpy(img).float()
+    img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype)
+    with torch.no_grad(), devices.autocast():
+        _, _, h_old, w_old = img.size()
+        h_pad = (h_old // window_size + 1) * window_size - h_old
+        w_pad = (w_old // window_size + 1) * window_size - w_old
+        img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
+        img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
+        output = inference(img, model, tile, tile_overlap, window_size, scale)
+        output = output[..., : h_old * scale, : w_old * scale]
+        output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+        if output.ndim == 3:
+            output = np.transpose(
+                output[[2, 1, 0], :, :], (1, 2, 0)
+            )  # CHW-RGB to HCW-BGR
+        output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8
+        return Image.fromarray(output, "RGB")
+
+
+def inference(img, model, tile, tile_overlap, window_size, scale):
+    # test the image tile by tile
+    b, c, h, w = img.size()
+    tile = min(tile, h, w)
+    assert tile % window_size == 0, "tile size should be a multiple of window_size"
+    sf = scale
+
+    stride = tile - tile_overlap
+    h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
+    w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
+    E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img)
+    W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir)
+
+    with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
+        for h_idx in h_idx_list:
+            for w_idx in w_idx_list:
+                in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+                out_patch = model(in_patch)
+                out_patch_mask = torch.ones_like(out_patch)
+
+                E[
+                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                ].add_(out_patch)
+                W[
+                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                ].add_(out_patch_mask)
+                pbar.update(1)
+    output = E.div_(W)
+
+    return output
+
+
+def on_ui_settings():
+    import gradio as gr
+
+    shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
+    shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)
diff --git a/extensions-builtin/SwinIR/swinir_model_arch.py b/extensions-builtin/SwinIR/swinir_model_arch.py
new file mode 100644
index 00000000..863f42db
--- /dev/null
+++ b/extensions-builtin/SwinIR/swinir_model_arch.py
@@ -0,0 +1,867 @@
+# -----------------------------------------------------------------------------------
+# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
+# Originally Written by Ze Liu, Modified by Jingyun Liang.
+# -----------------------------------------------------------------------------------
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+
+
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            attn_mask = self.calculate_mask(self.input_resolution)
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+
+    def calculate_mask(self, x_size):
+        # calculate attention mask for SW-MSA
+        H, W = x_size
+        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+        return attn_mask
+
+    def forward(self, x, x_size):
+        H, W = x_size
+        B, L, C = x.shape
+        # assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_x = x
+
+        # partition windows
+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+        if self.input_resolution == x_size:
+            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+        else:
+            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        nW = H * W / self.window_size / self.window_size
+        flops += nW * self.attn.flops(self.window_size * self.window_size)
+        # mlp
+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+        # norm2
+        flops += self.dim * H * W
+        return flops
+
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.dim
+        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        return flops
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, x_size):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, x_size)
+            else:
+                x = blk(x, x_size)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        if self.downsample is not None:
+            flops += self.downsample.flops()
+        return flops
+
+
+class RSTB(nn.Module):
+    """Residual Swin Transformer Block (RSTB).
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        img_size: Input image size.
+        patch_size: Patch size.
+        resi_connection: The convolutional block before residual connection.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 img_size=224, patch_size=4, resi_connection='1conv'):
+        super(RSTB, self).__init__()
+
+        self.dim = dim
+        self.input_resolution = input_resolution
+
+        self.residual_group = BasicLayer(dim=dim,
+                                         input_resolution=input_resolution,
+                                         depth=depth,
+                                         num_heads=num_heads,
+                                         window_size=window_size,
+                                         mlp_ratio=mlp_ratio,
+                                         qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                         drop=drop, attn_drop=attn_drop,
+                                         drop_path=drop_path,
+                                         norm_layer=norm_layer,
+                                         downsample=downsample,
+                                         use_checkpoint=use_checkpoint)
+
+        if resi_connection == '1conv':
+            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
+                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+            norm_layer=None)
+
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+            norm_layer=None)
+
+    def forward(self, x, x_size):
+        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
+
+    def flops(self):
+        flops = 0
+        flops += self.residual_group.flops()
+        H, W = self.input_resolution
+        flops += H * W * self.dim * self.dim * 9
+        flops += self.patch_embed.flops()
+        flops += self.patch_unembed.flops()
+
+        return flops
+
+
+class PatchEmbed(nn.Module):
+    r""" Image to Patch Embedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self):
+        flops = 0
+        H, W = self.img_size
+        if self.norm is not None:
+            flops += H * W * self.embed_dim
+        return flops
+
+
+class PatchUnEmbed(nn.Module):
+    r""" Image to Patch Unembedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+    def forward(self, x, x_size):
+        B, HW, C = x.shape
+        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
+        return x
+
+    def flops(self):
+        flops = 0
+        return flops
+
+
+class Upsample(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+        super(Upsample, self).__init__(*m)
+
+
+class UpsampleOneStep(nn.Sequential):
+    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+       Used in lightweight SR to save parameters.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+
+    """
+
+    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
+        self.num_feat = num_feat
+        self.input_resolution = input_resolution
+        m = []
+        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+        m.append(nn.PixelShuffle(scale))
+        super(UpsampleOneStep, self).__init__(*m)
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.num_feat * 3 * 9
+        return flops
+
+
+class SwinIR(nn.Module):
+    r""" SwinIR
+        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
+
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 64
+        patch_size (int | tuple(int)): Patch size. Default: 1
+        in_chans (int): Number of input image channels. Default: 3
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_size (int): Window size. Default: 7
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+        img_range: Image range. 1. or 255.
+        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
+    """
+
+    def __init__(self, img_size=64, patch_size=1, in_chans=3,
+                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
+                 **kwargs):
+        super(SwinIR, self).__init__()
+        num_in_ch = in_chans
+        num_out_ch = in_chans
+        num_feat = 64
+        self.img_range = img_range
+        if in_chans == 3:
+            rgb_mean = (0.4488, 0.4371, 0.4040)
+            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+        else:
+            self.mean = torch.zeros(1, 1, 1, 1)
+        self.upscale = upscale
+        self.upsampler = upsampler
+        self.window_size = window_size
+
+        #####################################################################################################
+        ################################### 1, shallow feature extraction ###################################
+        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
+
+        #####################################################################################################
+        ################################### 2, deep feature extraction ######################################
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = embed_dim
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # merge non-overlapping patches into image
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # build Residual Swin Transformer blocks (RSTB)
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = RSTB(dim=embed_dim,
+                         input_resolution=(patches_resolution[0],
+                                           patches_resolution[1]),
+                         depth=depths[i_layer],
+                         num_heads=num_heads[i_layer],
+                         window_size=window_size,
+                         mlp_ratio=self.mlp_ratio,
+                         qkv_bias=qkv_bias, qk_scale=qk_scale,
+                         drop=drop_rate, attn_drop=attn_drop_rate,
+                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
+                         norm_layer=norm_layer,
+                         downsample=None,
+                         use_checkpoint=use_checkpoint,
+                         img_size=img_size,
+                         patch_size=patch_size,
+                         resi_connection=resi_connection
+
+                         )
+            self.layers.append(layer)
+        self.norm = norm_layer(self.num_features)
+
+        # build the last conv layer in deep feature extraction
+        if resi_connection == '1conv':
+            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
+
+        #####################################################################################################
+        ################################ 3, high quality image reconstruction ################################
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.upsample = Upsample(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR (to save parameters)
+            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
+                                            (patches_resolution[0], patches_resolution[1]))
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR (less artifacts)
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            if self.upscale == 4:
+                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def check_image_size(self, x):
+        _, _, h, w = x.size()
+        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
+        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
+        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+        return x
+
+    def forward_features(self, x):
+        x_size = (x.shape[2], x.shape[3])
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers:
+            x = layer(x, x_size)
+
+        x = self.norm(x)  # B L C
+        x = self.patch_unembed(x, x_size)
+
+        return x
+
+    def forward(self, x):
+        H, W = x.shape[2:]
+        x = self.check_image_size(x)
+        
+        self.mean = self.mean.type_as(x)
+        x = (x - self.mean) * self.img_range
+
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.conv_last(self.upsample(x))
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.upsample(x)
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            if self.upscale == 4:
+                x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.conv_last(self.lrelu(self.conv_hr(x)))
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            x_first = self.conv_first(x)
+            res = self.conv_after_body(self.forward_features(x_first)) + x_first
+            x = x + self.conv_last(res)
+
+        x = x / self.img_range + self.mean
+
+        return x[:, :, :H*self.upscale, :W*self.upscale]
+
+    def flops(self):
+        flops = 0
+        H, W = self.patches_resolution
+        flops += H * W * 3 * self.embed_dim * 9
+        flops += self.patch_embed.flops()
+        for i, layer in enumerate(self.layers):
+            flops += layer.flops()
+        flops += H * W * 3 * self.embed_dim * self.embed_dim
+        flops += self.upsample.flops()
+        return flops
+
+
+if __name__ == '__main__':
+    upscale = 4
+    window_size = 8
+    height = (1024 // upscale // window_size + 1) * window_size
+    width = (720 // upscale // window_size + 1) * window_size
+    model = SwinIR(upscale=2, img_size=(height, width),
+                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
+                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+    print(model)
+    print(height, width, model.flops() / 1e9)
+
+    x = torch.randn((1, 3, height, width))
+    x = model(x)
+    print(x.shape)
diff --git a/extensions-builtin/SwinIR/swinir_model_arch_v2.py b/extensions-builtin/SwinIR/swinir_model_arch_v2.py
new file mode 100644
index 00000000..0e28ae6e
--- /dev/null
+++ b/extensions-builtin/SwinIR/swinir_model_arch_v2.py
@@ -0,0 +1,1017 @@
+# -----------------------------------------------------------------------------------
+# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/
+# Written by Conde and Choi et al.
+# -----------------------------------------------------------------------------------
+
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
+                 pretrained_window_size=[0, 0]):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.pretrained_window_size = pretrained_window_size
+        self.num_heads = num_heads
+
+        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
+
+        # mlp to generate continuous relative position bias
+        self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
+                                     nn.ReLU(inplace=True),
+                                     nn.Linear(512, num_heads, bias=False))
+
+        # get relative_coords_table
+        relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
+        relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
+        relative_coords_table = torch.stack(
+            torch.meshgrid([relative_coords_h,
+                            relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
+        if pretrained_window_size[0] > 0:
+            relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
+            relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
+        else:
+            relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
+            relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
+        relative_coords_table *= 8  # normalize to -8, 8
+        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
+            torch.abs(relative_coords_table) + 1.0) / np.log2(8)
+
+        self.register_buffer("relative_coords_table", relative_coords_table)
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=False)
+        if qkv_bias:
+            self.q_bias = nn.Parameter(torch.zeros(dim))
+            self.v_bias = nn.Parameter(torch.zeros(dim))
+        else:
+            self.q_bias = None
+            self.v_bias = None
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv_bias = None
+        if self.q_bias is not None:
+            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        # cosine attention
+        attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
+        logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()
+        attn = attn * logit_scale
+
+        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
+        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, ' \
+               f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+        pretrained_window_size (int): Window size in pre-training.
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
+            pretrained_window_size=to_2tuple(pretrained_window_size))
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            attn_mask = self.calculate_mask(self.input_resolution)
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+        
+    def calculate_mask(self, x_size):
+        # calculate attention mask for SW-MSA
+        H, W = x_size
+        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+        h_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        w_slices = (slice(0, -self.window_size),
+                    slice(-self.window_size, -self.shift_size),
+                    slice(-self.shift_size, None))
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+        return attn_mask        
+
+    def forward(self, x, x_size):
+        H, W = x_size
+        B, L, C = x.shape
+        #assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_x = x
+
+        # partition windows
+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+        if self.input_resolution == x_size:
+            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+        else:
+            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
+            
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+        x = x.view(B, H * W, C)
+        x = shortcut + self.drop_path(self.norm1(x))
+
+        # FFN
+        x = x + self.drop_path(self.norm2(self.mlp(x)))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        nW = H * W / self.window_size / self.window_size
+        flops += nW * self.attn.flops(self.window_size * self.window_size)
+        # mlp
+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+        # norm2
+        flops += self.dim * H * W
+        return flops
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(2 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.reduction(x)
+        x = self.norm(x)
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        flops += H * W * self.dim // 2
+        return flops    
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        pretrained_window_size (int): Local window size in pre-training.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 pretrained_window_size=0):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer,
+                                 pretrained_window_size=pretrained_window_size)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, x_size):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, x_size)
+            else:
+                x = blk(x, x_size)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        if self.downsample is not None:
+            flops += self.downsample.flops()
+        return flops
+
+    def _init_respostnorm(self):
+        for blk in self.blocks:
+            nn.init.constant_(blk.norm1.bias, 0)
+            nn.init.constant_(blk.norm1.weight, 0)
+            nn.init.constant_(blk.norm2.bias, 0)
+            nn.init.constant_(blk.norm2.weight, 0)
+            
+class PatchEmbed(nn.Module):
+    r""" Image to Patch Embedding
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        # assert H == self.img_size[0] and W == self.img_size[1],
+        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self):
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops           
+
+class RSTB(nn.Module):
+    """Residual Swin Transformer Block (RSTB).
+
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        img_size: Input image size.
+        patch_size: Patch size.
+        resi_connection: The convolutional block before residual connection.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+                 img_size=224, patch_size=4, resi_connection='1conv'):
+        super(RSTB, self).__init__()
+
+        self.dim = dim
+        self.input_resolution = input_resolution
+
+        self.residual_group = BasicLayer(dim=dim,
+                                         input_resolution=input_resolution,
+                                         depth=depth,
+                                         num_heads=num_heads,
+                                         window_size=window_size,
+                                         mlp_ratio=mlp_ratio,
+                                         qkv_bias=qkv_bias, 
+                                         drop=drop, attn_drop=attn_drop,
+                                         drop_path=drop_path,
+                                         norm_layer=norm_layer,
+                                         downsample=downsample,
+                                         use_checkpoint=use_checkpoint)
+
+        if resi_connection == '1conv':
+            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
+                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+            norm_layer=None)
+
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+            norm_layer=None)
+
+    def forward(self, x, x_size):
+        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
+
+    def flops(self):
+        flops = 0
+        flops += self.residual_group.flops()
+        H, W = self.input_resolution
+        flops += H * W * self.dim * self.dim * 9
+        flops += self.patch_embed.flops()
+        flops += self.patch_unembed.flops()
+
+        return flops
+
+class PatchUnEmbed(nn.Module):
+    r""" Image to Patch Unembedding
+
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+    def forward(self, x, x_size):
+        B, HW, C = x.shape
+        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
+        return x
+
+    def flops(self):
+        flops = 0
+        return flops
+
+
+class Upsample(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+        super(Upsample, self).__init__(*m)
+        
+class Upsample_hf(nn.Sequential):
+    """Upsample module.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+    """
+
+    def __init__(self, scale, num_feat):
+        m = []
+        if (scale & (scale - 1)) == 0:  # scale = 2^n
+            for _ in range(int(math.log(scale, 2))):
+                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+                m.append(nn.PixelShuffle(2))
+        elif scale == 3:
+            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+            m.append(nn.PixelShuffle(3))
+        else:
+            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+        super(Upsample_hf, self).__init__(*m)        
+
+
+class UpsampleOneStep(nn.Sequential):
+    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+       Used in lightweight SR to save parameters.
+
+    Args:
+        scale (int): Scale factor. Supported scales: 2^n and 3.
+        num_feat (int): Channel number of intermediate features.
+
+    """
+
+    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
+        self.num_feat = num_feat
+        self.input_resolution = input_resolution
+        m = []
+        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+        m.append(nn.PixelShuffle(scale))
+        super(UpsampleOneStep, self).__init__(*m)
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.num_feat * 3 * 9
+        return flops
+    
+    
+
+class Swin2SR(nn.Module):
+    r""" Swin2SR
+        A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
+
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 64
+        patch_size (int | tuple(int)): Patch size. Default: 1
+        in_chans (int): Number of input image channels. Default: 3
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_size (int): Window size. Default: 7
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+        img_range: Image range. 1. or 255.
+        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
+    """
+
+    def __init__(self, img_size=64, patch_size=1, in_chans=3,
+                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 window_size=7, mlp_ratio=4., qkv_bias=True, 
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
+                 **kwargs):
+        super(Swin2SR, self).__init__()
+        num_in_ch = in_chans
+        num_out_ch = in_chans
+        num_feat = 64
+        self.img_range = img_range
+        if in_chans == 3:
+            rgb_mean = (0.4488, 0.4371, 0.4040)
+            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+        else:
+            self.mean = torch.zeros(1, 1, 1, 1)
+        self.upscale = upscale
+        self.upsampler = upsampler
+        self.window_size = window_size
+
+        #####################################################################################################
+        ################################### 1, shallow feature extraction ###################################
+        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
+
+        #####################################################################################################
+        ################################### 2, deep feature extraction ######################################
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = embed_dim
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # merge non-overlapping patches into image
+        self.patch_unembed = PatchUnEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # build Residual Swin Transformer blocks (RSTB)
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = RSTB(dim=embed_dim,
+                         input_resolution=(patches_resolution[0],
+                                           patches_resolution[1]),
+                         depth=depths[i_layer],
+                         num_heads=num_heads[i_layer],
+                         window_size=window_size,
+                         mlp_ratio=self.mlp_ratio,
+                         qkv_bias=qkv_bias, 
+                         drop=drop_rate, attn_drop=attn_drop_rate,
+                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
+                         norm_layer=norm_layer,
+                         downsample=None,
+                         use_checkpoint=use_checkpoint,
+                         img_size=img_size,
+                         patch_size=patch_size,
+                         resi_connection=resi_connection
+
+                         )
+            self.layers.append(layer)
+            
+        if self.upsampler == 'pixelshuffle_hf':
+            self.layers_hf = nn.ModuleList()
+            for i_layer in range(self.num_layers):
+                layer = RSTB(dim=embed_dim,
+                             input_resolution=(patches_resolution[0],
+                                               patches_resolution[1]),
+                             depth=depths[i_layer],
+                             num_heads=num_heads[i_layer],
+                             window_size=window_size,
+                             mlp_ratio=self.mlp_ratio,
+                             qkv_bias=qkv_bias, 
+                             drop=drop_rate, attn_drop=attn_drop_rate,
+                             drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
+                             norm_layer=norm_layer,
+                             downsample=None,
+                             use_checkpoint=use_checkpoint,
+                             img_size=img_size,
+                             patch_size=patch_size,
+                             resi_connection=resi_connection
+
+                             )
+                self.layers_hf.append(layer)
+                        
+        self.norm = norm_layer(self.num_features)
+
+        # build the last conv layer in deep feature extraction
+        if resi_connection == '1conv':
+            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+        elif resi_connection == '3conv':
+            # to save parameters and memory
+            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
+                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
+                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
+
+        #####################################################################################################
+        ################################ 3, high quality image reconstruction ################################
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.upsample = Upsample(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+        elif self.upsampler == 'pixelshuffle_aux':
+            self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+            self.conv_before_upsample = nn.Sequential(
+                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                nn.LeakyReLU(inplace=True))
+            self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.conv_after_aux = nn.Sequential(
+                nn.Conv2d(3, num_feat, 3, 1, 1),
+                nn.LeakyReLU(inplace=True))            
+            self.upsample = Upsample(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            
+        elif self.upsampler == 'pixelshuffle_hf':
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.upsample = Upsample(upscale, num_feat)
+            self.upsample_hf = Upsample_hf(upscale, num_feat)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+            self.conv_before_upsample_hf = nn.Sequential(
+                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                nn.LeakyReLU(inplace=True))
+            self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR (to save parameters)
+            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
+                                            (patches_resolution[0], patches_resolution[1]))
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR (less artifacts)
+            assert self.upscale == 4, 'only support x4 now.'
+            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+                                                      nn.LeakyReLU(inplace=True))
+            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def check_image_size(self, x):
+        _, _, h, w = x.size()
+        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
+        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
+        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+        return x
+
+    def forward_features(self, x):
+        x_size = (x.shape[2], x.shape[3])
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers:
+            x = layer(x, x_size)
+
+        x = self.norm(x)  # B L C
+        x = self.patch_unembed(x, x_size)
+
+        return x
+    
+    def forward_features_hf(self, x):
+        x_size = (x.shape[2], x.shape[3])
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers_hf:
+            x = layer(x, x_size)
+
+        x = self.norm(x)  # B L C
+        x = self.patch_unembed(x, x_size)
+
+        return x    
+
+    def forward(self, x):
+        H, W = x.shape[2:]
+        x = self.check_image_size(x)
+
+        self.mean = self.mean.type_as(x)
+        x = (x - self.mean) * self.img_range
+
+        if self.upsampler == 'pixelshuffle':
+            # for classical SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.conv_last(self.upsample(x))
+        elif self.upsampler == 'pixelshuffle_aux':
+            bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
+            bicubic = self.conv_bicubic(bicubic)
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            aux = self.conv_aux(x) # b, 3, LR_H, LR_W
+            x = self.conv_after_aux(aux)
+            x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
+            x = self.conv_last(x)
+            aux = aux / self.img_range + self.mean
+        elif self.upsampler == 'pixelshuffle_hf':
+            # for classical SR with HF
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x_before = self.conv_before_upsample(x)
+            x_out = self.conv_last(self.upsample(x_before))
+            
+            x_hf = self.conv_first_hf(x_before)
+            x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
+            x_hf = self.conv_before_upsample_hf(x_hf)
+            x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
+            x = x_out + x_hf
+            x_hf = x_hf / self.img_range + self.mean
+
+        elif self.upsampler == 'pixelshuffledirect':
+            # for lightweight SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.upsample(x)
+        elif self.upsampler == 'nearest+conv':
+            # for real-world SR
+            x = self.conv_first(x)
+            x = self.conv_after_body(self.forward_features(x)) + x
+            x = self.conv_before_upsample(x)
+            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+            x = self.conv_last(self.lrelu(self.conv_hr(x)))
+        else:
+            # for image denoising and JPEG compression artifact reduction
+            x_first = self.conv_first(x)
+            res = self.conv_after_body(self.forward_features(x_first)) + x_first
+            x = x + self.conv_last(res)
+        
+        x = x / self.img_range + self.mean
+        if self.upsampler == "pixelshuffle_aux":
+            return x[:, :, :H*self.upscale, :W*self.upscale], aux
+        
+        elif self.upsampler == "pixelshuffle_hf":
+            x_out = x_out / self.img_range + self.mean
+            return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
+        
+        else:
+            return x[:, :, :H*self.upscale, :W*self.upscale]
+
+    def flops(self):
+        flops = 0
+        H, W = self.patches_resolution
+        flops += H * W * 3 * self.embed_dim * 9
+        flops += self.patch_embed.flops()
+        for i, layer in enumerate(self.layers):
+            flops += layer.flops()
+        flops += H * W * 3 * self.embed_dim * self.embed_dim
+        flops += self.upsample.flops()
+        return flops
+
+
+if __name__ == '__main__':
+    upscale = 4
+    window_size = 8
+    height = (1024 // upscale // window_size + 1) * window_size
+    width = (720 // upscale // window_size + 1) * window_size
+    model = Swin2SR(upscale=2, img_size=(height, width),
+                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
+                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+    print(model)
+    print(height, width, model.flops() / 1e9)
+
+    x = torch.randn((1, 3, height, width))
+    x = model(x)
+    print(x.shape)
\ No newline at end of file
diff --git a/modules/devices.py b/modules/devices.py
index d6a76844..f8cffae1 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -44,6 +44,15 @@ def get_optimal_device():
     return cpu
 
 
+def get_device_for(task):
+    from modules import shared
+
+    if task in shared.cmd_opts.use_cpu:
+        return cpu
+
+    return get_optimal_device()
+
+
 def torch_gc():
     if torch.cuda.is_available():
         with torch.cuda.device(get_cuda_device_string()):
@@ -67,7 +76,7 @@ def enable_tf32():
 errors.run(enable_tf32, "Enabling TF32")
 
 cpu = torch.device("cpu")
-device = device_interrogate = device_gfpgan = device_swinir = device_esrgan = device_scunet = device_codeformer = None
+device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
 dtype = torch.float16
 dtype_vae = torch.float16
 
diff --git a/modules/extensions.py b/modules/extensions.py
index db9c4200..b522125c 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -8,6 +8,7 @@ from modules import paths, shared
 
 extensions = []
 extensions_dir = os.path.join(paths.script_path, "extensions")
+extensions_builtin_dir = os.path.join(paths.script_path, "extensions-builtin")
 
 
 def active():
@@ -15,12 +16,13 @@ def active():
 
 
 class Extension:
-    def __init__(self, name, path, enabled=True):
+    def __init__(self, name, path, enabled=True, is_builtin=False):
         self.name = name
         self.path = path
         self.enabled = enabled
         self.status = ''
         self.can_update = False
+        self.is_builtin = is_builtin
 
         repo = None
         try:
@@ -79,11 +81,19 @@ def list_extensions():
     if not os.path.isdir(extensions_dir):
         return
 
-    for dirname in sorted(os.listdir(extensions_dir)):
-        path = os.path.join(extensions_dir, dirname)
-        if not os.path.isdir(path):
-            continue
+    paths = []
+    for dirname in [extensions_dir, extensions_builtin_dir]:
+        if not os.path.isdir(dirname):
+            return
 
-        extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions)
+        for extension_dirname in sorted(os.listdir(dirname)):
+            path = os.path.join(dirname, extension_dirname)
+            if not os.path.isdir(path):
+                continue
+
+            paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
+
+    for dirname, path, is_builtin in paths:
+        extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
         extensions.append(extension)
 
diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py
deleted file mode 100644
index 8c4db44a..00000000
--- a/modules/ldsr_model.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import os
-import sys
-import traceback
-
-from basicsr.utils.download_util import load_file_from_url
-
-from modules.upscaler import Upscaler, UpscalerData
-from modules.ldsr_model_arch import LDSR
-from modules import shared
-
-
-class UpscalerLDSR(Upscaler):
-    def __init__(self, user_path):
-        self.name = "LDSR"
-        self.user_path = user_path
-        self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
-        self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
-        super().__init__()
-        scaler_data = UpscalerData("LDSR", None, self)
-        self.scalers = [scaler_data]
-
-    def load_model(self, path: str):
-        # Remove incorrect project.yaml file if too big
-        yaml_path = os.path.join(self.model_path, "project.yaml")
-        old_model_path = os.path.join(self.model_path, "model.pth")
-        new_model_path = os.path.join(self.model_path, "model.ckpt")
-        if os.path.exists(yaml_path):
-            statinfo = os.stat(yaml_path)
-            if statinfo.st_size >= 10485760:
-                print("Removing invalid LDSR YAML file.")
-                os.remove(yaml_path)
-        if os.path.exists(old_model_path):
-            print("Renaming model from model.pth to model.ckpt")
-            os.rename(old_model_path, new_model_path)
-        model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
-                                   file_name="model.ckpt", progress=True)
-        yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
-                                  file_name="project.yaml", progress=True)
-
-        try:
-            return LDSR(model, yaml)
-
-        except Exception:
-            print("Error importing LDSR:", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-        return None
-
-    def do_upscale(self, img, path):
-        ldsr = self.load_model(path)
-        if ldsr is None:
-            print("NO LDSR!")
-            return img
-        ddim_steps = shared.opts.ldsr_steps
-        return ldsr.super_resolution(img, ddim_steps, self.scale)
diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
deleted file mode 100644
index 90e0a2f0..00000000
--- a/modules/ldsr_model_arch.py
+++ /dev/null
@@ -1,230 +0,0 @@
-import gc
-import time
-import warnings
-
-import numpy as np
-import torch
-import torchvision
-from PIL import Image
-from einops import rearrange, repeat
-from omegaconf import OmegaConf
-
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.util import instantiate_from_config, ismap
-
-warnings.filterwarnings("ignore", category=UserWarning)
-
-
-# Create LDSR Class
-class LDSR:
-    def load_model_from_config(self, half_attention):
-        print(f"Loading model from {self.modelPath}")
-        pl_sd = torch.load(self.modelPath, map_location="cpu")
-        sd = pl_sd["state_dict"]
-        config = OmegaConf.load(self.yamlPath)
-        model = instantiate_from_config(config.model)
-        model.load_state_dict(sd, strict=False)
-        model.cuda()
-        if half_attention:
-            model = model.half()
-
-        model.eval()
-        return {"model": model}
-
-    def __init__(self, model_path, yaml_path):
-        self.modelPath = model_path
-        self.yamlPath = yaml_path
-
-    @staticmethod
-    def run(model, selected_path, custom_steps, eta):
-        example = get_cond(selected_path)
-
-        n_runs = 1
-        guider = None
-        ckwargs = None
-        ddim_use_x0_pred = False
-        temperature = 1.
-        eta = eta
-        custom_shape = None
-
-        height, width = example["image"].shape[1:3]
-        split_input = height >= 128 and width >= 128
-
-        if split_input:
-            ks = 128
-            stride = 64
-            vqf = 4  #
-            model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
-                                        "vqf": vqf,
-                                        "patch_distributed_vq": True,
-                                        "tie_braker": False,
-                                        "clip_max_weight": 0.5,
-                                        "clip_min_weight": 0.01,
-                                        "clip_max_tie_weight": 0.5,
-                                        "clip_min_tie_weight": 0.01}
-        else:
-            if hasattr(model, "split_input_params"):
-                delattr(model, "split_input_params")
-
-        x_t = None
-        logs = None
-        for n in range(n_runs):
-            if custom_shape is not None:
-                x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
-                x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
-
-            logs = make_convolutional_sample(example, model,
-                                             custom_steps=custom_steps,
-                                             eta=eta, quantize_x0=False,
-                                             custom_shape=custom_shape,
-                                             temperature=temperature, noise_dropout=0.,
-                                             corrector=guider, corrector_kwargs=ckwargs, x_T=x_t,
-                                             ddim_use_x0_pred=ddim_use_x0_pred
-                                             )
-        return logs
-
-    def super_resolution(self, image, steps=100, target_scale=2, half_attention=False):
-        model = self.load_model_from_config(half_attention)
-
-        # Run settings
-        diffusion_steps = int(steps)
-        eta = 1.0
-
-        down_sample_method = 'Lanczos'
-
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        im_og = image
-        width_og, height_og = im_og.size
-        # If we can adjust the max upscale size, then the 4 below should be our variable
-        down_sample_rate = target_scale / 4
-        wd = width_og * down_sample_rate
-        hd = height_og * down_sample_rate
-        width_downsampled_pre = int(np.ceil(wd))
-        height_downsampled_pre = int(np.ceil(hd))
-
-        if down_sample_rate != 1:
-            print(
-                f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
-            im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
-        else:
-            print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
-        
-        # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
-        pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
-        im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
-        
-        logs = self.run(model["model"], im_padded, diffusion_steps, eta)
-
-        sample = logs["sample"]
-        sample = sample.detach().cpu()
-        sample = torch.clamp(sample, -1., 1.)
-        sample = (sample + 1.) / 2. * 255
-        sample = sample.numpy().astype(np.uint8)
-        sample = np.transpose(sample, (0, 2, 3, 1))
-        a = Image.fromarray(sample[0])
-
-        # remove padding
-        a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
-
-        del model
-        gc.collect()
-        torch.cuda.empty_cache()
-        return a
-
-
-def get_cond(selected_path):
-    example = dict()
-    up_f = 4
-    c = selected_path.convert('RGB')
-    c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
-    c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
-                                                    antialias=True)
-    c_up = rearrange(c_up, '1 c h w -> 1 h w c')
-    c = rearrange(c, '1 c h w -> 1 h w c')
-    c = 2. * c - 1.
-
-    c = c.to(torch.device("cuda"))
-    example["LR_image"] = c
-    example["image"] = c_up
-
-    return example
-
-
-@torch.no_grad()
-def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
-                    mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
-                    corrector_kwargs=None, x_t=None
-                    ):
-    ddim = DDIMSampler(model)
-    bs = shape[0]
-    shape = shape[1:]
-    print(f"Sampling with eta = {eta}; steps: {steps}")
-    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
-                                         normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
-                                         mask=mask, x0=x0, temperature=temperature, verbose=False,
-                                         score_corrector=score_corrector,
-                                         corrector_kwargs=corrector_kwargs, x_t=x_t)
-
-    return samples, intermediates
-
-
-@torch.no_grad()
-def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
-                              corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
-    log = dict()
-
-    z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
-                                        return_first_stage_outputs=True,
-                                        force_c_encode=not (hasattr(model, 'split_input_params')
-                                                            and model.cond_stage_key == 'coordinates_bbox'),
-                                        return_original_cond=True)
-
-    if custom_shape is not None:
-        z = torch.randn(custom_shape)
-        print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
-
-    z0 = None
-
-    log["input"] = x
-    log["reconstruction"] = xrec
-
-    if ismap(xc):
-        log["original_conditioning"] = model.to_rgb(xc)
-        if hasattr(model, 'cond_stage_key'):
-            log[model.cond_stage_key] = model.to_rgb(xc)
-
-    else:
-        log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
-        if model.cond_stage_model:
-            log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
-            if model.cond_stage_key == 'class_label':
-                log[model.cond_stage_key] = xc[model.cond_stage_key]
-
-    with model.ema_scope("Plotting"):
-        t0 = time.time()
-
-        sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
-                                                eta=eta,
-                                                quantize_x0=quantize_x0, mask=None, x0=z0,
-                                                temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
-                                                x_t=x_T)
-        t1 = time.time()
-
-        if ddim_use_x0_pred:
-            sample = intermediates['pred_x0'][-1]
-
-    x_sample = model.decode_first_stage(sample)
-
-    try:
-        x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
-        log["sample_noquant"] = x_sample_noquant
-        log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
-    except:
-        pass
-
-    log["sample"] = x_sample
-    log["time"] = t1 - t0
-
-    return log
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 7d2f0ade..e647f6fa 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -124,10 +124,9 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
 
 
 def load_upscalers():
-    sd = shared.script_path
     # We can only do this 'magic' method to dynamically load upscalers if they are referenced,
     # so we'll try to import any _model.py files before looking in __subclasses__
-    modules_dir = os.path.join(sd, "modules")
+    modules_dir = os.path.join(shared.script_path, "modules")
     for file in os.listdir(modules_dir):
         if "_model.py" in file:
             model_name = file.replace("_model.py", "")
@@ -136,22 +135,13 @@ def load_upscalers():
                 importlib.import_module(full_model)
             except:
                 pass
+
     datas = []
-    c_o = vars(shared.cmd_opts)
+    commandline_options = vars(shared.cmd_opts)
     for cls in Upscaler.__subclasses__():
         name = cls.__name__
-        module_name = cls.__module__
-        module = importlib.import_module(module_name)
-        class_ = getattr(module, name)
         cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
-        opt_string = None
-        try:
-            if cmd_name in c_o:
-                opt_string = c_o[cmd_name]
-        except:
-            pass
-        scaler = class_(opt_string)
-        for child in scaler.scalers:
-            datas.append(child)
+        scaler = cls(commandline_options.get(cmd_name, None))
+        datas += scaler.scalers
 
     shared.sd_upscalers = datas
diff --git a/modules/scunet_model.py b/modules/scunet_model.py
deleted file mode 100644
index 52360241..00000000
--- a/modules/scunet_model.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import os.path
-import sys
-import traceback
-
-import PIL.Image
-import numpy as np
-import torch
-from basicsr.utils.download_util import load_file_from_url
-
-import modules.upscaler
-from modules import devices, modelloader
-from modules.scunet_model_arch import SCUNet as net
-
-
-class UpscalerScuNET(modules.upscaler.Upscaler):
-    def __init__(self, dirname):
-        self.name = "ScuNET"
-        self.model_name = "ScuNET GAN"
-        self.model_name2 = "ScuNET PSNR"
-        self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
-        self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
-        self.user_path = dirname
-        super().__init__()
-        model_paths = self.find_models(ext_filter=[".pth"])
-        scalers = []
-        add_model2 = True
-        for file in model_paths:
-            if "http" in file:
-                name = self.model_name
-            else:
-                name = modelloader.friendly_name(file)
-            if name == self.model_name2 or file == self.model_url2:
-                add_model2 = False
-            try:
-                scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
-                scalers.append(scaler_data)
-            except Exception:
-                print(f"Error loading ScuNET model: {file}", file=sys.stderr)
-                print(traceback.format_exc(), file=sys.stderr)
-        if add_model2:
-            scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
-            scalers.append(scaler_data2)
-        self.scalers = scalers
-
-    def do_upscale(self, img: PIL.Image, selected_file):
-        torch.cuda.empty_cache()
-
-        model = self.load_model(selected_file)
-        if model is None:
-            return img
-
-        device = devices.device_scunet
-        img = np.array(img)
-        img = img[:, :, ::-1]
-        img = np.moveaxis(img, 2, 0) / 255
-        img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(device)
-
-        with torch.no_grad():
-            output = model(img)
-        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
-        output = 255. * np.moveaxis(output, 0, 2)
-        output = output.astype(np.uint8)
-        output = output[:, :, ::-1]
-        torch.cuda.empty_cache()
-        return PIL.Image.fromarray(output, 'RGB')
-
-    def load_model(self, path: str):
-        device = devices.device_scunet
-        if "http" in path:
-            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
-                                          progress=True)
-        else:
-            filename = path
-        if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
-            print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
-            return None
-
-        model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
-        model.load_state_dict(torch.load(filename), strict=True)
-        model.eval()
-        for k, v in model.named_parameters():
-            v.requires_grad = False
-        model = model.to(device)
-
-        return model
-
diff --git a/modules/scunet_model_arch.py b/modules/scunet_model_arch.py
deleted file mode 100644
index 43ca8d36..00000000
--- a/modules/scunet_model_arch.py
+++ /dev/null
@@ -1,265 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import torch
-import torch.nn as nn
-from einops import rearrange
-from einops.layers.torch import Rearrange
-from timm.models.layers import trunc_normal_, DropPath
-
-
-class WMSA(nn.Module):
-    """ Self-attention module in Swin Transformer
-    """
-
-    def __init__(self, input_dim, output_dim, head_dim, window_size, type):
-        super(WMSA, self).__init__()
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        self.head_dim = head_dim
-        self.scale = self.head_dim ** -0.5
-        self.n_heads = input_dim // head_dim
-        self.window_size = window_size
-        self.type = type
-        self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
-
-        self.relative_position_params = nn.Parameter(
-            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
-
-        self.linear = nn.Linear(self.input_dim, self.output_dim)
-
-        trunc_normal_(self.relative_position_params, std=.02)
-        self.relative_position_params = torch.nn.Parameter(
-            self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
-                                                                                                                 2).transpose(
-                0, 1))
-
-    def generate_mask(self, h, w, p, shift):
-        """ generating the mask of SW-MSA
-        Args:
-            shift: shift parameters in CyclicShift.
-        Returns:
-            attn_mask: should be (1 1 w p p),
-        """
-        # supporting square.
-        attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
-        if self.type == 'W':
-            return attn_mask
-
-        s = p - shift
-        attn_mask[-1, :, :s, :, s:, :] = True
-        attn_mask[-1, :, s:, :, :s, :] = True
-        attn_mask[:, -1, :, :s, :, s:] = True
-        attn_mask[:, -1, :, s:, :, :s] = True
-        attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
-        return attn_mask
-
-    def forward(self, x):
-        """ Forward pass of Window Multi-head Self-attention module.
-        Args:
-            x: input tensor with shape of [b h w c];
-            attn_mask: attention mask, fill -inf where the value is True;
-        Returns:
-            output: tensor shape [b h w c]
-        """
-        if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
-        x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
-        h_windows = x.size(1)
-        w_windows = x.size(2)
-        # square validation
-        # assert h_windows == w_windows
-
-        x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
-        qkv = self.embedding_layer(x)
-        q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
-        sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
-        # Adding learnable relative embedding
-        sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
-        # Using Attn Mask to distinguish different subwindows.
-        if self.type != 'W':
-            attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
-            sim = sim.masked_fill_(attn_mask, float("-inf"))
-
-        probs = nn.functional.softmax(sim, dim=-1)
-        output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
-        output = rearrange(output, 'h b w p c -> b w p (h c)')
-        output = self.linear(output)
-        output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
-
-        if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
-                                                 dims=(1, 2))
-        return output
-
-    def relative_embedding(self):
-        cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
-        relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
-        # negative is allowed
-        return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
-
-
-class Block(nn.Module):
-    def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
-        """ SwinTransformer Block
-        """
-        super(Block, self).__init__()
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        assert type in ['W', 'SW']
-        self.type = type
-        if input_resolution <= window_size:
-            self.type = 'W'
-
-        self.ln1 = nn.LayerNorm(input_dim)
-        self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.ln2 = nn.LayerNorm(input_dim)
-        self.mlp = nn.Sequential(
-            nn.Linear(input_dim, 4 * input_dim),
-            nn.GELU(),
-            nn.Linear(4 * input_dim, output_dim),
-        )
-
-    def forward(self, x):
-        x = x + self.drop_path(self.msa(self.ln1(x)))
-        x = x + self.drop_path(self.mlp(self.ln2(x)))
-        return x
-
-
-class ConvTransBlock(nn.Module):
-    def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
-        """ SwinTransformer and Conv Block
-        """
-        super(ConvTransBlock, self).__init__()
-        self.conv_dim = conv_dim
-        self.trans_dim = trans_dim
-        self.head_dim = head_dim
-        self.window_size = window_size
-        self.drop_path = drop_path
-        self.type = type
-        self.input_resolution = input_resolution
-
-        assert self.type in ['W', 'SW']
-        if self.input_resolution <= self.window_size:
-            self.type = 'W'
-
-        self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
-                                 self.type, self.input_resolution)
-        self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
-        self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
-
-        self.conv_block = nn.Sequential(
-            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
-            nn.ReLU(True),
-            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
-        )
-
-    def forward(self, x):
-        conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
-        conv_x = self.conv_block(conv_x) + conv_x
-        trans_x = Rearrange('b c h w -> b h w c')(trans_x)
-        trans_x = self.trans_block(trans_x)
-        trans_x = Rearrange('b h w c -> b c h w')(trans_x)
-        res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
-        x = x + res
-
-        return x
-
-
-class SCUNet(nn.Module):
-    # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
-    def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
-        super(SCUNet, self).__init__()
-        if config is None:
-            config = [2, 2, 2, 2, 2, 2, 2]
-        self.config = config
-        self.dim = dim
-        self.head_dim = 32
-        self.window_size = 8
-
-        # drop path rate for each layer
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
-
-        self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
-
-        begin = 0
-        self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
-                                       'W' if not i % 2 else 'SW', input_resolution)
-                        for i in range(config[0])] + \
-                       [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[0]
-        self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
-                                       'W' if not i % 2 else 'SW', input_resolution // 2)
-                        for i in range(config[1])] + \
-                       [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[1]
-        self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
-                                       'W' if not i % 2 else 'SW', input_resolution // 4)
-                        for i in range(config[2])] + \
-                       [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[2]
-        self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
-                                      'W' if not i % 2 else 'SW', input_resolution // 8)
-                       for i in range(config[3])]
-
-        begin += config[3]
-        self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
-                     [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
-                                     'W' if not i % 2 else 'SW', input_resolution // 4)
-                      for i in range(config[4])]
-
-        begin += config[4]
-        self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
-                     [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
-                                     'W' if not i % 2 else 'SW', input_resolution // 2)
-                      for i in range(config[5])]
-
-        begin += config[5]
-        self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
-                     [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
-                                     'W' if not i % 2 else 'SW', input_resolution)
-                      for i in range(config[6])]
-
-        self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
-
-        self.m_head = nn.Sequential(*self.m_head)
-        self.m_down1 = nn.Sequential(*self.m_down1)
-        self.m_down2 = nn.Sequential(*self.m_down2)
-        self.m_down3 = nn.Sequential(*self.m_down3)
-        self.m_body = nn.Sequential(*self.m_body)
-        self.m_up3 = nn.Sequential(*self.m_up3)
-        self.m_up2 = nn.Sequential(*self.m_up2)
-        self.m_up1 = nn.Sequential(*self.m_up1)
-        self.m_tail = nn.Sequential(*self.m_tail)
-        # self.apply(self._init_weights)
-
-    def forward(self, x0):
-
-        h, w = x0.size()[-2:]
-        paddingBottom = int(np.ceil(h / 64) * 64 - h)
-        paddingRight = int(np.ceil(w / 64) * 64 - w)
-        x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
-
-        x1 = self.m_head(x0)
-        x2 = self.m_down1(x1)
-        x3 = self.m_down2(x2)
-        x4 = self.m_down3(x3)
-        x = self.m_body(x4)
-        x = self.m_up3(x + x4)
-        x = self.m_up2(x + x3)
-        x = self.m_up1(x + x2)
-        x = self.m_tail(x + x1)
-
-        x = x[..., :h, :w]
-
-        return x
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
\ No newline at end of file
diff --git a/modules/shared.py b/modules/shared.py
index 8202d8e5..dc45fcaa 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -50,9 +50,6 @@ parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory wi
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
 parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
 parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
-parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
-parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
-parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
 parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
@@ -61,7 +58,7 @@ parser.add_argument("--opt-split-attention", action='store_true', help="force-en
 parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
-parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)
+parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -95,6 +92,7 @@ parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, req
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
 
 script_loading.preload_extensions(extensions.extensions_dir, parser)
+script_loading.preload_extensions(extensions.extensions_builtin_dir, parser)
 
 cmd_opts = parser.parse_args()
 
@@ -112,8 +110,8 @@ restricted_opts = {
 
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
-devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
-(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
+devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
+    (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
 
 device = devices.device
 weight_load_location = None if cmd_opts.lowram else "cpu"
@@ -326,9 +324,6 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
-    "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
-    "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
-    "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
     "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
 }))
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
deleted file mode 100644
index 483eabd4..00000000
--- a/modules/swinir_model.py
+++ /dev/null
@@ -1,157 +0,0 @@
-import contextlib
-import os
-
-import numpy as np
-import torch
-from PIL import Image
-from basicsr.utils.download_util import load_file_from_url
-from tqdm import tqdm
-
-from modules import modelloader, devices
-from modules.shared import cmd_opts, opts
-from modules.swinir_model_arch import SwinIR as net
-from modules.swinir_model_arch_v2 import Swin2SR as net2
-from modules.upscaler import Upscaler, UpscalerData
-
-
-class UpscalerSwinIR(Upscaler):
-    def __init__(self, dirname):
-        self.name = "SwinIR"
-        self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
-                         "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
-                         "-L_x4_GAN.pth "
-        self.model_name = "SwinIR 4x"
-        self.user_path = dirname
-        super().__init__()
-        scalers = []
-        model_files = self.find_models(ext_filter=[".pt", ".pth"])
-        for model in model_files:
-            if "http" in model:
-                name = self.model_name
-            else:
-                name = modelloader.friendly_name(model)
-            model_data = UpscalerData(name, model, self)
-            scalers.append(model_data)
-        self.scalers = scalers
-
-    def do_upscale(self, img, model_file):
-        model = self.load_model(model_file)
-        if model is None:
-            return img
-        model = model.to(devices.device_swinir)
-        img = upscale(img, model)
-        try:
-            torch.cuda.empty_cache()
-        except:
-            pass
-        return img
-
-    def load_model(self, path, scale=4):
-        if "http" in path:
-            dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
-            filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True)
-        else:
-            filename = path
-        if filename is None or not os.path.exists(filename):
-            return None
-        if filename.endswith(".v2.pth"):
-            model = net2(
-            upscale=scale,
-            in_chans=3,
-            img_size=64,
-            window_size=8,
-            img_range=1.0,
-            depths=[6, 6, 6, 6, 6, 6],
-            embed_dim=180,
-            num_heads=[6, 6, 6, 6, 6, 6],
-            mlp_ratio=2,
-            upsampler="nearest+conv",
-            resi_connection="1conv",
-            )
-            params = None
-        else:
-            model = net(
-                upscale=scale,
-                in_chans=3,
-                img_size=64,
-                window_size=8,
-                img_range=1.0,
-                depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
-                embed_dim=240,
-                num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
-                mlp_ratio=2,
-                upsampler="nearest+conv",
-                resi_connection="3conv",
-            )
-            params = "params_ema"
-
-        pretrained_model = torch.load(filename)
-        if params is not None:
-            model.load_state_dict(pretrained_model[params], strict=True)
-        else:
-            model.load_state_dict(pretrained_model, strict=True)
-        if not cmd_opts.no_half:
-            model = model.half()
-        return model
-
-
-def upscale(
-        img,
-        model,
-        tile=opts.SWIN_tile,
-        tile_overlap=opts.SWIN_tile_overlap,
-        window_size=8,
-        scale=4,
-):
-    img = np.array(img)
-    img = img[:, :, ::-1]
-    img = np.moveaxis(img, 2, 0) / 255
-    img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(devices.device_swinir)
-    with torch.no_grad(), devices.autocast():
-        _, _, h_old, w_old = img.size()
-        h_pad = (h_old // window_size + 1) * window_size - h_old
-        w_pad = (w_old // window_size + 1) * window_size - w_old
-        img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
-        img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
-        output = inference(img, model, tile, tile_overlap, window_size, scale)
-        output = output[..., : h_old * scale, : w_old * scale]
-        output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
-        if output.ndim == 3:
-            output = np.transpose(
-                output[[2, 1, 0], :, :], (1, 2, 0)
-            )  # CHW-RGB to HCW-BGR
-        output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8
-        return Image.fromarray(output, "RGB")
-
-
-def inference(img, model, tile, tile_overlap, window_size, scale):
-    # test the image tile by tile
-    b, c, h, w = img.size()
-    tile = min(tile, h, w)
-    assert tile % window_size == 0, "tile size should be a multiple of window_size"
-    sf = scale
-
-    stride = tile - tile_overlap
-    h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
-    w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
-    E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=devices.device_swinir).type_as(img)
-    W = torch.zeros_like(E, dtype=torch.half, device=devices.device_swinir)
-
-    with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
-        for h_idx in h_idx_list:
-            for w_idx in w_idx_list:
-                in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
-                out_patch = model(in_patch)
-                out_patch_mask = torch.ones_like(out_patch)
-
-                E[
-                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-                ].add_(out_patch)
-                W[
-                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-                ].add_(out_patch_mask)
-                pbar.update(1)
-    output = E.div_(W)
-
-    return output
diff --git a/modules/swinir_model_arch.py b/modules/swinir_model_arch.py
deleted file mode 100644
index 863f42db..00000000
--- a/modules/swinir_model_arch.py
+++ /dev/null
@@ -1,867 +0,0 @@
-# -----------------------------------------------------------------------------------
-# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
-# Originally Written by Ze Liu, Modified by Jingyun Liang.
-# -----------------------------------------------------------------------------------
-
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        H, W = x_size
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        return attn_mask
-
-    def forward(self, x, x_size):
-        H, W = x_size
-        B, L, C = x.shape
-        # assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
-        else:
-            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.dim
-        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        return flops
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
-                 img_size=224, patch_size=4, resi_connection='1conv'):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(dim=dim,
-                                         input_resolution=input_resolution,
-                                         depth=depth,
-                                         num_heads=num_heads,
-                                         window_size=window_size,
-                                         mlp_ratio=mlp_ratio,
-                                         qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                         drop=drop, attn_drop=attn_drop,
-                                         drop_path=drop_path,
-                                         norm_layer=norm_layer,
-                                         downsample=downsample,
-                                         use_checkpoint=use_checkpoint)
-
-        if resi_connection == '1conv':
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-    def forward(self, x, x_size):
-        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        H, W = self.input_resolution
-        flops += H * W * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        flops = 0
-        H, W = self.img_size
-        if self.norm is not None:
-            flops += H * W * self.embed_dim
-        return flops
-
-
-class PatchUnEmbed(nn.Module):
-    r""" Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.num_feat * 3 * 9
-        return flops
-
-
-class SwinIR(nn.Module):
-    r""" SwinIR
-        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
-                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
-                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
-                 **kwargs):
-        super(SwinIR, self).__init__()
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-        self.window_size = window_size
-
-        #####################################################################################################
-        ################################### 1, shallow feature extraction ###################################
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        #####################################################################################################
-        ################################### 2, deep feature extraction ######################################
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(dim=embed_dim,
-                         input_resolution=(patches_resolution[0],
-                                           patches_resolution[1]),
-                         depth=depths[i_layer],
-                         num_heads=num_heads[i_layer],
-                         window_size=window_size,
-                         mlp_ratio=self.mlp_ratio,
-                         qkv_bias=qkv_bias, qk_scale=qk_scale,
-                         drop=drop_rate, attn_drop=attn_drop_rate,
-                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                         norm_layer=norm_layer,
-                         downsample=None,
-                         use_checkpoint=use_checkpoint,
-                         img_size=img_size,
-                         patch_size=patch_size,
-                         resi_connection=resi_connection
-
-                         )
-            self.layers.append(layer)
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == '1conv':
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
-
-        #####################################################################################################
-        ################################ 3, high quality image reconstruction ################################
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
-                                            (patches_resolution[0], patches_resolution[1]))
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR (less artifacts)
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            if self.upscale == 4:
-                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-        
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            if self.upscale == 4:
-                x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-
-        x = x / self.img_range + self.mean
-
-        return x[:, :, :H*self.upscale, :W*self.upscale]
-
-    def flops(self):
-        flops = 0
-        H, W = self.patches_resolution
-        flops += H * W * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()
-        flops += H * W * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()
-        return flops
-
-
-if __name__ == '__main__':
-    upscale = 4
-    window_size = 8
-    height = (1024 // upscale // window_size + 1) * window_size
-    width = (720 // upscale // window_size + 1) * window_size
-    model = SwinIR(upscale=2, img_size=(height, width),
-                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
-                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
-    print(model)
-    print(height, width, model.flops() / 1e9)
-
-    x = torch.randn((1, 3, height, width))
-    x = model(x)
-    print(x.shape)
diff --git a/modules/swinir_model_arch_v2.py b/modules/swinir_model_arch_v2.py
deleted file mode 100644
index 0e28ae6e..00000000
--- a/modules/swinir_model_arch_v2.py
+++ /dev/null
@@ -1,1017 +0,0 @@
-# -----------------------------------------------------------------------------------
-# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/
-# Written by Conde and Choi et al.
-# -----------------------------------------------------------------------------------
-
-import math
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
-                 pretrained_window_size=[0, 0]):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.pretrained_window_size = pretrained_window_size
-        self.num_heads = num_heads
-
-        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
-
-        # mlp to generate continuous relative position bias
-        self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
-                                     nn.ReLU(inplace=True),
-                                     nn.Linear(512, num_heads, bias=False))
-
-        # get relative_coords_table
-        relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
-        relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
-        relative_coords_table = torch.stack(
-            torch.meshgrid([relative_coords_h,
-                            relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
-        if pretrained_window_size[0] > 0:
-            relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
-            relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
-        else:
-            relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
-            relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
-        relative_coords_table *= 8  # normalize to -8, 8
-        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
-            torch.abs(relative_coords_table) + 1.0) / np.log2(8)
-
-        self.register_buffer("relative_coords_table", relative_coords_table)
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=False)
-        if qkv_bias:
-            self.q_bias = nn.Parameter(torch.zeros(dim))
-            self.v_bias = nn.Parameter(torch.zeros(dim))
-        else:
-            self.q_bias = None
-            self.v_bias = None
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv_bias = None
-        if self.q_bias is not None:
-            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
-        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
-        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        # cosine attention
-        attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
-        logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()
-        attn = attn * logit_scale
-
-        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
-        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, ' \
-               f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-        pretrained_window_size (int): Window size in pre-training.
-    """
-
-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
-            pretrained_window_size=to_2tuple(pretrained_window_size))
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-        
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        H, W = x_size
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        return attn_mask        
-
-    def forward(self, x, x_size):
-        H, W = x_size
-        B, L, C = x.shape
-        #assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
-        else:
-            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-            
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-        x = shortcut + self.drop_path(self.norm1(x))
-
-        # FFN
-        x = x + self.drop_path(self.norm2(self.mlp(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(2 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.reduction(x)
-        x = self.norm(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        flops += H * W * self.dim // 2
-        return flops    
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        pretrained_window_size (int): Local window size in pre-training.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
-                 pretrained_window_size=0):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer,
-                                 pretrained_window_size=pretrained_window_size)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-    def _init_respostnorm(self):
-        for blk in self.blocks:
-            nn.init.constant_(blk.norm1.bias, 0)
-            nn.init.constant_(blk.norm1.weight, 0)
-            nn.init.constant_(blk.norm2.bias, 0)
-            nn.init.constant_(blk.norm2.weight, 0)
-            
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        # assert H == self.img_size[0] and W == self.img_size[1],
-        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        Ho, Wo = self.patches_resolution
-        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
-        if self.norm is not None:
-            flops += Ho * Wo * self.embed_dim
-        return flops           
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
-                 img_size=224, patch_size=4, resi_connection='1conv'):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(dim=dim,
-                                         input_resolution=input_resolution,
-                                         depth=depth,
-                                         num_heads=num_heads,
-                                         window_size=window_size,
-                                         mlp_ratio=mlp_ratio,
-                                         qkv_bias=qkv_bias, 
-                                         drop=drop, attn_drop=attn_drop,
-                                         drop_path=drop_path,
-                                         norm_layer=norm_layer,
-                                         downsample=downsample,
-                                         use_checkpoint=use_checkpoint)
-
-        if resi_connection == '1conv':
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
-            norm_layer=None)
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
-            norm_layer=None)
-
-    def forward(self, x, x_size):
-        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        H, W = self.input_resolution
-        flops += H * W * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-class PatchUnEmbed(nn.Module):
-    r""" Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-        
-class Upsample_hf(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample_hf, self).__init__(*m)        
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.num_feat * 3 * 9
-        return flops
-    
-    
-
-class Swin2SR(nn.Module):
-    r""" Swin2SR
-        A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
-                 window_size=7, mlp_ratio=4., qkv_bias=True, 
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
-                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
-                 **kwargs):
-        super(Swin2SR, self).__init__()
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-        self.window_size = window_size
-
-        #####################################################################################################
-        ################################### 1, shallow feature extraction ###################################
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        #####################################################################################################
-        ################################### 2, deep feature extraction ######################################
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(dim=embed_dim,
-                         input_resolution=(patches_resolution[0],
-                                           patches_resolution[1]),
-                         depth=depths[i_layer],
-                         num_heads=num_heads[i_layer],
-                         window_size=window_size,
-                         mlp_ratio=self.mlp_ratio,
-                         qkv_bias=qkv_bias, 
-                         drop=drop_rate, attn_drop=attn_drop_rate,
-                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                         norm_layer=norm_layer,
-                         downsample=None,
-                         use_checkpoint=use_checkpoint,
-                         img_size=img_size,
-                         patch_size=patch_size,
-                         resi_connection=resi_connection
-
-                         )
-            self.layers.append(layer)
-            
-        if self.upsampler == 'pixelshuffle_hf':
-            self.layers_hf = nn.ModuleList()
-            for i_layer in range(self.num_layers):
-                layer = RSTB(dim=embed_dim,
-                             input_resolution=(patches_resolution[0],
-                                               patches_resolution[1]),
-                             depth=depths[i_layer],
-                             num_heads=num_heads[i_layer],
-                             window_size=window_size,
-                             mlp_ratio=self.mlp_ratio,
-                             qkv_bias=qkv_bias, 
-                             drop=drop_rate, attn_drop=attn_drop_rate,
-                             drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                             norm_layer=norm_layer,
-                             downsample=None,
-                             use_checkpoint=use_checkpoint,
-                             img_size=img_size,
-                             patch_size=patch_size,
-                             resi_connection=resi_connection
-
-                             )
-                self.layers_hf.append(layer)
-                        
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == '1conv':
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
-
-        #####################################################################################################
-        ################################ 3, high quality image reconstruction ################################
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == 'pixelshuffle_aux':
-            self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                nn.LeakyReLU(inplace=True))
-            self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.conv_after_aux = nn.Sequential(
-                nn.Conv2d(3, num_feat, 3, 1, 1),
-                nn.LeakyReLU(inplace=True))            
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            
-        elif self.upsampler == 'pixelshuffle_hf':
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.upsample = Upsample(upscale, num_feat)
-            self.upsample_hf = Upsample_hf(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-            self.conv_before_upsample_hf = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                nn.LeakyReLU(inplace=True))
-            self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
-                                            (patches_resolution[0], patches_resolution[1]))
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR (less artifacts)
-            assert self.upscale == 4, 'only support x4 now.'
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-    
-    def forward_features_hf(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers_hf:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x    
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == 'pixelshuffle_aux':
-            bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
-            bicubic = self.conv_bicubic(bicubic)
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            aux = self.conv_aux(x) # b, 3, LR_H, LR_W
-            x = self.conv_after_aux(aux)
-            x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
-            x = self.conv_last(x)
-            aux = aux / self.img_range + self.mean
-        elif self.upsampler == 'pixelshuffle_hf':
-            # for classical SR with HF
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x_before = self.conv_before_upsample(x)
-            x_out = self.conv_last(self.upsample(x_before))
-            
-            x_hf = self.conv_first_hf(x_before)
-            x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
-            x_hf = self.conv_before_upsample_hf(x_hf)
-            x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
-            x = x_out + x_hf
-            x_hf = x_hf / self.img_range + self.mean
-
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-        
-        x = x / self.img_range + self.mean
-        if self.upsampler == "pixelshuffle_aux":
-            return x[:, :, :H*self.upscale, :W*self.upscale], aux
-        
-        elif self.upsampler == "pixelshuffle_hf":
-            x_out = x_out / self.img_range + self.mean
-            return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
-        
-        else:
-            return x[:, :, :H*self.upscale, :W*self.upscale]
-
-    def flops(self):
-        flops = 0
-        H, W = self.patches_resolution
-        flops += H * W * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()
-        flops += H * W * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()
-        return flops
-
-
-if __name__ == '__main__':
-    upscale = 4
-    window_size = 8
-    height = (1024 // upscale // window_size + 1) * window_size
-    width = (720 // upscale // window_size + 1) * window_size
-    model = Swin2SR(upscale=2, img_size=(height, width),
-                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
-                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
-    print(model)
-    print(height, width, model.flops() / 1e9)
-
-    x = torch.randn((1, 3, height, width))
-    x = model(x)
-    print(x.shape)
\ No newline at end of file
diff --git a/modules/ui.py b/modules/ui.py
index 2eb0b684..3acb9b48 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -28,7 +28,6 @@ import modules.codeformer_model
 import modules.generation_parameters_copypaste as parameters_copypaste
 import modules.gfpgan_model
 import modules.hypernetworks.ui
-import modules.ldsr_model
 import modules.scripts
 import modules.shared as shared
 import modules.styles
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 42667941..b487ac25 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -78,6 +78,12 @@ def extension_table():
     """
 
     for ext in extensions.extensions:
+        remote = ""
+        if ext.is_builtin:
+            remote = "built-in"
+        elif ext.remote:
+            remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
+
         if ext.can_update:
             ext_status = f"""<label><input class="gr-check-radio gr-checkbox" name="update_{html.escape(ext.name)}" checked="checked" type="checkbox">{html.escape(ext.status)}</label>"""
         else:
@@ -86,7 +92,7 @@ def extension_table():
         code += f"""
             <tr>
                 <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
-                <td><a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape(ext.remote or '')}</a></td>
+                <td>{remote}</td>
                 <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
             </tr>
     """
diff --git a/webui.py b/webui.py
index 16e7ec1a..78204d11 100644
--- a/webui.py
+++ b/webui.py
@@ -53,10 +53,11 @@ def initialize():
     codeformer.setup_model(cmd_opts.codeformer_models_path)
     gfpgan.setup_model(cmd_opts.gfpgan_models_path)
     shared.face_restorers.append(modules.face_restoration.FaceRestoration())
-    modelloader.load_upscalers()
 
     modules.scripts.load_scripts()
 
+    modelloader.load_upscalers()
+
     modules.sd_vae.refresh_vae_list()
     modules.sd_models.load_model()
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
@@ -177,6 +178,8 @@ def webui():
 
         print('Reloading custom scripts')
         modules.scripts.reload_scripts()
+        modelloader.load_upscalers()
+
         print('Reloading modules: modules.ui')
         importlib.reload(modules.ui)
         print('Refreshing Model List')
-- 
cgit v1.2.1


From 0d21624ceef52b843c731ddc7fdcd7b8d108a42e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 18:16:19 +0300
Subject: move #5216 to the extension

---
 extensions-builtin/LDSR/scripts/ldsr_model.py    |   1 +
 extensions-builtin/LDSR/sd_hijack_autoencoder.py | 286 +++++++++++++++++++++++
 modules/sd_hijack.py                             |   2 +-
 modules/sd_hijack_autoencoder.py                 | 286 -----------------------
 4 files changed, 288 insertions(+), 287 deletions(-)
 create mode 100644 extensions-builtin/LDSR/sd_hijack_autoencoder.py
 delete mode 100644 modules/sd_hijack_autoencoder.py

diff --git a/extensions-builtin/LDSR/scripts/ldsr_model.py b/extensions-builtin/LDSR/scripts/ldsr_model.py
index 841ecba0..1cef29a4 100644
--- a/extensions-builtin/LDSR/scripts/ldsr_model.py
+++ b/extensions-builtin/LDSR/scripts/ldsr_model.py
@@ -7,6 +7,7 @@ from basicsr.utils.download_util import load_file_from_url
 from modules.upscaler import Upscaler, UpscalerData
 from ldsr_model_arch import LDSR
 from modules import shared, script_callbacks
+import sd_hijack_autoencoder
 
 
 class UpscalerLDSR(Upscaler):
diff --git a/extensions-builtin/LDSR/sd_hijack_autoencoder.py b/extensions-builtin/LDSR/sd_hijack_autoencoder.py
new file mode 100644
index 00000000..8e03c7f8
--- /dev/null
+++ b/extensions-builtin/LDSR/sd_hijack_autoencoder.py
@@ -0,0 +1,286 @@
+# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
+# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
+# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
+
+import torch
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from contextlib import contextmanager
+from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
+from ldm.modules.diffusionmodules.model import Encoder, Decoder
+from ldm.util import instantiate_from_config
+
+import ldm.models.autoencoder
+
+class VQModel(pl.LightningModule):
+    def __init__(self,
+                 ddconfig,
+                 lossconfig,
+                 n_embed,
+                 embed_dim,
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 image_key="image",
+                 colorize_nlabels=None,
+                 monitor=None,
+                 batch_resize_range=None,
+                 scheduler_config=None,
+                 lr_g_factor=1.0,
+                 remap=None,
+                 sane_index_shape=False, # tell vector quantizer to return indices as bhw
+                 use_ema=False
+                 ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.n_embed = n_embed
+        self.image_key = image_key
+        self.encoder = Encoder(**ddconfig)
+        self.decoder = Decoder(**ddconfig)
+        self.loss = instantiate_from_config(lossconfig)
+        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
+                                        remap=remap,
+                                        sane_index_shape=sane_index_shape)
+        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        if colorize_nlabels is not None:
+            assert type(colorize_nlabels)==int
+            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+        if monitor is not None:
+            self.monitor = monitor
+        self.batch_resize_range = batch_resize_range
+        if self.batch_resize_range is not None:
+            print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
+
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+        self.scheduler_config = scheduler_config
+        self.lr_g_factor = lr_g_factor
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.parameters())
+            self.model_ema.copy_to(self)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def init_from_ckpt(self, path, ignore_keys=list()):
+        sd = torch.load(path, map_location="cpu")["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        missing, unexpected = self.load_state_dict(sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+            print(f"Unexpected Keys: {unexpected}")
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self)
+
+    def encode(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        quant, emb_loss, info = self.quantize(h)
+        return quant, emb_loss, info
+
+    def encode_to_prequant(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        return h
+
+    def decode(self, quant):
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+    def decode_code(self, code_b):
+        quant_b = self.quantize.embed_code(code_b)
+        dec = self.decode(quant_b)
+        return dec
+
+    def forward(self, input, return_pred_indices=False):
+        quant, diff, (_,_,ind) = self.encode(input)
+        dec = self.decode(quant)
+        if return_pred_indices:
+            return dec, diff, ind
+        return dec, diff
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+        if self.batch_resize_range is not None:
+            lower_size = self.batch_resize_range[0]
+            upper_size = self.batch_resize_range[1]
+            if self.global_step <= 4:
+                # do the first few batches with max size to avoid later oom
+                new_resize = upper_size
+            else:
+                new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
+            if new_resize != x.shape[2]:
+                x = F.interpolate(x, size=new_resize, mode="bicubic")
+            x = x.detach()
+        return x
+
+    def training_step(self, batch, batch_idx, optimizer_idx):
+        # https://github.com/pytorch/pytorch/issues/37142
+        # try not to fool the heuristics
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss, ind = self(x, return_pred_indices=True)
+
+        if optimizer_idx == 0:
+            # autoencode
+            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train",
+                                            predicted_indices=ind)
+
+            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return aeloss
+
+        if optimizer_idx == 1:
+            # discriminator
+            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train")
+            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return discloss
+
+    def validation_step(self, batch, batch_idx):
+        log_dict = self._validation_step(batch, batch_idx)
+        with self.ema_scope():
+            log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
+        return log_dict
+
+    def _validation_step(self, batch, batch_idx, suffix=""):
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss, ind = self(x, return_pred_indices=True)
+        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
+                                        self.global_step,
+                                        last_layer=self.get_last_layer(),
+                                        split="val"+suffix,
+                                        predicted_indices=ind
+                                        )
+
+        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
+                                            self.global_step,
+                                            last_layer=self.get_last_layer(),
+                                            split="val"+suffix,
+                                            predicted_indices=ind
+                                            )
+        rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
+        self.log(f"val{suffix}/rec_loss", rec_loss,
+                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+        self.log(f"val{suffix}/aeloss", aeloss,
+                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+        if version.parse(pl.__version__) >= version.parse('1.4.0'):
+            del log_dict_ae[f"val{suffix}/rec_loss"]
+        self.log_dict(log_dict_ae)
+        self.log_dict(log_dict_disc)
+        return self.log_dict
+
+    def configure_optimizers(self):
+        lr_d = self.learning_rate
+        lr_g = self.lr_g_factor*self.learning_rate
+        print("lr_d", lr_d)
+        print("lr_g", lr_g)
+        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
+                                  list(self.decoder.parameters())+
+                                  list(self.quantize.parameters())+
+                                  list(self.quant_conv.parameters())+
+                                  list(self.post_quant_conv.parameters()),
+                                  lr=lr_g, betas=(0.5, 0.9))
+        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+                                    lr=lr_d, betas=(0.5, 0.9))
+
+        if self.scheduler_config is not None:
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                },
+                {
+                    'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                },
+            ]
+            return [opt_ae, opt_disc], scheduler
+        return [opt_ae, opt_disc], []
+
+    def get_last_layer(self):
+        return self.decoder.conv_out.weight
+
+    def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.image_key)
+        x = x.to(self.device)
+        if only_inputs:
+            log["inputs"] = x
+            return log
+        xrec, _ = self(x)
+        if x.shape[1] > 3:
+            # colorize with random projection
+            assert xrec.shape[1] > 3
+            x = self.to_rgb(x)
+            xrec = self.to_rgb(xrec)
+        log["inputs"] = x
+        log["reconstructions"] = xrec
+        if plot_ema:
+            with self.ema_scope():
+                xrec_ema, _ = self(x)
+                if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
+                log["reconstructions_ema"] = xrec_ema
+        return log
+
+    def to_rgb(self, x):
+        assert self.image_key == "segmentation"
+        if not hasattr(self, "colorize"):
+            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+        x = F.conv2d(x, weight=self.colorize)
+        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+        return x
+
+
+class VQModelInterface(VQModel):
+    def __init__(self, embed_dim, *args, **kwargs):
+        super().__init__(embed_dim=embed_dim, *args, **kwargs)
+        self.embed_dim = embed_dim
+
+    def encode(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        return h
+
+    def decode(self, h, force_not_quantize=False):
+        # also go through quantization layer
+        if not force_not_quantize:
+            quant, emb_loss, info = self.quantize(h)
+        else:
+            quant = h
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+setattr(ldm.models.autoencoder, "VQModel", VQModel)
+setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 303b1397..95a17093 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -11,7 +11,7 @@ import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.hypernetworks import hypernetwork
 from modules.shared import opts, device, cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_autoencoder
+from modules import sd_hijack_clip, sd_hijack_open_clip
 
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
diff --git a/modules/sd_hijack_autoencoder.py b/modules/sd_hijack_autoencoder.py
deleted file mode 100644
index 8e03c7f8..00000000
--- a/modules/sd_hijack_autoencoder.py
+++ /dev/null
@@ -1,286 +0,0 @@
-# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
-# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
-# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
-
-import torch
-import pytorch_lightning as pl
-import torch.nn.functional as F
-from contextlib import contextmanager
-from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.util import instantiate_from_config
-
-import ldm.models.autoencoder
-
-class VQModel(pl.LightningModule):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 n_embed,
-                 embed_dim,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None,
-                 monitor=None,
-                 batch_resize_range=None,
-                 scheduler_config=None,
-                 lr_g_factor=1.0,
-                 remap=None,
-                 sane_index_shape=False, # tell vector quantizer to return indices as bhw
-                 use_ema=False
-                 ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.n_embed = n_embed
-        self.image_key = image_key
-        self.encoder = Encoder(**ddconfig)
-        self.decoder = Decoder(**ddconfig)
-        self.loss = instantiate_from_config(lossconfig)
-        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
-                                        remap=remap,
-                                        sane_index_shape=sane_index_shape)
-        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
-        if colorize_nlabels is not None:
-            assert type(colorize_nlabels)==int
-            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
-        if monitor is not None:
-            self.monitor = monitor
-        self.batch_resize_range = batch_resize_range
-        if self.batch_resize_range is not None:
-            print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
-
-        self.use_ema = use_ema
-        if self.use_ema:
-            self.model_ema = LitEma(self)
-            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-        self.scheduler_config = scheduler_config
-        self.lr_g_factor = lr_g_factor
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.parameters())
-            self.model_ema.copy_to(self)
-            if context is not None:
-                print(f"{context}: Switched to EMA weights")
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.parameters())
-                if context is not None:
-                    print(f"{context}: Restored training weights")
-
-    def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location="cpu")["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-            print(f"Unexpected Keys: {unexpected}")
-
-    def on_train_batch_end(self, *args, **kwargs):
-        if self.use_ema:
-            self.model_ema(self)
-
-    def encode(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        quant, emb_loss, info = self.quantize(h)
-        return quant, emb_loss, info
-
-    def encode_to_prequant(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        return h
-
-    def decode(self, quant):
-        quant = self.post_quant_conv(quant)
-        dec = self.decoder(quant)
-        return dec
-
-    def decode_code(self, code_b):
-        quant_b = self.quantize.embed_code(code_b)
-        dec = self.decode(quant_b)
-        return dec
-
-    def forward(self, input, return_pred_indices=False):
-        quant, diff, (_,_,ind) = self.encode(input)
-        dec = self.decode(quant)
-        if return_pred_indices:
-            return dec, diff, ind
-        return dec, diff
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
-        if self.batch_resize_range is not None:
-            lower_size = self.batch_resize_range[0]
-            upper_size = self.batch_resize_range[1]
-            if self.global_step <= 4:
-                # do the first few batches with max size to avoid later oom
-                new_resize = upper_size
-            else:
-                new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
-            if new_resize != x.shape[2]:
-                x = F.interpolate(x, size=new_resize, mode="bicubic")
-            x = x.detach()
-        return x
-
-    def training_step(self, batch, batch_idx, optimizer_idx):
-        # https://github.com/pytorch/pytorch/issues/37142
-        # try not to fool the heuristics
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss, ind = self(x, return_pred_indices=True)
-
-        if optimizer_idx == 0:
-            # autoencode
-            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train",
-                                            predicted_indices=ind)
-
-            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return aeloss
-
-        if optimizer_idx == 1:
-            # discriminator
-            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
-            return discloss
-
-    def validation_step(self, batch, batch_idx):
-        log_dict = self._validation_step(batch, batch_idx)
-        with self.ema_scope():
-            log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
-        return log_dict
-
-    def _validation_step(self, batch, batch_idx, suffix=""):
-        x = self.get_input(batch, self.image_key)
-        xrec, qloss, ind = self(x, return_pred_indices=True)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
-                                        self.global_step,
-                                        last_layer=self.get_last_layer(),
-                                        split="val"+suffix,
-                                        predicted_indices=ind
-                                        )
-
-        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
-                                            self.global_step,
-                                            last_layer=self.get_last_layer(),
-                                            split="val"+suffix,
-                                            predicted_indices=ind
-                                            )
-        rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
-        self.log(f"val{suffix}/rec_loss", rec_loss,
-                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
-        self.log(f"val{suffix}/aeloss", aeloss,
-                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
-        if version.parse(pl.__version__) >= version.parse('1.4.0'):
-            del log_dict_ae[f"val{suffix}/rec_loss"]
-        self.log_dict(log_dict_ae)
-        self.log_dict(log_dict_disc)
-        return self.log_dict
-
-    def configure_optimizers(self):
-        lr_d = self.learning_rate
-        lr_g = self.lr_g_factor*self.learning_rate
-        print("lr_d", lr_d)
-        print("lr_g", lr_g)
-        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quantize.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=lr_g, betas=(0.5, 0.9))
-        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
-                                    lr=lr_d, betas=(0.5, 0.9))
-
-        if self.scheduler_config is not None:
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                },
-                {
-                    'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                },
-            ]
-            return [opt_ae, opt_disc], scheduler
-        return [opt_ae, opt_disc], []
-
-    def get_last_layer(self):
-        return self.decoder.conv_out.weight
-
-    def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.image_key)
-        x = x.to(self.device)
-        if only_inputs:
-            log["inputs"] = x
-            return log
-        xrec, _ = self(x)
-        if x.shape[1] > 3:
-            # colorize with random projection
-            assert xrec.shape[1] > 3
-            x = self.to_rgb(x)
-            xrec = self.to_rgb(xrec)
-        log["inputs"] = x
-        log["reconstructions"] = xrec
-        if plot_ema:
-            with self.ema_scope():
-                xrec_ema, _ = self(x)
-                if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
-                log["reconstructions_ema"] = xrec_ema
-        return log
-
-    def to_rgb(self, x):
-        assert self.image_key == "segmentation"
-        if not hasattr(self, "colorize"):
-            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
-        x = F.conv2d(x, weight=self.colorize)
-        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
-        return x
-
-
-class VQModelInterface(VQModel):
-    def __init__(self, embed_dim, *args, **kwargs):
-        super().__init__(embed_dim=embed_dim, *args, **kwargs)
-        self.embed_dim = embed_dim
-
-    def encode(self, x):
-        h = self.encoder(x)
-        h = self.quant_conv(h)
-        return h
-
-    def decode(self, h, force_not_quantize=False):
-        # also go through quantization layer
-        if not force_not_quantize:
-            quant, emb_loss, info = self.quantize(h)
-        else:
-            quant = h
-        quant = self.post_quant_conv(quant)
-        dec = self.decoder(quant)
-        return dec
-
-setattr(ldm.models.autoencoder, "VQModel", VQModel)
-setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)
-- 
cgit v1.2.1


From 4b0dc206edbad90affe609ac0bf2e9be7e197674 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 18:45:51 +0300
Subject: use modelloader for #4956

---
 modules/interrogate.py | 22 ++++++++--------------
 1 file changed, 8 insertions(+), 14 deletions(-)

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 3a09b366..0068b81c 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -1,4 +1,3 @@
-import contextlib
 import os
 import sys
 import traceback
@@ -11,12 +10,9 @@ from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 
 import modules.shared as shared
-from modules import devices, paths, lowvram
+from modules import devices, paths, lowvram, modelloader
 
 blip_image_eval_size = 384
-blip_local_dir = os.path.join('models', 'Interrogator')
-blip_local_file = os.path.join(blip_local_dir, 'model_base_caption_capfilt_large.pth')
-blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
 clip_model_name = 'ViT-L/14'
 
 Category = namedtuple("Category", ["name", "topn", "items"])
@@ -49,16 +45,14 @@ class InterrogateModels:
     def load_blip_model(self):
         import models.blip
 
-        if not os.path.isfile(blip_local_file):
-            if not os.path.isdir(blip_local_dir):
-                os.mkdir(blip_local_dir)
+        files = modelloader.load_models(
+            model_path=os.path.join(paths.models_path, "BLIP"),
+            model_url='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth',
+            ext_filter=[".pth"],
+            download_name='model_base_caption_capfilt_large.pth',
+        )
 
-            print("Downloading BLIP...")
-            from requests import get as reqget
-            open(blip_local_file, 'wb').write(reqget(blip_model_url, allow_redirects=True).content)
-            print("BLIP downloaded to", blip_local_file + '.')
-
-        blip_model = models.blip.blip_decoder(pretrained=blip_local_file, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
+        blip_model = models.blip.blip_decoder(pretrained=files[0], image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
         blip_model.eval()
 
         return blip_model
-- 
cgit v1.2.1


From 60bd4d52a658838c5ee3f6ddfe8d4db55cf1d764 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 18:46:09 +0300
Subject: fix incorrect file extension filter for deepdanbooru models

---
 modules/deepbooru.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 31ec7e17..dfc83357 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -21,7 +21,7 @@ class DeepDanbooru:
         files = modelloader.load_models(
             model_path=os.path.join(paths.models_path, "torch_deepdanbooru"),
             model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt',
-            ext_filter=".pt",
+            ext_filter=[".pt"],
             download_name='model-resnet_custom_v3.pt',
         )
 
-- 
cgit v1.2.1


From cefb5d6d7dbb35e68467bb7965f7139abfaf290d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 3 Dec 2022 20:40:11 +0300
Subject: fix accessing options when they are not ready for SwinIR.

---
 extensions-builtin/SwinIR/scripts/swinir_model.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py
index 782769e2..9a74b253 100644
--- a/extensions-builtin/SwinIR/scripts/swinir_model.py
+++ b/extensions-builtin/SwinIR/scripts/swinir_model.py
@@ -99,11 +99,15 @@ class UpscalerSwinIR(Upscaler):
 def upscale(
         img,
         model,
-        tile=opts.SWIN_tile,
-        tile_overlap=opts.SWIN_tile_overlap,
+        tile=None,
+        tile_overlap=None,
         window_size=8,
         scale=4,
 ):
+    tile = tile or opts.SWIN_tile
+    tile_overlap = tile_overlap or opts.SWIN_tile_overlap
+
+
     img = np.array(img)
     img = img[:, :, ::-1]
     img = np.moveaxis(img, 2, 0) / 255
-- 
cgit v1.2.1


From 8504db51704d238cc7616f6bf59eb049d3eb101d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 4 Dec 2022 01:04:24 +0300
Subject: fix #4459 breaking inpainting when the option is not specified.

---
 modules/img2img.py | 17 +++++++++--------
 modules/ui.py      | 25 ++++++++++++++-----------
 2 files changed, 23 insertions(+), 19 deletions(-)

diff --git a/modules/img2img.py b/modules/img2img.py
index 830cfa15..81da4b13 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -4,7 +4,7 @@ import sys
 import traceback
 
 import numpy as np
-from PIL import Image, ImageOps, ImageFilter, ImageEnhance
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
 
 from modules import devices, sd_samplers
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
@@ -66,22 +66,23 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
     if is_inpaint:
         # Drawn mask
         if mask_mode == 0:
-            image = init_img_with_mask
-            is_mask_sketch = isinstance(image, dict)
+            is_mask_sketch = isinstance(init_img_with_mask, dict)
             is_mask_paint = not is_mask_sketch
             if is_mask_sketch:
                 # Sketch: mask iff. not transparent
-                image, mask = image["image"], image["mask"]
-                pred = np.array(mask)[..., -1] > 0
+                image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
+                alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
+                mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
             else:
                 # Color-sketch: mask iff. painted over
-                orig = init_img_with_mask_orig or image
+                image = init_img_with_mask
+                orig = init_img_with_mask_orig or init_img_with_mask
                 pred = np.any(np.array(image) != np.array(orig), axis=-1)
-            mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
-            if is_mask_paint:
+                mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
                 mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
                 blur = ImageFilter.GaussianBlur(mask_blur)
                 image = Image.composite(image.filter(blur), orig, mask.filter(blur))
+
             image = image.convert("RGB")
         # Uploaded mask
         else:
diff --git a/modules/ui.py b/modules/ui.py
index 3acb9b48..b2b8de90 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -791,23 +791,26 @@ def create_ui():
                         init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
 
                     with gr.TabItem('Inpaint', id='inpaint'):
-                        init_img_with_mask_orig = gr.State(None)
                         init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_inpaint_tool, image_mode="RGBA").style(height=480)
+                        init_img_with_mask_orig = gr.State(None)
 
-                        def update_orig(image, state):
-                            if image is not None:
-                                same_size = state is not None and state.size == image.size
-                                has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
-                                edited = same_size and has_exact_match
-                                return image if not edited or state is None else state
+                        use_color_sketch = cmd_opts.gradio_inpaint_tool == "color-sketch"
+                        if use_color_sketch:
+                            def update_orig(image, state):
+                                if image is not None:
+                                    same_size = state is not None and state.size == image.size
+                                    has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+                                    edited = same_size and has_exact_match
+                                    return image if not edited or state is None else state
+
+                            init_img_with_mask.change(update_orig, [init_img_with_mask, init_img_with_mask_orig], init_img_with_mask_orig)
 
-                        init_img_with_mask.change(update_orig, [init_img_with_mask, init_img_with_mask_orig], init_img_with_mask_orig)
                         init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
                         init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
 
-                        show_mask_alpha = cmd_opts.gradio_inpaint_tool == "color-sketch"
-                        mask_alpha = gr.Slider(label="Mask transparency", interactive=show_mask_alpha, visible=show_mask_alpha)
-                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+                        with gr.Row():
+                            mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
+                            mask_alpha = gr.Slider(label="Mask transparency", interactive=use_color_sketch, visible=use_color_sketch)
 
                         with gr.Row():
                             mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
-- 
cgit v1.2.1


From 44c46f0ed395967cd3830dd481a2db759fda5b3b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 4 Dec 2022 12:30:44 +0300
Subject: make it possible to merge inpainting model with non-inpainting one

---
 modules/extras.py | 27 +++++++++++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 6021a024..bc349d5e 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -247,6 +247,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
     teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
+    result_is_inpainting_model = False
 
     print(f"Loading {primary_model_info.filename}...")
     theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
@@ -280,8 +281,22 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
+            a = theta_0[key]
+            b = theta_1[key]
 
-            theta_0[key] = theta_func2(theta_0[key], theta_1[key], multiplier)
+            # this enables merging an inpainting model (A) with another one (B);
+            # where normal model would have 4 channels, for latenst space, inpainting model would
+            # have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9
+            if a.shape != b.shape and a.shape[0:1] + a.shape[2:] == b.shape[0:1] + b.shape[2:]:
+                if a.shape[1] == 4 and b.shape[1] == 9:
+                    raise RuntimeError("When merging inpainting model with a normal one, A must be the inpainting model.")
+
+                assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
+
+                theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
+                result_is_inpainting_model = True
+            else:
+                theta_0[key] = theta_func2(a, b, multiplier)
 
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
@@ -295,8 +310,16 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
 
-    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.' + checkpoint_format
+    filename = \
+        primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + \
+        secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + \
+        interp_method.replace(" ", "_") + \
+        '-merged.' +  \
+        ("inpainting." if result_is_inpainting_model else "") + \
+        checkpoint_format
+
     filename = filename if custom_name == '' else (custom_name + '.' + checkpoint_format)
+
     output_modelname = os.path.join(ckpt_dir, filename)
 
     print(f"Saving to {output_modelname}...")
-- 
cgit v1.2.1


From a8ae263c695c1da8d05b2140e2225babcfb0a387 Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Sun, 4 Dec 2022 13:42:19 +0000
Subject: Reinstate DDPM V1 to LDSR

---
 extensions-builtin/LDSR/ldsr_model_arch.py    |    1 +
 extensions-builtin/LDSR/scripts/ldsr_model.py |    2 +-
 extensions-builtin/LDSR/sd_hijack_ddpm_v1.py  | 1449 +++++++++++++++++++++++++
 3 files changed, 1451 insertions(+), 1 deletion(-)
 create mode 100644 extensions-builtin/LDSR/sd_hijack_ddpm_v1.py

diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py
index 90e0a2f0..a87d1ef9 100644
--- a/extensions-builtin/LDSR/ldsr_model_arch.py
+++ b/extensions-builtin/LDSR/ldsr_model_arch.py
@@ -22,6 +22,7 @@ class LDSR:
         pl_sd = torch.load(self.modelPath, map_location="cpu")
         sd = pl_sd["state_dict"]
         config = OmegaConf.load(self.yamlPath)
+        config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
         model = instantiate_from_config(config.model)
         model.load_state_dict(sd, strict=False)
         model.cuda()
diff --git a/extensions-builtin/LDSR/scripts/ldsr_model.py b/extensions-builtin/LDSR/scripts/ldsr_model.py
index 1cef29a4..5c96037d 100644
--- a/extensions-builtin/LDSR/scripts/ldsr_model.py
+++ b/extensions-builtin/LDSR/scripts/ldsr_model.py
@@ -7,7 +7,7 @@ from basicsr.utils.download_util import load_file_from_url
 from modules.upscaler import Upscaler, UpscalerData
 from ldsr_model_arch import LDSR
 from modules import shared, script_callbacks
-import sd_hijack_autoencoder
+import sd_hijack_autoencoder, sd_hijack_ddpm_v1
 
 
 class UpscalerLDSR(Upscaler):
diff --git a/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
new file mode 100644
index 00000000..5c0488e5
--- /dev/null
+++ b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
@@ -0,0 +1,1449 @@
+# This script is copied from the compvis/stable-diffusion repo (aka the SD V1 repo)
+# Original filename: ldm/models/diffusion/ddpm.py
+# The purpose to reinstate the old DDPM logic which works with VQ, whereas the V2 one doesn't
+# Some models such as LDSR require VQ to work correctly
+# The classes are suffixed with "V1" and added back to the "ldm.models.diffusion.ddpm" module
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager
+from functools import partial
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+
+import ldm.models.diffusion.ddpm
+
+__conditioning_keys__ = {'concat': 'c_concat',
+                         'crossattn': 'c_crossattn',
+                         'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+    return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+class DDPMV1(pl.LightningModule):
+    # classic DDPM with Gaussian diffusion, in image space
+    def __init__(self,
+                 unet_config,
+                 timesteps=1000,
+                 beta_schedule="linear",
+                 loss_type="l2",
+                 ckpt_path=None,
+                 ignore_keys=[],
+                 load_only_unet=False,
+                 monitor="val/loss",
+                 use_ema=True,
+                 first_stage_key="image",
+                 image_size=256,
+                 channels=3,
+                 log_every_t=100,
+                 clip_denoised=True,
+                 linear_start=1e-4,
+                 linear_end=2e-2,
+                 cosine_s=8e-3,
+                 given_betas=None,
+                 original_elbo_weight=0.,
+                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+                 l_simple_weight=1.,
+                 conditioning_key=None,
+                 parameterization="eps",  # all assuming fixed variance schedules
+                 scheduler_config=None,
+                 use_positional_encodings=False,
+                 learn_logvar=False,
+                 logvar_init=0.,
+                 ):
+        super().__init__()
+        assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
+        self.parameterization = parameterization
+        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+        self.cond_stage_model = None
+        self.clip_denoised = clip_denoised
+        self.log_every_t = log_every_t
+        self.first_stage_key = first_stage_key
+        self.image_size = image_size  # try conv?
+        self.channels = channels
+        self.use_positional_encodings = use_positional_encodings
+        self.model = DiffusionWrapperV1(unet_config, conditioning_key)
+        count_params(self.model, verbose=True)
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self.model)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        self.use_scheduler = scheduler_config is not None
+        if self.use_scheduler:
+            self.scheduler_config = scheduler_config
+
+        self.v_posterior = v_posterior
+        self.original_elbo_weight = original_elbo_weight
+        self.l_simple_weight = l_simple_weight
+
+        if monitor is not None:
+            self.monitor = monitor
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+
+        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+        self.loss_type = loss_type
+
+        self.learn_logvar = learn_logvar
+        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+        if self.learn_logvar:
+            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+
+
+    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        if exists(given_betas):
+            betas = given_betas
+        else:
+            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+                                       cosine_s=cosine_s)
+        alphas = 1. - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+        timesteps, = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.linear_start = linear_start
+        self.linear_end = linear_end
+        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        self.register_buffer('betas', to_torch(betas))
+        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+                    1. - alphas_cumprod) + self.v_posterior * betas
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.register_buffer('posterior_variance', to_torch(posterior_variance))
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+        self.register_buffer('posterior_mean_coef1', to_torch(
+            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+        self.register_buffer('posterior_mean_coef2', to_torch(
+            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+        if self.parameterization == "eps":
+            lvlb_weights = self.betas ** 2 / (
+                        2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+        elif self.parameterization == "x0":
+            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+        else:
+            raise NotImplementedError("mu not supported")
+        # TODO how to choose this term
+        lvlb_weights[0] = lvlb_weights[1]
+        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+        assert not torch.isnan(self.lvlb_weights).all()
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.model.parameters())
+            self.model_ema.copy_to(self.model)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.model.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+        sd = torch.load(path, map_location="cpu")
+        if "state_dict" in list(sd.keys()):
+            sd = sd["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+            sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+        if len(unexpected) > 0:
+            print(f"Unexpected Keys: {unexpected}")
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def predict_start_from_noise(self, x_t, t, noise):
+        return (
+                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+        )
+
+    def q_posterior(self, x_start, x_t, t):
+        posterior_mean = (
+                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, x, t, clip_denoised: bool):
+        model_out = self.model(x, t)
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+        b, *_, device = *x.shape, x.device
+        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+        noise = noise_like(x.shape, device, repeat_noise)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def p_sample_loop(self, shape, return_intermediates=False):
+        device = self.betas.device
+        b = shape[0]
+        img = torch.randn(shape, device=device)
+        intermediates = [img]
+        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+                                clip_denoised=self.clip_denoised)
+            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+                intermediates.append(img)
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, batch_size=16, return_intermediates=False):
+        image_size = self.image_size
+        channels = self.channels
+        return self.p_sample_loop((batch_size, channels, image_size, image_size),
+                                  return_intermediates=return_intermediates)
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+    def get_loss(self, pred, target, mean=True):
+        if self.loss_type == 'l1':
+            loss = (target - pred).abs()
+            if mean:
+                loss = loss.mean()
+        elif self.loss_type == 'l2':
+            if mean:
+                loss = torch.nn.functional.mse_loss(target, pred)
+            else:
+                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+        else:
+            raise NotImplementedError("unknown loss type '{loss_type}'")
+
+        return loss
+
+    def p_losses(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_out = self.model(x_noisy, t)
+
+        loss_dict = {}
+        if self.parameterization == "eps":
+            target = noise
+        elif self.parameterization == "x0":
+            target = x_start
+        else:
+            raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
+
+        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
+
+        log_prefix = 'train' if self.training else 'val'
+
+        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
+        loss_simple = loss.mean() * self.l_simple_weight
+
+        loss_vlb = (self.lvlb_weights[t] * loss).mean()
+        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
+
+        loss = loss_simple + self.original_elbo_weight * loss_vlb
+
+        loss_dict.update({f'{log_prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def forward(self, x, *args, **kwargs):
+        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        return self.p_losses(x, t, *args, **kwargs)
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = rearrange(x, 'b h w c -> b c h w')
+        x = x.to(memory_format=torch.contiguous_format).float()
+        return x
+
+    def shared_step(self, batch):
+        x = self.get_input(batch, self.first_stage_key)
+        loss, loss_dict = self(x)
+        return loss, loss_dict
+
+    def training_step(self, batch, batch_idx):
+        loss, loss_dict = self.shared_step(batch)
+
+        self.log_dict(loss_dict, prog_bar=True,
+                      logger=True, on_step=True, on_epoch=True)
+
+        self.log("global_step", self.global_step,
+                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        if self.use_scheduler:
+            lr = self.optimizers().param_groups[0]['lr']
+            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        return loss
+
+    @torch.no_grad()
+    def validation_step(self, batch, batch_idx):
+        _, loss_dict_no_ema = self.shared_step(batch)
+        with self.ema_scope():
+            _, loss_dict_ema = self.shared_step(batch)
+            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self.model)
+
+    def _get_rows_from_list(self, samples):
+        n_imgs_per_row = len(samples)
+        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+        log = dict()
+        x = self.get_input(batch, self.first_stage_key)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        x = x.to(self.device)[:N]
+        log["inputs"] = x
+
+        # get diffusion row
+        diffusion_row = list()
+        x_start = x[:n_row]
+
+        for t in range(self.num_timesteps):
+            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                t = t.to(self.device).long()
+                noise = torch.randn_like(x_start)
+                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+                diffusion_row.append(x_noisy)
+
+        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+        if sample:
+            # get denoise row
+            with self.ema_scope("Plotting"):
+                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+            log["samples"] = samples
+            log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.learn_logvar:
+            params = params + [self.logvar]
+        opt = torch.optim.AdamW(params, lr=lr)
+        return opt
+
+
+class LatentDiffusionV1(DDPMV1):
+    """main class"""
+    def __init__(self,
+                 first_stage_config,
+                 cond_stage_config,
+                 num_timesteps_cond=None,
+                 cond_stage_key="image",
+                 cond_stage_trainable=False,
+                 concat_mode=True,
+                 cond_stage_forward=None,
+                 conditioning_key=None,
+                 scale_factor=1.0,
+                 scale_by_std=False,
+                 *args, **kwargs):
+        self.num_timesteps_cond = default(num_timesteps_cond, 1)
+        self.scale_by_std = scale_by_std
+        assert self.num_timesteps_cond <= kwargs['timesteps']
+        # for backwards compatibility after implementation of DiffusionWrapper
+        if conditioning_key is None:
+            conditioning_key = 'concat' if concat_mode else 'crossattn'
+        if cond_stage_config == '__is_unconditional__':
+            conditioning_key = None
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        ignore_keys = kwargs.pop("ignore_keys", [])
+        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+        self.concat_mode = concat_mode
+        self.cond_stage_trainable = cond_stage_trainable
+        self.cond_stage_key = cond_stage_key
+        try:
+            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+        except:
+            self.num_downs = 0
+        if not scale_by_std:
+            self.scale_factor = scale_factor
+        else:
+            self.register_buffer('scale_factor', torch.tensor(scale_factor))
+        self.instantiate_first_stage(first_stage_config)
+        self.instantiate_cond_stage(cond_stage_config)
+        self.cond_stage_forward = cond_stage_forward
+        self.clip_denoised = False
+        self.bbox_tokenizer = None  
+
+        self.restarted_from_ckpt = False
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys)
+            self.restarted_from_ckpt = True
+
+    def make_cond_schedule(self, ):
+        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+        self.cond_ids[:self.num_timesteps_cond] = ids
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+        # only for very first batch
+        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+            # set rescale weight to 1./std of encodings
+            print("### USING STD-RESCALING ###")
+            x = super().get_input(batch, self.first_stage_key)
+            x = x.to(self.device)
+            encoder_posterior = self.encode_first_stage(x)
+            z = self.get_first_stage_encoding(encoder_posterior).detach()
+            del self.scale_factor
+            self.register_buffer('scale_factor', 1. / z.flatten().std())
+            print(f"setting self.scale_factor to {self.scale_factor}")
+            print("### USING STD-RESCALING ###")
+
+    def register_schedule(self,
+                          given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+        self.shorten_cond_schedule = self.num_timesteps_cond > 1
+        if self.shorten_cond_schedule:
+            self.make_cond_schedule()
+
+    def instantiate_first_stage(self, config):
+        model = instantiate_from_config(config)
+        self.first_stage_model = model.eval()
+        self.first_stage_model.train = disabled_train
+        for param in self.first_stage_model.parameters():
+            param.requires_grad = False
+
+    def instantiate_cond_stage(self, config):
+        if not self.cond_stage_trainable:
+            if config == "__is_first_stage__":
+                print("Using first stage also as cond stage.")
+                self.cond_stage_model = self.first_stage_model
+            elif config == "__is_unconditional__":
+                print(f"Training {self.__class__.__name__} as an unconditional model.")
+                self.cond_stage_model = None
+                # self.be_unconditional = True
+            else:
+                model = instantiate_from_config(config)
+                self.cond_stage_model = model.eval()
+                self.cond_stage_model.train = disabled_train
+                for param in self.cond_stage_model.parameters():
+                    param.requires_grad = False
+        else:
+            assert config != '__is_first_stage__'
+            assert config != '__is_unconditional__'
+            model = instantiate_from_config(config)
+            self.cond_stage_model = model
+
+    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+        denoise_row = []
+        for zd in tqdm(samples, desc=desc):
+            denoise_row.append(self.decode_first_stage(zd.to(self.device),
+                                                            force_not_quantize=force_no_decoder_quantization))
+        n_imgs_per_row = len(denoise_row)
+        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
+        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    def get_first_stage_encoding(self, encoder_posterior):
+        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+            z = encoder_posterior.sample()
+        elif isinstance(encoder_posterior, torch.Tensor):
+            z = encoder_posterior
+        else:
+            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+        return self.scale_factor * z
+
+    def get_learned_conditioning(self, c):
+        if self.cond_stage_forward is None:
+            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+                c = self.cond_stage_model.encode(c)
+                if isinstance(c, DiagonalGaussianDistribution):
+                    c = c.mode()
+            else:
+                c = self.cond_stage_model(c)
+        else:
+            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+        return c
+
+    def meshgrid(self, h, w):
+        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+        arr = torch.cat([y, x], dim=-1)
+        return arr
+
+    def delta_border(self, h, w):
+        """
+        :param h: height
+        :param w: width
+        :return: normalized distance to image border,
+         wtith min distance = 0 at border and max dist = 0.5 at image center
+        """
+        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+        arr = self.meshgrid(h, w) / lower_right_corner
+        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+        return edge_dist
+
+    def get_weighting(self, h, w, Ly, Lx, device):
+        weighting = self.delta_border(h, w)
+        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+                               self.split_input_params["clip_max_weight"], )
+        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+        if self.split_input_params["tie_braker"]:
+            L_weighting = self.delta_border(Ly, Lx)
+            L_weighting = torch.clip(L_weighting,
+                                     self.split_input_params["clip_min_tie_weight"],
+                                     self.split_input_params["clip_max_tie_weight"])
+
+            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+            weighting = weighting * L_weighting
+        return weighting
+
+    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
+        """
+        :param x: img of size (bs, c, h, w)
+        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+        """
+        bs, nc, h, w = x.shape
+
+        # number of crops in image
+        Ly = (h - kernel_size[0]) // stride[0] + 1
+        Lx = (w - kernel_size[1]) // stride[1] + 1
+
+        if uf == 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+        elif uf > 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+                                dilation=1, padding=0,
+                                stride=(stride[0] * uf, stride[1] * uf))
+            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+        elif df > 1 and uf == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+                                dilation=1, padding=0,
+                                stride=(stride[0] // df, stride[1] // df))
+            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+        else:
+            raise NotImplementedError
+
+        return fold, unfold, normalization, weighting
+
+    @torch.no_grad()
+    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+                  cond_key=None, return_original_cond=False, bs=None):
+        x = super().get_input(batch, k)
+        if bs is not None:
+            x = x[:bs]
+        x = x.to(self.device)
+        encoder_posterior = self.encode_first_stage(x)
+        z = self.get_first_stage_encoding(encoder_posterior).detach()
+
+        if self.model.conditioning_key is not None:
+            if cond_key is None:
+                cond_key = self.cond_stage_key
+            if cond_key != self.first_stage_key:
+                if cond_key in ['caption', 'coordinates_bbox']:
+                    xc = batch[cond_key]
+                elif cond_key == 'class_label':
+                    xc = batch
+                else:
+                    xc = super().get_input(batch, cond_key).to(self.device)
+            else:
+                xc = x
+            if not self.cond_stage_trainable or force_c_encode:
+                if isinstance(xc, dict) or isinstance(xc, list):
+                    # import pudb; pudb.set_trace()
+                    c = self.get_learned_conditioning(xc)
+                else:
+                    c = self.get_learned_conditioning(xc.to(self.device))
+            else:
+                c = xc
+            if bs is not None:
+                c = c[:bs]
+
+            if self.use_positional_encodings:
+                pos_x, pos_y = self.compute_latent_shifts(batch)
+                ckey = __conditioning_keys__[self.model.conditioning_key]
+                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
+
+        else:
+            c = None
+            xc = None
+            if self.use_positional_encodings:
+                pos_x, pos_y = self.compute_latent_shifts(batch)
+                c = {'pos_x': pos_x, 'pos_y': pos_y}
+        out = [z, c]
+        if return_first_stage_outputs:
+            xrec = self.decode_first_stage(z)
+            out.extend([x, xrec])
+        if return_original_cond:
+            out.append(xc)
+        return out
+
+    @torch.no_grad()
+    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+        if predict_cids:
+            if z.dim() == 4:
+                z = torch.argmax(z.exp(), dim=1).long()
+            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+            z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+        z = 1. / self.scale_factor * z
+
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                uf = self.split_input_params["vqf"]
+                bs, nc, h, w = z.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+                z = unfold(z)  # (bn, nc * prod(**ks), L)
+                # 1. Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                # 2. apply model loop over last dim
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+                                                                 force_not_quantize=predict_cids or force_not_quantize)
+                                   for i in range(z.shape[-1])]
+                else:
+
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+                                   for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
+                o = o * weighting
+                # Reverse 1. reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
+                return decoded
+            else:
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+                else:
+                    return self.first_stage_model.decode(z)
+
+        else:
+            if isinstance(self.first_stage_model, VQModelInterface):
+                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+            else:
+                return self.first_stage_model.decode(z)
+
+    # same as above but without decorator
+    def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+        if predict_cids:
+            if z.dim() == 4:
+                z = torch.argmax(z.exp(), dim=1).long()
+            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+            z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+        z = 1. / self.scale_factor * z
+
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                uf = self.split_input_params["vqf"]
+                bs, nc, h, w = z.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+                z = unfold(z)  # (bn, nc * prod(**ks), L)
+                # 1. Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                # 2. apply model loop over last dim
+                if isinstance(self.first_stage_model, VQModelInterface):  
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+                                                                 force_not_quantize=predict_cids or force_not_quantize)
+                                   for i in range(z.shape[-1])]
+                else:
+
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+                                   for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
+                o = o * weighting
+                # Reverse 1. reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
+                return decoded
+            else:
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+                else:
+                    return self.first_stage_model.decode(z)
+
+        else:
+            if isinstance(self.first_stage_model, VQModelInterface):
+                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+            else:
+                return self.first_stage_model.decode(z)
+
+    @torch.no_grad()
+    def encode_first_stage(self, x):
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                df = self.split_input_params["vqf"]
+                self.split_input_params['original_image_size'] = x.shape[-2:]
+                bs, nc, h, w = x.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
+                z = unfold(x)  # (bn, nc * prod(**ks), L)
+                # Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
+                               for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)
+                o = o * weighting
+
+                # Reverse reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization
+                return decoded
+
+            else:
+                return self.first_stage_model.encode(x)
+        else:
+            return self.first_stage_model.encode(x)
+
+    def shared_step(self, batch, **kwargs):
+        x, c = self.get_input(batch, self.first_stage_key)
+        loss = self(x, c)
+        return loss
+
+    def forward(self, x, c, *args, **kwargs):
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        if self.model.conditioning_key is not None:
+            assert c is not None
+            if self.cond_stage_trainable:
+                c = self.get_learned_conditioning(c)
+            if self.shorten_cond_schedule:  # TODO: drop this option
+                tc = self.cond_ids[t].to(self.device)
+                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+        return self.p_losses(x, c, t, *args, **kwargs)
+
+    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
+        def rescale_bbox(bbox):
+            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
+            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
+            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
+            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
+            return x0, y0, w, h
+
+        return [rescale_bbox(b) for b in bboxes]
+
+    def apply_model(self, x_noisy, t, cond, return_ids=False):
+
+        if isinstance(cond, dict):
+            # hybrid case, cond is exptected to be a dict
+            pass
+        else:
+            if not isinstance(cond, list):
+                cond = [cond]
+            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+            cond = {key: cond}
+
+        if hasattr(self, "split_input_params"):
+            assert len(cond) == 1  # todo can only deal with one conditioning atm
+            assert not return_ids  
+            ks = self.split_input_params["ks"]  # eg. (128, 128)
+            stride = self.split_input_params["stride"]  # eg. (64, 64)
+
+            h, w = x_noisy.shape[-2:]
+
+            fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
+
+            z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
+            # Reshape to img shape
+            z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+            z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
+
+            if self.cond_stage_key in ["image", "LR_image", "segmentation",
+                                       'bbox_img'] and self.model.conditioning_key:  # todo check for completeness
+                c_key = next(iter(cond.keys()))  # get key
+                c = next(iter(cond.values()))  # get value
+                assert (len(c) == 1)  # todo extend to list with more than one elem
+                c = c[0]  # get element
+
+                c = unfold(c)
+                c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
+
+            elif self.cond_stage_key == 'coordinates_bbox':
+                assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
+
+                # assuming padding of unfold is always 0 and its dilation is always 1
+                n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
+                full_img_h, full_img_w = self.split_input_params['original_image_size']
+                # as we are operating on latents, we need the factor from the original image size to the
+                # spatial latent size to properly rescale the crops for regenerating the bbox annotations
+                num_downs = self.first_stage_model.encoder.num_resolutions - 1
+                rescale_latent = 2 ** (num_downs)
+
+                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
+                # need to rescale the tl patch coordinates to be in between (0,1)
+                tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
+                                         rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
+                                        for patch_nr in range(z.shape[-1])]
+
+                # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
+                patch_limits = [(x_tl, y_tl,
+                                 rescale_latent * ks[0] / full_img_w,
+                                 rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
+                # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
+
+                # tokenize crop coordinates for the bounding boxes of the respective patches
+                patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
+                                      for bbox in patch_limits]  # list of length l with tensors of shape (1, 2)
+                print(patch_limits_tknzd[0].shape)
+                # cut tknzd crop position from conditioning
+                assert isinstance(cond, dict), 'cond must be dict to be fed into model'
+                cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
+                print(cut_cond.shape)
+
+                adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
+                adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
+                print(adapted_cond.shape)
+                adapted_cond = self.get_learned_conditioning(adapted_cond)
+                print(adapted_cond.shape)
+                adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
+                print(adapted_cond.shape)
+
+                cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
+
+            else:
+                cond_list = [cond for i in range(z.shape[-1])]  # Todo make this more efficient
+
+            # apply model by loop over crops
+            output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
+            assert not isinstance(output_list[0],
+                                  tuple)  # todo cant deal with multiple model outputs check this never happens
+
+            o = torch.stack(output_list, axis=-1)
+            o = o * weighting
+            # Reverse reshape to img shape
+            o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+            # stitch crops together
+            x_recon = fold(o) / normalization
+
+        else:
+            x_recon = self.model(x_noisy, t, **cond)
+
+        if isinstance(x_recon, tuple) and not return_ids:
+            return x_recon[0]
+        else:
+            return x_recon
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def _prior_bpd(self, x_start):
+        """
+        Get the prior KL term for the variational lower-bound, measured in
+        bits-per-dim.
+        This term can't be optimized, as it only depends on the encoder.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :return: a batch of [N] KL values (in bits), one per batch element.
+        """
+        batch_size = x_start.shape[0]
+        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+        return mean_flat(kl_prior) / np.log(2.0)
+
+    def p_losses(self, x_start, cond, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_output = self.apply_model(x_noisy, t, cond)
+
+        loss_dict = {}
+        prefix = 'train' if self.training else 'val'
+
+        if self.parameterization == "x0":
+            target = x_start
+        elif self.parameterization == "eps":
+            target = noise
+        else:
+            raise NotImplementedError()
+
+        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+        logvar_t = self.logvar[t].to(self.device)
+        loss = loss_simple / torch.exp(logvar_t) + logvar_t
+        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+        if self.learn_logvar:
+            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+            loss_dict.update({'logvar': self.logvar.data.mean()})
+
+        loss = self.l_simple_weight * loss.mean()
+
+        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+        loss += (self.original_elbo_weight * loss_vlb)
+        loss_dict.update({f'{prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+                        return_x0=False, score_corrector=None, corrector_kwargs=None):
+        t_in = t
+        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+        if score_corrector is not None:
+            assert self.parameterization == "eps"
+            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+        if return_codebook_ids:
+            model_out, logits = model_out
+
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        else:
+            raise NotImplementedError()
+
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+        if quantize_denoised:
+            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        if return_codebook_ids:
+            return model_mean, posterior_variance, posterior_log_variance, logits
+        elif return_x0:
+            return model_mean, posterior_variance, posterior_log_variance, x_recon
+        else:
+            return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+        b, *_, device = *x.shape, x.device
+        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+                                       return_codebook_ids=return_codebook_ids,
+                                       quantize_denoised=quantize_denoised,
+                                       return_x0=return_x0,
+                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+        if return_codebook_ids:
+            raise DeprecationWarning("Support dropped.")
+            model_mean, _, model_log_variance, logits = outputs
+        elif return_x0:
+            model_mean, _, model_log_variance, x0 = outputs
+        else:
+            model_mean, _, model_log_variance = outputs
+
+        noise = noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+        if return_codebook_ids:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+        if return_x0:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+        else:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+                              log_every_t=None):
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        timesteps = self.num_timesteps
+        if batch_size is not None:
+            b = batch_size if batch_size is not None else shape[0]
+            shape = [batch_size] + list(shape)
+        else:
+            b = batch_size = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=self.device)
+        else:
+            img = x_T
+        intermediates = []
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+                        total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+        if type(temperature) == float:
+            temperature = [temperature] * timesteps
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img, x0_partial = self.p_sample(img, cond, ts,
+                                            clip_denoised=self.clip_denoised,
+                                            quantize_denoised=quantize_denoised, return_x0=True,
+                                            temperature=temperature[i], noise_dropout=noise_dropout,
+                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(x0_partial)
+            if callback: callback(i)
+            if img_callback: img_callback(img, i)
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_loop(self, cond, shape, return_intermediates=False,
+                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, start_T=None,
+                      log_every_t=None):
+
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        device = self.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        intermediates = [img]
+        if timesteps is None:
+            timesteps = self.num_timesteps
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+
+        if mask is not None:
+            assert x0 is not None
+            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img = self.p_sample(img, cond, ts,
+                                clip_denoised=self.clip_denoised,
+                                quantize_denoised=quantize_denoised)
+            if mask is not None:
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(img)
+            if callback: callback(i)
+            if img_callback: img_callback(img, i)
+
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+               verbose=True, timesteps=None, quantize_denoised=False,
+               mask=None, x0=None, shape=None,**kwargs):
+        if shape is None:
+            shape = (batch_size, self.channels, self.image_size, self.image_size)
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+        return self.p_sample_loop(cond,
+                                  shape,
+                                  return_intermediates=return_intermediates, x_T=x_T,
+                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+                                  mask=mask, x0=x0)
+
+    @torch.no_grad()
+    def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
+
+        if ddim:
+            ddim_sampler = DDIMSampler(self)
+            shape = (self.channels, self.image_size, self.image_size)
+            samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
+                                                        shape,cond,verbose=False,**kwargs)
+
+        else:
+            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+                                                 return_intermediates=True,**kwargs)
+
+        return samples, intermediates
+
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+                   plot_diffusion_rows=True, **kwargs):
+
+        use_ddim = ddim_steps is not None
+
+        log = dict()
+        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+                                           return_first_stage_outputs=True,
+                                           force_c_encode=True,
+                                           return_original_cond=True,
+                                           bs=N)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        log["inputs"] = x
+        log["reconstruction"] = xrec
+        if self.model.conditioning_key is not None:
+            if hasattr(self.cond_stage_model, "decode"):
+                xc = self.cond_stage_model.decode(c)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ["caption"]:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
+                log["conditioning"] = xc
+            elif self.cond_stage_key == 'class_label':
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
+                log['conditioning'] = xc
+            elif isimage(xc):
+                log["conditioning"] = xc
+            if ismap(xc):
+                log["original_conditioning"] = self.to_rgb(xc)
+
+        if plot_diffusion_rows:
+            # get diffusion row
+            diffusion_row = list()
+            z_start = z[:n_row]
+            for t in range(self.num_timesteps):
+                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                    t = t.to(self.device).long()
+                    noise = torch.randn_like(z_start)
+                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+                    diffusion_row.append(self.decode_first_stage(z_noisy))
+
+            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
+            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+            log["diffusion_row"] = diffusion_grid
+
+        if sample:
+            # get denoise row
+            with self.ema_scope("Plotting"):
+                samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+                                                         ddim_steps=ddim_steps,eta=ddim_eta)
+                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+            x_samples = self.decode_first_stage(samples)
+            log["samples"] = x_samples
+            if plot_denoise_rows:
+                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+                log["denoise_row"] = denoise_grid
+
+            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+                    self.first_stage_model, IdentityFirstStage):
+                # also display when quantizing x0 while sampling
+                with self.ema_scope("Plotting Quantized Denoised"):
+                    samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+                                                             ddim_steps=ddim_steps,eta=ddim_eta,
+                                                             quantize_denoised=True)
+                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+                    #                                      quantize_denoised=True)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_x0_quantized"] = x_samples
+
+            if inpaint:
+                # make a simple center square
+                b, h, w = z.shape[0], z.shape[2], z.shape[3]
+                mask = torch.ones(N, h, w).to(self.device)
+                # zeros will be filled in
+                mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+                mask = mask[:, None, ...]
+                with self.ema_scope("Plotting Inpaint"):
+
+                    samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
+                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_inpainting"] = x_samples
+                log["mask"] = mask
+
+                # outpaint
+                with self.ema_scope("Plotting Outpaint"):
+                    samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
+                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_outpainting"] = x_samples
+
+        if plot_progressive_rows:
+            with self.ema_scope("Plotting Progressives"):
+                img, progressives = self.progressive_denoising(c,
+                                                               shape=(self.channels, self.image_size, self.image_size),
+                                                               batch_size=N)
+            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+            log["progressive_row"] = prog_row
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.cond_stage_trainable:
+            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+            params = params + list(self.cond_stage_model.parameters())
+        if self.learn_logvar:
+            print('Diffusion model optimizing logvar')
+            params.append(self.logvar)
+        opt = torch.optim.AdamW(params, lr=lr)
+        if self.use_scheduler:
+            assert 'target' in self.scheduler_config
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                }]
+            return [opt], scheduler
+        return opt
+
+    @torch.no_grad()
+    def to_rgb(self, x):
+        x = x.float()
+        if not hasattr(self, "colorize"):
+            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+        x = nn.functional.conv2d(x, weight=self.colorize)
+        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+        return x
+
+
+class DiffusionWrapperV1(pl.LightningModule):
+    def __init__(self, diff_model_config, conditioning_key):
+        super().__init__()
+        self.diffusion_model = instantiate_from_config(diff_model_config)
+        self.conditioning_key = conditioning_key
+        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
+
+    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
+        if self.conditioning_key is None:
+            out = self.diffusion_model(x, t)
+        elif self.conditioning_key == 'concat':
+            xc = torch.cat([x] + c_concat, dim=1)
+            out = self.diffusion_model(xc, t)
+        elif self.conditioning_key == 'crossattn':
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(x, t, context=cc)
+        elif self.conditioning_key == 'hybrid':
+            xc = torch.cat([x] + c_concat, dim=1)
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(xc, t, context=cc)
+        elif self.conditioning_key == 'adm':
+            cc = c_crossattn[0]
+            out = self.diffusion_model(x, t, y=cc)
+        else:
+            raise NotImplementedError()
+
+        return out
+
+
+class Layout2ImgDiffusionV1(LatentDiffusionV1):
+    # TODO: move all layout-specific hacks to this class
+    def __init__(self, cond_stage_key, *args, **kwargs):
+        assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
+        super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
+
+    def log_images(self, batch, N=8, *args, **kwargs):
+        logs = super().log_images(batch=batch, N=N, *args, **kwargs)
+
+        key = 'train' if self.training else 'validation'
+        dset = self.trainer.datamodule.datasets[key]
+        mapper = dset.conditional_builders[self.cond_stage_key]
+
+        bbox_imgs = []
+        map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
+        for tknzd_bbox in batch[self.cond_stage_key][:N]:
+            bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
+            bbox_imgs.append(bboximg)
+
+        cond_img = torch.stack(bbox_imgs, dim=0)
+        logs['bbox_image'] = cond_img
+        return logs
+
+setattr(ldm.models.diffusion.ddpm, "DDPMV1", DDPMV1)
+setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1)
+setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1)
+setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1)
-- 
cgit v1.2.1


From 681c450ecd8f0999cbaf562c5e734c7105320ad9 Mon Sep 17 00:00:00 2001
From: Mackerel <mackerel@chickatrice.com>
Date: Sun, 4 Dec 2022 01:13:36 -0500
Subject: extras.py: use as little RAM as possible, misc fixes

maximum of 2 models loaded at once. delete unneeded model before next
step. fix 'teritary' -> 'tertiary'. gracefully fail when "add
difference" is selected without a tertiary model
---
 modules/extras.py | 37 +++++++++++++++++++------------------
 1 file changed, 19 insertions(+), 18 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index bc349d5e..0ad8deec 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -62,7 +62,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     # Also keep track of original file names
     imageNameArr = []
     outputs = []
-    
+
     if extras_mode == 1:
         #convert file to pillow image
         for img in image_folder:
@@ -234,7 +234,7 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
+def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
     def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
@@ -246,30 +246,25 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
-    teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
+    tertiary_model_info = sd_models.checkpoints_list.get(tertiary_model_name, None)
     result_is_inpainting_model = False
 
-    print(f"Loading {primary_model_info.filename}...")
-    theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
-
-    print(f"Loading {secondary_model_info.filename}...")
-    theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
-
-    if teritary_model_info is not None:
-        print(f"Loading {teritary_model_info.filename}...")
-        theta_2 = sd_models.read_state_dict(teritary_model_info.filename, map_location='cpu')
-    else:
-        theta_2 = None
-
     theta_funcs = {
         "Weighted sum": (None, weighted_sum),
         "Add difference": (get_difference, add_difference),
     }
     theta_func1, theta_func2 = theta_funcs[interp_method]
 
-    print(f"Merging...")
+    if theta_func1 and not tertiary_model_info:
+        return ["Failed: Interpolation method requires a tertiary model."] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
+
+    print(f"Loading {secondary_model_info.filename}...")
+    theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
 
     if theta_func1:
+        print(f"Loading {tertiary_model_info.filename}...")
+        theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
+
         for key in tqdm.tqdm(theta_1.keys()):
             if 'model' in key:
                 if key in theta_2:
@@ -277,7 +272,12 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
                     theta_1[key] = theta_func1(theta_1[key], t2)
                 else:
                     theta_1[key] = torch.zeros_like(theta_1[key])
-    del theta_2
+        del theta_2
+
+    print(f"Loading {primary_model_info.filename}...")
+    theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
+
+    print("Merging...")
 
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
@@ -307,6 +307,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
             theta_0[key] = theta_1[key]
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
+    del theta_1
 
     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
 
@@ -332,5 +333,5 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     sd_models.list_models()
 
-    print(f"Checkpoint saved.")
+    print("Checkpoint saved.")
     return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
-- 
cgit v1.2.1


From 12ade469c8fb65468da072c6489283acfb420763 Mon Sep 17 00:00:00 2001
From: Pi Esposito <piero.skywalker@gmail.com>
Date: Sun, 4 Dec 2022 12:33:15 -0300
Subject: add queuing by default to avoid timeout on client side when
 share=True

---
 webui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/webui.py b/webui.py
index 78204d11..98f3c645 100644
--- a/webui.py
+++ b/webui.py
@@ -137,7 +137,7 @@ def webui():
 
         shared.demo = modules.ui.create_ui()
 
-        app, local_url, share_url = shared.demo.launch(
+        app, local_url, share_url = shared.demo.queue().launch(
             share=cmd_opts.share,
             server_name=server_name,
             server_port=cmd_opts.port,
-- 
cgit v1.2.1


From fcf372e5d044164931f0b779d0c63e9dcfbafef0 Mon Sep 17 00:00:00 2001
From: Pi Esposito <piero.skywalker@gmail.com>
Date: Sun, 4 Dec 2022 14:13:31 -0300
Subject: set default to avoid breaking stuff

---
 webui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/webui.py b/webui.py
index 98f3c645..c2d0c6be 100644
--- a/webui.py
+++ b/webui.py
@@ -137,7 +137,7 @@ def webui():
 
         shared.demo = modules.ui.create_ui()
 
-        app, local_url, share_url = shared.demo.queue().launch(
+        app, local_url, share_url = shared.demo.queue(default_enabled=False).launch(
             share=cmd_opts.share,
             server_name=server_name,
             server_port=cmd_opts.port,
-- 
cgit v1.2.1


From fa6478796a5c794b4141fce3e8cdb9ec5ea2b71f Mon Sep 17 00:00:00 2001
From: David Vorick <david@siasky.net>
Date: Mon, 5 Dec 2022 00:21:37 -0500
Subject: allow randomized seeds in prompt_matrix

---
 scripts/prompt_matrix.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/scripts/prompt_matrix.py b/scripts/prompt_matrix.py
index 5fd952e9..a543d51b 100644
--- a/scripts/prompt_matrix.py
+++ b/scripts/prompt_matrix.py
@@ -46,10 +46,11 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):
         put_at_start = gr.Checkbox(label='Put variable parts at start of prompt', value=False)
+        random_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)
 
-        return [put_at_start]
+        return [put_at_start, random_seeds]
 
-    def run(self, p, put_at_start):
+    def run(self, p, put_at_start, random_seeds):
         modules.processing.fix_seed(p)
 
         original_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt
@@ -73,7 +74,13 @@ class Script(scripts.Script):
         print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
 
         p.prompt = all_prompts
-        p.seed = [p.seed for _ in all_prompts]
+        if random_seeds:
+            base = p.seed
+            p.seed = []
+            for i in range(len(all_prompts)):
+                p.seed.append(base+1)
+        else:
+            p.seed = [p.seed for _ in all_prompts]
         p.prompt_for_display = original_prompt
         processed = process_images(p)
 
-- 
cgit v1.2.1


From 7057c72ae3f697381a6ccdd1527b954a1280cb40 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Mon, 5 Dec 2022 03:41:36 -0800
Subject: Add opt. to avoid sending size between interfaces.

---
 modules/generation_parameters_copypaste.py | 3 +--
 modules/shared.py                          | 1 +
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 44fe1a6c..e8d5250a 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -121,8 +121,7 @@ def run_bind():
 
             if send_generate_info and paste_fields[tab]["fields"] is not None:
                 if send_generate_info in paste_fields:
-                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Size-1', 'Size-2'] + (["Seed"] if shared.opts.send_seed else [])
-
+                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] +  (['Size-1', 'Size-2'] if shared.opts.send_size else []) + (["Seed"] if shared.opts.send_seed else [])
                     button.click(
                         fn=lambda *x: x,
                         inputs=[field for field, name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
diff --git a/modules/shared.py b/modules/shared.py
index dc45fcaa..ab9012af 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -395,6 +395,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
     "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
     "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
+    "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-- 
cgit v1.2.1


From 2eb5f103ab1b41477440cc391165ea7ef5f7f959 Mon Sep 17 00:00:00 2001
From: apolinario <joaopaulo.passos@gmail.com>
Date: Mon, 5 Dec 2022 16:30:15 +0100
Subject: Fix WebUI not working inside of iframes

---
 script.js | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/script.js b/script.js
index 8b3b67e3..d9424de9 100644
--- a/script.js
+++ b/script.js
@@ -1,5 +1,5 @@
 function gradioApp(){
-    return document.getElementsByTagName('gradio-app')[0].shadowRoot;
+    return document
 }
 
 function get_uiCurrentTab() {
@@ -82,4 +82,4 @@ function uiElementIsVisible(el) {
         }
     }
     return isVisible;
-}
\ No newline at end of file
+}
-- 
cgit v1.2.1


From 760a7836eb1f8645b7c32df82085383e1859c74d Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Sun, 4 Dec 2022 08:29:10 -0500
Subject: Use python3.10 for python_cmd if available

---
 webui-macos-env.sh | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/webui-macos-env.sh b/webui-macos-env.sh
index 68d1f754..21ca1dfd 100644
--- a/webui-macos-env.sh
+++ b/webui-macos-env.sh
@@ -4,6 +4,11 @@
 # Please modify webui-user.sh to change these instead of this file #
 ####################################################################
 
+if [[ -x "$(command -v python3.10)" ]]
+then
+    python_cmd="python3.10"
+fi
+
 export COMMANDLINE_ARGS="--skip-torch-cuda-test --no-half --use-cpu interrogate"
 export TORCH_COMMAND="pip install torch==1.12.1 torchvision==0.13.1"
 export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git"
-- 
cgit v1.2.1


From 4929503258d80abbc4b5f40da034298fe3803906 Mon Sep 17 00:00:00 2001
From: zhaohu xing <920232796@qq.com>
Date: Tue, 6 Dec 2022 09:03:55 +0800
Subject: fix bugs

Signed-off-by: zhaohu xing <920232796@qq.com>
---
 modules/devices.py   |  4 ++--
 modules/sd_hijack.py |  2 +-
 v2-inference.yaml    | 67 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 70 insertions(+), 3 deletions(-)
 create mode 100644 v2-inference.yaml

diff --git a/modules/devices.py b/modules/devices.py
index e69c1fe3..f00079c6 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -38,8 +38,8 @@ def get_optimal_device():
     if torch.cuda.is_available():
         return torch.device(get_cuda_device_string())
 
-    # if has_mps():
-    #     return torch.device("mps")
+    if has_mps():
+        return torch.device("mps")
 
     return cpu
 
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index edb8b420..cd65d356 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -28,7 +28,7 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
 # new memory efficient cross attention blocks do not support hypernets and we already
 # have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention
 ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention
-# ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
+ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
 
 # silence new console spam from SD2
 ldm.modules.attention.print = lambda *args: None
diff --git a/v2-inference.yaml b/v2-inference.yaml
new file mode 100644
index 00000000..0eb25395
--- /dev/null
+++ b/v2-inference.yaml
@@ -0,0 +1,67 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False # we set this to false because this is an inference only config
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"
\ No newline at end of file
-- 
cgit v1.2.1


From 5dcc22606d05ebe5ae89c990bd83a3eb068fcb78 Mon Sep 17 00:00:00 2001
From: zhaohu xing <920232796@qq.com>
Date: Tue, 6 Dec 2022 16:04:50 +0800
Subject: add hash and fix undo hijack bug

Signed-off-by: zhaohu xing <920232796@qq.com>
---
 .DS_Store            | Bin 0 -> 6148 bytes
 launch.py            |  10 ++++----
 modules/sd_hijack.py |   6 ++++-
 v2-inference-v.yaml  |  68 +++++++++++++++++++++++++++++++++++++++++++++++++++
 v2-inference.yaml    |  67 --------------------------------------------------
 5 files changed, 78 insertions(+), 73 deletions(-)
 create mode 100644 .DS_Store
 create mode 100644 v2-inference-v.yaml
 delete mode 100644 v2-inference.yaml

diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 00000000..5008ddfc
Binary files /dev/null and b/.DS_Store differ
diff --git a/launch.py b/launch.py
index 0d8f2776..0e1bbaf2 100644
--- a/launch.py
+++ b/launch.py
@@ -234,11 +234,11 @@ def prepare_enviroment():
 
     os.makedirs(dir_repos, exist_ok=True)
 
-    git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", )
-    git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", )
-    git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", )
-    git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", )
-    git_clone(blip_repo, repo_dir('BLIP'), "BLIP", )
+    git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
+    git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
+    git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
+    git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
+    git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
 
     if not is_installed("lpips"):
         run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 9b5890e7..9fed1b6f 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -112,7 +112,11 @@ class StableDiffusionModelHijack:
         self.layers = flatten(m)
 
     def undo_hijack(self, m):
-        if type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
+
+        if shared.text_model_name == "XLMR-Large":
+            m.cond_stage_model = m.cond_stage_model.wrapped 
+
+        elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped
 
             model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
diff --git a/v2-inference-v.yaml b/v2-inference-v.yaml
new file mode 100644
index 00000000..513cd635
--- /dev/null
+++ b/v2-inference-v.yaml
@@ -0,0 +1,68 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    parameterization: "v"
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False # we set this to false because this is an inference only config
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"
\ No newline at end of file
diff --git a/v2-inference.yaml b/v2-inference.yaml
deleted file mode 100644
index 0eb25395..00000000
--- a/v2-inference.yaml
+++ /dev/null
@@ -1,67 +0,0 @@
-model:
-  base_learning_rate: 1.0e-4
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False # we set this to false because this is an inference only config
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: True
-        use_fp16: True
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          #attn_type: "vanilla-xformers"
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
\ No newline at end of file
-- 
cgit v1.2.1


From 965fc5ac5a6ccdf38342e21c97183011a04e799e Mon Sep 17 00:00:00 2001
From: zhaohu xing <920232796@qq.com>
Date: Tue, 6 Dec 2022 16:15:15 +0800
Subject: delete a file

Signed-off-by: zhaohu xing <920232796@qq.com>
---
 .DS_Store         | Bin 6148 -> 0 bytes
 modules/shared.py |   2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)
 delete mode 100644 .DS_Store

diff --git a/.DS_Store b/.DS_Store
deleted file mode 100644
index 5008ddfc..00000000
Binary files a/.DS_Store and /dev/null differ
diff --git a/modules/shared.py b/modules/shared.py
index 522c56c1..8419b531 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -22,7 +22,7 @@ demo = None
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
 parser = argparse.ArgumentParser()
-parser.add_argument("--config", type=str, default="configs/altdiffusion/ad-inference.yaml", help="path to config which constructs model",)
+parser.add_argument("--config", type=str, default=os.path.join(script_path, "v1-inference.yaml"), help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
 parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
-- 
cgit v1.2.1


From 1075819b16ef328805dd946acaffd43efa2eb444 Mon Sep 17 00:00:00 2001
From: apolinario <joaopaulo.passos@gmail.com>
Date: Tue, 6 Dec 2022 15:13:41 +0100
Subject: Use shadowRoot if inside of an iframe and don't use it if outside

This makes sure it will work everywhere
---
 script.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script.js b/script.js
index d9424de9..49b6c557 100644
--- a/script.js
+++ b/script.js
@@ -1,5 +1,5 @@
 function gradioApp(){
-    return document
+    return !!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document
 }
 
 function get_uiCurrentTab() {
-- 
cgit v1.2.1


From 8eb638cdd3d08ad6e9373569fd81d0a6e8a63f16 Mon Sep 17 00:00:00 2001
From: apolinario <joaopaulo.passos@gmail.com>
Date: Tue, 6 Dec 2022 15:14:22 +0100
Subject: style change

---
 script.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script.js b/script.js
index 49b6c557..1c8fcbf1 100644
--- a/script.js
+++ b/script.js
@@ -1,5 +1,5 @@
 function gradioApp(){
-    return !!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document
+    return !!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document;
 }
 
 function get_uiCurrentTab() {
-- 
cgit v1.2.1


From a7b1ab28d562ef8a020be0129cbc4c521569cd24 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Sun, 4 Dec 2022 08:29:45 -0500
Subject: Set install_dir (per Ephil012's suggestion)

Set install_dir to a correct value for macOS. Thanks to @Ephil012 for pointing out this is needed.
---
 webui-macos-env.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/webui-macos-env.sh b/webui-macos-env.sh
index 21ca1dfd..95ca9c55 100644
--- a/webui-macos-env.sh
+++ b/webui-macos-env.sh
@@ -9,6 +9,7 @@ then
     python_cmd="python3.10"
 fi
 
+export install_dir="$HOME"
 export COMMANDLINE_ARGS="--skip-torch-cuda-test --no-half --use-cpu interrogate"
 export TORCH_COMMAND="pip install torch==1.12.1 torchvision==0.13.1"
 export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git"
-- 
cgit v1.2.1


From 8bc16b7582406c54fb2aff586ed1f8d28cb3b69d Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Sun, 4 Dec 2022 08:31:19 -0500
Subject: Add psutil if using macOS to requirements.txt

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 5f3d9623..05818aa6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -30,3 +30,4 @@ inflection
 GitPython
 torchsde
 safetensors
+psutil; sys_platform == 'darwin'
-- 
cgit v1.2.1


From 02f566f684caa4394caedac1f876e71f202a2bf9 Mon Sep 17 00:00:00 2001
From: Bwin4L <Bwin4L@users.noreply.github.com>
Date: Wed, 7 Dec 2022 16:14:05 +0100
Subject: Add bracket checking functionality to the txt2img/img2img prompts and
 negative prompts.

---
 .../javascript/prompt-bracket-checker.js           | 107 +++++++++++++++++++++
 1 file changed, 107 insertions(+)
 create mode 100644 extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js

diff --git a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
new file mode 100644
index 00000000..41d384c7
--- /dev/null
+++ b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
@@ -0,0 +1,107 @@
+// Stable Diffusion WebUI - Bracket checker
+// Version 1.0
+// By Hingashi no Florin/Bwin4L
+// Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
+// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
+
+function checkBrackets(evt) {
+  textArea = evt.target;
+  tabName = evt.target.parentElement.parentElement.id.split("_")[0];
+  counterElt = document.querySelector('gradio-app').shadowRoot.querySelector('#' + tabName + '_token_counter');
+
+  promptName = evt.target.parentElement.parentElement.id.includes('neg') ? ' negative' : '';
+
+  errorStringParen  = '(' + tabName + promptName + ' prompt) - Different number of opening and closing parentheses detected.\n';
+  errorStringSquare = '[' + tabName + promptName + ' prompt] - Different number of opening and closing square brackets detected.\n';
+  errorStringCurly  = '{' + tabName + promptName + ' prompt} - Different number of opening and closing curly brackets detected.\n';
+
+  openBracketRegExp = /\(/g;
+  closeBracketRegExp = /\)/g;
+
+  openSquareBracketRegExp = /\[/g;
+  closeSquareBracketRegExp = /\]/g;
+
+  openCurlyBracketRegExp = /\{/g;
+  closeCurlyBracketRegExp = /\}/g;
+
+  totalOpenBracketMatches = 0;
+  totalCloseBracketMatches = 0;
+  totalOpenSquareBracketMatches = 0;
+  totalCloseSquareBracketMatches = 0;
+  totalOpenCurlyBracketMatches = 0;
+  totalCloseCurlyBracketMatches = 0;
+
+  openBracketMatches = textArea.value.match(openBracketRegExp);
+  if(openBracketMatches) {
+    totalOpenBracketMatches = openBracketMatches.length;
+  }
+
+  closeBracketMatches = textArea.value.match(closeBracketRegExp);
+  if(closeBracketMatches) {
+    totalCloseBracketMatches = closeBracketMatches.length;
+  }
+
+  openSquareBracketMatches = textArea.value.match(openSquareBracketRegExp);
+  if(openSquareBracketMatches) {
+    totalOpenSquareBracketMatches = openSquareBracketMatches.length;
+  }
+
+  closeSquareBracketMatches = textArea.value.match(closeSquareBracketRegExp);
+  if(closeSquareBracketMatches) {
+    totalCloseSquareBracketMatches = closeSquareBracketMatches.length;
+  }
+
+  openCurlyBracketMatches = textArea.value.match(openCurlyBracketRegExp);
+  if(openCurlyBracketMatches) {
+    totalOpenCurlyBracketMatches = openCurlyBracketMatches.length;
+  }
+
+  closeCurlyBracketMatches = textArea.value.match(closeCurlyBracketRegExp);
+  if(closeCurlyBracketMatches) {
+    totalCloseCurlyBracketMatches = closeCurlyBracketMatches.length;
+  }
+
+  if(totalOpenBracketMatches != totalCloseBracketMatches) {
+    if(!counterElt.title.includes(errorStringParen)) {
+      counterElt.title += errorStringParen;
+    }
+  } else {
+    counterElt.title = counterElt.title.replace(errorStringParen, '');
+  }
+
+  if(totalOpenSquareBracketMatches != totalCloseSquareBracketMatches) {
+    if(!counterElt.title.includes(errorStringSquare)) {
+      counterElt.title += errorStringSquare;
+    }
+  } else {
+    counterElt.title = counterElt.title.replace(errorStringSquare, '');
+  }
+
+  if(totalOpenCurlyBracketMatches != totalCloseCurlyBracketMatches) {
+    if(!counterElt.title.includes(errorStringCurly)) {
+      counterElt.title += errorStringCurly;
+    }
+  } else {
+    counterElt.title = counterElt.title.replace(errorStringCurly, '');
+  }
+
+  if(counterElt.title != '') {
+    counterElt.style = 'color: #FF5555;';
+  } else {
+    counterElt.style = 'color: #55FF55;';
+  }
+}
+
+var shadowRootLoaded = setInterval(function() {
+  var shadowTextArea = document.querySelector('gradio-app').shadowRoot.querySelectorAll('#txt2img_prompt > label > textarea');
+  if(shadowTextArea.length < 1) {
+      return false;
+  }
+
+  clearInterval(shadowRootLoaded);
+
+  document.querySelector('gradio-app').shadowRoot.querySelector('#txt2img_prompt').onkeyup = checkBrackets;
+  document.querySelector('gradio-app').shadowRoot.querySelector('#txt2img_neg_prompt').onkeyup = checkBrackets;
+  document.querySelector('gradio-app').shadowRoot.querySelector('#img2img_prompt').onkeyup = checkBrackets;
+  document.querySelector('gradio-app').shadowRoot.querySelector('#img2img_neg_prompt').onkeyup = checkBrackets;
+}, 1000);
-- 
cgit v1.2.1


From 358a8628f6abb4ca1e1bfddf122687c6fb13be0c Mon Sep 17 00:00:00 2001
From: Andrew Ryan <andrewryanchama@gmail.com>
Date: Thu, 8 Dec 2022 07:09:09 +0000
Subject: Add latent upscale option to img2img

Recently, the option to do latent upscale was added to txt2img highres
fix. This feature runs by scaling the latent sample of the image, and
then running a second pass of img2img.

But, in this edition of highres fix, the image and parameters cannot be
changed between the first pass and second pass. We might want to do a
fixup in img2img before doing the second pass, or might want to run the
second pass at a different resolution.

This change adds the option for img2img to perform its upscale in latent
space, rather than image space, giving very similar results to highres
fix with latent upscale.  The result is not exactly the same because
there is an additional latent -> decoder -> image -> encoder -> latent
that won't happen in highres fix, but this conversion has relatively
small losses
---
 modules/processing.py | 6 +++++-
 modules/ui.py         | 2 +-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 3d2c4dc9..ab5a34d0 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -795,7 +795,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         for img in self.init_images:
             image = img.convert("RGB")
 
-            if crop_region is None:
+            if crop_region is None and self.resize_mode != 3:
                 image = images.resize_image(self.resize_mode, image, self.width, self.height)
 
             if image_mask is not None:
@@ -804,6 +804,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
                 self.overlay_images.append(image_masked.convert('RGBA'))
 
+            # crop_region is not none iif we are doing inpaint full res
             if crop_region is not None:
                 image = image.crop(crop_region)
                 image = images.resize_image(2, image, self.width, self.height)
@@ -840,6 +841,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
         self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
 
+        if self.resize_mode == 3:
+            self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+
         if image_mask is not None:
             init_mask = latent_mask
             latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
diff --git a/modules/ui.py b/modules/ui.py
index b2b8de90..fe4abe05 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -829,7 +829,7 @@ def create_ui():
                         img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
 
                 with gr.Row():
-                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill", "Upscale Latent Space"], type="index", value="Just resize")
 
                 steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
-- 
cgit v1.2.1


From 1ed4f0e22807f3afef925210182cbbee51f0cb2c Mon Sep 17 00:00:00 2001
From: Jay Smith <jayvsmith@gmail.com>
Date: Thu, 8 Dec 2022 18:14:35 -0600
Subject: Depth2img model support

---
 README.md             |  1 +
 modules/processing.py | 38 ++++++++++++++++++++++++++++++++++----
 modules/sd_models.py  | 46 ++++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 81 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 8a4ffade..55990581 100644
--- a/README.md
+++ b/README.md
@@ -135,6 +135,7 @@ The documentation was moved from this README over to the project's [wiki](https:
 - SwinIR - https://github.com/JingyunLiang/SwinIR
 - Swin2SR - https://github.com/mv-lab/swin2sr
 - LDSR - https://github.com/Hafiidz/latent-diffusion
+- MiDaS - https://github.com/isl-org/MiDaS
 - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
 - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
 - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
diff --git a/modules/processing.py b/modules/processing.py
index 3d2c4dc9..0417ffc5 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -21,7 +21,10 @@ import modules.face_restoration
 import modules.images as images
 import modules.styles
 import logging
+from ldm.data.util import AddMiDaS
+from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 
+from einops import repeat, rearrange
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
@@ -150,11 +153,26 @@ class StableDiffusionProcessing():
 
         return image_conditioning
 
-    def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):
-        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
-            # Dummy zero conditioning if we're not using inpainting model.
-            return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
+    def depth2img_image_conditioning(self, source_image):
+        # Use the AddMiDaS helper to Format our source image to suit the MiDaS model
+        transformer = AddMiDaS(model_type="dpt_hybrid")
+        transformed = transformer({"jpg": rearrange(source_image[0], "c h w -> h w c")})
+        midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
+        midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
+
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
+        conditioning = torch.nn.functional.interpolate(
+            self.sd_model.depth_model(midas_in),
+            size=conditioning_image.shape[2:],
+            mode="bicubic",
+            align_corners=False,
+        )
+
+        (depth_min, depth_max) = torch.aminmax(conditioning)
+        conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
+        return conditioning
 
+    def inpainting_image_conditioning(self, source_image, latent_image, image_mask = None):
         self.is_using_inpainting_conditioning = True
 
         # Handle the different mask inputs
@@ -191,6 +209,18 @@ class StableDiffusionProcessing():
 
         return image_conditioning
 
+    def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
+        # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
+        # identify itself with a field common to all models. The conditioning_key is also hybrid.
+        if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
+            return self.depth2img_image_conditioning(source_image)
+
+        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
+            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+
+        # Dummy zero conditioning if we're not using inpainting or depth model.
+        return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 283cf1cd..139952ba 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -7,6 +7,9 @@ import torch
 import re
 import safetensors.torch
 from omegaconf import OmegaConf
+from os import mkdir
+from urllib import request
+import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
@@ -36,6 +39,7 @@ def setup_model():
         os.makedirs(model_path)
 
     list_models()
+    enable_midas_autodownload()
 
 
 def checkpoint_tiles(): 
@@ -227,6 +231,48 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     sd_vae.load_vae(model, vae_file)
 
 
+def enable_midas_autodownload():
+    """
+    Gives the ldm.modules.midas.api.load_model function automatic downloading.
+
+    When the 512-depth-ema model, and other future models like it, is loaded,
+    it calls midas.api.load_model to load the associated midas depth model.
+    This function applies a wrapper to download the model to the correct
+    location automatically.
+    """
+
+    midas_path = os.path.join(models_path, 'midas')
+
+    # stable-diffusion-stability-ai hard-codes the midas model path to
+    # a location that differs from where other scripts using this model look.
+    # HACK: Overriding the path here.
+    for k, v in midas.api.ISL_PATHS.items():
+        file_name = os.path.basename(v)
+        midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
+
+    midas_urls = {
+        "dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
+        "dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
+        "midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
+        "midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
+    }
+
+    midas.api.load_model_inner = midas.api.load_model
+
+    def load_model_wrapper(model_type):
+        path = midas.api.ISL_PATHS[model_type]
+        if not os.path.exists(path):
+            if not os.path.exists(midas_path):
+                mkdir(midas_path)
+    
+            print(f"Downloading midas model weights for {model_type} to {path}")
+            request.urlretrieve(midas_urls[model_type], path)
+            print(f"{model_type} downloaded")
+
+        return midas.api.load_model_inner(model_type)
+
+    midas.api.load_model = load_model_wrapper
+
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
-- 
cgit v1.2.1


From ce04ba71b880aeb97b7d47d404ba4ea430891618 Mon Sep 17 00:00:00 2001
From: Ju1-js <40339350+Ju1-js@users.noreply.github.com>
Date: Thu, 8 Dec 2022 22:47:45 -0800
Subject: Make # settings changed message grammatically correct

Make the ": " in the settings changed message not show if 0 settings were changed.
"0 settings changed: ." -> "0 settings changed."
---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index b2b8de90..79bb3d1f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1450,7 +1450,7 @@ def create_ui():
             opts.save(shared.config_filename)
         except RuntimeError:
             return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
-        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
+        return opts.dumpjson(), f'{len(changed)} settings changed{": " if len(changed) > 0 else ""}{", ".join(changed)}.'
 
     def run_settings_single(value, key):
         if not opts.same_type(value, opts.data_labels[key].default):
-- 
cgit v1.2.1


From 9539c2045a2de9e900b96acbf67e41fafe93c6f6 Mon Sep 17 00:00:00 2001
From: ywx9 <yw.ninefold@gmail.com>
Date: Fri, 9 Dec 2022 23:03:06 +0900
Subject: Bug fix

---
 modules/api/api.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 54ee7cb0..89935a70 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -157,12 +157,7 @@ class Api:
         args.pop('include_init_images', None)  # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
         p = StableDiffusionProcessingImg2Img(**args)
 
-        imgs = []
-        for img in init_images:
-            img = decode_base64_to_image(img)
-            imgs = [img] * p.batch_size
-
-        p.init_images = imgs
+        p.init_images = [decode_base64_to_image(x) for x in init_images]
 
         shared.state.begin()
 
-- 
cgit v1.2.1


From 7dbfd8a7d8aefec7283b456c6f5b000ae4d3496d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 09:14:30 +0300
Subject: do not replace entire unet for the resolution hack

---
 modules/sd_hijack.py               |  5 +++--
 modules/sd_hijack_optimizations.py | 28 ----------------------------
 modules/sd_hijack_unet.py          | 30 ++++++++++++++++++++++++++++++
 3 files changed, 33 insertions(+), 30 deletions(-)
 create mode 100644 modules/sd_hijack_unet.py

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 92874a79..47dbc1b7 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -11,7 +11,7 @@ import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.hypernetworks import hypernetwork
 from modules.shared import opts, device, cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet
 
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
@@ -35,11 +35,12 @@ ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"]
 ldm.modules.attention.print = lambda *args: None
 ldm.modules.diffusionmodules.model.print = lambda *args: None
 
+
 def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_hijack_optimizations.patched_unet_forward
+    ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
 
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 8cd4c954..85909eb9 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -313,31 +313,3 @@ def xformers_attnblock_forward(self, x):
         return x + out
     except NotImplementedError:
         return cross_attention_attnblock_forward(self, x)
-
-def patched_unet_forward(self, x, timesteps=None, context=None, y=None,**kwargs):
-    assert (y is not None) == (
-        self.num_classes is not None
-    ), "must specify y if and only if the model is class-conditional"
-    hs = []
-    t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-    emb = self.time_embed(t_emb)
-
-    if self.num_classes is not None:
-        assert y.shape == (x.shape[0],)
-        emb = emb + self.label_emb(y)
-
-    h = x.type(self.dtype)
-    for module in self.input_blocks:
-        h = module(h, emb, context)
-        hs.append(h)
-    h = self.middle_block(h, emb, context)
-    for module in self.output_blocks:
-        if h.shape[-2:] != hs[-1].shape[-2:]:
-            h = F.interpolate(h, hs[-1].shape[-2:], mode="nearest")
-        h = torch.cat([h, hs.pop()], dim=1)
-        h = module(h, emb, context)
-    h = h.type(x.dtype)
-    if self.predict_codebook_ids:
-        return self.id_predictor(h)
-    else:
-        return self.out(h)
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
new file mode 100644
index 00000000..1b9d7757
--- /dev/null
+++ b/modules/sd_hijack_unet.py
@@ -0,0 +1,30 @@
+import torch
+
+
+class TorchHijackForUnet:
+    """
+    This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
+    this makes it possible to create pictures with dimensions that are muliples of 8 rather than 64
+    """
+
+    def __getattr__(self, item):
+        if item == 'cat':
+            return self.cat
+
+        if hasattr(torch, item):
+            return getattr(torch, item)
+
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+
+    def cat(self, tensors, *args, **kwargs):
+        if len(tensors) == 2:
+            a, b = tensors
+            if a.shape[-2:] != b.shape[-2:]:
+                a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
+
+            tensors = (a, b)
+
+        return torch.cat(tensors, *args, **kwargs)
+
+
+th = TorchHijackForUnet()
-- 
cgit v1.2.1


From 505ec7e4d960e7bea579182509050fafb10bd00c Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 09:17:39 +0300
Subject: cleanup some unneeded imports for hijack files

---
 modules/sd_hijack.py               | 10 ++--------
 modules/sd_hijack_optimizations.py |  3 ---
 2 files changed, 2 insertions(+), 11 deletions(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 47dbc1b7..690a9ec2 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -1,16 +1,10 @@
-import math
-import os
-import sys
-import traceback
 import torch
-import numpy as np
-from torch import einsum
 from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
-from modules import prompt_parser, devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
+from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.hypernetworks import hypernetwork
-from modules.shared import opts, device, cmd_opts
+from modules.shared import cmd_opts
 from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet
 
 from modules.sd_hijack_optimizations import invokeAI_mps_available
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 85909eb9..98123fbf 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -5,7 +5,6 @@ import importlib
 
 import torch
 from torch import einsum
-import torch.nn.functional as F
 
 from ldm.util import default
 from einops import rearrange
@@ -13,8 +12,6 @@ from einops import rearrange
 from modules import shared
 from modules.hypernetworks import hypernetwork
 
-from ldm.modules.diffusionmodules.util import timestep_embedding
-
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
     try:
-- 
cgit v1.2.1


From bab91b12798f67c19a2b14dab13a08d5d3e3de26 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 09:51:26 +0300
Subject: add Noise multiplier option to infotext

---
 modules/generation_parameters_copypaste.py | 1 +
 modules/processing.py                      | 8 ++++++--
 modules/shared.py                          | 2 +-
 3 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 44fe1a6c..53f34b0a 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -77,6 +77,7 @@ def integrate_settings_paste_fields(component_dict):
         'inpainting_mask_weight': 'Conditional mask weight',
         'sd_model_checkpoint': 'Model hash',
         'eta_noise_seed_delta': 'ENSD',
+        'initial_noise_multiplier': 'Noise multiplier',
     }
     settings_paste_fields = [
         (component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))
diff --git a/modules/processing.py b/modules/processing.py
index dd22a2fa..81400d14 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -764,7 +764,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: Any=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs):
+    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
         super().__init__(**kwargs)
 
         self.init_images = init_images
@@ -779,6 +779,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.inpaint_full_res = inpaint_full_res
         self.inpaint_full_res_padding = inpaint_full_res_padding
         self.inpainting_mask_invert = inpainting_mask_invert
+        self.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
         self.mask = None
         self.nmask = None
         self.image_conditioning = None
@@ -891,7 +892,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        x = x*shared.opts.initial_noise_multiplier
+
+        if self.initial_noise_multiplier != 1.0:
+            self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
+            x *= self.initial_noise_multiplier
 
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
 
diff --git a/modules/shared.py b/modules/shared.py
index 67f8f77b..200693fe 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -359,7 +359,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "initial_noise_multiplier": OptionInfo(1.0, "Multiply initial noise by this factor, may result in less or more detailed img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01 }),
+    "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01 }),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
-- 
cgit v1.2.1


From e11d0d43b1048c23b31b894baae6352d057332c6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 09:53:54 +0300
Subject: change color of the valid prompt to black - back to how it was

---
 .../prompt-bracket-checker/javascript/prompt-bracket-checker.js         | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
index 41d384c7..3f3bebcd 100644
--- a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
+++ b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
@@ -88,7 +88,7 @@ function checkBrackets(evt) {
   if(counterElt.title != '') {
     counterElt.style = 'color: #FF5555;';
   } else {
-    counterElt.style = 'color: #55FF55;';
+    counterElt.style = 'color: #000;';
   }
 }
 
-- 
cgit v1.2.1


From 7dab7c97597c9aa0e666a862619fbf6752519b3d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 11:20:43 +0300
Subject: repair #5438

---
 scripts/prompt_matrix.py | 14 ++++----------
 1 file changed, 4 insertions(+), 10 deletions(-)

diff --git a/scripts/prompt_matrix.py b/scripts/prompt_matrix.py
index a543d51b..c53ca28c 100644
--- a/scripts/prompt_matrix.py
+++ b/scripts/prompt_matrix.py
@@ -46,11 +46,11 @@ class Script(scripts.Script):
 
     def ui(self, is_img2img):
         put_at_start = gr.Checkbox(label='Put variable parts at start of prompt', value=False)
-        random_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)
+        different_seeds = gr.Checkbox(label='Use different seed for each picture', value=False)
 
-        return [put_at_start, random_seeds]
+        return [put_at_start, different_seeds]
 
-    def run(self, p, put_at_start, random_seeds):
+    def run(self, p, put_at_start, different_seeds):
         modules.processing.fix_seed(p)
 
         original_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt
@@ -74,13 +74,7 @@ class Script(scripts.Script):
         print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
 
         p.prompt = all_prompts
-        if random_seeds:
-            base = p.seed
-            p.seed = []
-            for i in range(len(all_prompts)):
-                p.seed.append(base+1)
-        else:
-            p.seed = [p.seed for _ in all_prompts]
+        p.seed = [p.seed + (i if different_seeds else 0) for i in range(len(all_prompts))]
         p.prompt_for_display = original_prompt
         processed = process_images(p)
 
-- 
cgit v1.2.1


From d06592267c745b4732026c4e0c499c9a4b3900a1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 13:46:18 +0300
Subject: use less javascript for this non-js-only implementation of the clear
 prompt button.

---
 javascript/ui.js  | 41 +++++------------------------------------
 modules/shared.py |  1 -
 modules/ui.py     | 23 +++++++++++------------
 3 files changed, 16 insertions(+), 49 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 951e8381..2cb280e5 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -131,44 +131,13 @@ function ask_for_style_name(_, prompt_text, negative_prompt_text) {
     return [name_, prompt_text, negative_prompt_text]
 }
 
-// returns css id for currently selected tab in ui
-function selected_tab_id() {
-    tabs = gradioApp().querySelectorAll('#tabs div.tabitem')
-
-    for(var tab = 0; tab < tabs.length; tab++) {
-        if (tabs[tab].style.display != "none") return tabs[tab].id
-
-    }
-
-}
-
-function clear_prompt() {
-
-if(confirm("Delete prompt?")) {
-
-    let pos_prompt = gradioApp().querySelector("#txt2img_prompt > label > textarea");
-    let neg_prompt = gradioApp().querySelector("#txt2img_neg_prompt > label > textarea");
-
-    if (selected_tab_id() == "tab_txt2img") {
-    } else {
-        pos_prompt = gradioApp().querySelector("#img2img_prompt > label > textarea");
-        neg_prompt = gradioApp().querySelector("#img2img_neg_prompt > label > textarea");
+function confirm_clear_prompt(prompt, negative_prompt) {
+    if(confirm("Delete prompt?")) {
+        prompt = ""
+        negative_prompt = ""
     }
 
-    pos_prompt.value = ""
-    neg_prompt.value = ""
-
-    //update prompt values on server-side
-    pos_prompt.dispatchEvent(
-        new Event("input", {bubbles: true})
-    )
-    neg_prompt.dispatchEvent(
-        new Event("input", {bubbles: true})
-    )
-
-    return true
-} else return false
-
+    return [prompt, negative_prompt]
 }
 
 
diff --git a/modules/shared.py b/modules/shared.py
index 4223c017..44922c91 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -401,7 +401,6 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
-    "clear_prompt_visible": OptionInfo(True, "Show clear prompt button"),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
     'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
diff --git a/modules/ui.py b/modules/ui.py
index c0a7ca8b..28481e33 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -403,16 +403,17 @@ def create_toprow(is_img2img):
             paste = gr.Button(value=paste_symbol, elem_id="paste")
             save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
             prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
-
-            clear_prompt_button = gr.Button(
-                value=clear_prompt_symbol,
-                elem_id="clear_prompt",
-                visible=opts.clear_prompt_visible
-            )
-
+            clear_prompt_button = gr.Button(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
             token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
             token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
 
+            clear_prompt_button.click(
+                fn=lambda *x: x,
+                _js="confirm_clear_prompt",
+                inputs=[prompt, negative_prompt],
+                outputs=[prompt, negative_prompt],
+            )
+
         button_interrogate = None
         button_deepbooru = None
         if is_img2img:
@@ -447,7 +448,7 @@ def create_toprow(is_img2img):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
                     prompt_style2.save_to_config = True
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button, clear_prompt_button
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
 
 
 def setup_progressbar(progressbar, preview, id_part, textinfo=None):
@@ -634,7 +635,7 @@ def create_ui():
     modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button, clear_prompt_button = create_toprow(is_img2img=False)
+        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -686,7 +687,6 @@ def create_ui():
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            connect_clear_prompt(clear_prompt_button)
 
             txt2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
@@ -793,7 +793,7 @@ def create_ui():
     modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button, clear_prompt_button = create_toprow(is_img2img=True)
+        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button = create_toprow(is_img2img=True)
 
 
         with gr.Row(elem_id='img2img_progress_row'):
@@ -884,7 +884,6 @@ def create_ui():
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-            connect_clear_prompt(clear_prompt_button)
 
             img2img_prompt_img.change(
                 fn=modules.images.image_data,
-- 
cgit v1.2.1


From 37139d8aac10bd13758f52e3d361f3d017c4ad46 Mon Sep 17 00:00:00 2001
From: apolinario <joaopaulo.passos+multimodal@gmail.com>
Date: Sat, 10 Dec 2022 12:51:40 +0100
Subject: No code repeat

---
 script.js | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/script.js b/script.js
index 1c8fcbf1..9748ec90 100644
--- a/script.js
+++ b/script.js
@@ -1,5 +1,6 @@
-function gradioApp(){
-    return !!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document;
+function gradioApp() {
+    const gradioShadowRoot = document.getElementsByTagName('gradio-app')[0].shadowRoot
+    return !!gradioShadowRoot ? gradioShadowRoot : document;
 }
 
 function get_uiCurrentTab() {
-- 
cgit v1.2.1


From 991e2dcee9d6baa66b5c0b1969c4c07407be933a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 14:54:02 +0300
Subject: remove NSFW filter and its dependency; if you still want it, find it
 in the extensions section

---
 modules/processing.py     |  7 +++----
 modules/safety.py         | 42 ------------------------------------------
 modules/scripts.py        | 20 ++++++++++++++++++++
 modules/shared.py         |  1 -
 requirements.txt          |  1 -
 requirements_versions.txt |  1 -
 6 files changed, 23 insertions(+), 49 deletions(-)
 delete mode 100644 modules/safety.py

diff --git a/modules/processing.py b/modules/processing.py
index 81400d14..056c9322 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -13,7 +13,7 @@ from skimage import exposure
 from typing import Any, Dict, List, Optional
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -571,9 +571,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
             devices.torch_gc()
 
-            if opts.filter_nsfw:
-                import modules.safety as safety
-                x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)
+            if p.scripts is not None:
+                p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
             for i, x_sample in enumerate(x_samples_ddim):
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
diff --git a/modules/safety.py b/modules/safety.py
deleted file mode 100644
index cff4b278..00000000
--- a/modules/safety.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import torch
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from transformers import AutoFeatureExtractor
-from PIL import Image
-
-import modules.shared as shared
-
-safety_model_id = "CompVis/stable-diffusion-safety-checker"
-safety_feature_extractor = None
-safety_checker = None
-
-def numpy_to_pil(images):
-    """
-    Convert a numpy image or a batch of images to a PIL image.
-    """
-    if images.ndim == 3:
-        images = images[None, ...]
-    images = (images * 255).round().astype("uint8")
-    pil_images = [Image.fromarray(image) for image in images]
-
-    return pil_images
-
-# check and replace nsfw content
-def check_safety(x_image):
-    global safety_feature_extractor, safety_checker
-
-    if safety_feature_extractor is None:
-        safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
-        safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
-
-    safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
-    x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
-
-    return x_checked_image, has_nsfw_concept
-
-
-def censor_batch(x):
-    x_samples_ddim_numpy = x.cpu().permute(0, 2, 3, 1).numpy()
-    x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim_numpy)
-    x = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
-
-    return x
diff --git a/modules/scripts.py b/modules/scripts.py
index b934d881..23ca195d 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -88,6 +88,17 @@ class Script:
 
         pass
 
+    def postprocess_batch(self, p, *args, **kwargs):
+        """
+        Same as process_batch(), but called for every batch after it has been generated.
+
+        **kwargs will have same items as process_batch, and also:
+          - batch_number - index of current batch, from 0 to number of batches-1
+          - images - torch tensor with all generated images, with values ranging from 0 to 1;
+        """
+
+        pass
+
     def postprocess(self, p, processed, *args):
         """
         This function is called after processing ends for AlwaysVisible scripts.
@@ -347,6 +358,15 @@ class ScriptRunner:
                 print(f"Error running postprocess: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
+    def postprocess_batch(self, p, images, **kwargs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.postprocess_batch(p, *script_args, images=images, **kwargs)
+            except Exception:
+                print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
     def before_component(self, component, **kwargs):
         for script in self.scripts:
             try:
diff --git a/modules/shared.py b/modules/shared.py
index 44922c91..272267c1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -367,7 +367,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
-    "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
diff --git a/requirements.txt b/requirements.txt
index 05818aa6..678acb4d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,5 @@
 accelerate
 basicsr
-diffusers
 fairscale==0.4.4
 fonts
 font-roboto
diff --git a/requirements_versions.txt b/requirements_versions.txt
index 035fa82f..185cd066 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -1,5 +1,4 @@
 transformers==4.19.2
-diffusers==0.3.0
 accelerate==0.12.0
 basicsr==1.4.2
 gfpgan==1.3.8
-- 
cgit v1.2.1


From 713c48ddd7f296fe064cf58af7baa31aa5fcffb3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 10 Dec 2022 15:05:22 +0300
Subject: add an 'installed' tag to extensions

---
 modules/ui_extensions.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index b487ac25..1434f25f 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -206,12 +206,13 @@ def refresh_available_extensions_from_data(hide_tags):
         if url is None:
             continue
 
+        existing = installed_extension_urls.get(normalize_git_url(url), None)
+        extension_tags = extension_tags + ["installed"] if existing else extension_tags
+
         if len([x for x in extension_tags if x in tags_to_hide]) > 0:
             hidden += 1
             continue
 
-        existing = installed_extension_urls.get(normalize_git_url(url), None)
-
         install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
 
         tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])
@@ -222,7 +223,11 @@ def refresh_available_extensions_from_data(hide_tags):
                 <td>{html.escape(description)}</td>
                 <td>{install_code}</td>
             </tr>
-    """
+        
+        """
+
+        for tag in [x for x in extension_tags if x not in tags]:
+            tags[tag] = tag
 
     code += """
         </tbody>
@@ -272,7 +277,7 @@ def create_ui():
                     install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
 
                 with gr.Row():
-                    hide_tags = gr.CheckboxGroup(value=["ads", "localization"], label="Hide extensions with tags", choices=["script", "ads", "localization"])
+                    hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
 
                 install_result = gr.HTML()
                 available_extensions_table = gr.HTML()
-- 
cgit v1.2.1


From 718dbe5e8282c8992afc4a67fe270b342013d282 Mon Sep 17 00:00:00 2001
From: Bwin4L <Bwin4L@users.noreply.github.com>
Date: Sat, 10 Dec 2022 14:51:11 +0100
Subject: Fix token counter color on dark theme

---
 .../prompt-bracket-checker/javascript/prompt-bracket-checker.js         | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
index 3f3bebcd..eccfb0f9 100644
--- a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
+++ b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
@@ -88,7 +88,7 @@ function checkBrackets(evt) {
   if(counterElt.title != '') {
     counterElt.style = 'color: #FF5555;';
   } else {
-    counterElt.style = 'color: #000;';
+    counterElt.style = '';
   }
 }
 
-- 
cgit v1.2.1


From 6df316c881b533731faa77494ea01533e35f8dc7 Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Sat, 10 Dec 2022 13:54:29 +0000
Subject: LDSR cache / optimization / opt_channelslast

---
 extensions-builtin/LDSR/ldsr_model_arch.py    | 40 +++++++++++++++++++--------
 extensions-builtin/LDSR/scripts/ldsr_model.py |  1 +
 2 files changed, 29 insertions(+), 12 deletions(-)

diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py
index a87d1ef9..9ec4e67e 100644
--- a/extensions-builtin/LDSR/ldsr_model_arch.py
+++ b/extensions-builtin/LDSR/ldsr_model_arch.py
@@ -11,25 +11,41 @@ from omegaconf import OmegaConf
 
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.util import instantiate_from_config, ismap
+from modules import shared, sd_hijack
 
 warnings.filterwarnings("ignore", category=UserWarning)
 
+cached_ldsr_model: torch.nn.Module = None
+
 
 # Create LDSR Class
 class LDSR:
     def load_model_from_config(self, half_attention):
-        print(f"Loading model from {self.modelPath}")
-        pl_sd = torch.load(self.modelPath, map_location="cpu")
-        sd = pl_sd["state_dict"]
-        config = OmegaConf.load(self.yamlPath)
-        config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
-        model = instantiate_from_config(config.model)
-        model.load_state_dict(sd, strict=False)
-        model.cuda()
-        if half_attention:
-            model = model.half()
-
-        model.eval()
+        global cached_ldsr_model
+
+        if shared.opts.ldsr_cached and cached_ldsr_model is not None:
+            print(f"Loading model from cache")
+            model: torch.nn.Module = cached_ldsr_model
+        else:
+            print(f"Loading model from {self.modelPath}")
+            pl_sd = torch.load(self.modelPath, map_location="cpu")
+            sd = pl_sd["state_dict"]
+            config = OmegaConf.load(self.yamlPath)
+            config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
+            model: torch.nn.Module = instantiate_from_config(config.model)
+            model.load_state_dict(sd, strict=False)
+            model = model.to(shared.device)
+            if half_attention:
+                model = model.half()
+            if shared.cmd_opts.opt_channelslast:
+                model = model.to(memory_format=torch.channels_last)
+
+            sd_hijack.model_hijack.hijack(model) # apply optimization
+            model.eval()
+
+            if shared.opts.ldsr_cached:
+                cached_ldsr_model = model
+
         return {"model": model}
 
     def __init__(self, model_path, yaml_path):
diff --git a/extensions-builtin/LDSR/scripts/ldsr_model.py b/extensions-builtin/LDSR/scripts/ldsr_model.py
index 5c96037d..29d5f94e 100644
--- a/extensions-builtin/LDSR/scripts/ldsr_model.py
+++ b/extensions-builtin/LDSR/scripts/ldsr_model.py
@@ -59,6 +59,7 @@ def on_ui_settings():
     import gradio as gr
 
     shared.opts.add_option("ldsr_steps", shared.OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}, section=('upscaling', "Upscaling")))
+    shared.opts.add_option("ldsr_cached", shared.OptionInfo(False, "Cache LDSR model in memory", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")))
 
 
 script_callbacks.on_ui_settings(on_ui_settings)
-- 
cgit v1.2.1


From 1581d5a1674fbbeaf047b79f3a138781d6322e6e Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Sat, 10 Dec 2022 14:07:27 +0000
Subject: Made device agnostic

---
 extensions-builtin/LDSR/ldsr_model_arch.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py
index 9ec4e67e..8b048ae0 100644
--- a/extensions-builtin/LDSR/ldsr_model_arch.py
+++ b/extensions-builtin/LDSR/ldsr_model_arch.py
@@ -110,7 +110,8 @@ class LDSR:
         down_sample_method = 'Lanczos'
 
         gc.collect()
-        torch.cuda.empty_cache()
+        if torch.cuda.is_available:
+            torch.cuda.empty_cache()
 
         im_og = image
         width_og, height_og = im_og.size
@@ -147,7 +148,9 @@ class LDSR:
 
         del model
         gc.collect()
-        torch.cuda.empty_cache()
+        if torch.cuda.is_available:
+            torch.cuda.empty_cache()
+
         return a
 
 
@@ -162,7 +165,7 @@ def get_cond(selected_path):
     c = rearrange(c, '1 c h w -> 1 h w c')
     c = 2. * c - 1.
 
-    c = c.to(torch.device("cuda"))
+    c = c.to(shared.device)
     example["LR_image"] = c
     example["image"] = c_up
 
-- 
cgit v1.2.1


From a1c8ad88283f7b3e861e4722c71e39bf71eec744 Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Sat, 10 Dec 2022 11:02:47 -0500
Subject: unload depth model if medvram/lowvram enabled

---
 modules/lowvram.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/modules/lowvram.py b/modules/lowvram.py
index aa464a95..042a0254 100644
--- a/modules/lowvram.py
+++ b/modules/lowvram.py
@@ -55,18 +55,20 @@ def setup_for_low_vram(sd_model, use_medvram):
     if hasattr(sd_model.cond_stage_model, 'model'):
         sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
 
-    # remove three big modules, cond, first_stage, and unet from the model and then
+    # remove four big modules, cond, first_stage, depth (if applicable), and unet from the model and then
     # send the model to GPU. Then put modules back. the modules will be in CPU.
-    stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None
+    stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), sd_model.model
+    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None, None
     sd_model.to(devices.device)
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored
+    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored
 
-    # register hooks for those the first two models
+    # register hooks for those the first three models
     sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
     sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
     sd_model.first_stage_model.encode = first_stage_model_encode_wrap
     sd_model.first_stage_model.decode = first_stage_model_decode_wrap
+    if sd_model.depth_model:
+        sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
     parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
 
     if hasattr(sd_model.cond_stage_model, 'model'):
-- 
cgit v1.2.1


From bd81a09eacf02dad095b98094ab936f276d0343f Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Sat, 10 Dec 2022 11:29:26 -0500
Subject: fix support for 2.0 inpainting model while maintaining support for
 1.5 inpainting model

---
 modules/sd_hijack_inpainting.py | 3 +--
 modules/sd_models.py            | 1 +
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 938f9a58..5018b047 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -324,12 +324,11 @@ def should_hijack_inpainting(checkpoint_info):
 
 def do_inpainting_hijack():
     # most of this stuff seems to no longer be needed because it is already included into SD2.0
-    # LatentInpaintDiffusion remains because SD2.0's LatentInpaintDiffusion can't be loaded without specifying a checkpoint
     # p_sample_plms is needed because PLMS can't work with dicts as conditionings
     # this file should be cleaned up later if weverything tuens out to work fine
 
     # ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
-    ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
+    # ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
 
     # ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
     # ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5b37f3fe..b64f573f 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -296,6 +296,7 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.use_ema = False
         sd_config.model.params.conditioning_key = "hybrid"
         sd_config.model.params.unet_config.params.in_channels = 9
+        sd_config.model.params.finetune_keys = None
 
         # Create a "fake" config with a different name so that we know to unload it when switching models.
         checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
-- 
cgit v1.2.1


From 8bcdd50461090a2dd238082b33f4c1423378ebbd Mon Sep 17 00:00:00 2001
From: wywywywy <wywywywy@gmail.com>
Date: Sat, 10 Dec 2022 18:57:18 +0000
Subject: Add safetensors support to LDSR

---
 extensions-builtin/LDSR/ldsr_model_arch.py    | 10 ++++++++--
 extensions-builtin/LDSR/scripts/ldsr_model.py |  8 ++++++--
 2 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py
index 8b048ae0..f5bd8ae4 100644
--- a/extensions-builtin/LDSR/ldsr_model_arch.py
+++ b/extensions-builtin/LDSR/ldsr_model_arch.py
@@ -1,3 +1,4 @@
+import os
 import gc
 import time
 import warnings
@@ -8,6 +9,7 @@ import torchvision
 from PIL import Image
 from einops import rearrange, repeat
 from omegaconf import OmegaConf
+import safetensors.torch
 
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.util import instantiate_from_config, ismap
@@ -28,8 +30,12 @@ class LDSR:
             model: torch.nn.Module = cached_ldsr_model
         else:
             print(f"Loading model from {self.modelPath}")
-            pl_sd = torch.load(self.modelPath, map_location="cpu")
-            sd = pl_sd["state_dict"]
+            _, extension = os.path.splitext(self.modelPath)
+            if extension.lower() == ".safetensors":
+                pl_sd = safetensors.torch.load_file(self.modelPath, device="cpu")
+            else:
+                pl_sd = torch.load(self.modelPath, map_location="cpu")
+            sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd
             config = OmegaConf.load(self.yamlPath)
             config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
             model: torch.nn.Module = instantiate_from_config(config.model)
diff --git a/extensions-builtin/LDSR/scripts/ldsr_model.py b/extensions-builtin/LDSR/scripts/ldsr_model.py
index 29d5f94e..b8cff29b 100644
--- a/extensions-builtin/LDSR/scripts/ldsr_model.py
+++ b/extensions-builtin/LDSR/scripts/ldsr_model.py
@@ -25,6 +25,7 @@ class UpscalerLDSR(Upscaler):
         yaml_path = os.path.join(self.model_path, "project.yaml")
         old_model_path = os.path.join(self.model_path, "model.pth")
         new_model_path = os.path.join(self.model_path, "model.ckpt")
+        safetensors_model_path = os.path.join(self.model_path, "model.safetensors")
         if os.path.exists(yaml_path):
             statinfo = os.stat(yaml_path)
             if statinfo.st_size >= 10485760:
@@ -33,8 +34,11 @@ class UpscalerLDSR(Upscaler):
         if os.path.exists(old_model_path):
             print("Renaming model from model.pth to model.ckpt")
             os.rename(old_model_path, new_model_path)
-        model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
-                                   file_name="model.ckpt", progress=True)
+        if os.path.exists(safetensors_model_path):
+            model = safetensors_model_path
+        else:
+            model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
+                                       file_name="model.ckpt", progress=True)
         yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
                                   file_name="project.yaml", progress=True)
 
-- 
cgit v1.2.1


From 303df25cc287365b43ede22e0c082ae06aa452b2 Mon Sep 17 00:00:00 2001
From: Bwin4L <Bwin4L@users.noreply.github.com>
Date: Sat, 10 Dec 2022 22:58:06 +0100
Subject: Make the generated image count only count new images in the currently
 active tab

---
 javascript/notification.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/javascript/notification.js b/javascript/notification.js
index f96de313..040a3afa 100644
--- a/javascript/notification.js
+++ b/javascript/notification.js
@@ -15,7 +15,7 @@ onUiUpdate(function(){
         }
     }
 
-    const galleryPreviews = gradioApp().querySelectorAll('img.h-full.w-full.overflow-hidden');
+    const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] img.h-full.w-full.overflow-hidden');
 
     if (galleryPreviews == null) return;
 
-- 
cgit v1.2.1


From 7b0a28f8ee937048f594211169565a361a692d2a Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Sun, 11 Dec 2022 02:17:14 -0800
Subject: fix hints file typo

---
 javascript/hints.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/javascript/hints.js b/javascript/hints.js
index 47e24616..28e1651b 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -17,7 +17,7 @@ titles = {
     "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
     "\u{1f4c2}": "Open images output directory",
     "\u{1f4be}": "Save style",
-    "\U0001F5D1": "Clear prompt"
+    "\U0001F5D1": "Clear prompt",
     "\u{1f4cb}": "Apply selected styles to current prompt",
 
     "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
-- 
cgit v1.2.1


From d5a9de16620869e0e5521a0a07d58f226ed661af Mon Sep 17 00:00:00 2001
From: repligator <114337591+repligator@users.noreply.github.com>
Date: Sun, 11 Dec 2022 09:39:56 -0500
Subject: Update hints.js - DPM adaptive

Added mouse-over for DPM adaptive
---
 javascript/hints.js | 1 +
 1 file changed, 1 insertion(+)

diff --git a/javascript/hints.js b/javascript/hints.js
index 47e24616..ab087530 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -6,6 +6,7 @@ titles = {
 	"GFPGAN": "Restore low quality faces using GFPGAN neural network",
 	"Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help",
 	"DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
+	"DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution", 
 
 	"Batch count": "How many batches of images to create",
 	"Batch size": "How many image to create in a single batch",
-- 
cgit v1.2.1


From 59c6511494c55a578eecdf71fb4590b6bd5d04a7 Mon Sep 17 00:00:00 2001
From: Dean van Dugteren <31391056+deanpress@users.noreply.github.com>
Date: Sun, 11 Dec 2022 17:08:51 +0100
Subject: fix: fallback model_checkpoint if it's empty

This fixes the following error when SD attempts to start with a deleted checkpoint:

```
Traceback (most recent call last):
  File "D:\Web\stable-diffusion-webui\launch.py", line 295, in <module>
    start()
  File "D:\Web\stable-diffusion-webui\launch.py", line 290, in start
    webui.webui()
  File "D:\Web\stable-diffusion-webui\webui.py", line 132, in webui
    initialize()
  File "D:\Web\stable-diffusion-webui\webui.py", line 62, in initialize
    modules.sd_models.load_model()
  File "D:\Web\stable-diffusion-webui\modules\sd_models.py", line 283, in load_model
    checkpoint_info = checkpoint_info or select_checkpoint()
  File "D:\Web\stable-diffusion-webui\modules\sd_models.py", line 117, in select_checkpoint
    checkpoint_info = checkpoints_list.get(model_checkpoint, None)
TypeError: unhashable type: 'list'
```
---
 modules/sd_models.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5b37f3fe..b6d75db7 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -111,6 +111,10 @@ def model_hash(filename):
 
 def select_checkpoint():
     model_checkpoint = shared.opts.sd_model_checkpoint
+    
+    if len(model_checkpoint) == 0:
+        model_checkpoint = shared.default_sd_model_file
+        
     checkpoint_info = checkpoints_list.get(model_checkpoint, None)
     if checkpoint_info is not None:
         return checkpoint_info
-- 
cgit v1.2.1


From ec0a48826fb41c1b1baab45a9030f7eb55568fd0 Mon Sep 17 00:00:00 2001
From: MrCheeze <fishycheeze@yahoo.ca>
Date: Sun, 11 Dec 2022 10:19:46 -0500
Subject: unconditionally set use_ema=False if value not specified (True never
 worked, and all configs except v1-inpainting-inference.yaml already correctly
 set it to False)

---
 modules/sd_models.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index b64f573f..f36b299f 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -293,7 +293,6 @@ def load_model(checkpoint_info=None):
     if should_hijack_inpainting(checkpoint_info):
         # Hardcoded config for now...
         sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
-        sd_config.model.params.use_ema = False
         sd_config.model.params.conditioning_key = "hybrid"
         sd_config.model.params.unet_config.params.in_channels = 9
         sd_config.model.params.finetune_keys = None
@@ -301,6 +300,9 @@ def load_model(checkpoint_info=None):
         # Create a "fake" config with a different name so that we know to unload it when switching models.
         checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
 
+    if not hasattr(sd_config.model.params, "use_ema"):
+        sd_config.model.params.use_ema = False
+
     do_inpainting_hijack()
 
     if shared.cmd_opts.no_half:
-- 
cgit v1.2.1


From 960293d6b24f380f5744c94c9a46acaae6cc8c04 Mon Sep 17 00:00:00 2001
From: Dean Hopkins <deanhopkins@gmail.com>
Date: Sun, 11 Dec 2022 19:16:44 +0000
Subject: API endpoint to refresh checkpoints

API endpoint to refresh checkpoints
---
 modules/api/api.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/modules/api/api.py b/modules/api/api.py
index 89935a70..14d0baaa 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -96,6 +96,7 @@ class Api:
         self.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
         self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
         self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
+        self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
 
     def add_api_route(self, path: str, endpoint, **kwargs):
         if shared.cmd_opts.api_auth:
@@ -321,6 +322,9 @@ class Api:
 
     def get_artists(self):
         return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
+    
+    def refresh_checkpoints(self):
+        shared.refresh_checkpoints()
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)
-- 
cgit v1.2.1


From 00ca6a6db4674713a10d1de312559cb924ed3c55 Mon Sep 17 00:00:00 2001
From: ThereforeGames <95403634+ThereforeGames@users.noreply.github.com>
Date: Sun, 11 Dec 2022 17:59:59 -0500
Subject: Add files via upload

---
 venv/Lib/site-packages/aenum/CHANGES               |  489 ++
 venv/Lib/site-packages/aenum/LICENSE               |   32 +
 venv/Lib/site-packages/aenum/__init__.py           | 4111 ++++++++++++
 .../aenum/__pycache__/__init__.cpython-39.pyc      |  Bin 0 -> 104964 bytes
 .../aenum/__pycache__/_py3.cpython-39.pyc          |  Bin 0 -> 686 bytes
 venv/Lib/site-packages/aenum/_py2.py               |    7 +
 venv/Lib/site-packages/aenum/_py3.py               |   12 +
 venv/Lib/site-packages/aenum/doc/aenum.rst         | 1568 +++++
 venv/Lib/site-packages/aenum/test.py               | 6832 ++++++++++++++++++++
 venv/Lib/site-packages/aenum/test_v3.py            | 1982 ++++++
 venv/Lib/site-packages/blendmodes/__init__.py      |    2 +
 .../blendmodes/__pycache__/__init__.cpython-39.pyc |  Bin 0 -> 285 bytes
 .../blendmodes/__pycache__/blend.cpython-39.pyc    |  Bin 0 -> 13302 bytes
 .../__pycache__/blendtype.cpython-39.pyc           |  Bin 0 -> 2850 bytes
 venv/Lib/site-packages/blendmodes/blend.py         |  511 ++
 venv/Lib/site-packages/blendmodes/blendtype.py     |   72 +
 venv/Lib/site-packages/blendmodes/imagetools.py    |   48 +
 17 files changed, 15666 insertions(+)
 create mode 100644 venv/Lib/site-packages/aenum/CHANGES
 create mode 100644 venv/Lib/site-packages/aenum/LICENSE
 create mode 100644 venv/Lib/site-packages/aenum/__init__.py
 create mode 100644 venv/Lib/site-packages/aenum/__pycache__/__init__.cpython-39.pyc
 create mode 100644 venv/Lib/site-packages/aenum/__pycache__/_py3.cpython-39.pyc
 create mode 100644 venv/Lib/site-packages/aenum/_py2.py
 create mode 100644 venv/Lib/site-packages/aenum/_py3.py
 create mode 100644 venv/Lib/site-packages/aenum/doc/aenum.rst
 create mode 100644 venv/Lib/site-packages/aenum/test.py
 create mode 100644 venv/Lib/site-packages/aenum/test_v3.py
 create mode 100644 venv/Lib/site-packages/blendmodes/__init__.py
 create mode 100644 venv/Lib/site-packages/blendmodes/__pycache__/__init__.cpython-39.pyc
 create mode 100644 venv/Lib/site-packages/blendmodes/__pycache__/blend.cpython-39.pyc
 create mode 100644 venv/Lib/site-packages/blendmodes/__pycache__/blendtype.cpython-39.pyc
 create mode 100644 venv/Lib/site-packages/blendmodes/blend.py
 create mode 100644 venv/Lib/site-packages/blendmodes/blendtype.py
 create mode 100644 venv/Lib/site-packages/blendmodes/imagetools.py

diff --git a/venv/Lib/site-packages/aenum/CHANGES b/venv/Lib/site-packages/aenum/CHANGES
new file mode 100644
index 00000000..e8799f6d
--- /dev/null
+++ b/venv/Lib/site-packages/aenum/CHANGES
@@ -0,0 +1,489 @@
+3.1.8
+=====
+
+recalculate bits used after all flags created (sometimes needed when a custom
+`__new__` is in place.
+
+
+3.1.7
+=====
+
+update flag creation to (possibly) add bitwise operator methods to newly
+created flags
+
+update extend_enum() to work with 3.11 flags
+
+
+3.1.6
+=====
+
+Update `dir()` on mixed enums to include mixed data type methods and
+attributes.
+
+Rename `enum_property` to `property` to match stdlib.  Recommended usage is
+`aenum.property` (prefix with module name).
+
+Remove quadritic creation behavior.
+
+
+BREAKING CHANGE BUG FIX that won't affect most people
+
+Enums with a custom `__new__` that:
+
+- use the enum machinery to generate the values; AND
+- have keyword arguments set to a default (like `None`)
+
+will fail to generate a missing value.  To fix: remove the default value and
+instead specify it on the member creation line.
+
+BREAKING CHANGE
+
+In Python 3.11 the `str()` of mixed enums will now match its `format()` which
+will be the normal `str()` of the data type -- so for an IntEnum you'll see
+`5` instead of `Perm.R|X`.  This affects IntEnum, StrEnum, and IntFlag.
+
+
+3.1.5
+=====
+
+fix support of `auto()` kwds
+
+
+3.1.3
+=====
+
+rename `aenum.property` to `aenum.enum_property`
+
+fix `enum_property` to work with `_init_` attributes
+
+
+3.1.2
+=====
+
+fix `extend_enum()` for unhashable values
+
+
+3.1.1
+=====
+
+fix `extend_enum()` for most cases
+
+
+3.1.0
+=====
+
+AddValue is similar to the old AutoNumber: it will always activate, but
+uses _generate_next_value_ to get the next value (so the user has some
+control over the return data type instead of always getting an int).
+
+
+BREAKING CHANGES
+
+AutoValue is gone.  It was superflous and its removal simplified the code.
+Simply put the fields needed in an `_init_` and `_generate_next_value_`
+will be called to supply the missing values (this is probably already what
+is happening).
+
+
+
+3.0.0
+=====
+
+standard Enum usage is unchanged
+
+BREAKING CHANGES
+
+Enum
+- the more esoteric method of creating Enums have been modified or removed
+  - AutoNumber setting is gone, inherit from AutoNumberEnum instead
+  - creating members without specifying anything is removed (if you don't
+    know what this means, you weren't doing it)
+
+Flag
+- unique flags are canonical (i.e. flags with powers of two values such as
+  1, 2, 4, 8, 16, etc.)
+- non-unique flags are aliases (i.e. values such as 3 or 7)
+- iteration of Flag and flag members only uses canonical flags
+
+
+ENHANCEMENTS
+
+Member creation has been redone to match Python 3.10's methods.  This also
+allows all supported Pythons (2.7, 3.3+) to use the __set_name__ and
+__init_subclass__ protocols (more robustly than in aenum 2.2.5)
+
+
+CHANGES
+
+enum_property() has been renamed to property() (old name still available, but
+deprecated).
+
+bin() replacement shows negative integers in twos-complement
+
+
+
+
+2.2.5
+=====
+
+call __init_subclass__ after members have been added, and in Pythons < 3.6
+call __set_name__ in Pythons < 3.6
+do not convert/disallow private names
+add iteration/len support to NamedConstant
+
+
+2.2.4
+=====
+
+add support to Constant to retrieve members by value
+
+    --> class K(Constant):
+    ...     one = 1
+    ...     two = 2
+
+    --> K.one
+    <K.one: 1>
+
+    --> K(1)
+    <K.one: 1>
+
+add pickle/deepcopy support to Constant
+
+add support for Constant to use other Constant values
+  (resulting members /are not/ the same)
+
+    --> class C(Constant)
+    ...     one = K.one
+    ...     three = 3
+
+    --> C.one == K.one
+    True
+
+    --> C.one is K.one
+    False
+
+AutoNumber and auto() now work together
+
+Enum members are now added to the class as enum_property, which supports
+unshadowing of parent class attributes when called on an Enum member:
+
+    --> class StrEnum(str, Enum):
+    ...     lower = 'lower'
+    ...     upper = 'upper'
+    ...     mixed = 'mixed'
+    
+    --> StrEnum.lower
+    <StrEnum.lower: 'lower'>
+
+    --> StrEnum.lower.upper()
+    'LOWER'
+
+    --> StrEnum.upper
+    <StrEnum.upper: 'upper'>
+
+    --> StrEnum.upper.upper()
+    'UPPER'
+
+
+2.2.3
+=====
+
+use members' type's methods __str__, __repr__, __format__, and
+__reduce_ex__ if directly assigned in Enum class body; i.e.:
+
+    --> class Color(str, Enum):
+    ...     red = 'red'
+    ...     green = 'green'
+    ...     blue = 'blue'
+    ...     __str__ = str.__str__
+
+    --> print(repr(Color.green))
+    <Color.green: 'green'>
+
+    --> print(Color.green)
+    green
+
+
+2.2.2
+=====
+
+replace _RouteClassAttributeToGetattr with enum_property (it is still
+available as an alias)
+
+support constant() and auto() being used together:
+
+    --> class Fruit(Flag):
+    ...    _order_ = 'apple banana lemon orange'
+    ...    apple = auto()
+    ...    banana = auto()
+    ...    lemon = auto()
+    ...    orange = auto()
+    ...    CitrusTypes = constant(lemon | orange)
+
+    --> list(Fruit)
+    [Fruit.apple, Fruit.banana, Fruit.lemon, Fruit.orange]
+
+    --> list(Fruit.CitrusTypes)
+    [Fruit.orange, Fruit.lemon]
+
+    --> Fruit.orange in Fruit.CitrusTypes
+    True
+
+
+2.2.1
+=====
+
+allow Enums to be called without a value
+
+    class Color(Enum):
+        black = 0
+        red = 1
+        green = 2
+        blue = 3
+        #
+        @classmethod
+        def _missing_value_(cls, value):
+            if value is no_arg:
+                return cls.black
+
+    >>> Color()
+    <Color.black: 0>
+
+allow Enum name use while constructing Enum (Python 3.4+ only)
+
+    --> class Color(Enum):
+    ...     _order_ = 'BLACK WHITE'
+    ...     BLACK = Color('black', '#000')
+    ...     WHITE = Color('white', '#fff')
+    ...     #
+    ...     def __init__(self, label, hex):
+    ...         self.label = label
+    ...         self.hex = hex
+
+
+2.2.0
+=====
+
+BREAKING CHANGE
+---------------
+In Python 3+ classes defined inside an Enum no longer become members by
+default; in Python 2 they still become members, but see below.
+
+For cross-compatibility and full control two decorators are provided:
+
+- @member  --> forces item to become a member
+- @nonmember  --> excludes item from becoming a member
+
+So to have an Enum that behaves the same in Python 2 and 3, use the
+decorators (and other compatibility shims):
+
+    class Color(Enum):
+
+        _order_ = 'red green blue'
+
+        red = 1
+        green = 2
+        blue = 3
+
+        @nonmember
+        class Shades(Enum):
+
+            _order_ = 'light medium dark'
+
+            light = 1
+            medium = 2
+            dark = 3
+
+
+2.1.4
+=====
+
+EnumMeta:
+- change __member_new__ to __new_member__ (as the stdlib enum does)
+- assign member name to enum() instances (an Enum helper for defining members)
+- handle empty iterables when using functional API
+- make auto() work with previous enum members
+- keep searching mixins until base class is found
+
+Enum:
+- fix bug in Flag checks (ensure it is a Flag before checking the name)
+- add multiple mixin support
+- do not allow blank names (functional API)
+- raise TypeError if _missing_* returns wrong type
+- fix __format__ to honor custom __str__
+
+extend_enum:
+- support stdlib Enums
+- use _generate_next_value_ if value not provided
+
+general:
+- standardize exception formatting
+- use getfullargspec() in Python 3 (avoids deprecation warnings)
+
+
+2.1.2
+=====
+
+when order is callable, save it for subclass use
+
+
+2.1.1
+=====
+
+correctly raise TypeError for non-Enum containment checks
+support combining names with | for Flag key access
+support _order_ being a callable
+
+
+2.1.0
+=====
+
+support Flags being combined with other data types:
+- add _create_pseudo_member_values_ 
+- add default __new__ and temporary _init_ 
+
+
+2.0.10
+======
+
+ensure _ignore_ is set when _settings_ specified in body which includes
+AutoValue
+
+make Flag members iterable
+
+
+2.0.9
+=====
+
+fix missing comma in __all__
+fix extend_enum with custom __new__ methods
+fix MultiValue with AutoNumber without _init_
+
+
+2.0.8
+=====
+
+extend_enum now handles aliases and multivalues correctly
+
+
+2.0.7
+=====
+
+support mixin types with extend_enum
+init and AutoNumber can now work together
+add test for new Enum using EnumMeta
+add tests for variations of multivalue and init
+prevent deletion of NamedConstant.constant
+
+
+2.0.6
+=====
+
+constants cannot be deleted (they already couldn't be changed)
+constants can be used to define other constants
+
+
+2.0.5
+=====
+
+_init_ and MultiValue can now work together
+
+
+2.0.4
+=====
+
+_init_ and AutoValue (and _generate_next_value_) can now work together to
+supply missing values even when some of the required values per member are
+absent
+
+
+2.0.3
+=====
+
+add _missing_value_ and _missing_name_ methods, deprecate _missing_
+make enum instances comparable
+
+
+2.0.2
+=====
+
+both EnumMeta.__getattr__ and Enum.__new__ fall back to _missing_
+
+
+2.0.1
+=====
+
+auto() now works with other data types
+AutoNumber supports legacy Enums (fixed regression)
+
+
+2.0.0
+=====
+
+Flag and IntFlag added.
+
+
+1.4.7
+=====
+
+fix %-interpolation bug
+defined SqlLiteEnum only if sqlite exists
+support pyflakes
+
+
+1.4.6
+=====
+
+version numbering error
+
+
+1.4.5
+=====
+
+revert AutoNumberEnum to custom __new__ instead of AutoNumber
+use _ignore_ to shield against AutoNumber magic
+inherit start and init settings from base Enums
+
+
+1.4.4
+=====
+
+enabled export as a decorator
+enabled _order_ to replace __order__
+enabled python2 support for settings, init, and start
+
+
+1.4.3
+=====
+
+support _ignore_ for dynamically creating class bodies
+
+
+1.4.2
+=====
+
+MultiValue, NoAlias, Unique, and init now work with Python 2
+
+
+1.4.1
+=====
+
+Py3: added Enum creation flags: Auto, MultiValue, NoAlias, Unique
+
+fixed extend_enum to honor Enum flags
+
+
+1.4.0
+=====
+
+When possible aenum inherits from Python's own enum.
+
+Breaking change: enum members now default to evaluating as True to maintain
+compatibility with the stdlib.
+
+Add your own __bool__ (__nonzero__ in Python 2) if need this behavior:
+
+    def __bool__(self):
+        return bool(self.value)
+    __nonzero__ = __bool__
+
diff --git a/venv/Lib/site-packages/aenum/LICENSE b/venv/Lib/site-packages/aenum/LICENSE
new file mode 100644
index 00000000..b20361ce
--- /dev/null
+++ b/venv/Lib/site-packages/aenum/LICENSE
@@ -0,0 +1,32 @@
+Copyright (c) 2015, 2016, 2017, 2018 Ethan Furman.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+    Redistributions of source code must retain the above
+    copyright notice, this list of conditions and the
+    following disclaimer.
+
+    Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following
+    disclaimer in the documentation and/or other materials
+    provided with the distribution.
+
+    Neither the name Ethan Furman nor the names of any
+    contributors may be used to endorse or promote products
+    derived from this software without specific prior written
+    permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/venv/Lib/site-packages/aenum/__init__.py b/venv/Lib/site-packages/aenum/__init__.py
new file mode 100644
index 00000000..26c504cc
--- /dev/null
+++ b/venv/Lib/site-packages/aenum/__init__.py
@@ -0,0 +1,4111 @@
+"""Python Advanced Enumerations & NameTuples"""
+from __future__ import print_function
+
+# imports
+import sys as _sys
+pyver = _sys.version_info[:2]
+PY2 = pyver < (3, )
+PY3 = pyver >= (3, )
+PY2_6 = (2, 6)
+PY3_3 = (3, 3)
+PY3_4 = (3, 4)
+PY3_5 = (3, 5)
+PY3_6 = (3, 6)
+PY3_11 = (3, 11)
+
+import re
+
+_bltin_property = property
+_bltin_bin = bin
+
+try:
+    from collections import OrderedDict
+except ImportError:
+    OrderedDict = dict
+from collections import defaultdict
+try:
+    import sqlite3
+except ImportError:
+    sqlite3 = None
+
+try:
+    RecursionError
+except NameError:
+    # python3.4
+    RecursionError = RuntimeError
+
+from operator import or_ as _or_, and_ as _and_, xor as _xor_, inv as _inv_
+from operator import abs as _abs_, add as _add_, floordiv as _floordiv_
+from operator import lshift as _lshift_, rshift as _rshift_, mod as _mod_
+from operator import mul as _mul_, neg as _neg_, pos as _pos_, pow as _pow_
+from operator import truediv as _truediv_, sub as _sub_
+
+if PY2:
+    from ._py2 import *
+if PY3:
+    from ._py3 import *
+
+obj_type = type
+
+__all__ = [
+        'NamedConstant', 'constant', 'skip', 'nonmember', 'member', 'no_arg',
+        'Enum', 'IntEnum', 'AutoNumberEnum', 'OrderedEnum', 'UniqueEnum',
+        'StrEnum', 'UpperStrEnum', 'LowerStrEnum',
+        'Flag', 'IntFlag',
+        'AddValue', 'MagicValue', 'MultiValue', 'NoAlias', 'Unique',
+        'AddValueEnum', 'MultiValueEnum', 'NoAliasEnum',
+        'enum', 'extend_enum', 'unique', 'property',
+        'NamedTuple', 'SqliteEnum',
+        'FlagBoundary', 'STRICT', 'CONFORM', 'EJECT', 'KEEP',
+        'add_stdlib_integration', 'remove_stdlib_integration'
+        ]
+
+if sqlite3 is None:
+    __all__.remove('SqliteEnum')
+
+version = 3, 1, 12, 1
+
+# shims
+try:
+    any
+except NameError:
+    def any(iterable):
+        for element in iterable:
+            if element:
+                return True
+        return False
+
+try:
+    unicode
+    unicode = unicode
+except NameError:
+    # In Python 3 unicode no longer exists (it's just str)
+    unicode = str
+
+try:
+    basestring
+    basestring = bytes, unicode
+except NameError:
+    # In Python 2 basestring is the ancestor of both str and unicode
+    # in Python 3 it's just str, but was missing in 3.1
+    basestring = str,
+
+try:
+    long
+    baseinteger = int, long
+except NameError:
+    baseinteger = int,
+    long = int
+# deprecated
+baseint = baseinteger
+
+try:
+    NoneType
+except NameError:
+    NoneType = type(None)
+
+try:
+    # derive from stdlib enum if possible
+    import enum
+    if hasattr(enum, 'version'):
+        raise ImportError('wrong version')
+    else:
+        from enum import EnumMeta as StdlibEnumMeta, Enum as StdlibEnum, IntEnum as StdlibIntEnum
+        StdlibFlag = StdlibIntFlag = StdlibStrEnum = StdlibReprEnum = None
+except ImportError:
+    StdlibEnumMeta = StdlibEnum = StdlibIntEnum = StdlibIntFlag = StdlibFlag = StdlibStrEnum = None
+
+if StdlibEnum:
+    try:
+        from enum import IntFlag as StdlibIntFlag, Flag as StdlibFlag
+    except ImportError:
+        pass
+    try:
+        from enum import StrEnum as StdlibStrEnum
+    except ImportError:
+        pass
+    try:
+        from enum import ReprEnum as StdlibReprEnum
+    except ImportError:
+        pass
+
+
+
+
+# helpers
+# will be exported later
+MagicValue = AddValue = MultiValue = NoAlias = Unique = None
+
+def _bit_count(num):
+    """
+    return number of set bits
+
+    Counting bits set, Brian Kernighan's way*
+
+        unsigned int v;          // count the number of bits set in v
+        unsigned int c;          // c accumulates the total bits set in v
+        for (c = 0; v; c++)
+        {   v &= v - 1;  }       //clear the least significant bit set
+
+    This method goes through as many iterations as there are set bits. So if we
+    have a 32-bit word with only the high bit set, then it will only go once
+    through the loop.
+
+    * The C Programming Language 2nd Ed., Kernighan & Ritchie, 1988.
+
+    This works because each subtraction "borrows" from the lowest 1-bit. For
+    example:
+
+          loop pass 1     loop pass 2
+          -----------     -----------
+               101000          100000
+             -      1        -      1
+             = 100111        = 011111
+             & 101000        & 100000
+             = 100000        =      0
+
+    It is an excellent technique for Python, since the size of the integer need
+    not be determined beforehand.
+
+    (from https://wiki.python.org/moin/BitManipulation)
+    """
+    count = 0
+    while num:
+        num &= num - 1
+        count += 1
+    return count
+
+def _is_single_bit(value):
+    """
+    True if only one bit set in value (should be an int)
+    """
+    if value == 0:
+        return False
+    value &= value - 1
+    return value == 0
+
+def _iter_bits_lsb(value):
+    """
+    Return each bit value one at a time.
+
+    >>> list(_iter_bits_lsb(6))
+    [2, 4]
+    """
+
+    while value:
+        bit = value & (~value + 1)
+        yield bit
+        value ^= bit
+
+def bin(value, max_bits=None):
+    """
+    Like built-in bin(), except negative values are represented in
+    twos-compliment, and the leading bit always indicates sign
+    (0=positive, 1=negative).
+
+    >>> bin(10)
+    '0b0 1010'
+    >>> bin(~10)   # ~10 is -11
+    '0b1 0101'
+    """
+
+    ceiling = 2 ** (value).bit_length()
+    if value >= 0:
+        s = _bltin_bin(value + ceiling).replace('1', '0', 1)
+    else:
+        s = _bltin_bin(~value ^ (ceiling - 1) + ceiling)
+    sign = s[:3]
+    digits = s[3:]
+    if max_bits is not None:
+        if len(digits) < max_bits:
+            digits = (sign[-1] * max_bits + digits)[-max_bits:]
+    return "%s %s" % (sign, digits)
+
+
+try:
+    from types import DynamicClassAttribute
+    base = DynamicClassAttribute
+except ImportError:
+    base = object
+    DynamicClassAttribute = None
+
+class property(base):
+    """
+    This is a descriptor, used to define attributes that act differently
+    when accessed through an enum member and through an enum class.
+    Instance access is the same as property(), but access to an attribute
+    through the enum class will look in the class' _member_map_.
+    """
+
+    # inherit from DynamicClassAttribute if we can in order to get `inspect`
+    # support
+
+    def __init__(self, fget=None, fset=None, fdel=None, doc=None):
+        self.fget = fget
+        self.fset = fset
+        self.fdel = fdel
+        # next two lines make property act the same as _bltin_property
+        self.__doc__ = doc or fget.__doc__
+        self.overwrite_doc = doc is None
+        # support for abstract methods
+        self.__isabstractmethod__ = bool(getattr(fget, '__isabstractmethod__', False))
+        # names, if possible
+
+    def getter(self, fget):
+        fdoc = fget.__doc__ if self.overwrite_doc else None
+        result = type(self)(fget, self.fset, self.fdel, fdoc or self.__doc__)
+        result.overwrite_doc = self.__doc__ is None
+        return result
+
+    def setter(self, fset):
+        fdoc = fget.__doc__ if self.overwrite_doc else None
+        result = type(self)(self.fget, fset, self.fdel, self.__doc__)
+        result.overwrite_doc = self.__doc__ is None
+        return result
+
+    def deleter(self, fdel):
+        fdoc = fget.__doc__ if self.overwrite_doc else None
+        result = type(self)(self.fget, self.fset, fdel, self.__doc__)
+        result.overwrite_doc = self.__doc__ is None
+        return result
+
+    def __repr__(self):
+        member = self.ownerclass._member_map_.get(self.name)
+        func = self.fget or self.fset or self.fdel
+        strings = []
+        if member:
+            strings.append('%r' % member)
+        if func:
+            strings.append('function=%s' % func.__name__)
+        return 'property(%s)' % ', '.join(strings)
+
+    def __get__(self, instance, ownerclass=None):
+        if instance is None:
+            try:
+                return ownerclass._member_map_[self.name]
+            except KeyError:
+                raise AttributeError(
+                        '%r has no attribute %r' % (ownerclass, self.name)
+                        )
+        else:
+            if self.fget is not None:
+                return self.fget(instance)
+            else:
+                if self.fset is not None:
+                    raise AttributeError(
+                            'cannot read attribute %r on %r' % (self.name, ownerclass)
+                            )
+                else:
+                    try:
+                        return instance.__dict__[self.name]
+                    except KeyError:
+                        raise AttributeError(
+                                '%r member has no attribute %r' % (ownerclass, self.name)
+                                )
+
+    def __set__(self, instance, value):
+        if self.fset is None:
+            if self.fget is not None:
+                raise AttributeError(
+                        "cannot set attribute %r on <aenum %r>" % (self.name, self.clsname)
+                        )
+            else:
+                instance.__dict__[self.name] = value
+        else:
+            return self.fset(instance, value)
+
+    def __delete__(self, instance):
+        if self.fdel is None:
+            if self.fget or self.fset:
+                raise AttributeError(
+                        "cannot delete attribute %r on <aenum %r>" % (self.name, self.clsname)
+                        )
+            elif self.name in instance.__dict__:
+                del instance.__dict__[self.name]
+            else:
+                raise AttributeError(
+                        "no attribute %r on <aenum %r> member" % (self.name, self.clsname)
+                        )
+        else:
+            return self.fdel(instance)
+
+    def __set_name__(self, ownerclass, name):
+        self.name = name
+        self.clsname = ownerclass.__name__
+        self.ownerclass = ownerclass
+
+_RouteClassAttributeToGetattr = property
+if DynamicClassAttribute is None:
+    DynamicClassAttribute = property
+# deprecated
+enum_property = property
+
+class NonMember(object):
+    """
+    Protects item from becaming an Enum member during class creation.
+    """
+    def __init__(self, value):
+        self.value = value
+
+    def __get__(self, instance, ownerclass=None):
+        return self.value
+skip = nonmember = NonMember
+
+class Member(object):
+    """
+    Forces item to became an Enum member during class creation.
+    """
+    def __init__(self, value):
+        self.value = value
+member = Member
+
+class SentinelType(type):
+    def __repr__(cls):
+        return '<%s>' % cls.__name__
+Sentinel = SentinelType('Sentinel', (object, ), {})
+
+def _is_descriptor(obj):
+    """Returns True if obj is a descriptor, False otherwise."""
+    return (
+            hasattr(obj, '__get__') or
+            hasattr(obj, '__set__') or
+            hasattr(obj, '__delete__'))
+
+
+def _is_dunder(name):
+    """Returns True if a __dunder__ name, False otherwise."""
+    return (len(name) > 4 and
+            name[:2] == name[-2:] == '__' and
+            name[2] != '_' and
+            name[-3] != '_')
+
+
+def _is_sunder(name):
+    """Returns True if a _sunder_ name, False otherwise."""
+    return (len(name) > 2 and
+            name[0] == name[-1] == '_' and
+            name[1] != '_' and
+            name[-2] != '_')
+
+def _is_internal_class(cls_name, obj):
+    # only 3.3 and up, always return False in 3.2 and below
+    if pyver < PY3_3:
+        return False
+    else:
+        qualname = getattr(obj, '__qualname__', False)
+        return not _is_descriptor(obj) and qualname and re.search(r"\.?%s\.\w+$" % cls_name, qualname)
+
+def _is_private_name(cls_name, name):
+    pattern = r'^_%s__\w+[^_]_?$' % (cls_name, )
+    return re.search(pattern, name)
+
+def _power_of_two(value):
+    if value < 1:
+        return False
+    return value == 2 ** _high_bit(value)
+
+def bits(num):
+    if num in (0, 1):
+        return str(num)
+    negative = False
+    if num < 0:
+        negative = True
+        num = ~num
+    result = bits(num>>1) + str(num&1)
+    if negative:
+        result = '1' + ''.join(['10'[d=='1'] for d in result])
+    return result
+
+
+def bit_count(num):
+    """
+        return number of set bits
+
+        Counting bits set, Brian Kernighan's way*
+
+            unsigned int v;          // count the number of bits set in v
+            unsigned int c;          // c accumulates the total bits set in v
+            for (c = 0; v; c++)
+            {   v &= v - 1;  }       //clear the least significant bit set
+
+        This method goes through as many iterations as there are set bits. So if we
+        have a 32-bit word with only the high bit set, then it will only go once
+        through the loop.
+
+        * The C Programming Language 2nd Ed., Kernighan & Ritchie, 1988.
+
+        This works because each subtraction "borrows" from the lowest 1-bit. For example:
+
+              loop pass 1     loop pass 2
+              -----------     -----------
+                   101000          100000
+                 -      1        -      1
+                 = 100111        = 011111
+                 & 101000        & 100000
+                 = 100000        =      0
+
+        It is an excellent technique for Python, since the size of the integer need not
+        be determined beforehand.
+    """
+    count = 0
+    while(num):
+        num &= num - 1
+        count += 1
+    return(count)
+
+def bit_len(num):
+    length = 0
+    while num:
+        length += 1
+        num >>= 1
+    return length
+
+def is_single_bit(num):
+    """
+    True if only one bit set in num (should be an int)
+    """
+    num &= num - 1
+    return num == 0
+
+def _make_class_unpicklable(obj):
+    """
+    Make the given obj un-picklable.
+
+    obj should be either a dictionary, on an Enum
+    """
+    def _break_on_call_reduce(self, proto):
+        raise TypeError('%r cannot be pickled' % self)
+    if isinstance(obj, dict):
+        obj['__reduce_ex__'] = _break_on_call_reduce
+        obj['__module__'] = '<unknown>'
+    else:
+        setattr(obj, '__reduce_ex__', _break_on_call_reduce)
+        setattr(obj, '__module__', '<unknown>')
+
+def _check_auto_args(method):
+    """check if new generate method supports *args and **kwds"""
+    if isinstance(method, staticmethod):
+        method = method.__get__(type)
+    method = getattr(method, 'im_func', method)
+    args, varargs, keywords, defaults = getargspec(method)
+    return varargs is not None and keywords is not None
+
+def _get_attr_from_chain(cls, attr):
+    sentinel = object()
+    for basecls in cls.mro():
+        obj = basecls.__dict__.get(attr, sentinel)
+        if obj is not sentinel:
+            return obj
+
+def _value(obj):
+    if isinstance(obj, (auto, constant)):
+        return obj.value
+    else:
+        return obj
+
+def enumsort(things):
+    """
+    sorts things by value if all same type; otherwise by name
+    """
+    if not things:
+        return things
+    sort_type = type(things[0])
+    if not issubclass(sort_type, tuple):
+        # direct sort or type error
+        if not all((type(v) is sort_type) for v in things[1:]):
+            raise TypeError('cannot sort items of different types')
+        return sorted(things)
+    else:
+        # expecting list of (name, value) tuples
+        sort_type = type(things[0][1])
+        try:
+            if all((type(v[1]) is sort_type) for v in things[1:]):
+                return sorted(things, key=lambda i: i[1])
+            else:
+                raise TypeError('try name sort instead')
+        except TypeError:
+            return sorted(things, key=lambda i: i[0])
+
+def export(collection, namespace=None):
+    """
+    export([collection,] namespace) -> Export members to target namespace.
+
+    If collection is not given, act as a decorator.
+    """
+    if namespace is None:
+        namespace = collection
+        def export_decorator(collection):
+            return export(collection, namespace)
+        return export_decorator
+    elif issubclass(collection, NamedConstant):
+        for n, c in collection.__dict__.items():
+            if isinstance(c, NamedConstant):
+                namespace[n] = c
+    elif issubclass(collection, Enum):
+        data = collection.__members__.items()
+        for n, m in data:
+            namespace[n] = m
+    else:
+        raise TypeError('%r is not a supported collection' % (collection,) )
+    return collection
+
+class _Addendum(object):
+    def __init__(self, dict, doc, ns):
+        # dict is the dict to update with functions
+        # doc is the docstring to put in the dict
+        # ns is the namespace to remove the function names from
+        self.dict = dict
+        self.ns = ns
+        self.added = set()
+    def __call__(self, func):
+        if isinstance(func, (staticmethod, classmethod)):
+            name = func.__func__.__name__
+        elif isinstance(func, (property, _bltin_property)):
+            name = (func.fget or func.fset or func.fdel).__name__
+        else:
+            name = func.__name__
+        self.dict[name] = func
+        self.added.add(name)
+    def resolve(self):
+        ns = self.ns
+        for name in self.added:
+            del ns[name]
+        return self.dict
+
+# Constant / NamedConstant
+
+class constant(object):
+    '''
+    Simple constant descriptor for NamedConstant and Enum use.
+    '''
+    def __init__(self, value, doc=None):
+        self.value = value
+        self.__doc__ = doc
+
+    def __get__(self, *args):
+        return self.value
+
+    def __repr__(self):
+        return '%s(%r)' % (self.__class__.__name__, self.value)
+
+    def __and__(self, other):
+        return _and_(self.value, _value(other))
+
+    def __rand__(self, other):
+        return _and_(_value(other), self.value)
+
+    def __invert__(self):
+        return _inv_(self.value)
+
+    def __or__(self, other):
+        return _or_(self.value, _value(other))
+
+    def __ror__(self, other):
+        return _or_(_value(other), self.value)
+
+    def __xor__(self, other):
+        return _xor_(self.value, _value(other))
+
+    def __rxor__(self, other):
+        return _xor_(_value(other), self.value)
+
+    def __abs__(self):
+        return _abs_(self.value)
+
+    def __add__(self, other):
+        return _add_(self.value, _value(other))
+
+    def __radd__(self, other):
+        return _add_(_value(other), self.value)
+
+    def __neg__(self):
+        return _neg_(self.value)
+
+    def __pos__(self):
+        return _pos_(self.value)
+
+    if PY2:
+        def __div__(self, other):
+            return _div_(self.value, _value(other))
+
+    def __rdiv__(self, other):
+        return _div_(_value(other), (self.value))
+
+    def __floordiv__(self, other):
+        return _floordiv_(self.value, _value(other))
+
+    def __rfloordiv__(self, other):
+        return _floordiv_(_value(other), self.value)
+
+    def __truediv__(self, other):
+        return _truediv_(self.value, _value(other))
+
+    def __rtruediv__(self, other):
+        return _truediv_(_value(other), self.value)
+
+    def __lshift__(self, other):
+        return _lshift_(self.value, _value(other))
+
+    def __rlshift__(self, other):
+        return _lshift_(_value(other), self.value)
+
+    def __rshift__(self, other):
+        return _rshift_(self.value, _value(other))
+
+    def __rrshift__(self, other):
+        return _rshift_(_value(other), self.value)
+
+    def __mod__(self, other):
+        return _mod_(self.value, _value(other))
+
+    def __rmod__(self, other):
+        return _mod_(_value(other), self.value)
+
+    def __mul__(self, other):
+        return _mul_(self.value, _value(other))
+
+    def __rmul__(self, other):
+        return _mul_(_value(other), self.value)
+
+    def __pow__(self, other):
+        return _pow_(self.value, _value(other))
+
+    def __rpow__(self, other):
+        return _pow_(_value(other), self.value)
+
+    def __sub__(self, other):
+        return _sub_(self.value, _value(other))
+
+    def __rsub__(self, other):
+        return _sub_(_value(other), self.value)
+
+    def __set_name__(self, ownerclass, name):
+        self.name = name
+        self.clsname = ownerclass.__name__
+
+
+NamedConstant = None
+
+class _NamedConstantDict(dict):
+    """Track constant order and ensure names are not reused.
+
+    NamedConstantMeta will use the names found in self._names as the
+    Constant names.
+    """
+    def __init__(self):
+        super(_NamedConstantDict, self).__init__()
+        self._names = []
+
+    def __setitem__(self, key, value):
+        """Changes anything not dundered or not a constant descriptor.
+
+        If an constant name is used twice, an error is raised; duplicate
+        values are not checked for.
+
+        Single underscore (sunder) names are reserved.
+        """
+        if _is_sunder(key):
+            raise ValueError(
+                    '_sunder_ names, such as %r, are reserved for future NamedConstant use'
+                    % (key, )
+                    )
+        elif _is_dunder(key):
+            pass
+        elif key in self._names:
+            # overwriting an existing constant?
+            raise TypeError('attempt to reuse name: %r' % (key, ))
+        elif isinstance(value, constant) or not _is_descriptor(value):
+            if key in self:
+                # overwriting a descriptor?
+                raise TypeError('%s already defined as: %r' % (key, self[key]))
+            self._names.append(key)
+        super(_NamedConstantDict, self).__setitem__(key, value)
+
+
+class NamedConstantMeta(type):
+    """
+    Block attempts to reassign NamedConstant attributes.
+    """
+
+    @classmethod
+    def __prepare__(metacls, cls, bases, **kwds):
+        return _NamedConstantDict()
+
+    def __new__(metacls, cls, bases, clsdict):
+        if type(clsdict) is dict:
+            original_dict = clsdict
+            clsdict = _NamedConstantDict()
+            for k, v in original_dict.items():
+                clsdict[k] = v
+        newdict = {}
+        constants = {}
+        for name, obj in clsdict.items():
+            if name in clsdict._names:
+                constants[name] = obj
+                continue
+            elif isinstance(obj, nonmember):
+                obj = obj.value
+            newdict[name] = obj
+        newcls = super(NamedConstantMeta, metacls).__new__(metacls, cls, bases, newdict)
+        newcls._named_constant_cache_ = {}
+        newcls._members_ = {}
+        for name, obj in constants.items():
+            new_k = newcls.__new__(newcls, name, obj)
+            newcls._members_[name] = new_k
+        return newcls
+
+    def __bool__(cls):
+        return True
+
+    def __delattr__(cls, attr):
+        cur_obj = cls.__dict__.get(attr)
+        if NamedConstant is not None and isinstance(cur_obj, NamedConstant):
+            raise AttributeError('cannot delete constant <%s.%s>' % (cur_obj.__class__.__name__, cur_obj._name_))
+        super(NamedConstantMeta, cls).__delattr__(attr)
+
+    def __iter__(cls):
+        return (k for k in cls._members_.values())
+
+    def __reversed__(cls):
+        return (k for k in reversed(cls._members_.values()))
+
+    def __len__(cls):
+        return len(cls._members_)
+
+    __nonzero__ = __bool__
+
+    def __setattr__(cls, name, value):
+        """Block attempts to reassign NamedConstants.
+        """
+        cur_obj = cls.__dict__.get(name)
+        if NamedConstant is not None and isinstance(cur_obj, NamedConstant):
+            raise AttributeError('cannot rebind constant <%s.%s>' % (cur_obj.__class__.__name__, cur_obj._name_))
+        super(NamedConstantMeta, cls).__setattr__(name, value)
+
+constant_dict = _Addendum(
+        dict=NamedConstantMeta.__prepare__('NamedConstant', (object, )),
+        doc="NamedConstants protection.\n\n    Derive from this class to lock NamedConstants.\n\n",
+        ns=globals(),
+        )
+
+@constant_dict
+def __new__(cls, name, value=None, doc=None):
+    if value is None:
+        # lookup, name is value
+        value = name
+        for name, obj in cls.__dict__.items():
+            if isinstance(obj, cls) and obj._value_ == value:
+                return obj
+        else:
+            raise ValueError('%r does not exist in %r' % (value, cls.__name__))
+    cur_obj = cls.__dict__.get(name)
+    if isinstance(cur_obj, NamedConstant):
+        raise AttributeError('cannot rebind constant <%s.%s>' % (cur_obj.__class__.__name__, cur_obj._name_))
+    elif isinstance(value, constant):
+        doc = doc or value.__doc__
+        value = value.value
+    metacls = cls.__class__
+    if isinstance(value, NamedConstant):
+        # constants from other classes are reduced to their actual value
+        value = value._value_
+    actual_type = type(value)
+    value_type = cls._named_constant_cache_.get(actual_type)
+    if value_type is None:
+        value_type = type(cls.__name__, (cls, type(value)), {})
+        cls._named_constant_cache_[type(value)] = value_type
+    obj = actual_type.__new__(value_type, value)
+    obj._name_ = name
+    obj._value_ = value
+    obj.__doc__ = doc
+    cls._members_[name] = obj
+    metacls.__setattr__(cls, name, obj)
+    return obj
+
+@constant_dict
+def __repr__(self):
+    return "<%s.%s: %r>" % (
+            self.__class__.__name__, self._name_, self._value_)
+
+@constant_dict
+def __reduce_ex__(self, proto):
+    return getattr, (self.__class__, self._name_)
+
+NamedConstant = NamedConstantMeta('NamedConstant', (object, ), constant_dict.resolve())
+Constant = NamedConstant
+del constant_dict
+
+# NamedTuple
+
+class _NamedTupleDict(OrderedDict):
+    """Track field order and ensure field names are not reused.
+
+    NamedTupleMeta will use the names found in self._field_names to translate
+    to indices.
+    """
+    def __init__(self, *args, **kwds):
+        self._field_names = []
+        super(_NamedTupleDict, self).__init__(*args, **kwds)
+
+    def __setitem__(self, key, value):
+        """Records anything not dundered or not a descriptor.
+
+        If a field name is used twice, an error is raised.
+
+        Single underscore (sunder) names are reserved.
+        """
+        if _is_sunder(key):
+            if key not in ('_size_', '_order_', '_fields_'):
+                raise ValueError(
+                        '_sunder_ names, such as %r, are reserved for future NamedTuple use'
+                        % (key, )
+                        )
+        elif _is_dunder(key):
+            if key == '__order__':
+                key = '_order_'
+        elif key in self._field_names:
+            # overwriting a field?
+            raise TypeError('attempt to reuse field name: %r' % (key, ))
+        elif not _is_descriptor(value):
+            if key in self:
+                # field overwriting a descriptor?
+                raise TypeError('%s already defined as: %r' % (key, self[key]))
+            self._field_names.append(key)
+        super(_NamedTupleDict, self).__setitem__(key, value)
+
+
+class _TupleAttributeAtIndex(object):
+
+    def __init__(self, name, index, doc, default):
+        self.name = name
+        self.index = index
+        if doc is undefined:
+            doc = None
+        self.__doc__ = doc
+        self.default = default
+
+    def __get__(self, instance, owner):
+        if instance is None:
+            return self
+        if len(instance) <= self.index:
+            raise AttributeError('%s instance has no value for %s' % (instance.__class__.__name__, self.name))
+        return instance[self.index]
+
+    def __repr__(self):
+        return '%s(%d)' % (self.__class__.__name__, self.index)
+
+
+class undefined(object):
+    def __repr__(self):
+        return 'undefined'
+    def __bool__(self):
+        return False
+    __nonzero__ = __bool__
+undefined = undefined()
+
+
+class TupleSize(NamedConstant):
+    fixed = constant('fixed', 'tuple length is static')
+    minimum = constant('minimum', 'tuple must be at least x long (x is calculated during creation')
+    variable = constant('variable', 'tuple length can be anything')
+
+class NamedTupleMeta(type):
+    """Metaclass for NamedTuple"""
+
+    @classmethod
+    def __prepare__(metacls, cls, bases, size=undefined, **kwds):
+        return _NamedTupleDict()
+
+    def __init__(cls, *args , **kwds):
+        super(NamedTupleMeta, cls).__init__(*args)
+
+    def __new__(metacls, cls, bases, clsdict, size=undefined, **kwds):
+        if bases == (object, ):
+            bases = (tuple, object)
+        elif tuple not in bases:
+            if object in bases:
+                index = bases.index(object)
+                bases = bases[:index] + (tuple, ) + bases[index:]
+            else:
+                bases = bases + (tuple, )
+        # include any fields from base classes
+        base_dict = _NamedTupleDict()
+        namedtuple_bases = []
+        for base in bases:
+            if isinstance(base, NamedTupleMeta):
+                namedtuple_bases.append(base)
+        i = 0
+        if namedtuple_bases:
+            for name, index, doc, default in metacls._convert_fields(*namedtuple_bases):
+                base_dict[name] = index, doc, default
+                i = max(i, index)
+        # construct properly ordered dict with normalized indexes
+        for k, v in clsdict.items():
+            base_dict[k] = v
+        original_dict = base_dict
+        if size is not undefined and '_size_' in original_dict:
+            raise TypeError('_size_ cannot be set if "size" is passed in header')
+        add_order = isinstance(clsdict, _NamedTupleDict)
+        clsdict = _NamedTupleDict()
+        clsdict.setdefault('_size_', size or TupleSize.fixed)
+        unnumbered = OrderedDict()
+        numbered = OrderedDict()
+        _order_ = original_dict.pop('_order_', [])
+        if _order_ :
+            _order_ = _order_.replace(',',' ').split()
+            add_order = False
+        # and process this class
+        for k, v in original_dict.items():
+            if k not in original_dict._field_names:
+                clsdict[k] = v
+            else:
+                # TODO:normalize v here
+                if isinstance(v, baseinteger):
+                    # assume an offset
+                    v = v, undefined, undefined
+                    i = v[0] + 1
+                    target = numbered
+                elif isinstance(v, basestring):
+                    # assume a docstring
+                    if add_order:
+                        v = i, v, undefined
+                        i += 1
+                        target = numbered
+                    else:
+                        v = undefined, v, undefined
+                        target = unnumbered
+                elif isinstance(v, tuple) and len(v) in (2, 3) and isinstance(v[0], baseinteger) and isinstance(v[1], (basestring, NoneType)):
+                    # assume an offset, a docstring, and (maybe) a default
+                    if len(v) == 2:
+                        v = v + (undefined, )
+                    v = v
+                    i = v[0] + 1
+                    target = numbered
+                elif isinstance(v, tuple) and len(v) in (1, 2) and isinstance(v[0], (basestring, NoneType)):
+                    # assume a docstring, and (maybe) a default
+                    if len(v) == 1:
+                        v = v + (undefined, )
+                    if add_order:
+                        v = (i, ) + v
+                        i += 1
+                        target = numbered
+                    else:
+                        v = (undefined, ) + v
+                        target = unnumbered
+                else:
+                    # refuse to guess further
+                    raise ValueError('not sure what to do with %s=%r (should be OFFSET [, DOC [, DEFAULT]])' % (k, v))
+                target[k] = v
+        # all index values have been normalized
+        # deal with _order_ (or lack thereof)
+        fields = []
+        aliases = []
+        seen = set()
+        max_len = 0
+        if not _order_:
+            if unnumbered:
+                raise ValueError("_order_ not specified and OFFSETs not declared for %r" % (unnumbered.keys(), ))
+            for name, (index, doc, default) in sorted(numbered.items(), key=lambda nv: (nv[1][0], nv[0])):
+                if index in seen:
+                    aliases.append(name)
+                else:
+                    fields.append(name)
+                    seen.add(index)
+                    max_len = max(max_len, index + 1)
+            offsets = numbered
+        else:
+            # check if any unnumbered not in _order_
+            missing = set(unnumbered) - set(_order_)
+            if missing:
+                raise ValueError("unable to order fields: %s (use _order_ or specify OFFSET" % missing)
+            offsets = OrderedDict()
+            # if any unnumbered, number them from their position in _order_
+            i = 0
+            for k in _order_:
+                try:
+                    index, doc, default = unnumbered.pop(k, None) or numbered.pop(k)
+                except IndexError:
+                    raise ValueError('%s (from _order_) not found in %s' % (k, cls))
+                if index is not undefined:
+                    i = index
+                if i in seen:
+                    aliases.append(k)
+                else:
+                    fields.append(k)
+                    seen.add(i)
+                offsets[k] = i, doc, default
+                i += 1
+                max_len = max(max_len, i)
+            # now handle anything in numbered
+            for k, (index, doc, default) in sorted(numbered.items(), key=lambda nv: (nv[1][0], nv[0])):
+                if index in seen:
+                    aliases.append(k)
+                else:
+                    fields.append(k)
+                    seen.add(index)
+                offsets[k] = index, doc, default
+                max_len = max(max_len, index+1)
+
+        # at this point fields and aliases should be ordered lists, offsets should be an
+        # OrdededDict with each value an int, str or None or undefined, default or None or undefined
+        assert len(fields) + len(aliases) == len(offsets), "number of fields + aliases != number of offsets"
+        assert set(fields) & set(offsets) == set(fields), "some fields are not in offsets: %s" % set(fields) & set(offsets)
+        assert set(aliases) & set(offsets) == set(aliases), "some aliases are not in offsets: %s" % set(aliases) & set(offsets)
+        for name, (index, doc, default) in offsets.items():
+            assert isinstance(index, baseinteger), "index for %s is not an int (%s:%r)" % (name, type(index), index)
+            assert isinstance(doc, (basestring, NoneType)) or doc is undefined, "doc is not a str, None, nor undefined (%s:%r)" % (name, type(doc), doc)
+
+        # create descriptors for fields
+        for name, (index, doc, default) in offsets.items():
+            clsdict[name] = _TupleAttributeAtIndex(name, index, doc, default)
+        clsdict['__slots__'] = ()
+
+        # create our new NamedTuple type
+        namedtuple_class = super(NamedTupleMeta, metacls).__new__(metacls, cls, bases, clsdict)
+        namedtuple_class._fields_ = fields
+        namedtuple_class._aliases_ = aliases
+        namedtuple_class._defined_len_ = max_len
+        return namedtuple_class
+
+    @staticmethod
+    def _convert_fields(*namedtuples):
+        "create list of index, doc, default triplets for cls in namedtuples"
+        all_fields = []
+        for cls in namedtuples:
+            base = len(all_fields)
+            for field in cls._fields_:
+                desc = getattr(cls, field)
+                all_fields.append((field, base+desc.index, desc.__doc__, desc.default))
+        return all_fields
+
+    def __add__(cls, other):
+        "A new NamedTuple is created by concatenating the _fields_ and adjusting the descriptors"
+        if not isinstance(other, NamedTupleMeta):
+            return NotImplemented
+        return NamedTupleMeta('%s%s' % (cls.__name__, other.__name__), (cls, other), {})
+
+    def __call__(cls, *args, **kwds):
+        """Creates a new NamedTuple class or an instance of a NamedTuple subclass.
+
+        NamedTuple should have args of (class_name, names, module)
+
+            `names` can be:
+
+                * A string containing member names, separated either with spaces or
+                  commas.  Values are auto-numbered from 1.
+                * An iterable of member names.  Values are auto-numbered from 1.
+                * An iterable of (member name, value) pairs.
+                * A mapping of member name -> value.
+
+                `module`, if set, will be stored in the new class' __module__ attribute;
+
+                Note: if `module` is not set this routine will attempt to discover the
+                calling module by walking the frame stack; if this is unsuccessful
+                the resulting class will not be pickleable.
+
+        subclass should have whatever arguments and/or keywords will be used to create an
+        instance of the subclass
+        """
+        if cls is NamedTuple:
+            original_args = args
+            original_kwds = kwds.copy()
+            # create a new subclass
+            try:
+                if 'class_name' in kwds:
+                    class_name = kwds.pop('class_name')
+                else:
+                    class_name, args = args[0], args[1:]
+                if 'names' in kwds:
+                    names = kwds.pop('names')
+                else:
+                    names, args = args[0], args[1:]
+                if 'module' in kwds:
+                    module = kwds.pop('module')
+                elif args:
+                    module, args = args[0], args[1:]
+                else:
+                    module = None
+                if 'type' in kwds:
+                    type = kwds.pop('type')
+                elif args:
+                    type, args = args[0], args[1:]
+                else:
+                    type = None
+
+            except IndexError:
+                raise TypeError('too few arguments to NamedTuple: %s, %s' % (original_args, original_kwds))
+            if args or kwds:
+                raise TypeError('too many arguments to NamedTuple: %s, %s' % (original_args, original_kwds))
+            if PY2:
+                # if class_name is unicode, attempt a conversion to ASCII
+                if isinstance(class_name, unicode):
+                    try:
+                        class_name = class_name.encode('ascii')
+                    except UnicodeEncodeError:
+                        raise TypeError('%r is not representable in ASCII' % (class_name, ))
+            # quick exit if names is a NamedTuple
+            if isinstance(names, NamedTupleMeta):
+                names.__name__ = class_name
+                if type is not None and type not in names.__bases__:
+                    names.__bases__ = (type, ) + names.__bases__
+                return names
+
+            metacls = cls.__class__
+            bases = (cls, )
+            clsdict = metacls.__prepare__(class_name, bases)
+
+            # special processing needed for names?
+            if isinstance(names, basestring):
+                names = names.replace(',', ' ').split()
+            if isinstance(names, (tuple, list)) and isinstance(names[0], basestring):
+                names = [(e, i) for (i, e) in enumerate(names)]
+            # Here, names is either an iterable of (name, index) or (name, index, doc, default) or a mapping.
+            item = None  # in case names is empty
+            for item in names:
+                if isinstance(item, basestring):
+                    # mapping
+                    field_name, field_index = item, names[item]
+                else:
+                    # non-mapping
+                    if len(item) == 2:
+                        field_name, field_index = item
+                    else:
+                        field_name, field_index = item[0], item[1:]
+                clsdict[field_name] = field_index
+            if type is not None:
+                if not isinstance(type, tuple):
+                    type = (type, )
+                bases = type + bases
+            namedtuple_class = metacls.__new__(metacls, class_name, bases, clsdict)
+
+            # TODO: replace the frame hack if a blessed way to know the calling
+            # module is ever developed
+            if module is None:
+                try:
+                    module = _sys._getframe(1).f_globals['__name__']
+                except (AttributeError, ValueError, KeyError):
+                    pass
+            if module is None:
+                _make_class_unpicklable(namedtuple_class)
+            else:
+                namedtuple_class.__module__ = module
+
+            return namedtuple_class
+        else:
+            # instantiate a subclass
+            namedtuple_instance = cls.__new__(cls, *args, **kwds)
+            if isinstance(namedtuple_instance, cls):
+                namedtuple_instance.__init__(*args, **kwds)
+            return namedtuple_instance
+
+    @_bltin_property
+    def __fields__(cls):
+        return list(cls._fields_)
+    # collections.namedtuple compatibility
+    _fields = __fields__
+
+    @_bltin_property
+    def __aliases__(cls):
+        return list(cls._aliases_)
+
+    def __repr__(cls):
+        return "<NamedTuple %r>" % (cls.__name__, )
+
+namedtuple_dict = _Addendum(
+        dict=NamedTupleMeta.__prepare__('NamedTuple', (object, )),
+        doc="NamedTuple base class.\n\n    Derive from this class to define new NamedTuples.\n\n",
+        ns=globals(),
+        )
+
+@namedtuple_dict
+def __new__(cls, *args, **kwds):
+    if cls._size_ is TupleSize.fixed and len(args) > cls._defined_len_:
+        raise TypeError('%d fields expected, %d received' % (cls._defined_len_, len(args)))
+    unknown = set(kwds) - set(cls._fields_) - set(cls._aliases_)
+    if unknown:
+        raise TypeError('unknown fields: %r' % (unknown, ))
+    final_args = list(args) + [undefined] * (len(cls.__fields__) - len(args))
+    for field, value in kwds.items():
+        index = getattr(cls, field).index
+        if final_args[index] != undefined:
+            raise TypeError('field %s specified more than once' % field)
+        final_args[index] = value
+    missing = []
+    for index, value in enumerate(final_args):
+        if value is undefined:
+            # look for default values
+            name = cls.__fields__[index]
+            default = getattr(cls, name).default
+            if default is undefined:
+                missing.append(name)
+            else:
+                final_args[index] = default
+    if missing:
+        if cls._size_ in (TupleSize.fixed, TupleSize.minimum):
+            raise TypeError('values not provided for field(s): %s' % ', '.join(missing))
+        while final_args and final_args[-1] is undefined:
+            final_args.pop()
+            missing.pop()
+        if cls._size_ is not TupleSize.variable or undefined in final_args:
+            raise TypeError('values not provided for field(s): %s' % ', '.join(missing))
+    return tuple.__new__(cls, tuple(final_args))
+
+@namedtuple_dict
+def __reduce_ex__(self, proto):
+    return self.__class__, tuple(getattr(self, f) for f in self._fields_)
+
+@namedtuple_dict
+def __repr__(self):
+    if len(self) == len(self._fields_):
+        return "%s(%s)" % (
+                self.__class__.__name__, ', '.join(['%s=%r' % (f, o) for f, o in zip(self._fields_, self)])
+                )
+    else:
+        return '%s(%s)' % (self.__class__.__name__, ', '.join([repr(o) for o in self]))
+
+@namedtuple_dict
+def __str__(self):
+    return "%s(%s)" % (
+            self.__class__.__name__, ', '.join(['%r' % (getattr(self, f), ) for f in self._fields_])
+            )
+
+@namedtuple_dict
+@_bltin_property
+def _fields_(self):
+    return list(self.__class__._fields_)
+
+    # compatibility methods with stdlib namedtuple
+@namedtuple_dict
+@_bltin_property
+def __aliases__(self):
+    return list(self.__class__._aliases_)
+
+@namedtuple_dict
+@_bltin_property
+def _fields(self):
+    return list(self.__class__._fields_)
+
+@namedtuple_dict
+@classmethod
+def _make(cls, iterable, new=None, len=None):
+    return cls.__new__(cls, *iterable)
+
+@namedtuple_dict
+def _asdict(self):
+    return OrderedDict(zip(self._fields_, self))
+
+@namedtuple_dict
+def _replace(self, **kwds):
+    current = self._asdict()
+    current.update(kwds)
+    return self.__class__(**current)
+
+NamedTuple = NamedTupleMeta('NamedTuple', (object, ), namedtuple_dict.resolve())
+del namedtuple_dict
+
+
+# Enum
+
+    # _init_ and value and AddValue
+    # -----------------------------
+    # by default, when defining a member everything after the = is "the value", everything is
+    #   passed to __new__, everything is passed to __init__
+    #
+    # if _init_ is present then
+    #   if `value` is not in _init_, everything is "the value", defaults apply
+    #   if `value` is in _init_, only the first thing after the = is the value, and the rest will
+    #       be passed to __init__
+    #   if fewer values are present for member assignment than _init_ calls for, _generate_next_value_
+    #       will be called in an attempt to generate them
+    #
+    # if AddValue is present then
+    #   _generate_next_value_ is always called, and any generated values are prepended to provided
+    #       values (custom _gnv_s can change that)
+    #   default _init_ rules apply
+
+
+    # Constants used in Enum
+
+@export(globals())
+class EnumConstants(NamedConstant):
+    AddValue = constant('addvalue', 'prepends value(s) from _generate_next_value_ to each member')
+    MagicValue = constant('magicvalue', 'calls _generate_next_value_ when no arguments are given')
+    MultiValue = constant('multivalue', 'each member can have several values')
+    NoAlias = constant('noalias', 'duplicate valued members are distinct, not aliased')
+    Unique = constant('unique', 'duplicate valued members are not allowed')
+    def __repr__(self):
+        return self._name_
+
+
+    # Dummy value for Enum as EnumType explicity checks for it, but of course until
+    # EnumType finishes running the first time the Enum class doesn't exist.  This
+    # is also why there are checks in EnumType like `if Enum is not None`.
+    #
+    # Ditto for Flag.
+
+Enum = ReprEnum = IntEnum = StrEnum = Flag = IntFlag = EJECT = KEEP = None
+
+class enum(object):
+    """
+    Helper class to track args, kwds.
+    """
+    def __init__(self, *args, **kwds):
+        self._args = args
+        self._kwds = dict(kwds.items())
+        self._hash = hash(args)
+        self.name = None
+
+    @_bltin_property
+    def args(self):
+        return self._args
+
+    @_bltin_property
+    def kwds(self):
+        return self._kwds.copy()
+
+    def __hash__(self):
+        return self._hash
+
+    def __eq__(self, other):
+        if not isinstance(other, self.__class__):
+            return NotImplemented
+        return self.args == other.args and self.kwds == other.kwds
+
+    def __ne__(self, other):
+        if not isinstance(other, self.__class__):
+            return NotImplemented
+        return self.args != other.args or self.kwds != other.kwds
+
+    def __repr__(self):
+        final = []
+        args = ', '.join(['%r' % (a, ) for a in self.args])
+        if args:
+            final.append(args)
+        kwds = ', '.join([('%s=%r') % (k, v) for k, v in enumsort(list(self.kwds.items()))])
+        if kwds:
+            final.append(kwds)
+        return '%s(%s)' % (self.__class__.__name__, ', '.join(final))
+
+_auto_null = SentinelType('no_value', (object, ), {})
+class auto(enum):
+    """
+    Instances are replaced with an appropriate value in Enum class suites.
+    """
+    enum_member = _auto_null
+    _value = _auto_null
+    _operations = []
+
+    def __and__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_and_, (self, other)))
+        return new_auto
+
+    def __rand__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_and_, (other, self)))
+        return new_auto
+
+    def __invert__(self):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_inv_, (self,)))
+        return new_auto
+
+    def __or__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_or_, (self, other)))
+        return new_auto
+
+    def __ror__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_or_, (other, self)))
+        return new_auto
+
+    def __xor__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_xor_, (self, other)))
+        return new_auto
+
+    def __rxor__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_xor_, (other, self)))
+        return new_auto
+
+    def __abs__(self):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_abs_, (self, )))
+        return new_auto
+
+    def __add__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_add_, (self, other)))
+        return new_auto
+
+    def __radd__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_add_, (other, self)))
+        return new_auto
+
+    def __neg__(self):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_neg_, (self, )))
+        return new_auto
+
+    def __pos__(self):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_pos_, (self, )))
+        return new_auto
+
+    if PY2:
+        def __div__(self, other):
+            new_auto = self.__class__()
+            new_auto._operations = self._operations[:]
+            new_auto._operations.append((_div_, (self, other)))
+            return new_auto
+
+    def __rdiv__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_div_, (other, self)))
+        return new_auto
+
+    def __floordiv__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_floordiv_, (self, other)))
+        return new_auto
+
+    def __rfloordiv__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_floordiv_, (other, self)))
+        return new_auto
+
+    def __truediv__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_truediv_, (self, other)))
+        return new_auto
+
+    def __rtruediv__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_truediv_, (other, self)))
+        return new_auto
+
+    def __lshift__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_lshift_, (self, other)))
+        return new_auto
+
+    def __rlshift__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_lshift_, (other, self)))
+        return new_auto
+
+    def __rshift__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_rshift_, (self, other)))
+        return new_auto
+
+    def __rrshift__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_rshift_, (other, self)))
+        return new_auto
+
+    def __mod__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_mod_, (self, other)))
+        return new_auto
+
+    def __rmod__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_mod_, (other, self)))
+        return new_auto
+
+    def __mul__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_mul_, (self, other)))
+        return new_auto
+
+    def __rmul__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_mul_, (other, self)))
+        return new_auto
+
+    def __pow__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_pow_, (self, other)))
+        return new_auto
+
+    def __rpow__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_pow_, (other, self)))
+        return new_auto
+
+    def __sub__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_sub_, (self, other)))
+        return new_auto
+
+    def __rsub__(self, other):
+        new_auto = self.__class__()
+        new_auto._operations = self._operations[:]
+        new_auto._operations.append((_sub_, (other, self)))
+        return new_auto
+
+    def __repr__(self):
+        if self._operations:
+            return 'auto(...)'
+        else:
+            return 'auto(%r, *%r, **%r)' % (self._value, self._args, self._kwds)
+
+    @_bltin_property
+    def value(self):
+        if self._value is not _auto_null and self._operations:
+            raise TypeError('auto() object out of sync')
+        elif self._value is _auto_null and not self._operations:
+            return self._value
+        elif self._value is not _auto_null:
+            return self._value
+        else:
+            return self._resolve()
+
+    @value.setter
+    def value(self, value):
+        if self._operations:
+            value = self._resolve(value)
+        self._value = value
+
+    def _resolve(self, base_value=None):
+            cls = self.__class__
+            for op, params in self._operations:
+                values = []
+                for param in params:
+                    if isinstance(param, cls):
+                        if param.value is _auto_null:
+                            if base_value is None:
+                                return _auto_null
+                            else:
+                                values.append(base_value)
+                        else:
+                            values.append(param.value)
+                    else:
+                        values.append(param)
+                value = op(*values)
+            self._operations[:] = []
+            self._value = value
+            return value
+
+
+class _EnumArgSpec(NamedTuple):
+    args = 0, 'all args except *args and **kwds'
+    varargs = 1, 'the name of the *args variable'
+    keywords = 2, 'the name of the **kwds variable'
+    defaults = 3, 'any default values'
+    required = 4, 'number of required values (no default available)'
+
+    def __new__(cls, _new_func):
+        argspec = getargspec(_new_func)
+        args, varargs, keywords, defaults = argspec
+        if defaults:
+            reqs = args[1:-len(defaults)]
+        else:
+            reqs = args[1:]
+        return tuple.__new__(_EnumArgSpec, (args, varargs, keywords, defaults, reqs))
+
+
+class _proto_member:
+    """
+    intermediate step for enum members between class execution and final creation
+    """
+
+    def __init__(self, value):
+        self.value = value
+
+    def __set_name__(self, enum_class, member_name):
+        """
+        convert each quasi-member into an instance of the new enum class
+        """
+        # first step: remove ourself from enum_class
+        delattr(enum_class, member_name)
+        # second step: create member based on enum_class
+        value = self.value
+        kwds = {}
+        args = ()
+        init_args = ()
+        extra_mv_args = ()
+        multivalue = None
+        if isinstance(value, tuple) and value and isinstance(value[0], auto):
+            multivalue = value
+            value = value[0]
+        if isinstance(value, auto) and value.value is _auto_null:
+            args = value.args
+            kwds = value.kwds
+        elif isinstance(value, auto):
+            kwds = value.kwds
+            args = (value.value, ) + value.args
+            value = value.value
+        elif isinstance(value, enum):
+            args = value.args
+            kwds = value.kwds
+        elif isinstance(value, Member):
+            value = value.value
+            args = (value, )
+        elif not isinstance(value, tuple):
+            args = (value, )
+        else:
+            args = value
+        if multivalue is not None:
+            value = (value, ) + multivalue[1:]
+            kwds = {}
+            args = value
+            del multivalue
+        # possibilities
+        #
+        # - no init, multivalue  -> __new__[0], __init__(*[:]), extra=[1:]
+        # - init w/o value, multivalue  -> __new__[0], __init__(*[:]), extra=[1:]
+        #
+        # - init w/value, multivalue  -> __new__[0], __init__(*[1:]),  extra=[1:]
+        #
+        # - init w/value, no multivalue  -> __new__[0], __init__(*[1:]), extra=[]
+        #
+        # - init w/o value, no multivalue  -> __new__[:], __init__(*[:]), extra=[]
+        # - no init, no multivalue  ->  __new__[:], __init__(*[:]), extra=[]
+        if enum_class._multivalue_ or 'value' in enum_class._creating_init_:
+            if enum_class._multivalue_:
+                # when multivalue is True, creating_init can be anything
+                mv_arg = args[0]
+                extra_mv_args = args[1:]
+                if 'value' in enum_class._creating_init_:
+                    init_args = args[1:]
+                else:
+                    init_args = args
+                args = args[0:1]
+                value = args[0]
+            else:
+                # 'value' is definitely in creating_init
+                if enum_class._auto_init_ and enum_class._new_args_:
+                    # we have a custom __new__ and an auto __init__
+                    # divvy up according to number of params in each
+                    init_args = args[-len(enum_class._creating_init_)+1:]
+                    if not enum_class._auto_args_:
+                        args = args[:len(enum_class._new_args_.args)]
+                    value = args[0]
+                elif enum_class._auto_init_:
+                    # don't pass in value
+                    init_args = args[1:]
+                    args = args[0:1]
+                    value = args[0]
+                elif enum_class._new_args_:
+                    # do not modify args
+                    value = args[0]
+                else:
+                    # keep all args for user-defined __init__
+                    # keep value as-is
+                    init_args = args
+        else:
+            # either no creating_init, or it doesn't have 'value'
+            init_args = args
+        if enum_class._member_type_ is tuple:   # special case for tuple enums
+            args = (args, )     # wrap it one more time
+        if not enum_class._use_args_:
+            enum_member = enum_class._new_member_(enum_class)
+            if not hasattr(enum_member, '_value_'):
+                enum_member._value_ = value
+        else:
+            enum_member = enum_class._new_member_(enum_class, *args, **kwds)
+            if not hasattr(enum_member, '_value_'):
+                if enum_class._member_type_ is object:
+                    enum_member._value_ = value
+                else:
+                    try:
+                        enum_member._value_ = enum_class._member_type_(*args, **kwds)
+                    except Exception:
+                        te = TypeError('_value_ not set in __new__, unable to create it')
+                        te.__cause__ = None
+                        raise te
+        value = enum_member._value_
+        enum_member._name_ = member_name
+        enum_member.__objclass__ = enum_class
+        enum_member.__init__(*init_args, **kwds)
+        enum_member._sort_order_ = len(enum_class._member_names_)
+        # If another member with the same value was already defined, the
+        # new member becomes an alias to the existing one.
+        if enum_class._noalias_:
+            # unless NoAlias was specified
+            enum_class._member_names_.append(member_name)
+        else:
+            nonunique = defaultdict(list)
+            try:
+                try:
+                    # try to do a fast lookup to avoid the quadratic loop
+                    enum_member = enum_class._value2member_map_[value]
+                    if enum_class._unique_:
+                        nonunique[enum_member.name].append(member_name)
+                except TypeError:
+                    # unhashable members are stored elsewhere
+                    for unhashable_value, canonical_member in enum_class._value2member_seq_:
+                        name = canonical_member.name
+                        if unhashable_value == enum_member._value_:
+                            if enum_class._unique_:
+                                nonunique[name].append(member_name)
+                            enum_member = canonical_member
+                            break
+                    else:
+                        raise KeyError
+            except KeyError:
+                # this could still be an alias if the value is multi-bit and the
+                # class is a flag class
+                if (
+                        Flag is None
+                        or not issubclass(enum_class, Flag)
+                    ):
+                    # no other instances found, record this member in _member_names_
+                    enum_class._member_names_.append(member_name)
+                elif (
+                        Flag is not None
+                        and issubclass(enum_class, Flag)
+                        and _is_single_bit(value)
+                    ):
+                    # no other instances found, record this member in _member_names_
+                    enum_class._member_names_.append(member_name)
+            if nonunique:
+                # duplicates not allowed if Unique specified
+                message = []
+                for name, aliases in nonunique.items():
+                    bad_aliases = ','.join(aliases)
+                    message.append('%s --> %s [%r]' % (name, bad_aliases, enum_class[name].value))
+                raise ValueError(
+                        '%s: duplicate names found: %s' %
+                            (enum_class.__name__, ';  '.join(message))
+                        )
+        # if self.value is an `auto()`, replace the value attribute with the new enum member
+        if isinstance(self.value, auto):
+            self.value.enum_member = enum_member
+        # get redirect in place before adding to _member_map_
+        # but check for other instances in parent classes first
+        need_override = False
+        descriptor = None
+        descriptor_property = None
+        for base in enum_class.__mro__[1:]:
+            descriptor = base.__dict__.get(member_name)
+            if descriptor is not None:
+                if isinstance(descriptor, (property, DynamicClassAttribute)):
+                    break
+                else:
+                    need_override = True
+                    if isinstance(descriptor, _bltin_property) and descriptor_property is None:
+                        descriptor_property = descriptor
+                    # keep looking for an enum.property
+        descriptor = descriptor or descriptor_property
+        if descriptor and not need_override:
+            # previous enum.property found, no further action needed
+            pass
+        else:
+            redirect = property()
+            redirect.__set_name__(enum_class, member_name)
+            if descriptor and need_override:
+                # previous enum.property found, but some other inherited
+                # attribute is in the way; copy fget, fset, fdel to this one
+                redirect.fget = descriptor.fget
+                redirect.fset = descriptor.fset
+                redirect.fdel = descriptor.fdel
+            setattr(enum_class, member_name, redirect)
+        # now add to _member_map_ (even aliases)
+        enum_class._member_map_[member_name] = enum_member
+        #
+        # process (possible) MultiValues
+        values = (value, ) + extra_mv_args
+        if enum_class._multivalue_ and mv_arg not in values:
+            values += (mv_arg, )
+        enum_member._values_ = values
+        for value in values:
+            # first check if value has already been used
+            if enum_class._multivalue_ and (
+                    value in enum_class._value2member_map_
+                    or any(v == value for (v, m) in enum_class._value2member_seq_)
+                    ):
+                raise ValueError('%r has already been used' % (value, ))
+            try:
+                # This may fail if value is not hashable. We can't add the value
+                # to the map, and by-value lookups for this value will be
+                # linear.
+                if enum_class._noalias_:
+                    raise TypeError('cannot use dict to store value')
+                enum_class._value2member_map_[value] = enum_member
+            except TypeError:
+                enum_class._value2member_seq_ += ((value, enum_member), )
+
+
+class _EnumDict(dict):
+    """Track enum member order and ensure member names are not reused.
+
+    EnumType will use the names found in self._member_names as the
+    enumeration member names.
+    """
+    def __init__(self, cls_name, settings, start, constructor_init, constructor_start, constructor_boundary):
+        super(_EnumDict, self).__init__()
+        self._cls_name = cls_name
+        self._constructor_init = constructor_init
+        self._constructor_start = constructor_start
+        self._constructor_boundary = constructor_boundary
+        self._generate_next_value = None
+        self._member_names = []
+        self._member_names_set = set()
+        self._settings = settings
+        self._addvalue = addvalue = AddValue in settings
+        self._magicvalue = MagicValue in settings
+        self._multivalue = MultiValue in settings
+        if self._addvalue and self._magicvalue:
+            raise TypeError('%r: AddValue and MagicValue are mutually exclusive' % cls_name)
+        if self._multivalue and self._magicvalue:
+            raise TypeError('%r: MultiValue and MagicValue are mutually exclusive' % cls_name)
+        self._start = start
+        self._addvalue_value = start
+        self._new_args = ()
+        self._auto_args = False
+        # when the magic turns off
+        self._locked = MagicValue not in settings
+        # if init fields are specified
+        self._init = []
+        # list of temporary names
+        self._ignore = []
+        if self._magicvalue:
+            self._ignore = ['property', 'staticmethod', 'classmethod']
+        self._ignore_init_done = False
+        # if _sunder_ values can be changed via the class body
+        self._allow_init = True
+        self._last_values = []
+
+    def __getitem__(self, key):
+        if key == self._cls_name and self._cls_name not in self:
+            return enum
+        elif (
+                self._locked
+                or key in self
+                or key in self._ignore
+                or _is_sunder(key)
+                or _is_dunder(key)
+                ):
+            return super(_EnumDict, self).__getitem__(key)
+        elif self._magicvalue:
+            value = self._generate_next_value(key, self._start, len(self._member_names), self._last_values[:])
+            self.__setitem__(key, value)
+            return value
+        else:
+            raise Exception('Magic is not set -- why am I here?')
+
+    def __setitem__(self, key, value):
+        """Changes anything not sundured, dundered, nor a descriptor.
+
+        If an enum member name is used twice, an error is raised; duplicate
+        values are not checked for.
+
+        Single underscore (sunder) names are reserved.
+        """
+        # Flag classes that have MagicValue and __new__ will get a generated _gnv_
+        if _is_internal_class(self._cls_name, value):
+            pass
+        elif _is_private_name(self._cls_name, key):
+            pass
+        elif _is_sunder(key):
+            if key not in (
+                    '_init_', '_settings_', '_order_', '_ignore_', '_start_',
+                    '_create_pseudo_member_', '_create_pseudo_member_values_',
+                    '_generate_next_value_', '_boundary_', '_numeric_repr_',
+                    '_missing_', '_missing_value_', '_missing_name_',
+                    '_iter_member_', '_iter_member_by_value_', '_iter_member_by_def_',
+                    ):
+                raise ValueError('%r: _sunder_ names, such as %r, are reserved for future Enum use'
+                        % (self._cls_name, key)
+                        )
+            elif not self._allow_init and key not in (
+                    'create_pseudo_member_', '_missing_', '_missing_value_', '_missing_name_',
+                ):
+                # sunder is used during creation, must be specified first
+                raise ValueError('%r: cannot set %r after init phase' % (self._cls_name, key))
+            elif key == '_ignore_':
+                if self._ignore_init_done:
+                    raise TypeError('%r: ignore can only be specified once' % self._cls_name)
+                if isinstance(value, basestring):
+                    value = value.split()
+                else:
+                    value = list(value)
+                self._ignore = value
+                already = set(value) & self._member_names_set
+                if already:
+                    raise ValueError('%r: _ignore_ cannot specify already set names %s' % (
+                            self._cls_name,
+                            ', '.join(repr(a) for a in already)
+                            ))
+                self._ignore_init_done = True
+            elif key == '_boundary_':
+                if self._constructor_boundary:
+                    raise TypeError('%r: boundary specified in constructor and class body' % self._cls_name)
+            elif key == '_start_':
+                if self._constructor_start:
+                    raise TypeError('%r: start specified in constructor and class body' % self._cls_name)
+                self._start = value
+            elif key == '_settings_':
+                if not isinstance(value, (set, tuple)):
+                    value = (value, )
+                if not isinstance(value, set):
+                    value = set(value)
+                self._settings |= value
+                if NoAlias in value and Unique in value:
+                    raise TypeError('%r: NoAlias and Unique are mutually exclusive' % self._cls_name)
+                elif MultiValue in value and NoAlias in value:
+                    raise TypeError('cannot specify both MultiValue and NoAlias' % self._cls_name)
+                allowed_settings = dict.fromkeys(['addvalue', 'magicvalue', 'noalias', 'unique', 'multivalue'])
+                for arg in self._settings:
+                    if arg not in allowed_settings:
+                        raise TypeError('%r: unknown qualifier %r (from %r)' % (self._cls_name, arg, value))
+                    allowed_settings[arg] = True
+                self._multivalue = allowed_settings['multivalue']
+                self._addvalue = allowed_settings['addvalue']
+                self._magicvalue = allowed_settings['magicvalue']
+                self._locked = not self._magicvalue
+                if self._magicvalue and not self._ignore_init_done:
+                    self._ignore = ['property', 'classmethod', 'staticmethod']
+                if self._addvalue and self._init and 'value' not in self._init:
+                    self._init.insert(0, 'value')
+                value = tuple(self._settings)
+            elif key == '_init_':
+                if self._constructor_init:
+                    raise TypeError('%r: init specified in constructor and in class body' % self._cls_name)
+                _init_ = value
+                if isinstance(_init_, basestring):
+                    _init_ = _init_.replace(',',' ').split()
+                if self._addvalue and 'value' not in self._init:
+                    self._init.insert(0, 'value')
+                if self._magicvalue:
+                    raise TypeError("%r: _init_ and MagicValue are mutually exclusive" % self._cls_name)
+                self._init = _init_
+                value = _init_
+            elif key == '_generate_next_value_':
+                gnv = value
+                if value is not None:
+                    if isinstance(value, staticmethod):
+                        gnv = value.__func__
+                    elif isinstance(value, classmethod):
+                        raise TypeError('%r: _generate_next_value must be a staticmethod, not a classmethod' % self._cls_name)
+                    else:
+                        gnv = value
+                        value = staticmethod(value)
+                    self._auto_args = _check_auto_args(value)
+                setattr(self, '_generate_next_value', gnv)
+        elif _is_dunder(key):
+            if key == '__order__':
+                key = '_order_'
+                if not self._allow_init:
+                    # _order_ is used during creation, must be specified first
+                    raise ValueError('%r: cannot set %r after init phase' % (self._cls_name, key))
+            elif key == '__new__':  # and self._new_to_init:
+                if isinstance(value, staticmethod):
+                    value = value.__func__
+                self._new_args = _EnumArgSpec(value)
+            elif key == '__init_subclass__':
+                if not isinstance(value, classmethod):
+                    value = classmethod(value)
+            if _is_descriptor(value):
+                self._locked = True
+        elif key in self._member_names_set:
+            # descriptor overwriting an enum?
+            raise TypeError('%r: attempt to reuse name: %r' % (self._cls_name, key))
+        elif key in self._ignore:
+            pass
+        elif not _is_descriptor(value):
+            self._allow_init = False
+            if key in self:
+                # enum overwriting a descriptor?
+                raise TypeError('%r: %s already defined as %r' % (self._cls_name, key, self[key]))
+            if type(value) is enum:
+                value.name = key
+                if self._addvalue:
+                    raise TypeError('%r: enum() and AddValue are incompatible' % self._cls_name)
+            elif self._addvalue and not self._multivalue:
+                # generate a value
+                value = self._gnv(key, value)
+            elif self._multivalue:
+                # make sure it's a tuple
+                if not isinstance(value, tuple):
+                    value = (value, )
+                if isinstance(value[0], auto):
+                    value = (self._convert_auto(key, value[0]), ) + value[1:]
+                if self._addvalue:
+                    value = self._gnv(key, value)
+            elif isinstance(value, auto):
+                value = self._convert_auto(key, value)
+            elif isinstance(value, tuple) and value and isinstance(value[0], auto):
+                value = (self._convert_auto(key, value[0]), ) + value[1:]
+            elif not isinstance(value, auto):
+                # call generate maybe if
+                # - init is specified; or
+                # - __new__ is specified;
+                # and either of them call for more values than are present
+                new_args = () or self._new_args and self._new_args.required
+                target_len = len(self._init or new_args)
+                if isinstance(value, tuple):
+                    source_len = len(value)
+                else:
+                    source_len = 1
+                multi_args = len(self._init) > 1 or new_args
+                if source_len < target_len :
+                    value = self._gnv(key, value)
+            else:
+                pass
+            if self._init:
+                if isinstance(value, auto):
+                    test_value = value.args
+                elif not isinstance(value, tuple):
+                    test_value = (value, )
+                else:
+                    test_value = value
+                if len(self._init) != len(test_value):
+                    raise TypeError(
+                            '%s.%s: number of fields provided do not match init [%r != %r]'
+                            % (self._cls_name, key, self._init, test_value)
+                        )
+            self._member_names.append(key)
+            self._member_names_set.add(key)
+        else:
+            # not a new member, turn off the autoassign magic
+            self._locked = True
+            self._allow_init = False
+        if not (_is_sunder(key) or _is_dunder(key) or _is_private_name(self._cls_name, key) or _is_descriptor(value)):
+            if isinstance(value, auto):
+                self._last_values.append(value.value)
+            elif isinstance(value, tuple) and value and isinstance(value[0], auto):
+                self._last_values.append(value[0].value)
+            elif isinstance(value, tuple):
+                if value:
+                    self._last_values.append(value[0])
+            else:
+                self._last_values.append(value)
+        super(_EnumDict, self).__setitem__(key, value)
+
+    def _convert_auto(self, key, value):
+        # if auto.args or auto.kwds, compare to _init_ and __new__ -- if lacking, call gnv
+        # if not auto.args|kwds but auto.value is _auto_null -- call gnv
+        if value.args or value.kwds or value.value is _auto_null:
+            if value.args or value.kwds:
+                values = value.args
+            else:
+                values = ()
+            new_args = () or self._new_args and self._new_args.required
+            target_len = len(self._init or new_args) or 1
+            if isinstance(values, tuple):
+                source_len = len(values)
+            else:
+                source_len = 1
+            multi_args = len(self._init) > 1 or new_args
+            if source_len < target_len :
+                values = self._gnv(key, values)
+                if value.args:
+                    value._args = values
+                else:
+                    value.value = values
+        return value
+
+    def _gnv(self, key, value):
+        # generate a value
+        if self._auto_args:
+            if not isinstance(value, tuple):
+                value = (value, )
+            value = self._generate_next_value(key, self._start, len(self._member_names), self._last_values[:], *value)
+        else:
+            value = self._generate_next_value(key, self._start, len(self._member_names), self._last_values[:])
+        if isinstance(value, tuple) and len(value) == 1:
+            value = value[0]
+        return value
+
+
+no_arg = SentinelType('no_arg', (object, ), {})
+class EnumType(type):
+    """Metaclass for Enum"""
+
+    @classmethod
+    def __prepare__(metacls, cls, bases, init=None, start=None, settings=(), boundary=None, **kwds):
+        metacls._check_for_existing_members_(cls, bases)
+        if Flag is None and cls == 'Flag':
+            initial_flag = True
+        else:
+            initial_flag = False
+        # settings are a combination of current and all past settings
+        constructor_init = init is not None
+        constructor_start = start is not None
+        constructor_boundary = boundary is not None
+        if not isinstance(settings, tuple):
+            settings = settings,
+        settings = set(settings)
+        generate = None
+        order = None
+        # inherit previous flags
+        member_type, first_enum = metacls._get_mixins_(cls, bases)
+        if first_enum is not None:
+            generate = getattr(first_enum, '_generate_next_value_', None)
+            generate = getattr(generate, 'im_func', generate)
+            settings |= metacls._get_settings_(bases)
+            init = init or first_enum._auto_init_[:]
+            order = first_enum._order_function_
+            if start is None:
+                start = first_enum._start_
+        else:
+            # first time through -- creating Enum itself
+            start = 1
+        # check for custom settings
+        if AddValue in settings and init and 'value' not in init:
+            if isinstance(init, list):
+                init.insert(0, 'value')
+            else:
+                init = 'value ' + init
+        if NoAlias in settings and Unique in settings:
+            raise TypeError('%r: NoAlias and Unique are mutually exclusive' % cls)
+        if MultiValue in settings and NoAlias in settings:
+            raise TypeError('%r: MultiValue and NoAlias are mutually exclusive' % cls)
+        allowed_settings = dict.fromkeys(['addvalue', 'magicvalue', 'noalias', 'unique', 'multivalue'])
+        for arg in settings:
+            if arg not in allowed_settings:
+                raise TypeError('%r: unknown qualifier %r' % (cls, arg))
+        enum_dict = _EnumDict(cls_name=cls, settings=settings, start=start, constructor_init=constructor_init, constructor_start=constructor_start, constructor_boundary=constructor_boundary)
+        enum_dict._member_type = member_type
+        enum_dict._base_type = ('enum', 'flag')[
+                Flag is None and cls == 'Flag'
+                or
+                Flag is not None and any(issubclass(b, Flag) for b in bases)
+                ]
+        if Flag is not None and any(b is Flag for b in bases) and member_type not in (baseinteger + (object, )):
+            if Flag in bases:
+                # when a non-int data type is mixed in with Flag, we end up
+                # needing two values for two `__new__`s:
+                # - the integer value for the Flag itself; and
+                # - the mix-in value for the mix-in
+                #
+                # we provide a default `_generate_next_value_` to supply the int
+                # argument, and a default `__new__` to keep the two straight
+                def _generate_next_value_(name, start, count, values, *args, **kwds):
+                    return (2 ** count, ) + args
+                enum_dict['_generate_next_value_'] = staticmethod(_generate_next_value_)
+                def __new__(cls, flag_value, type_value):
+                    obj = member_type.__new__(cls, type_value)
+                    obj._value_ = flag_value
+                    return obj
+                enum_dict['__new__'] = __new__
+            else:
+                try:
+                    enum_dict._new_args = _EnumArgSpec(first_enum.__new_member__)
+                except TypeError:
+                    pass
+        elif not initial_flag:
+            if hasattr(first_enum, '__new_member__'):
+                enum_dict._new_args = _EnumArgSpec(first_enum.__new_member__)
+            if generate:
+                enum_dict['_generate_next_value_'] = generate
+                enum_dict._inherited_gnv = True
+            if init is not None:
+                if isinstance(init, basestring):
+                    init = init.replace(',',' ').split()
+                enum_dict._init = init
+        elif hasattr(first_enum, '__new_member__'):
+            enum_dict._new_args = _EnumArgSpec(first_enum.__new_member__)
+        if order is not None:
+            enum_dict['_order_'] = staticmethod(order)
+        return enum_dict
+
+    def __init__(cls, *args , **kwds):
+        pass
+
+    def __new__(metacls, cls, bases, clsdict, init=None, start=None, settings=(), boundary=None, **kwds):
+        # handle py2 case first
+        if type(clsdict) is not _EnumDict:
+            # py2 and/or functional API gyrations
+            init = clsdict.pop('_init_', None)
+            start = clsdict.pop('_start_', None)
+            settings = clsdict.pop('_settings_', ())
+            _order_ = clsdict.pop('_order_', clsdict.pop('__order__', None))
+            _ignore_ = clsdict.pop('_ignore_', None)
+            _create_pseudo_member_ = clsdict.pop('_create_pseudo_member_', None)
+            _create_pseudo_member_values_ = clsdict.pop('_create_pseudo_member_values_', None)
+            _generate_next_value_ = clsdict.pop('_generate_next_value_', None)
+            _missing_ = clsdict.pop('_missing_', None)
+            _missing_value_ = clsdict.pop('_missing_value_', None)
+            _missing_name_ = clsdict.pop('_missing_name_', None)
+            _boundary_ = clsdict.pop('_boundary_', None)
+            _iter_member_ = clsdict.pop('_iter_member_', None)
+            _iter_member_by_value_ = clsdict.pop('_iter_member_by_value_', None)
+            _iter_member_by_def_ = clsdict.pop('_iter_member_by_def_', None)
+            __new__ = clsdict.pop('__new__', None)
+            __new__ = getattr(__new__, 'im_func', __new__)
+            __new__ = getattr(__new__, '__func__', __new__)
+            enum_members = dict([
+                    (k, v) for (k, v) in clsdict.items()
+                    if not (_is_sunder(k) or _is_dunder(k) or _is_private_name(cls, k) or _is_descriptor(v))
+                    ])
+            original_dict = clsdict
+            clsdict = metacls.__prepare__(cls, bases, init=init, start=start)
+            if settings:
+                clsdict['_settings_'] = settings
+            init = init or clsdict._init
+            if _order_ is None:
+                _order_ = clsdict.get('_order_')
+                if _order_ is not None:
+                    _order_ = _order_.__get__(cls)
+            if isinstance(original_dict, OrderedDict):
+                calced_order = original_dict
+            elif _order_ is None:
+                calced_order = [name for (name, value) in enumsort(list(enum_members.items()))]
+            elif isinstance(_order_, basestring):
+                calced_order = _order_ = _order_.replace(',', ' ').split()
+            elif callable(_order_):
+                if init:
+                    if not isinstance(init, basestring):
+                        init = ' '.join(init)
+                member = NamedTuple('member', init and 'name ' + init or ['name', 'value'])
+                calced_order = []
+                for name, value in enum_members.items():
+                    if init:
+                        if not isinstance(value, tuple):
+                            value = (value, )
+                        name_value = (name, ) + value
+                    else:
+                        name_value = tuple((name, value))
+                    if member._defined_len_ != len(name_value):
+                        raise TypeError('%d values expected (%s), %d received (%s)' % (
+                            member._defined_len_,
+                            ', '.join(member._fields_),
+                            len(name_value),
+                            ', '.join([repr(v) for v in name_value]),
+                            ))
+                    calced_order.append(member(*name_value))
+                calced_order = [m.name for m in sorted(calced_order, key=_order_)]
+            else:
+                calced_order = _order_
+            for name in (
+                    '_missing_', '_missing_value_', '_missing_name_',
+                    '_ignore_', '_create_pseudo_member_', '_create_pseudo_member_values_',
+                    '_generate_next_value_', '_order_', '__new__',
+                    '_missing_', '_missing_value_', '_missing_name_',
+                    '_boundary_',
+                    '_iter_member_', '_iter_member_by_value_', '_iter_member_by_def_',
+                ):
+                attr = locals()[name]
+                if attr is not None:
+                    clsdict[name] = attr
+            # now add members
+            for k in calced_order:
+                try:
+                    clsdict[k] = original_dict[k]
+                except KeyError:
+                    # this error will be handled when _order_ is checked
+                    pass
+            for k, v in original_dict.items():
+                if k not in calced_order:
+                    clsdict[k] = v
+            del _order_, _ignore_, _create_pseudo_member_, _create_pseudo_member_values_,
+            del _generate_next_value_, _missing_, _missing_value_, _missing_name_
+        #
+        # resume normal path
+        clsdict._locked = True
+        #
+        # check for illegal enum names (any others?)
+        member_names = clsdict._member_names
+        invalid_names = set(member_names) & set(['mro', ''])
+        if invalid_names:
+            raise ValueError('invalid enum member name(s): %s' % (
+                ', '.join(invalid_names), ))
+        _order_ = clsdict.pop('_order_', None)
+        if isinstance(_order_, basestring):
+            _order_ = _order_.replace(',',' ').split()
+        init = clsdict._init
+        start = clsdict._start
+        settings = clsdict._settings
+        creating_init = []
+        new_args = clsdict._new_args
+        auto_args = clsdict._auto_args
+        auto_init = False
+        if init is not None:
+            auto_init = True
+            creating_init = init[:]
+        if 'value' in creating_init and creating_init[0] != 'value':
+            raise TypeError("'value', if specified, must be the first item in 'init'")
+        magicvalue = MagicValue in settings
+        multivalue = MultiValue in settings
+        noalias = NoAlias in settings
+        unique = Unique in settings
+        # an Enum class cannot be mixed with other types (int, float, etc.) if
+        #   it has an inherited __new__ unless a new __new__ is defined (or
+        #   the resulting class will fail).
+        # an Enum class is final once enumeration items have been defined;
+        #
+        # remove any keys listed in _ignore_
+        clsdict.setdefault('_ignore_', []).append('_ignore_')
+        ignore = clsdict['_ignore_']
+        for key in ignore:
+            clsdict.pop(key, None)
+        #
+        boundary = boundary or clsdict.pop('_boundary_', None)
+        # convert to regular dict
+        clsdict = dict(clsdict.items())
+        member_type, first_enum = metacls._get_mixins_(cls, bases)
+        # get the method to create enum members
+        __new__, save_new, new_uses_args = metacls._find_new_(
+                clsdict,
+                member_type,
+                first_enum,
+                )
+        clsdict['_new_member_'] = staticmethod(__new__)
+        clsdict['_use_args_'] = new_uses_args
+        #
+        # convert future enum members into temporary _proto_members
+        # and record integer values in case this will be a Flag
+        flag_mask = 0
+        for name in member_names:
+            value = test_value = clsdict[name]
+            if isinstance(value, auto) and value.value is not _auto_null:
+                test_value = value.value
+            if isinstance(test_value, baseinteger):
+                flag_mask |= test_value
+            if isinstance(test_value, tuple) and test_value and isinstance(test_value[0], baseinteger):
+                flag_mask |= test_value[0]
+            clsdict[name] = _proto_member(value)
+        #
+        # temp stuff
+        clsdict['_creating_init_'] = creating_init
+        clsdict['_multivalue_'] = multivalue
+        clsdict['_magicvalue_'] = magicvalue
+        clsdict['_noalias_'] = noalias
+        clsdict['_unique_'] = unique
+        #
+        # house-keeping structures
+        clsdict['_member_names_'] = []
+        clsdict['_member_map_'] = OrderedDict()
+        clsdict['_member_type_'] = member_type
+        clsdict['_value2member_map_'] = {}
+        clsdict['_value2member_seq_'] = ()
+        clsdict['_settings_'] = settings
+        clsdict['_start_'] = start
+        clsdict['_auto_init_'] = init
+        clsdict['_new_args_'] = new_args
+        clsdict['_auto_args_'] = auto_args
+        clsdict['_order_function_'] = None
+        # now set the __repr__ for the value
+        clsdict['_value_repr_'] = metacls._find_data_repr_(cls, bases)
+        #
+        # Flag structures (will be removed if final class is not a Flag
+        clsdict['_boundary_'] = (
+                boundary
+                or getattr(first_enum, '_boundary_', None)
+                )
+        clsdict['_flag_mask_'] = flag_mask
+        clsdict['_all_bits_'] = 2 ** ((flag_mask).bit_length()) - 1
+        clsdict['_inverted_'] = None
+        #
+        # move skipped values out of the descriptor
+        for name, obj in clsdict.items():
+            if isinstance(obj, nonmember):
+                clsdict[name] = obj.value
+        #
+        # If a custom type is mixed into the Enum, and it does not know how
+        # to pickle itself, pickle.dumps will succeed but pickle.loads will
+        # fail.  Rather than have the error show up later and possibly far
+        # from the source, sabotage the pickle protocol for this class so
+        # that pickle.dumps also fails.
+        #
+        # However, if the new class implements its own __reduce_ex__, do not
+        # sabotage -- it's on them to make sure it works correctly.  We use
+        # __reduce_ex__ instead of any of the others as it is preferred by
+        # pickle over __reduce__, and it handles all pickle protocols.
+        unpicklable = False
+        if '__reduce_ex__' not in clsdict:
+            if member_type is not object:
+                methods = ('__getnewargs_ex__', '__getnewargs__',
+                        '__reduce_ex__', '__reduce__')
+                if not any(m in member_type.__dict__ for m in methods):
+                    _make_class_unpicklable(clsdict)
+                    unpicklable = True
+        #
+        # create a default docstring if one has not been provided
+        if '__doc__' not in clsdict:
+            clsdict['__doc__'] = 'An enumeration.'
+        #
+        # create our new Enum type
+        try:
+            exc = None
+            enum_class = type.__new__(metacls, cls, bases, clsdict)
+        except RuntimeError as e:
+            # any exceptions raised by _proto_member (aka member.__new__) will get converted to
+            # a RuntimeError, so get that original exception back and raise
+            # it instead
+            exc = e.__cause__ or e
+        if exc is not None:
+            raise exc
+        #
+        # if Python 3.5 or ealier, implement the __set_name__ and
+        # __init_subclass__ protocols
+        if pyver < PY3_6:
+            for name in member_names:
+                enum_class.__dict__[name].__set_name__(enum_class, name)
+            for name, obj in enum_class.__dict__.items():
+                if name in member_names:
+                    continue
+                if hasattr(obj, '__set_name__'):
+                    obj.__set_name__(enum_class, name)
+            if Enum is not None:
+                super(enum_class, enum_class).__init_subclass__()
+        #
+        # double check that repr and friends are not the mixin's or various
+        # things break (such as pickle)
+        #
+        # Also, special handling for ReprEnum
+        if ReprEnum is not None and ReprEnum in bases:
+            if member_type is object:
+                raise TypeError(
+                        'ReprEnum subclasses must be mixed with a data type (i.e.'
+                        ' int, str, float, etc.)'
+                        )
+            if '__format__' not in clsdict:
+                enum_class.__format__ = member_type.__format__
+                clsdict['__format__'] = enum_class.__format__
+            if '__str__' not in clsdict:
+                method = member_type.__str__
+                if method is object.__str__:
+                    # if member_type does not define __str__, object.__str__ will use
+                    # its __repr__ instead, so we'll also use its __repr__
+                    method = member_type.__repr__
+                enum_class.__str__ = method
+                clsdict['__str__'] = enum_class.__str__
+
+        for name in ('__repr__', '__str__', '__format__', '__reduce_ex__'):
+            if name in clsdict:
+                # class has defined/imported/copied the method
+                continue
+            class_method = getattr(enum_class, name)
+            obj_method = getattr(member_type, name, None)
+            enum_method = getattr(first_enum, name, None)
+            if obj_method is not None and obj_method == class_method:
+                if name == '__reduce_ex__' and unpicklable:
+                    continue
+                setattr(enum_class, name, enum_method)
+                clsdict[name] = enum_method
+        #
+        # for Flag, add __or__, __and__, __xor__, and __invert__
+        if Flag is not None and issubclass(enum_class, Flag):
+            for name in (
+                    '__or__', '__and__', '__xor__',
+                    '__ror__', '__rand__', '__rxor__',
+                    '__invert__'
+                ):
+                if name not in clsdict:
+                    setattr(enum_class, name, getattr(Flag, name))
+                    clsdict[name] = enum_method
+        #
+        # method resolution and int's are not playing nice
+        # Python's less than 2.6 use __cmp__
+        if pyver < PY2_6:
+            #
+            if issubclass(enum_class, int):
+                setattr(enum_class, '__cmp__', getattr(int, '__cmp__'))
+            #
+        elif PY2:
+            #
+            if issubclass(enum_class, int):
+                for method in (
+                        '__le__',
+                        '__lt__',
+                        '__gt__',
+                        '__ge__',
+                        '__eq__',
+                        '__ne__',
+                        '__hash__',
+                        ):
+                    setattr(enum_class, method, getattr(int, method))
+        #
+        # replace any other __new__ with our own (as long as Enum is not None,
+        # anyway) -- again, this is to support pickle
+        if Enum is not None:
+            # if the user defined their own __new__, save it before it gets
+            # clobbered in case they subclass later
+            if save_new:
+                setattr(enum_class, '__new_member__', enum_class.__dict__['__new__'])
+            setattr(enum_class, '__new__', Enum.__dict__['__new__'])
+        #
+        # _order_ checking is spread out into three/four steps
+        # - ensure _order_ is a list, not a string nor a function
+        # - if enum_class is a Flag:
+        #   - remove any non-single-bit flags from _order_
+        # - remove any aliases from _order_
+        # - check that _order_ and _member_names_ match
+        #
+        # _order_ step 1: ensure _order_ is a list
+        if _order_:
+            if isinstance(_order_, staticmethod):
+                _order_ = _order_.__func__
+            if callable(_order_):
+                # save order for future subclasses
+                enum_class._order_function_ = staticmethod(_order_)
+                # create ordered list for comparison
+                _order_ = [m.name for m in sorted(enum_class, key=_order_)]
+        #
+        # remove Flag structures if final class is not a Flag
+        if (
+                Flag is None and cls != 'Flag'
+                or Flag is not None and not issubclass(enum_class, Flag)
+            ):
+            delattr(enum_class, '_boundary_')
+            delattr(enum_class, '_flag_mask_')
+            delattr(enum_class, '_all_bits_')
+            delattr(enum_class, '_inverted_')
+        elif Flag is not None and issubclass(enum_class, Flag):
+            # ensure _all_bits_ is correct and there are no missing flags
+            single_bit_total = 0
+            multi_bit_total = 0
+            for flag in enum_class._member_map_.values():
+                if _is_single_bit(flag._value_):
+                    single_bit_total |= flag._value_
+                else:
+                    # multi-bit flags are considered aliases
+                    multi_bit_total |= flag._value_
+            if enum_class._boundary_ is not KEEP:
+                missed = list(_iter_bits_lsb(multi_bit_total & ~single_bit_total))
+                if missed:
+                    raise TypeError(
+                            'invalid Flag %r -- missing values: %s'
+                            % (cls, ', '.join((str(i) for i in missed)))
+                            )
+            enum_class._flag_mask_ = single_bit_total
+            enum_class._all_bits_ = 2 ** ((single_bit_total).bit_length()) - 1
+            #
+            # set correct __iter__
+            if [m._value_ for m in enum_class] != sorted([m._value_ for m in enum_class]):
+                enum_class._iter_member_ = enum_class._iter_member_by_def_
+            if _order_:
+                # _order_ step 2: remove any items from _order_ that are not single-bit
+                _order_ = [
+                        o
+                        for o in _order_
+                        if o not in enum_class._member_map_ or _is_single_bit(enum_class[o]._value_)
+                        ]
+        #
+        # check for constants with auto() values
+        for k, v in enum_class.__dict__.items():
+            if isinstance(v, constant) and isinstance(v.value, auto):
+                v.value = enum_class(v.value.value)
+        #
+        if _order_:
+            # _order_ step 3: remove aliases from _order_
+            _order_ = [
+                    o
+                    for o in _order_
+                    if (
+                        o not in enum_class._member_map_
+                        or
+                        (o in enum_class._member_map_ and o in enum_class._member_names_)
+                        )]
+            # _order_ step 4: verify that _order_ and _member_names_ match
+            if _order_ != enum_class._member_names_:
+                raise TypeError(
+                        'member order does not match _order_:\n%r\n%r'
+                        % (enum_class._member_names_, _order_)
+                        )
+        return enum_class
+
+    def __bool__(cls):
+        """
+        classes/types should always be True.
+        """
+        return True
+
+    def __call__(cls, value=no_arg, names=None, module=None, qualname=None, type=None, start=1, boundary=None):
+        """Either returns an existing member, or creates a new enum class.
+
+        This method is used both when an enum class is given a value to match
+        to an enumeration member (i.e. Color(3)) and for the functional API
+        (i.e. Color = Enum('Color', names='red green blue')).
+
+        When used for the functional API: `module`, if set, will be stored in
+        the new class' __module__ attribute; `type`, if set, will be mixed in
+        as the first base class.
+
+        Note: if `module` is not set this routine will attempt to discover the
+        calling module by walking the frame stack; if this is unsuccessful
+        the resulting class will not be pickleable.
+        """
+        if names is None:  # simple value lookup
+            return cls.__new__(cls, value)
+        # otherwise, functional API: we're creating a new Enum type
+        return cls._create_(value, names, module=module, qualname=qualname, type=type, start=start, boundary=boundary)
+
+    def __contains__(cls, member):
+        if not isinstance(member, Enum):
+            raise TypeError("%r (%r) is not an <aenum 'Enum'>" % (member, type(member)))
+        if not isinstance(member, cls):
+            return False
+        return True
+
+    def __delattr__(cls, attr):
+        # nicer error message when someone tries to delete an attribute
+        # (see issue19025).
+        if attr in cls._member_map_:
+            raise AttributeError(
+                    "%s: cannot delete Enum member %r." % (cls.__name__, attr),
+                    )
+        found_attr = _get_attr_from_chain(cls, attr)
+        if isinstance(found_attr, constant):
+            raise AttributeError(
+                    "%s: cannot delete constant %r" % (cls.__name__, attr),
+                    )
+        elif isinstance(found_attr, property):
+            raise AttributeError(
+                    "%s: cannot delete property %r" % (cls.__name__, attr),
+                    )
+        super(EnumType, cls).__delattr__(attr)
+
+    def __dir__(cls):
+        interesting = set(cls._member_names_ + [
+                    '__class__', '__contains__', '__doc__', '__getitem__',
+                    '__iter__', '__len__', '__members__', '__module__',
+                    '__name__',
+                    ])
+        if cls._new_member_ is not object.__new__:
+            interesting.add('__new__')
+        if cls.__init_subclass__ is not Enum.__init_subclass__:
+            interesting.add('__init_subclass__')
+        if hasattr(object, '__qualname__'):
+            interesting.add('__qualname__')
+        for method in ('__init__', '__format__', '__repr__', '__str__'):
+            if getattr(cls, method) not in (getattr(Enum, method), getattr(Flag, method)):
+                interesting.add(method)
+        if cls._member_type_ is object:
+            return sorted(interesting)
+        else:
+            # return whatever mixed-in data type has
+            return sorted(set(dir(cls._member_type_)) | interesting)
+
+    @_bltin_property
+    def __members__(cls):
+        """Returns a mapping of member name->value.
+
+        This mapping lists all enum members, including aliases. Note that this
+        is a copy of the internal mapping.
+        """
+        return cls._member_map_.copy()
+
+    def __getitem__(cls, name):
+        try:
+            return cls._member_map_[name]
+        except KeyError:
+            exc = _sys.exc_info()[1]
+        if Flag is not None and issubclass(cls, Flag) and '|' in name:
+            try:
+                # may be an __or__ed name
+                result = cls(0)
+                for n in name.split('|'):
+                    result |= cls[n]
+                return result
+            except KeyError:
+                raise exc
+        result = cls._missing_name_(name)
+        if isinstance(result, cls):
+            return result
+        else:
+            raise exc
+
+    def __iter__(cls):
+        return (cls._member_map_[name] for name in cls._member_names_)
+
+    def __reversed__(cls):
+        return (cls._member_map_[name] for name in reversed(cls._member_names_))
+
+    def __len__(cls):
+        return len(cls._member_names_)
+
+    __nonzero__ = __bool__
+
+    def __repr__(cls):
+        return "<aenum %r>" % (cls.__name__, )
+
+    def __setattr__(cls, name, value):
+        """Block attempts to reassign Enum members/constants.
+
+        A simple assignment to the class namespace only changes one of the
+        several possible ways to get an Enum member from the Enum class,
+        resulting in an inconsistent Enumeration.
+        """
+        member_map = cls.__dict__.get('_member_map_', {})
+        if name in member_map:
+            raise AttributeError(
+                    '%s: cannot rebind member %r.' % (cls.__name__, name),
+                    )
+        found_attr = _get_attr_from_chain(cls, name)
+        if isinstance(found_attr, constant):
+            raise AttributeError(
+                    "%s: cannot rebind constant %r" % (cls.__name__, name),
+                    )
+        elif isinstance(found_attr, property):
+            raise AttributeError(
+                    "%s: cannot rebind property %r" % (cls.__name__, name),
+                    )
+        super(EnumType, cls).__setattr__(name, value)
+
+    def _convert(cls, *args, **kwds):
+        import warnings
+        warnings.warn("_convert is deprecated and will be removed, use"
+                      " _convert_ instead.", DeprecationWarning, stacklevel=2)
+        return cls._convert_(*args, **kwds)
+
+    def _convert_(cls, name, module, filter, source=None, boundary=None, as_global=False):
+        """
+        Create a new Enum subclass that replaces a collection of global constants
+        """
+        # convert all constants from source (or module) that pass filter() to
+        # a new Enum called name, and export the enum and its members back to
+        # module;
+        # also, replace the __reduce_ex__ method so unpickling works in
+        # previous Python versions
+        module_globals = vars(_sys.modules[module])
+        if source:
+            source = vars(source)
+        else:
+            source = module_globals
+        members = [(key, source[key]) for key in source.keys() if filter(key)]
+        try:
+            # sort by value, name
+            members.sort(key=lambda t: (t[1], t[0]))
+        except TypeError:
+            # unless some values aren't comparable, in which case sort by just name
+            members.sort(key=lambda t: t[0])
+        cls = cls(name, members, module=module, boundary=boundary or KEEP)
+        cls.__reduce_ex__ = _reduce_ex_by_name
+        if as_global:
+            global_enum(cls)
+        else:
+            module_globals.update(cls.__members__)
+        module_globals[name] = cls
+        return cls
+
+    def _create_(cls, class_name, names, module=None, qualname=None, type=None, start=1, boundary=None):
+        """Convenience method to create a new Enum class.
+
+        `names` can be:
+
+        * A string containing member names, separated either with spaces or
+          commas.  Values are auto-numbered from 1.
+        * An iterable of member names.  Values are auto-numbered from 1.
+        * An iterable of (member name, value) pairs.
+        * A mapping of member name -> value.
+        """
+        if PY2:
+            # if class_name is unicode, attempt a conversion to ASCII
+            if isinstance(class_name, unicode):
+                try:
+                    class_name = class_name.encode('ascii')
+                except UnicodeEncodeError:
+                    raise TypeError('%r is not representable in ASCII' % (class_name, ))
+        metacls = cls.__class__
+        if type is None:
+            bases = (cls, )
+        else:
+            bases = (type, cls)
+        _, first_enum = cls._get_mixins_(class_name, bases)
+        generate = getattr(first_enum, '_generate_next_value_', None)
+        generate = getattr(generate, 'im_func', generate)
+        # special processing needed for names?
+        if isinstance(names, basestring):
+            names = names.replace(',', ' ').split()
+        if isinstance(names, (tuple, list)) and names and isinstance(names[0], basestring):
+            original_names, names = names, []
+            last_values = []
+            for count, name in enumerate(original_names):
+                value = generate(name, start, count, last_values[:])
+                last_values.append(value)
+                names.append((name, value))
+        # Here, names is either an iterable of (name, value) or a mapping.
+        item = None  # in case names is empty
+        clsdict = None
+        for item in names:
+            if clsdict is None:
+                # first time initialization
+                if isinstance(item, basestring):
+                    clsdict = {}
+                else:
+                    # remember the order
+                    clsdict = metacls.__prepare__(class_name, bases)
+            if isinstance(item, basestring):
+                member_name, member_value = item, names[item]
+            else:
+                member_name, member_value = item
+            clsdict[member_name] = member_value
+        if clsdict is None:
+            # in case names was empty
+            clsdict = metacls.__prepare__(class_name, bases)
+        enum_class = metacls.__new__(metacls, class_name, bases, clsdict, boundary=boundary)
+        # TODO: replace the frame hack if a blessed way to know the calling
+        # module is ever developed
+        if module is None:
+            try:
+                module = _sys._getframe(2).f_globals['__name__']
+            except (AttributeError, KeyError):
+                pass
+        if module is None:
+            _make_class_unpicklable(enum_class)
+        else:
+            enum_class.__module__ = module
+        if qualname is not None:
+            enum_class.__qualname__ = qualname
+        return enum_class
+
+    @classmethod
+    def _check_for_existing_members_(mcls, class_name, bases):
+        if Enum is None:
+            return
+        for chain in bases:
+            for base in chain.__mro__:
+                if issubclass(base, Enum) and base._member_names_:
+                    raise TypeError(
+                            "<aenum %r> cannot extend %r"
+                            % (class_name, base)
+                            )
+    @classmethod
+    def _get_mixins_(mcls, class_name, bases):
+        """Returns the type for creating enum members, and the first inherited
+        enum class.
+
+        bases: the tuple of bases that was given to __new__
+        """
+        if not bases or Enum is None:
+            return object, Enum
+
+        mcls._check_for_existing_members_(class_name, bases)
+
+        # ensure final parent class is an Enum derivative, find any concrete
+        # data type, and check that Enum has no members
+        first_enum = bases[-1]
+        if not issubclass(first_enum, Enum):
+            raise TypeError("new enumerations should be created as "
+                    "`EnumName([mixin_type, ...] [data_type,] enum_type)`")
+        member_type = mcls._find_data_type_(class_name, bases) or object
+        if first_enum._member_names_:
+            raise TypeError("cannot extend enumerations via subclassing")
+        #
+        return member_type, first_enum
+
+    @classmethod
+    def _find_data_repr_(mcls, class_name, bases):
+        for chain in bases:
+            for base in chain.__mro__:
+                if base is object:
+                    continue
+                elif issubclass(base, Enum):
+                    # if we hit an Enum, use it's _value_repr_
+                    return base._value_repr_
+                elif '__repr__' in base.__dict__:
+                    # this is our data repr
+                    return base.__dict__['__repr__']
+        return None
+
+    @classmethod
+    def _find_data_type_(mcls, class_name, bases):
+        data_types = set()
+        for chain in bases:
+            candidate = None
+            for base in chain.__mro__:
+                if base is object or base is StdlibEnum or base is StdlibFlag:
+                    continue
+                elif issubclass(base, Enum):
+                    if base._member_type_ is not object:
+                        data_types.add(base._member_type_)
+                elif '__new__' in base.__dict__:
+                    if issubclass(base, Enum):
+                        continue
+                    elif StdlibFlag is not None and issubclass(base, StdlibFlag):
+                        continue
+                    data_types.add(candidate or base)
+                    break
+                else:
+                    candidate = candidate or base
+        if len(data_types) > 1:
+            raise TypeError('%r: too many data types: %r' % (class_name, data_types))
+        elif data_types:
+            return data_types.pop()
+        else:
+            return None
+
+    @staticmethod
+    def _get_settings_(bases):
+        """Returns the combined _settings_ of all Enum base classes
+
+        bases: the tuple of bases given to __new__
+        """
+        settings = set()
+        for chain in bases:
+            for base in chain.__mro__:
+                if issubclass(base, Enum):
+                    for s in base._settings_:
+                        settings.add(s)
+        return settings
+
+    @classmethod
+    def _find_new_(mcls, clsdict, member_type, first_enum):
+        """Returns the __new__ to be used for creating the enum members.
+
+        clsdict: the class dictionary given to __new__
+        member_type: the data type whose __new__ will be used by default
+        first_enum: enumeration to check for an overriding __new__
+        """
+        # now find the correct __new__, checking to see of one was defined
+        # by the user; also check earlier enum classes in case a __new__ was
+        # saved as __new_member__
+        __new__ = clsdict.get('__new__', None)
+        #
+        # should __new__ be saved as __new_member__ later?
+        save_new = first_enum is not None and __new__ is not None
+        #
+        if __new__ is None:
+            # check all possibles for __new_member__ before falling back to
+            # __new__
+            for method in ('__new_member__', '__new__'):
+                for possible in (member_type, first_enum):
+                    target = getattr(possible, method, None)
+                    if target not in (
+                            None,
+                            None.__new__,
+                            object.__new__,
+                            Enum.__new__,
+                            StdlibEnum.__new__,
+                            ):
+                        __new__ = target
+                        break
+                if __new__ is not None:
+                    break
+            else:
+                __new__ = object.__new__
+        # if a non-object.__new__ is used then whatever value/tuple was
+        # assigned to the enum member name will be passed to __new__ and to the
+        # new enum member's __init__
+        if __new__ is object.__new__:
+            new_uses_args = False
+        else:
+            new_uses_args = True
+        #
+        return __new__, save_new, new_uses_args
+
+
+    # In order to support Python 2 and 3 with a single
+    # codebase we have to create the Enum methods separately
+    # and then use the `type(name, bases, dict)` method to
+    # create the class.
+
+EnumMeta = EnumType
+
+enum_dict = _Addendum(
+        dict=EnumType.__prepare__('Enum', (object, )),
+        doc="Generic enumeration.\n\n    Derive from this class to define new enumerations.\n\n",
+        ns=globals(),
+        )
+
+@enum_dict
+def __init__(self, *args, **kwds):
+    # auto-init method
+    _auto_init_ = self._auto_init_
+    if _auto_init_ is None:
+        return
+    if 'value' in _auto_init_:
+        # remove 'value' from _auto_init_ as it has already been handled
+        _auto_init_ = _auto_init_[1:]
+    if _auto_init_:
+        if len(_auto_init_) < len(args):
+            raise TypeError('%d arguments expected (%s), %d received (%s)'
+                    % (len(_auto_init_), _auto_init_, len(args), args))
+        for name, arg in zip(_auto_init_, args):
+            setattr(self, name, arg)
+        if len(args) < len(_auto_init_):
+            remaining_args = _auto_init_[len(args):]
+            for name in remaining_args:
+                value = kwds.pop(name, undefined)
+                if value is undefined:
+                    raise TypeError('missing value for: %r' % (name, ))
+                setattr(self, name, value)
+            if kwds:
+                # too many keyword arguments
+                raise TypeError('invalid keyword(s): %s' % ', '.join(kwds.keys()))
+
+@enum_dict
+def __new__(cls, value):
+    # all enum instances are actually created during class construction
+    # without calling this method; this method is called by the metaclass'
+    # __call__ (i.e. Color(3) ), and by pickle
+    if NoAlias in cls._settings_:
+        raise TypeError('NoAlias enumerations cannot be looked up by value')
+    if type(value) is cls:
+        # For lookups like Color(Color.red)
+        # value = value.value
+        return value
+    # by-value search for a matching enum member
+    # see if it's in the reverse mapping (for hashable values)
+    try:
+        if value in cls._value2member_map_:
+            return cls._value2member_map_[value]
+    except TypeError:
+        # not there, now do long search -- O(n) behavior
+        for name, member in cls._value2member_seq_:
+            if name == value:
+                return member
+    # still not found -- try _missing_ hook
+    try:
+        exc = None
+        result = cls._missing_value_(value)
+    except Exception as e:
+        exc = e
+        result = None
+    if isinstance(result, cls) or getattr(cls, '_boundary_', None) is EJECT:
+        return result
+    else:
+        if value is no_arg:
+            ve_exc = ValueError('%s() should be called with a value' % (cls.__name__, ))
+        else:
+            ve_exc = ValueError("%r is not a valid %s" % (value, cls.__name__))
+        if result is None and exc is None:
+            raise ve_exc
+        elif exc is None:
+            exc = TypeError(
+                    'error in %s._missing_: returned %r instead of None or a valid member'
+                    % (cls.__name__, result)
+                    )
+        if not isinstance(exc, ValueError):
+            exc.__cause__ = ve_exc
+        raise exc
+
+@enum_dict
+@classmethod
+def __init_subclass__(cls, **kwds):
+    if pyver < PY3_6:
+        # end of the line
+        if kwds:
+            raise TypeError('unconsumed keyword arguments: %r' % (kwds, ))
+    else:
+        super(Enum, cls).__init_subclass__(**kwds)
+
+@enum_dict
+@staticmethod
+def _generate_next_value_(name, start, count, last_values, *args, **kwds):
+    for last_value in reversed(last_values):
+        try:
+            new_value = last_value + 1
+            break
+        except TypeError:
+            pass
+    else:
+        new_value = start
+    if args:
+        return (new_value, ) + args
+    else:
+        return new_value
+
+@enum_dict
+@classmethod
+def _missing_(cls, value):
+    "deprecated, use _missing_value_ instead"
+    return None
+
+@enum_dict
+@classmethod
+def _missing_value_(cls, value):
+    "used for failed value access"
+    return cls._missing_(value)
+
+@enum_dict
+@classmethod
+def _missing_name_(cls, name):
+    "used for failed item access"
+    return None
+
+@enum_dict
+def __repr__(self):
+    v_repr = self.__class__._value_repr_ or self._value_.__class__.__repr__
+    return "<%s.%s: %s>" % (self.__class__.__name__, self._name_, v_repr(self._value_))
+
+@enum_dict
+def __str__(self):
+    return "%s.%s" % (self.__class__.__name__, self._name_)
+
+if PY3:
+    @enum_dict
+    def __dir__(self):
+        """
+        Returns all members and all public methods
+        """
+        if self.__class__._member_type_ is object:
+            interesting = set(['__class__', '__doc__', '__eq__', '__hash__', '__module__', 'name', 'value'])
+        else:
+            interesting = set(object.__dir__(self))
+        for name in getattr(self, '__dict__', []):
+            if name[0] != '_':
+                interesting.add(name)
+        for cls in self.__class__.mro():
+            for name, obj in cls.__dict__.items():
+                if name[0] == '_':
+                    continue
+                if isinstance(obj, property):
+                    # that's an enum.property
+                    if obj.fget is not None or name not in self._member_map_:
+                        interesting.add(name)
+                    else:
+                        # in case it was added by `dir(self)`
+                        interesting.discard(name)
+                else:
+                    interesting.add(name)
+        return sorted(interesting)
+
+@enum_dict
+def __format__(self, format_spec):
+    # mixed-in Enums should use the mixed-in type's __format__, otherwise
+    # we can get strange results with the Enum name showing up instead of
+    # the value
+
+    # pure Enum branch / overridden __str__ branch
+    overridden_str = self.__class__.__str__ != Enum.__str__
+    if self._member_type_ is object or overridden_str:
+        cls = str
+        val = str(self)
+    # mix-in branch
+    else:
+        cls = self._member_type_
+        val = self.value
+    return cls.__format__(val, format_spec)
+
+@enum_dict
+def __hash__(self):
+    return hash(self._name_)
+
+@enum_dict
+def __reduce_ex__(self, proto):
+    return self.__class__, (self._value_, )
+
+
+####################################
+# Python's less than 2.6 use __cmp__
+
+if pyver < PY2_6:
+
+    @enum_dict
+    def __cmp__(self, other):
+        if type(other) is self.__class__:
+            if self is other:
+                return 0
+            return -1
+        return NotImplemented
+        raise TypeError("unorderable types: %s() and %s()" % (self.__class__.__name__, other.__class__.__name__))
+
+else:
+
+    @enum_dict
+    def __le__(self, other):
+        raise TypeError("unorderable types: %s() <= %s()" % (self.__class__.__name__, other.__class__.__name__))
+
+    @enum_dict
+    def __lt__(self, other):
+        raise TypeError("unorderable types: %s() < %s()" % (self.__class__.__name__, other.__class__.__name__))
+
+    @enum_dict
+    def __ge__(self, other):
+        raise TypeError("unorderable types: %s() >= %s()" % (self.__class__.__name__, other.__class__.__name__))
+
+    @enum_dict
+    def __gt__(self, other):
+        raise TypeError("unorderable types: %s() > %s()" % (self.__class__.__name__, other.__class__.__name__))
+
+
+@enum_dict
+def __eq__(self, other):
+    if type(other) is self.__class__:
+        return self is other
+    return NotImplemented
+
+@enum_dict
+def __ne__(self, other):
+    if type(other) is self.__class__:
+        return self is not other
+    return NotImplemented
+
+@enum_dict
+def __hash__(self):
+    return hash(self._name_)
+
+@enum_dict
+def __reduce_ex__(self, proto):
+    return self.__class__, (self._value_, )
+
+
+# enum.property is used to provide access to the `name`, `value', etc.,
+# properties of enum members while keeping some measure of protection
+# from modification, while still allowing for an enumeration to have
+# members named `name`, `value`, etc..  This works because enumeration
+# members are not set directly on the enum class -- enum.property will
+# look them up in _member_map_.
+
+@enum_dict
+@property
+def name(self):
+    return self._name_
+
+@enum_dict
+@property
+def value(self):
+    return self._value_
+
+@enum_dict
+@property
+def values(self):
+    return self._values_
+
+def _reduce_ex_by_name(self, proto):
+    return self.name
+
+Enum = EnumType('Enum', (object, ), enum_dict.resolve())
+del enum_dict
+
+    # Enum has now been created
+
+class ReprEnum(Enum):
+    """
+    Only changes the repr(), leaving str() and format() to the mixed-in type.
+    """
+
+
+class IntEnum(int, ReprEnum):
+    """
+    Enum where members are also (and must be) ints
+    """
+
+
+class StrEnum(str, ReprEnum):
+    """
+    Enum where members are also (and must already be) strings
+
+    default value is member name, lower-cased
+    """
+
+    def __new__(cls, *values, **kwds):
+        if kwds:
+            raise TypeError('%r: keyword arguments not supported' % (cls.__name__))
+        if values:
+            if not isinstance(values[0], str):
+                raise TypeError('%s: values must be str [%r is a %r]' % (cls.__name__, values[0], type(values[0])))
+        value = str(*values)
+        member = str.__new__(cls, value)
+        member._value_ = value
+        return member
+
+    __str__ = str.__str__
+
+    def _generate_next_value_(name, start, count, last_values):
+        """
+        Return the lower-cased version of the member name.
+        """
+        return name.lower()
+
+
+class LowerStrEnum(StrEnum):
+    """
+    Enum where members are also (and must already be) lower-case strings
+
+    default value is member name, lower-cased
+    """
+
+    def __new__(cls, value, *args, **kwds):
+        obj = StrEnum.__new_member__(cls, value, *args, **kwds)
+        if value != value.lower():
+            raise ValueError('%r is not lower-case' % value)
+        return obj
+
+
+class UpperStrEnum(StrEnum):
+    """
+    Enum where members are also (and must already be) upper-case strings
+
+    default value is member name, upper-cased
+    """
+
+    def __new__(cls, value, *args, **kwds):
+        obj = StrEnum.__new_member__(cls, value, *args, **kwds)
+        if value != value.upper():
+            raise ValueError('%r is not upper-case' % value)
+        return obj
+
+    def _generate_next_value_(name, start, count, last_values, *args, **kwds):
+        return name.upper()
+
+
+if PY3:
+    class AutoEnum(Enum):
+        """
+        automatically use _generate_next_value_ when values are missing (Python 3 only)
+        """
+        _settings_ = MagicValue
+
+
+class AutoNumberEnum(Enum):
+    """
+    Automatically assign increasing values to members.
+
+    Py3: numbers match creation order
+    Py2: numbers are assigned alphabetically by member name
+         (unless `_order_` is specified)
+    """
+
+    def __new__(cls, *args, **kwds):
+        value = len(cls.__members__) + 1
+        if cls._member_type_ is int:
+            obj = int.__new__(cls, value)
+        elif cls._member_type_ is long:
+            obj = long.__new__(cls, value)
+        else:
+            obj = object.__new__(cls)
+        obj._value_ = value
+        return obj
+
+
+class AddValueEnum(Enum):
+    _settings_ = AddValue
+
+
+class MultiValueEnum(Enum):
+    """
+    Multiple values can map to each member.
+    """
+    _settings_ = MultiValue
+
+
+class NoAliasEnum(Enum):
+    """
+    Duplicate value members are distinct, but cannot be looked up by value.
+    """
+    _settings_ = NoAlias
+
+
+class OrderedEnum(Enum):
+    """
+    Add ordering based on values of Enum members.
+    """
+
+    def __ge__(self, other):
+        if self.__class__ is other.__class__:
+            return self._value_ >= other._value_
+        return NotImplemented
+
+    def __gt__(self, other):
+        if self.__class__ is other.__class__:
+            return self._value_ > other._value_
+        return NotImplemented
+
+    def __le__(self, other):
+        if self.__class__ is other.__class__:
+            return self._value_ <= other._value_
+        return NotImplemented
+
+    def __lt__(self, other):
+        if self.__class__ is other.__class__:
+            return self._value_ < other._value_
+        return NotImplemented
+
+
+if sqlite3:
+    class SqliteEnum(Enum):
+        def __conform__(self, protocol):
+            if protocol is sqlite3.PrepareProtocol:
+                return self.name
+
+
+class UniqueEnum(Enum):
+    """
+    Ensure no duplicate values exist.
+    """
+    _settings_ = Unique
+
+
+def convert(enum, name, module, filter, source=None):
+    """
+    Create a new Enum subclass that replaces a collection of global constants
+
+    enum: Enum, IntEnum, ...
+    name: name of new Enum
+    module: name of module (__name__ in global context)
+    filter: function that returns True if name should be converted to Enum member
+    source: namespace to check (defaults to 'module')
+    """
+    # convert all constants from source (or module) that pass filter() to
+    # a new Enum called name, and export the enum and its members back to
+    # module;
+    # also, replace the __reduce_ex__ method so unpickling works in
+    # previous Python versions
+    module_globals = vars(_sys.modules[module])
+    if source:
+        source = vars(source)
+    else:
+        source = module_globals
+    members = dict((name, value) for name, value in source.items() if filter(name))
+    enum = enum(name, members, module=module)
+    enum.__reduce_ex__ = _reduce_ex_by_name
+    module_globals.update(enum.__members__)
+    module_globals[name] = enum
+
+def extend_enum(enumeration, name, *args, **kwds):
+    """
+    Add a new member to an existing Enum.
+    """
+    # there are four possibilities:
+    # - extending an aenum Enum or 3.11+ enum Enum
+    # - extending an aenum Flag or 3.11+ enum Flag
+    # - extending a pre-3.11 stdlib Enum Flag
+    # - extending a 3.11+ stdlib Flag
+    #
+    # fail early if name is already in the enumeration
+    if (
+            name in enumeration.__dict__
+            or name in enumeration._member_map_
+            or name in [t[1] for t in getattr(enumeration, '_value2member_seq_', ())]
+        ):
+        raise TypeError('%r already in use as %r' % (name, enumeration.__dict__.get(name, enumeration[name])))
+    # and check for other instances in parent classes
+    descriptor = None
+    for base in enumeration.__mro__[1:]:
+        descriptor = base.__dict__.get(name)
+        if descriptor is not None:
+            if isinstance(descriptor, (property, DynamicClassAttribute)):
+                break
+            else:
+                raise TypeError('%r already in use in superclass %r' % (name, base.__name__))
+    try:
+        _member_map_ = enumeration._member_map_
+        _member_names_ = enumeration._member_names_
+        _member_type_ = enumeration._member_type_
+        _value2member_map_ = enumeration._value2member_map_
+        base_attributes = set([a for b in enumeration.mro() for a in b.__dict__])
+    except AttributeError:
+        raise TypeError('%r is not a supported Enum' % (enumeration, ))
+    try:
+        _value2member_seq_ = enumeration._value2member_seq_
+        _multi_value_ = MultiValue in enumeration._settings_
+        _no_alias_ = NoAlias in enumeration._settings_
+        _unique_ = Unique in enumeration._settings_
+        _auto_init_ = enumeration._auto_init_ or []
+    except AttributeError:
+        # standard Enum
+        _value2member_seq_ = []
+        _multi_value_ = False
+        _no_alias_ = False
+        _unique_ = False
+        _auto_init_ = []
+    if _multi_value_ and not args:
+        # must specify values for multivalue enums
+        raise ValueError('no values specified for MultiValue enum %r' % enumeration.__name__)
+    mt_new = _member_type_.__new__
+    _new = getattr(enumeration, '__new_member__', mt_new)
+    if not args:
+        last_values = [m.value for m in enumeration]
+        count = len(enumeration)
+        start = getattr(enumeration, '_start_', None)
+        if start is None:
+            start = last_values and (last_values[-1] + 1) or 1
+        _gnv = getattr(enumeration, '_generate_next_value_', None)
+        if _gnv is not None:
+            args = ( _gnv(name, start, count, last_values), )
+        else:
+            # must be a 3.4 or 3.5 Enum
+            args = (start, )
+    if _new is object.__new__:
+        new_uses_args = False
+    else:
+        new_uses_args = True
+    if len(args) == 1:
+        [value] = args
+    else:
+        value = args
+    more_values = ()
+    kwds = {}
+    if isinstance(value, enum):
+        args = value.args
+        kwds = value.kwds
+    if not isinstance(value, tuple):
+        args = (value, )
+    else:
+        args = value
+    # tease value out of auto-init if specified
+    if 'value' in _auto_init_:
+        if 'value' in kwds:
+            value = kwds.pop('value')
+        else:
+            value, args = args[0], args[1:]
+    elif _multi_value_:
+        value, more_values, args = args[0], args[1:], ()
+        if new_uses_args:
+            args = (value, )
+    if _member_type_ is tuple:
+        args = (args, )
+    if not new_uses_args:
+        new_member = _new(enumeration)
+        if not hasattr(new_member, '_value_'):
+            new_member._value_ = value
+    else:
+        new_member = _new(enumeration, *args, **kwds)
+        if not hasattr(new_member, '_value_'):
+            new_member._value_ = _member_type_(*args)
+    value = new_member._value_
+    if _multi_value_:
+        if 'value' in _auto_init_:
+            args = more_values
+        else:
+        # put all the values back into args for the init call
+            args = (value, ) + more_values
+    new_member._name_ = name
+    new_member.__objclass__ = enumeration.__class__
+    new_member.__init__(*args)
+    new_member._values_ = (value, ) + more_values
+    # do final checks before modifying enum structures:
+    # - is new member a flag?
+    #   - does the new member fit in the enum's declared _boundary_?
+    # - is new member an alias?
+    #
+    _all_bits_ = _flag_mask_ = None
+    if hasattr(enumeration, '_all_bits_'):
+        _all_bits_ = enumeration._all_bits_ | value
+        _flag_mask_ = enumeration._flag_mask_ | value
+        if enumeration._boundary_ != 'keep':
+            missed = list(_iter_bits_lsb(_flag_mask_ & ~_all_bits_))
+            if missed:
+                raise TypeError(
+                        'invalid Flag %r -- missing values: %s'
+                        % (cls, ', '.join((str(i) for i in missed)))
+                        )
+    # If another member with the same value was already defined, the
+    # new member becomes an alias to the existing one.
+    if _no_alias_:
+        # unless NoAlias was specified
+        return _finalize_extend_enum(enumeration, new_member, bits=_all_bits_, mask=_flag_mask_)
+    else:
+        # handle "normal" aliases
+        new_values = new_member._values_
+        for canonical_member in _member_map_.values():
+            canonical_values_ = getattr(canonical_member, '_values_', [canonical_member._value_])
+            for canonical_value in canonical_values_:
+                for new_value in new_values:
+                    if canonical_value == new_value:
+                        # name is an alias
+                        if _unique_ or _multi_value_:
+                            # aliases not allowed in Unique and MultiValue enums
+                            raise ValueError('%r is a duplicate of %r' % (new_member, canonical_member))
+                        else:
+                            # aliased name can be added, remaining checks irrelevant
+                            # aliases don't appear in member names (only in __members__ and _member_map_).
+                            return _finalize_extend_enum(enumeration, canonical_member, name=name, bits=_all_bits_, mask=_flag_mask_, is_alias=True)
+        # not a standard alias, but maybe a flag alias
+        if pyver < PY3_6:
+            flag_bases = Flag,
+        else:
+            flag_bases = Flag, StdlibFlag
+        if issubclass(enumeration, flag_bases) and hasattr(enumeration, '_all_bits_'):
+            # handle the new flag type
+            if _is_single_bit(value):
+                # a new member!  (an aliase would have been discovered in the previous loop)
+                return _finalize_extend_enum(enumeration, new_member, bits=_all_bits_, mask=_flag_mask_)
+            else:
+                # might be an 3.11 Flag alias
+                if value & enumeration._flag_mask_ == value and _value2member_map_.get(value) is not None:
+                    # yup, it's an alias to existing members... and its an alias of an alias
+                    canonical = _value2member_map_.get(value)
+                    return _finalize_extend_enum(enumeration, canonical, name=name, bits=_all_bits_, mask=_flag_mask_, is_alias=True)
+                else:
+                    return _finalize_extend_enum(enumeration, new_member, bits=_all_bits_, mask=_flag_mask_, is_alias=True)
+        else:
+            # if we get here, we have a brand new member
+            return _finalize_extend_enum(enumeration, new_member)
+
+def _finalize_extend_enum(enumeration, new_member, name=None, bits=None, mask=None, is_alias=False):
+    name = name or new_member.name
+    descriptor = None
+    for base in enumeration.__mro__[1:]:
+        descriptor = base.__dict__.get(name)
+        if descriptor is not None:
+            if isinstance(descriptor, (property, DynamicClassAttribute)):
+                break
+            else:
+                raise TypeError('%r already in use in superclass %r' % (name, base.__name__))
+    if not descriptor:
+        # get redirect in place before adding to _member_map_
+        redirect = property()
+        redirect.__set_name__(enumeration, name)
+        setattr(enumeration, name, redirect)
+    if not is_alias:
+        enumeration._member_names_.append(name)
+    enumeration._member_map_[name] = new_member
+    for v in getattr(new_member, '_values_', [new_member._value_]):
+        try:
+            enumeration._value2member_map_[v] = new_member
+        except TypeError:
+            enumeration._value2member_seq_ += ((v, new_member), )
+    if bits:
+        enumeration._all_bits_ = bits
+        enumeration._flag_mask_ = mask
+    return new_member
+
+def unique(enumeration):
+    """
+    Class decorator that ensures only unique members exist in an enumeration.
+    """
+    duplicates = []
+    for name, member in enumeration.__members__.items():
+        if name != member.name:
+            duplicates.append((name, member.name))
+    if duplicates:
+        duplicate_names = ', '.join(
+                ["%s -> %s" % (alias, name) for (alias, name) in duplicates]
+                )
+        raise ValueError('duplicate names found in %r: %s' %
+                (enumeration, duplicate_names)
+                )
+    return enumeration
+
+# Flag
+
+@export(globals())
+class FlagBoundary(StrEnum):
+    """
+    control how out of range values are handled
+    "strict" -> error is raised  [default]
+    "conform" -> extra bits are discarded
+    "eject" -> lose flag status (becomes a normal integer)
+    """
+    STRICT = auto()
+    CONFORM = auto()
+    EJECT = auto()
+    KEEP = auto()
+assert FlagBoundary.STRICT == 'strict', (FlagBoundary.STRICT, FlagBoundary.CONFORM)
+
+class Flag(Enum):
+    """
+    Generic flag enumeration.
+
+    Derive from this class to define new flag enumerations.
+    """
+
+    _boundary_ = STRICT
+    _numeric_repr_ = repr
+
+
+    def _generate_next_value_(name, start, count, last_values, *args, **kwds):
+        """
+        Generate the next value when not given.
+
+        name: the name of the member
+        start: the initital start value or None
+        count: the number of existing members
+        last_value: the last value assigned or None
+        """
+        if not count:
+            if args:
+                return ((1, start)[start is not None], ) + args
+            else:
+                return (1, start)[start is not None]
+        else:
+            last_value = max(last_values)
+            try:
+                high_bit = _high_bit(last_value)
+                result = 2 ** (high_bit+1)
+                if args:
+                    return (result,)  + args
+                else:
+                    return result
+            except Exception:
+                pass
+            raise TypeError('invalid Flag value: %r' % last_value)
+
+    @classmethod
+    def _iter_member_by_value_(cls, value):
+        """
+        Extract all members from the value in definition (i.e. increasing value) order.
+        """
+        for val in _iter_bits_lsb(value & cls._flag_mask_):
+            yield cls._value2member_map_.get(val)
+
+    _iter_member_ = _iter_member_by_value_
+
+    @classmethod
+    def _iter_member_by_def_(cls, value):
+        """
+        Extract all members from the value in definition order.
+        """
+        members = list(cls._iter_member_by_value_(value))
+        members.sort(key=lambda m: m._sort_order_)
+        for member in members:
+            yield member
+
+    @classmethod
+    def _missing_(cls, value):
+        """
+        return a member matching the given value, or None
+        """
+        return cls._create_pseudo_member_(value)
+
+    @classmethod
+    def _create_pseudo_member_(cls, *values):
+        """
+        Create a composite member.
+        """
+        value = values[0]
+        if not isinstance(value, baseinteger):
+            raise ValueError(
+                    "%r is not a valid %s" % (value, getattr(cls, '__qualname__', cls.__name__))
+                    )
+        # check boundaries
+        # - value must be in range (e.g. -16 <-> +15, i.e. ~15 <-> 15)
+        # - value must not include any skipped flags (e.g. if bit 2 is not
+        #   defined, then 0d10 is invalid)
+        neg_value = None
+        if (
+                not ~cls._all_bits_ <= value <= cls._all_bits_
+                or value & (cls._all_bits_ ^ cls._flag_mask_)
+            ):
+            if cls._boundary_ is STRICT:
+                max_bits = max(value.bit_length(), cls._flag_mask_.bit_length())
+                raise ValueError(
+                        "%s: invalid value: %r\n    given %s\n  allowed %s"
+                        % (cls.__name__, value, bin(value, max_bits), bin(cls._flag_mask_, max_bits))
+                        )
+            elif cls._boundary_ is CONFORM:
+                value = value & cls._flag_mask_
+            elif cls._boundary_ is EJECT:
+                return value
+            elif cls._boundary_ is KEEP:
+                if value < 0:
+                    value = (
+                            max(cls._all_bits_+1, 2**(value.bit_length()))
+                            + value
+                            )
+            else:
+                raise ValueError(
+                        'unknown flag boundary: %r' % (cls._boundary_, )
+                        )
+        if value < 0:
+            neg_value = value
+            value = cls._all_bits_ + 1 + value
+        # get members and unknown
+        unknown = value & ~cls._flag_mask_
+        members = list(cls._iter_member_(value))
+        if unknown and cls._boundary_ is not KEEP:
+            raise ValueError(
+                    '%s(%r) -->  unknown values %r [%s]'
+                    % (cls.__name__, value, unknown, bin(unknown))
+                    )
+        # let class adjust values
+        values = cls._create_pseudo_member_values_(members, *values)
+        __new__ = getattr(cls, '__new_member__', None)
+        if cls._member_type_ is object and not __new__:
+            # construct a singleton enum pseudo-member
+            pseudo_member = object.__new__(cls)
+        else:
+            pseudo_member = (__new__ or cls._member_type_.__new__)(cls, *values)
+        if not hasattr(pseudo_member, 'value'):
+            pseudo_member._value_ = value
+        if members:
+            pseudo_member._name_ = '|'.join([m._name_ for m in members])
+            if unknown:
+                pseudo_member._name_ += '|%s' % cls._numeric_repr_(unknown)
+        else:
+            pseudo_member._name_ = None
+        # use setdefault in case another thread already created a composite
+        # with this value, but only if all members are known
+        # note: zero is a special case -- add it
+        if not unknown:
+            pseudo_member = cls._value2member_map_.setdefault(value, pseudo_member)
+            if neg_value is not None:
+                cls._value2member_map_[neg_value] = pseudo_member
+        return pseudo_member
+
+
+    @classmethod
+    def _create_pseudo_member_values_(cls, members, *values):
+        """
+        Return values to be fed to __new__ to create new member.
+        """
+        if cls._member_type_ in (baseinteger + (object, )):
+            return values
+        elif len(values) < 2:
+            return values + (cls._member_type_(), )
+        else:
+            return values
+
+    def __contains__(self, other):
+        """
+        Returns True if self has at least the same flags set as other.
+        """
+        if not isinstance(other, self.__class__):
+            raise TypeError(
+                "unsupported operand type(s) for 'in': '%s' and '%s'" % (
+                    type(other).__name__, self.__class__.__name__))
+        if other._value_ == 0 or self._value_ == 0:
+            return False
+        return other._value_ & self._value_ == other._value_
+
+    def __iter__(self):
+        """
+        Returns flags in definition order.
+        """
+        for member in self._iter_member_(self._value_):
+            yield member
+
+    def __len__(self):
+        return _bit_count(self._value_)
+
+    def __repr__(self):
+        cls = self.__class__
+        if self._name_ is None:
+            # only zero is unnamed by default
+            return '<%s: %r>' % (cls.__name__, self._value_)
+        else:
+            return '<%s.%s: %r>' % (cls.__name__, self._name_, self._value_)
+
+    def __str__(self):
+        cls = self.__class__
+        if self._name_ is None:
+            return '%s(%s)' % (cls.__name__, self._value_)
+        else:
+            return '%s.%s' % (cls.__name__, self._name_)
+
+    if PY2:
+        def __nonzero__(self):
+            return bool(self._value_)
+    else:
+        def __bool__(self):
+            return bool(self._value_)
+
+    def __or__(self, other):
+        if isinstance(other, self.__class__):
+            other_value = other._value_
+        elif self._member_type_ is not object and isinstance(other, self._member_type_):
+            other_value = other
+        else:
+            return NotImplemented
+        return self.__class__(self._value_ | other_value)
+
+    def __and__(self, other):
+        if isinstance(other, self.__class__):
+            other_value = other._value_
+        elif self._member_type_ is not object and isinstance(other, self._member_type_):
+            other_value = other
+        else:
+            return NotImplemented
+        return self.__class__(self._value_ & other_value)
+
+    def __xor__(self, other):
+        if isinstance(other, self.__class__):
+            other_value = other._value_
+        elif self._member_type_ is not object and isinstance(other, self._member_type_):
+            other_value = other
+        else:
+            return NotImplemented
+        return self.__class__(self._value_ ^ other_value)
+
+    def __invert__(self):
+        if self._inverted_ is None:
+            if self._boundary_ is KEEP:
+                # use all bits
+                self._inverted_ = self.__class__(~self._value_)
+            else:
+                # calculate flags not in this member
+                self._inverted_ = self.__class__(self._flag_mask_ ^ self._value_)
+            self._inverted_._inverted_ = self
+        return self._inverted_
+
+    __ror__ = __or__
+    __rand__ = __and__
+    __rxor__ = __xor__
+
+
+
+class IntFlag(int, ReprEnum, Flag):
+    """Support for integer-based Flags"""
+
+    _boundary_ = EJECT
+
+
+def _high_bit(value):
+    """returns index of highest bit, or -1 if value is zero or negative"""
+    return value.bit_length() - 1
+
+def global_enum_repr(self):
+    """
+    use module.enum_name instead of class.enum_name
+
+    the module is the last module in case of a multi-module name
+    """
+    module = self.__class__.__module__.split('.')[-1]
+    return '%s.%s' % (module, self._name_)
+
+def global_flag_repr(self):
+    """
+    use module.flag_name instead of class.flag_name
+
+    the module is the last module in case of a multi-module name
+    """
+    module = self.__class__.__module__.split('.')[-1]
+    cls_name = self.__class__.__name__
+    if self._name_ is None:
+        return "%s.%s(%r)" % (module, cls_name, self._value_)
+    if _is_single_bit(self):
+        return '%s.%s' % (module, self._name_)
+    if self._boundary_ is not FlagBoundary.KEEP:
+        return '|'.join(['%s.%s' % (module, name) for name in self.name.split('|')])
+    else:
+        name = []
+        for n in self._name_.split('|'):
+            if n[0].isdigit():
+                name.append(n)
+            else:
+                name.append('%s.%s' % (module, n))
+        return '|'.join(name)
+
+def global_str(self):
+    """
+    use enum_name instead of class.enum_name
+    """
+    if self._name_ is None:
+        return "%s(%r)" % (cls_name, self._value_)
+    else:
+        return self._name_
+
+def global_enum(cls, update_str=False):
+    """
+    decorator that makes the repr() of an enum member reference its module
+    instead of its class; also exports all members to the enum's module's
+    global namespace
+    """
+    if issubclass(cls, Flag):
+        cls.__repr__ = global_flag_repr
+    else:
+        cls.__repr__ = global_enum_repr
+    if not issubclass(cls, ReprEnum) or update_str:
+        cls.__str__ = global_str
+    _sys.modules[cls.__module__].__dict__.update(cls.__members__)
+    return cls
+
+
+class module(object):
+
+    def __init__(self, cls, *args):
+        self.__name__ = cls.__name__
+        self._parent_module = cls.__module__
+        self.__all__ = []
+        all_objects = cls.__dict__
+        if not args:
+            args = [k for k, v in all_objects.items() if isinstance(v, (NamedConstant, Enum))]
+        for name in args:
+            self.__dict__[name] = all_objects[name]
+            self.__all__.append(name)
+
+    def register(self):
+        _sys.modules["%s.%s" % (self._parent_module, self.__name__)] = self
+
+if StdlibEnumMeta:
+
+    from _weakrefset import WeakSet
+
+    def __subclasscheck__(cls, subclass):
+        """
+        Override for issubclass(subclass, cls).
+        """
+        if not isinstance(subclass, type):
+            raise TypeError('issubclass() arg 1 must be a class (got %r)' % (subclass, ))
+        # Check cache
+        try:
+            cls.__dict__['_subclass_cache_']
+        except KeyError:
+            cls._subclass_cache_ = WeakSet()
+            cls._subclass_negative_cache_ = WeakSet()
+        except RecursionError:
+            import sys
+            exc, cls, tb = sys.exc_info()
+            exc = RecursionError('possible causes for endless recursion:\n    - __getattribute__ is not ignoring __dunder__ attibutes\n    - __instancecheck__ and/or __subclasscheck_ are (mutually) recursive\n    see `aenum.remove_stdlib_integration` for temporary work-around')
+            raise_from_none(exc)
+        if subclass in cls._subclass_cache_:
+            return True
+        # Check negative cache
+        elif subclass in cls._subclass_negative_cache_:
+            return False
+        if cls is subclass:
+            cls._subclass_cache_.add(subclass)
+            return True
+        # Check if it's a direct subclass
+        if cls in getattr(subclass, '__mro__', ()):
+            cls._subclass_cache_.add(subclass)
+            return True
+        # Check if it's an aenum.Enum|IntEnum|IntFlag|Flag subclass
+        if cls is StdlibIntFlag and issubclass(subclass, IntFlag):
+            cls._subclass_cache_.add(subclass)
+            return True
+        elif cls is StdlibFlag and issubclass(subclass, Flag):
+            cls._subclass_cache_.add(subclass)
+            return True
+        elif cls is StdlibIntEnum and issubclass(subclass, IntEnum):
+            cls._subclass_cache_.add(subclass)
+            return True
+        if cls is StdlibEnum and issubclass(subclass, Enum):
+            cls._subclass_cache_.add(subclass)
+            return True
+        # No dice; update negative cache
+        cls._subclass_negative_cache_.add(subclass)
+        return False
+
+    def __instancecheck__(cls, instance):
+        subclass = instance.__class__
+        try:
+            return cls.__subclasscheck__(subclass)
+        except RecursionError:
+            import sys
+            exc, cls, tb = sys.exc_info()
+            exc = RecursionError('possible causes for endless recursion:\n    - __getattribute__ is not ignoring __dunder__ attibutes\n    - __instancecheck__ and/or __subclasscheck_ are (mutually) recursive\n    see `aenum.remove_stdlib_integration` for temporary work-around')
+            raise_from_none(exc)
+
+    StdlibEnumMeta.__subclasscheck__ = __subclasscheck__
+    StdlibEnumMeta.__instancecheck__ = __instancecheck__
+
+def add_stdlib_integration():
+    if StdlibEnum:
+        StdlibEnumMeta.__subclasscheck__ = __subclasscheck__
+        StdlibEnumMeta.__instancecheck__ = __instancecheck__
+
+def remove_stdlib_integration():
+    """
+    Remove the __instancecheck__ and __subclasscheck__ overrides from the stdlib Enum.
+
+    Those overrides are in place so that code detecting stdlib enums will also detect
+    aenum enums.  If a buggy __getattribute__, __instancecheck__, or __subclasscheck__
+    is defined on a custom EnumMeta then RecursionErrors can result; using this
+    function after importing aenum will solve that problem, but the better solution is
+    to fix the buggy method.
+    """
+    if StdlibEnum:
+        del StdlibEnumMeta.__instancecheck__
+        del StdlibEnumMeta.__subclasscheck__
+
diff --git a/venv/Lib/site-packages/aenum/__pycache__/__init__.cpython-39.pyc b/venv/Lib/site-packages/aenum/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 00000000..176f78e2
Binary files /dev/null and b/venv/Lib/site-packages/aenum/__pycache__/__init__.cpython-39.pyc differ
diff --git a/venv/Lib/site-packages/aenum/__pycache__/_py3.cpython-39.pyc b/venv/Lib/site-packages/aenum/__pycache__/_py3.cpython-39.pyc
new file mode 100644
index 00000000..c289c1bf
Binary files /dev/null and b/venv/Lib/site-packages/aenum/__pycache__/_py3.cpython-39.pyc differ
diff --git a/venv/Lib/site-packages/aenum/_py2.py b/venv/Lib/site-packages/aenum/_py2.py
new file mode 100644
index 00000000..a6e7b290
--- /dev/null
+++ b/venv/Lib/site-packages/aenum/_py2.py
@@ -0,0 +1,7 @@
+from operator import div as _div_
+from inspect import getargspec
+
+def raise_with_traceback(exc, tb):
+    raise exc, None, tb
+
+__all__ = ['_div_', 'getargspec', 'raise_with_traceback']
diff --git a/venv/Lib/site-packages/aenum/_py3.py b/venv/Lib/site-packages/aenum/_py3.py
new file mode 100644
index 00000000..2edeefdf
--- /dev/null
+++ b/venv/Lib/site-packages/aenum/_py3.py
@@ -0,0 +1,12 @@
+from inspect import getfullargspec as _getfullargspec
+
+def getargspec(method):
+    args, varargs, keywords, defaults, _, _, _ = _getfullargspec(method)
+    return args, varargs, keywords, defaults
+
+def raise_with_traceback(exc, tb):
+    raise exc.with_traceback(tb)
+
+def raise_from_none(exc):
+    raise exc from None
+
diff --git a/venv/Lib/site-packages/aenum/doc/aenum.rst b/venv/Lib/site-packages/aenum/doc/aenum.rst
new file mode 100644
index 00000000..42ef0d3c
--- /dev/null
+++ b/venv/Lib/site-packages/aenum/doc/aenum.rst
@@ -0,0 +1,1568 @@
+``aenum`` --- support for advanced enumerations, namedtuples, and constants
+===========================================================================
+
+.. :synopsis:: enumerations are sets of symbolic names bound to unique,
+    constant values; namedtuples are fixed- or variable-length
+    tuples with the positions addressable by field name as well as by index;
+    constants are classes of named constants that cannot be rebound.
+.. :moduleauthor:: Ethan Furman <ethan@stoneleaf.us>
+
+----------------
+
+An ``Enum`` is a set of symbolic names (members) bound to unique, constant
+values.  Within an enumeration, the members can be compared by identity, and
+the enumeration itself can be iterated over.
+
+A ``NamedTuple`` is a class-based, fixed-length tuple with a name for each
+possible position accessible using attribute-access notation.
+
+A ``NamedConstant`` is a class whose members cannot be rebound;  it lacks all
+other ``Enum`` capabilities, however; consequently, it can have duplicate
+values.  There is also a ``module`` function that can insert the
+``NamedConstant`` class into ``sys.modules`` where it will appear to be a
+module whose top-level names cannot be rebound.
+
+.. note::
+    ``constant`` refers to names not being rebound; mutable objects can be
+     mutated.
+
+
+Module Contents
+---------------
+
+This module defines five enumeration classes that can be used to define unique
+sets of names and values, one ``Enum`` class decorator, one ``NamedTuple``
+class, one ``NamedConstant`` class, and several helpers.
+
+``NamedConstant``
+
+NamedConstant class for creating groups of constants.  These names cannot be
+rebound to other values.
+
+``Enum``
+
+Base class for creating enumerated constants.  See section `Enum Functional API`_
+for an alternate construction syntax.
+
+``AddValue``
+
+Flag specifying that ``_generate_next_value_`` should always be called to
+provide the initial value for an enum member.
+
+``MultiValue``
+
+Flag specifying that each item of tuple value is a separate value for that
+member; the first tuple item is the canonical one.
+
+``NoAlias``
+
+Flag specifying that duplicate valued members are distinct and not aliases;
+by-value lookups are disabled.
+
+``Unique``
+
+Flag specifying that duplicate valued members are not allowed.
+
+.. note::
+    The flags are inherited by the enumeration's subclasses.  To use them in
+    Python 2 assign to ``_settings_`` in the class body.
+
+``IntEnum``
+
+Base class for creating enumerated constants that are also subclasses of ``int``.
+
+``AutoNumberEnum``
+
+Derived class that automatically assigns an ``int`` value to each member.
+
+``OrderedEnum``
+
+Derived class that adds ``<``, ``<=``, ``>=``, and ``>`` methods to an ``Enum``.
+
+``UniqueEnum``
+
+Derived class that ensures only one name is bound to any one value.
+
+``unique``
+
+Enum class decorator that ensures only one name is bound to any one value.
+
+.. note::
+
+    the ``UniqueEnum`` class, the ``unique`` decorator, and the Unique
+    flag all do the same thing; you do not need to use more than one of
+    them at the same time.
+
+``NamedTuple``
+
+Base class for `creating NamedTuples`_, either by subclassing or via it's
+functional API.
+
+``constant``
+
+Descriptor to add constant values to an ``Enum``, or advanced constants to
+``NamedConstant``.
+
+``convert``
+
+Helper to transform target global variables into an ``Enum``.
+
+``enum``
+
+Helper for specifying keyword arguments when creating ``Enum`` members.
+
+``export``
+
+Helper for inserting ``Enum`` members and ``NamedConstant`` constants into a
+namespace (usually ``globals()``.
+
+``extend_enum``
+
+Helper for adding new ``Enum`` members, both stdlib and aenum.
+
+``module``
+
+Function to take a ``NamedConstant`` or ``Enum`` class and insert it into
+``sys.modules`` with the affect of a module whose top-level constant and
+member names cannot be rebound.
+
+``skip``
+
+Descriptor to add a normal (non-``Enum`` member) attribute to an ``Enum``
+or ``NamedConstant``.
+
+
+Creating an Enum
+----------------
+
+Enumerations are created using the ``class`` syntax, which makes them
+easy to read and write.  An alternative creation method is described in
+`Enum Functional API`_.  To define an enumeration, subclass ``Enum`` as
+follows::
+
+    >>> from aenum import Enum
+    >>> class Color(Enum):
+    ...     red = 1
+    ...     green = 2
+    ...     blue = 3
+
+*Nomenclature*
+
+  - The class ``Color`` is an *enumeration* (or *enum*)
+  - The attributes ``Color.red``, ``Color.green``, etc., are
+    *enumeration members* (or *enum members*).
+  - The enum members have *names* and *values* (the name of
+    ``Color.red`` is ``red``, the value of ``Color.blue`` is
+    ``3``, etc.)
+
+.. note::
+
+    Even though we use the ``class`` syntax to create Enums, Enums
+    are not normal Python classes.  See `How are Enums different?`_ for
+    more details.
+
+Enumeration members have human readable string representations::
+
+    >>> print(Color.red)
+    Color.red
+
+...while their ``repr`` has more information::
+
+    >>> print(repr(Color.red))
+    <Color.red: 1>
+
+The *type* of an enumeration member is the enumeration it belongs to::
+
+    >>> type(Color.red)
+    <aenum 'Color'>
+    >>> isinstance(Color.green, Color)
+    True
+
+Enumerations support iteration.  In Python 3.x definition order is used; in
+Python 2.x the definition order is not available, but class attribute
+``_order_`` is supported;  otherwise, value order is used if posible,
+otherwise alphabetical name order is used::
+
+    >>> class Shake(Enum):
+    ...   _order_ = 'vanilla chocolate cookies mint'  # only needed in 2.x
+    ...   vanilla = 7
+    ...   chocolate = 4
+    ...   cookies = 9
+    ...   mint = 3
+    ...
+    >>> for shake in Shake:
+    ...   print(shake)
+    ...
+    Shake.vanilla
+    Shake.chocolate
+    Shake.cookies
+    Shake.mint
+
+The ``_order_`` attribute is always removed, but in 3.x it is also used to
+verify that definition order is the same (useful for py2&3 code bases);
+however, in the stdlib version it will be ignored and not removed.
+
+.. note::
+
+    To maintain compatibility with Python 3.4 and 3.5, use __order__
+    instead (double leading and trailing underscores).
+
+Enumeration members are hashable, so they can be used in dictionaries and sets::
+
+    >>> apples = {}
+    >>> apples[Color.red] = 'red delicious'
+    >>> apples[Color.green] = 'granny smith'
+    >>> apples == {Color.red: 'red delicious', Color.green: 'granny smith'}
+    True
+
+In Python 3 the class syntax has a few extra advancements::
+
+    --> class Color(
+    ...         Enum,
+    ...         settings=(AddValue, MultiValue, NoAlias, Unique),
+    ...         init='field_name1 field_name2 ...',
+    ...         start=7,
+    ...         )
+    ...
+
+``start`` is used to specify the starting value for the first member::
+
+    --> class Count(Enum, start=11):
+    ...     eleven
+    ...     twelve
+    ...
+    --> Count.twelve.value == 12
+    True
+
+``init`` specifies the attribute names to store creation values to::
+
+    --> class Planet(Enum, init='mass radius'):
+    ...     MERCURY = (3.303e+23, 2.4397e6)
+    ...     EARTH   = (5.976e+24, 6.37814e6)
+    ...
+    --> Planet.EARTH.value
+    (5.976e+24, 6378140.0)
+    --> Planet.EARTH.radius
+    2.4397e6
+
+The various settings enable special behavior:
+
+- ``AddValue`` calls a user supplied ``_generate_next_value_`` to provide
+  the initial value
+- ``MultiValue`` allows multiple values per member instead of the usual 1
+- ``NoAlias`` allows different members to have the same value
+- ``Unique`` disallows different members to have the same value
+
+.. note::
+
+    To use these features in Python 2 use the _sundered_ versions of
+    the names in the class body:  ``_start_``, ``_init_``, ``_settings_``.
+
+
+Programmatic access to enumeration members and their attributes
+---------------------------------------------------------------
+
+Sometimes it's useful to access members in enumerations programmatically (i.e.
+situations where ``Color.red`` won't do because the exact color is not known
+at program-writing time).  ``Enum`` allows such access::
+
+    >>> Color(1)
+    <Color.red: 1>
+    >>> Color(3)
+    <Color.blue: 3>
+
+If you want to access enum members by *name*, use item access::
+
+    >>> Color['red']
+    <Color.red: 1>
+    >>> Color['green']
+    <Color.green: 2>
+
+If have an enum member and need its ``name`` or ``value``::
+
+    >>> member = Color.red
+    >>> member.name
+    'red'
+    >>> member.value
+    1
+
+
+Duplicating enum members and values
+-----------------------------------
+
+Having two enum members (or any other attribute) with the same name is invalid;
+in Python 3.x this would raise an error, but in Python 2.x the second member
+simply overwrites the first::
+
+    # python 2.x
+    --> class Shape(Enum):
+    ...   square = 2
+    ...   square = 3
+    ...
+    --> Shape.square
+    <Shape.square: 3>
+
+    # python 3.x
+    --> class Shape(Enum):
+    ...   square = 2
+    ...   square = 3
+    Traceback (most recent call last):
+    ...
+    TypeError: Attempted to reuse key: 'square'
+
+However, two enum members are allowed to have the same value.  Given two members
+A and B with the same value (and A defined first), B is an alias to A.  By-value
+lookup of the value of A and B will return A.  By-name lookup of B will also
+return A::
+
+    >>> class Shape(Enum):
+    ...   _order_ = 'square diamond circle'  # needed in 2.x
+    ...   square = 2
+    ...   diamond = 1
+    ...   circle = 3
+    ...   alias_for_square = 2
+    ...
+    >>> Shape.square
+    <Shape.square: 2>
+    >>> Shape.alias_for_square
+    <Shape.square: 2>
+    >>> Shape(2)
+    <Shape.square: 2>
+
+
+Allowing aliases is not always desirable.  ``unique`` can be used to ensure
+that none exist in a particular enumeration::
+
+    >>> from aenum import unique
+    >>> @unique
+    ... class Mistake(Enum):
+    ...   _order_ = 'one two three'  # only needed in 2.x
+    ...   one = 1
+    ...   two = 2
+    ...   three = 3
+    ...   four = 3
+    Traceback (most recent call last):
+    ...
+    ValueError: duplicate names found in <aenum 'Mistake'>: four -> three
+
+Iterating over the members of an enum does not provide the aliases::
+
+    >>> list(Shape)
+    [<Shape.square: 2>, <Shape.diamond: 1>, <Shape.circle: 3>]
+
+The special attribute ``__members__`` is a dictionary mapping names to members.
+It includes all names defined in the enumeration, including the aliases::
+
+    >>> for name, member in sorted(Shape.__members__.items()):
+    ...   name, member
+    ...
+    ('alias_for_square', <Shape.square: 2>)
+    ('circle', <Shape.circle: 3>)
+    ('diamond', <Shape.diamond: 1>)
+    ('square', <Shape.square: 2>)
+
+The ``__members__`` attribute can be used for detailed programmatic access to
+the enumeration members.  For example, finding all the aliases::
+
+    >>> [n for n, mbr in Shape.__members__.items() if mbr.name != n]
+    ['alias_for_square']
+
+Comparisons
+-----------
+
+Enumeration members are compared by identity::
+
+    >>> Color.red is Color.red
+    True
+    >>> Color.red is Color.blue
+    False
+    >>> Color.red is not Color.blue
+    True
+
+Ordered comparisons between enumeration values are *not* supported.  Enum
+members are not integers (but see `IntEnum`_ below)::
+
+    >>> Color.red < Color.blue
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+    TypeError: unorderable types: Color() < Color()
+
+.. warning::
+
+    In Python 2 *everything* is ordered, even though the ordering may not
+    make sense.  If you want your enumerations to have a sensible ordering
+    consider using an `OrderedEnum`_.
+
+
+Equality comparisons are defined though::
+
+    >>> Color.blue == Color.red
+    False
+    >>> Color.blue != Color.red
+    True
+    >>> Color.blue == Color.blue
+    True
+
+Comparisons against non-enumeration values will always compare not equal
+(again, ``IntEnum`` was explicitly designed to behave differently, see
+below)::
+
+    >>> Color.blue == 2
+    False
+
+
+Allowed members and attributes of enumerations
+----------------------------------------------
+
+The examples above use integers for enumeration values.  Using integers is
+short and handy (and provided by default by the `Enum Functional API`_), but not
+strictly enforced.  In the vast majority of use-cases, one doesn't care what
+the actual value of an enumeration is.  But if the value *is* important,
+enumerations can have arbitrary values.
+
+Enumerations are Python classes, and can have methods and special methods as
+usual.  If we have this enumeration::
+
+    >>> class Mood(Enum):
+    ...   funky = 1
+    ...   happy = 3
+    ...
+    ...   def describe(self):
+    ...     # self is the member here
+    ...     return self.name, self.value
+    ...
+    ...   def __str__(self):
+    ...     return 'my custom str! {0}'.format(self.value)
+    ...
+    ...   @classmethod
+    ...   def favorite_mood(cls):
+    ...     # cls here is the enumeration
+    ...     return cls.happy
+
+Then::
+
+    >>> Mood.favorite_mood()
+    <Mood.happy: 3>
+    >>> Mood.happy.describe()
+    ('happy', 3)
+    >>> str(Mood.funky)
+    'my custom str! 1'
+
+The rules for what is allowed are as follows: _sunder_ names (starting and
+ending with a single underscore) are reserved by enum and cannot be used;
+all other attributes defined within an enumeration will become members of this
+enumeration, with the exception of *__dunder__* names and descriptors (methods
+are also descriptors).
+
+.. note::
+
+    If your enumeration defines ``__new__`` and/or ``__init__`` then
+    whatever value(s) were given to the enum member will be passed into
+    those methods.  See `Planet`_ for an example.
+
+
+Restricted Enum subclassing
+---------------------------
+
+A new `Enum` class must have one base Enum class, up to one concrete
+data type, and as many `object`-based mixin classes as needed.  The
+order of these base classes is::
+
+    def EnumName([mix-in, ...,] [data-type,] base-enum):
+        pass
+
+Also, subclassing an enumeration is allowed only if the enumeration does not define
+
+any members.  So this is forbidden::
+
+    >>> class MoreColor(Color):
+    ...   pink = 17
+    Traceback (most recent call last):
+    ...
+    TypeError: <aenum 'MoreColor'> cannot extend <aenum 'Color'>
+
+But this is allowed::
+
+    >>> class Foo(Enum):
+    ...   def some_behavior(self):
+    ...     pass
+    ...
+    >>> class Bar(Foo):
+    ...   happy = 1
+    ...   sad = 2
+    ...
+
+Allowing subclassing of enums that define members would lead to a violation of
+some important invariants of types and instances.  On the other hand, it makes
+sense to allow sharing some common behavior between a group of enumerations.
+(See `OrderedEnum`_ for an example.)
+
+
+Pickling
+--------
+
+Enumerations can be pickled and unpickled::
+
+    >>> from aenum.test import Fruit
+    >>> from pickle import dumps, loads
+    >>> Fruit.tomato is loads(dumps(Fruit.tomato, 2))
+    True
+
+The usual restrictions for pickling apply: picklable enums must be defined in
+the top level of a module, since unpickling requires them to be importable
+from that module.
+
+.. note::
+
+    With pickle protocol version 4 (introduced in Python 3.4) it is possible
+    to easily pickle enums nested in other classes.
+
+
+
+Enum Functional API
+-------------------
+
+The ``Enum`` class is callable, providing the following functional API::
+
+    >>> Animal = Enum('Animal', 'ant bee cat dog')
+    >>> Animal
+    <aenum 'Animal'>
+    >>> Animal.ant
+    <Animal.ant: 1>
+    >>> Animal.ant.value
+    1
+    >>> list(Animal)
+    [<Animal.ant: 1>, <Animal.bee: 2>, <Animal.cat: 3>, <Animal.dog: 4>]
+
+The semantics of this API resemble ``namedtuple``. The first argument
+of the call to ``Enum`` is the name of the enumeration.
+
+The second argument is the *source* of enumeration member names.  It can be a
+whitespace-separated string of names, a sequence of names, a sequence of
+2-tuples with key/value pairs, or a mapping (e.g. dictionary) of names to
+values.  The last two options enable assigning arbitrary values to
+enumerations; the others auto-assign increasing integers starting with 1.  A
+new class derived from ``Enum`` is returned.  In other words, the above
+assignment to ``Animal`` is equivalent to::
+
+    >>> class Animals(Enum):
+    ...   ant = 1
+    ...   bee = 2
+    ...   cat = 3
+    ...   dog = 4
+
+Pickling enums created with the functional API can be tricky as frame stack
+implementation details are used to try and figure out which module the
+enumeration is being created in (e.g. it will fail if you use a utility
+function in separate module, and also may not work on IronPython or Jython).
+The solution is to specify the module name explicitly as follows::
+
+    >>> Animals = Enum('Animals', 'ant bee cat dog', module=__name__)
+
+Derived Enumerations
+--------------------
+
+IntEnum
+^^^^^^^
+
+A variation of ``Enum`` is provided which is also a subclass of
+``int``.  Members of an ``IntEnum`` can be compared to integers;
+by extension, integer enumerations of different types can also be compared
+to each other::
+
+    >>> from aenum import IntEnum
+    >>> class Shape(IntEnum):
+    ...   circle = 1
+    ...   square = 2
+    ...
+    >>> class Request(IntEnum):
+    ...   post = 1
+    ...   get = 2
+    ...
+    >>> Shape == 1
+    False
+    >>> Shape.circle == 1
+    True
+    >>> Shape.circle == Request.post
+    True
+
+However, they still can't be compared to standard ``Enum`` enumerations::
+
+    >>> class Shape(IntEnum):
+    ...   circle = 1
+    ...   square = 2
+    ...
+    >>> class Color(Enum):
+    ...   red = 1
+    ...   green = 2
+    ...
+    >>> Shape.circle == Color.red
+    False
+
+``IntEnum`` values behave like integers in other ways you'd expect::
+
+    >>> int(Shape.circle)
+    1
+    >>> ['a', 'b', 'c'][Shape.circle]
+    'b'
+    >>> [i for i in range(Shape.square)]
+    [0, 1]
+
+For the vast majority of code, ``Enum`` is strongly recommended,
+since ``IntEnum`` breaks some semantic promises of an enumeration (by
+being comparable to integers, and thus by transitivity to other
+unrelated enumerations).  It should be used only in special cases where
+there's no other choice; for example, when integer constants are
+replaced with enumerations and backwards compatibility is required with code
+that still expects integers.
+
+
+IntFlag
+^^^^^^^
+
+The next variation of ``Enum`` provided, ``IntFlag``, is also based
+on ``int``.  The difference being ``IntFlag`` members can be combined
+using the bitwise operators (&, \|, ^, ~) and the result is still an
+``IntFlag`` member.  However, as the name implies, ``IntFlag``
+members also subclass ``int`` and can be used wherever an ``int`` is
+used.  Any operation on an ``IntFlag`` member besides the bit-wise
+operations will lose the ``IntFlag`` membership.
+
+Sample ``IntFlag`` class::
+
+    >>> from aenum import IntFlag
+    >>> class Perm(IntFlag):
+    ...     _order_ = 'R W X'
+    ...     R = 4
+    ...     W = 2
+    ...     X = 1
+    ...
+    >>> Perm.R | Perm.W
+    <Perm.R|W: 6>
+    >>> Perm.R + Perm.W
+    6
+    >>> RW = Perm.R | Perm.W
+    >>> Perm.R in RW
+    True
+
+It is also possible to name the combinations::
+
+    >>> class Perm(IntFlag):
+    ...     _order_ = 'R W X'
+    ...     R = 4
+    ...     W = 2
+    ...     X = 1
+    ...     RWX = 7
+    >>> Perm.RWX
+    <Perm.RWX: 7>
+    >>> ~Perm.RWX
+    <Perm: 0>
+
+Another important difference between ``IntFlag`` and ``Enum`` is that
+if no flags are set (the value is 0), its boolean evaluation is ``False``::
+
+    >>> Perm.R & Perm.X
+    <Perm: 0>
+    >>> bool(Perm.R & Perm.X)
+    False
+
+Because ``IntFlag`` members are also subclasses of ``int`` they can
+be combined with them::
+
+    >>> Perm.X | 4
+    <Perm.R|X: 5>
+
+If the result is not a ``Flag`` then, depending on the ``_boundary_`` setting,
+an exception is raised (``STRICT``), the extra bits are lost (``CONFORM``), or
+it reverts to an int (``EJECT``):
+
+    >>> from aenum import STRICT, CONFORM, EJECT
+    >>> Perm._boundary_ = STRICT
+    >>> Perm.X | 8
+    Traceback (most recent call last):
+    ...
+    ValueError: Perm: invalid value: 9
+        given 0b0 1001
+      allowed 0b0 0111
+
+    >>> Perm._boundary_ = EJECT
+    >>> Perm.X | 8
+    9
+
+    >>> Perm._boundary_ = CONFORM
+    >>> Perm.X | 8
+    <Perm.X: 1>
+
+
+Flag
+^^^^
+
+The last variation is ``Flag``.  Like ``IntFlag``, ``Flag``
+members can be combined using the bitwise operators (&, \|, ^, ~).  Unlike
+``IntFlag``, they cannot be combined with, nor compared against, any
+other ``Flag`` enumeration, nor ``int``.  While it is possible to
+specify the values directly it is recommended to use ``auto`` as the
+value and let ``Flag`` select an appropriate value.
+
+Like ``IntFlag``, if a combination of ``Flag`` members results in no
+flags being set, the boolean evaluation is ``False``::
+
+    >>> from aenum import Flag, auto
+    >>> class Color(Flag):
+    ...     RED = auto()
+    ...     BLUE = auto()
+    ...     GREEN = auto()
+    ...
+    >>> Color.RED & Color.GREEN
+    <Color: 0>
+    >>> bool(Color.RED & Color.GREEN)
+    False
+
+Individual flags should have values that are powers of two (1, 2, 4, 8, ...),
+while combinations of flags won't::
+
+    --> class Color(Flag):
+    ...     RED = auto()
+    ...     BLUE = auto()
+    ...     GREEN = auto()
+    ...     WHITE = RED | BLUE | GREEN
+    ...
+    --> Color.WHITE
+    <Color.WHITE: 7>
+
+Giving a name to the "no flags set" condition does not change its boolean
+value::
+
+    >>> class Color(Flag):
+    ...     BLACK = 0
+    ...     RED = auto()
+    ...     BLUE = auto()
+    ...     GREEN = auto()
+    ...
+    >>> Color.BLACK
+    <Color.BLACK: 0>
+    >>> bool(Color.BLACK)
+    False
+
+Flags can be iterated over to retrieve the individual truthy flags in the value::
+
+    >>> class Color(Flag):
+    ...     _order_ = 'BLACK RED BLUE GREEN WHITE'
+    ...     BLACK = 0
+    ...     RED = auto()
+    ...     BLUE = auto()
+    ...     GREEN = auto()
+    ...     WHITE = RED | BLUE | GREEN
+    ...
+    >>> list(Color.GREEN)
+    [<Color.GREEN: 4>]
+    >>> list(Color.WHITE)
+    [<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 4>]
+
+.. note::
+
+    For the majority of new code, ``Enum`` and ``Flag`` are strongly
+    recommended, since ``IntEnum`` and ``IntFlag`` break some
+    semantic promises of an enumeration (by being comparable to integers, and
+    thus by transitivity to other unrelated enumerations).  ``IntEnum``
+    and ``IntFlag`` should be used only in cases where ``Enum`` and
+    ``Flag`` will not do; for example, when integer constants are replaced
+    with enumerations, or for interoperability with other systems.
+
+
+Others
+^^^^^^
+
+While ``IntEnum`` is part of the ``aenum`` module, it would be very
+simple to implement independently::
+
+    class MyIntEnum(int, Enum):
+        pass
+
+This demonstrates how similar derived enumerations can be defined; for example
+a ``MyStrEnum`` that mixes in ``str`` instead of ``int``.
+
+Some rules:
+
+1. When subclassing ``Enum``, mix-in types must appear before
+   ``Enum`` itself in the sequence of bases, as in the ``MyIntEnum``
+   example above.
+2. While ``Enum`` can have members of any type, once you mix in an
+   additional type, all the members must have values of that type or be
+   convertible into that type.  This restriction does not apply to mix-ins
+   which only add methods and don't specify another data type.
+3. When another data type is mixed in, the ``value`` attribute is *not the
+   same* as the enum member itself, although it is equivalant and will compare
+   equal.
+4. %-style formatting:  ``%s`` and ``%r`` call ``Enum``'s ``__str__`` and
+   ``__repr__`` respectively; other codes (such as ``%i`` or ``%h`` for
+   MyIntEnum) treat the enum member as its mixed-in type.
+5. ``str.__format__`` (or ``format``) will use the mixed-in
+   type's ``__format__``.  If the ``Enum``'s ``str`` or ``repr`` is desired
+   use the ``!s`` or ``!r`` ``str`` format codes.
+
+.. note::
+
+   If you override the ``__str__`` method, then it will be used to provide the
+   string portion of the ``format()`` call.
+
+.. note::
+
+   Prior to Python 3.4 there is a bug in ``str``'s %-formatting: ``int``
+   subclasses are printed as strings and not numbers when the ``%d``, ``%i``,
+   or ``%u`` codes are used.
+
+
+Extra Goodies
+-------------
+
+aenum supports a few extra techniques not found in the stdlib version.
+
+enum
+^^^^
+
+If you have several items to initialize your ``Enum`` members with and
+would like to use keyword arguments, the ``enum`` helper is for you::
+
+    >>> from aenum import enum
+    >>> class Presidents(Enum):
+    ...     Washington = enum('George Washington', circa=1776, death=1797)
+    ...     Jackson = enum('Andrew Jackson', circa=1830, death=1837)
+    ...     Lincoln = enum('Abraham Lincoln', circa=1860, death=1865)
+    ...
+    >>> Presidents.Lincoln
+    <Presidents.Lincoln: enum('Abraham Lincoln', circa=1860, death=1865)>
+
+extend_enum
+^^^^^^^^^^^
+
+For those rare cases when you need to create your ``Enum`` in pieces, you
+can use ``extend_enum`` to add new members after the initial creation
+(the new member is returned)::
+
+    >>> from aenum import extend_enum
+    >>> class Color(Enum):
+    ...     red = 1
+    ...     green = 2
+    ...     blue = 3
+    ...
+    >>> list(Color)
+    [<Color.red: 1>, <Color.green: 2>, <Color.blue: 3>]
+    >>> extend_enum(Color, 'opacity', 4)
+    <Color.opacity: 4>
+    >>> list(Color)
+    [<Color.red: 1>, <Color.green: 2>, <Color.blue: 3>, <Color.opacity: 4>]
+    >>> Color.opacity in Color
+    True
+    >>> Color.opacity.name == 'opacity'
+    True
+    >>> Color.opacity.value == 4
+    True
+    >>> Color(4)
+    <Color.opacity: 4>
+    >>> Color['opacity']
+    <Color.opacity: 4>
+
+    --> Color.__members__
+    OrderedDict([
+        ('red', <Color.red: 1>),
+        ('green', <Color.green: 2>),
+        ('blue', <Color.blue: 3>),
+        ('opacity', <Color.opacity: 4>)
+        ])
+
+constant
+^^^^^^^^
+
+If you need to have some constant value in your ``Enum`` that isn't a member,
+use ``constant``::
+
+    >>> from aenum import constant
+    >>> class Planet(Enum):
+    ...     MERCURY = (3.303e+23, 2.4397e6)
+    ...     EARTH   = (5.976e+24, 6.37814e6)
+    ...     JUPITER = (1.9e+27,   7.1492e7)
+    ...     URANUS  = (8.686e+25, 2.5559e7)
+    ...     G = constant(6.67300E-11)
+    ...     def __init__(self, mass, radius):
+    ...         self.mass = mass       # in kilograms
+    ...         self.radius = radius   # in meters
+    ...     @property
+    ...     def surface_gravity(self):
+    ...         # universal gravitational constant  (m3 kg-1 s-2)
+    ...         return self.G * self.mass / (self.radius * self.radius)
+    ...
+    >>> Planet.EARTH.value
+    (5.976e+24, 6378140.0)
+    >>> Planet.EARTH.surface_gravity
+    9.802652743337129
+    >>> Planet.G
+    6.673e-11
+    >>> Planet.G = 9
+    Traceback (most recent call last):
+    ...
+    AttributeError: Planet: cannot rebind constant 'G'
+
+skip
+^^^^
+
+If you need a standard attribute that is not converted into an ``Enum``
+member, use ``skip``::
+
+    >>> from aenum import skip
+    >>> class Color(Enum):
+    ...     red = 1
+    ...     green = 2
+    ...     blue = 3
+    ...     opacity = skip(0.45)
+    ...
+    >>> Color.opacity
+    0.45
+    >>> Color.opacity = 0.77
+    >>> Color.opacity
+    0.77
+
+start
+^^^^^
+
+``start`` can be used to turn on auto-numbering (useful for when you don't
+care which numbers are assigned as long as they are consistent and in order)
+The Python 3 version can look like this::
+
+    >>> class Color(Enum, start=1):                # doctest: +SKIP
+    ...     red, green, blue
+    ...
+    >>> Color.blue
+    <Color.blue: 3>
+
+This can also be done in Python 2, albeit not as elegantly (this also works in
+Python 3)::
+
+    >>> class Color(Enum):                         # doctest: +SKIP
+    ...     _start_ = 1
+    ...     red = auto()
+    ...     green = auto()
+    ...     blue = auto()
+    ...
+    >>> Color.blue
+    <Color.blue: 3>
+
+init
+^^^^
+
+If you need an ``__init__`` method that does nothing besides save its
+arguments, ``init`` is for you::
+
+    >>> class Planet(Enum, init='mass radius'):      # doctest: +SKIP
+    ...     MERCURY = (3.303e+23, 2.4397e6)
+    ...     EARTH   = (5.976e+24, 6.37814e6)
+    ...     JUPITER = (1.9e+27,   7.1492e7)
+    ...     URANUS  = (8.686e+25, 2.5559e7)
+    ...     G = constant(6.67300E-11)
+    ...     @property
+    ...     def surface_gravity(self):
+    ...         # universal gravitational constant  (m3 kg-1 s-2)
+    ...         return self.G * self.mass / (self.radius * self.radius)
+    ...
+    >>> Planet.JUPITER.value
+    (1.9e+27, 71492000.0)
+    >>> Planet.JUPITER.mass
+    1.9e+27
+
+.. note::
+
+   Just as with ``start`` above, in Python 2 you must put the keyword as a
+   _sunder_ in the class body -- ``_init_ = 'mass radius'``.
+
+init and missing values
+^^^^^^^^^^^^^^^^^^^^^^^
+
+If ``_init_`` calls for values that are not supplied, ``_generate_next_value_``
+will be called in an effort to generate them.  Here is an example in Python 2::
+
+    >>> from aenum import Enum
+    >>> class SelectionEnum(Enum):
+    ...     _init_ = 'db user'
+    ...     def __new__(cls, *args, **kwds):
+    ...         count = len(cls.__members__)
+    ...         obj = object.__new__(cls)
+    ...         obj._count = count
+    ...         obj._value_ = args
+    ...         return obj
+    ...     @staticmethod
+    ...     def _generate_next_value_(name, start, count, values, *args, **kwds):
+    ...         return (name, ) + args
+    ...
+    >>> class NotificationType(SelectionEnum):
+    ...     # usually, name is the same as db
+    ...     # but not for blanks
+    ...     blank = '', ''
+    ...     C = 'Catalog'
+    ...     S = 'Sheet'
+    ...     B = 'Both'
+    ...
+    >>> NotificationType.blank
+    <NotificationType.blank: ('', '')>
+    >>> NotificationType.B
+    <NotificationType.B: ('B', 'Both')>
+    >>> NotificationType.B.db
+    'B'
+    >>> NotificationType.B.user
+    'Both'
+
+combining Flag with other data types
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Flag does support being combined with other data types.  To support this you
+need to provide a ``_create_pseudo_member_values_`` method which will be called
+with the members in a composite flag.  You may also need to provide a custom
+``__new__`` method::
+
+    >>> class AnsiFlag(str, Flag):
+    ...     def __new__(cls, value, code):
+    ...         str_value = '\x1b[%sm' % code
+    ...         obj = str.__new__(cls, str_value)
+    ...         obj._value_ = value
+    ...         obj.code = code
+    ...         return obj
+    ...     @classmethod
+    ...     def _create_pseudo_member_values_(cls, members, *values):
+    ...         code = ';'.join(m.code for m in members)
+    ...         return values + (code, )
+    ...     _order_ = 'FG_Red FG_Green BG_Magenta BG_White'
+    ...     FG_Red = '31'             # ESC [ 31 m      # red
+    ...     FG_Green = '32'           # ESC [ 32 m      # green
+    ...     BG_Magenta = '45'         # ESC [ 35 m      # magenta
+    ...     BG_White = '47'           # ESC [ 37 m      # white
+    ...
+    >>> color = AnsiFlag.BG_White | AnsiFlag.FG_Red
+    >>> repr(color)
+    '<AnsiFlag.FG_Red|BG_White: 9>'
+    >>> str.__repr__(color)
+    "'\\x1b[31;47m'"
+
+.. note::
+
+   If you do not provide your own ``_create_pseudo_member_values_`` the flags
+   may still combine, but may be missing functionality.
+
+
+Decorators
+----------
+
+unique
+^^^^^^
+
+A ``class`` decorator specifically for enumerations.  It searches an
+enumeration's ``__members__`` gathering any aliases it finds; if any are
+found ``ValueError`` is raised with the details::
+
+    >>> @unique
+    ... class NoDupes(Enum):
+    ...    first = 'one'
+    ...    second = 'two'
+    ...    third = 'two'
+    Traceback (most recent call last):
+    ...
+    ValueError: duplicate names found in <aenum 'NoDupes'>: third -> second
+
+
+Interesting examples
+--------------------
+
+While ``Enum`` and ``IntEnum`` are expected to cover the majority of
+use-cases, they cannot cover them all.  Here are recipes for some different
+types of enumerations that can be used directly (the first three are included
+in the module), or as examples for creating one's own.
+
+
+AutoNumber
+^^^^^^^^^^
+
+Avoids having to specify the value for each enumeration member::
+
+    >>> class AutoNumber(Enum):
+    ...     def __new__(cls):
+    ...         value = len(cls.__members__) + 1
+    ...         obj = object.__new__(cls)
+    ...         obj._value_ = value
+    ...         return obj
+    ...
+    >>> class Color(AutoNumber):
+    ...     _order_ = "red green blue"  # only needed in 2.x
+    ...     red = ()
+    ...     green = ()
+    ...     blue = ()
+    ...
+    >>> Color.green.value == 2
+    True
+
+.. note::
+
+    The `__new__` method, if defined, is used during creation of the Enum
+    members; it is then replaced by Enum's `__new__` which is used after
+    class creation for lookup of existing members.  Due to the way Enums are
+    supposed to behave, there is no way to customize Enum's `__new__` without
+    modifying the class after it is created.
+
+
+UniqueEnum
+^^^^^^^^^^
+
+Raises an error if a duplicate member name is found instead of creating an
+alias::
+
+    >>> class UniqueEnum(Enum):
+    ...     def __init__(self, *args):
+    ...         cls = self.__class__
+    ...         if any(self.value == e.value for e in cls):
+    ...             a = self.name
+    ...             e = cls(self.value).name
+    ...             raise ValueError(
+    ...                     "aliases not allowed in UniqueEnum:  %r --> %r"
+    ...                     % (a, e))
+    ...
+    >>> class Color(UniqueEnum):
+    ...     _order_ = 'red green blue'
+    ...     red = 1
+    ...     green = 2
+    ...     blue = 3
+    ...     grene = 2
+    Traceback (most recent call last):
+    ...
+    ValueError: aliases not allowed in UniqueEnum:  'grene' --> 'green'
+
+
+OrderedEnum
+^^^^^^^^^^^
+
+An ordered enumeration that is not based on ``IntEnum`` and so maintains
+the normal ``Enum`` invariants (such as not being comparable to other
+enumerations)::
+
+    >>> class OrderedEnum(Enum):
+    ...     def __ge__(self, other):
+    ...         if self.__class__ is other.__class__:
+    ...             return self._value_ >= other._value_
+    ...         return NotImplemented
+    ...     def __gt__(self, other):
+    ...         if self.__class__ is other.__class__:
+    ...             return self._value_ > other._value_
+    ...         return NotImplemented
+    ...     def __le__(self, other):
+    ...         if self.__class__ is other.__class__:
+    ...             return self._value_ <= other._value_
+    ...         return NotImplemented
+    ...     def __lt__(self, other):
+    ...         if self.__class__ is other.__class__:
+    ...             return self._value_ < other._value_
+    ...         return NotImplemented
+    ...
+    >>> class Grade(OrderedEnum):
+    ...     __ordered__ = 'A B C D F'
+    ...     A = 5
+    ...     B = 4
+    ...     C = 3
+    ...     D = 2
+    ...     F = 1
+    ...
+    >>> Grade.C < Grade.A
+    True
+
+
+Planet
+^^^^^^
+
+If ``__new__`` or ``__init__`` is defined the value of the enum member
+will be passed to those methods::
+
+    >>> class Planet(Enum):
+    ...     MERCURY = (3.303e+23, 2.4397e6)
+    ...     VENUS   = (4.869e+24, 6.0518e6)
+    ...     EARTH   = (5.976e+24, 6.37814e6)
+    ...     MARS    = (6.421e+23, 3.3972e6)
+    ...     JUPITER = (1.9e+27,   7.1492e7)
+    ...     SATURN  = (5.688e+26, 6.0268e7)
+    ...     URANUS  = (8.686e+25, 2.5559e7)
+    ...     NEPTUNE = (1.024e+26, 2.4746e7)
+    ...     def __init__(self, mass, radius):
+    ...         self.mass = mass       # in kilograms
+    ...         self.radius = radius   # in meters
+    ...     @property
+    ...     def surface_gravity(self):
+    ...         # universal gravitational constant  (m3 kg-1 s-2)
+    ...         G = 6.67300E-11
+    ...         return G * self.mass / (self.radius * self.radius)
+    ...
+    >>> Planet.EARTH.value
+    (5.976e+24, 6378140.0)
+    >>> Planet.EARTH.surface_gravity
+    9.802652743337129
+
+
+How are Enums different?
+------------------------
+
+Enums have a custom metaclass that affects many aspects of both derived Enum
+classes and their instances (members).
+
+
+Enum Classes
+^^^^^^^^^^^^
+
+The ``EnumMeta`` metaclass is responsible for providing the
+``__contains__``, ``__dir__``, ``__iter__`` and other methods that
+allow one to do things with an ``Enum`` class that fail on a typical
+class, such as ``list(Color)`` or ``some_var in Color``.  ``EnumMeta`` is
+responsible for ensuring that various other methods on the final ``Enum``
+class are correct (such as ``__new__``, ``__getnewargs__``,
+``__str__`` and ``__repr__``).
+
+.. note::
+
+    ``__dir__`` is not changed in the Python 2 line as it messes up some
+    of the decorators included in the stdlib.
+
+
+Enum Members (aka instances)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The most interesting thing about Enum members is that they are singletons.
+``EnumMeta`` creates them all while it is creating the ``Enum``
+class itself, and then puts a custom ``__new__`` in place to ensure
+that no new ones are ever instantiated by returning only the existing
+member instances.
+
+
+Finer Points
+^^^^^^^^^^^^
+
+``Enum`` members are instances of an ``Enum`` class, but are not
+accessible as `EnumClass.member1.member2`.
+(changed in version 1.1.1 to be accessible)
+(changed in version 2.2.4 to be inaccessible)::
+
+    >>> class FieldTypes(Enum):
+    ...     name = 1
+    ...     value = 2
+    ...     size = 3
+    ...
+    >>> FieldTypes.size.value
+    3
+    >>> FieldTypes.size
+    <FieldTypes.size: 3>
+    >>> FieldTypes.value.size
+    Traceback (most recent call last):
+    ...
+    AttributeError: <aenum 'FieldTypes'> member has no attribute 'size'
+
+The ``__members__`` attribute is only available on the class.
+
+
+``__members__`` is always an ``OrderedDict``, with the order being the
+definition order in Python 3.x or the order in ``_order_`` in Python 2.7;
+if no ``_order_`` was specified in Python 2.7 then the order of
+``__members__`` is either increasing value or alphabetically by name.
+
+If you give your ``Enum`` subclass extra methods, like the `Planet`_
+class above, those methods will show up in a `dir` of the member,
+but not of the class (in Python 3.x)::
+
+    --> dir(Planet)
+    ['EARTH', 'JUPITER', 'MARS', 'MERCURY', 'NEPTUNE', 'SATURN', 'URANUS',
+     'VENUS', '__class__', '__doc__', '__members__', '__module__']
+    --> dir(Planet.EARTH)
+    ['__class__', '__doc__', '__module__', 'name', 'surface_gravity', 'value']
+
+A ``__new__`` method will only be used for the creation of the
+``Enum`` members -- after that it is replaced.  This means if you wish to
+change how ``Enum`` members are looked up you either have to write a
+helper function or a ``classmethod``.
+
+.. note::
+
+    If you create your own ``__new__`` you should set the ``_value_`` in it;
+    if you do not, aenum will try to, but will raise a ``TypeError`` if it
+    cannot.
+
+If the stdlib ``enum`` is available (Python 3.4+ and it hasn't been shadowed
+by, for example, ``enum34``) then aenum will be a subclass of it.
+
+To use the ``AddValue``, ``MultiValue``, ``NoAlias``, and ``Unique`` flags
+in Py2 or Py2/Py3 codebases, use ``_settings_ = ...`` in the class body.
+
+To use ``init`` in Py2 or Py2/Py3 codebases use ``_init_`` in the class body.
+
+To use ``start`` in Py2 or Py2/Py3 codebases use ``_start_`` in the class body.
+
+When creating class bodies dynamically, put any variables you need to use into
+``_ignore_``::
+
+    >>> from datetime import timedelta
+    >>> from aenum import NoAlias
+    >>> class Period(timedelta, Enum):
+    ...     '''
+    ...     different lengths of time
+    ...     '''
+    ...     _init_ = 'value period'
+    ...     _settings_ = NoAlias
+    ...     _ignore_ = 'Period i'
+    ...     Period = vars()
+    ...     for i in range(31):
+    ...         Period['day_%d' % i] = i, 'day'
+    ...     for i in range(15):
+    ...         Period['week_%d' % i] = i*7, 'week'
+    ...
+    >>> hasattr(Period, '_ignore_')
+    False
+    >>> hasattr(Period, 'Period')
+    False
+    >>> hasattr(Period, 'i')
+    False
+
+The name listed in ``_ignore_``, as well as ``_ignore_`` itself, will not be
+present in the final enumeration as neither attributes nor members.
+
+.. note::
+
+    except for __dunder__ attributes/methods, all _sunder_ attributes must
+    be before any thing else in the class body
+
+.. note::
+
+    all _sunder_ attributes that affect member creation are only looked up in
+    the last ``Enum`` class listed in the class header
+
+
+Creating NamedTuples
+--------------------
+
+Simple
+^^^^^^
+
+The most common way to create a new NamedTuple will be via the functional API::
+
+    >>> from aenum import NamedTuple
+    >>> Book = NamedTuple('Book', 'title author genre', module=__name__)
+
+This creates a ``NamedTuple`` called ``Book`` that will always contain three
+items, each of which is also addressable as ``title``, ``author``, or ``genre``.
+
+``Book`` instances can be created using positional or keyword argements or a
+mixture of the two::
+
+    >>> b1 = Book('Lord of the Rings', 'J.R.R. Tolkien', 'fantasy')
+    >>> b2 = Book(title='Jhereg', author='Steven Brust', genre='fantasy')
+    >>> b3 = Book('Empire', 'Orson Scott Card', genre='scifi')
+
+If too few or too many arguments are used a ``TypeError`` will be raised::
+
+    >>> b4 = Book('Hidden Empire')
+    Traceback (most recent call last):
+    ...
+    TypeError: values not provided for field(s): author, genre
+    >>> b5 = Book(genre='business')
+    Traceback (most recent call last):
+    ...
+    TypeError: values not provided for field(s): title, author
+
+As a ``class`` the above ``Book`` ``NamedTuple`` would look like::
+
+    >>> class Book(NamedTuple):
+    ...     title = 0
+    ...     author = 1
+    ...     genre = 2
+    ...
+
+For compatibility with the stdlib ``namedtuple``, NamedTuple also has the
+``_asdict``, ``_make``, and ``_replace`` methods, and the ``_fields``
+attribute, which all function similarly::
+
+    >>> class Point(NamedTuple):
+    ...     x = 0, 'horizontal coordinate', 1
+    ...     y = 1, 'vertical coordinate', -1
+    ...
+    >>> class Color(NamedTuple):
+    ...     r = 0, 'red component', 11
+    ...     g = 1, 'green component', 29
+    ...     b = 2, 'blue component', 37
+    ...
+    >>> Pixel = NamedTuple('Pixel', Point+Color, module=__name__)
+    >>> pixel = Pixel(99, -101, 255, 128, 0)
+
+    >>> pixel._asdict()
+    OrderedDict([('x', 99), ('y', -101), ('r', 255), ('g', 128), ('b', 0)])
+
+    >>> Point._make((4, 5))
+    Point(x=4, y=5)
+
+    >>> purple = Color(127, 0, 127)
+    >>> mid_gray = purple._replace(g=127)
+    >>> mid_gray
+    Color(r=127, g=127, b=127)
+
+    >>> pixel._fields
+    ['x', 'y', 'r', 'g', 'b']
+
+    >>> Pixel._fields
+    ['x', 'y', 'r', 'g', 'b']
+
+
+Advanced
+^^^^^^^^
+
+The simple method of creating ``NamedTuples`` requires always specifying all
+possible arguments when creating instances; failure to do so will raise
+exceptions::
+
+    >>> class Point(NamedTuple):
+    ...     x = 0
+    ...     y = 1
+    ...
+    >>> Point()
+    Traceback (most recent call last):
+    ...
+    TypeError: values not provided for field(s): x, y
+    >>> Point(1)
+    Traceback (most recent call last):
+    ...
+    TypeError: values not provided for field(s): y
+    >>> Point(y=2)
+    Traceback (most recent call last):
+    ...
+    TypeError: values not provided for field(s): x
+
+However, it is possible to specify both docstrings and default values when
+creating a ``NamedTuple`` using the class method::
+
+    >>> class Point(NamedTuple):
+    ...     x = 0, 'horizontal coordinate', 0
+    ...     y = 1, 'vertical coordinate', 0
+    ...
+    >>> Point()
+    Point(x=0, y=0)
+    >>> Point(1)
+    Point(x=1, y=0)
+    >>> Point(y=2)
+    Point(x=0, y=2)
+
+It is also possible to create ``NamedTuples`` that only have named attributes
+for certain fields; any fields without names can still be accessed by index::
+
+    >>> class Person(NamedTuple):
+    ...     fullname = 2
+    ...     phone = 5
+    ...
+    >>> p = Person('Ethan', 'Furman', 'Ethan Furman',
+    ...            'ethan at stoneleaf dot us',
+    ...            'ethan.furman', '999.555.1212')
+    >>> p
+    Person('Ethan', 'Furman', 'Ethan Furman', 'ethan at stoneleaf dot us',
+           'ethan.furman', '999.555.1212')
+    >>> p.fullname
+    'Ethan Furman'
+    >>> p.phone
+    '999.555.1212'
+    >>> p[0]
+    'Ethan'
+
+In the above example the last named field was also the last field possible; in
+those cases where you don't need to have the last possible field named, you can
+provide a ``_size_`` of ``TupleSize.minimum`` to declare that more fields are
+okay::
+
+    >>> from aenum import TupleSize
+    >>> class Person(NamedTuple):
+    ...     _size_ = TupleSize.minimum
+    ...     first = 0
+    ...     last = 1
+    ...
+
+or, optionally if using Python 3::
+
+    >>> class Person(NamedTuple, size=TupleSize.minimum):      # doctest: +SKIP
+    ...     first = 0
+    ...     last = 1
+
+and in use::
+
+    >>> Person('Ethan', 'Furman')
+    Person(first='Ethan', last='Furman')
+
+    >>> Person('Ethan', 'Furman', 'ethan.furman')
+    Person('Ethan', 'Furman', 'ethan.furman')
+
+    >>> Person('Ethan', 'Furman', 'ethan.furman', 'yay Python!')
+    Person('Ethan', 'Furman', 'ethan.furman', 'yay Python!')
+
+    >>> Person('Ethan')
+    Traceback (most recent call last):
+    ...
+    TypeError: values not provided for field(s): last
+
+Also, for those cases where even named fields may not be present, you can
+specify ``TupleSize.variable``::
+
+    >>> class Person(NamedTuple):
+    ...     _size_ = TupleSize.variable
+    ...     first = 0
+    ...     last = 1
+    ...
+
+    >>> Person('Ethan')
+    Person('Ethan')
+
+    >>> Person(last='Furman')
+    Traceback (most recent call last):
+    ...
+    TypeError: values not provided for field(s): first
+
+Creating new ``NamedTuples`` from existing ``NamedTuples`` is simple::
+
+    >>> Point = NamedTuple('Point', 'x y')
+    >>> Color = NamedTuple('Color', 'r g b')
+    >>> Pixel = NamedTuple('Pixel', Point+Color, module=__name__)
+    >>> Pixel
+    <NamedTuple 'Pixel'>
+
+The existing fields in the bases classes are renumbered to fit the new class,
+but keep their doc strings and default values.  If you use standard
+subclassing::
+
+    >>> Point = NamedTuple('Point', 'x y')
+    >>> class Pixel(Point):
+    ...     r = 2, 'red component', 11
+    ...     g = 3, 'green component', 29
+    ...     b = 4, 'blue component', 37
+    ...
+    >>> Pixel.__fields__
+    ['x', 'y', 'r', 'g', 'b']
+
+You must manage the numbering yourself.
+
+
+Creating NamedConstants
+-----------------------
+
+A ``NamedConstant`` class is created much like an ``Enum``::
+
+    >>> from aenum import NamedConstant
+    >>> class Konstant(NamedConstant):
+    ...     PI = 3.14159
+    ...     TAU = 2 * PI
+
+    >>> Konstant.PI
+    <Konstant.PI: 3.14159>
+
+    >> print(Konstant.PI)
+    3.14159
+
+    >>> Konstant.PI = 'apple'
+    Traceback (most recent call last):
+    ...
+    AttributeError: cannot rebind constant <Konstant.PI>
+
+    >>> del Konstant.PI
+    Traceback (most recent call last):
+    ...
+    AttributeError: cannot delete constant <Konstant.PI>
diff --git a/venv/Lib/site-packages/aenum/test.py b/venv/Lib/site-packages/aenum/test.py
new file mode 100644
index 00000000..224293cb
--- /dev/null
+++ b/venv/Lib/site-packages/aenum/test.py
@@ -0,0 +1,6832 @@
+# -*- coding: utf-8 -*-
+
+from __future__ import division, print_function
+import sys
+import aenum
+import doctest
+import os
+import shutil
+import tempfile
+import textwrap
+import unittest
+import uuid
+import warnings
+from aenum import EnumType, EnumMeta, Enum, IntEnum, StrEnum, LowerStrEnum, UpperStrEnum
+from aenum import AutoNumberEnum, MultiValueEnum, OrderedEnum, UniqueEnum, AddValueEnum, Flag, IntFlag
+from aenum import NamedTuple, TupleSize, NamedConstant, constant, NoAlias, AddValue, Unique
+from aenum import STRICT, CONFORM, EJECT, KEEP
+from aenum import _reduce_ex_by_name, unique, skip, extend_enum, auto, enum, MultiValue, member, nonmember, no_arg
+from aenum import basestring, baseinteger, unicode, enum_property
+from aenum import pyver, PY2, PY3, PY2_6, PY3_3, PY3_4, PY3_5, PY3_6, PY3_11
+from collections import OrderedDict
+from datetime import timedelta
+from pickle import dumps, loads, PicklingError, HIGHEST_PROTOCOL
+from operator import or_ as _or_, and_ as _and_, xor as _xor_, inv as _inv_
+from operator import abs as _abs_, add as _add_, floordiv as _floordiv_
+from operator import lshift as _lshift_, rshift as _rshift_, mod as _mod_
+from operator import mul as _mul_, neg as _neg_, pos as _pos_, pow as _pow_
+from operator import truediv as _truediv_, sub as _sub_
+if PY2:
+    from operator import div as _div_
+try:
+    import threading
+except ImportError:
+    threading = None
+
+try:
+    any
+except NameError:
+    from aenum import any
+
+MODULE = __name__
+SHORT_MODULE = MODULE.split('.')[-1]
+
+def load_tests(loader, tests, ignore):
+    tests.addTests(doctest.DocTestSuite(aenum))
+    tests.addTests(doctest.DocFileSuite(
+        'doc/aenum.rst',
+        package=aenum,
+        optionflags=doctest.ELLIPSIS|doctest.NORMALIZE_WHITESPACE,
+        ))
+    return tests
+
+class TestCase(unittest.TestCase):
+
+    def __init__(self, *args, **kwds):
+        regex = getattr(self, 'assertRaisesRegex', None)
+        if regex is None:
+            self.assertRaisesRegex = getattr(self, 'assertRaisesRegexp')
+        super(TestCase, self).__init__(*args, **kwds)
+
+
+# for pickle tests
+try:
+    class Stooges(Enum):
+        LARRY = 1
+        CURLY = 2
+        MOE = 3
+except Exception:
+    Stooges = sys.exc_info()[1]
+
+try:
+    class IntStooges(int, Enum):
+        LARRY = 1
+        CURLY = 2
+        MOE = 3
+except Exception:
+    IntStooges = sys.exc_info()[1]
+
+try:
+    class FloatStooges(float, Enum):
+        LARRY = 1.39
+        CURLY = 2.72
+        MOE = 3.142596
+except Exception:
+    FloatStooges = sys.exc_info()[1]
+
+try:
+    class FlagStooges(Flag):
+        LARRY = 1
+        CURLY = 2
+        MOE = 3
+except Exception as exc:
+    FlagStooges = exc
+
+try:
+    LifeForm = NamedTuple('LifeForm', 'branch genus species', module=__name__)
+except Exception:
+    LifeForm = sys.exc_info()[1]
+
+try:
+    class DeathForm(NamedTuple):
+        color = 0
+        rigidity = 1
+        odor = 2
+except Exception:
+    DeathForm = sys.exc_info()[1]
+
+# for pickle test and subclass tests
+try:
+    class Name(StrEnum):
+        BDFL = 'Guido van Rossum'
+        FLUFL = 'Barry Warsaw'
+except Exception:
+    Name = sys.exc_info()[1]
+
+try:
+    Question = Enum('Question', 'who what when where why', module=__name__)
+except Exception:
+    Question = sys.exc_info()[1]
+
+try:
+    Answer = Enum('Answer', 'him this then there because')
+except Exception:
+    Answer = sys.exc_info()[1]
+
+try:
+    class WhatsIt(NamedTuple):
+        def what(self):
+            return self[0]
+    class ThatsIt(WhatsIt):
+        blah = 0
+        bleh = 1
+except Exception:
+    ThatsIt = sys.exc_info()[1]
+
+# for doctests
+try:
+    class Fruit(Enum):
+        tomato = 1
+        banana = 2
+        cherry = 3
+except Exception:
+    pass
+
+def test_pickle_dump_load(assertion, source, target=None, protocol=(0, HIGHEST_PROTOCOL)):
+    start, stop = protocol
+    failures = []
+    for protocol in range(start, stop+1):
+        try:
+            if target is None:
+                assertion(loads(dumps(source, protocol=protocol)), source)
+            else:
+                assertion(loads(dumps(source, protocol=protocol)), target)
+        except Exception:
+            exc, tb = sys.exc_info()[1:]
+            failures.append('%2d: %s' %(protocol, exc))
+    if failures:
+        raise ValueError('Failed with protocols: %s' % ', '.join(failures))
+
+def test_pickle_exception(assertion, exception, obj,
+        protocol=(0, HIGHEST_PROTOCOL)):
+    start, stop = protocol
+    failures = []
+    for protocol in range(start, stop+1):
+        try:
+            assertion(exception, dumps, obj, protocol=protocol)
+        except Exception:
+            exc = sys.exc_info()[1]
+            failures.append('%d: %s %s' % (protocol, exc.__class__.__name__, exc))
+    if failures:
+        raise ValueError('Failed with protocols: %s' % ', '.join(failures))
+
+if PY3:
+    from aenum.test_v3 import TestEnumV3, TestOrderV3, TestNamedTupleV3, TestStackoverflowAnswersV3, TestIssuesV3, TestExtendEnumV3
+    from aenum import test_v3
+    test_v3.IntStooges = IntStooges
+    test_v3.test_pickle_exception = test_pickle_exception
+    test_v3.test_pickle_dump_load = test_pickle_dump_load
+
+# for subclassing tests
+
+class classproperty(object):
+
+    def __init__(self, fget=None, fset=None, fdel=None, doc=None):
+        self.fget = fget
+        self.fset = fset
+        self.fdel = fdel
+        if doc is None and fget is not None:
+            doc = fget.__doc__
+        self.__doc__ = doc
+
+    def __get__(self, instance, ownerclass):
+        return self.fget(ownerclass)
+
+
+# tests
+class TestOrder(TestCase):
+    """
+    Test _order_ extra/missing members.
+    """
+
+    def test_same_members(self):
+        class Color(Enum):
+            _order_ = 'red green blue'
+            red = 1
+            green = 2
+            blue = 3
+
+    def test_same_members_with_aliases(self):
+        class Color(Enum):
+            _order_ = 'red green blue'
+            red = 1
+            green = 2
+            blue = 3
+            verde = green
+
+    def test_order_has_extra_members(self):
+        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
+            class Color(Enum):
+                _order_ = 'red green blue purple'
+                red = 1
+                green = 2
+                blue = 3
+
+    def test_order_has_extra_members_with_aliases(self):
+        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
+            class Color(Enum):
+                _order_ = 'red green blue purple'
+                red = 1
+                green = 2
+                blue = 3
+                verde = green
+
+    def test_enum_has_extra_members(self):
+        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
+            class Color(Enum):
+                _order_ = 'red green blue'
+                red = 1
+                green = 2
+                blue = 3
+                purple = 4
+
+    def test_enum_has_extra_members_with_aliases(self):
+        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
+            class Color(Enum):
+                _order_ = 'red green blue'
+                red = 1
+                green = 2
+                blue = 3
+                purple = 4
+                verde = green
+
+    def test_same_members_flag(self):
+        class Color(Flag):
+            _order_ = 'red green blue'
+            red = 1
+            green = 2
+            blue = 4
+
+    def test_same_members_with_aliases_flag(self):
+        class Color(Flag):
+            _order_ = 'red green blue'
+            red = 1
+            green = 2
+            blue = 4
+            verde = green
+
+    def test_order_has_extra_members_flag(self):
+        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
+            class Color(Flag):
+                _order_ = 'red green blue purple'
+                red = 1
+                green = 2
+                blue = 4
+
+    def test_order_has_extra_members_with_aliases_flag(self):
+        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
+            class Color(Flag):
+                _order_ = 'red green blue purple'
+                red = 1
+                green = 2
+                blue = 4
+                verde = green
+
+    def test_enum_has_extra_members_flag(self):
+        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
+            class Color(Flag):
+                _order_ = 'red green blue'
+                red = 1
+                green = 2
+                blue = 4
+                purple = 8
+
+    def test_enum_has_extra_members_with_aliases_flag(self):
+        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
+            class Color(Flag):
+                _order_ = 'red green blue'
+                red = 1
+                green = 2
+                blue = 4
+                purple = 8
+                verde = green
+
+
+class TestAutoValue(TestCase):
+
+    def test_bare(self):
+        #
+        class BareEnum(Enum):
+            _order_ = 'ONE TWO THREE'
+            ONE = auto()
+            TWO = auto()
+            THREE = auto()
+        self.assertEqual(BareEnum.THREE.value, 3)
+        #
+        class BareIntEnum(IntEnum):
+            _order_ = 'ONE TWO THREE'
+            ONE = auto()
+            TWO = auto()
+            THREE = auto()
+        self.assertEqual(BareIntEnum.THREE, 3)
+        #
+        class BareFlag(Flag):
+            _order_ = 'ONE TWO THREE'
+            ONE = auto()
+            TWO = auto()
+            THREE = auto()
+        self.assertEqual(BareFlag.THREE.value, 4)
+        #
+        class BareIntFlag(IntFlag):
+            _order_ = 'ONE TWO THREE'
+            ONE = auto()
+            TWO = auto()
+            THREE = auto()
+        self.assertEqual(BareIntFlag.THREE, 4)
+
+    def test_init_only_final(self):
+        #
+        class InitEnumValue(Enum):
+            _init_ = 'value description'
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitEnumValue.THREE.value, 3)
+        self.assertEqual(InitEnumValue.THREE.description, 'a triangle')
+        #
+        class InitEnum(Enum):
+            _init_ = 'value description'
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitEnum.THREE.value, 3)
+        self.assertEqual(InitEnum.THREE.description, 'a triangle')
+        #
+        class InitIntEnum(IntEnum):
+            _init_ = 'value description'
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitIntEnum.THREE, 3)
+        self.assertEqual(InitIntEnum.THREE.description, 'a triangle')
+        #
+        class InitFlag(Flag):
+            _init_ = 'value description'
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitFlag.THREE.value, 4)
+        self.assertEqual(InitFlag.THREE.description, 'a triangle')
+        #
+        class InitIntFlag(IntFlag):
+            _init_ = 'value description'
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitIntFlag.THREE, 4)
+        self.assertEqual(InitIntFlag.THREE.description, 'a triangle')
+
+    def test_init_only_inherit(self):
+        #
+        class InitInheritEnum(Enum):
+            _init_ = 'value description'
+        #
+        class InitEnum(InitInheritEnum):
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitEnum.THREE.value, 3)
+        self.assertEqual(InitEnum.THREE.description, 'a triangle')
+        #
+        #
+        class InitInheritValueEnum(Enum):
+            _init_ = 'value description'
+        #
+        class InitEnum(InitInheritValueEnum):
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitEnum.THREE.value, 3)
+        self.assertEqual(InitEnum.THREE.description, 'a triangle')
+        #
+        class InitIntEnum(int, InitInheritValueEnum):
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitIntEnum.THREE, 3)
+        self.assertEqual(InitIntEnum.THREE.description, 'a triangle')
+        #
+        class InitInheritValueFlag(Flag):
+            _init_ = 'value description'
+        #
+        class InitFlag(InitInheritValueFlag):
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitFlag.THREE.value, 4)
+        self.assertEqual(InitFlag.THREE.description, 'a triangle')
+        #
+        class InitIntFlag(int, InitInheritValueFlag):
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitIntFlag.THREE, 4)
+        self.assertEqual(InitIntFlag.THREE.description, 'a triangle')
+
+    def test_new_only_final(self):
+        #
+        class NewFinalEnum(Enum):
+            _order_ = 'ONE TWO THREE'
+            def __new__(cls, value):
+                member = object.__new__(cls)
+                member._value_ = value
+                member.proof = 'NFE1'
+                return member
+            ONE = auto()
+            TWO = auto()
+            THREE = auto()
+        self.assertEqual(NewFinalEnum.THREE.value, 3)
+        self.assertEqual(NewFinalEnum.TWO.proof, 'NFE1')
+        #
+        class NewFinalIntEnum(IntEnum):
+            _order_ = 'ONE TWO THREE'
+            def __new__(cls, value):
+                member = int.__new__(cls, value)
+                member._value_ = value
+                member.proof = 'NFE2'
+                return member
+            ONE = auto()
+            TWO = auto()
+            THREE = auto()
+        self.assertEqual(NewFinalIntEnum.THREE, 3)
+        self.assertEqual(NewFinalIntEnum.TWO.proof, 'NFE2')
+        #
+        class NewFinalFlag(Flag):
+            _order_ = 'ONE TWO THREE'
+            def __new__(cls, value):
+                member = object.__new__(cls)
+                member._value_ = value
+                member.proof = 'NFE3'
+                return member
+            ONE = auto()
+            TWO = auto()
+            THREE = auto()
+        self.assertEqual(NewFinalFlag.THREE.value, 4)
+        self.assertEqual(NewFinalFlag.TWO.proof, 'NFE3')
+        #
+        class NewFinalIntFlag(IntFlag):
+            _order_ = 'ONE TWO THREE'
+            def __new__(cls, value):
+                member = int.__new__(cls, value)
+                member._value_ = value
+                member.proof = 'NFE4'
+                return member
+            ONE = auto()
+            TWO = auto()
+            THREE = auto()
+        self.assertEqual(NewFinalIntFlag.THREE, 4)
+        self.assertEqual(NewFinalIntFlag.TWO.proof, 'NFE4')
+        #
+        class NewFinalStrEnum(str, Enum):
+            #
+            _order_ = "AllReset Bright FG_Cyan BG_Black"
+            #
+            def __new__(cls, value, code, description):
+                str_value = '\x1b[%sm' % code
+                obj = str.__new__(cls, str_value)
+                obj._value_ = value
+                obj.code = code
+                obj.description = description
+                return obj
+            #
+            __str__ = str.__str__
+            #
+            AllReset = '0', 'reset all (colors and brightness)'
+            Bright = '1', 'bright lights!'
+            FG_Cyan = '36', 'cyan'
+            BG_Black = '40', 'black'
+        self.assertEqual(NewFinalStrEnum.FG_Cyan.value, 3)
+        self.assertEqual(NewFinalStrEnum.BG_Black.value, 4)
+        self.assertEqual(NewFinalStrEnum.AllReset.code, '0')
+        self.assertEqual(NewFinalStrEnum.Bright.description, 'bright lights!')
+        #
+        class NewFinalStrFlag(str, Flag):
+            #
+            _order_ = "AllReset Bright FG_Cyan BG_Black"
+            #
+            def __new__(cls, value, code, description):
+                str_value = '\x1b[%sm' % code
+                obj = str.__new__(cls, str_value)
+                obj._value_ = value
+                obj.code = code
+                obj.description = description
+                return obj
+            #
+            __str__ = str.__str__
+            #
+            AllReset = '0', 'reset all (colors and brightness)'
+            Bright = '1', 'bright lights!'
+            FG_Cyan = '36', 'cyan'
+            BG_Black = '40', 'black'
+        self.assertEqual(NewFinalStrFlag.FG_Cyan.value, 4)
+        self.assertEqual(NewFinalStrFlag.BG_Black.value, 8)
+        self.assertEqual(NewFinalStrFlag.AllReset.code, '0')
+        self.assertEqual(NewFinalStrFlag.Bright.description, 'bright lights!')
+
+    def test_new_only_inherited(self):
+        #
+        class NewInheritEnum(Enum):
+            def __new__(cls, value):
+                if cls._member_type_ is int:
+                    member = int.__new__(cls, value*2)
+                else:
+                    member = object.__new__(cls)
+                member._value_ = value * 2
+                member.proof = 'NIE'
+                return member
+        #
+        class NewFinalEnum(NewInheritEnum):
+            _order_ = 'ONE TWO THREE'
+            ONE = auto()
+            TWO = auto()
+            THREE = auto()
+        self.assertEqual(NewFinalEnum.THREE.value, 6)
+        self.assertEqual(NewFinalEnum.TWO.proof, 'NIE')
+        #
+        class NewFinalIntEnum(int, NewInheritEnum):
+            _order_ = 'ONE TWO THREE'
+            ONE = auto()
+            TWO = auto()
+            THREE = auto()
+        self.assertEqual(NewFinalIntEnum.THREE, 6)
+        self.assertEqual(NewFinalIntEnum.TWO.proof, 'NIE')
+        #
+        class NewInheritFlag(Flag):
+            def __new__(cls, value):
+                if cls._member_type_ is int:
+                    member = int.__new__(cls, value*2)
+                else:
+                    member = object.__new__(cls)
+                member._value_ = value * 2
+                member.proof = 'NIE'
+                return member
+        #
+        class NewFinalFlag(NewInheritFlag):
+            _order_ = 'ONE TWO THREE'
+            ONE = auto()
+            TWO = auto()
+            THREE = auto()
+        self.assertEqual(NewFinalFlag.THREE.value, 8)
+        self.assertEqual(NewFinalFlag.TWO.proof, 'NIE')
+        #
+        class NewFinalIntFlag(int, NewInheritFlag):
+            _order_ = 'ONE TWO THREE'
+            ONE = auto()
+            TWO = auto()
+            THREE = auto()
+        self.assertEqual(NewFinalIntFlag.THREE, 8)
+        self.assertEqual(NewFinalIntFlag.TWO.proof, 'NIE')
+
+    def test_init_new_only(self):
+        #
+        class InitNewEnum(Enum):
+            _init_ = "value description"
+            _order_ = 'ONE TWO THREE'
+            def __new__(cls, value, *args):
+                member = object.__new__(cls)
+                member._value_ = value
+                member.proof = 'INE1'
+                return member
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitNewEnum.THREE.value, 3)
+        self.assertEqual(InitNewEnum.THREE.description, 'a triangle')
+        self.assertEqual(InitNewEnum.TWO.proof, 'INE1')
+        #
+        class InitNewIntEnum(IntEnum):
+            _init_ = "value description"
+            _order_ = 'ONE TWO THREE'
+            def __new__(cls, value, *args):
+                member = int.__new__(cls, value)
+                member._value_ = value
+                member.proof = 'INE2'
+                return member
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitNewIntEnum.THREE, 3)
+        self.assertEqual(InitNewIntEnum.THREE.description, 'a triangle')
+        self.assertEqual(InitNewIntEnum.TWO.proof, 'INE2')
+        #
+        class InitNewFlag(Flag):
+            _init_ = "value description"
+            _order_ = 'ONE TWO THREE'
+            def __new__(cls, value, *args):
+                member = object.__new__(cls)
+                member._value_ = value
+                member.proof = 'INE3'
+                return member
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitNewFlag.THREE.value, 4)
+        self.assertEqual(InitNewFlag.THREE.description, 'a triangle')
+        self.assertEqual(InitNewFlag.TWO.proof, 'INE3')
+        #
+        class InitNewIntFlag(IntFlag):
+            _init_ = "value description"
+            _order_ = 'ONE TWO THREE'
+            def __new__(cls, value, *args):
+                member = int.__new__(cls, value)
+                member._value_ = value
+                member.proof = 'INE4'
+                return member
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitNewIntFlag.THREE, 4)
+        self.assertEqual(InitNewIntFlag.THREE.description, 'a triangle')
+        self.assertEqual(InitNewIntFlag.TWO.proof, 'INE4')
+
+    def test_init_new_inherit(self):
+        #
+        class InitNew(Enum):
+            _init_ = "value description"
+            def __new__(cls, value, *args):
+                member = object.__new__(cls)
+                member._value_ = value
+                member.proof = 'IN'
+                return member
+        #
+        class InitNewEnum(InitNew):
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitNewEnum.THREE.value, 3)
+        self.assertEqual(InitNewEnum.THREE.description, 'a triangle')
+        self.assertEqual(InitNewEnum.TWO.proof, 'IN')
+        #
+        class InitNewInt(Enum):
+            _init_ = "value description"
+            def __new__(cls, value, *args):
+                member = int.__new__(cls, value)
+                member._value_ = value
+                member.proof = 'IN'
+                return member
+        #
+        class InitNewIntEnum(int, InitNewInt):
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitNewIntEnum.THREE, 3)
+        self.assertEqual(InitNewIntEnum.THREE.description, 'a triangle')
+        self.assertEqual(InitNewIntEnum.TWO.proof, 'IN')
+        #
+        class InitNewFlagBase(Flag):
+            _init_ = "value description"
+            def __new__(cls, value, *args):
+                member = object.__new__(cls)
+                member._value_ = value
+                member.proof = 'IN'
+                return member
+        #
+        class InitNewFlag(InitNewFlagBase):
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitNewFlag.THREE.value, 4)
+        self.assertEqual(InitNewFlag.THREE.description, 'a triangle')
+        self.assertEqual(InitNewFlag.TWO.proof, 'IN')
+        #
+        class InitNewIntFlagBase(int, Flag):
+            _init_ = "value description"
+            def __new__(cls, value, *args):
+                member = int.__new__(cls, value)
+                member._value_ = value
+                member.proof = 'IN'
+                return member
+        #
+        class InitNewIntFlag(InitNewIntFlagBase):
+            _order_ = 'ONE TWO THREE'
+            ONE = 'the loneliest number'
+            TWO = 'the number with you'
+            THREE = 'a triangle'
+        self.assertEqual(InitNewIntFlag.THREE, 4)
+        self.assertEqual(InitNewIntFlag.THREE.description, 'a triangle')
+        self.assertEqual(InitNewIntFlag.TWO.proof, 'IN')
+
+
+class TestHelpers(TestCase):
+    # _is_descriptor, _is_sunder, _is_dunder
+
+    def test_is_descriptor(self):
+        class foo:
+            pass
+        for attr in ('__get__','__set__','__delete__'):
+            obj = foo()
+            self.assertFalse(aenum._is_descriptor(obj))
+            setattr(obj, attr, 1)
+            self.assertTrue(aenum._is_descriptor(obj))
+
+    def test_is_sunder(self):
+        for s in ('_a_', '_aa_'):
+            self.assertTrue(aenum._is_sunder(s))
+
+        for s in ('a', 'a_', '_a', '__a', 'a__', '__a__', '_a__', '__a_', '_',
+                '__', '___', '____', '_____',):
+            self.assertFalse(aenum._is_sunder(s))
+
+    def test_is_dunder(self):
+        for s in ('__a__', '__aa__'):
+            self.assertTrue(aenum._is_dunder(s))
+        for s in ('a', 'a_', '_a', '__a', 'a__', '_a_', '_a__', '__a_', '_',
+                '__', '___', '____', '_____',):
+            self.assertFalse(aenum._is_dunder(s))
+
+    def test_auto(self):
+        def tester(first, op, final, second=None):
+            if second is None:
+                left = auto()
+                value = op(left)
+                left.value = first
+                self.assertEqual(value.value, final,
+                        "%s %r -> %r != %r" % (op.__name__, first, value, final))
+            else:
+                left = first
+                right = auto()
+                value = op(left, right)
+                right.value = second
+                self.assertEqual(value.value, final,
+                        "forward: %r %s %r -> %r != %r" % (first, op.__name__, second, value.value, final))
+                left = auto()
+                right = second
+                value = op(left, right)
+                left.value = first
+                self.assertEqual(value.value, final,
+                        "reversed: %r %s %r -> %r != %r" % (second, op.__name__, first, value.value, final))
+        for args in (
+                (1, _abs_, abs(1)),
+                (-3, _abs_, abs(-3)),
+                (1, _add_, 1+2, 2),
+                (25, _floordiv_, 25 // 5, 5),
+                (49, _truediv_, 49 / 9, 9),
+                (6, _mod_, 6 % 9, 9),
+                (5, _lshift_, 5 << 2, 2),
+                (5, _rshift_, 5 >> 2, 2),
+                (3, _mul_, 3 * 6, 6),
+                (5, _neg_, -5),
+                (-4, _pos_, +(-4)),
+                (2, _pow_, 2**5, 5),
+                (7, _sub_, 7 - 10, 10),
+                (1, _or_, 1 | 2, 2),
+                (3, _xor_, 3 ^ 6, 6),
+                (3, _and_, 3 & 6, 6),
+                (7, _inv_, ~7),
+                ('a', _add_, 'a'+'b', 'b'),
+                ('a', _mul_, 'a' * 3, 3),
+                ):
+            tester(*args)
+        # operator.div is gone in 3
+        if PY2:
+            tester(12, _div_, 12 // 5, 5)
+        # strings are a pain
+        left = auto()
+        right = 'eggs'
+        value = _mod_(left, right)
+        left.value = 'I see 17 %s!'
+        self.assertEqual(value.value, 'I see 17 %s!' % 'eggs')
+
+    def test_constant(self):
+        errors = []
+        def tester(first, op, final, second=None):
+            if second is None:
+                primary = constant(first)
+                secondary = constant(op(primary))
+                if secondary.value != final:
+                    errors.append(
+                        "%s %r -> %r != %r" % (op.__name__, first, secondary.value, final),
+                        )
+            else:
+                left = constant(first)
+                right = second
+                value = op(left, right)
+                if value != final:
+                    errors.append(
+                        "forward: %r %s %r -> %r != %r" % (first, op.__name__, second, value, final),
+                        )
+                left = first
+                right = constant(second)
+                value = op(left, right)
+                if value != final:
+                    errors.append(
+                        "reversed: %r %s %r -> %r != %r" % (second, op.__name__, first, value, final),
+                        )
+        for args in (
+                (1, _abs_, abs(1)),
+                (-3, _abs_, abs(-3)),
+                (1, _add_, 1+2, 2),
+                (25, _floordiv_, 25 // 5, 5),
+                (49, _truediv_, 49 / 9, 9),
+                (6, _mod_, 6 % 9, 9),
+                (5, _lshift_, 5 << 2, 2),
+                (5, _rshift_, 5 >> 2, 2),
+                (3, _mul_, 3 * 6, 6),
+                (5, _neg_, -5),
+                (-4, _pos_, +(-4)),
+                (2, _pow_, 2**5, 5),
+                (7, _sub_, 7 - 10, 10),
+                (1, _or_, 1 | 2, 2),
+                (3, _xor_, 3 ^ 6, 6),
+                (3, _and_, 3 & 6, 6),
+                (7, _inv_, ~7),
+                ('a', _add_, 'a'+'b', 'b'),
+                ('a', _mul_, 'a' * 3, 3),
+                ):
+            tester(*args)
+        # operator.div is gone in 3
+        if PY2:
+            tester(12, _div_, 12 // 5, 5)
+        # strings are a pain
+        left = constant('I see 17 %s!')
+        right = 'eggs'
+        value = _mod_(left, right)
+        if value != 'I see 17 %s!' % 'eggs':
+            errors.append("'I see 17 eggs!' != %r" % value)
+        if errors:
+            print()
+            for error in errors:
+                print(error)
+            self.assertTrue(False)
+
+
+class TestEnumType(TestCase):
+
+    def test_immutability(self):
+        class Hah(object):
+            @classproperty
+            def all_values(cls):
+                return [m.value for m in cls]
+        class Huh(Hah, Enum):
+            one = 1
+            two = 2
+        self.assertRaisesRegex(AttributeError, 'cannot rebind property', setattr, Huh, 'value', 'boom')
+        self.assertRaisesRegex(AttributeError, 'cannot delete property', delattr, Huh, 'value')
+        self.assertRaisesRegex(AttributeError, 'cannot set attribute', setattr, Huh.one, 'value', 'boom')
+        self.assertRaisesRegex(AttributeError, 'cannot delete attribute', delattr, Huh.two, 'value')
+        self.assertEqual(Huh.one.value, 1)
+        self.assertEqual(Huh.two.value, 2)
+        self.assertEqual(Huh.all_values, [1, 2])
+        setattr(Huh, 'all_values', 99)
+        self.assertEqual(Huh.all_values, 99)
+
+    def test_enum_shadow_base(self):
+        class hohum(object):
+            def cyan(self):
+                "cyanize a color"
+                return self.value * 'cyan'
+            @property
+            def azure(self):
+                return 'azure ' + self.name
+        class Color(hohum, Enum):
+            red = 1
+            green = 2
+            blue = 3
+            cyan = 4
+            azure = 5
+        self.assertEqual(len(Color), 5)
+        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue, Color.cyan, Color.azure])
+        self.assertRaisesRegex(AttributeError, 'no attribute .cyan.', lambda: Color.blue.cyan)
+        self.assertEqual(Color.red.azure, 'azure red')
+
+
+class TestEnum(TestCase):
+
+    def setUp(self):
+        class Season(Enum):
+            SPRING = 1
+            SUMMER = 2
+            AUTUMN = 3
+            WINTER = 4
+        self.Season = Season
+
+        class Konstants(float, Enum):
+            E = 2.7182818
+            PI = 3.1415926
+            TAU = 2 * PI
+        self.Konstants = Konstants
+
+        class Grades(IntEnum):
+            A = 5
+            B = 4
+            C = 3
+            D = 2
+            F = 0
+        self.Grades = Grades
+
+        class Directional(str, Enum):
+            EAST = 'east'
+            WEST = 'west'
+            NORTH = 'north'
+            SOUTH = 'south'
+        self.Directional = Directional
+
+        from datetime import date
+        class Holiday(date, Enum):
+            NEW_YEAR = 2013, 1, 1
+            IDES_OF_MARCH = 2013, 3, 15
+        self.Holiday = Holiday
+
+    def test_set_name(self):
+        class Descriptor(object):
+            name = None
+            def __get__(self, instance, owner_class=None):
+                if instance is None:
+                    return self
+                else:
+                    return instance.__dict__[self.name]
+            def __set__(self, instance, value):
+                instance.__dict__[self.name] = value
+            def __set_name__(self, owner, name):
+                self.name = name
+        #
+        class AnEnum(Enum):
+            ONE = 'one'
+            two = Descriptor()
+        #
+        self.assertEqual(list(AnEnum), [AnEnum.ONE])
+        self.assertEqual(AnEnum.two.name, 'two')
+        AnEnum.ONE.two = 'three'
+        self.assertEqual(AnEnum.ONE.two, 'three')
+        self.assertEqual(AnEnum.ONE.__dict__['two'], 'three')
+
+    def test_private_names(self):
+        class Private(Enum):
+            __corporal = 'Radar'
+            __major_ = 'Hoolihan'
+        self.assertEqual(len(Private), 0)
+        self.assertEqual(Private._Private__corporal, 'Radar')
+        self.assertFalse(isinstance(Private._Private__corporal, Enum))
+        self.assertEqual(Private._Private__major_, 'Hoolihan')
+        self.assertFalse(isinstance(Private._Private__major_, Enum))
+
+    def test_new_with_keywords(self):
+        class Huh(IntEnum):
+            __order__ = 'PLAIN BOLD_ITALIC HIGHLIGHT'
+            def __new__(cls, docstring, open=None, close=None):
+                value = len(cls.__members__)
+                member = int.__new__(cls, value)
+                if open and close is None:
+                    close = open
+                member.open = open
+                member.close = close
+                member.__doc__ = docstring
+                member._value_ = value
+                return member
+            PLAIN           = 'normal'
+            BOLD_ITALIC     = '***really super important***', '***'
+            HIGHLIGHT       = 'please ==take notice==', '==', '=='
+        p = Huh.PLAIN
+        self.assertTrue(type(p) is Huh, type(p))
+        self.assertEqual(
+                (p.value, p.__doc__, p.open, p.close),
+                (0, 'normal', None, None),
+                )
+        bi = Huh.BOLD_ITALIC
+        self.assertEqual(
+                (bi.value, bi.__doc__, bi.open, bi.close),
+                (1, '***really super important***', '***', '***'),
+                )
+        h = Huh.HIGHLIGHT
+        self.assertEqual(
+                (h.value, h.__doc__, h.open, h.close),
+                (2, 'please ==take notice==', '==', '=='),
+                )
+
+    def test_members_is_ordereddict_if_ordered(self):
+        class Ordered(Enum):
+            __order__ = 'first second third'
+            first = 'bippity'
+            second = 'boppity'
+            third = 'boo'
+        self.assertTrue(type(Ordered.__members__) is OrderedDict)
+
+    def test_members_is_ordereddict_if_not_ordered(self):
+        class Unordered(Enum):
+            this = 'that'
+            these = 'those'
+        self.assertTrue(type(Unordered.__members__) is OrderedDict)
+
+    def test_enum_in_enum_out(self):
+        Season = self.Season
+        self.assertTrue(Season(Season.WINTER) is Season.WINTER)
+
+    def test_enum_value(self):
+        Season = self.Season
+        self.assertEqual(Season.SPRING.value, 1)
+
+    def test_intenum_value(self):
+        self.assertEqual(IntStooges.CURLY.value, 2)
+
+    def test_enum(self):
+        Season = self.Season
+        lst = list(Season)
+        self.assertEqual(len(lst), len(Season))
+        self.assertEqual(len(Season), 4, Season)
+        self.assertEqual(
+            [Season.SPRING, Season.SUMMER, Season.AUTUMN, Season.WINTER], lst)
+
+        for i, season in enumerate('SPRING SUMMER AUTUMN WINTER'.split()):
+            i += 1
+            e = Season(i)
+            self.assertEqual(e, getattr(Season, season))
+            self.assertEqual(e.value, i)
+            self.assertNotEqual(e, i)
+            self.assertEqual(e.name, season)
+            self.assertTrue(e in Season)
+            self.assertTrue(type(e) is Season)
+            self.assertTrue(isinstance(e, Season))
+            self.assertEqual(str(e), 'Season.' + season)
+            self.assertEqual(
+                    repr(e),
+                    '<Season.%s: %s>' % (season, i),
+                    )
+    def test_enum_helper(self):
+        e1 = enum(1, 2, three=9)
+        e2 = enum(1, 2, three=9)
+        e3 = enum(1, 2, 9)
+        self.assertTrue(e1 is not e2)
+        self.assertEqual(e1, e2)
+        self.assertNotEqual(e1, e3)
+        self.assertNotEqual(e2, e3)
+
+    def test_enum_in_enum(self):
+        #
+        class Level(Enum):
+            _order_ = 'DATA_CHECK DESIGN_CHECK ALERT'
+            #
+            def __new__(cls, *args, **kwds):
+                member = object.__new__(cls)
+                member._value_ = len(cls) + 1  # members are 1-based
+                return member
+            #
+            def __init__(self, prereq=None, dependent=None):
+                # create priority level lists
+                self.lower_priority_levels = list(self.__class__._member_map_.values())
+                self.greater_priority_levels = []
+                # update previous members' greater priority list
+                for member in self.lower_priority_levels:
+                    member.greater_priority_levels.append(self)
+                # and save prereq and dependent
+                self.prerequisite = prereq and self.__class__[prereq.name] or None
+                self.dependent = dependent and self.__class__[dependent.name] or None
+            #
+            DATA_CHECK = enum()
+            DESIGN_CHECK = enum(DATA_CHECK)
+            ALERT = enum(None, DATA_CHECK)
+        #
+        self.assertEqual(Level.DATA_CHECK.value, 1)
+        self.assertEqual(Level.DATA_CHECK.prerequisite, None)
+        self.assertEqual(Level.DATA_CHECK.dependent, None)
+        self.assertEqual(Level.DESIGN_CHECK.prerequisite, Level.DATA_CHECK)
+        self.assertEqual(Level.DESIGN_CHECK.dependent, None)
+        self.assertEqual(Level.ALERT.prerequisite, None)
+        self.assertEqual(Level.ALERT.dependent, Level.DATA_CHECK)
+
+    def test_value_name(self):
+        Season = self.Season
+        self.assertEqual(Season.SPRING.name, 'SPRING')
+        self.assertEqual(Season.SPRING.value, 1)
+        def set_name(obj, new_value):
+            obj.name = new_value
+        def set_value(obj, new_value):
+            obj.value = new_value
+        self.assertRaises(AttributeError, set_name, Season.SPRING, 'invierno', )
+        self.assertRaises(AttributeError, set_value, Season.SPRING, 2)
+
+    def test_attribute_deletion(self):
+        class Season(Enum):
+            SPRING = 1
+            SUMMER = 2
+            AUTUMN = 3
+            WINTER = 4
+
+            def spam(cls):
+                pass
+
+        self.assertTrue(hasattr(Season, 'spam'))
+        del Season.spam
+        self.assertFalse(hasattr(Season, 'spam'))
+
+        self.assertRaises(AttributeError, delattr, Season, 'SPRING')
+        self.assertRaises(AttributeError, delattr, Season, 'DRY')
+        self.assertRaises(AttributeError, delattr, Season.SPRING, 'name')
+
+    def test_bool_of_class(self):
+        class Empty(Enum):
+            pass
+        self.assertTrue(bool(Empty))
+
+    def test_bool_of_member(self):
+        class Count(Enum):
+            zero = 0
+            one = 1
+            two = 2
+        for member in Count:
+            self.assertTrue(bool(member))
+
+    def test_invalid_names(self):
+        def create_bad_class_1():
+            class Wrong(Enum):
+                mro = 9
+        def create_bad_class_2():
+            class Wrong(Enum):
+                _reserved_ = 3
+        self.assertRaises(ValueError, create_bad_class_1)
+        self.assertRaises(ValueError, create_bad_class_2)
+
+    def test_bool(self):
+        class Logic(Enum):
+            true = True
+            false = False
+            def __bool__(self):
+                return bool(self.value)
+            __nonzero__ = __bool__
+        self.assertTrue(Logic.true)
+        self.assertFalse(Logic.false)
+
+    def test_contains(self):
+        Season = self.Season
+        self.assertRaises(TypeError, lambda: 'AUTUMN' in Season)
+        self.assertTrue(Season.AUTUMN in Season)
+        self.assertRaises(TypeError, lambda: 3 not in Season)
+        val = Season(3)
+        self.assertTrue(val in Season)
+        #
+        class OtherEnum(Enum):
+            one = 1; two = 2
+        self.assertTrue(OtherEnum.two not in Season)
+        #
+        class Wierd(Enum):
+            this = [1, 2, 3]
+            that = (1, 2, 3)
+            those = {1: 1, 2: 2, 3: 3}
+        self.assertTrue(Wierd.this in Wierd)
+        self.assertRaises(TypeError, lambda: [1, 2, 3] in Wierd)
+        self.assertRaises(TypeError, lambda: {1: 1, 2: 2, 3: 3} in Wierd)
+
+    def test_member_contains(self):
+        self.assertRaises(TypeError, lambda: 'test' in self.Season.AUTUMN)
+
+    if pyver >= PY2_6:     # when `format` came into being
+
+        def test_format_enum(self):
+            Season = self.Season
+            self.assertEqual('{0}'.format(Season.SPRING),
+                             '{0}'.format(str(Season.SPRING)))
+            self.assertEqual( '{0:}'.format(Season.SPRING),
+                              '{0:}'.format(str(Season.SPRING)))
+            self.assertEqual('{0:20}'.format(Season.SPRING),
+                             '{0:20}'.format(str(Season.SPRING)))
+            self.assertEqual('{0:^20}'.format(Season.SPRING),
+                             '{0:^20}'.format(str(Season.SPRING)))
+            self.assertEqual('{0:>20}'.format(Season.SPRING),
+                             '{0:>20}'.format(str(Season.SPRING)))
+            self.assertEqual('{0:<20}'.format(Season.SPRING),
+                             '{0:<20}'.format(str(Season.SPRING)))
+
+        def test_custom_format(self):
+            class TestFloat(float, Enum):
+                one = 1.0
+                two = 2.0
+                def __format__(self, spec):
+                    return 'TestFloat success!'
+            self.assertEqual(str(TestFloat.one), 'TestFloat.one')
+            self.assertEqual('{0}'.format(TestFloat.one), 'TestFloat success!')
+
+        def test_format_with_custom_str(self):
+            class TestInt(int, Enum):
+                one = 1
+                two = 2
+                def __str__(self):
+                    return self.name * 3
+            self.assertEqual(str(TestInt.two), 'twotwotwo')
+            self.assertEqual('{0}'.format(TestInt.two), 'twotwotwo')
+
+        def assertFormatIsValue(self, spec, member):
+            self.assertEqual(spec.format(member), spec.format(member.value))
+
+        def test_format_enum_date(self):
+            Holiday = self.Holiday
+            self.assertFormatIsValue('{0}', Holiday.IDES_OF_MARCH)
+            self.assertFormatIsValue('{0:}', Holiday.IDES_OF_MARCH)
+            self.assertFormatIsValue('{0:20}', Holiday.IDES_OF_MARCH)
+            self.assertFormatIsValue('{0:^20}', Holiday.IDES_OF_MARCH)
+            self.assertFormatIsValue('{0:>20}', Holiday.IDES_OF_MARCH)
+            self.assertFormatIsValue('{0:<20}', Holiday.IDES_OF_MARCH)
+            self.assertFormatIsValue('{0:%Y %m}', Holiday.IDES_OF_MARCH)
+            self.assertFormatIsValue('{0:%Y %m %M:00}', Holiday.IDES_OF_MARCH)
+
+        def test_format_enum_float(self):
+            Konstants = self.Konstants
+            self.assertFormatIsValue('{0}', Konstants.TAU)
+            self.assertFormatIsValue('{0:}', Konstants.TAU)
+            self.assertFormatIsValue('{0:20}', Konstants.TAU)
+            self.assertFormatIsValue('{0:^20}', Konstants.TAU)
+            self.assertFormatIsValue('{0:>20}', Konstants.TAU)
+            self.assertFormatIsValue('{0:<20}', Konstants.TAU)
+            self.assertFormatIsValue('{0:n}', Konstants.TAU)
+            self.assertFormatIsValue('{0:5.2}', Konstants.TAU)
+            self.assertFormatIsValue('{0:f}', Konstants.TAU)
+
+        def test_format_enum_int(self):
+            Grades = self.Grades
+            self.assertFormatIsValue('{0}', Grades.C)
+            self.assertFormatIsValue('{0:}', Grades.C)
+            self.assertFormatIsValue('{0:20}', Grades.C)
+            self.assertFormatIsValue('{0:^20}', Grades.C)
+            self.assertFormatIsValue('{0:>20}', Grades.C)
+            self.assertFormatIsValue('{0:<20}', Grades.C)
+            self.assertFormatIsValue('{0:+}', Grades.C)
+            self.assertFormatIsValue('{0:08X}', Grades.C)
+            self.assertFormatIsValue('{0:b}', Grades.C)
+
+        def test_format_enum_str(self):
+            Directional = self.Directional
+            self.assertFormatIsValue('{0}', Directional.WEST)
+            self.assertFormatIsValue('{0:}', Directional.WEST)
+            self.assertFormatIsValue('{0:20}', Directional.WEST)
+            self.assertFormatIsValue('{0:^20}', Directional.WEST)
+            self.assertFormatIsValue('{0:>20}', Directional.WEST)
+            self.assertFormatIsValue('{0:<20}', Directional.WEST)
+
+    def test_hash(self):
+        Season = self.Season
+        dates = {}
+        dates[Season.WINTER] = '1225'
+        dates[Season.SPRING] = '0315'
+        dates[Season.SUMMER] = '0704'
+        dates[Season.AUTUMN] = '1031'
+        self.assertEqual(dates[Season.AUTUMN], '1031')
+
+    def test_enum_duplicates(self):
+        class Season(Enum):
+            __order__ = "SPRING SUMMER AUTUMN WINTER"
+            SPRING = 1
+            SUMMER = 2
+            AUTUMN = FALL = 3
+            WINTER = 4
+            ANOTHER_SPRING = 1
+        lst = list(Season)
+        self.assertEqual(
+            lst,
+            [Season.SPRING, Season.SUMMER,
+             Season.AUTUMN, Season.WINTER,
+            ])
+        self.assertTrue(Season.FALL is Season.AUTUMN)
+        self.assertEqual(Season.FALL.value, 3)
+        self.assertEqual(Season.AUTUMN.value, 3)
+        self.assertTrue(Season(3) is Season.AUTUMN)
+        self.assertTrue(Season(1) is Season.SPRING)
+        self.assertEqual(Season.FALL.name, 'AUTUMN')
+        self.assertEqual(
+                set([k for k,v in Season.__members__.items() if v.name != k]),
+                set(['FALL', 'ANOTHER_SPRING']),
+                )
+
+    def test_enum_with_value_name(self):
+        class Huh(Enum):
+            _order_ = 'name value'
+            name = 1
+            value = 2
+        self.assertEqual(
+            list(Huh),
+            [Huh.name, Huh.value],
+            )
+        self.assertTrue(type(Huh.name) is Huh)
+        self.assertEqual(Huh.name.name, 'name')
+        self.assertEqual(Huh.name.value, 1)
+
+    def test_intenum_from_scratch(self):
+        class phy(int, Enum):
+            pi = 3
+            tau = 2 * pi
+        self.assertTrue(phy.pi < phy.tau)
+
+    def test_intenum_inherited(self):
+        class IntEnum(int, Enum):
+            pass
+        class phy(IntEnum):
+            pi = 3
+            tau = 2 * pi
+        self.assertTrue(phy.pi < phy.tau)
+
+    def test_floatenum_from_scratch(self):
+        class phy(float, Enum):
+            pi = 3.1415926
+            tau = 2 * pi
+        self.assertTrue(phy.pi < phy.tau)
+
+    def test_floatenum_inherited(self):
+        class FloatEnum(float, Enum):
+            pass
+        class phy(FloatEnum):
+            pi = 3.1415926
+            tau = 2 * pi
+        self.assertTrue(phy.pi < phy.tau)
+
+    def test_strenum_from_scratch(self):
+        class phy(str, Enum):
+            pi = 'Pi'
+            tau = 'Tau'
+        self.assertTrue(phy.pi < phy.tau)
+
+    def test_intenum(self):
+        class WeekDay(IntEnum):
+            SUNDAY = 1
+            MONDAY = 2
+            TUESDAY = 3
+            WEDNESDAY = 4
+            THURSDAY = 5
+            FRIDAY = 6
+            SATURDAY = 7
+
+        self.assertEqual(['a', 'b', 'c'][WeekDay.MONDAY], 'c')
+        self.assertEqual([i for i in range(WeekDay.TUESDAY)], [0, 1, 2])
+
+        lst = list(WeekDay)
+        self.assertEqual(len(lst), len(WeekDay))
+        self.assertEqual(len(WeekDay), 7)
+        target = 'SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY'
+        target = target.split()
+        for i, weekday in enumerate(target):
+            i += 1
+            e = WeekDay(i)
+            self.assertEqual(e, i)
+            self.assertEqual(int(e), i)
+            self.assertEqual(e.name, weekday)
+            self.assertTrue(e in WeekDay)
+            self.assertEqual(lst.index(e)+1, i)
+            self.assertTrue(0 < e < 8)
+            self.assertTrue(type(e) is WeekDay)
+            self.assertTrue(isinstance(e, int))
+            self.assertTrue(isinstance(e, Enum))
+
+    def test_intenum_duplicates(self):
+        class WeekDay(IntEnum):
+            __order__ = 'SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY'
+            SUNDAY = 1
+            MONDAY = 2
+            TUESDAY = TEUSDAY = 3
+            WEDNESDAY = 4
+            THURSDAY = 5
+            FRIDAY = 6
+            SATURDAY = 7
+        self.assertTrue(WeekDay.TEUSDAY is WeekDay.TUESDAY)
+        self.assertEqual(WeekDay(3).name, 'TUESDAY')
+        self.assertEqual([k for k,v in WeekDay.__members__.items()
+                if v.name != k], ['TEUSDAY', ])
+
+    def test_floatenum_fromhex(self):
+        h = float.hex(FloatStooges.MOE.value)
+        self.assertIs(FloatStooges.fromhex(h), FloatStooges.MOE)
+        h = float.hex(FloatStooges.MOE.value + 0.01)
+        with self.assertRaises(ValueError):
+            FloatStooges.fromhex(h)
+
+    def test_pickle_enum(self):
+        if isinstance(Stooges, Exception):
+            raise Stooges
+        test_pickle_dump_load(self.assertTrue, Stooges.CURLY)
+        test_pickle_dump_load(self.assertTrue, Stooges)
+
+    def test_pickle_int(self):
+        if isinstance(IntStooges, Exception):
+            raise IntStooges
+        test_pickle_dump_load(self.assertTrue, IntStooges.CURLY)
+        test_pickle_dump_load(self.assertTrue, IntStooges)
+
+    def test_pickle_float(self):
+        if isinstance(FloatStooges, Exception):
+            raise FloatStooges
+        test_pickle_dump_load(self.assertTrue, FloatStooges.CURLY)
+        test_pickle_dump_load(self.assertTrue, FloatStooges)
+
+    def test_pickle_enum_function(self):
+        if isinstance(Answer, Exception):
+            raise Answer
+        test_pickle_dump_load(self.assertTrue, Answer.him)
+        test_pickle_dump_load(self.assertTrue, Answer)
+
+    def test_pickle_enum_function_with_module(self):
+        if isinstance(Question, Exception):
+            raise Question
+        test_pickle_dump_load(self.assertTrue, Question.who)
+        test_pickle_dump_load(self.assertTrue, Question)
+
+    def test_pickle_by_name(self):
+        class ReplaceGlobalInt(IntEnum):
+            ONE = 1
+            TWO = 2
+        ReplaceGlobalInt.__reduce_ex__ = _reduce_ex_by_name
+        for proto in range(HIGHEST_PROTOCOL):
+            self.assertEqual(ReplaceGlobalInt.TWO.__reduce_ex__(proto), 'TWO')
+
+    def test_exploding_pickle(self):
+        BadPickle = Enum('BadPickle', 'dill sweet bread-n-butter')
+        aenum._make_class_unpicklable(BadPickle)
+        globals()['BadPickle'] = BadPickle
+        test_pickle_exception(self.assertRaises, TypeError, BadPickle.dill)
+        test_pickle_exception(self.assertRaises, PicklingError, BadPickle)
+
+    def test_string_enum(self):
+        class SkillLevel(str, Enum):
+            master = 'what is the sound of one hand clapping?'
+            journeyman = 'why did the chicken cross the road?'
+            apprentice = 'knock, knock!'
+        self.assertEqual(SkillLevel.apprentice, 'knock, knock!')
+
+    def test_getattr_getitem(self):
+        class Period(Enum):
+            morning = 1
+            noon = 2
+            evening = 3
+            night = 4
+        self.assertTrue(Period(2) is Period.noon)
+        self.assertTrue(getattr(Period, 'night') is Period.night)
+        self.assertTrue(Period['morning'] is Period.morning)
+
+    def test_getattr_dunder(self):
+        Season = self.Season
+        self.assertTrue(getattr(Season, '__hash__'))
+
+    def test_iteration_order(self):
+        class Season(Enum):
+            __order__ = 'SUMMER WINTER AUTUMN SPRING'
+            SUMMER = 2
+            WINTER = 4
+            AUTUMN = 3
+            SPRING = 1
+        self.assertEqual(
+                list(Season),
+                [Season.SUMMER, Season.WINTER, Season.AUTUMN, Season.SPRING],
+                )
+
+    def test_iteration_order_reversed(self):
+        self.assertEqual(
+                list(reversed(self.Season)),
+                [self.Season.WINTER, self.Season.AUTUMN, self.Season.SUMMER,
+                 self.Season.SPRING]
+                )
+
+    def test_iteration_order_with_unorderable_values(self):
+        class Complex(Enum):
+            a = complex(7, 9)
+            b = complex(3.14, 2)
+            c = complex(1, -1)
+            d = complex(-77, 32)
+        self.assertEqual(
+                list(Complex),
+                [Complex.a, Complex.b, Complex.c, Complex.d],
+                )
+
+    def test_programatic_function_string(self):
+        SummerMonth = Enum('SummerMonth', 'june july august')
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate('june july august'.split()):
+            i += 1
+            e = SummerMonth(i)
+            self.assertEqual(int(e.value), i)
+            self.assertNotEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_string_with_start(self):
+        SummerMonth = Enum('SummerMonth', 'june july august', start=10)
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate('june july august'.split(), 10):
+            e = SummerMonth(i)
+            self.assertEqual(int(e.value), i)
+            self.assertNotEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_string_list(self):
+        SummerMonth = Enum('SummerMonth', ['june', 'july', 'august'])
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate('june july august'.split()):
+            i += 1
+            e = SummerMonth(i)
+            self.assertEqual(int(e.value), i)
+            self.assertNotEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_string_list_with_start(self):
+        SummerMonth = Enum('SummerMonth', ['june', 'july', 'august'], start=20)
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate('june july august'.split(), 20):
+            e = SummerMonth(i)
+            self.assertEqual(int(e.value), i)
+            self.assertNotEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_iterable(self):
+        SummerMonth = Enum(
+                'SummerMonth',
+                (('june', 1), ('july', 2), ('august', 3))
+                )
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate('june july august'.split()):
+            i += 1
+            e = SummerMonth(i)
+            self.assertEqual(int(e.value), i)
+            self.assertNotEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_from_dict(self):
+        SummerMonth = Enum(
+                'SummerMonth',
+                dict((('june', 1), ('july', 2), ('august', 3)))
+                )
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        if PY2:
+            self.assertEqual(
+                    [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                    lst,
+                    )
+        for i, month in enumerate('june july august'.split()):
+            i += 1
+            e = SummerMonth(i)
+            self.assertEqual(int(e.value), i)
+            self.assertNotEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_type(self):
+        SummerMonth = Enum('SummerMonth', 'june july august', type=int)
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate('june july august'.split()):
+            i += 1
+            e = SummerMonth(i)
+            self.assertEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_type_with_start(self):
+        SummerMonth = Enum('SummerMonth', 'june july august', type=int, start=30)
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate('june july august'.split(), 30):
+            e = SummerMonth(i)
+            self.assertEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_type_from_subclass(self):
+        SummerMonth = IntEnum('SummerMonth', 'june july august')
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate('june july august'.split()):
+            i += 1
+            e = SummerMonth(i)
+            self.assertEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_type_from_subclass_with_start(self):
+        SummerMonth = IntEnum('SummerMonth', 'june july august', start=40)
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate('june july august'.split(), 40):
+            e = SummerMonth(i)
+            self.assertEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_unicode(self):
+        SummerMonth = Enum('SummerMonth', unicode('june july august'))
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate(unicode('june july august').split()):
+            i += 1
+            e = SummerMonth(i)
+            self.assertEqual(int(e.value), i)
+            self.assertNotEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_unicode_list(self):
+        SummerMonth = Enum('SummerMonth', [unicode('june'), unicode('july'), unicode('august')])
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate(unicode('june july august').split()):
+            i += 1
+            e = SummerMonth(i)
+            self.assertEqual(int(e.value), i)
+            self.assertNotEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_unicode_iterable(self):
+        SummerMonth = Enum(
+                'SummerMonth',
+                ((unicode('june'), 1), (unicode('july'), 2), (unicode('august'), 3))
+                )
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate(unicode('june july august').split()):
+            i += 1
+            e = SummerMonth(i)
+            self.assertEqual(int(e.value), i)
+            self.assertNotEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_from_unicode_dict(self):
+        SummerMonth = Enum(
+                'SummerMonth',
+                dict(((unicode('june'), 1), (unicode('july'), 2), (unicode('august'), 3)))
+                )
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        if PY2:
+            self.assertEqual(
+                    [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                    lst,
+                    )
+        for i, month in enumerate(unicode('june july august').split()):
+            i += 1
+            e = SummerMonth(i)
+            self.assertEqual(int(e.value), i)
+            self.assertNotEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_unicode_type(self):
+        SummerMonth = Enum('SummerMonth', unicode('june july august'), type=int)
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate(unicode('june july august').split()):
+            i += 1
+            e = SummerMonth(i)
+            self.assertEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programatic_function_unicode_type_from_subclass(self):
+        SummerMonth = IntEnum('SummerMonth', unicode('june july august'))
+        lst = list(SummerMonth)
+        self.assertEqual(len(lst), len(SummerMonth))
+        self.assertEqual(len(SummerMonth), 3, SummerMonth)
+        self.assertEqual(
+                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                lst,
+                )
+        for i, month in enumerate(unicode('june july august').split()):
+            i += 1
+            e = SummerMonth(i)
+            self.assertEqual(e, i)
+            self.assertEqual(e.name, month)
+            self.assertTrue(e in SummerMonth)
+            self.assertTrue(type(e) is SummerMonth)
+
+    def test_programmatic_function_unicode_class(self):
+        if PY2:
+            class_names = unicode('SummerMonth'), 'S\xfcmm\xe9rM\xf6nth'.decode('latin1')
+        else:
+            class_names = 'SummerMonth', 'S\xfcmm\xe9rM\xf6nth'
+        for i, class_name in enumerate(class_names):
+            if PY2 and i == 1:
+                self.assertRaises(TypeError, Enum, class_name, unicode('june july august'))
+            else:
+                SummerMonth = Enum(class_name, unicode('june july august'))
+                lst = list(SummerMonth)
+                self.assertEqual(len(lst), len(SummerMonth))
+                self.assertEqual(len(SummerMonth), 3, SummerMonth)
+                self.assertEqual(
+                        [SummerMonth.june, SummerMonth.july, SummerMonth.august],
+                        lst,
+                        )
+                for i, month in enumerate(unicode('june july august').split()):
+                    i += 1
+                    e = SummerMonth(i)
+                    self.assertEqual(e.value, i)
+                    self.assertEqual(e.name, month)
+                    self.assertTrue(e in SummerMonth)
+                    self.assertTrue(type(e) is SummerMonth)
+
+    def test_subclassing(self):
+        if isinstance(Name, Exception):
+            raise Name
+        self.assertEqual(Name.BDFL, 'Guido van Rossum')
+        self.assertTrue(Name.BDFL, Name('Guido van Rossum'))
+        self.assertTrue(Name.BDFL is getattr(Name, 'BDFL'))
+        test_pickle_dump_load(self.assertTrue, Name.BDFL)
+
+    def test_extending(self):
+        def bad_extension():
+            class Color(Enum):
+                red = 1
+                green = 2
+                blue = 3
+            class MoreColor(Color):
+                cyan = 4
+                magenta = 5
+                yellow = 6
+        self.assertRaises(TypeError, bad_extension)
+
+    def test_exclude_methods(self):
+        class whatever(Enum):
+            this = 'that'
+            these = 'those'
+            def really(self):
+                return 'no, not %s' % self.value
+        self.assertFalse(type(whatever.really) is whatever)
+        self.assertEqual(whatever.this.really(), 'no, not that')
+
+    def test_wrong_inheritance_order(self):
+        def wrong_inherit():
+            class Wrong(Enum, str):
+                NotHere = 'error before this point'
+        self.assertRaises(TypeError, wrong_inherit)
+
+    def test_intenum_transitivity(self):
+        class number(IntEnum):
+            one = 1
+            two = 2
+            three = 3
+        class numero(IntEnum):
+            uno = 1
+            dos = 2
+            tres = 3
+        self.assertEqual(number.one, numero.uno)
+        self.assertEqual(number.two, numero.dos)
+        self.assertEqual(number.three, numero.tres)
+
+    def test_introspection(self):
+        class Number(IntEnum):
+            one = 100
+            two = 200
+        self.assertTrue(Number.one._member_type_ is int)
+        self.assertTrue(Number._member_type_ is int)
+        class String(str, Enum):
+            yarn = 'soft'
+            rope = 'rough'
+            wire = 'hard'
+        self.assertTrue(String.yarn._member_type_ is str)
+        self.assertTrue(String._member_type_ is str)
+        class Plain(Enum):
+            vanilla = 'white'
+            one = 1
+        self.assertTrue(Plain.vanilla._member_type_ is object)
+        self.assertTrue(Plain._member_type_ is object)
+
+    def test_wrong_enum_in_call(self):
+        class Monochrome(Enum):
+            black = 0
+            white = 1
+        class Gender(Enum):
+            male = 0
+            female = 1
+        self.assertRaises(ValueError, Monochrome, Gender.male)
+
+    def test_wrong_enum_in_mixed_call(self):
+        class Monochrome(IntEnum):
+            black = 0
+            white = 1
+        class Gender(Enum):
+            male = 0
+            female = 1
+        self.assertRaises(ValueError, Monochrome, Gender.male)
+
+    def test_mixed_enum_in_call_1(self):
+        class Monochrome(IntEnum):
+            black = 0
+            white = 1
+        class Gender(IntEnum):
+            male = 0
+            female = 1
+        self.assertTrue(Monochrome(Gender.female) is Monochrome.white)
+
+    def test_mixed_enum_in_call_2(self):
+        class Monochrome(Enum):
+            black = 0
+            white = 1
+        class Gender(IntEnum):
+            male = 0
+            female = 1
+        self.assertTrue(Monochrome(Gender.male) is Monochrome.black)
+
+    def test_flufl_enum(self):
+        class Fluflnum(Enum):
+            def __int__(self):
+                return int(self.value)
+        class MailManOptions(Fluflnum):
+            option1 = 1
+            option2 = 2
+            option3 = 3
+        self.assertEqual(int(MailManOptions.option1), 1)
+
+    def test_no_such_enum_member(self):
+        class Color(Enum):
+            red = 1
+            green = 2
+            blue = 3
+        self.assertRaises(ValueError, Color, 4)
+        self.assertRaises(KeyError, Color.__getitem__, 'chartreuse')
+
+    def test_new_repr(self):
+        class Color(Enum):
+            red = 1
+            green = 2
+            blue = 3
+            def __repr__(self):
+                return "don't you just love shades of %s?" % self.name
+        self.assertEqual(
+                repr(Color.blue),
+                "don't you just love shades of blue?",
+                )
+
+    def test_inherited_repr(self):
+        class MyEnum(Enum):
+            def __repr__(self):
+                return "My name is %s." % self.name
+        class MyIntEnum(int, MyEnum):
+            this = 1
+            that = 2
+            theother = 3
+        self.assertEqual(repr(MyIntEnum.that), "My name is that.")
+
+    def test_multiple_mixin_mro(self):
+        class auto_enum(EnumMeta):
+            def __new__(metacls, cls, bases, classdict):
+                original_dict = classdict
+                temp_dict = metacls.__prepare__(cls, bases, {})
+                if hasattr(original_dict, '_member_names'):
+                    for k in original_dict._member_names:
+                        temp_dict[k] = original_dict[k]
+                    sunders = [k for k in original_dict.keys() if aenum._is_sunder(k)]
+                else:
+                    sunders = []
+                    for k, v in original_dict.items():
+                        if aenum._is_sunder(k):
+                            sunders.append(k)
+                        temp_dict[k] = v
+                classdict = metacls.__prepare__(cls, bases, {})
+                i = 0
+                for k in sunders:
+                    classdict[k] = original_dict[k]
+                for k in temp_dict._member_names:
+                    v = original_dict[k]
+                    if v == ():
+                        v = i
+                    else:
+                        i = v
+                    i += 1
+                    classdict[k] = v
+                for k, v in original_dict.items():
+                    if k not in temp_dict._member_names and k not in sunders:
+                        classdict[k] = v
+                return super(auto_enum, metacls).__new__(
+                        metacls, cls, bases, classdict)
+
+        AutoNumberedEnum = auto_enum('AutoNumberedEnum', (Enum,), {})
+
+        AutoIntEnum = auto_enum('AutoIntEnum', (IntEnum,), {})
+
+        # class TestAutoNumber(AutoNumberedEnum):
+        #     a = ()
+        #     b = 3
+        #     c = ()
+        # self.assertEqual(TestAutoNumber.b.value, 3)
+        #
+        # if pyver >= 3.0:
+        #     self.assertEqual(
+        #         [TestAutoNumber.a.value, TestAutoNumber.b.value, TestAutoNumber.c.value],
+        #         [0, 3, 4],
+        #         )
+        #
+        # class TestAutoInt(AutoIntEnum):
+        #     a = ()
+        #     b = 3
+        #     c = ()
+        # self.assertEqual(TestAutoInt.b, 3)
+        #
+        # if pyver >= 3.0:
+        #     self.assertEqual(
+        #         [TestAutoInt.a.value, TestAutoInt.b.value, TestAutoInt.c.value],
+        #         [0, 3, 4],
+        #         )
+
+    def test_meta_reconfigure(self):
+
+        def identity(*args):
+            if len(args) == 1:
+                return args[0]
+            return args
+
+        JSONEnum = None
+
+        class JSONEnumMeta(EnumMeta):
+
+            @classmethod
+            def __prepare__(metacls, cls, bases, init=None, start=None, settings=()):
+                return {}
+
+            def __init__(cls, *args , **kwds):
+                super(JSONEnumMeta, cls).__init__(*args)
+
+            def __new__(metacls, cls, bases, clsdict, init=None, start=None, settings=()):
+                import json
+                members = []
+                if JSONEnum is not None:
+                    if '_file' not in clsdict:
+                        raise TypeError('_file is required')
+                    if '_name' not in clsdict:
+                        raise TypeError('_name is required')
+                    if '_value' not in clsdict:
+                        raise TypeError('_value is required')
+                    name_spec = clsdict.pop('_name')
+                    if not isinstance(name_spec, (tuple, list)):
+                        name_spec = (name_spec, )
+                    value_spec = clsdict.pop('_value')
+                    file = clsdict.pop('_file')
+                    with open(file) as f:
+                        json_data = json.load(f)
+                    for data in json_data:
+                        values = []
+                        name = data[name_spec[0]]
+                        for piece in name_spec[1:]:
+                            name = name[piece]
+                        for order, (value_path, func) in sorted(value_spec.items()):
+                            if not isinstance(value_path, (list, tuple)):
+                                value_path = (value_path, )
+                            value = data[value_path[0]]
+                            for piece in value_path[1:]:
+                                value = value[piece]
+                            if func is not None:
+                                value = func(value)
+                            values.append(value)
+                        values = tuple(values)
+                        members.append(
+                            (name, identity(*values))
+                            )
+                # get the real EnumDict
+                enum_dict = super(JSONEnumMeta, metacls).__prepare__(cls, bases, init, start, settings)
+                # transfer the original dict content, _items first
+                items = list(clsdict.items())
+                items.sort(key=lambda p: (0 if p[0][0] == '_' else 1, p))
+                for name, value in items:
+                    enum_dict[name] = value
+                # add the members
+                for name, value in members:
+                    enum_dict[name] = value
+                return super(JSONEnumMeta, metacls).__new__(metacls, cls, bases, enum_dict, init, start, settings)
+
+        # for use with both Python 2/3
+        JSONEnum = JSONEnumMeta('JsonEnum', (Enum, ), {})
+
+        test_file = os.path.join(tempdir, 'test_json.json')
+        with open(test_file, 'w') as f:
+            f.write(
+                '[{"name":"Afghanistan","alpha-2":"AF","country-code":"004","notes":{"description":"pretty"}},'
+                '{"name":"Åland Islands","alpha-2":"AX","country-code":"248","notes":{"description":"serene"}},'
+                '{"name":"Albania","alpha-2":"AL","country-code":"008","notes":{"description":"exciting"}},'
+                '{"name":"Algeria","alpha-2":"DZ","country-code":"012","notes":{"description":"scarce"}}]')
+
+        class Country(JSONEnum):
+            _init_ = 'abbr code country_name description'
+            _file = test_file
+            _name = 'alpha-2'
+            _value = {
+                    1: ('alpha-2', None),
+                    2: ('country-code', lambda c: int(c)),
+                    3: ('name', None),
+                    4: (('notes','description'), lambda s: s.title()),
+                    }
+
+        self.assertEqual([Country.AF, Country.AX, Country.AL, Country.DZ], list(Country))
+        self.assertEqual(Country.AF.abbr, 'AF')
+        self.assertEqual(Country.AX.code, 248)
+        self.assertEqual(Country.AL.country_name, 'Albania')
+        self.assertEqual(Country.DZ.description, 'Scarce')
+
+
+    def test_subclasses_with_getnewargs(self):
+        class NamedInt(int):
+            __qualname__ = 'NamedInt'  # needed for pickle protocol 4
+            def __new__(cls, *args):
+                _args = args
+                if len(args) < 1:
+                    raise TypeError("name and value must be specified")
+                name, args = args[0], args[1:]
+                self = int.__new__(cls, *args)
+                self._intname = name
+                self._args = _args
+                return self
+            def __getnewargs__(self):
+                return self._args
+            @property
+            def __name__(self):
+                return self._intname
+            def __repr__(self):
+                # repr() is updated to include the name and type info
+                return "%s(%r, %s)" % (type(self).__name__,
+                                             self.__name__,
+                                             int.__repr__(self))
+            def __str__(self):
+                # str() is unchanged, even if it relies on the repr() fallback
+                base = int
+                base_str = base.__str__
+                if base_str.__objclass__ is object:
+                    return base.__repr__(self)
+                return base_str(self)
+            # for simplicity, we only define one operator that
+            # propagates expressions
+            def __add__(self, other):
+                temp = int(self) + int( other)
+                if isinstance(self, NamedInt) and isinstance(other, NamedInt):
+                    return NamedInt(
+                        '(%s + %s)' % (self.__name__, other.__name__),
+                        temp )
+                else:
+                    return temp
+
+        class NEI(NamedInt, Enum):
+            __qualname__ = 'NEI'  # needed for pickle protocol 4
+            x = ('the-x', 1)
+            y = ('the-y', 2)
+
+        self.assertTrue(NEI.__new__ is Enum.__new__)
+        self.assertEqual(repr(NEI.x + NEI.y), "NamedInt('(the-x + the-y)', 3)")
+        globals()['NamedInt'] = NamedInt
+        globals()['NEI'] = NEI
+        NI5 = NamedInt('test', 5)
+        self.assertEqual(NI5, 5)
+        test_pickle_dump_load(self.assertTrue, NI5, 5)
+        self.assertEqual(NEI.y.value, 2)
+        test_pickle_dump_load(self.assertTrue, NEI.y)
+
+    def test_subclasses_with_reduce(self):
+        class NamedInt(int):
+            __qualname__ = 'NamedInt'       # needed for pickle protocol 4
+            def __new__(cls, *args):
+                _args = args
+                if len(args) < 1:
+                    raise TypeError("name and value must be specified")
+                name, args = args[0], args[1:]
+                self = int.__new__(cls, *args)
+                self._intname = name
+                self._args = _args
+                return self
+            def __reduce__(self):
+                return self.__class__, self._args
+            @property
+            def __name__(self):
+                return self._intname
+            def __repr__(self):
+                # repr() is updated to include the name and type info
+                return "%s(%r, %s)" % (type(self).__name__,
+                                             self.__name__,
+                                             int.__repr__(self))
+            def __str__(self):
+                # str() is unchanged, even if it relies on the repr() fallback
+                base = int
+                base_str = base.__str__
+                if base_str.__objclass__ is object:
+                    return base.__repr__(self)
+                return base_str(self)
+            # for simplicity, we only define one operator that
+            # propagates expressions
+            def __add__(self, other):
+                temp = int(self) + int( other)
+                if isinstance(self, NamedInt) and isinstance(other, NamedInt):
+                    return NamedInt(
+                        '(%s + %s)' % (self.__name__, other.__name__),
+                        temp )
+                else:
+                    return temp
+
+        class NEI(NamedInt, Enum):
+            __qualname__ = 'NEI'      # needed for pickle protocol 4
+            x = ('the-x', 1)
+            y = ('the-y', 2)
+
+
+        self.assertTrue(NEI.__new__ is Enum.__new__)
+        self.assertEqual(repr(NEI.x + NEI.y), "NamedInt('(the-x + the-y)', 3)")
+        globals()['NamedInt'] = NamedInt
+        globals()['NEI'] = NEI
+        NI5 = NamedInt('test', 5)
+        self.assertEqual(NI5, 5)
+        test_pickle_dump_load(self.assertEqual, NI5, 5)
+        self.assertEqual(NEI.y.value, 2)
+        test_pickle_dump_load(self.assertTrue, NEI.y)
+
+    def test_subclasses_with_reduce_ex(self):
+        class NamedInt(int):
+            __qualname__ = 'NamedInt'       # needed for pickle protocol 4
+            def __new__(cls, *args):
+                _args = args
+                if len(args) < 1:
+                    raise TypeError("name and value must be specified")
+                name, args = args[0], args[1:]
+                self = int.__new__(cls, *args)
+                self._intname = name
+                self._args = _args
+                return self
+            def __reduce_ex__(self, proto):
+                return self.__class__, self._args
+            @property
+            def __name__(self):
+                return self._intname
+            def __repr__(self):
+                # repr() is updated to include the name and type info
+                return "%s(%r, %s)" % (type(self).__name__,
+                                             self.__name__,
+                                             int.__repr__(self))
+            def __str__(self):
+                # str() is unchanged, even if it relies on the repr() fallback
+                base = int
+                base_str = base.__str__
+                if base_str.__objclass__ is object:
+                    return base.__repr__(self)
+                return base_str(self)
+            # for simplicity, we only define one operator that
+            # propagates expressions
+            def __add__(self, other):
+                temp = int(self) + int( other)
+                if isinstance(self, NamedInt) and isinstance(other, NamedInt):
+                    return NamedInt(
+                        '(%s + %s)' % (self.__name__, other.__name__),
+                        temp )
+                else:
+                    return temp
+
+        class NEI(NamedInt, Enum):
+            __qualname__ = 'NEI'      # needed for pickle protocol 4
+            x = ('the-x', 1)
+            y = ('the-y', 2)
+
+
+        self.assertTrue(NEI.__new__ is Enum.__new__)
+        self.assertEqual(repr(NEI.x + NEI.y), "NamedInt('(the-x + the-y)', 3)")
+        globals()['NamedInt'] = NamedInt
+        globals()['NEI'] = NEI
+        NI5 = NamedInt('test', 5)
+        self.assertEqual(NI5, 5)
+        test_pickle_dump_load(self.assertEqual, NI5, 5)
+        self.assertEqual(NEI.y.value, 2)
+        test_pickle_dump_load(self.assertTrue, NEI.y)
+
+    def test_subclasses_without_direct_pickle_support(self):
+        class NamedInt(int):
+            __qualname__ = 'NamedInt'
+            def __new__(cls, *args):
+                _args = args
+                name, args = args[0], args[1:]
+                if len(args) == 0:
+                    raise TypeError("name and value must be specified")
+                self = int.__new__(cls, *args)
+                self._intname = name
+                self._args = _args
+                return self
+            @property
+            def __name__(self):
+                return self._intname
+            def __repr__(self):
+                # repr() is updated to include the name and type info
+                return "%s(%r, %s)" % (type(self).__name__,
+                                             self.__name__,
+                                             int.__repr__(self))
+            def __str__(self):
+                # str() is unchanged, even if it relies on the repr() fallback
+                base = int
+                base_str = base.__str__
+                if base_str.__objclass__ is object:
+                    return base.__repr__(self)
+                return base_str(self)
+            # for simplicity, we only define one operator that
+            # propagates expressions
+            def __add__(self, other):
+                temp = int(self) + int( other)
+                if isinstance(self, NamedInt) and isinstance(other, NamedInt):
+                    return NamedInt(
+                        '(%s + %s)' % (self.__name__, other.__name__),
+                        temp )
+                else:
+                    return temp
+
+        class NEI(NamedInt, Enum):
+            __qualname__ = 'NEI'
+            x = ('the-x', 1)
+            y = ('the-y', 2)
+
+        self.assertTrue(NEI.__new__ is Enum.__new__)
+        self.assertEqual(repr(NEI.x + NEI.y), "NamedInt('(the-x + the-y)', 3)")
+        globals()['NamedInt'] = NamedInt
+        globals()['NEI'] = NEI
+        NI5 = NamedInt('test', 5)
+        self.assertEqual(NI5, 5)
+        self.assertEqual(NEI.y.value, 2)
+        test_pickle_exception(self.assertRaises, TypeError, NEI.x)
+        test_pickle_exception(self.assertRaises, PicklingError, NEI)
+
+    def test_subclasses_without_direct_pickle_support_using_name(self):
+        class NamedInt(int):
+            __qualname__ = 'NamedInt'
+            def __new__(cls, *args):
+                _args = args
+                name, args = args[0], args[1:]
+                if len(args) == 0:
+                    raise TypeError("name and value must be specified")
+                self = int.__new__(cls, *args)
+                self._intname = name
+                self._args = _args
+                return self
+            @property
+            def __name__(self):
+                return self._intname
+            def __repr__(self):
+                # repr() is updated to include the name and type info
+                return "%s(%r, %s)" % (type(self).__name__,
+                                             self.__name__,
+                                             int.__repr__(self))
+            def __str__(self):
+                # str() is unchanged, even if it relies on the repr() fallback
+                base = int
+                base_str = base.__str__
+                if base_str.__objclass__ is object:
+                    return base.__repr__(self)
+                return base_str(self)
+            # for simplicity, we only define one operator that
+            # propagates expressions
+            def __add__(self, other):
+                temp = int(self) + int( other)
+                if isinstance(self, NamedInt) and isinstance(other, NamedInt):
+                    return NamedInt(
+                        '(%s + %s)' % (self.__name__, other.__name__),
+                        temp )
+                else:
+                    return temp
+
+        class NEI(NamedInt, Enum):
+            __qualname__ = 'NEI'
+            x = ('the-x', 1)
+            y = ('the-y', 2)
+            def __reduce_ex__(self, proto):
+                return getattr, (self.__class__, self._name_)
+
+        self.assertTrue(NEI.__new__ is Enum.__new__)
+        self.assertEqual(repr(NEI.x + NEI.y), "NamedInt('(the-x + the-y)', 3)")
+        globals()['NamedInt'] = NamedInt
+        globals()['NEI'] = NEI
+        NI5 = NamedInt('test', 5)
+        self.assertEqual(NI5, 5)
+        self.assertEqual(NEI.y.value, 2)
+        test_pickle_dump_load(self.assertTrue, NEI.y)
+        test_pickle_dump_load(self.assertTrue, NEI)
+
+    def test_tuple_subclass(self):
+        class SomeTuple(tuple, Enum):
+            __qualname__ = 'SomeTuple'
+            first = (1, 'for the money')
+            second = (2, 'for the show')
+            third = (3, 'for the music')
+        self.assertTrue(type(SomeTuple.first) is SomeTuple)
+        self.assertTrue(isinstance(SomeTuple.second, tuple))
+        self.assertEqual(SomeTuple.third, (3, 'for the music'))
+        globals()['SomeTuple'] = SomeTuple
+        test_pickle_dump_load(self.assertTrue, SomeTuple.first)
+
+    # def test_duplicate_values_give_unique_enum_items(self):
+    #     class NumericEnum(AutoNumberEnum):
+    #         __order__ = 'enum_m enum_d enum_y'
+    #         enum_m = ()
+    #         enum_d = ()
+    #         enum_y = ()
+    #         def __int__(self):
+    #             return int(self._value_)
+    #     self.assertEqual(int(NumericEnum.enum_d), 2)
+    #     self.assertEqual(NumericEnum.enum_y.value, 3)
+    #     self.assertTrue(NumericEnum(1) is NumericEnum.enum_m)
+    #     self.assertEqual(
+    #         list(NumericEnum),
+    #         [NumericEnum.enum_m, NumericEnum.enum_d, NumericEnum.enum_y],
+    #         )
+
+    def test_inherited_new_from_enhanced_enum(self):
+        class AutoNumber2(Enum):
+            def __new__(cls):
+                value = len(cls.__members__) + 1
+                obj = object.__new__(cls)
+                obj._value_ = value
+                return obj
+            def __int__(self):
+                return int(self._value_)
+        class Color(AutoNumber2):
+            __order__ = 'red green blue'
+            red = ()
+            green = ()
+            blue = ()
+        self.assertEqual(len(Color), 3, "wrong number of elements: %d (should be %d)" % (len(Color), 3))
+        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
+        if PY3:
+            self.assertEqual(list(map(int, Color)), [1, 2, 3])
+
+    def test_inherited_new_from_mixed_enum(self):
+        class AutoNumber3(IntEnum):
+            def __new__(cls):
+                value = len(cls.__members__) + 11
+                obj = int.__new__(cls, value)
+                obj._value_ = value
+                return obj
+        class Color(AutoNumber3):
+            __order__ = 'red green blue'
+            red = ()
+            green = ()
+            blue = ()
+        self.assertEqual(len(Color), 3, "wrong number of elements: %d (should be %d)" % (len(Color), 3))
+        Color.red
+        Color.green
+        Color.blue
+        self.assertEqual(Color.blue, 13)
+
+    def test_equality(self):
+        class AlwaysEqual:
+            def __eq__(self, other):
+                return True
+        class OrdinaryEnum(Enum):
+            a = 1
+        self.assertEqual(AlwaysEqual(), OrdinaryEnum.a)
+        self.assertEqual(OrdinaryEnum.a, AlwaysEqual())
+
+    def test_ordered_mixin(self):
+        class Grade(OrderedEnum):
+            __order__ = 'A B C D F'
+            A = 5
+            B = 4
+            C = 3
+            D = 2
+            F = 1
+        self.assertEqual(list(Grade), [Grade.A, Grade.B, Grade.C, Grade.D, Grade.F])
+        self.assertTrue(Grade.A > Grade.B)
+        self.assertTrue(Grade.F <= Grade.C)
+        self.assertTrue(Grade.D < Grade.A)
+        self.assertTrue(Grade.B >= Grade.B)
+
+    def test_missing_deprecated(self):
+        class Label(Enum):
+            AnyApple = 0
+            RedApple = 1
+            GreenApple = 2
+            @classmethod
+            def _missing_(cls, name):
+                return cls.AnyApple
+
+        self.assertEqual(Label.AnyApple, Label(4))
+        with self.assertRaises(AttributeError):
+            Label.redapple
+        with self.assertRaises(KeyError):
+            Label['redapple']
+
+    def test_missing(self):
+        class Label(Enum):
+            AnyApple = 0
+            RedApple = 1
+            GreenApple = 2
+            @classmethod
+            def _missing_value_(cls, value):
+                return cls.AnyApple
+
+        self.assertEqual(Label.AnyApple, Label(4))
+        with self.assertRaises(AttributeError):
+            Label.redapple
+        with self.assertRaises(KeyError):
+            Label['redapple']
+
+    def test_missing_name(self):
+        class Label(Enum):
+            RedApple = 1
+            GreenApple = 2
+            @classmethod
+            def _missing_name_(cls, name):
+                for member in cls:
+                    if member.name.lower() == name.lower():
+                        return member
+
+        Label['redapple']
+        with self.assertRaises(AttributeError):
+            Label.redapple
+        with self.assertRaises(ValueError):
+            Label('redapple')
+
+    def test_missing_value_bad_input(self):
+        class Label(Enum):
+            AnyApple = 0
+            RedApple = 1
+            GreenApple = 2
+            @classmethod
+            def _missing_value_(cls, value):
+                return cls.AnyApple
+
+        self.assertEqual(Label.AnyApple, Label(4))
+        with self.assertRaises(KeyError):
+            Label[True]
+
+    def test_missing_name_bad_return(self):
+        class Label(Enum):
+            RedApple = 1
+            GreenApple = 2
+            @classmethod
+            def _missing_name_(cls, name):
+                return None
+
+        with self.assertRaises(AttributeError):
+            Label.redapple
+        with self.assertRaises(ValueError):
+            Label('redapple')
+        with self.assertRaises(KeyError):
+            Label['redapple']
+
+    def test_extending2(self):
+        def bad_extension():
+            class Shade(Enum):
+                def shade(self):
+                    print(self.name)
+            class Color(Shade):
+                red = 1
+                green = 2
+                blue = 3
+            class MoreColor(Color):
+                cyan = 4
+                magenta = 5
+                yellow = 6
+        self.assertRaises(TypeError, bad_extension)
+
+    def test_extending3(self):
+        class Shade(Enum):
+            def shade(self):
+                return self.name
+        class Color(Shade):
+            def hex(self):
+                return '%s hexlified!' % self.value
+        class MoreColor(Color):
+            cyan = 4
+            magenta = 5
+            yellow = 6
+        self.assertEqual(MoreColor.magenta.hex(), '5 hexlified!')
+
+    def test_extending5(self):
+        class Color(Enum):
+            _order_ = 'red green blue value'
+            red = 1
+            green = 2
+            blue = 3
+            value = 4
+        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue, Color.value])
+        self.assertEqual(Color.value.name, 'value')
+        self.assertEqual(Color.value.value, 4)
+        self.assertTrue(Color.value in Color)
+        self.assertEqual(Color(4), Color.value)
+        self.assertEqual(Color['value'], Color.value)
+        self.assertEqual(Color.red.value, 1)
+
+    CONTINUE = 100, 'Continue', 'Request received, please continue'
+    SWITCHING_PROTOCOLS = (101, 'Switching Protocols',
+            'Switching to new protocol; obey Upgrade header')
+    PROCESSING = 102, 'Processing'
+
+    def test_no_duplicates(self):
+        def bad_duplicates():
+            class Color1(UniqueEnum):
+                red = 1
+                green = 2
+                blue = 3
+            class Color2(UniqueEnum):
+                red = 1
+                green = 2
+                blue = 3
+                grene = 2
+        self.assertRaises(ValueError, bad_duplicates)
+
+    def test_no_duplicates_kinda(self):
+        class Silly(UniqueEnum):
+            one = 1
+            two = 'dos'
+            name = 3
+        class Sillier(IntEnum, UniqueEnum):
+            single = 1
+            name = 2
+            triple = 3
+            value = 4
+
+    def test_init(self):
+        class Planet(Enum):
+            MERCURY = (3.303e+23, 2.4397e6)
+            VENUS   = (4.869e+24, 6.0518e6)
+            EARTH   = (5.976e+24, 6.37814e6)
+            MARS    = (6.421e+23, 3.3972e6)
+            JUPITER = (1.9e+27,   7.1492e7)
+            SATURN  = (5.688e+26, 6.0268e7)
+            URANUS  = (8.686e+25, 2.5559e7)
+            NEPTUNE = (1.024e+26, 2.4746e7)
+            def __init__(self, mass, radius):
+                self.mass = mass       # in kilograms
+                self.radius = radius   # in meters
+            @property
+            def surface_gravity(self):
+                # universal gravitational constant  (m3 kg-1 s-2)
+                G = 6.67300E-11
+                return G * self.mass / (self.radius * self.radius)
+        self.assertEqual(round(Planet.EARTH.surface_gravity, 2), 9.80)
+        self.assertEqual(Planet.EARTH.value, (5.976e+24, 6.37814e6))
+
+    def test_init_and_shadowing_attribute(self):
+        class SelectionEnum(str, Enum):
+            _init_ = 'db user'
+            def __new__(cls, *args, **kwds):
+                count = len(cls.__members__)
+                obj = str.__new__(cls, args[0])
+                obj._count = count
+                obj._value_ = args
+                return obj
+            @staticmethod
+            def _generate_next_value_(name, start, count, values, *args, **kwds):
+                return (name, ) + args
+        class DeviceTypeSource(SelectionEnum):
+            _order_ = 'user system'
+            user = "User controlled"
+            system = "System controlled"
+        self.assertEqual(DeviceTypeSource.system.db, 'system')
+        self.assertEqual(DeviceTypeSource.system.user, 'System controlled')
+        self.assertEqual(DeviceTypeSource.user.db, 'user')
+        self.assertEqual(DeviceTypeSource.user.user, 'User controlled')
+
+    def test_nonhash_value(self):
+        class AutoNumberInAList(Enum):
+            def __new__(cls):
+                value = [len(cls.__members__) + 1]
+                obj = object.__new__(cls)
+                obj._value_ = value
+                return obj
+        class ColorInAList(AutoNumberInAList):
+            __order__ = 'red green blue'
+            red = ()
+            green = ()
+            blue = ()
+        self.assertEqual(list(ColorInAList), [ColorInAList.red, ColorInAList.green, ColorInAList.blue])
+        self.assertEqual(ColorInAList.red.value, [1])
+        self.assertEqual(ColorInAList([1]), ColorInAList.red)
+
+    def test_number_reset_and_order_cleanup(self):
+        class Confused(Enum):
+            _order_ = 'ONE TWO THREE UNO DOS TRES FOUR'
+            ONE = auto()
+            TWO = auto()
+            THREE = auto()
+            UNO = 1
+            DOS = auto()
+            TRES = auto()
+            FOUR = auto()
+        self.assertEqual(list(Confused), [Confused.ONE, Confused.TWO, Confused.THREE, Confused.FOUR])
+        self.assertIs(Confused.TWO, Confused.DOS)
+        self.assertEqual(Confused.DOS._value_, 2)
+        self.assertEqual(Confused.TRES._value_, 3)
+        self.assertEqual(Confused.FOUR._value_, 4)
+
+    def test_conflicting_types_resolved_in_new(self):
+        class LabelledIntEnum(int, Enum):
+            def __new__(cls, *args):
+                value, label = args
+                obj = int.__new__(cls, value)
+                obj.label = label
+                obj._value_ = value
+                return obj
+
+        class LabelledList(LabelledIntEnum):
+            unprocessed = (1, "Unprocessed")
+            payment_complete = (2, "Payment Complete")
+
+        self.assertEqual(LabelledList.unprocessed, 1)
+        self.assertEqual(LabelledList(1), LabelledList.unprocessed)
+        self.assertEqual(list(LabelledList), [LabelledList.unprocessed, LabelledList.payment_complete])
+
+    def test_auto_number(self):
+        class Color(Enum):
+            _order_ = 'red blue green'
+            red = auto()
+            blue = auto()
+            green = auto()
+
+        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
+        self.assertEqual(Color.red.value, 1)
+        self.assertEqual(Color.blue.value, 2)
+        self.assertEqual(Color.green.value, 3)
+
+    def test_auto_name(self):
+        class Color(Enum):
+            _order_ = 'red blue green'
+            def _generate_next_value_(name, start, count, last):
+                return name
+            red = auto()
+            blue = auto()
+            green = auto()
+
+        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
+        self.assertEqual(Color.red.value, 'red')
+        self.assertEqual(Color.blue.value, 'blue')
+        self.assertEqual(Color.green.value, 'green')
+
+    def test_auto_name_inherit(self):
+        class AutoNameEnum(Enum):
+            def _generate_next_value_(name, start, count, last):
+                return name
+        class Color(AutoNameEnum):
+            _order_ = 'red blue green'
+            red = auto()
+            blue = auto()
+            green = auto()
+
+        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
+        self.assertEqual(Color.red.value, 'red')
+        self.assertEqual(Color.blue.value, 'blue')
+        self.assertEqual(Color.green.value, 'green')
+
+    def test_auto_garbage(self):
+        class Color(Enum):
+            _order_ = 'red blue'
+            red = 'red'
+            blue = auto()
+        self.assertEqual(Color.blue.value, 1)
+
+    def test_auto_garbage_corrected(self):
+        class Color(Enum):
+            _order_ = 'red blue green'
+            red = 'red'
+            blue = 2
+            green = auto()
+
+        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
+        self.assertEqual(Color.red.value, 'red')
+        self.assertEqual(Color.blue.value, 2)
+        self.assertEqual(Color.green.value, 3)
+
+    def test_duplicate_auto(self):
+        #
+        class MoreDupes(Enum):
+            _order_ = 'A B C'
+            A = auto()
+            B = A,
+            C = auto()
+        self.assertEqual(list(MoreDupes), [MoreDupes.A, MoreDupes.B, MoreDupes.C])
+        self.assertEqual([m.value for m in MoreDupes], [1, (1, ), 2])
+        #
+        class Dupes(Enum):
+            _order_ = 'first second third'
+            first = primero = auto()
+            second = auto()
+            third = auto()
+        self.assertEqual([Dupes.first, Dupes.second, Dupes.third], list(Dupes))
+
+    def test_auto_value_with_auto(self):
+
+        class SelectionEnum(Enum):
+            _init_ = 'db user'
+            def __new__(cls, *args, **kwds):
+                count = len(cls.__members__)
+                obj = object.__new__(cls)
+                obj._count = count
+                obj._value_ = args
+                obj.db, obj.user = args
+                return obj
+            @staticmethod
+            def _generate_next_value_(name, start, count, values, *args, **kwds):
+                return (name, ) + args
+
+        class Test(SelectionEnum):
+            _order_ = 'this that'
+            this = auto('these')
+            that = auto('those')
+
+        self.assertEqual(list(Test), [Test.this, Test.that])
+        self.assertEqual(Test.this.name, 'this')
+        self.assertEqual(Test.this.value, ('this', 'these'))
+        self.assertEqual(Test.this.db, 'this')
+        self.assertEqual(Test.this.user, 'these')
+        self.assertEqual(Test.that.name, 'that')
+        self.assertEqual(Test.that.value, ('that', 'those'))
+        self.assertEqual(Test.that.db, 'that')
+        self.assertEqual(Test.that.user, 'those')
+
+    def test_auto_value_with_autovalue(self):
+
+        class SelectionEnum(Enum):
+            _init_ = 'db user'
+            def __new__(cls, *args, **kwds):
+                count = len(cls.__members__)
+                obj = object.__new__(cls)
+                obj._count = count
+                obj._value_ = args
+                return obj
+            @staticmethod
+            def _generate_next_value_(name, start, count, values, *args, **kwds):
+                return (name, ) + args
+
+        class Test(SelectionEnum):
+            _order_ = 'this that'
+            this = 'these'
+            that = 'those'
+
+        self.assertEqual(list(Test), [Test.this, Test.that])
+        self.assertEqual(Test.this.name, 'this')
+        self.assertEqual(Test.this.value, ('this', 'these'))
+        self.assertEqual(Test.this.db, 'this')
+        self.assertEqual(Test.this.user, 'these')
+        self.assertEqual(Test.that.name, 'that')
+        self.assertEqual(Test.that.value, ('that', 'those'))
+        self.assertEqual(Test.that.db, 'that')
+        self.assertEqual(Test.that.user, 'those')
+
+    def test_auto_and_kwds(self):
+        class Item(Enum):
+            _order_ = 'A B'
+            A = auto(size=100, req={'red': True})
+            B = auto(size=200, req={'red': False})
+            #
+            def __new__(cls, value, size, req):
+                obj = object.__new__(cls)
+                obj._value_ = value
+                obj.size = size
+                obj.req= req
+                return obj
+        self.assertEqual((Item.A.value, Item.A.size, Item.A.req), (1, 100, {'red': True}))
+        self.assertEqual((Item.B.value, Item.B.size, Item.B.req), (2, 200, {'red': False}))
+
+    def test_empty_with_functional_api(self):
+        empty = aenum.IntEnum('Foo', {})
+        self.assertEqual(len(empty), 0)
+
+    def test_auto_init(self):
+        class Planet(Enum):
+            _init_ = 'mass radius'
+            MERCURY = (3.303e+23, 2.4397e6)
+            VENUS   = (4.869e+24, 6.0518e6)
+            EARTH   = (5.976e+24, 6.37814e6)
+            MARS    = (6.421e+23, 3.3972e6)
+            JUPITER = (1.9e+27,   7.1492e7)
+            SATURN  = (5.688e+26, 6.0268e7)
+            URANUS  = (8.686e+25, 2.5559e7)
+            NEPTUNE = (1.024e+26, 2.4746e7)
+            @property
+            def surface_gravity(self):
+                # universal gravitational constant  (m3 kg-1 s-2)
+                G = 6.67300E-11
+                return G * self.mass / (self.radius * self.radius)
+        self.assertEqual(round(Planet.EARTH.surface_gravity, 2), 9.80)
+        self.assertEqual(Planet.EARTH.value, (5.976e+24, 6.37814e6))
+
+    def test_auto_init_with_value(self):
+        class Color(Enum):
+            _init_='value, rgb'
+            RED = 1, (1, 0, 0)
+            BLUE = 2, (0, 1, 0)
+            GREEN = 3, (0, 0, 1)
+        self.assertEqual(Color.RED.value, 1)
+        self.assertEqual(Color.BLUE.value, 2)
+        self.assertEqual(Color.GREEN.value, 3)
+        self.assertEqual(Color.RED.rgb, (1, 0, 0))
+        self.assertEqual(Color.BLUE.rgb, (0, 1, 0))
+        self.assertEqual(Color.GREEN.rgb, (0, 0, 1))
+
+    def test_noalias(self):
+        class Settings(Enum):
+            _settings_ = NoAlias
+            red = 1
+            rojo = 1
+        self.assertFalse(Settings.red is Settings.rojo)
+        self.assertRaises(TypeError, Settings, 1)
+
+    def test_auto_and_init(self):
+        class Field(int, Enum):
+            _order_ = 'TYPE START'
+            _init_ = 'value __doc__'
+            TYPE = "Char, Date, Logical, etc."
+            START = "Field offset in record"
+        self.assertEqual(Field.TYPE, 1)
+        self.assertEqual(Field.START, 2)
+        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
+        self.assertEqual(Field.START.__doc__, 'Field offset in record')
+
+    def test_auto_and_start(self):
+        class Field(IntEnum):
+            _order_ = 'TYPE START'
+            _start_ = 0
+            _init_ = 'value __doc__'
+            TYPE = "Char, Date, Logical, etc."
+            START = "Field offset in record"
+        self.assertEqual(Field.TYPE, 0)
+        self.assertEqual(Field.START, 1)
+        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
+        self.assertEqual(Field.START.__doc__, 'Field offset in record')
+
+    def test_auto_and_init_and_some_values(self):
+        class Field(int, Enum):
+            _order_ = 'TYPE START BLAH BELCH'
+            _init_ = 'value __doc__'
+            TYPE = "Char, Date, Logical, etc."
+            START = "Field offset in record"
+            BLAH = 5, "test blah"
+            BELCH = 'test belch'
+        self.assertEqual(Field.TYPE, 1)
+        self.assertEqual(Field.START, 2)
+        self.assertEqual(Field.BLAH, 5)
+        self.assertEqual(Field.BELCH, 6)
+        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
+        self.assertEqual(Field.START.__doc__, 'Field offset in record')
+        self.assertEqual(Field.BLAH.__doc__, 'test blah')
+        self.assertEqual(Field.BELCH.__doc__, 'test belch')
+
+    def test_auto_and_init_w_value_and_too_many_values(self):
+        with self.assertRaisesRegex(TypeError, r'Field\.BLAH: number of fields provided do not match init'):
+            class Field(int, Enum):
+                _order_ = 'TYPE START BLAH BELCH'
+                _init_ = 'value __doc__'
+                TYPE = 1, "Char, Date, Logical, etc."
+                START = 2, "Field offset in record"
+                BLAH = 5, 6, "test blah"
+                BELCH = 7, 'test belch'
+
+    def test_auto_and_init_and_some_complex_values(self):
+        class Field(int, Enum):
+            _order_ = 'TYPE START BLAH BELCH'
+            _init_ = 'value __doc__ help'
+            TYPE = "Char, Date, Logical, etc.", "fields composed of character data"
+            START = "Field offset in record", "where the data starts in the record"
+            BLAH = 5, "test blah", "some help"
+            BELCH = 'test belch', "some more help"
+        self.assertEqual(Field.TYPE, 1)
+        self.assertEqual(Field.START, 2)
+        self.assertEqual(Field.BLAH, 5)
+        self.assertEqual(Field.BELCH, 6)
+        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
+        self.assertEqual(Field.START.__doc__, 'Field offset in record')
+        self.assertEqual(Field.BLAH.__doc__, 'test blah')
+        self.assertEqual(Field.BELCH.__doc__, 'test belch')
+        self.assertEqual(Field.TYPE.help, "fields composed of character data")
+        self.assertEqual(Field.START.help, "where the data starts in the record")
+        self.assertEqual(Field.BLAH.help, "some help")
+        self.assertEqual(Field.BELCH.help, "some more help")
+
+    def test_auto_and_init_inherited(self):
+        class AutoEnum(IntEnum):
+            _start_ = 0
+            _init_ = 'value __doc__'
+        class Field(AutoEnum):
+            _order_ = 'TYPE START BLAH BELCH'
+            TYPE = "Char, Date, Logical, etc."
+            START = "Field offset in record"
+            BLAH = 5, "test blah"
+            BELCH = 'test belch'
+        self.assertEqual(Field.TYPE, 0)
+        self.assertEqual(Field.START, 1)
+        self.assertEqual(Field.BLAH, 5)
+        self.assertEqual(Field.BELCH, 6)
+        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
+        self.assertEqual(Field.START.__doc__, 'Field offset in record')
+        self.assertEqual(Field.BLAH.__doc__, 'test blah')
+        self.assertEqual(Field.BELCH.__doc__, 'test belch')
+
+    def test_missing_value_error(self):
+        with self.assertRaisesRegex(TypeError, r"_value_ not set in __new__"):
+            class Combined(str, Enum):
+                #
+                _init_ = 'value sequence'
+                _order_ = lambda m: m.sequence
+                #
+                def __new__(cls, value, *args):
+                    enum = str.__new__(cls, value)
+                    if '(' in value:
+                        fis_name, segment = value.split('(', 1)
+                        segment = segment.strip(' )')
+                    else:
+                        fis_name = value
+                        segment = None
+                    enum.fis_name = fis_name
+                    enum.segment = segment
+                    return enum
+                #
+                def __repr__(self):
+                    return "<%s.%s>" % (self.__class__.__name__, self._name_)
+                #
+                key_type      = 'An$(1,2)', 0
+                company_id    = 'An$(3,2)', 1
+                code          = 'An$(5,1)', 2
+                description   = 'Bn$',      3
+
+
+    def test_auto_and_enum(self):
+        class Foo(aenum.Flag):
+            _order_ = 'a b c'
+            a = aenum.auto()
+            b = a | aenum.auto()
+            c = 2
+
+        self.assertEqual([Foo.a, Foo.c], list(Foo))
+        self.assertEqual(Foo.a.value, 1)
+        self.assertEqual(Foo.b.value, 3)
+
+    def test_multiple_arg_auto(self):
+        class AutoName(Enum):
+            def _generate_next_value_(name, start, count, last, *args, **kwds):
+                return (name, ) + args
+        #
+        class Planet(AutoName):
+            _init_ = 'value mass radius'
+            MERCURY = auto(3.303e+23, 2.4397e6)
+            VENUS = auto(4.869e+24, 6.0518e6)
+        self.assertEqual(Planet.MERCURY.value, 'MERCURY')
+
+    def test_auto_w_multiple_arg(self):
+        class AutoName(Enum):
+            def _generate_next_value_(name, start, count, last, *args, **kwds):
+                return (name, ) + args
+        #
+        class Planet(AutoName):
+            _init_ = 'value mass radius'
+            MERCURY = auto(), 3.303e+23, 2.4397e6  # doesn't work
+            VENUS = auto(), 4.869e+24, 6.0518e6  # doesn't work
+        self.assertEqual(Planet.MERCURY.value, 'MERCURY')
+
+    def test_auto_gnv_and_init(self):
+        class AutoName(Enum):
+            def _generate_next_value_(name, start, count, last, *args, **kwds):
+                return (name, ) + args
+        #
+        class Planet(AutoName):
+            _init_ = 'value mass radius'
+            MERCURY = 3.303e+23, 2.4397e6  # doesn't work
+            VENUS = 4.869e+24, 6.0518e6  # doesn't work
+        self.assertEqual(Planet.MERCURY.value, 'MERCURY')
+
+    # def test_AutoNumberEnum_and_property(self):
+    #     class Color(aenum.AutoNumberEnum):
+    #         red = ()
+    #         green = ()
+    #         blue = ()
+    #         @property
+    #         def cap_name(self):
+    #             return self.name.title()
+    #     self.assertEqual(Color.blue.cap_name, 'Blue')
+
+    # def test_AutoNumberEnum(self):
+    #     class Color(aenum.AutoNumberEnum):
+    #         _order_ = 'red green blue'
+    #         red = ()
+    #         green = ()
+    #         blue = ()
+    #     self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
+    #     self.assertEqual(Color.red.value, 1)
+    #     self.assertEqual(Color.green.value, 2)
+    #     self.assertEqual(Color.blue.value, 3)
+
+    def test_MultiValue_with_init_wo_value(self):
+        class Color(Enum):
+            _init_ = 'color r g b'
+            _order_ = 'red green blue'
+            _settings_ = MultiValue
+            red = 'red', 1, 2, 3
+            green = 'green', 4, 5, 6
+            blue = 'blue', 7, 8, 9
+        self.assertEqual(Color.red.value, 'red')
+        self.assertEqual(Color.red.color, 'red')
+        self.assertEqual(Color.red.r, 1)
+        self.assertEqual(Color.red.g, 2)
+        self.assertEqual(Color.red.b, 3)
+        self.assertEqual(Color.green.value, 'green')
+        self.assertEqual(Color.green.color, 'green')
+        self.assertEqual(Color.green.r, 4)
+        self.assertEqual(Color.green.g, 5)
+        self.assertEqual(Color.green.b, 6)
+        self.assertEqual(Color.blue.value, 'blue')
+        self.assertEqual(Color.blue.color, 'blue')
+        self.assertEqual(Color.blue.r, 7)
+        self.assertEqual(Color.blue.g, 8)
+        self.assertEqual(Color.blue.b, 9)
+        self.assertIs(Color('red'), Color.red)
+        self.assertIs(Color(1), Color.red)
+        self.assertIs(Color(2), Color.red)
+        self.assertIs(Color(3), Color.red)
+        self.assertIs(Color('green'), Color.green)
+        self.assertIs(Color(4), Color.green)
+        self.assertIs(Color(5), Color.green)
+        self.assertIs(Color(6), Color.green)
+        self.assertIs(Color('blue'), Color.blue)
+        self.assertIs(Color(7), Color.blue)
+        self.assertIs(Color(8), Color.blue)
+        self.assertIs(Color(9), Color.blue)
+
+    def test_MultiValue_with_init_w_value(self):
+        class Color(Enum):
+            _init_ = 'value r g b'
+            _order_ = 'red green blue'
+            _settings_ = MultiValue
+            red = 'red', 1, 2, 3
+            green = 'green', 4, 5, 6
+            blue = 'blue', 7, 8, 9
+        self.assertEqual(Color.red.value, 'red')
+        self.assertEqual(Color.red.r, 1)
+        self.assertEqual(Color.red.g, 2)
+        self.assertEqual(Color.red.b, 3)
+        self.assertEqual(Color.green.value, 'green')
+        self.assertEqual(Color.green.r, 4)
+        self.assertEqual(Color.green.g, 5)
+        self.assertEqual(Color.green.b, 6)
+        self.assertEqual(Color.blue.value, 'blue')
+        self.assertEqual(Color.blue.r, 7)
+        self.assertEqual(Color.blue.g, 8)
+        self.assertEqual(Color.blue.b, 9)
+        self.assertIs(Color('red'), Color.red)
+        self.assertIs(Color(1), Color.red)
+        self.assertIs(Color(2), Color.red)
+        self.assertIs(Color(3), Color.red)
+        self.assertIs(Color('green'), Color.green)
+        self.assertIs(Color(4), Color.green)
+        self.assertIs(Color(5), Color.green)
+        self.assertIs(Color(6), Color.green)
+        self.assertIs(Color('blue'), Color.blue)
+        self.assertIs(Color(7), Color.blue)
+        self.assertIs(Color(8), Color.blue)
+        self.assertIs(Color(9), Color.blue)
+
+    def test_MultiValue_with_init_wo_value_w_autonumber(self):
+        class Color(AutoNumberEnum):
+            _init_ = 'color r g b'
+            _order_ = 'red green blue'
+            _settings_ = MultiValue
+            red = 'red', 10, 20, 30
+            green = 'green', 40, 50, 60
+            blue = 'blue', 70, 80, 90
+        self.assertEqual(Color.red.value, 1)
+        self.assertEqual(Color.red.color, 'red')
+        self.assertEqual(Color.red.r, 10)
+        self.assertEqual(Color.red.g, 20)
+        self.assertEqual(Color.red.b, 30)
+        self.assertEqual(Color.green.value, 2)
+        self.assertEqual(Color.green.color, 'green')
+        self.assertEqual(Color.green.r, 40)
+        self.assertEqual(Color.green.g, 50)
+        self.assertEqual(Color.green.b, 60)
+        self.assertEqual(Color.blue.value, 3)
+        self.assertEqual(Color.blue.color, 'blue')
+        self.assertEqual(Color.blue.r, 70)
+        self.assertEqual(Color.blue.g, 80)
+        self.assertEqual(Color.blue.b, 90)
+        self.assertIs(Color(1), Color.red)
+        self.assertIs(Color('red'), Color.red)
+        self.assertIs(Color(10), Color.red)
+        self.assertIs(Color(20), Color.red)
+        self.assertIs(Color(30), Color.red)
+        self.assertIs(Color(2), Color.green)
+        self.assertIs(Color('green'), Color.green)
+        self.assertIs(Color(40), Color.green)
+        self.assertIs(Color(50), Color.green)
+        self.assertIs(Color(60), Color.green)
+        self.assertIs(Color(3), Color.blue)
+        self.assertIs(Color('blue'), Color.blue)
+        self.assertIs(Color(70), Color.blue)
+        self.assertIs(Color(80), Color.blue)
+        self.assertIs(Color(90), Color.blue)
+
+    def test_multivalue_and_autonumber_wo_init_wo_value(self):
+        class Day(Enum):
+            _settings_ = MultiValue, AddValue
+            _order_ = 'one two three'
+            _start_ = 7
+            one = "21", "one"
+            two = "22", "two"
+            three = "23", "three"
+        self.assertEqual(Day.one.value, 7)
+        self.assertEqual(Day.two.value, 8)
+        self.assertEqual(Day.three.value, 9)
+        self.assertEqual(Day('21'), Day.one)
+        self.assertEqual(Day('one'), Day.one)
+
+    def test_multivalue_and_autonumber_wo_init_w_some_value(self):
+        class Color(Enum):
+            _settings_ = MultiValue, Unique
+            _order_ = 'BLACK RED BLUE YELLOW GREEN MAGENTA'
+            _init_ = "value description"
+            BLACK = -1, "Text0"
+            RED = -50, "Text1"
+            BLUE = auto(), "Text2"
+            YELLOW = auto(), "Text3"
+            GREEN = -70, "Text4"
+            MAGENTA = auto(), "Text5"
+        self.assertEqual(Color.BLACK.value, -1)
+        self.assertEqual(Color.RED.value, -50)
+        self.assertEqual(Color.BLUE.value, -49)
+        self.assertEqual(Color.YELLOW.value, -48)
+        self.assertEqual(Color.GREEN.value, -70)
+        self.assertEqual(Color.MAGENTA.value, -69)
+        self.assertEqual(Color(-1), Color.BLACK)
+        self.assertEqual(Color('Text2'), Color.BLUE)
+
+    def test_combine_new_settings_with_old_settings(self):
+        class Auto(Enum):
+            _settings_ = Unique
+        with self.assertRaises(ValueError):
+            class AutoUnique(Auto):
+                BLAH = auto()
+                BLUH = auto()
+                ICK = 1
+
+    def test_timedelta(self):
+        class Period(timedelta, Enum):
+            '''
+            different lengths of time
+            '''
+            _init_ = 'value period'
+            _settings_ = NoAlias
+            _ignore_ = 'Period i'
+            Period = vars()
+            for i in range(31):
+                Period['day_%d' % i] = i, 'day'
+            for i in range(15):
+                Period['week_%d' % i] = i*7, 'week'
+            for i in range(12):
+                Period['month_%d' % i] = i*30, 'month'
+            OneDay = day_1
+            OneWeek = week_1
+        self.assertFalse(hasattr(Period, '_ignore_'))
+        self.assertFalse(hasattr(Period, 'Period'))
+        self.assertFalse(hasattr(Period, 'i'))
+        self.assertTrue(isinstance(Period.day_1, timedelta))
+
+    def test_skip(self):
+        class enumA(Enum):
+            @skip
+            class enumB(Enum):
+                elementA = 'a'
+                elementB = 'b'
+            @skip
+            class enumC(Enum):
+                elementC = 'c'
+                elementD = 'd'
+        self.assertIs(enumA.enumB, enumA.__dict__['enumB'])
+
+    def test_nonmember(self):
+        class enumA(Enum):
+            @nonmember
+            class enumB(Enum):
+                elementA = 'a'
+                elementB = 'b'
+            @nonmember
+            class enumC(Enum):
+                elementC = 'c'
+                elementD = 'd'
+        self.assertIs(enumA.enumB, enumA.__dict__['enumB'])
+
+    def test_member_with_external_functions(self):
+        class Func(Enum):
+            _order_ = 'an_int a_str'
+            an_int = member(int)
+            a_str = member(str)
+            @classproperty
+            def types(cls):
+                return [m.value for m in list(cls)]
+            def __repr__(self):
+                return "<%s.%s>" % (self.__class__.__name__, self.name, )
+            def __call__(self, *args, **kwds):
+                return self.value(*args, **kwds)
+        #
+        self.assertEqual([Func.an_int, Func.a_str], list(Func))
+        self.assertEqual([int, str], Func.types)
+        self.assertEqual(Func.an_int(7), 7)
+        self.assertEqual(Func.a_str('BlahBlah'), 'BlahBlah')
+
+    def test_member_with_internal_functions(self):
+        class Func(Enum):
+            _order_ = 'haha hehe'
+            @member
+            def haha():
+                return 'haha'
+            @member
+            def hehe(name):
+                return 'hehe -- what a name!  %s!' % name
+            @classproperty
+            def types(cls):
+                return [m.value for m in list(cls)]
+            def __repr__(self):
+                return "<%s.%s>" % (self.__class__.__name__, self.name, )
+            def __call__(self, *args, **kwds):
+                return self.value(*args, **kwds)
+        #
+        self.assertEqual([Func.haha, Func.hehe], list(Func))
+        self.assertEqual([Func.haha.value, Func.hehe.value], Func.types)
+        self.assertEqual(Func.haha(), 'haha')
+        self.assertEqual(Func.hehe('BlahBlah'), 'hehe -- what a name!  BlahBlah!')
+
+    def test_constantness_of_constants(self):
+        class Universe(Enum):
+            PI = constant(3.141596)
+            G = constant(6.67300E-11)
+        self.assertEqual(Universe.PI, 3.141596)
+        self.assertRaisesRegex(AttributeError, r'cannot rebind constant', setattr, Universe, 'PI', 9)
+        self.assertRaisesRegex(AttributeError, r'cannot delete constant', delattr, Universe, 'PI')
+
+    def test_math_and_stuff_with_constants(self):
+        class Universe(Enum):
+            PI = constant(3.141596)
+            TAU = constant(2 * PI)
+        self.assertEqual(Universe.PI, 3.141596)
+        self.assertEqual(Universe.TAU, 2 * Universe.PI)
+
+    def test_constant_with_auto_is_updated(self):
+        class Fruit(Flag):
+            _order_ = 'apple banana lemon orange'
+            apple = auto()
+            banana = auto()
+            lemon = auto()
+            orange = auto()
+            CitrusTypes = constant(lemon | orange)
+        self.assertEqual(list(Fruit), [Fruit.apple, Fruit.banana, Fruit.lemon, Fruit.orange])
+        self.assertEqual(list(Fruit.CitrusTypes), [Fruit.lemon, Fruit.orange])
+        self.assertTrue(Fruit.orange in Fruit.CitrusTypes)
+
+
+    def test_order_as_function(self):
+        # first with _init_
+        class TestSequence(Enum):
+            _init_ = 'value, sequence'
+            _order_ = lambda member: member.sequence
+            item_id                  = 'An$(1,6)',      0     # Item Code
+            company_id               = 'An$(7,2)',      1     # Company Code
+            warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
+            company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
+            key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
+            available                = 'Zn$(1,1)',      5     # Available?
+            contract_item            = 'Bn(2,1)',       6     # Contract Item?
+            sales_category           = 'Fn',            7     # Sales Category
+            gl_category              = 'Rn$(5,1)',      8     # G/L Category
+            warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
+            inv_units                = 'Qn$(7,2)',     10     # Inv Units
+        for i, member in enumerate(TestSequence):
+            self.assertEqual(i, member.sequence)
+        ts = TestSequence
+        self.assertEqual(ts.item_id.name, 'item_id')
+        self.assertEqual(ts.item_id.value, 'An$(1,6)')
+        self.assertEqual(ts.item_id.sequence, 0)
+        self.assertEqual(ts.company_id.name, 'company_id')
+        self.assertEqual(ts.company_id.value, 'An$(7,2)')
+        self.assertEqual(ts.company_id.sequence, 1)
+        self.assertEqual(ts.warehouse_no.name, 'warehouse_no')
+        self.assertEqual(ts.warehouse_no.value, 'An$(9,4)')
+        self.assertEqual(ts.warehouse_no.sequence, 2)
+        self.assertEqual(ts.company.name, 'company')
+        self.assertEqual(ts.company.value, 'Hn$(13,6)')
+        self.assertEqual(ts.company.sequence, 3)
+        self.assertEqual(ts.key_type.name, 'key_type')
+        self.assertEqual(ts.key_type.value, 'Cn$(19,3)')
+        self.assertEqual(ts.key_type.sequence, 4)
+        self.assertEqual(ts.available.name, 'available')
+        self.assertEqual(ts.available.value, 'Zn$(1,1)')
+        self.assertEqual(ts.available.sequence, 5)
+        self.assertEqual(ts.contract_item.name, 'contract_item')
+        self.assertEqual(ts.contract_item.value, 'Bn(2,1)')
+        self.assertEqual(ts.contract_item.sequence, 6)
+        self.assertEqual(ts.sales_category.name, 'sales_category')
+        self.assertEqual(ts.sales_category.value, 'Fn')
+        self.assertEqual(ts.sales_category.sequence, 7)
+        self.assertEqual(ts.gl_category.name, 'gl_category')
+        self.assertEqual(ts.gl_category.value, 'Rn$(5,1)')
+        self.assertEqual(ts.gl_category.sequence, 8)
+        self.assertEqual(ts.warehouse_category.name, 'warehouse_category')
+        self.assertEqual(ts.warehouse_category.value, 'Sn$(6,1)')
+        self.assertEqual(ts.warehouse_category.sequence, 9)
+        self.assertEqual(ts.inv_units.name, 'inv_units')
+        self.assertEqual(ts.inv_units.value, 'Qn$(7,2)')
+        self.assertEqual(ts.inv_units.sequence, 10)
+        # and then without
+        class TestSequence(Enum):
+            _order_ = lambda member: member.value[1]
+            item_id                  = 'An$(1,6)',      0     # Item Code
+            company_id               = 'An$(7,2)',      1     # Company Code
+            warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
+            company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
+            key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
+            available                = 'Zn$(1,1)',      5     # Available?
+            contract_item            = 'Bn(2,1)',       6     # Contract Item?
+            sales_category           = 'Fn',            7     # Sales Category
+            gl_category              = 'Rn$(5,1)',      8     # G/L Category
+            warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
+            inv_units                = 'Qn$(7,2)',     10     # Inv Units
+        for i, member in enumerate(TestSequence):
+            self.assertEqual(i, member.value[1])
+        ts = TestSequence
+        self.assertEqual(ts.item_id.name, 'item_id')
+        self.assertEqual(ts.item_id.value, ('An$(1,6)', 0))
+        self.assertEqual(ts.company_id.name, 'company_id')
+        self.assertEqual(ts.company_id.value, ('An$(7,2)', 1))
+        self.assertEqual(ts.warehouse_no.name, 'warehouse_no')
+        self.assertEqual(ts.warehouse_no.value, ('An$(9,4)', 2))
+        self.assertEqual(ts.company.name, 'company')
+        self.assertEqual(ts.company.value, ('Hn$(13,6)', 3))
+        self.assertEqual(ts.key_type.name, 'key_type')
+        self.assertEqual(ts.key_type.value, ('Cn$(19,3)', 4))
+        self.assertEqual(ts.available.name, 'available')
+        self.assertEqual(ts.available.value, ('Zn$(1,1)', 5))
+        self.assertEqual(ts.contract_item.name, 'contract_item')
+        self.assertEqual(ts.contract_item.value, ('Bn(2,1)', 6))
+        self.assertEqual(ts.sales_category.name, 'sales_category')
+        self.assertEqual(ts.sales_category.value, ('Fn', 7))
+        self.assertEqual(ts.gl_category.name, 'gl_category')
+        self.assertEqual(ts.gl_category.value, ('Rn$(5,1)', 8))
+        self.assertEqual(ts.warehouse_category.name, 'warehouse_category')
+        self.assertEqual(ts.warehouse_category.value, ('Sn$(6,1)', 9))
+        self.assertEqual(ts.inv_units.name, 'inv_units')
+        self.assertEqual(ts.inv_units.value, ('Qn$(7,2)', 10))
+        # then with _init_ but without value
+        with self.assertRaises(TypeError):
+            class TestSequence(Enum):
+                _init_ = 'sequence'
+                _order_ = lambda member: member.sequence
+                item_id                  = 'An$(1,6)',      0     # Item Code
+                company_id               = 'An$(7,2)',      1     # Company Code
+                warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
+                company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
+                key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
+                available                = 'Zn$(1,1)',      5     # Available?
+                contract_item            = 'Bn(2,1)',       6     # Contract Item?
+                sales_category           = 'Fn',            7     # Sales Category
+                gl_category              = 'Rn$(5,1)',      8     # G/L Category
+                warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
+                inv_units                = 'Qn$(7,2)',     10     # Inv Units
+        # finally, out of order so Python 3 barfs
+        with self.assertRaises(TypeError):
+            class TestSequence(Enum):
+                _init_ = 'sequence'
+                _order_ = lambda member: member.sequence
+                item_id                  = 'An$(1,6)',      0     # Item Code
+                warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
+                company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
+                company_id               = 'An$(7,2)',      1     # Company Code
+                inv_units                = 'Qn$(7,2)',     10     # Inv Units
+                available                = 'Zn$(1,1)',      5     # Available?
+                contract_item            = 'Bn(2,1)',       6     # Contract Item?
+                sales_category           = 'Fn',            7     # Sales Category
+                key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
+                gl_category              = 'Rn$(5,1)',      8     # G/L Category
+                warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
+
+    def test_order_as_function_in_subclass(self):
+        #
+        class Parent(Enum):
+            _init_ = 'value sequence'
+            _order_ = lambda m: m.sequence
+        #
+        class Child(Parent):
+            item_id                  = 'An$(1,6)',      0     # Item Code
+            company_id               = 'An$(7,2)',      1     # Company Code
+            warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
+            company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
+            key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
+            available                = 'Zn$(1,1)',      5     # Available?
+            contract_item            = 'Bn(2,1)',       6     # Contract Item?
+            sales_category           = 'Fn',            7     # Sales Category
+            gl_category              = 'Rn$(5,1)',      8     # G/L Category
+            warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
+            inv_units                = 'Qn$(7,2)',     10     # Inv Units
+        #
+        for i, member in enumerate(Child):
+            self.assertEqual(i, member.sequence)
+        #
+        ts = Child
+        self.assertEqual(ts.item_id.name, 'item_id')
+        self.assertEqual(ts.item_id.value, 'An$(1,6)')
+        self.assertEqual(ts.item_id.sequence, 0)
+        self.assertEqual(ts.company_id.name, 'company_id')
+        self.assertEqual(ts.company_id.value, 'An$(7,2)')
+        self.assertEqual(ts.company_id.sequence, 1)
+        self.assertEqual(ts.warehouse_no.name, 'warehouse_no')
+        self.assertEqual(ts.warehouse_no.value, 'An$(9,4)')
+        self.assertEqual(ts.warehouse_no.sequence, 2)
+        self.assertEqual(ts.company.name, 'company')
+        self.assertEqual(ts.company.value, 'Hn$(13,6)')
+        self.assertEqual(ts.company.sequence, 3)
+        self.assertEqual(ts.key_type.name, 'key_type')
+        self.assertEqual(ts.key_type.value, 'Cn$(19,3)')
+        self.assertEqual(ts.key_type.sequence, 4)
+        self.assertEqual(ts.available.name, 'available')
+        self.assertEqual(ts.available.value, 'Zn$(1,1)')
+        self.assertEqual(ts.available.sequence, 5)
+        self.assertEqual(ts.contract_item.name, 'contract_item')
+        self.assertEqual(ts.contract_item.value, 'Bn(2,1)')
+        self.assertEqual(ts.contract_item.sequence, 6)
+        self.assertEqual(ts.sales_category.name, 'sales_category')
+        self.assertEqual(ts.sales_category.value, 'Fn')
+        self.assertEqual(ts.sales_category.sequence, 7)
+        self.assertEqual(ts.gl_category.name, 'gl_category')
+        self.assertEqual(ts.gl_category.value, 'Rn$(5,1)')
+        self.assertEqual(ts.gl_category.sequence, 8)
+        self.assertEqual(ts.warehouse_category.name, 'warehouse_category')
+        self.assertEqual(ts.warehouse_category.value, 'Sn$(6,1)')
+        self.assertEqual(ts.warehouse_category.sequence, 9)
+        self.assertEqual(ts.inv_units.name, 'inv_units')
+        self.assertEqual(ts.inv_units.value, 'Qn$(7,2)')
+        self.assertEqual(ts.inv_units.sequence, 10)
+
+        pass
+
+    def test_multiple_mixin(self):
+        class MaxMixin(object):
+            @classproperty
+            def MAX(cls):
+                max = len(cls)
+                cls.MAX = max
+                return max
+        class StrMixin(object):
+            def __str__(self):
+                return self._name_.lower()
+        class SomeEnum(Enum):
+            def behavior(self):
+                return 'booyah'
+        class AnotherEnum(Enum):
+            def behavior(self):
+                return 'nuhuh!'
+            def social(self):
+                return "what's up?"
+        class Color(MaxMixin, Enum):
+            _order_ = 'RED GREEN BLUE'
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(Color.RED.value, 1)
+        self.assertEqual(Color.GREEN.value, 2)
+        self.assertEqual(Color.BLUE.value, 3)
+        self.assertEqual(Color.MAX, 3)
+        self.assertEqual(str(Color.BLUE), 'Color.BLUE')
+        class Color(MaxMixin, StrMixin, Enum):
+            _order_ = 'RED GREEN BLUE'
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(Color.RED.value, 1)
+        self.assertEqual(Color.GREEN.value, 2)
+        self.assertEqual(Color.BLUE.value, 3)
+        self.assertEqual(Color.MAX, 3)
+        self.assertEqual(str(Color.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
+        class Color(StrMixin, MaxMixin, Enum):
+            _order_ = 'RED GREEN BLUE'
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(Color.RED.value, 1)
+        self.assertEqual(Color.GREEN.value, 2)
+        self.assertEqual(Color.BLUE.value, 3)
+        self.assertEqual(Color.MAX, 3)
+        self.assertEqual(str(Color.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
+        class CoolColor(StrMixin, SomeEnum, Enum):
+            _order_ = 'RED GREEN BLUE'
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(CoolColor.RED.value, 1)
+        self.assertEqual(CoolColor.GREEN.value, 2)
+        self.assertEqual(CoolColor.BLUE.value, 3)
+        self.assertEqual(str(CoolColor.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
+        self.assertEqual(CoolColor.RED.behavior(), 'booyah')
+        class CoolerColor(StrMixin, AnotherEnum, Enum):
+            _order_ = 'RED GREEN BLUE'
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(CoolerColor.RED.value, 1)
+        self.assertEqual(CoolerColor.GREEN.value, 2)
+        self.assertEqual(CoolerColor.BLUE.value, 3)
+        self.assertEqual(str(CoolerColor.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
+        self.assertEqual(CoolerColor.RED.behavior(), 'nuhuh!')
+        self.assertEqual(CoolerColor.RED.social(), "what's up?")
+        class CoolestColor(StrMixin, SomeEnum, AnotherEnum):
+            _order_ = 'RED GREEN BLUE'
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(CoolestColor.RED.value, 1)
+        self.assertEqual(CoolestColor.GREEN.value, 2)
+        self.assertEqual(CoolestColor.BLUE.value, 3)
+        self.assertEqual(str(CoolestColor.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
+        self.assertEqual(CoolestColor.RED.behavior(), 'booyah')
+        self.assertEqual(CoolestColor.RED.social(), "what's up?")
+        class ConfusedColor(StrMixin, AnotherEnum, SomeEnum):
+            _order_ = 'RED GREEN BLUE'
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(ConfusedColor.RED.value, 1)
+        self.assertEqual(ConfusedColor.GREEN.value, 2)
+        self.assertEqual(ConfusedColor.BLUE.value, 3)
+        self.assertEqual(str(ConfusedColor.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
+        self.assertEqual(ConfusedColor.RED.behavior(), 'nuhuh!')
+        self.assertEqual(ConfusedColor.RED.social(), "what's up?")
+        class ReformedColor(StrMixin, IntEnum, SomeEnum, AnotherEnum):
+            _order_ = 'RED GREEN BLUE'
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(ReformedColor.RED.value, 1)
+        self.assertEqual(ReformedColor.GREEN.value, 2)
+        self.assertEqual(ReformedColor.BLUE.value, 3)
+        self.assertEqual(str(ReformedColor.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
+        self.assertEqual(ReformedColor.RED.behavior(), 'booyah')
+        self.assertEqual(ConfusedColor.RED.social(), "what's up?")
+        self.assertTrue(issubclass(ReformedColor, int))
+
+    def test_multiple_inherited_mixin(self):
+        @unique
+        class Decision1(StrEnum):
+            REVERT = "REVERT"
+            REVERT_ALL = "REVERT_ALL"
+            RETRY = "RETRY"
+        class MyEnum(StrEnum):
+            pass
+        @unique
+        class Decision2(MyEnum):
+            REVERT = "REVERT"
+            REVERT_ALL = "REVERT_ALL"
+            RETRY = "RETRY"
+
+    def test_value_auto_assign(self):
+        class Some(Enum):
+            def __new__(cls, val):
+                return object.__new__(cls)
+            x = 1
+            y = 2
+        self.assertEqual(Some.x.value, 1)
+        self.assertEqual(Some.y.value, 2)
+
+    def test_enum_of_types(self):
+        """Support using Enum to refer to types deliberately."""
+        class MyTypes(Enum):
+            i = int
+            f = float
+            s = str
+        self.assertEqual(MyTypes.i.value, int)
+        self.assertEqual(MyTypes.f.value, float)
+        self.assertEqual(MyTypes.s.value, str)
+        class Foo:
+            pass
+        class Bar:
+            pass
+        class MyTypes2(Enum):
+            a = Foo
+            b = Bar
+        self.assertEqual(MyTypes2.a.value, Foo)
+        self.assertEqual(MyTypes2.b.value, Bar)
+        class SpamEnumNotInner:
+            pass
+        class SpamEnum(Enum):
+            spam = SpamEnumNotInner
+        self.assertEqual(SpamEnum.spam.value, SpamEnumNotInner)
+
+    if PY2:
+        def test_nested_classes_in_enum_do_become_members(self):
+            # manually set __qualname__ to remove testing framework noise
+            class Outer(Enum):
+                _order_ = 'a b Inner'
+                __qualname__ = "Outer"
+                a = 1
+                b = 2
+                class Inner(Enum):
+                    __qualname__ = "Outer.Inner"
+                    foo = 10
+                    bar = 11
+            self.assertTrue(isinstance(Outer.Inner, Outer))
+            self.assertEqual(Outer.a.value, 1)
+            self.assertEqual(Outer.Inner.value.foo.value, 10)
+            self.assertEqual(
+                list(Outer.Inner.value),
+                [Outer.Inner.value.foo, Outer.Inner.value.bar],
+                )
+            self.assertEqual(
+                list(Outer),
+                [Outer.a, Outer.b, Outer.Inner],
+                )
+
+        def test_really_nested_classes_in_enum_do_become_members(self):
+            class Outer(Enum):
+                _order_ = 'a b Inner'
+                a = 1
+                b = 2
+                class Inner(Enum):
+                    foo = 10
+                    bar = 11
+            self.assertTrue(isinstance(Outer.Inner, Outer))
+            self.assertEqual(Outer.a.value, 1)
+            self.assertEqual(Outer.Inner.value.foo.value, 10)
+            self.assertEqual(
+                list(Outer.Inner.value),
+                [Outer.Inner.value.foo, Outer.Inner.value.bar],
+                )
+            self.assertEqual(
+                list(Outer),
+                [Outer.a, Outer.b, Outer.Inner],
+                )
+
+    def test_nested_classes_in_enum_are_skipped_with_skip(self):
+        """Support locally-defined nested classes using @skip"""
+        # manually set __qualname__ to remove testing framework noise
+        class Outer(Enum):
+            __qualname__ = "Outer"
+            a = 1
+            b = 2
+            @skip
+            class Inner(Enum):
+                __qualname__ = "Outer.Inner"
+                foo = 10
+                bar = 11
+        self.assertTrue(isinstance(Outer.Inner, type))
+        self.assertEqual(Outer.a.value, 1)
+        self.assertEqual(Outer.Inner.foo.value, 10)
+        self.assertEqual(
+            list(Outer.Inner),
+            [Outer.Inner.foo, Outer.Inner.bar],
+            )
+        self.assertEqual(
+            list(Outer),
+            [Outer.a, Outer.b],
+            )
+
+    def test_really_nested_classes_in_enum_are_skipped_with_skip(self):
+        """Support locally-defined nested classes using @skip"""
+        class Outer(Enum):
+            a = 1
+            b = 2
+            @skip
+            class Inner(Enum):
+                foo = 10
+                bar = 11
+        self.assertTrue(isinstance(Outer.Inner, type))
+        self.assertEqual(Outer.a.value, 1)
+        self.assertEqual(Outer.Inner.foo.value, 10)
+        self.assertEqual(
+            list(Outer.Inner),
+            [Outer.Inner.foo, Outer.Inner.bar],
+            )
+        self.assertEqual(
+            list(Outer),
+            [Outer.a, Outer.b],
+            )
+
+    def test_enum_call_without_arg(self):
+        class Color(Enum):
+            black = 0
+            red = 1
+            green = 2
+            blue = 3
+            #
+            @classmethod
+            def _missing_value_(cls, value):
+                if value is no_arg:
+                    return cls.black
+        self.assertTrue(Color.red is Color(1))
+        self.assertTrue(Color.black is Color())
+
+    def test_init_subclass(self):
+        class MyEnum(Enum):
+            def __init_subclass__(cls, **kwds):
+                super(MyEnum, cls).__init_subclass__(**kwds)
+                self.assertFalse(cls.__dict__.get('_test', False))
+                cls._test1 = 'MyEnum'
+        #
+        class TheirEnum(MyEnum):
+            def __init_subclass__(cls, **kwds):
+                super(TheirEnum, cls).__init_subclass__(**kwds)
+                cls._test2 = 'TheirEnum'
+        class WhoseEnum(TheirEnum):
+            def __init_subclass__(cls, **kwds):
+                pass
+        class NoEnum(WhoseEnum):
+            ONE = 1
+        self.assertEqual(TheirEnum.__dict__['_test1'], 'MyEnum')
+        self.assertEqual(WhoseEnum.__dict__['_test1'], 'MyEnum')
+        self.assertEqual(WhoseEnum.__dict__['_test2'], 'TheirEnum')
+        self.assertFalse(NoEnum.__dict__.get('_test1', False))
+        self.assertFalse(NoEnum.__dict__.get('_test2', False))
+        #
+        class OurEnum(MyEnum):
+            def __init_subclass__(cls, **kwds):
+                cls._test2 = 'OurEnum'
+        class WhereEnum(OurEnum):
+            def __init_subclass__(cls, **kwds):
+                pass
+        class NeverEnum(WhereEnum):
+            ONE = 'one'
+        self.assertEqual(OurEnum.__dict__['_test1'], 'MyEnum')
+        self.assertFalse(WhereEnum.__dict__.get('_test1', False))
+        self.assertEqual(WhereEnum.__dict__['_test2'], 'OurEnum')
+        self.assertFalse(NeverEnum.__dict__.get('_test1', False))
+        self.assertFalse(NeverEnum.__dict__.get('_test2', False))
+
+
+class TestStrEnum(TestCase):
+
+    def test_set_name(self):
+        class Descriptor(object):
+            name = None
+            def __get__(self, instance, owner_class=None):
+                if instance is None:
+                    return self
+                else:
+                    return instance.__dict__[self.name]
+            def __set__(self, instance, value):
+                instance.__dict__[self.name] = value
+            def __set_name__(self, owner, name):
+                self.name = name
+        #
+        class AnEnum(Enum):
+            ONE = 'one'
+            two = Descriptor()
+        #
+        self.assertEqual(list(AnEnum), [AnEnum.ONE])
+        self.assertEqual(AnEnum.two.name, 'two')
+        AnEnum.ONE.two = 'three'
+        self.assertEqual(AnEnum.ONE.two, 'three')
+        self.assertEqual(AnEnum.ONE.__dict__['two'], 'three')
+
+    def test_private_names(self):
+        class Private(Enum):
+            __corporal = 'Radar'
+            __major_ = 'Hoolihan'
+        self.assertEqual(len(Private), 0)
+        self.assertEqual(Private._Private__corporal, 'Radar')
+        self.assertFalse(isinstance(Private._Private__corporal, Enum))
+        self.assertEqual(Private._Private__major_, 'Hoolihan')
+        self.assertFalse(isinstance(Private._Private__major_, Enum))
+
+    def test_strenum_inherited_methods(self):
+        class phy(StrEnum):
+            pi = 'Pi'
+            tau = 'Tau'
+        self.assertTrue(phy.pi < phy.tau)
+        self.assertEqual(phy.pi.upper(), 'PI')
+        self.assertEqual(phy.tau.count('a'), 1)
+
+    def test_strict_strenum(self):
+        for uhoh in (object, object(), [], Enum, 9):
+            with self.assertRaisesRegex(TypeError, r'values must be str'):
+                class Huh(StrEnum):
+                    huh = uhoh
+        class Either(StrEnum):
+            _order_ = 'this that Those lower upper'
+            this = auto()
+            that = 'That'
+            Those = auto()
+            lower = 'lower'
+            upper = 'UPPER'
+        self.assertEqual([m.value for m in Either], ['this', 'That', 'those', 'lower', 'UPPER'])
+        #
+        with self.assertRaisesRegex(ValueError, r' is not lower-case'):
+            class Huh(LowerStrEnum):
+                huh = 'What'
+        #
+        class Lower(LowerStrEnum):
+            _order_ = 'this that Those lower upper'
+            this = auto()
+            that = 'that'
+            Those = auto()
+            lower = 'lower'
+            upper = 'upper'
+        self.assertEqual([m.value for m in Lower], ['this', 'that', 'those', 'lower', 'upper'])
+        #
+        with self.assertRaisesRegex(ValueError, r' is not upper-case'):
+            class Huh(UpperStrEnum):
+                huh = 'What'
+        #
+        class Upper(UpperStrEnum):
+            _order_ = 'this that Those lower upper'
+            this = auto()
+            that = 'THAT'
+            Those = auto()
+            lower = 'LOWER'
+            upper = 'UPPER'
+        self.assertEqual([m.value for m in Upper], ['THIS', 'THAT', 'THOSE', 'LOWER', 'UPPER'])
+
+    def test_init_subclass(self):
+        class MyEnum(StrEnum):
+            def __init_subclass__(cls, **kwds):
+                super(MyEnum, cls).__init_subclass__(**kwds)
+                self.assertFalse(cls.__dict__.get('_test', False))
+                cls._test1 = 'MyEnum'
+        #
+        class TheirEnum(MyEnum):
+            def __init_subclass__(cls, **kwds):
+                super(TheirEnum, cls).__init_subclass__(**kwds)
+                cls._test2 = 'TheirEnum'
+        class WhoseEnum(TheirEnum):
+            def __init_subclass__(cls, **kwds):
+                pass
+        class NoEnum(WhoseEnum):
+            ONE = 'one'
+        self.assertEqual(TheirEnum.__dict__['_test1'], 'MyEnum')
+        self.assertEqual(WhoseEnum.__dict__['_test1'], 'MyEnum')
+        self.assertEqual(WhoseEnum.__dict__['_test2'], 'TheirEnum')
+        self.assertFalse(NoEnum.__dict__.get('_test1', False))
+        self.assertFalse(NoEnum.__dict__.get('_test2', False))
+        #
+        class OurEnum(MyEnum):
+            def __init_subclass__(cls, **kwds):
+                cls._test2 = 'OurEnum'
+        class WhereEnum(OurEnum):
+            def __init_subclass__(cls, **kwds):
+                pass
+        class NeverEnum(WhereEnum):
+            ONE = 'one'
+        self.assertEqual(OurEnum.__dict__['_test1'], 'MyEnum')
+        self.assertFalse(WhereEnum.__dict__.get('_test1', False))
+        self.assertEqual(WhereEnum.__dict__['_test2'], 'OurEnum')
+        self.assertFalse(NeverEnum.__dict__.get('_test1', False))
+        self.assertFalse(NeverEnum.__dict__.get('_test2', False))
+
+
+class TestFlag(TestCase):
+    """Tests of the Flags."""
+
+    def setUp(self):
+        class Perm(Flag):
+            _order_ = 'R W X'
+            R, W, X = 4, 2, 1
+        self.Perm = Perm
+        #
+        class Color(Flag):
+            BLACK = 0
+            RED = 1
+            ROJO = 1
+            GREEN = 2
+            BLUE = 4
+            PURPLE = RED|BLUE
+            WHITE = RED|GREEN|BLUE
+            BLANCO = RED|GREEN|BLUE
+        self.Color = Color
+        #
+        class Fun(Flag):
+            _order_ = 'ONE TWO FOUR EIGHT'
+            ONE = auto()
+            TWO = auto()
+            THREE = ONE | TWO
+            FOUR = auto()
+            FIVE = FOUR | ONE
+            SIX = FOUR | TWO
+            SEVEN = FOUR | TWO | ONE
+            EIGHT = auto()
+        self.Fun = Fun
+        #
+        class TermColor(str, Flag):
+            def __new__(cls, value, code):
+                str_value = '\x1b[%sm' % code
+                obj = str.__new__(cls, str_value)
+                obj._value_ = value
+                obj.code = code
+                return obj
+            #
+            @classmethod
+            def _create_pseudo_member_values_(cls, members, *values):
+                code = ';'.join(m.code for m in members)
+                return values + (code, )
+            #
+            AllReset = '0'           # ESC [ 0 m       # reset all (colors and brightness)
+            Bright = '1'          # ESC [ 1 m       # bright
+            Dim = '2'             # ESC [ 2 m       # dim (looks same as normal brightness)
+            Underline = '4'
+            Normal = '22'         # ESC [ 22 m      # normal brightness
+                                #
+                                # # FOREGROUND - 30s  BACKGROUND - 40s:
+            FG_Black = '30'           # ESC [ 30 m      # black
+            FG_Red = '31'             # ESC [ 31 m      # red
+            FG_Green = '32'           # ESC [ 32 m      # green
+            FG_Yellow = '33'          # ESC [ 33 m      # yellow
+            FG_Blue = '34'            # ESC [ 34 m      # blue
+            FG_Magenta = '35'         # ESC [ 35 m      # magenta
+            FG_Cyan = '36'            # ESC [ 36 m      # cyan
+            FG_White = '37'           # ESC [ 37 m      # white
+            FG_Reset = '39'           # ESC [ 39 m      # reset
+                                    #
+            BG_Black = '40'           # ESC [ 30 m      # black
+            BG_Red = '41'             # ESC [ 31 m      # red
+            BG_Green = '42'           # ESC [ 32 m      # green
+            BG_Yellow = '43'          # ESC [ 33 m      # yellow
+            BG_Blue = '44'            # ESC [ 34 m      # blue
+            BG_Magenta = '45'         # ESC [ 35 m      # magenta
+            BG_Cyan = '46'            # ESC [ 36 m      # cyan
+            BG_White = '47'           # ESC [ 37 m      # white
+            BG_Reset = '49'           # ESC [ 39 m      # reset
+            #
+            __str__ = str.__str__
+            #
+            def __repr__(self):
+                if self._name_ is not None:
+                    return '<%s.%s>' % (self.__class__.__name__, self._name_)
+                else:
+                    return '<%s: %s>' % (self.__class__.__name__, '|'.join([m.name for m in Flag.__iter__(self)]))
+            #
+            def __enter__(self):
+                print(self.AllReset, end='', verbose=0)
+                return self
+            #
+            def __exit__(self, *args):
+                print(self.AllReset, end='', verbose=0)
+        self.TermColor = TermColor
+        #
+        class Open(Flag):
+            RO = 0
+            WO = 1
+            RW = 2
+            AC = 3
+            CE = 1<<19
+        self.Open = Open
+
+    def test_set_name(self):
+        class Descriptor(object):
+            name = None
+            def __get__(self, instance, owner_class=None):
+                if instance is None:
+                    return self
+                else:
+                    return instance.__dict__[self.name]
+            def __set__(self, instance, value):
+                instance.__dict__[self.name] = value
+            def __set_name__(self, owner, name):
+                self.name = name
+        #
+        class AnEnum(Enum):
+            ONE = 1
+            two = Descriptor()
+        #
+        self.assertEqual(list(AnEnum), [AnEnum.ONE])
+        self.assertEqual(AnEnum.two.name, 'two')
+        AnEnum.ONE.two = 'three'
+        self.assertEqual(AnEnum.ONE.two, 'three')
+        self.assertEqual(AnEnum.ONE.__dict__['two'], 'three')
+
+    def test_new_with_keywords(self):
+        class Huh(IntFlag):
+            __order__ = 'PLAIN BOLD_ITALIC HIGHLIGHT'
+            def __new__(cls, docstring, open=None, close=None):
+                if cls.__members__:
+                    value = 2 ** (len(cls.__members__)-1)
+                else:
+                    value = 0
+                member = int.__new__(cls, value)
+                if open and close is None:
+                    close = open
+                member.open = open
+                member.close = close
+                member.__doc__ = docstring
+                member._value_ = value
+                return member
+            PLAIN           = 'normal'
+            BOLD_ITALIC     = '***really super important***', '***'
+            HIGHLIGHT       = 'please ==take notice==', '==', '=='
+        p = Huh.PLAIN
+        self.assertTrue(type(p) is Huh, type(p))
+        self.assertEqual(
+                (p.value, p.__doc__, p.open, p.close),
+                (0, 'normal', None, None),
+                )
+        bi = Huh.BOLD_ITALIC
+        self.assertEqual(
+                (bi.value, bi.__doc__, bi.open, bi.close),
+                (1, '***really super important***', '***', '***'),
+                )
+        h = Huh.HIGHLIGHT
+        self.assertEqual(
+                (h.value, h.__doc__, h.open, h.close),
+                (2, 'please ==take notice==', '==', '=='),
+                )
+
+    def test_private_names(self):
+        class Private(Enum):
+            __corporal = 'Radar'
+            __major_ = 'Hoolihan'
+        self.assertEqual(len(Private), 0)
+        self.assertEqual(Private._Private__corporal, 'Radar')
+        self.assertFalse(isinstance(Private._Private__corporal, Enum))
+        self.assertEqual(Private._Private__major_, 'Hoolihan')
+        self.assertFalse(isinstance(Private._Private__major_, Enum))
+
+    def test_auto_alias(self):
+        Fun = self.Fun
+        self.assertEqual(
+                list(Fun),
+                [Fun.ONE, Fun.TWO, Fun.FOUR, Fun.EIGHT],
+                )
+        self.assertEqual(Fun.THREE._value_, 3)
+        self.assertEqual(repr(Fun.SEVEN), '<Fun.SEVEN: 7>')
+        self.assertEqual(list(Fun.SEVEN), [Fun.ONE, Fun.TWO, Fun.FOUR])
+
+    def test_str_is_str_str(self):
+        red, white = self.TermColor.FG_Red, self.TermColor.BG_White
+        barber = red | white
+        self.assertEqual(barber, '\x1b[31;47m')
+        self.assertEqual(barber.value, red.value | white.value)
+        self.assertEqual(barber.code, ';'.join([red.code, white.code]))
+        self.assertEqual(repr(barber), '<TermColor.FG_Red|BG_White>')
+        self.assertEqual(str(barber), '\x1b[31;47m')
+
+    def test_membership(self):
+        Color = self.Color
+        Open = self.Open
+        self.assertRaises(TypeError, lambda: 'BLACK' in Color)
+        self.assertRaises(TypeError, lambda: 'RO' in Open)
+        self.assertTrue(Color.BLACK in Color)
+        self.assertTrue(Open.RO in Open)
+        self.assertFalse(Color.BLACK in Open)
+        self.assertFalse(Open.RO in Color)
+        self.assertRaises(TypeError, lambda: 0 in Color)
+        self.assertRaises(TypeError, lambda: 0 in Open)
+
+    def test_member_contains(self):
+        Color = self.Color
+        self.assertRaises(TypeError, lambda: 'test' in Color.BLUE)
+        self.assertRaises(TypeError, lambda: 2 in Color.BLUE)
+        self.assertTrue(Color.BLUE in Color.BLUE)
+        self.assertTrue(Color.BLUE in Color['RED|GREEN|BLUE'])
+
+    def test_member_length(self):
+        self.assertEqual(self.Color.__len__(self.Color.BLACK), 0)
+        self.assertEqual(self.Color.__len__(self.Color.GREEN), 1)
+        self.assertEqual(self.Color.__len__(self.Color.PURPLE), 2)
+        self.assertEqual(self.Color.__len__(self.Color.BLANCO), 3)
+
+    def test_number_reset_and_order_cleanup(self):
+        class Confused(Flag):
+            _order_ = 'ONE TWO FOUR DOS EIGHT SIXTEEN'
+            ONE = auto()
+            TWO = auto()
+            FOUR = auto()
+            DOS = 2
+            EIGHT = auto()
+            SIXTEEN = auto()
+        self.assertEqual(
+                list(Confused),
+                [Confused.ONE, Confused.TWO, Confused.FOUR, Confused.EIGHT, Confused.SIXTEEN])
+        self.assertIs(Confused.TWO, Confused.DOS)
+        self.assertEqual(Confused.DOS._value_, 2)
+        self.assertEqual(Confused.EIGHT._value_, 8)
+        self.assertEqual(Confused.SIXTEEN._value_, 16)
+
+    def test_str(self):
+        Perm = self.Perm
+        self.assertEqual(str(Perm.R), 'Perm.R')
+        self.assertEqual(str(Perm.W), 'Perm.W')
+        self.assertEqual(str(Perm.X), 'Perm.X')
+        self.assertEqual(str(Perm.R | Perm.W), 'Perm.R|W')
+        self.assertEqual(str(Perm.R | Perm.W | Perm.X), 'Perm.R|W|X')
+        self.assertEqual(str(Perm(0)), 'Perm(0)')
+        self.assertEqual(str(~Perm.R), 'Perm.W|X')
+        self.assertEqual(str(~Perm.W), 'Perm.R|X')
+        self.assertEqual(str(~Perm.X), 'Perm.R|W')
+        self.assertEqual(str(~(Perm.R | Perm.W)), 'Perm.X')
+        self.assertEqual(str(~(Perm.R | Perm.W | Perm.X)), 'Perm(0)')
+        self.assertEqual(str(Perm(-1)), 'Perm.R|W|X')
+        self.assertEqual(str(Perm(~0)), 'Perm.R|W|X')
+
+        Open = self.Open
+        self.assertEqual(str(Open.RO), 'Open.RO')
+        self.assertEqual(str(Open.WO), 'Open.WO')
+        self.assertEqual(str(Open.AC), 'Open.AC')
+        self.assertEqual(str(Open.RO | Open.CE), 'Open.CE')
+        self.assertEqual(str(Open.WO | Open.CE), 'Open.WO|CE')
+        self.assertEqual(str(~Open.RO), 'Open.WO|RW|CE')
+        self.assertEqual(str(~Open.WO), 'Open.RW|CE')
+        self.assertEqual(str(~Open.AC), 'Open.CE')
+        self.assertEqual(str(~(Open.RO | Open.CE)), 'Open.AC')
+        self.assertEqual(str(~(Open.WO | Open.CE)), 'Open.RW')
+
+    def test_repr(self):
+        Perm = self.Perm
+        self.assertEqual(repr(Perm.R), '<Perm.R: 4>')
+        self.assertEqual(repr(Perm.W), '<Perm.W: 2>')
+        self.assertEqual(repr(Perm.X), '<Perm.X: 1>')
+        self.assertEqual(repr(Perm.R | Perm.W), '<Perm.R|W: 6>')
+        self.assertEqual(repr(Perm.R | Perm.W | Perm.X), '<Perm.R|W|X: 7>')
+        self.assertEqual(repr(Perm(0)), '<Perm: 0>')
+        self.assertEqual(repr(~Perm.R), '<Perm.W|X: 3>')
+        self.assertEqual(repr(~Perm.W), '<Perm.R|X: 5>')
+        self.assertEqual(repr(~Perm.X), '<Perm.R|W: 6>')
+        self.assertEqual(repr(~(Perm.R | Perm.W)), '<Perm.X: 1>')
+        self.assertEqual(repr(~(Perm.R | Perm.W | Perm.X)), '<Perm: 0>')
+        self.assertEqual(repr(Perm(~0)), '<Perm.R|W|X: 7>')
+
+        Open = self.Open
+        self.assertEqual(repr(Open.RO), '<Open.RO: 0>')
+        self.assertEqual(repr(Open.WO), '<Open.WO: 1>')
+        self.assertEqual(repr(Open.AC), '<Open.AC: 3>')
+        self.assertEqual(repr(Open.RO | Open.CE), '<Open.CE: 524288>')
+        self.assertEqual(repr(Open.WO | Open.CE), '<Open.WO|CE: 524289>')
+        self.assertEqual(repr(~Open.RO), '<Open.WO|RW|CE: 524291>')
+        self.assertEqual(repr(~Open.WO), '<Open.RW|CE: 524290>')
+        self.assertEqual(repr(~Open.AC), '<Open.CE: 524288>')
+        self.assertEqual(repr(~(Open.RO | Open.CE)), '<Open.AC: 3>')
+        self.assertEqual(repr(~(Open.WO | Open.CE)), '<Open.RW: 2>')
+
+    def test_name_lookup(self):
+        Color = self.Color
+        self.assertTrue(Color.RED is Color['RED'])
+        self.assertTrue(Color.RED|Color.GREEN is Color['RED|GREEN'])
+        self.assertTrue(Color.PURPLE is Color['RED|BLUE'])
+
+    def test_or(self):
+        Perm = self.Perm
+        for i in Perm:
+            for j in Perm:
+                self.assertEqual((i | j), Perm(i.value | j.value))
+                self.assertEqual((i | j).value, i.value | j.value)
+                self.assertIs(type(i | j), Perm)
+        for i in Perm:
+            self.assertIs(i | i, i)
+        Open = self.Open
+        self.assertIs(Open.RO | Open.CE, Open.CE)
+
+    def test_and(self):
+        Perm = self.Perm
+        RW = Perm.R | Perm.W
+        RX = Perm.R | Perm.X
+        WX = Perm.W | Perm.X
+        RWX = Perm.R | Perm.W | Perm.X
+        values = list(Perm) + [RW, RX, WX, RWX, Perm(0)]
+        for i in values:
+            for j in values:
+                self.assertEqual((i & j).value, i.value & j.value)
+                self.assertIs(type(i & j), Perm)
+        for i in Perm:
+            self.assertIs(i & i, i)
+            self.assertIs(i & RWX, i)
+            self.assertIs(RWX & i, i)
+        Open = self.Open
+        self.assertIs(Open.RO & Open.CE, Open.RO)
+
+    def test_xor(self):
+        Perm = self.Perm
+        for i in Perm:
+            for j in Perm:
+                self.assertEqual((i ^ j).value, i.value ^ j.value)
+                self.assertIs(type(i ^ j), Perm)
+        for i in Perm:
+            self.assertIs(i ^ Perm(0), i)
+            self.assertIs(Perm(0) ^ i, i)
+        Open = self.Open
+        self.assertIs(Open.RO ^ Open.CE, Open.CE)
+        self.assertIs(Open.CE ^ Open.CE, Open.RO)
+
+    def test_invert(self):
+        Perm = self.Perm
+        RW = Perm.R | Perm.W
+        RX = Perm.R | Perm.X
+        WX = Perm.W | Perm.X
+        RWX = Perm.R | Perm.W | Perm.X
+        values = list(Perm) + [RW, RX, WX, RWX, Perm(0)]
+        for i in values:
+            self.assertIs(type(~i), Perm)
+            self.assertEqual(~~i, i)
+        for i in Perm:
+            self.assertIs(~~i, i)
+        Open = self.Open
+        self.assertIs(Open.WO & ~Open.WO, Open.RO)
+        self.assertIs((Open.WO|Open.CE) & ~Open.WO, Open.CE)
+
+    def test_bool(self):
+        Perm = self.Perm
+        for f in Perm:
+            self.assertTrue(f)
+        Open = self.Open
+        for f in Open:
+            self.assertEqual(bool(f.value), bool(f))
+
+    def test_doc_flag(self):
+        class DocFlag(Flag):
+            _init_ = 'value __doc__'
+            _start_ = 0
+            # def __new__(cls, value, doc=None):
+            #     # if doc is None and isinstance(value, basestring):
+            #     #     value, doc = doc, value
+            #     # if value is None:
+            #     #     if not len(cls):
+            #     #         value = 0
+            #     #     else:
+            #     #         value = 2 ** (len(cls) -1)
+            #     # if not isinstance(value, baseinteger):
+            #     #     raise TypeError("%r is not a valid %s value" % (value, cls.__name__))
+            #     obj = object.__new__(cls)
+            #     # if doc is None, don't mess with the value
+            #     if doc:
+            #         value = value >> 1
+            #     obj._value_ = value
+            #     obj.__doc__ = doc
+            #     return obj
+        #
+        class AddressSegment(DocFlag):
+            _order_ = 'UNKNOWN PO PO_TYPE NUMBER PREORD NAME STREET POSTORD SECONDARY_TYPE SECONDARY_NUMBER AND'
+            UNKNOWN = "unable to determine address element type"
+            PO = "post office delivery"
+            PO_TYPE = "box or drawer"
+            NUMBER = "main unit designator"
+            PREORD = "N S E W etc"
+            NAME = "street name"
+            STREET = "st ave blvd etc"
+            POSTORD = "N S E W etc"
+            SECONDARY_TYPE = "apt bldg floor etc"
+            SECONDARY_NUMBER = "secondary unit designator"
+            AND = "& indicates a corner address"
+        AS = AddressSegment
+        self.assertEqual(AS.NAME._value_, 16)
+        self.assertEqual(AS.STREET._value_, 32)
+        self.assertEqual(AS.SECONDARY_TYPE._value_, 128)
+        self.assertEqual((AS.NAME | AS.STREET)._value_, 48, "%r is not 48" % (AS.NAME | AS.STREET))
+                
+    def test_iteration(self):
+        C = self.Color
+        self.assertEqual(list(C), [C.RED, C.GREEN, C.BLUE])
+        self.assertEqual(list(C.PURPLE), [C.RED, C.BLUE])
+
+    def test_member_iteration(self):
+        C = self.Color
+        self.assertEqual(list(C.BLACK), [])
+        self.assertEqual(list(C.RED), [C.RED])
+        self.assertEqual(list(C.PURPLE), [C.RED, C.BLUE])
+
+    def test_programatic_function_string(self):
+        Perm = Flag('Perm', 'R W X')
+        lst = list(Perm)
+        self.assertEqual(len(lst), len(Perm))
+        self.assertEqual(len(Perm), 3, Perm)
+        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
+        for i, n in enumerate('R W X'.split()):
+            v = 1<<i
+            e = Perm(v)
+            self.assertEqual(e.value, v)
+            self.assertEqual(type(e.value), int)
+            self.assertEqual(e.name, n)
+            self.assertIn(e, Perm)
+            self.assertIs(type(e), Perm)
+
+    def test_programatic_function_string_with_start(self):
+        Perm = Flag('Perm', 'R W X', start=8)
+        lst = list(Perm)
+        self.assertEqual(len(lst), len(Perm))
+        self.assertEqual(len(Perm), 3, Perm)
+        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
+        for i, n in enumerate('R W X'.split()):
+            v = 8<<i
+            e = Perm(v)
+            self.assertEqual(e.value, v)
+            self.assertEqual(type(e.value), int)
+            self.assertEqual(e.name, n)
+            self.assertIn(e, Perm)
+            self.assertIs(type(e), Perm)
+
+    def test_programatic_function_string_list(self):
+        Perm = Flag('Perm', ['R', 'W', 'X'])
+        lst = list(Perm)
+        self.assertEqual(len(lst), len(Perm))
+        self.assertEqual(len(Perm), 3, Perm)
+        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
+        for i, n in enumerate('R W X'.split()):
+            v = 1<<i
+            e = Perm(v)
+            self.assertEqual(e.value, v)
+            self.assertEqual(type(e.value), int)
+            self.assertEqual(e.name, n)
+            self.assertIn(e, Perm)
+            self.assertIs(type(e), Perm)
+
+    def test_programatic_function_iterable(self):
+        Perm = Flag('Perm', (('R', 2), ('W', 8), ('X', 32)))
+        lst = list(Perm)
+        self.assertEqual(len(lst), len(Perm))
+        self.assertEqual(len(Perm), 3, Perm)
+        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
+        for i, n in enumerate('R W X'.split()):
+            v = 1<<(2*i+1)
+            e = Perm(v)
+            self.assertEqual(e.value, v)
+            self.assertEqual(type(e.value), int)
+            self.assertEqual(e.name, n)
+            self.assertIn(e, Perm)
+            self.assertIs(type(e), Perm)
+
+    def test_programatic_function_from_dict(self):
+        Perm = Flag('Perm', OrderedDict((('R', 2), ('W', 8), ('X', 32))))
+        lst = list(Perm)
+        self.assertEqual(len(lst), len(Perm))
+        self.assertEqual(len(Perm), 3, Perm)
+        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
+        for i, n in enumerate('R W X'.split()):
+            v = 1<<(2*i+1)
+            e = Perm(v)
+            self.assertEqual(e.value, v)
+            self.assertEqual(type(e.value), int)
+            self.assertEqual(e.name, n)
+            self.assertIn(e, Perm)
+            self.assertIs(type(e), Perm)
+
+    def test_programatic_function_empty_list(self):
+        Perm = IntFlag('Perm', [])
+        self.assertEqual(len(list(Perm)), len(Perm))
+        self.assertEqual(len(Perm), 0)
+        Thing = Enum('Thing', [])
+        self.assertEqual(len(list(Thing)), len(Thing))
+        self.assertEqual(len(Thing), 0)
+
+    def test_programatic_function_empty_tuple(self):
+        Perm = IntFlag('Perm', ())
+        self.assertEqual(len(list(Perm)), len(Perm))
+        self.assertEqual(len(Perm), 0)
+        Thing = Enum('Thing', ())
+        self.assertEqual(len(list(Thing)), len(Thing))
+        self.assertEqual(len(Thing), 0)
+
+    def test_pickle(self):
+        if isinstance(FlagStooges, Exception):
+            raise FlagStooges
+        test_pickle_dump_load(self.assertIs, FlagStooges.CURLY|FlagStooges.MOE)
+        test_pickle_dump_load(self.assertIs, FlagStooges)
+
+    def test_containment(self):
+        Perm = self.Perm
+        R, W, X = Perm
+        RW = R | W
+        RX = R | X
+        WX = W | X
+        RWX = R | W | X
+        self.assertTrue(R in RW)
+        self.assertTrue(R in RX)
+        self.assertTrue(R in RWX)
+        self.assertTrue(W in RW)
+        self.assertTrue(W in WX)
+        self.assertTrue(W in RWX)
+        self.assertTrue(X in RX)
+        self.assertTrue(X in WX)
+        self.assertTrue(X in RWX)
+        self.assertFalse(R in WX)
+        self.assertFalse(W in RX)
+        self.assertFalse(X in RW)
+
+    def test_auto_number(self):
+        class Color(Flag):
+            _order_ = 'red blue green'
+            red = auto()
+            blue = auto()
+            green = auto()
+
+        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
+        self.assertEqual(Color.red.value, 1)
+        self.assertEqual(Color.blue.value, 2)
+        self.assertEqual(Color.green.value, 4)
+
+    def test_auto_number_garbage(self):
+        with self.assertRaisesRegex(TypeError, r'invalid Flag value: .not an int.'):
+            class Color(Flag):
+                _order_ = 'red blue'
+                red = 'not an int'
+                blue = auto()
+
+    def test_auto_w_pending(self):
+        class Required(Flag):
+            _order_ = 'TO_S FROM_S'
+            NONE = 0
+            TO_S = auto()
+            FROM_S = auto()
+            BOTH = TO_S | FROM_S
+        self.assertEqual(Required.TO_S.value, 1)
+        self.assertEqual(Required.FROM_S.value, 2)
+        self.assertEqual(Required.BOTH.value, 3)
+
+    def test_duplicate_auto(self):
+        class Dupes(Enum):
+            _order_ = 'first second third'
+            first = primero = auto()
+            second = auto()
+            third = auto()
+        self.assertEqual([Dupes.first, Dupes.second, Dupes.third], list(Dupes))
+
+    def test_bizarre(self):
+        with self.assertRaisesRegex(TypeError, r"invalid Flag 'Bizarre' -- missing values: 1, 2"):
+            class Bizarre(Flag):
+                b = 3
+                c = 4
+                d = 6
+
+    def test_multiple_mixin(self):
+        class AllMixin(object):
+            @classproperty
+            def ALL(cls):
+                members = list(cls)
+                all_value = None
+                if members:
+                    all_value = members[0]
+                    for member in members[1:]:
+                        all_value |= member
+                cls.ALL = all_value
+                return all_value
+        class StrMixin(object):
+            def __str__(self):
+                return self._name_.lower()
+        class Color(AllMixin, Flag):
+            _order_ = 'RED GREEN BLUE'
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(Color.RED.value, 1)
+        self.assertEqual(Color.GREEN.value, 2)
+        self.assertEqual(Color.BLUE.value, 4)
+        self.assertEqual(Color.ALL.value, 7)
+        self.assertEqual(str(Color.BLUE), 'Color.BLUE')
+        class Color(AllMixin, StrMixin, Flag):
+            _order_ = 'RED GREEN BLUE'
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(Color.RED.value, 1)
+        self.assertEqual(Color.GREEN.value, 2)
+        self.assertEqual(Color.BLUE.value, 4)
+        self.assertEqual(Color.ALL.value, 7)
+        self.assertEqual(str(Color.BLUE), 'blue')
+        class Color(StrMixin, AllMixin, Flag):
+            _order_ = 'RED GREEN BLUE'
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(Color.RED.value, 1)
+        self.assertEqual(Color.GREEN.value, 2)
+        self.assertEqual(Color.BLUE.value, 4)
+        self.assertEqual(Color.ALL.value, 7)
+        self.assertEqual(str(Color.BLUE), 'blue')
+
+    @unittest.skipUnless(threading, 'Threading required for this test.')
+    def test_unique_composite(self):
+        # override __eq__ to be identity only
+        class TestFlag(Flag):
+            _order_ = 'one two three four five six seven eight'
+            one = auto()
+            two = auto()
+            three = auto()
+            four = auto()
+            five = auto()
+            six = auto()
+            seven = auto()
+            eight = auto()
+            def __eq__(self, other):
+                return self is other
+            def __hash__(self):
+                return hash(self._value_)
+        # have multiple threads competing to complete the composite members
+        seen = set()
+        failed = [False]
+        def cycle_enum():
+            # nonlocal failed
+            try:
+                for i in range(256):
+                    seen.add(TestFlag(i))
+            except Exception:
+                failed[0] = True
+        threads = [
+                threading.Thread(target=cycle_enum)
+                for _ in range(8)
+                ]
+        for t in threads:
+            t.start()
+        for t in threads:
+            t.join()
+        # check that only 248 members were created (8 were created originally)
+        self.assertFalse(
+                failed[0],
+                'at least one thread failed while creating composite members')
+        self.assertEqual(256, len(seen), 'too many composite members created')
+
+    def test_init_with_autovalue_and_generate_next_value(self):
+        class Color(Flag):
+            _init_ = 'value code'
+            def _generate_next_value_(name, start, count, last_values, *args, **kwds):
+                if not count:
+                    return ((1, start)[start is not None], ) + args
+                error = False
+                for last_value in reversed(last_values):
+                    try:
+                        high_bit = aenum._high_bit(last_value)
+                        break
+                    except Exception:
+                        error = True
+                        break
+                if error:
+                    raise TypeError('Invalid Flag value: %r' % (last_value, ))
+                return (2 ** (high_bit+1), ) + args
+            # TODO: actually test _create_pseudo_member
+            @classmethod
+            def _create_pseudo_member_(cls, value):
+                members = list(cls._iter_member_(value))
+                pseudo_member = super(Color, cls)._create_pseudo_member_(value)
+                pseudo_member.code = ';'.join(m.code for m in members)
+                return pseudo_member
+            AllReset = '0'           # ESC [ 0 m       # reset all (colors and brightness)
+            Bright = '1'          # ESC [ 1 m       # bright
+            Dim = '2'             # ESC [ 2 m       # dim (looks same as normal brightness)
+            Underline = '4'
+            Normal = '22'         # ESC [ 22 m      # normal brightness
+        # if we got here, we're good
+
+    def test_autovalue_and_generate_next_value(self):
+        class Color(str, Flag):
+            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
+            def __new__(cls, value, code):
+                str_value = '\x1b[%sm' % code
+                obj = str.__new__(cls, str_value)
+                obj._value_ = value
+                obj.code = code
+                return obj
+            @staticmethod
+            def _generate_next_value_(name, start, count, values, *args, **kwds):
+                return (2 ** count, ) + args
+            # TODO: actually test _create_pseudo_member
+            @classmethod
+            def _create_pseudo_member_(cls, value):
+                # calculate the code
+                members = list(cls._iter_member_(value))
+                code = ';'.join(m.code for m in members)
+                pseudo_member = super(Color, cls)._create_pseudo_member_(value, code)
+                return pseudo_member
+            #
+                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
+            FG_Black = '30'           # ESC [ 30 m      # black
+            FG_Red = '31'             # ESC [ 31 m      # red
+            FG_Green = '32'           # ESC [ 32 m      # green
+            FG_Blue = '34'            # ESC [ 34 m      # blue
+                                      #
+            BG_Yellow = '43'          # ESC [ 33 m      # yellow
+            BG_Magenta = '45'         # ESC [ 35 m      # magenta
+            BG_Cyan = '46'            # ESC [ 36 m      # cyan
+            BG_White = '47'           # ESC [ 37 m      # white
+        # if we got here, we're good
+
+    def test_subclass(self):
+        class Color(str, Flag):
+            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
+            def __new__(cls, value, code):
+                str_value = '\x1b[%sm' % code
+                obj = str.__new__(cls, str_value)
+                obj._value_ = value
+                obj.code = code
+                return obj
+            @staticmethod
+            def _generate_next_value_(name, start, count, values, *args, **kwds):
+                return (2 ** count, ) + args
+            @classmethod
+            def _create_pseudo_member_(cls, value):
+                # calculate the code
+                members = list(cls._iter_member_(value))
+                code = ';'.join(m.code for m in members)
+                pseudo_member = super(Color, cls)._create_pseudo_member_(value, code)
+                return pseudo_member
+            #
+                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
+            FG_Black = '30'           # ESC [ 30 m      # black
+            FG_Red = '31'             # ESC [ 31 m      # red
+            FG_Green = '32'           # ESC [ 32 m      # green
+            FG_Blue = '34'            # ESC [ 34 m      # blue
+                                      #
+            BG_Yellow = '43'          # ESC [ 33 m      # yellow
+            BG_Magenta = '45'         # ESC [ 35 m      # magenta
+            BG_Cyan = '46'            # ESC [ 36 m      # cyan
+            BG_White = '47'           # ESC [ 37 m      # white
+        self.assertTrue(isinstance(Color.FG_Black, Color))
+        self.assertTrue(isinstance(Color.FG_Black, str))
+        self.assertEqual(Color.FG_Black, '\x1b[30m')
+        self.assertEqual(Color.FG_Black.code, '30')
+
+    def test_sub_subclass_1(self):
+        class StrFlag(str, Flag):
+            def __new__(cls, value, code):
+                str_value = '\x1b[%sm' % code
+                obj = str.__new__(cls, str_value)
+                obj._value_ = value
+                obj.code = code
+                return obj
+            @classmethod
+            def _create_pseudo_member_(cls, value):
+                # calculate the code
+                members = list(cls._iter_member_(value))
+                code = ';'.join(m.code for m in members)
+                pseudo_member = super(Color, cls)._create_pseudo_member_(value, code)
+                return pseudo_member
+        class Color(StrFlag):
+            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
+                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
+            FG_Black = '30'           # ESC [ 30 m      # black
+            FG_Red = '31'             # ESC [ 31 m      # red
+            FG_Green = '32'           # ESC [ 32 m      # green
+            FG_Blue = '34'            # ESC [ 34 m      # blue
+                                      #
+            BG_Yellow = '43'          # ESC [ 33 m      # yellow
+            BG_Magenta = '45'         # ESC [ 35 m      # magenta
+            BG_Cyan = '46'            # ESC [ 36 m      # cyan
+            BG_White = '47'           # ESC [ 37 m      # white
+        self.assertTrue(isinstance(Color.FG_Black, Color))
+        self.assertTrue(isinstance(Color.FG_Black, str))
+        self.assertEqual(Color.FG_Black, '\x1b[30m')
+        self.assertEqual(Color.FG_Black.code, '30')
+
+    def test_sub_subclass_2(self):
+        class StrFlag(str, Flag):
+            @staticmethod
+            def _generate_next_value_(name, start, count, values, *args, **kwds):
+                return (2 ** count, ) + args
+            @classmethod
+            def _create_pseudo_member_(cls, value):
+                # calculate the code
+                members = list(cls._iter_member_(value))
+                code = ';'.join(m.code for m in members)
+                pseudo_member = super(Color, cls)._create_pseudo_member_(value, code)
+                return pseudo_member
+            #
+        class Color(StrFlag):
+            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
+            def __new__(cls, value, code):
+                str_value = '\x1b[%sm' % code
+                obj = str.__new__(cls, str_value)
+                obj._value_ = value
+                obj.code = code
+                return obj
+                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
+            FG_Black = '30'           # ESC [ 30 m      # black
+            FG_Red = '31'             # ESC [ 31 m      # red
+            FG_Green = '32'           # ESC [ 32 m      # green
+            FG_Blue = '34'            # ESC [ 34 m      # blue
+                                      #
+            BG_Yellow = '43'          # ESC [ 33 m      # yellow
+            BG_Magenta = '45'         # ESC [ 35 m      # magenta
+            BG_Cyan = '46'            # ESC [ 36 m      # cyan
+            BG_White = '47'           # ESC [ 37 m      # white
+        self.assertTrue(isinstance(Color.FG_Black, Color))
+        self.assertTrue(isinstance(Color.FG_Black, str))
+        self.assertEqual(Color.FG_Black, '\x1b[30m')
+        self.assertEqual(Color.FG_Black.code, '30')
+
+    def test_sub_subclass_3(self):
+        class StrFlag(str, Flag):
+            def __new__(cls, value, code):
+                str_value = '\x1b[%sm' % code
+                obj = str.__new__(cls, str_value)
+                obj._value_ = value
+                obj.code = code
+                return obj
+            @classmethod
+            def _create_pseudo_member_(cls, value):
+                # calculate the code
+                members = list(cls._iter_member_(value))
+                code = ';'.join(m.code for m in members)
+                pseudo_member = super(Color, cls)._create_pseudo_member_(value, code)
+                return pseudo_member
+            #
+        class Color(StrFlag):
+            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
+                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
+            FG_Black = '30'           # ESC [ 30 m      # black
+            FG_Red = '31'             # ESC [ 31 m      # red
+            FG_Green = '32'           # ESC [ 32 m      # green
+            FG_Blue = '34'            # ESC [ 34 m      # blue
+                                      #
+            BG_Yellow = '43'          # ESC [ 33 m      # yellow
+            BG_Magenta = '45'         # ESC [ 35 m      # magenta
+            BG_Cyan = '46'            # ESC [ 36 m      # cyan
+            BG_White = '47'           # ESC [ 37 m      # white
+        self.assertTrue(isinstance(Color.FG_Black, Color))
+        self.assertTrue(isinstance(Color.FG_Black, str))
+        self.assertEqual(Color.FG_Black, '\x1b[30m')
+        self.assertEqual(Color.FG_Black.code, '30')
+
+    def test_sub_subclass_4(self):
+        class StrFlag(str, Flag):
+            def __new__(cls, value, code):
+                str_value = '\x1b[%sm' % code
+                obj = str.__new__(cls, str_value)
+                obj._value_ = value
+                obj.code = code
+                return obj
+            @classmethod
+            def _create_pseudo_member_values_(cls, members, *values):
+                code = ';'.join(m.code for m in members)
+                return values + (code, )
+            #
+        class Color(StrFlag):
+            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
+                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
+            FG_Black = '30'           # ESC [ 30 m      # black
+            FG_Red = '31'             # ESC [ 31 m      # red
+            FG_Green = '32'           # ESC [ 32 m      # green
+            FG_Blue = '34'            # ESC [ 34 m      # blue
+                                      #
+            BG_Yellow = '43'          # ESC [ 33 m      # yellow
+            BG_Magenta = '45'         # ESC [ 35 m      # magenta
+            BG_Cyan = '46'            # ESC [ 36 m      # cyan
+            BG_White = '47'           # ESC [ 37 m      # white
+            #
+            def __repr__(self):
+                return '<%s.%s>' % (self.__class__.__name__, self._name_)
+        self.assertTrue(isinstance(Color.FG_Black, Color))
+        self.assertTrue(isinstance(Color.FG_Black, str))
+        self.assertEqual(Color.FG_Black, '\x1b[30m')
+        self.assertEqual(Color.FG_Black.code, '30')
+        colors = Color.BG_Magenta | Color.FG_Black
+        self.assertTrue(isinstance(colors, Color))
+        self.assertTrue(isinstance(colors, str))
+        self.assertEqual(colors, '\x1b[30;45m')
+        self.assertEqual(colors.code, '30;45')
+        self.assertEqual(repr(colors), '<Color.FG_Black|BG_Magenta>')
+
+    def test_sub_subclass_with_new_new(self):
+        class StrFlag(str, Flag):
+            def __new__(cls, value, code):
+                str_value = '\x1b[%sm' % code
+                obj = str.__new__(cls, str_value)
+                obj._value_ = value
+                obj.code = code
+                return obj
+            @classmethod
+            def _create_pseudo_member_(cls, value):
+                # calculate the code
+                members = list(cls._iter_member_(value))
+                code = ';'.join(m.code for m in members)
+                pseudo_member = super(StrFlag, cls)._create_pseudo_member_(value, code)
+                return pseudo_member
+            #
+        class Color(StrFlag):
+            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
+            def __new__(cls, value, string, abbr):
+                str_value = (abbr or '').title()
+                obj = str.__new__(cls, str_value)
+                obj._value_ = value
+                obj.code = string
+                obj.abbr = abbr
+                return obj
+                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
+            FG_Black = '30', 'blk'           # ESC [ 30 m      # black
+            FG_Red = '31', 'red'             # ESC [ 31 m      # red
+            FG_Green = '32', 'grn'           # ESC [ 32 m      # green
+            FG_Blue = '34', 'blu'            # ESC [ 34 m      # blue
+                                      #
+            BG_Yellow = '43', 'ylw'          # ESC [ 33 m      # yellow
+            BG_Magenta = '45', 'mag'         # ESC [ 35 m      # magenta
+            BG_Cyan = '46', 'cyn'            # ESC [ 36 m      # cyan
+            BG_White = '47', 'wht'           # ESC [ 37 m      # white
+            #
+            def __repr__(self):
+                if self._name_ is not None:
+                    return '<%s.%s>' % (self.__class__.__name__, self._name_)
+                else:
+                    return '<%s: %s>' % (self.__class__.__name__, '|'.join([m.name for m in self]))
+        self.assertTrue(isinstance(Color.FG_Black, Color))
+        self.assertTrue(isinstance(Color.FG_Black, str))
+        self.assertEqual(Color.FG_Black, 'Blk', str.__repr__(Color.FG_Black))
+        self.assertEqual(Color.FG_Black.abbr, 'blk')
+
+    def test_subclass_with_default_new(self):
+        class MyFlag(str, Flag):
+            _order_ = 'this these theother'
+            this = 'that'
+            these = 'those'
+            theother = 'thingimibobs'
+        self.assertEqual(MyFlag.this, 'that')
+        self.assertEqual(MyFlag.this.value, 1)
+        self.assertEqual(MyFlag.these, 'those')
+        self.assertEqual(MyFlag.these.value, 2)
+        self.assertEqual(MyFlag.theother, 'thingimibobs')
+        self.assertEqual(MyFlag.theother.value, 4)
+
+    def test_subclass_a_bunch(self):
+        class Color(str, Flag):
+            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
+            def __new__(cls, value, code):
+                str_value = '\x1b[%sm' % code
+                obj = str.__new__(cls, str_value)
+                obj._value_ = value
+                obj.code = code
+                return obj
+            @staticmethod
+            def _generate_next_value_(name, start, count, values, *args, **kwds):
+                return (2 ** count, ) + args
+            @classmethod
+            def _create_pseudo_member_(cls, value):
+                # calculate the code
+                members = list(cls._iter_member_(value))
+                code = ';'.join(m.code for m in members)
+                pseudo_member = super(Color, cls)._create_pseudo_member_(value, code)
+                return pseudo_member
+            #
+                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
+            FG_Black = '30'           # ESC [ 30 m      # black
+            FG_Red = '31'             # ESC [ 31 m      # red
+            FG_Green = '32'           # ESC [ 32 m      # green
+            FG_Blue = '34'            # ESC [ 34 m      # blue
+                                      #
+            BG_Yellow = '43'          # ESC [ 33 m      # yellow
+            BG_Magenta = '45'         # ESC [ 35 m      # magenta
+            BG_Cyan = '46'            # ESC [ 36 m      # cyan
+            BG_White = '47'           # ESC [ 37 m      # white
+            #
+            def __repr__(self):
+                if self._name_ is not None:
+                    return '<%s.%s>' % (self.__class__.__name__, self._name_)
+                else:
+                    return '<%s: %s>' % (self.__class__.__name__, '|'.join([m.name for m in self]))
+        #
+        Purple = Color.BG_Magenta | Color.FG_Blue
+        self.assertTrue(isinstance(Purple, Color))
+        self.assertTrue(isinstance(Purple, str))
+        self.assertIs(Purple, Color.BG_Magenta | Color.FG_Blue)
+        self.assertEqual(Purple, '\x1b[34;45m')
+        self.assertEqual(Purple.code, '34;45')
+        self.assertEqual(Purple.name, 'FG_Blue|BG_Magenta')
+
+    def test_init_subclass(self):
+        class MyEnum(Flag):
+            def __init_subclass__(cls, **kwds):
+                super(MyEnum, cls).__init_subclass__(**kwds)
+                self.assertFalse(cls.__dict__.get('_test', False))
+                cls._test1 = 'MyEnum'
+        #
+        class TheirEnum(MyEnum):
+            def __init_subclass__(cls, **kwds):
+                super(TheirEnum, cls).__init_subclass__(**kwds)
+                cls._test2 = 'TheirEnum'
+        class WhoseEnum(TheirEnum):
+            def __init_subclass__(cls, **kwds):
+                pass
+        class NoEnum(WhoseEnum):
+            ONE = 1
+        self.assertEqual(TheirEnum.__dict__['_test1'], 'MyEnum')
+        self.assertEqual(WhoseEnum.__dict__['_test1'], 'MyEnum')
+        self.assertEqual(WhoseEnum.__dict__['_test2'], 'TheirEnum')
+        self.assertFalse(NoEnum.__dict__.get('_test1', False))
+        self.assertFalse(NoEnum.__dict__.get('_test2', False))
+        #
+        class OurEnum(MyEnum):
+            def __init_subclass__(cls, **kwds):
+                cls._test2 = 'OurEnum'
+        class WhereEnum(OurEnum):
+            def __init_subclass__(cls, **kwds):
+                pass
+        class NeverEnum(WhereEnum):
+            ONE = 1
+        self.assertEqual(OurEnum.__dict__['_test1'], 'MyEnum')
+        self.assertFalse(WhereEnum.__dict__.get('_test1', False))
+        self.assertEqual(WhereEnum.__dict__['_test2'], 'OurEnum')
+        self.assertFalse(NeverEnum.__dict__.get('_test1', False))
+        self.assertFalse(NeverEnum.__dict__.get('_test2', False))
+
+    def test_int_long_conversion(self):
+        class Perm(Flag):
+            EXEC    = 1 << 0
+            WRITE   = 1 << 1
+            READ    = 1 << 2
+            MSB32   = 1 << 31
+            MSB64   = 1 << 63
+
+        # 32-bit system test
+        self.assertEqual(Perm.MSB32, Perm(0x80000000))
+        self.assertEqual(Perm.WRITE|Perm.MSB32, Perm(0x80000002))
+
+        # 64-bit system test
+        self.assertEqual(Perm.MSB64, Perm(0x8000000000000000))
+        self.assertEqual(Perm.MSB64|Perm.WRITE, Perm(0x8000000000000002))
+
+
+class TestIntFlag(TestCase):
+    """Tests of the IntFlags."""
+
+    def setUp(self):
+        #
+        class Perm(IntFlag):
+            _order_ = 'R W X'
+            R = 1 << 2
+            W = 1 << 1
+            X = 1 << 0
+        #
+        class Color(IntFlag):
+            BLACK = 0
+            RED = 1
+            GREEN = 2
+            BLUE = 4
+            PURPLE = RED|BLUE
+        #
+        class Open(IntFlag):
+            "not a good flag candidate"
+            RO = 0
+            WO = 1
+            RW = 2
+            AC = 3
+            CE = 1<<19
+        #
+        self.Perm = Perm
+        self.Color = Color
+        self.Open = Open
+
+    def test_set_name(self):
+        class Descriptor(object):
+            name = None
+            def __get__(self, instance, owner_class=None):
+                if instance is None:
+                    return self
+                else:
+                    return instance.__dict__[self.name]
+            def __set__(self, instance, value):
+                instance.__dict__[self.name] = value
+            def __set_name__(self, owner, name):
+                self.name = name
+        #
+        class AnEnum(Enum):
+            ONE = 1
+            two = Descriptor()
+        #
+        self.assertEqual(list(AnEnum), [AnEnum.ONE])
+        self.assertEqual(AnEnum.two.name, 'two')
+        AnEnum.ONE.two = 'three'
+        self.assertEqual(AnEnum.ONE.two, 'three')
+        self.assertEqual(AnEnum.ONE.__dict__['two'], 'three')
+
+    def test_private_names(self):
+        class Private(Enum):
+            __corporal = 'Radar'
+            __major_ = 'Hoolihan'
+        self.assertEqual(len(Private), 0)
+        self.assertEqual(Private._Private__corporal, 'Radar')
+        self.assertFalse(isinstance(Private._Private__corporal, Enum))
+        self.assertEqual(Private._Private__major_, 'Hoolihan')
+        self.assertFalse(isinstance(Private._Private__major_, Enum))
+
+    def test_membership(self):
+        Color = self.Color
+        Open = self.Open
+        self.assertRaises(TypeError, lambda: 'GREEN' in Color)
+        self.assertRaises(TypeError, lambda: 'RW' in Open)
+        self.assertTrue(Color.GREEN in Color)
+        self.assertTrue(Open.RW in Open)
+        self.assertFalse(Color.GREEN in Open)
+        self.assertFalse(Open.RW in Color)
+        self.assertRaises(TypeError, lambda: 2 in Color)
+        self.assertRaises(TypeError, lambda: 2 in Open)
+
+    def test_member_contains(self):
+        Color = self.Color
+        self.assertRaises(TypeError, lambda: 'test' in Color.RED)
+        self.assertRaises(TypeError, lambda: 1 in Color.RED)
+        self.assertTrue(Color.RED in Color.RED)
+        self.assertTrue(Color.RED in Color.PURPLE)
+
+    def test_name_lookup(self):
+        Color = self.Color
+        self.assertTrue(Color.RED is Color['RED'])
+        self.assertTrue(Color.RED|Color.GREEN is Color['RED|GREEN'])
+        self.assertTrue(Color.PURPLE is Color['RED|BLUE'])
+
+    def test_type(self):
+        Perm = self.Perm
+        Open = self.Open
+        for f in Perm:
+            self.assertTrue(isinstance(f, Perm))
+            self.assertEqual(f, f.value)
+        self.assertTrue(isinstance(Perm.W | Perm.X, Perm))
+        self.assertEqual(Perm.W | Perm.X, 3)
+        for f in Open:
+            self.assertTrue(isinstance(f, Open))
+            self.assertEqual(f, f.value)
+        self.assertTrue(isinstance(Open.WO | Open.RW, Open))
+        self.assertEqual(Open.WO | Open.RW, 3)
+
+
+    def test_str(self):
+        Perm = self.Perm
+        self.assertEqual(str(Perm.R), '4')
+        self.assertEqual(str(Perm.W), '2')
+        self.assertEqual(str(Perm.X), '1')
+        self.assertEqual(str(Perm.R | Perm.W), '6')
+        self.assertEqual(str(Perm.R | Perm.W | Perm.X), '7')
+        self.assertEqual(str(Perm(0)), '0')
+        self.assertEqual(str(~Perm.R), '3')
+        self.assertEqual(str(~Perm.W), '5')
+        self.assertEqual(str(~Perm.X), '6')
+        self.assertEqual(str(~(Perm.R | Perm.W)), '1')
+        self.assertEqual(str(~(Perm.R | Perm.W | Perm.X)), '0')
+        self.assertEqual(str(Perm(~0)), '7')
+
+        Open = self.Open
+        self.assertEqual(str(Open.RO), '0')
+        self.assertEqual(str(Open.WO), '1')
+        self.assertEqual(str(Open.AC), '3')
+        self.assertEqual(str(Open.RO | Open.CE), '524288')
+        self.assertEqual(str(Open.WO | Open.CE), '524289')
+        self.assertEqual(str(~Open.RO), '524291')
+        self.assertEqual(str(~Open.WO), '524290')
+        self.assertEqual(str(~Open.AC), '524288')
+        self.assertEqual(str(~(Open.RO | Open.CE)), '3')
+        self.assertEqual(str(~(Open.WO | Open.CE)), '2')
+
+    def test_repr_strict(self):
+        class Perm(IntFlag):
+            _order_ = 'R W X'
+            R = 1 << 2
+            W = 1 << 1
+            X = 1 << 0
+        Perm._boundary_ = aenum.STRICT
+        self.assertEqual(repr(Perm.R), '<Perm.R: 4>')
+        self.assertEqual(repr(Perm.W), '<Perm.W: 2>')
+        self.assertEqual(repr(Perm.X), '<Perm.X: 1>')
+        self.assertEqual(repr(Perm.R | Perm.W), '<Perm.R|W: 6>')
+        self.assertEqual(repr(Perm.R | Perm.W | Perm.X), '<Perm.R|W|X: 7>')
+        self.assertEqual(repr(Perm(0)), '<Perm: 0>')
+        self.assertEqual(repr(~Perm.R), '<Perm.W|X: 3>')
+        self.assertEqual(repr(~Perm.W), '<Perm.R|X: 5>')
+        self.assertEqual(repr(~Perm.X), '<Perm.R|W: 6>')
+        self.assertEqual(repr(~(Perm.R | Perm.W)), '<Perm.X: 1>')
+        self.assertEqual(repr(~(Perm.R | Perm.W | Perm.X)), '<Perm: 0>')
+        #
+        with self.assertRaisesRegex(ValueError, r'invalid value: 12'):
+            repr(Perm.R | 8)
+        with self.assertRaisesRegex(ValueError, r'invalid value: 12'):
+            repr(~(Perm.R | 8))
+        with self.assertRaisesRegex(ValueError, r'invalid value: -9'):
+            repr(Perm(~8))
+
+    def test_repr_conform(self):
+        class Perm(IntFlag):
+            _order_ = 'R W X'
+            R = 1 << 2
+            W = 1 << 1
+            X = 1 << 0
+        Perm._boundary_ = aenum.CONFORM
+        self.assertEqual(repr(Perm.R), '<Perm.R: 4>')
+        self.assertEqual(repr(Perm.W), '<Perm.W: 2>')
+        self.assertEqual(repr(Perm.X), '<Perm.X: 1>')
+        self.assertEqual(repr(Perm.R | Perm.W), '<Perm.R|W: 6>')
+        self.assertEqual(repr(Perm.R | Perm.W | Perm.X), '<Perm.R|W|X: 7>')
+        self.assertEqual(repr(Perm(0)), '<Perm: 0>')
+        self.assertEqual(repr(~Perm.R), '<Perm.W|X: 3>')
+        self.assertEqual(repr(~Perm.W), '<Perm.R|X: 5>')
+        self.assertEqual(repr(~Perm.X), '<Perm.R|W: 6>')
+        self.assertEqual(repr(~(Perm.R | Perm.W)), '<Perm.X: 1>')
+        self.assertEqual(repr(~(Perm.R | Perm.W | Perm.X)), '<Perm: 0>')
+        self.assertEqual(repr(Perm.R | 8), '<Perm.R: 4>')
+        self.assertEqual(repr(Perm(8)), '<Perm: 0>')
+        self.assertEqual(repr(~(Perm.R | 8)), '<Perm.W|X: 3>')
+        self.assertEqual(repr(Perm(~8)), '<Perm.R|W|X: 7>')
+
+    def test_repr_eject(self):
+        class Perm(IntFlag):
+            _order_ = 'R W X'
+            _boundary_ = EJECT
+            R = 1 << 2
+            W = 1 << 1
+            X = 1 << 0
+        self.assertEqual(repr(Perm.R), '<Perm.R: 4>')
+        self.assertEqual(repr(Perm.W), '<Perm.W: 2>')
+        self.assertEqual(repr(Perm.X), '<Perm.X: 1>')
+        self.assertEqual(repr(Perm.R | Perm.W), '<Perm.R|W: 6>')
+        self.assertEqual(repr(Perm.R | Perm.W | Perm.X), '<Perm.R|W|X: 7>')
+        self.assertEqual(repr(Perm(0)), '<Perm: 0>')
+        self.assertEqual(repr(~Perm.R), '<Perm.W|X: 3>')
+        self.assertEqual(repr(~Perm.W), '<Perm.R|X: 5>')
+        self.assertEqual(repr(~Perm.X), '<Perm.R|W: 6>')
+        self.assertEqual(repr(~(Perm.R | Perm.W)), '<Perm.X: 1>')
+        self.assertEqual(repr(~(Perm.R | Perm.W | Perm.X)), '<Perm: 0>')
+        self.assertEqual(repr(Perm.R | 8), '12')
+        self.assertEqual(repr(Perm(8)), '8')
+        self.assertEqual(repr(~(Perm.R | 8)), '-13')
+        self.assertEqual(repr(Perm(~8)), '-9')
+
+    def test_repr_open(self):
+        class Open(IntFlag):
+            "not a good flag candidate"
+            RO = 0
+            WO = 1
+            RW = 2
+            AC = 3
+            CE = 1<<19
+        Open._boundary_ = aenum.STRICT
+        self.assertEqual(repr(Open.RO), '<Open.RO: 0>')
+        self.assertEqual(repr(Open.WO), '<Open.WO: 1>')
+        self.assertEqual(repr(Open.AC), '<Open.AC: 3>')
+        self.assertEqual(repr(Open.RO | Open.CE), '<Open.CE: 524288>')
+        self.assertEqual(repr(Open.WO | Open.CE), '<Open.WO|CE: 524289>')
+        self.assertEqual(repr(~Open.RO), '<Open.WO|RW|CE: 524291>')
+        self.assertEqual(repr(~Open.WO), '<Open.RW|CE: 524290>')
+        self.assertEqual(repr(~Open.AC), '<Open.CE: 524288>')
+        self.assertEqual(repr(~(Open.RO | Open.CE)), '<Open.AC: 3>')
+        self.assertEqual(repr(~(Open.WO | Open.CE)), '<Open.RW: 2>')
+        with self.assertRaisesRegex(ValueError, r'invalid value: -5'):
+            repr(Open(~4))
+        with self.assertRaisesRegex(ValueError, r'invalid value: 4'):
+            repr(Open(4))
+        #
+        class Open(IntFlag):
+            "not a good flag candidate"
+            RO = 0
+            WO = 1
+            RW = 2
+            AC = 3
+            CE = 1<<19
+        Open._boundary_ = aenum.CONFORM
+        self.assertEqual(repr(Open.RO), '<Open.RO: 0>')
+        self.assertEqual(repr(Open.WO), '<Open.WO: 1>')
+        self.assertEqual(repr(Open.AC), '<Open.AC: 3>')
+        self.assertEqual(repr(Open.RO | Open.CE), '<Open.CE: 524288>')
+        self.assertEqual(repr(Open.WO | Open.CE), '<Open.WO|CE: 524289>')
+        self.assertEqual(repr(~Open.RO), '<Open.WO|RW|CE: 524291>')
+        self.assertEqual(repr(~Open.WO), '<Open.RW|CE: 524290>')
+        self.assertEqual(repr(~Open.AC), '<Open.CE: 524288>')
+        self.assertEqual(repr(~(Open.RO | Open.CE)), '<Open.AC: 3>')
+        self.assertEqual(repr(~(Open.WO | Open.CE)), '<Open.RW: 2>')
+        self.assertEqual(repr(Open(~4)), '<Open.WO|RW|CE: 524291>')
+        self.assertEqual(repr(Open(4)), '<Open.RO: 0>')
+        #
+        class Open(IntFlag):
+            "not a good flag candidate"
+            RO = 0
+            WO = 1
+            RW = 2
+            AC = 3
+            CE = 1<<19
+        Open._boundary_ = aenum.EJECT
+        self.assertEqual(repr(Open.RO), '<Open.RO: 0>')
+        self.assertEqual(repr(Open.WO), '<Open.WO: 1>')
+        self.assertEqual(repr(Open.AC), '<Open.AC: 3>')
+        self.assertEqual(repr(Open.RO | Open.CE), '<Open.CE: 524288>')
+        self.assertEqual(repr(Open.WO | Open.CE), '<Open.WO|CE: 524289>')
+        self.assertEqual(repr(~Open.RO), '<Open.WO|RW|CE: 524291>')
+        self.assertEqual(repr(~Open.WO), '<Open.RW|CE: 524290>')
+        self.assertEqual(repr(~Open.AC), '<Open.CE: 524288>')
+        self.assertEqual(repr(~(Open.RO | Open.CE)), '<Open.AC: 3>')
+        self.assertEqual(repr(~(Open.WO | Open.CE)), '<Open.RW: 2>')
+        self.assertEqual(repr(Open(~4)), '-5')
+        self.assertEqual(repr(Open(4)), '4')
+
+    def test_or(self):
+        Perm = self.Perm
+        for i in Perm:
+            for j in Perm:
+                self.assertEqual(i | j, i.value | j.value)
+                self.assertEqual((i | j).value, i.value | j.value)
+                self.assertIs(type(i | j), Perm)
+            for j in range(8):
+                self.assertEqual(i | j, i.value | j)
+                self.assertEqual((i | j).value, i.value | j)
+                self.assertIs(type(i | j), Perm)
+                self.assertEqual(j | i, j | i.value)
+                self.assertEqual((j | i).value, j | i.value)
+                self.assertIs(type(j | i), Perm)
+        for i in Perm:
+            self.assertIs(i | i, i)
+            self.assertIs(i | 0, i)
+            self.assertIs(0 | i, i)
+        Open = self.Open
+        self.assertIs(Open.RO | Open.CE, Open.CE)
+
+    def test_and(self):
+        Perm = self.Perm
+        RW = Perm.R | Perm.W
+        RX = Perm.R | Perm.X
+        WX = Perm.W | Perm.X
+        RWX = Perm.R | Perm.W | Perm.X
+        values = list(Perm) + [RW, RX, WX, RWX, Perm(0)]
+        for i in values:
+            for j in values:
+                self.assertEqual(i & j, i.value & j.value, 'i is %r, j is %r' % (i, j))
+                self.assertEqual((i & j).value, i.value & j.value, 'i is %r, j is %r' % (i, j))
+                self.assertIs(type(i & j), Perm, 'i is %r, j is %r' % (i, j))
+            for j in range(8):
+                self.assertEqual(i & j, i.value & j)
+                self.assertEqual((i & j).value, i.value & j)
+                self.assertIs(type(i & j), Perm)
+                self.assertEqual(j & i, j & i.value)
+                self.assertEqual((j & i).value, j & i.value)
+                self.assertIs(type(j & i), Perm)
+        for i in Perm:
+            self.assertIs(i & i, i)
+            self.assertIs(i & 7, i)
+            self.assertIs(7 & i, i)
+        Open = self.Open
+        self.assertIs(Open.RO & Open.CE, Open.RO)
+
+    def test_xor(self):
+        Perm = self.Perm
+        for i in Perm:
+            for j in Perm:
+                self.assertEqual(i ^ j, i.value ^ j.value)
+                self.assertEqual((i ^ j).value, i.value ^ j.value)
+                self.assertIs(type(i ^ j), Perm)
+            for j in range(8):
+                self.assertEqual(i ^ j, i.value ^ j)
+                self.assertEqual((i ^ j).value, i.value ^ j)
+                self.assertIs(type(i ^ j), Perm)
+                self.assertEqual(j ^ i, j ^ i.value)
+                self.assertEqual((j ^ i).value, j ^ i.value)
+                self.assertIs(type(j ^ i), Perm)
+        for i in Perm:
+            self.assertIs(i ^ 0, i)
+            self.assertIs(0 ^ i, i)
+        Open = self.Open
+        self.assertIs(Open.RO ^ Open.CE, Open.CE)
+        self.assertIs(Open.CE ^ Open.CE, Open.RO)
+
+    def test_invert(self):
+        Perm = self.Perm
+        RW = Perm.R | Perm.W
+        RX = Perm.R | Perm.X
+        WX = Perm.W | Perm.X
+        RWX = Perm.R | Perm.W | Perm.X
+        values = list(Perm) + [RW, RX, WX, RWX, Perm(0)]
+        for i in values:
+            self.assertEqual(~i, (~i).value)
+            self.assertIs(type(~i), Perm)
+            self.assertEqual(~~i, i)
+        for i in Perm:
+            self.assertIs(~~i, i)
+        Open = self.Open
+        self.assertIs(Open.WO & ~Open.WO, Open.RO)
+        self.assertIs((Open.WO|Open.CE) & ~Open.WO, Open.CE)
+
+    def test_iter(self):
+        Perm = self.Perm
+        NoPerm = Perm.R ^ Perm.R
+        RWX = Perm.R | Perm.W | Perm.X
+        self.assertEqual(list(NoPerm), [])
+        self.assertEqual(list(Perm.R), [Perm.R])
+        self.assertEqual(list(RWX), [Perm.R, Perm.W, Perm.X])
+
+    def test_programatic_function_string(self):
+        Perm = IntFlag('Perm', 'R W X')
+        lst = list(Perm)
+        self.assertEqual(len(lst), len(Perm))
+        self.assertEqual(len(Perm), 3, Perm)
+        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
+        for i, n in enumerate('R W X'.split()):
+            v = 1<<i
+            e = Perm(v)
+            self.assertEqual(e.value, v)
+            self.assertEqual(type(e.value), int)
+            self.assertEqual(e, v)
+            self.assertEqual(e.name, n)
+            self.assertIn(e, Perm)
+            self.assertIs(type(e), Perm)
+
+    def test_programatic_function_string_with_start(self):
+        Perm = IntFlag('Perm', 'R W X', start=8)
+        lst = list(Perm)
+        self.assertEqual(len(lst), len(Perm))
+        self.assertEqual(len(Perm), 3, Perm)
+        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
+        for i, n in enumerate('R W X'.split()):
+            v = 8<<i
+            e = Perm(v)
+            self.assertEqual(e.value, v)
+            self.assertEqual(type(e.value), int)
+            self.assertEqual(e, v)
+            self.assertEqual(e.name, n)
+            self.assertIn(e, Perm)
+            self.assertIs(type(e), Perm)
+
+    def test_programatic_function_string_list(self):
+        Perm = IntFlag('Perm', ['R', 'W', 'X'])
+        lst = list(Perm)
+        self.assertEqual(len(lst), len(Perm))
+        self.assertEqual(len(Perm), 3, Perm)
+        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
+        for i, n in enumerate('R W X'.split()):
+            v = 1<<i
+            e = Perm(v)
+            self.assertEqual(e.value, v)
+            self.assertEqual(type(e.value), int)
+            self.assertEqual(e, v)
+            self.assertEqual(e.name, n)
+            self.assertIn(e, Perm)
+            self.assertIs(type(e), Perm)
+
+    def test_programatic_function_iterable(self):
+        Perm = IntFlag('Perm', (('R', 2), ('W', 8), ('X', 32)))
+        lst = list(Perm)
+        self.assertEqual(len(lst), len(Perm))
+        self.assertEqual(len(Perm), 3, Perm)
+        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
+        for i, n in enumerate('R W X'.split()):
+            v = 1<<(2*i+1)
+            e = Perm(v)
+            self.assertEqual(e.value, v)
+            self.assertEqual(type(e.value), int)
+            self.assertEqual(e, v)
+            self.assertEqual(e.name, n)
+            self.assertIn(e, Perm)
+            self.assertIs(type(e), Perm)
+
+    def test_programatic_function_from_dict(self):
+        Perm = IntFlag('Perm', OrderedDict((('R', 2), ('W', 8), ('X', 32))))
+        lst = list(Perm)
+        self.assertEqual(len(lst), len(Perm))
+        self.assertEqual(len(Perm), 3, Perm)
+        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
+        for i, n in enumerate('R W X'.split()):
+            v = 1<<(2*i+1)
+            e = Perm(v)
+            self.assertEqual(e.value, v)
+            self.assertEqual(type(e.value), int)
+            self.assertEqual(e, v)
+            self.assertEqual(e.name, n)
+            self.assertIn(e, Perm)
+            self.assertIs(type(e), Perm)
+
+    def test_containment(self):
+        Perm = self.Perm
+        R, W, X = Perm
+        RW = R | W
+        RX = R | X
+        WX = W | X
+        RWX = R | W | X
+        self.assertTrue(R in RW)
+        self.assertTrue(R in RX)
+        self.assertTrue(R in RWX)
+        self.assertTrue(W in RW)
+        self.assertTrue(W in WX)
+        self.assertTrue(W in RWX)
+        self.assertTrue(X in RX)
+        self.assertTrue(X in WX)
+        self.assertTrue(X in RWX)
+        self.assertFalse(R in WX)
+        self.assertFalse(W in RX)
+        self.assertFalse(X in RW)
+
+    def test_bool(self):
+        Perm = self.Perm
+        for f in Perm:
+            self.assertTrue(f)
+        Open = self.Open
+        for f in Open:
+            self.assertEqual(bool(f.value), bool(f))
+
+    def test_multiple_mixin(self):
+        class AllMixin(object):
+            @classproperty
+            def ALL(cls):
+                members = list(cls)
+                all_value = None
+                if members:
+                    all_value = members[0]
+                    for member in members[1:]:
+                        all_value |= member
+                cls.ALL = all_value
+                return all_value
+        class StrMixin(object):
+            def __str__(self):
+                return self._name_.lower()
+        class Color(AllMixin, IntFlag):
+            _order_ = 'RED GREEN BLUE'
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(Color.RED.value, 1)
+        self.assertEqual(Color.GREEN.value, 2)
+        self.assertEqual(Color.BLUE.value, 4)
+        self.assertEqual(Color.ALL.value, 7)
+        self.assertEqual(str(Color.BLUE), '4')
+        class Color(AllMixin, StrMixin, IntFlag):
+            _order_ = 'RED GREEN BLUE'
+            __str__ = StrMixin.__str__
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(Color.RED.value, 1)
+        self.assertEqual(Color.GREEN.value, 2)
+        self.assertEqual(Color.BLUE.value, 4)
+        self.assertEqual(Color.ALL.value, 7)
+        self.assertEqual(str(Color.BLUE), 'blue')
+        class Color(StrMixin, AllMixin, IntFlag):
+            _order_ = 'RED GREEN BLUE'
+            __str__ = StrMixin.__str__
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        self.assertEqual(Color.RED.value, 1)
+        self.assertEqual(Color.GREEN.value, 2)
+        self.assertEqual(Color.BLUE.value, 4)
+        self.assertEqual(Color.ALL.value, 7)
+        self.assertEqual(str(Color.BLUE), 'blue')
+
+    @unittest.skipUnless(threading, 'Threading required for this test.')
+    def test_unique_composite(self):
+        # override __eq__ to be identity only
+        class TestFlag(IntFlag):
+            _order_ = 'one two three four five six seven eight'
+            one = auto()
+            two = auto()
+            three = auto()
+            four = auto()
+            five = auto()
+            six = auto()
+            seven = auto()
+            eight = auto()
+            def __eq__(self, other):
+                return self is other
+            def __hash__(self):
+                return hash(self._value_)
+        # have multiple threads competing to complete the composite members
+        seen = set()
+        failed = [False]
+        def cycle_enum():
+            # nonlocal failed
+            try:
+                for i in range(256):
+                    seen.add(TestFlag(i))
+            except Exception:
+                failed[0] = True
+        threads = [
+                threading.Thread(target=cycle_enum)
+                for _ in range(8)
+                ]
+        for t in threads:
+            t.start()
+        for t in threads:
+            t.join()
+        # check that only 248 members were created (8 were created originally)
+        self.assertFalse(
+                failed[0],
+                'at least one thread failed while creating composite members')
+        self.assertEqual(256, len(seen), 'too many composite members created')
+
+    def test_init_subclass(self):
+        class MyEnum(IntEnum):
+            def __init_subclass__(cls, **kwds):
+                super(MyEnum, cls).__init_subclass__(**kwds)
+                self.assertFalse(cls.__dict__.get('_test', False))
+                cls._test1 = 'MyEnum'
+        #
+        class TheirEnum(MyEnum):
+            def __init_subclass__(cls, **kwds):
+                super(TheirEnum, cls).__init_subclass__(**kwds)
+                cls._test2 = 'TheirEnum'
+        class WhoseEnum(TheirEnum):
+            def __init_subclass__(cls, **kwds):
+                pass
+        class NoEnum(WhoseEnum):
+            ONE = 1
+        self.assertEqual(TheirEnum.__dict__['_test1'], 'MyEnum')
+        self.assertEqual(WhoseEnum.__dict__['_test1'], 'MyEnum')
+        self.assertEqual(WhoseEnum.__dict__['_test2'], 'TheirEnum')
+        self.assertFalse(NoEnum.__dict__.get('_test1', False))
+        self.assertFalse(NoEnum.__dict__.get('_test2', False))
+        #
+        class OurEnum(MyEnum):
+            def __init_subclass__(cls, **kwds):
+                cls._test2 = 'OurEnum'
+        class WhereEnum(OurEnum):
+            def __init_subclass__(cls, **kwds):
+                pass
+        class NeverEnum(WhereEnum):
+            ONE = 1
+        self.assertEqual(OurEnum.__dict__['_test1'], 'MyEnum')
+        self.assertFalse(WhereEnum.__dict__.get('_test1', False))
+        self.assertEqual(WhereEnum.__dict__['_test2'], 'OurEnum')
+        self.assertFalse(NeverEnum.__dict__.get('_test1', False))
+        self.assertFalse(NeverEnum.__dict__.get('_test2', False))
+
+
+
+class TestEmptyAndNonLatinStrings(unittest.TestCase):
+
+    def test_empty_string(self):
+        with self.assertRaises(ValueError):
+            empty_abc = Enum('empty_abc', ('', 'B', 'C'))
+
+    def test_non_latin_character_string(self):
+        greek_abc = Enum('greek_abc', ('\u03B1', 'B', 'C'))
+        item = getattr(greek_abc, '\u03B1')
+        self.assertEqual(item.value, 1)
+
+    def test_non_latin_number_string(self):
+        hebrew_123 = Enum('hebrew_123', ('\u05D0', '2', '3'))
+        item = getattr(hebrew_123, '\u05D0')
+        self.assertEqual(item.value, 1)
+
+
+class TestUnique(TestCase):
+    """2.4 doesn't allow class decorators, use function syntax."""
+
+    def test_unique_clean(self):
+        class Clean(Enum):
+            one = 1
+            two = 'dos'
+            tres = 4.0
+        unique(Clean)
+        class Cleaner(IntEnum):
+            single = 1
+            double = 2
+            triple = 3
+        unique(Cleaner)
+
+    def test_unique_dirty(self):
+        try:
+            class Dirty(Enum):
+                __order__ = 'one two'
+                one = 1
+                two = 'dos'
+                tres = 1
+            unique(Dirty)
+        except ValueError:
+            exc = sys.exc_info()[1]
+            message = exc.args[0]
+        self.assertTrue('tres -> one' in message)
+
+        try:
+            class Dirtier(IntEnum):
+                __order__ = 'single triple'
+                single = 1
+                double = 1
+                triple = 3
+                turkey = 3
+            unique(Dirtier)
+        except ValueError:
+            exc = sys.exc_info()[1]
+            message = exc.args[0]
+        self.assertTrue('double -> single' in message)
+        self.assertTrue('turkey -> triple' in message)
+
+    def test_unique_with_name(self):
+        @unique
+        class Silly(Enum):
+            one = 1
+            two = 'dos'
+            name = 3
+        @unique
+        class Sillier(IntEnum):
+            single = 1
+            name = 2
+            triple = 3
+            value = 4
+
+
+class TestNamedTuple(TestCase):
+
+    def test_explicit_indexing(self):
+        class Person(NamedTuple):
+            age = 0
+            first = 1
+            last = 2
+        p1 = Person(17, 'John', 'Doe')
+        p2 = Person(21, 'Jane', 'Doe')
+        self.assertEqual(p1[0], 17)
+        self.assertEqual(p1[1], 'John')
+        self.assertEqual(p1[2], 'Doe')
+        self.assertEqual(p2[0], 21)
+        self.assertEqual(p2[1], 'Jane')
+        self.assertEqual(p2[2], 'Doe')
+        self.assertEqual(p1.age, 17)
+        self.assertEqual(p1.first, 'John')
+        self.assertEqual(p1.last, 'Doe')
+        self.assertEqual(p2.age, 21)
+        self.assertEqual(p2.first, 'Jane')
+        self.assertEqual(p2.last, 'Doe')
+
+    def test_implicit_indexing(self):
+        class Person(NamedTuple):
+            __order__ = "age first last"
+            age = "person's age"
+            first = "person's first name"
+            last = "person's last name"
+        p1 = Person(17, 'John', 'Doe')
+        p2 = Person(21, 'Jane', 'Doe')
+        self.assertEqual(p1[0], 17)
+        self.assertEqual(p1[1], 'John')
+        self.assertEqual(p1[2], 'Doe')
+        self.assertEqual(p2[0], 21)
+        self.assertEqual(p2[1], 'Jane')
+        self.assertEqual(p2[2], 'Doe')
+        self.assertEqual(p1.age, 17)
+        self.assertEqual(p1.first, 'John')
+        self.assertEqual(p1.last, 'Doe')
+        self.assertEqual(p2.age, 21)
+        self.assertEqual(p2.first, 'Jane')
+        self.assertEqual(p2.last, 'Doe')
+
+    def test_mixed_indexing(self):
+        class Person(NamedTuple):
+            __order__ = "age last cars"
+            age = "person's age"
+            last = 2, "person's last name"
+            cars = "person's cars"
+        p1 = Person(17, 'John', 'Doe', 3)
+        p2 = Person(21, 'Jane', 'Doe', 9)
+        self.assertEqual(p1[0], 17)
+        self.assertEqual(p1[1], 'John')
+        self.assertEqual(p1[2], 'Doe')
+        self.assertEqual(p1[3], 3)
+        self.assertEqual(p2[0], 21)
+        self.assertEqual(p2[1], 'Jane')
+        self.assertEqual(p2[2], 'Doe')
+        self.assertEqual(p2[3], 9)
+        self.assertEqual(p1.age, 17)
+        self.assertEqual(p1.last, 'Doe')
+        self.assertEqual(p1.cars, 3)
+        self.assertEqual(p2.age, 21)
+        self.assertEqual(p2.last, 'Doe')
+        self.assertEqual(p2.cars, 9)
+
+    def test_issubclass(self):
+        class Person(NamedTuple):
+            age = 0
+            first = 1
+            last = 2
+        self.assertTrue(issubclass(Person, NamedTuple))
+        self.assertTrue(issubclass(Person, tuple))
+
+    def test_isinstance(self):
+        class Person(NamedTuple):
+            age = 0
+            first = 1
+            last = 2
+        p1 = Person(17, 'John', 'Doe')
+        self.assertTrue(isinstance(p1, Person))
+        self.assertTrue(isinstance(p1, NamedTuple))
+        self.assertTrue(isinstance(p1, tuple))
+
+    def test_explicit_indexing_after_functional_api(self):
+        Person = NamedTuple('Person', (('age', 0), ('first', 1), ('last', 2)))
+        p1 = Person(17, 'John', 'Doe')
+        p2 = Person(21, 'Jane', 'Doe')
+        self.assertEqual(p1[0], 17)
+        self.assertEqual(p1[1], 'John')
+        self.assertEqual(p1[2], 'Doe')
+        self.assertEqual(p2[0], 21)
+        self.assertEqual(p2[1], 'Jane')
+        self.assertEqual(p2[2], 'Doe')
+        self.assertEqual(p1.age, 17)
+        self.assertEqual(p1.first, 'John')
+        self.assertEqual(p1.last, 'Doe')
+        self.assertEqual(p2.age, 21)
+        self.assertEqual(p2.first, 'Jane')
+        self.assertEqual(p2.last, 'Doe')
+
+    def test_implicit_indexing_after_functional_api(self):
+        Person = NamedTuple('Person', 'age first last')
+        p1 = Person(17, 'John', 'Doe')
+        p2 = Person(21, 'Jane', 'Doe')
+        self.assertEqual(p1[0], 17)
+        self.assertEqual(p1[1], 'John')
+        self.assertEqual(p1[2], 'Doe')
+        self.assertEqual(p2[0], 21)
+        self.assertEqual(p2[1], 'Jane')
+        self.assertEqual(p2[2], 'Doe')
+        self.assertEqual(p1.age, 17)
+        self.assertEqual(p1.first, 'John')
+        self.assertEqual(p1.last, 'Doe')
+        self.assertEqual(p2.age, 21)
+        self.assertEqual(p2.first, 'Jane')
+        self.assertEqual(p2.last, 'Doe')
+
+    def test_mixed_indexing_after_functional_api(self):
+        Person = NamedTuple('Person', (('age', 0), ('last', 2), ('cars', 3)))
+        p1 = Person(17, 'John', 'Doe', 3)
+        p2 = Person(21, 'Jane', 'Doe', 9)
+        self.assertEqual(p1[0], 17)
+        self.assertEqual(p1[1], 'John')
+        self.assertEqual(p1[2], 'Doe')
+        self.assertEqual(p1[3], 3)
+        self.assertEqual(p2[0], 21)
+        self.assertEqual(p2[1], 'Jane')
+        self.assertEqual(p2[2], 'Doe')
+        self.assertEqual(p2[3], 9)
+        self.assertEqual(p1.age, 17)
+        self.assertEqual(p1.last, 'Doe')
+        self.assertEqual(p1.cars, 3)
+        self.assertEqual(p2.age, 21)
+        self.assertEqual(p2.last, 'Doe')
+        self.assertEqual(p2.cars, 9)
+
+    def test_issubclass_after_functional_api(self):
+        Person = NamedTuple('Person', 'age first last')
+        self.assertTrue(issubclass(Person, NamedTuple))
+        self.assertTrue(issubclass(Person, tuple))
+
+    def test_isinstance_after_functional_api(self):
+        Person = NamedTuple('Person', 'age first last')
+        p1 = Person(17, 'John', 'Doe')
+        self.assertTrue(isinstance(p1, Person))
+        self.assertTrue(isinstance(p1, NamedTuple))
+        self.assertTrue(isinstance(p1, tuple))
+
+    def test_creation_with_all_keywords(self):
+        Person = NamedTuple('Person', 'age first last')
+        p1 = Person(age=17, first='John', last='Doe')
+        self.assertEqual(p1[0], 17)
+        self.assertEqual(p1[1], 'John')
+        self.assertEqual(p1[2], 'Doe')
+        self.assertEqual(p1.age, 17)
+        self.assertEqual(p1.first, 'John')
+        self.assertEqual(p1.last, 'Doe')
+
+    def test_creation_with_some_keywords(self):
+        Person = NamedTuple('Person', 'age first last')
+        p1 = Person(17, first='John', last='Doe')
+        self.assertEqual(p1[0], 17)
+        self.assertEqual(p1[1], 'John')
+        self.assertEqual(p1[2], 'Doe')
+        self.assertEqual(p1.age, 17)
+        self.assertEqual(p1.first, 'John')
+        self.assertEqual(p1.last, 'Doe')
+        p1 = Person(17, last='Doe', first='John')
+        self.assertEqual(p1[0], 17)
+        self.assertEqual(p1[1], 'John')
+        self.assertEqual(p1[2], 'Doe')
+        self.assertEqual(p1.age, 17)
+        self.assertEqual(p1.first, 'John')
+        self.assertEqual(p1.last, 'Doe')
+
+    def test_custom_new(self):
+        class Book(NamedTuple):
+            title = 0
+            author = 1
+            genre = 2
+            def __new__(cls, string):
+                args = [s.strip() for s in string.split(';')]
+                return super(Book, cls).__new__(cls, *tuple(args))
+        b1 = Book('The Last Mohican; John Doe; Historical')
+        self.assertEqual(b1.title, 'The Last Mohican')
+        self.assertEqual(b1.author, 'John Doe')
+        self.assertEqual(b1.genre, 'Historical')
+
+    def test_defaults_in_class(self):
+        class Character(NamedTuple):
+            name = 0
+            gender = 1, None, 'male'
+            klass = 2, None, 'fighter'
+        for char in (
+                {'name':'John Doe'},
+                {'name':'William Pickney', 'klass':'scholar'},
+                {'name':'Sarah Doughtery', 'gender':'female'},
+                {'name':'Sissy Moonbeam', 'gender':'female', 'klass':'sorceress'},
+                ):
+            c = Character(**char)
+            for name, value in (('name', None), ('gender','male'), ('klass','fighter')):
+                if name in char:
+                    value = char[name]
+                self.assertEqual(getattr(c, name), value)
+
+    def test_defaults_in_class_that_are_falsey(self):
+        class Point(NamedTuple):
+            x = 0, 'horizondal coordinate', 0
+            y = 1, 'vertical coordinate', 0
+        p = Point()
+        self.assertEqual(p.x, 0)
+        self.assertEqual(p.y, 0)
+
+    def test_pickle_namedtuple_with_module(self):
+        if isinstance(LifeForm, Exception):
+            raise LifeForm
+        lf = LifeForm('this', 'that', 'theother')
+        test_pickle_dump_load(self.assertEqual, lf)
+
+    def test_pickle_namedtuple_without_module(self):
+        if isinstance(DeathForm, Exception):
+            raise DeathForm
+        df = DeathForm('sickly green', '2x4', 'foul')
+        test_pickle_dump_load(self.assertEqual, df)
+
+    def test_subclassing(self):
+        if isinstance(ThatsIt, Exception):
+            raise ThatsIt
+        ti = ThatsIt('Henry', 'Weinhardt')
+        self.assertEqual(ti.blah, 'Henry')
+        self.assertTrue(ti.what(), 'Henry')
+        test_pickle_dump_load(self.assertEqual, ti)
+
+    def test_contains(self):
+        Book = NamedTuple('Book', 'title author genre')
+        b = Book('Teckla', 'Steven Brust', 'fantasy')
+        self.assertTrue('Teckla' in b)
+        self.assertTrue('Steven Brust' in b)
+        self.assertTrue('fantasy' in b)
+
+    def test_fixed_size(self):
+        class Book(NamedTuple):
+            _size_ = TupleSize.fixed
+            title = 0
+            author = 1
+            genre = 2
+        b = Book('Teckla', 'Steven Brust', 'fantasy')
+        self.assertTrue('Teckla' in b)
+        self.assertTrue('Steven Brust' in b)
+        self.assertTrue('fantasy' in b)
+        self.assertEqual(b.title, 'Teckla')
+        self.assertEqual(b.author, 'Steven Brust')
+        self.assertRaises(TypeError, Book, 'Teckla', 'Steven Brust')
+        self.assertRaises(TypeError, Book, 'Teckla')
+
+    def test_minimum_size(self):
+        class Book(NamedTuple):
+            _size_ = TupleSize.minimum
+            title = 0
+            author = 1
+        b = Book('Teckla', 'Steven Brust', 'fantasy')
+        self.assertTrue('Teckla' in b)
+        self.assertTrue('Steven Brust' in b)
+        self.assertTrue('fantasy' in b)
+        self.assertEqual(b.title, 'Teckla')
+        self.assertEqual(b.author, 'Steven Brust')
+        b = Book('Teckla', 'Steven Brust')
+        self.assertTrue('Teckla' in b)
+        self.assertTrue('Steven Brust' in b)
+        self.assertEqual(b.title, 'Teckla')
+        self.assertEqual(b.author, 'Steven Brust')
+        self.assertRaises(TypeError, Book, 'Teckla')
+
+    def test_variable_size(self):
+        class Book(NamedTuple):
+            _size_ = TupleSize.variable
+            title = 0
+            author = 1
+            genre = 2
+        b = Book('Teckla', 'Steven Brust', 'fantasy')
+        self.assertTrue('Teckla' in b)
+        self.assertTrue('Steven Brust' in b)
+        self.assertTrue('fantasy' in b)
+        self.assertEqual(b.title, 'Teckla')
+        self.assertEqual(b.author, 'Steven Brust')
+        self.assertEqual(b.genre, 'fantasy')
+        b = Book('Teckla', 'Steven Brust')
+        self.assertTrue('Teckla' in b)
+        self.assertTrue('Steven Brust' in b)
+        self.assertEqual(b.title, 'Teckla')
+        self.assertEqual(b.author, 'Steven Brust')
+        self.assertRaises(AttributeError, getattr, b, 'genre')
+        self.assertRaises(TypeError, Book, title='Teckla', genre='fantasy')
+        self.assertRaises(TypeError, Book, author='Steven Brust')
+
+    def test_combining_namedtuples(self):
+        class Point(NamedTuple):
+            x = 0, 'horizontal coordinate', 1
+            y = 1, 'vertical coordinate', -1
+        class Color(NamedTuple):
+            r = 0, 'red component', 11
+            g = 1, 'green component', 29
+            b = 2, 'blue component', 37
+        Pixel1 = NamedTuple('Pixel', Point+Color, module=__name__)
+        class Pixel2(Point, Color):
+            "a colored dot"
+        class Pixel3(Point):
+            r = 2, 'red component', 11
+            g = 3, 'green component', 29
+            b = 4, 'blue component', 37
+        self.assertEqual(Pixel1._fields_, 'x y r g b'.split())
+        self.assertEqual(Pixel1.x.__doc__, 'horizontal coordinate')
+        self.assertEqual(Pixel1.x.default, 1)
+        self.assertEqual(Pixel1.y.__doc__, 'vertical coordinate')
+        self.assertEqual(Pixel1.y.default, -1)
+        self.assertEqual(Pixel1.r.__doc__, 'red component')
+        self.assertEqual(Pixel1.r.default, 11)
+        self.assertEqual(Pixel1.g.__doc__, 'green component')
+        self.assertEqual(Pixel1.g.default, 29)
+        self.assertEqual(Pixel1.b.__doc__, 'blue component')
+        self.assertEqual(Pixel1.b.default, 37)
+        self.assertEqual(Pixel2._fields_, 'x y r g b'.split())
+        self.assertEqual(Pixel2.x.__doc__, 'horizontal coordinate')
+        self.assertEqual(Pixel2.x.default, 1)
+        self.assertEqual(Pixel2.y.__doc__, 'vertical coordinate')
+        self.assertEqual(Pixel2.y.default, -1)
+        self.assertEqual(Pixel2.r.__doc__, 'red component')
+        self.assertEqual(Pixel2.r.default, 11)
+        self.assertEqual(Pixel2.g.__doc__, 'green component')
+        self.assertEqual(Pixel2.g.default, 29)
+        self.assertEqual(Pixel2.b.__doc__, 'blue component')
+        self.assertEqual(Pixel2.b.default, 37)
+        self.assertEqual(Pixel3._fields_, 'x y r g b'.split())
+        self.assertEqual(Pixel3.x.__doc__, 'horizontal coordinate')
+        self.assertEqual(Pixel3.x.default, 1)
+        self.assertEqual(Pixel3.y.__doc__, 'vertical coordinate')
+        self.assertEqual(Pixel3.y.default, -1)
+        self.assertEqual(Pixel3.r.__doc__, 'red component')
+        self.assertEqual(Pixel3.r.default, 11)
+        self.assertEqual(Pixel3.g.__doc__, 'green component')
+        self.assertEqual(Pixel3.g.default, 29)
+        self.assertEqual(Pixel3.b.__doc__, 'blue component')
+        self.assertEqual(Pixel3.b.default, 37)
+
+    def test_function_api_type(self):
+        class Tester(NamedTuple):
+            def howdy(self):
+                return 'backwards', list(reversed(self))
+        Testee = NamedTuple('Testee', 'a c e', type=Tester)
+        t = Testee(1, 2, 3)
+        self.assertEqual(t.howdy(), ('backwards', [3, 2, 1]))
+
+    def test_asdict(self):
+        class Point(NamedTuple):
+            x = 0, 'horizontal coordinate', 1
+            y = 1, 'vertical coordinate', -1
+        class Color(NamedTuple):
+            r = 0, 'red component', 11
+            g = 1, 'green component', 29
+            b = 2, 'blue component', 37
+        Pixel = NamedTuple('Pixel', Point+Color, module=__name__)
+        pixel = Pixel(99, -101, 255, 128, 0)
+        self.assertEqual(pixel._asdict(), {'x':99, 'y':-101, 'r':255, 'g':128, 'b':0})
+
+    def test_make(self):
+        class Point(NamedTuple):
+            x = 0, 'horizontal coordinate', 1
+            y = 1, 'vertical coordinate', -1
+        self.assertEqual(Point(4, 5), (4, 5))
+        self.assertEqual(Point._make((4, 5)), (4, 5))
+
+    def test_replace(self):
+        class Color(NamedTuple):
+            r = 0, 'red component', 11
+            g = 1, 'green component', 29
+            b = 2, 'blue component', 37
+        purple = Color(127, 0, 127)
+        mid_gray = purple._replace(g=127)
+        self.assertEqual(mid_gray, (127, 127, 127))
+
+
+class TestNamedConstant(TestCase):
+
+    def test_constantness(self):
+        class K(NamedConstant):
+            PI = 3.141596
+            TAU = 2 * PI
+        self.assertEqual(K.PI, 3.141596)
+        self.assertEqual(K.TAU, 2 * K.PI)
+        with self.assertRaisesRegex(AttributeError, r'cannot rebind constant'):
+            K.PI = 9
+        with self.assertRaisesRegex(AttributeError, r'cannot delete constant'):
+            del K.PI
+        with self.assertRaisesRegex(AttributeError, r'cannot rebind constant'):
+            K('PI', 3)
+        self.assertTrue(K.PI in K)
+        self.assertTrue(K.TAU in K)
+
+    def test_duplicates(self):
+        class CardNumber(NamedConstant):
+            ACE      = 11
+            TWO      = 2
+            THREE    = 3
+            FOUR     = 4
+            FIVE     = 5
+            SIX      = 6
+            SEVEN    = 7
+            EIGHT    = 8
+            NINE     = 9
+            TEN      = 10
+            JACK     = 10
+            QUEEN    = 10
+            KING     = 10
+        self.assertFalse(CardNumber.TEN is CardNumber.JACK)
+        self.assertEqual(CardNumber.TEN, CardNumber.JACK)
+        self.assertEqual(CardNumber.TEN, 10)
+
+    def test_extend_constants(self):
+        class CardSuit(NamedConstant):
+            HEARTS = 1
+            SPADES = 2
+            DIAMONTS = 3
+            CLUBS = 4
+        self.assertEqual(CardSuit.HEARTS, 1)
+        stars = CardSuit('STARS', 5)
+        self.assertIs(stars, CardSuit.STARS)
+        self.assertEqual(CardSuit.STARS, 5)
+        self.assertTrue(CardSuit.STARS in CardSuit)
+
+    def test_constant_with_docstring(self):
+        class Stuff(NamedConstant):
+            Artifact = constant(7, "lucky number!")
+            Bowling = 11
+            HillWomp = constant(29, 'blah blah')
+        self.assertEqual(Stuff.Artifact, 7)
+        self.assertEqual(Stuff.Artifact.__doc__, 'lucky number!')
+        self.assertEqual(Stuff.Bowling, 11)
+        self.assertEqual(Stuff.Bowling.__doc__, None)
+        self.assertEqual(Stuff.HillWomp, 29)
+        self.assertEqual(Stuff.HillWomp.__doc__, 'blah blah')
+
+    def test_deep_copy(self):
+        import copy
+        class APITypes(aenum.Constant):
+            STRING = "string"
+            INT = "int"
+        APITypes('string')
+        d = {"first": APITypes.STRING}
+        copy.deepcopy(d)
+        self.assertTrue(d['first'] is APITypes.STRING)
+
+    def test_subclass_w_same_value(self):
+        class Foo(aenum.Constant):
+            BLA = 'bla1'
+            ABA = 'aba1'
+        class Bar(aenum.Constant):
+            BLA = Foo.BLA
+            ABA = 'aba2'
+        self.assertEqual(Foo.BLA, Bar.BLA)
+        self.assertFalse(Foo.BLA is Bar.BLA)
+
+
+class TestStarImport(TestCase):
+
+    def test_all_exports_names(self):
+        scope = {}
+        exec('from aenum import *', scope, scope)
+        self.assertIn('Enum', scope)
+
+class TestStackoverflowAnswers(TestCase):
+
+    def test_self_referential_directions(self):
+        # https://stackoverflow.com/a/64000706/208880
+        class Directions(Enum):
+            _order_ = 'NORTH WEST SOUTH EAST'
+            #
+            NORTH = 1, 0
+            WEST = 0, 1
+            SOUTH = -1, 0
+            EAST = 0, -1
+            #
+            def __init__(self, x, y):
+                self.x = x
+                self.y = y
+                if len(self.__class__):
+                    # make links
+                    all = list(self.__class__)
+                    left, right = all[0], all[-1]
+                    self.left = left
+                    self.right = right
+                    left.right = self
+                    right.left = self
+        #
+        D = Directions
+        self.assertEqual(D.NORTH.value, (1, 0))
+        self.assertTrue(D.NORTH.left is D.WEST)
+        self.assertTrue(D.SOUTH.right is D.WEST)
+
+    def test_self_referential_rock_paper_scissors(self):
+        # https://stackoverflow.com/a/57085357/208880
+        class RPS(Enum):
+            _order_ = 'Rock, Paper, Scissors'
+            #
+            Rock = "rock"
+            Paper = "paper"
+            Scissors = "scissors"
+            #
+            def __init__(self, value):
+                if len(self.__class__):
+                    # make links
+                    all = list(self.__class__)
+                    first, previous = all[0], all[-1]
+                    first.beats = self
+                    self.beats = previous
+        #
+        self.assertTrue(RPS.Rock.beats is RPS.Scissors)
+        self.assertTrue(RPS.Scissors.beats is RPS.Paper)
+        self.assertTrue(RPS.Paper.beats is RPS.Rock)
+
+    def test_arduino_headers(self):
+        # https://stackoverflow.com/q/65048495/208880
+        class CHeader(Enum):
+            def __init_subclass__(cls, **kwds):
+                # write Enums to C header file
+                cls_name = cls.__name__
+                header_path = getattr(cls, '_%s__header' % cls_name)
+                with open(header_path, 'w') as fh:
+                    fh.write('initial header stuff here\n')
+                    for enum in cls:
+                        fh.write('#define %s %r\n' % (enum.name, enum.value))
+        class Arduino(CHeader):
+            _order_ = 'ONE TWO'
+            __header = os.path.join(tempdir, 'arduino.h')
+            ONE = 1
+            TWO = 2
+        with open(os.path.join(tempdir, 'arduino.h')) as fh:
+                data = fh.read()
+        self.assertEqual(textwrap.dedent("""\
+                initial header stuff here
+                #define ONE 1
+                #define TWO 2
+                """),
+                data,
+                )
+
+    def test_lowercase_compare(self):
+        # https://stackoverflow.com/q/65139026/208880
+        class CompareLowerCase(Enum):
+            def __init_subclass__(cls, **kwds):
+                super(CompareLowerCase, cls).__init_subclass__(**kwds)
+                cls.lowered_names = set([m.name.lower() for m in cls])
+            @classmethod
+            def has_name(cls, name):
+                return name.lower() in cls.lowered_names
+        #
+        class LabelEnum(CompareLowerCase, StrEnum):
+            ENUM_ONE = "Enum One"
+            ENUM_TWO = "Enum Two"
+            ENUM_THREE = "Enum Three"
+            FOUR = "FOUR"
+            FIVE = "FIVE"
+            SIX = "SIX"
+        #
+        self.assertTrue(LabelEnum.has_name('Enum_Three'))
+
+
+class TestExtendEnum(TestCase):
+
+    def test_extend_enum_plain(self):
+        class Color(Enum):
+            red = 1
+            green = 2
+            blue = 3
+        self.assertRaisesRegex(TypeError, 'already in use as', extend_enum, Color, 'blue', 5)
+        #
+        extend_enum(Color, 'brown', 4)
+        self.assertEqual(Color.brown.name, 'brown')
+        self.assertEqual(Color.brown.value, 4)
+        self.assertTrue(Color.brown in Color)
+        self.assertEqual(Color(4), Color.brown)
+        self.assertEqual(Color['brown'], Color.brown)
+        self.assertEqual(len(Color), 4)
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, 5)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(5), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+        self.assertEqual(len(Color), 5)
+
+    def test_extend_enum_alias(self):
+        class Color(Enum):
+            red = 1
+            green = 2
+            blue = 3
+        extend_enum(Color, 'rojo', 1)
+        self.assertEqual(Color.rojo.name, 'red')
+        self.assertEqual(Color.rojo.value, 1)
+        self.assertTrue(Color.rojo in Color)
+        self.assertEqual(Color(1), Color.rojo)
+        self.assertEqual(Color['rojo'], Color.red)
+        self.assertEqual(len(Color), 3)
+
+    def test_extend_enum_unique(self):
+        class Color(UniqueEnum):
+            red = 1
+            green = 2
+            blue = 3
+        self.assertRaisesRegex(ValueError, r'<Color.rojo: 1> is a duplicate of <Color.red: 1>', extend_enum, Color, 'rojo', 1)
+        #
+        self.assertEqual(Color.red.name, 'red')
+        self.assertEqual(Color.red.value, 1)
+        self.assertTrue(Color.red in Color)
+        self.assertEqual(Color(1), Color.red)
+        self.assertEqual(Color['red'], Color.red)
+        self.assertEqual(Color.green.name, 'green')
+        self.assertEqual(Color.green.value, 2)
+        self.assertTrue(Color.green in Color)
+        self.assertEqual(Color(2), Color.green)
+        self.assertEqual(Color['blue'], Color.blue)
+        self.assertEqual(Color.blue.name, 'blue')
+        self.assertEqual(Color.blue.value, 3)
+        self.assertTrue(Color.blue in Color)
+        self.assertEqual(Color(3), Color.blue)
+        self.assertEqual(len(Color), 3)
+        #
+        extend_enum(Color, 'brown', 4)
+        self.assertEqual(Color.brown.name, 'brown')
+        self.assertEqual(Color.brown.value, 4)
+        self.assertTrue(Color.brown in Color)
+        self.assertEqual(Color(4), Color.brown)
+        self.assertEqual(Color['brown'], Color.brown)
+        self.assertEqual(len(Color), 4)
+        #
+        self.assertRaisesRegex(ValueError, '', extend_enum, Color, 'verde', 2)
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, 5)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(5), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+        self.assertEqual(len(Color), 5)
+
+
+    def test_extend_enum_shadow_property(self):
+        class Color(Enum):
+            red = 1
+            green = 2
+            blue = 3
+        extend_enum(Color, 'value', 4)
+        self.assertEqual(Color.value.name, 'value')
+        self.assertEqual(Color.value.value, 4)
+        self.assertTrue(Color.value in Color)
+        self.assertEqual(Color(4), Color.value)
+        self.assertEqual(Color['value'], Color.value)
+        self.assertEqual(len(Color), 4)
+        self.assertEqual(Color.red.value, 1)
+
+    def test_extend_enum_shadow_base(self):
+        class hohum(object):
+            def cyan(self):
+                "cyanize a color"
+                return self.value
+        class Color(hohum, Enum):
+            red = 1
+            green = 2
+            blue = 3
+        self.assertRaisesRegex(TypeError, r'already in use in superclass', extend_enum, Color, 'cyan', 4)
+        self.assertEqual(len(Color), 3)
+        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
+
+    def test_extend_enum_multivalue(self):
+        class Color(MultiValueEnum):
+            red = 1, 4, 7
+            green = 2, 5, 8
+            blue = 3, 6, 9
+        extend_enum(Color, 'brown', 10, 20)
+        self.assertEqual(Color.brown.name, 'brown')
+        self.assertEqual(Color.brown.value, 10)
+        self.assertTrue(Color.brown in Color)
+        self.assertEqual(Color(10), Color.brown)
+        self.assertEqual(Color(20), Color.brown)
+        self.assertEqual(Color['brown'], Color.brown)
+        self.assertEqual(len(Color), 4)
+        #
+        self.assertRaisesRegex(ValueError, 'no values specified for MultiValue enum', extend_enum, Color, 'mauve')
+
+    def test_extend_enum_multivalue_alias(self):
+        class Color(MultiValueEnum):
+            red = 1, 4, 7
+            green = 2, 5, 8
+            blue = 3, 6, 9
+        self.assertRaisesRegex(ValueError, r'<Color.rojo: 7> is a duplicate of <Color.red: 1>', extend_enum, Color, 'rojo', 7)
+        self.assertEqual(Color.red.name, 'red')
+        self.assertEqual(Color.red.value, 1)
+        self.assertTrue(Color.red in Color)
+        self.assertEqual(Color(1), Color.red)
+        self.assertEqual(Color(4), Color.red)
+        self.assertEqual(Color(7), Color.red)
+        self.assertEqual(Color['red'], Color.red)
+        self.assertEqual(Color.green.name, 'green')
+        self.assertEqual(Color.green.value, 2)
+        self.assertTrue(Color.green in Color)
+        self.assertEqual(Color(2), Color.green)
+        self.assertEqual(Color(5), Color.green)
+        self.assertEqual(Color(8), Color.green)
+        self.assertEqual(Color['blue'], Color.blue)
+        self.assertEqual(Color.blue.name, 'blue')
+        self.assertEqual(Color.blue.value, 3)
+        self.assertTrue(Color.blue in Color)
+        self.assertEqual(Color(3), Color.blue)
+        self.assertEqual(Color(6), Color.blue)
+        self.assertEqual(Color(9), Color.blue)
+        self.assertEqual(len(Color), 3)
+
+    def test_extend_enum_multivalue_str(self):
+        class M(str, MultiValueEnum):
+            VALUE_1 = 'value_1', 'VALUE_1'
+            VALUE_2 = 'value_2', 'VALUE_2'
+            VALUE_3 = 'value_3', 'VALUE_3'
+        self.assertTrue(M._member_type_ is str)
+        extend_enum(M, 'VALUE_4', 'value_4', 'VALUE_4')
+        self.assertEqual(list(M), [M.VALUE_1, M.VALUE_2, M.VALUE_3, M.VALUE_4])
+        self.assertTrue(M('value_4') is M.VALUE_4)
+        self.assertTrue(M('VALUE_4') is M.VALUE_4)
+        self.assertTrue(M.VALUE_4.name == 'VALUE_4')
+        self.assertTrue(M.VALUE_4.value == 'value_4')
+
+    def test_extend_intenum(self):
+        class Index(IntEnum):
+            DeviceType    = 0x1000
+            ErrorRegister = 0x1001
+
+        for name, value in (
+                ('ControlWord', 0x6040),
+                ('StatusWord', 0x6041),
+                ('OperationMode', 0x6060),
+                ):
+            extend_enum(Index, name, value)
+
+        self.assertEqual(len(Index), 5)
+        self.assertEqual(list(Index), [Index.DeviceType, Index.ErrorRegister, Index.ControlWord, Index.StatusWord, Index.OperationMode])
+        self.assertEqual(Index.DeviceType.value, 0x1000)
+        self.assertEqual(Index.StatusWord.value, 0x6041)
+
+    def test_extend_multi_init(self):
+        try:
+            from http import HTTPStatus
+            length = len(HTTPStatus)
+        except ImportError:
+            class HTTPStatus(IntEnum):
+                def __new__(cls, value, phrase, description):
+                    obj = int.__new__(cls, value)
+                    obj._value_ = value
+
+                    obj.phrase = phrase
+                    obj.description = description
+                    return obj
+                CONTINUE = 100, 'Continue', 'Request received, please continue'
+                SWITCHING_PROTOCOLS = 101, 'Switching Protocols', 'Switching to new protocol; obey Upgrade header'
+                PROCESSING = 102, 'Processing', ''
+            length = 3
+        extend_enum(HTTPStatus, 'BAD_SPAM', 513, 'Too greasy', 'for a train')
+        extend_enum(HTTPStatus, 'BAD_EGGS', 514, 'Too green', '')
+        self.assertEqual(len(HTTPStatus), length+2)
+        self.assertEqual(
+                list(HTTPStatus)[-2:],
+                [HTTPStatus.BAD_SPAM, HTTPStatus.BAD_EGGS],
+                )
+        self.assertEqual(HTTPStatus.BAD_SPAM.value, 513)
+        self.assertEqual(HTTPStatus.BAD_SPAM.name, 'BAD_SPAM')
+        self.assertEqual(HTTPStatus.BAD_SPAM.phrase, 'Too greasy')
+        self.assertEqual(HTTPStatus.BAD_SPAM.description, 'for a train')
+        self.assertEqual(HTTPStatus.BAD_EGGS.value, 514)
+        self.assertEqual(HTTPStatus.BAD_EGGS.name, 'BAD_EGGS')
+        self.assertEqual(HTTPStatus.BAD_EGGS.phrase, 'Too green')
+        self.assertEqual(HTTPStatus.BAD_EGGS.description, '')
+
+    def test_extend_flag(self):
+        class Color(Flag):
+            BLACK = 0
+            RED = 1
+            GREEN = 2
+            BLUE = 4
+        extend_enum(Color, 'MAGENTA')
+        self.assertTrue(Color(8) is Color.MAGENTA)
+        self.assertTrue(isinstance(Color.MAGENTA, Color))
+        self.assertEqual(Color.MAGENTA.value, 8)
+        extend_enum(Color, 'PURPLE', 11)
+        self.assertTrue(Color(11) is Color.PURPLE)
+        self.assertTrue(isinstance(Color.PURPLE, Color))
+        self.assertEqual(Color.PURPLE.value, 11)
+        self.assertTrue(issubclass(Color, Flag))
+
+    def test_extend_flag_backwards(self):
+        class Color(Flag):
+            BLACK = 0
+            RED = 1
+            GREEN = 2
+            BLUE = 4
+        extend_enum(Color, 'PURPLE', 11)
+        self.assertTrue(Color(11) is Color.PURPLE)
+        self.assertTrue(isinstance(Color.PURPLE, Color))
+        self.assertEqual(Color.PURPLE.value, 11)
+        self.assertTrue(issubclass(Color, Flag))
+        #
+        extend_enum(Color, 'MAGENTA')
+        self.assertTrue(Color(8) is Color.MAGENTA)
+        self.assertTrue(isinstance(Color.MAGENTA, Color))
+        self.assertEqual(Color.MAGENTA.value, 8)
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, 16)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(16), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+        self.assertEqual(len(Color), 5)
+
+    def test_extend_intflag(self):
+        class Color(IntFlag):
+            BLACK = 0
+            RED = 1
+            GREEN = 2
+            BLUE = 4
+        extend_enum(Color, 'MAGENTA')
+        self.assertTrue(Color(8) is Color.MAGENTA)
+        self.assertTrue(isinstance(Color.MAGENTA, Color))
+        self.assertEqual(Color.MAGENTA.value, 8)
+        extend_enum(Color, 'PURPLE', 11)
+        self.assertTrue(Color(11) is Color.PURPLE)
+        self.assertTrue(isinstance(Color.PURPLE, Color))
+        self.assertEqual(Color.PURPLE.value, 11)
+        self.assertTrue(issubclass(Color, Flag))
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, 16)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(16), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+        self.assertEqual(len(Color), 5)
+
+    def test_extend_intflag_backwards(self):
+        class Color(IntFlag):
+            BLACK = 0
+            RED = 1
+            GREEN = 2
+            BLUE = 4
+        extend_enum(Color, 'PURPLE', 11)
+        self.assertTrue(Color(11) is Color.PURPLE)
+        self.assertTrue(isinstance(Color.PURPLE, Color))
+        self.assertEqual(Color.PURPLE.value, 11)
+        self.assertTrue(issubclass(Color, Flag))
+        #
+        extend_enum(Color, 'MAGENTA')
+        self.assertTrue(Color(8) is Color.MAGENTA)
+        self.assertTrue(isinstance(Color.MAGENTA, Color))
+        self.assertEqual(Color.MAGENTA.value, 8)
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, 16)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(16), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+        self.assertEqual(len(Color), 5)
+
+    def test_extend_strenum(self):
+        class Color(StrEnum):
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        extend_enum(Color, 'BLACK')
+        self.assertEqual(Color.BLACK.name, 'BLACK')
+        self.assertEqual(Color.BLACK.value, 'black')
+        self.assertEqual(len(Color), 4)
+
+
+class TestIssues(TestCase):
+
+    def test_auto_multi_int(self):
+        class Measurement(int, MultiValueEnum, AddValueEnum):
+            _order_ = 'one two three'
+            _start_ = 0
+            one = "20110721"
+            two = "20120911"
+            three = "20110518"
+        self.assertEqual([m.value for m in Measurement], [0, 1, 2])
+        self.assertEqual([m.name for m in Measurement], ['one', 'two', 'three'])
+        self.assertIs(Measurement('20110721'), Measurement.one)
+        self.assertIs(Measurement(0), Measurement.one)
+        self.assertIs(Measurement('20120911'), Measurement.two)
+        self.assertIs(Measurement(1), Measurement.two)
+        self.assertIs(Measurement('20110518'), Measurement.three)
+        self.assertIs(Measurement(2), Measurement.three)
+
+    def test_auto_kwds(self):
+        class Item(Enum):
+            _order_ = 'A B'
+            A = auto(size=100, requirements={})
+            B = auto(size=200, requirements={A: 1})
+            #
+            def __new__(cls, value, size, requirements):
+                obj = object.__new__(cls)
+                obj._value_ = value
+                obj.size = size
+                # fix requirements
+                new_requirements = {}
+                for k, v in requirements.items():
+                    if isinstance(k, auto):
+                        k = k.enum_member
+                    new_requirements[k] = v
+                obj.requirements = new_requirements
+                return obj
+        self.assertEqual((Item.A.value, Item.A.size, Item.A.requirements), (1, 100, {}))
+        self.assertEqual((Item.B.value, Item.B.size, Item.B.requirements), (2, 200, {Item.A: 1}))
+
+    def test_extend_flag(self):
+        class FlagTest(Flag): # Or IntFlag
+            NONE = 0
+            LOW = 1
+            MID = 2
+        extend_enum(FlagTest, 'HIGH', 4)
+        self.assertEqual(FlagTest.LOW | FlagTest.HIGH, FlagTest(5))
+        self.assertEqual((FlagTest.LOW | FlagTest.HIGH).value, 5)
+        
+    def test_extend_unhashable(self):
+        class TestEnum(Enum):
+            ABC = {
+                'id': 0,
+                'value': 'abc'
+                }
+            DEF = {
+                'id': 1,
+                'value': 'def'
+                }
+        rand = uuid.uuid4().hex
+        new_value = {
+            'id': 99,
+            'value': 'new',
+            }
+        extend_enum(TestEnum, rand, new_value)
+
+
+
+# Test conversion of global constants
+# These are unordered here on purpose to ensure that declaration order
+# makes no difference.
+CONVERT_TEST_NAME_D = 5
+CONVERT_TEST_NAME_C = 5
+CONVERT_TEST_NAME_B = 5
+CONVERT_TEST_NAME_A = 5  # This one should sort first.
+CONVERT_TEST_NAME_E = 5
+CONVERT_TEST_NAME_F = 5
+CONVERT_TEST_SIGABRT = 4 # and this one
+CONVERT_TEST_SIGIOT = 4
+CONVERT_TEST_EIO = 7
+CONVERT_TEST_EBUS = 7    # and this one
+
+CONVERT_STRING_TEST_NAME_D = 5
+CONVERT_STRING_TEST_NAME_C = 5
+CONVERT_STRING_TEST_NAME_B = 5
+CONVERT_STRING_TEST_NAME_A = 5  # This one should sort first.
+CONVERT_STRING_TEST_NAME_E = 5
+CONVERT_STRING_TEST_NAME_F = 5
+
+# global names for StrEnum._convert_ test
+CONVERT_STR_TEST_2 = 'goodbye'
+CONVERT_STR_TEST_1 = 'hello'
+
+# We also need values that cannot be compared:
+UNCOMPARABLE_A = 5
+UNCOMPARABLE_C = (9, 1)  # naming order is broken on purpose
+UNCOMPARABLE_B = 'value'
+
+COMPLEX_C = 1j
+COMPLEX_A = 2j
+COMPLEX_B = 3j
+
+
+class TestConvert(TestCase):
+
+    def tearDown(self):
+        # Reset the module-level test variables to their original integer
+        # values, otherwise the already created enum values get converted
+        # instead.
+        g = globals()
+        for suffix in ['A', 'B', 'C', 'D', 'E', 'F']:
+            g['CONVERT_TEST_NAME_%s' % suffix] = 5
+            g['CONVERT_STRING_TEST_NAME_%s' % suffix] = 5
+        for suffix, value in (('A', 5), ('B', (9, 1)), ('C', 'value')):
+            g['UNCOMPARABLE_%s' % suffix] = value
+        for suffix, value in (('A', 2j), ('B', 3j), ('C', 1j)):
+            g['COMPLEX_%s' % suffix] = value
+        for suffix, value in (('1', 'hello'), ('2', 'goodbye')):
+            g['CONVERT_STR_TEST_%s' % suffix] = value
+        g['CONVERT_TEST_SIGABRT'] = 4
+        g['CONVERT_TEST_SIGIOT'] = 4
+        g['CONVERT_TEST_EIO'] = 7
+        g['CONVERT_TEST_EBUS'] = 7
+
+    def test_convert_value_lookup_priority(self):
+        test_type = IntEnum._convert_(
+                'UnittestConvert',
+                MODULE,
+                filter=lambda x: x.startswith('CONVERT_TEST_'))
+        # We don't want the reverse lookup value to vary when there are
+        # multiple possible names for a given value.  It should always
+        # report the first lexigraphical name in that case.
+        self.assertEqual(test_type(5).name, 'CONVERT_TEST_NAME_A')
+        self.assertEqual(test_type(4).name, 'CONVERT_TEST_SIGABRT')
+        self.assertEqual(test_type(7).name, 'CONVERT_TEST_EBUS')
+        self.assertEqual(
+                list(test_type),
+                [
+                    test_type.CONVERT_TEST_SIGABRT,
+                    test_type.CONVERT_TEST_NAME_A,
+                    test_type.CONVERT_TEST_EBUS,
+                    ],
+                )
+
+    def test_convert_int(self):
+        test_type = IntEnum._convert_(
+                'UnittestConvert',
+                MODULE,
+                filter=lambda x: x.startswith('CONVERT_TEST_'))
+        # Ensure that test_type has all of the desired names and values.
+        self.assertEqual(test_type.CONVERT_TEST_NAME_F,
+                         test_type.CONVERT_TEST_NAME_A)
+        self.assertEqual(test_type.CONVERT_TEST_NAME_B, 5)
+        self.assertEqual(test_type.CONVERT_TEST_NAME_C, 5)
+        self.assertEqual(test_type.CONVERT_TEST_NAME_D, 5)
+        self.assertEqual(test_type.CONVERT_TEST_NAME_E, 5)
+        # Ensure that test_type only picked up names matching the filter.
+        int_dir = dir(int) + [
+                'CONVERT_TEST_NAME_A', 'CONVERT_TEST_NAME_B', 'CONVERT_TEST_NAME_C',
+                'CONVERT_TEST_NAME_D', 'CONVERT_TEST_NAME_E', 'CONVERT_TEST_NAME_F',
+                'CONVERT_TEST_SIGABRT', 'CONVERT_TEST_SIGIOT',
+                'CONVERT_TEST_EIO', 'CONVERT_TEST_EBUS',
+                ]
+        extra = [name for name in dir(test_type) if name not in enum_dir(test_type)]
+        missing = [name for name in enum_dir(test_type) if name not in dir(test_type)]
+        self.assertEqual(
+                extra + missing,
+                [],
+                msg='extra names: %r;  missing names: %r' % (extra, missing),
+                )
+
+    @unittest.skipUnless(PY3, 'everything is comparable on Python 2')
+    def test_convert_uncomparable(self):
+        uncomp = Enum._convert_(
+                'Uncomparable',
+                MODULE,
+                filter=lambda x: x.startswith('UNCOMPARABLE_'))
+        # Should be ordered by `name` only:
+        self.assertEqual(
+            list(uncomp),
+            [uncomp.UNCOMPARABLE_A, uncomp.UNCOMPARABLE_B, uncomp.UNCOMPARABLE_C],
+            list(uncomp),
+            )
+
+    @unittest.skipUnless(PY3, 'everything is comparable on Python 2')
+    def test_convert_complex(self):
+        uncomp = Enum._convert_(
+            'Uncomparable',
+            MODULE,
+            filter=lambda x: x.startswith('COMPLEX_'))
+        # Should be ordered by `name` only:
+        self.assertEqual(
+            list(uncomp),
+            [uncomp.COMPLEX_A, uncomp.COMPLEX_B, uncomp.COMPLEX_C],
+            )
+
+    def test_convert_str(self):
+        test_type = StrEnum._convert_(
+                'UnittestConvert',
+                MODULE,
+                filter=lambda x: x.startswith('CONVERT_STR_'),
+                as_global=True)
+        # Ensure that test_type has all of the desired names and values.
+        self.assertEqual(test_type.CONVERT_STR_TEST_1, 'hello')
+        self.assertEqual(test_type.CONVERT_STR_TEST_2, 'goodbye')
+        # Ensure that test_type only picked up names matching the filter.
+        extra = [name for name in dir(test_type) if name not in enum_dir(test_type)]
+        missing = [name for name in enum_dir(test_type) if name not in dir(test_type)]
+        self.assertEqual(
+                extra + missing,
+                [],
+                msg='extra names: %r;  missing names: %r' % (extra, missing),
+                )
+        self.assertEqual(repr(test_type.CONVERT_STR_TEST_1), '%s.CONVERT_STR_TEST_1' % SHORT_MODULE)
+        self.assertEqual(str(test_type.CONVERT_STR_TEST_2), 'goodbye')
+        self.assertEqual(format(test_type.CONVERT_STR_TEST_1), 'hello')
+
+    def test_convert_repr_and_str(self):
+        test_type = IntEnum._convert_(
+                'UnittestConvert',
+                MODULE,
+                filter=lambda x: x.startswith('CONVERT_STRING_TEST_'),
+                as_global=True)
+        self.assertEqual(repr(test_type.CONVERT_STRING_TEST_NAME_A), '%s.CONVERT_STRING_TEST_NAME_A' % SHORT_MODULE)
+        self.assertEqual(str(test_type.CONVERT_STRING_TEST_NAME_A), '5')
+        self.assertEqual(format(test_type.CONVERT_STRING_TEST_NAME_A), '5')
+
+# helpers
+
+def enum_dir(cls):
+        interesting = set(cls._member_names_ + [
+                    '__class__', '__contains__', '__doc__', '__getitem__',
+                    '__iter__', '__len__', '__members__', '__module__',
+                    '__name__',
+                    ])
+        if cls._new_member_ is not object.__new__:
+            interesting.add('__new__')
+        if cls.__init_subclass__ is not Enum.__init_subclass__:
+            interesting.add('__init_subclass__')
+        if hasattr(object, '__qualname__'):
+            interesting.add('__qualname__')
+        for method in ('__init__', '__format__', '__repr__', '__str__'):
+            if getattr(cls, method) not in (getattr(Enum, method), getattr(Flag, method)):
+                interesting.add(method)
+        if cls._member_type_ is object:
+            return sorted(interesting)
+        else:
+            # return whatever mixed-in data type has
+            return sorted(set(dir(cls._member_type_)) | interesting)
+
+def member_dir(member):
+    if member.__class__._member_type_ is object:
+        allowed = set(['__class__', '__doc__', '__eq__', '__hash__', '__module__', 'name', 'value'])
+    else:
+        allowed = set(dir(member))
+    for cls in member.__class__.mro():
+        for name, obj in cls.__dict__.items():
+            if name[0] == '_':
+                continue
+            if isinstance(obj, enum.property):
+                if obj.fget is not None or name not in member._member_map_:
+                    allowed.add(name)
+                else:
+                    allowed.discard(name)
+            else:
+                allowed.add(name)
+    return sorted(allowed)
+
+
+
+if __name__ == '__main__':
+    tempdir = tempfile.mkdtemp()
+    test = None
+    try:
+        if PY3:
+            test_v3.tempdir = tempdir
+        test = unittest.main(exit=False)
+        sys.stdout.flush()
+        for name, reason in test.result.skipped:
+            print("%s: %s" % (name, reason))
+    finally:
+        shutil.rmtree(tempdir, True)
+        if test:
+            sys.exit(len(test.result.errors or test.result.failures) and 1 or 0)
+
diff --git a/venv/Lib/site-packages/aenum/test_v3.py b/venv/Lib/site-packages/aenum/test_v3.py
new file mode 100644
index 00000000..62453df1
--- /dev/null
+++ b/venv/Lib/site-packages/aenum/test_v3.py
@@ -0,0 +1,1982 @@
+from . import EnumMeta, Enum, IntEnum, Flag, IntFlag, StrEnum, UniqueEnum, AutoEnum, AddValueEnum
+from . import NamedTuple, TupleSize, MagicValue, AddValue, NoAlias, Unique, MultiValue
+from . import AutoNumberEnum,MultiValueEnum, OrderedEnum, unique, skip, extend_enum, auto
+from . import StdlibEnumMeta, StdlibEnum, StdlibIntEnum, StdlibFlag, StdlibIntFlag, StdlibStrEnum
+from . import pyver, PY3_3, PY3_4, PY3_5, PY3_6, PY3_11
+from . import add_stdlib_integration, remove_stdlib_integration
+
+from collections import OrderedDict
+from datetime import timedelta
+from pickle import dumps, loads, PicklingError, HIGHEST_PROTOCOL
+from unittest import TestCase, main
+
+import os
+import sys
+import tempfile
+import textwrap
+import unittest
+
+try:
+    import pyparsing
+except (ImportError, SyntaxError):
+    pyparsing = None
+
+try:
+    RecursionError
+except NameError:
+    # python3.4
+    RecursionError = RuntimeError
+
+class TestEnumV3(TestCase):
+
+    def setUp(self):
+        class Season(Enum):
+            SPRING = 1
+            SUMMER = 2
+            AUTUMN = 3
+            WINTER = 4
+        self.Season = Season
+
+        class Konstants(float, Enum):
+            E = 2.7182818
+            PI = 3.1415926
+            TAU = 2 * PI
+        self.Konstants = Konstants
+
+        class Grades(IntEnum):
+            A = 5
+            B = 4
+            C = 3
+            D = 2
+            F = 0
+        self.Grades = Grades
+
+        class Directional(str, Enum):
+            EAST = 'east'
+            WEST = 'west'
+            NORTH = 'north'
+            SOUTH = 'south'
+        self.Directional = Directional
+
+        from datetime import date
+        class Holiday(date, Enum):
+            NEW_YEAR = 2013, 1, 1
+            IDES_OF_MARCH = 2013, 3, 15
+        self.Holiday = Holiday
+
+    @unittest.skipUnless(StdlibEnumMeta, 'Stdlib enum not available')
+    def test_stdlib_inheritence(self):
+        # 3.4
+        self.assertTrue(issubclass(self.Season, StdlibEnum))
+        self.assertTrue(isinstance(self.Season.SPRING, StdlibEnum))
+        #
+        if pyver >= PY3_6:
+            class AFlag(Flag):
+                one = 1
+            self.assertTrue(issubclass(AFlag, StdlibEnum))
+            self.assertTrue(isinstance(AFlag.one, StdlibEnum))
+            self.assertTrue(issubclass(AFlag, StdlibFlag))
+            self.assertTrue(isinstance(AFlag.one, StdlibFlag))
+            #
+            class AnIntFlag(IntFlag):
+                one = 1
+            self.assertTrue(issubclass(AnIntFlag, StdlibEnum))
+            self.assertTrue(isinstance(AnIntFlag.one, StdlibEnum))
+            self.assertTrue(issubclass(AnIntFlag, StdlibFlag))
+            self.assertTrue(isinstance(AnIntFlag.one, StdlibFlag))
+            self.assertTrue(issubclass(AnIntFlag, StdlibIntFlag))
+            self.assertTrue(isinstance(AnIntFlag.one, StdlibIntFlag))
+        #
+        if pyver >= PY3_11:
+            class AStrEnum(StrFlag):
+                one = '1'
+            self.assertTrue(issubclass(AStrEnum, StdlibEnum))
+            self.assertTrue(isinstance(AStrEnum.one, StdlibEnum))
+            self.assertTrue(issubclass(AStrEnum, StdlibStrEnum))
+            self.assertTrue(isinstance(AStrEnum.one, StdlibStrEnum))
+
+    @unittest.skipUnless(StdlibEnumMeta, 'Stdlib enum not available')
+    def test_stdlib_bad_getattribute(self):
+        class BadEnumType(StdlibEnumMeta):
+            def __getattribute__(cls, name):
+                obj = super().__getattribute__(name)
+                if isinstance(obj, cls):
+                    obj.deprecate()
+                return obj
+        with self.assertRaisesRegex(RecursionError, 'endless recursion'):
+            class BaseEnum(StdlibEnum):
+                pass
+            class BadEnum(BaseEnum, metaclass=BadEnumType):
+                FOO = 'bar'
+        try:
+            remove_stdlib_integration()
+            class OkayEnum(StdlibEnum, metaclass=BadEnumType):
+                FOO = 'bar'
+        finally:
+            add_stdlib_integration()
+
+    @unittest.skipUnless(pyver >= PY3_5, '__qualname__ requires python 3.5 or greater')
+    def test_pickle_enum_function_with_qualname(self):
+        Theory = Enum('Theory', 'rule law supposition', qualname='spanish_inquisition')
+        globals()['spanish_inquisition'] = Theory
+        test_pickle_dump_load(self.assertTrue, Theory.rule)
+        test_pickle_dump_load(self.assertTrue, Theory)
+
+    def test_auto_init(self):
+        class Planet(Enum, init='mass radius'):
+            MERCURY = (3.303e+23, 2.4397e6)
+            VENUS   = (4.869e+24, 6.0518e6)
+            EARTH   = (5.976e+24, 6.37814e6)
+            MARS    = (6.421e+23, 3.3972e6)
+            JUPITER = (1.9e+27,   7.1492e7)
+            SATURN  = (5.688e+26, 6.0268e7)
+            URANUS  = (8.686e+25, 2.5559e7)
+            NEPTUNE = (1.024e+26, 2.4746e7)
+            @property
+            def surface_gravity(self):
+                # universal gravitational constant  (m3 kg-1 s-2)
+                G = 6.67300E-11
+                return G * self.mass / (self.radius * self.radius)
+        self.assertEqual(round(Planet.EARTH.surface_gravity, 2), 9.80)
+        self.assertEqual(Planet.EARTH.value, (5.976e+24, 6.37814e6))
+
+    def test_auto_init_with_value(self):
+        class Color(Enum, init='value, rgb'):
+            RED = 1, (1, 0, 0)
+            BLUE = 2, (0, 1, 0)
+            GREEN = 3, (0, 0, 1)
+        self.assertEqual(Color.RED.value, 1)
+        self.assertEqual(Color.BLUE.value, 2)
+        self.assertEqual(Color.GREEN.value, 3)
+        self.assertEqual(Color.RED.rgb, (1, 0, 0))
+        self.assertEqual(Color.BLUE.rgb, (0, 1, 0))
+        self.assertEqual(Color.GREEN.rgb, (0, 0, 1))
+
+    def test_auto_turns_off(self):
+        with self.assertRaises(NameError):
+            class Color(Enum, settings=MagicValue):
+                red
+                green
+                blue
+                def hello(self):
+                    print('Hello!  My serial is %s.' % self.value)
+                rose
+        with self.assertRaises(NameError):
+            class Color(Enum, settings=MagicValue):
+                red
+                green
+                blue
+                def __init__(self, *args):
+                    pass
+                rose
+
+    def test_magic(self):
+        class Color(Enum, settings=MagicValue):
+            red, green, blue
+        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
+        self.assertEqual(Color.red.value, 1)
+
+    def test_ignore_not_overridden(self):
+        with self.assertRaisesRegex(TypeError, 'object is not callable'):
+            class Color(Flag):
+                _ignore_ = 'irrelevent'
+                _settings_ = MagicValue
+                @property
+                def shade(self):
+                    print('I am light', self.name.lower())
+
+    def test_magic_start(self):
+        class Color(Enum, settings=MagicValue, start=0):
+            red, green, blue
+        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
+        self.assertEqual(Color.red.value, 0)
+
+    def test_dir_on_class(self):
+        Season = self.Season
+        self.assertEqual(
+            set(dir(Season)),
+            set(['__class__', '__doc__', '__members__', '__module__',
+                'SPRING', 'SUMMER', 'AUTUMN', 'WINTER',
+                '__init_subclass__', '__name__', '__getitem__', '__len__',
+                '__contains__', '__iter__', '__qualname__',
+                ]))
+
+    def test_dir_on_item(self):
+        Season = self.Season
+        self.assertEqual(
+            set(dir(Season.WINTER)),
+            set(['__class__', '__doc__', '__eq__', '__hash__', '__module__', 'name', 'value', 'values']),
+            )
+
+    def test_dir_with_added_behavior(self):
+        class Test(Enum):
+            this = 'that'
+            these = 'those'
+            def wowser(self):
+                return ("Wowser! I'm %s!" % self.name)
+        self.assertEqual(
+                set(dir(Test)),
+                set([
+                    '__class__', '__doc__', '__members__', '__module__', 'this', 'these',
+                    '__init_subclass__', '__name__', '__getitem__', '__len__',
+                    '__contains__', '__iter__', '__qualname__',
+                    ]))
+        self.assertEqual(
+                set(dir(Test.this)),
+                set(['__class__', '__doc__', '__eq__', '__hash__', '__module__', 'name', 'value', 'values', 'wowser']),
+                )
+
+    def test_dir_on_sub_with_behavior_on_super(self):
+        # see issue22506
+        class SuperEnum(Enum):
+            def invisible(self):
+                return "did you see me?"
+        class SubEnum(SuperEnum):
+            sample = 5
+        self.assertEqual(
+                set(dir(SubEnum.sample)),
+                set(['__class__', '__doc__', '__eq__', '__hash__', '__module__', 'name', 'value', 'values', 'invisible']),
+                )
+
+    def test_members_are_always_ordered(self):
+        class AlwaysOrdered(Enum):
+            first = 1
+            second = 2
+            third = 3
+        self.assertTrue(type(AlwaysOrdered.__members__) is OrderedDict)
+
+    def test_comparisons(self):
+        def bad_compare():
+            Season.SPRING > 4
+        Season = self.Season
+        self.assertNotEqual(Season.SPRING, 1)
+        self.assertRaises(TypeError, bad_compare)
+
+        class Part(Enum):
+            SPRING = 1
+            CLIP = 2
+            BARREL = 3
+
+        self.assertNotEqual(Season.SPRING, Part.SPRING)
+        def bad_compare():
+            Season.SPRING < Part.CLIP
+        self.assertRaises(TypeError, bad_compare)
+
+    def test_duplicate_name(self):
+        with self.assertRaises(TypeError):
+            class Color1(Enum):
+                red = 1
+                green = 2
+                blue = 3
+                red = 4
+
+        with self.assertRaises(TypeError):
+            class Color2(Enum):
+                red = 1
+                green = 2
+                blue = 3
+                def red(self):
+                    return 'red'
+
+        with self.assertRaises(TypeError):
+            class Color3(Enum):
+                @property
+                def red(self):
+                    return 'redder'
+                red = 1
+                green = 2
+                blue = 3
+
+    def test_duplicate_value_with_unique(self):
+        with self.assertRaises(ValueError):
+            class Color(Enum, settings=Unique):
+                red = 1
+                green = 2
+                blue = 3
+                rojo = 1
+
+    def test_duplicate_value_with_noalias(self):
+        class Color(Enum, settings=NoAlias):
+            red = 1
+            green = 2
+            blue = 3
+            rojo = 1
+        self.assertFalse(Color.red is Color.rojo)
+        self.assertEqual(Color.red.value, 1)
+        self.assertEqual(Color.rojo.value, 1)
+        self.assertEqual(len(Color), 4)
+        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue, Color.rojo])
+
+    def test_noalias_value_lookup(self):
+        class Color(Enum, settings=NoAlias):
+            red = 1
+            green = 2
+            blue = 3
+            rojo = 1
+        self.assertRaises(TypeError, Color, 2)
+
+    def test_multivalue(self):
+        class Color(Enum, settings=MultiValue):
+            red = 1, 'red'
+            green = 2, 'green'
+            blue = 3, 'blue'
+        self.assertEqual(Color.red.value, 1)
+        self.assertIs(Color('green'), Color.green)
+        self.assertEqual(Color.blue.values, (3, 'blue'))
+
+    def test_multivalue_with_duplicate_values(self):
+        with self.assertRaises(ValueError):
+            class Color(Enum, settings=MultiValue):
+                red = 1, 'red'
+                green = 2, 'green'
+                blue = 3, 'blue', 'red'
+
+    def test_multivalue_with_duplicate_values_and_noalias(self):
+        with self.assertRaises(TypeError):
+            class Color(Enum, settings=(MultiValue, NoAlias)):
+                red = 1, 'red'
+                green = 2, 'green'
+                blue = 3, 'blue', 'red'
+
+    def test_multivalue_and_auto(self):
+        with self.assertRaisesRegex(TypeError, r'MultiValue and MagicValue are mutually exclusive'):
+            class Color(Enum, settings=(MultiValue, MagicValue)):
+                red
+                green = 3, 'green'
+                blue
+
+    def test_autonumber_and_init(self):
+        class Field(IntEnum, settings=AddValue, init='__doc__'):
+            TYPE = "Char, Date, Logical, etc."
+            START = "Field offset in record"
+        self.assertEqual(Field.TYPE, 1)
+        self.assertEqual(Field.START, 2)
+        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
+        self.assertEqual(Field.START.__doc__, 'Field offset in record')
+        self.assertFalse(hasattr(Field, '_order_'))
+
+    def test_autovalue_and_init(self):
+        class Field(IntEnum, init='value __doc__'):
+            TYPE = "Char, Date, Logical, etc."
+            START = "Field offset in record"
+        self.assertEqual(Field.TYPE, 1)
+        self.assertEqual(Field.START.__doc__, 'Field offset in record')
+
+    def test_autonumber_and_start(self):
+        class Field(IntEnum, init='__doc__', settings=AddValue, start=0):
+            TYPE = "Char, Date, Logical, etc."
+            START = "Field offset in record"
+        self.assertEqual(Field.TYPE, 0)
+        self.assertEqual(Field.START, 1)
+        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
+        self.assertEqual(Field.START.__doc__, 'Field offset in record')
+
+    def test_autonumber_and_init_and_some_values(self):
+        class Field(IntEnum, init='value __doc__'):
+            TYPE = "Char, Date, Logical, etc."
+            START = "Field offset in record"
+            BLAH = 5, "test blah"
+            BELCH = 'test belch'
+        self.assertEqual(Field.TYPE, 1)
+        self.assertEqual(Field.START, 2)
+        self.assertEqual(Field.BLAH, 5)
+        self.assertEqual(Field.BELCH, 6)
+        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
+        self.assertEqual(Field.START.__doc__, 'Field offset in record')
+        self.assertEqual(Field.BLAH.__doc__, 'test blah')
+        self.assertEqual(Field.BELCH.__doc__, 'test belch')
+
+    def test_autonumber_with_irregular_values(self):
+        class Point(AutoNumberEnum, init='x y'):
+            first = 7, 9
+            second = 11, 13
+        self.assertEqual(Point.first.value, 1)
+        self.assertEqual(Point.first.x, 7)
+        self.assertEqual(Point.first.y, 9)
+        self.assertEqual(Point.second.value, 2)
+        self.assertEqual(Point.second.x, 11)
+        self.assertEqual(Point.second.y, 13)
+        with self.assertRaisesRegex(TypeError, '.*number of fields provided do not match init ...x., .y.. != .3, 11, 13..'):
+            class Point(AutoNumberEnum, init='x y'):
+                first = 7, 9
+                second = 3, 11, 13
+        class Color(AutoNumberEnum, init='__doc__'):
+            # interactions between AutoNumberEnum and _generate_next_value_ may not be pretty
+            red = ()
+            green = 'red'
+            blue = ()
+        self.assertTrue(Color.red.__doc__, 1)
+        self.assertEqual(Color.green.__doc__, 'red')
+        self.assertTrue(Color.blue.__doc__, 2)
+
+    def test_autonumber_and_property(self):
+        with self.assertRaises(TypeError):
+            class Color(AutoEnum):
+                _ignore_ = ()
+                red = ()
+                green = ()
+                blue = ()
+                @property
+                def cap_name(self) -> str:
+                    return self.name.title()
+
+    def test_autoenum(self):
+        class Color(AutoEnum):
+            red
+            green
+            blue
+        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
+        self.assertEqual([m.value for m in Color], [1, 2, 3])
+        self.assertEqual([m.name for m in Color], ['red', 'green', 'blue'])
+
+    def test_autoenum_with_str(self):
+        class Color(AutoEnum):
+            def _generate_next_value_(name, start, count, last_values):
+                return name
+            red
+            green
+            blue
+        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
+        self.assertEqual([m.value for m in Color], ['red', 'green', 'blue'])
+        self.assertEqual([m.name for m in Color], ['red', 'green', 'blue'])
+
+    def test_autoenum_and_default_ignore(self):
+        class Color(AutoEnum):
+            red
+            green
+            blue
+            @property
+            def cap_name(self):
+                return self.name.title()
+        self.assertEqual(Color.blue.cap_name, 'Blue')
+
+    def test_autonumber_and_overridden_ignore(self):
+        with self.assertRaises(TypeError):
+            class Color(AutoEnum):
+                _ignore_ = 'staticmethod'
+                red
+                green
+                blue
+                @property
+                def cap_name(self) -> str:
+                    return self.name.title()
+
+    def test_autonumber_and_multiple_assignment(self):
+        class Color(AutoEnum):
+            _ignore_ = 'property'
+            red
+            green
+            blue = cyan
+            @property
+            def cap_name(self) -> str:
+                return self.name.title()
+        self.assertEqual(Color.blue.cap_name, 'Cyan')
+
+    def test_multivalue_and_autonumber_inherited(self):
+        class Measurement(int, Enum, settings=(MultiValue, AddValue), start=0):
+            one = "20110721"
+            two = "20120911"
+            three = "20110518"
+        M = Measurement
+        self.assertEqual(M.one, 0)
+        self.assertTrue(M.one is M(0) is M('20110721'))
+
+    def test_combine_new_settings_with_old_settings(self):
+        class Auto(Enum, settings=Unique):
+            pass
+        with self.assertRaises(ValueError):
+            class AutoUnique(Auto, settings=MagicValue):
+                BLAH
+                BLUH
+                ICK = 1
+
+    def test_timedelta(self):
+        class Period(timedelta, Enum):
+            '''
+            different lengths of time
+            '''
+            _init_ = 'value period'
+            _settings_ = NoAlias
+            _ignore_ = 'Period i'
+            Period = vars()
+            for i in range(31):
+                Period['day_%d' % i] = i, 'day'
+            for i in range(15):
+                Period['week_%d' % i] = i*7, 'week'
+            for i in range(12):
+                Period['month_%d' % i] = i*30, 'month'
+            OneDay = day_1
+            OneWeek = week_1
+        self.assertFalse(hasattr(Period, '_ignore_'))
+        self.assertFalse(hasattr(Period, 'Period'))
+        self.assertFalse(hasattr(Period, 'i'))
+        self.assertTrue(isinstance(Period.day_1, timedelta))
+
+    def test_extend_enum_plain(self):
+        class Color(UniqueEnum):
+            red = 1
+            green = 2
+            blue = 3
+        extend_enum(Color, 'brown', 4)
+        self.assertEqual(Color.brown.name, 'brown')
+        self.assertEqual(Color.brown.value, 4)
+        self.assertTrue(Color.brown in Color)
+        self.assertEqual(len(Color), 4)
+
+    def test_extend_enum_shadow(self):
+        class Color(UniqueEnum):
+            red = 1
+            green = 2
+            blue = 3
+        extend_enum(Color, 'value', 4)
+        self.assertEqual(Color.value.name, 'value')
+        self.assertEqual(Color.value.value, 4)
+        self.assertTrue(Color.value in Color)
+        self.assertEqual(len(Color), 4)
+        self.assertEqual(Color.red.value, 1)
+
+    def test_extend_enum_generate(self):
+        class Foo(AutoEnum):
+            def _generate_next_value_(name, start, count, values, *args, **kwds):
+                return name
+            a
+            b
+        #
+        extend_enum(Foo, 'c')
+        self.assertEqual(Foo.a.value, 'a')
+        self.assertEqual(Foo.b.value, 'b')
+        self.assertEqual(Foo.c.value, 'c')
+
+    def test_extend_enum_unique_with_duplicate(self):
+        with self.assertRaises(ValueError):
+            class Color(Enum, settings=Unique):
+                red = 1
+                green = 2
+                blue = 3
+            extend_enum(Color, 'value', 1)
+
+    def test_extend_enum_multivalue_with_duplicate(self):
+        with self.assertRaises(ValueError):
+            class Color(Enum, settings=MultiValue):
+                red = 1, 'rojo'
+                green = 2, 'verde'
+                blue = 3, 'azul'
+            extend_enum(Color, 'value', 2)
+
+    def test_extend_enum_noalias_with_duplicate(self):
+        class Color(Enum, settings=NoAlias):
+            red = 1
+            green = 2
+            blue = 3
+        extend_enum(Color, 'value', 3, )
+        self.assertRaises(TypeError, Color, 3)
+        self.assertFalse(Color.value is Color.blue)
+        self.assertTrue(Color.value.value, 3)
+
+    def test_no_duplicates(self):
+        def bad_duplicates():
+            class Color(UniqueEnum):
+                red = 1
+                green = 2
+                blue = 3
+            class Color(UniqueEnum):
+                red = 1
+                green = 2
+                blue = 3
+                grene = 2
+        self.assertRaises(ValueError, bad_duplicates)
+
+    def test_no_duplicates_kinda(self):
+        class Silly(UniqueEnum):
+            one = 1
+            two = 'dos'
+            name = 3
+        class Sillier(IntEnum, UniqueEnum):
+            single = 1
+            name = 2
+            triple = 3
+            value = 4
+
+    def test_auto_number(self):
+        class Color(Enum, settings=MagicValue):
+            red
+            blue
+            green
+
+        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
+        self.assertEqual(Color.red.value, 1)
+        self.assertEqual(Color.blue.value, 2)
+        self.assertEqual(Color.green.value, 3)
+
+    def test_auto_name(self):
+        class Color(Enum, settings=MagicValue):
+            def _generate_next_value_(name, start, count, last):
+                return name
+            red
+            blue
+            green
+
+        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
+        self.assertEqual(Color.red.value, 'red')
+        self.assertEqual(Color.blue.value, 'blue')
+        self.assertEqual(Color.green.value, 'green')
+
+    def test_auto_name_inherit(self):
+        class AutoNameEnum(Enum):
+            def _generate_next_value_(name, start, count, last):
+                return name
+        class Color(AutoNameEnum, settings=MagicValue):
+            red
+            blue
+            green
+
+        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
+        self.assertEqual(Color.red.value, 'red')
+        self.assertEqual(Color.blue.value, 'blue')
+        self.assertEqual(Color.green.value, 'green')
+
+    def test_auto_garbage(self):
+        class Color(Enum):
+            _settings_ = MagicValue
+            red = 'red'
+            blue
+        self.assertEqual(Color.blue.value, 1)
+
+    def test_auto_garbage_corrected(self):
+        class Color(Enum, settings=MagicValue):
+            red = 'red'
+            blue = 2
+            green
+
+        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
+        self.assertEqual(Color.red.value, 'red')
+        self.assertEqual(Color.blue.value, 2)
+        self.assertEqual(Color.green.value, 3)
+
+    def test_duplicate_auto(self):
+        class Dupes(Enum, settings=MagicValue):
+            first = primero
+            second
+            third
+        self.assertEqual([Dupes.first, Dupes.second, Dupes.third], list(Dupes))
+
+    def test_order_as_function(self):
+        # first with _init_
+        class TestSequence(Enum):
+            _init_ = 'value, sequence'
+            _order_ = lambda member: member.sequence
+            item_id                  = 'An$(1,6)',      0     # Item Code
+            company_id               = 'An$(7,2)',      1     # Company Code
+            warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
+            company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
+            key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
+            available                = 'Zn$(1,1)',      5     # Available?
+            contract_item            = 'Bn(2,1)',       6     # Contract Item?
+            sales_category           = 'Fn',            7     # Sales Category
+            gl_category              = 'Rn$(5,1)',      8     # G/L Category
+            warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
+            inv_units                = 'Qn$(7,2)',     10     # Inv Units
+        for i, member in enumerate(TestSequence):
+            self.assertEqual(i, member.sequence)
+        ts = TestSequence
+        self.assertEqual(ts.item_id.name, 'item_id')
+        self.assertEqual(ts.item_id.value, 'An$(1,6)')
+        self.assertEqual(ts.item_id.sequence, 0)
+        self.assertEqual(ts.company_id.name, 'company_id')
+        self.assertEqual(ts.company_id.value, 'An$(7,2)')
+        self.assertEqual(ts.company_id.sequence, 1)
+        self.assertEqual(ts.warehouse_no.name, 'warehouse_no')
+        self.assertEqual(ts.warehouse_no.value, 'An$(9,4)')
+        self.assertEqual(ts.warehouse_no.sequence, 2)
+        self.assertEqual(ts.company.name, 'company')
+        self.assertEqual(ts.company.value, 'Hn$(13,6)')
+        self.assertEqual(ts.company.sequence, 3)
+        self.assertEqual(ts.key_type.name, 'key_type')
+        self.assertEqual(ts.key_type.value, 'Cn$(19,3)')
+        self.assertEqual(ts.key_type.sequence, 4)
+        self.assertEqual(ts.available.name, 'available')
+        self.assertEqual(ts.available.value, 'Zn$(1,1)')
+        self.assertEqual(ts.available.sequence, 5)
+        self.assertEqual(ts.contract_item.name, 'contract_item')
+        self.assertEqual(ts.contract_item.value, 'Bn(2,1)')
+        self.assertEqual(ts.contract_item.sequence, 6)
+        self.assertEqual(ts.sales_category.name, 'sales_category')
+        self.assertEqual(ts.sales_category.value, 'Fn')
+        self.assertEqual(ts.sales_category.sequence, 7)
+        self.assertEqual(ts.gl_category.name, 'gl_category')
+        self.assertEqual(ts.gl_category.value, 'Rn$(5,1)')
+        self.assertEqual(ts.gl_category.sequence, 8)
+        self.assertEqual(ts.warehouse_category.name, 'warehouse_category')
+        self.assertEqual(ts.warehouse_category.value, 'Sn$(6,1)')
+        self.assertEqual(ts.warehouse_category.sequence, 9)
+        self.assertEqual(ts.inv_units.name, 'inv_units')
+        self.assertEqual(ts.inv_units.value, 'Qn$(7,2)')
+        self.assertEqual(ts.inv_units.sequence, 10)
+        # and then without
+        class TestSequence(Enum):
+            _order_ = lambda member: member.value[1]
+            item_id                  = 'An$(1,6)',      0     # Item Code
+            company_id               = 'An$(7,2)',      1     # Company Code
+            warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
+            company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
+            key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
+            available                = 'Zn$(1,1)',      5     # Available?
+            contract_item            = 'Bn(2,1)',       6     # Contract Item?
+            sales_category           = 'Fn',            7     # Sales Category
+            gl_category              = 'Rn$(5,1)',      8     # G/L Category
+            warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
+            inv_units                = 'Qn$(7,2)',     10     # Inv Units
+        for i, member in enumerate(TestSequence):
+            self.assertEqual(i, member.value[1])
+        ts = TestSequence
+        self.assertEqual(ts.item_id.name, 'item_id')
+        self.assertEqual(ts.item_id.value, ('An$(1,6)', 0))
+        self.assertEqual(ts.company_id.name, 'company_id')
+        self.assertEqual(ts.company_id.value, ('An$(7,2)', 1))
+        self.assertEqual(ts.warehouse_no.name, 'warehouse_no')
+        self.assertEqual(ts.warehouse_no.value, ('An$(9,4)', 2))
+        self.assertEqual(ts.company.name, 'company')
+        self.assertEqual(ts.company.value, ('Hn$(13,6)', 3))
+        self.assertEqual(ts.key_type.name, 'key_type')
+        self.assertEqual(ts.key_type.value, ('Cn$(19,3)', 4))
+        self.assertEqual(ts.available.name, 'available')
+        self.assertEqual(ts.available.value, ('Zn$(1,1)', 5))
+        self.assertEqual(ts.contract_item.name, 'contract_item')
+        self.assertEqual(ts.contract_item.value, ('Bn(2,1)', 6))
+        self.assertEqual(ts.sales_category.name, 'sales_category')
+        self.assertEqual(ts.sales_category.value, ('Fn', 7))
+        self.assertEqual(ts.gl_category.name, 'gl_category')
+        self.assertEqual(ts.gl_category.value, ('Rn$(5,1)', 8))
+        self.assertEqual(ts.warehouse_category.name, 'warehouse_category')
+        self.assertEqual(ts.warehouse_category.value, ('Sn$(6,1)', 9))
+        self.assertEqual(ts.inv_units.name, 'inv_units')
+        self.assertEqual(ts.inv_units.value, ('Qn$(7,2)', 10))
+        # then with _init_ but without value
+        with self.assertRaises(TypeError):
+            class TestSequence(Enum):
+                _init_ = 'sequence'
+                _order_ = lambda member: member.sequence
+                item_id                  = 'An$(1,6)',      0     # Item Code
+                company_id               = 'An$(7,2)',      1     # Company Code
+                warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
+                company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
+                key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
+                available                = 'Zn$(1,1)',      5     # Available?
+                contract_item            = 'Bn(2,1)',       6     # Contract Item?
+                sales_category           = 'Fn',            7     # Sales Category
+                gl_category              = 'Rn$(5,1)',      8     # G/L Category
+                warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
+                inv_units                = 'Qn$(7,2)',     10     # Inv Units
+        # finally, out of order so Python 3 barfs
+        with self.assertRaises(TypeError):
+            class TestSequence(Enum):
+                _init_ = 'sequence'
+                _order_ = lambda member: member.sequence
+                item_id                  = 'An$(1,6)',      0     # Item Code
+                warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
+                company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
+                company_id               = 'An$(7,2)',      1     # Company Code
+                inv_units                = 'Qn$(7,2)',     10     # Inv Units
+                available                = 'Zn$(1,1)',      5     # Available?
+                contract_item            = 'Bn(2,1)',       6     # Contract Item?
+                sales_category           = 'Fn',            7     # Sales Category
+                key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
+                gl_category              = 'Rn$(5,1)',      8     # G/L Category
+                warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
+
+    if pyver >= PY3_3:
+        def test_missing(self):
+            class Color(Enum):
+                red = 1
+                green = 2
+                blue = 3
+                @classmethod
+                def _missing_(cls, item):
+                    if item == 'three':
+                        return cls.blue
+                    elif item == 'bad return':
+                        # trigger internal error
+                        return 5
+                    elif item == 'error out':
+                        raise ZeroDivisionError
+                    else:
+                        # trigger not found
+                        return None
+            self.assertIs(Color('three'), Color.blue)
+            self.assertRaises(ValueError, Color, 7)
+            try:
+                Color('bad return')
+            except TypeError as exc:
+                self.assertTrue(isinstance(exc.__cause__, ValueError))
+            else:
+                raise Exception('Exception not raised.')
+            try:
+                Color('error out')
+            except ZeroDivisionError as exc:
+                self.assertTrue(isinstance(exc.__cause__, ValueError))
+            else:
+                raise Exception('Exception not raised.')
+
+    def test_enum_of_types(self):
+        """Support using Enum to refer to types deliberately."""
+        class MyTypes(Enum):
+            i = int
+            f = float
+            s = str
+        self.assertEqual(MyTypes.i.value, int)
+        self.assertEqual(MyTypes.f.value, float)
+        self.assertEqual(MyTypes.s.value, str)
+        class Foo:
+            pass
+        class Bar:
+            pass
+        class MyTypes2(Enum):
+            a = Foo
+            b = Bar
+        self.assertEqual(MyTypes2.a.value, Foo)
+        self.assertEqual(MyTypes2.b.value, Bar)
+        class SpamEnumNotInner:
+            pass
+        class SpamEnum(Enum):
+            spam = SpamEnumNotInner
+        self.assertEqual(SpamEnum.spam.value, SpamEnumNotInner)
+
+    def test_nested_classes_in_enum_do_not_create_members(self):
+        """Support locally-defined nested classes."""
+        # manually set __qualname__ to remove testing framework noise
+        class Outer(Enum):
+            __qualname__ = "Outer"
+            a = 1
+            b = 2
+            class Inner(Enum):
+                __qualname__ = "Outer.Inner"
+                foo = 10
+                bar = 11
+        self.assertTrue(isinstance(Outer.Inner, type))
+        self.assertEqual(Outer.a.value, 1)
+        self.assertEqual(Outer.Inner.foo.value, 10)
+        self.assertEqual(
+            list(Outer.Inner),
+            [Outer.Inner.foo, Outer.Inner.bar],
+            )
+        self.assertEqual(
+            list(Outer),
+            [Outer.a, Outer.b],
+            )
+
+    if pyver == PY3_4:
+        def test_class_nested_enum_and_pickle_protocol_four(self):
+            # would normally just have this directly in the class namespace
+            class NestedEnum(Enum):
+                twigs = 'common'
+                shiny = 'rare'
+
+            self.__class__.NestedEnum = NestedEnum
+            self.NestedEnum.__qualname__ = '%s.NestedEnum' % self.__class__.__name__
+            test_pickle_exception(
+                    self.assertRaises, PicklingError, self.NestedEnum.twigs,
+                    protocol=(0, 3))
+            test_pickle_dump_load(self.assertTrue, self.NestedEnum.twigs,
+                    protocol=(4, HIGHEST_PROTOCOL))
+
+    elif pyver >= PY3_5:
+        def test_class_nested_enum_and_pickle_protocol_four(self):
+            # would normally just have this directly in the class namespace
+            class NestedEnum(Enum):
+                twigs = 'common'
+                shiny = 'rare'
+
+            self.__class__.NestedEnum = NestedEnum
+            self.NestedEnum.__qualname__ = '%s.NestedEnum' % self.__class__.__name__
+            test_pickle_dump_load(self.assertTrue, self.NestedEnum.twigs,
+                    protocol=(0, HIGHEST_PROTOCOL))
+
+    if pyver >= PY3_4:
+        def test_enum_injection(self):
+            class Color(Enum):
+                _order_ = 'BLACK WHITE'
+                BLACK = Color('black', '#000')
+                WHITE = Color('white', '#fff')
+
+                def __init__(self, label, hex):
+                    self.label = label
+                    self.hex = hex
+
+            self.assertEqual([Color.BLACK, Color.WHITE], list(Color))
+            self.assertEqual(Color.WHITE.hex, '#fff')
+            self.assertEqual(Color.BLACK.label, 'black')
+
+        def test_subclasses_with_getnewargs_ex(self):
+            class NamedInt(int):
+                __qualname__ = 'NamedInt'       # needed for pickle protocol 4
+                def __new__(cls, *args):
+                    _args = args
+                    if len(args) < 2:
+                        raise TypeError("name and value must be specified")
+                    name, args = args[0], args[1:]
+                    self = int.__new__(cls, *args)
+                    self._intname = name
+                    self._args = _args
+                    return self
+                def __getnewargs_ex__(self):
+                    return self._args, {}
+                @property
+                def __name__(self):
+                    return self._intname
+                def __repr__(self):
+                    # repr() is updated to include the name and type info
+                    return "{}({!r}, {})".format(type(self).__name__,
+                                                 self.__name__,
+                                                 int.__repr__(self))
+                def __str__(self):
+                    # str() is unchanged, even if it relies on the repr() fallback
+                    base = int
+                    base_str = base.__str__
+                    if base_str.__objclass__ is object:
+                        return base.__repr__(self)
+                    return base_str(self)
+                # for simplicity, we only define one operator that
+                # propagates expressions
+                def __add__(self, other):
+                    temp = int(self) + int( other)
+                    if isinstance(self, NamedInt) and isinstance(other, NamedInt):
+                        return NamedInt(
+                            '({0} + {1})'.format(self.__name__, other.__name__),
+                            temp )
+                    else:
+                        return temp
+
+            class NEI(NamedInt, Enum):
+                __qualname__ = 'NEI'      # needed for pickle protocol 4
+                x = ('the-x', 1)
+                y = ('the-y', 2)
+
+
+            self.assertIs(NEI.__new__, Enum.__new__)
+            self.assertEqual(repr(NEI.x + NEI.y), "NamedInt('(the-x + the-y)', 3)")
+            globals()['NamedInt'] = NamedInt
+            globals()['NEI'] = NEI
+            NI5 = NamedInt('test', 5)
+            self.assertEqual(NI5, 5)
+            test_pickle_dump_load(self.assertEqual, NI5, 5, protocol=(4, HIGHEST_PROTOCOL))
+            self.assertEqual(NEI.y.value, 2)
+            test_pickle_dump_load(self.assertTrue, NEI.y, protocol=(4, HIGHEST_PROTOCOL))
+
+
+class TestOrderV3(TestCase):
+    """
+    Test definition order versus _order_ order.
+    """
+
+    def test_same_members(self):
+        class Color(Enum):
+            _order_ = 'red green blue'
+            red = 1
+            green = 2
+            blue = 3
+
+    def test_same_members_with_aliases(self):
+        class Color(Enum):
+            _order_ = 'red green blue'
+            red = 1
+            green = 2
+            blue = 3
+            verde = green
+
+    def test_same_members_wrong_order(self):
+        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
+            class Color(Enum):
+                _order_ = 'red green blue'
+                red = 1
+                blue = 3
+                green = 2
+
+    def test_order_has_extra_members(self):
+        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
+            class Color(Enum):
+                _order_ = 'red green blue purple'
+                red = 1
+                green = 2
+                blue = 3
+
+    def test_order_has_extra_members_with_aliases(self):
+        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
+            class Color(Enum):
+                _order_ = 'red green blue purple'
+                red = 1
+                green = 2
+                blue = 3
+                verde = green
+
+    def test_enum_has_extra_members(self):
+        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
+            class Color(Enum):
+                _order_ = 'red green blue'
+                red = 1
+                green = 2
+                blue = 3
+                purple = 4
+
+    def test_enum_has_extra_members_with_aliases(self):
+        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
+            class Color(Enum):
+                _order_ = 'red green blue'
+                red = 1
+                green = 2
+                blue = 3
+                purple = 4
+                verde = green
+
+    def test_same_members_flag(self):
+        class Color(Flag):
+            _order_ = 'red green blue'
+            red = 1
+            green = 2
+            blue = 4
+
+    def test_same_members_with_aliases_flag(self):
+        class Color(Flag):
+            _order_ = 'red green blue'
+            red = 1
+            green = 2
+            blue = 4
+            verde = green
+
+    def test_same_members_wrong_order_falg(self):
+        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
+            class Color(Flag):
+                _order_ = 'red green blue'
+                red = 1
+                blue = 4
+                green = 2
+
+    def test_order_has_extra_members_flag(self):
+        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
+            class Color(Flag):
+                _order_ = 'red green blue purple'
+                red = 1
+                green = 2
+                blue = 4
+
+    def test_order_has_extra_members_with_aliases_flag(self):
+        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
+            class Color(Flag):
+                _order_ = 'red green blue purple'
+                red = 1
+                green = 2
+                blue = 4
+                verde = green
+
+    def test_enum_has_extra_members_flag(self):
+        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
+            class Color(Flag):
+                _order_ = 'red green blue'
+                red = 1
+                green = 2
+                blue = 4
+                purple = 8
+
+    def test_enum_has_extra_members_with_aliases_flag(self):
+        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
+            class Color(Flag):
+                _order_ = 'red green blue'
+                red = 1
+                green = 2
+                blue = 4
+                purple = 8
+                verde = green
+
+
+class TestNamedTupleV3(TestCase):
+
+    def test_fixed_size(self):
+        class Book(NamedTuple, size=TupleSize.fixed):
+            title = 0
+            author = 1
+            genre = 2
+        b = Book('Teckla', 'Steven Brust', 'fantasy')
+        self.assertTrue('Teckla' in b)
+        self.assertTrue('Steven Brust' in b)
+        self.assertTrue('fantasy' in b)
+        self.assertEqual(b.title, 'Teckla')
+        self.assertEqual(b.author, 'Steven Brust')
+        self.assertRaises(TypeError, Book, 'Teckla', 'Steven Brust')
+        self.assertRaises(TypeError, Book, 'Teckla')
+
+    def test_minimum_size(self):
+        class Book(NamedTuple, size=TupleSize.minimum):
+            title = 0
+            author = 1
+        b = Book('Teckla', 'Steven Brust', 'fantasy')
+        self.assertTrue('Teckla' in b)
+        self.assertTrue('Steven Brust' in b)
+        self.assertTrue('fantasy' in b)
+        self.assertEqual(b.title, 'Teckla')
+        self.assertEqual(b.author, 'Steven Brust')
+        self.assertEqual(b[2], 'fantasy')
+        b = Book('Teckla', 'Steven Brust')
+        self.assertTrue('Teckla' in b)
+        self.assertTrue('Steven Brust' in b)
+        self.assertEqual(b.title, 'Teckla')
+        self.assertEqual(b.author, 'Steven Brust')
+        self.assertRaises(TypeError, Book, 'Teckla')
+
+    def test_variable_size(self):
+        class Book(NamedTuple, size=TupleSize.variable):
+            title = 0
+            author = 1
+            genre = 2
+        b = Book('Teckla', 'Steven Brust', 'fantasy')
+        self.assertTrue('Teckla' in b)
+        self.assertTrue('Steven Brust' in b)
+        self.assertTrue('fantasy' in b)
+        self.assertEqual(b.title, 'Teckla')
+        self.assertEqual(b.author, 'Steven Brust')
+        self.assertEqual(b.genre, 'fantasy')
+        b = Book('Teckla', 'Steven Brust')
+        self.assertTrue('Teckla' in b)
+        self.assertTrue('Steven Brust' in b)
+        self.assertEqual(b.title, 'Teckla')
+        self.assertEqual(b.author, 'Steven Brust')
+        self.assertRaises(AttributeError, getattr, b, 'genre')
+        self.assertRaises(TypeError, Book, title='Teckla', genre='fantasy')
+        self.assertRaises(TypeError, Book, author='Steven Brust')
+
+
+
+class TestStackoverflowAnswersV3(TestCase):
+
+    def test_self_referential_directions(self):
+        # https://stackoverflow.com/a/64000706/208880
+        class Directions(Enum):
+            #
+            NORTH = 1, 0
+            WEST = 0, 1
+            SOUTH = -1, 0
+            EAST = 0, -1
+            #
+            def __init__(self, x, y):
+                self.x = x
+                self.y = y
+                if len(self.__class__):
+                    # make links
+                    all = list(self.__class__)
+                    left, right = all[0], all[-1]
+                    self.left = left
+                    self.right = right
+                    left.right = self
+                    right.left = self
+        #
+        D = Directions
+        self.assertEqual(D.NORTH.value, (1, 0))
+        self.assertTrue(D.NORTH.left is D.WEST)
+        self.assertTrue(D.SOUTH.right is D.WEST)
+
+    def test_self_referential_rock_paper_scissors(self):
+        # https://stackoverflow.com/a/57085357/208880
+        class RPS(Enum):
+            #
+            Rock = "rock"
+            Paper = "paper"
+            Scissors = "scissors"
+            #
+            def __init__(self, value):
+                if len(self.__class__):
+                    # make links
+                    all = list(self.__class__)
+                    first, previous = all[0], all[-1]
+                    first.beats = self
+                    self.beats = previous
+        #
+        self.assertTrue(RPS.Rock.beats is RPS.Scissors)
+        self.assertTrue(RPS.Scissors.beats is RPS.Paper)
+        self.assertTrue(RPS.Paper.beats is RPS.Rock)
+
+    def test_arduino_headers(self):
+        # https://stackoverflow.com/q/65048495/208880
+        class CHeader(Enum):
+            def __init_subclass__(cls, **kwds):
+                # write Enums to C header file
+                cls_name = cls.__name__
+                header_path = getattr(cls, '_%s__header' % cls_name)
+                with open(header_path, 'w') as fh:
+                    fh.write('initial header stuff here\n')
+                    for enum in cls:
+                        fh.write('#define %s %r\n' % (enum.name, enum.value))
+        class Arduino(CHeader):
+            __header = os.path.join(tempdir, 'arduino.h')
+            ONE = 1
+            TWO = 2
+        with open(os.path.join(tempdir, 'arduino.h')) as fh:
+                data = fh.read()
+        self.assertEqual(textwrap.dedent("""\
+                initial header stuff here
+                #define ONE 1
+                #define TWO 2
+                """),
+                data,
+                )
+
+    def test_create_C_like_Enum(self):
+        # https://stackoverflow.com/a/35965438/208880
+        class Id(Enum, settings=MagicValue, start=0):
+            #
+            NONE  # 0x0
+            HEARTBEAT  # 0x1
+            FLUID_TRANSFER_REQUEST
+            FLUID_TRANSFER_STATUS_MSG
+            FLUID_TRANSFER_ERROR_MSG
+            # ...
+            #
+            # Camera App Messages
+            START_SENDING_PICTURES = 0x010000
+            STOP_SENDING_PICTURES
+            START_RECORDING_VIDEO_REQ
+            STOP_RECORDING_VIDEO_REQ
+            # ...
+            #
+            # Sensor Calibration
+            VOLUME_REQUEST = 0x020000
+            START_CAL
+            CLI_COMMAND_REQUEST
+            CLI_COMMAND_RESPONSE
+            #
+            # File Mananger
+            NEW_DELIVERY_REQ = 0x30000
+            GET_DELIVERY_FILE_REQ
+            GET_FILE_REQ
+            #
+            ACK_NACK
+            RESPONSE
+            #
+            LAST_ID
+        #
+        self.assertEqual(Id.NONE.value, 0)
+        self.assertEqual(Id.FLUID_TRANSFER_ERROR_MSG.value, 4)
+        self.assertEqual(Id.START_SENDING_PICTURES.value, 0x010000)
+        self.assertEqual(Id.STOP_RECORDING_VIDEO_REQ.value, 0x010003)
+        self.assertEqual(Id.START_CAL.value, 0x020001)
+        self.assertEqual(Id.LAST_ID.value, 0x30005)
+
+
+    @unittest.skipUnless(pyparsing, 'pyparsing not installed')
+    def test_c_header_scanner(self):
+        # https://stackoverflow.com/questions/58732872/208880
+        with open(os.path.join(tempdir, 'c_plus_plus.h'), 'w') as fh:
+            fh.write("""
+                    stuff before
+                    enum hello {
+                        Zero,
+                        One,
+                        Two,
+                        Three,
+                        Five=5,
+                        Six,
+                        Ten=10
+                        };
+                    in the middle
+                    enum blah
+                        {
+                        alpha,
+                        beta,
+                        gamma = 10 ,
+                        zeta = 50
+                        };
+                    at the end
+                    """)
+        from pyparsing import Group, Optional, Suppress, Word, ZeroOrMore
+        from pyparsing import alphas, alphanums, nums
+        #
+        CPPEnum = None
+        class CPPEnumType(EnumMeta):
+            #
+            @classmethod
+            def __prepare__(metacls, clsname, bases, **kwds):
+                # return a standard dictionary for the initial processing
+                return {}
+            #
+            def __init__(clsname, *args , **kwds):
+                super(CPPEnumType, clsname).__init__(*args)
+            #
+            def __new__(metacls, clsname, bases, clsdict, **kwds):
+                if CPPEnum is None:
+                    # first time through, ignore the rest
+                    enum_dict = super(CPPEnumType, metacls).__prepare__(clsname, bases, **kwds)
+                    enum_dict.update(clsdict)
+                    return super(CPPEnumType, metacls).__new__(metacls, clsname, bases, enum_dict, **kwds)
+                members = []
+                #
+                # remove _file and _name using `pop()` as they will cause problems in EnumMeta
+                try:
+                    file = clsdict.pop('_file')
+                except KeyError:
+                    raise TypeError('_file not specified')
+                cpp_enum_name = clsdict.pop('_name', clsname.lower())
+                with open(file) as fh:
+                    file_contents = fh.read()
+                #
+                # syntax we don't want to see in the final parse tree
+                LBRACE, RBRACE, EQ, COMMA = map(Suppress, "{}=,")
+                _enum = Suppress("enum")
+                identifier = Word(alphas, alphanums + "_")
+                integer = Word(nums)
+                enumValue = Group(identifier("name") + Optional(EQ + integer("value")))
+                enumList = Group(enumValue + ZeroOrMore(COMMA + enumValue))
+                enum = _enum + identifier("enum") + LBRACE + enumList("names") + RBRACE
+                #
+                # find the cpp_enum_name ignoring other syntax and other enums
+                for item, start, stop in enum.scanString(file_contents):
+                    if item.enum != cpp_enum_name:
+                        continue
+                    id = 0
+                    for entry in item.names:
+                        if entry.value != "":
+                            id = int(entry.value)
+                        members.append((entry.name.upper(), id))
+                        id += 1
+                #
+                # get the real EnumDict
+                enum_dict = super(CPPEnumType, metacls).__prepare__(clsname, bases, **kwds)
+                # transfer the original dict content, names starting with '_' first
+                items = list(clsdict.items())
+                items.sort(key=lambda p: (0 if p[0][0] == '_' else 1, p))
+                for name, value in items:
+                    enum_dict[name] = value
+                # add the members
+                for name, value in members:
+                    enum_dict[name] = value
+                return super(CPPEnumType, metacls).__new__(metacls, clsname, bases, enum_dict, **kwds)
+        #
+        class CPPEnum(IntEnum, metaclass=CPPEnumType):
+            pass
+        #
+        class Hello(CPPEnum):
+            _file = os.path.join(tempdir, 'c_plus_plus.h')
+        #
+        class Blah(CPPEnum):
+            _file = os.path.join(tempdir, 'c_plus_plus.h')
+            _name = 'blah'
+        #
+        self.assertEqual(
+                list(Hello),
+                [Hello.ZERO, Hello.ONE, Hello.TWO, Hello.THREE, Hello.FIVE, Hello.SIX, Hello.TEN],
+                )
+        self.assertEqual(Hello.ZERO.value, 0)
+        self.assertEqual(Hello.THREE.value, 3)
+        self.assertEqual(Hello.SIX.value, 6)
+        self.assertEqual(Hello.TEN.value, 10)
+        #
+        self.assertEqual(
+                list(Blah),
+                [Blah.ALPHA, Blah.BETA, Blah.GAMMA, Blah.ZETA],
+                )
+        self.assertEqual(Blah.ALPHA.value, 0)
+        self.assertEqual(Blah.BETA.value, 1)
+        self.assertEqual(Blah.GAMMA.value, 10)
+        self.assertEqual(Blah.ZETA.value, 50)
+
+class TestIssuesV3(TestCase):
+    """
+    Problems that were stated in issues.
+    """
+
+    def test_auto_multi_int_1(self):
+        class Measurement(int, AddValueEnum, MultiValueEnum, start=0):
+            one = "20110721"
+            two = "20120911"
+            three = "20110518"
+        self.assertEqual([m.value for m in Measurement], [0, 1, 2])
+        self.assertEqual([m.name for m in Measurement], ['one', 'two', 'three'])
+        self.assertIs(Measurement(0), Measurement.one)
+        self.assertIs(Measurement('20110721'), Measurement.one)
+        self.assertIs(Measurement(1), Measurement.two)
+        self.assertIs(Measurement('20120911'), Measurement.two)
+        self.assertIs(Measurement(2), Measurement.three)
+        self.assertIs(Measurement('20110518'), Measurement.three)
+
+    def test_auto_multi_int_2(self):
+        class Measurement(int, Enum, settings=(MultiValue, AddValue), start=0):
+            one = "20110721"
+            two = "20120911"
+            three = "20110518"
+        self.assertEqual([m.value for m in Measurement], [0, 1, 2])
+        self.assertEqual([m.name for m in Measurement], ['one', 'two', 'three'])
+        self.assertIs(Measurement(0), Measurement.one)
+        self.assertIs(Measurement('20110721'), Measurement.one)
+        self.assertIs(Measurement(1), Measurement.two)
+        self.assertIs(Measurement('20120911'), Measurement.two)
+        self.assertIs(Measurement(2), Measurement.three)
+        self.assertIs(Measurement('20110518'), Measurement.three)
+
+    def test_extend_enum_with_init(self):
+        class Color(Enum, settings=MultiValue, init='foo bar'):
+            red = '1', 'yes'
+            green = '2', 'no'
+            blue = '3', 'maybe'
+        self.assertEqual(Color.red.value, '1')
+        self.assertEqual(Color.red.foo, '1')
+        self.assertEqual(Color.red.bar, 'yes')
+        extend_enum(Color, 'opacity', '4', 'never')
+        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue, Color.opacity])
+        self.assertEqual(Color.opacity.value, '4')
+        self.assertEqual(Color.opacity.name, 'opacity')
+        self.assertTrue(Color('4') is Color.opacity)
+        self.assertTrue(Color('never') is Color.opacity)
+
+class TestExtendEnumV3(TestCase):
+
+    def test_extend_enum_plain(self):
+        class Color(Enum):
+            red = 1
+            green = 2
+            blue = 3
+        self.assertRaisesRegex(TypeError, '.blue. already in use as property..Color.blue: 3.', extend_enum, Color, 'blue', 5)
+        #
+        extend_enum(Color, 'brown', 4)
+        self.assertEqual(Color.brown.name, 'brown')
+        self.assertEqual(Color.brown.value, 4)
+        self.assertTrue(Color.brown in Color)
+        self.assertEqual(Color(4), Color.brown)
+        self.assertEqual(Color['brown'], Color.brown)
+        self.assertEqual(len(Color), 4)
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, 5)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(5), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+        self.assertEqual(len(Color), 5)
+
+    def test_extend_enum_alias(self):
+        class Color(Enum):
+            red = 1
+            green = 2
+            blue = 3
+        extend_enum(Color, 'rojo', 1)
+        self.assertEqual(Color.rojo.name, 'red')
+        self.assertEqual(Color.rojo.value, 1)
+        self.assertTrue(Color.rojo in Color)
+        self.assertEqual(Color(1), Color.rojo)
+        self.assertEqual(Color['rojo'], Color.red)
+        self.assertEqual(len(Color), 3)
+
+    def test_extend_enum_unique(self):
+        class Color(UniqueEnum):
+            red = 1
+            green = 2
+            blue = 3
+        self.assertRaisesRegex(ValueError, r'<Color.rojo: 1> is a duplicate of <Color.red: 1>', extend_enum, Color, 'rojo', 1)
+        #
+        self.assertEqual(Color.red.name, 'red')
+        self.assertEqual(Color.red.value, 1)
+        self.assertTrue(Color.red in Color)
+        self.assertEqual(Color(1), Color.red)
+        self.assertEqual(Color['red'], Color.red)
+        self.assertEqual(Color.green.name, 'green')
+        self.assertEqual(Color.green.value, 2)
+        self.assertTrue(Color.green in Color)
+        self.assertEqual(Color(2), Color.green)
+        self.assertEqual(Color['blue'], Color.blue)
+        self.assertEqual(Color.blue.name, 'blue')
+        self.assertEqual(Color.blue.value, 3)
+        self.assertTrue(Color.blue in Color)
+        self.assertEqual(Color(3), Color.blue)
+        self.assertEqual(len(Color), 3)
+        #
+        extend_enum(Color, 'brown', 4)
+        self.assertEqual(Color.brown.name, 'brown')
+        self.assertEqual(Color.brown.value, 4)
+        self.assertTrue(Color.brown in Color)
+        self.assertEqual(Color(4), Color.brown)
+        self.assertEqual(Color['brown'], Color.brown)
+        self.assertEqual(len(Color), 4)
+        #
+        self.assertRaisesRegex(ValueError, '<Color.verde: 2> is a duplicate of <Color.green: 2>', extend_enum, Color, 'verde', 2)
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, 5)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(5), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+        self.assertEqual(len(Color), 5)
+
+
+    def test_extend_enum_shadow_property(self):
+        class Color(Enum):
+            red = 1
+            green = 2
+            blue = 3
+        extend_enum(Color, 'value', 4)
+        self.assertEqual(Color.value.name, 'value')
+        self.assertEqual(Color.value.value, 4)
+        self.assertTrue(Color.value in Color)
+        self.assertEqual(Color(4), Color.value)
+        self.assertEqual(Color['value'], Color.value)
+        self.assertEqual(len(Color), 4)
+        self.assertEqual(Color.red.value, 1)
+
+    def test_extend_enum_shadow_base(self):
+        class hohum(object):
+            def cyan(self):
+                "cyanize a color"
+                return self.value
+        class Color(hohum, Enum):
+            red = 1
+            green = 2
+            blue = 3
+        self.assertRaisesRegex(TypeError, r'already in use in superclass', extend_enum, Color, 'cyan', 4)
+        self.assertEqual(len(Color), 3)
+        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
+
+    def test_extend_enum_multivalue(self):
+        class Color(MultiValueEnum):
+            red = 1, 4, 7
+            green = 2, 5, 8
+            blue = 3, 6, 9
+        extend_enum(Color, 'brown', 10, 20)
+        self.assertEqual(Color.brown.name, 'brown')
+        self.assertEqual(Color.brown.value, 10)
+        self.assertTrue(Color.brown in Color)
+        self.assertEqual(Color(10), Color.brown)
+        self.assertEqual(Color(20), Color.brown)
+        self.assertEqual(Color['brown'], Color.brown)
+        self.assertEqual(len(Color), 4)
+        #
+        self.assertRaisesRegex(ValueError, 'no values specified for MultiValue enum', extend_enum, Color, 'mauve')
+
+    def test_extend_enum_multivalue_alias(self):
+        class Color(MultiValueEnum):
+            red = 1, 4, 7
+            green = 2, 5, 8
+            blue = 3, 6, 9
+        self.assertRaisesRegex(ValueError, r'<Color.rojo: 7> is a duplicate of <Color.red: 1>', extend_enum, Color, 'rojo', 7)
+        self.assertEqual(Color.red.name, 'red')
+        self.assertEqual(Color.red.value, 1)
+        self.assertTrue(Color.red in Color)
+        self.assertEqual(Color(1), Color.red)
+        self.assertEqual(Color(4), Color.red)
+        self.assertEqual(Color(7), Color.red)
+        self.assertEqual(Color['red'], Color.red)
+        self.assertEqual(Color.green.name, 'green')
+        self.assertEqual(Color.green.value, 2)
+        self.assertTrue(Color.green in Color)
+        self.assertEqual(Color(2), Color.green)
+        self.assertEqual(Color(5), Color.green)
+        self.assertEqual(Color(8), Color.green)
+        self.assertEqual(Color['blue'], Color.blue)
+        self.assertEqual(Color.blue.name, 'blue')
+        self.assertEqual(Color.blue.value, 3)
+        self.assertTrue(Color.blue in Color)
+        self.assertEqual(Color(3), Color.blue)
+        self.assertEqual(Color(6), Color.blue)
+        self.assertEqual(Color(9), Color.blue)
+        self.assertEqual(len(Color), 3)
+
+    def test_extend_enum_multivalue_str(self):
+        class M(str, MultiValueEnum):
+            VALUE_1 = 'value_1', 'VALUE_1'
+            VALUE_2 = 'value_2', 'VALUE_2'
+            VALUE_3 = 'value_3', 'VALUE_3'
+        self.assertTrue(M._member_type_ is str)
+        extend_enum(M, 'VALUE_4', 'value_4', 'VALUE_4')
+        self.assertEqual(list(M), [M.VALUE_1, M.VALUE_2, M.VALUE_3, M.VALUE_4])
+        self.assertTrue(M('value_4') is M.VALUE_4)
+        self.assertTrue(M('VALUE_4') is M.VALUE_4)
+        self.assertTrue(M.VALUE_4.name == 'VALUE_4')
+        self.assertTrue(M.VALUE_4.value == 'value_4')
+
+    def test_extend_intenum(self):
+        class Index(IntEnum):
+            DeviceType    = 0x1000
+            ErrorRegister = 0x1001
+
+        for name, value in (
+                ('ControlWord', 0x6040),
+                ('StatusWord', 0x6041),
+                ('OperationMode', 0x6060),
+                ):
+            extend_enum(Index, name, value)
+
+        self.assertEqual(len(Index), 5)
+        self.assertEqual(list(Index), [Index.DeviceType, Index.ErrorRegister, Index.ControlWord, Index.StatusWord, Index.OperationMode])
+        self.assertEqual(Index.DeviceType.value, 0x1000)
+        self.assertEqual(Index.StatusWord.value, 0x6041)
+
+    def test_extend_multi_init(self):
+        class HTTPStatus(IntEnum):
+            def __new__(cls, value, phrase, description):
+                obj = int.__new__(cls, value)
+                obj._value_ = value
+
+                obj.phrase = phrase
+                obj.description = description
+                return obj
+            CONTINUE = 100, 'Continue', 'Request received, please continue'
+            SWITCHING_PROTOCOLS = 101, 'Switching Protocols', 'Switching to new protocol; obey Upgrade header'
+            PROCESSING = 102, 'Processing', ''
+        length = 3
+        extend_enum(HTTPStatus, 'BAD_SPAM', 513, 'Too greasy', 'for a train')
+        extend_enum(HTTPStatus, 'BAD_EGGS', 514, 'Too green', '')
+        self.assertEqual(len(HTTPStatus), length+2)
+        self.assertEqual(
+                list(HTTPStatus)[-2:],
+                [HTTPStatus.BAD_SPAM, HTTPStatus.BAD_EGGS],
+                )
+        self.assertEqual(HTTPStatus.BAD_SPAM.value, 513)
+        self.assertEqual(HTTPStatus.BAD_SPAM.name, 'BAD_SPAM')
+        self.assertEqual(HTTPStatus.BAD_SPAM.phrase, 'Too greasy')
+        self.assertEqual(HTTPStatus.BAD_SPAM.description, 'for a train')
+        self.assertEqual(HTTPStatus.BAD_EGGS.value, 514)
+        self.assertEqual(HTTPStatus.BAD_EGGS.name, 'BAD_EGGS')
+        self.assertEqual(HTTPStatus.BAD_EGGS.phrase, 'Too green')
+        self.assertEqual(HTTPStatus.BAD_EGGS.description, '')
+
+    def test_extend_flag(self):
+        class Color(Flag):
+            BLACK = 0
+            RED = 1
+            GREEN = 2
+            BLUE = 4
+        extend_enum(Color, 'MAGENTA')
+        self.assertTrue(Color(8) is Color.MAGENTA)
+        self.assertTrue(isinstance(Color.MAGENTA, Color))
+        self.assertEqual(Color.MAGENTA.value, 8)
+        extend_enum(Color, 'PURPLE', 11)
+        self.assertTrue(Color(11) is Color.PURPLE)
+        self.assertTrue(isinstance(Color.PURPLE, Color))
+        self.assertEqual(Color.PURPLE.value, 11)
+        self.assertTrue(issubclass(Color, Flag))
+
+    def test_extend_flag_backwards(self):
+        class Color(Flag):
+            BLACK = 0
+            RED = 1
+            GREEN = 2
+            BLUE = 4
+        extend_enum(Color, 'PURPLE', 11)
+        self.assertTrue(Color(11) is Color.PURPLE)
+        self.assertTrue(isinstance(Color.PURPLE, Color))
+        self.assertEqual(Color.PURPLE.value, 11)
+        self.assertTrue(issubclass(Color, Flag))
+        #
+        extend_enum(Color, 'MAGENTA')
+        self.assertTrue(Color(8) is Color.MAGENTA)
+        self.assertTrue(isinstance(Color.MAGENTA, Color))
+        self.assertEqual(Color.MAGENTA.value, 8)
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, 16)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(16), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+        self.assertEqual(len(Color), 5)
+
+    def test_extend_intflag(self):
+        class Color(IntFlag):
+            BLACK = 0
+            RED = 1
+            GREEN = 2
+            BLUE = 4
+        extend_enum(Color, 'MAGENTA')
+        self.assertTrue(Color(8) is Color.MAGENTA)
+        self.assertTrue(isinstance(Color.MAGENTA, Color))
+        self.assertEqual(Color.MAGENTA.value, 8)
+        extend_enum(Color, 'PURPLE', 11)
+        self.assertTrue(Color(11) is Color.PURPLE)
+        self.assertTrue(isinstance(Color.PURPLE, Color))
+        self.assertEqual(Color.PURPLE.value, 11)
+        self.assertTrue(issubclass(Color, Flag))
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, 16)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(16), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+        self.assertEqual(len(Color), 5)
+
+    def test_extend_intflag_backwards(self):
+        class Color(IntFlag):
+            BLACK = 0
+            RED = 1
+            GREEN = 2
+            BLUE = 4
+        extend_enum(Color, 'PURPLE', 11)
+        self.assertTrue(Color(11) is Color.PURPLE)
+        self.assertTrue(isinstance(Color.PURPLE, Color))
+        self.assertEqual(Color.PURPLE.value, 11)
+        self.assertTrue(issubclass(Color, Flag))
+        #
+        extend_enum(Color, 'MAGENTA')
+        self.assertTrue(Color(8) is Color.MAGENTA)
+        self.assertTrue(isinstance(Color.MAGENTA, Color))
+        self.assertEqual(Color.MAGENTA.value, 8)
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, 16)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(16), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+        self.assertEqual(len(Color), 5)
+
+    def test_extend_strenum(self):
+        class Color(StrEnum):
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        extend_enum(Color, 'BLACK')
+        self.assertEqual(Color.BLACK.name, 'BLACK')
+        self.assertEqual(Color.BLACK.value, 'black')
+        self.assertEqual(len(Color), 4)
+
+    @unittest.skipUnless(StdlibEnum, 'Stdlib Enum not available')
+    def test_extend_enum_stdlib(self):
+        class Color(StdlibEnum):
+            red = 1
+            green = 2
+            blue = 3
+        self.assertEqual(getattr(Color.red, '_values_', None), None)
+        extend_enum(Color, 'brown', 4)
+        self.assertEqual(Color.brown.name, 'brown')
+        self.assertEqual(Color.brown.value, 4)
+        self.assertTrue(Color.brown in Color)
+        self.assertEqual(Color(4), Color.brown)
+        self.assertEqual(Color['brown'], Color.brown)
+        self.assertEqual(len(Color), 4)
+
+    @unittest.skipUnless(StdlibEnum, 'Stdlib Enum not available')
+    def test_extend_enum_plain_stdlib(self):
+        class Color(StdlibEnum):
+            red = 1
+            green = 2
+            blue = 3
+        self.assertRaisesRegex(TypeError, 'already in use as', extend_enum, Color, 'blue', 5)
+        #
+        extend_enum(Color, 'brown', 4)
+        self.assertEqual(Color.brown.name, 'brown')
+        self.assertEqual(Color.brown.value, 4)
+        self.assertTrue(Color.brown in Color)
+        self.assertEqual(Color(4), Color.brown)
+        self.assertEqual(Color['brown'], Color.brown)
+        self.assertEqual(len(Color), 4)
+        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue, Color.brown])
+        self.assertEqual([c.value for c in Color], [1, 2, 3, 4])
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, 5)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(5), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+        self.assertEqual(len(Color), 5)
+
+    @unittest.skipUnless(StdlibEnum, 'Stdlib Enum not available')
+    def test_extend_enum_alias_stdlib(self):
+        class Color(StdlibEnum):
+            red = 1
+            green = 2
+            blue = 3
+        extend_enum(Color, 'rojo', 1)
+        self.assertEqual(Color.rojo.name, 'red')
+        self.assertEqual(Color.rojo.value, 1)
+        self.assertTrue(Color.rojo in Color)
+        self.assertEqual(Color(1), Color.rojo)
+        self.assertEqual(Color['rojo'], Color.red)
+        self.assertEqual(len(Color), 3)
+
+    @unittest.skipUnless(StdlibEnum, 'Stdlib Enum not available')
+    def test_extend_enum_shadow_property_stdlib(self):
+        class Color(StdlibEnum):
+            red = 1
+            green = 2
+            blue = 3
+        extend_enum(Color, 'value', 4)
+        self.assertEqual(Color.value.name, 'value')
+        self.assertEqual(Color.value.value, 4)
+        self.assertTrue(Color.value in Color)
+        self.assertEqual(Color(4), Color.value)
+        self.assertEqual(Color['value'], Color.value)
+        self.assertEqual(len(Color), 4)
+        self.assertEqual(Color.red.value, 1)
+
+    @unittest.skipUnless(StdlibEnum, 'Stdlib Enum not available')
+    def test_extend_enum_shadow_base_stdlib(self):
+        class hohum(object):
+            def cyan(self):
+                "cyanize a color"
+                return self.value
+        class Color(hohum, StdlibEnum):
+            red = 1
+            green = 2
+            blue = 3
+        self.assertRaisesRegex(TypeError, r'already in use in superclass', extend_enum, Color, 'cyan', 4)
+        self.assertEqual(len(Color), 3)
+        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
+
+    @unittest.skipUnless(StdlibIntEnum, 'Stdlib IntEnum not available')
+    def test_extend_intenum_stdlib(self):
+        class Index(StdlibIntEnum):
+            DeviceType    = 0x1000
+            ErrorRegister = 0x1001
+
+        for name, value in (
+                ('ControlWord', 0x6040),
+                ('StatusWord', 0x6041),
+                ('OperationMode', 0x6060),
+                ):
+            extend_enum(Index, name, value)
+
+        self.assertEqual(len(Index), 5)
+        self.assertEqual(list(Index), [Index.DeviceType, Index.ErrorRegister, Index.ControlWord, Index.StatusWord, Index.OperationMode])
+        self.assertEqual(Index.DeviceType.value, 0x1000)
+        self.assertEqual(Index.StatusWord.value, 0x6041)
+
+    @unittest.skipUnless(StdlibIntEnum, 'Stdlib IntEnum not available')
+    def test_extend_multi_init_stdlib(self):
+        class HTTPStatus(StdlibIntEnum):
+            def __new__(cls, value, phrase, description):
+                obj = int.__new__(cls, value)
+                obj._value_ = value
+
+                obj.phrase = phrase
+                obj.description = description
+                return obj
+            CONTINUE = 100, 'Continue', 'Request received, please continue'
+            SWITCHING_PROTOCOLS = 101, 'Switching Protocols', 'Switching to new protocol; obey Upgrade header'
+            PROCESSING = 102, 'Processing', ''
+        length = 3
+        extend_enum(HTTPStatus, 'BAD_SPAM', 513, 'Too greasy', 'for a train')
+        extend_enum(HTTPStatus, 'BAD_EGGS', 514, 'Too green', '')
+        self.assertEqual(len(HTTPStatus), length+2)
+        self.assertEqual(
+                list(HTTPStatus)[-2:],
+                [HTTPStatus.BAD_SPAM, HTTPStatus.BAD_EGGS],
+                )
+        self.assertEqual(HTTPStatus.BAD_SPAM.value, 513)
+        self.assertEqual(HTTPStatus.BAD_SPAM.name, 'BAD_SPAM')
+        self.assertEqual(HTTPStatus.BAD_SPAM.phrase, 'Too greasy')
+        self.assertEqual(HTTPStatus.BAD_SPAM.description, 'for a train')
+        self.assertEqual(HTTPStatus.BAD_EGGS.value, 514)
+        self.assertEqual(HTTPStatus.BAD_EGGS.name, 'BAD_EGGS')
+        self.assertEqual(HTTPStatus.BAD_EGGS.phrase, 'Too green')
+        self.assertEqual(HTTPStatus.BAD_EGGS.description, '')
+
+    @unittest.skipUnless(StdlibFlag, 'Stdlib Flag not available')
+    def test_extend_flag_stdlib(self):
+        class Color(StdlibFlag):
+            BLACK = 0
+            RED = 1
+            GREEN = 2
+            BLUE = 4
+        extend_enum(Color, 'MAGENTA')
+        self.assertTrue(Color(8) is Color.MAGENTA)
+        self.assertTrue(isinstance(Color.MAGENTA, Color))
+        self.assertEqual(Color.MAGENTA.value, 8)
+        extend_enum(Color, 'PURPLE', 11)
+        self.assertTrue(Color(11) is Color.PURPLE)
+        self.assertTrue(isinstance(Color.PURPLE, Color))
+        self.assertEqual(Color.PURPLE.value, 11)
+        self.assertTrue(issubclass(Color, StdlibFlag))
+
+    @unittest.skipUnless(StdlibFlag, 'Stdlib Flag not available')
+    def test_extend_flag_backwards_stdlib(self):
+        class Color(StdlibFlag):
+            BLACK = 0
+            RED = 1
+            GREEN = 2
+            BLUE = 4
+        extend_enum(Color, 'PURPLE', 11)
+        self.assertTrue(Color(11) is Color.PURPLE)
+        self.assertTrue(isinstance(Color.PURPLE, Color))
+        self.assertEqual(Color.PURPLE.value, 11)
+        self.assertTrue(issubclass(Color, StdlibFlag))
+        #
+        extend_enum(Color, 'MAGENTA')
+        self.assertTrue(Color(16) is Color.MAGENTA)
+        self.assertTrue(isinstance(Color.MAGENTA, Color))
+        self.assertEqual(Color.MAGENTA.value,16)
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, 32)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(32), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+
+    @unittest.skipUnless(StdlibIntFlag, 'Stdlib IntFlag not available')
+    def test_extend_intflag_stdlib(self):
+        class Color(StdlibIntFlag):
+            BLACK = 0
+            RED = 1
+            GREEN = 2
+            BLUE = 4
+        extend_enum(Color, 'MAGENTA')
+        self.assertTrue(Color(8) is Color.MAGENTA)
+        self.assertTrue(isinstance(Color.MAGENTA, Color))
+        self.assertEqual(Color.MAGENTA.value, 8)
+        extend_enum(Color, 'PURPLE', 11)
+        self.assertTrue(Color(11) is Color.PURPLE)
+        self.assertTrue(isinstance(Color.PURPLE, Color))
+        self.assertEqual(Color.PURPLE.value, 11)
+        self.assertTrue(issubclass(Color, StdlibFlag))
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, 16)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(16), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+
+    @unittest.skipUnless(StdlibIntFlag, 'Stdlib IntFlag not available')
+    def test_extend_intflag_backwards_stdlib(self):
+        class Color(StdlibIntFlag):
+            BLACK = 0
+            RED = 1
+            GREEN = 2
+            BLUE = 4
+        if pyver >= PY3_11:
+            # flags make more sense in 3.11
+            length = 5
+            MAGENTA = 8
+            mauve = 16
+        else:
+            length = 7
+            MAGENTA = 16
+            mauve = 32
+        extend_enum(Color, 'PURPLE', 11)
+        self.assertTrue(Color(11) is Color.PURPLE)
+        self.assertTrue(isinstance(Color.PURPLE, Color))
+        self.assertEqual(Color.PURPLE.value, 11)
+        self.assertTrue(issubclass(Color, StdlibFlag))
+        #
+        extend_enum(Color, 'MAGENTA')
+        self.assertTrue(Color(MAGENTA) is Color.MAGENTA)
+        self.assertTrue(isinstance(Color.MAGENTA, Color))
+        self.assertEqual(Color.MAGENTA.value, MAGENTA)
+        #
+        extend_enum(Color, 'mauve')
+        self.assertEqual(Color.mauve.name, 'mauve')
+        self.assertEqual(Color.mauve.value, mauve)
+        self.assertTrue(Color.mauve in Color)
+        self.assertEqual(Color(mauve), Color.mauve)
+        self.assertEqual(Color['mauve'], Color.mauve)
+        self.assertEqual(len(Color), length, list(Color))
+
+    @unittest.skipUnless(StdlibStrEnum, 'Stdlib StrEnum not available')
+    def test_extend_strenum_stdlib(self):
+        class Color(StrEnum):
+            RED = auto()
+            GREEN = auto()
+            BLUE = auto()
+        extend_enum(Color, 'BLACK')
+        self.assertEqual(Color.BLACK.name, 'BLACK')
+        self.assertEqual(Color.BLACK.value, 'black')
+        self.assertEqual(len(Color), 4)
+
+
+if __name__ == '__main__':
+    raise RuntimeError("'test_v3.py' should not be run by itself; it's included in 'test.py'")
diff --git a/venv/Lib/site-packages/blendmodes/__init__.py b/venv/Lib/site-packages/blendmodes/__init__.py
new file mode 100644
index 00000000..2b6e91ad
--- /dev/null
+++ b/venv/Lib/site-packages/blendmodes/__init__.py
@@ -0,0 +1,2 @@
+"""Use this module to apply a number of blending modes to a background and foreground image
+"""
diff --git a/venv/Lib/site-packages/blendmodes/__pycache__/__init__.cpython-39.pyc b/venv/Lib/site-packages/blendmodes/__pycache__/__init__.cpython-39.pyc
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diff --git a/venv/Lib/site-packages/blendmodes/__pycache__/blendtype.cpython-39.pyc b/venv/Lib/site-packages/blendmodes/__pycache__/blendtype.cpython-39.pyc
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diff --git a/venv/Lib/site-packages/blendmodes/blend.py b/venv/Lib/site-packages/blendmodes/blend.py
new file mode 100644
index 00000000..4165bfa7
--- /dev/null
+++ b/venv/Lib/site-packages/blendmodes/blend.py
@@ -0,0 +1,511 @@
+"""Provide blending functions and types.
+
+Adapted from https://github.com/addisonElliott/pypdn/blob/master/pypdn/reader.py
+and https://gitlab.com/inklabapp/pyora/-/blob/master/pyora/BlendNonSep.py
+MIT License Copyright (c) 2020 FredHappyface
+
+Credits to:
+
+MIT License Copyright (c) 2019 Paul Jewell
+For implementing blending from the Open Raster Image Spec
+
+MIT License Copyright (c) 2018 Addison Elliott
+For implementing blending from Paint.NET
+
+MIT License Copyright (c) 2017 pashango
+For implementing a number of blending functions used by other popular image
+editors
+"""
+
+from __future__ import annotations
+
+import warnings
+
+import numpy as np
+from PIL import Image
+
+from .blendtype import BlendType
+
+
+def normal(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.NORMAL."""
+	del background  # we don't care about this
+	return foreground
+
+
+def multiply(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.MULTIPLY."""
+	return np.clip(foreground * background, 0.0, 1.0)
+
+
+def additive(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.ADDITIVE."""
+	return np.minimum(background + foreground, 1.0)
+
+
+def colourburn(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.COLOURBURN."""
+	with np.errstate(divide="ignore"):
+		return np.where(
+			foreground != 0.0, np.maximum(1.0 - ((1.0 - background) / foreground), 0.0), 0.0
+		)
+
+
+def colourdodge(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.COLOURDODGE."""
+	with np.errstate(divide="ignore"):
+		return np.where(foreground != 1.0, np.minimum(background / (1.0 - foreground), 1.0), 1.0)
+
+
+def reflect(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.REFLECT."""
+	with np.errstate(divide="ignore"):
+		return np.where(
+			foreground != 1.0, np.minimum((background ** 2) / (1.0 - foreground), 1.0), 1.0
+		)
+
+
+def glow(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.GLOW."""
+	with np.errstate(divide="ignore"):
+		return np.where(
+			background != 1.0, np.minimum((foreground ** 2) / (1.0 - background), 1.0), 1.0
+		)
+
+
+def overlay(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.OVERLAY."""
+	return np.where(
+		background < 0.5,
+		2 * background * foreground,
+		1.0 - (2 * (1.0 - background) * (1.0 - foreground)),
+	)
+
+
+def difference(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.DIFFERENCE."""
+	return np.abs(background - foreground)
+
+
+def negation(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.NEGATION."""
+	return np.maximum(background - foreground, 0.0)
+
+
+def lighten(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.LIGHTEN."""
+	return np.maximum(background, foreground)
+
+
+def darken(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.DARKEN."""
+	return np.minimum(background, foreground)
+
+
+def screen(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.SCREEN."""
+	return background + foreground - background * foreground
+
+
+def xor(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.XOR."""
+	# XOR requires int values so convert to uint8
+	with warnings.catch_warnings():
+		warnings.simplefilter("ignore")
+		return imageIntToFloat(imageFloatToInt(background) ^ imageFloatToInt(foreground))
+
+
+def softlight(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.SOFTLIGHT."""
+	return (1.0 - background) * background * foreground + background * (
+		1.0 - (1.0 - background) * (1.0 - foreground)
+	)
+
+
+def hardlight(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.HARDLIGHT."""
+	return np.where(
+		foreground < 0.5,
+		np.minimum(background * 2 * foreground, 1.0),
+		np.minimum(1.0 - ((1.0 - background) * (1.0 - (foreground - 0.5) * 2.0)), 1.0),
+	)
+
+
+def grainextract(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.GRAINEXTRACT."""
+	return np.clip(background - foreground + 0.5, 0.0, 1.0)
+
+
+def grainmerge(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.GRAINMERGE."""
+	return np.clip(background + foreground - 0.5, 0.0, 1.0)
+
+
+def divide(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.DIVIDE."""
+	return np.minimum((256.0 / 255.0 * background) / (1.0 / 255.0 + foreground), 1.0)
+
+
+def pinlight(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.PINLIGHT."""
+	return np.minimum(background, 2 * foreground) * (foreground < 0.5) + np.maximum(
+		background, 2 * (foreground - 0.5)
+	) * (foreground >= 0.5)
+
+
+def vividlight(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.VIVIDLIGHT."""
+	return colourburn(background, foreground * 2) * (foreground < 0.5) + colourdodge(
+		background, 2 * (foreground - 0.5)
+	) * (foreground >= 0.5)
+
+
+def exclusion(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.EXCLUSION."""
+	return background + foreground - (2.0 * background * foreground)
+
+
+def _lum(colours: np.ndarray) -> np.ndarray:
+	"""Luminosity.
+
+	:param colours: x by x by 3 matrix of rgb color components of pixels
+	:return: x by x by 3 matrix of luminosity of pixels
+	"""
+	return (colours[:, :, 0] * 0.299) + (colours[:, :, 1] * 0.587) + (colours[:, :, 2] * 0.114)
+
+
+def _setLum(originalColours: np.ndarray, newLuminosity: np.ndarray) -> np.ndarray:
+	"""Set a new luminosity value for the matrix of color."""
+	_colours = originalColours.copy()
+	_luminosity = _lum(_colours)
+	deltaLum = newLuminosity - _luminosity
+	_colours[:, :, 0] += deltaLum
+	_colours[:, :, 1] += deltaLum
+	_colours[:, :, 2] += deltaLum
+	_luminosity = _lum(_colours)
+	_minColours = np.min(_colours, axis=2)
+	_MaxColours = np.max(_colours, axis=2)
+	for i in range(_colours.shape[0]):
+		for j in range(_colours.shape[1]):
+			_colour = _colours[i][j]
+			newLuminosity = _luminosity[i, j]
+			minColour = _minColours[i, j]
+			maxColour = _MaxColours[i, j]
+			if minColour < 0:
+				_colours[i][j] = newLuminosity + (
+					((_colour - newLuminosity) * newLuminosity) / (newLuminosity - minColour)
+				)
+			if maxColour > 1:
+				_colours[i][j] = newLuminosity + (
+					((_colour - newLuminosity) * (1 - newLuminosity)) / (maxColour - newLuminosity)
+				)
+	return _colours
+
+
+def _sat(colours: np.ndarray) -> np.ndarray:
+	"""Saturation.
+
+	:param colours: x by x by 3 matrix of rgb color components of pixels
+	:return: int of saturation of pixels
+	"""
+	return np.max(colours, axis=2) - np.min(colours, axis=2)
+
+
+def _setSat(originalColours: np.ndarray, newSaturation: np.ndarray) -> np.ndarray:
+	"""Set a new saturation value for the matrix of color.
+
+	The current implementation cannot be vectorized in an efficient manner,
+	so it is very slow,
+	O(m*n) at least. This might be able to be improved with openCL if that is
+	the direction that the lib takes.
+	:param c: x by x by 3 matrix of rgb color components of pixels
+	:param s: int of the new saturation value for the matrix
+	:return: x by x by 3 matrix of luminosity of pixels
+	"""
+	_colours = originalColours.copy()
+	for i in range(_colours.shape[0]):
+		for j in range(_colours.shape[1]):
+			_colour = _colours[i][j]
+			minI = 0
+			midI = 1
+			maxI = 2
+			if _colour[midI] < _colour[minI]:
+				minI, midI = midI, minI
+			if _colour[maxI] < _colour[midI]:
+				midI, maxI = maxI, midI
+			if _colour[midI] < _colour[minI]:
+				minI, midI = midI, minI
+			if _colour[maxI] - _colour[minI] > 0.0:
+				_colours[i][j][midI] = ((_colour[midI] - _colour[minI]) * newSaturation[i, j]) / (
+					_colour[maxI] - _colour[minI]
+				)
+				_colours[i][j][maxI] = newSaturation[i, j]
+			else:
+				_colours[i][j][midI] = 0
+				_colours[i][j][maxI] = 0
+			_colours[i][j][minI] = 0
+	return _colours
+
+
+def hue(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.HUE."""
+	return _setLum(_setSat(foreground, _sat(background)), _lum(background))
+
+
+def saturation(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.SATURATION."""
+	return _setLum(_setSat(background, _sat(foreground)), _lum(background))
+
+
+def colour(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.COLOUR."""
+	return _setLum(foreground, _lum(background))
+
+
+def luminosity(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
+	"""BlendType.LUMINOSITY."""
+	return _setLum(background, _lum(foreground))
+
+
+def destin(
+	backgroundAlpha: np.ndarray,
+	foregroundAlpha: np.ndarray,
+	backgroundColour: np.ndarray,
+	foregroundColour: np.ndarray,
+):
+	"""'clip' composite mode.
+
+	All parts of 'layer above' which are alpha in 'layer below' will be made
+	also alpha in 'layer above'
+	(to whatever degree of alpha they were)
+
+	Destination which overlaps the source, replaces the source.
+
+	Fa = 0; Fb = αs
+	co = αb x Cb x αs
+	αo = αb x αs
+	"""
+	del foregroundColour  # Not used by function
+	outAlpha = backgroundAlpha * foregroundAlpha
+	with np.errstate(divide="ignore", invalid="ignore"):
+		outRGB = np.divide(
+			np.multiply((backgroundAlpha * foregroundAlpha)[:, :, None], backgroundColour),
+			outAlpha[:, :, None],
+		)
+	return outRGB, outAlpha
+
+
+def destout(
+	backgroundAlpha: np.ndarray,
+	foregroundAlpha: np.ndarray,
+	backgroundColour: np.ndarray,
+	foregroundColour: np.ndarray,
+):
+	"""Reverse 'Clip' composite mode.
+
+	All parts of 'layer below' which are alpha in 'layer above' will be made
+	also alpha in 'layer below'
+	(to whatever degree of alpha they were)
+
+	"""
+	del foregroundColour  # Not used by function
+	outAlpha = backgroundAlpha * (1 - foregroundAlpha)
+	with np.errstate(divide="ignore", invalid="ignore"):
+		outRGB = np.divide(
+			np.multiply((backgroundAlpha * (1 - foregroundAlpha))[:, :, None], backgroundColour),
+			outAlpha[:, :, None],
+		)
+	return outRGB, outAlpha
+
+
+def destatop(
+	backgroundAlpha: np.ndarray,
+	foregroundAlpha: np.ndarray,
+	backgroundColour: np.ndarray,
+	foregroundColour: np.ndarray,
+):
+	"""Place the layer below above the 'layer above' in places where the 'layer above' exists...
+
+	where 'layer below' does not exist, but 'layer above' does, place 'layer-above'
+
+	"""
+	outAlpha = (foregroundAlpha * (1 - backgroundAlpha)) + (backgroundAlpha * foregroundAlpha)
+	with np.errstate(divide="ignore", invalid="ignore"):
+		outRGB = np.divide(
+			np.multiply((foregroundAlpha * (1 - backgroundAlpha))[:, :, None], foregroundColour)
+			+ np.multiply((backgroundAlpha * foregroundAlpha)[:, :, None], backgroundColour),
+			outAlpha[:, :, None],
+		)
+	return outRGB, outAlpha
+
+
+def srcatop(
+	backgroundAlpha: np.ndarray,
+	foregroundAlpha: np.ndarray,
+	backgroundColour: np.ndarray,
+	foregroundColour: np.ndarray,
+):
+	"""Place the layer below above the 'layer above' in places where the 'layer above' exists."""
+	outAlpha = (foregroundAlpha * backgroundAlpha) + (backgroundAlpha * (1 - foregroundAlpha))
+	with np.errstate(divide="ignore", invalid="ignore"):
+		outRGB = np.divide(
+			np.multiply((foregroundAlpha * backgroundAlpha)[:, :, None], foregroundColour)
+			+ np.multiply((backgroundAlpha * (1 - foregroundAlpha))[:, :, None], backgroundColour),
+			outAlpha[:, :, None],
+		)
+
+	return outRGB, outAlpha
+
+
+def imageIntToFloat(image: np.ndarray) -> np.ndarray:
+	"""Convert a numpy array representing an image to an array of floats.
+
+	Args:
+		image (np.ndarray): numpy array of ints
+
+	Returns:
+		np.ndarray: numpy array of floats
+	"""
+	return image / 255
+
+
+def imageFloatToInt(image: np.ndarray) -> np.ndarray:
+	"""Convert a numpy array representing an image to an array of ints.
+
+	Args:
+		image (np.ndarray): numpy array of floats
+
+	Returns:
+		np.ndarray: numpy array of ints
+	"""
+	return (image * 255).astype(np.uint8)
+
+
+def blend(background: np.ndarray, foreground: np.ndarray, blendType: BlendType) -> np.ndarray:
+	"""Blend pixels.
+
+	Args:
+		background (np.ndarray): background
+		foreground (np.ndarray): foreground
+		blendType (BlendType): the blend type
+
+	Returns:
+		np.ndarray: new array representing the image
+
+	background: np.ndarray,
+	foreground: np.ndarray and the return are in the form
+
+		[[[0. 0. 0.]
+		[0. 0. 0.]
+		[0. 0. 0.]
+		...
+		[0. 0. 0.]
+		[0. 0. 0.]
+		[0. 0. 0.]]
+
+		...
+
+		[[0. 0. 0.]
+		[0. 0. 0.]
+		[0. 0. 0.]
+		...
+		[0. 0. 0.]
+		[0. 0. 0.]
+		[0. 0. 0.]]]
+	"""
+	blendLookup = {
+		BlendType.NORMAL: normal,
+		BlendType.MULTIPLY: multiply,
+		BlendType.COLOURBURN: colourburn,
+		BlendType.COLOURDODGE: colourdodge,
+		BlendType.REFLECT: reflect,
+		BlendType.OVERLAY: overlay,
+		BlendType.DIFFERENCE: difference,
+		BlendType.LIGHTEN: lighten,
+		BlendType.DARKEN: darken,
+		BlendType.SCREEN: screen,
+		BlendType.SOFTLIGHT: softlight,
+		BlendType.HARDLIGHT: hardlight,
+		BlendType.GRAINEXTRACT: grainextract,
+		BlendType.GRAINMERGE: grainmerge,
+		BlendType.DIVIDE: divide,
+		BlendType.HUE: hue,
+		BlendType.SATURATION: saturation,
+		BlendType.COLOUR: colour,
+		BlendType.LUMINOSITY: luminosity,
+		BlendType.XOR: xor,
+		BlendType.NEGATION: negation,
+		BlendType.PINLIGHT: pinlight,
+		BlendType.VIVIDLIGHT: vividlight,
+		BlendType.EXCLUSION: exclusion,
+	}
+
+	if blendType not in blendLookup:
+		return normal(background, foreground)
+	return blendLookup[blendType](background, foreground)
+
+
+def blendLayers(
+	background: Image.Image,
+	foreground: Image.Image,
+	blendType: BlendType | tuple[str, ...],
+	opacity: float = 1.0,
+) -> Image.Image:
+	"""Blend layers using numpy array.
+
+	Args:
+		background (Image.Image): background layer
+		foreground (Image.Image): foreground layer (must be same size as background)
+		blendType (BlendType): The blendtype
+		opacity (float): The opacity of the foreground image
+
+	Returns:
+		Image.Image: combined image
+	"""
+	# Convert the Image.Image to a numpy array
+	npForeground: np.ndarray = imageIntToFloat(np.array(foreground.convert("RGBA")))
+	npBackground: np.ndarray = imageIntToFloat(np.array(background.convert("RGBA")))
+
+	# Get the alpha from the layers
+	backgroundAlpha = npBackground[:, :, 3]
+	foregroundAlpha = npForeground[:, :, 3] * opacity
+	combinedAlpha = backgroundAlpha * foregroundAlpha
+
+	# Get the colour from the layers
+	backgroundColor = npBackground[:, :, 0:3]
+	foregroundColor = npForeground[:, :, 0:3]
+
+	# Some effects require alpha
+	alphaFunc = {
+		BlendType.DESTIN: destin,
+		BlendType.DESTOUT: destout,
+		BlendType.SRCATOP: srcatop,
+		BlendType.DESTATOP: destatop,
+	}
+
+	if blendType in alphaFunc:
+		return Image.fromarray(
+			imageFloatToInt(
+				np.clip(
+					np.dstack(
+						alphaFunc[blendType](
+							backgroundAlpha, foregroundAlpha, backgroundColor, foregroundColor
+						)
+					),
+					a_min=0,
+					a_max=1,
+				)
+			)
+		)
+
+	# Get the colours and the alpha for the new image
+	colorComponents = (
+		(backgroundAlpha - combinedAlpha)[:, :, None] * backgroundColor
+		+ (foregroundAlpha - combinedAlpha)[:, :, None] * foregroundColor
+		+ combinedAlpha[:, :, None] * blend(backgroundColor, foregroundColor, blendType)
+	)
+	alphaComponent = backgroundAlpha + foregroundAlpha - combinedAlpha
+
+	return Image.fromarray(
+		imageFloatToInt(np.clip(np.dstack((colorComponents, alphaComponent)), a_min=0, a_max=1))
+	)
diff --git a/venv/Lib/site-packages/blendmodes/blendtype.py b/venv/Lib/site-packages/blendmodes/blendtype.py
new file mode 100644
index 00000000..1bde12a6
--- /dev/null
+++ b/venv/Lib/site-packages/blendmodes/blendtype.py
@@ -0,0 +1,72 @@
+"""Specify supported blend types."""
+
+from __future__ import annotations
+
+from aenum import MultiValueEnum
+
+
+class BlendType(str, MultiValueEnum):
+	"""Specify supported blend types.
+
+	NORMAL = "bm:normal", "normal"
+	MULTIPLY = "bm:multiply", "multiply"
+	ADDITIVE = "bm:additive", "additive"
+	COLOURBURN = "bm:colourburn", "colourburn"
+	COLOURDODGE = "bm:colourdodge", "colourdodge"
+	REFLECT = "bm:reflect", "reflect"
+	GLOW = "bm:glow", "glow"
+	OVERLAY = "bm:overlay", "overlay"
+	DIFFERENCE = "bm:difference", "difference"
+	NEGATION = "bm:negation", "negation"
+	LIGHTEN = "bm:lighten", "lighten"
+	DARKEN = "bm:darken", "darken"
+	SCREEN = "bm:screen", "screen"
+	XOR = "bm:xor", "xor"
+	SOFTLIGHT = "bm:softlight", "softlight"
+	HARDLIGHT = "bm:hardlight", "hardlight"
+	GRAINEXTRACT = "bm:grainextract", "grainextract"
+	GRAINMERGE = "bm:grainmerge", "grainmerge"
+	DIVIDE = "bm:divide", "divide"
+	HUE = "bm:hue", "hue"
+	SATURATION = "bm:saturation", "saturation"
+	COLOUR = "bm:colour", "colour"
+	LUMINOSITY = "bm:luminosity", "luminosity"
+	PINLIGHT = "bm:pinlight", "pinlight"
+	VIVIDLIGHT = "bm:vividlight", "vividlight"
+	EXCLUSION = "bm:exclusion", "exclusion"
+	DESTIN = "bm:destin", "destin"
+	DESTOUT = "bm:destout", "destout"
+	SRCATOP = "bm:srcatop", "srcatop"
+	DESTATOP = "bm:destatop", "destatop"
+	"""
+
+	NORMAL = "bm:normal", "normal"
+	MULTIPLY = "bm:multiply", "multiply"
+	ADDITIVE = "bm:additive", "additive"
+	COLOURBURN = "bm:colourburn", "colourburn"
+	COLOURDODGE = "bm:colourdodge", "colourdodge"
+	REFLECT = "bm:reflect", "reflect"
+	GLOW = "bm:glow", "glow"
+	OVERLAY = "bm:overlay", "overlay"
+	DIFFERENCE = "bm:difference", "difference"
+	NEGATION = "bm:negation", "negation"
+	LIGHTEN = "bm:lighten", "lighten"
+	DARKEN = "bm:darken", "darken"
+	SCREEN = "bm:screen", "screen"
+	XOR = "bm:xor", "xor"
+	SOFTLIGHT = "bm:softlight", "softlight"
+	HARDLIGHT = "bm:hardlight", "hardlight"
+	GRAINEXTRACT = "bm:grainextract", "grainextract"
+	GRAINMERGE = "bm:grainmerge", "grainmerge"
+	DIVIDE = "bm:divide", "divide"
+	HUE = "bm:hue", "hue"
+	SATURATION = "bm:saturation", "saturation"
+	COLOUR = "bm:colour", "colour"
+	LUMINOSITY = "bm:luminosity", "luminosity"
+	PINLIGHT = "bm:pinlight", "pinlight"
+	VIVIDLIGHT = "bm:vividlight", "vividlight"
+	EXCLUSION = "bm:exclusion", "exclusion"
+	DESTIN = "bm:destin", "destin"
+	DESTOUT = "bm:destout", "destout"
+	SRCATOP = "bm:srcatop", "srcatop"
+	DESTATOP = "bm:destatop", "destatop"
diff --git a/venv/Lib/site-packages/blendmodes/imagetools.py b/venv/Lib/site-packages/blendmodes/imagetools.py
new file mode 100644
index 00000000..834b7c86
--- /dev/null
+++ b/venv/Lib/site-packages/blendmodes/imagetools.py
@@ -0,0 +1,48 @@
+"""Do stuff to images to prepare them.
+"""
+from __future__ import annotations
+
+import warnings
+
+from deprecation import deprecated
+from PIL import Image
+
+
+@deprecated(deprecated_in="2021.1", removed_in="", details="use renderWAlphaOffset")
+def rasterImageOA(  # pylint:disable=missing-function-docstring
+	image: Image.Image, size: tuple[int, int], alpha: float = 1.0, offsets: tuple[int, int] = (0, 0)
+) -> Image.Image:
+	warnings.warn(
+		"Call to deprecated function rasterImageOA.", category=DeprecationWarning, stacklevel=2
+	)
+	return renderWAlphaOffset(image, size, alpha, offsets)
+
+
+@deprecated(deprecated_in="2021.1", removed_in="", details="use renderWAlphaOffset")
+def rasterImageOffset(  # pylint:disable=missing-function-docstring
+	image: Image.Image, size: tuple[int, int], offsets: tuple[int, int] = (0, 0)
+) -> Image.Image:
+	warnings.warn(
+		"Call to deprecated function rasterImageOffset.", category=DeprecationWarning, stacklevel=2
+	)
+	return renderWAlphaOffset(image, size, 1, offsets)
+
+
+def renderWAlphaOffset(
+	image: Image.Image, size: tuple[int, int], alpha: float = 1.0, offsets: tuple[int, int] = (0, 0)
+) -> Image.Image:
+	"""Render an image with offset and alpha to a given size.
+
+	Args:
+		image (Image.Image): pil image to draw
+		size (tuple[int, int]): width, height as a tuple
+		alpha (float, optional): alpha transparency. Defaults to 1.0.
+		offsets (tuple[int, int], optional): x, y offsets as a tuple.
+		Defaults to (0, 0).
+
+	Returns:
+		Image.Image: new image
+	"""
+	imageOffset = Image.new("RGBA", size)
+	imageOffset.paste(image.convert("RGBA"), offsets, image.convert("RGBA"))
+	return Image.blend(Image.new("RGBA", size), imageOffset, alpha)
-- 
cgit v1.2.1


From 2e8b5418e3cd4e9212f2fcdb36305d7a40f97916 Mon Sep 17 00:00:00 2001
From: ThereforeGames <95403634+ThereforeGames@users.noreply.github.com>
Date: Sun, 11 Dec 2022 18:03:36 -0500
Subject: Improve color correction with luminosity blend

---
 modules/processing.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 24c537d1..bc841837 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -27,6 +27,7 @@ from ldm.data.util import AddMiDaS
 from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 
 from einops import repeat, rearrange
+from blendmodes.blend import blendLayers, BlendType
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
@@ -39,17 +40,19 @@ def setup_color_correction(image):
     return correction_target
 
 
-def apply_color_correction(correction, image):
+def apply_color_correction(correction, original_image):
     logging.info("Applying color correction.")
     image = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
         cv2.cvtColor(
-            np.asarray(image),
+            np.asarray(original_image),
             cv2.COLOR_RGB2LAB
         ),
         correction,
         channel_axis=2
     ), cv2.COLOR_LAB2RGB).astype("uint8"))
-
+    
+    image = blendLayers(image, original_image, BlendType.LUMINOSITY)
+    
     return image
 
 
-- 
cgit v1.2.1


From 9170224d230fb22387e955d0b40ceda116f688b6 Mon Sep 17 00:00:00 2001
From: ThereforeGames <95403634+ThereforeGames@users.noreply.github.com>
Date: Sun, 11 Dec 2022 18:06:22 -0500
Subject: Delete venv/Lib/site-packages directory

---
 venv/Lib/site-packages/aenum/CHANGES               |  489 --
 venv/Lib/site-packages/aenum/LICENSE               |   32 -
 venv/Lib/site-packages/aenum/__init__.py           | 4111 ------------
 .../aenum/__pycache__/__init__.cpython-39.pyc      |  Bin 104964 -> 0 bytes
 .../aenum/__pycache__/_py3.cpython-39.pyc          |  Bin 686 -> 0 bytes
 venv/Lib/site-packages/aenum/_py2.py               |    7 -
 venv/Lib/site-packages/aenum/_py3.py               |   12 -
 venv/Lib/site-packages/aenum/doc/aenum.rst         | 1568 -----
 venv/Lib/site-packages/aenum/test.py               | 6832 --------------------
 venv/Lib/site-packages/aenum/test_v3.py            | 1982 ------
 venv/Lib/site-packages/blendmodes/__init__.py      |    2 -
 .../blendmodes/__pycache__/__init__.cpython-39.pyc |  Bin 285 -> 0 bytes
 .../blendmodes/__pycache__/blend.cpython-39.pyc    |  Bin 13302 -> 0 bytes
 .../__pycache__/blendtype.cpython-39.pyc           |  Bin 2850 -> 0 bytes
 venv/Lib/site-packages/blendmodes/blend.py         |  511 --
 venv/Lib/site-packages/blendmodes/blendtype.py     |   72 -
 venv/Lib/site-packages/blendmodes/imagetools.py    |   48 -
 17 files changed, 15666 deletions(-)
 delete mode 100644 venv/Lib/site-packages/aenum/CHANGES
 delete mode 100644 venv/Lib/site-packages/aenum/LICENSE
 delete mode 100644 venv/Lib/site-packages/aenum/__init__.py
 delete mode 100644 venv/Lib/site-packages/aenum/__pycache__/__init__.cpython-39.pyc
 delete mode 100644 venv/Lib/site-packages/aenum/__pycache__/_py3.cpython-39.pyc
 delete mode 100644 venv/Lib/site-packages/aenum/_py2.py
 delete mode 100644 venv/Lib/site-packages/aenum/_py3.py
 delete mode 100644 venv/Lib/site-packages/aenum/doc/aenum.rst
 delete mode 100644 venv/Lib/site-packages/aenum/test.py
 delete mode 100644 venv/Lib/site-packages/aenum/test_v3.py
 delete mode 100644 venv/Lib/site-packages/blendmodes/__init__.py
 delete mode 100644 venv/Lib/site-packages/blendmodes/__pycache__/__init__.cpython-39.pyc
 delete mode 100644 venv/Lib/site-packages/blendmodes/__pycache__/blend.cpython-39.pyc
 delete mode 100644 venv/Lib/site-packages/blendmodes/__pycache__/blendtype.cpython-39.pyc
 delete mode 100644 venv/Lib/site-packages/blendmodes/blend.py
 delete mode 100644 venv/Lib/site-packages/blendmodes/blendtype.py
 delete mode 100644 venv/Lib/site-packages/blendmodes/imagetools.py

diff --git a/venv/Lib/site-packages/aenum/CHANGES b/venv/Lib/site-packages/aenum/CHANGES
deleted file mode 100644
index e8799f6d..00000000
--- a/venv/Lib/site-packages/aenum/CHANGES
+++ /dev/null
@@ -1,489 +0,0 @@
-3.1.8
-=====
-
-recalculate bits used after all flags created (sometimes needed when a custom
-`__new__` is in place.
-
-
-3.1.7
-=====
-
-update flag creation to (possibly) add bitwise operator methods to newly
-created flags
-
-update extend_enum() to work with 3.11 flags
-
-
-3.1.6
-=====
-
-Update `dir()` on mixed enums to include mixed data type methods and
-attributes.
-
-Rename `enum_property` to `property` to match stdlib.  Recommended usage is
-`aenum.property` (prefix with module name).
-
-Remove quadritic creation behavior.
-
-
-BREAKING CHANGE BUG FIX that won't affect most people
-
-Enums with a custom `__new__` that:
-
-- use the enum machinery to generate the values; AND
-- have keyword arguments set to a default (like `None`)
-
-will fail to generate a missing value.  To fix: remove the default value and
-instead specify it on the member creation line.
-
-BREAKING CHANGE
-
-In Python 3.11 the `str()` of mixed enums will now match its `format()` which
-will be the normal `str()` of the data type -- so for an IntEnum you'll see
-`5` instead of `Perm.R|X`.  This affects IntEnum, StrEnum, and IntFlag.
-
-
-3.1.5
-=====
-
-fix support of `auto()` kwds
-
-
-3.1.3
-=====
-
-rename `aenum.property` to `aenum.enum_property`
-
-fix `enum_property` to work with `_init_` attributes
-
-
-3.1.2
-=====
-
-fix `extend_enum()` for unhashable values
-
-
-3.1.1
-=====
-
-fix `extend_enum()` for most cases
-
-
-3.1.0
-=====
-
-AddValue is similar to the old AutoNumber: it will always activate, but
-uses _generate_next_value_ to get the next value (so the user has some
-control over the return data type instead of always getting an int).
-
-
-BREAKING CHANGES
-
-AutoValue is gone.  It was superflous and its removal simplified the code.
-Simply put the fields needed in an `_init_` and `_generate_next_value_`
-will be called to supply the missing values (this is probably already what
-is happening).
-
-
-
-3.0.0
-=====
-
-standard Enum usage is unchanged
-
-BREAKING CHANGES
-
-Enum
-- the more esoteric method of creating Enums have been modified or removed
-  - AutoNumber setting is gone, inherit from AutoNumberEnum instead
-  - creating members without specifying anything is removed (if you don't
-    know what this means, you weren't doing it)
-
-Flag
-- unique flags are canonical (i.e. flags with powers of two values such as
-  1, 2, 4, 8, 16, etc.)
-- non-unique flags are aliases (i.e. values such as 3 or 7)
-- iteration of Flag and flag members only uses canonical flags
-
-
-ENHANCEMENTS
-
-Member creation has been redone to match Python 3.10's methods.  This also
-allows all supported Pythons (2.7, 3.3+) to use the __set_name__ and
-__init_subclass__ protocols (more robustly than in aenum 2.2.5)
-
-
-CHANGES
-
-enum_property() has been renamed to property() (old name still available, but
-deprecated).
-
-bin() replacement shows negative integers in twos-complement
-
-
-
-
-2.2.5
-=====
-
-call __init_subclass__ after members have been added, and in Pythons < 3.6
-call __set_name__ in Pythons < 3.6
-do not convert/disallow private names
-add iteration/len support to NamedConstant
-
-
-2.2.4
-=====
-
-add support to Constant to retrieve members by value
-
-    --> class K(Constant):
-    ...     one = 1
-    ...     two = 2
-
-    --> K.one
-    <K.one: 1>
-
-    --> K(1)
-    <K.one: 1>
-
-add pickle/deepcopy support to Constant
-
-add support for Constant to use other Constant values
-  (resulting members /are not/ the same)
-
-    --> class C(Constant)
-    ...     one = K.one
-    ...     three = 3
-
-    --> C.one == K.one
-    True
-
-    --> C.one is K.one
-    False
-
-AutoNumber and auto() now work together
-
-Enum members are now added to the class as enum_property, which supports
-unshadowing of parent class attributes when called on an Enum member:
-
-    --> class StrEnum(str, Enum):
-    ...     lower = 'lower'
-    ...     upper = 'upper'
-    ...     mixed = 'mixed'
-    
-    --> StrEnum.lower
-    <StrEnum.lower: 'lower'>
-
-    --> StrEnum.lower.upper()
-    'LOWER'
-
-    --> StrEnum.upper
-    <StrEnum.upper: 'upper'>
-
-    --> StrEnum.upper.upper()
-    'UPPER'
-
-
-2.2.3
-=====
-
-use members' type's methods __str__, __repr__, __format__, and
-__reduce_ex__ if directly assigned in Enum class body; i.e.:
-
-    --> class Color(str, Enum):
-    ...     red = 'red'
-    ...     green = 'green'
-    ...     blue = 'blue'
-    ...     __str__ = str.__str__
-
-    --> print(repr(Color.green))
-    <Color.green: 'green'>
-
-    --> print(Color.green)
-    green
-
-
-2.2.2
-=====
-
-replace _RouteClassAttributeToGetattr with enum_property (it is still
-available as an alias)
-
-support constant() and auto() being used together:
-
-    --> class Fruit(Flag):
-    ...    _order_ = 'apple banana lemon orange'
-    ...    apple = auto()
-    ...    banana = auto()
-    ...    lemon = auto()
-    ...    orange = auto()
-    ...    CitrusTypes = constant(lemon | orange)
-
-    --> list(Fruit)
-    [Fruit.apple, Fruit.banana, Fruit.lemon, Fruit.orange]
-
-    --> list(Fruit.CitrusTypes)
-    [Fruit.orange, Fruit.lemon]
-
-    --> Fruit.orange in Fruit.CitrusTypes
-    True
-
-
-2.2.1
-=====
-
-allow Enums to be called without a value
-
-    class Color(Enum):
-        black = 0
-        red = 1
-        green = 2
-        blue = 3
-        #
-        @classmethod
-        def _missing_value_(cls, value):
-            if value is no_arg:
-                return cls.black
-
-    >>> Color()
-    <Color.black: 0>
-
-allow Enum name use while constructing Enum (Python 3.4+ only)
-
-    --> class Color(Enum):
-    ...     _order_ = 'BLACK WHITE'
-    ...     BLACK = Color('black', '#000')
-    ...     WHITE = Color('white', '#fff')
-    ...     #
-    ...     def __init__(self, label, hex):
-    ...         self.label = label
-    ...         self.hex = hex
-
-
-2.2.0
-=====
-
-BREAKING CHANGE
----------------
-In Python 3+ classes defined inside an Enum no longer become members by
-default; in Python 2 they still become members, but see below.
-
-For cross-compatibility and full control two decorators are provided:
-
-- @member  --> forces item to become a member
-- @nonmember  --> excludes item from becoming a member
-
-So to have an Enum that behaves the same in Python 2 and 3, use the
-decorators (and other compatibility shims):
-
-    class Color(Enum):
-
-        _order_ = 'red green blue'
-
-        red = 1
-        green = 2
-        blue = 3
-
-        @nonmember
-        class Shades(Enum):
-
-            _order_ = 'light medium dark'
-
-            light = 1
-            medium = 2
-            dark = 3
-
-
-2.1.4
-=====
-
-EnumMeta:
-- change __member_new__ to __new_member__ (as the stdlib enum does)
-- assign member name to enum() instances (an Enum helper for defining members)
-- handle empty iterables when using functional API
-- make auto() work with previous enum members
-- keep searching mixins until base class is found
-
-Enum:
-- fix bug in Flag checks (ensure it is a Flag before checking the name)
-- add multiple mixin support
-- do not allow blank names (functional API)
-- raise TypeError if _missing_* returns wrong type
-- fix __format__ to honor custom __str__
-
-extend_enum:
-- support stdlib Enums
-- use _generate_next_value_ if value not provided
-
-general:
-- standardize exception formatting
-- use getfullargspec() in Python 3 (avoids deprecation warnings)
-
-
-2.1.2
-=====
-
-when order is callable, save it for subclass use
-
-
-2.1.1
-=====
-
-correctly raise TypeError for non-Enum containment checks
-support combining names with | for Flag key access
-support _order_ being a callable
-
-
-2.1.0
-=====
-
-support Flags being combined with other data types:
-- add _create_pseudo_member_values_ 
-- add default __new__ and temporary _init_ 
-
-
-2.0.10
-======
-
-ensure _ignore_ is set when _settings_ specified in body which includes
-AutoValue
-
-make Flag members iterable
-
-
-2.0.9
-=====
-
-fix missing comma in __all__
-fix extend_enum with custom __new__ methods
-fix MultiValue with AutoNumber without _init_
-
-
-2.0.8
-=====
-
-extend_enum now handles aliases and multivalues correctly
-
-
-2.0.7
-=====
-
-support mixin types with extend_enum
-init and AutoNumber can now work together
-add test for new Enum using EnumMeta
-add tests for variations of multivalue and init
-prevent deletion of NamedConstant.constant
-
-
-2.0.6
-=====
-
-constants cannot be deleted (they already couldn't be changed)
-constants can be used to define other constants
-
-
-2.0.5
-=====
-
-_init_ and MultiValue can now work together
-
-
-2.0.4
-=====
-
-_init_ and AutoValue (and _generate_next_value_) can now work together to
-supply missing values even when some of the required values per member are
-absent
-
-
-2.0.3
-=====
-
-add _missing_value_ and _missing_name_ methods, deprecate _missing_
-make enum instances comparable
-
-
-2.0.2
-=====
-
-both EnumMeta.__getattr__ and Enum.__new__ fall back to _missing_
-
-
-2.0.1
-=====
-
-auto() now works with other data types
-AutoNumber supports legacy Enums (fixed regression)
-
-
-2.0.0
-=====
-
-Flag and IntFlag added.
-
-
-1.4.7
-=====
-
-fix %-interpolation bug
-defined SqlLiteEnum only if sqlite exists
-support pyflakes
-
-
-1.4.6
-=====
-
-version numbering error
-
-
-1.4.5
-=====
-
-revert AutoNumberEnum to custom __new__ instead of AutoNumber
-use _ignore_ to shield against AutoNumber magic
-inherit start and init settings from base Enums
-
-
-1.4.4
-=====
-
-enabled export as a decorator
-enabled _order_ to replace __order__
-enabled python2 support for settings, init, and start
-
-
-1.4.3
-=====
-
-support _ignore_ for dynamically creating class bodies
-
-
-1.4.2
-=====
-
-MultiValue, NoAlias, Unique, and init now work with Python 2
-
-
-1.4.1
-=====
-
-Py3: added Enum creation flags: Auto, MultiValue, NoAlias, Unique
-
-fixed extend_enum to honor Enum flags
-
-
-1.4.0
-=====
-
-When possible aenum inherits from Python's own enum.
-
-Breaking change: enum members now default to evaluating as True to maintain
-compatibility with the stdlib.
-
-Add your own __bool__ (__nonzero__ in Python 2) if need this behavior:
-
-    def __bool__(self):
-        return bool(self.value)
-    __nonzero__ = __bool__
-
diff --git a/venv/Lib/site-packages/aenum/LICENSE b/venv/Lib/site-packages/aenum/LICENSE
deleted file mode 100644
index b20361ce..00000000
--- a/venv/Lib/site-packages/aenum/LICENSE
+++ /dev/null
@@ -1,32 +0,0 @@
-Copyright (c) 2015, 2016, 2017, 2018 Ethan Furman.
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions
-are met:
-
-    Redistributions of source code must retain the above
-    copyright notice, this list of conditions and the
-    following disclaimer.
-
-    Redistributions in binary form must reproduce the above
-    copyright notice, this list of conditions and the following
-    disclaimer in the documentation and/or other materials
-    provided with the distribution.
-
-    Neither the name Ethan Furman nor the names of any
-    contributors may be used to endorse or promote products
-    derived from this software without specific prior written
-    permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
-LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
diff --git a/venv/Lib/site-packages/aenum/__init__.py b/venv/Lib/site-packages/aenum/__init__.py
deleted file mode 100644
index 26c504cc..00000000
--- a/venv/Lib/site-packages/aenum/__init__.py
+++ /dev/null
@@ -1,4111 +0,0 @@
-"""Python Advanced Enumerations & NameTuples"""
-from __future__ import print_function
-
-# imports
-import sys as _sys
-pyver = _sys.version_info[:2]
-PY2 = pyver < (3, )
-PY3 = pyver >= (3, )
-PY2_6 = (2, 6)
-PY3_3 = (3, 3)
-PY3_4 = (3, 4)
-PY3_5 = (3, 5)
-PY3_6 = (3, 6)
-PY3_11 = (3, 11)
-
-import re
-
-_bltin_property = property
-_bltin_bin = bin
-
-try:
-    from collections import OrderedDict
-except ImportError:
-    OrderedDict = dict
-from collections import defaultdict
-try:
-    import sqlite3
-except ImportError:
-    sqlite3 = None
-
-try:
-    RecursionError
-except NameError:
-    # python3.4
-    RecursionError = RuntimeError
-
-from operator import or_ as _or_, and_ as _and_, xor as _xor_, inv as _inv_
-from operator import abs as _abs_, add as _add_, floordiv as _floordiv_
-from operator import lshift as _lshift_, rshift as _rshift_, mod as _mod_
-from operator import mul as _mul_, neg as _neg_, pos as _pos_, pow as _pow_
-from operator import truediv as _truediv_, sub as _sub_
-
-if PY2:
-    from ._py2 import *
-if PY3:
-    from ._py3 import *
-
-obj_type = type
-
-__all__ = [
-        'NamedConstant', 'constant', 'skip', 'nonmember', 'member', 'no_arg',
-        'Enum', 'IntEnum', 'AutoNumberEnum', 'OrderedEnum', 'UniqueEnum',
-        'StrEnum', 'UpperStrEnum', 'LowerStrEnum',
-        'Flag', 'IntFlag',
-        'AddValue', 'MagicValue', 'MultiValue', 'NoAlias', 'Unique',
-        'AddValueEnum', 'MultiValueEnum', 'NoAliasEnum',
-        'enum', 'extend_enum', 'unique', 'property',
-        'NamedTuple', 'SqliteEnum',
-        'FlagBoundary', 'STRICT', 'CONFORM', 'EJECT', 'KEEP',
-        'add_stdlib_integration', 'remove_stdlib_integration'
-        ]
-
-if sqlite3 is None:
-    __all__.remove('SqliteEnum')
-
-version = 3, 1, 12, 1
-
-# shims
-try:
-    any
-except NameError:
-    def any(iterable):
-        for element in iterable:
-            if element:
-                return True
-        return False
-
-try:
-    unicode
-    unicode = unicode
-except NameError:
-    # In Python 3 unicode no longer exists (it's just str)
-    unicode = str
-
-try:
-    basestring
-    basestring = bytes, unicode
-except NameError:
-    # In Python 2 basestring is the ancestor of both str and unicode
-    # in Python 3 it's just str, but was missing in 3.1
-    basestring = str,
-
-try:
-    long
-    baseinteger = int, long
-except NameError:
-    baseinteger = int,
-    long = int
-# deprecated
-baseint = baseinteger
-
-try:
-    NoneType
-except NameError:
-    NoneType = type(None)
-
-try:
-    # derive from stdlib enum if possible
-    import enum
-    if hasattr(enum, 'version'):
-        raise ImportError('wrong version')
-    else:
-        from enum import EnumMeta as StdlibEnumMeta, Enum as StdlibEnum, IntEnum as StdlibIntEnum
-        StdlibFlag = StdlibIntFlag = StdlibStrEnum = StdlibReprEnum = None
-except ImportError:
-    StdlibEnumMeta = StdlibEnum = StdlibIntEnum = StdlibIntFlag = StdlibFlag = StdlibStrEnum = None
-
-if StdlibEnum:
-    try:
-        from enum import IntFlag as StdlibIntFlag, Flag as StdlibFlag
-    except ImportError:
-        pass
-    try:
-        from enum import StrEnum as StdlibStrEnum
-    except ImportError:
-        pass
-    try:
-        from enum import ReprEnum as StdlibReprEnum
-    except ImportError:
-        pass
-
-
-
-
-# helpers
-# will be exported later
-MagicValue = AddValue = MultiValue = NoAlias = Unique = None
-
-def _bit_count(num):
-    """
-    return number of set bits
-
-    Counting bits set, Brian Kernighan's way*
-
-        unsigned int v;          // count the number of bits set in v
-        unsigned int c;          // c accumulates the total bits set in v
-        for (c = 0; v; c++)
-        {   v &= v - 1;  }       //clear the least significant bit set
-
-    This method goes through as many iterations as there are set bits. So if we
-    have a 32-bit word with only the high bit set, then it will only go once
-    through the loop.
-
-    * The C Programming Language 2nd Ed., Kernighan & Ritchie, 1988.
-
-    This works because each subtraction "borrows" from the lowest 1-bit. For
-    example:
-
-          loop pass 1     loop pass 2
-          -----------     -----------
-               101000          100000
-             -      1        -      1
-             = 100111        = 011111
-             & 101000        & 100000
-             = 100000        =      0
-
-    It is an excellent technique for Python, since the size of the integer need
-    not be determined beforehand.
-
-    (from https://wiki.python.org/moin/BitManipulation)
-    """
-    count = 0
-    while num:
-        num &= num - 1
-        count += 1
-    return count
-
-def _is_single_bit(value):
-    """
-    True if only one bit set in value (should be an int)
-    """
-    if value == 0:
-        return False
-    value &= value - 1
-    return value == 0
-
-def _iter_bits_lsb(value):
-    """
-    Return each bit value one at a time.
-
-    >>> list(_iter_bits_lsb(6))
-    [2, 4]
-    """
-
-    while value:
-        bit = value & (~value + 1)
-        yield bit
-        value ^= bit
-
-def bin(value, max_bits=None):
-    """
-    Like built-in bin(), except negative values are represented in
-    twos-compliment, and the leading bit always indicates sign
-    (0=positive, 1=negative).
-
-    >>> bin(10)
-    '0b0 1010'
-    >>> bin(~10)   # ~10 is -11
-    '0b1 0101'
-    """
-
-    ceiling = 2 ** (value).bit_length()
-    if value >= 0:
-        s = _bltin_bin(value + ceiling).replace('1', '0', 1)
-    else:
-        s = _bltin_bin(~value ^ (ceiling - 1) + ceiling)
-    sign = s[:3]
-    digits = s[3:]
-    if max_bits is not None:
-        if len(digits) < max_bits:
-            digits = (sign[-1] * max_bits + digits)[-max_bits:]
-    return "%s %s" % (sign, digits)
-
-
-try:
-    from types import DynamicClassAttribute
-    base = DynamicClassAttribute
-except ImportError:
-    base = object
-    DynamicClassAttribute = None
-
-class property(base):
-    """
-    This is a descriptor, used to define attributes that act differently
-    when accessed through an enum member and through an enum class.
-    Instance access is the same as property(), but access to an attribute
-    through the enum class will look in the class' _member_map_.
-    """
-
-    # inherit from DynamicClassAttribute if we can in order to get `inspect`
-    # support
-
-    def __init__(self, fget=None, fset=None, fdel=None, doc=None):
-        self.fget = fget
-        self.fset = fset
-        self.fdel = fdel
-        # next two lines make property act the same as _bltin_property
-        self.__doc__ = doc or fget.__doc__
-        self.overwrite_doc = doc is None
-        # support for abstract methods
-        self.__isabstractmethod__ = bool(getattr(fget, '__isabstractmethod__', False))
-        # names, if possible
-
-    def getter(self, fget):
-        fdoc = fget.__doc__ if self.overwrite_doc else None
-        result = type(self)(fget, self.fset, self.fdel, fdoc or self.__doc__)
-        result.overwrite_doc = self.__doc__ is None
-        return result
-
-    def setter(self, fset):
-        fdoc = fget.__doc__ if self.overwrite_doc else None
-        result = type(self)(self.fget, fset, self.fdel, self.__doc__)
-        result.overwrite_doc = self.__doc__ is None
-        return result
-
-    def deleter(self, fdel):
-        fdoc = fget.__doc__ if self.overwrite_doc else None
-        result = type(self)(self.fget, self.fset, fdel, self.__doc__)
-        result.overwrite_doc = self.__doc__ is None
-        return result
-
-    def __repr__(self):
-        member = self.ownerclass._member_map_.get(self.name)
-        func = self.fget or self.fset or self.fdel
-        strings = []
-        if member:
-            strings.append('%r' % member)
-        if func:
-            strings.append('function=%s' % func.__name__)
-        return 'property(%s)' % ', '.join(strings)
-
-    def __get__(self, instance, ownerclass=None):
-        if instance is None:
-            try:
-                return ownerclass._member_map_[self.name]
-            except KeyError:
-                raise AttributeError(
-                        '%r has no attribute %r' % (ownerclass, self.name)
-                        )
-        else:
-            if self.fget is not None:
-                return self.fget(instance)
-            else:
-                if self.fset is not None:
-                    raise AttributeError(
-                            'cannot read attribute %r on %r' % (self.name, ownerclass)
-                            )
-                else:
-                    try:
-                        return instance.__dict__[self.name]
-                    except KeyError:
-                        raise AttributeError(
-                                '%r member has no attribute %r' % (ownerclass, self.name)
-                                )
-
-    def __set__(self, instance, value):
-        if self.fset is None:
-            if self.fget is not None:
-                raise AttributeError(
-                        "cannot set attribute %r on <aenum %r>" % (self.name, self.clsname)
-                        )
-            else:
-                instance.__dict__[self.name] = value
-        else:
-            return self.fset(instance, value)
-
-    def __delete__(self, instance):
-        if self.fdel is None:
-            if self.fget or self.fset:
-                raise AttributeError(
-                        "cannot delete attribute %r on <aenum %r>" % (self.name, self.clsname)
-                        )
-            elif self.name in instance.__dict__:
-                del instance.__dict__[self.name]
-            else:
-                raise AttributeError(
-                        "no attribute %r on <aenum %r> member" % (self.name, self.clsname)
-                        )
-        else:
-            return self.fdel(instance)
-
-    def __set_name__(self, ownerclass, name):
-        self.name = name
-        self.clsname = ownerclass.__name__
-        self.ownerclass = ownerclass
-
-_RouteClassAttributeToGetattr = property
-if DynamicClassAttribute is None:
-    DynamicClassAttribute = property
-# deprecated
-enum_property = property
-
-class NonMember(object):
-    """
-    Protects item from becaming an Enum member during class creation.
-    """
-    def __init__(self, value):
-        self.value = value
-
-    def __get__(self, instance, ownerclass=None):
-        return self.value
-skip = nonmember = NonMember
-
-class Member(object):
-    """
-    Forces item to became an Enum member during class creation.
-    """
-    def __init__(self, value):
-        self.value = value
-member = Member
-
-class SentinelType(type):
-    def __repr__(cls):
-        return '<%s>' % cls.__name__
-Sentinel = SentinelType('Sentinel', (object, ), {})
-
-def _is_descriptor(obj):
-    """Returns True if obj is a descriptor, False otherwise."""
-    return (
-            hasattr(obj, '__get__') or
-            hasattr(obj, '__set__') or
-            hasattr(obj, '__delete__'))
-
-
-def _is_dunder(name):
-    """Returns True if a __dunder__ name, False otherwise."""
-    return (len(name) > 4 and
-            name[:2] == name[-2:] == '__' and
-            name[2] != '_' and
-            name[-3] != '_')
-
-
-def _is_sunder(name):
-    """Returns True if a _sunder_ name, False otherwise."""
-    return (len(name) > 2 and
-            name[0] == name[-1] == '_' and
-            name[1] != '_' and
-            name[-2] != '_')
-
-def _is_internal_class(cls_name, obj):
-    # only 3.3 and up, always return False in 3.2 and below
-    if pyver < PY3_3:
-        return False
-    else:
-        qualname = getattr(obj, '__qualname__', False)
-        return not _is_descriptor(obj) and qualname and re.search(r"\.?%s\.\w+$" % cls_name, qualname)
-
-def _is_private_name(cls_name, name):
-    pattern = r'^_%s__\w+[^_]_?$' % (cls_name, )
-    return re.search(pattern, name)
-
-def _power_of_two(value):
-    if value < 1:
-        return False
-    return value == 2 ** _high_bit(value)
-
-def bits(num):
-    if num in (0, 1):
-        return str(num)
-    negative = False
-    if num < 0:
-        negative = True
-        num = ~num
-    result = bits(num>>1) + str(num&1)
-    if negative:
-        result = '1' + ''.join(['10'[d=='1'] for d in result])
-    return result
-
-
-def bit_count(num):
-    """
-        return number of set bits
-
-        Counting bits set, Brian Kernighan's way*
-
-            unsigned int v;          // count the number of bits set in v
-            unsigned int c;          // c accumulates the total bits set in v
-            for (c = 0; v; c++)
-            {   v &= v - 1;  }       //clear the least significant bit set
-
-        This method goes through as many iterations as there are set bits. So if we
-        have a 32-bit word with only the high bit set, then it will only go once
-        through the loop.
-
-        * The C Programming Language 2nd Ed., Kernighan & Ritchie, 1988.
-
-        This works because each subtraction "borrows" from the lowest 1-bit. For example:
-
-              loop pass 1     loop pass 2
-              -----------     -----------
-                   101000          100000
-                 -      1        -      1
-                 = 100111        = 011111
-                 & 101000        & 100000
-                 = 100000        =      0
-
-        It is an excellent technique for Python, since the size of the integer need not
-        be determined beforehand.
-    """
-    count = 0
-    while(num):
-        num &= num - 1
-        count += 1
-    return(count)
-
-def bit_len(num):
-    length = 0
-    while num:
-        length += 1
-        num >>= 1
-    return length
-
-def is_single_bit(num):
-    """
-    True if only one bit set in num (should be an int)
-    """
-    num &= num - 1
-    return num == 0
-
-def _make_class_unpicklable(obj):
-    """
-    Make the given obj un-picklable.
-
-    obj should be either a dictionary, on an Enum
-    """
-    def _break_on_call_reduce(self, proto):
-        raise TypeError('%r cannot be pickled' % self)
-    if isinstance(obj, dict):
-        obj['__reduce_ex__'] = _break_on_call_reduce
-        obj['__module__'] = '<unknown>'
-    else:
-        setattr(obj, '__reduce_ex__', _break_on_call_reduce)
-        setattr(obj, '__module__', '<unknown>')
-
-def _check_auto_args(method):
-    """check if new generate method supports *args and **kwds"""
-    if isinstance(method, staticmethod):
-        method = method.__get__(type)
-    method = getattr(method, 'im_func', method)
-    args, varargs, keywords, defaults = getargspec(method)
-    return varargs is not None and keywords is not None
-
-def _get_attr_from_chain(cls, attr):
-    sentinel = object()
-    for basecls in cls.mro():
-        obj = basecls.__dict__.get(attr, sentinel)
-        if obj is not sentinel:
-            return obj
-
-def _value(obj):
-    if isinstance(obj, (auto, constant)):
-        return obj.value
-    else:
-        return obj
-
-def enumsort(things):
-    """
-    sorts things by value if all same type; otherwise by name
-    """
-    if not things:
-        return things
-    sort_type = type(things[0])
-    if not issubclass(sort_type, tuple):
-        # direct sort or type error
-        if not all((type(v) is sort_type) for v in things[1:]):
-            raise TypeError('cannot sort items of different types')
-        return sorted(things)
-    else:
-        # expecting list of (name, value) tuples
-        sort_type = type(things[0][1])
-        try:
-            if all((type(v[1]) is sort_type) for v in things[1:]):
-                return sorted(things, key=lambda i: i[1])
-            else:
-                raise TypeError('try name sort instead')
-        except TypeError:
-            return sorted(things, key=lambda i: i[0])
-
-def export(collection, namespace=None):
-    """
-    export([collection,] namespace) -> Export members to target namespace.
-
-    If collection is not given, act as a decorator.
-    """
-    if namespace is None:
-        namespace = collection
-        def export_decorator(collection):
-            return export(collection, namespace)
-        return export_decorator
-    elif issubclass(collection, NamedConstant):
-        for n, c in collection.__dict__.items():
-            if isinstance(c, NamedConstant):
-                namespace[n] = c
-    elif issubclass(collection, Enum):
-        data = collection.__members__.items()
-        for n, m in data:
-            namespace[n] = m
-    else:
-        raise TypeError('%r is not a supported collection' % (collection,) )
-    return collection
-
-class _Addendum(object):
-    def __init__(self, dict, doc, ns):
-        # dict is the dict to update with functions
-        # doc is the docstring to put in the dict
-        # ns is the namespace to remove the function names from
-        self.dict = dict
-        self.ns = ns
-        self.added = set()
-    def __call__(self, func):
-        if isinstance(func, (staticmethod, classmethod)):
-            name = func.__func__.__name__
-        elif isinstance(func, (property, _bltin_property)):
-            name = (func.fget or func.fset or func.fdel).__name__
-        else:
-            name = func.__name__
-        self.dict[name] = func
-        self.added.add(name)
-    def resolve(self):
-        ns = self.ns
-        for name in self.added:
-            del ns[name]
-        return self.dict
-
-# Constant / NamedConstant
-
-class constant(object):
-    '''
-    Simple constant descriptor for NamedConstant and Enum use.
-    '''
-    def __init__(self, value, doc=None):
-        self.value = value
-        self.__doc__ = doc
-
-    def __get__(self, *args):
-        return self.value
-
-    def __repr__(self):
-        return '%s(%r)' % (self.__class__.__name__, self.value)
-
-    def __and__(self, other):
-        return _and_(self.value, _value(other))
-
-    def __rand__(self, other):
-        return _and_(_value(other), self.value)
-
-    def __invert__(self):
-        return _inv_(self.value)
-
-    def __or__(self, other):
-        return _or_(self.value, _value(other))
-
-    def __ror__(self, other):
-        return _or_(_value(other), self.value)
-
-    def __xor__(self, other):
-        return _xor_(self.value, _value(other))
-
-    def __rxor__(self, other):
-        return _xor_(_value(other), self.value)
-
-    def __abs__(self):
-        return _abs_(self.value)
-
-    def __add__(self, other):
-        return _add_(self.value, _value(other))
-
-    def __radd__(self, other):
-        return _add_(_value(other), self.value)
-
-    def __neg__(self):
-        return _neg_(self.value)
-
-    def __pos__(self):
-        return _pos_(self.value)
-
-    if PY2:
-        def __div__(self, other):
-            return _div_(self.value, _value(other))
-
-    def __rdiv__(self, other):
-        return _div_(_value(other), (self.value))
-
-    def __floordiv__(self, other):
-        return _floordiv_(self.value, _value(other))
-
-    def __rfloordiv__(self, other):
-        return _floordiv_(_value(other), self.value)
-
-    def __truediv__(self, other):
-        return _truediv_(self.value, _value(other))
-
-    def __rtruediv__(self, other):
-        return _truediv_(_value(other), self.value)
-
-    def __lshift__(self, other):
-        return _lshift_(self.value, _value(other))
-
-    def __rlshift__(self, other):
-        return _lshift_(_value(other), self.value)
-
-    def __rshift__(self, other):
-        return _rshift_(self.value, _value(other))
-
-    def __rrshift__(self, other):
-        return _rshift_(_value(other), self.value)
-
-    def __mod__(self, other):
-        return _mod_(self.value, _value(other))
-
-    def __rmod__(self, other):
-        return _mod_(_value(other), self.value)
-
-    def __mul__(self, other):
-        return _mul_(self.value, _value(other))
-
-    def __rmul__(self, other):
-        return _mul_(_value(other), self.value)
-
-    def __pow__(self, other):
-        return _pow_(self.value, _value(other))
-
-    def __rpow__(self, other):
-        return _pow_(_value(other), self.value)
-
-    def __sub__(self, other):
-        return _sub_(self.value, _value(other))
-
-    def __rsub__(self, other):
-        return _sub_(_value(other), self.value)
-
-    def __set_name__(self, ownerclass, name):
-        self.name = name
-        self.clsname = ownerclass.__name__
-
-
-NamedConstant = None
-
-class _NamedConstantDict(dict):
-    """Track constant order and ensure names are not reused.
-
-    NamedConstantMeta will use the names found in self._names as the
-    Constant names.
-    """
-    def __init__(self):
-        super(_NamedConstantDict, self).__init__()
-        self._names = []
-
-    def __setitem__(self, key, value):
-        """Changes anything not dundered or not a constant descriptor.
-
-        If an constant name is used twice, an error is raised; duplicate
-        values are not checked for.
-
-        Single underscore (sunder) names are reserved.
-        """
-        if _is_sunder(key):
-            raise ValueError(
-                    '_sunder_ names, such as %r, are reserved for future NamedConstant use'
-                    % (key, )
-                    )
-        elif _is_dunder(key):
-            pass
-        elif key in self._names:
-            # overwriting an existing constant?
-            raise TypeError('attempt to reuse name: %r' % (key, ))
-        elif isinstance(value, constant) or not _is_descriptor(value):
-            if key in self:
-                # overwriting a descriptor?
-                raise TypeError('%s already defined as: %r' % (key, self[key]))
-            self._names.append(key)
-        super(_NamedConstantDict, self).__setitem__(key, value)
-
-
-class NamedConstantMeta(type):
-    """
-    Block attempts to reassign NamedConstant attributes.
-    """
-
-    @classmethod
-    def __prepare__(metacls, cls, bases, **kwds):
-        return _NamedConstantDict()
-
-    def __new__(metacls, cls, bases, clsdict):
-        if type(clsdict) is dict:
-            original_dict = clsdict
-            clsdict = _NamedConstantDict()
-            for k, v in original_dict.items():
-                clsdict[k] = v
-        newdict = {}
-        constants = {}
-        for name, obj in clsdict.items():
-            if name in clsdict._names:
-                constants[name] = obj
-                continue
-            elif isinstance(obj, nonmember):
-                obj = obj.value
-            newdict[name] = obj
-        newcls = super(NamedConstantMeta, metacls).__new__(metacls, cls, bases, newdict)
-        newcls._named_constant_cache_ = {}
-        newcls._members_ = {}
-        for name, obj in constants.items():
-            new_k = newcls.__new__(newcls, name, obj)
-            newcls._members_[name] = new_k
-        return newcls
-
-    def __bool__(cls):
-        return True
-
-    def __delattr__(cls, attr):
-        cur_obj = cls.__dict__.get(attr)
-        if NamedConstant is not None and isinstance(cur_obj, NamedConstant):
-            raise AttributeError('cannot delete constant <%s.%s>' % (cur_obj.__class__.__name__, cur_obj._name_))
-        super(NamedConstantMeta, cls).__delattr__(attr)
-
-    def __iter__(cls):
-        return (k for k in cls._members_.values())
-
-    def __reversed__(cls):
-        return (k for k in reversed(cls._members_.values()))
-
-    def __len__(cls):
-        return len(cls._members_)
-
-    __nonzero__ = __bool__
-
-    def __setattr__(cls, name, value):
-        """Block attempts to reassign NamedConstants.
-        """
-        cur_obj = cls.__dict__.get(name)
-        if NamedConstant is not None and isinstance(cur_obj, NamedConstant):
-            raise AttributeError('cannot rebind constant <%s.%s>' % (cur_obj.__class__.__name__, cur_obj._name_))
-        super(NamedConstantMeta, cls).__setattr__(name, value)
-
-constant_dict = _Addendum(
-        dict=NamedConstantMeta.__prepare__('NamedConstant', (object, )),
-        doc="NamedConstants protection.\n\n    Derive from this class to lock NamedConstants.\n\n",
-        ns=globals(),
-        )
-
-@constant_dict
-def __new__(cls, name, value=None, doc=None):
-    if value is None:
-        # lookup, name is value
-        value = name
-        for name, obj in cls.__dict__.items():
-            if isinstance(obj, cls) and obj._value_ == value:
-                return obj
-        else:
-            raise ValueError('%r does not exist in %r' % (value, cls.__name__))
-    cur_obj = cls.__dict__.get(name)
-    if isinstance(cur_obj, NamedConstant):
-        raise AttributeError('cannot rebind constant <%s.%s>' % (cur_obj.__class__.__name__, cur_obj._name_))
-    elif isinstance(value, constant):
-        doc = doc or value.__doc__
-        value = value.value
-    metacls = cls.__class__
-    if isinstance(value, NamedConstant):
-        # constants from other classes are reduced to their actual value
-        value = value._value_
-    actual_type = type(value)
-    value_type = cls._named_constant_cache_.get(actual_type)
-    if value_type is None:
-        value_type = type(cls.__name__, (cls, type(value)), {})
-        cls._named_constant_cache_[type(value)] = value_type
-    obj = actual_type.__new__(value_type, value)
-    obj._name_ = name
-    obj._value_ = value
-    obj.__doc__ = doc
-    cls._members_[name] = obj
-    metacls.__setattr__(cls, name, obj)
-    return obj
-
-@constant_dict
-def __repr__(self):
-    return "<%s.%s: %r>" % (
-            self.__class__.__name__, self._name_, self._value_)
-
-@constant_dict
-def __reduce_ex__(self, proto):
-    return getattr, (self.__class__, self._name_)
-
-NamedConstant = NamedConstantMeta('NamedConstant', (object, ), constant_dict.resolve())
-Constant = NamedConstant
-del constant_dict
-
-# NamedTuple
-
-class _NamedTupleDict(OrderedDict):
-    """Track field order and ensure field names are not reused.
-
-    NamedTupleMeta will use the names found in self._field_names to translate
-    to indices.
-    """
-    def __init__(self, *args, **kwds):
-        self._field_names = []
-        super(_NamedTupleDict, self).__init__(*args, **kwds)
-
-    def __setitem__(self, key, value):
-        """Records anything not dundered or not a descriptor.
-
-        If a field name is used twice, an error is raised.
-
-        Single underscore (sunder) names are reserved.
-        """
-        if _is_sunder(key):
-            if key not in ('_size_', '_order_', '_fields_'):
-                raise ValueError(
-                        '_sunder_ names, such as %r, are reserved for future NamedTuple use'
-                        % (key, )
-                        )
-        elif _is_dunder(key):
-            if key == '__order__':
-                key = '_order_'
-        elif key in self._field_names:
-            # overwriting a field?
-            raise TypeError('attempt to reuse field name: %r' % (key, ))
-        elif not _is_descriptor(value):
-            if key in self:
-                # field overwriting a descriptor?
-                raise TypeError('%s already defined as: %r' % (key, self[key]))
-            self._field_names.append(key)
-        super(_NamedTupleDict, self).__setitem__(key, value)
-
-
-class _TupleAttributeAtIndex(object):
-
-    def __init__(self, name, index, doc, default):
-        self.name = name
-        self.index = index
-        if doc is undefined:
-            doc = None
-        self.__doc__ = doc
-        self.default = default
-
-    def __get__(self, instance, owner):
-        if instance is None:
-            return self
-        if len(instance) <= self.index:
-            raise AttributeError('%s instance has no value for %s' % (instance.__class__.__name__, self.name))
-        return instance[self.index]
-
-    def __repr__(self):
-        return '%s(%d)' % (self.__class__.__name__, self.index)
-
-
-class undefined(object):
-    def __repr__(self):
-        return 'undefined'
-    def __bool__(self):
-        return False
-    __nonzero__ = __bool__
-undefined = undefined()
-
-
-class TupleSize(NamedConstant):
-    fixed = constant('fixed', 'tuple length is static')
-    minimum = constant('minimum', 'tuple must be at least x long (x is calculated during creation')
-    variable = constant('variable', 'tuple length can be anything')
-
-class NamedTupleMeta(type):
-    """Metaclass for NamedTuple"""
-
-    @classmethod
-    def __prepare__(metacls, cls, bases, size=undefined, **kwds):
-        return _NamedTupleDict()
-
-    def __init__(cls, *args , **kwds):
-        super(NamedTupleMeta, cls).__init__(*args)
-
-    def __new__(metacls, cls, bases, clsdict, size=undefined, **kwds):
-        if bases == (object, ):
-            bases = (tuple, object)
-        elif tuple not in bases:
-            if object in bases:
-                index = bases.index(object)
-                bases = bases[:index] + (tuple, ) + bases[index:]
-            else:
-                bases = bases + (tuple, )
-        # include any fields from base classes
-        base_dict = _NamedTupleDict()
-        namedtuple_bases = []
-        for base in bases:
-            if isinstance(base, NamedTupleMeta):
-                namedtuple_bases.append(base)
-        i = 0
-        if namedtuple_bases:
-            for name, index, doc, default in metacls._convert_fields(*namedtuple_bases):
-                base_dict[name] = index, doc, default
-                i = max(i, index)
-        # construct properly ordered dict with normalized indexes
-        for k, v in clsdict.items():
-            base_dict[k] = v
-        original_dict = base_dict
-        if size is not undefined and '_size_' in original_dict:
-            raise TypeError('_size_ cannot be set if "size" is passed in header')
-        add_order = isinstance(clsdict, _NamedTupleDict)
-        clsdict = _NamedTupleDict()
-        clsdict.setdefault('_size_', size or TupleSize.fixed)
-        unnumbered = OrderedDict()
-        numbered = OrderedDict()
-        _order_ = original_dict.pop('_order_', [])
-        if _order_ :
-            _order_ = _order_.replace(',',' ').split()
-            add_order = False
-        # and process this class
-        for k, v in original_dict.items():
-            if k not in original_dict._field_names:
-                clsdict[k] = v
-            else:
-                # TODO:normalize v here
-                if isinstance(v, baseinteger):
-                    # assume an offset
-                    v = v, undefined, undefined
-                    i = v[0] + 1
-                    target = numbered
-                elif isinstance(v, basestring):
-                    # assume a docstring
-                    if add_order:
-                        v = i, v, undefined
-                        i += 1
-                        target = numbered
-                    else:
-                        v = undefined, v, undefined
-                        target = unnumbered
-                elif isinstance(v, tuple) and len(v) in (2, 3) and isinstance(v[0], baseinteger) and isinstance(v[1], (basestring, NoneType)):
-                    # assume an offset, a docstring, and (maybe) a default
-                    if len(v) == 2:
-                        v = v + (undefined, )
-                    v = v
-                    i = v[0] + 1
-                    target = numbered
-                elif isinstance(v, tuple) and len(v) in (1, 2) and isinstance(v[0], (basestring, NoneType)):
-                    # assume a docstring, and (maybe) a default
-                    if len(v) == 1:
-                        v = v + (undefined, )
-                    if add_order:
-                        v = (i, ) + v
-                        i += 1
-                        target = numbered
-                    else:
-                        v = (undefined, ) + v
-                        target = unnumbered
-                else:
-                    # refuse to guess further
-                    raise ValueError('not sure what to do with %s=%r (should be OFFSET [, DOC [, DEFAULT]])' % (k, v))
-                target[k] = v
-        # all index values have been normalized
-        # deal with _order_ (or lack thereof)
-        fields = []
-        aliases = []
-        seen = set()
-        max_len = 0
-        if not _order_:
-            if unnumbered:
-                raise ValueError("_order_ not specified and OFFSETs not declared for %r" % (unnumbered.keys(), ))
-            for name, (index, doc, default) in sorted(numbered.items(), key=lambda nv: (nv[1][0], nv[0])):
-                if index in seen:
-                    aliases.append(name)
-                else:
-                    fields.append(name)
-                    seen.add(index)
-                    max_len = max(max_len, index + 1)
-            offsets = numbered
-        else:
-            # check if any unnumbered not in _order_
-            missing = set(unnumbered) - set(_order_)
-            if missing:
-                raise ValueError("unable to order fields: %s (use _order_ or specify OFFSET" % missing)
-            offsets = OrderedDict()
-            # if any unnumbered, number them from their position in _order_
-            i = 0
-            for k in _order_:
-                try:
-                    index, doc, default = unnumbered.pop(k, None) or numbered.pop(k)
-                except IndexError:
-                    raise ValueError('%s (from _order_) not found in %s' % (k, cls))
-                if index is not undefined:
-                    i = index
-                if i in seen:
-                    aliases.append(k)
-                else:
-                    fields.append(k)
-                    seen.add(i)
-                offsets[k] = i, doc, default
-                i += 1
-                max_len = max(max_len, i)
-            # now handle anything in numbered
-            for k, (index, doc, default) in sorted(numbered.items(), key=lambda nv: (nv[1][0], nv[0])):
-                if index in seen:
-                    aliases.append(k)
-                else:
-                    fields.append(k)
-                    seen.add(index)
-                offsets[k] = index, doc, default
-                max_len = max(max_len, index+1)
-
-        # at this point fields and aliases should be ordered lists, offsets should be an
-        # OrdededDict with each value an int, str or None or undefined, default or None or undefined
-        assert len(fields) + len(aliases) == len(offsets), "number of fields + aliases != number of offsets"
-        assert set(fields) & set(offsets) == set(fields), "some fields are not in offsets: %s" % set(fields) & set(offsets)
-        assert set(aliases) & set(offsets) == set(aliases), "some aliases are not in offsets: %s" % set(aliases) & set(offsets)
-        for name, (index, doc, default) in offsets.items():
-            assert isinstance(index, baseinteger), "index for %s is not an int (%s:%r)" % (name, type(index), index)
-            assert isinstance(doc, (basestring, NoneType)) or doc is undefined, "doc is not a str, None, nor undefined (%s:%r)" % (name, type(doc), doc)
-
-        # create descriptors for fields
-        for name, (index, doc, default) in offsets.items():
-            clsdict[name] = _TupleAttributeAtIndex(name, index, doc, default)
-        clsdict['__slots__'] = ()
-
-        # create our new NamedTuple type
-        namedtuple_class = super(NamedTupleMeta, metacls).__new__(metacls, cls, bases, clsdict)
-        namedtuple_class._fields_ = fields
-        namedtuple_class._aliases_ = aliases
-        namedtuple_class._defined_len_ = max_len
-        return namedtuple_class
-
-    @staticmethod
-    def _convert_fields(*namedtuples):
-        "create list of index, doc, default triplets for cls in namedtuples"
-        all_fields = []
-        for cls in namedtuples:
-            base = len(all_fields)
-            for field in cls._fields_:
-                desc = getattr(cls, field)
-                all_fields.append((field, base+desc.index, desc.__doc__, desc.default))
-        return all_fields
-
-    def __add__(cls, other):
-        "A new NamedTuple is created by concatenating the _fields_ and adjusting the descriptors"
-        if not isinstance(other, NamedTupleMeta):
-            return NotImplemented
-        return NamedTupleMeta('%s%s' % (cls.__name__, other.__name__), (cls, other), {})
-
-    def __call__(cls, *args, **kwds):
-        """Creates a new NamedTuple class or an instance of a NamedTuple subclass.
-
-        NamedTuple should have args of (class_name, names, module)
-
-            `names` can be:
-
-                * A string containing member names, separated either with spaces or
-                  commas.  Values are auto-numbered from 1.
-                * An iterable of member names.  Values are auto-numbered from 1.
-                * An iterable of (member name, value) pairs.
-                * A mapping of member name -> value.
-
-                `module`, if set, will be stored in the new class' __module__ attribute;
-
-                Note: if `module` is not set this routine will attempt to discover the
-                calling module by walking the frame stack; if this is unsuccessful
-                the resulting class will not be pickleable.
-
-        subclass should have whatever arguments and/or keywords will be used to create an
-        instance of the subclass
-        """
-        if cls is NamedTuple:
-            original_args = args
-            original_kwds = kwds.copy()
-            # create a new subclass
-            try:
-                if 'class_name' in kwds:
-                    class_name = kwds.pop('class_name')
-                else:
-                    class_name, args = args[0], args[1:]
-                if 'names' in kwds:
-                    names = kwds.pop('names')
-                else:
-                    names, args = args[0], args[1:]
-                if 'module' in kwds:
-                    module = kwds.pop('module')
-                elif args:
-                    module, args = args[0], args[1:]
-                else:
-                    module = None
-                if 'type' in kwds:
-                    type = kwds.pop('type')
-                elif args:
-                    type, args = args[0], args[1:]
-                else:
-                    type = None
-
-            except IndexError:
-                raise TypeError('too few arguments to NamedTuple: %s, %s' % (original_args, original_kwds))
-            if args or kwds:
-                raise TypeError('too many arguments to NamedTuple: %s, %s' % (original_args, original_kwds))
-            if PY2:
-                # if class_name is unicode, attempt a conversion to ASCII
-                if isinstance(class_name, unicode):
-                    try:
-                        class_name = class_name.encode('ascii')
-                    except UnicodeEncodeError:
-                        raise TypeError('%r is not representable in ASCII' % (class_name, ))
-            # quick exit if names is a NamedTuple
-            if isinstance(names, NamedTupleMeta):
-                names.__name__ = class_name
-                if type is not None and type not in names.__bases__:
-                    names.__bases__ = (type, ) + names.__bases__
-                return names
-
-            metacls = cls.__class__
-            bases = (cls, )
-            clsdict = metacls.__prepare__(class_name, bases)
-
-            # special processing needed for names?
-            if isinstance(names, basestring):
-                names = names.replace(',', ' ').split()
-            if isinstance(names, (tuple, list)) and isinstance(names[0], basestring):
-                names = [(e, i) for (i, e) in enumerate(names)]
-            # Here, names is either an iterable of (name, index) or (name, index, doc, default) or a mapping.
-            item = None  # in case names is empty
-            for item in names:
-                if isinstance(item, basestring):
-                    # mapping
-                    field_name, field_index = item, names[item]
-                else:
-                    # non-mapping
-                    if len(item) == 2:
-                        field_name, field_index = item
-                    else:
-                        field_name, field_index = item[0], item[1:]
-                clsdict[field_name] = field_index
-            if type is not None:
-                if not isinstance(type, tuple):
-                    type = (type, )
-                bases = type + bases
-            namedtuple_class = metacls.__new__(metacls, class_name, bases, clsdict)
-
-            # TODO: replace the frame hack if a blessed way to know the calling
-            # module is ever developed
-            if module is None:
-                try:
-                    module = _sys._getframe(1).f_globals['__name__']
-                except (AttributeError, ValueError, KeyError):
-                    pass
-            if module is None:
-                _make_class_unpicklable(namedtuple_class)
-            else:
-                namedtuple_class.__module__ = module
-
-            return namedtuple_class
-        else:
-            # instantiate a subclass
-            namedtuple_instance = cls.__new__(cls, *args, **kwds)
-            if isinstance(namedtuple_instance, cls):
-                namedtuple_instance.__init__(*args, **kwds)
-            return namedtuple_instance
-
-    @_bltin_property
-    def __fields__(cls):
-        return list(cls._fields_)
-    # collections.namedtuple compatibility
-    _fields = __fields__
-
-    @_bltin_property
-    def __aliases__(cls):
-        return list(cls._aliases_)
-
-    def __repr__(cls):
-        return "<NamedTuple %r>" % (cls.__name__, )
-
-namedtuple_dict = _Addendum(
-        dict=NamedTupleMeta.__prepare__('NamedTuple', (object, )),
-        doc="NamedTuple base class.\n\n    Derive from this class to define new NamedTuples.\n\n",
-        ns=globals(),
-        )
-
-@namedtuple_dict
-def __new__(cls, *args, **kwds):
-    if cls._size_ is TupleSize.fixed and len(args) > cls._defined_len_:
-        raise TypeError('%d fields expected, %d received' % (cls._defined_len_, len(args)))
-    unknown = set(kwds) - set(cls._fields_) - set(cls._aliases_)
-    if unknown:
-        raise TypeError('unknown fields: %r' % (unknown, ))
-    final_args = list(args) + [undefined] * (len(cls.__fields__) - len(args))
-    for field, value in kwds.items():
-        index = getattr(cls, field).index
-        if final_args[index] != undefined:
-            raise TypeError('field %s specified more than once' % field)
-        final_args[index] = value
-    missing = []
-    for index, value in enumerate(final_args):
-        if value is undefined:
-            # look for default values
-            name = cls.__fields__[index]
-            default = getattr(cls, name).default
-            if default is undefined:
-                missing.append(name)
-            else:
-                final_args[index] = default
-    if missing:
-        if cls._size_ in (TupleSize.fixed, TupleSize.minimum):
-            raise TypeError('values not provided for field(s): %s' % ', '.join(missing))
-        while final_args and final_args[-1] is undefined:
-            final_args.pop()
-            missing.pop()
-        if cls._size_ is not TupleSize.variable or undefined in final_args:
-            raise TypeError('values not provided for field(s): %s' % ', '.join(missing))
-    return tuple.__new__(cls, tuple(final_args))
-
-@namedtuple_dict
-def __reduce_ex__(self, proto):
-    return self.__class__, tuple(getattr(self, f) for f in self._fields_)
-
-@namedtuple_dict
-def __repr__(self):
-    if len(self) == len(self._fields_):
-        return "%s(%s)" % (
-                self.__class__.__name__, ', '.join(['%s=%r' % (f, o) for f, o in zip(self._fields_, self)])
-                )
-    else:
-        return '%s(%s)' % (self.__class__.__name__, ', '.join([repr(o) for o in self]))
-
-@namedtuple_dict
-def __str__(self):
-    return "%s(%s)" % (
-            self.__class__.__name__, ', '.join(['%r' % (getattr(self, f), ) for f in self._fields_])
-            )
-
-@namedtuple_dict
-@_bltin_property
-def _fields_(self):
-    return list(self.__class__._fields_)
-
-    # compatibility methods with stdlib namedtuple
-@namedtuple_dict
-@_bltin_property
-def __aliases__(self):
-    return list(self.__class__._aliases_)
-
-@namedtuple_dict
-@_bltin_property
-def _fields(self):
-    return list(self.__class__._fields_)
-
-@namedtuple_dict
-@classmethod
-def _make(cls, iterable, new=None, len=None):
-    return cls.__new__(cls, *iterable)
-
-@namedtuple_dict
-def _asdict(self):
-    return OrderedDict(zip(self._fields_, self))
-
-@namedtuple_dict
-def _replace(self, **kwds):
-    current = self._asdict()
-    current.update(kwds)
-    return self.__class__(**current)
-
-NamedTuple = NamedTupleMeta('NamedTuple', (object, ), namedtuple_dict.resolve())
-del namedtuple_dict
-
-
-# Enum
-
-    # _init_ and value and AddValue
-    # -----------------------------
-    # by default, when defining a member everything after the = is "the value", everything is
-    #   passed to __new__, everything is passed to __init__
-    #
-    # if _init_ is present then
-    #   if `value` is not in _init_, everything is "the value", defaults apply
-    #   if `value` is in _init_, only the first thing after the = is the value, and the rest will
-    #       be passed to __init__
-    #   if fewer values are present for member assignment than _init_ calls for, _generate_next_value_
-    #       will be called in an attempt to generate them
-    #
-    # if AddValue is present then
-    #   _generate_next_value_ is always called, and any generated values are prepended to provided
-    #       values (custom _gnv_s can change that)
-    #   default _init_ rules apply
-
-
-    # Constants used in Enum
-
-@export(globals())
-class EnumConstants(NamedConstant):
-    AddValue = constant('addvalue', 'prepends value(s) from _generate_next_value_ to each member')
-    MagicValue = constant('magicvalue', 'calls _generate_next_value_ when no arguments are given')
-    MultiValue = constant('multivalue', 'each member can have several values')
-    NoAlias = constant('noalias', 'duplicate valued members are distinct, not aliased')
-    Unique = constant('unique', 'duplicate valued members are not allowed')
-    def __repr__(self):
-        return self._name_
-
-
-    # Dummy value for Enum as EnumType explicity checks for it, but of course until
-    # EnumType finishes running the first time the Enum class doesn't exist.  This
-    # is also why there are checks in EnumType like `if Enum is not None`.
-    #
-    # Ditto for Flag.
-
-Enum = ReprEnum = IntEnum = StrEnum = Flag = IntFlag = EJECT = KEEP = None
-
-class enum(object):
-    """
-    Helper class to track args, kwds.
-    """
-    def __init__(self, *args, **kwds):
-        self._args = args
-        self._kwds = dict(kwds.items())
-        self._hash = hash(args)
-        self.name = None
-
-    @_bltin_property
-    def args(self):
-        return self._args
-
-    @_bltin_property
-    def kwds(self):
-        return self._kwds.copy()
-
-    def __hash__(self):
-        return self._hash
-
-    def __eq__(self, other):
-        if not isinstance(other, self.__class__):
-            return NotImplemented
-        return self.args == other.args and self.kwds == other.kwds
-
-    def __ne__(self, other):
-        if not isinstance(other, self.__class__):
-            return NotImplemented
-        return self.args != other.args or self.kwds != other.kwds
-
-    def __repr__(self):
-        final = []
-        args = ', '.join(['%r' % (a, ) for a in self.args])
-        if args:
-            final.append(args)
-        kwds = ', '.join([('%s=%r') % (k, v) for k, v in enumsort(list(self.kwds.items()))])
-        if kwds:
-            final.append(kwds)
-        return '%s(%s)' % (self.__class__.__name__, ', '.join(final))
-
-_auto_null = SentinelType('no_value', (object, ), {})
-class auto(enum):
-    """
-    Instances are replaced with an appropriate value in Enum class suites.
-    """
-    enum_member = _auto_null
-    _value = _auto_null
-    _operations = []
-
-    def __and__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_and_, (self, other)))
-        return new_auto
-
-    def __rand__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_and_, (other, self)))
-        return new_auto
-
-    def __invert__(self):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_inv_, (self,)))
-        return new_auto
-
-    def __or__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_or_, (self, other)))
-        return new_auto
-
-    def __ror__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_or_, (other, self)))
-        return new_auto
-
-    def __xor__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_xor_, (self, other)))
-        return new_auto
-
-    def __rxor__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_xor_, (other, self)))
-        return new_auto
-
-    def __abs__(self):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_abs_, (self, )))
-        return new_auto
-
-    def __add__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_add_, (self, other)))
-        return new_auto
-
-    def __radd__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_add_, (other, self)))
-        return new_auto
-
-    def __neg__(self):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_neg_, (self, )))
-        return new_auto
-
-    def __pos__(self):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_pos_, (self, )))
-        return new_auto
-
-    if PY2:
-        def __div__(self, other):
-            new_auto = self.__class__()
-            new_auto._operations = self._operations[:]
-            new_auto._operations.append((_div_, (self, other)))
-            return new_auto
-
-    def __rdiv__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_div_, (other, self)))
-        return new_auto
-
-    def __floordiv__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_floordiv_, (self, other)))
-        return new_auto
-
-    def __rfloordiv__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_floordiv_, (other, self)))
-        return new_auto
-
-    def __truediv__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_truediv_, (self, other)))
-        return new_auto
-
-    def __rtruediv__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_truediv_, (other, self)))
-        return new_auto
-
-    def __lshift__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_lshift_, (self, other)))
-        return new_auto
-
-    def __rlshift__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_lshift_, (other, self)))
-        return new_auto
-
-    def __rshift__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_rshift_, (self, other)))
-        return new_auto
-
-    def __rrshift__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_rshift_, (other, self)))
-        return new_auto
-
-    def __mod__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_mod_, (self, other)))
-        return new_auto
-
-    def __rmod__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_mod_, (other, self)))
-        return new_auto
-
-    def __mul__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_mul_, (self, other)))
-        return new_auto
-
-    def __rmul__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_mul_, (other, self)))
-        return new_auto
-
-    def __pow__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_pow_, (self, other)))
-        return new_auto
-
-    def __rpow__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_pow_, (other, self)))
-        return new_auto
-
-    def __sub__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_sub_, (self, other)))
-        return new_auto
-
-    def __rsub__(self, other):
-        new_auto = self.__class__()
-        new_auto._operations = self._operations[:]
-        new_auto._operations.append((_sub_, (other, self)))
-        return new_auto
-
-    def __repr__(self):
-        if self._operations:
-            return 'auto(...)'
-        else:
-            return 'auto(%r, *%r, **%r)' % (self._value, self._args, self._kwds)
-
-    @_bltin_property
-    def value(self):
-        if self._value is not _auto_null and self._operations:
-            raise TypeError('auto() object out of sync')
-        elif self._value is _auto_null and not self._operations:
-            return self._value
-        elif self._value is not _auto_null:
-            return self._value
-        else:
-            return self._resolve()
-
-    @value.setter
-    def value(self, value):
-        if self._operations:
-            value = self._resolve(value)
-        self._value = value
-
-    def _resolve(self, base_value=None):
-            cls = self.__class__
-            for op, params in self._operations:
-                values = []
-                for param in params:
-                    if isinstance(param, cls):
-                        if param.value is _auto_null:
-                            if base_value is None:
-                                return _auto_null
-                            else:
-                                values.append(base_value)
-                        else:
-                            values.append(param.value)
-                    else:
-                        values.append(param)
-                value = op(*values)
-            self._operations[:] = []
-            self._value = value
-            return value
-
-
-class _EnumArgSpec(NamedTuple):
-    args = 0, 'all args except *args and **kwds'
-    varargs = 1, 'the name of the *args variable'
-    keywords = 2, 'the name of the **kwds variable'
-    defaults = 3, 'any default values'
-    required = 4, 'number of required values (no default available)'
-
-    def __new__(cls, _new_func):
-        argspec = getargspec(_new_func)
-        args, varargs, keywords, defaults = argspec
-        if defaults:
-            reqs = args[1:-len(defaults)]
-        else:
-            reqs = args[1:]
-        return tuple.__new__(_EnumArgSpec, (args, varargs, keywords, defaults, reqs))
-
-
-class _proto_member:
-    """
-    intermediate step for enum members between class execution and final creation
-    """
-
-    def __init__(self, value):
-        self.value = value
-
-    def __set_name__(self, enum_class, member_name):
-        """
-        convert each quasi-member into an instance of the new enum class
-        """
-        # first step: remove ourself from enum_class
-        delattr(enum_class, member_name)
-        # second step: create member based on enum_class
-        value = self.value
-        kwds = {}
-        args = ()
-        init_args = ()
-        extra_mv_args = ()
-        multivalue = None
-        if isinstance(value, tuple) and value and isinstance(value[0], auto):
-            multivalue = value
-            value = value[0]
-        if isinstance(value, auto) and value.value is _auto_null:
-            args = value.args
-            kwds = value.kwds
-        elif isinstance(value, auto):
-            kwds = value.kwds
-            args = (value.value, ) + value.args
-            value = value.value
-        elif isinstance(value, enum):
-            args = value.args
-            kwds = value.kwds
-        elif isinstance(value, Member):
-            value = value.value
-            args = (value, )
-        elif not isinstance(value, tuple):
-            args = (value, )
-        else:
-            args = value
-        if multivalue is not None:
-            value = (value, ) + multivalue[1:]
-            kwds = {}
-            args = value
-            del multivalue
-        # possibilities
-        #
-        # - no init, multivalue  -> __new__[0], __init__(*[:]), extra=[1:]
-        # - init w/o value, multivalue  -> __new__[0], __init__(*[:]), extra=[1:]
-        #
-        # - init w/value, multivalue  -> __new__[0], __init__(*[1:]),  extra=[1:]
-        #
-        # - init w/value, no multivalue  -> __new__[0], __init__(*[1:]), extra=[]
-        #
-        # - init w/o value, no multivalue  -> __new__[:], __init__(*[:]), extra=[]
-        # - no init, no multivalue  ->  __new__[:], __init__(*[:]), extra=[]
-        if enum_class._multivalue_ or 'value' in enum_class._creating_init_:
-            if enum_class._multivalue_:
-                # when multivalue is True, creating_init can be anything
-                mv_arg = args[0]
-                extra_mv_args = args[1:]
-                if 'value' in enum_class._creating_init_:
-                    init_args = args[1:]
-                else:
-                    init_args = args
-                args = args[0:1]
-                value = args[0]
-            else:
-                # 'value' is definitely in creating_init
-                if enum_class._auto_init_ and enum_class._new_args_:
-                    # we have a custom __new__ and an auto __init__
-                    # divvy up according to number of params in each
-                    init_args = args[-len(enum_class._creating_init_)+1:]
-                    if not enum_class._auto_args_:
-                        args = args[:len(enum_class._new_args_.args)]
-                    value = args[0]
-                elif enum_class._auto_init_:
-                    # don't pass in value
-                    init_args = args[1:]
-                    args = args[0:1]
-                    value = args[0]
-                elif enum_class._new_args_:
-                    # do not modify args
-                    value = args[0]
-                else:
-                    # keep all args for user-defined __init__
-                    # keep value as-is
-                    init_args = args
-        else:
-            # either no creating_init, or it doesn't have 'value'
-            init_args = args
-        if enum_class._member_type_ is tuple:   # special case for tuple enums
-            args = (args, )     # wrap it one more time
-        if not enum_class._use_args_:
-            enum_member = enum_class._new_member_(enum_class)
-            if not hasattr(enum_member, '_value_'):
-                enum_member._value_ = value
-        else:
-            enum_member = enum_class._new_member_(enum_class, *args, **kwds)
-            if not hasattr(enum_member, '_value_'):
-                if enum_class._member_type_ is object:
-                    enum_member._value_ = value
-                else:
-                    try:
-                        enum_member._value_ = enum_class._member_type_(*args, **kwds)
-                    except Exception:
-                        te = TypeError('_value_ not set in __new__, unable to create it')
-                        te.__cause__ = None
-                        raise te
-        value = enum_member._value_
-        enum_member._name_ = member_name
-        enum_member.__objclass__ = enum_class
-        enum_member.__init__(*init_args, **kwds)
-        enum_member._sort_order_ = len(enum_class._member_names_)
-        # If another member with the same value was already defined, the
-        # new member becomes an alias to the existing one.
-        if enum_class._noalias_:
-            # unless NoAlias was specified
-            enum_class._member_names_.append(member_name)
-        else:
-            nonunique = defaultdict(list)
-            try:
-                try:
-                    # try to do a fast lookup to avoid the quadratic loop
-                    enum_member = enum_class._value2member_map_[value]
-                    if enum_class._unique_:
-                        nonunique[enum_member.name].append(member_name)
-                except TypeError:
-                    # unhashable members are stored elsewhere
-                    for unhashable_value, canonical_member in enum_class._value2member_seq_:
-                        name = canonical_member.name
-                        if unhashable_value == enum_member._value_:
-                            if enum_class._unique_:
-                                nonunique[name].append(member_name)
-                            enum_member = canonical_member
-                            break
-                    else:
-                        raise KeyError
-            except KeyError:
-                # this could still be an alias if the value is multi-bit and the
-                # class is a flag class
-                if (
-                        Flag is None
-                        or not issubclass(enum_class, Flag)
-                    ):
-                    # no other instances found, record this member in _member_names_
-                    enum_class._member_names_.append(member_name)
-                elif (
-                        Flag is not None
-                        and issubclass(enum_class, Flag)
-                        and _is_single_bit(value)
-                    ):
-                    # no other instances found, record this member in _member_names_
-                    enum_class._member_names_.append(member_name)
-            if nonunique:
-                # duplicates not allowed if Unique specified
-                message = []
-                for name, aliases in nonunique.items():
-                    bad_aliases = ','.join(aliases)
-                    message.append('%s --> %s [%r]' % (name, bad_aliases, enum_class[name].value))
-                raise ValueError(
-                        '%s: duplicate names found: %s' %
-                            (enum_class.__name__, ';  '.join(message))
-                        )
-        # if self.value is an `auto()`, replace the value attribute with the new enum member
-        if isinstance(self.value, auto):
-            self.value.enum_member = enum_member
-        # get redirect in place before adding to _member_map_
-        # but check for other instances in parent classes first
-        need_override = False
-        descriptor = None
-        descriptor_property = None
-        for base in enum_class.__mro__[1:]:
-            descriptor = base.__dict__.get(member_name)
-            if descriptor is not None:
-                if isinstance(descriptor, (property, DynamicClassAttribute)):
-                    break
-                else:
-                    need_override = True
-                    if isinstance(descriptor, _bltin_property) and descriptor_property is None:
-                        descriptor_property = descriptor
-                    # keep looking for an enum.property
-        descriptor = descriptor or descriptor_property
-        if descriptor and not need_override:
-            # previous enum.property found, no further action needed
-            pass
-        else:
-            redirect = property()
-            redirect.__set_name__(enum_class, member_name)
-            if descriptor and need_override:
-                # previous enum.property found, but some other inherited
-                # attribute is in the way; copy fget, fset, fdel to this one
-                redirect.fget = descriptor.fget
-                redirect.fset = descriptor.fset
-                redirect.fdel = descriptor.fdel
-            setattr(enum_class, member_name, redirect)
-        # now add to _member_map_ (even aliases)
-        enum_class._member_map_[member_name] = enum_member
-        #
-        # process (possible) MultiValues
-        values = (value, ) + extra_mv_args
-        if enum_class._multivalue_ and mv_arg not in values:
-            values += (mv_arg, )
-        enum_member._values_ = values
-        for value in values:
-            # first check if value has already been used
-            if enum_class._multivalue_ and (
-                    value in enum_class._value2member_map_
-                    or any(v == value for (v, m) in enum_class._value2member_seq_)
-                    ):
-                raise ValueError('%r has already been used' % (value, ))
-            try:
-                # This may fail if value is not hashable. We can't add the value
-                # to the map, and by-value lookups for this value will be
-                # linear.
-                if enum_class._noalias_:
-                    raise TypeError('cannot use dict to store value')
-                enum_class._value2member_map_[value] = enum_member
-            except TypeError:
-                enum_class._value2member_seq_ += ((value, enum_member), )
-
-
-class _EnumDict(dict):
-    """Track enum member order and ensure member names are not reused.
-
-    EnumType will use the names found in self._member_names as the
-    enumeration member names.
-    """
-    def __init__(self, cls_name, settings, start, constructor_init, constructor_start, constructor_boundary):
-        super(_EnumDict, self).__init__()
-        self._cls_name = cls_name
-        self._constructor_init = constructor_init
-        self._constructor_start = constructor_start
-        self._constructor_boundary = constructor_boundary
-        self._generate_next_value = None
-        self._member_names = []
-        self._member_names_set = set()
-        self._settings = settings
-        self._addvalue = addvalue = AddValue in settings
-        self._magicvalue = MagicValue in settings
-        self._multivalue = MultiValue in settings
-        if self._addvalue and self._magicvalue:
-            raise TypeError('%r: AddValue and MagicValue are mutually exclusive' % cls_name)
-        if self._multivalue and self._magicvalue:
-            raise TypeError('%r: MultiValue and MagicValue are mutually exclusive' % cls_name)
-        self._start = start
-        self._addvalue_value = start
-        self._new_args = ()
-        self._auto_args = False
-        # when the magic turns off
-        self._locked = MagicValue not in settings
-        # if init fields are specified
-        self._init = []
-        # list of temporary names
-        self._ignore = []
-        if self._magicvalue:
-            self._ignore = ['property', 'staticmethod', 'classmethod']
-        self._ignore_init_done = False
-        # if _sunder_ values can be changed via the class body
-        self._allow_init = True
-        self._last_values = []
-
-    def __getitem__(self, key):
-        if key == self._cls_name and self._cls_name not in self:
-            return enum
-        elif (
-                self._locked
-                or key in self
-                or key in self._ignore
-                or _is_sunder(key)
-                or _is_dunder(key)
-                ):
-            return super(_EnumDict, self).__getitem__(key)
-        elif self._magicvalue:
-            value = self._generate_next_value(key, self._start, len(self._member_names), self._last_values[:])
-            self.__setitem__(key, value)
-            return value
-        else:
-            raise Exception('Magic is not set -- why am I here?')
-
-    def __setitem__(self, key, value):
-        """Changes anything not sundured, dundered, nor a descriptor.
-
-        If an enum member name is used twice, an error is raised; duplicate
-        values are not checked for.
-
-        Single underscore (sunder) names are reserved.
-        """
-        # Flag classes that have MagicValue and __new__ will get a generated _gnv_
-        if _is_internal_class(self._cls_name, value):
-            pass
-        elif _is_private_name(self._cls_name, key):
-            pass
-        elif _is_sunder(key):
-            if key not in (
-                    '_init_', '_settings_', '_order_', '_ignore_', '_start_',
-                    '_create_pseudo_member_', '_create_pseudo_member_values_',
-                    '_generate_next_value_', '_boundary_', '_numeric_repr_',
-                    '_missing_', '_missing_value_', '_missing_name_',
-                    '_iter_member_', '_iter_member_by_value_', '_iter_member_by_def_',
-                    ):
-                raise ValueError('%r: _sunder_ names, such as %r, are reserved for future Enum use'
-                        % (self._cls_name, key)
-                        )
-            elif not self._allow_init and key not in (
-                    'create_pseudo_member_', '_missing_', '_missing_value_', '_missing_name_',
-                ):
-                # sunder is used during creation, must be specified first
-                raise ValueError('%r: cannot set %r after init phase' % (self._cls_name, key))
-            elif key == '_ignore_':
-                if self._ignore_init_done:
-                    raise TypeError('%r: ignore can only be specified once' % self._cls_name)
-                if isinstance(value, basestring):
-                    value = value.split()
-                else:
-                    value = list(value)
-                self._ignore = value
-                already = set(value) & self._member_names_set
-                if already:
-                    raise ValueError('%r: _ignore_ cannot specify already set names %s' % (
-                            self._cls_name,
-                            ', '.join(repr(a) for a in already)
-                            ))
-                self._ignore_init_done = True
-            elif key == '_boundary_':
-                if self._constructor_boundary:
-                    raise TypeError('%r: boundary specified in constructor and class body' % self._cls_name)
-            elif key == '_start_':
-                if self._constructor_start:
-                    raise TypeError('%r: start specified in constructor and class body' % self._cls_name)
-                self._start = value
-            elif key == '_settings_':
-                if not isinstance(value, (set, tuple)):
-                    value = (value, )
-                if not isinstance(value, set):
-                    value = set(value)
-                self._settings |= value
-                if NoAlias in value and Unique in value:
-                    raise TypeError('%r: NoAlias and Unique are mutually exclusive' % self._cls_name)
-                elif MultiValue in value and NoAlias in value:
-                    raise TypeError('cannot specify both MultiValue and NoAlias' % self._cls_name)
-                allowed_settings = dict.fromkeys(['addvalue', 'magicvalue', 'noalias', 'unique', 'multivalue'])
-                for arg in self._settings:
-                    if arg not in allowed_settings:
-                        raise TypeError('%r: unknown qualifier %r (from %r)' % (self._cls_name, arg, value))
-                    allowed_settings[arg] = True
-                self._multivalue = allowed_settings['multivalue']
-                self._addvalue = allowed_settings['addvalue']
-                self._magicvalue = allowed_settings['magicvalue']
-                self._locked = not self._magicvalue
-                if self._magicvalue and not self._ignore_init_done:
-                    self._ignore = ['property', 'classmethod', 'staticmethod']
-                if self._addvalue and self._init and 'value' not in self._init:
-                    self._init.insert(0, 'value')
-                value = tuple(self._settings)
-            elif key == '_init_':
-                if self._constructor_init:
-                    raise TypeError('%r: init specified in constructor and in class body' % self._cls_name)
-                _init_ = value
-                if isinstance(_init_, basestring):
-                    _init_ = _init_.replace(',',' ').split()
-                if self._addvalue and 'value' not in self._init:
-                    self._init.insert(0, 'value')
-                if self._magicvalue:
-                    raise TypeError("%r: _init_ and MagicValue are mutually exclusive" % self._cls_name)
-                self._init = _init_
-                value = _init_
-            elif key == '_generate_next_value_':
-                gnv = value
-                if value is not None:
-                    if isinstance(value, staticmethod):
-                        gnv = value.__func__
-                    elif isinstance(value, classmethod):
-                        raise TypeError('%r: _generate_next_value must be a staticmethod, not a classmethod' % self._cls_name)
-                    else:
-                        gnv = value
-                        value = staticmethod(value)
-                    self._auto_args = _check_auto_args(value)
-                setattr(self, '_generate_next_value', gnv)
-        elif _is_dunder(key):
-            if key == '__order__':
-                key = '_order_'
-                if not self._allow_init:
-                    # _order_ is used during creation, must be specified first
-                    raise ValueError('%r: cannot set %r after init phase' % (self._cls_name, key))
-            elif key == '__new__':  # and self._new_to_init:
-                if isinstance(value, staticmethod):
-                    value = value.__func__
-                self._new_args = _EnumArgSpec(value)
-            elif key == '__init_subclass__':
-                if not isinstance(value, classmethod):
-                    value = classmethod(value)
-            if _is_descriptor(value):
-                self._locked = True
-        elif key in self._member_names_set:
-            # descriptor overwriting an enum?
-            raise TypeError('%r: attempt to reuse name: %r' % (self._cls_name, key))
-        elif key in self._ignore:
-            pass
-        elif not _is_descriptor(value):
-            self._allow_init = False
-            if key in self:
-                # enum overwriting a descriptor?
-                raise TypeError('%r: %s already defined as %r' % (self._cls_name, key, self[key]))
-            if type(value) is enum:
-                value.name = key
-                if self._addvalue:
-                    raise TypeError('%r: enum() and AddValue are incompatible' % self._cls_name)
-            elif self._addvalue and not self._multivalue:
-                # generate a value
-                value = self._gnv(key, value)
-            elif self._multivalue:
-                # make sure it's a tuple
-                if not isinstance(value, tuple):
-                    value = (value, )
-                if isinstance(value[0], auto):
-                    value = (self._convert_auto(key, value[0]), ) + value[1:]
-                if self._addvalue:
-                    value = self._gnv(key, value)
-            elif isinstance(value, auto):
-                value = self._convert_auto(key, value)
-            elif isinstance(value, tuple) and value and isinstance(value[0], auto):
-                value = (self._convert_auto(key, value[0]), ) + value[1:]
-            elif not isinstance(value, auto):
-                # call generate maybe if
-                # - init is specified; or
-                # - __new__ is specified;
-                # and either of them call for more values than are present
-                new_args = () or self._new_args and self._new_args.required
-                target_len = len(self._init or new_args)
-                if isinstance(value, tuple):
-                    source_len = len(value)
-                else:
-                    source_len = 1
-                multi_args = len(self._init) > 1 or new_args
-                if source_len < target_len :
-                    value = self._gnv(key, value)
-            else:
-                pass
-            if self._init:
-                if isinstance(value, auto):
-                    test_value = value.args
-                elif not isinstance(value, tuple):
-                    test_value = (value, )
-                else:
-                    test_value = value
-                if len(self._init) != len(test_value):
-                    raise TypeError(
-                            '%s.%s: number of fields provided do not match init [%r != %r]'
-                            % (self._cls_name, key, self._init, test_value)
-                        )
-            self._member_names.append(key)
-            self._member_names_set.add(key)
-        else:
-            # not a new member, turn off the autoassign magic
-            self._locked = True
-            self._allow_init = False
-        if not (_is_sunder(key) or _is_dunder(key) or _is_private_name(self._cls_name, key) or _is_descriptor(value)):
-            if isinstance(value, auto):
-                self._last_values.append(value.value)
-            elif isinstance(value, tuple) and value and isinstance(value[0], auto):
-                self._last_values.append(value[0].value)
-            elif isinstance(value, tuple):
-                if value:
-                    self._last_values.append(value[0])
-            else:
-                self._last_values.append(value)
-        super(_EnumDict, self).__setitem__(key, value)
-
-    def _convert_auto(self, key, value):
-        # if auto.args or auto.kwds, compare to _init_ and __new__ -- if lacking, call gnv
-        # if not auto.args|kwds but auto.value is _auto_null -- call gnv
-        if value.args or value.kwds or value.value is _auto_null:
-            if value.args or value.kwds:
-                values = value.args
-            else:
-                values = ()
-            new_args = () or self._new_args and self._new_args.required
-            target_len = len(self._init or new_args) or 1
-            if isinstance(values, tuple):
-                source_len = len(values)
-            else:
-                source_len = 1
-            multi_args = len(self._init) > 1 or new_args
-            if source_len < target_len :
-                values = self._gnv(key, values)
-                if value.args:
-                    value._args = values
-                else:
-                    value.value = values
-        return value
-
-    def _gnv(self, key, value):
-        # generate a value
-        if self._auto_args:
-            if not isinstance(value, tuple):
-                value = (value, )
-            value = self._generate_next_value(key, self._start, len(self._member_names), self._last_values[:], *value)
-        else:
-            value = self._generate_next_value(key, self._start, len(self._member_names), self._last_values[:])
-        if isinstance(value, tuple) and len(value) == 1:
-            value = value[0]
-        return value
-
-
-no_arg = SentinelType('no_arg', (object, ), {})
-class EnumType(type):
-    """Metaclass for Enum"""
-
-    @classmethod
-    def __prepare__(metacls, cls, bases, init=None, start=None, settings=(), boundary=None, **kwds):
-        metacls._check_for_existing_members_(cls, bases)
-        if Flag is None and cls == 'Flag':
-            initial_flag = True
-        else:
-            initial_flag = False
-        # settings are a combination of current and all past settings
-        constructor_init = init is not None
-        constructor_start = start is not None
-        constructor_boundary = boundary is not None
-        if not isinstance(settings, tuple):
-            settings = settings,
-        settings = set(settings)
-        generate = None
-        order = None
-        # inherit previous flags
-        member_type, first_enum = metacls._get_mixins_(cls, bases)
-        if first_enum is not None:
-            generate = getattr(first_enum, '_generate_next_value_', None)
-            generate = getattr(generate, 'im_func', generate)
-            settings |= metacls._get_settings_(bases)
-            init = init or first_enum._auto_init_[:]
-            order = first_enum._order_function_
-            if start is None:
-                start = first_enum._start_
-        else:
-            # first time through -- creating Enum itself
-            start = 1
-        # check for custom settings
-        if AddValue in settings and init and 'value' not in init:
-            if isinstance(init, list):
-                init.insert(0, 'value')
-            else:
-                init = 'value ' + init
-        if NoAlias in settings and Unique in settings:
-            raise TypeError('%r: NoAlias and Unique are mutually exclusive' % cls)
-        if MultiValue in settings and NoAlias in settings:
-            raise TypeError('%r: MultiValue and NoAlias are mutually exclusive' % cls)
-        allowed_settings = dict.fromkeys(['addvalue', 'magicvalue', 'noalias', 'unique', 'multivalue'])
-        for arg in settings:
-            if arg not in allowed_settings:
-                raise TypeError('%r: unknown qualifier %r' % (cls, arg))
-        enum_dict = _EnumDict(cls_name=cls, settings=settings, start=start, constructor_init=constructor_init, constructor_start=constructor_start, constructor_boundary=constructor_boundary)
-        enum_dict._member_type = member_type
-        enum_dict._base_type = ('enum', 'flag')[
-                Flag is None and cls == 'Flag'
-                or
-                Flag is not None and any(issubclass(b, Flag) for b in bases)
-                ]
-        if Flag is not None and any(b is Flag for b in bases) and member_type not in (baseinteger + (object, )):
-            if Flag in bases:
-                # when a non-int data type is mixed in with Flag, we end up
-                # needing two values for two `__new__`s:
-                # - the integer value for the Flag itself; and
-                # - the mix-in value for the mix-in
-                #
-                # we provide a default `_generate_next_value_` to supply the int
-                # argument, and a default `__new__` to keep the two straight
-                def _generate_next_value_(name, start, count, values, *args, **kwds):
-                    return (2 ** count, ) + args
-                enum_dict['_generate_next_value_'] = staticmethod(_generate_next_value_)
-                def __new__(cls, flag_value, type_value):
-                    obj = member_type.__new__(cls, type_value)
-                    obj._value_ = flag_value
-                    return obj
-                enum_dict['__new__'] = __new__
-            else:
-                try:
-                    enum_dict._new_args = _EnumArgSpec(first_enum.__new_member__)
-                except TypeError:
-                    pass
-        elif not initial_flag:
-            if hasattr(first_enum, '__new_member__'):
-                enum_dict._new_args = _EnumArgSpec(first_enum.__new_member__)
-            if generate:
-                enum_dict['_generate_next_value_'] = generate
-                enum_dict._inherited_gnv = True
-            if init is not None:
-                if isinstance(init, basestring):
-                    init = init.replace(',',' ').split()
-                enum_dict._init = init
-        elif hasattr(first_enum, '__new_member__'):
-            enum_dict._new_args = _EnumArgSpec(first_enum.__new_member__)
-        if order is not None:
-            enum_dict['_order_'] = staticmethod(order)
-        return enum_dict
-
-    def __init__(cls, *args , **kwds):
-        pass
-
-    def __new__(metacls, cls, bases, clsdict, init=None, start=None, settings=(), boundary=None, **kwds):
-        # handle py2 case first
-        if type(clsdict) is not _EnumDict:
-            # py2 and/or functional API gyrations
-            init = clsdict.pop('_init_', None)
-            start = clsdict.pop('_start_', None)
-            settings = clsdict.pop('_settings_', ())
-            _order_ = clsdict.pop('_order_', clsdict.pop('__order__', None))
-            _ignore_ = clsdict.pop('_ignore_', None)
-            _create_pseudo_member_ = clsdict.pop('_create_pseudo_member_', None)
-            _create_pseudo_member_values_ = clsdict.pop('_create_pseudo_member_values_', None)
-            _generate_next_value_ = clsdict.pop('_generate_next_value_', None)
-            _missing_ = clsdict.pop('_missing_', None)
-            _missing_value_ = clsdict.pop('_missing_value_', None)
-            _missing_name_ = clsdict.pop('_missing_name_', None)
-            _boundary_ = clsdict.pop('_boundary_', None)
-            _iter_member_ = clsdict.pop('_iter_member_', None)
-            _iter_member_by_value_ = clsdict.pop('_iter_member_by_value_', None)
-            _iter_member_by_def_ = clsdict.pop('_iter_member_by_def_', None)
-            __new__ = clsdict.pop('__new__', None)
-            __new__ = getattr(__new__, 'im_func', __new__)
-            __new__ = getattr(__new__, '__func__', __new__)
-            enum_members = dict([
-                    (k, v) for (k, v) in clsdict.items()
-                    if not (_is_sunder(k) or _is_dunder(k) or _is_private_name(cls, k) or _is_descriptor(v))
-                    ])
-            original_dict = clsdict
-            clsdict = metacls.__prepare__(cls, bases, init=init, start=start)
-            if settings:
-                clsdict['_settings_'] = settings
-            init = init or clsdict._init
-            if _order_ is None:
-                _order_ = clsdict.get('_order_')
-                if _order_ is not None:
-                    _order_ = _order_.__get__(cls)
-            if isinstance(original_dict, OrderedDict):
-                calced_order = original_dict
-            elif _order_ is None:
-                calced_order = [name for (name, value) in enumsort(list(enum_members.items()))]
-            elif isinstance(_order_, basestring):
-                calced_order = _order_ = _order_.replace(',', ' ').split()
-            elif callable(_order_):
-                if init:
-                    if not isinstance(init, basestring):
-                        init = ' '.join(init)
-                member = NamedTuple('member', init and 'name ' + init or ['name', 'value'])
-                calced_order = []
-                for name, value in enum_members.items():
-                    if init:
-                        if not isinstance(value, tuple):
-                            value = (value, )
-                        name_value = (name, ) + value
-                    else:
-                        name_value = tuple((name, value))
-                    if member._defined_len_ != len(name_value):
-                        raise TypeError('%d values expected (%s), %d received (%s)' % (
-                            member._defined_len_,
-                            ', '.join(member._fields_),
-                            len(name_value),
-                            ', '.join([repr(v) for v in name_value]),
-                            ))
-                    calced_order.append(member(*name_value))
-                calced_order = [m.name for m in sorted(calced_order, key=_order_)]
-            else:
-                calced_order = _order_
-            for name in (
-                    '_missing_', '_missing_value_', '_missing_name_',
-                    '_ignore_', '_create_pseudo_member_', '_create_pseudo_member_values_',
-                    '_generate_next_value_', '_order_', '__new__',
-                    '_missing_', '_missing_value_', '_missing_name_',
-                    '_boundary_',
-                    '_iter_member_', '_iter_member_by_value_', '_iter_member_by_def_',
-                ):
-                attr = locals()[name]
-                if attr is not None:
-                    clsdict[name] = attr
-            # now add members
-            for k in calced_order:
-                try:
-                    clsdict[k] = original_dict[k]
-                except KeyError:
-                    # this error will be handled when _order_ is checked
-                    pass
-            for k, v in original_dict.items():
-                if k not in calced_order:
-                    clsdict[k] = v
-            del _order_, _ignore_, _create_pseudo_member_, _create_pseudo_member_values_,
-            del _generate_next_value_, _missing_, _missing_value_, _missing_name_
-        #
-        # resume normal path
-        clsdict._locked = True
-        #
-        # check for illegal enum names (any others?)
-        member_names = clsdict._member_names
-        invalid_names = set(member_names) & set(['mro', ''])
-        if invalid_names:
-            raise ValueError('invalid enum member name(s): %s' % (
-                ', '.join(invalid_names), ))
-        _order_ = clsdict.pop('_order_', None)
-        if isinstance(_order_, basestring):
-            _order_ = _order_.replace(',',' ').split()
-        init = clsdict._init
-        start = clsdict._start
-        settings = clsdict._settings
-        creating_init = []
-        new_args = clsdict._new_args
-        auto_args = clsdict._auto_args
-        auto_init = False
-        if init is not None:
-            auto_init = True
-            creating_init = init[:]
-        if 'value' in creating_init and creating_init[0] != 'value':
-            raise TypeError("'value', if specified, must be the first item in 'init'")
-        magicvalue = MagicValue in settings
-        multivalue = MultiValue in settings
-        noalias = NoAlias in settings
-        unique = Unique in settings
-        # an Enum class cannot be mixed with other types (int, float, etc.) if
-        #   it has an inherited __new__ unless a new __new__ is defined (or
-        #   the resulting class will fail).
-        # an Enum class is final once enumeration items have been defined;
-        #
-        # remove any keys listed in _ignore_
-        clsdict.setdefault('_ignore_', []).append('_ignore_')
-        ignore = clsdict['_ignore_']
-        for key in ignore:
-            clsdict.pop(key, None)
-        #
-        boundary = boundary or clsdict.pop('_boundary_', None)
-        # convert to regular dict
-        clsdict = dict(clsdict.items())
-        member_type, first_enum = metacls._get_mixins_(cls, bases)
-        # get the method to create enum members
-        __new__, save_new, new_uses_args = metacls._find_new_(
-                clsdict,
-                member_type,
-                first_enum,
-                )
-        clsdict['_new_member_'] = staticmethod(__new__)
-        clsdict['_use_args_'] = new_uses_args
-        #
-        # convert future enum members into temporary _proto_members
-        # and record integer values in case this will be a Flag
-        flag_mask = 0
-        for name in member_names:
-            value = test_value = clsdict[name]
-            if isinstance(value, auto) and value.value is not _auto_null:
-                test_value = value.value
-            if isinstance(test_value, baseinteger):
-                flag_mask |= test_value
-            if isinstance(test_value, tuple) and test_value and isinstance(test_value[0], baseinteger):
-                flag_mask |= test_value[0]
-            clsdict[name] = _proto_member(value)
-        #
-        # temp stuff
-        clsdict['_creating_init_'] = creating_init
-        clsdict['_multivalue_'] = multivalue
-        clsdict['_magicvalue_'] = magicvalue
-        clsdict['_noalias_'] = noalias
-        clsdict['_unique_'] = unique
-        #
-        # house-keeping structures
-        clsdict['_member_names_'] = []
-        clsdict['_member_map_'] = OrderedDict()
-        clsdict['_member_type_'] = member_type
-        clsdict['_value2member_map_'] = {}
-        clsdict['_value2member_seq_'] = ()
-        clsdict['_settings_'] = settings
-        clsdict['_start_'] = start
-        clsdict['_auto_init_'] = init
-        clsdict['_new_args_'] = new_args
-        clsdict['_auto_args_'] = auto_args
-        clsdict['_order_function_'] = None
-        # now set the __repr__ for the value
-        clsdict['_value_repr_'] = metacls._find_data_repr_(cls, bases)
-        #
-        # Flag structures (will be removed if final class is not a Flag
-        clsdict['_boundary_'] = (
-                boundary
-                or getattr(first_enum, '_boundary_', None)
-                )
-        clsdict['_flag_mask_'] = flag_mask
-        clsdict['_all_bits_'] = 2 ** ((flag_mask).bit_length()) - 1
-        clsdict['_inverted_'] = None
-        #
-        # move skipped values out of the descriptor
-        for name, obj in clsdict.items():
-            if isinstance(obj, nonmember):
-                clsdict[name] = obj.value
-        #
-        # If a custom type is mixed into the Enum, and it does not know how
-        # to pickle itself, pickle.dumps will succeed but pickle.loads will
-        # fail.  Rather than have the error show up later and possibly far
-        # from the source, sabotage the pickle protocol for this class so
-        # that pickle.dumps also fails.
-        #
-        # However, if the new class implements its own __reduce_ex__, do not
-        # sabotage -- it's on them to make sure it works correctly.  We use
-        # __reduce_ex__ instead of any of the others as it is preferred by
-        # pickle over __reduce__, and it handles all pickle protocols.
-        unpicklable = False
-        if '__reduce_ex__' not in clsdict:
-            if member_type is not object:
-                methods = ('__getnewargs_ex__', '__getnewargs__',
-                        '__reduce_ex__', '__reduce__')
-                if not any(m in member_type.__dict__ for m in methods):
-                    _make_class_unpicklable(clsdict)
-                    unpicklable = True
-        #
-        # create a default docstring if one has not been provided
-        if '__doc__' not in clsdict:
-            clsdict['__doc__'] = 'An enumeration.'
-        #
-        # create our new Enum type
-        try:
-            exc = None
-            enum_class = type.__new__(metacls, cls, bases, clsdict)
-        except RuntimeError as e:
-            # any exceptions raised by _proto_member (aka member.__new__) will get converted to
-            # a RuntimeError, so get that original exception back and raise
-            # it instead
-            exc = e.__cause__ or e
-        if exc is not None:
-            raise exc
-        #
-        # if Python 3.5 or ealier, implement the __set_name__ and
-        # __init_subclass__ protocols
-        if pyver < PY3_6:
-            for name in member_names:
-                enum_class.__dict__[name].__set_name__(enum_class, name)
-            for name, obj in enum_class.__dict__.items():
-                if name in member_names:
-                    continue
-                if hasattr(obj, '__set_name__'):
-                    obj.__set_name__(enum_class, name)
-            if Enum is not None:
-                super(enum_class, enum_class).__init_subclass__()
-        #
-        # double check that repr and friends are not the mixin's or various
-        # things break (such as pickle)
-        #
-        # Also, special handling for ReprEnum
-        if ReprEnum is not None and ReprEnum in bases:
-            if member_type is object:
-                raise TypeError(
-                        'ReprEnum subclasses must be mixed with a data type (i.e.'
-                        ' int, str, float, etc.)'
-                        )
-            if '__format__' not in clsdict:
-                enum_class.__format__ = member_type.__format__
-                clsdict['__format__'] = enum_class.__format__
-            if '__str__' not in clsdict:
-                method = member_type.__str__
-                if method is object.__str__:
-                    # if member_type does not define __str__, object.__str__ will use
-                    # its __repr__ instead, so we'll also use its __repr__
-                    method = member_type.__repr__
-                enum_class.__str__ = method
-                clsdict['__str__'] = enum_class.__str__
-
-        for name in ('__repr__', '__str__', '__format__', '__reduce_ex__'):
-            if name in clsdict:
-                # class has defined/imported/copied the method
-                continue
-            class_method = getattr(enum_class, name)
-            obj_method = getattr(member_type, name, None)
-            enum_method = getattr(first_enum, name, None)
-            if obj_method is not None and obj_method == class_method:
-                if name == '__reduce_ex__' and unpicklable:
-                    continue
-                setattr(enum_class, name, enum_method)
-                clsdict[name] = enum_method
-        #
-        # for Flag, add __or__, __and__, __xor__, and __invert__
-        if Flag is not None and issubclass(enum_class, Flag):
-            for name in (
-                    '__or__', '__and__', '__xor__',
-                    '__ror__', '__rand__', '__rxor__',
-                    '__invert__'
-                ):
-                if name not in clsdict:
-                    setattr(enum_class, name, getattr(Flag, name))
-                    clsdict[name] = enum_method
-        #
-        # method resolution and int's are not playing nice
-        # Python's less than 2.6 use __cmp__
-        if pyver < PY2_6:
-            #
-            if issubclass(enum_class, int):
-                setattr(enum_class, '__cmp__', getattr(int, '__cmp__'))
-            #
-        elif PY2:
-            #
-            if issubclass(enum_class, int):
-                for method in (
-                        '__le__',
-                        '__lt__',
-                        '__gt__',
-                        '__ge__',
-                        '__eq__',
-                        '__ne__',
-                        '__hash__',
-                        ):
-                    setattr(enum_class, method, getattr(int, method))
-        #
-        # replace any other __new__ with our own (as long as Enum is not None,
-        # anyway) -- again, this is to support pickle
-        if Enum is not None:
-            # if the user defined their own __new__, save it before it gets
-            # clobbered in case they subclass later
-            if save_new:
-                setattr(enum_class, '__new_member__', enum_class.__dict__['__new__'])
-            setattr(enum_class, '__new__', Enum.__dict__['__new__'])
-        #
-        # _order_ checking is spread out into three/four steps
-        # - ensure _order_ is a list, not a string nor a function
-        # - if enum_class is a Flag:
-        #   - remove any non-single-bit flags from _order_
-        # - remove any aliases from _order_
-        # - check that _order_ and _member_names_ match
-        #
-        # _order_ step 1: ensure _order_ is a list
-        if _order_:
-            if isinstance(_order_, staticmethod):
-                _order_ = _order_.__func__
-            if callable(_order_):
-                # save order for future subclasses
-                enum_class._order_function_ = staticmethod(_order_)
-                # create ordered list for comparison
-                _order_ = [m.name for m in sorted(enum_class, key=_order_)]
-        #
-        # remove Flag structures if final class is not a Flag
-        if (
-                Flag is None and cls != 'Flag'
-                or Flag is not None and not issubclass(enum_class, Flag)
-            ):
-            delattr(enum_class, '_boundary_')
-            delattr(enum_class, '_flag_mask_')
-            delattr(enum_class, '_all_bits_')
-            delattr(enum_class, '_inverted_')
-        elif Flag is not None and issubclass(enum_class, Flag):
-            # ensure _all_bits_ is correct and there are no missing flags
-            single_bit_total = 0
-            multi_bit_total = 0
-            for flag in enum_class._member_map_.values():
-                if _is_single_bit(flag._value_):
-                    single_bit_total |= flag._value_
-                else:
-                    # multi-bit flags are considered aliases
-                    multi_bit_total |= flag._value_
-            if enum_class._boundary_ is not KEEP:
-                missed = list(_iter_bits_lsb(multi_bit_total & ~single_bit_total))
-                if missed:
-                    raise TypeError(
-                            'invalid Flag %r -- missing values: %s'
-                            % (cls, ', '.join((str(i) for i in missed)))
-                            )
-            enum_class._flag_mask_ = single_bit_total
-            enum_class._all_bits_ = 2 ** ((single_bit_total).bit_length()) - 1
-            #
-            # set correct __iter__
-            if [m._value_ for m in enum_class] != sorted([m._value_ for m in enum_class]):
-                enum_class._iter_member_ = enum_class._iter_member_by_def_
-            if _order_:
-                # _order_ step 2: remove any items from _order_ that are not single-bit
-                _order_ = [
-                        o
-                        for o in _order_
-                        if o not in enum_class._member_map_ or _is_single_bit(enum_class[o]._value_)
-                        ]
-        #
-        # check for constants with auto() values
-        for k, v in enum_class.__dict__.items():
-            if isinstance(v, constant) and isinstance(v.value, auto):
-                v.value = enum_class(v.value.value)
-        #
-        if _order_:
-            # _order_ step 3: remove aliases from _order_
-            _order_ = [
-                    o
-                    for o in _order_
-                    if (
-                        o not in enum_class._member_map_
-                        or
-                        (o in enum_class._member_map_ and o in enum_class._member_names_)
-                        )]
-            # _order_ step 4: verify that _order_ and _member_names_ match
-            if _order_ != enum_class._member_names_:
-                raise TypeError(
-                        'member order does not match _order_:\n%r\n%r'
-                        % (enum_class._member_names_, _order_)
-                        )
-        return enum_class
-
-    def __bool__(cls):
-        """
-        classes/types should always be True.
-        """
-        return True
-
-    def __call__(cls, value=no_arg, names=None, module=None, qualname=None, type=None, start=1, boundary=None):
-        """Either returns an existing member, or creates a new enum class.
-
-        This method is used both when an enum class is given a value to match
-        to an enumeration member (i.e. Color(3)) and for the functional API
-        (i.e. Color = Enum('Color', names='red green blue')).
-
-        When used for the functional API: `module`, if set, will be stored in
-        the new class' __module__ attribute; `type`, if set, will be mixed in
-        as the first base class.
-
-        Note: if `module` is not set this routine will attempt to discover the
-        calling module by walking the frame stack; if this is unsuccessful
-        the resulting class will not be pickleable.
-        """
-        if names is None:  # simple value lookup
-            return cls.__new__(cls, value)
-        # otherwise, functional API: we're creating a new Enum type
-        return cls._create_(value, names, module=module, qualname=qualname, type=type, start=start, boundary=boundary)
-
-    def __contains__(cls, member):
-        if not isinstance(member, Enum):
-            raise TypeError("%r (%r) is not an <aenum 'Enum'>" % (member, type(member)))
-        if not isinstance(member, cls):
-            return False
-        return True
-
-    def __delattr__(cls, attr):
-        # nicer error message when someone tries to delete an attribute
-        # (see issue19025).
-        if attr in cls._member_map_:
-            raise AttributeError(
-                    "%s: cannot delete Enum member %r." % (cls.__name__, attr),
-                    )
-        found_attr = _get_attr_from_chain(cls, attr)
-        if isinstance(found_attr, constant):
-            raise AttributeError(
-                    "%s: cannot delete constant %r" % (cls.__name__, attr),
-                    )
-        elif isinstance(found_attr, property):
-            raise AttributeError(
-                    "%s: cannot delete property %r" % (cls.__name__, attr),
-                    )
-        super(EnumType, cls).__delattr__(attr)
-
-    def __dir__(cls):
-        interesting = set(cls._member_names_ + [
-                    '__class__', '__contains__', '__doc__', '__getitem__',
-                    '__iter__', '__len__', '__members__', '__module__',
-                    '__name__',
-                    ])
-        if cls._new_member_ is not object.__new__:
-            interesting.add('__new__')
-        if cls.__init_subclass__ is not Enum.__init_subclass__:
-            interesting.add('__init_subclass__')
-        if hasattr(object, '__qualname__'):
-            interesting.add('__qualname__')
-        for method in ('__init__', '__format__', '__repr__', '__str__'):
-            if getattr(cls, method) not in (getattr(Enum, method), getattr(Flag, method)):
-                interesting.add(method)
-        if cls._member_type_ is object:
-            return sorted(interesting)
-        else:
-            # return whatever mixed-in data type has
-            return sorted(set(dir(cls._member_type_)) | interesting)
-
-    @_bltin_property
-    def __members__(cls):
-        """Returns a mapping of member name->value.
-
-        This mapping lists all enum members, including aliases. Note that this
-        is a copy of the internal mapping.
-        """
-        return cls._member_map_.copy()
-
-    def __getitem__(cls, name):
-        try:
-            return cls._member_map_[name]
-        except KeyError:
-            exc = _sys.exc_info()[1]
-        if Flag is not None and issubclass(cls, Flag) and '|' in name:
-            try:
-                # may be an __or__ed name
-                result = cls(0)
-                for n in name.split('|'):
-                    result |= cls[n]
-                return result
-            except KeyError:
-                raise exc
-        result = cls._missing_name_(name)
-        if isinstance(result, cls):
-            return result
-        else:
-            raise exc
-
-    def __iter__(cls):
-        return (cls._member_map_[name] for name in cls._member_names_)
-
-    def __reversed__(cls):
-        return (cls._member_map_[name] for name in reversed(cls._member_names_))
-
-    def __len__(cls):
-        return len(cls._member_names_)
-
-    __nonzero__ = __bool__
-
-    def __repr__(cls):
-        return "<aenum %r>" % (cls.__name__, )
-
-    def __setattr__(cls, name, value):
-        """Block attempts to reassign Enum members/constants.
-
-        A simple assignment to the class namespace only changes one of the
-        several possible ways to get an Enum member from the Enum class,
-        resulting in an inconsistent Enumeration.
-        """
-        member_map = cls.__dict__.get('_member_map_', {})
-        if name in member_map:
-            raise AttributeError(
-                    '%s: cannot rebind member %r.' % (cls.__name__, name),
-                    )
-        found_attr = _get_attr_from_chain(cls, name)
-        if isinstance(found_attr, constant):
-            raise AttributeError(
-                    "%s: cannot rebind constant %r" % (cls.__name__, name),
-                    )
-        elif isinstance(found_attr, property):
-            raise AttributeError(
-                    "%s: cannot rebind property %r" % (cls.__name__, name),
-                    )
-        super(EnumType, cls).__setattr__(name, value)
-
-    def _convert(cls, *args, **kwds):
-        import warnings
-        warnings.warn("_convert is deprecated and will be removed, use"
-                      " _convert_ instead.", DeprecationWarning, stacklevel=2)
-        return cls._convert_(*args, **kwds)
-
-    def _convert_(cls, name, module, filter, source=None, boundary=None, as_global=False):
-        """
-        Create a new Enum subclass that replaces a collection of global constants
-        """
-        # convert all constants from source (or module) that pass filter() to
-        # a new Enum called name, and export the enum and its members back to
-        # module;
-        # also, replace the __reduce_ex__ method so unpickling works in
-        # previous Python versions
-        module_globals = vars(_sys.modules[module])
-        if source:
-            source = vars(source)
-        else:
-            source = module_globals
-        members = [(key, source[key]) for key in source.keys() if filter(key)]
-        try:
-            # sort by value, name
-            members.sort(key=lambda t: (t[1], t[0]))
-        except TypeError:
-            # unless some values aren't comparable, in which case sort by just name
-            members.sort(key=lambda t: t[0])
-        cls = cls(name, members, module=module, boundary=boundary or KEEP)
-        cls.__reduce_ex__ = _reduce_ex_by_name
-        if as_global:
-            global_enum(cls)
-        else:
-            module_globals.update(cls.__members__)
-        module_globals[name] = cls
-        return cls
-
-    def _create_(cls, class_name, names, module=None, qualname=None, type=None, start=1, boundary=None):
-        """Convenience method to create a new Enum class.
-
-        `names` can be:
-
-        * A string containing member names, separated either with spaces or
-          commas.  Values are auto-numbered from 1.
-        * An iterable of member names.  Values are auto-numbered from 1.
-        * An iterable of (member name, value) pairs.
-        * A mapping of member name -> value.
-        """
-        if PY2:
-            # if class_name is unicode, attempt a conversion to ASCII
-            if isinstance(class_name, unicode):
-                try:
-                    class_name = class_name.encode('ascii')
-                except UnicodeEncodeError:
-                    raise TypeError('%r is not representable in ASCII' % (class_name, ))
-        metacls = cls.__class__
-        if type is None:
-            bases = (cls, )
-        else:
-            bases = (type, cls)
-        _, first_enum = cls._get_mixins_(class_name, bases)
-        generate = getattr(first_enum, '_generate_next_value_', None)
-        generate = getattr(generate, 'im_func', generate)
-        # special processing needed for names?
-        if isinstance(names, basestring):
-            names = names.replace(',', ' ').split()
-        if isinstance(names, (tuple, list)) and names and isinstance(names[0], basestring):
-            original_names, names = names, []
-            last_values = []
-            for count, name in enumerate(original_names):
-                value = generate(name, start, count, last_values[:])
-                last_values.append(value)
-                names.append((name, value))
-        # Here, names is either an iterable of (name, value) or a mapping.
-        item = None  # in case names is empty
-        clsdict = None
-        for item in names:
-            if clsdict is None:
-                # first time initialization
-                if isinstance(item, basestring):
-                    clsdict = {}
-                else:
-                    # remember the order
-                    clsdict = metacls.__prepare__(class_name, bases)
-            if isinstance(item, basestring):
-                member_name, member_value = item, names[item]
-            else:
-                member_name, member_value = item
-            clsdict[member_name] = member_value
-        if clsdict is None:
-            # in case names was empty
-            clsdict = metacls.__prepare__(class_name, bases)
-        enum_class = metacls.__new__(metacls, class_name, bases, clsdict, boundary=boundary)
-        # TODO: replace the frame hack if a blessed way to know the calling
-        # module is ever developed
-        if module is None:
-            try:
-                module = _sys._getframe(2).f_globals['__name__']
-            except (AttributeError, KeyError):
-                pass
-        if module is None:
-            _make_class_unpicklable(enum_class)
-        else:
-            enum_class.__module__ = module
-        if qualname is not None:
-            enum_class.__qualname__ = qualname
-        return enum_class
-
-    @classmethod
-    def _check_for_existing_members_(mcls, class_name, bases):
-        if Enum is None:
-            return
-        for chain in bases:
-            for base in chain.__mro__:
-                if issubclass(base, Enum) and base._member_names_:
-                    raise TypeError(
-                            "<aenum %r> cannot extend %r"
-                            % (class_name, base)
-                            )
-    @classmethod
-    def _get_mixins_(mcls, class_name, bases):
-        """Returns the type for creating enum members, and the first inherited
-        enum class.
-
-        bases: the tuple of bases that was given to __new__
-        """
-        if not bases or Enum is None:
-            return object, Enum
-
-        mcls._check_for_existing_members_(class_name, bases)
-
-        # ensure final parent class is an Enum derivative, find any concrete
-        # data type, and check that Enum has no members
-        first_enum = bases[-1]
-        if not issubclass(first_enum, Enum):
-            raise TypeError("new enumerations should be created as "
-                    "`EnumName([mixin_type, ...] [data_type,] enum_type)`")
-        member_type = mcls._find_data_type_(class_name, bases) or object
-        if first_enum._member_names_:
-            raise TypeError("cannot extend enumerations via subclassing")
-        #
-        return member_type, first_enum
-
-    @classmethod
-    def _find_data_repr_(mcls, class_name, bases):
-        for chain in bases:
-            for base in chain.__mro__:
-                if base is object:
-                    continue
-                elif issubclass(base, Enum):
-                    # if we hit an Enum, use it's _value_repr_
-                    return base._value_repr_
-                elif '__repr__' in base.__dict__:
-                    # this is our data repr
-                    return base.__dict__['__repr__']
-        return None
-
-    @classmethod
-    def _find_data_type_(mcls, class_name, bases):
-        data_types = set()
-        for chain in bases:
-            candidate = None
-            for base in chain.__mro__:
-                if base is object or base is StdlibEnum or base is StdlibFlag:
-                    continue
-                elif issubclass(base, Enum):
-                    if base._member_type_ is not object:
-                        data_types.add(base._member_type_)
-                elif '__new__' in base.__dict__:
-                    if issubclass(base, Enum):
-                        continue
-                    elif StdlibFlag is not None and issubclass(base, StdlibFlag):
-                        continue
-                    data_types.add(candidate or base)
-                    break
-                else:
-                    candidate = candidate or base
-        if len(data_types) > 1:
-            raise TypeError('%r: too many data types: %r' % (class_name, data_types))
-        elif data_types:
-            return data_types.pop()
-        else:
-            return None
-
-    @staticmethod
-    def _get_settings_(bases):
-        """Returns the combined _settings_ of all Enum base classes
-
-        bases: the tuple of bases given to __new__
-        """
-        settings = set()
-        for chain in bases:
-            for base in chain.__mro__:
-                if issubclass(base, Enum):
-                    for s in base._settings_:
-                        settings.add(s)
-        return settings
-
-    @classmethod
-    def _find_new_(mcls, clsdict, member_type, first_enum):
-        """Returns the __new__ to be used for creating the enum members.
-
-        clsdict: the class dictionary given to __new__
-        member_type: the data type whose __new__ will be used by default
-        first_enum: enumeration to check for an overriding __new__
-        """
-        # now find the correct __new__, checking to see of one was defined
-        # by the user; also check earlier enum classes in case a __new__ was
-        # saved as __new_member__
-        __new__ = clsdict.get('__new__', None)
-        #
-        # should __new__ be saved as __new_member__ later?
-        save_new = first_enum is not None and __new__ is not None
-        #
-        if __new__ is None:
-            # check all possibles for __new_member__ before falling back to
-            # __new__
-            for method in ('__new_member__', '__new__'):
-                for possible in (member_type, first_enum):
-                    target = getattr(possible, method, None)
-                    if target not in (
-                            None,
-                            None.__new__,
-                            object.__new__,
-                            Enum.__new__,
-                            StdlibEnum.__new__,
-                            ):
-                        __new__ = target
-                        break
-                if __new__ is not None:
-                    break
-            else:
-                __new__ = object.__new__
-        # if a non-object.__new__ is used then whatever value/tuple was
-        # assigned to the enum member name will be passed to __new__ and to the
-        # new enum member's __init__
-        if __new__ is object.__new__:
-            new_uses_args = False
-        else:
-            new_uses_args = True
-        #
-        return __new__, save_new, new_uses_args
-
-
-    # In order to support Python 2 and 3 with a single
-    # codebase we have to create the Enum methods separately
-    # and then use the `type(name, bases, dict)` method to
-    # create the class.
-
-EnumMeta = EnumType
-
-enum_dict = _Addendum(
-        dict=EnumType.__prepare__('Enum', (object, )),
-        doc="Generic enumeration.\n\n    Derive from this class to define new enumerations.\n\n",
-        ns=globals(),
-        )
-
-@enum_dict
-def __init__(self, *args, **kwds):
-    # auto-init method
-    _auto_init_ = self._auto_init_
-    if _auto_init_ is None:
-        return
-    if 'value' in _auto_init_:
-        # remove 'value' from _auto_init_ as it has already been handled
-        _auto_init_ = _auto_init_[1:]
-    if _auto_init_:
-        if len(_auto_init_) < len(args):
-            raise TypeError('%d arguments expected (%s), %d received (%s)'
-                    % (len(_auto_init_), _auto_init_, len(args), args))
-        for name, arg in zip(_auto_init_, args):
-            setattr(self, name, arg)
-        if len(args) < len(_auto_init_):
-            remaining_args = _auto_init_[len(args):]
-            for name in remaining_args:
-                value = kwds.pop(name, undefined)
-                if value is undefined:
-                    raise TypeError('missing value for: %r' % (name, ))
-                setattr(self, name, value)
-            if kwds:
-                # too many keyword arguments
-                raise TypeError('invalid keyword(s): %s' % ', '.join(kwds.keys()))
-
-@enum_dict
-def __new__(cls, value):
-    # all enum instances are actually created during class construction
-    # without calling this method; this method is called by the metaclass'
-    # __call__ (i.e. Color(3) ), and by pickle
-    if NoAlias in cls._settings_:
-        raise TypeError('NoAlias enumerations cannot be looked up by value')
-    if type(value) is cls:
-        # For lookups like Color(Color.red)
-        # value = value.value
-        return value
-    # by-value search for a matching enum member
-    # see if it's in the reverse mapping (for hashable values)
-    try:
-        if value in cls._value2member_map_:
-            return cls._value2member_map_[value]
-    except TypeError:
-        # not there, now do long search -- O(n) behavior
-        for name, member in cls._value2member_seq_:
-            if name == value:
-                return member
-    # still not found -- try _missing_ hook
-    try:
-        exc = None
-        result = cls._missing_value_(value)
-    except Exception as e:
-        exc = e
-        result = None
-    if isinstance(result, cls) or getattr(cls, '_boundary_', None) is EJECT:
-        return result
-    else:
-        if value is no_arg:
-            ve_exc = ValueError('%s() should be called with a value' % (cls.__name__, ))
-        else:
-            ve_exc = ValueError("%r is not a valid %s" % (value, cls.__name__))
-        if result is None and exc is None:
-            raise ve_exc
-        elif exc is None:
-            exc = TypeError(
-                    'error in %s._missing_: returned %r instead of None or a valid member'
-                    % (cls.__name__, result)
-                    )
-        if not isinstance(exc, ValueError):
-            exc.__cause__ = ve_exc
-        raise exc
-
-@enum_dict
-@classmethod
-def __init_subclass__(cls, **kwds):
-    if pyver < PY3_6:
-        # end of the line
-        if kwds:
-            raise TypeError('unconsumed keyword arguments: %r' % (kwds, ))
-    else:
-        super(Enum, cls).__init_subclass__(**kwds)
-
-@enum_dict
-@staticmethod
-def _generate_next_value_(name, start, count, last_values, *args, **kwds):
-    for last_value in reversed(last_values):
-        try:
-            new_value = last_value + 1
-            break
-        except TypeError:
-            pass
-    else:
-        new_value = start
-    if args:
-        return (new_value, ) + args
-    else:
-        return new_value
-
-@enum_dict
-@classmethod
-def _missing_(cls, value):
-    "deprecated, use _missing_value_ instead"
-    return None
-
-@enum_dict
-@classmethod
-def _missing_value_(cls, value):
-    "used for failed value access"
-    return cls._missing_(value)
-
-@enum_dict
-@classmethod
-def _missing_name_(cls, name):
-    "used for failed item access"
-    return None
-
-@enum_dict
-def __repr__(self):
-    v_repr = self.__class__._value_repr_ or self._value_.__class__.__repr__
-    return "<%s.%s: %s>" % (self.__class__.__name__, self._name_, v_repr(self._value_))
-
-@enum_dict
-def __str__(self):
-    return "%s.%s" % (self.__class__.__name__, self._name_)
-
-if PY3:
-    @enum_dict
-    def __dir__(self):
-        """
-        Returns all members and all public methods
-        """
-        if self.__class__._member_type_ is object:
-            interesting = set(['__class__', '__doc__', '__eq__', '__hash__', '__module__', 'name', 'value'])
-        else:
-            interesting = set(object.__dir__(self))
-        for name in getattr(self, '__dict__', []):
-            if name[0] != '_':
-                interesting.add(name)
-        for cls in self.__class__.mro():
-            for name, obj in cls.__dict__.items():
-                if name[0] == '_':
-                    continue
-                if isinstance(obj, property):
-                    # that's an enum.property
-                    if obj.fget is not None or name not in self._member_map_:
-                        interesting.add(name)
-                    else:
-                        # in case it was added by `dir(self)`
-                        interesting.discard(name)
-                else:
-                    interesting.add(name)
-        return sorted(interesting)
-
-@enum_dict
-def __format__(self, format_spec):
-    # mixed-in Enums should use the mixed-in type's __format__, otherwise
-    # we can get strange results with the Enum name showing up instead of
-    # the value
-
-    # pure Enum branch / overridden __str__ branch
-    overridden_str = self.__class__.__str__ != Enum.__str__
-    if self._member_type_ is object or overridden_str:
-        cls = str
-        val = str(self)
-    # mix-in branch
-    else:
-        cls = self._member_type_
-        val = self.value
-    return cls.__format__(val, format_spec)
-
-@enum_dict
-def __hash__(self):
-    return hash(self._name_)
-
-@enum_dict
-def __reduce_ex__(self, proto):
-    return self.__class__, (self._value_, )
-
-
-####################################
-# Python's less than 2.6 use __cmp__
-
-if pyver < PY2_6:
-
-    @enum_dict
-    def __cmp__(self, other):
-        if type(other) is self.__class__:
-            if self is other:
-                return 0
-            return -1
-        return NotImplemented
-        raise TypeError("unorderable types: %s() and %s()" % (self.__class__.__name__, other.__class__.__name__))
-
-else:
-
-    @enum_dict
-    def __le__(self, other):
-        raise TypeError("unorderable types: %s() <= %s()" % (self.__class__.__name__, other.__class__.__name__))
-
-    @enum_dict
-    def __lt__(self, other):
-        raise TypeError("unorderable types: %s() < %s()" % (self.__class__.__name__, other.__class__.__name__))
-
-    @enum_dict
-    def __ge__(self, other):
-        raise TypeError("unorderable types: %s() >= %s()" % (self.__class__.__name__, other.__class__.__name__))
-
-    @enum_dict
-    def __gt__(self, other):
-        raise TypeError("unorderable types: %s() > %s()" % (self.__class__.__name__, other.__class__.__name__))
-
-
-@enum_dict
-def __eq__(self, other):
-    if type(other) is self.__class__:
-        return self is other
-    return NotImplemented
-
-@enum_dict
-def __ne__(self, other):
-    if type(other) is self.__class__:
-        return self is not other
-    return NotImplemented
-
-@enum_dict
-def __hash__(self):
-    return hash(self._name_)
-
-@enum_dict
-def __reduce_ex__(self, proto):
-    return self.__class__, (self._value_, )
-
-
-# enum.property is used to provide access to the `name`, `value', etc.,
-# properties of enum members while keeping some measure of protection
-# from modification, while still allowing for an enumeration to have
-# members named `name`, `value`, etc..  This works because enumeration
-# members are not set directly on the enum class -- enum.property will
-# look them up in _member_map_.
-
-@enum_dict
-@property
-def name(self):
-    return self._name_
-
-@enum_dict
-@property
-def value(self):
-    return self._value_
-
-@enum_dict
-@property
-def values(self):
-    return self._values_
-
-def _reduce_ex_by_name(self, proto):
-    return self.name
-
-Enum = EnumType('Enum', (object, ), enum_dict.resolve())
-del enum_dict
-
-    # Enum has now been created
-
-class ReprEnum(Enum):
-    """
-    Only changes the repr(), leaving str() and format() to the mixed-in type.
-    """
-
-
-class IntEnum(int, ReprEnum):
-    """
-    Enum where members are also (and must be) ints
-    """
-
-
-class StrEnum(str, ReprEnum):
-    """
-    Enum where members are also (and must already be) strings
-
-    default value is member name, lower-cased
-    """
-
-    def __new__(cls, *values, **kwds):
-        if kwds:
-            raise TypeError('%r: keyword arguments not supported' % (cls.__name__))
-        if values:
-            if not isinstance(values[0], str):
-                raise TypeError('%s: values must be str [%r is a %r]' % (cls.__name__, values[0], type(values[0])))
-        value = str(*values)
-        member = str.__new__(cls, value)
-        member._value_ = value
-        return member
-
-    __str__ = str.__str__
-
-    def _generate_next_value_(name, start, count, last_values):
-        """
-        Return the lower-cased version of the member name.
-        """
-        return name.lower()
-
-
-class LowerStrEnum(StrEnum):
-    """
-    Enum where members are also (and must already be) lower-case strings
-
-    default value is member name, lower-cased
-    """
-
-    def __new__(cls, value, *args, **kwds):
-        obj = StrEnum.__new_member__(cls, value, *args, **kwds)
-        if value != value.lower():
-            raise ValueError('%r is not lower-case' % value)
-        return obj
-
-
-class UpperStrEnum(StrEnum):
-    """
-    Enum where members are also (and must already be) upper-case strings
-
-    default value is member name, upper-cased
-    """
-
-    def __new__(cls, value, *args, **kwds):
-        obj = StrEnum.__new_member__(cls, value, *args, **kwds)
-        if value != value.upper():
-            raise ValueError('%r is not upper-case' % value)
-        return obj
-
-    def _generate_next_value_(name, start, count, last_values, *args, **kwds):
-        return name.upper()
-
-
-if PY3:
-    class AutoEnum(Enum):
-        """
-        automatically use _generate_next_value_ when values are missing (Python 3 only)
-        """
-        _settings_ = MagicValue
-
-
-class AutoNumberEnum(Enum):
-    """
-    Automatically assign increasing values to members.
-
-    Py3: numbers match creation order
-    Py2: numbers are assigned alphabetically by member name
-         (unless `_order_` is specified)
-    """
-
-    def __new__(cls, *args, **kwds):
-        value = len(cls.__members__) + 1
-        if cls._member_type_ is int:
-            obj = int.__new__(cls, value)
-        elif cls._member_type_ is long:
-            obj = long.__new__(cls, value)
-        else:
-            obj = object.__new__(cls)
-        obj._value_ = value
-        return obj
-
-
-class AddValueEnum(Enum):
-    _settings_ = AddValue
-
-
-class MultiValueEnum(Enum):
-    """
-    Multiple values can map to each member.
-    """
-    _settings_ = MultiValue
-
-
-class NoAliasEnum(Enum):
-    """
-    Duplicate value members are distinct, but cannot be looked up by value.
-    """
-    _settings_ = NoAlias
-
-
-class OrderedEnum(Enum):
-    """
-    Add ordering based on values of Enum members.
-    """
-
-    def __ge__(self, other):
-        if self.__class__ is other.__class__:
-            return self._value_ >= other._value_
-        return NotImplemented
-
-    def __gt__(self, other):
-        if self.__class__ is other.__class__:
-            return self._value_ > other._value_
-        return NotImplemented
-
-    def __le__(self, other):
-        if self.__class__ is other.__class__:
-            return self._value_ <= other._value_
-        return NotImplemented
-
-    def __lt__(self, other):
-        if self.__class__ is other.__class__:
-            return self._value_ < other._value_
-        return NotImplemented
-
-
-if sqlite3:
-    class SqliteEnum(Enum):
-        def __conform__(self, protocol):
-            if protocol is sqlite3.PrepareProtocol:
-                return self.name
-
-
-class UniqueEnum(Enum):
-    """
-    Ensure no duplicate values exist.
-    """
-    _settings_ = Unique
-
-
-def convert(enum, name, module, filter, source=None):
-    """
-    Create a new Enum subclass that replaces a collection of global constants
-
-    enum: Enum, IntEnum, ...
-    name: name of new Enum
-    module: name of module (__name__ in global context)
-    filter: function that returns True if name should be converted to Enum member
-    source: namespace to check (defaults to 'module')
-    """
-    # convert all constants from source (or module) that pass filter() to
-    # a new Enum called name, and export the enum and its members back to
-    # module;
-    # also, replace the __reduce_ex__ method so unpickling works in
-    # previous Python versions
-    module_globals = vars(_sys.modules[module])
-    if source:
-        source = vars(source)
-    else:
-        source = module_globals
-    members = dict((name, value) for name, value in source.items() if filter(name))
-    enum = enum(name, members, module=module)
-    enum.__reduce_ex__ = _reduce_ex_by_name
-    module_globals.update(enum.__members__)
-    module_globals[name] = enum
-
-def extend_enum(enumeration, name, *args, **kwds):
-    """
-    Add a new member to an existing Enum.
-    """
-    # there are four possibilities:
-    # - extending an aenum Enum or 3.11+ enum Enum
-    # - extending an aenum Flag or 3.11+ enum Flag
-    # - extending a pre-3.11 stdlib Enum Flag
-    # - extending a 3.11+ stdlib Flag
-    #
-    # fail early if name is already in the enumeration
-    if (
-            name in enumeration.__dict__
-            or name in enumeration._member_map_
-            or name in [t[1] for t in getattr(enumeration, '_value2member_seq_', ())]
-        ):
-        raise TypeError('%r already in use as %r' % (name, enumeration.__dict__.get(name, enumeration[name])))
-    # and check for other instances in parent classes
-    descriptor = None
-    for base in enumeration.__mro__[1:]:
-        descriptor = base.__dict__.get(name)
-        if descriptor is not None:
-            if isinstance(descriptor, (property, DynamicClassAttribute)):
-                break
-            else:
-                raise TypeError('%r already in use in superclass %r' % (name, base.__name__))
-    try:
-        _member_map_ = enumeration._member_map_
-        _member_names_ = enumeration._member_names_
-        _member_type_ = enumeration._member_type_
-        _value2member_map_ = enumeration._value2member_map_
-        base_attributes = set([a for b in enumeration.mro() for a in b.__dict__])
-    except AttributeError:
-        raise TypeError('%r is not a supported Enum' % (enumeration, ))
-    try:
-        _value2member_seq_ = enumeration._value2member_seq_
-        _multi_value_ = MultiValue in enumeration._settings_
-        _no_alias_ = NoAlias in enumeration._settings_
-        _unique_ = Unique in enumeration._settings_
-        _auto_init_ = enumeration._auto_init_ or []
-    except AttributeError:
-        # standard Enum
-        _value2member_seq_ = []
-        _multi_value_ = False
-        _no_alias_ = False
-        _unique_ = False
-        _auto_init_ = []
-    if _multi_value_ and not args:
-        # must specify values for multivalue enums
-        raise ValueError('no values specified for MultiValue enum %r' % enumeration.__name__)
-    mt_new = _member_type_.__new__
-    _new = getattr(enumeration, '__new_member__', mt_new)
-    if not args:
-        last_values = [m.value for m in enumeration]
-        count = len(enumeration)
-        start = getattr(enumeration, '_start_', None)
-        if start is None:
-            start = last_values and (last_values[-1] + 1) or 1
-        _gnv = getattr(enumeration, '_generate_next_value_', None)
-        if _gnv is not None:
-            args = ( _gnv(name, start, count, last_values), )
-        else:
-            # must be a 3.4 or 3.5 Enum
-            args = (start, )
-    if _new is object.__new__:
-        new_uses_args = False
-    else:
-        new_uses_args = True
-    if len(args) == 1:
-        [value] = args
-    else:
-        value = args
-    more_values = ()
-    kwds = {}
-    if isinstance(value, enum):
-        args = value.args
-        kwds = value.kwds
-    if not isinstance(value, tuple):
-        args = (value, )
-    else:
-        args = value
-    # tease value out of auto-init if specified
-    if 'value' in _auto_init_:
-        if 'value' in kwds:
-            value = kwds.pop('value')
-        else:
-            value, args = args[0], args[1:]
-    elif _multi_value_:
-        value, more_values, args = args[0], args[1:], ()
-        if new_uses_args:
-            args = (value, )
-    if _member_type_ is tuple:
-        args = (args, )
-    if not new_uses_args:
-        new_member = _new(enumeration)
-        if not hasattr(new_member, '_value_'):
-            new_member._value_ = value
-    else:
-        new_member = _new(enumeration, *args, **kwds)
-        if not hasattr(new_member, '_value_'):
-            new_member._value_ = _member_type_(*args)
-    value = new_member._value_
-    if _multi_value_:
-        if 'value' in _auto_init_:
-            args = more_values
-        else:
-        # put all the values back into args for the init call
-            args = (value, ) + more_values
-    new_member._name_ = name
-    new_member.__objclass__ = enumeration.__class__
-    new_member.__init__(*args)
-    new_member._values_ = (value, ) + more_values
-    # do final checks before modifying enum structures:
-    # - is new member a flag?
-    #   - does the new member fit in the enum's declared _boundary_?
-    # - is new member an alias?
-    #
-    _all_bits_ = _flag_mask_ = None
-    if hasattr(enumeration, '_all_bits_'):
-        _all_bits_ = enumeration._all_bits_ | value
-        _flag_mask_ = enumeration._flag_mask_ | value
-        if enumeration._boundary_ != 'keep':
-            missed = list(_iter_bits_lsb(_flag_mask_ & ~_all_bits_))
-            if missed:
-                raise TypeError(
-                        'invalid Flag %r -- missing values: %s'
-                        % (cls, ', '.join((str(i) for i in missed)))
-                        )
-    # If another member with the same value was already defined, the
-    # new member becomes an alias to the existing one.
-    if _no_alias_:
-        # unless NoAlias was specified
-        return _finalize_extend_enum(enumeration, new_member, bits=_all_bits_, mask=_flag_mask_)
-    else:
-        # handle "normal" aliases
-        new_values = new_member._values_
-        for canonical_member in _member_map_.values():
-            canonical_values_ = getattr(canonical_member, '_values_', [canonical_member._value_])
-            for canonical_value in canonical_values_:
-                for new_value in new_values:
-                    if canonical_value == new_value:
-                        # name is an alias
-                        if _unique_ or _multi_value_:
-                            # aliases not allowed in Unique and MultiValue enums
-                            raise ValueError('%r is a duplicate of %r' % (new_member, canonical_member))
-                        else:
-                            # aliased name can be added, remaining checks irrelevant
-                            # aliases don't appear in member names (only in __members__ and _member_map_).
-                            return _finalize_extend_enum(enumeration, canonical_member, name=name, bits=_all_bits_, mask=_flag_mask_, is_alias=True)
-        # not a standard alias, but maybe a flag alias
-        if pyver < PY3_6:
-            flag_bases = Flag,
-        else:
-            flag_bases = Flag, StdlibFlag
-        if issubclass(enumeration, flag_bases) and hasattr(enumeration, '_all_bits_'):
-            # handle the new flag type
-            if _is_single_bit(value):
-                # a new member!  (an aliase would have been discovered in the previous loop)
-                return _finalize_extend_enum(enumeration, new_member, bits=_all_bits_, mask=_flag_mask_)
-            else:
-                # might be an 3.11 Flag alias
-                if value & enumeration._flag_mask_ == value and _value2member_map_.get(value) is not None:
-                    # yup, it's an alias to existing members... and its an alias of an alias
-                    canonical = _value2member_map_.get(value)
-                    return _finalize_extend_enum(enumeration, canonical, name=name, bits=_all_bits_, mask=_flag_mask_, is_alias=True)
-                else:
-                    return _finalize_extend_enum(enumeration, new_member, bits=_all_bits_, mask=_flag_mask_, is_alias=True)
-        else:
-            # if we get here, we have a brand new member
-            return _finalize_extend_enum(enumeration, new_member)
-
-def _finalize_extend_enum(enumeration, new_member, name=None, bits=None, mask=None, is_alias=False):
-    name = name or new_member.name
-    descriptor = None
-    for base in enumeration.__mro__[1:]:
-        descriptor = base.__dict__.get(name)
-        if descriptor is not None:
-            if isinstance(descriptor, (property, DynamicClassAttribute)):
-                break
-            else:
-                raise TypeError('%r already in use in superclass %r' % (name, base.__name__))
-    if not descriptor:
-        # get redirect in place before adding to _member_map_
-        redirect = property()
-        redirect.__set_name__(enumeration, name)
-        setattr(enumeration, name, redirect)
-    if not is_alias:
-        enumeration._member_names_.append(name)
-    enumeration._member_map_[name] = new_member
-    for v in getattr(new_member, '_values_', [new_member._value_]):
-        try:
-            enumeration._value2member_map_[v] = new_member
-        except TypeError:
-            enumeration._value2member_seq_ += ((v, new_member), )
-    if bits:
-        enumeration._all_bits_ = bits
-        enumeration._flag_mask_ = mask
-    return new_member
-
-def unique(enumeration):
-    """
-    Class decorator that ensures only unique members exist in an enumeration.
-    """
-    duplicates = []
-    for name, member in enumeration.__members__.items():
-        if name != member.name:
-            duplicates.append((name, member.name))
-    if duplicates:
-        duplicate_names = ', '.join(
-                ["%s -> %s" % (alias, name) for (alias, name) in duplicates]
-                )
-        raise ValueError('duplicate names found in %r: %s' %
-                (enumeration, duplicate_names)
-                )
-    return enumeration
-
-# Flag
-
-@export(globals())
-class FlagBoundary(StrEnum):
-    """
-    control how out of range values are handled
-    "strict" -> error is raised  [default]
-    "conform" -> extra bits are discarded
-    "eject" -> lose flag status (becomes a normal integer)
-    """
-    STRICT = auto()
-    CONFORM = auto()
-    EJECT = auto()
-    KEEP = auto()
-assert FlagBoundary.STRICT == 'strict', (FlagBoundary.STRICT, FlagBoundary.CONFORM)
-
-class Flag(Enum):
-    """
-    Generic flag enumeration.
-
-    Derive from this class to define new flag enumerations.
-    """
-
-    _boundary_ = STRICT
-    _numeric_repr_ = repr
-
-
-    def _generate_next_value_(name, start, count, last_values, *args, **kwds):
-        """
-        Generate the next value when not given.
-
-        name: the name of the member
-        start: the initital start value or None
-        count: the number of existing members
-        last_value: the last value assigned or None
-        """
-        if not count:
-            if args:
-                return ((1, start)[start is not None], ) + args
-            else:
-                return (1, start)[start is not None]
-        else:
-            last_value = max(last_values)
-            try:
-                high_bit = _high_bit(last_value)
-                result = 2 ** (high_bit+1)
-                if args:
-                    return (result,)  + args
-                else:
-                    return result
-            except Exception:
-                pass
-            raise TypeError('invalid Flag value: %r' % last_value)
-
-    @classmethod
-    def _iter_member_by_value_(cls, value):
-        """
-        Extract all members from the value in definition (i.e. increasing value) order.
-        """
-        for val in _iter_bits_lsb(value & cls._flag_mask_):
-            yield cls._value2member_map_.get(val)
-
-    _iter_member_ = _iter_member_by_value_
-
-    @classmethod
-    def _iter_member_by_def_(cls, value):
-        """
-        Extract all members from the value in definition order.
-        """
-        members = list(cls._iter_member_by_value_(value))
-        members.sort(key=lambda m: m._sort_order_)
-        for member in members:
-            yield member
-
-    @classmethod
-    def _missing_(cls, value):
-        """
-        return a member matching the given value, or None
-        """
-        return cls._create_pseudo_member_(value)
-
-    @classmethod
-    def _create_pseudo_member_(cls, *values):
-        """
-        Create a composite member.
-        """
-        value = values[0]
-        if not isinstance(value, baseinteger):
-            raise ValueError(
-                    "%r is not a valid %s" % (value, getattr(cls, '__qualname__', cls.__name__))
-                    )
-        # check boundaries
-        # - value must be in range (e.g. -16 <-> +15, i.e. ~15 <-> 15)
-        # - value must not include any skipped flags (e.g. if bit 2 is not
-        #   defined, then 0d10 is invalid)
-        neg_value = None
-        if (
-                not ~cls._all_bits_ <= value <= cls._all_bits_
-                or value & (cls._all_bits_ ^ cls._flag_mask_)
-            ):
-            if cls._boundary_ is STRICT:
-                max_bits = max(value.bit_length(), cls._flag_mask_.bit_length())
-                raise ValueError(
-                        "%s: invalid value: %r\n    given %s\n  allowed %s"
-                        % (cls.__name__, value, bin(value, max_bits), bin(cls._flag_mask_, max_bits))
-                        )
-            elif cls._boundary_ is CONFORM:
-                value = value & cls._flag_mask_
-            elif cls._boundary_ is EJECT:
-                return value
-            elif cls._boundary_ is KEEP:
-                if value < 0:
-                    value = (
-                            max(cls._all_bits_+1, 2**(value.bit_length()))
-                            + value
-                            )
-            else:
-                raise ValueError(
-                        'unknown flag boundary: %r' % (cls._boundary_, )
-                        )
-        if value < 0:
-            neg_value = value
-            value = cls._all_bits_ + 1 + value
-        # get members and unknown
-        unknown = value & ~cls._flag_mask_
-        members = list(cls._iter_member_(value))
-        if unknown and cls._boundary_ is not KEEP:
-            raise ValueError(
-                    '%s(%r) -->  unknown values %r [%s]'
-                    % (cls.__name__, value, unknown, bin(unknown))
-                    )
-        # let class adjust values
-        values = cls._create_pseudo_member_values_(members, *values)
-        __new__ = getattr(cls, '__new_member__', None)
-        if cls._member_type_ is object and not __new__:
-            # construct a singleton enum pseudo-member
-            pseudo_member = object.__new__(cls)
-        else:
-            pseudo_member = (__new__ or cls._member_type_.__new__)(cls, *values)
-        if not hasattr(pseudo_member, 'value'):
-            pseudo_member._value_ = value
-        if members:
-            pseudo_member._name_ = '|'.join([m._name_ for m in members])
-            if unknown:
-                pseudo_member._name_ += '|%s' % cls._numeric_repr_(unknown)
-        else:
-            pseudo_member._name_ = None
-        # use setdefault in case another thread already created a composite
-        # with this value, but only if all members are known
-        # note: zero is a special case -- add it
-        if not unknown:
-            pseudo_member = cls._value2member_map_.setdefault(value, pseudo_member)
-            if neg_value is not None:
-                cls._value2member_map_[neg_value] = pseudo_member
-        return pseudo_member
-
-
-    @classmethod
-    def _create_pseudo_member_values_(cls, members, *values):
-        """
-        Return values to be fed to __new__ to create new member.
-        """
-        if cls._member_type_ in (baseinteger + (object, )):
-            return values
-        elif len(values) < 2:
-            return values + (cls._member_type_(), )
-        else:
-            return values
-
-    def __contains__(self, other):
-        """
-        Returns True if self has at least the same flags set as other.
-        """
-        if not isinstance(other, self.__class__):
-            raise TypeError(
-                "unsupported operand type(s) for 'in': '%s' and '%s'" % (
-                    type(other).__name__, self.__class__.__name__))
-        if other._value_ == 0 or self._value_ == 0:
-            return False
-        return other._value_ & self._value_ == other._value_
-
-    def __iter__(self):
-        """
-        Returns flags in definition order.
-        """
-        for member in self._iter_member_(self._value_):
-            yield member
-
-    def __len__(self):
-        return _bit_count(self._value_)
-
-    def __repr__(self):
-        cls = self.__class__
-        if self._name_ is None:
-            # only zero is unnamed by default
-            return '<%s: %r>' % (cls.__name__, self._value_)
-        else:
-            return '<%s.%s: %r>' % (cls.__name__, self._name_, self._value_)
-
-    def __str__(self):
-        cls = self.__class__
-        if self._name_ is None:
-            return '%s(%s)' % (cls.__name__, self._value_)
-        else:
-            return '%s.%s' % (cls.__name__, self._name_)
-
-    if PY2:
-        def __nonzero__(self):
-            return bool(self._value_)
-    else:
-        def __bool__(self):
-            return bool(self._value_)
-
-    def __or__(self, other):
-        if isinstance(other, self.__class__):
-            other_value = other._value_
-        elif self._member_type_ is not object and isinstance(other, self._member_type_):
-            other_value = other
-        else:
-            return NotImplemented
-        return self.__class__(self._value_ | other_value)
-
-    def __and__(self, other):
-        if isinstance(other, self.__class__):
-            other_value = other._value_
-        elif self._member_type_ is not object and isinstance(other, self._member_type_):
-            other_value = other
-        else:
-            return NotImplemented
-        return self.__class__(self._value_ & other_value)
-
-    def __xor__(self, other):
-        if isinstance(other, self.__class__):
-            other_value = other._value_
-        elif self._member_type_ is not object and isinstance(other, self._member_type_):
-            other_value = other
-        else:
-            return NotImplemented
-        return self.__class__(self._value_ ^ other_value)
-
-    def __invert__(self):
-        if self._inverted_ is None:
-            if self._boundary_ is KEEP:
-                # use all bits
-                self._inverted_ = self.__class__(~self._value_)
-            else:
-                # calculate flags not in this member
-                self._inverted_ = self.__class__(self._flag_mask_ ^ self._value_)
-            self._inverted_._inverted_ = self
-        return self._inverted_
-
-    __ror__ = __or__
-    __rand__ = __and__
-    __rxor__ = __xor__
-
-
-
-class IntFlag(int, ReprEnum, Flag):
-    """Support for integer-based Flags"""
-
-    _boundary_ = EJECT
-
-
-def _high_bit(value):
-    """returns index of highest bit, or -1 if value is zero or negative"""
-    return value.bit_length() - 1
-
-def global_enum_repr(self):
-    """
-    use module.enum_name instead of class.enum_name
-
-    the module is the last module in case of a multi-module name
-    """
-    module = self.__class__.__module__.split('.')[-1]
-    return '%s.%s' % (module, self._name_)
-
-def global_flag_repr(self):
-    """
-    use module.flag_name instead of class.flag_name
-
-    the module is the last module in case of a multi-module name
-    """
-    module = self.__class__.__module__.split('.')[-1]
-    cls_name = self.__class__.__name__
-    if self._name_ is None:
-        return "%s.%s(%r)" % (module, cls_name, self._value_)
-    if _is_single_bit(self):
-        return '%s.%s' % (module, self._name_)
-    if self._boundary_ is not FlagBoundary.KEEP:
-        return '|'.join(['%s.%s' % (module, name) for name in self.name.split('|')])
-    else:
-        name = []
-        for n in self._name_.split('|'):
-            if n[0].isdigit():
-                name.append(n)
-            else:
-                name.append('%s.%s' % (module, n))
-        return '|'.join(name)
-
-def global_str(self):
-    """
-    use enum_name instead of class.enum_name
-    """
-    if self._name_ is None:
-        return "%s(%r)" % (cls_name, self._value_)
-    else:
-        return self._name_
-
-def global_enum(cls, update_str=False):
-    """
-    decorator that makes the repr() of an enum member reference its module
-    instead of its class; also exports all members to the enum's module's
-    global namespace
-    """
-    if issubclass(cls, Flag):
-        cls.__repr__ = global_flag_repr
-    else:
-        cls.__repr__ = global_enum_repr
-    if not issubclass(cls, ReprEnum) or update_str:
-        cls.__str__ = global_str
-    _sys.modules[cls.__module__].__dict__.update(cls.__members__)
-    return cls
-
-
-class module(object):
-
-    def __init__(self, cls, *args):
-        self.__name__ = cls.__name__
-        self._parent_module = cls.__module__
-        self.__all__ = []
-        all_objects = cls.__dict__
-        if not args:
-            args = [k for k, v in all_objects.items() if isinstance(v, (NamedConstant, Enum))]
-        for name in args:
-            self.__dict__[name] = all_objects[name]
-            self.__all__.append(name)
-
-    def register(self):
-        _sys.modules["%s.%s" % (self._parent_module, self.__name__)] = self
-
-if StdlibEnumMeta:
-
-    from _weakrefset import WeakSet
-
-    def __subclasscheck__(cls, subclass):
-        """
-        Override for issubclass(subclass, cls).
-        """
-        if not isinstance(subclass, type):
-            raise TypeError('issubclass() arg 1 must be a class (got %r)' % (subclass, ))
-        # Check cache
-        try:
-            cls.__dict__['_subclass_cache_']
-        except KeyError:
-            cls._subclass_cache_ = WeakSet()
-            cls._subclass_negative_cache_ = WeakSet()
-        except RecursionError:
-            import sys
-            exc, cls, tb = sys.exc_info()
-            exc = RecursionError('possible causes for endless recursion:\n    - __getattribute__ is not ignoring __dunder__ attibutes\n    - __instancecheck__ and/or __subclasscheck_ are (mutually) recursive\n    see `aenum.remove_stdlib_integration` for temporary work-around')
-            raise_from_none(exc)
-        if subclass in cls._subclass_cache_:
-            return True
-        # Check negative cache
-        elif subclass in cls._subclass_negative_cache_:
-            return False
-        if cls is subclass:
-            cls._subclass_cache_.add(subclass)
-            return True
-        # Check if it's a direct subclass
-        if cls in getattr(subclass, '__mro__', ()):
-            cls._subclass_cache_.add(subclass)
-            return True
-        # Check if it's an aenum.Enum|IntEnum|IntFlag|Flag subclass
-        if cls is StdlibIntFlag and issubclass(subclass, IntFlag):
-            cls._subclass_cache_.add(subclass)
-            return True
-        elif cls is StdlibFlag and issubclass(subclass, Flag):
-            cls._subclass_cache_.add(subclass)
-            return True
-        elif cls is StdlibIntEnum and issubclass(subclass, IntEnum):
-            cls._subclass_cache_.add(subclass)
-            return True
-        if cls is StdlibEnum and issubclass(subclass, Enum):
-            cls._subclass_cache_.add(subclass)
-            return True
-        # No dice; update negative cache
-        cls._subclass_negative_cache_.add(subclass)
-        return False
-
-    def __instancecheck__(cls, instance):
-        subclass = instance.__class__
-        try:
-            return cls.__subclasscheck__(subclass)
-        except RecursionError:
-            import sys
-            exc, cls, tb = sys.exc_info()
-            exc = RecursionError('possible causes for endless recursion:\n    - __getattribute__ is not ignoring __dunder__ attibutes\n    - __instancecheck__ and/or __subclasscheck_ are (mutually) recursive\n    see `aenum.remove_stdlib_integration` for temporary work-around')
-            raise_from_none(exc)
-
-    StdlibEnumMeta.__subclasscheck__ = __subclasscheck__
-    StdlibEnumMeta.__instancecheck__ = __instancecheck__
-
-def add_stdlib_integration():
-    if StdlibEnum:
-        StdlibEnumMeta.__subclasscheck__ = __subclasscheck__
-        StdlibEnumMeta.__instancecheck__ = __instancecheck__
-
-def remove_stdlib_integration():
-    """
-    Remove the __instancecheck__ and __subclasscheck__ overrides from the stdlib Enum.
-
-    Those overrides are in place so that code detecting stdlib enums will also detect
-    aenum enums.  If a buggy __getattribute__, __instancecheck__, or __subclasscheck__
-    is defined on a custom EnumMeta then RecursionErrors can result; using this
-    function after importing aenum will solve that problem, but the better solution is
-    to fix the buggy method.
-    """
-    if StdlibEnum:
-        del StdlibEnumMeta.__instancecheck__
-        del StdlibEnumMeta.__subclasscheck__
-
diff --git a/venv/Lib/site-packages/aenum/__pycache__/__init__.cpython-39.pyc b/venv/Lib/site-packages/aenum/__pycache__/__init__.cpython-39.pyc
deleted file mode 100644
index 176f78e2..00000000
Binary files a/venv/Lib/site-packages/aenum/__pycache__/__init__.cpython-39.pyc and /dev/null differ
diff --git a/venv/Lib/site-packages/aenum/__pycache__/_py3.cpython-39.pyc b/venv/Lib/site-packages/aenum/__pycache__/_py3.cpython-39.pyc
deleted file mode 100644
index c289c1bf..00000000
Binary files a/venv/Lib/site-packages/aenum/__pycache__/_py3.cpython-39.pyc and /dev/null differ
diff --git a/venv/Lib/site-packages/aenum/_py2.py b/venv/Lib/site-packages/aenum/_py2.py
deleted file mode 100644
index a6e7b290..00000000
--- a/venv/Lib/site-packages/aenum/_py2.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from operator import div as _div_
-from inspect import getargspec
-
-def raise_with_traceback(exc, tb):
-    raise exc, None, tb
-
-__all__ = ['_div_', 'getargspec', 'raise_with_traceback']
diff --git a/venv/Lib/site-packages/aenum/_py3.py b/venv/Lib/site-packages/aenum/_py3.py
deleted file mode 100644
index 2edeefdf..00000000
--- a/venv/Lib/site-packages/aenum/_py3.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from inspect import getfullargspec as _getfullargspec
-
-def getargspec(method):
-    args, varargs, keywords, defaults, _, _, _ = _getfullargspec(method)
-    return args, varargs, keywords, defaults
-
-def raise_with_traceback(exc, tb):
-    raise exc.with_traceback(tb)
-
-def raise_from_none(exc):
-    raise exc from None
-
diff --git a/venv/Lib/site-packages/aenum/doc/aenum.rst b/venv/Lib/site-packages/aenum/doc/aenum.rst
deleted file mode 100644
index 42ef0d3c..00000000
--- a/venv/Lib/site-packages/aenum/doc/aenum.rst
+++ /dev/null
@@ -1,1568 +0,0 @@
-``aenum`` --- support for advanced enumerations, namedtuples, and constants
-===========================================================================
-
-.. :synopsis:: enumerations are sets of symbolic names bound to unique,
-    constant values; namedtuples are fixed- or variable-length
-    tuples with the positions addressable by field name as well as by index;
-    constants are classes of named constants that cannot be rebound.
-.. :moduleauthor:: Ethan Furman <ethan@stoneleaf.us>
-
-----------------
-
-An ``Enum`` is a set of symbolic names (members) bound to unique, constant
-values.  Within an enumeration, the members can be compared by identity, and
-the enumeration itself can be iterated over.
-
-A ``NamedTuple`` is a class-based, fixed-length tuple with a name for each
-possible position accessible using attribute-access notation.
-
-A ``NamedConstant`` is a class whose members cannot be rebound;  it lacks all
-other ``Enum`` capabilities, however; consequently, it can have duplicate
-values.  There is also a ``module`` function that can insert the
-``NamedConstant`` class into ``sys.modules`` where it will appear to be a
-module whose top-level names cannot be rebound.
-
-.. note::
-    ``constant`` refers to names not being rebound; mutable objects can be
-     mutated.
-
-
-Module Contents
----------------
-
-This module defines five enumeration classes that can be used to define unique
-sets of names and values, one ``Enum`` class decorator, one ``NamedTuple``
-class, one ``NamedConstant`` class, and several helpers.
-
-``NamedConstant``
-
-NamedConstant class for creating groups of constants.  These names cannot be
-rebound to other values.
-
-``Enum``
-
-Base class for creating enumerated constants.  See section `Enum Functional API`_
-for an alternate construction syntax.
-
-``AddValue``
-
-Flag specifying that ``_generate_next_value_`` should always be called to
-provide the initial value for an enum member.
-
-``MultiValue``
-
-Flag specifying that each item of tuple value is a separate value for that
-member; the first tuple item is the canonical one.
-
-``NoAlias``
-
-Flag specifying that duplicate valued members are distinct and not aliases;
-by-value lookups are disabled.
-
-``Unique``
-
-Flag specifying that duplicate valued members are not allowed.
-
-.. note::
-    The flags are inherited by the enumeration's subclasses.  To use them in
-    Python 2 assign to ``_settings_`` in the class body.
-
-``IntEnum``
-
-Base class for creating enumerated constants that are also subclasses of ``int``.
-
-``AutoNumberEnum``
-
-Derived class that automatically assigns an ``int`` value to each member.
-
-``OrderedEnum``
-
-Derived class that adds ``<``, ``<=``, ``>=``, and ``>`` methods to an ``Enum``.
-
-``UniqueEnum``
-
-Derived class that ensures only one name is bound to any one value.
-
-``unique``
-
-Enum class decorator that ensures only one name is bound to any one value.
-
-.. note::
-
-    the ``UniqueEnum`` class, the ``unique`` decorator, and the Unique
-    flag all do the same thing; you do not need to use more than one of
-    them at the same time.
-
-``NamedTuple``
-
-Base class for `creating NamedTuples`_, either by subclassing or via it's
-functional API.
-
-``constant``
-
-Descriptor to add constant values to an ``Enum``, or advanced constants to
-``NamedConstant``.
-
-``convert``
-
-Helper to transform target global variables into an ``Enum``.
-
-``enum``
-
-Helper for specifying keyword arguments when creating ``Enum`` members.
-
-``export``
-
-Helper for inserting ``Enum`` members and ``NamedConstant`` constants into a
-namespace (usually ``globals()``.
-
-``extend_enum``
-
-Helper for adding new ``Enum`` members, both stdlib and aenum.
-
-``module``
-
-Function to take a ``NamedConstant`` or ``Enum`` class and insert it into
-``sys.modules`` with the affect of a module whose top-level constant and
-member names cannot be rebound.
-
-``skip``
-
-Descriptor to add a normal (non-``Enum`` member) attribute to an ``Enum``
-or ``NamedConstant``.
-
-
-Creating an Enum
-----------------
-
-Enumerations are created using the ``class`` syntax, which makes them
-easy to read and write.  An alternative creation method is described in
-`Enum Functional API`_.  To define an enumeration, subclass ``Enum`` as
-follows::
-
-    >>> from aenum import Enum
-    >>> class Color(Enum):
-    ...     red = 1
-    ...     green = 2
-    ...     blue = 3
-
-*Nomenclature*
-
-  - The class ``Color`` is an *enumeration* (or *enum*)
-  - The attributes ``Color.red``, ``Color.green``, etc., are
-    *enumeration members* (or *enum members*).
-  - The enum members have *names* and *values* (the name of
-    ``Color.red`` is ``red``, the value of ``Color.blue`` is
-    ``3``, etc.)
-
-.. note::
-
-    Even though we use the ``class`` syntax to create Enums, Enums
-    are not normal Python classes.  See `How are Enums different?`_ for
-    more details.
-
-Enumeration members have human readable string representations::
-
-    >>> print(Color.red)
-    Color.red
-
-...while their ``repr`` has more information::
-
-    >>> print(repr(Color.red))
-    <Color.red: 1>
-
-The *type* of an enumeration member is the enumeration it belongs to::
-
-    >>> type(Color.red)
-    <aenum 'Color'>
-    >>> isinstance(Color.green, Color)
-    True
-
-Enumerations support iteration.  In Python 3.x definition order is used; in
-Python 2.x the definition order is not available, but class attribute
-``_order_`` is supported;  otherwise, value order is used if posible,
-otherwise alphabetical name order is used::
-
-    >>> class Shake(Enum):
-    ...   _order_ = 'vanilla chocolate cookies mint'  # only needed in 2.x
-    ...   vanilla = 7
-    ...   chocolate = 4
-    ...   cookies = 9
-    ...   mint = 3
-    ...
-    >>> for shake in Shake:
-    ...   print(shake)
-    ...
-    Shake.vanilla
-    Shake.chocolate
-    Shake.cookies
-    Shake.mint
-
-The ``_order_`` attribute is always removed, but in 3.x it is also used to
-verify that definition order is the same (useful for py2&3 code bases);
-however, in the stdlib version it will be ignored and not removed.
-
-.. note::
-
-    To maintain compatibility with Python 3.4 and 3.5, use __order__
-    instead (double leading and trailing underscores).
-
-Enumeration members are hashable, so they can be used in dictionaries and sets::
-
-    >>> apples = {}
-    >>> apples[Color.red] = 'red delicious'
-    >>> apples[Color.green] = 'granny smith'
-    >>> apples == {Color.red: 'red delicious', Color.green: 'granny smith'}
-    True
-
-In Python 3 the class syntax has a few extra advancements::
-
-    --> class Color(
-    ...         Enum,
-    ...         settings=(AddValue, MultiValue, NoAlias, Unique),
-    ...         init='field_name1 field_name2 ...',
-    ...         start=7,
-    ...         )
-    ...
-
-``start`` is used to specify the starting value for the first member::
-
-    --> class Count(Enum, start=11):
-    ...     eleven
-    ...     twelve
-    ...
-    --> Count.twelve.value == 12
-    True
-
-``init`` specifies the attribute names to store creation values to::
-
-    --> class Planet(Enum, init='mass radius'):
-    ...     MERCURY = (3.303e+23, 2.4397e6)
-    ...     EARTH   = (5.976e+24, 6.37814e6)
-    ...
-    --> Planet.EARTH.value
-    (5.976e+24, 6378140.0)
-    --> Planet.EARTH.radius
-    2.4397e6
-
-The various settings enable special behavior:
-
-- ``AddValue`` calls a user supplied ``_generate_next_value_`` to provide
-  the initial value
-- ``MultiValue`` allows multiple values per member instead of the usual 1
-- ``NoAlias`` allows different members to have the same value
-- ``Unique`` disallows different members to have the same value
-
-.. note::
-
-    To use these features in Python 2 use the _sundered_ versions of
-    the names in the class body:  ``_start_``, ``_init_``, ``_settings_``.
-
-
-Programmatic access to enumeration members and their attributes
----------------------------------------------------------------
-
-Sometimes it's useful to access members in enumerations programmatically (i.e.
-situations where ``Color.red`` won't do because the exact color is not known
-at program-writing time).  ``Enum`` allows such access::
-
-    >>> Color(1)
-    <Color.red: 1>
-    >>> Color(3)
-    <Color.blue: 3>
-
-If you want to access enum members by *name*, use item access::
-
-    >>> Color['red']
-    <Color.red: 1>
-    >>> Color['green']
-    <Color.green: 2>
-
-If have an enum member and need its ``name`` or ``value``::
-
-    >>> member = Color.red
-    >>> member.name
-    'red'
-    >>> member.value
-    1
-
-
-Duplicating enum members and values
------------------------------------
-
-Having two enum members (or any other attribute) with the same name is invalid;
-in Python 3.x this would raise an error, but in Python 2.x the second member
-simply overwrites the first::
-
-    # python 2.x
-    --> class Shape(Enum):
-    ...   square = 2
-    ...   square = 3
-    ...
-    --> Shape.square
-    <Shape.square: 3>
-
-    # python 3.x
-    --> class Shape(Enum):
-    ...   square = 2
-    ...   square = 3
-    Traceback (most recent call last):
-    ...
-    TypeError: Attempted to reuse key: 'square'
-
-However, two enum members are allowed to have the same value.  Given two members
-A and B with the same value (and A defined first), B is an alias to A.  By-value
-lookup of the value of A and B will return A.  By-name lookup of B will also
-return A::
-
-    >>> class Shape(Enum):
-    ...   _order_ = 'square diamond circle'  # needed in 2.x
-    ...   square = 2
-    ...   diamond = 1
-    ...   circle = 3
-    ...   alias_for_square = 2
-    ...
-    >>> Shape.square
-    <Shape.square: 2>
-    >>> Shape.alias_for_square
-    <Shape.square: 2>
-    >>> Shape(2)
-    <Shape.square: 2>
-
-
-Allowing aliases is not always desirable.  ``unique`` can be used to ensure
-that none exist in a particular enumeration::
-
-    >>> from aenum import unique
-    >>> @unique
-    ... class Mistake(Enum):
-    ...   _order_ = 'one two three'  # only needed in 2.x
-    ...   one = 1
-    ...   two = 2
-    ...   three = 3
-    ...   four = 3
-    Traceback (most recent call last):
-    ...
-    ValueError: duplicate names found in <aenum 'Mistake'>: four -> three
-
-Iterating over the members of an enum does not provide the aliases::
-
-    >>> list(Shape)
-    [<Shape.square: 2>, <Shape.diamond: 1>, <Shape.circle: 3>]
-
-The special attribute ``__members__`` is a dictionary mapping names to members.
-It includes all names defined in the enumeration, including the aliases::
-
-    >>> for name, member in sorted(Shape.__members__.items()):
-    ...   name, member
-    ...
-    ('alias_for_square', <Shape.square: 2>)
-    ('circle', <Shape.circle: 3>)
-    ('diamond', <Shape.diamond: 1>)
-    ('square', <Shape.square: 2>)
-
-The ``__members__`` attribute can be used for detailed programmatic access to
-the enumeration members.  For example, finding all the aliases::
-
-    >>> [n for n, mbr in Shape.__members__.items() if mbr.name != n]
-    ['alias_for_square']
-
-Comparisons
------------
-
-Enumeration members are compared by identity::
-
-    >>> Color.red is Color.red
-    True
-    >>> Color.red is Color.blue
-    False
-    >>> Color.red is not Color.blue
-    True
-
-Ordered comparisons between enumeration values are *not* supported.  Enum
-members are not integers (but see `IntEnum`_ below)::
-
-    >>> Color.red < Color.blue
-    Traceback (most recent call last):
-      File "<stdin>", line 1, in <module>
-    TypeError: unorderable types: Color() < Color()
-
-.. warning::
-
-    In Python 2 *everything* is ordered, even though the ordering may not
-    make sense.  If you want your enumerations to have a sensible ordering
-    consider using an `OrderedEnum`_.
-
-
-Equality comparisons are defined though::
-
-    >>> Color.blue == Color.red
-    False
-    >>> Color.blue != Color.red
-    True
-    >>> Color.blue == Color.blue
-    True
-
-Comparisons against non-enumeration values will always compare not equal
-(again, ``IntEnum`` was explicitly designed to behave differently, see
-below)::
-
-    >>> Color.blue == 2
-    False
-
-
-Allowed members and attributes of enumerations
-----------------------------------------------
-
-The examples above use integers for enumeration values.  Using integers is
-short and handy (and provided by default by the `Enum Functional API`_), but not
-strictly enforced.  In the vast majority of use-cases, one doesn't care what
-the actual value of an enumeration is.  But if the value *is* important,
-enumerations can have arbitrary values.
-
-Enumerations are Python classes, and can have methods and special methods as
-usual.  If we have this enumeration::
-
-    >>> class Mood(Enum):
-    ...   funky = 1
-    ...   happy = 3
-    ...
-    ...   def describe(self):
-    ...     # self is the member here
-    ...     return self.name, self.value
-    ...
-    ...   def __str__(self):
-    ...     return 'my custom str! {0}'.format(self.value)
-    ...
-    ...   @classmethod
-    ...   def favorite_mood(cls):
-    ...     # cls here is the enumeration
-    ...     return cls.happy
-
-Then::
-
-    >>> Mood.favorite_mood()
-    <Mood.happy: 3>
-    >>> Mood.happy.describe()
-    ('happy', 3)
-    >>> str(Mood.funky)
-    'my custom str! 1'
-
-The rules for what is allowed are as follows: _sunder_ names (starting and
-ending with a single underscore) are reserved by enum and cannot be used;
-all other attributes defined within an enumeration will become members of this
-enumeration, with the exception of *__dunder__* names and descriptors (methods
-are also descriptors).
-
-.. note::
-
-    If your enumeration defines ``__new__`` and/or ``__init__`` then
-    whatever value(s) were given to the enum member will be passed into
-    those methods.  See `Planet`_ for an example.
-
-
-Restricted Enum subclassing
----------------------------
-
-A new `Enum` class must have one base Enum class, up to one concrete
-data type, and as many `object`-based mixin classes as needed.  The
-order of these base classes is::
-
-    def EnumName([mix-in, ...,] [data-type,] base-enum):
-        pass
-
-Also, subclassing an enumeration is allowed only if the enumeration does not define
-
-any members.  So this is forbidden::
-
-    >>> class MoreColor(Color):
-    ...   pink = 17
-    Traceback (most recent call last):
-    ...
-    TypeError: <aenum 'MoreColor'> cannot extend <aenum 'Color'>
-
-But this is allowed::
-
-    >>> class Foo(Enum):
-    ...   def some_behavior(self):
-    ...     pass
-    ...
-    >>> class Bar(Foo):
-    ...   happy = 1
-    ...   sad = 2
-    ...
-
-Allowing subclassing of enums that define members would lead to a violation of
-some important invariants of types and instances.  On the other hand, it makes
-sense to allow sharing some common behavior between a group of enumerations.
-(See `OrderedEnum`_ for an example.)
-
-
-Pickling
---------
-
-Enumerations can be pickled and unpickled::
-
-    >>> from aenum.test import Fruit
-    >>> from pickle import dumps, loads
-    >>> Fruit.tomato is loads(dumps(Fruit.tomato, 2))
-    True
-
-The usual restrictions for pickling apply: picklable enums must be defined in
-the top level of a module, since unpickling requires them to be importable
-from that module.
-
-.. note::
-
-    With pickle protocol version 4 (introduced in Python 3.4) it is possible
-    to easily pickle enums nested in other classes.
-
-
-
-Enum Functional API
--------------------
-
-The ``Enum`` class is callable, providing the following functional API::
-
-    >>> Animal = Enum('Animal', 'ant bee cat dog')
-    >>> Animal
-    <aenum 'Animal'>
-    >>> Animal.ant
-    <Animal.ant: 1>
-    >>> Animal.ant.value
-    1
-    >>> list(Animal)
-    [<Animal.ant: 1>, <Animal.bee: 2>, <Animal.cat: 3>, <Animal.dog: 4>]
-
-The semantics of this API resemble ``namedtuple``. The first argument
-of the call to ``Enum`` is the name of the enumeration.
-
-The second argument is the *source* of enumeration member names.  It can be a
-whitespace-separated string of names, a sequence of names, a sequence of
-2-tuples with key/value pairs, or a mapping (e.g. dictionary) of names to
-values.  The last two options enable assigning arbitrary values to
-enumerations; the others auto-assign increasing integers starting with 1.  A
-new class derived from ``Enum`` is returned.  In other words, the above
-assignment to ``Animal`` is equivalent to::
-
-    >>> class Animals(Enum):
-    ...   ant = 1
-    ...   bee = 2
-    ...   cat = 3
-    ...   dog = 4
-
-Pickling enums created with the functional API can be tricky as frame stack
-implementation details are used to try and figure out which module the
-enumeration is being created in (e.g. it will fail if you use a utility
-function in separate module, and also may not work on IronPython or Jython).
-The solution is to specify the module name explicitly as follows::
-
-    >>> Animals = Enum('Animals', 'ant bee cat dog', module=__name__)
-
-Derived Enumerations
---------------------
-
-IntEnum
-^^^^^^^
-
-A variation of ``Enum`` is provided which is also a subclass of
-``int``.  Members of an ``IntEnum`` can be compared to integers;
-by extension, integer enumerations of different types can also be compared
-to each other::
-
-    >>> from aenum import IntEnum
-    >>> class Shape(IntEnum):
-    ...   circle = 1
-    ...   square = 2
-    ...
-    >>> class Request(IntEnum):
-    ...   post = 1
-    ...   get = 2
-    ...
-    >>> Shape == 1
-    False
-    >>> Shape.circle == 1
-    True
-    >>> Shape.circle == Request.post
-    True
-
-However, they still can't be compared to standard ``Enum`` enumerations::
-
-    >>> class Shape(IntEnum):
-    ...   circle = 1
-    ...   square = 2
-    ...
-    >>> class Color(Enum):
-    ...   red = 1
-    ...   green = 2
-    ...
-    >>> Shape.circle == Color.red
-    False
-
-``IntEnum`` values behave like integers in other ways you'd expect::
-
-    >>> int(Shape.circle)
-    1
-    >>> ['a', 'b', 'c'][Shape.circle]
-    'b'
-    >>> [i for i in range(Shape.square)]
-    [0, 1]
-
-For the vast majority of code, ``Enum`` is strongly recommended,
-since ``IntEnum`` breaks some semantic promises of an enumeration (by
-being comparable to integers, and thus by transitivity to other
-unrelated enumerations).  It should be used only in special cases where
-there's no other choice; for example, when integer constants are
-replaced with enumerations and backwards compatibility is required with code
-that still expects integers.
-
-
-IntFlag
-^^^^^^^
-
-The next variation of ``Enum`` provided, ``IntFlag``, is also based
-on ``int``.  The difference being ``IntFlag`` members can be combined
-using the bitwise operators (&, \|, ^, ~) and the result is still an
-``IntFlag`` member.  However, as the name implies, ``IntFlag``
-members also subclass ``int`` and can be used wherever an ``int`` is
-used.  Any operation on an ``IntFlag`` member besides the bit-wise
-operations will lose the ``IntFlag`` membership.
-
-Sample ``IntFlag`` class::
-
-    >>> from aenum import IntFlag
-    >>> class Perm(IntFlag):
-    ...     _order_ = 'R W X'
-    ...     R = 4
-    ...     W = 2
-    ...     X = 1
-    ...
-    >>> Perm.R | Perm.W
-    <Perm.R|W: 6>
-    >>> Perm.R + Perm.W
-    6
-    >>> RW = Perm.R | Perm.W
-    >>> Perm.R in RW
-    True
-
-It is also possible to name the combinations::
-
-    >>> class Perm(IntFlag):
-    ...     _order_ = 'R W X'
-    ...     R = 4
-    ...     W = 2
-    ...     X = 1
-    ...     RWX = 7
-    >>> Perm.RWX
-    <Perm.RWX: 7>
-    >>> ~Perm.RWX
-    <Perm: 0>
-
-Another important difference between ``IntFlag`` and ``Enum`` is that
-if no flags are set (the value is 0), its boolean evaluation is ``False``::
-
-    >>> Perm.R & Perm.X
-    <Perm: 0>
-    >>> bool(Perm.R & Perm.X)
-    False
-
-Because ``IntFlag`` members are also subclasses of ``int`` they can
-be combined with them::
-
-    >>> Perm.X | 4
-    <Perm.R|X: 5>
-
-If the result is not a ``Flag`` then, depending on the ``_boundary_`` setting,
-an exception is raised (``STRICT``), the extra bits are lost (``CONFORM``), or
-it reverts to an int (``EJECT``):
-
-    >>> from aenum import STRICT, CONFORM, EJECT
-    >>> Perm._boundary_ = STRICT
-    >>> Perm.X | 8
-    Traceback (most recent call last):
-    ...
-    ValueError: Perm: invalid value: 9
-        given 0b0 1001
-      allowed 0b0 0111
-
-    >>> Perm._boundary_ = EJECT
-    >>> Perm.X | 8
-    9
-
-    >>> Perm._boundary_ = CONFORM
-    >>> Perm.X | 8
-    <Perm.X: 1>
-
-
-Flag
-^^^^
-
-The last variation is ``Flag``.  Like ``IntFlag``, ``Flag``
-members can be combined using the bitwise operators (&, \|, ^, ~).  Unlike
-``IntFlag``, they cannot be combined with, nor compared against, any
-other ``Flag`` enumeration, nor ``int``.  While it is possible to
-specify the values directly it is recommended to use ``auto`` as the
-value and let ``Flag`` select an appropriate value.
-
-Like ``IntFlag``, if a combination of ``Flag`` members results in no
-flags being set, the boolean evaluation is ``False``::
-
-    >>> from aenum import Flag, auto
-    >>> class Color(Flag):
-    ...     RED = auto()
-    ...     BLUE = auto()
-    ...     GREEN = auto()
-    ...
-    >>> Color.RED & Color.GREEN
-    <Color: 0>
-    >>> bool(Color.RED & Color.GREEN)
-    False
-
-Individual flags should have values that are powers of two (1, 2, 4, 8, ...),
-while combinations of flags won't::
-
-    --> class Color(Flag):
-    ...     RED = auto()
-    ...     BLUE = auto()
-    ...     GREEN = auto()
-    ...     WHITE = RED | BLUE | GREEN
-    ...
-    --> Color.WHITE
-    <Color.WHITE: 7>
-
-Giving a name to the "no flags set" condition does not change its boolean
-value::
-
-    >>> class Color(Flag):
-    ...     BLACK = 0
-    ...     RED = auto()
-    ...     BLUE = auto()
-    ...     GREEN = auto()
-    ...
-    >>> Color.BLACK
-    <Color.BLACK: 0>
-    >>> bool(Color.BLACK)
-    False
-
-Flags can be iterated over to retrieve the individual truthy flags in the value::
-
-    >>> class Color(Flag):
-    ...     _order_ = 'BLACK RED BLUE GREEN WHITE'
-    ...     BLACK = 0
-    ...     RED = auto()
-    ...     BLUE = auto()
-    ...     GREEN = auto()
-    ...     WHITE = RED | BLUE | GREEN
-    ...
-    >>> list(Color.GREEN)
-    [<Color.GREEN: 4>]
-    >>> list(Color.WHITE)
-    [<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 4>]
-
-.. note::
-
-    For the majority of new code, ``Enum`` and ``Flag`` are strongly
-    recommended, since ``IntEnum`` and ``IntFlag`` break some
-    semantic promises of an enumeration (by being comparable to integers, and
-    thus by transitivity to other unrelated enumerations).  ``IntEnum``
-    and ``IntFlag`` should be used only in cases where ``Enum`` and
-    ``Flag`` will not do; for example, when integer constants are replaced
-    with enumerations, or for interoperability with other systems.
-
-
-Others
-^^^^^^
-
-While ``IntEnum`` is part of the ``aenum`` module, it would be very
-simple to implement independently::
-
-    class MyIntEnum(int, Enum):
-        pass
-
-This demonstrates how similar derived enumerations can be defined; for example
-a ``MyStrEnum`` that mixes in ``str`` instead of ``int``.
-
-Some rules:
-
-1. When subclassing ``Enum``, mix-in types must appear before
-   ``Enum`` itself in the sequence of bases, as in the ``MyIntEnum``
-   example above.
-2. While ``Enum`` can have members of any type, once you mix in an
-   additional type, all the members must have values of that type or be
-   convertible into that type.  This restriction does not apply to mix-ins
-   which only add methods and don't specify another data type.
-3. When another data type is mixed in, the ``value`` attribute is *not the
-   same* as the enum member itself, although it is equivalant and will compare
-   equal.
-4. %-style formatting:  ``%s`` and ``%r`` call ``Enum``'s ``__str__`` and
-   ``__repr__`` respectively; other codes (such as ``%i`` or ``%h`` for
-   MyIntEnum) treat the enum member as its mixed-in type.
-5. ``str.__format__`` (or ``format``) will use the mixed-in
-   type's ``__format__``.  If the ``Enum``'s ``str`` or ``repr`` is desired
-   use the ``!s`` or ``!r`` ``str`` format codes.
-
-.. note::
-
-   If you override the ``__str__`` method, then it will be used to provide the
-   string portion of the ``format()`` call.
-
-.. note::
-
-   Prior to Python 3.4 there is a bug in ``str``'s %-formatting: ``int``
-   subclasses are printed as strings and not numbers when the ``%d``, ``%i``,
-   or ``%u`` codes are used.
-
-
-Extra Goodies
--------------
-
-aenum supports a few extra techniques not found in the stdlib version.
-
-enum
-^^^^
-
-If you have several items to initialize your ``Enum`` members with and
-would like to use keyword arguments, the ``enum`` helper is for you::
-
-    >>> from aenum import enum
-    >>> class Presidents(Enum):
-    ...     Washington = enum('George Washington', circa=1776, death=1797)
-    ...     Jackson = enum('Andrew Jackson', circa=1830, death=1837)
-    ...     Lincoln = enum('Abraham Lincoln', circa=1860, death=1865)
-    ...
-    >>> Presidents.Lincoln
-    <Presidents.Lincoln: enum('Abraham Lincoln', circa=1860, death=1865)>
-
-extend_enum
-^^^^^^^^^^^
-
-For those rare cases when you need to create your ``Enum`` in pieces, you
-can use ``extend_enum`` to add new members after the initial creation
-(the new member is returned)::
-
-    >>> from aenum import extend_enum
-    >>> class Color(Enum):
-    ...     red = 1
-    ...     green = 2
-    ...     blue = 3
-    ...
-    >>> list(Color)
-    [<Color.red: 1>, <Color.green: 2>, <Color.blue: 3>]
-    >>> extend_enum(Color, 'opacity', 4)
-    <Color.opacity: 4>
-    >>> list(Color)
-    [<Color.red: 1>, <Color.green: 2>, <Color.blue: 3>, <Color.opacity: 4>]
-    >>> Color.opacity in Color
-    True
-    >>> Color.opacity.name == 'opacity'
-    True
-    >>> Color.opacity.value == 4
-    True
-    >>> Color(4)
-    <Color.opacity: 4>
-    >>> Color['opacity']
-    <Color.opacity: 4>
-
-    --> Color.__members__
-    OrderedDict([
-        ('red', <Color.red: 1>),
-        ('green', <Color.green: 2>),
-        ('blue', <Color.blue: 3>),
-        ('opacity', <Color.opacity: 4>)
-        ])
-
-constant
-^^^^^^^^
-
-If you need to have some constant value in your ``Enum`` that isn't a member,
-use ``constant``::
-
-    >>> from aenum import constant
-    >>> class Planet(Enum):
-    ...     MERCURY = (3.303e+23, 2.4397e6)
-    ...     EARTH   = (5.976e+24, 6.37814e6)
-    ...     JUPITER = (1.9e+27,   7.1492e7)
-    ...     URANUS  = (8.686e+25, 2.5559e7)
-    ...     G = constant(6.67300E-11)
-    ...     def __init__(self, mass, radius):
-    ...         self.mass = mass       # in kilograms
-    ...         self.radius = radius   # in meters
-    ...     @property
-    ...     def surface_gravity(self):
-    ...         # universal gravitational constant  (m3 kg-1 s-2)
-    ...         return self.G * self.mass / (self.radius * self.radius)
-    ...
-    >>> Planet.EARTH.value
-    (5.976e+24, 6378140.0)
-    >>> Planet.EARTH.surface_gravity
-    9.802652743337129
-    >>> Planet.G
-    6.673e-11
-    >>> Planet.G = 9
-    Traceback (most recent call last):
-    ...
-    AttributeError: Planet: cannot rebind constant 'G'
-
-skip
-^^^^
-
-If you need a standard attribute that is not converted into an ``Enum``
-member, use ``skip``::
-
-    >>> from aenum import skip
-    >>> class Color(Enum):
-    ...     red = 1
-    ...     green = 2
-    ...     blue = 3
-    ...     opacity = skip(0.45)
-    ...
-    >>> Color.opacity
-    0.45
-    >>> Color.opacity = 0.77
-    >>> Color.opacity
-    0.77
-
-start
-^^^^^
-
-``start`` can be used to turn on auto-numbering (useful for when you don't
-care which numbers are assigned as long as they are consistent and in order)
-The Python 3 version can look like this::
-
-    >>> class Color(Enum, start=1):                # doctest: +SKIP
-    ...     red, green, blue
-    ...
-    >>> Color.blue
-    <Color.blue: 3>
-
-This can also be done in Python 2, albeit not as elegantly (this also works in
-Python 3)::
-
-    >>> class Color(Enum):                         # doctest: +SKIP
-    ...     _start_ = 1
-    ...     red = auto()
-    ...     green = auto()
-    ...     blue = auto()
-    ...
-    >>> Color.blue
-    <Color.blue: 3>
-
-init
-^^^^
-
-If you need an ``__init__`` method that does nothing besides save its
-arguments, ``init`` is for you::
-
-    >>> class Planet(Enum, init='mass radius'):      # doctest: +SKIP
-    ...     MERCURY = (3.303e+23, 2.4397e6)
-    ...     EARTH   = (5.976e+24, 6.37814e6)
-    ...     JUPITER = (1.9e+27,   7.1492e7)
-    ...     URANUS  = (8.686e+25, 2.5559e7)
-    ...     G = constant(6.67300E-11)
-    ...     @property
-    ...     def surface_gravity(self):
-    ...         # universal gravitational constant  (m3 kg-1 s-2)
-    ...         return self.G * self.mass / (self.radius * self.radius)
-    ...
-    >>> Planet.JUPITER.value
-    (1.9e+27, 71492000.0)
-    >>> Planet.JUPITER.mass
-    1.9e+27
-
-.. note::
-
-   Just as with ``start`` above, in Python 2 you must put the keyword as a
-   _sunder_ in the class body -- ``_init_ = 'mass radius'``.
-
-init and missing values
-^^^^^^^^^^^^^^^^^^^^^^^
-
-If ``_init_`` calls for values that are not supplied, ``_generate_next_value_``
-will be called in an effort to generate them.  Here is an example in Python 2::
-
-    >>> from aenum import Enum
-    >>> class SelectionEnum(Enum):
-    ...     _init_ = 'db user'
-    ...     def __new__(cls, *args, **kwds):
-    ...         count = len(cls.__members__)
-    ...         obj = object.__new__(cls)
-    ...         obj._count = count
-    ...         obj._value_ = args
-    ...         return obj
-    ...     @staticmethod
-    ...     def _generate_next_value_(name, start, count, values, *args, **kwds):
-    ...         return (name, ) + args
-    ...
-    >>> class NotificationType(SelectionEnum):
-    ...     # usually, name is the same as db
-    ...     # but not for blanks
-    ...     blank = '', ''
-    ...     C = 'Catalog'
-    ...     S = 'Sheet'
-    ...     B = 'Both'
-    ...
-    >>> NotificationType.blank
-    <NotificationType.blank: ('', '')>
-    >>> NotificationType.B
-    <NotificationType.B: ('B', 'Both')>
-    >>> NotificationType.B.db
-    'B'
-    >>> NotificationType.B.user
-    'Both'
-
-combining Flag with other data types
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Flag does support being combined with other data types.  To support this you
-need to provide a ``_create_pseudo_member_values_`` method which will be called
-with the members in a composite flag.  You may also need to provide a custom
-``__new__`` method::
-
-    >>> class AnsiFlag(str, Flag):
-    ...     def __new__(cls, value, code):
-    ...         str_value = '\x1b[%sm' % code
-    ...         obj = str.__new__(cls, str_value)
-    ...         obj._value_ = value
-    ...         obj.code = code
-    ...         return obj
-    ...     @classmethod
-    ...     def _create_pseudo_member_values_(cls, members, *values):
-    ...         code = ';'.join(m.code for m in members)
-    ...         return values + (code, )
-    ...     _order_ = 'FG_Red FG_Green BG_Magenta BG_White'
-    ...     FG_Red = '31'             # ESC [ 31 m      # red
-    ...     FG_Green = '32'           # ESC [ 32 m      # green
-    ...     BG_Magenta = '45'         # ESC [ 35 m      # magenta
-    ...     BG_White = '47'           # ESC [ 37 m      # white
-    ...
-    >>> color = AnsiFlag.BG_White | AnsiFlag.FG_Red
-    >>> repr(color)
-    '<AnsiFlag.FG_Red|BG_White: 9>'
-    >>> str.__repr__(color)
-    "'\\x1b[31;47m'"
-
-.. note::
-
-   If you do not provide your own ``_create_pseudo_member_values_`` the flags
-   may still combine, but may be missing functionality.
-
-
-Decorators
-----------
-
-unique
-^^^^^^
-
-A ``class`` decorator specifically for enumerations.  It searches an
-enumeration's ``__members__`` gathering any aliases it finds; if any are
-found ``ValueError`` is raised with the details::
-
-    >>> @unique
-    ... class NoDupes(Enum):
-    ...    first = 'one'
-    ...    second = 'two'
-    ...    third = 'two'
-    Traceback (most recent call last):
-    ...
-    ValueError: duplicate names found in <aenum 'NoDupes'>: third -> second
-
-
-Interesting examples
---------------------
-
-While ``Enum`` and ``IntEnum`` are expected to cover the majority of
-use-cases, they cannot cover them all.  Here are recipes for some different
-types of enumerations that can be used directly (the first three are included
-in the module), or as examples for creating one's own.
-
-
-AutoNumber
-^^^^^^^^^^
-
-Avoids having to specify the value for each enumeration member::
-
-    >>> class AutoNumber(Enum):
-    ...     def __new__(cls):
-    ...         value = len(cls.__members__) + 1
-    ...         obj = object.__new__(cls)
-    ...         obj._value_ = value
-    ...         return obj
-    ...
-    >>> class Color(AutoNumber):
-    ...     _order_ = "red green blue"  # only needed in 2.x
-    ...     red = ()
-    ...     green = ()
-    ...     blue = ()
-    ...
-    >>> Color.green.value == 2
-    True
-
-.. note::
-
-    The `__new__` method, if defined, is used during creation of the Enum
-    members; it is then replaced by Enum's `__new__` which is used after
-    class creation for lookup of existing members.  Due to the way Enums are
-    supposed to behave, there is no way to customize Enum's `__new__` without
-    modifying the class after it is created.
-
-
-UniqueEnum
-^^^^^^^^^^
-
-Raises an error if a duplicate member name is found instead of creating an
-alias::
-
-    >>> class UniqueEnum(Enum):
-    ...     def __init__(self, *args):
-    ...         cls = self.__class__
-    ...         if any(self.value == e.value for e in cls):
-    ...             a = self.name
-    ...             e = cls(self.value).name
-    ...             raise ValueError(
-    ...                     "aliases not allowed in UniqueEnum:  %r --> %r"
-    ...                     % (a, e))
-    ...
-    >>> class Color(UniqueEnum):
-    ...     _order_ = 'red green blue'
-    ...     red = 1
-    ...     green = 2
-    ...     blue = 3
-    ...     grene = 2
-    Traceback (most recent call last):
-    ...
-    ValueError: aliases not allowed in UniqueEnum:  'grene' --> 'green'
-
-
-OrderedEnum
-^^^^^^^^^^^
-
-An ordered enumeration that is not based on ``IntEnum`` and so maintains
-the normal ``Enum`` invariants (such as not being comparable to other
-enumerations)::
-
-    >>> class OrderedEnum(Enum):
-    ...     def __ge__(self, other):
-    ...         if self.__class__ is other.__class__:
-    ...             return self._value_ >= other._value_
-    ...         return NotImplemented
-    ...     def __gt__(self, other):
-    ...         if self.__class__ is other.__class__:
-    ...             return self._value_ > other._value_
-    ...         return NotImplemented
-    ...     def __le__(self, other):
-    ...         if self.__class__ is other.__class__:
-    ...             return self._value_ <= other._value_
-    ...         return NotImplemented
-    ...     def __lt__(self, other):
-    ...         if self.__class__ is other.__class__:
-    ...             return self._value_ < other._value_
-    ...         return NotImplemented
-    ...
-    >>> class Grade(OrderedEnum):
-    ...     __ordered__ = 'A B C D F'
-    ...     A = 5
-    ...     B = 4
-    ...     C = 3
-    ...     D = 2
-    ...     F = 1
-    ...
-    >>> Grade.C < Grade.A
-    True
-
-
-Planet
-^^^^^^
-
-If ``__new__`` or ``__init__`` is defined the value of the enum member
-will be passed to those methods::
-
-    >>> class Planet(Enum):
-    ...     MERCURY = (3.303e+23, 2.4397e6)
-    ...     VENUS   = (4.869e+24, 6.0518e6)
-    ...     EARTH   = (5.976e+24, 6.37814e6)
-    ...     MARS    = (6.421e+23, 3.3972e6)
-    ...     JUPITER = (1.9e+27,   7.1492e7)
-    ...     SATURN  = (5.688e+26, 6.0268e7)
-    ...     URANUS  = (8.686e+25, 2.5559e7)
-    ...     NEPTUNE = (1.024e+26, 2.4746e7)
-    ...     def __init__(self, mass, radius):
-    ...         self.mass = mass       # in kilograms
-    ...         self.radius = radius   # in meters
-    ...     @property
-    ...     def surface_gravity(self):
-    ...         # universal gravitational constant  (m3 kg-1 s-2)
-    ...         G = 6.67300E-11
-    ...         return G * self.mass / (self.radius * self.radius)
-    ...
-    >>> Planet.EARTH.value
-    (5.976e+24, 6378140.0)
-    >>> Planet.EARTH.surface_gravity
-    9.802652743337129
-
-
-How are Enums different?
-------------------------
-
-Enums have a custom metaclass that affects many aspects of both derived Enum
-classes and their instances (members).
-
-
-Enum Classes
-^^^^^^^^^^^^
-
-The ``EnumMeta`` metaclass is responsible for providing the
-``__contains__``, ``__dir__``, ``__iter__`` and other methods that
-allow one to do things with an ``Enum`` class that fail on a typical
-class, such as ``list(Color)`` or ``some_var in Color``.  ``EnumMeta`` is
-responsible for ensuring that various other methods on the final ``Enum``
-class are correct (such as ``__new__``, ``__getnewargs__``,
-``__str__`` and ``__repr__``).
-
-.. note::
-
-    ``__dir__`` is not changed in the Python 2 line as it messes up some
-    of the decorators included in the stdlib.
-
-
-Enum Members (aka instances)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The most interesting thing about Enum members is that they are singletons.
-``EnumMeta`` creates them all while it is creating the ``Enum``
-class itself, and then puts a custom ``__new__`` in place to ensure
-that no new ones are ever instantiated by returning only the existing
-member instances.
-
-
-Finer Points
-^^^^^^^^^^^^
-
-``Enum`` members are instances of an ``Enum`` class, but are not
-accessible as `EnumClass.member1.member2`.
-(changed in version 1.1.1 to be accessible)
-(changed in version 2.2.4 to be inaccessible)::
-
-    >>> class FieldTypes(Enum):
-    ...     name = 1
-    ...     value = 2
-    ...     size = 3
-    ...
-    >>> FieldTypes.size.value
-    3
-    >>> FieldTypes.size
-    <FieldTypes.size: 3>
-    >>> FieldTypes.value.size
-    Traceback (most recent call last):
-    ...
-    AttributeError: <aenum 'FieldTypes'> member has no attribute 'size'
-
-The ``__members__`` attribute is only available on the class.
-
-
-``__members__`` is always an ``OrderedDict``, with the order being the
-definition order in Python 3.x or the order in ``_order_`` in Python 2.7;
-if no ``_order_`` was specified in Python 2.7 then the order of
-``__members__`` is either increasing value or alphabetically by name.
-
-If you give your ``Enum`` subclass extra methods, like the `Planet`_
-class above, those methods will show up in a `dir` of the member,
-but not of the class (in Python 3.x)::
-
-    --> dir(Planet)
-    ['EARTH', 'JUPITER', 'MARS', 'MERCURY', 'NEPTUNE', 'SATURN', 'URANUS',
-     'VENUS', '__class__', '__doc__', '__members__', '__module__']
-    --> dir(Planet.EARTH)
-    ['__class__', '__doc__', '__module__', 'name', 'surface_gravity', 'value']
-
-A ``__new__`` method will only be used for the creation of the
-``Enum`` members -- after that it is replaced.  This means if you wish to
-change how ``Enum`` members are looked up you either have to write a
-helper function or a ``classmethod``.
-
-.. note::
-
-    If you create your own ``__new__`` you should set the ``_value_`` in it;
-    if you do not, aenum will try to, but will raise a ``TypeError`` if it
-    cannot.
-
-If the stdlib ``enum`` is available (Python 3.4+ and it hasn't been shadowed
-by, for example, ``enum34``) then aenum will be a subclass of it.
-
-To use the ``AddValue``, ``MultiValue``, ``NoAlias``, and ``Unique`` flags
-in Py2 or Py2/Py3 codebases, use ``_settings_ = ...`` in the class body.
-
-To use ``init`` in Py2 or Py2/Py3 codebases use ``_init_`` in the class body.
-
-To use ``start`` in Py2 or Py2/Py3 codebases use ``_start_`` in the class body.
-
-When creating class bodies dynamically, put any variables you need to use into
-``_ignore_``::
-
-    >>> from datetime import timedelta
-    >>> from aenum import NoAlias
-    >>> class Period(timedelta, Enum):
-    ...     '''
-    ...     different lengths of time
-    ...     '''
-    ...     _init_ = 'value period'
-    ...     _settings_ = NoAlias
-    ...     _ignore_ = 'Period i'
-    ...     Period = vars()
-    ...     for i in range(31):
-    ...         Period['day_%d' % i] = i, 'day'
-    ...     for i in range(15):
-    ...         Period['week_%d' % i] = i*7, 'week'
-    ...
-    >>> hasattr(Period, '_ignore_')
-    False
-    >>> hasattr(Period, 'Period')
-    False
-    >>> hasattr(Period, 'i')
-    False
-
-The name listed in ``_ignore_``, as well as ``_ignore_`` itself, will not be
-present in the final enumeration as neither attributes nor members.
-
-.. note::
-
-    except for __dunder__ attributes/methods, all _sunder_ attributes must
-    be before any thing else in the class body
-
-.. note::
-
-    all _sunder_ attributes that affect member creation are only looked up in
-    the last ``Enum`` class listed in the class header
-
-
-Creating NamedTuples
---------------------
-
-Simple
-^^^^^^
-
-The most common way to create a new NamedTuple will be via the functional API::
-
-    >>> from aenum import NamedTuple
-    >>> Book = NamedTuple('Book', 'title author genre', module=__name__)
-
-This creates a ``NamedTuple`` called ``Book`` that will always contain three
-items, each of which is also addressable as ``title``, ``author``, or ``genre``.
-
-``Book`` instances can be created using positional or keyword argements or a
-mixture of the two::
-
-    >>> b1 = Book('Lord of the Rings', 'J.R.R. Tolkien', 'fantasy')
-    >>> b2 = Book(title='Jhereg', author='Steven Brust', genre='fantasy')
-    >>> b3 = Book('Empire', 'Orson Scott Card', genre='scifi')
-
-If too few or too many arguments are used a ``TypeError`` will be raised::
-
-    >>> b4 = Book('Hidden Empire')
-    Traceback (most recent call last):
-    ...
-    TypeError: values not provided for field(s): author, genre
-    >>> b5 = Book(genre='business')
-    Traceback (most recent call last):
-    ...
-    TypeError: values not provided for field(s): title, author
-
-As a ``class`` the above ``Book`` ``NamedTuple`` would look like::
-
-    >>> class Book(NamedTuple):
-    ...     title = 0
-    ...     author = 1
-    ...     genre = 2
-    ...
-
-For compatibility with the stdlib ``namedtuple``, NamedTuple also has the
-``_asdict``, ``_make``, and ``_replace`` methods, and the ``_fields``
-attribute, which all function similarly::
-
-    >>> class Point(NamedTuple):
-    ...     x = 0, 'horizontal coordinate', 1
-    ...     y = 1, 'vertical coordinate', -1
-    ...
-    >>> class Color(NamedTuple):
-    ...     r = 0, 'red component', 11
-    ...     g = 1, 'green component', 29
-    ...     b = 2, 'blue component', 37
-    ...
-    >>> Pixel = NamedTuple('Pixel', Point+Color, module=__name__)
-    >>> pixel = Pixel(99, -101, 255, 128, 0)
-
-    >>> pixel._asdict()
-    OrderedDict([('x', 99), ('y', -101), ('r', 255), ('g', 128), ('b', 0)])
-
-    >>> Point._make((4, 5))
-    Point(x=4, y=5)
-
-    >>> purple = Color(127, 0, 127)
-    >>> mid_gray = purple._replace(g=127)
-    >>> mid_gray
-    Color(r=127, g=127, b=127)
-
-    >>> pixel._fields
-    ['x', 'y', 'r', 'g', 'b']
-
-    >>> Pixel._fields
-    ['x', 'y', 'r', 'g', 'b']
-
-
-Advanced
-^^^^^^^^
-
-The simple method of creating ``NamedTuples`` requires always specifying all
-possible arguments when creating instances; failure to do so will raise
-exceptions::
-
-    >>> class Point(NamedTuple):
-    ...     x = 0
-    ...     y = 1
-    ...
-    >>> Point()
-    Traceback (most recent call last):
-    ...
-    TypeError: values not provided for field(s): x, y
-    >>> Point(1)
-    Traceback (most recent call last):
-    ...
-    TypeError: values not provided for field(s): y
-    >>> Point(y=2)
-    Traceback (most recent call last):
-    ...
-    TypeError: values not provided for field(s): x
-
-However, it is possible to specify both docstrings and default values when
-creating a ``NamedTuple`` using the class method::
-
-    >>> class Point(NamedTuple):
-    ...     x = 0, 'horizontal coordinate', 0
-    ...     y = 1, 'vertical coordinate', 0
-    ...
-    >>> Point()
-    Point(x=0, y=0)
-    >>> Point(1)
-    Point(x=1, y=0)
-    >>> Point(y=2)
-    Point(x=0, y=2)
-
-It is also possible to create ``NamedTuples`` that only have named attributes
-for certain fields; any fields without names can still be accessed by index::
-
-    >>> class Person(NamedTuple):
-    ...     fullname = 2
-    ...     phone = 5
-    ...
-    >>> p = Person('Ethan', 'Furman', 'Ethan Furman',
-    ...            'ethan at stoneleaf dot us',
-    ...            'ethan.furman', '999.555.1212')
-    >>> p
-    Person('Ethan', 'Furman', 'Ethan Furman', 'ethan at stoneleaf dot us',
-           'ethan.furman', '999.555.1212')
-    >>> p.fullname
-    'Ethan Furman'
-    >>> p.phone
-    '999.555.1212'
-    >>> p[0]
-    'Ethan'
-
-In the above example the last named field was also the last field possible; in
-those cases where you don't need to have the last possible field named, you can
-provide a ``_size_`` of ``TupleSize.minimum`` to declare that more fields are
-okay::
-
-    >>> from aenum import TupleSize
-    >>> class Person(NamedTuple):
-    ...     _size_ = TupleSize.minimum
-    ...     first = 0
-    ...     last = 1
-    ...
-
-or, optionally if using Python 3::
-
-    >>> class Person(NamedTuple, size=TupleSize.minimum):      # doctest: +SKIP
-    ...     first = 0
-    ...     last = 1
-
-and in use::
-
-    >>> Person('Ethan', 'Furman')
-    Person(first='Ethan', last='Furman')
-
-    >>> Person('Ethan', 'Furman', 'ethan.furman')
-    Person('Ethan', 'Furman', 'ethan.furman')
-
-    >>> Person('Ethan', 'Furman', 'ethan.furman', 'yay Python!')
-    Person('Ethan', 'Furman', 'ethan.furman', 'yay Python!')
-
-    >>> Person('Ethan')
-    Traceback (most recent call last):
-    ...
-    TypeError: values not provided for field(s): last
-
-Also, for those cases where even named fields may not be present, you can
-specify ``TupleSize.variable``::
-
-    >>> class Person(NamedTuple):
-    ...     _size_ = TupleSize.variable
-    ...     first = 0
-    ...     last = 1
-    ...
-
-    >>> Person('Ethan')
-    Person('Ethan')
-
-    >>> Person(last='Furman')
-    Traceback (most recent call last):
-    ...
-    TypeError: values not provided for field(s): first
-
-Creating new ``NamedTuples`` from existing ``NamedTuples`` is simple::
-
-    >>> Point = NamedTuple('Point', 'x y')
-    >>> Color = NamedTuple('Color', 'r g b')
-    >>> Pixel = NamedTuple('Pixel', Point+Color, module=__name__)
-    >>> Pixel
-    <NamedTuple 'Pixel'>
-
-The existing fields in the bases classes are renumbered to fit the new class,
-but keep their doc strings and default values.  If you use standard
-subclassing::
-
-    >>> Point = NamedTuple('Point', 'x y')
-    >>> class Pixel(Point):
-    ...     r = 2, 'red component', 11
-    ...     g = 3, 'green component', 29
-    ...     b = 4, 'blue component', 37
-    ...
-    >>> Pixel.__fields__
-    ['x', 'y', 'r', 'g', 'b']
-
-You must manage the numbering yourself.
-
-
-Creating NamedConstants
------------------------
-
-A ``NamedConstant`` class is created much like an ``Enum``::
-
-    >>> from aenum import NamedConstant
-    >>> class Konstant(NamedConstant):
-    ...     PI = 3.14159
-    ...     TAU = 2 * PI
-
-    >>> Konstant.PI
-    <Konstant.PI: 3.14159>
-
-    >> print(Konstant.PI)
-    3.14159
-
-    >>> Konstant.PI = 'apple'
-    Traceback (most recent call last):
-    ...
-    AttributeError: cannot rebind constant <Konstant.PI>
-
-    >>> del Konstant.PI
-    Traceback (most recent call last):
-    ...
-    AttributeError: cannot delete constant <Konstant.PI>
diff --git a/venv/Lib/site-packages/aenum/test.py b/venv/Lib/site-packages/aenum/test.py
deleted file mode 100644
index 224293cb..00000000
--- a/venv/Lib/site-packages/aenum/test.py
+++ /dev/null
@@ -1,6832 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from __future__ import division, print_function
-import sys
-import aenum
-import doctest
-import os
-import shutil
-import tempfile
-import textwrap
-import unittest
-import uuid
-import warnings
-from aenum import EnumType, EnumMeta, Enum, IntEnum, StrEnum, LowerStrEnum, UpperStrEnum
-from aenum import AutoNumberEnum, MultiValueEnum, OrderedEnum, UniqueEnum, AddValueEnum, Flag, IntFlag
-from aenum import NamedTuple, TupleSize, NamedConstant, constant, NoAlias, AddValue, Unique
-from aenum import STRICT, CONFORM, EJECT, KEEP
-from aenum import _reduce_ex_by_name, unique, skip, extend_enum, auto, enum, MultiValue, member, nonmember, no_arg
-from aenum import basestring, baseinteger, unicode, enum_property
-from aenum import pyver, PY2, PY3, PY2_6, PY3_3, PY3_4, PY3_5, PY3_6, PY3_11
-from collections import OrderedDict
-from datetime import timedelta
-from pickle import dumps, loads, PicklingError, HIGHEST_PROTOCOL
-from operator import or_ as _or_, and_ as _and_, xor as _xor_, inv as _inv_
-from operator import abs as _abs_, add as _add_, floordiv as _floordiv_
-from operator import lshift as _lshift_, rshift as _rshift_, mod as _mod_
-from operator import mul as _mul_, neg as _neg_, pos as _pos_, pow as _pow_
-from operator import truediv as _truediv_, sub as _sub_
-if PY2:
-    from operator import div as _div_
-try:
-    import threading
-except ImportError:
-    threading = None
-
-try:
-    any
-except NameError:
-    from aenum import any
-
-MODULE = __name__
-SHORT_MODULE = MODULE.split('.')[-1]
-
-def load_tests(loader, tests, ignore):
-    tests.addTests(doctest.DocTestSuite(aenum))
-    tests.addTests(doctest.DocFileSuite(
-        'doc/aenum.rst',
-        package=aenum,
-        optionflags=doctest.ELLIPSIS|doctest.NORMALIZE_WHITESPACE,
-        ))
-    return tests
-
-class TestCase(unittest.TestCase):
-
-    def __init__(self, *args, **kwds):
-        regex = getattr(self, 'assertRaisesRegex', None)
-        if regex is None:
-            self.assertRaisesRegex = getattr(self, 'assertRaisesRegexp')
-        super(TestCase, self).__init__(*args, **kwds)
-
-
-# for pickle tests
-try:
-    class Stooges(Enum):
-        LARRY = 1
-        CURLY = 2
-        MOE = 3
-except Exception:
-    Stooges = sys.exc_info()[1]
-
-try:
-    class IntStooges(int, Enum):
-        LARRY = 1
-        CURLY = 2
-        MOE = 3
-except Exception:
-    IntStooges = sys.exc_info()[1]
-
-try:
-    class FloatStooges(float, Enum):
-        LARRY = 1.39
-        CURLY = 2.72
-        MOE = 3.142596
-except Exception:
-    FloatStooges = sys.exc_info()[1]
-
-try:
-    class FlagStooges(Flag):
-        LARRY = 1
-        CURLY = 2
-        MOE = 3
-except Exception as exc:
-    FlagStooges = exc
-
-try:
-    LifeForm = NamedTuple('LifeForm', 'branch genus species', module=__name__)
-except Exception:
-    LifeForm = sys.exc_info()[1]
-
-try:
-    class DeathForm(NamedTuple):
-        color = 0
-        rigidity = 1
-        odor = 2
-except Exception:
-    DeathForm = sys.exc_info()[1]
-
-# for pickle test and subclass tests
-try:
-    class Name(StrEnum):
-        BDFL = 'Guido van Rossum'
-        FLUFL = 'Barry Warsaw'
-except Exception:
-    Name = sys.exc_info()[1]
-
-try:
-    Question = Enum('Question', 'who what when where why', module=__name__)
-except Exception:
-    Question = sys.exc_info()[1]
-
-try:
-    Answer = Enum('Answer', 'him this then there because')
-except Exception:
-    Answer = sys.exc_info()[1]
-
-try:
-    class WhatsIt(NamedTuple):
-        def what(self):
-            return self[0]
-    class ThatsIt(WhatsIt):
-        blah = 0
-        bleh = 1
-except Exception:
-    ThatsIt = sys.exc_info()[1]
-
-# for doctests
-try:
-    class Fruit(Enum):
-        tomato = 1
-        banana = 2
-        cherry = 3
-except Exception:
-    pass
-
-def test_pickle_dump_load(assertion, source, target=None, protocol=(0, HIGHEST_PROTOCOL)):
-    start, stop = protocol
-    failures = []
-    for protocol in range(start, stop+1):
-        try:
-            if target is None:
-                assertion(loads(dumps(source, protocol=protocol)), source)
-            else:
-                assertion(loads(dumps(source, protocol=protocol)), target)
-        except Exception:
-            exc, tb = sys.exc_info()[1:]
-            failures.append('%2d: %s' %(protocol, exc))
-    if failures:
-        raise ValueError('Failed with protocols: %s' % ', '.join(failures))
-
-def test_pickle_exception(assertion, exception, obj,
-        protocol=(0, HIGHEST_PROTOCOL)):
-    start, stop = protocol
-    failures = []
-    for protocol in range(start, stop+1):
-        try:
-            assertion(exception, dumps, obj, protocol=protocol)
-        except Exception:
-            exc = sys.exc_info()[1]
-            failures.append('%d: %s %s' % (protocol, exc.__class__.__name__, exc))
-    if failures:
-        raise ValueError('Failed with protocols: %s' % ', '.join(failures))
-
-if PY3:
-    from aenum.test_v3 import TestEnumV3, TestOrderV3, TestNamedTupleV3, TestStackoverflowAnswersV3, TestIssuesV3, TestExtendEnumV3
-    from aenum import test_v3
-    test_v3.IntStooges = IntStooges
-    test_v3.test_pickle_exception = test_pickle_exception
-    test_v3.test_pickle_dump_load = test_pickle_dump_load
-
-# for subclassing tests
-
-class classproperty(object):
-
-    def __init__(self, fget=None, fset=None, fdel=None, doc=None):
-        self.fget = fget
-        self.fset = fset
-        self.fdel = fdel
-        if doc is None and fget is not None:
-            doc = fget.__doc__
-        self.__doc__ = doc
-
-    def __get__(self, instance, ownerclass):
-        return self.fget(ownerclass)
-
-
-# tests
-class TestOrder(TestCase):
-    """
-    Test _order_ extra/missing members.
-    """
-
-    def test_same_members(self):
-        class Color(Enum):
-            _order_ = 'red green blue'
-            red = 1
-            green = 2
-            blue = 3
-
-    def test_same_members_with_aliases(self):
-        class Color(Enum):
-            _order_ = 'red green blue'
-            red = 1
-            green = 2
-            blue = 3
-            verde = green
-
-    def test_order_has_extra_members(self):
-        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
-            class Color(Enum):
-                _order_ = 'red green blue purple'
-                red = 1
-                green = 2
-                blue = 3
-
-    def test_order_has_extra_members_with_aliases(self):
-        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
-            class Color(Enum):
-                _order_ = 'red green blue purple'
-                red = 1
-                green = 2
-                blue = 3
-                verde = green
-
-    def test_enum_has_extra_members(self):
-        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
-            class Color(Enum):
-                _order_ = 'red green blue'
-                red = 1
-                green = 2
-                blue = 3
-                purple = 4
-
-    def test_enum_has_extra_members_with_aliases(self):
-        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
-            class Color(Enum):
-                _order_ = 'red green blue'
-                red = 1
-                green = 2
-                blue = 3
-                purple = 4
-                verde = green
-
-    def test_same_members_flag(self):
-        class Color(Flag):
-            _order_ = 'red green blue'
-            red = 1
-            green = 2
-            blue = 4
-
-    def test_same_members_with_aliases_flag(self):
-        class Color(Flag):
-            _order_ = 'red green blue'
-            red = 1
-            green = 2
-            blue = 4
-            verde = green
-
-    def test_order_has_extra_members_flag(self):
-        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
-            class Color(Flag):
-                _order_ = 'red green blue purple'
-                red = 1
-                green = 2
-                blue = 4
-
-    def test_order_has_extra_members_with_aliases_flag(self):
-        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
-            class Color(Flag):
-                _order_ = 'red green blue purple'
-                red = 1
-                green = 2
-                blue = 4
-                verde = green
-
-    def test_enum_has_extra_members_flag(self):
-        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
-            class Color(Flag):
-                _order_ = 'red green blue'
-                red = 1
-                green = 2
-                blue = 4
-                purple = 8
-
-    def test_enum_has_extra_members_with_aliases_flag(self):
-        with self.assertRaisesRegex(TypeError, r'member order does not match _order_'):
-            class Color(Flag):
-                _order_ = 'red green blue'
-                red = 1
-                green = 2
-                blue = 4
-                purple = 8
-                verde = green
-
-
-class TestAutoValue(TestCase):
-
-    def test_bare(self):
-        #
-        class BareEnum(Enum):
-            _order_ = 'ONE TWO THREE'
-            ONE = auto()
-            TWO = auto()
-            THREE = auto()
-        self.assertEqual(BareEnum.THREE.value, 3)
-        #
-        class BareIntEnum(IntEnum):
-            _order_ = 'ONE TWO THREE'
-            ONE = auto()
-            TWO = auto()
-            THREE = auto()
-        self.assertEqual(BareIntEnum.THREE, 3)
-        #
-        class BareFlag(Flag):
-            _order_ = 'ONE TWO THREE'
-            ONE = auto()
-            TWO = auto()
-            THREE = auto()
-        self.assertEqual(BareFlag.THREE.value, 4)
-        #
-        class BareIntFlag(IntFlag):
-            _order_ = 'ONE TWO THREE'
-            ONE = auto()
-            TWO = auto()
-            THREE = auto()
-        self.assertEqual(BareIntFlag.THREE, 4)
-
-    def test_init_only_final(self):
-        #
-        class InitEnumValue(Enum):
-            _init_ = 'value description'
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitEnumValue.THREE.value, 3)
-        self.assertEqual(InitEnumValue.THREE.description, 'a triangle')
-        #
-        class InitEnum(Enum):
-            _init_ = 'value description'
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitEnum.THREE.value, 3)
-        self.assertEqual(InitEnum.THREE.description, 'a triangle')
-        #
-        class InitIntEnum(IntEnum):
-            _init_ = 'value description'
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitIntEnum.THREE, 3)
-        self.assertEqual(InitIntEnum.THREE.description, 'a triangle')
-        #
-        class InitFlag(Flag):
-            _init_ = 'value description'
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitFlag.THREE.value, 4)
-        self.assertEqual(InitFlag.THREE.description, 'a triangle')
-        #
-        class InitIntFlag(IntFlag):
-            _init_ = 'value description'
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitIntFlag.THREE, 4)
-        self.assertEqual(InitIntFlag.THREE.description, 'a triangle')
-
-    def test_init_only_inherit(self):
-        #
-        class InitInheritEnum(Enum):
-            _init_ = 'value description'
-        #
-        class InitEnum(InitInheritEnum):
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitEnum.THREE.value, 3)
-        self.assertEqual(InitEnum.THREE.description, 'a triangle')
-        #
-        #
-        class InitInheritValueEnum(Enum):
-            _init_ = 'value description'
-        #
-        class InitEnum(InitInheritValueEnum):
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitEnum.THREE.value, 3)
-        self.assertEqual(InitEnum.THREE.description, 'a triangle')
-        #
-        class InitIntEnum(int, InitInheritValueEnum):
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitIntEnum.THREE, 3)
-        self.assertEqual(InitIntEnum.THREE.description, 'a triangle')
-        #
-        class InitInheritValueFlag(Flag):
-            _init_ = 'value description'
-        #
-        class InitFlag(InitInheritValueFlag):
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitFlag.THREE.value, 4)
-        self.assertEqual(InitFlag.THREE.description, 'a triangle')
-        #
-        class InitIntFlag(int, InitInheritValueFlag):
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitIntFlag.THREE, 4)
-        self.assertEqual(InitIntFlag.THREE.description, 'a triangle')
-
-    def test_new_only_final(self):
-        #
-        class NewFinalEnum(Enum):
-            _order_ = 'ONE TWO THREE'
-            def __new__(cls, value):
-                member = object.__new__(cls)
-                member._value_ = value
-                member.proof = 'NFE1'
-                return member
-            ONE = auto()
-            TWO = auto()
-            THREE = auto()
-        self.assertEqual(NewFinalEnum.THREE.value, 3)
-        self.assertEqual(NewFinalEnum.TWO.proof, 'NFE1')
-        #
-        class NewFinalIntEnum(IntEnum):
-            _order_ = 'ONE TWO THREE'
-            def __new__(cls, value):
-                member = int.__new__(cls, value)
-                member._value_ = value
-                member.proof = 'NFE2'
-                return member
-            ONE = auto()
-            TWO = auto()
-            THREE = auto()
-        self.assertEqual(NewFinalIntEnum.THREE, 3)
-        self.assertEqual(NewFinalIntEnum.TWO.proof, 'NFE2')
-        #
-        class NewFinalFlag(Flag):
-            _order_ = 'ONE TWO THREE'
-            def __new__(cls, value):
-                member = object.__new__(cls)
-                member._value_ = value
-                member.proof = 'NFE3'
-                return member
-            ONE = auto()
-            TWO = auto()
-            THREE = auto()
-        self.assertEqual(NewFinalFlag.THREE.value, 4)
-        self.assertEqual(NewFinalFlag.TWO.proof, 'NFE3')
-        #
-        class NewFinalIntFlag(IntFlag):
-            _order_ = 'ONE TWO THREE'
-            def __new__(cls, value):
-                member = int.__new__(cls, value)
-                member._value_ = value
-                member.proof = 'NFE4'
-                return member
-            ONE = auto()
-            TWO = auto()
-            THREE = auto()
-        self.assertEqual(NewFinalIntFlag.THREE, 4)
-        self.assertEqual(NewFinalIntFlag.TWO.proof, 'NFE4')
-        #
-        class NewFinalStrEnum(str, Enum):
-            #
-            _order_ = "AllReset Bright FG_Cyan BG_Black"
-            #
-            def __new__(cls, value, code, description):
-                str_value = '\x1b[%sm' % code
-                obj = str.__new__(cls, str_value)
-                obj._value_ = value
-                obj.code = code
-                obj.description = description
-                return obj
-            #
-            __str__ = str.__str__
-            #
-            AllReset = '0', 'reset all (colors and brightness)'
-            Bright = '1', 'bright lights!'
-            FG_Cyan = '36', 'cyan'
-            BG_Black = '40', 'black'
-        self.assertEqual(NewFinalStrEnum.FG_Cyan.value, 3)
-        self.assertEqual(NewFinalStrEnum.BG_Black.value, 4)
-        self.assertEqual(NewFinalStrEnum.AllReset.code, '0')
-        self.assertEqual(NewFinalStrEnum.Bright.description, 'bright lights!')
-        #
-        class NewFinalStrFlag(str, Flag):
-            #
-            _order_ = "AllReset Bright FG_Cyan BG_Black"
-            #
-            def __new__(cls, value, code, description):
-                str_value = '\x1b[%sm' % code
-                obj = str.__new__(cls, str_value)
-                obj._value_ = value
-                obj.code = code
-                obj.description = description
-                return obj
-            #
-            __str__ = str.__str__
-            #
-            AllReset = '0', 'reset all (colors and brightness)'
-            Bright = '1', 'bright lights!'
-            FG_Cyan = '36', 'cyan'
-            BG_Black = '40', 'black'
-        self.assertEqual(NewFinalStrFlag.FG_Cyan.value, 4)
-        self.assertEqual(NewFinalStrFlag.BG_Black.value, 8)
-        self.assertEqual(NewFinalStrFlag.AllReset.code, '0')
-        self.assertEqual(NewFinalStrFlag.Bright.description, 'bright lights!')
-
-    def test_new_only_inherited(self):
-        #
-        class NewInheritEnum(Enum):
-            def __new__(cls, value):
-                if cls._member_type_ is int:
-                    member = int.__new__(cls, value*2)
-                else:
-                    member = object.__new__(cls)
-                member._value_ = value * 2
-                member.proof = 'NIE'
-                return member
-        #
-        class NewFinalEnum(NewInheritEnum):
-            _order_ = 'ONE TWO THREE'
-            ONE = auto()
-            TWO = auto()
-            THREE = auto()
-        self.assertEqual(NewFinalEnum.THREE.value, 6)
-        self.assertEqual(NewFinalEnum.TWO.proof, 'NIE')
-        #
-        class NewFinalIntEnum(int, NewInheritEnum):
-            _order_ = 'ONE TWO THREE'
-            ONE = auto()
-            TWO = auto()
-            THREE = auto()
-        self.assertEqual(NewFinalIntEnum.THREE, 6)
-        self.assertEqual(NewFinalIntEnum.TWO.proof, 'NIE')
-        #
-        class NewInheritFlag(Flag):
-            def __new__(cls, value):
-                if cls._member_type_ is int:
-                    member = int.__new__(cls, value*2)
-                else:
-                    member = object.__new__(cls)
-                member._value_ = value * 2
-                member.proof = 'NIE'
-                return member
-        #
-        class NewFinalFlag(NewInheritFlag):
-            _order_ = 'ONE TWO THREE'
-            ONE = auto()
-            TWO = auto()
-            THREE = auto()
-        self.assertEqual(NewFinalFlag.THREE.value, 8)
-        self.assertEqual(NewFinalFlag.TWO.proof, 'NIE')
-        #
-        class NewFinalIntFlag(int, NewInheritFlag):
-            _order_ = 'ONE TWO THREE'
-            ONE = auto()
-            TWO = auto()
-            THREE = auto()
-        self.assertEqual(NewFinalIntFlag.THREE, 8)
-        self.assertEqual(NewFinalIntFlag.TWO.proof, 'NIE')
-
-    def test_init_new_only(self):
-        #
-        class InitNewEnum(Enum):
-            _init_ = "value description"
-            _order_ = 'ONE TWO THREE'
-            def __new__(cls, value, *args):
-                member = object.__new__(cls)
-                member._value_ = value
-                member.proof = 'INE1'
-                return member
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitNewEnum.THREE.value, 3)
-        self.assertEqual(InitNewEnum.THREE.description, 'a triangle')
-        self.assertEqual(InitNewEnum.TWO.proof, 'INE1')
-        #
-        class InitNewIntEnum(IntEnum):
-            _init_ = "value description"
-            _order_ = 'ONE TWO THREE'
-            def __new__(cls, value, *args):
-                member = int.__new__(cls, value)
-                member._value_ = value
-                member.proof = 'INE2'
-                return member
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitNewIntEnum.THREE, 3)
-        self.assertEqual(InitNewIntEnum.THREE.description, 'a triangle')
-        self.assertEqual(InitNewIntEnum.TWO.proof, 'INE2')
-        #
-        class InitNewFlag(Flag):
-            _init_ = "value description"
-            _order_ = 'ONE TWO THREE'
-            def __new__(cls, value, *args):
-                member = object.__new__(cls)
-                member._value_ = value
-                member.proof = 'INE3'
-                return member
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitNewFlag.THREE.value, 4)
-        self.assertEqual(InitNewFlag.THREE.description, 'a triangle')
-        self.assertEqual(InitNewFlag.TWO.proof, 'INE3')
-        #
-        class InitNewIntFlag(IntFlag):
-            _init_ = "value description"
-            _order_ = 'ONE TWO THREE'
-            def __new__(cls, value, *args):
-                member = int.__new__(cls, value)
-                member._value_ = value
-                member.proof = 'INE4'
-                return member
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitNewIntFlag.THREE, 4)
-        self.assertEqual(InitNewIntFlag.THREE.description, 'a triangle')
-        self.assertEqual(InitNewIntFlag.TWO.proof, 'INE4')
-
-    def test_init_new_inherit(self):
-        #
-        class InitNew(Enum):
-            _init_ = "value description"
-            def __new__(cls, value, *args):
-                member = object.__new__(cls)
-                member._value_ = value
-                member.proof = 'IN'
-                return member
-        #
-        class InitNewEnum(InitNew):
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitNewEnum.THREE.value, 3)
-        self.assertEqual(InitNewEnum.THREE.description, 'a triangle')
-        self.assertEqual(InitNewEnum.TWO.proof, 'IN')
-        #
-        class InitNewInt(Enum):
-            _init_ = "value description"
-            def __new__(cls, value, *args):
-                member = int.__new__(cls, value)
-                member._value_ = value
-                member.proof = 'IN'
-                return member
-        #
-        class InitNewIntEnum(int, InitNewInt):
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitNewIntEnum.THREE, 3)
-        self.assertEqual(InitNewIntEnum.THREE.description, 'a triangle')
-        self.assertEqual(InitNewIntEnum.TWO.proof, 'IN')
-        #
-        class InitNewFlagBase(Flag):
-            _init_ = "value description"
-            def __new__(cls, value, *args):
-                member = object.__new__(cls)
-                member._value_ = value
-                member.proof = 'IN'
-                return member
-        #
-        class InitNewFlag(InitNewFlagBase):
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitNewFlag.THREE.value, 4)
-        self.assertEqual(InitNewFlag.THREE.description, 'a triangle')
-        self.assertEqual(InitNewFlag.TWO.proof, 'IN')
-        #
-        class InitNewIntFlagBase(int, Flag):
-            _init_ = "value description"
-            def __new__(cls, value, *args):
-                member = int.__new__(cls, value)
-                member._value_ = value
-                member.proof = 'IN'
-                return member
-        #
-        class InitNewIntFlag(InitNewIntFlagBase):
-            _order_ = 'ONE TWO THREE'
-            ONE = 'the loneliest number'
-            TWO = 'the number with you'
-            THREE = 'a triangle'
-        self.assertEqual(InitNewIntFlag.THREE, 4)
-        self.assertEqual(InitNewIntFlag.THREE.description, 'a triangle')
-        self.assertEqual(InitNewIntFlag.TWO.proof, 'IN')
-
-
-class TestHelpers(TestCase):
-    # _is_descriptor, _is_sunder, _is_dunder
-
-    def test_is_descriptor(self):
-        class foo:
-            pass
-        for attr in ('__get__','__set__','__delete__'):
-            obj = foo()
-            self.assertFalse(aenum._is_descriptor(obj))
-            setattr(obj, attr, 1)
-            self.assertTrue(aenum._is_descriptor(obj))
-
-    def test_is_sunder(self):
-        for s in ('_a_', '_aa_'):
-            self.assertTrue(aenum._is_sunder(s))
-
-        for s in ('a', 'a_', '_a', '__a', 'a__', '__a__', '_a__', '__a_', '_',
-                '__', '___', '____', '_____',):
-            self.assertFalse(aenum._is_sunder(s))
-
-    def test_is_dunder(self):
-        for s in ('__a__', '__aa__'):
-            self.assertTrue(aenum._is_dunder(s))
-        for s in ('a', 'a_', '_a', '__a', 'a__', '_a_', '_a__', '__a_', '_',
-                '__', '___', '____', '_____',):
-            self.assertFalse(aenum._is_dunder(s))
-
-    def test_auto(self):
-        def tester(first, op, final, second=None):
-            if second is None:
-                left = auto()
-                value = op(left)
-                left.value = first
-                self.assertEqual(value.value, final,
-                        "%s %r -> %r != %r" % (op.__name__, first, value, final))
-            else:
-                left = first
-                right = auto()
-                value = op(left, right)
-                right.value = second
-                self.assertEqual(value.value, final,
-                        "forward: %r %s %r -> %r != %r" % (first, op.__name__, second, value.value, final))
-                left = auto()
-                right = second
-                value = op(left, right)
-                left.value = first
-                self.assertEqual(value.value, final,
-                        "reversed: %r %s %r -> %r != %r" % (second, op.__name__, first, value.value, final))
-        for args in (
-                (1, _abs_, abs(1)),
-                (-3, _abs_, abs(-3)),
-                (1, _add_, 1+2, 2),
-                (25, _floordiv_, 25 // 5, 5),
-                (49, _truediv_, 49 / 9, 9),
-                (6, _mod_, 6 % 9, 9),
-                (5, _lshift_, 5 << 2, 2),
-                (5, _rshift_, 5 >> 2, 2),
-                (3, _mul_, 3 * 6, 6),
-                (5, _neg_, -5),
-                (-4, _pos_, +(-4)),
-                (2, _pow_, 2**5, 5),
-                (7, _sub_, 7 - 10, 10),
-                (1, _or_, 1 | 2, 2),
-                (3, _xor_, 3 ^ 6, 6),
-                (3, _and_, 3 & 6, 6),
-                (7, _inv_, ~7),
-                ('a', _add_, 'a'+'b', 'b'),
-                ('a', _mul_, 'a' * 3, 3),
-                ):
-            tester(*args)
-        # operator.div is gone in 3
-        if PY2:
-            tester(12, _div_, 12 // 5, 5)
-        # strings are a pain
-        left = auto()
-        right = 'eggs'
-        value = _mod_(left, right)
-        left.value = 'I see 17 %s!'
-        self.assertEqual(value.value, 'I see 17 %s!' % 'eggs')
-
-    def test_constant(self):
-        errors = []
-        def tester(first, op, final, second=None):
-            if second is None:
-                primary = constant(first)
-                secondary = constant(op(primary))
-                if secondary.value != final:
-                    errors.append(
-                        "%s %r -> %r != %r" % (op.__name__, first, secondary.value, final),
-                        )
-            else:
-                left = constant(first)
-                right = second
-                value = op(left, right)
-                if value != final:
-                    errors.append(
-                        "forward: %r %s %r -> %r != %r" % (first, op.__name__, second, value, final),
-                        )
-                left = first
-                right = constant(second)
-                value = op(left, right)
-                if value != final:
-                    errors.append(
-                        "reversed: %r %s %r -> %r != %r" % (second, op.__name__, first, value, final),
-                        )
-        for args in (
-                (1, _abs_, abs(1)),
-                (-3, _abs_, abs(-3)),
-                (1, _add_, 1+2, 2),
-                (25, _floordiv_, 25 // 5, 5),
-                (49, _truediv_, 49 / 9, 9),
-                (6, _mod_, 6 % 9, 9),
-                (5, _lshift_, 5 << 2, 2),
-                (5, _rshift_, 5 >> 2, 2),
-                (3, _mul_, 3 * 6, 6),
-                (5, _neg_, -5),
-                (-4, _pos_, +(-4)),
-                (2, _pow_, 2**5, 5),
-                (7, _sub_, 7 - 10, 10),
-                (1, _or_, 1 | 2, 2),
-                (3, _xor_, 3 ^ 6, 6),
-                (3, _and_, 3 & 6, 6),
-                (7, _inv_, ~7),
-                ('a', _add_, 'a'+'b', 'b'),
-                ('a', _mul_, 'a' * 3, 3),
-                ):
-            tester(*args)
-        # operator.div is gone in 3
-        if PY2:
-            tester(12, _div_, 12 // 5, 5)
-        # strings are a pain
-        left = constant('I see 17 %s!')
-        right = 'eggs'
-        value = _mod_(left, right)
-        if value != 'I see 17 %s!' % 'eggs':
-            errors.append("'I see 17 eggs!' != %r" % value)
-        if errors:
-            print()
-            for error in errors:
-                print(error)
-            self.assertTrue(False)
-
-
-class TestEnumType(TestCase):
-
-    def test_immutability(self):
-        class Hah(object):
-            @classproperty
-            def all_values(cls):
-                return [m.value for m in cls]
-        class Huh(Hah, Enum):
-            one = 1
-            two = 2
-        self.assertRaisesRegex(AttributeError, 'cannot rebind property', setattr, Huh, 'value', 'boom')
-        self.assertRaisesRegex(AttributeError, 'cannot delete property', delattr, Huh, 'value')
-        self.assertRaisesRegex(AttributeError, 'cannot set attribute', setattr, Huh.one, 'value', 'boom')
-        self.assertRaisesRegex(AttributeError, 'cannot delete attribute', delattr, Huh.two, 'value')
-        self.assertEqual(Huh.one.value, 1)
-        self.assertEqual(Huh.two.value, 2)
-        self.assertEqual(Huh.all_values, [1, 2])
-        setattr(Huh, 'all_values', 99)
-        self.assertEqual(Huh.all_values, 99)
-
-    def test_enum_shadow_base(self):
-        class hohum(object):
-            def cyan(self):
-                "cyanize a color"
-                return self.value * 'cyan'
-            @property
-            def azure(self):
-                return 'azure ' + self.name
-        class Color(hohum, Enum):
-            red = 1
-            green = 2
-            blue = 3
-            cyan = 4
-            azure = 5
-        self.assertEqual(len(Color), 5)
-        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue, Color.cyan, Color.azure])
-        self.assertRaisesRegex(AttributeError, 'no attribute .cyan.', lambda: Color.blue.cyan)
-        self.assertEqual(Color.red.azure, 'azure red')
-
-
-class TestEnum(TestCase):
-
-    def setUp(self):
-        class Season(Enum):
-            SPRING = 1
-            SUMMER = 2
-            AUTUMN = 3
-            WINTER = 4
-        self.Season = Season
-
-        class Konstants(float, Enum):
-            E = 2.7182818
-            PI = 3.1415926
-            TAU = 2 * PI
-        self.Konstants = Konstants
-
-        class Grades(IntEnum):
-            A = 5
-            B = 4
-            C = 3
-            D = 2
-            F = 0
-        self.Grades = Grades
-
-        class Directional(str, Enum):
-            EAST = 'east'
-            WEST = 'west'
-            NORTH = 'north'
-            SOUTH = 'south'
-        self.Directional = Directional
-
-        from datetime import date
-        class Holiday(date, Enum):
-            NEW_YEAR = 2013, 1, 1
-            IDES_OF_MARCH = 2013, 3, 15
-        self.Holiday = Holiday
-
-    def test_set_name(self):
-        class Descriptor(object):
-            name = None
-            def __get__(self, instance, owner_class=None):
-                if instance is None:
-                    return self
-                else:
-                    return instance.__dict__[self.name]
-            def __set__(self, instance, value):
-                instance.__dict__[self.name] = value
-            def __set_name__(self, owner, name):
-                self.name = name
-        #
-        class AnEnum(Enum):
-            ONE = 'one'
-            two = Descriptor()
-        #
-        self.assertEqual(list(AnEnum), [AnEnum.ONE])
-        self.assertEqual(AnEnum.two.name, 'two')
-        AnEnum.ONE.two = 'three'
-        self.assertEqual(AnEnum.ONE.two, 'three')
-        self.assertEqual(AnEnum.ONE.__dict__['two'], 'three')
-
-    def test_private_names(self):
-        class Private(Enum):
-            __corporal = 'Radar'
-            __major_ = 'Hoolihan'
-        self.assertEqual(len(Private), 0)
-        self.assertEqual(Private._Private__corporal, 'Radar')
-        self.assertFalse(isinstance(Private._Private__corporal, Enum))
-        self.assertEqual(Private._Private__major_, 'Hoolihan')
-        self.assertFalse(isinstance(Private._Private__major_, Enum))
-
-    def test_new_with_keywords(self):
-        class Huh(IntEnum):
-            __order__ = 'PLAIN BOLD_ITALIC HIGHLIGHT'
-            def __new__(cls, docstring, open=None, close=None):
-                value = len(cls.__members__)
-                member = int.__new__(cls, value)
-                if open and close is None:
-                    close = open
-                member.open = open
-                member.close = close
-                member.__doc__ = docstring
-                member._value_ = value
-                return member
-            PLAIN           = 'normal'
-            BOLD_ITALIC     = '***really super important***', '***'
-            HIGHLIGHT       = 'please ==take notice==', '==', '=='
-        p = Huh.PLAIN
-        self.assertTrue(type(p) is Huh, type(p))
-        self.assertEqual(
-                (p.value, p.__doc__, p.open, p.close),
-                (0, 'normal', None, None),
-                )
-        bi = Huh.BOLD_ITALIC
-        self.assertEqual(
-                (bi.value, bi.__doc__, bi.open, bi.close),
-                (1, '***really super important***', '***', '***'),
-                )
-        h = Huh.HIGHLIGHT
-        self.assertEqual(
-                (h.value, h.__doc__, h.open, h.close),
-                (2, 'please ==take notice==', '==', '=='),
-                )
-
-    def test_members_is_ordereddict_if_ordered(self):
-        class Ordered(Enum):
-            __order__ = 'first second third'
-            first = 'bippity'
-            second = 'boppity'
-            third = 'boo'
-        self.assertTrue(type(Ordered.__members__) is OrderedDict)
-
-    def test_members_is_ordereddict_if_not_ordered(self):
-        class Unordered(Enum):
-            this = 'that'
-            these = 'those'
-        self.assertTrue(type(Unordered.__members__) is OrderedDict)
-
-    def test_enum_in_enum_out(self):
-        Season = self.Season
-        self.assertTrue(Season(Season.WINTER) is Season.WINTER)
-
-    def test_enum_value(self):
-        Season = self.Season
-        self.assertEqual(Season.SPRING.value, 1)
-
-    def test_intenum_value(self):
-        self.assertEqual(IntStooges.CURLY.value, 2)
-
-    def test_enum(self):
-        Season = self.Season
-        lst = list(Season)
-        self.assertEqual(len(lst), len(Season))
-        self.assertEqual(len(Season), 4, Season)
-        self.assertEqual(
-            [Season.SPRING, Season.SUMMER, Season.AUTUMN, Season.WINTER], lst)
-
-        for i, season in enumerate('SPRING SUMMER AUTUMN WINTER'.split()):
-            i += 1
-            e = Season(i)
-            self.assertEqual(e, getattr(Season, season))
-            self.assertEqual(e.value, i)
-            self.assertNotEqual(e, i)
-            self.assertEqual(e.name, season)
-            self.assertTrue(e in Season)
-            self.assertTrue(type(e) is Season)
-            self.assertTrue(isinstance(e, Season))
-            self.assertEqual(str(e), 'Season.' + season)
-            self.assertEqual(
-                    repr(e),
-                    '<Season.%s: %s>' % (season, i),
-                    )
-    def test_enum_helper(self):
-        e1 = enum(1, 2, three=9)
-        e2 = enum(1, 2, three=9)
-        e3 = enum(1, 2, 9)
-        self.assertTrue(e1 is not e2)
-        self.assertEqual(e1, e2)
-        self.assertNotEqual(e1, e3)
-        self.assertNotEqual(e2, e3)
-
-    def test_enum_in_enum(self):
-        #
-        class Level(Enum):
-            _order_ = 'DATA_CHECK DESIGN_CHECK ALERT'
-            #
-            def __new__(cls, *args, **kwds):
-                member = object.__new__(cls)
-                member._value_ = len(cls) + 1  # members are 1-based
-                return member
-            #
-            def __init__(self, prereq=None, dependent=None):
-                # create priority level lists
-                self.lower_priority_levels = list(self.__class__._member_map_.values())
-                self.greater_priority_levels = []
-                # update previous members' greater priority list
-                for member in self.lower_priority_levels:
-                    member.greater_priority_levels.append(self)
-                # and save prereq and dependent
-                self.prerequisite = prereq and self.__class__[prereq.name] or None
-                self.dependent = dependent and self.__class__[dependent.name] or None
-            #
-            DATA_CHECK = enum()
-            DESIGN_CHECK = enum(DATA_CHECK)
-            ALERT = enum(None, DATA_CHECK)
-        #
-        self.assertEqual(Level.DATA_CHECK.value, 1)
-        self.assertEqual(Level.DATA_CHECK.prerequisite, None)
-        self.assertEqual(Level.DATA_CHECK.dependent, None)
-        self.assertEqual(Level.DESIGN_CHECK.prerequisite, Level.DATA_CHECK)
-        self.assertEqual(Level.DESIGN_CHECK.dependent, None)
-        self.assertEqual(Level.ALERT.prerequisite, None)
-        self.assertEqual(Level.ALERT.dependent, Level.DATA_CHECK)
-
-    def test_value_name(self):
-        Season = self.Season
-        self.assertEqual(Season.SPRING.name, 'SPRING')
-        self.assertEqual(Season.SPRING.value, 1)
-        def set_name(obj, new_value):
-            obj.name = new_value
-        def set_value(obj, new_value):
-            obj.value = new_value
-        self.assertRaises(AttributeError, set_name, Season.SPRING, 'invierno', )
-        self.assertRaises(AttributeError, set_value, Season.SPRING, 2)
-
-    def test_attribute_deletion(self):
-        class Season(Enum):
-            SPRING = 1
-            SUMMER = 2
-            AUTUMN = 3
-            WINTER = 4
-
-            def spam(cls):
-                pass
-
-        self.assertTrue(hasattr(Season, 'spam'))
-        del Season.spam
-        self.assertFalse(hasattr(Season, 'spam'))
-
-        self.assertRaises(AttributeError, delattr, Season, 'SPRING')
-        self.assertRaises(AttributeError, delattr, Season, 'DRY')
-        self.assertRaises(AttributeError, delattr, Season.SPRING, 'name')
-
-    def test_bool_of_class(self):
-        class Empty(Enum):
-            pass
-        self.assertTrue(bool(Empty))
-
-    def test_bool_of_member(self):
-        class Count(Enum):
-            zero = 0
-            one = 1
-            two = 2
-        for member in Count:
-            self.assertTrue(bool(member))
-
-    def test_invalid_names(self):
-        def create_bad_class_1():
-            class Wrong(Enum):
-                mro = 9
-        def create_bad_class_2():
-            class Wrong(Enum):
-                _reserved_ = 3
-        self.assertRaises(ValueError, create_bad_class_1)
-        self.assertRaises(ValueError, create_bad_class_2)
-
-    def test_bool(self):
-        class Logic(Enum):
-            true = True
-            false = False
-            def __bool__(self):
-                return bool(self.value)
-            __nonzero__ = __bool__
-        self.assertTrue(Logic.true)
-        self.assertFalse(Logic.false)
-
-    def test_contains(self):
-        Season = self.Season
-        self.assertRaises(TypeError, lambda: 'AUTUMN' in Season)
-        self.assertTrue(Season.AUTUMN in Season)
-        self.assertRaises(TypeError, lambda: 3 not in Season)
-        val = Season(3)
-        self.assertTrue(val in Season)
-        #
-        class OtherEnum(Enum):
-            one = 1; two = 2
-        self.assertTrue(OtherEnum.two not in Season)
-        #
-        class Wierd(Enum):
-            this = [1, 2, 3]
-            that = (1, 2, 3)
-            those = {1: 1, 2: 2, 3: 3}
-        self.assertTrue(Wierd.this in Wierd)
-        self.assertRaises(TypeError, lambda: [1, 2, 3] in Wierd)
-        self.assertRaises(TypeError, lambda: {1: 1, 2: 2, 3: 3} in Wierd)
-
-    def test_member_contains(self):
-        self.assertRaises(TypeError, lambda: 'test' in self.Season.AUTUMN)
-
-    if pyver >= PY2_6:     # when `format` came into being
-
-        def test_format_enum(self):
-            Season = self.Season
-            self.assertEqual('{0}'.format(Season.SPRING),
-                             '{0}'.format(str(Season.SPRING)))
-            self.assertEqual( '{0:}'.format(Season.SPRING),
-                              '{0:}'.format(str(Season.SPRING)))
-            self.assertEqual('{0:20}'.format(Season.SPRING),
-                             '{0:20}'.format(str(Season.SPRING)))
-            self.assertEqual('{0:^20}'.format(Season.SPRING),
-                             '{0:^20}'.format(str(Season.SPRING)))
-            self.assertEqual('{0:>20}'.format(Season.SPRING),
-                             '{0:>20}'.format(str(Season.SPRING)))
-            self.assertEqual('{0:<20}'.format(Season.SPRING),
-                             '{0:<20}'.format(str(Season.SPRING)))
-
-        def test_custom_format(self):
-            class TestFloat(float, Enum):
-                one = 1.0
-                two = 2.0
-                def __format__(self, spec):
-                    return 'TestFloat success!'
-            self.assertEqual(str(TestFloat.one), 'TestFloat.one')
-            self.assertEqual('{0}'.format(TestFloat.one), 'TestFloat success!')
-
-        def test_format_with_custom_str(self):
-            class TestInt(int, Enum):
-                one = 1
-                two = 2
-                def __str__(self):
-                    return self.name * 3
-            self.assertEqual(str(TestInt.two), 'twotwotwo')
-            self.assertEqual('{0}'.format(TestInt.two), 'twotwotwo')
-
-        def assertFormatIsValue(self, spec, member):
-            self.assertEqual(spec.format(member), spec.format(member.value))
-
-        def test_format_enum_date(self):
-            Holiday = self.Holiday
-            self.assertFormatIsValue('{0}', Holiday.IDES_OF_MARCH)
-            self.assertFormatIsValue('{0:}', Holiday.IDES_OF_MARCH)
-            self.assertFormatIsValue('{0:20}', Holiday.IDES_OF_MARCH)
-            self.assertFormatIsValue('{0:^20}', Holiday.IDES_OF_MARCH)
-            self.assertFormatIsValue('{0:>20}', Holiday.IDES_OF_MARCH)
-            self.assertFormatIsValue('{0:<20}', Holiday.IDES_OF_MARCH)
-            self.assertFormatIsValue('{0:%Y %m}', Holiday.IDES_OF_MARCH)
-            self.assertFormatIsValue('{0:%Y %m %M:00}', Holiday.IDES_OF_MARCH)
-
-        def test_format_enum_float(self):
-            Konstants = self.Konstants
-            self.assertFormatIsValue('{0}', Konstants.TAU)
-            self.assertFormatIsValue('{0:}', Konstants.TAU)
-            self.assertFormatIsValue('{0:20}', Konstants.TAU)
-            self.assertFormatIsValue('{0:^20}', Konstants.TAU)
-            self.assertFormatIsValue('{0:>20}', Konstants.TAU)
-            self.assertFormatIsValue('{0:<20}', Konstants.TAU)
-            self.assertFormatIsValue('{0:n}', Konstants.TAU)
-            self.assertFormatIsValue('{0:5.2}', Konstants.TAU)
-            self.assertFormatIsValue('{0:f}', Konstants.TAU)
-
-        def test_format_enum_int(self):
-            Grades = self.Grades
-            self.assertFormatIsValue('{0}', Grades.C)
-            self.assertFormatIsValue('{0:}', Grades.C)
-            self.assertFormatIsValue('{0:20}', Grades.C)
-            self.assertFormatIsValue('{0:^20}', Grades.C)
-            self.assertFormatIsValue('{0:>20}', Grades.C)
-            self.assertFormatIsValue('{0:<20}', Grades.C)
-            self.assertFormatIsValue('{0:+}', Grades.C)
-            self.assertFormatIsValue('{0:08X}', Grades.C)
-            self.assertFormatIsValue('{0:b}', Grades.C)
-
-        def test_format_enum_str(self):
-            Directional = self.Directional
-            self.assertFormatIsValue('{0}', Directional.WEST)
-            self.assertFormatIsValue('{0:}', Directional.WEST)
-            self.assertFormatIsValue('{0:20}', Directional.WEST)
-            self.assertFormatIsValue('{0:^20}', Directional.WEST)
-            self.assertFormatIsValue('{0:>20}', Directional.WEST)
-            self.assertFormatIsValue('{0:<20}', Directional.WEST)
-
-    def test_hash(self):
-        Season = self.Season
-        dates = {}
-        dates[Season.WINTER] = '1225'
-        dates[Season.SPRING] = '0315'
-        dates[Season.SUMMER] = '0704'
-        dates[Season.AUTUMN] = '1031'
-        self.assertEqual(dates[Season.AUTUMN], '1031')
-
-    def test_enum_duplicates(self):
-        class Season(Enum):
-            __order__ = "SPRING SUMMER AUTUMN WINTER"
-            SPRING = 1
-            SUMMER = 2
-            AUTUMN = FALL = 3
-            WINTER = 4
-            ANOTHER_SPRING = 1
-        lst = list(Season)
-        self.assertEqual(
-            lst,
-            [Season.SPRING, Season.SUMMER,
-             Season.AUTUMN, Season.WINTER,
-            ])
-        self.assertTrue(Season.FALL is Season.AUTUMN)
-        self.assertEqual(Season.FALL.value, 3)
-        self.assertEqual(Season.AUTUMN.value, 3)
-        self.assertTrue(Season(3) is Season.AUTUMN)
-        self.assertTrue(Season(1) is Season.SPRING)
-        self.assertEqual(Season.FALL.name, 'AUTUMN')
-        self.assertEqual(
-                set([k for k,v in Season.__members__.items() if v.name != k]),
-                set(['FALL', 'ANOTHER_SPRING']),
-                )
-
-    def test_enum_with_value_name(self):
-        class Huh(Enum):
-            _order_ = 'name value'
-            name = 1
-            value = 2
-        self.assertEqual(
-            list(Huh),
-            [Huh.name, Huh.value],
-            )
-        self.assertTrue(type(Huh.name) is Huh)
-        self.assertEqual(Huh.name.name, 'name')
-        self.assertEqual(Huh.name.value, 1)
-
-    def test_intenum_from_scratch(self):
-        class phy(int, Enum):
-            pi = 3
-            tau = 2 * pi
-        self.assertTrue(phy.pi < phy.tau)
-
-    def test_intenum_inherited(self):
-        class IntEnum(int, Enum):
-            pass
-        class phy(IntEnum):
-            pi = 3
-            tau = 2 * pi
-        self.assertTrue(phy.pi < phy.tau)
-
-    def test_floatenum_from_scratch(self):
-        class phy(float, Enum):
-            pi = 3.1415926
-            tau = 2 * pi
-        self.assertTrue(phy.pi < phy.tau)
-
-    def test_floatenum_inherited(self):
-        class FloatEnum(float, Enum):
-            pass
-        class phy(FloatEnum):
-            pi = 3.1415926
-            tau = 2 * pi
-        self.assertTrue(phy.pi < phy.tau)
-
-    def test_strenum_from_scratch(self):
-        class phy(str, Enum):
-            pi = 'Pi'
-            tau = 'Tau'
-        self.assertTrue(phy.pi < phy.tau)
-
-    def test_intenum(self):
-        class WeekDay(IntEnum):
-            SUNDAY = 1
-            MONDAY = 2
-            TUESDAY = 3
-            WEDNESDAY = 4
-            THURSDAY = 5
-            FRIDAY = 6
-            SATURDAY = 7
-
-        self.assertEqual(['a', 'b', 'c'][WeekDay.MONDAY], 'c')
-        self.assertEqual([i for i in range(WeekDay.TUESDAY)], [0, 1, 2])
-
-        lst = list(WeekDay)
-        self.assertEqual(len(lst), len(WeekDay))
-        self.assertEqual(len(WeekDay), 7)
-        target = 'SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY'
-        target = target.split()
-        for i, weekday in enumerate(target):
-            i += 1
-            e = WeekDay(i)
-            self.assertEqual(e, i)
-            self.assertEqual(int(e), i)
-            self.assertEqual(e.name, weekday)
-            self.assertTrue(e in WeekDay)
-            self.assertEqual(lst.index(e)+1, i)
-            self.assertTrue(0 < e < 8)
-            self.assertTrue(type(e) is WeekDay)
-            self.assertTrue(isinstance(e, int))
-            self.assertTrue(isinstance(e, Enum))
-
-    def test_intenum_duplicates(self):
-        class WeekDay(IntEnum):
-            __order__ = 'SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY'
-            SUNDAY = 1
-            MONDAY = 2
-            TUESDAY = TEUSDAY = 3
-            WEDNESDAY = 4
-            THURSDAY = 5
-            FRIDAY = 6
-            SATURDAY = 7
-        self.assertTrue(WeekDay.TEUSDAY is WeekDay.TUESDAY)
-        self.assertEqual(WeekDay(3).name, 'TUESDAY')
-        self.assertEqual([k for k,v in WeekDay.__members__.items()
-                if v.name != k], ['TEUSDAY', ])
-
-    def test_floatenum_fromhex(self):
-        h = float.hex(FloatStooges.MOE.value)
-        self.assertIs(FloatStooges.fromhex(h), FloatStooges.MOE)
-        h = float.hex(FloatStooges.MOE.value + 0.01)
-        with self.assertRaises(ValueError):
-            FloatStooges.fromhex(h)
-
-    def test_pickle_enum(self):
-        if isinstance(Stooges, Exception):
-            raise Stooges
-        test_pickle_dump_load(self.assertTrue, Stooges.CURLY)
-        test_pickle_dump_load(self.assertTrue, Stooges)
-
-    def test_pickle_int(self):
-        if isinstance(IntStooges, Exception):
-            raise IntStooges
-        test_pickle_dump_load(self.assertTrue, IntStooges.CURLY)
-        test_pickle_dump_load(self.assertTrue, IntStooges)
-
-    def test_pickle_float(self):
-        if isinstance(FloatStooges, Exception):
-            raise FloatStooges
-        test_pickle_dump_load(self.assertTrue, FloatStooges.CURLY)
-        test_pickle_dump_load(self.assertTrue, FloatStooges)
-
-    def test_pickle_enum_function(self):
-        if isinstance(Answer, Exception):
-            raise Answer
-        test_pickle_dump_load(self.assertTrue, Answer.him)
-        test_pickle_dump_load(self.assertTrue, Answer)
-
-    def test_pickle_enum_function_with_module(self):
-        if isinstance(Question, Exception):
-            raise Question
-        test_pickle_dump_load(self.assertTrue, Question.who)
-        test_pickle_dump_load(self.assertTrue, Question)
-
-    def test_pickle_by_name(self):
-        class ReplaceGlobalInt(IntEnum):
-            ONE = 1
-            TWO = 2
-        ReplaceGlobalInt.__reduce_ex__ = _reduce_ex_by_name
-        for proto in range(HIGHEST_PROTOCOL):
-            self.assertEqual(ReplaceGlobalInt.TWO.__reduce_ex__(proto), 'TWO')
-
-    def test_exploding_pickle(self):
-        BadPickle = Enum('BadPickle', 'dill sweet bread-n-butter')
-        aenum._make_class_unpicklable(BadPickle)
-        globals()['BadPickle'] = BadPickle
-        test_pickle_exception(self.assertRaises, TypeError, BadPickle.dill)
-        test_pickle_exception(self.assertRaises, PicklingError, BadPickle)
-
-    def test_string_enum(self):
-        class SkillLevel(str, Enum):
-            master = 'what is the sound of one hand clapping?'
-            journeyman = 'why did the chicken cross the road?'
-            apprentice = 'knock, knock!'
-        self.assertEqual(SkillLevel.apprentice, 'knock, knock!')
-
-    def test_getattr_getitem(self):
-        class Period(Enum):
-            morning = 1
-            noon = 2
-            evening = 3
-            night = 4
-        self.assertTrue(Period(2) is Period.noon)
-        self.assertTrue(getattr(Period, 'night') is Period.night)
-        self.assertTrue(Period['morning'] is Period.morning)
-
-    def test_getattr_dunder(self):
-        Season = self.Season
-        self.assertTrue(getattr(Season, '__hash__'))
-
-    def test_iteration_order(self):
-        class Season(Enum):
-            __order__ = 'SUMMER WINTER AUTUMN SPRING'
-            SUMMER = 2
-            WINTER = 4
-            AUTUMN = 3
-            SPRING = 1
-        self.assertEqual(
-                list(Season),
-                [Season.SUMMER, Season.WINTER, Season.AUTUMN, Season.SPRING],
-                )
-
-    def test_iteration_order_reversed(self):
-        self.assertEqual(
-                list(reversed(self.Season)),
-                [self.Season.WINTER, self.Season.AUTUMN, self.Season.SUMMER,
-                 self.Season.SPRING]
-                )
-
-    def test_iteration_order_with_unorderable_values(self):
-        class Complex(Enum):
-            a = complex(7, 9)
-            b = complex(3.14, 2)
-            c = complex(1, -1)
-            d = complex(-77, 32)
-        self.assertEqual(
-                list(Complex),
-                [Complex.a, Complex.b, Complex.c, Complex.d],
-                )
-
-    def test_programatic_function_string(self):
-        SummerMonth = Enum('SummerMonth', 'june july august')
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate('june july august'.split()):
-            i += 1
-            e = SummerMonth(i)
-            self.assertEqual(int(e.value), i)
-            self.assertNotEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_string_with_start(self):
-        SummerMonth = Enum('SummerMonth', 'june july august', start=10)
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate('june july august'.split(), 10):
-            e = SummerMonth(i)
-            self.assertEqual(int(e.value), i)
-            self.assertNotEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_string_list(self):
-        SummerMonth = Enum('SummerMonth', ['june', 'july', 'august'])
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate('june july august'.split()):
-            i += 1
-            e = SummerMonth(i)
-            self.assertEqual(int(e.value), i)
-            self.assertNotEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_string_list_with_start(self):
-        SummerMonth = Enum('SummerMonth', ['june', 'july', 'august'], start=20)
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate('june july august'.split(), 20):
-            e = SummerMonth(i)
-            self.assertEqual(int(e.value), i)
-            self.assertNotEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_iterable(self):
-        SummerMonth = Enum(
-                'SummerMonth',
-                (('june', 1), ('july', 2), ('august', 3))
-                )
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate('june july august'.split()):
-            i += 1
-            e = SummerMonth(i)
-            self.assertEqual(int(e.value), i)
-            self.assertNotEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_from_dict(self):
-        SummerMonth = Enum(
-                'SummerMonth',
-                dict((('june', 1), ('july', 2), ('august', 3)))
-                )
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        if PY2:
-            self.assertEqual(
-                    [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                    lst,
-                    )
-        for i, month in enumerate('june july august'.split()):
-            i += 1
-            e = SummerMonth(i)
-            self.assertEqual(int(e.value), i)
-            self.assertNotEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_type(self):
-        SummerMonth = Enum('SummerMonth', 'june july august', type=int)
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate('june july august'.split()):
-            i += 1
-            e = SummerMonth(i)
-            self.assertEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_type_with_start(self):
-        SummerMonth = Enum('SummerMonth', 'june july august', type=int, start=30)
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate('june july august'.split(), 30):
-            e = SummerMonth(i)
-            self.assertEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_type_from_subclass(self):
-        SummerMonth = IntEnum('SummerMonth', 'june july august')
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate('june july august'.split()):
-            i += 1
-            e = SummerMonth(i)
-            self.assertEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_type_from_subclass_with_start(self):
-        SummerMonth = IntEnum('SummerMonth', 'june july august', start=40)
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate('june july august'.split(), 40):
-            e = SummerMonth(i)
-            self.assertEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_unicode(self):
-        SummerMonth = Enum('SummerMonth', unicode('june july august'))
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate(unicode('june july august').split()):
-            i += 1
-            e = SummerMonth(i)
-            self.assertEqual(int(e.value), i)
-            self.assertNotEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_unicode_list(self):
-        SummerMonth = Enum('SummerMonth', [unicode('june'), unicode('july'), unicode('august')])
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate(unicode('june july august').split()):
-            i += 1
-            e = SummerMonth(i)
-            self.assertEqual(int(e.value), i)
-            self.assertNotEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_unicode_iterable(self):
-        SummerMonth = Enum(
-                'SummerMonth',
-                ((unicode('june'), 1), (unicode('july'), 2), (unicode('august'), 3))
-                )
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate(unicode('june july august').split()):
-            i += 1
-            e = SummerMonth(i)
-            self.assertEqual(int(e.value), i)
-            self.assertNotEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_from_unicode_dict(self):
-        SummerMonth = Enum(
-                'SummerMonth',
-                dict(((unicode('june'), 1), (unicode('july'), 2), (unicode('august'), 3)))
-                )
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        if PY2:
-            self.assertEqual(
-                    [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                    lst,
-                    )
-        for i, month in enumerate(unicode('june july august').split()):
-            i += 1
-            e = SummerMonth(i)
-            self.assertEqual(int(e.value), i)
-            self.assertNotEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_unicode_type(self):
-        SummerMonth = Enum('SummerMonth', unicode('june july august'), type=int)
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate(unicode('june july august').split()):
-            i += 1
-            e = SummerMonth(i)
-            self.assertEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programatic_function_unicode_type_from_subclass(self):
-        SummerMonth = IntEnum('SummerMonth', unicode('june july august'))
-        lst = list(SummerMonth)
-        self.assertEqual(len(lst), len(SummerMonth))
-        self.assertEqual(len(SummerMonth), 3, SummerMonth)
-        self.assertEqual(
-                [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                lst,
-                )
-        for i, month in enumerate(unicode('june july august').split()):
-            i += 1
-            e = SummerMonth(i)
-            self.assertEqual(e, i)
-            self.assertEqual(e.name, month)
-            self.assertTrue(e in SummerMonth)
-            self.assertTrue(type(e) is SummerMonth)
-
-    def test_programmatic_function_unicode_class(self):
-        if PY2:
-            class_names = unicode('SummerMonth'), 'S\xfcmm\xe9rM\xf6nth'.decode('latin1')
-        else:
-            class_names = 'SummerMonth', 'S\xfcmm\xe9rM\xf6nth'
-        for i, class_name in enumerate(class_names):
-            if PY2 and i == 1:
-                self.assertRaises(TypeError, Enum, class_name, unicode('june july august'))
-            else:
-                SummerMonth = Enum(class_name, unicode('june july august'))
-                lst = list(SummerMonth)
-                self.assertEqual(len(lst), len(SummerMonth))
-                self.assertEqual(len(SummerMonth), 3, SummerMonth)
-                self.assertEqual(
-                        [SummerMonth.june, SummerMonth.july, SummerMonth.august],
-                        lst,
-                        )
-                for i, month in enumerate(unicode('june july august').split()):
-                    i += 1
-                    e = SummerMonth(i)
-                    self.assertEqual(e.value, i)
-                    self.assertEqual(e.name, month)
-                    self.assertTrue(e in SummerMonth)
-                    self.assertTrue(type(e) is SummerMonth)
-
-    def test_subclassing(self):
-        if isinstance(Name, Exception):
-            raise Name
-        self.assertEqual(Name.BDFL, 'Guido van Rossum')
-        self.assertTrue(Name.BDFL, Name('Guido van Rossum'))
-        self.assertTrue(Name.BDFL is getattr(Name, 'BDFL'))
-        test_pickle_dump_load(self.assertTrue, Name.BDFL)
-
-    def test_extending(self):
-        def bad_extension():
-            class Color(Enum):
-                red = 1
-                green = 2
-                blue = 3
-            class MoreColor(Color):
-                cyan = 4
-                magenta = 5
-                yellow = 6
-        self.assertRaises(TypeError, bad_extension)
-
-    def test_exclude_methods(self):
-        class whatever(Enum):
-            this = 'that'
-            these = 'those'
-            def really(self):
-                return 'no, not %s' % self.value
-        self.assertFalse(type(whatever.really) is whatever)
-        self.assertEqual(whatever.this.really(), 'no, not that')
-
-    def test_wrong_inheritance_order(self):
-        def wrong_inherit():
-            class Wrong(Enum, str):
-                NotHere = 'error before this point'
-        self.assertRaises(TypeError, wrong_inherit)
-
-    def test_intenum_transitivity(self):
-        class number(IntEnum):
-            one = 1
-            two = 2
-            three = 3
-        class numero(IntEnum):
-            uno = 1
-            dos = 2
-            tres = 3
-        self.assertEqual(number.one, numero.uno)
-        self.assertEqual(number.two, numero.dos)
-        self.assertEqual(number.three, numero.tres)
-
-    def test_introspection(self):
-        class Number(IntEnum):
-            one = 100
-            two = 200
-        self.assertTrue(Number.one._member_type_ is int)
-        self.assertTrue(Number._member_type_ is int)
-        class String(str, Enum):
-            yarn = 'soft'
-            rope = 'rough'
-            wire = 'hard'
-        self.assertTrue(String.yarn._member_type_ is str)
-        self.assertTrue(String._member_type_ is str)
-        class Plain(Enum):
-            vanilla = 'white'
-            one = 1
-        self.assertTrue(Plain.vanilla._member_type_ is object)
-        self.assertTrue(Plain._member_type_ is object)
-
-    def test_wrong_enum_in_call(self):
-        class Monochrome(Enum):
-            black = 0
-            white = 1
-        class Gender(Enum):
-            male = 0
-            female = 1
-        self.assertRaises(ValueError, Monochrome, Gender.male)
-
-    def test_wrong_enum_in_mixed_call(self):
-        class Monochrome(IntEnum):
-            black = 0
-            white = 1
-        class Gender(Enum):
-            male = 0
-            female = 1
-        self.assertRaises(ValueError, Monochrome, Gender.male)
-
-    def test_mixed_enum_in_call_1(self):
-        class Monochrome(IntEnum):
-            black = 0
-            white = 1
-        class Gender(IntEnum):
-            male = 0
-            female = 1
-        self.assertTrue(Monochrome(Gender.female) is Monochrome.white)
-
-    def test_mixed_enum_in_call_2(self):
-        class Monochrome(Enum):
-            black = 0
-            white = 1
-        class Gender(IntEnum):
-            male = 0
-            female = 1
-        self.assertTrue(Monochrome(Gender.male) is Monochrome.black)
-
-    def test_flufl_enum(self):
-        class Fluflnum(Enum):
-            def __int__(self):
-                return int(self.value)
-        class MailManOptions(Fluflnum):
-            option1 = 1
-            option2 = 2
-            option3 = 3
-        self.assertEqual(int(MailManOptions.option1), 1)
-
-    def test_no_such_enum_member(self):
-        class Color(Enum):
-            red = 1
-            green = 2
-            blue = 3
-        self.assertRaises(ValueError, Color, 4)
-        self.assertRaises(KeyError, Color.__getitem__, 'chartreuse')
-
-    def test_new_repr(self):
-        class Color(Enum):
-            red = 1
-            green = 2
-            blue = 3
-            def __repr__(self):
-                return "don't you just love shades of %s?" % self.name
-        self.assertEqual(
-                repr(Color.blue),
-                "don't you just love shades of blue?",
-                )
-
-    def test_inherited_repr(self):
-        class MyEnum(Enum):
-            def __repr__(self):
-                return "My name is %s." % self.name
-        class MyIntEnum(int, MyEnum):
-            this = 1
-            that = 2
-            theother = 3
-        self.assertEqual(repr(MyIntEnum.that), "My name is that.")
-
-    def test_multiple_mixin_mro(self):
-        class auto_enum(EnumMeta):
-            def __new__(metacls, cls, bases, classdict):
-                original_dict = classdict
-                temp_dict = metacls.__prepare__(cls, bases, {})
-                if hasattr(original_dict, '_member_names'):
-                    for k in original_dict._member_names:
-                        temp_dict[k] = original_dict[k]
-                    sunders = [k for k in original_dict.keys() if aenum._is_sunder(k)]
-                else:
-                    sunders = []
-                    for k, v in original_dict.items():
-                        if aenum._is_sunder(k):
-                            sunders.append(k)
-                        temp_dict[k] = v
-                classdict = metacls.__prepare__(cls, bases, {})
-                i = 0
-                for k in sunders:
-                    classdict[k] = original_dict[k]
-                for k in temp_dict._member_names:
-                    v = original_dict[k]
-                    if v == ():
-                        v = i
-                    else:
-                        i = v
-                    i += 1
-                    classdict[k] = v
-                for k, v in original_dict.items():
-                    if k not in temp_dict._member_names and k not in sunders:
-                        classdict[k] = v
-                return super(auto_enum, metacls).__new__(
-                        metacls, cls, bases, classdict)
-
-        AutoNumberedEnum = auto_enum('AutoNumberedEnum', (Enum,), {})
-
-        AutoIntEnum = auto_enum('AutoIntEnum', (IntEnum,), {})
-
-        # class TestAutoNumber(AutoNumberedEnum):
-        #     a = ()
-        #     b = 3
-        #     c = ()
-        # self.assertEqual(TestAutoNumber.b.value, 3)
-        #
-        # if pyver >= 3.0:
-        #     self.assertEqual(
-        #         [TestAutoNumber.a.value, TestAutoNumber.b.value, TestAutoNumber.c.value],
-        #         [0, 3, 4],
-        #         )
-        #
-        # class TestAutoInt(AutoIntEnum):
-        #     a = ()
-        #     b = 3
-        #     c = ()
-        # self.assertEqual(TestAutoInt.b, 3)
-        #
-        # if pyver >= 3.0:
-        #     self.assertEqual(
-        #         [TestAutoInt.a.value, TestAutoInt.b.value, TestAutoInt.c.value],
-        #         [0, 3, 4],
-        #         )
-
-    def test_meta_reconfigure(self):
-
-        def identity(*args):
-            if len(args) == 1:
-                return args[0]
-            return args
-
-        JSONEnum = None
-
-        class JSONEnumMeta(EnumMeta):
-
-            @classmethod
-            def __prepare__(metacls, cls, bases, init=None, start=None, settings=()):
-                return {}
-
-            def __init__(cls, *args , **kwds):
-                super(JSONEnumMeta, cls).__init__(*args)
-
-            def __new__(metacls, cls, bases, clsdict, init=None, start=None, settings=()):
-                import json
-                members = []
-                if JSONEnum is not None:
-                    if '_file' not in clsdict:
-                        raise TypeError('_file is required')
-                    if '_name' not in clsdict:
-                        raise TypeError('_name is required')
-                    if '_value' not in clsdict:
-                        raise TypeError('_value is required')
-                    name_spec = clsdict.pop('_name')
-                    if not isinstance(name_spec, (tuple, list)):
-                        name_spec = (name_spec, )
-                    value_spec = clsdict.pop('_value')
-                    file = clsdict.pop('_file')
-                    with open(file) as f:
-                        json_data = json.load(f)
-                    for data in json_data:
-                        values = []
-                        name = data[name_spec[0]]
-                        for piece in name_spec[1:]:
-                            name = name[piece]
-                        for order, (value_path, func) in sorted(value_spec.items()):
-                            if not isinstance(value_path, (list, tuple)):
-                                value_path = (value_path, )
-                            value = data[value_path[0]]
-                            for piece in value_path[1:]:
-                                value = value[piece]
-                            if func is not None:
-                                value = func(value)
-                            values.append(value)
-                        values = tuple(values)
-                        members.append(
-                            (name, identity(*values))
-                            )
-                # get the real EnumDict
-                enum_dict = super(JSONEnumMeta, metacls).__prepare__(cls, bases, init, start, settings)
-                # transfer the original dict content, _items first
-                items = list(clsdict.items())
-                items.sort(key=lambda p: (0 if p[0][0] == '_' else 1, p))
-                for name, value in items:
-                    enum_dict[name] = value
-                # add the members
-                for name, value in members:
-                    enum_dict[name] = value
-                return super(JSONEnumMeta, metacls).__new__(metacls, cls, bases, enum_dict, init, start, settings)
-
-        # for use with both Python 2/3
-        JSONEnum = JSONEnumMeta('JsonEnum', (Enum, ), {})
-
-        test_file = os.path.join(tempdir, 'test_json.json')
-        with open(test_file, 'w') as f:
-            f.write(
-                '[{"name":"Afghanistan","alpha-2":"AF","country-code":"004","notes":{"description":"pretty"}},'
-                '{"name":"Åland Islands","alpha-2":"AX","country-code":"248","notes":{"description":"serene"}},'
-                '{"name":"Albania","alpha-2":"AL","country-code":"008","notes":{"description":"exciting"}},'
-                '{"name":"Algeria","alpha-2":"DZ","country-code":"012","notes":{"description":"scarce"}}]')
-
-        class Country(JSONEnum):
-            _init_ = 'abbr code country_name description'
-            _file = test_file
-            _name = 'alpha-2'
-            _value = {
-                    1: ('alpha-2', None),
-                    2: ('country-code', lambda c: int(c)),
-                    3: ('name', None),
-                    4: (('notes','description'), lambda s: s.title()),
-                    }
-
-        self.assertEqual([Country.AF, Country.AX, Country.AL, Country.DZ], list(Country))
-        self.assertEqual(Country.AF.abbr, 'AF')
-        self.assertEqual(Country.AX.code, 248)
-        self.assertEqual(Country.AL.country_name, 'Albania')
-        self.assertEqual(Country.DZ.description, 'Scarce')
-
-
-    def test_subclasses_with_getnewargs(self):
-        class NamedInt(int):
-            __qualname__ = 'NamedInt'  # needed for pickle protocol 4
-            def __new__(cls, *args):
-                _args = args
-                if len(args) < 1:
-                    raise TypeError("name and value must be specified")
-                name, args = args[0], args[1:]
-                self = int.__new__(cls, *args)
-                self._intname = name
-                self._args = _args
-                return self
-            def __getnewargs__(self):
-                return self._args
-            @property
-            def __name__(self):
-                return self._intname
-            def __repr__(self):
-                # repr() is updated to include the name and type info
-                return "%s(%r, %s)" % (type(self).__name__,
-                                             self.__name__,
-                                             int.__repr__(self))
-            def __str__(self):
-                # str() is unchanged, even if it relies on the repr() fallback
-                base = int
-                base_str = base.__str__
-                if base_str.__objclass__ is object:
-                    return base.__repr__(self)
-                return base_str(self)
-            # for simplicity, we only define one operator that
-            # propagates expressions
-            def __add__(self, other):
-                temp = int(self) + int( other)
-                if isinstance(self, NamedInt) and isinstance(other, NamedInt):
-                    return NamedInt(
-                        '(%s + %s)' % (self.__name__, other.__name__),
-                        temp )
-                else:
-                    return temp
-
-        class NEI(NamedInt, Enum):
-            __qualname__ = 'NEI'  # needed for pickle protocol 4
-            x = ('the-x', 1)
-            y = ('the-y', 2)
-
-        self.assertTrue(NEI.__new__ is Enum.__new__)
-        self.assertEqual(repr(NEI.x + NEI.y), "NamedInt('(the-x + the-y)', 3)")
-        globals()['NamedInt'] = NamedInt
-        globals()['NEI'] = NEI
-        NI5 = NamedInt('test', 5)
-        self.assertEqual(NI5, 5)
-        test_pickle_dump_load(self.assertTrue, NI5, 5)
-        self.assertEqual(NEI.y.value, 2)
-        test_pickle_dump_load(self.assertTrue, NEI.y)
-
-    def test_subclasses_with_reduce(self):
-        class NamedInt(int):
-            __qualname__ = 'NamedInt'       # needed for pickle protocol 4
-            def __new__(cls, *args):
-                _args = args
-                if len(args) < 1:
-                    raise TypeError("name and value must be specified")
-                name, args = args[0], args[1:]
-                self = int.__new__(cls, *args)
-                self._intname = name
-                self._args = _args
-                return self
-            def __reduce__(self):
-                return self.__class__, self._args
-            @property
-            def __name__(self):
-                return self._intname
-            def __repr__(self):
-                # repr() is updated to include the name and type info
-                return "%s(%r, %s)" % (type(self).__name__,
-                                             self.__name__,
-                                             int.__repr__(self))
-            def __str__(self):
-                # str() is unchanged, even if it relies on the repr() fallback
-                base = int
-                base_str = base.__str__
-                if base_str.__objclass__ is object:
-                    return base.__repr__(self)
-                return base_str(self)
-            # for simplicity, we only define one operator that
-            # propagates expressions
-            def __add__(self, other):
-                temp = int(self) + int( other)
-                if isinstance(self, NamedInt) and isinstance(other, NamedInt):
-                    return NamedInt(
-                        '(%s + %s)' % (self.__name__, other.__name__),
-                        temp )
-                else:
-                    return temp
-
-        class NEI(NamedInt, Enum):
-            __qualname__ = 'NEI'      # needed for pickle protocol 4
-            x = ('the-x', 1)
-            y = ('the-y', 2)
-
-
-        self.assertTrue(NEI.__new__ is Enum.__new__)
-        self.assertEqual(repr(NEI.x + NEI.y), "NamedInt('(the-x + the-y)', 3)")
-        globals()['NamedInt'] = NamedInt
-        globals()['NEI'] = NEI
-        NI5 = NamedInt('test', 5)
-        self.assertEqual(NI5, 5)
-        test_pickle_dump_load(self.assertEqual, NI5, 5)
-        self.assertEqual(NEI.y.value, 2)
-        test_pickle_dump_load(self.assertTrue, NEI.y)
-
-    def test_subclasses_with_reduce_ex(self):
-        class NamedInt(int):
-            __qualname__ = 'NamedInt'       # needed for pickle protocol 4
-            def __new__(cls, *args):
-                _args = args
-                if len(args) < 1:
-                    raise TypeError("name and value must be specified")
-                name, args = args[0], args[1:]
-                self = int.__new__(cls, *args)
-                self._intname = name
-                self._args = _args
-                return self
-            def __reduce_ex__(self, proto):
-                return self.__class__, self._args
-            @property
-            def __name__(self):
-                return self._intname
-            def __repr__(self):
-                # repr() is updated to include the name and type info
-                return "%s(%r, %s)" % (type(self).__name__,
-                                             self.__name__,
-                                             int.__repr__(self))
-            def __str__(self):
-                # str() is unchanged, even if it relies on the repr() fallback
-                base = int
-                base_str = base.__str__
-                if base_str.__objclass__ is object:
-                    return base.__repr__(self)
-                return base_str(self)
-            # for simplicity, we only define one operator that
-            # propagates expressions
-            def __add__(self, other):
-                temp = int(self) + int( other)
-                if isinstance(self, NamedInt) and isinstance(other, NamedInt):
-                    return NamedInt(
-                        '(%s + %s)' % (self.__name__, other.__name__),
-                        temp )
-                else:
-                    return temp
-
-        class NEI(NamedInt, Enum):
-            __qualname__ = 'NEI'      # needed for pickle protocol 4
-            x = ('the-x', 1)
-            y = ('the-y', 2)
-
-
-        self.assertTrue(NEI.__new__ is Enum.__new__)
-        self.assertEqual(repr(NEI.x + NEI.y), "NamedInt('(the-x + the-y)', 3)")
-        globals()['NamedInt'] = NamedInt
-        globals()['NEI'] = NEI
-        NI5 = NamedInt('test', 5)
-        self.assertEqual(NI5, 5)
-        test_pickle_dump_load(self.assertEqual, NI5, 5)
-        self.assertEqual(NEI.y.value, 2)
-        test_pickle_dump_load(self.assertTrue, NEI.y)
-
-    def test_subclasses_without_direct_pickle_support(self):
-        class NamedInt(int):
-            __qualname__ = 'NamedInt'
-            def __new__(cls, *args):
-                _args = args
-                name, args = args[0], args[1:]
-                if len(args) == 0:
-                    raise TypeError("name and value must be specified")
-                self = int.__new__(cls, *args)
-                self._intname = name
-                self._args = _args
-                return self
-            @property
-            def __name__(self):
-                return self._intname
-            def __repr__(self):
-                # repr() is updated to include the name and type info
-                return "%s(%r, %s)" % (type(self).__name__,
-                                             self.__name__,
-                                             int.__repr__(self))
-            def __str__(self):
-                # str() is unchanged, even if it relies on the repr() fallback
-                base = int
-                base_str = base.__str__
-                if base_str.__objclass__ is object:
-                    return base.__repr__(self)
-                return base_str(self)
-            # for simplicity, we only define one operator that
-            # propagates expressions
-            def __add__(self, other):
-                temp = int(self) + int( other)
-                if isinstance(self, NamedInt) and isinstance(other, NamedInt):
-                    return NamedInt(
-                        '(%s + %s)' % (self.__name__, other.__name__),
-                        temp )
-                else:
-                    return temp
-
-        class NEI(NamedInt, Enum):
-            __qualname__ = 'NEI'
-            x = ('the-x', 1)
-            y = ('the-y', 2)
-
-        self.assertTrue(NEI.__new__ is Enum.__new__)
-        self.assertEqual(repr(NEI.x + NEI.y), "NamedInt('(the-x + the-y)', 3)")
-        globals()['NamedInt'] = NamedInt
-        globals()['NEI'] = NEI
-        NI5 = NamedInt('test', 5)
-        self.assertEqual(NI5, 5)
-        self.assertEqual(NEI.y.value, 2)
-        test_pickle_exception(self.assertRaises, TypeError, NEI.x)
-        test_pickle_exception(self.assertRaises, PicklingError, NEI)
-
-    def test_subclasses_without_direct_pickle_support_using_name(self):
-        class NamedInt(int):
-            __qualname__ = 'NamedInt'
-            def __new__(cls, *args):
-                _args = args
-                name, args = args[0], args[1:]
-                if len(args) == 0:
-                    raise TypeError("name and value must be specified")
-                self = int.__new__(cls, *args)
-                self._intname = name
-                self._args = _args
-                return self
-            @property
-            def __name__(self):
-                return self._intname
-            def __repr__(self):
-                # repr() is updated to include the name and type info
-                return "%s(%r, %s)" % (type(self).__name__,
-                                             self.__name__,
-                                             int.__repr__(self))
-            def __str__(self):
-                # str() is unchanged, even if it relies on the repr() fallback
-                base = int
-                base_str = base.__str__
-                if base_str.__objclass__ is object:
-                    return base.__repr__(self)
-                return base_str(self)
-            # for simplicity, we only define one operator that
-            # propagates expressions
-            def __add__(self, other):
-                temp = int(self) + int( other)
-                if isinstance(self, NamedInt) and isinstance(other, NamedInt):
-                    return NamedInt(
-                        '(%s + %s)' % (self.__name__, other.__name__),
-                        temp )
-                else:
-                    return temp
-
-        class NEI(NamedInt, Enum):
-            __qualname__ = 'NEI'
-            x = ('the-x', 1)
-            y = ('the-y', 2)
-            def __reduce_ex__(self, proto):
-                return getattr, (self.__class__, self._name_)
-
-        self.assertTrue(NEI.__new__ is Enum.__new__)
-        self.assertEqual(repr(NEI.x + NEI.y), "NamedInt('(the-x + the-y)', 3)")
-        globals()['NamedInt'] = NamedInt
-        globals()['NEI'] = NEI
-        NI5 = NamedInt('test', 5)
-        self.assertEqual(NI5, 5)
-        self.assertEqual(NEI.y.value, 2)
-        test_pickle_dump_load(self.assertTrue, NEI.y)
-        test_pickle_dump_load(self.assertTrue, NEI)
-
-    def test_tuple_subclass(self):
-        class SomeTuple(tuple, Enum):
-            __qualname__ = 'SomeTuple'
-            first = (1, 'for the money')
-            second = (2, 'for the show')
-            third = (3, 'for the music')
-        self.assertTrue(type(SomeTuple.first) is SomeTuple)
-        self.assertTrue(isinstance(SomeTuple.second, tuple))
-        self.assertEqual(SomeTuple.third, (3, 'for the music'))
-        globals()['SomeTuple'] = SomeTuple
-        test_pickle_dump_load(self.assertTrue, SomeTuple.first)
-
-    # def test_duplicate_values_give_unique_enum_items(self):
-    #     class NumericEnum(AutoNumberEnum):
-    #         __order__ = 'enum_m enum_d enum_y'
-    #         enum_m = ()
-    #         enum_d = ()
-    #         enum_y = ()
-    #         def __int__(self):
-    #             return int(self._value_)
-    #     self.assertEqual(int(NumericEnum.enum_d), 2)
-    #     self.assertEqual(NumericEnum.enum_y.value, 3)
-    #     self.assertTrue(NumericEnum(1) is NumericEnum.enum_m)
-    #     self.assertEqual(
-    #         list(NumericEnum),
-    #         [NumericEnum.enum_m, NumericEnum.enum_d, NumericEnum.enum_y],
-    #         )
-
-    def test_inherited_new_from_enhanced_enum(self):
-        class AutoNumber2(Enum):
-            def __new__(cls):
-                value = len(cls.__members__) + 1
-                obj = object.__new__(cls)
-                obj._value_ = value
-                return obj
-            def __int__(self):
-                return int(self._value_)
-        class Color(AutoNumber2):
-            __order__ = 'red green blue'
-            red = ()
-            green = ()
-            blue = ()
-        self.assertEqual(len(Color), 3, "wrong number of elements: %d (should be %d)" % (len(Color), 3))
-        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
-        if PY3:
-            self.assertEqual(list(map(int, Color)), [1, 2, 3])
-
-    def test_inherited_new_from_mixed_enum(self):
-        class AutoNumber3(IntEnum):
-            def __new__(cls):
-                value = len(cls.__members__) + 11
-                obj = int.__new__(cls, value)
-                obj._value_ = value
-                return obj
-        class Color(AutoNumber3):
-            __order__ = 'red green blue'
-            red = ()
-            green = ()
-            blue = ()
-        self.assertEqual(len(Color), 3, "wrong number of elements: %d (should be %d)" % (len(Color), 3))
-        Color.red
-        Color.green
-        Color.blue
-        self.assertEqual(Color.blue, 13)
-
-    def test_equality(self):
-        class AlwaysEqual:
-            def __eq__(self, other):
-                return True
-        class OrdinaryEnum(Enum):
-            a = 1
-        self.assertEqual(AlwaysEqual(), OrdinaryEnum.a)
-        self.assertEqual(OrdinaryEnum.a, AlwaysEqual())
-
-    def test_ordered_mixin(self):
-        class Grade(OrderedEnum):
-            __order__ = 'A B C D F'
-            A = 5
-            B = 4
-            C = 3
-            D = 2
-            F = 1
-        self.assertEqual(list(Grade), [Grade.A, Grade.B, Grade.C, Grade.D, Grade.F])
-        self.assertTrue(Grade.A > Grade.B)
-        self.assertTrue(Grade.F <= Grade.C)
-        self.assertTrue(Grade.D < Grade.A)
-        self.assertTrue(Grade.B >= Grade.B)
-
-    def test_missing_deprecated(self):
-        class Label(Enum):
-            AnyApple = 0
-            RedApple = 1
-            GreenApple = 2
-            @classmethod
-            def _missing_(cls, name):
-                return cls.AnyApple
-
-        self.assertEqual(Label.AnyApple, Label(4))
-        with self.assertRaises(AttributeError):
-            Label.redapple
-        with self.assertRaises(KeyError):
-            Label['redapple']
-
-    def test_missing(self):
-        class Label(Enum):
-            AnyApple = 0
-            RedApple = 1
-            GreenApple = 2
-            @classmethod
-            def _missing_value_(cls, value):
-                return cls.AnyApple
-
-        self.assertEqual(Label.AnyApple, Label(4))
-        with self.assertRaises(AttributeError):
-            Label.redapple
-        with self.assertRaises(KeyError):
-            Label['redapple']
-
-    def test_missing_name(self):
-        class Label(Enum):
-            RedApple = 1
-            GreenApple = 2
-            @classmethod
-            def _missing_name_(cls, name):
-                for member in cls:
-                    if member.name.lower() == name.lower():
-                        return member
-
-        Label['redapple']
-        with self.assertRaises(AttributeError):
-            Label.redapple
-        with self.assertRaises(ValueError):
-            Label('redapple')
-
-    def test_missing_value_bad_input(self):
-        class Label(Enum):
-            AnyApple = 0
-            RedApple = 1
-            GreenApple = 2
-            @classmethod
-            def _missing_value_(cls, value):
-                return cls.AnyApple
-
-        self.assertEqual(Label.AnyApple, Label(4))
-        with self.assertRaises(KeyError):
-            Label[True]
-
-    def test_missing_name_bad_return(self):
-        class Label(Enum):
-            RedApple = 1
-            GreenApple = 2
-            @classmethod
-            def _missing_name_(cls, name):
-                return None
-
-        with self.assertRaises(AttributeError):
-            Label.redapple
-        with self.assertRaises(ValueError):
-            Label('redapple')
-        with self.assertRaises(KeyError):
-            Label['redapple']
-
-    def test_extending2(self):
-        def bad_extension():
-            class Shade(Enum):
-                def shade(self):
-                    print(self.name)
-            class Color(Shade):
-                red = 1
-                green = 2
-                blue = 3
-            class MoreColor(Color):
-                cyan = 4
-                magenta = 5
-                yellow = 6
-        self.assertRaises(TypeError, bad_extension)
-
-    def test_extending3(self):
-        class Shade(Enum):
-            def shade(self):
-                return self.name
-        class Color(Shade):
-            def hex(self):
-                return '%s hexlified!' % self.value
-        class MoreColor(Color):
-            cyan = 4
-            magenta = 5
-            yellow = 6
-        self.assertEqual(MoreColor.magenta.hex(), '5 hexlified!')
-
-    def test_extending5(self):
-        class Color(Enum):
-            _order_ = 'red green blue value'
-            red = 1
-            green = 2
-            blue = 3
-            value = 4
-        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue, Color.value])
-        self.assertEqual(Color.value.name, 'value')
-        self.assertEqual(Color.value.value, 4)
-        self.assertTrue(Color.value in Color)
-        self.assertEqual(Color(4), Color.value)
-        self.assertEqual(Color['value'], Color.value)
-        self.assertEqual(Color.red.value, 1)
-
-    CONTINUE = 100, 'Continue', 'Request received, please continue'
-    SWITCHING_PROTOCOLS = (101, 'Switching Protocols',
-            'Switching to new protocol; obey Upgrade header')
-    PROCESSING = 102, 'Processing'
-
-    def test_no_duplicates(self):
-        def bad_duplicates():
-            class Color1(UniqueEnum):
-                red = 1
-                green = 2
-                blue = 3
-            class Color2(UniqueEnum):
-                red = 1
-                green = 2
-                blue = 3
-                grene = 2
-        self.assertRaises(ValueError, bad_duplicates)
-
-    def test_no_duplicates_kinda(self):
-        class Silly(UniqueEnum):
-            one = 1
-            two = 'dos'
-            name = 3
-        class Sillier(IntEnum, UniqueEnum):
-            single = 1
-            name = 2
-            triple = 3
-            value = 4
-
-    def test_init(self):
-        class Planet(Enum):
-            MERCURY = (3.303e+23, 2.4397e6)
-            VENUS   = (4.869e+24, 6.0518e6)
-            EARTH   = (5.976e+24, 6.37814e6)
-            MARS    = (6.421e+23, 3.3972e6)
-            JUPITER = (1.9e+27,   7.1492e7)
-            SATURN  = (5.688e+26, 6.0268e7)
-            URANUS  = (8.686e+25, 2.5559e7)
-            NEPTUNE = (1.024e+26, 2.4746e7)
-            def __init__(self, mass, radius):
-                self.mass = mass       # in kilograms
-                self.radius = radius   # in meters
-            @property
-            def surface_gravity(self):
-                # universal gravitational constant  (m3 kg-1 s-2)
-                G = 6.67300E-11
-                return G * self.mass / (self.radius * self.radius)
-        self.assertEqual(round(Planet.EARTH.surface_gravity, 2), 9.80)
-        self.assertEqual(Planet.EARTH.value, (5.976e+24, 6.37814e6))
-
-    def test_init_and_shadowing_attribute(self):
-        class SelectionEnum(str, Enum):
-            _init_ = 'db user'
-            def __new__(cls, *args, **kwds):
-                count = len(cls.__members__)
-                obj = str.__new__(cls, args[0])
-                obj._count = count
-                obj._value_ = args
-                return obj
-            @staticmethod
-            def _generate_next_value_(name, start, count, values, *args, **kwds):
-                return (name, ) + args
-        class DeviceTypeSource(SelectionEnum):
-            _order_ = 'user system'
-            user = "User controlled"
-            system = "System controlled"
-        self.assertEqual(DeviceTypeSource.system.db, 'system')
-        self.assertEqual(DeviceTypeSource.system.user, 'System controlled')
-        self.assertEqual(DeviceTypeSource.user.db, 'user')
-        self.assertEqual(DeviceTypeSource.user.user, 'User controlled')
-
-    def test_nonhash_value(self):
-        class AutoNumberInAList(Enum):
-            def __new__(cls):
-                value = [len(cls.__members__) + 1]
-                obj = object.__new__(cls)
-                obj._value_ = value
-                return obj
-        class ColorInAList(AutoNumberInAList):
-            __order__ = 'red green blue'
-            red = ()
-            green = ()
-            blue = ()
-        self.assertEqual(list(ColorInAList), [ColorInAList.red, ColorInAList.green, ColorInAList.blue])
-        self.assertEqual(ColorInAList.red.value, [1])
-        self.assertEqual(ColorInAList([1]), ColorInAList.red)
-
-    def test_number_reset_and_order_cleanup(self):
-        class Confused(Enum):
-            _order_ = 'ONE TWO THREE UNO DOS TRES FOUR'
-            ONE = auto()
-            TWO = auto()
-            THREE = auto()
-            UNO = 1
-            DOS = auto()
-            TRES = auto()
-            FOUR = auto()
-        self.assertEqual(list(Confused), [Confused.ONE, Confused.TWO, Confused.THREE, Confused.FOUR])
-        self.assertIs(Confused.TWO, Confused.DOS)
-        self.assertEqual(Confused.DOS._value_, 2)
-        self.assertEqual(Confused.TRES._value_, 3)
-        self.assertEqual(Confused.FOUR._value_, 4)
-
-    def test_conflicting_types_resolved_in_new(self):
-        class LabelledIntEnum(int, Enum):
-            def __new__(cls, *args):
-                value, label = args
-                obj = int.__new__(cls, value)
-                obj.label = label
-                obj._value_ = value
-                return obj
-
-        class LabelledList(LabelledIntEnum):
-            unprocessed = (1, "Unprocessed")
-            payment_complete = (2, "Payment Complete")
-
-        self.assertEqual(LabelledList.unprocessed, 1)
-        self.assertEqual(LabelledList(1), LabelledList.unprocessed)
-        self.assertEqual(list(LabelledList), [LabelledList.unprocessed, LabelledList.payment_complete])
-
-    def test_auto_number(self):
-        class Color(Enum):
-            _order_ = 'red blue green'
-            red = auto()
-            blue = auto()
-            green = auto()
-
-        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
-        self.assertEqual(Color.red.value, 1)
-        self.assertEqual(Color.blue.value, 2)
-        self.assertEqual(Color.green.value, 3)
-
-    def test_auto_name(self):
-        class Color(Enum):
-            _order_ = 'red blue green'
-            def _generate_next_value_(name, start, count, last):
-                return name
-            red = auto()
-            blue = auto()
-            green = auto()
-
-        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
-        self.assertEqual(Color.red.value, 'red')
-        self.assertEqual(Color.blue.value, 'blue')
-        self.assertEqual(Color.green.value, 'green')
-
-    def test_auto_name_inherit(self):
-        class AutoNameEnum(Enum):
-            def _generate_next_value_(name, start, count, last):
-                return name
-        class Color(AutoNameEnum):
-            _order_ = 'red blue green'
-            red = auto()
-            blue = auto()
-            green = auto()
-
-        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
-        self.assertEqual(Color.red.value, 'red')
-        self.assertEqual(Color.blue.value, 'blue')
-        self.assertEqual(Color.green.value, 'green')
-
-    def test_auto_garbage(self):
-        class Color(Enum):
-            _order_ = 'red blue'
-            red = 'red'
-            blue = auto()
-        self.assertEqual(Color.blue.value, 1)
-
-    def test_auto_garbage_corrected(self):
-        class Color(Enum):
-            _order_ = 'red blue green'
-            red = 'red'
-            blue = 2
-            green = auto()
-
-        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
-        self.assertEqual(Color.red.value, 'red')
-        self.assertEqual(Color.blue.value, 2)
-        self.assertEqual(Color.green.value, 3)
-
-    def test_duplicate_auto(self):
-        #
-        class MoreDupes(Enum):
-            _order_ = 'A B C'
-            A = auto()
-            B = A,
-            C = auto()
-        self.assertEqual(list(MoreDupes), [MoreDupes.A, MoreDupes.B, MoreDupes.C])
-        self.assertEqual([m.value for m in MoreDupes], [1, (1, ), 2])
-        #
-        class Dupes(Enum):
-            _order_ = 'first second third'
-            first = primero = auto()
-            second = auto()
-            third = auto()
-        self.assertEqual([Dupes.first, Dupes.second, Dupes.third], list(Dupes))
-
-    def test_auto_value_with_auto(self):
-
-        class SelectionEnum(Enum):
-            _init_ = 'db user'
-            def __new__(cls, *args, **kwds):
-                count = len(cls.__members__)
-                obj = object.__new__(cls)
-                obj._count = count
-                obj._value_ = args
-                obj.db, obj.user = args
-                return obj
-            @staticmethod
-            def _generate_next_value_(name, start, count, values, *args, **kwds):
-                return (name, ) + args
-
-        class Test(SelectionEnum):
-            _order_ = 'this that'
-            this = auto('these')
-            that = auto('those')
-
-        self.assertEqual(list(Test), [Test.this, Test.that])
-        self.assertEqual(Test.this.name, 'this')
-        self.assertEqual(Test.this.value, ('this', 'these'))
-        self.assertEqual(Test.this.db, 'this')
-        self.assertEqual(Test.this.user, 'these')
-        self.assertEqual(Test.that.name, 'that')
-        self.assertEqual(Test.that.value, ('that', 'those'))
-        self.assertEqual(Test.that.db, 'that')
-        self.assertEqual(Test.that.user, 'those')
-
-    def test_auto_value_with_autovalue(self):
-
-        class SelectionEnum(Enum):
-            _init_ = 'db user'
-            def __new__(cls, *args, **kwds):
-                count = len(cls.__members__)
-                obj = object.__new__(cls)
-                obj._count = count
-                obj._value_ = args
-                return obj
-            @staticmethod
-            def _generate_next_value_(name, start, count, values, *args, **kwds):
-                return (name, ) + args
-
-        class Test(SelectionEnum):
-            _order_ = 'this that'
-            this = 'these'
-            that = 'those'
-
-        self.assertEqual(list(Test), [Test.this, Test.that])
-        self.assertEqual(Test.this.name, 'this')
-        self.assertEqual(Test.this.value, ('this', 'these'))
-        self.assertEqual(Test.this.db, 'this')
-        self.assertEqual(Test.this.user, 'these')
-        self.assertEqual(Test.that.name, 'that')
-        self.assertEqual(Test.that.value, ('that', 'those'))
-        self.assertEqual(Test.that.db, 'that')
-        self.assertEqual(Test.that.user, 'those')
-
-    def test_auto_and_kwds(self):
-        class Item(Enum):
-            _order_ = 'A B'
-            A = auto(size=100, req={'red': True})
-            B = auto(size=200, req={'red': False})
-            #
-            def __new__(cls, value, size, req):
-                obj = object.__new__(cls)
-                obj._value_ = value
-                obj.size = size
-                obj.req= req
-                return obj
-        self.assertEqual((Item.A.value, Item.A.size, Item.A.req), (1, 100, {'red': True}))
-        self.assertEqual((Item.B.value, Item.B.size, Item.B.req), (2, 200, {'red': False}))
-
-    def test_empty_with_functional_api(self):
-        empty = aenum.IntEnum('Foo', {})
-        self.assertEqual(len(empty), 0)
-
-    def test_auto_init(self):
-        class Planet(Enum):
-            _init_ = 'mass radius'
-            MERCURY = (3.303e+23, 2.4397e6)
-            VENUS   = (4.869e+24, 6.0518e6)
-            EARTH   = (5.976e+24, 6.37814e6)
-            MARS    = (6.421e+23, 3.3972e6)
-            JUPITER = (1.9e+27,   7.1492e7)
-            SATURN  = (5.688e+26, 6.0268e7)
-            URANUS  = (8.686e+25, 2.5559e7)
-            NEPTUNE = (1.024e+26, 2.4746e7)
-            @property
-            def surface_gravity(self):
-                # universal gravitational constant  (m3 kg-1 s-2)
-                G = 6.67300E-11
-                return G * self.mass / (self.radius * self.radius)
-        self.assertEqual(round(Planet.EARTH.surface_gravity, 2), 9.80)
-        self.assertEqual(Planet.EARTH.value, (5.976e+24, 6.37814e6))
-
-    def test_auto_init_with_value(self):
-        class Color(Enum):
-            _init_='value, rgb'
-            RED = 1, (1, 0, 0)
-            BLUE = 2, (0, 1, 0)
-            GREEN = 3, (0, 0, 1)
-        self.assertEqual(Color.RED.value, 1)
-        self.assertEqual(Color.BLUE.value, 2)
-        self.assertEqual(Color.GREEN.value, 3)
-        self.assertEqual(Color.RED.rgb, (1, 0, 0))
-        self.assertEqual(Color.BLUE.rgb, (0, 1, 0))
-        self.assertEqual(Color.GREEN.rgb, (0, 0, 1))
-
-    def test_noalias(self):
-        class Settings(Enum):
-            _settings_ = NoAlias
-            red = 1
-            rojo = 1
-        self.assertFalse(Settings.red is Settings.rojo)
-        self.assertRaises(TypeError, Settings, 1)
-
-    def test_auto_and_init(self):
-        class Field(int, Enum):
-            _order_ = 'TYPE START'
-            _init_ = 'value __doc__'
-            TYPE = "Char, Date, Logical, etc."
-            START = "Field offset in record"
-        self.assertEqual(Field.TYPE, 1)
-        self.assertEqual(Field.START, 2)
-        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
-        self.assertEqual(Field.START.__doc__, 'Field offset in record')
-
-    def test_auto_and_start(self):
-        class Field(IntEnum):
-            _order_ = 'TYPE START'
-            _start_ = 0
-            _init_ = 'value __doc__'
-            TYPE = "Char, Date, Logical, etc."
-            START = "Field offset in record"
-        self.assertEqual(Field.TYPE, 0)
-        self.assertEqual(Field.START, 1)
-        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
-        self.assertEqual(Field.START.__doc__, 'Field offset in record')
-
-    def test_auto_and_init_and_some_values(self):
-        class Field(int, Enum):
-            _order_ = 'TYPE START BLAH BELCH'
-            _init_ = 'value __doc__'
-            TYPE = "Char, Date, Logical, etc."
-            START = "Field offset in record"
-            BLAH = 5, "test blah"
-            BELCH = 'test belch'
-        self.assertEqual(Field.TYPE, 1)
-        self.assertEqual(Field.START, 2)
-        self.assertEqual(Field.BLAH, 5)
-        self.assertEqual(Field.BELCH, 6)
-        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
-        self.assertEqual(Field.START.__doc__, 'Field offset in record')
-        self.assertEqual(Field.BLAH.__doc__, 'test blah')
-        self.assertEqual(Field.BELCH.__doc__, 'test belch')
-
-    def test_auto_and_init_w_value_and_too_many_values(self):
-        with self.assertRaisesRegex(TypeError, r'Field\.BLAH: number of fields provided do not match init'):
-            class Field(int, Enum):
-                _order_ = 'TYPE START BLAH BELCH'
-                _init_ = 'value __doc__'
-                TYPE = 1, "Char, Date, Logical, etc."
-                START = 2, "Field offset in record"
-                BLAH = 5, 6, "test blah"
-                BELCH = 7, 'test belch'
-
-    def test_auto_and_init_and_some_complex_values(self):
-        class Field(int, Enum):
-            _order_ = 'TYPE START BLAH BELCH'
-            _init_ = 'value __doc__ help'
-            TYPE = "Char, Date, Logical, etc.", "fields composed of character data"
-            START = "Field offset in record", "where the data starts in the record"
-            BLAH = 5, "test blah", "some help"
-            BELCH = 'test belch', "some more help"
-        self.assertEqual(Field.TYPE, 1)
-        self.assertEqual(Field.START, 2)
-        self.assertEqual(Field.BLAH, 5)
-        self.assertEqual(Field.BELCH, 6)
-        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
-        self.assertEqual(Field.START.__doc__, 'Field offset in record')
-        self.assertEqual(Field.BLAH.__doc__, 'test blah')
-        self.assertEqual(Field.BELCH.__doc__, 'test belch')
-        self.assertEqual(Field.TYPE.help, "fields composed of character data")
-        self.assertEqual(Field.START.help, "where the data starts in the record")
-        self.assertEqual(Field.BLAH.help, "some help")
-        self.assertEqual(Field.BELCH.help, "some more help")
-
-    def test_auto_and_init_inherited(self):
-        class AutoEnum(IntEnum):
-            _start_ = 0
-            _init_ = 'value __doc__'
-        class Field(AutoEnum):
-            _order_ = 'TYPE START BLAH BELCH'
-            TYPE = "Char, Date, Logical, etc."
-            START = "Field offset in record"
-            BLAH = 5, "test blah"
-            BELCH = 'test belch'
-        self.assertEqual(Field.TYPE, 0)
-        self.assertEqual(Field.START, 1)
-        self.assertEqual(Field.BLAH, 5)
-        self.assertEqual(Field.BELCH, 6)
-        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
-        self.assertEqual(Field.START.__doc__, 'Field offset in record')
-        self.assertEqual(Field.BLAH.__doc__, 'test blah')
-        self.assertEqual(Field.BELCH.__doc__, 'test belch')
-
-    def test_missing_value_error(self):
-        with self.assertRaisesRegex(TypeError, r"_value_ not set in __new__"):
-            class Combined(str, Enum):
-                #
-                _init_ = 'value sequence'
-                _order_ = lambda m: m.sequence
-                #
-                def __new__(cls, value, *args):
-                    enum = str.__new__(cls, value)
-                    if '(' in value:
-                        fis_name, segment = value.split('(', 1)
-                        segment = segment.strip(' )')
-                    else:
-                        fis_name = value
-                        segment = None
-                    enum.fis_name = fis_name
-                    enum.segment = segment
-                    return enum
-                #
-                def __repr__(self):
-                    return "<%s.%s>" % (self.__class__.__name__, self._name_)
-                #
-                key_type      = 'An$(1,2)', 0
-                company_id    = 'An$(3,2)', 1
-                code          = 'An$(5,1)', 2
-                description   = 'Bn$',      3
-
-
-    def test_auto_and_enum(self):
-        class Foo(aenum.Flag):
-            _order_ = 'a b c'
-            a = aenum.auto()
-            b = a | aenum.auto()
-            c = 2
-
-        self.assertEqual([Foo.a, Foo.c], list(Foo))
-        self.assertEqual(Foo.a.value, 1)
-        self.assertEqual(Foo.b.value, 3)
-
-    def test_multiple_arg_auto(self):
-        class AutoName(Enum):
-            def _generate_next_value_(name, start, count, last, *args, **kwds):
-                return (name, ) + args
-        #
-        class Planet(AutoName):
-            _init_ = 'value mass radius'
-            MERCURY = auto(3.303e+23, 2.4397e6)
-            VENUS = auto(4.869e+24, 6.0518e6)
-        self.assertEqual(Planet.MERCURY.value, 'MERCURY')
-
-    def test_auto_w_multiple_arg(self):
-        class AutoName(Enum):
-            def _generate_next_value_(name, start, count, last, *args, **kwds):
-                return (name, ) + args
-        #
-        class Planet(AutoName):
-            _init_ = 'value mass radius'
-            MERCURY = auto(), 3.303e+23, 2.4397e6  # doesn't work
-            VENUS = auto(), 4.869e+24, 6.0518e6  # doesn't work
-        self.assertEqual(Planet.MERCURY.value, 'MERCURY')
-
-    def test_auto_gnv_and_init(self):
-        class AutoName(Enum):
-            def _generate_next_value_(name, start, count, last, *args, **kwds):
-                return (name, ) + args
-        #
-        class Planet(AutoName):
-            _init_ = 'value mass radius'
-            MERCURY = 3.303e+23, 2.4397e6  # doesn't work
-            VENUS = 4.869e+24, 6.0518e6  # doesn't work
-        self.assertEqual(Planet.MERCURY.value, 'MERCURY')
-
-    # def test_AutoNumberEnum_and_property(self):
-    #     class Color(aenum.AutoNumberEnum):
-    #         red = ()
-    #         green = ()
-    #         blue = ()
-    #         @property
-    #         def cap_name(self):
-    #             return self.name.title()
-    #     self.assertEqual(Color.blue.cap_name, 'Blue')
-
-    # def test_AutoNumberEnum(self):
-    #     class Color(aenum.AutoNumberEnum):
-    #         _order_ = 'red green blue'
-    #         red = ()
-    #         green = ()
-    #         blue = ()
-    #     self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
-    #     self.assertEqual(Color.red.value, 1)
-    #     self.assertEqual(Color.green.value, 2)
-    #     self.assertEqual(Color.blue.value, 3)
-
-    def test_MultiValue_with_init_wo_value(self):
-        class Color(Enum):
-            _init_ = 'color r g b'
-            _order_ = 'red green blue'
-            _settings_ = MultiValue
-            red = 'red', 1, 2, 3
-            green = 'green', 4, 5, 6
-            blue = 'blue', 7, 8, 9
-        self.assertEqual(Color.red.value, 'red')
-        self.assertEqual(Color.red.color, 'red')
-        self.assertEqual(Color.red.r, 1)
-        self.assertEqual(Color.red.g, 2)
-        self.assertEqual(Color.red.b, 3)
-        self.assertEqual(Color.green.value, 'green')
-        self.assertEqual(Color.green.color, 'green')
-        self.assertEqual(Color.green.r, 4)
-        self.assertEqual(Color.green.g, 5)
-        self.assertEqual(Color.green.b, 6)
-        self.assertEqual(Color.blue.value, 'blue')
-        self.assertEqual(Color.blue.color, 'blue')
-        self.assertEqual(Color.blue.r, 7)
-        self.assertEqual(Color.blue.g, 8)
-        self.assertEqual(Color.blue.b, 9)
-        self.assertIs(Color('red'), Color.red)
-        self.assertIs(Color(1), Color.red)
-        self.assertIs(Color(2), Color.red)
-        self.assertIs(Color(3), Color.red)
-        self.assertIs(Color('green'), Color.green)
-        self.assertIs(Color(4), Color.green)
-        self.assertIs(Color(5), Color.green)
-        self.assertIs(Color(6), Color.green)
-        self.assertIs(Color('blue'), Color.blue)
-        self.assertIs(Color(7), Color.blue)
-        self.assertIs(Color(8), Color.blue)
-        self.assertIs(Color(9), Color.blue)
-
-    def test_MultiValue_with_init_w_value(self):
-        class Color(Enum):
-            _init_ = 'value r g b'
-            _order_ = 'red green blue'
-            _settings_ = MultiValue
-            red = 'red', 1, 2, 3
-            green = 'green', 4, 5, 6
-            blue = 'blue', 7, 8, 9
-        self.assertEqual(Color.red.value, 'red')
-        self.assertEqual(Color.red.r, 1)
-        self.assertEqual(Color.red.g, 2)
-        self.assertEqual(Color.red.b, 3)
-        self.assertEqual(Color.green.value, 'green')
-        self.assertEqual(Color.green.r, 4)
-        self.assertEqual(Color.green.g, 5)
-        self.assertEqual(Color.green.b, 6)
-        self.assertEqual(Color.blue.value, 'blue')
-        self.assertEqual(Color.blue.r, 7)
-        self.assertEqual(Color.blue.g, 8)
-        self.assertEqual(Color.blue.b, 9)
-        self.assertIs(Color('red'), Color.red)
-        self.assertIs(Color(1), Color.red)
-        self.assertIs(Color(2), Color.red)
-        self.assertIs(Color(3), Color.red)
-        self.assertIs(Color('green'), Color.green)
-        self.assertIs(Color(4), Color.green)
-        self.assertIs(Color(5), Color.green)
-        self.assertIs(Color(6), Color.green)
-        self.assertIs(Color('blue'), Color.blue)
-        self.assertIs(Color(7), Color.blue)
-        self.assertIs(Color(8), Color.blue)
-        self.assertIs(Color(9), Color.blue)
-
-    def test_MultiValue_with_init_wo_value_w_autonumber(self):
-        class Color(AutoNumberEnum):
-            _init_ = 'color r g b'
-            _order_ = 'red green blue'
-            _settings_ = MultiValue
-            red = 'red', 10, 20, 30
-            green = 'green', 40, 50, 60
-            blue = 'blue', 70, 80, 90
-        self.assertEqual(Color.red.value, 1)
-        self.assertEqual(Color.red.color, 'red')
-        self.assertEqual(Color.red.r, 10)
-        self.assertEqual(Color.red.g, 20)
-        self.assertEqual(Color.red.b, 30)
-        self.assertEqual(Color.green.value, 2)
-        self.assertEqual(Color.green.color, 'green')
-        self.assertEqual(Color.green.r, 40)
-        self.assertEqual(Color.green.g, 50)
-        self.assertEqual(Color.green.b, 60)
-        self.assertEqual(Color.blue.value, 3)
-        self.assertEqual(Color.blue.color, 'blue')
-        self.assertEqual(Color.blue.r, 70)
-        self.assertEqual(Color.blue.g, 80)
-        self.assertEqual(Color.blue.b, 90)
-        self.assertIs(Color(1), Color.red)
-        self.assertIs(Color('red'), Color.red)
-        self.assertIs(Color(10), Color.red)
-        self.assertIs(Color(20), Color.red)
-        self.assertIs(Color(30), Color.red)
-        self.assertIs(Color(2), Color.green)
-        self.assertIs(Color('green'), Color.green)
-        self.assertIs(Color(40), Color.green)
-        self.assertIs(Color(50), Color.green)
-        self.assertIs(Color(60), Color.green)
-        self.assertIs(Color(3), Color.blue)
-        self.assertIs(Color('blue'), Color.blue)
-        self.assertIs(Color(70), Color.blue)
-        self.assertIs(Color(80), Color.blue)
-        self.assertIs(Color(90), Color.blue)
-
-    def test_multivalue_and_autonumber_wo_init_wo_value(self):
-        class Day(Enum):
-            _settings_ = MultiValue, AddValue
-            _order_ = 'one two three'
-            _start_ = 7
-            one = "21", "one"
-            two = "22", "two"
-            three = "23", "three"
-        self.assertEqual(Day.one.value, 7)
-        self.assertEqual(Day.two.value, 8)
-        self.assertEqual(Day.three.value, 9)
-        self.assertEqual(Day('21'), Day.one)
-        self.assertEqual(Day('one'), Day.one)
-
-    def test_multivalue_and_autonumber_wo_init_w_some_value(self):
-        class Color(Enum):
-            _settings_ = MultiValue, Unique
-            _order_ = 'BLACK RED BLUE YELLOW GREEN MAGENTA'
-            _init_ = "value description"
-            BLACK = -1, "Text0"
-            RED = -50, "Text1"
-            BLUE = auto(), "Text2"
-            YELLOW = auto(), "Text3"
-            GREEN = -70, "Text4"
-            MAGENTA = auto(), "Text5"
-        self.assertEqual(Color.BLACK.value, -1)
-        self.assertEqual(Color.RED.value, -50)
-        self.assertEqual(Color.BLUE.value, -49)
-        self.assertEqual(Color.YELLOW.value, -48)
-        self.assertEqual(Color.GREEN.value, -70)
-        self.assertEqual(Color.MAGENTA.value, -69)
-        self.assertEqual(Color(-1), Color.BLACK)
-        self.assertEqual(Color('Text2'), Color.BLUE)
-
-    def test_combine_new_settings_with_old_settings(self):
-        class Auto(Enum):
-            _settings_ = Unique
-        with self.assertRaises(ValueError):
-            class AutoUnique(Auto):
-                BLAH = auto()
-                BLUH = auto()
-                ICK = 1
-
-    def test_timedelta(self):
-        class Period(timedelta, Enum):
-            '''
-            different lengths of time
-            '''
-            _init_ = 'value period'
-            _settings_ = NoAlias
-            _ignore_ = 'Period i'
-            Period = vars()
-            for i in range(31):
-                Period['day_%d' % i] = i, 'day'
-            for i in range(15):
-                Period['week_%d' % i] = i*7, 'week'
-            for i in range(12):
-                Period['month_%d' % i] = i*30, 'month'
-            OneDay = day_1
-            OneWeek = week_1
-        self.assertFalse(hasattr(Period, '_ignore_'))
-        self.assertFalse(hasattr(Period, 'Period'))
-        self.assertFalse(hasattr(Period, 'i'))
-        self.assertTrue(isinstance(Period.day_1, timedelta))
-
-    def test_skip(self):
-        class enumA(Enum):
-            @skip
-            class enumB(Enum):
-                elementA = 'a'
-                elementB = 'b'
-            @skip
-            class enumC(Enum):
-                elementC = 'c'
-                elementD = 'd'
-        self.assertIs(enumA.enumB, enumA.__dict__['enumB'])
-
-    def test_nonmember(self):
-        class enumA(Enum):
-            @nonmember
-            class enumB(Enum):
-                elementA = 'a'
-                elementB = 'b'
-            @nonmember
-            class enumC(Enum):
-                elementC = 'c'
-                elementD = 'd'
-        self.assertIs(enumA.enumB, enumA.__dict__['enumB'])
-
-    def test_member_with_external_functions(self):
-        class Func(Enum):
-            _order_ = 'an_int a_str'
-            an_int = member(int)
-            a_str = member(str)
-            @classproperty
-            def types(cls):
-                return [m.value for m in list(cls)]
-            def __repr__(self):
-                return "<%s.%s>" % (self.__class__.__name__, self.name, )
-            def __call__(self, *args, **kwds):
-                return self.value(*args, **kwds)
-        #
-        self.assertEqual([Func.an_int, Func.a_str], list(Func))
-        self.assertEqual([int, str], Func.types)
-        self.assertEqual(Func.an_int(7), 7)
-        self.assertEqual(Func.a_str('BlahBlah'), 'BlahBlah')
-
-    def test_member_with_internal_functions(self):
-        class Func(Enum):
-            _order_ = 'haha hehe'
-            @member
-            def haha():
-                return 'haha'
-            @member
-            def hehe(name):
-                return 'hehe -- what a name!  %s!' % name
-            @classproperty
-            def types(cls):
-                return [m.value for m in list(cls)]
-            def __repr__(self):
-                return "<%s.%s>" % (self.__class__.__name__, self.name, )
-            def __call__(self, *args, **kwds):
-                return self.value(*args, **kwds)
-        #
-        self.assertEqual([Func.haha, Func.hehe], list(Func))
-        self.assertEqual([Func.haha.value, Func.hehe.value], Func.types)
-        self.assertEqual(Func.haha(), 'haha')
-        self.assertEqual(Func.hehe('BlahBlah'), 'hehe -- what a name!  BlahBlah!')
-
-    def test_constantness_of_constants(self):
-        class Universe(Enum):
-            PI = constant(3.141596)
-            G = constant(6.67300E-11)
-        self.assertEqual(Universe.PI, 3.141596)
-        self.assertRaisesRegex(AttributeError, r'cannot rebind constant', setattr, Universe, 'PI', 9)
-        self.assertRaisesRegex(AttributeError, r'cannot delete constant', delattr, Universe, 'PI')
-
-    def test_math_and_stuff_with_constants(self):
-        class Universe(Enum):
-            PI = constant(3.141596)
-            TAU = constant(2 * PI)
-        self.assertEqual(Universe.PI, 3.141596)
-        self.assertEqual(Universe.TAU, 2 * Universe.PI)
-
-    def test_constant_with_auto_is_updated(self):
-        class Fruit(Flag):
-            _order_ = 'apple banana lemon orange'
-            apple = auto()
-            banana = auto()
-            lemon = auto()
-            orange = auto()
-            CitrusTypes = constant(lemon | orange)
-        self.assertEqual(list(Fruit), [Fruit.apple, Fruit.banana, Fruit.lemon, Fruit.orange])
-        self.assertEqual(list(Fruit.CitrusTypes), [Fruit.lemon, Fruit.orange])
-        self.assertTrue(Fruit.orange in Fruit.CitrusTypes)
-
-
-    def test_order_as_function(self):
-        # first with _init_
-        class TestSequence(Enum):
-            _init_ = 'value, sequence'
-            _order_ = lambda member: member.sequence
-            item_id                  = 'An$(1,6)',      0     # Item Code
-            company_id               = 'An$(7,2)',      1     # Company Code
-            warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
-            company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
-            key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
-            available                = 'Zn$(1,1)',      5     # Available?
-            contract_item            = 'Bn(2,1)',       6     # Contract Item?
-            sales_category           = 'Fn',            7     # Sales Category
-            gl_category              = 'Rn$(5,1)',      8     # G/L Category
-            warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
-            inv_units                = 'Qn$(7,2)',     10     # Inv Units
-        for i, member in enumerate(TestSequence):
-            self.assertEqual(i, member.sequence)
-        ts = TestSequence
-        self.assertEqual(ts.item_id.name, 'item_id')
-        self.assertEqual(ts.item_id.value, 'An$(1,6)')
-        self.assertEqual(ts.item_id.sequence, 0)
-        self.assertEqual(ts.company_id.name, 'company_id')
-        self.assertEqual(ts.company_id.value, 'An$(7,2)')
-        self.assertEqual(ts.company_id.sequence, 1)
-        self.assertEqual(ts.warehouse_no.name, 'warehouse_no')
-        self.assertEqual(ts.warehouse_no.value, 'An$(9,4)')
-        self.assertEqual(ts.warehouse_no.sequence, 2)
-        self.assertEqual(ts.company.name, 'company')
-        self.assertEqual(ts.company.value, 'Hn$(13,6)')
-        self.assertEqual(ts.company.sequence, 3)
-        self.assertEqual(ts.key_type.name, 'key_type')
-        self.assertEqual(ts.key_type.value, 'Cn$(19,3)')
-        self.assertEqual(ts.key_type.sequence, 4)
-        self.assertEqual(ts.available.name, 'available')
-        self.assertEqual(ts.available.value, 'Zn$(1,1)')
-        self.assertEqual(ts.available.sequence, 5)
-        self.assertEqual(ts.contract_item.name, 'contract_item')
-        self.assertEqual(ts.contract_item.value, 'Bn(2,1)')
-        self.assertEqual(ts.contract_item.sequence, 6)
-        self.assertEqual(ts.sales_category.name, 'sales_category')
-        self.assertEqual(ts.sales_category.value, 'Fn')
-        self.assertEqual(ts.sales_category.sequence, 7)
-        self.assertEqual(ts.gl_category.name, 'gl_category')
-        self.assertEqual(ts.gl_category.value, 'Rn$(5,1)')
-        self.assertEqual(ts.gl_category.sequence, 8)
-        self.assertEqual(ts.warehouse_category.name, 'warehouse_category')
-        self.assertEqual(ts.warehouse_category.value, 'Sn$(6,1)')
-        self.assertEqual(ts.warehouse_category.sequence, 9)
-        self.assertEqual(ts.inv_units.name, 'inv_units')
-        self.assertEqual(ts.inv_units.value, 'Qn$(7,2)')
-        self.assertEqual(ts.inv_units.sequence, 10)
-        # and then without
-        class TestSequence(Enum):
-            _order_ = lambda member: member.value[1]
-            item_id                  = 'An$(1,6)',      0     # Item Code
-            company_id               = 'An$(7,2)',      1     # Company Code
-            warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
-            company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
-            key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
-            available                = 'Zn$(1,1)',      5     # Available?
-            contract_item            = 'Bn(2,1)',       6     # Contract Item?
-            sales_category           = 'Fn',            7     # Sales Category
-            gl_category              = 'Rn$(5,1)',      8     # G/L Category
-            warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
-            inv_units                = 'Qn$(7,2)',     10     # Inv Units
-        for i, member in enumerate(TestSequence):
-            self.assertEqual(i, member.value[1])
-        ts = TestSequence
-        self.assertEqual(ts.item_id.name, 'item_id')
-        self.assertEqual(ts.item_id.value, ('An$(1,6)', 0))
-        self.assertEqual(ts.company_id.name, 'company_id')
-        self.assertEqual(ts.company_id.value, ('An$(7,2)', 1))
-        self.assertEqual(ts.warehouse_no.name, 'warehouse_no')
-        self.assertEqual(ts.warehouse_no.value, ('An$(9,4)', 2))
-        self.assertEqual(ts.company.name, 'company')
-        self.assertEqual(ts.company.value, ('Hn$(13,6)', 3))
-        self.assertEqual(ts.key_type.name, 'key_type')
-        self.assertEqual(ts.key_type.value, ('Cn$(19,3)', 4))
-        self.assertEqual(ts.available.name, 'available')
-        self.assertEqual(ts.available.value, ('Zn$(1,1)', 5))
-        self.assertEqual(ts.contract_item.name, 'contract_item')
-        self.assertEqual(ts.contract_item.value, ('Bn(2,1)', 6))
-        self.assertEqual(ts.sales_category.name, 'sales_category')
-        self.assertEqual(ts.sales_category.value, ('Fn', 7))
-        self.assertEqual(ts.gl_category.name, 'gl_category')
-        self.assertEqual(ts.gl_category.value, ('Rn$(5,1)', 8))
-        self.assertEqual(ts.warehouse_category.name, 'warehouse_category')
-        self.assertEqual(ts.warehouse_category.value, ('Sn$(6,1)', 9))
-        self.assertEqual(ts.inv_units.name, 'inv_units')
-        self.assertEqual(ts.inv_units.value, ('Qn$(7,2)', 10))
-        # then with _init_ but without value
-        with self.assertRaises(TypeError):
-            class TestSequence(Enum):
-                _init_ = 'sequence'
-                _order_ = lambda member: member.sequence
-                item_id                  = 'An$(1,6)',      0     # Item Code
-                company_id               = 'An$(7,2)',      1     # Company Code
-                warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
-                company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
-                key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
-                available                = 'Zn$(1,1)',      5     # Available?
-                contract_item            = 'Bn(2,1)',       6     # Contract Item?
-                sales_category           = 'Fn',            7     # Sales Category
-                gl_category              = 'Rn$(5,1)',      8     # G/L Category
-                warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
-                inv_units                = 'Qn$(7,2)',     10     # Inv Units
-        # finally, out of order so Python 3 barfs
-        with self.assertRaises(TypeError):
-            class TestSequence(Enum):
-                _init_ = 'sequence'
-                _order_ = lambda member: member.sequence
-                item_id                  = 'An$(1,6)',      0     # Item Code
-                warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
-                company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
-                company_id               = 'An$(7,2)',      1     # Company Code
-                inv_units                = 'Qn$(7,2)',     10     # Inv Units
-                available                = 'Zn$(1,1)',      5     # Available?
-                contract_item            = 'Bn(2,1)',       6     # Contract Item?
-                sales_category           = 'Fn',            7     # Sales Category
-                key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
-                gl_category              = 'Rn$(5,1)',      8     # G/L Category
-                warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
-
-    def test_order_as_function_in_subclass(self):
-        #
-        class Parent(Enum):
-            _init_ = 'value sequence'
-            _order_ = lambda m: m.sequence
-        #
-        class Child(Parent):
-            item_id                  = 'An$(1,6)',      0     # Item Code
-            company_id               = 'An$(7,2)',      1     # Company Code
-            warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
-            company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
-            key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
-            available                = 'Zn$(1,1)',      5     # Available?
-            contract_item            = 'Bn(2,1)',       6     # Contract Item?
-            sales_category           = 'Fn',            7     # Sales Category
-            gl_category              = 'Rn$(5,1)',      8     # G/L Category
-            warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
-            inv_units                = 'Qn$(7,2)',     10     # Inv Units
-        #
-        for i, member in enumerate(Child):
-            self.assertEqual(i, member.sequence)
-        #
-        ts = Child
-        self.assertEqual(ts.item_id.name, 'item_id')
-        self.assertEqual(ts.item_id.value, 'An$(1,6)')
-        self.assertEqual(ts.item_id.sequence, 0)
-        self.assertEqual(ts.company_id.name, 'company_id')
-        self.assertEqual(ts.company_id.value, 'An$(7,2)')
-        self.assertEqual(ts.company_id.sequence, 1)
-        self.assertEqual(ts.warehouse_no.name, 'warehouse_no')
-        self.assertEqual(ts.warehouse_no.value, 'An$(9,4)')
-        self.assertEqual(ts.warehouse_no.sequence, 2)
-        self.assertEqual(ts.company.name, 'company')
-        self.assertEqual(ts.company.value, 'Hn$(13,6)')
-        self.assertEqual(ts.company.sequence, 3)
-        self.assertEqual(ts.key_type.name, 'key_type')
-        self.assertEqual(ts.key_type.value, 'Cn$(19,3)')
-        self.assertEqual(ts.key_type.sequence, 4)
-        self.assertEqual(ts.available.name, 'available')
-        self.assertEqual(ts.available.value, 'Zn$(1,1)')
-        self.assertEqual(ts.available.sequence, 5)
-        self.assertEqual(ts.contract_item.name, 'contract_item')
-        self.assertEqual(ts.contract_item.value, 'Bn(2,1)')
-        self.assertEqual(ts.contract_item.sequence, 6)
-        self.assertEqual(ts.sales_category.name, 'sales_category')
-        self.assertEqual(ts.sales_category.value, 'Fn')
-        self.assertEqual(ts.sales_category.sequence, 7)
-        self.assertEqual(ts.gl_category.name, 'gl_category')
-        self.assertEqual(ts.gl_category.value, 'Rn$(5,1)')
-        self.assertEqual(ts.gl_category.sequence, 8)
-        self.assertEqual(ts.warehouse_category.name, 'warehouse_category')
-        self.assertEqual(ts.warehouse_category.value, 'Sn$(6,1)')
-        self.assertEqual(ts.warehouse_category.sequence, 9)
-        self.assertEqual(ts.inv_units.name, 'inv_units')
-        self.assertEqual(ts.inv_units.value, 'Qn$(7,2)')
-        self.assertEqual(ts.inv_units.sequence, 10)
-
-        pass
-
-    def test_multiple_mixin(self):
-        class MaxMixin(object):
-            @classproperty
-            def MAX(cls):
-                max = len(cls)
-                cls.MAX = max
-                return max
-        class StrMixin(object):
-            def __str__(self):
-                return self._name_.lower()
-        class SomeEnum(Enum):
-            def behavior(self):
-                return 'booyah'
-        class AnotherEnum(Enum):
-            def behavior(self):
-                return 'nuhuh!'
-            def social(self):
-                return "what's up?"
-        class Color(MaxMixin, Enum):
-            _order_ = 'RED GREEN BLUE'
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(Color.RED.value, 1)
-        self.assertEqual(Color.GREEN.value, 2)
-        self.assertEqual(Color.BLUE.value, 3)
-        self.assertEqual(Color.MAX, 3)
-        self.assertEqual(str(Color.BLUE), 'Color.BLUE')
-        class Color(MaxMixin, StrMixin, Enum):
-            _order_ = 'RED GREEN BLUE'
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(Color.RED.value, 1)
-        self.assertEqual(Color.GREEN.value, 2)
-        self.assertEqual(Color.BLUE.value, 3)
-        self.assertEqual(Color.MAX, 3)
-        self.assertEqual(str(Color.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
-        class Color(StrMixin, MaxMixin, Enum):
-            _order_ = 'RED GREEN BLUE'
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(Color.RED.value, 1)
-        self.assertEqual(Color.GREEN.value, 2)
-        self.assertEqual(Color.BLUE.value, 3)
-        self.assertEqual(Color.MAX, 3)
-        self.assertEqual(str(Color.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
-        class CoolColor(StrMixin, SomeEnum, Enum):
-            _order_ = 'RED GREEN BLUE'
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(CoolColor.RED.value, 1)
-        self.assertEqual(CoolColor.GREEN.value, 2)
-        self.assertEqual(CoolColor.BLUE.value, 3)
-        self.assertEqual(str(CoolColor.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
-        self.assertEqual(CoolColor.RED.behavior(), 'booyah')
-        class CoolerColor(StrMixin, AnotherEnum, Enum):
-            _order_ = 'RED GREEN BLUE'
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(CoolerColor.RED.value, 1)
-        self.assertEqual(CoolerColor.GREEN.value, 2)
-        self.assertEqual(CoolerColor.BLUE.value, 3)
-        self.assertEqual(str(CoolerColor.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
-        self.assertEqual(CoolerColor.RED.behavior(), 'nuhuh!')
-        self.assertEqual(CoolerColor.RED.social(), "what's up?")
-        class CoolestColor(StrMixin, SomeEnum, AnotherEnum):
-            _order_ = 'RED GREEN BLUE'
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(CoolestColor.RED.value, 1)
-        self.assertEqual(CoolestColor.GREEN.value, 2)
-        self.assertEqual(CoolestColor.BLUE.value, 3)
-        self.assertEqual(str(CoolestColor.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
-        self.assertEqual(CoolestColor.RED.behavior(), 'booyah')
-        self.assertEqual(CoolestColor.RED.social(), "what's up?")
-        class ConfusedColor(StrMixin, AnotherEnum, SomeEnum):
-            _order_ = 'RED GREEN BLUE'
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(ConfusedColor.RED.value, 1)
-        self.assertEqual(ConfusedColor.GREEN.value, 2)
-        self.assertEqual(ConfusedColor.BLUE.value, 3)
-        self.assertEqual(str(ConfusedColor.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
-        self.assertEqual(ConfusedColor.RED.behavior(), 'nuhuh!')
-        self.assertEqual(ConfusedColor.RED.social(), "what's up?")
-        class ReformedColor(StrMixin, IntEnum, SomeEnum, AnotherEnum):
-            _order_ = 'RED GREEN BLUE'
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(ReformedColor.RED.value, 1)
-        self.assertEqual(ReformedColor.GREEN.value, 2)
-        self.assertEqual(ReformedColor.BLUE.value, 3)
-        self.assertEqual(str(ReformedColor.BLUE), 'blue', '%r is not %r' % (str(Color.BLUE), 'blue'))
-        self.assertEqual(ReformedColor.RED.behavior(), 'booyah')
-        self.assertEqual(ConfusedColor.RED.social(), "what's up?")
-        self.assertTrue(issubclass(ReformedColor, int))
-
-    def test_multiple_inherited_mixin(self):
-        @unique
-        class Decision1(StrEnum):
-            REVERT = "REVERT"
-            REVERT_ALL = "REVERT_ALL"
-            RETRY = "RETRY"
-        class MyEnum(StrEnum):
-            pass
-        @unique
-        class Decision2(MyEnum):
-            REVERT = "REVERT"
-            REVERT_ALL = "REVERT_ALL"
-            RETRY = "RETRY"
-
-    def test_value_auto_assign(self):
-        class Some(Enum):
-            def __new__(cls, val):
-                return object.__new__(cls)
-            x = 1
-            y = 2
-        self.assertEqual(Some.x.value, 1)
-        self.assertEqual(Some.y.value, 2)
-
-    def test_enum_of_types(self):
-        """Support using Enum to refer to types deliberately."""
-        class MyTypes(Enum):
-            i = int
-            f = float
-            s = str
-        self.assertEqual(MyTypes.i.value, int)
-        self.assertEqual(MyTypes.f.value, float)
-        self.assertEqual(MyTypes.s.value, str)
-        class Foo:
-            pass
-        class Bar:
-            pass
-        class MyTypes2(Enum):
-            a = Foo
-            b = Bar
-        self.assertEqual(MyTypes2.a.value, Foo)
-        self.assertEqual(MyTypes2.b.value, Bar)
-        class SpamEnumNotInner:
-            pass
-        class SpamEnum(Enum):
-            spam = SpamEnumNotInner
-        self.assertEqual(SpamEnum.spam.value, SpamEnumNotInner)
-
-    if PY2:
-        def test_nested_classes_in_enum_do_become_members(self):
-            # manually set __qualname__ to remove testing framework noise
-            class Outer(Enum):
-                _order_ = 'a b Inner'
-                __qualname__ = "Outer"
-                a = 1
-                b = 2
-                class Inner(Enum):
-                    __qualname__ = "Outer.Inner"
-                    foo = 10
-                    bar = 11
-            self.assertTrue(isinstance(Outer.Inner, Outer))
-            self.assertEqual(Outer.a.value, 1)
-            self.assertEqual(Outer.Inner.value.foo.value, 10)
-            self.assertEqual(
-                list(Outer.Inner.value),
-                [Outer.Inner.value.foo, Outer.Inner.value.bar],
-                )
-            self.assertEqual(
-                list(Outer),
-                [Outer.a, Outer.b, Outer.Inner],
-                )
-
-        def test_really_nested_classes_in_enum_do_become_members(self):
-            class Outer(Enum):
-                _order_ = 'a b Inner'
-                a = 1
-                b = 2
-                class Inner(Enum):
-                    foo = 10
-                    bar = 11
-            self.assertTrue(isinstance(Outer.Inner, Outer))
-            self.assertEqual(Outer.a.value, 1)
-            self.assertEqual(Outer.Inner.value.foo.value, 10)
-            self.assertEqual(
-                list(Outer.Inner.value),
-                [Outer.Inner.value.foo, Outer.Inner.value.bar],
-                )
-            self.assertEqual(
-                list(Outer),
-                [Outer.a, Outer.b, Outer.Inner],
-                )
-
-    def test_nested_classes_in_enum_are_skipped_with_skip(self):
-        """Support locally-defined nested classes using @skip"""
-        # manually set __qualname__ to remove testing framework noise
-        class Outer(Enum):
-            __qualname__ = "Outer"
-            a = 1
-            b = 2
-            @skip
-            class Inner(Enum):
-                __qualname__ = "Outer.Inner"
-                foo = 10
-                bar = 11
-        self.assertTrue(isinstance(Outer.Inner, type))
-        self.assertEqual(Outer.a.value, 1)
-        self.assertEqual(Outer.Inner.foo.value, 10)
-        self.assertEqual(
-            list(Outer.Inner),
-            [Outer.Inner.foo, Outer.Inner.bar],
-            )
-        self.assertEqual(
-            list(Outer),
-            [Outer.a, Outer.b],
-            )
-
-    def test_really_nested_classes_in_enum_are_skipped_with_skip(self):
-        """Support locally-defined nested classes using @skip"""
-        class Outer(Enum):
-            a = 1
-            b = 2
-            @skip
-            class Inner(Enum):
-                foo = 10
-                bar = 11
-        self.assertTrue(isinstance(Outer.Inner, type))
-        self.assertEqual(Outer.a.value, 1)
-        self.assertEqual(Outer.Inner.foo.value, 10)
-        self.assertEqual(
-            list(Outer.Inner),
-            [Outer.Inner.foo, Outer.Inner.bar],
-            )
-        self.assertEqual(
-            list(Outer),
-            [Outer.a, Outer.b],
-            )
-
-    def test_enum_call_without_arg(self):
-        class Color(Enum):
-            black = 0
-            red = 1
-            green = 2
-            blue = 3
-            #
-            @classmethod
-            def _missing_value_(cls, value):
-                if value is no_arg:
-                    return cls.black
-        self.assertTrue(Color.red is Color(1))
-        self.assertTrue(Color.black is Color())
-
-    def test_init_subclass(self):
-        class MyEnum(Enum):
-            def __init_subclass__(cls, **kwds):
-                super(MyEnum, cls).__init_subclass__(**kwds)
-                self.assertFalse(cls.__dict__.get('_test', False))
-                cls._test1 = 'MyEnum'
-        #
-        class TheirEnum(MyEnum):
-            def __init_subclass__(cls, **kwds):
-                super(TheirEnum, cls).__init_subclass__(**kwds)
-                cls._test2 = 'TheirEnum'
-        class WhoseEnum(TheirEnum):
-            def __init_subclass__(cls, **kwds):
-                pass
-        class NoEnum(WhoseEnum):
-            ONE = 1
-        self.assertEqual(TheirEnum.__dict__['_test1'], 'MyEnum')
-        self.assertEqual(WhoseEnum.__dict__['_test1'], 'MyEnum')
-        self.assertEqual(WhoseEnum.__dict__['_test2'], 'TheirEnum')
-        self.assertFalse(NoEnum.__dict__.get('_test1', False))
-        self.assertFalse(NoEnum.__dict__.get('_test2', False))
-        #
-        class OurEnum(MyEnum):
-            def __init_subclass__(cls, **kwds):
-                cls._test2 = 'OurEnum'
-        class WhereEnum(OurEnum):
-            def __init_subclass__(cls, **kwds):
-                pass
-        class NeverEnum(WhereEnum):
-            ONE = 'one'
-        self.assertEqual(OurEnum.__dict__['_test1'], 'MyEnum')
-        self.assertFalse(WhereEnum.__dict__.get('_test1', False))
-        self.assertEqual(WhereEnum.__dict__['_test2'], 'OurEnum')
-        self.assertFalse(NeverEnum.__dict__.get('_test1', False))
-        self.assertFalse(NeverEnum.__dict__.get('_test2', False))
-
-
-class TestStrEnum(TestCase):
-
-    def test_set_name(self):
-        class Descriptor(object):
-            name = None
-            def __get__(self, instance, owner_class=None):
-                if instance is None:
-                    return self
-                else:
-                    return instance.__dict__[self.name]
-            def __set__(self, instance, value):
-                instance.__dict__[self.name] = value
-            def __set_name__(self, owner, name):
-                self.name = name
-        #
-        class AnEnum(Enum):
-            ONE = 'one'
-            two = Descriptor()
-        #
-        self.assertEqual(list(AnEnum), [AnEnum.ONE])
-        self.assertEqual(AnEnum.two.name, 'two')
-        AnEnum.ONE.two = 'three'
-        self.assertEqual(AnEnum.ONE.two, 'three')
-        self.assertEqual(AnEnum.ONE.__dict__['two'], 'three')
-
-    def test_private_names(self):
-        class Private(Enum):
-            __corporal = 'Radar'
-            __major_ = 'Hoolihan'
-        self.assertEqual(len(Private), 0)
-        self.assertEqual(Private._Private__corporal, 'Radar')
-        self.assertFalse(isinstance(Private._Private__corporal, Enum))
-        self.assertEqual(Private._Private__major_, 'Hoolihan')
-        self.assertFalse(isinstance(Private._Private__major_, Enum))
-
-    def test_strenum_inherited_methods(self):
-        class phy(StrEnum):
-            pi = 'Pi'
-            tau = 'Tau'
-        self.assertTrue(phy.pi < phy.tau)
-        self.assertEqual(phy.pi.upper(), 'PI')
-        self.assertEqual(phy.tau.count('a'), 1)
-
-    def test_strict_strenum(self):
-        for uhoh in (object, object(), [], Enum, 9):
-            with self.assertRaisesRegex(TypeError, r'values must be str'):
-                class Huh(StrEnum):
-                    huh = uhoh
-        class Either(StrEnum):
-            _order_ = 'this that Those lower upper'
-            this = auto()
-            that = 'That'
-            Those = auto()
-            lower = 'lower'
-            upper = 'UPPER'
-        self.assertEqual([m.value for m in Either], ['this', 'That', 'those', 'lower', 'UPPER'])
-        #
-        with self.assertRaisesRegex(ValueError, r' is not lower-case'):
-            class Huh(LowerStrEnum):
-                huh = 'What'
-        #
-        class Lower(LowerStrEnum):
-            _order_ = 'this that Those lower upper'
-            this = auto()
-            that = 'that'
-            Those = auto()
-            lower = 'lower'
-            upper = 'upper'
-        self.assertEqual([m.value for m in Lower], ['this', 'that', 'those', 'lower', 'upper'])
-        #
-        with self.assertRaisesRegex(ValueError, r' is not upper-case'):
-            class Huh(UpperStrEnum):
-                huh = 'What'
-        #
-        class Upper(UpperStrEnum):
-            _order_ = 'this that Those lower upper'
-            this = auto()
-            that = 'THAT'
-            Those = auto()
-            lower = 'LOWER'
-            upper = 'UPPER'
-        self.assertEqual([m.value for m in Upper], ['THIS', 'THAT', 'THOSE', 'LOWER', 'UPPER'])
-
-    def test_init_subclass(self):
-        class MyEnum(StrEnum):
-            def __init_subclass__(cls, **kwds):
-                super(MyEnum, cls).__init_subclass__(**kwds)
-                self.assertFalse(cls.__dict__.get('_test', False))
-                cls._test1 = 'MyEnum'
-        #
-        class TheirEnum(MyEnum):
-            def __init_subclass__(cls, **kwds):
-                super(TheirEnum, cls).__init_subclass__(**kwds)
-                cls._test2 = 'TheirEnum'
-        class WhoseEnum(TheirEnum):
-            def __init_subclass__(cls, **kwds):
-                pass
-        class NoEnum(WhoseEnum):
-            ONE = 'one'
-        self.assertEqual(TheirEnum.__dict__['_test1'], 'MyEnum')
-        self.assertEqual(WhoseEnum.__dict__['_test1'], 'MyEnum')
-        self.assertEqual(WhoseEnum.__dict__['_test2'], 'TheirEnum')
-        self.assertFalse(NoEnum.__dict__.get('_test1', False))
-        self.assertFalse(NoEnum.__dict__.get('_test2', False))
-        #
-        class OurEnum(MyEnum):
-            def __init_subclass__(cls, **kwds):
-                cls._test2 = 'OurEnum'
-        class WhereEnum(OurEnum):
-            def __init_subclass__(cls, **kwds):
-                pass
-        class NeverEnum(WhereEnum):
-            ONE = 'one'
-        self.assertEqual(OurEnum.__dict__['_test1'], 'MyEnum')
-        self.assertFalse(WhereEnum.__dict__.get('_test1', False))
-        self.assertEqual(WhereEnum.__dict__['_test2'], 'OurEnum')
-        self.assertFalse(NeverEnum.__dict__.get('_test1', False))
-        self.assertFalse(NeverEnum.__dict__.get('_test2', False))
-
-
-class TestFlag(TestCase):
-    """Tests of the Flags."""
-
-    def setUp(self):
-        class Perm(Flag):
-            _order_ = 'R W X'
-            R, W, X = 4, 2, 1
-        self.Perm = Perm
-        #
-        class Color(Flag):
-            BLACK = 0
-            RED = 1
-            ROJO = 1
-            GREEN = 2
-            BLUE = 4
-            PURPLE = RED|BLUE
-            WHITE = RED|GREEN|BLUE
-            BLANCO = RED|GREEN|BLUE
-        self.Color = Color
-        #
-        class Fun(Flag):
-            _order_ = 'ONE TWO FOUR EIGHT'
-            ONE = auto()
-            TWO = auto()
-            THREE = ONE | TWO
-            FOUR = auto()
-            FIVE = FOUR | ONE
-            SIX = FOUR | TWO
-            SEVEN = FOUR | TWO | ONE
-            EIGHT = auto()
-        self.Fun = Fun
-        #
-        class TermColor(str, Flag):
-            def __new__(cls, value, code):
-                str_value = '\x1b[%sm' % code
-                obj = str.__new__(cls, str_value)
-                obj._value_ = value
-                obj.code = code
-                return obj
-            #
-            @classmethod
-            def _create_pseudo_member_values_(cls, members, *values):
-                code = ';'.join(m.code for m in members)
-                return values + (code, )
-            #
-            AllReset = '0'           # ESC [ 0 m       # reset all (colors and brightness)
-            Bright = '1'          # ESC [ 1 m       # bright
-            Dim = '2'             # ESC [ 2 m       # dim (looks same as normal brightness)
-            Underline = '4'
-            Normal = '22'         # ESC [ 22 m      # normal brightness
-                                #
-                                # # FOREGROUND - 30s  BACKGROUND - 40s:
-            FG_Black = '30'           # ESC [ 30 m      # black
-            FG_Red = '31'             # ESC [ 31 m      # red
-            FG_Green = '32'           # ESC [ 32 m      # green
-            FG_Yellow = '33'          # ESC [ 33 m      # yellow
-            FG_Blue = '34'            # ESC [ 34 m      # blue
-            FG_Magenta = '35'         # ESC [ 35 m      # magenta
-            FG_Cyan = '36'            # ESC [ 36 m      # cyan
-            FG_White = '37'           # ESC [ 37 m      # white
-            FG_Reset = '39'           # ESC [ 39 m      # reset
-                                    #
-            BG_Black = '40'           # ESC [ 30 m      # black
-            BG_Red = '41'             # ESC [ 31 m      # red
-            BG_Green = '42'           # ESC [ 32 m      # green
-            BG_Yellow = '43'          # ESC [ 33 m      # yellow
-            BG_Blue = '44'            # ESC [ 34 m      # blue
-            BG_Magenta = '45'         # ESC [ 35 m      # magenta
-            BG_Cyan = '46'            # ESC [ 36 m      # cyan
-            BG_White = '47'           # ESC [ 37 m      # white
-            BG_Reset = '49'           # ESC [ 39 m      # reset
-            #
-            __str__ = str.__str__
-            #
-            def __repr__(self):
-                if self._name_ is not None:
-                    return '<%s.%s>' % (self.__class__.__name__, self._name_)
-                else:
-                    return '<%s: %s>' % (self.__class__.__name__, '|'.join([m.name for m in Flag.__iter__(self)]))
-            #
-            def __enter__(self):
-                print(self.AllReset, end='', verbose=0)
-                return self
-            #
-            def __exit__(self, *args):
-                print(self.AllReset, end='', verbose=0)
-        self.TermColor = TermColor
-        #
-        class Open(Flag):
-            RO = 0
-            WO = 1
-            RW = 2
-            AC = 3
-            CE = 1<<19
-        self.Open = Open
-
-    def test_set_name(self):
-        class Descriptor(object):
-            name = None
-            def __get__(self, instance, owner_class=None):
-                if instance is None:
-                    return self
-                else:
-                    return instance.__dict__[self.name]
-            def __set__(self, instance, value):
-                instance.__dict__[self.name] = value
-            def __set_name__(self, owner, name):
-                self.name = name
-        #
-        class AnEnum(Enum):
-            ONE = 1
-            two = Descriptor()
-        #
-        self.assertEqual(list(AnEnum), [AnEnum.ONE])
-        self.assertEqual(AnEnum.two.name, 'two')
-        AnEnum.ONE.two = 'three'
-        self.assertEqual(AnEnum.ONE.two, 'three')
-        self.assertEqual(AnEnum.ONE.__dict__['two'], 'three')
-
-    def test_new_with_keywords(self):
-        class Huh(IntFlag):
-            __order__ = 'PLAIN BOLD_ITALIC HIGHLIGHT'
-            def __new__(cls, docstring, open=None, close=None):
-                if cls.__members__:
-                    value = 2 ** (len(cls.__members__)-1)
-                else:
-                    value = 0
-                member = int.__new__(cls, value)
-                if open and close is None:
-                    close = open
-                member.open = open
-                member.close = close
-                member.__doc__ = docstring
-                member._value_ = value
-                return member
-            PLAIN           = 'normal'
-            BOLD_ITALIC     = '***really super important***', '***'
-            HIGHLIGHT       = 'please ==take notice==', '==', '=='
-        p = Huh.PLAIN
-        self.assertTrue(type(p) is Huh, type(p))
-        self.assertEqual(
-                (p.value, p.__doc__, p.open, p.close),
-                (0, 'normal', None, None),
-                )
-        bi = Huh.BOLD_ITALIC
-        self.assertEqual(
-                (bi.value, bi.__doc__, bi.open, bi.close),
-                (1, '***really super important***', '***', '***'),
-                )
-        h = Huh.HIGHLIGHT
-        self.assertEqual(
-                (h.value, h.__doc__, h.open, h.close),
-                (2, 'please ==take notice==', '==', '=='),
-                )
-
-    def test_private_names(self):
-        class Private(Enum):
-            __corporal = 'Radar'
-            __major_ = 'Hoolihan'
-        self.assertEqual(len(Private), 0)
-        self.assertEqual(Private._Private__corporal, 'Radar')
-        self.assertFalse(isinstance(Private._Private__corporal, Enum))
-        self.assertEqual(Private._Private__major_, 'Hoolihan')
-        self.assertFalse(isinstance(Private._Private__major_, Enum))
-
-    def test_auto_alias(self):
-        Fun = self.Fun
-        self.assertEqual(
-                list(Fun),
-                [Fun.ONE, Fun.TWO, Fun.FOUR, Fun.EIGHT],
-                )
-        self.assertEqual(Fun.THREE._value_, 3)
-        self.assertEqual(repr(Fun.SEVEN), '<Fun.SEVEN: 7>')
-        self.assertEqual(list(Fun.SEVEN), [Fun.ONE, Fun.TWO, Fun.FOUR])
-
-    def test_str_is_str_str(self):
-        red, white = self.TermColor.FG_Red, self.TermColor.BG_White
-        barber = red | white
-        self.assertEqual(barber, '\x1b[31;47m')
-        self.assertEqual(barber.value, red.value | white.value)
-        self.assertEqual(barber.code, ';'.join([red.code, white.code]))
-        self.assertEqual(repr(barber), '<TermColor.FG_Red|BG_White>')
-        self.assertEqual(str(barber), '\x1b[31;47m')
-
-    def test_membership(self):
-        Color = self.Color
-        Open = self.Open
-        self.assertRaises(TypeError, lambda: 'BLACK' in Color)
-        self.assertRaises(TypeError, lambda: 'RO' in Open)
-        self.assertTrue(Color.BLACK in Color)
-        self.assertTrue(Open.RO in Open)
-        self.assertFalse(Color.BLACK in Open)
-        self.assertFalse(Open.RO in Color)
-        self.assertRaises(TypeError, lambda: 0 in Color)
-        self.assertRaises(TypeError, lambda: 0 in Open)
-
-    def test_member_contains(self):
-        Color = self.Color
-        self.assertRaises(TypeError, lambda: 'test' in Color.BLUE)
-        self.assertRaises(TypeError, lambda: 2 in Color.BLUE)
-        self.assertTrue(Color.BLUE in Color.BLUE)
-        self.assertTrue(Color.BLUE in Color['RED|GREEN|BLUE'])
-
-    def test_member_length(self):
-        self.assertEqual(self.Color.__len__(self.Color.BLACK), 0)
-        self.assertEqual(self.Color.__len__(self.Color.GREEN), 1)
-        self.assertEqual(self.Color.__len__(self.Color.PURPLE), 2)
-        self.assertEqual(self.Color.__len__(self.Color.BLANCO), 3)
-
-    def test_number_reset_and_order_cleanup(self):
-        class Confused(Flag):
-            _order_ = 'ONE TWO FOUR DOS EIGHT SIXTEEN'
-            ONE = auto()
-            TWO = auto()
-            FOUR = auto()
-            DOS = 2
-            EIGHT = auto()
-            SIXTEEN = auto()
-        self.assertEqual(
-                list(Confused),
-                [Confused.ONE, Confused.TWO, Confused.FOUR, Confused.EIGHT, Confused.SIXTEEN])
-        self.assertIs(Confused.TWO, Confused.DOS)
-        self.assertEqual(Confused.DOS._value_, 2)
-        self.assertEqual(Confused.EIGHT._value_, 8)
-        self.assertEqual(Confused.SIXTEEN._value_, 16)
-
-    def test_str(self):
-        Perm = self.Perm
-        self.assertEqual(str(Perm.R), 'Perm.R')
-        self.assertEqual(str(Perm.W), 'Perm.W')
-        self.assertEqual(str(Perm.X), 'Perm.X')
-        self.assertEqual(str(Perm.R | Perm.W), 'Perm.R|W')
-        self.assertEqual(str(Perm.R | Perm.W | Perm.X), 'Perm.R|W|X')
-        self.assertEqual(str(Perm(0)), 'Perm(0)')
-        self.assertEqual(str(~Perm.R), 'Perm.W|X')
-        self.assertEqual(str(~Perm.W), 'Perm.R|X')
-        self.assertEqual(str(~Perm.X), 'Perm.R|W')
-        self.assertEqual(str(~(Perm.R | Perm.W)), 'Perm.X')
-        self.assertEqual(str(~(Perm.R | Perm.W | Perm.X)), 'Perm(0)')
-        self.assertEqual(str(Perm(-1)), 'Perm.R|W|X')
-        self.assertEqual(str(Perm(~0)), 'Perm.R|W|X')
-
-        Open = self.Open
-        self.assertEqual(str(Open.RO), 'Open.RO')
-        self.assertEqual(str(Open.WO), 'Open.WO')
-        self.assertEqual(str(Open.AC), 'Open.AC')
-        self.assertEqual(str(Open.RO | Open.CE), 'Open.CE')
-        self.assertEqual(str(Open.WO | Open.CE), 'Open.WO|CE')
-        self.assertEqual(str(~Open.RO), 'Open.WO|RW|CE')
-        self.assertEqual(str(~Open.WO), 'Open.RW|CE')
-        self.assertEqual(str(~Open.AC), 'Open.CE')
-        self.assertEqual(str(~(Open.RO | Open.CE)), 'Open.AC')
-        self.assertEqual(str(~(Open.WO | Open.CE)), 'Open.RW')
-
-    def test_repr(self):
-        Perm = self.Perm
-        self.assertEqual(repr(Perm.R), '<Perm.R: 4>')
-        self.assertEqual(repr(Perm.W), '<Perm.W: 2>')
-        self.assertEqual(repr(Perm.X), '<Perm.X: 1>')
-        self.assertEqual(repr(Perm.R | Perm.W), '<Perm.R|W: 6>')
-        self.assertEqual(repr(Perm.R | Perm.W | Perm.X), '<Perm.R|W|X: 7>')
-        self.assertEqual(repr(Perm(0)), '<Perm: 0>')
-        self.assertEqual(repr(~Perm.R), '<Perm.W|X: 3>')
-        self.assertEqual(repr(~Perm.W), '<Perm.R|X: 5>')
-        self.assertEqual(repr(~Perm.X), '<Perm.R|W: 6>')
-        self.assertEqual(repr(~(Perm.R | Perm.W)), '<Perm.X: 1>')
-        self.assertEqual(repr(~(Perm.R | Perm.W | Perm.X)), '<Perm: 0>')
-        self.assertEqual(repr(Perm(~0)), '<Perm.R|W|X: 7>')
-
-        Open = self.Open
-        self.assertEqual(repr(Open.RO), '<Open.RO: 0>')
-        self.assertEqual(repr(Open.WO), '<Open.WO: 1>')
-        self.assertEqual(repr(Open.AC), '<Open.AC: 3>')
-        self.assertEqual(repr(Open.RO | Open.CE), '<Open.CE: 524288>')
-        self.assertEqual(repr(Open.WO | Open.CE), '<Open.WO|CE: 524289>')
-        self.assertEqual(repr(~Open.RO), '<Open.WO|RW|CE: 524291>')
-        self.assertEqual(repr(~Open.WO), '<Open.RW|CE: 524290>')
-        self.assertEqual(repr(~Open.AC), '<Open.CE: 524288>')
-        self.assertEqual(repr(~(Open.RO | Open.CE)), '<Open.AC: 3>')
-        self.assertEqual(repr(~(Open.WO | Open.CE)), '<Open.RW: 2>')
-
-    def test_name_lookup(self):
-        Color = self.Color
-        self.assertTrue(Color.RED is Color['RED'])
-        self.assertTrue(Color.RED|Color.GREEN is Color['RED|GREEN'])
-        self.assertTrue(Color.PURPLE is Color['RED|BLUE'])
-
-    def test_or(self):
-        Perm = self.Perm
-        for i in Perm:
-            for j in Perm:
-                self.assertEqual((i | j), Perm(i.value | j.value))
-                self.assertEqual((i | j).value, i.value | j.value)
-                self.assertIs(type(i | j), Perm)
-        for i in Perm:
-            self.assertIs(i | i, i)
-        Open = self.Open
-        self.assertIs(Open.RO | Open.CE, Open.CE)
-
-    def test_and(self):
-        Perm = self.Perm
-        RW = Perm.R | Perm.W
-        RX = Perm.R | Perm.X
-        WX = Perm.W | Perm.X
-        RWX = Perm.R | Perm.W | Perm.X
-        values = list(Perm) + [RW, RX, WX, RWX, Perm(0)]
-        for i in values:
-            for j in values:
-                self.assertEqual((i & j).value, i.value & j.value)
-                self.assertIs(type(i & j), Perm)
-        for i in Perm:
-            self.assertIs(i & i, i)
-            self.assertIs(i & RWX, i)
-            self.assertIs(RWX & i, i)
-        Open = self.Open
-        self.assertIs(Open.RO & Open.CE, Open.RO)
-
-    def test_xor(self):
-        Perm = self.Perm
-        for i in Perm:
-            for j in Perm:
-                self.assertEqual((i ^ j).value, i.value ^ j.value)
-                self.assertIs(type(i ^ j), Perm)
-        for i in Perm:
-            self.assertIs(i ^ Perm(0), i)
-            self.assertIs(Perm(0) ^ i, i)
-        Open = self.Open
-        self.assertIs(Open.RO ^ Open.CE, Open.CE)
-        self.assertIs(Open.CE ^ Open.CE, Open.RO)
-
-    def test_invert(self):
-        Perm = self.Perm
-        RW = Perm.R | Perm.W
-        RX = Perm.R | Perm.X
-        WX = Perm.W | Perm.X
-        RWX = Perm.R | Perm.W | Perm.X
-        values = list(Perm) + [RW, RX, WX, RWX, Perm(0)]
-        for i in values:
-            self.assertIs(type(~i), Perm)
-            self.assertEqual(~~i, i)
-        for i in Perm:
-            self.assertIs(~~i, i)
-        Open = self.Open
-        self.assertIs(Open.WO & ~Open.WO, Open.RO)
-        self.assertIs((Open.WO|Open.CE) & ~Open.WO, Open.CE)
-
-    def test_bool(self):
-        Perm = self.Perm
-        for f in Perm:
-            self.assertTrue(f)
-        Open = self.Open
-        for f in Open:
-            self.assertEqual(bool(f.value), bool(f))
-
-    def test_doc_flag(self):
-        class DocFlag(Flag):
-            _init_ = 'value __doc__'
-            _start_ = 0
-            # def __new__(cls, value, doc=None):
-            #     # if doc is None and isinstance(value, basestring):
-            #     #     value, doc = doc, value
-            #     # if value is None:
-            #     #     if not len(cls):
-            #     #         value = 0
-            #     #     else:
-            #     #         value = 2 ** (len(cls) -1)
-            #     # if not isinstance(value, baseinteger):
-            #     #     raise TypeError("%r is not a valid %s value" % (value, cls.__name__))
-            #     obj = object.__new__(cls)
-            #     # if doc is None, don't mess with the value
-            #     if doc:
-            #         value = value >> 1
-            #     obj._value_ = value
-            #     obj.__doc__ = doc
-            #     return obj
-        #
-        class AddressSegment(DocFlag):
-            _order_ = 'UNKNOWN PO PO_TYPE NUMBER PREORD NAME STREET POSTORD SECONDARY_TYPE SECONDARY_NUMBER AND'
-            UNKNOWN = "unable to determine address element type"
-            PO = "post office delivery"
-            PO_TYPE = "box or drawer"
-            NUMBER = "main unit designator"
-            PREORD = "N S E W etc"
-            NAME = "street name"
-            STREET = "st ave blvd etc"
-            POSTORD = "N S E W etc"
-            SECONDARY_TYPE = "apt bldg floor etc"
-            SECONDARY_NUMBER = "secondary unit designator"
-            AND = "& indicates a corner address"
-        AS = AddressSegment
-        self.assertEqual(AS.NAME._value_, 16)
-        self.assertEqual(AS.STREET._value_, 32)
-        self.assertEqual(AS.SECONDARY_TYPE._value_, 128)
-        self.assertEqual((AS.NAME | AS.STREET)._value_, 48, "%r is not 48" % (AS.NAME | AS.STREET))
-                
-    def test_iteration(self):
-        C = self.Color
-        self.assertEqual(list(C), [C.RED, C.GREEN, C.BLUE])
-        self.assertEqual(list(C.PURPLE), [C.RED, C.BLUE])
-
-    def test_member_iteration(self):
-        C = self.Color
-        self.assertEqual(list(C.BLACK), [])
-        self.assertEqual(list(C.RED), [C.RED])
-        self.assertEqual(list(C.PURPLE), [C.RED, C.BLUE])
-
-    def test_programatic_function_string(self):
-        Perm = Flag('Perm', 'R W X')
-        lst = list(Perm)
-        self.assertEqual(len(lst), len(Perm))
-        self.assertEqual(len(Perm), 3, Perm)
-        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
-        for i, n in enumerate('R W X'.split()):
-            v = 1<<i
-            e = Perm(v)
-            self.assertEqual(e.value, v)
-            self.assertEqual(type(e.value), int)
-            self.assertEqual(e.name, n)
-            self.assertIn(e, Perm)
-            self.assertIs(type(e), Perm)
-
-    def test_programatic_function_string_with_start(self):
-        Perm = Flag('Perm', 'R W X', start=8)
-        lst = list(Perm)
-        self.assertEqual(len(lst), len(Perm))
-        self.assertEqual(len(Perm), 3, Perm)
-        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
-        for i, n in enumerate('R W X'.split()):
-            v = 8<<i
-            e = Perm(v)
-            self.assertEqual(e.value, v)
-            self.assertEqual(type(e.value), int)
-            self.assertEqual(e.name, n)
-            self.assertIn(e, Perm)
-            self.assertIs(type(e), Perm)
-
-    def test_programatic_function_string_list(self):
-        Perm = Flag('Perm', ['R', 'W', 'X'])
-        lst = list(Perm)
-        self.assertEqual(len(lst), len(Perm))
-        self.assertEqual(len(Perm), 3, Perm)
-        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
-        for i, n in enumerate('R W X'.split()):
-            v = 1<<i
-            e = Perm(v)
-            self.assertEqual(e.value, v)
-            self.assertEqual(type(e.value), int)
-            self.assertEqual(e.name, n)
-            self.assertIn(e, Perm)
-            self.assertIs(type(e), Perm)
-
-    def test_programatic_function_iterable(self):
-        Perm = Flag('Perm', (('R', 2), ('W', 8), ('X', 32)))
-        lst = list(Perm)
-        self.assertEqual(len(lst), len(Perm))
-        self.assertEqual(len(Perm), 3, Perm)
-        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
-        for i, n in enumerate('R W X'.split()):
-            v = 1<<(2*i+1)
-            e = Perm(v)
-            self.assertEqual(e.value, v)
-            self.assertEqual(type(e.value), int)
-            self.assertEqual(e.name, n)
-            self.assertIn(e, Perm)
-            self.assertIs(type(e), Perm)
-
-    def test_programatic_function_from_dict(self):
-        Perm = Flag('Perm', OrderedDict((('R', 2), ('W', 8), ('X', 32))))
-        lst = list(Perm)
-        self.assertEqual(len(lst), len(Perm))
-        self.assertEqual(len(Perm), 3, Perm)
-        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
-        for i, n in enumerate('R W X'.split()):
-            v = 1<<(2*i+1)
-            e = Perm(v)
-            self.assertEqual(e.value, v)
-            self.assertEqual(type(e.value), int)
-            self.assertEqual(e.name, n)
-            self.assertIn(e, Perm)
-            self.assertIs(type(e), Perm)
-
-    def test_programatic_function_empty_list(self):
-        Perm = IntFlag('Perm', [])
-        self.assertEqual(len(list(Perm)), len(Perm))
-        self.assertEqual(len(Perm), 0)
-        Thing = Enum('Thing', [])
-        self.assertEqual(len(list(Thing)), len(Thing))
-        self.assertEqual(len(Thing), 0)
-
-    def test_programatic_function_empty_tuple(self):
-        Perm = IntFlag('Perm', ())
-        self.assertEqual(len(list(Perm)), len(Perm))
-        self.assertEqual(len(Perm), 0)
-        Thing = Enum('Thing', ())
-        self.assertEqual(len(list(Thing)), len(Thing))
-        self.assertEqual(len(Thing), 0)
-
-    def test_pickle(self):
-        if isinstance(FlagStooges, Exception):
-            raise FlagStooges
-        test_pickle_dump_load(self.assertIs, FlagStooges.CURLY|FlagStooges.MOE)
-        test_pickle_dump_load(self.assertIs, FlagStooges)
-
-    def test_containment(self):
-        Perm = self.Perm
-        R, W, X = Perm
-        RW = R | W
-        RX = R | X
-        WX = W | X
-        RWX = R | W | X
-        self.assertTrue(R in RW)
-        self.assertTrue(R in RX)
-        self.assertTrue(R in RWX)
-        self.assertTrue(W in RW)
-        self.assertTrue(W in WX)
-        self.assertTrue(W in RWX)
-        self.assertTrue(X in RX)
-        self.assertTrue(X in WX)
-        self.assertTrue(X in RWX)
-        self.assertFalse(R in WX)
-        self.assertFalse(W in RX)
-        self.assertFalse(X in RW)
-
-    def test_auto_number(self):
-        class Color(Flag):
-            _order_ = 'red blue green'
-            red = auto()
-            blue = auto()
-            green = auto()
-
-        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
-        self.assertEqual(Color.red.value, 1)
-        self.assertEqual(Color.blue.value, 2)
-        self.assertEqual(Color.green.value, 4)
-
-    def test_auto_number_garbage(self):
-        with self.assertRaisesRegex(TypeError, r'invalid Flag value: .not an int.'):
-            class Color(Flag):
-                _order_ = 'red blue'
-                red = 'not an int'
-                blue = auto()
-
-    def test_auto_w_pending(self):
-        class Required(Flag):
-            _order_ = 'TO_S FROM_S'
-            NONE = 0
-            TO_S = auto()
-            FROM_S = auto()
-            BOTH = TO_S | FROM_S
-        self.assertEqual(Required.TO_S.value, 1)
-        self.assertEqual(Required.FROM_S.value, 2)
-        self.assertEqual(Required.BOTH.value, 3)
-
-    def test_duplicate_auto(self):
-        class Dupes(Enum):
-            _order_ = 'first second third'
-            first = primero = auto()
-            second = auto()
-            third = auto()
-        self.assertEqual([Dupes.first, Dupes.second, Dupes.third], list(Dupes))
-
-    def test_bizarre(self):
-        with self.assertRaisesRegex(TypeError, r"invalid Flag 'Bizarre' -- missing values: 1, 2"):
-            class Bizarre(Flag):
-                b = 3
-                c = 4
-                d = 6
-
-    def test_multiple_mixin(self):
-        class AllMixin(object):
-            @classproperty
-            def ALL(cls):
-                members = list(cls)
-                all_value = None
-                if members:
-                    all_value = members[0]
-                    for member in members[1:]:
-                        all_value |= member
-                cls.ALL = all_value
-                return all_value
-        class StrMixin(object):
-            def __str__(self):
-                return self._name_.lower()
-        class Color(AllMixin, Flag):
-            _order_ = 'RED GREEN BLUE'
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(Color.RED.value, 1)
-        self.assertEqual(Color.GREEN.value, 2)
-        self.assertEqual(Color.BLUE.value, 4)
-        self.assertEqual(Color.ALL.value, 7)
-        self.assertEqual(str(Color.BLUE), 'Color.BLUE')
-        class Color(AllMixin, StrMixin, Flag):
-            _order_ = 'RED GREEN BLUE'
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(Color.RED.value, 1)
-        self.assertEqual(Color.GREEN.value, 2)
-        self.assertEqual(Color.BLUE.value, 4)
-        self.assertEqual(Color.ALL.value, 7)
-        self.assertEqual(str(Color.BLUE), 'blue')
-        class Color(StrMixin, AllMixin, Flag):
-            _order_ = 'RED GREEN BLUE'
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(Color.RED.value, 1)
-        self.assertEqual(Color.GREEN.value, 2)
-        self.assertEqual(Color.BLUE.value, 4)
-        self.assertEqual(Color.ALL.value, 7)
-        self.assertEqual(str(Color.BLUE), 'blue')
-
-    @unittest.skipUnless(threading, 'Threading required for this test.')
-    def test_unique_composite(self):
-        # override __eq__ to be identity only
-        class TestFlag(Flag):
-            _order_ = 'one two three four five six seven eight'
-            one = auto()
-            two = auto()
-            three = auto()
-            four = auto()
-            five = auto()
-            six = auto()
-            seven = auto()
-            eight = auto()
-            def __eq__(self, other):
-                return self is other
-            def __hash__(self):
-                return hash(self._value_)
-        # have multiple threads competing to complete the composite members
-        seen = set()
-        failed = [False]
-        def cycle_enum():
-            # nonlocal failed
-            try:
-                for i in range(256):
-                    seen.add(TestFlag(i))
-            except Exception:
-                failed[0] = True
-        threads = [
-                threading.Thread(target=cycle_enum)
-                for _ in range(8)
-                ]
-        for t in threads:
-            t.start()
-        for t in threads:
-            t.join()
-        # check that only 248 members were created (8 were created originally)
-        self.assertFalse(
-                failed[0],
-                'at least one thread failed while creating composite members')
-        self.assertEqual(256, len(seen), 'too many composite members created')
-
-    def test_init_with_autovalue_and_generate_next_value(self):
-        class Color(Flag):
-            _init_ = 'value code'
-            def _generate_next_value_(name, start, count, last_values, *args, **kwds):
-                if not count:
-                    return ((1, start)[start is not None], ) + args
-                error = False
-                for last_value in reversed(last_values):
-                    try:
-                        high_bit = aenum._high_bit(last_value)
-                        break
-                    except Exception:
-                        error = True
-                        break
-                if error:
-                    raise TypeError('Invalid Flag value: %r' % (last_value, ))
-                return (2 ** (high_bit+1), ) + args
-            # TODO: actually test _create_pseudo_member
-            @classmethod
-            def _create_pseudo_member_(cls, value):
-                members = list(cls._iter_member_(value))
-                pseudo_member = super(Color, cls)._create_pseudo_member_(value)
-                pseudo_member.code = ';'.join(m.code for m in members)
-                return pseudo_member
-            AllReset = '0'           # ESC [ 0 m       # reset all (colors and brightness)
-            Bright = '1'          # ESC [ 1 m       # bright
-            Dim = '2'             # ESC [ 2 m       # dim (looks same as normal brightness)
-            Underline = '4'
-            Normal = '22'         # ESC [ 22 m      # normal brightness
-        # if we got here, we're good
-
-    def test_autovalue_and_generate_next_value(self):
-        class Color(str, Flag):
-            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
-            def __new__(cls, value, code):
-                str_value = '\x1b[%sm' % code
-                obj = str.__new__(cls, str_value)
-                obj._value_ = value
-                obj.code = code
-                return obj
-            @staticmethod
-            def _generate_next_value_(name, start, count, values, *args, **kwds):
-                return (2 ** count, ) + args
-            # TODO: actually test _create_pseudo_member
-            @classmethod
-            def _create_pseudo_member_(cls, value):
-                # calculate the code
-                members = list(cls._iter_member_(value))
-                code = ';'.join(m.code for m in members)
-                pseudo_member = super(Color, cls)._create_pseudo_member_(value, code)
-                return pseudo_member
-            #
-                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
-            FG_Black = '30'           # ESC [ 30 m      # black
-            FG_Red = '31'             # ESC [ 31 m      # red
-            FG_Green = '32'           # ESC [ 32 m      # green
-            FG_Blue = '34'            # ESC [ 34 m      # blue
-                                      #
-            BG_Yellow = '43'          # ESC [ 33 m      # yellow
-            BG_Magenta = '45'         # ESC [ 35 m      # magenta
-            BG_Cyan = '46'            # ESC [ 36 m      # cyan
-            BG_White = '47'           # ESC [ 37 m      # white
-        # if we got here, we're good
-
-    def test_subclass(self):
-        class Color(str, Flag):
-            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
-            def __new__(cls, value, code):
-                str_value = '\x1b[%sm' % code
-                obj = str.__new__(cls, str_value)
-                obj._value_ = value
-                obj.code = code
-                return obj
-            @staticmethod
-            def _generate_next_value_(name, start, count, values, *args, **kwds):
-                return (2 ** count, ) + args
-            @classmethod
-            def _create_pseudo_member_(cls, value):
-                # calculate the code
-                members = list(cls._iter_member_(value))
-                code = ';'.join(m.code for m in members)
-                pseudo_member = super(Color, cls)._create_pseudo_member_(value, code)
-                return pseudo_member
-            #
-                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
-            FG_Black = '30'           # ESC [ 30 m      # black
-            FG_Red = '31'             # ESC [ 31 m      # red
-            FG_Green = '32'           # ESC [ 32 m      # green
-            FG_Blue = '34'            # ESC [ 34 m      # blue
-                                      #
-            BG_Yellow = '43'          # ESC [ 33 m      # yellow
-            BG_Magenta = '45'         # ESC [ 35 m      # magenta
-            BG_Cyan = '46'            # ESC [ 36 m      # cyan
-            BG_White = '47'           # ESC [ 37 m      # white
-        self.assertTrue(isinstance(Color.FG_Black, Color))
-        self.assertTrue(isinstance(Color.FG_Black, str))
-        self.assertEqual(Color.FG_Black, '\x1b[30m')
-        self.assertEqual(Color.FG_Black.code, '30')
-
-    def test_sub_subclass_1(self):
-        class StrFlag(str, Flag):
-            def __new__(cls, value, code):
-                str_value = '\x1b[%sm' % code
-                obj = str.__new__(cls, str_value)
-                obj._value_ = value
-                obj.code = code
-                return obj
-            @classmethod
-            def _create_pseudo_member_(cls, value):
-                # calculate the code
-                members = list(cls._iter_member_(value))
-                code = ';'.join(m.code for m in members)
-                pseudo_member = super(Color, cls)._create_pseudo_member_(value, code)
-                return pseudo_member
-        class Color(StrFlag):
-            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
-                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
-            FG_Black = '30'           # ESC [ 30 m      # black
-            FG_Red = '31'             # ESC [ 31 m      # red
-            FG_Green = '32'           # ESC [ 32 m      # green
-            FG_Blue = '34'            # ESC [ 34 m      # blue
-                                      #
-            BG_Yellow = '43'          # ESC [ 33 m      # yellow
-            BG_Magenta = '45'         # ESC [ 35 m      # magenta
-            BG_Cyan = '46'            # ESC [ 36 m      # cyan
-            BG_White = '47'           # ESC [ 37 m      # white
-        self.assertTrue(isinstance(Color.FG_Black, Color))
-        self.assertTrue(isinstance(Color.FG_Black, str))
-        self.assertEqual(Color.FG_Black, '\x1b[30m')
-        self.assertEqual(Color.FG_Black.code, '30')
-
-    def test_sub_subclass_2(self):
-        class StrFlag(str, Flag):
-            @staticmethod
-            def _generate_next_value_(name, start, count, values, *args, **kwds):
-                return (2 ** count, ) + args
-            @classmethod
-            def _create_pseudo_member_(cls, value):
-                # calculate the code
-                members = list(cls._iter_member_(value))
-                code = ';'.join(m.code for m in members)
-                pseudo_member = super(Color, cls)._create_pseudo_member_(value, code)
-                return pseudo_member
-            #
-        class Color(StrFlag):
-            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
-            def __new__(cls, value, code):
-                str_value = '\x1b[%sm' % code
-                obj = str.__new__(cls, str_value)
-                obj._value_ = value
-                obj.code = code
-                return obj
-                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
-            FG_Black = '30'           # ESC [ 30 m      # black
-            FG_Red = '31'             # ESC [ 31 m      # red
-            FG_Green = '32'           # ESC [ 32 m      # green
-            FG_Blue = '34'            # ESC [ 34 m      # blue
-                                      #
-            BG_Yellow = '43'          # ESC [ 33 m      # yellow
-            BG_Magenta = '45'         # ESC [ 35 m      # magenta
-            BG_Cyan = '46'            # ESC [ 36 m      # cyan
-            BG_White = '47'           # ESC [ 37 m      # white
-        self.assertTrue(isinstance(Color.FG_Black, Color))
-        self.assertTrue(isinstance(Color.FG_Black, str))
-        self.assertEqual(Color.FG_Black, '\x1b[30m')
-        self.assertEqual(Color.FG_Black.code, '30')
-
-    def test_sub_subclass_3(self):
-        class StrFlag(str, Flag):
-            def __new__(cls, value, code):
-                str_value = '\x1b[%sm' % code
-                obj = str.__new__(cls, str_value)
-                obj._value_ = value
-                obj.code = code
-                return obj
-            @classmethod
-            def _create_pseudo_member_(cls, value):
-                # calculate the code
-                members = list(cls._iter_member_(value))
-                code = ';'.join(m.code for m in members)
-                pseudo_member = super(Color, cls)._create_pseudo_member_(value, code)
-                return pseudo_member
-            #
-        class Color(StrFlag):
-            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
-                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
-            FG_Black = '30'           # ESC [ 30 m      # black
-            FG_Red = '31'             # ESC [ 31 m      # red
-            FG_Green = '32'           # ESC [ 32 m      # green
-            FG_Blue = '34'            # ESC [ 34 m      # blue
-                                      #
-            BG_Yellow = '43'          # ESC [ 33 m      # yellow
-            BG_Magenta = '45'         # ESC [ 35 m      # magenta
-            BG_Cyan = '46'            # ESC [ 36 m      # cyan
-            BG_White = '47'           # ESC [ 37 m      # white
-        self.assertTrue(isinstance(Color.FG_Black, Color))
-        self.assertTrue(isinstance(Color.FG_Black, str))
-        self.assertEqual(Color.FG_Black, '\x1b[30m')
-        self.assertEqual(Color.FG_Black.code, '30')
-
-    def test_sub_subclass_4(self):
-        class StrFlag(str, Flag):
-            def __new__(cls, value, code):
-                str_value = '\x1b[%sm' % code
-                obj = str.__new__(cls, str_value)
-                obj._value_ = value
-                obj.code = code
-                return obj
-            @classmethod
-            def _create_pseudo_member_values_(cls, members, *values):
-                code = ';'.join(m.code for m in members)
-                return values + (code, )
-            #
-        class Color(StrFlag):
-            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
-                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
-            FG_Black = '30'           # ESC [ 30 m      # black
-            FG_Red = '31'             # ESC [ 31 m      # red
-            FG_Green = '32'           # ESC [ 32 m      # green
-            FG_Blue = '34'            # ESC [ 34 m      # blue
-                                      #
-            BG_Yellow = '43'          # ESC [ 33 m      # yellow
-            BG_Magenta = '45'         # ESC [ 35 m      # magenta
-            BG_Cyan = '46'            # ESC [ 36 m      # cyan
-            BG_White = '47'           # ESC [ 37 m      # white
-            #
-            def __repr__(self):
-                return '<%s.%s>' % (self.__class__.__name__, self._name_)
-        self.assertTrue(isinstance(Color.FG_Black, Color))
-        self.assertTrue(isinstance(Color.FG_Black, str))
-        self.assertEqual(Color.FG_Black, '\x1b[30m')
-        self.assertEqual(Color.FG_Black.code, '30')
-        colors = Color.BG_Magenta | Color.FG_Black
-        self.assertTrue(isinstance(colors, Color))
-        self.assertTrue(isinstance(colors, str))
-        self.assertEqual(colors, '\x1b[30;45m')
-        self.assertEqual(colors.code, '30;45')
-        self.assertEqual(repr(colors), '<Color.FG_Black|BG_Magenta>')
-
-    def test_sub_subclass_with_new_new(self):
-        class StrFlag(str, Flag):
-            def __new__(cls, value, code):
-                str_value = '\x1b[%sm' % code
-                obj = str.__new__(cls, str_value)
-                obj._value_ = value
-                obj.code = code
-                return obj
-            @classmethod
-            def _create_pseudo_member_(cls, value):
-                # calculate the code
-                members = list(cls._iter_member_(value))
-                code = ';'.join(m.code for m in members)
-                pseudo_member = super(StrFlag, cls)._create_pseudo_member_(value, code)
-                return pseudo_member
-            #
-        class Color(StrFlag):
-            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
-            def __new__(cls, value, string, abbr):
-                str_value = (abbr or '').title()
-                obj = str.__new__(cls, str_value)
-                obj._value_ = value
-                obj.code = string
-                obj.abbr = abbr
-                return obj
-                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
-            FG_Black = '30', 'blk'           # ESC [ 30 m      # black
-            FG_Red = '31', 'red'             # ESC [ 31 m      # red
-            FG_Green = '32', 'grn'           # ESC [ 32 m      # green
-            FG_Blue = '34', 'blu'            # ESC [ 34 m      # blue
-                                      #
-            BG_Yellow = '43', 'ylw'          # ESC [ 33 m      # yellow
-            BG_Magenta = '45', 'mag'         # ESC [ 35 m      # magenta
-            BG_Cyan = '46', 'cyn'            # ESC [ 36 m      # cyan
-            BG_White = '47', 'wht'           # ESC [ 37 m      # white
-            #
-            def __repr__(self):
-                if self._name_ is not None:
-                    return '<%s.%s>' % (self.__class__.__name__, self._name_)
-                else:
-                    return '<%s: %s>' % (self.__class__.__name__, '|'.join([m.name for m in self]))
-        self.assertTrue(isinstance(Color.FG_Black, Color))
-        self.assertTrue(isinstance(Color.FG_Black, str))
-        self.assertEqual(Color.FG_Black, 'Blk', str.__repr__(Color.FG_Black))
-        self.assertEqual(Color.FG_Black.abbr, 'blk')
-
-    def test_subclass_with_default_new(self):
-        class MyFlag(str, Flag):
-            _order_ = 'this these theother'
-            this = 'that'
-            these = 'those'
-            theother = 'thingimibobs'
-        self.assertEqual(MyFlag.this, 'that')
-        self.assertEqual(MyFlag.this.value, 1)
-        self.assertEqual(MyFlag.these, 'those')
-        self.assertEqual(MyFlag.these.value, 2)
-        self.assertEqual(MyFlag.theother, 'thingimibobs')
-        self.assertEqual(MyFlag.theother.value, 4)
-
-    def test_subclass_a_bunch(self):
-        class Color(str, Flag):
-            _order_ = 'FG_Black FG_Red FG_Green FG_Blue BG_Yellow BG_Magenta BG_Cyan BG_White'
-            def __new__(cls, value, code):
-                str_value = '\x1b[%sm' % code
-                obj = str.__new__(cls, str_value)
-                obj._value_ = value
-                obj.code = code
-                return obj
-            @staticmethod
-            def _generate_next_value_(name, start, count, values, *args, **kwds):
-                return (2 ** count, ) + args
-            @classmethod
-            def _create_pseudo_member_(cls, value):
-                # calculate the code
-                members = list(cls._iter_member_(value))
-                code = ';'.join(m.code for m in members)
-                pseudo_member = super(Color, cls)._create_pseudo_member_(value, code)
-                return pseudo_member
-            #
-                                      # # FOREGROUND - 30s  BACKGROUND - 40s:
-            FG_Black = '30'           # ESC [ 30 m      # black
-            FG_Red = '31'             # ESC [ 31 m      # red
-            FG_Green = '32'           # ESC [ 32 m      # green
-            FG_Blue = '34'            # ESC [ 34 m      # blue
-                                      #
-            BG_Yellow = '43'          # ESC [ 33 m      # yellow
-            BG_Magenta = '45'         # ESC [ 35 m      # magenta
-            BG_Cyan = '46'            # ESC [ 36 m      # cyan
-            BG_White = '47'           # ESC [ 37 m      # white
-            #
-            def __repr__(self):
-                if self._name_ is not None:
-                    return '<%s.%s>' % (self.__class__.__name__, self._name_)
-                else:
-                    return '<%s: %s>' % (self.__class__.__name__, '|'.join([m.name for m in self]))
-        #
-        Purple = Color.BG_Magenta | Color.FG_Blue
-        self.assertTrue(isinstance(Purple, Color))
-        self.assertTrue(isinstance(Purple, str))
-        self.assertIs(Purple, Color.BG_Magenta | Color.FG_Blue)
-        self.assertEqual(Purple, '\x1b[34;45m')
-        self.assertEqual(Purple.code, '34;45')
-        self.assertEqual(Purple.name, 'FG_Blue|BG_Magenta')
-
-    def test_init_subclass(self):
-        class MyEnum(Flag):
-            def __init_subclass__(cls, **kwds):
-                super(MyEnum, cls).__init_subclass__(**kwds)
-                self.assertFalse(cls.__dict__.get('_test', False))
-                cls._test1 = 'MyEnum'
-        #
-        class TheirEnum(MyEnum):
-            def __init_subclass__(cls, **kwds):
-                super(TheirEnum, cls).__init_subclass__(**kwds)
-                cls._test2 = 'TheirEnum'
-        class WhoseEnum(TheirEnum):
-            def __init_subclass__(cls, **kwds):
-                pass
-        class NoEnum(WhoseEnum):
-            ONE = 1
-        self.assertEqual(TheirEnum.__dict__['_test1'], 'MyEnum')
-        self.assertEqual(WhoseEnum.__dict__['_test1'], 'MyEnum')
-        self.assertEqual(WhoseEnum.__dict__['_test2'], 'TheirEnum')
-        self.assertFalse(NoEnum.__dict__.get('_test1', False))
-        self.assertFalse(NoEnum.__dict__.get('_test2', False))
-        #
-        class OurEnum(MyEnum):
-            def __init_subclass__(cls, **kwds):
-                cls._test2 = 'OurEnum'
-        class WhereEnum(OurEnum):
-            def __init_subclass__(cls, **kwds):
-                pass
-        class NeverEnum(WhereEnum):
-            ONE = 1
-        self.assertEqual(OurEnum.__dict__['_test1'], 'MyEnum')
-        self.assertFalse(WhereEnum.__dict__.get('_test1', False))
-        self.assertEqual(WhereEnum.__dict__['_test2'], 'OurEnum')
-        self.assertFalse(NeverEnum.__dict__.get('_test1', False))
-        self.assertFalse(NeverEnum.__dict__.get('_test2', False))
-
-    def test_int_long_conversion(self):
-        class Perm(Flag):
-            EXEC    = 1 << 0
-            WRITE   = 1 << 1
-            READ    = 1 << 2
-            MSB32   = 1 << 31
-            MSB64   = 1 << 63
-
-        # 32-bit system test
-        self.assertEqual(Perm.MSB32, Perm(0x80000000))
-        self.assertEqual(Perm.WRITE|Perm.MSB32, Perm(0x80000002))
-
-        # 64-bit system test
-        self.assertEqual(Perm.MSB64, Perm(0x8000000000000000))
-        self.assertEqual(Perm.MSB64|Perm.WRITE, Perm(0x8000000000000002))
-
-
-class TestIntFlag(TestCase):
-    """Tests of the IntFlags."""
-
-    def setUp(self):
-        #
-        class Perm(IntFlag):
-            _order_ = 'R W X'
-            R = 1 << 2
-            W = 1 << 1
-            X = 1 << 0
-        #
-        class Color(IntFlag):
-            BLACK = 0
-            RED = 1
-            GREEN = 2
-            BLUE = 4
-            PURPLE = RED|BLUE
-        #
-        class Open(IntFlag):
-            "not a good flag candidate"
-            RO = 0
-            WO = 1
-            RW = 2
-            AC = 3
-            CE = 1<<19
-        #
-        self.Perm = Perm
-        self.Color = Color
-        self.Open = Open
-
-    def test_set_name(self):
-        class Descriptor(object):
-            name = None
-            def __get__(self, instance, owner_class=None):
-                if instance is None:
-                    return self
-                else:
-                    return instance.__dict__[self.name]
-            def __set__(self, instance, value):
-                instance.__dict__[self.name] = value
-            def __set_name__(self, owner, name):
-                self.name = name
-        #
-        class AnEnum(Enum):
-            ONE = 1
-            two = Descriptor()
-        #
-        self.assertEqual(list(AnEnum), [AnEnum.ONE])
-        self.assertEqual(AnEnum.two.name, 'two')
-        AnEnum.ONE.two = 'three'
-        self.assertEqual(AnEnum.ONE.two, 'three')
-        self.assertEqual(AnEnum.ONE.__dict__['two'], 'three')
-
-    def test_private_names(self):
-        class Private(Enum):
-            __corporal = 'Radar'
-            __major_ = 'Hoolihan'
-        self.assertEqual(len(Private), 0)
-        self.assertEqual(Private._Private__corporal, 'Radar')
-        self.assertFalse(isinstance(Private._Private__corporal, Enum))
-        self.assertEqual(Private._Private__major_, 'Hoolihan')
-        self.assertFalse(isinstance(Private._Private__major_, Enum))
-
-    def test_membership(self):
-        Color = self.Color
-        Open = self.Open
-        self.assertRaises(TypeError, lambda: 'GREEN' in Color)
-        self.assertRaises(TypeError, lambda: 'RW' in Open)
-        self.assertTrue(Color.GREEN in Color)
-        self.assertTrue(Open.RW in Open)
-        self.assertFalse(Color.GREEN in Open)
-        self.assertFalse(Open.RW in Color)
-        self.assertRaises(TypeError, lambda: 2 in Color)
-        self.assertRaises(TypeError, lambda: 2 in Open)
-
-    def test_member_contains(self):
-        Color = self.Color
-        self.assertRaises(TypeError, lambda: 'test' in Color.RED)
-        self.assertRaises(TypeError, lambda: 1 in Color.RED)
-        self.assertTrue(Color.RED in Color.RED)
-        self.assertTrue(Color.RED in Color.PURPLE)
-
-    def test_name_lookup(self):
-        Color = self.Color
-        self.assertTrue(Color.RED is Color['RED'])
-        self.assertTrue(Color.RED|Color.GREEN is Color['RED|GREEN'])
-        self.assertTrue(Color.PURPLE is Color['RED|BLUE'])
-
-    def test_type(self):
-        Perm = self.Perm
-        Open = self.Open
-        for f in Perm:
-            self.assertTrue(isinstance(f, Perm))
-            self.assertEqual(f, f.value)
-        self.assertTrue(isinstance(Perm.W | Perm.X, Perm))
-        self.assertEqual(Perm.W | Perm.X, 3)
-        for f in Open:
-            self.assertTrue(isinstance(f, Open))
-            self.assertEqual(f, f.value)
-        self.assertTrue(isinstance(Open.WO | Open.RW, Open))
-        self.assertEqual(Open.WO | Open.RW, 3)
-
-
-    def test_str(self):
-        Perm = self.Perm
-        self.assertEqual(str(Perm.R), '4')
-        self.assertEqual(str(Perm.W), '2')
-        self.assertEqual(str(Perm.X), '1')
-        self.assertEqual(str(Perm.R | Perm.W), '6')
-        self.assertEqual(str(Perm.R | Perm.W | Perm.X), '7')
-        self.assertEqual(str(Perm(0)), '0')
-        self.assertEqual(str(~Perm.R), '3')
-        self.assertEqual(str(~Perm.W), '5')
-        self.assertEqual(str(~Perm.X), '6')
-        self.assertEqual(str(~(Perm.R | Perm.W)), '1')
-        self.assertEqual(str(~(Perm.R | Perm.W | Perm.X)), '0')
-        self.assertEqual(str(Perm(~0)), '7')
-
-        Open = self.Open
-        self.assertEqual(str(Open.RO), '0')
-        self.assertEqual(str(Open.WO), '1')
-        self.assertEqual(str(Open.AC), '3')
-        self.assertEqual(str(Open.RO | Open.CE), '524288')
-        self.assertEqual(str(Open.WO | Open.CE), '524289')
-        self.assertEqual(str(~Open.RO), '524291')
-        self.assertEqual(str(~Open.WO), '524290')
-        self.assertEqual(str(~Open.AC), '524288')
-        self.assertEqual(str(~(Open.RO | Open.CE)), '3')
-        self.assertEqual(str(~(Open.WO | Open.CE)), '2')
-
-    def test_repr_strict(self):
-        class Perm(IntFlag):
-            _order_ = 'R W X'
-            R = 1 << 2
-            W = 1 << 1
-            X = 1 << 0
-        Perm._boundary_ = aenum.STRICT
-        self.assertEqual(repr(Perm.R), '<Perm.R: 4>')
-        self.assertEqual(repr(Perm.W), '<Perm.W: 2>')
-        self.assertEqual(repr(Perm.X), '<Perm.X: 1>')
-        self.assertEqual(repr(Perm.R | Perm.W), '<Perm.R|W: 6>')
-        self.assertEqual(repr(Perm.R | Perm.W | Perm.X), '<Perm.R|W|X: 7>')
-        self.assertEqual(repr(Perm(0)), '<Perm: 0>')
-        self.assertEqual(repr(~Perm.R), '<Perm.W|X: 3>')
-        self.assertEqual(repr(~Perm.W), '<Perm.R|X: 5>')
-        self.assertEqual(repr(~Perm.X), '<Perm.R|W: 6>')
-        self.assertEqual(repr(~(Perm.R | Perm.W)), '<Perm.X: 1>')
-        self.assertEqual(repr(~(Perm.R | Perm.W | Perm.X)), '<Perm: 0>')
-        #
-        with self.assertRaisesRegex(ValueError, r'invalid value: 12'):
-            repr(Perm.R | 8)
-        with self.assertRaisesRegex(ValueError, r'invalid value: 12'):
-            repr(~(Perm.R | 8))
-        with self.assertRaisesRegex(ValueError, r'invalid value: -9'):
-            repr(Perm(~8))
-
-    def test_repr_conform(self):
-        class Perm(IntFlag):
-            _order_ = 'R W X'
-            R = 1 << 2
-            W = 1 << 1
-            X = 1 << 0
-        Perm._boundary_ = aenum.CONFORM
-        self.assertEqual(repr(Perm.R), '<Perm.R: 4>')
-        self.assertEqual(repr(Perm.W), '<Perm.W: 2>')
-        self.assertEqual(repr(Perm.X), '<Perm.X: 1>')
-        self.assertEqual(repr(Perm.R | Perm.W), '<Perm.R|W: 6>')
-        self.assertEqual(repr(Perm.R | Perm.W | Perm.X), '<Perm.R|W|X: 7>')
-        self.assertEqual(repr(Perm(0)), '<Perm: 0>')
-        self.assertEqual(repr(~Perm.R), '<Perm.W|X: 3>')
-        self.assertEqual(repr(~Perm.W), '<Perm.R|X: 5>')
-        self.assertEqual(repr(~Perm.X), '<Perm.R|W: 6>')
-        self.assertEqual(repr(~(Perm.R | Perm.W)), '<Perm.X: 1>')
-        self.assertEqual(repr(~(Perm.R | Perm.W | Perm.X)), '<Perm: 0>')
-        self.assertEqual(repr(Perm.R | 8), '<Perm.R: 4>')
-        self.assertEqual(repr(Perm(8)), '<Perm: 0>')
-        self.assertEqual(repr(~(Perm.R | 8)), '<Perm.W|X: 3>')
-        self.assertEqual(repr(Perm(~8)), '<Perm.R|W|X: 7>')
-
-    def test_repr_eject(self):
-        class Perm(IntFlag):
-            _order_ = 'R W X'
-            _boundary_ = EJECT
-            R = 1 << 2
-            W = 1 << 1
-            X = 1 << 0
-        self.assertEqual(repr(Perm.R), '<Perm.R: 4>')
-        self.assertEqual(repr(Perm.W), '<Perm.W: 2>')
-        self.assertEqual(repr(Perm.X), '<Perm.X: 1>')
-        self.assertEqual(repr(Perm.R | Perm.W), '<Perm.R|W: 6>')
-        self.assertEqual(repr(Perm.R | Perm.W | Perm.X), '<Perm.R|W|X: 7>')
-        self.assertEqual(repr(Perm(0)), '<Perm: 0>')
-        self.assertEqual(repr(~Perm.R), '<Perm.W|X: 3>')
-        self.assertEqual(repr(~Perm.W), '<Perm.R|X: 5>')
-        self.assertEqual(repr(~Perm.X), '<Perm.R|W: 6>')
-        self.assertEqual(repr(~(Perm.R | Perm.W)), '<Perm.X: 1>')
-        self.assertEqual(repr(~(Perm.R | Perm.W | Perm.X)), '<Perm: 0>')
-        self.assertEqual(repr(Perm.R | 8), '12')
-        self.assertEqual(repr(Perm(8)), '8')
-        self.assertEqual(repr(~(Perm.R | 8)), '-13')
-        self.assertEqual(repr(Perm(~8)), '-9')
-
-    def test_repr_open(self):
-        class Open(IntFlag):
-            "not a good flag candidate"
-            RO = 0
-            WO = 1
-            RW = 2
-            AC = 3
-            CE = 1<<19
-        Open._boundary_ = aenum.STRICT
-        self.assertEqual(repr(Open.RO), '<Open.RO: 0>')
-        self.assertEqual(repr(Open.WO), '<Open.WO: 1>')
-        self.assertEqual(repr(Open.AC), '<Open.AC: 3>')
-        self.assertEqual(repr(Open.RO | Open.CE), '<Open.CE: 524288>')
-        self.assertEqual(repr(Open.WO | Open.CE), '<Open.WO|CE: 524289>')
-        self.assertEqual(repr(~Open.RO), '<Open.WO|RW|CE: 524291>')
-        self.assertEqual(repr(~Open.WO), '<Open.RW|CE: 524290>')
-        self.assertEqual(repr(~Open.AC), '<Open.CE: 524288>')
-        self.assertEqual(repr(~(Open.RO | Open.CE)), '<Open.AC: 3>')
-        self.assertEqual(repr(~(Open.WO | Open.CE)), '<Open.RW: 2>')
-        with self.assertRaisesRegex(ValueError, r'invalid value: -5'):
-            repr(Open(~4))
-        with self.assertRaisesRegex(ValueError, r'invalid value: 4'):
-            repr(Open(4))
-        #
-        class Open(IntFlag):
-            "not a good flag candidate"
-            RO = 0
-            WO = 1
-            RW = 2
-            AC = 3
-            CE = 1<<19
-        Open._boundary_ = aenum.CONFORM
-        self.assertEqual(repr(Open.RO), '<Open.RO: 0>')
-        self.assertEqual(repr(Open.WO), '<Open.WO: 1>')
-        self.assertEqual(repr(Open.AC), '<Open.AC: 3>')
-        self.assertEqual(repr(Open.RO | Open.CE), '<Open.CE: 524288>')
-        self.assertEqual(repr(Open.WO | Open.CE), '<Open.WO|CE: 524289>')
-        self.assertEqual(repr(~Open.RO), '<Open.WO|RW|CE: 524291>')
-        self.assertEqual(repr(~Open.WO), '<Open.RW|CE: 524290>')
-        self.assertEqual(repr(~Open.AC), '<Open.CE: 524288>')
-        self.assertEqual(repr(~(Open.RO | Open.CE)), '<Open.AC: 3>')
-        self.assertEqual(repr(~(Open.WO | Open.CE)), '<Open.RW: 2>')
-        self.assertEqual(repr(Open(~4)), '<Open.WO|RW|CE: 524291>')
-        self.assertEqual(repr(Open(4)), '<Open.RO: 0>')
-        #
-        class Open(IntFlag):
-            "not a good flag candidate"
-            RO = 0
-            WO = 1
-            RW = 2
-            AC = 3
-            CE = 1<<19
-        Open._boundary_ = aenum.EJECT
-        self.assertEqual(repr(Open.RO), '<Open.RO: 0>')
-        self.assertEqual(repr(Open.WO), '<Open.WO: 1>')
-        self.assertEqual(repr(Open.AC), '<Open.AC: 3>')
-        self.assertEqual(repr(Open.RO | Open.CE), '<Open.CE: 524288>')
-        self.assertEqual(repr(Open.WO | Open.CE), '<Open.WO|CE: 524289>')
-        self.assertEqual(repr(~Open.RO), '<Open.WO|RW|CE: 524291>')
-        self.assertEqual(repr(~Open.WO), '<Open.RW|CE: 524290>')
-        self.assertEqual(repr(~Open.AC), '<Open.CE: 524288>')
-        self.assertEqual(repr(~(Open.RO | Open.CE)), '<Open.AC: 3>')
-        self.assertEqual(repr(~(Open.WO | Open.CE)), '<Open.RW: 2>')
-        self.assertEqual(repr(Open(~4)), '-5')
-        self.assertEqual(repr(Open(4)), '4')
-
-    def test_or(self):
-        Perm = self.Perm
-        for i in Perm:
-            for j in Perm:
-                self.assertEqual(i | j, i.value | j.value)
-                self.assertEqual((i | j).value, i.value | j.value)
-                self.assertIs(type(i | j), Perm)
-            for j in range(8):
-                self.assertEqual(i | j, i.value | j)
-                self.assertEqual((i | j).value, i.value | j)
-                self.assertIs(type(i | j), Perm)
-                self.assertEqual(j | i, j | i.value)
-                self.assertEqual((j | i).value, j | i.value)
-                self.assertIs(type(j | i), Perm)
-        for i in Perm:
-            self.assertIs(i | i, i)
-            self.assertIs(i | 0, i)
-            self.assertIs(0 | i, i)
-        Open = self.Open
-        self.assertIs(Open.RO | Open.CE, Open.CE)
-
-    def test_and(self):
-        Perm = self.Perm
-        RW = Perm.R | Perm.W
-        RX = Perm.R | Perm.X
-        WX = Perm.W | Perm.X
-        RWX = Perm.R | Perm.W | Perm.X
-        values = list(Perm) + [RW, RX, WX, RWX, Perm(0)]
-        for i in values:
-            for j in values:
-                self.assertEqual(i & j, i.value & j.value, 'i is %r, j is %r' % (i, j))
-                self.assertEqual((i & j).value, i.value & j.value, 'i is %r, j is %r' % (i, j))
-                self.assertIs(type(i & j), Perm, 'i is %r, j is %r' % (i, j))
-            for j in range(8):
-                self.assertEqual(i & j, i.value & j)
-                self.assertEqual((i & j).value, i.value & j)
-                self.assertIs(type(i & j), Perm)
-                self.assertEqual(j & i, j & i.value)
-                self.assertEqual((j & i).value, j & i.value)
-                self.assertIs(type(j & i), Perm)
-        for i in Perm:
-            self.assertIs(i & i, i)
-            self.assertIs(i & 7, i)
-            self.assertIs(7 & i, i)
-        Open = self.Open
-        self.assertIs(Open.RO & Open.CE, Open.RO)
-
-    def test_xor(self):
-        Perm = self.Perm
-        for i in Perm:
-            for j in Perm:
-                self.assertEqual(i ^ j, i.value ^ j.value)
-                self.assertEqual((i ^ j).value, i.value ^ j.value)
-                self.assertIs(type(i ^ j), Perm)
-            for j in range(8):
-                self.assertEqual(i ^ j, i.value ^ j)
-                self.assertEqual((i ^ j).value, i.value ^ j)
-                self.assertIs(type(i ^ j), Perm)
-                self.assertEqual(j ^ i, j ^ i.value)
-                self.assertEqual((j ^ i).value, j ^ i.value)
-                self.assertIs(type(j ^ i), Perm)
-        for i in Perm:
-            self.assertIs(i ^ 0, i)
-            self.assertIs(0 ^ i, i)
-        Open = self.Open
-        self.assertIs(Open.RO ^ Open.CE, Open.CE)
-        self.assertIs(Open.CE ^ Open.CE, Open.RO)
-
-    def test_invert(self):
-        Perm = self.Perm
-        RW = Perm.R | Perm.W
-        RX = Perm.R | Perm.X
-        WX = Perm.W | Perm.X
-        RWX = Perm.R | Perm.W | Perm.X
-        values = list(Perm) + [RW, RX, WX, RWX, Perm(0)]
-        for i in values:
-            self.assertEqual(~i, (~i).value)
-            self.assertIs(type(~i), Perm)
-            self.assertEqual(~~i, i)
-        for i in Perm:
-            self.assertIs(~~i, i)
-        Open = self.Open
-        self.assertIs(Open.WO & ~Open.WO, Open.RO)
-        self.assertIs((Open.WO|Open.CE) & ~Open.WO, Open.CE)
-
-    def test_iter(self):
-        Perm = self.Perm
-        NoPerm = Perm.R ^ Perm.R
-        RWX = Perm.R | Perm.W | Perm.X
-        self.assertEqual(list(NoPerm), [])
-        self.assertEqual(list(Perm.R), [Perm.R])
-        self.assertEqual(list(RWX), [Perm.R, Perm.W, Perm.X])
-
-    def test_programatic_function_string(self):
-        Perm = IntFlag('Perm', 'R W X')
-        lst = list(Perm)
-        self.assertEqual(len(lst), len(Perm))
-        self.assertEqual(len(Perm), 3, Perm)
-        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
-        for i, n in enumerate('R W X'.split()):
-            v = 1<<i
-            e = Perm(v)
-            self.assertEqual(e.value, v)
-            self.assertEqual(type(e.value), int)
-            self.assertEqual(e, v)
-            self.assertEqual(e.name, n)
-            self.assertIn(e, Perm)
-            self.assertIs(type(e), Perm)
-
-    def test_programatic_function_string_with_start(self):
-        Perm = IntFlag('Perm', 'R W X', start=8)
-        lst = list(Perm)
-        self.assertEqual(len(lst), len(Perm))
-        self.assertEqual(len(Perm), 3, Perm)
-        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
-        for i, n in enumerate('R W X'.split()):
-            v = 8<<i
-            e = Perm(v)
-            self.assertEqual(e.value, v)
-            self.assertEqual(type(e.value), int)
-            self.assertEqual(e, v)
-            self.assertEqual(e.name, n)
-            self.assertIn(e, Perm)
-            self.assertIs(type(e), Perm)
-
-    def test_programatic_function_string_list(self):
-        Perm = IntFlag('Perm', ['R', 'W', 'X'])
-        lst = list(Perm)
-        self.assertEqual(len(lst), len(Perm))
-        self.assertEqual(len(Perm), 3, Perm)
-        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
-        for i, n in enumerate('R W X'.split()):
-            v = 1<<i
-            e = Perm(v)
-            self.assertEqual(e.value, v)
-            self.assertEqual(type(e.value), int)
-            self.assertEqual(e, v)
-            self.assertEqual(e.name, n)
-            self.assertIn(e, Perm)
-            self.assertIs(type(e), Perm)
-
-    def test_programatic_function_iterable(self):
-        Perm = IntFlag('Perm', (('R', 2), ('W', 8), ('X', 32)))
-        lst = list(Perm)
-        self.assertEqual(len(lst), len(Perm))
-        self.assertEqual(len(Perm), 3, Perm)
-        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
-        for i, n in enumerate('R W X'.split()):
-            v = 1<<(2*i+1)
-            e = Perm(v)
-            self.assertEqual(e.value, v)
-            self.assertEqual(type(e.value), int)
-            self.assertEqual(e, v)
-            self.assertEqual(e.name, n)
-            self.assertIn(e, Perm)
-            self.assertIs(type(e), Perm)
-
-    def test_programatic_function_from_dict(self):
-        Perm = IntFlag('Perm', OrderedDict((('R', 2), ('W', 8), ('X', 32))))
-        lst = list(Perm)
-        self.assertEqual(len(lst), len(Perm))
-        self.assertEqual(len(Perm), 3, Perm)
-        self.assertEqual(lst, [Perm.R, Perm.W, Perm.X])
-        for i, n in enumerate('R W X'.split()):
-            v = 1<<(2*i+1)
-            e = Perm(v)
-            self.assertEqual(e.value, v)
-            self.assertEqual(type(e.value), int)
-            self.assertEqual(e, v)
-            self.assertEqual(e.name, n)
-            self.assertIn(e, Perm)
-            self.assertIs(type(e), Perm)
-
-    def test_containment(self):
-        Perm = self.Perm
-        R, W, X = Perm
-        RW = R | W
-        RX = R | X
-        WX = W | X
-        RWX = R | W | X
-        self.assertTrue(R in RW)
-        self.assertTrue(R in RX)
-        self.assertTrue(R in RWX)
-        self.assertTrue(W in RW)
-        self.assertTrue(W in WX)
-        self.assertTrue(W in RWX)
-        self.assertTrue(X in RX)
-        self.assertTrue(X in WX)
-        self.assertTrue(X in RWX)
-        self.assertFalse(R in WX)
-        self.assertFalse(W in RX)
-        self.assertFalse(X in RW)
-
-    def test_bool(self):
-        Perm = self.Perm
-        for f in Perm:
-            self.assertTrue(f)
-        Open = self.Open
-        for f in Open:
-            self.assertEqual(bool(f.value), bool(f))
-
-    def test_multiple_mixin(self):
-        class AllMixin(object):
-            @classproperty
-            def ALL(cls):
-                members = list(cls)
-                all_value = None
-                if members:
-                    all_value = members[0]
-                    for member in members[1:]:
-                        all_value |= member
-                cls.ALL = all_value
-                return all_value
-        class StrMixin(object):
-            def __str__(self):
-                return self._name_.lower()
-        class Color(AllMixin, IntFlag):
-            _order_ = 'RED GREEN BLUE'
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(Color.RED.value, 1)
-        self.assertEqual(Color.GREEN.value, 2)
-        self.assertEqual(Color.BLUE.value, 4)
-        self.assertEqual(Color.ALL.value, 7)
-        self.assertEqual(str(Color.BLUE), '4')
-        class Color(AllMixin, StrMixin, IntFlag):
-            _order_ = 'RED GREEN BLUE'
-            __str__ = StrMixin.__str__
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(Color.RED.value, 1)
-        self.assertEqual(Color.GREEN.value, 2)
-        self.assertEqual(Color.BLUE.value, 4)
-        self.assertEqual(Color.ALL.value, 7)
-        self.assertEqual(str(Color.BLUE), 'blue')
-        class Color(StrMixin, AllMixin, IntFlag):
-            _order_ = 'RED GREEN BLUE'
-            __str__ = StrMixin.__str__
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        self.assertEqual(Color.RED.value, 1)
-        self.assertEqual(Color.GREEN.value, 2)
-        self.assertEqual(Color.BLUE.value, 4)
-        self.assertEqual(Color.ALL.value, 7)
-        self.assertEqual(str(Color.BLUE), 'blue')
-
-    @unittest.skipUnless(threading, 'Threading required for this test.')
-    def test_unique_composite(self):
-        # override __eq__ to be identity only
-        class TestFlag(IntFlag):
-            _order_ = 'one two three four five six seven eight'
-            one = auto()
-            two = auto()
-            three = auto()
-            four = auto()
-            five = auto()
-            six = auto()
-            seven = auto()
-            eight = auto()
-            def __eq__(self, other):
-                return self is other
-            def __hash__(self):
-                return hash(self._value_)
-        # have multiple threads competing to complete the composite members
-        seen = set()
-        failed = [False]
-        def cycle_enum():
-            # nonlocal failed
-            try:
-                for i in range(256):
-                    seen.add(TestFlag(i))
-            except Exception:
-                failed[0] = True
-        threads = [
-                threading.Thread(target=cycle_enum)
-                for _ in range(8)
-                ]
-        for t in threads:
-            t.start()
-        for t in threads:
-            t.join()
-        # check that only 248 members were created (8 were created originally)
-        self.assertFalse(
-                failed[0],
-                'at least one thread failed while creating composite members')
-        self.assertEqual(256, len(seen), 'too many composite members created')
-
-    def test_init_subclass(self):
-        class MyEnum(IntEnum):
-            def __init_subclass__(cls, **kwds):
-                super(MyEnum, cls).__init_subclass__(**kwds)
-                self.assertFalse(cls.__dict__.get('_test', False))
-                cls._test1 = 'MyEnum'
-        #
-        class TheirEnum(MyEnum):
-            def __init_subclass__(cls, **kwds):
-                super(TheirEnum, cls).__init_subclass__(**kwds)
-                cls._test2 = 'TheirEnum'
-        class WhoseEnum(TheirEnum):
-            def __init_subclass__(cls, **kwds):
-                pass
-        class NoEnum(WhoseEnum):
-            ONE = 1
-        self.assertEqual(TheirEnum.__dict__['_test1'], 'MyEnum')
-        self.assertEqual(WhoseEnum.__dict__['_test1'], 'MyEnum')
-        self.assertEqual(WhoseEnum.__dict__['_test2'], 'TheirEnum')
-        self.assertFalse(NoEnum.__dict__.get('_test1', False))
-        self.assertFalse(NoEnum.__dict__.get('_test2', False))
-        #
-        class OurEnum(MyEnum):
-            def __init_subclass__(cls, **kwds):
-                cls._test2 = 'OurEnum'
-        class WhereEnum(OurEnum):
-            def __init_subclass__(cls, **kwds):
-                pass
-        class NeverEnum(WhereEnum):
-            ONE = 1
-        self.assertEqual(OurEnum.__dict__['_test1'], 'MyEnum')
-        self.assertFalse(WhereEnum.__dict__.get('_test1', False))
-        self.assertEqual(WhereEnum.__dict__['_test2'], 'OurEnum')
-        self.assertFalse(NeverEnum.__dict__.get('_test1', False))
-        self.assertFalse(NeverEnum.__dict__.get('_test2', False))
-
-
-
-class TestEmptyAndNonLatinStrings(unittest.TestCase):
-
-    def test_empty_string(self):
-        with self.assertRaises(ValueError):
-            empty_abc = Enum('empty_abc', ('', 'B', 'C'))
-
-    def test_non_latin_character_string(self):
-        greek_abc = Enum('greek_abc', ('\u03B1', 'B', 'C'))
-        item = getattr(greek_abc, '\u03B1')
-        self.assertEqual(item.value, 1)
-
-    def test_non_latin_number_string(self):
-        hebrew_123 = Enum('hebrew_123', ('\u05D0', '2', '3'))
-        item = getattr(hebrew_123, '\u05D0')
-        self.assertEqual(item.value, 1)
-
-
-class TestUnique(TestCase):
-    """2.4 doesn't allow class decorators, use function syntax."""
-
-    def test_unique_clean(self):
-        class Clean(Enum):
-            one = 1
-            two = 'dos'
-            tres = 4.0
-        unique(Clean)
-        class Cleaner(IntEnum):
-            single = 1
-            double = 2
-            triple = 3
-        unique(Cleaner)
-
-    def test_unique_dirty(self):
-        try:
-            class Dirty(Enum):
-                __order__ = 'one two'
-                one = 1
-                two = 'dos'
-                tres = 1
-            unique(Dirty)
-        except ValueError:
-            exc = sys.exc_info()[1]
-            message = exc.args[0]
-        self.assertTrue('tres -> one' in message)
-
-        try:
-            class Dirtier(IntEnum):
-                __order__ = 'single triple'
-                single = 1
-                double = 1
-                triple = 3
-                turkey = 3
-            unique(Dirtier)
-        except ValueError:
-            exc = sys.exc_info()[1]
-            message = exc.args[0]
-        self.assertTrue('double -> single' in message)
-        self.assertTrue('turkey -> triple' in message)
-
-    def test_unique_with_name(self):
-        @unique
-        class Silly(Enum):
-            one = 1
-            two = 'dos'
-            name = 3
-        @unique
-        class Sillier(IntEnum):
-            single = 1
-            name = 2
-            triple = 3
-            value = 4
-
-
-class TestNamedTuple(TestCase):
-
-    def test_explicit_indexing(self):
-        class Person(NamedTuple):
-            age = 0
-            first = 1
-            last = 2
-        p1 = Person(17, 'John', 'Doe')
-        p2 = Person(21, 'Jane', 'Doe')
-        self.assertEqual(p1[0], 17)
-        self.assertEqual(p1[1], 'John')
-        self.assertEqual(p1[2], 'Doe')
-        self.assertEqual(p2[0], 21)
-        self.assertEqual(p2[1], 'Jane')
-        self.assertEqual(p2[2], 'Doe')
-        self.assertEqual(p1.age, 17)
-        self.assertEqual(p1.first, 'John')
-        self.assertEqual(p1.last, 'Doe')
-        self.assertEqual(p2.age, 21)
-        self.assertEqual(p2.first, 'Jane')
-        self.assertEqual(p2.last, 'Doe')
-
-    def test_implicit_indexing(self):
-        class Person(NamedTuple):
-            __order__ = "age first last"
-            age = "person's age"
-            first = "person's first name"
-            last = "person's last name"
-        p1 = Person(17, 'John', 'Doe')
-        p2 = Person(21, 'Jane', 'Doe')
-        self.assertEqual(p1[0], 17)
-        self.assertEqual(p1[1], 'John')
-        self.assertEqual(p1[2], 'Doe')
-        self.assertEqual(p2[0], 21)
-        self.assertEqual(p2[1], 'Jane')
-        self.assertEqual(p2[2], 'Doe')
-        self.assertEqual(p1.age, 17)
-        self.assertEqual(p1.first, 'John')
-        self.assertEqual(p1.last, 'Doe')
-        self.assertEqual(p2.age, 21)
-        self.assertEqual(p2.first, 'Jane')
-        self.assertEqual(p2.last, 'Doe')
-
-    def test_mixed_indexing(self):
-        class Person(NamedTuple):
-            __order__ = "age last cars"
-            age = "person's age"
-            last = 2, "person's last name"
-            cars = "person's cars"
-        p1 = Person(17, 'John', 'Doe', 3)
-        p2 = Person(21, 'Jane', 'Doe', 9)
-        self.assertEqual(p1[0], 17)
-        self.assertEqual(p1[1], 'John')
-        self.assertEqual(p1[2], 'Doe')
-        self.assertEqual(p1[3], 3)
-        self.assertEqual(p2[0], 21)
-        self.assertEqual(p2[1], 'Jane')
-        self.assertEqual(p2[2], 'Doe')
-        self.assertEqual(p2[3], 9)
-        self.assertEqual(p1.age, 17)
-        self.assertEqual(p1.last, 'Doe')
-        self.assertEqual(p1.cars, 3)
-        self.assertEqual(p2.age, 21)
-        self.assertEqual(p2.last, 'Doe')
-        self.assertEqual(p2.cars, 9)
-
-    def test_issubclass(self):
-        class Person(NamedTuple):
-            age = 0
-            first = 1
-            last = 2
-        self.assertTrue(issubclass(Person, NamedTuple))
-        self.assertTrue(issubclass(Person, tuple))
-
-    def test_isinstance(self):
-        class Person(NamedTuple):
-            age = 0
-            first = 1
-            last = 2
-        p1 = Person(17, 'John', 'Doe')
-        self.assertTrue(isinstance(p1, Person))
-        self.assertTrue(isinstance(p1, NamedTuple))
-        self.assertTrue(isinstance(p1, tuple))
-
-    def test_explicit_indexing_after_functional_api(self):
-        Person = NamedTuple('Person', (('age', 0), ('first', 1), ('last', 2)))
-        p1 = Person(17, 'John', 'Doe')
-        p2 = Person(21, 'Jane', 'Doe')
-        self.assertEqual(p1[0], 17)
-        self.assertEqual(p1[1], 'John')
-        self.assertEqual(p1[2], 'Doe')
-        self.assertEqual(p2[0], 21)
-        self.assertEqual(p2[1], 'Jane')
-        self.assertEqual(p2[2], 'Doe')
-        self.assertEqual(p1.age, 17)
-        self.assertEqual(p1.first, 'John')
-        self.assertEqual(p1.last, 'Doe')
-        self.assertEqual(p2.age, 21)
-        self.assertEqual(p2.first, 'Jane')
-        self.assertEqual(p2.last, 'Doe')
-
-    def test_implicit_indexing_after_functional_api(self):
-        Person = NamedTuple('Person', 'age first last')
-        p1 = Person(17, 'John', 'Doe')
-        p2 = Person(21, 'Jane', 'Doe')
-        self.assertEqual(p1[0], 17)
-        self.assertEqual(p1[1], 'John')
-        self.assertEqual(p1[2], 'Doe')
-        self.assertEqual(p2[0], 21)
-        self.assertEqual(p2[1], 'Jane')
-        self.assertEqual(p2[2], 'Doe')
-        self.assertEqual(p1.age, 17)
-        self.assertEqual(p1.first, 'John')
-        self.assertEqual(p1.last, 'Doe')
-        self.assertEqual(p2.age, 21)
-        self.assertEqual(p2.first, 'Jane')
-        self.assertEqual(p2.last, 'Doe')
-
-    def test_mixed_indexing_after_functional_api(self):
-        Person = NamedTuple('Person', (('age', 0), ('last', 2), ('cars', 3)))
-        p1 = Person(17, 'John', 'Doe', 3)
-        p2 = Person(21, 'Jane', 'Doe', 9)
-        self.assertEqual(p1[0], 17)
-        self.assertEqual(p1[1], 'John')
-        self.assertEqual(p1[2], 'Doe')
-        self.assertEqual(p1[3], 3)
-        self.assertEqual(p2[0], 21)
-        self.assertEqual(p2[1], 'Jane')
-        self.assertEqual(p2[2], 'Doe')
-        self.assertEqual(p2[3], 9)
-        self.assertEqual(p1.age, 17)
-        self.assertEqual(p1.last, 'Doe')
-        self.assertEqual(p1.cars, 3)
-        self.assertEqual(p2.age, 21)
-        self.assertEqual(p2.last, 'Doe')
-        self.assertEqual(p2.cars, 9)
-
-    def test_issubclass_after_functional_api(self):
-        Person = NamedTuple('Person', 'age first last')
-        self.assertTrue(issubclass(Person, NamedTuple))
-        self.assertTrue(issubclass(Person, tuple))
-
-    def test_isinstance_after_functional_api(self):
-        Person = NamedTuple('Person', 'age first last')
-        p1 = Person(17, 'John', 'Doe')
-        self.assertTrue(isinstance(p1, Person))
-        self.assertTrue(isinstance(p1, NamedTuple))
-        self.assertTrue(isinstance(p1, tuple))
-
-    def test_creation_with_all_keywords(self):
-        Person = NamedTuple('Person', 'age first last')
-        p1 = Person(age=17, first='John', last='Doe')
-        self.assertEqual(p1[0], 17)
-        self.assertEqual(p1[1], 'John')
-        self.assertEqual(p1[2], 'Doe')
-        self.assertEqual(p1.age, 17)
-        self.assertEqual(p1.first, 'John')
-        self.assertEqual(p1.last, 'Doe')
-
-    def test_creation_with_some_keywords(self):
-        Person = NamedTuple('Person', 'age first last')
-        p1 = Person(17, first='John', last='Doe')
-        self.assertEqual(p1[0], 17)
-        self.assertEqual(p1[1], 'John')
-        self.assertEqual(p1[2], 'Doe')
-        self.assertEqual(p1.age, 17)
-        self.assertEqual(p1.first, 'John')
-        self.assertEqual(p1.last, 'Doe')
-        p1 = Person(17, last='Doe', first='John')
-        self.assertEqual(p1[0], 17)
-        self.assertEqual(p1[1], 'John')
-        self.assertEqual(p1[2], 'Doe')
-        self.assertEqual(p1.age, 17)
-        self.assertEqual(p1.first, 'John')
-        self.assertEqual(p1.last, 'Doe')
-
-    def test_custom_new(self):
-        class Book(NamedTuple):
-            title = 0
-            author = 1
-            genre = 2
-            def __new__(cls, string):
-                args = [s.strip() for s in string.split(';')]
-                return super(Book, cls).__new__(cls, *tuple(args))
-        b1 = Book('The Last Mohican; John Doe; Historical')
-        self.assertEqual(b1.title, 'The Last Mohican')
-        self.assertEqual(b1.author, 'John Doe')
-        self.assertEqual(b1.genre, 'Historical')
-
-    def test_defaults_in_class(self):
-        class Character(NamedTuple):
-            name = 0
-            gender = 1, None, 'male'
-            klass = 2, None, 'fighter'
-        for char in (
-                {'name':'John Doe'},
-                {'name':'William Pickney', 'klass':'scholar'},
-                {'name':'Sarah Doughtery', 'gender':'female'},
-                {'name':'Sissy Moonbeam', 'gender':'female', 'klass':'sorceress'},
-                ):
-            c = Character(**char)
-            for name, value in (('name', None), ('gender','male'), ('klass','fighter')):
-                if name in char:
-                    value = char[name]
-                self.assertEqual(getattr(c, name), value)
-
-    def test_defaults_in_class_that_are_falsey(self):
-        class Point(NamedTuple):
-            x = 0, 'horizondal coordinate', 0
-            y = 1, 'vertical coordinate', 0
-        p = Point()
-        self.assertEqual(p.x, 0)
-        self.assertEqual(p.y, 0)
-
-    def test_pickle_namedtuple_with_module(self):
-        if isinstance(LifeForm, Exception):
-            raise LifeForm
-        lf = LifeForm('this', 'that', 'theother')
-        test_pickle_dump_load(self.assertEqual, lf)
-
-    def test_pickle_namedtuple_without_module(self):
-        if isinstance(DeathForm, Exception):
-            raise DeathForm
-        df = DeathForm('sickly green', '2x4', 'foul')
-        test_pickle_dump_load(self.assertEqual, df)
-
-    def test_subclassing(self):
-        if isinstance(ThatsIt, Exception):
-            raise ThatsIt
-        ti = ThatsIt('Henry', 'Weinhardt')
-        self.assertEqual(ti.blah, 'Henry')
-        self.assertTrue(ti.what(), 'Henry')
-        test_pickle_dump_load(self.assertEqual, ti)
-
-    def test_contains(self):
-        Book = NamedTuple('Book', 'title author genre')
-        b = Book('Teckla', 'Steven Brust', 'fantasy')
-        self.assertTrue('Teckla' in b)
-        self.assertTrue('Steven Brust' in b)
-        self.assertTrue('fantasy' in b)
-
-    def test_fixed_size(self):
-        class Book(NamedTuple):
-            _size_ = TupleSize.fixed
-            title = 0
-            author = 1
-            genre = 2
-        b = Book('Teckla', 'Steven Brust', 'fantasy')
-        self.assertTrue('Teckla' in b)
-        self.assertTrue('Steven Brust' in b)
-        self.assertTrue('fantasy' in b)
-        self.assertEqual(b.title, 'Teckla')
-        self.assertEqual(b.author, 'Steven Brust')
-        self.assertRaises(TypeError, Book, 'Teckla', 'Steven Brust')
-        self.assertRaises(TypeError, Book, 'Teckla')
-
-    def test_minimum_size(self):
-        class Book(NamedTuple):
-            _size_ = TupleSize.minimum
-            title = 0
-            author = 1
-        b = Book('Teckla', 'Steven Brust', 'fantasy')
-        self.assertTrue('Teckla' in b)
-        self.assertTrue('Steven Brust' in b)
-        self.assertTrue('fantasy' in b)
-        self.assertEqual(b.title, 'Teckla')
-        self.assertEqual(b.author, 'Steven Brust')
-        b = Book('Teckla', 'Steven Brust')
-        self.assertTrue('Teckla' in b)
-        self.assertTrue('Steven Brust' in b)
-        self.assertEqual(b.title, 'Teckla')
-        self.assertEqual(b.author, 'Steven Brust')
-        self.assertRaises(TypeError, Book, 'Teckla')
-
-    def test_variable_size(self):
-        class Book(NamedTuple):
-            _size_ = TupleSize.variable
-            title = 0
-            author = 1
-            genre = 2
-        b = Book('Teckla', 'Steven Brust', 'fantasy')
-        self.assertTrue('Teckla' in b)
-        self.assertTrue('Steven Brust' in b)
-        self.assertTrue('fantasy' in b)
-        self.assertEqual(b.title, 'Teckla')
-        self.assertEqual(b.author, 'Steven Brust')
-        self.assertEqual(b.genre, 'fantasy')
-        b = Book('Teckla', 'Steven Brust')
-        self.assertTrue('Teckla' in b)
-        self.assertTrue('Steven Brust' in b)
-        self.assertEqual(b.title, 'Teckla')
-        self.assertEqual(b.author, 'Steven Brust')
-        self.assertRaises(AttributeError, getattr, b, 'genre')
-        self.assertRaises(TypeError, Book, title='Teckla', genre='fantasy')
-        self.assertRaises(TypeError, Book, author='Steven Brust')
-
-    def test_combining_namedtuples(self):
-        class Point(NamedTuple):
-            x = 0, 'horizontal coordinate', 1
-            y = 1, 'vertical coordinate', -1
-        class Color(NamedTuple):
-            r = 0, 'red component', 11
-            g = 1, 'green component', 29
-            b = 2, 'blue component', 37
-        Pixel1 = NamedTuple('Pixel', Point+Color, module=__name__)
-        class Pixel2(Point, Color):
-            "a colored dot"
-        class Pixel3(Point):
-            r = 2, 'red component', 11
-            g = 3, 'green component', 29
-            b = 4, 'blue component', 37
-        self.assertEqual(Pixel1._fields_, 'x y r g b'.split())
-        self.assertEqual(Pixel1.x.__doc__, 'horizontal coordinate')
-        self.assertEqual(Pixel1.x.default, 1)
-        self.assertEqual(Pixel1.y.__doc__, 'vertical coordinate')
-        self.assertEqual(Pixel1.y.default, -1)
-        self.assertEqual(Pixel1.r.__doc__, 'red component')
-        self.assertEqual(Pixel1.r.default, 11)
-        self.assertEqual(Pixel1.g.__doc__, 'green component')
-        self.assertEqual(Pixel1.g.default, 29)
-        self.assertEqual(Pixel1.b.__doc__, 'blue component')
-        self.assertEqual(Pixel1.b.default, 37)
-        self.assertEqual(Pixel2._fields_, 'x y r g b'.split())
-        self.assertEqual(Pixel2.x.__doc__, 'horizontal coordinate')
-        self.assertEqual(Pixel2.x.default, 1)
-        self.assertEqual(Pixel2.y.__doc__, 'vertical coordinate')
-        self.assertEqual(Pixel2.y.default, -1)
-        self.assertEqual(Pixel2.r.__doc__, 'red component')
-        self.assertEqual(Pixel2.r.default, 11)
-        self.assertEqual(Pixel2.g.__doc__, 'green component')
-        self.assertEqual(Pixel2.g.default, 29)
-        self.assertEqual(Pixel2.b.__doc__, 'blue component')
-        self.assertEqual(Pixel2.b.default, 37)
-        self.assertEqual(Pixel3._fields_, 'x y r g b'.split())
-        self.assertEqual(Pixel3.x.__doc__, 'horizontal coordinate')
-        self.assertEqual(Pixel3.x.default, 1)
-        self.assertEqual(Pixel3.y.__doc__, 'vertical coordinate')
-        self.assertEqual(Pixel3.y.default, -1)
-        self.assertEqual(Pixel3.r.__doc__, 'red component')
-        self.assertEqual(Pixel3.r.default, 11)
-        self.assertEqual(Pixel3.g.__doc__, 'green component')
-        self.assertEqual(Pixel3.g.default, 29)
-        self.assertEqual(Pixel3.b.__doc__, 'blue component')
-        self.assertEqual(Pixel3.b.default, 37)
-
-    def test_function_api_type(self):
-        class Tester(NamedTuple):
-            def howdy(self):
-                return 'backwards', list(reversed(self))
-        Testee = NamedTuple('Testee', 'a c e', type=Tester)
-        t = Testee(1, 2, 3)
-        self.assertEqual(t.howdy(), ('backwards', [3, 2, 1]))
-
-    def test_asdict(self):
-        class Point(NamedTuple):
-            x = 0, 'horizontal coordinate', 1
-            y = 1, 'vertical coordinate', -1
-        class Color(NamedTuple):
-            r = 0, 'red component', 11
-            g = 1, 'green component', 29
-            b = 2, 'blue component', 37
-        Pixel = NamedTuple('Pixel', Point+Color, module=__name__)
-        pixel = Pixel(99, -101, 255, 128, 0)
-        self.assertEqual(pixel._asdict(), {'x':99, 'y':-101, 'r':255, 'g':128, 'b':0})
-
-    def test_make(self):
-        class Point(NamedTuple):
-            x = 0, 'horizontal coordinate', 1
-            y = 1, 'vertical coordinate', -1
-        self.assertEqual(Point(4, 5), (4, 5))
-        self.assertEqual(Point._make((4, 5)), (4, 5))
-
-    def test_replace(self):
-        class Color(NamedTuple):
-            r = 0, 'red component', 11
-            g = 1, 'green component', 29
-            b = 2, 'blue component', 37
-        purple = Color(127, 0, 127)
-        mid_gray = purple._replace(g=127)
-        self.assertEqual(mid_gray, (127, 127, 127))
-
-
-class TestNamedConstant(TestCase):
-
-    def test_constantness(self):
-        class K(NamedConstant):
-            PI = 3.141596
-            TAU = 2 * PI
-        self.assertEqual(K.PI, 3.141596)
-        self.assertEqual(K.TAU, 2 * K.PI)
-        with self.assertRaisesRegex(AttributeError, r'cannot rebind constant'):
-            K.PI = 9
-        with self.assertRaisesRegex(AttributeError, r'cannot delete constant'):
-            del K.PI
-        with self.assertRaisesRegex(AttributeError, r'cannot rebind constant'):
-            K('PI', 3)
-        self.assertTrue(K.PI in K)
-        self.assertTrue(K.TAU in K)
-
-    def test_duplicates(self):
-        class CardNumber(NamedConstant):
-            ACE      = 11
-            TWO      = 2
-            THREE    = 3
-            FOUR     = 4
-            FIVE     = 5
-            SIX      = 6
-            SEVEN    = 7
-            EIGHT    = 8
-            NINE     = 9
-            TEN      = 10
-            JACK     = 10
-            QUEEN    = 10
-            KING     = 10
-        self.assertFalse(CardNumber.TEN is CardNumber.JACK)
-        self.assertEqual(CardNumber.TEN, CardNumber.JACK)
-        self.assertEqual(CardNumber.TEN, 10)
-
-    def test_extend_constants(self):
-        class CardSuit(NamedConstant):
-            HEARTS = 1
-            SPADES = 2
-            DIAMONTS = 3
-            CLUBS = 4
-        self.assertEqual(CardSuit.HEARTS, 1)
-        stars = CardSuit('STARS', 5)
-        self.assertIs(stars, CardSuit.STARS)
-        self.assertEqual(CardSuit.STARS, 5)
-        self.assertTrue(CardSuit.STARS in CardSuit)
-
-    def test_constant_with_docstring(self):
-        class Stuff(NamedConstant):
-            Artifact = constant(7, "lucky number!")
-            Bowling = 11
-            HillWomp = constant(29, 'blah blah')
-        self.assertEqual(Stuff.Artifact, 7)
-        self.assertEqual(Stuff.Artifact.__doc__, 'lucky number!')
-        self.assertEqual(Stuff.Bowling, 11)
-        self.assertEqual(Stuff.Bowling.__doc__, None)
-        self.assertEqual(Stuff.HillWomp, 29)
-        self.assertEqual(Stuff.HillWomp.__doc__, 'blah blah')
-
-    def test_deep_copy(self):
-        import copy
-        class APITypes(aenum.Constant):
-            STRING = "string"
-            INT = "int"
-        APITypes('string')
-        d = {"first": APITypes.STRING}
-        copy.deepcopy(d)
-        self.assertTrue(d['first'] is APITypes.STRING)
-
-    def test_subclass_w_same_value(self):
-        class Foo(aenum.Constant):
-            BLA = 'bla1'
-            ABA = 'aba1'
-        class Bar(aenum.Constant):
-            BLA = Foo.BLA
-            ABA = 'aba2'
-        self.assertEqual(Foo.BLA, Bar.BLA)
-        self.assertFalse(Foo.BLA is Bar.BLA)
-
-
-class TestStarImport(TestCase):
-
-    def test_all_exports_names(self):
-        scope = {}
-        exec('from aenum import *', scope, scope)
-        self.assertIn('Enum', scope)
-
-class TestStackoverflowAnswers(TestCase):
-
-    def test_self_referential_directions(self):
-        # https://stackoverflow.com/a/64000706/208880
-        class Directions(Enum):
-            _order_ = 'NORTH WEST SOUTH EAST'
-            #
-            NORTH = 1, 0
-            WEST = 0, 1
-            SOUTH = -1, 0
-            EAST = 0, -1
-            #
-            def __init__(self, x, y):
-                self.x = x
-                self.y = y
-                if len(self.__class__):
-                    # make links
-                    all = list(self.__class__)
-                    left, right = all[0], all[-1]
-                    self.left = left
-                    self.right = right
-                    left.right = self
-                    right.left = self
-        #
-        D = Directions
-        self.assertEqual(D.NORTH.value, (1, 0))
-        self.assertTrue(D.NORTH.left is D.WEST)
-        self.assertTrue(D.SOUTH.right is D.WEST)
-
-    def test_self_referential_rock_paper_scissors(self):
-        # https://stackoverflow.com/a/57085357/208880
-        class RPS(Enum):
-            _order_ = 'Rock, Paper, Scissors'
-            #
-            Rock = "rock"
-            Paper = "paper"
-            Scissors = "scissors"
-            #
-            def __init__(self, value):
-                if len(self.__class__):
-                    # make links
-                    all = list(self.__class__)
-                    first, previous = all[0], all[-1]
-                    first.beats = self
-                    self.beats = previous
-        #
-        self.assertTrue(RPS.Rock.beats is RPS.Scissors)
-        self.assertTrue(RPS.Scissors.beats is RPS.Paper)
-        self.assertTrue(RPS.Paper.beats is RPS.Rock)
-
-    def test_arduino_headers(self):
-        # https://stackoverflow.com/q/65048495/208880
-        class CHeader(Enum):
-            def __init_subclass__(cls, **kwds):
-                # write Enums to C header file
-                cls_name = cls.__name__
-                header_path = getattr(cls, '_%s__header' % cls_name)
-                with open(header_path, 'w') as fh:
-                    fh.write('initial header stuff here\n')
-                    for enum in cls:
-                        fh.write('#define %s %r\n' % (enum.name, enum.value))
-        class Arduino(CHeader):
-            _order_ = 'ONE TWO'
-            __header = os.path.join(tempdir, 'arduino.h')
-            ONE = 1
-            TWO = 2
-        with open(os.path.join(tempdir, 'arduino.h')) as fh:
-                data = fh.read()
-        self.assertEqual(textwrap.dedent("""\
-                initial header stuff here
-                #define ONE 1
-                #define TWO 2
-                """),
-                data,
-                )
-
-    def test_lowercase_compare(self):
-        # https://stackoverflow.com/q/65139026/208880
-        class CompareLowerCase(Enum):
-            def __init_subclass__(cls, **kwds):
-                super(CompareLowerCase, cls).__init_subclass__(**kwds)
-                cls.lowered_names = set([m.name.lower() for m in cls])
-            @classmethod
-            def has_name(cls, name):
-                return name.lower() in cls.lowered_names
-        #
-        class LabelEnum(CompareLowerCase, StrEnum):
-            ENUM_ONE = "Enum One"
-            ENUM_TWO = "Enum Two"
-            ENUM_THREE = "Enum Three"
-            FOUR = "FOUR"
-            FIVE = "FIVE"
-            SIX = "SIX"
-        #
-        self.assertTrue(LabelEnum.has_name('Enum_Three'))
-
-
-class TestExtendEnum(TestCase):
-
-    def test_extend_enum_plain(self):
-        class Color(Enum):
-            red = 1
-            green = 2
-            blue = 3
-        self.assertRaisesRegex(TypeError, 'already in use as', extend_enum, Color, 'blue', 5)
-        #
-        extend_enum(Color, 'brown', 4)
-        self.assertEqual(Color.brown.name, 'brown')
-        self.assertEqual(Color.brown.value, 4)
-        self.assertTrue(Color.brown in Color)
-        self.assertEqual(Color(4), Color.brown)
-        self.assertEqual(Color['brown'], Color.brown)
-        self.assertEqual(len(Color), 4)
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, 5)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(5), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-        self.assertEqual(len(Color), 5)
-
-    def test_extend_enum_alias(self):
-        class Color(Enum):
-            red = 1
-            green = 2
-            blue = 3
-        extend_enum(Color, 'rojo', 1)
-        self.assertEqual(Color.rojo.name, 'red')
-        self.assertEqual(Color.rojo.value, 1)
-        self.assertTrue(Color.rojo in Color)
-        self.assertEqual(Color(1), Color.rojo)
-        self.assertEqual(Color['rojo'], Color.red)
-        self.assertEqual(len(Color), 3)
-
-    def test_extend_enum_unique(self):
-        class Color(UniqueEnum):
-            red = 1
-            green = 2
-            blue = 3
-        self.assertRaisesRegex(ValueError, r'<Color.rojo: 1> is a duplicate of <Color.red: 1>', extend_enum, Color, 'rojo', 1)
-        #
-        self.assertEqual(Color.red.name, 'red')
-        self.assertEqual(Color.red.value, 1)
-        self.assertTrue(Color.red in Color)
-        self.assertEqual(Color(1), Color.red)
-        self.assertEqual(Color['red'], Color.red)
-        self.assertEqual(Color.green.name, 'green')
-        self.assertEqual(Color.green.value, 2)
-        self.assertTrue(Color.green in Color)
-        self.assertEqual(Color(2), Color.green)
-        self.assertEqual(Color['blue'], Color.blue)
-        self.assertEqual(Color.blue.name, 'blue')
-        self.assertEqual(Color.blue.value, 3)
-        self.assertTrue(Color.blue in Color)
-        self.assertEqual(Color(3), Color.blue)
-        self.assertEqual(len(Color), 3)
-        #
-        extend_enum(Color, 'brown', 4)
-        self.assertEqual(Color.brown.name, 'brown')
-        self.assertEqual(Color.brown.value, 4)
-        self.assertTrue(Color.brown in Color)
-        self.assertEqual(Color(4), Color.brown)
-        self.assertEqual(Color['brown'], Color.brown)
-        self.assertEqual(len(Color), 4)
-        #
-        self.assertRaisesRegex(ValueError, '', extend_enum, Color, 'verde', 2)
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, 5)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(5), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-        self.assertEqual(len(Color), 5)
-
-
-    def test_extend_enum_shadow_property(self):
-        class Color(Enum):
-            red = 1
-            green = 2
-            blue = 3
-        extend_enum(Color, 'value', 4)
-        self.assertEqual(Color.value.name, 'value')
-        self.assertEqual(Color.value.value, 4)
-        self.assertTrue(Color.value in Color)
-        self.assertEqual(Color(4), Color.value)
-        self.assertEqual(Color['value'], Color.value)
-        self.assertEqual(len(Color), 4)
-        self.assertEqual(Color.red.value, 1)
-
-    def test_extend_enum_shadow_base(self):
-        class hohum(object):
-            def cyan(self):
-                "cyanize a color"
-                return self.value
-        class Color(hohum, Enum):
-            red = 1
-            green = 2
-            blue = 3
-        self.assertRaisesRegex(TypeError, r'already in use in superclass', extend_enum, Color, 'cyan', 4)
-        self.assertEqual(len(Color), 3)
-        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
-
-    def test_extend_enum_multivalue(self):
-        class Color(MultiValueEnum):
-            red = 1, 4, 7
-            green = 2, 5, 8
-            blue = 3, 6, 9
-        extend_enum(Color, 'brown', 10, 20)
-        self.assertEqual(Color.brown.name, 'brown')
-        self.assertEqual(Color.brown.value, 10)
-        self.assertTrue(Color.brown in Color)
-        self.assertEqual(Color(10), Color.brown)
-        self.assertEqual(Color(20), Color.brown)
-        self.assertEqual(Color['brown'], Color.brown)
-        self.assertEqual(len(Color), 4)
-        #
-        self.assertRaisesRegex(ValueError, 'no values specified for MultiValue enum', extend_enum, Color, 'mauve')
-
-    def test_extend_enum_multivalue_alias(self):
-        class Color(MultiValueEnum):
-            red = 1, 4, 7
-            green = 2, 5, 8
-            blue = 3, 6, 9
-        self.assertRaisesRegex(ValueError, r'<Color.rojo: 7> is a duplicate of <Color.red: 1>', extend_enum, Color, 'rojo', 7)
-        self.assertEqual(Color.red.name, 'red')
-        self.assertEqual(Color.red.value, 1)
-        self.assertTrue(Color.red in Color)
-        self.assertEqual(Color(1), Color.red)
-        self.assertEqual(Color(4), Color.red)
-        self.assertEqual(Color(7), Color.red)
-        self.assertEqual(Color['red'], Color.red)
-        self.assertEqual(Color.green.name, 'green')
-        self.assertEqual(Color.green.value, 2)
-        self.assertTrue(Color.green in Color)
-        self.assertEqual(Color(2), Color.green)
-        self.assertEqual(Color(5), Color.green)
-        self.assertEqual(Color(8), Color.green)
-        self.assertEqual(Color['blue'], Color.blue)
-        self.assertEqual(Color.blue.name, 'blue')
-        self.assertEqual(Color.blue.value, 3)
-        self.assertTrue(Color.blue in Color)
-        self.assertEqual(Color(3), Color.blue)
-        self.assertEqual(Color(6), Color.blue)
-        self.assertEqual(Color(9), Color.blue)
-        self.assertEqual(len(Color), 3)
-
-    def test_extend_enum_multivalue_str(self):
-        class M(str, MultiValueEnum):
-            VALUE_1 = 'value_1', 'VALUE_1'
-            VALUE_2 = 'value_2', 'VALUE_2'
-            VALUE_3 = 'value_3', 'VALUE_3'
-        self.assertTrue(M._member_type_ is str)
-        extend_enum(M, 'VALUE_4', 'value_4', 'VALUE_4')
-        self.assertEqual(list(M), [M.VALUE_1, M.VALUE_2, M.VALUE_3, M.VALUE_4])
-        self.assertTrue(M('value_4') is M.VALUE_4)
-        self.assertTrue(M('VALUE_4') is M.VALUE_4)
-        self.assertTrue(M.VALUE_4.name == 'VALUE_4')
-        self.assertTrue(M.VALUE_4.value == 'value_4')
-
-    def test_extend_intenum(self):
-        class Index(IntEnum):
-            DeviceType    = 0x1000
-            ErrorRegister = 0x1001
-
-        for name, value in (
-                ('ControlWord', 0x6040),
-                ('StatusWord', 0x6041),
-                ('OperationMode', 0x6060),
-                ):
-            extend_enum(Index, name, value)
-
-        self.assertEqual(len(Index), 5)
-        self.assertEqual(list(Index), [Index.DeviceType, Index.ErrorRegister, Index.ControlWord, Index.StatusWord, Index.OperationMode])
-        self.assertEqual(Index.DeviceType.value, 0x1000)
-        self.assertEqual(Index.StatusWord.value, 0x6041)
-
-    def test_extend_multi_init(self):
-        try:
-            from http import HTTPStatus
-            length = len(HTTPStatus)
-        except ImportError:
-            class HTTPStatus(IntEnum):
-                def __new__(cls, value, phrase, description):
-                    obj = int.__new__(cls, value)
-                    obj._value_ = value
-
-                    obj.phrase = phrase
-                    obj.description = description
-                    return obj
-                CONTINUE = 100, 'Continue', 'Request received, please continue'
-                SWITCHING_PROTOCOLS = 101, 'Switching Protocols', 'Switching to new protocol; obey Upgrade header'
-                PROCESSING = 102, 'Processing', ''
-            length = 3
-        extend_enum(HTTPStatus, 'BAD_SPAM', 513, 'Too greasy', 'for a train')
-        extend_enum(HTTPStatus, 'BAD_EGGS', 514, 'Too green', '')
-        self.assertEqual(len(HTTPStatus), length+2)
-        self.assertEqual(
-                list(HTTPStatus)[-2:],
-                [HTTPStatus.BAD_SPAM, HTTPStatus.BAD_EGGS],
-                )
-        self.assertEqual(HTTPStatus.BAD_SPAM.value, 513)
-        self.assertEqual(HTTPStatus.BAD_SPAM.name, 'BAD_SPAM')
-        self.assertEqual(HTTPStatus.BAD_SPAM.phrase, 'Too greasy')
-        self.assertEqual(HTTPStatus.BAD_SPAM.description, 'for a train')
-        self.assertEqual(HTTPStatus.BAD_EGGS.value, 514)
-        self.assertEqual(HTTPStatus.BAD_EGGS.name, 'BAD_EGGS')
-        self.assertEqual(HTTPStatus.BAD_EGGS.phrase, 'Too green')
-        self.assertEqual(HTTPStatus.BAD_EGGS.description, '')
-
-    def test_extend_flag(self):
-        class Color(Flag):
-            BLACK = 0
-            RED = 1
-            GREEN = 2
-            BLUE = 4
-        extend_enum(Color, 'MAGENTA')
-        self.assertTrue(Color(8) is Color.MAGENTA)
-        self.assertTrue(isinstance(Color.MAGENTA, Color))
-        self.assertEqual(Color.MAGENTA.value, 8)
-        extend_enum(Color, 'PURPLE', 11)
-        self.assertTrue(Color(11) is Color.PURPLE)
-        self.assertTrue(isinstance(Color.PURPLE, Color))
-        self.assertEqual(Color.PURPLE.value, 11)
-        self.assertTrue(issubclass(Color, Flag))
-
-    def test_extend_flag_backwards(self):
-        class Color(Flag):
-            BLACK = 0
-            RED = 1
-            GREEN = 2
-            BLUE = 4
-        extend_enum(Color, 'PURPLE', 11)
-        self.assertTrue(Color(11) is Color.PURPLE)
-        self.assertTrue(isinstance(Color.PURPLE, Color))
-        self.assertEqual(Color.PURPLE.value, 11)
-        self.assertTrue(issubclass(Color, Flag))
-        #
-        extend_enum(Color, 'MAGENTA')
-        self.assertTrue(Color(8) is Color.MAGENTA)
-        self.assertTrue(isinstance(Color.MAGENTA, Color))
-        self.assertEqual(Color.MAGENTA.value, 8)
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, 16)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(16), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-        self.assertEqual(len(Color), 5)
-
-    def test_extend_intflag(self):
-        class Color(IntFlag):
-            BLACK = 0
-            RED = 1
-            GREEN = 2
-            BLUE = 4
-        extend_enum(Color, 'MAGENTA')
-        self.assertTrue(Color(8) is Color.MAGENTA)
-        self.assertTrue(isinstance(Color.MAGENTA, Color))
-        self.assertEqual(Color.MAGENTA.value, 8)
-        extend_enum(Color, 'PURPLE', 11)
-        self.assertTrue(Color(11) is Color.PURPLE)
-        self.assertTrue(isinstance(Color.PURPLE, Color))
-        self.assertEqual(Color.PURPLE.value, 11)
-        self.assertTrue(issubclass(Color, Flag))
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, 16)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(16), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-        self.assertEqual(len(Color), 5)
-
-    def test_extend_intflag_backwards(self):
-        class Color(IntFlag):
-            BLACK = 0
-            RED = 1
-            GREEN = 2
-            BLUE = 4
-        extend_enum(Color, 'PURPLE', 11)
-        self.assertTrue(Color(11) is Color.PURPLE)
-        self.assertTrue(isinstance(Color.PURPLE, Color))
-        self.assertEqual(Color.PURPLE.value, 11)
-        self.assertTrue(issubclass(Color, Flag))
-        #
-        extend_enum(Color, 'MAGENTA')
-        self.assertTrue(Color(8) is Color.MAGENTA)
-        self.assertTrue(isinstance(Color.MAGENTA, Color))
-        self.assertEqual(Color.MAGENTA.value, 8)
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, 16)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(16), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-        self.assertEqual(len(Color), 5)
-
-    def test_extend_strenum(self):
-        class Color(StrEnum):
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        extend_enum(Color, 'BLACK')
-        self.assertEqual(Color.BLACK.name, 'BLACK')
-        self.assertEqual(Color.BLACK.value, 'black')
-        self.assertEqual(len(Color), 4)
-
-
-class TestIssues(TestCase):
-
-    def test_auto_multi_int(self):
-        class Measurement(int, MultiValueEnum, AddValueEnum):
-            _order_ = 'one two three'
-            _start_ = 0
-            one = "20110721"
-            two = "20120911"
-            three = "20110518"
-        self.assertEqual([m.value for m in Measurement], [0, 1, 2])
-        self.assertEqual([m.name for m in Measurement], ['one', 'two', 'three'])
-        self.assertIs(Measurement('20110721'), Measurement.one)
-        self.assertIs(Measurement(0), Measurement.one)
-        self.assertIs(Measurement('20120911'), Measurement.two)
-        self.assertIs(Measurement(1), Measurement.two)
-        self.assertIs(Measurement('20110518'), Measurement.three)
-        self.assertIs(Measurement(2), Measurement.three)
-
-    def test_auto_kwds(self):
-        class Item(Enum):
-            _order_ = 'A B'
-            A = auto(size=100, requirements={})
-            B = auto(size=200, requirements={A: 1})
-            #
-            def __new__(cls, value, size, requirements):
-                obj = object.__new__(cls)
-                obj._value_ = value
-                obj.size = size
-                # fix requirements
-                new_requirements = {}
-                for k, v in requirements.items():
-                    if isinstance(k, auto):
-                        k = k.enum_member
-                    new_requirements[k] = v
-                obj.requirements = new_requirements
-                return obj
-        self.assertEqual((Item.A.value, Item.A.size, Item.A.requirements), (1, 100, {}))
-        self.assertEqual((Item.B.value, Item.B.size, Item.B.requirements), (2, 200, {Item.A: 1}))
-
-    def test_extend_flag(self):
-        class FlagTest(Flag): # Or IntFlag
-            NONE = 0
-            LOW = 1
-            MID = 2
-        extend_enum(FlagTest, 'HIGH', 4)
-        self.assertEqual(FlagTest.LOW | FlagTest.HIGH, FlagTest(5))
-        self.assertEqual((FlagTest.LOW | FlagTest.HIGH).value, 5)
-        
-    def test_extend_unhashable(self):
-        class TestEnum(Enum):
-            ABC = {
-                'id': 0,
-                'value': 'abc'
-                }
-            DEF = {
-                'id': 1,
-                'value': 'def'
-                }
-        rand = uuid.uuid4().hex
-        new_value = {
-            'id': 99,
-            'value': 'new',
-            }
-        extend_enum(TestEnum, rand, new_value)
-
-
-
-# Test conversion of global constants
-# These are unordered here on purpose to ensure that declaration order
-# makes no difference.
-CONVERT_TEST_NAME_D = 5
-CONVERT_TEST_NAME_C = 5
-CONVERT_TEST_NAME_B = 5
-CONVERT_TEST_NAME_A = 5  # This one should sort first.
-CONVERT_TEST_NAME_E = 5
-CONVERT_TEST_NAME_F = 5
-CONVERT_TEST_SIGABRT = 4 # and this one
-CONVERT_TEST_SIGIOT = 4
-CONVERT_TEST_EIO = 7
-CONVERT_TEST_EBUS = 7    # and this one
-
-CONVERT_STRING_TEST_NAME_D = 5
-CONVERT_STRING_TEST_NAME_C = 5
-CONVERT_STRING_TEST_NAME_B = 5
-CONVERT_STRING_TEST_NAME_A = 5  # This one should sort first.
-CONVERT_STRING_TEST_NAME_E = 5
-CONVERT_STRING_TEST_NAME_F = 5
-
-# global names for StrEnum._convert_ test
-CONVERT_STR_TEST_2 = 'goodbye'
-CONVERT_STR_TEST_1 = 'hello'
-
-# We also need values that cannot be compared:
-UNCOMPARABLE_A = 5
-UNCOMPARABLE_C = (9, 1)  # naming order is broken on purpose
-UNCOMPARABLE_B = 'value'
-
-COMPLEX_C = 1j
-COMPLEX_A = 2j
-COMPLEX_B = 3j
-
-
-class TestConvert(TestCase):
-
-    def tearDown(self):
-        # Reset the module-level test variables to their original integer
-        # values, otherwise the already created enum values get converted
-        # instead.
-        g = globals()
-        for suffix in ['A', 'B', 'C', 'D', 'E', 'F']:
-            g['CONVERT_TEST_NAME_%s' % suffix] = 5
-            g['CONVERT_STRING_TEST_NAME_%s' % suffix] = 5
-        for suffix, value in (('A', 5), ('B', (9, 1)), ('C', 'value')):
-            g['UNCOMPARABLE_%s' % suffix] = value
-        for suffix, value in (('A', 2j), ('B', 3j), ('C', 1j)):
-            g['COMPLEX_%s' % suffix] = value
-        for suffix, value in (('1', 'hello'), ('2', 'goodbye')):
-            g['CONVERT_STR_TEST_%s' % suffix] = value
-        g['CONVERT_TEST_SIGABRT'] = 4
-        g['CONVERT_TEST_SIGIOT'] = 4
-        g['CONVERT_TEST_EIO'] = 7
-        g['CONVERT_TEST_EBUS'] = 7
-
-    def test_convert_value_lookup_priority(self):
-        test_type = IntEnum._convert_(
-                'UnittestConvert',
-                MODULE,
-                filter=lambda x: x.startswith('CONVERT_TEST_'))
-        # We don't want the reverse lookup value to vary when there are
-        # multiple possible names for a given value.  It should always
-        # report the first lexigraphical name in that case.
-        self.assertEqual(test_type(5).name, 'CONVERT_TEST_NAME_A')
-        self.assertEqual(test_type(4).name, 'CONVERT_TEST_SIGABRT')
-        self.assertEqual(test_type(7).name, 'CONVERT_TEST_EBUS')
-        self.assertEqual(
-                list(test_type),
-                [
-                    test_type.CONVERT_TEST_SIGABRT,
-                    test_type.CONVERT_TEST_NAME_A,
-                    test_type.CONVERT_TEST_EBUS,
-                    ],
-                )
-
-    def test_convert_int(self):
-        test_type = IntEnum._convert_(
-                'UnittestConvert',
-                MODULE,
-                filter=lambda x: x.startswith('CONVERT_TEST_'))
-        # Ensure that test_type has all of the desired names and values.
-        self.assertEqual(test_type.CONVERT_TEST_NAME_F,
-                         test_type.CONVERT_TEST_NAME_A)
-        self.assertEqual(test_type.CONVERT_TEST_NAME_B, 5)
-        self.assertEqual(test_type.CONVERT_TEST_NAME_C, 5)
-        self.assertEqual(test_type.CONVERT_TEST_NAME_D, 5)
-        self.assertEqual(test_type.CONVERT_TEST_NAME_E, 5)
-        # Ensure that test_type only picked up names matching the filter.
-        int_dir = dir(int) + [
-                'CONVERT_TEST_NAME_A', 'CONVERT_TEST_NAME_B', 'CONVERT_TEST_NAME_C',
-                'CONVERT_TEST_NAME_D', 'CONVERT_TEST_NAME_E', 'CONVERT_TEST_NAME_F',
-                'CONVERT_TEST_SIGABRT', 'CONVERT_TEST_SIGIOT',
-                'CONVERT_TEST_EIO', 'CONVERT_TEST_EBUS',
-                ]
-        extra = [name for name in dir(test_type) if name not in enum_dir(test_type)]
-        missing = [name for name in enum_dir(test_type) if name not in dir(test_type)]
-        self.assertEqual(
-                extra + missing,
-                [],
-                msg='extra names: %r;  missing names: %r' % (extra, missing),
-                )
-
-    @unittest.skipUnless(PY3, 'everything is comparable on Python 2')
-    def test_convert_uncomparable(self):
-        uncomp = Enum._convert_(
-                'Uncomparable',
-                MODULE,
-                filter=lambda x: x.startswith('UNCOMPARABLE_'))
-        # Should be ordered by `name` only:
-        self.assertEqual(
-            list(uncomp),
-            [uncomp.UNCOMPARABLE_A, uncomp.UNCOMPARABLE_B, uncomp.UNCOMPARABLE_C],
-            list(uncomp),
-            )
-
-    @unittest.skipUnless(PY3, 'everything is comparable on Python 2')
-    def test_convert_complex(self):
-        uncomp = Enum._convert_(
-            'Uncomparable',
-            MODULE,
-            filter=lambda x: x.startswith('COMPLEX_'))
-        # Should be ordered by `name` only:
-        self.assertEqual(
-            list(uncomp),
-            [uncomp.COMPLEX_A, uncomp.COMPLEX_B, uncomp.COMPLEX_C],
-            )
-
-    def test_convert_str(self):
-        test_type = StrEnum._convert_(
-                'UnittestConvert',
-                MODULE,
-                filter=lambda x: x.startswith('CONVERT_STR_'),
-                as_global=True)
-        # Ensure that test_type has all of the desired names and values.
-        self.assertEqual(test_type.CONVERT_STR_TEST_1, 'hello')
-        self.assertEqual(test_type.CONVERT_STR_TEST_2, 'goodbye')
-        # Ensure that test_type only picked up names matching the filter.
-        extra = [name for name in dir(test_type) if name not in enum_dir(test_type)]
-        missing = [name for name in enum_dir(test_type) if name not in dir(test_type)]
-        self.assertEqual(
-                extra + missing,
-                [],
-                msg='extra names: %r;  missing names: %r' % (extra, missing),
-                )
-        self.assertEqual(repr(test_type.CONVERT_STR_TEST_1), '%s.CONVERT_STR_TEST_1' % SHORT_MODULE)
-        self.assertEqual(str(test_type.CONVERT_STR_TEST_2), 'goodbye')
-        self.assertEqual(format(test_type.CONVERT_STR_TEST_1), 'hello')
-
-    def test_convert_repr_and_str(self):
-        test_type = IntEnum._convert_(
-                'UnittestConvert',
-                MODULE,
-                filter=lambda x: x.startswith('CONVERT_STRING_TEST_'),
-                as_global=True)
-        self.assertEqual(repr(test_type.CONVERT_STRING_TEST_NAME_A), '%s.CONVERT_STRING_TEST_NAME_A' % SHORT_MODULE)
-        self.assertEqual(str(test_type.CONVERT_STRING_TEST_NAME_A), '5')
-        self.assertEqual(format(test_type.CONVERT_STRING_TEST_NAME_A), '5')
-
-# helpers
-
-def enum_dir(cls):
-        interesting = set(cls._member_names_ + [
-                    '__class__', '__contains__', '__doc__', '__getitem__',
-                    '__iter__', '__len__', '__members__', '__module__',
-                    '__name__',
-                    ])
-        if cls._new_member_ is not object.__new__:
-            interesting.add('__new__')
-        if cls.__init_subclass__ is not Enum.__init_subclass__:
-            interesting.add('__init_subclass__')
-        if hasattr(object, '__qualname__'):
-            interesting.add('__qualname__')
-        for method in ('__init__', '__format__', '__repr__', '__str__'):
-            if getattr(cls, method) not in (getattr(Enum, method), getattr(Flag, method)):
-                interesting.add(method)
-        if cls._member_type_ is object:
-            return sorted(interesting)
-        else:
-            # return whatever mixed-in data type has
-            return sorted(set(dir(cls._member_type_)) | interesting)
-
-def member_dir(member):
-    if member.__class__._member_type_ is object:
-        allowed = set(['__class__', '__doc__', '__eq__', '__hash__', '__module__', 'name', 'value'])
-    else:
-        allowed = set(dir(member))
-    for cls in member.__class__.mro():
-        for name, obj in cls.__dict__.items():
-            if name[0] == '_':
-                continue
-            if isinstance(obj, enum.property):
-                if obj.fget is not None or name not in member._member_map_:
-                    allowed.add(name)
-                else:
-                    allowed.discard(name)
-            else:
-                allowed.add(name)
-    return sorted(allowed)
-
-
-
-if __name__ == '__main__':
-    tempdir = tempfile.mkdtemp()
-    test = None
-    try:
-        if PY3:
-            test_v3.tempdir = tempdir
-        test = unittest.main(exit=False)
-        sys.stdout.flush()
-        for name, reason in test.result.skipped:
-            print("%s: %s" % (name, reason))
-    finally:
-        shutil.rmtree(tempdir, True)
-        if test:
-            sys.exit(len(test.result.errors or test.result.failures) and 1 or 0)
-
diff --git a/venv/Lib/site-packages/aenum/test_v3.py b/venv/Lib/site-packages/aenum/test_v3.py
deleted file mode 100644
index 62453df1..00000000
--- a/venv/Lib/site-packages/aenum/test_v3.py
+++ /dev/null
@@ -1,1982 +0,0 @@
-from . import EnumMeta, Enum, IntEnum, Flag, IntFlag, StrEnum, UniqueEnum, AutoEnum, AddValueEnum
-from . import NamedTuple, TupleSize, MagicValue, AddValue, NoAlias, Unique, MultiValue
-from . import AutoNumberEnum,MultiValueEnum, OrderedEnum, unique, skip, extend_enum, auto
-from . import StdlibEnumMeta, StdlibEnum, StdlibIntEnum, StdlibFlag, StdlibIntFlag, StdlibStrEnum
-from . import pyver, PY3_3, PY3_4, PY3_5, PY3_6, PY3_11
-from . import add_stdlib_integration, remove_stdlib_integration
-
-from collections import OrderedDict
-from datetime import timedelta
-from pickle import dumps, loads, PicklingError, HIGHEST_PROTOCOL
-from unittest import TestCase, main
-
-import os
-import sys
-import tempfile
-import textwrap
-import unittest
-
-try:
-    import pyparsing
-except (ImportError, SyntaxError):
-    pyparsing = None
-
-try:
-    RecursionError
-except NameError:
-    # python3.4
-    RecursionError = RuntimeError
-
-class TestEnumV3(TestCase):
-
-    def setUp(self):
-        class Season(Enum):
-            SPRING = 1
-            SUMMER = 2
-            AUTUMN = 3
-            WINTER = 4
-        self.Season = Season
-
-        class Konstants(float, Enum):
-            E = 2.7182818
-            PI = 3.1415926
-            TAU = 2 * PI
-        self.Konstants = Konstants
-
-        class Grades(IntEnum):
-            A = 5
-            B = 4
-            C = 3
-            D = 2
-            F = 0
-        self.Grades = Grades
-
-        class Directional(str, Enum):
-            EAST = 'east'
-            WEST = 'west'
-            NORTH = 'north'
-            SOUTH = 'south'
-        self.Directional = Directional
-
-        from datetime import date
-        class Holiday(date, Enum):
-            NEW_YEAR = 2013, 1, 1
-            IDES_OF_MARCH = 2013, 3, 15
-        self.Holiday = Holiday
-
-    @unittest.skipUnless(StdlibEnumMeta, 'Stdlib enum not available')
-    def test_stdlib_inheritence(self):
-        # 3.4
-        self.assertTrue(issubclass(self.Season, StdlibEnum))
-        self.assertTrue(isinstance(self.Season.SPRING, StdlibEnum))
-        #
-        if pyver >= PY3_6:
-            class AFlag(Flag):
-                one = 1
-            self.assertTrue(issubclass(AFlag, StdlibEnum))
-            self.assertTrue(isinstance(AFlag.one, StdlibEnum))
-            self.assertTrue(issubclass(AFlag, StdlibFlag))
-            self.assertTrue(isinstance(AFlag.one, StdlibFlag))
-            #
-            class AnIntFlag(IntFlag):
-                one = 1
-            self.assertTrue(issubclass(AnIntFlag, StdlibEnum))
-            self.assertTrue(isinstance(AnIntFlag.one, StdlibEnum))
-            self.assertTrue(issubclass(AnIntFlag, StdlibFlag))
-            self.assertTrue(isinstance(AnIntFlag.one, StdlibFlag))
-            self.assertTrue(issubclass(AnIntFlag, StdlibIntFlag))
-            self.assertTrue(isinstance(AnIntFlag.one, StdlibIntFlag))
-        #
-        if pyver >= PY3_11:
-            class AStrEnum(StrFlag):
-                one = '1'
-            self.assertTrue(issubclass(AStrEnum, StdlibEnum))
-            self.assertTrue(isinstance(AStrEnum.one, StdlibEnum))
-            self.assertTrue(issubclass(AStrEnum, StdlibStrEnum))
-            self.assertTrue(isinstance(AStrEnum.one, StdlibStrEnum))
-
-    @unittest.skipUnless(StdlibEnumMeta, 'Stdlib enum not available')
-    def test_stdlib_bad_getattribute(self):
-        class BadEnumType(StdlibEnumMeta):
-            def __getattribute__(cls, name):
-                obj = super().__getattribute__(name)
-                if isinstance(obj, cls):
-                    obj.deprecate()
-                return obj
-        with self.assertRaisesRegex(RecursionError, 'endless recursion'):
-            class BaseEnum(StdlibEnum):
-                pass
-            class BadEnum(BaseEnum, metaclass=BadEnumType):
-                FOO = 'bar'
-        try:
-            remove_stdlib_integration()
-            class OkayEnum(StdlibEnum, metaclass=BadEnumType):
-                FOO = 'bar'
-        finally:
-            add_stdlib_integration()
-
-    @unittest.skipUnless(pyver >= PY3_5, '__qualname__ requires python 3.5 or greater')
-    def test_pickle_enum_function_with_qualname(self):
-        Theory = Enum('Theory', 'rule law supposition', qualname='spanish_inquisition')
-        globals()['spanish_inquisition'] = Theory
-        test_pickle_dump_load(self.assertTrue, Theory.rule)
-        test_pickle_dump_load(self.assertTrue, Theory)
-
-    def test_auto_init(self):
-        class Planet(Enum, init='mass radius'):
-            MERCURY = (3.303e+23, 2.4397e6)
-            VENUS   = (4.869e+24, 6.0518e6)
-            EARTH   = (5.976e+24, 6.37814e6)
-            MARS    = (6.421e+23, 3.3972e6)
-            JUPITER = (1.9e+27,   7.1492e7)
-            SATURN  = (5.688e+26, 6.0268e7)
-            URANUS  = (8.686e+25, 2.5559e7)
-            NEPTUNE = (1.024e+26, 2.4746e7)
-            @property
-            def surface_gravity(self):
-                # universal gravitational constant  (m3 kg-1 s-2)
-                G = 6.67300E-11
-                return G * self.mass / (self.radius * self.radius)
-        self.assertEqual(round(Planet.EARTH.surface_gravity, 2), 9.80)
-        self.assertEqual(Planet.EARTH.value, (5.976e+24, 6.37814e6))
-
-    def test_auto_init_with_value(self):
-        class Color(Enum, init='value, rgb'):
-            RED = 1, (1, 0, 0)
-            BLUE = 2, (0, 1, 0)
-            GREEN = 3, (0, 0, 1)
-        self.assertEqual(Color.RED.value, 1)
-        self.assertEqual(Color.BLUE.value, 2)
-        self.assertEqual(Color.GREEN.value, 3)
-        self.assertEqual(Color.RED.rgb, (1, 0, 0))
-        self.assertEqual(Color.BLUE.rgb, (0, 1, 0))
-        self.assertEqual(Color.GREEN.rgb, (0, 0, 1))
-
-    def test_auto_turns_off(self):
-        with self.assertRaises(NameError):
-            class Color(Enum, settings=MagicValue):
-                red
-                green
-                blue
-                def hello(self):
-                    print('Hello!  My serial is %s.' % self.value)
-                rose
-        with self.assertRaises(NameError):
-            class Color(Enum, settings=MagicValue):
-                red
-                green
-                blue
-                def __init__(self, *args):
-                    pass
-                rose
-
-    def test_magic(self):
-        class Color(Enum, settings=MagicValue):
-            red, green, blue
-        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
-        self.assertEqual(Color.red.value, 1)
-
-    def test_ignore_not_overridden(self):
-        with self.assertRaisesRegex(TypeError, 'object is not callable'):
-            class Color(Flag):
-                _ignore_ = 'irrelevent'
-                _settings_ = MagicValue
-                @property
-                def shade(self):
-                    print('I am light', self.name.lower())
-
-    def test_magic_start(self):
-        class Color(Enum, settings=MagicValue, start=0):
-            red, green, blue
-        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
-        self.assertEqual(Color.red.value, 0)
-
-    def test_dir_on_class(self):
-        Season = self.Season
-        self.assertEqual(
-            set(dir(Season)),
-            set(['__class__', '__doc__', '__members__', '__module__',
-                'SPRING', 'SUMMER', 'AUTUMN', 'WINTER',
-                '__init_subclass__', '__name__', '__getitem__', '__len__',
-                '__contains__', '__iter__', '__qualname__',
-                ]))
-
-    def test_dir_on_item(self):
-        Season = self.Season
-        self.assertEqual(
-            set(dir(Season.WINTER)),
-            set(['__class__', '__doc__', '__eq__', '__hash__', '__module__', 'name', 'value', 'values']),
-            )
-
-    def test_dir_with_added_behavior(self):
-        class Test(Enum):
-            this = 'that'
-            these = 'those'
-            def wowser(self):
-                return ("Wowser! I'm %s!" % self.name)
-        self.assertEqual(
-                set(dir(Test)),
-                set([
-                    '__class__', '__doc__', '__members__', '__module__', 'this', 'these',
-                    '__init_subclass__', '__name__', '__getitem__', '__len__',
-                    '__contains__', '__iter__', '__qualname__',
-                    ]))
-        self.assertEqual(
-                set(dir(Test.this)),
-                set(['__class__', '__doc__', '__eq__', '__hash__', '__module__', 'name', 'value', 'values', 'wowser']),
-                )
-
-    def test_dir_on_sub_with_behavior_on_super(self):
-        # see issue22506
-        class SuperEnum(Enum):
-            def invisible(self):
-                return "did you see me?"
-        class SubEnum(SuperEnum):
-            sample = 5
-        self.assertEqual(
-                set(dir(SubEnum.sample)),
-                set(['__class__', '__doc__', '__eq__', '__hash__', '__module__', 'name', 'value', 'values', 'invisible']),
-                )
-
-    def test_members_are_always_ordered(self):
-        class AlwaysOrdered(Enum):
-            first = 1
-            second = 2
-            third = 3
-        self.assertTrue(type(AlwaysOrdered.__members__) is OrderedDict)
-
-    def test_comparisons(self):
-        def bad_compare():
-            Season.SPRING > 4
-        Season = self.Season
-        self.assertNotEqual(Season.SPRING, 1)
-        self.assertRaises(TypeError, bad_compare)
-
-        class Part(Enum):
-            SPRING = 1
-            CLIP = 2
-            BARREL = 3
-
-        self.assertNotEqual(Season.SPRING, Part.SPRING)
-        def bad_compare():
-            Season.SPRING < Part.CLIP
-        self.assertRaises(TypeError, bad_compare)
-
-    def test_duplicate_name(self):
-        with self.assertRaises(TypeError):
-            class Color1(Enum):
-                red = 1
-                green = 2
-                blue = 3
-                red = 4
-
-        with self.assertRaises(TypeError):
-            class Color2(Enum):
-                red = 1
-                green = 2
-                blue = 3
-                def red(self):
-                    return 'red'
-
-        with self.assertRaises(TypeError):
-            class Color3(Enum):
-                @property
-                def red(self):
-                    return 'redder'
-                red = 1
-                green = 2
-                blue = 3
-
-    def test_duplicate_value_with_unique(self):
-        with self.assertRaises(ValueError):
-            class Color(Enum, settings=Unique):
-                red = 1
-                green = 2
-                blue = 3
-                rojo = 1
-
-    def test_duplicate_value_with_noalias(self):
-        class Color(Enum, settings=NoAlias):
-            red = 1
-            green = 2
-            blue = 3
-            rojo = 1
-        self.assertFalse(Color.red is Color.rojo)
-        self.assertEqual(Color.red.value, 1)
-        self.assertEqual(Color.rojo.value, 1)
-        self.assertEqual(len(Color), 4)
-        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue, Color.rojo])
-
-    def test_noalias_value_lookup(self):
-        class Color(Enum, settings=NoAlias):
-            red = 1
-            green = 2
-            blue = 3
-            rojo = 1
-        self.assertRaises(TypeError, Color, 2)
-
-    def test_multivalue(self):
-        class Color(Enum, settings=MultiValue):
-            red = 1, 'red'
-            green = 2, 'green'
-            blue = 3, 'blue'
-        self.assertEqual(Color.red.value, 1)
-        self.assertIs(Color('green'), Color.green)
-        self.assertEqual(Color.blue.values, (3, 'blue'))
-
-    def test_multivalue_with_duplicate_values(self):
-        with self.assertRaises(ValueError):
-            class Color(Enum, settings=MultiValue):
-                red = 1, 'red'
-                green = 2, 'green'
-                blue = 3, 'blue', 'red'
-
-    def test_multivalue_with_duplicate_values_and_noalias(self):
-        with self.assertRaises(TypeError):
-            class Color(Enum, settings=(MultiValue, NoAlias)):
-                red = 1, 'red'
-                green = 2, 'green'
-                blue = 3, 'blue', 'red'
-
-    def test_multivalue_and_auto(self):
-        with self.assertRaisesRegex(TypeError, r'MultiValue and MagicValue are mutually exclusive'):
-            class Color(Enum, settings=(MultiValue, MagicValue)):
-                red
-                green = 3, 'green'
-                blue
-
-    def test_autonumber_and_init(self):
-        class Field(IntEnum, settings=AddValue, init='__doc__'):
-            TYPE = "Char, Date, Logical, etc."
-            START = "Field offset in record"
-        self.assertEqual(Field.TYPE, 1)
-        self.assertEqual(Field.START, 2)
-        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
-        self.assertEqual(Field.START.__doc__, 'Field offset in record')
-        self.assertFalse(hasattr(Field, '_order_'))
-
-    def test_autovalue_and_init(self):
-        class Field(IntEnum, init='value __doc__'):
-            TYPE = "Char, Date, Logical, etc."
-            START = "Field offset in record"
-        self.assertEqual(Field.TYPE, 1)
-        self.assertEqual(Field.START.__doc__, 'Field offset in record')
-
-    def test_autonumber_and_start(self):
-        class Field(IntEnum, init='__doc__', settings=AddValue, start=0):
-            TYPE = "Char, Date, Logical, etc."
-            START = "Field offset in record"
-        self.assertEqual(Field.TYPE, 0)
-        self.assertEqual(Field.START, 1)
-        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
-        self.assertEqual(Field.START.__doc__, 'Field offset in record')
-
-    def test_autonumber_and_init_and_some_values(self):
-        class Field(IntEnum, init='value __doc__'):
-            TYPE = "Char, Date, Logical, etc."
-            START = "Field offset in record"
-            BLAH = 5, "test blah"
-            BELCH = 'test belch'
-        self.assertEqual(Field.TYPE, 1)
-        self.assertEqual(Field.START, 2)
-        self.assertEqual(Field.BLAH, 5)
-        self.assertEqual(Field.BELCH, 6)
-        self.assertEqual(Field.TYPE.__doc__, 'Char, Date, Logical, etc.')
-        self.assertEqual(Field.START.__doc__, 'Field offset in record')
-        self.assertEqual(Field.BLAH.__doc__, 'test blah')
-        self.assertEqual(Field.BELCH.__doc__, 'test belch')
-
-    def test_autonumber_with_irregular_values(self):
-        class Point(AutoNumberEnum, init='x y'):
-            first = 7, 9
-            second = 11, 13
-        self.assertEqual(Point.first.value, 1)
-        self.assertEqual(Point.first.x, 7)
-        self.assertEqual(Point.first.y, 9)
-        self.assertEqual(Point.second.value, 2)
-        self.assertEqual(Point.second.x, 11)
-        self.assertEqual(Point.second.y, 13)
-        with self.assertRaisesRegex(TypeError, '.*number of fields provided do not match init ...x., .y.. != .3, 11, 13..'):
-            class Point(AutoNumberEnum, init='x y'):
-                first = 7, 9
-                second = 3, 11, 13
-        class Color(AutoNumberEnum, init='__doc__'):
-            # interactions between AutoNumberEnum and _generate_next_value_ may not be pretty
-            red = ()
-            green = 'red'
-            blue = ()
-        self.assertTrue(Color.red.__doc__, 1)
-        self.assertEqual(Color.green.__doc__, 'red')
-        self.assertTrue(Color.blue.__doc__, 2)
-
-    def test_autonumber_and_property(self):
-        with self.assertRaises(TypeError):
-            class Color(AutoEnum):
-                _ignore_ = ()
-                red = ()
-                green = ()
-                blue = ()
-                @property
-                def cap_name(self) -> str:
-                    return self.name.title()
-
-    def test_autoenum(self):
-        class Color(AutoEnum):
-            red
-            green
-            blue
-        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
-        self.assertEqual([m.value for m in Color], [1, 2, 3])
-        self.assertEqual([m.name for m in Color], ['red', 'green', 'blue'])
-
-    def test_autoenum_with_str(self):
-        class Color(AutoEnum):
-            def _generate_next_value_(name, start, count, last_values):
-                return name
-            red
-            green
-            blue
-        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
-        self.assertEqual([m.value for m in Color], ['red', 'green', 'blue'])
-        self.assertEqual([m.name for m in Color], ['red', 'green', 'blue'])
-
-    def test_autoenum_and_default_ignore(self):
-        class Color(AutoEnum):
-            red
-            green
-            blue
-            @property
-            def cap_name(self):
-                return self.name.title()
-        self.assertEqual(Color.blue.cap_name, 'Blue')
-
-    def test_autonumber_and_overridden_ignore(self):
-        with self.assertRaises(TypeError):
-            class Color(AutoEnum):
-                _ignore_ = 'staticmethod'
-                red
-                green
-                blue
-                @property
-                def cap_name(self) -> str:
-                    return self.name.title()
-
-    def test_autonumber_and_multiple_assignment(self):
-        class Color(AutoEnum):
-            _ignore_ = 'property'
-            red
-            green
-            blue = cyan
-            @property
-            def cap_name(self) -> str:
-                return self.name.title()
-        self.assertEqual(Color.blue.cap_name, 'Cyan')
-
-    def test_multivalue_and_autonumber_inherited(self):
-        class Measurement(int, Enum, settings=(MultiValue, AddValue), start=0):
-            one = "20110721"
-            two = "20120911"
-            three = "20110518"
-        M = Measurement
-        self.assertEqual(M.one, 0)
-        self.assertTrue(M.one is M(0) is M('20110721'))
-
-    def test_combine_new_settings_with_old_settings(self):
-        class Auto(Enum, settings=Unique):
-            pass
-        with self.assertRaises(ValueError):
-            class AutoUnique(Auto, settings=MagicValue):
-                BLAH
-                BLUH
-                ICK = 1
-
-    def test_timedelta(self):
-        class Period(timedelta, Enum):
-            '''
-            different lengths of time
-            '''
-            _init_ = 'value period'
-            _settings_ = NoAlias
-            _ignore_ = 'Period i'
-            Period = vars()
-            for i in range(31):
-                Period['day_%d' % i] = i, 'day'
-            for i in range(15):
-                Period['week_%d' % i] = i*7, 'week'
-            for i in range(12):
-                Period['month_%d' % i] = i*30, 'month'
-            OneDay = day_1
-            OneWeek = week_1
-        self.assertFalse(hasattr(Period, '_ignore_'))
-        self.assertFalse(hasattr(Period, 'Period'))
-        self.assertFalse(hasattr(Period, 'i'))
-        self.assertTrue(isinstance(Period.day_1, timedelta))
-
-    def test_extend_enum_plain(self):
-        class Color(UniqueEnum):
-            red = 1
-            green = 2
-            blue = 3
-        extend_enum(Color, 'brown', 4)
-        self.assertEqual(Color.brown.name, 'brown')
-        self.assertEqual(Color.brown.value, 4)
-        self.assertTrue(Color.brown in Color)
-        self.assertEqual(len(Color), 4)
-
-    def test_extend_enum_shadow(self):
-        class Color(UniqueEnum):
-            red = 1
-            green = 2
-            blue = 3
-        extend_enum(Color, 'value', 4)
-        self.assertEqual(Color.value.name, 'value')
-        self.assertEqual(Color.value.value, 4)
-        self.assertTrue(Color.value in Color)
-        self.assertEqual(len(Color), 4)
-        self.assertEqual(Color.red.value, 1)
-
-    def test_extend_enum_generate(self):
-        class Foo(AutoEnum):
-            def _generate_next_value_(name, start, count, values, *args, **kwds):
-                return name
-            a
-            b
-        #
-        extend_enum(Foo, 'c')
-        self.assertEqual(Foo.a.value, 'a')
-        self.assertEqual(Foo.b.value, 'b')
-        self.assertEqual(Foo.c.value, 'c')
-
-    def test_extend_enum_unique_with_duplicate(self):
-        with self.assertRaises(ValueError):
-            class Color(Enum, settings=Unique):
-                red = 1
-                green = 2
-                blue = 3
-            extend_enum(Color, 'value', 1)
-
-    def test_extend_enum_multivalue_with_duplicate(self):
-        with self.assertRaises(ValueError):
-            class Color(Enum, settings=MultiValue):
-                red = 1, 'rojo'
-                green = 2, 'verde'
-                blue = 3, 'azul'
-            extend_enum(Color, 'value', 2)
-
-    def test_extend_enum_noalias_with_duplicate(self):
-        class Color(Enum, settings=NoAlias):
-            red = 1
-            green = 2
-            blue = 3
-        extend_enum(Color, 'value', 3, )
-        self.assertRaises(TypeError, Color, 3)
-        self.assertFalse(Color.value is Color.blue)
-        self.assertTrue(Color.value.value, 3)
-
-    def test_no_duplicates(self):
-        def bad_duplicates():
-            class Color(UniqueEnum):
-                red = 1
-                green = 2
-                blue = 3
-            class Color(UniqueEnum):
-                red = 1
-                green = 2
-                blue = 3
-                grene = 2
-        self.assertRaises(ValueError, bad_duplicates)
-
-    def test_no_duplicates_kinda(self):
-        class Silly(UniqueEnum):
-            one = 1
-            two = 'dos'
-            name = 3
-        class Sillier(IntEnum, UniqueEnum):
-            single = 1
-            name = 2
-            triple = 3
-            value = 4
-
-    def test_auto_number(self):
-        class Color(Enum, settings=MagicValue):
-            red
-            blue
-            green
-
-        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
-        self.assertEqual(Color.red.value, 1)
-        self.assertEqual(Color.blue.value, 2)
-        self.assertEqual(Color.green.value, 3)
-
-    def test_auto_name(self):
-        class Color(Enum, settings=MagicValue):
-            def _generate_next_value_(name, start, count, last):
-                return name
-            red
-            blue
-            green
-
-        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
-        self.assertEqual(Color.red.value, 'red')
-        self.assertEqual(Color.blue.value, 'blue')
-        self.assertEqual(Color.green.value, 'green')
-
-    def test_auto_name_inherit(self):
-        class AutoNameEnum(Enum):
-            def _generate_next_value_(name, start, count, last):
-                return name
-        class Color(AutoNameEnum, settings=MagicValue):
-            red
-            blue
-            green
-
-        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
-        self.assertEqual(Color.red.value, 'red')
-        self.assertEqual(Color.blue.value, 'blue')
-        self.assertEqual(Color.green.value, 'green')
-
-    def test_auto_garbage(self):
-        class Color(Enum):
-            _settings_ = MagicValue
-            red = 'red'
-            blue
-        self.assertEqual(Color.blue.value, 1)
-
-    def test_auto_garbage_corrected(self):
-        class Color(Enum, settings=MagicValue):
-            red = 'red'
-            blue = 2
-            green
-
-        self.assertEqual(list(Color), [Color.red, Color.blue, Color.green])
-        self.assertEqual(Color.red.value, 'red')
-        self.assertEqual(Color.blue.value, 2)
-        self.assertEqual(Color.green.value, 3)
-
-    def test_duplicate_auto(self):
-        class Dupes(Enum, settings=MagicValue):
-            first = primero
-            second
-            third
-        self.assertEqual([Dupes.first, Dupes.second, Dupes.third], list(Dupes))
-
-    def test_order_as_function(self):
-        # first with _init_
-        class TestSequence(Enum):
-            _init_ = 'value, sequence'
-            _order_ = lambda member: member.sequence
-            item_id                  = 'An$(1,6)',      0     # Item Code
-            company_id               = 'An$(7,2)',      1     # Company Code
-            warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
-            company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
-            key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
-            available                = 'Zn$(1,1)',      5     # Available?
-            contract_item            = 'Bn(2,1)',       6     # Contract Item?
-            sales_category           = 'Fn',            7     # Sales Category
-            gl_category              = 'Rn$(5,1)',      8     # G/L Category
-            warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
-            inv_units                = 'Qn$(7,2)',     10     # Inv Units
-        for i, member in enumerate(TestSequence):
-            self.assertEqual(i, member.sequence)
-        ts = TestSequence
-        self.assertEqual(ts.item_id.name, 'item_id')
-        self.assertEqual(ts.item_id.value, 'An$(1,6)')
-        self.assertEqual(ts.item_id.sequence, 0)
-        self.assertEqual(ts.company_id.name, 'company_id')
-        self.assertEqual(ts.company_id.value, 'An$(7,2)')
-        self.assertEqual(ts.company_id.sequence, 1)
-        self.assertEqual(ts.warehouse_no.name, 'warehouse_no')
-        self.assertEqual(ts.warehouse_no.value, 'An$(9,4)')
-        self.assertEqual(ts.warehouse_no.sequence, 2)
-        self.assertEqual(ts.company.name, 'company')
-        self.assertEqual(ts.company.value, 'Hn$(13,6)')
-        self.assertEqual(ts.company.sequence, 3)
-        self.assertEqual(ts.key_type.name, 'key_type')
-        self.assertEqual(ts.key_type.value, 'Cn$(19,3)')
-        self.assertEqual(ts.key_type.sequence, 4)
-        self.assertEqual(ts.available.name, 'available')
-        self.assertEqual(ts.available.value, 'Zn$(1,1)')
-        self.assertEqual(ts.available.sequence, 5)
-        self.assertEqual(ts.contract_item.name, 'contract_item')
-        self.assertEqual(ts.contract_item.value, 'Bn(2,1)')
-        self.assertEqual(ts.contract_item.sequence, 6)
-        self.assertEqual(ts.sales_category.name, 'sales_category')
-        self.assertEqual(ts.sales_category.value, 'Fn')
-        self.assertEqual(ts.sales_category.sequence, 7)
-        self.assertEqual(ts.gl_category.name, 'gl_category')
-        self.assertEqual(ts.gl_category.value, 'Rn$(5,1)')
-        self.assertEqual(ts.gl_category.sequence, 8)
-        self.assertEqual(ts.warehouse_category.name, 'warehouse_category')
-        self.assertEqual(ts.warehouse_category.value, 'Sn$(6,1)')
-        self.assertEqual(ts.warehouse_category.sequence, 9)
-        self.assertEqual(ts.inv_units.name, 'inv_units')
-        self.assertEqual(ts.inv_units.value, 'Qn$(7,2)')
-        self.assertEqual(ts.inv_units.sequence, 10)
-        # and then without
-        class TestSequence(Enum):
-            _order_ = lambda member: member.value[1]
-            item_id                  = 'An$(1,6)',      0     # Item Code
-            company_id               = 'An$(7,2)',      1     # Company Code
-            warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
-            company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
-            key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
-            available                = 'Zn$(1,1)',      5     # Available?
-            contract_item            = 'Bn(2,1)',       6     # Contract Item?
-            sales_category           = 'Fn',            7     # Sales Category
-            gl_category              = 'Rn$(5,1)',      8     # G/L Category
-            warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
-            inv_units                = 'Qn$(7,2)',     10     # Inv Units
-        for i, member in enumerate(TestSequence):
-            self.assertEqual(i, member.value[1])
-        ts = TestSequence
-        self.assertEqual(ts.item_id.name, 'item_id')
-        self.assertEqual(ts.item_id.value, ('An$(1,6)', 0))
-        self.assertEqual(ts.company_id.name, 'company_id')
-        self.assertEqual(ts.company_id.value, ('An$(7,2)', 1))
-        self.assertEqual(ts.warehouse_no.name, 'warehouse_no')
-        self.assertEqual(ts.warehouse_no.value, ('An$(9,4)', 2))
-        self.assertEqual(ts.company.name, 'company')
-        self.assertEqual(ts.company.value, ('Hn$(13,6)', 3))
-        self.assertEqual(ts.key_type.name, 'key_type')
-        self.assertEqual(ts.key_type.value, ('Cn$(19,3)', 4))
-        self.assertEqual(ts.available.name, 'available')
-        self.assertEqual(ts.available.value, ('Zn$(1,1)', 5))
-        self.assertEqual(ts.contract_item.name, 'contract_item')
-        self.assertEqual(ts.contract_item.value, ('Bn(2,1)', 6))
-        self.assertEqual(ts.sales_category.name, 'sales_category')
-        self.assertEqual(ts.sales_category.value, ('Fn', 7))
-        self.assertEqual(ts.gl_category.name, 'gl_category')
-        self.assertEqual(ts.gl_category.value, ('Rn$(5,1)', 8))
-        self.assertEqual(ts.warehouse_category.name, 'warehouse_category')
-        self.assertEqual(ts.warehouse_category.value, ('Sn$(6,1)', 9))
-        self.assertEqual(ts.inv_units.name, 'inv_units')
-        self.assertEqual(ts.inv_units.value, ('Qn$(7,2)', 10))
-        # then with _init_ but without value
-        with self.assertRaises(TypeError):
-            class TestSequence(Enum):
-                _init_ = 'sequence'
-                _order_ = lambda member: member.sequence
-                item_id                  = 'An$(1,6)',      0     # Item Code
-                company_id               = 'An$(7,2)',      1     # Company Code
-                warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
-                company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
-                key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
-                available                = 'Zn$(1,1)',      5     # Available?
-                contract_item            = 'Bn(2,1)',       6     # Contract Item?
-                sales_category           = 'Fn',            7     # Sales Category
-                gl_category              = 'Rn$(5,1)',      8     # G/L Category
-                warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
-                inv_units                = 'Qn$(7,2)',     10     # Inv Units
-        # finally, out of order so Python 3 barfs
-        with self.assertRaises(TypeError):
-            class TestSequence(Enum):
-                _init_ = 'sequence'
-                _order_ = lambda member: member.sequence
-                item_id                  = 'An$(1,6)',      0     # Item Code
-                warehouse_no             = 'An$(9,4)',      2     # Warehouse Number
-                company                  = 'Hn$(13,6)',     3     # 4 SPACES + COMPANY
-                company_id               = 'An$(7,2)',      1     # Company Code
-                inv_units                = 'Qn$(7,2)',     10     # Inv Units
-                available                = 'Zn$(1,1)',      5     # Available?
-                contract_item            = 'Bn(2,1)',       6     # Contract Item?
-                sales_category           = 'Fn',            7     # Sales Category
-                key_type                 = 'Cn$(19,3)',     4     # Key Type = '1**'
-                gl_category              = 'Rn$(5,1)',      8     # G/L Category
-                warehouse_category       = 'Sn$(6,1)',      9     # Warehouse Category
-
-    if pyver >= PY3_3:
-        def test_missing(self):
-            class Color(Enum):
-                red = 1
-                green = 2
-                blue = 3
-                @classmethod
-                def _missing_(cls, item):
-                    if item == 'three':
-                        return cls.blue
-                    elif item == 'bad return':
-                        # trigger internal error
-                        return 5
-                    elif item == 'error out':
-                        raise ZeroDivisionError
-                    else:
-                        # trigger not found
-                        return None
-            self.assertIs(Color('three'), Color.blue)
-            self.assertRaises(ValueError, Color, 7)
-            try:
-                Color('bad return')
-            except TypeError as exc:
-                self.assertTrue(isinstance(exc.__cause__, ValueError))
-            else:
-                raise Exception('Exception not raised.')
-            try:
-                Color('error out')
-            except ZeroDivisionError as exc:
-                self.assertTrue(isinstance(exc.__cause__, ValueError))
-            else:
-                raise Exception('Exception not raised.')
-
-    def test_enum_of_types(self):
-        """Support using Enum to refer to types deliberately."""
-        class MyTypes(Enum):
-            i = int
-            f = float
-            s = str
-        self.assertEqual(MyTypes.i.value, int)
-        self.assertEqual(MyTypes.f.value, float)
-        self.assertEqual(MyTypes.s.value, str)
-        class Foo:
-            pass
-        class Bar:
-            pass
-        class MyTypes2(Enum):
-            a = Foo
-            b = Bar
-        self.assertEqual(MyTypes2.a.value, Foo)
-        self.assertEqual(MyTypes2.b.value, Bar)
-        class SpamEnumNotInner:
-            pass
-        class SpamEnum(Enum):
-            spam = SpamEnumNotInner
-        self.assertEqual(SpamEnum.spam.value, SpamEnumNotInner)
-
-    def test_nested_classes_in_enum_do_not_create_members(self):
-        """Support locally-defined nested classes."""
-        # manually set __qualname__ to remove testing framework noise
-        class Outer(Enum):
-            __qualname__ = "Outer"
-            a = 1
-            b = 2
-            class Inner(Enum):
-                __qualname__ = "Outer.Inner"
-                foo = 10
-                bar = 11
-        self.assertTrue(isinstance(Outer.Inner, type))
-        self.assertEqual(Outer.a.value, 1)
-        self.assertEqual(Outer.Inner.foo.value, 10)
-        self.assertEqual(
-            list(Outer.Inner),
-            [Outer.Inner.foo, Outer.Inner.bar],
-            )
-        self.assertEqual(
-            list(Outer),
-            [Outer.a, Outer.b],
-            )
-
-    if pyver == PY3_4:
-        def test_class_nested_enum_and_pickle_protocol_four(self):
-            # would normally just have this directly in the class namespace
-            class NestedEnum(Enum):
-                twigs = 'common'
-                shiny = 'rare'
-
-            self.__class__.NestedEnum = NestedEnum
-            self.NestedEnum.__qualname__ = '%s.NestedEnum' % self.__class__.__name__
-            test_pickle_exception(
-                    self.assertRaises, PicklingError, self.NestedEnum.twigs,
-                    protocol=(0, 3))
-            test_pickle_dump_load(self.assertTrue, self.NestedEnum.twigs,
-                    protocol=(4, HIGHEST_PROTOCOL))
-
-    elif pyver >= PY3_5:
-        def test_class_nested_enum_and_pickle_protocol_four(self):
-            # would normally just have this directly in the class namespace
-            class NestedEnum(Enum):
-                twigs = 'common'
-                shiny = 'rare'
-
-            self.__class__.NestedEnum = NestedEnum
-            self.NestedEnum.__qualname__ = '%s.NestedEnum' % self.__class__.__name__
-            test_pickle_dump_load(self.assertTrue, self.NestedEnum.twigs,
-                    protocol=(0, HIGHEST_PROTOCOL))
-
-    if pyver >= PY3_4:
-        def test_enum_injection(self):
-            class Color(Enum):
-                _order_ = 'BLACK WHITE'
-                BLACK = Color('black', '#000')
-                WHITE = Color('white', '#fff')
-
-                def __init__(self, label, hex):
-                    self.label = label
-                    self.hex = hex
-
-            self.assertEqual([Color.BLACK, Color.WHITE], list(Color))
-            self.assertEqual(Color.WHITE.hex, '#fff')
-            self.assertEqual(Color.BLACK.label, 'black')
-
-        def test_subclasses_with_getnewargs_ex(self):
-            class NamedInt(int):
-                __qualname__ = 'NamedInt'       # needed for pickle protocol 4
-                def __new__(cls, *args):
-                    _args = args
-                    if len(args) < 2:
-                        raise TypeError("name and value must be specified")
-                    name, args = args[0], args[1:]
-                    self = int.__new__(cls, *args)
-                    self._intname = name
-                    self._args = _args
-                    return self
-                def __getnewargs_ex__(self):
-                    return self._args, {}
-                @property
-                def __name__(self):
-                    return self._intname
-                def __repr__(self):
-                    # repr() is updated to include the name and type info
-                    return "{}({!r}, {})".format(type(self).__name__,
-                                                 self.__name__,
-                                                 int.__repr__(self))
-                def __str__(self):
-                    # str() is unchanged, even if it relies on the repr() fallback
-                    base = int
-                    base_str = base.__str__
-                    if base_str.__objclass__ is object:
-                        return base.__repr__(self)
-                    return base_str(self)
-                # for simplicity, we only define one operator that
-                # propagates expressions
-                def __add__(self, other):
-                    temp = int(self) + int( other)
-                    if isinstance(self, NamedInt) and isinstance(other, NamedInt):
-                        return NamedInt(
-                            '({0} + {1})'.format(self.__name__, other.__name__),
-                            temp )
-                    else:
-                        return temp
-
-            class NEI(NamedInt, Enum):
-                __qualname__ = 'NEI'      # needed for pickle protocol 4
-                x = ('the-x', 1)
-                y = ('the-y', 2)
-
-
-            self.assertIs(NEI.__new__, Enum.__new__)
-            self.assertEqual(repr(NEI.x + NEI.y), "NamedInt('(the-x + the-y)', 3)")
-            globals()['NamedInt'] = NamedInt
-            globals()['NEI'] = NEI
-            NI5 = NamedInt('test', 5)
-            self.assertEqual(NI5, 5)
-            test_pickle_dump_load(self.assertEqual, NI5, 5, protocol=(4, HIGHEST_PROTOCOL))
-            self.assertEqual(NEI.y.value, 2)
-            test_pickle_dump_load(self.assertTrue, NEI.y, protocol=(4, HIGHEST_PROTOCOL))
-
-
-class TestOrderV3(TestCase):
-    """
-    Test definition order versus _order_ order.
-    """
-
-    def test_same_members(self):
-        class Color(Enum):
-            _order_ = 'red green blue'
-            red = 1
-            green = 2
-            blue = 3
-
-    def test_same_members_with_aliases(self):
-        class Color(Enum):
-            _order_ = 'red green blue'
-            red = 1
-            green = 2
-            blue = 3
-            verde = green
-
-    def test_same_members_wrong_order(self):
-        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
-            class Color(Enum):
-                _order_ = 'red green blue'
-                red = 1
-                blue = 3
-                green = 2
-
-    def test_order_has_extra_members(self):
-        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
-            class Color(Enum):
-                _order_ = 'red green blue purple'
-                red = 1
-                green = 2
-                blue = 3
-
-    def test_order_has_extra_members_with_aliases(self):
-        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
-            class Color(Enum):
-                _order_ = 'red green blue purple'
-                red = 1
-                green = 2
-                blue = 3
-                verde = green
-
-    def test_enum_has_extra_members(self):
-        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
-            class Color(Enum):
-                _order_ = 'red green blue'
-                red = 1
-                green = 2
-                blue = 3
-                purple = 4
-
-    def test_enum_has_extra_members_with_aliases(self):
-        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
-            class Color(Enum):
-                _order_ = 'red green blue'
-                red = 1
-                green = 2
-                blue = 3
-                purple = 4
-                verde = green
-
-    def test_same_members_flag(self):
-        class Color(Flag):
-            _order_ = 'red green blue'
-            red = 1
-            green = 2
-            blue = 4
-
-    def test_same_members_with_aliases_flag(self):
-        class Color(Flag):
-            _order_ = 'red green blue'
-            red = 1
-            green = 2
-            blue = 4
-            verde = green
-
-    def test_same_members_wrong_order_falg(self):
-        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
-            class Color(Flag):
-                _order_ = 'red green blue'
-                red = 1
-                blue = 4
-                green = 2
-
-    def test_order_has_extra_members_flag(self):
-        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
-            class Color(Flag):
-                _order_ = 'red green blue purple'
-                red = 1
-                green = 2
-                blue = 4
-
-    def test_order_has_extra_members_with_aliases_flag(self):
-        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
-            class Color(Flag):
-                _order_ = 'red green blue purple'
-                red = 1
-                green = 2
-                blue = 4
-                verde = green
-
-    def test_enum_has_extra_members_flag(self):
-        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
-            class Color(Flag):
-                _order_ = 'red green blue'
-                red = 1
-                green = 2
-                blue = 4
-                purple = 8
-
-    def test_enum_has_extra_members_with_aliases_flag(self):
-        with self.assertRaisesRegex(TypeError, 'member order does not match _order_'):
-            class Color(Flag):
-                _order_ = 'red green blue'
-                red = 1
-                green = 2
-                blue = 4
-                purple = 8
-                verde = green
-
-
-class TestNamedTupleV3(TestCase):
-
-    def test_fixed_size(self):
-        class Book(NamedTuple, size=TupleSize.fixed):
-            title = 0
-            author = 1
-            genre = 2
-        b = Book('Teckla', 'Steven Brust', 'fantasy')
-        self.assertTrue('Teckla' in b)
-        self.assertTrue('Steven Brust' in b)
-        self.assertTrue('fantasy' in b)
-        self.assertEqual(b.title, 'Teckla')
-        self.assertEqual(b.author, 'Steven Brust')
-        self.assertRaises(TypeError, Book, 'Teckla', 'Steven Brust')
-        self.assertRaises(TypeError, Book, 'Teckla')
-
-    def test_minimum_size(self):
-        class Book(NamedTuple, size=TupleSize.minimum):
-            title = 0
-            author = 1
-        b = Book('Teckla', 'Steven Brust', 'fantasy')
-        self.assertTrue('Teckla' in b)
-        self.assertTrue('Steven Brust' in b)
-        self.assertTrue('fantasy' in b)
-        self.assertEqual(b.title, 'Teckla')
-        self.assertEqual(b.author, 'Steven Brust')
-        self.assertEqual(b[2], 'fantasy')
-        b = Book('Teckla', 'Steven Brust')
-        self.assertTrue('Teckla' in b)
-        self.assertTrue('Steven Brust' in b)
-        self.assertEqual(b.title, 'Teckla')
-        self.assertEqual(b.author, 'Steven Brust')
-        self.assertRaises(TypeError, Book, 'Teckla')
-
-    def test_variable_size(self):
-        class Book(NamedTuple, size=TupleSize.variable):
-            title = 0
-            author = 1
-            genre = 2
-        b = Book('Teckla', 'Steven Brust', 'fantasy')
-        self.assertTrue('Teckla' in b)
-        self.assertTrue('Steven Brust' in b)
-        self.assertTrue('fantasy' in b)
-        self.assertEqual(b.title, 'Teckla')
-        self.assertEqual(b.author, 'Steven Brust')
-        self.assertEqual(b.genre, 'fantasy')
-        b = Book('Teckla', 'Steven Brust')
-        self.assertTrue('Teckla' in b)
-        self.assertTrue('Steven Brust' in b)
-        self.assertEqual(b.title, 'Teckla')
-        self.assertEqual(b.author, 'Steven Brust')
-        self.assertRaises(AttributeError, getattr, b, 'genre')
-        self.assertRaises(TypeError, Book, title='Teckla', genre='fantasy')
-        self.assertRaises(TypeError, Book, author='Steven Brust')
-
-
-
-class TestStackoverflowAnswersV3(TestCase):
-
-    def test_self_referential_directions(self):
-        # https://stackoverflow.com/a/64000706/208880
-        class Directions(Enum):
-            #
-            NORTH = 1, 0
-            WEST = 0, 1
-            SOUTH = -1, 0
-            EAST = 0, -1
-            #
-            def __init__(self, x, y):
-                self.x = x
-                self.y = y
-                if len(self.__class__):
-                    # make links
-                    all = list(self.__class__)
-                    left, right = all[0], all[-1]
-                    self.left = left
-                    self.right = right
-                    left.right = self
-                    right.left = self
-        #
-        D = Directions
-        self.assertEqual(D.NORTH.value, (1, 0))
-        self.assertTrue(D.NORTH.left is D.WEST)
-        self.assertTrue(D.SOUTH.right is D.WEST)
-
-    def test_self_referential_rock_paper_scissors(self):
-        # https://stackoverflow.com/a/57085357/208880
-        class RPS(Enum):
-            #
-            Rock = "rock"
-            Paper = "paper"
-            Scissors = "scissors"
-            #
-            def __init__(self, value):
-                if len(self.__class__):
-                    # make links
-                    all = list(self.__class__)
-                    first, previous = all[0], all[-1]
-                    first.beats = self
-                    self.beats = previous
-        #
-        self.assertTrue(RPS.Rock.beats is RPS.Scissors)
-        self.assertTrue(RPS.Scissors.beats is RPS.Paper)
-        self.assertTrue(RPS.Paper.beats is RPS.Rock)
-
-    def test_arduino_headers(self):
-        # https://stackoverflow.com/q/65048495/208880
-        class CHeader(Enum):
-            def __init_subclass__(cls, **kwds):
-                # write Enums to C header file
-                cls_name = cls.__name__
-                header_path = getattr(cls, '_%s__header' % cls_name)
-                with open(header_path, 'w') as fh:
-                    fh.write('initial header stuff here\n')
-                    for enum in cls:
-                        fh.write('#define %s %r\n' % (enum.name, enum.value))
-        class Arduino(CHeader):
-            __header = os.path.join(tempdir, 'arduino.h')
-            ONE = 1
-            TWO = 2
-        with open(os.path.join(tempdir, 'arduino.h')) as fh:
-                data = fh.read()
-        self.assertEqual(textwrap.dedent("""\
-                initial header stuff here
-                #define ONE 1
-                #define TWO 2
-                """),
-                data,
-                )
-
-    def test_create_C_like_Enum(self):
-        # https://stackoverflow.com/a/35965438/208880
-        class Id(Enum, settings=MagicValue, start=0):
-            #
-            NONE  # 0x0
-            HEARTBEAT  # 0x1
-            FLUID_TRANSFER_REQUEST
-            FLUID_TRANSFER_STATUS_MSG
-            FLUID_TRANSFER_ERROR_MSG
-            # ...
-            #
-            # Camera App Messages
-            START_SENDING_PICTURES = 0x010000
-            STOP_SENDING_PICTURES
-            START_RECORDING_VIDEO_REQ
-            STOP_RECORDING_VIDEO_REQ
-            # ...
-            #
-            # Sensor Calibration
-            VOLUME_REQUEST = 0x020000
-            START_CAL
-            CLI_COMMAND_REQUEST
-            CLI_COMMAND_RESPONSE
-            #
-            # File Mananger
-            NEW_DELIVERY_REQ = 0x30000
-            GET_DELIVERY_FILE_REQ
-            GET_FILE_REQ
-            #
-            ACK_NACK
-            RESPONSE
-            #
-            LAST_ID
-        #
-        self.assertEqual(Id.NONE.value, 0)
-        self.assertEqual(Id.FLUID_TRANSFER_ERROR_MSG.value, 4)
-        self.assertEqual(Id.START_SENDING_PICTURES.value, 0x010000)
-        self.assertEqual(Id.STOP_RECORDING_VIDEO_REQ.value, 0x010003)
-        self.assertEqual(Id.START_CAL.value, 0x020001)
-        self.assertEqual(Id.LAST_ID.value, 0x30005)
-
-
-    @unittest.skipUnless(pyparsing, 'pyparsing not installed')
-    def test_c_header_scanner(self):
-        # https://stackoverflow.com/questions/58732872/208880
-        with open(os.path.join(tempdir, 'c_plus_plus.h'), 'w') as fh:
-            fh.write("""
-                    stuff before
-                    enum hello {
-                        Zero,
-                        One,
-                        Two,
-                        Three,
-                        Five=5,
-                        Six,
-                        Ten=10
-                        };
-                    in the middle
-                    enum blah
-                        {
-                        alpha,
-                        beta,
-                        gamma = 10 ,
-                        zeta = 50
-                        };
-                    at the end
-                    """)
-        from pyparsing import Group, Optional, Suppress, Word, ZeroOrMore
-        from pyparsing import alphas, alphanums, nums
-        #
-        CPPEnum = None
-        class CPPEnumType(EnumMeta):
-            #
-            @classmethod
-            def __prepare__(metacls, clsname, bases, **kwds):
-                # return a standard dictionary for the initial processing
-                return {}
-            #
-            def __init__(clsname, *args , **kwds):
-                super(CPPEnumType, clsname).__init__(*args)
-            #
-            def __new__(metacls, clsname, bases, clsdict, **kwds):
-                if CPPEnum is None:
-                    # first time through, ignore the rest
-                    enum_dict = super(CPPEnumType, metacls).__prepare__(clsname, bases, **kwds)
-                    enum_dict.update(clsdict)
-                    return super(CPPEnumType, metacls).__new__(metacls, clsname, bases, enum_dict, **kwds)
-                members = []
-                #
-                # remove _file and _name using `pop()` as they will cause problems in EnumMeta
-                try:
-                    file = clsdict.pop('_file')
-                except KeyError:
-                    raise TypeError('_file not specified')
-                cpp_enum_name = clsdict.pop('_name', clsname.lower())
-                with open(file) as fh:
-                    file_contents = fh.read()
-                #
-                # syntax we don't want to see in the final parse tree
-                LBRACE, RBRACE, EQ, COMMA = map(Suppress, "{}=,")
-                _enum = Suppress("enum")
-                identifier = Word(alphas, alphanums + "_")
-                integer = Word(nums)
-                enumValue = Group(identifier("name") + Optional(EQ + integer("value")))
-                enumList = Group(enumValue + ZeroOrMore(COMMA + enumValue))
-                enum = _enum + identifier("enum") + LBRACE + enumList("names") + RBRACE
-                #
-                # find the cpp_enum_name ignoring other syntax and other enums
-                for item, start, stop in enum.scanString(file_contents):
-                    if item.enum != cpp_enum_name:
-                        continue
-                    id = 0
-                    for entry in item.names:
-                        if entry.value != "":
-                            id = int(entry.value)
-                        members.append((entry.name.upper(), id))
-                        id += 1
-                #
-                # get the real EnumDict
-                enum_dict = super(CPPEnumType, metacls).__prepare__(clsname, bases, **kwds)
-                # transfer the original dict content, names starting with '_' first
-                items = list(clsdict.items())
-                items.sort(key=lambda p: (0 if p[0][0] == '_' else 1, p))
-                for name, value in items:
-                    enum_dict[name] = value
-                # add the members
-                for name, value in members:
-                    enum_dict[name] = value
-                return super(CPPEnumType, metacls).__new__(metacls, clsname, bases, enum_dict, **kwds)
-        #
-        class CPPEnum(IntEnum, metaclass=CPPEnumType):
-            pass
-        #
-        class Hello(CPPEnum):
-            _file = os.path.join(tempdir, 'c_plus_plus.h')
-        #
-        class Blah(CPPEnum):
-            _file = os.path.join(tempdir, 'c_plus_plus.h')
-            _name = 'blah'
-        #
-        self.assertEqual(
-                list(Hello),
-                [Hello.ZERO, Hello.ONE, Hello.TWO, Hello.THREE, Hello.FIVE, Hello.SIX, Hello.TEN],
-                )
-        self.assertEqual(Hello.ZERO.value, 0)
-        self.assertEqual(Hello.THREE.value, 3)
-        self.assertEqual(Hello.SIX.value, 6)
-        self.assertEqual(Hello.TEN.value, 10)
-        #
-        self.assertEqual(
-                list(Blah),
-                [Blah.ALPHA, Blah.BETA, Blah.GAMMA, Blah.ZETA],
-                )
-        self.assertEqual(Blah.ALPHA.value, 0)
-        self.assertEqual(Blah.BETA.value, 1)
-        self.assertEqual(Blah.GAMMA.value, 10)
-        self.assertEqual(Blah.ZETA.value, 50)
-
-class TestIssuesV3(TestCase):
-    """
-    Problems that were stated in issues.
-    """
-
-    def test_auto_multi_int_1(self):
-        class Measurement(int, AddValueEnum, MultiValueEnum, start=0):
-            one = "20110721"
-            two = "20120911"
-            three = "20110518"
-        self.assertEqual([m.value for m in Measurement], [0, 1, 2])
-        self.assertEqual([m.name for m in Measurement], ['one', 'two', 'three'])
-        self.assertIs(Measurement(0), Measurement.one)
-        self.assertIs(Measurement('20110721'), Measurement.one)
-        self.assertIs(Measurement(1), Measurement.two)
-        self.assertIs(Measurement('20120911'), Measurement.two)
-        self.assertIs(Measurement(2), Measurement.three)
-        self.assertIs(Measurement('20110518'), Measurement.three)
-
-    def test_auto_multi_int_2(self):
-        class Measurement(int, Enum, settings=(MultiValue, AddValue), start=0):
-            one = "20110721"
-            two = "20120911"
-            three = "20110518"
-        self.assertEqual([m.value for m in Measurement], [0, 1, 2])
-        self.assertEqual([m.name for m in Measurement], ['one', 'two', 'three'])
-        self.assertIs(Measurement(0), Measurement.one)
-        self.assertIs(Measurement('20110721'), Measurement.one)
-        self.assertIs(Measurement(1), Measurement.two)
-        self.assertIs(Measurement('20120911'), Measurement.two)
-        self.assertIs(Measurement(2), Measurement.three)
-        self.assertIs(Measurement('20110518'), Measurement.three)
-
-    def test_extend_enum_with_init(self):
-        class Color(Enum, settings=MultiValue, init='foo bar'):
-            red = '1', 'yes'
-            green = '2', 'no'
-            blue = '3', 'maybe'
-        self.assertEqual(Color.red.value, '1')
-        self.assertEqual(Color.red.foo, '1')
-        self.assertEqual(Color.red.bar, 'yes')
-        extend_enum(Color, 'opacity', '4', 'never')
-        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue, Color.opacity])
-        self.assertEqual(Color.opacity.value, '4')
-        self.assertEqual(Color.opacity.name, 'opacity')
-        self.assertTrue(Color('4') is Color.opacity)
-        self.assertTrue(Color('never') is Color.opacity)
-
-class TestExtendEnumV3(TestCase):
-
-    def test_extend_enum_plain(self):
-        class Color(Enum):
-            red = 1
-            green = 2
-            blue = 3
-        self.assertRaisesRegex(TypeError, '.blue. already in use as property..Color.blue: 3.', extend_enum, Color, 'blue', 5)
-        #
-        extend_enum(Color, 'brown', 4)
-        self.assertEqual(Color.brown.name, 'brown')
-        self.assertEqual(Color.brown.value, 4)
-        self.assertTrue(Color.brown in Color)
-        self.assertEqual(Color(4), Color.brown)
-        self.assertEqual(Color['brown'], Color.brown)
-        self.assertEqual(len(Color), 4)
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, 5)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(5), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-        self.assertEqual(len(Color), 5)
-
-    def test_extend_enum_alias(self):
-        class Color(Enum):
-            red = 1
-            green = 2
-            blue = 3
-        extend_enum(Color, 'rojo', 1)
-        self.assertEqual(Color.rojo.name, 'red')
-        self.assertEqual(Color.rojo.value, 1)
-        self.assertTrue(Color.rojo in Color)
-        self.assertEqual(Color(1), Color.rojo)
-        self.assertEqual(Color['rojo'], Color.red)
-        self.assertEqual(len(Color), 3)
-
-    def test_extend_enum_unique(self):
-        class Color(UniqueEnum):
-            red = 1
-            green = 2
-            blue = 3
-        self.assertRaisesRegex(ValueError, r'<Color.rojo: 1> is a duplicate of <Color.red: 1>', extend_enum, Color, 'rojo', 1)
-        #
-        self.assertEqual(Color.red.name, 'red')
-        self.assertEqual(Color.red.value, 1)
-        self.assertTrue(Color.red in Color)
-        self.assertEqual(Color(1), Color.red)
-        self.assertEqual(Color['red'], Color.red)
-        self.assertEqual(Color.green.name, 'green')
-        self.assertEqual(Color.green.value, 2)
-        self.assertTrue(Color.green in Color)
-        self.assertEqual(Color(2), Color.green)
-        self.assertEqual(Color['blue'], Color.blue)
-        self.assertEqual(Color.blue.name, 'blue')
-        self.assertEqual(Color.blue.value, 3)
-        self.assertTrue(Color.blue in Color)
-        self.assertEqual(Color(3), Color.blue)
-        self.assertEqual(len(Color), 3)
-        #
-        extend_enum(Color, 'brown', 4)
-        self.assertEqual(Color.brown.name, 'brown')
-        self.assertEqual(Color.brown.value, 4)
-        self.assertTrue(Color.brown in Color)
-        self.assertEqual(Color(4), Color.brown)
-        self.assertEqual(Color['brown'], Color.brown)
-        self.assertEqual(len(Color), 4)
-        #
-        self.assertRaisesRegex(ValueError, '<Color.verde: 2> is a duplicate of <Color.green: 2>', extend_enum, Color, 'verde', 2)
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, 5)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(5), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-        self.assertEqual(len(Color), 5)
-
-
-    def test_extend_enum_shadow_property(self):
-        class Color(Enum):
-            red = 1
-            green = 2
-            blue = 3
-        extend_enum(Color, 'value', 4)
-        self.assertEqual(Color.value.name, 'value')
-        self.assertEqual(Color.value.value, 4)
-        self.assertTrue(Color.value in Color)
-        self.assertEqual(Color(4), Color.value)
-        self.assertEqual(Color['value'], Color.value)
-        self.assertEqual(len(Color), 4)
-        self.assertEqual(Color.red.value, 1)
-
-    def test_extend_enum_shadow_base(self):
-        class hohum(object):
-            def cyan(self):
-                "cyanize a color"
-                return self.value
-        class Color(hohum, Enum):
-            red = 1
-            green = 2
-            blue = 3
-        self.assertRaisesRegex(TypeError, r'already in use in superclass', extend_enum, Color, 'cyan', 4)
-        self.assertEqual(len(Color), 3)
-        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
-
-    def test_extend_enum_multivalue(self):
-        class Color(MultiValueEnum):
-            red = 1, 4, 7
-            green = 2, 5, 8
-            blue = 3, 6, 9
-        extend_enum(Color, 'brown', 10, 20)
-        self.assertEqual(Color.brown.name, 'brown')
-        self.assertEqual(Color.brown.value, 10)
-        self.assertTrue(Color.brown in Color)
-        self.assertEqual(Color(10), Color.brown)
-        self.assertEqual(Color(20), Color.brown)
-        self.assertEqual(Color['brown'], Color.brown)
-        self.assertEqual(len(Color), 4)
-        #
-        self.assertRaisesRegex(ValueError, 'no values specified for MultiValue enum', extend_enum, Color, 'mauve')
-
-    def test_extend_enum_multivalue_alias(self):
-        class Color(MultiValueEnum):
-            red = 1, 4, 7
-            green = 2, 5, 8
-            blue = 3, 6, 9
-        self.assertRaisesRegex(ValueError, r'<Color.rojo: 7> is a duplicate of <Color.red: 1>', extend_enum, Color, 'rojo', 7)
-        self.assertEqual(Color.red.name, 'red')
-        self.assertEqual(Color.red.value, 1)
-        self.assertTrue(Color.red in Color)
-        self.assertEqual(Color(1), Color.red)
-        self.assertEqual(Color(4), Color.red)
-        self.assertEqual(Color(7), Color.red)
-        self.assertEqual(Color['red'], Color.red)
-        self.assertEqual(Color.green.name, 'green')
-        self.assertEqual(Color.green.value, 2)
-        self.assertTrue(Color.green in Color)
-        self.assertEqual(Color(2), Color.green)
-        self.assertEqual(Color(5), Color.green)
-        self.assertEqual(Color(8), Color.green)
-        self.assertEqual(Color['blue'], Color.blue)
-        self.assertEqual(Color.blue.name, 'blue')
-        self.assertEqual(Color.blue.value, 3)
-        self.assertTrue(Color.blue in Color)
-        self.assertEqual(Color(3), Color.blue)
-        self.assertEqual(Color(6), Color.blue)
-        self.assertEqual(Color(9), Color.blue)
-        self.assertEqual(len(Color), 3)
-
-    def test_extend_enum_multivalue_str(self):
-        class M(str, MultiValueEnum):
-            VALUE_1 = 'value_1', 'VALUE_1'
-            VALUE_2 = 'value_2', 'VALUE_2'
-            VALUE_3 = 'value_3', 'VALUE_3'
-        self.assertTrue(M._member_type_ is str)
-        extend_enum(M, 'VALUE_4', 'value_4', 'VALUE_4')
-        self.assertEqual(list(M), [M.VALUE_1, M.VALUE_2, M.VALUE_3, M.VALUE_4])
-        self.assertTrue(M('value_4') is M.VALUE_4)
-        self.assertTrue(M('VALUE_4') is M.VALUE_4)
-        self.assertTrue(M.VALUE_4.name == 'VALUE_4')
-        self.assertTrue(M.VALUE_4.value == 'value_4')
-
-    def test_extend_intenum(self):
-        class Index(IntEnum):
-            DeviceType    = 0x1000
-            ErrorRegister = 0x1001
-
-        for name, value in (
-                ('ControlWord', 0x6040),
-                ('StatusWord', 0x6041),
-                ('OperationMode', 0x6060),
-                ):
-            extend_enum(Index, name, value)
-
-        self.assertEqual(len(Index), 5)
-        self.assertEqual(list(Index), [Index.DeviceType, Index.ErrorRegister, Index.ControlWord, Index.StatusWord, Index.OperationMode])
-        self.assertEqual(Index.DeviceType.value, 0x1000)
-        self.assertEqual(Index.StatusWord.value, 0x6041)
-
-    def test_extend_multi_init(self):
-        class HTTPStatus(IntEnum):
-            def __new__(cls, value, phrase, description):
-                obj = int.__new__(cls, value)
-                obj._value_ = value
-
-                obj.phrase = phrase
-                obj.description = description
-                return obj
-            CONTINUE = 100, 'Continue', 'Request received, please continue'
-            SWITCHING_PROTOCOLS = 101, 'Switching Protocols', 'Switching to new protocol; obey Upgrade header'
-            PROCESSING = 102, 'Processing', ''
-        length = 3
-        extend_enum(HTTPStatus, 'BAD_SPAM', 513, 'Too greasy', 'for a train')
-        extend_enum(HTTPStatus, 'BAD_EGGS', 514, 'Too green', '')
-        self.assertEqual(len(HTTPStatus), length+2)
-        self.assertEqual(
-                list(HTTPStatus)[-2:],
-                [HTTPStatus.BAD_SPAM, HTTPStatus.BAD_EGGS],
-                )
-        self.assertEqual(HTTPStatus.BAD_SPAM.value, 513)
-        self.assertEqual(HTTPStatus.BAD_SPAM.name, 'BAD_SPAM')
-        self.assertEqual(HTTPStatus.BAD_SPAM.phrase, 'Too greasy')
-        self.assertEqual(HTTPStatus.BAD_SPAM.description, 'for a train')
-        self.assertEqual(HTTPStatus.BAD_EGGS.value, 514)
-        self.assertEqual(HTTPStatus.BAD_EGGS.name, 'BAD_EGGS')
-        self.assertEqual(HTTPStatus.BAD_EGGS.phrase, 'Too green')
-        self.assertEqual(HTTPStatus.BAD_EGGS.description, '')
-
-    def test_extend_flag(self):
-        class Color(Flag):
-            BLACK = 0
-            RED = 1
-            GREEN = 2
-            BLUE = 4
-        extend_enum(Color, 'MAGENTA')
-        self.assertTrue(Color(8) is Color.MAGENTA)
-        self.assertTrue(isinstance(Color.MAGENTA, Color))
-        self.assertEqual(Color.MAGENTA.value, 8)
-        extend_enum(Color, 'PURPLE', 11)
-        self.assertTrue(Color(11) is Color.PURPLE)
-        self.assertTrue(isinstance(Color.PURPLE, Color))
-        self.assertEqual(Color.PURPLE.value, 11)
-        self.assertTrue(issubclass(Color, Flag))
-
-    def test_extend_flag_backwards(self):
-        class Color(Flag):
-            BLACK = 0
-            RED = 1
-            GREEN = 2
-            BLUE = 4
-        extend_enum(Color, 'PURPLE', 11)
-        self.assertTrue(Color(11) is Color.PURPLE)
-        self.assertTrue(isinstance(Color.PURPLE, Color))
-        self.assertEqual(Color.PURPLE.value, 11)
-        self.assertTrue(issubclass(Color, Flag))
-        #
-        extend_enum(Color, 'MAGENTA')
-        self.assertTrue(Color(8) is Color.MAGENTA)
-        self.assertTrue(isinstance(Color.MAGENTA, Color))
-        self.assertEqual(Color.MAGENTA.value, 8)
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, 16)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(16), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-        self.assertEqual(len(Color), 5)
-
-    def test_extend_intflag(self):
-        class Color(IntFlag):
-            BLACK = 0
-            RED = 1
-            GREEN = 2
-            BLUE = 4
-        extend_enum(Color, 'MAGENTA')
-        self.assertTrue(Color(8) is Color.MAGENTA)
-        self.assertTrue(isinstance(Color.MAGENTA, Color))
-        self.assertEqual(Color.MAGENTA.value, 8)
-        extend_enum(Color, 'PURPLE', 11)
-        self.assertTrue(Color(11) is Color.PURPLE)
-        self.assertTrue(isinstance(Color.PURPLE, Color))
-        self.assertEqual(Color.PURPLE.value, 11)
-        self.assertTrue(issubclass(Color, Flag))
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, 16)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(16), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-        self.assertEqual(len(Color), 5)
-
-    def test_extend_intflag_backwards(self):
-        class Color(IntFlag):
-            BLACK = 0
-            RED = 1
-            GREEN = 2
-            BLUE = 4
-        extend_enum(Color, 'PURPLE', 11)
-        self.assertTrue(Color(11) is Color.PURPLE)
-        self.assertTrue(isinstance(Color.PURPLE, Color))
-        self.assertEqual(Color.PURPLE.value, 11)
-        self.assertTrue(issubclass(Color, Flag))
-        #
-        extend_enum(Color, 'MAGENTA')
-        self.assertTrue(Color(8) is Color.MAGENTA)
-        self.assertTrue(isinstance(Color.MAGENTA, Color))
-        self.assertEqual(Color.MAGENTA.value, 8)
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, 16)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(16), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-        self.assertEqual(len(Color), 5)
-
-    def test_extend_strenum(self):
-        class Color(StrEnum):
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        extend_enum(Color, 'BLACK')
-        self.assertEqual(Color.BLACK.name, 'BLACK')
-        self.assertEqual(Color.BLACK.value, 'black')
-        self.assertEqual(len(Color), 4)
-
-    @unittest.skipUnless(StdlibEnum, 'Stdlib Enum not available')
-    def test_extend_enum_stdlib(self):
-        class Color(StdlibEnum):
-            red = 1
-            green = 2
-            blue = 3
-        self.assertEqual(getattr(Color.red, '_values_', None), None)
-        extend_enum(Color, 'brown', 4)
-        self.assertEqual(Color.brown.name, 'brown')
-        self.assertEqual(Color.brown.value, 4)
-        self.assertTrue(Color.brown in Color)
-        self.assertEqual(Color(4), Color.brown)
-        self.assertEqual(Color['brown'], Color.brown)
-        self.assertEqual(len(Color), 4)
-
-    @unittest.skipUnless(StdlibEnum, 'Stdlib Enum not available')
-    def test_extend_enum_plain_stdlib(self):
-        class Color(StdlibEnum):
-            red = 1
-            green = 2
-            blue = 3
-        self.assertRaisesRegex(TypeError, 'already in use as', extend_enum, Color, 'blue', 5)
-        #
-        extend_enum(Color, 'brown', 4)
-        self.assertEqual(Color.brown.name, 'brown')
-        self.assertEqual(Color.brown.value, 4)
-        self.assertTrue(Color.brown in Color)
-        self.assertEqual(Color(4), Color.brown)
-        self.assertEqual(Color['brown'], Color.brown)
-        self.assertEqual(len(Color), 4)
-        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue, Color.brown])
-        self.assertEqual([c.value for c in Color], [1, 2, 3, 4])
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, 5)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(5), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-        self.assertEqual(len(Color), 5)
-
-    @unittest.skipUnless(StdlibEnum, 'Stdlib Enum not available')
-    def test_extend_enum_alias_stdlib(self):
-        class Color(StdlibEnum):
-            red = 1
-            green = 2
-            blue = 3
-        extend_enum(Color, 'rojo', 1)
-        self.assertEqual(Color.rojo.name, 'red')
-        self.assertEqual(Color.rojo.value, 1)
-        self.assertTrue(Color.rojo in Color)
-        self.assertEqual(Color(1), Color.rojo)
-        self.assertEqual(Color['rojo'], Color.red)
-        self.assertEqual(len(Color), 3)
-
-    @unittest.skipUnless(StdlibEnum, 'Stdlib Enum not available')
-    def test_extend_enum_shadow_property_stdlib(self):
-        class Color(StdlibEnum):
-            red = 1
-            green = 2
-            blue = 3
-        extend_enum(Color, 'value', 4)
-        self.assertEqual(Color.value.name, 'value')
-        self.assertEqual(Color.value.value, 4)
-        self.assertTrue(Color.value in Color)
-        self.assertEqual(Color(4), Color.value)
-        self.assertEqual(Color['value'], Color.value)
-        self.assertEqual(len(Color), 4)
-        self.assertEqual(Color.red.value, 1)
-
-    @unittest.skipUnless(StdlibEnum, 'Stdlib Enum not available')
-    def test_extend_enum_shadow_base_stdlib(self):
-        class hohum(object):
-            def cyan(self):
-                "cyanize a color"
-                return self.value
-        class Color(hohum, StdlibEnum):
-            red = 1
-            green = 2
-            blue = 3
-        self.assertRaisesRegex(TypeError, r'already in use in superclass', extend_enum, Color, 'cyan', 4)
-        self.assertEqual(len(Color), 3)
-        self.assertEqual(list(Color), [Color.red, Color.green, Color.blue])
-
-    @unittest.skipUnless(StdlibIntEnum, 'Stdlib IntEnum not available')
-    def test_extend_intenum_stdlib(self):
-        class Index(StdlibIntEnum):
-            DeviceType    = 0x1000
-            ErrorRegister = 0x1001
-
-        for name, value in (
-                ('ControlWord', 0x6040),
-                ('StatusWord', 0x6041),
-                ('OperationMode', 0x6060),
-                ):
-            extend_enum(Index, name, value)
-
-        self.assertEqual(len(Index), 5)
-        self.assertEqual(list(Index), [Index.DeviceType, Index.ErrorRegister, Index.ControlWord, Index.StatusWord, Index.OperationMode])
-        self.assertEqual(Index.DeviceType.value, 0x1000)
-        self.assertEqual(Index.StatusWord.value, 0x6041)
-
-    @unittest.skipUnless(StdlibIntEnum, 'Stdlib IntEnum not available')
-    def test_extend_multi_init_stdlib(self):
-        class HTTPStatus(StdlibIntEnum):
-            def __new__(cls, value, phrase, description):
-                obj = int.__new__(cls, value)
-                obj._value_ = value
-
-                obj.phrase = phrase
-                obj.description = description
-                return obj
-            CONTINUE = 100, 'Continue', 'Request received, please continue'
-            SWITCHING_PROTOCOLS = 101, 'Switching Protocols', 'Switching to new protocol; obey Upgrade header'
-            PROCESSING = 102, 'Processing', ''
-        length = 3
-        extend_enum(HTTPStatus, 'BAD_SPAM', 513, 'Too greasy', 'for a train')
-        extend_enum(HTTPStatus, 'BAD_EGGS', 514, 'Too green', '')
-        self.assertEqual(len(HTTPStatus), length+2)
-        self.assertEqual(
-                list(HTTPStatus)[-2:],
-                [HTTPStatus.BAD_SPAM, HTTPStatus.BAD_EGGS],
-                )
-        self.assertEqual(HTTPStatus.BAD_SPAM.value, 513)
-        self.assertEqual(HTTPStatus.BAD_SPAM.name, 'BAD_SPAM')
-        self.assertEqual(HTTPStatus.BAD_SPAM.phrase, 'Too greasy')
-        self.assertEqual(HTTPStatus.BAD_SPAM.description, 'for a train')
-        self.assertEqual(HTTPStatus.BAD_EGGS.value, 514)
-        self.assertEqual(HTTPStatus.BAD_EGGS.name, 'BAD_EGGS')
-        self.assertEqual(HTTPStatus.BAD_EGGS.phrase, 'Too green')
-        self.assertEqual(HTTPStatus.BAD_EGGS.description, '')
-
-    @unittest.skipUnless(StdlibFlag, 'Stdlib Flag not available')
-    def test_extend_flag_stdlib(self):
-        class Color(StdlibFlag):
-            BLACK = 0
-            RED = 1
-            GREEN = 2
-            BLUE = 4
-        extend_enum(Color, 'MAGENTA')
-        self.assertTrue(Color(8) is Color.MAGENTA)
-        self.assertTrue(isinstance(Color.MAGENTA, Color))
-        self.assertEqual(Color.MAGENTA.value, 8)
-        extend_enum(Color, 'PURPLE', 11)
-        self.assertTrue(Color(11) is Color.PURPLE)
-        self.assertTrue(isinstance(Color.PURPLE, Color))
-        self.assertEqual(Color.PURPLE.value, 11)
-        self.assertTrue(issubclass(Color, StdlibFlag))
-
-    @unittest.skipUnless(StdlibFlag, 'Stdlib Flag not available')
-    def test_extend_flag_backwards_stdlib(self):
-        class Color(StdlibFlag):
-            BLACK = 0
-            RED = 1
-            GREEN = 2
-            BLUE = 4
-        extend_enum(Color, 'PURPLE', 11)
-        self.assertTrue(Color(11) is Color.PURPLE)
-        self.assertTrue(isinstance(Color.PURPLE, Color))
-        self.assertEqual(Color.PURPLE.value, 11)
-        self.assertTrue(issubclass(Color, StdlibFlag))
-        #
-        extend_enum(Color, 'MAGENTA')
-        self.assertTrue(Color(16) is Color.MAGENTA)
-        self.assertTrue(isinstance(Color.MAGENTA, Color))
-        self.assertEqual(Color.MAGENTA.value,16)
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, 32)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(32), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-
-    @unittest.skipUnless(StdlibIntFlag, 'Stdlib IntFlag not available')
-    def test_extend_intflag_stdlib(self):
-        class Color(StdlibIntFlag):
-            BLACK = 0
-            RED = 1
-            GREEN = 2
-            BLUE = 4
-        extend_enum(Color, 'MAGENTA')
-        self.assertTrue(Color(8) is Color.MAGENTA)
-        self.assertTrue(isinstance(Color.MAGENTA, Color))
-        self.assertEqual(Color.MAGENTA.value, 8)
-        extend_enum(Color, 'PURPLE', 11)
-        self.assertTrue(Color(11) is Color.PURPLE)
-        self.assertTrue(isinstance(Color.PURPLE, Color))
-        self.assertEqual(Color.PURPLE.value, 11)
-        self.assertTrue(issubclass(Color, StdlibFlag))
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, 16)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(16), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-
-    @unittest.skipUnless(StdlibIntFlag, 'Stdlib IntFlag not available')
-    def test_extend_intflag_backwards_stdlib(self):
-        class Color(StdlibIntFlag):
-            BLACK = 0
-            RED = 1
-            GREEN = 2
-            BLUE = 4
-        if pyver >= PY3_11:
-            # flags make more sense in 3.11
-            length = 5
-            MAGENTA = 8
-            mauve = 16
-        else:
-            length = 7
-            MAGENTA = 16
-            mauve = 32
-        extend_enum(Color, 'PURPLE', 11)
-        self.assertTrue(Color(11) is Color.PURPLE)
-        self.assertTrue(isinstance(Color.PURPLE, Color))
-        self.assertEqual(Color.PURPLE.value, 11)
-        self.assertTrue(issubclass(Color, StdlibFlag))
-        #
-        extend_enum(Color, 'MAGENTA')
-        self.assertTrue(Color(MAGENTA) is Color.MAGENTA)
-        self.assertTrue(isinstance(Color.MAGENTA, Color))
-        self.assertEqual(Color.MAGENTA.value, MAGENTA)
-        #
-        extend_enum(Color, 'mauve')
-        self.assertEqual(Color.mauve.name, 'mauve')
-        self.assertEqual(Color.mauve.value, mauve)
-        self.assertTrue(Color.mauve in Color)
-        self.assertEqual(Color(mauve), Color.mauve)
-        self.assertEqual(Color['mauve'], Color.mauve)
-        self.assertEqual(len(Color), length, list(Color))
-
-    @unittest.skipUnless(StdlibStrEnum, 'Stdlib StrEnum not available')
-    def test_extend_strenum_stdlib(self):
-        class Color(StrEnum):
-            RED = auto()
-            GREEN = auto()
-            BLUE = auto()
-        extend_enum(Color, 'BLACK')
-        self.assertEqual(Color.BLACK.name, 'BLACK')
-        self.assertEqual(Color.BLACK.value, 'black')
-        self.assertEqual(len(Color), 4)
-
-
-if __name__ == '__main__':
-    raise RuntimeError("'test_v3.py' should not be run by itself; it's included in 'test.py'")
diff --git a/venv/Lib/site-packages/blendmodes/__init__.py b/venv/Lib/site-packages/blendmodes/__init__.py
deleted file mode 100644
index 2b6e91ad..00000000
--- a/venv/Lib/site-packages/blendmodes/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""Use this module to apply a number of blending modes to a background and foreground image
-"""
diff --git a/venv/Lib/site-packages/blendmodes/__pycache__/__init__.cpython-39.pyc b/venv/Lib/site-packages/blendmodes/__pycache__/__init__.cpython-39.pyc
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diff --git a/venv/Lib/site-packages/blendmodes/__pycache__/blend.cpython-39.pyc b/venv/Lib/site-packages/blendmodes/__pycache__/blend.cpython-39.pyc
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diff --git a/venv/Lib/site-packages/blendmodes/__pycache__/blendtype.cpython-39.pyc b/venv/Lib/site-packages/blendmodes/__pycache__/blendtype.cpython-39.pyc
deleted file mode 100644
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diff --git a/venv/Lib/site-packages/blendmodes/blend.py b/venv/Lib/site-packages/blendmodes/blend.py
deleted file mode 100644
index 4165bfa7..00000000
--- a/venv/Lib/site-packages/blendmodes/blend.py
+++ /dev/null
@@ -1,511 +0,0 @@
-"""Provide blending functions and types.
-
-Adapted from https://github.com/addisonElliott/pypdn/blob/master/pypdn/reader.py
-and https://gitlab.com/inklabapp/pyora/-/blob/master/pyora/BlendNonSep.py
-MIT License Copyright (c) 2020 FredHappyface
-
-Credits to:
-
-MIT License Copyright (c) 2019 Paul Jewell
-For implementing blending from the Open Raster Image Spec
-
-MIT License Copyright (c) 2018 Addison Elliott
-For implementing blending from Paint.NET
-
-MIT License Copyright (c) 2017 pashango
-For implementing a number of blending functions used by other popular image
-editors
-"""
-
-from __future__ import annotations
-
-import warnings
-
-import numpy as np
-from PIL import Image
-
-from .blendtype import BlendType
-
-
-def normal(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.NORMAL."""
-	del background  # we don't care about this
-	return foreground
-
-
-def multiply(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.MULTIPLY."""
-	return np.clip(foreground * background, 0.0, 1.0)
-
-
-def additive(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.ADDITIVE."""
-	return np.minimum(background + foreground, 1.0)
-
-
-def colourburn(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.COLOURBURN."""
-	with np.errstate(divide="ignore"):
-		return np.where(
-			foreground != 0.0, np.maximum(1.0 - ((1.0 - background) / foreground), 0.0), 0.0
-		)
-
-
-def colourdodge(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.COLOURDODGE."""
-	with np.errstate(divide="ignore"):
-		return np.where(foreground != 1.0, np.minimum(background / (1.0 - foreground), 1.0), 1.0)
-
-
-def reflect(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.REFLECT."""
-	with np.errstate(divide="ignore"):
-		return np.where(
-			foreground != 1.0, np.minimum((background ** 2) / (1.0 - foreground), 1.0), 1.0
-		)
-
-
-def glow(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.GLOW."""
-	with np.errstate(divide="ignore"):
-		return np.where(
-			background != 1.0, np.minimum((foreground ** 2) / (1.0 - background), 1.0), 1.0
-		)
-
-
-def overlay(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.OVERLAY."""
-	return np.where(
-		background < 0.5,
-		2 * background * foreground,
-		1.0 - (2 * (1.0 - background) * (1.0 - foreground)),
-	)
-
-
-def difference(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.DIFFERENCE."""
-	return np.abs(background - foreground)
-
-
-def negation(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.NEGATION."""
-	return np.maximum(background - foreground, 0.0)
-
-
-def lighten(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.LIGHTEN."""
-	return np.maximum(background, foreground)
-
-
-def darken(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.DARKEN."""
-	return np.minimum(background, foreground)
-
-
-def screen(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.SCREEN."""
-	return background + foreground - background * foreground
-
-
-def xor(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.XOR."""
-	# XOR requires int values so convert to uint8
-	with warnings.catch_warnings():
-		warnings.simplefilter("ignore")
-		return imageIntToFloat(imageFloatToInt(background) ^ imageFloatToInt(foreground))
-
-
-def softlight(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.SOFTLIGHT."""
-	return (1.0 - background) * background * foreground + background * (
-		1.0 - (1.0 - background) * (1.0 - foreground)
-	)
-
-
-def hardlight(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.HARDLIGHT."""
-	return np.where(
-		foreground < 0.5,
-		np.minimum(background * 2 * foreground, 1.0),
-		np.minimum(1.0 - ((1.0 - background) * (1.0 - (foreground - 0.5) * 2.0)), 1.0),
-	)
-
-
-def grainextract(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.GRAINEXTRACT."""
-	return np.clip(background - foreground + 0.5, 0.0, 1.0)
-
-
-def grainmerge(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.GRAINMERGE."""
-	return np.clip(background + foreground - 0.5, 0.0, 1.0)
-
-
-def divide(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.DIVIDE."""
-	return np.minimum((256.0 / 255.0 * background) / (1.0 / 255.0 + foreground), 1.0)
-
-
-def pinlight(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.PINLIGHT."""
-	return np.minimum(background, 2 * foreground) * (foreground < 0.5) + np.maximum(
-		background, 2 * (foreground - 0.5)
-	) * (foreground >= 0.5)
-
-
-def vividlight(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.VIVIDLIGHT."""
-	return colourburn(background, foreground * 2) * (foreground < 0.5) + colourdodge(
-		background, 2 * (foreground - 0.5)
-	) * (foreground >= 0.5)
-
-
-def exclusion(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.EXCLUSION."""
-	return background + foreground - (2.0 * background * foreground)
-
-
-def _lum(colours: np.ndarray) -> np.ndarray:
-	"""Luminosity.
-
-	:param colours: x by x by 3 matrix of rgb color components of pixels
-	:return: x by x by 3 matrix of luminosity of pixels
-	"""
-	return (colours[:, :, 0] * 0.299) + (colours[:, :, 1] * 0.587) + (colours[:, :, 2] * 0.114)
-
-
-def _setLum(originalColours: np.ndarray, newLuminosity: np.ndarray) -> np.ndarray:
-	"""Set a new luminosity value for the matrix of color."""
-	_colours = originalColours.copy()
-	_luminosity = _lum(_colours)
-	deltaLum = newLuminosity - _luminosity
-	_colours[:, :, 0] += deltaLum
-	_colours[:, :, 1] += deltaLum
-	_colours[:, :, 2] += deltaLum
-	_luminosity = _lum(_colours)
-	_minColours = np.min(_colours, axis=2)
-	_MaxColours = np.max(_colours, axis=2)
-	for i in range(_colours.shape[0]):
-		for j in range(_colours.shape[1]):
-			_colour = _colours[i][j]
-			newLuminosity = _luminosity[i, j]
-			minColour = _minColours[i, j]
-			maxColour = _MaxColours[i, j]
-			if minColour < 0:
-				_colours[i][j] = newLuminosity + (
-					((_colour - newLuminosity) * newLuminosity) / (newLuminosity - minColour)
-				)
-			if maxColour > 1:
-				_colours[i][j] = newLuminosity + (
-					((_colour - newLuminosity) * (1 - newLuminosity)) / (maxColour - newLuminosity)
-				)
-	return _colours
-
-
-def _sat(colours: np.ndarray) -> np.ndarray:
-	"""Saturation.
-
-	:param colours: x by x by 3 matrix of rgb color components of pixels
-	:return: int of saturation of pixels
-	"""
-	return np.max(colours, axis=2) - np.min(colours, axis=2)
-
-
-def _setSat(originalColours: np.ndarray, newSaturation: np.ndarray) -> np.ndarray:
-	"""Set a new saturation value for the matrix of color.
-
-	The current implementation cannot be vectorized in an efficient manner,
-	so it is very slow,
-	O(m*n) at least. This might be able to be improved with openCL if that is
-	the direction that the lib takes.
-	:param c: x by x by 3 matrix of rgb color components of pixels
-	:param s: int of the new saturation value for the matrix
-	:return: x by x by 3 matrix of luminosity of pixels
-	"""
-	_colours = originalColours.copy()
-	for i in range(_colours.shape[0]):
-		for j in range(_colours.shape[1]):
-			_colour = _colours[i][j]
-			minI = 0
-			midI = 1
-			maxI = 2
-			if _colour[midI] < _colour[minI]:
-				minI, midI = midI, minI
-			if _colour[maxI] < _colour[midI]:
-				midI, maxI = maxI, midI
-			if _colour[midI] < _colour[minI]:
-				minI, midI = midI, minI
-			if _colour[maxI] - _colour[minI] > 0.0:
-				_colours[i][j][midI] = ((_colour[midI] - _colour[minI]) * newSaturation[i, j]) / (
-					_colour[maxI] - _colour[minI]
-				)
-				_colours[i][j][maxI] = newSaturation[i, j]
-			else:
-				_colours[i][j][midI] = 0
-				_colours[i][j][maxI] = 0
-			_colours[i][j][minI] = 0
-	return _colours
-
-
-def hue(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.HUE."""
-	return _setLum(_setSat(foreground, _sat(background)), _lum(background))
-
-
-def saturation(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.SATURATION."""
-	return _setLum(_setSat(background, _sat(foreground)), _lum(background))
-
-
-def colour(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.COLOUR."""
-	return _setLum(foreground, _lum(background))
-
-
-def luminosity(background: np.ndarray, foreground: np.ndarray) -> np.ndarray:
-	"""BlendType.LUMINOSITY."""
-	return _setLum(background, _lum(foreground))
-
-
-def destin(
-	backgroundAlpha: np.ndarray,
-	foregroundAlpha: np.ndarray,
-	backgroundColour: np.ndarray,
-	foregroundColour: np.ndarray,
-):
-	"""'clip' composite mode.
-
-	All parts of 'layer above' which are alpha in 'layer below' will be made
-	also alpha in 'layer above'
-	(to whatever degree of alpha they were)
-
-	Destination which overlaps the source, replaces the source.
-
-	Fa = 0; Fb = αs
-	co = αb x Cb x αs
-	αo = αb x αs
-	"""
-	del foregroundColour  # Not used by function
-	outAlpha = backgroundAlpha * foregroundAlpha
-	with np.errstate(divide="ignore", invalid="ignore"):
-		outRGB = np.divide(
-			np.multiply((backgroundAlpha * foregroundAlpha)[:, :, None], backgroundColour),
-			outAlpha[:, :, None],
-		)
-	return outRGB, outAlpha
-
-
-def destout(
-	backgroundAlpha: np.ndarray,
-	foregroundAlpha: np.ndarray,
-	backgroundColour: np.ndarray,
-	foregroundColour: np.ndarray,
-):
-	"""Reverse 'Clip' composite mode.
-
-	All parts of 'layer below' which are alpha in 'layer above' will be made
-	also alpha in 'layer below'
-	(to whatever degree of alpha they were)
-
-	"""
-	del foregroundColour  # Not used by function
-	outAlpha = backgroundAlpha * (1 - foregroundAlpha)
-	with np.errstate(divide="ignore", invalid="ignore"):
-		outRGB = np.divide(
-			np.multiply((backgroundAlpha * (1 - foregroundAlpha))[:, :, None], backgroundColour),
-			outAlpha[:, :, None],
-		)
-	return outRGB, outAlpha
-
-
-def destatop(
-	backgroundAlpha: np.ndarray,
-	foregroundAlpha: np.ndarray,
-	backgroundColour: np.ndarray,
-	foregroundColour: np.ndarray,
-):
-	"""Place the layer below above the 'layer above' in places where the 'layer above' exists...
-
-	where 'layer below' does not exist, but 'layer above' does, place 'layer-above'
-
-	"""
-	outAlpha = (foregroundAlpha * (1 - backgroundAlpha)) + (backgroundAlpha * foregroundAlpha)
-	with np.errstate(divide="ignore", invalid="ignore"):
-		outRGB = np.divide(
-			np.multiply((foregroundAlpha * (1 - backgroundAlpha))[:, :, None], foregroundColour)
-			+ np.multiply((backgroundAlpha * foregroundAlpha)[:, :, None], backgroundColour),
-			outAlpha[:, :, None],
-		)
-	return outRGB, outAlpha
-
-
-def srcatop(
-	backgroundAlpha: np.ndarray,
-	foregroundAlpha: np.ndarray,
-	backgroundColour: np.ndarray,
-	foregroundColour: np.ndarray,
-):
-	"""Place the layer below above the 'layer above' in places where the 'layer above' exists."""
-	outAlpha = (foregroundAlpha * backgroundAlpha) + (backgroundAlpha * (1 - foregroundAlpha))
-	with np.errstate(divide="ignore", invalid="ignore"):
-		outRGB = np.divide(
-			np.multiply((foregroundAlpha * backgroundAlpha)[:, :, None], foregroundColour)
-			+ np.multiply((backgroundAlpha * (1 - foregroundAlpha))[:, :, None], backgroundColour),
-			outAlpha[:, :, None],
-		)
-
-	return outRGB, outAlpha
-
-
-def imageIntToFloat(image: np.ndarray) -> np.ndarray:
-	"""Convert a numpy array representing an image to an array of floats.
-
-	Args:
-		image (np.ndarray): numpy array of ints
-
-	Returns:
-		np.ndarray: numpy array of floats
-	"""
-	return image / 255
-
-
-def imageFloatToInt(image: np.ndarray) -> np.ndarray:
-	"""Convert a numpy array representing an image to an array of ints.
-
-	Args:
-		image (np.ndarray): numpy array of floats
-
-	Returns:
-		np.ndarray: numpy array of ints
-	"""
-	return (image * 255).astype(np.uint8)
-
-
-def blend(background: np.ndarray, foreground: np.ndarray, blendType: BlendType) -> np.ndarray:
-	"""Blend pixels.
-
-	Args:
-		background (np.ndarray): background
-		foreground (np.ndarray): foreground
-		blendType (BlendType): the blend type
-
-	Returns:
-		np.ndarray: new array representing the image
-
-	background: np.ndarray,
-	foreground: np.ndarray and the return are in the form
-
-		[[[0. 0. 0.]
-		[0. 0. 0.]
-		[0. 0. 0.]
-		...
-		[0. 0. 0.]
-		[0. 0. 0.]
-		[0. 0. 0.]]
-
-		...
-
-		[[0. 0. 0.]
-		[0. 0. 0.]
-		[0. 0. 0.]
-		...
-		[0. 0. 0.]
-		[0. 0. 0.]
-		[0. 0. 0.]]]
-	"""
-	blendLookup = {
-		BlendType.NORMAL: normal,
-		BlendType.MULTIPLY: multiply,
-		BlendType.COLOURBURN: colourburn,
-		BlendType.COLOURDODGE: colourdodge,
-		BlendType.REFLECT: reflect,
-		BlendType.OVERLAY: overlay,
-		BlendType.DIFFERENCE: difference,
-		BlendType.LIGHTEN: lighten,
-		BlendType.DARKEN: darken,
-		BlendType.SCREEN: screen,
-		BlendType.SOFTLIGHT: softlight,
-		BlendType.HARDLIGHT: hardlight,
-		BlendType.GRAINEXTRACT: grainextract,
-		BlendType.GRAINMERGE: grainmerge,
-		BlendType.DIVIDE: divide,
-		BlendType.HUE: hue,
-		BlendType.SATURATION: saturation,
-		BlendType.COLOUR: colour,
-		BlendType.LUMINOSITY: luminosity,
-		BlendType.XOR: xor,
-		BlendType.NEGATION: negation,
-		BlendType.PINLIGHT: pinlight,
-		BlendType.VIVIDLIGHT: vividlight,
-		BlendType.EXCLUSION: exclusion,
-	}
-
-	if blendType not in blendLookup:
-		return normal(background, foreground)
-	return blendLookup[blendType](background, foreground)
-
-
-def blendLayers(
-	background: Image.Image,
-	foreground: Image.Image,
-	blendType: BlendType | tuple[str, ...],
-	opacity: float = 1.0,
-) -> Image.Image:
-	"""Blend layers using numpy array.
-
-	Args:
-		background (Image.Image): background layer
-		foreground (Image.Image): foreground layer (must be same size as background)
-		blendType (BlendType): The blendtype
-		opacity (float): The opacity of the foreground image
-
-	Returns:
-		Image.Image: combined image
-	"""
-	# Convert the Image.Image to a numpy array
-	npForeground: np.ndarray = imageIntToFloat(np.array(foreground.convert("RGBA")))
-	npBackground: np.ndarray = imageIntToFloat(np.array(background.convert("RGBA")))
-
-	# Get the alpha from the layers
-	backgroundAlpha = npBackground[:, :, 3]
-	foregroundAlpha = npForeground[:, :, 3] * opacity
-	combinedAlpha = backgroundAlpha * foregroundAlpha
-
-	# Get the colour from the layers
-	backgroundColor = npBackground[:, :, 0:3]
-	foregroundColor = npForeground[:, :, 0:3]
-
-	# Some effects require alpha
-	alphaFunc = {
-		BlendType.DESTIN: destin,
-		BlendType.DESTOUT: destout,
-		BlendType.SRCATOP: srcatop,
-		BlendType.DESTATOP: destatop,
-	}
-
-	if blendType in alphaFunc:
-		return Image.fromarray(
-			imageFloatToInt(
-				np.clip(
-					np.dstack(
-						alphaFunc[blendType](
-							backgroundAlpha, foregroundAlpha, backgroundColor, foregroundColor
-						)
-					),
-					a_min=0,
-					a_max=1,
-				)
-			)
-		)
-
-	# Get the colours and the alpha for the new image
-	colorComponents = (
-		(backgroundAlpha - combinedAlpha)[:, :, None] * backgroundColor
-		+ (foregroundAlpha - combinedAlpha)[:, :, None] * foregroundColor
-		+ combinedAlpha[:, :, None] * blend(backgroundColor, foregroundColor, blendType)
-	)
-	alphaComponent = backgroundAlpha + foregroundAlpha - combinedAlpha
-
-	return Image.fromarray(
-		imageFloatToInt(np.clip(np.dstack((colorComponents, alphaComponent)), a_min=0, a_max=1))
-	)
diff --git a/venv/Lib/site-packages/blendmodes/blendtype.py b/venv/Lib/site-packages/blendmodes/blendtype.py
deleted file mode 100644
index 1bde12a6..00000000
--- a/venv/Lib/site-packages/blendmodes/blendtype.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""Specify supported blend types."""
-
-from __future__ import annotations
-
-from aenum import MultiValueEnum
-
-
-class BlendType(str, MultiValueEnum):
-	"""Specify supported blend types.
-
-	NORMAL = "bm:normal", "normal"
-	MULTIPLY = "bm:multiply", "multiply"
-	ADDITIVE = "bm:additive", "additive"
-	COLOURBURN = "bm:colourburn", "colourburn"
-	COLOURDODGE = "bm:colourdodge", "colourdodge"
-	REFLECT = "bm:reflect", "reflect"
-	GLOW = "bm:glow", "glow"
-	OVERLAY = "bm:overlay", "overlay"
-	DIFFERENCE = "bm:difference", "difference"
-	NEGATION = "bm:negation", "negation"
-	LIGHTEN = "bm:lighten", "lighten"
-	DARKEN = "bm:darken", "darken"
-	SCREEN = "bm:screen", "screen"
-	XOR = "bm:xor", "xor"
-	SOFTLIGHT = "bm:softlight", "softlight"
-	HARDLIGHT = "bm:hardlight", "hardlight"
-	GRAINEXTRACT = "bm:grainextract", "grainextract"
-	GRAINMERGE = "bm:grainmerge", "grainmerge"
-	DIVIDE = "bm:divide", "divide"
-	HUE = "bm:hue", "hue"
-	SATURATION = "bm:saturation", "saturation"
-	COLOUR = "bm:colour", "colour"
-	LUMINOSITY = "bm:luminosity", "luminosity"
-	PINLIGHT = "bm:pinlight", "pinlight"
-	VIVIDLIGHT = "bm:vividlight", "vividlight"
-	EXCLUSION = "bm:exclusion", "exclusion"
-	DESTIN = "bm:destin", "destin"
-	DESTOUT = "bm:destout", "destout"
-	SRCATOP = "bm:srcatop", "srcatop"
-	DESTATOP = "bm:destatop", "destatop"
-	"""
-
-	NORMAL = "bm:normal", "normal"
-	MULTIPLY = "bm:multiply", "multiply"
-	ADDITIVE = "bm:additive", "additive"
-	COLOURBURN = "bm:colourburn", "colourburn"
-	COLOURDODGE = "bm:colourdodge", "colourdodge"
-	REFLECT = "bm:reflect", "reflect"
-	GLOW = "bm:glow", "glow"
-	OVERLAY = "bm:overlay", "overlay"
-	DIFFERENCE = "bm:difference", "difference"
-	NEGATION = "bm:negation", "negation"
-	LIGHTEN = "bm:lighten", "lighten"
-	DARKEN = "bm:darken", "darken"
-	SCREEN = "bm:screen", "screen"
-	XOR = "bm:xor", "xor"
-	SOFTLIGHT = "bm:softlight", "softlight"
-	HARDLIGHT = "bm:hardlight", "hardlight"
-	GRAINEXTRACT = "bm:grainextract", "grainextract"
-	GRAINMERGE = "bm:grainmerge", "grainmerge"
-	DIVIDE = "bm:divide", "divide"
-	HUE = "bm:hue", "hue"
-	SATURATION = "bm:saturation", "saturation"
-	COLOUR = "bm:colour", "colour"
-	LUMINOSITY = "bm:luminosity", "luminosity"
-	PINLIGHT = "bm:pinlight", "pinlight"
-	VIVIDLIGHT = "bm:vividlight", "vividlight"
-	EXCLUSION = "bm:exclusion", "exclusion"
-	DESTIN = "bm:destin", "destin"
-	DESTOUT = "bm:destout", "destout"
-	SRCATOP = "bm:srcatop", "srcatop"
-	DESTATOP = "bm:destatop", "destatop"
diff --git a/venv/Lib/site-packages/blendmodes/imagetools.py b/venv/Lib/site-packages/blendmodes/imagetools.py
deleted file mode 100644
index 834b7c86..00000000
--- a/venv/Lib/site-packages/blendmodes/imagetools.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""Do stuff to images to prepare them.
-"""
-from __future__ import annotations
-
-import warnings
-
-from deprecation import deprecated
-from PIL import Image
-
-
-@deprecated(deprecated_in="2021.1", removed_in="", details="use renderWAlphaOffset")
-def rasterImageOA(  # pylint:disable=missing-function-docstring
-	image: Image.Image, size: tuple[int, int], alpha: float = 1.0, offsets: tuple[int, int] = (0, 0)
-) -> Image.Image:
-	warnings.warn(
-		"Call to deprecated function rasterImageOA.", category=DeprecationWarning, stacklevel=2
-	)
-	return renderWAlphaOffset(image, size, alpha, offsets)
-
-
-@deprecated(deprecated_in="2021.1", removed_in="", details="use renderWAlphaOffset")
-def rasterImageOffset(  # pylint:disable=missing-function-docstring
-	image: Image.Image, size: tuple[int, int], offsets: tuple[int, int] = (0, 0)
-) -> Image.Image:
-	warnings.warn(
-		"Call to deprecated function rasterImageOffset.", category=DeprecationWarning, stacklevel=2
-	)
-	return renderWAlphaOffset(image, size, 1, offsets)
-
-
-def renderWAlphaOffset(
-	image: Image.Image, size: tuple[int, int], alpha: float = 1.0, offsets: tuple[int, int] = (0, 0)
-) -> Image.Image:
-	"""Render an image with offset and alpha to a given size.
-
-	Args:
-		image (Image.Image): pil image to draw
-		size (tuple[int, int]): width, height as a tuple
-		alpha (float, optional): alpha transparency. Defaults to 1.0.
-		offsets (tuple[int, int], optional): x, y offsets as a tuple.
-		Defaults to (0, 0).
-
-	Returns:
-		Image.Image: new image
-	"""
-	imageOffset = Image.new("RGBA", size)
-	imageOffset.paste(image.convert("RGBA"), offsets, image.convert("RGBA"))
-	return Image.blend(Image.new("RGBA", size), imageOffset, alpha)
-- 
cgit v1.2.1


From 9bcf4165f89e6706a25ef0c32149d1fd3f4e102a Mon Sep 17 00:00:00 2001
From: ThereforeGames <95403634+ThereforeGames@users.noreply.github.com>
Date: Sun, 11 Dec 2022 18:09:18 -0500
Subject: Update requirements.txt

---
 requirements.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index 678acb4d..85e51575 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,5 @@
+aenum
+blendmodes
 accelerate
 basicsr
 fairscale==0.4.4
-- 
cgit v1.2.1


From cb64439f41bfaec168704751609d4afa47d6f2e9 Mon Sep 17 00:00:00 2001
From: Fionn Langhans <fionn.langhans@gmail.com>
Date: Mon, 12 Dec 2022 21:18:25 +0100
Subject: Bugfix: Use /usr/bin/env bash instead of just /bin/bash

The problem: Some Linux distrubutions, like NixOS, use a
non-standard filesystem. This causes the bash program to not
be at /bin/bash (though /usr/bin/env is always there).
---
 webui.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/webui.sh b/webui.sh
index 683c97d3..04ecbf76 100755
--- a/webui.sh
+++ b/webui.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 #################################################
 # Please do not make any changes to this file,  #
 # change the variables in webui-user.sh instead #
-- 
cgit v1.2.1


From 27c0504bc4d17eec6e58148ab33c75f5ed2e6f00 Mon Sep 17 00:00:00 2001
From: David Vorick <david@siasky.net>
Date: Tue, 13 Dec 2022 12:03:16 -0500
Subject: add support for prompts, negative prompts, and sampler-by-name in
 text file script

---
 scripts/prompts_from_file.py | 20 ++++++++++++++++++--
 1 file changed, 18 insertions(+), 2 deletions(-)

diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 32fe6bdb..6e118ddb 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -9,6 +9,7 @@ import shlex
 import modules.scripts as scripts
 import gradio as gr
 
+from modules import sd_samplers
 from modules.processing import Processed, process_images
 from PIL import Image
 from modules.shared import opts, cmd_opts, state
@@ -44,6 +45,7 @@ prompt_tags = {
     "seed_resize_from_h": process_int_tag,
     "seed_resize_from_w": process_int_tag,
     "sampler_index": process_int_tag,
+    "sampler_name": process_string_tag,
     "batch_size": process_int_tag,
     "n_iter": process_int_tag,
     "steps": process_int_tag,
@@ -66,14 +68,28 @@ def cmdargs(line):
         arg = args[pos]
 
         assert arg.startswith("--"), f'must start with "--": {arg}'
+        assert pos+1 < len(args), f'missing argument for command line option {arg}'
+
         tag = arg[2:]
 
+        if tag == "prompt" or tag == "negative_prompt":
+            pos += 1
+            prompt = args[pos]
+            pos += 1
+            while pos < len(args) and not args[pos].startswith("--"):
+                prompt += " "
+                prompt += args[pos]
+                pos += 1
+            res[tag] = prompt
+            continue
+
+
         func = prompt_tags.get(tag, None)
         assert func, f'unknown commandline option: {arg}'
 
-        assert pos+1 < len(args), f'missing argument for command line option {arg}'
-
         val = args[pos+1]
+        if tag == "sampler_name":
+            val = sd_samplers.samplers_map.get(val.lower(), None)
 
         res[tag] = func(val)
 
-- 
cgit v1.2.1


From 7077428209cd02f7da23ef843e5027e960f6aa39 Mon Sep 17 00:00:00 2001
From: space-nuko <24979496+space-nuko@users.noreply.github.com>
Date: Tue, 13 Dec 2022 13:05:40 -0800
Subject: Save hypernetwork hash in infotext

---
 modules/processing.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 24c537d1..6dd7491b 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -314,7 +314,7 @@ class Processed:
 
         return json.dumps(obj)
 
-    def infotext(self,  p: StableDiffusionProcessing, index):
+    def infotext(self, p: StableDiffusionProcessing, index):
         return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
 
 
@@ -429,6 +429,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
         "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
         "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name),
+        "Hypernet hash": (None if shared.loaded_hypernetwork is None else sd_models.model_hash(shared.loaded_hypernetwork.filename)),
         "Hypernet strength": (None if shared.loaded_hypernetwork is None or shared.opts.sd_hypernetwork_strength >= 1 else shared.opts.sd_hypernetwork_strength),
         "Batch size": (None if p.batch_size < 2 else p.batch_size),
         "Batch pos": (None if p.batch_size < 2 else position_in_batch),
@@ -446,7 +447,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
-    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if  p.all_negative_prompts[index] else ""
+    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else ""
 
     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
-- 
cgit v1.2.1


From 9d5948e5f7324b98fa7445accb2fe14487ff809d Mon Sep 17 00:00:00 2001
From: space-nuko <24979496+space-nuko@users.noreply.github.com>
Date: Tue, 13 Dec 2022 14:25:16 -0800
Subject: Correctly restore hypernetwork from hash

---
 modules/generation_parameters_copypaste.py | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 565e342d..e4e1d41c 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -14,6 +14,7 @@ re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
 re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
+re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
 type_of_gr_update = type(gr.update())
 paste_fields = {}
 bind_list = []
@@ -139,6 +140,30 @@ def run_bind():
             )
 
 
+def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
+    """Determines the config parameter name to use for the hypernet based on the parameters in the infotext.
+
+    Example: an infotext provides "Hypernet: ke-ta" and "Hypernet hash: 1234abcd". For the "Hypernet" config
+    parameter this means there should be an entry that looks like "ke-ta-10000(1234abcd)" to set it to.
+
+    If the infotext has no hash, then a hypernet with the same name and the most steps will be selected instead.
+    """
+    hypernet_name = hypernet_name.lower()
+    if hypernet_hash is not None:
+        # Try to match the hash in the name
+        for hypernet_key in shared.hypernetworks.keys():
+            result = re_hypernet_hash.search(hypernet_key)
+            if result is not None and result[1] == hypernet_hash:
+                return hypernet_key
+    else:
+        # Fall back to a hypernet with the same name
+        for hypernet_key in shared.hypernetworks.keys():
+            if hypernet_key.lower().startswith(hypernet_name):
+                return hypernet_key
+
+    return None
+
+
 def parse_generation_parameters(x: str):
     """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
@@ -188,6 +213,11 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     if "Clip skip" not in res:
         res["Clip skip"] = "1"
 
+    if "Hypernet" in res:
+        hypernet_name = res["Hypernet"]
+        hypernet_hash = res.get("Hypernet hash", None)
+        res["Hypernet"] = find_hypernetwork_key(hypernet_name, hypernet_hash)
+
     return res
 
 
-- 
cgit v1.2.1


From 1fcb9595143fc352240635959ea5b9929c02dca6 Mon Sep 17 00:00:00 2001
From: space-nuko <24979496+space-nuko@users.noreply.github.com>
Date: Tue, 13 Dec 2022 14:30:54 -0800
Subject: Correctly restore default hypernetwork strength

---
 modules/generation_parameters_copypaste.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index e4e1d41c..a33f8d5c 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -213,6 +213,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     if "Clip skip" not in res:
         res["Clip skip"] = "1"
 
+    if "Hypernet strength" not in res:
+        res["Hypernet strength"] = "1"
+
     if "Hypernet" in res:
         hypernet_name = res["Hypernet"]
         hypernet_hash = res.get("Hypernet hash", None)
-- 
cgit v1.2.1


From 5f407ebd61bb5c1ca025c5d7fa642e32ac0526ce Mon Sep 17 00:00:00 2001
From: space-nuko <24979496+space-nuko@users.noreply.github.com>
Date: Tue, 13 Dec 2022 14:32:26 -0800
Subject: Fix comment

---
 modules/generation_parameters_copypaste.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index a33f8d5c..fbd91300 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -146,7 +146,7 @@ def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
     Example: an infotext provides "Hypernet: ke-ta" and "Hypernet hash: 1234abcd". For the "Hypernet" config
     parameter this means there should be an entry that looks like "ke-ta-10000(1234abcd)" to set it to.
 
-    If the infotext has no hash, then a hypernet with the same name and the most steps will be selected instead.
+    If the infotext has no hash, then a hypernet with the same name will be selected instead.
     """
     hypernet_name = hypernet_name.lower()
     if hypernet_hash is not None:
-- 
cgit v1.2.1


From 957e15c4642199e0792eba817a15e244246fb012 Mon Sep 17 00:00:00 2001
From: Yuval Aboulafia <yuval.abou@gmail.com>
Date: Wed, 14 Dec 2022 20:59:33 +0200
Subject: Correct singleton comparisons

---
 modules/extras.py | 2 +-
 modules/ngrok.py  | 4 ++--
 modules/ui.py     | 8 ++++++--
 3 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 0ad8deec..69b85465 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -188,7 +188,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         for op in extras_ops:
             image, info = op(image, info)
 
-        if opts.use_original_name_batch and image_name != None:
+        if opts.use_original_name_batch and image_name is not None:
             basename = os.path.splitext(os.path.basename(image_name))[0]
         else:
             basename = ''
diff --git a/modules/ngrok.py b/modules/ngrok.py
index 64c9a3c2..3df2c06b 100644
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -2,7 +2,7 @@ from pyngrok import ngrok, conf, exception
 
 def connect(token, port, region):
     account = None
-    if token == None:
+    if token is None:
         token = 'None'
     else:
         if ':' in token:
@@ -14,7 +14,7 @@ def connect(token, port, region):
         auth_token=token, region=region
     )
     try:
-        if account == None:
+        if account is None:
             public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
         else:
             public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True, auth=account).public_url
diff --git a/modules/ui.py b/modules/ui.py
index 28481e33..c4bb186d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -49,10 +49,14 @@ if not cmd_opts.share and not cmd_opts.listen:
     gradio.utils.version_check = lambda: None
     gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
 
-if cmd_opts.ngrok != None:
+if cmd_opts.ngrok is not None:
     import modules.ngrok as ngrok
     print('ngrok authtoken detected, trying to connect...')
-    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
+    ngrok.connect(
+        cmd_opts.ngrok,
+        cmd_opts.port if cmd_opts.port is not None else 7860,
+        cmd_opts.ngrok_region
+        )
 
 
 def gr_show(visible=True):
-- 
cgit v1.2.1


From c0355caefe3d82e304e6d832699d581fc8f9fbf9 Mon Sep 17 00:00:00 2001
From: Jim Hays <jim@emporatitle.com>
Date: Wed, 14 Dec 2022 21:01:32 -0500
Subject: Fix various typos

---
 README.md                                      |  4 ++--
 javascript/contextMenus.js                     | 24 ++++++++++++------------
 javascript/progressbar.js                      | 12 ++++++------
 javascript/ui.js                               |  2 +-
 modules/api/api.py                             | 18 +++++++++---------
 modules/api/models.py                          |  2 +-
 modules/images.py                              |  4 ++--
 modules/processing.py                          | 14 +++++++-------
 modules/safe.py                                |  4 ++--
 modules/scripts.py                             |  4 ++--
 modules/sd_hijack_inpainting.py                |  6 +++---
 modules/sd_hijack_unet.py                      |  2 +-
 modules/textual_inversion/dataset.py           | 10 +++++-----
 modules/textual_inversion/textual_inversion.py | 16 ++++++++--------
 scripts/prompt_matrix.py                       | 10 +++++-----
 webui.py                                       |  4 ++--
 16 files changed, 68 insertions(+), 68 deletions(-)

diff --git a/README.md b/README.md
index 55990581..556000fb 100644
--- a/README.md
+++ b/README.md
@@ -82,8 +82,8 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - Use VAEs
 - Estimated completion time in progress bar
 - API
-- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. 
-- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
+- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
+- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
 - [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
 
 ## Installation and Running
diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js
index fe67c42e..11bcce1b 100644
--- a/javascript/contextMenus.js
+++ b/javascript/contextMenus.js
@@ -9,7 +9,7 @@ contextMenuInit = function(){
 
   function showContextMenu(event,element,menuEntries){
     let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
-    let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop; 
+    let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
 
     let oldMenu = gradioApp().querySelector('#context-menu')
     if(oldMenu){
@@ -61,15 +61,15 @@ contextMenuInit = function(){
 
   }
 
-  function appendContextMenuOption(targetEmementSelector,entryName,entryFunction){
-    
-    currentItems = menuSpecs.get(targetEmementSelector)
-    
+  function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
+
+    currentItems = menuSpecs.get(targetElementSelector)
+
     if(!currentItems){
       currentItems = []
-      menuSpecs.set(targetEmementSelector,currentItems);
+      menuSpecs.set(targetElementSelector,currentItems);
     }
-    let newItem = {'id':targetEmementSelector+'_'+uid(), 
+    let newItem = {'id':targetElementSelector+'_'+uid(),
                    'name':entryName,
                    'func':entryFunction,
                    'isNew':true}
@@ -97,7 +97,7 @@ contextMenuInit = function(){
       if(source.id && source.id.indexOf('check_progress')>-1){
         return
       }
-      
+
       let oldMenu = gradioApp().querySelector('#context-menu')
       if(oldMenu){
         oldMenu.remove()
@@ -117,7 +117,7 @@ contextMenuInit = function(){
       })
     });
     eventListenerApplied=true
-  
+
   }
 
   return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
@@ -152,8 +152,8 @@ addContextMenuEventListener = initResponse[2];
     generateOnRepeat('#img2img_generate','#img2img_interrupt');
   })
 
-  let cancelGenerateForever = function(){ 
-    clearInterval(window.generateOnRepeatInterval) 
+  let cancelGenerateForever = function(){
+    clearInterval(window.generateOnRepeatInterval)
   }
 
   appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
@@ -162,7 +162,7 @@ addContextMenuEventListener = initResponse[2];
   appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
 
   appendContextMenuOption('#roll','Roll three',
-    function(){ 
+    function(){
       let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
       setTimeout(function(){rollbutton.click()},100)
       setTimeout(function(){rollbutton.click()},200)
diff --git a/javascript/progressbar.js b/javascript/progressbar.js
index d58737c4..d6323ed9 100644
--- a/javascript/progressbar.js
+++ b/javascript/progressbar.js
@@ -3,7 +3,7 @@ global_progressbars = {}
 galleries = {}
 galleryObservers = {}
 
-// this tracks laumnches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running
+// this tracks launches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running
 timeoutIds = {}
 
 function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
@@ -20,21 +20,21 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
 
     var skip = id_skip ? gradioApp().getElementById(id_skip) : null
     var interrupt = gradioApp().getElementById(id_interrupt)
-    
+
     if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){
         if(progressbar.innerText){
             let newtitle = '[' + progressbar.innerText.trim() + '] Stable Diffusion';
             if(document.title != newtitle){
-                document.title =  newtitle;          
+                document.title =  newtitle;
             }
         }else{
             let newtitle = 'Stable Diffusion'
             if(document.title != newtitle){
-                document.title =  newtitle;          
+                document.title =  newtitle;
             }
         }
     }
-    
+
 	if(progressbar!= null && progressbar != global_progressbars[id_progressbar]){
 	    global_progressbars[id_progressbar] = progressbar
 
@@ -63,7 +63,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
                         skip.style.display = "none"
                     }
                     interrupt.style.display = "none"
-			
+
                     //disconnect observer once generation finished, so user can close selected image if they want
                     if (galleryObservers[id_gallery]) {
                         galleryObservers[id_gallery].disconnect();
diff --git a/javascript/ui.js b/javascript/ui.js
index 2cb280e5..587dd782 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -100,7 +100,7 @@ function create_submit_args(args){
 
     // As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
     // This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate.
-    // I don't know why gradio is seding outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
+    // I don't know why gradio is sending outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
     // If gradio at some point stops sending outputs, this may break something
     if(Array.isArray(res[res.length - 3])){
         res[res.length - 3] = null
diff --git a/modules/api/api.py b/modules/api/api.py
index 89935a70..33845045 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -67,10 +67,10 @@ def encode_pil_to_base64(image):
 class Api:
     def __init__(self, app: FastAPI, queue_lock: Lock):
         if shared.cmd_opts.api_auth:
-            self.credenticals = dict()
+            self.credentials = dict()
             for auth in shared.cmd_opts.api_auth.split(","):
                 user, password = auth.split(":")
-                self.credenticals[user] = password
+                self.credentials[user] = password
 
         self.router = APIRouter()
         self.app = app
@@ -93,7 +93,7 @@ class Api:
         self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
         self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
         self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
-        self.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
+        self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
         self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
         self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
 
@@ -102,9 +102,9 @@ class Api:
             return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
         return self.app.add_api_route(path, endpoint, **kwargs)
 
-    def auth(self, credenticals: HTTPBasicCredentials = Depends(HTTPBasic())):
-        if credenticals.username in self.credenticals:
-            if compare_digest(credenticals.password, self.credenticals[credenticals.username]):
+    def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())):
+        if credentials.username in self.credentials:
+            if compare_digest(credentials.password, self.credentials[credentials.username]):
                 return True
 
         raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
@@ -239,7 +239,7 @@ class Api:
     def interrogateapi(self, interrogatereq: InterrogateRequest):
         image_b64 = interrogatereq.image
         if image_b64 is None:
-            raise HTTPException(status_code=404, detail="Image not found") 
+            raise HTTPException(status_code=404, detail="Image not found")
 
         img = decode_base64_to_image(image_b64)
         img = img.convert('RGB')
@@ -252,7 +252,7 @@ class Api:
                 processed = deepbooru.model.tag(img)
             else:
                 raise HTTPException(status_code=404, detail="Model not found")
-        
+
         return InterrogateResponse(caption=processed)
 
     def interruptapi(self):
@@ -308,7 +308,7 @@ class Api:
     def get_realesrgan_models(self):
         return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
 
-    def get_promp_styles(self):
+    def get_prompt_styles(self):
         styleList = []
         for k in shared.prompt_styles.styles:
             style = shared.prompt_styles.styles[k]
diff --git a/modules/api/models.py b/modules/api/models.py
index f77951fc..a22bc6b3 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -128,7 +128,7 @@ class ExtrasBaseRequest(BaseModel):
     upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.")
     upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
     upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
-    upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the choosen size?")
+    upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
     upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
diff --git a/modules/images.py b/modules/images.py
index 8146f580..93a14289 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -429,7 +429,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
         basename (`str`):
             The base filename which will be applied to `filename pattern`.
-        seed, prompt, short_filename, 
+        seed, prompt, short_filename,
         extension (`str`):
             Image file extension, default is `png`.
         pngsectionname (`str`):
@@ -590,7 +590,7 @@ def read_info_from_image(image):
 Negative prompt: {json_info["uc"]}
 Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
         except Exception:
-            print(f"Error parsing NovelAI iamge generation parameters:", file=sys.stderr)
+            print(f"Error parsing NovelAI image generation parameters:", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
     return geninfo, items
diff --git a/modules/processing.py b/modules/processing.py
index 24c537d1..fe7f4faf 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -147,11 +147,11 @@ class StableDiffusionProcessing():
 
         # The "masked-image" in this case will just be all zeros since the entire image is masked.
         image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
+        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
 
         # Add the fake full 1s mask to the first dimension.
         image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-        image_conditioning = image_conditioning.to(x.dtype)            
+        image_conditioning = image_conditioning.to(x.dtype)
 
         return image_conditioning
 
@@ -199,7 +199,7 @@ class StableDiffusionProcessing():
             source_image * (1.0 - conditioning_mask),
             getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
         )
-        
+
         # Encode the new masked image using first stage of network.
         conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
 
@@ -537,7 +537,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         for n in range(p.n_iter):
             if state.skipped:
                 state.skipped = False
-            
+
             if state.interrupted:
                 break
 
@@ -612,7 +612,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image.info["parameters"] = text
                 output_images.append(image)
 
-            del x_samples_ddim 
+            del x_samples_ddim
 
             devices.torch_gc()
 
@@ -704,7 +704,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
-        """saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images"""
+        """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
         def save_intermediate(image, index):
             if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
                 return
@@ -720,7 +720,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
 
-            # Avoid making the inpainting conditioning unless necessary as 
+            # Avoid making the inpainting conditioning unless necessary as
             # this does need some extra compute to decode / encode the image again.
             if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
                 image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
diff --git a/modules/safe.py b/modules/safe.py
index 10460ad0..20e9d2fa 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -80,7 +80,7 @@ def check_pt(filename, extra_handler):
         # new pytorch format is a zip file
         with zipfile.ZipFile(filename) as z:
             check_zip_filenames(filename, z.namelist())
-            
+
             # find filename of data.pkl in zip file: '<directory name>/data.pkl'
             data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)]
             if len(data_pkl_filenames) == 0:
@@ -108,7 +108,7 @@ def load(filename, *args, **kwargs):
 
 def load_with_extra(filename, extra_handler=None, *args, **kwargs):
     """
-    this functon is intended to be used by extensions that want to load models with
+    this function is intended to be used by extensions that want to load models with
     some extra classes in them that the usual unpickler would find suspicious.
 
     Use the extra_handler argument to specify a function that takes module and field name as text,
diff --git a/modules/scripts.py b/modules/scripts.py
index 23ca195d..722f8685 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -36,7 +36,7 @@ class Script:
     def ui(self, is_img2img):
         """this function should create gradio UI elements. See https://gradio.app/docs/#components
         The return value should be an array of all components that are used in processing.
-        Values of those returned componenbts will be passed to run() and process() functions.
+        Values of those returned components will be passed to run() and process() functions.
         """
 
         pass
@@ -47,7 +47,7 @@ class Script:
 
         This function should return:
          - False if the script should not be shown in UI at all
-         - True if the script should be shown in UI if it's scelected in the scripts drowpdown
+         - True if the script should be shown in UI if it's selected in the scripts dropdown
          - script.AlwaysVisible if the script should be shown in UI at all times
          """
 
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 938f9a58..d72f83fd 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -209,7 +209,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
         else:
             x_in = torch.cat([x] * 2)
             t_in = torch.cat([t] * 2)
-            
+
             if isinstance(c, dict):
                 assert isinstance(unconditional_conditioning, dict)
                 c_in = dict()
@@ -278,7 +278,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
     x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
 
     return x_prev, pred_x0, e_t
-    
+
 # =================================================================================================
 # Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
 # Adapted from:
@@ -326,7 +326,7 @@ def do_inpainting_hijack():
     # most of this stuff seems to no longer be needed because it is already included into SD2.0
     # LatentInpaintDiffusion remains because SD2.0's LatentInpaintDiffusion can't be loaded without specifying a checkpoint
     # p_sample_plms is needed because PLMS can't work with dicts as conditionings
-    # this file should be cleaned up later if weverything tuens out to work fine
+    # this file should be cleaned up later if everything turns out to work fine
 
     # ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
     ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
index 1b9d7757..18daf8c1 100644
--- a/modules/sd_hijack_unet.py
+++ b/modules/sd_hijack_unet.py
@@ -4,7 +4,7 @@ import torch
 class TorchHijackForUnet:
     """
     This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
-    this makes it possible to create pictures with dimensions that are muliples of 8 rather than 64
+    this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
     """
 
     def __getattr__(self, item):
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 2dc64c3c..88d68c76 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -28,9 +28,9 @@ class DatasetEntry:
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'):        
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'):
         re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
-        
+
         self.placeholder_token = placeholder_token
 
         self.width = width
@@ -50,14 +50,14 @@ class PersonalizedBase(Dataset):
 
         self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
 
-        
+
         self.shuffle_tags = shuffle_tags
         self.tag_drop_out = tag_drop_out
 
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
             if shared.state.interrupted:
-                raise Exception("inturrupted")
+                raise Exception("interrupted")
             try:
                 image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
             except Exception:
@@ -144,7 +144,7 @@ class PersonalizedDataLoader(DataLoader):
             self.collate_fn = collate_wrapper_random
         else:
             self.collate_fn = collate_wrapper
-        
+
 
 class BatchLoader:
     def __init__(self, data):
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e28c357a..daf3997b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -133,7 +133,7 @@ class EmbeddingDatabase:
 
                 process_file(fullfn, fn)
             except Exception:
-                print(f"Error loading emedding {fn}:", file=sys.stderr)
+                print(f"Error loading embedding {fn}:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
                 continue
 
@@ -194,7 +194,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             csv_writer.writeheader()
 
         epoch = (step - 1) // epoch_len
-        epoch_step = (step - 1) % epoch_len 
+        epoch_step = (step - 1) % epoch_len
 
         csv_writer.writerow({
             "step": step,
@@ -270,9 +270,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
    # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     old_parallel_processing_allowed = shared.parallel_processing_allowed
-    
+
     pin_memory = shared.opts.pin_memory
-    
+
     ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
 
     latent_sampling_method = ds.latent_sampling_method
@@ -295,12 +295,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     loss_step = 0
     _loss_step = 0 #internal
 
-    
+
     last_saved_file = "<none>"
     last_saved_image = "<none>"
     forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
-    
+
     pbar = tqdm.tqdm(total=steps - initial_step)
     try:
         for i in range((steps-initial_step) * gradient_step):
@@ -327,10 +327,10 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                     c = shared.sd_model.cond_stage_model(batch.cond_text)
                     loss = shared.sd_model(x, c)[0] / gradient_step
                     del x
-                    
+
                     _loss_step += loss.item()
                 scaler.scale(loss).backward()
-                
+
                 # go back until we reach gradient accumulation steps
                 if (j + 1) % gradient_step != 0:
                     continue
diff --git a/scripts/prompt_matrix.py b/scripts/prompt_matrix.py
index c53ca28c..4c79eaef 100644
--- a/scripts/prompt_matrix.py
+++ b/scripts/prompt_matrix.py
@@ -18,7 +18,7 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
     ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
     hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
 
-    first_pocessed = None
+    first_processed = None
 
     state.job_count = len(xs) * len(ys)
 
@@ -27,17 +27,17 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
             state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
 
             processed = cell(x, y)
-            if first_pocessed is None:
-                first_pocessed = processed
+            if first_processed is None:
+                first_processed = processed
 
             res.append(processed.images[0])
 
     grid = images.image_grid(res, rows=len(ys))
     grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
 
-    first_pocessed.images = [grid]
+    first_processed.images = [grid]
 
-    return first_pocessed
+    return first_processed
 
 
 class Script(scripts.Script):
diff --git a/webui.py b/webui.py
index c2d0c6be..4b32e77d 100644
--- a/webui.py
+++ b/webui.py
@@ -153,8 +153,8 @@ def webui():
 
         # gradio uses a very open CORS policy via app.user_middleware, which makes it possible for
         # an attacker to trick the user into opening a malicious HTML page, which makes a request to the
-        # running web ui and do whatever the attcker wants, including installing an extension and
-        # runnnig its code. We disable this here. Suggested by RyotaK.
+        # running web ui and do whatever the attacker wants, including installing an extension and
+        # running its code. We disable this here. Suggested by RyotaK.
         app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
 
         setup_cors(app)
-- 
cgit v1.2.1


From 35e1017e3ea0a3ad9ec28c9b447200a70a65c0ae Mon Sep 17 00:00:00 2001
From: Akiba <akiba@anzu.link>
Date: Fri, 16 Dec 2022 20:43:09 +0800
Subject: fix: xformers

---
 modules/import_hook.py | 18 ++++++++++++++++++
 webui.py               |  1 +
 2 files changed, 19 insertions(+)
 create mode 100644 modules/import_hook.py

diff --git a/modules/import_hook.py b/modules/import_hook.py
new file mode 100644
index 00000000..eb10e4fd
--- /dev/null
+++ b/modules/import_hook.py
@@ -0,0 +1,18 @@
+import builtins
+import sys
+
+old_import = builtins.__import__
+IMPORT_BLACKLIST = []
+
+
+if "xformers" not in "".join(sys.argv):
+    IMPORT_BLACKLIST.append("xformers")
+
+
+def import_hook(*args, **kwargs):
+    if args[0] in IMPORT_BLACKLIST:
+        raise ImportError("Import of %s is blacklisted" % args[0])
+    return old_import(*args, **kwargs)
+
+
+builtins.__import__ = import_hook
diff --git a/webui.py b/webui.py
index c2d0c6be..18ee5a3d 100644
--- a/webui.py
+++ b/webui.py
@@ -8,6 +8,7 @@ from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
 
+from modules import import_hook
 from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call
 from modules.paths import script_path
 
-- 
cgit v1.2.1


From 8b0703b8fcdab153958b11f0dd5e5b6b58565fed Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Fri, 16 Dec 2022 08:18:29 -0800
Subject: Add a workaround patch for DPM2 a issue

DPM2 a and DPM2 a Karras samplers are both affected by an issue described by https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/3483 and can be resolved by a workaround suggested by the k-diffusion author at https://github.com/crowsonkb/k-diffusion/issues/43#issuecomment-1305195666
---
 modules/sd_samplers.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 4c123d3b..b8e0ce53 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -494,6 +494,9 @@ class KDiffusionSampler:
 
         x = x * sigmas[0]
 
+        if self.funcname == "sample_dpm_2_ancestral": # workaround dpm2 a issue
+            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+
         extra_params_kwargs = self.initialize(p)
         if 'sigma_min' in inspect.signature(self.func).parameters:
             extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
-- 
cgit v1.2.1


From 180fdf7809ea18de2d3b04618846d5a4e33c002e Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Fri, 16 Dec 2022 08:42:00 -0800
Subject: apply to DPM2 (non-ancestral) as well

---
 modules/sd_samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b8e0ce53..ae3d8bfa 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -494,7 +494,7 @@ class KDiffusionSampler:
 
         x = x * sigmas[0]
 
-        if self.funcname == "sample_dpm_2_ancestral": # workaround dpm2 a issue
+        if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
             sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
 
         extra_params_kwargs = self.initialize(p)
-- 
cgit v1.2.1


From b7c478c3ebb2b1844efd5d6bddb69095dd10808f Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Sat, 17 Dec 2022 00:45:43 -0500
Subject: fix: Modify font size when unable to fit in plot

This prevents scenarios where text without line breaks will start overlapping with each other when generating X/Y plots. This is most evident when generating X/Y plots with checkpoints, as most don't contain spaces and sometimes include extra information such as the epoch, making it extra long.
---
 modules/images.py | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index 8146f580..ad97980c 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -136,8 +136,19 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
                 lines.append(word)
         return lines
 
-    def draw_texts(drawing, draw_x, draw_y, lines):
+    def get_font(fontsize):
+        try:
+            return ImageFont.truetype(opts.font or Roboto, fontsize)
+        except Exception:
+            return ImageFont.truetype(Roboto, fontsize)
+
+    def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
         for i, line in enumerate(lines):
+            fnt = initial_fnt
+            fontsize = initial_fontsize
+            while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0:
+                fontsize -= 1
+                fnt = get_font(fontsize)
             drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
 
             if not line.is_active:
@@ -148,10 +159,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
     fontsize = (width + height) // 25
     line_spacing = fontsize // 2
 
-    try:
-        fnt = ImageFont.truetype(opts.font or Roboto, fontsize)
-    except Exception:
-        fnt = ImageFont.truetype(Roboto, fontsize)
+    fnt = get_font(fontsize)
 
     color_active = (0, 0, 0)
     color_inactive = (153, 153, 153)
@@ -178,6 +186,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
         for line in texts:
             bbox = calc_d.multiline_textbbox((0, 0), line.text, font=fnt)
             line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1])
+            line.allowed_width = allowed_width
 
     hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]
     ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in
@@ -194,13 +203,13 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
         x = pad_left + width * col + width / 2
         y = pad_top / 2 - hor_text_heights[col] / 2
 
-        draw_texts(d, x, y, hor_texts[col])
+        draw_texts(d, x, y, hor_texts[col], fnt, fontsize)
 
     for row in range(rows):
         x = pad_left / 2
         y = pad_top + height * row + height / 2 - ver_text_heights[row] / 2
 
-        draw_texts(d, x, y, ver_texts[row])
+        draw_texts(d, x, y, ver_texts[row], fnt, fontsize)
 
     return result
 
-- 
cgit v1.2.1


From 16b4509fa60ec03102b2452b41799dafccd35970 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Sat, 17 Dec 2022 03:21:19 -0500
Subject: Add numpy fix for MPS on PyTorch 1.12.1

When saving training results with torch.save(), an exception is thrown:
"RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead."

So for MPS, check if Tensor.requires_grad and detach() if necessary.
---
 modules/devices.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/modules/devices.py b/modules/devices.py
index f8cffae1..800510b7 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -125,7 +125,16 @@ def layer_norm_fix(*args, **kwargs):
     return orig_layer_norm(*args, **kwargs)
 
 
+# MPS workaround for https://github.com/pytorch/pytorch/issues/90532
+orig_tensor_numpy = torch.Tensor.numpy
+def numpy_fix(self, *args, **kwargs):
+    if self.requires_grad:
+        self = self.detach()
+    return orig_tensor_numpy(self, *args, **kwargs)
+
+
 # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
 if has_mps() and version.parse(torch.__version__) < version.parse("1.13"):
     torch.Tensor.to = tensor_to_fix
     torch.nn.functional.layer_norm = layer_norm_fix
+    torch.Tensor.numpy = numpy_fix
-- 
cgit v1.2.1


From cca16373def60bfc6d159a3c2dca91d0ba48112a Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Sat, 17 Dec 2022 03:24:54 -0500
Subject: Add attributes used by MPS

---
 modules/safe.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/modules/safe.py b/modules/safe.py
index 10460ad0..7c89c4c2 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -37,16 +37,16 @@ class RestrictedUnpickler(pickle.Unpickler):
 
         if module == 'collections' and name == 'OrderedDict':
             return getattr(collections, name)
-        if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
+        if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter', '_rebuild_device_tensor_from_numpy']:
             return getattr(torch._utils, name)
-        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage']:
+        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32']:
             return getattr(torch, name)
         if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
             return getattr(torch.nn.modules.container, name)
-        if module == 'numpy.core.multiarray' and name == 'scalar':
-            return numpy.core.multiarray.scalar
-        if module == 'numpy' and name == 'dtype':
-            return numpy.dtype
+        if module == 'numpy.core.multiarray' and name in ['scalar', '_reconstruct']:
+            return getattr(numpy.core.multiarray, name)
+        if module == 'numpy' and name in ['dtype', 'ndarray']:
+            return getattr(numpy, name)
         if module == '_codecs' and name == 'encode':
             return encode
         if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':
-- 
cgit v1.2.1


From a26fe85056cf0dacef2d78cccf6ab100fd16da1c Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Sat, 17 Dec 2022 04:31:03 -0800
Subject: Add upscaler name as a suffix.

---
 modules/extras.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/modules/extras.py b/modules/extras.py
index bc349d5e..9b60e360 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -193,8 +193,13 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         else:
             basename = ''
 
+        # Add upscaler name as a suffix.
+        suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}"
+        if extras_upscaler_2 and extras_upscaler_2_visibility:
+            suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"
+
         images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
+                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo
-- 
cgit v1.2.1


From a7a039d53a69f8c32cb889fe322e769b238fec27 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Sat, 17 Dec 2022 06:28:51 -0800
Subject: Add option to include upscaler name in filename.

---
 modules/extras.py | 5 +++--
 modules/shared.py | 1 +
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 9b60e360..074a7c22 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -194,8 +194,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
             basename = ''
 
         # Add upscaler name as a suffix.
-        suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}"
-        if extras_upscaler_2 and extras_upscaler_2_visibility:
+        suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
+        # Add second upscaler if applicable.
+        if suffix and extras_upscaler_2 and extras_upscaler_2_visibility:
             suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"
 
         images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
diff --git a/modules/shared.py b/modules/shared.py
index dc45fcaa..218894e8 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -293,6 +293,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
 
     "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
+    "use_upscaler_name_as_suffix": OptionInfo(False, "Add upscaler name to the end of filename in the extras tab"),
     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
     "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
 
-- 
cgit v1.2.1


From 6fd91c9179f51dd2f73f03eeabd12bfd081941c5 Mon Sep 17 00:00:00 2001
From: timntorres <timothynarcisotorres@gmail.com>
Date: Sat, 17 Dec 2022 08:59:02 -0800
Subject: Update OptionInfo to match preexisting option.

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 218894e8..230c377e 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -293,7 +293,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
 
     "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
-    "use_upscaler_name_as_suffix": OptionInfo(False, "Add upscaler name to the end of filename in the extras tab"),
+    "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
     "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
 
-- 
cgit v1.2.1


From 35f0698ae85571f688e82447c989b75e4fe81b0d Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Sun, 18 Dec 2022 20:45:30 +0800
Subject: Dirty fix for missing PIL supported file extensions

---
 launch.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/launch.py b/launch.py
index ad9ddd5a..581a21ff 100644
--- a/launch.py
+++ b/launch.py
@@ -7,6 +7,7 @@ import shlex
 import platform
 import argparse
 import json
+from PIL import Image
 
 dir_repos = "repositories"
 dir_extensions = "extensions"
-- 
cgit v1.2.1


From c02ef0f4286c618d30ee028778f58ca7809c7d93 Mon Sep 17 00:00:00 2001
From: Billy Cao <aliencaocao@gmail.com>
Date: Sun, 18 Dec 2022 20:51:59 +0800
Subject: Fix PIL being imported before its installed (for new users only)

---
 launch.py         | 1 -
 modules/shared.py | 1 +
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/launch.py b/launch.py
index 581a21ff..ad9ddd5a 100644
--- a/launch.py
+++ b/launch.py
@@ -7,7 +7,6 @@ import shlex
 import platform
 import argparse
 import json
-from PIL import Image
 
 dir_repos = "repositories"
 dir_extensions = "extensions"
diff --git a/modules/shared.py b/modules/shared.py
index c36ee211..734ea2fe 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -5,6 +5,7 @@ import os
 import sys
 import time
 
+from PIL import Image
 import gradio as gr
 import tqdm
 
-- 
cgit v1.2.1


From 492052b5df657c3280f433fec047667246694bdb Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Sun, 18 Dec 2022 10:47:02 -0500
Subject: feat: Add upscale latent, VAE, styles to X/Y plot

Adds upscale latent space for hires., VAE, and Styles as new axis options to the X/Y plot.
---
 scripts/xy_grid.py | 44 +++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 43 insertions(+), 1 deletion(-)

diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index d402c281..3e0b2805 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -10,13 +10,16 @@ import numpy as np
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import images, sd_samplers
+from modules import images, paths, sd_samplers
 from modules.hypernetworks import hypernetwork
 from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.sd_samplers
 import modules.sd_models
+import modules.sd_vae
+import glob
+import os
 import re
 
 
@@ -114,6 +117,38 @@ def apply_clip_skip(p, x, xs):
     opts.data["CLIP_stop_at_last_layers"] = x
 
 
+def apply_upscale_latent_space(p, x, xs):
+    if x.lower().strip() != '0':
+        opts.data["use_scale_latent_for_hires_fix"] = True
+    else:
+        opts.data["use_scale_latent_for_hires_fix"] = False
+
+
+def find_vae(name: str):
+    if name.lower() in ['auto', 'none']:
+        return name
+    else:
+        vae_path = os.path.abspath(os.path.join(paths.models_path, 'VAE'))
+        found = glob.glob(os.path.join(vae_path, f'**/{name}.*pt'), recursive=True)
+        if found:
+            return found[0]
+        else:
+            return 'auto'
+
+
+def apply_vae(p, x, xs):
+    if x.lower().strip() == 'none':
+        modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file='None')
+    else:
+        found = find_vae(x)
+        if found:
+            v = modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=found)
+
+
+def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
+    p.styles = x.split(',')
+
+
 def format_value_add_label(p, opt, x):
     if type(x) == float:
         x = round(x, 8)
@@ -167,7 +202,10 @@ axis_options = [
     AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
     AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
     AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
+    AxisOption("Upscale latent space for hires.", str, apply_upscale_latent_space, format_value_add_label, None),
     AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight"), format_value_add_label, None),
+    AxisOption("VAE", str, apply_vae, format_value_add_label, None),
+    AxisOption("Styles", str, apply_styles, format_value_add_label, None),
 ]
 
 
@@ -229,14 +267,18 @@ class SharedSettingsStackHelper(object):
         self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
         self.hypernetwork = opts.sd_hypernetwork
         self.model = shared.sd_model
+        self.use_scale_latent_for_hires_fix = opts.use_scale_latent_for_hires_fix
+        self.vae = opts.sd_vae
   
     def __exit__(self, exc_type, exc_value, tb):
         modules.sd_models.reload_model_weights(self.model)
+        modules.sd_vae.reload_vae_weights(self.model, vae_file=find_vae(self.vae))
 
         hypernetwork.load_hypernetwork(self.hypernetwork)
         hypernetwork.apply_strength()
 
         opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
+        opts.data["use_scale_latent_for_hires_fix"] = self.use_scale_latent_for_hires_fix
 
 
 re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
-- 
cgit v1.2.1


From 7ba9bc2fdbfae8115294962510492faafeb48573 Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin" <git_commits@alexgoodwin.dev>
Date: Sun, 18 Dec 2022 19:16:42 -0800
Subject: fix dpm2 in img2img as well

---
 modules/sd_samplers.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index ae3d8bfa..1a1b8919 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -454,6 +454,9 @@ class KDiffusionSampler:
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
 
+        if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
+            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+
         sigma_sched = sigmas[steps - t_enc - 1:]
         xi = x + noise * sigma_sched[0]
         
-- 
cgit v1.2.1


From 22f1527fa79a03dbc8b1a4eec3b22369a877f4bd Mon Sep 17 00:00:00 2001
From: Philpax <me@philpax.me>
Date: Tue, 20 Dec 2022 20:36:49 +1100
Subject: feat(api): add override_settings_restore_afterwards

---
 modules/processing.py | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 24c537d1..f7335da2 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -77,7 +77,7 @@ class StableDiffusionProcessing():
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
     """
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, sampler_index: int = None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None):
         if sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
@@ -118,6 +118,7 @@ class StableDiffusionProcessing():
         self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
         self.s_noise = s_noise or opts.s_noise
         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
+        self.override_settings_restore_afterwards = override_settings_restore_afterwards
         self.is_using_inpainting_conditioning = False
 
         if not seed_enable_extras:
@@ -147,11 +148,11 @@ class StableDiffusionProcessing():
 
         # The "masked-image" in this case will just be all zeros since the entire image is masked.
         image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
+        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
 
         # Add the fake full 1s mask to the first dimension.
         image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-        image_conditioning = image_conditioning.to(x.dtype)            
+        image_conditioning = image_conditioning.to(x.dtype)
 
         return image_conditioning
 
@@ -199,7 +200,7 @@ class StableDiffusionProcessing():
             source_image * (1.0 - conditioning_mask),
             getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
         )
-        
+
         # Encode the new masked image using first stage of network.
         conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
 
@@ -463,12 +464,14 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
         res = process_images_inner(p)
 
-    finally:  # restore opts to original state
-        for k, v in stored_opts.items():
-            setattr(opts, k, v)
-            if k == 'sd_hypernetwork': shared.reload_hypernetworks()
-            if k == 'sd_model_checkpoint': sd_models.reload_model_weights()
-            if k == 'sd_vae': sd_vae.reload_vae_weights()
+    finally:
+        # restore opts to original state
+        if p.override_settings_restore_afterwards:
+            for k, v in stored_opts.items():
+                setattr(opts, k, v)
+                if k == 'sd_hypernetwork': shared.reload_hypernetworks()
+                if k == 'sd_model_checkpoint': sd_models.reload_model_weights()
+                if k == 'sd_vae': sd_vae.reload_vae_weights()
 
     return res
 
@@ -537,7 +540,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         for n in range(p.n_iter):
             if state.skipped:
                 state.skipped = False
-            
+
             if state.interrupted:
                 break
 
@@ -612,7 +615,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image.info["parameters"] = text
                 output_images.append(image)
 
-            del x_samples_ddim 
+            del x_samples_ddim
 
             devices.torch_gc()
 
@@ -720,7 +723,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
 
-            # Avoid making the inpainting conditioning unless necessary as 
+            # Avoid making the inpainting conditioning unless necessary as
             # this does need some extra compute to decode / encode the image again.
             if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
                 image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
-- 
cgit v1.2.1


From d050bb78636236338440768f871a0f2bb9f277f6 Mon Sep 17 00:00:00 2001
From: Florian Chrometz <1118875+florianchrometz@users.noreply.github.com>
Date: Tue, 20 Dec 2022 20:19:50 +0100
Subject: Set httpcore version in requirements - fixes #4833

---
 requirements_versions.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements_versions.txt b/requirements_versions.txt
index 185cd066..7208aeba 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -26,3 +26,4 @@ inflection==0.5.1
 GitPython==3.1.27
 torchsde==0.2.5
 safetensors==0.2.5
+httpcore<=0.15
-- 
cgit v1.2.1


From 35b1775b32a07f1b7c9dccad61f7aa77027a00fa Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Mon, 19 Dec 2022 17:25:14 -0500
Subject: Use other MPS optimization for large q.shape[0] * q.shape[1]

Check if q.shape[0] * q.shape[1] is 2**18 or larger and use the lower memory usage MPS optimization if it is. This should prevent most crashes that were occurring at certain resolutions (e.g. 1024x1024, 2048x512, 512x2048).

Also included is a change to check slice_size and prevent it from being divisible by 4096 which also results in a crash. Otherwise a crash can occur at 1024x512 or 512x1024 resolution.
---
 modules/sd_hijack_optimizations.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 98123fbf..02c87f40 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -127,7 +127,7 @@ def check_for_psutil():
 
 invokeAI_mps_available = check_for_psutil()
 
-# -- Taken from https://github.com/invoke-ai/InvokeAI --
+# -- Taken from https://github.com/invoke-ai/InvokeAI and modified --
 if invokeAI_mps_available:
     import psutil
     mem_total_gb = psutil.virtual_memory().total // (1 << 30)
@@ -152,14 +152,16 @@ def einsum_op_slice_1(q, k, v, slice_size):
     return r
 
 def einsum_op_mps_v1(q, k, v):
-    if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
+    if q.shape[0] * q.shape[1] <= 2**16: # (512x512) max q.shape[1]: 4096
         return einsum_op_compvis(q, k, v)
     else:
         slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
+        if slice_size % 4096 == 0:
+            slice_size -= 1
         return einsum_op_slice_1(q, k, v, slice_size)
 
 def einsum_op_mps_v2(q, k, v):
-    if mem_total_gb > 8 and q.shape[1] <= 4096:
+    if mem_total_gb > 8 and q.shape[0] * q.shape[1] <= 2**16:
         return einsum_op_compvis(q, k, v)
     else:
         return einsum_op_slice_0(q, k, v, 1)
@@ -188,7 +190,7 @@ def einsum_op(q, k, v):
         return einsum_op_cuda(q, k, v)
 
     if q.device.type == 'mps':
-        if mem_total_gb >= 32:
+        if mem_total_gb >= 32 and q.shape[0] % 32 != 0 and q.shape[0] * q.shape[1] < 2**18:
             return einsum_op_mps_v1(q, k, v)
         return einsum_op_mps_v2(q, k, v)
 
-- 
cgit v1.2.1


From 13e0295ab682299e3280eb6ff28be0870f2bc57c Mon Sep 17 00:00:00 2001
From: Akiba <akiba@anzu.link>
Date: Sat, 24 Dec 2022 11:17:21 +0800
Subject: fix: xformers use importlib

---
 modules/import_hook.py | 15 +--------------
 1 file changed, 1 insertion(+), 14 deletions(-)

diff --git a/modules/import_hook.py b/modules/import_hook.py
index eb10e4fd..7403135d 100644
--- a/modules/import_hook.py
+++ b/modules/import_hook.py
@@ -1,18 +1,5 @@
-import builtins
 import sys
 
-old_import = builtins.__import__
-IMPORT_BLACKLIST = []
-
 
 if "xformers" not in "".join(sys.argv):
-    IMPORT_BLACKLIST.append("xformers")
-
-
-def import_hook(*args, **kwargs):
-    if args[0] in IMPORT_BLACKLIST:
-        raise ImportError("Import of %s is blacklisted" % args[0])
-    return old_import(*args, **kwargs)
-
-
-builtins.__import__ = import_hook
+    sys.modules["xformers"] = None
-- 
cgit v1.2.1


From 0c747d4013f41f6c887a63d256af884aa8872f91 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 07:57:56 +0300
Subject: add a comment for disable xformers hack

---
 modules/import_hook.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/import_hook.py b/modules/import_hook.py
index 7403135d..28c67dfa 100644
--- a/modules/import_hook.py
+++ b/modules/import_hook.py
@@ -1,5 +1,5 @@
 import sys
 
-
-if "xformers" not in "".join(sys.argv):
+# this will break any attempt to import xformers which will prevent stability diffusion repo from trying to use it
+if "--xformers" not in "".join(sys.argv):
     sys.modules["xformers"] = None
-- 
cgit v1.2.1


From 399b229783a7b5fddab0a258740b4d59d668ee12 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 09:03:45 +0300
Subject: eliminate duplicated code add an option to samplers for skipping next
 to last sigma

---
 modules/sd_samplers.py | 31 ++++++++++++++-----------------
 1 file changed, 14 insertions(+), 17 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 1a1b8919..d26e48dc 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -23,16 +23,16 @@ samplers_k_diffusion = [
     ('Euler', 'sample_euler', ['k_euler'], {}),
     ('LMS', 'sample_lms', ['k_lms'], {}),
     ('Heun', 'sample_heun', ['k_heun'], {}),
-    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
     ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
     ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
-    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
-    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
     ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
     ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
     ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
@@ -444,9 +444,7 @@ class KDiffusionSampler:
 
         return extra_params_kwargs
 
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps, t_enc = setup_img2img_steps(p, steps)
-
+    def get_sigmas(self, p, steps):
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
@@ -454,9 +452,16 @@ class KDiffusionSampler:
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
 
-        if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
+        if self.config is not None and self.config.options.get('discard_next_to_last_sigma', False):
             sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
 
+        return sigmas
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps, t_enc = setup_img2img_steps(p, steps)
+
+        sigmas = self.get_sigmas(p, steps)
+
         sigma_sched = sigmas[steps - t_enc - 1:]
         xi = x + noise * sigma_sched[0]
         
@@ -488,18 +493,10 @@ class KDiffusionSampler:
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
         steps = steps or p.steps
 
-        if p.sampler_noise_scheduler_override:
-            sigmas = p.sampler_noise_scheduler_override(steps)
-        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
-        else:
-            sigmas = self.model_wrap.get_sigmas(steps)
+        sigmas = self.get_sigmas(p, steps)
 
         x = x * sigmas[0]
 
-        if self.funcname in ['sample_dpm_2_ancestral', 'sample_dpm_2']:
-            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-
         extra_params_kwargs = self.initialize(p)
         if 'sigma_min' in inspect.signature(self.func).parameters:
             extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
-- 
cgit v1.2.1


From 1d9eaf94e32e2d6b817a6796ffa7b0a814580015 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 09:27:41 +0300
Subject: add blendmodes to requirements_versions, remove aenum as it's already
 required by blendmodes

---
 requirements.txt          | 1 -
 requirements_versions.txt | 1 +
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 85e51575..5bed694e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,3 @@
-aenum
 blendmodes
 accelerate
 basicsr
diff --git a/requirements_versions.txt b/requirements_versions.txt
index 7208aeba..c126c8c4 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -1,3 +1,4 @@
+blendmodes==2022
 transformers==4.19.2
 accelerate==0.12.0
 basicsr==1.4.2
-- 
cgit v1.2.1


From 9441c28c947588d756e279a8cd5db6c0b4a8d2e4 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 09:46:35 +0300
Subject: add an option for img2img background color

---
 modules/images.py     | 11 +++++++++++
 modules/processing.py |  2 +-
 modules/shared.py     |  1 +
 modules/ui.py         |  2 +-
 4 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index ad97980c..8bcbc8d9 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -622,3 +622,14 @@ def image_data(data):
         pass
 
     return '', None
+
+
+def flatten(img, bgcolor):
+    """replaces transparency with bgcolor (example: "#ffffff"), returning an RGB mode image with no transparency"""
+
+    if img.mode == "RGBA":
+        background = Image.new('RGBA', img.size, bgcolor)
+        background.paste(img, mask=img)
+        img = background
+
+    return img.convert('RGB')
diff --git a/modules/processing.py b/modules/processing.py
index bc841837..7c4bcd74 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -832,7 +832,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             self.color_corrections = []
         imgs = []
         for img in self.init_images:
-            image = img.convert("RGB")
+            image = images.flatten(img, opts.img2img_background_color)
 
             if crop_region is None:
                 image = images.resize_image(self.resize_mode, image, self.width, self.height)
diff --git a/modules/shared.py b/modules/shared.py
index 215c1358..dcce9299 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -363,6 +363,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01 }),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
+    "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", gr.ColorPicker, {}),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
diff --git a/modules/ui.py b/modules/ui.py
index 28481e33..76919b0f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -812,7 +812,7 @@ def create_ui():
 
                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
                     with gr.TabItem('img2img', id='img2img'):
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool, image_mode="RGBA").style(height=480)
 
                     with gr.TabItem('Inpaint', id='inpaint'):
                         init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_inpaint_tool, image_mode="RGBA").style(height=480)
-- 
cgit v1.2.1


From c0a8401b5a8368d03bb14fc63abbdedb1e802d8d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 11:12:17 +0300
Subject: rename the option for img2img latent upscale

---
 modules/processing.py | 2 +-
 modules/ui.py         | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 75b0067c..d2288f26 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -846,7 +846,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
                 self.overlay_images.append(image_masked.convert('RGBA'))
 
-            # crop_region is not none iif we are doing inpaint full res
+            # crop_region is not None if we are doing inpaint full res
             if crop_region is not None:
                 image = image.crop(crop_region)
                 image = images.resize_image(2, image, self.width, self.height)
diff --git a/modules/ui.py b/modules/ui.py
index faba69a4..9dec61d5 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -857,7 +857,7 @@ def create_ui():
                         img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
 
                 with gr.Row():
-                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill", "Upscale Latent Space"], type="index", value="Just resize")
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
 
                 steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
                 sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
-- 
cgit v1.2.1


From f23a822f1c9cb3bd2e8772c75af429e06515eaef Mon Sep 17 00:00:00 2001
From: Philpax <me@philpax.me>
Date: Sat, 24 Dec 2022 20:45:16 +1100
Subject: feat(api): include job_timestamp in progress

---
 modules/shared.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/shared.py b/modules/shared.py
index 8ea3b441..f356dbf7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -171,6 +171,7 @@ class State:
             "interrupted": self.skipped,
             "job": self.job,
             "job_count": self.job_count,
+            "job_timestamp": self.job_timestamp,
             "job_no": self.job_no,
             "sampling_step": self.sampling_step,
             "sampling_steps": self.sampling_steps,
-- 
cgit v1.2.1


From 11dd79e346bd780bc5c3119df962e7a9c20f2493 Mon Sep 17 00:00:00 2001
From: AbstractQbit <38468635+AbstractQbit@users.noreply.github.com>
Date: Sat, 24 Dec 2022 14:00:17 +0300
Subject: Add an option for faster low quality previews

---
 modules/sd_samplers.py | 23 ++++++++++++++++-------
 modules/shared.py      |  5 +++--
 2 files changed, 19 insertions(+), 9 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d26e48dc..fbb56af4 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -106,20 +106,29 @@ def setup_img2img_steps(p, steps=None):
     return steps, t_enc
 
 
-def single_sample_to_image(sample):
-    x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
+def single_sample_to_image(sample, approximation=False):
+    if approximation:
+        # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
+        coefs = torch.tensor(
+            [[ 0.298,  0.207,  0.208],
+             [ 0.187,  0.286,  0.173],
+             [-0.158,  0.189,  0.264],
+             [-0.184, -0.271, -0.473]]).to(sample.device)
+        x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs)
+    else:
+        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
     x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
     x_sample = x_sample.astype(np.uint8)
     return Image.fromarray(x_sample)
 
 
-def sample_to_image(samples, index=0):
-    return single_sample_to_image(samples[index])
+def sample_to_image(samples, index=0, approximation=False):
+    return single_sample_to_image(samples[index], approximation)
 
 
-def samples_to_image_grid(samples):
-    return images.image_grid([single_sample_to_image(sample) for sample in samples])
+def samples_to_image_grid(samples, approximation=False):
+    return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
 
 
 def store_latent(decoded):
@@ -127,7 +136,7 @@ def store_latent(decoded):
 
     if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
         if not shared.parallel_processing_allowed:
-            shared.state.current_image = sample_to_image(decoded)
+            shared.state.current_image = sample_to_image(decoded, approximation=opts.show_progress_approximate)
 
 
 class InterruptedException(BaseException):
diff --git a/modules/shared.py b/modules/shared.py
index 8ea3b441..1067b1d3 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -212,9 +212,9 @@ class State:
 
         import modules.sd_samplers
         if opts.show_progress_grid:
-            self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent)
+            self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent, approximation=opts.show_progress_approximate)
         else:
-            self.current_image = modules.sd_samplers.sample_to_image(self.current_latent)
+            self.current_image = modules.sd_samplers.sample_to_image(self.current_latent, approximation=opts.show_progress_approximate)
 
         self.current_image_sampling_step = self.sampling_step
 
@@ -391,6 +391,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
+    "show_progress_approximate": OptionInfo(False, "Calculate small previews using fast linear approximation instead of VAE"),
     "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
-- 
cgit v1.2.1


From 6247f21a637399900643a4915e8a223688e0ed22 Mon Sep 17 00:00:00 2001
From: Philpax <me@philpax.me>
Date: Sat, 24 Dec 2022 22:04:53 +1100
Subject: fix(api): don't save extras output to disk

---
 modules/api/api.py |  6 +++---
 modules/extras.py  | 17 +++++++++--------
 2 files changed, 12 insertions(+), 11 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 3257445d..b43dd16b 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -181,7 +181,7 @@ class Api:
         reqDict['image'] = decode_base64_to_image(reqDict['image'])
 
         with self.queue_lock:
-            result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", **reqDict)
+            result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
 
         return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
 
@@ -197,7 +197,7 @@ class Api:
         reqDict.pop('imageList')
 
         with self.queue_lock:
-            result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", **reqDict)
+            result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
 
         return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
 
@@ -322,7 +322,7 @@ class Api:
 
     def get_artists(self):
         return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
-    
+
     def refresh_checkpoints(self):
         shared.refresh_checkpoints()
 
diff --git a/modules/extras.py b/modules/extras.py
index 6fa7d856..68939dea 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -55,7 +55,7 @@ class LruCache(OrderedDict):
 cached_images: LruCache = LruCache(max_size=5)
 
 
-def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool):
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
     devices.torch_gc()
 
     imageArr = []
@@ -193,14 +193,15 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         else:
             basename = ''
 
-        # Add upscaler name as a suffix.
-        suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
-        # Add second upscaler if applicable.
-        if suffix and extras_upscaler_2 and extras_upscaler_2_visibility:
-            suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"
+        if save_output:
+            # Add upscaler name as a suffix.
+            suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
+            # Add second upscaler if applicable.
+            if suffix and extras_upscaler_2 and extras_upscaler_2_visibility:
+                suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"
 
-        images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
-                          no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
+            images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
+                            no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
 
         if opts.enable_pnginfo:
             image.info = existing_pnginfo
-- 
cgit v1.2.1


From 5a650055de3792223a91925aba8130ebdee29e35 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?linuxmobile=20=28=20=E3=83=AA=E3=83=8A=E3=83=83=E3=82=AF?=
 =?UTF-8?q?=E3=82=B9=20=29?= <bdiez19@gmail.com>
Date: Sat, 24 Dec 2022 09:25:35 -0300
Subject: Removed lenght in sd_model at line 115

Commit eba60a4 is what is causing this error, delete the length check in sd_model starting at line 115 and it's fine.

https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/5971#issuecomment-1364507379
---
 modules/sd_models.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 1254e5ae..6ca06211 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -111,9 +111,6 @@ def model_hash(filename):
 
 def select_checkpoint():
     model_checkpoint = shared.opts.sd_model_checkpoint
-    
-    if len(model_checkpoint) == 0:
-        model_checkpoint = shared.default_sd_model_file
         
     checkpoint_info = checkpoints_list.get(model_checkpoint, None)
     if checkpoint_info is not None:
-- 
cgit v1.2.1


From 03d7b394539558f6f560155d87a4fc66eb675e30 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 12:40:32 +0300
Subject: added an option to filter out deepbooru tags

---
 modules/deepbooru.py | 4 +++-
 modules/shared.py    | 1 +
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index dfc83357..122fce7f 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -79,7 +79,9 @@ class DeepDanbooru:
 
         res = []
 
-        for tag in tags:
+        filtertags = set([x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")])
+
+        for tag in [x for x in tags if x not in filtertags]:
             probability = probability_dict[tag]
             tag_outformat = tag
             if use_spaces:
diff --git a/modules/shared.py b/modules/shared.py
index 8ea3b441..a75de535 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -386,6 +386,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
     "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
     "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
     "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
+    "deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
 }))
 
 options_templates.update(options_section(('ui', "User interface"), {
-- 
cgit v1.2.1


From 0b8acce6a9a1418fa88a506450cd1b92e2d48986 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 18:38:16 +0300
Subject: separate part of denoiser code into a function to make it easier for
 extensions to override it

---
 modules/sd_samplers.py | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index d26e48dc..8efe74df 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -288,6 +288,16 @@ class CFGDenoiser(torch.nn.Module):
         self.init_latent = None
         self.step = 0
 
+    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        denoised = torch.clone(denoised_uncond)
+
+        for i, conds in enumerate(conds_list):
+            for cond_index, weight in conds:
+                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+
+        return denoised
+
     def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
         if state.interrupted or state.skipped:
             raise InterruptedException
@@ -329,12 +339,7 @@ class CFGDenoiser(torch.nn.Module):
 
             x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
 
-        denoised_uncond = x_out[-uncond.shape[0]:]
-        denoised = torch.clone(denoised_uncond)
-
-        for i, conds in enumerate(conds_list):
-            for cond_index, weight in conds:
-                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+        denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
 
         if self.mask is not None:
             denoised = self.init_latent * self.mask + self.nmask * denoised
-- 
cgit v1.2.1


From 3bf5591efe9a9f219c6088be322a87adc4f48f95 Mon Sep 17 00:00:00 2001
From: Yuval Aboulafia <yuval.abou@gmail.com>
Date: Sat, 24 Dec 2022 21:35:29 +0200
Subject: fix F541 f-string without any placeholders

---
 extensions-builtin/LDSR/ldsr_model_arch.py     | 2 +-
 modules/codeformer/vqgan_arch.py               | 4 ++--
 modules/hypernetworks/hypernetwork.py          | 4 ++--
 modules/images.py                              | 2 +-
 modules/interrogate.py                         | 2 +-
 modules/safe.py                                | 8 ++++----
 modules/sd_models.py                           | 8 ++++----
 modules/sd_vae.py                              | 2 +-
 modules/textual_inversion/textual_inversion.py | 2 +-
 scripts/prompts_from_file.py                   | 2 +-
 10 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py
index f5bd8ae4..0ad49f4e 100644
--- a/extensions-builtin/LDSR/ldsr_model_arch.py
+++ b/extensions-builtin/LDSR/ldsr_model_arch.py
@@ -26,7 +26,7 @@ class LDSR:
         global cached_ldsr_model
 
         if shared.opts.ldsr_cached and cached_ldsr_model is not None:
-            print(f"Loading model from cache")
+            print("Loading model from cache")
             model: torch.nn.Module = cached_ldsr_model
         else:
             print(f"Loading model from {self.modelPath}")
diff --git a/modules/codeformer/vqgan_arch.py b/modules/codeformer/vqgan_arch.py
index c06c590c..e7293683 100644
--- a/modules/codeformer/vqgan_arch.py
+++ b/modules/codeformer/vqgan_arch.py
@@ -382,7 +382,7 @@ class VQAutoEncoder(nn.Module):
                 self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
                 logger.info(f'vqgan is loaded from: {model_path} [params]')
             else:
-                raise ValueError(f'Wrong params!')
+                raise ValueError('Wrong params!')
 
 
     def forward(self, x):
@@ -431,7 +431,7 @@ class VQGANDiscriminator(nn.Module):
             elif 'params' in chkpt:
                 self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
             else:
-                raise ValueError(f'Wrong params!')
+                raise ValueError('Wrong params!')
 
     def forward(self, x):
         return self.main(x)
\ No newline at end of file
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index c406ffb3..9d3034ae 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -277,7 +277,7 @@ def load_hypernetwork(filename):
             print(traceback.format_exc(), file=sys.stderr)
     else:
         if shared.loaded_hypernetwork is not None:
-            print(f"Unloading hypernetwork")
+            print("Unloading hypernetwork")
 
         shared.loaded_hypernetwork = None
 
@@ -417,7 +417,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
 
     initial_step = hypernetwork.step or 0
     if initial_step >= steps:
-        shared.state.textinfo = f"Model has already been trained beyond specified max steps"
+        shared.state.textinfo = "Model has already been trained beyond specified max steps"
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
diff --git a/modules/images.py b/modules/images.py
index 809ad9f7..31d4528d 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -599,7 +599,7 @@ def read_info_from_image(image):
 Negative prompt: {json_info["uc"]}
 Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
         except Exception:
-            print(f"Error parsing NovelAI image generation parameters:", file=sys.stderr)
+            print("Error parsing NovelAI image generation parameters:", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
     return geninfo, items
diff --git a/modules/interrogate.py b/modules/interrogate.py
index 0068b81c..46935210 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -172,7 +172,7 @@ class InterrogateModels:
                             res += ", " + match
 
         except Exception:
-            print(f"Error interrogating", file=sys.stderr)
+            print("Error interrogating", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
             res += "<error>"
 
diff --git a/modules/safe.py b/modules/safe.py
index 479c8b86..1d4c20b9 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -137,15 +137,15 @@ def load_with_extra(filename, extra_handler=None, *args, **kwargs):
     except pickle.UnpicklingError:
         print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
-        print(f"-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
-        print(f"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
+        print("-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
+        print("You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
         return None
 
     except Exception:
         print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
-        print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
-        print(f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
+        print("\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
+        print("You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
         return None
 
     return unsafe_torch_load(filename, *args, **kwargs)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6ca06211..ecdd91c5 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -117,13 +117,13 @@ def select_checkpoint():
         return checkpoint_info
 
     if len(checkpoints_list) == 0:
-        print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
+        print("No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
         if shared.cmd_opts.ckpt is not None:
             print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
         print(f" - directory {model_path}", file=sys.stderr)
         if shared.cmd_opts.ckpt_dir is not None:
             print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
-        print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
+        print("Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
         exit(1)
 
     checkpoint_info = next(iter(checkpoints_list.values()))
@@ -324,7 +324,7 @@ def load_model(checkpoint_info=None):
 
     script_callbacks.model_loaded_callback(sd_model)
 
-    print(f"Model loaded.")
+    print("Model loaded.")
     return sd_model
 
 
@@ -359,5 +359,5 @@ def reload_model_weights(sd_model=None, info=None):
     if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
         sd_model.to(devices.device)
 
-    print(f"Weights loaded.")
+    print("Weights loaded.")
     return sd_model
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 25638a83..3856418e 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -208,5 +208,5 @@ def reload_vae_weights(sd_model=None, vae_file="auto"):
     if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
         sd_model.to(devices.device)
 
-    print(f"VAE Weights loaded.")
+    print("VAE Weights loaded.")
     return sd_model
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index daf3997b..f6112578 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -263,7 +263,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 
     initial_step = embedding.step or 0
     if initial_step >= steps:
-        shared.state.textinfo = f"Model has already been trained beyond specified max steps"
+        shared.state.textinfo = "Model has already been trained beyond specified max steps"
         return embedding, filename
     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
 
diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py
index 6e118ddb..e8386ed2 100644
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@@ -140,7 +140,7 @@ class Script(scripts.Script):
                 try:
                     args = cmdargs(line)
                 except Exception:
-                    print(f"Error parsing line [line] as commandline:", file=sys.stderr)
+                    print(f"Error parsing line {line} as commandline:", file=sys.stderr)
                     print(traceback.format_exc(), file=sys.stderr)
                     args = {"prompt": line}
             else:
-- 
cgit v1.2.1


From 56e557c6ff8a6480887c9c585bf908045ee8e791 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 22:39:00 +0300
Subject: added cheap NN approximation for VAE

---
 javascript/hints.js        |   5 +++-
 models/VAE-approx/model.pt | Bin 0 -> 213777 bytes
 modules/sd_samplers.py     |  29 +++++++++++++----------
 modules/sd_vae_approx.py   |  58 +++++++++++++++++++++++++++++++++++++++++++++
 modules/shared.py          |   6 ++---
 5 files changed, 81 insertions(+), 17 deletions(-)
 create mode 100644 models/VAE-approx/model.pt
 create mode 100644 modules/sd_vae_approx.py

diff --git a/javascript/hints.js b/javascript/hints.js
index a739a177..63e17e05 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -97,7 +97,10 @@ titles = {
 
     "Learning rate": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
 
-    "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc."
+    "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.",
+
+    "Approx NN": "Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resoluton and lower quality.",
+    "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resoluton and extremely low quality."
 }
 
 
diff --git a/models/VAE-approx/model.pt b/models/VAE-approx/model.pt
new file mode 100644
index 00000000..8bda9d6e
Binary files /dev/null and b/models/VAE-approx/model.pt differ
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 27ef4ff8..177b5338 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -9,7 +9,7 @@ import k_diffusion.sampling
 import torchsde._brownian.brownian_interval
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
-from modules import prompt_parser, devices, processing, images
+from modules import prompt_parser, devices, processing, images, sd_vae_approx
 
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -106,28 +106,31 @@ def setup_img2img_steps(p, steps=None):
     return steps, t_enc
 
 
-def single_sample_to_image(sample, approximation=False):
-    if approximation:
-        # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
-        coefs = torch.tensor(
-            [[ 0.298,  0.207,  0.208],
-             [ 0.187,  0.286,  0.173],
-             [-0.158,  0.189,  0.264],
-             [-0.184, -0.271, -0.473]]).to(sample.device)
-        x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs)
+approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
+
+
+def single_sample_to_image(sample, approximation=None):
+    if approximation is None:
+        approximation = approximation_indexes.get(opts.show_progress_type, 0)
+
+    if approximation == 2:
+        x_sample = sd_vae_approx.cheap_approximation(sample)
+    elif approximation == 1:
+        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
     else:
         x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
+
     x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
     x_sample = x_sample.astype(np.uint8)
     return Image.fromarray(x_sample)
 
 
-def sample_to_image(samples, index=0, approximation=False):
+def sample_to_image(samples, index=0, approximation=None):
     return single_sample_to_image(samples[index], approximation)
 
 
-def samples_to_image_grid(samples, approximation=False):
+def samples_to_image_grid(samples, approximation=None):
     return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
 
 
@@ -136,7 +139,7 @@ def store_latent(decoded):
 
     if opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
         if not shared.parallel_processing_allowed:
-            shared.state.current_image = sample_to_image(decoded, approximation=opts.show_progress_approximate)
+            shared.state.current_image = sample_to_image(decoded)
 
 
 class InterruptedException(BaseException):
diff --git a/modules/sd_vae_approx.py b/modules/sd_vae_approx.py
new file mode 100644
index 00000000..0a58542d
--- /dev/null
+++ b/modules/sd_vae_approx.py
@@ -0,0 +1,58 @@
+import os
+
+import torch
+from torch import nn
+from modules import devices, paths
+
+sd_vae_approx_model = None
+
+
+class VAEApprox(nn.Module):
+    def __init__(self):
+        super(VAEApprox, self).__init__()
+        self.conv1 = nn.Conv2d(4, 8, (7, 7))
+        self.conv2 = nn.Conv2d(8, 16, (5, 5))
+        self.conv3 = nn.Conv2d(16, 32, (3, 3))
+        self.conv4 = nn.Conv2d(32, 64, (3, 3))
+        self.conv5 = nn.Conv2d(64, 32, (3, 3))
+        self.conv6 = nn.Conv2d(32, 16, (3, 3))
+        self.conv7 = nn.Conv2d(16, 8, (3, 3))
+        self.conv8 = nn.Conv2d(8, 3, (3, 3))
+
+    def forward(self, x):
+        extra = 11
+        x = nn.functional.interpolate(x, (x.shape[2] * 2, x.shape[3] * 2))
+        x = nn.functional.pad(x, (extra, extra, extra, extra))
+
+        for layer in [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5, self.conv6, self.conv7, self.conv8, ]:
+            x = layer(x)
+            x = nn.functional.leaky_relu(x, 0.1)
+
+        return x
+
+
+def model():
+    global sd_vae_approx_model
+
+    if sd_vae_approx_model is None:
+        sd_vae_approx_model = VAEApprox()
+        sd_vae_approx_model.load_state_dict(torch.load(os.path.join(paths.models_path, "VAE-approx", "model.pt")))
+        sd_vae_approx_model.eval()
+        sd_vae_approx_model.to(devices.device, devices.dtype)
+
+    return sd_vae_approx_model
+
+
+def cheap_approximation(sample):
+    # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
+
+    coefs = torch.tensor([
+        [0.298, 0.207, 0.208],
+        [0.187, 0.286, 0.173],
+        [-0.158, 0.189, 0.264],
+        [-0.184, -0.271, -0.473],
+    ]).to(sample.device)
+
+    x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs)
+
+    return x_sample
diff --git a/modules/shared.py b/modules/shared.py
index eb3e5aec..3cc3c724 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -212,9 +212,9 @@ class State:
 
         import modules.sd_samplers
         if opts.show_progress_grid:
-            self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent, approximation=opts.show_progress_approximate)
+            self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent)
         else:
-            self.current_image = modules.sd_samplers.sample_to_image(self.current_latent, approximation=opts.show_progress_approximate)
+            self.current_image = modules.sd_samplers.sample_to_image(self.current_latent)
 
         self.current_image_sampling_step = self.sampling_step
 
@@ -392,7 +392,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
-    "show_progress_approximate": OptionInfo(False, "Calculate small previews using fast linear approximation instead of VAE"),
+    "show_progress_type": OptionInfo("Full", "Image creation progress mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
     "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
-- 
cgit v1.2.1


From c5bdba2089dc7060be2631bcbc83313b6358cbf2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 24 Dec 2022 22:41:35 +0300
Subject: change wording a bit

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index 3cc3c724..d4ddeea0 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -392,7 +392,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
-    "show_progress_type": OptionInfo("Full", "Image creation progress mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
+    "show_progress_type": OptionInfo("Full", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}),
     "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
-- 
cgit v1.2.1


From 5f1dfbbc959855fd90ba80c0c76301d2063772fa Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 24 Dec 2022 18:02:22 -0500
Subject: implement train api

---
 modules/api/api.py                    | 94 ++++++++++++++++++++++++++++++++++-
 modules/api/models.py                 |  9 ++++
 modules/hypernetworks/hypernetwork.py | 26 ++++++++++
 modules/hypernetworks/ui.py           | 31 ++----------
 4 files changed, 132 insertions(+), 28 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index b43dd16b..1ceba75d 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -10,13 +10,17 @@ from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers, deepbooru
+from modules import sd_samplers, deepbooru, sd_hijack
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.extras import run_extras, run_pnginfo
+from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
+from modules.textual_inversion.preprocess import preprocess
+from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
 from PIL import PngImagePlugin,Image
 from modules.sd_models import checkpoints_list
 from modules.realesrgan_model import get_realesrgan_models
+from modules import devices
 from typing import List
 
 def upscaler_to_index(name: str):
@@ -97,6 +101,11 @@ class Api:
         self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
         self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
         self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
+        self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse)
+        self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse)
+        self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=PreprocessResponse)
+        self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse)
+        self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse)
 
     def add_api_route(self, path: str, endpoint, **kwargs):
         if shared.cmd_opts.api_auth:
@@ -326,6 +335,89 @@ class Api:
     def refresh_checkpoints(self):
         shared.refresh_checkpoints()
 
+    def create_embedding(self, args: dict):
+        try:
+            shared.state.begin()
+            filename = create_embedding(**args) # create empty embedding
+            sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
+            shared.state.end()
+            return CreateResponse(info = "create embedding filename: {filename}".format(filename = filename))
+        except AssertionError as e:
+            shared.state.end()
+            return TrainResponse(info = "create embedding error: {error}".format(error = e))
+
+    def create_hypernetwork(self, args: dict):
+        try:
+            shared.state.begin()
+            filename = create_hypernetwork(**args) # create empty embedding
+            shared.state.end()
+            return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename))
+        except AssertionError as e:
+            shared.state.end()
+            return TrainResponse(info = "create hypernetwork error: {error}".format(error = e))
+
+    def preprocess(self, args: dict):
+        try:
+            shared.state.begin()
+            preprocess(**args) # quick operation unless blip/booru interrogation is enabled
+            shared.state.end()
+            return PreprocessResponse(info = 'preprocess complete')
+        except KeyError as e:
+            shared.state.end()
+            return PreprocessResponse(info = "preprocess error: invalid token: {error}".format(error = e))
+        except AssertionError as e:
+            shared.state.end()
+            return PreprocessResponse(info = "preprocess error: {error}".format(error = e))
+        except FileNotFoundError as e:
+            shared.state.end()
+            return PreprocessResponse(info = 'preprocess error: {error}'.format(error = e))
+
+    def train_embedding(self, args: dict):
+        try:
+            shared.state.begin()
+            apply_optimizations = shared.opts.training_xattention_optimizations
+            error = None
+            filename = ''
+            if not apply_optimizations:
+                sd_hijack.undo_optimizations()
+            try:
+                embedding, filename = train_embedding(**args) # can take a long time to complete
+            except Exception as e:
+                error = e
+            finally:
+                if not apply_optimizations:
+                    sd_hijack.apply_optimizations()
+                shared.state.end()
+            return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
+        except AssertionError as msg:
+            shared.state.end()
+            return TrainResponse(info = "train embedding error: {msg}".format(msg = msg))
+
+    def train_hypernetwork(self, args: dict):
+        try:
+            shared.state.begin()
+            initial_hypernetwork = shared.loaded_hypernetwork
+            apply_optimizations = shared.opts.training_xattention_optimizations
+            error = None
+            filename = ''
+            if not apply_optimizations:
+                sd_hijack.undo_optimizations()
+            try:
+                hypernetwork, filename = train_hypernetwork(*args)
+            except Exception as e:
+                error = e
+            finally:
+                shared.loaded_hypernetwork = initial_hypernetwork
+                shared.sd_model.cond_stage_model.to(devices.device)
+                shared.sd_model.first_stage_model.to(devices.device)
+                if not apply_optimizations:
+                    sd_hijack.apply_optimizations()
+                shared.state.end()
+            return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
+        except AssertionError as msg:
+            shared.state.end()
+            return TrainResponse(info = "train embedding error: {error}".format(error = error))
+
     def launch(self, server_name, port):
         self.app.include_router(self.router)
         uvicorn.run(self.app, host=server_name, port=port)
diff --git a/modules/api/models.py b/modules/api/models.py
index a22bc6b3..c446ce7a 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -175,6 +175,15 @@ class InterrogateRequest(BaseModel):
 class InterrogateResponse(BaseModel):
     caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
 
+class TrainResponse(BaseModel):
+    info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.")
+
+class CreateResponse(BaseModel):
+    info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
+
+class PreprocessResponse(BaseModel):
+    info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
+
 fields = {}
 for key, metadata in opts.data_labels.items():
     value = opts.data.get(key)
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index c406ffb3..3182ff03 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -378,6 +378,32 @@ def report_statistics(loss_info:dict):
             print(e)
 
 
+def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
+    # Remove illegal characters from name.
+    name = "".join( x for x in name if (x.isalnum() or x in "._- "))
+
+    fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
+    if not overwrite_old:
+        assert not os.path.exists(fn), f"file {fn} already exists"
+
+    if type(layer_structure) == str:
+        layer_structure = [float(x.strip()) for x in layer_structure.split(",")]
+
+    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
+        name=name,
+        enable_sizes=[int(x) for x in enable_sizes],
+        layer_structure=layer_structure,
+        activation_func=activation_func,
+        weight_init=weight_init,
+        add_layer_norm=add_layer_norm,
+        use_dropout=use_dropout,
+    )
+    hypernet.save(fn)
+
+    shared.reload_hypernetworks()
+
+    return fn
+
 
 def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index c2d4b51c..e7f9e593 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -3,39 +3,16 @@ import os
 import re
 
 import gradio as gr
-import modules.textual_inversion.preprocess
-import modules.textual_inversion.textual_inversion
+import modules.hypernetworks.hypernetwork
 from modules import devices, sd_hijack, shared
-from modules.hypernetworks import hypernetwork
 
 not_available = ["hardswish", "multiheadattention"]
-keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
+keys = list(x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
 
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
-    # Remove illegal characters from name.
-    name = "".join( x for x in name if (x.isalnum() or x in "._- "))
+    filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout)
 
-    fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
-    if not overwrite_old:
-        assert not os.path.exists(fn), f"file {fn} already exists"
-
-    if type(layer_structure) == str:
-        layer_structure = [float(x.strip()) for x in layer_structure.split(",")]
-
-    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
-        name=name,
-        enable_sizes=[int(x) for x in enable_sizes],
-        layer_structure=layer_structure,
-        activation_func=activation_func,
-        weight_init=weight_init,
-        add_layer_norm=add_layer_norm,
-        use_dropout=use_dropout,
-    )
-    hypernet.save(fn)
-
-    shared.reload_hypernetworks()
-
-    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
+    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {filename}", ""
 
 
 def train_hypernetwork(*args):
-- 
cgit v1.2.1


From f60c24f8121186f8d85f1096a96ddf685f625d04 Mon Sep 17 00:00:00 2001
From: eaglgenes101 <eaglgenes101@gmail.com>
Date: Sat, 24 Dec 2022 22:16:01 -0500
Subject: Add CSS classes for the settings panels

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 9dec61d5..65af8966 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -657,7 +657,7 @@ def create_ui():
                 setup_progressbar(progressbar, txt2img_preview, 'txt2img')
 
         with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
+            with gr.Column(variant='panel', elem_id="txt2img_settings"):
                 steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
                 sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
 
@@ -812,7 +812,7 @@ def create_ui():
                 setup_progressbar(progressbar, img2img_preview, 'img2img')
 
         with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
+            with gr.Column(variant='panel', elem_id="img2img_settings"):
 
                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
                     with gr.TabItem('img2img', id='img2img'):
-- 
cgit v1.2.1


From 61a273236ffd1366456cac7040e30972ca65dc2c Mon Sep 17 00:00:00 2001
From: Allen Benz <allenbenz@users.noreply.github.com>
Date: Sat, 24 Dec 2022 20:23:12 -0800
Subject: Fix clip interrogate from the webui

A recent change made the image RGBA, which makes the clip interrogator unhappy.
deepbooru and calling the interrogator from the api already do the conversion so this is the only place that needed it.
---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 9dec61d5..7bf5abd9 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -270,7 +270,7 @@ def apply_styles(prompt, prompt_neg, style1_name, style2_name):
 
 
 def interrogate(image):
-    prompt = shared.interrogator.interrogate(image)
+    prompt = shared.interrogator.interrogate(image.convert("RGB"))
 
     return gr_show(True) if prompt is None else prompt
 
-- 
cgit v1.2.1


From 8eef9d8e782aa0655241e43f67059aa7bef3bdca Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 25 Dec 2022 09:03:56 +0300
Subject: a way to add an exception to unpickler without explicitly calling
 load_with_extra

---
 modules/safe.py | 39 ++++++++++++++++++++++++++++++++++++++-
 1 file changed, 38 insertions(+), 1 deletion(-)

diff --git a/modules/safe.py b/modules/safe.py
index 479c8b86..ec23a53c 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -103,7 +103,7 @@ def check_pt(filename, extra_handler):
 
 
 def load(filename, *args, **kwargs):
-    return load_with_extra(filename, *args, **kwargs)
+    return load_with_extra(filename, extra_handler=global_extra_handler, *args, **kwargs)
 
 
 def load_with_extra(filename, extra_handler=None, *args, **kwargs):
@@ -151,5 +151,42 @@ def load_with_extra(filename, extra_handler=None, *args, **kwargs):
     return unsafe_torch_load(filename, *args, **kwargs)
 
 
+class Extra:
+    """
+    A class for temporarily setting the global handler for when you can't explicitly call load_with_extra
+    (because it's not your code making the torch.load call). The intended use is like this:
+
+```
+import torch
+from modules import safe
+
+def handler(module, name):
+    if module == 'torch' and name in ['float64', 'float16']:
+        return getattr(torch, name)
+
+    return None
+
+with safe.Extra(handler):
+    x = torch.load('model.pt')
+```
+    """
+
+    def __init__(self, handler):
+        self.handler = handler
+
+    def __enter__(self):
+        global global_extra_handler
+
+        assert global_extra_handler is None, 'already inside an Extra() block'
+        global_extra_handler = self.handler
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        global global_extra_handler
+
+        global_extra_handler = None
+
+
 unsafe_torch_load = torch.load
 torch.load = load
+global_extra_handler = None
+
-- 
cgit v1.2.1


From c6f347b81f584b6c0d44af7a209983284dbb52d2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 25 Dec 2022 09:47:24 +0300
Subject: fix ruined SD upscale

---
 scripts/sd_upscale.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
index 28bd96b3..e8c80a6c 100644
--- a/scripts/sd_upscale.py
+++ b/scripts/sd_upscale.py
@@ -35,8 +35,9 @@ class Script(scripts.Script):
         seed = p.seed
 
         init_img = p.init_images[0]
+        init_img = images.flatten(init_img, opts.img2img_background_color)
 
-        if (upscaler.name != "None"):
+        if upscaler.name != "None":
             img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)
         else:
             img = init_img
-- 
cgit v1.2.1


From 5be9387b230794a8c771120577cb213490c940c0 Mon Sep 17 00:00:00 2001
From: Philpax <me@philpax.me>
Date: Sun, 25 Dec 2022 21:45:44 +1100
Subject: fix(api): only begin/end state in lock

---
 modules/api/api.py | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 1ceba75d..59b81c93 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -130,14 +130,12 @@ class Api:
         if populate.sampler_name:
             populate.sampler_index = None  # prevent a warning later on
         p = StableDiffusionProcessingTxt2Img(**vars(populate))
-        # Override object param
-
-        shared.state.begin()
 
         with self.queue_lock:
+            shared.state.begin()
             processed = process_images(p)
+            shared.state.end()
 
-        shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images))
 
@@ -169,12 +167,10 @@ class Api:
 
         p.init_images = [decode_base64_to_image(x) for x in init_images]
 
-        shared.state.begin()
-
         with self.queue_lock:
+            shared.state.begin()
             processed = process_images(p)
-
-        shared.state.end()
+            shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images))
 
-- 
cgit v1.2.1


From 893933e05ad267778111b4fad6d1ecb80937afdf Mon Sep 17 00:00:00 2001
From: hitomi <huahuohe@foxmail.com>
Date: Sun, 25 Dec 2022 20:49:25 +0800
Subject: Add memory cache for VAE weights

---
 modules/sd_vae.py | 31 +++++++++++++++++++++++++------
 modules/shared.py |  1 +
 2 files changed, 26 insertions(+), 6 deletions(-)

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 3856418e..ac71d62d 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -1,5 +1,6 @@
 import torch
 import os
+import collections
 from collections import namedtuple
 from modules import shared, devices, script_callbacks
 from modules.paths import models_path
@@ -30,6 +31,7 @@ base_vae = None
 loaded_vae_file = None
 checkpoint_info = None
 
+checkpoints_loaded = collections.OrderedDict()
 
 def get_base_vae(model):
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
@@ -149,13 +151,30 @@ def load_vae(model, vae_file=None):
     global first_load, vae_dict, vae_list, loaded_vae_file
     # save_settings = False
 
+    cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
+
     if vae_file:
-        assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
-        print(f"Loading VAE weights from: {vae_file}")
-        store_base_vae(model)
-        vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
-        vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-        _load_vae_dict(model, vae_dict_1)
+        if cache_enabled and vae_file in checkpoints_loaded:
+            # use vae checkpoint cache
+            print(f"Loading VAE weights [{get_filename(vae_file)}] from cache")
+            store_base_vae(model)
+            _load_vae_dict(model, checkpoints_loaded[vae_file])
+        else:
+            assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
+            print(f"Loading VAE weights from: {vae_file}")
+            store_base_vae(model)
+            vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
+            vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
+            _load_vae_dict(model, vae_dict_1)
+
+            if cache_enabled:
+                # cache newly loaded vae
+                checkpoints_loaded[vae_file] = vae_dict_1.copy()
+
+        # clean up cache if limit is reached
+        if cache_enabled:
+            while len(checkpoints_loaded) > shared.opts.sd_vae_checkpoint_cache + 1: # we need to count the current model
+                checkpoints_loaded.popitem(last=False)  # LRU
 
         # If vae used is not in dict, update it
         # It will be removed on refresh though
diff --git a/modules/shared.py b/modules/shared.py
index d4ddeea0..671d30e1 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -356,6 +356,7 @@ options_templates.update(options_section(('training', "Training"), {
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+    "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
     "sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
-- 
cgit v1.2.1


From 4af3ca5393151d61363c30eef4965e694eeac15e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 26 Dec 2022 10:11:28 +0300
Subject: make it so that blank ENSD does not break image generation

---
 modules/processing.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index 4a406084..0a9a8f95 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -338,13 +338,14 @@ def slerp(val, low, high):
 
 
 def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
+    eta_noise_seed_delta = opts.eta_noise_seed_delta or 0
     xs = []
 
     # if we have multiple seeds, this means we are working with batch size>1; this then
     # enables the generation of additional tensors with noise that the sampler will use during its processing.
     # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
     # produce the same images as with two batches [100], [101].
-    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
+    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or eta_noise_seed_delta > 0):
         sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
     else:
         sampler_noises = None
@@ -384,8 +385,8 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
         if sampler_noises is not None:
             cnt = p.sampler.number_of_needed_noises(p)
 
-            if opts.eta_noise_seed_delta > 0:
-                torch.manual_seed(seed + opts.eta_noise_seed_delta)
+            if eta_noise_seed_delta > 0:
+                torch.manual_seed(seed + eta_noise_seed_delta)
 
             for j in range(cnt):
                 sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
-- 
cgit v1.2.1


From ae955b0146a52ea2474c79655ede0d361829ef63 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 26 Dec 2022 09:53:26 -0500
Subject: fix rgba to rgb when using jpeg output

---
 modules/images.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/images.py b/modules/images.py
index 31d4528d..962a955d 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -525,6 +525,9 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
 
         elif extension.lower() in (".jpg", ".jpeg", ".webp"):
+            if image_to_save.mode == 'RGBA':
+                image_to_save = image_to_save.convert("RGB")
+
             image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
 
             if opts.enable_pnginfo and info is not None:
-- 
cgit v1.2.1


From 5ba04f9ec050a66e918571f07e8863f157f05b44 Mon Sep 17 00:00:00 2001
From: Nicolas Patry <patry.nicolas@protonmail.com>
Date: Wed, 21 Dec 2022 13:45:58 +0100
Subject: Attempting to solve slow loads for `safetensors`.

Fixes #5893
---
 modules/sd_models.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ecdd91c5..cd938656 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -168,7 +168,10 @@ def get_state_dict_from_checkpoint(pl_sd):
 def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
     _, extension = os.path.splitext(checkpoint_file)
     if extension.lower() == ".safetensors":
-        pl_sd = safetensors.torch.load_file(checkpoint_file, device=map_location or shared.weight_load_location)
+        device = map_location or shared.weight_load_location
+        if device is None:
+            device = "cuda:0" if torch.cuda.is_available() else "cpu"
+        pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
     else:
         pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
 
-- 
cgit v1.2.1


From 5a523d13050a5ede43c473767f29dfe2e391136a Mon Sep 17 00:00:00 2001
From: Nicolas Patry <patry.nicolas@protonmail.com>
Date: Tue, 27 Dec 2022 11:27:40 +0100
Subject: Version 0.2.7 Fixes Windows SAFETENSORS_FAST_GPU path.

---
 requirements_versions.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements_versions.txt b/requirements_versions.txt
index c126c8c4..52e98818 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -26,5 +26,5 @@ lark==1.1.2
 inflection==0.5.1
 GitPython==3.1.27
 torchsde==0.2.5
-safetensors==0.2.5
+safetensors==0.2.7
 httpcore<=0.15
-- 
cgit v1.2.1


From 5958bbd244703f7c248a91e86dea5d52acc85505 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 30 Dec 2022 19:36:36 -0500
Subject: add additional memory states

---
 modules/memmon.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/modules/memmon.py b/modules/memmon.py
index 9fb9b687..a7060f58 100644
--- a/modules/memmon.py
+++ b/modules/memmon.py
@@ -71,10 +71,13 @@ class MemUsageMonitor(threading.Thread):
     def read(self):
         if not self.disabled:
             free, total = torch.cuda.mem_get_info()
+            self.data["free"] = free
             self.data["total"] = total
 
             torch_stats = torch.cuda.memory_stats(self.device)
+            self.data["active"] = torch_stats["active.all.current"]
             self.data["active_peak"] = torch_stats["active_bytes.all.peak"]
+            self.data["reserved"] = torch_stats["reserved_bytes.all.current"]
             self.data["reserved_peak"] = torch_stats["reserved_bytes.all.peak"]
             self.data["system_peak"] = total - self.data["min_free"]
 
-- 
cgit v1.2.1


From d3aa2a48e1e896b6ffafda5367200a4bbd46b0d7 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 30 Dec 2022 19:38:53 -0500
Subject: remove unnecessary console message

---
 modules/sd_hijack_inpainting.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index bb5499b3..06b75772 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -178,7 +178,7 @@ def sample_plms(self,
     # sampling
     C, H, W = shape
     size = (batch_size, C, H, W)
-    print(f'Data shape for PLMS sampling is {size}')
+    # print(f'Data shape for PLMS sampling is {size}') # remove unnecessary message
 
     samples, intermediates = self.plms_sampling(conditioning, size,
                                                 callback=callback,
-- 
cgit v1.2.1


From 463048344fc036b262aa132584b65ee6e9fec6cf Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Fri, 30 Dec 2022 19:41:47 -0500
Subject: fix shared state dictionary

---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index d4ddeea0..9a13fb60 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -168,7 +168,7 @@ class State:
     def dict(self):
         obj = {
             "skipped": self.skipped,
-            "interrupted": self.skipped,
+            "interrupted": self.interrupted,
             "job": self.job,
             "job_count": self.job_count,
             "job_no": self.job_no,
-- 
cgit v1.2.1


From fef98723b2b1c7a9893ead41bbefcb36192babd6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 31 Dec 2022 12:44:26 +0300
Subject: set sd_model for API later, inside the lock, to prevent multiple
 requests with different models ending up with incorrect results #5877 #6012

---
 modules/api/api.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 59b81c93..11daff0d 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -121,7 +121,6 @@ class Api:
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         populate = txt2imgreq.copy(update={ # Override __init__ params
-            "sd_model": shared.sd_model,
             "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True
@@ -129,9 +128,10 @@ class Api:
         )
         if populate.sampler_name:
             populate.sampler_index = None  # prevent a warning later on
-        p = StableDiffusionProcessingTxt2Img(**vars(populate))
 
         with self.queue_lock:
+            p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **vars(populate))
+
             shared.state.begin()
             processed = process_images(p)
             shared.state.end()
@@ -151,7 +151,6 @@ class Api:
             mask = decode_base64_to_image(mask)
 
         populate = img2imgreq.copy(update={ # Override __init__ params
-            "sd_model": shared.sd_model,
             "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True,
@@ -163,11 +162,11 @@ class Api:
 
         args = vars(populate)
         args.pop('include_init_images', None)  # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
-        p = StableDiffusionProcessingImg2Img(**args)
-
-        p.init_images = [decode_base64_to_image(x) for x in init_images]
 
         with self.queue_lock:
+            p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)
+            p.init_images = [decode_base64_to_image(x) for x in init_images]
+
             shared.state.begin()
             processed = process_images(p)
             shared.state.end()
-- 
cgit v1.2.1


From 65be1df7bb55b21a3d76630a397c820218cbd12a Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 31 Dec 2022 07:46:04 -0500
Subject: initialize result so not to cause exception on empty results

---
 modules/interrogate.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 46935210..6f761c5a 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -135,7 +135,7 @@ class InterrogateModels:
         return caption[0]
 
     def interrogate(self, pil_image):
-        res = None
+        res = ""
 
         try:
 
-- 
cgit v1.2.1


From f34c7341720fb2059992926c9f9ae6ff25f7385b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 31 Dec 2022 18:06:35 +0300
Subject: alt-diffusion integration

---
 configs/alt-diffusion-inference.yaml   | 72 ++++++++++++++++++++++++++++++++++
 configs/altdiffusion/ad-inference.yaml | 72 ----------------------------------
 configs/v1-inference.yaml              | 70 +++++++++++++++++++++++++++++++++
 modules/sd_hijack.py                   | 18 +++++----
 modules/sd_hijack_clip.py              | 14 +++----
 modules/sd_hijack_xlmr.py              | 34 ++++++++++++++++
 modules/shared.py                      | 10 +----
 v1-inference.yaml                      | 70 ---------------------------------
 8 files changed, 192 insertions(+), 168 deletions(-)
 create mode 100644 configs/alt-diffusion-inference.yaml
 delete mode 100644 configs/altdiffusion/ad-inference.yaml
 create mode 100644 configs/v1-inference.yaml
 create mode 100644 modules/sd_hijack_xlmr.py
 delete mode 100644 v1-inference.yaml

diff --git a/configs/alt-diffusion-inference.yaml b/configs/alt-diffusion-inference.yaml
new file mode 100644
index 00000000..cfbee72d
--- /dev/null
+++ b/configs/alt-diffusion-inference.yaml
@@ -0,0 +1,72 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: modules.xlmr.BertSeriesModelWithTransformation
+      params:
+        name: "XLMR-Large"
\ No newline at end of file
diff --git a/configs/altdiffusion/ad-inference.yaml b/configs/altdiffusion/ad-inference.yaml
deleted file mode 100644
index cfbee72d..00000000
--- a/configs/altdiffusion/ad-inference.yaml
+++ /dev/null
@@ -1,72 +0,0 @@
-model:
-  base_learning_rate: 1.0e-04
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false   # Note: different from the one we trained before
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [ 10000 ]
-        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
-        f_start: [ 1.e-6 ]
-        f_max: [ 1. ]
-        f_min: [ 1. ]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_heads: 8
-        use_spatial_transformer: True
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: True
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: modules.xlmr.BertSeriesModelWithTransformation
-      params:
-        name: "XLMR-Large"
\ No newline at end of file
diff --git a/configs/v1-inference.yaml b/configs/v1-inference.yaml
new file mode 100644
index 00000000..d4effe56
--- /dev/null
+++ b/configs/v1-inference.yaml
@@ -0,0 +1,70 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index bce23b03..edcbaf52 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -5,7 +5,7 @@ import modules.textual_inversion.textual_inversion
 from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
 
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
@@ -68,6 +68,7 @@ def fix_checkpoint():
     ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = sd_hijack_checkpoint.ResBlock_forward
     ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = sd_hijack_checkpoint.AttentionBlock_forward
 
+
 class StableDiffusionModelHijack:
     fixes = None
     comments = []
@@ -79,21 +80,22 @@ class StableDiffusionModelHijack:
 
     def hijack(self, m):
 
-        if shared.text_model_name == "XLMR-Large":
+        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
             model_embeddings = m.cond_stage_model.roberta.embeddings
             model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
-            m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
-            
+            m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
+
         elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
             model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
             model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
             m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
-            apply_optimizations()
+
         elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder:
             m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
             m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
-            apply_optimizations()
-        
+
+        apply_optimizations()
+
         self.clip = m.cond_stage_model
         
         fix_checkpoint()
@@ -109,7 +111,7 @@ class StableDiffusionModelHijack:
 
     def undo_hijack(self, m):
 
-        if shared.text_model_name == "XLMR-Large":
+        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
             m.cond_stage_model = m.cond_stage_model.wrapped 
 
         elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index 9ea6e1ce..6ec50cca 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -4,7 +4,6 @@ import torch
 
 from modules import prompt_parser, devices
 from modules.shared import opts
-import modules.shared as shared
 
 def get_target_prompt_token_count(token_count):
     return math.ceil(max(token_count, 1) / 75) * 75
@@ -177,9 +176,6 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
 
     def forward(self, text):
-        if shared.text_model_name == "XLMR-Large":
-            return self.wrapped.encode(text)
-
         use_old = opts.use_old_emphasis_implementation
         if use_old:
             batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
@@ -257,13 +253,13 @@ class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
     def __init__(self, wrapped, hijack):
         super().__init__(wrapped, hijack)
         self.tokenizer = wrapped.tokenizer
-        if shared.text_model_name == "XLMR-Large":
-            self.comma_token = None 
-        else :
-            self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
+
+        vocab = self.tokenizer.get_vocab()
+
+        self.comma_token = vocab.get(',</w>', None)
 
         self.token_mults = {}
-        tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
+        tokens_with_parens = [(k, v) for k, v in vocab.items() if '(' in k or ')' in k or '[' in k or ']' in k]
         for text, ident in tokens_with_parens:
             mult = 1.0
             for c in text:
diff --git a/modules/sd_hijack_xlmr.py b/modules/sd_hijack_xlmr.py
new file mode 100644
index 00000000..4ac51c38
--- /dev/null
+++ b/modules/sd_hijack_xlmr.py
@@ -0,0 +1,34 @@
+import open_clip.tokenizer
+import torch
+
+from modules import sd_hijack_clip, devices
+from modules.shared import opts
+
+
+class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):
+    def __init__(self, wrapped, hijack):
+        super().__init__(wrapped, hijack)
+
+        self.id_start = wrapped.config.bos_token_id
+        self.id_end = wrapped.config.eos_token_id
+        self.id_pad = wrapped.config.pad_token_id
+
+        self.comma_token = self.tokenizer.get_vocab().get(',', None)  # alt diffusion doesn't have </w> bits for comma
+
+    def encode_with_transformers(self, tokens):
+        # there's no CLIP Skip here because all hidden layers have size of 1024 and the last one uses a
+        # trained layer to transform those 1024 into 768 for unet; so you can't choose which transformer
+        # layer to work with - you have to use the last
+
+        attention_mask = (tokens != self.id_pad).to(device=tokens.device, dtype=torch.int64)
+        features = self.wrapped(input_ids=tokens, attention_mask=attention_mask)
+        z = features['projection_state']
+
+        return z
+
+    def encode_embedding_init_text(self, init_text, nvpt):
+        embedding_layer = self.wrapped.roberta.embeddings
+        ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
+        embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
+
+        return embedded
diff --git a/modules/shared.py b/modules/shared.py
index 2b31e717..715b9169 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -23,7 +23,7 @@ demo = None
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
 parser = argparse.ArgumentParser()
-parser.add_argument("--config", type=str, default=os.path.join(script_path, "v1-inference.yaml"), help="path to config which constructs model",)
+parser.add_argument("--config", type=str, default=os.path.join(script_path, "configs/v1-inference.yaml"), help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
 parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
@@ -108,14 +108,6 @@ restricted_opts = {
     "outdir_txt2img_grids",
     "outdir_save",
 }
-from omegaconf import OmegaConf
-config = OmegaConf.load(f"{cmd_opts.config}")
-# XLMR-Large
-try:
-    text_model_name = config.model.params.cond_stage_config.params.name
-
-except :
-    text_model_name = "stable_diffusion"
 
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
diff --git a/v1-inference.yaml b/v1-inference.yaml
deleted file mode 100644
index d4effe56..00000000
--- a/v1-inference.yaml
+++ /dev/null
@@ -1,70 +0,0 @@
-model:
-  base_learning_rate: 1.0e-04
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false   # Note: different from the one we trained before
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [ 10000 ]
-        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
-        f_start: [ 1.e-6 ]
-        f_max: [ 1. ]
-        f_min: [ 1. ]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_heads: 8
-        use_spatial_transformer: True
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: True
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
-- 
cgit v1.2.1


From f55ac33d446185680604e872ceda2ae858821d5c Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Sat, 31 Dec 2022 11:27:02 -0500
Subject: validate textual inversion embeddings

---
 modules/sd_models.py                           |  3 ++
 modules/textual_inversion/textual_inversion.py | 43 +++++++++++++++++++++++---
 modules/ui.py                                  |  2 --
 3 files changed, 41 insertions(+), 7 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ecdd91c5..ebd4dff7 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -325,6 +325,9 @@ def load_model(checkpoint_info=None):
     script_callbacks.model_loaded_callback(sd_model)
 
     print("Model loaded.")
+
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload = True) # Reload embeddings after model load as they may or may not fit the model
+
     return sd_model
 
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index f6112578..103ace60 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -23,6 +23,8 @@ class Embedding:
         self.vec = vec
         self.name = name
         self.step = step
+        self.shape = None
+        self.vectors = 0
         self.cached_checksum = None
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
@@ -57,8 +59,10 @@ class EmbeddingDatabase:
     def __init__(self, embeddings_dir):
         self.ids_lookup = {}
         self.word_embeddings = {}
+        self.skipped_embeddings = []
         self.dir_mtime = None
         self.embeddings_dir = embeddings_dir
+        self.expected_shape = -1
 
     def register_embedding(self, embedding, model):
 
@@ -75,14 +79,35 @@ class EmbeddingDatabase:
 
         return embedding
 
-    def load_textual_inversion_embeddings(self):
+    def get_expected_shape(self):
+        expected_shape = -1 # initialize with unknown
+        idx = torch.tensor(0).to(shared.device)
+        if expected_shape == -1:
+            try: # matches sd15 signature
+                first_embedding = shared.sd_model.cond_stage_model.wrapped.transformer.text_model.embeddings.token_embedding.wrapped(idx)
+                expected_shape = first_embedding.shape[0]
+            except:
+                pass
+        if expected_shape == -1:
+            try: # matches sd20 signature
+                first_embedding = shared.sd_model.cond_stage_model.wrapped.model.token_embedding.wrapped(idx)
+                expected_shape = first_embedding.shape[0]
+            except:
+                pass
+        if expected_shape == -1:
+            print('Could not determine expected embeddings shape from model')
+        return expected_shape
+
+    def load_textual_inversion_embeddings(self, force_reload = False):
         mt = os.path.getmtime(self.embeddings_dir)
-        if self.dir_mtime is not None and mt <= self.dir_mtime:
+        if not force_reload and self.dir_mtime is not None and mt <= self.dir_mtime:
             return
 
         self.dir_mtime = mt
         self.ids_lookup.clear()
         self.word_embeddings.clear()
+        self.skipped_embeddings = []
+        self.expected_shape = self.get_expected_shape()
 
         def process_file(path, filename):
             name = os.path.splitext(filename)[0]
@@ -122,7 +147,14 @@ class EmbeddingDatabase:
             embedding.step = data.get('step', None)
             embedding.sd_checkpoint = data.get('sd_checkpoint', None)
             embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
-            self.register_embedding(embedding, shared.sd_model)
+            embedding.vectors = vec.shape[0]
+            embedding.shape = vec.shape[-1]
+
+            if (self.expected_shape == -1) or (self.expected_shape == embedding.shape):
+                self.register_embedding(embedding, shared.sd_model)
+            else:
+                self.skipped_embeddings.append(name)
+                # print('Skipping embedding {name}: shape was {shape} expected {expected}'.format(name = name, shape = embedding.shape, expected = self.expected_shape))
 
         for fn in os.listdir(self.embeddings_dir):
             try:
@@ -137,8 +169,9 @@ class EmbeddingDatabase:
                 print(traceback.format_exc(), file=sys.stderr)
                 continue
 
-        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
-        print("Embeddings:", ', '.join(self.word_embeddings.keys()))
+        print("Textual inversion embeddings {num} loaded: {val}".format(num = len(self.word_embeddings), val = ', '.join(self.word_embeddings.keys())))
+        if (len(self.skipped_embeddings) > 0):
+            print("Textual inversion embeddings {num} skipped: {val}".format(num = len(self.skipped_embeddings), val = ', '.join(self.skipped_embeddings)))
 
     def find_embedding_at_position(self, tokens, offset):
         token = tokens[offset]
diff --git a/modules/ui.py b/modules/ui.py
index 57ee0465..397dd804 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1157,8 +1157,6 @@ def create_ui():
             with gr.Column(variant='panel'):
                 submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
 
-    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
-
     with gr.Blocks(analytics_enabled=False) as train_interface:
         with gr.Row().style(equal_height=False):
             gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
-- 
cgit v1.2.1


From bdbe09827b39be63c9c0b3636132ca58da38ebf6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 31 Dec 2022 22:49:09 +0300
Subject: changed embedding accepted shape detection to use existing code and
 support the new alt-diffusion model, and reformatted messages a bit #6149

---
 modules/textual_inversion/textual_inversion.py | 30 ++++++--------------------
 1 file changed, 6 insertions(+), 24 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 103ace60..66f40367 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -80,23 +80,8 @@ class EmbeddingDatabase:
         return embedding
 
     def get_expected_shape(self):
-        expected_shape = -1 # initialize with unknown
-        idx = torch.tensor(0).to(shared.device)
-        if expected_shape == -1:
-            try: # matches sd15 signature
-                first_embedding = shared.sd_model.cond_stage_model.wrapped.transformer.text_model.embeddings.token_embedding.wrapped(idx)
-                expected_shape = first_embedding.shape[0]
-            except:
-                pass
-        if expected_shape == -1:
-            try: # matches sd20 signature
-                first_embedding = shared.sd_model.cond_stage_model.wrapped.model.token_embedding.wrapped(idx)
-                expected_shape = first_embedding.shape[0]
-            except:
-                pass
-        if expected_shape == -1:
-            print('Could not determine expected embeddings shape from model')
-        return expected_shape
+        vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
+        return vec.shape[1]
 
     def load_textual_inversion_embeddings(self, force_reload = False):
         mt = os.path.getmtime(self.embeddings_dir)
@@ -112,8 +97,6 @@ class EmbeddingDatabase:
         def process_file(path, filename):
             name = os.path.splitext(filename)[0]
 
-            data = []
-
             if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
                 embed_image = Image.open(path)
                 if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
@@ -150,11 +133,10 @@ class EmbeddingDatabase:
             embedding.vectors = vec.shape[0]
             embedding.shape = vec.shape[-1]
 
-            if (self.expected_shape == -1) or (self.expected_shape == embedding.shape):
+            if self.expected_shape == -1 or self.expected_shape == embedding.shape:
                 self.register_embedding(embedding, shared.sd_model)
             else:
                 self.skipped_embeddings.append(name)
-                # print('Skipping embedding {name}: shape was {shape} expected {expected}'.format(name = name, shape = embedding.shape, expected = self.expected_shape))
 
         for fn in os.listdir(self.embeddings_dir):
             try:
@@ -169,9 +151,9 @@ class EmbeddingDatabase:
                 print(traceback.format_exc(), file=sys.stderr)
                 continue
 
-        print("Textual inversion embeddings {num} loaded: {val}".format(num = len(self.word_embeddings), val = ', '.join(self.word_embeddings.keys())))
-        if (len(self.skipped_embeddings) > 0):
-            print("Textual inversion embeddings {num} skipped: {val}".format(num = len(self.skipped_embeddings), val = ', '.join(self.skipped_embeddings)))
+        print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
+        if len(self.skipped_embeddings) > 0:
+            print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings)}")
 
     def find_embedding_at_position(self, tokens, offset):
         token = tokens[offset]
-- 
cgit v1.2.1


From f4535f6e4f001314bd155bc6e1b6908e02792b9a Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 31 Dec 2022 23:40:55 +0300
Subject: make it so that memory/embeddings info is displayed in a separate UI
 element from generation parameters, and is preserved when you change the
 displayed infotext by clicking on gallery images

---
 modules/img2img.py    |  2 +-
 modules/processing.py |  5 +++--
 modules/txt2img.py    |  2 +-
 modules/ui.py         | 31 +++++++++++++++++--------------
 4 files changed, 22 insertions(+), 18 deletions(-)

diff --git a/modules/img2img.py b/modules/img2img.py
index 81da4b13..ca58b5d8 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -162,4 +162,4 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
     if opts.do_not_show_images:
         processed.images = []
 
-    return processed.images, generation_info_js, plaintext_to_html(processed.info)
+    return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)
diff --git a/modules/processing.py b/modules/processing.py
index 0a9a8f95..42dc19ea 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -239,7 +239,7 @@ class StableDiffusionProcessing():
 
 
 class Processed:
-    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
+    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
         self.images = images_list
         self.prompt = p.prompt
         self.negative_prompt = p.negative_prompt
@@ -247,6 +247,7 @@ class Processed:
         self.subseed = subseed
         self.subseed_strength = p.subseed_strength
         self.info = info
+        self.comments = comments
         self.width = p.width
         self.height = p.height
         self.sampler_name = p.sampler_name
@@ -646,7 +647,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     devices.torch_gc()
 
-    res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
+    res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
 
     if p.scripts is not None:
         p.scripts.postprocess(p, res)
diff --git a/modules/txt2img.py b/modules/txt2img.py
index c8f81176..7f61e19a 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -59,4 +59,4 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     if opts.do_not_show_images:
         processed.images = []
 
-    return processed.images, generation_info_js, plaintext_to_html(processed.info)
+    return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)
diff --git a/modules/ui.py b/modules/ui.py
index 397dd804..f550ad00 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -159,7 +159,7 @@ def save_files(js_data, images, do_make_zip, index):
                     zip_file.writestr(filenames[i], f.read())
         fullfns.insert(0, zip_filepath)
 
-    return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+    return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
 
 
 
@@ -593,6 +593,8 @@ Requested path was: {f}
 
                     with gr.Group():
                         html_info = gr.HTML()
+                        html_log = gr.HTML()
+
                         generation_info = gr.Textbox(visible=False)
                         if tabname == 'txt2img' or tabname == 'img2img':
                             generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
@@ -615,16 +617,16 @@ Requested path was: {f}
                             ],
                             outputs=[
                                 download_files,
-                                html_info,
-                                html_info,
-                                html_info,
+                                html_log,
                             ]
                         )
                 else:
                     html_info_x = gr.HTML()
                     html_info = gr.HTML()
+                    html_log = gr.HTML()
+
                 parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
-                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
+                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
 
 
 def create_ui():
@@ -686,14 +688,14 @@ def create_ui():
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
 
-            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+            txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
             parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
 
             txt2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
+                fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
                 _js="submit",
                 inputs=[
                     txt2img_prompt,
@@ -720,7 +722,8 @@ def create_ui():
                 outputs=[
                     txt2img_gallery,
                     generation_info,
-                    html_info
+                    html_info,
+                    html_log,
                 ],
                 show_progress=False,
             )
@@ -799,7 +802,6 @@ def create_ui():
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button = create_toprow(is_img2img=True)
 
-
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
 
@@ -883,7 +885,7 @@ def create_ui():
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui()
 
-            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
+            img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
             parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
@@ -915,7 +917,7 @@ def create_ui():
             )
 
             img2img_args = dict(
-                fn=wrap_gradio_gpu_call(modules.img2img.img2img),
+                fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
                 _js="submit_img2img",
                 inputs=[
                     dummy_component,
@@ -954,7 +956,8 @@ def create_ui():
                 outputs=[
                     img2img_gallery,
                     generation_info,
-                    html_info
+                    html_info,
+                    html_log,
                 ],
                 show_progress=False,
             )
@@ -1078,10 +1081,10 @@ def create_ui():
                 with gr.Group():
                     upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
 
-            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
+            result_images, html_info_x, html_info, html_log = create_output_panel("extras", opts.outdir_extras_samples)
 
         submit.click(
-            fn=wrap_gradio_gpu_call(modules.extras.run_extras),
+            fn=wrap_gradio_gpu_call(modules.extras.run_extras, extra_outputs=[None, '']),
             _js="get_extras_tab_index",
             inputs=[
                 dummy_component,
-- 
cgit v1.2.1


From 360feed9b55fb03060c236773867b08b4265645d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 00:38:58 +0300
Subject: HAPPY NEW YEAR

make save to zip into its own button instead of a checkbox
---
 modules/ui.py | 30 ++++++++++++++++++++++--------
 style.css     |  6 ++++++
 2 files changed, 28 insertions(+), 8 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index f550ad00..279b5110 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -570,13 +570,14 @@ Requested path was: {f}
 
             generation_info = None
             with gr.Column():
-                with gr.Row():
+                with gr.Row(elem_id=f"image_buttons_{tabname}"):
+                    open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder')
+
                     if tabname != "extras":
                         save = gr.Button('Save', elem_id=f'save_{tabname}')
+                        save_zip = gr.Button('Zip', elem_id=f'save_zip_{tabname}')
 
                     buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
-                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
 
                 open_folder_button.click(
                     fn=lambda: open_folder(opts.outdir_samples or outdir),
@@ -585,9 +586,6 @@ Requested path was: {f}
                 )
 
                 if tabname != "extras":
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
                     with gr.Row():
                         download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
@@ -608,11 +606,11 @@ Requested path was: {f}
 
                         save.click(
                             fn=wrap_gradio_call(save_files),
-                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
+                            _js="(x, y, z, w) => [x, y, false, selected_gallery_index()]",
                             inputs=[
                                 generation_info,
                                 result_gallery,
-                                do_make_zip,
+                                html_info,
                                 html_info,
                             ],
                             outputs=[
@@ -620,6 +618,22 @@ Requested path was: {f}
                                 html_log,
                             ]
                         )
+
+                        save_zip.click(
+                            fn=wrap_gradio_call(save_files),
+                            _js="(x, y, z, w) => [x, y, true, selected_gallery_index()]",
+                            inputs=[
+                                generation_info,
+                                result_gallery,
+                                html_info,
+                                html_info,
+                            ],
+                            outputs=[
+                                download_files,
+                                html_log,
+                            ]
+                        )
+
                 else:
                     html_info_x = gr.HTML()
                     html_info = gr.HTML()
diff --git a/style.css b/style.css
index 3ad78006..f245f674 100644
--- a/style.css
+++ b/style.css
@@ -568,6 +568,12 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
     font-size: 95%;
 }
 
+#image_buttons_txt2img button, #image_buttons_img2img button, #image_buttons_extras button{
+    min-width: auto;
+    padding-left: 0.5em;
+    padding-right: 0.5em;
+}
+
 /* The following handles localization for right-to-left (RTL) languages like Arabic.
 The rtl media type will only be activated by the logic in javascript/localization.js.
 If you change anything above, you need to make sure it is RTL compliant by just running
-- 
cgit v1.2.1


From 29a3a7eb13478297bc7093971b48827ab8246f45 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 01:19:10 +0300
Subject: show sampler selection in dropdown, add option selection to revert to
 old radio group

---
 modules/shared.py |  1 +
 modules/ui.py     | 22 +++++++++++++++-------
 2 files changed, 16 insertions(+), 7 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 715b9169..948b9542 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -406,6 +406,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+    "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
     'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
diff --git a/modules/ui.py b/modules/ui.py
index 279b5110..c7b8ea5d 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -643,6 +643,19 @@ Requested path was: {f}
                 return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
 
 
+def create_sampler_and_steps_selection(choices, tabname):
+    if opts.samplers_in_dropdown:
+        with gr.Row(elem_id=f"sampler_selection_{tabname}"):
+            sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20)
+    else:
+        with gr.Group(elem_id=f"sampler_selection_{tabname}"):
+            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20)
+            sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+
+    return steps, sampler_index
+
+
 def create_ui():
     import modules.img2img
     import modules.txt2img
@@ -660,9 +673,6 @@ def create_ui():
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
 
-
-
-
         with gr.Row(elem_id='txt2img_progress_row'):
             with gr.Column(scale=1):
                 pass
@@ -674,8 +684,7 @@ def create_ui():
 
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel', elem_id="txt2img_settings"):
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
+                steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
 
                 with gr.Group():
                     width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
@@ -875,8 +884,7 @@ def create_ui():
                 with gr.Row():
                     resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
 
-                steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
-                sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
+                steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
 
                 with gr.Group():
                     width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
-- 
cgit v1.2.1


From 210449b374d522c94a67fe54289a9eb515933a9f Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 02:41:15 +0300
Subject: fix 'RuntimeError: Expected all tensors to be on the same device'
 error preventing models from loading on lowvram/medvram.

---
 modules/sd_hijack_clip.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index 6ec50cca..ca92b142 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -298,6 +298,6 @@ class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
     def encode_embedding_init_text(self, init_text, nvpt):
         embedding_layer = self.wrapped.transformer.text_model.embeddings
         ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
-        embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
+        embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0)
 
         return embedded
-- 
cgit v1.2.1


From a939e82a0b982517aa212197a0e5f6d11daec7d0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 03:24:58 +0300
Subject: fix weird padding for sampler dropdown in chrome

---
 style.css | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/style.css b/style.css
index f245f674..4b98b84d 100644
--- a/style.css
+++ b/style.css
@@ -245,11 +245,6 @@ input[type="range"]{
     margin: 0.5em 0 -0.3em 0;
 }
 
-#txt2img_sampling label{
-    padding-left: 0.6em;
-    padding-right: 0.6em;
-}
-
 #mask_bug_info {
   text-align: center;
   display: block;
-- 
cgit v1.2.1


From 16b9661d2741b241c3964fcbd56559c078b84822 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 09:51:37 +0300
Subject: change karras scheduler sigmas to values recommended by SD from old
 0.1 to 10 with an option to revert to old

---
 modules/sd_samplers.py | 4 +++-
 modules/shared.py      | 6 +++++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 177b5338..e904d860 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -465,7 +465,9 @@ class KDiffusionSampler:
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+
+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
 
diff --git a/modules/shared.py b/modules/shared.py
index 948b9542..7f430b93 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -368,13 +368,17 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", gr.ColorPicker, {}),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
-    "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))
 
+options_templates.update(options_section(('compatibility', "Compatibility"), {
+    "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
+    "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
+}))
+
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
     "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
     "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
-- 
cgit v1.2.1


From 11d432d92d63660c516540dcb48faac87669b4f0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 10:35:38 +0300
Subject: add refresh buttons to checkpoint merger

---
 modules/ui.py | 6 ++++++
 style.css     | 2 +-
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index c7b8ea5d..4cc2ce4f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1167,8 +1167,14 @@ def create_ui():
 
                 with gr.Row():
                     primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+                    create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
+
                     secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+                    create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
+
                     tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+                    create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
+
                 custom_name = gr.Textbox(label="Custom Name (Optional)")
                 interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
                 interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
diff --git a/style.css b/style.css
index 4b98b84d..516ef7bf 100644
--- a/style.css
+++ b/style.css
@@ -496,7 +496,7 @@ input[type="range"]{
     padding: 0;
 }
 
-#refresh_sd_model_checkpoint, #refresh_sd_vae, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
+#refresh_sd_model_checkpoint, #refresh_sd_vae, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization, #refresh_checkpoint_A, #refresh_checkpoint_B, #refresh_checkpoint_C{
     max-width: 2.5em;
     min-width: 2.5em;
     height: 2.4em;
-- 
cgit v1.2.1


From 76f256fe8f844641f4e9b41f35c7dd2cba5090d6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 11:08:39 +0300
Subject: Bump gradio version #YOLO

---
 modules/ui_tempdir.py     | 3 ++-
 requirements.txt          | 2 +-
 requirements_versions.txt | 2 +-
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py
index 07210d14..8d519310 100644
--- a/modules/ui_tempdir.py
+++ b/modules/ui_tempdir.py
@@ -15,7 +15,8 @@ Savedfile = namedtuple("Savedfile", ["name"])
 def save_pil_to_file(pil_image, dir=None):
     already_saved_as = getattr(pil_image, 'already_saved_as', None)
     if already_saved_as and os.path.isfile(already_saved_as):
-        shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(os.path.dirname(already_saved_as))}
+        shared.demo.temp_file_sets[0] = shared.demo.temp_file_sets[0] | {os.path.abspath(already_saved_as)}
+
         file_obj = Savedfile(already_saved_as)
         return file_obj
 
diff --git a/requirements.txt b/requirements.txt
index 5bed694e..e2c3876b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,7 +5,7 @@ fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
-gradio==3.9
+gradio==3.15.0
 invisible-watermark
 numpy
 omegaconf
diff --git a/requirements_versions.txt b/requirements_versions.txt
index c126c8c4..836523ba 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -3,7 +3,7 @@ transformers==4.19.2
 accelerate==0.12.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.9
+gradio==3.15.0
 numpy==1.23.3
 Pillow==9.2.0
 realesrgan==0.3.0
-- 
cgit v1.2.1


From b46b97fa297b3a4a654da77cf98a775a2bcab4c7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 11:38:17 +0300
Subject: more fixes for gradio update

---
 modules/generation_parameters_copypaste.py | 2 +-
 modules/ui_tempdir.py                      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index fbd91300..54b3372d 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -38,7 +38,7 @@ def quote(text):
 def image_from_url_text(filedata):
     if type(filedata) == dict and filedata["is_file"]:
         filename = filedata["name"]
-        is_in_right_dir = any(Path(temp_dir).resolve() in Path(filename).resolve().parents for temp_dir in shared.demo.temp_dirs)
+        is_in_right_dir = any([filename in fileset for fileset in shared.demo.temp_file_sets])
         assert is_in_right_dir, 'trying to open image file outside of allowed directories'
 
         return Image.open(filename)
diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py
index 8d519310..363d449d 100644
--- a/modules/ui_tempdir.py
+++ b/modules/ui_tempdir.py
@@ -45,7 +45,7 @@ def on_tmpdir_changed():
 
     os.makedirs(shared.opts.temp_dir, exist_ok=True)
 
-    shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(shared.opts.temp_dir)}
+    shared.demo.temp_file_sets[0] = shared.demo.temp_file_sets[0] | {os.path.abspath(shared.opts.temp_dir)}
 
 
 def cleanup_tmpdr():
-- 
cgit v1.2.1


From e5f1a37cb9b537d95b2df47c96b4a4f7242fd294 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 13:08:40 +0300
Subject: make refresh buttons look more nice

---
 modules/ui.py            |  6 +++---
 modules/ui_components.py | 18 ++++++++++++++++++
 style.css                | 28 +++++++++++++++++++++-------
 3 files changed, 42 insertions(+), 10 deletions(-)
 create mode 100644 modules/ui_components.py

diff --git a/modules/ui.py b/modules/ui.py
index 4cc2ce4f..32fa80d1 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -19,7 +19,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, ui_components
 from modules.paths import script_path
 
 from modules.shared import opts, cmd_opts, restricted_opts
@@ -532,7 +532,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
 
         return gr.update(**(args or {}))
 
-    refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
+    refresh_button = ui_components.ToolButton(value=refresh_symbol, elem_id=elem_id)
     refresh_button.click(
         fn=refresh,
         inputs=[],
@@ -1476,7 +1476,7 @@ def create_ui():
                 res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
                 create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
             else:
-                with gr.Row(variant="compact"):
+                with ui_components.FormRow():
                     res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
                     create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
         else:
diff --git a/modules/ui_components.py b/modules/ui_components.py
new file mode 100644
index 00000000..d0519d2d
--- /dev/null
+++ b/modules/ui_components.py
@@ -0,0 +1,18 @@
+import gradio as gr
+
+
+class ToolButton(gr.Button, gr.components.FormComponent):
+    """Small button with single emoji as text, fits inside gradio forms"""
+
+    def __init__(self, **kwargs):
+        super().__init__(variant="tool", **kwargs)
+
+    def get_block_name(self):
+        return "button"
+
+
+class FormRow(gr.Row, gr.components.FormComponent):
+    """Same as gr.Row but fits inside gradio forms"""
+
+    def get_block_name(self):
+        return "row"
diff --git a/style.css b/style.css
index 516ef7bf..f168571e 100644
--- a/style.css
+++ b/style.css
@@ -496,13 +496,6 @@ input[type="range"]{
     padding: 0;
 }
 
-#refresh_sd_model_checkpoint, #refresh_sd_vae, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization, #refresh_checkpoint_A, #refresh_checkpoint_B, #refresh_checkpoint_C{
-    max-width: 2.5em;
-    min-width: 2.5em;
-    height: 2.4em;
-}
-
-
 canvas[key="mask"] {
     z-index: 12 !important;
     filter: invert();
@@ -569,6 +562,27 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
     padding-right: 0.5em;
 }
 
+.gr-form{
+    background-color: white;
+}
+
+.dark .gr-form{
+    background-color: rgb(31 41 55 / var(--tw-bg-opacity));
+}
+
+.gr-button-tool{
+    max-width: 2.5em;
+    min-width: 2.5em !important;
+    height: 2.4em;
+    margin: 0.55em 0;
+}
+
+#quicksettings .gr-button-tool{
+    margin: 0;
+}
+
+
+
 /* The following handles localization for right-to-left (RTL) languages like Arabic.
 The rtl media type will only be activated by the logic in javascript/localization.js.
 If you change anything above, you need to make sure it is RTL compliant by just running
-- 
cgit v1.2.1


From 5f12b23b8bb7fca585a3a1e844881d06f171364e Mon Sep 17 00:00:00 2001
From: AlUlkesh <99896447+AlUlkesh@users.noreply.github.com>
Date: Wed, 28 Dec 2022 22:18:19 +0100
Subject: Adding image numbers on grids

New grid option in settings enables adding of image numbers on grids. This makes identifying the images, especially in larger batches, much easier.

Revert "Adding image numbers on grids"

This reverts commit 3530c283b4b1d3a3cab40efbffe4cf2697938b6f.

Implements Callback for image grid loop

Necessary to make "Add image's number to its picture in the grid" extension possible.
---
 modules/images.py           |  1 +
 modules/script_callbacks.py | 20 ++++++++++++++++++++
 2 files changed, 21 insertions(+)

diff --git a/modules/images.py b/modules/images.py
index 31d4528d..5afd3891 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -43,6 +43,7 @@ def image_grid(imgs, batch_size=1, rows=None):
     grid = Image.new('RGB', size=(cols * w, rows * h), color='black')
 
     for i, img in enumerate(imgs):
+        script_callbacks.image_grid_loop_callback(img)
         grid.paste(img, box=(i % cols * w, i // cols * h))
 
     return grid
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 8e22f875..0c854407 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -51,6 +51,11 @@ class UiTrainTabParams:
         self.txt2img_preview_params = txt2img_preview_params
 
 
+class ImageGridLoopParams:
+    def __init__(self, img):
+        self.img = img
+
+
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
 callback_map = dict(
     callbacks_app_started=[],
@@ -63,6 +68,7 @@ callback_map = dict(
     callbacks_cfg_denoiser=[],
     callbacks_before_component=[],
     callbacks_after_component=[],
+    callbacks_image_grid_loop=[],
 )
 
 
@@ -154,6 +160,12 @@ def after_component_callback(component, **kwargs):
         except Exception:
             report_exception(c, 'after_component_callback')
 
+def image_grid_loop_callback(component, **kwargs):
+    for c in callback_map['callbacks_image_grid_loop']:
+        try:
+            c.callback(component, **kwargs)
+        except Exception:
+            report_exception(c, 'image_grid_loop')
 
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
@@ -255,3 +267,11 @@ def on_before_component(callback):
 def on_after_component(callback):
     """register a function to be called after a component is created. See on_before_component for more."""
     add_callback(callback_map['callbacks_after_component'], callback)
+
+
+def on_image_grid_loop(callback):
+    """register a function to be called inside the image grid loop.
+    The callback is called with one argument:
+       - params: ImageGridLoopParams - parameters to be used inside the image grid loop.
+    """
+    add_callback(callback_map['callbacks_image_grid_loop'], callback)
-- 
cgit v1.2.1


From 524d532b387732d4d32f237e792c7f201a934400 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 14:07:40 +0300
Subject: moved roll artist to built-in extensions

---
 .../roll-artist/scripts/roll-artist.py             | 50 ++++++++++++++++++++++
 modules/ui.py                                      | 37 ++--------------
 2 files changed, 53 insertions(+), 34 deletions(-)
 create mode 100644 extensions-builtin/roll-artist/scripts/roll-artist.py

diff --git a/extensions-builtin/roll-artist/scripts/roll-artist.py b/extensions-builtin/roll-artist/scripts/roll-artist.py
new file mode 100644
index 00000000..c3bc1fd0
--- /dev/null
+++ b/extensions-builtin/roll-artist/scripts/roll-artist.py
@@ -0,0 +1,50 @@
+import random
+
+from modules import script_callbacks, shared
+import gradio as gr
+
+art_symbol = '\U0001f3a8'  # 🎨
+global_prompt = None
+related_ids = {"txt2img_prompt", "txt2img_clear_prompt", "img2img_prompt", "img2img_clear_prompt" }
+
+
+def roll_artist(prompt):
+    allowed_cats = set([x for x in shared.artist_db.categories() if len(shared.opts.random_artist_categories)==0 or x in shared.opts.random_artist_categories])
+    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
+
+    return prompt + ", " + artist.name if prompt != '' else artist.name
+
+
+def add_roll_button(prompt):
+    roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
+
+    roll.click(
+        fn=roll_artist,
+        _js="update_txt2img_tokens",
+        inputs=[
+            prompt,
+        ],
+        outputs=[
+            prompt,
+        ]
+    )
+
+
+def after_component(component, **kwargs):
+    global global_prompt
+
+    elem_id = kwargs.get('elem_id', None)
+    if elem_id not in related_ids:
+        return
+
+    if elem_id == "txt2img_prompt":
+        global_prompt = component
+    elif elem_id == "txt2img_clear_prompt":
+        add_roll_button(global_prompt)
+    elif elem_id == "img2img_prompt":
+        global_prompt = component
+    elif elem_id == "img2img_clear_prompt":
+        add_roll_button(global_prompt)
+
+
+script_callbacks.on_after_component(after_component)
diff --git a/modules/ui.py b/modules/ui.py
index 32fa80d1..27da2c2c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -80,7 +80,6 @@ css_hide_progressbar = """
 # Important that they exactly match script.js for tooltip to work.
 random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
 reuse_symbol = '\u267b\ufe0f'  # ♻️
-art_symbol = '\U0001f3a8'  # 🎨
 paste_symbol = '\u2199\ufe0f'  # ↙
 folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
@@ -234,13 +233,6 @@ def check_progress_call_initial(id_part):
     return check_progress_call(id_part)
 
 
-def roll_artist(prompt):
-    allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
-    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
-
-    return prompt + ", " + artist.name if prompt != '' else artist.name
-
-
 def visit(x, func, path=""):
     if hasattr(x, 'children'):
         for c in x.children:
@@ -403,7 +395,6 @@ def create_toprow(is_img2img):
                         )
 
         with gr.Column(scale=1, elem_id="roll_col"):
-            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
             paste = gr.Button(value=paste_symbol, elem_id="paste")
             save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
             prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
@@ -452,7 +443,7 @@ def create_toprow(is_img2img):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
                     prompt_style2.save_to_config = True
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
+    return prompt, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
 
 
 def setup_progressbar(progressbar, preview, id_part, textinfo=None):
@@ -668,7 +659,7 @@ def create_ui():
     modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
+        txt2img_prompt, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _,txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
         txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -771,16 +762,6 @@ def create_ui():
                 outputs=[hr_options],
             )
 
-            roll.click(
-                fn=roll_artist,
-                _js="update_txt2img_tokens",
-                inputs=[
-                    txt2img_prompt,
-                ],
-                outputs=[
-                    txt2img_prompt,
-                ]
-            )
 
             txt2img_paste_fields = [
                 (txt2img_prompt, "Prompt"),
@@ -823,7 +804,7 @@ def create_ui():
     modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button = create_toprow(is_img2img=True)
+        img2img_prompt, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button = create_toprow(is_img2img=True)
 
         with gr.Row(elem_id='img2img_progress_row'):
             img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
@@ -999,18 +980,6 @@ def create_ui():
                 outputs=[img2img_prompt],
             )
 
-
-            roll.click(
-                fn=roll_artist,
-                _js="update_img2img_tokens",
-                inputs=[
-                    img2img_prompt,
-                ],
-                outputs=[
-                    img2img_prompt,
-                ]
-            )
-
             prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
             style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
             style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
-- 
cgit v1.2.1


From e672cfb07418a1a3130d3bf21c14a0d3819f81fb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 18:37:37 +0300
Subject: rework of callback for #6094

---
 modules/images.py           | 10 ++++++----
 modules/script_callbacks.py | 26 +++++++++++++++-----------
 2 files changed, 21 insertions(+), 15 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index 719aaf3b..f84fd485 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -39,12 +39,14 @@ def image_grid(imgs, batch_size=1, rows=None):
 
     cols = math.ceil(len(imgs) / rows)
 
+    params = script_callbacks.ImageGridLoopParams(imgs, cols, rows)
+    script_callbacks.image_grid_callback(params)
+
     w, h = imgs[0].size
-    grid = Image.new('RGB', size=(cols * w, rows * h), color='black')
+    grid = Image.new('RGB', size=(params.cols * w, params.rows * h), color='black')
 
-    for i, img in enumerate(imgs):
-        script_callbacks.image_grid_loop_callback(img)
-        grid.paste(img, box=(i % cols * w, i // cols * h))
+    for i, img in enumerate(params.imgs):
+        grid.paste(img, box=(i % params.cols * w, i // params.cols * h))
 
     return grid
 
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index 0c854407..de69fd9f 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -52,8 +52,10 @@ class UiTrainTabParams:
 
 
 class ImageGridLoopParams:
-    def __init__(self, img):
-        self.img = img
+    def __init__(self, imgs, cols, rows):
+        self.imgs = imgs
+        self.cols = cols
+        self.rows = rows
 
 
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
@@ -68,7 +70,7 @@ callback_map = dict(
     callbacks_cfg_denoiser=[],
     callbacks_before_component=[],
     callbacks_after_component=[],
-    callbacks_image_grid_loop=[],
+    callbacks_image_grid=[],
 )
 
 
@@ -160,12 +162,14 @@ def after_component_callback(component, **kwargs):
         except Exception:
             report_exception(c, 'after_component_callback')
 
-def image_grid_loop_callback(component, **kwargs):
-    for c in callback_map['callbacks_image_grid_loop']:
+
+def image_grid_callback(params: ImageGridLoopParams):
+    for c in callback_map['callbacks_image_grid']:
         try:
-            c.callback(component, **kwargs)
+            c.callback(params)
         except Exception:
-            report_exception(c, 'image_grid_loop')
+            report_exception(c, 'image_grid')
+
 
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
@@ -269,9 +273,9 @@ def on_after_component(callback):
     add_callback(callback_map['callbacks_after_component'], callback)
 
 
-def on_image_grid_loop(callback):
-    """register a function to be called inside the image grid loop.
+def on_image_grid(callback):
+    """register a function to be called before making an image grid.
     The callback is called with one argument:
-       - params: ImageGridLoopParams - parameters to be used inside the image grid loop.
+       - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified.
     """
-    add_callback(callback_map['callbacks_image_grid_loop'], callback)
+    add_callback(callback_map['callbacks_image_grid'], callback)
-- 
cgit v1.2.1


From a005fccddd5a37c57f1afe5234660b59b9a41508 Mon Sep 17 00:00:00 2001
From: me <25877290+Kryptortio@users.noreply.github.com>
Date: Sun, 1 Jan 2023 14:51:12 +0100
Subject: Add a lot more elem_id/HTML id, modified some that were duplicates
 for seed section

---
 modules/generation_parameters_copypaste.py |   2 +-
 modules/ui.py                              | 254 ++++++++++++++---------------
 style.css                                  |  12 +-
 3 files changed, 134 insertions(+), 134 deletions(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 54b3372d..8e7f0df0 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -93,7 +93,7 @@ def integrate_settings_paste_fields(component_dict):
 def create_buttons(tabs_list):
     buttons = {}
     for tab in tabs_list:
-        buttons[tab] = gr.Button(f"Send to {tab}")
+        buttons[tab] = gr.Button(f"Send to {tab}", elem_id=f"{tab}_tab")
     return buttons
 
 
diff --git a/modules/ui.py b/modules/ui.py
index 27da2c2c..7070ea15 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -272,17 +272,17 @@ def interrogate_deepbooru(image):
     return gr_show(True) if prompt is None else prompt
 
 
-def create_seed_inputs():
+def create_seed_inputs(target_interface):
     with gr.Row():
         with gr.Box():
-            with gr.Row(elem_id='seed_row'):
-                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
+            with gr.Row(elem_id=target_interface + '_seed_row'):
+                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
                 seed.style(container=False)
-                random_seed = gr.Button(random_symbol, elem_id='random_seed')
-                reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
+                random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed')
+                reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed')
 
-        with gr.Box(elem_id='subseed_show_box'):
-            seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
+        with gr.Box(elem_id=target_interface + '_subseed_show_box'):
+            seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
 
     # Components to show/hide based on the 'Extra' checkbox
     seed_extras = []
@@ -290,17 +290,17 @@ def create_seed_inputs():
     with gr.Row(visible=False) as seed_extra_row_1:
         seed_extras.append(seed_extra_row_1)
         with gr.Box():
-            with gr.Row(elem_id='subseed_row'):
-                subseed = gr.Number(label='Variation seed', value=-1)
+            with gr.Row(elem_id=target_interface + '_subseed_row'):
+                subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
                 subseed.style(container=False)
-                random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
-                reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
-        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
+                random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed')
+                reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
+        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
 
     with gr.Row(visible=False) as seed_extra_row_2:
         seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0)
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0)
+        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w')
+        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h')
 
     random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
     random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
@@ -678,28 +678,28 @@ def create_ui():
                 steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
 
                 with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
-                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
+                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
+                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
 
                 with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
-                    enable_hr = gr.Checkbox(label='Highres. fix', value=False)
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
+                    tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
+                    enable_hr = gr.Checkbox(label='Highres. fix', value=False, elem_id="txt2img_enable_hr")
 
                 with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass width", value=0)
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass height", value=0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
+                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass width", value=0, elem_id="txt2img_firstphase_width")
+                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass height", value=0, elem_id="txt2img_firstphase_height")
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
 
                 with gr.Row(equal_height=True):
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
 
-                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
+                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
 
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
 
-                with gr.Group():
+                with gr.Group(elem_id="txt2img_script_container"):
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
 
             txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
@@ -821,10 +821,10 @@ def create_ui():
             with gr.Column(variant='panel', elem_id="img2img_settings"):
 
                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
-                    with gr.TabItem('img2img', id='img2img'):
+                    with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab"):
                         init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool, image_mode="RGBA").style(height=480)
 
-                    with gr.TabItem('Inpaint', id='inpaint'):
+                    with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab"):
                         init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_inpaint_tool, image_mode="RGBA").style(height=480)
                         init_img_with_mask_orig = gr.State(None)
 
@@ -843,24 +843,24 @@ def create_ui():
                         init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
 
                         with gr.Row():
-                            mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
-                            mask_alpha = gr.Slider(label="Mask transparency", interactive=use_color_sketch, visible=use_color_sketch)
+                            mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
+                            mask_alpha = gr.Slider(label="Mask transparency", interactive=use_color_sketch, visible=use_color_sketch, elem_id="img2img_mask_alpha")
 
                         with gr.Row():
                             mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
-                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
+                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
 
-                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
+                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
 
                         with gr.Row():
-                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
-                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
+                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False, elem_id="img2img_inpaint_full_res")
+                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
 
-                    with gr.TabItem('Batch img2img', id='batch'):
+                    with gr.TabItem('Batch img2img', id='batch', elem_id="img2img_batch_tab"):
                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
                         gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
-                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
-                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
+                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
+                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
 
                 with gr.Row():
                     resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
@@ -872,20 +872,20 @@ def create_ui():
                     height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
 
                 with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
-                    tiling = gr.Checkbox(label='Tiling', value=False)
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
+                    tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
 
                 with gr.Row():
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
+                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
 
                 with gr.Group():
-                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
+                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
 
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
 
-                with gr.Group():
+                with gr.Group(elem_id="img2img_script_container"):
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui()
 
             img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
@@ -1032,45 +1032,45 @@ def create_ui():
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
                 with gr.Tabs(elem_id="mode_extras"):
-                    with gr.TabItem('Single Image'):
-                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
+                    with gr.TabItem('Single Image', elem_id="extras_single_tab"):
+                        extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
 
-                    with gr.TabItem('Batch Process'):
-                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
+                    with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab"):
+                        image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
 
-                    with gr.TabItem('Batch from Directory'):
-                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")
-                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
-                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
+                    with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab"):
+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
+                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
+                        show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
 
                 submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
 
                 with gr.Tabs(elem_id="extras_resize_mode"):
-                    with gr.TabItem('Scale by'):
-                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
-                    with gr.TabItem('Scale to'):
+                    with gr.TabItem('Scale by', elem_id="extras_scale_by_tab"):
+                        upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
+                    with gr.TabItem('Scale to', elem_id="extras_scale_to_tab"):
                         with gr.Group():
                             with gr.Row():
-                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
-                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
-                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
+                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
+                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
+                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
 
                 with gr.Group():
                     extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
                 with gr.Group():
                     extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
-                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
+                    extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1, elem_id="extras_upscaler_2_visibility")
 
                 with gr.Group():
-                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
+                    gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan, elem_id="extras_gfpgan_visibility")
 
                 with gr.Group():
-                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
-                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
+                    codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer, elem_id="extras_codeformer_visibility")
+                    codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer, elem_id="extras_codeformer_weight")
 
                 with gr.Group():
-                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
+                    upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False, elem_id="extras_upscale_before_face_fix")
 
             result_images, html_info_x, html_info, html_log = create_output_panel("extras", opts.outdir_extras_samples)
 
@@ -1117,7 +1117,7 @@ def create_ui():
 
             with gr.Column(variant='panel'):
                 html = gr.HTML()
-                generation_info = gr.Textbox(visible=False)
+                generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
                 html2 = gr.HTML()
                 with gr.Row():
                     buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
@@ -1144,13 +1144,13 @@ def create_ui():
                     tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
                     create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
 
-                custom_name = gr.Textbox(label="Custom Name (Optional)")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
-                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
+                custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
+                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
+                interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
 
                 with gr.Row():
-                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format")
-                    save_as_half = gr.Checkbox(value=False, label="Save as float16")
+                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
+                    save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
 
                 modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
 
@@ -1165,58 +1165,58 @@ def create_ui():
             with gr.Tabs(elem_id="train_tabs"):
 
                 with gr.Tab(label="Create embedding"):
-                    new_embedding_name = gr.Textbox(label="Name")
-                    initialization_text = gr.Textbox(label="Initialization text", value="*")
-                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
-                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")
+                    new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
+                    initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
+                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
+                    overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
 
                     with gr.Row():
                         with gr.Column(scale=3):
                             gr.HTML(value="")
 
                         with gr.Column():
-                            create_embedding = gr.Button(value="Create embedding", variant='primary')
+                            create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
 
                 with gr.Tab(label="Create hypernetwork"):
-                    new_hypernetwork_name = gr.Textbox(label="Name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"])
-                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
-                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
-                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
-                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
+                    new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
+                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
+                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
+                    new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
+                    new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
+                    overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
 
                     with gr.Row():
                         with gr.Column(scale=3):
                             gr.HTML(value="")
 
                         with gr.Column():
-                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
+                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
 
                 with gr.Tab(label="Preprocess images"):
-                    process_src = gr.Textbox(label='Source directory')
-                    process_dst = gr.Textbox(label='Destination directory')
-                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
-                    process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
-                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"])
+                    process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
+                    process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
+                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
+                    process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
+                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
 
                     with gr.Row():
-                        process_flip = gr.Checkbox(label='Create flipped copies')
-                        process_split = gr.Checkbox(label='Split oversized images')
-                        process_focal_crop = gr.Checkbox(label='Auto focal point crop')
-                        process_caption = gr.Checkbox(label='Use BLIP for caption')
-                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True)
+                        process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
+                        process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
+                        process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
+                        process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
+                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
 
                     with gr.Row(visible=False) as process_split_extra_row:
-                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
+                        process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
+                        process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
 
                     with gr.Row(visible=False) as process_focal_crop_row:
-                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
-                        process_focal_crop_debug = gr.Checkbox(label='Create debug image')
+                        process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
+                        process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
+                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
+                        process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1224,8 +1224,8 @@ def create_ui():
 
                         with gr.Column():
                             with gr.Row():
-                                interrupt_preprocessing = gr.Button("Interrupt")
-                            run_preprocess = gr.Button(value="Preprocess", variant='primary')
+                                interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
+                            run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
 
                     process_split.change(
                         fn=lambda show: gr_show(show),
@@ -1248,31 +1248,31 @@ def create_ui():
                         train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
                         create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
                     with gr.Row():
-                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
-                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
-
-                    batch_size = gr.Number(label='Batch size', value=1, precision=0)
-                    gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0)
-                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
-                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
-                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-                    training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512)
-                    training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512)
-                    steps = gr.Number(label='Max steps', value=100000, precision=0)
-                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
-                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
+                        embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
+                        hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
+
+                    batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
+                    gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
+                    dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
+                    log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
+                    template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"), elem_id="train_template_file")
+                    training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
+                    training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
+                    steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
+                    create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
+                    save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
                     with gr.Row():
-                        shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False)
-                        tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0)
+                        shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
+                        tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
                     with gr.Row():
-                        latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'])
+                        latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
 
                     with gr.Row():
-                        interrupt_training = gr.Button(value="Interrupt")
-                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
-                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
+                        interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
+                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
+                        train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
 
                 params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
 
@@ -1490,7 +1490,7 @@ def create_ui():
         return gr.update(value=value), opts.dumpjson()
 
     with gr.Blocks(analytics_enabled=False) as settings_interface:
-        settings_submit = gr.Button(value="Apply settings", variant='primary')
+        settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
         result = gr.HTML()
 
         settings_cols = 3
@@ -1541,8 +1541,8 @@ def create_ui():
             download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
 
         with gr.Row():
-            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
-            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
+            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
+            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary', elem_id="settings_restart_gradio")
 
         request_notifications.click(
             fn=lambda: None,
diff --git a/style.css b/style.css
index f168571e..924d4ae7 100644
--- a/style.css
+++ b/style.css
@@ -73,7 +73,7 @@
     margin-right: auto;
 }
 
-#random_seed, #random_subseed, #reuse_seed, #reuse_subseed, #open_folder{
+[id$=_random_seed], [id$=_random_subseed], [id$=_reuse_seed], [id$=_reuse_subseed], #open_folder{
     min-width: auto;
     flex-grow: 0;
     padding-left: 0.25em;
@@ -84,27 +84,27 @@
     display: none;
 }
 
-#seed_row, #subseed_row{
+[id$=_seed_row], [id$=_subseed_row]{
     gap: 0.5rem;
 }
 
-#subseed_show_box{
+[id$=_subseed_show_box]{
     min-width: auto;
     flex-grow: 0;
 }
 
-#subseed_show_box > div{
+[id$=_subseed_show_box] > div{
     border: 0;
     height: 100%;
 }
 
-#subseed_show{
+[id$=_subseed_show]{
     min-width: auto;
     flex-grow: 0;
     padding: 0;
 }
 
-#subseed_show label{
+[id$=_subseed_show] label{
     height: 100%;
 }
 
-- 
cgit v1.2.1


From 311354c0bb8930ea939d6aa6b3edd50c69301320 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 2 Jan 2023 00:38:09 +0300
Subject: fix the issue with training on SD2.0

---
 modules/sd_models.py                           | 2 ++
 modules/textual_inversion/textual_inversion.py | 3 +--
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ebd4dff7..bff8d6c9 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -228,6 +228,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    model.logvar = model.logvar.to(devices.device)  # fix for training
+
     sd_vae.delete_base_vae()
     sd_vae.clear_loaded_vae()
     vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 66f40367..1e5722e7 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -282,7 +282,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
         return embedding, filename
     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
 
-   # dataset loading may take a while, so input validations and early returns should be done before this
+    # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     old_parallel_processing_allowed = shared.parallel_processing_allowed
 
@@ -310,7 +310,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     loss_step = 0
     _loss_step = 0 #internal
 
-
     last_saved_file = "<none>"
     last_saved_image = "<none>"
     forced_filename = "<none>"
-- 
cgit v1.2.1


From b5819d9bf1794071139c640b5f1e72c84a0e051a Mon Sep 17 00:00:00 2001
From: Philpax <me@philpax.me>
Date: Mon, 2 Jan 2023 10:17:33 +1100
Subject: feat(api): add /sdapi/v1/embeddings

---
 modules/api/api.py    | 8 ++++++++
 modules/api/models.py | 3 +++
 2 files changed, 11 insertions(+)

diff --git a/modules/api/api.py b/modules/api/api.py
index 11daff0d..30bf3dac 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -100,6 +100,7 @@ class Api:
         self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
         self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
         self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
+        self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=EmbeddingsResponse)
         self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
         self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse)
         self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse)
@@ -327,6 +328,13 @@ class Api:
     def get_artists(self):
         return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
 
+    def get_embeddings(self):
+        db = sd_hijack.model_hijack.embedding_db
+        return {
+            "loaded": sorted(db.word_embeddings.keys()),
+            "skipped": sorted(db.skipped_embeddings),
+        }
+
     def refresh_checkpoints(self):
         shared.refresh_checkpoints()
 
diff --git a/modules/api/models.py b/modules/api/models.py
index c446ce7a..a8472dc9 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -249,3 +249,6 @@ class ArtistItem(BaseModel):
     score: float = Field(title="Score")
     category: str = Field(title="Category")
 
+class EmbeddingsResponse(BaseModel):
+    loaded: List[str] = Field(title="Loaded", description="Embeddings loaded for the current model")
+    skipped: List[str] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
\ No newline at end of file
-- 
cgit v1.2.1


From c65909ad16a1962129114c6251de092f49479b06 Mon Sep 17 00:00:00 2001
From: Philpax <me@philpax.me>
Date: Mon, 2 Jan 2023 12:21:22 +1100
Subject: feat(api): return more data for embeddings

---
 modules/api/api.py                             | 17 +++++++++++++++--
 modules/api/models.py                          | 11 +++++++++--
 modules/textual_inversion/textual_inversion.py |  8 ++++----
 3 files changed, 28 insertions(+), 8 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 30bf3dac..9c670f00 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -330,9 +330,22 @@ class Api:
 
     def get_embeddings(self):
         db = sd_hijack.model_hijack.embedding_db
+
+        def convert_embedding(embedding):
+            return {
+                "step": embedding.step,
+                "sd_checkpoint": embedding.sd_checkpoint,
+                "sd_checkpoint_name": embedding.sd_checkpoint_name,
+                "shape": embedding.shape,
+                "vectors": embedding.vectors,
+            }
+
+        def convert_embeddings(embeddings):
+            return {embedding.name: convert_embedding(embedding) for embedding in embeddings.values()}
+
         return {
-            "loaded": sorted(db.word_embeddings.keys()),
-            "skipped": sorted(db.skipped_embeddings),
+            "loaded": convert_embeddings(db.word_embeddings),
+            "skipped": convert_embeddings(db.skipped_embeddings),
         }
 
     def refresh_checkpoints(self):
diff --git a/modules/api/models.py b/modules/api/models.py
index a8472dc9..4a632c68 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -249,6 +249,13 @@ class ArtistItem(BaseModel):
     score: float = Field(title="Score")
     category: str = Field(title="Category")
 
+class EmbeddingItem(BaseModel):
+    step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
+    sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available")
+    sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead")
+    shape: int = Field(title="Shape", description="The length of each individual vector in the embedding")
+    vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
+
 class EmbeddingsResponse(BaseModel):
-    loaded: List[str] = Field(title="Loaded", description="Embeddings loaded for the current model")
-    skipped: List[str] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
\ No newline at end of file
+    loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
+    skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
\ No newline at end of file
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 1e5722e7..fd253477 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -59,7 +59,7 @@ class EmbeddingDatabase:
     def __init__(self, embeddings_dir):
         self.ids_lookup = {}
         self.word_embeddings = {}
-        self.skipped_embeddings = []
+        self.skipped_embeddings = {}
         self.dir_mtime = None
         self.embeddings_dir = embeddings_dir
         self.expected_shape = -1
@@ -91,7 +91,7 @@ class EmbeddingDatabase:
         self.dir_mtime = mt
         self.ids_lookup.clear()
         self.word_embeddings.clear()
-        self.skipped_embeddings = []
+        self.skipped_embeddings.clear()
         self.expected_shape = self.get_expected_shape()
 
         def process_file(path, filename):
@@ -136,7 +136,7 @@ class EmbeddingDatabase:
             if self.expected_shape == -1 or self.expected_shape == embedding.shape:
                 self.register_embedding(embedding, shared.sd_model)
             else:
-                self.skipped_embeddings.append(name)
+                self.skipped_embeddings[name] = embedding
 
         for fn in os.listdir(self.embeddings_dir):
             try:
@@ -153,7 +153,7 @@ class EmbeddingDatabase:
 
         print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
         if len(self.skipped_embeddings) > 0:
-            print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings)}")
+            print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
 
     def find_embedding_at_position(self, tokens, offset):
         token = tokens[offset]
-- 
cgit v1.2.1


From ef27a18b6b7cb1a8eebdc9b2e88d25baf2c2414d Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 2 Jan 2023 19:42:10 +0300
Subject: Hires fix rework

---
 modules/generation_parameters_copypaste.py | 32 ++++++++++++++
 modules/images.py                          | 24 +++++++++--
 modules/processing.py                      | 68 ++++++++++++------------------
 modules/shared.py                          |  7 ++-
 modules/txt2img.py                         |  6 +--
 modules/ui.py                              | 15 +++----
 scripts/xy_grid.py                         |  4 +-
 7 files changed, 96 insertions(+), 60 deletions(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 8e7f0df0..d6fa822b 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -1,5 +1,6 @@
 import base64
 import io
+import math
 import os
 import re
 from pathlib import Path
@@ -164,6 +165,35 @@ def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
     return None
 
 
+def restore_old_hires_fix_params(res):
+    """for infotexts that specify old First pass size parameter, convert it into
+    width, height, and hr scale"""
+
+    firstpass_width = res.get('First pass size-1', None)
+    firstpass_height = res.get('First pass size-2', None)
+
+    if firstpass_width is None or firstpass_height is None:
+        return
+
+    firstpass_width, firstpass_height = int(firstpass_width), int(firstpass_height)
+    width = int(res.get("Size-1", 512))
+    height = int(res.get("Size-2", 512))
+
+    if firstpass_width == 0 or firstpass_height == 0:
+        # old algorithm for auto-calculating first pass size
+        desired_pixel_count = 512 * 512
+        actual_pixel_count = width * height
+        scale = math.sqrt(desired_pixel_count / actual_pixel_count)
+        firstpass_width = math.ceil(scale * width / 64) * 64
+        firstpass_height = math.ceil(scale * height / 64) * 64
+
+    hr_scale = width / firstpass_width if firstpass_width > 0 else height / firstpass_height
+
+    res['Size-1'] = firstpass_width
+    res['Size-2'] = firstpass_height
+    res['Hires upscale'] = hr_scale
+
+
 def parse_generation_parameters(x: str):
     """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
@@ -221,6 +251,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         hypernet_hash = res.get("Hypernet hash", None)
         res["Hypernet"] = find_hypernetwork_key(hypernet_name, hypernet_hash)
 
+    restore_old_hires_fix_params(res)
+
     return res
 
 
diff --git a/modules/images.py b/modules/images.py
index f84fd485..c3a5fc8b 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -230,16 +230,32 @@ def draw_prompt_matrix(im, width, height, all_prompts):
     return draw_grid_annotations(im, width, height, hor_texts, ver_texts)
 
 
-def resize_image(resize_mode, im, width, height):
+def resize_image(resize_mode, im, width, height, upscaler_name=None):
+    """
+    Resizes an image with the specified resize_mode, width, and height.
+
+    Args:
+        resize_mode: The mode to use when resizing the image.
+            0: Resize the image to the specified width and height.
+            1: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess.
+            2: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image.
+        im: The image to resize.
+        width: The width to resize the image to.
+        height: The height to resize the image to.
+        upscaler_name: The name of the upscaler to use. If not provided, defaults to opts.upscaler_for_img2img.
+    """
+
+    upscaler_name = upscaler_name or opts.upscaler_for_img2img
+
     def resize(im, w, h):
-        if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None" or im.mode == 'L':
+        if upscaler_name is None or upscaler_name == "None" or im.mode == 'L':
             return im.resize((w, h), resample=LANCZOS)
 
         scale = max(w / im.width, h / im.height)
 
         if scale > 1.0:
-            upscalers = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img]
-            assert len(upscalers) > 0, f"could not find upscaler named {opts.upscaler_for_img2img}"
+            upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name]
+            assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}"
 
             upscaler = upscalers[0]
             im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
diff --git a/modules/processing.py b/modules/processing.py
index 42dc19ea..4654570c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -658,14 +658,18 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs):
+    def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.denoising_strength = denoising_strength
-        self.firstphase_width = firstphase_width
-        self.firstphase_height = firstphase_height
-        self.truncate_x = 0
-        self.truncate_y = 0
+        self.hr_scale = hr_scale
+        self.hr_upscaler = hr_upscaler
+
+        if firstphase_width != 0 or firstphase_height != 0:
+            print("firstphase_width/firstphase_height no longer supported; use hr_scale", file=sys.stderr)
+            self.hr_scale = self.width / firstphase_width
+            self.width = firstphase_width
+            self.height = firstphase_height
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
@@ -674,47 +678,29 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
-            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
-
-            if self.firstphase_width == 0 or self.firstphase_height == 0:
-                desired_pixel_count = 512 * 512
-                actual_pixel_count = self.width * self.height
-                scale = math.sqrt(desired_pixel_count / actual_pixel_count)
-                self.firstphase_width = math.ceil(scale * self.width / 64) * 64
-                self.firstphase_height = math.ceil(scale * self.height / 64) * 64
-                firstphase_width_truncated = int(scale * self.width)
-                firstphase_height_truncated = int(scale * self.height)
-
-            else:
-
-                width_ratio = self.width / self.firstphase_width
-                height_ratio = self.height / self.firstphase_height
-
-                if width_ratio > height_ratio:
-                    firstphase_width_truncated = self.firstphase_width
-                    firstphase_height_truncated = self.firstphase_width * self.height / self.width
-                else:
-                    firstphase_width_truncated = self.firstphase_height * self.width / self.height
-                    firstphase_height_truncated = self.firstphase_height
-
-            self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
-            self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
+            self.extra_generation_params["Hires upscale"] = self.hr_scale
+            if self.hr_upscaler is not None:
+                self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
+        latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_default_mode
+        if self.enable_hr and latent_scale_mode is None:
+            assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}"
+
+        x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
+
         if not self.enable_hr:
-            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
             return samples
 
-        x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x, self.firstphase_width, self.firstphase_height))
-
-        samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
+        target_width = int(self.width * self.hr_scale)
+        target_height = int(self.height * self.hr_scale)
 
-        """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
         def save_intermediate(image, index):
+            """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
+
             if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
                 return
 
@@ -723,11 +709,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
 
-        if opts.use_scale_latent_for_hires_fix:
+        if latent_scale_mode is not None:
             for i in range(samples.shape[0]):
                 save_intermediate(samples, i)
 
-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode)
 
             # Avoid making the inpainting conditioning unless necessary as
             # this does need some extra compute to decode / encode the image again.
@@ -747,7 +733,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
                 save_intermediate(image, i)
 
-                image = images.resize_image(0, image, self.width, self.height)
+                image = images.resize_image(0, image, target_width, target_height, upscaler_name=self.hr_upscaler)
                 image = np.array(image).astype(np.float32) / 255.0
                 image = np.moveaxis(image, 2, 0)
                 batch_images.append(image)
@@ -764,7 +750,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
-        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
 
         # GC now before running the next img2img to prevent running out of memory
         x = None
diff --git a/modules/shared.py b/modules/shared.py
index 7f430b93..b65559ee 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -327,7 +327,6 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
-    "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
@@ -545,6 +544,12 @@ opts = Options()
 if os.path.exists(config_filename):
     opts.load(config_filename)
 
+latent_upscale_default_mode = "Latent"
+latent_upscale_modes = {
+    "Latent": "bilinear",
+    "Latent (nearest)": "nearest",
+}
+
 sd_upscalers = []
 
 sd_model = None
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 7f61e19a..e189a899 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -8,7 +8,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -33,8 +33,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         tiling=tiling,
         enable_hr=enable_hr,
         denoising_strength=denoising_strength if enable_hr else None,
-        firstphase_width=firstphase_width if enable_hr else None,
-        firstphase_height=firstphase_height if enable_hr else None,
+        hr_scale=hr_scale,
+        hr_upscaler=hr_upscaler,
     )
 
     p.scripts = modules.scripts.scripts_txt2img
diff --git a/modules/ui.py b/modules/ui.py
index 7070ea15..27cd9ddd 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -684,11 +684,11 @@ def create_ui():
                 with gr.Row():
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
                     tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
-                    enable_hr = gr.Checkbox(label='Highres. fix', value=False, elem_id="txt2img_enable_hr")
+                    enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
 
                 with gr.Row(visible=False) as hr_options:
-                    firstphase_width = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass width", value=0, elem_id="txt2img_firstphase_width")
-                    firstphase_height = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass height", value=0, elem_id="txt2img_firstphase_height")
+                    hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
+                    hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
 
                 with gr.Row(equal_height=True):
@@ -729,8 +729,8 @@ def create_ui():
                     width,
                     enable_hr,
                     denoising_strength,
-                    firstphase_width,
-                    firstphase_height,
+                    hr_scale,
+                    hr_upscaler,
                 ] + custom_inputs,
 
                 outputs=[
@@ -762,7 +762,6 @@ def create_ui():
                 outputs=[hr_options],
             )
 
-
             txt2img_paste_fields = [
                 (txt2img_prompt, "Prompt"),
                 (txt2img_negative_prompt, "Negative prompt"),
@@ -781,8 +780,8 @@ def create_ui():
                 (denoising_strength, "Denoising strength"),
                 (enable_hr, lambda d: "Denoising strength" in d),
                 (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
-                (firstphase_width, "First pass size-1"),
-                (firstphase_height, "First pass size-2"),
+                (hr_scale, "Hires upscale"),
+                (hr_upscaler, "Hires upscaler"),
                 *modules.scripts.scripts_txt2img.infotext_fields
             ]
             parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 3e0b2805..f92f9776 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -202,7 +202,7 @@ axis_options = [
     AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
     AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
     AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
-    AxisOption("Upscale latent space for hires.", str, apply_upscale_latent_space, format_value_add_label, None),
+    AxisOption("Hires upscaler", str, apply_field("hr_upscaler"), format_value_add_label, None),
     AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight"), format_value_add_label, None),
     AxisOption("VAE", str, apply_vae, format_value_add_label, None),
     AxisOption("Styles", str, apply_styles, format_value_add_label, None),
@@ -267,7 +267,6 @@ class SharedSettingsStackHelper(object):
         self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
         self.hypernetwork = opts.sd_hypernetwork
         self.model = shared.sd_model
-        self.use_scale_latent_for_hires_fix = opts.use_scale_latent_for_hires_fix
         self.vae = opts.sd_vae
   
     def __exit__(self, exc_type, exc_value, tb):
@@ -278,7 +277,6 @@ class SharedSettingsStackHelper(object):
         hypernetwork.apply_strength()
 
         opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
-        opts.data["use_scale_latent_for_hires_fix"] = self.use_scale_latent_for_hires_fix
 
 
 re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
-- 
cgit v1.2.1


From 4dbde228ff48dbb105241b1ed25c21ce3f87d182 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 2 Jan 2023 20:01:16 +0300
Subject: make it possible to use fractional values for SD upscale.

---
 modules/upscaler.py   | 6 +++---
 scripts/sd_upscale.py | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/modules/upscaler.py b/modules/upscaler.py
index c4e6e6bd..231680cb 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -53,10 +53,10 @@ class Upscaler:
     def do_upscale(self, img: PIL.Image, selected_model: str):
         return img
 
-    def upscale(self, img: PIL.Image, scale: int, selected_model: str = None):
+    def upscale(self, img: PIL.Image, scale, selected_model: str = None):
         self.scale = scale
-        dest_w = img.width * scale
-        dest_h = img.height * scale
+        dest_w = int(img.width * scale)
+        dest_h = int(img.height * scale)
 
         for i in range(3):
             shape = (img.width, img.height)
diff --git a/scripts/sd_upscale.py b/scripts/sd_upscale.py
index e8c80a6c..9739545c 100644
--- a/scripts/sd_upscale.py
+++ b/scripts/sd_upscale.py
@@ -19,7 +19,7 @@ class Script(scripts.Script):
     def ui(self, is_img2img):
         info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
         overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)
-        scale_factor = gr.Slider(minimum=1, maximum=4, step=1, label='Scale Factor', value=2)
+        scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0)
         upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
         return [info, overlap, upscaler_index, scale_factor]
-- 
cgit v1.2.1


From 84dd7e8e2495c4fc2997e97f8267aa831eb90d11 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 2 Jan 2023 20:30:02 +0300
Subject: error out with a readable message in chwewckpoint merger for
 incompatible tensor shapes (ie when trying to merge SD1.5 with SD2.0)

---
 modules/extras.py | 2 ++
 modules/ui.py     | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/extras.py b/modules/extras.py
index 68939dea..5e270250 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -303,6 +303,8 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
                 theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
                 result_is_inpainting_model = True
             else:
+                assert a.shape == b.shape, f'Incompatible shapes for layer {key}: A is {a.shape}, and B is {b.shape}'
+
                 theta_0[key] = theta_func2(a, b, multiplier)
 
             if save_as_half:
diff --git a/modules/ui.py b/modules/ui.py
index 27cd9ddd..67a51888 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1663,7 +1663,7 @@ def create_ui():
                 print("Error loading/saving model file:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
                 modules.sd_models.list_models()  # to remove the potentially missing models from the list
-                return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
+                return [f"Error merging checkpoints: {e}"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)]
             return results
 
         modelmerger_merge.click(
-- 
cgit v1.2.1


From 8d12a729b8b036cb765cf2d87576d5ae256135c8 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 2 Jan 2023 20:46:51 +0300
Subject: fix possible error with accessing nonexistent setting

---
 modules/ui.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/ui.py b/modules/ui.py
index 67a51888..9350a80f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -491,7 +491,7 @@ def apply_setting(key, value):
         return
 
     valtype = type(opts.data_labels[key].default)
-    oldval = opts.data[key]
+    oldval = opts.data.get(key, None)
     opts.data[key] = valtype(value) if valtype != type(None) else value
     if oldval != value and opts.data_labels[key].onchange is not None:
         opts.data_labels[key].onchange()
-- 
cgit v1.2.1


From 251ecee6949c36e9df1d99a950b3e1af2b5fa2b6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 2 Jan 2023 22:44:46 +0300
Subject: make "send to" buttons send actual dimension of the sent image rather
 than fields

---
 javascript/ui.js                           |  4 +--
 modules/generation_parameters_copypaste.py | 58 ++++++++++++++++++++----------
 2 files changed, 42 insertions(+), 20 deletions(-)

diff --git a/javascript/ui.js b/javascript/ui.js
index 587dd782..d0c054d9 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -19,7 +19,7 @@ function selected_gallery_index(){
 
 function extract_image_from_gallery(gallery){
     if(gallery.length == 1){
-        return gallery[0]
+        return [gallery[0]]
     }
 
     index = selected_gallery_index()
@@ -28,7 +28,7 @@ function extract_image_from_gallery(gallery){
         return [null]
     }
 
-    return gallery[index];
+    return [gallery[index]];
 }
 
 function args_to_array(args){
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index d6fa822b..ec60319a 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -103,35 +103,57 @@ def bind_buttons(buttons, send_image, send_generate_info):
     bind_list.append([buttons, send_image, send_generate_info])
 
 
+def send_image_and_dimensions(x):
+    if isinstance(x, Image.Image):
+        img = x
+    else:
+        img = image_from_url_text(x)
+
+    if shared.opts.send_size and isinstance(img, Image.Image):
+        w = img.width
+        h = img.height
+    else:
+        w = gr.update()
+        h = gr.update()
+
+    return img, w, h
+
+
 def run_bind():
-    for buttons, send_image, send_generate_info in bind_list:
+    for buttons, source_image_component, send_generate_info in bind_list:
         for tab in buttons:
             button = buttons[tab]
-            if send_image and paste_fields[tab]["init_img"]:
-                if type(send_image) == gr.Gallery:
-                    button.click(
-                        fn=lambda x: image_from_url_text(x),
-                        _js="extract_image_from_gallery",
-                        inputs=[send_image],
-                        outputs=[paste_fields[tab]["init_img"]],
-                    )
+            destination_image_component = paste_fields[tab]["init_img"]
+            fields = paste_fields[tab]["fields"]
+
+            destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
+            destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
+
+            if source_image_component and destination_image_component:
+                if isinstance(source_image_component, gr.Gallery):
+                    func = send_image_and_dimensions if destination_width_component else image_from_url_text
+                    jsfunc = "extract_image_from_gallery"
                 else:
-                    button.click(
-                        fn=lambda x: x,
-                        inputs=[send_image],
-                        outputs=[paste_fields[tab]["init_img"]],
-                    )
+                    func = send_image_and_dimensions if destination_width_component else lambda x: x
+                    jsfunc = None
+
+                button.click(
+                    fn=func,
+                    _js=jsfunc,
+                    inputs=[source_image_component],
+                    outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
+                )
 
-            if send_generate_info and paste_fields[tab]["fields"] is not None:
+            if send_generate_info and fields is not None:
                 if send_generate_info in paste_fields:
-                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] +  (['Size-1', 'Size-2'] if shared.opts.send_size else []) + (["Seed"] if shared.opts.send_seed else [])
+                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else [])
                     button.click(
                         fn=lambda *x: x,
                         inputs=[field for field, name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
-                        outputs=[field for field, name in paste_fields[tab]["fields"] if name in paste_field_names],
+                        outputs=[field for field, name in fields if name in paste_field_names],
                     )
                 else:
-                    connect_paste(button, paste_fields[tab]["fields"], send_generate_info)
+                    connect_paste(button, fields, send_generate_info)
 
             button.click(
                 fn=None,
-- 
cgit v1.2.1


From 1d7a31def8b5f4c348e2dd07536ac56cb4350614 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 06:21:53 +0300
Subject: make edit fields for sliders not get hidden by slider's label when
 there's not enough space

---
 style.css | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/style.css b/style.css
index 924d4ae7..77551dd7 100644
--- a/style.css
+++ b/style.css
@@ -509,7 +509,7 @@ canvas[key="mask"] {
     position: absolute;
     right: 0.5em;
     top: -0.6em;
-    z-index: 200;
+    z-index: 400;
     width: 8em;
 }
 #quicksettings .gr-box > div > div > input.gr-text-input {
-- 
cgit v1.2.1


From 269f6e867651cadef40d2c939a79d13291280bcd Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 07:20:20 +0300
Subject: change settings UI to use vertical tabs

---
 modules/ui.py | 45 +++++++++++++++++----------------------------
 style.css     | 27 +++++++++++++++++++++++++++
 2 files changed, 44 insertions(+), 28 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index 9350a80f..f8c973ba 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1489,41 +1489,34 @@ def create_ui():
         return gr.update(value=value), opts.dumpjson()
 
     with gr.Blocks(analytics_enabled=False) as settings_interface:
-        settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
-        result = gr.HTML()
+        with gr.Row():
+            settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
+            restart_gradio = gr.Button(value='Restart UI', variant='primary', elem_id="settings_restart_gradio")
 
-        settings_cols = 3
-        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
+        result = gr.HTML(elem_id="settings_result")
 
         quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
         quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
 
         quicksettings_list = []
 
-        cols_displayed = 0
-        items_displayed = 0
         previous_section = None
-        column = None
-        with gr.Row(elem_id="settings").style(equal_height=False):
+        current_tab = None
+        with gr.Tabs(elem_id="settings"):
             for i, (k, item) in enumerate(opts.data_labels.items()):
                 section_must_be_skipped = item.section[0] is None
 
                 if previous_section != item.section and not section_must_be_skipped:
-                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
-                        if column is not None:
-                            column.__exit__()
+                    elem_id, text = item.section
 
-                        column = gr.Column(variant='panel')
-                        column.__enter__()
+                    if current_tab is not None:
+                        current_tab.__exit__()
 
-                        items_displayed = 0
-                        cols_displayed += 1
+                    current_tab = gr.TabItem(elem_id="settings_{}".format(elem_id), label=text)
+                    current_tab.__enter__()
 
                     previous_section = item.section
 
-                    elem_id, text = item.section
-                    gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='<h1 class="gr-button-lg">{}</h1>'.format(text))
-
                 if k in quicksettings_names and not shared.cmd_opts.freeze_settings:
                     quicksettings_list.append((i, k, item))
                     components.append(dummy_component)
@@ -1533,15 +1526,14 @@ def create_ui():
                     component = create_setting_component(k)
                     component_dict[k] = component
                     components.append(component)
-                    items_displayed += 1
 
-        with gr.Row():
-            request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
-            download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+            if current_tab is not None:
+                current_tab.__exit__()
 
-        with gr.Row():
-            reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
-            restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary', elem_id="settings_restart_gradio")
+            with gr.TabItem("Actions"):
+                request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+                download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+                reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
 
         request_notifications.click(
             fn=lambda: None,
@@ -1578,9 +1570,6 @@ def create_ui():
             outputs=[],
         )
 
-        if column is not None:
-            column.__exit__()
-
     interfaces = [
         (txt2img_interface, "txt2img", "txt2img"),
         (img2img_interface, "img2img", "img2img"),
diff --git a/style.css b/style.css
index 77551dd7..7df4d960 100644
--- a/style.css
+++ b/style.css
@@ -241,6 +241,33 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500,  label.block s
     z-index: 200;
 }
 
+#settings{
+    display: block;
+}
+
+#settings > div{
+    border: none;
+    margin-left: 10em;
+}
+
+#settings > div.flex-wrap{
+    float: left;
+    display: block;
+    margin-left: 0;
+    width: 10em;
+}
+
+#settings > div.flex-wrap button{
+    display: block;
+    border: none;
+    text-align: left;
+}
+
+#settings_result{
+    height: 1.4em;
+    margin: 0 1.2em;
+}
+
 input[type="range"]{
     margin: 0.5em 0 -0.3em 0;
 }
-- 
cgit v1.2.1


From 18c03cdeac6272734b0c09afd3fbe47d1372dd07 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 09:04:29 +0300
Subject: styling rework to make things more compact

---
 modules/ui.py            | 121 ++++++++++++++++++++++++-----------------------
 modules/ui_components.py |   7 +++
 style.css                |  35 ++++++++------
 3 files changed, 89 insertions(+), 74 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index f8c973ba..f787b518 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -19,7 +19,8 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, ui_components
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru
+from modules.ui_components import FormRow, FormGroup, ToolButton
 from modules.paths import script_path
 
 from modules.shared import opts, cmd_opts, restricted_opts
@@ -273,31 +274,27 @@ def interrogate_deepbooru(image):
 
 
 def create_seed_inputs(target_interface):
-    with gr.Row():
-        with gr.Box():
-            with gr.Row(elem_id=target_interface + '_seed_row'):
-                seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
-                seed.style(container=False)
-                random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed')
-                reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed')
-
-        with gr.Box(elem_id=target_interface + '_subseed_show_box'):
+    with FormRow(elem_id=target_interface + '_seed_row'):
+        seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
+        seed.style(container=False)
+        random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed')
+        reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed')
+
+        with gr.Group(elem_id=target_interface + '_subseed_show_box'):
             seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
 
     # Components to show/hide based on the 'Extra' checkbox
     seed_extras = []
 
-    with gr.Row(visible=False) as seed_extra_row_1:
+    with FormRow(visible=False, elem_id=target_interface + '_subseed_row') as seed_extra_row_1:
         seed_extras.append(seed_extra_row_1)
-        with gr.Box():
-            with gr.Row(elem_id=target_interface + '_subseed_row'):
-                subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
-                subseed.style(container=False)
-                random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed')
-                reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
+        subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
+        subseed.style(container=False)
+        random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed')
+        reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
         subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
 
-    with gr.Row(visible=False) as seed_extra_row_2:
+    with FormRow(visible=False) as seed_extra_row_2:
         seed_extras.append(seed_extra_row_2)
         seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w')
         seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h')
@@ -523,7 +520,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
 
         return gr.update(**(args or {}))
 
-    refresh_button = ui_components.ToolButton(value=refresh_symbol, elem_id=elem_id)
+    refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
     refresh_button.click(
         fn=refresh,
         inputs=[],
@@ -636,11 +633,11 @@ Requested path was: {f}
 
 def create_sampler_and_steps_selection(choices, tabname):
     if opts.samplers_in_dropdown:
-        with gr.Row(elem_id=f"sampler_selection_{tabname}"):
+        with FormRow(elem_id=f"sampler_selection_{tabname}"):
             sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
             steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20)
     else:
-        with gr.Group(elem_id=f"sampler_selection_{tabname}"):
+        with FormGroup(elem_id=f"sampler_selection_{tabname}"):
             steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20)
             sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
 
@@ -677,29 +674,29 @@ def create_ui():
             with gr.Column(variant='panel', elem_id="txt2img_settings"):
                 steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
 
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
+                with FormRow():
+                    with gr.Column(elem_id="txt2img_column_size", scale=4):
+                        width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
+                        height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
+                    with gr.Column(elem_id="txt2img_column_batch"):
+                        batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                        batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
 
-                with gr.Row():
+                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
+
+                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
+
+                with FormRow(elem_id="txt2img_checkboxes"):
                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
                     tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
                     enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
 
-                with gr.Row(visible=False) as hr_options:
+                with FormRow(visible=False) as hr_options:
                     hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
                     hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
 
-                with gr.Row(equal_height=True):
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
-
-                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
-
-                with gr.Group(elem_id="txt2img_script_container"):
+                with FormGroup(elem_id="txt2img_script_container"):
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
 
             txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
@@ -816,7 +813,7 @@ def create_ui():
                 img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
                 setup_progressbar(progressbar, img2img_preview, 'img2img')
 
-        with gr.Row().style(equal_height=False):
+        with FormRow().style(equal_height=False):
             with gr.Column(variant='panel', elem_id="img2img_settings"):
 
                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
@@ -841,19 +838,23 @@ def create_ui():
                         init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
                         init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
 
-                        with gr.Row():
+                        with FormRow():
                             mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
                             mask_alpha = gr.Slider(label="Mask transparency", interactive=use_color_sketch, visible=use_color_sketch, elem_id="img2img_mask_alpha")
 
-                        with gr.Row():
-                            mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
-                            inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
+                        with FormRow():
+                            mask_mode = gr.Radio(label="Mask source", choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
+                            inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
 
-                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
+                        with FormRow():
+                            inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
 
-                        with gr.Row():
-                            inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False, elem_id="img2img_inpaint_full_res")
-                            inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
+                        with FormRow():
+                            with gr.Column():
+                                inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
+
+                            with gr.Column(scale=4):
+                                inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
 
                     with gr.TabItem('Batch img2img', id='batch', elem_id="img2img_batch_tab"):
                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
@@ -861,30 +862,30 @@ def create_ui():
                         img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
                         img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
 
-                with gr.Row():
-                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
+                with FormRow():
+                    resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
 
                 steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
 
-                with gr.Group():
-                    width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
-                    height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
-
-                with gr.Row():
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
-                    tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
+                with FormRow():
+                    with gr.Column(elem_id="img2img_column_size", scale=4):
+                        width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+                        height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+                    with gr.Column(elem_id="img2img_column_batch"):
+                        batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                        batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
 
-                with gr.Row():
-                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
-                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
-
-                with gr.Group():
+                with FormGroup():
                     cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
                     denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
 
                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
 
-                with gr.Group(elem_id="img2img_script_container"):
+                with FormRow(elem_id="img2img_checkboxes"):
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
+                    tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
+
+                with FormGroup(elem_id="img2img_script_container"):
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui()
 
             img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
@@ -1444,7 +1445,7 @@ def create_ui():
                 res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
                 create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
             else:
-                with ui_components.FormRow():
+                with FormRow():
                     res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
                     create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
         else:
diff --git a/modules/ui_components.py b/modules/ui_components.py
index d0519d2d..91eb0e3d 100644
--- a/modules/ui_components.py
+++ b/modules/ui_components.py
@@ -16,3 +16,10 @@ class FormRow(gr.Row, gr.components.FormComponent):
 
     def get_block_name(self):
         return "row"
+
+
+class FormGroup(gr.Group, gr.components.FormComponent):
+    """Same as gr.Row but fits inside gradio forms"""
+
+    def get_block_name(self):
+        return "group"
diff --git a/style.css b/style.css
index 7df4d960..86a265f6 100644
--- a/style.css
+++ b/style.css
@@ -74,7 +74,8 @@
 }
 
 [id$=_random_seed], [id$=_random_subseed], [id$=_reuse_seed], [id$=_reuse_subseed], #open_folder{
-    min-width: auto;
+    min-width: 2.3em;
+    height: 2.5em;
     flex-grow: 0;
     padding-left: 0.25em;
     padding-right: 0.25em;
@@ -86,6 +87,7 @@
 
 [id$=_seed_row], [id$=_subseed_row]{
     gap: 0.5rem;
+    padding: 0.6em;
 }
 
 [id$=_subseed_show_box]{
@@ -206,24 +208,24 @@ button{
 
 fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500,  label.block span{
     position: absolute;
-    top: -0.6em;
+    top: -0.5em;
     line-height: 1.2em;
-    padding: 0 0.5em;
-    margin: 0;
+    padding: 0;
+    margin: 0 0.5em;
 
     background-color: white;
-    border-top: 1px solid #eee;
-    border-left: 1px solid #eee;
-    border-right: 1px solid #eee;
+    box-shadow: 0 0 5px 5px white;
 
     z-index: 300;
 }
 
 .dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{
     background-color: rgb(31, 41, 55);
-    border-top: 1px solid rgb(55 65 81);
-    border-left: 1px solid rgb(55 65 81);
-    border-right: 1px solid rgb(55 65 81);
+    box-shadow: 0 0 5px 5px rgb(31, 41, 55);
+}
+
+#txt2img_column_batch, #img2img_column_batch{
+    min-width: min(13.5em, 100%) !important;
 }
 
 #settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{
@@ -232,10 +234,6 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500,  label.block s
     margin-right: 8em;
 }
 
-.gr-panel div.flex-col div.justify-between label span{
-    margin: 0;
-}
-
 #settings .gr-panel div.flex-col div.justify-between div{
     position: relative;
     z-index: 200;
@@ -609,6 +607,15 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
 }
 
 
+#img2img_settings > div.gr-form, #txt2img_settings > div.gr-form {
+    padding-top: 0.9em;
+}
+
+#img2img_settings div.gr-form .gr-form, #txt2img_settings div.gr-form .gr-form{
+    border: none;
+    padding-bottom: 0.5em;
+}
+
 
 /* The following handles localization for right-to-left (RTL) languages like Arabic.
 The rtl media type will only be activated by the logic in javascript/localization.js.
-- 
cgit v1.2.1


From 2bc86712ec16cada01a2353f1d978c1aabc84dbb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 09:13:35 +0300
Subject: make quicksettings UI elements appear in same order as they are
 listed in the setting

---
 modules/ui.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index f787b518..d7b911da 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1497,7 +1497,7 @@ def create_ui():
         result = gr.HTML(elem_id="settings_result")
 
         quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
-        quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
+        quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'}
 
         quicksettings_list = []
 
@@ -1604,7 +1604,7 @@ def create_ui():
 
     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
         with gr.Row(elem_id="quicksettings"):
-            for i, k, item in quicksettings_list:
+            for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
                 component = create_setting_component(k, is_quicksettings=True)
                 component_dict[k] = component
 
-- 
cgit v1.2.1


From 9d4eff097deff6153c4023f158bd9fbd4f3e88b3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 10:01:06 +0300
Subject: add a button to show all setting pages

---
 javascript/ui.js | 11 +++++++++++
 modules/ui.py    |  2 ++
 2 files changed, 13 insertions(+)

diff --git a/javascript/ui.js b/javascript/ui.js
index d0c054d9..34406f3f 100644
--- a/javascript/ui.js
+++ b/javascript/ui.js
@@ -188,6 +188,17 @@ onUiUpdate(function(){
 		img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
 		img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
 	}
+
+    show_all_pages = gradioApp().getElementById('settings_show_all_pages')
+    settings_tabs = gradioApp().querySelector('#settings div')
+    if(show_all_pages && settings_tabs){
+        settings_tabs.appendChild(show_all_pages)
+        show_all_pages.onclick = function(){
+            gradioApp().querySelectorAll('#settings > div').forEach(function(elem){
+                elem.style.display = "block";
+            })
+        }
+    }
 })
 
 let txt2img_textarea, img2img_textarea = undefined;
diff --git a/modules/ui.py b/modules/ui.py
index d7b911da..2c92c422 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1536,6 +1536,8 @@ def create_ui():
                 download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
                 reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
 
+            gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
+
         request_notifications.click(
             fn=lambda: None,
             inputs=[],
-- 
cgit v1.2.1


From a1cf55a9d1c82f8e56c00d549bca5c8fa069f412 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 10:39:21 +0300
Subject: add option to reorder items in main UI

---
 modules/shared.py |  13 ++++++
 modules/ui.py     | 130 +++++++++++++++++++++++++++++++++++-------------------
 2 files changed, 97 insertions(+), 46 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index b65559ee..23657a93 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -109,6 +109,17 @@ restricted_opts = {
     "outdir_save",
 }
 
+ui_reorder_categories = [
+    "sampler",
+    "dimensions",
+    "cfg",
+    "seed",
+    "checkboxes",
+    "hires_fix",
+    "batch",
+    "scripts",
+]
+
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
@@ -410,7 +421,9 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
     "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
+    "dimensions_and_batch_together": OptionInfo(True, "Show Witdth/Height and Batch sliders in same row"),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
+    'ui_reorder': OptionInfo(", ".join(ui_reorder_categories), "txt2img/ing2img UI item order"),
     'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
 
diff --git a/modules/ui.py b/modules/ui.py
index 2c92c422..f2e7c0d6 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -644,6 +644,13 @@ def create_sampler_and_steps_selection(choices, tabname):
     return steps, sampler_index
 
 
+def ordered_ui_categories():
+    user_order = {x.strip(): i for i, x in enumerate(shared.opts.ui_reorder.split(","))}
+
+    for i, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] + 1000)):
+        yield category
+
+
 def create_ui():
     import modules.img2img
     import modules.txt2img
@@ -672,32 +679,48 @@ def create_ui():
 
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel', elem_id="txt2img_settings"):
-                steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
-
-                with FormRow():
-                    with gr.Column(elem_id="txt2img_column_size", scale=4):
-                        width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
-                        height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
-                    with gr.Column(elem_id="txt2img_column_batch"):
-                        batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
-                        batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
-
-                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
-
-                with FormRow(elem_id="txt2img_checkboxes"):
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
-                    tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
-                    enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
+                for category in ordered_ui_categories():
+                    if category == "sampler":
+                        steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
 
-                with FormRow(visible=False) as hr_options:
-                    hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
-                    hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
-
-                with FormGroup(elem_id="txt2img_script_container"):
-                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+                    elif category == "dimensions":
+                        with FormRow():
+                            with gr.Column(elem_id="txt2img_column_size", scale=4):
+                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
+                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
+
+                            if opts.dimensions_and_batch_together:
+                                with gr.Column(elem_id="txt2img_column_batch"):
+                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+                    elif category == "cfg":
+                        cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
+
+                    elif category == "seed":
+                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
+
+                    elif category == "checkboxes":
+                        with FormRow(elem_id="txt2img_checkboxes"):
+                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
+                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
+                            enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
+
+                    elif category == "hires_fix":
+                        with FormRow(visible=False, elem_id="txt2img_hires_fix") as hr_options:
+                            hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
+                            hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
+                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
+
+                    elif category == "batch":
+                        if not opts.dimensions_and_batch_together:
+                            with FormRow(elem_id="txt2img_column_batch"):
+                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+                    elif category == "scripts":
+                        with FormGroup(elem_id="txt2img_script_container"):
+                            custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
 
             txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
             parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
@@ -865,28 +888,43 @@ def create_ui():
                 with FormRow():
                     resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
 
-                steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
-
-                with FormRow():
-                    with gr.Column(elem_id="img2img_column_size", scale=4):
-                        width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
-                        height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
-                    with gr.Column(elem_id="img2img_column_batch"):
-                        batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
-                        batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
-
-                with FormGroup():
-                    cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
-                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
-
-                seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
+                for category in ordered_ui_categories():
+                    if category == "sampler":
+                        steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
 
-                with FormRow(elem_id="img2img_checkboxes"):
-                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
-                    tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
-
-                with FormGroup(elem_id="img2img_script_container"):
-                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+                    elif category == "dimensions":
+                        with FormRow():
+                            with gr.Column(elem_id="img2img_column_size", scale=4):
+                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+
+                            if opts.dimensions_and_batch_together:
+                                with gr.Column(elem_id="img2img_column_batch"):
+                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                                    batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+                    elif category == "cfg":
+                        with FormGroup():
+                            cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
+
+                    elif category == "seed":
+                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
+
+                    elif category == "checkboxes":
+                        with FormRow(elem_id="img2img_checkboxes"):
+                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
+                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
+
+                    elif category == "batch":
+                        if not opts.dimensions_and_batch_together:
+                            with FormRow(elem_id="img2img_column_batch"):
+                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+                    elif category == "scripts":
+                        with FormGroup(elem_id="img2img_script_container"):
+                            custom_inputs = modules.scripts.scripts_img2img.setup_ui()
 
             img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
             parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
-- 
cgit v1.2.1


From fda1ed184381fdf8aa81be4f64e77787f3fac1b2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 12:01:32 +0300
Subject: some minor improvements for dark mode UI

---
 style.css | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/style.css b/style.css
index 86a265f6..7296ce91 100644
--- a/style.css
+++ b/style.css
@@ -208,20 +208,20 @@ button{
 
 fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500,  label.block span{
     position: absolute;
-    top: -0.5em;
+    top: -0.7em;
     line-height: 1.2em;
     padding: 0;
     margin: 0 0.5em;
 
     background-color: white;
-    box-shadow: 0 0 5px 5px white;
+    box-shadow:  6px 0 6px 0px white, -6px 0 6px 0px white;
 
     z-index: 300;
 }
 
 .dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{
     background-color: rgb(31, 41, 55);
-    box-shadow: 0 0 5px 5px rgb(31, 41, 55);
+    box-shadow: 6px 0 6px 0px rgb(31, 41, 55), -6px 0 6px 0px rgb(31, 41, 55);
 }
 
 #txt2img_column_batch, #img2img_column_batch{
-- 
cgit v1.2.1


From 9a3b0ee960b0c61c4f60e3081ae6f2098533d393 Mon Sep 17 00:00:00 2001
From: hithereai <121192995+hithereai@users.noreply.github.com>
Date: Tue, 3 Jan 2023 11:22:06 +0200
Subject: update req.txt

The old 'opencv-python' package is very limiting in terms of optical flow - so I propose a package change to 'opencv-contrib-python', which has more cv2.optflow methods.

These are needed for optical flow trickery in auto1111 and its extensions, and it cannot be installed by an extension as only a single package of opencv needs to be installed for optical flow to work properly. Change of the main one is Inevitable.
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index e2c3876b..4f09385f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,7 +9,7 @@ gradio==3.15.0
 invisible-watermark
 numpy
 omegaconf
-opencv-python
+opencv-contrib-python
 requests
 piexif
 Pillow
-- 
cgit v1.2.1


From c0ee1488702d5a6ae35fbf7e0422f9f685394920 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 14:18:48 +0300
Subject: add support for running with gradio 3.9 installed

---
 modules/generation_parameters_copypaste.py |  4 ++--
 modules/ui_tempdir.py                      | 23 +++++++++++++++++++++--
 2 files changed, 23 insertions(+), 4 deletions(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index ec60319a..d94f11a3 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -7,7 +7,7 @@ from pathlib import Path
 
 import gradio as gr
 from modules.shared import script_path
-from modules import shared
+from modules import shared, ui_tempdir
 import tempfile
 from PIL import Image
 
@@ -39,7 +39,7 @@ def quote(text):
 def image_from_url_text(filedata):
     if type(filedata) == dict and filedata["is_file"]:
         filename = filedata["name"]
-        is_in_right_dir = any([filename in fileset for fileset in shared.demo.temp_file_sets])
+        is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
         assert is_in_right_dir, 'trying to open image file outside of allowed directories'
 
         return Image.open(filename)
diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py
index 363d449d..21945235 100644
--- a/modules/ui_tempdir.py
+++ b/modules/ui_tempdir.py
@@ -1,6 +1,7 @@
 import os
 import tempfile
 from collections import namedtuple
+from pathlib import Path
 
 import gradio as gr
 
@@ -12,10 +13,28 @@ from modules import shared
 Savedfile = namedtuple("Savedfile", ["name"])
 
 
+def register_tmp_file(gradio, filename):
+    if hasattr(gradio, 'temp_file_sets'):  # gradio 3.15
+        gradio.temp_file_sets[0] = gradio.temp_file_sets[0] | {os.path.abspath(filename)}
+
+    if hasattr(gradio, 'temp_dirs'):  # gradio 3.9
+        gradio.temp_dirs = gradio.temp_dirs | {os.path.abspath(os.path.dirname(filename))}
+
+
+def check_tmp_file(gradio, filename):
+    if hasattr(gradio, 'temp_file_sets'):
+        return any([filename in fileset for fileset in gradio.temp_file_sets])
+
+    if hasattr(gradio, 'temp_dirs'):
+        return any(Path(temp_dir).resolve() in Path(filename).resolve().parents for temp_dir in gradio.temp_dirs)
+
+    return False
+
+
 def save_pil_to_file(pil_image, dir=None):
     already_saved_as = getattr(pil_image, 'already_saved_as', None)
     if already_saved_as and os.path.isfile(already_saved_as):
-        shared.demo.temp_file_sets[0] = shared.demo.temp_file_sets[0] | {os.path.abspath(already_saved_as)}
+        register_tmp_file(shared.demo, already_saved_as)
 
         file_obj = Savedfile(already_saved_as)
         return file_obj
@@ -45,7 +64,7 @@ def on_tmpdir_changed():
 
     os.makedirs(shared.opts.temp_dir, exist_ok=True)
 
-    shared.demo.temp_file_sets[0] = shared.demo.temp_file_sets[0] | {os.path.abspath(shared.opts.temp_dir)}
+    register_tmp_file(shared.demo, os.path.join(shared.opts.temp_dir, "x"))
 
 
 def cleanup_tmpdr():
-- 
cgit v1.2.1


From bddebe09edeb6a18f2c06986d5658a7be3a563ea Mon Sep 17 00:00:00 2001
From: Shondoit <shondoit@gmail.com>
Date: Tue, 3 Jan 2023 10:26:37 +0100
Subject: Save Optimizer next to TI embedding

Also add check to load only .PT and .BIN files as embeddings. (since we add .optim files in the same directory)
---
 modules/shared.py                              |  2 +-
 modules/textual_inversion/textual_inversion.py | 40 ++++++++++++++++++++------
 2 files changed, 33 insertions(+), 9 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 23657a93..c541d18c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -355,7 +355,7 @@ options_templates.update(options_section(('system', "System"), {
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
-    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fd253477..16176e90 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -28,6 +28,7 @@ class Embedding:
         self.cached_checksum = None
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
+        self.optimizer_state_dict = None
 
     def save(self, filename):
         embedding_data = {
@@ -41,6 +42,13 @@ class Embedding:
 
         torch.save(embedding_data, filename)
 
+        if shared.opts.save_optimizer_state and self.optimizer_state_dict is not None:
+            optimizer_saved_dict = {
+                'hash': self.checksum(),
+                'optimizer_state_dict': self.optimizer_state_dict,
+            }
+            torch.save(optimizer_saved_dict, filename + '.optim')
+
     def checksum(self):
         if self.cached_checksum is not None:
             return self.cached_checksum
@@ -95,9 +103,10 @@ class EmbeddingDatabase:
         self.expected_shape = self.get_expected_shape()
 
         def process_file(path, filename):
-            name = os.path.splitext(filename)[0]
+            name, ext = os.path.splitext(filename)
+            ext = ext.upper()
 
-            if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
+            if ext in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
                 embed_image = Image.open(path)
                 if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
                     data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
@@ -105,8 +114,10 @@ class EmbeddingDatabase:
                 else:
                     data = extract_image_data_embed(embed_image)
                     name = data.get('name', name)
-            else:
+            elif ext in ['.BIN', '.PT']:
                 data = torch.load(path, map_location="cpu")
+            else:
+                return
 
             # textual inversion embeddings
             if 'string_to_param' in data:
@@ -300,6 +311,20 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 
     embedding.vec.requires_grad = True
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)
+    if shared.opts.save_optimizer_state:
+        optimizer_state_dict = None
+        if os.path.exists(filename + '.optim'):
+            optimizer_saved_dict = torch.load(filename + '.optim', map_location='cpu')
+            if embedding.checksum() == optimizer_saved_dict.get('hash', None):
+                optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
+    
+        if optimizer_state_dict is not None:
+            optimizer.load_state_dict(optimizer_state_dict)
+            print("Loaded existing optimizer from checkpoint")
+        else:
+            print("No saved optimizer exists in checkpoint")
+
+
     scaler = torch.cuda.amp.GradScaler()
 
     batch_size = ds.batch_size
@@ -366,9 +391,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                     # Before saving, change name to match current checkpoint.
                     embedding_name_every = f'{embedding_name}-{steps_done}'
                     last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
-                    #if shared.opts.save_optimizer_state:
-                        #embedding.optimizer_state_dict = optimizer.state_dict()
-                    save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
+                    save_embedding(embedding, optimizer, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
                     embedding_yet_to_be_embedded = True
 
                 write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, steps_per_epoch, {
@@ -458,7 +481,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
         filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
-        save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
+        save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
     except Exception:
         print(traceback.format_exc(), file=sys.stderr)
         pass
@@ -470,7 +493,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     return embedding, filename
 
-def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True):
+def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True):
     old_embedding_name = embedding.name
     old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None
     old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
@@ -481,6 +504,7 @@ def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cache
         if remove_cached_checksum:
             embedding.cached_checksum = None
         embedding.name = embedding_name
+        embedding.optimizer_state_dict = optimizer.state_dict()
         embedding.save(filename)
     except:
         embedding.sd_checkpoint = old_sd_checkpoint
-- 
cgit v1.2.1


From e9fb9bb0c25f59109a816fc53c385bed58965c24 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 17:40:20 +0300
Subject: fix hires fix not working in API when user does not specify upscaler

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 4654570c..a172af0b 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -685,7 +685,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
-        latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_default_mode
+        latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
         if self.enable_hr and latent_scale_mode is None:
             assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}"
 
-- 
cgit v1.2.1


From aaa4c2aacbb6523077334093c81bd475d757f7a1 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 3 Jan 2023 09:45:16 -0500
Subject: add api logging

---
 modules/api/api.py | 24 +++++++++++++++++++++++-
 modules/shared.py  |  1 +
 2 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 9c670f00..53135470 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -1,11 +1,12 @@
 import base64
 import io
 import time
+import datetime
 import uvicorn
 from threading import Lock
 from io import BytesIO
 from gradio.processing_utils import decode_base64_to_file
-from fastapi import APIRouter, Depends, FastAPI, HTTPException
+from fastapi import APIRouter, Depends, FastAPI, HTTPException, Request, Response
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from secrets import compare_digest
 
@@ -67,6 +68,26 @@ def encode_pil_to_base64(image):
         bytes_data = output_bytes.getvalue()
     return base64.b64encode(bytes_data)
 
+def init_api_middleware(app: FastAPI):
+    @app.middleware("http")
+    async def log_and_time(req: Request, call_next):
+        ts = time.time()
+        res: Response = await call_next(req)
+        duration = str(round(time.time() - ts, 4))
+        res.headers["X-Process-Time"] = duration
+        if shared.cmd_opts.api_log:
+            print('API {t} {code} {prot}/{ver} {method} {p} {cli} {duration}'.format(
+                t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),
+                code = res.status_code,
+                ver = req.scope.get('http_version', '0.0'),
+                cli = req.scope.get('client', ('0:0.0.0', 0))[0],
+                prot = req.scope.get('scheme', 'err'),
+                method = req.scope.get('method', 'err'),
+                p = req.scope.get('path', 'err'),
+                duration = duration,
+            ))
+        return res
+
 
 class Api:
     def __init__(self, app: FastAPI, queue_lock: Lock):
@@ -78,6 +99,7 @@ class Api:
 
         self.router = APIRouter()
         self.app = app
+        init_api_middleware(self.app)
         self.queue_lock = queue_lock
         self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
         self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
diff --git a/modules/shared.py b/modules/shared.py
index 23657a93..2a03d716 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -82,6 +82,7 @@ parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencode
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
 parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-- 
cgit v1.2.1


From 1d9dc48efda2e8da6d13fc62e65500198a9b041c Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 3 Jan 2023 10:21:51 -0500
Subject: init job and add info to model merge

---
 modules/extras.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 5e270250..7e222313 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -242,6 +242,9 @@ def run_pnginfo(image):
 
 
 def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
+    shared.state.begin()
+    shared.state.job = 'model-merge'
+
     def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
@@ -263,8 +266,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
     theta_func1, theta_func2 = theta_funcs[interp_method]
 
     if theta_func1 and not tertiary_model_info:
+        shared.state.textinfo = "Failed: Interpolation method requires a tertiary model."
+        shared.state.end()
         return ["Failed: Interpolation method requires a tertiary model."] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
 
+    shared.state.textinfo = f"Loading {secondary_model_info.filename}..."
     print(f"Loading {secondary_model_info.filename}...")
     theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
 
@@ -281,6 +287,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
                     theta_1[key] = torch.zeros_like(theta_1[key])
         del theta_2
 
+    shared.state.textinfo = f"Loading {primary_model_info.filename}..."
     print(f"Loading {primary_model_info.filename}...")
     theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
 
@@ -291,6 +298,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
             a = theta_0[key]
             b = theta_1[key]
 
+            shared.state.textinfo = f'Merging layer {key}'
             # this enables merging an inpainting model (A) with another one (B);
             # where normal model would have 4 channels, for latenst space, inpainting model would
             # have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9
@@ -303,8 +311,6 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
                 theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
                 result_is_inpainting_model = True
             else:
-                assert a.shape == b.shape, f'Incompatible shapes for layer {key}: A is {a.shape}, and B is {b.shape}'
-
                 theta_0[key] = theta_func2(a, b, multiplier)
 
             if save_as_half:
@@ -332,6 +338,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
 
     output_modelname = os.path.join(ckpt_dir, filename)
 
+    shared.state.textinfo = f"Saving to {output_modelname}..."
     print(f"Saving to {output_modelname}...")
 
     _, extension = os.path.splitext(output_modelname)
@@ -343,4 +350,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
     sd_models.list_models()
 
     print("Checkpoint saved.")
+    shared.state.textinfo = "Checkpoint saved to " + output_modelname
+    shared.state.end()
+
     return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
-- 
cgit v1.2.1


From 192ddc04d6de0d780f73aa5fbaa8c66cd4642e1c Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 3 Jan 2023 10:34:51 -0500
Subject: add job info to modules

---
 modules/extras.py                              | 17 +++++++++++++----
 modules/hypernetworks/hypernetwork.py          |  1 +
 modules/textual_inversion/preprocess.py        |  1 +
 modules/textual_inversion/textual_inversion.py |  1 +
 4 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/modules/extras.py b/modules/extras.py
index 7e222313..d665440a 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -58,6 +58,9 @@ cached_images: LruCache = LruCache(max_size=5)
 def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
     devices.torch_gc()
 
+    shared.state.begin()
+    shared.state.job = 'extras'
+
     imageArr = []
     # Also keep track of original file names
     imageNameArr = []
@@ -94,6 +97,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     # Extra operation definitions
 
     def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        shared.state.job = 'extras-gfpgan'
         restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
         res = Image.fromarray(restored_img)
 
@@ -104,6 +108,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         return (res, info)
 
     def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        shared.state.job = 'extras-codeformer'
         restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
         res = Image.fromarray(restored_img)
 
@@ -114,6 +119,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         return (res, info)
 
     def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
+        shared.state.job = 'extras-upscale'
         upscaler = shared.sd_upscalers[scaler_index]
         res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
         if mode == 1 and crop:
@@ -180,6 +186,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     for image, image_name in zip(imageArr, imageNameArr):
         if image is None:
             return outputs, "Please select an input image.", ''
+
+        shared.state.textinfo = f'Processing image {image_name}'
+        
         existing_pnginfo = image.info or {}
 
         image = image.convert("RGB")
@@ -193,6 +202,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         else:
             basename = ''
 
+        if opts.enable_pnginfo: # append info before save
+            image.info = existing_pnginfo
+            image.info["extras"] = info
+
         if save_output:
             # Add upscaler name as a suffix.
             suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
@@ -203,10 +216,6 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
             images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
                             no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
 
-        if opts.enable_pnginfo:
-            image.info = existing_pnginfo
-            image.info["extras"] = info
-
         if extras_mode != 2 or show_extras_results :
             outputs.append(image)
 
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 109e8078..450fecac 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -417,6 +417,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
     shared.loaded_hypernetwork = Hypernetwork()
     shared.loaded_hypernetwork.load(path)
 
+    shared.state.job = "train-hypernetwork"
     shared.state.textinfo = "Initializing hypernetwork training..."
     shared.state.job_count = steps
 
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 56b9b2eb..feb876c6 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -124,6 +124,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
 
     files = listfiles(src)
 
+    shared.state.job = "preprocess"
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
 
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index fd253477..2c1251d6 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -245,6 +245,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     create_image_every = create_image_every or 0
     validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
 
+    shared.state.job = "train-embedding"
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
 
-- 
cgit v1.2.1


From 2d5a5076bb2a0c05cc27d75a1bcadab7f32a46d0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 18:38:21 +0300
Subject: Make it so that upscalers are not repeated when restarting UI.

---
 modules/modelloader.py | 20 ++++++++++++++++++++
 webui.py               | 14 +++++++-------
 2 files changed, 27 insertions(+), 7 deletions(-)

diff --git a/modules/modelloader.py b/modules/modelloader.py
index e647f6fa..6a1a7ac8 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -123,6 +123,23 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
         pass
 
 
+builtin_upscaler_classes = []
+forbidden_upscaler_classes = set()
+
+
+def list_builtin_upscalers():
+    load_upscalers()
+
+    builtin_upscaler_classes.clear()
+    builtin_upscaler_classes.extend(Upscaler.__subclasses__())
+
+
+def forbid_loaded_nonbuiltin_upscalers():
+    for cls in Upscaler.__subclasses__():
+        if cls not in builtin_upscaler_classes:
+            forbidden_upscaler_classes.add(cls)
+
+
 def load_upscalers():
     # We can only do this 'magic' method to dynamically load upscalers if they are referenced,
     # so we'll try to import any _model.py files before looking in __subclasses__
@@ -139,6 +156,9 @@ def load_upscalers():
     datas = []
     commandline_options = vars(shared.cmd_opts)
     for cls in Upscaler.__subclasses__():
+        if cls in forbidden_upscaler_classes:
+            continue
+
         name = cls.__name__
         cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
         scaler = cls(commandline_options.get(cmd_name, None))
diff --git a/webui.py b/webui.py
index 3aee8792..c7d55a97 100644
--- a/webui.py
+++ b/webui.py
@@ -1,4 +1,5 @@
 import os
+import sys
 import threading
 import time
 import importlib
@@ -55,8 +56,8 @@ def initialize():
     gfpgan.setup_model(cmd_opts.gfpgan_models_path)
     shared.face_restorers.append(modules.face_restoration.FaceRestoration())
 
+    modelloader.list_builtin_upscalers()
     modules.scripts.load_scripts()
-
     modelloader.load_upscalers()
 
     modules.sd_vae.refresh_vae_list()
@@ -169,23 +170,22 @@ def webui():
         modules.script_callbacks.app_started_callback(shared.demo, app)
 
         wait_on_server(shared.demo)
+        print('Restarting UI...')
 
         sd_samplers.set_samplers()
 
-        print('Reloading extensions')
         extensions.list_extensions()
 
         localization.list_localizations(cmd_opts.localizations_dir)
 
-        print('Reloading custom scripts')
+        modelloader.forbid_loaded_nonbuiltin_upscalers()
         modules.scripts.reload_scripts()
         modelloader.load_upscalers()
 
-        print('Reloading modules: modules.ui')
-        importlib.reload(modules.ui)
-        print('Refreshing Model List')
+        for module in [module for name, module in sys.modules.items() if name.startswith("modules.ui")]:
+            importlib.reload(module)
+
         modules.sd_models.list_models()
-        print('Restarting Gradio')
 
 
 if __name__ == "__main__":
-- 
cgit v1.2.1


From 8f96f9289981a66741ba770d14f3d27ce335a0fb Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 18:39:14 +0300
Subject: call script callbacks for reloaded model after loading embeddings

---
 modules/sd_models.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index bff8d6c9..b98b05fc 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -324,12 +324,12 @@ def load_model(checkpoint_info=None):
     sd_model.eval()
     shared.sd_model = sd_model
 
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model
+
     script_callbacks.model_loaded_callback(sd_model)
 
     print("Model loaded.")
 
-    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload = True) # Reload embeddings after model load as they may or may not fit the model
-
     return sd_model
 
 
-- 
cgit v1.2.1


From cec209981ee988536c2521297baf9bc1b256005f Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 3 Jan 2023 10:58:52 -0500
Subject: log only sdapi

---
 modules/api/api.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 53135470..78751c57 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -68,22 +68,23 @@ def encode_pil_to_base64(image):
         bytes_data = output_bytes.getvalue()
     return base64.b64encode(bytes_data)
 
-def init_api_middleware(app: FastAPI):
+def api_middleware(app: FastAPI):
     @app.middleware("http")
     async def log_and_time(req: Request, call_next):
         ts = time.time()
         res: Response = await call_next(req)
         duration = str(round(time.time() - ts, 4))
         res.headers["X-Process-Time"] = duration
-        if shared.cmd_opts.api_log:
-            print('API {t} {code} {prot}/{ver} {method} {p} {cli} {duration}'.format(
+        endpoint = req.scope.get('path', 'err')
+        if shared.cmd_opts.api_log and endpoint.startswith('/sdapi'):
+            print('API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}'.format(
                 t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),
                 code = res.status_code,
                 ver = req.scope.get('http_version', '0.0'),
                 cli = req.scope.get('client', ('0:0.0.0', 0))[0],
                 prot = req.scope.get('scheme', 'err'),
                 method = req.scope.get('method', 'err'),
-                p = req.scope.get('path', 'err'),
+                endpoint = endpoint,
                 duration = duration,
             ))
         return res
-- 
cgit v1.2.1


From d8d206c1685d1e7027d4af82ed18d106f41d1cc4 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Tue, 3 Jan 2023 11:01:04 -0500
Subject: add state to interrogate

---
 modules/interrogate.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 6f761c5a..738d8ff7 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -136,7 +136,8 @@ class InterrogateModels:
 
     def interrogate(self, pil_image):
         res = ""
-
+        shared.state.begin()
+        shared.state.job = 'interrogate'
         try:
 
             if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -177,5 +178,6 @@ class InterrogateModels:
             res += "<error>"
 
         self.unload()
+        shared.state.end()
 
         return res
-- 
cgit v1.2.1


From 82cfc227d735c140447d5b8dca29a71ee9bde127 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 20:23:17 +0300
Subject: added licenses screen to settings added footer removed unused
 inpainting code

---
 README.md                       |   2 +
 html/footer.html                |   9 +
 html/licenses.html              | 392 ++++++++++++++++++++++++++++++++++++++++
 modules/sd_hijack_inpainting.py | 232 ------------------------
 modules/ui.py                   |  15 +-
 style.css                       |  11 ++
 6 files changed, 427 insertions(+), 234 deletions(-)
 create mode 100644 html/footer.html
 create mode 100644 html/licenses.html

diff --git a/README.md b/README.md
index 556000fb..88250a6b 100644
--- a/README.md
+++ b/README.md
@@ -127,6 +127,8 @@ Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC
 The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
 
 ## Credits
+Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
+
 - Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
 - k-diffusion - https://github.com/crowsonkb/k-diffusion.git
 - GFPGAN - https://github.com/TencentARC/GFPGAN.git
diff --git a/html/footer.html b/html/footer.html
new file mode 100644
index 00000000..a8f2adf7
--- /dev/null
+++ b/html/footer.html
@@ -0,0 +1,9 @@
+<div>
+        <a href="/docs">API</a>
+         • 
+        <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">Github</a>
+         • 
+        <a href="https://gradio.app">Gradio</a>
+         • 
+        <a href="/" onclick="javascript:gradioApp().getElementById('settings_restart_gradio').click(); return false">Reload UI</a>
+</div>
diff --git a/html/licenses.html b/html/licenses.html
new file mode 100644
index 00000000..9eeaa072
--- /dev/null
+++ b/html/licenses.html
@@ -0,0 +1,392 @@
+<style>
+    #licenses h2 {font-size: 1.2em; font-weight: bold; margin-bottom: 0.2em;}
+    #licenses small {font-size: 0.95em; opacity: 0.85;}
+    #licenses pre { margin: 1em 0 2em 0;}
+</style>
+
+<h2><a href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">CodeFormer</a></h2>
+<small>Parts of CodeFormer code had to be copied to be compatible with GFPGAN.</small>
+<pre>
+S-Lab License 1.0
+
+Copyright 2022 S-Lab
+
+Redistribution and use for non-commercial purpose in source and
+binary forms, with or without modification, are permitted provided
+that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in
+   the documentation and/or other materials provided with the
+   distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+In the event that redistribution and/or use for commercial purpose in
+source or binary forms, with or without modification is required,
+please contact the contributor(s) of the work.
+</pre>
+
+
+<h2><a href="https://github.com/victorca25/iNNfer/blob/main/LICENSE">ESRGAN</a></h2>
+<small>Code for architecture and reading models copied.</small>
+<pre>
+MIT License
+
+Copyright (c) 2021 victorca25
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+</pre>
+
+<h2><a href="https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE">Real-ESRGAN</a></h2>
+<small>Some code is copied to support ESRGAN models.</small>
+<pre>
+BSD 3-Clause License
+
+Copyright (c) 2021, Xintao Wang
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+</pre>
+
+<h2><a href="https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE">InvokeAI</a></h2>
+<small>Some code for compatibility with OSX is taken from lstein's repository.</small>
+<pre>
+MIT License
+
+Copyright (c) 2022 InvokeAI Team
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+</pre>
+
+<h2><a href="https://github.com/Hafiidz/latent-diffusion/blob/main/LICENSE">LDSR</a></h2>
+<small>Code added by contirubtors, most likely copied from this repository.</small>
+<pre>
+MIT License
+
+Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+</pre>
+
+<h2><a href="https://github.com/pharmapsychotic/clip-interrogator/blob/main/LICENSE">CLIP Interrogator</a></h2>
+<small>Some small amounts of code borrowed and reworked.</small>
+<pre>
+MIT License
+
+Copyright (c) 2022 pharmapsychotic
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+</pre>
+
+<h2><a href="https://github.com/JingyunLiang/SwinIR/blob/main/LICENSE">SwinIR</a></h2>
+<small>Code added by contirubtors, most likely copied from this repository.</small>
+
+<pre>
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
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+      and distribution as defined by Sections 1 through 9 of this document.
+
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+
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+
+      "You" (or "Your") shall mean an individual or Legal Entity
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+
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+      source, and configuration files.
+
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+
+      "Work" shall mean the work of authorship, whether in Source or
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+
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+   Copyright [2021] [SwinIR Authors]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+</pre>
+
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 06b75772..3c214a35 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -12,191 +12,6 @@ from ldm.models.diffusion.ddpm import LatentDiffusion
 from ldm.models.diffusion.plms import PLMSSampler
 from ldm.models.diffusion.ddim import DDIMSampler, noise_like
 
-# =================================================================================================
-# Monkey patch DDIMSampler methods from RunwayML repo directly.
-# Adapted from:
-# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
-# =================================================================================================
-@torch.no_grad()
-def sample_ddim(self,
-            S,
-            batch_size,
-            shape,
-            conditioning=None,
-            callback=None,
-            normals_sequence=None,
-            img_callback=None,
-            quantize_x0=False,
-            eta=0.,
-            mask=None,
-            x0=None,
-            temperature=1.,
-            noise_dropout=0.,
-            score_corrector=None,
-            corrector_kwargs=None,
-            verbose=True,
-            x_T=None,
-            log_every_t=100,
-            unconditional_guidance_scale=1.,
-            unconditional_conditioning=None,
-            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-            **kwargs
-            ):
-    if conditioning is not None:
-        if isinstance(conditioning, dict):
-            ctmp = conditioning[list(conditioning.keys())[0]]
-            while isinstance(ctmp, list):
-                ctmp = ctmp[0]
-            cbs = ctmp.shape[0]
-            if cbs != batch_size:
-                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-        else:
-            if conditioning.shape[0] != batch_size:
-                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-    # sampling
-    C, H, W = shape
-    size = (batch_size, C, H, W)
-    print(f'Data shape for DDIM sampling is {size}, eta {eta}')
-
-    samples, intermediates = self.ddim_sampling(conditioning, size,
-                                                callback=callback,
-                                                img_callback=img_callback,
-                                                quantize_denoised=quantize_x0,
-                                                mask=mask, x0=x0,
-                                                ddim_use_original_steps=False,
-                                                noise_dropout=noise_dropout,
-                                                temperature=temperature,
-                                                score_corrector=score_corrector,
-                                                corrector_kwargs=corrector_kwargs,
-                                                x_T=x_T,
-                                                log_every_t=log_every_t,
-                                                unconditional_guidance_scale=unconditional_guidance_scale,
-                                                unconditional_conditioning=unconditional_conditioning,
-                                                )
-    return samples, intermediates
-
-@torch.no_grad()
-def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                    unconditional_guidance_scale=1., unconditional_conditioning=None):
-    b, *_, device = *x.shape, x.device
-
-    if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-        e_t = self.model.apply_model(x, t, c)
-    else:
-        x_in = torch.cat([x] * 2)
-        t_in = torch.cat([t] * 2)
-        if isinstance(c, dict):
-            assert isinstance(unconditional_conditioning, dict)
-            c_in = dict()
-            for k in c:
-                if isinstance(c[k], list):
-                    c_in[k] = [
-                        torch.cat([unconditional_conditioning[k][i], c[k][i]])
-                        for i in range(len(c[k]))
-                    ]
-                else:
-                    c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
-        else:
-            c_in = torch.cat([unconditional_conditioning, c])
-        e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-        e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-    if score_corrector is not None:
-        assert self.model.parameterization == "eps"
-        e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-    # select parameters corresponding to the currently considered timestep
-    a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-    a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-    sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-    sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-    # current prediction for x_0
-    pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-    if quantize_denoised:
-        pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-    # direction pointing to x_t
-    dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-    noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-    if noise_dropout > 0.:
-        noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-    x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-    return x_prev, pred_x0
-
-
-# =================================================================================================
-# Monkey patch PLMSSampler methods.
-# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes.
-# Adapted from:
-# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py
-# =================================================================================================
-@torch.no_grad()
-def sample_plms(self,
-            S,
-            batch_size,
-            shape,
-            conditioning=None,
-            callback=None,
-            normals_sequence=None,
-            img_callback=None,
-            quantize_x0=False,
-            eta=0.,
-            mask=None,
-            x0=None,
-            temperature=1.,
-            noise_dropout=0.,
-            score_corrector=None,
-            corrector_kwargs=None,
-            verbose=True,
-            x_T=None,
-            log_every_t=100,
-            unconditional_guidance_scale=1.,
-            unconditional_conditioning=None,
-            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-            **kwargs
-            ):
-    if conditioning is not None:
-        if isinstance(conditioning, dict):
-            ctmp = conditioning[list(conditioning.keys())[0]]
-            while isinstance(ctmp, list):
-                ctmp = ctmp[0]
-            cbs = ctmp.shape[0]
-            if cbs != batch_size:
-                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-        else:
-            if conditioning.shape[0] != batch_size:
-                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-    # sampling
-    C, H, W = shape
-    size = (batch_size, C, H, W)
-    # print(f'Data shape for PLMS sampling is {size}') # remove unnecessary message
-
-    samples, intermediates = self.plms_sampling(conditioning, size,
-                                                callback=callback,
-                                                img_callback=img_callback,
-                                                quantize_denoised=quantize_x0,
-                                                mask=mask, x0=x0,
-                                                ddim_use_original_steps=False,
-                                                noise_dropout=noise_dropout,
-                                                temperature=temperature,
-                                                score_corrector=score_corrector,
-                                                corrector_kwargs=corrector_kwargs,
-                                                x_T=x_T,
-                                                log_every_t=log_every_t,
-                                                unconditional_guidance_scale=unconditional_guidance_scale,
-                                                unconditional_conditioning=unconditional_conditioning,
-                                                )
-    return samples, intermediates
-
 
 @torch.no_grad()
 def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
@@ -280,44 +95,6 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
 
     return x_prev, pred_x0, e_t
 
-# =================================================================================================
-# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
-# Adapted from:
-# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py
-# =================================================================================================
-
-@torch.no_grad()
-def get_unconditional_conditioning(self, batch_size, null_label=None):
-    if null_label is not None:
-        xc = null_label
-        if isinstance(xc, ListConfig):
-            xc = list(xc)
-        if isinstance(xc, dict) or isinstance(xc, list):
-            c = self.get_learned_conditioning(xc)
-        else:
-            if hasattr(xc, "to"):
-                xc = xc.to(self.device)
-            c = self.get_learned_conditioning(xc)
-    else:
-        # todo: get null label from cond_stage_model
-        raise NotImplementedError()
-    c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
-    return c
-
-
-class LatentInpaintDiffusion(LatentDiffusion):
-    def __init__(
-        self,
-        concat_keys=("mask", "masked_image"),
-        masked_image_key="masked_image",
-        *args,
-        **kwargs,
-    ):
-        super().__init__(*args, **kwargs)
-        self.masked_image_key = masked_image_key
-        assert self.masked_image_key in concat_keys
-        self.concat_keys = concat_keys
-
 
 def should_hijack_inpainting(checkpoint_info):
     ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
@@ -326,15 +103,6 @@ def should_hijack_inpainting(checkpoint_info):
 
 
 def do_inpainting_hijack():
-    # most of this stuff seems to no longer be needed because it is already included into SD2.0
     # p_sample_plms is needed because PLMS can't work with dicts as conditionings
-    # this file should be cleaned up later if everything turns out to work fine
-
-    # ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
-    # ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
-
-    # ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
-    # ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
 
     ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
-    # ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms
diff --git a/modules/ui.py b/modules/ui.py
index f2e7c0d6..d941cb5f 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1529,8 +1529,10 @@ def create_ui():
 
     with gr.Blocks(analytics_enabled=False) as settings_interface:
         with gr.Row():
-            settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
-            restart_gradio = gr.Button(value='Restart UI', variant='primary', elem_id="settings_restart_gradio")
+            with gr.Column(scale=6):
+                settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
+            with gr.Column():
+                restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
 
         result = gr.HTML(elem_id="settings_result")
 
@@ -1574,6 +1576,11 @@ def create_ui():
                 download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
                 reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
 
+            if os.path.exists("html/licenses.html"):
+                with open("html/licenses.html", encoding="utf8") as file:
+                    with gr.TabItem("Licenses"):
+                        gr.HTML(file.read(), elem_id="licenses")
+
             gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
 
         request_notifications.click(
@@ -1659,6 +1666,10 @@ def create_ui():
         if os.path.exists(os.path.join(script_path, "notification.mp3")):
             audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
 
+        if os.path.exists("html/footer.html"):
+            with open("html/footer.html", encoding="utf8") as file:
+                gr.HTML(file.read(), elem_id="footer")
+
         text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
         settings_submit.click(
             fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
diff --git a/style.css b/style.css
index 7296ce91..2116ec3c 100644
--- a/style.css
+++ b/style.css
@@ -616,6 +616,17 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
     padding-bottom: 0.5em;
 }
 
+footer {
+    display: none !important;
+}
+
+#footer{
+    text-align: center;
+}
+
+#footer div{
+    display: inline-block;
+}
 
 /* The following handles localization for right-to-left (RTL) languages like Arabic.
 The rtl media type will only be activated by the logic in javascript/localization.js.
-- 
cgit v1.2.1


From 7c89f3718f9f078113833a88a86f02d3205855b4 Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Tue, 3 Jan 2023 12:46:48 -0500
Subject: Add image paste fallback

Fixes Firefox pasting support
(and possibly other browsers)
---
 javascript/dragdrop.js | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/javascript/dragdrop.js b/javascript/dragdrop.js
index 3ed1cb3c..fe008924 100644
--- a/javascript/dragdrop.js
+++ b/javascript/dragdrop.js
@@ -9,11 +9,19 @@ function dropReplaceImage( imgWrap, files ) {
         return;
     }
 
+    const tmpFile = files[0];
+
     imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click();
     const callback = () => {
         const fileInput = imgWrap.querySelector('input[type="file"]');
         if ( fileInput ) {
-            fileInput.files = files;
+            if ( files.length === 0 ) {
+                files = new DataTransfer();
+                files.items.add(tmpFile);
+                fileInput.files = files.files;
+            } else {
+                fileInput.files = files;
+            }
             fileInput.dispatchEvent(new Event('change'));   
         }
     };
-- 
cgit v1.2.1


From 3e22e294135ed0327ce9d9738655ff03c53df3c0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 3 Jan 2023 21:49:24 +0300
Subject: fix broken send to extras button

---
 modules/generation_parameters_copypaste.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index d94f11a3..4baf4d9a 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -37,7 +37,10 @@ def quote(text):
 
 
 def image_from_url_text(filedata):
-    if type(filedata) == dict and filedata["is_file"]:
+    if type(filedata) == list and len(filedata) > 0 and type(filedata[0]) == dict and filedata[0].get("is_file", False):
+        filedata = filedata[0]
+
+    if type(filedata) == dict and filedata.get("is_file", False):
         filename = filedata["name"]
         is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
         assert is_in_right_dir, 'trying to open image file outside of allowed directories'
-- 
cgit v1.2.1


From 917b5bd8d0cd47c9dc241c1852ccd440a8c61668 Mon Sep 17 00:00:00 2001
From: Max Weber <mii7303@gmail.com>
Date: Tue, 3 Jan 2023 18:19:56 -0700
Subject: ui: save dropdown sampling method to the ui-config

---
 modules/ui.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/ui.py b/modules/ui.py
index d941cb5f..bfc93634 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -635,6 +635,7 @@ def create_sampler_and_steps_selection(choices, tabname):
     if opts.samplers_in_dropdown:
         with FormRow(elem_id=f"sampler_selection_{tabname}"):
             sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
+            sampler_index.save_to_config = True
             steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20)
     else:
         with FormGroup(elem_id=f"sampler_selection_{tabname}"):
-- 
cgit v1.2.1


From 4fc81542077af73610279ad7b6b26e38718a0f81 Mon Sep 17 00:00:00 2001
From: Gerschel <Gerschel_Payne@hotmail.com>
Date: Tue, 3 Jan 2023 23:25:34 -0800
Subject: better targetting, class tabs was autoassigned

I moved a preset manager into quicksettings, this function
was targeting my component instead of the tabs. This is
because class tabs is autoassigned, while element id #tabs
is not, this allows a tabbed component to live in the quicksettings.
---
 script.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script.js b/script.js
index 9748ec90..0e117d06 100644
--- a/script.js
+++ b/script.js
@@ -4,7 +4,7 @@ function gradioApp() {
 }
 
 function get_uiCurrentTab() {
-    return gradioApp().querySelector('.tabs button:not(.border-transparent)')
+    return gradioApp().querySelector('#tabs button:not(.border-transparent)')
 }
 
 function get_uiCurrentTabContent() {
-- 
cgit v1.2.1


From e5b7ee910e7bb88f08e8876b5732cb034c6fe529 Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Wed, 4 Jan 2023 04:22:01 -0500
Subject: fix: Save full res of intermediate step

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index a172af0b..93e75ba6 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -705,7 +705,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 return
 
             if not isinstance(image, Image.Image):
-                image = sd_samplers.sample_to_image(image, index)
+                image = sd_samplers.sample_to_image(image, index, approximation=0)
 
             images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
 
-- 
cgit v1.2.1


From 02d7abf5141431b9a3a8a189bb3136c71abd5e79 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 12:35:07 +0300
Subject: helpful error message when trying to load 2.0 without config failing
 to load model weights from settings won't break generation for currently
 loaded model anymore

---
 modules/errors.py    | 25 +++++++++++++++++++++++--
 modules/sd_models.py | 26 ++++++++++++++++++--------
 modules/shared.py    |  9 +++++++--
 webui.py             | 12 ++++++++++--
 4 files changed, 58 insertions(+), 14 deletions(-)

diff --git a/modules/errors.py b/modules/errors.py
index 372dc51a..a668c014 100644
--- a/modules/errors.py
+++ b/modules/errors.py
@@ -2,9 +2,30 @@ import sys
 import traceback
 
 
+def print_error_explanation(message):
+    lines = message.strip().split("\n")
+    max_len = max([len(x) for x in lines])
+
+    print('=' * max_len, file=sys.stderr)
+    for line in lines:
+        print(line, file=sys.stderr)
+    print('=' * max_len, file=sys.stderr)
+
+
+def display(e: Exception, task):
+    print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr)
+    print(traceback.format_exc(), file=sys.stderr)
+
+    message = str(e)
+    if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:
+        print_error_explanation("""
+The most likely cause of this is you are trying to load Stable Diffusion 2.0 model without specifying its connfig file.
+See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20 for how to solve this.
+        """)
+
+
 def run(code, task):
     try:
         code()
     except Exception as e:
-        print(f"{task}: {type(e).__name__}", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+        display(task, e)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index b98b05fc..6846b74a 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -278,6 +278,7 @@ def enable_midas_autodownload():
 
     midas.api.load_model = load_model_wrapper
 
+
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
@@ -312,6 +313,7 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.unet_config.params.use_fp16 = False
 
     sd_model = instantiate_from_config(sd_config.model)
+
     load_model_weights(sd_model, checkpoint_info)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -336,10 +338,12 @@ def load_model(checkpoint_info=None):
 def reload_model_weights(sd_model=None, info=None):
     from modules import lowvram, devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
- 
+
     if not sd_model:
         sd_model = shared.sd_model
 
+    current_checkpoint_info = sd_model.sd_checkpoint_info
+
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
@@ -356,13 +360,19 @@ def reload_model_weights(sd_model=None, info=None):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_model_weights(sd_model, checkpoint_info)
-
-    sd_hijack.model_hijack.hijack(sd_model)
-    script_callbacks.model_loaded_callback(sd_model)
-
-    if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
-        sd_model.to(devices.device)
+    try:
+        load_model_weights(sd_model, checkpoint_info)
+    except Exception as e:
+        print("Failed to load checkpoint, restoring previous")
+        load_model_weights(sd_model, current_checkpoint_info)
+        raise
+    finally:
+        sd_hijack.model_hijack.hijack(sd_model)
+        script_callbacks.model_loaded_callback(sd_model)
+
+        if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+            sd_model.to(devices.device)
 
     print("Weights loaded.")
+
     return sd_model
diff --git a/modules/shared.py b/modules/shared.py
index 23657a93..7588c47b 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -14,7 +14,7 @@ import modules.interrogate
 import modules.memmon
 import modules.styles
 import modules.devices as devices
-from modules import localization, sd_vae, extensions, script_loading
+from modules import localization, sd_vae, extensions, script_loading, errors
 from modules.paths import models_path, script_path, sd_path
 
 
@@ -494,7 +494,12 @@ class Options:
             return False
 
         if self.data_labels[key].onchange is not None:
-            self.data_labels[key].onchange()
+            try:
+                self.data_labels[key].onchange()
+            except Exception as e:
+                errors.display(e, f"changing setting {key} to {value}")
+                setattr(self, key, oldval)
+                return False
 
         return True
 
diff --git a/webui.py b/webui.py
index c7d55a97..13375e71 100644
--- a/webui.py
+++ b/webui.py
@@ -9,7 +9,7 @@ from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
 
-from modules import import_hook
+from modules import import_hook, errors
 from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call
 from modules.paths import script_path
 
@@ -61,7 +61,15 @@ def initialize():
     modelloader.load_upscalers()
 
     modules.sd_vae.refresh_vae_list()
-    modules.sd_models.load_model()
+
+    try:
+        modules.sd_models.load_model()
+    except Exception as e:
+        errors.display(e, "loading stable diffusion model")
+        print("", file=sys.stderr)
+        print("Stable diffusion model failed to load, exiting", file=sys.stderr)
+        exit(1)
+
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
     shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
     shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
-- 
cgit v1.2.1


From 8d8a05a3bbb50fdfeab51679a919d2487bd97976 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 12:47:42 +0300
Subject: find configs for models at runtime rather than when starting

---
 modules/sd_hijack_inpainting.py |  5 ++++-
 modules/sd_models.py            | 31 ++++++++++++++++++-------------
 2 files changed, 22 insertions(+), 14 deletions(-)

diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 3c214a35..31d2c898 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -97,8 +97,11 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
 
 
 def should_hijack_inpainting(checkpoint_info):
+    from modules import sd_models
+
     ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
-    cfg_basename = os.path.basename(checkpoint_info.config).lower()
+    cfg_basename = os.path.basename(sd_models.find_checkpoint_config(checkpoint_info)).lower()
+
     return "inpainting" in ckpt_basename and not "inpainting" in cfg_basename
 
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6846b74a..6dca4ddf 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -20,7 +20,7 @@ from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inp
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
 checkpoints_list = {}
 checkpoints_loaded = collections.OrderedDict()
 
@@ -48,6 +48,14 @@ def checkpoint_tiles():
     return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key)
 
 
+def find_checkpoint_config(info):
+    config = os.path.splitext(info.filename)[0] + ".yaml"
+    if os.path.exists(config):
+        return config
+
+    return shared.cmd_opts.config
+
+
 def list_models():
     checkpoints_list.clear()
     model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"])
@@ -73,7 +81,7 @@ def list_models():
     if os.path.exists(cmd_ckpt):
         h = model_hash(cmd_ckpt)
         title, short_model_name = modeltitle(cmd_ckpt, h)
-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
         shared.opts.data['sd_model_checkpoint'] = title
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
@@ -81,12 +89,7 @@ def list_models():
         h = model_hash(filename)
         title, short_model_name = modeltitle(filename, h)
 
-        basename, _ = os.path.splitext(filename)
-        config = basename + ".yaml"
-        if not os.path.exists(config):
-            config = shared.cmd_opts.config
-
-        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
+        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
 
 
 def get_closet_checkpoint_match(searchString):
@@ -282,9 +285,10 @@ def enable_midas_autodownload():
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
+    checkpoint_config = find_checkpoint_config(checkpoint_info)
 
-    if checkpoint_info.config != shared.cmd_opts.config:
-        print(f"Loading config from: {checkpoint_info.config}")
+    if checkpoint_config != shared.cmd_opts.config:
+        print(f"Loading config from: {checkpoint_config}")
 
     if shared.sd_model:
         sd_hijack.model_hijack.undo_hijack(shared.sd_model)
@@ -292,7 +296,7 @@ def load_model(checkpoint_info=None):
         gc.collect()
         devices.torch_gc()
 
-    sd_config = OmegaConf.load(checkpoint_info.config)
+    sd_config = OmegaConf.load(checkpoint_config)
     
     if should_hijack_inpainting(checkpoint_info):
         # Hardcoded config for now...
@@ -302,7 +306,7 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.finetune_keys = None
 
         # Create a "fake" config with a different name so that we know to unload it when switching models.
-        checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
+        checkpoint_info = checkpoint_info._replace(config=checkpoint_config.replace(".yaml", "-inpainting.yaml"))
 
     if not hasattr(sd_config.model.params, "use_ema"):
         sd_config.model.params.use_ema = False
@@ -343,11 +347,12 @@ def reload_model_weights(sd_model=None, info=None):
         sd_model = shared.sd_model
 
     current_checkpoint_info = sd_model.sd_checkpoint_info
+    checkpoint_config = find_checkpoint_config(current_checkpoint_info)
 
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
-    if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
+    if checkpoint_config != find_checkpoint_config(checkpoint_info) or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         del sd_model
         checkpoints_loaded.clear()
         load_model(checkpoint_info)
-- 
cgit v1.2.1


From 96cf15bedecbed97ef9b70b8413d543a9aee5adf Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Wed, 4 Jan 2023 05:12:06 -0500
Subject: Add new latent upscale modes

---
 modules/shared.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 7588c47b..a10f69a9 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -564,8 +564,11 @@ if os.path.exists(config_filename):
 
 latent_upscale_default_mode = "Latent"
 latent_upscale_modes = {
-    "Latent": "bilinear",
-    "Latent (nearest)": "nearest",
+    "Latent": {"mode": "bilinear", "antialias": False},
+    "Latent (antialiased)": {"mode": "bilinear", "antialias": True},
+    "Latent (bicubic)": {"mode": "bicubic", "antialias": False},
+    "Latent (bicubic, antialiased)": {"mode": "bicubic", "antialias": True},
+    "Latent (nearest)": {"mode": "nearest", "antialias": False},
 }
 
 sd_upscalers = []
-- 
cgit v1.2.1


From 15fd0b8bc4734ea85bca1acfb12b51465ab9817d Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Wed, 4 Jan 2023 05:12:54 -0500
Subject: Update processing.py

---
 modules/processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index a172af0b..7c72b56a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -713,7 +713,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             for i in range(samples.shape[0]):
                 save_intermediate(samples, i)
 
-            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode)
+            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
 
             # Avoid making the inpainting conditioning unless necessary as
             # this does need some extra compute to decode / encode the image again.
-- 
cgit v1.2.1


From 4ec6470a1a2d9430b91266426f995e48f59564e1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 13:26:23 +0300
Subject: fix checkpoint list API

---
 modules/api/api.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index 9c670f00..2b1f180c 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -18,7 +18,7 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_
 from modules.textual_inversion.preprocess import preprocess
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
 from PIL import PngImagePlugin,Image
-from modules.sd_models import checkpoints_list
+from modules.sd_models import checkpoints_list, find_checkpoint_config
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
 from typing import List
@@ -303,7 +303,7 @@ class Api:
         return upscalers
 
     def get_sd_models(self):
-        return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": x.config} for x in checkpoints_list.values()]
+        return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
 
     def get_hypernetworks(self):
         return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
-- 
cgit v1.2.1


From b2151b934fe0a3613570c6abd7615d3788fd1c8f Mon Sep 17 00:00:00 2001
From: MMaker <mmaker@mmaker.moe>
Date: Wed, 4 Jan 2023 05:36:18 -0500
Subject: Rename bicubic antialiased option

Comma was causing the the value in PNG info to be quoted, which causes the upscaler dropdown option to be blank when sending to UI
---
 modules/shared.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/shared.py b/modules/shared.py
index a10f69a9..c1b20081 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -567,7 +567,7 @@ latent_upscale_modes = {
     "Latent": {"mode": "bilinear", "antialias": False},
     "Latent (antialiased)": {"mode": "bilinear", "antialias": True},
     "Latent (bicubic)": {"mode": "bicubic", "antialias": False},
-    "Latent (bicubic, antialiased)": {"mode": "bicubic", "antialias": True},
+    "Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True},
     "Latent (nearest)": {"mode": "nearest", "antialias": False},
 }
 
-- 
cgit v1.2.1


From 3bd737767b071878ea980e94b8705f603bcf545e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 14:20:32 +0300
Subject: disable broken API logging

---
 modules/api/api.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modules/api/api.py b/modules/api/api.py
index a6c1d6ed..6267afdc 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -100,7 +100,6 @@ class Api:
 
         self.router = APIRouter()
         self.app = app
-        init_api_middleware(self.app)
         self.queue_lock = queue_lock
         self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
         self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
-- 
cgit v1.2.1


From 0cd6399b8b1699b8b7acad6f0ad2988111fe618e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 14:29:13 +0300
Subject: fix broken inpainting model

---
 modules/sd_models.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 6dca4ddf..a568823d 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -305,9 +305,6 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.unet_config.params.in_channels = 9
         sd_config.model.params.finetune_keys = None
 
-        # Create a "fake" config with a different name so that we know to unload it when switching models.
-        checkpoint_info = checkpoint_info._replace(config=checkpoint_config.replace(".yaml", "-inpainting.yaml"))
-
     if not hasattr(sd_config.model.params, "use_ema"):
         sd_config.model.params.use_ema = False
 
-- 
cgit v1.2.1


From 11b8160a086c434d5baf4971edda46e6d2126800 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 4 Jan 2023 06:36:57 -0500
Subject: fix typo

---
 modules/api/api.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/modules/api/api.py b/modules/api/api.py
index 6267afdc..48a70a44 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -101,6 +101,7 @@ class Api:
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
+        api_middleware(self.app)
         self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
         self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
-- 
cgit v1.2.1


From 642142556d8ecdea9beb86d7618b628b1803ab98 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 15:09:53 +0300
Subject: use commandline-supplied cuda device name instead of cuda:0 for
 safetensors PR that doesn't fix anything

---
 modules/sd_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/sd_models.py b/modules/sd_models.py
index ee918f24..76a89e88 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -173,7 +173,7 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
     if extension.lower() == ".safetensors":
         device = map_location or shared.weight_load_location
         if device is None:
-            device = "cuda:0" if torch.cuda.is_available() else "cpu"
+            device = devices.get_cuda_device_string() if torch.cuda.is_available() else "cpu"
         pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
     else:
         pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
-- 
cgit v1.2.1


From 21ee77db314ede7ccbb18787962347c09a4df0c7 Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Wed, 4 Jan 2023 08:04:38 -0500
Subject: add cross-attention info

---
 modules/sd_hijack.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index edcbaf52..fa2cd4bb 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -35,26 +35,35 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
+    
+    optimization_method = None
 
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
+        optimization_method = 'xformers'
     elif cmd_opts.opt_split_attention_v1:
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+        optimization_method = 'V1'
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
         if not invokeAI_mps_available and shared.device.type == 'mps':
             print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
             print("Applying v1 cross attention optimization.")
             ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
+            optimization_method = 'V1'
         else:
             print("Applying cross attention optimization (InvokeAI).")
             ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
+            optimization_method = 'InvokeAI'
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
         print("Applying cross attention optimization (Doggettx).")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
+        optimization_method = 'Doggettx'
+
+    return optimization_method
 
 
 def undo_optimizations():
@@ -75,6 +84,7 @@ class StableDiffusionModelHijack:
     layers = None
     circular_enabled = False
     clip = None
+    optimization_method = None
 
     embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
 
@@ -94,7 +104,7 @@ class StableDiffusionModelHijack:
             m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
             m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
 
-        apply_optimizations()
+        self.optimization_method = apply_optimizations()
 
         self.clip = m.cond_stage_model
         
-- 
cgit v1.2.1


From 1cfd8aec4ae5a6ca1afd67b44cb4ef6dd14d8c34 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 16:05:42 +0300
Subject: make it possible to work with opts.show_progress_every_n_steps = -1
 with medvram

---
 modules/shared.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/modules/shared.py b/modules/shared.py
index 4fcc6edd..54a6ba23 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -214,12 +214,13 @@ class State:
 
     """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
     def set_current_image(self):
+        if not parallel_processing_allowed:
+            return
+
         if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0:
             self.do_set_current_image()
 
     def do_set_current_image(self):
-        if not parallel_processing_allowed:
-            return
         if self.current_latent is None:
             return
 
@@ -231,6 +232,7 @@ class State:
 
         self.current_image_sampling_step = self.sampling_step
 
+
 state = State()
 
 artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
-- 
cgit v1.2.1


From 4d66bf2c0d27702cc83b9cc57ebb1f359d18d938 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 17:24:46 +0300
Subject: add infotext to "-before-highres-fix" images

---
 modules/processing.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index fd7c7015..c03e77e7 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -136,6 +136,7 @@ class StableDiffusionProcessing():
         self.all_negative_prompts = None
         self.all_seeds = None
         self.all_subseeds = None
+        self.iteration = 0
 
     def txt2img_image_conditioning(self, x, width=None, height=None):
         if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
@@ -544,6 +545,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             state.job_count = p.n_iter
 
         for n in range(p.n_iter):
+            p.iteration = n
+
             if state.skipped:
                 state.skipped = False
 
@@ -707,7 +710,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             if not isinstance(image, Image.Image):
                 image = sd_samplers.sample_to_image(image, index, approximation=0)
 
-            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
+            info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
+            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
 
         if latent_scale_mode is not None:
             for i in range(samples.shape[0]):
-- 
cgit v1.2.1


From 184e670126f5fc50ba56fa0fedcf0cf60e45ed7e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 17:45:01 +0300
Subject: fix the merge

---
 modules/textual_inversion/textual_inversion.py | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 5421a758..8731ea5d 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -251,6 +251,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
     if save_model_every or create_image_every:
         assert log_directory, "Log directory is empty"
 
+
 def create_dummy_mask(x, width=None, height=None):
     if shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}:
 
@@ -380,17 +381,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                     break
 
                 with devices.autocast():
-                    # c = stack_conds(batch.cond).to(devices.device)
-                    # mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)
-                    # print(mask)
-                    # c[:, 1:1+embedding.vec.shape[0]] = embedding.vec.to(devices.device, non_blocking=pin_memory)
-                    
-                    
-                    if img_c is None:
-                        img_c = create_dummy_mask(c, training_width, training_height)
-                    
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
                     c = shared.sd_model.cond_stage_model(batch.cond_text)
+
+                    if img_c is None:
+                        img_c = create_dummy_mask(c, training_width, training_height)
+
                     cond = {"c_concat": [img_c], "c_crossattn": [c]}
                     loss = shared.sd_model(x, cond)[0] / gradient_step
                     del x
-- 
cgit v1.2.1


From 525cea924562afd676f55470095268a0f6fca59e Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 17:58:07 +0300
Subject: use shared function from processing for creating dummy mask when
 training inpainting model

---
 modules/processing.py                          | 39 +++++++++++++-------------
 modules/textual_inversion/textual_inversion.py | 33 ++++++----------------
 2 files changed, 29 insertions(+), 43 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index c03e77e7..c7264aff 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -76,6 +76,24 @@ def apply_overlay(image, paste_loc, index, overlays):
     return image
 
 
+def txt2img_image_conditioning(sd_model, x, width, height):
+    if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
+        # Dummy zero conditioning if we're not using inpainting model.
+        # Still takes up a bit of memory, but no encoder call.
+        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+        return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+
+    # The "masked-image" in this case will just be all zeros since the entire image is masked.
+    image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+    image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+
+    # Add the fake full 1s mask to the first dimension.
+    image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+    image_conditioning = image_conditioning.to(x.dtype)
+
+    return image_conditioning
+
+
 class StableDiffusionProcessing():
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
@@ -139,26 +157,9 @@ class StableDiffusionProcessing():
         self.iteration = 0
 
     def txt2img_image_conditioning(self, x, width=None, height=None):
-        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
-            # Dummy zero conditioning if we're not using inpainting model.
-            # Still takes up a bit of memory, but no encoder call.
-            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-            return x.new_zeros(x.shape[0], 5, 1, 1)
+        self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
 
-        self.is_using_inpainting_conditioning = True
-
-        height = height or self.height
-        width = width or self.width
-
-        # The "masked-image" in this case will just be all zeros since the entire image is masked.
-        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
-
-        # Add the fake full 1s mask to the first dimension.
-        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-        image_conditioning = image_conditioning.to(x.dtype)
-
-        return image_conditioning
+        return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height)
 
     def depth2img_image_conditioning(self, source_image):
         # Use the AddMiDaS helper to Format our source image to suit the MiDaS model
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 8731ea5d..2250e41b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -252,26 +252,6 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
         assert log_directory, "Log directory is empty"
 
 
-def create_dummy_mask(x, width=None, height=None):
-    if shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}:
-
-        # The "masked-image" in this case will just be all zeros since the entire image is masked.
-        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = shared.sd_model.get_first_stage_encoding(shared.sd_model.encode_first_stage(image_conditioning))
-
-        # Add the fake full 1s mask to the first dimension.
-        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-        image_conditioning = image_conditioning.to(x.dtype)
-
-    else:
-        # Dummy zero conditioning if we're not using inpainting model.
-        # Still takes up a bit of memory, but no encoder call.
-        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-        image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
-
-    return image_conditioning
-
-
 def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
@@ -346,7 +326,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
         else:
             print("No saved optimizer exists in checkpoint")
 
-
     scaler = torch.cuda.amp.GradScaler()
 
     batch_size = ds.batch_size
@@ -362,7 +341,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
 
+    is_training_inpainting_model = shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}
     img_c = None
+
     pbar = tqdm.tqdm(total=steps - initial_step)
     try:
         for i in range((steps-initial_step) * gradient_step):
@@ -384,10 +365,14 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
                     c = shared.sd_model.cond_stage_model(batch.cond_text)
 
-                    if img_c is None:
-                        img_c = create_dummy_mask(c, training_width, training_height)
+                    if is_training_inpainting_model:
+                        if img_c is None:
+                            img_c = processing.txt2img_image_conditioning(shared.sd_model, c, training_width, training_height)
+
+                        cond = {"c_concat": [img_c], "c_crossattn": [c]}
+                    else:
+                        cond = c
 
-                    cond = {"c_concat": [img_c], "c_crossattn": [c]}
                     loss = shared.sd_model(x, cond)[0] / gradient_step
                     del x
 
-- 
cgit v1.2.1


From a8eb9e3bf814f72293e474c11e9ff0098859a942 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 18:20:38 +0300
Subject: Revert "Merge pull request #3791 from shirayu/fix/filename"

This reverts commit eed58279e7cb0e873ebd88a29609f9bab0f1f3af, reversing
changes made to 4ae960b01c6711c66985479f14809dc7fa549fc2.
---
 modules/images.py | 16 ++++------------
 1 file changed, 4 insertions(+), 12 deletions(-)

diff --git a/modules/images.py b/modules/images.py
index 2967fa9a..c3a5fc8b 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -447,14 +447,6 @@ def get_next_sequence_number(path, basename):
     return result + 1
 
 
-def truncate_fullpath(full_path, encoding='utf-8'):
-    dir_name, full_name = os.path.split(full_path)
-    file_name, file_ext = os.path.splitext(full_name)
-    max_length = os.statvfs(dir_name).f_namemax
-    file_name_truncated = file_name.encode(encoding)[:max_length - len(file_ext)].decode(encoding, 'ignore')
-    return os.path.join(dir_name , file_name_truncated + file_ext)
-
-
 def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
     """Save an image.
 
@@ -495,7 +487,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
 
     if save_to_dirs:
         dirname = namegen.apply(opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /')
-        path = truncate_fullpath(os.path.join(path, dirname))
+        path = os.path.join(path, dirname)
 
     os.makedirs(path, exist_ok=True)
 
@@ -519,13 +511,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             fullfn = None
             for i in range(500):
                 fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
-                fullfn = truncate_fullpath(os.path.join(path, f"{fn}{file_decoration}.{extension}"))
+                fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
                 if not os.path.exists(fullfn):
                     break
         else:
-            fullfn = truncate_fullpath(os.path.join(path, f"{file_decoration}.{extension}"))
+            fullfn = os.path.join(path, f"{file_decoration}.{extension}")
     else:
-        fullfn = truncate_fullpath(os.path.join(path, f"{forced_filename}.{extension}"))
+        fullfn = os.path.join(path, f"{forced_filename}.{extension}")
 
     pnginfo = existing_info or {}
     if info is not None:
-- 
cgit v1.2.1


From 3dae545a03f5102ba5d9c3f27bb6241824c5a916 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 4 Jan 2023 18:42:51 +0300
Subject: rename weirdly named variables from #3176

---
 modules/ui.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/modules/ui.py b/modules/ui.py
index e4859020..184af7ad 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -162,16 +162,14 @@ def save_files(js_data, images, do_make_zip, index):
     return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
 
 
-
-
-def calc_time_left(progress, threshold, label, force_display, showTime):
+def calc_time_left(progress, threshold, label, force_display, show_eta):
     if progress == 0:
         return ""
     else:
         time_since_start = time.time() - shared.state.time_start
         eta = (time_since_start/progress)
         eta_relative = eta-time_since_start
-        if (eta_relative > threshold and showTime) or force_display:
+        if (eta_relative > threshold and show_eta) or force_display:
             if eta_relative > 3600:
                 return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
             elif eta_relative > 60:
@@ -194,9 +192,9 @@ def check_progress_call(id_part):
         progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
 
     # Show progress percentage and time left at the same moment, and base it also on steps done
-    showPBText = progress >= 0.01 or shared.state.sampling_step >= 10
+    show_eta = progress >= 0.01 or shared.state.sampling_step >= 10
 
-    time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display, showPBText )
+    time_left = calc_time_left(progress, 1, " ETA: ", shared.state.time_left_force_display, show_eta)
     if time_left != "":
         shared.state.time_left_force_display = True
 
@@ -204,7 +202,7 @@ def check_progress_call(id_part):
 
     progressbar = ""
     if opts.show_progressbar:
-        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if showPBText else ""}</div></div>"""
+        progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:visible;width:{progress * 100}%;white-space:nowrap;">{"&nbsp;" * 2 + str(int(progress*100))+"%" + time_left if show_eta else ""}</div></div>"""
 
     image = gr_show(False)
     preview_visibility = gr_show(False)
-- 
cgit v1.2.1